From 3f84e1d831ce28310dca809d99604ee300be3467 Mon Sep 17 00:00:00 2001
From: Marco Realacci <marco@marcorealacci.me>
Date: Thu, 4 Apr 2024 23:24:06 +0200
Subject: [PATCH] Adapt to new bot structure

---
 Bot/LICENSE                                   |   674 -
 Bot/README.md                                 |    35 -
 {Data => data/config}/motd.txt                |     0
 data/questions/diritto_unive_inf.json         |  1244 ++
 data/questions/ium_unive.json                 |  3081 ++++
 data/questions/ogas.json                      |  1066 ++
 data/questions/sicurezza.json                 | 11975 ++++++++++++++++
 data/questions/sicurezza_appello1.json        |   754 +
 data/questions/so1.json                       |  3107 ++++
 data/questions/so1_new.json                   |  2927 ++++
 data/questions/so1_unive.json                 |  1193 ++
 data/questions/so2.json                       |  2740 ++++
 .../Bot}/AccessControl/AccessManager.cs       |     0
 {Bot => legacy/Bot}/ModuleLoader/IModule.cs   |     0
 .../Bot}/ModuleLoader/ModuleLoader.cs         |     0
 .../Bot}/Modules/OttoLinux/BotGame.cs         |     0
 .../Bot}/Modules/OttoLinux/OttoReverse.cs     |     0
 .../Bot}/Modules/OttoLinux/OttoScore.cs       |     0
 .../Bot}/Modules/OttoLinux/PhotoServer.cs     |     0
 .../Bot}/Modules/OttoLinux/Question.cs        |     0
 .../Bot}/Modules/OttoLinux/WebReverse.cs      |     0
 {Bot => legacy/Bot}/Program.cs                |     0
 {Bot => legacy/Bot}/SoUnBot.csproj            |     0
 {Bot => legacy/Bot}/Telegram/TelegramBot.cs   |     0
 .../Debug/net8.0/JetBrains.Annotations.dll    |   Bin 0 -> 93184 bytes
 .../Bot/bin/Debug/net8.0/Newtonsoft.Json.dll  |   Bin 0 -> 695336 bytes
 legacy/Bot/bin/Debug/net8.0/SoUnBot           |   Bin 0 -> 77288 bytes
 legacy/Bot/bin/Debug/net8.0/SoUnBot.deps.json |   104 +
 legacy/Bot/bin/Debug/net8.0/SoUnBot.dll       |   Bin 0 -> 50688 bytes
 legacy/Bot/bin/Debug/net8.0/SoUnBot.pdb       |   Bin 0 -> 23980 bytes
 .../Debug/net8.0/SoUnBot.runtimeconfig.json   |    12 +
 .../Telegram.Bot.Extensions.Polling.dll       |   Bin 0 -> 26624 bytes
 legacy/Bot/bin/Debug/net8.0/Telegram.Bot.dll  |   Bin 0 -> 305152 bytes
 ...CoreApp,Version=v8.0.AssemblyAttributes.cs |     4 +
 .../obj/Debug/net8.0/SoUnBot.AssemblyInfo.cs  |    22 +
 .../net8.0/SoUnBot.AssemblyInfoInputs.cache   |     1 +
 ....GeneratedMSBuildEditorConfig.editorconfig |    13 +
 .../Debug/net8.0/SoUnBot.GlobalUsings.g.cs    |     8 +
 .../Bot/obj/Debug/net8.0/SoUnBot.assets.cache |   Bin 0 -> 3539 bytes
 .../SoUnBot.csproj.AssemblyReference.cache    |   Bin 0 -> 1446 bytes
 .../Debug/net8.0/SoUnBot.csproj.CopyComplete  |     0
 .../SoUnBot.csproj.CoreCompileInputs.cache    |     1 +
 .../SoUnBot.csproj.FileListAbsolute.txt       |    21 +
 legacy/Bot/obj/Debug/net8.0/SoUnBot.dll       |   Bin 0 -> 50688 bytes
 .../net8.0/SoUnBot.genruntimeconfig.cache     |     1 +
 legacy/Bot/obj/Debug/net8.0/SoUnBot.pdb       |   Bin 0 -> 23980 bytes
 .../obj/Debug/net8.0/SoUnBot.sourcelink.json  |     1 +
 legacy/Bot/obj/Debug/net8.0/apphost           |   Bin 0 -> 77288 bytes
 legacy/Bot/obj/Debug/net8.0/ref/SoUnBot.dll   |   Bin 0 -> 11776 bytes
 .../Bot/obj/Debug/net8.0/refint/SoUnBot.dll   |   Bin 0 -> 11776 bytes
 .../Bot/obj/SoUnBot.csproj.nuget.dgspec.json  |    81 +
 legacy/Bot/obj/SoUnBot.csproj.nuget.g.props   |    15 +
 legacy/Bot/obj/SoUnBot.csproj.nuget.g.targets |     2 +
 legacy/Bot/obj/project.assets.json            |   280 +
 legacy/Bot/obj/project.nuget.cache            |    15 +
 legacy/Bot/obj/project.packagespec.json       |     1 +
 legacy/Bot/obj/rider.project.model.nuget.info |     1 +
 legacy/Bot/obj/rider.project.restore.info     |     1 +
 {Bot => legacy/Bot}/run.sh                    |     0
 {Data => legacy/Data}/Images/25.png           |   Bin
 {Data => legacy/Data}/Images/26.png           |   Bin
 {Data => legacy/Data}/Images/27.png           |   Bin
 {Data => legacy/Data}/Images/35.png           |   Bin
 {Data => legacy/Data}/Images/36.png           |   Bin
 {Data => legacy/Data}/Images/37.png           |   Bin
 {Data => legacy/Data}/Images/38.png           |   Bin
 {Data => legacy/Data}/Images/39.png           |   Bin
 {Data => legacy/Data}/Images/40.png           |   Bin
 {Data => legacy/Data}/Images/56.png           |   Bin
 {Data => legacy/Data}/Images/57.png           |   Bin
 {Data => legacy/Data}/Images/58.png           |   Bin
 {Data => legacy/Data}/Images/59.png           |   Bin
 {Data => legacy/Data}/Images/60.png           |   Bin
 {Data => legacy/Data}/Images/61.png           |   Bin
 {Data => legacy/Data}/Images/62.png           |   Bin
 .../Data}/Images/FDS/1positive0negative.png   |   Bin
 .../Data}/Images/FDS/accuracy80.png           |   Bin
 .../Data}/Images/FDS/matrixwhatcanwesay.png   |   Bin
 .../Data}/Questions/Domande Sicurezza.old     |     0
 .../Data}/Questions/diritto_unive_inf.txt     |     0
 .../Questions/ingsw/0000_102/correct.txt      |     0
 .../Data}/Questions/ingsw/0000_102/quest.txt  |     0
 .../Data}/Questions/ingsw/0000_102/wrong1.txt |     0
 .../Data}/Questions/ingsw/0000_102/wrong2.txt |     0
 .../Data}/Questions/ingsw/0000_2/correct.txt  |     0
 .../Data}/Questions/ingsw/0000_2/quest.txt    |     0
 .../Data}/Questions/ingsw/0000_2/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0000_2/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0000_3/correct.txt  |     0
 .../Data}/Questions/ingsw/0000_3/quest.txt    |     0
 .../Data}/Questions/ingsw/0000_3/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0000_3/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0000_32/correct.txt |     0
 .../Data}/Questions/ingsw/0000_32/quest.txt   |     0
 .../Data}/Questions/ingsw/0000_32/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0000_32/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0000_4/correct.txt  |     0
 .../Data}/Questions/ingsw/0000_4/quest.txt    |     0
 .../Data}/Questions/ingsw/0000_4/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0000_4/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0000_7/correct.txt  |     0
 .../Data}/Questions/ingsw/0000_7/quest.txt    |     0
 .../Data}/Questions/ingsw/0000_7/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0000_7/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0000_8/correct.txt  |     0
 .../Data}/Questions/ingsw/0000_8/quest.txt    |     0
 .../Data}/Questions/ingsw/0000_8/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0000_8/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0120_0/correct.txt  |     0
 .../Data}/Questions/ingsw/0120_0/quest.txt    |     0
 .../Data}/Questions/ingsw/0120_0/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0120_0/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0120_1/correct.txt  |     0
 .../Data}/Questions/ingsw/0120_1/quest.txt    |     0
 .../Data}/Questions/ingsw/0120_1/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0120_1/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0120_10/correct.txt |     0
 .../Data}/Questions/ingsw/0120_10/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_10/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_10/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_11/correct.txt |     0
 .../Data}/Questions/ingsw/0120_11/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_11/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_11/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_12/correct.txt |     0
 .../Data}/Questions/ingsw/0120_12/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_12/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_12/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_13/correct.txt |     0
 .../Data}/Questions/ingsw/0120_13/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_13/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_13/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_14/correct.txt |     0
 .../Data}/Questions/ingsw/0120_14/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_14/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_14/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_15/correct.txt |     0
 .../Data}/Questions/ingsw/0120_15/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_15/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_15/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_16/correct.txt |     0
 .../Data}/Questions/ingsw/0120_16/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_16/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_16/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_17/correct.txt |     0
 .../Data}/Questions/ingsw/0120_17/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_17/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_17/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_18/correct.txt |     0
 .../Data}/Questions/ingsw/0120_18/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_18/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_18/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_19/correct.txt |     0
 .../Data}/Questions/ingsw/0120_19/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_19/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_19/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_2/correct.txt  |     0
 .../Data}/Questions/ingsw/0120_2/quest.txt    |     0
 .../Data}/Questions/ingsw/0120_2/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0120_2/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0120_20/correct.txt |     0
 .../Data}/Questions/ingsw/0120_20/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_20/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_20/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_21/correct.txt |     0
 .../Data}/Questions/ingsw/0120_21/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_21/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_21/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_22/quest.txt   |     0
 .../Data/Questions/ingsw/0120_22}/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_22/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_22/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0120_23/correct.txt |     0
 .../Data}/Questions/ingsw/0120_23/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_23/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_23/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_24/correct.txt |     0
 .../Data}/Questions/ingsw/0120_24/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_24/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_24/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_25/quest.txt   |     0
 .../Data/Questions/ingsw/0120_25}/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_25/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_25/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0120_26/correct.txt |     0
 .../Data}/Questions/ingsw/0120_26/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_26/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_26/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_27/correct.txt |     0
 .../Data}/Questions/ingsw/0120_27/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_27/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_27/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_28/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_28/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_28/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_28/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0120_29/correct.txt |     0
 .../Data}/Questions/ingsw/0120_29/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_29/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_29/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_3/correct.txt  |     0
 .../Data}/Questions/ingsw/0120_3/quest.txt    |     0
 .../Data}/Questions/ingsw/0120_3/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0120_3/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0120_30/correct.txt |     0
 .../Data}/Questions/ingsw/0120_30/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_30/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_30/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_31/correct.txt |     0
 .../Data}/Questions/ingsw/0120_31/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_31/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_31/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_32/correct.txt |     0
 .../Data}/Questions/ingsw/0120_32/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_32/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_32/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_33/correct.txt |     0
 .../Data}/Questions/ingsw/0120_33/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_33/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_33/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_34/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_34/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_34/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_34/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0120_35/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_35/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_35/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_35/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0120_36/correct.txt |     0
 .../Data}/Questions/ingsw/0120_36/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_36/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_36/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_37/correct.txt |     0
 .../Data}/Questions/ingsw/0120_37/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_37/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_37/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_38/correct.txt |     0
 .../Data}/Questions/ingsw/0120_38/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_38/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_38/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_39/quest.txt   |     0
 .../Data/Questions/ingsw/0120_39}/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_39/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_39/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0120_4/correct.txt  |     0
 .../Data}/Questions/ingsw/0120_4/quest.txt    |     0
 .../Data}/Questions/ingsw/0120_4/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0120_4/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0120_40/correct.txt |     0
 .../Data}/Questions/ingsw/0120_40/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_40/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_40/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_41/correct.txt |     0
 .../Data}/Questions/ingsw/0120_41/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_41/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_41/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_42/correct.txt |     0
 .../Data}/Questions/ingsw/0120_42/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_42/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_42/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_43/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_43/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_43/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_43/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0120_44/correct.txt |     0
 .../Data}/Questions/ingsw/0120_44/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_44/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_44/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_45/quest.txt   |     0
 .../Data/Questions/ingsw/0120_45}/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_45/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_45/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0120_46/correct.txt |     0
 .../Data}/Questions/ingsw/0120_46/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_46/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_46/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_47/correct.txt |     0
 .../Data}/Questions/ingsw/0120_47/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_47/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_47/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_48/correct.txt |     0
 .../Data}/Questions/ingsw/0120_48/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_48/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_48/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_49/correct.txt |     0
 .../Data}/Questions/ingsw/0120_49/quest.txt   |     0
 .../Data}/Questions/ingsw/0120_49/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0120_49/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0120_5/quest.txt    |     0
 .../Data}/Questions/ingsw/0120_5/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0120_5/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0120_5/wrong3.txt   |     0
 .../Data}/Questions/ingsw/0120_6/correct.txt  |     0
 .../Data}/Questions/ingsw/0120_6/quest.txt    |     0
 .../Data}/Questions/ingsw/0120_6/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0120_6/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0120_7/correct.txt  |     0
 .../Data}/Questions/ingsw/0120_7/quest.txt    |     0
 .../Data}/Questions/ingsw/0120_7/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0120_7/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0120_8/correct.txt  |     0
 .../Data}/Questions/ingsw/0120_8/quest.txt    |     0
 .../Data}/Questions/ingsw/0120_8/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0120_8/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0120_9/correct.txt  |     0
 .../Data}/Questions/ingsw/0120_9/quest.txt    |     0
 .../Data}/Questions/ingsw/0120_9/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0120_9/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0121_34/correct.txt |     0
 .../Data}/Questions/ingsw/0121_34/quest.txt   |     0
 .../Data}/Questions/ingsw/0121_34/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0121_34/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_0/correct.txt  |     0
 .../Data}/Questions/ingsw/0210_0/quest.txt    |     0
 .../Data}/Questions/ingsw/0210_0/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0210_0/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0210_1/quest.txt    |     0
 .../Data}/Questions/ingsw/0210_1/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0210_1/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0210_1/wrong3.txt   |     0
 .../Data}/Questions/ingsw/0210_10/correct.txt |     0
 .../Data}/Questions/ingsw/0210_10/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_10/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_10/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_11/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_11/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_11/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_11/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0210_12/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_12/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_12/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_12/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0210_13/correct.txt |     0
 .../Data}/Questions/ingsw/0210_13/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_13/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_13/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_14/correct.txt |     0
 .../Data}/Questions/ingsw/0210_14/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_14/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_14/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_15/correct.txt |     0
 .../Data}/Questions/ingsw/0210_15/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_15/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_15/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_16/correct.txt |     0
 .../Data}/Questions/ingsw/0210_16/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_16/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_16/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_17/correct.txt |     0
 .../Data}/Questions/ingsw/0210_17/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_17/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_17/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_18/correct.txt |     0
 .../Data}/Questions/ingsw/0210_18/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_18/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_18/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_19/correct.txt |     0
 .../Data}/Questions/ingsw/0210_19/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_19/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_19/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_2/quest.txt    |     0
 .../Data/Questions/ingsw/0210_2}/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0210_2/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0210_2/wrong3.txt   |     0
 .../Data}/Questions/ingsw/0210_20/correct.txt |     0
 .../Data}/Questions/ingsw/0210_20/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_20/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_20/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_21/correct.txt |     0
 .../Data}/Questions/ingsw/0210_21/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_21/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_21/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_22/correct.txt |     0
 .../Data}/Questions/ingsw/0210_22/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_22/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_22/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_23/correct.txt |     0
 .../Data}/Questions/ingsw/0210_23/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_23/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_23/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_24/correct.txt |     0
 .../Data}/Questions/ingsw/0210_24/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_24/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_24/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_25/correct.txt |     0
 .../Data}/Questions/ingsw/0210_25/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_25/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_25/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_26/correct.txt |     0
 .../Data}/Questions/ingsw/0210_26/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_26/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_26/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_27/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_27/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_27/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_27/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0210_28/quest.txt   |     0
 .../Data/Questions/ingsw/0210_28}/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_28/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_28/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0210_29/correct.txt |     0
 .../Data}/Questions/ingsw/0210_29/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_29/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_29/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_3/quest.txt    |     0
 .../Data}/Questions/ingsw/0210_3/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0210_3/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0210_3/wrong3.txt   |     0
 .../Data}/Questions/ingsw/0210_30/correct.txt |     0
 .../Data}/Questions/ingsw/0210_30/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_30/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_30/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_31/correct.txt |     0
 .../Data}/Questions/ingsw/0210_31/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_31/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_31/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_32/correct.txt |     0
 .../Data}/Questions/ingsw/0210_32/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_32/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_32/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_33/correct.txt |     0
 .../Data}/Questions/ingsw/0210_33/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_33/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_33/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_34/quest.txt   |     0
 .../Data/Questions/ingsw/0210_34}/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_34/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_34/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0210_35/correct.txt |     0
 .../Data}/Questions/ingsw/0210_35/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_35/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_35/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_36/correct.txt |     0
 .../Data}/Questions/ingsw/0210_36/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_36/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_36/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_37/quest.txt   |     0
 .../Data/Questions/ingsw/0210_37}/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_37/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_37/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0210_38/correct.txt |     0
 .../Data}/Questions/ingsw/0210_38/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_38/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_38/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_39/correct.txt |     0
 .../Data}/Questions/ingsw/0210_39/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_39/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_39/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_4/correct.txt  |     0
 .../Data}/Questions/ingsw/0210_4/quest.txt    |     0
 .../Data}/Questions/ingsw/0210_4/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0210_4/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0210_40/correct.txt |     0
 .../Data}/Questions/ingsw/0210_40/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_40/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_40/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_41/correct.txt |     0
 .../Data}/Questions/ingsw/0210_41/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_41/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_41/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_42/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_42/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_42/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_42/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0210_43/correct.txt |     0
 .../Data}/Questions/ingsw/0210_43/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_43/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_43/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_44/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_44/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_44/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_44/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0210_45/correct.txt |     0
 .../Data}/Questions/ingsw/0210_45/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_45/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_45/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_46/correct.txt |     0
 .../Data}/Questions/ingsw/0210_46/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_46/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_46/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_47/correct.txt |     0
 .../Data}/Questions/ingsw/0210_47/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_47/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_47/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_48/correct.txt |     0
 .../Data}/Questions/ingsw/0210_48/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_48/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_48/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_49/correct.txt |     0
 .../Data}/Questions/ingsw/0210_49/quest.txt   |     0
 .../Data}/Questions/ingsw/0210_49/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0210_49/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0210_5/correct.txt  |     0
 .../Data}/Questions/ingsw/0210_5/quest.txt    |     0
 .../Data}/Questions/ingsw/0210_5/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0210_5/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0210_6/correct.txt  |     0
 .../Data}/Questions/ingsw/0210_6/quest.txt    |     0
 .../Data}/Questions/ingsw/0210_6/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0210_6/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0210_7/correct.txt  |     0
 .../Data}/Questions/ingsw/0210_7/quest.txt    |     0
 .../Data}/Questions/ingsw/0210_7/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0210_7/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0210_8/correct.txt  |     0
 .../Data}/Questions/ingsw/0210_8/quest.txt    |     0
 .../Data}/Questions/ingsw/0210_8/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0210_8/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0210_9/quest.txt    |     0
 .../Data}/Questions/ingsw/0210_9/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0210_9/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0210_9/wrong3.txt   |     0
 .../Data}/Questions/ingsw/0221_18/correct.txt |     0
 .../Data}/Questions/ingsw/0221_18/quest.txt   |     0
 .../Data}/Questions/ingsw/0221_18/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0221_18/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0221_28/correct.txt |     0
 .../Data}/Questions/ingsw/0221_28/quest.txt   |     0
 .../Data}/Questions/ingsw/0221_28/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0221_28/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0221_32/correct.txt |     0
 .../Data}/Questions/ingsw/0221_32/quest.txt   |     0
 .../Data}/Questions/ingsw/0221_32/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0221_32/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0222_24/correct.txt |     0
 .../Data}/Questions/ingsw/0222_24/quest.txt   |     0
 .../Data}/Questions/ingsw/0222_24/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0222_24/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0222_27/correct.txt |     0
 .../Data}/Questions/ingsw/0222_27/quest.txt   |     0
 .../Data}/Questions/ingsw/0222_27/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0222_27/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0222_33/correct.txt |     0
 .../Data}/Questions/ingsw/0222_33/quest.txt   |     0
 .../Data}/Questions/ingsw/0222_33/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0222_33/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0222_35/correct.txt |     0
 .../Data}/Questions/ingsw/0222_35/quest.txt   |     0
 .../Data}/Questions/ingsw/0222_35/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0222_35/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0222_39/correct.txt |     0
 .../Data}/Questions/ingsw/0222_39/quest.txt   |     0
 .../Data}/Questions/ingsw/0222_39/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0222_39/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0222_41/correct.txt |     0
 .../Data}/Questions/ingsw/0222_41/quest.txt   |     0
 .../Data}/Questions/ingsw/0222_41/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0222_41/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0222_5/correct.txt  |     0
 .../Data}/Questions/ingsw/0222_5/quest.txt    |     0
 .../Data}/Questions/ingsw/0222_5/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0222_5/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0222_50/correct.txt |     0
 .../Data}/Questions/ingsw/0222_50/quest.txt   |     0
 .../Data}/Questions/ingsw/0222_50/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0222_50/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0222_7/correct.txt  |     0
 .../Data}/Questions/ingsw/0222_7/quest.txt    |     0
 .../Data}/Questions/ingsw/0222_7/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0222_7/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0321_1/correct.txt  |     0
 .../Data}/Questions/ingsw/0321_1/quest.txt    |     0
 .../Data}/Questions/ingsw/0321_1/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0321_1/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0321_10/correct.txt |     0
 .../Data}/Questions/ingsw/0321_10/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_10/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_10/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_11/correct.txt |     0
 .../Data}/Questions/ingsw/0321_11/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_11/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_11/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_12/correct.txt |     0
 .../Data}/Questions/ingsw/0321_12/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_12/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_12/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_13/correct.txt |     0
 .../Data}/Questions/ingsw/0321_13/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_13/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_13/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_14/correct.txt |     0
 .../Data}/Questions/ingsw/0321_14/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_14/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_14/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_15/correct.txt |     0
 .../Data}/Questions/ingsw/0321_15/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_15/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_15/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_16/correct.txt |     0
 .../Data}/Questions/ingsw/0321_16/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_16/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_16/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_17/correct.txt |     0
 .../Data}/Questions/ingsw/0321_17/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_17/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_17/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_18/correct.txt |     0
 .../Data}/Questions/ingsw/0321_18/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_18/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_18/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_19/correct.txt |     0
 .../Data}/Questions/ingsw/0321_19/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_19/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_19/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_2/correct.txt  |     0
 .../Data}/Questions/ingsw/0321_2/quest.txt    |     0
 .../Data}/Questions/ingsw/0321_2/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0321_2/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0321_20/correct.txt |     0
 .../Data}/Questions/ingsw/0321_20/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_20/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_20/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_21/correct.txt |     0
 .../Data}/Questions/ingsw/0321_21/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_21/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_21/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_23/correct.txt |     0
 .../Data}/Questions/ingsw/0321_23/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_23/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_23/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_24/correct.txt |     0
 .../Data}/Questions/ingsw/0321_24/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_24/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_24/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_27/correct.txt |     0
 .../Data}/Questions/ingsw/0321_27/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_27/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_27/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_28/correct.txt |     0
 .../Data}/Questions/ingsw/0321_28/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_28/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_28/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_29/correct.txt |     0
 .../Data}/Questions/ingsw/0321_29/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_29/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_29/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_30/correct.txt |     0
 .../Data}/Questions/ingsw/0321_30/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_30/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_30/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_31/correct.txt |     0
 .../Data}/Questions/ingsw/0321_31/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_31/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_31/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_32/correct.txt |     0
 .../Data}/Questions/ingsw/0321_32/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_32/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_32/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_36/correct.txt |     0
 .../Data}/Questions/ingsw/0321_36/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_36/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_36/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_37/correct.txt |     0
 .../Data}/Questions/ingsw/0321_37/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_37/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_37/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_38/correct.txt |     0
 .../Data}/Questions/ingsw/0321_38/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_38/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_38/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_4/correct.txt  |     0
 .../Data}/Questions/ingsw/0321_4/quest.txt    |     0
 .../Data}/Questions/ingsw/0321_4/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0321_4/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0321_40/correct.txt |     0
 .../Data}/Questions/ingsw/0321_40/quest.txt   |     0
 .../Data}/Questions/ingsw/0321_40/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0321_40/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0321_8/correct.txt  |     0
 .../Data}/Questions/ingsw/0321_8/quest.txt    |     0
 .../Data}/Questions/ingsw/0321_8/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0321_8/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0321_9/correct.txt  |     0
 .../Data}/Questions/ingsw/0321_9/quest.txt    |     0
 .../Data}/Questions/ingsw/0321_9/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0321_9/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0324_0/correct.txt  |     0
 .../Data}/Questions/ingsw/0324_0/quest.txt    |     0
 .../Data}/Questions/ingsw/0324_0/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0324_0/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0324_1/quest.txt    |     0
 .../Data}/Questions/ingsw/0324_1/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0324_1/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0324_1/wrong3.txt   |     0
 .../Data}/Questions/ingsw/0324_10/correct.txt |     0
 .../Data}/Questions/ingsw/0324_10/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_10/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_10/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_11/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_11/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_11/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_11/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0324_12/correct.txt |     0
 .../Data}/Questions/ingsw/0324_12/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_12/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_12/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_13/correct.txt |     0
 .../Data}/Questions/ingsw/0324_13/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_13/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_13/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_14/correct.txt |     0
 .../Data}/Questions/ingsw/0324_14/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_14/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_14/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_15/correct.txt |     0
 .../Data}/Questions/ingsw/0324_15/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_15/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_15/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_16/correct.txt |     0
 .../Data}/Questions/ingsw/0324_16/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_16/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_16/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_17/correct.txt |     0
 .../Data}/Questions/ingsw/0324_17/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_17/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_17/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_18/correct.txt |     0
 .../Data}/Questions/ingsw/0324_18/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_18/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_18/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_19/correct.txt |     0
 .../Data}/Questions/ingsw/0324_19/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_19/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_19/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_2/correct.txt  |     0
 .../Data}/Questions/ingsw/0324_2/quest.txt    |     0
 .../Data}/Questions/ingsw/0324_2/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0324_2/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0324_20/correct.txt |     0
 .../Data}/Questions/ingsw/0324_20/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_20/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_20/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_21/correct.txt |     0
 .../Data}/Questions/ingsw/0324_21/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_21/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_21/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_22/correct.txt |     0
 .../Data}/Questions/ingsw/0324_22/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_22/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_22/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_23/correct.txt |     0
 .../Data}/Questions/ingsw/0324_23/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_23/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_23/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_24/correct.txt |     0
 .../Data}/Questions/ingsw/0324_24/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_24/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_24/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_25/correct.txt |     0
 .../Data}/Questions/ingsw/0324_25/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_25/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_25/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_26/quest.txt   |     0
 .../Data/Questions/ingsw/0324_26}/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_26/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_26/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0324_27/correct.txt |     0
 .../Data}/Questions/ingsw/0324_27/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_27/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_27/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_28/correct.txt |     0
 .../Data}/Questions/ingsw/0324_28/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_28/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_28/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_29/correct.txt |     0
 .../Data}/Questions/ingsw/0324_29/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_29/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_29/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_3/correct.txt  |     0
 .../Data}/Questions/ingsw/0324_3/quest.txt    |     0
 .../Data}/Questions/ingsw/0324_3/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0324_3/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0324_30/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_30/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_30/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_30/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0324_31/correct.txt |     0
 .../Data}/Questions/ingsw/0324_31/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_31/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_31/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_32/correct.txt |     0
 .../Data}/Questions/ingsw/0324_32/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_32/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_32/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_33/correct.txt |     0
 .../Data}/Questions/ingsw/0324_33/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_33/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_33/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_34/correct.txt |     0
 .../Data}/Questions/ingsw/0324_34/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_34/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_34/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_35/quest.txt   |     0
 .../Data/Questions/ingsw/0324_35}/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_35/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_35/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0324_36/correct.txt |     0
 .../Data}/Questions/ingsw/0324_36/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_36/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_36/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_37/correct.txt |     0
 .../Data}/Questions/ingsw/0324_37/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_37/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_37/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_38/correct.txt |     0
 .../Data}/Questions/ingsw/0324_38/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_38/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_38/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_39/correct.txt |     0
 .../Data}/Questions/ingsw/0324_39/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_39/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_39/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_4/correct.txt  |     0
 .../Data}/Questions/ingsw/0324_4/quest.txt    |     0
 .../Data}/Questions/ingsw/0324_4/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0324_4/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0324_40/correct.txt |     0
 .../Data}/Questions/ingsw/0324_40/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_40/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_40/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_41/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_41/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_41/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_41/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0324_42/quest.txt   |     0
 .../Data/Questions/ingsw/0324_42}/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_42/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_42/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0324_43/correct.txt |     0
 .../Data}/Questions/ingsw/0324_43/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_43/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_43/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_44/correct.txt |     0
 .../Data}/Questions/ingsw/0324_44/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_44/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_44/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_45/correct.txt |     0
 .../Data}/Questions/ingsw/0324_45/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_45/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_45/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_46/correct.txt |     0
 .../Data}/Questions/ingsw/0324_46/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_46/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_46/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_47/quest.txt   |     0
 .../Data/Questions/ingsw/0324_47}/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_47/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_47/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0324_48/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_48/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_48/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_48/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0324_49/correct.txt |     0
 .../Data}/Questions/ingsw/0324_49/quest.txt   |     0
 .../Data}/Questions/ingsw/0324_49/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0324_49/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0324_5/correct.txt  |     0
 .../Data}/Questions/ingsw/0324_5/quest.txt    |     0
 .../Data}/Questions/ingsw/0324_5/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0324_5/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0324_6/correct.txt  |     0
 .../Data}/Questions/ingsw/0324_6/quest.txt    |     0
 .../Data}/Questions/ingsw/0324_6/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0324_6/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0324_7/correct.txt  |     0
 .../Data}/Questions/ingsw/0324_7/quest.txt    |     0
 .../Data}/Questions/ingsw/0324_7/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0324_7/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0324_8/correct.txt  |     0
 .../Data}/Questions/ingsw/0324_8/quest.txt    |     0
 .../Data}/Questions/ingsw/0324_8/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0324_8/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0324_9/correct.txt  |     0
 .../Data}/Questions/ingsw/0324_9/quest.txt    |     0
 .../Data}/Questions/ingsw/0324_9/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0324_9/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0422-16/correct.txt |     0
 .../Data}/Questions/ingsw/0422-16/quest.txt   |     0
 .../Data}/Questions/ingsw/0422-16/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0422-16/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_0/quest.txt    |     0
 .../Data/Questions/ingsw/0613_0}/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0613_0/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0613_0/wrong3.txt   |     0
 .../Data}/Questions/ingsw/0613_1/correct.txt  |     0
 .../Data}/Questions/ingsw/0613_1/quest.txt    |     0
 .../Data}/Questions/ingsw/0613_1/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0613_1/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0613_10/correct.txt |     0
 .../Data}/Questions/ingsw/0613_10/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_10/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_10/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_11/correct.txt |     0
 .../Data}/Questions/ingsw/0613_11/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_11/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_11/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_12/correct.txt |     0
 .../Data}/Questions/ingsw/0613_12/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_12/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_12/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_13/correct.txt |     0
 .../Data}/Questions/ingsw/0613_13/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_13/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_13/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_14/quest.txt   |     0
 .../Data/Questions/ingsw/0613_14}/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_14/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_14/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0613_15/correct.txt |     0
 .../Data}/Questions/ingsw/0613_15/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_15/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_15/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_16/correct.txt |     0
 .../Data}/Questions/ingsw/0613_16/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_16/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_16/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_17/correct.txt |     0
 .../Data}/Questions/ingsw/0613_17/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_17/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_17/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_18/correct.txt |     0
 .../Data}/Questions/ingsw/0613_18/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_18/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_18/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_19/quest.txt   |     0
 .../Data/Questions/ingsw/0613_19}/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_19/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_19/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0613_2/quest.txt    |     0
 .../Data/Questions/ingsw/0613_2/wrong1.txt    |     0
 .../Data/Questions/ingsw/0613_2/wrong2.txt    |     0
 legacy/Data/Questions/ingsw/0613_2/wrong3.txt |     0
 .../Data}/Questions/ingsw/0613_20/correct.txt |     0
 .../Data}/Questions/ingsw/0613_20/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_20/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_20/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_21/correct.txt |     0
 .../Data}/Questions/ingsw/0613_21/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_21/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_21/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_22/correct.txt |     0
 .../Data}/Questions/ingsw/0613_22/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_22/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_22/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_23/correct.txt |     0
 .../Data}/Questions/ingsw/0613_23/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_23/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_23/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_24/correct.txt |     0
 .../Data}/Questions/ingsw/0613_24/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_24/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_24/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_25/correct.txt |     0
 .../Data}/Questions/ingsw/0613_25/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_25/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_25/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_26/correct.txt |     0
 .../Data}/Questions/ingsw/0613_26/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_26/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_26/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_27/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_27/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_27/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_27/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0613_28/correct.txt |     0
 .../Data}/Questions/ingsw/0613_28/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_28/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_28/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_29/correct.txt |     0
 .../Data}/Questions/ingsw/0613_29/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_29/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_29/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_3/correct.txt  |     0
 .../Data}/Questions/ingsw/0613_3/quest.txt    |     0
 .../Data}/Questions/ingsw/0613_3/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0613_3/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0613_30/correct.txt |     0
 .../Data}/Questions/ingsw/0613_30/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_30/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_30/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_31/correct.txt |     0
 .../Data}/Questions/ingsw/0613_31/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_31/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_31/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_32/correct.txt |     0
 .../Data}/Questions/ingsw/0613_32/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_32/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_32/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_33/correct.txt |     0
 .../Data}/Questions/ingsw/0613_33/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_33/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_33/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_34/correct.txt |     0
 .../Data}/Questions/ingsw/0613_34/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_34/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_34/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_35/correct.txt |     0
 .../Data}/Questions/ingsw/0613_35/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_35/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_35/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_36/correct.txt |     0
 .../Data}/Questions/ingsw/0613_36/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_36/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_36/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_37/correct.txt |     0
 .../Data}/Questions/ingsw/0613_37/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_37/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_37/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_38/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_38/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_38/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_38/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0613_39/correct.txt |     0
 .../Data}/Questions/ingsw/0613_39/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_39/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_39/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_4/correct.txt  |     0
 .../Data}/Questions/ingsw/0613_4/quest.txt    |     0
 .../Data}/Questions/ingsw/0613_4/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0613_4/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0613_40/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_40/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_40/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_40/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0613_41/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_41/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_41/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_41/wrong3.txt  |     0
 .../Data}/Questions/ingsw/0613_42/correct.txt |     0
 .../Data}/Questions/ingsw/0613_42/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_42/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_42/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_43/correct.txt |     0
 .../Data}/Questions/ingsw/0613_43/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_43/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_43/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_44/correct.txt |     0
 .../Data}/Questions/ingsw/0613_44/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_44/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_44/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_45/correct.txt |     0
 .../Data}/Questions/ingsw/0613_45/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_45/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_45/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_46/correct.txt |     0
 .../Data}/Questions/ingsw/0613_46/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_46/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_46/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_47/correct.txt |     0
 .../Data}/Questions/ingsw/0613_47/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_47/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_47/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_48/correct.txt |     0
 .../Data}/Questions/ingsw/0613_48/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_48/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_48/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_49/correct.txt |     0
 .../Data}/Questions/ingsw/0613_49/quest.txt   |     0
 .../Data}/Questions/ingsw/0613_49/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0613_49/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0613_5/correct.txt  |     0
 .../Data}/Questions/ingsw/0613_5/quest.txt    |     0
 .../Data}/Questions/ingsw/0613_5/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0613_5/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0613_6/correct.txt  |     0
 .../Data}/Questions/ingsw/0613_6/quest.txt    |     0
 .../Data}/Questions/ingsw/0613_6/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0613_6/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0613_7/correct.txt  |     0
 .../Data}/Questions/ingsw/0613_7/quest.txt    |     0
 .../Data}/Questions/ingsw/0613_7/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0613_7/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0613_8/correct.txt  |     0
 .../Data}/Questions/ingsw/0613_8/quest.txt    |     0
 .../Data}/Questions/ingsw/0613_8/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0613_8/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0613_9/quest.txt    |     0
 .../Data}/Questions/ingsw/0613_9/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0613_9/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0613_9/wrong3.txt   |     0
 .../Data}/Questions/ingsw/0621_0/correct.txt  |     0
 .../Data}/Questions/ingsw/0621_0/quest.txt    |     0
 .../Data}/Questions/ingsw/0621_0/wrong0.txt   |     0
 .../Data}/Questions/ingsw/0621_0/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0621_1/correct.txt  |     0
 .../Data}/Questions/ingsw/0621_1/quest.txt    |     0
 .../Data}/Questions/ingsw/0621_1/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0621_1/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0621_10/correct.txt |     0
 .../Data}/Questions/ingsw/0621_10/quest.txt   |     0
 .../Data}/Questions/ingsw/0621_10/wrong0.txt  |     0
 .../Data}/Questions/ingsw/0621_10/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0621_13/correct.txt |     0
 .../Data}/Questions/ingsw/0621_13/quest.txt   |     0
 .../Data}/Questions/ingsw/0621_13/wrong0.txt  |     0
 .../Data}/Questions/ingsw/0621_13/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0621_14/correct.txt |     0
 .../Data}/Questions/ingsw/0621_14/quest.txt   |     0
 .../Data}/Questions/ingsw/0621_14/wrong0.txt  |     0
 .../Data}/Questions/ingsw/0621_14/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0621_17/correct.txt |     0
 .../Data}/Questions/ingsw/0621_17/quest.txt   |     0
 .../Data}/Questions/ingsw/0621_17/wrong0.txt  |     0
 .../Data}/Questions/ingsw/0621_17/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0621_19/correct.txt |     0
 .../Data}/Questions/ingsw/0621_19/quest.txt   |     0
 .../Data}/Questions/ingsw/0621_19/wrong0.txt  |     0
 .../Data}/Questions/ingsw/0621_19/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0621_2/correct.txt  |     0
 .../Data}/Questions/ingsw/0621_2/quest.txt    |     0
 .../Data}/Questions/ingsw/0621_2/wrong0.txt   |     0
 .../Data}/Questions/ingsw/0621_2/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0621_21/correct.txt |     0
 .../Data}/Questions/ingsw/0621_21/quest.txt   |     0
 .../Data}/Questions/ingsw/0621_21/wrong0.txt  |     0
 .../Data}/Questions/ingsw/0621_21/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0621_22/correct.txt |     0
 .../Data}/Questions/ingsw/0621_22/quest.txt   |     0
 .../Data}/Questions/ingsw/0621_22/wrong0.txt  |     0
 .../Data}/Questions/ingsw/0621_22/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0621_24/correct.txt |     0
 .../Data}/Questions/ingsw/0621_24/quest.txt   |     0
 .../Data}/Questions/ingsw/0621_24/wrong0.txt  |     0
 .../Data}/Questions/ingsw/0621_24/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0621_3/correct.txt  |     0
 .../Data}/Questions/ingsw/0621_3/quest.txt    |     0
 .../Data}/Questions/ingsw/0621_3/wrong0.txt   |     0
 .../Data}/Questions/ingsw/0621_3/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0621_32/correct.txt |     0
 .../Data}/Questions/ingsw/0621_32/quest.txt   |     0
 .../Data}/Questions/ingsw/0621_32/wrong0.txt  |     0
 .../Data}/Questions/ingsw/0621_32/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0621_35/correct.txt |     0
 .../Data}/Questions/ingsw/0621_35/quest.txt   |     0
 .../Data}/Questions/ingsw/0621_35/wrong0.txt  |     0
 .../Data}/Questions/ingsw/0621_35/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0621_36/correct.txt |     0
 .../Data}/Questions/ingsw/0621_36/quest.txt   |     0
 .../Data}/Questions/ingsw/0621_36/wrong0.txt  |     0
 .../Data}/Questions/ingsw/0621_36/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0621_39/correct.txt |     0
 .../Data}/Questions/ingsw/0621_39/quest.txt   |     0
 .../Data}/Questions/ingsw/0621_39/wrong0.txt  |     0
 .../Data}/Questions/ingsw/0621_39/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0621_6/correct.txt  |     0
 .../Data}/Questions/ingsw/0621_6/quest.txt    |     0
 .../Data}/Questions/ingsw/0621_6/wrong0.txt   |     0
 .../Data}/Questions/ingsw/0621_6/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0621_6/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0621_9/correct.txt  |     0
 .../Data}/Questions/ingsw/0621_9/quest.txt    |     0
 .../Data}/Questions/ingsw/0621_9/wrong0.txt   |     0
 .../Data}/Questions/ingsw/0621_9/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0622_1/correct.txt  |     0
 .../Data}/Questions/ingsw/0622_1/quest.txt    |     0
 .../Data}/Questions/ingsw/0622_1/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0622_1/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0622_2/correct.txt  |     0
 .../Data}/Questions/ingsw/0622_2/quest.txt    |     0
 .../Data}/Questions/ingsw/0622_2/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0622_2/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0622_3/correct.txt  |     0
 .../Data}/Questions/ingsw/0622_3/quest.txt    |     0
 .../Data}/Questions/ingsw/0622_3/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0622_3/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0622_4/correct.txt  |     0
 .../Data}/Questions/ingsw/0622_4/quest.txt    |     0
 .../Data}/Questions/ingsw/0622_4/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0622_4/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0622_5/correct.txt  |     0
 .../Data}/Questions/ingsw/0622_5/quest.txt    |     0
 .../Data}/Questions/ingsw/0622_5/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0622_5/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0622_6/correct.txt  |     0
 .../Data}/Questions/ingsw/0622_6/quest.txt    |     0
 .../Data}/Questions/ingsw/0622_6/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0622_6/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0622_7/correct.txt  |     0
 .../Data}/Questions/ingsw/0622_7/quest.txt    |     0
 .../Data}/Questions/ingsw/0622_7/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0622_7/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0622_8/correct.txt  |     0
 .../Data}/Questions/ingsw/0622_8/quest.txt    |     0
 .../Data}/Questions/ingsw/0622_8/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0622_8/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0622_9/correct.txt  |     0
 .../Data}/Questions/ingsw/0622_9/quest.txt    |     0
 .../Data}/Questions/ingsw/0622_9/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0622_9/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0721_1/correct.txt  |     0
 .../Data}/Questions/ingsw/0721_1/quest.txt    |     0
 .../Data}/Questions/ingsw/0721_1/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0721_1/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0721_10/correct.txt |     0
 .../Data}/Questions/ingsw/0721_10/quest.txt   |     0
 .../Data}/Questions/ingsw/0721_10/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0721_10/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0721_13/correct.txt |     0
 .../Data}/Questions/ingsw/0721_13/quest.txt   |     0
 .../Data}/Questions/ingsw/0721_13/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0721_13/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0721_15/correct.txt |     0
 .../Data}/Questions/ingsw/0721_15/quest.txt   |     0
 .../Data}/Questions/ingsw/0721_15/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0721_15/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0721_17/correct.txt |     0
 .../Data}/Questions/ingsw/0721_17/quest.txt   |     0
 .../Data}/Questions/ingsw/0721_17/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0721_17/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0721_18/correct.txt |     0
 .../Data}/Questions/ingsw/0721_18/quest.txt   |     0
 .../Data}/Questions/ingsw/0721_18/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0721_18/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0721_19/correct.txt |     0
 .../Data}/Questions/ingsw/0721_19/quest.txt   |     0
 .../Data}/Questions/ingsw/0721_19/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0721_19/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0721_21/correct.txt |     0
 .../Data}/Questions/ingsw/0721_21/quest.txt   |     0
 .../Data}/Questions/ingsw/0721_21/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0721_21/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0721_28/correct.txt |     0
 .../Data}/Questions/ingsw/0721_28/quest.txt   |     0
 .../Data}/Questions/ingsw/0721_28/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0721_28/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0721_29/correct.txt |     0
 .../Data}/Questions/ingsw/0721_29/quest.txt   |     0
 .../Data}/Questions/ingsw/0721_29/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0721_29/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0721_32/correct.txt |     0
 .../Data}/Questions/ingsw/0721_32/quest.txt   |     0
 .../Data}/Questions/ingsw/0721_32/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0721_32/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0721_33/correct.txt |     0
 .../Data}/Questions/ingsw/0721_33/quest.txt   |     0
 .../Data}/Questions/ingsw/0721_33/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0721_33/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0721_34/correct.txt |     0
 .../Data}/Questions/ingsw/0721_34/quest.txt   |     0
 .../Data}/Questions/ingsw/0721_34/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0721_34/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0721_36/correct.txt |     0
 .../Data}/Questions/ingsw/0721_36/quest.txt   |     0
 .../Data}/Questions/ingsw/0721_36/wrong1.txt  |     0
 .../Data}/Questions/ingsw/0721_36/wrong2.txt  |     0
 .../Data}/Questions/ingsw/0721_4/correct.txt  |     0
 .../Data}/Questions/ingsw/0721_4/quest.txt    |     0
 .../Data}/Questions/ingsw/0721_4/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0721_4/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0721_5/correct.txt  |     0
 .../Data}/Questions/ingsw/0721_5/quest.txt    |     0
 .../Data}/Questions/ingsw/0721_5/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0721_5/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0721_6/correct.txt  |     0
 .../Data}/Questions/ingsw/0721_6/quest.txt    |     0
 .../Data}/Questions/ingsw/0721_6/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0721_6/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0721_8/correct.txt  |     0
 .../Data}/Questions/ingsw/0721_8/quest.txt    |     0
 .../Data}/Questions/ingsw/0721_8/wrong1.txt   |     0
 .../Data}/Questions/ingsw/0721_8/wrong2.txt   |     0
 .../Data}/Questions/ingsw/0722_1/correct.txt  |     0
 .../Data}/Questions/ingsw/0722_1/quest.txt    |     0
 .../Data}/Questions/ingsw/0722_1/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0722_1/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0722_10/correct.txt |     0
 .../Data}/Questions/ingsw/0722_10/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_10/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_10/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_11/correct.txt |     0
 .../Data}/Questions/ingsw/0722_11/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_11/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_11/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_12/correct.txt |     0
 .../Data}/Questions/ingsw/0722_12/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_12/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_12/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_13/correct.txt |     0
 .../Data}/Questions/ingsw/0722_13/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_13/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_13/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_14/correct.txt |     0
 .../Data}/Questions/ingsw/0722_14/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_14/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_14/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_15/correct.txt |     0
 .../Data}/Questions/ingsw/0722_15/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_15/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_15/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_16/correct.txt |     0
 .../Data}/Questions/ingsw/0722_16/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_16/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_16/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_17/correct.txt |     0
 .../Data}/Questions/ingsw/0722_17/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_17/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_17/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_18/correct.txt |     0
 .../Data}/Questions/ingsw/0722_18/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_18/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_18/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_19/correct.txt |     0
 .../Data}/Questions/ingsw/0722_19/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_19/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_19/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_2/correct.txt  |     0
 .../Data}/Questions/ingsw/0722_2/quest.txt    |     0
 .../Data}/Questions/ingsw/0722_2/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0722_2/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0722_20/correct.txt |     0
 .../Data}/Questions/ingsw/0722_20/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_20/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_20/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_21/correct.txt |     0
 .../Data}/Questions/ingsw/0722_21/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_21/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_21/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_22/correct.txt |     0
 .../Data}/Questions/ingsw/0722_22/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_22/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_22/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_23/correct.txt |     0
 .../Data}/Questions/ingsw/0722_23/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_23/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_23/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_24/correct.txt |     0
 .../Data}/Questions/ingsw/0722_24/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_24/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_24/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_25/correct.txt |     0
 .../Data}/Questions/ingsw/0722_25/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_25/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_25/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_26/correct.txt |     0
 .../Data}/Questions/ingsw/0722_26/quest.txt   |     0
 .../Data}/Questions/ingsw/0722_26/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0722_26/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0722_3/correct.txt  |     0
 .../Data}/Questions/ingsw/0722_3/quest.txt    |     0
 .../Data}/Questions/ingsw/0722_3/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0722_3/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0722_4/correct.txt  |     0
 .../Data}/Questions/ingsw/0722_4/quest.txt    |     0
 .../Data}/Questions/ingsw/0722_4/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0722_4/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0722_5/correct.txt  |     0
 .../Data}/Questions/ingsw/0722_5/quest.txt    |     0
 .../Data}/Questions/ingsw/0722_5/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0722_5/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0722_6/correct.txt  |     0
 .../Data}/Questions/ingsw/0722_6/quest.txt    |     0
 .../Data}/Questions/ingsw/0722_6/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0722_6/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0722_7/correct.txt  |     0
 .../Data}/Questions/ingsw/0722_7/quest.txt    |     0
 .../Data}/Questions/ingsw/0722_7/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0722_7/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0722_8/correct.txt  |     0
 .../Data}/Questions/ingsw/0722_8/quest.txt    |     0
 .../Data}/Questions/ingsw/0722_8/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0722_8/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0722_9/correct.txt  |     0
 .../Data}/Questions/ingsw/0722_9/quest.txt    |     0
 .../Data}/Questions/ingsw/0722_9/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0722_9/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0922_10/correct.txt |     0
 .../Data}/Questions/ingsw/0922_10/quest.txt   |     0
 .../Data}/Questions/ingsw/0922_10/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0922_10/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0922_11/correct.txt |     0
 .../Data}/Questions/ingsw/0922_11/quest.txt   |     0
 .../Data}/Questions/ingsw/0922_11/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0922_11/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0922_12/correct.txt |     0
 .../Data}/Questions/ingsw/0922_12/quest.txt   |     0
 .../Data}/Questions/ingsw/0922_12/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0922_12/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0922_13/correct.txt |     0
 .../Data}/Questions/ingsw/0922_13/quest.txt   |     0
 .../Data}/Questions/ingsw/0922_13/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0922_13/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0922_14/correct.txt |     0
 .../Data}/Questions/ingsw/0922_14/quest.txt   |     0
 .../Data}/Questions/ingsw/0922_14/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0922_14/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0922_15/correct.txt |     0
 .../Data}/Questions/ingsw/0922_15/quest.txt   |     0
 .../Data}/Questions/ingsw/0922_15/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0922_15/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0922_16/correct.txt |     0
 .../Data}/Questions/ingsw/0922_16/quest.txt   |     0
 .../Data}/Questions/ingsw/0922_16/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0922_16/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0922_17/correct.txt |     0
 .../Data}/Questions/ingsw/0922_17/quest.txt   |     0
 .../Data}/Questions/ingsw/0922_17/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0922_17/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0922_18/correct.txt |     0
 .../Data}/Questions/ingsw/0922_18/quest.txt   |     0
 .../Data}/Questions/ingsw/0922_18/wrong 1.txt |     0
 .../Data}/Questions/ingsw/0922_18/wrong 2.txt |     0
 .../Data}/Questions/ingsw/0922_3/correct.txt  |     0
 .../Data}/Questions/ingsw/0922_3/quest.txt    |     0
 .../Data}/Questions/ingsw/0922_3/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0922_3/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0922_4/correct.txt  |     0
 .../Data}/Questions/ingsw/0922_4/quest.txt    |     0
 .../Data}/Questions/ingsw/0922_4/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0922_4/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0922_5/correct.txt  |     0
 .../Data}/Questions/ingsw/0922_5/quest.txt    |     0
 .../Data}/Questions/ingsw/0922_5/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0922_5/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0922_6/correct.txt  |     0
 .../Data}/Questions/ingsw/0922_6/quest.txt    |     0
 .../Data}/Questions/ingsw/0922_6/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0922_6/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0922_7/correct.txt  |     0
 .../Data}/Questions/ingsw/0922_7/quest.txt    |     0
 .../Data}/Questions/ingsw/0922_7/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0922_7/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0922_8/correct.txt  |     0
 .../Data}/Questions/ingsw/0922_8/quest.txt    |     0
 .../Data}/Questions/ingsw/0922_8/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0922_8/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/0922_9/correct.txt  |     0
 .../Data}/Questions/ingsw/0922_9/quest.txt    |     0
 .../Data}/Questions/ingsw/0922_9/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/0922_9/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/10/correct.txt      |     0
 .../Data}/Questions/ingsw/10/quest.txt        |     0
 .../Data}/Questions/ingsw/10/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/10/wrong.txt        |     0
 .../Data}/Questions/ingsw/11/correct.txt      |     0
 .../Data}/Questions/ingsw/11/quest.txt        |     0
 .../Data}/Questions/ingsw/11/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/11/wrong.txt        |     0
 .../Data}/Questions/ingsw/1122_1/correct.txt  |     0
 .../Data}/Questions/ingsw/1122_1/quest.txt    |     0
 .../Data}/Questions/ingsw/1122_1/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/1122_1/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/1122_10/correct.txt |     0
 .../Data}/Questions/ingsw/1122_10/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_10/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_10/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_11/correct.txt |     0
 .../Data}/Questions/ingsw/1122_11/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_11/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_11/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_12/correct.txt |     0
 .../Data}/Questions/ingsw/1122_12/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_12/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_12/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_13/correct.txt |     0
 .../Data}/Questions/ingsw/1122_13/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_13/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_13/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_14/correct.txt |     0
 .../Data}/Questions/ingsw/1122_14/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_14/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_14/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_15/correct.txt |     0
 .../Data}/Questions/ingsw/1122_15/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_15/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_15/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_16/correct.txt |     0
 .../Data}/Questions/ingsw/1122_16/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_16/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_16/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_19/correct.txt |     0
 .../Data}/Questions/ingsw/1122_19/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_19/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_19/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_2/correct.txt  |     0
 .../Data}/Questions/ingsw/1122_2/quest.txt    |     0
 .../Data}/Questions/ingsw/1122_2/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/1122_2/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/1122_20/correct.txt |     0
 .../Data}/Questions/ingsw/1122_20/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_20/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_20/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_21/correct.txt |     0
 .../Data}/Questions/ingsw/1122_21/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_21/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_21/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_22/correct.txt |     0
 .../Data}/Questions/ingsw/1122_22/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_22/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_22/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_23/correct.txt |     0
 .../Data}/Questions/ingsw/1122_23/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_23/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_23/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_24/correct.txt |     0
 .../Data}/Questions/ingsw/1122_24/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_24/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_24/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_25/correct.txt |     0
 .../Data}/Questions/ingsw/1122_25/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_25/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_25/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_26/correct.txt |     0
 .../Data}/Questions/ingsw/1122_26/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_26/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_26/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_27/correct.txt |     0
 .../Data}/Questions/ingsw/1122_27/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_27/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_27/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_28/correct.txt |     0
 .../Data}/Questions/ingsw/1122_28/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_28/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_28/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_29/correct.txt |     0
 .../Data}/Questions/ingsw/1122_29/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_29/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_29/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_3/correct.txt  |     0
 .../Data}/Questions/ingsw/1122_3/quest.txt    |     0
 .../Data}/Questions/ingsw/1122_3/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/1122_3/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/1122_30/correct.txt |     0
 .../Data}/Questions/ingsw/1122_30/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_30/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_30/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_31/correct.txt |     0
 .../Data}/Questions/ingsw/1122_31/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_31/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_31/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_33/correct.txt |     0
 .../Data}/Questions/ingsw/1122_33/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_33/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_33/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_34/correct.txt |     0
 .../Data}/Questions/ingsw/1122_34/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_34/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_34/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_35/correct.txt |     0
 .../Data}/Questions/ingsw/1122_35/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_35/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_35/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_36/correct.txt |     0
 .../Data}/Questions/ingsw/1122_36/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_36/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_36/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_37/correct.txt |     0
 .../Data}/Questions/ingsw/1122_37/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_37/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_37/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_38/correct.txt |     0
 .../Data}/Questions/ingsw/1122_38/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_38/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_38/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_39/correct.txt |     0
 .../Data}/Questions/ingsw/1122_39/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_39/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_39/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_4/correct.txt  |     0
 .../Data}/Questions/ingsw/1122_4/quest.txt    |     0
 .../Data}/Questions/ingsw/1122_4/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/1122_4/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/1122_40/correct.txt |     0
 .../Data}/Questions/ingsw/1122_40/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_40/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_40/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_42/correct.txt |     0
 .../Data}/Questions/ingsw/1122_42/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_42/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_42/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_43/correct.txt |     0
 .../Data}/Questions/ingsw/1122_43/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_43/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_43/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_44/correct.txt |     0
 .../Data}/Questions/ingsw/1122_44/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_44/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_44/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_45/correct.txt |     0
 .../Data}/Questions/ingsw/1122_45/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_45/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_45/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_46/correct.txt |     0
 .../Data}/Questions/ingsw/1122_46/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_46/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_46/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_47/correct.txt |     0
 .../Data}/Questions/ingsw/1122_47/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_47/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_47/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_48/correct.txt |     0
 .../Data}/Questions/ingsw/1122_48/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_48/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_48/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_49/correct.txt |     0
 .../Data}/Questions/ingsw/1122_49/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_49/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_49/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_5/correct.txt  |     0
 .../Data}/Questions/ingsw/1122_5/quest.txt    |     0
 .../Data}/Questions/ingsw/1122_5/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/1122_5/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/1122_50/correct.txt |     0
 .../Data}/Questions/ingsw/1122_50/quest.txt   |     0
 .../Data}/Questions/ingsw/1122_50/wrong 1.txt |     0
 .../Data}/Questions/ingsw/1122_50/wrong 2.txt |     0
 .../Data}/Questions/ingsw/1122_6/correct.txt  |     0
 .../Data}/Questions/ingsw/1122_6/quest.txt    |     0
 .../Data}/Questions/ingsw/1122_6/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/1122_6/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/1122_7/correct.txt  |     0
 .../Data}/Questions/ingsw/1122_7/quest.txt    |     0
 .../Data}/Questions/ingsw/1122_7/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/1122_7/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/1122_8/correct.txt  |     0
 .../Data}/Questions/ingsw/1122_8/quest.txt    |     0
 .../Data}/Questions/ingsw/1122_8/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/1122_8/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/1122_9/correct.txt  |     0
 .../Data}/Questions/ingsw/1122_9/quest.txt    |     0
 .../Data}/Questions/ingsw/1122_9/wrong 1.txt  |     0
 .../Data}/Questions/ingsw/1122_9/wrong 2.txt  |     0
 .../Data}/Questions/ingsw/12/correct.txt      |     0
 .../Data}/Questions/ingsw/12/quest.txt        |     0
 .../Data}/Questions/ingsw/12/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/12/wrong.txt        |     0
 .../Data}/Questions/ingsw/16/correct.txt      |     0
 .../Data}/Questions/ingsw/16/quest.txt        |     0
 .../Data}/Questions/ingsw/16/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/16/wrong.txt        |     0
 .../Data}/Questions/ingsw/17/correct.txt      |     0
 .../Data}/Questions/ingsw/17/quest.txt        |     0
 .../Data}/Questions/ingsw/17/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/17/wrong.txt        |     0
 .../Data}/Questions/ingsw/19/correct.txt      |     0
 .../Data}/Questions/ingsw/19/quest.txt        |     0
 .../Data}/Questions/ingsw/19/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/19/wrong.txt        |     0
 .../Data}/Questions/ingsw/2/correct.txt       |     0
 .../Data}/Questions/ingsw/2/quest.txt         |     0
 .../Data}/Questions/ingsw/2/wrong 2.txt       |     0
 .../Data}/Questions/ingsw/2/wrong.txt         |     0
 .../Data}/Questions/ingsw/20/correct.txt      |     0
 .../Data}/Questions/ingsw/20/quest.txt        |     0
 .../Data}/Questions/ingsw/20/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/20/wrong.txt        |     0
 .../Data}/Questions/ingsw/21/correct.txt      |     0
 .../Data}/Questions/ingsw/21/quest.txt        |     0
 .../Data}/Questions/ingsw/21/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/21/wrong.txt        |     0
 .../Data}/Questions/ingsw/22/correct.txt      |     0
 .../Data}/Questions/ingsw/22/quest.txt        |     0
 .../Data}/Questions/ingsw/22/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/22/wrong.txt        |     0
 .../Data}/Questions/ingsw/24/correct.txt      |     0
 .../Data}/Questions/ingsw/24/quest.txt        |     0
 .../Data}/Questions/ingsw/24/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/24/wrong.txt        |     0
 .../Data}/Questions/ingsw/25/correct.txt      |     0
 .../Data}/Questions/ingsw/25/quest.txt        |     0
 .../Data}/Questions/ingsw/25/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/25/wrong.txt        |     0
 .../Data}/Questions/ingsw/26/correct.txt      |     0
 .../Data}/Questions/ingsw/26/quest.txt        |     0
 .../Data}/Questions/ingsw/26/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/26/wrong.txt        |     0
 .../Data}/Questions/ingsw/32/correct.txt      |     0
 .../Data}/Questions/ingsw/32/quest.txt        |     0
 .../Data}/Questions/ingsw/32/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/32/wrong.txt        |     0
 .../Data}/Questions/ingsw/33/correct.txt      |     0
 .../Data}/Questions/ingsw/33/quest.txt        |     0
 .../Data}/Questions/ingsw/33/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/33/wrong.txt        |     0
 .../Data}/Questions/ingsw/34/correct.txt      |     0
 .../Data}/Questions/ingsw/34/quest.txt        |     0
 .../Data}/Questions/ingsw/34/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/34/wrong.txt        |     0
 .../Data}/Questions/ingsw/35/correct.txt      |     0
 .../Data}/Questions/ingsw/35/quest.txt        |     0
 .../Data}/Questions/ingsw/35/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/35/wrong.txt        |     0
 .../Data}/Questions/ingsw/39/correct.txt      |     0
 .../Data}/Questions/ingsw/39/quest.txt        |     0
 .../Data}/Questions/ingsw/39/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/39/wrong.txt        |     0
 .../Data}/Questions/ingsw/4/correct.txt       |     0
 .../Data}/Questions/ingsw/4/quest.txt         |     0
 .../Data}/Questions/ingsw/4/wrong 2.txt       |     0
 .../Data}/Questions/ingsw/4/wrong.txt         |     0
 .../Data}/Questions/ingsw/43/correct.txt      |     0
 .../Data}/Questions/ingsw/43/quest.txt        |     0
 .../Data}/Questions/ingsw/43/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/43/wrong.txt        |     0
 .../Data}/Questions/ingsw/44/correct.txt      |     0
 .../Data}/Questions/ingsw/44/quest.txt        |     0
 .../Data}/Questions/ingsw/44/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/44/wrong.txt        |     0
 .../Data}/Questions/ingsw/45/correct.txt      |     0
 .../Data}/Questions/ingsw/45/quest.txt        |     0
 .../Data}/Questions/ingsw/45/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/45/wrong.txt        |     0
 .../Data}/Questions/ingsw/46/correct.txt      |     0
 .../Data}/Questions/ingsw/46/quest.txt        |     0
 .../Data}/Questions/ingsw/46/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/46/wrong.txt        |     0
 .../Data}/Questions/ingsw/47/correct.txt      |     0
 .../Data}/Questions/ingsw/47/quest.txt        |     0
 .../Data}/Questions/ingsw/47/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/47/wrong.txt        |     0
 .../Data}/Questions/ingsw/48/correct.txt      |     0
 .../Data}/Questions/ingsw/48/quest.txt        |     0
 .../Data}/Questions/ingsw/48/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/48/wrong.txt        |     0
 .../Data}/Questions/ingsw/49/correct.txt      |     0
 .../Data}/Questions/ingsw/49/quest.txt        |     0
 .../Data}/Questions/ingsw/49/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/49/wrong.txt        |     0
 .../Data}/Questions/ingsw/5/correct.txt       |     0
 .../Data}/Questions/ingsw/5/quest.txt         |     0
 .../Data}/Questions/ingsw/5/wrong 2.txt       |     0
 .../Data}/Questions/ingsw/5/wrong.txt         |     0
 .../Data}/Questions/ingsw/50/correct.txt      |     0
 .../Data}/Questions/ingsw/50/quest.txt        |     0
 .../Data}/Questions/ingsw/50/wrong 2.txt      |     0
 .../Data}/Questions/ingsw/50/wrong.txt        |     0
 .../Data}/Questions/ingsw/69420/correct.txt   |     0
 .../Data}/Questions/ingsw/69420/quest.txt     |     0
 .../Data}/Questions/ingsw/69420/wrong 2.txt   |     0
 .../Data}/Questions/ingsw/69420/wrong 3.txt   |     0
 .../Data}/Questions/ingsw/69420/wrong.txt     |     0
 .../Data}/Questions/ingsw/8/correct.txt       |     0
 .../Data}/Questions/ingsw/8/quest.txt         |     0
 .../Data}/Questions/ingsw/8/wrong 2.txt       |     0
 .../Data}/Questions/ingsw/8/wrong.txt         |     0
 .../Data}/Questions/ingsw/9/correct.txt       |     0
 .../Data}/Questions/ingsw/9/quest.txt         |     0
 .../Data}/Questions/ingsw/9/wrong 2.txt       |     0
 .../Data}/Questions/ingsw/9/wrong.txt         |     0
 {Data => legacy/Data}/Questions/ium_unive.txt |     0
 {Data => legacy/Data}/Questions/ogas.txt      |     0
 {Data => legacy/Data}/Questions/sicurezza.txt |     0
 .../Data}/Questions/sicurezza_appello1.txt    |     0
 {Data => legacy/Data}/Questions/so1.txt       |     0
 {Data => legacy/Data}/Questions/so1_new.json  |     0
 {Data => legacy/Data}/Questions/so1_unive.txt |     0
 {Data => legacy/Data}/Questions/so2.txt       |     0
 legacy/Data/ingsw/0000_102/correct.txt        |     1 +
 legacy/Data/ingsw/0000_102/quest.txt          |    20 +
 legacy/Data/ingsw/0000_102/wrong1.txt         |     1 +
 legacy/Data/ingsw/0000_102/wrong2.txt         |     1 +
 legacy/Data/ingsw/0000_2/correct.txt          |     1 +
 legacy/Data/ingsw/0000_2/quest.txt            |    57 +
 legacy/Data/ingsw/0000_2/wrong1.txt           |     1 +
 legacy/Data/ingsw/0000_2/wrong2.txt           |     1 +
 legacy/Data/ingsw/0000_3/correct.txt          |     1 +
 legacy/Data/ingsw/0000_3/quest.txt            |    45 +
 legacy/Data/ingsw/0000_3/wrong1.txt           |     1 +
 legacy/Data/ingsw/0000_3/wrong2.txt           |     1 +
 legacy/Data/ingsw/0000_32/correct.txt         |     1 +
 legacy/Data/ingsw/0000_32/quest.txt           |    22 +
 legacy/Data/ingsw/0000_32/wrong1.txt          |     1 +
 legacy/Data/ingsw/0000_32/wrong2.txt          |     1 +
 legacy/Data/ingsw/0000_4/correct.txt          |     1 +
 legacy/Data/ingsw/0000_4/quest.txt            |     1 +
 legacy/Data/ingsw/0000_4/wrong1.txt           |     1 +
 legacy/Data/ingsw/0000_4/wrong2.txt           |     1 +
 legacy/Data/ingsw/0000_7/correct.txt          |     1 +
 legacy/Data/ingsw/0000_7/quest.txt            |     1 +
 legacy/Data/ingsw/0000_7/wrong1.txt           |     1 +
 legacy/Data/ingsw/0000_7/wrong2.txt           |     1 +
 legacy/Data/ingsw/0000_8/correct.txt          |     1 +
 legacy/Data/ingsw/0000_8/quest.txt            |     1 +
 legacy/Data/ingsw/0000_8/wrong1.txt           |     1 +
 legacy/Data/ingsw/0000_8/wrong2.txt           |     1 +
 legacy/Data/ingsw/0120_0/correct.txt          |     1 +
 legacy/Data/ingsw/0120_0/quest.txt            |    11 +
 legacy/Data/ingsw/0120_0/wrong1.txt           |     1 +
 legacy/Data/ingsw/0120_0/wrong2.txt           |     1 +
 legacy/Data/ingsw/0120_1/correct.txt          |    17 +
 legacy/Data/ingsw/0120_1/quest.txt            |     3 +
 legacy/Data/ingsw/0120_1/wrong1.txt           |    17 +
 legacy/Data/ingsw/0120_1/wrong2.txt           |    17 +
 legacy/Data/ingsw/0120_10/correct.txt         |    16 +
 legacy/Data/ingsw/0120_10/quest.txt           |     4 +
 legacy/Data/ingsw/0120_10/wrong1.txt          |    16 +
 legacy/Data/ingsw/0120_10/wrong2.txt          |    16 +
 legacy/Data/ingsw/0120_11/correct.txt         |     1 +
 legacy/Data/ingsw/0120_11/quest.txt           |     1 +
 legacy/Data/ingsw/0120_11/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_11/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_12/correct.txt         |     1 +
 legacy/Data/ingsw/0120_12/quest.txt           |     6 +
 legacy/Data/ingsw/0120_12/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_12/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_13/correct.txt         |     1 +
 legacy/Data/ingsw/0120_13/quest.txt           |     9 +
 legacy/Data/ingsw/0120_13/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_13/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_14/correct.txt         |     1 +
 legacy/Data/ingsw/0120_14/quest.txt           |     6 +
 legacy/Data/ingsw/0120_14/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_14/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_15/correct.txt         |     1 +
 legacy/Data/ingsw/0120_15/quest.txt           |     1 +
 legacy/Data/ingsw/0120_15/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_15/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_16/correct.txt         |     1 +
 legacy/Data/ingsw/0120_16/quest.txt           |     2 +
 legacy/Data/ingsw/0120_16/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_16/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_17/correct.txt         |     1 +
 legacy/Data/ingsw/0120_17/quest.txt           |     9 +
 legacy/Data/ingsw/0120_17/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_17/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_18/correct.txt         |     1 +
 legacy/Data/ingsw/0120_18/quest.txt           |     9 +
 legacy/Data/ingsw/0120_18/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_18/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_19/correct.txt         |     1 +
 legacy/Data/ingsw/0120_19/quest.txt           |     1 +
 legacy/Data/ingsw/0120_19/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_19/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_2/correct.txt          |     1 +
 legacy/Data/ingsw/0120_2/quest.txt            |    11 +
 legacy/Data/ingsw/0120_2/wrong1.txt           |     1 +
 legacy/Data/ingsw/0120_2/wrong2.txt           |     1 +
 legacy/Data/ingsw/0120_20/correct.txt         |     1 +
 legacy/Data/ingsw/0120_20/quest.txt           |     8 +
 legacy/Data/ingsw/0120_20/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_20/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_21/correct.txt         |     1 +
 legacy/Data/ingsw/0120_21/quest.txt           |     2 +
 legacy/Data/ingsw/0120_21/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_21/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_22/quest.txt           |    32 +
 legacy/Data/ingsw/0120_22/wrong1.txt          |     0
 legacy/Data/ingsw/0120_22/wrong2.txt          |     0
 legacy/Data/ingsw/0120_22/wrong3.txt          |     0
 legacy/Data/ingsw/0120_23/correct.txt         |     1 +
 legacy/Data/ingsw/0120_23/quest.txt           |    11 +
 legacy/Data/ingsw/0120_23/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_23/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_24/correct.txt         |     1 +
 legacy/Data/ingsw/0120_24/quest.txt           |     9 +
 legacy/Data/ingsw/0120_24/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_24/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_25/quest.txt           |    37 +
 legacy/Data/ingsw/0120_25/wrong1.txt          |     0
 legacy/Data/ingsw/0120_25/wrong2.txt          |     0
 legacy/Data/ingsw/0120_25/wrong3.txt          |     0
 legacy/Data/ingsw/0120_26/correct.txt         |     1 +
 legacy/Data/ingsw/0120_26/quest.txt           |     9 +
 legacy/Data/ingsw/0120_26/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_26/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_27/correct.txt         |    17 +
 legacy/Data/ingsw/0120_27/quest.txt           |     3 +
 legacy/Data/ingsw/0120_27/wrong1.txt          |    17 +
 legacy/Data/ingsw/0120_27/wrong2.txt          |    17 +
 legacy/Data/ingsw/0120_28/quest.txt           |     2 +
 legacy/Data/ingsw/0120_28/wrong1.txt          |    38 +
 legacy/Data/ingsw/0120_28/wrong2.txt          |    33 +
 legacy/Data/ingsw/0120_28/wrong3.txt          |    33 +
 legacy/Data/ingsw/0120_29/correct.txt         |    15 +
 legacy/Data/ingsw/0120_29/quest.txt           |     5 +
 legacy/Data/ingsw/0120_29/wrong1.txt          |    16 +
 legacy/Data/ingsw/0120_29/wrong2.txt          |    15 +
 legacy/Data/ingsw/0120_3/correct.txt          |     5 +
 legacy/Data/ingsw/0120_3/quest.txt            |     4 +
 legacy/Data/ingsw/0120_3/wrong1.txt           |     5 +
 legacy/Data/ingsw/0120_3/wrong2.txt           |     5 +
 legacy/Data/ingsw/0120_30/correct.txt         |     1 +
 legacy/Data/ingsw/0120_30/quest.txt           |    22 +
 legacy/Data/ingsw/0120_30/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_30/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_31/correct.txt         |     1 +
 legacy/Data/ingsw/0120_31/quest.txt           |    11 +
 legacy/Data/ingsw/0120_31/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_31/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_32/correct.txt         |     1 +
 legacy/Data/ingsw/0120_32/quest.txt           |     8 +
 legacy/Data/ingsw/0120_32/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_32/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_33/correct.txt         |     1 +
 legacy/Data/ingsw/0120_33/quest.txt           |    16 +
 legacy/Data/ingsw/0120_33/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_33/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_34/quest.txt           |     2 +
 legacy/Data/ingsw/0120_34/wrong1.txt          |    35 +
 legacy/Data/ingsw/0120_34/wrong2.txt          |    36 +
 legacy/Data/ingsw/0120_34/wrong3.txt          |    37 +
 legacy/Data/ingsw/0120_35/quest.txt           |     4 +
 legacy/Data/ingsw/0120_35/wrong1.txt          |     6 +
 legacy/Data/ingsw/0120_35/wrong2.txt          |     6 +
 legacy/Data/ingsw/0120_35/wrong3.txt          |     6 +
 legacy/Data/ingsw/0120_36/correct.txt         |     1 +
 legacy/Data/ingsw/0120_36/quest.txt           |    12 +
 legacy/Data/ingsw/0120_36/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_36/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_37/correct.txt         |     1 +
 legacy/Data/ingsw/0120_37/quest.txt           |    13 +
 legacy/Data/ingsw/0120_37/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_37/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_38/correct.txt         |    17 +
 legacy/Data/ingsw/0120_38/quest.txt           |     3 +
 legacy/Data/ingsw/0120_38/wrong1.txt          |    17 +
 legacy/Data/ingsw/0120_38/wrong2.txt          |    17 +
 legacy/Data/ingsw/0120_39/quest.txt           |    35 +
 legacy/Data/ingsw/0120_39/wrong1.txt          |     0
 legacy/Data/ingsw/0120_39/wrong2.txt          |     0
 legacy/Data/ingsw/0120_39/wrong3.txt          |     0
 legacy/Data/ingsw/0120_4/correct.txt          |     1 +
 legacy/Data/ingsw/0120_4/quest.txt            |    16 +
 legacy/Data/ingsw/0120_4/wrong1.txt           |     1 +
 legacy/Data/ingsw/0120_4/wrong2.txt           |     1 +
 legacy/Data/ingsw/0120_40/correct.txt         |     1 +
 legacy/Data/ingsw/0120_40/quest.txt           |     8 +
 legacy/Data/ingsw/0120_40/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_40/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_41/correct.txt         |     1 +
 legacy/Data/ingsw/0120_41/quest.txt           |     4 +
 legacy/Data/ingsw/0120_41/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_41/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_42/correct.txt         |     1 +
 legacy/Data/ingsw/0120_42/quest.txt           |    11 +
 legacy/Data/ingsw/0120_42/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_42/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_43/quest.txt           |     2 +
 legacy/Data/ingsw/0120_43/wrong1.txt          |    32 +
 legacy/Data/ingsw/0120_43/wrong2.txt          |    36 +
 legacy/Data/ingsw/0120_43/wrong3.txt          |    36 +
 legacy/Data/ingsw/0120_44/correct.txt         |     1 +
 legacy/Data/ingsw/0120_44/quest.txt           |    21 +
 legacy/Data/ingsw/0120_44/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_44/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_45/quest.txt           |    35 +
 legacy/Data/ingsw/0120_45/wrong1.txt          |     0
 legacy/Data/ingsw/0120_45/wrong2.txt          |     0
 legacy/Data/ingsw/0120_45/wrong3.txt          |     0
 legacy/Data/ingsw/0120_46/correct.txt         |     1 +
 legacy/Data/ingsw/0120_46/quest.txt           |     9 +
 legacy/Data/ingsw/0120_46/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_46/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_47/correct.txt         |     1 +
 legacy/Data/ingsw/0120_47/quest.txt           |     1 +
 legacy/Data/ingsw/0120_47/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_47/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_48/correct.txt         |    17 +
 legacy/Data/ingsw/0120_48/quest.txt           |     3 +
 legacy/Data/ingsw/0120_48/wrong1.txt          |    17 +
 legacy/Data/ingsw/0120_48/wrong2.txt          |    17 +
 legacy/Data/ingsw/0120_49/correct.txt         |     1 +
 legacy/Data/ingsw/0120_49/quest.txt           |     6 +
 legacy/Data/ingsw/0120_49/wrong1.txt          |     1 +
 legacy/Data/ingsw/0120_49/wrong2.txt          |     1 +
 legacy/Data/ingsw/0120_5/quest.txt            |     2 +
 legacy/Data/ingsw/0120_5/wrong1.txt           |    35 +
 legacy/Data/ingsw/0120_5/wrong2.txt           |    37 +
 legacy/Data/ingsw/0120_5/wrong3.txt           |    28 +
 legacy/Data/ingsw/0120_6/correct.txt          |     1 +
 legacy/Data/ingsw/0120_6/quest.txt            |     1 +
 legacy/Data/ingsw/0120_6/wrong1.txt           |     1 +
 legacy/Data/ingsw/0120_6/wrong2.txt           |     1 +
 legacy/Data/ingsw/0120_7/correct.txt          |    15 +
 legacy/Data/ingsw/0120_7/quest.txt            |     5 +
 legacy/Data/ingsw/0120_7/wrong1.txt           |    15 +
 legacy/Data/ingsw/0120_7/wrong2.txt           |    15 +
 legacy/Data/ingsw/0120_8/correct.txt          |     1 +
 legacy/Data/ingsw/0120_8/quest.txt            |    11 +
 legacy/Data/ingsw/0120_8/wrong1.txt           |     1 +
 legacy/Data/ingsw/0120_8/wrong2.txt           |     1 +
 legacy/Data/ingsw/0120_9/correct.txt          |     1 +
 legacy/Data/ingsw/0120_9/quest.txt            |     9 +
 legacy/Data/ingsw/0120_9/wrong1.txt           |     1 +
 legacy/Data/ingsw/0120_9/wrong2.txt           |     1 +
 legacy/Data/ingsw/0121_34/correct.txt         |     1 +
 legacy/Data/ingsw/0121_34/quest.txt           |    53 +
 legacy/Data/ingsw/0121_34/wrong1.txt          |     1 +
 legacy/Data/ingsw/0121_34/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_0/correct.txt          |     1 +
 legacy/Data/ingsw/0210_0/quest.txt            |    22 +
 legacy/Data/ingsw/0210_0/wrong1.txt           |     1 +
 legacy/Data/ingsw/0210_0/wrong2.txt           |     1 +
 legacy/Data/ingsw/0210_1/quest.txt            |     5 +
 legacy/Data/ingsw/0210_1/wrong1.txt           |     6 +
 legacy/Data/ingsw/0210_1/wrong2.txt           |     3 +
 legacy/Data/ingsw/0210_1/wrong3.txt           |    40 +
 legacy/Data/ingsw/0210_10/correct.txt         |     1 +
 legacy/Data/ingsw/0210_10/quest.txt           |    16 +
 legacy/Data/ingsw/0210_10/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_10/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_11/quest.txt           |     4 +
 legacy/Data/ingsw/0210_11/wrong1.txt          |     9 +
 legacy/Data/ingsw/0210_11/wrong2.txt          |     9 +
 legacy/Data/ingsw/0210_11/wrong3.txt          |     9 +
 legacy/Data/ingsw/0210_12/quest.txt           |     2 +
 legacy/Data/ingsw/0210_12/wrong1.txt          |    38 +
 legacy/Data/ingsw/0210_12/wrong2.txt          |    35 +
 legacy/Data/ingsw/0210_12/wrong3.txt          |    35 +
 legacy/Data/ingsw/0210_13/correct.txt         |    17 +
 legacy/Data/ingsw/0210_13/quest.txt           |     3 +
 legacy/Data/ingsw/0210_13/wrong1.txt          |    17 +
 legacy/Data/ingsw/0210_13/wrong2.txt          |    17 +
 legacy/Data/ingsw/0210_14/correct.txt         |     1 +
 legacy/Data/ingsw/0210_14/quest.txt           |     9 +
 legacy/Data/ingsw/0210_14/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_14/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_15/correct.txt         |    17 +
 legacy/Data/ingsw/0210_15/quest.txt           |     3 +
 legacy/Data/ingsw/0210_15/wrong1.txt          |    17 +
 legacy/Data/ingsw/0210_15/wrong2.txt          |    17 +
 legacy/Data/ingsw/0210_16/correct.txt         |    17 +
 legacy/Data/ingsw/0210_16/quest.txt           |     3 +
 legacy/Data/ingsw/0210_16/wrong1.txt          |    17 +
 legacy/Data/ingsw/0210_16/wrong2.txt          |    17 +
 legacy/Data/ingsw/0210_17/correct.txt         |     1 +
 legacy/Data/ingsw/0210_17/quest.txt           |    13 +
 legacy/Data/ingsw/0210_17/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_17/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_18/correct.txt         |     1 +
 legacy/Data/ingsw/0210_18/quest.txt           |     1 +
 legacy/Data/ingsw/0210_18/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_18/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_19/correct.txt         |     1 +
 legacy/Data/ingsw/0210_19/quest.txt           |    12 +
 legacy/Data/ingsw/0210_19/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_19/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_2/quest.txt            |    36 +
 legacy/Data/ingsw/0210_2/wrong1.txt           |     0
 legacy/Data/ingsw/0210_2/wrong2.txt           |     0
 legacy/Data/ingsw/0210_2/wrong3.txt           |     0
 legacy/Data/ingsw/0210_20/correct.txt         |     1 +
 legacy/Data/ingsw/0210_20/quest.txt           |     1 +
 legacy/Data/ingsw/0210_20/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_20/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_21/correct.txt         |    15 +
 legacy/Data/ingsw/0210_21/quest.txt           |     5 +
 legacy/Data/ingsw/0210_21/wrong1.txt          |    15 +
 legacy/Data/ingsw/0210_21/wrong2.txt          |    15 +
 legacy/Data/ingsw/0210_22/correct.txt         |     1 +
 legacy/Data/ingsw/0210_22/quest.txt           |    16 +
 legacy/Data/ingsw/0210_22/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_22/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_23/correct.txt         |     1 +
 legacy/Data/ingsw/0210_23/quest.txt           |     9 +
 legacy/Data/ingsw/0210_23/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_23/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_24/correct.txt         |     1 +
 legacy/Data/ingsw/0210_24/quest.txt           |     6 +
 legacy/Data/ingsw/0210_24/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_24/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_25/correct.txt         |     5 +
 legacy/Data/ingsw/0210_25/quest.txt           |     4 +
 legacy/Data/ingsw/0210_25/wrong1.txt          |     5 +
 legacy/Data/ingsw/0210_25/wrong2.txt          |     5 +
 legacy/Data/ingsw/0210_26/correct.txt         |     1 +
 legacy/Data/ingsw/0210_26/quest.txt           |     6 +
 legacy/Data/ingsw/0210_26/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_26/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_27/quest.txt           |     2 +
 legacy/Data/ingsw/0210_27/wrong1.txt          |    36 +
 legacy/Data/ingsw/0210_27/wrong2.txt          |    32 +
 legacy/Data/ingsw/0210_27/wrong3.txt          |    37 +
 legacy/Data/ingsw/0210_28/quest.txt           |    38 +
 legacy/Data/ingsw/0210_28/wrong1.txt          |     0
 legacy/Data/ingsw/0210_28/wrong2.txt          |     0
 legacy/Data/ingsw/0210_28/wrong3.txt          |     0
 legacy/Data/ingsw/0210_29/correct.txt         |     1 +
 legacy/Data/ingsw/0210_29/quest.txt           |     2 +
 legacy/Data/ingsw/0210_29/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_29/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_3/quest.txt            |     4 +
 legacy/Data/ingsw/0210_3/wrong1.txt           |    14 +
 legacy/Data/ingsw/0210_3/wrong2.txt           |    14 +
 legacy/Data/ingsw/0210_3/wrong3.txt           |    15 +
 legacy/Data/ingsw/0210_30/correct.txt         |     1 +
 legacy/Data/ingsw/0210_30/quest.txt           |    13 +
 legacy/Data/ingsw/0210_30/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_30/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_31/correct.txt         |     1 +
 legacy/Data/ingsw/0210_31/quest.txt           |     9 +
 legacy/Data/ingsw/0210_31/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_31/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_32/correct.txt         |     1 +
 legacy/Data/ingsw/0210_32/quest.txt           |    13 +
 legacy/Data/ingsw/0210_32/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_32/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_33/correct.txt         |     1 +
 legacy/Data/ingsw/0210_33/quest.txt           |     9 +
 legacy/Data/ingsw/0210_33/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_33/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_34/quest.txt           |    34 +
 legacy/Data/ingsw/0210_34/wrong1.txt          |     0
 legacy/Data/ingsw/0210_34/wrong2.txt          |     0
 legacy/Data/ingsw/0210_34/wrong3.txt          |     0
 legacy/Data/ingsw/0210_35/correct.txt         |     1 +
 legacy/Data/ingsw/0210_35/quest.txt           |     1 +
 legacy/Data/ingsw/0210_35/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_35/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_36/correct.txt         |     5 +
 legacy/Data/ingsw/0210_36/quest.txt           |     4 +
 legacy/Data/ingsw/0210_36/wrong1.txt          |     5 +
 legacy/Data/ingsw/0210_36/wrong2.txt          |     5 +
 legacy/Data/ingsw/0210_37/quest.txt           |    36 +
 legacy/Data/ingsw/0210_37/wrong1.txt          |     0
 legacy/Data/ingsw/0210_37/wrong2.txt          |     0
 legacy/Data/ingsw/0210_37/wrong3.txt          |     0
 legacy/Data/ingsw/0210_38/correct.txt         |     1 +
 legacy/Data/ingsw/0210_38/quest.txt           |    21 +
 legacy/Data/ingsw/0210_38/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_38/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_39/correct.txt         |     1 +
 legacy/Data/ingsw/0210_39/quest.txt           |     6 +
 legacy/Data/ingsw/0210_39/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_39/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_4/correct.txt          |     1 +
 legacy/Data/ingsw/0210_4/quest.txt            |    12 +
 legacy/Data/ingsw/0210_4/wrong1.txt           |     1 +
 legacy/Data/ingsw/0210_4/wrong2.txt           |     1 +
 legacy/Data/ingsw/0210_40/correct.txt         |     1 +
 legacy/Data/ingsw/0210_40/quest.txt           |     9 +
 legacy/Data/ingsw/0210_40/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_40/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_41/correct.txt         |     1 +
 legacy/Data/ingsw/0210_41/quest.txt           |    13 +
 legacy/Data/ingsw/0210_41/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_41/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_42/quest.txt           |     5 +
 legacy/Data/ingsw/0210_42/wrong1.txt          |     6 +
 legacy/Data/ingsw/0210_42/wrong2.txt          |     3 +
 legacy/Data/ingsw/0210_42/wrong3.txt          |    39 +
 legacy/Data/ingsw/0210_43/correct.txt         |    16 +
 legacy/Data/ingsw/0210_43/quest.txt           |     4 +
 legacy/Data/ingsw/0210_43/wrong1.txt          |    16 +
 legacy/Data/ingsw/0210_43/wrong2.txt          |    16 +
 legacy/Data/ingsw/0210_44/quest.txt           |     2 +
 legacy/Data/ingsw/0210_44/wrong1.txt          |    34 +
 legacy/Data/ingsw/0210_44/wrong2.txt          |    35 +
 legacy/Data/ingsw/0210_44/wrong3.txt          |    32 +
 legacy/Data/ingsw/0210_45/correct.txt         |    15 +
 legacy/Data/ingsw/0210_45/quest.txt           |     5 +
 legacy/Data/ingsw/0210_45/wrong1.txt          |    16 +
 legacy/Data/ingsw/0210_45/wrong2.txt          |    15 +
 legacy/Data/ingsw/0210_46/correct.txt         |     9 +
 legacy/Data/ingsw/0210_46/quest.txt           |     4 +
 legacy/Data/ingsw/0210_46/wrong1.txt          |     9 +
 legacy/Data/ingsw/0210_46/wrong2.txt          |     9 +
 legacy/Data/ingsw/0210_47/correct.txt         |     1 +
 legacy/Data/ingsw/0210_47/quest.txt           |     8 +
 legacy/Data/ingsw/0210_47/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_47/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_48/correct.txt         |     1 +
 legacy/Data/ingsw/0210_48/quest.txt           |     8 +
 legacy/Data/ingsw/0210_48/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_48/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_49/correct.txt         |     1 +
 legacy/Data/ingsw/0210_49/quest.txt           |    12 +
 legacy/Data/ingsw/0210_49/wrong1.txt          |     1 +
 legacy/Data/ingsw/0210_49/wrong2.txt          |     1 +
 legacy/Data/ingsw/0210_5/correct.txt          |    17 +
 legacy/Data/ingsw/0210_5/quest.txt            |     3 +
 legacy/Data/ingsw/0210_5/wrong1.txt           |    17 +
 legacy/Data/ingsw/0210_5/wrong2.txt           |    17 +
 legacy/Data/ingsw/0210_6/correct.txt          |    15 +
 legacy/Data/ingsw/0210_6/quest.txt            |     5 +
 legacy/Data/ingsw/0210_6/wrong1.txt           |    16 +
 legacy/Data/ingsw/0210_6/wrong2.txt           |    15 +
 legacy/Data/ingsw/0210_7/correct.txt          |     1 +
 legacy/Data/ingsw/0210_7/quest.txt            |     8 +
 legacy/Data/ingsw/0210_7/wrong1.txt           |     1 +
 legacy/Data/ingsw/0210_7/wrong2.txt           |     1 +
 legacy/Data/ingsw/0210_8/correct.txt          |     1 +
 legacy/Data/ingsw/0210_8/quest.txt            |    10 +
 legacy/Data/ingsw/0210_8/wrong1.txt           |     1 +
 legacy/Data/ingsw/0210_8/wrong2.txt           |     1 +
 legacy/Data/ingsw/0210_9/quest.txt            |     2 +
 legacy/Data/ingsw/0210_9/wrong1.txt           |    36 +
 legacy/Data/ingsw/0210_9/wrong2.txt           |    35 +
 legacy/Data/ingsw/0210_9/wrong3.txt           |    32 +
 legacy/Data/ingsw/0221_18/correct.txt         |     1 +
 legacy/Data/ingsw/0221_18/quest.txt           |     1 +
 legacy/Data/ingsw/0221_18/wrong1.txt          |     1 +
 legacy/Data/ingsw/0221_18/wrong2.txt          |     1 +
 legacy/Data/ingsw/0221_28/correct.txt         |     1 +
 legacy/Data/ingsw/0221_28/quest.txt           |     1 +
 legacy/Data/ingsw/0221_28/wrong1.txt          |     1 +
 legacy/Data/ingsw/0221_28/wrong2.txt          |     1 +
 legacy/Data/ingsw/0221_32/correct.txt         |     1 +
 legacy/Data/ingsw/0221_32/quest.txt           |     1 +
 legacy/Data/ingsw/0221_32/wrong1.txt          |     1 +
 legacy/Data/ingsw/0221_32/wrong2.txt          |     1 +
 legacy/Data/ingsw/0222_24/correct.txt         |     1 +
 legacy/Data/ingsw/0222_24/quest.txt           |    12 +
 legacy/Data/ingsw/0222_24/wrong1.txt          |     1 +
 legacy/Data/ingsw/0222_24/wrong2.txt          |     1 +
 legacy/Data/ingsw/0222_27/correct.txt         |     1 +
 legacy/Data/ingsw/0222_27/quest.txt           |    13 +
 legacy/Data/ingsw/0222_27/wrong1.txt          |     1 +
 legacy/Data/ingsw/0222_27/wrong2.txt          |     1 +
 legacy/Data/ingsw/0222_33/correct.txt         |     1 +
 legacy/Data/ingsw/0222_33/quest.txt           |    45 +
 legacy/Data/ingsw/0222_33/wrong1.txt          |     1 +
 legacy/Data/ingsw/0222_33/wrong2.txt          |     1 +
 legacy/Data/ingsw/0222_35/correct.txt         |     1 +
 legacy/Data/ingsw/0222_35/quest.txt           |    52 +
 legacy/Data/ingsw/0222_35/wrong1.txt          |     1 +
 legacy/Data/ingsw/0222_35/wrong2.txt          |     1 +
 legacy/Data/ingsw/0222_39/correct.txt         |     1 +
 legacy/Data/ingsw/0222_39/quest.txt           |    55 +
 legacy/Data/ingsw/0222_39/wrong1.txt          |     1 +
 legacy/Data/ingsw/0222_39/wrong2.txt          |     1 +
 legacy/Data/ingsw/0222_41/correct.txt         |     1 +
 legacy/Data/ingsw/0222_41/quest.txt           |    55 +
 legacy/Data/ingsw/0222_41/wrong1.txt          |     1 +
 legacy/Data/ingsw/0222_41/wrong2.txt          |     1 +
 legacy/Data/ingsw/0222_5/correct.txt          |     1 +
 legacy/Data/ingsw/0222_5/quest.txt            |    15 +
 legacy/Data/ingsw/0222_5/wrong1.txt           |     1 +
 legacy/Data/ingsw/0222_5/wrong2.txt           |     1 +
 legacy/Data/ingsw/0222_50/correct.txt         |     1 +
 legacy/Data/ingsw/0222_50/quest.txt           |    14 +
 legacy/Data/ingsw/0222_50/wrong1.txt          |     1 +
 legacy/Data/ingsw/0222_50/wrong2.txt          |     1 +
 legacy/Data/ingsw/0222_7/correct.txt          |     1 +
 legacy/Data/ingsw/0222_7/quest.txt            |    13 +
 legacy/Data/ingsw/0222_7/wrong1.txt           |     1 +
 legacy/Data/ingsw/0222_7/wrong2.txt           |     1 +
 legacy/Data/ingsw/0321_1/correct.txt          |     1 +
 legacy/Data/ingsw/0321_1/quest.txt            |     1 +
 legacy/Data/ingsw/0321_1/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0321_1/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0321_10/correct.txt         |     1 +
 legacy/Data/ingsw/0321_10/quest.txt           |     1 +
 legacy/Data/ingsw/0321_10/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_10/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_11/correct.txt         |     1 +
 legacy/Data/ingsw/0321_11/quest.txt           |     1 +
 legacy/Data/ingsw/0321_11/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_11/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_12/correct.txt         |     1 +
 legacy/Data/ingsw/0321_12/quest.txt           |     1 +
 legacy/Data/ingsw/0321_12/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_12/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_13/correct.txt         |     1 +
 legacy/Data/ingsw/0321_13/quest.txt           |     1 +
 legacy/Data/ingsw/0321_13/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_13/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_14/correct.txt         |    68 +
 legacy/Data/ingsw/0321_14/quest.txt           |     2 +
 legacy/Data/ingsw/0321_14/wrong 1.txt         |    68 +
 legacy/Data/ingsw/0321_14/wrong 2.txt         |    67 +
 legacy/Data/ingsw/0321_15/correct.txt         |     1 +
 legacy/Data/ingsw/0321_15/quest.txt           |     1 +
 legacy/Data/ingsw/0321_15/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_15/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_16/correct.txt         |    40 +
 legacy/Data/ingsw/0321_16/quest.txt           |     1 +
 legacy/Data/ingsw/0321_16/wrong 1.txt         |    40 +
 legacy/Data/ingsw/0321_16/wrong 2.txt         |    39 +
 legacy/Data/ingsw/0321_17/correct.txt         |     1 +
 legacy/Data/ingsw/0321_17/quest.txt           |    31 +
 legacy/Data/ingsw/0321_17/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_17/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_18/correct.txt         |     1 +
 legacy/Data/ingsw/0321_18/quest.txt           |     1 +
 legacy/Data/ingsw/0321_18/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_18/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_19/correct.txt         |     1 +
 legacy/Data/ingsw/0321_19/quest.txt           |     1 +
 legacy/Data/ingsw/0321_19/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_19/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_2/correct.txt          |     1 +
 legacy/Data/ingsw/0321_2/quest.txt            |     1 +
 legacy/Data/ingsw/0321_2/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0321_2/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0321_20/correct.txt         |    69 +
 legacy/Data/ingsw/0321_20/quest.txt           |     2 +
 legacy/Data/ingsw/0321_20/wrong 1.txt         |    67 +
 legacy/Data/ingsw/0321_20/wrong 2.txt         |    68 +
 legacy/Data/ingsw/0321_21/correct.txt         |     1 +
 legacy/Data/ingsw/0321_21/quest.txt           |     1 +
 legacy/Data/ingsw/0321_21/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_21/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_23/correct.txt         |     1 +
 legacy/Data/ingsw/0321_23/quest.txt           |     1 +
 legacy/Data/ingsw/0321_23/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_23/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_24/correct.txt         |     1 +
 legacy/Data/ingsw/0321_24/quest.txt           |     1 +
 legacy/Data/ingsw/0321_24/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_24/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_27/correct.txt         |     1 +
 legacy/Data/ingsw/0321_27/quest.txt           |     1 +
 legacy/Data/ingsw/0321_27/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_27/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_28/correct.txt         |     1 +
 legacy/Data/ingsw/0321_28/quest.txt           |     1 +
 legacy/Data/ingsw/0321_28/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_28/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_29/correct.txt         |     1 +
 legacy/Data/ingsw/0321_29/quest.txt           |    31 +
 legacy/Data/ingsw/0321_29/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_29/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_30/correct.txt         |    26 +
 legacy/Data/ingsw/0321_30/quest.txt           |     1 +
 legacy/Data/ingsw/0321_30/wrong 1.txt         |    26 +
 legacy/Data/ingsw/0321_30/wrong 2.txt         |    25 +
 legacy/Data/ingsw/0321_31/correct.txt         |     1 +
 legacy/Data/ingsw/0321_31/quest.txt           |     1 +
 legacy/Data/ingsw/0321_31/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_31/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_32/correct.txt         |     1 +
 legacy/Data/ingsw/0321_32/quest.txt           |     1 +
 legacy/Data/ingsw/0321_32/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_32/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_36/correct.txt         |     1 +
 legacy/Data/ingsw/0321_36/quest.txt           |    21 +
 legacy/Data/ingsw/0321_36/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_36/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_37/correct.txt         |    27 +
 legacy/Data/ingsw/0321_37/quest.txt           |     1 +
 legacy/Data/ingsw/0321_37/wrong 1.txt         |    28 +
 legacy/Data/ingsw/0321_37/wrong 2.txt         |    27 +
 legacy/Data/ingsw/0321_38/correct.txt         |     1 +
 legacy/Data/ingsw/0321_38/quest.txt           |     1 +
 legacy/Data/ingsw/0321_38/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_38/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_4/correct.txt          |     1 +
 legacy/Data/ingsw/0321_4/quest.txt            |     1 +
 legacy/Data/ingsw/0321_4/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0321_4/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0321_40/correct.txt         |     1 +
 legacy/Data/ingsw/0321_40/quest.txt           |     1 +
 legacy/Data/ingsw/0321_40/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0321_40/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0321_8/correct.txt          |    53 +
 legacy/Data/ingsw/0321_8/quest.txt            |     1 +
 legacy/Data/ingsw/0321_8/wrong 1.txt          |    54 +
 legacy/Data/ingsw/0321_8/wrong 2.txt          |    53 +
 legacy/Data/ingsw/0321_9/correct.txt          |     1 +
 legacy/Data/ingsw/0321_9/quest.txt            |     1 +
 legacy/Data/ingsw/0321_9/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0321_9/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0324_0/correct.txt          |     1 +
 legacy/Data/ingsw/0324_0/quest.txt            |     9 +
 legacy/Data/ingsw/0324_0/wrong1.txt           |     1 +
 legacy/Data/ingsw/0324_0/wrong2.txt           |     1 +
 legacy/Data/ingsw/0324_1/quest.txt            |     4 +
 legacy/Data/ingsw/0324_1/wrong1.txt           |     8 +
 legacy/Data/ingsw/0324_1/wrong2.txt           |     2 +
 legacy/Data/ingsw/0324_1/wrong3.txt           |    46 +
 legacy/Data/ingsw/0324_10/correct.txt         |     1 +
 legacy/Data/ingsw/0324_10/quest.txt           |     1 +
 legacy/Data/ingsw/0324_10/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_10/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_11/quest.txt           |     2 +
 legacy/Data/ingsw/0324_11/wrong1.txt          |    35 +
 legacy/Data/ingsw/0324_11/wrong2.txt          |    34 +
 legacy/Data/ingsw/0324_11/wrong3.txt          |    37 +
 legacy/Data/ingsw/0324_12/correct.txt         |     1 +
 legacy/Data/ingsw/0324_12/quest.txt           |     9 +
 legacy/Data/ingsw/0324_12/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_12/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_13/correct.txt         |     1 +
 legacy/Data/ingsw/0324_13/quest.txt           |     8 +
 legacy/Data/ingsw/0324_13/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_13/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_14/correct.txt         |     1 +
 legacy/Data/ingsw/0324_14/quest.txt           |     9 +
 legacy/Data/ingsw/0324_14/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_14/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_15/correct.txt         |     1 +
 legacy/Data/ingsw/0324_15/quest.txt           |    22 +
 legacy/Data/ingsw/0324_15/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_15/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_16/correct.txt         |     1 +
 legacy/Data/ingsw/0324_16/quest.txt           |     1 +
 legacy/Data/ingsw/0324_16/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_16/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_17/correct.txt         |     1 +
 legacy/Data/ingsw/0324_17/quest.txt           |    13 +
 legacy/Data/ingsw/0324_17/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_17/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_18/correct.txt         |     1 +
 legacy/Data/ingsw/0324_18/quest.txt           |     8 +
 legacy/Data/ingsw/0324_18/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_18/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_19/correct.txt         |     1 +
 legacy/Data/ingsw/0324_19/quest.txt           |    12 +
 legacy/Data/ingsw/0324_19/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_19/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_2/correct.txt          |     1 +
 legacy/Data/ingsw/0324_2/quest.txt            |     9 +
 legacy/Data/ingsw/0324_2/wrong1.txt           |     1 +
 legacy/Data/ingsw/0324_2/wrong2.txt           |     1 +
 legacy/Data/ingsw/0324_20/correct.txt         |     1 +
 legacy/Data/ingsw/0324_20/quest.txt           |    13 +
 legacy/Data/ingsw/0324_20/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_20/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_21/correct.txt         |     1 +
 legacy/Data/ingsw/0324_21/quest.txt           |     8 +
 legacy/Data/ingsw/0324_21/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_21/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_22/correct.txt         |     1 +
 legacy/Data/ingsw/0324_22/quest.txt           |     9 +
 legacy/Data/ingsw/0324_22/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_22/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_23/correct.txt         |     1 +
 legacy/Data/ingsw/0324_23/quest.txt           |    11 +
 legacy/Data/ingsw/0324_23/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_23/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_24/correct.txt         |     1 +
 legacy/Data/ingsw/0324_24/quest.txt           |     1 +
 legacy/Data/ingsw/0324_24/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_24/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_25/correct.txt         |     1 +
 legacy/Data/ingsw/0324_25/quest.txt           |     1 +
 legacy/Data/ingsw/0324_25/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_25/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_26/quest.txt           |    19 +
 legacy/Data/ingsw/0324_26/wrong1.txt          |     0
 legacy/Data/ingsw/0324_26/wrong2.txt          |     0
 legacy/Data/ingsw/0324_26/wrong3.txt          |     0
 legacy/Data/ingsw/0324_27/correct.txt         |    17 +
 legacy/Data/ingsw/0324_27/quest.txt           |     3 +
 legacy/Data/ingsw/0324_27/wrong1.txt          |    17 +
 legacy/Data/ingsw/0324_27/wrong2.txt          |    17 +
 legacy/Data/ingsw/0324_28/correct.txt         |    16 +
 legacy/Data/ingsw/0324_28/quest.txt           |     4 +
 legacy/Data/ingsw/0324_28/wrong1.txt          |    16 +
 legacy/Data/ingsw/0324_28/wrong2.txt          |    16 +
 legacy/Data/ingsw/0324_29/correct.txt         |     1 +
 legacy/Data/ingsw/0324_29/quest.txt           |     1 +
 legacy/Data/ingsw/0324_29/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_29/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_3/correct.txt          |     1 +
 legacy/Data/ingsw/0324_3/quest.txt            |    11 +
 legacy/Data/ingsw/0324_3/wrong1.txt           |     1 +
 legacy/Data/ingsw/0324_3/wrong2.txt           |     1 +
 legacy/Data/ingsw/0324_30/quest.txt           |     4 +
 legacy/Data/ingsw/0324_30/wrong1.txt          |    14 +
 legacy/Data/ingsw/0324_30/wrong2.txt          |    15 +
 legacy/Data/ingsw/0324_30/wrong3.txt          |    14 +
 legacy/Data/ingsw/0324_31/correct.txt         |    17 +
 legacy/Data/ingsw/0324_31/quest.txt           |     3 +
 legacy/Data/ingsw/0324_31/wrong1.txt          |    17 +
 legacy/Data/ingsw/0324_31/wrong2.txt          |    17 +
 legacy/Data/ingsw/0324_32/correct.txt         |     1 +
 legacy/Data/ingsw/0324_32/quest.txt           |     6 +
 legacy/Data/ingsw/0324_32/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_32/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_33/correct.txt         |     1 +
 legacy/Data/ingsw/0324_33/quest.txt           |    21 +
 legacy/Data/ingsw/0324_33/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_33/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_34/correct.txt         |    15 +
 legacy/Data/ingsw/0324_34/quest.txt           |     5 +
 legacy/Data/ingsw/0324_34/wrong1.txt          |    15 +
 legacy/Data/ingsw/0324_34/wrong2.txt          |    15 +
 legacy/Data/ingsw/0324_35/quest.txt           |    39 +
 legacy/Data/ingsw/0324_35/wrong1.txt          |     0
 legacy/Data/ingsw/0324_35/wrong2.txt          |     0
 legacy/Data/ingsw/0324_35/wrong3.txt          |     0
 legacy/Data/ingsw/0324_36/correct.txt         |     1 +
 legacy/Data/ingsw/0324_36/quest.txt           |     6 +
 legacy/Data/ingsw/0324_36/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_36/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_37/correct.txt         |     1 +
 legacy/Data/ingsw/0324_37/quest.txt           |    12 +
 legacy/Data/ingsw/0324_37/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_37/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_38/correct.txt         |     1 +
 legacy/Data/ingsw/0324_38/quest.txt           |     2 +
 legacy/Data/ingsw/0324_38/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_38/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_39/correct.txt         |    15 +
 legacy/Data/ingsw/0324_39/quest.txt           |     5 +
 legacy/Data/ingsw/0324_39/wrong1.txt          |    15 +
 legacy/Data/ingsw/0324_39/wrong2.txt          |    16 +
 legacy/Data/ingsw/0324_4/correct.txt          |     1 +
 legacy/Data/ingsw/0324_4/quest.txt            |     9 +
 legacy/Data/ingsw/0324_4/wrong1.txt           |     1 +
 legacy/Data/ingsw/0324_4/wrong2.txt           |     1 +
 legacy/Data/ingsw/0324_40/correct.txt         |     1 +
 legacy/Data/ingsw/0324_40/quest.txt           |     9 +
 legacy/Data/ingsw/0324_40/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_40/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_41/quest.txt           |     2 +
 legacy/Data/ingsw/0324_41/wrong1.txt          |    36 +
 legacy/Data/ingsw/0324_41/wrong2.txt          |    36 +
 legacy/Data/ingsw/0324_41/wrong3.txt          |    34 +
 legacy/Data/ingsw/0324_42/quest.txt           |    35 +
 legacy/Data/ingsw/0324_42/wrong1.txt          |     0
 legacy/Data/ingsw/0324_42/wrong2.txt          |     0
 legacy/Data/ingsw/0324_42/wrong3.txt          |     0
 legacy/Data/ingsw/0324_43/correct.txt         |     1 +
 legacy/Data/ingsw/0324_43/quest.txt           |    13 +
 legacy/Data/ingsw/0324_43/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_43/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_44/correct.txt         |     1 +
 legacy/Data/ingsw/0324_44/quest.txt           |     6 +
 legacy/Data/ingsw/0324_44/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_44/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_45/correct.txt         |    15 +
 legacy/Data/ingsw/0324_45/quest.txt           |     5 +
 legacy/Data/ingsw/0324_45/wrong1.txt          |    15 +
 legacy/Data/ingsw/0324_45/wrong2.txt          |    16 +
 legacy/Data/ingsw/0324_46/correct.txt         |    17 +
 legacy/Data/ingsw/0324_46/quest.txt           |     3 +
 legacy/Data/ingsw/0324_46/wrong1.txt          |    17 +
 legacy/Data/ingsw/0324_46/wrong2.txt          |    17 +
 legacy/Data/ingsw/0324_47/quest.txt           |    18 +
 legacy/Data/ingsw/0324_47/wrong1.txt          |     0
 legacy/Data/ingsw/0324_47/wrong2.txt          |     0
 legacy/Data/ingsw/0324_47/wrong3.txt          |     0
 legacy/Data/ingsw/0324_48/quest.txt           |     4 +
 legacy/Data/ingsw/0324_48/wrong1.txt          |     8 +
 legacy/Data/ingsw/0324_48/wrong2.txt          |     2 +
 legacy/Data/ingsw/0324_48/wrong3.txt          |    49 +
 legacy/Data/ingsw/0324_49/correct.txt         |     1 +
 legacy/Data/ingsw/0324_49/quest.txt           |     1 +
 legacy/Data/ingsw/0324_49/wrong1.txt          |     1 +
 legacy/Data/ingsw/0324_49/wrong2.txt          |     1 +
 legacy/Data/ingsw/0324_5/correct.txt          |     1 +
 legacy/Data/ingsw/0324_5/quest.txt            |    10 +
 legacy/Data/ingsw/0324_5/wrong1.txt           |     1 +
 legacy/Data/ingsw/0324_5/wrong2.txt           |     1 +
 legacy/Data/ingsw/0324_6/correct.txt          |     1 +
 legacy/Data/ingsw/0324_6/quest.txt            |    13 +
 legacy/Data/ingsw/0324_6/wrong1.txt           |     1 +
 legacy/Data/ingsw/0324_6/wrong2.txt           |     1 +
 legacy/Data/ingsw/0324_7/correct.txt          |     5 +
 legacy/Data/ingsw/0324_7/quest.txt            |     4 +
 legacy/Data/ingsw/0324_7/wrong1.txt           |     5 +
 legacy/Data/ingsw/0324_7/wrong2.txt           |     5 +
 legacy/Data/ingsw/0324_8/correct.txt          |    17 +
 legacy/Data/ingsw/0324_8/quest.txt            |     3 +
 legacy/Data/ingsw/0324_8/wrong1.txt           |    17 +
 legacy/Data/ingsw/0324_8/wrong2.txt           |    17 +
 legacy/Data/ingsw/0324_9/correct.txt          |     1 +
 legacy/Data/ingsw/0324_9/quest.txt            |     4 +
 legacy/Data/ingsw/0324_9/wrong1.txt           |     1 +
 legacy/Data/ingsw/0324_9/wrong2.txt           |     1 +
 legacy/Data/ingsw/0422-16/correct.txt         |     1 +
 legacy/Data/ingsw/0422-16/quest.txt           |    20 +
 legacy/Data/ingsw/0422-16/wrong1.txt          |     1 +
 legacy/Data/ingsw/0422-16/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_0/quest.txt            |    35 +
 legacy/Data/ingsw/0613_0/wrong1.txt           |     0
 legacy/Data/ingsw/0613_0/wrong2.txt           |     0
 legacy/Data/ingsw/0613_0/wrong3.txt           |     0
 legacy/Data/ingsw/0613_1/correct.txt          |     1 +
 legacy/Data/ingsw/0613_1/quest.txt            |     9 +
 legacy/Data/ingsw/0613_1/wrong1.txt           |     1 +
 legacy/Data/ingsw/0613_1/wrong2.txt           |     1 +
 legacy/Data/ingsw/0613_10/correct.txt         |     1 +
 legacy/Data/ingsw/0613_10/quest.txt           |    31 +
 legacy/Data/ingsw/0613_10/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_10/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_11/correct.txt         |     1 +
 legacy/Data/ingsw/0613_11/quest.txt           |     1 +
 legacy/Data/ingsw/0613_11/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_11/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_12/correct.txt         |     1 +
 legacy/Data/ingsw/0613_12/quest.txt           |     6 +
 legacy/Data/ingsw/0613_12/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_12/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_13/correct.txt         |     1 +
 legacy/Data/ingsw/0613_13/quest.txt           |    13 +
 legacy/Data/ingsw/0613_13/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_13/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_14/quest.txt           |    13 +
 legacy/Data/ingsw/0613_14/wrong1.txt          |     0
 legacy/Data/ingsw/0613_14/wrong2.txt          |     0
 legacy/Data/ingsw/0613_14/wrong3.txt          |     0
 legacy/Data/ingsw/0613_15/correct.txt         |    17 +
 legacy/Data/ingsw/0613_15/quest.txt           |     3 +
 legacy/Data/ingsw/0613_15/wrong1.txt          |    17 +
 legacy/Data/ingsw/0613_15/wrong2.txt          |    17 +
 legacy/Data/ingsw/0613_16/correct.txt         |    17 +
 legacy/Data/ingsw/0613_16/quest.txt           |     3 +
 legacy/Data/ingsw/0613_16/wrong1.txt          |    17 +
 legacy/Data/ingsw/0613_16/wrong2.txt          |    17 +
 legacy/Data/ingsw/0613_17/correct.txt         |    15 +
 legacy/Data/ingsw/0613_17/quest.txt           |     5 +
 legacy/Data/ingsw/0613_17/wrong1.txt          |    16 +
 legacy/Data/ingsw/0613_17/wrong2.txt          |    15 +
 legacy/Data/ingsw/0613_18/correct.txt         |     1 +
 legacy/Data/ingsw/0613_18/quest.txt           |     2 +
 legacy/Data/ingsw/0613_18/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_18/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_19/quest.txt           |    37 +
 legacy/Data/ingsw/0613_19/wrong1.txt          |     0
 legacy/Data/ingsw/0613_19/wrong2.txt          |     0
 legacy/Data/ingsw/0613_19/wrong3.txt          |     0
 legacy/Data/ingsw/0613_2/quest.txt            |    19 +
 legacy/Data/ingsw/0613_2/wrong1.txt           |     0
 legacy/Data/ingsw/0613_2/wrong2.txt           |     0
 legacy/Data/ingsw/0613_2/wrong3.txt           |     0
 legacy/Data/ingsw/0613_20/correct.txt         |     1 +
 legacy/Data/ingsw/0613_20/quest.txt           |     6 +
 legacy/Data/ingsw/0613_20/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_20/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_21/correct.txt         |     1 +
 legacy/Data/ingsw/0613_21/quest.txt           |     1 +
 legacy/Data/ingsw/0613_21/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_21/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_22/correct.txt         |     1 +
 legacy/Data/ingsw/0613_22/quest.txt           |    13 +
 legacy/Data/ingsw/0613_22/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_22/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_23/correct.txt         |    15 +
 legacy/Data/ingsw/0613_23/quest.txt           |     5 +
 legacy/Data/ingsw/0613_23/wrong1.txt          |    16 +
 legacy/Data/ingsw/0613_23/wrong2.txt          |    15 +
 legacy/Data/ingsw/0613_24/correct.txt         |     1 +
 legacy/Data/ingsw/0613_24/quest.txt           |    13 +
 legacy/Data/ingsw/0613_24/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_24/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_25/correct.txt         |     1 +
 legacy/Data/ingsw/0613_25/quest.txt           |     9 +
 legacy/Data/ingsw/0613_25/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_25/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_26/correct.txt         |     1 +
 legacy/Data/ingsw/0613_26/quest.txt           |     8 +
 legacy/Data/ingsw/0613_26/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_26/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_27/quest.txt           |     4 +
 legacy/Data/ingsw/0613_27/wrong1.txt          |     8 +
 legacy/Data/ingsw/0613_27/wrong2.txt          |     2 +
 legacy/Data/ingsw/0613_27/wrong3.txt          |    49 +
 legacy/Data/ingsw/0613_28/correct.txt         |     1 +
 legacy/Data/ingsw/0613_28/quest.txt           |    11 +
 legacy/Data/ingsw/0613_28/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_28/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_29/correct.txt         |     1 +
 legacy/Data/ingsw/0613_29/quest.txt           |     4 +
 legacy/Data/ingsw/0613_29/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_29/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_3/correct.txt          |     1 +
 legacy/Data/ingsw/0613_3/quest.txt            |     9 +
 legacy/Data/ingsw/0613_3/wrong1.txt           |     1 +
 legacy/Data/ingsw/0613_3/wrong2.txt           |     1 +
 legacy/Data/ingsw/0613_30/correct.txt         |     1 +
 legacy/Data/ingsw/0613_30/quest.txt           |    11 +
 legacy/Data/ingsw/0613_30/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_30/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_31/correct.txt         |     1 +
 legacy/Data/ingsw/0613_31/quest.txt           |    12 +
 legacy/Data/ingsw/0613_31/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_31/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_32/correct.txt         |     1 +
 legacy/Data/ingsw/0613_32/quest.txt           |    13 +
 legacy/Data/ingsw/0613_32/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_32/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_33/correct.txt         |     5 +
 legacy/Data/ingsw/0613_33/quest.txt           |     4 +
 legacy/Data/ingsw/0613_33/wrong1.txt          |     5 +
 legacy/Data/ingsw/0613_33/wrong2.txt          |     5 +
 legacy/Data/ingsw/0613_34/correct.txt         |     1 +
 legacy/Data/ingsw/0613_34/quest.txt           |     1 +
 legacy/Data/ingsw/0613_34/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_34/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_35/correct.txt         |    15 +
 legacy/Data/ingsw/0613_35/quest.txt           |     5 +
 legacy/Data/ingsw/0613_35/wrong1.txt          |    15 +
 legacy/Data/ingsw/0613_35/wrong2.txt          |    15 +
 legacy/Data/ingsw/0613_36/correct.txt         |     1 +
 legacy/Data/ingsw/0613_36/quest.txt           |     9 +
 legacy/Data/ingsw/0613_36/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_36/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_37/correct.txt         |     1 +
 legacy/Data/ingsw/0613_37/quest.txt           |    16 +
 legacy/Data/ingsw/0613_37/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_37/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_38/quest.txt           |     2 +
 legacy/Data/ingsw/0613_38/wrong1.txt          |    35 +
 legacy/Data/ingsw/0613_38/wrong2.txt          |    34 +
 legacy/Data/ingsw/0613_38/wrong3.txt          |    35 +
 legacy/Data/ingsw/0613_39/correct.txt         |     1 +
 legacy/Data/ingsw/0613_39/quest.txt           |     6 +
 legacy/Data/ingsw/0613_39/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_39/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_4/correct.txt          |     1 +
 legacy/Data/ingsw/0613_4/quest.txt            |    12 +
 legacy/Data/ingsw/0613_4/wrong1.txt           |     1 +
 legacy/Data/ingsw/0613_4/wrong2.txt           |     1 +
 legacy/Data/ingsw/0613_40/quest.txt           |     2 +
 legacy/Data/ingsw/0613_40/wrong1.txt          |    36 +
 legacy/Data/ingsw/0613_40/wrong2.txt          |    36 +
 legacy/Data/ingsw/0613_40/wrong3.txt          |    35 +
 legacy/Data/ingsw/0613_41/quest.txt           |     4 +
 legacy/Data/ingsw/0613_41/wrong1.txt          |     6 +
 legacy/Data/ingsw/0613_41/wrong2.txt          |     6 +
 legacy/Data/ingsw/0613_41/wrong3.txt          |     6 +
 legacy/Data/ingsw/0613_42/correct.txt         |     1 +
 legacy/Data/ingsw/0613_42/quest.txt           |    29 +
 legacy/Data/ingsw/0613_42/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_42/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_43/correct.txt         |    17 +
 legacy/Data/ingsw/0613_43/quest.txt           |     3 +
 legacy/Data/ingsw/0613_43/wrong1.txt          |    17 +
 legacy/Data/ingsw/0613_43/wrong2.txt          |    17 +
 legacy/Data/ingsw/0613_44/correct.txt         |     1 +
 legacy/Data/ingsw/0613_44/quest.txt           |     1 +
 legacy/Data/ingsw/0613_44/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_44/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_45/correct.txt         |     1 +
 legacy/Data/ingsw/0613_45/quest.txt           |    21 +
 legacy/Data/ingsw/0613_45/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_45/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_46/correct.txt         |     1 +
 legacy/Data/ingsw/0613_46/quest.txt           |    30 +
 legacy/Data/ingsw/0613_46/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_46/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_47/correct.txt         |     1 +
 legacy/Data/ingsw/0613_47/quest.txt           |     1 +
 legacy/Data/ingsw/0613_47/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_47/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_48/correct.txt         |     1 +
 legacy/Data/ingsw/0613_48/quest.txt           |     9 +
 legacy/Data/ingsw/0613_48/wrong1.txt          |     1 +
 legacy/Data/ingsw/0613_48/wrong2.txt          |     1 +
 legacy/Data/ingsw/0613_49/correct.txt         |    16 +
 legacy/Data/ingsw/0613_49/quest.txt           |     4 +
 legacy/Data/ingsw/0613_49/wrong1.txt          |    16 +
 legacy/Data/ingsw/0613_49/wrong2.txt          |    16 +
 legacy/Data/ingsw/0613_5/correct.txt          |     1 +
 legacy/Data/ingsw/0613_5/quest.txt            |    29 +
 legacy/Data/ingsw/0613_5/wrong1.txt           |     1 +
 legacy/Data/ingsw/0613_5/wrong2.txt           |     1 +
 legacy/Data/ingsw/0613_6/correct.txt          |    17 +
 legacy/Data/ingsw/0613_6/quest.txt            |     3 +
 legacy/Data/ingsw/0613_6/wrong1.txt           |    17 +
 legacy/Data/ingsw/0613_6/wrong2.txt           |    17 +
 legacy/Data/ingsw/0613_7/correct.txt          |     1 +
 legacy/Data/ingsw/0613_7/quest.txt            |    22 +
 legacy/Data/ingsw/0613_7/wrong1.txt           |     1 +
 legacy/Data/ingsw/0613_7/wrong2.txt           |     1 +
 legacy/Data/ingsw/0613_8/correct.txt          |     1 +
 legacy/Data/ingsw/0613_8/quest.txt            |     1 +
 legacy/Data/ingsw/0613_8/wrong1.txt           |     1 +
 legacy/Data/ingsw/0613_8/wrong2.txt           |     1 +
 legacy/Data/ingsw/0613_9/quest.txt            |     4 +
 legacy/Data/ingsw/0613_9/wrong1.txt           |     8 +
 legacy/Data/ingsw/0613_9/wrong2.txt           |     4 +
 legacy/Data/ingsw/0613_9/wrong3.txt           |    46 +
 legacy/Data/ingsw/0621_0/correct.txt          |    14 +
 legacy/Data/ingsw/0621_0/quest.txt            |     4 +
 legacy/Data/ingsw/0621_0/wrong0.txt           |    14 +
 legacy/Data/ingsw/0621_0/wrong1.txt           |    14 +
 legacy/Data/ingsw/0621_1/correct.txt          |    14 +
 legacy/Data/ingsw/0621_1/quest.txt            |     1 +
 legacy/Data/ingsw/0621_1/wrong1.txt           |    13 +
 legacy/Data/ingsw/0621_1/wrong2.txt           |    13 +
 legacy/Data/ingsw/0621_10/correct.txt         |     1 +
 legacy/Data/ingsw/0621_10/quest.txt           |    13 +
 legacy/Data/ingsw/0621_10/wrong0.txt          |     1 +
 legacy/Data/ingsw/0621_10/wrong1.txt          |     1 +
 legacy/Data/ingsw/0621_13/correct.txt         |     1 +
 legacy/Data/ingsw/0621_13/quest.txt           |     1 +
 legacy/Data/ingsw/0621_13/wrong0.txt          |     1 +
 legacy/Data/ingsw/0621_13/wrong1.txt          |     1 +
 legacy/Data/ingsw/0621_14/correct.txt         |     1 +
 legacy/Data/ingsw/0621_14/quest.txt           |     1 +
 legacy/Data/ingsw/0621_14/wrong0.txt          |     1 +
 legacy/Data/ingsw/0621_14/wrong1.txt          |     1 +
 legacy/Data/ingsw/0621_17/correct.txt         |    13 +
 legacy/Data/ingsw/0621_17/quest.txt           |     5 +
 legacy/Data/ingsw/0621_17/wrong0.txt          |    14 +
 legacy/Data/ingsw/0621_17/wrong1.txt          |    13 +
 legacy/Data/ingsw/0621_19/correct.txt         |     1 +
 legacy/Data/ingsw/0621_19/quest.txt           |    13 +
 legacy/Data/ingsw/0621_19/wrong0.txt          |     1 +
 legacy/Data/ingsw/0621_19/wrong1.txt          |     1 +
 legacy/Data/ingsw/0621_2/correct.txt          |     1 +
 legacy/Data/ingsw/0621_2/quest.txt            |     1 +
 legacy/Data/ingsw/0621_2/wrong0.txt           |     1 +
 legacy/Data/ingsw/0621_2/wrong1.txt           |     1 +
 legacy/Data/ingsw/0621_21/correct.txt         |     1 +
 legacy/Data/ingsw/0621_21/quest.txt           |     1 +
 legacy/Data/ingsw/0621_21/wrong0.txt          |     1 +
 legacy/Data/ingsw/0621_21/wrong1.txt          |     1 +
 legacy/Data/ingsw/0621_22/correct.txt         |     1 +
 legacy/Data/ingsw/0621_22/quest.txt           |     1 +
 legacy/Data/ingsw/0621_22/wrong0.txt          |     1 +
 legacy/Data/ingsw/0621_22/wrong1.txt          |     1 +
 legacy/Data/ingsw/0621_24/correct.txt         |     1 +
 legacy/Data/ingsw/0621_24/quest.txt           |    17 +
 legacy/Data/ingsw/0621_24/wrong0.txt          |     1 +
 legacy/Data/ingsw/0621_24/wrong1.txt          |     1 +
 legacy/Data/ingsw/0621_3/correct.txt          |     1 +
 legacy/Data/ingsw/0621_3/quest.txt            |     1 +
 legacy/Data/ingsw/0621_3/wrong0.txt           |     1 +
 legacy/Data/ingsw/0621_3/wrong1.txt           |     1 +
 legacy/Data/ingsw/0621_32/correct.txt         |     1 +
 legacy/Data/ingsw/0621_32/quest.txt           |    17 +
 legacy/Data/ingsw/0621_32/wrong0.txt          |     1 +
 legacy/Data/ingsw/0621_32/wrong1.txt          |     1 +
 legacy/Data/ingsw/0621_35/correct.txt         |     1 +
 legacy/Data/ingsw/0621_35/quest.txt           |     1 +
 legacy/Data/ingsw/0621_35/wrong0.txt          |     1 +
 legacy/Data/ingsw/0621_35/wrong1.txt          |     1 +
 legacy/Data/ingsw/0621_36/correct.txt         |    15 +
 legacy/Data/ingsw/0621_36/quest.txt           |     3 +
 legacy/Data/ingsw/0621_36/wrong0.txt          |    15 +
 legacy/Data/ingsw/0621_36/wrong1.txt          |    15 +
 legacy/Data/ingsw/0621_39/correct.txt         |     1 +
 legacy/Data/ingsw/0621_39/quest.txt           |     1 +
 legacy/Data/ingsw/0621_39/wrong0.txt          |     1 +
 legacy/Data/ingsw/0621_39/wrong1.txt          |     1 +
 legacy/Data/ingsw/0621_6/correct.txt          |    16 +
 legacy/Data/ingsw/0621_6/quest.txt            |     3 +
 legacy/Data/ingsw/0621_6/wrong0.txt           |    16 +
 legacy/Data/ingsw/0621_6/wrong1.txt           |    16 +
 legacy/Data/ingsw/0621_6/wrong2.txt           |    16 +
 legacy/Data/ingsw/0621_9/correct.txt          |     1 +
 legacy/Data/ingsw/0621_9/quest.txt            |     1 +
 legacy/Data/ingsw/0621_9/wrong0.txt           |     1 +
 legacy/Data/ingsw/0621_9/wrong1.txt           |     1 +
 legacy/Data/ingsw/0622_1/correct.txt          |     1 +
 legacy/Data/ingsw/0622_1/quest.txt            |     1 +
 legacy/Data/ingsw/0622_1/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0622_1/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0622_2/correct.txt          |     1 +
 legacy/Data/ingsw/0622_2/quest.txt            |     1 +
 legacy/Data/ingsw/0622_2/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0622_2/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0622_3/correct.txt          |     1 +
 legacy/Data/ingsw/0622_3/quest.txt            |     1 +
 legacy/Data/ingsw/0622_3/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0622_3/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0622_4/correct.txt          |     1 +
 legacy/Data/ingsw/0622_4/quest.txt            |     1 +
 legacy/Data/ingsw/0622_4/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0622_4/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0622_5/correct.txt          |     1 +
 legacy/Data/ingsw/0622_5/quest.txt            |     1 +
 legacy/Data/ingsw/0622_5/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0622_5/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0622_6/correct.txt          |     1 +
 legacy/Data/ingsw/0622_6/quest.txt            |     1 +
 legacy/Data/ingsw/0622_6/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0622_6/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0622_7/correct.txt          |     1 +
 legacy/Data/ingsw/0622_7/quest.txt            |     1 +
 legacy/Data/ingsw/0622_7/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0622_7/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0622_8/correct.txt          |     1 +
 legacy/Data/ingsw/0622_8/quest.txt            |     2 +
 legacy/Data/ingsw/0622_8/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0622_8/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0622_9/correct.txt          |     1 +
 legacy/Data/ingsw/0622_9/quest.txt            |     1 +
 legacy/Data/ingsw/0622_9/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0622_9/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0721_1/correct.txt          |     1 +
 legacy/Data/ingsw/0721_1/quest.txt            |    13 +
 legacy/Data/ingsw/0721_1/wrong1.txt           |     1 +
 legacy/Data/ingsw/0721_1/wrong2.txt           |     1 +
 legacy/Data/ingsw/0721_10/correct.txt         |     1 +
 legacy/Data/ingsw/0721_10/quest.txt           |     1 +
 legacy/Data/ingsw/0721_10/wrong1.txt          |     1 +
 legacy/Data/ingsw/0721_10/wrong2.txt          |     1 +
 legacy/Data/ingsw/0721_13/correct.txt         |    18 +
 legacy/Data/ingsw/0721_13/quest.txt           |     4 +
 legacy/Data/ingsw/0721_13/wrong1.txt          |    18 +
 legacy/Data/ingsw/0721_13/wrong2.txt          |    18 +
 legacy/Data/ingsw/0721_15/correct.txt         |     1 +
 legacy/Data/ingsw/0721_15/quest.txt           |     1 +
 legacy/Data/ingsw/0721_15/wrong1.txt          |     1 +
 legacy/Data/ingsw/0721_15/wrong2.txt          |     1 +
 legacy/Data/ingsw/0721_17/correct.txt         |     1 +
 legacy/Data/ingsw/0721_17/quest.txt           |    13 +
 legacy/Data/ingsw/0721_17/wrong1.txt          |     1 +
 legacy/Data/ingsw/0721_17/wrong2.txt          |     1 +
 legacy/Data/ingsw/0721_18/correct.txt         |    16 +
 legacy/Data/ingsw/0721_18/quest.txt           |     3 +
 legacy/Data/ingsw/0721_18/wrong1.txt          |    16 +
 legacy/Data/ingsw/0721_18/wrong2.txt          |    16 +
 legacy/Data/ingsw/0721_19/correct.txt         |    35 +
 legacy/Data/ingsw/0721_19/quest.txt           |     4 +
 legacy/Data/ingsw/0721_19/wrong1.txt          |    35 +
 legacy/Data/ingsw/0721_19/wrong2.txt          |    35 +
 legacy/Data/ingsw/0721_21/correct.txt         |     1 +
 legacy/Data/ingsw/0721_21/quest.txt           |     1 +
 legacy/Data/ingsw/0721_21/wrong1.txt          |     1 +
 legacy/Data/ingsw/0721_21/wrong2.txt          |     1 +
 legacy/Data/ingsw/0721_28/correct.txt         |    23 +
 legacy/Data/ingsw/0721_28/quest.txt           |     2 +
 legacy/Data/ingsw/0721_28/wrong1.txt          |    23 +
 legacy/Data/ingsw/0721_28/wrong2.txt          |    23 +
 legacy/Data/ingsw/0721_29/correct.txt         |    21 +
 legacy/Data/ingsw/0721_29/quest.txt           |     2 +
 legacy/Data/ingsw/0721_29/wrong1.txt          |    21 +
 legacy/Data/ingsw/0721_29/wrong2.txt          |    21 +
 legacy/Data/ingsw/0721_32/correct.txt         |     1 +
 legacy/Data/ingsw/0721_32/quest.txt           |     1 +
 legacy/Data/ingsw/0721_32/wrong1.txt          |     1 +
 legacy/Data/ingsw/0721_32/wrong2.txt          |     1 +
 legacy/Data/ingsw/0721_33/correct.txt         |    23 +
 legacy/Data/ingsw/0721_33/quest.txt           |     2 +
 legacy/Data/ingsw/0721_33/wrong1.txt          |    23 +
 legacy/Data/ingsw/0721_33/wrong2.txt          |    23 +
 legacy/Data/ingsw/0721_34/correct.txt         |     1 +
 legacy/Data/ingsw/0721_34/quest.txt           |     1 +
 legacy/Data/ingsw/0721_34/wrong1.txt          |     1 +
 legacy/Data/ingsw/0721_34/wrong2.txt          |     1 +
 legacy/Data/ingsw/0721_36/correct.txt         |     1 +
 legacy/Data/ingsw/0721_36/quest.txt           |     1 +
 legacy/Data/ingsw/0721_36/wrong1.txt          |     1 +
 legacy/Data/ingsw/0721_36/wrong2.txt          |     1 +
 legacy/Data/ingsw/0721_4/correct.txt          |    21 +
 legacy/Data/ingsw/0721_4/quest.txt            |     4 +
 legacy/Data/ingsw/0721_4/wrong1.txt           |    21 +
 legacy/Data/ingsw/0721_4/wrong2.txt           |    21 +
 legacy/Data/ingsw/0721_5/correct.txt          |     1 +
 legacy/Data/ingsw/0721_5/quest.txt            |     2 +
 legacy/Data/ingsw/0721_5/wrong1.txt           |     1 +
 legacy/Data/ingsw/0721_5/wrong2.txt           |     1 +
 legacy/Data/ingsw/0721_6/correct.txt          |    14 +
 legacy/Data/ingsw/0721_6/quest.txt            |     1 +
 legacy/Data/ingsw/0721_6/wrong1.txt           |    14 +
 legacy/Data/ingsw/0721_6/wrong2.txt           |    14 +
 legacy/Data/ingsw/0721_8/correct.txt          |    35 +
 legacy/Data/ingsw/0721_8/quest.txt            |     4 +
 legacy/Data/ingsw/0721_8/wrong1.txt           |    35 +
 legacy/Data/ingsw/0721_8/wrong2.txt           |    35 +
 legacy/Data/ingsw/0722_1/correct.txt          |     1 +
 legacy/Data/ingsw/0722_1/quest.txt            |    19 +
 legacy/Data/ingsw/0722_1/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0722_1/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0722_10/correct.txt         |     1 +
 legacy/Data/ingsw/0722_10/quest.txt           |    20 +
 legacy/Data/ingsw/0722_10/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_10/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_11/correct.txt         |     1 +
 legacy/Data/ingsw/0722_11/quest.txt           |    22 +
 legacy/Data/ingsw/0722_11/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_11/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_12/correct.txt         |     1 +
 legacy/Data/ingsw/0722_12/quest.txt           |    11 +
 legacy/Data/ingsw/0722_12/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_12/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_13/correct.txt         |     1 +
 legacy/Data/ingsw/0722_13/quest.txt           |    15 +
 legacy/Data/ingsw/0722_13/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_13/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_14/correct.txt         |     1 +
 legacy/Data/ingsw/0722_14/quest.txt           |    17 +
 legacy/Data/ingsw/0722_14/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_14/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_15/correct.txt         |     1 +
 legacy/Data/ingsw/0722_15/quest.txt           |    16 +
 legacy/Data/ingsw/0722_15/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_15/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_16/correct.txt         |     1 +
 legacy/Data/ingsw/0722_16/quest.txt           |    17 +
 legacy/Data/ingsw/0722_16/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_16/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_17/correct.txt         |     1 +
 legacy/Data/ingsw/0722_17/quest.txt           |    16 +
 legacy/Data/ingsw/0722_17/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_17/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_18/correct.txt         |     1 +
 legacy/Data/ingsw/0722_18/quest.txt           |    16 +
 legacy/Data/ingsw/0722_18/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_18/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_19/correct.txt         |     1 +
 legacy/Data/ingsw/0722_19/quest.txt           |    17 +
 legacy/Data/ingsw/0722_19/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_19/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_2/correct.txt          |     5 +
 legacy/Data/ingsw/0722_2/quest.txt            |     7 +
 legacy/Data/ingsw/0722_2/wrong 1.txt          |     5 +
 legacy/Data/ingsw/0722_2/wrong 2.txt          |     5 +
 legacy/Data/ingsw/0722_20/correct.txt         |     1 +
 legacy/Data/ingsw/0722_20/quest.txt           |    15 +
 legacy/Data/ingsw/0722_20/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_20/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_21/correct.txt         |     1 +
 legacy/Data/ingsw/0722_21/quest.txt           |    20 +
 legacy/Data/ingsw/0722_21/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_21/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_22/correct.txt         |     1 +
 legacy/Data/ingsw/0722_22/quest.txt           |    16 +
 legacy/Data/ingsw/0722_22/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_22/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_23/correct.txt         |     1 +
 legacy/Data/ingsw/0722_23/quest.txt           |    15 +
 legacy/Data/ingsw/0722_23/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_23/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_24/correct.txt         |     1 +
 legacy/Data/ingsw/0722_24/quest.txt           |    22 +
 legacy/Data/ingsw/0722_24/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_24/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_25/correct.txt         |     9 +
 legacy/Data/ingsw/0722_25/quest.txt           |     7 +
 legacy/Data/ingsw/0722_25/wrong 1.txt         |     9 +
 legacy/Data/ingsw/0722_25/wrong 2.txt         |     9 +
 legacy/Data/ingsw/0722_26/correct.txt         |     1 +
 legacy/Data/ingsw/0722_26/quest.txt           |    15 +
 legacy/Data/ingsw/0722_26/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0722_26/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0722_3/correct.txt          |     1 +
 legacy/Data/ingsw/0722_3/quest.txt            |    15 +
 legacy/Data/ingsw/0722_3/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0722_3/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0722_4/correct.txt          |     1 +
 legacy/Data/ingsw/0722_4/quest.txt            |    15 +
 legacy/Data/ingsw/0722_4/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0722_4/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0722_5/correct.txt          |     1 +
 legacy/Data/ingsw/0722_5/quest.txt            |    17 +
 legacy/Data/ingsw/0722_5/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0722_5/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0722_6/correct.txt          |     1 +
 legacy/Data/ingsw/0722_6/quest.txt            |    15 +
 legacy/Data/ingsw/0722_6/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0722_6/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0722_7/correct.txt          |     1 +
 legacy/Data/ingsw/0722_7/quest.txt            |    14 +
 legacy/Data/ingsw/0722_7/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0722_7/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0722_8/correct.txt          |     1 +
 legacy/Data/ingsw/0722_8/quest.txt            |    15 +
 legacy/Data/ingsw/0722_8/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0722_8/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0722_9/correct.txt          |     1 +
 legacy/Data/ingsw/0722_9/quest.txt            |    16 +
 legacy/Data/ingsw/0722_9/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0722_9/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0922_10/correct.txt         |    69 +
 legacy/Data/ingsw/0922_10/quest.txt           |     2 +
 legacy/Data/ingsw/0922_10/wrong 1.txt         |    67 +
 legacy/Data/ingsw/0922_10/wrong 2.txt         |    69 +
 legacy/Data/ingsw/0922_11/correct.txt         |     1 +
 legacy/Data/ingsw/0922_11/quest.txt           |    19 +
 legacy/Data/ingsw/0922_11/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0922_11/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0922_12/correct.txt         |     1 +
 legacy/Data/ingsw/0922_12/quest.txt           |    17 +
 legacy/Data/ingsw/0922_12/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0922_12/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0922_13/correct.txt         |     1 +
 legacy/Data/ingsw/0922_13/quest.txt           |    15 +
 legacy/Data/ingsw/0922_13/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0922_13/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0922_14/correct.txt         |    71 +
 legacy/Data/ingsw/0922_14/quest.txt           |     2 +
 legacy/Data/ingsw/0922_14/wrong 1.txt         |    71 +
 legacy/Data/ingsw/0922_14/wrong 2.txt         |    69 +
 legacy/Data/ingsw/0922_15/correct.txt         |     1 +
 legacy/Data/ingsw/0922_15/quest.txt           |    16 +
 legacy/Data/ingsw/0922_15/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0922_15/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0922_16/correct.txt         |     1 +
 legacy/Data/ingsw/0922_16/quest.txt           |    75 +
 legacy/Data/ingsw/0922_16/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0922_16/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0922_17/correct.txt         |    69 +
 legacy/Data/ingsw/0922_17/quest.txt           |     2 +
 legacy/Data/ingsw/0922_17/wrong 1.txt         |    74 +
 legacy/Data/ingsw/0922_17/wrong 2.txt         |    69 +
 legacy/Data/ingsw/0922_18/correct.txt         |     1 +
 legacy/Data/ingsw/0922_18/quest.txt           |    75 +
 legacy/Data/ingsw/0922_18/wrong 1.txt         |     1 +
 legacy/Data/ingsw/0922_18/wrong 2.txt         |     1 +
 legacy/Data/ingsw/0922_3/correct.txt          |     1 +
 legacy/Data/ingsw/0922_3/quest.txt            |    77 +
 legacy/Data/ingsw/0922_3/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0922_3/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0922_4/correct.txt          |     1 +
 legacy/Data/ingsw/0922_4/quest.txt            |    16 +
 legacy/Data/ingsw/0922_4/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0922_4/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0922_5/correct.txt          |    67 +
 legacy/Data/ingsw/0922_5/quest.txt            |     2 +
 legacy/Data/ingsw/0922_5/wrong 1.txt          |    69 +
 legacy/Data/ingsw/0922_5/wrong 2.txt          |    71 +
 legacy/Data/ingsw/0922_6/correct.txt          |     1 +
 legacy/Data/ingsw/0922_6/quest.txt            |    73 +
 legacy/Data/ingsw/0922_6/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0922_6/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0922_7/correct.txt          |     1 +
 legacy/Data/ingsw/0922_7/quest.txt            |    15 +
 legacy/Data/ingsw/0922_7/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0922_7/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0922_8/correct.txt          |     1 +
 legacy/Data/ingsw/0922_8/quest.txt            |    18 +
 legacy/Data/ingsw/0922_8/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0922_8/wrong 2.txt          |     1 +
 legacy/Data/ingsw/0922_9/correct.txt          |     1 +
 legacy/Data/ingsw/0922_9/quest.txt            |    17 +
 legacy/Data/ingsw/0922_9/wrong 1.txt          |     1 +
 legacy/Data/ingsw/0922_9/wrong 2.txt          |     1 +
 legacy/Data/ingsw/10/correct.txt              |     1 +
 legacy/Data/ingsw/10/quest.txt                |    26 +
 legacy/Data/ingsw/10/wrong 2.txt              |     1 +
 legacy/Data/ingsw/10/wrong.txt                |     1 +
 legacy/Data/ingsw/11/correct.txt              |     1 +
 legacy/Data/ingsw/11/quest.txt                |     4 +
 legacy/Data/ingsw/11/wrong 2.txt              |     1 +
 legacy/Data/ingsw/11/wrong.txt                |     1 +
 legacy/Data/ingsw/1122_1/correct.txt          |    44 +
 legacy/Data/ingsw/1122_1/quest.txt            |     2 +
 legacy/Data/ingsw/1122_1/wrong 1.txt          |    77 +
 legacy/Data/ingsw/1122_1/wrong 2.txt          |    67 +
 legacy/Data/ingsw/1122_10/correct.txt         |     5 +
 legacy/Data/ingsw/1122_10/quest.txt           |     7 +
 legacy/Data/ingsw/1122_10/wrong 1.txt         |    10 +
 legacy/Data/ingsw/1122_10/wrong 2.txt         |     9 +
 legacy/Data/ingsw/1122_11/correct.txt         |     1 +
 legacy/Data/ingsw/1122_11/quest.txt           |    17 +
 legacy/Data/ingsw/1122_11/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_11/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_12/correct.txt         |     1 +
 legacy/Data/ingsw/1122_12/quest.txt           |    76 +
 legacy/Data/ingsw/1122_12/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_12/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_13/correct.txt         |     1 +
 legacy/Data/ingsw/1122_13/quest.txt           |     8 +
 legacy/Data/ingsw/1122_13/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_13/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_14/correct.txt         |    19 +
 legacy/Data/ingsw/1122_14/quest.txt           |     7 +
 legacy/Data/ingsw/1122_14/wrong 1.txt         |    29 +
 legacy/Data/ingsw/1122_14/wrong 2.txt         |    29 +
 legacy/Data/ingsw/1122_15/correct.txt         |     1 +
 legacy/Data/ingsw/1122_15/quest.txt           |    12 +
 legacy/Data/ingsw/1122_15/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_15/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_16/correct.txt         |     1 +
 legacy/Data/ingsw/1122_16/quest.txt           |     7 +
 legacy/Data/ingsw/1122_16/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_16/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_19/correct.txt         |     1 +
 legacy/Data/ingsw/1122_19/quest.txt           |    75 +
 legacy/Data/ingsw/1122_19/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_19/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_2/correct.txt          |    15 +
 legacy/Data/ingsw/1122_2/quest.txt            |     9 +
 legacy/Data/ingsw/1122_2/wrong 1.txt          |    17 +
 legacy/Data/ingsw/1122_2/wrong 2.txt          |    17 +
 legacy/Data/ingsw/1122_20/correct.txt         |     1 +
 legacy/Data/ingsw/1122_20/quest.txt           |    19 +
 legacy/Data/ingsw/1122_20/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_20/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_21/correct.txt         |    17 +
 legacy/Data/ingsw/1122_21/quest.txt           |     5 +
 legacy/Data/ingsw/1122_21/wrong 1.txt         |    19 +
 legacy/Data/ingsw/1122_21/wrong 2.txt         |    19 +
 legacy/Data/ingsw/1122_22/correct.txt         |     1 +
 legacy/Data/ingsw/1122_22/quest.txt           |    14 +
 legacy/Data/ingsw/1122_22/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_22/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_23/correct.txt         |     1 +
 legacy/Data/ingsw/1122_23/quest.txt           |    29 +
 legacy/Data/ingsw/1122_23/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_23/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_24/correct.txt         |    17 +
 legacy/Data/ingsw/1122_24/quest.txt           |     5 +
 legacy/Data/ingsw/1122_24/wrong 1.txt         |    19 +
 legacy/Data/ingsw/1122_24/wrong 2.txt         |    19 +
 legacy/Data/ingsw/1122_25/correct.txt         |     1 +
 legacy/Data/ingsw/1122_25/quest.txt           |    10 +
 legacy/Data/ingsw/1122_25/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_25/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_26/correct.txt         |     1 +
 legacy/Data/ingsw/1122_26/quest.txt           |    15 +
 legacy/Data/ingsw/1122_26/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_26/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_27/correct.txt         |     1 +
 legacy/Data/ingsw/1122_27/quest.txt           |    13 +
 legacy/Data/ingsw/1122_27/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_27/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_28/correct.txt         |     9 +
 legacy/Data/ingsw/1122_28/quest.txt           |     7 +
 legacy/Data/ingsw/1122_28/wrong 1.txt         |    13 +
 legacy/Data/ingsw/1122_28/wrong 2.txt         |    13 +
 legacy/Data/ingsw/1122_29/correct.txt         |     1 +
 legacy/Data/ingsw/1122_29/quest.txt           |    70 +
 legacy/Data/ingsw/1122_29/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_29/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_3/correct.txt          |     1 +
 legacy/Data/ingsw/1122_3/quest.txt            |     3 +
 legacy/Data/ingsw/1122_3/wrong 1.txt          |     1 +
 legacy/Data/ingsw/1122_3/wrong 2.txt          |     1 +
 legacy/Data/ingsw/1122_30/correct.txt         |     1 +
 legacy/Data/ingsw/1122_30/quest.txt           |    10 +
 legacy/Data/ingsw/1122_30/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_30/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_31/correct.txt         |    17 +
 legacy/Data/ingsw/1122_31/quest.txt           |     5 +
 legacy/Data/ingsw/1122_31/wrong 1.txt         |    19 +
 legacy/Data/ingsw/1122_31/wrong 2.txt         |    19 +
 legacy/Data/ingsw/1122_33/correct.txt         |     1 +
 legacy/Data/ingsw/1122_33/quest.txt           |    17 +
 legacy/Data/ingsw/1122_33/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_33/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_34/correct.txt         |     1 +
 legacy/Data/ingsw/1122_34/quest.txt           |    13 +
 legacy/Data/ingsw/1122_34/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_34/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_35/correct.txt         |     1 +
 legacy/Data/ingsw/1122_35/quest.txt           |     3 +
 legacy/Data/ingsw/1122_35/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_35/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_36/correct.txt         |     1 +
 legacy/Data/ingsw/1122_36/quest.txt           |    26 +
 legacy/Data/ingsw/1122_36/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_36/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_37/correct.txt         |    17 +
 legacy/Data/ingsw/1122_37/quest.txt           |     9 +
 legacy/Data/ingsw/1122_37/wrong 1.txt         |    19 +
 legacy/Data/ingsw/1122_37/wrong 2.txt         |    20 +
 legacy/Data/ingsw/1122_38/correct.txt         |     1 +
 legacy/Data/ingsw/1122_38/quest.txt           |    29 +
 legacy/Data/ingsw/1122_38/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_38/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_39/correct.txt         |     1 +
 legacy/Data/ingsw/1122_39/quest.txt           |    14 +
 legacy/Data/ingsw/1122_39/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_39/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_4/correct.txt          |    45 +
 legacy/Data/ingsw/1122_4/quest.txt            |     2 +
 legacy/Data/ingsw/1122_4/wrong 1.txt          |    67 +
 legacy/Data/ingsw/1122_4/wrong 2.txt          |    69 +
 legacy/Data/ingsw/1122_40/correct.txt         |     1 +
 legacy/Data/ingsw/1122_40/quest.txt           |    13 +
 legacy/Data/ingsw/1122_40/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_40/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_42/correct.txt         |     1 +
 legacy/Data/ingsw/1122_42/quest.txt           |    17 +
 legacy/Data/ingsw/1122_42/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_42/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_43/correct.txt         |     1 +
 legacy/Data/ingsw/1122_43/quest.txt           |    11 +
 legacy/Data/ingsw/1122_43/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_43/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_44/correct.txt         |     1 +
 legacy/Data/ingsw/1122_44/quest.txt           |    15 +
 legacy/Data/ingsw/1122_44/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_44/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_45/correct.txt         |     1 +
 legacy/Data/ingsw/1122_45/quest.txt           |     8 +
 legacy/Data/ingsw/1122_45/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_45/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_46/correct.txt         |     1 +
 legacy/Data/ingsw/1122_46/quest.txt           |    14 +
 legacy/Data/ingsw/1122_46/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_46/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_47/correct.txt         |     1 +
 legacy/Data/ingsw/1122_47/quest.txt           |    11 +
 legacy/Data/ingsw/1122_47/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_47/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_48/correct.txt         |     1 +
 legacy/Data/ingsw/1122_48/quest.txt           |    24 +
 legacy/Data/ingsw/1122_48/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_48/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_49/correct.txt         |     1 +
 legacy/Data/ingsw/1122_49/quest.txt           |    12 +
 legacy/Data/ingsw/1122_49/wrong 1.txt         |     1 +
 legacy/Data/ingsw/1122_49/wrong 2.txt         |     1 +
 legacy/Data/ingsw/1122_5/correct.txt          |     1 +
 legacy/Data/ingsw/1122_5/quest.txt            |    68 +
 legacy/Data/ingsw/1122_5/wrong 1.txt          |     1 +
 legacy/Data/ingsw/1122_5/wrong 2.txt          |     1 +
 legacy/Data/ingsw/1122_50/correct.txt         |    69 +
 legacy/Data/ingsw/1122_50/quest.txt           |     2 +
 legacy/Data/ingsw/1122_50/wrong 1.txt         |    69 +
 legacy/Data/ingsw/1122_50/wrong 2.txt         |    71 +
 legacy/Data/ingsw/1122_6/correct.txt          |     1 +
 legacy/Data/ingsw/1122_6/quest.txt            |     1 +
 legacy/Data/ingsw/1122_6/wrong 1.txt          |     1 +
 legacy/Data/ingsw/1122_6/wrong 2.txt          |     1 +
 legacy/Data/ingsw/1122_7/correct.txt          |     1 +
 legacy/Data/ingsw/1122_7/quest.txt            |    10 +
 legacy/Data/ingsw/1122_7/wrong 1.txt          |     1 +
 legacy/Data/ingsw/1122_7/wrong 2.txt          |     1 +
 legacy/Data/ingsw/1122_8/correct.txt          |     1 +
 legacy/Data/ingsw/1122_8/quest.txt            |     8 +
 legacy/Data/ingsw/1122_8/wrong 1.txt          |     1 +
 legacy/Data/ingsw/1122_8/wrong 2.txt          |     1 +
 legacy/Data/ingsw/1122_9/correct.txt          |    44 +
 legacy/Data/ingsw/1122_9/quest.txt            |     2 +
 legacy/Data/ingsw/1122_9/wrong 1.txt          |    71 +
 legacy/Data/ingsw/1122_9/wrong 2.txt          |    76 +
 legacy/Data/ingsw/12/correct.txt              |     1 +
 legacy/Data/ingsw/12/quest.txt                |     2 +
 legacy/Data/ingsw/12/wrong 2.txt              |     1 +
 legacy/Data/ingsw/12/wrong.txt                |     1 +
 legacy/Data/ingsw/16/correct.txt              |     1 +
 legacy/Data/ingsw/16/quest.txt                |     3 +
 legacy/Data/ingsw/16/wrong 2.txt              |     1 +
 legacy/Data/ingsw/16/wrong.txt                |     1 +
 legacy/Data/ingsw/17/correct.txt              |    25 +
 legacy/Data/ingsw/17/quest.txt                |    10 +
 legacy/Data/ingsw/17/wrong 2.txt              |    19 +
 legacy/Data/ingsw/17/wrong.txt                |    25 +
 legacy/Data/ingsw/19/correct.txt              |     1 +
 legacy/Data/ingsw/19/quest.txt                |     2 +
 legacy/Data/ingsw/19/wrong 2.txt              |     1 +
 legacy/Data/ingsw/19/wrong.txt                |     1 +
 legacy/Data/ingsw/2/correct.txt               |     1 +
 legacy/Data/ingsw/2/quest.txt                 |     2 +
 legacy/Data/ingsw/2/wrong 2.txt               |     1 +
 legacy/Data/ingsw/2/wrong.txt                 |     1 +
 legacy/Data/ingsw/20/correct.txt              |    47 +
 legacy/Data/ingsw/20/quest.txt                |     3 +
 legacy/Data/ingsw/20/wrong 2.txt              |    47 +
 legacy/Data/ingsw/20/wrong.txt                |    47 +
 legacy/Data/ingsw/21/correct.txt              |     1 +
 legacy/Data/ingsw/21/quest.txt                |     2 +
 legacy/Data/ingsw/21/wrong 2.txt              |     1 +
 legacy/Data/ingsw/21/wrong.txt                |     1 +
 legacy/Data/ingsw/22/correct.txt              |    23 +
 legacy/Data/ingsw/22/quest.txt                |     5 +
 legacy/Data/ingsw/22/wrong 2.txt              |    21 +
 legacy/Data/ingsw/22/wrong.txt                |    23 +
 legacy/Data/ingsw/24/correct.txt              |     1 +
 legacy/Data/ingsw/24/quest.txt                |     2 +
 legacy/Data/ingsw/24/wrong 2.txt              |     1 +
 legacy/Data/ingsw/24/wrong.txt                |     1 +
 legacy/Data/ingsw/25/correct.txt              |     1 +
 legacy/Data/ingsw/25/quest.txt                |     3 +
 legacy/Data/ingsw/25/wrong 2.txt              |     1 +
 legacy/Data/ingsw/25/wrong.txt                |     1 +
 legacy/Data/ingsw/26/correct.txt              |     1 +
 legacy/Data/ingsw/26/quest.txt                |     2 +
 legacy/Data/ingsw/26/wrong 2.txt              |     1 +
 legacy/Data/ingsw/26/wrong.txt                |     1 +
 legacy/Data/ingsw/32/correct.txt              |     1 +
 legacy/Data/ingsw/32/quest.txt                |     3 +
 legacy/Data/ingsw/32/wrong 2.txt              |     1 +
 legacy/Data/ingsw/32/wrong.txt                |     1 +
 legacy/Data/ingsw/33/correct.txt              |     1 +
 legacy/Data/ingsw/33/quest.txt                |    17 +
 legacy/Data/ingsw/33/wrong 2.txt              |     1 +
 legacy/Data/ingsw/33/wrong.txt                |     1 +
 legacy/Data/ingsw/34/correct.txt              |    21 +
 legacy/Data/ingsw/34/quest.txt                |     3 +
 legacy/Data/ingsw/34/wrong 2.txt              |    22 +
 legacy/Data/ingsw/34/wrong.txt                |    19 +
 legacy/Data/ingsw/35/correct.txt              |     1 +
 legacy/Data/ingsw/35/quest.txt                |     1 +
 legacy/Data/ingsw/35/wrong 2.txt              |     1 +
 legacy/Data/ingsw/35/wrong.txt                |     1 +
 legacy/Data/ingsw/39/correct.txt              |     1 +
 legacy/Data/ingsw/39/quest.txt                |     1 +
 legacy/Data/ingsw/39/wrong 2.txt              |     1 +
 legacy/Data/ingsw/39/wrong.txt                |     1 +
 legacy/Data/ingsw/4/correct.txt               |     1 +
 legacy/Data/ingsw/4/quest.txt                 |     1 +
 legacy/Data/ingsw/4/wrong 2.txt               |     1 +
 legacy/Data/ingsw/4/wrong.txt                 |     1 +
 legacy/Data/ingsw/43/correct.txt              |    15 +
 legacy/Data/ingsw/43/quest.txt                |     6 +
 legacy/Data/ingsw/43/wrong 2.txt              |    15 +
 legacy/Data/ingsw/43/wrong.txt                |    15 +
 legacy/Data/ingsw/44/correct.txt              |    20 +
 legacy/Data/ingsw/44/quest.txt                |     8 +
 legacy/Data/ingsw/44/wrong 2.txt              |    20 +
 legacy/Data/ingsw/44/wrong.txt                |    22 +
 legacy/Data/ingsw/45/correct.txt              |     1 +
 legacy/Data/ingsw/45/quest.txt                |     2 +
 legacy/Data/ingsw/45/wrong 2.txt              |     1 +
 legacy/Data/ingsw/45/wrong.txt                |     1 +
 legacy/Data/ingsw/46/correct.txt              |     1 +
 legacy/Data/ingsw/46/quest.txt                |     3 +
 legacy/Data/ingsw/46/wrong 2.txt              |     1 +
 legacy/Data/ingsw/46/wrong.txt                |     1 +
 legacy/Data/ingsw/47/correct.txt              |     1 +
 legacy/Data/ingsw/47/quest.txt                |     3 +
 legacy/Data/ingsw/47/wrong 2.txt              |     1 +
 legacy/Data/ingsw/47/wrong.txt                |     1 +
 legacy/Data/ingsw/48/correct.txt              |     1 +
 legacy/Data/ingsw/48/quest.txt                |     1 +
 legacy/Data/ingsw/48/wrong 2.txt              |     1 +
 legacy/Data/ingsw/48/wrong.txt                |     1 +
 legacy/Data/ingsw/49/correct.txt              |     1 +
 legacy/Data/ingsw/49/quest.txt                |     1 +
 legacy/Data/ingsw/49/wrong 2.txt              |     1 +
 legacy/Data/ingsw/49/wrong.txt                |     1 +
 legacy/Data/ingsw/5/correct.txt               |     1 +
 legacy/Data/ingsw/5/quest.txt                 |     2 +
 legacy/Data/ingsw/5/wrong 2.txt               |     1 +
 legacy/Data/ingsw/5/wrong.txt                 |     1 +
 legacy/Data/ingsw/50/correct.txt              |     1 +
 legacy/Data/ingsw/50/quest.txt                |     2 +
 legacy/Data/ingsw/50/wrong 2.txt              |     1 +
 legacy/Data/ingsw/50/wrong.txt                |     1 +
 legacy/Data/ingsw/69420/correct.txt           |     2 +
 legacy/Data/ingsw/69420/quest.txt             |     1 +
 legacy/Data/ingsw/69420/wrong 2.txt           |     2 +
 legacy/Data/ingsw/69420/wrong 3.txt           |     2 +
 legacy/Data/ingsw/69420/wrong.txt             |     2 +
 legacy/Data/ingsw/8/correct.txt               |     1 +
 legacy/Data/ingsw/8/quest.txt                 |     1 +
 legacy/Data/ingsw/8/wrong 2.txt               |     1 +
 legacy/Data/ingsw/8/wrong.txt                 |     1 +
 legacy/Data/ingsw/9/correct.txt               |     1 +
 legacy/Data/ingsw/9/quest.txt                 |     1 +
 legacy/Data/ingsw/9/wrong 2.txt               |     1 +
 legacy/Data/ingsw/9/wrong.txt                 |     1 +
 legacy/Data/motd.txt                          |    36 +
 Dockerfile => legacy/Dockerfile               |     0
 README.md => legacy/README.md                 |     0
 {Utils => legacy/Utils}/check-ingsw-photos.sh |     0
 {Utils => legacy/Utils}/find_duplicates.py    |     0
 {Utils => legacy/Utils}/make_questions.py     |     0
 .../Utils}/moodle-scraper/README.md           |     0
 .../Utils}/moodle-scraper/scraper.py          |     0
 .../docker-compose.yml                        |     0
 scripts/docker-compose.yml                    |    15 +
 3414 files changed, 41525 insertions(+), 709 deletions(-)
 delete mode 100644 Bot/LICENSE
 delete mode 100644 Bot/README.md
 rename {Data => data/config}/motd.txt (100%)
 create mode 100644 data/questions/diritto_unive_inf.json
 create mode 100644 data/questions/ium_unive.json
 create mode 100644 data/questions/ogas.json
 create mode 100644 data/questions/sicurezza.json
 create mode 100644 data/questions/sicurezza_appello1.json
 create mode 100644 data/questions/so1.json
 create mode 100644 data/questions/so1_new.json
 create mode 100644 data/questions/so1_unive.json
 create mode 100644 data/questions/so2.json
 rename {Bot => legacy/Bot}/AccessControl/AccessManager.cs (100%)
 rename {Bot => legacy/Bot}/ModuleLoader/IModule.cs (100%)
 rename {Bot => legacy/Bot}/ModuleLoader/ModuleLoader.cs (100%)
 rename {Bot => legacy/Bot}/Modules/OttoLinux/BotGame.cs (100%)
 rename {Bot => legacy/Bot}/Modules/OttoLinux/OttoReverse.cs (100%)
 rename {Bot => legacy/Bot}/Modules/OttoLinux/OttoScore.cs (100%)
 rename {Bot => legacy/Bot}/Modules/OttoLinux/PhotoServer.cs (100%)
 rename {Bot => legacy/Bot}/Modules/OttoLinux/Question.cs (100%)
 rename {Bot => legacy/Bot}/Modules/OttoLinux/WebReverse.cs (100%)
 rename {Bot => legacy/Bot}/Program.cs (100%)
 rename {Bot => legacy/Bot}/SoUnBot.csproj (100%)
 rename {Bot => legacy/Bot}/Telegram/TelegramBot.cs (100%)
 create mode 100755 legacy/Bot/bin/Debug/net8.0/JetBrains.Annotations.dll
 create mode 100755 legacy/Bot/bin/Debug/net8.0/Newtonsoft.Json.dll
 create mode 100755 legacy/Bot/bin/Debug/net8.0/SoUnBot
 create mode 100644 legacy/Bot/bin/Debug/net8.0/SoUnBot.deps.json
 create mode 100644 legacy/Bot/bin/Debug/net8.0/SoUnBot.dll
 create mode 100644 legacy/Bot/bin/Debug/net8.0/SoUnBot.pdb
 create mode 100644 legacy/Bot/bin/Debug/net8.0/SoUnBot.runtimeconfig.json
 create mode 100755 legacy/Bot/bin/Debug/net8.0/Telegram.Bot.Extensions.Polling.dll
 create mode 100755 legacy/Bot/bin/Debug/net8.0/Telegram.Bot.dll
 create mode 100644 legacy/Bot/obj/Debug/net8.0/.NETCoreApp,Version=v8.0.AssemblyAttributes.cs
 create mode 100644 legacy/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfo.cs
 create mode 100644 legacy/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfoInputs.cache
 create mode 100644 legacy/Bot/obj/Debug/net8.0/SoUnBot.GeneratedMSBuildEditorConfig.editorconfig
 create mode 100644 legacy/Bot/obj/Debug/net8.0/SoUnBot.GlobalUsings.g.cs
 create mode 100644 legacy/Bot/obj/Debug/net8.0/SoUnBot.assets.cache
 create mode 100644 legacy/Bot/obj/Debug/net8.0/SoUnBot.csproj.AssemblyReference.cache
 rename Data/Questions/ingsw/0120_22/wrong1.txt => legacy/Bot/obj/Debug/net8.0/SoUnBot.csproj.CopyComplete (100%)
 create mode 100644 legacy/Bot/obj/Debug/net8.0/SoUnBot.csproj.CoreCompileInputs.cache
 create mode 100644 legacy/Bot/obj/Debug/net8.0/SoUnBot.csproj.FileListAbsolute.txt
 create mode 100644 legacy/Bot/obj/Debug/net8.0/SoUnBot.dll
 create mode 100644 legacy/Bot/obj/Debug/net8.0/SoUnBot.genruntimeconfig.cache
 create mode 100644 legacy/Bot/obj/Debug/net8.0/SoUnBot.pdb
 create mode 100644 legacy/Bot/obj/Debug/net8.0/SoUnBot.sourcelink.json
 create mode 100755 legacy/Bot/obj/Debug/net8.0/apphost
 create mode 100644 legacy/Bot/obj/Debug/net8.0/ref/SoUnBot.dll
 create mode 100644 legacy/Bot/obj/Debug/net8.0/refint/SoUnBot.dll
 create mode 100644 legacy/Bot/obj/SoUnBot.csproj.nuget.dgspec.json
 create mode 100644 legacy/Bot/obj/SoUnBot.csproj.nuget.g.props
 create mode 100644 legacy/Bot/obj/SoUnBot.csproj.nuget.g.targets
 create mode 100644 legacy/Bot/obj/project.assets.json
 create mode 100644 legacy/Bot/obj/project.nuget.cache
 create mode 100644 legacy/Bot/obj/project.packagespec.json
 create mode 100644 legacy/Bot/obj/rider.project.model.nuget.info
 create mode 100644 legacy/Bot/obj/rider.project.restore.info
 rename {Bot => legacy/Bot}/run.sh (100%)
 rename {Data => legacy/Data}/Images/25.png (100%)
 rename {Data => legacy/Data}/Images/26.png (100%)
 rename {Data => legacy/Data}/Images/27.png (100%)
 rename {Data => legacy/Data}/Images/35.png (100%)
 rename {Data => legacy/Data}/Images/36.png (100%)
 rename {Data => legacy/Data}/Images/37.png (100%)
 rename {Data => legacy/Data}/Images/38.png (100%)
 rename {Data => legacy/Data}/Images/39.png (100%)
 rename {Data => legacy/Data}/Images/40.png (100%)
 rename {Data => legacy/Data}/Images/56.png (100%)
 rename {Data => legacy/Data}/Images/57.png (100%)
 rename {Data => legacy/Data}/Images/58.png (100%)
 rename {Data => legacy/Data}/Images/59.png (100%)
 rename {Data => legacy/Data}/Images/60.png (100%)
 rename {Data => legacy/Data}/Images/61.png (100%)
 rename {Data => legacy/Data}/Images/62.png (100%)
 rename {Data => legacy/Data}/Images/FDS/1positive0negative.png (100%)
 rename {Data => legacy/Data}/Images/FDS/accuracy80.png (100%)
 rename {Data => legacy/Data}/Images/FDS/matrixwhatcanwesay.png (100%)
 rename {Data => legacy/Data}/Questions/Domande Sicurezza.old (100%)
 rename {Data => legacy/Data}/Questions/diritto_unive_inf.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_102/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_102/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_102/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_102/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_2/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_2/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_2/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_2/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_3/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_3/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_3/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_3/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_32/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_32/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_32/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_32/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_4/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_4/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_4/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_4/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_7/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_7/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_7/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_7/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_8/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_8/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_8/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0000_8/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_0/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_0/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_0/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_0/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_1/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_1/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_1/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_1/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_10/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_10/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_10/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_10/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_11/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_11/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_11/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_11/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_12/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_12/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_12/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_12/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_13/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_13/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_13/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_13/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_14/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_14/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_14/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_14/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_15/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_15/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_15/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_15/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_16/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_16/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_16/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_16/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_17/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_17/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_17/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_17/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_18/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_18/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_18/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_18/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_19/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_19/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_19/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_19/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_2/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_2/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_2/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_2/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_20/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_20/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_20/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_20/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_21/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_21/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_21/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_21/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_22/quest.txt (100%)
 rename {Data/Questions/ingsw/0120_25 => legacy/Data/Questions/ingsw/0120_22}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_22/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_22/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_23/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_23/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_23/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_23/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_24/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_24/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_24/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_24/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_25/quest.txt (100%)
 rename {Data/Questions/ingsw/0120_39 => legacy/Data/Questions/ingsw/0120_25}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_25/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_25/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_26/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_26/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_26/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_26/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_27/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_27/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_27/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_27/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_28/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_28/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_28/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_28/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_29/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_29/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_29/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_29/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_3/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_3/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_3/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_3/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_30/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_30/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_30/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_30/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_31/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_31/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_31/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_31/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_32/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_32/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_32/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_32/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_33/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_33/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_33/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_33/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_34/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_34/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_34/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_34/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_35/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_35/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_35/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_35/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_36/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_36/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_36/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_36/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_37/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_37/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_37/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_37/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_38/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_38/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_38/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_38/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_39/quest.txt (100%)
 rename {Data/Questions/ingsw/0120_45 => legacy/Data/Questions/ingsw/0120_39}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_39/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_39/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_4/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_4/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_4/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_4/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_40/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_40/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_40/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_40/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_41/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_41/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_41/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_41/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_42/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_42/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_42/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_42/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_43/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_43/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_43/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_43/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_44/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_44/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_44/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_44/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_45/quest.txt (100%)
 rename {Data/Questions/ingsw/0210_2 => legacy/Data/Questions/ingsw/0120_45}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_45/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_45/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_46/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_46/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_46/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_46/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_47/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_47/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_47/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_47/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_48/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_48/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_48/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_48/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_49/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_49/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_49/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_49/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_5/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_5/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_5/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_5/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_6/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_6/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_6/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_6/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_7/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_7/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_7/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_7/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_8/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_8/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_8/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_8/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_9/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_9/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_9/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0120_9/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0121_34/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0121_34/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0121_34/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0121_34/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_0/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_0/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_0/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_0/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_1/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_1/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_1/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_1/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_10/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_10/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_10/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_10/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_11/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_11/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_11/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_11/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_12/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_12/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_12/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_12/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_13/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_13/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_13/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_13/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_14/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_14/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_14/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_14/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_15/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_15/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_15/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_15/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_16/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_16/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_16/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_16/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_17/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_17/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_17/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_17/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_18/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_18/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_18/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_18/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_19/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_19/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_19/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_19/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_2/quest.txt (100%)
 rename {Data/Questions/ingsw/0210_28 => legacy/Data/Questions/ingsw/0210_2}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_2/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_2/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_20/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_20/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_20/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_20/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_21/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_21/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_21/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_21/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_22/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_22/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_22/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_22/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_23/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_23/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_23/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_23/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_24/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_24/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_24/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_24/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_25/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_25/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_25/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_25/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_26/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_26/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_26/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_26/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_27/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_27/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_27/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_27/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_28/quest.txt (100%)
 rename {Data/Questions/ingsw/0210_34 => legacy/Data/Questions/ingsw/0210_28}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_28/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_28/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_29/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_29/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_29/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_29/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_3/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_3/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_3/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_3/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_30/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_30/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_30/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_30/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_31/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_31/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_31/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_31/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_32/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_32/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_32/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_32/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_33/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_33/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_33/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_33/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_34/quest.txt (100%)
 rename {Data/Questions/ingsw/0210_37 => legacy/Data/Questions/ingsw/0210_34}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_34/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_34/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_35/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_35/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_35/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_35/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_36/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_36/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_36/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_36/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_37/quest.txt (100%)
 rename {Data/Questions/ingsw/0324_26 => legacy/Data/Questions/ingsw/0210_37}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_37/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_37/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_38/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_38/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_38/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_38/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_39/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_39/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_39/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_39/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_4/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_4/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_4/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_4/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_40/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_40/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_40/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_40/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_41/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_41/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_41/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_41/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_42/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_42/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_42/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_42/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_43/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_43/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_43/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_43/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_44/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_44/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_44/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_44/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_45/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_45/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_45/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_45/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_46/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_46/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_46/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_46/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_47/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_47/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_47/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_47/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_48/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_48/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_48/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_48/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_49/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_49/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_49/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_49/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_5/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_5/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_5/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_5/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_6/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_6/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_6/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_6/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_7/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_7/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_7/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_7/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_8/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_8/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_8/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_8/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_9/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_9/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_9/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0210_9/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0221_18/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0221_18/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0221_18/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0221_18/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0221_28/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0221_28/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0221_28/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0221_28/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0221_32/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0221_32/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0221_32/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0221_32/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_24/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_24/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_24/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_24/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_27/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_27/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_27/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_27/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_33/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_33/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_33/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_33/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_35/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_35/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_35/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_35/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_39/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_39/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_39/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_39/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_41/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_41/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_41/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_41/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_5/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_5/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_5/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_5/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_50/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_50/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_50/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_50/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_7/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_7/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_7/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0222_7/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_1/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_1/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_1/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_1/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_10/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_10/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_10/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_10/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_11/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_11/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_11/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_11/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_12/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_12/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_12/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_12/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_13/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_13/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_13/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_13/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_14/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_14/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_14/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_14/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_15/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_15/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_15/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_15/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_16/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_16/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_16/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_16/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_17/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_17/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_17/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_17/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_18/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_18/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_18/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_18/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_19/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_19/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_19/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_19/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_2/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_2/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_2/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_2/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_20/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_20/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_20/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_20/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_21/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_21/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_21/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_21/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_23/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_23/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_23/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_23/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_24/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_24/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_24/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_24/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_27/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_27/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_27/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_27/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_28/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_28/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_28/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_28/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_29/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_29/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_29/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_29/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_30/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_30/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_30/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_30/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_31/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_31/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_31/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_31/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_32/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_32/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_32/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_32/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_36/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_36/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_36/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_36/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_37/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_37/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_37/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_37/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_38/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_38/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_38/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_38/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_4/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_4/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_4/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_4/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_40/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_40/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_40/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_40/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_8/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_8/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_8/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_8/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_9/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_9/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_9/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0321_9/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_0/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_0/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_0/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_0/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_1/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_1/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_1/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_1/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_10/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_10/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_10/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_10/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_11/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_11/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_11/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_11/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_12/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_12/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_12/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_12/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_13/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_13/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_13/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_13/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_14/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_14/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_14/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_14/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_15/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_15/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_15/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_15/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_16/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_16/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_16/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_16/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_17/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_17/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_17/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_17/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_18/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_18/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_18/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_18/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_19/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_19/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_19/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_19/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_2/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_2/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_2/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_2/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_20/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_20/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_20/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_20/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_21/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_21/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_21/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_21/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_22/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_22/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_22/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_22/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_23/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_23/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_23/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_23/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_24/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_24/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_24/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_24/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_25/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_25/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_25/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_25/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_26/quest.txt (100%)
 rename {Data/Questions/ingsw/0324_35 => legacy/Data/Questions/ingsw/0324_26}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_26/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_26/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_27/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_27/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_27/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_27/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_28/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_28/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_28/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_28/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_29/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_29/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_29/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_29/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_3/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_3/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_3/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_3/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_30/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_30/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_30/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_30/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_31/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_31/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_31/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_31/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_32/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_32/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_32/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_32/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_33/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_33/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_33/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_33/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_34/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_34/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_34/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_34/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_35/quest.txt (100%)
 rename {Data/Questions/ingsw/0324_42 => legacy/Data/Questions/ingsw/0324_35}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_35/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_35/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_36/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_36/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_36/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_36/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_37/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_37/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_37/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_37/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_38/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_38/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_38/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_38/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_39/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_39/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_39/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_39/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_4/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_4/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_4/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_4/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_40/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_40/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_40/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_40/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_41/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_41/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_41/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_41/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_42/quest.txt (100%)
 rename {Data/Questions/ingsw/0324_47 => legacy/Data/Questions/ingsw/0324_42}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_42/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_42/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_43/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_43/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_43/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_43/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_44/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_44/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_44/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_44/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_45/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_45/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_45/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_45/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_46/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_46/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_46/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_46/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_47/quest.txt (100%)
 rename {Data/Questions/ingsw/0613_0 => legacy/Data/Questions/ingsw/0324_47}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_47/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_47/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_48/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_48/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_48/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_48/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_49/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_49/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_49/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_49/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_5/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_5/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_5/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_5/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_6/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_6/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_6/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_6/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_7/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_7/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_7/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_7/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_8/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_8/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_8/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_8/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_9/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_9/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_9/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0324_9/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0422-16/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0422-16/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0422-16/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0422-16/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_0/quest.txt (100%)
 rename {Data/Questions/ingsw/0613_14 => legacy/Data/Questions/ingsw/0613_0}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_0/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_0/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_1/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_1/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_1/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_1/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_10/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_10/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_10/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_10/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_11/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_11/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_11/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_11/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_12/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_12/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_12/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_12/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_13/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_13/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_13/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_13/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_14/quest.txt (100%)
 rename {Data/Questions/ingsw/0613_19 => legacy/Data/Questions/ingsw/0613_14}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_14/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_14/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_15/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_15/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_15/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_15/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_16/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_16/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_16/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_16/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_17/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_17/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_17/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_17/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_18/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_18/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_18/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_18/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_19/quest.txt (100%)
 rename {Data/Questions/ingsw/0613_2 => legacy/Data/Questions/ingsw/0613_19}/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_19/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_19/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_2/quest.txt (100%)
 rename Data/Questions/ingsw/0613_2/wrong2.txt => legacy/Data/Questions/ingsw/0613_2/wrong1.txt (100%)
 rename Data/Questions/ingsw/0613_2/wrong3.txt => legacy/Data/Questions/ingsw/0613_2/wrong2.txt (100%)
 create mode 100644 legacy/Data/Questions/ingsw/0613_2/wrong3.txt
 rename {Data => legacy/Data}/Questions/ingsw/0613_20/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_20/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_20/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_20/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_21/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_21/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_21/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_21/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_22/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_22/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_22/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_22/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_23/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_23/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_23/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_23/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_24/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_24/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_24/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_24/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_25/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_25/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_25/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_25/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_26/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_26/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_26/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_26/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_27/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_27/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_27/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_27/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_28/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_28/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_28/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_28/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_29/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_29/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_29/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_29/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_3/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_3/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_3/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_3/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_30/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_30/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_30/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_30/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_31/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_31/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_31/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_31/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_32/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_32/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_32/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_32/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_33/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_33/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_33/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_33/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_34/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_34/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_34/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_34/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_35/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_35/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_35/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_35/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_36/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_36/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_36/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_36/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_37/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_37/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_37/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_37/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_38/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_38/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_38/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_38/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_39/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_39/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_39/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_39/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_4/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_4/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_4/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_4/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_40/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_40/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_40/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_40/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_41/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_41/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_41/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_41/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_42/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_42/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_42/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_42/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_43/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_43/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_43/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_43/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_44/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_44/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_44/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_44/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_45/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_45/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_45/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_45/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_46/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_46/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_46/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_46/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_47/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_47/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_47/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_47/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_48/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_48/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_48/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_48/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_49/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_49/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_49/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_49/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_5/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_5/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_5/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_5/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_6/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_6/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_6/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_6/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_7/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_7/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_7/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_7/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_8/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_8/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_8/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_8/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_9/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_9/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_9/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0613_9/wrong3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_0/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_0/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_0/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_0/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_1/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_1/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_1/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_1/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_10/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_10/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_10/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_10/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_13/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_13/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_13/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_13/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_14/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_14/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_14/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_14/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_17/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_17/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_17/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_17/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_19/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_19/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_19/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_19/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_2/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_2/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_2/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_2/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_21/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_21/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_21/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_21/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_22/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_22/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_22/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_22/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_24/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_24/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_24/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_24/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_3/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_3/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_3/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_3/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_32/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_32/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_32/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_32/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_35/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_35/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_35/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_35/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_36/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_36/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_36/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_36/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_39/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_39/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_39/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_39/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_6/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_6/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_6/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_6/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_6/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_9/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_9/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_9/wrong0.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0621_9/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_1/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_1/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_1/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_1/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_2/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_2/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_2/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_2/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_3/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_3/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_3/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_3/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_4/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_4/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_4/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_4/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_5/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_5/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_5/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_5/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_6/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_6/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_6/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_6/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_7/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_7/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_7/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_7/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_8/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_8/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_8/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_8/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_9/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_9/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_9/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0622_9/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_1/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_1/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_1/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_1/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_10/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_10/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_10/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_10/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_13/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_13/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_13/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_13/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_15/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_15/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_15/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_15/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_17/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_17/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_17/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_17/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_18/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_18/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_18/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_18/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_19/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_19/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_19/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_19/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_21/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_21/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_21/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_21/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_28/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_28/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_28/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_28/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_29/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_29/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_29/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_29/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_32/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_32/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_32/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_32/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_33/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_33/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_33/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_33/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_34/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_34/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_34/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_34/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_36/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_36/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_36/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_36/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_4/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_4/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_4/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_4/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_5/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_5/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_5/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_5/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_6/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_6/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_6/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_6/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_8/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_8/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_8/wrong1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0721_8/wrong2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_1/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_1/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_1/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_1/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_10/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_10/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_10/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_10/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_11/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_11/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_11/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_11/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_12/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_12/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_12/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_12/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_13/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_13/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_13/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_13/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_14/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_14/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_14/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_14/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_15/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_15/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_15/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_15/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_16/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_16/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_16/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_16/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_17/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_17/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_17/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_17/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_18/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_18/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_18/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_18/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_19/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_19/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_19/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_19/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_2/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_2/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_2/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_2/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_20/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_20/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_20/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_20/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_21/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_21/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_21/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_21/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_22/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_22/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_22/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_22/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_23/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_23/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_23/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_23/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_24/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_24/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_24/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_24/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_25/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_25/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_25/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_25/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_26/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_26/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_26/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_26/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_3/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_3/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_3/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_3/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_4/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_4/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_4/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_4/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_5/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_5/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_5/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_5/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_6/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_6/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_6/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_6/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_7/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_7/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_7/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_7/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_8/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_8/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_8/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_8/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_9/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_9/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_9/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0722_9/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_10/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_10/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_10/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_10/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_11/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_11/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_11/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_11/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_12/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_12/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_12/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_12/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_13/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_13/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_13/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_13/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_14/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_14/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_14/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_14/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_15/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_15/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_15/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_15/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_16/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_16/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_16/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_16/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_17/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_17/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_17/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_17/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_18/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_18/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_18/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_18/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_3/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_3/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_3/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_3/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_4/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_4/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_4/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_4/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_5/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_5/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_5/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_5/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_6/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_6/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_6/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_6/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_7/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_7/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_7/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_7/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_8/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_8/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_8/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_8/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_9/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_9/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_9/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/0922_9/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/10/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/10/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/10/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/10/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/11/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/11/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/11/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/11/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_1/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_1/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_1/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_1/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_10/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_10/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_10/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_10/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_11/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_11/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_11/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_11/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_12/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_12/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_12/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_12/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_13/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_13/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_13/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_13/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_14/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_14/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_14/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_14/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_15/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_15/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_15/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_15/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_16/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_16/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_16/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_16/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_19/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_19/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_19/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_19/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_2/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_2/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_2/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_2/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_20/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_20/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_20/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_20/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_21/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_21/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_21/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_21/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_22/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_22/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_22/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_22/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_23/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_23/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_23/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_23/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_24/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_24/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_24/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_24/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_25/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_25/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_25/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_25/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_26/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_26/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_26/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_26/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_27/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_27/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_27/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_27/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_28/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_28/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_28/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_28/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_29/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_29/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_29/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_29/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_3/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_3/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_3/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_3/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_30/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_30/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_30/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_30/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_31/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_31/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_31/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_31/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_33/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_33/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_33/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_33/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_34/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_34/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_34/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_34/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_35/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_35/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_35/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_35/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_36/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_36/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_36/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_36/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_37/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_37/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_37/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_37/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_38/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_38/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_38/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_38/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_39/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_39/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_39/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_39/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_4/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_4/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_4/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_4/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_40/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_40/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_40/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_40/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_42/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_42/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_42/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_42/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_43/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_43/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_43/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_43/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_44/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_44/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_44/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_44/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_45/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_45/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_45/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_45/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_46/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_46/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_46/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_46/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_47/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_47/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_47/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_47/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_48/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_48/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_48/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_48/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_49/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_49/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_49/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_49/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_5/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_5/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_5/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_5/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_50/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_50/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_50/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_50/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_6/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_6/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_6/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_6/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_7/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_7/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_7/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_7/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_8/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_8/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_8/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_8/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_9/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_9/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_9/wrong 1.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/1122_9/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/12/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/12/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/12/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/12/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/16/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/16/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/16/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/16/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/17/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/17/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/17/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/17/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/19/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/19/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/19/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/19/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/2/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/2/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/2/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/2/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/20/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/20/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/20/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/20/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/21/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/21/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/21/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/21/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/22/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/22/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/22/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/22/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/24/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/24/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/24/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/24/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/25/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/25/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/25/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/25/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/26/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/26/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/26/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/26/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/32/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/32/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/32/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/32/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/33/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/33/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/33/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/33/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/34/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/34/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/34/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/34/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/35/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/35/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/35/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/35/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/39/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/39/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/39/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/39/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/4/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/4/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/4/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/4/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/43/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/43/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/43/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/43/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/44/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/44/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/44/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/44/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/45/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/45/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/45/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/45/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/46/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/46/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/46/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/46/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/47/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/47/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/47/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/47/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/48/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/48/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/48/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/48/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/49/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/49/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/49/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/49/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/5/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/5/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/5/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/5/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/50/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/50/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/50/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/50/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/69420/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/69420/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/69420/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/69420/wrong 3.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/69420/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/8/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/8/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/8/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/8/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/9/correct.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/9/quest.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/9/wrong 2.txt (100%)
 rename {Data => legacy/Data}/Questions/ingsw/9/wrong.txt (100%)
 rename {Data => legacy/Data}/Questions/ium_unive.txt (100%)
 rename {Data => legacy/Data}/Questions/ogas.txt (100%)
 rename {Data => legacy/Data}/Questions/sicurezza.txt (100%)
 rename {Data => legacy/Data}/Questions/sicurezza_appello1.txt (100%)
 rename {Data => legacy/Data}/Questions/so1.txt (100%)
 rename {Data => legacy/Data}/Questions/so1_new.json (100%)
 rename {Data => legacy/Data}/Questions/so1_unive.txt (100%)
 rename {Data => legacy/Data}/Questions/so2.txt (100%)
 create mode 100644 legacy/Data/ingsw/0000_102/correct.txt
 create mode 100644 legacy/Data/ingsw/0000_102/quest.txt
 create mode 100644 legacy/Data/ingsw/0000_102/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0000_102/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0000_2/correct.txt
 create mode 100644 legacy/Data/ingsw/0000_2/quest.txt
 create mode 100644 legacy/Data/ingsw/0000_2/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0000_2/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0000_3/correct.txt
 create mode 100644 legacy/Data/ingsw/0000_3/quest.txt
 create mode 100644 legacy/Data/ingsw/0000_3/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0000_3/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0000_32/correct.txt
 create mode 100644 legacy/Data/ingsw/0000_32/quest.txt
 create mode 100644 legacy/Data/ingsw/0000_32/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0000_32/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0000_4/correct.txt
 create mode 100644 legacy/Data/ingsw/0000_4/quest.txt
 create mode 100644 legacy/Data/ingsw/0000_4/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0000_4/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0000_7/correct.txt
 create mode 100644 legacy/Data/ingsw/0000_7/quest.txt
 create mode 100644 legacy/Data/ingsw/0000_7/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0000_7/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0000_8/correct.txt
 create mode 100644 legacy/Data/ingsw/0000_8/quest.txt
 create mode 100644 legacy/Data/ingsw/0000_8/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0000_8/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_0/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_0/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_0/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_0/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_1/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_1/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_1/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_1/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_10/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_10/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_10/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_10/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_11/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_11/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_11/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_11/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_12/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_12/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_12/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_12/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_13/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_13/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_13/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_13/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_14/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_14/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_14/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_14/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_15/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_15/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_15/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_15/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_16/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_16/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_16/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_16/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_17/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_17/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_17/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_17/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_18/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_18/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_18/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_18/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_19/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_19/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_19/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_19/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_2/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_2/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_2/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_2/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_20/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_20/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_20/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_20/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_21/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_21/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_21/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_21/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_22/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_22/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_22/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_22/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0120_23/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_23/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_23/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_23/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_24/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_24/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_24/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_24/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_25/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_25/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_25/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_25/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0120_26/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_26/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_26/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_26/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_27/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_27/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_27/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_27/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_28/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_28/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_28/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_28/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0120_29/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_29/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_29/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_29/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_3/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_3/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_3/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_3/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_30/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_30/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_30/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_30/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_31/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_31/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_31/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_31/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_32/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_32/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_32/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_32/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_33/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_33/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_33/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_33/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_34/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_34/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_34/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_34/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0120_35/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_35/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_35/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_35/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0120_36/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_36/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_36/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_36/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_37/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_37/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_37/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_37/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_38/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_38/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_38/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_38/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_39/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_39/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_39/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_39/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0120_4/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_4/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_4/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_4/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_40/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_40/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_40/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_40/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_41/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_41/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_41/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_41/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_42/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_42/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_42/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_42/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_43/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_43/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_43/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_43/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0120_44/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_44/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_44/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_44/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_45/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_45/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_45/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_45/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0120_46/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_46/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_46/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_46/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_47/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_47/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_47/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_47/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_48/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_48/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_48/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_48/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_49/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_49/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_49/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_49/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_5/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_5/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_5/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_5/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0120_6/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_6/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_6/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_6/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_7/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_7/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_7/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_7/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_8/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_8/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_8/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_8/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0120_9/correct.txt
 create mode 100644 legacy/Data/ingsw/0120_9/quest.txt
 create mode 100644 legacy/Data/ingsw/0120_9/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0120_9/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0121_34/correct.txt
 create mode 100644 legacy/Data/ingsw/0121_34/quest.txt
 create mode 100644 legacy/Data/ingsw/0121_34/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0121_34/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_0/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_0/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_0/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_0/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_1/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_1/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_1/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_1/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0210_10/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_10/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_10/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_10/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_11/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_11/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_11/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_11/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0210_12/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_12/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_12/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_12/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0210_13/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_13/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_13/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_13/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_14/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_14/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_14/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_14/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_15/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_15/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_15/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_15/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_16/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_16/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_16/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_16/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_17/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_17/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_17/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_17/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_18/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_18/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_18/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_18/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_19/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_19/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_19/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_19/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_2/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_2/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_2/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_2/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0210_20/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_20/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_20/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_20/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_21/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_21/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_21/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_21/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_22/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_22/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_22/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_22/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_23/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_23/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_23/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_23/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_24/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_24/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_24/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_24/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_25/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_25/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_25/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_25/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_26/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_26/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_26/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_26/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_27/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_27/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_27/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_27/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0210_28/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_28/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_28/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_28/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0210_29/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_29/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_29/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_29/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_3/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_3/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_3/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_3/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0210_30/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_30/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_30/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_30/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_31/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_31/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_31/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_31/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_32/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_32/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_32/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_32/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_33/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_33/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_33/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_33/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_34/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_34/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_34/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_34/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0210_35/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_35/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_35/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_35/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_36/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_36/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_36/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_36/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_37/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_37/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_37/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_37/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0210_38/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_38/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_38/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_38/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_39/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_39/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_39/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_39/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_4/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_4/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_4/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_4/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_40/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_40/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_40/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_40/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_41/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_41/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_41/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_41/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_42/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_42/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_42/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_42/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0210_43/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_43/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_43/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_43/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_44/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_44/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_44/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_44/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0210_45/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_45/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_45/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_45/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_46/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_46/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_46/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_46/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_47/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_47/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_47/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_47/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_48/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_48/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_48/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_48/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_49/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_49/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_49/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_49/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_5/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_5/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_5/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_5/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_6/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_6/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_6/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_6/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_7/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_7/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_7/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_7/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_8/correct.txt
 create mode 100644 legacy/Data/ingsw/0210_8/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_8/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_8/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_9/quest.txt
 create mode 100644 legacy/Data/ingsw/0210_9/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0210_9/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0210_9/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0221_18/correct.txt
 create mode 100644 legacy/Data/ingsw/0221_18/quest.txt
 create mode 100644 legacy/Data/ingsw/0221_18/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0221_18/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0221_28/correct.txt
 create mode 100644 legacy/Data/ingsw/0221_28/quest.txt
 create mode 100644 legacy/Data/ingsw/0221_28/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0221_28/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0221_32/correct.txt
 create mode 100644 legacy/Data/ingsw/0221_32/quest.txt
 create mode 100644 legacy/Data/ingsw/0221_32/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0221_32/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0222_24/correct.txt
 create mode 100644 legacy/Data/ingsw/0222_24/quest.txt
 create mode 100644 legacy/Data/ingsw/0222_24/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0222_24/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0222_27/correct.txt
 create mode 100644 legacy/Data/ingsw/0222_27/quest.txt
 create mode 100644 legacy/Data/ingsw/0222_27/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0222_27/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0222_33/correct.txt
 create mode 100644 legacy/Data/ingsw/0222_33/quest.txt
 create mode 100644 legacy/Data/ingsw/0222_33/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0222_33/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0222_35/correct.txt
 create mode 100644 legacy/Data/ingsw/0222_35/quest.txt
 create mode 100644 legacy/Data/ingsw/0222_35/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0222_35/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0222_39/correct.txt
 create mode 100644 legacy/Data/ingsw/0222_39/quest.txt
 create mode 100644 legacy/Data/ingsw/0222_39/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0222_39/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0222_41/correct.txt
 create mode 100644 legacy/Data/ingsw/0222_41/quest.txt
 create mode 100644 legacy/Data/ingsw/0222_41/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0222_41/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0222_5/correct.txt
 create mode 100644 legacy/Data/ingsw/0222_5/quest.txt
 create mode 100644 legacy/Data/ingsw/0222_5/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0222_5/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0222_50/correct.txt
 create mode 100644 legacy/Data/ingsw/0222_50/quest.txt
 create mode 100644 legacy/Data/ingsw/0222_50/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0222_50/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0222_7/correct.txt
 create mode 100644 legacy/Data/ingsw/0222_7/quest.txt
 create mode 100644 legacy/Data/ingsw/0222_7/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0222_7/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0321_1/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_1/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_1/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_1/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_10/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_10/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_10/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_10/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_11/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_11/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_11/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_11/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_12/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_12/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_12/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_12/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_13/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_13/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_13/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_13/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_14/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_14/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_14/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_14/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_15/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_15/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_15/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_15/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_16/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_16/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_16/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_16/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_17/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_17/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_17/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_17/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_18/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_18/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_18/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_18/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_19/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_19/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_19/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_19/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_2/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_2/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_2/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_2/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_20/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_20/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_20/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_20/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_21/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_21/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_21/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_21/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_23/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_23/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_23/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_23/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_24/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_24/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_24/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_24/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_27/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_27/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_27/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_27/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_28/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_28/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_28/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_28/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_29/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_29/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_29/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_29/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_30/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_30/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_30/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_30/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_31/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_31/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_31/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_31/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_32/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_32/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_32/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_32/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_36/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_36/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_36/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_36/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_37/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_37/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_37/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_37/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_38/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_38/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_38/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_38/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_4/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_4/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_4/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_4/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_40/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_40/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_40/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_40/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_8/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_8/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_8/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_8/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0321_9/correct.txt
 create mode 100644 legacy/Data/ingsw/0321_9/quest.txt
 create mode 100644 legacy/Data/ingsw/0321_9/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0321_9/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0324_0/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_0/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_0/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_0/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_1/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_1/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_1/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_1/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0324_10/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_10/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_10/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_10/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_11/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_11/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_11/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_11/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0324_12/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_12/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_12/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_12/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_13/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_13/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_13/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_13/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_14/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_14/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_14/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_14/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_15/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_15/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_15/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_15/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_16/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_16/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_16/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_16/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_17/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_17/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_17/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_17/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_18/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_18/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_18/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_18/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_19/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_19/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_19/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_19/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_2/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_2/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_2/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_2/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_20/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_20/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_20/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_20/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_21/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_21/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_21/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_21/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_22/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_22/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_22/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_22/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_23/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_23/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_23/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_23/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_24/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_24/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_24/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_24/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_25/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_25/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_25/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_25/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_26/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_26/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_26/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_26/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0324_27/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_27/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_27/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_27/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_28/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_28/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_28/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_28/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_29/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_29/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_29/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_29/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_3/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_3/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_3/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_3/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_30/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_30/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_30/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_30/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0324_31/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_31/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_31/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_31/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_32/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_32/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_32/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_32/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_33/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_33/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_33/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_33/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_34/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_34/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_34/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_34/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_35/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_35/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_35/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_35/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0324_36/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_36/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_36/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_36/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_37/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_37/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_37/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_37/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_38/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_38/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_38/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_38/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_39/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_39/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_39/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_39/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_4/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_4/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_4/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_4/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_40/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_40/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_40/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_40/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_41/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_41/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_41/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_41/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0324_42/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_42/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_42/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_42/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0324_43/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_43/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_43/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_43/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_44/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_44/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_44/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_44/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_45/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_45/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_45/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_45/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_46/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_46/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_46/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_46/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_47/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_47/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_47/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_47/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0324_48/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_48/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_48/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_48/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0324_49/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_49/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_49/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_49/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_5/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_5/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_5/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_5/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_6/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_6/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_6/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_6/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_7/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_7/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_7/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_7/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_8/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_8/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_8/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_8/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0324_9/correct.txt
 create mode 100644 legacy/Data/ingsw/0324_9/quest.txt
 create mode 100644 legacy/Data/ingsw/0324_9/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0324_9/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0422-16/correct.txt
 create mode 100644 legacy/Data/ingsw/0422-16/quest.txt
 create mode 100644 legacy/Data/ingsw/0422-16/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0422-16/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_0/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_0/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_0/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_0/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0613_1/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_1/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_1/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_1/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_10/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_10/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_10/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_10/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_11/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_11/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_11/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_11/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_12/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_12/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_12/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_12/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_13/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_13/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_13/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_13/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_14/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_14/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_14/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_14/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0613_15/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_15/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_15/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_15/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_16/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_16/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_16/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_16/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_17/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_17/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_17/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_17/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_18/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_18/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_18/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_18/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_19/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_19/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_19/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_19/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0613_2/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_2/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_2/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_2/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0613_20/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_20/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_20/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_20/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_21/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_21/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_21/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_21/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_22/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_22/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_22/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_22/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_23/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_23/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_23/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_23/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_24/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_24/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_24/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_24/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_25/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_25/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_25/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_25/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_26/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_26/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_26/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_26/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_27/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_27/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_27/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_27/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0613_28/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_28/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_28/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_28/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_29/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_29/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_29/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_29/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_3/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_3/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_3/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_3/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_30/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_30/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_30/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_30/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_31/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_31/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_31/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_31/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_32/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_32/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_32/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_32/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_33/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_33/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_33/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_33/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_34/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_34/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_34/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_34/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_35/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_35/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_35/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_35/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_36/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_36/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_36/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_36/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_37/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_37/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_37/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_37/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_38/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_38/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_38/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_38/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0613_39/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_39/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_39/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_39/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_4/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_4/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_4/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_4/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_40/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_40/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_40/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_40/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0613_41/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_41/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_41/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_41/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0613_42/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_42/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_42/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_42/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_43/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_43/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_43/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_43/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_44/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_44/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_44/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_44/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_45/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_45/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_45/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_45/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_46/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_46/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_46/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_46/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_47/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_47/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_47/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_47/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_48/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_48/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_48/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_48/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_49/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_49/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_49/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_49/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_5/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_5/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_5/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_5/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_6/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_6/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_6/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_6/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_7/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_7/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_7/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_7/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_8/correct.txt
 create mode 100644 legacy/Data/ingsw/0613_8/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_8/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_8/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_9/quest.txt
 create mode 100644 legacy/Data/ingsw/0613_9/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0613_9/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0613_9/wrong3.txt
 create mode 100644 legacy/Data/ingsw/0621_0/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_0/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_0/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_0/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_1/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_1/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_1/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_1/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0621_10/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_10/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_10/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_10/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_13/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_13/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_13/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_13/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_14/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_14/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_14/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_14/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_17/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_17/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_17/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_17/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_19/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_19/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_19/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_19/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_2/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_2/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_2/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_2/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_21/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_21/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_21/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_21/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_22/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_22/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_22/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_22/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_24/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_24/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_24/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_24/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_3/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_3/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_3/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_3/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_32/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_32/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_32/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_32/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_35/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_35/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_35/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_35/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_36/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_36/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_36/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_36/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_39/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_39/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_39/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_39/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_6/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_6/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_6/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_6/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0621_6/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0621_9/correct.txt
 create mode 100644 legacy/Data/ingsw/0621_9/quest.txt
 create mode 100644 legacy/Data/ingsw/0621_9/wrong0.txt
 create mode 100644 legacy/Data/ingsw/0621_9/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0622_1/correct.txt
 create mode 100644 legacy/Data/ingsw/0622_1/quest.txt
 create mode 100644 legacy/Data/ingsw/0622_1/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0622_1/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0622_2/correct.txt
 create mode 100644 legacy/Data/ingsw/0622_2/quest.txt
 create mode 100644 legacy/Data/ingsw/0622_2/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0622_2/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0622_3/correct.txt
 create mode 100644 legacy/Data/ingsw/0622_3/quest.txt
 create mode 100644 legacy/Data/ingsw/0622_3/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0622_3/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0622_4/correct.txt
 create mode 100644 legacy/Data/ingsw/0622_4/quest.txt
 create mode 100644 legacy/Data/ingsw/0622_4/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0622_4/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0622_5/correct.txt
 create mode 100644 legacy/Data/ingsw/0622_5/quest.txt
 create mode 100644 legacy/Data/ingsw/0622_5/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0622_5/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0622_6/correct.txt
 create mode 100644 legacy/Data/ingsw/0622_6/quest.txt
 create mode 100644 legacy/Data/ingsw/0622_6/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0622_6/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0622_7/correct.txt
 create mode 100644 legacy/Data/ingsw/0622_7/quest.txt
 create mode 100644 legacy/Data/ingsw/0622_7/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0622_7/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0622_8/correct.txt
 create mode 100644 legacy/Data/ingsw/0622_8/quest.txt
 create mode 100644 legacy/Data/ingsw/0622_8/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0622_8/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0622_9/correct.txt
 create mode 100644 legacy/Data/ingsw/0622_9/quest.txt
 create mode 100644 legacy/Data/ingsw/0622_9/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0622_9/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0721_1/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_1/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_1/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_1/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_10/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_10/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_10/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_10/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_13/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_13/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_13/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_13/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_15/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_15/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_15/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_15/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_17/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_17/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_17/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_17/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_18/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_18/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_18/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_18/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_19/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_19/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_19/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_19/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_21/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_21/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_21/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_21/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_28/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_28/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_28/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_28/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_29/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_29/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_29/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_29/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_32/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_32/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_32/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_32/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_33/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_33/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_33/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_33/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_34/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_34/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_34/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_34/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_36/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_36/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_36/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_36/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_4/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_4/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_4/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_4/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_5/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_5/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_5/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_5/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_6/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_6/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_6/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_6/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0721_8/correct.txt
 create mode 100644 legacy/Data/ingsw/0721_8/quest.txt
 create mode 100644 legacy/Data/ingsw/0721_8/wrong1.txt
 create mode 100644 legacy/Data/ingsw/0721_8/wrong2.txt
 create mode 100644 legacy/Data/ingsw/0722_1/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_1/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_1/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_1/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_10/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_10/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_10/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_10/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_11/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_11/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_11/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_11/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_12/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_12/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_12/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_12/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_13/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_13/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_13/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_13/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_14/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_14/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_14/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_14/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_15/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_15/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_15/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_15/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_16/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_16/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_16/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_16/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_17/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_17/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_17/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_17/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_18/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_18/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_18/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_18/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_19/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_19/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_19/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_19/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_2/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_2/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_2/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_2/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_20/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_20/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_20/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_20/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_21/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_21/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_21/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_21/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_22/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_22/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_22/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_22/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_23/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_23/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_23/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_23/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_24/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_24/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_24/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_24/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_25/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_25/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_25/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_25/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_26/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_26/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_26/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_26/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_3/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_3/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_3/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_3/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_4/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_4/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_4/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_4/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_5/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_5/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_5/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_5/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_6/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_6/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_6/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_6/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_7/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_7/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_7/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_7/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_8/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_8/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_8/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_8/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0722_9/correct.txt
 create mode 100644 legacy/Data/ingsw/0722_9/quest.txt
 create mode 100644 legacy/Data/ingsw/0722_9/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0722_9/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_10/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_10/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_10/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_10/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_11/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_11/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_11/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_11/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_12/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_12/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_12/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_12/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_13/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_13/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_13/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_13/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_14/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_14/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_14/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_14/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_15/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_15/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_15/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_15/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_16/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_16/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_16/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_16/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_17/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_17/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_17/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_17/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_18/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_18/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_18/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_18/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_3/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_3/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_3/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_3/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_4/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_4/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_4/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_4/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_5/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_5/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_5/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_5/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_6/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_6/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_6/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_6/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_7/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_7/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_7/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_7/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_8/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_8/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_8/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_8/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/0922_9/correct.txt
 create mode 100644 legacy/Data/ingsw/0922_9/quest.txt
 create mode 100644 legacy/Data/ingsw/0922_9/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/0922_9/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/10/correct.txt
 create mode 100644 legacy/Data/ingsw/10/quest.txt
 create mode 100644 legacy/Data/ingsw/10/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/10/wrong.txt
 create mode 100644 legacy/Data/ingsw/11/correct.txt
 create mode 100644 legacy/Data/ingsw/11/quest.txt
 create mode 100644 legacy/Data/ingsw/11/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/11/wrong.txt
 create mode 100644 legacy/Data/ingsw/1122_1/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_1/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_1/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_1/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_10/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_10/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_10/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_10/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_11/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_11/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_11/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_11/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_12/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_12/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_12/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_12/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_13/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_13/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_13/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_13/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_14/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_14/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_14/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_14/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_15/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_15/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_15/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_15/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_16/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_16/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_16/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_16/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_19/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_19/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_19/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_19/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_2/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_2/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_2/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_2/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_20/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_20/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_20/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_20/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_21/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_21/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_21/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_21/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_22/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_22/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_22/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_22/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_23/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_23/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_23/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_23/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_24/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_24/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_24/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_24/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_25/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_25/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_25/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_25/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_26/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_26/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_26/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_26/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_27/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_27/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_27/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_27/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_28/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_28/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_28/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_28/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_29/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_29/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_29/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_29/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_3/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_3/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_3/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_3/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_30/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_30/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_30/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_30/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_31/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_31/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_31/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_31/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_33/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_33/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_33/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_33/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_34/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_34/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_34/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_34/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_35/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_35/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_35/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_35/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_36/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_36/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_36/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_36/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_37/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_37/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_37/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_37/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_38/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_38/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_38/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_38/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_39/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_39/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_39/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_39/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_4/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_4/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_4/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_4/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_40/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_40/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_40/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_40/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_42/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_42/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_42/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_42/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_43/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_43/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_43/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_43/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_44/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_44/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_44/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_44/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_45/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_45/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_45/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_45/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_46/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_46/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_46/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_46/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_47/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_47/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_47/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_47/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_48/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_48/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_48/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_48/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_49/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_49/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_49/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_49/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_5/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_5/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_5/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_5/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_50/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_50/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_50/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_50/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_6/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_6/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_6/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_6/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_7/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_7/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_7/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_7/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_8/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_8/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_8/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_8/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/1122_9/correct.txt
 create mode 100644 legacy/Data/ingsw/1122_9/quest.txt
 create mode 100644 legacy/Data/ingsw/1122_9/wrong 1.txt
 create mode 100644 legacy/Data/ingsw/1122_9/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/12/correct.txt
 create mode 100644 legacy/Data/ingsw/12/quest.txt
 create mode 100644 legacy/Data/ingsw/12/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/12/wrong.txt
 create mode 100644 legacy/Data/ingsw/16/correct.txt
 create mode 100644 legacy/Data/ingsw/16/quest.txt
 create mode 100644 legacy/Data/ingsw/16/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/16/wrong.txt
 create mode 100644 legacy/Data/ingsw/17/correct.txt
 create mode 100644 legacy/Data/ingsw/17/quest.txt
 create mode 100644 legacy/Data/ingsw/17/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/17/wrong.txt
 create mode 100644 legacy/Data/ingsw/19/correct.txt
 create mode 100644 legacy/Data/ingsw/19/quest.txt
 create mode 100644 legacy/Data/ingsw/19/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/19/wrong.txt
 create mode 100644 legacy/Data/ingsw/2/correct.txt
 create mode 100644 legacy/Data/ingsw/2/quest.txt
 create mode 100644 legacy/Data/ingsw/2/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/2/wrong.txt
 create mode 100644 legacy/Data/ingsw/20/correct.txt
 create mode 100644 legacy/Data/ingsw/20/quest.txt
 create mode 100644 legacy/Data/ingsw/20/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/20/wrong.txt
 create mode 100644 legacy/Data/ingsw/21/correct.txt
 create mode 100644 legacy/Data/ingsw/21/quest.txt
 create mode 100644 legacy/Data/ingsw/21/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/21/wrong.txt
 create mode 100644 legacy/Data/ingsw/22/correct.txt
 create mode 100644 legacy/Data/ingsw/22/quest.txt
 create mode 100644 legacy/Data/ingsw/22/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/22/wrong.txt
 create mode 100644 legacy/Data/ingsw/24/correct.txt
 create mode 100644 legacy/Data/ingsw/24/quest.txt
 create mode 100644 legacy/Data/ingsw/24/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/24/wrong.txt
 create mode 100644 legacy/Data/ingsw/25/correct.txt
 create mode 100644 legacy/Data/ingsw/25/quest.txt
 create mode 100644 legacy/Data/ingsw/25/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/25/wrong.txt
 create mode 100644 legacy/Data/ingsw/26/correct.txt
 create mode 100644 legacy/Data/ingsw/26/quest.txt
 create mode 100644 legacy/Data/ingsw/26/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/26/wrong.txt
 create mode 100644 legacy/Data/ingsw/32/correct.txt
 create mode 100644 legacy/Data/ingsw/32/quest.txt
 create mode 100644 legacy/Data/ingsw/32/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/32/wrong.txt
 create mode 100644 legacy/Data/ingsw/33/correct.txt
 create mode 100644 legacy/Data/ingsw/33/quest.txt
 create mode 100644 legacy/Data/ingsw/33/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/33/wrong.txt
 create mode 100644 legacy/Data/ingsw/34/correct.txt
 create mode 100644 legacy/Data/ingsw/34/quest.txt
 create mode 100644 legacy/Data/ingsw/34/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/34/wrong.txt
 create mode 100644 legacy/Data/ingsw/35/correct.txt
 create mode 100644 legacy/Data/ingsw/35/quest.txt
 create mode 100644 legacy/Data/ingsw/35/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/35/wrong.txt
 create mode 100644 legacy/Data/ingsw/39/correct.txt
 create mode 100644 legacy/Data/ingsw/39/quest.txt
 create mode 100644 legacy/Data/ingsw/39/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/39/wrong.txt
 create mode 100644 legacy/Data/ingsw/4/correct.txt
 create mode 100644 legacy/Data/ingsw/4/quest.txt
 create mode 100644 legacy/Data/ingsw/4/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/4/wrong.txt
 create mode 100644 legacy/Data/ingsw/43/correct.txt
 create mode 100644 legacy/Data/ingsw/43/quest.txt
 create mode 100644 legacy/Data/ingsw/43/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/43/wrong.txt
 create mode 100644 legacy/Data/ingsw/44/correct.txt
 create mode 100644 legacy/Data/ingsw/44/quest.txt
 create mode 100644 legacy/Data/ingsw/44/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/44/wrong.txt
 create mode 100644 legacy/Data/ingsw/45/correct.txt
 create mode 100644 legacy/Data/ingsw/45/quest.txt
 create mode 100644 legacy/Data/ingsw/45/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/45/wrong.txt
 create mode 100644 legacy/Data/ingsw/46/correct.txt
 create mode 100644 legacy/Data/ingsw/46/quest.txt
 create mode 100644 legacy/Data/ingsw/46/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/46/wrong.txt
 create mode 100644 legacy/Data/ingsw/47/correct.txt
 create mode 100644 legacy/Data/ingsw/47/quest.txt
 create mode 100644 legacy/Data/ingsw/47/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/47/wrong.txt
 create mode 100644 legacy/Data/ingsw/48/correct.txt
 create mode 100644 legacy/Data/ingsw/48/quest.txt
 create mode 100644 legacy/Data/ingsw/48/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/48/wrong.txt
 create mode 100644 legacy/Data/ingsw/49/correct.txt
 create mode 100644 legacy/Data/ingsw/49/quest.txt
 create mode 100644 legacy/Data/ingsw/49/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/49/wrong.txt
 create mode 100644 legacy/Data/ingsw/5/correct.txt
 create mode 100644 legacy/Data/ingsw/5/quest.txt
 create mode 100644 legacy/Data/ingsw/5/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/5/wrong.txt
 create mode 100644 legacy/Data/ingsw/50/correct.txt
 create mode 100644 legacy/Data/ingsw/50/quest.txt
 create mode 100644 legacy/Data/ingsw/50/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/50/wrong.txt
 create mode 100644 legacy/Data/ingsw/69420/correct.txt
 create mode 100644 legacy/Data/ingsw/69420/quest.txt
 create mode 100644 legacy/Data/ingsw/69420/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/69420/wrong 3.txt
 create mode 100644 legacy/Data/ingsw/69420/wrong.txt
 create mode 100644 legacy/Data/ingsw/8/correct.txt
 create mode 100644 legacy/Data/ingsw/8/quest.txt
 create mode 100644 legacy/Data/ingsw/8/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/8/wrong.txt
 create mode 100644 legacy/Data/ingsw/9/correct.txt
 create mode 100644 legacy/Data/ingsw/9/quest.txt
 create mode 100644 legacy/Data/ingsw/9/wrong 2.txt
 create mode 100644 legacy/Data/ingsw/9/wrong.txt
 create mode 100644 legacy/Data/motd.txt
 rename Dockerfile => legacy/Dockerfile (100%)
 rename README.md => legacy/README.md (100%)
 rename {Utils => legacy/Utils}/check-ingsw-photos.sh (100%)
 rename {Utils => legacy/Utils}/find_duplicates.py (100%)
 rename {Utils => legacy/Utils}/make_questions.py (100%)
 rename {Utils => legacy/Utils}/moodle-scraper/README.md (100%)
 rename {Utils => legacy/Utils}/moodle-scraper/scraper.py (100%)
 rename docker-compose.yml => legacy/docker-compose.yml (100%)
 create mode 100644 scripts/docker-compose.yml

diff --git a/Bot/LICENSE b/Bot/LICENSE
deleted file mode 100644
index e72bfdd..0000000
--- a/Bot/LICENSE
+++ /dev/null
@@ -1,674 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
-                            Preamble
-
-  The GNU General Public License is a free, copyleft license for
-software and other kinds of works.
-
-  The licenses for most software and other practical works are designed
-to take away your freedom to share and change the works.  By contrast,
-the GNU General Public License is intended to guarantee your freedom to
-share and change all versions of a program--to make sure it remains free
-software for all its users.  We, the Free Software Foundation, use the
-GNU General Public License for most of our software; it applies also to
-any other work released this way by its authors.  You can apply it to
-your programs, too.
-
-  When we speak of free software, we are referring to freedom, not
-price.  Our General Public Licenses are designed to make sure that you
-have the freedom to distribute copies of free software (and charge for
-them if you wish), that you receive source code or can get it if you
-want it, that you can change the software or use pieces of it in new
-free programs, and that you know you can do these things.
-
-  To protect your rights, we need to prevent others from denying you
-these rights or asking you to surrender the rights.  Therefore, you have
-certain responsibilities if you distribute copies of the software, or if
-you modify it: responsibilities to respect the freedom of others.
-
-  For example, if you distribute copies of such a program, whether
-gratis or for a fee, you must pass on to the recipients the same
-freedoms that you received.  You must make sure that they, too, receive
-or can get the source code.  And you must show them these terms so they
-know their rights.
-
-  Developers that use the GNU GPL protect your rights with two steps:
-(1) assert copyright on the software, and (2) offer you this License
-giving you legal permission to copy, distribute and/or modify it.
-
-  For the developers' and authors' protection, the GPL clearly explains
-that there is no warranty for this free software.  For both users' and
-authors' sake, the GPL requires that modified versions be marked as
-changed, so that their problems will not be attributed erroneously to
-authors of previous versions.
-
-  Some devices are designed to deny users access to install or run
-modified versions of the software inside them, although the manufacturer
-can do so.  This is fundamentally incompatible with the aim of
-protecting users' freedom to change the software.  The systematic
-pattern of such abuse occurs in the area of products for individuals to
-use, which is precisely where it is most unacceptable.  Therefore, we
-have designed this version of the GPL to prohibit the practice for those
-products.  If such problems arise substantially in other domains, we
-stand ready to extend this provision to those domains in future versions
-of the GPL, as needed to protect the freedom of users.
-
-  Finally, every program is threatened constantly by software patents.
-States should not allow patents to restrict development and use of
-software on general-purpose computers, but in those that do, we wish to
-avoid the special danger that patents applied to a free program could
-make it effectively proprietary.  To prevent this, the GPL assures that
-patents cannot be used to render the program non-free.
-
-  The precise terms and conditions for copying, distribution and
-modification follow.
-
-                       TERMS AND CONDITIONS
-
-  0. Definitions.
-
-  "This License" refers to version 3 of the GNU General Public License.
-
-  "Copyright" also means copyright-like laws that apply to other kinds of
-works, such as semiconductor masks.
-
-  "The Program" refers to any copyrightable work licensed under this
-License.  Each licensee is addressed as "you".  "Licensees" and
-"recipients" may be individuals or organizations.
-
-  To "modify" a work means to copy from or adapt all or part of the work
-in a fashion requiring copyright permission, other than the making of an
-exact copy.  The resulting work is called a "modified version" of the
-earlier work or a work "based on" the earlier work.
-
-  A "covered work" means either the unmodified Program or a work based
-on the Program.
-
-  To "propagate" a work means to do anything with it that, without
-permission, would make you directly or secondarily liable for
-infringement under applicable copyright law, except executing it on a
-computer or modifying a private copy.  Propagation includes copying,
-distribution (with or without modification), making available to the
-public, and in some countries other activities as well.
-
-  To "convey" a work means any kind of propagation that enables other
-parties to make or receive copies.  Mere interaction with a user through
-a computer network, with no transfer of a copy, is not conveying.
-
-  An interactive user interface displays "Appropriate Legal Notices"
-to the extent that it includes a convenient and prominently visible
-feature that (1) displays an appropriate copyright notice, and (2)
-tells the user that there is no warranty for the work (except to the
-extent that warranties are provided), that licensees may convey the
-work under this License, and how to view a copy of this License.  If
-the interface presents a list of user commands or options, such as a
-menu, a prominent item in the list meets this criterion.
-
-  1. Source Code.
-
-  The "source code" for a work means the preferred form of the work
-for making modifications to it.  "Object code" means any non-source
-form of a work.
-
-  A "Standard Interface" means an interface that either is an official
-standard defined by a recognized standards body, or, in the case of
-interfaces specified for a particular programming language, one that
-is widely used among developers working in that language.
-
-  The "System Libraries" of an executable work include anything, other
-than the work as a whole, that (a) is included in the normal form of
-packaging a Major Component, but which is not part of that Major
-Component, and (b) serves only to enable use of the work with that
-Major Component, or to implement a Standard Interface for which an
-implementation is available to the public in source code form.  A
-"Major Component", in this context, means a major essential component
-(kernel, window system, and so on) of the specific operating system
-(if any) on which the executable work runs, or a compiler used to
-produce the work, or an object code interpreter used to run it.
-
-  The "Corresponding Source" for a work in object code form means all
-the source code needed to generate, install, and (for an executable
-work) run the object code and to modify the work, including scripts to
-control those activities.  However, it does not include the work's
-System Libraries, or general-purpose tools or generally available free
-programs which are used unmodified in performing those activities but
-which are not part of the work.  For example, Corresponding Source
-includes interface definition files associated with source files for
-the work, and the source code for shared libraries and dynamically
-linked subprograms that the work is specifically designed to require,
-such as by intimate data communication or control flow between those
-subprograms and other parts of the work.
-
-  The Corresponding Source need not include anything that users
-can regenerate automatically from other parts of the Corresponding
-Source.
-
-  The Corresponding Source for a work in source code form is that
-same work.
-
-  2. Basic Permissions.
-
-  All rights granted under this License are granted for the term of
-copyright on the Program, and are irrevocable provided the stated
-conditions are met.  This License explicitly affirms your unlimited
-permission to run the unmodified Program.  The output from running a
-covered work is covered by this License only if the output, given its
-content, constitutes a covered work.  This License acknowledges your
-rights of fair use or other equivalent, as provided by copyright law.
-
-  You may make, run and propagate covered works that you do not
-convey, without conditions so long as your license otherwise remains
-in force.  You may convey covered works to others for the sole purpose
-of having them make modifications exclusively for you, or provide you
-with facilities for running those works, provided that you comply with
-the terms of this License in conveying all material for which you do
-not control copyright.  Those thus making or running the covered works
-for you must do so exclusively on your behalf, under your direction
-and control, on terms that prohibit them from making any copies of
-your copyrighted material outside their relationship with you.
-
-  Conveying under any other circumstances is permitted solely under
-the conditions stated below.  Sublicensing is not allowed; section 10
-makes it unnecessary.
-
-  3. Protecting Users' Legal Rights From Anti-Circumvention Law.
-
-  No covered work shall be deemed part of an effective technological
-measure under any applicable law fulfilling obligations under article
-11 of the WIPO copyright treaty adopted on 20 December 1996, or
-similar laws prohibiting or restricting circumvention of such
-measures.
-
-  When you convey a covered work, you waive any legal power to forbid
-circumvention of technological measures to the extent such circumvention
-is effected by exercising rights under this License with respect to
-the covered work, and you disclaim any intention to limit operation or
-modification of the work as a means of enforcing, against the work's
-users, your or third parties' legal rights to forbid circumvention of
-technological measures.
-
-  4. Conveying Verbatim Copies.
-
-  You may convey verbatim copies of the Program's source code as you
-receive it, in any medium, provided that you conspicuously and
-appropriately publish on each copy an appropriate copyright notice;
-keep intact all notices stating that this License and any
-non-permissive terms added in accord with section 7 apply to the code;
-keep intact all notices of the absence of any warranty; and give all
-recipients a copy of this License along with the Program.
-
-  You may charge any price or no price for each copy that you convey,
-and you may offer support or warranty protection for a fee.
-
-  5. Conveying Modified Source Versions.
-
-  You may convey a work based on the Program, or the modifications to
-produce it from the Program, in the form of source code under the
-terms of section 4, provided that you also meet all of these conditions:
-
-    a) The work must carry prominent notices stating that you modified
-    it, and giving a relevant date.
-
-    b) The work must carry prominent notices stating that it is
-    released under this License and any conditions added under section
-    7.  This requirement modifies the requirement in section 4 to
-    "keep intact all notices".
-
-    c) You must license the entire work, as a whole, under this
-    License to anyone who comes into possession of a copy.  This
-    License will therefore apply, along with any applicable section 7
-    additional terms, to the whole of the work, and all its parts,
-    regardless of how they are packaged.  This License gives no
-    permission to license the work in any other way, but it does not
-    invalidate such permission if you have separately received it.
-
-    d) If the work has interactive user interfaces, each must display
-    Appropriate Legal Notices; however, if the Program has interactive
-    interfaces that do not display Appropriate Legal Notices, your
-    work need not make them do so.
-
-  A compilation of a covered work with other separate and independent
-works, which are not by their nature extensions of the covered work,
-and which are not combined with it such as to form a larger program,
-in or on a volume of a storage or distribution medium, is called an
-"aggregate" if the compilation and its resulting copyright are not
-used to limit the access or legal rights of the compilation's users
-beyond what the individual works permit.  Inclusion of a covered work
-in an aggregate does not cause this License to apply to the other
-parts of the aggregate.
-
-  6. Conveying Non-Source Forms.
-
-  You may convey a covered work in object code form under the terms
-of sections 4 and 5, provided that you also convey the
-machine-readable Corresponding Source under the terms of this License,
-in one of these ways:
-
-    a) Convey the object code in, or embodied in, a physical product
-    (including a physical distribution medium), accompanied by the
-    Corresponding Source fixed on a durable physical medium
-    customarily used for software interchange.
-
-    b) Convey the object code in, or embodied in, a physical product
-    (including a physical distribution medium), accompanied by a
-    written offer, valid for at least three years and valid for as
-    long as you offer spare parts or customer support for that product
-    model, to give anyone who possesses the object code either (1) a
-    copy of the Corresponding Source for all the software in the
-    product that is covered by this License, on a durable physical
-    medium customarily used for software interchange, for a price no
-    more than your reasonable cost of physically performing this
-    conveying of source, or (2) access to copy the
-    Corresponding Source from a network server at no charge.
-
-    c) Convey individual copies of the object code with a copy of the
-    written offer to provide the Corresponding Source.  This
-    alternative is allowed only occasionally and noncommercially, and
-    only if you received the object code with such an offer, in accord
-    with subsection 6b.
-
-    d) Convey the object code by offering access from a designated
-    place (gratis or for a charge), and offer equivalent access to the
-    Corresponding Source in the same way through the same place at no
-    further charge.  You need not require recipients to copy the
-    Corresponding Source along with the object code.  If the place to
-    copy the object code is a network server, the Corresponding Source
-    may be on a different server (operated by you or a third party)
-    that supports equivalent copying facilities, provided you maintain
-    clear directions next to the object code saying where to find the
-    Corresponding Source.  Regardless of what server hosts the
-    Corresponding Source, you remain obligated to ensure that it is
-    available for as long as needed to satisfy these requirements.
-
-    e) Convey the object code using peer-to-peer transmission, provided
-    you inform other peers where the object code and Corresponding
-    Source of the work are being offered to the general public at no
-    charge under subsection 6d.
-
-  A separable portion of the object code, whose source code is excluded
-from the Corresponding Source as a System Library, need not be
-included in conveying the object code work.
-
-  A "User Product" is either (1) a "consumer product", which means any
-tangible personal property which is normally used for personal, family,
-or household purposes, or (2) anything designed or sold for incorporation
-into a dwelling.  In determining whether a product is a consumer product,
-doubtful cases shall be resolved in favor of coverage.  For a particular
-product received by a particular user, "normally used" refers to a
-typical or common use of that class of product, regardless of the status
-of the particular user or of the way in which the particular user
-actually uses, or expects or is expected to use, the product.  A product
-is a consumer product regardless of whether the product has substantial
-commercial, industrial or non-consumer uses, unless such uses represent
-the only significant mode of use of the product.
-
-  "Installation Information" for a User Product means any methods,
-procedures, authorization keys, or other information required to install
-and execute modified versions of a covered work in that User Product from
-a modified version of its Corresponding Source.  The information must
-suffice to ensure that the continued functioning of the modified object
-code is in no case prevented or interfered with solely because
-modification has been made.
-
-  If you convey an object code work under this section in, or with, or
-specifically for use in, a User Product, and the conveying occurs as
-part of a transaction in which the right of possession and use of the
-User Product is transferred to the recipient in perpetuity or for a
-fixed term (regardless of how the transaction is characterized), the
-Corresponding Source conveyed under this section must be accompanied
-by the Installation Information.  But this requirement does not apply
-if neither you nor any third party retains the ability to install
-modified object code on the User Product (for example, the work has
-been installed in ROM).
-
-  The requirement to provide Installation Information does not include a
-requirement to continue to provide support service, warranty, or updates
-for a work that has been modified or installed by the recipient, or for
-the User Product in which it has been modified or installed.  Access to a
-network may be denied when the modification itself materially and
-adversely affects the operation of the network or violates the rules and
-protocols for communication across the network.
-
-  Corresponding Source conveyed, and Installation Information provided,
-in accord with this section must be in a format that is publicly
-documented (and with an implementation available to the public in
-source code form), and must require no special password or key for
-unpacking, reading or copying.
-
-  7. Additional Terms.
-
-  "Additional permissions" are terms that supplement the terms of this
-License by making exceptions from one or more of its conditions.
-Additional permissions that are applicable to the entire Program shall
-be treated as though they were included in this License, to the extent
-that they are valid under applicable law.  If additional permissions
-apply only to part of the Program, that part may be used separately
-under those permissions, but the entire Program remains governed by
-this License without regard to the additional permissions.
-
-  When you convey a copy of a covered work, you may at your option
-remove any additional permissions from that copy, or from any part of
-it.  (Additional permissions may be written to require their own
-removal in certain cases when you modify the work.)  You may place
-additional permissions on material, added by you to a covered work,
-for which you have or can give appropriate copyright permission.
-
-  Notwithstanding any other provision of this License, for material you
-add to a covered work, you may (if authorized by the copyright holders of
-that material) supplement the terms of this License with terms:
-
-    a) Disclaiming warranty or limiting liability differently from the
-    terms of sections 15 and 16 of this License; or
-
-    b) Requiring preservation of specified reasonable legal notices or
-    author attributions in that material or in the Appropriate Legal
-    Notices displayed by works containing it; or
-
-    c) Prohibiting misrepresentation of the origin of that material, or
-    requiring that modified versions of such material be marked in
-    reasonable ways as different from the original version; or
-
-    d) Limiting the use for publicity purposes of names of licensors or
-    authors of the material; or
-
-    e) Declining to grant rights under trademark law for use of some
-    trade names, trademarks, or service marks; or
-
-    f) Requiring indemnification of licensors and authors of that
-    material by anyone who conveys the material (or modified versions of
-    it) with contractual assumptions of liability to the recipient, for
-    any liability that these contractual assumptions directly impose on
-    those licensors and authors.
-
-  All other non-permissive additional terms are considered "further
-restrictions" within the meaning of section 10.  If the Program as you
-received it, or any part of it, contains a notice stating that it is
-governed by this License along with a term that is a further
-restriction, you may remove that term.  If a license document contains
-a further restriction but permits relicensing or conveying under this
-License, you may add to a covered work material governed by the terms
-of that license document, provided that the further restriction does
-not survive such relicensing or conveying.
-
-  If you add terms to a covered work in accord with this section, you
-must place, in the relevant source files, a statement of the
-additional terms that apply to those files, or a notice indicating
-where to find the applicable terms.
-
-  Additional terms, permissive or non-permissive, may be stated in the
-form of a separately written license, or stated as exceptions;
-the above requirements apply either way.
-
-  8. Termination.
-
-  You may not propagate or modify a covered work except as expressly
-provided under this License.  Any attempt otherwise to propagate or
-modify it is void, and will automatically terminate your rights under
-this License (including any patent licenses granted under the third
-paragraph of section 11).
-
-  However, if you cease all violation of this License, then your
-license from a particular copyright holder is reinstated (a)
-provisionally, unless and until the copyright holder explicitly and
-finally terminates your license, and (b) permanently, if the copyright
-holder fails to notify you of the violation by some reasonable means
-prior to 60 days after the cessation.
-
-  Moreover, your license from a particular copyright holder is
-reinstated permanently if the copyright holder notifies you of the
-violation by some reasonable means, this is the first time you have
-received notice of violation of this License (for any work) from that
-copyright holder, and you cure the violation prior to 30 days after
-your receipt of the notice.
-
-  Termination of your rights under this section does not terminate the
-licenses of parties who have received copies or rights from you under
-this License.  If your rights have been terminated and not permanently
-reinstated, you do not qualify to receive new licenses for the same
-material under section 10.
-
-  9. Acceptance Not Required for Having Copies.
-
-  You are not required to accept this License in order to receive or
-run a copy of the Program.  Ancillary propagation of a covered work
-occurring solely as a consequence of using peer-to-peer transmission
-to receive a copy likewise does not require acceptance.  However,
-nothing other than this License grants you permission to propagate or
-modify any covered work.  These actions infringe copyright if you do
-not accept this License.  Therefore, by modifying or propagating a
-covered work, you indicate your acceptance of this License to do so.
-
-  10. Automatic Licensing of Downstream Recipients.
-
-  Each time you convey a covered work, the recipient automatically
-receives a license from the original licensors, to run, modify and
-propagate that work, subject to this License.  You are not responsible
-for enforcing compliance by third parties with this License.
-
-  An "entity transaction" is a transaction transferring control of an
-organization, or substantially all assets of one, or subdividing an
-organization, or merging organizations.  If propagation of a covered
-work results from an entity transaction, each party to that
-transaction who receives a copy of the work also receives whatever
-licenses to the work the party's predecessor in interest had or could
-give under the previous paragraph, plus a right to possession of the
-Corresponding Source of the work from the predecessor in interest, if
-the predecessor has it or can get it with reasonable efforts.
-
-  You may not impose any further restrictions on the exercise of the
-rights granted or affirmed under this License.  For example, you may
-not impose a license fee, royalty, or other charge for exercise of
-rights granted under this License, and you may not initiate litigation
-(including a cross-claim or counterclaim in a lawsuit) alleging that
-any patent claim is infringed by making, using, selling, offering for
-sale, or importing the Program or any portion of it.
-
-  11. Patents.
-
-  A "contributor" is a copyright holder who authorizes use under this
-License of the Program or a work on which the Program is based.  The
-work thus licensed is called the contributor's "contributor version".
-
-  A contributor's "essential patent claims" are all patent claims
-owned or controlled by the contributor, whether already acquired or
-hereafter acquired, that would be infringed by some manner, permitted
-by this License, of making, using, or selling its contributor version,
-but do not include claims that would be infringed only as a
-consequence of further modification of the contributor version.  For
-purposes of this definition, "control" includes the right to grant
-patent sublicenses in a manner consistent with the requirements of
-this License.
-
-  Each contributor grants you a non-exclusive, worldwide, royalty-free
-patent license under the contributor's essential patent claims, to
-make, use, sell, offer for sale, import and otherwise run, modify and
-propagate the contents of its contributor version.
-
-  In the following three paragraphs, a "patent license" is any express
-agreement or commitment, however denominated, not to enforce a patent
-(such as an express permission to practice a patent or covenant not to
-sue for patent infringement).  To "grant" such a patent license to a
-party means to make such an agreement or commitment not to enforce a
-patent against the party.
-
-  If you convey a covered work, knowingly relying on a patent license,
-and the Corresponding Source of the work is not available for anyone
-to copy, free of charge and under the terms of this License, through a
-publicly available network server or other readily accessible means,
-then you must either (1) cause the Corresponding Source to be so
-available, or (2) arrange to deprive yourself of the benefit of the
-patent license for this particular work, or (3) arrange, in a manner
-consistent with the requirements of this License, to extend the patent
-license to downstream recipients.  "Knowingly relying" means you have
-actual knowledge that, but for the patent license, your conveying the
-covered work in a country, or your recipient's use of the covered work
-in a country, would infringe one or more identifiable patents in that
-country that you have reason to believe are valid.
-
-  If, pursuant to or in connection with a single transaction or
-arrangement, you convey, or propagate by procuring conveyance of, a
-covered work, and grant a patent license to some of the parties
-receiving the covered work authorizing them to use, propagate, modify
-or convey a specific copy of the covered work, then the patent license
-you grant is automatically extended to all recipients of the covered
-work and works based on it.
-
-  A patent license is "discriminatory" if it does not include within
-the scope of its coverage, prohibits the exercise of, or is
-conditioned on the non-exercise of one or more of the rights that are
-specifically granted under this License.  You may not convey a covered
-work if you are a party to an arrangement with a third party that is
-in the business of distributing software, under which you make payment
-to the third party based on the extent of your activity of conveying
-the work, and under which the third party grants, to any of the
-parties who would receive the covered work from you, a discriminatory
-patent license (a) in connection with copies of the covered work
-conveyed by you (or copies made from those copies), or (b) primarily
-for and in connection with specific products or compilations that
-contain the covered work, unless you entered into that arrangement,
-or that patent license was granted, prior to 28 March 2007.
-
-  Nothing in this License shall be construed as excluding or limiting
-any implied license or other defenses to infringement that may
-otherwise be available to you under applicable patent law.
-
-  12. No Surrender of Others' Freedom.
-
-  If conditions are imposed on you (whether by court order, agreement or
-otherwise) that contradict the conditions of this License, they do not
-excuse you from the conditions of this License.  If you cannot convey a
-covered work so as to satisfy simultaneously your obligations under this
-License and any other pertinent obligations, then as a consequence you may
-not convey it at all.  For example, if you agree to terms that obligate you
-to collect a royalty for further conveying from those to whom you convey
-the Program, the only way you could satisfy both those terms and this
-License would be to refrain entirely from conveying the Program.
-
-  13. Use with the GNU Affero General Public License.
-
-  Notwithstanding any other provision of this License, you have
-permission to link or combine any covered work with a work licensed
-under version 3 of the GNU Affero General Public License into a single
-combined work, and to convey the resulting work.  The terms of this
-License will continue to apply to the part which is the covered work,
-but the special requirements of the GNU Affero General Public License,
-section 13, concerning interaction through a network will apply to the
-combination as such.
-
-  14. Revised Versions of this License.
-
-  The Free Software Foundation may publish revised and/or new versions of
-the GNU General Public License from time to time.  Such new versions will
-be similar in spirit to the present version, but may differ in detail to
-address new problems or concerns.
-
-  Each version is given a distinguishing version number.  If the
-Program specifies that a certain numbered version of the GNU General
-Public License "or any later version" applies to it, you have the
-option of following the terms and conditions either of that numbered
-version or of any later version published by the Free Software
-Foundation.  If the Program does not specify a version number of the
-GNU General Public License, you may choose any version ever published
-by the Free Software Foundation.
-
-  If the Program specifies that a proxy can decide which future
-versions of the GNU General Public License can be used, that proxy's
-public statement of acceptance of a version permanently authorizes you
-to choose that version for the Program.
-
-  Later license versions may give you additional or different
-permissions.  However, no additional obligations are imposed on any
-author or copyright holder as a result of your choosing to follow a
-later version.
-
-  15. Disclaimer of Warranty.
-
-  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
-APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
-HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
-OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
-THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
-IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
-ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
-
-  16. Limitation of Liability.
-
-  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    <one line to give the program's name and a brief idea of what it does.>
-    Copyright (C) <year>  <name of author>
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
-  If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
-    <program>  Copyright (C) <year>  <name of author>
-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<https://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<https://www.gnu.org/licenses/why-not-lgpl.html>.
\ No newline at end of file
diff --git a/Bot/README.md b/Bot/README.md
deleted file mode 100644
index 196e4b0..0000000
--- a/Bot/README.md
+++ /dev/null
@@ -1,35 +0,0 @@
-# so-un-bot-for-real
-
-The code behind so-un-bot
-
-Nothing is documnted so far.
-I will try to write a documentation on my free time.
-Have fun!
-
-## How to run
-
-#### Prepare the environment
-```
-$ dotnet restore
-```
-
-#### Option 1: using the .NET runtime (and SDK) in your host
-```
-$ dotnet run
-```
-*This will compile the project in debug mode*
-
-#### Option 2: in Docker
-```
-$ dotnet publish -c Release
-$ docker build -t sounbot .
-$ docker run -d --name='sounbot' -v '/path/to/datadir':'/App/ACL':'rw' 'sobot:latest'
-```
-
-Remember to change `/path/to/datadir` with your own data directory!
-
-## How to debug
-
-Just open the project in **Visual Studio** or **VSCode** with the **.NET Extension Pack** extension.
-
-You will need the .NET 6.0 SDK: https://dotnet.microsoft.com/en-us/download/dotnet/6.0
diff --git a/Data/motd.txt b/data/config/motd.txt
similarity index 100%
rename from Data/motd.txt
rename to data/config/motd.txt
diff --git a/data/questions/diritto_unive_inf.json b/data/questions/diritto_unive_inf.json
new file mode 100644
index 0000000..661d7dd
--- /dev/null
+++ b/data/questions/diritto_unive_inf.json
@@ -0,0 +1,1244 @@
+[
+  {
+    "quest": "1) Esiste un codice di diritto dell’informatica?",
+    "answers": [
+      {
+        "answer": "No.",
+        "image": ""
+      },
+      {
+        "answer": "Si",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "2) Cosa si intende con principio di neutralità della rete?",
+    "answers": [
+      {
+        "answer": "Che opera nella rete non deve discriminare politicamente",
+        "image": ""
+      },
+      {
+        "answer": "Che chi opera nella rete non deve discriminare tra tecnologie di accesso.",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "A metà degli anni ‘70",
+        "image": ""
+      },
+      {
+        "answer": "A metà degli anni ‘90",
+        "image": ""
+      },
+      {
+        "answer": "Dal 2023",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "4) Perché di solito un internet provider non chiede un canone all’utente?",
+    "answers": [
+      {
+        "answer": "Perché vende le informazioni sugli interessi dell'utente",
+        "image": ""
+      },
+      {
+        "answer": "Perché riceve dallo Stato un apposito finanziamento per far funzionare l rete",
+        "image": ""
+      },
+      {
+        "answer": "Perché ricava gli utili da altri servizi che offre agli utenti",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "5) Un file è un documento valido?",
+    "answers": [
+      {
+        "answer": "Si",
+        "image": ""
+      },
+      {
+        "answer": "Si ma solo se firmato digitalmente.",
+        "image": ""
+      },
+      {
+        "answer": "No",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "6) Cosa è la dematerializzazione dei titoli finanziari?",
+    "answers": [
+      {
+        "answer": "Il fatto che ormai nessuno è più interessato a questi documenti",
+        "image": ""
+      },
+      {
+        "answer": "Il fatto che si sta passando dalla moneta cartacea a quella digitale (bitcoins e simili)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il fatto che i registri di carta sono ora digitali",
+        "image": ""
+      },
+      {
+        "answer": "Il fatto che chiunque può accedere telematicamente ai registri digitali.",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Si, digitale",
+        "image": ""
+      },
+      {
+        "answer": "Si ma privata",
+        "image": ""
+      },
+      {
+        "answer": "No, sono un mezzo di scambio.",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "9) Il commercio elettronico riguarda?",
+    "answers": [
+      {
+        "answer": "Consumatori e/o imprese.",
+        "image": ""
+      },
+      {
+        "answer": "solo i consumatori",
+        "image": ""
+      },
+      {
+        "answer": "solo le imprese",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "10) Per effettuare una comunicazione commerciale:",
+    "answers": [
+      {
+        "answer": "occorre il preventivo consenso del destinatario.",
+        "image": ""
+      },
+      {
+        "answer": "occorre che siano dirette solo ad imprese, non a consumatori",
+        "image": ""
+      },
+      {
+        "answer": "occorre che chiariscano di essere comunicazioni commerciali",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "11) A cosa servono i marchi di qualità?",
+    "answers": [
+      {
+        "answer": "Per attestare la qualità del prodotto",
+        "image": ""
+      },
+      {
+        "answer": "Per acquistare la fiducia dei cliente sulla bontà del prodotto",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Si sempre.",
+        "image": ""
+      },
+      {
+        "answer": "Si ma solo se sono opere che hanno carattere creativo",
+        "image": ""
+      },
+      {
+        "answer": "Si ogni volta che sono brevettate",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "13) Esistono norme penali contro l’uso improprio del software?",
+    "answers": [
+      {
+        "answer": "Si ma riguardano solo gli hacker",
+        "image": ""
+      },
+      {
+        "answer": "Si ma riguardano solo la violazione del diritto d’autore.",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Si ",
+        "image": ""
+      },
+      {
+        "answer": "Si se si tratta di tutela dei minori o di terrorismo",
+        "image": ""
+      },
+      {
+        "answer": "No",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "15) Quali regole disciplinano i social network?",
+    "answers": [
+      {
+        "answer": "Il contratto ed eventuali norme di legge",
+        "image": ""
+      },
+      {
+        "answer": "L’apposita disciplina legale e poi il contratto.",
+        "image": ""
+      },
+      {
+        "answer": "Non ci sono regole",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "16) Da quando è la legge scritta a tutelare la privacy?",
+    "answers": [
+      {
+        "answer": "Dagli anni ‘50",
+        "image": ""
+      },
+      {
+        "answer": "Dagli anni ‘70",
+        "image": ""
+      },
+      {
+        "answer": "Dagli anni ‘90",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "17) Chi è il \"responsabile\" nel trattamento dei dati personali?",
+    "answers": [
+      {
+        "answer": "Chi paga i danni se non ci sono le autorizzazioni",
+        "image": ""
+      },
+      {
+        "answer": "Chi è titolare delle modalità di trattamento",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Nel codice delle telecomunicazioni",
+        "image": ""
+      },
+      {
+        "answer": "Nel codice penale",
+        "image": ""
+      },
+      {
+        "answer": "Nel codice civile",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "19) Le regole del codice dell'amministrazione digitale sui documenti informatici valgono per i privati?",
+    "answers": [
+      {
+        "answer": "No",
+        "image": ""
+      },
+      {
+        "answer": "Si, ma solo se autorizzati",
+        "image": ""
+      },
+      {
+        "answer": "Si",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "20) Si può imporre ad un'amministrazione di rispondere via pec alle istanze dei privati?",
+    "answers": [
+      {
+        "answer": "Si",
+        "image": ""
+      },
+      {
+        "answer": "No",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "21) Quali mezzi di comunicazione hanno la data certa opponibile a tutti?",
+    "answers": [
+      {
+        "answer": "La mail e il fax",
+        "image": ""
+      },
+      {
+        "answer": "La pec",
+        "image": ""
+      },
+      {
+        "answer": "La pec ed il fax.",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "22) Cosa è il processo civile telematico?",
+    "answers": [
+      {
+        "answer": "Il processo civile in cui il deposito degli atti avviene in forma telematica.",
+        "image": ""
+      },
+      {
+        "answer": "Il processo civile che ha per oggetto una lite informatica",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il bitcoin",
+        "image": ""
+      },
+      {
+        "answer": "Carte di debito e di credito",
+        "image": ""
+      },
+      {
+        "answer": "Un valore monetario memorizzato elettronicamente.",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "24) I bitcoins hanno valore stabile?",
+    "answers": [
+      {
+        "answer": "Tendenzialmente si",
+        "image": ""
+      },
+      {
+        "answer": "Tendenzialmente no;",
+        "image": ""
+      },
+      {
+        "answer": "Non esistono dati rilevati",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "25) I pagamenti mediante bitcoins sono tracciati?",
+    "answers": [
+      {
+        "answer": "Si, ma non i loro autori",
+        "image": ""
+      },
+      {
+        "answer": "No",
+        "image": ""
+      },
+      {
+        "answer": "No ma sono tracciati i loro autori",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "26) Si possono fornire ai consumatori beni non richiesti?",
+    "answers": [
+      {
+        "answer": "No, occorre un previo ordine di acquisto",
+        "image": ""
+      },
+      {
+        "answer": "No, a meno che il venditore li offra gratuitamente",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "È una dichiarazione di un terzo circa l'affidabilità di un soggetto",
+        "image": ""
+      },
+      {
+        "answer": "È un marchio che protegge un software o un sito web",
+        "image": ""
+      },
+      {
+        "answer": "È un marchio rilasciato dallo stato ad imprenditori che hanno certe qualifiche",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "28) Le banche dati sono tutelate?",
+    "answers": [
+      {
+        "answer": "Si sempre.",
+        "image": ""
+      },
+      {
+        "answer": "Si ogni volta che hanno carattere creativo",
+        "image": ""
+      },
+      {
+        "answer": "Si ogni volta che sono brevettate",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "29) Esiste un' autorità centrale che governa Internet?",
+    "answers": [
+      {
+        "answer": "Si",
+        "image": ""
+      },
+      {
+        "answer": "Si ma non in Italia",
+        "image": ""
+      },
+      {
+        "answer": "No",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "30) Cosa si intende per privacy?",
+    "answers": [
+      {
+        "answer": "Il diritto di mantenere il controllo sulle proprie informazioni",
+        "image": ""
+      },
+      {
+        "answer": "Il diritto alla proprietà privata",
+        "image": ""
+      },
+      {
+        "answer": "Il diritto alla non ingerenza nella sfera sessuale",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "31) I privati possono raccogliere i dati personali altrui?",
+    "answers": [
+      {
+        "answer": "No",
+        "image": ""
+      },
+      {
+        "answer": "Si, ma solo se autorizzati",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Civile",
+        "image": ""
+      },
+      {
+        "answer": "Penale.",
+        "image": ""
+      },
+      {
+        "answer": "Amministrativa",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "33) I documenti informatici?",
+    "answers": [
+      {
+        "answer": "Sono validi nei soli casi previsti dalla legge",
+        "image": ""
+      },
+      {
+        "answer": "Sono validi se con firma digitale.",
+        "image": ""
+      },
+      {
+        "answer": "Sono validi a tutti gli effetti",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "34) Quando un documento inviato a mezzo posta certificata si considera consegnato?",
+    "answers": [
+      {
+        "answer": "Quando viene inviato dal proprio server",
+        "image": ""
+      },
+      {
+        "answer": "Quando arriva al server del destinatario",
+        "image": ""
+      },
+      {
+        "answer": "Quando viene letto dal destinatario",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "35) Chi emette i bitcoins?",
+    "answers": [
+      {
+        "answer": "Le autorità dei vari paesi controllano le loro emissioni",
+        "image": ""
+      },
+      {
+        "answer": "Le banche centrali",
+        "image": ""
+      },
+      {
+        "answer": "Colui che li ha inventati",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "36) I bitcoins sono convertibili in denaro?",
+    "answers": [
+      {
+        "answer": "Si",
+        "image": ""
+      },
+      {
+        "answer": "No",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Per qualsiasi transazione effettuata con strumenti telematici",
+        "image": ""
+      },
+      {
+        "answer": "Per le transazioni con strumenti telematici tra consumatori",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Per qualsiasi transazione effettuata con strumenti telematici",
+        "image": ""
+      },
+      {
+        "answer": "Per le transazioni con strumenti telematici tra consumatori",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Qualsiasi transazione effettuata con strumenti telematici con imprese",
+        "image": ""
+      },
+      {
+        "answer": "Le transazioni con strumenti telematici tra consumatori",
+        "image": ""
+      },
+      {
+        "answer": "Le transazioni con strumenti telematici tra consumatori ed imprese",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "40) Quando inizia il trattamento dei dati personali?",
+    "answers": [
+      {
+        "answer": "Da quando inizia la raccolta dei dati",
+        "image": ""
+      },
+      {
+        "answer": "Da quando l’interessato rilascia il consenso",
+        "image": ""
+      },
+      {
+        "answer": "Da quando i dati vengono elaborati",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "41) I codici di autoregolamento sono obbligatori?",
+    "answers": [
+      {
+        "answer": "Si",
+        "image": ""
+      },
+      {
+        "answer": "Si, se lo dice la legge",
+        "image": ""
+      },
+      {
+        "answer": "No",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "42) Scannerizzo ed appongo la mia firma ad un documento, questa è una firma elettronica avanzata?",
+    "answers": [
+      {
+        "answer": "Si",
+        "image": ""
+      },
+      {
+        "answer": "Si se poi non la contesto",
+        "image": ""
+      },
+      {
+        "answer": "No",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "43) Si può concludere un contratto via mail?\t",
+    "answers": [
+      {
+        "answer": "Si.",
+        "image": ""
+      },
+      {
+        "answer": "Si se poi non lo contestano",
+        "image": ""
+      },
+      {
+        "answer": "No",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "45) Se compro il pc ed il software è in licenza:",
+    "answers": [
+      {
+        "answer": "posso acquistare il software pagando un ulteriore somma",
+        "image": ""
+      },
+      {
+        "answer": "Il software non è mio.",
+        "image": ""
+      },
+      {
+        "answer": "ho pagato e quindi anche il software è mio",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "46) Cos’è il fascicolo telematico?",
+    "answers": [
+      {
+        "answer": "È il fascicolo digitale del processo civile.",
+        "image": ""
+      },
+      {
+        "answer": "Il fascicolo delle comunicazioni digitali con la pubblica amministrazione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Sì pagando un’apposita royalties",
+        "image": ""
+      },
+      {
+        "answer": "Si ma solo se pattuito all’inizio del contratto",
+        "image": ""
+      },
+      {
+        "answer": "Si ma senza commercializzarlo e per l’uso del programma stesso.",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "48) Cosa è una wireless community network?",
+    "answers": [
+      {
+        "answer": "Un insieme di persone che sostiene il diritto alla libertà su internet",
+        "image": ""
+      },
+      {
+        "answer": "Un insieme di persone che crea una rete di comunicazioni wireless.",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Non sono regole giuridiche",
+        "image": ""
+      },
+      {
+        "answer": "Sono regole locali",
+        "image": ""
+      },
+      {
+        "answer": "Sono regole contrattuali.",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "50) Cliccando su accetto sull’iscrizione ad un social network si conclude un contratto?",
+    "answers": [
+      {
+        "answer": "Si",
+        "image": ""
+      },
+      {
+        "answer": "Si ma poi serve un documento scritto",
+        "image": ""
+      },
+      {
+        "answer": "No",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "51) Quando sono protette le banche dati?",
+    "answers": [
+      {
+        "answer": "Sempre se hanno carattere creativo",
+        "image": ""
+      },
+      {
+        "answer": "Sempre se sono brevettate",
+        "image": ""
+      },
+      {
+        "answer": "Sempre se sono rese pubbliche.",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "52) Cosa si intende per deterritorializzazione?",
+    "answers": [
+      {
+        "answer": "La perdita di sovranità derivante della tecnologia informatica.",
+        "image": ""
+      },
+      {
+        "answer": "Il fatto che tra gli stati stanno venendo meno i conflitti",
+        "image": ""
+      },
+      {
+        "answer": "Il fatto che ognuno può installare un provider nello stato che preferisce",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Si",
+        "image": ""
+      },
+      {
+        "answer": "No",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "54) Un documento elettronico privo di firma digitale?",
+    "answers": [
+      {
+        "answer": "Non è valido",
+        "image": ""
+      },
+      {
+        "answer": "È valido",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "No se svolge un ruolo meramente passivo nelle loro attività.",
+        "image": ""
+      },
+      {
+        "answer": "No ma solo se ha messo delle regole contrattuali che li vietino",
+        "image": ""
+      },
+      {
+        "answer": "Si",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "56) Cos’è il trattamento dei dati personali?",
+    "answers": [
+      {
+        "answer": "La raccolta dei dati personali di un soggetto tramite strumenti informatici",
+        "image": ""
+      },
+      {
+        "answer": "La raccolta, l’elaborazione e la conservazione dei dati personali di un soggetto tramite strumenti informatici.",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Si",
+        "image": ""
+      },
+      {
+        "answer": "Si ma solo se vi è coinvolgimento umano nella decisione",
+        "image": ""
+      },
+      {
+        "answer": "No",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "58) Le criptovalute sono monete?",
+    "answers": [
+      {
+        "answer": "Si",
+        "image": ""
+      },
+      {
+        "answer": "No, sono beni digitali.",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "59) Cosa si intende con dematerializzazione?",
+    "answers": [
+      {
+        "answer": "il fatto che esistono beni immateriali",
+        "image": ""
+      },
+      {
+        "answer": "Il fatto che sempre più beni stanno assumendo forma digitale anziché materiale.",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Che molti rapporti digitali sono regolati quasi interamente da contratti , mancando regole specifiche.",
+        "image": ""
+      },
+      {
+        "answer": "che il contratto fa parte delle fonti legali di regole",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l’obbligo di utilizzare una medisca tecnologia informatica",
+        "image": ""
+      },
+      {
+        "answer": "L’obbligo di non discriminare tra diverse tecnologie.",
+        "image": ""
+      },
+      {
+        "answer": "il divieto di discriminazione di ogni tipo tramite social network",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "62) Cos’è il digital divide?",
+    "answers": [
+      {
+        "answer": "La scarsa distribuzione di risorse e conoscenze informatiche.",
+        "image": ""
+      },
+      {
+        "answer": "L’utilizzo di pc e softwares non aggiornati",
+        "image": ""
+      },
+      {
+        "answer": "l’insieme dei rischi connessi all’utilizzo di strumenti informativi",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "63) L’acquisto di competenze digitali:",
+    "answers": [
+      {
+        "answer": "È oggetto di leggi che lo agevolano.",
+        "image": ""
+      },
+      {
+        "answer": "dipende solo dalla volontà di ognuno",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "si sempre ma solo se è maggiorenne",
+        "image": ""
+      },
+      {
+        "answer": "si ma va tolta se lo chiede",
+        "image": ""
+      },
+      {
+        "answer": "Si ma dopo aver avuto il suo consenso.",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "65) Il cyberbullismo: ",
+    "answers": [
+      {
+        "answer": "è una questione di maleducazione",
+        "image": ""
+      },
+      {
+        "answer": "È un comportamento vietato dalla legge",
+        "image": ""
+      },
+      {
+        "answer": "è un comportamento regolato dai singoli social network",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "66) Cos’è lo SPID?",
+    "answers": [
+      {
+        "answer": "È un sistema pubblico di identificazione di oggetti.",
+        "image": ""
+      },
+      {
+        "answer": "è un sistema pubblico di attribuzione di posta certificata",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l’indirizzo pec che indica dove siamo residenti",
+        "image": ""
+      },
+      {
+        "answer": "l'indirizzo pec che dobbiamo aver per i nostri rapporti con il fisco",
+        "image": ""
+      },
+      {
+        "answer": "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/ium_unive.json b/data/questions/ium_unive.json
new file mode 100644
index 0000000..0ba2f92
--- /dev/null
+++ b/data/questions/ium_unive.json
@@ -0,0 +1,3081 @@
+[
+  {
+    "quest": "1) L’interaction framework descrive 4 fasi del ciclo interattivo, tra le quali troviamo:",
+    "answers": [
+      {
+        "answer": "l’osservazione e la memorizzazione",
+        "image": ""
+      },
+      {
+        "answer": "la sintesi e l’articolazione",
+        "image": ""
+      },
+      {
+        "answer": "l’articolazione e la presentazione",
+        "image": ""
+      },
+      {
+        "answer": "la prestazione e la virtualizzazione",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "2) Qual èil significato dell’euristica visibilità dello stato del sistema?",
+    "answers": [
+      {
+        "answer": "che i dispositivi di input del sistema devono essere posizionati in modo da essere sempre visibili agli utenti",
+        "image": ""
+      },
+      {
+        "answer": "che in ogni momento il sistema deve tenere informato l’utente su quello che sta succedendo",
+        "image": ""
+      },
+      {
+        "answer": " che i dispositivi di output del sistema devono essere posizionati a non più di 2 metri dall’utente",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l’uso dell’ambiente fisico come interfaccia per l’informazione digitale",
+        "image": ""
+      },
+      {
+        "answer": "l’utilizzo esclusivo di schermi di schermi di grande dimensione, per permettere una comunicazione parallela a più utenti",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "invisibilità di una parte degli oggetti e feedback veloce",
+        "image": ""
+      },
+      {
+        "answer": "sostituzione di linguaggi di comando basati su numero con linguaggi di comando basati su parole comuni",
+        "image": ""
+      },
+      {
+        "answer": "reversibilità delle azioni",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "che il sistema deve essere percepito dall’utente come una controparte umana",
+        "image": ""
+      },
+      {
+        "answer": "che il sistema informatico non deve mai effettuare previsioni sbagliate",
+        "image": ""
+      },
+      {
+        "answer": "che il sistema informatico deve eseguire previsioni il più spesso possibile corrette",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "paradigmi di interazione in cui vengono presentati all’utente in sequenza elementi reali ed elementi virtuali",
+        "image": ""
+      },
+      {
+        "answer": "paradigmi di interazione in cui vengono presentati all’utente contemporaneamente sia elementi reali che virtuali",
+        "image": ""
+      },
+      {
+        "answer": "tutta la gamma di combinazioni possibili tra realtà e virtualità",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "è fondamentale dare il dettaglio dei consumi in tempo reale",
+        "image": ""
+      },
+      {
+        "answer": "è importante mostrare dati sui consumi a lungo termine",
+        "image": ""
+      },
+      {
+        "answer": "non c’è alcun vantaggio dal punto di vista del miglioramento delle abitudini derivante dalla competizione tra i vicini",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "una storyboard nella qualevengono proposti gli screenshot dell’interfaccia senza alcun ordinamento particolare",
+        "image": ""
+      },
+      {
+        "answer": "una storyboard nella quale viene data una descrizioneil più possibilecompleta di tutti glistati dell’interazionee delle loro relazioni",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "richiedere un consumo di energia molto basso, di provenienza esclusivamente rinnovabile",
+        "image": ""
+      },
+      {
+        "answer": "fornire agli utenti informazioni riguardo alle conseguenze per l’ambiente del loro comportamento",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "lo studio dei tempi di interazione con un sistema pervasivo",
+        "image": ""
+      },
+      {
+        "answer": "lo studio del significato della gestualità umana",
+        "image": ""
+      },
+      {
+        "answer": "lo studio delle distanze interpersonali",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la disponibilità e l’omogeneità di operazioni non distruttive, come l’undo",
+        "image": ""
+      },
+      {
+        "answer": "la disponibilità di linee guida riconosciute per il controllo gestuale",
+        "image": ""
+      },
+      {
+        "answer": "la disponibilità di gesti consistenti e omogenei attraverso le diverse piattaforme di utilizzo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la differenza tra la formulazione delle azioni dell’utente e le azioni consentite",
+        "image": ""
+      },
+      {
+        "answer": "la differenza tra la presentazione dello stato del sistema e le aspettative dell’utente",
+        "image": ""
+      },
+      {
+        "answer": "la differenza tra la presentazione dello stato del sistema e le azioni consentite",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "è fondamentale dare il dettaglio dei consumi in tempo reale",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "è importante mostrare dati sui consumi a lungo termine",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "le azioni che non richiedono un’interazione verbale da parte dell’utente",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "le azioni che non richiedono un’interazione gestuale da parte dell’utente",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "paradigmi di interazione in cui vengono presentati all’utente in sequenza elementi reali ed elementi virtuali",
+        "image": ""
+      },
+      {
+        "answer": "paradigmi di interazione in cui vengono presentati all’utente contemporaneamente siaelementi reali che virtuali",
+        "image": ""
+      },
+      {
+        "answer": "tutta la gamma di combinazioni possibili tra realtà e virtualità",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "una storyboard caratterizzata da uno schema lineare per mostrare i cambiamenti di stato dell’interfaccia a seguito dell’azione diretta dell’utente",
+        "image": ""
+      },
+      {
+        "answer": "una storyboard nella quale vengono proposti gli screenshot dell’interfaccia senza alcun ordinamento particolare",
+        "image": ""
+      },
+      {
+        "answer": "una storyboard nella quale viene data una descrizione il più possibile completa di tutti gli stati dell’interazione e delle loro relazioni",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "che agli utenti deve essere impedito di selezionare per errore le funzionalità del sistema",
+        "image": ""
+      },
+      {
+        "answer": "che tutti i sistemi dovrebbero fornire la libertà di essere utilizzati,anche da parte di utenti non registrati",
+        "image": ""
+      },
+      {
+        "answer": "che agli utenti deve essere fornite uscite di emergenza nel caso in cui selezionino determinate funzionalità per errore",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la versatilità e la collaborazione",
+        "image": ""
+      },
+      {
+        "answer": "il parallelismo spaziale e la scalabilità",
+        "image": ""
+      },
+      {
+        "answer": "il pensiero tangibile e l’uso delle affordances",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "19) I parametri che definiscono l’engagement dell’utente comprendono:",
+    "answers": [
+      {
+        "answer": "l’attenzione focalizzata e la durabilità",
+        "image": ""
+      },
+      {
+        "answer": "l’usabilità percepita e l’effervescenza",
+        "image": ""
+      },
+      {
+        "answer": "il coinvolgimento percepito e l’accessibilità",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "20) Il cosiddetto affective computing denota:",
+    "answers": [
+      {
+        "answer": "la computazione che influenza le emozioni degli utenti",
+        "image": ""
+      },
+      {
+        "answer": "l’attrazione emotiva degli utenti verso i nuovi prodotti hardware",
+        "image": ""
+      },
+      {
+        "answer": "la computazione che sorge dalle emozioni dell’unità di calcolo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "dell’incapacità dei cosiddetti nativi digitali ad effettuare manipolazioni con entrambe le mani",
+        "image": ""
+      },
+      {
+        "answer": "della mancanza di linee guida riconosciute per il controllo gestuale",
+        "image": ""
+      },
+      {
+        "answer": "dellalimitata espressività delle interfacce gestuali",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l’implementazione fittizia di interfacce, costruita in modo tale che l’utente abbia l’impressione di interagire con un sistema realmente funzionante",
+        "image": ""
+      },
+      {
+        "answer": "l’utilizzo di tecniche di storytelling (narrazione interattiva) per la didattica dell’informatica",
+        "image": ""
+      },
+      {
+        "answer": "un’implementazione ridondante e fantasiosa di un’interfaccia utente, effettuata allo scopo di permettere molteplici modalità di interazione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la disponibilità e l’omogeneità di operazioni non distruttive, come l’undo",
+        "image": ""
+      },
+      {
+        "answer": "la ridotta espressività del linguaggio di input",
+        "image": ""
+      },
+      {
+        "answer": "la disponibilità di gesti consistenti e omogenei attraverso le diverse piattaforme di utilizzo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "permettere di svolgere la stessa azione attraverso modalità diverse, scegliendo ad esempio una modalitàgrafica piuttosto che una a linea di comando",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "gli oggetti, le azioni e le opzioni che fanno parte dell’interazione vanno resi visibili",
+        "image": ""
+      },
+      {
+        "answer": "non vanno seguitele convenzioni delle piattaforme su cui si sta lavorando",
+        "image": ""
+      },
+      {
+        "answer": "i widget utilizzati per l'interazione dovrebbero essere di grandi dimensioni e posti a breve distanza",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "per la descrizione del contesto in cui avviene l’interazione",
+        "image": ""
+      },
+      {
+        "answer": "per la presentazione degli screenshots dell’interfaccia senza alcun ordinamento particolare",
+        "image": ""
+      },
+      {
+        "answer": "per la definizione di percorsi non-lineari che descrivono le transizioni di stato dell’interfaccia",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l’incapacità del sistema uditivo di distinguere, in determinate condizioni, suoni di livello diverso vicini nel tempo",
+        "image": ""
+      },
+      {
+        "answer": "la capacità del sistema uditivo di filtrare i suoni in un ambiente rumoroso",
+        "image": ""
+      },
+      {
+        "answer": "l’incapacità del sistema uditivo di distinguere suoni bassi con frequenze molto diverse",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "che ha lo stesso layout fisico di una tastiera QWERTY",
+        "image": ""
+      },
+      {
+        "answer": "che ha il layout e lo stesso numero di tasti di una tastiera di pianoforte",
+        "image": ""
+      },
+      {
+        "answer": "per avviare alla digitazione del testo i bambini di età pre-scolare",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il linguaggio dei compitidell’utente viene tradottoinlinguaggio di input",
+        "image": ""
+      },
+      {
+        "answer": "Dopo l’esecuzione il sistema cambia stato e lo comunica attraverso il linguaggio di output",
+        "image": ""
+      },
+      {
+        "answer": "L’utente osserva l’output e valuta i risultati",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "utilizzare forme specifi che suggeriscano come svolgere la funzione",
+        "image": ""
+      },
+      {
+        "answer": "utilizzare in forma seriale le stesse forme per mappare funzioni diverse",
+        "image": ""
+      },
+      {
+        "answer": "utilizzare solo dispositivi sostenibili economicamente",
+        "image": ""
+      },
+      {
+        "answer": "utilizzare solo dispositivi che richiedano una forza fisica limitata",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "31) Che cosa si intende per scheumorfismo?",
+    "answers": [
+      {
+        "answer": "E’ un ornamento apposto su un oggetto (digitale) allo scopo di richiamare le caratteristiche di un altro oggetto",
+        "image": ""
+      },
+      {
+        "answer": "E’ un termine che indica le interfacce visuali caratterizzate da uno stile astratto",
+        "image": ""
+      },
+      {
+        "answer": "E’ un termine che indica le interfacce realizzate in economia di mezzi",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "non sono indipendenti l’uno dall’altro",
+        "image": ""
+      },
+      {
+        "answer": "comprendono il coinvolgimento percepito e la reversibilità delle azioni",
+        "image": ""
+      },
+      {
+        "answer": "comprendono la consistenzae la durabilità",
+        "image": ""
+      },
+      {
+        "answer": "non comprendono l’usabilità percepita",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "33) Con il termine interazione a manipolazione diretta:",
+    "answers": [
+      {
+        "answer": "i si riferisce ad un paradigma di interazione basato sull’invisibilità degli oggetti",
+        "image": ""
+      },
+      {
+        "answer": "ci si riferisce ad un paradigma di interazione nel quale le azioni sono irreversibili",
+        "image": ""
+      },
+      {
+        "answer": "ci si riferisce ad un paradigma di interazione basato su WIMP",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "un prototipo orizzontale",
+        "image": ""
+      },
+      {
+        "answer": "un prototipo verticale",
+        "image": ""
+      },
+      {
+        "answer": "uno scenario",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "dal grande costo delle essenze necessarie",
+        "image": ""
+      },
+      {
+        "answer": "dall’impossibilità di ottenere il risultato utilizzando una miscela di pochi odori di base",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la disponibilità e l’omogeneità di operazioni non distruttive, come l’undo",
+        "image": ""
+      },
+      {
+        "answer": "la disponibilità di gesti consistenti e omogenei attraverso le diverse piattaforme di utilizzo",
+        "image": ""
+      },
+      {
+        "answer": "la mancanza di linee guida riconosciute per il controllo gestuale",
+        "image": ""
+      },
+      {
+        "answer": "la ridotta espressività del linguaggio di input",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "37) Il layout del tastierino numerico delle tastiere di computer deriva:",
+    "answers": [
+      {
+        "answer": "da uno dei primi studi sul layout dei tastierini numerici di Bell Labs",
+        "image": ""
+      },
+      {
+        "answer": "dal layout dei calcolatori meccanici",
+        "image": ""
+      },
+      {
+        "answer": "dal layout delle tastiere telefoniche",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "utilizzare forme specifiche che suggeriscano come svolgere la funzione",
+        "image": ""
+      },
+      {
+        "answer": "utilizzare in forma seriale le stesse forme per mappare funzioni diverse",
+        "image": ""
+      },
+      {
+        "answer": "utilizzare solo dispositivi sostenibili economicamente",
+        "image": ""
+      },
+      {
+        "answer": "utilizzare solo dispositivi che richiedano una forza fisica limitata",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "39) L'euristica Consistenza e Standard indica che:",
+    "answers": [
+      {
+        "answer": "gli oggetti, le azioni e le opzioni che fanno parte dell’interazione vanno resi visibili",
+        "image": ""
+      },
+      {
+        "answer": "non vanno seguitele convenzioni delle piattaforme su cui si sta lavorando",
+        "image": ""
+      },
+      {
+        "answer": "i widget utilizzati per l'interazione dovrebbero essere di grandi dimensioni e posti a breve distanza",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "E’ un termine che indica le interfacce visuali caratterizzate da uno stile astratto",
+        "image": ""
+      },
+      {
+        "answer": "E’ un ornamento apposto su un oggetto (digitale) allo scopo di richiamare le caratteristiche di un altro",
+        "image": ""
+      },
+      {
+        "answer": "E’ un termine utilizzato per caratterizzare tutte le tipologie di interfacce tangibili",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "il concetto di engagementè utilizzato solo nella progettazione di sistemi di gioco interattivi",
+        "image": ""
+      },
+      {
+        "answer": "sono due concetti separati che vengono utilizzati in aree di applicazione completamente diverse",
+        "image": ""
+      },
+      {
+        "answer": "l'usabilità percepita è parte della definizione del concetto di engagement(secondo la definizione di O'Brien)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il linguaggio dei compitidell’utente viene tradottoinlinguaggio di input",
+        "image": ""
+      },
+      {
+        "answer": "Dopo l’esecuzione il sistema cambia stato e lo comunica attraverso il linguaggio di output",
+        "image": ""
+      },
+      {
+        "answer": "L’utente osserva l’output e valuta i risultati",
+        "image": ""
+      },
+      {
+        "answer": "L'inputvienetradotto nel linguaggio di base del sistema per attivare l’esecuzione",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "43) Un’interfaccia tangibile",
+    "answers": [
+      {
+        "answer": "è un’interfaccia utente nella quale l’utente interagiscecon l’informazione digitale trasmettendo l’input attraverso oggetti fisici",
+        "image": ""
+      },
+      {
+        "answer": "è un’interfaccia utente nella quale l’utente interagisce con gli oggetti tangibili trasmettendo l’input attraverso la mediazione dioggetti digitali",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "vengono aperti automaticamente al passaggio del puntatore sui titoli",
+        "image": ""
+      },
+      {
+        "answer": "vengono aperti posizionando il puntatore sui titoli e facendo click",
+        "image": ""
+      },
+      {
+        "answer": "vengono aperti posizionando il puntatore sui titoli e rilasciando il tasto del mouse",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "45) Un campoX3D",
+    "answers": [
+      {
+        "answer": "è un’area in cui viene partizionato lo spazio della scena 3D",
+        "image": ""
+      },
+      {
+        "answer": "è un’entità di secondo livello che definisce lo stato di un nodo",
+        "image": ""
+      },
+      {
+        "answer": "è un’entità di primolivello per definire primitivegeometrichedi interazione",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "46) In un prototipo verticale di un'interfaccia",
+    "answers": [
+      {
+        "answer": "viene ridotto il livello di funzionalità del sistema, dando luogo a un'interfaccia con features non completamente implementate",
+        "image": ""
+      },
+      {
+        "answer": "viene ridotto il numero di features considerate, mostrando il funzionamento del sistema solo lungo percorsi precedentemente pianificati",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l'utente riceve un doppio output, dall'interfaccia e dal sistema",
+        "image": ""
+      },
+      {
+        "answer": "l'utente opera un doppio input, verso l'interfaccia e verso il sistema",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "definisce un modello di interazione uomo calcolatore basato sulla specifica di 7 fasi corrispondenti alle attività dell’utente",
+        "image": ""
+      },
+      {
+        "answer": "definisce un modello di interazione uomo calcolatore basato sulla specifica di 7 golfi corrispondenti ai punti critici dell’interazione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "i secondi sistemi si differenziano dai primi per un’espressività più limitata",
+        "image": ""
+      },
+      {
+        "answer": "i primi sistemi si differenziano dai secondi per una maggiore standardizzazione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la comunicazione di un maggior numero di informazioni",
+        "image": ""
+      },
+      {
+        "answer": "la diminuzione del coinvolgimento emotivo dell’utente",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "si da spazio all’aspetto dell’improvvisazione tipico del comportamento umano",
+        "image": ""
+      },
+      {
+        "answer": "le azioni derivano da scopi pre-pianificati",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l’interfaccia non viene percepita comeuna mediazione con il sistema sottostante, ma come il sistema",
+        "image": ""
+      },
+      {
+        "answer": "l’interfaccia dovrebbe essere costruita come un modello in scala del mondo",
+        "image": ""
+      },
+      {
+        "answer": "e azioni non sono reversibili, come succede appunto nel mondo reale",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "53) La tastiera DVORAK",
+    "answers": [
+      {
+        "answer": "condivide con la tastiera QWERTY lo stesso layout fisico dei tasti",
+        "image": ""
+      },
+      {
+        "answer": "diminuisce la fatica di input e raddoppia la velocità di input",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "lascalabilità",
+        "image": ""
+      },
+      {
+        "answer": "a mancanza di relazione con il contesto",
+        "image": ""
+      },
+      {
+        "answer": "il parallelismo spaziale",
+        "image": ""
+      },
+      {
+        "answer": "la mancanza di affordance",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "55) Una finestra di dialogo modale",
+    "answers": [
+      {
+        "answer": "permette di continuare in ogni momento l’interazione con le altre componenti dell’interfaccia che non fanno parte della finestra di dialogo",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "è un altro modo per riferirsi alla realtà virtuale immersiva",
+        "image": ""
+      },
+      {
+        "answer": "descrive tutto il continuum nel quale si collocano tutte le diverse miscele di realtà e virtualità",
+        "image": ""
+      },
+      {
+        "answer": "è un altro modo per riferirsi alla realtà aumentata",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "57) L’affordance di un oggetto consiste",
+    "answers": [
+      {
+        "answer": "nella capacità dell’oggetto di suggerire, attraverso il proprio nome, le possibilità di interazione",
+        "image": ""
+      },
+      {
+        "answer": "nel grado di convenienza economica dell’oggetto",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "che è vantaggioso per l’interazione rendere gli oggetti e le azioni visibili",
+        "image": ""
+      },
+      {
+        "answer": "che è vantaggioso per l’interazione richiedere all’utente di memorizzare azioni e opzioni",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "definire oggetti interattivi di piccole dimensioni e posti a grande distanza tra di loro",
+        "image": ""
+      },
+      {
+        "answer": "definire oggetti interattivi di grandidimensioni e posti a piccoladistanza tra di loro",
+        "image": ""
+      },
+      {
+        "answer": "definire oggetti interattivi di grandidimensioni e posti a grande distanza tra di loro",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "definire un meccanismo di navigazione per gli utenti del mondo interattivo",
+        "image": ""
+      },
+      {
+        "answer": "definire un meccanismo di rotazione automatica per il nodo a cui è applicato",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "che è necessario seguire nella progettazione unnumero consistente di standard",
+        "image": ""
+      },
+      {
+        "answer": "che è necessario seguire le convenzioni delle piattaforme su cui si lavora",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la versatilità e la malleabilità",
+        "image": ""
+      },
+      {
+        "answer": "la relazione con il contesto e l’utilizzo delle affordances",
+        "image": ""
+      },
+      {
+        "answer": "il pensiero tangibile e la scalabilità",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "63)La realtà aumentata",
+    "answers": [
+      {
+        "answer": "prevede l’esclusiva possibilità di sincronizzazione gli elementi virtuali con l’occhio dell’utente",
+        "image": ""
+      },
+      {
+        "answer": "prevede la possibilità di sincronizzazione gli oggetti virtuali con una telecamera non coincidente con il punto di vista dell’utente",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la differenza tra l’osservazione della risposta del sistema e la prestazione",
+        "image": ""
+      },
+      {
+        "answer": "la differenza tra la formulazione delle azioni dell’utente e le azioni consentite",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "permettono di aumentare leggermente la produttività",
+        "image": ""
+      },
+      {
+        "answer": "diminuiscono lo spazio necessario per l’interfaccia",
+        "image": ""
+      },
+      {
+        "answer": "permettono di accedere nello stesso tempo ad un numero molto elevato di elementi",
+        "image": ""
+      },
+      {
+        "answer": "garantiscono un aumento della soddisfazione soggettiva degli utenti",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "66)In un prototipo orizzontaledi un'interfaccia",
+    "answers": [
+      {
+        "answer": "viene ridotto il numero di features considerate, mostrando il funzionamento del sistema solo lungo percorsi precedentemente pianificati",
+        "image": ""
+      },
+      {
+        "answer": "viene ridotto il numero di features considerate, ma quelle selezionate vengono pienamente implementate",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "piccoli widget che forniscono informazioni aggiuntive oltre all’indicazione del tempo",
+        "image": ""
+      },
+      {
+        "answer": "una situazione di blocco dell’interfaccia che provoca problemi persistenti agli utenti",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "ilcoinvolgimento emotivo del soggetto coinvolto",
+        "image": ""
+      },
+      {
+        "answer": "l’impegno cognitivo del soggetto coinvolto",
+        "image": ""
+      },
+      {
+        "answer": "Il grado di novità dell’esperienza con il sistema interattivo percepito dal soggetto coinvolto",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l’uso dell’ambiente fisico come interfaccia per l’informazione digitale",
+        "image": ""
+      },
+      {
+        "answer": "l’utilizzo esclusivo di schermi di schermi di grande dimensione, per permettere una comunicazione parallela a più utenti",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l'utilizzo della sola mano sinistra",
+        "image": ""
+      },
+      {
+        "answer": "una minore velocità nella digitazione",
+        "image": ""
+      },
+      {
+        "answer": "l'utilizzo della sola mano destra",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la scalabilità",
+        "image": ""
+      },
+      {
+        "answer": "la mancanza di relazione con il contesto",
+        "image": ""
+      },
+      {
+        "answer": "il parallelismo spaziale",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "è uno storyboard nel quale vengono proposti gli screenshots dell’interfaccia senza alcun ordinamento particolare",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "il tempo motorio è funzione del canale sensoriale e aumenta in caso di segnali misti (es. segnale uditivo + segnale visivo)",
+        "image": ""
+      },
+      {
+        "answer": "il tempo motorio è funzione dell’età e della salute dell’individuo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "la situazione negativa derivante all’incapacità di avere una visione complessiva dello stato del sistema con cui più operatori interagiscono",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la fase di conversione del linguaggio di base in linguaggio di output",
+        "image": ""
+      },
+      {
+        "answer": "la fase di conversione del linguaggio del compito in linguaggio di input",
+        "image": ""
+      },
+      {
+        "answer": "la fase di conversione del linguaggio di output in linguaggio del compito",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "l'utilizzo di una configurazione di computer più potente di quella che verrà utilizzata come target per il rilascio dell’applicazione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "il sistema dovrebbe parlare il linguaggio dell'utente",
+        "image": ""
+      },
+      {
+        "answer": "nel processo di interazione gli oggetti, le azioni e le opzioni vanno rese visibili",
+        "image": ""
+      },
+      {
+        "answer": "ogni elemento informativo presentato nella finestra di output compete con gli altri e ne diminuisce la visibilità",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "per la definizione di percorsi non-lineari che descrivono le transizioni di stato dell’interfaccia",
+        "image": ""
+      },
+      {
+        "answer": "per la descrizione del contesto in cui avviene l’interazione",
+        "image": ""
+      },
+      {
+        "answer": "per la presentazione degli screenshots dell’interfaccia senza alcun ordinamento particolar",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Dopo l’esecuzione il sistema cambia stato e lo comunica attraverso il linguaggio di output",
+        "image": ""
+      },
+      {
+        "answer": "L’utente osserva l’output e valuta i risultati",
+        "image": ""
+      },
+      {
+        "answer": "Il compito dell’utentevienearticolato all’interno del linguaggio di input",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l’esteticae la visibilità degli oggetti",
+        "image": ""
+      },
+      {
+        "answer": "la durabilità e le azioni incrementali",
+        "image": ""
+      },
+      {
+        "answer": "il coinvolgimento percepitoe l'attenzione focalizzata",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "è indipendente dal canale sensoriale",
+        "image": ""
+      },
+      {
+        "answer": "dipende dalla salute del soggetto",
+        "image": ""
+      },
+      {
+        "answer": "dipende dall'età del soggetto",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "82) Le interfaccea manipolazione diretta si caratterizzano per:",
+    "answers": [
+      {
+        "answer": "l'irreversibilità delle azioni",
+        "image": ""
+      },
+      {
+        "answer": "l'invisibilità degli oggetti",
+        "image": ""
+      },
+      {
+        "answer": "l'utilizzo di linguaggi di comando",
+        "image": ""
+      },
+      {
+        "answer": "la correttezza sintattica di tutte le azioni",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "84) Il concetto di affordanceesprime:",
+    "answers": [
+      {
+        "answer": "la capacità di un oggetto fisico di suggerire, tramite la propria forma, la funzione a cui è preposto",
+        "image": ""
+      },
+      {
+        "answer": "la possibilità di produzionedi un oggetto fisico a basso costo, per permettere una distribuzione ampia",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "prevedono che un elevato numero di utenti esaminino gli aspetti di usabilità di un prodotto",
+        "image": ""
+      },
+      {
+        "answer": "prevedono un’accurata analisi del dispositivo prima della release finale, ottenuta anche attraverso il disassemblaggio e l’ispezione delle sue componenti",
+        "image": ""
+      },
+      {
+        "answer": "prevedono che un numero limitato di specialisti esaminino gli aspetti di usabilità di un prodotto",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "il sistema di eco-feedback dovrebbe dare solo informazioni generali, lasciando agli utenti il compito di elaborare strategie di azione giornaliere",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "il sistema di eco-feedback dovrebbe includere una vista a volo d’uccello dei consumi della famiglia",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l'informatica pervasiva richiede che l'utente comunichi esplicitamente al sistema che cosa fare",
+        "image": ""
+      },
+      {
+        "answer": "l'informatica interattiva richiede necessariamente un ambiente dotato di sensori",
+        "image": ""
+      },
+      {
+        "answer": "l'informatica interattiva prevede che l'utente possa essere ignaro del fatto che stia",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "usabilità ed engagement sono due concetti separati che vengono utilizzati in ambiti applicativi completamente diversi",
+        "image": ""
+      },
+      {
+        "answer": "il concetto di usabilità viene compreso nella definizione del concetto di engagement (secondo la definizione di O'Brien)",
+        "image": ""
+      },
+      {
+        "answer": "il concetto di engagement viene compreso nella definizione del concetto di usabilità (secondo la definizione ISO)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "vanno predisposte uscite di emergenza per lasciare lo stato dell'interazione in cui ci si trova",
+        "image": ""
+      },
+      {
+        "answer": "l'interfaccia non deve seguire le convenzioni del mondo reale",
+        "image": ""
+      },
+      {
+        "answer": "gli utenti non devono preoccuparsi se le parole o icone diverse usate nell'interfaccia indicano la stessa cosa",
+        "image": ""
+      },
+      {
+        "answer": " 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": [
+      {
+        "answer": "l'utilizzo di una voce narrante che descrive lo scenario dell'interfaccia",
+        "image": ""
+      },
+      {
+        "answer": "l'utilizzo di uno schema non lineare per mostrare i cambiamenti di stato dell’interfaccia a seguito delle azioni dell'utente",
+        "image": ""
+      },
+      {
+        "answer": "l'utilizzo di uno schema lineare per mostrare i cambiamenti di stato dell’interfaccia",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "per avere solamente un’interfaccia di input",
+        "image": ""
+      },
+      {
+        "answer": "perché il feedback per l’operatore non deriva solo dall’interfaccia di output",
+        "image": ""
+      },
+      {
+        "answer": "perché il feedback per l’operatore deriva solo dall’osservazione diretta del mondo reale",
+        "image": ""
+      },
+      {
+        "answer": "per avere solamente un’interfaccia di output",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "92) Il paradigma a manipolazione diretta si distingue dal paradigma linguistico:",
+    "answers": [
+      {
+        "answer": "perché è più difficile da apprendere",
+        "image": ""
+      },
+      {
+        "answer": "per la possibilità di applicare contemporaneamente un determinato comando a più oggetti",
+        "image": ""
+      },
+      {
+        "answer": "per l'implicita correttezza sintattica di tutte le azioni che si possono compiere",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "dalla trasposizione del layout utilizzato nelle calcolatrici meccaniche",
+        "image": ""
+      },
+      {
+        "answer": "da uno studio specifico sulla fatica di utilizzo svolto da IBM",
+        "image": ""
+      },
+      {
+        "answer": "dalla versione estesa della tastiera DVORAK",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "si riferisce alle modalità di utilizzo del canale sonoro per la comunicazione a grande distanza in sistemi pervasivi",
+        "image": ""
+      },
+      {
+        "answer": "si riferisce alla possibilità di fornire agli utenti informazioni sulle conseguenze delle loro azioni per l'ambiente",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "presentazione",
+        "image": ""
+      },
+      {
+        "answer": "prestazione",
+        "image": ""
+      },
+      {
+        "answer": "articolazione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "è essenziale dare informazioni sui consumi in tempo reale",
+        "image": ""
+      },
+      {
+        "answer": "è importante dare informazioni chiare su come cambiare i comportamenti",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "indica che il sistema deve eseguire almeno il 5% di previsioni corrette ed utili",
+        "image": ""
+      },
+      {
+        "answer": "indica che è necessario un quoziente intellettivo minimo (espresso come valore QI) per poter interagire con il sistema",
+        "image": ""
+      },
+      {
+        "answer": "indica che il sistema non deve causare problemi nel caso in cui l'azione del sistema derivi da una previsione sbagliata",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "osservazione",
+        "image": ""
+      },
+      {
+        "answer": "articolazione",
+        "image": ""
+      },
+      {
+        "answer": "presentazione",
+        "image": ""
+      },
+      {
+        "answer": "prestazione",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "99) Definire quale dei seguenti parametri non viene considerato nella definizione di engagement secondo O'Brien:",
+    "answers": [
+      {
+        "answer": "coinvolgimento percepito",
+        "image": ""
+      },
+      {
+        "answer": "costo",
+        "image": ""
+      },
+      {
+        "answer": "durabilità",
+        "image": ""
+      },
+      {
+        "answer": "attenzione focalizzata",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "100) Nell’ambito delle tecniche di prototipazione di un’interfaccia, una branching storyboard si distingue per:",
+    "answers": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "l'utilizzo di uno schema non lineare per mostrare i cambiamenti di stato dell’interfaccia a seguito delle sole azioni esplicite dell'utente",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la maggiore espressività nell'input",
+        "image": ""
+      },
+      {
+        "answer": "la maggiore consistenza",
+        "image": ""
+      },
+      {
+        "answer": "la minore scopribilità",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la maggiore velocità rispetto alle soluzioni concorrent",
+        "image": ""
+      },
+      {
+        "answer": "la minore fatica rispetto alle soluzioni concorrent",
+        "image": ""
+      },
+      {
+        "answer": "l'ordinamento ottimale del layout",
+        "image": ""
+      },
+      {
+        "answer": "'inerzia tecnologica",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "103) Le interfacce industriali si distinguono dalle interfacce a manipolazione diretta:",
+    "answers": [
+      {
+        "answer": "per avere solamente un’interfaccia di input",
+        "image": ""
+      },
+      {
+        "answer": "perché il feedback per l’operatore non deriva solo dall’interfaccia di output",
+        "image": ""
+      },
+      {
+        "answer": "per avere solamente un’interfaccia di output",
+        "image": ""
+      },
+      {
+        "answer": " 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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "il feedback basato su rappresentazioni artistiche è un'ottima alternativa alla visualizzazione di informazione dettagliata",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "dipende dalle culture.",
+        "image": ""
+      },
+      {
+        "answer": "non cambia a seconda delle culture.",
+        "image": ""
+      },
+      {
+        "answer": "non cambia (ma possono cambiare i nomi a seconda delle culture).",
+        "image": ""
+      },
+      {
+        "answer": "é correlato alle condizioni climatiche.",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "106) E' corretto dire che il software Graffiti per il riconoscimento della scrittura:",
+    "answers": [
+      {
+        "answer": "permette il riconoscimento di qualsiasi stile di scrittura individuale.",
+        "image": ""
+      },
+      {
+        "answer": "è basato esclusivamente sulla semplificazione dei caratteri.",
+        "image": ""
+      },
+      {
+        "answer": " si avvale della standardizzazione del tratto.",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la durabilità e la visibilità degli oggetti",
+        "image": ""
+      },
+      {
+        "answer": "il coinvolgimento percepito e le azioni incrementali",
+        "image": ""
+      },
+      {
+        "answer": "l'estetica e l'attenzione focalizzata",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "è bene permettere all'utente di personalizzare lo svolgimento delle azioni frequenti",
+        "image": ""
+      },
+      {
+        "answer": "nell'interazione è preferibile il riconoscimento al ricordo",
+        "image": ""
+      },
+      {
+        "answer": "il sistema non dovrebbe corrispondere al mondo reale",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "il sistema di eco-feedback dovrebbe includere una vista a volo d’uccello dei consumi della famiglia",
+        "image": ""
+      },
+      {
+        "answer": "il sistema di eco-feedback, per ragioni di privacy, non dovrebbe stimolare una mutua",
+        "image": ""
+      },
+      {
+        "answer": "il sistema di eco-feedback dovrebbe dare solo informazioni generali, lasciando agli utenti il compito di elaborare strategie di azione giornaliere",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la durabilità e la visibilità degli oggetti",
+        "image": ""
+      },
+      {
+        "answer": "il coinvolgimento percepito e le azioni incrementali",
+        "image": ""
+      },
+      {
+        "answer": "l'estetica e l'attenzione focalizzata",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "è bene permettere all'utente di personalizzare lo svolgimento delle azioni frequenti",
+        "image": ""
+      },
+      {
+        "answer": "nell'interazione è preferibile il riconoscimento al ricordo",
+        "image": ""
+      },
+      {
+        "answer": "il sistema non dovrebbe corrispondere al mondo reale",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "il sistema di eco-feedback dovrebbe includere una vista a volo d’uccello dei consumi della famiglia",
+        "image": ""
+      },
+      {
+        "answer": "il sistema di eco-feedback, per ragioni di privacy, non dovrebbe stimolare una mutua",
+        "image": ""
+      },
+      {
+        "answer": "il sistema di eco-feedback dovrebbe dare solo informazioni generali, lasciando agli utenti il compito di elaborare strategie di azione giornaliere",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "per la descrizione del contesto in cui avviene l’interazione",
+        "image": ""
+      },
+      {
+        "answer": "per la definizione di percorsi lineari che descrivono le transizioni di stato dell’interfaccia",
+        "image": ""
+      },
+      {
+        "answer": "per una migliore accuratezza nella rappresentazione dell’interfaccia",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "è dipendente dal canale sensoriale",
+        "image": ""
+      },
+      {
+        "answer": "dipende dalla salute del soggetto",
+        "image": ""
+      },
+      {
+        "answer": "diminuisce quando lo stimolo avviene su un canale misto (es. uditivo e tattile",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "il compito dell’utente viene articolato all’interno del linguaggio di input",
+        "image": ""
+      },
+      {
+        "answer": "dopo l’esecuzione il sistema cambia stato e lo comunica attraverso il linguaggio di output",
+        "image": ""
+      },
+      {
+        "answer": "l’utente osserva l’output e valuta i risultati",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "prevedono che un elevato numero di utenti esaminino gli aspetti di usabilità di un prodotto",
+        "image": ""
+      },
+      {
+        "answer": "prevedono che un numero limitato di specialisti esaminino gli aspetti di usabilità di un prodotto",
+        "image": ""
+      },
+      {
+        "answer": "prevedono che i valutatori dell’usabilità raccolgano dati attraverso un numero sostanzioso di questionari composti di domande aperte e chiuse",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la manipolazione di oggetti visibili",
+        "image": ""
+      },
+      {
+        "answer": "l’impossibilità di accedere in modo diretto alle funzioni del sistema",
+        "image": ""
+      },
+      {
+        "answer": "la possibilità di applicare lo stesso comando contemporaneamente a più oggetti",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l’interazione dell’uomo con un dispositivo smart, basata sull’uso di un set di app",
+        "image": ""
+      },
+      {
+        "answer": "l’interazione di un uomo con il suo ambiente e con gli artefatti inseriti all’interno di esso,",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la capacità del sistema uditivo di comprendere una conversazione che comprende una miscela di termini appartenenti a lingue diverse",
+        "image": ""
+      },
+      {
+        "answer": "la capacità del sistema uditivo di filtrare i suoni ricevuti, per permettere ad esempio di focalizzarsi su una conversazione in un ambiente rumoroso",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "è dipendente dal canale sensoriale",
+        "image": ""
+      },
+      {
+        "answer": "dipende dalla salute del soggetto",
+        "image": ""
+      },
+      {
+        "answer": "diminuisce quando lo stimolo avviene su un canale misto (es. uditivo e tattile",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "per la descrizione del contesto in cui avviene l’interazione",
+        "image": ""
+      },
+      {
+        "answer": "per la definizione di percorsi lineari che descrivono le transizioni di stato dell’interfaccia",
+        "image": ""
+      },
+      {
+        "answer": "per una migliore accuratezza nella rappresentazione dell’interfaccia",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "il compito dell’utente viene articolato all’interno del linguaggio di input",
+        "image": ""
+      },
+      {
+        "answer": "dopo l’esecuzione il sistema cambia stato e lo comunica attraverso il linguaggio di output",
+        "image": ""
+      },
+      {
+        "answer": "l’utente osserva l’output e valuta i risultati",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "prevedono che un elevato numero di utenti esaminino gli aspetti di usabilità di un prodotto",
+        "image": ""
+      },
+      {
+        "answer": "prevedono che un numero limitato di specialisti esaminino gli aspetti di usabilità di un prodotto",
+        "image": ""
+      },
+      {
+        "answer": "prevedono che i valutatori dell’usabilità raccolgano dati attraverso un numero sostanzioso di questionari composti di domande aperte e chiuse",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la manipolazione di oggetti visibili",
+        "image": ""
+      },
+      {
+        "answer": "l’impossibilità di accedere in modo diretto alle funzioni del sistema",
+        "image": ""
+      },
+      {
+        "answer": "la possibilità di applicare lo stesso comando contemporaneamente a più oggetti",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l’interazione dell’uomo con un dispositivo smart, basata sull’uso di un set di app",
+        "image": ""
+      },
+      {
+        "answer": "l’interazione di un uomo con il suo ambiente e con gli artefatti inseriti all’interno di esso,",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la capacità del sistema uditivo di comprendere una conversazione che comprende una miscela di termini appartenenti a lingue diverse",
+        "image": ""
+      },
+      {
+        "answer": "la capacità del sistema uditivo di filtrare i suoni ricevuti, per permettere ad esempio di focalizzarsi su una conversazione in un ambiente rumoroso",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la sintesi e l’articolazione;",
+        "image": ""
+      },
+      {
+        "answer": "l’elaborazione e la manipolazione;",
+        "image": ""
+      },
+      {
+        "answer": "la sequenza e la presentazione;",
+        "image": ""
+      },
+      {
+        "answer": "l’osservazione e la prestazione",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "128) Qual è il significato dell’euristica controllo dell’utente e libertà?",
+    "answers": [
+      {
+        "answer": "che gli utenti non devono avere la possibilità di selezionare funzionalità del sistema per errore;",
+        "image": ""
+      },
+      {
+        "answer": "che agli utenti devono essere disponibili funzioni di undo;",
+        "image": ""
+      },
+      {
+        "answer": "che agli utenti non devono essere disponibili funzioni di redo;",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l’impossibilità di riconoscere la vocedi una persona in ambienti molto rumorosi;",
+        "image": ""
+      },
+      {
+        "answer": "l’incapacità del sistema uditivo di differenziare suoni vicini per intensità e frequenza;",
+        "image": ""
+      },
+      {
+        "answer": "l’incapacità del sistema visuale di discriminare l’identità di una persona in ambienti poco illuminati;",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "allo svolgimento di attività che hanno un inizio ed una fine ben definiti;",
+        "image": ""
+      },
+      {
+        "answer": "alla promozione di attività informali e non strutturate;",
+        "image": ""
+      },
+      {
+        "answer": "all’uso giornaliero e continuativo di alcune applicazioni nel contesto lavorativo;",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "una situazione che si verifica quando l’utente deve accedere ad un’interfaccia con un visore monoculare;",
+        "image": ""
+      },
+      {
+        "answer": "una situazione che si verifica quando l’operatore si astrae volontariamente dalla visualizzazione generale dell’interfaccia per concentrarsi sul suo lavoro;",
+        "image": ""
+      },
+      {
+        "answer": "una situazione che si verifica quando l’utente ha accesso a informazioni riservate;",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "irreversibilità delle azioni;",
+        "image": ""
+      },
+      {
+        "answer": "azioni non incrementali;",
+        "image": ""
+      },
+      {
+        "answer": "presenza di widget che vengono utilizzati come oggetti di input e di output;",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "una bassa espressività;",
+        "image": ""
+      },
+      {
+        "answer": "la disponibilitàdi linee guida riconosciute;",
+        "image": ""
+      },
+      {
+        "answer": "la proposta di nuove convenzioni che ignorano le esistenti;",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l’osservazione e la prenotazione;",
+        "image": ""
+      },
+      {
+        "answer": "l’analisi e la collaborazione;",
+        "image": ""
+      },
+      {
+        "answer": "la sintesi e l’articolazione;",
+        "image": ""
+      },
+      {
+        "answer": "la presentazionee la prestazione",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "136) Qual è il significato dell’euristica riconoscimento anziché ricordo?",
+    "answers": [
+      {
+        "answer": "che,per favorire l’interazione,gli oggetti e le azioni dell’interazionestessavanno resi visibili;",
+        "image": ""
+      },
+      {
+        "answer": "che è auspicabile che sistema interattivo sia in grado di riconoscere i pattern di interazione dell’utente;",
+        "image": ""
+      },
+      {
+        "answer": "che è auspicabile che sistema interattivo sia in grado di riconoscere il profilo dell’utente;",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "una situazione che si verifica quando l’utente ha accesso a informazioni riservate;",
+        "image": ""
+      },
+      {
+        "answer": "una situazione che si verifica quando l’operatore si astrae volontariamente dalla visualizzazione generale dell’interfaccia per concentrarsi sul suo lavoro;",
+        "image": ""
+      },
+      {
+        "answer": "una situazione che si verifica quando l’utente ha accesso solo a visualizzazioni parziali del sistema;",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "uno stato di frastornamento legato all’eccesso di stimoli uditivi di un ambiente;",
+        "image": ""
+      },
+      {
+        "answer": "la capacità del sistema sensoriale di miscelare stimoli da canali diversi (es. visuale e tattile) per aumentare la velocità di ricezione dell’informazione;",
+        "image": ""
+      },
+      {
+        "answer": "uno stato di frastornamento legato all’eccesso di stimoli visuali di un ambiente;",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "una bassa espressività;",
+        "image": ""
+      },
+      {
+        "answer": "la proposta di nuove convenzioni che ignorano le esistenti;",
+        "image": ""
+      },
+      {
+        "answer": "la disponibilità di linee guida riconosciute;",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "l’output esplicito costituisce un canale di comunicazione diretto tra utente e ambiente, che non passa attraversol’applicazione di informatica pervasiva;",
+        "image": ""
+      },
+      {
+        "answer": "l’output esplicitonon dovrebbe mai affiancare l’output esplicito dell’interfaccia;",
+        "image": ""
+      },
+      {
+        "answer": "l’input implicito viene comunque visto dall’utente come un’interazione con il sistema informatico;",
+        "image": ""
+      },
+      {
+        "answer": "l’input implicito può affiancare l’input esplicito dell’utente",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "142) Ilparadigma a manipolazione diretta è caratterizzato da:",
+    "answers": [
+      {
+        "answer": "sostituzione di azioni per la manipolazione di oggetti visibili con linguaggi di comando;",
+        "image": ""
+      },
+      {
+        "answer": "correttezza sintattica di tutte le azioni;",
+        "image": ""
+      },
+      {
+        "answer": "chiara separazionedel linguaggio di input e del linguaggio di output;",
+        "image": ""
+      },
+      {
+        "answer": "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
new file mode 100644
index 0000000..21dcad7
--- /dev/null
+++ b/data/questions/ogas.json
@@ -0,0 +1,1066 @@
+[
+  {
+    "quest": "1) I manager sono dei visionari e hanno un'alta propensione al rischio",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "3) Il budget è l'output dell'attività di programmazione",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "8) La fattibilità economica è quella fase della pianificazione il cui output è il piano operativo",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "9) Il pensiero visuale è una tecnica di progettazione dei modelli di business",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "10) Il business model CANVAS è strutturato in sette blocchi",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "12) Il manager, così come l'imprenditore, potrebbe rischiare il proprio capitale",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "14) La condizione di vitalità dell'impresa è RICAVI >= COSTI",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "17) L'equilibrio finanziario è rappresentato dall'equazione ∑ Ricavi = ∑ Costi + margine di profitto equo",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "21) Il capitale di rischio rappresenta i mezzi monetari apportati da terzi finanziatori",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "22) L'attitudine all'imprenditorialità si manifesta",
+    "answers": [
+      {
+        "answer": "nell'ideazione dell'attività intrapresa dall'imprenditore",
+        "image": ""
+      },
+      {
+        "answer": "nell'avviamento e gestione dell'attività oggetto dell'impresa",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "rimane in capo ai soci, nei limiti delle azioni sottoscritte",
+        "image": ""
+      },
+      {
+        "answer": "viene delegato al consiglio di amministrazione",
+        "image": ""
+      },
+      {
+        "answer": "è assunto dal top management",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "24) L'interesse patrimoniale consiste",
+    "answers": [
+      {
+        "answer": "nel compenso spettante al titolare per la messa a disposizione del capitale",
+        "image": ""
+      },
+      {
+        "answer": "nel compenso per il rischio sopportato con l'investimento",
+        "image": ""
+      },
+      {
+        "answer": "nel compenso per l'attività che il titolare svolge in azienda",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "25) La redditività coincide con l'economicità",
+    "answers": [
+      {
+        "answer": "in ogni caso",
+        "image": ""
+      },
+      {
+        "answer": "nel caso in cui il reddito realizzato sia anche equo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "all'ottimale combinazione dei fattori produttivi",
+        "image": ""
+      },
+      {
+        "answer": "all'ordinata sequenza ed alla correta attuazione delle operazioni di gestione",
+        "image": ""
+      },
+      {
+        "answer": "all'instaurazione di corretti rapporti con l'ambiente di riferimento",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "27) Il budget è",
+    "answers": [
+      {
+        "answer": "il documento sintetico che scaturisce al termine del processo di programmazione",
+        "image": ""
+      },
+      {
+        "answer": "il documento sintetico che scaturisce al termine del processo di pianificazione",
+        "image": ""
+      },
+      {
+        "answer": "uno strumento di analisi dei problemi gestionali",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "28) Il gruppo autarchicoera caratterizzato da una politica rivolta",
+    "answers": [
+      {
+        "answer": "all'oggi e al domani proprio e all'oggi e al domani altrui",
+        "image": ""
+      },
+      {
+        "answer": "all'oggi proprio e al domani altrui",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "all'attuazione della produzione, mediante il principio del minimo mezzo",
+        "image": ""
+      },
+      {
+        "answer": "all'acquisizione dei beni necessari per il consumo",
+        "image": ""
+      },
+      {
+        "answer": "alla selezione dei bisogni e all'individuazione delle vie più convenienti per la produzione",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "30) Le imprese sono",
+    "answers": [
+      {
+        "answer": "aziende che producono per il mercato",
+        "image": ""
+      },
+      {
+        "answer": "aziende che producono per gli associati",
+        "image": ""
+      },
+      {
+        "answer": "aziende che producono per la collettività",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "31) Lo scambio è una fase economica che ha origine",
+    "answers": [
+      {
+        "answer": "nel momento dell'introduzione della moneta",
+        "image": ""
+      },
+      {
+        "answer": "nel momento in cui il gruppo economico da chiuso diventa aperto",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "lo scopo di lucro",
+        "image": ""
+      },
+      {
+        "answer": "l'elemento personale",
+        "image": ""
+      },
+      {
+        "answer": "l'elemento patrimoniale",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "33) Il concepimento delle linee strategico-tattiche si riferisce all'impegno umano in azienda",
+    "answers": [
+      {
+        "answer": "di tipo esecutivo",
+        "image": ""
+      },
+      {
+        "answer": "di tipo volitivo",
+        "image": ""
+      },
+      {
+        "answer": "di tipo direttivo",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "34) Il passaggio da capitale generico a capitale disponibile si attua",
+    "answers": [
+      {
+        "answer": "con la progettazione dell'azienda",
+        "image": ""
+      },
+      {
+        "answer": "con l'ingresso del capitale in azienda",
+        "image": ""
+      },
+      {
+        "answer": "con l'ingresso in azienda dei fattori produttivi",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "35) La cognizione post-operativa, si riferisce",
+    "answers": [
+      {
+        "answer": "alla fase del controllo dell'esito della gestione",
+        "image": ""
+      },
+      {
+        "answer": "alla fase esecutiva della gestione",
+        "image": ""
+      },
+      {
+        "answer": "alla fase di pianificazione della gestione",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "36) Il passaggio da capitale inerte a capitale attivato si ha",
+    "answers": [
+      {
+        "answer": "quando il capitale monetario viene rimborsato al titolare",
+        "image": ""
+      },
+      {
+        "answer": "quando il capitale monetario viene convertito nei fattori produttivi con esso acquisiti",
+        "image": ""
+      },
+      {
+        "answer": "con l'applicazione delle capacità potenziali del lavoro alle utilità del capitale",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "37) Il soggetto istituzionale è",
+    "answers": [
+      {
+        "answer": "colui che svolge il lavoro direttivo in azienda",
+        "image": ""
+      },
+      {
+        "answer": "colui che progetta l'azienda e le linee fondamentali dell'attività",
+        "image": ""
+      },
+      {
+        "answer": "colui che svolge il lavoro esecutivo in azienda",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "38) Il soggetto giuridico, in un'azienda individuale",
+    "answers": [
+      {
+        "answer": "coincide sempre con il soggetto economico",
+        "image": ""
+      },
+      {
+        "answer": "non può mai coincidere con il soggetto economico",
+        "image": ""
+      },
+      {
+        "answer": "è rappresentato dal titolare dell'azienda",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": "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"
+  },
+  {
+    "quest": "39) Nelle società per azioni, il rischio aziendale",
+    "answers": [
+      {
+        "answer": "rimane in capo ai soci, nei limiti delle azioni sottoscritte",
+        "image": ""
+      },
+      {
+        "answer": "viene delegato al consiglio di amministrazione",
+        "image": ""
+      },
+      {
+        "answer": "è assunto dal top management",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "40) Il capitale di rischio rappresenta",
+    "answers": [
+      {
+        "answer": "mezzi monetari apportati da terzi finanziatori",
+        "image": ""
+      },
+      {
+        "answer": "mezzi monetari apportati dal titolare",
+        "image": ""
+      },
+      {
+        "answer": "l'insieme dei mezzi monetari e del lavoro del titolare",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "41) Le combinazioni di processi sono",
+    "answers": [
+      {
+        "answer": "raggruppamenti di operazioni di gestione",
+        "image": ""
+      },
+      {
+        "answer": "raggruppamenti di funzioni amministrative",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "ai soci",
+        "image": ""
+      },
+      {
+        "answer": "al consiglio di amministrazione",
+        "image": ""
+      },
+      {
+        "answer": "alla società",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "43) La redditività coincide con l'economicità",
+    "answers": [
+      {
+        "answer": "in ogni caso",
+        "image": ""
+      },
+      {
+        "answer": "nel caso in cui il reddito realizzato sia anche equo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "all'ottimale combinazione dei fattori produttivi",
+        "image": ""
+      },
+      {
+        "answer": "all'ordinata sequenza ed alla corretta attuazione delle operazioni di gestione",
+        "image": ""
+      },
+      {
+        "answer": "all'instaurazione di corretti rapporti con l'ambiente di riferimento",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "46) Affinché si abbia equilibrio economico, i ricavi",
+    "answers": [
+      {
+        "answer": "devono limitarsi a reintegrare i costi storici",
+        "image": ""
+      },
+      {
+        "answer": "devono coprire anche i costi prospettici di ricostituzione",
+        "image": ""
+      },
+      {
+        "answer": "devono coprire anche i costi prospettici di sviluppo",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "47) L'ordine",
+    "answers": [
+      {
+        "answer": "è sinonimo di economicità",
+        "image": ""
+      },
+      {
+        "answer": "è il presupposto dell'economicità",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": [
+      {
+        "answer": "So rispondere",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "50) DOMANDA APERTA: Il business plan: descrizione e scopi",
+    "answers": [
+      {
+        "answer": "So rispondere",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "51) DOMANDA APERTA: Progettare un modello di business. Finalità e tecniche",
+    "answers": [
+      {
+        "answer": "So rispondere",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "52) Il business plan è",
+    "answers": [
+      {
+        "answer": "non conosco la risposta (a)",
+        "image": ""
+      },
+      {
+        "answer": "il documento sintetico che scaturisce al termine del processo di pianificazione",
+        "image": ""
+      },
+      {
+        "answer": "non conosco la risposta (c)",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "53) Il break-even operativo",
+    "answers": [
+      {
+        "answer": "rappresenta il punto di pareggio tra costi totali e ricavi totali, espresso in volumi di vendita",
+        "image": ""
+      },
+      {
+        "answer": "non conosco la risposta (b)",
+        "image": ""
+      },
+      {
+        "answer": "non conosco la risposta (c)",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "54) Organizzazione è un concetto polisemico",
+    "answers": [
+      {
+        "answer": "con molteplici significati e due accezioni prevalenti",
+        "image": ""
+      },
+      {
+        "answer": "con molteplici significati e un' accezione prevalente",
+        "image": ""
+      },
+      {
+        "answer": "con molteplici significati e molte accezioni",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "55) Le due grandi dimensioni del problema organizzativo",
+    "answers": [
+      {
+        "answer": "divisione del lavoro e controllo",
+        "image": ""
+      },
+      {
+        "answer": "divisione del lavoro e coordinamento",
+        "image": ""
+      },
+      {
+        "answer": "coordinamento e potere",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "56) DOMANDA APERTA: Si descrivano le funzioni del business plan",
+    "answers": [
+      {
+        "answer": "So rispondere",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "57) Cosa hai appreso durante il corso?",
+    "answers": [
+      {
+        "answer": "Se non investi in azioni non sei nessuno",
+        "image": ""
+      },
+      {
+        "answer": "Se non hai l'Audi non sei nessuno",
+        "image": ""
+      },
+      {
+        "answer": "Se non hai la Amex Platinum non sei nessuno",
+        "image": ""
+      },
+      {
+        "answer": "Miriam",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le risposte sono corrette",
+        "image": ""
+      }
+    ],
+    "correct": 4,
+    "image": ""
+  },
+  {
+    "quest": "58) Cosa hai appreso durante il corso?",
+    "answers": [
+      {
+        "answer": "Berlusconi usa delle scorciatoie",
+        "image": ""
+      },
+      {
+        "answer": "I conquistatori inglesi sono violenti",
+        "image": ""
+      },
+      {
+        "answer": "Saremo schiavi della Cina",
+        "image": ""
+      },
+      {
+        "answer": "Il voto dell'esame è espresso in trentesimi",
+        "image": ""
+      },
+      {
+        "answer": "Gli ospedali offrono servizi di problem solving",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le risposte sono corrette",
+        "image": ""
+      }
+    ],
+    "correct": 5,
+    "image": ""
+  },
+  {
+    "quest": "45) Cosa hai appreso durante il corso?",
+    "answers": [
+      {
+        "answer": "Gli imprenditori hanno le visioni, in pratica pippano",
+        "image": ""
+      },
+      {
+        "answer": "L'attività del progettista comprende pratiche di proiezione astrale per \"estendere il pensiero\"",
+        "image": ""
+      },
+      {
+        "answer": "Uno di noi sarà il nuovo Steve Jobs (no niente poi ci ha ripensato e ha detto che non starebbe ad informatica)",
+        "image": ""
+      },
+      {
+        "answer": "POPI (https://popipopi.win) è la miglior idea del 2023",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le risposte sono corrette",
+        "image": ""
+      }
+    ],
+    "correct": 4,
+    "image": ""
+  },
+  {
+    "quest": "59) Cosa hai appreso durante il corso?",
+    "answers": [
+      {
+        "answer": "Che la prof vuole un personal trainer figo, un azzardo",
+        "image": ""
+      },
+      {
+        "answer": "Che il cane della prof ce l'ha con gli attrezzi da palestra",
+        "image": ""
+      },
+      {
+        "answer": "I foulard Gucci sono meglio dei Rolex e delle Mercedes",
+        "image": ""
+      },
+      {
+        "answer": "I vegani sono facilmente abbindolati dai guru",
+        "image": ""
+      },
+      {
+        "answer": "I lavoratori hanno troppi diritti",
+        "image": ""
+      },
+      {
+        "answer": "La televisione è un mezzo del mezzo",
+        "image": ""
+      },
+      {
+        "answer": "I mezzi sono essi stessi un mezzo",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le risposte sono corrette",
+        "image": ""
+      }
+    ],
+    "correct": 7,
+    "image": ""
+  },
+  {
+    "quest": "60) L'orale è:",
+    "answers": [
+      {
+        "answer": "Obbligatorio",
+        "image": ""
+      },
+      {
+        "answer": "Facoltativo",
+        "image": ""
+      },
+      {
+        "answer": "FACOLTATIVO",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  }
+]
\ No newline at end of file
diff --git a/data/questions/sicurezza.json b/data/questions/sicurezza.json
new file mode 100644
index 0000000..8369a45
--- /dev/null
+++ b/data/questions/sicurezza.json
@@ -0,0 +1,11975 @@
+[
+  {
+    "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": [
+      {
+        "answer": "Intrusion Prevention System (IPS)",
+        "image": ""
+      },
+      {
+        "answer": "Firewall",
+        "image": ""
+      },
+      {
+        "answer": "Encryption tool",
+        "image": ""
+      },
+      {
+        "answer": "digital immune system",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "reflector DDoS",
+        "image": ""
+      },
+      {
+        "answer": "blended",
+        "image": ""
+      },
+      {
+        "answer": "internal resource",
+        "image": ""
+      },
+      {
+        "answer": "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 confidence whether that behavior is not legitimate user behavior",
+    "answers": [
+      {
+        "answer": "Signature-based",
+        "image": ""
+      },
+      {
+        "answer": "Statistical anomaly",
+        "image": ""
+      },
+      {
+        "answer": "Heuristic",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Phishing attack",
+        "image": ""
+      },
+      {
+        "answer": "drive-by-download",
+        "image": ""
+      },
+      {
+        "answer": "Cross-site scripting (XSS)",
+        "image": ""
+      },
+      {
+        "answer": "Denial of Service (DoS) attack",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "central manager module",
+        "image": ""
+      },
+      {
+        "answer": "host agent module",
+        "image": ""
+      },
+      {
+        "answer": "intruder alert module",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "protocol",
+        "image": ""
+      },
+      {
+        "answer": "attavk",
+        "image": ""
+      },
+      {
+        "answer": "countermeasure",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "SQL injection",
+        "image": ""
+      },
+      {
+        "answer": "off-by-one",
+        "image": ""
+      },
+      {
+        "answer": "Cross-site scripting (XSS)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "timestamp",
+        "image": ""
+      },
+      {
+        "answer": "backward reply",
+        "image": ""
+      },
+      {
+        "answer": "challenge-response",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "underflow/underrun/underwrite",
+        "image": ""
+      },
+      {
+        "answer": "overflow/overrun/overwrite",
+        "image": ""
+      },
+      {
+        "answer": "bypass/overwrite/override",
+        "image": ""
+      },
+      {
+        "answer": "breach/infiltration/compromise",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "417) A consequence of a buffer overflow error is __________ ",
+    "answers": [
+      {
+        "answer": "loss of data connectivity and communication",
+        "image": ""
+      },
+      {
+        "answer": "corruption of data used by the program, unexpected transfer of control int he program, and possible memory access violation",
+        "image": ""
+      },
+      {
+        "answer": "system shutdown and restart",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "incident response plan",
+        "image": ""
+      },
+      {
+        "answer": "access control list",
+        "image": ""
+      },
+      {
+        "answer": "security policy",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "CBC",
+        "image": ""
+      },
+      {
+        "answer": "CTR",
+        "image": ""
+      },
+      {
+        "answer": "CFB",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Address Space Layout Randomization (ASLR)",
+        "image": ""
+      },
+      {
+        "answer": "Data Execution Prevention (DEP)",
+        "image": ""
+      },
+      {
+        "answer": "Control Flow Integrity (CFI)",
+        "image": ""
+      },
+      {
+        "answer": "stackguard",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Encryption key",
+        "image": ""
+      },
+      {
+        "answer": "Generic decryption",
+        "image": ""
+      },
+      {
+        "answer": "Firewall",
+        "image": ""
+      },
+      {
+        "answer": "Intrusion Detection System (IDS)",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "344) Unlike heuristics or fingerprint 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": [
+      {
+        "answer": "mobile code",
+        "image": ""
+      },
+      {
+        "answer": "digital immune system",
+        "image": ""
+      },
+      {
+        "answer": "generic decryption",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "secure coding practices",
+        "image": ""
+      },
+      {
+        "answer": "guard pages",
+        "image": ""
+      },
+      {
+        "answer": "encrypted tunnels",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Gauge",
+        "image": ""
+      },
+      {
+        "answer": "Resource utilization",
+        "image": ""
+      },
+      {
+        "answer": "Counter",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Firewall",
+        "image": ""
+      },
+      {
+        "answer": "Shellcode",
+        "image": ""
+      },
+      {
+        "answer": "Antivirus software",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Intrusion Detection Message Exchange Requirements",
+        "image": ""
+      },
+      {
+        "answer": "Network Security Protocol Standards",
+        "image": ""
+      },
+      {
+        "answer": "Firewall Configuration Best Practices",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "buffer overflow",
+        "image": ""
+      },
+      {
+        "answer": "Null pointer dereference",
+        "image": ""
+      },
+      {
+        "answer": "Division by zero",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "vulnerable",
+        "image": ""
+      },
+      {
+        "answer": "computationally secure",
+        "image": ""
+      },
+      {
+        "answer": "unbreakable",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Anti-virus software",
+        "image": ""
+      },
+      {
+        "answer": "Data encryption",
+        "image": ""
+      },
+      {
+        "answer": "Intrusion Detection",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Cryptographic hash function",
+        "image": ""
+      },
+      {
+        "answer": "Shellcode",
+        "image": ""
+      },
+      {
+        "answer": "Key exchange algorithm",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Heap buffer overflow",
+        "image": ""
+      },
+      {
+        "answer": "Integer overflow",
+        "image": ""
+      },
+      {
+        "answer": "Format string vulnerability",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Stream Cipher (SC)",
+        "image": ""
+      },
+      {
+        "answer": "Counter (CTR)",
+        "image": ""
+      },
+      {
+        "answer": "Cipher Block Chaining (CBC)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "poison packet",
+        "image": ""
+      },
+      {
+        "answer": "slashdot",
+        "image": ""
+      },
+      {
+        "answer": "backscatter traffic",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "confidentiality",
+        "image": ""
+      },
+      {
+        "answer": "authentication",
+        "image": ""
+      },
+      {
+        "answer": "security",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Electronic Codebook mode",
+        "image": ""
+      },
+      {
+        "answer": "Cipher Block Chaining mode",
+        "image": ""
+      },
+      {
+        "answer": "Counter mode",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Data Encryption Standard (DES)",
+        "image": ""
+      },
+      {
+        "answer": "Rivest Cipher 4 (RC4)",
+        "image": ""
+      },
+      {
+        "answer": "Blowfish",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "reactive password checking",
+        "image": ""
+      },
+      {
+        "answer": "computer-generated password",
+        "image": ""
+      },
+      {
+        "answer": "proactive password checking",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "reactive password checking",
+        "image": ""
+      },
+      {
+        "answer": "proactive password checking",
+        "image": ""
+      },
+      {
+        "answer": "computer-generated password",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "mean and standard deviation",
+        "image": ""
+      },
+      {
+        "answer": "Markoprocess",
+        "image": ""
+      },
+      {
+        "answer": "multivariate",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Signature",
+        "image": ""
+      },
+      {
+        "answer": "Statistical",
+        "image": ""
+      },
+      {
+        "answer": "Heuristic",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Ransomware",
+        "image": ""
+      },
+      {
+        "answer": "Spyware",
+        "image": ""
+      },
+      {
+        "answer": "shellcode",
+        "image": ""
+      },
+      {
+        "answer": "Rootkit",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "CBC and ECB",
+        "image": ""
+      },
+      {
+        "answer": "OFB and CTR",
+        "image": ""
+      },
+      {
+        "answer": "ECB and OFB",
+        "image": ""
+      },
+      {
+        "answer": "CTR and CBC",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "322) The _________ prevents duplicate passwords from being visible in the password file. Even if two users choose the same password, those passwords will be assigned at different times\nSelect one:",
+    "answers": [
+      {
+        "answer": "honeypot",
+        "image": ""
+      },
+      {
+        "answer": "salt",
+        "image": ""
+      },
+      {
+        "answer": "rule based intrusion detection",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Intrusion Prevention System (IPS)",
+        "image": ""
+      },
+      {
+        "answer": "Honeytokens",
+        "image": ""
+      },
+      {
+        "answer": "Guard pages",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "boot sector infector",
+        "image": ""
+      },
+      {
+        "answer": "file infector",
+        "image": ""
+      },
+      {
+        "answer": "macro virus",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "SYN spoofing attacks",
+        "image": ""
+      },
+      {
+        "answer": "indirect flooding attacks",
+        "image": ""
+      },
+      {
+        "answer": "ICMP attacks",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Misfeasor",
+        "image": ""
+      },
+      {
+        "answer": "Emissary",
+        "image": ""
+      },
+      {
+        "answer": "Clandestine User",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "hybrid",
+        "image": ""
+      },
+      {
+        "answer": "community",
+        "image": ""
+      },
+      {
+        "answer": "private",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "host-based IDS ",
+        "image": ""
+      },
+      {
+        "answer": "security intrusion",
+        "image": ""
+      },
+      {
+        "answer": "network-based IDS ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Man-in-the-middle attack",
+        "image": ""
+      },
+      {
+        "answer": "Social engineering attack",
+        "image": ""
+      },
+      {
+        "answer": "blended attack",
+        "image": ""
+      },
+      {
+        "answer": "Phishing attack",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Privacy",
+        "image": ""
+      },
+      {
+        "answer": "System Integrity",
+        "image": ""
+      },
+      {
+        "answer": "Avvailability",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "firewall configuration",
+        "image": ""
+      },
+      {
+        "answer": "intrusion",
+        "image": ""
+      },
+      {
+        "answer": "network segmentation",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "stacking",
+        "image": ""
+      },
+      {
+        "answer": "shellcode",
+        "image": ""
+      },
+      {
+        "answer": "no-execute",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Fingerprint-based scanners",
+        "image": ""
+      },
+      {
+        "answer": "Behavior-blocking software",
+        "image": ""
+      },
+      {
+        "answer": "Generic decryption technology",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Host-based IDS",
+        "image": ""
+      },
+      {
+        "answer": "Intrusion Prevention System",
+        "image": ""
+      },
+      {
+        "answer": "Firewal",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "data source ",
+        "image": ""
+      },
+      {
+        "answer": "sensor",
+        "image": ""
+      },
+      {
+        "answer": "operator ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Data Integrity",
+        "image": ""
+      },
+      {
+        "answer": "Confidentiality",
+        "image": ""
+      },
+      {
+        "answer": "Availability",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Identification",
+        "image": ""
+      },
+      {
+        "answer": "Message authentication",
+        "image": ""
+      },
+      {
+        "answer": "Verification",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "DNS amplification attack",
+        "image": ""
+      },
+      {
+        "answer": "SYN spoofing attack",
+        "image": ""
+      },
+      {
+        "answer": "basic flooding attack",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Audit control",
+        "image": ""
+      },
+      {
+        "answer": "Resource control",
+        "image": ""
+      },
+      {
+        "answer": "System control",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "deeply embedded system",
+        "image": ""
+      },
+      {
+        "answer": "constrained device",
+        "image": ""
+      },
+      {
+        "answer": "lattice device",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Encryption tool",
+        "image": ""
+      },
+      {
+        "answer": "Spyware",
+        "image": ""
+      },
+      {
+        "answer": "rootkit",
+        "image": ""
+      },
+      {
+        "answer": "Firewall",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "overwrite",
+        "image": ""
+      },
+      {
+        "answer": "overflow",
+        "image": ""
+      },
+      {
+        "answer": "overrun",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Exploit",
+        "image": ""
+      },
+      {
+        "answer": "shellcode",
+        "image": ""
+      },
+      {
+        "answer": "Payload",
+        "image": ""
+      },
+      {
+        "answer": "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 significant deviations\nSelect one:",
+    "answers": [
+      {
+        "answer": "Threshold",
+        "image": ""
+      },
+      {
+        "answer": "Profile-based anomaly",
+        "image": ""
+      },
+      {
+        "answer": "Statistical anomaly",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "XTS-AES",
+        "image": ""
+      },
+      {
+        "answer": "S-AES",
+        "image": ""
+      },
+      {
+        "answer": "3DES",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Profile based detection ",
+        "image": ""
+      },
+      {
+        "answer": "Signature detection",
+        "image": ""
+      },
+      {
+        "answer": "Threshold detection ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Agent",
+        "image": ""
+      },
+      {
+        "answer": "Collector",
+        "image": ""
+      },
+      {
+        "answer": "Analyzer",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Sobig.f",
+        "image": ""
+      },
+      {
+        "answer": "Mydoom",
+        "image": ""
+      },
+      {
+        "answer": "Slammer",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "File signature matching",
+        "image": ""
+      },
+      {
+        "answer": "Generic Decryption",
+        "image": ""
+      },
+      {
+        "answer": "Behavioral analysis",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "First generation",
+        "image": ""
+      },
+      {
+        "answer": "Fourth generation",
+        "image": ""
+      },
+      {
+        "answer": "Second generation",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "ransomware",
+        "image": ""
+      },
+      {
+        "answer": "shellcode",
+        "image": ""
+      },
+      {
+        "answer": "rootkit",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Firewall",
+        "image": ""
+      },
+      {
+        "answer": "Encryption tool",
+        "image": ""
+      },
+      {
+        "answer": "Rootkit",
+        "image": ""
+      },
+      {
+        "answer": "anti-virus",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Aleph One",
+        "image": ""
+      },
+      {
+        "answer": "L0phtcrack",
+        "image": ""
+      },
+      {
+        "answer": "Acid Burn",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "vulnerability",
+        "image": ""
+      },
+      {
+        "answer": "countermeasure",
+        "image": ""
+      },
+      {
+        "answer": "risk",
+        "image": ""
+      },
+      {
+        "answer": "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 file checker in Windows\nSelect one:",
+    "answers": [
+      {
+        "answer": "Mydoom",
+        "image": ""
+      },
+      {
+        "answer": "Warezov",
+        "image": ""
+      },
+      {
+        "answer": "Slammer",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Heap buffer overflow",
+        "image": ""
+      },
+      {
+        "answer": "Global buffer overflow",
+        "image": ""
+      },
+      {
+        "answer": "stack buffer",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "cipher feedback mode",
+        "image": ""
+      },
+      {
+        "answer": "counter mode",
+        "image": ""
+      },
+      {
+        "answer": "electronic codebook mode",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Clandestine User",
+        "image": ""
+      },
+      {
+        "answer": "Mole",
+        "image": ""
+      },
+      {
+        "answer": "Masquerader",
+        "image": ""
+      },
+      {
+        "answer": "Misfeasor",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "300) What are possible locations for NIDS sensors?",
+    "answers": [
+      {
+        "answer": "inside the external firewall",
+        "image": ""
+      },
+      {
+        "answer": "between the external firewall and the Internet",
+        "image": ""
+      },
+      {
+        "answer": "before internal servers and database resources",
+        "image": ""
+      },
+      {
+        "answer": "before the workstation networks",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "application owner",
+        "image": ""
+      },
+      {
+        "answer": "end user other than application owner",
+        "image": ""
+      },
+      {
+        "answer": "foreign key",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "user education",
+        "image": ""
+      },
+      {
+        "answer": "proactive password checking",
+        "image": ""
+      },
+      {
+        "answer": "reactive password checking",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Private key",
+        "image": ""
+      },
+      {
+        "answer": "Key exchange protocol",
+        "image": ""
+      },
+      {
+        "answer": "Key distribution technique ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "intrusion detection ",
+        "image": ""
+      },
+      {
+        "answer": "IDS",
+        "image": ""
+      },
+      {
+        "answer": "criminal enterprise ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "risk",
+        "image": ""
+      },
+      {
+        "answer": "vulnerability",
+        "image": ""
+      },
+      {
+        "answer": "asset",
+        "image": ""
+      },
+      {
+        "answer": "attack",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "1297)________ includes data processing and storage equipment,transmission and networking facilities,and offline storage media.",
+    "answers": [
+      {
+        "answer": "Supporting facilities",
+        "image": ""
+      },
+      {
+        "answer": "Physical facilities",
+        "image": ""
+      },
+      {
+        "answer": "Information system hardware",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "echo",
+        "image": ""
+      },
+      {
+        "answer": "reflection",
+        "image": ""
+      },
+      {
+        "answer": "poison packet",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Cipher Block Chaining",
+        "image": ""
+      },
+      {
+        "answer": "Counter",
+        "image": ""
+      },
+      {
+        "answer": "Cipher Feedback",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "client attack",
+        "image": ""
+      },
+      {
+        "answer": "eavesdropping attack",
+        "image": ""
+      },
+      {
+        "answer": "host attack",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "multipartite",
+        "image": ""
+      },
+      {
+        "answer": "backdoor",
+        "image": ""
+      },
+      {
+        "answer": "hatch",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "PHP attack",
+        "image": ""
+      },
+      {
+        "answer": "Format string injection attack",
+        "image": ""
+      },
+      {
+        "answer": "XSS attack",
+        "image": ""
+      },
+      {
+        "answer": "Injection attack",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "105) Cryptographic systems are generically classified by _________",
+    "answers": [
+      {
+        "answer": "the type of operations used for transforming plaintext to ciphertext",
+        "image": ""
+      },
+      {
+        "answer": "the number of keys used",
+        "image": ""
+      },
+      {
+        "answer": "the way in which the plaintext is processed",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "identification step",
+        "image": ""
+      },
+      {
+        "answer": "verification step",
+        "image": ""
+      },
+      {
+        "answer": "authentication step",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Clandestine User",
+        "image": ""
+      },
+      {
+        "answer": "Masquerader",
+        "image": ""
+      },
+      {
+        "answer": "Sniffer",
+        "image": ""
+      },
+      {
+        "answer": "Misfeasor",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "646) __________ houses cross-connects and active equipment for distributing cable to the equipment distribution area",
+    "answers": [
+      {
+        "answer": "Main distribution area",
+        "image": ""
+      },
+      {
+        "answer": "Equipment distribution area",
+        "image": ""
+      },
+      {
+        "answer": "Horizontal distribution area",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Profile-based",
+        "image": ""
+      },
+      {
+        "answer": "Statistical",
+        "image": ""
+      },
+      {
+        "answer": "Behavioral",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Traffic Analysis",
+        "image": ""
+      },
+      {
+        "answer": "Payload Inspection",
+        "image": ""
+      },
+      {
+        "answer": "Signature Detection",
+        "image": ""
+      },
+      {
+        "answer": "Anomaly Detection",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "784) IPsec can assure that _________",
+    "answers": [
+      {
+        "answer": "a router advertisement comes from an authorized router",
+        "image": ""
+      },
+      {
+        "answer": "a routing update is not forged",
+        "image": ""
+      },
+      {
+        "answer": "a redirect message comes from the router to which the initial packet was sent",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "counter mode attack",
+        "image": ""
+      },
+      {
+        "answer": "ciphertext stealing",
+        "image": ""
+      },
+      {
+        "answer": "meet-in-the-middle attack",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "hacker",
+        "image": ""
+      },
+      {
+        "answer": "administrator",
+        "image": ""
+      },
+      {
+        "answer": "analyst",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "use longer keys",
+        "image": ""
+      },
+      {
+        "answer": "use shorter keys",
+        "image": ""
+      },
+      {
+        "answer": "use more keys",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": " integrity",
+        "image": ""
+      },
+      {
+        "answer": "encryption",
+        "image": ""
+      },
+      {
+        "answer": " analysis",
+        "image": ""
+      },
+      {
+        "answer": " 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": [
+      {
+        "answer": "digital signature",
+        "image": ""
+      },
+      {
+        "answer": "keystream",
+        "image": ""
+      },
+      {
+        "answer": "one way hash function",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Session key",
+        "image": ""
+      },
+      {
+        "answer": "Subkey",
+        "image": ""
+      },
+      {
+        "answer": "Key distribution technique",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "keystream",
+        "image": ""
+      },
+      {
+        "answer": "digital signature",
+        "image": ""
+      },
+      {
+        "answer": "secure hash",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "moderate",
+        "image": ""
+      },
+      {
+        "answer": "high",
+        "image": ""
+      },
+      {
+        "answer": "normal",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "central manager agent ",
+        "image": ""
+      },
+      {
+        "answer": "LAN monitor agent",
+        "image": ""
+      },
+      {
+        "answer": "host agent ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "known-plaintext",
+        "image": ""
+      },
+      {
+        "answer": "chosen-plaintext",
+        "image": ""
+      },
+      {
+        "answer": "chosen ciphertext",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Collision attacks",
+        "image": ""
+      },
+      {
+        "answer": "Preimage attacks",
+        "image": ""
+      },
+      {
+        "answer": "Timing attacks",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "MMUs",
+        "image": ""
+      },
+      {
+        "answer": "Heaps",
+        "image": ""
+      },
+      {
+        "answer": "Guard Pages",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "1091) The _______ category is a transitional stage between awareness and training.",
+    "answers": [
+      {
+        "answer": "roles and responsibilities relative to IT systems",
+        "image": ""
+      },
+      {
+        "answer": "security basics and literacy",
+        "image": ""
+      },
+      {
+        "answer": "education and experience",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "host-based IDS ",
+        "image": ""
+      },
+      {
+        "answer": "security intrusion",
+        "image": ""
+      },
+      {
+        "answer": "network-based IDS ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "State-sponsored organizations ",
+        "image": ""
+      },
+      {
+        "answer": "Activists",
+        "image": ""
+      },
+      {
+        "answer": "Cyber criminals ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "CBC",
+        "image": ""
+      },
+      {
+        "answer": "CTR",
+        "image": ""
+      },
+      {
+        "answer": "OFB",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Firewall",
+        "image": ""
+      },
+      {
+        "answer": "Router",
+        "image": ""
+      },
+      {
+        "answer": "Antivirus software",
+        "image": ""
+      },
+      {
+        "answer": "Encryption key",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "eavesdropping attack",
+        "image": ""
+      },
+      {
+        "answer": "denial-of-service attack",
+        "image": ""
+      },
+      {
+        "answer": "client attack",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Obfuscated",
+        "image": ""
+      },
+      {
+        "answer": "Scripting",
+        "image": ""
+      },
+      {
+        "answer": "Legacy",
+        "image": ""
+      },
+      {
+        "answer": "Mobile",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "command-line interface",
+        "image": ""
+      },
+      {
+        "answer": "graphical user interface",
+        "image": ""
+      },
+      {
+        "answer": "administrator console",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Initialization phase",
+        "image": ""
+      },
+      {
+        "answer": "Recovery phase",
+        "image": ""
+      },
+      {
+        "answer": "propagation",
+        "image": ""
+      },
+      {
+        "answer": "Termination phase",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Profile based detection ",
+        "image": ""
+      },
+      {
+        "answer": "Signature detection",
+        "image": ""
+      },
+      {
+        "answer": "Threshold detection ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "mode of operation",
+        "image": ""
+      },
+      {
+        "answer": "hash function",
+        "image": ""
+      },
+      {
+        "answer": "cryptanalysis",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "eavesdropping attack",
+        "image": ""
+      },
+      {
+        "answer": "denial-of-service attack",
+        "image": ""
+      },
+      {
+        "answer": "client attack",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "administrator",
+        "image": ""
+      },
+      {
+        "answer": "database relations manager",
+        "image": ""
+      },
+      {
+        "answer": "application owner",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "three",
+        "image": ""
+      },
+      {
+        "answer": "five",
+        "image": ""
+      },
+      {
+        "answer": "seven",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "resource management",
+        "image": ""
+      },
+      {
+        "answer": "access management",
+        "image": ""
+      },
+      {
+        "answer": "privilege management",
+        "image": ""
+      },
+      {
+        "answer": "policy management",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "325) _________ involves counting the number of occurrences of a specific event type over an interval of time\nSelect one:",
+    "answers": [
+      {
+        "answer": "Threshold detection",
+        "image": ""
+      },
+      {
+        "answer": "Rule-based detection",
+        "image": ""
+      },
+      {
+        "answer": "Resource usage",
+        "image": ""
+      },
+      {
+        "answer": "Profile-based system",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "282) _________ simulate human brain operation with neurons and synapse between them that classify observed data",
+    "answers": [
+      {
+        "answer": "Antivirus software",
+        "image": ""
+      },
+      {
+        "answer": "Intrusion prevention systems",
+        "image": ""
+      },
+      {
+        "answer": "Neural networks",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Network-based IDS",
+        "image": ""
+      },
+      {
+        "answer": "Intrusion Prevention System",
+        "image": ""
+      },
+      {
+        "answer": "Firewall",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "active attack",
+        "image": ""
+      },
+      {
+        "answer": "inside attack",
+        "image": ""
+      },
+      {
+        "answer": "outside attack",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "HTTP",
+        "image": ""
+      },
+      {
+        "answer": "Reflection attacks",
+        "image": ""
+      },
+      {
+        "answer": "SYN flooding",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Stack frames",
+        "image": ""
+      },
+      {
+        "answer": "Heap blocks",
+        "image": ""
+      },
+      {
+        "answer": "guard pages",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "07) __________ is a procedure that allows communicating parties to verify that received or stored messages are authentic",
+    "answers": [
+      {
+        "answer": "Cryptanalysis",
+        "image": ""
+      },
+      {
+        "answer": "Decryption",
+        "image": ""
+      },
+      {
+        "answer": "Message authentication",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Counter",
+        "image": ""
+      },
+      {
+        "answer": "Cipher Block Chaining",
+        "image": ""
+      },
+      {
+        "answer": "Output Feedback",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Exploit",
+        "image": ""
+      },
+      {
+        "answer": "Encryption algorithm",
+        "image": ""
+      },
+      {
+        "answer": "infection mechanism",
+        "image": ""
+      },
+      {
+        "answer": "Payload",
+        "image": ""
+      },
+      {
+        "answer": "Backdoor",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "1122) the key exchange protocol is vulnerable to a __________ attack because it does not authenticate the participants.",
+    "answers": [
+      {
+        "answer": "one-way function",
+        "image": ""
+      },
+      {
+        "answer": "time complexity",
+        "image": ""
+      },
+      {
+        "answer": "chosen ciphertext",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "cardholder",
+        "image": ""
+      },
+      {
+        "answer": "auditor",
+        "image": ""
+      },
+      {
+        "answer": "issuer",
+        "image": ""
+      },
+      {
+        "answer": "processor",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "378) A consequence of a buffer overflow error is:",
+    "answers": [
+      {
+        "answer": "possibly memory access violation",
+        "image": ""
+      },
+      {
+        "answer": "corruption of data used by the program",
+        "image": ""
+      },
+      {
+        "answer": "unexpected transfer of control in the program",
+        "image": ""
+      },
+      {
+        "answer": "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 field",
+    "answers": [
+      {
+        "answer": "Action",
+        "image": ""
+      },
+      {
+        "answer": "Subject",
+        "image": ""
+      },
+      {
+        "answer": "Resource-usage",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Trojan horse",
+        "image": ""
+      },
+      {
+        "answer": "Adware",
+        "image": ""
+      },
+      {
+        "answer": "virus",
+        "image": ""
+      },
+      {
+        "answer": "Worm",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "three-way handshake",
+        "image": ""
+      },
+      {
+        "answer": "UDP flood",
+        "image": ""
+      },
+      {
+        "answer": "SYN spoofing attack",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "data authenticator ",
+        "image": ""
+      },
+      {
+        "answer": "strong hash function",
+        "image": ""
+      },
+      {
+        "answer": "weak hash function ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Perturbation",
+        "image": ""
+      },
+      {
+        "answer": "Inference",
+        "image": ""
+      },
+      {
+        "answer": "Compromise",
+        "image": ""
+      },
+      {
+        "answer": "Partitioning",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "283) A ________ IDS monitors traffic at selected points on a network or interconnected set of networks",
+    "answers": [
+      {
+        "answer": "host-based (HIDS)",
+        "image": ""
+      },
+      {
+        "answer": "cloud-based (CIDS)",
+        "image": ""
+      },
+      {
+        "answer": "application-based (AIDS)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Constraints",
+        "image": ""
+      },
+      {
+        "answer": "Mutually Exclusive Roles",
+        "image": ""
+      },
+      {
+        "answer": "Cardinality",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "AES",
+        "image": ""
+      },
+      {
+        "answer": "ECC",
+        "image": ""
+      },
+      {
+        "answer": "Blowfish",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Code Red Worm",
+        "image": ""
+      },
+      {
+        "answer": "Slammer Worm",
+        "image": ""
+      },
+      {
+        "answer": "Morris Internet Worm",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "421) __________ aim to prevent or detect buffer overflows by instrumenting programs when they are compiled",
+    "answers": [
+      {
+        "answer": "threat modeling",
+        "image": ""
+      },
+      {
+        "answer": "compile-time defenses",
+        "image": ""
+      },
+      {
+        "answer": "runtime patching",
+        "image": ""
+      },
+      {
+        "answer": "post-incident analysis",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "308) Metrics that are useful for profile-based intrusion detection are: counter, gauge, resource utilization, and _______",
+    "answers": [
+      {
+        "answer": "network bandwidth",
+        "image": ""
+      },
+      {
+        "answer": "packet loss rate",
+        "image": ""
+      },
+      {
+        "answer": "system uptime",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "RFID",
+        "image": ""
+      },
+      {
+        "answer": "NtRU",
+        "image": ""
+      },
+      {
+        "answer": "EPC",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "repudiation",
+        "image": ""
+      },
+      {
+        "answer": "masquerade",
+        "image": ""
+      },
+      {
+        "answer": "inference",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Traffic padding",
+        "image": ""
+      },
+      {
+        "answer": "Traffic integrity",
+        "image": ""
+      },
+      {
+        "answer": "Traffic control",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Signature Detection",
+        "image": ""
+      },
+      {
+        "answer": "Statistical Analysis",
+        "image": ""
+      },
+      {
+        "answer": "Log Monitoring",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "775) ______ is the recommended technique for wireless network security",
+    "answers": [
+      {
+        "answer": "Using encryption",
+        "image": ""
+      },
+      {
+        "answer": "Using anti-virus and anti-spyware software",
+        "image": ""
+      },
+      {
+        "answer": "Turning off identifier broadcasting",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "passive sensor ",
+        "image": ""
+      },
+      {
+        "answer": "analysis sensor",
+        "image": ""
+      },
+      {
+        "answer": "LAN sensor ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Trojan horse",
+        "image": ""
+      },
+      {
+        "answer": "Ransomware",
+        "image": ""
+      },
+      {
+        "answer": "Crimeware",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "SYN flood",
+        "image": ""
+      },
+      {
+        "answer": "DNS amplification",
+        "image": ""
+      },
+      {
+        "answer": "poison packet",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "security attack",
+        "image": ""
+      },
+      {
+        "answer": "security mechanism",
+        "image": ""
+      },
+      {
+        "answer": "security policy",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Encryption phase",
+        "image": ""
+      },
+      {
+        "answer": "Stealth phase",
+        "image": ""
+      },
+      {
+        "answer": "Payload phase",
+        "image": ""
+      },
+      {
+        "answer": "triggering",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Adobe",
+        "image": ""
+      },
+      {
+        "answer": "Animoto",
+        "image": ""
+      },
+      {
+        "answer": "Malware",
+        "image": ""
+      },
+      {
+        "answer": "Prezi",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "106) A symmetric encryption scheme has five ingredients: plaintext, encryption algorithm, ciphertext, decryption algorithm and _________",
+    "answers": [
+      {
+        "answer": "password",
+        "image": ""
+      },
+      {
+        "answer": "hash",
+        "image": ""
+      },
+      {
+        "answer": "secret key",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Client",
+        "image": ""
+      },
+      {
+        "answer": "Data owner",
+        "image": ""
+      },
+      {
+        "answer": "User",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "block",
+        "image": ""
+      },
+      {
+        "answer": "counter (CTR)",
+        "image": ""
+      },
+      {
+        "answer": "stream",
+        "image": ""
+      },
+      {
+        "answer": "substitution",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "714) To counter threats to remote user authentication, systems generally rely on some form of ___________ protocol",
+    "answers": [
+      {
+        "answer": "eavesdropping",
+        "image": ""
+      },
+      {
+        "answer": "Trojan horse",
+        "image": ""
+      },
+      {
+        "answer": "challenge-response",
+        "image": ""
+      },
+      {
+        "answer": "denial-of-service",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "1104) Plaintext is recovered from the ciphertext using the paired key and _____________ .",
+    "answers": [
+      {
+        "answer": "a digital signature",
+        "image": ""
+      },
+      {
+        "answer": "a recovery encryption",
+        "image": ""
+      },
+      {
+        "answer": "a decryption algorithm",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "authentication",
+        "image": ""
+      },
+      {
+        "answer": "firewall implementation",
+        "image": ""
+      },
+      {
+        "answer": "intrusion detection",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Data section",
+        "image": ""
+      },
+      {
+        "answer": "Cache",
+        "image": ""
+      },
+      {
+        "answer": "heap",
+        "image": ""
+      },
+      {
+        "answer": "Register file",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "369) _________ aim to prevent or detect buffer overflows by instrumenting programs when they are compiled",
+    "answers": [
+      {
+        "answer": "Run-time defenses",
+        "image": ""
+      },
+      {
+        "answer": "Compile-time defenses",
+        "image": ""
+      },
+      {
+        "answer": "Shellcodes",
+        "image": ""
+      },
+      {
+        "answer": "All of these answers",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "821) Masquerade, falsification, and repudiation are threat actions that cause __________ threat consequences\nSelect one:",
+    "answers": [
+      {
+        "answer": "unauthorized disclosure",
+        "image": ""
+      },
+      {
+        "answer": "disruption",
+        "image": ""
+      },
+      {
+        "answer": "deception",
+        "image": ""
+      },
+      {
+        "answer": "usurpation",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "348) _________ are used to attack networked computer systems with a large volume of traffic to carry out a denial-of-service attack\nSelect one:",
+    "answers": [
+      {
+        "answer": "Bots",
+        "image": ""
+      },
+      {
+        "answer": "Exploits",
+        "image": ""
+      },
+      {
+        "answer": "Keyloggers",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "script kiddie",
+        "image": ""
+      },
+      {
+        "answer": "journeyman",
+        "image": ""
+      },
+      {
+        "answer": "novice",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "21) A concept that evolved out of requirements for military information security is ______ ",
+    "answers": [
+      {
+        "answer": "reliable input",
+        "image": ""
+      },
+      {
+        "answer": "mandatory access control",
+        "image": ""
+      },
+      {
+        "answer": "open and closed policies",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Antivirus software",
+        "image": ""
+      },
+      {
+        "answer": "Honeypots",
+        "image": ""
+      },
+      {
+        "answer": "Firewalls",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "logic bomb",
+        "image": ""
+      },
+      {
+        "answer": "trapdoor",
+        "image": ""
+      },
+      {
+        "answer": "worm",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "username",
+        "image": ""
+      },
+      {
+        "answer": "key",
+        "image": ""
+      },
+      {
+        "answer": "password",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "471) A bot can use a __________ to capture keystrokes on the infected machine to retrieve sensitive information",
+    "answers": [
+      {
+        "answer": "Antivirus software",
+        "image": ""
+      },
+      {
+        "answer": "Encryption key",
+        "image": ""
+      },
+      {
+        "answer": "keylogger",
+        "image": ""
+      },
+      {
+        "answer": "Firewall",
+        "image": ""
+      },
+      {
+        "answer": "Rootkit",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "828) The assurance that data received are exactly as sent by an authorized entity is __________\nSelect one:",
+    "answers": [
+      {
+        "answer": "data integrity",
+        "image": ""
+      },
+      {
+        "answer": "data confidentiality",
+        "image": ""
+      },
+      {
+        "answer": "authentication",
+        "image": ""
+      },
+      {
+        "answer": "access control",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "833) The _________ prevents or inhibits the normal use or management of communications facilities\nSelect one:",
+    "answers": [
+      {
+        "answer": "passive attack",
+        "image": ""
+      },
+      {
+        "answer": "denial of service",
+        "image": ""
+      },
+      {
+        "answer": "masquerade",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "504) _______ bandwidth attacks attempt to take advantage of the disproportionally large resource consumption at a server",
+    "answers": [
+      {
+        "answer": "Application-based",
+        "image": ""
+      },
+      {
+        "answer": "System-based",
+        "image": ""
+      },
+      {
+        "answer": "Random",
+        "image": ""
+      },
+      {
+        "answer": "Amplification",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "499) ______ relates to the capacity of the network links connecting a server to the wider Internet",
+    "answers": [
+      {
+        "answer": "Application resource",
+        "image": ""
+      },
+      {
+        "answer": "Network bandwidth",
+        "image": ""
+      },
+      {
+        "answer": "System payload",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Sparrow",
+        "image": ""
+      },
+      {
+        "answer": "Falcon",
+        "image": ""
+      },
+      {
+        "answer": "Hawk",
+        "image": ""
+      },
+      {
+        "answer": "canary",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Alpha One",
+        "image": ""
+      },
+      {
+        "answer": "Code Red Worm",
+        "image": ""
+      },
+      {
+        "answer": "Slammer Worm",
+        "image": ""
+      },
+      {
+        "answer": "Morris Internet Worm",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "423) _________ is a form of overflow attack",
+    "answers": [
+      {
+        "answer": "heap overflows, return to system call, and replacement stack frame",
+        "image": ""
+      },
+      {
+        "answer": "Cross-site scripting (XSS)",
+        "image": ""
+      },
+      {
+        "answer": "SQL injection",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Melissa Worm",
+        "image": ""
+      },
+      {
+        "answer": "Sasser Worm",
+        "image": ""
+      },
+      {
+        "answer": "Nimda Worm",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Active Sensor",
+        "image": ""
+      },
+      {
+        "answer": "Probe",
+        "image": ""
+      },
+      {
+        "answer": "Inline Sensor",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "relational database",
+        "image": ""
+      },
+      {
+        "answer": "query set",
+        "image": ""
+      },
+      {
+        "answer": "DBMS",
+        "image": ""
+      },
+      {
+        "answer": "perturbation",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "329) A ______ attack is an attempt to prevent legitimate users of a service from using that service",
+    "answers": [
+      {
+        "answer": "Man-in-the-middle",
+        "image": ""
+      },
+      {
+        "answer": "Phishing",
+        "image": ""
+      },
+      {
+        "answer": "Denial of service (DOS)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "trailing",
+        "image": ""
+      },
+      {
+        "answer": "spidering",
+        "image": ""
+      },
+      {
+        "answer": "spoofing",
+        "image": ""
+      },
+      {
+        "answer": "crowding",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "271) _________ is a document that describes the application level protocol for exchanging data between intrusion detection entities",
+    "answers": [
+      {
+        "answer": "RFC 4767 ",
+        "image": ""
+      },
+      {
+        "answer": "RFC 4766",
+        "image": ""
+      },
+      {
+        "answer": "RFC 4765 ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "User",
+        "image": ""
+      },
+      {
+        "answer": "Client",
+        "image": ""
+      },
+      {
+        "answer": "Data owner",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "stealth",
+        "image": ""
+      },
+      {
+        "answer": "polymorphic",
+        "image": ""
+      },
+      {
+        "answer": "encrypted",
+        "image": ""
+      },
+      {
+        "answer": "metamorphic",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "157) ________ attacks have several approaches, all equivalent in effort to factoring the product of two primes",
+    "answers": [
+      {
+        "answer": "Mathematical ",
+        "image": ""
+      },
+      {
+        "answer": "Statistical",
+        "image": ""
+      },
+      {
+        "answer": "Brute-force",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1043) Which stages does a virus have?",
+    "answers": [
+      {
+        "answer": "Dormant phase",
+        "image": ""
+      },
+      {
+        "answer": "Propagation phase - i.e. attachment to email",
+        "image": ""
+      },
+      {
+        "answer": "Triggering phase",
+        "image": ""
+      },
+      {
+        "answer": "Execution phase",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "LAN monitor agent ",
+        "image": ""
+      },
+      {
+        "answer": "host agent",
+        "image": ""
+      },
+      {
+        "answer": "central manager agent ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "645) A ___________ is the portion of the data center that houses data processing equipment",
+    "answers": [
+      {
+        "answer": "computer room",
+        "image": ""
+      },
+      {
+        "answer": "main distribution area",
+        "image": ""
+      },
+      {
+        "answer": "entrance room",
+        "image": ""
+      },
+      {
+        "answer": "horizontal distribution",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "377) The ________________ used a buffer overflow exploit in the \"fingerd\" as one of its attack mechanisms",
+    "answers": [
+      {
+        "answer": "Morris Internet Worm",
+        "image": ""
+      },
+      {
+        "answer": "Sasser Worm",
+        "image": ""
+      },
+      {
+        "answer": "Code Red Worm",
+        "image": ""
+      },
+      {
+        "answer": "Slammer Worm",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "470) A __________ is a collection of bots capable of acting in a coordinated manner",
+    "answers": [
+      {
+        "answer": "botnet",
+        "image": ""
+      },
+      {
+        "answer": "Firewall",
+        "image": ""
+      },
+      {
+        "answer": "Encryption algorithm",
+        "image": ""
+      },
+      {
+        "answer": "Intrusion Detection System (IDS)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "76) In the first instance of multiple encryption plaintext is converted to __________ using the encryption algorithm",
+    "answers": [
+      {
+        "answer": "ciphertext",
+        "image": ""
+      },
+      {
+        "answer": "S-AES mode",
+        "image": ""
+      },
+      {
+        "answer": "Triple DES",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "92) The exact substitutions and transformations performed by the algorithm depend on the ________",
+    "answers": [
+      {
+        "answer": "ciphertext",
+        "image": ""
+      },
+      {
+        "answer": "decryption algorithm",
+        "image": ""
+      },
+      {
+        "answer": "secret key",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "substitution",
+        "image": ""
+      },
+      {
+        "answer": "block",
+        "image": ""
+      },
+      {
+        "answer": "stream",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "verified",
+        "image": ""
+      },
+      {
+        "answer": "authenticated",
+        "image": ""
+      },
+      {
+        "answer": "identified",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "307) Password files can be protected in one of two ways: One-way function or ______",
+    "answers": [
+      {
+        "answer": "biometric authentication",
+        "image": ""
+      },
+      {
+        "answer": "access control",
+        "image": ""
+      },
+      {
+        "answer": "encryption",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "EFT",
+        "image": ""
+      },
+      {
+        "answer": "POS",
+        "image": ""
+      },
+      {
+        "answer": "BTM",
+        "image": ""
+      },
+      {
+        "answer": "ATF",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "012) Digital signatures and key management are the two most important applications of __________ encryption",
+    "answers": [
+      {
+        "answer": "private-key",
+        "image": ""
+      },
+      {
+        "answer": "public-key",
+        "image": ""
+      },
+      {
+        "answer": "preimage resistant ",
+        "image": ""
+      },
+      {
+        "answer": "advanced",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "647) __________ encompasses intrusion detection, prevention and response",
+    "answers": [
+      {
+        "answer": "Intrusion management",
+        "image": ""
+      },
+      {
+        "answer": "Security assessments",
+        "image": ""
+      },
+      {
+        "answer": "Database access control",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "disruption",
+        "image": ""
+      },
+      {
+        "answer": "exposure",
+        "image": ""
+      },
+      {
+        "answer": "corruption",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "secret key ",
+        "image": ""
+      },
+      {
+        "answer": "digital signature",
+        "image": ""
+      },
+      {
+        "answer": "keystream",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "one-fourth ",
+        "image": ""
+      },
+      {
+        "answer": "half",
+        "image": ""
+      },
+      {
+        "answer": "two-thirds ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "PEP ",
+        "image": ""
+      },
+      {
+        "answer": "DDI",
+        "image": ""
+      },
+      {
+        "answer": "IDEP ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "RFC 2828",
+        "image": ""
+      },
+      {
+        "answer": "RFC 2493",
+        "image": ""
+      },
+      {
+        "answer": "RFC 2298",
+        "image": ""
+      },
+      {
+        "answer": "RFC 2328",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "370) In 2003, the _______ exploited a buffer overflow in Microsoft SQL Server 2000",
+    "answers": [
+      {
+        "answer": "Slammer worm",
+        "image": ""
+      },
+      {
+        "answer": "Sasser worm",
+        "image": ""
+      },
+      {
+        "answer": "Morris Internet Worm",
+        "image": ""
+      },
+      {
+        "answer": "Code Red Worm",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "410) The __________ used a buffer overflow exploit in fingerd as one of its attack mechanisms",
+    "answers": [
+      {
+        "answer": "Conficker Worm",
+        "image": ""
+      },
+      {
+        "answer": "Morris Internet Worm",
+        "image": ""
+      },
+      {
+        "answer": "Stuxnet Worm",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "substitution",
+        "image": ""
+      },
+      {
+        "answer": "block",
+        "image": ""
+      },
+      {
+        "answer": "stream",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "140) Which of the following scenario requires a security protocol:",
+    "answers": [
+      {
+        "answer": "log in to mail.google.com",
+        "image": ""
+      },
+      {
+        "answer": "connecting to work from home using a VPN",
+        "image": ""
+      },
+      {
+        "answer": "All the previous answers",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "274) The broad classes of intruders are: cyber criminals, state-sponsored organizations, _________ , and others",
+    "answers": [
+      {
+        "answer": "terrorists",
+        "image": ""
+      },
+      {
+        "answer": "script kiddies",
+        "image": ""
+      },
+      {
+        "answer": "activists",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "RFC 4949",
+        "image": ""
+      },
+      {
+        "answer": "RFC 2298",
+        "image": ""
+      },
+      {
+        "answer": "RFC 2493",
+        "image": ""
+      },
+      {
+        "answer": "RFC 2328",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "427) The buffer is located __________ ",
+    "answers": [
+      {
+        "answer": "in the heap",
+        "image": ""
+      },
+      {
+        "answer": "in the stack",
+        "image": ""
+      },
+      {
+        "answer": "in the data section of the process",
+        "image": ""
+      },
+      {
+        "answer": "in the register",
+        "image": ""
+      },
+      {
+        "answer": "All of the above",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "712) __________ systems identify features of the hand, including shape, and lengths and widths of fingers",
+    "answers": [
+      {
+        "answer": "Signature",
+        "image": ""
+      },
+      {
+        "answer": "Hand geometry",
+        "image": ""
+      },
+      {
+        "answer": "Fingerprint",
+        "image": ""
+      },
+      {
+        "answer": "Palm print",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "155) Which of the following feature can only be provided by public-key cryptography?",
+    "answers": [
+      {
+        "answer": "Data integrity",
+        "image": ""
+      },
+      {
+        "answer": "Confidentiality",
+        "image": ""
+      },
+      {
+        "answer": "Digital signatures",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Morris",
+        "image": ""
+      },
+      {
+        "answer": "Slammer",
+        "image": ""
+      },
+      {
+        "answer": "Code Red",
+        "image": ""
+      },
+      {
+        "answer": "Heartbleed",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "707) Recognition by fingerprint, retina, and face are examples of __________",
+    "answers": [
+      {
+        "answer": "face recognition",
+        "image": ""
+      },
+      {
+        "answer": "dynamic biometrics",
+        "image": ""
+      },
+      {
+        "answer": "static biometrics authentication",
+        "image": ""
+      },
+      {
+        "answer": "token",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "306) The three classes of intruders identified by Anderson are: Masquerader, Misfeasor, and____",
+    "answers": [
+      {
+        "answer": "Insider threat",
+        "image": ""
+      },
+      {
+        "answer": "Social engineer",
+        "image": ""
+      },
+      {
+        "answer": "clandestine",
+        "image": ""
+      },
+      {
+        "answer": "Cybercriminal",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "513) When a DoS attack is detected, the first step is to _______",
+    "answers": [
+      {
+        "answer": "identify the attack",
+        "image": ""
+      },
+      {
+        "answer": "analyze the response",
+        "image": ""
+      },
+      {
+        "answer": "design blocking filters",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "309) Two types of audit records used are Detection-specific audit records and ____ audit records",
+    "answers": [
+      {
+        "answer": "system uptime",
+        "image": ""
+      },
+      {
+        "answer": "native",
+        "image": ""
+      },
+      {
+        "answer": "network bandwidth",
+        "image": ""
+      },
+      {
+        "answer": "packet loss rate",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "102) A ________ is a key used between entities for the purpose of distributing session keys",
+    "answers": [
+      {
+        "answer": "permanent key",
+        "image": ""
+      },
+      {
+        "answer": "session key",
+        "image": ""
+      },
+      {
+        "answer": "distribution key",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "record",
+        "image": ""
+      },
+      {
+        "answer": "standard",
+        "image": ""
+      },
+      {
+        "answer": "field",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1113) Defensive programming is sometimes referred to as _________.",
+    "answers": [
+      {
+        "answer": "variable programming",
+        "image": ""
+      },
+      {
+        "answer": "secure programming",
+        "image": ""
+      },
+      {
+        "answer": "interpretive programming",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Authorization",
+        "image": ""
+      },
+      {
+        "answer": "Authentication",
+        "image": ""
+      },
+      {
+        "answer": "Control",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "ROM",
+        "image": ""
+      },
+      {
+        "answer": "heap",
+        "image": ""
+      },
+      {
+        "answer": "address space",
+        "image": ""
+      },
+      {
+        "answer": "shell",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "117) With ______ encryption each vulnerable communications link is equipped on both ends with an encryption device",
+    "answers": [
+      {
+        "answer": "network",
+        "image": ""
+      },
+      {
+        "answer": "end-to-end",
+        "image": ""
+      },
+      {
+        "answer": "link",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "91) _________ is the original message or data that is fed into the algorithm as input",
+    "answers": [
+      {
+        "answer": "Plaintext",
+        "image": ""
+      },
+      {
+        "answer": "Encryption algorithm",
+        "image": ""
+      },
+      {
+        "answer": "Decryption algorithm",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "second",
+        "image": ""
+      },
+      {
+        "answer": "third",
+        "image": ""
+      },
+      {
+        "answer": "fourth",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "266) A (n) __________ is a hacker with minimal technical skill who primarily uses existing attack toolkits",
+    "answers": [
+      {
+        "answer": "Master ",
+        "image": ""
+      },
+      {
+        "answer": "Apprentice",
+        "image": ""
+      },
+      {
+        "answer": "Journeyman ",
+        "image": ""
+      },
+      {
+        "answer": "Activist",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "424) The __________ used a buffer overflow exploit in \"fingerd\" as one of its attack",
+    "answers": [
+      {
+        "answer": "Code Red Worm",
+        "image": ""
+      },
+      {
+        "answer": "Stuxnet Worm",
+        "image": ""
+      },
+      {
+        "answer": "Morris Internet Worm",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "285) 12.The functional components of an _________ are: data source, sensor, analyzer, administration, manager, and operator",
+    "answers": [
+      {
+        "answer": "IDS",
+        "image": ""
+      },
+      {
+        "answer": "IPS",
+        "image": ""
+      },
+      {
+        "answer": "SIEM",
+        "image": ""
+      },
+      {
+        "answer": "Firewall",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "139) The purposes of a security protocol include:",
+    "answers": [
+      {
+        "answer": "Authentication",
+        "image": ""
+      },
+      {
+        "answer": "Key-exchange",
+        "image": ""
+      },
+      {
+        "answer": "Negotiate crypto algorithms and parameters",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1082) The first widely used occurrence of the buffer overflow attack was the _______.",
+    "answers": [
+      {
+        "answer": "Code Red Worm",
+        "image": ""
+      },
+      {
+        "answer": "Morris Internet Worm",
+        "image": ""
+      },
+      {
+        "answer": "Sasser Worm",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "DSD",
+        "image": ""
+      },
+      {
+        "answer": "RBAC",
+        "image": ""
+      },
+      {
+        "answer": "ABAC",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "protocol ",
+        "image": ""
+      },
+      {
+        "answer": "direction",
+        "image": ""
+      },
+      {
+        "answer": "action ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "foreign key",
+        "image": ""
+      },
+      {
+        "answer": "query",
+        "image": ""
+      },
+      {
+        "answer": "primary key",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "AES",
+        "image": ""
+      },
+      {
+        "answer": "SHA-1 ",
+        "image": ""
+      },
+      {
+        "answer": "MD5",
+        "image": ""
+      },
+      {
+        "answer": "RSA",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "428) _________ is a tool used to automatically identify potentially vulnerable programs",
+    "answers": [
+      {
+        "answer": "Code obfuscation",
+        "image": ""
+      },
+      {
+        "answer": "Encryption",
+        "image": ""
+      },
+      {
+        "answer": "fuzzing",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Nine",
+        "image": ""
+      },
+      {
+        "answer": "Seven",
+        "image": ""
+      },
+      {
+        "answer": "Three",
+        "image": ""
+      },
+      {
+        "answer": "Five",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "467) A __________ virus is explicitly designed to hide itself from detection by anti-virus software",
+    "answers": [
+      {
+        "answer": "Adware",
+        "image": ""
+      },
+      {
+        "answer": "Spyware",
+        "image": ""
+      },
+      {
+        "answer": "Rootkit",
+        "image": ""
+      },
+      {
+        "answer": "stealth",
+        "image": ""
+      },
+      {
+        "answer": "Ransomware",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "116) With _________ encryption the encryption process is carried out at the two end systems",
+    "answers": [
+      {
+        "answer": "point-to-point",
+        "image": ""
+      },
+      {
+        "answer": "intermediary",
+        "image": ""
+      },
+      {
+        "answer": "centralized",
+        "image": ""
+      },
+      {
+        "answer": "end-to-end",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "119) All encryption algorithms are based on two general principles: substitution and _________",
+    "answers": [
+      {
+        "answer": "compression",
+        "image": ""
+      },
+      {
+        "answer": "expansion",
+        "image": ""
+      },
+      {
+        "answer": "transposition",
+        "image": ""
+      },
+      {
+        "answer": "permutation",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "01) The original message or data that is fed into the algorithm is __________",
+    "answers": [
+      {
+        "answer": "encryption algorithm ",
+        "image": ""
+      },
+      {
+        "answer": "secret key",
+        "image": ""
+      },
+      {
+        "answer": "decryption algorithm ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "ECB",
+        "image": ""
+      },
+      {
+        "answer": "OFB",
+        "image": ""
+      },
+      {
+        "answer": "CFB",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "634) Security specifically tailored to databases is an increasingly important component of an overall organizational security strategy",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "650) __________ specifies the minimum requirements for telecommunications infrastructure of data centers",
+    "answers": [
+      {
+        "answer": "TIA-492",
+        "image": ""
+      },
+      {
+        "answer": "RFC-4949",
+        "image": ""
+      },
+      {
+        "answer": "NIST-7883",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "943) Four stages of viruses",
+    "answers": [
+      {
+        "answer": "Dormant phase",
+        "image": ""
+      },
+      {
+        "answer": "Propagation phase - i.e. attachment to email",
+        "image": ""
+      },
+      {
+        "answer": "Triggering phase",
+        "image": ""
+      },
+      {
+        "answer": "Execution phase",
+        "image": ""
+      },
+      {
+        "answer": "All of the above",
+        "image": ""
+      }
+    ],
+    "correct": 4,
+    "image": ""
+  },
+  {
+    "quest": "437) __________ defenses aim to detect and abort attacks in existing programs",
+    "answers": [
+      {
+        "answer": "Code signing",
+        "image": ""
+      },
+      {
+        "answer": "run-time",
+        "image": ""
+      },
+      {
+        "answer": "Compile-time defenses",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "82) The simplest form of multiple encryption has __________ encryption stages and __________ keys",
+    "answers": [
+      {
+        "answer": "three, two",
+        "image": ""
+      },
+      {
+        "answer": "four, two",
+        "image": ""
+      },
+      {
+        "answer": "two, three",
+        "image": ""
+      },
+      {
+        "answer": "two, two",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "304) Statistical approaches attempt to define proper behavior and rule-based approaches attempt to define normal or expected behavior",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "17) __________ is verification that the credentials of a user or other system entity are valid",
+    "answers": [
+      {
+        "answer": "Adequacy",
+        "image": ""
+      },
+      {
+        "answer": "Authentication",
+        "image": ""
+      },
+      {
+        "answer": "Authorization",
+        "image": ""
+      },
+      {
+        "answer": "Audit",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "711) The most common means of human-to-human identification are __________",
+    "answers": [
+      {
+        "answer": "facial characteristics",
+        "image": ""
+      },
+      {
+        "answer": "signatures",
+        "image": ""
+      },
+      {
+        "answer": "retinal patterns",
+        "image": ""
+      },
+      {
+        "answer": "fingerprints",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1155) In relational database parlance,the basic building block is a __________,which is a flat table.",
+    "answers": [
+      {
+        "answer": "attribute",
+        "image": ""
+      },
+      {
+        "answer": "tuple",
+        "image": ""
+      },
+      {
+        "answer": "primary key",
+        "image": ""
+      },
+      {
+        "answer": "relation",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "1159) Using forged source addresses is known as _________.",
+    "answers": [
+      {
+        "answer": "source address spoofing",
+        "image": ""
+      },
+      {
+        "answer": "a three-way address",
+        "image": ""
+      },
+      {
+        "answer": "random dropping",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "AES",
+        "image": ""
+      },
+      {
+        "answer": "3DES",
+        "image": ""
+      },
+      {
+        "answer": "CES",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "SHA",
+        "image": ""
+      },
+      {
+        "answer": "RSA",
+        "image": ""
+      },
+      {
+        "answer": "AES",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Rabin",
+        "image": ""
+      },
+      {
+        "answer": "Rijnedel",
+        "image": ""
+      },
+      {
+        "answer": "Hillman",
+        "image": ""
+      },
+      {
+        "answer": "ElGamal",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "1052) TCB Design Principles",
+    "answers": [
+      {
+        "answer": "Least Privilege",
+        "image": ""
+      },
+      {
+        "answer": "Economy",
+        "image": ""
+      },
+      {
+        "answer": "Open Design",
+        "image": ""
+      },
+      {
+        "answer": "Complete Mediation",
+        "image": ""
+      },
+      {
+        "answer": "Fail-safe defaults",
+        "image": ""
+      },
+      {
+        "answer": "Ease of Use",
+        "image": ""
+      },
+      {
+        "answer": "All of the above",
+        "image": ""
+      }
+    ],
+    "correct": 6,
+    "image": ""
+  },
+  {
+    "quest": "438) The __________ project produces a free, multiplatform 4.4BSD-based UNIX-like operating system",
+    "answers": [
+      {
+        "answer": "Linux",
+        "image": ""
+      },
+      {
+        "answer": "Windows",
+        "image": ""
+      },
+      {
+        "answer": "OpenBSD",
+        "image": ""
+      },
+      {
+        "answer": "macOS",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "spam",
+        "image": ""
+      },
+      {
+        "answer": "phishing",
+        "image": ""
+      },
+      {
+        "answer": "DDoS",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "411) In 2003 the _________ exploited a buffer overflow in Microsoft SQL Server 2000",
+    "answers": [
+      {
+        "answer": "Code Red Worm",
+        "image": ""
+      },
+      {
+        "answer": "Mydoom Worm",
+        "image": ""
+      },
+      {
+        "answer": "Blaster Worm",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "831) A loss of _________ is the unauthorized disclosure of information\nSelect one:",
+    "answers": [
+      {
+        "answer": "integrity",
+        "image": ""
+      },
+      {
+        "answer": "availability",
+        "image": ""
+      },
+      {
+        "answer": "confidentiality",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "DES",
+        "image": ""
+      },
+      {
+        "answer": "RC4",
+        "image": ""
+      },
+      {
+        "answer": "AES",
+        "image": ""
+      },
+      {
+        "answer": "RSA",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1090) Security awareness,training,and education programs may be needed to\ncomply with regulations and contractual obligations.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "twelve",
+        "image": ""
+      },
+      {
+        "answer": "six",
+        "image": ""
+      },
+      {
+        "answer": "nine",
+        "image": ""
+      },
+      {
+        "answer": "three",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "436) __________ defenses aim to harden programs to resist attacks in new programs",
+    "answers": [
+      {
+        "answer": "Machine code",
+        "image": ""
+      },
+      {
+        "answer": "Obfuscated",
+        "image": ""
+      },
+      {
+        "answer": "Self-modifying",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "55) __________ is the first function in the propagation phase for a network worm",
+    "answers": [
+      {
+        "answer": "Propagating",
+        "image": ""
+      },
+      {
+        "answer": "Fingerprinting",
+        "image": ""
+      },
+      {
+        "answer": "Keylogging",
+        "image": ""
+      },
+      {
+        "answer": "Spear phishing",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "717) A __________ attack involves an adversary repeating a previously captured user response",
+    "answers": [
+      {
+        "answer": "client",
+        "image": ""
+      },
+      {
+        "answer": "replay",
+        "image": ""
+      },
+      {
+        "answer": "Trojan horse",
+        "image": ""
+      },
+      {
+        "answer": "eavesdropping",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "578) A(n) __________ is a structured collection of data stored for use by one or more applications",
+    "answers": [
+      {
+        "answer": "attribute",
+        "image": ""
+      },
+      {
+        "answer": "database",
+        "image": ""
+      },
+      {
+        "answer": "tuple",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "120) The three most important symmetric block ciphers are: 3DES, AES, and _____",
+    "answers": [
+      {
+        "answer": "Serpent",
+        "image": ""
+      },
+      {
+        "answer": "Data Encryption Standard (DES)",
+        "image": ""
+      },
+      {
+        "answer": "Blowfish",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "278) An IDS comprises three logical components: analyzers, user interface and _____",
+    "answers": [
+      {
+        "answer": "sensors",
+        "image": ""
+      },
+      {
+        "answer": "firewalls",
+        "image": ""
+      },
+      {
+        "answer": "routers",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "698) Depending on the application, user authentication on a biometric system involves either verification or identification",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "382) A stack buffer overflow attack is also referred to as ______",
+    "answers": [
+      {
+        "answer": "buffer overrunning",
+        "image": ""
+      },
+      {
+        "answer": "stack framing",
+        "image": ""
+      },
+      {
+        "answer": "heap overflowing",
+        "image": ""
+      },
+      {
+        "answer": "stack smashing",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "78) Another important mode, XTS-AES, has been standardized by the __________ Security in Storage Working Group",
+    "answers": [
+      {
+        "answer": "NIST",
+        "image": ""
+      },
+      {
+        "answer": "IEEE",
+        "image": ""
+      },
+      {
+        "answer": "ITIL",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1105) A major advance in symmetric cryptography occurred with the\ndevelopment of the rotor encryption/decryption machine.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "26) A __________ is a named job function within the organization that controls this computer system",
+    "answers": [
+      {
+        "answer": "user",
+        "image": ""
+      },
+      {
+        "answer": "role",
+        "image": ""
+      },
+      {
+        "answer": "permission",
+        "image": ""
+      },
+      {
+        "answer": "session",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1126) the Secure Hash Algorithm design closely models, and is based on, the hash function __________ .",
+    "answers": [
+      {
+        "answer": "MD5",
+        "image": ""
+      },
+      {
+        "answer": "FIPS 180",
+        "image": ""
+      },
+      {
+        "answer": "RFC 4634",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "DSS",
+        "image": ""
+      },
+      {
+        "answer": "RSA",
+        "image": ""
+      },
+      {
+        "answer": "SHA",
+        "image": ""
+      },
+      {
+        "answer": "AES",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "349) Malicious software that needs a host program is referred to as _________ \nSelect one:",
+    "answers": [
+      {
+        "answer": "blended",
+        "image": ""
+      },
+      {
+        "answer": "parasitic",
+        "image": ""
+      },
+      {
+        "answer": "logic bomb",
+        "image": ""
+      },
+      {
+        "answer": "flooders",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "701) Identifiers should be assigned carefully because authenticated identities are the basis for other security services",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "79) The _________ and _________ block cipher modes of operation are used for authentication",
+    "answers": [
+      {
+        "answer": "OFB, CTR",
+        "image": ""
+      },
+      {
+        "answer": "CBC, CFB",
+        "image": ""
+      },
+      {
+        "answer": "CFB, OFB",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "107) _________ is the process of attempting to discover the plaintext or key",
+    "answers": [
+      {
+        "answer": "Cryptanalysis",
+        "image": ""
+      },
+      {
+        "answer": "Steganography",
+        "image": ""
+      },
+      {
+        "answer": "Cryptography",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "02) The __________ is the encryption algorithm run in reverse",
+    "answers": [
+      {
+        "answer": "decryption algorithm",
+        "image": ""
+      },
+      {
+        "answer": "plaintext",
+        "image": ""
+      },
+      {
+        "answer": "ciphertext ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "340) Mobile phone worms communicate through Bluetooth wireless connections or via the _________ \nSelect one:",
+    "answers": [
+      {
+        "answer": "SQL",
+        "image": ""
+      },
+      {
+        "answer": "TRW",
+        "image": ""
+      },
+      {
+        "answer": "PWC",
+        "image": ""
+      },
+      {
+        "answer": "MMS",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "367) ____________ is a form of overflow attack",
+    "answers": [
+      {
+        "answer": "Heap overflows",
+        "image": ""
+      },
+      {
+        "answer": "Replacement stack frame",
+        "image": ""
+      },
+      {
+        "answer": "Return to system call",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1149) A loss of _________ is the unauthorized disclosure of information.",
+    "answers": [
+      {
+        "answer": "confidentiality",
+        "image": ""
+      },
+      {
+        "answer": "authenticity",
+        "image": ""
+      },
+      {
+        "answer": "integrity",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "319) The main advantage of the use of statistical profiles is that a prior knowledge of security flaws is not required",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1100) The Diffie-Hellman algorithm depends for its effectiveness on the\ndifficulty of computing discrete logarithms.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "014) An important element in many computer security services and\napplications is the use of cryptographic algorithms",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "52) The __________ is when the virus function is performed",
+    "answers": [
+      {
+        "answer": "dormant phase",
+        "image": ""
+      },
+      {
+        "answer": "propagation phase",
+        "image": ""
+      },
+      {
+        "answer": "triggering phase",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1171) In a biometric scheme some physical characteristic of the individual is\nmapped into a digital representation.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "418) A stack buffer overflow is also referred to as ___________ ",
+    "answers": [
+      {
+        "answer": "data leakage",
+        "image": ""
+      },
+      {
+        "answer": "stack smashing",
+        "image": ""
+      },
+      {
+        "answer": "heap hijacking",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "ECB",
+        "image": ""
+      },
+      {
+        "answer": "CFB",
+        "image": ""
+      },
+      {
+        "answer": "CBC",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1075) the Rijndael developers designed the expansion key algorithm to\nbe resistant to known cryptanalytic attacks.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "137) Issued as RFC 2104, _______ has been chosen as the mandatory-to-implement MAC for IP Security",
+    "answers": [
+      {
+        "answer": "SHA-256",
+        "image": ""
+      },
+      {
+        "answer": "HMAC",
+        "image": ""
+      },
+      {
+        "answer": "MD5",
+        "image": ""
+      },
+      {
+        "answer": "AES",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1084) Restoring the plaintext from the ciphertext is __________ .",
+    "answers": [
+      {
+        "answer": "deciphering",
+        "image": ""
+      },
+      {
+        "answer": " transposition",
+        "image": ""
+      },
+      {
+        "answer": " steganography",
+        "image": ""
+      },
+      {
+        "answer": " 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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "49) The term \"computer virus\" is attributed to __________",
+    "answers": [
+      {
+        "answer": "Herman Hollerith",
+        "image": ""
+      },
+      {
+        "answer": "Fred Cohen",
+        "image": ""
+      },
+      {
+        "answer": "Charles Babbage",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "842) Security mechanisms typically do not involve more than one particular\nalgorithm or protocol\nTrue or False",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "227) The IT security management process ends with the implementation of controls and the training of personnel",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "913) each layer of code needs appropriate hardening measures in place to provide appropriate security services",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1117) The Biba models deals with confidentiality and is concerned with\nunauthorized disclosure of information.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "28) __________ refers to setting a maximum number with respect to roles",
+    "answers": [
+      {
+        "answer": "Cardinality",
+        "image": ""
+      },
+      {
+        "answer": "Prerequisite",
+        "image": ""
+      },
+      {
+        "answer": "Exclusive",
+        "image": ""
+      },
+      {
+        "answer": "Hierarchy",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "152) is the original message or data that is fed into the encryption process as input",
+    "answers": [
+      {
+        "answer": "Hash",
+        "image": ""
+      },
+      {
+        "answer": "Key",
+        "image": ""
+      },
+      {
+        "answer": "Plaintext ",
+        "image": ""
+      },
+      {
+        "answer": "Ciphertext",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "1092) The approach taken by Kerberos is using authentication software tied\nto a secure authentication server.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "DAC",
+        "image": ""
+      },
+      {
+        "answer": "RBAC",
+        "image": ""
+      },
+      {
+        "answer": "MAC",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "409) A buffer can be located _________",
+    "answers": [
+      {
+        "answer": "in the heap",
+        "image": ""
+      },
+      {
+        "answer": "on the stack",
+        "image": ""
+      },
+      {
+        "answer": "in the data section of the process",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "47) __________ are used to send large volumes of unwanted e-mail",
+    "answers": [
+      {
+        "answer": "Rootkits",
+        "image": ""
+      },
+      {
+        "answer": "Spammer programs",
+        "image": ""
+      },
+      {
+        "answer": "Downloaders",
+        "image": ""
+      },
+      {
+        "answer": "Auto-rooters",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "263) The ________ is responsible for determining if an intrusion has occurred",
+    "answers": [
+      {
+        "answer": "analyzer ",
+        "image": ""
+      },
+      {
+        "answer": "host",
+        "image": ""
+      },
+      {
+        "answer": "user interface ",
+        "image": ""
+      },
+      {
+        "answer": "sensor",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "899) The OpenSSL heartbleed vulnerability would have been prevented if OpenSSL had been implemented in Java",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "103) The _______ module performs end-to-end encryption and obtains session keys on behalf of users",
+    "answers": [
+      {
+        "answer": "PKM",
+        "image": ""
+      },
+      {
+        "answer": "RCM",
+        "image": ""
+      },
+      {
+        "answer": "SSM",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "93) The _________ is the encryption algorithm run in reverse",
+    "answers": [
+      {
+        "answer": "decryption algorithm",
+        "image": ""
+      },
+      {
+        "answer": "ciphertext",
+        "image": ""
+      },
+      {
+        "answer": "plaintext",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "708) A __________ is a password guessing program",
+    "answers": [
+      {
+        "answer": "password hash",
+        "image": ""
+      },
+      {
+        "answer": "password cracker",
+        "image": ""
+      },
+      {
+        "answer": "password biometric",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1489) __________ controls access based on comparing security labels with security clearances.",
+    "answers": [
+      {
+        "answer": "MAC",
+        "image": ""
+      },
+      {
+        "answer": "DAC",
+        "image": ""
+      },
+      {
+        "answer": "RBAC",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "508) A characteristic of reflection attacks is the lack of _______ traffic",
+    "answers": [
+      {
+        "answer": "backscatter",
+        "image": ""
+      },
+      {
+        "answer": "network",
+        "image": ""
+      },
+      {
+        "answer": "three-way",
+        "image": ""
+      },
+      {
+        "answer": "botnet",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "313) Penetration identification is an approach developed to detect deviation from previous usage patterns",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "691) Identification is the means of establishing the validity of a claimed identity provided by a user",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "779) The most significant source of risk in wireless networks in the underlying communications medium",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "53) During the __________ the virus is idle",
+    "answers": [
+      {
+        "answer": "dormant phase",
+        "image": ""
+      },
+      {
+        "answer": "propagation phase",
+        "image": ""
+      },
+      {
+        "answer": "triggering phase",
+        "image": ""
+      },
+      {
+        "answer": "execution phase",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "503) TCP uses the _______ to establish a connection",
+    "answers": [
+      {
+        "answer": "zombie",
+        "image": ""
+      },
+      {
+        "answer": "SYN cookie",
+        "image": ""
+      },
+      {
+        "answer": "directed broadcast",
+        "image": ""
+      },
+      {
+        "answer": "three-way handshake",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "97) For stream-oriented transmission over noisy channel you would typically use _______ mode",
+    "answers": [
+      {
+        "answer": "ECB",
+        "image": ""
+      },
+      {
+        "answer": "CTR",
+        "image": ""
+      },
+      {
+        "answer": "OFB",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "66) Given the potential vulnerability of DES to a brute-force attack, an alternative has been found",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "758) A packet filtering firewall is typically configured to filter packets going in both directions",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1085) the process of converting from plaintext to ciphertext is known as\ndeciphering or decryption.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "99) For general-purpose stream-oriented transmission you would typically use _______ mode",
+    "answers": [
+      {
+        "answer": "CTR",
+        "image": ""
+      },
+      {
+        "answer": "CFB",
+        "image": ""
+      },
+      {
+        "answer": "ECB",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "03) __________ is the scrambled message produced as output",
+    "answers": [
+      {
+        "answer": "Plaintext",
+        "image": ""
+      },
+      {
+        "answer": "Ciphertext",
+        "image": ""
+      },
+      {
+        "answer": "Secret key ",
+        "image": ""
+      },
+      {
+        "answer": "Cryptanalysis",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "42) Packet sniffers are mostly used to retrieve sensitive information like usernames and passwords",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "236) The primary purpose of an IDS is to detect intrusions, log suspicious events, and send alerts",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "696) User authentication is the basis for most types of access control and for user accountability",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "59) The ideal solution to the threat of malware is __________",
+    "answers": [
+      {
+        "answer": "identification",
+        "image": ""
+      },
+      {
+        "answer": "removal",
+        "image": ""
+      },
+      {
+        "answer": "detection",
+        "image": ""
+      },
+      {
+        "answer": "prevention",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "98) For general-purpose block-oriented transmission you would typically use _______ mode",
+    "answers": [
+      {
+        "answer": "CBC",
+        "image": ""
+      },
+      {
+        "answer": "CTR",
+        "image": ""
+      },
+      {
+        "answer": "CFB",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "128) It is a good idea to use sequentially increasing numbers as challenges in security protocols",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "893) Security mechanisms typically do not involve more than one particular algorithm or protocol",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "341) Backdoors become threats when unscrupulous programmers use them to gain unauthorized access",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "894) The first step in devising security services and mechanisms is to develop a security policy",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "67) A number of Internet based applications have adopted two-key 3DES, including PGP and S/MIME",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "386) Buffer overflow exploits are no longer a major source of concern to security practitioners",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "936) Both static and dynamic analyses are needed in order to fully understand malware behaviors",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "151) In general, public key based encryption is much slower than symmetric key based encryption",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "011) Transmitted data stored locally are referred to as __________ ",
+    "answers": [
+      {
+        "answer": "ciphertext ",
+        "image": ""
+      },
+      {
+        "answer": "DES",
+        "image": ""
+      },
+      {
+        "answer": "data at rest ",
+        "image": ""
+      },
+      {
+        "answer": "ECC",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "727) Hardware is the most vulnerable to attack and the least susceptible to automated controls",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1109) The foundation of a security auditing facility is the initial capture of\nthe audit data.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "699) Enrollment creates an association between a user and the user's biometric characteristics",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "222) Organizational security objectives identify what IT security outcomes should be achieved",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "221) SQL injection is yet another example that illustrates the importance of input validation",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "203) Compared with WEP, WPA2 has more flexible authentication and stronger encryption schemes",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "165) Network-based intrusion detection makes use of signature detection and anomaly detection",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "125) The strong collision resistance property subsumes the weak collision resistance property",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "13) Traditional RBAC systems define the access rights of individual users and groups of users",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "200) Most browsers come equipped with SSL and most Web servers have implemented the protocol",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "690) User authentication is the fundamental building block and the primary line of defense",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "6) Security labels indicate which system entities are eligible to access certain resources",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1096) A malicious driver can potentially bypass many security controls to\ninstall malware.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "639) In a relational database rows are referred to as _________",
+    "answers": [
+      {
+        "answer": "relations",
+        "image": ""
+      },
+      {
+        "answer": "attributes",
+        "image": ""
+      },
+      {
+        "answer": "views",
+        "image": ""
+      },
+      {
+        "answer": "tuples",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "312) Password crackers rely on the fact that some people choose easily guessable passwords",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1161) An important aspect of a distributed firewall configuration is security\nmonitoring.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1133) An important characteristic of the MAC algorithm is that it needs\nto be reversible.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1086) A loss of integrity is the unauthorized modification or destruction\nof information.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "3) An auditing function monitors and keeps a record of user accesses to system resources",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "224) Legal and regulatory constraints may require specific approaches to risk assessment",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "568) T/F: Two disadvantages to database encryption are key management and inflexibility",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1146) SSO enables a user to access all network resources after a single\nauthentication.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "333) Viruses, logic bombs, and backdoors are examples of independent malicious software",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "225) One asset may have multiple threats and a single threat may target multiple assets",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "14) A constraint is a defined relationship among roles or a condition related to roles",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1167) The __________ is what the virus \"does\".",
+    "answers": [
+      {
+        "answer": "infection mechanism",
+        "image": ""
+      },
+      {
+        "answer": "trigger",
+        "image": ""
+      },
+      {
+        "answer": "logic bomb",
+        "image": ""
+      },
+      {
+        "answer": "payload",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "56) Unsolicited bulk e-mail is referred to as __________",
+    "answers": [
+      {
+        "answer": "spam",
+        "image": ""
+      },
+      {
+        "answer": "propagating",
+        "image": ""
+      },
+      {
+        "answer": "phishing",
+        "image": ""
+      },
+      {
+        "answer": "crimeware",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "575) The two commands that SQL provides for managing access rights are ALLOW and DENY",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "71) Cipher Block Chaining is a simple way to satisfy the security deficiencies of ECB",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1110) Although important,security auditing is not a key element in computer\nsecurity.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "933) A bot is a computer compromised by malware and under the control of a bot master",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "379) A buffer overflow error is not likely to lead to eventual program termination. ",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "700) An individual's signature is not unique enough to use in biometric applications",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "505) _______ is a text-based protocol with a syntax similar to that of HTTP",
+    "answers": [
+      {
+        "answer": "RIP",
+        "image": ""
+      },
+      {
+        "answer": "DIP",
+        "image": ""
+      },
+      {
+        "answer": "SIP",
+        "image": ""
+      },
+      {
+        "answer": "HIP",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "431) Data is simply an array of _________ ",
+    "answers": [
+      {
+        "answer": "characters",
+        "image": ""
+      },
+      {
+        "answer": "integers",
+        "image": ""
+      },
+      {
+        "answer": "floating-point numbers",
+        "image": ""
+      },
+      {
+        "answer": "bytes",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "915) The default configuration for many operating systems usually maximizes security",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "141) Symmetric encryption is also referred to as secret-key or single-key encryption",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1081) The buffer overflow type of attack is one of the least commonly seen\nattacks.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "23) A(n) __________ is a resource to which access is controlled",
+    "answers": [
+      {
+        "answer": "object",
+        "image": ""
+      },
+      {
+        "answer": "owner",
+        "image": ""
+      },
+      {
+        "answer": "world",
+        "image": ""
+      },
+      {
+        "answer": "subject",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "159) _________ was the first published public-key algorithm",
+    "answers": [
+      {
+        "answer": "ElGamal",
+        "image": ""
+      },
+      {
+        "answer": "DSA",
+        "image": ""
+      },
+      {
+        "answer": "Diffie-Hellman",
+        "image": ""
+      },
+      {
+        "answer": "RSA",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "1132) A __________ is an algorithm that requires the use of a secret key.",
+    "answers": [
+      {
+        "answer": "DAA",
+        "image": ""
+      },
+      {
+        "answer": "SHA",
+        "image": ""
+      },
+      {
+        "answer": "GCM",
+        "image": ""
+      },
+      {
+        "answer": "MAC",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "914) it is possible for a system to be compromised during the installation process",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1136) the digital signature function does not include the authentication\nfunction.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "19) __________ is the traditional method of implementing access control",
+    "answers": [
+      {
+        "answer": "MAC",
+        "image": ""
+      },
+      {
+        "answer": "RBAC",
+        "image": ""
+      },
+      {
+        "answer": "DAC",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "31) Malicious software aims to trick users into revealing sensitive personal data",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1147) The authentication function determines who is trusted for a given purpose.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "73) Cipher Feedback Mode conforms to the typical construction of a stream cipher",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "168) The secret key is one of the inputs to a symmetric-key encryption algorithm",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1080) Buffer overflow attacks result from careless programming in\napplications.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "70) Cipher Feedback (CFB  is used for the secure transmission of single values)",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1103) Much of the theory of public-key cryptosystems is based on\nnumber theory.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "22) A __________ is an entity capable of accessing objects",
+    "answers": [
+      {
+        "answer": "group",
+        "image": ""
+      },
+      {
+        "answer": "object",
+        "image": ""
+      },
+      {
+        "answer": "subject",
+        "image": ""
+      },
+      {
+        "answer": "owner",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "334) In addition to propagation a worm usually performs some unwanted function",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "388) The buffer overflow type of attack is one of the most common attacks seen",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "104) Public-key encryption was developed in the late ________",
+    "answers": [
+      {
+        "answer": "1950s",
+        "image": ""
+      },
+      {
+        "answer": "1970s",
+        "image": ""
+      },
+      {
+        "answer": "1960s",
+        "image": ""
+      },
+      {
+        "answer": "1980s",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "164) A common location for a NIDS sensor is just inside the external firewall",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1165) Those who hack into computers do so for the thrill of it or for status.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "9) An access right describes the way in which a subject may access an object",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "4) External devices such as firewalls cannot provide access control services",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1094) The authentication server shares a unique secret key with each server.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "233) The IDS component responsible for collecting data is the user interface",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "130) In security protocol, an obvious security risk is that of impersonation",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "50) Computer viruses first appeared in the early __________",
+    "answers": [
+      {
+        "answer": "1960s",
+        "image": ""
+      },
+      {
+        "answer": "1970s",
+        "image": ""
+      },
+      {
+        "answer": "1980s",
+        "image": ""
+      },
+      {
+        "answer": "1990s",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "24) The final permission bit is the _________ bit",
+    "answers": [
+      {
+        "answer": "superuser",
+        "image": ""
+      },
+      {
+        "answer": "kernel",
+        "image": ""
+      },
+      {
+        "answer": "set user",
+        "image": ""
+      },
+      {
+        "answer": "sticky",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "1079) the XtS-AES mode is based on the concept of a tweakable block\ncipher.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "591) T/F: Fixed server roles operate at the level of an individual database",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1154) Public-key algorithms are based on simple operations on bit patterns.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "40) In addition to propagating, a worm usually carries some form of payload",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1144) For end-to-end encryption over a network, manual delivery is\nawkward.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1093) X.509 provides a format for use in revoking a key before it expires.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "387) Shellcode must be able to run no matter where in memory it is located",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "563) T/F: Encryption becomes the last line of defense in database security",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "324) One important element of intrusion prevention is password management",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1164) Snort can perform intrusion prevention but not intrusion detection.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "68) The sender is the only one who needs to know an initialization vector",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "693) Many users choose a password that is too short or too easy to guess",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "374) Stack buffer overflow attacks were first seen in the Aleph One Worm",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "199) In IPSec, the sequence number is used for preventing replay attacks",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "372) There are several generic restrictions on the content of shellcode",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "583) T/F: A query language provides a uniform interface to the database",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "166) Symmetric encryption is used primarily to provide confidentiality",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "900) ASLR(if implemented correctly) can prevent return-to-libc attacks",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "399) Shellcode is not specific to a particular processor architecture",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1143) Manual delivery of a key is not reasonable for link encryption.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "839) The \"A\" in the CIA triad stands for \"authenticity\"\nTrue or False",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "389) Buffer overflow attacks are one of the most common attacks seen",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "206) In iOS, an app can run its own dynamic, run-time generated code",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "336) Macro viruses infect documents, not executable portions of code",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "732) Like the MAC, a hash function also takes a secret key as input",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "235) Intruders typically use steps from a common attack methodology",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "587) T/F: A foreign key value can appear multiple times in a table",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "209) In Android, all apps have to be reviewed and signed by Google",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "121) SHA is perhaps the most widely used family of hash functions",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "735) The advantage of a stream cipher is that you can reuse keys",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "015) Some form of protocol is needed for public-key distribution",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "584) T/F: A single countermeasure is sufficient for SQLi attacks",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "205) In iOS, each file is encrypted using a unique, per-file key",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "213) Malicious JavaScripts is a major threat to browser security",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "142) The ciphertext-only attack is the easiest to defend against",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "318) Insider attacks are among the easiest to detect and prevent",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "244) An intruder can also be referred to as a hacker or cracker",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1139) the main elements of a RFID system are tags and readers.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "219) SQL injection attacks only lead to information disclosure",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "879) External attacks are the only threats to dataase security",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "938) Some APT attacks last for years before they are detected",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1) Access control is the central element of computer security",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "65) S-AES is the most widely used multiple encryption scheme",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "631) A data center generally includes backup power supplies",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1168) Programmers use backdoors to debug and test programs.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "864) ?No write down? is also referred to as the *-property",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1141) the Iot depends heavily on deeply embedded systems.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "135) Kerberos does not support inter-realm authentication",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1151) The \"A\" in the CIA triad stands for \"authenticity\".",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "136) SHA-1 produces a hash value of _______ bits",
+    "answers": [
+      {
+        "answer": "256",
+        "image": ""
+      },
+      {
+        "answer": "512",
+        "image": ""
+      },
+      {
+        "answer": "160",
+        "image": ""
+      },
+      {
+        "answer": "128",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "39) E-mail is a common method for spreading macro viruses",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "35) It is not possible to spread a virus via a USB stick",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1097) Lower layer security does not impact upper layers.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "62) There are no practical cryptanalytic attacks on 3DES",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "32) Keyware captures keystrokes on a compromised system",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "04) The secret key is input to the encryption algorithm",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "253) Anomaly detection is effective against misfeasors",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "883) A macro virus infects executable protions of code",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "38) A macro virus infects executable portions of code",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1098) The direct flame is the only threat from fire.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "702) A smart card contains an entire microprocessor",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "346) Malware is another name for Malicious Software",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "641) A _________ is a virtual table",
+    "answers": [
+      {
+        "answer": "tuple",
+        "image": ""
+      },
+      {
+        "answer": "query",
+        "image": ""
+      },
+      {
+        "answer": "view",
+        "image": ""
+      },
+      {
+        "answer": "DBMS",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "7) Reliable input is an access control requirement",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "835) Threats are attacks carried out\nTrue or False",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "201) Even web searches have (often) been in HTTPS",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "134) The ticket-granting ticket is never expired",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "146) Timing attacks are only applicable to RSA",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "1076) InvSubBytes is the inverse of ShiftRows.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "208) In iOS, each app runs in its own sandbox",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1153) Public-key cryptography is asymmetric.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "327) Bot programs are activated by a trigger",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1148) A user may belong to multiple groups.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "122) SHA-1 is considered to be very secure",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "703) Keylogging is a form of host attack",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "697) Memory cards store and process data",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "44) Every bot has a distinct IP address",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "1150) Threats are attacks carried out.",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "144) AES uses a Feistel structure",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "787) Search engines support HTTPS",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "False",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "204) iOS has no vulnerability",
+    "answers": [
+      {
+        "answer": "True",
+        "image": ""
+      },
+      {
+        "answer": "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
new file mode 100644
index 0000000..e206c47
--- /dev/null
+++ b/data/questions/sicurezza_appello1.json
@@ -0,0 +1,754 @@
+[
+  {
+    "quest": "1) L'input affidabile (reliable) è un requisito per il controllo degli accessi",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "cardinalità (cardinality)",
+        "image": ""
+      },
+      {
+        "answer": "vincoli (constraints)",
+        "image": ""
+      },
+      {
+        "answer": "ruoli che si escludono a vicenda (mutually exclusive roles)",
+        "image": ""
+      },
+      {
+        "answer": "prerequisiti (prerequisites)",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "3) _______________ è il metodo tradizionale per implementare il controllo degli accessi",
+    "answers": [
+      {
+        "answer": "MBAC",
+        "image": ""
+      },
+      {
+        "answer": "RBAC",
+        "image": ""
+      },
+      {
+        "answer": "DAC",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "5) Le minacce (thrats) sono attacchi effettuati",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "6) Masquerade, falsificazione e ripudio sono azioni di minaccia che causano conseguenze di minaccia di __________",
+    "answers": [
+      {
+        "answer": "Inganno (deception)",
+        "image": ""
+      },
+      {
+        "answer": "Usurpazione (usurpation)",
+        "image": ""
+      },
+      {
+        "answer": "Divulgazione non autorizzata (unauthorized disclosure)",
+        "image": ""
+      },
+      {
+        "answer": "Interruzione (disruption)",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "7) La \"A\" nella triade CIA sta per \"autenticità\"",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Contromisura",
+        "image": ""
+      },
+      {
+        "answer": "Vulnerabilità",
+        "image": ""
+      },
+      {
+        "answer": "Avversario",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Fase di autnticazione (authentication step)",
+        "image": ""
+      },
+      {
+        "answer": "Fase di conferma (confirmation step)",
+        "image": ""
+      },
+      {
+        "answer": "Fase di identificazione (identification step)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "11) Un attacco __________ coinvolge un avversario che ripete una risposta dell'utente acquisita in precedenza",
+    "answers": [
+      {
+        "answer": "Eavesdropping",
+        "image": ""
+      },
+      {
+        "answer": "Client",
+        "image": ""
+      },
+      {
+        "answer": "Replay",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "13) Due svantaggi della crittografia dei database sono la gestione delle chiavi e la poca flessibilità",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "compromesso (compromise)",
+        "image": ""
+      },
+      {
+        "answer": "inferenza (inference)",
+        "image": ""
+      },
+      {
+        "answer": "perturbazione (perturbation)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "16) La crittografia è l'ultima linea di difesa nella sicurezza del database",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "pacchetto di echo (echo packet)",
+        "image": ""
+      },
+      {
+        "answer": "reflection attack",
+        "image": ""
+      },
+      {
+        "answer": "attacco flash flood (flash flood attack)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "poison packet attack",
+        "image": ""
+      },
+      {
+        "answer": "SYN spoofing attack",
+        "image": ""
+      },
+      {
+        "answer": "DNS amplification attack",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "scanner basati su fingerprint (fingerprint-based scanners)",
+        "image": ""
+      },
+      {
+        "answer": "scanner che bloccano il comportamento (behavior-blocking scanners)",
+        "image": ""
+      },
+      {
+        "answer": "tecnologie di decrittazione generica (generic decryption technology)",
+        "image": ""
+      },
+      {
+        "answer": "scanner euristici (heuristic scanners)",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "23) Un macro virus infetta porzioni eseguibili di codice",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "worm",
+        "image": ""
+      },
+      {
+        "answer": "trojan horse",
+        "image": ""
+      },
+      {
+        "answer": "logic bomb",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "MMU",
+        "image": ""
+      },
+      {
+        "answer": "pagine di protezione (guard pages)",
+        "image": ""
+      },
+      {
+        "answer": "Heaps",
+        "image": ""
+      },
+      {
+        "answer": "Tutto quanto elencato precedentemente",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "26) Una conseguenza di un errore di buffer overflow è la / il / __________",
+    "answers": [
+      {
+        "answer": "corruzione dei dati utilizzati dal programma",
+        "image": ""
+      },
+      {
+        "answer": "trasferimento imprevisto del controllo del programma",
+        "image": ""
+      },
+      {
+        "answer": "possibile violazione dell'accesso alla memoria",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "rilevamento delle intrusioni (intrusion detection)",
+        "image": ""
+      },
+      {
+        "answer": "IDS basato sulla rete (network-based IDS)",
+        "image": ""
+      },
+      {
+        "answer": "intrusione nella sicurezza (security intrusion)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "rilevamento basato sul profilo (profile-based detection)",
+        "image": ""
+      },
+      {
+        "answer": "rilevamento delle anomalie (anomaly detection)",
+        "image": ""
+      },
+      {
+        "answer": "rilevamento della firma (signature detection)",
+        "image": ""
+      },
+      {
+        "answer": "rilevamento di soglia (threshold detection)",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "31) Un sensore NIDS è solitamente posizionato all'interno del firewall esterno",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "33) SHA-1 produce un valore di hash di ______ bits",
+    "answers": [
+      {
+        "answer": "384",
+        "image": ""
+      },
+      {
+        "answer": "160",
+        "image": ""
+      },
+      {
+        "answer": "180",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "35) Gli attacchi di tipo Timing (timing attacks) sono applicabili solo a RSA",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "matematici",
+        "image": ""
+      },
+      {
+        "answer": "a testo cifrato scelto",
+        "image": ""
+      },
+      {
+        "answer": "di Timing",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "CBC (Cipher Block Chaining)",
+        "image": ""
+      },
+      {
+        "answer": "OFB (Output Feedback)",
+        "image": ""
+      },
+      {
+        "answer": "ECB (Electronic Code Book)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "CFB (Cipher Feedback)",
+        "image": ""
+      },
+      {
+        "answer": "OFB (Output Feedback)",
+        "image": ""
+      },
+      {
+        "answer": "CBC (Cipher Block Chaining)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "40) AES usa una struttura di Feistel",
+    "answers": [
+      {
+        "answer": "V",
+        "image": ""
+      },
+      {
+        "answer": "F",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  }
+]
\ No newline at end of file
diff --git a/data/questions/so1.json b/data/questions/so1.json
new file mode 100644
index 0000000..85a0fdc
--- /dev/null
+++ b/data/questions/so1.json
@@ -0,0 +1,3107 @@
+[
+  {
+    "quest": "1) Quale delle seguenti affermazioni sulle directory di un file system è vera?",
+    "answers": [
+      {
+        "answer": "È sempre necessario identificare un file di un file system fornendone il path assoluto",
+        "image": ""
+      },
+      {
+        "answer": "È sempre necessario identificare un file di un file system fornendone il path relativo alla directory corrente",
+        "image": ""
+      },
+      {
+        "answer": "È sempre possibile dare lo stesso nome a file diversi",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "2) UNSAFE Quale delle seguenti affermazioni sulla concorrenza tra processi o thread è falsa?",
+    "answers": [
+      {
+        "answer": "La disabilitazione delle interruzioni impedisce la creazione di nuove interruzioni",
+        "image": ""
+      },
+      {
+        "answer": "L'abuso della disabilitazione delle interruzioni fa diminuire la multiprogrammazione, a parità di numero di processi",
+        "image": ""
+      },
+      {
+        "answer": "Se un processo può disabilitare le interruzioni tramite un'istruzione macchina dedicata, allora può far diminuire l'uso del processore",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Lo scheduler ha, tra i suoi obiettivi, quello di minimizzare il numero di processi che rispettano la propria deadline",
+        "image": ""
+      },
+      {
+        "answer": "Lo scheduler ha, tra i suoi obiettivi, quello di minimizzare il volume di lavoro nel tempo",
+        "image": ""
+      },
+      {
+        "answer": "Lo scheduler ha, tra i suoi obiettivi, quello di massimizzare il tempo di risposta",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Se un processo è Zombie, allora è terminato ma il suo process control block è ancora in memoria",
+        "image": ""
+      },
+      {
+        "answer": "Asleep in Memory coincide con Blocked",
+        "image": ""
+      },
+      {
+        "answer": "Ha anche uno stato Zombie: serve per tutti i processi che sono terminati",
+        "image": ""
+      },
+      {
+        "answer": "Ha 9 stati (10 con Exit)",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "5) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è falsa? ",
+    "answers": [
+      {
+        "answer": "Quando un indirizzo non viene trovato nel translation lookaside buffer, è necessario consultare la normale tabella delle pagine",
+        "image": ""
+      },
+      {
+        "answer": "Il translation lookaside buffer è una particolare cache, ma non è completamente trasparente al sistema operativo",
+        "image": ""
+      },
+      {
+        "answer": "Il translation lookaside buffer permette di accedere direttamente al contenuto degli indirizzi di memoria virtuali usati più di recente",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Per \"disponibilità\" dell'hardware si intende la garanzia che le workstation restino sempre fisse in un posto",
+        "image": ""
+      },
+      {
+        "answer": "Per \"confidenzialità\" dei dati si intende la garanzia che essi non possano essere generati automaticamente",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "Avviene direttamente su disco, altrimenti si rischia il deadlock per interferenze con il DMA",
+        "image": ""
+      },
+      {
+        "answer": "Nel caso ci siano più buffer, vanno gestiti come nel problema dei lettori/scrittori",
+        "image": ""
+      },
+      {
+        "answer": "Può consistere nel completare un'istruzione di output I (è una 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": [
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "Può essere usato per visualizzare le possibilità di deadlock, ma solo se i processi richiedono al massimo 2 risorse",
+        "image": ""
+      },
+      {
+        "answer": "Può essere usato per determinare quando uno dei due processi va in esecuzione a discapito dell'altro",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "Andando dall'alto in basso, cresce il costo",
+        "image": ""
+      },
+      {
+        "answer": "Andando dall'alto in basso, diminuisce la capacità",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il puntatore alla tabella delle pagine",
+        "image": ""
+      },
+      {
+        "answer": "L’identificatore del thread",
+        "image": ""
+      },
+      {
+        "answer": "Lo stato o modalità",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "Il quanto di tempo ottimale per lo scheduler round-robin è maggiore del tipico tempo di completa esecuzione di un processo interattivo",
+        "image": ""
+      },
+      {
+        "answer": "Lo scheduler First Come First Served favorisce i processi I/O-bound",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Diversi segmenti possono avere diverse lunghezze",
+        "image": ""
+      },
+      {
+        "answer": "Differentemente dalla paginazione, il programmatore assembler di un processo non interagisce esplicitamente con la gestione dei segmenti",
+        "image": ""
+      },
+      {
+        "answer": "Per accedere ad un indirizzo contenuto in un segmento di un processo, tale segmento dovrà essere posizionato in memoria principale",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Se al passo 3 viene trovato un processo non marcato che soddisfi la condizione Qik ≤ wik, allora c'è un deadlock",
+        "image": ""
+      },
+      {
+        "answer": "I processi marcati sono quelli che non sono coinvolti in un deadlock",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Viene chiamato in causa esclusivamente quando viene creato un nuovo processo",
+        "image": ""
+      },
+      {
+        "answer": "Avendo le necessarie informazioni, una tipica strategia è mantenere una giusta proporzione, stabilita a priori, tra processi I/O-bound e CPU-bound",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il difetto principale del prepaging è che potrebbe portare in memoria pagine cui poi non si fa riferimento",
+        "image": ""
+      },
+      {
+        "answer": "Placement policy e replacement policy sono sinonimi ed indicano lo stesso insieme di metodologie",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Non deve essere fatta alcuna assunzione sulla velocità di esecuzione dei processi coinvolti",
+        "image": ""
+      },
+      {
+        "answer": "Se un processo fa richiesta di entrare nella sezione critica, deve poterlo fare subito",
+        "image": ""
+      },
+      {
+        "answer": "Se un processo non fa richiesta di entrare nella sezione critica, deve comunque accordarsi all'esecuzione degli altri processi",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il principio di località afferma che poche pagine saranno sempre sufficienti per eseguire ogni processo senza thrashing",
+        "image": ""
+      },
+      {
+        "answer": "Il thrashing si verifica quando l'overhead dovuto alla gestione della paginazione è molto basso",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "La paginazione con memoria virtuale funziona bene nonostante il principio di località",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "20) UNSAFE (sbagliata secondo Gabriele Pelissetto) Quale delle seguenti affermazioni sullo scambio messaggi per la gestione della concorrenza è vera? ",
+    "answers": [
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "L'implementazione delle primitive per lo scambio messaggi non è garantita atomica dal sistema operativo",
+        "image": ""
+      },
+      {
+        "answer": "Se un processo chiama receive, finché il messaggio non viene ricevuto, tutti gli altri processi che proveranno a chiamare receive verranno bloccati",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "I dati possono essere ricavati dai metadati",
+        "image": ""
+      },
+      {
+        "answer": "I metadati possono essere ricavati dai dati",
+        "image": ""
+      },
+      {
+        "answer": "I file system, che adottano il metodo journaling, mantengono un log per le operazioni di sola scrittura da effettuare, realizzandole in seguito",
+        "image": ""
+      },
+      {
+        "answer": "Un volume coincide sempre con un disco, quindi se un computer ha 2 dischi avrà 2 volumi",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "22) (UNSAFE secondo Gabriele Pelissetto vd. gruppo 5 di slide, slide 11) Quale delle seguenti affermazioni sui dispositivi di I/O è vera?",
+    "answers": [
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "Il data rate confronta le velocità di 2 diversi dispositivi di I/O",
+        "image": ""
+      },
+      {
+        "answer": "Ciascun dispositivo di I/O può essere usato solo da un ben determinato tipo di applicazioni",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Se viene usata la lista di blocchi liberi, c'è un overhead di spazio, contrariamente alla concatenazione di blocchi liberi",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "Se ci sono blocchi da 1kB, e il disco contiene 1TB, l'occupazione dovuta alla lista di blocchi liberi è dell'1%",
+        "image": ""
+      },
+      {
+        "answer": "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) 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?",
+    "answers": [
+      {
+        "answer": "Salvataggio del contesto del programma",
+        "image": ""
+      },
+      {
+        "answer": "Aggiornamento delle strutture dati per la gestione della memoria",
+        "image": ""
+      },
+      {
+        "answer": "Spostamento del process control block nella coda appropriata (ready, blocked, ready/suspend)",
+        "image": ""
+      },
+      {
+        "answer": "Scelta di un altro processo da eseguire",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "25) Quale delle seguenti affermazioni è vera?",
+    "answers": [
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "È responsabile dell'accensione del computer ",
+        "image": ""
+      },
+      {
+        "answer": "Viene swappato dal disco alla memoria principale ad ogni context switch ",
+        "image": ""
+      },
+      {
+        "answer": "È responsabile, tra le altre cose, della gestione dei processori",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "27) Quale delle seguenti affermazioni sul controllo di accesso è vera?",
+    "answers": [
+      {
+        "answer": "Nel controllo di accesso basato su ruoli, ad ogni ruolo è assegnato un utente",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "Nel controllo di accesso basato su ruoli, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-ruoli-oggetti",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Se uno scheduler è non-preemptive, permette sempre ai suoi processi di essere eseguiti sul processore, senza interruzioni, fino al loro completamento",
+        "image": ""
+      },
+      {
+        "answer": "Se uno scheduler è non-preemptive, è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready",
+        "image": ""
+      },
+      {
+        "answer": "Se uno scheduler è preemptive, non è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Con lo scheduler Shortest Process Next, i processi con una grande immagine su RAM potrebbero soffrire di starvation",
+        "image": ""
+      },
+      {
+        "answer": "Lo scheduler round-robin virtuale migliora il round-robin classico, facendo sì che i processi I/O-bound non vengano sfavoriti",
+        "image": ""
+      },
+      {
+        "answer": "Lo scheduler First Come First Served \"degenera\" nello scheduler round-robin se il quanto di tempo è troppo lungo",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "30) Quale delle seguenti affermazioni sugli indirizzi di memoria principale è vera?",
+    "answers": [
+      {
+        "answer": "Un indirizzo fisico fa sempre riferimento alla memoria secondaria",
+        "image": ""
+      },
+      {
+        "answer": "Per rispettare il requisito di rilocazione, occorre trasformare indirizzi fisici in logici",
+        "image": ""
+      },
+      {
+        "answer": "Gli indirizzi relativi sono usati nella paginazione",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "31) Quale delle seguenti affermazioni sui termini tipici della concorrenza è falsa?",
+    "answers": [
+      {
+        "answer": "Una sezione critica è una porzione di memoria che contiene almeno una variabile condivisa tra più processi",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Il requisito di mutua esclusione prevede che un solo processo possa eseguire un certo segmento di codice o accedere ad una determinata risorsa",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Nessuna delle altre opzioni contiene elementi del process control block",
+        "image": ""
+      },
+      {
+        "answer": "Le informazioni sul contesto del processo, aggiornate ad ogni istruzione eseguita",
+        "image": ""
+      },
+      {
+        "answer": "L'intera immagine del processo in memoria",
+        "image": ""
+      },
+      {
+        "answer": "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 file system?",
+    "answers": [
+      {
+        "answer": "Il gruppo cui appartiene l'utente che ha creato il file",
+        "image": ""
+      },
+      {
+        "answer": "La data di creazione del file",
+        "image": ""
+      },
+      {
+        "answer": "Autorizzazioni per l'accesso al file",
+        "image": ""
+      },
+      {
+        "answer": "Dimensione del file",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "34) Quale delle seguenti affermazioni sugli algoritmi di scheduling per i dischi è vera?",
+    "answers": [
+      {
+        "answer": "L'algoritmo random ha la stessa funzione dell'algoritmo ottimo dei rimpiazzamenti di pagina: ha delle prestazioni ottime non raggiungibili dagli altri algoritmi",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "L'algoritmo C-SCAN deriva da SCAN, ed è stato sviluppato per evitare di favorire le richieste di tracce ai bordi del disco",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "L'algoritmo C-SCAN deriva da SCAN, ed è stato sviluppato per evitare di favorire le richieste di tracce ai bordi del disco",
+        "image": ""
+      },
+      {
+        "answer": "Per valutare le prestazioni dell'algoritmo con priorità è sufficiente fornire il ruolo degli utenti dei processi che effettuano le richieste",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "È possibile che ci sia frammentazione interna",
+        "image": ""
+      },
+      {
+        "answer": "La compattazione permette di memorizzare file che altrimenti non potrebbero esserlo (pur essendo la loro dimensione minore di quella dello spazio libero)",
+        "image": ""
+      },
+      {
+        "answer": "Non è necessaria la preallocazione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Frame e pagine devono avere la stessa dimensione",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le pagine di un processo dovranno essere, prima o poi, posizionate in un frame",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Nel controllo di accesso basato su ruoli, ad ogni ruolo è assegnato un utente",
+        "image": ""
+      },
+      {
+        "answer": "Nel controllo di accesso basato su ruoli, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-ruoli-oggetti",
+        "image": ""
+      },
+      {
+        "answer": "Nel controllo di accesso discrezionale, prima di stabilire se un'operazione è lecita, è necessario consultare una tabella soggetti-oggetti",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "39) Quale delle seguenti affermazioni è falsa? ",
+    "answers": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Un grafo dell'allocazione delle risorse è un grafo diretto aciclico",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il difetto principale del prepaging è che potrebbe portare in memoria pagine cui poi non si fa riferimento",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "Il difetto principale del paging on demand è che, dopo una prima fase di assestamento, causa molti page fault",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "Un settore di un disco magnetico a testina mobile è l'area di una corona circolare del disco stesso",
+        "image": ""
+      },
+      {
+        "answer": "Una traccia di un disco magnetico a testina mobile è l'area compresa da 2 raggi del disco stesso",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Semafori generali e semafori binari hanno lo stesso potere computazionale (ovvero, permettono di risolvere gli stessi problemi)",
+        "image": ""
+      },
+      {
+        "answer": "Le primitive sui semafori sono in grado di mettere un processo in blocked, senza usare, a tal proposito, il busy-waiting",
+        "image": ""
+      },
+      {
+        "answer": "Per implementare le primitive sui semafori, servono un contatore ed una coda, che saranno condivisi da tutti i semafori usati",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "L'algoritmo LIFO è il più equo nei confronti dei processi che effettuano le richieste al disco",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Disabilitando gli interrupt, è possibile scrivere processi che non soffrano di starvation",
+        "image": ""
+      },
+      {
+        "answer": "Usando i semafori di qualsiasi tipo, è possibile scrivere processi che non soffrano di starvation",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Decide quali processi, tra quelli appena creati, possono essere ammessi in memoria principale per l'esecuzione",
+        "image": ""
+      },
+      {
+        "answer": "Avendo le necessarie informazioni, una tipica strategia è mantenere una giusta proporzione, stabilita a priori, tra processi I/O-bound e CPU-bound",
+        "image": ""
+      },
+      {
+        "answer": "Viene chiamato in causa esclusivamente quando viene creato un nuovo processo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Se ci sono blocchi da 1kB, e il disco contiene 1TB, l'occupazione dovuta alla lista di blocchi liberi è dell'1%",
+        "image": ""
+      },
+      {
+        "answer": "Se viene usata la lista di blocchi liberi, tale lista viene interamente mantenuta in memoria principale",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "È sempre necessario identificare un file di un file system fornendone il path relativo alla directory corrente",
+        "image": ""
+      },
+      {
+        "answer": "È sempre possibile dare lo stesso nome a file diversi",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "La memoria cache è direttamente indirizzabile in assembler",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "È possibile che, in un dato istante, la cache e la memoria RAM non siano coerenti tra loro",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Può essere usato per determinare quando uno dei due processi sperimenta un page fault",
+        "image": ""
+      },
+      {
+        "answer": "Può essere usato per visualizzare le possibilità di deadlock, ma solo se i processi richiedono al massimo 2 risorse",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Con il partizionamento fisso, le partizioni devono avere tutte la stessa dimensione",
+        "image": ""
+      },
+      {
+        "answer": "Con il buddy system, ogni indirizzo di memoria può ricadere in 2 porzioni",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Se uno scheduler è preemptive e vi è più di 1 processo ready, non è possibile che un processo monopolizzi il processore",
+        "image": ""
+      },
+      {
+        "answer": "Per avere un trattamento equo sui processi, è sufficiente usare uno scheduler preemptive",
+        "image": ""
+      },
+      {
+        "answer": "Se uno scheduler è non-preemptive, permette sempre ai suoi processi di essere eseguiti senza interruzioni sul processore fino al loro completamento",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Blocked ==> Running",
+        "image": ""
+      },
+      {
+        "answer": "Running ==> Ready",
+        "image": ""
+      },
+      {
+        "answer": "Blocked ==> Exit",
+        "image": ""
+      },
+      {
+        "answer": "Blocked ==> Ready",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "56) Quale delle seguenti affermazioni sul metodo di allocazione indicizzata dei file è vera? ",
+    "answers": [
+      {
+        "answer": "Il consolidamento permette sempre di ridurre la dimensione dell'indice",
+        "image": ""
+      },
+      {
+        "answer": "Se usato con porzioni di dimensione variabile, i blocchi indice devono contenere anche la lunghezza di ogni porzione",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Se uno scheduler è non-preemptive, permette sempre ai suoi processi di essere eseguiti sul processore, senza interruzioni, fino al loro completamento",
+        "image": ""
+      },
+      {
+        "answer": "Se uno scheduler è non-preemptive, è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready",
+        "image": ""
+      },
+      {
+        "answer": "Se uno scheduler è preemptive, non è possibile che un processo monopolizzi il processore, anche in presenza di altri processi ready",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Non deve essere fatta alcuna assunzione sulla velocità di esecuzione dei processi coinvolti",
+        "image": ""
+      },
+      {
+        "answer": "Se un processo non fa richiesta di entrare nella sezione critica, deve comunque sincronizzarsi all'esecuzione degli altri processi",
+        "image": ""
+      },
+      {
+        "answer": "Se un processo è nella sezione critica, occorre che rilasci subito la sezione critica stessa",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il resource balancing è un criterio di sistema non prestazionale",
+        "image": ""
+      },
+      {
+        "answer": "Il rispetto delle deadline è un criterio utente prestazionale",
+        "image": ""
+      },
+      {
+        "answer": "Il throughput è un criterio di sistema prestazionale",
+        "image": ""
+      },
+      {
+        "answer": "La predictability è un criterio utente prestazionale",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "61) Quale delle seguenti affermazioni sugli interrupt (o eccezioni) è falsa? ",
+    "answers": [
+      {
+        "answer": "Devono essere gestiti da opportuno software di sistema",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Normalmente, non vengono gestiti dal programmatore dell'applicazione che li ha causati",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Sono basate sul busy-waiting, ovvero sul fatto che un processo si mette autonomamente in stato blocked",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "Non riescono ad evitare il manifestarsi del deadlock, a meno che non sia presente un sistema a priorità",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "Per la terminazione normale di un processo, è tipicamente prevista un'apposita system call, come ad esempio exit",
+        "image": ""
+      },
+      {
+        "answer": "Un processo può morire quando si effettua il process spawning",
+        "image": ""
+      },
+      {
+        "answer": "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) (UNSAFE le decisioni dello scheduler possono influenzare la risposta) Quale delle seguenti affermazioni sui meccanismi software per la gestione della concorrenza è vera? ",
+    "answers": [
+      {
+        "answer": "Sia l'algoritmo di Dekker che quello di Peterson possono mettere in blocked uno dei 2 processi, quando ciò si rivela necessario",
+        "image": ""
+      },
+      {
+        "answer": "Sia l'algoritmo di Dekker che quello di Peterson non funzionano se l'hardware sottostante riordina gli accessi in memoria",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": 3,
+    "image": ""
+  },
+  {
+    "quest": "65) Quale delle seguenti affermazioni sugli i-node di Unix è falsa? ",
+    "answers": [
+      {
+        "answer": "Ogni directory è identificata da un i-node",
+        "image": ""
+      },
+      {
+        "answer": "Per modificare una directory, un utente deve aprire il file speciale corrispondente e poi modificarlo opportunamente",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Ogni directory può contenere molti i-node",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "66) Quale delle seguenti affermazioni è falsa? ",
+    "answers": [
+      {
+        "answer": "Nel caso di un sistema operativo a kernel separato, la gestione dei process switch è a sua volta un processo",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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) (corretta secondo @loryspat e @notherealmarco, da controllare) Quale delle seguenti affermazioni sulla paginazione della memoria è vera? ",
+    "answers": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Con la paginazione con memoria virtuale, una sola pagina di ogni processo ready o in esecuzione è inizialmente caricata in memoria principale",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il consolidamento permette di memorizzare file che altrimenti non potrebbero esserlo (pur essendo la loro dimensione minore di quella dello spazio libero)",
+        "image": ""
+      },
+      {
+        "answer": "La tabella di allocazione dei file deve contenere l'intera catena",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "70) (corretta secondo Simone Sestito e @loryspat) Quale delle seguenti affermazioni sul deadlock è falsa? ",
+    "answers": [
+      {
+        "answer": "Affinchè ci sia un deadlock, sono necessarie le condizioni di attesa circolare, hold-and-wait, mutua esclusione e no preemption",
+        "image": ""
+      },
+      {
+        "answer": "Per prevenire il deadlock, è necessario cercare di impedire almeno una delle 3 condizioni di mutua esclusione, hold-and-wait e no preemption",
+        "image": ""
+      },
+      {
+        "answer": "Affinchè il deadlock sia possibile, sono necessarie le condizioni di mutua esclusione, hold-and-wait e no preemption",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "La modalità di un processo utente è sempre la modalità di sistema",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "Per ogni processo, il resident set contiene lo stesso numero di pagine",
+        "image": ""
+      },
+      {
+        "answer": "Un tipico algoritmo per il replacement scope è quello dell'orologio",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "L'istruzione exchange non può ricevere costanti in input su nessun suo argomento, mentre per l'istruzione compare_and_swap questo non vale",
+        "image": ""
+      },
+      {
+        "answer": "Le istruzioni speciali exchange e compare_and_swap sono garantite atomiche dal sistema operativo",
+        "image": ""
+      },
+      {
+        "answer": "Per realizzare opportunamente l'istruzione compare_and_swap è sufficiente disabilitare le interruzioni",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il response time è un criterio utente prestazionale",
+        "image": ""
+      },
+      {
+        "answer": "Il turnaround time (normalizzato o no) è un criterio utente prestazionale",
+        "image": ""
+      },
+      {
+        "answer": "Il throughput è un criterio di sistema non prestazionale",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Usa il metodo di allocazione contiguo",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "77) UNSAFE Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è falsa?",
+    "answers": [
+      {
+        "answer": "Il translation lookaside buffer, su alcuni processori, contiene un campo per il PID dei processi",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il contatore di programma",
+        "image": ""
+      },
+      {
+        "answer": "La priorità",
+        "image": ""
+      },
+      {
+        "answer": "I dati contenuti nella porzione di memoria a lui dedicata",
+        "image": ""
+      },
+      {
+        "answer": "Informazioni sullo stato delle risorse",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "79) UNSAFE Quale delle seguenti affermazioni, riguardanti la classificazione delle risorse di un sistema operativo e la loro relazione con il deadlock, è vera?",
+    "answers": [
+      {
+        "answer": "Nel caso delle risorse consumabili, se c'è un deadlock allora è stata richiesta almeno una risorsa già detenuta da un altro processo",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Nel caso delle risorse riusabili, se c'è un deadlock allora è stata richiesta almeno una risorsa non ancora creata",
+        "image": ""
+      },
+      {
+        "answer": "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) 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?",
+    "answers": [
+      {
+        "answer": "Dal punto di vista del processore, ogni processo esegue sempre le proprie istruzioni senza interruzioni",
+        "image": ""
+      },
+      {
+        "answer": "Per realizzare correttamente un process switch, il SO avrà necessità di usare le informazioni sul contesto contenute nel process control block",
+        "image": ""
+      },
+      {
+        "answer": "Dal punto di vista di ogni processo, l'esecuzione avviene in interleaving con gli altri processi",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "L'hardware deve anche cercare il numero di pagina nelle entries della tabella delle pagine del processo in esecuzione. ",
+        "image": ""
+      },
+      {
+        "answer": "L'hardware deve anche estrarre dall'indirizzo virtuale il numero di pagina virtuale; tale operazione è equivalente ad una divisione intera",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Apertura",
+        "image": ""
+      },
+      {
+        "answer": "Connessione",
+        "image": ""
+      },
+      {
+        "answer": "Posizionamento (seek)",
+        "image": ""
+      },
+      {
+        "answer": "Lock/Unlock",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "83) Quale delle seguenti affermazioni è falsa?",
+    "answers": [
+      {
+        "answer": "Diversi thread di uno stesso processo condividono lo stesso thread identifier",
+        "image": ""
+      },
+      {
+        "answer": "Tra le funzioni di sistema per i thread, è tipicamente prevista una funzione per bloccare e sbloccare esplicitamente i thread stessi",
+        "image": ""
+      },
+      {
+        "answer": "Diversi thread di uno stesso processo condividono lo stesso process identifier",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Deve minimizzare il tempo di risposta, tenendo presenti eventuali priorità",
+        "image": ""
+      },
+      {
+        "answer": "Deve servire il maggior numero di utenti possibile, tenendo presenti eventuali livelli di accesso",
+        "image": ""
+      },
+      {
+        "answer": "Deve dare accesso alle risorse in modo equo ed egualitario tra tutti i processi",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Il metodo più permissivo nei confronti delle richieste di risorse è quello che consiste nel prevenire il deadlock",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Lo scheduler ha, tra i suoi obiettivi, quello dell'equità tra i processi, a meno che non siano definite delle priorità",
+        "image": ""
+      },
+      {
+        "answer": "Lo scheduler va scritto in modo che il suo overhead sia basso",
+        "image": ""
+      },
+      {
+        "answer": "Lo scheduler ha, tra i suoi obiettivi, quello di evitare il deadlock",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Con l'assegnamento statico, si dà un processore a caso tra quelli liberi ai processi che mantengono un uso della RAM pressoché costante",
+        "image": ""
+      },
+      {
+        "answer": "Assegnando i processi del sistema operativo con l'assegnamento dinamico, si rischia di creare un bottleneck su un solo processore",
+        "image": ""
+      },
+      {
+        "answer": "Uno svantaggio dell'assegnamento statico è il suo overhead maggiore rispetto a quello dinamico",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "La matrice C - A può contenere elementi negativi, ma le matrici C ed A contengono solo elementi non negativi",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "All'inizio e alla fine di ogni invocazione dell'algoritmo, Vi = Ri - ∑j = 1, ..., nAi, j",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il throughput è definito come il numero di processi completati per unità di tempo",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "I puntatori a tripla indirezione di un i-node vengono usati solo se la dimensione del file lo richiede",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "Ad ogni file effettivamente memorizzato su disco può essere associato un solo numero di i-node",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "94) UNSAFE Quale delle seguenti affermazioni sul modello dei processi a 7 stati è vera?",
+    "answers": [
+      {
+        "answer": "Nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "Gli stati Ready, New e Blocked del modello a 5 stati vengono sdoppiati, e ne viene creata una versione Suspend",
+        "image": ""
+      },
+      {
+        "answer": "Un processo è Suspend quando scade il timeout del dispatcher",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Una traccia di un disco è l'area compresa tra 2 raggi del disco stesso",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni è corretta",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "98) UNSAFE Assumendo un sistema monoprocessore, quale delle seguenti affermazioni sugli algoritmi di scheduling è vera?",
+    "answers": [
+      {
+        "answer": "L'exponential averaging permette di stimare la dimensione dell'immagine di un processo, a partire dalle precedenti immagini di quello stesso processo",
+        "image": ""
+      },
+      {
+        "answer": "La funzione di decisione dello scheduler Highest Response Ratio Next considera tanto il tempo di esecuzione stimato quanto il tempo trascorso in attesa",
+        "image": ""
+      },
+      {
+        "answer": "L'exponential averaging è una tecnica applicabile dal solo scheduler Short Process Next",
+        "image": ""
+      },
+      {
+        "answer": "La funzione di decisione dello scheduler Shortest Remaining Time considera tanto il tempo di esecuzione richiesto quanto il tempo trascorso in attesa",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "100) Quale delle seguenti affermazioni è vera sulla memoria virtuale con paginazione a segmentazione?",
+    "answers": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Un indirizzo virtuale contiene anche un bit per indicare se la pagina corrispondente è o no in memoria principale",
+        "image": ""
+      },
+      {
+        "answer": "La tabella delle pagine di un processo contiene una pagina speciale dove è memorizzato il process control block del processo stesso",
+        "image": ""
+      },
+      {
+        "answer": "Ogni entry di una tabella delle pagine contiene un numero di pagina ed un offset",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "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?",
+    "answers": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Per avere un overhead accettabile, occorre demandare all'hardware la traduzione degli indirizzi ed il prelievo, il posizionamento e la sostituzione delle pagine",
+        "image": ""
+      },
+      {
+        "answer": "Per avere un overhead accettabile, occorre demandare al software anche la traduzione degli indirizzi",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "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? ",
+    "answers": [
+      {
+        "answer": "I sistemi time-sharing puntavano a minimizzare l'uso del processore",
+        "image": ""
+      },
+      {
+        "answer": "Nei sistemi time-sharing, le direttive al sistema operativo arrivavano dai comandi digitati su terminali",
+        "image": ""
+      },
+      {
+        "answer": "Nei sistemi batch, le direttive al sistema operativo arrivavano dai comandi del job control language, che erano non-interattivi",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling a feedback classico di Unix",
+        "image": ""
+      },
+      {
+        "answer": "Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling Virtual Round-Robin",
+        "image": ""
+      },
+      {
+        "answer": "Non ci sono sufficienti informazioni per determinare come si comporterebbe l'algoritmo di scheduling Round-Robin",
+        "image": ""
+      },
+      {
+        "answer": "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, fino a che non terminano tutti. Quale delle seguenti affermazioni è falsa?",
+    "answers": [
+      {
+        "answer": "Gli unici 2 processi che non sono serviti subito (ovvero, appena arrivati) sono P3 e P5",
+        "image": ""
+      },
+      {
+        "answer": "Il tempo medio di attesa è tra 10 ed 11 ms",
+        "image": ""
+      },
+      {
+        "answer": "Il processo con il più lungo tempo di attesa è P1",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "La soluzione non implementa correttamente la mutua esclusione, in quanto key deve essere una variabile globale",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "La soluzione implementa correttamente la mutua esclusione",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": "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"
+  },
+  {
+    "quest": "103) Quale delle seguenti affermazioni sulla memoria virtuale con paginazione è falsa?",
+    "answers": [
+      {
+        "answer": "Diminuire la dimensione delle pagine ha effetti positivi sul numero di pagine che possono trovarsi in memoria principale",
+        "image": ""
+      },
+      {
+        "answer": "Aumentare la dimensione delle pagine ha effetti positivi sulla frammentazione interna",
+        "image": ""
+      },
+      {
+        "answer": "Diminuire la dimensione delle pagine ha effetti negativi sulla dimensione della tabella delle pagine",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "La disabilitazione delle interruzioni impedisce la creazione di nuove interruzioni",
+        "image": ""
+      },
+      {
+        "answer": "Se un processo utente può disabilitare le interruzioni tramite un'istruzione macchina dedicata, allora può far diminuire l'uso utile del processore",
+        "image": ""
+      },
+      {
+        "answer": "La disabilitazione delle interruzioni non funziona ai fini della concorrenza (gestione sezioni critiche) su sistemi con più processori o più core",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "il kernel rimane in memoria durante l'intera sessione del computer",
+        "image": ""
+      },
+      {
+        "answer": "il kernel è costituito da vari moduli che non possono essere caricati nel sistema operativo in esecuzione",
+        "image": ""
+      },
+      {
+        "answer": "il kernel è la prima parte del sistema operativo a essere caricata in memoria durante l'avvio",
+        "image": ""
+      },
+      {
+        "answer": "Il kernel è il programma che costituisce il nucleo centrale del sistema operativo.",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "106) UNSAFE In generale, la CPU puo’ eseguire un'istruzione soltanto quando gli operandi si trovano:",
+    "answers": [
+      {
+        "answer": "In RAM, o in un livello qualsiasi della cache o nella memoria secondaria o nei registri CPU",
+        "image": ""
+      },
+      {
+        "answer": "In RAM o in un livello qualsiasi della cache o nei registri CPU",
+        "image": ""
+      },
+      {
+        "answer": "Nella cache di livello 1 (L1 cache) o nei registri CPU",
+        "image": ""
+      },
+      {
+        "answer": "Nei registri della CPU",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "107) Il PCB (Process Control Block) e’:",
+    "answers": [
+      {
+        "answer": "Un campo dello stato di un processo che definisce quali operazioni di controllo dei dispositivi a blocchi sono state fatte dal processo",
+        "image": ""
+      },
+      {
+        "answer": "Una struttura dati mantenuta dal sistema operativo che contiene tutte le informazioni necessarie all’esecuzione, sospensione e ripresa dell’esecuzione di un processo",
+        "image": ""
+      },
+      {
+        "answer": "Una struttura dati mantenuta dal sistema operativo che contiene l’intera immagine di un processo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il sistema operativo deve effettuare un process switch ed un mode switch per eseguire la funzione richiesta",
+        "image": ""
+      },
+      {
+        "answer": "Il sistema operativo deve effettuare soltanto un process switch per eseguire la funzione richiesta",
+        "image": ""
+      },
+      {
+        "answer": "Il sistema operativo deve effettuare soltanto un mode switch per eseguire la funzione richiesta",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il dispatcher e’ una componente del medium term scheduler",
+        "image": ""
+      },
+      {
+        "answer": "Il dispatcher si occupa di decidere l’ordine di sospensione dei processi",
+        "image": ""
+      },
+      {
+        "answer": "Il dispatcher si occupa di scambiare i processi in esecuzione sulla CPU (process switch)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il sistema operativo copia immediatamente il contenuto della scrittura dalla memoria del processo direttamente alla memoria del dispositivo di I/O",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Il sistema operativo copia quando piu’ opportuno il contenuto della scrittura dalla memoria del processo direttamente alla memoria del dispositivo di I/O",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre opzioni e’ corretta",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "111) L’algoritmo di scheduling C-SCAN:",
+    "answers": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Puo’ portare a starvation per alcuni processi",
+        "image": ""
+      },
+      {
+        "answer": "E’ meno fair (equo) dell’algoritmo SCAN",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Ogni utente dev’essere in grado di creare, cancellare, leggere, scrivere e modificare un file",
+        "image": ""
+      },
+      {
+        "answer": "Ogni utente deve poter accedere, in modo controllato, ai file di un altro utente",
+        "image": ""
+      },
+      {
+        "answer": "Ogni utente deve poter mantenere una copia di backup dei propri file",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le opzioni sono requisiti",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "113) Una sezione critica è un segmento di programma:",
+    "answers": [
+      {
+        "answer": "Che e’ racchiuso tra una coppia di operazioni di semaforo semWait e semSignal",
+        "image": ""
+      },
+      {
+        "answer": "In cui si accede a risorse condivise",
+        "image": ""
+      },
+      {
+        "answer": "Che evita i deadlock",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Garantisce la non-starvation",
+        "image": ""
+      },
+      {
+        "answer": "Non richiede nessun supporto dal SO.",
+        "image": ""
+      },
+      {
+        "answer": "Richiede supporto dal SO",
+        "image": ""
+      },
+      {
+        "answer": "E' deterministico.",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "115) Quale delle affermazioni è vera riguardo al Translation lookaside buffer per la gestione della memoria?",
+    "answers": [
+      {
+        "answer": "Nel Translation lookaside buffer ci sono tag e chiavi con l'aiuto dei quali viene effettuata la mappatura.",
+        "image": ""
+      },
+      {
+        "answer": "Il TLB hit è una condizione in cui la voce desiderata viene trovata nel TLB.",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Tutte le opzioni sono vere.",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "116) Quale delle seguenti affermazioni sul long-term scheduler e’ vera:",
+    "answers": [
+      {
+        "answer": "Si occupa della decisione di quali processi debbano essere ammessi all’esecuzione nel sistema",
+        "image": ""
+      },
+      {
+        "answer": "Si occupa dell’organizzazione di lungo termine dell’ordine di esecuzione dei processi nella CPU",
+        "image": ""
+      },
+      {
+        "answer": "Si occupa dell’implementazione della funzione di swapping dei processi alla memoria secondaria",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Un processo puo’ essere spostato allo stato suspended dallo stato blocked e ready",
+        "image": ""
+      },
+      {
+        "answer": "Un processo puo’ essere spostato dallo stato running allo stato ready o exit",
+        "image": ""
+      },
+      {
+        "answer": "Un processo puo’ essere spostato dallo stato blocked solo allo stato ready",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "I/O programmato, perche’ consente al programmatore di fare uno scheduling esatto delle operazioni di I/O nei momenti piu’ opportuni",
+        "image": ""
+      },
+      {
+        "answer": "I/O basato su DMA (Accesso Diretto alla Memoria), perche’ la CPU deve soltanto occuparsi del trasferimento dei dati",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "A richiede ed ottiene accesso ad R1, B richiede accesso ad R2. A richiede accesso ad R2. B richiede accesso ad R1",
+        "image": ""
+      },
+      {
+        "answer": "A richiede ed ottiene accesso ad R2, B richiede accesso ad R1 ed R2. A richiede ed ottiene accesso ad R1",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la preallocazione è più efficiente nell'utilizzo dello spazio su disco",
+        "image": ""
+      },
+      {
+        "answer": "nessuna delle opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "NTFS può, ove possibile, includere direttamente i dati di un file nella master file table",
+        "image": ""
+      },
+      {
+        "answer": "NTFS non prevede la possibilità di avere record estesi",
+        "image": ""
+      },
+      {
+        "answer": "nessuna delle altre opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "In un sistema a rilocazione con indirizzi logici, non è necessario avere hardware dedicato per effettuare la rilocazione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il SO impiega la maggior parte del suo tempo a swappare pezzi di processi, anziché ad eseguire istruzioni",
+        "image": ""
+      },
+      {
+        "answer": "provoca il deterioramento o il crollo delle prestazioni del computer",
+        "image": ""
+      },
+      {
+        "answer": "quasi ogni richiesta di pagine da luogo ad una page fault",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le opzioni sono vere",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "124) Il sistema di partizionamento fisso per la memoria principale:",
+    "answers": [
+      {
+        "answer": "Permette di avere partizioni di lunghezza diversa e di modificarle a runtime",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "Consente una efficiente della memoria se ci sono molti processi di piccole dimensioni ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Semplice da effettuare",
+        "image": ""
+      },
+      {
+        "answer": "Versatilità",
+        "image": ""
+      },
+      {
+        "answer": "Velocità di computazione in real time degli hash",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il tipo di processi che gestiscono",
+        "image": ""
+      },
+      {
+        "answer": "La frequenza di esecuzione",
+        "image": ""
+      },
+      {
+        "answer": "La lunghezza delle loro code",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle opzioni è corretta",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "127) Quale dei seguenti NON è un vantaggio della multiprogrammazione?",
+    "answers": [
+      {
+        "answer": "Riduzione dei tempi di risposta",
+        "image": ""
+      },
+      {
+        "answer": "Possibilità di assegnare priorità ai lavori",
+        "image": ""
+      },
+      {
+        "answer": "Aumento del throughput",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Lo stack del processo",
+        "image": ""
+      },
+      {
+        "answer": "Il bus di sistema",
+        "image": ""
+      },
+      {
+        "answer": "Nessuno ",
+        "image": ""
+      },
+      {
+        "answer": "Program Counter",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "129) Quale dei seguenti NON è un valido schema di prevenzione del deadlock?",
+    "answers": [
+      {
+        "answer": "Rilasciare tutte le risorse prima di richiederne una nuova",
+        "image": ""
+      },
+      {
+        "answer": "Non chiedere mai una risorsa dopo averne rilasciate altre",
+        "image": ""
+      },
+      {
+        "answer": "Si definisce un ordinamento crescente delle risorse, una risorsa viene data solo se esegue quelle che il processo già detiene",
+        "image": ""
+      },
+      {
+        "answer": "Richiedere e allocare tutte le risorse necessarie prima dell’esecuzione",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "130) UNSAFE Quale dei seguenti NON è vero riguardo l’algoritmo di Dekker per gestire la concorrenza?",
+    "answers": [
+      {
+        "answer": "Non usa busy waiting",
+        "image": ""
+      },
+      {
+        "answer": "Garantisce la non-starvation",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le opzioni elencate",
+        "image": ""
+      },
+      {
+        "answer": "Garantisce il non-deadlock",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "131) Quale delle seguenti non è una tabella di controllo del sistema operativo?",
+    "answers": [
+      {
+        "answer": "Tabella dei processi sospesi",
+        "image": ""
+      },
+      {
+        "answer": "Tabelle di memoria",
+        "image": ""
+      },
+      {
+        "answer": "Tabelle di controllo di accesso",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "La cpu sospende l’esecuzione del codice corrente, ed avvia l’handler del nuovo interrupt ricevuto",
+        "image": ""
+      },
+      {
+        "answer": "La cpu completa l’esecuzione del codice corrente, e successivamente avvia l’handler del nuovo interrupt ricevuto",
+        "image": ""
+      },
+      {
+        "answer": "La cpu gestisce entrambi gli handler in parallelo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Produzione",
+        "image": ""
+      },
+      {
+        "answer": "Throughput",
+        "image": ""
+      },
+      {
+        "answer": "Capacità",
+        "image": ""
+      },
+      {
+        "answer": "Nessuno",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "134) Quale dei seguenti sono obiettivi per un file Management System?",
+    "answers": [
+      {
+        "answer": "Tutte le opzioni elencate",
+        "image": ""
+      },
+      {
+        "answer": "Fornire supporto per l’I/O da più utenti in contemporanea",
+        "image": ""
+      },
+      {
+        "answer": "Minimizzare i dati persi o distrutti",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "La tabella di allocazione contiene l'indirizzo del primo blocco del file, e ciascun blocco contiene l'indirizzo del prossimo blocco del file",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "L'algoritmo SCAN può portare a starvation delle richieste",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "L'algoritmo Minimo Tempo di Servizio non richiede di conoscere la posizione della testina del disco per operare",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Short term scheduling",
+        "image": ""
+      },
+      {
+        "answer": "Long term scheduling",
+        "image": ""
+      },
+      {
+        "answer": "Disk scheduling",
+        "image": ""
+      },
+      {
+        "answer": "File scheduling",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "138) Nei sistemi operativi che usano paginazione SEMPLICE per la gestione della memoria",
+    "answers": [
+      {
+        "answer": "ai processi devono essere allocati frame di memoria necessariamente contigui per poter consentire l'esecuzione del processo",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "nessuna delle altre opzioni è corretta",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "139) Nei sistemi operativi che usano journaling logico",
+    "answers": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "nessuna delle opzioni è corretta",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "140) Il sistema operativo linux per la gestione dei file",
+    "answers": [
+      {
+        "answer": "nessuna delle altre opzioni è corretta",
+        "image": ""
+      },
+      {
+        "answer": "utilizza un sistema misto di allocazione contigua e concatenata in modo da minimizzare l'overhead di sistema e massimizzare le performance",
+        "image": ""
+      },
+      {
+        "answer": "utilizza un sistema di allocazione concatenata basato sulla struttura dati conosciuta come inode",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "possono esistere hard link a file non più esistenti, ad esempio se il file a cui l'hard link puntava viene cancellato",
+        "image": ""
+      },
+      {
+        "answer": "nessuna delle altre risposte è corretta",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  }
+]
\ No newline at end of file
diff --git a/data/questions/so1_new.json b/data/questions/so1_new.json
new file mode 100644
index 0000000..baeb7d3
--- /dev/null
+++ b/data/questions/so1_new.json
@@ -0,0 +1,2927 @@
+[
+  {
+    "quest": "Il sistema operativo",
+    "answers": [
+      {
+        "answer": "Coincide con il kernel",
+        "image": ""
+      },
+      {
+        "answer": "Costituisce l'interfaccia tra la macchina fisica (hardware) e le applicazioni utente",
+        "image": ""
+      },
+      {
+        "answer": "È soggetto alle politiche di scheduling",
+        "image": ""
+      },
+      {
+        "answer": "Risiede in memoria principale anche in seguito allo shutdown della macchina",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "In un sistema operativo microkernel",
+    "answers": [
+      {
+        "answer": "Alcune delle funzionalità sono implementate in spazio utente anziché all'interno del kernel",
+        "image": ""
+      },
+      {
+        "answer": "I processi utente possono interagire direttamente con il sistema,evitando l'uso di system call",
+        "image": ""
+      },
+      {
+        "answer": "La comunicazione tra le varie componenti del sistema è più efficiente",
+        "image": ""
+      },
+      {
+        "answer": "Non sono previsti meccanismi di protezione  ",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "In un sistema operativo strutturato secondo un approccio microkernel",
+    "answers": [
+      {
+        "answer": "Non necessita di avere due modalità di utilizzo della CPU (user vs.kernel mode)",
+        "image": ""
+      },
+      {
+        "answer": "Non necessita di meccanismi di comunicazione tra porzioni diverse del sistema operativo",
+        "image": ""
+      },
+      {
+        "answer": "E' più efficiente di un sistema monolitico",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Sono composte da un codice operativo e da zero o più operandi",
+        "image": ""
+      },
+      {
+        "answer": "Sono definite da uno specifico linguaggio macchina",
+        "image": ""
+      },
+      {
+        "answer": "Sono un'astrazione dell'architettura hardware",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le risposte precedenti sono corrette",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "I registri interni della CPU e la cache sono unità di memoria:",
+    "answers": [
+      {
+        "answer": "Non volatili",
+        "image": ""
+      },
+      {
+        "answer": "Gestite interamente dall'architettura a livello hardware",
+        "image": ""
+      },
+      {
+        "answer": "Gestite interamente dal sistema operativo",
+        "image": ""
+      },
+      {
+        "answer": "Molto economiche e altamente performanti",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "La transizione da user a kernel mode avviene quando:",
+    "answers": [
+      {
+        "answer": "Un programma esegue una chiamata di funzione",
+        "image": ""
+      },
+      {
+        "answer": "Si avvia il computer (bootstrap)",
+        "image": ""
+      },
+      {
+        "answer": "Si esegue la prima istruzione di un programma",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Contiene dei registri che ne indicano lo stato",
+        "image": ""
+      },
+      {
+        "answer": "Contiene dei registri che ne consentono il controllo da parte della CPU",
+        "image": ""
+      },
+      {
+        "answer": "Contiene dei registri per lo scambio di dati con la CPU",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le risposte precedenti sono corrette",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "Le chiamate di sistema:",
+    "answers": [
+      {
+        "answer": "Sono sempre bloccanti",
+        "image": ""
+      },
+      {
+        "answer": "Causano la terminazione del processo in corso e l'avvio di un nuovo processo",
+        "image": ""
+      },
+      {
+        "answer": "Devono essere implementate in spazio utente",
+        "image": ""
+      },
+      {
+        "answer": "Devono essere implementate in spazio kernel",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "Una chiamata di sistema bloccante",
+    "answers": [
+      {
+        "answer": "Sposta in coda pronti (ready) il processo che la esegue",
+        "image": ""
+      },
+      {
+        "answer": "Interrompe definitivamente il processo che la esegue",
+        "image": ""
+      },
+      {
+        "answer": "Interrompe temporaneamente il processo che la esegue",
+        "image": ""
+      },
+      {
+        "answer": "Necessità che il processo che la esegue ne verifichi periodicamente l'esito (polling)",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "Il system call handler:",
+    "answers": [
+      {
+        "answer": "È invocato dallo scheduler del sistema operativo",
+        "image": ""
+      },
+      {
+        "answer": "Viene invocato alla scadenza del quanto temporale",
+        "image": ""
+      },
+      {
+        "answer": "Viene eseguito in spazio utente",
+        "image": ""
+      },
+      {
+        "answer": "Gestisce le chiamate di sistema tramite la system call table",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": " Il codice generico del system call handler:",
+    "answers": [
+      {
+        "answer": "Viene eseguito in spazio utente",
+        "image": ""
+      },
+      {
+        "answer": "È indicizzato tramite la interrupt vector table (IVT)",
+        "image": ""
+      },
+      {
+        "answer": "Viene invocato alla scadenza del quanto temporale",
+        "image": ""
+      },
+      {
+        "answer": "Viene invocato dallo scheduler del sistema operativo",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "L'interrupt vector table(IVT):",
+    "answers": [
+      {
+        "answer": "Si aggiorna dinamicamente ad ogni interruzione",
+        "image": ""
+      },
+      {
+        "answer": "E' una struttura dati che contiene puntatori ai vari gestori(handler) delle interruzioni",
+        "image": ""
+      },
+      {
+        "answer": "E' una struttura dati che è associata a ciascun processo",
+        "image": ""
+      },
+      {
+        "answer": "E' una struttura dati che contiene puntatori a codici di errori",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "La system-call table:",
+    "answers": [
+      {
+        "answer": "Contiene tante entry quanto sono le chiamate di sistema supportare",
+        "image": ""
+      },
+      {
+        "answer": "Contiene tante entry quante sono le interruzioni supportare",
+        "image": ""
+      },
+      {
+        "answer": "Contiene tante entry quanti sono i dispositivi di I/O presenti nel sistema",
+        "image": ""
+      },
+      {
+        "answer": "Contiene tante entry quanti sono i processi in esecuzione",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "La system-call table è una struttura dati gestita:",
+    "answers": [
+      {
+        "answer": "Dai dispositivi di I/O",
+        "image": ""
+      },
+      {
+        "answer": "Dal processo utente",
+        "image": ""
+      },
+      {
+        "answer": "Sia dal kernel del sistema operativo che dal processo utente",
+        "image": ""
+      },
+      {
+        "answer": "Dal kernel del sistema operativo",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "Se si cambia l'implementazione di una chiamata di sistema esistente:",
+    "answers": [
+      {
+        "answer": "E' sempre necessario modificare il codice utente che ne fa uso",
+        "image": ""
+      },
+      {
+        "answer": "Non è mai necessario modificare il codice utente che ne fa uso",
+        "image": ""
+      },
+      {
+        "answer": "Non è necessario modificare il codice utente che ne fa uso, a patto che cambi anche l'interfaccia (API) della chiamata di sistema",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "1*10^3",
+        "image": ""
+      },
+      {
+        "answer": "Decido di NON rispondere a questa domanda",
+        "image": ""
+      },
+      {
+        "answer": "25*10^3",
+        "image": ""
+      },
+      {
+        "answer": "1*10^6",
+        "image": ""
+      },
+      {
+        "answer": "25*10^6",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "Data una CPU multicore con 𝑚unità(cores), il numero di processi/thread che ad un certo istante si trovano nella “coda” di esecuzione(running):",
+    "answers": [
+      {
+        "answer": "Può essere superiore a 𝑚",
+        "image": ""
+      },
+      {
+        "answer": "E’ esattamente pari a 𝑚",
+        "image": ""
+      },
+      {
+        "answer": "I dati sono insufficienti per rispondere alla domanda",
+        "image": ""
+      },
+      {
+        "answer": "E' al massimo pari a 𝑚",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "La creazione di un nuovo processo da parte di un processo avviene tramite:",
+    "answers": [
+      {
+        "answer": "Una chiamata di sistema",
+        "image": ""
+      },
+      {
+        "answer": "Una chiamata di funzione",
+        "image": ""
+      },
+      {
+        "answer": "L'invio di un interruzione",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle risposte precedenti è corretta",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "Il sistema operativo tiene traccia dello stato di un processo tramite:",
+    "answers": [
+      {
+        "answer": "Un'apposita area dedicata e protetta della memoria principale",
+        "image": ""
+      },
+      {
+        "answer": "Un apposito registro interno della CPU",
+        "image": ""
+      },
+      {
+        "answer": "Un'apposita area dedicata e protetta della memoria cache",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Riceve un segnale di interruzione da parte di un dispositivo di I/O",
+        "image": ""
+      },
+      {
+        "answer": "Fa richiesta di input da parte dell’utente",
+        "image": ""
+      },
+      {
+        "answer": "Fa richiesta di una pagina che non è presente in memoria principale",
+        "image": ""
+      },
+      {
+        "answer": "Esegue una chiamata di funzione",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "Un processo in esecuzione sulla CPU passa in stato waiting quando:",
+    "answers": [
+      {
+        "answer": "Riceve un segnale da parte di un dispositivo di I/O",
+        "image": ""
+      },
+      {
+        "answer": "Termina il quanto di tempo ad esso assegnato",
+        "image": ""
+      },
+      {
+        "answer": "Apre una connessione di rete (ad es., un socket TCP)",
+        "image": ""
+      },
+      {
+        "answer": "Esegue una chiamata di funzione",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "Un processo in esecuzione sulla CPU passa in stato waiting quando:",
+    "answers": [
+      {
+        "answer": "Fa richiesta di input da parte dell'utente",
+        "image": ""
+      },
+      {
+        "answer": "Esegue una chiamata di funzione",
+        "image": ""
+      },
+      {
+        "answer": "Termina il quanto di tempo ad esso assegnato",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Termina il quanto di tempo ad esso assegnato",
+        "image": ""
+      },
+      {
+        "answer": "L'utente trascina il dispositivo di puntamento(e.g. mouse)",
+        "image": ""
+      },
+      {
+        "answer": "Esegue una chiamata di funzione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "8",
+        "image": ""
+      },
+      {
+        "answer": "7",
+        "image": ""
+      },
+      {
+        "answer": "4",
+        "image": ""
+      },
+      {
+        "answer": "3",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "I processi CPU-bound che non eseguono richieste di I/O:",
+    "answers": [
+      {
+        "answer": "Hanno una priorità alta",
+        "image": ""
+      },
+      {
+        "answer": "Hanno una priorità bassa",
+        "image": ""
+      },
+      {
+        "answer": "Sono processi mediamente brevi",
+        "image": ""
+      },
+      {
+        "answer": "Possono non rilasciare mai la CPU volontariamente",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "Lo scheduler della CPU si attiva:",
+    "answers": [
+      {
+        "answer": "Quando un processo tenta di eseguire una scrittura su discord",
+        "image": ""
+      },
+      {
+        "answer": "Quando il codice di un programma esegue una divisione per zero",
+        "image": ""
+      },
+      {
+        "answer": "Quando scade il quanto di tempo",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le risposte precedenti sono corrette",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "Lo scheduling preemptive(basato su time slice o quanto temporale):",
+    "answers": [
+      {
+        "answer": "Da la priorità ai processi CPU-bound",
+        "image": ""
+      },
+      {
+        "answer": "Si attiva solamenta alla scadenza del quanto temporale(time slice)",
+        "image": ""
+      },
+      {
+        "answer": "Si attiva solamente a fronte di una chiamata di sistema",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "L'impiego dei multi-threading consente di migliorare la latenza del sistema",
+        "image": ""
+      },
+      {
+        "answer": "L'impiego del multi-threading consente di diminuire il tempo di completamente di ciascun processo",
+        "image": ""
+      },
+      {
+        "answer": "L'impiego del multi-threading consente di migliorare il throughput del sistema",
+        "image": ""
+      },
+      {
+        "answer": "L'impiego dei multi-threading non costituisce alcun vantaggio",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "In caso di scheduling preemptive, lo scheduler interviene:",
+    "answers": [
+      {
+        "answer": "Quando un processo passa dallo stato running allo stato waiting",
+        "image": ""
+      },
+      {
+        "answer": "Quando un processo passa dallo stato running allo stato ready",
+        "image": ""
+      },
+      {
+        "answer": "Quando un processo passa dallo stato waiting allo stato ready",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "13",
+        "image": ""
+      },
+      {
+        "answer": "2",
+        "image": ""
+      },
+      {
+        "answer": "I dati sono insufficienti per rispondere alla domanda",
+        "image": ""
+      },
+      {
+        "answer": "15",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "Se un processo arriva nella coda dei pronti all’istante 𝑡0 = 3 e termina all’istante 𝑡𝑓 = 25, il tempo di attesa equivale a",
+    "answers": [
+      {
+        "answer": "3",
+        "image": ""
+      },
+      {
+        "answer": "22",
+        "image": ""
+      },
+      {
+        "answer": "25",
+        "image": ""
+      },
+      {
+        "answer": "I dati sono insufficienti per rispondere alla domanda",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "I thread di uno stesso processo condividono:",
+    "answers": [
+      {
+        "answer": "Lo stack",
+        "image": ""
+      },
+      {
+        "answer": "Le variabili globali",
+        "image": ""
+      },
+      {
+        "answer": "I valori dei registri della CPU",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle informazioni elencate sopra",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "Lo user thread:",
+    "answers": [
+      {
+        "answer": "Necessita del supporto di una opportuna thread table a livello kernel",
+        "image": ""
+      },
+      {
+        "answer": "E' la più piccola unità schedulabile sulla CPU dal sistema operativo",
+        "image": ""
+      },
+      {
+        "answer": "E' gestito in spazio utente tramite un'apposita libreria",
+        "image": ""
+      },
+      {
+        "answer": "Coincide sempre con uno ed un solo kernel thread",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "Nel modello di thread mapping cosiddetto one-to-one:",
+    "answers": [
+      {
+        "answer": "Consente di gestire i thread tramite un'apposita libreria a livello utente",
+        "image": ""
+      },
+      {
+        "answer": "Può essere implementato solo su sistemi multiprocessore",
+        "image": ""
+      },
+      {
+        "answer": "Causa il blocco di tutti i thread di un processo se anche uno solo di questi thread esegue una chiamata di sistema bloccante",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Molti user thread possono essere distribuiti su più CPU (se presenti)",
+        "image": ""
+      },
+      {
+        "answer": "L'effetto di una chiamata bloccante da parte di uno user thread non blocca gli altri thread da cui è composto il processo",
+        "image": ""
+      },
+      {
+        "answer": "Molti user thread sono mappati su un singolo kernel thread",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Non prevede alcun limite al numero di kernel thread",
+        "image": ""
+      },
+      {
+        "answer": "Può essere implementato solo su sistemi multiprocessore",
+        "image": ""
+      },
+      {
+        "answer": "Causa il blocco di tutti i thread di un processo se anche uno solo di questi thread esegue una chiamata di sistema bloccante",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Vengono eseguiti processi single-threaded su CPU multicore",
+        "image": ""
+      },
+      {
+        "answer": "Vengono eseguiti processi multi-threaded su CPU single core",
+        "image": ""
+      },
+      {
+        "answer": "Vengono eseguiti processi multi-threaded su CPU multicore",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le risposte precedenti sono corrette",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "Si parla di concorrenza quando:",
+    "answers": [
+      {
+        "answer": "Vengono eseguiti processi multi-threaded su CPU single core",
+        "image": ""
+      },
+      {
+        "answer": "Vengono eseguiti processi single-threaded su CPU single core",
+        "image": ""
+      },
+      {
+        "answer": "Vengono eseguiti processi single-threaded su CPU multicore",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "È più lenta poiché i thread sono gestiti da librerie di alto livello",
+        "image": ""
+      },
+      {
+        "answer": "È più veloce poiché i thread non eseguono context switch",
+        "image": ""
+      },
+      {
+        "answer": "È più veloce poiché i thread condividono lo stesso spazio di indirizzamento",
+        "image": ""
+      },
+      {
+        "answer": "Non c'è alcuna differenza sostanziale in termini di performance",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "Il kernel thread:",
+    "answers": [
+      {
+        "answer": "Coincide sempre con uno ed un solo user thread",
+        "image": ""
+      },
+      {
+        "answer": "È gestito in spazio utente tramite un'apposita libreria",
+        "image": ""
+      },
+      {
+        "answer": "È la più piccola unità schedulabile sulla CPU dal sistema operativo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "La lock sia inizialmente libera",
+        "image": ""
+      },
+      {
+        "answer": "La lock venga acquisita prima dell'ingresso nella sezione critica",
+        "image": ""
+      },
+      {
+        "answer": "La lock venga rilasciata dopo l'uscita dalla sezione critica",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le condizioni precedenti devono essere verificate",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "L'acquisizione di una lock:",
+    "answers": [
+      {
+        "answer": "Deve avvenire in modo atomico, evitando che lo scheduler interrompa l'acquisizione",
+        "image": ""
+      },
+      {
+        "answer": "Necessita obbligatoriamente del supporto di istruzioni hardware atomiche",
+        "image": ""
+      },
+      {
+        "answer": "Necessita obbligatoriamente che il sistema operativo disabiliti le interruzioni",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle risposte precedenti è corretta",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "Un semaforo può essere utilizzato per:",
+    "answers": [
+      {
+        "answer": "Forzare le politiche di scheduling tra processi/thread",
+        "image": ""
+      },
+      {
+        "answer": "Accedere al codice del kernel",
+        "image": ""
+      },
+      {
+        "answer": "Lo scambio di messaggi tra processi/thread",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Decrementa il valore del semaforo a 1 e blocca il processo che ha eseguito l'invocazione",
+        "image": ""
+      },
+      {
+        "answer": "Incrementa il valore del semaforo a 3 e fa proseguire il processo che ha eseguito l'invocazione (al netto delle politiche di scheduling)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "È un'istruzione atomica che consente di implementare le primitive di sincronizzazione",
+        "image": ""
+      },
+      {
+        "answer": "È un'istruzione atomica che consente di disabilitare le interruzioni",
+        "image": ""
+      },
+      {
+        "answer": "È un'istruzione atomica che consente di aggiornare i valori di più registri simultaneamente",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Si riferiscono a codice utente e codice di sistema (rispettivamente)",
+        "image": ""
+      },
+      {
+        "answer": "Nel caso di starvation tutto il sistema è completamente bloccato",
+        "image": ""
+      },
+      {
+        "answer": "Non vi è alcuna differenza",
+        "image": ""
+      },
+      {
+        "answer": "Nel caso di deadlock tutto il sistema è completamente bloccato",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "Con il termine address binding si intende:",
+    "answers": [
+      {
+        "answer": "Il processo di traduzione da indirizzi logici a indirizzi fisici",
+        "image": ""
+      },
+      {
+        "answer": "Il processo di inizializzazione delle variabili globali di un programma",
+        "image": ""
+      },
+      {
+        "answer": "Il processo di collegamento tra il codice compilato ed eventuali librerie esterne",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle risposte precedenti è corretta",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "Lo swapping consente di:",
+    "answers": [
+      {
+        "answer": "Implementare la rilocazione dinamica del codice di un processo",
+        "image": ""
+      },
+      {
+        "answer": "Risolvere il problema della frammentazione esterna",
+        "image": ""
+      },
+      {
+        "answer": "Trasferire temporaneamente su disco i processi che non sono attualmente in esecuzione",
+        "image": ""
+      },
+      {
+        "answer": "Scambiare le aree di memoria occupate da due o più processi",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "La gestione 'paginata' della memoria (paging):",
+    "answers": [
+      {
+        "answer": "Prevede che lo spazio di indirizzamento logico di un processo sia non-contiguo e suddiviso in blocchi di dimensioni fissate (pages)",
+        "image": ""
+      },
+      {
+        "answer": "Non richiede alcun supporto hardware per essere implementata in modo efficiente",
+        "image": ""
+      },
+      {
+        "answer": "Prevede che lo spazio di indirizzamento fisico di un processo sia non-contiguo e suddiviso in blocchi di dimensioni fissate (frames)",
+        "image": ""
+      },
+      {
+        "answer": "Risolve il problema della frammentazione interna",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "La cache TLB (Translation Look-aside Buffer)",
+    "answers": [
+      {
+        "answer": "E' condivisa tra tutti i processi del sistema",
+        "image": ""
+      },
+      {
+        "answer": "Consente una traduzione mediamente più rapida degli indirizzi logici",
+        "image": ""
+      },
+      {
+        "answer": "Contiene un sottoinsieme delle entry della page table",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le risposte precedenti sono corrette",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "La dimensione (i.e., il numero di entry) della page table:",
+    "answers": [
+      {
+        "answer": "È direttamente proporzionale alla dimensione (fissata) delle pagine",
+        "image": ""
+      },
+      {
+        "answer": "Si adatta a seconda delle richieste di accesso alla memoria di ciascun processo",
+        "image": ""
+      },
+      {
+        "answer": "Dipende dalla dimensione (fissata) delle pagine",
+        "image": ""
+      },
+      {
+        "answer": "Varia dinamicamente a seconda del processo",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "La dimensione (i.e., il numero di entry) della page table:",
+    "answers": [
+      {
+        "answer": "Varia dinamicamente a seconda del processo",
+        "image": ""
+      },
+      {
+        "answer": "E' direttamente proporzionale alla dimensione (fissata)",
+        "image": ""
+      },
+      {
+        "answer": "E' inversamente proporzionale alla dimensione (fissata) delle pagine",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "576",
+        "image": ""
+      },
+      {
+        "answer": "552",
+        "image": ""
+      },
+      {
+        "answer": "600",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Allocare l'intero blocco al processo, sprecando 211 bytes(frammentazione interna)",
+        "image": ""
+      },
+      {
+        "answer": " Allocare la porzione del blocco necessaria al processo e aggiungere alla lista dei blocchi liberi i 211 bytes rimanente(frammentazione esterna)",
+        "image": ""
+      },
+      {
+        "answer": "Attendere che vi sia un blocco di dimensione multipla rispetto a quella del processo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Attendere che vi sia un blocco di dimensione inferiore adatto a contenere il processo",
+        "image": ""
+      },
+      {
+        "answer": "Allocare l'intero blocco al processo, sprecando 20 bytes(frammentazione interna)",
+        "image": ""
+      },
+      {
+        "answer": "Attendere che vi sia un blocco di dimensione multipla rispetto a quella dei processi",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "blocco A",
+        "image": ""
+      },
+      {
+        "answer": "blocco C",
+        "image": ""
+      },
+      {
+        "answer": "blocco B",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "blocco B",
+        "image": ""
+      },
+      {
+        "answer": "Non è possibile soddisfare la richiesta, pertanto P dovrà attendere",
+        "image": ""
+      },
+      {
+        "answer": "C e i restati 25 KiB vengono allocati su A",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "blocco A",
+        "image": ""
+      },
+      {
+        "answer": "blocco D",
+        "image": ""
+      },
+      {
+        "answer": "blocco B",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "blocco A",
+        "image": ""
+      },
+      {
+        "answer": "blocco F",
+        "image": ""
+      },
+      {
+        "answer": "blocco E",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "blocco B",
+        "image": ""
+      },
+      {
+        "answer": "blocco C e i restanti 25 Kib vengono allocati su A",
+        "image": ""
+      },
+      {
+        "answer": "blocco E",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "blocco A",
+        "image": ""
+      },
+      {
+        "answer": "blocco B",
+        "image": ""
+      },
+      {
+        "answer": "blocco C",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "p=6; offset=5",
+        "image": ""
+      },
+      {
+        "answer": "b.p=7; offset=5",
+        "image": ""
+      },
+      {
+        "answer": "p=5; offset=7",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "p=5; offset=12",
+        "image": ""
+      },
+      {
+        "answer": "I dati sono insufficienti per rispondere alla domanda",
+        "image": ""
+      },
+      {
+        "answer": "p=13; offset=0",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "p=3; offset=7",
+        "image": ""
+      },
+      {
+        "answer": "I dati sono insufficienti per rispondere alla domanda",
+        "image": ""
+      },
+      {
+        "answer": "p=7; offset=3",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Attendere che vi sia un blocco di dimensione multipla rispetto a quella del processo",
+        "image": ""
+      },
+      {
+        "answer": "Allocare l'intero blocco al processo, sprecando 7 bytes (frammentazione interna)",
+        "image": ""
+      },
+      {
+        "answer": "Attendere che vi sia un blocco di dimensione inferiore adatto a contenere il processo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "2",
+        "image": ""
+      },
+      {
+        "answer": "9",
+        "image": ""
+      },
+      {
+        "answer": "11",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "10",
+        "image": ""
+      },
+      {
+        "answer": "11",
+        "image": ""
+      },
+      {
+        "answer": "12",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "I dati sono insufficienti per rispondere al problema",
+        "image": ""
+      },
+      {
+        "answer": "13",
+        "image": ""
+      },
+      {
+        "answer": "64",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "32",
+        "image": ""
+      },
+      {
+        "answer": "2048",
+        "image": ""
+      },
+      {
+        "answer": "8",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "a",
+        "image": ""
+      },
+      {
+        "answer": "b",
+        "image": ""
+      },
+      {
+        "answer": "c",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "32 KiB",
+        "image": ""
+      },
+      {
+        "answer": "64 KiB",
+        "image": ""
+      },
+      {
+        "answer": "2 MiB",
+        "image": ""
+      },
+      {
+        "answer": "Non esiste un limite fisico alla memoria supportata dal sistema",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "La memoria virtuale consente di:",
+    "answers": [
+      {
+        "answer": "Aumentare l'efficienza delle operazioni di I/O",
+        "image": ""
+      },
+      {
+        "answer": "Mantenere allocate in memoria fisica solo alcune pagine dello spazio di indirizzamento logico di un processo",
+        "image": ""
+      },
+      {
+        "answer": "Diminuire il grado di multiprogrammazione del sistema",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il processo di cui fa parte l'istruzione termina",
+        "image": ""
+      },
+      {
+        "answer": "Le istruzioni idempotenti non possono generare page fault",
+        "image": ""
+      },
+      {
+        "answer": "L'istruzione non verrà più eseguita una volta effettuato il ritorno dalla gestione del page fault",
+        "image": ""
+      },
+      {
+        "answer": "L'istruzione verrà nuovamente eseguita al ritorno dalla gestione del page fault",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": ".Il problema della frammentazione esterna:",
+    "answers": [
+      {
+        "answer": "Necessita di un supporto hardware per essere risolto",
+        "image": ""
+      },
+      {
+        "answer": "Non è risolvibile a meno di un riavvio del sistema",
+        "image": ""
+      },
+      {
+        "answer": "E’ una conseguenza del vincolo di allocazione contigua della memoria",
+        "image": ""
+      },
+      {
+        "answer": "Causa un’interruzione hardware",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "Il problema della frammentazione esterna",
+    "answers": [
+      {
+        "answer": "Non è risolvibile a meno di un riavvio del sistema",
+        "image": ""
+      },
+      {
+        "answer": "Causa un’interruzione hardware",
+        "image": ""
+      },
+      {
+        "answer": "Necessita di un supporto hardware per essere risolto",
+        "image": ""
+      },
+      {
+        "answer": "E’ dovuto all’ allocazione/deallocazione di blocchi contigui di memoria",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "Il working set è:",
+    "answers": [
+      {
+        "answer": "Fissato per ogni quanto di tempo",
+        "image": ""
+      },
+      {
+        "answer": "Relativamente grande rispetto all’intero spazio di indirizzamento di un processo",
+        "image": ""
+      },
+      {
+        "answer": "Relativamente piccolo rispetto all’intero spazio di indirizzamento di un processo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Incrementare una variabile di tipo contatore",
+        "image": ""
+      },
+      {
+        "answer": "Aggiornare il valore del timestamp con quello corrente",
+        "image": ""
+      },
+      {
+        "answer": "Impostare un bit di validità",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "4",
+        "image": ""
+      },
+      {
+        "answer": "5",
+        "image": ""
+      },
+      {
+        "answer": "6",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "10",
+        "image": ""
+      },
+      {
+        "answer": "7",
+        "image": ""
+      },
+      {
+        "answer": "9",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "2",
+        "image": ""
+      },
+      {
+        "answer": "4",
+        "image": ""
+      },
+      {
+        "answer": "5",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "6",
+        "image": ""
+      },
+      {
+        "answer": "7",
+        "image": ""
+      },
+      {
+        "answer": "4",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "6",
+        "image": ""
+      },
+      {
+        "answer": "7",
+        "image": ""
+      },
+      {
+        "answer": "5",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "7",
+        "image": ""
+      },
+      {
+        "answer": "8",
+        "image": ""
+      },
+      {
+        "answer": "6",
+        "image": ""
+      },
+      {
+        "answer": "5",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "L'allocazione contigua di un file su disco:",
+    "answers": [
+      {
+        "answer": "È ottima sia per l'accesso diretto (random) che per quello sequenziale",
+        "image": ""
+      },
+      {
+        "answer": "Presenta il problema della frammentazione",
+        "image": ""
+      },
+      {
+        "answer": "Necessita il mantenimento dei blocchi liberi all'interno di una opportuna struttura dati",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Un CD/DVD-ROM in sola lettura",
+        "image": ""
+      },
+      {
+        "answer": "Un disco magnetico",
+        "image": ""
+      },
+      {
+        "answer": "Un disco a stato solido",
+        "image": ""
+      },
+      {
+        "answer": "In nessuno dei casi precedenti",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "In un disco magnetico, il seek time:",
+    "answers": [
+      {
+        "answer": "È il tempo necessario al disco per posizionare le proprie testine su uno specifico settore",
+        "image": ""
+      },
+      {
+        "answer": "Include il tempo di trasferimento alla memoria principale",
+        "image": ""
+      },
+      {
+        "answer": "È il tempo necessario al disco per posizionare le proprie testine su uno specifico cilindro",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "7.5 GB",
+        "image": ""
+      },
+      {
+        "answer": "75 GB",
+        "image": ""
+      },
+      {
+        "answer": "750 MB",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "~30.5 nsec",
+        "image": ""
+      },
+      {
+        "answer": "~30.5 microsec",
+        "image": ""
+      },
+      {
+        "answer": "~3.05 microsec",
+        "image": ""
+      },
+      {
+        "answer": "~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": [
+      {
+        "answer": "~150.025 microsec",
+        "image": ""
+      },
+      {
+        "answer": "~15.025 nsec",
+        "image": ""
+      },
+      {
+        "answer": "~150.025 nsec",
+        "image": ""
+      },
+      {
+        "answer": "~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": [
+      {
+        "answer": "~0.02%",
+        "image": ""
+      },
+      {
+        "answer": "~0.2%",
+        "image": ""
+      },
+      {
+        "answer": "~0.002%",
+        "image": ""
+      },
+      {
+        "answer": "~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": [
+      {
+        "answer": "I dati sono insufficienti per rispondere alla domanda",
+        "image": ""
+      },
+      {
+        "answer": "~0,00024%",
+        "image": ""
+      },
+      {
+        "answer": " ~0,000024%",
+        "image": ""
+      },
+      {
+        "answer": " ~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": [
+      {
+        "answer": "86",
+        "image": ""
+      },
+      {
+        "answer": "49",
+        "image": ""
+      },
+      {
+        "answer": "123",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "290",
+        "image": ""
+      },
+      {
+        "answer": "240",
+        "image": ""
+      },
+      {
+        "answer": "238",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "9.375 MB",
+        "image": ""
+      },
+      {
+        "answer": "7.5 MB",
+        "image": ""
+      },
+      {
+        "answer": "937.5 KB",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "9375 MB",
+        "image": ""
+      },
+      {
+        "answer": "70 MB",
+        "image": ""
+      },
+      {
+        "answer": "70 KB",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "7 msec",
+        "image": ""
+      },
+      {
+        "answer": "2 msec",
+        "image": ""
+      },
+      {
+        "answer": "16 msec",
+        "image": ""
+      },
+      {
+        "answer": "I dati sono insufficiente per rispondere alla domanda",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "La tabella globale dei file aperti (global open file table):",
+    "answers": [
+      {
+        "answer": "È condivisa tra tutti i processi",
+        "image": ""
+      },
+      {
+        "answer": "Contiene una entry per ciascun file in uso",
+        "image": ""
+      },
+      {
+        "answer": "Mantiene un contatore per ciascun file in uso",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le risposte precedenti sono corrette",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "La tabella locale dei file aperti (local open file table):",
+    "answers": [
+      {
+        "answer": "Contiene informazioni di protezione di ciascun file riferita da un processo",
+        "image": ""
+      },
+      {
+        "answer": "Contiene un puntatore alla locazione sul disco di ciascun file riferito da un processo",
+        "image": ""
+      },
+      {
+        "answer": "Contiene un puntatore alla tabella globale dei file aperti per ciascun file riferito da un processo",
+        "image": ""
+      },
+      {
+        "answer": "E’ condivisa tra più processi",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "Un possibile esempio di applicazione che necessita accesso sequenziale ad un file è:",
+    "answers": [
+      {
+        "answer": "Un compilatore",
+        "image": ""
+      },
+      {
+        "answer": "Un sistema di ricerca all'interno di una base di dati",
+        "image": ""
+      },
+      {
+        "answer": "Un sistema di ricerca di contatti telefonici",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "E’ di piccole dimensioni,indipendentemente dal modo in cui viene acceduto",
+        "image": ""
+      },
+      {
+        "answer": "E’ di grandi dimensioni, indipendentemente dal modo in cui viene acceduto",
+        "image": ""
+      },
+      {
+        "answer": "E’ di grandi dimensioni ed è tipicamente acceduto in modo sequenziale",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "E’ di piccolo dimensioni, indipendentemente dal modo in cui viene acceduto",
+        "image": ""
+      },
+      {
+        "answer": "E’ di grandi dimensioni ed è tipicamente acceduto in modo casuale(diretto)",
+        "image": ""
+      },
+      {
+        "answer": "E’ di grandi dimensioni, indipendentemente dal modo in cui viene acceduto",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Consente al solo proprietario del file di esercitare diritti di lettura ed esecuzione (sul file)",
+        "image": ""
+      },
+      {
+        "answer": "Consente al solo proprietario del file di esercitare diritti di scrittura ed esecuzione (sul file)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Consente al solo proprietario del file di esercitare diritti di lettura e scrittura (sul file)",
+        "image": ""
+      },
+      {
+        "answer": "Consente al solo proprietario del file di esercitare diritti di lettura ed esecuzione (sul file)",
+        "image": ""
+      },
+      {
+        "answer": "Non dà alcun diritto al proprietario del file",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Consente al proprietario del file di esercitare tutti i diritti(sul file) e fornisce agli altri utenti solo diritti di scrittura ed esecuzione",
+        "image": ""
+      },
+      {
+        "answer": "Consente al proprietario del file di esercitare tutti i diritti(sul file) e fornisce agli altri utenti solo diritti di lettura ed scrittura",
+        "image": ""
+      },
+      {
+        "answer": "Consente al proprietario del file di esercitare tutti i diritti(sul file) e fornisce agli altri utenti solo diritti di lettura ed esecuzione",
+        "image": ""
+      },
+      {
+        "answer": "Consente a chiunque di esercitare tutti i diritti (sul file)",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "Il comando UNIX ln file_1 file_2",
+    "answers": [
+      {
+        "answer": "Crea un hard link con il file file_2(sorgente) in cui nome è file_1(destinazione)",
+        "image": ""
+      },
+      {
+        "answer": "Crea un hard link con il file file_1(sorgente) il cui nome è file_2(destinazione)",
+        "image": ""
+      },
+      {
+        "answer": "Crea un soft link con il file file_1(sorgente) il cui nome è file_2(destinazione)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Crea un hard link con il file file_2(sorgente) in cui nome è file_1(destinazione)",
+        "image": ""
+      },
+      {
+        "answer": "Crea un hard link con il file file_1(sorgente) il cui nome è file_2(destinazione)",
+        "image": ""
+      },
+      {
+        "answer": "Crea un soft link con il file file_1(sorgente) il cui nome è file_2(destinazione)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "~20.2 KB",
+        "image": ""
+      },
+      {
+        "answer": "~20.2 MB",
+        "image": ""
+      },
+      {
+        "answer": "~20.7 KB",
+        "image": ""
+      },
+      {
+        "answer": "~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": [
+      {
+        "answer": "595",
+        "image": ""
+      },
+      {
+        "answer": "558",
+        "image": ""
+      },
+      {
+        "answer": "650",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "650",
+        "image": ""
+      },
+      {
+        "answer": "522",
+        "image": ""
+      },
+      {
+        "answer": "638",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "76",
+        "image": ""
+      },
+      {
+        "answer": "87",
+        "image": ""
+      },
+      {
+        "answer": "46",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "5",
+        "image": ""
+      },
+      {
+        "answer": "20",
+        "image": ""
+      },
+      {
+        "answer": "15",
+        "image": ""
+      },
+      {
+        "answer": "I dati sono insufficiente per rispondere alla domanda",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": "iVBORw0KGgoAAAANSUhEUgAAAxwAAAKyCAYAAABfZtgiAAAMP2lDQ1BJQ0MgUHJvZmlsZQAASImVVwdYU8kWnltSSUIJICAl9CaISAkgJYQWQHoRbIQkQCgxBoKIvSwquHaxgA1dFVHsgNgRO4ti74sFBWVdLNiVNymg677yvfm+ufPff87858y5M/feAUDzBF8qzUW1AMiTFMjiQgNZo1JSWaSngAYYgAJYwIAvyJdyYmIiASz97d/LuxsAUbRXnRRa/+z/r0VbKMoXAIDEQJwuzBfkQXwAALxSIJUVAEBU8JaTCqQKDCvQlcEAIZ6vwJkqXKnA6Sq8R2mTEMeFuBkAMo3Pl2UCwLgMeVahIBNqMHogdpEIxRIANFkQ++XlTRBCnAaxHbSRQqzQZ6f/oJP5N830AU0+P3MAq+aiLOQgcb40lz/5/0zH/y55ufJ+Hzaw0rJkYXGKOcO83cqZEKHANIi7JelR0RDrQPxBLFTaQ4xSs+RhiSp71FiQz4U5A/oQuwj5QREQG0McIsmNilTz6RniEB7EcIWgReICXgLEBhDPF+UHx6ttNsomxKl9oXUZMi5HzZ/jy5R+Fb4eyHMSOWr911kinlofYxRnJSRDTIXYqlCcFAUxA2Ln/Jz4CLXNiOIsblS/jUwep4jfCuI4kSQ0UKWPFWbIQuLU9qV5+f3zxTZmiXlRaryvICshTJUfrFnAV8YP54JdFkk4if06ovxRkf1zEYqCglVzxzpFksR4tc4HaUFgnGosTpXmxqjtcQtRbqiCt4DYLb8wXj0WTyqAC1Klj2dIC2ISVHHixdn88BhVPPgSEAm4IAjuODms6WACyAbi1u76bnin6gkBfCADmUAEnNRM/4hkZY8EXuNBMfgTIhHIHxgXqOwVgULIfx1gVVcnkKHsLVSOyAFPIc4DESAX3suVoyQD3pLAE8iI/+GdD6sAxpsLq6L/3/P97HeGA5lINSPv98jS7LckBhODiGHEEKI9boT74T54JLwGwOqKs3Gv/nl8tyc8JbQRHhGuE9oJt8eLZ8t+inIkaIf6IepcpP+YC9wGarrjgbgvVIfKuD5uBJxwN+iHg/tDz+6Q5arjVmSF9ZP232bww9NQ21FcKChlECWAYvfzSIYDw31ARZHrH/OjijV9IN/cgZ6f/XN/yL4QthE/W2Lzsf3YWewkdh47gtUDFnYca8BasKMKPLC6nihXV7+3OGU8OVBH/A9//U9Wkcl8lxqXLpcvqr4CUZHiHQ24E6STZeLMrAIWB34RRCyeROA8hOXq4uoKgOL7onp9vYlVfjcQ/Zbv3Jw/APA93tfXd/g7F34cgL2ecPsf+s7ZseGnQwOAc4cEclmhisMVFwJ8S2jCnWYITIElsIPzcQUewAcEgGAQDqJBAkgB42D0WXCdy8AkMBXMAiWgDCwBK8FasAFsBtvBLrAP1IMj4CQ4Ay6Cy+A6uAtXTwd4AXrAO/AZQRASQkeYiCFihlgjjogrwkb8kGAkEolDUpA0JBORIHJkKjIHKUOWIWuRTUg1shc5hJxEziNtyG3kIdKFvEY+oRhKQ3VRE9QGHYqyUQ4agSagY9FMdCJajM5FF6Gr0Sp0J1qHnkQvotfRdvQF2osBTAPTx8wxJ4yNcbFoLBXLwGTYdKwUK8eqsFqsET7nq1g71o19xIk4E2fhTnAFh+GJuACfiE/HF+Jr8e14Hd6MX8Uf4j34NwKdYExwJHgTeIRRhEzCJEIJoZywlXCQcBrupQ7COyKRqE+0JXrCvZhCzCZOIS4kriPuJp4gthEfE3tJJJIhyZHkS4om8UkFpBLSGtJO0nHSFVIH6QNZg2xGdiWHkFPJEvJscjl5B/kY+Qr5GfkzRYtiTfGmRFOElMmUxZQtlEbKJUoH5TNVm2pL9aUmULOps6irqbXU09R71DcaGhoWGl4asRpijZkaqzX2aJzTeKjxkaZDc6BxaWNoctoi2jbaCdpt2hs6nW5DD6Cn0gvoi+jV9FP0B/QPDCbDmcFjCBkzGBWMOsYVxktNiqa1JkdznGaxZrnmfs1Lmt1aFC0bLa4WX2u6VoXWIa2bWr3aTO1h2tHaedoLtXdon9fu1CHp2OgE6wh15ups1jml85iJMS2ZXKaAOYe5hXma2aFL1LXV5elm65bp7tJt1e3R09Fz00vSK9Kr0Duq166P6dvo8/Rz9Rfr79O/of9pkMkgziDRoAWDagddGfTeYLBBgIHIoNRgt8F1g0+GLMNgwxzDpYb1hveNcCMHo1ijSUbrjU4bdQ/WHewzWDC4dPC+wXeMUWMH4zjjKcabjVuMe01MTUJNpCZrTE6ZdJvqmwaYZpuuMD1m2mXGNPMzE5utMDtu9pylx+KwclmrWc2sHnNj8zBzufkm81bzzxa2FokWsy12W9y3pFqyLTMsV1g2WfZYmVmNtJpqVWN1x5pizbbOsl5lfdb6vY2tTbLNPJt6m05bA1uebbFtje09O7qdv91Euyq7a/ZEe7Z9jv06+8sOqIO7Q5ZDhcMlR9TRw1HsuM6xbQhhiNcQyZCqITedaE4cp0KnGqeHzvrOkc6zneudXw61Gpo6dOnQs0O/ubi75Lpscbk7TGdY+LDZwxqHvXZ1cBW4VrheG04fHjJ8xvCG4a/cHN1Ebuvdbrkz3Ue6z3Nvcv/q4ekh86j16PK08kzzrPS8ydZlx7AXss95EbwCvWZ4HfH66O3hXeC9z/svHyefHJ8dPp0jbEeIRmwZ8djXwpfvu8m33Y/ll+a30a/d39yf71/l/yjAMkAYsDXgGceek83ZyXkZ6BIoCzwY+J7rzZ3GPRGEBYUGlQa1BusEJwavDX4QYhGSGVIT0hPqHjol9EQYISwibGnYTZ4JT8Cr5vWEe4ZPC2+OoEXER6yNeBTpECmLbByJjgwfuXzkvSjrKElUfTSI5kUvj74fYxszMeZwLDE2JrYi9mncsLipcWfjmfHj43fEv0sITFiccDfRLlGe2JSkmTQmqTrpfXJQ8rLk9lFDR00bdTHFKEWc0pBKSk1K3ZraOzp49MrRHWPcx5SMuTHWdmzR2PPjjMbljjs6XnM8f/z+NEJactqOtC/8aH4Vvzedl16Z3iPgClYJXggDhCuEXSJf0TLRswzfjGUZnZm+mcszu7L8s8qzusVc8Vrxq+yw7A3Z73Oic7bl9OUm5+7OI+el5R2S6EhyJM0TTCcUTWiTOkpLpO0TvSeunNgji5BtzUfyx+Y3FOjCH/kWuZ38F/nDQr/CisIPk5Im7S/SLpIUtUx2mLxg8rPikOLfpuBTBFOapppPnTX14TTOtE3Tkenp05tmWM6YO6NjZujM7bOos3Jm/T7bZfay2W/nJM9pnGsyd+bcx7+E/lJTwiiRldyc5zNvw3x8vnh+64LhC9Ys+FYqLL1Q5lJWXvZloWDhhV+H/br6175FGYtaF3ssXr+EuESy5MZS/6Xbl2kvK172ePnI5XUrWCtKV7xdOX7l+XK38g2rqKvkq9pXR65uWGO1ZsmaL2uz1l6vCKzYXWlcuaDy/TrhuivrA9bXbjDZULbh00bxxlubQjfVVdlUlW8mbi7c/HRL0pazv7F/q95qtLVs69dtkm3t2+O2N1d7VlfvMN6xuAatkdd07Ryz8/KuoF0NtU61m3br7y7bA/bI9zzfm7b3xr6IfU372ftrD1gfqDzIPFhah9RNruupz6pvb0hpaDsUfqip0afx4GHnw9uOmB+pOKp3dPEx6rG5x/qOFx/vPSE90X0y8+TjpvFNd0+NOnWtOba59XTE6XNnQs6cOss5e/yc77kj573PH7rAvlB/0eNiXYt7y8Hf3X8/2OrRWnfJ81LDZa/LjW0j2o5d8b9y8mrQ1TPXeNcuXo+63nYj8catm2Nutt8S3uq8nXv71Z3CO5/vzrxHuFd6X+t++QPjB1V/2P+xu92j/ejDoIctj+If3X0sePziSf6TLx1zn9Kflj8ze1bd6dp5pCuk6/Lz0c87XkhffO4u+VP7z8qXdi8P/BXwV0vPqJ6OV7JXfa8XvjF8s+2t29um3pjeB+/y3n1+X/rB8MP2j+yPZz8lf3r2edIX0pfVX+2/Nn6L+HavL6+vT8qX8ZW/AhisaEYGAK+3AUBPAYAJz2fU0arzn7IgqjOrEoH/hFVnRGXxAKAW/r/HdsO/m5sA7NkCj19QX3MMADF0ABK8ADp8+EDtP6spz5WKQoTngI3BX9Pz0sG/Kaoz5w9x/9wChaob+Ln9F+VvfFhA5t1MAAAAimVYSWZNTQAqAAAACAAEARoABQAAAAEAAAA+ARsABQAAAAEAAABGASgAAwAAAAEAAgAAh2kABAAAAAEAAABOAAAAAAAAAJAAAAABAAAAkAAAAAEAA5KGAAcAAAASAAAAeKACAAQAAAABAAADHKADAAQAAAABAAACsgAAAABBU0NJSQAAAFNjcmVlbnNob3SIBbLyAAAACXBIWXMAABYlAAAWJQFJUiTwAAAB1mlUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iWE1QIENvcmUgNi4wLjAiPgogICA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPgogICAgICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgICAgICAgICB4bWxuczpleGlmPSJodHRwOi8vbnMuYWRvYmUuY29tL2V4aWYvMS4wLyI+CiAgICAgICAgIDxleGlmOlBpeGVsWURpbWVuc2lvbj42OTA8L2V4aWY6UGl4ZWxZRGltZW5zaW9uPgogICAgICAgICA8ZXhpZjpQaXhlbFhEaW1lbnNpb24+Nzk2PC9leGlmOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6VXNlckNvbW1lbnQ+U2NyZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+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": [
+      {
+        "answer": "A",
+        "image": ""
+      },
+      {
+        "answer": "B",
+        "image": ""
+      },
+      {
+        "answer": "C",
+        "image": ""
+      },
+      {
+        "answer": "D",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": "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"
+  },
+  {
+    "quest": "Data la porzione di codice in figura, indicare il corrispondente albero dei, indicare il corrispondente albero dei processi generati:",
+    "answers": [
+      {
+        "answer": "A",
+        "image": ""
+      },
+      {
+        "answer": "B",
+        "image": ""
+      },
+      {
+        "answer": "C",
+        "image": ""
+      },
+      {
+        "answer": "D",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": "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"
+  },
+  {
+    "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": [
+      {
+        "answer": "FCFS",
+        "image": ""
+      },
+      {
+        "answer": "RR",
+        "image": ""
+      },
+      {
+        "answer": "SJF",
+        "image": ""
+      },
+      {
+        "answer": "Tutte e tre le politiche garantiscono al processo C lo stesso tempo di attesa",
+        "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 round robin con time slice = 3, nessuna attività di I/O e context switch trascurabile:",
+    "answers": [
+      {
+        "answer": "6.5",
+        "image": ""
+      },
+      {
+        "answer": "6.75",
+        "image": ""
+      },
+      {
+        "answer": "7.15",
+        "image": ""
+      },
+      {
+        "answer": "5,85",
+        "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 Round Robin con time slice q= 4. Nel calcolo, si consideri il tempo necessario ad eseguire il context switch trascurabile:",
+    "answers": [
+      {
+        "answer": "4.85",
+        "image": ""
+      },
+      {
+        "answer": "4.25",
+        "image": ""
+      },
+      {
+        "answer": "4.5",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "6",
+        "image": ""
+      },
+      {
+        "answer": "5.75",
+        "image": ""
+      },
+      {
+        "answer": "4.5",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "4.5",
+        "image": ""
+      },
+      {
+        "answer": "5.5",
+        "image": ""
+      },
+      {
+        "answer": "7.5",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "4.5",
+        "image": ""
+      },
+      {
+        "answer": "5.25",
+        "image": ""
+      },
+      {
+        "answer": "4",
+        "image": ""
+      },
+      {
+        "answer": "4.25",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": "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"
+  },
+  {
+    "quest": "",
+    "answers": [
+      {
+        "answer": "72",
+        "image": ""
+      },
+      {
+        "answer": "73",
+        "image": ""
+      },
+      {
+        "answer": "74",
+        "image": ""
+      },
+      {
+        "answer": "I dati sono insufficienti per rispondere alla domanda",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": "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"
+  },
+  {
+    "quest": "",
+    "answers": [
+      {
+        "answer": "23",
+        "image": ""
+      },
+      {
+        "answer": "24",
+        "image": ""
+      },
+      {
+        "answer": "25",
+        "image": ""
+      },
+      {
+        "answer": "I dati sono insufficienti per rispondere alla domanda",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": "iVBORw0KGgoAAAANSUhEUgAABQAAAAF+CAYAAADdg7dVAAAMP2lDQ1BJQ0MgUHJvZmlsZQAASImVVwdYU8kWnltSSUIJICAl9CaISAkgJYQWQHoRbIQkQCgxBoKIvSwquHaxgA1dFVHsgNgRO4ti74sFBWVdLNiVNymg677yvfm+ufPff87858y5M/feAUDzBF8qzUW1AMiTFMjiQgNZo1JSWaSngAYYgAJYwIAvyJdyYmIiASz97d/LuxsAUbRXnRRa/+z/r0VbKMoXAIDEQJwuzBfkQXwAALxSIJUVAEBU8JaTCqQKDCvQlcEAIZ6vwJkqXKnA6Sq8R2mTEMeFuBkAMo3Pl2UCwLgMeVahIBNqMHogdpEIxRIANFkQ++XlTRBCnAaxHbSRQqzQZ6f/oJP5N830AU0+P3MAq+aiLOQgcb40lz/5/0zH/y55ufJ+Hzaw0rJkYXGKOcO83cqZEKHANIi7JelR0RDrQPxBLFTaQ4xSs+RhiSp71FiQz4U5A/oQuwj5QREQG0McIsmNilTz6RniEB7EcIWgReICXgLEBhDPF+UHx6ttNsomxKl9oXUZMi5HzZ/jy5R+Fb4eyHMSOWr911kinlofYxRnJSRDTIXYqlCcFAUxA2Ln/Jz4CLXNiOIsblS/jUwep4jfCuI4kSQ0UKWPFWbIQuLU9qV5+f3zxTZmiXlRaryvICshTJUfrFnAV8YP54JdFkk4if06ovxRkf1zEYqCglVzxzpFksR4tc4HaUFgnGosTpXmxqjtcQtRbqiCt4DYLb8wXj0WTyqAC1Klj2dIC2ISVHHixdn88BhVPPgSEAm4IAjuODms6WACyAbi1u76bnin6gkBfCADmUAEnNRM/4hkZY8EXuNBMfgTIhHIHxgXqOwVgULIfx1gVVcnkKHsLVSOyAFPIc4DESAX3suVoyQD3pLAE8iI/+GdD6sAxpsLq6L/3/P97HeGA5lINSPv98jS7LckBhODiGHEEKI9boT74T54JLwGwOqKs3Gv/nl8tyc8JbQRHhGuE9oJt8eLZ8t+inIkaIf6IepcpP+YC9wGarrjgbgvVIfKuD5uBJxwN+iHg/tDz+6Q5arjVmSF9ZP232bww9NQ21FcKChlECWAYvfzSIYDw31ARZHrH/OjijV9IN/cgZ6f/XN/yL4QthE/W2Lzsf3YWewkdh47gtUDFnYca8BasKMKPLC6nihXV7+3OGU8OVBH/A9//U9Wkcl8lxqXLpcvqr4CUZHiHQ24E6STZeLMrAIWB34RRCyeROA8hOXq4uoKgOL7onp9vYlVfjcQ/Zbv3Jw/APA93tfXd/g7F34cgL2ecPsf+s7ZseGnQwOAc4cEclmhisMVFwJ8S2jCnWYITIElsIPzcQUewAcEgGAQDqJBAkgB42D0WXCdy8AkMBXMAiWgDCwBK8FasAFsBtvBLrAP1IMj4CQ4Ay6Cy+A6uAtXTwd4AXrAO/AZQRASQkeYiCFihlgjjogrwkb8kGAkEolDUpA0JBORIHJkKjIHKUOWIWuRTUg1shc5hJxEziNtyG3kIdKFvEY+oRhKQ3VRE9QGHYqyUQ4agSagY9FMdCJajM5FF6Gr0Sp0J1qHnkQvotfRdvQF2osBTAPTx8wxJ4yNcbFoLBXLwGTYdKwUK8eqsFqsET7nq1g71o19xIk4E2fhTnAFh+GJuACfiE/HF+Jr8e14Hd6MX8Uf4j34NwKdYExwJHgTeIRRhEzCJEIJoZywlXCQcBrupQ7COyKRqE+0JXrCvZhCzCZOIS4kriPuJp4gthEfE3tJJJIhyZHkS4om8UkFpBLSGtJO0nHSFVIH6QNZg2xGdiWHkFPJEvJscjl5B/kY+Qr5GfkzRYtiTfGmRFOElMmUxZQtlEbKJUoH5TNVm2pL9aUmULOps6irqbXU09R71DcaGhoWGl4asRpijZkaqzX2aJzTeKjxkaZDc6BxaWNoctoi2jbaCdpt2hs6nW5DD6Cn0gvoi+jV9FP0B/QPDCbDmcFjCBkzGBWMOsYVxktNiqa1JkdznGaxZrnmfs1Lmt1aFC0bLa4WX2u6VoXWIa2bWr3aTO1h2tHaedoLtXdon9fu1CHp2OgE6wh15ups1jml85iJMS2ZXKaAOYe5hXma2aFL1LXV5elm65bp7tJt1e3R09Fz00vSK9Kr0Duq166P6dvo8/Rz9Rfr79O/of9pkMkgziDRoAWDagddGfTeYLBBgIHIoNRgt8F1g0+GLMNgwxzDpYb1hveNcCMHo1ijSUbrjU4bdQ/WHewzWDC4dPC+wXeMUWMH4zjjKcabjVuMe01MTUJNpCZrTE6ZdJvqmwaYZpuuMD1m2mXGNPMzE5utMDtu9pylx+KwclmrWc2sHnNj8zBzufkm81bzzxa2FokWsy12W9y3pFqyLTMsV1g2WfZYmVmNtJpqVWN1x5pizbbOsl5lfdb6vY2tTbLNPJt6m05bA1uebbFtje09O7qdv91Euyq7a/ZEe7Z9jv06+8sOqIO7Q5ZDhcMlR9TRw1HsuM6xbQhhiNcQyZCqITedaE4cp0KnGqeHzvrOkc6zneudXw61Gpo6dOnQs0O/ubi75Lpscbk7TGdY+LDZwxqHvXZ1cBW4VrheG04fHjJ8xvCG4a/cHN1Ebuvdbrkz3Ue6z3Nvcv/q4ekh86j16PK08kzzrPS8ydZlx7AXss95EbwCvWZ4HfH66O3hXeC9z/svHyefHJ8dPp0jbEeIRmwZ8djXwpfvu8m33Y/ll+a30a/d39yf71/l/yjAMkAYsDXgGceek83ZyXkZ6BIoCzwY+J7rzZ3GPRGEBYUGlQa1BusEJwavDX4QYhGSGVIT0hPqHjol9EQYISwibGnYTZ4JT8Cr5vWEe4ZPC2+OoEXER6yNeBTpECmLbByJjgwfuXzkvSjrKElUfTSI5kUvj74fYxszMeZwLDE2JrYi9mncsLipcWfjmfHj43fEv0sITFiccDfRLlGe2JSkmTQmqTrpfXJQ8rLk9lFDR00bdTHFKEWc0pBKSk1K3ZraOzp49MrRHWPcx5SMuTHWdmzR2PPjjMbljjs6XnM8f/z+NEJactqOtC/8aH4Vvzedl16Z3iPgClYJXggDhCuEXSJf0TLRswzfjGUZnZm+mcszu7L8s8qzusVc8Vrxq+yw7A3Z73Oic7bl9OUm5+7OI+el5R2S6EhyJM0TTCcUTWiTOkpLpO0TvSeunNgji5BtzUfyx+Y3FOjCH/kWuZ38F/nDQr/CisIPk5Im7S/SLpIUtUx2mLxg8rPikOLfpuBTBFOapppPnTX14TTOtE3Tkenp05tmWM6YO6NjZujM7bOos3Jm/T7bZfay2W/nJM9pnGsyd+bcx7+E/lJTwiiRldyc5zNvw3x8vnh+64LhC9Ys+FYqLL1Q5lJWXvZloWDhhV+H/br6175FGYtaF3ssXr+EuESy5MZS/6Xbl2kvK172ePnI5XUrWCtKV7xdOX7l+XK38g2rqKvkq9pXR65uWGO1ZsmaL2uz1l6vCKzYXWlcuaDy/TrhuivrA9bXbjDZULbh00bxxlubQjfVVdlUlW8mbi7c/HRL0pazv7F/q95qtLVs69dtkm3t2+O2N1d7VlfvMN6xuAatkdd07Ryz8/KuoF0NtU61m3br7y7bA/bI9zzfm7b3xr6IfU372ftrD1gfqDzIPFhah9RNruupz6pvb0hpaDsUfqip0afx4GHnw9uOmB+pOKp3dPEx6rG5x/qOFx/vPSE90X0y8+TjpvFNd0+NOnWtOba59XTE6XNnQs6cOss5e/yc77kj573PH7rAvlB/0eNiXYt7y8Hf3X8/2OrRWnfJ81LDZa/LjW0j2o5d8b9y8mrQ1TPXeNcuXo+63nYj8catm2Nutt8S3uq8nXv71Z3CO5/vzrxHuFd6X+t++QPjB1V/2P+xu92j/ejDoIctj+If3X0sePziSf6TLx1zn9Kflj8ze1bd6dp5pCuk6/Lz0c87XkhffO4u+VP7z8qXdi8P/BXwV0vPqJ6OV7JXfa8XvjF8s+2t29um3pjeB+/y3n1+X/rB8MP2j+yPZz8lf3r2edIX0pfVX+2/Nn6L+HavL6+vT8qX8ZW/AhisaEYGAK+3AUBPAYAJz2fU0arzn7IgqjOrEoH/hFVnRGXxAKAW/r/HdsO/m5sA7NkCj19QX3MMADF0ABK8ADp8+EDtP6spz5WKQoTngI3BX9Pz0sG/Kaoz5w9x/9wChaob+Ln9F+VvfFhA5t1MAAAAimVYSWZNTQAqAAAACAAEARoABQAAAAEAAAA+ARsABQAAAAEAAABGASgAAwAAAAEAAgAAh2kABAAAAAEAAABOAAAAAAAAAJAAAAABAAAAkAAAAAEAA5KGAAcAAAASAAAAeKACAAQAAAABAAAFAKADAAQAAAABAAABfgAAAABBU0NJSQAAAFNjcmVlbnNob3QaRZMvAAAACXBIWXMAABYlAAAWJQFJUiTwAAAB12lUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iWE1QIENvcmUgNi4wLjAiPgogICA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPgogICAgICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgICAgICAgICB4bWxuczpleGlmPSJodHRwOi8vbnMuYWRvYmUuY29tL2V4aWYvMS4wLyI+CiAgICAgICAgIDxleGlmOlBpeGVsWURpbWVuc2lvbj4zODI8L2V4aWY6UGl4ZWxZRGltZW5zaW9uPgogICAgICAgICA8ZXhpZjpQaXhlbFhEaW1lbnNpb24+MTI4MDwvZXhpZjpQaXhlbFhEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlVzZXJDb21tZW50PlNjcmVlbnNob3Q8L2V4aWY6VXNlckNvbW1lbnQ+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgrMv2fBAAAAHGlET1QAAAACAAAAAAAAAL8AAAAoAAAAvwAAAL8AAL3TNQonvQAAQABJREFUeAHsXQXYHEXS7rgHSIIcHizooYce/DjksBzuEtzdD5egwd0l+OFOgEOCE0LgODTBgwRJIEZk/nqH1KSmt8d2e77v203V8+z2SOvb3TXd1dXVrQIio6QIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCDYlAKxUANmS9aqEUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFIEQARUAakNQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQaGAEVADZw5WrRFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBFQBqG1AEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBoYARUANnDlatEUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAEVAGobUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEGhgBFQA2cOVq0RQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUARUAahtQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQaGIGqBYBjx441Tz/9tBk6dKj54YcfzOjRo03nzp3NHHPMYeaee26zzjrrmJVWWsm0atUqE7433njD/P7776G/hRZayCy44IKZYdSDHwR8Yz9kyBAzadKkMHOzzz67WWaZZfxkVGNpcgQmTpxoRowYYT755BMzZcoUs9hii5lFF13UdOzYscnzogk2HgLV8J5DDjnEjBw5MgTj/PPPN4svvnhuYF5//XUzbty40P/CCy9sFlhggdxh1WMlAr54/ccff2y+/vrrygTEk9atW5t27dqFv/bt25tevXqZueaay7Rt21b4aszLb7/91nz44YexwnXr1s387W9/iz3Tm8ZEYMKECWaPPfYIeVfPnj3NzTffHBW07L6z9957m++++y4c11977bWmTZs2UdqNdjFmzBjzzDPPmGHDhpnvv/8+nNPMOuusZskll4x++GaAFykpAopAy0MgjVem5dZn3y+LZ2aNl+V4DONiyGEaibLK30hlbbKyBAXpt99+C3bfffeABuMBZTL1N+eccwYDBgwISCCUmspyyy0XxbPGGmuk+tWXfhHwjT0xnagut9xyS7+Z1diaBIHHHnssQLuggW5Ul9zX8WyJJZYI7r///ibJiybSuAgU5T0///xzQBPQsE2SECigRaMYOCSkDi677LLg008/jT3nm6WWWipqz2uvvTY/VrdKBHzx+gMOOCCqF+YzeVzwomWXXTa48cYbM8cYVRaxRQS7+uqrK/Dp0KFDs+WNhE7BoYceWij9asIUSqCBPR911FFR/Z9xxhmxkpbdd2T8F1xwQSztRrl57733gs033zygxYQI5yT+QwugAQkJG6XoWo4WhoDyydoqJI1XumIuo++XxTOzxst/+ctfIv7ViHPvrPK76lefpSNg0l/H37711lsBPoBJH8ek5wjz2WefxSMTd7JiV111VfFGL8tGwDf2viaFZZdb469EgLR4g/XXXz93/+7bt28watSoyoj0iSKQA4GivOeee+6J2ua6664bS+Hll18OhUH4Bt13332xd3wjBYB///vf+bG6VSLgi9fLAXPSGCLr+dJLL92wvKilCABp10dwzDHHBBC+oz7yUDVh8sQ7s/ghrYdo0WO++eYLxo8fHyt62X3nxx9/DGaZZZawvjt16pS4uBLLVJ3cYMHo8MMPdy50ZvGbHXfcMSDNyDopqWazpSOgfLL2GsrilTKFMvt+WTwza7wsBYBY0Gg0yip/o5W3KcqTbxRHOXn88cejgZ/8OGIVHhOBFVdcMdQM6tKlSzRJk/5oy1VAavXOMmnFOmFpkoe+sfc1KWySwmsiMQTw0ZB9Fte0rT9YYYUVwv7t6tsQxEybNi0Wj94oAnkQKMp7+vfvH7XP8847L0qCtvYGZGoieqcCwAiaUi988XofQgzwKiw0kjmSUsvcHJE3twAQ/J22nQa05TrqY8A7jaoJkxbfzPjujz/+CMiESoT5bbfdVgFDU/Qd8FoeF5Bpn4b43pMpiGCzzTaLysXlK+Jih9M333xTUSf6QBHIi4DyybxIpfvLwys5hqbo+2XwzKzxsgoAuYbVzYtA+ihueixgUmQHI/axnH/++cPtVuhMNr377rvBNttsE5uU4cNKdkxsr+H9nnvuGSy//PLhb9ddd3X60YflIOAbe1+TwnJKq7EmIXDppZfG+jcEfxdeeGGAlTKmyZMnB+eee24oFJQD5UbdGsTlVrccBIrynnnmmSdqo/jGMP3nP/+JnqNdqgCQkSnX9cXrfQkxUPfQ6Gk0am4BIIR/kt/zdRrO1YRJi29mfHf66adHuEPDFeNwm5qi75BdrUBOLq+77jo7G3V3v8suu0TYcnuuxoUmOcZFSopANQgon6wGtcoweXglh2qKvl8Gz8waL0se3YgagFnl5/pVNz8CuQSATz31VOxjiYlYnpUvKQXHxxV2A7/66qv8uVOfdYeAr0lh3RW8zjPcu3fvWB9Hn0+iBx54IOYXHx4lRaBMBIYPHx61OfAYSSoAlGg03bUvXp8kxDjooIOC4447Ltx2CsEe/G299dZB9+7do7ZgT9qxTbHRzBI0twDw+uuvd+Kd1tKqCZMW38z2jozSx9o5zB+4qKn6zkUXXRS1ASz+17PQK0noAl6C7e1QQoAtWdj6GzRoUADNc7Y9a/Mb3F911VWuqtFnikAmAsonMyHK9JCXVyKipuz7Tc0zG10AmNkQ1ENhBHIJAM8555zo448P3g033JA7oQ022CAWFh1QqXER8DUpbFyEWl7Jvvjii1gfhfZuFtnbZxpt0p1Vfn3ftAjQib9RG8UhVJJUACjRaLprX7w+SYgBu0guwjZfCAfltm85Mb/77rtdwer2mQoA67bqqs44tOq5Tc8xxxwxTXwZaVP1HfRFHDzDeYJgrB4JBxLKiTKXBy4WQd98801nsf773/8G8847b1R+GQ5b85QUgWoQUAFgNajFw+TllU3d95uaZ0q+1ogagPFa1zsfCLRCJPQxSyUaZBha5Yr8vPLKK2a11VaL7tMuiMEZHIvNdPTRRxvSDOTb0P36668Ndc7wumPHjoY0DGPva7mhLYyGJojmk08+MV9++aUhLURD9gjNIossYlZZZRVDJ3/lih7Hi3/44Yfmgw8+CF1aETRkn8X89a9/DeMjW4ip8fzyyy+GTrEM/fTq1cuQYeXwGuV+++23zauvvmp++umnMD6yuRbmLytOTnDkyJGGNGQMHbRivv32W0MDFUNbtsMfrtOoKPZ0wqZ57bXXDNypU6eG7WD11Vc3s846a5gM6g55ANFJRObf//53eJ329/nnnxva0hf+aGU5xIAGVSEGNMlLCxpiCmxBs88+uyHtEEMTRPPwww+HdY56JiG0WXDBBUM/9h/SA26oW/wQlmzdhW0Q7QN1nNZGyqxX5LXWdmeX13VPtoUMrXpHr9BnSd06undd0FZgQ9o50asnnnjCbLzxxtF93guJX48ePcxss80WBiUNYwM+g77RrVu3sI/RdpuwbafFjT5WS3soA29fPAjlrqWvSNxq4Rm+4inCe+hwGvPss8+GSd91111mu+22MxMnTjRoJy+88EKsvV5++eVRW0R7QrsC0TY6QxO58Bpt6aWXXgqvaQU5bGc0+Qt5MA2kDB0YYtZbbz0DPp9EtbY1xOurPhFXrbwMcUgqg9fL+Pn6wAMPNFdeeSXfRi4NoMO+Hz2wLjCuAK+yacCAATHeJHkM+6UDFQxp+xj0gyeffDJ08Y0gG2eGTjlnbxUu2tyLL74Y1hvGEyC0K3wn+vTpk/qtqIhs+gMyo2KGDRtmhg4damiHhKGTjcPv6kILLRT6uOaaa8x+++0XC07CmLD9y4e0CGPokAj5yHTt2tWQrbLYM74hm8yGTtLm29Al0w8G7R/E70n7zJxwwgnhM/mH9sFEQqqwrqoJw3HYrg+si9Q9xrlJhLEOxgeoI/CQnj17hvWEukrCNymurOf4XqDu0RZAqHs5/pbhy+47Mq0tttgiHFfhGZnsCbGQ72u99skLk/Jyyy23GFpAqniN9vvRRx9F49gKD/SATgwN5wwYI9hEBxCYv/3tb/bj2L2P9oz+jX4uifsentE28TCfQ4YMMbSwG84ByD57yNPSvmUyPnzX3n///XC+BB6HeQn4CL6l4I34LqaNiRGXb17E+XPFi/kcE8YUdCCYeeedd8I+tOaaa4Z5ds0jEBd4OcaYmDeQZqvZZJNNwr7N8WW54Afgg5jzID6kA96A32KLLRbycRfu9cwn076RTcknUTdFeGVz9H2fPDNrvEwLshFvIAGgeeihh7Kab6731c5fwH8QFoQ5NdkQTk0PfObXX39N9J9V/tTI9aUbgTxSxH/961+xla8iNr+w5RfbdvbZZ5/g+OOPDx577LGKJLOMW1YEyPGAPrbBySefnLjaR2gEJBwKVfyJaSXGSA043A5AH78YBgjPP7zL0oqkwUHkf+DAgQENvAMc1c0n6nFc7OLEN5xsmUY4lXnttdeO4uWw0iWhWAANTuDhorzYk+Az6Nevn1PrAgfBYMUBKx5FtEJIOBjAkLLMr7ymj2hwxx13uLIdPYNtHA6DrRg0+a841Y0GK8FJJ50UhcHFxx9/HBqBxjsO73Kx3cXVZjmyMuoVcftqd5zPNBcH/GDbC04Ahq1PtKssOuOMM2K4IY5qSOJ31llnhacMytNaZZ3AhMBuu+2Wakag2vZQBt6+eBBw9dFXEI8PnuErnry8B7yS+SR4DQ0UkIVwa6hsH67rE088MfSLP9muYLsJ8e68885Onoa4cOjB7bffHoW3L6pta4jHV30iLl+8DHGByuD1f8bs/i+qxcSx0KJDjAdx/WOsIUnyGPaDfrDvvvs6wx9xxBEyeHhNE+FQ6zBtHIA2ivbE7bMiEusBDJcfddRRFd8rziMJFANsfc+rAbjhhhtWlAenlSbRDjvsUOEfcTBtu+22Fe85b7bL459qwnB67PrEukjdu+oddvegfQybuHaZ+X6BBRYISIjM2a/ZBc/huOE+99xziXGW3XdkwrfeemssX7QgI19Xfe2TF2ZlAoeYSGz5GuaK8hAJD6PwJBQL0F8wFkKbTSKf7VkehMV5R71g/AIzCUnmEfAtw5bmNMLppaSgkdrWkSYpMIS8kwSiidH55kWckGu8jneklBAeAsWYSBfzCDk+/d///heQ4D6qR+kXWq4Yh2YRdrLJ8YuMQ17T4mPYPsDrJdUzn0T5bF7ZHHwSeBbhlc3R933yTNneVl11VdmcwmvfGoC1zl+K5gfyGO472GFmU1b5bf96n41Ari3Ad955Z1QxqCDY2bn33nudRomzk6z04btiIXR0Dfy4cdkuBlEuwsRKTvLscPY9rR4FtBLkiio2+aTV9IC0y2KY2nHhHh+7JPsitEIXkNZdZhwcLz7IUIG2KQ/2pCmTKy3ShozlBwJOF0FQKAdSnMckF5MYWhlwRRXQSnmUJj6qSdvCTjvttCg8BKIsUEhK036OgZGLpFDBR70iDZ/tzpVnH89I2y/CHVhVe/qmxI+0uipOmrTrAfcQypI2hrMY1bSHMvD2xYN89hVfPMNXPHl4Dyr5kUceidoa+CYTtoG62od8liQAxCTGPthKhpPXSZPDatqaz/oEDj55GeLzzesRZxZVK8SA0EnWE1+TxlQsSclj2A8Ws/jadoGppEcffTTARM72l3SPyfaDDz4oo6i4HjFiRK4xCgRPpMVSkTYmqjb5nnRXM0mtJowsh2+si9S9Xe8Yy2FRLKme5XOMOzApdo2xZPnyXMuJatr2X8RVdt+R+cUYTI6bIECuhXzzwqy8QAjjEuTiW4DFoDyErcAXX3xxuJAGoVsW+W7P22+/fUV7vOKKK4J//OMfFc9l+8Q12igOdnMR7YIpxOMQH+1iCgVvrvh88yJOAwuAslwoE7aj086x2HPpB9d4P3jw4ODpp58O822/t+95QYPTlS4UWWz/WfcwhSXbSz3zSZRV8srm4pOok7y8sjn6PvLnk2dmjZeLCtyQvyTyMX8pmh+pPKQCwKSa8fs8lwAQkmAMRGwmhwYJbTZ8FGuhrIZdJG50dJfQDsIpCJ0wabQFVSgXTkGVhDLLfHHZMaCEthRW+rFCz8/ZRRjJ6DlO10AUYSDkQ3769u3rxJi2P4aadRwPXOQNQhBOEy7sk+CUHHycMCikLUmx9/Djso0ky+haVYB9OHvQBAHwuuuuG64CrrHGGrFBocxTkgDQFh4hDFbksLpJ27oC2gJdoRWx6aabSgiiazkJl2nLawwSaKtXGIa2BlTggra90047BUceeWRYJtriUJE+6sm1yuuzXpFB3+0uLLTnP9quHsMQGhDVUhJ+0FzFqeEYaGCigdV2Wac4iOi3336rSLZoeygDb188CIXz1Vd88Qxf8aBsWbwHfkBS0HfKKaeEz/B3ySWXhBrQMh60EbQpaEbjJwfySW0NYdDewGMgYLQnGZj00jafKF2+KNrWEM5XfSIu37ysDF6PfGZRtUIM1JfkCXwtF3uQdlq9cxh28a2QfB6HL+AZvy/iYmzkImhL0BbKquLk9BtRAFgG1kXqXtY7BHmLLrpo4TrCbpdaiLZ4xmzt2cJsO+6y+46dHha5uQ1i90Yt5JMX5skHbdON8s5lgPvPf/4zT/DCfspozy4BoCxL1jXG8qNHj46VBW3O/oZmxcPvIQS040PkTSUA5HzkccHH7W97UjiM92Fb2KakRaekeORzaHIz1SIALKNdFeGT8hvZXHwSOBbhlU3d97me4frimbKPuubqRQVuMo/y2tf8pWh+VAAoa6FprnMJAJGV+++/P5V5QgCFCTvts48NoPMUI6th54mD/dhbEyFAgwajJGz5xUqYZM7QpsPklunggw+ueI/TT21CeW0BGSamNrkYLIRoUEeXhNNXMbiXecO2VklktyL2nuzAVJzKhkmGPIUI8UHIaFMW9vaHCkJPW/sKE1FbIIn0XAJAqc0DP9CscG2zw3YFqO5LHLDlyybXJHyllVYKNSfxoYRQFMJaJlvz8JBDDonVO/vD9gZbUAwNP5t81ivi9t3u7PzWeo8Pvq1dCwFNteTCD/Vna9KiPuyTiiHstqloeygDb188yGdf8cUzfMWDesviPVy3cmsA2YXkx5GLgbrkE/fdd1/0Tl642hpveQe/ZMJ2IphgkHG6NCeKtjWf9Ym8+uZlvnk945nlFhViQFvHtX2V6wt8X5Kr3tmv7a611lpRULJJlqn5niYcxEIZtIttsrcFyTxgQo3t6Tavk35w3RQCQGje2+km3bOgvZowwKcsrIvUvawnaP26ygozFGRTLVh55ZUTFz7zmNCQaclrjHtkumSfV76uuC6779gJku3fWP6gDV4N+eaFefKABXCJLV9jDOibymrPWQJACK7QPpO2AqPM9hZXzGsYC3bB17CABg36U089NZw7JMVpL7gAy+YSAEJo71LM4HJJF3M+jC3s+Rb7kfMGlAljhKRFAcwzMZ/D98OeD3J8EAQx1TOflN/I5uKTwLEIr2zKvs91zK4vnpk1Xi4qcOP82a6v+UvR/KgA0K6J8u9zCwCRFQi7bE0cZm62S4ZZQ/s2ZGA1sxRZDTszgukeyAh6xYAdW5qSaK+99op9+CDkBJEx4NhzDPpgFymJMCmVEwF8KG0Bhj0QJQOxidtFgLPE016htDsotlIkkVx9IGO0FVs107Cng1Ni+YD9IzIQ7Ezqu+++qxh02AJACI/kZB6Ypdm3gdYNJlGMxeKLLx5gdUKSPQmHNhqOhXcRtqnKjz1WUeTE3w6DE+E4bbh2PcC/z3oto93ZZarlHoJznBAsMcHAB3VfLdn4YeKLlT0XYdsc+iKnj7ZBBvRjXou0hzLw9sWDfPcVXzzDVzyotDTew5UKHsD1Df7j0q6uVgAILVKp9cNpwoUQidOFa/My+CnS1nzXp29e5pvXA5+8lCTEwMIW7FoddthhoWb2LrvsEm75gZBM1o28Bj+y+YfNY6R/bCGCcBeLO1hwogM3omzbvI7DwR8WFfEtRFr4hpFxfGeeMFmS3xg6QMC5WIa4MR6Ri5DYRpw06W4KASDaGB0mENpv5rJLF+/4R0btQ9yqCYOAZWCNeIvUPfyDyJh/gHYky4prCBakwAtjQle9YztmtWRvL6QDjlKjKrvv2InDJrPExd45Y/t33fvmha40XM9ctjRRFth49E1ltec0ASAEe3ToTVgUjJOxU0nWFV/b41g6zK3CHxaDbIKmH8wbcDzs0oGQttcmFwBivikXW7BNm/Nnu5h3QOmAxxLY0eIS2qFvS0qyOQsb+Rj3McEGrMvGIMYLTPXMJ/kb2Zx8EjgW4ZVN2fe5jtn1wTMRV9Z4uajAjfMnXV/zF8RZND8qAJQ10TTXhQSAyBIm4RiIZ9lckEwXq0GYbCdRVsNOCmc/h1Fima5L402GgfYd+4dgk1eyrrvuuug53u+///4ymPPa/jDDLockeyAK2yBJhE4oP0jQCJCEfHK+4d50003ydewaAtCzzz473P6LlWlbgJaGPTQZZTo4VCWNbIZsT5ohYJXxYbtvFsH2ngxjCwztSTgMSicRBLVYpaPTHgMIQ2HIN40wcZMCSLRjm3zWaxntzs5vLfeuyYZrW3mRNGz8WJMkKQ5odMr2QCddxbwWaQ9l4O2LB/nuK754hq94UGlpvIcrFdrPXN+YVLmoWgFgllF0yYP/7//+ryLpIm3Nd3365mW+eX0FWCkPXHyF67yo6xJI2DyG4wQvkQRhHn8fMUGTiw0cBsI/l9AYWom2ZjSHoRPpo2RsDVr2kyQ0sicP7L8pBICcaTppOeqDnD7cNCoSpiyskb8idc/lsRc5UFZsHcROB5vwzLWtsFotQHtbLMbbaVR237HTxsF0sg1g109R8s0L86aPcbDMO1+7dqDkjdPlr8z2bM8zuAy2thryhcVROQlnv7bADhqQ/I5dfNvwXZWLF4gTh92gzrG7CGPxpAOPmlIDECY6sFgvCfl2lR3lw4KSTS6bsBB2SsJCNxZ+MM+BYgV2CWCBRwr/2L9L4IRvh031yCf5G9mcfBI4FuGVTdX37frFvQ+eiXiyxsuyveNQzmrI1/wFaRfNjwoAq6mx2sKkj+JS4sbqNwQo2IoD+0n84UhywaTlQFhGndWwpd+0a3slC9tpswirP3S8dMybvXIGg5hZhC2Ksuz2tgJ7IGprLtnxQ0OQ48N2E0mY/PE7djEAwAoVNAyKUBr2UnsQ6fAqf1L8GPjIwbAtABwwYEAs3/JkrqQ4sQLOZYTLq0/s356E56krhMVH2x7ccJzsQitG2r7ElgibfNZrGe3Ozm8198DK1pZFXcgDFqqJF2EkfvhguAZTMu6hQ4fG2gO0hCQVaQ9l4O2LB/nuK754hq94UGdpvIfrFMaAuf8nCYerEQB26dIlEvZwWrYrDwmBOQCbirQ13/Up8+KDl/nm9TJ/Wde+hBj2N5fTlTyG2xK0QGD4Pomk4JnDwIVmSRJhsU365Wu5cIYxEz+Xrj2B5TSgpWK3M4RrJAFgWVgDw2rqHvaAZd3gGpqiSYRxge1f2vtKCud6ju2bHBfaKLTl0qjsvmOnDTuhnD+4SbaZ7XDyvkxeKNOxr88888xY3rkc9pjSDlf0vsz2nCQATNqZI7+fXF5op0my64P9wYUQDAofEFRhPJyXmlIAmCTswKKdLAuuMT9x7Zg66aSTKvxijFCU8C2GYNSlAYrt2TYVEQCW2a7qjU8CxyK8sqn6vl2/uPfBMxFP1ni5qMANcdrka/6CeIvmRwWAdm2Uf1+1AFBmDYIUrITC6DWYsWv7BJgvtAYhcLMpq2Hb/pPu7YHbyJEjk7ymPpf2d2AnIg9hoA4Gzx8cW1tMMlgITLNIMjcciiEJacEAM6clXWC80UYbhSt0tq0+GQdfp2EvDxLBVqQ8JDu9LQDEIR8yr7CDAftxaT9bSHPUUUfFsiEnR9DWyxLqxQJPv8Hg6fXXXw9gbwfq/Ph42zbAkO8sAWCt9VpGu3OVt8gzHLRhr7QBi6RTkYvEDb+yX+DwlSzC4E22Ifu0qCLtoQy8ffEg333FF8/wFQ/qOY334D0mvxiEc33bCzXwA6pGAIjtfFmE1X1O294OhLBF2prv+kzKe7W8zDevT8qf63mtQgwISnbbbbcAJgpcJHkM1ycW2NIICwvsl11MHrMW2FxpyS13rokmNE3TyG47yE8jCQDLwhqYuuojq+5dmpzoHzfeeKPzZ9sKRv2gnqshaUvZ1kByxVd237HThIADuye4T8hT2W2/Sfd2e/YxDkxKSz7HrhzOt3SlgF76r/a6zPbsEgCizSQRdtnIsuIafUJS0sKFHQ73MOGDhRZ7J46MD9dNKQDEmN1FrpORk/r+BRdcUIET5hNZBFNPsDmMAwRxWrvcNWDjh++HTUUEgGW2q3rjk8CxCK9sqr5v1y/uffBMxJM1XpZz7yShOOJJI1/zF6RRND8qAEyrmXLeeREA2lmDHRsY+ZUTKGaGrpXUrIZtx590j6PWOR0wW1ZVTvKf9FwK8pC3vCQnhPbJqJLBQriXRXIQagsAERbbl6UAg8ttu7CbByO+OI7cRWnYS+03e9DgigvPZL5tASA+kHb+it5vscUWsaQl5jj9OS/hVChoryYJUl35yhIA1lqvZbS7vHi4/GFw4zqt0nUYiit8nmeyX2BikIcglOf6sbVji7SHMvD2xYPK6Cu+eIaveNJ4D9qB3I6Q1rerEQDmmbjK71eWADAtfyhLGfWJeEE+eJlvXv9nzvL9VyvEwJaqfffdN9W8CHIgeQzzDSxqpJGtEYlwWBTKInyfOA12MWlmsgfY8CPfsz/pYoLLcbGbVwCIb1wS4cRajo9de+ESYYtMUjmtImHKwhp5qabu5feFcSnq5v2WMV7sSpMj9reN/Ui37L4j0+JrHPjHeOB7W5TK5IVpebnrrruifHP+4dqmANLiyPOuzPbsEgBizJ1E9iFnKK89lsfCyXbbbefERuJkX+P76FLoQF5cAkAfvAjzOjsfSaetY+HF9rvGGms4obryyisr/CYJALEoDruRcvxip+O6x3jTJuWT+cb8Nm64L8Irm6rvu/KJZ7XyTMQh21tZpwD7mr8gvyoABAotm0oRAMoi27aFwBhh2F1SVsOWftOupfAJhsKrJal1YtvLSItTau3BYL0kORC1bfpJf3wty+ISAMIftuRiEC8FGa4PD57h9Kr//ve/HH3kpmEv7TxCEJSHsPLAebAFgLC9x++qde2JuxT4gHllEew8wTZknvTlpBj+swSAtdZrGe0uC4+k9zB2LlfYUH6s/F911VVJQap6LvtF0hY+O2KpnWkPZou0hzLwlv22Fh5URl8Bjj54hq940ngP0pD2P23NX7xnqkYAmKevFhEAZvGeMurTJy/zzeu5bvK4SUIM2Pd8/vnnwx/qGJNNmACAZjsmYXlJ8hjm+y6bWTI+bG1kv+zm0Rp1mUqQGn4ubWpsNU8jTHA5D+zmFQBifJBEru2BzSEALAtrlLto3dsa5ox3URe7MIoSNEVkOhAkZVHZfceVPsajnM9evXq5vKQ+K4MXpiY4/SVOd+d8S3f99dfPEzz0A/6DLbSwIwfzNNCIt6nM9uwSAOI7lUT4bsqy4toeMyEslCVcgks7rOseGnA2uQSAPniRSwCYtIU77wIH8o5xrV02lwAQdgBl+7fDpN3DDJZNRQSAZbareuKTwLAor2yqvm/XL9/LNlMNz0Q8WePlogI3zpt0fc1fEGfR/KgGoKyJprnOFACCQeGDh+0IEDTBBlRRsrWI7AMwshp23vTk6ju2BckT9fLGAX9pAoa0eGSDh8BNkmSweSafsiMmCQA5fhjihW0hnNwFrYikjxAOOrGNSqdhLztklpYC50Xa3bAFgBDecd4gTILafdEfjKJLKiLwwWDNNelBnoANVlWgUQJ1cWwJxsooTgrlPJctACyj3Ums8l5je4d90iYmsUk2PPPG6/In+8U+++zj8hJ7hjqRxvntOinSHsrA2xcPKqOvSCBr4Rm+4knjPUhDbq1LO7CjHgSAvuvTNy/zzetlG8m6ThJiuGw1ZcXlei95DPPy/fbbz+U1egZtFfbLbp4tma7FJWlzCzyO42MXfCiNbLs8CJdXAJi2Fcg1SW8OAWBZWAPTonUPEyJSGM51VNR12QxNq2N+J7+7aYId9l923+F0pCvtU2OHSVHyzQvzpg8hl2uLJsai9rg4KU5s9ZRtAWZfoEEoF9fLbM8uAaCrz3L+8woA2T/GeFtttVVMu0qW13UNjVnbPriLt/jgRS4BYJJtYJcAEEI0F+URAKKMcnxpYwHBDrQOMadxHTrhEigWEQCW2a7qjU+iDovwyqbq+662hWe18kzEkTVelvKHtL6GuJLI1/wF8cv82KaaXOlLzXuX/6zyu+LUZ+kIZAoAzzvvvNgHz3WCUnoSQQAbG5JZgulJ8lWx9sc5zcg3pw8/w4YNC8aMGcOPwpNiOb8QDOUhCBshdORwtqq5ZLC+BYAyfxCQwKD46aefHki7Tpyvyy67THpPZSpSoxETjjz29eS2ZFsACGPCnA+4WE2rleQHOUsL59Zbb42ljzwcfvjh4eAtqWxywAjhpk0+6xUnFDM+vtqdnd+sewjnsVLJ+YCLiW+Skfqs+LLeS/yytuUhrm+++SaWNwyKJBVpD2Xg7YsHldFXJE7yuijPkGHlddF40vi+rGf0wbTFnHoQAPquT9+8zDevl+0i67psIYbkMczXDj300NRs2f2YwyWZ0uDIJP/hMFLb0B5PwQ/4bdqhYK5Jf14BYJomGgSTnEd2XcKEpElq0jcTWBQJUxbWyEc1dQ+hFuPBLoQ8sK+Z9yfHk8hHXpJpI+9ZVHbfcaXfo0ePCB+XSR9XGPnMNy+UcWddyy1uXLdwsxYEEC9O+pYLJTI8zFUwldmeXbwgbdxUVADIZYCGNTT7YJ8b5i3k3EaWm68feOABDhq6LgGgD17UnALAa6+9Nmr3XG64GIO+/fbbsfmRfdI1/GHHiU3KJ2fMu21ssu6L8sqm6PtJea6VZyLetPEy3kuBW7UCQJt3VStDsfOT1vfhF2MJ2bdVAAhUyqdMASBWhCSzw172tIGfK8u77757LA6cVispq2FLv2nX9ipOHo0lfNy4fDzgghYYP4MLm1dZZBvSxUESkuRAtFYBIGyzYcABTbU0zRikD4GfLIu9rSQNe9suCE46TiMMGqR2li0AtI+Nh53ILMJAGsa3H3zwwfCgGXtgLSdcWQLAPfbYI4YFtqenEXCW2MF2jU0+67WMdmfnN+1+8ODBFdoP6B84xaoskvjBbiaESGlk8yMIuiUVaQ9l4O2LB/nuK754hq94UGdpvAd9nvsejHmnUT0IAH3Xp29e5pvXp9WX/a5sIYbkMdymMNBNo+uuuy5qfxwGbpLBecTlmvQhDIR+TP/+97+d8d55553sJeZCU9d1qJpLAOjSbscioIugESFtKHEZXQJAaNjwe+liG1YSFQlTFtbIWzV17zo8YPXVV08qavDkk08GL7/8cvDLL78k+sn7AhrtjDHsE2dR2X3HTh+ax1IYhAM8ipJvXlgk/XvvvTfCl3GGi/4EW6ppBEG+DMPXtn3YMttzWQJAjN0hxMIOG4yp7MX50aNHB3fffXeQJEQ57bTTYtCVxYukkIDxbyoNQOyw4jTZdfFLAIFTwNkPu65FfeWTsWZT6KYor2yKvu8qgA+eiXjTxst470MA6Gv+gvzIHVZZMo+PPvoo1l9UAAgEy6dMASAYv9SCAjO76KKLcucMHxbYw2MmCPfzzz+Phc9q2DHPKTfSaDzSsbcH2kG/+uqrWL4gqARdeOGFseeYHGUR7IjIMmICK0kORLM6A8IlbQHGyZhSQ8sllJLp4lqqStunYKVhj8GALBMGH2l01llnxfzbAsB77rkn9h5bQbKEyXac9jbRIgIfbMuW5bGFiXbZbG0Nl+FVX/WKtMtod3aZku7Rz+3t49BizdJ4SYov73OJH+oGxnrTCHUg6/Ddd9+NeS/SHsrA2xcP8tlXfPEMX/FwhaXxHjnYvvTSSzmI04UtJtkmMFFxkWxreXiwTxuAPusTZfPNy3zzehf+Sc/KFmLIeud2knWKOTS95HeWw2Es5NKGxgnVMJPB/qQrDeVjq6B8x9fQZrC30QEvaCqyH+m6BIA4CVn6wTW2s9rjLcQLe6u2X9y7JrS33HKL02/aFu0iYcrCGuWspu7tHSuM0+OPP44oY/TYY4/FsIGGGIQkWd+xWCTiRm7zg8kd8Nw0Krvv2GmjnTMecLME6XZ43Pvmha40kp5hgTGpn6JPYQJsEwS70BCU5ZbX9q6aMtuzbwEgbKNJMzdcLp4L2Vgk2cg88cQTY17L4kXNKQB04eTaUozdCtJ8CWPq4tnKJ9PH+7FGZd0U5ZVN0fetLIa3PngmIkobL+O9DwGgr/kL8iO3PSNvaQuGAwYMiPFXFQACwfIpUwCILNiDUKwAwi4NVOLTCANl2QjACF0Gd7Madloa8h0k7faA76mnnpJeYtewdcHMGS6MjoMgDJEquygvtEySCOrvMh5owWEQIEnmK8/kM0kAiDjXW2+9KD3YL3nxxRdlUrFrqPDKFVt7u0Aa9vjYS+EvPr5JNiCxYmgLem0B4IQJEypOLrYHTzLzWBWw48QqpaQiAh97S/SXX34po4pdQ4gAbGW9AiubfNZrGe3Ozm/S/d577x0rK+x2pm1LS4qn6HOJH7DGanoSX7EnDrYwG2kXaQ9l4O2LB/nuK754hq94UFdJvAcYSv4LPpBG0NyQ/RSr7y6SbS0PD/YpAPRdn755mW9e78I/6VnZQgxZ79xOsgSAyKvLhhTCQzAILaa33norwGFJ0PiBLTCOW7rQ1LTJdRAIwkDb+umnnw75Lg5Kw7ZTGZe8dk0mXbYCEQaLhCwEHDlyZIVJFhmvSwAI7UTph69xGjxs5eJETGjASSoapiysq6n7H374ITbu4fLCxpfcVfLBBx9UCOLZ7/PPPy/hyH1tCx+TxlscYdl9h9Nh19ZgTeK17N/l+uaFrjTSnqGPuQRJXHfYiYD2iL4LgVtS34Z/LK67BOFltWffAkAoaEjbW4wB5gwQYqOumLBYj77OfqRrC8fL4kWuemsqDUDblj3Kj/y88847DFGIVxrfxthGkvLJ5yUcha6L8kpE3hR93y6ED56JOJPGy5yeDwGgr/kL8gTzEJJHQMjnImjQ2weZqgDQhZT/Zwa2lrIIfmTj4krFKanQ1EDFQtCGQSsGPpdffnmA0/DkdlBmltDWsCmrYdv+0+7RwTl/cDFYxxHvkqD5ZQ+c8NGXHztsr5XxoCzQfJQaa+gsmAjYHyXXiZVyIJpn8pkmAMSpVzJvqAfbBgfKC9uGcoszwqBckrKwP+ecc2JpYdvQ7bffLqMIsIoojznnvNkCQATCVl5+zy7qwhY24SAK+xRaF25FBD72qiRW6m0NN6wSoXx220VekZZNPusVcftud3Z+XffQUJFCYpQVmpao+7y/4cOHu6LOfCbx4/aANitPCkefg7aeLZC1DxNCYkXaA/yXgbcvHuSzr/jiGb7iAfZJvAftkduCq88hrCRbqwqntcI0Ak5sHDVqVORVtjUXL4k8Tr/wKQBElD7rswxe5pvX23gm3dvfYq5718Q6KY6057LeOe48AkAIzewFKA6fx8WYybUtFKY0bF6WJz7pxyUAtPmO9I/rNEEG+3UJAGEagt8nuRgHSSoapiysq617e9FblhtjnYUXXjgRE9iWrZYgUJVpye3jrjjL7jt2mrCZLPOXtohqh5X3PnmhjDfvtS08kGXKe40xYpIJnrLas28BIPA69thjY3Uqy4/xPsbJMGlkK3OwPyzWwaSApLJ4kT3XQh6aSgDYv3//RJywyIL3Li1Bxgkudp1JUj4p0Sh2XZRXcuxl931Oh11fPDNpvMzpSBlNtTYAEZfdd6uVocDElmz72I0waNCgaM4NuRJsYGJrvPSHaxUAcq2W6+YSACILWOVw2aKxKy7t3hY+cdGyGjb7y+ui8dj5wEcK2iuQStsrXmiYWM2XBOGea8Wne/fuAQ6DAMN3NdyVVlqp4mOIeOVANM/kM00AOH78+NgWYS4rJsuwX4OTqHBysC3UwcRYCjmRryzsobaLMnEa7OJgCBj2tLei8Xu4LgEg0nTZ2EEdoMw4hUhixfHBHs4nn3yC4DEqIvDBYM3GBNteYZQaQltMqiEI5jRtF4wQK6aSZF5rrVfE67vdybwmXUutLrvMee+TBmFJafJziZ9MCwM9aDlhK4XUBmM/9uEfHF+R9oAwZeHtgwchf776ii+e4SselC2J95xyyilRH9x///3hNZWQJ3sFkduJPIBBtrU8fdW3ABCF8FWfZfCyMnh9asVNf1m2EEPWO7eLPAJAZA8aX9UI6/A9cy1QMB62/VHOl8t1pe8SAGK7aJpgyhW3/cwlAMQ3T+4EsMPgHuWRVE2YMrCutu5//PHHwDY34Sq3/QxagtAKrZaw2IWxDseL8VAald137LRXXnnlKG/g37WQL15YTR6AMzRYGeeiLsaR9kK4nY8y2nMZAkAoRKy22mpVYYFxmtSKZQzK4kXNKQDEwqSLFxdpO9jFIkn5pESj2HVRXsmxN0Xf57Tg+uKZSeNlTsuXABDx+Zi/QLkGB9/Y/QNjdZf9YelPBYBcq+W6uQWAyAbsbVXzocAE/qabbkosSVbDTgyY8ALaZAceeGBFw5MNjK8xsIVU2kVYvbdPLONwLhcCL1ujjOOVA9E8k880ASDihPTcpXXnyheeYUXAlbc82AMHaXA1KQ3YjJNqv0kCQAghjzjiiArNyaR4IaSDNqOLigp8XKeiJaWLwZZtJw42OyT5rlfE7bPdybwmXdsC8SQ80p77EABCoC4/YknpYbtykj2Jou2hLLx98SCffcUXz/AVTxLvgW1QrntoiuShpNV5LIQwFe2rZQgAfdanb14GnHzzesY+zS1biCHrndtVXgEg8o1tmEUEaxjwZx0choWHY445JmrnnC/bxaKrvYUIflwCQOQVh5G5Bt12vNjeaJsugR+XABDxYnHMjkPe24eLVRvGN9a11D2EGNjJIsuZdo2dGFkHpQGXLJJbCCFQTDsYq+y+I/MKYYXcGXHSSSfJ14WvffLCwolPD3DbbbcFqLe0erXfYdyNcHnId3suQwCIckDbGkoNdlnT7jFew46AJCqDFzWnABDlPPXUU3NhhAVqjD1s/Pr161cBVzW81Xe7qkc+CSCL8Eob+LL7PtLzyTOTxstcLjl3qkUDEPH5mr9gi3ue8QgW/Q877LCov6gAkGu1XNfYtuqykoP0HCtfGPBhldtmcPIexnZhCwI2VdIIgiMO5zpoIS1s2jvsLcd2QteqDVbw8DG1VbJd8WESuuSSSzrjQb4xUURDTyPJYPMIAKXATU5iZRoQ6EGdWR7ywTjChcYatBhhtwP15qK82MOw7cCBA51CRwwEcFAKmMZOO+0U1SWEp2kEu0GYLGEyI/PN19h+hRPJ0g7rqEbgA/t+vXv3dqYJgTDsVMIYKggn4HJ+4NqaZ2XUK2Pmo91xXEkuBn6uQZUsc57rpIMXktLl5zZ+MJiLSSjarp0uNFHtFVSOh91q2gOHLQNvXzzIR19BOX3wDF/xuHgPTj3l9ojJZt5toBAIQwhoryxCgMJktzV+nuT27ds3aoNoezbV0tZ81adPXsblK4PXc9wuF8I4u6+j/0NA4INkvXM60P4pQhjIwwSIa1KHODHGwEEeZ599dqhVnDduCOFchxJg0QvjE2wlRNqcb3Zlu7bTwpZ4LMTZ2u4Ii0kCxgMgmGvh+NhNEgCiTRx88MEV/hEOC7y2BiDiryYMwvnE2kfdX3vttQHGbEk7YCCkg2AANmV9EL5DXB9w8Q1JorL7jkwXi/gyX67DcKT/vNe+eGHe9Gx/+MbgFFtXW5Hlxe4QmDdCuy5CPtuzDwEg5gQuwhwBWstyEV+Wn6/xjcW2xqx5HdLwzYtcc86kxWeXHcakAx1vvfXWWNtGWdGvXQRb8UnzB/BynDYLgukpxoxdCEOw+CNJ+aREo9h1EV7pirnsvu+TZ7rGy7JMchxRqwCQ4/Uxf8FiKL6f9i4djPOXXXbZaBEBBwlxP7Hn2chPVvk5z+rmR6AVvBLoVRFtvTJkoB1ahObbb781uCdtIkPbQw0xSENbTquK13cgYrCGPkSGVmcNNUJDBw0YGqwbYsaFkkI8ZPjZ0BYPQ0zc0IDAkF0MQ8ddF4rHt2f6cJuRI0eG+UI90AfaUMcyNKAxNJHymhxNtEMcyUabIeZpVlxxRUPbNA1NgKpOB1jS9t6wjkgAEOJKk2uz4IILes8/ZxJpAjOUg+zYGFoFNrTl1CBdYkzsrUW4LbXd+QCHBPRhvSMu+kgYWjUOo0WZ6XCHsL+RXZWwnzUVPykDb8Tpgwf56iu+eIaveLgt0QEwhuyChLckBDG0iMCvcrnAGXyetHcMCTtC3uybB+bKSE5PPuqzLF5WBq/PCUuL9kaLQoYO1zK0eGjQ/sHD8MN3t1oi4ZEhm0aGbFYa0oAN4yMBXrXRheFIeGjINms4NiPTJeGYjBbcavpW02KcoZ0g5sMPPwzHeSRMyBz/VBOGC14G1hx3URdD5REjRhjajWBQXxj3YYxC2qGGFjGLRpfoH/0Z40qMT0AkyDAkVEj031Qv8H0eMmRImBzaKNl+9pq0D15Ya4bQ3mjCauhEboN2S6c6h2NC1DEttNcavWlJ7TmtMLRQGOYVPARzClrkD9s62jvGY0XHyGXworT8l/0ObRW8gOe/JHwJ50KktFB10soni0Pnk1eW0ffL5pnFEasuhI/5C+LA2AFjEtKiNquvvroXnlpdiTQUEKhJAKgQKgKKgCJQCwJJAsBa4tSwioAioAgoAopAvSJAWyvNfvvtF2YfCxhYZCeNpGYrDgS+WDhnIk0xQ7uA+FZdRUARUASaBYGWxisZBOWZjIS6LRUBFQC21JrRfCkCMwECKgCcCSpZi6gIKAKKgCKQGwE6VTXUPIPgD0S2iA3ZTc4d3rdH2mpsLrjggjBaaH3SCeu+k9D4FAFFQBEojEBL45VcAOWZjIS6LRUBFQC21JrRfCkCMwECKgCcCSpZi6gIKAKKgCJQCIFLL73UHHrooWEYbJmCmRSygVYoDh+esd0ZW2Bh8gV03333ma222spH1BqHIqAIKAI1I9BSeCUXRHkmI6FuS0ZABYAtuXY0b4pAgyOgAsAGr2AtniKgCCgCikBhBKDZAhvLsDkNOuecc8yxxx5bOJ5aA9DpjOaSSy4Jo1l33XXN4MGDTa32KWvNk4ZXBBQBRYARaCm8kvOjPJORULclI6ACwJZcO5o3RaDBEVABYINXsBZPEVAEFAFFoCoEsNUWB27A2D0OocDhMzgcqakIBx3A9h8m2DgIAgcs4SAIJUVAEVAEWhICzc0rGQvlmYyEui0dARUAtvQa0vwpAg2MgAoAG7hytWiKgCKgCCgCNSFwyimnmNNPPz2M4/DDDzcDBw6sKb4igXfYYQdz1113hUGuvvpqs++++xYJrn4VAUVAEWgyBJqTV3IhlWcyEuq2dARUANjSa0jzpwg0MAKffvqpwfHwoC5dupjevXs3cGm1aIqAIqAIKAKKQH4EJk+eHG79HT9+vJlrrrnMqaeemj9wDT6nTZtmDjzwQBMEQaj1d9xxx9UQmwZVBBQBRaBcBJqLV3KplGcyEurWAwIqAKyHWtI8KgKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCVSKgAsAqgdNgioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAopAPSCgAsB6qCXNoyKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAiUCUCKgCsEjgNpggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAI1AMCKgCsh1rSPCoCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAlUioALAKoHTYIqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKQD0goALAeqglzaMioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIlAlAioArBI4DaYIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCNQDAioArIda0jwqAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAJVIqACwCqB02CKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCikA9IKACwHqoJc2jIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCJQJQIqAKwSOA2mCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAjUAwIqAKyHWtI8KgKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCVSIw0wkAhwwZYiZNmhTCNfvss5tlllkmBt0bb7xhfv/99/DZQgstZBZccMHY+0a6QVmHDRsWFalVq1amf//+pk2bNtEzvVAEFIEZCGTxjxk+/V3JNBdffHEz99xzxyLPeh/zrDeKQBMh4Otb6iueJip2XSVT79jWe/7rqrFoZhUBRUARUAQUAUWgIRCY6QSA88wzj/n222/Dyttyyy3Nv//971hFLr/88pFQbI011jAvv/xy7H0j3dx5551mxx13jBXp9ttvNzvttFPsmd4oArUiMHXqVHPVVVeZvn37moUXXrjW6JotfBb/KCNjEPiNGjUqjNrFs7Lel5EnjVMRyOrTvr6lvuLJW2NZ5cobTz34a2psfWNS7/n3jYfGpwgoAoqAIqAIKAKKQCYCwUxGNFkOCJTwR5PpitIvt9xy0ftVV1214n2jPTjhhBOi8gKXpZZaKpg2bVqjFVPL04wIkBA9WHbZZcN2dt999zVjTmpPOot/1J5CZQx/+ctfoj66+eabV3jIel8RQB8oAjUikKdP+/qW+oonT5HzlCtPPPXipymxLQOTes9/GZhonIqAIqAIKAKKgCKgCKQhoBqAKRqAJAA0r776aqYQtZ49UOMw//znP81DDz0UFQNakdA0UlIEakUAW7TQj9DOQCQANFtttVWt0TZb+ObWACQBYKyvAgipAeh632xgacINiUDePi21s2r5lvqKJ6sy8pYrK556et9U2JaFSb3nvyxcNF5FQBFQBBQBRUARUASSEFABoCUA3GuvvczQoUNDvGAf8JZbbknCrmGew+bh6quvbt57772wTCuuuKJ56623GqZ8WpDmQ+CFF14wa6+9dpQBFQBGUOS+yBLwZb3PnZB6VARyIJC3T/v6lvqKJ6toecuVFU89vW8qbMvCpN7zXxYuGq8ioAgoAoqAIqAIKAJJCKgA0BIAJgHV6M8///xzs+aaa5pffvklLOrgwYNDza1GL7eWr1wEGm1SrRqA5bYXjb3lI9BofZoRb9RycfnUVQQUAUVAEVAEFAFFQBFQBFQAqAJA7QWKQGkINNqkWgWApTUVjbhOEGi0Ps2wN2q5uHzqKgKKgCKgCCgCioAioAgoAlUJAKdMmWL+85//mE8++cR8+eWXpl27duHJnosssohZZZVVTNu2bXMhO2HCBPPhhx+aDz74IHTbtGljsO32r3/9axhf69atc8UjPX366afmtddeM3Bxmt9qq60Wbm+dddZZQ29ZE/ivv/7aTJo0KfTbsWNHA/9p5AsLpAEtvHfffTf8TZ48OcSBjFwb4NqqVau0bNT0rjnS9YVbrW0IGo8///xziF+vXr3MLLPMEl6jHnAC9FdffRW25/nnn9+st956zhNsP/vsM/PMM8+EfQFtf/HFFzd06IWhA1US60Wm26NHDzPbbBzJAiYAAEAASURBVLOFfr/55hvzyiuvmLffftt069YtTO/vf/+7mXfeeRPjcr2oFRcZ58iRI83w4cMNyokTtJGXJZdcMvwl5WvixIkGZcGkes8994yiu/zyy83GG28c3qPMKLuL0P6RHvgDfj/88IPp0qVL2B/BY8An8vIZV/xJz2rlH654ay1L1hbfrPeuPDV1n6+mDbnyzc985h/t7PXXXzcjRowwMIewwgorhN+N+eabL0wOvOqLL74Ir/GtAy+QhHfwA0IbnWuuueTriuuffvrJ/Prrr+HzPP7hsZbySl4jeRy+c+AzsHOLPOG7i7Lje+P69hbt00W/pRVATX9QJJ5q2lnRcuF7wZrys88+u+nevXvInx5++OFwTAT8NthgA7PggguGJfDZPiQWSXglPcf4ivPEfmR8ecY7tX5XfLXFIvn/8ccfzdixYzlIiAGwUFIEFAFFQBFQBBQBRWCmRCDthBD7HQ2Ug5NPPjmQp04SaNEJlbimwW8waNCggIRvdvDoniZLwWWXXRaQACAWVsaFdzfccEMUJuuChIhBv379AhKUVcRJk5kAp2fSIDCgyXL0vpZTgH1hgXLRoRvBnHPOGeVL4oDrnj17BnfccUcWBIXfN0e6vnDz1Yb+9re/RbhfeumlAU0WArJZFz2TdYG2tffee0c4wy8doOJscwi32267BaNHj478ywuZ7llnnRWQ4Ck8gVmmx9ckdAjjImGkjMJ57QsXRE52IBOx4LzRBDg455xzAtSrpGOOOcaJIYeDe+KJJ8og4fXHH38cbLbZZgEJ91LDkxAmeOyxxyrCV/vAF/+Q6fsqi+S3tZ4C3NR9vpY2JLHka5/5R52j/+L7INslX5NALKBFruDiiy+O3pMAhbMSuVn1E3mcfoFvJKeBtp5GPsorec3AgQMDEnIG+Pa1b98+ygfnBy4JPgOchmtT0T7t64TWPPHU0s6KlmvppZeOcLvqqqsCWtSoaEPgXyeddFIIoc/2QfZ5o7RlneW5JuGvXaVBHmwRyNd3xVdb5ILkyb89tqLFJA6uriKgCCgCioAioAgoAjMdAjidMxdB+CAHb1kDzgMOOMAZLybFcgCdFc8mm2wSkNaRMy5++NJLLwWk4Zc5MMaETqZXrQDQFxYQSO6+++6xPMn82dc77rhjQJojXOyq3eZK1xduPtsQaelF+B977LHBEkssEd3b+PM9JtEoCybK/CzJ/b//+z9nPcl0t9tuu4C0hjLjgtCLtOGc8eGhT1zef//9XH2Ky73hhhsGpFEU5e2ggw7KLI8tAIQgMUkowenY7tFHHx2lWe2FT/7BefBZliwBQtZ75Kk5+nytbYixLCP/EHDl+WZgIYo0f6O27EMAKBehkgSAPutL8poTTjghIA3aqDx2f+J7CLAg3JJUtE/nEc7I+JOus+KptZ0VLddCCy0U4bftttsmLgCddtppYZHy9E9Z9rT20RwCQJ/fFV9tkfHKahvwR5r0UX2hfasAkNFTVxFQBBQBRUARUARmRgRyCQD/+OMPp9AOAjUIsDCAtoVrGGhBo0oStITkgI0nGxgU7rrrrsHOO+8c9OnTJzZYgx+EwQq0i2h7TdC5c+dYmE6dOgXrrrtusO+++wZrrLFGolChGgGgLyxQFtoKGcs3ykpbR4P+/fuHmma0HatCs2DTTTd1wVDoWXOk6ws3321ITki4PbK78MILBxtttFGFcA6TYymwo61T4aQamoNoexyeXTr5tqJ+ktKFRt0ee+wRatXtsMMOQdeuXWPx0Zb04LfffquIzycuiAvCRs4/XNrqG9BW3uD4448PINynLbix9/Bz9913R/m65JJLQu1Bu7+j3MAJP6nh+84771TEN8cccwQ77bRTcOSRR4Z9GYIYW1sLdUFmCKJ0i1745h9I33dZsgQIWe+Rp6bu8z7aEPLN5DP/tJ02oK23sfaGPow2iW9GUj9GG28qAaDP8ibxGvQdfLf79u0boK/J/o5rCE4giGQq2qdl31911VU5msJuWjw+2lnRckkBoI0Z30NbnLYjh2XN0z8lKGkCQLTPRRddNPGH8RPqdPnll6+oz7XWWksmE16nYQsPPr8riM9XW0RcoKz8w48KAIGCkiKgCCgCioAioAgoAn8ikEsAeMYZZ8QGkxhQ3XnnnTEMseX3wgsvjPmDhgUGkEwHH3xwxfsHHniAX0fuLbfcUiHUwyDdRViB50E3XAyAbS0pTMhtgQb8ViMA9IXFI488Ess32UILbr/99ooikm25cAuwLOMTTzxR4S/vg+ZK1xduvtuQa0KCiRK0HpiwZQ6aOrIO+BqTZ7KjxF4DspFUIWzZfvvto/d84Up3pZVWqtB2fe+994LevXvH0oYQziafuLz44oux9A488MCA7NjFkpw2bVpw0UUXxfwBC5vIVmjMj0sYijC2JuwhhxwS4x0cL/CwFxug1VQt+eYfyIfvsmQJELLeN0ef99mGfOcffJ/7L1x8M7AdWBL6v0vI3RQCQN/ldfEaLJD973//k0UOnnrqqaBDhw4xbLC91aa8fTqPcMaO23WfFo/Pdpa3XC4BIHg3NCbvueeecKEEi5lMWf2T/bGbJgBkP1nuXnvtFatHtNvvv/++IlgatvDs87uC+Hy3xaz8I030byyc4bfAAgs4F9DgT0kRUAQUAUVAEVAEFIGZAYFMASAm/vZWKWyZSyJ74Hn//feHXj/66KPYgBR2zexJl4yTDkKIba0hQ9sVwhHYZ5ITOWzXIgPdMpro+rvvvgsQh/RfVADoCwtslZR2oKAt8Nxzz0V5tS9gH05qltEhEwE06opSc6XrC7cy2pA9IcG2XtdECdocZDg81n6wXdgWjKFOsE0bAl1ua9g6b5OdLoR848ePt72F93Q4QYD+wvGhLYwbNy7y6xsXW1grtYCiRKdfYIs+5wv42Nur8kyqEUYKHqAtBAFjEr355ptRmkgbdtyqoTL4RxllyRIgpL1vrj7vqw35zj8EyNxe4ULzLcmsAvqYLXwvWwDou7zoFzavWWyxxWLb9WXfweKbxMfVt/L0acSZRzgj0066TovHVztD2nnLZQsAIVQaM2ZMUvZjNpNdNjztgLUKAGFTVtYhHWwVYJu0i9Kw9f1dQfq+22Ja/l3l1WeKgCKgCCgCioAioAjM7AhkCgCfffbZ2GDSpeUjQYRWAQ8+sX2R7eBcd9110XO833///WUw5zU0pzguuFdccUXMH7QC5XscUJJG0JyS/osKAH1hAaGozIc8WCIp/7B1JsOkCQyT4miudH3hVkYbsickadqV0CSQdeDSXmXsYROP/bqMr9vpyu2wHId0sf2W44P70EMPRa9944I+K9O66aaborTsCywGnH322eH2XxjitwXTeSbVEPZDIwnCFQgRb775ZjuZ2D2Eg1IgDqyroTL4RxllSRPwodxp75urz/tqQ77zbwuMoMWaRrfddlusL5QtAPRdXpTN5jWPPvpoYpGxoCFNatAJ5BV+8/RpBPIlnEmLx1c7Q37zlssWAOKgljRK65+ucLUIALGLQB6Ehm3eTz/9tCuZ8Fkatr6/K0jQd1tMy39iofWFIqAIKAKKgCKgCCgCMzECmQLA4447LjYBwjahLHr11Vdj2yLhHzb+pFABhyhkka2tgW2BkqT2EeJ2aW5J/9DOkTbEigoAfWExYMCAGBaPP/64zKbz+oUXXoiFueaaa5z+0h42V7q+cCujDckJCbTskrTwgOs666wTq4NRo0Ylwi2F17A3ZpNMFxNElyahDDN06NBY2tiWy+QbFwixZF/FNba0QTiKLc5FKO+kmuMEDmnaf/AHzT1psww226qhMviHzIevsmQJENLeN1ef99WGfOcfNmG5bePAGWmiQtYdX8O0hcS3bAGg7/KiHJLXoOxSe5jLKV1oCDJGK6+8snwVXuft076EM2nx+GpnKFjectkCwKyxjGw/ZWoAIv/2IUpXX311Rf3JB2nY+v6uIF3fbTEt/7Kceq0IKAKKgCKgCCgCioAi8CcCreDQYD+RaOJvBg0aFL2nrZCGJkHRfd4LGjQbhAXRlmLzyy+/ZAalyZch4+yGDgAJ/ZKmjyEBZBSObIEZEhKG97S919A2nOhd0gWtrhsS3ISvSQBoaPU+5pWMZ5thw4aFz2groiFhZvTeFxakzWVuvPHGKF463dfQltHo3nVB2zDNrbfeGr066qijzPnnnx/d57lornR94VZGG6ITqc1///vfED7E/9lnnyVCSQewmMceeyx8TxpohoSFiX7pIBdDmnPhexIAGrIjGPMr06XDLczgwYNj7+0bOvjDoI0zkU1C8/DDD4e3vnFBvyN7SYYE6pxc5KI/0snGhg4pMKQNbEgrMnrnuiDBtSEBXfSKbACarbbaKrpPuwCPIGGfIXtshrajhb/XXnvN0IQ7FgzxP//887FneW7K4B9J6dZSFsmzSIBgSPszlkza++bq877akO/802E25ptvvgnxIzMMYfuKgem4QftCOwbh28ffMfaahj/7kS76Fp1sHz6S/RgPfJcXcUpeQ4cMGVoIw+NEInt25u233w7f00FU0TUHyNun076lHFceNy0eX+0M+chbLjocypBZhjDr+A6QQNWQ1l1iUXy2j6REaOeFIeF2bFxFhyeZCy64IClI+DwNW9/fFSTouy2m5T+14PpSEVAEFAFFQBFQBBSBmRWBLEnoBhtsAAFh+IP2nL3FLys8v8dWFI4Hq7Z5iQahUTjY2pEktYCwspyHYI+N81FUA9AXFmuuuWaUB85LUXeLLbbIU9yYn+ZK1xduZbQhqZFAk4kYXvaN1BiDrcA0wkm+XKdZGoA49TkPSVucUjOnDFywld+2f8blkS7sUZ566qmJdtTyatVw+d94440Apx/POeecEX4yPdc1CWg4eCG3DP4hM+CrLFkaRGnvm6vPAwcfbchn/qFZKvsKtp3nIXmoCwkAK4Kk4V/hmR6QQChq2zhcSJLP8nK8ksetuOKK/DjRld9InERvU94+7Us7KyseH+0MZcxbLjkmIYGWDU/Fvc/2URE5PYB9Y7RLyRsxPiDhqMt77FkatrKv+Bqz+W6LafmPFVRvFAFFQBFQBBQBRUARUARCBDK3AMvJAIxJV0sQgvAAdbXVVssdDSYsHA6HfEgibaToXZbwhsNhCw7HV1QA6AsLe7DO+SnirrLKKlyk3G5zpesLtzLakJyQuOxdSXDLEgDaW9tlmvIaQkduI8g3Uxm4IG5sqd96661jQhNO33YXXXTRgDQpOUuRm3dSja3XsC9qx+u6l4I7vK9WAFgG/0DBfZclS4CQ9r65+jw3gFrbkM/8Y/u6bE952w2223M45MemNPxtv7hPEwD6LC+nXYTHIYzk1/UgAESea21niCMvr5ICQCxuZZHP9mGnhe3cOIGY2ydc1FnWNm+OJ02AVsZ3xXdbTMs/l1FdRUARUAQUAUVAEVAEFIEZCGRuAe7Xr1+05QzbXGgSZejEThpnFqP5558/2rpHg0BDp9LlikBunyEhQ7gdkAPKrVTVbOcqugXYFxbYWvz666+HxaBDD8y5557LRcrtAhfSlMrtHx6bK11fuJXRhuSWJBIAGjrUIhFTuQWYhHHmyy+/TPRbZAvwPvvsY8imY2JceEHaS+F2eLIrF/rDtkTe9loGLjIzdLK2eeSRRwzZqjR0oIv56aef5Ovomg79McOHDzekORg9y7OtDtv46LTRMI0o4PQLxIk6WnbZZQ227GJ7In4oM2/llFjY4dPuy+AfZZRF8sCiW4Cbq8/buFfbhnznn4QxhjSmwuyhPb377rt2VivuJf8iAV3qFmB7S29FZPSAFrIMnTwcvrL9+y4vEinC4+CftIsNnbSNS9PStwCHmRR/1bYzRJGHV8Gf3AJMAkBDh2zgcSLJ/mvXtytQWvuQ/sFrMIZhUxB4hy3uGFsgzTyUtoW2jO+K77aYlv885Vc/ioAioAgoAoqAIqAIzHQIzJAFuq/IjkxsdZlspLk9iqfwQ3b0ArLJFz3FdisCN/zhdOA8BAPt8kQ7GHCXJLUDSSiZeXgAwsptjUU1AH1hscsuu0RYABNs4WkKaq50feFWRhsqopFQlgYg2dPLrH4SdsXaDLbJMpWBC8dtu9hWRsKB4PTTTw+WWWaZWJ7Qli+77LJYkDxaNWTbsiKeww8/PNQoTDoQRJ5USjYJY2nmvSmDf5RRliwNorT3zdXn0+qgSBvynX+5xRZb6vMQtMv525WlAbjRRhulRon2LA+isrcA+y4vMlOEx8F/PWoAIt82FWlnCJuHV8FfLRqAtbYPpM900EEHRe0S7RPjKoy7ilCaBl0Z3xXfbTEt/0VwUL+KgCKgCCgCioAioAjMLAhkbgG+6qqrYoNMWm3OxIZWeaMwGPCB9t133+gZBquw25NFpI0VC7PNNtvEgmy33Xax9zg1OI3oIIUAJ73yZK6oANAXFmeccUaUB+SFtKvSsh2+gzCVDg4JHnzwweCdd96JCVczA0/30Fzp+sKtjDZUZEJSlgAQti0xWU0j9Dtut3AhgGPyjQsdUBCQpl9wxRVXBM888wwn43Qh8JP5AkaS8kyqpb1ExHXJJZfIKCqukT+ZJoQ61VAZ/KOMsqQJ+FDutPfN1ed9tSHf+adDNmJthw6VSW06OOEVi0vc3lwCQLk1P8uMAB1mE8WFOG0BoO/yonBFeBz815MA0Fc7Q7nz8Cr4KyoA9Nk+kD7owgsvjLUjCJUfffTRP18W+E8ToPn+riBbvttiWv4LwKBeFQFFQBFQBBQBRUARmGkQyBQAQhDAkx+4WXaTMGGS/mFAHWQPWDH5zqL1118/FhcEYJLuuOOO2Pvtt99evq64Puuss2L+iwoAfWFxzz33xPIBe35Jmk5cCDvvtG2UX+V2mytdX7iV0YaKTEjKEgCiv9x1112p9UhbA2NthrYuRv594jJp0qSgffv2UVp5hGuwDcp9frHFFovyhQvaVhe9g5+777479h43sB/I4eFKzeEKz/TgvPPOi/kHNtVQGfyjjLKkCfhQ7rT3zdHnfbYh3/m344PdyTTab7/9Ym3NJQBEm+f2i7qgbfqJUQ4YMCDyizC2ANDOn49vQxEeh4xnCQDz9GnE40s4kxSPz3aG/OYtV1EBoM/2gXzSSeqxnRFoR1mLJgjnoiRs4dfnd4XT9t0W0/LPaaqrCCgCioAioAgoAoqAIjADgUwB4JQpU2KrthhsPvXUUzNisK622mqr2ATnoYceCn2MHj066NGjR/QOW3ux4p5EDzzwQOQXaUJzj+z7xLyPHTs2kFsBsQr+yiuvxPzwDbbZ4hARxMW/ogJAX1jAGL3cioz82FsnOd9woTVi5/3tt9+WXnJdN1e6vnArow0VmZCUKQBcYoklgt9//91Zj7ZQwBay+cZlvfXWi/oI2agMXnzxRWe+8BDb/eU2fXs7M07C5f4G9+qrr66Iy95KTLYVK/zwAwgQkScZJyaB1VAZ/KOMsqQJ+FDutPfN1ed9tSHf+cdCC9mTjLWfJOEJhPJSYxxtziUAXGeddWLxQcjnoieffLLiQB1bAOi7vMhHER4H/1kCwDx9GvH4Es6kxeOrnSG/ectVVADos31gfCMPL0KbPPTQQ5H9qigNW9/fFWTQd1tMyz8DAlMy6Ff84+fqKgKKgCKgCCgCioAiMDMikCkABChk5Do2wYGG0JVXXhnDC1o7BxxwQMwftjZi0MWELYVy4o7J1UUXXRTTfoOwCNugpJ0khDnqqKM4mph7zjnnxOLs1KlTcPvtt8f8YJsXGceO+UOcRQWAiNQXFtjKK7HANfCzT+977rnnAjLGHfObtc0sVnjrprnS9YWb7zZUZEJSpgAQ9Y+t859++mlUYxBWQAvDFni5tnr5xIUOJIm1N5y6C4G8TbA3Jbf7owzIhyScDCzbeZ8+fcJtxUOHDg1GjRoVet1tt91ifnCyJh2SIKMJt0ijX9sCGcSNCXm15Jt/lFGWNAEfyp31vjn6vM825Dv/9nZ6tCEsXCEdLLbQYTeBvVWY27BLAAgBIr+HCwHNoEGDojYM+53XX399aKNN+sO1LQBEffoubxEeh/SzBIB5+jTiySOcgb8sSovHZzvLW66iAkBf7eOTTz4JevXqFWtrdChQ8MMPPwRYNPn444/D9os2nPSjA5xicKdhC48+vyuIz3dbzMo/0uzWrVsMM+ClpAgoAoqAIqAIKAKKwMyKQC4BIMDBRMWevECjDyvwWOGGQXX5HpOgt956K4YrhHvSoDr77969ewBD/thuCEPW/JxdOvUz+OOPP2Jx8Q22W+E9+2V3rrnmCmBw296Sx+/hViMARLo+sEA8//jHPyryDdwwAdtiiy1ig2XO95xzzhlgIlALNVe6PnDz3YaKTEjKFgCijiH4hhYZnVAa05jl+peHf8g24BOX8ePHx4QAnDYmvmg7dGJvQKeDxjT/4AfCPSnwR/4QV9u2bSvaOfzvvPPOYRFgZ1BqEeJdz549AxyIAME/hGpYTOB82C4WJGDfsxryzT/KKEuWgC/rPXBp6j7vsw2VkX9oTdntKM+9S9gMYXWXLl0q4kO7x4JUWrwuAaDv8hbhcUg7SwCYp08jnjzCGfjLorR4fLazvOUqKgD01T5OPvnk1LaU1s74HRZXJaVhC38+vyuIz3dbzMo/0lQBIFBQUgQUAUVAEVAEFAFF4E8EcgsAoZl24IEH5hqAYlsuNCBc9Msvv4QTex6QZrkQ0tnaQHa8iBO2CbPiwinCcjtOtQJAX1hAWHLEEUdUaDsmlQNCkaKn/NlY4b650vWFm882VGRCUpYAEEJvKcRJqv+999471b6YT1ygteTSmk3KGzRRkvpp//79nX0TQkQmCPqS4rafw9anbZ/qlltu4agKuz75BxL3XRbZNoCzTVnv4b85+rzPNlRG/qGVJ+1d2u0MmrcXXHBBKOzmdxA4uOjOO+90CgE5HLunnHJKcNhhh0VtPUkA6LO8RXgcypYlAISfPH06j3AGcWVRVjw+21mechUVAKJ8PtqHDwHgmWeeGYM7C1t49vld8d0W8+RfBYCxKtcbRUARUAQUAUVAEZjJEcgtAGScYMcI2/7sbYmY4ECLB5NzHASSRdjmtOSSSzrjQVxrrbVWOGjOioffw87LwIEDnUILCFhw6AgEUDvttFM0+YKGkU0QEvJkLetwAV9YvPzyy8HKK68cO22S8wAX9v9w6mvW4Qh2WbLumytdX7j5aENFJiRFBIAHHXRQ1I6whdYmO92vv/462HDDDZ0CCWi4wg5gXvKBC9KCQA+TTnnIh2yXEJ5Ao/f888+PbeO38wktO0ysbW0oTMwkwb6fbRuT08OiAg4FwoEyoC+++CLCF36SNCNl/GnXvvgHp+GzLIssskhUVpcAMOs95wluU/d5X22Iy+A7/1hQgVCkX79+4bcDGtjg+xDSDRkyJExW2nWEpnoS4WR7mGewNV6h1Qu7g9iuCjrxxBOj+sxqtz7Ka/OapPzzc7mYJoX0/B5unj5d5Fsq47av88Tjq53lKVc1AkCUqdb2YR9+xLyxiGsLAPNgy/Xh47viuy3myb8UAKJv+h5HMT7qKgKKgCKgCCgCioAiUA8ItEImaQBZmGjCbMhmjnnvvfcMDaoMHWJgFl98cUNboQrFhXg++OAD8/777xvabmJoq5+hQw7MfPPNVyge9kwD+DBPZEvNkJF/s+KKKxraTmlIYMlevLu+sED5aXtviCvZ6gmxoMmGIbtThoQt3vPNETZXur5w892GGJcyXRKih/WMNEhoYF566aUwOZSFjNGHfWKeeeYJ+wJtra0qK75wIVuEZuTIkWEf/fbbbw0J8gwJNAxN5gq1S+QH/IJO7zSktRb2cbtdoy0iLfRfsmtlSHhqSABj0A9IkFIVDkUC+eQfzV2WtHI3dZ/31Ya4TE2Zf7TBH3/8MUyaTDMYEoRwNpwu2jmd0m2GDx9uSIvWrL766oaE6E6/eR82ZXnz5gn+8vTpIvHV6tdXOyuzXGW0j1pxKxIe+fc5ZiuStvpVBBQBRUARUAQUAUVAEagNgaoFgLUlq6EVgZkbgSQB4MyNipZeESgXATp53hx99NGhYB0LTXTwkpl99tkTE33zzTcNaWdH78l2oLn44ouje71QBBQBRUARUAQUAUVAEVAEFAFFoF4QUAFgvdSU5rOhEFABYENVpxamThB48cUXDW3jjXJLW30NnUQf3csLOnjK0BbYSFMX7+gEbkOmAKQ3vVYEFAFFQBFQBBQBRUARUAQUAUWgLhBQAWBdVJNmstEQUAFgo9WolqceEPj5558NHaYUZbVDhw7mlVdeCQV90UO6wDb1Y445xlx66aXRY7Ila0aNGmXgKikCioAioAgoAoqAIqAIKAKKgCJQbwioALDeakzz2xAIqACwIapRC1GHCMCO38MPPxzlnA6vMuutt15oa5IOmzF0qqyhg4rMd999F/nBBZ0abPbcc8/YM71RBBQBRUARUAQUAUVAEVAEFAFFoF4QUAFgvdSU5rOhEFABYENVpxamjhDA4VCrrLKK+fDDD3PlGgLCY4891gwYMCCXf/WkCCgCioAioAgoAoqAIqAIKAKKQEtEQAWALbFWNE8Nj4AKABu+irWALRgBnOoL239XXnmlGTNmTGJOISg85ZRTTN++fRP96AtFQBFQBBQBRUARUAQUAUVAEVAE6gEBFQDWQy1pHhsOgU8//dRMnDgxLFeXLl1M7969G66MWiBFoKUjAOEfbAB+/vnn4W/cuHFmrrnmMnPPPbdZY401TJ8+fVp6ETR/ioAioAgoAoqAIqAIKAKKgCKgCORCQAWAuWBST4qAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKQH0ioALA+qw3zbUioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIpALARUA5oJJPSkCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAvWJgAoA67PeNNeKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCikAuBFQAmAsm9aQIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCNQnAioArM9601wrAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAK5EFABYC6Y1JMioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIlCfCKgAsD7rTXOtCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAjkQkAFgLlgUk+KgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCikB9IqACwPqsN821IqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKQCwEVAOaCST0pAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAL1iYAKAOuz3jTXioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAopALgRUAJgLJvWkCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAjUJwIqAKzPesvM9ccff2y+/vrrVH+tW7c27dq1C3/t27c3vXr1MnPNNZdp27ZtarhaX0796gsz5qA9TDB1imlFafW4f7AxlBfQ5OFDzZT3h+dLokN706pTF9O6c2fTqkcv027pv1I8bfKFTfAVjB9vxl9/uTHBNNN63vlNp612TPBJj6dONb/ut7OZOuobykdnM+u1d5jWs/VI9l/gzZQP3jOTh72dP0QrY1p16WpazzKradVtFtNmwd6mNWFSKxXCo9bEZvLwE+68mdrdnyC0W3k103aRPjM5IuUXf8KECWaPPfYw48aNMz179jQ330x1MJ3AP8FH81CnTp1M165dw98cc8xhunTpkidYhZ+JEyeaESNGmE8++cRMmTLFLLbYYmbRRRc1HTt2rPBrP9h7773Nd999Z5D+tddea9q0qY0XcvzKixiJxnCVpzdGPZZRipbGD1HGn3/+2Xz00Ufm888/N7PMMouZf/75zZJLLklDxj/HjGk4lMUT09JszncteQyh35HmbBmatiKgCCgCcQRUABjHo2HuDjzwQHPllVcWLg8GVcsss4w59NBDzU477WQgGPRKQRAKzSa//XoYbadtdzFdjzklSmLcVReZ8TdcEd0XuWjdaw7T8R/9TMfNtyYB2EJFgkZ+fz/3VDPh3tvD+3YrrmJmvWZQ9M51MfbYg8ykZ58MX3XYcBPT/exLXN4KPxt/67Vm3KXnFQ4nA7RZaFHTZY/9TYe+m8vHha6L4lEocvUcQ+DHlRczZtq08Fm7ZVc0s95wd+y93vhH4OijjzYXXHBBGPEZZ5xh/vWvf0WJXH755ebggw+O7vNeYAFl4403DgWLm222WbjAkhX28ccfNyeeeKIZPnw4NYE/2wCHAU/u06ePOeuss8w///lPflzhSp6PMh155JEVfqp5oLyoGtRabhjl6S23bpo7Zy2FHwKHhx56yBx77LGh8M/GZZ555jG77babOf7448NFF/s935fFEzn+lua25DGEfkdaWmvR/CgCisDMjIAKABu09uXAp9oiLr300uaZZ54JtQKrjcMON/H+O81vZ58UPm7Vrbvp8eBzodYa+6tFAMhxwO12+oUkDNxCPsq8nvTEw2bsyTRpJiElKI8AcOrXX5qft9mIVBcnh2FmGXiNab/WeuF1LX8+Bkucfof1NjbdB1waaVny8yy3Gjyy4tT3yQjIwXvbpZczs918X7JnfVMzAm+++aZZbbXVSJF3qplvvvnCiSY0+ZiqFQByeLjQxnvuuefMUkstJR9H1z/99JPZfvvtzeDBpAWdg/r27WtuvPFGJ08ePXq0WWSRRcyYMWMMyvHee++ZhRdeOEes6V6UF6XjU09vlafXU201bV5bAj9EiSdNmmT69etnnnzyz4XVNBR69+5t7rvvPrPCCis4vZXFE52JtYCHLXkMod+RFtBANAuKgCKgCExHQAWADdoUfAgAAQ22nw0ZMsTMPvvsNSM17YfvSFi2sQnG/R7G1eWw403nnfeMxetLAEiqi7Ql907aFrxsLP6km0mDnzBjTziMNLCmRl7yCADh+feLB5gJt98Qhms9+5ymx71PmlZdu0XxVHPhc7CE9DuTJmCXA/NrBNWCRzXl1TDGtOTBe6PVz2QS2K+44oqhkAxlu+2228zOO+8cK6YPASAixCT1jTfeCE0sxBKgmy222MI8/PDDscedyaTB4osvblq1amU+/PDDcHuy9LDuuuuGAkO8t+n88883xxxzTPh4nXXWMc8++2wYj+2vyL3yoiJotVy/ytNbbt00d85aCj8EDjDJIE0x4BlM00AL+ocffgjNMmDRhukvf/mLgfASWoEuKoMnutJpCc9a8hhCvyMtoYVoHhQBRUAR+BMBFQA2aEvwJQAEPIcffrgZOHBgzUiNPWp/M+k/z4TxtJ5jLtOTtP8gqJNkCwA7rL2B6dx/f+nlz2vS0gtoEg97RlO/GGHG33SVmfbzTzF/rXvObno89Jxp1XGGVk/MA90g/LjLz6Ntv7TVd7rmH/vJKwCcNuZX8/Nm/0dxjQuDdtp6J9P1uNM4mqpce7DUabtdTdcjT0yMK8Thu2/N1K8+NxPuuNlMfufNuN927UzPx17KtAvoA494wnqXF4GWPHjPW4Z68YftvieffHKYXWg6Y+utLVCzBYDgqdh2ZlNAfOOPP/4w44mXwH7fOeecY7744ouYt7XXXts8//zzsWeXXXaZOeSQQ6JnEPwhXzC/wPb7YAcQvPe0004L42fPSVt8YUNwoYUWMqNGjQq9XnfddWavvfbiYFW5youqgq3FBFKe3mKqosVmpCXwQ4CDhZhdd901wql79+4h/+vfv3/En8GrISQcOnRo5G/HHXc0gwa5zbWUwROjhFvYRUseQ+h3pIU1Fs2OIqAIzNQIqACwQas/SQB40EEHhTZTYGcKq77YboFV1aefftqMHTvWiQa2k2Fii1XYamnKZx+bX7b7RxQcdv9g/88mWwDYee+DTZd9D7W9VdxD+PfbGcebP14ioaKgUAtwhb+JJzMu/3j1JdqO/C8zjQ7xcFFeASDCjrv8AjP+5qv/jAbCtkdeMLBJWC1VDJZ27G+6HnFC7ujG33a9GXfJOTH/XQ462nTefd/YM3njEw8Zr17nQ6AlD97zlaA+fIHPYcsv87t77rnHbLPNNhWZtwWAV1xxhTnggAMq/NkPsAUX/NeekH755ZdhuuwfgrqRI0fyrXnqqafMhhtuGN3LiwcffDBm/w9aL99++630El1ffPHF4aINHsBg/meffVbTwU4zKy+a/N4w8xtMQhC16tDRdD/vCtNm/gXD+3r5U55eLzXVfPlsKfwQCKy11lrmpZdeisC49957zdZbbx3d88X3339vsHCDLb4gHGb3zTffJO5U8c0TOR8tzW3JY4iZ9TvS0tqI5kcRUAQUASCgAsAGbQdJAkAM9rp1q9ye+uOPP5rTTz/dYJILjRab7r77brPtttvaj3Pf/3basWbiI/8O/bdq38H0HPymaUUaLzZVKwAM46Htuz9vuYGBXT6mLgeT0Gu3uNArGDvG/D7wLDPx0fvZm9MtIgCcRtp3P5EWIGsRFt1ya2eg1sES4sMJxZPfei2KusNGm5nuZ10U3fNFGXhw3OrmR6AlD97zl6Ll+7zwwgvNUUcdFWYUNvogSGONO5n7agWAHMeqq65qXn/9db41kodCGLjAAgtE7yCAhCAyjTbffHPzyCOPRF6g5edalPntt9/CiTAWd0AQREJDplqaWXkReCd4KBPsqHbYYMYiFj9via7y9JZYKy0zTy2BHwIZ8OF55503Gn/ChAHspybRCSecYAYMGBC9hrkDmEdwkW+e6EqjJTxryWOImfU70hLaheZBEVAEFAEbARUA2og0yH1RASAXe++99zbXX38930YuBlrHHXdcdF/kYtroH/4Ujk0/KKPDOhua7ue7TyiuSQBImRp/3WVm3DUzTuJt/3/rm1kunK6ZNz3TEx97wPx2ytEVRcDW3Qn3zdhGUkQAiMh+2X1rM+X9YWG8rbrPQltuXzatxKECFQmmPPAxWLKxaPfXFcysN1YKGcrCI6V4zfJq6rdfmykffRCl3X6VNUgI3SW6d15ge+cLg6NJSSvSNGj/93UqvAZ/TDJTR3xqpn7+mZny+QhyR5hpY34Jt5+37tmLbFEuZ9qttKppM+/8FWH5QZ7BO7S5JEGLzN66Kt/rdRwBbKkFZl999VX4Yr/99jNXXXVV3NP0u1oFgNdcc41B/EyHHXaYueiiPwXw9lY38Nw994zbQ+Vw7J577rkxHvzEE0+Epw3ze+lK24LLL798bLuc9JfnemblRfUsAPTN05Xv5Okp9eenpfBDIIddKNj+C+0+EEwfwPxMEl177bVm331nLO7eeuutZpddKneVcHgfPLERxhCMR1O79fwdUf7X1K1F01MEFIGyEVABYNkIN1P81QoAcfIaTpq0aZ999jGY0FZDse2xFAG00KCN5qJaBYCw5ff7uadEUbddYmkz220PRve4sCdHrWftYbqdeq5pv+qa5sdVF4/8FhUA2ttuux51kum0/W5RfEUufAyWcNoyTl1mggBq1qtv59vILQuPKAHrAlvrxl12nvW04G3r1qbrIceatksukzvgmMP2Nn+8/Hzkv9sJZ5qOW24f3bsuJr/7tvl1z+2iV20WWtT0uOeJ6B4Xk5582Px+6XkGh9ykUpu2pvMue5nO+xxsoAVrUx4BoC3smzBhgunYsaMdld4nIABtOHnYBzRMoGnioloFgNDWg9Ye06abbhpp8EF4d9ddd4VaL9B8weQVh5Kk0ZlnnmlOOumkyMvjjz/u5NXwYAsY07RjoggTLhqZFyUUOXxcrQCwufibLItvnq58R6LbONcthR9KRLEbBSeYL7PMMolbeuEfwkFs7WVKM6EAPz54Yj2PIZqbL9Xzd0T5H/cydRUBRaBREFABYKPUpFWOagWAN954o1MTJU1Txkq64vanTdY0074fFT5P2/4LD7UKAH+/iE7kHXRDlIcOG25iup89QyMQL3hyBC29zjvsHgrpWnXrbgxpB9UiAMTq8M+brx2l3XapZc1st/y57Tl6mPOi5sESaa79tOlaEe5IFsJICCVtKgsPOx2+/2PIf8yYQ2s7mABxdTv1PNNx0y052kwXW9CxFZ2p3fJ/M7NeN0NAys+lawtRuxxyjOm86z5/eqH2MuaIfcwfr7wog2Ret1tmeTPrTfdW+FMBYAUk3h9gixgfxpG2/RcJ1yoAvPTSS8MDPbgQOPDjkkvivIjf5XGxMIMFGibYbk06nR12CFE+HE4C2mGHHcwdd9zBQQu5jcyL0oCoVgDYXPxNlsU3T9cJsES3ca7rlR9i0WTllVcO7f6hNmADEFrdc845Z2Ll+OCJ9TyGaG6+VM/fEeV/id1KXygCikCdIqACwDqtuKxsVysA3Gyzzcyjjz5aET1OoeRTMytepjyY+uXnZJdv/chH2vZfeKpFABiM+938vP0msUM9uux/uOm854FR+riAfSRMkDpusU18C2iNAkDE/cuu/zRTPngPl8a0bmN6PfeWadW125/3Bf5rGSwFv401Y2mL8x8vPhtLsdvJ55iOm1ca1C4Tj1gGpt8010A0IG25nzZaNTqtmfbOmh4P/8e0+cs8rmwakp6Y0RuvFraX0APVZ8/H6STl6Ye7THriYTP2pCNiYSFIbr/aWuRn9jCdqV+MrDyRmULMcuUtpv3Ka8TCqgAwBof3G2hLzjbbbOHBR4g8a1GjVgHgBhtsYAYPHhyVA1vWYGKhGnrttdfMaqutFgWF/cDPP/88unddQOPwscceC19hYvzddxkaqq5I6Fkj86KEIoeP61kA6Jun6wQ4raXU57t65Yc4/Rensb///vsR8DgVGIvXWVQrT6znMURzjbu4Tur5O6L8j2tRXUVAEWgUBFQA2Cg1aZWjqABw3Lhx4eT0zjvdGlFJJ2VayVbcYgsqtKiYuhx2vOm8c7Ktq2oFgMHvv9EpwCeYSc/Gt2fOcsn1pv0aa3Py6a4HAaCtgTjLwGtM+7XWS0/X8dYeLHXcasfkU4CnTjMQfk77ebSZ/N47ZsLtN8QOQkH0bRZc2PS4gw4QaN/ekVrCIw94uGKe9uMPZuyJh1Gex7le537WZb/DTPs13ds3kyL57fTjzcSHZ2jfdTngSNO5//5O75Oee8qMPWaG8BjtCO2J6ZfdtjRT/jucb02HvlsYbCu27T5Ofv9d89sJh5mp3341wy8dJoBDBSTlEQD26dPHoK+CsPX3ww8/rOmEV5l+o19DGAehHBO2hMntwPyc3VoEgPLUSY7vrbfeytzmy36lCy2+v//97+bNN9+MHuM098suuyy6d12cd9555thjZ2i8YsK81FJLubymPmtkXpRW8GoFgM3J39LKE76rkqcr38lEtu481As//P333w0WT6D1Bx76wgsvxLBebrnlwmfdu9MujgzywRPrdQzR3Hypnr8jyv8yOpa+VgQUgbpDQAWAdVdl+TKcJADE8/YkBMJJv1gBHj9+vPn6669DI/HYIuEinBoMw8ydqjjQYuzxh5hJzzweRZt1kqItAOyw8eamy94HReGji2mBmfbTj2bqD9+bqZ9+ZCY8cJeB5pukdsuuaGa9/i4667qVfJx8XeXkSEY44Y6b6YThM6NHnWiLcdcj/xXd572wB0t5wzn9kebarNfcbrDltRB5wKNQek3gefKwt8yve82w+9dmoUXIpt+MbZUyC2OO3C88AISfdT/3ctNhvY3D28nD3zG/9t+GX5k28y9oetxNwmfaiuQi2B6E/SAm1/bwPAJADq9ucQTsUyMxkVxrrbUSIyoiAPz1119DPvrRRx+FxutfeeWVWLz9+vUzDzzwQOxZnptp06aZ7bff3tx77wyhNfjxJ598krrdDXFjMUee/ostyQcffHCeZGN+GpkXTX7nTfPbOTNsxsYKTt9HKbTHuzYLLxbzgptW9D3tfuZA02aBhSretbgHDcjTWxzGdZKheuGHb7/9tllppZWcqK6//vrmvvvuM7PMMovzvf3QB0/UMYSNar77Rv6O5ENAfSkCioAi0HIQUAFgy6kLrzlJEgBWk0i1E0ek9QttyZ1CAjqmWW+6z7RbZjm+rXBtAWCFh7wP2rYzsw16yLR1TNgSo/AwOZr0PGmNHS20xuhgkVkuvykxyaQXvgZLrXv0Ik2zSwwONClMHvAonGYTBMCWdGxNZ5pt0MOmbZ8l+TZ0p435lbYL05bLKZPD+9azzGZ6PklCnekCPmynGX/LdaRp+YWZ9uP3psvBR8+wDRiL6c8baKiOXnv56E3bRfqY2e56LLrHhQoAY3B4v7Ft6I0YMcL07t07MR1bAAiPWDyxCYspk6efcG6/w33Xrl3N//73PzPvvPO6Xqc+c/Hxu+++22y77bap4fByyJAhoeYge8y7TY79s9vIvKi5t8Uxxk3mNihPbzL8GiiheuGHEPBts82MxTZZBdiaCTuG2P47//zzy1fOa188UccQTnhTHzbydyS14PpSEVAEFIEWiIAKAFtgpfjIkmviWE28tRquhxAFmnpMPR8fYlrPMSffVrg+BIA4aKTrv84yHf/RryL+1AceJkdTPhhOdgC3jJJp22epUBAZPch5UdNgiQbFEGh13HwbwmCLqmwQhtn0gEfO4japt/E3XW3GXXFBlGannfY0XQ8/PrrHhX2adKftdjVdjz455ie6IZxMa9IyJU1LF4Vbs996zYz915HGTJsaemnTmzQP741rHqoA0IWev2fQIoE2CQgTx4kTJzoFepyiSwDI7/K62JaGkzZhe6oITaE2tf/++5vrr5+x5RzhTzzxRIPTgPPQl19+aWArkEmeQszP8riNzItUALgKaYcPytMM1E+DIVAv/PCll14KD0/CqcDYsTJs2LDQtiq0o5nmnnvu8NkSSyzBj5yuN56oYwgnvmkPG/k7klZufacIKAKKQEtEQAWALbFWPOSpVgEgJsi77rpruLLaunXr6nJEmjE/rkoDsqkkIAG1aWtmf/UDEpQkx1eLALBVp86mwwabmM677GkgYClMHgReEPb8tOGqUdKt55iLDo54ObrPe2EPltou9dfYoRHBpIkmIC21P159kWz//RSLtu0SS5vuZ10cbkuNvSh64wGPokk2hf9ptG0cJySzMO7/2bsSeKvGrr/uPM+3AQ0UiYpSSqOvkEbRrESTRqRoopTQXDTQKJXQJCoSDXrx9lKUSEqDlGi48zye8z3rue3n7H3O3me+5+xz7lq/320/8/Dfp7WfvfYaMKgHv0ey32XGoF7cn6K0HjUtQalOuqKPndI/z0DJxfNQ+hf+nSv7+/uC1ERcSQAooPBYAoVh+AKIVLVqVfj337LI5FoLcEUAiCZpAwYMgHHjxlnVMlSbG31eocaLPOIvths/fjygDyt7CYWI6CeytLRM6NysWTPAYCKOkj/zopLfjkHO2wtUIcFAGiUnfxN1aP4bmJQs8vIERgYPva+VvEifaT/l6foEW9+r8hV+qIYiuljAQCBnzpwR1W3btoX9+/eLvFrCXTyRzhBq6Fov8+fniPWdUy0hQAgQAvpDgASA+rsnblmRswLApKQk6NmzJ39xrVPH0t+RI4vDF6iUdo1Fl8CqN0LSZ9+IvFrCXACIpquhTUwCNUUfZuaLEXYDmU8sfDkLrn2bphaWop9Wxh0vRyj0bMkc7TPH/ZyYyWClg0zo6SCZH5Yi+g1WDwLC5uFtl7+lmCEwPhHi12+DoBurKcodyrgDD5UJiw8dZNpwY1VqHCuKHj+NCXw7OdbpeuvM54ZA0UGTM3F5VF7zyNXBde6ABAygokIYFbBg+2bI37jOIvCKSnNRRAJAAYXHEpGRkdzvKU7YtGlT+OGHH6zObS4A7Ny5M++n1iksLAxQ2y8xMRHuuusuQKfhznw4QaEkznP06FHFNOiv64033lCU2ZOpXr06902IbWvVqgVnz561p5uijT/zIsVGzTLOBgHRA38z24opW0483TQBpXwFAV/gh9awRH+rGAAENbklQhPfFi1aSFnVqzt4Ig7sa2cIb/OlivocUf0RUiEhQAgQAl5GgASAXr4B5TW9lgBw+/bt/EUV50UtP3xxxT88DN50003cX5W71sS/knZqKYazxxzWXAAY+fSzEDV8jBijXBNuejkyN3uudIj5QNQwD9Xaj92HpesD5H/4Hgs+ohQQoFA0gflcDGD31ilyEx7mc+vB7K5w7xeQNckUECG8S3eImV6mXZXLhKl5q5eKZWMQFwzmYk4oKMSAIqj1aYsCYuMABeISkQBQQsIzV9T8CJEFaEEh22effWZ1cnMB4Ntvvw2jRo2y2seVyt9++w06deoktBRxrKCgIMB1jBgxwqmhGzduzAM8Yefk5GS4ds3kjsHeAf2ZF1nDwFkBoB74m+a+yomna85HFbpEwBf4oT3ATZo0CebMmSOaLliwgH+8FgUqCXfwRBzW184Q3uZLFfU5ovITpCJCgBAgBLyOAAkAvX4LymcBWgLArKwswCiSniBjUSGktGDacNcpqHpNSPxkn5RVvfqDADCl9V1gzM/j+0PBT/L+n1T3aq3Q0cMSjpWzcCbkf7hGMWxomwcgbv4yq2bXig7yTDm9LHr7IMq3yDQnUzq0YEK5DJ4NiIyCpD0/sIieYZDWrZ0p+ifTMsXgH4HxCXJkuF/LjEG9Te1ktQFh4Uwj9TYIvrUuBNdhf3XrQUj9hnAN/y+QD0AZUp5NxsfHgxTpHKP/YhRga+RJAeDXX38Njz32mFgfrgs/ymzcuBG6du1qbZlW61AT8Y8//uBt6taty4ORWO2gUunPvEhlu6KIBIACCkr4IQJ65of2wr1jxw7o1q2baI4faPBDjTVyB0/k4/vYGcLb566K+hyx9lukOkKAECAEvIUACQC9hXw5z6sHASBuMaXtPWDMzuK7DYiJheSvj1jdua8LAC2EnrVYsIfNymAPVgG4XunMYYmFIoX0gT2h5JTJbxUOFzXieYgc+ow90yrblJMAsOSP31kE3ZXKuRzMBTDNqMhBI5zz9Xh9rpx5MyB/03oxc+y8d5iPr0qQMdgUcTCsbXvAcnPKXbEI8lYtURRj0JWIvk8ywd/tFgJX8yjAQTVrQeLHXyn6UxAQBRxuz6CD+JMnT/Jx69WrB8ePH7c6h6cEgJ9//jl0796deQ0oEutBH4U7d+4EdNTvCqFLh7S0ND6EPT6y1ObyZ16ktl+pzFkBoF74m7QPxbWceLpiDsr4BAJ644eoAY0mvBidHV0hvPvuuxAcHGwVy2+++Qbuv/9+0Qb9rq5fb3qmiwpZwh08URrOl84Q3uZLFfU5Iv1W6EoIEAKEgJ4QIAGgnu6GG9eiFwFgWq8OPDCCtLVKPzBzWCa80SJfFwAarl6G1E4mZ/Ah9zaH+GXva21Xs9ypwxIbreTMKUgfwKIfM2GgIGb6mMh82DkcGMXPXxZLTp2A9P6PCJgwanQA850o16KMW7gSQtu0E22kRMawflB85JCUhcjBIyFqFIvyq0HFP//IzYWl6qAaN0Pitr1Sll9JAKiAw+0ZFIAdOHCAj1ulShW4fPmy1Tk8IQDct28fjxAs92NVv359QKFgjRo1rK7PViUG/0CzZyPzS4rUr18/HpHYVj/z+orKi5wVAJrjp6u8n/N0XWGt88XojR+an1n37NkDDz74oFUUly9fzqOlS43mzZsHL774opS1uLqLJ0oD0xlCQsL2taI+R2wjQy0IAUKAEPA8AiQA9DzmHpnR/DAlTepJE2CcM2PkACg+/D9pekjcccBqYApfFwAW/3KEaZD1FvsN69gNYl9bIPL2Jpw9LOH4qJmGGmpyCrnrHoh/dxM6fpQXW09XgJfF9H5dAb+MI2FAGTTfNaSW+UlDbUAeHVhFYI2m7ajtKVFU7KWWAAA9h0lEQVTili+ZgLW2lFVemQAm66UxULhnlyjH4Cz4f0FOJACUo+H+NArAPvroIz4wapbk5uZCKAvSo0XlLQBMTU3lwULwKlHLli258A+jCLtKly5dgmrVTEGAXnjhBZg/f77Dw1ZUXkQCQId/KtTBhxDQGz/csmUL9O5tOjsNGTIEVq9erYmowWAAdOWAWoMS2QoC4i6eKM2HVzpDyNHQTlfU54g2IlRDCBAChID3ECABoPewL9eZ9SIAzJ45FQq2lb1044ZjX1sIYR1NWlfmIPi6ADDv/dWQu2i22BYGMMFAJo6SK4clYIK7NCbYKj13WjFt9ITpENH7CUWZ1UwFEABi9N6c+a+pwhA5YChEjZmkWpfavhkL/mES3MQteQ9Cm7dWbZszZzrkb9mgqAtMTIakr75XlNkjAJRrimHn8PBwxRiU0UZg2rRpMGPGDNHg4MGD0Lx5c5E3T5S3AHDYsGGwatUqMW2jRo3gu+++477/RKELiW3btkGPHj3ECKgtM3z4cJG3N1FReVHpv5cga+wwARPyAq3/46KR3hNO8nTiO3q/sY6vT2/8EF0VVKpUCVCwh4RR1Pfv368w8ZXvcuHChYAfNSTCjyZXrlzhQe2kMvOru3iifFxfO0PI1+7JtC8/R4j/efKXQnMRAoSAJxAgAaAnUPbCHHoRABZ9sw8yx5leOsN79IOYyaaXcHNofF0AmPXiSCg8sEdsK2H9JxB8ZwORtzfh0mGJTVL8y1FmctqHBZ0oO0zjvBjoInHrlxBYuap9y3DyZdG+wfXRypCZAakdmBBIbjJ9fWmJW3Zrmk1nPjcYig5+IzYRWOUGiJ2zlAX7uFuUoTl4LosmXLBtoygTCaZ5VungCZHFhD0CQIzcLaf8/HwSAsoBsZI+dOgQNGvWTLSYO3cujB8/XuTNE+UpAPz++++hRQsWhOa6eS7OjQLBWrVqmS9DM48Rgxs00OYt48aNgzfffFP0v3DhAlSvXl3k7U0QL7IXKR9o5yRPJ77jA/fWwSXqiR9KSx85ciTghwqJKleuDMuWLeM+UqUy1NyeMGECL5fzTwyY1KcPO/NYIXfxRPkUvnaGkK/dk2lffo4Q//PkL4XmIgQIAU8gQAJAT6DshTn0IgA0FuRD6gP3grGwgKOAARISNn6uiYivCwBTH2oKhvQyp/uBlaqUmZCaCW00Ny+rcPWwhEPlzGWaZ5uVmmc8KvDCFbKZrCSdfFm0MqIuq7ImPguF+75QrC243t2QsO5jRZk8U7h7B2RNGScv4umgWrexQCLJYMxIh5KzLPqqTABr3jhx8xeA7SUiAaCERPlc8WXxhhtu4FoiOANGj/z00081JytPASD6tkL/f64QOskfPHiw5hAo7MSXfKSGDRvC0aNHNdtaqyBeZA0dH6tzkqfTC7CP3Wc7lqsnfigtt7CwkGtlm/MqjNyLQUuuXbsGx44dg5ycHKkLvw4aNAjWrFmjKFPLuIsnmo/tS2cI87V7Ku/LzxHif576ldA8hAAh4CkESADoKaQ9PI9eBIC47UxmRlX07f4yBJgwLGnPIQiMT1BFxJcFgGhym9a7o9hXePe+EPPS6yLvSMIdhyVjXi6k9XwYUBNNTrGzFkPYQ53kReppJ18W1QfTb2nRfw9A5pihigVGMy3VCKatao2yX3kRCnZpC5DkfUNbtAGM/Jv/0VpRbB44hASAAppySwwdOpRHl8QJkpOTuTAQTc3UqDwFgAkJCZCRkaE2rd1l1gSA+IKcmJjIFFvLggFNnTpVYf5s9ySsIfEiR9DSeVsneTq9AOv8vjq5PL3wQ/ny//zzT+jVqxf89NNP8mLVNPpyRTPg6dOn29SEdydPNF+ML50hzNfuqbwvP0eI/3nqV0LzEAKEgKcQIAGgp5D28DxoIoER0eSEDu8zMzNtHpTkfdyRRhPI7JlTxFDR46ZARL+BIi9PmAsAoye+ChG9+sublF+aaWulPsR8u2Wm8zlCW7WFuLdMPrpsTZy7eC5/WZbaYV8cwxkq+GwbZE+fILpGPTseIp8ymVKLChsJcxNsbM4DgqzZbKMnq3YRD9sT6KSFoRRSO7cBw7UrfEEBoWGQ9OX/ICAm1voCGT54n3LfWQiGlKuWbdn/t5A7GvD7hpGES078AulPdhftQu9rDXFL3xP51A4txDjB9RtCwtqtok5KREdH8+AVUp5MgCUk7Ltu374dHn2URcm+Trt374aHH35Yyiqu5gLAdevWwZNPPqlo40wmOzsb4uPjha8rZ8bAPps2bVI4zZePs3btWkCtGIkOHz4MTZo0kbIOXYkXOQSXvhs7ydOJ7+j7tjq7Oj3wQ7W1ox/AlStXwpQpU0AeJElqG8QCc6H/VuTRd99tcrsh1atd3ckTLcb3oTOExdo9VODLzxHifx76kdA0hAAh4DEESADoMagr7kSoiZba5X4wZpVpvKDZI5o/+hWVMiFSx5YsMEQK31ZQ9ZqQ+PFXzJN1kF9tkzajggDTsir9+0LZ35V/mHZrIqCpe1CNmuVy//HFCIM5oAYbmkQR2Y9ACdOAqlOnDqCWCVLPnj0Bo0/6G7Vq1UpEx0SzN/Q5SEQIuIIA8R1X0NNnX73zQzRTPn/+PPz+++/843UpO2fVq1ePmwM7GgBL1zzRw2cIff4a9b0q4n/6vj+0OkKAEHAMARIAOoYXtXYSgbxVSyB3xSLRO37tx4qACaLCRxNFLPBHJgsAIlHM1FkQ3q2XlKUrIeA2BIYMGcL9HXXp0gV27tzptnErykArVqyAESNG8O2iVvSlS5e4MNVf9n/y5En+gizt57PPPoPOnTtLWboSAk4hQHzHKdh038nf+SHeAOKJuv8Z6n6BxP90f4togYQAIeAAAiQAdAAsauo8AsbsLKYF2AaMuWXOm8O79YaYqTOdH1BnPdGHHPqBQQqseiMkfcp8HjL/NESEgDsRQIFf3759IS8vD15//XV4+eWX3Tl8hRirqKiIR9tFwR/SggULAKND+gthZOP58+fz7TRq1AiOHDniL1ujfXgJAeI7XgLeA9P6Oz9ECIkneuCH5MdTEP/z45tLWyMEKigCJACsoDfeG9vOfXsB5L23rGzqkBBI3PoVBN1U3RtLceucJb8dg/SneogxPeq3UMxKCX9H4MyZM1C3bl1AMyj0IYdmnRgdkchxBBYvXgxjxozhHatVqwanT5/2uG9Ux1dtu0dKSgrUrl0bsrKyeOOtW7dCjx4m3mR7BGpBCCgRIL6jxMMfc/7KD/FeEU/0x1+s5/ZE/M9zWNNMhAAh4DkESADoOawr/EzGnGxI69MJDFf+5ViEte8CsTPf8nlcMob1h+IjP/B9BNe5AxLWf0Lafz5/V/W5AQxYgYF8MABEzZrMxyCRUwig1kvjxo3h+PHjvP/s2bNh4sSJTo2lp07PP/88LFpU5mqhXbt2sHfvXjCPYKin9dJafAMB4ju+cZ+cXaW/8kPEg3iis78K6ichQPxPQoKuhAAh4C8IkADQX+6kj+yj6PvvIPOZgWWrDQiAhHXbIPjOBj6yestlFn37NWSOfbqsIjiECf/YfpgQkIgQKA8E0PQX/dYFk3m5y/CiaSwGyEBH+HFxcXD27FlISkpyeVxvDXDu3Dnu+w9f5jFq4a+//go333yzt5ZD8/oRAsR3/OhmamzF3/ghbpN4osbNpmKHECD+5xBc1JgQIAR8AAESAPrATfK3JWbPnAoF2z7i2wppch/EL9/gm1s0lEJa3y5Qeu40X3/U8DEQ+fSzvrkXWjUhUAERmDZtGsyYMYPvfOzYsbBw4UKfReHxxx+HjRs38vUvX76cR4r22c3QwgkBQsDjCPgTP0TwiCd6/CdEExIChAAhQAj4AAIkAPSBm+RvSzQyLabcpXPBWFIKgfEJEDXKNx3wG7MyIWdpmbP9oGrVIbL/EICgIH+7XbQfQsBvESguLuamv/iFv2rVqjB9+nSf3KvBYIDRo0eD0WjkWn+TJk3yyX3QogkBQsB7CPgLP0QEiSd673dEMxMChAAhQAjoGwESAOr7/tDqCAFCgBAgBAgBQoAQIAQIAUKAECAECAFCgBAgBAgBlxAgAaBL8FFnQoAQIAQIAUKAECAECAFCgBAgBAgBQoAQIAQIAUJA3wiQAFDf94dWRwgQAoQAIUAIEAKEACFACBAChAAhQAgQAoQAIUAIuIQACQBdgo86EwKEACFACBAChAAhQAgQAoQAIUAIEAKEACFACBAC+kaABID6vj+0OkKAECAECAFCgBAgBAgBQoAQIAQIAUKAECAECAFCwCUESADoEnzUmRAgBAgBQoAQIAQIAUKAECAECAFCgBAgBAgBQoAQ0DcCJADU9/2h1REChAAhQAgQAoQAIUAIEAKEACFACBAChAAhQAgQAi4hQAJAl+CjzoQAIUAIEAKEACFACBAChAAhQAgQAoQAIUAIEAKEgL4RIAGgvu8PrY4QIAQIAUKAECAECAFCgBAgBAgBQoAQIAQIAUKAEHAJARIAugQfdSYECAFCgBAgBAgBQoAQIAQIAUKAECAECAFCgBAgBPSNAAkA9X1/aHWEACFACBAChAAhQAgQAoQAIUAIEAKEACFACBAChIBLCJAA0CX4qDMhQAgQAoQAIUAIEAKEACFACBAChAAhQAgQAoQAIaBvBEgAqO/7Q6sjBAgBQoAQIAQIAUKAECAECAFCgBAgBAgBQoAQIARcQoAEgC7B55udMzMzYc+ePfDzzz/DlStXICUlBeLj4+HOO+8UfzVr1oTAwMBy3WDpxb8g85lBYCwtgYDgYEjcthfYpDbnLL10EQq//AxK//4LSv+9BIYr/wIEBELQTdUhqFoNCKpeE8Ie7ASBlSrbHMtWA2NeHuStXgpgNEAgGzuiRz/tLqWlkDHiCb6mgIhIiF/5IQQmJGq3d6Cm5MSvUPzzT/b3CGCQREVDYFw8BMTEQdDNt0BgYrL9/a+3NOZkQ8n5c1B64TzHG0pKIKjGzRBUsxYE31yLjR3r8JjUwT4E8j9ay353ZW1DmjaH4Ftvt68jtbIbgfz8fBg0aBDk5uZCUlISrF271q6+Jez/wf79++HUqVPw119/wcWLFyEgIABq167N/+rUqQMtWrRwmocWFBTAuXPn4PTp0+y/XAngeLfddhuEh4fbXN/TTz8Nly9fhsqVK8PKlSshKCjIZh97GhAPsgclfbRx6LmljyXTKnSAgF75YVpaGue158+fh7i4OKhRowY/q9p7Ri0vnqiDW6a6BH84O9DzRvXWUiEhQAgQAm5BgASAboHRNwY5fvw4vPzyy7Br1y7+Umlt1fiy+c4778CDDz5orZnzdUYjF5YV//QDHyOi9wCInjDN6nhFB/ZA3qb1UPzj90wwcl0yotEjIDQMwrv3hciBwyEw2XlBYM6c6ZC/ZQOfJaRxM4hf8YHGjGXFWROfgcJ9u3kmrH1niJ25yGp7eyvz1q+E3MVz7W2u2i6o1m0QNWgkhHV8RLVeXmjMy4W8tcshf8MaMBYVyqtM6aBgiOjzJESNGAMBkVGmckq5BYFrTesAGAx8rJC7G0P8u5vcMi4NYkJg/PjxMH/+fF7w2muvwZQpU0yVKil8AUWhGgoK//2XfXiwQvhB5ZVXXoFevXrZLQhE3ow8+pdffmG3vuzeS1Pgy+7tt98Ob7zxBjz22GNSscV19OjRnHdjBe7thRdesGjjTAHxIGdQ804fR59b3lklzao3BPTGD7dv3w4TJ07kwj9zrG666SZ46qmnYPLkyRAdHW1erciXF09UTKKjjD+cHeh5o6MfFC2FECAE/A4BEgD63S213FAp00zDg92iRYssXiotWytL+vXrBwsXLoQqVaooK1zMFWz7CLJnTuWjoBZZ4qf7ubaa2rDGwgLImTsDCrZvVqu2WhYQHgFxS9ZASKN7rbZTqyz8YgdkvcJenq8LG+0RAJb+fQHSej0MUFzMh4xbuAJC2zygNrxDZe44DEkThj3QAWJnLdbUtsQvr5nPPw2GtBSpi9VrYKUqbLxFENKwidV2VOkYAvJDfHD9hpCwdqtjA1BrqwgcPnwYmjdvDsgfq1evzl8yIyIiNPu8//77MHLkSK4tqNlIpaJhw4awe/duqzw0NTUV+vbtC3v3Mi1oO6hjx46wZs0aqFq1qkVr1Oi+9dZbATW9cT+//vor10q0aOhgAfEgBwHzUnNnnlteWipNqyME9MQPCwsL4dFHH+V80xZEt9xyC2zduhXuuecezablxRM1J/RyhT+cHeh54+UfEU1PCBACfo0ACQD9+vYC5DETVnyx3Llzp9M7ReHfkSNH4MYbb3R6DHlHw9XLTEjWAYy5Obw46vnJEPnEEHkTkTakXoPMZwdDyR+/izKRYBpoQVVugMAb2LqYmRyaBhtSropqKYGmsPHLN0DwHfWlIpvXwr1fQNZLzzMNrFLR1h4BIDbOeWsW05x7l/dD4Vjilt0QEB0jxnEm4c7DEM4fyTQBo0ZbagbhPUnv9wjD8oJymaGhEFzjFmZXHMDNgVEoK6fAylUhcePnEBAbJy+mtAsI+MMh3oXtl2vXYiagb9y4MReO4UQo3HviiSdU50QTXNQgQc0/cwoJCQF0l3DzzTczmX8xN9v9+++/2TcDpYbyXXfdBQcOHICEhATzIXi+W7dusGPHDkVdZGQk1K1bl5sWnzx50kLw2K5dOy4wRNNjc5o3bx5MmDCBF7dt2xb27dvHxzFv50ieeJAjaHmnrSvPLe+smGbVAwJ644folgG1rOWEHztQA/rq1avwxx9/8A83Uv0NN9wAKMBErUAtKg+eqDWXt8v94exAzxtv/4pofkKAEPBrBNiLCpEfIzBgwAB8E3X5r1WrVkZ2SHQLUpkvjDBebVyb/6V0bGk0Fhaqj2swGDOeGSjaij4PNTXmrllmNORkW/Qr+uWoep92TYyl165YtDcvMOTmGrPnTDNebXKrxbzpw/qZN1fNl2akG6+1vkv0z571imo7Rwpz160Q4yEO2XNfNTLfiZp/huwsY/Hpk8aC/buN6UP7KvpyHO+rayxNvWaxhKypLyjbNqtrzFn+luIeGQoLjDlvLzBeZXXSPcFr5uTnLMajAucRuHrvbQLftKd6OD8Q9bRAYMaMGYIn1q9f38jMbS3aSAVTp04VbSVeyjQGjUyj2sh8B0rNxJWZ7xp79Ohh0adZs2aqPHTx4sWKtkzwZ1ywYIGRCR7FmMh758yZY8Q6aQ14ZSa+oo08wXx5GdlLsWi7atUqebVTaeJBTsHmkU7ueG55ZKE0iS4R0BM/XL9+veBbyONiY2ONq1evVvDoY8eOGZnGn6Ids1axim158ESrE3qx0h/ODvS88eIPiKYmBAgBv0eANADZCcNfad26dTBw4EDV7YUyjS7UDLz33nu5lgl+VUUtEeyDJnFqtGzZMhgxYoRald1lJWf/gPQ+nUR79PuH/v/UKH/jOsiZ/5qiCk154xattulzLmfhG5D/4XuKvpEDR0DUMy8qyuSZov99y8ySp4CBBRZRI3s1ALFv7tL53IceH4dpCSXt/I9LvgjNv4ZG9BsM0eNeUlumalne+6shd9FsRV3UM+O5j0SpEH39pfxfI4CiIqkIYmbMh/BOj4q8PCE34+blgUGQ/M3PgGbXRK4j4A9f8V1Hwf0jZGVlcZNfvCJt3ryZ++lTm+ngwYPQpk0bBU985JFHeJ+wsDC1LqJs9uzZ3D+VKGCJDRs2QP/+/eVFUKtWLfjzzz9F2Zdffgnt27cXeXni008/Vfj/Q82Xf/75R95EpN966y0YO3Ysz6PT/LNnz0IwC7bkLBEPcha58u3nzudW+a6URtcjAnrjh8hvv/32WwHVli1boGfPniIvJTCAHft4w4PYYRlqY1+6dAkqVaokNbG4upsnWkygkwJ/ODvQ80YnPyZaBiFACPglAiQA9MvbijKcIm6WpuaoHn2m4EtvkyaWPttOnDgBDz/8MKAZmzmhL6ujR4+aFzuUz351IhTs/Jj3wUAdSXsPM2FepMUYxqxMSO3QQhGAAn3MxS15DwKs+OkSAzETvKzxo6CQBQ6RCH0NJn3+ncV8OBcKDAs+2yY1Vb06IgA0XP4HUrveL/wHapncqk6kUujqYQiHxAjFPIDK9fHDHu4KsW+8KWbDgCwZw03CiZB7WNCTldaDnqQ/1QNKfjsmxoh/byuENGgo8pRwHgF/OMQ7v/vy68m06+DFF8s+BGCkXBSgqUXKZZ//ePTdM2fOiMV07doVPv74Y/6yKQqtJIYNGwZM+060MOehFy5c4CbEUgMMGIK82RqhAFLu0gF5vJovwOzsbP4yjP60kD744ANAn67OEvEgZ5Ern37l8dwqn5XSqHpGQE/8EHlxtWrVhAsFdF+A0da16KWXXoJZs2aJavyIja4RtMjdPFFrHm+X+8PZgZ433v4V0fyEACHgzwiQANBP766W9h++8J46dQri4+M1d45O45m5GjCTCYs2hw4d4lqDFhV2FKB/Pi4Uux4gI6xte4id945qT0vtskBI3LQLgm65VbW9WmHpn2cgrXdHIYTDNtGTZ0BED+VLcMHnn0D2tPEWQ0T07A/5W00CMEcEgDhY+sCeUHL8Zz4u+sbjwkd7hJcWK2G+HM2iADuqAYhD5q1aArkrFonRQ+66B+LXmIQNuSsXQx77kyhq1AsQOXiklFW9ooYmampKFPPS6zz6spTX87X0n7+h5NQJscTQZi1tapZiQJii/+wVLygBTOsgtFVbMYaUQG3K0nNnoPT8WSg5f45dz4EhM51rRwYmJUMIC+oR0uQ+CKpWQ+picbXnEI9aXXJCbTI1n3DyNhU5jf78EKOLFy9yGFCjGTWb1Qh99uELqEQYUAPxRq07e+ny5ctcwIcfZCTCF1ppXPQ9+OSTT0pVwEzdYMiQISKvlmCmwDBp0iRR9cUXX0CHDh1EXp6Q+xZs1KgR9+Uqr3ckTTzIEbTKv627n1vES8r/nultBr3xw6+++orzQ9TuQ8IAdJIWsxp26Jd1+PDhooqZDwNzeyPyagl38ER/ODuoYaOnMl9+3hAv1dMvidZCCBACagiQAFANFT8ow6+gX3/9tcVO5s6dyyMCW1SYFcidMEdHR0OLFi2gdevW8NRTT3HzObPmdmUVZrGsB2qfoRaaGmU8/TgUHz0sqsI6doPY1xaIvL2JzDFDofjnHyHk7nt4lFqMgBtUs5aiu/mLVGB8IsRMnwOh97WGa/fVFW0dFQCam91GvzgVIvo+JcZzJOGOwxBGXUbBqkQogMLgKBIV7tkFhd/sA8O1K2BITWEmxi9DaPPWUrXqNfu1lxTRmWOmzYHwrj1U22oVFv/6M+QumatVbV95YCBEPzcRgu9sYF971gojHRd9Z/o/Yo/wsvjYT5AxpI+YI6jWbZC4+QuRx0Th7h2Qs3guYLAbq8SC2EQOGAqRw54F1IY1J3sEgObCPhTah4eHmw9F+esIoBacPNiHXBhnDpK59t64ceMAtWUcpcGDB8P27duhZcuWnIf27t1baP2h8G7jxo1cCxG1X/AFFoOTWKPXX38dmF9C0WTXrl2AUYHVyFzAaEtDRm0MqcyfeZC0R3uv3uJZ8vW5+7lFvESObsVI640fSqhfu3aNB2hq0KCBVZNeFA6iWa9E1twnSG3cwRP94ewg4aF19TaP8+XnDfFSrV8VlRMChIBeECABoF7uhBvXgRHdUMMPIwDLKTk5Gc6fPw9RUVHyYtU0mgLv2bMHWPAPQLM1NRM51Y5WClM7twbDlX95C2vmv4a0VEh9+D6F5l7c4nchtAUzqXWU0J8hRslkAiItkl6kUEsv8vGBXEiH5sIYWdgVASB+JU575P/EtMH17oaEdR+LvCMJlw9DTHMttUsbgT/OjcJIFEo6TSxCclr3h6D0b1PE4ASmpRlcu45DQxb99wCgoNZVipk+F8K7dLd7GDRFR5N0idC/ZPwqk4BUKpdfzYWoUc9NgMgnh5U1Yb+XzHHDoOjgN/IuNtMhDRpB/HtbLNqRANACEpcL5B9GrJn/oh9U9CWVnp4u5vzpp5+AOZ4XeXsTOFYg4z/mLwX29jdvh8K+3bt3i2L036rl9yozMxNwn5IG4uOPPw4ffvih6OtIwp95kCM4YFtv8Sz5Ot393DL/fdLHBDna/pn2ZX6IH0yaNm3K/f7h3UEfgKjZXaVKFas3yx080R/ODlZBYpXe5nG+/LwhXmrr10X1hAAh4G0ESADo7TtQDvMfPnyYH4zMh37sscdg2zbrfu7M+7grX3rhPBMWPSiGs2b+W3z8GGQMlGmRYXCJA0dsm2eK0R1LoC8lfJkK79ZLOYeLAkBcRfqTj0HJiV/LFoT72P8jBETHOLZA1tqVw5AxOwuymIlzEdPuk1PMK7Mh/BFL59ryNtbS+R9/CDmzXhFNAiIiIfk/zOTZirBVNJYlvHXQNDJtORQ0G/Nyy1bDBMWJOw5A0A03yVYnSzIzzpQOzQF/L5zY/Uza9a0I7lL4xQ7ImjpO1oHJnpkgObR5G9amEp+n9K8/FZqtUuO4d9ZBaNOWUpZfSQCogMPlDAo0EhISQPKJZ838F53Joy8qifCDSmpqKhfkSWXeuH7//ffQvHlzMXXNmjX5Rx1RoJLo0qULfP7557wGX47RLNkZ8mce5Cge3uJZ8nW6+7lFL61ydP0/7cv88MiRI9wa5fjx4+JGodXKmjVrRN5awlWe6A9nB2v4YJ23eZwvP2+Il9r6dVE9IUAIeBsBEgB6+w6Uw/zoRL5PH5OZojTFc889B4sWmXzASeWeuJr79It6fjJEPqHu66rw6694AA9pXcG33g4JG8teYKUyj1zdIADMeXMW5H/wrlhu3MIVENrmAZG3N2F+GApnfgw1owCXGsCYmwOGtBQo/vUo5G94V6Glh3MG3VwbEj/cCcCiQTtDpSxScsaAx8CQkSa6hz3QEWLnLBF5exOGa1ch6+Xn2ZqvC+Ls7WjWLmrE8xDa2uSzzaxaNZs9YzIU7DBp31nze1i4/0vImjBajBPa8v94RGqpIP2p7iwgyi9SFtBsHc2KzYPWoIA7+6XnofSfi6a2D3WC2FmLRR4T9ggAb7/9dsi9jhua/p48edKlSK+KBfhZZu/evfDQQw+JXaEpmNwcWFSwBPo6RT+oEj344INcI1rKe+OKWnyokY0feCR65plnYMkS6//n0O3DxIkmTVd8aa5Xr540hN1Xf+ZBdoNwvaE3eZbNtTr53CJeYhNZv2rgS/wwJycH0N8fav39+OOP8J///EdxL9BKBctiY5nlhh3kDp7o62cHWzB5m8f58vOGeKmtXxfVEwKEgLcRIAGgt+9AOcy/YsUKQO0Wc5o3b56IfmleV975rMnPAfqYkwiFHWFM6KFG+Zs3QM7c6aIKNaNQQ8rj5OSLlHyd+R+uZRGGXxdFEczEOPqFKSJvb8L8MGRvP9V2THMtfsUGQJNXZ8iQkc794JX+dc7UPTiE+cJjQVpq3GIq84EU+ofMGNpXrDSo1q1sHybzSlHBEpkvjOABQKSy2DlLAX1KIhX/chQyBveSqhgON7OgNV+gXZIokyfQ9yD6EZJIzTzcHgGg1J+uthEwjxiJL4xt2rRR7fjpp58CakxL1L9/f9iwweQvUyr31NVgMEDfvn1hyxaTsDomJgZOnz5t0+Tto48+UkT/Xbx4MTz77LMOL514kMOQeaeDG55b3lk4zepJBHyJH6L7hSZNmqjCgx9ntm7dCnFxcar1aoXu4Il0dlBD1n1l9LxxH5Y0EiFACBAC5giQANAcET/Iz5o1C/BwZ074Aosvst6g9L6doeTMKTF1/HtbIaRBQ5GXJ/JWsmi1K02aiihkQWGLx8kNL1KFXzOtsfEyrTEWWCRu6XsOb8Vdh6HAxGSmabYIMKCJM4TmshkjByg03XCc6AnTIKL3AGeG9HofNE1HE3WJEj7YAcG33yll+dWQmcHMhZnpZUkxzwfGJUDS7oNCwIfmMnnrVjFNy794EJWoZ8ebfAMqRirLGHOyIeX/GokaNS1XEgAKeNySMPedd+7cObjlFnWBtXl0SXs07dyySI1BRo8eDe+8o4yYvmnTJsCAIrbov//9L9cclNo5Yion9cEr8SA5GjpOu+G5pePd0dLchIAv8UMU8PXqZfrAJocAzS3RlyGa/9aoUUNepZl2F0+ks4MmxC5X0PPGZQhpAEKAECAENBEgAaAmNL5b8cYbb8CUKZZaZqgZiJEtvUEoPDGkXhNTJ+36LwRWriLy8kT+h+8xrbk3RBFGoo1b4rjQTAzgbMINL1IlJ35hfgC7ixUE314PEj7YLvL2Jlw6DLEDMgq0wh/pBeGdujnlgxDXabh6hWmtDYWSP35XLDv80d4QM2WmosyXMnnvLYfct+eLJUf0HwLRYyeLPCbyt3wAOXOmibKIPk9C9PhXRF6RYL8bCAxgf0GKYinDTbN//B6yprzAQC3lxUG3MM3DLUrNQxIASoi554oaJKhJgoQvjQUFBcwCXt0EHgNlyD+WYCTfd981mfK7Z0W2Rylhv6WRI0fC6tWrFY1ffvllwGjA9tCFCxdE1GFsj/6vdu5k5v8OEvEgBwHzVnM3PLe8tXSa13MI+BI//Pbbb7n7GowKjMHtfv75Z0ATZtSMlujGG2/kZXfccYdUpHl1G0+ks4Mmxq5W0PPGVQSpPyFACBAC2giQAFAbG5+tQU0R1Bgxp1deeQVeffVV8+Lyz7MItNfuY4eyUiYYQQoKhkr/O6EZLKLwy53MJ9zYsrbs3+A7G0DC+k9E3mMJN7xIobAntf19YsmBlauywBHfiby9CfPDUHC9uxRBI4yFBWBkWmpF//uG+f5LVQwbfEd9iH3jLW6WqqhwIFNy9g/IfG6IIoowdg/v2gNips7SvJcOTOG1pijYxAjJkjAuMLly2T2SBTPJGNSL+1OUFqmmJSjVSVf0oVP65xkouXgeSv/Cv3Nlf7KoyVJbEgBKSJTfFQNm4IsfUtWqVeHff8sikqvN+PXXX3OtEqnukUcege3bHRfcS/2duaLfK9R6kUf8xXHGjx8P6MPKXkIhIvqHxGjESOjbEIOJOEr+yoMM6WmQv36lXXBEPD5I88OVXQN4opEbnlueWCbN4V0EfI0fmqN18OBBHgjkzJkzoqpt27awf/9+kddKuIsn0tlBC2HXy/31eeM6MjQCIUAIEAKuI0ACQNcx1N0IaBqG/qLMaciQIRaaJOZtyiOP0QpT2jUWQwdWvRGSPvtG5M0TxT/9ABnDTabKgYlJkPTVD+bNyj/vjhcpFH62rMdCqhWVrZdpHFU6yISfDpL5YSii32D1ICBsHt52+VuKGQLjEyF+/TYIurGaotyeTPHh/0Hm+FGAZqtyCn+sD8RMfs1l4V/xoYNMG84k8JXP4Ug6evw0Tb+StsZB4WbRwf+IZvKovOYRrIPr3AEJGEBFhTA6YMH2zZC/cZ1F4BWV5qKIBIACinJLREZGAka+RGratCn88IM2T/n999/hzjtNZuD33nsvDwxSboszGxiFk507d4ajR48qatC1A2p4O0rVq1eHv//+m3erVasWnD171tEhLEyA/YUHoWA+rUd7u/CQBP964FmaC3bHc0tzcKrwFwR8iR9qYX7q1CnAACCozS0Rmve2aNFCympe3cETcXB/PTt4m8f585lX80dJFYQAIUAIeAgBEgB6CGhPToMvtvfdZ9I6k+Z2JJIlvniiryj8oorO8Fu2bAlBQeomjdL4Wlf+lbRTS1Ftywy29J+/Ie2R/xPtMZGwaRcE166jKLMng2MVbNkAgTdVh9DG90HQLbXt6VbWxk0vUubmz5UOMV+IGuahWouz+zB0fQBzM2osDmL4JTDfiwFMEGIvFXz+CWS/xvxJXvd9J/WLGjkWIodYaplK9Y5c0X9e5pihjnRRbRszfS6EdzGZW6s20igs3PsFZE0yBUbAcXA8pFwmTM1bbfJBiUFcMJiLOaGgEAOKoNanLQqIjQMUjEtEAkAJifK5osZHiCwgCwrXPvvsM83JsrOzISEhQWjNBQcHQ1paGmDgDUcJhY4ffPAB3H333XDPPffY5KO//fYbdOrUSWgr4nzIe5cuXaoa3Mme9TRu3BiOHDnCmyYnJ8O1ayZ3DPb0xzb+yoOcEQDqgWdp3jc3Pbc0x6cKn0fAl/ihLbAnTZoEc+bMEc0WLFgA48aNE3mthDt4Io7tr2cHb/M4f33eaP0eqZwQIAQIAU8iQAJAT6LtobmKi4shPj6e+0qRT4kvkRg1Usvxvbztiy++CHiQkqhSpUqAZnB4sJJrxkj11q7GokJIacG04K5TUPWakPjJPimrekWNDHmU2ehxL0NEv0Gqba0V5q1iAUVWLBJNeHTWbXtF3mrCTS9SKa3vAmN+Hp8KBT/J+3+yOq1apaOHIRwjZ+FMyP9wjWK40DYPQNz8ZXZp7Zljxwdi0X5jprzhtKBNsZjrGW8fNPkymOZkSocWTCiXwbMBkVGQtOcHCAgNg7Ru7aD0n4tlq2X7x+AfgfEJ11dfdkH/lhmDepvayWoDwsKZ8PU2CL61LgTXYX9160FI/YZwDf9PkA9AGVLlm0SemJlZJnTF6L8YBdga3X///fDNNyZN5R07dkDXrl2tdVGt27x5M/Tp04fXxcbG8sAdq1atUm2Lpsf4wUVaJzZCTZ2NGzc6Nbc0ye233w5//PEHz9atWxdQw9FR8lceVPrvJUjrer9dcCRu+ZJ/RNIFz9JasZueW1rDU7l/IOAL/NAepJEvd+vWTTQdNWoUvP322yKvlXAHT+Rj++nZwds8zl+fN1q/RyonBAgBQsCTCJAA0JNoe3Cu9u3bw549eyxmHDFiBCxbxgRAVig3Nxfq1KkD//zzj0Wrffv2KXxjWTTQKEhpew8Ys7N4bUBMLCR/XaaNotEcchfP5RonUj3XkNr4OarCSEV2XdO6P8QivP4p2oY91IlFwV0s8lYTbniRshB+1mLBHjbvtjqtWqUzhyFgguD0gT2h5NRviiGjRjwPkUOfUZSZZ3KXzGNRbVcoivG+xbFozCFNbZvXKDrayGBQkbx1K220sl4dwH4XkYNGsJfzW603tFKbM28G5G9aL1rEznsHApMqQcZgU/TBsLbtAcvNCYXMKDCVEwZdiej7JBP83W4hcDWPAhxUsxYkfvyVvDtQEBAFHC5n0Dn8yZMn+Tj16tWD48ePWx1z4cKF8MILL4g2PXv2hC1btoi8vQn8cCIPuvHcc89xh/bm/T///HPo3r078xZQJKrQVyH2RYf9rlBSUhLXYMQx7PWTZT6fP/Og4l+YqTXj91YpKBBC7m7Mm+iFZ6mu1w3PLdVxqdCvENAjP0TtZzThxQjt6AYBAy+h9rU1wo80+LFGogEDBsD69abnuFRufnUHT5TG9Mezg7d5nD8/b6TfDV0JAUKAEPAWAiQA9Bby5Tzv1q1buQN582nCwsIAI6qhTystwgPUhg0bLKrxwHjihOP+63CgtF4deEAEadBKPzAzWCvCvOJfjjDBS2+pOb9i1FWMvmovFX6xA7KmKk1B4pdvgJAmlubRqmO64UXKcPUypHZqJYYPubc5xC97X+TtTTh1GGKDl5w5BekDHgUUBgpippCJzIedlrAs7923IXfZm6I5JtB3YNyid7n2i6LCjzIlp05Aev9HxI7COz0KAcx3olyLMm7hSght0060kRIZw/pB8ZFDUhYiB4+EqFEm4ZGouJ4o/vlHbi4slatpppIAUELHPVcUfB04cIAPVqVKFbh8+bLVgdFP3q23KgXKyDtbtTL9f7Y6AKtETTs0/UWtbInwJddcixo/rGB0Xrkvq/r16wMKBWvUqCF1deqKwT/Q/NnI/JEi9evXj5skOzoY8SBHEfNSezc8t7y0cprWgwjokR9i8DoMYicRfsRG1zXWaPny5TxSutRm3rx5gBYs1shdPFGag84OEhLuu9Lzxn1Y0kiEACFACJgjQAJAc0T8JG8wGABNHOQR0qStoRDwrbfesvAnlZGRAZMnTwY8UKnRkiVL4JlnrGuOqfXDsoyRAwCDSUiUuOOAzYAUmaMHQtEP30ldICAqGuIWLLdLgFdy8jfIGNIHMDquRGpaVlKd6tUNL1Lmgsywjt0g9jWTabXqvCqFzh6GcCg1U96Qu+6B+Hc3AQQEKGYrOfEL1xoE9vuRCDXY4t5ey7XhpDJ/vab36wr45RspIDoG0HwXzXuRUBuQR3BWEVyjiTtqe0okmQpKecWVCWKyXhoDhXt2iWIUsOL/CTmRAFCOhutpFHx99NFHfCDUKkFN51AWlMcamX8MQcEdCutQM88WIT/FYCPodkGi1q1bK8yKsTw1NZXzarxKhD5XUfgXFxcnFTl9vXTpElSrVk30R63G+fPni7y9CeJB9iLl5XZueG55eQc0vQcQ0CM/RA3r3r1NH36H2Ahch+dcdOeAWoMS2RMExF08UZoTr3R2kKPhepqeN65jSCMQAoQAIaCFAAkAtZDxg3L8etqhQwcmyzEJc+TbqlmzJtcERMf26KgeX2y1nMPji+jFixedcoKPc2bPnAoF28pevjEf+9pCCOto0rbCMnMq/fMspD3eRRmAgmmyxDBNwPBH2SFRI5AGOmXOmf8aGFKuKoaMmToLwruZzDkVlWoZN7xI5b2/GnIXzRajRw0fA5FPPyvy9iZcOQyhaVsaE2yVnjMJInDe6AnTIaL3E6YlsN9J+sAeUHLiV1EWdFMNSHj/E0DfhRWBMHov/nbUKHLAUIgaM0mtClLbN2PBP0wCnLgl70Fo89aqbXPmTId8FphGToGJySzS9ffyIrtMgOUaY9g5PDxcMQZlTAhMmzYNZsyYIQoOHjwIzZs3F3m1BJqh4YcUDAoiEfJNDCCCGnpa9Oeff8LAgQMthH1qfgSHDRsGcp+AjRo1gu+++477/tMa35Hybdu2QY8ePUQX/MAzfPhwkbc3QTzIXqS83M7J5xbxEi/fNw9Pr0d+iIGW0N+0dGYNDAyE/fv3K0x85TCZu2nAc+qVK1cAP3JbI3fxRPkcvnZ2kK9dj2lfft4QL9XjL4rWRAgQAnIESAAoR8MP0+aHPGe2iOZju3btsmmKYW3som/2QeY400tneI9+EDPZ9DKu1Tf3nQWQt8bSZ2FglRsgosfjEHxHAwisVBmMTPhScv4cFH65E4qP/WQxHAq6UODlEDn5IiWfI+vFkVB4YI8oSlj/CQTf2UDk7U24dBhik6CPq4yhfVjQCZMwGANdJG79EgIrl2kz5W/9AHJmT1MsKYj5LAy+7Q5FmbVM+MNdmInsA9aa6LrOkJkBqR2YUEhmsiktOHHLbk2z6cznBkPRwW+kpoC/z1j0l1j/blGG5uC5LJpwwbaNokwkmCZapYMnRBYT9mgABphpcKIgn4SAChhF5tChQ9CsWTORnzt3LowfP17ktRKo+Yx+++SEAZXQtx/6VG3YsCFUrlwZ0tPTuY/BTz75BBYvXgyFhSaNUOyLwsepU6fKh4Hvv/8eWrRgwWeum+diJQoEa9WqpWhnLYMRgxs00OYpGLjpzTdNJv0XLlyA6tWrWxtStY54kCos+it08rlFvER/t7I8V6RHfoj7HTlypMIKBXkr+q1G/6gSofb2hAkTeLmcd2KwJCngktRW7eounigf29fODvK16zHty88b4qV6/EXRmggBQkCOAAkA5Wj4YRoPR1OmTIGZM2c6tTt8kL3//vvQv39/p/pLnYwF+ZD6wL3CJBfNShMwqIcdpCUEtKMrb1IW+fYdTY1BzXGcfJGSj5f6UFMwpKfxosBKVcpMSM2ENvL2WmlXD0M4bs5cpnm2Wal5xrFZuIJPm9qlDRguWwZ+0VqTWnnU6Bd5MA61Ol8py5r4LBTu+0Kx3OB6d0PCuo8VZfJM4W7mb3KK0t8k1gfVuo2ZDieDMSMdSs7+oRDAyvtjOnHzF7y9VE4CQAkJ91yRF95www1cQwRHxMiRn376qV2Do8sEfGmUv2zKO+JHErmfP3kdpgcPHswd2puXo38r1Lx2hdBRPo6vRSj0xJd9JBRWHj16VKup1XLiQVbh0U+lk88temnVzy30xEr0yA9x3/jhBDWzzfkUamKjH2q0Ujl27Bjk5OQoYBo0aBCsWbNGUaaVcRdPNB/fl84O5mvXW96XnzfES/X2a6L1EAKEgDkCJAA0R8RP8xjUA30/Xb2qNIu1tl30GzVr1ix44gmZmai1DjbqMscOg6Jv95e1YkKwpD2HIDA+wUavsmoUXOUumQvG/Dy72mMj9N+GQUMihz0LAeERdvcTDZ18kZL6o8ltWu+OUhbCu/eFmJdeF3lHEu44DBnzciGt58OAmmhywqjIoS3aQMr9DeXFTqX9QQBY9N8DkDlmqGL/0UxbNYJprVqj7FdehIJd9gmUEG/0SZn/0VoxpHngEBIACmjclhg6dKgQxCUnJ3NhIJqZ2UPoP3DUqFGAvv3spdjYWK5lOGnSJNVolgkJCQ6NpzavNQEgviQnJiYK4SRqIMrNoNXG0yojHqSFjM7KnXxu0Uurzu6jB5ajN34obRldKPTq1Qt++snSmkNqI13RnyuebadPn26X9rs7eaK0BunqS2cHac16vfry84Z4qV5/VbQuQoAQkBAgAaCERAW4oh8rNAXbvHkzYCRKLUIfV2gah4dDW75UtMZQK0fTx+yZU0RV9LgpENFvoMjbShhzsqFgx8fMf9r7UHrxL83mARGRENahK0Q9/Rwzb62i2c5mBTOXTX2I+XbLTOdNQ1u1hbi3VtnsJjXIXTwX8BAjEfbFMZyhgs+2Qfb0CaJr1LPjIfIpk0m1qLCRMDfFxuYYECR29mJADUC5ibCNoVSrY16ZDeGP9FSt85lCQymkdmbakNeu8CUHhIZB0pf/g4CYWOtbYL8XvE+57yy08D/JOzIz3xBmso73DSMJ84ArT5rMmkLvaw1xS98Tc6R2aCHGCa7fEBLWbhV1UiI6OpoHs5DyZAIsIaF+3b59Ozz66KOicvfu3fDwww+LvK1EXl4ej5C+dOlS+PVXk69M837oiwq18l5++WVISkoyr+Z55Mfx8fHC35VqIzsKN23apHCcL++ydu1aQM0YiQ4fPgxNmjSRsg5diQc5BJf3Gjv53CJe4r1b5q2Z9cQPzTFAP4ArV67kFizyAElSO3TDgJqCyIsx0rq95E6eaDGnD50dLNauswJfft4QL9XZj4mWQwgQAhYIkADQApKKUfDXX3/B77//zjVgMjMz4cYbb+R+p2rXru2WyJNqKKIGWmqX+8GYVaZBg+aRaPboMDFTPjSrNVz5l2uzlV69AgHsK3BQ9ZoQVOMW7hPQPLqtw3O42qGUCZE6tmSBIVL4SLi2xI+/ctwM2dV1UH/PI8D8B5b+faHs78o/TMs1EdDkPahGzXK5//iShEEdUKNNK4iP50HQ54wlTDuqTp06gBomSD179gSMPOkM4UspBkbCP4wqiWbAODb+VaniwocHZxaj0adVq1YiQiaavaHPQSJCQAsB4iVayPhnuS/wQzRVPn/+PD+v4lm1lJ2t6tWrx82BnfF3q2ue6OGzg3/+qvWxK+Kl+rgPtApCgBBQR4AEgOq4UGk5IZC3agnkrlgkRo9f+7EiUIKo8PFEEQv8kckCgEjkcARiqSNdCQEbCAwZMoT7PurSpQvs3LnTRmuqXrFiBQ/egUiEMq1MFN6h8NTf6OTJk/wlWdoXRi7u3LmzlKUrIWCBAPESC0j8vqCi8EO8kcQT/f7nrJsNEi/Vza2ghRAChIAKAiQAVAGFisoPAWN2Fjc1NeaWOXAO79YbYqY6F6Ck/Fbp+sjoQw79wSAFVr0Rkj5lvg+ZliIRIeBOBFDg17dvX0DT1Ndff52bnLpzfH8cq6ioiGs7o+APacGCBTzAh7/tFd04zJ8/n2+rUaNGcOTIEX/bIu3HjQgQL3EjmD40VEXhh3hLiCf60A/Th5dKvNSHbx4tnRCoIAiQALCC3Gg9bTP37QWQ996ysiUxs7nErV9B0E3V9bREl9ZS8tsxSH+qhxgjeuKrENHLtSjKYjBKEALXEThz5gzUrVuXm0ShLzk078RIiUS2EVi8eDGMGTOGN8RgR6dPn7bLgbztkfXRIiUlBdCdQ1ZWFl/Q1q1boUcPE0/SxyppFXpBgHiJXu6Ed9bh7/wQUSWe6J3fVkWblXhpRbvjtF9CwDcRIAGgb943n141BvNI69OJ+/DDjYS17wKxM9/y6T3JF58xrD8UH/mBFwXXuQMS1n9C2n9ygCjtNgQwgAX6RcJAEBi8h8g+BFDrpXHjxnD8+HHeYfbs2TBx4kT7OvtAq+effx4WLSpztdCuXTvYu3cvc4sa4AMrpyV6CwHiJd5C3vvz+js/RISJJ3r/d1ZRVkC8tKLcadonIeC7CJAA0HfvnU+vvOj77yDzmYFle2AvpgnrtkHwnQ18ek+4+KJvv4bMsU+X7SM4hAn/2L6YEJCIECgPBND0F/3YBZN5ucPwokksBsZAR/gYtffs2bOaEXsdHtyLHc6dO8d9/+FLPUYjxGjFN998sxdXRFP7AgLES3zhLpXfGv2VHyJixBPL73dDI1siQLzUEhMqIQQIAX0hQAJAfd2PCrWa7JlToWDbR3zPIU3ug/jlG3x7/4ZSSOvbBUrPneb7iBo+BiKffta390SrJwT8GIFp06bBjBkz+A7Hjh0LCxcu9PndPv7447Bx40a+j+XLl/MI0T6/KdoAIUAIlDsC/sgPETTiieX+06EJCAFCgBAgBHwIARIA+tDN8relGpn2Uu7SuWAsKYXA+ASIGjXOp7dozMqEnKVlTveDqlWHyP5DAIKCfHpPtHhCwJ8RKC4u5qa/+MW+atWqMH36dJ/ersFggNGjR4PRaORaf5MmTfLp/dDiCQFCwHMI+Bs/ROSIJ3ru90MzEQKEACFACPgGAiQA9I37RKskBAgBQoAQIAQIAUKAECAECAFCgBAgBAgBQoAQIAScQoAEgE7BRp0IAUKAECAECAFCgBAgBAgBQoAQIAQIAUKAECAECAHfQIAEgL5xn2iVhAAhQAgQAoQAIUAIEAKEACFACBAChAAhQAgQAoSAUwj8PwAAAP//U3HjIgAAQABJREFU7F0HvBXF1R+aDcGCLVbs2AsKKjZiw65gxYJijSWJscRYo4gaawS7xooSDeIHtqjRxN4VNBYsCEZR4T3qew948N585784y+zc3Z3de2d373vvzO937+zuzJw5858zM2fPTmknyQl2jAAjwAgwAowAI8AIMAKMACPACDACjAAjwAgwAowAI9AqEWjHBsBWWa9cKEaAEWAEGAFGgBFgBBgBRoARYAQYAUaAEWAEGAFGwEOADYAsCIwAI8AIMAKMACPACDACjAAjwAgwAowAI8AIMAKMQCtGgA2ArbhyuWiMACPACDACjAAjwAgwAowAI8AIMAKMACPACDACjAAbAFkGGAFGgBFgBBgBRoARYAQYAUaAEWAEGAFGgBFgBBiBVowAGwBbceVy0RgBRoARYAQYAUaAEWAEGAFGgBFgBBgBRoARYAQYATYAsgwwAowAI8AIMAKMACPACDACjAAjwAgwAowAI8AIMAKtGAE2ALbiyuWiMQKMACPACDACjAAjwAgwAowAI8AIMAKMACPACDACbABkGWAEGAFGgBFgBBgBRoARYAQYAUaAEWAEGAFGgBFgBFoxAhUbAH/88Ufx+eef+xAtu+yyolevXv49XzACLhF44403xPz58z2SK6+8sthiiy1cki+c1jvvvCPq6+s9PtZff32xzjrrFM5TOQws/OwTsWDcB8mTthOiXedlRfvllhftuiwnOnRfV7RfcaXk6SNiyoYG0XDvrULIZtF+zbXF0gMGRsRsOY+LwlbWzRELJ00UTd9NEk3fTxZi4ULRYe3uosM664mO3dejeutaVSC+++67oq6uzuNpvfXWE927d68q/piZfBBo6XLgin9XdPKptZadC2O9qP5aiz5TrdI4d+QDpNss4q5Trx1Fxw02LpRVlvtC4W/Tmecte2+//bZooPcLuA022ECsvfbaFeFv419/9+3Ro4dYffXVK8rPVWIb367yYTruEajYADho0CDx0EMP+Zx16tRJfPfdd2K11Vbzn/EFI+AKgTXWWENMmTLFI9e/f3/xxBNPuCJdFXQ233xz8emnn3q87L777uLf//53VfCVlomGh+4W9cOuS5ssEL/DehuKzif+Riy570GB52lu6v7yZzH3HyO8JJ169hbL3/VImuRVGTdvbGVDvWh44E4xd8R9QjYuMr6XANOho1j6yONF59N/J9ot07kkuIgH22yzjRg3bpyXdZ8+fcTrr79eBBucZ8EI5C0HTU1N4o477hD77ruvwEecSp0r/l3RKac8rjEph4c80xSJdZ7ltOXVWvQZWzmLCp/WayMhmpu97Dtt1VMs/7fHimLFy5flvlD423TmecsejHATJkzwMN9zzz3Fiy++WBH+Nv5h8MOEK7hqeve18V0RKJw4UwQqMgDOnj1b/OpXv/Kt4IrTIUOGiEsuuUTdss8IOEOgLRkAd955Z/Haa685wy5PQi6MVIrfJffoJ7peM0yI9u3Vo0T+/OfGitmXnUtfyBd9ImcDYClsNmwx23DW708RzdNrShOHPGm/8qpUV7eITltvFxKa7yNdMdlhhx3EW2+9lS8DnFtVIJCnHOAr/ZlnninGjx8vRo0aJQYMGFAxBq74d0UnbYGywCQtD3nHLwrrvMtpy083ALZkfcZWzqLCdQNgx823Fis8MKooVrx8We4Lhb9NZ5637OkGwL59+4qXX365Ivxt/OsGwIMOOkiMGTOmovxcJbbx7SofpuMegYoMgPfcc4849dRTS7jCVNiJEyeKDh06lITxA0agEgTYAFgJevmldWkABNfL0EzAzmeSMS+hm/+v58Tsi35PX8eb/BRsAPShCFxEYSvr68SMgQeJph++C8QXSywhOq69Lq3ZbuctB5bz5wXC26+ymljx78+Idl2XCzzP+4YVk7wRr8788pIDLIWBoVn+8sGBDYBCZIVJdUraYq7ykrnFOVbnFRsAs60XNgBmiy9TbzkI5N3nsgFwkWzkjXvLkcjq57QiAyCUXezxEebGjh0rDjzwwLAgfsYIlI0AGwDLhi7XhKYBEMtDlz334kgesFdf009TRNP/Jom5jz4gFnz0XjAubS3Q7ZnXrPsCgk79rdfRsl9a6vvLi7gi1FoNgFlhO+ey88S8Z/9PwSdEx05kiD3dW5YNIyAclgQ33HubaHjoHtoTcIEfd8m99xddr77Fvy/i4uSTTxYffvihlzX2Cn3wwQeLYIPzLBiBvOTglVdeEdi2QTlXBkBX/Luio8qXxM8KkyR5FxmnCKyLLG9U3mwAjELGzfNqMwCy3LupV6aSHoG8Zc+1AdDGf7XOALTxnb4mOUVeCJRtAPzss8/EZptt5vN53XXXiT/96U8Ce73AYf+bZ5991g/nC0bABQJsAHSBYvY0SgyAAweLZf9wUeKMGx6+V9Tfcm0gfuezzhfLnHBa4Jl+0/jWa2LO1ZeI5h9/0B/7163WAJgBtjDs1ey+jRCNjT5+Xa68QSy13yH+vX4xb/RIwv7SxY/adxArvTpOtFtq6cXP+IoRaMUItFVjV1yVMiZx6LT+MDYAZlvH1WYAzLa0TJ0RqB4EXBsAbSWrVgOgjW8Or14EyjYAnnfeeeLGG2/0S/bDDz+IE044wd8Isz3t1/X111+LddelpWLsGAFHCLAB0BGQGZOp1AAI9maefqxY8P7bPqdL7nOg6Dr0Zv9eXcjZs0TdTUPFvKdHq0ehPhsAF8Niw3bBB++Imacd4yfotC0doHJ3/AEqMwYNEAs/He+nWf7+UaLTFlv793zBCLRmBNjYVVq7jEkpJm3pCRsAs61tNgBmiy9TZwSiEGADYBQy/LylIFCWAXDBggVizTXXFFOnTvXKieVVH3/8sXcaME4FVu6Pf/yjuPba4CweFRblf/vttx6tb775xjvtFflsuumm3g/XaVwltCZPniwWLlzoZde5c2frqca1tbVi5syZkfFnzJghpk+f7oWvtNJKYrnlFu2Phc3CcTrl//73P9GxY0fvKPE99tgj9PRAYIKThnDKMuKiA9pqq60CMzGT4DNp0iRvk3LkjbrccsstxdZbb+0dZd6O9vWKc1HlmD9/vvjggw+8jfaBBWhuu+22Hk0Yg5M6GI1xvDp8zCbdcccdxU477SSWX355j0RaA+DcuXPFF198ITBjFT72pYS8gj+c0JiGt6RlQDzIzn/+8x/x1VdfefWF07GRH46L7927t1d/YfSiFOZZs2Z5+L733nsC+OLwHczAhayk3WuzkvoP4znsmQsDYMM9w0X9XYuXkXbaclux/H2Pl2Q375knxZzLzy95vvRhx4i5oxYbrbI2AOaBKwqZB7b1dw8TDfRTrvMZ54plBv9G3Yb6dTcMEXP/vniZbZeLrhJL9T8qNG7Sh5X04d9//71AvwS31FJLCfQdcQ59IfpY9BP4YXxD3490aLPoN9DvRrms+8a8+jKMUygL3Morryy6du3qYYFtPdCfoQ/ba6+9RPfu3b04YX8u2wLqBFuNYF/huro6b1zBuLDWWmt5WaOvxXgNh34WexDrLo0clCNv8+bNE/gACmPXSSed5Gd96623in79+nn3K6ywglhxxRW967T4puHfzzzkwkbHpfymxSSEXVGJDKXF2GXZURYb1mHlTfqsElyS5mHGa6v6TNOU78XCCZ/5cCzRu4/9lHvaeqTxlX/5e4G2oz5piZ37+jTUBWbZN00kPXfSN2LhpInkTxTNs2Z4s+bbd1tJdKJDPTptt4PosGawP1Pp4WdhACynD1Q8pZX7Ssdcla/Nz+t9ztV7EMqTNTY4xHPatGk+dOuttx5t69zOv4+6wPsneINbgraCUeOwGb9S/tP24XnLXpQBEP0zDpwbN26cZyfp1auX93695JJLmhAF7m38lzMDMI+xwsa367E1ABrfVIYAbVid2o0ePRrHavq/Cy64wKMxZ84cucwyy/jP6eVBUoeYiP77778vaf8cP61OX12DHhkUJSmXsTRd0CIDi88LnbgTmx8C6aXIj097H5bE33777f3wYcOGSep4I8tLnbA85ZRTfBqIe+ihh0o8V1joPhldZU1NjR8/6uKJJ56Qq666aigN0OvWrZt89NFHo5J7z/Vy3HTTTZJeyCQdSS5pIAilS4ODJANnLE0EkoFOHnLIIaFlJCOdRB3QgCWpE/TzQb5RjhRWOXz4cEkvXn58HTNcI+xvf/tbFImynkM2L7vsMqnLj5kvZOWRRx6RZOAsyYOMej6/dGqeh++xxx4bigvorrbaanLEiBEldMIeuKj/MLphz+ofvEtO7bm+/5tz49CwaLHPZg+9xE8PWjNOOyY0/tynRwfi1eyxvZz/2stSLlgQeD7j1IGh6St9mCeu4DUPbOe98IycdckfPMxrD9tHzn/zVStMs6/8UwDvuWNHWdNERXDRh9NHDb8t0X61UVnJL7/8UqLPJuOeH99ss7gnw5J85plnIulk1Tfm3ZfRRwgfhzvuuEOSIUuiD9YxAVaXXnppCRYu2wLGBIx7Zt6KD/qII8kgKf/617/6vJFRsoSnJHJQibxB/1E8RfkXX3yxz1dafJPw7xOPubDRcSm/aTHR2XYhQ2kxdll2lMWGtV7epNcucEmal4rX1vWZmb87OTimPTFSQRPpN457P5Cm9vB+JXHnPTdG1uzbJxBP15f8614by7rh18vm+eHvPFO339CnMX3QgJJ80jyopA9U+SSVe1djrsrX5uv6uOv3OZfvQShHXtgMHjw4MG7RhAUbjPKnn36SNOHAT0cTEErSuOI/bR+et+xtvPHGPg50CrCkSTxSH0d0XQDvx2eccUas7cLGfxoZznOssPGtY+K6rZQIHz9IhQC+UqV2BxxwgC/4EHLaaN2nccwxxwTCbAYlJPzvf/8raYZXIJ3eeMzrvffeO9Kw6IpWmsaGMuiGqTADoG7YoZmRcpNNNrGWF40FnQqMaCYG5v1uu+0GNkIdDGe0PNtKQ9EcOHCgpNmMobT0clx00UWSZsZY6eJlES+SUe61115LVP944VM8wo8yAGIA0gcPPU3Y9f777y+nTJkSxV7i53EDQFi+GBBMp+NLM0UlzX4NlDmMDp7RHpwmKf/eZf37RC0XFRupmptlzX47+8otFOI5118ZmqsyAE7ru62kmWuyefasRfEyNgAWgSsKlie2oYCHPWxaKGsP7huorwVfTwiLaX3mqg+3KSZgBB+Uoj5eRLW1888/P7QMett11TcW0ZfRTAC/zzniiCMiPz5cccUVPg6u2wI+GiXRCfARBy8hqq7KMQBWKm9nnXWWn7/iw/R1A2BafJPIsV8RMRc2Oi7lNy0mYNulDKXF2GXZURYb1oiT1LnEJWmeiMf6jJT4iOUb4/AR8uSjrBCaHy4xXvuOdJKZZ58YoKnTj7qeccJhPgn9wpUBsNI+UPGURO5djrkqX5uf5fucq7EeZcgTG9qjPzBu6ZNOovC8+eabA2keeOCBQFSX/Kftw/OWPd0AiIlPuoyZ47+6hzGMZuUFMFM3Nv51+lFG7CLGChvfrsdWhRf7lSOQ2gAIQ4n+BQBGGd3961//CnQQu+yyix5cco0vjJhZoRoIfFrqK2kpjaRDRTyrOS29CoQjzmOPPZYprSSNTWcgjQFQLyuuaWmo3GeffbzZXHoYDGeY4aWe0TI2z+CGmZJLL720/1yF06mDOkv+NS1DKolLS4clvgCh06eluiWzLGDkDXN6Y1b5wgevkAU6/EWussoqJfl16dLFU/BNmjQ1PzBrFLRQtl//+tfytNNOk3369Il8QQ8zAEKe9A5J8Qi+jz/+eInZdHrHrcKRBjNtynWNjY2hRkdgAuMrXohMAybyxmxQ3UXhi7iYAYt6gdHVnBUDIwYtm9ZJ+dcu698narmoxEgFA97Mc04tUZLnjvlHaK7Ns2bKhkfvl831dcHwjA2AReCKAuaJbRDQ6LuGUY8E6mvazltImuIanSAixOV4oPcDYTMAP/roo5J+Cn0XPmKde+65Xv8D45LZ1tDX0TKYkhJEtd1y+8ai+jJd8Vb9o+ljNjotFfMxcNkWoCDT0utA3WDsw7iHMSFq/AOPaQ2ALuTtlltu8XjS5Q28QB7AK376TPO0+Op0w+TYrwTLhY2OS/lNiwlYdylDaTF2WXaUxYY14iR1LnFJmifrM4uQam5okNN22XLx2LbdBnLhlO+jYaQVT/gQ6Rvytt9INk372Y8/79kxi8N+WSExbfdt5Kw//U7OufEqOXvInzwjo5/+lzi4n/9O6UoaFwZAF32gKqBN7l2PuSpfm5/X+1y5Yz34zxsbvO/Q9ib+OIsPbpCFOLfNNtv48ZdddllvhZKK75r/tH143rIX9h6JcR+6C97nMcED76+6vQThdC6CRP9qOhv/SWS4iLHCxrfrsdXEje/LRyC1AfCaa67xOwAIM2ap6Q7LGk2DHr4uRblXX301QO/MM8+klXsLAtGbaTaQ+eUBhibTuaSVpLHp+ZdjANx111296d6KDpbTYvYgcDV/KC+ttVdRJe0HVaIwH3VU6dfJp556KkCL9iEKXTJKewt6S4D1fJ977jk/P3UR1phhrPv8889VFM9//vnnJe15EMgbS8lMhxkmep7oVGn/rUA0DCymTCFNmAHw7LPPDtDDoPbkk08G6OHmwQcfLDE84qWlXDdkyJBAvjB4jhwZXC6CtkEH5wTimYNuGL6YBYjlGWgHytE+iSUzQ0HbdK7r36QfdW8aqWZffalsnjc3/FdfL5um/iwXfPGpbPjHiJKZZFB+awfsLWnab1R24c8zNAAWhSsKWhXYaojjhQjLrvWXllkXnKXFSH7psg+3KSbmrOjf/va3oQrwJ598UmK8x1d/04W13Ur6xqL6sjDFe7vttvNmcT/++OPexzl8SFHOdVtAv26OCVgOrDvMjAz7MJjWAOhS3rCESuc76oNcWnxtcqzjEndto+NafsFLUkxcy1BajF2X3YZ1XD3pYa5x0WnHXbM+sxid2VdcGBjb6v92++JA42reS/8MxJ3525MCMaYff2ggHNtswMhousZPxsnaA3cPxr3wbDOadGEAdNkH2uTe9ZhbAkjEgzze5yoZ68F2Edhgko0+ZmHpaJTDe7we98QTTwxEdc1/2j48b9kLMwBiYo05w++ll14qebe+8847A9jhxsa/TYaLGitsfLseW0uA4wdlI5DaALjhhhv6nQC+dvz88+KvW4oL7A2kdxSY/RTlTEUDU1ijHJZqKrqwqtMm7YGoLmnZGlsgY7pJawDEst4w7DCrwvxigOXCplEU+WOZLgx6ChNML9Yd9l/U9ybErI2XX6a90SIcZpDpMwtpk9OSLxVmY95oo40il2PDyKZ4g4/9nHSH/Zv0cCznoo1f9Sj+NfaeoM3oA/FNA+CECRMC4bQhvLe3oE/EuHjzzTcDy9tAv5ylwKgbc7kaljVHuZNPPjnAJ/bUVM7EF1/owmYbIT5exnX8TDyyqH/Fp803jVS6cSj1NX1Fb/zwXVuWpeEZGQCLxBWFrApsf0G7acZ0Wdt/r8CLytTePeTCyRNL6yPBE5d9eJxigrFD/0CBmVW6gd1klQ7fCbQ1sy9DfLPtVtI3FtWXoRym4r3OOutIOoQIQSXOdVuAsVXv0zAjM2o7inr6cICv6Xr8tAZAl/KW1NiVBl8AHifHJRUS88BGx6X8KjaSYOJahpB3Woxdl92GtcInzs8Cl7j8VBjrMwqJRX7jR+8Fxrfaw/cJRtDuZv7htEDcef9a/BG9cfyHgbDaQ2n/tJCZQIoc9jHWdaXpx5fuee3CAOiyD4yT+yzGXIWVzc/6fa6SsR68F4WNqWeE6TUKW2xdpY+1MBwrlwX/afvwvGXPNABiJR8m8YQ5TGLR9/DHKkdztmUc/6AZJ8NFjRXgy8a367EVebJzg0AqA6D5pShqHToMSbqww7AS1TCwj5Deqdx///2RJYNR5eqrr/aW/2JGlDmN1iWtuMYWxmBaA2DY7DpF1+xYwmawqbjYD1Hhhz3jdGce1pJkjwfsb6XowTcNhmZjfvrpp/UsA9dQJPVDYXCohe4w407PC4dnxDnza5Vp8LrnnnsC9H7zm9/EkfPCMGtS5+G2226zpjEj4AuPTiNsdqqeBrMlVXxMo9f30jLxxczMOKfja+4DmUX9x/Gih7kyUtXs1Vs2vv+2Tjr5dUYGwCJxReGrAlviA0uuzRkNeGFpeOyh5HVkxHTZh8cpJjD+44s9DEb44GLuZWOw5RkH9Y8j6HdNZ7bdSvrGovoylMlUvONmBbhuC+bLKGb+x7mHH37Y70vRp6Y1ALqUtyTGrrT4In6cHMdhY4bZ6LiUX5V3EkxcyxDyTiPDiO+67DaskafNZYGLLU+Esz5TihKMdboxDqsVTNc0c4bExy8Vr+bX2wUMfPNf/7ecccrARYd/0FJijONxrnnObJ8WaE4/cr+S6C4MgC77wDi5z2LMLQEk4kHW73OVjPVguUhs8F6m3kfwUZRObS1BEauXYLRS8TCxRHdZ8J+2D89b9sz39Iceitd7zZUNpszE8Q+s42S4qLECfNn4dj22Ik92bhBIZQA0p/jGvRhgeavqLODffffdoRyj49Dj4RrLi2AgwzLXNM4lrbjGFsZTGgMgZqY1hEz7V3RxopCOyY8//qiCSnzdgIW9B3RnLtfGpq8298orrwTyvuuuoJJiNmbMwohz+DKmykLHoQei6jM6ESdsRqSeAF+Z9P24TAMg9vhTecHHJtY2Z844wTLAtO7CCy8M5IvlzzZHx8QHlnSr+Dq+qE/TyK3iKV8/JMTcjzOL+lf52vyKjFSkHE8/5iAyJD0soQSX7TIyABaJK7CoBmybfv5JTj/6gMALCl5SsIdRJc5lH25TTBSf+FARN/sP8TBbWd/bFPu6mU5vu+h/Kukbi+rLUCZT8Y7rR123BeyZo/pw7GtqfiU3MceLiT5WpzUAupS3JMautPgiflI5NrEx7210XMqvyjsJJq5lCHmnkWHEd112G9bI0+aywMWWJ8JZnylFqf6+OwJj3Zybri6J1PD4iGCc6xYfklQSmcYcSQdnRbmm2mly3vNP0RLfjXyatYeVzjx0YQB02QcmlXtXY24UfuZzfYyImriip0nzPlfpWK/ni+u8scH+tGrMhR/2rm5+FLjqqqtMtv17V/yn7cPzlj3dAIiVWtBF4pw5Fprbp9n4j5PhosYKlNfGt+uxNQ5jDkuHQDtEp0ZvdXPmzBEkgIJeary4tMeZ+OGHHwQp6aFpaSafoBlYfhgdNCE++OAD/15dUKMR1HgEGX/UI9+njb8FzWoStLGloFlVghqcHxZ24ZIWDQCCDG9eNjRgiDFjxoRl6T9DGWj5qHdP+/iJsWPH+mG4oFNpxaeffuo9o45NfPPNN4Fw/YYOehDPPPOM94hmnQgyFurBgWs6yEMAazgyGAmaaemH00Eq4r777vPv6XRfQUuG/fuwC1qCLehLhh903nnnieuvv96/18tBh1IIMsr5YWEXtHeUX++mDJDBSpABzktGs0QFLTMLIxF4ptcLGQAFGaH9cOBKS6i9e1qSK+hLlh8WdQGZgZzRhrheFJrZI8iAFxU99DkZrMUjjzzih4EHehH179Nc6PhC3mk/xNjkaB80M9eLQ0vFBe2V5cfPov594paLhofuFvXDrvNjddxsS7FErz7+vZw/T8hZM0XjW6+K5um1/nNcdNxkc9F16F9Fh7W7B56nvqE6nbZDDz9Zp569xfJ3La4nPyDlRZG4gtWisV34zZdi1m9PEs0/L+ofFXxLHThAdLn0GiHat1ePUvsu+3DasFqMGzfO44GW+Aoyulv5QZ9Bxj5Be8wJWh7j/Wi/TUFGsEBaMgCKf//734FnetuttG8sqi9DgWgpi5g4caJXNow/GPNpRn+grOrGdVugWQaeXgH6NMvAqwuVV5SPuqAPV14w+l01Bqj4cXLgUt7AA3hRjvYAFAMGDFC3vp8GXySK498nmuDCRsel/Cp2kmDiWoaQd1qMXZfdhrXCJ87PApe4/FQY6zMKicV+89SfRe0BuwrR3OQ9bL/SKqLbs68HxrqZJx4uFnzykZ9ohUfGio4bb+rfh100T5sqmr79Wiz83yTRNBm/iYt+339XEr3DuhuIFf/xz8Dzab02Ip6avWcdN99arPDAqEB4khuXfWA5cl/JmJukfIijvze4fp+rdKyPK0Me2OCdkQ6b9N/tafKOP54q3mjyj6AtnbxbmoQh6PBGgbHa5irhP20fnrfs0RZZnn4IDPbcc09Bq7Vi4YA9AXKo3Omnny7uuOMOdWsd5+NkuKixAszbcHc9tvqA8UXlCCS1F5pLkihnmfaHgwvCHJZEmnv5hNHGnnR//vOfI/cEAm1XtOKs7WFloMbp44GDPEynW8GpwZjBgXt9Zhz2Coxz2IhVYWXOAMQJzCqsXP/ggw8OZK+Xo2fPnoGwsBvsS6jyxmnDutNn1IBuEqfTM2cAYk9KlRe+SiR1+pcm7HeV1u21115+vpihaJu1F0dfxxen/dqcPtMWe0XqLov61+nHXZuz1ObcODQ8Ou1TWX/PcP8rt798hg6VWPiDfQZnONFfnmY0A7BIXFGyIrFtfPdNOW23rUvqa/ZVdCiG5QtobF1pga76cNuXSZXlu+++K48++mi56qqr+u1Y9SNRPhl6VHLf19tupX1jUX0ZCqP3h6S8+eULu3DZFjALUy83lmgncfrKBDIAliSxyYEreTO/8Cc5BMSGLwpj47+kwBEPbHRcyq9iIQkmLmVI5ZtGhpHGddltWCs+4/wscInLT4WxPqOQCPozzx4cGPf0U3kXTv42EIbZ8VEOh340jHwg9LAzpf+E+VnNAASfrvrApHLvasyNwth8nuX7XKVjvclr3tggf30MxfZdZODz2cJKBhxqqHShsO1P/Mh04Yr/tH143rKnzwBMsr0W9BusalA4mvqNjf84GS5qrEC92/h2PbbqssbXlSEgkibfcccdfcFVApzWRycT5bD887DDDgu8AETRx0EkNJsuipS3lLRSWnGNLSzjNAZAcy88k54rAyBehqIwTPrcNELpjdlWDpRLN9iZBkCaeefzZzOKKowwfV/xbhoAYQBVYZDXpA4DuEqHg0jSOr2Myy23XNrkgfhp8Y0zAGZR/wFmY24SG6l+odHwyH0BBRoKcO0R+9I+c/FLzGNYwFqKAM0Zpw6MjZ40sEhcwWNR2M59enRgjyP1klJ/761JoUscz8V4YFNMsA0D9utUbT/O1z9WIJ7NAFhp31hUX4YK0hVvGAPinMu2gC0/9DoIwziMlzPPPNNPB35MZ5MDxHchb0mMXcgrDb6In4R/xLM5G520Y48+7plju+IlCSYuZUjlmxZj12W3Ya34jPOzwCUuPxWm1yvrMwoVKee9+GxAn5h9+fl+YN0dNwfCGh693w/TL2AoxL7GauyM86f13TYQL0sDIHh00Qfa5N71mKtjG3dd5Psc+NLbVFRfWRQ24M/c+glLSpWj1U3++IrxeeTIkSoo4LvmP20fnrfs6QbAs88uPaE7AM4vNzRb1McSB8/pzsZ/nAwXNVaAfxvfrsdWHTO+rgyBREuA6euQoL3GqO1X5rCcCMuGycgSSYhOgRV0nLWg/eoE7TsgamuDywNVQjo8QXz88ceCZg6qRyV+JbT06bZhS3rNzFAmOq3QexwWX58GSy+Hgg40MUn49/oSYJoBKOgUWD/MvIhbAoxlb++8846XhDa6F3/5y1/M5NZ74ECzY/x4acqBRLTvn6ATNL30NPD5y4HxQF82Xc5yL3MJ8Nprr+0v1aNOR9Cx9V6+tj+9rsm47C3/s6XRww855BB/iTiWytGLrKDNdPUoia/T4hu3BDiL+k9aEHOZ6tIDB4tl/3BRbPK6m64Wcx9dvGQdkZfYdQ+x3A00TZ6WHaR2GS0BLhJXYFAEtg33DBf1d90SrIKOnUSXS4aKpQ7oH3zu8K6SPjxuaQKWPdGJd95YY7KLsQXtcKutthLYpgDbGOCH/gXjFxwZp2KXANv6eNCI6xuL6svAl770hgyA4oUXXsDjUOe6LZCSK+jEdy8vYD9+/PjQfPWHev9LinCqJcA6HVxXIm9JlrsijzT4In6cHCM8qbPRSTv2xMmv4ikJJq5lCHmnxdh12W1YK3zi/CxwictPhentifUZhQr5jY2ipt9OQs5epOe3W6az6PbiO6LdEkuK6Qf/WjRN+WWbCBoXu/3zTdF++eB7TnPtNDHzxCMWx9NIt1tyKdFh/Q1Fxw16iI4b0a/HZqITLemdttNm/rLjrJYAa2x4l5X0gXFyn8WYa/Ieda/r+GHvZ2Y6l+9zoG3rK4vEBvyRGUHg3UdtTaW/P2H7rX/+c9HSc2ythKWs2DZJd1nwn7YPz1v29CXA5nJeHRt1TXsjerjRTEDvEbbk0reOiuMfCXQZNpexFzVWgC8b367HVuTJzhECSeyH5smwOCgBhyck+eG0IGLV/5kbX8blT52KJOORvPLKK+UWW2zh01D0hg8fHpc8EJaWlm5t32ef0s13deKY2qsfTmFbAmybHeJqBuBxxx0XwIxerHS2y7p2ac3XZ97h9CngaHP6UnFzBiCmVCvZwOm6SRw2mceUd5UOm9Cndeeee66fHnRoELWSQBzan0zSvoeBuGnxjZsBmEX9B5iNuUk7S80j1dgopw88KPDVG1/IsUS4LJfRDMAicQUOeWNbN+y6kjqZtvs2svGdN8qqlnITpe3D475M4sQ21eaVf84553gzy6P6obgTt1GmtG03blZAUX0ZypHmy7vrtqAvZaGXDbBjdaSA+nWJL+Gmi5MDM65+n1beksx2A/00+CJ+ufwjre5sdFzKr8o3CSauZQh5p8XYddltWCt84vwscInLT4WxPqOQKPXn0MEe+qy9eS8/LxvHfxh4Nuu835QmpCd1d/41EA90Zl9xoVww4bPQ7TPMU4Br+5fOxnZxCEgos788TNsHxsl9FmNuHO96WJHvc+AjbqxHeJHYIH84HOyhdCH4eL/HrFCaPOI/J0PXosjGfxb8p+3D85Y9fQYgVhzaHN75dHwvu+yyQJI4/hFRl2GshNNdUWMFeLDx7Xps1cvN15UhAMt/rMOJPubeSLRBemwaPfDqq68OCD0aje7o4AyJE4Zuu+02SZto6kEl1zD46Q0IhjLduaSFvfdUXjaDHW0W78dFmmoxAA4ZMiTAF82s1OEKvYZBig4Okf/3f/8nP/roo4oNVHED35FHHhngDwNOnKODaCROUFb1YhoATzvtND8McbCvic3RTMxAmsMPP9yWpCScNnIN0KADYErimA/oq4ifBh2kcmk7yzgDYBb1r/i0+WUZqYjogq++kFN36BFUlOl+4cSvbFmWhmdkACwSVxQyT2yxvFd/4cF17YG7UX18XYq3gycu+/A4xUTfOxV9xS233BLLPfhS/Q58GKpMl7btxvWNRfVlKFMaxdt1W6DNrAM4R+0brLDHCcX4eKTqJq0B0KW8JTF2pcUX8ePkWOGQxLfRcSm/ip8kmLiWIeSdRoYR33XZbVgjT5vLAhdbnghnfSYapQVffBoYD2dfeq7E/sb6GDn/lZdCCcw45ehAvLrbbgiNpx42fvReIH7toXuoIN93YQB02QfGyX0WY64PhOWiyPc5sBY31iO8SGyQPxzGUn0iCybemH0BrShbFNn4z4L/tH143rKnGwAhX1EfjhVUjz32mK+nQF+hQ1VUkOfH8Y8IcQbAosYK8GXj2/XYijzZuUHAagCEIUgp1/DNPeFsbNCSqcAXBNCAwQ9uPh0AoG+KGfZSZdLHniSKn4022sgPdkkLREFb5YOGB0NolDOP4K4WA+Djjz/ul0HVna2TGjp0aCDNqaeeGii2y8b86KOPBvI66qijAnmZNyZvpgHwxhtvDNCDgdHm6PSmQBoYP9M6yLOSFfi2vasw0Orx9b0x0+IbZwDMov6TYlOukQr06+8eFlB8oVzPOJEMswlmiAb4y8gAWCSuHj4P3hXAJ/KAlQAYi27SYLvg0/FSf8FAPUw/cj/ZVDM1hHLlj1z34XGKCfaR1dugORPXLM11110XiE9LLswoTo0IRfVlKFQaxdt1WzDpYY/GOIcZCXo9pjEAupY3cx8lKPxhLg2+SB8nx2H0o57Z6KQde2wvteAjCSZmnUPHrFRPSYux67LbsI6qI/15Frjo9KOuWZ+JQmbRcxzwoQx+OBCrZu8d/Htcy4ULQwlM23FTPx7Sx35EI11n1oVnB+Ljw5vp9PF5+qABZrD13nUfGCf3WYy51gL+EqHI9zmwYOsri8RGxxCr3dR4ipn1+koE9JFRLgv+0/bhecuebgAEZq+++moUPN54RlvK+Ngi/gcffBCIH8c/IsYZAIsaK8CXjW/XYyvyZOcGAasBUD90AUJ7663pN3zXl7SChj5ddo899vAbBaYaxzUiTKHVl2v269cvgIJLWn379vX5As/6pqh6prQ3QsnBJdViAMSm6vqSWZQjbtk0ZjLiEAzEUz+zk3LZmGfPni31ZXX4+vTmm2/q8PrXWL5s8mYaAGtqauSKK67o8w5ZwQyEKPfkk0/6cVFezC6k/U+iokc+X0gKn44LaD3//POR8QcMGBDId8yYMX5cnY5t5ikSxRkAs6h/n1HLRSUGQBzeUXt4v4DyC4W54bGHLbkawRkZAIvEFSXMBVvafmH6cYcE6qD2oL6yedZMA2S3ty778DjFxNxSgvZZjSwIDDn6Mhi0b9A2Xdq2G/dSUFRfhjKlUbxdtwUYfkxFOWp25t///vfAjHDUSxoDIMrqUt5w+iF4UL8777wTWZS4NPgicZwclxCPeWCj41J+FRtJMHEtQ8g7Lcauy27DWuET52eBS1x+Koz1GYVEuI8TfJUB0PTr/rr48AQzdc1evQLp5r8ZbTCYc+3lgbjIB4eHmK5SAyDouewD4+Q+izHXxCPqvsj3OfAUN9YjvEhskL9yGFPV+GX6119/vYpW4mfBf9o+PG/ZMw2AtCegt2S6BBx6cO+99wZwNW0XSBPHP8LjDIBFjRVJ+HY9tiJPdm4QiDUA0mafAeMWDCTTpk1LnfMTTzwREP6OHTtKTDuHu+uuuwJhOG0RhhnTYb80fdkkOicsG9adS1p46dA7QJxYi9OQ6KAPL0vMbESjxl5zejxcV4sBEIyaMzjB3xlnnCFxtLvuXn75ZUkbzwfKEmaAct2Yr7322kCedFCMHDFihM6axDKwNddcMxAP5TANgEgEmdDrAzJ78803B2YUQMHFlGl9ujvSnHfeeYF809zQRvmBfDGz9fbbbw+QwCwjYK/zt84660h03sqlxTfOAAiarutf8WnzKzJSEXFvX53tNwwowdN22VI2/fyjLevF4RkZAJFBUbgi7zywbfjHiAD2eAGpPXwfOeui3yf+zX/lX2A3lXPZh8cpVIMGDQq0Q5x2q/p2xTD2P0JfpG87oNoulFPTpW27tpeCovqytIq367aALRQUzsrHRxPkg49UdECYNJcKq3hpDYAu5e3TTz8N8I0XBGxr8uGHH0roUsqlxTdOjhXNJL6Njmv5BU9JMXEtQ2kxdl12G9ZJ6gtxXOOSNF/WZ6KRapo5o3SbEhofMUbGbVUy8+wTA2NqzX47y8ZPxgUygn4ze+jFgXi+kXHHTQJxcePCAOiyD4yT+yzG3BJAIh4U+T4HlmxjfZHY6JBhT3RzogXGVryzx+0hnwX/afvwvGXPNAACJzq4LLAHPHRILKXWJy7hYzIdUKnD7l3H8Y8IcQZAhBc1Vtj4dj22oqzs3CAQawCkU2MDCq258WRSFhobG6V+/DUaCgwwcDg6XO8clSKPxr/ffvtJOqlR4th0vQEhDhqfbjhxTQsvg507dw6UH/miI4SRSvEZ5leTARC4AEeTTxg0gfvBBx9cMnsNcbHv41dfle675roxY2k1nbBZwt9qq60mMR3dnFqulyPMAAjjnr4pvIrftWtXSSfment3hRltwQPktBKHelf5KR8zEvGFFV8gsam9eg4fdfD+++8HskyLr80ACOIu6z/AbMxNpUYqkJ7zl9Kv4DPPCS5Jj2GBNhRcEFCkZ5w6MDZ62sAicAWPeWBbs/8uAez8l5BfXnSS3Nffd0daSJ2OB3GKCQwz5pjSrVs3ic2U8SEACi2M83p71a9h4MeepLpL23b1cQ9jnOmK6svSKt7g23Vb+N3vfheJvV4P5jV4N12cHLjWP6AfmDzh/thjj/XZSotvHP8+0QQXNjqu5RcsAd8kmCCuSxlKi7HrstuwRnmTOpe4JM0T8VifiUZr1gVnlYyP04/vH52AQuY9N6YkDcZRrHaYcfqxcvpR+wcMemFj7MJvvgzk4cIA6LIPjJP7LMbcABgxN0W+z4Et21hfJDYmbGeeeWbJGBb2Tquny4L/tH143rIXZgBUYz8OP8XyaX3LMhV21lln6dD513H8I5LNAIg4RYwVNr5dj60oJzs3CMQaAE0B/8c//lF2rn/4wx8CnQo2zcQLDhxm04XN8FINxvRhiDRnayjGXNIaOXJkqBHQ5Ofyyy+Xv//97/3yhXWWaRqBvmQaOMU5ffNVGCzDHAylwN+c8WaWQ93jRRgzLsNcmnIgvW3gQ5wZM2Z4++ap/KN8nNCrT+UPMwAqeuapSFE08Rx0ouQJ9JI6zKoMGzzD8sbSZ8woNV1afJMYAF3Wv8lv1L0LI1VzfZ2s2bdPidI874VnorINPs/YAFgErihg1tg2180pwTzsZcT2rBwDIMrnqg+3KSYw9IW1zbBn2J/U3JfP3MQ5bdtN2jfm3ZelVbxRZ1m0Bcyw1/cINusFX9JvuOEG7wOhCkOdm84mB67kDfkOHjw4VKZ0A29afG38m+WNurfRyUJ+k2KCeC5lKC3GrstuwzqqjsKeu8QljH7UM9ZnopCh/ctf/3fJGNkwqlSfMyng0BDbuKnCMWNwzg1DAvHNg0NcGADBo6s+0Cb3rsdcE9+4+6Le58BTkrG+SGx03DApQY2nyh89erQeJfTaNf9p+/C8ZU+3jwwcOFCuv/76Jbgp/ODj/fuKK66QmBUY5mz8JzEAFjFW2Ph2PbaGYcfPykMg0gD4xRdfBIQZlmxMDy7XmUtB0CDeeOMNnxwMMDgWO8xijrh4EcDMLuxDYNsg2iUtnCSLpbDmV2w0ZuxVhOnzcBdffLGP19FHH+2XS12kaQRpDID4mqA6GSyfjnOvv/667NWrV+DURJUWPqZ+Y7py3Ib4acoBXnAghspDfwky+YRs3XTTTaGGYMzYw4EeUEiPOeYYnx5ejOMcpkRvuummJft3KX5gPINS4NphX0gsVzf3DUO+mHUEYwIOAglzafHFJvmqPJjFGOdc1H8cfT1s7lNPBBTX+gfC98PS04RdYxmpUoiV7x0IEhbZfEYDbc2vt/PTz/zdyWYMJ/d54gqGs8YWy5D0FwuFe1p/7pjyPxi56MPxwUC1jbBDO4Al9vcz90lVaWCkxyFB6tCqyZMn+/QQx+zn07bdpH0j+MyzL0ureIM/5Vy3BXyIuuqqq+QhhxzijQ2YNY26xAc3pT/o+w9hlrfpksiBC3lDvpjRDiOguUqgS5cuPltp8U3Cv0885sJGJyv5TYKJzrYLGUqLseuy27DWy5v02gUuSfPS47E+o6Pxy3XTQlnTbydft8ABH82zZ4VENB6RToJxsWafHf20gXGVlvnOGHyEVCcJewdxabPuZ555QoCgTqecQ0B0Yi76wCRy73LM1flPcl3E+xz4SjrWF4mNjh+Wsio9aKWVVkq8Osol/2n78LxlTzcAwrCHbT6wlcySSy7pYwcMYSuAjoJttuKcjX/MKlR1YluNmedYYePb9dgahyGHpUOgHaKTUFWNI+Oe+PbbbwWtkRe0T6AgRVqQoU2QEAkyAqbi0yUtMlCJ8ePHi48//ljQbEWx0047CTJWpuKnWiLTzEtBy3sFGWVFbW2toKVugjpbQfsnpcbYdZnoZUF88skn4uuvvxZ0SIjo2bOnoMFIkDGt7KxQd5999pknUyg7ykunggmaXVk2zSQJkS8wRnnIgCw22WQTQRvFCpqpmSR5ZnGquf4zK3QOhBlX9yC77MOjuEO9YcxBn0OHgQj6kCJIYfP6RFLeopIV8ryovixtYfNsC6gv2pvYY5G2tBBkLE3Lrh/flbyhntDv0ymbgr7ce2NNWv3FZ6qVXKTFJE8ZakkQF4UL6zMOpYT03Kbvv1v0+3mKaL/8iqLjBhuLDmuvI0T78nVdFxy66gPjeCl6zIUsV+v7XNHYxNVbkrBq5z9r/jDmv/fee967H94zaWKGoA+ASaBzHgdlrdZ3feeFZYKpEag6A2DqEnACRoARYAQYAUaAEWjxCNCp7eL888/3PtBAeaZDkwTtHxxZLijaNKvdD6e9A8Vf//pX/54vGAFGgBFgBBgBRoARYAQYAUZgMQJsAFyMBV8xAowAI8AIMAKMQEEIvPrqq4KW8fq501JfQae4+/f6BR3aJGhbCe9Lu3r+9NNPC9pCQ92yzwgwAowAI8AIMAKMACPACDACGgJsANTA4EtGgBFgBBgBRoARKAaB6dOnCzqEys+c9tMRb775pmfo8x/SBZbZXHDBBWLYsGH+Y9orVtA+PAI+O0aAEWAEGAFGgBFgBBgBRoARKEWADYClmPATRoARYAQYAUaAESgAAezjN3bsWD9nOjhJ7LHHHt6+jHQwi6ATKwUdTCB++uknPw4u6NRgcdJJJwWe8Q0jwAgwAowAI8AIMAKMACPACCxGgA2Ai7HgK0aAEWAEGAFGgBEoEAEc/tS7d2/xxRdfJOICBsI//vGP4pprrkkUnyMxAowAI8AIMAKMACPACDACbRUBNgC21ZrncjMCjAAjwAgwAlWIAE71xd5/t99+u5g1a1YkhzAUXn755WLfffeNjMMBjAAjwAgwAowAI8AIMAKMACOwCAE2ALIkMAKMACPACDACjEDVIQDjH/YAnDRpkverr68Xq622mlh99dVFnz59xMYbb1x1PDNDjAAjwAgwAowAI8AIMAKMQLUiwAbAaq0Z5osRYAQYAUaAEWAEGAFGgBFgBBgBRoARYAQYAUaAEXCAABsAHYDIJBgBRoARYAQYAUaAEWAEGAFGgBFgBBgBRoARYAQYgWpFgA2A1VozzBcjwAgwAowAI8AIMAKMACPACDACjAAjwAgwAowAI+AAATYAOgCRSTACjAAjwAgwAowAI8AIMAKMACPACDACjAAjwAgwAtWKABsAq7VmmC9GgBFgBBgBRoARYAQYAUaAEWAEGAFGgBFgBBgBRsABAmwAdAAik2AEGAFGgBFgBBgBRoARYAQYAUaAEWAEGAFGgBFgBKoVATYAVmvNMF+MACPACDACjAAjwAgwAowAI8AIMAKMACPACDACjIADBNgA6ABEJsEIMAKMACPACDACjAAjwAgwAowAI8AIMAKMACPACFQrAmwArNaaYb4YAUaAEWAEGAFGgBFgBBgBRoARYAQYAUaAEWAEGAEHCLAB0AGITIIRYAQYAUaAEWAEGAFGgBFgBBgBRoARYAQYAUaAEahWBNgAWK01w3wxAowAI8AIMAKMACPACDACjAAjwAgwAowAI8AIMAIOEGADoAMQmQQjwAgwAowAI8AIMAKMACPACDACjAAjwAgwAowAI1CtCLABsFprhvliBBgBRoARYAQYAUaAEWAEGAFGgBFgBBgBRoARYAQcIMAGQAcgtgQS7777rqirq/NYXW+99UT37t1bAtuZ89hScWmpfKsKfeONN8T8+fO92x49eojVV19dBRXqF4FrXnnmlY+qwGqtY8Uf+4wAEMi7XWSNuqybIxZOmiiavpskmr6fLMTChaLD2t1Fh3XWEx27ryfadelaMQvN02vE/H8+7dNZ6sD+Tuj6BPkigAD3pQE4Mr1pbGwUI0aMoGaz0M9n6623Fr169fLvXV3Y+p7WXu+tvXyu5ITptDwEbG275ZWodXHM9UP1Kdm1CQRIgZGobvz69OnTJsqcpJAtFZeWyreqk1/96le+PPbv3189LtwvAte88swrH1WJ1VrHir9q9+kFVA4fPlx+/fXX1c5qi+Yv73aRFVjN9XWy7rYb5LQdN5VTe64f/uu1sZxz41CJuJW4mb89KUB/4XeTKiHHaS0IcF9qAchhcENDg+zZs6evn0Bn3nDDDSX6Y9fO1ve09npv7eVzLS9Mr+UgYGvbLacklXFarXos14+UbACsTLZbTGpd2HfYYYcWw3fWjLZUXFoq36o+dcXvoIMOUo8L94vANa8888pHVWK11rHir5r9119/XW611VbeS+ioUaOqmdUWz1ve7SILwBZ8+rGs2at3wCgXaQQk42BNv51k40fvlcVKw+MjSvJhA2BZUCZOxH1pYqicRPz+++/laqutFjACPvroo05o60RsfU9rr/fWXj69rvm6bSFga9ttAY1q1mO5fqTkJcD0ea8tuG222UaMGzfOKyoZAMVbb73VFoptLWNLxaWl8q0qBEt+f/zxR++WDIBizJgxKqhQvwhc88ozr3xUBVZrHSv+qtXH0gT00aQgeiySAVAMGDCgWtlt8Xzl3S5cAybr68SMgQeJph++C5JeYgnRce11hWjXzlsOLOfPC4S3X2U1seLfnxHtui4XeB5300RLi2ccc5Awaa345Euiw1rrxCXlsAoQ4L60AvDKTPrOO++I3Xbbzd+qZPPNNxcff/wxNad2ZVIsTWbre1p7vbf28pXWOD9pKwjY2nZrx6Ha9di2Xj+QPzYAtvZW+Ev5Tj75ZPHhhx96d1tssYV48MEH20jJ44vZUnFpqXyr2qhWxa8IXPPKM698qr2OFX/V6r/yyiti991399ljA6APRSYXebcL14WYc9l5Yt6z/7eYbMdOYpkTTxedT/yNEGQEhJON80XDvbeJhofuoT0BF/hxl9x7f9H16lv8+9gL2hNtxgkDxMIvPi2JxgbAEkicPqjW8dJpIauQGPYCPO6443zOnnzySXHIIYf495Ve2Pqe1l7vrb18lcoHp2+5CNjadsstWTLOq12Pbev1g1pkA2AyWeZYjAAj4BABVvwcglmlpLiOy6uYalecyisVp8oCARj2anbfRgg6uEC5LlfeIJbaL9xIMW/0SDHn6ktVVCHadxArvTpOtFtq6cXPIq7qb71BNDxwZ2goGwBDYXH2kPtSZ1CmJvSnP/1J0F6sXjp8mHn66cWH36QmljJBa6/31l6+lNXN0RmBVoMA67HVX5VsAKz+OmIOGYFWhwArfq2uSksKxHVcAkmiB6w4JYKJIxECCz54R8w87Rgfi07b9hbL3/2Ifx92MWMQzeL7dLwftPz9o0SnLbb278MuFnz4nph5OuXT3BwWLNgAGAqLs4fclzqDskURau313trL16KEjZllBBwiwHqsQzAzIpXIADhjxgwxffp0j4WVVlpJLLfcoj1jxo8fL2iTR/G///1PdOzYUay99tpijz32EOuvv34Ju99884148cUXxXfffefF7dGjh6BNzsVmm21WEjfuwaRJkwTyxW/BggViyy23FLSZo9hggw0S7c1RLWWZO3eu+OKLL8Rnn33m+R06dBBYmovyAL/27dvHweDVB8oCt/LKK4uuXbuKqVOnirFjx4qvvvrKw2OvvfYS3bt39+LQpsb+XiZLLbWUWGONNbznUX/AFnUGHvED7c6dO3vpevfu7fGKOq/U6fWx4oorihVWWMEj+cMPP4g333xTfPDBB6JLly4eJjvvvLNYc801Y7OEnLrERedPl/358+d7vGEvxdraWq/ett12Ww93W93pBaATksR//vMfr87QNjp16uSVFfIMnKMwttWnzrcLXHWecV2pfJSj+FXa9s0yhN2nwdWVPMTlqYeF8Rv3DH2Kav+Ip9PKow+w1bEuozqWLW1ciSpH2j5i3rx5Av0eFKeTTjrJr5GZl8MAAEAASURBVNpbb71V9OvXz7tH/4j2HOXyaCN6eauxb3E9BkRhbXteaR9po4/w+ruHiQb6Kdf5jHPFMoNp6W+Mq7thiJj798VbgHS56CqxVP+jIlPIujli+tEHiOYff/DitFt6GdFxw43Fgo8/8tMUbQCcOHGieOONNwR84I49NPv06ePrE6ptgWHoMXTAg887LiZPniwwFsOFhXsB2h/G/JkzZ3pPksSvVBZsfanGmn+ZR1/gZ0YX3377rbdHHvTGKVOmeLrapptuKvCz6W06HVxXilfaPsB1/ia9qHvbmFxOvet5zZ49W0ybNs1/tN566yV6T4IuijqAW4K2EVhrrbV8GvpFpfWUtnx5vzfpZVXXebUrfZxtjfpR3npe3rIT17b1MCVXSX1dr3dFJyzvctu2GmvL1WMrraewsoQ907ELex+Kan9p9fqwvKvmWZKTaLbffnv/NKxhw4ZJGlAkTYX3n1Fh/GvaIFeecsopPlnEPfTQQyWe6/HU9aBBg2RNTY0fP+riiSeekKuuumooDdDq1q2bTHJKV9FlUUdi04tcZFkQ9re//S0KCu85bUjsp7/jjjskvSRKMjz5z4AJGY/kpZde6sVPeuLNl19+KQ888EAvraqjMJ+MvfKZZ56J5TFJoF4fQ4cOlV9//bUko3CgHCp/Mo5JyAsZnCNJu8ZF5++mm26SdXV1sn///pKUolAeSVGSOPnI5qiTlJdddpnUT0FT5VQ+GQHlI488IpuamkrI2epT59sFrooBV/Khl9t2CrCrtq/KEOenwdWVPMTl2bNnz1A5UzIS55PSGChqXD56xLzqWJfRljyu6OWoRCYuuOACa11ffPHFelX513m2Eb281di3uB4DfJATXrhqP0mym/fCM3LWJX+QM047RtYeto+c/+ar1mSzr/xT4BTfuWPjT5kGff1E4YZRj8jZl50XeFbUKcATJkyQtCdbie6DfhE655577ilJkZfnn3++37ag35guzXiEtBibVd8bRk/RdyULafjLsy9AOd9///3I9wGFEX2kltdee62E3hPnXOGVtg9QPLnKX9Gz+bYxOU29h+U1ePBgX05RF/SxOSxa4NlPP/0kycjgp6NJHYFw3LjCKWn5inpv0gued7vSx9nWoh8VUY6iZCeubbvS613R0eW80rZdrh7rqp70ssRdx9UP0untrxK9Po6HosNw0qDV6QaZP/7xj3KTTTbxBwc1wJs+AIOhBgYRM8y8p5O2InmgL1jyhBNOsNJQNAcOHCjpy2wkvSLLgoalKyaK5yh///33l/QlNbQs9CXPx+SII46INLBeccUVXnqbsCMSFLQow1YUj1CsK3F6fRx55JGSvsz75YrKE8ZHmpUYmq1rXHT+LrroIkmz8qz8wfAKo2yUQ7vQO5eocqrnZ5xxRgkpW33qfLvAFQy4lI8kip/rtl8CYsiDNLi6koe4PF0O8HH5KCjyrGNdRlvyuKKXoxKZOOuss6x9i2kALKKN6OWtxr7F9Rig2kYS32X7SZJf6jhNC2XtwX0DxrsFX0+IJDPv+acCcWeePdiLWw0GwFdffVXSDFRrm6FVFZJO0fbjhRnskoxHOkg0cymWHuK6lIUk/BXRF/z3v/+Vyy+/vI+F0lmi/L333lvSDAodSv/aJV5p+wDX9eUXynJhG5OT1HtcFs8++2ygbvTJGVHpbr755kCaBx54IBDVZT0lKV+R700oeBHtCvnq42xr0Y/yLkeRshPXtl3p9a7oQN7gXLTtcvRYl/W0qCT2/7j6QWq9/VWi19s5KS5GagOgObDTclW5zz77lBhuYATRjTk0xdIznmDm4NJLLx0YYECTTjoMRYGWPpXEpaXDEl+2MJjRssuSr78HHHBAKC081Cs1z7Lgy6cucCpv8HP88cfLY489Vm688cYlZUUaWMZNpys4ipbp4ws4Lcvwkup50/IYk5z86KOPSvJeZZVV5DHHHCPPPfdcedppp0l8CQybZUjLBUroJX0QVR/4YnziiSd6HdLRRx8tl1122QB/tIRZzpkzpyQb17hE8Qf5xovFvvvuK4GTiT0tW/YUB5PBxsbGUCMwaMHQjc4T1yY9fDnTna0+o/guF1fX8pFE8XPd9nX8oq7LxbVceQAfcXmi3W244YaRP/QZkBc60r5EZnbddddAMePyQcS86zhKRiH7LWlciSpHWpm45ZZbvJk0ej0BC9DHuImfOTO8iDYSVd5q6VtcjwGBRhRz47r9xGRVdhBm7+mz+abtvIWkKeah9Jp+miKn7b6NH7/m19vJppqpXtyiDYC0fKdEJ8AqEOhSmJWKj6K0LUpJn4j2lIcB0LUsVON4CZ0WH2N1XYWW+kravkDSwRkSHy5pW5tAOOI+9thjJfLmGq+0fYDr/EsKGPFA7+vD9PIk9R5B2nuMdwfoyqqOYKy1zcLUdQno3Vj1opxrnGzlK/q9CeUuYoxFvlHjLOqyNehHWZejaNmJa9uu9HpXdCBvrtp2Wj3WdT2hLElcXP0gfVT7S6vXJ+GlqDhlGwDxcgmrrXIYJKBYqYFG92EkgcKmHK3xLulUjzrqKBXs+0899VSAHr72jhgxwg9XF7S3oLcEWM/zueeeU8EBP6xS8yjL2WefHSgLBuInn3wywBtuHnzwQbnMMssE4qJBmS5Mwdluu+28mWePP/64p4TBqKicTdjNWZa//e1vQxWFTz75pMRABet4uS6sPlAOc+Yj8l133XUDuEDJNJ1rXML4+/Wvfy0///zzQNbPP/+8XHLJJQP8YVm26YYMGRKIA0PhyJEjA9Gw5PfGG28MxDMVN1t9hvFdCa6u5cOm+GXR9gMgR9yUg2sl8gA2bHlGsBp4TEfaB+SF9v6TP//8cyCOLZ+86zhMRvPoi13LVlg5KpEJLNPSx7Koj2OuyxEQlpibsPJWU9/iegyIgSIQ5Lr9BIg7uFk45XtZs8f2vkEPhsBZF5wVTpnGICwr1o2F8176px+3aAPg4YcfHmgjMFqYHyKxPBgfT/S2hOs8DICuZaEax0vMwNSxPfPMMyXtG+XLCC6am5ulOaMM7wOmc41X2j7Adf5m+aLubWOyrd6j6OrPoSfr9YSlrFEOMzr1uPgIrzvXONnKV/R7U1FjLDAPG2dbi36URzmKlh1b29bbVdR1Er0+Kq3+PAkd1207qR7rup70csdd2+onrP1VotfH8VJUWFkGQCzrNV8uUQDMNtP3jsBAguXCplKAuFimqy/fwJJI3WGZgL7PCmazvfzyy3qUwDX2jtNnFtIhIxKzrUxnVmoeZYEiqg+q2MuODv8wWfPv6fCLwJJefMk2DWKmgrPOOuvIWbNm+TTMizhhpwM+AsYrfImE4hbl3nvvvUB5sMdjuc6sDxj5GhoaQsnRBt8S2CksUd/19fWBuC5xAWGTv4022ihyCQuMt4o3+CYuaAfmcpnXXnstwL9+Y3bao0eP9oPj6hORTL4rwTUL+YhT/LJq+z54MRdpca1EHhQbtjxVvCgfM150uaNDmiQUedPF5ZN3HYM3U0bz6IuzkC2zHJXKRBLFKYtymPISdW+Wt9r6FtdjQBQO+vMs2o9Ov9LrphnTZW3/vQIGvam9e8iFkyeGkq5/8O5AXBj8dFekAZA24Q/0dxhTo7Z9wR7U5pibtQEwC1moxvHS/JiJpZJRDtvZqDEK7wjASLks8ErTB2SRvyqbzY8bk5E2rt5ttFW4+f5h6qUqHnws0VT1BB9GXuWywCmufCbfeb83FTnGAnNznG0t+lEe5ShadlB/traNOHEuqV4fRwNhSehk0baT6LFZ1JMNDxVuqx+z/VWq16t8q8kvywAYNbsOBTOXsYbNclMAYD8QNdiYG9bD2KHC4CfZu0Lf6BlpwgyGZqXmUZZ77rknUJbf/OY3CoJIHzMi9fLfdtttgbimghP3VQ8J44QdBkdYtjFrCMqZuedHIGO6gXFQN7aiHst1Zn2YS9xMulheouMyZsyYQBSXuICwyd/TTz8dyE+/gYFPn71JpxbrwfKll14K8B72JVxPgFmGqqxYiqH2c0ScuPpEuMl3JbhmIR9xil9WbR+42FxaXCuRB8WLLU8VL8zHjGj9gCVMT3/hhRfCosbKTN51DAZNGc2jL85CtsxyVCoTSRSnLMoRKjQhD83yVlvf4noMCIGg5FEW7ackkzIfNNfXyenHHxow6GFmX8NjD4VSXDDhMzl1hx5+/JoDdpXNdcHtNoo0AGI7DDUuwlcHnYUWhh7isC09ftYGwCxkoRrHS+gjOq73339/VBVIfOi8+uqrveW/ODRE/zifBV5p+oAs8o8Ewgiwjf1x9W6Qir2FLqrqCitVcDCO6bDyBEu4VTxMwNBdFjjFla/o96Yix1jgbo6zrUU/yqMcRcsO6s/WthEnyqXR66No4HlSOlm07SR6bBb1FIeHHmarH7P9VarX63lXy3VqAyC+wkTN0EKh+vbt6w8gGEh+/PHHyLLqRq7OnTsH4l1zzTUBOtjM1ubo2OlAmrvuuqskiV6peZUF+9KoQRV+3Cm2imEsedXTYEmu7kwFx0bTJuyKNoxYcbP/EO+rr74K7HuHvanKdXp9QBkImy2q0/7www8DuGDZie5c46Lzh/owZxzqeeMaXwlUvfXq1SsQfOGFF/phiINlwzb31ltvBZbPq/i2+tT5doGryteVfMQpflm1fVWGOD8NrpXKg+LDlqeKZ/oYYM1De+68804zmn+fNJ886hhM6TKaV1+chWzp5XAhE0kUpyzK4QuK5UIvbzX2La7HAAscJcGu2k8J4TIeNP38k5x+9AG+MU8t6Z09pHT7DJBvnj9P1h7eb3H87TaQje+/XZJzkQZAnBqvxlj4+vYyJYzSg+nTpwf6yawNgDoPrmShGsdLvDTq9YBrbDuDF3xs81OOc4VX2j5A8eoqf0XP5tvG5Lh6t9HWw/GRRq+ru+++Ww/2rs0P1FdddVVJHPXAFU5x5Sv6vanIMRY46+Nsa9GP8ipH0bKD+rO1bcQJc2n1+jAaeFYuHVdtO4kem0U9ReFhPrfVj97+0HdW8u5v5l0t96kNgBhY45w+1R+zxOIc9pdQg5JpADSPr8fpvjggIe5nCtN55wWXrYAXvVLzKou+dx2WoyRx2LwXs3kUPuYsO13BAc42o51N2MN4guL8zjvvyIcfflhecsklEvvuYPq24kn5rgyAOGTE5rDMROUL31TmXeOiyws2ubc5/VQmHFCjOxyoovOOJfPlOlt96ny7wDWMz0rkI07xy6rth5XBfJYG10rlQeVty1PF031sIbDCCisE5AmH9cS5cvLJqo7Bpy6jefXFWciWXg4XMpFEccqiHHGyo4fp5a3GvsX1GKCXPe11Je0nbV5mfJzuW7PfzouNeTTrDwbA2X++IPLgjznXXxmIP+fmq02y3n2RBkB9jMXhckmcvp2MqTMgfdx4FEY/ySnAZrpKZCGOv6L6Auioq666amAMUvoN6gWHA2L/vy+++MKEItF9JXil7QPCGKok/zB6Yc9sY3JcvYfRi3qGA/PwjqXqB3uwmW7QoEF+OA77s00qUOkrwSmufEW/NxXVrhSu+jjbWvSjvMpRtOygDm1tW9Wz7pej1+vp1bUrOpW07SR6bBb1pDCw+bb60dufC73exk8R4akNgNhsOc7pBkAYi+JcnAFwl1128QcjNWil9Q8++OCS7PVKzassuiEPQpfU6UoM9vjTnR62+eab60Gh1zZhV4neffddiVN3oxS7sDpwZQDEgJvE6Xv6mLPsXOOiywtePGwOe1kqjEwD4F577eWHQcHSl8HY6JrhtvrU+XaBq8rflXzEKX5ZtX1Vhjg/Da6VyoPiw5aniqf8n376yVuur+QMPvo6LOGJc0nzyaOOwacuo3n1xVnIll4OFzKRRHHKohxxsqOH6eWtxr7F9Riglz3Jtav2kySvqDiN774pp+22dcCY5xn/rroo0vg3/81X5VSa8admCdYeQYc10F7MYa5IA6C+TBGnYSZx2OJE9Zd5GgBdyUK1jpfYpkR/iVMYmz725P7zn/8cuVejqkNXeKXtA1znr+jZfNuYHFfvNtpmuL7JP7YNmTx5sh8Fs1twIJ2qN3PCgR/xlwtX9RRXvqLfm4ocYwGzPs62Fv0or3IULTuoP1vbRhzdlavX6zRwXSkdV207iR6bRT2ZeETd2+pHb38u9PooPop8ntoAaO5rZjLvygCI/ejUYFSu37t3b5O9QKeaV1n0L2877rhjCU9RDyB0quyY7aM7XcGBYcnmbMKOZd3Yk07lF+evssoqgXiuDIDmMueoMumzENFIdecaF70TsMkL+IgzAOphOKihEmerT51vF7i6lo84xS+rtp8E7zS4VioPih9bnioefCjqOHFVb58wNNumpyOtLZ886xj86DJqw7Kax5U05UC59X7A/EiA8CSKU5FtRC9vNfYtrscA1EkS57r9JMkzLM7cp0dLHPChDHnKr7/31rDo/rPpAw8KpKnZfxc585xTQ381+/YJxJ1x+rF+vIZH7vdpZnGB/aJV/4c9p5M4ffZ9HgZA17JQreMlsMeBgIcddlhgxYqqH9PHqcyffvppSZW5xittH+A6/5ICRjywjclx9R5BMvKxuUUSlrgq98gjj/htCnU2cuRIFRTwXeMUV76i35uKHGMBuj7OthX9yJWeV7TsoP5sbRtxlKtEr1c04FdCx3XbTqLHZlFPOh5x17b6SdP+kI9Nr4/jpaiwdsiYOvxYRzPMBA3aXhzqiARt6BsZ/4ADDhDPPPOMF06GGvHdd99FxqXZA4I2DvbCSRBEXV2dH5dOohW0/NS7p4MpxF/+8hc/LOkFLdMQNJstEL2Isqy99tqCptN7fJBQCTqhM8BT1A34pz0UvWBSnMSXX37pR6Uv34JOxfXuyQAoaON/Pyzsgr68iHHjxnlBwJb2lvOj0cwhQSeDCTry3n+mLujwCQHMttpqK7HlllsKMj54P5Tphx9+8KKRAVD8+9//VklS+Xp9nHrqqYL2bYxNT0udBS0vEbRPQWjeLnFBBjp/NtlHfJqRKOiUZFwKerkXH3zwgXeNv0MOOUTQoSXePX2BFbRPjqANmf3wNBdx9Qk6Ot+V4pqFfOiyTXs6+biA96zaPmjbXBpcK5UHxYstTxUP9dC/f38xduxY9UjQbBivnwSeNheXT951DF51GbVhWc3jSppyoNxxfQTC6UVNoE9VbtSoUWLAgAHq1vOLbCN6eauxb3E9BgSAj7jJov1EZBX7uOGe4aL+rluCcTp2El0uGSqWOqB/8LlxN/2wfUTTpG+Mp+lvl9r/UNHliuvTJ0yYQm8/ZEAQU6ZMsabU+w8yAAb6UCTWx6OwcDMD+iAr6ORh77EZPwtZ0PmrpvFSx4WWi3k6JO3XLWg/OVFbW6sH+9fQKT/++GNBMwe9Z1nglaYPyCJ/v7CWi7gxGUnj6t1CuiQYr3p4j/jmm0VtXH8XoY//4p///KeXhlbYeO8d0LN1lwVOceUr+r2pyDEWuOvjbFvRj/R+uhL7QdGyg/qztW3EgUO7qkSvX0SlMjpZtO0kemwW9aTwsPm2+knT/pCXrpeY7/42XgoLT2J5TGMJdWXBP+644wJfpDCt1YUroiz68hOc5prEzZs3L3C6Z58+fQLJ0n7hjLN2P/TQQwGsSRjlOeec432pjdpbUD/tdrfddgvwluZGr49+/fpZk5LRMcArlivrziUuoKvzR4OwnlXoddxXAOzRBmzVjxSxUBr6Q8Qhw62cNWuW/tj6dUnnu1Jcs5CPuC+/WbX9AIARN3HtBEl0XCuVB8WCLU8VD/ufKtmBj74EspHUxeWTdx2D5zRYVvO4kqYcKHdcH4HwJF9Oi2wjenmrsW9xPQagTmwui/Zjy9MMrxt2XWBWHmb+Tdt9G9n4zhtm1ND72gF7l6RXswfT+FgenKXDftCqH8RSxvkRy5R1HrbYYgs/jW0GIPaui3PQibCFh+LBpJeFLFTreBmFE71QSvoQKq+88kqpY68wGz58uJ80C7zS9AFZ5O8XznIRNyYjaVy9W0iHBuNgD1UH8HHYIGZw0iQL//npp58emjYLnOLKV/R7U5FjLCpAH2dtumZr0Y9claNo2UH92do24sBVqtcvolIZnSzadhI9Not6UnjYfFv9pGl/yMum19v4KSK8apcADxkyxB+QMFDR7DQrPjCS3HffffL//u//5EcffVRiNAGBNJXqqjM67bTTAmXBvik2R7MsA2lwAIfu0ig4SBcn7PpejMD6lltu0bMquaYv7gHesFdGuU6vD+xzCMUxztHsp0DeUDB15xIX0NX5sw3CiB/XCdxxxx0B3lEWm6OvEH4a8KJcXH0ijs53pbhmIR9xil9WbV9hF+enwbVSeVB82PJEvBtvvNGXA7RRvIDGHUuvaOt+XD551zH40mXUhqWrvjgL2UpTDpQ7ro9AeBLFKYtyIO8kTi9vNfYtrseAJJhk0X6S5KviYHmvaaSrPXA3uXDi1yqK1Z917uneicE4Ndj2m9Z320B+NXtu76eZ9+Kz1rwqiYC95NAHqh9Oo41zWNqEjyUqvmmwQ1p9WxFbXzRhwgSfFmia9LKQhWocL6EH0kw/edttt8kXX3wxrgokDH4Kf/joz5XLAq80fUAW+auy2fy4MRlp4+rdRjssHAd76MZr6M+mXoqD/8JcFjjFla/o96Yix1jgr4+ztj6ptehHrspRtOyg/mxtG3Fc6PUu6GTRtpPosVnUE/BI4mz1k6b9IT+bXp+Ep7zjVK0B8PHHHw8oDNjPL2o2mgJt6NChgTS0PEkF+X6aSnXVGZmN/Mgjj/T5ibrYc889A2WBYVN3aRQcpIsTduzLoitn5mwzPV9cX3fddYH4NFXejJL4Xq8P8PD3v/89Ni3y0nkdP358IL5LXEBY5882CCN+XCcAZVnn3bZ3IpQ1PT42cVYurj4RR+e7UlyzkI84xS+rtq+wi/PT4FqpPCg+bHnSEtDAbGDUp81Ir2jrflw+edcx+NJl1Ialq744C9lKUw6UO66PQLi5V9Njjz2GxwGXRTkCGcTc6OWtxr7F9RgQA4UflEX78YlbLhZ8Ol5O3X7DgEFu+pH7yaaaqZaU5QcXeQjIE088ERgX99tvv9iCmPqXabBD4o022sinibGJthiJpIm90/Rx2aSXhSxU23iJWZdLLLGEj0OSj8DY91jhBryVywKvNH1AFvmrstn8uDEZaePq3UY7KhwzXFU90DI4qc/CQd8e5bLAKa58ZrvN+72pyDEWdaCPs21FP3Kl5xUtO6g/W9t2pde7oJNF206ix2ZRT8A+ibPVT5r2h/xsen0SnvKOU7UGQNofreR0MX3ZgAkUvsrioAw1sMGn/dfMaIV0qjU1NXLFFVf0ecOyFVjHo9yTTz7px0U5OnXqJHEct+7SKDhIFyfs5hIN2rdRzypwjZdRfbkA+APtcp3eyEBrk002kbQXZCg5c0DWlUiVwCUuoKnzZxuEET+uE1i4cGGAHsr7/PPPI1moo32/AnJA+wf68eLqE5F0vivFNQv5iFP8smr7PngxF2lwrVQeFBtxeWJ2C+3FE5CD3/3udyppKj8un7zrGIzrMmrD0pVimIVspSkHyh3XRyAcp7ChzarfnXfeiccBl0U5AhnE3Ojlrca+xfUYEAOFH5RF+/GJx13QjPnpxx0SMP7VHtRXNs+aGZeq4rByDICQWf1XLhNYJUB7EvvtAzIYNY5OmzZNmoeWmQY78NG3b98APf2ABJ1P2iet5LALk14WslCN4+Uee+zhYwad8NVXX9WhClxjKxPovapP07cOyAKvNH1AFvkHCh9zEzcmI1lcvceQjQ3CR3ZVD6Z//fXXR6bNAqe48hX93lTkGItK0MfZtqIfudLzipYd1F9c23al17uik0XbTqLHZlFPwD6Ji6sfpE/T/hDfptcjTrW5qjUAAigs5TUHqDPOOKPkxMuXX35Z0maSgbhRHWaaSnXVGaEsWCahlwVGvZtvvjkwqxEGIkw716foI81555XuqZNGwUH+ccI+aNCgAG84VZg2uEYy30HpHjFihGeM1MuBa/BSrtPrQ9HFstevv168dAkzP/GlwDQ8hi2BdIkLyqTzFyVTetltnQAd1hLAGl/Rb7/9dp2Et3Qdcq7wgI+ldlBIlIurT8TR+VZ0ysU1C/mIU/zAfxZtH3RtLg2uLuQB/ETl+dVXX0n9xEvUI20AL6dOnSphpKdDgSQ+fMT9aCN2v8hR+SBCEXWsy6gNS5d9sWvZSlMOYG3rI3BKpmqz8HHSKZbYffjhh5IOhQIJz7kuh6Jr8/XyKj6rqW9xPQbY8EB4Fu0nSb4N/xgRMP5hGXDt4fvIWRf9PvFv/iv/SpJVIE45BkAlK8rXx7MA8QQ30PkUHfjQp+gAsUBKtBeMm3o8XJsGOyTCjGo9Hj664GRUpQdh7+F77703sJRYxTfpZSEL1TheAm+FAXwYWvHx2nTYp1bfygRxoQ8rlwVeafqALPJXZbP5cWMy0trq3UY/LBz7i5uTJVAnHTt2lHF7rWeBk618Rb83FTXGot70cbat6Ecu9byiZSeqbbvS613Rgaxl0baT6rGu6wnlSeKi6kelTdP+kMam1yu61eRXtQEQQGF5h65k4BrKGcA++OCDA52kirfqqqtKNI4wl6ZSXXZGMO5hur3iUfldu3aVOEQDSyj0fWpUOJ26KxsbG0uKkkbBQeI4YcfLpf51Fnl369ZNYhNcGB/ROYQp0opHGLHmzJlTwmOSB3p9KHrwYQTFVwk6eTgwe1LFMQ//UHm5xAU0df5sgzDiJ+kE8MKgyqF8zBDFF3XMRKBT2ALhkPf3338f5H0XV5+IpPOt8oBfDq5ZyIdN8UMZXLd90LS5NLi6koeoPC+77LKAHOj1mPQaHxSUi8oH4UXUsS6jNixd9sUor0vZSlMO5G3rI7BnGV7Gwur42GOPBQnfuSyHT9RyoZdX57Fa+hbXY4AFDi84i/aTJN+a/XcpMQCaewHa7uvvuyNJVoE4RRsAwcwRRxxR0kbwwQSzA3v06FGi0yhZNQ12oAVDX+fOnUvooR0uvfTSJc8VLfgmvSxkoRrHS/RTel+mMEH7Q7906KGHSjoNsaQe8EFDN/5mgVeaPiCL/CFTSVzcmIz0Seo9ST5mnDPPPLNEpk05NtNkgZOtfEW/NwGDIsZY5KuPs21FP3Kp5xUtO1Ft25Ve74oOZC2Ltp1Uj3VdTyhPEhdVPyptmvaHNPpYiHGvJbiqNwBCUfjDH/5QMitOKRumD8NV3MmYaSrVZWcEYZgxY4ZnVDN5jrqno8H9L9CmMKVRcJDWJuww9EXxYT4/6qijSjYvffDBB00WE93r9QEDqK4QmPmq+1NOOSVyjx7XuOj82QZhFDhJJ1BfXy/DFDBVPt3HacuYiWA6W33qfLvA1bV86PWMWW1hznXbD8vDfJYGV1fyEJWniwEep/4pF5WPCs+7jnUZtWHpui92KVtpygGsk/QRgwcPDu2PTcXCZTmUHNh8vbzV2Le4HgNseKhw1+1H0Y3ym+vmVGz8g3GwpRoAsU9fEszN5cJRho6RI0eGGgH18RjXl19+ufz973/vt88wekn4UnST6FPVOl5iZuSaa67pY6HKFOVjrFezKnW5do1X2j7Adf562eKubWNyknqPox8Vhg/KZh2NHj06Krr/3DVOScpX5HsTCl7EGIt89XG2rehHrvW8ImUnqm270utd0YGswblu26CZVI91WU/IN4mLqh+VNk37Q5oker2iXS1+oQZA/fhrLB+Ic6+//rrs1auXXHLJJUsGLgxkmNKOE61sB1ikqdQ0nVGasmBa+aabblqypFUNyLvuuquEMhrn0io4ffr08XGLOrQD+/utu+66fjzFD3wYonAwCQ6ygJs8eXIgXtSMvLgyIMysj++//17uvffegQ2mFR+YDYl9AOOca1xM/uLyRhgO9lD8mi/rZlrsJ4SlMebSZqTHjEy8GOAgkDBnq0+T70pxBQ8u5WODDTbwcYoyAKpyu2r7il6cnxbXOFoISyIPUXmah+0ouUrj6wbAqHz0MuRZx6aM6nyY11n1xS5kK005UK4kMgHjBpQnc/ZRly5dTGi8exflCCUc8tAsb7X1La7HgBAIIh+5bD+RmfwS0PTzjyWHf9hm+4WFzx3zD1tWJeF1t1y72Pi4Qw/ZNHNGSRzzgTnDDi/WLhz2xg2b8QfjNFYxYE/h1Vdf3R9vwgx2io/PP/9c4mXbnIGL2a0wJKplxhdffLFPL0r3cSkL1TpeAjcY9PAyqh/yoY9RWCGC1S/YXy7uID+XeKXtA1AOl/mDXhJnG5PT1HuS/PQ4WF2j6gkzZ8NWGunx1bVLnNKUr4j3JlVm+HmOscjPHGfxLMq1Fv0oq3IUITtRbduVXu+Kji5TLts26KbVY13Uk16euOuo+lFp0rQ/pEmi1yva1eK3AyM0CLQYR9NFBS3vFbS+XND+VoKWpgoa7EX37t0FKRotphxglPbiEJ999pn473//K1AulIUOthBrrbVWYeUAH99++62gPfgE7TMmyDAraCmuhzEpwc75IgOYV5cgTIq3eO2117w8gA1tIurhs8Yaa3i40NIR5/lXA0GUFfL8ySefCHrxEHQQiqAXGkEvTGWzlxWuecuHDkBravt6uartusg6LgqLapUt9A3oF+jUTUGzJbyxIW6cy6McrbFvcSl3bbH9JMXv7rvvFqeddpogg4OgAzqSJksUjwxR4r333vP0FzI4CfwwnsJBh5gyZYp3TQZAMXbsWO866g/tbvz48eLjjz8WNMNN7LTTToIMXFHRI58XKQt59AV6wcm452EPfRZY08cLQUZTQS9SiXXzIvFCWYrOX8ezmq+LxAlts8j3przbVTXIQWspc9GyUw11aeMhi7adVo/lerLVkpvwFmcAdFNsplItCES9TFYLfy2VD8a1pdYc880IVDcC3LdUd/1UM3cnnXSSuO+++8QBBxwgnnrqqdxYTWsAzI0xzogRYAQYAUaAEWAEGIGcEWADYM6Ac3ZBBPhlMoiHqzvG1RWSTIcRYAR0BLhv0dHg66QIwOBH21oI2hxc0NYEgpbRJk1acTw2AFYMIRNgBBgBRoARYAQYgVaCABsAW0lFttRi8MtkNjXHuGaDK1NlBNo6Aty3tHUJSF9+bCmCbS2ampoEnXIv3n77bZHnlh5sAExfZ5yCEWAEGAFGgBFgBFonAmwAbJ312mJKxS+T2VQV45oNrkyVEWjrCHDf0tYloLzy77PPPoIOaRO00bi333F5VMpLxQbA8nDjVIwAI8AIMAKMACPQ+hBgA2Drq9MWVSJ+mcymuhjXbHBlqoxAW0eA+5a2LgHllR9Lf3GAjTqYozwq5aViA2B5uHEqRoARYAQYAUaAEWh9CLABsPXVaYsqEb9MZlNdjGs2uDJVRqCtI8B9S1uXgJZXft0AeOihh4rRo0e3vEIwx4wAI8AIMAKMACPACDhAgA2ADkBkEuUjgL2BcOQ3XOfOncW6665bPjFO6SPAuPpQ8AUjwAg4RID7FodgMqlcEJgwYYJYsGCBl9fKK68sVl111Vzy5UwYAUaAEWAEGAFGgBGoNgTYAFhtNcL8MAKMACPACDACjAAjwAgwAowAI8AIMAKMACPACDACDhFgA6BDMJkUI8AIMAKMACPACDACjAAjwAgwAowAI8AIMAKMACNQbQiwAbDaaoT5YQQYAUaAEWAEGAFGgBFgBBgBRoARYAQYAUaAEWAEHCLABkCHYDIpRoARYAQYAUaAEWAEGAFGgBFgBBgBRoARYAQYAUag2hBgA2C11QjzwwgwAowAI8AIMAKMACPACDACjAAjwAgwAowAI8AIOESADYAOwWRSjAAjwAgwAowAI8AIMAKMACPACDACjAAjwAgwAoxAtSHABsBqqxHmhxFgBBgBRoARYAQYAUaAEWAEGAFGgBFgBBgBRoARcIgAGwAdgsmkGAFGgBFgBBgBRoARYAQYAUaAEWAEGAFGgBFgBBiBakOADYDVViPMDyPACDACjAAjwAgwAowAI8AIMAKMACPACDACjAAj4BABNgA6BJNJMQKMACPACDACjAAjwAgwAowAI8AIMAKMACPACDAC1YYAGwCrrUaYH0aAEWAEGAFGgBFgBBgBRoARYAQYAUaAEWAEGAFGwCECbAB0CCaTYgQYAUaAEWAEGAFGgBFgBBgBRoARYAQYAUaAEWAEqg0BNgBWW40wP4wAI8AIMAKMACPACDACjAAjwAgwAowAI8AIMAKMgEMEWrwBcO7IB4SQixDp1GtH0XGDjR3CUz6pN954Q8yfP98j0KNHD7H66quXT4xTRiLw7rvvirq6Oi98vfXWE927d4+M25YCdPlbeeWVxRZbbJFp8fX8wuQ9LrypqUlceumlPn9rrLGGOPPMM/17vmhdCHCbzac+9TYX1ge4qocvv/xSfP/997GFat++vejUqZP3W2KJJcRKK60kVlttNdGxY8fYdK0xMK96icNu1qxZ4sUXXxTjxo0TP//8s6ipqRHLL7+82HTTTf3fOuusI1Bv7BYjoNdd2Di3OGZ1XC1cuFB89tln3g99wMYbbyzWXHNNZ8y56kOcMdRKCLV0XFs6/61EjBIVo6X1aYkKlSJSW5TVuXPnihNPPFHU19eLbt26iQceeCAFYsGouvywnhnEpurvZAt3U7ffUE7tub73mzH4iKopza9+9SuYJb1f//79K+aLFDk5fPhw+fXXX1dMqzUR2HrrrX2c+/Tp05qKVlFZyODs4+JC/mzM2OQ9LpwM5T6vaDM9e/a0ZcfhLRgBbrP5VJ6tD3BVD2eccUag/apxz+aTcUlutdVW8r777pPoA9qKy6tewvD85JNP5EEHHSTJ8Gqtsw033FCSkTCMTJt9FjeOVRMot99+u9xxxx3l0ksvXVLPZHyXw4YNkwsWLKiYZVd9SFJG2ooenDeuSfFPGq+l85+0nK0hXkvp07LCui3K6nnnneePC0OGDKkI2rz0GdYzK6qm0MQi9GkLeqgbAKcPGlA1nOudKhTuStzrr7/uvSjhhWrUqFGVkGp1afXOe4cddmh15Su3QLZOuVy6Uels8h4XzgbAKFRb53Nus/nUq60PcFUP5SpmuoFw8803lz/++GM+wBScS171ohcThpNzzjlHwuiq457keuDAgfKnn37SybXZ67hxrBpAoRkdEvWVpF5pVYCkmZ8Vse2qD0nCRFvSg/PENQn2aeO0dP7Tlrclx6/2Pi1rbNuarNKMR9mhQwdvjFhrrbVkQ0NDRRDnpc+wnllRNYUmZgNgKCyVP3TVqb7zzjuyXbt2vkLHBsBg3bS1zjtY+ug7W6ccnbK8EJu8x4WbBsDtttuuPCY4VYtAgNtsPtVk6wNc1YMLxQwGC8w4mzp1aj7gFJhLXvWiigij0IEHHujrEEmMQ2acVVddVf7www+KZJv148axokGhpdwSRj2z7mipstxtt93kKqusUhK27777yubm5rJZd9WH2Bhoa3pwXrjacC83vKXzX265W2K6au7T8sCzLclqY2NjYIx4+OGHK4Y4L32G9cyKq6qEABsASyBx88BVp/qf//wnoLSxATBYPyeddJLcZpttvN/xxx8fDGzDd7ZO2TU0NnmPC8cLCKakb7DBBp6s42WFXetFgNtsPnVr6wNc1YMrxQyGC8xSa+0ur3pROB533HEBHcI0ECW933nnnZ0sG1V8tUQ/bhwrujzHHHNMoJ5pP2T5yiuvBNjCcvtll102EO+ee+4JxElz46oPseXZ1vTgvHC14V5ueEvnv9xyt8R01dyn5YFnW5LVK6+80u/7seqiko8/qm7y0mdYz1SIu/Nb/CEg03ptJERzM+mwQnTcfGuxwgOjvOui/6hRCFrS5LFBS4DFmDFjymKJFDix++67+2nJACgGDBjg3/MFIxCGAA7SmDJlihdEewCKJ554Iiyas2c2ebeFg5HDDz9cQL4PPfRQMXr0aGe8MSFGoC0ikFcfgAN7aM+xEojPOussQcYGGp6bBe035h2KRTP8xAsvvCBmz55dEh8PaM8yMXHiRO+AkNAIreBhXvUCqB588EFxwgknhKKGw1iOOuoosf322wscaIG6eemll7w0OJgpzN1xxx3i9NNPDwtqE8+SjGNFAEHLY8Uuu+ziZ40DXKA7wjfdU089JaCTKtevXz/x3HPPqduq9FkPrspqYaZaAQLV2qe1AmirqgjQuWjJr697Pf744947V6VM5qXPsJ5ZaU2FpHdnSyyGUmvfA7CtffksRopaX640qPtfevI+BCRsz0vbV8bvvvtOYnNy6qLkX/7yl9ZXIVwiRiBnBPLqA6K+zJLCGVpiLPMl42Bgawu0e/V77LHHQtO1lod51Qu2VtD7XYUv/HXXXVe+9957oZB++umnkk6K9etDT4flUm3Z6XiGjXNFYTNo0KBAfZFBL5YVbLOh6nWppZaSWCZezY714GquHeatJSNQrX1aS8a0Gnm/4YYb/D4f20FgX2AXLi99hvVMF7UVpBE7A7Bpyvdi4YTPSE9Y5Jbo3Ue0W6azug33pRSNr/wLS4u98HadOokldu5bElc2zhdNE78WTZO+EQsnTSR/omieNUO0W2pp0b7bSqITzebrtN0OosOaa5ek1R9kNQMQMxa++eYb8cUXX3g/fB3v3LmzgLW7d+/egvZaEXSSns5K4JoaRUUzAOfNmydozx3vKy5NUfZp33rrrQJfbOFWWGEFseKKK/ph5sWkSZPE+PHjvR/Ks+WWWwpS4AUttRS0r6AZPXA/ffp0MWPGDO8Zjvbu2rWrN0Ng7Nix4quvvvJo7LXXXoKWmXhxEBdp4MiQI5ZbbjnvGvnj6/T//vc/D6+1115b7LHHHmL99df3wvU/4E2nDgoyBnlxMSuBTokUm222mR4tcP399997M0vwkBRZr370CFF80cuR+OCDD8Rbb70lamtrPWy23XZbr1y0UbpOwnpdCc5W4hER6DRo8fbbbwv4mK1BJ/6JnXbaSSy//PJeinK+ylRSDpu8x4Wj3nfddVdvxiJkDTJDSklEyRc/roTfxVSE+Pbbb8XHH3/stXfMmqSXX7Hpppt6P1ynda74UvnSQC3oBchrd2gbnahPRftBO0ZfFNUPTZ48WSAtHPqu1VZbTZEM9dEOZs6c6YWZ8fV2Fpo45iFtOOz3E4im0wprsyapuXPnen3wZ5995vmgh/4X/RlwsLVX7gOECJsFnLYezHpR91FfZvHFuUuXLipaiX/KKaeIe++9t+T5NddcIy688MKS5+U8cN0Wk/BQad/sql6iZv+R8i8mTJjgjxVhZaLTgr2+BW3PdLSJuDdr0Hye9r7a+92w8sSNY2Hx8SxrGYSuSHs0+jM7+vbtK15++eUodrzn999/v3jyySc9XQ362p///GexzDLLxKYJC0wjq+XUd1o9OK3eymPkolp1PUamkYswuYp6Vq4uZNLLSqfI+n0H5dDrCu9/eA+Ew/vim2++6b3XYNyFbkTbNnj6rBch4q/IPq2cPiGsGJXQiZJV6C/Tpk3zs1tvvfWs782IDB0d79twmGWPWXdRLuuxQeWLdgP+8R4Oh1n8mM2fxFWLPsN6ZpLaShknaA8M3s383clyas/1/d/cJ0YGI4TcNY5734+PtLWH9yuJNe+5MbJm3z6BeHo+/nWvjWXd8Otl8/x5JTTUA9czAL/88ktvw2x6qfat5QRpyTUZsuQzzzyj2CjxK/2qcsEFF5TkafJx8cUXl+SLB7TcU2LTbjO+uu/WrZt89NFHQ9Oqh9gfQMWnjkKS4bHkBEFgdOmll3pJaBmRH3/YsGGSOk5JS5f9Z4oWfBxqQi9/KisvLi37jJwRgq/bUafV2TZw1fm66aabZF1dncSMOOqYQ3nDqUg4bS6Jc4Fzknz0OGQEkYccckgoVjjhEbMSMPOGBnW/fLYZgC7KYZP3uHAagL19iRDntdde04sbeu2CXxB+//33I2VUySsZJOW1114r6UUklBf9oSu+FE3kedlll0XO4gGP2DfxkUcekWQEVsl8Pw5zP5J2ofZgBF0cGqC7nj17+vKksEnqY2an7mxtVsUlpUUOHz5ckoIbmTfC/va3v6kkoT73AcLr80xwktaDmc68T/tlVqXHLKUwGTr11FNVlLJ9120xCSOu+mZX9UKGoFB8r7vuuiTFkbR02E+PfeP23ntvOWTIEInZ2pW4au9348qWpk/NSwaxz5/ejmg5flwRnIYlkdVK6jutHpxWb01TnwCOx8hF70I2PTmJXKQRxEp1IZVXFjpFnu87KIeuzwwdOlSSgUbSJIlAH6D6A/pYLPH+RIYfBUGJn6YNuOrTKukT9AK4oBMlq4MHDw5gilnINvfTTz/5J+yiDmiyS2gSVziGEg95OGLEiEBZ6ANRSKzgo2rTZ1jPDNaPi7vYQ0Dmjh0VMNLNOPkoa56zh14SSFP/4F2L0yxYIGeefWIg3Df2aYZG89mMEw5bTMO4cmkAxIt+lGFIdaimf/755xscLbpN06mGEcASKTMv8940AMLwoyvtZnzzfuD/t3ct8L8N1X5Ir+vqgeIqIjmoi0rX47gSchGh5HESLuVRCEciyru8n3mFlPcj1yvJK92EcvN2qFwOScQ5B8frcjz2Xd/9b/ZZM7/ZM7N/v7V/v9///1/z+fz/e//2nj2z5juz1l6z9po1X/5yQR4/oeoL+lpQ1b/pppsGDU4o78ADDyyf5y+gvfbaq1h66aWr5/167W8Y5PBigjJhr9Ud6zaGqBPetlGcrn322acgj6lkXTBswuhZlyRxrqsjdB3GMfLwS9JPnlFOnjoDoGQ7UuM9dZ++XNaORYuFJL1TpkzJwtKOR0x+saQulCTpsuWDL7iiZ+moO+Ll6KcU5n5+bjQetAEQH2L4ZK6u3fb6euutV5D3pt+k8rfKgOE0AMJwa/uPH+nrdLAfcy62wYs59UrK5tQ7LYce7PZHHl0d+MIYj49gOQlLgY877rjyQwkmzhJp2OVuqo05MrXfYxAfZzn/0KoVpxmgB5M5LL2XTqmx2mt/N9WDm+qtOf3JMdN35GxniJienBoXHNPUuYQuhDra0in6Od9BO7g+s9lmmxW0ssPhfy4L7DkcVny5gLKQcnhAUqb1KhNGqC4KqXLqxurVV1/t4MqdViwN/vHYY491nvnpT3/qZJHE0Sk48YN/DMxZ/jts+gya160BcCzpmYlubnw7agB88+WXi2mrLjvbYPepjxSvP/F4fSU0QZ62+idn5/+3CcUb056q8r9y9RWz7/3D4DftM58oZn5n1+KFow8pnj/4OwWMjL4BEL9fvS3slSVlALzrrrscxoXgBKNgZ7U99tij2GGHHUprPrysrFDFES/B0BfxHKFaARM4Of7440vvJC6cUB+EPzzr8Od7vtDSYIc25KcltAW+ZEB40RLXDi++9ddfP1B74RgAeXv5OTz5yPW6fJ6/lHgenJMrerH22mt3vKiAHX95IRYNDHRoGwWD72hLaAdkjs9KK63U0ZY6ulA3DGXrrrtu2c8+zeRCX3rSdRRIFyRxDpUfukZLVTomdMBojTXWKMfmKqusUmu8rjMASrYjNd5T90Nt9q9J0YuvyVCIeJ8j5hV2A/vOd75TvmhoialzH3nrYpNJ0WXbi8l7yPiF8QoDPyZFvpEX9OFLNE9NMY9NbiD/llhiidq/JZdcsqQJO3JzXHFOy7s5WUWKZ9E/PI8tD7yMnb6/8pWvFKjPXrdHPBMyVKgMGE4DIN49tu/40X5UcgZN5g9pXsypVlo287Efeqfl0ETLdIPYwtN+UGnY5W4OLjkytd9jEAZzyz/w1ER64YUXikMOOaSU1/YejvjYijFw22235TQ3mSc2ViX6u6kezA2AvN38nOutOf3JQdB35GwDIDCt05Nj44LjmTqX0oX6pVMAkzbnO8CrTp/BapVtttmmXLEyadKkjt2+KSxQKRd8zHN4QEqmScgE0C9VDsqqG6vQJYGZlR1wvkC9scT1X8hi/2ObFI4xGvx7L5Md5+1vf3vVjtQH1mHUZ9Cmbg2AY0XP9PtV4nfUAIgKnj9wb8cg99KP65cXvPKra5y8z33zqw6Nz2z1Bef+zO9OLmBk9NOs++4uZnz+M27evXfxs5W/pQyAvufcN7/5zSCzU2ycjsk3PMv8lCNU/WdCv+F2bAUQjiEjGJ6jnd2cfBQbooDbr58oxl6BJcC8zFDA6JAihcDR8Iyj3YNKYwkm4jaFXkqY+OOrm00QhvAs4nXbcxjisBTUJorR0WFoox0L7e3qWCe8bYYQXTCa/fGPf7RZyuO1117rCEnQhS/rfpLG2S+/7je8MC1WOMIA4n/RgxHbN2whb8gAKN2O1HhP3a9rt70uSe9NN93kYEmxJQqK2WGrKo+0c2nhf83DGPWTJF22bCyz430NJfuCC9zwC1jye/TRRzv5fAWlKeaxyY2lLXX82te+5tBEMUKLp56a/REIz6d4dpdddnHKQLsoXlVH1RTjrMMojgmjn1QGhGVAqh98HOt+N1XM8B7ABIWPcX6O90s3qQ1ezKFDWjZL9As+VnBM7Tn0mkGlYZe7ObikZOogxiC8n23/fuhDHyo3d4ERwl4LHfEBFO+Z0AeTHBxsnthYlezvXD24qd6a6k/bTnvUd2SenhwbFxbLnKOULtQPnaIf8x1gFtJnMDfzV0BgrorNnjj/4wO3n1I8ICnTpGSCVDnAIjZWgRfHD0t36xI8EnleGGN5ksSRl5s6x5yf03XOOedEHxlGfQYEj3c9M9ppXd5MGgBn3fUHxxA3Y5O1a6t6bvIOTt5Xbpi9E9mse+507s34Aq2NJ0+XuvTqb2908j+z1ReDWSUMgFgawS3k+OoOA0Bdwu55nKFCX9VTQrWubP96juKDpYk8Ngm+cMbW+CNmBPewo802qCvcvvAVKSiWM2fO9MmrfvsvJXxp9if+yAyPQQri7+CH5cK+AQZ5sTwZhkyLNZZE+ikmvJHXp2vChAm1SzlhULB14ej3axs4++0J/aZNVxy6EPeMgl2HshaIQUEbtjj5fQNgG+1IjffU/WBj/nFRml5fqYRbfl3ikyuMW76MSpou0AA+8Jd5x+Ii+ga3Sy+9tGpKU8x7ndwgHg3nH9oIqFymURH0j5MYz9LmBE4ZiGGD5Wt1CUvHIe9svRj7viKsMmAwBkAY1nffffdit912K72Ut9xyywJLUTAubH/5Rxi78cW6aWqDF3NokJbNqDPGHzk0Ic+pp54axPjII4/MLUI83zDL3dzGxmTqoMYgDA+WjyD//A+89l7oiPdHLyk2VqX6G/Tl6MHI11RvjfUnyvOTviOLIqUnA7PYuPAxrfstpQv1Q6fo13wHWPn6DIx8de/MqVOnFtChLO9j3ufv+B3jAWmZJiUTpMoBnrGx6o8df06I523CUnCLM44wUtokjaMtN+foGzERM7YuDas+A3rrDIDjQc+s669erycNgKgAxjq+LPe1P93fUe8bzz1bPL3iUlW+6Wt8yjHwvXrzr4tnt/vyyOYftJTYiQ3YUVpRvPnC81VZqPuZzT4XyFUUEgZATCLhFQZvFUzy/XX7fsUwDnIDGuKD+SkmVP28sd85ig8m/Vzw5MQqQOxC/oxvMPQVqdiXD9Dvv5RCXoW2nf7yvZB3j80LbC2d/mYCyBMT3rjv03XVVVfhcjBB4eBxk2gHLSdfGzg7FdT8gFeTxQBHbAwRS77A9w2AbbQjNd5T92PtkaYXSww5nrQjYm31ML794Ac/KJf/IuAwN5RL0wUifvWrXzm0hbwOObHwZLVtwZIDvnyyKea9TG7gbcwNcfAwue666zip1XmMZ08//fSqPWjX17/+9eq5uhN4BlsMcDzppJOcrCoDBmMA5H2Se+4vY3c6MvKjDV6MVFfdkpbNKDjGH1XFiRPIrBDmoVU3s60CAABAAElEQVQBiaLEbg+z3M1tZEymDmoMIrRLqK/x8RQxHLEcHB9RLrzwQmds2WdiE8IULrGxKtXfoCFHD0a+pnprrD9Rnp/0HTnykTKmJwOz2LjwMa37LaUL9UOn6Nd8B1j5+owfCsrHE6FtLK/jeMUVVzhZYjwgLdOkZIJUOQAiNVYxD7T4wVGIdmF28MMPrMhBGCGbDw45PEnjyMtOnfvLjmEUrkvDqs+A3joDoMW8yXG06Zl1/dXr9SwD4EtnnuIY41445gcd9b588bluniNGNofoyIgLWG73Rn1Q6TdmTCteufbnZNybUJU540thz0MJAyCnEUagmPcf8sJKjviAdsAhZp2fYkLVzxv7naP4HHrooRUtoAnBS1PJ3znuRz9im7XQw74iFdtFCnXxlxK+ONV9kUJeHpAU9D755JO4HEx8cj/33HN35EkJb04X6vK/fvkFwkPQ9usKK6zg3G4DZ6eCmh/cCw20hTwr+aPwUuOxKn0DYBvtSI331H1Ov38uTS8M/raP7RHL2aHEYel5bpKmC/XuvffeDm1Ymp5Kv/vd75zl8zZ/U8y7ndxARvmbJ8EDqS7FeBYx/myf4JiSO6gDS134M/4yR5UBo8MA6Pdb3fgJXW+DF0P1+NekZTPKj/GHX3/db8R/4zxhz/33fN3zbVwfZrmb296YTB3UGPQ/qKKvsWv73/72t45mwRMFS9PseMARK0Cg93aTYmNVqr9BV44ejHxN9dZYf6I8P+k7cgSRmJ6MHLFx4WNa91tKF2pbp+jnfAdYcX0G4zfFu3feeafD7/CY4inGA9IyTUomSJUDHFJj1d9E4rTTTuPwlee+sRrvX56kceRlp87xLrDyHh/p8Q6oS8Oqz4BeKQPgaNQz6/qr1+tzoAAaHNH05tNPmRnrf9qYN98o8805//vNfFffbMycc1bPPbfNJua1++6qfr/3vCvNXEt+tPodOnlz2tPmjUceMq//9VHzxl/wN3Xk7/HHOrK/ZbGPmHl/dk3H9WkrTCC63iyvz/WvHzfv/eklHXm6vUCWfkPGPkNx7Ay5Apd/v//97w1NSp0iyQBofv3rXzvXSFEwZNgqr22wwQaGvro493N/kKHOoHybKAag2Xjjje3P8khfeMyZZ55ZXaPdfQ19/a1+h05o2aM5++yzq1vf+ta3DC0Pqn5TDBlDXwrK3+TtaMhwZkh4VPf9E9q0wNCugeVlUsLMww8/7GepflNQTvOLX/yi/I2yyVhY3fNPaAMTQx5a5WUyABqKH+VkoaCr5u677y6v0dJtQ8YQ5z6ni4LkGjKOOff9HxRLw9xxxx3lZfqyXp3jQhs4+/WHftOGD4aMHOUtWuJjaCl2KJtzjY8/MgAa8uCs7rfRDl5faLyn7lfEBU6k6aWvdYaC+xoypHbURhvRmNVWW83QVzND3neGJlcdeewFabpQLhkizXnnnWerMLRk3pBncvW7yUlTzIEJLZ8tq6BYnebKK69MVkceiIY2oDGQlTbRpknmqKOOsj87jjGehexAm5FoKbRTbkdB/7iA/kS/UTyr8gp5DRsynFbZVQYY48sAgBPrhwq8jBOaUJiTTz45I2c4C94rNEkr32H04SKcKXG1DV5MVFnelpbNKFSiX9Af6Bc/kfe4Ie8J/3Jffg+z3M0FICZTh2EMoh30MabUiSi0SrBZ0Ldwjzavq+5Dh6IN46rfuSexsSrV36AlRw9GvqZ6a6w/UZ6f9B05gkhMT0aO2LjwMa37LaULta1T9HO+A6y4PrPmmmuaG264oQ7C8jptCGQwb7DJ1+1iPCAt06RkglQ5wCQ1VjHnpM0qyzkw8lPIhVIe4dwm2kPA0NL48id0GNpIw5BHoL09sLkjCKDwXZWsRzusXaIijp0Mqz4DEseznsm6SPY014L43C7bVt54/q68r//lEefeM5PCO8uiLmz68fIFPy1mbLi68wxfYhw675cHIJZLIED5AgssAMNo1h8Z6DpgjH1V6cgcuZDz5XPVVVfNojPWng033NChgl5qVZn0wnHuhX7wr1IkUENZqmv8KwNiZ8QS/1rdqwcgvoSkEuIMWpywtIanNnDm5dedc29T4JyTeDt8D8A22pEa76n7sTa1QS+WzvoBkm2/8yO8Iw444IAyHqVPYxt0rbXWWtX4gxcnX3Ls15/63RRzUgSrurFZTyoh3iQZJ6tngBvkCCln0UdjX1yxdNjij3y5icsrxCvlicsmlQGzkYn1w+xc6bNuv8wiVhl2l0acnV5TG7yYQ5O0bEadEv2C5Z6Wj/iRJnQ5zWotz7DK3dwGx2TqoMbgxIkTnb7efvvtk80hA7HzzPnnn598JpQhNVYl+hv15ujByMffAzl6a6w/UZ6f9B05ggjXL309GTlS48LHNfRbShdqW6fo53wHOHF9hhwkQtB1XONxpf2VTTEeaEOmSckEqXJyxirfJBRedNgp1yasKkPsYvueDYUEawNHW3/qyMOV+X3vPzus+gzoHM96pt9PUr+zlgCjsleuv9ox2D2//54VDS+ecqxz7+Xzf1Ld4ycwFE5fa0Unb8jYh2vTVv+kk69tAyCWrCLelmXi2JEzCfIN2gDoT8JjtNfdW3HFFXlXOYoUXsSpxF9Kfuw8/9lBGQBTdIHOmGLTBs4+NqHf5N1UjcuUsmGfJy+86hnfANhGO2JKBGhK3bd0h45t0It6sJT6S1/6UsEVxDr+WGKJJQrycHXIa4MuPv6wWUIvqSnmTSY3UHqw8xzHCxOB1BJ7tCemcMHIb8tceeWVs5vPlzlgkxyemsgmPMf7wJ/ctNHnnNa6c2kZgHpi/VBHR+h6nWKGWEPkGV/+YQKPpepYjoTdy8krIVRU19e0X1zoaKVCxUeWn3D87Gc/62aM/EJfkVdYuYkLQob0umOsrWoY5a6lLXWMydRBjcG1117b6eucZd633HKL88x3v/vdVNOD93NkSK/9jYq7MQDm6K2x/gw1WN+RI6jE3pHIkTMuQvjya7yOXnShQesUkvMd4MP1mdzljHC0sO8BPM9TjAfakmkSMgFtkCgnZ6z6IbOwpNcmWrFTYQuML7jgAnurOraFY1VBzQmWh9t+xxFjMZZUz4yhU3Q4PHBsc899W0u8xnbvZi0BpoYZM2uWmb7ORFM8/1z5c45/mtvMd/1tZo63vd08s+Ea5o0n/rEsdq63mvmuudXM+Z73lvnsvzdnTDPPbbPp7Hz2Bh3nePs7zFsWX8LM9ZGlzFwT6G+pj5m30nLeaRM/Vi07bnMJMNyJaXcfQ9t0M6pGTimwfulyjeURcI+F6zv+FllkEUMxVspMZAAc6BJgLH297bbbSlpoExNz+OGHd7QjdYEUG0Oej1U2vpSCFClDAf2re6ET7pZOhjZDmyeEspXX+BJgejFV7smhBySXAKfoQv30hcTQLs8lKTT5d5YAt4FzqM3+Nb7shILLlsvS/Tz+b4xJLJtB8pf/tdEOjB/rWi69BLgNejletKNyyfsUO9NQLA8zY8YMfrs6hyy49957DXkOltfaoGujjTaqwgVgaSTFJDQUeLiiockJ7xN/2UeoHDKcGdp5u7wVyw95iTHFlwhjuQNkEOpMpdiSC8hVG2KBFFUzZcqUVHHlfd5WMtaWYRvsg01kE54ZDzIA7Yz1A+7nprqlGQgzQV/Gc4vpKV8bvJhDkLRsRp0S/UKKf7mE3g+vAf0AYU2sDIu1EWFBjj766CoLQmhAtk+ePNl89KPx8C7VQ5GTYZK7ETKdW1zO+O+5QY1B2snXUJyqis5zzjmnDCVRXQicQHflS9RopYUTRibwSPBSk7HabX+j4m6WAOforbw/Y+8823h9R44gEXtHIkeTcWGx9Y9SutCgdQrJ+Q4w4voMefsaMvj70Dm/KaZ9GSIF7wQkf77KeaDfMq0XmcAb2Us5OWOVzDAGeqUNbcV1U4QKuuaakfBkCFuDeRBC0vA0qHcDaABNNmxUaPkyp3NY9RnQOJ71TN5HoudN7Isv0MYe3GPvlRuvLWbdc6dzbea3wrs2vnjqcU4+lPP8gXsXr/35AWyh00GGvwvwjC+GvdAkNgGhWHiOlZwALnbffffS46duQxC+CxbFDOugP/ZVpSNz5ELOl88tt9zSoR9L83pNfClFzpdU/lUq5Wkn+UUs9fWmCV3AjH919L1/2sA5p5+4dxN2oaobk7wsvrzV9wBsox2p8Z66z2n3z9ug16/D/sbyVTIAFwcddFCxzDLLOHwFufDDH/7QZi3aoIvi5zl1ksJR1Vd3gjwUw6mgl7yThWMOL5FYwpjiG8fElgDvvPPODo3YfRj156YYz2I3duCMP5Sbk1555RVnB2KKSeg8pjJgMJuAkAHQ6Yc2f7TBizn0Sstm1BnjjxyabB68ty0v8eOOO+5os9QeKe5RQRPD4PMIeC6dBi13c9vDZSpNlp3HBjUG/Z0b999/f4eu0A9/YwBsuNBN6nasNulv0JWjByNfU72V96e+I0feuyn9HTjH9GTc73Zc4FmbpHShQesUkvMdYMP1GezwmkrYDIjLf4S44onzwCBlWlOZwNvAz5uWkztW/Y21sPkcPBDpo1qFb927dVDvBuCCMEa2/zF2YmmY9Zm6lSbjQc+M9Vkv97KXAKOS1/50v2PEe/57exQvHP1959qrvwkrh89uN8nJ9+JJR0XpnnXXH5z8M76wZjC/hAGQx5kDo0ChiiUKlF8xFPJjfb+fYkLVzxv7naP4HHzwwQ495MkYK7K8B2MBbRxSXH755cVdd93VYTxoqkjxl1JKgZB8IaaEdxO6AExMsWkD52RHUYbNNtvM6V+8eGIJy+uwM5kV+r4BsI12pMZ76n6sPdL0gn8xiT3ppJOK66+/PlZ1afCzOOLIXeil6QIhp5xyStVvqI+87KL04SZiHVka+QueL/tI8STisNkycKwzAJJHkJMPRsOrrroqSSPPEONZxITjdCDOSyqRt7HzzCabbOI8ojJg7BsA2+BFZxDV/JCWzagmxh81ZAQv/+xnP3P4wvIVPiIh1nEsYVd0m58fafOI2GPRe8Msd6OEs5ux99igxqC/PC0Ug4o1oTz1Y0SeeOKJfpas37GxKtXfICRHD0a+pnqrviOBmmtUSukKyB/Tk3E/Ni5wPydJ6UKD1ikk5zvAjesziHcMg1csQYfkMhwft3nqp0yTkglS5QCH3LFKK1Ocj+TA0R+jtAqGQ1udD+rdAALI47Pqf+xtEEvDrM8MgwFwkP0Y67du7zUyAKISbPBhvQCnrfbxYvp/rFT9xjkFignSMm3lj1b58PzrUx8K5isvkjfKzL13cfLP+PxqwfwSBkDE9+IC0vek8Ss+4ogjnPzk3utn6SnmGS/MV+4uuugifrs8v/jiix16sMY85SX2/e9/33nGDxzdVJHiL6WUAiH5QkwJ7yZ0AcyYYtMGzh2dGbiAAN18fG6++eaBXLMv+X3rGwDbaEdMiQBlqfuzqe88k6T31VdfLWiXxArPkPHepwDxZyz+EyZMqG5L0mULhWHS1oUjXt6xBKWE50ewYptAq70H/BEPpC4hponNi2PIAEg7kDuedsiX+lgSqi/Gs76BEQpJKiGuGacdHzZ4Uhkw9g2AbfAiH0N159KyGfXE+KOOjtB1TAwpZITDG5ZPYATE5MVPtJt3AS8Gm88/cg9o/9nY72GXuzHa+b3Ye2xQYxCxGfmmdfCcnjp1Kie743z11Vd3+phCLXTkyblQN1Yl+xt05OjByNdUb9V3JFBzjUop/R35Y3oy7teNC9zLTVK60KB1Csn5DrDj+gzkM4z5sYT5KZfj99xzj5O9XzJNSiZIlWNBaDJWebxVWjpccO9S9EtdGtS7AfTA49P2P+KdA7+6NMz6zDAYAAfZj3V91sv1xgZA7OBrDYD+8cXjZgfG9ImavtYKznOv3nqTn6X6/cJh+zt5UQ82DwklCQOgv9TvscceC1VVXoMBjrv8grEgQPwUE6p+3thvfKm3zIvjqaee2pGd4oR17GgaU9Th7YNA+bzcO+64wym3qSLFX0opBULyhZgS3k3oAgAxxaYNnB3Qa37AxZkvOYfX1a233hrMjeXfft/6BsA22pEa76n7wcb846I0vWuuuWY19sHLN91UL4uwvBa7flle4UsupOlCczGZ42MW9V577bW18Gy88cYVbciLjRds8id5PHCxzYMjxS/p2AjFNwBivPEAwahr11135cVkn8d4dvr06cW8885btQnYw/ujLl122WVVXtAEz1eKB+Nk53imZBMeHA8yAO2M9QPu56ZhUMza4MWc9kvLZtQp1S8oi2L3Ol4L4BH+Bw8SbISEVRD4sERx/pz7PC8+hPSy3GaY5S6wykmx99igxiDo3m233Zx+oxiNwZ3rkdf3DMV46zbFxqpUf4O2HD0Y+ZrqrfqOBGquUanXdyTKi40L3M9JUrrQoHUKyfkOcOP6DOQzvLIRsiGUfIMF/4Bt8/dTpknJBKlygEGTsep7TvP345FHHmkh7TgO8t2w3377Oe+GurkjiB5mfWY865kdA0roQmMD4BvPPVs8vdJSHQa6Ea++/60l67ldtnGemf65fy9m3efGjXrjqSeL57+/r5OvMjKuHF56ImEA3HrrrR0GQewcCobvtAVf088991xnaaVlfigdfooJVT9v7Dd2HrX14LjkkkuWyxYRw4WCjVaPYikvz4dzMIy/K+eNN95YUFBcJ2/ohd9UkeIvpVB5FaF0IvlCTAnvJnSBxtjkH/elcUaZOemwww5z+gxbu2M88oSdHymwt5MP48A3AOIZ6XakxnvqPm9H6FySXuySyHkFu3rDkOQnxLXjy2vxDJYN8yRJly0Xk3ZOHzwWTz75ZHu7PMJL2X8hYjIPRcMmPzYUDHjYsczKNsSGOeOMM8pYe7w+nHMDIG0aUMw///wOTYgV8/TTTxf4WPLggw8W+KgQ+6ONVSxZSYULGHN6YNQ79thjHa9mTA7gjs/jFuIZ2rigqseeqAwIy4CU7LT4pY7+OLR914uxKFVn6H4bvBiqx78mLZul+sXS6U8AbP80OYIHU+ESbH11x2GXu3V08+up99igxiBiUc0333yO3IR3iu/tgw/D3AMeYwAGwW5TbKxK9neuHtxUb9V35EjPS78jY+OiyViT0oUGqVNIzneAHe8rK8Ohpz700OxVdVgBBs9H31klFK6lnzJNSiZIlQM8m4xVxJv2HSzQB/CsS8XeH9S7AcuS7TjBESsYY2lY9ZnxrmfG+qzbe40NgKho5rd37jDSPbPVF6M0vPLLKzqegXFvxibrFM/u+JXimc3XK7gxrzL8UR57/vrDD3bUwZ95ZuuNO+7nXIBiy718wCRQphC4ExNKGAgxueZMxM+hUCHuGk8pocrzxs5pF78O7xxbN+L08PS5z32ug0ZM+mHU2nDDDYMvDiwdwQTfT00VKf5SGssGQOAkibOPe91vLN+k3ac7+nfBBRcs4JbuL2O3YwTHkAFQuh2p8Z66X9dufl0Kd/AUN/RarDDmUQftCF5gAxhfJsD4zg1sljYpumx5OMIAZ+myR3jG4csnvBZoZy/nPvj89ttv50WUhr65557byYeyoKzAgGzLDR25AVDCgABjnU0phQvGPSyv8Ol617veVWDDJSzbxjI3/z74Y9asWbaa6thENuEhPjb8jYBsoW30uS277igtA1L9UEeHf31YFDPQpf3i905RGs732WefDn7x+afuN+Sg/7Gps5b0ldEgd1OtyHmPDWIMgu5LL720wNJuvx/xwRee66FNXXJCLMQwickQyf7O1YOb6q34GKbvSNeolNLfMR5S78jYuIiNp9A9CV1okDpFPwyA4Hl8DMVqtmWXXdZZRWHlgb/5h8W6nzJNSiZIlQMMmo5V2o22Q8ZyfdniGjoO4t0AYzAPEQFbQCypnhlDZ+TeIPoxTVXzHF0ZAF+9+deVUc4a516+5Lxk7dg0xOZPHeEx+MJRBzv5QxuHSBgAQTgMfVZQpo5YKuPHlTjrrLOc9ucIVeeByI9tt902SJs/OYVxYvLkyR1eMXXtgZGzbvfOpopUk0m25AsxJbyb0IUuSCk2yCOJM8rLTYjPxAO61vUrdkHlS1vqDICS7UiN99T9HAwk6YX3W8hbsg5TeLxZzzmfVkm6bNnw3A0pGiH6sDwcnn2hdMEFFwQnOH452DmSLyPjCo2EARA7qNmU4lnkw1j3d07zaea/Mcbr+kdlQPgjQE4/2D6LHYfJANgGL8babu9JymapfrG02eM555xTwNuZ803qHDISz0mlYZe7qXbmvMcGNQZBOz4CLbbYYll9vOmmmxaYSPeSUmNVsr9z9OCmeivaru/I4TYASulCg9IpJOc7GK9cn8GHUC6T6uT5dtttVxsDmj/v7wKM+pAkZZqUTJAqJyXDRhCY/R8y1scZH19ykiSOOfXZPF/96lcrmrGaJ7VxzDDqM6pn2t6UO3ZlACzeeL2Yvs7EyjiHDT7efH5mmipaRvt/V/ysmL72ytWzjiGQlvk+u+2mhd1J+LX773HyPbfTf3bUwcvq1gPQFor4fnXKEybZCDaPwLRIf/nLXyqGgjDwv67w4Nt1QtXWmzrCIg/lx/famWeeeYKP3nzzzcUKK6wQ/BoMWuHCjB2MYpudNFWk+Esp9QWxyQtx5513rnDG5MVPMHZZYRzajKUJXSibG9h8A6tftwTOfpmp33BBP+aYY4LGKygD+KIPhWmLLbaocIEhJZYk2pEa76n7Mfr8exL0okwYjGDc4pt82LGEIzx74YmG2B6pTXVQnhRdKMsmxOfD8g5/KQfog2cOPkZgI5BYwk664El4/vH24YvxcsstV2A5BdK+++5b3efyzN/0iJeRe84NgCme5W3BsgnEswq1H3V/+tOfLidw/Bn/XGWAKY2pPi5N+sF/lv/ec889q3FjxwN4B8ruoFIbvJhqi5RsluqXEL1Yln3ggQc6k0jbZ/yIFQ/YGRZtkk6jQe7WtbnJe2wQYxB0Y/K21VZbOV4fvG8RM+y0007LeqfV4WCv54xVqf7O0YOb6q22HfqO/Fglw1P6OzBL6ck548Jin3uU0IVQV791Csn5Duj39ZnHH3+8wM7f/tJ+8DxWRSAOYCwNQqZJyQSJcroZq/CytDIVBrXQypMY5v1+N/jLj8FLqTRs+ozqmakea35/DjxCA7m/6bXXzBuPPzby99QTZs73zGvm+siS5i2LfMiYOd/SX1q82shV3DzyyCOG4ikYim9lyOhkyK3akGJhaMLs5e7vT2JIc9999xnaxcfQVxuz8MILGxL6tUSgLbS811D8FEMxuAwp9WU7Fl100ehztQXqjSACg8CZlOFyLGCc0qTOLL/88oZeSoYMJUEacy4Ooh05dNXlkaKXjHslz9NOiOaJJ54wZGg3ZBgzpGh1xSdSdPF2g/fBx+B/MuQZmsSZpZZaytDyJZ4teo4yKCaUuffeew159piJEycaMn5GnxmWm6D9gQceMOgj4AtZRgGtSxk4DDS20eepdrUhA1J1jrb72i/1PUYfMQ0ZPgzFjzP0MdDQEtFSP1h88cX7IhdGg9ytRy//ziDGoKWOjAOGPFYMjh/4wAfK/sW7bRBJqr+b6sFN2qrvyCZoDSYv+qhXXQiUD7tOUYcufRAu24/7ZKw1v/3tb8usaA9tllPqSeB16EcUtqaumJ6uS8k0KZkgVU5PoHTxsBSOqapRD8YDbBtItPGXodivqcfK+6pnpmHqVz+mKWmWYzAGwGY0am5FQBFQBBQBRUARUAQUAUVAEVAEFAFFYFwiUGcAHJdgaKOzEaCVPmbHHXcs88NxiJZQG/JezH5eM449BNQAOPb6VFukCCgCioAioAgoAoqAIqAIKAKKgCIwRhBQA+AY6cg+N4OWKZce4DD8IdE+Bob2DOgzFVrdMCGgBsBh6g2lRRFQBBQBRUARUAQUAUVAEVAEFAFFQBFgCKgBkIGhp40QOOGEE8yuu+5aPoMwQAgR9o53vKNRGZp57CCgBsCx05faEkVAEVAEFAFFQBFQBBQBRUARUAQUgTGGgBoAx1iH9rE58AJEvHjE0kY67LDDzF577dVHCrSqYUJADYDD1BtKiyKgCCgCioAioAgoAoqAIqAIKAKKgCLAEFADIANDTxsjcOedd5oVV1yx3EwPmwA+/PDDZr755mtcjj4w+hFQA+Do70NtgSKgCCgCioAioAgoAoqAIqAIKAKKwBhFQA2AY7Rj+9is/fff3xx00EFljbvvvrs55phj+li7VjUsCKgBcFh6QulQBBQBRUARUAQUAUVAEVAEFAFFQBFQBDwEHnroIfPKK6+UV+eee26z2GKLeTn0pyIQR+C1114rl/6+/PLLZsEFFzQHHHBA/AG9OyYRUAPgmOxWbZQioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIjCCgBoAdSQoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAJjGAE1AI7hztWmKQKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCagDUMaAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCIxhBNQAOIY7V5umCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAioAVDHgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAiMIYRUAPgGO5cbZoioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAGQB0DioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAorAGEZADYBjuHO1aYqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgBoAdQwoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAJjGAE1AI7hztWmKQKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCagDUMaAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCIxhBNQAOIY7V5umCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAj0bACcOXOmuf76683dd99tnnrqKTN9+nTznve8x3z0ox+t/j70oQ+ZOeecM4n2dtttZ/7+97+b97///ea0004zb3nLW5LPaAZFQBHoROC2224zL730Unlj8cUXN+DB0ZxuueUW8+qrr5ZNeN/73meWWWaZVpvD61tqqaXMQgst1Gp9WrgiII0AH8P94Jlc+v/nf/7HvPjii2X2D3/4w2bRRRd1HuV0K+850OgPRaAVBDjPhWRFimdbIWqUFvr666+bG2+80fz5z382f/nLX8xf//pXM8cccxjoYfibMGGCmThxYtacKAeCiy++uNSNIEdXXXXVnEfE8jz++OPmwQcfzCrvne98p/nnf/7n8g9zvLnnnjvruVSmQbYftA0Cg1deecVMnTrV/O///q/BeMOYWmKJJcw73vGOFFx9vd82LygOI935zDPPlPLm0UcfNe9+97vNIossUtpfcuwu0gNiEPwg2f4HHnjA3HHHHaVcA38BwyWXXLLksY9//ONm6aWXloZscOUVXab77ruv2GCDDYq55pqrIOqjfySYCjISJmv6xje+UZVz1FFHJfNrBkVAEQgj8LGPfazipc985jPhTKPoKhngqvZ88YtfbJ3yf/mXf+lrfbxBpDQVP/zhD4uHHnqIX9ZzRaARAv3mmVziSImqeGuVVVbpeGyQvNdBjF4YagRUVsp0T0pWpHhWhorRXcojjzxSfOc73ym4/KqbG5GDRHHhhRcWb7zxRk+N/ulPf1rJ0s0226ynsrp5GHpKXRtj1zFvXH/99Yv/+q//KmbNmtVN1eUzg24/iOgnBr/4xS8K8CIZJTpwxzUyThSXXnpp13hKPdg2LygOIz11+eWXF2Sc6hgL4L0PfOADxT777FO88MILUt2aVU4/+UGy/WS4LL7yla8U9LEmiKeVZ1tssUXxt7/9LQuLYc9kmhIIhWv33XcPCiALUN3xy1/+ckEefrVVTps2rSDrdQk+fS3SCXAtUnpDEYgjwA2A//7v/x7PPArupiYo0k3gSjw+dPQr3XzzzcVyyy1XysBLLrmkX9VqPWMQgX7zTC6E3Jiw0kordTw2KN7rIEQvDDUCKivluiclK1I8K0fJ6Czp7LPPLsijLTpxDM2LgGtsThRD4/777y9otVVV52gyAHIsyBuwmDJlSqypwXvD0H4Q1q3BowkGtLKu+OxnP1v1NX82dL7uuusWTz75ZBC3ti+2yQuKw0jvkedjsc4662SNh8UWW6wgj7a2u70qvx/8IN1+8oAv/umf/ikLT/AbeTIXV155ZdXm0XrSyABISwqLz3/+89kghQTTAgssELWeHnHEEVX5q6++evHmm2+OVmyVbkVgYAioAbA36AdhhKBl287XJzUA9taH4/3p1KR+UPikjAmD4L1BYaH1doeAysrucKt7KiUrUjxbV+5Yv/7aa68V22+/fTVn4XOet771rcVHPvKR0nCz2mqrFQsvvLDzfrd5l1122YKWsDWCipYXFwsuuKBT72g1AAIHGCngAJKbhqX9oFfC4JHCAB+h7XixRxgsPvnJTxbLL7980Pi8xhpr9HX+3A9eUBxGOOQ///M/O8YD5AHkDLxAKXyacx86FTzc+pH6wQ+S7Z8xY0Ypmy1f4Qj8Jk2aVBx55JEFbFI491e74uMLPF1Hc2pkANxyyy2dQcUBa3IOjyQIi1D6v//7P8eF/vTTTw9l02uKgCIQQUANgBFwMm4Nwgjx3//93458VQNgRkdplloEUpP62gdbvvHVr361+MQnPlH+bbXVVh21DYL3OojQC0ONgMpK2e5JyYoUz8pSM3pK+973vue8szEPgqHv+OOPL+Aw4ad777232HjjjTueWXHFFWvnRH4ZWDpMcRo7yhgGA+BOO+1UULzIjj8Y7H/7298W1157bXHKKacUFJO6g/7cUDXD1H70jW/wkMbghBNOcLCC4e/oo48usBrPJsynDz/88A4vpn6G0mqbFxSHkd6GhyW3t7zrXe8qzjjjDMfYe88995TGYZ4PKzD7kdrmB+n2b7755g6edR6Td955Z0ExqZ28oRA2/cBYqo5sAyCPtcAHFc7f9ra3FVDk0fGI9XfeeecV2267bYcVmj+Hl0BdOvbYYyuQKZhl9ouxrjy9rgiMNwTUANhbjw/CCKGT2t76TJ92EUhN6t3cw/NrELw3PK1XSnIQUFmZg1J+ntEqK/JbKJ8Ty8Z8Txt4KGF5Wiodeuih1RzHzovOPffc6GOIO7Xhhht2PGefHwYD4EknnRRtg7353HPPFYilZWm3x8cee8xm6TgOY/tBpG/wkMYABgmLD44wotalyy67zMmLd2k/Uj94QXEY6Una6Mfp45/97GfBLkZogfnnn7/KC4/kp59+OphX8mLb/CDZ/pdffrmgjXMqjCDPb7311lo4aMOjDm9b2vy2Nv+w38gyANLum45XHhdGYMo//OEPwXYiRsMHP/jBClz+HJYU1KXnn3++ePvb3149B4OiJkVAEchHQA2A+ViFcg7CCKGT2lBP6LVuERitk/pB8F63GOtzg0FAZaUs7qNVVsiikF8aQhNheS+f0yA8UpMNLbbbbjvn+dic6Mc//rET74/Xa89HkwHQIg3PR0s/jhdddJG95RyHtf0gsluDh21gDAPaRdrBZ5NNNrGP1R79MF1txwLsBy8oDiPdDSM436QCYdJiCZsScf761a9+Fcsucq9NfpBu/zXXXOPgk+MluddeeznPXHXVVSK4DaKQOVApDZBoOuusswytue7Ig63csdU9rYXuuGcv0G7BhgScoaW99lJ1JFdx82//9m/Vb35CX7oMBVksL9FyIUPul/x29jkZEw3Flqjyf/jDHzbEQNXvuhP6EmXIrbq8TR6Ohtz667IabL1NLrflH56hmB6GXuaGFIRkXdi++tlnny3LJrd+Q+68hqz0ZduxBTXKWGuttcyiiy5a5mma3yca9D388MPmT3/6U/mHuih4saEdg8p+WmaZZQytdfcfq36DVtCARF8Xyi3HcU5G4nLr7N/97neG1tSXGFB8ipL+2Fbk0uWBFp4w7tBWbO2NI1n4DdqIPlp88cXLLb55folzigtgaKlHifMTTzxhyAhebslOO7+V503qIDd/Q5Mdg7GAMUlfcUq6MS7AV3V99a//+q+GDPBlVbTk3tDyi/J85syZZT+R0b7sJ5psGzIWmjXXXLPEpgltvYx7vx7a8db8/ve/NzjSznhm5ZVXNhMnTqxkC8YnsESiXYAN7R7nFEEKggFWSBjPFA/Due//wBilr9Dl5VB+mhAZUpzK+/RV31xxxRV+Ec7vXviKPAYMvdjMb37zG0NLrapyTzzxREOBfsvf733ve828885b3fNPJPvCLzv0W3KMo3xJ+rvlGb+dvcqOtmVbrzwj3V6/vLrfFIumfF/gPn19Ld89PG9T3uPP1p0P83jF+5iWyJmpU6eaF1980eC9CflndQ6MZ8g3JMh/WhXhNFNa9jmFsx+98KgUL4xGWQkIe5Ulvep9vcqKFM9K9S8bbh2nvYw/FIZ5AOYDNkGnhj7YTYJORpPv6lHatLDU96BP5Sby0DG0FNaQ0bB65MYbb3TKxY0bbrihnANUmegEdNOSS0NLQQ3t8lneIgOgoeWxPFvr59BRdtlll6oe8n4z3/jGN6rfqZMf/ehHZscdd6yy7bbbboZWgFW/cTLM7Qd9bWJwzjnnGFpdh2rKREs9HR3RXudHWgps9t577+rSL3/5y0qPrC4KnvSDFxSHEVlz3XXXleOBvM7KHjzmmGMMbcpa25unnXaa2WGHHar7tHzWUCi36ncbJ23yg3T799xzT0PL5CsYyNnMkBGw+h06IY9Ls+mmm1a38Pwee+xR/R5VJzlWR1iZqVEdfwiOmJPIeFg9i91T/uM//qM4+OCDi5i7t7/Ou1vLNZYic9pJWCVJhussXEHtc2QcCT6DLeyxqYnN5x/nm2++4vzzzw8+ay+SoaZ6HsuiiXk6dlhG8EnEV0Bqmt/WA9dVfBnyA1n6NGPJNbZYr0tksK3oJeFT0ISlIINMuQzcLwu/EQ8Fu/XVJenybD00aSq/zJHhpKLXpw/38GVRKt1+++0F4pj49fDfiN1y2GGHJZeJYBnJfvvtV+t5izLxBRresWQw62iC7wGIfoptcY4AsqklKLYSiXFvyyLDbLHRRhs5X7UsXmQ4LrCkBh7BKQ+Fpl5DwM7WA77wU255Enz17W9/u6LF0uQf9913X5/E8rdkXwQr8C5KjnEULUl/rzxjmyolO9qSbVI8I91eW17qmNpQIJf3UvXg/jCPV/TjF77whY73veV9bA5AH36K4447rpIPZLToaHZTvFKyz69AgkeleGE0yUrgKCVLutX7pGRFimel+tcfe/gtMf5Qjq+r97Iczvfemzx5MqponLbZZpuCPuyVujnmU2Tk7CjD91KBTn3TTTeV+bh+Oxo9ALGTppV3OK6//vqjqv0gtlePpxgGV199dRleCzsAkwNB+T7rAMi7gLk1xxRltJn6wQuKg9uDkF2wiaRkGBnUnbEQWz7u1tD9rzb5wVIl1X766FqQc0eB+JLQLbDLdCphYxDOX2RgTz0ytPeTS4Dh0h7aHhlry2FUyElYCgwlFso4FKKchBgRiC1ogcYuLN0kCA5bBo4QVqnEYxDiGcQ/5AkGCW7U5OWHzuFWivaEEnkkVvSRVTloCEGZBx54YPl40/x4CAYnjmWIRv8aWcZD5BbcsLTPPvsUvvu6Xw5+w+hYF/NRujwQDaMMV5hDNPFr6623XkHeZcH25l6cMmVKcokGrxNGcCytD6W//vWvBVeo+XOhc/ri2lEMxxW8CuUh9Kx/LWbUlxz3IBhBobGTkk+D/xsTYX4NBmc/NZ0Ec4NitwZAKb7aeeednfbxttpz3wAo3Rc+nqHfkmNcmn4JnkGbJWUH50EJWQn6JHlGur0oLyeljAlNebmuzmEer/goliP7MMHHB0grByQMgCnZZ/GU5FEpXhgtshIYSsqSbvQ+SVmR4lmp/rVjD0fJ8Yfy5plnnoqPwE+pyTOeCSXMYbjhDWXdcccdoazJaygLSyhjyRoA8ZEWmzrwzUU4HaPRAIjNUqxsw/Gb3/xmBxTD3H4Q26vBIweDDlAiF2jViINpt+M8UkV1q9+8UFWccTLeccByWVo5VY0FxACEc1PbaVj4oY32w9mG2zxg2yAP7LYhba38pAEQOzpxAW3P8eW67QTDjK0PX++6SRBQnAmgdMNTJJawQ6GtFx6LvqHTFyzIu9xyy5Ubn8DAiK3Z4b1ky8Ax9GULNHDFjufn51jzT0uYSpKb5r/rrrscOlAuLd0uA/CS22pB7sHlBMOnFwM75KHJFT1OI/LDULPuuuuW5fN7OIfyBYXOT9LloW+5smrpQD3YqAZecEsuuWQHJngGY6WbhDrhOWnrwhGxL7FzHmIwwEBHy46d+8gTincCg3vIeAlsYXTGBMg3iKEsfMHgqQ5X5IUXIsYjBJnf7zAU03IhXlR1LjnuEdPD/7BAy2iKNdZYoxyT2F2pzmg9DAZASb6CAgjPUX/cog9xHX++p6pkX1QdHDmRHOOoRpJ+KZ6Rlh11PNitrJTmGen2RoaPc4uP85VWWsm5hx8SBsBhHq/w8qGwA877AIGowed4H+MIWQhZ7f/10wAoyaNSvDAaZCXGsDRvNdX7pGVFimel+hfY2SQ5/lCmlAGQlkM7fIk5RWgVhm1Hr0dM2rGxBC0j7yhqtBsA4dnGZRwtWexo4zC3H8T2avDIwaADlJoLFILJwfNDtNtym6nfvJDblvGOAz5I+PNIeBv3Iw0DP7TVfnh6c3lFoVr6AWlrdSQNgDBS8Abb89CXGmkqsa25rQ9HfNHvJvmBMLGkoC6hDl6nzzQ///nPnftw3w8tncRuyFgCzMsKuYqGFLtPfepTpcfcxRdfXBqRYLSyqWl+31MR/Qbl1E8Uq7HDsASvFT+FFD0Ybf74xz86WeFqzDdyAQ5Y3uwn6fIoHomDOZQz7IzlJ4pr2WGAwuSim4TlGLyfd9ppp46dq/GV1/cshbHUT777PpTWCy64wMkGZZNivzh1+obtEK52CQH/4kxx98pl2px+lO0n6XEPb1deJ4yyFKPRqRZGNt+wimeGwQAozVdoOMITcEwuueQSBw/7Q7ovbLmxo+QYl6ZfimekZUeIB3uRldI8I93e2Pjh91LGBAkD4DCPV8gvzueQfViqyRO8x0IfjfplAJTmUWleGGZZiX6U5q2mep+0rEjxrHT/So8/9An4DM4A+INhpFvPDYrX6fAvDDiDSqPZAOjrw5CJWCXWJA26/aC1F4OHBAYWL6wo8lcOwWGgzTRMvGDbOR5xgCzDvA1OPauttpojn8BXkN8U/91C1OpxEPzQdvsxZ951110dXBEmzoZiaBXQFgtPGgBPPfVUp9FWccU66LYT4ufZ+nD0vZxy66eNSpxyYt6L/g4vvIMhWHj8HHjmUdDeWjLgScW/5C+11FIdu4T5ih0UkxijNskP129uhIO3BTf++IRjN2eOdwgnX9GbMGFC7VJWGNn6WZ7fz3B59idWvM3Y7pvvqEQbsHS1FNg3QIQ8HW293KsVAoS759NGEh3LwrCMpy597Wtfc/C99NJLq6x+P0HpDXl04gEYmnk/+QY26XGPuFa8PihxFOS8op2f4Osv+oXn9+lD/qZGg9QyuFh5bfAV2pAzqZXuC9Sbk6TGuDT9UjzThuzwebAXWSnNM220N2ccIU/KmBDjvdw6hnW84kMbl2Xwxq8LD4KlfosttpiTvx8GQGkeRZ9J8gLKG2ZZ2QZvNdH7pGUF8E7xrGT/tjH+0AaphA/KnIe32GILqaIblzNoA1iTyT5tFFNA/uHDJm3w5mAIPBELumkadPtB76AxAA1wCsAOwXxcwnmg7SWfw8QL4xkHGM553/NzfKCo0zGAmXQaBD+02X7MMbbeeusOfEOOMtJYtl1e0gD4gx/8oKPhGFwhrzdpYhEnhw9k3xuvSX20E2pVFoxieBn5CUIUSzdtnTD28QQDi72HY048QcTS48/4BkNfsYt5J4KWJvlh4ILHCSYNMDb5sQx523AO4yA3WCJOnZ98RS+2BTYYhy/zRB/4SbK8008/3cH661//ul9dx+/NN9/ceQZLLZomxGfkffyTn/yktggY9MBT8KyF0MLyRZsQ1JWXE/IQtHlxhNelzY+l6jZOJO75uMIjNZZ4P+ELEk/S496PeYLNTmLJ9+AdtAGwDb5C+3MmtdJ9EcOd35Ma49L0S/FMG7LD58FeZKU0z7TRXj5eYucpY4KEAXBYx6tvmIQHSCzR7oeVjIes74cBUJpH0T5JXkB5wywr2+CtJnqftKwA3imelezfNsYf2iCVaOdahyfb9rKK0T1oA5g/2YeMQugW/w8f462uGjpCf0Uc36Zp0O0HvYPGADQgzJCPayjEEPJKpmHiBbRrvOJAO9N29L8dD3ByQRxhhIXoRxoEP7TVftiEEFvVYmmP/XCA60dfJQ2AhxxySEfjAQIYv+2EAWsBx7Eujl4OHYihxcsKxZrwJ5NoO0+HHnqoU0bO7kq/+c1vnGd83HzFLvUSbJrf0g9jXMz7D/nw5RgeCRYnxCLyk6/o8YDEfl78hteLLW+FFVboyCJZHmL82bpwTGEJYnyPjG6WtsMgxOvFOZZtY8l3KG5LBwj/uLD33ns75eTs2IRYF4jD4SeOK+JNcUOjnxe/+SYhiDHIk/S4516QwIq2tOfVdZzD447HKhy0AZATKMVXKDNnUivdF7wtsXOpMS5NvxTPtCE7OA9inPciK6V5po32xsYPv5cyJkgYAId1vCK2qX1XYJIcCsfBsYICyvHohwFQmkfRHkleQHnDLCvb4K0mep+0rADeKZ6V7N82xh/aIJXOO++8iofBy9tuu61U0Y3LGbQBLDTZt/It94gVHljy3U0adPtB8yAxgP7prwQC7v6mcd1gm/PMsPDCeMcBKxU33njj4oADDih3s4XzDp8zYUxg1VNsRVxOf+fkGQQ/tNX+Y445xpH1MKZ2uxI1B7t+50kaAOERFRLkKa8diYaAqeG5ZuvHpgXdJqwR54G3P/3pT3cUxd08wTy+AQkveksLjtjdF1//Yn++Mvitb33LqZcrdvC+SxnpmuZ3KvvHDyy3ROwGeBd897vfLV3HF154YadtaF/KAIjNJFJp+eWXr8rF5ih+4opjr+Xx5VKIiZeTsPEHgvLbfg15PabKQRnYpMaWwY8I7r722muX8f/8GHd+uVhKwp+1G7/4+XJ+c1wR+yaVwA+27qWXXtrJLj3ueWwrKH85iU+Ch8kAyGnvha9QTs6kVrovOP2xc6kxLk2/FM+0ITs4D/Yq26R5po32xsYPv5cyJnBe32CDDfij2efDOl75hmT+CoO6xvGYPv0wAErzKNolyQsob5hlZRu81UTvk5YVwDvFs5L928b4QxukElbxWF0Jx25llAQ9gzaA9TLZf/e7313OnaZOndo1FINuPwgfFAaY04Y2ysGqs36lYeAFxSHc27fccosTsgyyavXVVw9nFrw6KH7wm9Br+7GLMN84Csa/2Oo+v/7R8DtpALzwwgudl5198WGH034kviQXSlAviQfuR2dyl1h4Z/DODhmCVl111SAWFpOc44Ybbug0gSt22LUnlZrmt+VhN+dJkybVGqpCtKcMgDDupRIPSpsyAPZaHjfkQWHNTRxTxGDsJmE5Llf8Q3jiGuJA4itNKCbDWmutVY0vGKBTXnsxOrlCnmM4jxkApcc99zIFnTmJj6NhMgBK8RUwyJnUSvdFDvY2j8QYl6ZfimfakB2cB3uVbdI800Z77ThJHVPGBAkDIGgYtvGKj3scd4TnyElcb+mHAVCaR9FGSV5AecMsK3kfS+khXEdJ6YnSsgJ4p3hWsn/bGH9og1SCFw3X76CbDCoN2gDmT/bhfYrwC6G/ww47rDj55JMLzCmBIbybe02Dbj/oHwQGTzzxRPGJT3zCGYcYk6GNG3vFOPb8oHlBcYj1TlFurAgnFC6vYBhrMw2CH+raA6ebbtuPZb4cN6w2GmspaQDELqEcBHveZOerO++8s1huueWK3XbbrcCSWHydz00wGtk6559//tzHgvn85bhYamCT78rs77yKfFC+LS3dHn1jDFfsMJlNpab5X3755QKx5HLo5Yoj8qcMgKGYfj793HCTMgD2Wh738GyyPTf3UoRC0W3CUtYvfelLziSvDvcllliiuP/++52qOFb4OtpL4gp5Dq4xA6D0uOcCGUpMTsJXdovlMBgApfkKGORMaqX7Igd7nqfXMS5NvxTPtCE7mvIgb4svK6V5po328nESO08ZE6QMgKBhmMYrwkFYGVb3fg3hhl3l7XPgHz81xSu1AZI0j4JeSV5AecMsK9vgrSZ6n7SsAN4pnpXs3zbGH9oglbDJG1+ZBINvbOO3WL3QIxAzEh8Sm8yLbJmDNoD5k/1uYmjbtnRzHHT7QXO/MZgyZUqxyCKLVO8EvBswHk855ZRuIOzpmUHyguKQ13X+xqZtb17Rb35IodBt+7HnhNW78E6fMWNGqqpRd38OUEyNrE20DNfQckpDLyonDwkcQzHjDHk9OddDP2jZq6FBV92i5VCGJvRm8uTJhmKPVddDJ7R80Tz44IPlLfKeMvRVP5Qt6xqaSoYX8/DDD5f5SWkxJETKczKSmWuuuaY8R3uffPJJQ4qUUy7tomto6Wx5De0//PDDnfs5P0j5NuSJV2VdfPHFDbnAl7/JAGiuu+666l7opEl++sJmaCdfQ/E1OoqioLuGviQbMswaivlmPvWpT5V/9GIx5Ppa5icDoPn1r3/tPItnyHBVXiPDkqFNLZz7/g+K+2dod+HyMk1qzR133OFkkSwPtNOy7bJ83rdOhYEf6BP0NxLGhx1vgaxZl2gpaIk5xYg0FFfSkOAIPoc+uPfeeyseol3QzBVXXFHmJQ9VQxNGQxvWBJ9NXWyKKy0zMxRHoSyWlgAb+rJXVSE97mkZnKEvd2X5tAyulCNVZTUnGItkwC/vkgHQ0GY5Tk7eh5///OfNlVde6dz3f5DiaMgLs7wcys/Lg6yy/YIH2uArlIv2oZ020W55huJ62J/lUbovnMIb/Oh2jEvTL8UzbciOpjwYk5XSPNNGe3OHDxn9zd13312NZ4pj6jwa4z0nY4MfwzJeyVhnaGfGknK8d++5555kK/gYJ+OIodAQzjMcr5AsczLTj5Tsk+ZR1C/JCyhvmGVlG7zVRO+TlhXAO8Wzkv3bxvhDGyQT15dQLvQN8F7TdPHFFxsKMl8+RuFQzKabbmrIIJhdzLzzzmtoQ8MyP8oh77rsZyUynnjiiWaXXXapiiIDoKGNGKrfbZ8Muv1oXz8xwHwMc7qZM2dW0NIGfmW/dzP+qkJ6OBkELygO+R0G2UQrD6sHwJ/g07ZSP/khpw3dtp+/h2jptKHl7jnVja48OSZLvsyKWldZRXfcccfk4y+++GIZfJI/Z8+x6UYqkYCv6pNYv+5vaoJNIOAlwL/o1bVryy23rGhBGyS2WG/yZRdYNcl/9tlnO/SC5t133730PKuLNRjbDRb1S37plS4PS6rs2MKuYjkJQdixHNw+hyDtkgnLHMgAWhx00EEFj81j68PXEpv22GOPig7cJ0O1vVV7RB6aTBekEDh5mvZTzANQetxzj0vsyF03FnmD+PLqlAcgYi7GEurjAXJJcerIHvOqaYOvQECOV4t0X3Q0vIsLTca4NP1SPNOG7GjKgzEPQGmeaaO9uUMn5U0U473cOmL5Bjle+fLG3Di1fKkXvKP8xPGSkH3SPAp6JXkB5Q2zrGyDt5rofdKyAnineFayf9sYf2iDZIIXjdXhcMTKj24SdA9eTtNN6AbtATdob59Btx993i8MrrrqOT9dugAAEc9JREFUqnJ3ZT5eFlxwwXJ+0c3Yk3qm37wwnnGA1yM2EoVXG/YXwD4JqeSvfIR8bTO1yQ/9bD/Xq7Cp51hMySXAaHTdFsuYvMN1PZYAHBdY9tzfaCBUBlziuXEGm270mrCxB5/8wzAD12lLF47YICOUDj74YCdfzs5VMMyceeaZxeWXX17cddddHYaaJoodaGqSn7uwol3HH398qFnVNcRT4DhgsuInSUUPZUuWt8MOOzj0IwZUKpEHo/PMJptsknqk4z5wgzEbyx+uv/76jvv8gi8cETPFJn8c0pcLe6v2SF/eK/qBpU1NcY0ZAKXHvb+tOozwsYQgv29961urdoYMgHwTm9SS5z//+c9VWRjvTQ2AbfAV2p8zqZXuixju/J7UGJemX4pn2pAdTXkwZgCU5pk22svHS+w8ZUzgile3AfaHdbwidjJ/xyLESixBX4GeZZ8JGQClZZ80j6J9kryA8oZZVrbBW030PmlZAbxTPCvZv22MP7RBMj300EMVT1rehC7ZJCF+GtdrUI4fFiZV3qANYL4+q0uATTkPSPVb0/s33HBDRywz6P48jn3TMqXy95MXxjsO5L3nyJ3UfBN97OvIiG3XZmpTJvSz/eTZXMB+hT/YcMZiyjIA4os5dqyzLzp+hHKKAeYncksv4EnH8/JzDJJUevzxx53n4e0hkfCV3NKCr+v8iy0UmbpE7vrVc3ge8fxS3kvf//73nWe23357p/gmih0ebJIfceZsO3H0vcQcQujHEUcc4eQnF1g/i7giL6k4+l+ioAinEmJZcoyaMvqrr77qfJULGU19GhDfz9Y5YcKE6jaMiPY6jrQctLoXOsHkkOdHsHibmuIaMwBKj/vzzz/foXvzzTe3ZAePPg+FDIDA0WIBA0Lsyxhif9q8ODY1ALbBV2i4/6Xuoosu6sBDui86KghckBzj0vRL8UwbsqMpD8YMgNI800Z7A0MneCllTOjVADjM49Uf/4jPG0u+DhUyAErLPp9GCT1HkheA17DKStDWBm810fukZQXalOJZyf5tY/yhDdLJd26gcEYFhZLJqgbzI1+PyNEd/cLVAPjeSpfL0fd9/CR+t2nwAH3Tp08v5ptvvqqd0FmxUim0kaBEe7opox+8oDgUhS8bU5uxwnaDscLnO/3eBETyo8BoaH83/DOoZ7IMgCCOYtM5nnN8QOEcu6fCDR7eMZjQU5w/Z9Dx/DCA5ATNpThfThmnnnqqCE51OxuDxph1HEG8+VJE5I8ZMuFpxF/QyE8x8Jw2NFHs8GCT/P6S08cee8ypm/+AsYEvgwatUPr8JKnooWzJ8vCC4EvG4T0KT4G6dNlllznjC19jKVZUXfba62uuuWZVDjCkWHq1ebFkl3u1rrPOOlVeeLxyPNAH1157bXXfP6H4cFW9yEtx6qosvJyURxweihkApcc9eJ8vNYdH7q233lrRzk+wzN7noZABEOEBgIH94xv88PIozmfHJi1NDYBt8BVohDe1pR/HkLyT7guOTexcaoxL0y/FM23IjqY8GDMASvNMG+2NjR9+L2VM6NUAiLqGdbzigyE2RON8XueZDz3F9xAKGQClZZ80j6I/JHkB5Q2zrGyDt5rofdKyAnineFayf9sYf2gDwr2gbPuHa70keBnPM888Di9jHpRa0UAxvx19y8qCnBUfPr1cN8o1gNn226NfZpPfbRu/UrR0036Uadtuj6l6YvfbxmC77bZzxhicVl566aUYScl7tt32mHwgkaEfvKA4FOVGFHwFI85jc1z/YxRsL5CDfrLjwB79+01+t8kP2IijjfaH2mexwDGEWeiZ0XYt2wCIhu23336OILIvriZHKLQ5bquoD/HqeNkxAxby5yZ0Jn9x2Dqwm1cqrh+W8tr89gi3VF8gU8DIjp2aQoaYJood2tck/9Zbb+3QiliO/lcjfCE499xzOyYaaBvq8pOkooeypcvD1wbbLzhivB177LGOpyaMBlhmwgUJ8tJmNX5zs34jJgOvE7spw7joJ8Tq40t28Yz/dQSGdl7W2972tuLkk092ioInp+8KDcUTgsqmprjGDIAoU3rcH3bYYU473/nOd5bj0NKPI5bHffCDH3TyAZuQARCTaI4bdkLEzt52vNPGNsUZZ5xRIDYkz4fzpgbANvgK7cXyH04bbYBUykrsos49C6T7AnWnkuQYl6ZfimekZUdTHowZANE/0jwj3d7UGLL3U8YECQPgMI9XTPQ5n+McH3PAF/hoSJtHFf5SYZs/ZACUln3oJ2keleaFYZaVwE+at5rofahfWlakeFa6f6XHHzDxjXVPP/00LveUTjjhhA5exkdg2qih/HiL+OJI+LCMj5x77rmns6Tf8jVCEXWT+Dwm1wBo67RHrjc2paHNyX4OLd20H+XattvjsGJAG2Q5DgOgF6vIwN+5f7TRYAeUtt322Ev7beFt8oLiYFEuOlZWYr4JZymesPcC5ojc2QR9jY+KoWTHgT32Mh7algn+qgiJ9vuY4N1gscAxd08Bv5xh/93IAIiv1/vss48DDAcpdY7BCGNTbqJdEau6oIBIpp122qkq29IdMgSE6vzc5z7X8SwMDpjA0W47jlHLlr3AAgsUtGtyR3FNFbsm+WFo9QUAXMkRBBTGLhgyYDiyNPpHGJ8Qf40naUVPujwY93jQdNsm2mGtoN2qCiyzCBmBaBfkYtasWbyp2ee0Q3bZ97Yue0RfYaxAGaQdkDv6AgaekKDFOLRl2CM8G+HVAm8PBI+313HE2Lv99tsdepvimjIAonDJcY8lusCctwPnCGqMJfr+8hieL2QAhKEPW7XzfDiHUR/GRf86/x3i+5gRog2+Ar4YR6CX02bPscSCJ8m+4OXWnUuPcWn6JXhGWnY05cGUAVCaZ6TbWzd2/OspY0KM9/yy6n4P+3jdddddg3xu+b3uiHeKn6Rlny1fkkeleWGYZSXwk+atJnof6peWFSmele5ftEFy/KG8NgyAKBcfmH09m/Ov78XL7+F82223RTFdpW4MYH79IR00l5i2J/spOrppP8ocLRhwT3af5tzfP/7xjztg9J/tZQzwwtviBcVhNspwYArNcTGf3Gijjcolv6E5LlZn1iXJ8dC2TGij/T4uagD0EWG/zznnnAJWV3/QxH7DkwfP5SY/8P/3vve93Eez8sFg4tN76aWXZj0LYTl58uQODzK/PPsbRjd4f4VSU8WuaX4Y+iwdqSOWbvsuw2eddZZDtrSiJ10eiEV8FX8nuVjbYVCynmJOYxv8gIdZyFutrl4Et6+rE96kIQN1qCwspYWnm5+a4ppjAJQc96AX/YQ4h6F28WuIYcGXuYUMgCjvggsuCBoBeVk433///YvddtutqrepARB1SfMVykTCZMCnF79hQOZJui942XXnkmNcmn4JnkG7JWVHUx5MGQAtfZI8I9neunHjX08ZEyQMgKhzmMcr6INHMj6yhfgd1+BJdNRRR5W8b/PUfQiVlH2gDUmSR9vghWGWlcBPkrea6n22filZkeLZNvpXcvwBj7YMgCgbcRf9D7OWZ+uO+CiNlSixeMUoO5a6MYD59ADnblPbk/0UXd20H2WOFgyajim/XfjdTwMgsG2DFxQHIDs7IYwA3+091O/2GpwKsGtwjM9tXnuM5Z1NRfisHzJBuv1+S9QA6CPi/UackQMPPDDo7WYHEY7wMDvxxBMbr6H+yU9+4gjpP/zhDx4Fvf9cdtllqzrmn3/+xh5gN998cwEvRb5LH287Xk5w7Y9tvtFUsWuaHyghvp8fu9DSCQMSNsJAMH0k7Cpl7+E4adKk8rr911TR4wqob8RAmdLlWTpxxDISBGb2Yxva9sHohYmTVIJBD8vk+SYfti4cMdnDlxvEmUxtHgOaEK8OS4ZD9OOLMwy22AgklJriimD0llZ45sWSxLi35eNrzjHHHBM0nuIrFpa1wLizxRZbVPTFtrHHzs9Yau970mG5N2JvYWkg0r777luV549x3OebHtXtRCrJV6gTCZMBTGx9r0VMXkJJsi9C5fvXpMe4NP298Axvq4TsaMqDKVlp6ZPmGZQr0V5LX+rIg1KHNprK4b1UHfb+sI9XfBg85JBDyi/3+IAEb25ggg8UNlg3jzkKL/a6JCX7/PIleLQNXhh2WWlxlOCtbvQ+1C8lK1I820b/Wvwkxh/K4gZA6AcxvdzW3eQIPQX6BedXq1PxI/RDhDdCrMheE//o7K8SqCvbXykhOdn3HQbqaJC63k37UfdowABzbD9MER9HueehzeQk2x/qS0leUBxCCBcFwndhA1Z/gxg7LjBvxFyozvmIlyo5HnwDYFsyQbL9HAuc493A55BYmTYW0xxoFA2YnhIZjQwpn4biXRgCziy00EKGFBaz+OKLG3rZdVU2DVxDCnD5LO1CZygeWFfl9OMhWu5haHmvobg0hoJUGjJ6lu2neD2GjD79ICFZB2h85JFHDG3ZbiiWoiEPTkOKSkknvWSSz4/mDKQEmwceeMBMmTLFAAf0D+2caBZeeOFWmkXGvRJr1EfBcQ0ZcwwZnwwpyV2NB9CPsUXBpQ0JJbP00kubpZZaypDQboX+3EIlxz1N5sr2YXzSC9/Q1y1DBnpDL7Fccpx8wOyee+4xFP/EkJJoJk6c2LUscgr2frTFV6Af/U07mhryiCrHakyWSPaF18TgT+kxLk2/FM+gnH7KjiDYNReleQbVDHN7a2DIujzs4zXWCLyrp02bVmahECOGDEqx7GUftiH7pHk02ogGN4ddVtqmDJK32pAVtl39Og7r+Au1H/MA+jhb/pEnsqFlwKXOCb2TwgGFHunrtdNOO83ssMMOhhwfKtnSVwKGoLLxjkG/2q+8MDLY28IBJpxHH320tMHA/kKGsXKuiXkifVDM5rR+jYdsgjIzSrU/s7oxlU3EACiNyJ/+9KfSyGHLveqqq8x6661nf+pREVAEFAFFQBFQBBSBUYUA7dhnaCOAyhhAgbrN+973vto20MoHQ6sMqvsUO9Acd9xx1W89UQQUAUWgKQK0yZA588wzzfrrr29+/vOfN318TOQf7xiM9/bbQaw4jCChONgRMX6OQ2kAhIJMsW/KXqAlk4Z2wRw/PaItVQQUAUVAEVAEFIExh8BNN91kaBlv1S5a6msocHr1m5/QplSGwmaU3t/2un4MtUjoURFQBLpBAAY/Ch9jaCMdQ+EHDIVD6aaYUf3MeMdgvLffDl7FYQQJxcGOiPF1HDoDIMXFKJcOYxkg0iWXXGI23njj8dUr2lpFQBFQBBQBRUARGFMIPPPMM4Zi9lRtovjB5tZbby0NfdVFOsHS/29/+9vmhBNOqC5TTFTz5JNPGhw1KQKKgCLQFAGEWEH4GCwTpI0VytBKtHto02JGdf7xjsF4b78dvIrDCBKKgx0R4+84dAZAfBE//vjjy55YY401zA033GBow4Px1zPaYkVAEVAEFAFFQBEYUwggjt+VV15ZtQn6zZprrlnG5KVNuQxihtFmNubvf/97lQcntGuwwTIdTYqAIqAIdIvA2muvXcZqp80hynjY3ZYzmp8b7xiM9/bbsas4jCChONgRMb6OQ2UApK2dy9h/WPqCr9wIgo+NNDQpAoqAIqAIKAKKgCIw2hHA6gZsbIZYxzkJBsK99trLHHrooTnZNY8ioAgoArUIYOkvNhTDhnLjNY13DMZ7++24VxxGkFAc7IgYX8ehMgBOmjTJXHjhhWUPnHrqqeUuVeOrO7S1ioAioAgoAoqAIjCWEcCuvoj9d/LJJ5feOHVthaFw//33N+uuu25dFr2uCCgCioAioAgoAoqAIqAIZCMwNAbAN9980+y0004GWzrD62/vvffOboRmVAQUAUVAEVAEFAFFYDQhMHPmzDIG4KOPPmrw99JLL5kFF1zQLLTQQmaVVVYx4y0+12jqO6VVEVAEFAFFQBFQBBSB0YjA0BgARyN4SrMioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIjDsCKgBcNh7SOlTBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAR6QEANgD2Ap48qAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKALDjsD/A1IPqYWUcju2AAAAAElFTkSuQmCC"
+  },
+  {
+    "quest": "",
+    "answers": [
+      {
+        "answer": "18",
+        "image": ""
+      },
+      {
+        "answer": "19",
+        "image": ""
+      },
+      {
+        "answer": "20",
+        "image": ""
+      },
+      {
+        "answer": "I dati sono insufficienti per rispondere alla domanda",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": "iVBORw0KGgoAAAANSUhEUgAABP4AAAGACAYAAAA9EKzWAAAMP2lDQ1BJQ0MgUHJvZmlsZQAASImVVwdYU8kWnltSSUIJICAl9CaISAkgJYQWQHoRbIQkQCgxBoKIvSwquHaxgA1dFVHsgNgRO4ti74sFBWVdLNiVNymg677yvfm+ufPff87858y5M/feAUDzBF8qzUW1AMiTFMjiQgNZo1JSWaSngAYYgAJYwIAvyJdyYmIiASz97d/LuxsAUbRXnRRa/+z/r0VbKMoXAIDEQJwuzBfkQXwAALxSIJUVAEBU8JaTCqQKDCvQlcEAIZ6vwJkqXKnA6Sq8R2mTEMeFuBkAMo3Pl2UCwLgMeVahIBNqMHogdpEIxRIANFkQ++XlTRBCnAaxHbSRQqzQZ6f/oJP5N830AU0+P3MAq+aiLOQgcb40lz/5/0zH/y55ufJ+Hzaw0rJkYXGKOcO83cqZEKHANIi7JelR0RDrQPxBLFTaQ4xSs+RhiSp71FiQz4U5A/oQuwj5QREQG0McIsmNilTz6RniEB7EcIWgReICXgLEBhDPF+UHx6ttNsomxKl9oXUZMi5HzZ/jy5R+Fb4eyHMSOWr911kinlofYxRnJSRDTIXYqlCcFAUxA2Ln/Jz4CLXNiOIsblS/jUwep4jfCuI4kSQ0UKWPFWbIQuLU9qV5+f3zxTZmiXlRaryvICshTJUfrFnAV8YP54JdFkk4if06ovxRkf1zEYqCglVzxzpFksR4tc4HaUFgnGosTpXmxqjtcQtRbqiCt4DYLb8wXj0WTyqAC1Klj2dIC2ISVHHixdn88BhVPPgSEAm4IAjuODms6WACyAbi1u76bnin6gkBfCADmUAEnNRM/4hkZY8EXuNBMfgTIhHIHxgXqOwVgULIfx1gVVcnkKHsLVSOyAFPIc4DESAX3suVoyQD3pLAE8iI/+GdD6sAxpsLq6L/3/P97HeGA5lINSPv98jS7LckBhODiGHEEKI9boT74T54JLwGwOqKs3Gv/nl8tyc8JbQRHhGuE9oJt8eLZ8t+inIkaIf6IepcpP+YC9wGarrjgbgvVIfKuD5uBJxwN+iHg/tDz+6Q5arjVmSF9ZP232bww9NQ21FcKChlECWAYvfzSIYDw31ARZHrH/OjijV9IN/cgZ6f/XN/yL4QthE/W2Lzsf3YWewkdh47gtUDFnYca8BasKMKPLC6nihXV7+3OGU8OVBH/A9//U9Wkcl8lxqXLpcvqr4CUZHiHQ24E6STZeLMrAIWB34RRCyeROA8hOXq4uoKgOL7onp9vYlVfjcQ/Zbv3Jw/APA93tfXd/g7F34cgL2ecPsf+s7ZseGnQwOAc4cEclmhisMVFwJ8S2jCnWYITIElsIPzcQUewAcEgGAQDqJBAkgB42D0WXCdy8AkMBXMAiWgDCwBK8FasAFsBtvBLrAP1IMj4CQ4Ay6Cy+A6uAtXTwd4AXrAO/AZQRASQkeYiCFihlgjjogrwkb8kGAkEolDUpA0JBORIHJkKjIHKUOWIWuRTUg1shc5hJxEziNtyG3kIdKFvEY+oRhKQ3VRE9QGHYqyUQ4agSagY9FMdCJajM5FF6Gr0Sp0J1qHnkQvotfRdvQF2osBTAPTx8wxJ4yNcbFoLBXLwGTYdKwUK8eqsFqsET7nq1g71o19xIk4E2fhTnAFh+GJuACfiE/HF+Jr8e14Hd6MX8Uf4j34NwKdYExwJHgTeIRRhEzCJEIJoZywlXCQcBrupQ7COyKRqE+0JXrCvZhCzCZOIS4kriPuJp4gthEfE3tJJJIhyZHkS4om8UkFpBLSGtJO0nHSFVIH6QNZg2xGdiWHkFPJEvJscjl5B/kY+Qr5GfkzRYtiTfGmRFOElMmUxZQtlEbKJUoH5TNVm2pL9aUmULOps6irqbXU09R71DcaGhoWGl4asRpijZkaqzX2aJzTeKjxkaZDc6BxaWNoctoi2jbaCdpt2hs6nW5DD6Cn0gvoi+jV9FP0B/QPDCbDmcFjCBkzGBWMOsYVxktNiqa1JkdznGaxZrnmfs1Lmt1aFC0bLa4WX2u6VoXWIa2bWr3aTO1h2tHaedoLtXdon9fu1CHp2OgE6wh15ups1jml85iJMS2ZXKaAOYe5hXma2aFL1LXV5elm65bp7tJt1e3R09Fz00vSK9Kr0Duq166P6dvo8/Rz9Rfr79O/of9pkMkgziDRoAWDagddGfTeYLBBgIHIoNRgt8F1g0+GLMNgwxzDpYb1hveNcCMHo1ijSUbrjU4bdQ/WHewzWDC4dPC+wXeMUWMH4zjjKcabjVuMe01MTUJNpCZrTE6ZdJvqmwaYZpuuMD1m2mXGNPMzE5utMDtu9pylx+KwclmrWc2sHnNj8zBzufkm81bzzxa2FokWsy12W9y3pFqyLTMsV1g2WfZYmVmNtJpqVWN1x5pizbbOsl5lfdb6vY2tTbLNPJt6m05bA1uebbFtje09O7qdv91Euyq7a/ZEe7Z9jv06+8sOqIO7Q5ZDhcMlR9TRw1HsuM6xbQhhiNcQyZCqITedaE4cp0KnGqeHzvrOkc6zneudXw61Gpo6dOnQs0O/ubi75Lpscbk7TGdY+LDZwxqHvXZ1cBW4VrheG04fHjJ8xvCG4a/cHN1Ebuvdbrkz3Ue6z3Nvcv/q4ekh86j16PK08kzzrPS8ydZlx7AXss95EbwCvWZ4HfH66O3hXeC9z/svHyefHJ8dPp0jbEeIRmwZ8djXwpfvu8m33Y/ll+a30a/d39yf71/l/yjAMkAYsDXgGceek83ZyXkZ6BIoCzwY+J7rzZ3GPRGEBYUGlQa1BusEJwavDX4QYhGSGVIT0hPqHjol9EQYISwibGnYTZ4JT8Cr5vWEe4ZPC2+OoEXER6yNeBTpECmLbByJjgwfuXzkvSjrKElUfTSI5kUvj74fYxszMeZwLDE2JrYi9mncsLipcWfjmfHj43fEv0sITFiccDfRLlGe2JSkmTQmqTrpfXJQ8rLk9lFDR00bdTHFKEWc0pBKSk1K3ZraOzp49MrRHWPcx5SMuTHWdmzR2PPjjMbljjs6XnM8f/z+NEJactqOtC/8aH4Vvzedl16Z3iPgClYJXggDhCuEXSJf0TLRswzfjGUZnZm+mcszu7L8s8qzusVc8Vrxq+yw7A3Z73Oic7bl9OUm5+7OI+el5R2S6EhyJM0TTCcUTWiTOkpLpO0TvSeunNgji5BtzUfyx+Y3FOjCH/kWuZ38F/nDQr/CisIPk5Im7S/SLpIUtUx2mLxg8rPikOLfpuBTBFOapppPnTX14TTOtE3Tkenp05tmWM6YO6NjZujM7bOos3Jm/T7bZfay2W/nJM9pnGsyd+bcx7+E/lJTwiiRldyc5zNvw3x8vnh+64LhC9Ys+FYqLL1Q5lJWXvZloWDhhV+H/br6175FGYtaF3ssXr+EuESy5MZS/6Xbl2kvK172ePnI5XUrWCtKV7xdOX7l+XK38g2rqKvkq9pXR65uWGO1ZsmaL2uz1l6vCKzYXWlcuaDy/TrhuivrA9bXbjDZULbh00bxxlubQjfVVdlUlW8mbi7c/HRL0pazv7F/q95qtLVs69dtkm3t2+O2N1d7VlfvMN6xuAatkdd07Ryz8/KuoF0NtU61m3br7y7bA/bI9zzfm7b3xr6IfU372ftrD1gfqDzIPFhah9RNruupz6pvb0hpaDsUfqip0afx4GHnw9uOmB+pOKp3dPEx6rG5x/qOFx/vPSE90X0y8+TjpvFNd0+NOnWtOba59XTE6XNnQs6cOss5e/yc77kj573PH7rAvlB/0eNiXYt7y8Hf3X8/2OrRWnfJ81LDZa/LjW0j2o5d8b9y8mrQ1TPXeNcuXo+63nYj8catm2Nutt8S3uq8nXv71Z3CO5/vzrxHuFd6X+t++QPjB1V/2P+xu92j/ejDoIctj+If3X0sePziSf6TLx1zn9Kflj8ze1bd6dp5pCuk6/Lz0c87XkhffO4u+VP7z8qXdi8P/BXwV0vPqJ6OV7JXfa8XvjF8s+2t29um3pjeB+/y3n1+X/rB8MP2j+yPZz8lf3r2edIX0pfVX+2/Nn6L+HavL6+vT8qX8ZW/AhisaEYGAK+3AUBPAYAJz2fU0arzn7IgqjOrEoH/hFVnRGXxAKAW/r/HdsO/m5sA7NkCj19QX3MMADF0ABK8ADp8+EDtP6spz5WKQoTngI3BX9Pz0sG/Kaoz5w9x/9wChaob+Ln9F+VvfFhA5t1MAAAAimVYSWZNTQAqAAAACAAEARoABQAAAAEAAAA+ARsABQAAAAEAAABGASgAAwAAAAEAAgAAh2kABAAAAAEAAABOAAAAAAAAAJAAAAABAAAAkAAAAAEAA5KGAAcAAAASAAAAeKACAAQAAAABAAAE/qADAAQAAAABAAABgAAAAABBU0NJSQAAAFNjcmVlbnNob3RPyv8/AAAACXBIWXMAABYlAAAWJQFJUiTwAAAB12lUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iWE1QIENvcmUgNi4wLjAiPgogICA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPgogICAgICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgICAgICAgICB4bWxuczpleGlmPSJodHRwOi8vbnMuYWRvYmUuY29tL2V4aWYvMS4wLyI+CiAgICAgICAgIDxleGlmOlBpeGVsWURpbWVuc2lvbj4zODQ8L2V4aWY6UGl4ZWxZRGltZW5zaW9uPgogICAgICAgICA8ZXhpZjpQaXhlbFhEaW1lbnNpb24+MTI3ODwvZXhpZjpQaXhlbFhEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlVzZXJDb21tZW50PlNjcmVlbnNob3Q8L2V4aWY6VXNlckNvbW1lbnQ+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgr0s1DoAAAAHGlET1QAAAACAAAAAAAAAMAAAAAoAAAAwAAAAMAAAL41oeC1jwAAQABJREFUeAHsXQWcHUXS77gnEAEO96CHHnrw4ZDDcrhLcHc/XIIGd5fghzsBDglOCIHj0CRokCBJiBGZr/4DNanp12Pv9ey+3a36/XZ73kxPy7+7a7qrq6pbBURGSRFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUASaFQKtVPDXrNpTK6MIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCIQIqOBPO4IioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAItAMEVDBXzNsVK2SIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKggj/tA4qAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKQDNEQAV/zbBRtUqKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAK/rQPKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCzRCBmgR/48ePN88884wZNmyY+eGHH8zYsWNN586dzRxzzGHmnntus+6665qVV17ZtGrVKhO6N9980/z2229hvIUXXtgsuOCCme9oBD8I+MZ+6NChZurUqWHh+vTpY5Zddlk/BdVUGhyBKVOmmJEjR5pPP/3UTJ8+3Sy++OJmscUWMx07dmzwsmiGzQ+BanjPoYceakaNGhWCccEFF5glllgiNzBvvPGGmThxYhh/kUUWMQsssEDudzViJQK+eP0nn3xivv7668oMxJ3WrVubdu3ahX/t27c3vXv3NnPNNZdp27atiNU8L7/99lvz0UcfxSrXrVs387e//S12T380TwQmT55s9txzz5B39erVy9xyyy1RRcseO/vss4/57rvvwnn9ddddZ9q0aRPl3dwuxo0bZ5599lkzfPhw8/3334drmtlmm80stdRS0R++GeBFSoqAIlB/CKTxyrTS+hz7ZfHMrPmynI9hXgw5THOirPo3p7qWWpegCpowYUKwxx57BDQJD6hwqX9zzjlnMHDgwIAEQak5Lb/88lE6a665ZmpcfegXAd/YE7OJ2nKrrbbyW1hNrUEQePzxxwP0C5rgRm3JYx33llxyyeCBBx5okLJoJs0XgaK85+effw5o4Rn2SRL+BLRZFAOHhNPB5ZdfHnz22Wex+/xj6aWXjvrzOuusw7c1rBIBX7z+wAMPjNqF+UyeELxoueWWC2666abMOUaVVayL16655poKfDp06NBoZSNhU3DYYYcVyr+adwpl0IwjH3300VH7n3nmmbGalj12ZPoXXnhhLO/m8uP9998Ptthii4A2ESKck/gPbXwGJBxsLlXXetQZAsona2uQNF7pSrmMsV8Wz8yaL//lL3+J+FdzXHtn1d/VvnqvEgFTeSv9zttvvx3gw5f0UUy6j3c+//zzxMRlg6622mqJ8fSBfwR8Y+9rMei/pppiFgKktRtssMEGucd3v379gjFjxmQlq88VAScCRXnPvffeG/XN9dZbL5bmK6+8EgqB8A26//77Y8/4hxT8/f3vf+fbGlaJgC9eLyfKSXOIrPvLLLNMs+VF9SL4IyuP4Nhjjw0gdEd75KFq3smTbkuJQ1oO0WbHfPPNF0yaNClW9bLHzo8//hj06NEjbO9OnTolbqrECtVEfmCj6IgjjnBucGbxm5122ikgTcgmUlMtZr0joHyy9hbK4pUyhzLHflk8M2u+LAV/2MhobpRV/+ZW37Lqk2/m9mfuTzzxRDThkx9F7LpjAbDSSiuFmkBdunSJFmcyHplWBaQ+76yLNqgTlga56Rt7X4vBBqm8ZhJDAB8LOWZxTeb7wYorrhiOb9fYhgBm5syZsXT0hyKQB4GivGfAgAFR/zz//POjLMiENyCXEtEzFfxF0JR64YvX+xBegFdhg5HcjpRa58ZIvLEFf+DvZF4akGl1NMaAdxpV805aei3x2e+//x6Qq5QI89tvv70ChoYYO+C1PC8gFz7N4ntPLh+CzTffPKoX169ICIumb775pqJN9IYikBcB5ZN5kUqPl4dXcgoNMfbL4JlZ82UV/HELa5iGQPrMTbwJ5kR+LmIfyfnnnz80q8Igsum9994Ltt1229hiDB9U8lNiRw1/77XXXsEKK6wQ/u22227OOHqzHAR8Y+9rMVhObTXVJAQuu+yy2PiGwO+iiy4KsDPGNG3atOC8884LhYFygtxcTYC43hqWg0BR3jPPPPNEfRTfGKb//Oc/0X30SxX8MTLlhr54vS/hBdoeGjzNjRpb8Aehn+T3fJ2GczXvpKXXEp+dccYZEe7QaMU83KaGGDvkNyuQi8rrr7/eLkaT+73rrrtG2HJ/riaE5jjmRUqKQDUIKJ+sBrXKd/LwSn6rIcZ+GTwza74seXRz1PjLqj+3r4bpCOQW/D399NOxjyQWYHl2uqTUGx9V+AX86quv0kulT5s0Ar4Wg00ahCZY+IUWWig2xjHmk+jBBx+MxcUHR0kRKBOBESNGRH0OPEaSCv4kGg137YvXJwkvDj744OD4448PzUsh0EO8bbbZJujevXvUF+zFOswRm5v7gcYW/N1www1OvNN6WjXvpKXX0p6Rs/lYP4ebAxc11Ni5+OKLoz6ATf+mLOxKEraAl8CMHcoH8BULX36DBw8OoGnOvmVtfoPfV199tatp9J4ikImA8slMiDIj5OWVSKghx35D88zmLvjL7AgaIRcCuQV/5557bvTRx4fuxhtvzJUBIm244YaxdzHwlJovAr4Wg80Xofqr2RdffBEbo9DWzSLbTKa5Lbaz6q/PGxYBOsE36qM4XEqSCv4kGg137YvXJwkv4PfIRTDnhVBQmnfLBfk999zjeq3J3lPBX5NtuqoLDi167tNzzDFHTPNeJtpQYwdjEQfKcJkgEGuKhIMG5QKZ64MQm59vvfWWs1r//e9/g3nnnTeqv3wPJnhKikA1CKjgrxrU4u/k5ZUNPfYbmmdKvtYcNf7ira6/qkWgFV6kD1gm0eTC0K5WFO/VV181q6++evQ77YIYm8Hx1kzHHHOMIU1A/hmGX3/9taFBGV537NjRkEZh7HktP8hU0dDC0Hz66afmyy+/NKR1aMjfoFl00UXNqquuaugkr1zJ45jwjz76yHz44YdhSDuAhvyvmL/+9a9heuTrMDWdX375xdCplGGc3r17G3KYHF6j3u+884557bXXzE8//RSmRz7VwvJlpckZjho1ypBGjKEDVMy3335raIJiyDQ7/MN1GhXFnk7MNK+//rpBOGPGjLAfrLHGGma22WYLs0HboQwgOlnI/Pvf/w6v0/6NHj3akOle+Ec7ySEGNJkKMaDFXdqrIabAFtSnTx9D2iCGFobmkUceCdsc7UzCZ7PggguGcex/yA+4oW3xh3fJl13YB9E/0MZpfaTMdkVZa+13dn1dv8l3kKFd7ugRxiypVUe/XRdk8mtIGyd69OSTT5pNNtkk+p33QuLXs2dPM/vss4evkkaxAZ/B2OjWrVs4xsisJuzbaWljjNXSH8rA2xcPQr1rGSsSt1p4hq90ivAeOnTGPPfcc2HWd999t9l+++3NlClTDPrJiy++GOuvV1xxRdQX0Z/Qr0BkLmdoARdeoy+9/PLL4TXtGIf9jBZ9IQ+mCZShg0DM+uuvb8Dnk6jWvoZ0fbUn0qqVlyENSWXwepk+Xx900EHmqquu4p9RSBPncOxHN6wLzCvAq2waOHBgjDdJHsNx6aAEQ9o9BuPgqaeeCkN8I8iHmaFTyzlaRYg+99JLL4XthvkECP0K34m+ffumfisqEvvzBrlLMcOHDzfDhg0zZBFh6KTi8Lu68MILhzGuvfZas//++8deJyFM2P/lTdp8MXT4g7xlunbtasgXWewe/yCfy4ZOxuafYUguHgz6P4ifk7aZOfHEE8N78h/6BxMJp8K2quYdTsMOfWBdpO0xz00izHUwP0AbgYf06tUrbCe0VRK+SWll3cf3Am2PvgBC28v5t3y/7LEj89pyyy3DeRXukWueEAv5vNZrn7wwqSy33nqroY2jisfovx9//HE0j62IQDfoBNBwzYA5gk10sID529/+Zt+O/fbRnzG+Mc4l8djDPTIHD8s5dOhQQxu64RqA/K+HPC3tWybTw3ftgw8+CNdL4HFYl4CP4FsK3ojvYtqcGGn55kVcPle6WM8xYU5BB32Zd999NxxDa621Vlhm1zoCaYGXY46JdQNpsppNN900HNucXlYIfgA+iDUP0kM+4A34W3zxxUM+7sK9KfPJtG9kQ/JJtE0RXtkYY98nz8yaL9NGbMQbSPBnHn744azum+t5tesX8B+8C8KamnwEp+YHPvPrr78mxs+qf2ri+nAWAnklhv/6179iO11FfHrBtBfmOfvuu29wwgknBI8//nhFtllOKyteyHGDPrLBKaeckri7RygEJBQKVfmJWSWmSB03VPunj14MA7zPf3iWpQVJk4Io/qBBgwKacAc4cptPyOO0OMQJbjipMo1wyvI666wTpcvvypCEYQE0NoGHi/JiTwLPoH///k4tCxzwgh0G7HAU0QIhoWAAB8myvPKaPp7BnXfe6Sp2dA++b/gdmFzQor/ilDaapAQnn3xy9A4uPvnkk9C5M57x+64QZi2uPsuJldGuSNtXv+NypoU4uAfmLTjRF7480a+y6Mwzz4zhhjSqIYnf2WefHZ4aKE9flW0CVwG77757qruAavtDGXj74kHA1cdYQTo+eIavdPLyHvBK5pPgNTRBQBFCE1DZP1zXJ510UhgX/2S/gm8mpLvLLrs4eRrSwmEGd9xxR/S+fVFtX0M6vtoTafniZUgLVAav/yNl9/+iWkucCm02xHgQtz/mGpIkj+E4GAf77bef8/0jjzxSvh5e0wI41DJMmwegj6I/cf+sSMS6AYfkRx99dMX3istIgsQAJu55Nf422mijivrg9NEk2nHHHSviIw2m7bbbruI5l80Oef5TzTucH4c+sS7S9q52h189aBvD561dZ/69wAILBCQ85uLXHILncNoIn3/++cQ0yx47MuPbbrstVi7aiJGPq772yQuzCoHDSSS2fA23RHmIhIbR+yQMCzBeMBdCn00in/1ZHnDFZUe7YP4CdwhJbhDwLYPpchrhNFJSzEjt68iTFBdC3kmC0MTkfPMizsg1X8czUkYID3diTGSIdYScn/7vf/8LSGAftaOMC61WzEOzCJZrcv4i05DXtOkY9g/weklNmU+ifjavbAw+CTyL8MrGGPs+eabsb6uttprsTuG1b42/WtcvRcsDeQyPHViU2ZRVfzu+/nYjkNvU96677ooaBA0DPzr33Xef09mwO6v0u74bFMJG14SPO5UdYvLkIiyo5OLOfs/+TbtFAe38uJKKLTpp9zwgbbIYpnZa+I2PXJL/ENqRC0jLLjMNThcfYqg625QHe9KMyZUXaT/GygPBposgIJQTKC5jUojFC+0EuJIKaGc8yhMf0yTzr9NPPz16H4JQFiQk5Wnfx4TIRVKY4KNdkYfPfucqs497pN0X4Q6sqj1NU+JHWlwVJ0fa7YDfEMaS9oWzGtX0hzLw9sWDfI4VXzzDVzp5eA8a+dFHH436GvgmE8w9Xf1D3ksS/GHxYh9YJd+T10mLwmr6ms/2BA4+eRnS883rkWYWVSu8gLBJthNfk4ZULEvJYzgONrH42g6BqaTHHnsswALOjpf0G4vshx56SCZRcT1y5MhccxQInEhrpSJvLFBt8r3YrmZxWs07sh6+sS7S9na7Yy6HzbCkdpb3Me/AYtg1x5L1y3MtF6hpZr5Iq+yxI8uLOZicN0FwXAv55oVZZYHwxSXAxbcAm0B5CCa/l1xySbiBBmFbFvnuzzvssENFf7zyyiuDf/zjHxX3Zf/ENfooDmxzEVm9FOJxSI+slkKBmys937yI88DGn6wX6gSzc7IUi92XcXCN50OGDAmeeeaZsNz2c/s3b2RwvjKEAosdP+s3XF7J/tKU+STqKnllY/FJtEleXtkYYx/l88kzs+bLRQVtKF8S+Vi/FC2PVBpSwV9Sy9R+P7fgD5JfTEBs5oaOCO01fAxroawOXSRtDHCXsA5CKQibsFi0BVSoF041lYQ6y3Jx3TGRhHYUdvaxI8/3OcQ7ksFzmq4JKN6BcA/l6devnxNjMnMMNek4HYQoG4QfnCdC+B/BqTf4KGEySKZHseeI4/J9JOvo2kWA/zd7sgTB73rrrRfu+q255pqxyaAsU5LgzxYa4R3swGE3k8y3AjJ1rtCC2GyzzSQE0bVcfMu85TUmB2TSFb5DJgAVuKBv77zzzsFRRx0V1olMGSryRzu5dnV9tisK6LvfhZX2/I/M0mMYQuOhWkrCD5qqOAUcEwwsMLC7LtsUBwxNmDChItui/aEMvH3xIFTO11jxxTN8pYO6ZfEexAFJAd+pp54a3sO/Sy+9NNR4lumgj6BPQRMaf3ICn9TX8A76G3gMBIv24gKLXTLnifLli6J9De/5ak+k5ZuXlcHrUc4sqlZ4gfaSPIGv5SYP8k5rd36HQ3wrJJ/HoQq4x8+LhJgbuQjaEWQqWVWanH9zFPyVgXWRtpftDgHeYostVriNYN1SC5EpZ8yXni3EttMue+zY+WFzm/sgrDVqIZ+8ME85yBw3KjvXAeE///nPPK8XjlNGf3YJ/mRdsq4xlx87dmysLuhz9jc0Kx1+DuGfnR4SbyjBH5cjTwg+bn/bk97DfB++g21K2mxKSkfeh+Y2Uy2CvzL6VRE+Kb+RjcUngWMRXtnQY5/bGaEvninHqGutXlTQJssor32tX4qWRwV/shXKu84t+EMRHnjggVSmCcETFupkRx+bOOcpflaHzpMGx7FNECE4g8aiJJj2YudLMmVoz2FRy3TIIYdUPMdppjahvrZgDAtSm1yMFcIzqJ1LwmmqmNTLssF8VRL5pYg9Jz8vFaesYXEhTxVCehAu2pSFvf2BgrDT1rbCAtQWRCI/l+BPau8gDjQpXOZ0MEuAir7EAaZdNrkW3yuvvHKoKYkPJIShENIy2ZqGhx56aKzdOR7MGGwBMTT6bPLZrkjbd7+zy1vrb3zobW1aCGaqJRd+aD9bcxbtYZ88DCG3TUX7Qxl4++JBPseKL57hKx20Wxbv4baVJgDk95FvRyEm6JJP3H///dEzeeHqa2zaDn7JBLMhuFqQabo0JYr2NZ/tibL65mW+eT3jmRUWFV5AO8dlpsrtBb4vydXuHNcO11577ehV8jmWqemeJhTEBhm0iW2yzX9kGbCQhhm6zetkHFw3hOAPmvZ2vkm/WcBezTvApyysi7S9bCdo+brqCncT5DMtWGWVVRI3PPO4ypB5yWvMe2S+5H9XPq64Lnvs2BmSb99Y+aD9XQ355oV5yoCNb4ktX2MO6JvK6s9Zgj8IrNA/k0x+UWfblBXrGsaCQ/A1bJxBY/60004L1w5JadobLcCysQR/ENa7FDK4XjLEmg9zC3u9xXHkugF1whwhaTMA60ys5/D9sNeDnB4EQExNmU/Kb2Rj8UngWIRXNuTY5zbm0BfPzJovFxW0cfns0Nf6pWh5VPBnt0Q5vwsJ/lAECLlszRtmanZIDldD/zXkODWz9FkdOjOBPyOQc/OKiTpMl5Jo7733jn3wINwEkZPf2H1M9uD3KImwGJULAHwgbcGFPQElx6+JZiHAWeJp70jaAxMmE0kkdxvIyWyFSWYa9nQgSqwc8G9Ejn+dWX333XcVkw1b8AehkVzEA7M0/zXQssHiibFYYoklAuxGSLIX39A+w/HuLoI5qvzIY9dELvjtd3DCG+eN0G4HxPfZrmX0O7tOtfyGwBwn/kpMMOFB21dLNn5Y8GInz0Uwj8NY5PzRN8gxfixqkf5QBt6+eJDvseKLZ/hKB42Wxnu4UcEDuL3Bf1za1NUK/qA1KrV8OE+EEB5xvghtXoY4Rfqa7/b0zct883rgk5eShBfY0ILfqsMPPzzUxN51111D0x4Ix2TbyGvwI5t/2DxGxoepEIS62NTBRhMdpBEV2+Z1/B7iYTMR30LkhW8YOb13lgmLJPmNoYMBnJtkSBvzEbn5CHPhpMV2Qwj+0MfokIDQPzPXXYZ4xn/krD7ErZp38GIZWCPdIm2P+CBy0h+gH8m64hoCBSnowpzQ1e4wu6yWbDNCOrgoNamyx46dOXwuS1xsSxk7vuu3b17oysN1z+UrE3WBD0ffVFZ/ThP8QaBHh9mEVcE8GZZJsq342p7H0iFtFfGwCWQTNPvgxoDT4ZAOerSjNrjgD+tNuckCc2wunx1i3QFlA55LwILFJazD2JaU5FMWPvAx72OCj1eXD0HMF5iaMp/kb2Rj8kngWIRXNuTY5zbm0AfPRFpZ8+WigjYunwx9rV+QZtHyqOBPtkR514UFfygKFt+YgGf5VJDMFrs/WGQnUVaHTnrPvg9nwzJfl4abfAfadhwfAk3eubr++uuj+3h+wAEHyNec1/YHGX43JNkTUPj+SCIMPvkhggaAJJSTy43w5ptvlo9j1xB8nnPOOaGZL3aibcFZGvbQXJT54LCUNLIZsb1YhmBVpgez3iyCbz35ji0otBffcBSdRBDQYleOTm8MIASFg940woJNCh7Rj23y2a5l9Du7vLX8di0yXObjRfKw8WPNkaQ0oMEp+wOdXBWLWqQ/lIG3Lx7ke6z44hm+0kGjpfEeblRoO3N7YzHlomoFf1nOziUP/r//+7+KrIv0Nd/t6ZuX+eb1FWCl3HDxFW7zoqFLEGHzGE4TvEQShHj8fcTCTG4y8DsQ+rmExdBCtDWh+R06YT7KxtaY5ThJwiJ70cDxG0Lwx4Wmk5OjMcj5I0yjIu+UhTXKV6TtuT725gbqChNBWDbYhHsu88Fqtf5s81fMt9Oo7LFj540D52QfgJVPUfLNC/Pmj3mwLDtfuyxO8qbpildmf7bXGVwHWzsN5cKmqFx8c1xbUAeNR37GIb5t+K7KTQukiUNs0OawJsJcPOkgo4bU+IMrDmzSS0K5XXVH/bCRZJPL5yuEnJKwwY0NH6xzoFABqwBs7EihH8d3CZrw7bCpKfJJ/kY2Jp8EjkV4ZUONfbt98dsHz0Q6WfNl2d9x2GY15Gv9gryLlkcFf9W0WPF30mduGelhtxuCE5jcwD8SfzCSQjBnOQGWyWd1aBk37dreuYLZbBZht4eOiY5Fs3fK4Ogyi2CKKOtumw/YE1BbU8lOHxqBnB7MSiRh0cfPOMSHHztS0CgoQmnYS21B5MO7+knpY8IjJ8G24G/gwIGxcsuTtpLSxI431xEh7zZxfHvxnaet8C4+1vakhtPkEFow0rclTB9s8tmuZfQ7u7zV/AZWtnYs2kIenFBNunhH4ocPhWsSJdMeNmxYrD9AK0hSkf5QBt6+eJDvseKLZ/hKB22Wxnu4TeHkl8d/klC4GsFfly5dIiEP52WH8vAPmP3bVKSv+W5PWRYfvMw3r5fly7r2Jbywv7mcr+Qx3Jeg9QGH9kkkBc78DkJokiQRNtlkXL6WG2aYM/F9GdoLV84DWil2P8N7zUnwVxbWwLCatoe/X9k2uIZmaBJhXmDHl/68kt5z3YeZJqeFPgrtuDQqe+zYecMPKJcPYZLvZfs9+btMXijzsa/POuusWNm5Hvac0n6v6O8y+3OS4C/JEkd+P7m+0EaTZLcHx0MI4RcUPSCgwnw4LzWk4C9JyIHNOlkXXGN94rKQOvnkkyviYo5QlPAthkDUpfEJM2ybigj+yuxXTY1PAscivLKhxr7dvvjtg2cinaz5clFBG9K0ydf6BekWLY8K/uzWKOd3TYI/WSQIULDzCWfWYMIuMwkwXWgJQtBmU1aHtuMn/bYnbKNGjUqKmnpf+teBH4g8hAk6GDt/aGztMMlYISjNIsnUcNiFJOQFx8qclwyB8cYbbxzuyNm++GQafJ2GvTwgBCZHeUgOdlvwh8M7ZFnh5wL+4dL+bOHM0UcfHSuGXBRBOy9LmBd7+c8fmDS98cYbAfzpQG0fH23bxxfKnSX4q7Vdy+h3rvoWuYcDNOydNWCRdMpxkbQRV44LHKqSRZi0yT5kn/5UpD+UgbcvHuR7rPjiGb7SQTun8R48x6IXk29ub3uDBnFA1Qj+YLaXRdjN57xtsx+8W6Sv+W7PpLJXy8t88/qk8rnu1yq8gIBk9913D+CKwEWSx3B7YmMtjbChwHE5xKIxa2PNlZc0rXMtMKFZmkZ230F5mpPgryysgamrPbLa3qW5ifFx0003Of9sX8BoH7RzNSR9JdsaR670yh47dp4QbMBagseEPGXdjpv02+7PPuaBSXnJ+7DC4XLLUArmZfxqr8vszy7BH/pMEsGqRtYV1xgTkpI2LOz38BuuerDBYlveyPRw3ZCCP8zZXeQ66Thp7F944YUVOGE9kUVw6QSfwjgYEKevSysBGz98P2wqIvgrs181NT4JHIvwyoYa+3b74rcPnol0subLcu2dJAxHOmnka/2CPIqWRwV/aS3j75k3wZ9dJPipgfNeuXBiJujaOc3q0Hb6Sb9xZDrnAybLKslJ8ZPuSwEeypaX5ELQPulUMlYI9bJITj5twR/ehZmyFFxwve0QfvHgnBfHirsoDXup7WZPFlxp4Z4sty34w4fRLl/R31tuuWUsa4k5TnPOSzjlCdqqSQJUV7myBH+1tmsZ/S4vHq54mNS4Tp90HXLiej/PPTkusCDIQxDGc/vY2rBF+kMZePviQWWMFV88w1c6abwH/UCaHaSN7WoEf3kWrPL7lSX4Sysf6lJGeyJdkA9e5pvX/1GyfP+rFV7AdGq//fZLdSOCEkgew3wDmxlpZGtA4j1sBmURvk+cB4dYLDPZE2vEkc85ngyxsOW0OMwr+MM3LolwAi2nx6G9YYl3iyxOOa8i75SFNcpSTdvL7wvjUjTM+y1jvDiUrkXsbxvHkWHZY0fmxdc4yI/xwPe2KJXJC9PKcvfdd0fl5vIjtE3+09LI86zM/uwS/GHOnUT24WWorz2Xx4bJ9ttv78RG4mRf4/voUuRAWVyCPx+8COs6uxxJp6djw8WOu+aaazqhuuqqqyriJgn+sBkOv5By/mLn4/qN+aZNyifzzflt3PC7CK9sqLHvKifu1cozkYbsb2Wd6utr/YLyquAPKNQflSb4k1W1fQeBIcJhu6SsDi3jpl1LoRMcgFdLUsvE9oeRlqbU0oMjeklyAmr77JPx+FrWxSX4QzyY3mLyLgUYrg8O7uE0qv/+97+cfBSmYS/9OEIAlIew08BlsAV/8K3Hz6oN7QW7FPSAaWUR/DjB92Oe/OViGPGzBH+1tmsZ/S4Lj6TncGIud9RQf+z0X3311UmvVHVfjoskUz07YamNaU9ii/SHMvCW47YWHlTGWAGOPniGr3TSeA/ykP49bU1fPGeqRvCXZ6wWEfxl8Z4y2tMnL/PN67lt8oRJwgv473zhhRfCP7QxFpkw9YcmOxZfeUnyGOb7Lp9YMj2YMHJcDvNoibpcIkiNPpf2NEzK0wgLWy4Dh3kFf5gfJJHLDLAxBH9lYY16F217W6Oc8S4awuqiKEEzROYDAVIWlT12XPljPsrl7N27tytK6r0yeGFqhn8+xGntXG4ZbrDBBnleD+OA/8BUFn7i4IYGGvA2ldmfXYI/fKeSCN9NWVdc23MmvAslCZfA0n7X9Rsabza5BH8+eJFL8Jdkqp13YwNlx7zWrptL8Ac/f7L/2++k/Ya7K5uKCP7K7FdNiU8Cw6K8sqHGvt2+/Fv2mWp4JtLJmi8XFbRx2WToa/2CNIuWRzX+ZEuUd51L8AfGhA8dzA4gYIKPp6Jkaw3ZB1tkdei8+cnddpj/yBPy8qaBeGmChbR0ZEeHoE2SZKx5Fp1yACYJ/jh9ONiF7yCcxAUtiKSPDw4wsZ1Fp2EvB2KWVgKXRfrVsAV/ENpx2SBEgnp90T84O5dURNCDSZprsYMyARvsokCDBGrhMP3FTihO/uQyly34K6PfSazyXsOMwz45E4vXJB+dedN1xZPjYt9993VFid1Dm0in+3abFOkPZeDtiweVMVYkkLXwDF/ppPEe5CFN6NIO4mgKgj/f7embl/nm9bKPZF0nCS9cvpiy0nI9lzyGefn+++/vihrdg3YKx+Uwj+mla1NJ+tQCj+P0OAQfSiPb7w7eyyv4SzP5cS3OG0PwVxbWwLRo28NViBSCcxsVDV0+QdPamJ/J726aQIfjlz12OB8ZSv/TsCgpSr55Yd78IdxymWJiLmrPi5PShEmn7Atw7wKNQbmpXmZ/dgn+XGOWy59X8MfxMcfbeuutY9pUsr6ua2jI2v6/XbzFBy9yCf6SfP+6BH8Qnrkoj+APdZTzSxsLCHSgZYg1jeswCZcgsYjgr8x+1dT4JNqwCK9sqLHv6lu4VyvPRBpZ82Upf0gba0griXytX5C+LI/tksmVv9S0d8XPqr8rTb1XiUAuwd/5558f+9C5TkSqTDp+Bz40JJMEs5Pkq0Htj3Ka827OH3GGDx8ejBs3jm+FJ79yeSEQykMQMkLYyO/ZKuWSsfoW/MnyQTACR+FnnHFGIP02cbkuv/xyGT2VmUgNRiw08vjPk+bHtuAPToK5HAixe1YryQ9xltbNbbfdFssfZTjiiCPCSVtS3eREEUJNm3y2K04cZnx89Tu7vFm/IZTHziSXAyEWvEnO57PSy3ou8csyv0Na33zzTaxsmAxJKtIfysDbFw8qY6xInOR1UZ4h35XXRdNJ4/uynTEG0zZxmoLgz3d7+uZlvnm97BdZ12ULLySPYb522GGHpRbLHsf8XpLLDE5M8h9+R2oX2vMpxAG/TTvsy7XYzyv4S9M8g0CSy8ihS4iQtDhN+mYCiyLvlIU1ylFN20OYxXhwCOEO/Gfm/ZPzSZQjL8m8UfYsKnvsuPLv2bNnhI/LdY/rHXnPNy+UaWddS1M2bluEWRsBSBcnd8sNEvk+3FIwldmfXbwgbd5UVPDHdYBGNTT54H8bbizk2kbWm68ffPBBfjUMXYI/H7yoMQV/1113XdTvud4IMQd95513Yusj++RqxIOFiU3KJ2etu21ssn4X5ZUNMfaTylwrz0S6afNlPJeCtmoFfzbvqlaGYpcnbewjLuYScmyr4A+olEO5BH/YAZJMDrbqaRM+V1H32GOPWBo4fVZSVoeWcdOu7V2bPBpK+Khx/XiiBa0vvocQPq2yyHaQiwMiJMkJaK2CP/hew0QDmmlpmjDIH4I+WRfbfCQNe9vvB04uTiNMFqQ2li34s49/hx/ILMIEGk61H3roofAAGXtCLRdaWYK/PffcM4YFzNDTCDhL7OCbxiaf7VpGv7PLm/Z7yJAhFdoOGB84laoskvjBLyaER2lk8yMIuCUV6Q9l4O2LB/keK754hq900GZpvAdjnscenHSnUVMQ/PluT9+8zDevT2sv+1nZwgvJY7hPYYKbRtdff33U//gdhEmO5JGWa7GHdyDsY/r3v//tTPeuu+7iKLEQmrmuw9Jcgj+XNjs2/1wEDQjpI4nr6BL8QaOGn8sQ5lZJVOSdsrBG2appe9ehAGussUZSVYOnnnoqeOWVV4JffvklMU7eB9BgZ4zhfziLyh47dv7QNJZCIBzMUZR888Ii+d93330RvowzQown+EpNIwjw5Tt8bft/LbM/lyX4w9wdwitY1GBOZW/Kjx07NrjnnnuCJOHJ6aefHoOuLF4khQOMf0Np/MGiivPk0MUvAQRO9eY4HLo285VPxrpNoR9FeWVDjH1XBXzwTKSbNl/Gcx+CP1/rF5RHWlRlyTw+/vjj2HhRwR8QLIdyCf7A8KXWE5jYxRdfnLtE+KDA3x0zP4SjR4+OvZ/VoWORU35IZ/DIxzYDtF/96quvYuWCgBJ00UUXxe5jUZRF8BMi64iFqyQ5Ac0aBHgvydQXJ11KjSyXMErmi2upEm2fapWGPSYBsk6YdKTR2WefHYtvC/7uvffe2HOYfGQJke00bXPQIoIemF/L+thCRLtutnaGy6Gqr3ZF3mX0O7tOSb8xzm0zcWitZmm4JKWX977ED20DJ7xphDaQbfjee+/FohfpD2Xg7YsH+RwrvniGr3S4wdJ4j5xkX3bZZfyKM4SvJdknsEBxkexreXiwTx9/PtsTdfPNy3zzehf+SffKFl7Idud+knUqOTS75HeW38NcyKX9jBOn4Q6D48lQOsCHSaB8xtfQXrDN5YAXNBM5jgxdgj+cbCzj4Bpmq/Z8C+nCn6odF79dC9lbb73VGTfNFLvIO2VhjXpW0/a2hQrj9MQTTyDJGD3++OMxbKARBuFI1ncsloj4Ic354FoHPDeNyh47dt7o54wHwiwBuv0+fvvmha48ku5hYzFpnGJMYeFrEwS60AiU9ZbXthVNmf3Zt+APvs+kOxuuF6+FbCySfGCedNJJsahl8aLGFPy5cHKZDsM6QbopYUxdPFv5ZPp8P9aprB9FeWVDjH2riOFPHzwTCaXNl/Hch+DP1/oF5ZHmzShb2kbhwIEDY/xVBX9AsBwykydPzpWyPfnEjh/8zkD1PY0wQZaNDwbocqSb1aHT8pDPIFm3J3pPP/20jBK7hi8LZsoI4UwcBCGIVM1FfaFVkkRQc5fpQOsNH39Jslx5Fp1Jgj+kuf7660f5wT/JSy+9JLOKXUNVV+7Q2mYBadjjIy+FvvjoJvl4xA6hLeC1BX/ob9IUGJjZkyZZeOwC2GliV1JSEUGPbfr85ZdfyqRi1xAeAFvZrsDKJp/tWka/s8ub9HufffaJ1RV+OdPMz5LSKXpf4gessXuexFfsBYMtxEbeRfpDGXj74kG+x4ovnuErHbRVEu8BhpL/gg+kETQ15DjFbruLZF/Lw4N9Cv58t6dvXuab17vwT7pXtvBCtjv3kyzBH8rq8hGF9yEQhNbS22+/HeAQJGj4wNcXpy1DaGba5DrgA+9Au/qZZ54J+S4OQIN5qUxLXrsWkS5fgHgHm4Ms/Bs1alSQJNhCXJfgD9qIMm++xunu8IWLEy6h8Sap6DtlYV1N2//www+xeQ/XFz68pBXJhx9+WCGA57gvvPCChCP3td02SfMtTrDsscP5cGhrrCbxWo7vCn3zQlceafcwxlwCJG47WB6gP2LsQtCWNLYRH5vqLgF4Wf3Zt+APihnStxZjgDUDhNdoKyZs0mOscxwZ2kLxsniRq90aSuPP9lWP+qM87777LkMU4pXGtzG3kaR88gUJR6HrorwSiTfE2Lcr4YNnIs2k+TLn50Pw52v9gjLBDYTkERDuuQga8/YBpSr4cyHl5x61iQl9ZmUlB39LslNxY+LUU2hmoEEhYMNkFROeK664IsDpdtLsE++ASUI7w6asDm3HT/uNgc3lQ4hJOo5qlwRNL3vChI+9/MjBjFamg7pA01FqqGGQYAFgf4xcJ1DKCWieRWea4A+nWMmyoR1sHxuoL3wXSlNmvIN6ScrC/txzz43lBfOgO+64QyYRYNdQHlfOZbMFf3gJJrv8nEO0hS1kwgET9qmyLtyKCHrsXUjszNsabdgVQv3svouyIi+bfLYr0vbd7+zyun5DI0UKh1FXaFai7fP+jRgxwpV05j2JH/cH9Fl58jfGHLTzbEGsfUgQMivSHxC/DLx98SCfY8UXz/CVDrBP4j3oj9wXXGMO70qytahw+ipcIOAExjFjxkRRZV9z8ZIo4p8XPgV/SNJne5bBy3zzehvPpN/2t5jb3rWgTkoj7b5sd047j+APwjJ744nfzxNizuQy/4TLDJuX5UlPxnEJ/my+I+PjOk2AwXFdgj+4gODnSSHmQZKKvlMW1tW2vb3ZLeuNuc4iiyySiAl8x1ZLEKTKvKSZuCvNsseOnSd8IsvypW2e2u/K3z55oUw377UtNJB1ynuNOWKSq52y+rNvwR/wOu6442JtKuuP+T7myXBdZCtxcDxs0sF1gKSyeJG91kIZGkrwN2DAgEScsLmC5y6tQMYJIazMJCmflGgUuy7KKzn1ssc+58OhL56ZNF/mfKSMploff0jLHrvVylDgSkv2fVgfDB48OFpzQ64EH5cwgZfxcK2CP25V/yHhm0/wh6yxq+HyNWM3WNpvW+jEVcrq0Bwvb4hOY5cDHydoq0AKbe9woUNi914ShHquHZ7u3bsHOOQBjN7VYVdeeeWKjyDSlRPQPIvONMHfpEmTYqbAXFcskuGfBidL4SRgW5iDBbEUbqJcWdhDPRd14jw4xIEPcNhpm5zxc4QuwR/ydPnQQRugzjhVSGLF6cHfzaefforXY1RE0INJmo0JzFvhbBrCWiymIQDmPO0QDBA7pJJkWWttV6Tru9/JsiZdSy0uu855fydNvpLy5PsSP5kXJnjQaoLJhNT+4jj2oR6cXpH+gHfKwtsHD0L5fI0VXzzDVzqoWxLvOfXUU6MxeMABByBqKqFM9o4h9xN5sILsa3nGqm/BHyrhqz3L4GVl8PrUhvvzYdnCC9nu3C/yCP5QPGh4VSOkw/fMtTHBeNj+RblcrtCVv0vwB7PQNIGUK237nkvwh2+e1Py338Fv1EdSNe+UgXW1bf/jjz8GtlsJV73te9AKhBZotYRNLsx1OF3Mh9Ko7LFj573KKqtEZQP/roV88cJqygCcobHKOBcNMY+0N8DtcpTRn8sQ/EERYvXVV68KC8zTpBYsY1AWL2pMwR82JF28uEjfgdWKJOWTEo1i10V5JafeEGOf80Loi2cmzZc5L1+CP6TnY/0CpRocaGOPD8zVXf6FZTwV/HGr+g8J5/yCP2QPf1rVfCCwcL/55psTa5DVoRNfTHgA7bGDDjqoosPJjsXXmNBCCu0i7NbbJ5Dxe64Qgi5bg4zTlRPQPIvONMEf0oS03KVl5yoX7mEHwFW2PNgDB+lINSkP+IST6r1Jgj8IH4888sgKTcmkdCGcg/aii4oKelynnCXli0mW7QcOPjkk+W5XpO2z38myJl3bgvAkPNLu+xD8QZAuP15J+cEsOclfRNH+UBbevniQz7Hii2f4SieJ98D3J7c9NEPyUNJuPDZAmIqO1TIEfz7b0zcvA06+eT1jnxaWLbyQ7c79Kq/gD+WGuWURgRom+lkHgmHD4dhjj436OZfLDrHZapsKIY5L8Iey4pAx12TbThdmjLaLEsRxCf6QLjbF7DTkb/vQsGrf8Y11LW0P4QUsV2Q9065heZF1ABpwySJpKghBYtqBV2WPHVlWCCmkJcTJJ58sHxe+9skLC2f+5wu33357gHZLa1f7GebdeC8P+e7PZQj+UA9oV0OZwa5r2m/M12ABkERl8KLGFPyhnqeddloujLAxjbmHjV///v0r4KqGt/ruV02RTwLIIrzSBr7ssY/8fPLMpPky10uunWrR+EN6vtYvMGXPMx/BZv/hhx8ejRcV/HGr+g8NmKjtiy4rG0jLsdOFiR52tW3GJn/DiS58PcBnShpBYMTvuQ5QSHs37Rlsx2E26NqlwY4dPqK26rUrPSw+l1pqKWc6KDcWiOjgaSQZax7BnxS0ycWrzAOCPKgty8M7GEeE0FCD1iL8cqDdXJQXezisHTRokFPYiAkADkABs9h5552jtoTQNI3gFwiLJCxiZLn5GmZWOGEs7RCOagQ98N9n+xrkPCEIhh9KODkF4URbfobQ1jQro10ZMx/9jtNKCjHhc02mZJ3zXCcdqJCUL9+38YMjXCw+0XftfKF5au+YcjocVtMf+N0y8PbFg3yMFdTTB8/wlY6L9+AUU+6PWGTmNfeEIBjCP3snEYITJruv8f2ksF+/flEfRN+zqZa+5qs9ffIyrl8ZvJ7TdoUQwtljHeMfggEfJNud84G2TxHCBB6uPlyLOaSJOQYO6DjnnHNCLeK8aUP45jpsAJtdmJ/AZBB5c7k5lP3azgum79iAs7Xb8S4WB5gPgOCWhdPjMEnwhz5xyCGHVMTHe9jYtTX+kH417+A9n1j7aPvrrrsuwJwtyeIFwjkIBOAz1gfhO8TtgRDfkCQqe+zIfLF5L8vlOuRGxs977YsX5s3PjodvDE6ldfUVWV9Yg8CNEfp1EfLZn30I/rAmcBHWCNBSlpv3sv58jW8szBez1nXIwzcvcq05kzadXX4Wkw5qvO2222J9G3XFuHYRfMEnrR/Ay3F6LAguphgzDiEEwaaPJOWTEo1i10V4pSvlsse+T57pmi/LOsl5RK2CP07Xx/oFm6D4ftpWOZjnL7fcctHmAQ4I4nFir7NRnqz6c5k1TEegFR4T0FUTmVgZcrxuSAPEfPvttwa/SXvIkBmoIcZoyLS06rR9vkiM1dAHyNBurKHOZ+gAAUOTdENMuFA2SIccOhsy5TDEvA1NBAz5vTB0bHWhdHxHpg+2GTVqVFgutAN9mA0NKEMTGUMLKK/Z0QI7xJF8sBlimmallVYyZI5paOFTdT7Aksx4wzaihX+IKy2qzYILLui9/FxI5AnMUA/yU2No19eQaalBvsSQOFpdhPXa73yAQ4L5sN2RFn0cDO0Sh8miznRoQzjeyG9KOM4aip+UgTfS9MGDfI0VXzzDVzrcl+hgF0N+P8KfJPwwtHnAj3KFwBl8nrR1DAk5Qt7smwfmKkjOSD7asyxeVgavzwlLXUejzSBDh2YZ2jQ06P/gYfjDd7daIqGRIZ9FhnxSGtJ4DdMjwV21yYXvkdDQkO/VcG5GLkrCORlttNX0raZNOEOWH+ajjz4K53kkRMic/1TzDle8DKw57aIhpssjR440ZH1g0F6Y92GOQtqghjYviyaXGB/jGfNKzE9AJMAwJExIjN9QD/B9Hjp0aJgd+ij5dvaatQ9eWGuB0N9ooWrohG2DfkunNIdzQrQxbbDXmrypp/6cVhnaIAzLCh6CNQVt7od9Hf0d87Gic+QyeFFa+ct+hr4KXsDrXxK6hGshUlaoOmvlk8Wh88kryxj7ZfPM4ohV94aP9QvSwNwBcxLSmjZrrLGGF55aXY1a7ls1C/5aLnRac0VAEfCBQJLgz0famoYioAgoAoqAItDUECATSrP//vuHxcbGBTbXSQOp0aoBQS82zJlIM8yQ1Q//1FARUAQUgUZBoN54JYOgPJOR0LCeEFDBXz21hpZFEWiBCKjgrwU2ulZZEVAEFAFFIBEBOiU11DSDwA9EvoYN+UVOjF/2AzIpNhdeeGGYDbQ86cT0srPU9BUBRUARyESg3nglF1h5JiOhYT0hoIK/emoNLYsi0AIRUMFfC2x0rbIioAgoAopAKgKXXXaZOeyww8I4MI2COxTycZb6ThkPYdYMU1e4dgHdf//9Zuutty4jK01TEVAEFIHCCNQLr+SCK89kJDSsNwRU8FdvLaLlUQRaGAIq+GthDa7VVQQUAUVAEchEAJos8KEMn9Kgc8891xx33HGZ7/mOQKctmksvvTRMdr311jNDhgwxtfqf9F1GTU8RUARaLgL1wiu5BZRnMhIa1hsCKvirtxbR8igCLQwBFfy1sAbX6ioCioAioAjkQgAmtThIA07scbgEDpXBoUcNRTjAAL79sLDGAQ84OAkHPCgpAoqAIlBPCDQ2r2QslGcyEhrWIwIq+KvHVtEyKQItCAEV/LWgxtaqKgKKgCKgCBRC4NRTTzVnnHFG+M4RRxxhBg0aVOj9WiLvuOOO5u677w6TuOaaa8x+++1XS3L6riKgCCgCpSHQmLySK6U8k5HQsB4RUMFfPbaKlkkRaEEIfPbZZwbHvIO6dOliFlpooRZUe62qIqAIKAKKgCKQjMC0adNCE99JkyaZueaay5x22mnJkT0+mTlzpjnooINMEAShlt/xxx/vMXVNShFQBBQBvwg0Fq/kWijPZCQ0rFcEVPBXry2j5VIEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBGpAQAV/NYCnryoCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAvWKgAr+6rVltFyKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCikANCKjgrwbw9FVFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRaBeEVDBX722jJZLEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBGoAQEV/NUAnr6qCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAjUKwIq+KvXltFyKQKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCNSCggr8awNNXFQFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBSBekVABX/12jJaLkVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFoAYEVPBXA3j6qiKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAiUK8IqOCvXltGy6UIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCNSAgAr+agBPX1UEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBOoVARX81WvLaLkUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFIEaEGiRgr+hQ4eaqVOnhrD16dPHLLvssjEI33zzTfPbb7+F9xZeeGGz4IILxp43px+o6/Dhw6MqtWrVygwYMMC0adMmuqcXioAiMAuBLP4xK6a/K5nnEkssYeaee+5Y4lnPY5H1hyLQQAj4+pb6SqeBqt2ksmnq2Db18jepzqKFVQQUAUVAEVAEFIEmi0CLFPzNM8885ttvvw0bbauttjL//ve/Yw24wgorRMKwNddc07zyyiux583px1133WV22mmnWJXuuOMOs/POO8fu6Q9FoFYEZsyYYa6++mrTr18/s8gii9SaXKO9n8U/yigYBH1jxowJk3bxrKznZZRJ01QEssa0r2+pr3TytlhWvfKm0xTiNTS2vjFp6uX3jYempwgoAoqAIqAIKAKKgBOBoAUSLZIDAiP8o0V0BQLLL7989Hy11VareN7cbpx44olRfYHL0ksvHcycObO5VVPr04gIkPA8WG655cJ+dv/99zdiSWrPOot/1J5DZQp/+ctfojG6xRZbVETIel7xgt5QBGpEIM+Y9vUt9ZVOnirnqVeedJpKnIbEtgxMmnr5y8BE01QEFAFFQBFQBBQBRcBGQDX+MjT+SPBnXnvtNafQtLncpE5h/vnPf5qHH344qhK0IKFZpKQI1IoATLEwjtDPQCT4M1tvvXWtyTba+42t8UeCv9hYBRBS48/1vNHA0oybJQJ5x7TUxqrlW+ornazGyFuvrHSa0vOGwrYsTJp6+cvCRdNVBBQBRUARUAQUAUVAIqCCP4fgb++99zbDhg0LcYL/v1tvvVVi1iyv4dNwjTXWMO+//35Yv5VWWsm8/fbbzbKuWqmGReDFF18066yzTpSpCv4iKHJfZAn2sp7nzkgjKgI5EMg7pn19S32lk1W1vPXKSqcpPW8obMvCpKmXvyxcNF1FQBFQBBQBRUARUAQkAir4cwj+JEAt6Xr06NFmrbXWMr/88ktY7SFDhoSaWi0JA62rfwSa22JaNf789xFNsWkh0NzGNKPfXOvF9dNQEVAEFAFFQBFQBBQBRaBlIqCCPxX8tcyer7VuMASa22JaBX8N1nU0ozpFoLmNaYa5udaL66ehIqAIKAKKgCKgCCgCikDLRKBqwd/06dPNf/7zH/Ppp5+aL7/80rRr1y48qXPRRRc1q666qmnbtm0uRCdPnmw++ugj8+GHH4ZhmzZtDMxr//rXv4bptW7dOlc6MtJnn31mXn/9dYMQp/OtvvrqoRnrbLPNFkbLWrh//fXXZurUqWHcjh07GsRPI19YIA9o3b333nvh37Rp00IcyHm1Aa6tWrVKK0ZNzxojX1+41dqHoOH4888/h/j17t3b9OjRI7xGO+BE56+++irsz/PPP79Zf/31nSfSfv755+bZZ58NxwL6/hJLLGHoMAtDB6UktovMt2fPnmb22WcP437zzTfm1VdfNe+8847p1q1bmN/f//53M++88yam5XpQKy4yzVGjRpkRI0YY1BMnYqMsS/sCGEIAAEAASURBVC21VPiXVK4pU6YY1AWL6b322itK7oorrjCbbLJJ+Bt1Rt1dhP6P/MAf8PfDDz+YLl26hOMRPAZ8Ii+fcaWfdK9W/uFKt9a6ZJnyZj13lamhx3w1fchVbr7ns/zoZ2+88YYZOXKkgduDFVdcMfxuzDfffGF24FVffPFFeI1vHXiBJDxDHBD66FxzzSUfV1z/9NNP5tdffw3v54mPiLXUV/IayePwnQOfgR9blAnfXdQd3xvXt7fomC76La0A6s8bRdKppp8VrRe+F6wZ36dPH9O9e/eQPz3yyCPhnAj4bbjhhmbBBRcMa+Czf0gskvBKuo/5FZeJ48j08sx3av2u+OqLRcr/448/mvHjx/MrIQbAQkkRUAQUAUVAEVAEFIEWg4B92kfWb5ogB6ecckogT5EksKITJ3FNk95g8ODBAQndEpOjRVJw+eWXB7Twj70r08KzG2+8MTEN+wEJD4P+/fsHJCCrSJMWMQFOw6TJX0CL5Oh5Laf6+sIC9aDDNII555wzKpfEAde9evUK7rzzTrvKNf9ujHx94earD/3tb3+LcL/ssssCWiQE5JMuuifbAn1rn332iXBHXDoYxdnn8N7uu+8ejB07NoovL2S+Z599dkACp/BEZZkfX5OwIUyLhJAyCee1L1yQOPl5TMSCy0YL3+Dcc88N0K6Sjj32WCeG/B7Ck046Sb4SXn/yySfB5ptvHpBQL/V9Er4Ejz/+eMX71d7wxT9k/r7qIvltraf6NvSYr6UPSSz52mf50eYYv/g+yH7J1yQIC2hzK7jkkkui5yQ44aJEYVb7RBH/vMA3kvNAX08jH/WVvGbQoEEBCTcDfPvat28flYPLg5AEngFOt7Wp6Jj2deJqnnRq6WdF67XMMstEuF199dUBbWZU9CHwr5NPPjmE0Gf/IP+7Ud6yzfJck9DXbtIgD7Z4ydd3xVdf5IrkKb89t6JNJH5dQ0VAEVAEFAFFQBFQBFoEAjhpMzdB6CAnbVkTzQMPPNCZNhbDcuKclc6mm24akJaRMy2++fLLLwek0Zc5IcZCTuZXreDPFxYQRO6xxx6xMsny2dc77bRTQJoiXO2qw8bK1xduPvsQaeVF+B933HHBkksuGf228effWDyjLlgg872k8P/+7/+c7STz3X777QPSEspMC8Iu0n5zpoebPnH54IMPco0prvdGG20UkAZRVLaDDz44sz624A8CxCRhBOdjh8ccc0yUZ7UXPvkHl8FnXbIEB1nPUabGGPO19iHGsozyQ7CV55uBDSjS9I36sg/Bn9x8ShL8+WwvyWtOPPHEgDRmo/rY44l/Q3AFoZakomM6j1BGpp90nZVOrf2saL0WXnjhCL/tttsucePn9NNPD6uUZ3zKuqf1j8YQ/Pn8rvjqi4xXVt9APNKcj9oL/VsFf4yehoqAIqAIKAKKgCLQUhDILfj7/fffncI6CNIguMLE2RaqYYIFDSpJ0AqSEzVeZGAyuNtuuwW77LJL0Ldv39gkDXHwDnacXURmNEHnzp1j73Tq1ClYb731gv322y9Yc801E4UJ1Qj+fGGBupDJY6zcqCuZiAYDBgwINcvI7KpCk2CzzTZzwVDoXmPk6ws3331ILkS4P3K4yCKLBBtvvHGFUA6LYimoIxOpcDENTUH0PX6fQzrJtqJ9kvKFBt2ee+4ZatHtuOOOQdeuXWPpkel5MGHChIr0fOKCtCBk5PIjJJPegEx2gxNOOCGAUJ9MbWPPEeeee+6JynXppZeG2oL2eEe9gRP+pEbvu+++W5HeHHPMEey8887BUUcdFY5lCGBs7Sy0BbkbiPIteuGbfyB/33XJEhxkPUeZGnrM++hDKDeTz/KT2WxAJrax/oYxjD6Jb0bSOEYfbyjBn8/6JvEajB18t/v16xdgrMnxjmsITCCAZCo6puXYX2211TiZwmFaOj76WdF6ScGfjRn/hnY4mR2Hdc0zPiUoaYI/9M/FFlss8Q/zJ7TpCiusUNGea6+9tswmvE7DFhF8fleQnq++iLRAWeVHHBX8AQUlRUARUAQUAUVAEWjJCOQW/J155pmxSSQmUnfddVcMO5j2XnTRRbF40KjAxJHpkEMOqXj+4IMP8uMovPXWWyuEeZicuwg77jzZRoiJr60VhYW4LchA3GoEf76wePTRR2PlJl9nwR133FFRRfIdF5r6yjo++eSTFfHy3misfH3h5rsPuRYiWCBBy4EJpnHQzJFtwNdYNJOfJI4akA+kCiHLDjvsED3nC1e+K6+8coV26/vvvx8stNBCsbwhfLPJJy4vvfRSLL+DDjooID91sSxnzpwZXHzxxbF4wMIm8gUai+MSguIdW/P10EMPjfEOThd42JsM0GKqlnzzD5TDd12yBAdZzxtjzPvsQ77LD77P4xchvhkw+5WE8e8SbjeE4M93fV28Bhtj//vf/2SVg6effjro0KFDDBuYsdqUd0znEcrYabt+p6Xjs5/lrZdL8AfeDQ3Je++9N9wgwSYmU9b45Hgcpgn+OE5WuPfee8faEf32+++/r3gtDVtE9vldQXq++2JW+ZEnxjc2zPC3wAILODfOEE9JEVAEFAFFQBFQBBSB5opALsEfFvy2SRRM45LInnA+8MADYdSPP/44NhGF3zJ7sSXTpAMOYiY05EC7QigC/0tyAQezLHK8LZOJrr/77rsAacj4RQV/vrCASaT08wTtgOeffz4qq30B/29Sk4wOjwigQVeUGitfX7iV0YfshQjMd10LJGhvkEPwWP+BWbAtEEObwBwbglzuazCRt8nOF8K9SZMm2dHC33ToQIDxwumhL0ycODGK6xsXW0grtX6iTP+8gCk+lwv42GZUeRbTeEcKHKAdBMFiEr311ltRnsgbftqqoTL4Rxl1yRIcpD1vrDHvqw/5Lj8Ex9xfEULTLcl9AsaYLXQvW/Dnu74YFzavWXzxxWNm+XLsYNNN4uMaW3nGNNLMI5SReSddp6Xjq58h77z1sgV/ECaNGzcuqfgxn8guH532i7UK/uAzVrYhHVgVwBzaRWnY+v6uIH/ffTGt/K766j1FQBFQBBQBRUARUARaIgK5BH/PPfdcbBLp0uqR4EGLgCedMFNkPzfXX399dB/PDzjgAPma8xqaUpwWwiuvvDIWD1qA8jkOHkkjaErJ+EUFf76wgDBUlkMeGJFUfvgyk++kCQqT0misfH3hVkYfshciadqU0ByQbeDSVmXs4fOO47qcqtv5SrNXTkOGMLPl9BA+/PDD0WPfuGDMyrxuvvnmKC/7ApsA55xzTmjmCwf7tkA6z2IaQn5oIEGoAuHhLbfcYmcT+w2hoBSEA+tqqAz+UUZd0gR7qHfa88Ya8776kO/y24IiaK2m0e233x4bC2UL/nzXF3Wzec1jjz2WWGVsZEjXGXSieEXcPGMaL/kSyqSl46ufobx562UL/nAASxqljU/Xe7UI/mA1IA84gzn3M88848omvJeGre/vCjL03RfTyp9YaX2gCCgCioAioAgoAopAC0Mgl+Dv+OOPjy18YA6URa+99lrM/BHx4cNPChNwOEIW2doZMP+TJLWNkLZLU0vGhzaO9BFWVPDnC4uBAwfGsHjiiSdkMZ3XL774Yuyda6+91hkv7WZj5esLtzL6kFyIQKsuSesOuK677rqxNhgzZkwi3FJoDX9iNsl8sTB0aQ7Kd4YNGxbLG+a3TL5xgfBKjlVcw3QNQlGYMhehvItpThM4pGn7IR409aRPMvhkq4bK4B+yHL7qkiU4SHveWGPeVx/yXX74fOW+jYNkpCsK2XZ8DRcWEt+yBX++64t6SF6DukttYa6nDKERyBitssoq8lF4nXdM+xLKpKXjq5+hYnnrZQv+suYysv+UqfGH8tuHI11zzTUV7SdvpGHr+7uCfH33xbTyy3rqtSKgCCgCioAioAgoAi0ZgVaoPE3wU4kW/Gbw4MFRHDJ5NLT4iX7nvaDJssG7IDIdNr/88kvmq7ToMuR03dDBHmFc0uwxJHiM3iNfX4aEg+FvMuM1ZG4TPUu6oN10QwKb8DEJ/gzt1seiklNsM3z48PAemRwaEmJGz31hQdpb5qabborSpdN6DZmGRr9dF2RuaW677bbo0dFHH20uuOCC6Heei8bK1xduZfQhOmHa/Pe//w3hQ/qff/55IpR0sIp5/PHHw+ekcWZISJgYlw5oMaQpFz4nwZ8hP4GxuDJfOrTCDBkyJPbc/kEHehj0cSbyOWgeeeSR8KdvXDDuyB+SIUE6ZxeFGI90UrGhwwcMaf8a0oKMnrkuSGBtSDAXPSIff2brrbeOfqddgEeQkM+QvzVDZmfh3+uvv25ooR17Dem/8MILsXt5fpTBP5LyraUukmeR4MCQtmcsm7TnjTXmffUh3+WnQ2rMN998E+JH7hbC/hUD0/ED/Qv9GIRvH3/HOGoa/hxHhhhbdFJ9eEuOY9zwXV+kKXkNHR5kaAMMtxOJ/NWZd955J3xOB0xF1/xC3jGd9i3ltPKEaen46mcoR9560aFPhtwvhEXHd4AEqYa07BKr4rN/JGVClhaGhNqxeRUdimQuvPDCpFfC+2nY+v6uIEPffTGt/KkV14eKgCKgCCgCioAioAi0JATySD033HBDCAfDP2jL2aZ8edJAHJiccDrYpc1LNPmM3oMvHUlS6wc7yXkI/ta4HEU1/nxhsdZaa0Vl4LIUDbfccss81Y3Faax8feFWRh+SGgi0iIjhZf+QGmLwBZhGOJmX2zRL4w+nOOch6WtTauKUgQtM9m3/ZlwfGcLf5GmnnZboJy2vFg3X/8033wxwmvGcc84Z4Sfzc12TYIZfLxSWwT9kAXzVJUtjKO15Y4154OCjD/ksPzRJ5ViBeXkekoe1kOCv4pU0/Csi0w0SBEV9G4cGSfJZX05X8riVVlqJbyeG8huJk+VtyjumfWljZaXjo5+hjnnrJeckJMiy4an47bN/VCRON+C/GP1S8kbMD0go6ooeu5eGrRwrvuZsvvtiWvljFdUfioAioAgoAoqAIqAItGAEcpn6ykUAnERXSxB+8MR09dVXz50MFir8Hg7vkETaR9GzLKENvwdTG06vqODPFxb2JJ3LUyRcddVVuUq5w8bK1xduZfQhuRBx+bOS4JYl+LNN2GWe8hrCRu4jKDdTGbggbZjOb7PNNjFhCedvh4sttlhAmpNcpCjMu5iGiTX8h9rpun5LgR2eVyv4K4N/oOK+65IlOEh73lhjnjtArX3IZ/lhpi77U95+A7N6fg/lsSkNfzsufqcJ/nzWl/MuwuPwjuTXTUHwhzLX2s+QRl5eJQV/2NTKIp/9w84LZts4UZj7J0K0WZY5N6eTJjgr47viuy+mlZ/rqKEioAgoAoqAIqAIKAItHYFcpr79+/ePTMtgzkKLJ0MncNL8shjNP//8kYkeTf4MnTKXKwFpJkPChdDsj1+UJlPVmG0VNfX1hQVMiN94442wGnSYgTnvvPO4SrlD4EKaUbnjI2Jj5esLtzL6kDQ9IsGfocMqEjGVpr4khDNffvllYtwipr777ruvIZ+NiWnhAWkrhWbv5DcujAfzQzZvLQMXWRg6Kds8+uijhnxRGjqoxfz000/ycXRNh/mYESNGGNIUjO7lMZ+DuR6dHhrmEb345wXSRBstt9xyBqa5MEPEH+rMJpsSC/v9tN9l8I8y6iJ5YFFT38Ya8zbu1fYh3+UnIYwhDamweOhP7733nl3Uit+Sf5FgLtXU1zbdrUiMbtAGlqGThMNHdnzf9UUmRXgc4pM2saGTs3Fp6t3UNyyk+FdtP0MSeXgV4klTXxL8GTo8A7cTSY5fu71dL6X1DxkfvAZzGHb5gGcwZcfcAnnmoTRT2TK+K777Ylr589Rf4ygCioAioAgoAoqAItAiEMgj+SQ/MbHdZPKBlvka4pCfvIB87kVxYVZFoIZ/OO03D8HxujyhDo7ZJUltQBJGZh4KgHel+WJRjT9fWOy6664RFsAEpjoNQY2Vry/cyuhDRTQQytL4I395mc1PQq5Yn4E5LFMZuHDadgjzMRIKBGeccUaw7LLLxsqEvnz55ZfHXsmjRUO+KyvSOeKII0INwqSDPuTJo+RzMJZn3h9l8I8y6pKlMZT2vLHGfFobFOlDvssvTWlhOp+HoE3O364sjb+NN944NUn0Z3nAlG3q67u+KEwRHof4TVHjD+W2qUg/w7t5eBXi1aLxV2v/QP5MBx98cNQv0T8xr8K8qwilacyV8V3x3RfTyl8EB42rCCgCioAioAgoAopAc0Ygl6nv1VdfHZtc0u5yJia0qxu9g4keaL/99ovuYZIKvzxZRNpXsXe23Xbb2Cvbb7997DlOAU4jOiAhwMmtvIgrKvjzhcWZZ54ZlQFlIW2qtGKHzyBEpQNBgoceeih49913Y0LVzJf/jNBY+frCrYw+VGQhUpbgD74rsUhNI4w77rcIIXhj8o0LHTwQkGZfcOWVVwbPPvssZ+MMIeiT5QJGkvIspqU/RKR16aWXyiQqrlE+mSeEOdVQGfyjjLqkCfZQ77TnjTXmffUh3+WnwzNifYcOi0ntOjixFZtK3N9cgj9pgp/lLoAOqYnSQpq24M93fVG5IjwO8ZuS4M9XP0O98/AqxCsq+PPZP5A/6KKLLor1IwiTH3vssT8eFvifJjjz/V1BsXz3xbTyF4BBoyoCioAioAgoAoqAItCsEcgl+IMAgBc9CLP8ImGhJOPDMTrInqhi0Z1FG2ywQSwtCL4k3XnnnbHnO+ywg3xccX322WfH4hcV/PnC4t57742VA/76kjSbuBJ22ck8lB/lDhsrX1+4ldGHiixEyhL8Ybzcfffdqe1IJoCxPkMmilF8n7hMnTo1aN++fZRXHqEafH/ymF988cWjcuGCzOeiZ4hzzz33xJ7jB/wD8vsIpaZwRWS6cf7558fiA5tqqAz+UUZd0gR7qHfa88YY8z77kO/y2+nBr2Qa7b///rG+5hL8oc9z/0VbkDl+YpIDBw6M4uIdW/Bnl8/Ht6EIj0PBswR/ecY00vEllElKx2c/Q3nz1quo4M9n/0A56WT0mCUE+lHWZgnec1EStojr87vCefvui2nl5zw1VAQUAUVAEVAEFAFFoKUjkEvwN3369NguLSaZTz/9dCJ2W2+9dWxh8/DDD4dxx44dG/Ts2TN6BhNe7LAn0YMPPhjFRZ7Q1CP/PbHo48ePD6TJH3a9X3311Vgc/gFzWhwOgrT4r6jgzxcWcDIvTY5RHttEksuNEFoidtnfeecdGSXXdWPl6wu3MvpQkYVImYK/JZdcMvjtt9+c7WgLA2zhmm9c1l9//WiMkA/K4KWXXnKWCzdh1i/N8W2zZZxsy+MN4TXXXFORlm0yTL4TK+LwDQgOUSaZJhZ/1VAZ/KOMuqQJ9lDvtOeNNeZ99SHf5ccGC/mLjPWfJKEJhPFSQxx9ziX4W3fddWPpQbjnoqeeeqrioBxb8Oe7vihHER6H+FmCvzxjGun4EsqkpeOrn6G8eetVVPDns39gfiMPJUKfPOyww1D8qigNW9/fFRTQd19MKz8DApcxGFf8x/c1VAQUAUVAEVAEFAFFoKUgkEvwBzDIeXVsYQONoKuuuiqGE7R0DjzwwFg8mDBissUE00G5YMei6uKLL45pu0FIBHMn6QcJ7xx99NGcTCw899xzY2l26tQpuOOOO2JxYM5FTq9j8ZBmUcEfEvWFBUx2JRa4Bn72aXzPP/98QE62Y3GzzMlilbd+NFa+vnDz3YeKLETKFPyh/WEi/9lnn0UtBiEFtC5sQZfLpMsnLnTQSKy/4RRdCOJtgj8padaPOqAcknDSr+znffv2Dc2Hhw0bFowZMyaMuvvuu8fi4KRMOvxAJhOaQmNc24IYpI2FeLXkm3+UUZc0wR7qnfW8Mca8zz7ku/y22Tz6EDaskA82WegQm8A2CeY+7BL8QXDIzxFCMDN48OCoD8M/5w033BD6YJPxcG0L/tCevutbhMch/yzBX54xjXTyCGUQL4vS0vHZz/LWq6jgz1f/+PTTT4PevXvH+hod9hP88MMPATZLPvnkk7D/og8n/dHBTDG407BFRJ/fFaTnuy9mlR95duvWLYYZ8FJSBBQBRUARUAQUAUWgJSGQW/AHULBAsRct0ODDjjt2tOEoXT7H4uftt9+O4QmhnnSUzvG7d+8ewEE/zArhoJrvc0ineAa///57LC3+AbMqPOe4HM4111wBHGnbpnf8HGE1gj/k6wMLpPOPf/yjotzADQuvLbfcMjZJ5nLPOeecARYAtVBj5esDN999qMhCpGzBH9oYAm9ojdGJozENWW5/eaiH7AM+cZk0aVJs8c95Y8GLvkMn8AZ02mdM0w9xINSTgn6UD2m1bdu2op8j/i677BJWAX4EpdYgnvXq1SvAQQcQ+EOYhk0ELocdYiMC/jurId/8o4y6ZAn2sp4Dl4Ye8z77UBnlh5aU3Y/y/HYJmSGk7tKlS0V66PfYiEpL1yX4813fIjwOeWcJ/vKMaaSTRyiDeFmUlo7Pfpa3XkUFf776xymnnJLal9L6GT/DpqqkNGwRz+d3Ben57otZ5UeeKvgDCkqKgCKgCCgCioAi0JIRKCT4gybaQQcdlGviCfNbaDy46JdffgkX9DwRzQohnLO1f+x0kSZ8D2alhVOBpdlNtYI/X1hASHLkkUdWaDcm1QPCkKKn9tlY4Xdj5esLN599qMhCpCzBH4TdUniT1P777LNPqv8wn7hAS8mlJZtUNmieJI3TAQMGOMcmhIdMEPAlpW3fhy9P2//UrbfeykkVDn3yD2Tuuy6ybwBnm7KeI35jjHmffaiM8kMLT/qztPsZNG0vvPDCUMjNzyBocNFdd93lFP7xexyeeuqpweGHHx719STBn8/6FuFxqFuW4A9x8ozpPEIZpJVFWen47Gd56lVU8If6+egfPgR/Z511VgzuLGwR2ed3xXdfzFN+FfzFmlx/KAKKgCKgCCgCikALRKCQ4I/xgZ8imPfZ5odY2EBrB4tyHPCRRTBnWmqppZzpIK211147nCxnpcPP4cdl0KBBTmEFBCs4TASCp5133jladEGjyCYIB3mRlnVogC8sXnnllWCVVVaJnR7JZUAI/344xTXr0AO7Llm/GytfX7j56ENFFiJFBH8HH3xw1I9gKmuTne/XX38dbLTRRk5BBDRa4ecvL/nABXlBkIfFpjy8Q/ZLCE2gwXvBBRfEzPXtckKrDgtqW/sJCzJJ8N9n+77k/LCZgMN+cFAM6IsvvojwRZwkTUiZftq1L/7Befisy6KLLhrV1SX4y3rOZULY0GPeVx/iOvguPzZSIAzp379/+O2AxjX4PoRzQ4cODbOVfhuhmZ5EOKkebhhsDVdo8cKvIMxSQSeddFLUnln91kd9bV6TVH6+LzfRpHCenyPMM6aLfEtl2vZ1nnR89bM89apG8Ic61do/7EONmDcWCW3BXx5suT18fFd898U85ZeCP4xN3/MoxkdDRUARUAQUAUVAEVAE6hWBVigYTRqrIlooG/KJY95//31DkylDhxOYJZZYwpDJU6H0kM6HH35oPvjgA0NmJYZM+gwdXmDmm2++QulwZJq4h2UiX2mGnPeblVZayZDZpCFBJUfxHvrCAvUnM94QV/LFE2JBiwxDfqUMCVm8l5sTbKx8feHmuw8xLmWGJDwP2xl5kLDAvPzyy2F2qAs5mQ/HxDzzzBOOBTKhraoovnAhX4Nm1KhR4Rj99ttvDQnwDAkyDC3iCvVLlAf8gk7jNKSlFo5xu1+jLyIvjF/yW2VIaGpI8GIwDkiAUhUORV7yyT8auy5p9W7oMe+rD3GdGrL86IM//vhjmDW5YDAkAOFiOEP0czp124wYMcKQ1qxZY401DAnPnXHz3mzI+uYtE+LlGdNF0qs1rq9+Vma9yugfteJW5H2U3+ecrUjeGlcRUAQUAUVAEVAEFAFFoDgCNQn+imenbygCigAjkCT44+caKgKKgH8E6CR5c8wxx4QCdWww0YFKpk+fPokZvfXWW4a0saPn5BvQXHLJJdFvvVAEFAFFQBFQBBQBRUARUAQUAUWgnhFQwV89t46WrVkjoIK/Zt28Wrk6ReCll14yZK4blY5Meg2dLB/9lhd0oJQhU9dIMxfP6ERtQyb/MppeKwKKgCKgCCgCioAioAgoAoqAIlC3CKjgr26bRgvW3BFQwV9zb2GtXz0i8PPPPxs6JCkqWocOHcyrr74aCviim3QBc/Rjjz3WXHbZZdFt8hVrxowZYxAqKQKKgCKgCCgCioAioAgoAoqAItAUEFDBX1NoJS1js0RABX/Nslm1Uk0AAfjpe+SRR6KS0qFUZv311w99SdIhMoZOiTV0AJH57rvvoji4oFOAzV577RW7pz8UAUVAEVAEFAFFQBFQBBQBRUARqGcEVPBXz62jZWvWCKjgr1k3r1aujhHAoU+rrrqq+eijj3KVEoLB4447zgwcODBXfI2kCCgCioAioAgoAoqAIqAIKAKKQL0goIK/emkJLUeLQ0AFfy2uybXCdYQATumFb7+rrrrKjBs3LrFkEBCeeuqppl+/folx9IEioAgoAoqAIqAIKAKKgCKgCCgC9YqACv7qtWW0XM0egc8++8xMmTIlrGeXLl3MQgst1OzrrBVUBOoNAQj94ONv9OjR4d/EiRPNXHPNZeaee26z5pprmr59+9ZbkbU8ioAioAgoAoqAIqAIKAKKgCKgCORGQAV/uaHSiIqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKQNNBQAV/TaettKSKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCikBuBFTwlxsqjagIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCDQdBFTw13TaSkuqCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAjkRkAFf7mh0oiKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCikDTQUAFf02nrbSkioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAopAbgRU8JcbKo2oCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAg0HQRU8Nd02kpLqggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAI5EZABX+5odKIioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAopA00FABX9Np620pIqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKQG4EVPCXGyqNqAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAINB0EVPDXdNpKS6oIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCORGQAV/uaHSiIqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKQNNBQAV/TaetCpf0k08+MV9//XXqe61btzbt2rUL/9q3b2969+5t5pprLtO2bdvU92p9OOOrL8y4g/c0wYzpphXl1fOBIcZQWUDTRgwz0z8YkS+LDu1Nq05dTOvOnU2rnr1Nu2X+Sum0yfduQqxg0iQz6YYrjAlmmtbzzm86bb1TQky6PWOG+XX/XcyMMd9QOTqb2a6707SevWdy/AJPpn/4vpk2/J38b7QyplWXrqZ1j9lMq249TJsFFzKtCZNaqRAetWbWwt+ffNct1O/+AKHdKqubtov2beGI+K0++CH4Yh7q1KmT6dq1a/g3xxxzmC5duuR5rSLO5MmTzZ577mkmTpxoevXqZW655ZaKOPLGlClTzMiRI82nn35qpk+fbhZffHGz2GKLmY4dO8pozut99tnHfPfddwblve6660ybNrXxQs5EeREj0TxC5enNox3LqEUav2oM/ok6/vzzz+bjjz82o0ePNj169DDzzz+/WWqppWjK+MecMQ2HsnhiWp6N+aye5xD6HWnMnqF5KwKKgCJAcoKASIFonggcdNBB5qqrripcOUymll12WXPYYYeZnXfe2UAg6JWoy0FYNu2dN8JkO223q+l67KlRFhOvvthMuvHK6HeRi9a95zAd/9HfdNxiGxJ8LVzk1Sjub+edZibfd0f4u91Kq5rZrh0cPXNdjD/uYDP1uafCRx022tR0P+dSV7TC9ybddp2ZeNn5hd+TL7RZeDHTZc8DTId+W8jbha6L4lEocY0cQ+DHVRY3ZubM8F675VYys914T+y5/qgNgSuuuMIccsghhRPBRsgmm2wSCvA233zzcKMkbyLHHHOMufDCC8PoZ555pvnXv/7lfPWJJ54wJ510khkxYgR1gT/6AEcET+7bt685++yzzT//+U++XRFKno88jzrqqIo41dxQXlQNavX7jvL0+m2bxi5ZGr9qaP758MMPm+OOOy4U+tm4zDPPPGb33Xc3J5xwQrg5Yz/n32XxRE6/3sJ6nkPod6TeeouWRxFQBFoaAir4a8YtLic81VZzmWWWMc8++2yoBVhtGvZ7Ux64y0w45+Twdqtu3U3Ph54PtdQ4Xi2CP04DYbczLiIh4JbyVub11CcfMeNPocXyn/LwPIK/GV9/aX7edmNSVZwWpt9j0LWm/drrZ+aVFcHHJInz6LD+Jqb7wMsirUq+nxVWg0dWmvo8GQE5aW+7zPJm9lvuT46sTwojUO3CVWYEbbrnn3/eLL300vK28/qtt94yq6++OikGzzDzzTdfuICFJqGkn376yeywww5myBDSes5B/fr1MzfddJOTJ48dO9YsuuiiZty4cQb5vP/++2aRRRbJkWp6FOVF6fg0pafK05tSazVsWbP4VUPxz6lTp5r+/fubp576Y0M1DYWFFlrI3H///WbFFVd0RiuLJzozq4Ob9TyH0O9IHXQQLYIioAi0aARU8NeMm9+H4A/wwMxs6NChpk+fPjWjNfOH70hItokJJv4WptXl8BNM5132iqXrS/BHqopkensXmf8uF0s/6cfUIU+a8SceThpXM6IoeQR/iPzbJQPN5DtuDN9r3WdO0/O+p0yrrt2idKq58DlJQv6dSfOvy0H5NYBqwaOa+uo7xtTzpL05tI+PhStwwGLzzTffDF0jJOEyjTYCVlpppVD4hji333672WWXXSqib7nlluaRRx6J3e9MrguWWGIJ06pVK/PRRx+FZsIywnrrrRcKCvHcpgsuuMAce+yx4e11113XPPfcc2E6drwiv5UXFUGrfuMqT6/ftmnskuXhVw3FP+EawXaJABc00Hr+4YcfQncN2Exh+stf/mIgtIQWoIvK4ImufOrhXj3PIfQ7Ug89RMugCCgCLRkBFfw149b3JfgDREcccYQZNGhQzWiNP/oAM/U/z4bptJ5jLtOLtP0goJNkC/46rLOh6TzgABnlj2vSygtocQ1/RTO+GGkm3Xy1mfnzT7F4rXv1MT0fft606hjXspGR8P7EK84n814y6bUs3/MK/maO+9X8vPn/UVkmhkl32mZn0/X402U2ha/tSVKn7XczXY86KTGdEIfvvjUzvhptJt95i5n27lvxuO3amV6Pv5zp988HHvGM9VdeBOp50p63DvUcz164gkfCXMwmeMD4/fffzSTiDfC3d+6555ovvvgiFm2dddYxL7zwQuye/AGz3lNOOSW8Bc1pmPDagrrLL7/cHHroodFrEPjhPbhZYP988PMH3nv66aeH5eHISaa88BG48MILmzFjxoRRr7/+erP33nvza1WFyouqgq1uXlKeXjdNUbcFycOvGoJ/YoNkt912i3Dq3r17yP8GDBgQ8U/wUggHhw0bFsXbaaedzODBbrcsZfDEKOM6u6jnOYR+R+qss2hxFAFFoMUhoIK/ZtzkSYK/gw8+OPSJAj9S2OWFWQV2UZ955hkzfvx4JyIwG8MCGLuu1dL0zz8xv2z/j+h1+PWDfz+bbMFf530OMV32O8yOVvEbQr8JZ55gfn+ZhImCQq2/Ff8m7sy6/P21l8ns+F9mJh3O4aK8gj+8O/GKC82kW675IxkI2R590cDnYLVUMUnaaYDpeuSJuZObdPsNZuKl58bidzn4GNN5j/1i9+QPn3jIdPU6HwL1PGnPV4P6jmUvXK+88kpz4IEHZhYaprPgp/bC8ssvvwxNeO0EwEdh2sv89N577zXbbrutHS0U0I0aNSq6//TTT5uNNtoo+i0vHnrooZh/P2i5fPvttzJKdH3JJZeEmzW4AUf4n3/+eU0HNrVUXjTt/eFmAlw/ELXq0NF0P/9K02b+BcPfTeWf8vSm0lKNV868/Koh+Ofaa69tXn755QiM++67z2yzzTbRb774/vvvDTZUYMoLwiF133zzTaJlim+eyOWot7Ce5xAt9TtSb31Ey6MIKAItFwEV/DXjtk8S/GGS161bpRnqjz/+aM444wyDxbDrzJd77rnHbLfddlUjNuH048yUR/8dvt+qfQfTa8hbphVpuNhUreAvTIfMdH/eakMDv3tMXQ4hYdfucWFXMH6c+W3Q2WbKYw9wNGdYRPA3k7TtfiKtP9YaLGpaaxeg1kkS0gsPUXn79SjpDhtvbrqffXH0my/KwIPT1jA/AvU8ac9fi/qNWe3ClWu02mqrmTfeeIN/miSeeNFFF5mjjz46jAefgBDQsQYfvwyh4QILLMA/Q8EgBIRptMUWW5hHH300igKtPtdmzIQJE8IFMDZ1QBBYQiOmWmqpvGga8U7wUCb4Se2w4azNK75fj6Hy9HpslfosUx5+hZKXzT/BJ+edd95o/glXBfCnmkQnnniiGThwYPQYbg3gBsFFvnmiK496uFfPc4iW+h2ph36hZVAEFAFFAAio4K8Z94Oigj+GYp999jE33HAD/4xCTLCOP/746HeRi5ljf/hDKPbnARgd1t3IdL/AfeJwTYI/KtSk6y83E6+ddbJu+//bwPS46E9NvD8LPeXxB82EU4+pqAJMdCffP8tcpIjgD4n9ssc2ZvoHw8N0W3XvQaa1r5hWljP/ikwTbviYJNlYtPvrima2myqFC2XhkVC1Rrs949uvzfSPP4zyb7/qmiR87hL9dl7A7PPFIdFipBVpFrT/+7oVUYPfp5oZIz8zM0Z/bqaPHknhSDNz3C+hmXnrXr3J1+Typt3Kq5k2885f8S7fyDNph/aWJJh12iak8rlez0Kg1oXrtddea/bff/8owcMPP9xcfHFckA7TXLTJV199FcZD/Kuvvjp6hy9skzbw3L32ivs75bgcnnfeeTEe/OSTT4anDfNzGUrfgSussELMLE7Gy3PdUnlRUxb8+ebpynfyjJSmFycvv0LNyuafsDqBmS+0+UBwcQA3M0l03XXXmf32m7Wpe9ttt5ldd620IuH3ffDE5jCHYDwaOmzK3xHlfw3dWzQ/RUARKAMBFfyVgWqdpFmt4A8nqeHkSJv23Xdfg4VvNRQzg6UEoHUG7TMX1Sr4g6++3847NUq67ZLLmNlvfyj6jQt7UdR6tp6m22nnmfarrWV+XG2JKG5RwZ9tXtv16JNNpx12j9IrcuFjkoTTk3GKMhMET7Ndcwf/jMKy8IgysC5gQjfx8vOtuwV/tm5tuh56nGm71LK5Xxx3+D7m91deiOJ3O/Es03GrHaLfrotp771jft1r++hRm4UXMz3vfTL6jYupTz1ifrvsfIPDa1KpTVvTede9Ted9DzHQerUpj+DPFvJNnjzZdOzY0U5KfzsQqHXhCm07aN0xbbbZZjENPNyHdp08xAMaK9BcsQlCu7vvvjvUBoSmCxatOAwkjc466yxz8sknR1GeeOIJJ69GBFuwmKYNEyWYcNGceVFClcPb1Qr+Gou/ybr45unKdyS6zec6L79CjRuCfyIfWJ/gRPJll1020XQX8SAUhAkvU5qrBMTxwROb8hyisflSU/6OKP/jUaahIqAINGUEVPDXlFsvo+zVCv5uuukmp+ZJkuZKRjHCxz9tupaZ+f2Y8DrNzBcRahX8/XYxnbA7+MYwL/zrsNGmpvs5szQAcY8XRdDK67zjHqFwrlW37saQtk4tgj/sBv+8xTrIIqS2Sy9nZr/1D/Nmvpc3rHmSRJpqP222doQ78oUQEsJIm8rCw86Hf/8+9D9m3GG1HTiAtLqddr7puNlWnGxmCFNzmJwztVvhb2a262cJRvm+DG3haZdDjzWdd9v3jyjUX8Ydua/5/dWX5CuZ1+2WXcHMdvN9FfFU8FcBidcbtS5cL7vssvDgDS4UDua49NI4b4GpGR/6kWTmy+8XDbEhg40ZJvhmTTptHX4JkT8OKQHtuOOO5s477+RXC4XNmRelAVGt4K+x+Jusi2+ergtfiW7zuS7CrxqCf+ZFFpslq6yySujXD+/Axx+0rOecc87EJHzwxKY8h2hsvtSUvyPK/xKHlT5QBBSBJoSACv6aUGMVLWq1gr/NN9/cPPbYYxXZ4VRJPqWy4mHKjRlfjia/extEMdLMfBGpFsFfMPE38/MOm8YO6+hywBGm814HRfnjAv6PsDDquOW2cVPPGgV/SPuX3f5ppn/4Pi6Nad3G9H7+bdOqa7c/fhf4X8skKZgw3ownU+bfX3oulmO3U841HbeodJRdJh6xAvz5o7EmoAFpx/208WrR6ctkI2t6PvIf0+Yv87iKaUhqYsZusnrYX8II1J69nqCTkf88tGXqk4+Y8ScfGXsXAuT2q69NcfqE+cz4YlTlCcv0Ro+rbjXtV1kz9q4K/mJweP9R68J1ww03NEOGDInKBVMzuEZggvbl7LPPHh6YhHu1bJZwmhy+/vrrZvXVV+efoX/A0aNHR79dF9BIfPzxx8NHWBB/912GRqorEbrXnHlRQpXD201Z8Oebp+vCN62nNM1nRflV2fwzL4o4zRensX/wwQfRKzjlF5vWWVQrT2zKc4jGmndxmzTl74jyP25FDRUBRaApI6CCv6bcehllLyr4mzhxYriIvesutwZU0smUGcUITU2hNcXU5fATTOddkn1ZVSv4C36bQKf6nmimPhc3w+xx6Q2m/ZrrcPbpoQfBn61x2GPQtab92uun5+t4ak+SOm69U/KpvjNmGgg9Z/481kx7/10z+Y4bYwecIPk2Cy5iet5JBwO0b+/ILeGWBzxcKc/88Qcz/qTDqcwTXY9z3+uy/+Gm/VqVZpRpCUw44wQz5ZFZ2nZdDjzKdB5wgPOVqc8/bcYfO0tojH6E/sT0y+5bmen/HcE/TYd+WxqYD9t+Had98J6ZcOLhZsa3X82KS4cE4LAASXkEf3379jUYqyCY+H700Uc1ndgq82/u17UsXOWpkIzT22+/HTPPhVAQwkEmmJZJs1++XzSE1t7f//5389Zbb0Wv4nT2yy+/PPrtujj//PPNccfN0nDFQnnppZd2RU2915x5UVrFqxX8NSZ/S6tP+KxKnq58JxPZJhehKL8qm38mAfjbb78ZbLJAyw8898UXX4xFXX755cN73buT1UYG+eCJTXUO0dh8qSl/R5T/ZQwsfawIKAJNAgEV/DWJZqqukEmCP9xvT8IfnNyLHd9JkyaZr7/+OnT+DlMIF+EUYDhc7lTFQRXjTzjUTH32iSjZrJMRbcFfh022MF32OTh6P7qYGZiZP/1oZvzwvZnx2cdm8oN3G2i6SWq33EpmthvupmNsWsnbyddVLopkgpPvvIVODD4rutWJTIm7HvWv6HfeC3uSlPc9ZzzSVJvt2jsMTFsLkQc8CuXXAJGnDX/b/Lr3LL9+bRZelHz2zTKflEUYd9T+4cEefK/7eVeYDutvEv6cNuJd8+uAbfmRaTP/gqbnPSR0JpMjF8G3IPwDMbnMwPMI/vh9DYsjUGTh+uuvv4Z88eOPPw6dzL/66quxDPv3728efPDB2D37lEksUNdee+1YnKI/Zs6caXbYYQdz332zhNXgx59++mmqWRvywSaOPM0XpsqHHHJI0SJUaPwVTkC+UGe8aNq7b5kJ587yCSuLSh/ImLAez9ossngsCn60ou9p97MGmTYLLFzxrO5uNEOeXncYN5ECFeVXZfPPJNjeeecds/LKKzsfb7DBBub+++83PXr0cD63b/rgiTqHsFHN91vntPlw0liKgCKgCJSFgAr+ykK2DtJNEvxVU7RqF4zI6xcyvZ1Ogjmm2W6+37Rbdnn+WRHagr+KCHlvtG1nZh/8sGnrWKglJuFhUTT1BdISO0ZoidGBIT2uuDkxy6QHviZJrXv2Js2ySw0OKilMHvAonGcDvADTc5igM80++BHTtu9S/DMMZ477lcyCybRy+rTwd+ses5teT5Hw50/BHsxmJt16PWlWfmFm/vi96XLIMbN8/8VS+uMHNFLHrrNC9KTton3N7Hc/Hv3GhQr+YnB4/2EvXJEBNkFswqbItD9PILef4XfXrl3N//73PzPvvPPGHts++EaOHGkWWmihWJyiP1x8/J577jHbbbddZlJDhw4NNQU5Yl5zOI7PYXPmRY1t/sYYN1jYTHl6g+HXjDIqyq/K5p9J0EKwt+22szbZZDyYYMJPIcx8559/fvnIee2LJ+ocwglv6s3m/B1Jrbg+VAQUAUWgThBQwV+dNEQZxXAtGKvJx+XAvkg6EJ5AM4+p1xNDTes55uSfFaEPwR8OEOn6r7NNx3/0r0g/9YaHRdH0D0eQn7+tomza9l06FEBGN3Je1DRJoskwBFkdt9iWMNiyKh+DYTE94JGzug0abdLN15iJV14Y5dlp571M1yNOiH7jwj4dutP2u5mux5wSixP9IJxMa9IqJW0mF4Um2G+/bsb/6yhjZs4Io7RZiDQN74trGqrgz4Wev3uuhWvR1GFOhpMw4SvKJmilQDsFhAXplClTnIJF+z3X7+nUpw444ABzww2zTMsR76STTjI43TcPffnll6EvQI7rOoWYn6WFzZkXqeBvVdIGH5zW/PqsmSJQlF+VzT+TYH755ZfDQ5Rwyi8sVIYPHx76WoU2NNPcc88d3ltyySX5ljP0xhN1DuHEN+1mc/6OpNVbnykCioAiUC8IqOCvXlqihHLUKvjDwnW33XYLd1Jbt25dXQlJc+bH1WgiNoMEI6A2bU2f1z4kAUlyerUI/lp16mw6bLip6bzrXgaClcLkQdAFIc9PG60WZd16jrnoQIhXot95L+xJUtul/xo7DCKYOsUEpJX2+2svkW+/n2LJtl1yGdP97EtC89PYg6I/POBRNMuGiD+TzMNx4jEL4XBYR9hGol/+uue2ob9ELo9LK5CfcQgfOjNGfWamfzXazPgCfyP/+Pv6S44ShSr4i6BosItaFq4wJdt1113NkUf+P3tXAS9F2fXP7e5LiJSggAIKgjT4gUqH0oIoJanSJSCISoOUklKiNNKg5Iu+vISAKCItgihxu3P3e85zmdmZ3dm8G7N7z/n9LvN0/Gc588yZEyONavGVK1cO8MUSqWTJkvDvvwWRzK3dIPq0Qg0XaQRfHGPMmDGAPqosJRQeoh/I/PwCYXPdunUBg4RYS57Mi/J+vwhpX8xThAQDZORd+V2sQzNf75hYMS9NYKRv/3qNpEXqTHsoT1cn2OpelbX8ytH80xq00PUCBvi4ceOG2K1p06Zw9OhRMa+UsBdPpDOEErqmyzz5OWJ651RLCBAChIA6ECDBnzrug0NWYavgLyYmBjp37sxfcCtVMvRnZM1i8cUprlktsYt3yVIQs/eEmFdK6Av+0ETVv7ZOkCbrw8x5MWKuN/N5hS9lvhWfMap1JetnLGOPlyIUdjZkDvSZQ35OzJSw2Ekm7LSS9A9JQT36Kgf3YPPwtssWyGbwjoyGyPU7wKdUaVm5VRl74KEwYe6Zk0z7bYRCjXVFoWOmMEFva+s6PW6d/EE/yDmpcxIujbKrH4nat9KzEIWBURQIo/xl7doCmZvWGQRUUWguFpHgT4TCaQn9F9c2bdpAnTp1FOcPCAgA1O6Ljo6G559/HtC5t7kPIMHBwdxvKg6I454+fVpxbFOFKCzEdV24cEHWDP1xffbZZ7IySzJlypThvgqxbYUKFeDmzZuWdJO18WReJNuoXsbW4B5q4G96W9FlHcTTdRNQyl0QsJZfOZp/Wosb+l/FwB6oWS0QmvI2aNBAyCpe7cETcWB3O0O4mi8V1eeI4o+QCgkBQoAQcAECJPhzAejOmtKY4G/Xrl38hRbXgVp9+IKLf3gIfPLJJ7n/KnutkX8Vbd1QHM4Ss1d9wV/wu+9DyMBh4hgOTdjppUjfvLnYGebj0IgZqLH9WHxIejxA5rdrWFARuWAAhaFRzKeiF7u3NpGd8NCfWw3mddmHD0DKeF2gg8C2HSFsaoE2VToTomasWiIuG4OzYJAWfUIBIQYKQS1Pc+QVHgEoCBeIBH8CEs676r+4fvHFFzBkyBC7LAA1SfwkgV1QeLd3716rxv7999+hdevWotYgdvbx8QFc96BBg6waS2hcq1YtHrgJ87GxsfDokc7tgtDG3NWTeZGpvdsq+FMDfzO6LwfxdKPzUYUqEbCFXzmSf9oK0vjx42HWrFli93nz5vGP1mKBQsIePBGHdbczhKv5UlF9jij8BKmIECAECAGXIECCP5fA7pxJjQn+UlJSAKNCOoO0OdkQ14Bpvz0mnzLlIPq7I0JW8eoJgr+4xs+DNjOD7w8FPrFHzynu1VShtYckHCtt/nTI/Ha1bFj/Jq9AxNylJs2rZR2kGQe9JLr6AMq3yDQl41o2YMK4JJ71Cg6BmEOnWYTOAEjo0EwXzZNplWJQD+/IKCky3G9lUp+uunaSWq+AQKaB+gz4Pl0FfCuxvypVwa9aDXiE/xfIx58EKecmHf3iGhkZCUJkdIzmi1F9LaVjx47BG2+8IfbHfvgxZtOmTdCuXTtLhzFoh5qK165d4+VVqlThQUkMGpkp8GReZGrrJPgzhQ7VuTsC1vIrR/NPW/DcvXs3dOjQQeyKH3Lwg44psgdP5OO72RnC1eeuovocMfVbpDpCgBAgBJyJAAn+nIm2k+dSg+APtxzX9EXQpqbw3XuFhUPssfMmkXB3wZ+BsLMCC+KwRR7EwSQAjyttOSSxUKSQ2Lsz5F3V+aXC4UIGDYfg/u9ZMq28jYMEf3nX/mARcVfI57Iy58U0oYL7DLLNl+PjudLmTIPMzevFmcPnfMl8eBWDpL66CIIBTZsDlutT+vKFkLFysawYg6kEdX+bCfwqGwha9aP6+pSrANHbf5D1p+AeMjjsnnH0iys6lr9y5Qpfd9WqVeHSpUsW7WHfvn3QsWNH5h0gR2yPPgL37NkD6IC/MISuGxISEvgQlvjAUprLk3mR0n6FMlsFf2rhb8I+ZFcH8XTZHJRxCwSs5VeO5p+o8YymuhgNHV0efPXVV+Dr62sSyxMnTsDLL78stkE/rOvX657pYoUkYQ+eKAznTmcIV/OlovocEX4rdCUECAFCwNUIkODP1XfAgfOrRfCX0KUlD3ggbLXYaWb2yoQ2xsjdBX+ah/chvrXOybvfS/UhcunXxrZrtNymQxIbLe/GVUjsxaIZMyGgSMwEMZr5qLM64ImHvyTmXb0MiT3bizBhFGgv5htRqjUZMX8F+DdpJrYREkkDekDu+TNCFoL7DoaQISxqrxHK/eVnbhYsVPuULQ/ROw4LWX4lwZ8MDrtnHP3iioK148eP83WXKFEC7t+/b3YPR44c4RGCpX6qqlWrBigMLFu2rNn+phpgUA80P9Yyv6NIPXr04BGJTfVRqiuqvMhWwZ8Shqop83Cerhqc3WAh1vIrR/NP/TProUOH4NVXXzWJ5LJly3j0c6HRnDlzYPTo0ULW4GovnigMTGcIAQnz16L6HDGPDLUgBAgBQsA5CJDgzzk4u2QW/UOUsAhnmvrinEmDe0Hu2f8J00P07uMmA064u+Av99fzTGOsq7jfgFYdIPyTeWLe0oSthyQcHzXRUCNNSn7PvwiRX21Gx47SYtPpIvCSmNijHeCXcCQMFINmupr4Aj9oqP3Ho/0qCKrRhB21OwWK3vo9E6xWFLLyKxO8pHw4DLIP7RfLMegIKJClAAA96UlEQVQK/l+QEgn+pGjYP+3oF1cUrG3cuJEvHDVV0tPTwZ8F9zFG8fHxPGgIXgVq2LAhF/phFOHC0r1796B0aV1wn1GjRsHcuXOtHrao8iIS/Fn9U6EOboSAtfzK0fxz69at0LWr7uzUr18/WLVqlVFENRoNoEsF1BIUyFxwD3vxRGE+vNIZQoqG8XRRfY4YR4RqCAFCgBBwLgIk+HMu3k6dTS2Cv9TpkyFrR8HLMAIQ/sl8CGil07LSB8XdBX8ZX6+C9IUzxW1hYBIMUGItFeaQBExgl8AEWvm3rsumDR07FYK6viUrM5kpAoI/jMabNvcTRRiCe/WHkGHjFevim9dlQT10ApuIxWvAv35jxbZps6ZC5tYNsjrv6FiI+eGUrMwSwZ9UMww7BwYGysagjHEEHP3iOmXKFJg2bZq4gJMnT0L9+vXFvH5iwIABsHLlSrG4Zs2a8NNPP3HffmJhIRI7duyATp06iSOgdszAgQPFvKWJosqL8v+9BykjBogwIS8w9n9cbKT2hI08nfiO2m+s9euzll85mn+iS4JixYoBCvSQMIr60aNHZaa80l3Onz8f8GOGQPix5MGDBzxYnVCmf7UXT5SO625nCOnanZl25+cI8T9n/lJoLkKAEHAUAiT4cxSyKhhXLYK/nBNHIHmk7mUzsFMPCJugeznWh8rdBX8powdD9vFD4rai1n8Hvs9VF/OWJgp1SGKT5P56gZmWdmPBJAoO0TgvBrCI3vY9eBcvadkybHxJtGxwdbTSJCdBfEsmnJGaRj9eWvTWg0bNo5M/6As5J0+Im/Au8QSEz1rCgni8IJah2Xc6iw6ctWOTWCYmmCZYsZOXxSwmLBH8YSRuKWVmZpLwTwqIibSjX1zPnDkDdevWFVcwe/ZsGDNmjJiXJk6dOgUNGrDgMo/NcLEOBYEVKlSQNjOZxgjA1asb5y0jR46Ezz//XBzjzp07UKZMGTFvaYJ4kaVIuUE7G3k68R03uLdWLtEafoVDO5p/4hyDBw8G/EAhUPHixWHp0qXcB6pQhprUY8eO5eVS/omBkLp1Y2ceE2Qvniidwt3OENK1OzPtzs8R4n/O/KXQXIQAIeAoBEjw5yhkVTCuWgR/2qxMiH/lJdBmZ3FUMPBB1KZ9RhFyd8Ff/Gt1QJNY4Ezfu1iJAlNRPWGN0c1LKgp7SMKh0mYzTbMtck0zHuV3/nLJTCaSNr4kmhhRlVUp496H7CMHZGvzrfoCRK3bLiuTZrIP7oaUSSOlRTztU+EZFiAkFrRJiZB3k0VTlQhe9RtHbzkA2F4gEvwJSDjm6ugXV3wJfeKJJ7jWCe4Ao03u3LlTcTPouwr9+xWG0Pl93759jQ6BQkh8uUeqUaMGXLhwwWhbUxXEi0yh42Z1NvJ0evF1s/tswXKt4Vc4nKP5J86RnZ3NtaT1eRVG4sVgJI8ePYKLFy9CWloaNhepT58+sHr1ajFvLGEvnqg/vjudIfTX7qy8Oz9HiP8561dC8xAChIAjESDBnyPRdfHYahH8IQzJzFwq58ejBYgwIVjMoTPgHRmliJA7C/7QtDahaytxX4Edu0PYh5+KeWsS9jgkaTPSIaFzC0DNMymFz1gEAa+1lhYpp218SVQeTL2lOf89DsnD+ssWGMq0UoOYdqopSv1oNGTtVxbs6Pfzb9AEMJJv5sa1YpV+QBAS/InQOCThjBfX/v3782iUuIHY2FguBESTNX2KioqCpKQk/WKr8qYEf/hiHB0dzRRZC4L8TJ48WWaGbM1ExIusQUvlbW3k6fTiq/L7auPyLOVXOLwz+CfO8+eff0KXLl3g3LlzmDVJ6EsVzX2nTp1qVvPdnjxRf1HudIbQX7uz8u78HCH+56xfCc1DCBACjkSABH+ORNfFY6MpBEY4kxI6mk9OTjZ7QJL2sUcaTR1Tp08ShwodOQmCevQW89KEvuAvdNzHENSlp7SJ49JMOyv+Nea7LTmRz+HfqClELND54DI3cfqi2YCHG4GwL45hC2Xt3QGpU8eKXUPeHwPB7+hMpsUKMwl9U2tszgN9rN5ipierLiQe5idQSQtNPsS3aQKaRw/4grz8AyDm+/+BV1i46QUyfPA+pX85HzRxDw3bsv9vfs9W5/cNIwPnXf4VEt/uKLbzr9cYIpasEfPxLRuI4/hWqwFRa7eJdUIiNDSUB40Q8mTqKyBh/qr/4rpu3Tp4++23zXe0osWuXbvg9ddZVO3HdPDgQWjRooWQ5dfU1FSIjIwUfVnJKq3IbN68WeYMX9p17dq1gFowAp09exZq164tZK26Ei+yCi51N7aRpxPfUfdttXV1lvArYWxn8E9hLvTzt2LFCpg0aRJIgx8J9T4s4Bb6T8U1vfCCzr2GUK90tSdPNBjfjc4QBmt3UoE7P0eI/znpR0LTEAKEgEMRIMGfQ+GlwQUEUPMsvu3LoE0p0HBB80Y0c/QoymfCo1YNWcCHOL4tnzLlIHr7D8xDtY9HbZM2o4AA06rK//tOwd+Df5g2azSgSbtP2XIOuf/4QoRBGlCjDE2fiNSDQB7TqKpUqRLXWsFVde7cGTBapbOpUaNGYrRLNG9Dn4JEhEBhECC+Uxj01NlXLfzKGDpojnz79m34448/+EfrfHbOqlq1Kjf7tTawlap5opPPEMbwpnLjCBD/M44N1RAChIB7IECCP/e4Tx6xyoyViyF9+UJxL5Frt8sCIYgVbprIYQE9kllgD4HCJs+AwA5dhCxdCQG7IdCvXz/uz6ht27awZ88eu41LA9kHgeXLl8OgQYP4YKhlfe/ePS6ktc/o5ke5cuUKfzEWWu7duxfatGkjZOlKCNiEAPEdm2BTfSdX8ytnAEQ80Rkoe/YcxP88+/7S7giBooAACf6Kwl1WyR61qSlM668JaNMLnDIHdugKYZOnq2R1hV8G+ohDPy9I3iVLQcxO5tOQ+Z8hIgTsiQAK+rp37w4ZGRnw6aefwsSJE+05PI1lBwRycnJ4dF4U+CHNmzcPMJqkswgjCc+dO5dPV7NmTTh//ryzpqZ5PBQB4jseemPZtlzNr5yBLPFEZ6DsuXMQ//Pce0s7IwSKEgIk+CtKd1sFe03/Yh5krFlasBI/P4je9gP4PFlGBSsr3BLyfr8Iie90Egdxql9CcVZKeDoCN27cgCpVqgCaO6GPODTfxGiHROpDYNGiRTBs2DC+sNKlS8P169ed4ls1Li4OKlasCCkpKXzubdu2QadOOt6kPqRoRWpHgPiO2u9Q4dfnKn5V+JWbH4F4onmMqIVxBIj/GceGaggBQsC9ECDBn3vdL7dfrTYtFRK6tQbNg3/5XgKat4Xw6Qvcfl9JA3pC7vnTfB++lZ6FqPXfkbaf299VdW4AA0VggB4M7FCuHPMhSKRKBFCLplatWnDp0iW+vpkzZ8K4ceMcvtbhw4fDwoUFLhWaNWsGhw8fBv2IhA5fBE3gcQgQ3/G4WyrbkKv4lWwRDsoQT3QQsEVoWOJ/Rehm01YJAQ9GgAR/Hnxz1bq1nFM/QfJ7vQuW5+UFUet2gO9z1dW6XLPryvnxGCSPeLegna8fE/qx/TDhHxEh4AgE0MQX/cb5khm5I+C165hoYouBNdCBfkREBNy8eRNiYmLsOod0sFu3bnHffvgSj1EIf/vtNyhfvry0CaUJAZsQIL5jE2xu1cnZ/MoZ4BBPdAbKnj8H8T/Pv8e0Q0KgKCBAgr+icJdVuMfU6ZMha8dGvjK/2vUgctkGFa7SgiVp8iGhe1vIv3WdNw4ZOAyC333fgo7UhBAgBIoCAlOmTIFp06bxrY4YMQLmz5/vsG2/+eabsGnTJj7+smXLeORnh01GAxMChIDHIeBMfuUM8IgnOgNlmoMQIAQIAULAHRAgwZ873CUPXKOWaS2lL5kN2rx88I6MgpAhznN8b084tSnJkLakwIm+T+kyENyzH4CPjz2noLEIAULAjRHIzc3lJr6oMVCyZEmYOnWqQ3aj0Whg6NChoNVquZbf+PHjHTIPDUoIEAKei4Cz+JUzECSe6AyUaQ5CgBAgBAgBd0GABH/ucqdonYQAIUAIEAKEACFACBAChAAhQAgQAoQAIUAIEAKEgBUIkODPCrCoKSFACBAChAAhQAgQAoQAIUAIEAKEACFACBAChAAh4C4IkODPXe4UrZMQIAQIAUKAECAECAFCgBAgBAgBQoAQIAQIAUKAELACARL8WQEWNSUECAFCgBAgBAgBQoAQIAQIAUKAECAECAFCgBAgBNwFARL8ucudonUSAoQAIUAIEAKEACFACBAChAAhQAgQAoQAIUAIEAJWIECCPyvAoqaEACFACBAChAAhQAgQAoQAIUAIEAKEACFACBAChIC7IECCP3e5U7ROQoAQIAQIAUKAECAECAFCgBAgBAgBQoAQIAQIAULACgRI8GcFWNSUECAECAFCgBAgBAgBQoAQIAQIAUKAECAECAFCgBBwFwRI8Ocud4rWSQgQAoQAIUAIEAKEACFACBAChAAhQAgQAoQAIUAIWIEACf6sAIuaEgKEACFACBAChAAhQAgQAoQAIUAIEAKEACFACBAC7oIACf7c5U7ROgkBQoAQIAQIAUKAECAECAFCgBAgBAgBQoAQIAQIASsQIMGfFWBRU0KAECAECAFCgBAgBAgBQoAQIAQIAUKAECAECAFCwF0QIMGfu9wpWichQAgQAoQAIUAIEAKEACFACBAChAAhQAgQAoQAIWAFAiT4swIsT2qanJwMhw4dgl9++QUePHgAcXFxEBkZCc8995z4V65cOfD29nbotvPv/gXJ7/UBbX4eePn6QvSOw8AmNTtn/r27kP39Xsj/+y/I//ceaB78C+DlDT5PlgGf0mXBp0w5CHi1NXgXK252LHMNtBkZkLFqCYBWA95s7KBOPYx3yc+HpEFv8TV5BQVD5IpvwTsq2nh7K2ryLv8Gub+cs7yHF4MkJBS8IyLBKywCfMo/Bd7RsZb3f9xSm5YKebdvQf6d2xxvyMsDn7LlwadcBfAtX4GNHW71mNTBMgQyN65lv7uCtn516oPv05Ut60itLEYgMzMT+vTpA+np6RATEwNr1661qG8e+39w9OhRuHr1Kvz1119w9+5d8PLygooVK/K/SpUqQYMGDWzmoVlZWXDr1i24fv06+y+XBzjeM888A4GBgWbX9+6778L9+/ehePHisGLFCvDx8THbx5IGxIMsQUkdbax6bqljybQKFSCgVn6YkJDAee3t27chIiICypYty8+qlp5RHcUTVXDLFJfgCWcHet4o3loqJAQIAULAZgRI8GczdO7Z8dKlSzBx4kTYv38/f5k0tQt8yfzyyy/h1VdfNdXM9jqtlgvJcs+d5mMEde0FoWOnmBwv5/ghyNi8HnJ/PsUEIo8lIkZ6ePkHQGDH7hDceyB4x9ouAEybNRUyt27gs/jVqguRy78xMmNBccq49yD7yEGeCWjeBsKnLzTZ3tLKjPUrIH3RbEubK7bzqfAMhPQZDAGt2ivWSwu1GemQsXYZZG5YDdqcbGmVLu3jC0Hd3oaQQcPAKzhEV04puyDwqE4lAI2Gj+X3Qi2I/GqzXcalQXQIjBkzBubOncsLPvnkE5g0aZKuUiGFL54oTEMB4b//sg8OJgg/pHz00UfQpUsXiwWAyJuRR//666/s1hfce2EKfMmtXLkyfPbZZ/DGG28IxQbXoUOHct6NFbi3UaNGGbSxpYB4kC2ouaaPtc8t16ySZlUbAmrjh7t27YJx48ZxoZ8+Vk8++SS88847MGHCBAgNDdWvluUdxRNlk6go4wlnB3reqOgHRUshBAgBj0CABH8ecRvNbyKfaaLhgW7hwoUGL5Pmevfo0QPmz58PJUqUMNfUqvqsHRshdfpk3ge1xqJ3HuXaaUqDaLOzIG32NMjatUWp2mSZV2AQRCxeDX41XzLZTqky+8BuSPmIvTQ/FjJaIvjL//sOJHRpAZCby4eMmL8c/Ju8ojS8VWX2OAQJEwa80hLCZywyql2JX1qTh78LmoQ4oYvJq3exEmy8heBXo7bJdlRpHQLSw7tvtRoQtXabdQNQa5MInD17FurXrw/IH8uUKcNfLoOCgoz2+frrr2Hw4MFcO9BoI4WKGjVqwMGDB03y0Pj4eOjevTscPsy0ni2gVq1awerVq6FkyZIGrVGD++mnnwbU7Mb9/Pbbb1wL0aChlQXEg6wEzEXNbXluuWipNK2KEFATP8zOzobXX3+d801zED311FOwbds2ePHFF402dRRPNDqhiys84exAzxsX/4hoekKAEPA4BEjw53G31HBDGcxUFV8o9+zZY1hpYQkK/c6fPw+lSpWysIfpZpqH95lwrCVo09N4w5DhEyD4rX6KnTTxjyD5/b6Qd+0Pw3qmceZT4gnwfoKti5nDoQmwJu6hQTs0eY1ctgF8n61mUGesIPvwAUj5cDjTuMoXm1gi+MPGaQtmME25r3g/FIpFbz0IXqFh4ji2JOx5CML5g5nmX8hQQ00gvCeJPdozLO/Il+nvD75ln2L2w17c7BeFsVLyLl4SojftA6/wCGkxpQuBgCcc3guxfYd2zWWC+Vq1anGhGE6EQr233npLcU40tUWNEdT00yc/Pz9Atwjly5dnsv5cbp77999/s28Fco3k559/Ho4fPw5RUVH6Q/B8hw4dYPfu3bK64OBgqFKlCjchvnLlioHAsVmzZlxQiCbG+jRnzhwYO3YsL27atCkcOXKEj6Pfzpo88SBr0HJN28I8t1yzYppVDQiojR+i+wXUqpYSfuRAjeeHDx/CtWvX+Acbof6JJ54AFFyiFqAxcgRPNDaXq8s94exAzxtX/4pofkKAEPA4BNjLCZGHI9CrVy98Ay30X6NGjbTscGgXtJJHDdI+rFWR/8W1aqjVZmcrj6vRaJPe6y22Ffu8VkebvnqpVpOWatAv59cLyn2a1dbmP3pg0F6/QJOerk2dNUX7sPbTBvMmDuih31wxn5+UqH3U+Hmxf+qMjxTbWVOYvm65OB7ikDr7Yy3zjWj0T5Oaos29fkWbdfSgNrF/d1lfjmO9Ktr8+EcGS0iZPEretm4VbdqyBbJ7pMnO0qZ9MU/7kNUJ9wSvyRM+MBiPCmxH4OFLz4j4JrzTyfaBqKcBAtOmTRN5YrVq1bTMrNagjVAwefJksa3AS5mGoJZpUGuZb0ChmXhlZrraTp06GfSpW7euIg9dtGiRrC0T+GnnzZunZQJHcUzkvbNmzdJinbAGvDJTXrGNNMF8dWnZy7DYduXKldJqm9LEg2yCzSmd7PHccspCaRJVIqAmfrh+/XqRbyGPCw8P165atUrGoy9evKhlGn6ydsw6xSS2juCJJid0YaUnnB3oeePCHxBNTQgQAh6JAGn8sVOFJ9O6deugd+/eilv0ZxpcqAn40ksvca0S/IqKWiHYB03flGjp0qUwaNAgpSqLy/JuXoPEbq3F9ujXD/37KVHmpnWQNvcTWRWa7EYsXGXWp1za/M8g89s1sr7BvQdByHujZWXSTM7/fmTmx5NAwwKGKJGlGn/YN33JXO4jj4/DtIJi9vynUL4G9b9+BvXoC6EjP1RapmJZxterIH3hTFldyHtjuA9EoRB9+cX9X02AnByhCMKmzYXA1q+LeWlCaq7Ny719IPbEL4Dm1USFR8ATvtoXHgX7j5CSksJNe/GKtGXLFu6HT2mmkydPQpMmTWQ8sX379rxPQECAUhexbObMmdz/lFjAEhs2bICePXtKi6BChQrw559/imXff/89NG/eXMxLEzt37pT590NNl3/++UfaREwvWLAARowYwfPoDP/mzZvgy4Io2UrEg2xFzrH97PnccuxKaXQ1IqA2foj89scffxSh2rp1K3Tu3FnMCwkMTMc+2vDgdFiG2tf37t2DYsWKCU0MrvbmiQYTqKTAE84O9LxRyY+JlkEIEAIegwAJ/jzmVhpuJIcJb9D8TMkBPfpEwZfd2rUNfbJdvnwZWrRoAWiupk/oq+rChQv6xVblUz8eB1l7tvM+GIAj5vBZJsQLNhhDm5IM8S0byAJLoA+5iMVrwMuEHy5xIGZqlzJmCGSzgCACoS/BmH0/GcyHc6GgMGvvDqGp4tUawZ/m/j8Q3+5l0T+gMdNaxYkUCgt7CMIhMeIwD4zyePyAFu0g/LPPxdkw0ErSQJ1Qwu9FFsxkhelgJonvdIK83y+KY0Su2QZ+1WuIeUrYjoAnHN5t373jejJtOhg9uuADAEa+RcGZUuRb9rmPR9O9ceOGuJh27drB9u3b+UumWGgiMWDAAGDadmILfR56584dbiosNMBAIMibTREKHqWuG5DHK/n6S01N5S/B6C8L6ZtvvgH02WorEQ+yFTnH9HPEc8sxK6VR1YyAmvgh8uLSpUuLrhLQTQFGTzdGH374IcyYMUOsxo/X6ALBGNmbJxqbx9XlnnB2oOeNq39FND8hQAh4GgIk+PO0OyrZjzFtP3zRvXr1KkRGRkpay5PoDJ6ZpQEzjZBXsNyZM2e4lqBBhQUF6H+PC8MeB74IaNocwud8qdjTUJvMG6I37wefp55WbK9UmP/nDUjo2koUvmGb0AnTIKiT/OU3a993kDpljMEQQZ17QuY2neDLGsEfDpbYuzPkXfqFj4u+77jQ0RKhpcFKAOxxCMpYuRjSly8UR/d7/kWIXK0TMqSvWAQZ7E+gkCGjILjvYCGreEWNTNTMFCjsw095NGUhr+Zr/j9/Q97Vy+IS/es2NKtJioFecv5zWHwx8WJaBv6NmopjCAnUnsy/dQPyb9+EvNu32PUWaJITuTakd0ws+LFgHX6164FP6bJCF4OrJYd31OKSEmqPKfl8k7Ypymn014cY3b17l8OAGsyoyaxE6JMPXzwFwkAZiDdq2VlK9+/f54I9/BAjEL7ICuOib8G3335bqAJm0gb9+vUT80oJZvIL48ePF6sOHDgALVu2FPPShNR3YM2aNbmvVmm9NWniQdag5fi29n5uES9x/D1T2wxq44c//PAD54eozYeEgeUErWUl7NDv6sCBA8UqZiYMzL2NmFdK2IMnesLZQQkbNZW58/OGeKmafkm0FkKAEBAQIMGfgIQHXvGr57Fjxwx2Nnv2bB7h16BCr0DqXDk0NBQaNGgAjRs3hnfeeYebyek1tygrM39lPVDbDLXOlCjp3Tch98JZsSqgVQcI/2SemLc0kTysP+T+8jP4vfAijzqLEW19ylWQddd/gfKOjIawqbPAv15jeFSvitjWWsGfvnlt6OjJENT9HXE8axL2OARhFGUUqAqEgicMeiJQ9qH9kH3iCGgePQBNfBwzJZ4I/vUbC9WK19RPPpRFWw6bMgsC23VSbGusMPe3XyB98Wxj1ZaVe3tD6AfjwPe56pa1Z60wcnHOT7r/I5YILXMvnoOkft3EOXwqPAPRWw6IeUxkH9wNaYtmAwaxMUksOE1wr/4QPOB9QO1XfbJE8Kcv5ENhfWBgoP5QlH+MAGq9SYN4SIVw+iDpa+uNHDkSUDvGWurbty/s2rULGjZsyHlo165dRS0/FNpt2rSJax2itgu+uGLQEVP06aefAvM7KDbZv38/YJRfJdIXLJrTiFEaQyjzZB4k7NHSq6t4lnR99n5uES+Rols00mrjhwLqjx494oGXqlevbtJ0F4WCaL4rkCk3CUIbe/BETzg7CHgYu7qax7nz84Z4qbFfFZUTAoSAKxEgwZ8r0Xfg3BihDTX6MKKvlGJjY+H27dsQEhIiLVZMo8nvoUOHgAX1ADRPUzKFU+xoojC+TWPQPPiXtzBl5qtJiIf4FvVkmnoRi74C/wbMdNZaQn+FGPWSCYaMkfAChVp5wW/25sI5NAvGSMGFEfzhV+GE9v8nTutb9QWIWrddzFuTKPQhiGmqxbdtIuKPc6MQEoWRNhOLeJzQ8TXI/1sXATiKaWX6Vqxk1ZA5/z0OKKAtLIVNnQ2BbTtaPAyanKPpuUDoPzJypU4wKpRLr/rC05APxkLw2wMKmrDfS/LIAZBz8oS0i9m0X/WaELlmq0E7EvwZQFLoAukHEVNmvujnFH1FJSYminOeO3cOmEN5MW9pAsfyZvxH/2XA0v767VDId/DgQbEY/bMa82uVnJwMuE9B4/DNN9+Eb7/9VuxrTcKTeZA1OGBbV/Es6Trt/dzS/33SRwQp2p6Zdmd+iB9K6tSpw/364d1BH3+oyV2iRAmTN8sePNETzg4mQWKVruZx7vy8IV5q7tdF9YQAIeAKBEjw5wrUnTDn2bNn+YFIf6o33ngDduww7cdOv4+98vl3bjMh0avicKbMfHMvXYSk3hKtMQwacfy8eTNMcXTrEugrCV+iAjt0kc9RSMEfriLx7Tcg7/JvBQvCfRz9GbxCw6xbIGtdmEOQNjUFUpgpcw7T5pNS2EczIbC9odNsaRtT6czt30LajI/EJl5BwRD7H2babELIKjaWJFx1wNQy7TgUMGsz0gtWwwTE0buPg88TT0pWJ0kyc824lvUBfy+c2P2M2f+jGLQl+8BuSJk8UtKByZyZANm/fhPWphifJ/+vP2WarELjiC/XgX+dhkKWX0nwJ4Oj0BkUZERFRYHg886UmS86iUdfUwLhh5T4+HguwBPKXHE9deoU1K9fX5y6XLly/GOOWKCQaNu2Lezbt4/X4Esxmh/bQp7Mg6zFw1U8S7pOez+36GVViq7np92ZH54/f55bn1y6dEm8UWilsnr1ajFvKlFYnugJZwdT+GCdq3mcOz9viJea+3VRPSFACLgCARL8uQJ1J8yJzuG7ddOZIwpTfvDBB7Bwoc7Hm1DujKu+z76Q4RMg+C1lX1bZx37ggTmEdfk+XRmiNhW8uAplTrnaQfCX9vkMyPzmK3G5EfOXg3+TV8S8pQn9Q1Ag81NoNKpvvga06WmgSYiD3N8uQOaGr2RaeTinT/mKEP3tHgAW3dkWymeRj5N6vQGapASxe8ArrSB81mIxb2lC8+ghpEwcztb8WABnaUe9diGDhoN/Y51PNr1qxWzqtAmQtVunbWfKr2H20e8hZexQcRz/hv/HI0wLBYnvdGSBTn4VsoDm6Wg+rB+MBgXbqR8Oh/x/7uravtYawmcsEvOYsETwV7lyZUh/jBua+F65cqVQkVtlC/CwzOHDh+G1114Td4UmX1KzX7GCJdCXKfo5FejVV1/lGtBC3hVX1NpDDWz8sCPQe++9B4sXm/4/h+4dxo3Tabbiy3LVqlWFISy+ejIPshiExw1dybPMrtXG5xbxErPIelQDd+KHaWlpgP78UMvv559/hv/85z+ye4FWKVgWHs4sNSwge/BEdz87mIPJ1TzOnZ83xEvN/bqonhAgBFyBAAn+XIG6E+Zcvnw5oDaLPs2ZM0eMZqlf5+h8yoQPAH3ICYRCjgAm7FCizC0bIG32VLEKNaFQI8rpZOMLlHSdmd+uZRGDPxWLgpgpceioSWLe0oT+IcjSfortmKZa5PINgKattpAmKZH7ucv/65auu68f83XHgq+UfUpX5gYp9P+Y1L+7uFKfCk+zfejMKMUKlkgeNYgH9hDKwmctAfQZiZT76wVI6ttFqGI4lGfBaA6g/ZFYJk2gb0H0EySQkhm4JYI/oT9dzSOgHwESXxSbNGmi2HHnzp2AGtIC9ezZEzZs0PnDFMqdddVoNNC9e3fYulUnpA4LC4Pr16+bNW3buHGjLJrvokWL4P3337d66cSDrIbMNR3s8NxyzcJpVmci4E78EN0s1K5dWxEe/Cizbds2iIiIUKxXKrQHT6SzgxKy9iuj5439sKSRCAFCgBBABEjw56G/gxkzZgAe6vQJX1zxBdYVlNi9DeTduCpOHblmG/hVryHmpYmMFSz67AqdZiIKV1DI4nSywwtU9jGmJTZGoiXGAoZELFlj9VbsdQjyjo5lmmULAQOV2EJoFps0uJdMsw3HCR07BYK69rJlSJf3QRN0NEUXKOqb3eBb+Tkhy6+a5CRmFsxMLPNyed47IgpiDp4UBXtoFpOxbiXTrPyLB0cJeX+MzvefbKSCjDYtFeL+r6ZYo6TVSoI/ER67JPR94926dQueekpZUK0fLdISzTq7LNLIIEOHDoUvv5RHQN+8eTNgoBBz9N///pdrCgrtrDGJE/rglXiQFA0Vp+3w3FLx7mhpdkLAnfghCva6dNF9WJNCgGaV6KsQzXzLli0rrTKathdPpLODUYgLXUHPm0JDSAMQAoQAISBDgAR/Mjg8J/PZZ5/BpEmGWmWoCYiRKl1BKDTRxD8Sp47Z/1/wLl5CzEsTmd+uYVpyn4lFGFk2YrH1wjJxAFsTdniByrv8K/Pz11FcgW/lqhD1zS4xb2miUIcgdjBGQVZg+y4Q2LqDTT4GcZ2ahw+Yllp/yLv2h2zZga93hbBJ02Vl7pTJWLMM0r+YKy45qGc/CB0xQcxjInPrN5A2a4pYFtTtbQgd85GYlyXY7wa8vdifj6xYyHAT7J9PQcqkUQzUfF7s8xTTNNwq1zQkwZ+AmH2uqDGCmiNI+LKYlZXFLN2VTd0xAIb0IwlG5v3qK53Jvn1WZH6UPPZbGjx4MKxatUrWeOLEiYDRfS2hO3fuiFGEsT36t9qzh5n5W0nEg6wEzFXN7fDcctXSaV7nIeBO/PDHH3/kbmowyi8Grfvll18ATZVRE1qgUqVK8bJnn31WKDJ6tRtPpLODUYwLW0HPm8IiSP0JAUKAEJAjQII/OR4ek0PNENQQ0aePPvoIPv74Y/1ix+dZRNlH9dhhLJ8JRJB8fKHY/y4bDQKR/f0e5vNtREFb9q/vc9Uhav13Yt5pCTu8QKGQJ755PXHJ3sVLsoAQP4l5SxP6hyDfqs/LgkFos7NAy7TScv53gvn2i5cN6/tsNQj/bAE3P5VVWJHJu3kNkj/oJ4sKjN0D23WCsMkzjN5LK6ZwWVMUaGLEY0EI5x1bvOAeSYKUJPXpwv0lCotU0goU6oQr+sjJ//MG5N29Dfl/4d+tgj9JFGShLQn+BCQcd8VAGPjCh1SyZEn499+CCONKMx47doxrkQh17du3h127rBfYC/1tuaJfK9RykUbwxXHGjBkD6KPKUkLhIfp/xOjCSOi7EIOEWEueyoM0iQmQuX6FRXAEvdnH6AcriwZwRiM7PLecsUyaw7UIuBs/1Efr5MmTPMDHjRs3xKqmTZvC0aNHxbyxhL14Ip0djCFc+HJPfd4UHhkagRAgBAgB2xAgwZ9tuKm+F5qAoT8oferXr5+B5oh+G0fkMfpgXLNa4tDeJUtBzN4TYl4/kXvuNCQN1Jkke0fHQMwPp/WbOT5vjxcoFHo2rMpCpOUUrJdpGBU7yYSeVpL+ISioR1/l4B5sHt522QLZDN6R0RC5fgf4lCotK7ckk3v2f5A8ZgigeaqUAt/oBmETPim00C/3zEmm/aYT9ErnsCYdOmaKUb+R5sZBoWbOyf+IzaRRdvUjUvtWehaiMDCKAmG0v6xdWyBz0zqDgCoKzcUiEvyJUDgsERwcDBjJEqlOnTpw+rRxnvLHH3/Ac8/pzL1feuklHvDDYYvTGxiFkm3atIELFy7IatCFA2p0W0tlypSBv//+m3erUKEC3Lx509ohDEx9PYUHoUA+oVNzi/AQBP5q4FlGF2yP55bRwanCUxBwJ35oDPOrV68CBvZA7W2B0Iy3QYMGQtbo1R48EQf31LODq3mcJ595jf4oqYIQIAQIAQciQII/B4LryqHxhbZePZ2WmbAWayJT4gsn+oLCL6jo5L5hw4bg46NsuiiMb+zKv4q2bihWmzN3zf/nb0ho/39ie0xEbd4PvhUrycosyeBYWVs3gPeTZcC/Vj3weaqiJd0K2tjpBUrfzLnYGebr0IgZqLHFWXwIejyAvrk0Fvsw/KKYb0UvJgCxlLL2fQepnzB/kY992wn9QgaPgOB+hlqlQr01V/SPlzysvzVdFNuGTZ0NgW11ZtWKjYwUZh8+ACnjdQEPcBwcDymdCVEzVul8TGJwFgzSok8oIMRAIajlaY68wiMABeICkeBPQMIxV9Tw8JMEWkGh2t69e41OlpqaClFRUaKWnK+vLyQkJAAG1LCWUNj4zTffwAsvvAAvvviiWT76+++/Q+vWrUXtRJwPee+SJUsUgzZZsp5atWrB+fPnedPY2Fh49EjndsGS/tjGU3mQLYI/NfAso/fNTs8to+NThdsj4E780BzY48ePh1mzZonN5s2bByNHjhTzxhL24Ik4tqeeHVzN4zz1eWPs90jlhAAhQAg4GgES/DkaYReNn5ubC5GRkdwXinQJ+PKIUSCNObSXth09ejTgAUqgYsWKAZq74YFKqgkj1Ju6anOyIa4B03p7TD5lykH0d0eErOIVNTCkUWNDR06EoB59FNuaKsxYyQKFLF8oNuHRVnccFvMmE3Z6gYpr/DxoMzP4VCjwiT16zuS0SpXWHoJwjLT50yHz29Wy4fybvAIRc5dapKWnjx0fiEXvDZv0mc0CNtliHmdcfcDky2CaknEtGzBhXBLPegWHQMyh0+DlHwAJHZpB/j93C1bL9o9BPbwjox6vvuCC/iuT+nTVtZPUegUEMqHrM+D7dBXwrcT+qlQFv2o14BH+nyAffxKkHJtEnpicXCBsxWi+GNXXFL388stw4oROM3n37t3Qrl07U10U67Zs2QLdunXjdeHh4Twgx8qVKxXbookxfmgR1omNUDNn06ZNNs0tTFK5cmW4du0az1apUgVQo9Fa8lQelP/vPUho97JFcERv/Z5/PFIFzzK2Yjs9t4wNT+WegYA78ENLkEa+3KFDB7HpkCFD4IsvvhDzxhL24Il8bA89O7iax3nq88bY75HKCQFCgBBwNAIk+HM0wi4cv3nz5nDo0CGDFQwaNAiWLmWCHxOUnp4OlSpVgn/++ceg1ZEjR2S+rwwaGCmIa/oiaFNTeK1XWDjEHivQPjHSHNIXzeYaJkI914jatA9VX4Qii64JHV9jEVv/FNsGvNaaRbVdJOZNJuzwAmUg9KzAgjhsOWhyWqVKWw5BwATAib07Q97V32VDhgwaDsH935OV6WfSF89hUWqXy4rxvkWw6Mp+dcyb0cg6mslgsJCMdSvMtDJd7cV+F8F9BrGX8qdNNzRRmzZnGmRuXi+2CJ/zJXjHFIOkvrpoggFNmwOW6xMKl1FQKiUMphLU/W0m8KtsIGjVj+rrU64CRG//QdodKLiHDI5CZ9Dp+5UrV/g4VatWhUuXLpkcc/78+TBq1CixTefOnWHr1q1i3tIEfjCRBtP44IMPuKN6/f779u2Djh07Mq8AOWIV+iLEvuiIvzAUExPDNRZxDEv9YOnP58k8KPdXZlLN+L1J8vEGvxdq8SZq4VmK67XDc0txXCr0KATUyA9R2xlNdTHiOro7wIBKqG1tivDjDH6kEahXr16wfr3uOS6U61/twROFMT3x7OBqHufJzxvhd0NXQoAQIASciQAJ/pyJtpPn2rZtG3cMrz9tQEAAYIQ09FlljPDgtGHDBoNqPChevmy9fzocKKFLSx7oQBi02Glm7mpCiJf763kmcOkqNOdXjKKK0VQtpewDuyFlstzkI3LZBvCrbWgGrTimHV6gNA/vQ3zrRuLwfi/Vh8ilX4t5SxM2HYLY4Hk3rkJir9cBhYAiMZPHaOajzpiQLOOrLyB96edic0ygb8CIhV9xbRdZhQdl8q5ehsSe7cUdBbZ+HbyYb0Sp1mTE/BXg36SZ2EZIJA3oAbnnzwhZCO47GEKG6IRGYsXjRO4vP3OzYKFcSROVBH8COva5osDr+PHjfLASJUrA/fv3TQ6MfvCeflouSEbe2aiR7v+zyQFYJWrWoYkvamELhC+3+lrT+EEFo+1KfVVVq1YNUBhYtmxZoatNVwzqgWbOWuZvFKlHjx7c9NjawYgHWYuYi9rb4bnlopXTtE5EQI38EIPSYXA6gfDjNbqoMUXLli3jkc+FNnPmzAG0WDFF9uKJwhx0dhCQsN+Vnjf2w5JGIgQIAUIAESDBnwf/DjQaDaApgzTimbBdFP4tWLDAwF9UUlISTJgwAfAgpUSLFy+G994zrSmm1A/Lkgb3AgwSIVD07uNmA00kD+0NOad/ErqAV0goRMxbZpHgLu/K75DUrxtgtFuBlLSqhDrFqx1eoPQFmAGtOkD4JzoTasV5FQptPQThUEomu37PvwiRX21mXMBLNlve5V+5liCw349AqLEW8cVarv0mlHnqNbFHO8Av3UheoWGAZrpoxouE2n88IrOCwBpN2VG7UyDBJFDIy65MAJPy4TDIPrRfLEbBKv6fkBIJ/qRoFD6NAq+NGzfygVCLBDWb/VmwHVOk/xEEBXYopENNPHOE/BSDiKB7BYEaN24sMx/G8vj4eM6r8SoQ+lRFoV9ERIRQZPP13r17ULp0abE/ajHOnTtXzFuaIB5kKVIubmeH55aLd0DTOwEBNfJD1Kju2lX3wbefmYB0eM5Ftw2oJSiQJcE97MUThTnxSmcHKRqFT9PzpvAY0giEACFACEgRIMGfFA0PTOPX0pYtWzIZjk6II91muXLluOYfOqxHB/T4QmvM6Tu+gN69e9cm5/Y4Z+r0yZC1o+ClG/Phn8yHgFY67Sos06f8P29Cwptt5YElmOZKGNP8C3ydHQ6NBMhAZ8tpcz8BTdxD2ZBhk2dAYAed2aasUiljhxeojK9XQfrCmeLoIQOHQfC774t5SxOFOQShCVsCE2jl39IJIHDe0LFTIajrW7olsN9JYu9OkHf5N7HM58myEPX1d4C+CYsCYTRe/O0oUXCv/hAybLxSFcQ3r8uCeugENxGL14B//caKbdNmTYVMFnBGSt7RsSxy9SlpkUWmvlINMewcGBgoG4MyOgSmTJkC06ZNEwtOnjwJ9evXF/NKCTQ3ww8oGOxDIOSbGBgENfKM0Z9//gm9e/c2EPIp+QkcMGAASH3+1axZE3766Sfu28/Y+NaU79ixAzp16iR2wQ87AwcOFPOWJogHWYqUi9vZ+NwiXuLi++bk6dXIDzGAEvqTFs6s3t7ecPToUZkprxQmfXcMeE598OAB4MdtU2Qvniidw93ODtK1qzHtzs8b4qVq/EXRmggBQoAEf0XgN6B/uLNly2gmtn//frMmF6bGzjlxBJJH6l42Azv1gLAJupdwY33Tv5wHGasNfRJ6l3gCgjq9Cb7PVgfvYsVBy4QuebdvQfb3eyD34jmD4VDAhYIuq8jGFyjpHCmjB0P28UNiUdT678D3uepi3tJEoQ5BbBL0YZXUvxsLJqETAmMAi+ht34N38QLtpcxt30DazCmyJfkwn4S+zzwrKzOVCWzRlpnCvmKqiarrNMlJEN+SCYMkppnCgqO3HjRqHp38QV/IOXlCaAr4+wxHf4jVXhDL0Ow7nUUHztqxSSwTE0zzrNjJy2IWE5Zo/HnpaWyiAJ+EfzIYxcyZM2egbt26Yn727NkwZswYMW8sgZrO6JdPShgoCX33oc/UGjVqQPHixSExMZH7EPzuu+9g0aJFkJ2t0wDFvih0nDx5snQYOHXqFDRowILKPDbDxUoUBFaoUEHWzlQGIwBXr26cp2BAps8/15nu37lzB8qUKWNqSMU64kGKsKiv0MbnFvES9d1KR65IjfwQ9zt48GCZ1QnyVvRLjf5PBUJt7bFjx/JyKe/EIEhCICWhrdLVXjxROra7nR2ka1dj2p2fN8RL1fiLojURAoQACf6KwG8AD0WTJk2C6dOn27RbfIB9/fXX0LNnT5v6C520WZkQ/8pLouktmo9GYbAOC8iY8M+CrrxJQSTbL41qCBodx8YXKOl48a/VAU1iAi/yLlaiwFRUT1gjbW8sXdhDEI6bNptpmm2Ra5pxbOYv59PGt20CmvuGAV2MrUmpPGToaB5kQ6nOXcpSxr0P2UcOyJbrW/UFiFq3XVYmzWQfZP4kJ8n9SWK9T4VnmIlwLGiTEiHv5jWZ4FXaH9PRWw7w9kI5Cf4EJOxzRV74xBNPcI0QHBEjQe7cudOiwdE1Ar4sSl8ypR3x44jUj5+0DtN9+/bljur1y9F/FWpaF4bQAT6Ob4xQ2Ikv+UgopLxw4YKxpibLiQeZhEc9lTY+t+hlVT230BkrUSM/xH3jBxPUxNbnU6h5jX6m0Srl4sWLkJaWJoOpT58+sHr1almZsYy9eKL++O50dtBfu9ry7vy8IV6qtl8TrYcQIAQQARL8FaHfAQbrQN9ODx/KzV9NQYB+oWbMmAFvvSUxBzXVwUxd8ogBkPPj0YJWTPgVc+gMeEdGmelVUI0Cq/TFs0GbmWFRe2yE/tkwGEjwgPfBKzDI4n5iQxtfoIT+aFqb0LWVkIXAjt0h7MNPxbw1CXscgrQZ6ZDQuQWg5pmUMMqxf4MmEPdyDWmxTWlPEPzl/Pc4JA/rL9t/KNNODWJaqqYo9aPRkLXfMkES4o0+JzM3rhWH1A8IQoI/ERq7Jfr37y8K4GJjY7kQEM3JLCH0DzhkyBBA332WUnh4ONcqHD9+vGJ0yqioKKvGU5rXlOAPX46jo6NFoSRqHErNnZXGM1ZGPMgYMiort/G5RS+rKruPTliO2vihsGV0ldClSxc4d87QekNoI1zRXyuebadOnWqRtrs9eaKwBuHqTmcHYc1qvbrz84Z4qVp/VbQuQqBoI0CCvyJ2/9FPFZp8bdmyBTCypDFCH1ZoAoeHQnO+UoyNoVSOJo6p0yeJVaEjJ0FQj95i3lxCm5YKWbu3M/9oX0P+3b+MNvcKCoaAlu0g5N0PmBlrCaPtzFYws9j415jvtuRE3tS/UVOIWLDSbDehQfqi2YCHF4GwL45hC2Xt3QGpU8eKXUPeHwPB7+hMp8UKMwl9k2tsjoE+wmcuAtT4k5oCmxlKsTrso5kQ2L6zYp3bFGryIb4N03589IAv2cs/AGK+/x94hYWb3gL7veB9Sv9yvoF/Sd6RmfP6MdN0vG8YGZgHUnlbZ77kX68xRCxZI84R37KBOI5vtRoQtXabWCckQkNDeZAKIU+mvgISytddu3bB66+/LlYePHgQWrRoIebNJTIyMnjE8yVLlsBvv+l8Yer3Q19TqIU3ceJEiImJ0a/meeTHkZGRoj8rxUYWFG7evFnmEF/aZe3atYCaMAKdPXsWateuLWStuhIPsgou1zW28blFvMR1t8xVM6uJH+pjgH7+VqxYwS1WpIGPhHbobgE1A5EXY+R0S8mePNFgTjc6OxisXWUF7vy8IV6qsh8TLYcQIAQ4AiT4K8I/hL/++gv++OMPrvGSnJwMpUqV4n6lKlasaJdIkkrQosZZfNuXQZtSoDGDZpBo3mg1MZM9NJ/VPPiXa6/lP3wAXuyrr0+ZcuBT9inu808/Wq3VcxS2Qz4THrVqyAI+xPGRcG3R23+w3ty4sOug/s5HgPkHzP/7TsHfg3+YVms0oGm7T9lyDrn/+HKEwRpQg81YcB7ng6DOGfOYNlSlSpUANUqQOnfuDBhJ0hbCl1EMeIR/GCUSzX1xbPwrUaIQHxxsWYyRPo0aNRIjXqJ5G/oUJCIEjCFAvMQYMp5Z7g78EE2Sb9++zc+reFbNZ2erqlWrcrNfW/zZqponOvns4Jm/anXsinipOu4DrYIQIAR0CJDgT4cFpZyEQMbKxZC+fKE4W+Ta7bIACGKFmydyWECPZBbYQyCrIwoLHelKCJhBoF+/fty3Udu2bWHPnj1mWlP18uXLeVAORMKfaWGi0A6Fpp5GV65c4S/Hwr4wEnGbNm2ELF0JAQMEiJcYQOLxBUWFH+KNJJ7o8T9n1WyQeKlqbgUthBAgBB4jQII/+ik4HQFtago3KdWmFzhmDuzQFcIm2xZ4xOmLt2JC9BGH/l6QvEuWgpidzLch00okIgTsiQAK+rp37w5ogvrpp59y01J7ju+JY+Xk5HDtZhT4Ic2bN48H7vC0vaK7hrlz5/Jt1axZE86fP+9pW6T92BEB4iV2BNONhioq/BBvCfFEN/phuvFSiZe68c2jpRMCHowACf48+OaqeWvpX8yDjDVLC5bIzOOit/0APk+WUfOSrVpb3u8XIfGdTmKf0HEfQ1CXwkVFFgejBCHwGIEbN25AlSpVuOkT+opDM06MfEhkHoFFixbBsGHDeEMMYnT9+nWLHMObH1kdLeLi4gDdNqSkpPAFbdu2DTp10vEkdaySVqEWBIiXqOVOuGYdns4PEVXiia75bRW1WYmXFrU7TvslBNwHARL8uc+98qiVYpCOhG6tuY8+3FhA87YQPn2Bx+wxaUBPyD1/mu/Ht9KzELX+O9L285i7q66NYGAK9HuEAR4wKA+RZQiglkutWrXg0qVLvMPMmTNh3LhxlnV2g1bDhw+HhQsLXCo0a9YMDh8+zNyeernBymmJrkKAeImrkHf9vJ7ODxFh4omu/50VlRUQLy0qd5r2SQi4FwIk+HOv++VRq8059RMkv9e7YE/shTRq3Q7wfa662+8x58djkDzi3YJ9+PoxoR/bFxP+ERECjkAATXzRT50vmZFbDS+avmLAC3Rwj1F4b968aTQCr9WDu7DDrVu3uG8/fJnH6IIYfbh8+fIuXBFN7Q4IEC9xh7vkuDV6Kj9ExIgnOu53QyMbIkC81BATKiEECAHXI0CCP9ffgyK9gtTpkyFrx0aOgV/tehC5bIN746HJh4TubSH/1nW+j5CBwyD43ffde0+0ekLAgxGYMmUKTJs2je9wxIgRMH/+fLff7ZtvvgmbNm3i+1i2bBmP+Oz2m6INEAKEgMMR8ER+iKART3T4T4cmIAQIAUKAEFA5AiT4U/kN8vTlaZm2UvqS2aDNywfvyCgIGTLSrbesTUmGtCUFzvR9SpeB4J79AHx83HpPtHhCwJMRyM3N5Sa++IW+ZMmSMHXqVLferkajgaFDh4JWq+VafuPHj3fr/dDiCQFCwHkIeBo/ROSIJzrv90MzEQKEACFACKgXARL8qffe0MoIAUKAECAECAFCgBAgBAgBQoAQIAQIAUKAECAECAGbESDBn83QUUdCgBAgBAgBQoAQIAQIAUKAECAECAFCgBAgBAgBQkC9CJDgT733hlZGCBAChAAhQAgQAoQAIUAIEAKEACFACBAChAAhQAjYjAAJ/myGjjoSAoQAIUAIEAKEACFACBAChAAhQAgQAoQAIUAIEALqRYAEf+q9N7QyQoAQIAQIAUKAECAECAFCgBAgBAgBQoAQIAQIAULAZgRI8GczdNSRECAECAFCgBAgBAgBQoAQIAQIAUKAECAECAFCgBBQLwL/DwAA//+iZsQ3AABAAElEQVTsXQe4FjXWDs2GYEGFtSD2XlEs2Fjsa0GxLfbuiq7rWtbV34q9rqJib6uyuooLiq5lde0VFV2xIYpdermXcuHe/OcdzJDJNzOZ+SYz8917T57n3plvkpycvDlJzpxJTtpICoIDI8AIMAKMACPACDACjAAjwAgwAowAI8AIMAKMACPACLQoBNqw4a9FtSdXhhFgBBgBRoARYAQYAUaAEWAEGAFGgBFgBBgBRoAR8BBgwx8LAiPACDACjAAjwAgwAowAI8AIMAKMACPACDACjAAj0AIRYMNfC2xUrhIjwAgwAowAI8AIMAKMACPACDACjAAjwAgwAowAI8CGP5YBRoARYAQYAUaAEWAEGAFGgBFgBBgBRoARYAQYAUagBSLAhr8W2KhcJUaAEWAEGAFGgBFgBBgBRoARYAQYAUaAEWAEGAFGwInh76effhKffvqpj+aSSy4pevXq5f/mG0bAJQKvv/66mDt3rkdy+eWXFxtttJFL8qXTevvtt0V9fb3HxxprrCFWXXXV0nmqhoH5Yz4W8z4clTxrGyHadFxStF1qadGm01KiXY/VRNtll0uePyKlnDVLzLrrZiFkk2i7cnexeP8BESmbz+OysJV1M8X8b8aJxm+/EY3fjxdi/nzRrnsP0W7V1UX7HqtTu3WuKRDfeecdUVdX5/G0+uqrix49etQUf8xMMQg0dzlwxb8rOsW0WvMuhbFe0H4tRZ+pVWmcPfQ+0m0WcNeh1zai/ZrrlMoqy32p8LfqwouWvbfeekvMovcLhDXXXFN07949E/42/vV333XXXVesuOKKmcpzldnGt6tymI4bBJwY/o488kjxwAMP+Bx16NBBfPvtt6Jbt27+M75hBFwhsNJKK4kff/zRI7f//vuLxx9/3BXpmqCz4YYbik8++cTjZaeddhIvvfRSTfCVlolZD9wh6m+6Om22QPp2q68lOh79B7HoHvsEnqf5UXfVRWL2Px/0snTouZVY+vaH0mSvybRFYytn1YtZ990mZj94j5ANC4zuFcC0ay8WP/gI0fGk00SbJTpWRJfxYLPNNhMffvihV3Tv3r3Fa6+9VgYbXGbJCBQtB42NjWLIkCFijz32EPh4kzW44t8VnWrq4xqTangoMk+ZWBdZT1tZLUWfsdWzrPiJvdYWoqnJK77DJj3F0nc/UhYrXrks96XC36oLL1r2YHz7/PPPPcx33nln8fzzz2fC38Y/DH1YaIVQS+++Nr4zgcKZnSOQ2fA3Y8YM8Zvf/Ma3eisOBw0aJP7v//5P/eQrI+AMgdZk+Ntuu+3Eq6++6gy7Igm5ME4pfhftu7vofMVNQrRtqx4lus59ZoSYccEZ9EV8wSdxNvxVwmbDFqsLp//peNE0ZVJl5pAnbZfvSm11o+iw6RYhscU+0hWSrbfeWrz55pvFMsCl1QQCRcoBvsoPHDhQjB49Wjz22GOif//+mTFwxb8rOmkrlAcmaXkoOn1ZWBddT1t5uuGvOesztnqWFa8b/tpvuKlY5r7HymLFK5flvlT4W3XhRcuebvjr06ePePHFFzPhb+NfN/zts88+Yvjw4ZnKc5XZxrercpiOGwQyG/7uvPNOccIJJ1RwgyWv48aNE+3atauI4weMQBYE2PCXBb3i8ro0/IHrJWjlX8eBZMRLGOa+8IyYce6f6Gt4o5+DDX8+FIGbKGxlfZ2YOmAf0fjDt4H0YpFFRPvuq9He7Dbetl85d04gvu0K3cSy/xgp2nReKvC86B+skBSNeG2WV5QcYMsLDMzy1w8NbPgTIi9MalPSFnJVlMwtLLE279jwl2+7sOEvX3yZevNBoOgxlw1/C2SjaNybj0TWJqeZDX9QcuHDIyyMGDFC7L333mFR/IwRqBoBNvxVDV2hGU3DH7aBLnnGeZE8wBdf488/isbvvhGzH75PzPvg3WBaciHQZeSrVr9/oFN/89W0vZe29P76Aq4ItVTDX17YzrzgTDHn6X8p+IRo34EMsCd5269h/EPA1t9Zd90iZj1wJ/n8m+enXXTX34nOl9/o/y7j5rjjjhPvv/++VzR8gd5///1lsMFlloxAUXLw8ssvC7hnUMGV4c8V/67oqPolueaFSZKyy0xTBtZl1jeqbDb8RSHj5nmtGf5Y7t20K1NJj0DRsufa8Gfjv1ZX/Nn4Tt+SnCNPBDIZ/saMGSM22GADn7+rr75a/PWvfxXw5YIA/zZPP/20H883jIALBNjw5wLF/GlUGP4GHCOW/PO5iQue9fe7RP2NVwbSdzzlLLHEUScGnuk/Gt58Vcy8/P9E008/6I/9+xZr+MsBWxj0Ju20mRANDT5+nS65Viy2Zz//t34zZ9hQwv78hY/athPLvfKhaLPY4guf8R0j0IIRaK1GrrgmZUzi0Gn5cWz4y7eNa83wl29tmTojUDsIuDb82WpWq4Y/G98cX1sIZDL8nXnmmeK6667za/TDDz+Io446yndw2Zb8cY0dO1asthptCePACDhCgA1/joDMmUxWwx/Ym3bSYWLee2/5nC66296i82U3+L/VjZwxXdRdf5mY89Qw9Sj0yoa/hbDYsJ036m0x7cRD/QwdNqeDUe6IPxhl6pH9xfxPRvt5lr73MdFho03933zDCLRkBNjIVdm6jEklJq3pCRv+8m1tNvzliy9TZwSiEGDDXxQy/LyWEaja8Ddv3jyx8soriwkTJnj1wzaqjz76yDvdF6f8qvCXv/xFXHllcNWOiou6fv311x6tr776yju9FeWsv/763h/u04QstMaPHy/mz5/vFdexY0frKcWTJ08W06ZNi0w/depUMWXKFC9+ueWWE0sttcD/FZyA47TJ7777TrRv3947Erxv376hpwECE5wchFOTkRYDzyabbBJYeZkEn2+++cZzPo6y0ZYbb7yx2HTTTb0jyduQ3664EFWPuXPnilGjRnkO9IEFaG6++eYeTRiBkwYYi3FMOq5YPbrNNtuIbbfdViy99NIeibSGv9mzZ4vPPvtMYIUqrvA7CXkFfzhxMQ1vSeuAdJCd//73v+LLL7/02gunXaM8HPu+1VZbee0XRi9KUZ4+fbqH77vvviuALw7VwYpbyEpaX5pZ2j+M57BnLgx/s+4cLOpvX7hdtMPGm4ul73m0org5I58QMy88q+L54gccKmY/ttBYlbfhrwhcUckisK2/4yYxi/5U6HjyGWKJY/6gfoZe664dJGb/Y+F22k7nXioW2/+Q0LRJH2YZw7///nuBcQlhscUWExg74gLGQoyxGCfwh/kNYz/yoc9i3MC4GxXyHhuLGsswT6EuCMsvv7zo3LmzhwXcd2A8wxi2yy67iB49enhpwv657AtoE7gUgd/guro6b17BvLDKKqt4RWOsxXyNgHEWPob1kEYOqpG3OXPmCHz4hJHr2GOP9Yu++eabxe677+79XmaZZcSyyy7r3afFNw3/fuEhNzY6LuU3LSYh7IosMpQWY5d1R11sWIfVN+mzLLgkLcNM11r1mcYfvxfzPx/jw7HIVr3tp9aTi5GGl1/wfX22oTFpke36+DTUDVbVN44jPfebr8T8b8bRdZxomj7VWyXftstyogMd1tFhi61Fu5WD45nKj2sehr9qxkDFU1q5zzrnqnJt16Le51y9B6E+eWODwzknTpzoQ7f66quT2+Y2/u+oG7x/gjeERcjli5qHzfRZ+U87hhcte1GGP4zPOEjuww8/9OwkvXr18t6vF110UROiwG8b/9Ws+CtirrDx7XpuDYDGP9IjQE6oqwrDhg3DMZn+39lnn+3RmTlzplxiiSX85/TSIGkgTFTGe++9J8k/jp9Xp6/uQY8MiZKUyliaLmiRYcXnhU7QiS0PkfQy5Kcn34YV6bfccks//qabbpI04EbWlwZfefzxx/s0kHa//faTeK6w0K9kbJWTJk3y00fdPP7447Jr166hNECvS5cu8uGHH47K7j3X63H99ddLehGTdLS4pAkglC5NCpIMm7E0EUmGOdmvX7/QOpJxTqINaKKSNPj55aDcqECKqhw8eLCkFy4/vY4Z7hF39913R5Go6jlk84ILLpC6/JjlQlYeeughSYbNijLImOfzS6fgefgedthhobiAbrdu3eSDDz5YQSfsgYv2D6Mb9qz+/tvlhJ5r+H8zr7ssLFnssxmX/Z+fH7SmnnhoaPrZTw0LpJvUd0s599UXpZw3L/B86gkDQvNnfVgkruC1CGznPDdSTv+/P3uYTz5gNzn3jVesMM245K8BvGePeMyaJyqBizGcPmb4fYn80UYVJb/44guJMZuMen56s8/iNxmU5MiRIyPp5DU2Fj2W0ccHH4chQ4ZIMmBJjME6JsDq/PPPr8DCZV/AnIB5zyxb8UEfbyQZIuXf/vY3nzcyRlbwlEQOssgb9B/FU9T1vPPO8/lKi28S/n3iMTc2Oi7lNy0mOtsuZCgtxi7rjrrYsNbrm/TeBS5Jy1LpWrs+M+2044Jz2uNDFTSR14YP3wvkmXzg7hVp5zwzXE7ao3cgna4v+fe91pF1g6+RTXPD33kmbLmWT2PKkf0ryknzIMsYqMpJKveu5lxVru2q6+Ou3+dcvgehHkVhc8wxxwTmLVqoYINR/vzzz5IWGvj5aOFBRR5X/Kcdw4uWvXXWWcfHgU71lbR4R+rziK4L4P345JNPjrVd2PhPI8NFzhU2vnVMXPeVCuHjB1YE8EWqqrDXXnv5Ag/hJgfqPp1DDz00EGczJCHj//73P0krugL59E5j3u+6666RBkVXtNJ0MtRBN0iFGf50gw6thJTrrbeetb7oJBhMYDwzMTB/77jjjmAjNMBgRtuwrTQUzQEDBkhavRhKS6/HueeeK2kljJUuXhLxAhkVXn311UTtjxc9xSOuUYY/TDz6pKHnCbv/3e9+J3/88cco9hI/jxv4w8rFRGAGHV9aGSpptWugzmF08Ix8bJqk/N8u298narnJbJxqapKT9tzOV2qhCM+85pLQUpXhb2KfzSWtVJNNM6YvSJez4a8MXFGxIrENBTzsYeN8OXnfPoH2mjf287CU1meuxnCbQgJG8CEp6qNFVF8766yzQuug911XY2MZYxl9+ffHnIMOOijyo8PFF1/s4+C6L+BjURKdAB9v8PKh2qoaw19WeTvllFP88hUf5lU3/KXFN4kc+w0Rc2Oj41J+02ICtl3KUFqMXdYddbFhjTRJg0tckpaJdKzPSImPV74RDh8fjzvECqH5wRLztR9IJ5l26tEBmjr9qPupRx3gk9BvXBn+so6Biqckcu9yzlXl2q55vs+5mutRhyKxIR/8gXlLX2wShecNN9wQyHPfffcFkrrkP+0YXrTs6YY/LHjSZcyc/9VvGMFoFV4AM/XDxr9OP8p4XcZcYePb9dyq8OJrdQhUZfiDgUS3+MMYo4cXXnghMDBsv/32enTFPb4oYiWF6hi40pZeSVtmJB0W4lnJaYtVIB5pHnnkkVxpJelkOgNpDH96XXFPW0Dlbrvt5q3e0uNgMMOKLvWMtqt5hjasjFx88cX95yqeThHUWfLvabtRRVraIizxxQeDPW3JrVhVAeNuWNA7sSoXV/AKWaBDXeQKK6xQUV6nTp08xd6kSUvwA6tEQQt1++1vfytPPPFE2bt378gX8zDDH+RJH4gUj+D7iCOOkFg9pw/YKh55sLKm2tDQ0BBqbAQmMLriRcg0XKJsrP7UQxS+SIsVr2gXGFvNVTAwXtD2aJ2Uf++y/X2ilpssxikY7qadfkKFcjx7+D9DS22aPk3Oevhe2VRfF4zP2fBXBq6oYJHYBgGN/jXrsYcC7TVxu40kLWmNzhAR43I+0MeBsBV/H3zwQcU4hbELH6/OOOMMb/yBUcnsaxjraLtLRQ2i+m61Y2NZY5mucKvx0bxi9TltCfMxcNkXoBjTFutA22Duw7yHOSFq/gOPaQ1/LuTtxhtv9HjS5Q28QB7AK/70leVp8dXphsmx3wiWGxsdl/KbFhOw7lKG0mLssu6oiw1rpEkaXOKStEzWZxYg1TRrlpy4/cYL57Yt1pTzf/w+Gkba4YQPkL4Bb8u1ZePEX/z0c54evjDu1x0RE3faTE7/62ly5nWXyhmD/uoZF/38v6bB77lvV+6ccWH4czEGqgra5N71nKvKtV2Lep+rdq4H/0Vjg/cdcmPiz7P40AZZiAubbbaZn37JJZf0diSp9K75TzuGFy17Ye+RmPehu+B9Hgs78P6q20sQT+ceSIyvZrDxn0SGy5grbHy7nltN3Ph3OgSqMvxdccUVfseHEGNVmh6wfdE05OFrUlR45ZVXAvQGDhxIO/TmBZI30eof80sDDExmcEkrSSfTy6/G8LfDDjt4y7oVHWybxWpB4Gr+ob60l14lleTvqUJRPuSQyq+RTz75ZIAW+RkK3RpKvgO9rb56uc8884xfnroJ68Qw0n366acqiXd99tlnJfk0CJSNLWNmwIoSvUwMpuRfK5AME4opU8gTZvg79dRTA/QwmT3xxBMBevhx//33Vxgc8bJSbRg0aFCgXBg6hw4NbgtB36ADcQLpzMk2DF+s+sM2DPQDFcgPYsVKUNA2g+v2N+lH/TaNUzMuP182zZkd/ldfLxsn/CLnffaJnPXPBytWjkHpndx/V0nLfKOKC3+eo+GvLFxR0ZrAVkMcL0LYXq2/rEw/+xQtRfJbl2O4TSExV0H/8Y9/DFV8P/744wqjPb7ymyGs72YZG8say8IU7i222MJbtf3oo496H+XwAUUF130B47o5J2Dbrx6wEjLsg2Baw59LecNWKZ3vqA9xafG1ybGOS9y9jY5r+QUvSTFxLUNpMXZddxvWce2kx7nGRacdd8/6zEJ0Zlx8TmBuq7/71oWRxt2c//w7kHbaH48NpJhyxH6BeLjTgHHRDA0ffygn771TMO05p5rJpAvDn8sx0Cb3rufcCkAiHhTxPpdlrgfbZWCDxTX6nIUtolEB7/F62qOPPjqQ1DX/acfwomUvzPCHBTXmir7//Oc/Fe/Wt912WwA7/LDxb5PhsuYKG9+u59YK4PhBKgSqMvyttdZafufH141ffln4NUuVDt8/+gCB1U5RwVQwsFQ1KmBLpqILKzo5Xw8kdUnL1skCBdOPtIY/bN8Nww6rKMwvBNgWbBpDUT6248KQpzDBMmI9wL+i7nsQqzRefJF8n0UErBjTVxKS89KKLxNmJ1577bUjt13DuKZ4wxX+mvQA/0x6PLZtkUNXPYl/D98S5GQ+kN40/H3++eeBeHL07vkO9IkYN2+88UZgGxvoV7PlF21jbkvD9uWocNxxxwX4hM9MFUx88UUubHUR0uMlXMfPxCOP9ld82q6mcUo3CqW+p6/mDe+/YyuyMj4nw1+ZuKKSNYHtr2g3Tp0iJ++/S+AFZcJW68r548dVtkeCJy7H8DiFBHOH/mECK6l0w7rJKh2qE+hr5liG9GbfzTI2ljWWoR6mwr3qqqtKOlwIURXBdV+AkVUf07ACM8rtRD19MMDXcz19WsOfS3lLauRKgy8Aj5PjigaJeWCj41J+FRtJMHEtQyg7Lcau627DWuETd80Dl7jyVBzrMwqJBdeGD94NzG+TD9wtmED7Ne3PJwbSznlh4cfzhtHvB+Im70f+0UJW/ihy8FOs60pTjqj0ae3C8OdyDIyT+zzmXIWV7Zr3+1yWuR68l4WNqWeE6TUKW7io0udaGIxVyIP/tGN40bJnGv6wcw+Ld8ICFq/oPvqxq9FcXRnHP2jGyXBZcwX4svHtem5FmRyqRyC14c/8MhS1zxwGJF3IYVCJ6hDwE6QPJvfee29kjWBMufzyy71tvlgBZS6XdUkrrpOFMZjW8Be2mk7RNQeUsBVrKi38HSr84BNOD+YhLEl8OMB/laKHq2koNDvxU089pRcZuIcCqR/2gsMq9IAVdnpZOBQjLphfp0xD15133hmg94c//CGOnBeHVZI6D7fccos1j5kAX3R0GmGrUfU8WB2p0mO5vO4ry8QXKzHjgo6v6ecxj/aP40WPc2WcmrTLVrLhvbd00snvczL8lYkrKl8T2BIf2FptrmDAi8qsRx5I3kZGSpdjeJxCAqM/vtDDUIQPLaavGoMtzyiofxTBuGsGs+9mGRvLGstQJ1PhjlsF4LovmC+hWOkfF/7+97/7YynG1LSGP5fylsTIlRZfpI+T4zhszDgbHZfyq8pOgolrGULZaWQY6V3X3YY1yrSFPHCxlYl41mcqUYKRTjfCYXeCGRqnTZX46KXSTfrtFgHD3tzXXpJTjx+w4FAP2jKMeTwuNM2c4dMCzSkH71mR3IXhz+UYGCf3ecy5FYBEPMj7fS7LXA+Wy8QG72XqfQQfQ+kU1goUsVsJxiqVDgtK9JAH/2nH8KJlz3xPf+CBeL3X3Mlgykwc/8A6TobLmivAl41v13MryuRQPQKpDX/mUt64FwJsY1WDBK533HFHKKcYMPR0uMc2IhjGsJ01TXBJK66ThfGUxvCHlWizQpb3K7o4IUjH5KefflJRFVfdcAXfAnowt2XDmastvPzyy4Gyb789qJyYnRirLuICvoSputCx5oGk+gpOpAlbAalnwFcl3d+WafiDDz9VFq5wTm0L5goTbPdLG84555xAudjmbAt03Htg67ZKr+OL9jSN2yqduuqHf5j+NvNof1Wu7ZrJOEVK8ZRD9yED0t8llN+qQ06GvzJxBRa1gG3jLz/LKb/fK/BigpcT+CjKElyO4TaFRPGJDxRxq/2QDquTdd+l8NtmBr3vYvzJMjaWNZahTqbCHTeOuu4L8ImjxnD4LTW/ipuY44VEn6vTGv5cylsSI1dafJE+qRyb2Ji/bXRcyq8qOwkmrmUIZaeRYaR3XXcb1ijTFvLAxVYm4lmfqUSp/p4hgblu5vWXVySa9eiDwTRXLzz8qCIxzTmSDsSKCo2TJ8o5zz5JW3nX9mlOPqBypaELw5/LMTCp3Luac6PwM5/rc0TUghU9T5r3uaxzvV4u7ovGBv5n1ZyLa9i7uvkx4NJLLzXZ9n+74j/tGF607OmGP+zMgi4SF8y50HSTZuM/TobLmitQXxvfrufWOIw5zo5AGyShjp4ozJw5U5DgCXqZ8dKTDzPxww8/CFLOQ/PTyj1BK678ODpAQowaNcr/rW6oswjqNIKMPuqRfyWH3oJWMQlyWCloFZWgjubHhd24pEUDvyCDm1cMTRRi+PDhYUX6z1AH2ibq/SY/fWLEiBF+HG7olFnxySefeM9oQBNfffVVIF7/QQc4iJEjR3qPaJWJICOhHh24pwM6BLBGIEORoJWVfjwdkCLuuece/zed1itoa7D/O+yGtloL+nLhR5155pnimmuu8X/r9aDDJgQZ4/y4sBvyDeW3uykDZKgSZHjzstGqUEHbycJIBJ7p7UKGP0HGZz8euNJWae83bb0V9OXKj4u6gcxAzsjRrZeEVvIIMtxFJQ99ToZq8dBDD/lx4IFeQP3faW50fCHv5O8wNjv6B63E9dLQlnBBvrD89Hm0v0/ccjPrgTtE/U1X+6nab7CxWKRXb/+3nDtHyOnTRMObr4imKZP957hpv96GovNlfxPtuvcIPE/9g9p04tbr+tk69NxKLH37wnbyI1LelIkrWC0b2/lffSGm//FY0fTLgvFRwbfY3v1Fp/OvEKJtW/Uo9dXlGE6OqMWHH37o8UBbeQUZ2638YMwgI58gH3KCtsF4f+RPU5DxK5CXDH/ipZdeCjzT+27WsbGssQwVoi0rYty4cV7dMP9gzqcV/IG6qh+u+wKtKvD0CtCnVQVeW6iyoq5oC/pg5UVj3FVzgEofJwcu5Q08gBcVyMef6N+/v/rpX9Pgi0xx/PtEE9zY6LiUX8VOEkxcyxDKToux67rbsFb4xF3zwCWuPBXH+oxCYuG1acIvYvJeOwjR1Og9bLvcCqLL068F5rppRx8o5n38gZ9pmYdGiPbrrO//DrtpmjhBNH49Vsz/7hvROB5/4xb8ff9tRfJ2q60plv3nvwPPJ/Zam3hq8p6133BTscx9jwXik/xwOQZWI/dZ5twk9UMa/b3B9ftc1rk+rg5FYIN3RjpE0n+3p0U7/nyqeKNFP4JcN3k/afGFoEMZBeZqW8jCf9oxvGjZI1dYnn4IDHbeeWdBu7Ni4YA9AXKowkknnSSGDBmiflrn+TgZLmuuAPM23F3PrT5gfFMdAnbb4MIU5tYjKlGm/cOBBGEBWx9NXz1htOFz7qKLLor0+QParmjFWdfD6kCd0scDB3SYQbd6U0cxowO/9ZVw8AUYF+BgVWFlrvjDicoqrtrrvvvuGyher0fPnj0DcWE/4HdQlY3Tg/Wgr6AB3SRBp2eu+IPPSVUWvkIkDfqXJfizSht22WUXv1ysSLSt0oujr+OL03ttQV9ZC1+Qesij/XX6cffmqrSZ110Wnpz8UNbfOdj/qu1vk6HDIub/YF+xGU7016c5rfgrE1fUrExsG955Q07ccdOK9ppxKR12YfniGdtWWqSrMdz2JVIV+c4778jf//73smvXrn4/VuNI1JUMPCq7f9X7btaxsayxDJXRx0NS2vz6hd247AtYdanXG1uxkwR9JwIZ/iqy2OTAlbyZX/STHO5hwxeVsfFfUeGIBzY6LuVXsZAEE5cypMpNI8PI47ruNqwVn3HXPHCJK0/FsT6jkAhep516TGDe00/ZnT/+60AcVsNHBRzmMWvofaGHmCn9J+ya14o/8OlqDEwq967m3CiMzed5vs9lnetNXovGBuXrcyjcdJFhz2cLOxdwWKHShcLcnPiJ6cYV/2nH8KJlT1/xl8SNFvQb7GJQOJr6jY3/OBkua65Au9v4dj236rLG9+kREGmybLPNNr7AKsFNe8XgEhWwzfOAAw4IKP5R9HHACK2eiyLlbRnNSiuuk4UVnMbwZ/q6M+m5MvzhJSgKw6TPTeOT3olt9UC9dEOdafijlXY+fzZjqMIIy/QV76bhD4ZPFQd5TRowcat8OGAkbdDruNRSS6XNHkifFt84w18e7R9gNuZHYuPUrzRmPXRPQHGG4jv5oD3Ij1z8VvIYFrBnIkBz6gkDYpMnjSwTV/BYFraznxoW8GGkXk7q77o5KXSJ07mYD2wKCdwtwB+n6vtxV/0jBdLZDH9Zx8ayxjI0kK5wwwgQF1z2Bbj20NsgDOMwXgYOHOjnAz9msMkB0ruQtyRGLpSVBl+kT8I/0tmCjU7auUef98y5XfGSBBOXMqTKTYux67rbsFZ8xl3zwCWuPBWntyvrMwoVKec8/3RAn5hx4Vl+ZN2QGwJxsx6+14/Tb2AghN9iNXfGXSf22TyQLk/DH3h0MQba5N71nKtjG3df5vsc+NL7VNRYWRY24M908YStoyrQbiZ/fsX8PHToUBUVuLrmP+0YXrTs6Ya/U0+tPHE7AM6vP2h1qI8lDpTTg43/OBkua64A/za+Xc+tOmZ8nx6BxFt96WuQIF9i1OezBWwbwvZgMq5EEqJTXQUdSy3IH50gvwJi8uTgNkCVkQ5FEB999JGglYLqUcU1Cy19WW3Y1l2zMNSJTh/0Hoel15e70kuhoINKTBL+b32rL634E3Sqqx9n3sRt9cX2trffftvLQg7sxVVXXWVmt/4GDrQaxk+Xph7IRH79BJ2I6eWnCc/f9osH+vboarZ1mVt9u3fv7m/Jo8FG0PHzXrm2f3pbk1HZ2+Zny6PH9+vXz98Kji1x9AIryEmuniTxfVp847b65tH+SStibkddfMAxYsk/nxubve76y8XshxduTUfiRXboK5a6lpbD0/aC1CGnrb5l4goMysB21p2DRf3tNwaboH0H0en/LhOL7bV/8LnDX1nG8LgtCNjeRCfYeXONyS7mFvTDTTbZRMAdAdwV4A/jC+YvBDJKxW71tY3xoBE3NpY1loEvfYsNGf7Ec889h8ehwXVfIOVW0AnuXlnAfvTo0aHl6g/18ZcU4FRbfXU6uM8ib0m2taKMNPgifZwcIz5psNFJO/fEya/iKQkmrmUIZafF2HXdbVgrfOKueeASV56K0/sT6zMKFbo2NIhJu28r5IwFen6bJTqKLs+/LdossqiYsu9vReOPv7qDoHmxy7/fEG2XDr7nNE2eKKYdfdDCdBrpNosuJtqtsZZov+a6ov3a9LfuBqIDbd2duO0G/vbivLb6amx4t1nGwDi5z2PONXmP+q3r+GHvZ2Y+l+9zoG0bK8vEBvyR+UDg3Ue5oNLfn+Bm69//XrDFHC6UsGUV7pH0kAf/acfwomVP3+prbtvVsVH35PvQw41W/nmP4HpLdxEVxz8y6DJsblcva64AXza+Xc+tKJNDBgSS2grNk15xAAIORUjyh9N/iEX/z3RoGccDDSaSjEbykksukRtttJFPQ9EbPHhwXPZAXFpaunV9t90qnerqxLGEVz90wrbV17YaxNWKv8MPPzyAGb1Q6WxXde/Seq+vtMNpUsDRFvQt4eaKPyydVrKB03KTBDiPx9J2lQ/O5dOGM844w88POjR5WkkgDfkfk+TXMJA2Lb5xK/7yaP8AszE/0q5K80g1NMgpA/YJfOXGF3FsBa4q5LTir0xcgUPR2NbddHVFm0zcaTPZ8PbrVTVLtZnSjuFxXyJxApvq8+p6+umneyvJo8ahuBO0Uae0fTduFUBZYxnqkeZLu+u+oG9ZoZcMsGMNpHj6bYkv32aIkwMzrf47rbwlWd0G+mnwRfpq+UdePdjouJRfVW4STFzLEMpOi7HrutuwVvjEXfPAJa48Fcf6jEKi8jqTDuzQV+nNefFZ2TD6/cCz6Wf+oTIjPam77W+BdKAz4+Jz5LzPx4S6yTBP9Z28f+XqaxeHe4Qy++vDtGNgnNznMefG8a7Hlfk+Bz7i5nrEl4kNykfAgR1KF8IV7/dYBUqLRvznZOBakNj4nwf/acfwomVPX/GHHYa2gHc+Hd8LLrggkCWOfyTUZRg73/RQ1lwBHmx8u55b9XrzfXoEYOW3BpzQY/o+Isfn1nwqweWXXx4QdnQWPdCBGBInBt1yyy2SnGPqURX3MPTpHQcGMj24pAXfeqosm6GOnMD7aZGnVgx/gwYNCvBFKyl1uELvYYiiA0Hkv/71L/nBBx9kNkzFTXgHH3xwgD9MNHGBDpiROBFZtYtp+DvxxBP9OKSB3xJboJWXgTwHHnigLUtFPDloDdCgg10q0pgP6CuInwcDowppB8k4w18e7a/4tF2rMk4R0XlffiYnbL1uUEGm3/PHfWkrsjI+J8NfmbiikkVii228+osO7ifvvSO1x9hKvB08cTmGxykkum9UjBU33nhjLPfgS407uMJAZYa0fTdubCxrLEOd0ijcrvsCOakO4BzlF1hhjxOH8dFItU1aw59LeUti5EqLL9LHybHCIcnVRsel/Cp+kmDiWoZQdhoZRnrXdbdhjTJtIQ9cbGUinvWZaJTmffZJYD6ccf4ZEv6L9Tly7sv/CSUw9fjfB9LV3XJtaDr1sOGDdwPpJ+/XV0X5VxeGP5djYJzc5zHn+kBYbsp8nwNrcXM94svEBuUjYC7VF7BgwY05FtAOsgWJjf958J92DC9a9nTDH+Qr6oOxguqRRx7x9RToK3RYioryrnH8I0Gc4a+suQJ82fh2PbeiTA7VI5DI8AcDkFKqcTV9vtmKp61RgS8GoAFDH8JccuyvO7sMe5ky6cPniOJn7bXX9qNd0gJR0FbloMPBABoVzKO0a8Xw9+ijj/p1UG1nG5wuu+yyQJ4TTjghUG2Xnfjhhx8OlHXIIYcEyjJ/mLyZhr/rrrsuQA+GRVug05gCeWD0TBsgz0pWcLX5psIEq6fXfV+mxTfO8JdH+yfFplrjFOjX33FTQOGFUj31aDLIJlgRGuAvJ8Nfmbh6+Nx/ewCfyINTAmAs+JEG23mfjJb6iwXaYcrBe8rGSRNCKGd/5HoMj1NI4CdW74PmyluzNldffXUgPW2tMJM4NR6UNZahUmkUbtd9waQHH4xxASsQ9HZMY/hzLW+mnyQo+mEhDb7IHyfHYfSjntnopJ17bC+z4CMJJmabQ8fMqqekxdh13W1YR7WR/jwPXHT6Ufesz0Qhs+A5Du5Qhj4cdDVp163937iX8+eHEpi4zfp+OuSP/XhGus70c04NpMcHNzPo8/OUI/ub0dbfrsfAOLnPY861VvDXBGW+z4EF21hZJjY6htjdpuZTrKTXdx5gjIwKefCfdgwvWvZ0wx8we+WVV6Lg8eYzch3jY4v0o0aNCqSP4x8J4wx/Zc0V4MvGt+u5FWVyqB6BRIY//TAFCOvNN6d35K5vXQUNfVls3759/c6AJcVxnQdLZfVtmbvvvnug9i5p9enTx+cLPOvOTvVCyfdBxYEktWL4g7N0fWss6hG3PRorF3G4BdKpP3NwctmJZ8yYIfXtc/ja9MYbb+jw+vfYpmzyZhr+Jk2aJJdddlmfd8gKVhxEhSeeeMJPi/piNSH5N4lKHvl8Pil6Oi6g9eyzz0am79+/f6Dc4cOH+2l1OraVpsgUZ/jLo/19Ri03WQx/OJRj8oG7B5ReKMqzHvm7pVQjOifDX5m4ooaFYEtuFqYc3i/QBpP36SObpk8zQHb70+UYHqeQmK4jyI9qZEVgwNG3u6B/g7YZ0vbduJeBssYy1CmNwu26L8DgYyrIUasx//GPfwRWgKNd0hj+UFeX8obTDMGD+rvttttQREVIgy8yx8lxBfGYBzY6LuVXsZEEE9cyhLLTYuy67jasFT5x1zxwiStPxbE+o5AIv+JEXmX4M691f1t4KIKZe9IuvQL55r4RbSiYeeWFgbQoB4eCmCGr4Q/0XI6BcXKfx5xr4hH1u8z3OfAUN9cjvkxsUL4KmFPV/GVer7nmGpWs4poH/2nH8KJlzzT8kc8/b2t0BTj04K677grgatoukCeOf8THGf7KmiuS8O16bkWZHKpHwGr4IyeeAaMWDCMTJ05MXeLjjz8eEPr27dtLLC9HuP322wNxOD0RBhkzwB+avj0SgxK2B+vBJS28bOgDH06gxelGdICHVyRWMqIzw5ecng73tWL4A6Pmik3wd/LJJ0sc0a6HF198UZJD+UBdwgxPrjvxlVdeGSiTDoCRDz74oM6axHavlVdeOZAO9TANf8gEmdDbAzJ7ww03BFYQQLHF0mh9WTvynHnmmYFy0/wgB/iBcrGS9dZbbw2QwKoiYK/zt+qqq0oM2iqkxTfO8Aearttf8Wm7ZjJOEXHPb86WawWU34nbbywbf/nJVvTC+JwMfyigLFxRdhHYzvrngwHs8eIx+cDd5PRz/5T4b+7LL4DdVMHlGB6nSB155JGBfojTa9XYrhiGfyOMRbp7AdV3oZSaIW3ftb0MlDWWpVW4XfcFuEpQOKsrPpagHHycooO/pLklWKVLa/hzKW+ffPJJgG+8GMB9yfvvvy+hS6mQFt84OVY0k1xtdFzLL3hKiolrGUqLseu627BO0l5I4xqXpOWyPhONVOO0qZXuSGh+xBwZ55Jk2qlHB+bUSXtuJxs+/jBQEPSbGZedF0jnGxe3WS+QFj9cGP5cjoFxcp/HnFsBSMSDMt/nwJJtri8TGx0y+Dw3F1hgbsU7e5yP+Dz4TzuGFy17puEPONGBZAEf79AhsWVaX7CEj8h08KQOu3cfxz8SxBn+EF/WXGHj2/XcirpyqB4Bq+GPToENKLKmQ8mkRTc0NEj9GGt0EBheEHAEuD4oKgUenX7PPfeUdPKixPHnesdBGnQ63WDimhZeAjt27BioP8rFAAjjlOIz7FpLhj/gAhxNPmHIBO777rtvxWo1pIVfxy+/rPSr5roTYws1nZhZwV+3bt0klp2bS8j1eoQZ/mDU0529q/SdO3eWdAKu55srzFgLHiCnWQLaXZWnrliBiC+q+OIIZ/XqOa5og/feey9QZFp8bYY/EHfZ/gFmY35kNU6B9MyrKr96Tzs9uPU8hgVyGDgvoEBPPWFAbPK0kWXgCh6LwHbS77YPYOe/fPz6gpPkd/09Q9JC6nQ+iFNIYJAx55QuXbpIOEnGBwAosjDK6/1Vv4dhHz5H9ZC27+rzHuY4M5Q1lqVVuMG3675w2mmnRWKvt4N5D97NECcHrvUP6AcmT/h92GGH+WylxTeOf59oghsbHdfyC5aAbxJMkNalDKXF2HXdbVijvkmDS1ySlol0rM9EozX97FMq5scpR+wfnYFi5jwzvCIP5lHsbph60mFyyiG/CxjywubY+V99ESjDheHP5RgYJ/d5zLkBMGJ+lPk+B7Zsc32Z2JiwDRw4sGIOC3un1fPlwX/aMbxo2Qsz/Km5H4eaYpu07ppMxZ1yyik6dP59HP9IZDP8IU0Zc4WNb9dzK+rJoXoErIY/U7D/+c9/Vl3an//858BgAmeYeLFBwOq5sBVdqqOYVxggzdUZijGXtIYOHRpq/DP5ufDCC+Wf/vQnv35hg2Qa4de3RgOnuKA7VYWhMizAQAr8zRVuZj3Ub7wAY4VlWEhTD+S3TXhIM3XqVM8vnio/6ooTd/Ul+2GGP0XPPOUoiiaeg06UPIFe0oBVlGGTZljZ2OKMFaRmSItvEsOfy/Y3+Y367cI41VRfJyft0btCWZ7z3MioYoPPczb8lYErKpg3tk11MyswD3sJsT2rxvCH+rkaw20KCQx8YX0z7Bn8j5p+90znzGn7btKxseixLK3CjTbLoy9gRb3uA9hsF3w5v/baa70PgyoObW4Gmxy4kjeUe8wxx4TKlG7YTYuvjX+zvlG/bXTykN+kmCCdSxlKi7HrutuwjmqjsOcucQmjH/WM9ZkoZMg/+WsvVcyRsx6r1OdMCjgMxDZvqnisEJx57aBAevNAEBeGP/Doagy0yb3rOdfEN+53We9z4CnJXF8mNjpuWIyg5lN1HTZsmJ4k9N41/2nH8KJlT7ePDBgwQK6xxhoVuCn8cMX798UXXyyxCjAs2PhPYvgrY66w8e16bg3Djp8lRyDW8PfZZ58FhBiWaywDrjaYWz7QEV5//XWfHAwvON46zEKOtHgBwEou+BmwOX52SQsnw2LLq/nVGp0YvoiwTB7hvPPO8/H6/e9/79dL3aQR/jSGP3w9UIMLtknHhddee0326tUrcAqiyosrlnhjWXKco/s09QAvOOhClaG//Jh8Qrauv/76UAMwVujhoA4oooceeqhPDy/EcQFLn9dff/0K/1yKHxjNoAy4DvD7iG3ppl8wlItVRjAi4ICPsJAWXzi/V/XBqsW44KL94+jrcbOffDygsNbfF+7vSs8Tdo/tokoRVlfvoI+wxOYzmmAn/XYLP/+0044zUzj5XSSuYDhvbLHdSH+hULinvc4eXv2HIhdjOD4UqL4RdhgHsIT/PtMPqsoD4zwO/1GHUY0fP96nhzTmOJ+27yYdG8FnkWNZWoUb/Kngui/gA9Sll14q+/Xr580NWCWNtsSHNqU/6P6FsKrbDEnkwIW8oVysYIfxz9wV0KlTJ5+ttPgm4d8nHnNjo5OX/CbBRGfbhQylxdh13W1Y6/VNeu8Cl6Rl6elYn9HR+PW+cb6ctPu2vm6BgzuaZkwPSWg8Ip0E8+Kk3bbx8wbmVdrOO/WYg6Q6Gdg7YEtbZT9t4FEBgjqdag730Im5GAOTyL3LOVfnP8l9Ge9z4CvpXF8mNjp+2LKq9KDlllsu8W4ol/ynHcOLlj3d8AeDHtx5wGXMoosu6mMHDGErgI4Cd1pxwcY/VhGqNrHtvixyrrDx7XpujcOQ4+wItEESEqSaCmTUE19//bWgPfCC/AAKUqAFGdgECY8g418qXl3SIsOUGD16tPjoo48ErU4U2267rSAjZSp+aiUxrbQUtI1XkDFWTJ48WdCWNkGDrCD/SKkxdl0nekkQH3/8sRg7dqygwz9Ez549BU1CgoxoVReFthszZownU6g76kunfAlaTVk1zSQZUS4wRn3IcCzWW289QQ5gBa3MTJI9tzS13P65VboAwoyre5BdjuFR3KHdMOdgzKFDPgR9QBGkqHljIiltUdlKeV7WWJa2skX2BbQX+R72WCTXFYKMpGnZ9dO7kje0E8Z9OjVT0Jd6b65Jq7/4TLWQm7SYFClDzQnisnBhfcahlJCe2/j9twv+fvlRtF16WdF+zXVEu+6rCtG2el3XBYeuxsA4XsqecyHLtfo+VzY2ce2WJK7W+c+bP8z57777rvfuh/dMWpAh6MNfEuicp0Fda/Vd33llmWAiBGrS8JeIc07ECDACjAAjwAgwAi0KATqFXZx11lnehxkozXQYkiD/wJF1hIJNq9j9ePINKP72t7/5v/mGEWAEGAFGgBFgBBgBRoARaO0IsOGvtUsA158RYAQYAUaAEagRBF555RVB23V9bmhLr6BT2f3f+g0dxiTIfYT3ZV09f+qppwS5ylA/+coIMAKMACPACDACjAAjwAi0egTY8NfqRYABYAQYAUaAEWAEagOBKVOmCDpcymeG/OWIN954wzPw+Q/pBttpzj77bHHTTTf5j8kXrCA/OwJXDowAI8AIMAKMACPACDACjAAjsAABNvyxJDACjAAjwAgwAoxAzSAAP30jRozw+aEDkUTfvn09v4t04IqgEygFHTggfv75Zz8NbugUYHHssccGnvEPRoARYAQYAUaAEWAEGAFGoLUjwIa/1i4BXH9GgBFgBBgBRqCGEMChTltttZX47LPPEnEFw+Bf/vIXccUVVyRKz4kYAUaAEWAEGAFGgBFgBBiB1oQAG/5aU2tzXRkBRoARYAQYgWaAAE7phW+/W2+9VUyfPj2SYxgIL7zwQrHHHntEpuEIRoARYAQYAUaAEWAEGAFGoDUjwIa/1tz6XHdGgBFgBBgBRqCGEYDRDz7+vvnmG++vvr5edOvWTay44oqid+/eYp111qlh7pk1RoARYAQYAUaAEWAEGAFGoHwE2PBXfhswB4wAI8AIMAKMACPACDACjAAjwAgwAowAI8AIMAKMgHME2PDnHFImyAgwAowAI8AIMAKMACPACDACjAAjwAgwAowAI8AIlI8AG/7KbwPmgBFgBBgBRoARYAQYAUaAEWAEGAFGgBFgBBgBRoARcI4AG/6cQ8oEGQFGgBFgBBgBRoARYAQYAUaAEWAEGAFGgBFgBBiB8hFgw1/5bcAcMAKMACPACDACjAAjwAgwAowAI8AIMAKMACPACDACzhFgw59zSJkgI8AIMAKMACPACDACjAAjwAgwAowAI8AIMAKMACNQPgJs+Cu/DZgDRoARYAQYAUaAEWAEGAFGgBFgBBgBRoARYAQYAUbAOQJs+HMOKRNkBBgBRoARYAQYAUaAEWAEGAFGgBFgBBgBRoARYATKR4ANf+W3AXPACDACjAAjwAgwAowAI8AIMAKMACPACDACjAAjwAg4R4ANf84hZYKMACPACDACjAAjwAgwAowAI8AIMAKMACPACDACjED5CLDhr/w2YA4YAUaAEWAEGAFGgBFgBBgBRoARYAQYAUaAEWAEGAHnCLDhzzmkTJARYAQYAUaAEWAEGAFGgBFgBBgBRoARYAQYAUaAESgfATb8ld8GzAEjwAgwAowAI8AIMAKMACPACDACjAAjwAgwAowAI+AcATb8OYe0dgm+8847oq6uzmNw9dVXFz169KhdZgvkrLni0lz5Vk37+uuvi7lz53o/1113XbHiiiuqqFKvZeBaVJlFlaMasFbbWPHHV0YACBTdL4pGXc6aJWbddbMQskm0Xbm7WLz/gFQsyIa5onHsF2L+F5+Kxm+/EW2WWkq0W6WHaL/ehqLdb1ZKRYsTV4cAj6XV4VZNroaGBvHggw+K+fPn+9k33XRT0atXL/+3qxvb2NPS272l18+VnDCd5oeArW83vxq1LI5bbftIDq0GAVJcJHVb7693796tpt62ijZXXJor36o9fvOb3/jyuP/++6vHpV/LwLWoMosqRzVirbax4q/Wr/TiKQcPHizHjh1b66w2a/6K7hdFgzXzygvlhJ5reH9TTxiQvPh582TdrdfLCb3W8fMrOt51y7Xk9LNPkfM+H5OcJqesCgEeS6uCrapMs2bNkj179vT1E+jNa621lsR47DrYxp6W3u4tvX6u5YXpNR8EbH27+dQkG6e1qse21vYR2ZqTczcnBHQh33rrrZsT67ny2lxxaa58q8bUFb599tlHPS79WgauRZVZVDmqEWu1jRV/tXx97bXX5CabbOK9fD722GO1zGqz563oflEkYHOeHi4nbLGmb7hLavib//VXcsqAffx8AYPfr0ZE/9k268k5LzxTZLVaXVk8lhbb5N9//73s1q1bwPj38MMPO2fCNva09HZv6fVzLjBMsNkgYOvbzaYiGRitZT22tbYPG/4yCHRzy9pahdzWTs0Vl+bKt2qPWlX4ysC1qDKLKqfW21jxV6vXt99+W7Zp08Z/6WTDX74tVXS/yLc2C6nPef5pOWHLtQPGuySGv6a5c+Tkfr8N5FNGvsn79FkQpxkT1eq/Oc8MX1g43zlFoFbnS6eVrDFib731llx00UX9cXjDDTeUTU1NTrm0jT0tvd1bev2cCgsTa1YI2Pp2s6pMFczWuh7bWtuHffy1rC3rsbU57rjjxPvvv++l2WijjcT9998fm761RDZXXJor30qu4NPvp59+8n7Sij8xfPhwFVXqtQxciyqzqHJUA9ZqGyv+avX68ssvi5122slnjwx/on///v5vvnGLQNH9wi33ldTg06/+5qvF7H8+RJsUYbdYGDr03EosfTs9jwn1Q24Qs+6+JZCiQ69tReeLrhFtV+jqPW+aOEHMuOAMMe/dN/10bZdaRiz7xAuiTeel/Gd84wYBHkvd4JiWCnz9HX744X62J554QvTr18//nfXGNva09HZv6fXLKh+cv/kiYOvbzbdmyTivdT22tbYPG/6SyS+nYgQYAccIsMLnGNAaJMdtXF2j1LrCVF2tOFcRCDS8+aqYefn/iaaffggtzmb4a/zxezGl/y5CzJvn519s7/6i0/lXCNG2rf/Mu2lqEjP+coqY+9Jz/vPFDzpMLHn2Rf5vvnGDAI+lbnCshspf//pXQb5Wvaz4IPPUU09VQ6aqPC293Vt6/apqdM7ECLQABFiPrc1GZMNfbbYLc8UItHgEWOFr8U3sndRci6s6ax15VphqvYVqjz85Y7qou/4yMeepYbHM2Qx/c57+l5h5wZk+jbbLLCuWHUar+Dp19p/pN41kYJx6wG5Czp3jPUb6Ls+9LUSbNnoyvs+IAM+XGQFsptlberu39Po1U7FjthmBzAiwHpsZwlwIJDb8TZ06VUyZMsVjYrnllhNLLbVgK8fo0aMFOW8U3333nWjfvr3o3r276Nu3r1hjjTUqGP7qq6/E888/L7799lsv7brrrivIebnYYIMNKtLGPfjmm28EysXfPPoqvfHGGwvaqy3WXHNN0jXtymat1GX27Nnis88+E2PGjPGu7dq1E9iCi/oAv7bm13UDFLQH6oKw/PLLi86dO4sJEyaIESNGiC+//NLDY5dddhE9evTw0pCzYjF37lzvfrHFFhMrrbSSdx/1D9iizcAj/kC7Y8eOXr6tttrK4xVtnjXo7bHsssuKZZZZxiP5ww8/iDfeeEOMGjVKdOrUycNku+22EyuvvHJska5x0fnTZR9Ygrc333xTTJ482Wu3zTff3MPd1nZ6BejEI/Hf//7XazP0jQ4dOnh1hTwD5yiMbe2p8+0CV51n3GeVj2oUvqx936xD2O80uLqSh7gy9bgwfuOeYUxR/R/pdFpFjAG2NtZlVMeyuc0rUfVIO0bMmTNHYNyDwnTsscf6TXvzzTeL3Xff3fuN8RH9OSoU0Uf0+tbi2OJ6DojC2vY86xhpo6/Hzxn5hJh54Vn6I+9+8QMOFbMfW7i112b4q7/lOjHr3iE+nY6nniWWOPJE/3fYzYxz/yTmPrdwFdQyDz8p2q+9XljS3J6NGzdOvP766wJX4E4HmInevXv7+oTqW2AAd5lyAAAAQABJREFUegwd3BDgZfz48QJzcVR8IDH9wJw/bdq0xOmzyoJtLDX5w+8ixgK93K+//lp89NFHnt74448/erra+uuvL/Bn09t0OrjPilfaMcB1+Sa9qN+2ObmadtfLmjFjhpg4caL/aPXVV0/0ngRdFG2AsMgii4hVVlnFp6HfZG2ntPUr+r1Jr6u6L6pf6fNsS9SPitbzipaduL6txym5SnrV9XpXdMLKrrZvq7m2Wj02azuF1SXsmY5d2PtQVP9Lq9eHlV3qs6T+Grfcckvfwe1NN90kaSKRtOTdf0aV8O/hlPz444/3SSPtfvvtF3BWrqc/8sgj5aRJk/z0UTePP/647Nq1q1+OTgP3Xbp0kUlO3Sq7Lupoa3qBi6wL4u6+++4oKLzncDSsMBgyZIikl0NJBif/GeLIaCTPP/98L31SR5ZffPGF3Hvvvb28in7YlYy8cuTIkbE8JonU2+Oyyy6TY8eOlWQMDtRDlU9GMQl5IUNzJGnXuOj8XX/99bKurk7uv//+kpShUB5JQZI4ycgWaHCUF1xwgdSdG6t6qisZ/+RDDz0kGxsbK8jZ2lPn2wWuigFX8qHX23aqr6u+r+oQd02Dqyt5iCuzZ8+eoXKmZCTuSspioKpx5egJi2pjXUab87yi1yOLTJx99tnWtj7vvPP0pvLvi+wjen1rcWxxPQf4ICe8cdV/EhbnJZv91LDAYRyT+m4p5776opTz5gWe2w73mPbnEwPp5775qpWNuttvDOSZ9WC87mIlmCLB559/LsnnWoXug3ERuujOO+8sSYGXZ511lt+3oN+YIc18hLyYm9XYG0ZP0XclC2n4K3IsQD3fe++9yPcBhRF9nJZXXnmlhN4TF1zhlXYMUDy5Kl/Rs11tc3Kadg8r65hjjvHlFG1BH5nDkgWe/fzzz5KMC34+WswRiMcPVzglrV9Z7016xYvuV/o821L0ozLqUZbsxPVtV3q9Kzq6nGft29Xqsa7aSa9L3H1c+yCf3v+y6PVxPJQRl/hUX90Q85e//EWut956/qSgJnbzCqBgoIEhxIwzf++4446R9acvVvKoo46y0lA0BwwYIOlLbCS9MuuCDqUrJIrnqOvvfvc7SV9OQ+tCX+58TA466KBIw+rFF1/s5bcJORJBMYsyaEXxCIU6S9Db4+CDD5b0Jd6vV1SZMDrSKsTQYl3jovN37rnnSlqFZ+UPBlcYY6MC+oU+qETVUz0/+eSTK0jZ2lPn2wWuYMClfCRR+Fz3/QoQQx6kwdWVPMSV6XJijytHQVFkG+sy2pznFb0eWWTilFNOsY4tpuGvjD6i17cWxxbXc4DqG0muLvtPkvJUGmX4m9hnc1l/x02yacb0BVEpDX9TDu8XMOLNHzdWFRF5rbvl2kCemddcEpnWZcQrr7wiacWptc/QLgpJh+P46cIMdUnmI513WqkUSw9pXcpCEv7KGAv+97//yaWXXtrHQuksUdddd91V0ooJHUr/3iVeaccA1+3lV8pyY5uTk7R7XBFPP/10oG30RRlR+W644YZAnvvuuy+Q1GU7Jalfme9NqHgZ/Qrl6vNsS9GPiq5HmbIT17dd6fWu6EDeEFz07Wr0WJfttKAm9v9x7YPcev/LotfbOSk2RVWGP3NCp22pcrfddqsw2MD4oRtxaCmlZzTBSsHFF188MLGAJp1cGFp72uJUkZa2CEt8ycIkRtsrK7727rXXXqG08FBvzCLrgi+duqCpssHPEUccIQ877DC5zjrrVNQVeWAJN4Ou2Cha5hVfvGn7hZdVL5u2wZjk5AcffFBR9gorrCAPPfRQecYZZ8gTTzxR4stf2KpC2hZQQS/pg6j2wBfio48+2huIfv/738sll1wywB9tVZYzZ86sKMY1LlH8Qb7xQrHHHntI4GRiT9uTPYXBZLChoSHU+AtaMHBj0MS9SQ9fyvRga88ovqvF1bV8JFH4XPd9Hb+o+2pxrVYewEdcmeh3a621VuQfxgzIy2abbVYhMzvssEOgmnHlIGHRbRwlo5D95jSvRNUjrUzceOON3soZvZ2ABehj3sSfuRK8jD4SVd9aGVtczwGBThTzw3X/iSmqIqpp+jQ56+F7ZVN9XTAupeFv+hknBYx4De+8EaQX8mvmDZcH8sy44MyQVG4f0TadCp0Auz6gS2EVKj6GkvuTijER/akIw59rWajF+RI6LT7C6roKbemV5KZA0oEYEh8syX1NIB5pH3nkkQphcI1X2jHAdfkVFYx4oI/1YXp5knaPIO09xrsDdGXVRjDS2lZd6roE9G7sclHBNU62+pX93oR6lzHHotyoeRZt2RL0o7zrUbbsxPVtV3q9KzqQN1d9O60e67qdUJckIa59kD+q/6XV65PwUmSaTIY/vFTCSqsCJgcoVGqC0a8wjkBRU4H2cFcMpocccoiK9q9PPvlkgB6+7j744IN+vLoh34HeVl+9zGeeeUZFB65hjVlEXU499dRAXTABP/HEEwHe8OP++++XSyyxRCAtOpIZwhSbLbbYwltp9uijj3rKF4yJKtiE3FxV+cc//jFUQfj4448rDFOwhlcbwtoD9TBXOqLc1VZbLYALlEszuMYljL/f/va38tNPPw0U/eyzz8pFF100wB+2X5th0KBBgTQwEA4dOjSQDFt7r7vuukA6U2GztWcY31lwdS0fNoUvj74fADniRzW4ZpEHsGErM4LVwGM6mj4gL+TbT/7yyy+BNLZyim7jMBktYix2LVth9cgiE9iOpc9lUR/FXNcjICwxP8LqW0tji+s5IAaKQJTr/hMgXu2PlIa/upuDq/fq777FWvLU4w4JGP6mnX6CNU/WBAceeGCgj8BYYX6AxDZgfDTR+xLuizD8uZaFWpwvseJSx3bgwIG0s3xeoGmbmpqkuYIM7wNmcI1X2jHAdflm/aJ+2+ZkW7tH0dWfQ0/W2wlbVqMCVnDqafHxXQ+ucbLVr+z3prLmWGAeNs+2FP2oiHqULTu2vq33q6j7JHp9VF79eRI6rvt2Uj3WdTvp9Y67t7VPWP/LotfH8VJkXNWGP2zfNV8qwThWl+m+ITCBYFuwqQwgLbbj6ts0sPVRD9gOoPtRweq1F18knzURAb7h9JWEdHiIxOoqM5iNWURdoIDqkyl81dGhHiZr/m861CKwdRdfrk1DmKnYrLrqqnL69F+39/iUFt7ECTkd3BEwWuHLIxS2qPDuu+8G6gMfjtUGsz1g3Js1a1YoOXLcLYGdwhLtXV9fH0jrEhcQNvlbe+21I7eqwGireMPVxAX9wNwW8+qr0T6UzMF62LBhfl3j2jOM7yy45iEfcQpfXn3fBy/mJi2uWeRBsWErU6WLumKFiy53dPiShAJvhrhyim5j8Gb2rSLG4jxky6xHVplIojDlUQ9TXqJ+m/WttbHF9RwQhYP+PI/+o9Ov+j6l4W/2yH8FjHgTd9hENk4IfkDQeYEfwQk91wj8wRCYZyDn+oHxDnNqlHsX+Jg259y8DX95yEItzpfmR0xsiYwKcFuj5ii8IwAjFfLAK80YkEf5qm62a9ycjLxx7W6jreLN9w9TL1XpcMVWTNVOuMK4q0IeOMXVz+S76PemMudYYG7Osy1FPyqiHmXLDtrP1reRJi4k1evjaCAuCZ08+nYSPTaPdrLhoeJt7WP2v6x6vSq37GvVhr+o1XSokLldNWxVm6o4/H2oScZ0RA8jh4rDNYlvCt2BM/KEGQrNxiyiLnfeeWegLn/4wx8UBJFXrIDU63/LLcEv76ZiE/cVD4XECTkMjbBkY5UQlDLTp4fJJIyCupEV7VhtMNvD3Mpm0sU2Eh2X4cOHB5K4xAWETf6eeuqpQHn6Dxj29NWadAqxHi3/85//BHgP+/KtZ8CqQlVXbLlQ/hqRJq49EW/ynQXXPOQjTuHLq+8DF1tIi2sWeVC82MpU6cKuWAGNjyJKTrAM/bnnngtLGiszRbcxGDRltIixOA/ZMuuRVSaSKEx51CNUaEIemvWttbHF9RwQAkHFozz6T0Uh1TxIafhr/OVnOXG7jYKGvGMOko2//FRR+rzPPpGTfrd9IC2MgFOO7F+R1uUDuL1Q4x2u6gCzqDJwiJaePm/DXx6yUIvzJfQRHdd77703qgkkPnBefvnl3jZfHAaif5TPA680Y0Ae5UcCYUTY5v64djdIxf6ELqraCjtTcOCNGbDTBFu1VTosvNBDHjjF1a/s96Yy51jgbs6zLUU/KqIeZcsO2s/Wt5EmKqTR66No4HlSOnn07SR6bB7tFIeHHmdrH7P/ZdXr9bLLvK/K8IevLlErslCZPn36+BMHJpCffqpUGFWldeNWx44d1WPvesUVVwTowEmtLdDx0YE8t99+e0UWvTGLqgv8zqjJFNe4U2kVw9jaqufB1ls9mIqNjaZNyBVtGK/iVvsh3ZdffhnwawffU9UGvT2gBIStDtVpv//++wFcsL1ED65x0flDe5grDPWycY+vAqrdevXqFYg+55xz/DikwfZgW3jzzTcD2+RVelt76ny7wFWV60o+4hS+vPq+qkPcNQ2uWeVB8WErU6Uzr5hYzcN4brvtNjOZ/ztpOUW0MZjSZbSosTgP2dLr4UImkihMedTDFxTLjV7fWhxbXM8BFjgqol31nwrC1TxIafhDEfUP3FlhzMOhIXW3/U3Ofek5Oee5kXLmdZfJCVuvW5EOhr9pfziiGk4T58Ep8GqOxVV3IxNGZMqUKYFxMm/Dn86DK1moxfkSL4t6O+Ae7mXwYg93PtUEV3ilHQMUr67KV/RsV9ucHNfuNtp6PD7O6G11xx136NHevflh+tJLL61Iox64wimufmW/N5U5xwJnfZ5tKfpRUfUoW3bQfra+jTRhIa1eH0YDz6ql46pvJ9Fj82inKDzM57b20fsfxs4s7/5m2WX+boPCqULWQCfRik8++cRLRxOq+OqrryLz0MEaYuTIkV48rQoTZCSMTEsHdAj6SujFk+FPkJ9APy2t7BL33HOP/5tO6xW0Ndj/HXZDWw3EAw884EedeeaZ4pprrvF/46aMugAz2gbt8UHbTgR9bQvwFPaDvr4JOhBFkHNeL5pW1QkyFPlJybmroK2v3m/gTEIpaOWPH2/ekA8c8eGHH3qPaSuvIIOSmaTiN/gkI58gX46CluR6f2+99ZYgI2MgLRn+xEsvvRR4lvSH3h50eIh44YUXYrPSgR6Ctj77aUiJFyNGjPB/u8ZF54+c1wtaEu2XFXZDvq7EqFGjvCg6eMa/xwNSisVDDz3kZ4NM0CpL/3eaG1t76ny7wDWMtyzyQaciCvoo4JGlFzlBKzf9IvLq+34BMTdpcM0qD4oNW5kqnX6l1aCid+/egbGEDuER1157rZ4scF9NOXm1MRjTZbQ5zyt6PVzIBH3AEhhTVSAff4JOJVU/vWuZfUSvby2OLa7ngADwKX9k6T8piwpPTvrDxK3X9eM69NxKLH37wjnIj9BvSPeYeng/Mf+LT/WnkfftVuouGn/41o9ftO/uovNVN/u/Xd/ocyx0JDIyWYsgX3+C3MF46UydAQ/j5qMw4nRggiD3K5H0wvJkkYU4/soaC6CjAgdy+1NRXbTLjjvuKOhgBEE7GwTtBKpIY3uQBa+0Y0AYL1nKD6MX9sw2J8e1exi9qGd4t6LDFr33BKQhH2sC84weyMeXIHc13iM6xE+MHz9e0ApAPUnofRac4upX9ntTWf1KgazPsy1FPyqqHmXLDtrQ1rdVO+vXavR6Pb+6d0UnS99Oosfm0U4KA9vV1j56/3Oh19v4KSw+qdVRt3wSWLHZdF8e2MsfF+A4lirr/Zkr/rbffns/TqVJe913330rii+jLth+p3iHlTlpoE7h54MPPz3ocSSgelTovc26rTK98847Eqfodu3a1S9b8R51pZdUlT31VW8PnNScJOg+e8xVda5x0fnD0em2AF+VCiecOK2HXXbZxY/D6cj6dhc9XZJ7W3vqfLvAVfHkSj7ivvTm1fdVHeKuaXDNKg+KD1uZKp26/vzzz962fCVnuGKsw1aduJC0nCLaGHzqMtqc5xW9Hi5kIsmX0jL7iF7fWhxbXM8BcX0qLM5V/wmjnfpZFSv+UAZOCZ5+1smhK/p0n35TjthfNnzwbiDdjEsqD91KzXdMBn07Ihl4YlIujIIrEzVeFrniz5Us1Op8CXck5qFrCmf9Cp/bF110UaQvRtVSrvBKOwa4Ll/Rs11tc3Jcu9tom/G68364ByHDnp8Eq1lw0JxqM5v7HlftFFe/st+bypxj0TD6PNtS9KOi6lG27KD9bH0bafRQrV6v08B9Vjqu+nYSPTaPdjLxiPptax+9/7nQ66P4KPp5VVt9Tb9lJtOuDH/wN6cmoWqvW221lcleYDAtqi4waqo6bLPNNhU8RT2AsKl8yyyzTCCZrtjAoGQLNiHH9m34nFPlxV1XWGGFQDpXhj9zO3NUnWBQVvyhc+rBNS5657fJC/iIM/zpcTiAIUuwtafOtwtcXctHnMKXV99PgncaXLPKg+LHVqZKhysUdJygquQfVxiYbcvQkddWTpFtDH50GbVhWcvzSpp6oN76OGB+HEB8EoWpzD6i17cWxxbXcwDaJElw3X+SlGlNU6XhT9GdPfyfcupRB8iJvTcMGPcm7baNrBtyg2yqr5NzX/9vIG7m9Zer7Llc4Q9ajX/wKZ0kHHrooX6eIgx/rmWhVudLYI+D/g444ACpv8Sp9jGvOGWZdg9VNJlrvNKOAa7Lr6hgxAPbnBzX7hEkIx+brpCwlVUF2oni9w+02dChQ1VU4Ooap7j6lf3eVOYcC9D1eba16Eeu9LyyZQftZ+vbSKNCFr1e0cA1Cx3XfTuJHptHO+l4xN3b2idN/0M5Nr0+jpci46ra6ksDkCBHvTQ3hAd9qy8ZaMS33y7cAmLmiNvqi+2ob7/9tpeFDpwQV111lZnd+hvLyGn1WiCdvnyzqLp0797d3x5LwiToxM0AT1E/9GXw2KqCLbcq6FsZyPAnyKG/igq90peWyK2+2LJBJ30JOrq+Ii8dKuFtydtkk03ExhtvLLDNBn+o0w8//OClJ8Ofk62+J5xwgiC/jBU86A/I/6C3BZr8EISW7RIXFJBGXpCeViAKOvUYt8Lc6tuvXz9/Syu2ZWOLEjla9tKm/RfXnqCl850V1zzkQ5dtc6tvXn0/CcZpcLWNHygvTh4UP7YyVTq0w/777x/Y2o6tOBgngactxJVTdBuDV11GbVjW8rySph6ot00mkmyRKLOP6PWtxbHF9RyANrOFPPqPrcxE8dVs9Q0j3NQoGr8ZJ5rIpUi7rr8RbZdfQZBvES/l7H8+JOquutDP1encQWKx/YN6lx/p4EbvP2Q48LfcxpHWxw/bVt+weJM2fYgVdJKw99hMn4cs1Op8qeNCvhQ9HZL8cQvyFycmT56sR/v30Ck/+ugjQSsFvWd54JVmDMijfL+ylpu4ORlZ49rdQroiml4uBd4jlKsm/V0E27H//e9/e3ngjghuWLBdWw954BRXv7Lfm8qcY4G7Ps+2Fv1IH6ez2A/Klh20n61vIw0C+lUWvX4BlWx08ujbSfTYPNpJ4WG72tonTf9DWbpeYr7723gpND6plTGN5dOVxf7www8PfIHC8lUXoYy66NtMcDprkjBnzpzAaZ3kzyuQLe0XzTjrNvlFDGBNQihPP/1078ts1EEf+um15MslwFuaH3p7kD8Ya1YyNgZ4xbZkPbjEBXR1/mjy1YsKvY+z+pMPtgDvpICF0tAfIg35ZpTTp0/XH1u/Jul8Z8U1D/mI+9KbV98PABjxI66fIIuOa1Z5UCzYylTpTjnllID8YCyBbCQNceUU3cbgOQ2WtTyvpKkH6h03RiA+yZfSMvuIXt9aHFtczwFoE1vIo//YykwUn3HFX5IycJiHvv13/ldfJMlWdRry9+yPg9iyOHfuXCutjTbayM9jW/G32267xdKDTgRXHdCT8GfSy0MWanW+jAKKXiQlfQCVl1xyidSxV5gNHjzYz5oHXmnGgDzK9ytnuYmbk5E1rt0tpEOjcWCHagNccYggVmzS4gr/+UknnRSaNw+c4upX9ntTmXMsGkCfZ226ZkvRj1zVo2zZQfvZ+jbSIGTV6xdQyUYnj76dRI/No50UHrarrX3S9D+UZdPrbfwUFV/TW30HDRrkT0SYoGg1mhUXGEfoQBD5r3/9S37wwQcVxhIQSNOYrgahE088MVAX+EWxBVpVGchz4IEHBrKkUWyQMU7IdV+LwPrGG28MlGX+IKfWAd7gC6PaoLcH/BhCYYwLdJBHoGwolnpwiQvo6vzZJl+kj+v8Q4YMCfCOutgCfXXw84AXFeLaE2l0vrPimod8xCl8efV9hV3cNQ2uWeVB8WErE+muu+46Xw7QR/HiGXe8vKKtX+PKKbqNwZcuozYsXY3FechWmnqg3nFjBOKTKEx51ANlJwl6fWtxbHE9ByTBJI/+k6Rca5qUhr/GCb/I6ef+SU498VA5+cDd5Ox/PRpbxPwfvpMTtljTN/zh9F9JhrE8A3zFYQxUfzhdNi5gCxM+kqj0pqEOeXX3IbaxiA4682mBpkkvD1moxfkSeiBOgr3lllvk888/H9cEEoY+hT+uGM9VyAOvNGNAHuWrutmucXMy8sa1u412WDwdzBcwWkN/NvVS2kEQllXmgVNc/cp+bypzjkUD6POsbUxqKfqRq3qULTtoP1vfRhoXer0LOnn07SR6bB7tBDySBFv7pOl/KM+m1yfhqYg0NW34e/TRRwOKAvz1Ra0+U2BddtllgTy0DUlF+dc0jelqEDI798EHH+zzE3Wz8847B+oCg6Ye0ig2yBcn5PC7oitl5uoyvVzcX3311YH0tCTeTJL4t94e4OEf//hHbF6UpfM6evToQHqXuICwzp9t8kX6uM4PJVnn3eYbEUqanh7OmVWIa0+k0fnOimse8hGn8OXV9xV2cdc0uGaVB8WHrUw61TWw+hftaTPOK9r6Na6cotsYfOkyasPS1Vich2ylqQfqHTdGIN70xfTII4/gcSDkUY9AATE/9PrW4tjieg6IgcKPyqP/+MSz3KQ0/M37fIxvxMMqvql/ODy29LrB1wTST//rH2PTu4h8/PHHA/PinnvuGUvW1L9MQx0yr7322j5NzE3kSiSSJnyj6fOySS8PWai1+RKrLBdZZBEfhyQff+HXWOEGvFXIA680Y0Ae5au62a5xczLyxrW7jXZUPFa0qnag7W5SX3WDsT0q5IFTXP3Mflv0e1OZcyzaQJ9nW4t+5ErPK1t20H62vu1Kr3dBJ4++nUSPzaOdgH2SYGufNP0P5dn0+iQ8FZGmpg1/5P+s4rQwfXuACRC+wuIADDWh4Tpq1CgzWSmD6aRJk+Syyy7r84btKbCGR4UnnnjCT4t6dOjQQZL/lEDyNIoNMsYJubkVg/wyBsrSf+AlVN8WAP5Au9qgdy7QWm+99WRdXV0oOXMi1pVHlcElLqCp82ebfJE+rvPPnz8/QA/1ffbZZ5EtNPTv3z8gB8OHD/fTxbUnEul8Z8U1D/mIU/jy6vs+eDE3aXDNKg+KjbgysZqFfO0E5OC0005TWVNd48opuo3BuC6jNixdKYR5yFaaeqDecWME4nGqGvqs+rvtttvwOBDyqEeggJgfen1rcWxxPQfEQOFH5dF/fOJZblIa/rBaDwd3+Ft3t1xLNrwTvqJu9sgn5ASK19POG/t5JLeQWf0vMqElArsCyOew3z8gg1Hz6MSJE6V5GJlpqENxffr0CdDTDz7Q2SE/aBWHWJj08pCFWpwv+/bt62MGnfCVV17RoQrcw2UJ9F41pukuAvLAK80YkEf5gcrH/Iibk5Etrt1jyMZG4eO6agfzes0110TmzQOnuPqV/d5U5hyLRtDn2daiH7nS88qWHbRfXN92pde7opNH306ix+bRTsA+SYhrH+RP0/+Q3qbXI00thJo2/AEgbNk1J6aTTz654gTLF198UZKTyEDaqIEyTWO6GoRQF2yH0OsCY94NN9CpeNq2GBiGsLxc9x+DPGeeeSZIBEIaxQYZ44T8yCOPDPCGU4LJcXWgPCjbDz74oGeE1OuBe/BSbdDbQ9HF9taxY8f6JIERvgyYBsewrY4ucQEDOn9RMuUzSje2zk+HsASwxlfzW2+9VSfhbVGHnCs8cMWWOigiKsS1J9LofCs61eKah3zEKXzgP4++D7q2kAZXF/IAfqLK/PLLL6V+giXakQ5CkRMmTJAwztNhPxIfPOL+yMG6X+WocpCgjDbWZdSGpcux2LVspakHsLaNETj1UvVZXHFyKbbSvf/++5KcroOEF1zXQ9G1XfX6Kj5raWxxPQfY8EB8Hv0nSbnWNGkNf0RwxqXnLjTm0aq/Sbv0knNeeMYvav7XX8n6u24OGv0oHbYIxwUlK+qqz2dx+cLioPMpOrhCn6KDwQJJ0V8wb+rpcG8a6pAJK6j1dPjYgpNOlR4E38J33XVXYMuwSm/Sy0MWanG+BN4KA1xhYMVHazPAD63usgRpoQ+rkAdeacaAPMpXdbNd4+Zk5LW1u41+WDz8h5uLJNAmOJU5zpd6HjjZ6lf2e1NZcyzaTZ9nW4t+5FLPK1t2ovq2K73eFR3IWh59O6ke67qdUJ8kIap9VN40/Q95bHq9olv2teYNfwAI2zh05QL3UMoA8r777hsYHFW6rl27SnSKsJCmMV0OQjDqYVm94lFdO3fuLHE4BrZK6H5oVDydoCsbGhoqqpJGsUHmOCHHS6X+NRZld+nSRcK5LYyOGBTCFGjFI4xXM2fOrOAxyQO9PRQ9XGH8xFcIOkk4sFpSpTEP9VBlucQFNHX+bJMv0ifp/HhRUPVQV6wIxRd0rDygU9UC8ZD39957D+T9ENeeSKTzrcrAtRpc85APm8KHOrju+6BpC2lwdSUPUWVecMEFATnQ2zHpPT4kqBBVDuLLaGNdRm1YuhyLUV+XspWmHijbNkbAJxlewsLa+LDDDgMJP7ish0/UcqPXV+exVsYW13OABQ4vOo/+k6Rca5oqDH+NU6fISXv0Dhj/sKpv4o6bykm/3aLiOeImH7SHRL64oMsK7rMY/lDOQQcdVNFH8KEEqwHXXXfdCp1GlW8a6kALBr6OHTtW0EM/XHzxxSueK1q4mvTykIVanC8xTuljmcIE/Q/j0n777SfpdMOKdsCHDL3t88ArzRiQR/mQqSQhbk5G/iTtnqQcM83AgQMrZNqUYzNPHjjZ6lf2exMwKGOORbn6PNta9COXel7ZshPVt13p9a7oQNby6NtJ9VjX7YT6JAlR7aPypul/yKPPhZj3ajU0C8MfFIQ///nPFavglJJhXmGwijvpMk1juhyEIARTp071jGkmz1G/6Yhv/4uzKURpFBvktQk5DHxRfJjPDznkkAqnpPfff7/JYqLfenvA8KkrAma56vfxxx8f6YPHNS46f7bJFxVO0vnr6+tlmOKl6qdfcXoyVh6YwdaeOt8ucHUtH3o7YxVbWHDd98PKMJ+lwdWVPESV6WJixyl+KkSVo+KLbmNdRm1Yuh6LXcpWmnoA6yRjxDHHHBM6HpsKhct6KDmwXfX61uLY4noOsOGh4l33H0U307UKwx/Ka/jwPTlx+41DjXz+1l4y+HlGvwN3l42TJ1nZ1Oc13EN2swT44UuCubktOMrAMXTo0FDjn8n3hRdeKP/0pz/5/TOMXhK+FN0k+lStzpdYCbnyyiv7WKg6RV0x16tVlHrbu8Yr7Rjguny9bnH3tjk5SbvH0Y+Kw4dks42GDRsWldx/7hqnJPUr870JFS9jjkW5+jzbWvQj13pembIT1bdd6fWu6EDWEFz3bdBMqse6bCeUmyREtY/Km6b/IU8SvV7RLvNauuFPP8Ya2wTiwmuvvSZ79eolF1100YoJCxMYlq7jhCrbwRRpGjPNIJSmLlg+vv7661dsXVUT8Q477CChhMaFtIpN7969fdyiDuOA/77VVlvNT6f4wRUGKBw4ggMqEMaPHx9IF7UCL64OiDPb4/vvv5e77rprwHG04gOrH+HnLy64xsXkL65sxOHADsWv+ZJu5oW/IGyBMbcwIz9WYOKFAAd8hAVbe5p8Z8UVPLiUjzXXXNPHKcrwp+rtqu8renHXtLjG0UJcEnmIKtM8REfJVZqrbviLKkevQ5FtbMqozod5n9dY7EK20tQD9UoiEzBqQGkyVxt16tTJhMb77aIeoYRDHpr1rbWxxfUcEAJB5COX/SeykDQR5KJDX6U37bTjEudunPiLnHHhWYFTe3Wj36Q9t5OzHn1QSjroIUkwV9RlNfypMuH7NmyFH4zS2LUAn8ErrriiP9+EGeoUrU8//VTiJdtccYvVrDAgqu3E5513nk8vSvdxKQu1Ol8CNxjy8BKqH96hz1HYEYLdLvAfp7u2UZirq0u80o4B4MFl+apOtqttTk7T7rayzHjsplHthJWyYTuLzDz47RKnNPUr471Jr3+RcyzKNedZnRfzvqXoR3nVowzZierbrvR6V3R0WXLZt0E3rR7rop30+sTdR7WPypOm/yFPEr1e0S7z2gaF08DfrAItCxW0jVfQ/nFB/qsEbUEVNMmLHj16CFIwmlVdyNeGGDNmjPjf//4nUC/UhQ6sEKusskpp9QAfX3/9tSAfe4L8iAkyyAracuthTMqvc77I8OW1JQiTwi1effVVrwxgQ85BPXxWWmklDxfaIuK8/FogiLpCnj/++GNBLxyCDjgR9CIj6EWpavbywrVo+dABaEl9X69Xrd2X2cZlYVGrsoWxAeMCnaIpaHWENzfEzXNF1KMlji0u5a6l9R85Y7qYP/5r0Th+nJCz6kW7biuJdt17iHarrCpEu3apoLvjjjvEiSeeKMjQIOjgjVR5bYnJACXeffddT38hQ5PAH+ZTBOgQP/74o3dPhj8xYsQI7z7qH/rd6NGjxUcffSRoRZvYdtttBRm2opJHPi9TFooYC/SKk1HPwx76LLCmjxaCjKWCXqAS6+Zl4oW6lF2+jmct35eJE/pmme9NRferWpCDllLnsmWnFtrSxkMefTutHsvtZGul6uObpeGv+upyzlpEIOolshZ5bU48Ma7NqbWYV0ag+SDAY0vzaata4/TYY48V99xzj9hrr73Ek08+WRh7aQ1/hTHGBTECjAAjwAgwAowAI1AAAmz4KwBkLiIeAX6JjMen2ljGtVrkOB8jwAjEIcBjSxw6HBeFAAx95L5CkNNvQS4IBG2XjUrq/Dkb/pxDygQZAUaAEWAEGAFGoBkhwIa/ZtRYLZVVfonMp2UZ13xwZaqMQGtHgMeW1i4B6esP1yFwX9HY2Cjo1Hrx1ltviSJdd7DhL32bcQ5GgBFgBBgBRoARaDkIsOGv5bRls60Jv0Tm03SMaz64MlVGoLUjwGNLa5eA6uq/2267CTp8TZADcc+fcXVUqsvFhr/qcONcjAAjwAgwAowAI9AyEGDDX8tox2ZdC36JzKf5GNd8cGWqjEBrR4DHltYuAdXVH1t8cTCNOnCjOirV5WLDX3W4cS5GgBFgBBgBRoARaBkIsOGvZbRjs64Fv0Tm03yMaz64MlVGoLUjwGNLa5eA5ld/3fC33377iWHDhjW/SjDHjAAjwAgwAowAI8AIVIkAG/6qBI6zuUMAvn9wdDdCx44dxWqrreaOeCumxLi24sbnqjMCOSLAY0uO4DLpXBD4/PPPxbx58zzayy+/vOjatWsu5TBRRoARYAQYAUaAEWAEahEBNvzVYqswT4wAI8AIMAKMACPACDACjAAjwAgwAowAI8AIMAKMQEYE2PCXEUDOzggwAowAI8AIMAKMACPACDACjAAjwAgwAowAI8AI1CICbPirxVZhnhgBRoARYAQYAUaAEWAEGAFGgBFgBBgBRoARYAQYgYwIsOEvI4CcnRFgBBgBRoARYAQYAUaAEWAEGAFGgBFgBBgBRoARqEUE2PBXi63CPDECjAAjwAgwAowAI8AIMAKMACPACDACjAAjwAgwAhkRYMNfRgA5OyPACDACjAAjwAgwAowAI8AIMAKMACPACDACjAAjUIsIsOGvFluFeWIEGAFGgBFgBBgBRoARYAQYAUaAEWAEGAFGgBFgBDIiwIa/jABydkaAEWAEGAFGgBFgBBgBRoARYAQYAUaAEWAEGAFGoBYRYMNfLbYK88QIMAKMACPACDACjAAjwAgwAowAI8AIMAKMACPACGREgA1/GQHk7IwAI8AIMAKMACPACDACjAAjwAgwAowAI8AIMAKMQC0iwIa/WmwV5okRYAQYAUaAEWAEGAFGgBFgBBgBRoARYAQYAUaAEciIABv+MgLI2RkBRoARYAQYAUaAEWAEGAFGgBFgBBgBRoARYAQYgVpEgA1/tdgqzBMjwAgwAowAI8AIMAKMACPACDACjAAjwAgwAowAI5ARgRZh+Js99D4h5AIkOvTaRrRfc52MsLjJ/vrrr4u5c+d6xNZdd12x4ooruiHMVAIIvPPOO6Kurs57tvrqq4sePXoE4lvrD13+ll9+ebHRRhvlCoVeXpi8x8U3NjaK888/3+dvpZVWEgMHDvR/803LQoD7bDHtqfe5sDHAVTt88cUX4vvvv4+tVNu2bUWHDh28v0UWWUQst9xyolu3bqJ9+/ax+VpiZFHtEofd9OnTxfPPPy8+/PBD8csvv4hJkyaJpZdeWqy//vr+36qrrirQbhwWIqC3Xdg8tzBlbdzNnz9fjBkzxvvDGLDOOuuIlVde2RlzrsYQZwy1EELNHdfmzn8LEaNE1WhuY1qiSqVI1Bpldfbs2eLoo48W9fX1okuXLuK+++5LgVgwqS4/rGcGsanJX7IFhAlbriUn9FzD+5t6zEE1U6Pf/OY3MEd6f/vvv39mvkiBk4MHD5Zjx47NTKslEdh00019nHv37t2SqpapLmRo9nFxIX82ZmzyHhdPBnKfV/SZnj172orj+GaMAPfZYhrPNga4aoeTTz450H/VvGe7klFJbrLJJvKee+6RGANaSyiqXcLw/Pjjj+U+++wjyeBqbbO11lpLknEwjEyrfRY3j9USKLfeeqvcZptt5OKLL17RzmR0lzfddJOcN29eZpZdjSFJGWktenDRuCbFP2m65s5/0nq2hHTNZUzLC+vWKKtnnnmmPy8MGjQoE7RF6TOsZ2ZqJj+z8O+a8Y1u+JtyZP+aqYk+mELRzhJee+017wUJL1KPPfZYFlItLq8+aG+99dYtrn7VVsg2GFdLNyqfTd7j4tnwF4Vqy3zOfbaYdrWNAa7aoVqFTDcMbrjhhvKnn34qBpiSSymqXfRqwmBy+umnSxhbddyT3A8YMED+/PPPOrlWex83j9UCKLSCQ6K9krQr7QKQtNIzE9uuxpAkTLQmPbhIXJNgnzZNc+c/bX2bc/paH9Pyxra1ySqtcJTt2rXz5ohVVllFzpo1KxPERekzrGdmaiY/Mxv+fCjc37gaTN9++23Zpk0bX5Fjw1+wrVrboB2sffQv22AcnbO6GJu8x8Wbhr8tttiiOiY4V7NAgPtsMc1kGwNctYMLhQyGCqwwmzBhQjHglFhKUe2iqghj0N577+3rEEmMQmaarl27yh9++EGRbLXXuHmsbFBoy7aEMc9sO9qSLHfccUe5wgorVMTtsccesqmpqWrWXY0hNgZamx5cFK423KuNb+78V1vv5pivlse0IvBsTbLa0NAQmCP+/ve/Z4a4KH2G9czMTeURYMOfGxxDqbgaTP/73/8GlDU2/AXhPvbYY+Vmm23m/R1xxBHByFb8yzYYu4bGJu9x8XjxwNLzNddc05N1vKRwaLkIcJ8tpm1tY4CrdnClkMFggVVpLT0U1S4Kx8MPPzygQ5iGoaS/t9tuOyfbQxVfzfEaN4+VXZ9DDz000M7k71i+/PLLAbawrX7JJZcMpLvzzjsDadL8cDWG2MpsbXpwUbjacK82vrnzX229m2O+Wh7TisCzNcnqJZdc4o/92GWR5aOPapui9BnWMxXi2a4t4nCPib3WFqKpiXRXIdpvuKlY5r7HvPuy/1FnELR1yWODtvqK4cOHV8USKW5ip5128vOS4U/079/f/803jEAYAjgg48cff/SiyMefePzxx8OSOXtmk3dbPBg58MADBeR7v/32E8OGDXPGGxNiBFojAkWNATiIh3yKVUB8yimnCDIy0PTcJMifmHfYFa3oE88995yYMWNGRXo8IJ9kYty4cd7BH6EJWsDDotoFUN1///3iqKOOCkUNh6wccsghYssttxQ4qAJt85///MfLgwOXwsKQIUPESSedFBbVKp4lmcfKAIK2wYrtt9/eLxoHs0B3xNUMTz75pIBOqsLuu+8unnnmGfWzJq+sB9dkszBTLQCBWh3TWgC0NVUF6Fy0tdfXvR599FHvnSsrk0XpM6xnZm2pX/NnsxvWRu6W7uOvtX3prA2pav5c0GTuf9kp+nCPMJ+Wtq+K3377rYTTcRqa5FVXXdX8G4BrwAiUjEBRY0DUl1hSNEMRwHZeMgoGXFig36u/Rx55JDRfS3lYVLvAhYI+7ip8cV1ttdXku+++GwrpJ598IunkV7899HzYFtWag45n2DxXFjZHHnlkoL3IkBfLCtxpqHZdbLHFJLaD13JgPbiWW4d5a84I1OqY1pwxrUXer732Wn/Mh9sH+P11EYrSZ1jPdNFaUlpX/DX++L2Y//kY0g8WhEW26i3aLNFR/Qy/SikaXn4B24i9+DYdOohFtutTkVY2zBWN48aKxm++EvO/GUfXcaJp+lTRZrHFRdsuy4kOtHqvwxZbi3Yrd6/Iqz/Ia8UfVih89dVX4rPPPvP+8DW8Y8eOAtbtrbbaSpAvFUEn4+msBO6pM2Ra8TdnzhxBPnW8r7a0FNmnffPNNwt8oUVYZpllxLLLLuvHmTfffPONGD16tPeH+my88caCFHdBWyoF+Q00kwd+T5kyRUydOtV7hiO6O3fu7K0IGDFihPjyyy89Grvssoug7SReGqRFHgQy4IilllrKu0f5+Br93XffeXh1795d9O3bV6yxxhpevP4PeNMpgoKMQF5arEKgUx/FBhtsoCcL3H///ffeShI8JAXWax89QRRf9FIkRo0aJd58800xefJkD5vNN9/cqxc5QNdJWO+z4GwlHpGATncWb731lsAVqzPoBD+x7bbbiqWXXtrLUc1XmCz1sMl7XDzafYcddvBWKELWIDOkjETUfOHjLPwupCLE119/LT766COvv2OVJL30ivXXX9/7w33a4IovVS5N0IJefLx+h77RgcZU9B/0Y4xFUePQ+PHjBfIiYOzq1q2bIhl6RT+YNm2aF2em1/tZaOaYh+RI2B8nkEynFdZnTVKzZ8/2xuAxY8Z4V9DD+IvxDDjY+iuPAUKErfpN2w5mu6jfUV9i8YW5U6dOKlnF9fjjjxd33XVXxfMrrrhCnHPOORXPq3ngui8m4SHr2OyqXaJW+5HSLz7//HN/rgirE53+640t6HtmIOfg3ipB83na37U+7obVJ24eC0uPZ3nLIHRF8sHor+To06ePePHFF6PY8Z7fe++94oknnvB0NehrF110kVhiiSVi84RFppHVato7rR6cVm/lOXJBq7qeI9PIRZhcRT2rVhcy6eWlU+T9voN66G2F9z+8ByLgffGNN97w3msw70I3IvcMnj7rJYj4V+aYVs2YEFaNLHSiZBX6y8SJE/3iVl99det7MxJDR8f7NgJW1WOVXVTIe25Q5aLfgH+8hyNg1T5W7ycJtaLPsJ6ZpLUSpLHZD6eddpyc0HMN/2/240NtWWTDh+/56ZF38oG7V+SZ88xwOWmP3oF0ejn+fa91ZN3ga2TT3DkVNNQD1yv+vvjiC88RNr1M+9ZxgrLingxYcuTIkYqNimvWryhnn312RZkmH+edd15FuXhA2zolnHGb6dXvLl26yIcffjg0r3qI/f8qPQ0QkgyOFScCAqPzzz/fy0Lbhfz0N910k6QBU9IWZf+ZooUrDiuhlz5VlJeWtndGrgDB1+yo0+dsjll1vq6//npZV1cnsQKOBuRQ3nDKEU6PSxJc4JykHD0NGT9kv379QrHCiY1YhYCVNjSZ+/WzrfhzUQ+bvMfF08Tr+R1CmldffVWvbui9C35B+L333ouUUSWvZIiUV155paQXkFBe9Ieu+FI0UeYFF1wQuWoHPMIv4kMPPSTJ+Kuy+dc4zP1E2o3ysQi6OAxADz179vTlSWGT9IqVnHqw9VmVlpQVOXjwYEmKbWTZiLv77rtVltArjwHCG/NMcJK2g5nP/J32S6zKj1VJYTJ0wgknqCRVX133xSSMuBqbXbULGYBC8b366quTVEfSFmE/P/zC7brrrnLQoEESq7OzhFofd+PqlmZMLUoG4cdP70e07T6uCk7jkshqlvZOqwen1VvTtCeA4zlywbuQTU9OIhdpBDGrLqTKykOnKPJ9B/XQ9ZnLLrtMkmFG0uKIwBigxgP6SCzx/kQGHwVBxTVNH3A1pmUZE/QKuKATJavHHHNMAFOsOraF/2/vOuAmKYp9gxh5GACFh4IgcoAKqCjhEBEQAYmKhBMBQQkKCBwiCEpGcpQkIJKTPJKIJPGJgPIkc6AiHIhIujvgCOdxhHn1n7Pnqnt7umd2a3bn+7b6ft/N7ExPd/W/u2uqa6qqn3766WLHXPQBGbkEH5HCMVh44OJ5553ntIU+DAVyuZfaJs+onOn2T7e/kpt7/PuqSx3l3PPf3ixZ14uH/Mh55pWzfzbrmddey17YeWvnfqHkYwpG/9rz3/zarDK8M0nFHxb4ZQohy0j94x577OFRNPNnHWYaKgCuUH5d/m9f8QeFDxfW/fz+769//esZWfiEqs/o60BR/yabbBJUNKG8Aw44IH+ev3j23HPPbMkllyye9+u1v6GIwwsJQoS9VnYs2/ChjGnbRnG69t5774wspJJ1QaEJZWdZksS5rI7QdSjFyKIvST9ZQjl5yhR/ku1IjffUffpSWToWLRaS9E6YMKESlnY8YtEL17lQkqTLlo95wQU8S0fZES9FP6Uw9/NzZfGgFX/4AMMXcWXtttfXWWedjKw1/Sblv5UHtFPxB4Wt7T9+pK/RwX6scrGJuVilXknenHqnVaEHu/eRBVcHvlDC4+NXlQSX3+OOOy7/QIIFs0RqO99NtbEKT+33GMRHWT5/yEvFaQbowSIOLvbSKTVWe+3vunJwXbm1Sn9yzPQdOcsIIiYnp8YFxzR1LiELoY6mZIp+rnfQDi7PbLrpphl5cjjzn/MCew5DFZ8voCykKnNAkqf1yhNmUp1lUuWUjdVrrrnGwZUbq1ga/OOxxx7rPHPWWWc5WSRxdApO/OAfAau4+bZNnkHzulX8jSY5M9HNlW4nFX9vTpuWTVp56VmKus98NHv9ySfKC6eF8aRVPz0r/2fHZG9MeqbIP/2aK2fd+4+ib9IXPpVN/eEu2UtHH5y9eNAPMygXfcUffr96e9gKS0rxd/fddzsTFgwTEwQ7pe2+++7Z9ttvn2vvYVVlmSmOePmFvoBXYaYFMIGT448/PrdG4kwJ9YHpw5IOf76lC7kAO7QhP7nKZvhyAaZFrqwdVnvrrrtuoPbMUfzx9vJzWO6RiXX+PH8Z8Tw4J5PzbM011+x4QQE7/tJCrBko5tA2CvLe0ZbQjsYcnxVWWKGjLWV0oW4oyNZee+28n32ayVQ+t5zrKJAuSOIcKj90jVxSOhZywGi11VbLx+ZKK61UqrQuU/xJtiM13lP3Q232r0nRi6/HEIR4nyOmFXb3+uEPf5i/YMiV1LmPvGWxx6Tosu3Foj2k9MJ4hWIfiyFfuQv68OWZp7qYxxY14H+LLbZY6d/iiy+e04QdtjmuOCc3bk5Wlpqz6B+ex5aHuYydu7/xjW9kqM9et0c8E1JQKA9op+IP7x7bd/xoPyY5g6biD+m5WKVaad7Mx37onVaFJnLHDWILy/pBpbbz3Sq4VOGp/R6DUJTb+QPLTKSXXnopO/jgg3N+be/hiI+sGAO33357leYm88TGqkR/15WDueKPt5ufc7m1Sn9yEPQdOUvxB0zL5OTYuOB4ps6lZKF+yRTApMn1DvAqk2fgnbL11lvnHirjxo3r2L2bwv/kfMHHvMockOJpEjwB9EuVg7LKxipkSWBmeQeMLlBvLHH5F7zY/8gmhWOMBv/eNNLjvP3tby/akfqw2kZ5Bm3qVvE3WuRMv1+7/Z1U/KHgFw/Yy1HEvfLzcjeC6b+91sn7wve+5dD23JZfce5P/dH4DMpFP824/55synpfcPPutbOfLf8tpfjzLeW+973vBSc5xb7pWHTDksxPVZip/0zoN8yLLePBMaT8wnO0U5uTj2I/ZDDv9RPF0Mvg6svLDAWCDglQCAgNSzjaDShXkmABblPoZYQFP76y2QQmCEsiXrc9hwIOLp82UQyODgUb7UBobxfHMqZtM4TogrLsL3/5i82SH6+77jqHOYIufEn3kzTOfvllv2F1abHCEYoP/wselNe+Qgt5Q4o/6Xakxnvqflm77XVJem+++WYHS4odkVFMDltVfsQ29/7XO4xRP0nSZcuGOx3vawjXF17ohlmAa+/RRx/t5PMFk7qYxxY1lrbU8dvf/rZDE8UAzZ55ZtbHHzyfmrM777yzUwbaRfGoOqqmGGYdynAsFP2kPCDMA1L94ONY9ruuQIb3ABYmfIzzc7xfuklNzMUqdEjzZol+wUcKjqk9h1wzqNR2vlsFlxRPHcQYhLWz7d8Pf/jD+aYtUD7Ya6EjPnziPRP6UFIFB5snNlYl+7uqHFxXbk31p22nPeo7spqcHBsXFssqRylZqB8yRT/WO8AsJM9gbeZ7PGCtik2c+PzHh20/peaAJE+T4glS5QCL2FgFXhw/uOiWJVgg8rxQwvIkiSMvN3WONT+n69xzz40+0kZ5BgQPu5wZ7bQaNysp/mbc/WdHATdl4zVLq3hh/PZO3uk3ztpZbMa9dzn3pnyFfN/JsqUsvfqHm5z8z2351WBWCcUfXCC4Rhxf2bHwL0vYDY9PpNBX9BQzLSvbv15F4IELIo89gi+aMR9+xITgFnW0iQZ1hdsXvgAFgXLq1Kk+ecVv/2WEL8v+gh+ZYSFIwfkd/OAW7CtekBduyFBgWqzh+uinGNNGXp+uMWPGlLpsQpFg68LR79cmcPbbE/pNm6k4dCGuGQWxDmXNEGOCNmJx8vuKvybakRrvqfvBxvznojS9vjAJ8/uyxBdVGLfcXUqaLtCAeeC7c8fiHvqKtssuu6xoSl3Me13UIN4Mnz+0wU/ujlEQ9J+T2JylTQecMhCjBm5qZQku4uB3tl6MfV8AVh4wGMUfFOq77bZbtuuuu+ZWyVtssUUGlxOMC9tf/hFKbnyhrpuamItVaJDmzagzNj+q0IQ8p556ahDjI488smoR4vnazHerNjbGUwc1BqFwsPMI/M//sGvvhY54f/SSYmNVqr9BXxU5GPnqyq2x/kR5ftJ3ZJal5GRgFhsXPqZlv6VkoX7IFP1a7wArX56Bcq/snTlx4sQMMpSd+1j3+Tt4x+aANE+T4glS5QDP2Fj1x46/JsTzNsHl2+KMI5STNknjaMutcvSVl4gJW5baKs+A3jLF3zDImWX91c31Soo/FAwlHXe/fe2vD3TU98YLz2fPLr9EkW/yap9xFHuv3vK77Pltvz5zUw9yGXZi/3WUlmVvvvRiURbqfm7TLwdyZZmE4g+LR1iBwToFi3vfL9+vGEpBrjhD/C8/xZipnzf2u4rAg8U+ZzhVYhEgNiF/xlcU+gJU7EsH6PdfRiErQttO300vZM1j8wJbS6e/SQDyxJg27vt0XX311bgcTBA0eFwk2hHLydcEzk4FJT9gxWQxwBEbPsSSz+h9xV8T7UiN99T9WHuk6YUrIceTdjgsrR5Kt5/85Ce5my8CCXMFuTRdIOK3v/2tQ1vIypATC8tV2xa4FnA3yb9msy4AAEAASURBVLqY97KogXUxV8DBouT666/npBbnsTl7+umnF+1Bu77zne8Uz5WdwBLYYoDjSSed5GRVHjAYxR/vk6rnvru605GRH03MxUh1xS1p3oyCY/OjqDhxAp4VwjzkBZAoSux2m/lu1UbGeOqgxiBCuIT6Gh9NEaMRbt/4eHLRRRc5Y8s+E1sIpnCJjVWp/gYNVeRg5Ksrt8b6E+X5Sd+RMz9OxuRkYBYbFz6mZb+lZKF+yBT9Wu8AK1+e8UM++XgihI2d6zheeeWVTpbYHJDmaVI8QaocAJEaq1gHWvxgIES7Kjv44Qc8cBAuyOaDIQ5P0jjyslPnvnsxlMFlqa3yDOgtU/xZzOscR5qcWdZf3VyvrPh75cxTHCXcS8f8pKO+aZec5+Y5YuamDx0ZcQFudW+UB4t+Y8qkbPp1vyKl3piizClfC1saSij+OI1Q/sSs/ZAXWnHE/7MDDTHp/BRjpn7e2O8qAs+hhx5a0AKaEJQ0lfyd4H72M7YJCz3sC1CxXaFQF38Z4QtT2Rco5OWBRkHvU089hcvBxBf1c845Z0eeFNPmdKEu/2uXXyAsAm2/Lrfccs7tJnB2Kij5wa3OQFvIkpI/Cqs0HovSV/w10Y7UeE/d5/T759L0QtFv+9ge4bYO4Q0u5lWTNF2od6+99nJogwt6Kv3xj3903ORt/rqYd7uoAY/yN0WCxVFZis1ZxPCzfYJjiu+gDri08Gd8d0blASND8ef3W9n4CV1vYi6G6vGvSfNmlB+bH379Zb8R343PCXvuv+fLnm/iepv5btX2xnjqoMag/yEVfY1d2P/1r391NAuWJ3BBs+MBR3h8QO7tJsXGqlR/g64qcjDy1ZVbY/2J8vyk78iZiMTkZOSIjQsf07LfUrJQ0zJFP9c7wIrLMxi/qbl71113OfMdFlI8xeaANE+T4glS5QCH1Fj1N4c47bTTOHz5ua+kxvuXJ2kcedmpc7wLLL/Hx3m8A8pSW+UZ0Cul+BuJcmZZf3VzfTY8RAMimd589hkzZd3PG/PmG3ne2ef9gJnnmluMmX324tkXtt7YvHb/3cXv951/lZlj8Y8Vv0Mnb0561rzx6MPm9X8+Zt74B/4mzvx74vGO7G9Z5KNm7l9e23F90nJjiK438+tzfOKT5n1nXdqRp9sLpNk3pOQzFKfOkMlv/venP/3J0GLUKZIUf+Z3v/udc40EBEMKrfza+uuvb+gri3O/6g9S0BmUbxPF+DMbbbSR/Zkf6YuOOfPMM4trtFuvoa+9xe/QCbk3mnPOOae49f3vf9+QG1Dxm2LEGPoykP8m60ZDCjNDTKO475/QZgSGdgHML5PwZR555BE/S/Gbgm2aX//61/lvlE1KwuKef0IbkxiyyMovk+LPUHwoJwsFUzX33HNPfo1ctA0pQZz7nC4KfmtIKebc939QrAxz55135pfpS3pxjgtN4OzXH/pNGzkYUm7kt8iVx5DLdSibc42PP1L8GbLYLO430Q5eX2i8p+4XxAVOpOmlr3OGgvYaUqB21EYbzJhVVlnF0FcyQ9Z2hhZVHXnsBWm6UC4pIM35559vqzDkGm/IErn4XeekLubAhNxk8yooFqe56qqrktWRxaGhjWUMeKVNtBmSOeqoo+zPjmNszoJ3oM1I5PLslNtR0H8uoD/RbxSvKr9CVsKGFKZFduUBxvg8AODE+qEAr8IJLSTMySefXCFnOAveK7Q4y99h9MEinClxtYm5mKgyvy3Nm1GoRL+gP9AvfiJrcUPWEv7lvvxuM9+tCkCMp7ZhDKId9BEml4kohEqwWZC3cI82pSvuQ4aijeCK31VPYmNVqr9BSxU5GPnqyq2x/kR5ftJ35ExEYnIycsTGhY9p2W8pWahpmaKf6x1gxeWZ1Vdf3dx4441lEObXaaMfg3WDTb5sF5sD0jxNiidIlQNMUmMVa07ahDJfAyM/hVbI+RHObaI9Agy5wOc/IcPQBhmGLADt7YGtHUEAhekqeD3aYfUSBXHspK3yDEgcZjmTdVHvp3W0hS/svE1hfefvsvv6Px517j03LrxTLOrDZh7TLjwrm7LBqs4z3JU4dN4viz+4RSDw+HzzzQelaKU/Usx1QBn7itKROXKhypfOlVdeuRKdsfZssMEGDhX0MivKpBeNcy/0g3+FIkYaylJc418VEBsjlvjX6V4t/vDlI5UQR9DiBBcanprAmZdfds6tS4FzlcTb4Vv8NdGO1HhP3Y+1qQl64SLrBz62/c6PsIbYf//983iTPo1N0LXGGmsU4w9Wm9y12K8/9bsu5iQAFnVjE55UQjxJUkoWzwA38BESyqKPxr6wwkXY4o98VRPnV4hHyhPnTcoDZiET64dZudJn3X6JRSwy7BaNODq9pibmYhWapHkz6pToF7h12nnEj7SQq9KsxvK0le9WbXCMpw5qDI4dO9bp6+222y7ZHFIMO89ccMEFyWdCGVJjVaK/UW8VORj5+Hugitwa60+U5yd9R85EhMuXvpyMHKlx4eMa+i0lCzUtU/RzvQOcuDxDhhEh6Dqu8bjRvidTbA40wdOkeIJUOVXGKt/8E1Zz2PnWJniRITaxfc+GQn81gaOtP3XkYcn8vvefbas8AzqHWc70+6mX35VdfVHJ9BuucRR1L+63R1H3y6cc69ybdsEvinv8BArCyWss7+QNKflwbdKqn3byNa34g2sq4mnZyRs78smBfINW/PmL7xjtZfeWX3553lWOAIUXcCrxl5EfG89/dlCKvxRdoDMm0DSBs49N6DdZMxXjMiVk2OfJ6q54xlf8NdGOmPAAmlL3Ld2hYxP0oh64TH/ta1/LuGBYNj8WW2yxjCxaHfKaoIuPP2yC0Euqi3mdRQ2EHewkx/HCAiDlSo/2xAQtKPdtmSuuuGLl5nN3Bmx+w1Md3oTneB/4i5om+pzTWnYuzQNQT6wfyugIXS8TyBBLiCzh8z8s3OGSDrcj7EZOVgihorq+pv3iQkeeCcU8svMJxy9+8Ytuxsgv9BVZgeWbsyA0SK87wNqq2sh3LW2pY4ynDmoMrrnmmk5fV3HnvvXWW51nfvSjH6WaHrxfhYf02t+ouBvFXxW5NdafoQbrO3ImKrF3JHJUGRchfPk1XkcvstCgZQrJ9Q7w4fJMVbdFGFjY9wCe5yk2B5riaRI8AW2QKKfKWPVDY8F11yby0CmwBcYXXnihvVUcm8KxqKDkBG7gtt9xxFiMJZUzY+hkHYYOHNuq576uJV6j/N3Krr7UIGNmzDCT1xprshdfyH/O9q45zTw33G5me9vbzXMbrGbeePI/7q9zvNXMc+1tZvb3vi/PZ/97c8ok88LWm8zKZ2/Qcba3v8O8ZdHFzBwfXcLMMYb+lvi4eSu57U4a+/HCvbhJV1+YDdNuPYa222ZUzTylgPm5aTXcIGAGCxN3/C200EKGYqjkmUjxN1BXX7i43n777TkttDmJOfzwwzvakbpAAo0hS8ciG3eZIAHKUKD+4l7ohJufk4LN0KYIoWz5Ne7qSy+kwgw59ICkq2+KLtRPX0QM7dqck0KLfsfVtwmcQ232r3H3Egoam7uf+3n83xiTcI9B8t38mmgHxo81IZd29W2CXo4X7ZCcz32KjWkoVoeZMmUKv12cgxfcd999hiwF82tN0LXhhhsWYQHgAkkxBw0FFC5oqHPC+8R37wiVQwozQztp57di+cEvMaa4KzDcGsCDUGcqxVwrwFdtKAUSUM2ECRNSxeX3eVtJSZuHZ7AP1uFNeGYYeADaGesH3K+aylwwEE6CvoRXLaanfE3MxSoESfNm1CnRLyTw567yfhgNyAcIX2J5WKyNCP9x9NFHF1kQKgO8ffz48eZjH4uHcSkeipy0ie9GyHRucT7jv+cGNQZpZ15DcagKOs8999w8ZERxIXAC2ZW7opFnhRMuJvBI8FKdsdptf6Piblx9q8itvD9j7zzbeH1HzkQi9o5EjjrjwmLrH6VkoUHLFJLrHWDE5Rmy7jWk6Pehc35TzPo8FAreCUj+epXPgX7ztF54Am9kL+VUGaukfjGQK20IKy6bIiTQtdfODEOG8DRYByH0DE+DejeABtBkw0OF3JQ5nW2VZ0DjMMuZvI96Pq+rS3yJNuzgFnrTb7oum3HvXc61qd8P78L48qnHOflQzosH7JW99rcHsSVOByn+rr5Tvhq2OpPY3INi3TlacQI222233XILn7KNPviuVhQTrIP+2FeUjsyRC1W+dG6xxRYO/XDB6zVxl4kqX075V6iUZZ3kF7DU15o6dAEz/pXRt/ZpAucq/cStmbCrVNmY5GVxN1bf4q+JdqTGe+o+p90/b4Jevw77G26qpPjNDjzwwGyppZZy5hX4wk9/+lObNWuCLoqP59RJgkZRX9kJ8lCMpoxe7k4WjjmsQmIJY4pvCBNz9d1pp50cGrGbMOqvmmJzFrurA2f8odwqafr06c6OwhRz0HlMecBgNvcgxZ/TD03+aGIuVqFXmjejztj8qEKTzYP3tp1L/LjDDjvYLKVHimuU0YIw+DwCmUunQfPdqu3hPJUWyc5jgxqD/k6M++23n0NX6Icf8B8bKXSTuh2rdfobdFWRg5GvrtzK+1PfkTPfuyn5HTjH5GTc73Zc4FmbpGShQcsUkusdYMPlGezYmkrY5Ifzf4Sy4onPgUHytLo8gbeBn9ctp+pY9TfMwqZysDikj2kFvmXv1kG9G4ALwhXZ/sfYiaU2yzNlniXDIGfG+qzuvVquvij8tb8+4CjvXvzx7tlLRx/iXHv192Gh8Pltxzn5Xj7pqCi9M+7+s5N/yldWD+aXUPzxOHKYIBCkYokC4BcTCfnhv++nGDP188Z+VxF4DjroIIceslyMFZnfg5KANgTJrrjiiuzuu+/uUBrUFaD4yyglOEi+CFNMuw5dACYm0DSBc7KjKMOmm27q9C9eOLEENzrsNGaZva/4a6IdqfGeuh9rjzS9mL9YvJ500knZDTfcEKs6V/RZHHHkpvLSdIGQU045peg31EdWdVH6cBOxjCyN/MXO3TtScxJx1mwZOJYp/sgCyMkHZeHVV1+dpJFniM1ZxHzjdCCOSyqRdbHzzMYbb+w8ojxg9Cv+mpiLziAq+SHNm1FNbH6UkBG8/Mtf/tKZF3Ze4eMRYhnHEnY5t/n5kTaFiD0WvddmvhslnN2MvccGNQZ9N7RQjCnWhPzUjwF54okn+lkq/Y6NVan+BiFV5GDkqyu36jsSqLnKpJSsgPwxORn3Y+MC96skKVlo0DKF5HoHuHF5BvGMoeiKJciQnIfjozZP/eRpUjxBqhzgUHWskieK83EcOPpjlLxeOLTF+aDeDSCALDyL/sfeBbHUZnmmDYq/QfZjrN/q3Kut+EPh2LjDWv1NWuWT2eQvrVD8xjkFggnSMGnFjxX58PzrEx8O5ssvkvXJ1L12dvJPWW+VYH4JxR/id3HG6FvO+BUfccQRTn4y4/Wz9BTTjBfmC3UXX3wxv52fX3LJJQ498CFPWYUdcsghzjN+QOi6AhR/GaUEB8kXYYpp16ELYMYEmiZw7ujMwAUE3ubjc7PNNgvkmnXJ71tf8ddEO2LCAyhL3Z9FfeeZJL3Yyp52PSzwDCntfQoQX8biP2bMmOK2JF22UCgkbV044qUdSxBGeH4EIbYJtNp7wB/xPsoSYpbYvDiGFH+0o7hjWYd8qY8kofpic9ZXLEIQSSXELeO044MGT8oDRr/ir4m5yMdQ2bk0b0Y9sflRRkfoOhaEFBrCmRt2nkD5h0WLn2h37gxWCzaff+QWz/6zsd9t57sx2vm92HtsUGMQsRf5ZnSwlJ44cSInu+N81VVXdfqYQip05KlyoWysSvY36KgiByNfXblV35FAzVUmpeR35I/JybhfNi5wr2qSkoUGLVNIrneAHZdnwJ+hxI8lrE85H7/33nud7P3iaVI8QaocC0KdscrjqZKLcMatSdEvZWlQ7wbQAwtP2/+IZw78ylKb5Zk2KP4G2Y9lfVb3eleKP+zIaxV//vHl42YFvPSJmbzGcs5zr952s5+l+P3SYfs5eVEPNgUJJQnFn+/S9/jjj4eqyq9B8cZNezGhwDj8FGOmft7Yb3yZt5MWx1NPPbUjO8UB69ihNCagw7oHAfB5uXfeeadTbl0Bir+MUoKD5IswxbTr0AUAYgJNEzg7oJf8gCkzdy2HldVtt90WzA03b79vfcVfE+1IjffU/WBj/nNRmt7VV1+9GPuYyzffXM6L4EaLXbzsXOGuFdJ0oblYxPExi3qvu+66Ung22mijgjbkxYYKNvmLOx6Q2ObBkeKTdGxw4iv+MN544F/Utcsuu/BiKp/H5uzkyZOzueeeu2gTsIe1R1m6/PLLi7ygCZauFO/Fyc7xTPEmPDgMPADtjPUD7ldNbRDImpiLVdovzZtRp1S/oCyKzetYKWCO8D9YjGCDI3g94IMSxfFz7vO8+ADSi1tNm/kusKqSYu+xQY1B0L3rrrs6/UYxGIM70SOvbwmK8dZtio1Vqf4GbVXkYOSrK7fqOxKoucqkXt+RKC82LnC/SpKShQYtU0iud4Abl2fAn2GFjdAMoeQrKviHa5u/nzxNiidIlQMM6oxV31Kavx+PPPJIC2nHcZDvhn333dd5N5StHUF0m+WZYZYzOwZUDxe6Uvy98cLz2bMrLNGhmJtpxff3UnJe2Hlr55nJX/5cNuN+Ny7UG888lb14yD5OvkK5uGLYxURC8bfVVls5EwOxcSjIvdMWfD0/77zzHBdKO+khbPgpxkz9vLHf2EnU1oPj4osvnrsnIkYLBREtHoXLLs+Hc0wUf5fNm266KaNgt07e0Iu+rgDFX0ah8gpC6UTyRZhi2nXoAo2xRT/uS+OMMqukww47zOkzbNGO8cgTdnKkgN1OPowDX/GHZ6TbkRrvqfu8HaFzSXqx6yGfK9ilGwokPyFuHXejxTNwD+ZJki5bLhbrnD5YKJ588sn2dn6EVbL/IsQiHgKGTX7sJyjusAOZ5W2I/XLGGWfksfR4fTjnij/aDCCbd955HZoQC+bZZ5/N8JHkoYceyvAxIfZHG6ZYspKCFjDm9ECZd+yxxzpWzFgUwOyexyXEM7QhQVGPPVEeEOYBKd5p8Usd/XFo+64XJVGqztD9JuZiqB7/mjRvluoXS6cv+Nv+qXPEHEyFRbD1lR3bznfL6ObXU++xQY1BxJqaZ555HL4JaxTfugcfhLnFO8YAFIHdpthYlezvqnJwXblV35Eze176HRkbF3XGmpQsNEiZQnK9A+x4X1keDjn14YdnedHB4wuWjr6RSigsSz95mhRPkCoHeNYZq4gn7RtWoA9gSZeKrT+odwPcj+04wREei7HUVnlm2OXMWJ/VudeV4g8VTP3BTh3Kuee2/Gq07um/ubLjGSj1pmy8Vvb8Dt/InttsnYwr8QqFH+Wx568/8lBHHfyZ57baqON+lQsQaLlVDyYHhCgE5MRCEopBLKr55OHnEKQQV42nFDPleWPntCtfhzWOrRtxeHj68pe/3EEjFvtQZm2wwQbBFwZcRLCw91NdAYq/jEaz4g84SeLs4172G26atJt0R//OP//8GczPfXd1O0ZwDCn+pNuRGu+p+2Xt5telcMec4gpeixXGPOqgHb4zbOzi8wQo3blizdImRZctD0co3ixd9ghLOHzphJUC7dTl3Mc8v+OOO3gRuYJvzjnndPKhLAgpUBzbckNHrviTUBxASWdTStCCUg9uFD5d7373uzNspAT3bLiz+fcxP2bMmGGrKY51eBMe4mPD3+DHFtpEn9uyy47SPCDVD2V0+NfbIpCBLu0Xv3eyXGG+9957d8wXf/6U/QYf9D8yddaSvjIS+G6qFVXeY4MYg6D7sssuy+DC7fcjPvTCUj20WUuVUAoxTGI8RLK/q8rBdeVWfATTd6SrTErJ7xgPqXdkbFzExlPonoQsNEiZoh+KP8x5fASF99rSSy/teE1YfuBv6mGx7idPk+IJUuUAg7pjlXaX7eCxXF62uIaOg3g3QAnMQ0FAFxBLKmfG0Jl5bxD9mKaqWo6uFX+v3vK7QhlnlXLTLj0/WSs2A7H5U0dYCL501EFO/tCGIBKKPxAOBZ9lkKkjXGL8uBFnn3220/4qzNR5IPJjm222CdLmL0qhlBg/fnyHFUxZe6DcLNuNs64AVWdxLfkiTDHtOnShC1ICDfJI4ozyqibEX+KBWsv6FbuacheWMsWfZDtS4z11vwoGkvTC2i1kHVmGKSzcrKWcT6skXbZsWOqGBIwQfXADhyVfKF144YXBhY1fDnaC5O5iXJCRUPxhRzSbUnMW+TDW/Z3QfJr5b4zxsv5RHhBW/lfpB9tnsWObFH9NzMVY2+09Sd4s1S+WNns899xzM1g383mTOgePxHNSqe18N9XOKu+xQY1B0I6PP4ssskilPt5kk00yLKB7SamxKtnfVeTgunIr2q7vyHYr/qRkoUHJFJLrHYxXLs/gAyjnSWX8fNttty2N8cyf93f1RX1IkjxNiidIlZPiYTMRmPU/eKyPMz66VEmSOFapz+b51re+VdAM753UhjBtlGdUzrS92duxa8Vf9sbr2eS1xhZKOWzc8eaLU9PUkLvsv6/8ZTZ5zRWLZx0FILnzPr/NJpndGfi1B+518r2w4zc76uBldWvxZwtF/L4yoQmLawSRR8BZpH/84x/FRAIT8L+m8KDaZczU1ps6QgMPoce30plrrrmCj95yyy3ZcsstF/z6C1phqowdiWKbmNQVoPjLKPXFsM6LcKeddipwxqLFT1ByWSYc2mSlDl0omyvWfMWqX7cEzn6Zqd8wNT/mmGOCSisIAfiCD0Fp8803L3CBAiWWJNqRGu+p+zH6/HsS9KJMKIqg1OKbd9ixhCMseWF5htgdqc1yUJ4UXSjLJsTfgxuH77IB+mCJg48Q2OAjlrAzLuYkLP14+/CFeJlllsngNoG0zz77FPc5P/M3M+JlVD3nir/UnOVtgXsE4lWF2o+6P//5z+cLN/6Mf648wORKVB+XOv3gP8t/77HHHsW4seMBcwdC7qBSE3Mx1RYp3izVLyF64X59wAEHOItH22f8CA8H7PSKNkmnkcB3y9pc5z02iDEIurFo23LLLR0rD963iAl22mmnVXqnleFgr1cZq1L9XUUOriu32nboO/LjBQ9Pye/ALCUnVxkXFvuqRwlZCHX1W6aQXO+Afl+eeeKJJzLs5O278GPOwwsCcf5iaRA8TYonSJTTzViFVaXlqVCkhTxNYpj3+93guxljLqVS2+QZlTNTPVbt/mzIRoO3/+m118wbTzw+8++ZJ83s753bzPHRxc1bFvqwMbO/pf/0sBrJJNw8+uijhuIlGIpfZUjZZMh82pBAYWihzHL2/5Qmorn//vsN7cpj6CuNWXDBBQ0x+1JC0BZy4zUUH8VQjC1DwnzejoUXXjj6XGmBeiOIwCBwJiE4HwsYp7SYM8suu6yhl5EhBUmQxioXB9GOKnSV5ZGil5R6+ZynnQ3Nk08+aUjBbkghZkjA6mqeSNHF2425j3mM+U8KPEOLN7PEEksYclPi2aLnKINiPpn77rvPkCWPGTt2rCGlZ/SZttwE7Q8++KBBHwFf8DIKVJ3zwDbQ2ESfp9rVBA9I1TnS7mu/lPcYfbw0pPAwFB/O0EdAQ66guXyw6KKL9oUvjAS+W45e9TuDGIOWOlIKGLJQMTh+8IMfzPsX77ZBJKn+risH12mrviProDWYvOijXmUhUN52maIMXfoQnLcf90lJa/7whz/kWdEe2gQnl5Mw1yEfUXiasmJ6ui7F06R4glQ5PYHSxcNSOKaqRj0YD9BtINGGXoZiu6Yey++rnJmGqV/9mKYknWNwir80bZpDEVAEFAFFQBFQBBQBRUARUAQUAUVAERh6BMoUf0MPjAIQRYA8e8wOO+yQ54HBELlKG7JWjD6jN0cfAqr4G319qi1SBBQBRUARUAQUAUVAEVAEFAFFQBEYRQio4m8UdWYfm0LuyLnFNxR+SLRPgaE9AfpIgVbVBgRU8deGXlAaFAFFQBFQBBQBRUARUAQUAUVAEVAEFIESBFTxVwKMXk4icMIJJ5hddtklz4dwPwgF9o53vCP5nGYYPQio4m/09KW2RBFQBBQBRUARUAQUAUVAEVAEFAFFYBQioIq/UdipfWoSrP4QDx6xspEOO+wws+eee/apdq2mDQio4q8NvaA0KAKKgCKgCCgCioAioAgoAoqAIqAIKAIlCKjirwQYvVwJgbvuusssv/zy+SZ52NzvkUceMfPMM0+lZzXTyEdAFX8jvw+1BYqAIqAIKAKKgCKgCCgCioAioAgoAqMYAVX8jeLO7VPT9ttvP3PggQfmte22227mmGOO6VPNWs2gEVDF36B7QOtXBBQBRUARUAQUAUVAEVAEFAFFQBFQBCIIPPzww2b69Ol5jjnnnNMsssgikdx6SxHoROC1117LXXynTZtm5p9/frP//vt3ZtIroxIBVfyNym7VRikCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgMOwKq+Bv2EaDtVwQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAERiVCKjib1R2qzZKEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFYNgRUMXfsI8Abb8ioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAorAqERAFX+jslu1UYqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKALDjoAq/oZ9BGj7FQFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBEYlAqr4G5Xdqo1SBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARGHYEVPE37CNA268IKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCIwKhFQxd+o7FZtlCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCisCwI6CKv2EfAdp+RUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUgVGJgCr+RmW3aqMUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEhh0BVfwN+wjQ9isCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAiMSgREFH9Tp041N9xwg7nnnnvMM888YyZPnmze+973mo997GPF34c//GEz++yzJ0HcdtttzdNPP20+8IEPmNNOO8285S1vST6jGRQBRaATgdtvv9288sor+Y1FF13UYA6O5HTrrbeaV199NW/C+9//frPUUks12hxe3xJLLGEWWGCBRuvTwhUBaQT4GO7HnKlK///93/+Zl19+Oc/+kY98xCy88MLOo5xunXsONPpDEWgEAT7nQrwiNWcbIWqEFvr666+bm266yfztb38z//jHP8w///lPM9tssxnIYfgbM2aMGTt2bKU1URUILrnkklw2Ah9deeWVqzwilueJJ54wDz30UKXy3vnOd5r/+q//yv+wxptzzjkrPZfKNMj2p2hreixMnz7dTJw40fz97383qAtja7HFFjPveMc7UqSJ3x/EWBj29j/33HM5n3nsscfMe97zHrPQQgvlepcq+hbxARApsOl5IInDgw8+aO68886cr2FeAcvFF188n1uf/OQnzZJLLhlp6Qi4lfWQ7r///mz99dfP5phjjoyaGv0jRpSRcjBZ23e/+92inKOOOiqZXzMoAopAGIGPf/zjxVz6whe+EM40gq6S4q1oz1e/+tXGKf/v//7vvtbHG0QvyeynP/1p9vDDD/PLeq4I1EKg33OmKnEkPBVza6WVVup4bJBzr4MYvdBqBJRXynRPilek5qwMFSO7lEcffTT74Q9/mHH+VbY2IsOI7KKLLsreeOONnhp91llnFbx000037amsbh6GnFLWxth1rBvXXXfd7H/+53+yGTNmdFN1/syg219GeNNj4de//nWGOUlKiQ78cY2UE9lll11WRl4j1/s5Foa9/VdccUVGyqiOvsec++AHP5jtvffe2UsvvdRIP9cptOl5IIkDKa6zb3zjGxl9pAniavnZ5ptvnv3rX/+qA0Or8ppuqIGgtdtuuwUZjgWm7Pj1r389I4u+0monTZqUkdY6B52+DunCtxQpvaEIxBHgir/Pfe5z8cwj4G5qYSLdBC684wNHv9Itt9ySLbPMMjkPvPTSS/tVrdYzChHo95ypCiFXIqywwgodjw1q7nUQohdajYDySrnuSfGK1JyVo2RklnTOOedkZMEWXTCG1kXANbYmiqHxwAMPZORdVdQ5khR/HAuy/ssmTJgQa2rwXhvaHyKsybFAHnXZF7/4xaLPOY6h87XXXjt76qmnQmSKX+tW8cfpTo2FYW8/WThma621VqX+X2SRRTKyXBPv56oFNjkPpHEgi/fsXe96VyVcMV7Jcjm76qqrqkLRqny1FX/kOpitt956lcHhE9qezzfffFFt6RFHHFGUv+qqq2Zvvvlmq0BTYhSBkYCAKv5666VBKB/IPdv52qSKv976cNifTi3mB4VPSokwiLk3KCy03u4QUF7ZHW5lT6V4RWrOlpU72q+/9tpr2XbbbVesWew6B8e3vvWt2Uc/+tFcUbPKKqtkCy64oPN+t3mXXnrpjFzVakFFbsTZ/PPP79Q7UhV/wAFKChh+VE1taT+ntx9jAR+h7bixRygsPv3pT2fLLrtsUPm82mqr9WUdLaH4S42FYW//N7/5zY7+Bx8Af4GVJ4VHc+5DloIlWz9TP+aBJA5TpkzJebOdTzgCx3HjxmVHHnlkBp0Uzn3vVnx0gUXjSEu1FX9bbLGFM6g4UHXOYYGEwRFK//73vx1T+dNPPz2UTa8pAopABAFV/EXAqXBrEMqH//3f/3X4qyr+KnSUZilFILWYL32w4Rvf+ta3sk996lP535ZbbtlR2yDmXgcReqHVCCivlO2eFK9IzVlZakZOaT/+8Y+ddzbWQVDwHX/88RkMJfx03333ZRtttFHHM8svv3zpmsgvAy7CFIexo4w2KP523HHHjOJBdvxBUf+HP/whu+6667JTTjklo5jTHfRXDUnTpvbzvml6LJxwwgkOZlD4HX300Rm88GzCuvrwww/vsF7qR+gsX/EnPRaGvf2woON6lne/+93ZGWec4Sh177333lwJzPPB07Kfqel5II3DZptt5uBaZil51113ZRRz2skbClXTT6y7qauW4o/HUuCDCudve9vbMgjwmPiI5Xf++edn22yzTYf2mT8H5l+Wjj322AJcClZZ+YVYVp5eVwSGDQFV/PXW44NQPuhitrc+06ddBFKLeTd3e34NYu61p/VKSRUElFdWQal6npHKK6q3UD4n3MN8CxtYJMENLZUOPfTQYo1j10XnnXde9DHEldpggw06nrPPt0Hxd9JJJ0XbYG++8MILGWJlWdrt8fHHH7dZOo5tbL8lsh9jAQoJixOOUKKWpcsvv9zJi3dq08lX/EmPhWFvP23c4/TpL3/5y2CXInTAvPPOW+SF5fGzzz4bzCt9sR/zQBKHadOmZbQRToEV+Pltt91WCgttZNRhVUub2pbmb+ONyoo/2k3TscLjzAeT8c9//nOwfYjB8KEPfagAlT8H14Gy9OKLL2Zvf/vbi+egSNSkCCgC1RFQxV91rEI5B6F80MVsqCf0WrcIjNTF/CDmXrcY63ODQUB5pSzuI5VXyKJQvTSEIIIbL1/TIAxSnY0qtt12W+f52Jro5z//uRPPj9drz0eS4s8iDUtHSz+OF198sb3lHNvafhDZj7FAu0M7OG288cYOPqEffliupmP9dav4s7THxsKwtx9Kb77pBMKgxRI2GeLz6re//W0su8i9fswDaRyuvfZaB6cq1pF77rmn88zVV18tgl+/CpkNFdHgSKazzz7bkE91Rz5syY4t68nXueOevUC7/xqa0IZceO2l4kgm4eazn/1s8Zuf0JctQ8ET80vkFmTIzJLfrnxOSkRDsSOK/B/5yEcMTaDid9kJfXkyZDad3yaLRkPm+2VZDbbSJhPb/A/PUMwOQy9xQ4JBsi5sQ/3888/nZZP5viHzXUPa+bzt2EoaZayxxhpm4YUXzvPUze8TDfoeeeQR89e//jX/Q10UlNjQTkB5Py211FKGfNn9x4rfoBU0INFXhXwLcZyTcjjfAvuPf/yjIZ/5HAOKO5HTH9taXLo80MITxh3aii26cSSNvkEb0UeLLrpovlU3zy9xTn7/hlw6cpyffPJJQ8rvfIt12sktP69TB5nxG1rkGIwFjEn6epPTjXGBeVXWV5/4xCcMKd7zqsi13pCbRX4+derUvJ9IWZ/3Ey2yDSkJzeqrr55jU4e2Xsa9Xw/tYGv+9Kc/GRxppzuz4oormrFjxxa8BeMTWCLRrr6GdoNziiDBwAArJIxninvh3Pd/YIzSV+f8cig/LYQMCUr5ffqKb6688kq/COd3L/OKLAQMvdDM73//e0MuVUW5J554oqFAvvnv973vfWbuuecu7vknkn3hlx36LTnGUb4k/d3OGb+dvfKOpnlbr3NGur1+eWW/KeZM/r7Afframr97eN66c48/W3be5vGK9zG5wpmJEyeal19+2eC9Cf5nZQ6MZ/A3JPB/8oJwminN+5zC2Y9e5qjUXBiJvBIQ9spLepX7euUVqTkr1b9suHWc9jL+UBjWAVgP2ASZGvJgNwkyGS2+i0dpM8Jc3oM8VTWRZY4hl1dDysLikZtuuskpFzduvPHGfA1QZKIT0E0udYZcPQ3t3pnfIsWfITdYnq3xc8goO++8c1EPWXmZ7373u8Xv1MnPfvYzs8MOOxTZdt11V0MeX8VvnLS5/aCvH2Ph3HPPNeRVh+ryRC6ejqxor/Mjufyavfbaq7j0m9/8ppAni4uCJ02OhWFv//XXX5/3P1mX5T12zDHHGNpktbT3TjvtNLP99tsX98k91lCotuJ3Eyf9mAfSOOyxxx6G3OALOMjIzJDyr/gdOiFLS7PJJpsUt/D87rvvXvxu/UlVDSO0y9SYjj8EPaySSGlYPIvdUL70pS9lBx10UBYz6/b9uLvVWMPlmNNOgzNJMkxlYfJpnyOlSPAZbEWPzUpsPv84zzzzZBdccEHwWXuRFDTF83B/JubZsWMygkrCbx6pbn5bD0xU8QXID1Dp0wzXamyVXpZIUVvQS8wno4VKRoqY3N3bLwu/Ee8Eu++VJenybD20WMpdz0lhUtDr04d7+JIole64444McUr8evhvxGY57LDDku4gcBfZd999Sy1tUSa+OMMalhRlHU3wLf7QT7GtyhEgNuVqYiuRGPe2LFLIZhtuuKHzNcviRQrjDK4zsABOWSTUtRICdrYezAs/VS1PYl794Ac/KGixNPnHffbZxycx/y3ZF8EKvIuSYxxFS9Lf65yxTZXiHU3xNqk5I91eW17qmNoooOrcS9WD+20er+jHr3zlKx3vezv3EfSfPvhkxx13XMEfSFnR0ey6eKV4n1+BxByVmgsjiVcCRyle0q3cJ8UrUnNWqn/9sYffEuMP5fiyei/ub7613vjx41FF7bT11ltn9EEvl82xniLlZkcZvlUKZOqbb745z8fl25Fo8YedMS2/w3HdddcdUe0Hsf0YC9dcc00eTgs7+pIBQf5e6wDKu4A1NscWZTSZerX4i42FYW+/7TfwLOhCUryLFOhO38fcwm3ZvR77MQ8sjVI40MfWjIw6MsSPhGyBXaNTCRt+8HlFCvXUI626X8nVF6broW2O4UMOZUKVBJdfCK8QwiEIVUmIAYHYgRZg7KrSTQLDsGXgiMGZSjzGIJ5BfEOeoIjgykxefugc5qNoTyiRBWJBH2mRgwoQlHnAAQfkj9fNj4egaOJYhmj0r5EmPERuxhVKe++9d+abZ/vl4DeUjWUxHaXLA9FQxnBBOUQTv7bOOutkZE0WbG/VixMmTEi6YvA6ofyGC30o/fOf/8y4IM2fC53TF9aOYjiumKsQFkLP+tdiynzJcQ+CEewZOyP5NPi/sQDm16Bo9lPdxS9XJHar+JOaVzvttJPTPt5We+4r/qT7wscz9FtyjEvTLzFn0GZJ3sHnoASvBH2Sc0a6vSivSkopEerO5bI62zxe8TGsCu/Dwh4fHi0fkFD8pXifxVNyjkrNhZHCK4GhJC/pRu6T5BWpOSvVv3bs4Sg5/lDeXHPNVcwjzKfU4hnPhBLWMFzhhrLuvPPOUNbkNZQFF7lYsoo/fJzFJg180xBOx0hU/GETFMvbcPze977XAUWb29/vsdABTuQCeYs42HY73iNVOLd6VfxVGQtOhYkfw9p+uMOSh1TR94jxB2OmJlMb50ETOMDIhus8oNsgi+smoRUvu5LiDzs0ccZsz/GluukEhYytD1/rukkYkHwSQNiGZUgsYcdBWy8sFH0Fp89QkHeZZZbJNzSBYhFbq8NayZaBY+hLFmjgAh3Pz8/h20+uSjnJdfPffffdDh0ol1y088C6ZJ6akTlwvrDw6cWADllkcgGP04j8UNCsvfbaefn8Hs4hdEGQ85N0eehbLqRaOlAPNqCB1dviiy/egQmewVjpJqFOWEraunBEbEvshIdYC1DMkXuxcx95QvFMoGgPKS2BLZTNWPj4ijCUhS8WPJXhirywOsR4BAPz+x0KYnIL4kUV55LjHjE7/A8K5C6TrbbaavmYxG5JZcrqNij+JOcVBB5YivrjFn2I6/jzLVMl+6Lo4MiJ5BhHNZL0S80Zad5RNge75ZXSc0a6vZHh49zi43yFFVZw7uGHhOKvzeMVVj0UXsB5HyDANOY53sc4gheCV/t//VT8Sc5RqbkwEnglxrD03Kor90nzitSclepfYGeT5PhDmVKKP3J7duYl1hQhrwvbjl6PWLRjowRyF+8oaqQr/mDBxnkcuSh2tLHN7e/3WOgAp+QChVxycP0w7aLcdOpV8VdlLFRtw7C2Hx8g/PUjrIqbTm2bB03hAMtuzq8oJEvT0IqXX0nxB+UEb6g9D32ZkaYQ25Lb+nDEF/xukh/oEq4DZQl18Dr9SfOrX/3KuQ8z/ZCLJHY3hqsvLytkEhoS6D7zmc/kFnKXXHJJrjyCssqmuvl9y0T0G4RSP1Esxg6FEqxU/BQS8KCs+ctf/uJkhWkx36AFOMCN2U/S5VG8EQdzCGXY4cpPFLeyQ/GERUU3CW4XvJ+xjT3FfHOKwldd35IUSlI/+eb5EFYvvPBCJxuETIrt4tTpK7RDuFoXAf6FmeLq5e7YnH6U7SfpcQ/rVl4nlLEUg9GpFso1X6GKZ9qg+JOeV2g4whBwTC699FIHD/tDui9subGj5BiXpl9qzkjzjtAc7IVXSs8Z6fbGxg+/l1IiSCj+2jxewb/4PAfvg0smT7AWC30s6pfiT3qOSs+FNvNK9KP03Kor90nzitScle5f6fGHPsE8gxEA/qAI6dZSg+JxOvMXCotBpZGs+PPlYfBEeIXVSYNuf5vGgsUNnkS+xxAMBppOvSj+JMaCbd8wtR88DOs1GPGsssoqDl/CfALfprjuFprGjoOeB03jgDXzLrvs4uCLcHA25EJjwDZQcCXF36mnnuo01gqs8HNuOiE+nq0PR9+qqWr9tAGJU07MWtHfsYV3LBgKj48DSzwKxltKBiyn+Jf7JZZYomPXL1+gg0ASm6h18sO0myvfYF3BlT4+4didmeMdwskX8MaMGVPqsgrlWj/L8/sZJs7+goq3Gdt2852SaGOVrlx+fcVDyLLR1sutWME4uPk9lIVQ4HHM4K5Tlr797W87eS+77LIiq99PEHZDFpx4AApmXqevWJMe94hbxeuD8EbBywva+Qm+9qJfeH6fPuSvqyxIubvFymtiXqENVRaz0n2BeqskqTEuTb/UnGmCd/hzsBdeKT1nmmhvlXGEPCklQmzuVa2jreMVH9g4L4P1fVkYELj0LbLIIk7+fij+pOco+kxyLqC8NvPKJuZWHblPmlcA79SclezfJsYf2iCV8CGZz+HNN99cquja5Qxa8VVH2UMbwGTgf/igSRu3ORgCT8R6rpsG3f42jQVgB6MA7PjLxyeMB5p29UTdgx4Lw9h+KMp5X/NzfJAoky2AlWQa9DxoEgesMbbaaqsOnEMGMpKYNlVWJcXfT37yk44GY3CFrNykCUUcHD6Qfeu7OvXRzqZFWVCG4SXkJzBNuGjaOqHk4wmKFXsPxyrxAhErjz/jKwp9gS5mjQha6uSHYgsWJlgsQMnkxyrkbcM5lIJcUYk4dH7yBbzYVtaYMNydE33gJ8nyTj/9dAfr73znO351Hb8322wz5xm4VNRNiL/I+/gXv/hFaRFQ5GFOwZIWzApuijYhaCsvJ2QRaPPiCCtLmx8u6TYOJO75uMICNZZ4P+HLEU/S496P5YFNTGLJt9gdtOKviXmF9ldZzEr3RQx3fk9qjEvTLzVnmuAd/hzshVdKz5km2svHS+w8pUSQUPy1dbz6CklYOsQS7WZY8Hjw+n4o/qTnKNonORdQXpt5ZRNzq47cJ80rgHdqzkr2bxPjD22QSrQTrTMn+2FNVUb7oBVfvrIHPAohWvw/fIS3smroCPkVcXrrpkG3v01jAdghrJCPbyikUF2cq+Qf9FgYxvbTDrMd/W37H0YtiA+MsA9Np0HPg6ZwgE4IsVMtpvbYD8O3pvqskuLv4IMP7mg0Go+ObjphwFqgcSyLk1eFDsTI4mWFYkn4i0i0nadDDz3UKaPKLkm///3vnWd83HyBLvXyq5vf0g8lXMzaD/nwpRgWCBYnxBryky/g8UDDfl78hpWLLW+55ZbryCJZHmL42bpwTGEJYnwLjG5c2KEI4vXiHO7ZcO0OxWXpAOE/F/baay+nnCo7MSGWBeIr+InjinhSXMHo58VvvvkHYgjyJD3uudUjsKIt6nl1HeewsOOxCAet+OMESs0rlFllMSvdF7wtsXOpMS5Nv9ScaYJ38DmIcd4Lr5SeM020NzZ++L2UEkFC8dfW8YrYpfZdgcVxKOwGxwqCJ8ejH4o/6TmK9kjOBZTXZl7ZxNyqI/dJ8wrgnZqzkv3bxPhDG6TS+eefX8xhzOVtttlGquja5Qxa8RVS9lj+VvUIjw64dneTBt3+towFyKG+BxDw9zeF6wbjqs8MciwMa/vhkbjRRhtl+++/f74rLYx1+FoJYwDeTTHPt6r9G8s36HnQFA7HHHOMw+uhTO3W8zSGXz/vVVL8wQIqxMBTVjoSDcFkhqWarR+bEXSb4APOA2p//vOf7yiKm3Ni8viKI7zgLS04YrdefO2L/flC4Pe//32nXi7QwdoupZyrm9+p7D8/4FYJn3xYE/zoRz/KTcMXXHBBp21oX0rxh00iUmnZZZctysWmJ37iAmOv5XG3KLjMVknY0APB9m2/hqwcU+WgDGw+Y8vgRwRtX3PNNfP4fn4MO79cuIzwZ+2GLn6+Kr85rohtk0qYD7buJZdc0skuPe557CoIfVUSX/y2SfHHae9lXqGcKotZ6b7g9MfOpca4NP1Sc6YJ3sHnYK+8TXrONNHe2Pjh91JKBD7X119/ff5o5fO2jle+0ZjvUVDWOB67px+KP+k5inZJzgWU12Ze2cTcqiP3SfMK4J2as5L928T4QxukErx2rKyEY7c8SoKeQSu+elH2vOc978nXThMnTuwaikG3vw1jAWvb0EY48DbrZxrUWBj29vt9fOuttzohycCjVl11VT+b6O82zAO/Qb3igF2B+YZQUPrFvPn8+tv6u5Li76KLLnJecvaFhx1L+5G46y2En14SD8iPTuQmsLDG4J0cUgCtvPLKQSwsJlWOG2ywgdMELtBhN55UqpvflofdmceNG1eqoArRnlL8QamXSjzIbErx12t5XIEHQbVq4pgixmI3CW63XOAP4YlriPOIrzOh2AtrrLFGMb6geE5Z6cXo5IJ4FYV5TPEnPe65VSnorJL4OGqT4k9qXgGDKotZ6b6ogr3NIzHGpemXmjNN8A4+B3vlbdJzpon22nGSOqaUCBKKP9DQtvGKj3ocd4ThqJK43NIPxZ/0HEUbJecCymszr+R9LCWHcBklJSdK8wrgnZqzkv3bxPhDG6QSrGe4fAfZZFBp0IovX9kDa1OEWQj9HXbYYdnJJ5+cYU0JDGHN3GsadPsHPRaefPLJ7FOf+pQzHjE2Qxsz9op16vlBjIVhb39Zn8DIBEYnnE9BEdZUGvQ8KGtXLzjAnZfjB++i0ZAqKf6w6ydvvD2vs5PVXXfdlS2zzDLZrrvumsH1FV/jqyYoi2yd8847b9XHgvl8t1u4FNjkm6r6O6kiH4RuS0u3R18JwwU6LGJTqW7+adOmZYgVV4VeLjAif0rxF4rZ59PPFTYpxV+v5XGLzjrbbHOrRAgS3Sa4rH7ta19zFndluC+22GLZAw884FTFscLX0F4SF8Sr4BpT/EmPe/5CgtBSJeGrusWyDYo/6XkFDKosZqX7ogr2PE+vY1yafqk50wTvqDsHeVt8Xik9Z5poLx8nsfOUEkFK8Qca2jReEfbB8rCy92sIN+wSb5/D/PFTXbxSGxtJz1HQKzkXUF6beWUTc6uO3CfNK4B3as5K9m8T4w9tkErYvI17IkHRG9vQLVYv5AjEhMQHxDrrIlvmoBVfvrKnmxjZti3dHAfd/kGOhQkTJmQLLbRQ8W7AOwLj8pRTTukGyp6f6fdYGPb2pzrM36i0yc0oBjkPmsIBe0pYuQvv9ClTpqSqGhH3ZwOV1LBoIndbQ26Thl5QTj5iMIZiwhmycnKuh36Qe6uhQVfcIrcnQwt5M378eEOxxYrroRNyUzQPPfRQfouspQx9xQ9lq3QNzSWFi3nkkUfy/CSsGGIe+Tkpx8y1116bn6O9Tz31lCEByimXdsU15CKbX0P7Dz/8cOd+lR8kdBuyvCuyLrroooZM3fPfpPgz119/fXEvdFInP31RM7Qzr6H4GR1FUTBdQ1+ODSlkDcV0M5/5zGfyP3qRGDJxzfOT4s/87ne/c57FM6Swyq+RQsnQZhXOff8HxfUztFtwfpkWs+bOO+90skiWB9rJPTsvn/etU2HgB/oE/Y2E8WHHWyBrpUvk8pljTjEgDcWNNMQwgs+hD+67775iDtGuZubKK6/M85JFqqGFoqGNaILPpi7WxZXcyQzFSciLJVdfQ19wiiqkxz25uxn6UpeXT+5uOR8pKis5wVgkxX1+lxR/hjbBcXLyPlxvvfXMVVdd5dz3f5DAaMjqMr8cys/LA6+y/YIHmphXKBftQzttot3vDMXvsD/zo3RfOIXX+NHtGJemX2rONME76s7BGK+UnjNNtLfq8CFlv7nnnnuK8UxxSp1HY3PPyVjjR1vGKynpDO2wmFOO9+69996bbAUf46QUMRQCwnmG4xXiZU5m+pHifdJzFPVLzgWU12Ze2cTcqiP3SfMK4J2as5L928T4QxskE5eXUC7kDcy9uumSSy4xFDw+f4zCnphNNtnEkCKwcjFzzz23oY0K8/woh6zpKj8rkfHEE080O++8c1EUKf4MbTBR/G76ZNDtR/sGMRawLsPaburUqQXEtEFf3v/djMOikB5O+jkWhr39VboJPIk8DIusmJeYn02lQcyDKm3pFgf+HiJXaUPuzFWqa3+equpJ7k5FrSq0oDvssEOyiJdffjkPLsmfs+fYTCOViLEX9Un4qfublWBzB1gF8C94Ze3aYostClrQBokt0ut8yQVWdfKfc845Dr2gebfddsstzcpiCcZ2d0X9kl92pcuD65QdW9glrEpCcHW4fdvnEHxdMsGdgRSf2YEHHpjx2Du2Pnwls2n33Xcv6MB9UlDbW6VH5KFFdEYCgJOnbj/FLP6kxz23sMQO22VjkTeIu1GnLP4QUzGWUB8PgEuCUkf2mBVNE/MKBFSxYpHui46Gd3GhzhiXpl9qzjTBO+rOwZjFn/ScaaK9VYdOynooNveq1hHLN8jxyt0Yq8ah5a5csIbyE8dLgvdJz1HQKzkXUF6beWUTc6sVjCWwAAAS8UlEQVSO3CfNK4B3as5K9m8T4w9tkEywnrEyHI7w9OgmQfbg5dTdXG7QFm/9tvLyMR50+0FPv8fC1Vdfne+azMfN/PPPn68zfHz6+btfY2EY2w/rRmwMCis+7BuA/Q9SyfdwBF9tMvVjHvQTBy5XYbPO0ZIqufqisWVbJWPRDhP1WAJgnEHZc38DgVAZMH3nShlsptFrwoYdfNEPhQxMoy1dOGLji1A66KCDnHxVdqKCQubMM8/Mrrjiiuzuu+/uUNDUEehAU5383FQV7Tr++ONDzSquIV4CxwGLFD9JCngoW7K87bff3qEfMZ5SiSwWnWc23njj1CMd94EblNhwc7jhhhs67vML/ssRMVFs8schfamwt0qP9KW9oB9Y2lQX15jiT3rc+9ujQ/keSwje+9a3vrVoZ0jxxzenSbk2/+1vfyvKwnivq/hrYl6h/VUWs9J9EcOd35Ma49L0S82ZJnhH3TkYU/xJz5km2svHS+w8pUTgAle3gfPbOl4RG5m/YxFKJZYgr0DOss+EFH/SvE96jqJ9knMB5bWZVzYxt+rIfdK8Anin5qxk/zYx/tAGyfTwww8Xc9LOTciSdRLiYnG5BuX44V9S5Q1a8eXLs8Pm6ov+6edYuPHGGztit2ENwOPVp8ZMU/f7MRaGtf1krefwm9Q6E33sy8aIWddk6sc86CcOZMmcQX+FP+hwRkuqrPjDF3LsQGdfcPwIoRQDzE9kfp7Bco7n5edgEqn0xBNPOM/DukMi4au4pQVf0/kXWggwZYnM8ovn8Dzi9aWslQ455BDnme22284pvo5Ahwfr5EccOdtOHH2rMIcQ+nHEEUc4+cnU1c8iLsBLCoz+FwcIwKmEWJUco7oT/NVXX3W+voWUpT4NiN9n6xwzZkxxG8pDex1Hcvss7oVOsCjk+REE3qa6uMYUf9Lj/oILLnDo3myzzSzZwaM/h0KKP+BosYDiIPZFDLE9bV4c6yr+mphXaLj/he7iiy/uwEO6LzoqCFyQHOPS9EvNmSZ4R905GFP8Sc+ZJtobGDrBSyklQq+KvzaPV3/8I/5uLPkyVEjxJ837fBol5BzJuQC82sorQVsTc6uO3CfNK9Cm1JyV7N8mxh/aIJ18owYKW5RRyJhK1WB95MsRVWRHv3BV/L2vkOWqyPs+flK/+zEWJk+enM0zzzxFeyG7wkMptFGgVLvqlNO04m+Y2+/zxNTmqtDZYGzwdU6Tm3vYcdL0PBgpOFg82nisrPgD8RR7zrGU4wMK59gNFebusIbBQp7i+DmDjueH4qNKMFyK4+WUceqpp4rgWLZTMWiMacURnJu7HCJ/TIEJyyL+YkZ+inHntKGOQIcH6+T3XUsff/xxp27+A0oG7u4MWiHs+UlSwEPZkuXhxcBdw2EtCsuAsnT55Zc74wtfXykWVFn20uurr756UQ4wpFh5pXnhmsutWNdaa60iLyxcOR7og+uuu664759Q/LeiXuSlOHRFFl5OygIOD8UUf9LjHnOfu5TDAve2224raOcncKf351BI8YcwAMDA/vGNe3h5FMezY/OVuoq/JuYVaIT1tKUfxxC/k+4Ljk3sXGqMS9MvNWea4B1152BM8Sc9Z5pob2z88HspJUKvij/U1dbxig+F2OiMz/MyS3zIKb5FUEjxJ837pOco+kNyLqC8NvPKJuZWHblPmlcA79SclezfJsYf2oCwLijb/uFaLwlWxXPNNZczl7EOSnkwUExvR96yvKCKh4dPL5eNqiq+bPvt0S+zzu+mlT0pWrppP8q0bbfHVD2p+/0YC9tuu60z1mC08sorr6RIi9637bfHaObEzabHwjC3HxtLcE9FnMfWtv7HJ+hcwP/8ZPvdHv37dX83PQ+awiHUTosJjiHsQs+MhGu1FH9o0L777uswHvvCqnOEIFvFTBX1IR4dLzumuEL+qgmdyF8Ytg7szpWK2weXXZvfHmF+6jNgCgTZseNSSAFTR6BD++rk32qrrRxaEavR/zqELwPnnXdexwIDbUNdfpIU8FC2dHlwNbD9giPG27HHHutYZkJZAHcSzkiRlzah8Ztb6TdiL/A6sTsylIp+Qiw+7pqLZ3zXCCjYeVlve9vbspNPPtkpCpabvskzBE4wKJvq4hpT/KFM6XF/2GGHOe185zvfmY9DSz+OcIP70Ic+5OQDNiHFHxbPHDfsbIiduu14pw1rsjPOOCND7EeeD+d1FX9NzCu0F24+nDba2CjnldgVnVsSSPcF6k4lyTEuTb/UnJHmHXXnYEzxh/6RnjPS7U2NIXs/pUSQUPy1ebxigc/nOc7xEQfzAh8LaVOozHcJtvlDij9p3od+kp6j0nOhzbwS+EnPrTpyH+qX5hWpOSvdv9LjD5j4Srpnn30Wl3tKJ5xwQsdcxsdf2ngh/2iL+OFI+KCMj5t77LGH47pv5zVCDnWT+DqmquLP1mmPXG6sS0PTyp4UPd20H2XatttjLxhYGpscC7QBlmMwALrhPYZ5XvWPNhK0pBZH23577AWHJsfCsLcfHeZb/2OdCeMonrCnAtaG3LgEfYuPiKFk+90ee+l/W36T8wB1NIGDpd0e8W6wmOBYdc8A+3ybj7UVf/havffeezuAcHBS5xiMUDJVTbTLYVEXBA/JtOOOOxZlW7pDCoBQnV/+8pc7noWiAQs32kXHUWbZsuebb76MdkHuKK6uQFcnPxSsPgOAqTiCfELJBQUGFEaWRv8IpRPiq/EkLeBJlwelHg+GbttEO6ZltOtQBneKkPKHdjXOZsyYwZta+Zx2vM773tZlj+grjBUIgbSjcUdfQLETYrQYh7YMe4QlI6xYYN2BoPD2Oo4Ye3fccYdDb11cU4o/FC457uGKC8x5O3COIMVwxffdYHi+kOIPCj5suc7z4RzKfCgV/ev8d2jex5QPTcwr4ItxBHo5bfYcJvQ8SfYFL7fsXHqMS9MvMWekeUfdOZhS/EnPGen2lo0d/3pKiRCbe35ZZb/bPl532WWX4Dy3873siHeKn6R5ny1fco5Kz4U280rgJz236sh9qF+aV6TmrHT/og2S4w/lNaH4Q7n4sOzL2Xz++la7/B7Ot9lmGxTTVepG8eXXH5JBqxLTpLKnCg3dtB/lSmLA6WxqLHALdp/2qr9//vOfc1Lzc//Zto6FYW8/OgsGS6G1LdaRG264Ye7aG1rbwguzLEn2P6+jqXmAOprAgdOOc1X8+YjQ73PPPTeDttkfNLHfsNzBc1WTH9D/xz/+cdVHK+WDosSn97LLLqv0LJjj+PHjOyzG/PLsbyjbYO0VSnUFurr5oeCzdKSOcNH2TYTPPvtsh2xpAU+6PBCL+Cn+znCxtkORZC3DnMbW+AGLspB1Wlm9CFpfViesR0OK6VBZcJmFZZuf6uJaRfEnOe5BL/oJcQxD7eLXEKuCu7OFFH8o78ILLwwq/3hZON9vv/2yXXfdtai3ruIPdUnPK5SJhEWATy9+Q3HMk3Rf8LLLziXHuDT9EnMG7ZbkHXXnYErxZ+mTnDOS7S0bN/71lBJBQvGHOts8XkEfLJDxcS0033ENlkNHHXVUPvdtnrIPoJK8D7QhSc7RJuZCm3kl8JOcW3XlPlu/FK9Izdkm+ldy/AGPphR/KBtxFf0PsnbOlh3xMRqeJ7F4xCg7lrpRfPn0AOdukyr+OpFrYizUHVt+H+P3SFb8DXv77ShDmAC+a3uon+01GBFgF+DY/LZ57TGW19JQ9djEPLB1S+Ngy7VHVfxZJLwj4ogccMABQes2O4hwhEXZiSeeWNtH+he/+IUjEP/5z3/2KOj959JLL13UMe+889a2+LrlllsyWCXyXfd42/FShgl/bFONugJd3fxACfH7/NiElk4ojrDBBYLkI2F3KHsPx3HjxuXX7X91BTwuePrKC5QpXZ6lE0e4iyDgsh+70LYPyi4smKQSFHlwh+ebd9i6cMQiD19sEEcytSkMaEI8OrgGh+jHF2YoarHBRyjVxRVB5i2tsMSLJYlxb8vH15tjjjkmqDTF1yu4r0Cps/nmmxf0xbalx07OcKn3Lefg1o3YWnABRNpnn32K8vwxjvt8M6OynUUl5xXqRMIiAAta30oRi5ZQkuyLUPn+NekxLk1/L3OGt1WCd9SdgyleaemTnjMoV6K9lr7UkQedDm0gVWXupeqw99s+XvFB8OCDD86/2OPDEay3gQk+TNhg3DymKKzWy5IU7/PLl5ijTcyFtvNKi6PE3OpG7kP9UrwiNWeb6F+Ln8T4Q1lc8Qf5ICaX27rrHCGnQL7g89XKVPwI+RBhjBALstfEPzb7XgFlZfueEb0s9n3Fn28oUEaD1PVu2o+6JTEItUVyLGCt7Ycl4uOp6nloszhJHJoaC8Pefn98ITwXNlT1N3qx4wDrRayByoyNeHmS/c/LteeS88CWaY+SONgy7RHvBr6GhCfaaEmzoSE0WHpOpCwyJHQaimdhCDCzwAILGBJUzKKLLmroJddV+TRwDQm++bO0q5yheF9dldOPh8itw5Abr6G4M4aCTxpSdubtp3g8hpQ9/SAhWQdofPTRRw1tuW0oVqIhi01DAkpOJ71Uks+P5Awk/JoHH3zQTJgwwQAH9A/thGgWXHDBRppFSr0ca9RHwU4NKXEMKZ0MCcddjQfQj7FFQaMNMSOz5JJLmiWWWMIQ026E/qqFSo57WsTl7cP4pBe9oa9ahhTzhl5iVclx8gGze++911BcE0PCoRk7dmzXvMgp2PvR1LwC/ehv2qHUkAVUPlZjvESyL7wmBn9Kj3Fp+qXmDMrpJ+8Igl1yUXrOoJo2t7cEhkqX2z5eY43Au3rSpEl5FgolYkiRFMue92ETvE96jkYbUeNm23mlbcog51YTvMK2q1/Hto6/UPuxDqCPsvkfWR4bcvfNZU7InRT2J/RIX6+ddtppZvvttzdk8FDwlr4S0ILK+oWBjoUWdHaEhH6NgwgJXd2C+uaxxx7LdS/Qu5AiLF9jYn1IHxArl9mv9jc1D6RwqAzYCM8opviTxuGvf/1rrtyw5V599dVmnXXWsT/1qAgoAoqAIqAIKAKKwIhCgHbiMxTgv1ACUCBu8/73v7+0DeTpYMiroLhPsQHNcccdV/zWE0VAEVAE6iJAmweZM88806y77rrmV7/6Vd3HR0V+xWBmNw47Dtp+5QWjgqFVbERrFX8QjCm2Td4Mco00tKtlxSZpNkVAEVAEFAFFQBFQBNqHwM0332zIXbcgjFx6DQXCLn7zE9psylB4jNza217Xj6AWCT0qAopANwhA0UdhYgxtkGMozIChsCfdFDOin1EMZnbfsOOg7VdeMKIZWRfEt1LxR3EvchdhuPshXXrppWajjTbqonn6iCKgCCgCioAioAgoAu1A4LnnnjMUm6cghuIDm9tuuy1X8BUX6QQu/j/4wQ/MCSecUFymmKfmqaeeMjhqUgQUAUWgLgIIpYIwMXALpA0T8hBKtCto3WJGdH7FYGb3DTsO2n7lBSOakXVJfCsVf/gCfvzxx+dNWm211cyNN95oaCODLpuojykCioAioAgoAoqAItAOBBCn76qrriqIgXyz+uqr5zF3abMtg5hgtEmNefrpp4s8OKFdgA3ckjQpAoqAItAtAmuuuWYei502e8jjXXdbzkh+TjGY2XvDjoO2X3nBSOZj3dDeOsUfbdGcx/aDiwu+aiO4PTbI0KQIKAKKgCKgCCgCisBIRwDeDNiwDLGMqyQoBvfcc09z6KGHVsmueRQBRUARKEUALr7YKAwbxQ1rUgxm9vyw46DtV14wbDywdYq/cePGmYsuuijvh1NPPTXfdWrYOkXbqwgoAoqAIqAIKAKjFwHs0ovYfieffHJufVPWUigI99tvP7P22muXZdHrioAioAgoAoqAIqAIKAKKQBSBVin+3nzzTbPjjjsabM0MK7+99torSrzeVAQUAUVAEVAEFAFFYKQiMHXq1DzG32OPPWbw98orr5j555/fLLDAAmallVYywxZ/a6T2o9KtCCgCioAioAgoAopAmxFoleKvzUApbYqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKwEhCQBV/I6m3lFZFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRaAiAqr4qwiUZlMEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBEYSAqr4G0m9pbQqAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAIVEVDFX0WgNJsioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIjCSEBhqxd/E7H7zJv3T1F8E5pttITOXeV9/K9XaFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBEYMgSGWvG37xubmunZtCHr8sE3d7O3jDefnm3VwROiFCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAiMIoRUMWfKv76PrxV8dd3yLVCRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARGEIE/h+DUtYtmGefPQAAAABJRU5ErkJggg=="
+  },
+  {
+    "quest": "Il seguente Resource Allocation Graph (RAG) mostra un sistema il cui stato:",
+    "answers": [
+      {
+        "answer": "Dipende dalle scelte dello scheduler del sistema operativo",
+        "image": ""
+      },
+      {
+        "answer": "Presenta deadlock",
+        "image": ""
+      },
+      {
+        "answer": "Non presenta deadlock",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Dipende dalle scelte dello scheduler del sistema operativo",
+        "image": ""
+      },
+      {
+        "answer": "Presente deadlock",
+        "image": ""
+      },
+      {
+        "answer": "Non presenta deadlock",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Sicuramente presenta deadlock",
+        "image": ""
+      },
+      {
+        "answer": "Potrebbe presentare deadlock",
+        "image": ""
+      },
+      {
+        "answer": "Sicuramente non presenta deadlock",
+        "image": ""
+      },
+      {
+        "answer": "E’ impossibile rispondere",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": "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"
+  },
+  {
+    "quest": "Il seguente Resource Allocation Graph(RAG) mostra un sistema che:",
+    "answers": [
+      {
+        "answer": "Sicuramente presenta deadlock",
+        "image": ""
+      },
+      {
+        "answer": "Potrebbe presentare deadlock",
+        "image": ""
+      },
+      {
+        "answer": "Sicuramente non presenta deadlock",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "76",
+        "image": ""
+      },
+      {
+        "answer": "87",
+        "image": ""
+      },
+      {
+        "answer": "46",
+        "image": ""
+      },
+      {
+        "answer": "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
new file mode 100644
index 0000000..7aeb716
--- /dev/null
+++ b/data/questions/so1_unive.json
@@ -0,0 +1,1193 @@
+[
+  {
+    "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": [
+      {
+        "answer": "\"ad una riduzione della\" e \"può ridurre\"",
+        "image": ""
+      },
+      {
+        "answer": "\"ad un aumento della\" e \"può aumentare\"",
+        "image": ""
+      },
+      {
+        "answer": "\"ad un aumento della\" e \"può ridurre\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "\"non memorizza\" e \"numero di pagine riferite\"",
+        "image": ""
+      },
+      {
+        "answer": "\"non memorizza\" e \"numero di page frame\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "\"l'ultitima pagina riferita\" e \"nodi\"",
+        "image": ""
+      },
+      {
+        "answer": "\"qualsiasi pagina riferita\" e \"entità\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "\"bit di residenza, di protezione, di lunghezza\" e \"overflow del segmento / protezione del segmento\"",
+        "image": ""
+      },
+      {
+        "answer": "\"bit di validità\" e \"validità del segmento\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "non ignorano i comportamenti dei singoli processi.",
+        "image": ""
+      },
+      {
+        "answer": "tengono conto dello stato del processo",
+        "image": ""
+      },
+      {
+        "answer": "ignorano i comportamenti dei singoli processi.",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "6) La strategia di sostituzione di pagina a orologio _____",
+    "answers": [
+      {
+        "answer": "è una variante della strategia FIFO",
+        "image": ""
+      },
+      {
+        "answer": "è una variante della strategia LIFO",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "tasso di page fault",
+        "image": ""
+      },
+      {
+        "answer": "grado di multiprogrammazione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "tempo limite",
+        "image": ""
+      },
+      {
+        "answer": "insieme di pagine riferite",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "lista circolare",
+        "image": ""
+      },
+      {
+        "answer": "coda di massima priorità",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "\"può essere tutta o in parte inserita in memoria associativa\" e \"bit di modifica e di riferimento\"",
+        "image": ""
+      },
+      {
+        "answer": "\"può essere inserita interamente in memoria secondaria\" e \"bit di modifica e di riferimento\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "\"può ridurre\" e \"può ridurre\" e \"aumentare\"",
+        "image": ""
+      },
+      {
+        "answer": "\"può ridurre\" e \"può ridurre\" e \"dimunuire\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "\"indipendentemente dalla CPU\" e \"sono ridotte\"",
+        "image": ""
+      },
+      {
+        "answer": "\"tramite la CPU\" e \"sono ridotte\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "record non bloccati",
+        "image": ""
+      },
+      {
+        "answer": "indipendenza logica",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "rimane valido",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "\"un minor spreco di spazio\" e \"un maggior spreco di tempo\"",
+        "image": ""
+      },
+      {
+        "answer": "\"un minor spreco di spazio\" e \"un minor spreco di tempo\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "il solo aumentare dei puntatori ai blocchi",
+        "image": ""
+      },
+      {
+        "answer": "il solo aumentare dei blocchi",
+        "image": ""
+      },
+      {
+        "answer": "#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": [
+      {
+        "answer": "\"fisicamente adiacenti\" e \"buone\" e \"frammentazione interna\"",
+        "image": ""
+      },
+      {
+        "answer": "\"fisicamente adiacenti\" e \"scarse\" e \"frammentazione esterna\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "il backup logico che memorizza solo i dati del file system che sono stati modificati rispetto al backup precedente",
+        "image": ""
+      },
+      {
+        "answer": "dati critici o sensibili",
+        "image": ""
+      },
+      {
+        "answer": "risorse limitate",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "\"esportazione delle copie\" e \"memorizzazione dei dati critici o sensibili\" e \"garantire i dati in ogni copia\"",
+        "image": ""
+      },
+      {
+        "answer": "\"ridondanza con copie multiple\" e \"memorizzazione dei soli dati modificati all’ultimo backup\" e \"mantenere la struttura del file system\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "complessità esponenziale",
+        "image": ""
+      },
+      {
+        "answer": "la necessità di performance discrete",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "\"non sono visibili\" e \"sotto livello\" e \"nel nucleo\"",
+        "image": ""
+      },
+      {
+        "answer": "\"non sono visibili\" e \"sotto livello\" e \"a livello applicazione\"",
+        "image": ""
+      },
+      {
+        "answer": "\"visibili\" e \"sotto livello\" e \"nel nucleo\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "\"ridondanza\" e \"distribuzione e partizione sulle copie dei dischi trasparenti\"",
+        "image": ""
+      },
+      {
+        "answer": "\"trasparenza\" e \"copie scalabili orizzontalmente\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "\"aumenta inizialmente i tempi di risposta a favore di una maggiore organizzazione dei dati per accessi futuri\" e \"permettono di velocizzare la ricerca\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "tempo più breve di seek in una direzione preferita",
+        "image": ""
+      },
+      {
+        "answer": "tempo più lungo di seek in una direzione preferita",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "della deviazione del cilindro tra le tracce",
+        "image": ""
+      },
+      {
+        "answer": "della fremmentazione",
+        "image": ""
+      },
+      {
+        "answer": "della latenza casuale",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "nessuna delle altre",
+        "image": ""
+      },
+      {
+        "answer": "throughput maggiore",
+        "image": ""
+      },
+      {
+        "answer": "throughput minore",
+        "image": ""
+      },
+      {
+        "answer": "latenza maggiore",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "27) L’algoritmo di scheduling del disco SCAN si basa su una ricerca del cilindro  ______",
+    "answers": [
+      {
+        "answer": "cercando i dati più vicino alle posizioni correnti delle teste di lettura/scrittura del disco.",
+        "image": ""
+      },
+      {
+        "answer": "in direzioni variabili quando si raggiunge un estremo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "risorse logiche",
+        "image": ""
+      },
+      {
+        "answer": "risorse fisiche",
+        "image": ""
+      },
+      {
+        "answer": "collegamenti simbolici",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "29) Ogni oggetto nel sistema Windows: ",
+    "answers": [
+      {
+        "answer": "condivide delle risorse con altri oggetti",
+        "image": ""
+      },
+      {
+        "answer": "nessuna delle due",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "\"hardware e critiche, chiamate differite, chiamate di procedura asincrone, passivo\" e \"Priority Scheduling\"",
+        "image": ""
+      },
+      {
+        "answer": "\"passivo, chiamate di procedura asincrone, chiamate differite, hardware e critiche\" e \" priorità di code round robin\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "Le tradizionali chiamate di sistema",
+        "image": ""
+      },
+      {
+        "answer": "le interfacce utente",
+        "image": ""
+      },
+      {
+        "answer": "nessuna delle precedenti",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "32) Nel sistema operativo Windows, il livello Executive si trova:",
+    "answers": [
+      {
+        "answer": "sopra al livello nucleo",
+        "image": ""
+      },
+      {
+        "answer": "sotto al livello applicazione di sistema",
+        "image": ""
+      },
+      {
+        "answer": "sotto al livello applicazioni utenti",
+        "image": ""
+      },
+      {
+        "answer": "nel Kernel Executive Layer",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "\"algoritmi di scheduling\" e \"in colonne\" e \"blocchi\"",
+        "image": ""
+      },
+      {
+        "answer": "\"paginazione\" e \"secondo uno schema creato al momento\"  e \"aree di utilizzo\" ",
+        "image": ""
+      },
+      {
+        "answer": "\"paginazione\" e \"su tre o quattro livelli\" e \"tre zone e in pagine\"",
+        "image": ""
+      },
+      {
+        "answer": "nessuna delle precedenti",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "34) Il sistema opeartivo Linux: ",
+    "answers": [
+      {
+        "answer": "e di tipo microkernel ",
+        "image": ""
+      },
+      {
+        "answer": "è di tipo monolitico, ma con componenti modulari",
+        "image": ""
+      },
+      {
+        "answer": "ha un kernel organizzato a livelli",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "35) Nel sistema sistema opeartivo Linux con porting si intende:  ",
+    "answers": [
+      {
+        "answer": "l'esportazione di componenti del kernel",
+        "image": ""
+      },
+      {
+        "answer": "l'aggiunta di feature da un kernel all'altro",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "III Generazione",
+        "image": ""
+      },
+      {
+        "answer": "IV Generazione",
+        "image": ""
+      },
+      {
+        "answer": "I generazione",
+        "image": ""
+      },
+      {
+        "answer": "V Generaione",
+        "image": ""
+      },
+      {
+        "answer": "II Generazine",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "37) [GENERAZIONI] Definizione e uso della memoria virutale:",
+    "answers": [
+      {
+        "answer": "III Generazione",
+        "image": ""
+      },
+      {
+        "answer": "IV Generazione",
+        "image": ""
+      },
+      {
+        "answer": "I generazione",
+        "image": ""
+      },
+      {
+        "answer": "V Generaione",
+        "image": ""
+      },
+      {
+        "answer": "II Generazine",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "38) [GENERAZIONI] Interfacce grafiche: IV Generazione ",
+    "answers": [
+      {
+        "answer": "III Generazione",
+        "image": ""
+      },
+      {
+        "answer": "IV Generazione",
+        "image": ""
+      },
+      {
+        "answer": "I generazione",
+        "image": ""
+      },
+      {
+        "answer": "V Generaione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "\"anche utente\" e \"anche utente\" e \"solo nucleo\"",
+        "image": ""
+      },
+      {
+        "answer": "\"solo nucleo\" e \"solo nucleo\" e \"solo nucleo\"",
+        "image": ""
+      },
+      {
+        "answer": "\"solo utente\" e \"solo nucleo\" e \"anche utente\"",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "40) [architetture ideali] efficienza e prestazioni:",
+    "answers": [
+      {
+        "answer": "monolitico",
+        "image": ""
+      },
+      {
+        "answer": "microkernel",
+        "image": ""
+      },
+      {
+        "answer": "a livelli",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "41) [architetture ideali] flessibilità e modificabilità:",
+    "answers": [
+      {
+        "answer": "monolitico",
+        "image": ""
+      },
+      {
+        "answer": "microkernel",
+        "image": ""
+      },
+      {
+        "answer": "a livelli",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "42) [architetture ideali] isolamento delle funzioni:",
+    "answers": [
+      {
+        "answer": "monolitico",
+        "image": ""
+      },
+      {
+        "answer": "microkernel",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "record definiti",
+        "image": ""
+      },
+      {
+        "answer": "record non bloccati ",
+        "image": ""
+      },
+      {
+        "answer": "record bloccati ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "\"rimane valido\" e \"diventa non più valido\"",
+        "image": ""
+      },
+      {
+        "answer": "\"rimane valido\" e \"diventa valido\"",
+        "image": ""
+      },
+      {
+        "answer": "\"diventa non più valido\" e \"rimane valido\"",
+        "image": ""
+      },
+      {
+        "answer": "\"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": [
+      {
+        "answer": "no, nella V generazione",
+        "image": ""
+      },
+      {
+        "answer": "no, nella III generazione",
+        "image": ""
+      },
+      {
+        "answer": "no, nella II generazione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "no, nella V generazione",
+        "image": ""
+      },
+      {
+        "answer": "no, nella III generazione",
+        "image": ""
+      },
+      {
+        "answer": "no, nella II generazione",
+        "image": ""
+      },
+      {
+        "answer": "sì, nella IV generazione",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "47) Considerare un sistema operativo della IV generazione, include la caratteristica \"memoria virtuale\"?",
+    "answers": [
+      {
+        "answer": "no, nella V generazione",
+        "image": ""
+      },
+      {
+        "answer": "no, nella III generazione",
+        "image": ""
+      },
+      {
+        "answer": "no, nella II generazione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "no, nella V generazione",
+        "image": ""
+      },
+      {
+        "answer": "no, nella III generazione",
+        "image": ""
+      },
+      {
+        "answer": "no, nella II generazione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "aumenta il livello di comunicazione fra moduli",
+        "image": ""
+      },
+      {
+        "answer": "usa un approccio a livelli",
+        "image": ""
+      },
+      {
+        "answer": "supporta funzioni per connessione alla rete",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "aumenta il livello di comunicazione fra moduli",
+        "image": ""
+      },
+      {
+        "answer": "gestisce un sistema connesso alla rete",
+        "image": ""
+      },
+      {
+        "answer": "usa un approccio a livelli",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "il sistema time-sharing prevede anche multiprogrammazione",
+        "image": ""
+      },
+      {
+        "answer": "il sistema time-sharing dipende dal livello di multiprogrammazione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "passa dallo stato \"Esecuzione\" allo stato \"Pronto\"",
+        "image": ""
+      },
+      {
+        "answer": "passa dallo stato \"Esecuzione\" allo stato \"Bloccato\"",
+        "image": ""
+      },
+      {
+        "answer": "passa dallo stato \"Bloccato\" allo stato \"Pronto\"",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "passa dallo stato \"Esecuzione\" allo stato \"Pronto\"",
+        "image": ""
+      },
+      {
+        "answer": "passa dallo stato \"Esecuzione\" allo stato \"Bloccato\"",
+        "image": ""
+      },
+      {
+        "answer": "passa dallo stato \"Bloccato\" allo stato \"Pronto\"",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "passa dallo stato \"Esecuzione\" allo stato \"Pronto\"",
+        "image": ""
+      },
+      {
+        "answer": "passa dallo stato \"Esecuzione\" allo stato \"Bloccato\"",
+        "image": ""
+      },
+      {
+        "answer": "passa dallo stato \"Bloccato\" allo stato \"Pronto\"",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "dipendenti l'uno dall'altro ",
+        "image": ""
+      },
+      {
+        "answer": "anche fisici",
+        "image": ""
+      },
+      {
+        "answer": "fisicamente adiacenti",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "di ottenere ottime prestazioni",
+        "image": ""
+      },
+      {
+        "answer": "frammentazione interna ",
+        "image": ""
+      },
+      {
+        "answer": "di ottenere scarse prestazioni",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "di ottenere ottime prestazioni",
+        "image": ""
+      },
+      {
+        "answer": "frammentazione interna ",
+        "image": ""
+      },
+      {
+        "answer": "frammentazione esterna",
+        "image": ""
+      },
+      {
+        "answer": "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
new file mode 100644
index 0000000..1f0ed6d
--- /dev/null
+++ b/data/questions/so2.json
@@ -0,0 +1,2740 @@
+[
+  {
+    "quest": "1. A quanti gruppi può appartenere un utente nel SO Linux?",
+    "answers": [
+      {
+        "answer": "Ad almeno un gruppo",
+        "image": ""
+      },
+      {
+        "answer": "Ad un solo gruppo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il comando genera un errore perché per aggiungere un utente ad un gruppo si può utilizzare solo il comando addgroup ",
+        "image": ""
+      },
+      {
+        "answer": "Se \"utente1\" non appartiene al gruppo \"studente\" lo aggiunge a tale gruppo altrimenti non lo aggiunge",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Da errore perché utente1 non esiste",
+        "image": ""
+      },
+      {
+        "answer": "Crea utente1 e, oltre a creare il gruppo utente1 lo aggiunge al gruppo studente",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "C2 da errore \"comando non trovato\"",
+        "image": ""
+      },
+      {
+        "answer": "C1 e C2 sono equivalenti",
+        "image": ""
+      },
+      {
+        "answer": "C2 esegue una setUID mentre C1 no",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "5. Quale è la differenza tra i comandi sudo e su",
+    "answers": [
+      {
+        "answer": "sudo è un comando che permette di eseguire altri comandi come root; su è una scorciatoia per invocare il comando sudo",
+        "image": ""
+      },
+      {
+        "answer": "su è un comando che permette di cambiare utente. sudo è un camando che permette di eseguire altri comandi come super-utente ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "5",
+        "image": ""
+      },
+      {
+        "answer": "7",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "touch -at202006021200 filename",
+        "image": ""
+      },
+      {
+        "answer": "touch -cat202006021200 filename",
+        "image": ""
+      },
+      {
+        "answer": "touch -ct202006021200 filename",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "8. Quale è il risultato del comando touch nomefile?",
+    "answers": [
+      {
+        "answer": "Crea un file vuoto con nome nomefile",
+        "image": ""
+      },
+      {
+        "answer": "Aggiorna, al tempo corrente, gli atttributi atime e mtime di nomefile ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il bit SetGid è settato",
+        "image": ""
+      },
+      {
+        "answer": "Lo sticky bit non è settatto",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "tree -d 3 dir1",
+        "image": ""
+      },
+      {
+        "answer": "tree -L 3 dir1",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "tree -d ~",
+        "image": ""
+      },
+      {
+        "answer": "tree -d -L 3 /home/myhomedir",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "dd if=filein of=fileout bs=100 count=1",
+        "image": ""
+      },
+      {
+        "answer": "dd if=filein of=fileout bs=1 skip=1 count=100",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "dd if=filein of=fileout bs=1 skip=10 count=100",
+        "image": ""
+      },
+      {
+        "answer": "dd if=filein of=fileout bs=100 seek=10 count=1",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "1",
+        "image": ""
+      },
+      {
+        "answer": "Nessuno, da errore",
+        "image": ""
+      },
+      {
+        "answer": "3",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "15. Quanti file system principali ha linux?",
+    "answers": [
+      {
+        "answer": "dipende dal numero di filesystem mondati al boot",
+        "image": ""
+      },
+      {
+        "answer": "1",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "/etc/mdev",
+        "image": ""
+      },
+      {
+        "answer": "/etc/mtab",
+        "image": ""
+      },
+      {
+        "answer": "/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": [
+      {
+        "answer": "Per consentire a qualsiasi utente di modificare la propria password",
+        "image": ""
+      },
+      {
+        "answer": "Per evitare che un utente possa cancellare il file eseguibile passwd",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "merge_sorted_list",
+        "image": ""
+      },
+      {
+        "answer": "entrambi",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "perché file2.o è stato ottimizzato, per occupare meno spazio in memoria, rispetto a file1.o",
+        "image": ""
+      },
+      {
+        "answer": "perché file2.o contiene informazioni aggiuntive rispetto a file1.o utili per il debug",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il processo xterm viene prima mandato in esecuzione in background e poi riportato in foreground",
+        "image": ""
+      },
+      {
+        "answer": "Il processo xterm viene mandato in esecuzione in background ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il processo xterm viene terminato con segnale SIGSTOP",
+        "image": ""
+      },
+      {
+        "answer": "Il processo xterm viene mandato in esecuzione in background",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "gcc -Wall hw1.c -o hw.out",
+        "image": ""
+      },
+      {
+        "answer": "gcc -Wall hw1.c hw2.c -o hw.out",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "no, nel primo caso A B C vengono caricati in argv, nel secondo caso vengono inviati sullo stdin",
+        "image": ""
+      },
+      {
+        "answer": "dipende dalla logica del codice",
+        "image": ""
+      },
+      {
+        "answer": "si sono equivalenti",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "24.\tQuale è la differenza tra thread posix e processo linux (uscito 2 volte)",
+    "answers": [
+      {
+        "answer": "Thread concorrenti condividono codice, segmento dati e file; i processi concorrenti pure",
+        "image": ""
+      },
+      {
+        "answer": "Thread concorrenti condividono lo stack; i processi concorrenti anche",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "jobs -p",
+        "image": ""
+      },
+      {
+        "answer": "ps -p -u",
+        "image": ""
+      },
+      {
+        "answer": "jobs",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "26. Quale di queste stringhe non è valida come identificatore in C?",
+    "answers": [
+      {
+        "answer": "_voltage",
+        "image": ""
+      },
+      {
+        "answer": "rerun",
+        "image": ""
+      },
+      {
+        "answer": "x-axis",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "27. Quale di queste stringe è valida come identificatore in C?",
+    "answers": [
+      {
+        "answer": "_voltage",
+        "image": ""
+      },
+      {
+        "answer": "x-ray",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Copia esattamente n caratteri della stringa b nella stringa a e restituisce a",
+        "image": ""
+      },
+      {
+        "answer": "Concatena al piò n caratteri della stringa  b alla stringa a e restituisce a",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Copia al piò n caratteri della stringa b in a e restituisce a",
+        "image": ""
+      },
+      {
+        "answer": "Copia esattamente n caratteri della stringa b nella stringa a e restituisce a",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "pA -> x = pB -> x; pA -> y = pB -> y;",
+        "image": ""
+      },
+      {
+        "answer": "pA = &pB",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Legge una sola volta da stdin e  poi termina, qualunque sia l'input",
+        "image": ""
+      },
+      {
+        "answer": "Legge  da stdin senza mai terminare",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "<code>0x55c2b1268421</code>",
+        "image": ""
+      },
+      {
+        "answer": "l'incremento della variabile prt genera un errore di segmentazione in fase di esecuzione",
+        "image": ""
+      },
+      {
+        "answer": "<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": [
+      {
+        "answer": "aaaaa bbbbb ccccc eeeee",
+        "image": ""
+      },
+      {
+        "answer": "aaaaa bbbbb ccccc ddddd",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "for(i=0;i<10;i++) free(mptr1[i]);",
+        "image": ""
+      },
+      {
+        "answer": "for(i=0;i<10;i++) free(mptr1[i]); free(mptr1);",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "free(mptr);",
+        "image": ""
+      },
+      {
+        "answer": "for(i=0;i<10;i++) free(mptr[i]);",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "perché cerca il file \"stdio.h\" nella directory corrente",
+        "image": ""
+      },
+      {
+        "answer": "La compilazione non genera errori a meno che il file non esista nel filesystem",
+        "image": ""
+      },
+      {
+        "answer": "perché il file stdio.h potrebbe non esistere",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "37. Quale delle seguenti dichiarazioni di variabile inizializza una stringa?",
+    "answers": [
+      {
+        "answer": "<code>char r[10] = {`L´,`9´,` ´,`4´,`a´,`p`,`r´};</code>",
+        "image": ""
+      },
+      {
+        "answer": "<code>char r[] = ``L9 4apr´´;</code>",
+        "image": ""
+      },
+      {
+        "answer": "<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": [
+      {
+        "answer": "Il programma genera  un errore in fase di esecuzione",
+        "image": ""
+      },
+      {
+        "answer": "Il programma genera errore in fase di compilazione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Stampa il valore di num almeno una  volta",
+        "image": ""
+      },
+      {
+        "answer": "Cicla infinitamente se num è diverso da 0",
+        "image": ""
+      },
+      {
+        "answer": "Popipopi S.p.A. > CD Click s.r.l.",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "0",
+        "image": ""
+      },
+      {
+        "answer": "-10",
+        "image": ""
+      },
+      {
+        "answer": "-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": [
+      {
+        "answer": "Il frammento_1 non produce alcun output sul terminale",
+        "image": ""
+      },
+      {
+        "answer": "La loro esecuzione produce sul terminale due stringhe identiche",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Producono stringhe diverse e la prima la invia su stdout mentre la seconda su stderr.",
+        "image": ""
+      },
+      {
+        "answer": "Inviano la stessa stringa su stdout",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "void *calloc( size_t nmemb, size_t size );",
+        "image": ""
+      },
+      {
+        "answer": "void *alloca( size_t size );",
+        "image": ""
+      },
+      {
+        "answer": "void *malloc( size_t size );",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "45. Un processo può allocare memoria nello stack?",
+    "answers": [
+      {
+        "answer": "no un processo può allocare memoria sono nell'heap",
+        "image": ""
+      },
+      {
+        "answer": "si mediante la funziona di libreria malloc(3)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "_exit(2) chiude tutti i file descriptor mentre exit(3) no",
+        "image": ""
+      },
+      {
+        "answer": "_exit(2) non invoca gli handler registrati con  atexit e on_exit mentre exit(3) li invoca",
+        "image": ""
+      },
+      {
+        "answer": "_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": [
+      {
+        "answer": "parent pid, timer, contatori risorse ",
+        "image": ""
+      },
+      {
+        "answer": "working directory, descrittori dei file, memoria condivisa",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Blocco_3",
+        "image": ""
+      },
+      {
+        "answer": "Blocco_1",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Blocco_3",
+        "image": ""
+      },
+      {
+        "answer": "Blocco_1",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "attende la terminazione di qualunque processo figlio il cui gruppo ID del processo sia diverso da quello del processo chiamante",
+        "image": ""
+      },
+      {
+        "answer": "attende la terminazione di qualunque processo figlio il cui gruppo ID  sia uguale a quello del processo chiamante (ovvero il processo padre)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la stringa \"joined\" è inviata su stdout solo quando  il thread creato a riga 3 è terminato",
+        "image": ""
+      },
+      {
+        "answer": "la stringa \"joined\" è inviata su stdout quando entrambi i thread sono terminati",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "R1 e R2 producono lo stesso risultato",
+        "image": ""
+      },
+      {
+        "answer": "R2 dopo aver allocato la memoria la inizializza, mentre R1 no",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "si, la realloc modifica sempre l'indirizzo di partenza dell'area di memoria ridimensionata",
+        "image": ""
+      },
+      {
+        "answer": "no, strptr1 è sempre uguale a strptr",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "sigaction(2)",
+        "image": ""
+      },
+      {
+        "answer": "sigaction(2) seguita da una fork(2) che esegue l’handler del segnale",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "open( \"filename\", O_RDWR | O_CREAT | S_IRUSR | S_IWUSR);",
+        "image": ""
+      },
+      {
+        "answer": "open(\"filename\",O_RDWR | O_CREAT, S_IRUSR & S_IWUSR);",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Se il file non esiste viene creato ed aperto in lettura, se invece esiste ritorna errore",
+        "image": ""
+      },
+      {
+        "answer": "Se il file non esiste lo crea e lo apre in lettura, altrimenti lo apre in lettura",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "mette un lock mandatory in scrittura sul file file",
+        "image": ""
+      },
+      {
+        "answer": "mette un lock advisory in scrittura sul file file",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Dopo l’esecuzione di C1 e C2 fd1 contiene lo stesso valore",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Dopo l’eseccuzione di C1 fd1 contiene lo stesso valore di  fd; mentre  dopo l’esecuzione di  C2  fd1 contiene il valore del piu’ 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": [
+      {
+        "answer": "buf.st_ino è uguale a buf1.st_ino",
+        "image": ""
+      },
+      {
+        "answer": "buf.st_ino è diverso da buf1.st_ino",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "filename = dentry --> d_name;",
+        "image": ""
+      },
+      {
+        "answer": "filename = dentry.filename;",
+        "image": ""
+      },
+      {
+        "answer": "filename = dentry --> filename;",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "61. Quali attributi di processo sono preservati dalla system call execve(2)?",
+    "answers": [
+      {
+        "answer": "Memory locks",
+        "image": ""
+      },
+      {
+        "answer": "Timer",
+        "image": ""
+      },
+      {
+        "answer": "Umask",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "62. Si consideri la system call execve(2). Quale delle seguenti affermazioni è corretta?",
+    "answers": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "è necessario invocare sempre la msync(2) se non si vogliono perdere le modifiche fatte in memoria.",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "In un determinato istante, non possono esserci 2 processi distinti con lo stesso PID",
+        "image": ""
+      },
+      {
+        "answer": "Per creare i PID dei processi si usano dei numeri interi che crescono sempre",
+        "image": ""
+      },
+      {
+        "answer": "In istanti diversi, possono esserci 2 processi distinti con lo stesso PID",
+        "image": ""
+      },
+      {
+        "answer": "Ogni processo può conoscere il suo PID",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "66. Quale delle seguenti affermazioni sui processi Linux è vera?",
+    "answers": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Un processo diventa zombie se termina prima di almeno uno dei processi che abbia eventualmente creato",
+        "image": ""
+      },
+      {
+        "answer": "Ogni processo può conoscere il proprio PID, ma non quello del processo che l'ha creato",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Digitare un comando sulla shell genera sempre un nuovo processo",
+        "image": ""
+      },
+      {
+        "answer": "Esistono file che non possono essere eseguiti per diventare processi",
+        "image": ""
+      },
+      {
+        "answer": "Affinché un file possa diventare un processo è necessario che abbia i permessi di esecuzione",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Eseguendo k volte un file eseguibile, si generano k diversi processi",
+        "image": ""
+      },
+      {
+        "answer": "Per poter lanciare un file eseguibile, è prima necessario aspettare che il comando precedente sia terminato",
+        "image": ""
+      },
+      {
+        "answer": "Tutti i processi sono sempre in stato di RUNNING",
+        "image": ""
+      },
+      {
+        "answer": "Un processo è sempre un'istanza di uno script bash",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "69. Un programma scritto in linguaggio C:",
+    "answers": [
+      {
+        "answer": "Rappresenta le stringhe ESCLUSIVAMENTE come array di caratteri terminate dal carattere ‘\\n’",
+        "image": ""
+      },
+      {
+        "answer": "Rappresenta le stringhe ESCLUSIVAMENTE come array di caratteri terminate dal carattere ‘^M’",
+        "image": ""
+      },
+      {
+        "answer": "Rappresenta le stringhe ESCLUSIVAMENTE come array di caratteri terminate dal carattere ‘0’",
+        "image": ""
+      },
+      {
+        "answer": "Rappresenta le stringhe come array di caratteri terminate dal carattere ‘\\0’",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "70. Quale delle seguenti affermazioni è vera?",
+    "answers": [
+      {
+        "answer": "Linus Torvalds ha riscritto i pacchetti di Unix, creando i pacchetti GNU",
+        "image": ""
+      },
+      {
+        "answer": "Tutte le opzioni sono false",
+        "image": ""
+      },
+      {
+        "answer": "Linus Torvalds ha scritto il primo kernel di Linux all'inizio degli anni '80",
+        "image": ""
+      },
+      {
+        "answer": "Richard Stallman ha descritto per primo la licenza GPL",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "71. Quali delle seguenti affermazioni è vera?",
+    "answers": [
+      {
+        "answer": "A. Nessuna delle opzioni è vera",
+        "image": ""
+      },
+      {
+        "answer": "È possibile montare un filesystem solo se è dichiarato nel file /etc/fstab",
+        "image": ""
+      },
+      {
+        "answer": "È possibile montare un filesystem solo se è dichiarato nel file /etc/mtab",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "<code>int fd = open(NOMEFILE, O_WRONLY | O_CREAT, 0666);</code>",
+        "image": ""
+      },
+      {
+        "answer": "<code>int fd = open(NOMEFILE, O_WRONLY | O_TRUNC);</code>",
+        "image": ""
+      },
+      {
+        "answer": "<code>int fd = open(NOMEFILE, O_WRONLY);</code>",
+        "image": ""
+      },
+      {
+        "answer": "<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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Per richiedere un lock su un file (o su una porzione di esso), occorre chiamare la syscall ioctl",
+        "image": ""
+      },
+      {
+        "answer": "È possibile usare la syscall select sia in modo bloccante che in modo non bloccante",
+        "image": ""
+      },
+      {
+        "answer": "Le syscall ioctl e fcntl ammettono 2 o 3 argomenti, a seconda dell'operazione",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "74. 11. (☢ UNSAFE, segnalate a @notherealmarco se è corretta o meno 🙏) Quale delle seguenti affermazioni sui segnali Linux è vera?",
+    "answers": [
+      {
+        "answer": "Tutti i segnali, se non opportunamente catturati, provocano la terminazione del processo, con l'eccezione del segnale STOP",
+        "image": ""
+      },
+      {
+        "answer": "Per un processo è sempre possibile ridefinire il comportamento di un qualsiasi segnale",
+        "image": ""
+      },
+      {
+        "answer": "È possibile per un qualunque processo inviare un segnale ad un qualsiasi altro processo dello stesso utente",
+        "image": ""
+      },
+      {
+        "answer": "Nessuna delle altre affermazioni è vera",
+        "image": ""
+      }
+    ],
+    "correct": 3,
+    "image": ""
+  },
+  {
+    "quest": "75. 12. Quale delle seguenti affermazioni sugli errori delle syscall di Linux è vera?",
+    "answers": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "Per stampare su stdout la spiegazione di un errore verificatosi in una syscall è sufficiente chiamare perror",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "a",
+        "image": ""
+      },
+      {
+        "answer": "b",
+        "image": ""
+      },
+      {
+        "answer": "c",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Genera sempre errore in fase di esecuzione perché non c'è alcun controllo sulla dimensione delle stringhe",
+        "image": ""
+      },
+      {
+        "answer": "Concatena la stringa src a dest e restituisce dest",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Prepara una sezione critica (ovvero dopo la sigprocmask può inizare la sezione critica)",
+        "image": ""
+      },
+      {
+        "answer": "Disabilita tutti i segnali tranne SIGINT e SIGUSR1",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "myfile e mylink hanno dimensione diversa",
+        "image": ""
+      },
+      {
+        "answer": "myfile e mylink hanno lo stesso numero di inode",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "dir1/dir11/dir112/filename",
+        "image": ""
+      },
+      {
+        "answer": "<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": [
+      {
+        "answer": "<code>adduser utente1; adduser utente1 studente</code>",
+        "image": ""
+      },
+      {
+        "answer": "<code>adduser utente1 utente1 studente</code>",
+        "image": ""
+      },
+      {
+        "answer": "<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": [
+      {
+        "answer": "ptr = vect;",
+        "image": ""
+      },
+      {
+        "answer": "ptr = &vect",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "<code>gcc -Wall hw1.c hw2.c -o hw.out</code> oppure <code>gcc -Wall hw1.c -o hw.out</code>",
+        "image": ""
+      },
+      {
+        "answer": "<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": [
+      {
+        "answer": "Blocco_1",
+        "image": ""
+      },
+      {
+        "answer": "Blocco_3",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Termina solo se n1 è uguale a n2",
+        "image": ""
+      },
+      {
+        "answer": "Quando si raggiunge il più grande tra n1 e n2",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "groups id",
+        "image": ""
+      },
+      {
+        "answer": "coda dei segnali",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "<code>printf(\"New File mode %x\\n\", s.st_mode);</code>",
+        "image": ""
+      },
+      {
+        "answer": "<code>printf(\"New File mode %o\\n\", s.st_mode);</code>",
+        "image": ""
+      },
+      {
+        "answer": "<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": [
+      {
+        "answer": "r = r1 = 4",
+        "image": ""
+      },
+      {
+        "answer": "r=6 e r1=4",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "<code>(x & y) == 7</code>",
+        "image": ""
+      },
+      {
+        "answer": "<code>(x | y) == 7</code>",
+        "image": ""
+      },
+      {
+        "answer": "<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": [
+      {
+        "answer": "<code>ls -lc nomefile</code>",
+        "image": ""
+      },
+      {
+        "answer": "<code>ls -lu nomefile</code>",
+        "image": ""
+      },
+      {
+        "answer": "<code>ls -la nomefile</code>",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "90. Quali attributi del processo sono preservati dalla funzione di libreria execve()?",
+    "answers": [
+      {
+        "answer": "Memory locks",
+        "image": ""
+      },
+      {
+        "answer": "Timer",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il bit SetUid non è settato",
+        "image": ""
+      },
+      {
+        "answer": "Lo sticky bit è settato",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il processo xterm viene messo nello stato  stopped (T)",
+        "image": ""
+      },
+      {
+        "answer": "Il processo xterm viene terminato con segnale SIGINT",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "(x & y) == (x && y)",
+        "image": ""
+      },
+      {
+        "answer": "(x && y) == 7",
+        "image": ""
+      },
+      {
+        "answer": "(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": [
+      {
+        "answer": "Copia la stringa src in dest e restituisce dest",
+        "image": ""
+      },
+      {
+        "answer": "Concatena la stringa src a dest e restituisce dest",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "<code>cp -n10 -i100 filein fileout</code>",
+        "image": ""
+      },
+      {
+        "answer": "<code>dd if=filein of=fileout bs=1 skip=10 count=100</code>",
+        "image": ""
+      },
+      {
+        "answer": "<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": [
+      {
+        "answer": "myfile e mylink hanno un diverso numero di inode",
+        "image": ""
+      },
+      {
+        "answer": "myfile e mylink hanno lo stesso numero di inode",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Se S_ISLINK(s) == 1",
+        "image": ""
+      },
+      {
+        "answer": "Se s.st_size == 0",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "<code>groups nomeutente</code>",
+        "image": ""
+      },
+      {
+        "answer": "<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": [
+      {
+        "answer": "Legge ripetutamente numeri interi da stdin",
+        "image": ""
+      },
+      {
+        "answer": "Legge una sola volta da stdin e poi termina",
+        "image": ""
+      },
+      {
+        "answer": "Legge da stdin senza mai terminare",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  },
+  {
+    "quest": "100. Quale delle seguenti funzioni di libreria non alloca nell'heap?",
+    "answers": [
+      {
+        "answer": "calloc",
+        "image": ""
+      },
+      {
+        "answer": "malloc",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Termina una sezione critica precedentemente iniziata",
+        "image": ""
+      },
+      {
+        "answer": "Disabilita tutti i segnali tranne SIGINT e SIGUSR1",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "stat nomefile",
+        "image": ""
+      },
+      {
+        "answer": "ls -la nomefile",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "<code>0x55c2b1268421</code>",
+        "image": ""
+      },
+      {
+        "answer": "<code>0x55c2b1268428</code>",
+        "image": ""
+      },
+      {
+        "answer": "<code>0x55c2b1268424</code>",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "104. Che cosa si intende per sudoer nel gergo Linux?",
+    "answers": [
+      {
+        "answer": "Un comando per essere aggiunti al gruppo sudo",
+        "image": ""
+      },
+      {
+        "answer": "Un gruppo che permette ai suoi membri di eseguire comandi come super-utente",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "0644",
+        "image": ""
+      },
+      {
+        "answer": "0022",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Nessuna operazione",
+        "image": ""
+      },
+      {
+        "answer": "Operazioni di scrittura ed e possibile visualizzarne il contenuto senza vedere gli attributi dei file",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "ls -l -n nomefile",
+        "image": ""
+      },
+      {
+        "answer": "stat -f nomefile",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Dimensione della directory espressa in numero di blocchi su disco",
+        "image": ""
+      },
+      {
+        "answer": "Dimensione della directory espressa in numero di file contenuti in essa e in tutte le sotto-directory",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "5",
+        "image": ""
+      },
+      {
+        "answer": "10",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "r < r1",
+        "image": ""
+      },
+      {
+        "answer": "r > r1",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il comando produce un file oggetto a partire da un file precompilato",
+        "image": ""
+      },
+      {
+        "answer": "Il comando produce un file oggetto",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Un file eseguibile a.out",
+        "image": ""
+      },
+      {
+        "answer": "Nulla, la sintassi è sbagliata",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "stampa il valore di num almeno una volta",
+        "image": ""
+      },
+      {
+        "answer": "cicla infinitamente se num != 0",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Visualizza su stdout tutti i gruppi a cui appartiene l'utente \"nomeutente\", incluso il gruppo \"nomeutente\" (se esiste)",
+        "image": ""
+      },
+      {
+        "answer": "Visualizza su stdout la lista dei gruppi a cui appartiene il gruppo \"nomeutente\" (se esiste)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Attende la terminazione di qualunque processo figlio il cui gruppo ID del processo sia diverso da quello del processo chiamante",
+        "image": ""
+      },
+      {
+        "answer": "Attende la terminazione di un qualunque processo figlio",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "mkdir /dir1/dir2/dir3",
+        "image": ""
+      },
+      {
+        "answer": "mkdir -p /dir1/dir2/dir3",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "filename e link1 hanno lo stesso inode",
+        "image": ""
+      },
+      {
+        "answer": "link1 occupa zero blocchi su disco anche se filename ne occupa un numero diverso da 0",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "# cd ../studente/download",
+        "image": ""
+      },
+      {
+        "answer": "# cd Immagini/../Immagini/faces/",
+        "image": ""
+      },
+      {
+        "answer": "# 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": [
+      {
+        "answer": "rwx",
+        "image": ""
+      },
+      {
+        "answer": "r-x",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Sempre, se invochiamo il comando <code>make sort_file_int</code>",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "0",
+        "image": ""
+      },
+      {
+        "answer": "2",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Sì",
+        "image": ""
+      },
+      {
+        "answer": "Viene eseguita solo riga 10 poi genera errore ed il programma termina",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "stampa il valore di num se num è diverso da 0",
+        "image": ""
+      },
+      {
+        "answer": "Il ciclo do-while entra in un loop infinito",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "ptrnum = (int *) 10;",
+        "image": ""
+      },
+      {
+        "answer": "ptrnum = 10;",
+        "image": ""
+      },
+      {
+        "answer": "*ptrnum = 10;",
+        "image": ""
+      }
+    ],
+    "correct": 2,
+    "image": ""
+  },
+  {
+    "quest": "126. Quale dei seguenti dichiarazioni di variabile perché non valida, generando quindi un errore di compilazione?",
+    "answers": [
+      {
+        "answer": "int goto=1;",
+        "image": ""
+      },
+      {
+        "answer": "int goTo=1;",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il ciclo non termina. La scanf va in errore quando viene letta la w",
+        "image": ""
+      },
+      {
+        "answer": "5",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "11",
+        "image": ""
+      },
+      {
+        "answer": "L'esecuziuone del frammento di codice non termina perché Il ciclo entra in un loop infinito",
+        "image": ""
+      },
+      {
+        "answer": "10",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "129. In quale situazione le system call dup(2) e dup2(2) hanno lo stesso comportamento?",
+    "answers": [
+      {
+        "answer": "Nel caso in cui gli passiamo gli stessi parametri",
+        "image": ""
+      },
+      {
+        "answer": "Nel casa in cui invochiamo la dup2(2) settando a NULL il valore del nuovo file descriptor",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Groups id",
+        "image": ""
+      },
+      {
+        "answer": "Memory mapping",
+        "image": ""
+      },
+      {
+        "answer": "File locks",
+        "image": ""
+      }
+    ],
+    "correct": 1,
+    "image": ""
+  },
+  {
+    "quest": "131. Quale attributi di un processo non sono ereditati dal processo figlio?",
+    "answers": [
+      {
+        "answer": "Descrittori dei file; terminale di controllo; memoria condivisa",
+        "image": ""
+      },
+      {
+        "answer": "I timer, i record lock e i memory lock; i contatori delle risorse ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Sì, mediante la funziona di libreria malloc(3) e calloc(3)",
+        "image": ""
+      },
+      {
+        "answer": "Sì, mediante le funzioni di libreria malloc(3), calloc(3) e alloca(3)",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "No. l'area di memoria allocata nello stack viene liberata automaticamente",
+        "image": ""
+      },
+      {
+        "answer": "Sì, ma mediante la chiamata di funzione dealloca(3) e non mediante la free(3) ",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il valore zero essendo la system call terminata con successo",
+        "image": ""
+      },
+      {
+        "answer": "Il codice di terminazione (con successo) in quanto non c'è una effettiva differenza tra codice di errore o di terminazione con successo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Se il file non esiste viene creato ed aperto in scrittura, se invece esiste ritorna errore",
+        "image": ""
+      },
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "stat -f nomefile",
+        "image": ""
+      },
+      {
+        "answer": "ls -l -i nomefile",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Aggiorna, al tempo corrente, gli atttributi atime e mtime di nomefile  ",
+        "image": ""
+      },
+      {
+        "answer": "Crea un file vuoto con nome nomefile in sostituzione dell'esistente",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "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": ""
+      },
+      {
+        "answer": "perché il file stdio.h potrebbe non esistere nella directory /usr/include, dove la direttiva dice di cercarlo",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Un file identico a helloworld.c",
+        "image": ""
+      },
+      {
+        "answer": "L'input per il debugger relativo al file helloworld.c",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "<code>if (s1==s2) { printf(\"stringhe uguali\") }</code>",
+        "image": ""
+      },
+      {
+        "answer": "<code>if strcmp(s1,s2) == 0 { printf(\"stringhe uguali\") }</code>",
+        "image": ""
+      },
+      {
+        "answer": "<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": [
+      {
+        "answer": "Quando il valore di i è uguale a n1",
+        "image": ""
+      },
+      {
+        "answer": "Quando il valore di i è uguale a n2",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "Il frammento_1 non produce alcun output sul terminale",
+        "image": ""
+      },
+      {
+        "answer": "La loro esecuzione produce sul terminale due stringhe identiche",
+        "image": ""
+      },
+      {
+        "answer": "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": [
+      {
+        "answer": "la stringa \"joined\" è inviata su stdout solo quando  il thread creato a riga 3 è terminato",
+        "image": ""
+      },
+      {
+        "answer": "la stringa \"joined\" è inviata su stdout quando entrambi i thread sono terminati",
+        "image": ""
+      },
+      {
+        "answer": "la stringa \"joined\" è inviata su stdout quando uno dei due thread (non importa quale) è terminato",
+        "image": ""
+      }
+    ],
+    "correct": 0,
+    "image": ""
+  }
+]
\ No newline at end of file
diff --git a/Bot/AccessControl/AccessManager.cs b/legacy/Bot/AccessControl/AccessManager.cs
similarity index 100%
rename from Bot/AccessControl/AccessManager.cs
rename to legacy/Bot/AccessControl/AccessManager.cs
diff --git a/Bot/ModuleLoader/IModule.cs b/legacy/Bot/ModuleLoader/IModule.cs
similarity index 100%
rename from Bot/ModuleLoader/IModule.cs
rename to legacy/Bot/ModuleLoader/IModule.cs
diff --git a/Bot/ModuleLoader/ModuleLoader.cs b/legacy/Bot/ModuleLoader/ModuleLoader.cs
similarity index 100%
rename from Bot/ModuleLoader/ModuleLoader.cs
rename to legacy/Bot/ModuleLoader/ModuleLoader.cs
diff --git a/Bot/Modules/OttoLinux/BotGame.cs b/legacy/Bot/Modules/OttoLinux/BotGame.cs
similarity index 100%
rename from Bot/Modules/OttoLinux/BotGame.cs
rename to legacy/Bot/Modules/OttoLinux/BotGame.cs
diff --git a/Bot/Modules/OttoLinux/OttoReverse.cs b/legacy/Bot/Modules/OttoLinux/OttoReverse.cs
similarity index 100%
rename from Bot/Modules/OttoLinux/OttoReverse.cs
rename to legacy/Bot/Modules/OttoLinux/OttoReverse.cs
diff --git a/Bot/Modules/OttoLinux/OttoScore.cs b/legacy/Bot/Modules/OttoLinux/OttoScore.cs
similarity index 100%
rename from Bot/Modules/OttoLinux/OttoScore.cs
rename to legacy/Bot/Modules/OttoLinux/OttoScore.cs
diff --git a/Bot/Modules/OttoLinux/PhotoServer.cs b/legacy/Bot/Modules/OttoLinux/PhotoServer.cs
similarity index 100%
rename from Bot/Modules/OttoLinux/PhotoServer.cs
rename to legacy/Bot/Modules/OttoLinux/PhotoServer.cs
diff --git a/Bot/Modules/OttoLinux/Question.cs b/legacy/Bot/Modules/OttoLinux/Question.cs
similarity index 100%
rename from Bot/Modules/OttoLinux/Question.cs
rename to legacy/Bot/Modules/OttoLinux/Question.cs
diff --git a/Bot/Modules/OttoLinux/WebReverse.cs b/legacy/Bot/Modules/OttoLinux/WebReverse.cs
similarity index 100%
rename from Bot/Modules/OttoLinux/WebReverse.cs
rename to legacy/Bot/Modules/OttoLinux/WebReverse.cs
diff --git a/Bot/Program.cs b/legacy/Bot/Program.cs
similarity index 100%
rename from Bot/Program.cs
rename to legacy/Bot/Program.cs
diff --git a/Bot/SoUnBot.csproj b/legacy/Bot/SoUnBot.csproj
similarity index 100%
rename from Bot/SoUnBot.csproj
rename to legacy/Bot/SoUnBot.csproj
diff --git a/Bot/Telegram/TelegramBot.cs b/legacy/Bot/Telegram/TelegramBot.cs
similarity index 100%
rename from Bot/Telegram/TelegramBot.cs
rename to legacy/Bot/Telegram/TelegramBot.cs
diff --git a/legacy/Bot/bin/Debug/net8.0/JetBrains.Annotations.dll b/legacy/Bot/bin/Debug/net8.0/JetBrains.Annotations.dll
new file mode 100755
index 0000000000000000000000000000000000000000..6361fca55e2f5a4ea2b2877bb57e16bd2b2a3f63
GIT binary patch
literal 93184
zcmeIb3!GfXRVQ3MFFkBIvSrzcAMw>l$&BPtk0jf&ElaXSGb3y4d3mNK*(gTt>Ao}5
zQBU`{yGPPkRwAQ>5R&jFS>XG?M&V%|frTuDu-UcO2@psK@Z0c9c3E(c2?SWkw}d4m
zA^HCQQ;&OZ_wAmCMos{at?FCvs#B*<ojP^u)X=wmpK4Z0wcy`3zM<5I@XNmg;x=~9
zBl?=#K75V(;F?cv`cPu<lbcf0xw1D~Do&NsGhQZLC=@H+gzuH+3SO?@^^J^sGsUc*
zT(@rR7E|<SzfyyVX7!=_hgh8ay`<W`s}ehunn)<MT8Fir$G3;?XYi}kdiieB?PdVm
zFMlr~9^r|PB+#$N-_E4`Kln9Xom&0sTN7%8-zfKoTp=csKhG!B)%bb&EeVwh1uyvN
zxu#3f)$kt<WlC23GZpx+ejM#Zob(m#4*z*dJ)SI;OBn=8+t5bU22U2hp}zwdu4Kv2
z7m<){Wt#~D_2!y%2N@my`%Cnne{5TmYE|FYq15&Jm0BaAO1<Alaj#9NEc$=mgf;qk
zeN*RkO0C^i?{|-mZ)!e=dRvg}toha1f@ri~qU|q`RQwpDmThmbrM&yK80vG3YiiNo
zRq?M2#ml$EE45)qi@F6JLG9O?!kU_;gg2}`$24tH>bCfFEGe39;r_Oo4#@8to0@Ov
zyaA)R?Wpa_IgBiNac-4KM=#+c{`RIDJ8xq006>@+5^sL1;fDc~X!(6()2%mlwlhB}
zc3-zP>#(2+Ymo^}xXR?EmrW4Al+o$RFkdJ`zJfA<fh=R4$xAQGF#hUhqEixWe{ZJ0
z9q1t+3quuSDEaO0tv0`WES&%KCcpW$$b0rSTaJ97{hkew4+Lzo0rFvM3dRTEmX^tH
z5WLw0%Wn|8#RSW55c~!cEWbhU8%?nM2EmxSQl<H=hBXFCqRnr0^i~sXeygLmnP~HC
zA$4VbgFfAEs+Hd$xXT2a-|EJ0H__&|I{FS1ZGIc~A*l3zlg<7fpr6PpAHazo8z3JR
zCl>n~%%OuQ2Xm;`)JHGpknzViH)v*`Np64p=?|i-yoXF``3;)z0D{qk!zPwqHbMMB
zSpz0meuLl#861>FFUoqzB$il|W&H8osSmWXgQlDz{u2E15!7zts|S79WRu^cO*ft!
zM}VWgU;?Cr_IK14E+1&^!!|%Z5HMx~<O2aLPue3N2mr&N1LOk%M{Iz6AmFGCkPoHQ
z!SM$VCb%l$?`r%zb!T!%@{S#M?7WvDttt=q6P{9=-=fq#pgdp1BC>hBQpy#k%1qID
z4@$)E<|E^(3*TgYHa~D=0Jxy`!vC38_z&ib6Y%q|2`kyLTYoaKhE;wgafd>A{KN{U
z9>G7fOZ_tbF+Ub|btC>gg#=6kGNbn39|m9i4C6ng9@wCN+tkhLw_o3;-g_P0*Q~wi
z`q!xcbUoG(^~BXTZfH~Iu6rKthu6`ay@u}N@_kO+`>$olp0#v;b2Z)nyfF>;(i*zo
zef3Q!^G~ni_qsJK{~smxrBw{MdOh7!8|Z%YD!NUIZUQb_xS9@p_%E?u{rc6z8~dH_
z_l>Vf;(pion#K34#@8agA6TRFwTf@(D*Bq#D)D{t8tq#xzOPEkX0=v)>!p-dj1Fsm
zcr8=5s%yn}@-^Cbo%lYxj=navQG9>Dn!eQ-9p?K%Nx24m41J%LxHall@%h&=<yv){
z_<mn%xLUmdKBfNo`r(a_s%zBk;=6q#eb<X`z4}fm<tF3XEctF$Nh#%d@!g_!if^0r
z;TE+Ea9gkbO5$!+cS+pWu4V17SNDkT6VmtBt9!+Fn?UI{wO4%qMWA$>+9$pv>zS_|
z*v1}xL42FlA@MyczAfNX8Fx;6Z%_}4?*^&wjcQPQzb$bcYFK>#C=l#aqvHD+>EBj0
zF1~+~+P4AAS>~Mr&)e0b;`?st(e3Kn#J5%8dAm9;z6VXpH;M13q(`@_$Heyz<NJ2;
ztunq7;@fCSd0c!yE3Lj=rN!50;wHrRAyY#}eE-|{vf}%al-Z@G#CMhSVY|wS?>_1K
zcJ*fXl={t89NQ$Q2KzTJ<8X)Zr6k`SYDQB2fTX-r6(wcA!0cY*`w1E8d)2JOT{Lk|
zi0@~mlzUZ4O4-uJ`tDU_iQ6P`_Z#2K;ybA3CGK`9<)C^}d@aUzR(yXZ^&M1CiSH4C
z(m{1jeCLhtE#kW<V{}lxReYV&zdrRH;`?KPS)aNfz7Gm?`_$WXoItQoy<L2tlDK~L
z4)J|JN;#z7DZahZ>Ic-j#CJ$qHlV&od_}4KLG^v&dyk|XR6iuXzm#?ksvj2LBU0v|
zdawBQ8{hlHcdfK@Q2ht-%}UD#)sKkpW2TfJ72mf@?ZfKF#do))98u4zP3lh)Uxxdr
zxD(A^#rLm@`w<EGL<?hgw0s@zPb<1dni7dk>fx4FxW$$=k?*#)akVaS^!%MSw8DMs
zM!IJ=(EaOGbgy6Cnh4UYLCE?Ibf-7cofS6=cayrM^~)-uzAp9tP&2=OyP35gmhUs-
z-k(^PNT|<C3qQCB_w6WilM1`K*V_I0ruD9EuV1wu?Ri4cJ#howQMjAbyPGz;G&iY@
z2)|a`c5%1CwY}?8Hz9VnxQE5PU)=lPZc?`<ZbHlNl0FVetKKirE(&a~L+>`JX6d_b
z+omRl6eZ1ei9I4A-z^~td?(c1lK0(;DgTdzyo~RJ`a}8t7x{i3BbZR{M#%~F65yFo
zK58HJxSoCIYriCG+#?a+;}PF<#5e1F>rv8MwQu!D+HOlUsdu<IuAblPeCy%+ag*|K
zq-<6{ZG6)kHpBM;<NG7={hG_yfw(_*zU_%t^=0R~Be6<(n-u=ohkFvM)nVsrgEV3^
z;(H|Gd))cfBV|$hR{uO&wpN`E#jRCOJKw&<RqA~v<)3Z5EwN7hgz<g!`d;{c#-#*z
zp?=x<9zfiGH7QT2-o!QPw_RKt*3?fq-yq`t)TA6<HI{gd`n-z+FQmTce8&*?73X^l
zl)9zeLL!s6R^6n1tN-fylZg$g-S}?aI1Ar4m$FT5P<J}tF~se6zQ+>RtHaKhNo-W#
zu6<JGjcU^PSmuqY;8M1M-#g`e>*0I5NxAoi+Y+x;?=imXZs>*YhmCLZ4G+Ng<1Swt
z_?4fI_&yNv{hITwN6L?B-|A;J+?KdWebV^;WJ51}pEka~+VB8;f9mpq&s3j}`2H^9
z`@hb&9w}R~_XeJ=-iaD+QR}o%X3i~Yqlx<tiM!SKzJJ4da9M9OzC&$uiCb0D_{Q53
z;JNNLzOMn7Ua#&qzEwA@2N!f$`=o~3)Tr~V2X}VDr2HiEd1@*Y=c!YnxJ~LUp}0-z
zJ)yXE^~0gKcJ=;H+-CI)p}5WJH$!n-)Qh3GE$WX#ac@xn!^O3MZ~SxTTMys=XHxz)
zu;7jApIuy=dZS8gvADY)zV+I-nkhTfYh7F$b`PHOt%t8Wl(JLZ?c&;0r@BAldm!Q)
ziTI91d?zBlsfe!_@tuzN-Wu_}BjWqsi0^&Qw;t{MY3*B0s<>5sAQZP%eIyjOO?}M8
zwW)3Dlg>Av*rxu(r2O?&=M%T9mt9;NxbnYszVAfbS53<EtKO67Qq7PO*)^a`T@~?d
zi1==f_%=J=52BP^+PB(k`_aU9wa578Fn-(BLE}3Qyxy)JGQM{qE~&<i@4bjisyAt$
z;C#2rMtu2*uM+V+74f|-;(NFAJ(K8GKdF7I-v?OlR6l2YSy0-Y>Q`LKHtbeD9P$0P
zi0^luZ#`0eR{K`}$m-h?yVU25ZyqbcF7*YM@&Wk1?0oCh9cmQ>ail05_dC^Vv=4pX
zsP0s6Fman#-Imy`wi{m#DR-;8T*~!ox9T%-U%u|P#NBEz6nD2e?tB}ut9rtu97dUY
z)V%RMjWYMBr(H_yoz*)dzVDCtek9_1HsX6O;`@)<xB8!?hTky0F2H(^dcpYitr~>y
zcU`{q>OS>Z=WD}$?{m(#9=<P{e8<&oiGAwJp}2kO8j2PG&n(7azq-Nr{=AjG*Bjs8
zwXRqDRfqBIMauhCxABdxUa#&~_ZZ(-kneyxV0`P^)~f?*!1zAc+MDQ64;$a_wLSpf
zw;A7eZtP7QR1?Pc?8XP+J867Fz=>W}HonaD55V`7O^Nw{ordv!5c8oAy-BD~x6ysw
zD!NZV3eluK3pe`xe;{N@DD8eh(*Ny7hR|(NpMmTofgKIq<5JEw5_?cwemAM$I}!S}
z;fF=Oqs#vlsa=2X4}FK*@EIw&vHQQI%&@EbsNFx1c0MQWUDBg)`4<t=q$(2oSJL+%
zkv{6Ye}V8Oby{+@Negwa={BkF36-{1N|+YNoQ816!Z2J{-1i#wCCI9-g-b5wOB>dz
zt#C;T=-w-CkGKQkJ}mB|;-<yT!QF^m?Uy#(te%#T?}yu_o)h;sB;=j)y&72m2aw(z
zP@jg|r~U`r!|KoA4yu>orX>6kao;4dk4tP;eF3rmS^XW{=Oy-|68k&q9}x22CHylI
z{wEUtlBEAD3IAIO|DuF{Ny5L3GB>HS2;TyU-lx%Dx=D4F`V6S}2K70(IDBbJs?BN*
z+}qUwxOb=#xc8|y!R=Mgz<o&lGTd?XmvE1(mgc0&sIARCQgW|4smjfmAL^;*J}G}d
z+_c1IBsL?lPfN%JguJi$f`mLPAr~d&qJ%sLIo7AqbGG>fabHC2GtDnb?8l|#&q??t
zgg@7ONx~Berf*K1RKL>PoM1~l3E3p>W`uvDd9#FXNeCRoJs`0?;`SoV`<i<tO`pU*
zF5zi$Gl+ezIU})IiG5naFChH$%@-v6or$lY4bMvWMTGx-^F;}NPQqVA$eNZHCFG-0
z!pE^=`Lw!(@atMGNqBS9Np*8ebJJg|yWp}HYnqs5Gs4%jY?hE-eE(QWuY3<kY^Lb~
z(&Xg(d=uetK|(Gz?Mb}9<s#DmB3zdCoTPaXA<s3xC?OwjA~ar-T$d33`Q}R!{<4IB
zL0r`=ux(~+bF;LnnYlJ2<hka}60$`?wu;*$>3b3TVoR^Y4oLXp5}p<}BY86to0ZsX
z^Y13m3;BLp(p-=lFG!kqHWLodO87;Df1>50gg+<Y&rA3V;=agSEiX#!$0hc25`Ia-
zFG={zQp*=4Hqj!zYhkYD7WS^Cg}qxN?k0)dEU}v<c8i4fNO-S=_eyx5gg-9fX>l_W
zn~~V8#GaS%r^UU1Sk-z#V&5jQ?`)AiO6*0%u4%m}vG14I=Op$8iG5M>z9_LDmDrDq
z`#FidBzZ4M?8_4V1#uIt($iLsT63%Pv{ia4t|zgZ#NCWE*f>d=EfRY`!h6K+MeKF0
zy%IYhv1tj<Abd@0M#6Iv{<MT&knjr<{!R&hM#7&J_oBpJl-TDa_IU|^5#g%!MG617
zguf)=mn8iq34d9_ztBp!HMcQ+bDQ+5P5LG7W`y6|x>>@vNXXVUrtd}gn$}(kACQoA
z8{w0Y@Qj3KCH;8`zku*9trsNxZ4&;BgkMDX_STCM{(cF6Ucz5&<E;Fsgj_-z%q&Us
zvV=6Rl2KSCtzE^|_O5!D+TGfV@Xt5*O8Dceq&*Unk&vvUydZfmNXRpi<|0CXEfVs)
zguEo~=fu4vv6m!Pt!9}Cahq44R8y_ZtJwxmLN<xJ8R4_7n<e~!g!G8pi}3l@UI|Z2
zNJc_367sZ!TtLWov|f;qXC&lVaW6{vMG1dFLS973%`GoV$V(FPIdLx`{9Ub=Bs{T3
zFm???n%77>C1jJhn-M<Wx>>?|B%~K1H@Eak$m0@{7B?d)GZOx^gj_(#54B#9kY^<1
zS#d8)_(chSK|)@XkQXK7B?<YQxR)gSl7uJLN;}tb?3&lIotq?NGeXcp2{|AkJ>vFC
z%3cXiOGrjSG7|E%gj_(#bIlhd<QWNhR@{pS$5>1F3lj1oLS|cEl#ojjdr7|4Rnn@f
zPO1;IK7Td8)jGP5i~GE|=dWSNGvYok?n~mT*GT&HbUksO7xyJ`4_wRe^Wr`u?n~kx
zxQ^kEi+f;$e2aTtTy;G|4v71>xaY-vM%?GceMwxkktscK4~YA?xaY-vM%?GceMww(
z1JiF(Jn>r%UU3by!LG&`+d70?gAhszuLWOm9YQxE<OWFOUW?GzA@pX1-l{f0a&rSD
zG&d>_F`E$6u5JQPbTjy-TOb3u71E8@t6fNO2U6UH6!##-{YY^LF%Lo-@DO;bLG12_
z)a^Kn+73OlBu<)ksAI_W2y!X)Lfg+Ipl`K`Zhphh!~J7%|EGjs+Q{(Vx&D{nKB?%A
zt@>5CiH#qEyL%&4+#=rxZeaS38<_I_8~FXV8~A;feE+bx$J+iILavgOe<<ZQc7IP&
zhF#rS?f&Mf-$H3Ww(1jbx3v8c+_y>k9~Jk<q!-iD=0ClT?KvlX(Rp8zUe8FHuSx8f
zguGoszAA2y<UOI7cX~D5*GhOwzOR+<Pqng-KedtWl$8G=)ZT<~ZkBOxg}WAKvQ6qL
zjCm7II<JCzBQT)}<N6x7H)G73FqRwOZU#0qVT5mlyA@;Jgz>u>?sklK6UGncsj3@p
z6L$8S;NFEC3GCfB<NH2fMH5akI}n1MDZcMV842+FTk+ikEWnNoZj<Uqc}?m8l+=XN
z^PO-X0zNbWQ|^L0qV9$}hTTV#I)YN0fGzvrPNKXfHI0&*z~}bDEubXqo={SgnnOtm
zbqX$aOeim*&cJO_=TT}Cb|xcmzf(O7_Z?~+?suc?CiQM$W0QIh&O9OESI6Q00M0R+
z)DNl?aDNnMlTGT!z}Y9%{{y#4J)?ZMKZ&!)CUuc#j_Unzo77LCMG2fPoy7Og@Jvzt
zEZipSf^ed!ehzMv`gw&jL-h-Ao769YM^C5^z->~$gtjN3WiY2&&MWm7tC}%0xPw!G
z_pt0~B|w}vsGn`MKBo8zc7^hHWS~&leW%(xRLsuh{rzfS{KTQ2!SQ~6q{fcO@9>eq
zLHdVBQYYm5aB65!J%WD^j*kqh-tlquV9(K>@!qk4(Udxs&d>QLPN*rra^i5MGJDi7
zO{i)6G3KYU5;f*OF_$a(Syi$hdu{6dCr_L>n9iJpa>k*YpU<kjHgRptp<-z!UFn&u
z_@yy_HlNP;wP}X@aydN}6Jc`HjUOny+0Rt`>|nYuH5Xe#e>PW1PXKT=O&(6q#Dq$J
z;-f}UL3~UqJry4@nufx#kFLi@YuGH1sJB=sS4!zzp%Pz6YJN7RAETw>Y_aTT<6{q|
z%YJ+eYO9or`MQw+XRcVN7oE>zI1t~H$zVw1Q<*VM`7^+VN=(zo{d6faT|d630=1xt
zxwx!-Kv*>3R7|Pt@@Tp;U5f~ZO2wJl(Ec;CCG-KEsf~MZu3X7Y<}zvawKh6ctPL9|
z_m@h=Qf<Webg@+N3uFGIU-An8TWylwk`F*>JebQ=ikL|C69cC*{-L>ie!PNAF{K?T
z`w!&`*+cpCR7`9tT>@RG6FHQwWTwY+Gr4@a<eC#xh{o%fju5%hnM!SpuC_MZ(3PIk
z>0C^%-r~%RUx<kqE%}qVGquHy&rQ||pS7d?kUw9WucuHf%+D0(%A8hysZyJy;8)6(
zbRnBAY3f-^_EZ`4HYS*4*bq2LV!s(4rGFA#6~t84R8Zxr$fD45O9`bE=V5-*6-nZV
zpPD3Dcj1=usaZn;CAFb?s-mf%ghgnXE>q}#G<-59Y&J|PC1;fIsZ4|pssbB5TrB98
zlho)2gy^31G)xF3g{6IJ!Vp1QnWcNI$$b+(Xh=ZzR8P4K48X*X1|w4_k<M024pDYY
zV1=z80GUr0Dml#B?6F*B`cUqSpG6%Y{xa#ub5n(MWv&!WIb5utj*Y0IwqX>q6ju5l
zCAvr!ron1-ZX%z{^o$NT+AEm?Ur%{<=v1Z$gen|X_Gc#Y^Fw|molRFlF?I+n*AV0x
zEN0UA+>=6BYhy9rXLEVK^nhRRON3L95e?`=9fH9@=vey6V(C~ZSMkw^>0&5-5HpC_
zQaWFqzzuVCgjPz`6S+E0N*$P)1xe@RDbIr430lMD$C2(J*nJ%#6XbmADEYK8a5kZ^
zK7V3vYKja^kj@~^VNjoNFgrl-r*f5W@Ic{I@uXjhOvs=t=>Vs@9ikvg4>8zKU$}!v
zMDKJiPh7RrCWssb!Unz}KV2vTK0u~i1q)FSV+NaxRxZ=$=lv;+ZxF33w-_6Qj~C~#
z+|`EnqM5a!7&=TPv?yzcY;BUUA{dL>U|m0{X0cQ+wl7x(M>Ag=Ju*=)=D`I9%|>xG
z*d4?kuFT|}Pq4uGNb!aV8}K+)F+*TV0MaoZ9V}GE$*?-+C@AenDW}mn2r>)+`PS3L
z(#asgk-K!zI5n6nJdv6!6#P)wK*gWwO&1RO!{9=K{0ts0R-%EAq-XMs7_ZFd{gFw2
zmJJ>SIc=hYU|X=|SSVR-0V%&UlLPVexqkBwUr@H4C5J#)un{7^R~tD{&?^=q)A^cI
z!^Qr>+zbg)Y-V(4GB-6>9WA{CFVw@!gf&@(nrc?sa=S_faojN2F4S&)G!qbMfu=^n
z3q-;lX>&tb+vPsLoGImIqg|2$5BqtrKxDnbpc?b1{4*eMprs)I4y7}tVo<nXC5GTs
zF6)<q;Njw!Uo1s}^n5dz0E!<ZmNtY)x+7#k5}kP{T}ET7L&xVdr*|~xpB9chNC4!_
zP0m{muXmc1H%oE}KED_$2)K<Q>zSR+&-Xtuhvlj=KLUtE6X`be$PkW$oRMu0szPj^
zU9HMNFeiS0-X~A%0z=WlmAeR~S3^OaF+ZO^BVVE9y;ZD6kkd2{tF;CaS6z;@;Z7ZH
zg#p<(4Of1Gv~jA1Q?MM2D7c{f!ig7^P%nZQ)htMV00PpKk%E6LJ#VNA7q)}*eg0&6
zE?<={b-K7<It?nenZp9MG&F=t78Qr6z-sVx+|UQj5v(3e&tvro0?kYjdWKuIK}1kl
zbyx@_)lreQ(Gw)VvXQl`a(+Gtx5SZMN*6*AV{>3sXRswjcqKQH;~WTQo7IewolB7@
zvg)-FX@_*D$o2?(=@u-NwflNCP$~QQNi{x?$vu-a?MND$jKx7Rl6fJsB`m&tJm^;r
z66ED%KyS;*Y(8&Fl57+Ns7X2`=w$T4a!X7g4A8qLCmWF1h&VvKIV>D-4fQ9DXhd?P
zimFnSFC!O_V(oX5#6rb4Su;GzOxc#)mrG9-ijYSHK^7{SK=`F`zjP`G$%0f^BX5wZ
zYeQuRgV|U|@Srjc;xw0v(cq-g)q=Ey#S#h&YQ)d#!jcJP@i7uO7m{6QKnCK9aE_1^
z1KU$4(TIns;%;jQyfT)5`5q`t``9xS;njjHscI-z_#x1%6XnoeH^%{DCS76w`J?xV
zS}LOVonQ&n$OOfjp$<wdx)cdD%%g}!BrXzQh?pr{L?kF%{~npma^u966(mYwfUxHE
zpUDA%3R5<et<oYCDcpQAR0~if)O_mDu=vEpMuM2CMu?)))nXMb^8V+qe&tIa`|Ojw
z?|9pzZ~FD)|M-il#Y-esH+w44ijQlrrT3avuPJd2Tm;<Pyee_a>Q9{A@QqJy|6dQE
ze+I`Ks^vUv3!u2RCOGhqjrZ*21w!78d&mF1@27v}oiARq>6*8E{2iMg{Os57`0zjN
zp8vrQ|Hogw?vMYe>nGp)8?XQLZ$0wfk+1&v-0a`H_QA2Irhn@rAMW|o=g+<SU+({|
z@3_-D`42zPdePs$=O6lx|Jpxo{Fh%CPj>#qU-z{C*e~3D>h`I>x#1tb_wSxKx5fY3
zbANZ^7r*BZr%KO$ZR~{?X4^ma#m|1^4_d|^oZR!fEzs#wEtK-NuyrkHmTKX}y%wGj
z(5G6U0;ZbUu4!t!rKzo{x$U}#o7=7(#J|y|w$&|d*Pj0jotNo+8GyM4|85~f)~=;{
z)l~%1S~^z=oSG2dL~j!TcHP4*p1ST~f!(0EqpOhX+QDl_5pnGxol)di-G;=dsulk8
zpV6L|#q+ZEd|5nSZsSm(g`H?er@96Inp!$h0UQ(tXB0uJTk(Oi=)NrOmsj%$lYg)0
zsHB>1I95u}VuRCvCgTg|nVK#YPnQ!&*Cgq7L(4EZ+@HcOQ6z0$hVJh>wJW&;C0uj;
zA-iuen=uh#U6W9295u!D(}Y?DhRMewp>Wed?GG&pUZn_&9;9t$-%DpQVB0hEUUAYJ
z^T(&tC9rv31~d(6%CnjiFDvz$gj&06$F7~pJ8-9U2Tm{(YNz$ze&@uF-Mh1syR*CR
z-g(bGcV~9&+;!Kkd+yx5>yC*X{;oUj$=-1%65oYuu(u@Cjjov<`{|8BLQYBn6i%qy
zg2<pXp|<0~YC^4pz^7Oy@tQwU%J1f}PI*sv_Y_Fn+(Z&opxc!ad_@*TB*it-g!<?S
z!u+O$+W269>fl(<!0`Bqp5ft<R8MMPWO!WCJkRU2O#Bk><MKm~=<5>d{2ETSRMWbi
ze7<;k2uxLOHt#18!%Q(T$b5i>C?w%We3Eaxl}V8HYMCcU%iox^enLHNxCBRsyS$K?
zw#&2F1LWa<U0yF*3F*MTf<FfaE#Kv7xfevjDacF;`!bokCwAU3Iho1s%4V~7?UaVJ
z95yY|$9o$Sifv?z)|iC^XJ}~fCvclvcTUte;~?`i;1|;sWPTpdl-dYZ`RL;>%RMi1
z2p<RR$4efgFd=To;^`fiT~uVJ+1asgzc-!ES30&nMpUlw%#UT=B0$B5{7UI8N<K)h
z`0+e{IY<nMY3&;m>T9CLTGPV_M3DKzu)os1%^Mm>d4u3j3T1!Y+I4Gti?j2k+|+c%
z>&$HRcJ0`?+jA)E5ubt1r%#_wzS*x#XqFCG&5Wbm<;#hU2Wvf8!u^SP54(M=blEO%
zvgG@m$QexIDZh&o9W1Sf8C@<Gyy8R!+#!}gFO3O4JHKu%l2&N-r(B$@oKBZO4GLK=
zT`m_hBBAxN#SAw61-*6mCLum3d!3bO-)kQ?Ioh{MEm=REU$+)!f*5JzywjMFVD>$n
zG2t{zwuxP&Jo!0_I9zldhb^WGW*4}W*R4h4!3uRrQ@dz;2#Yv=ed*aOg|+4BE-#B!
zU7wV7d1aB{62@KZNq4d2fssbT(AylEF5L~<Cy59nzy~cbU@9}@^mK71+Ls(!Gzp;-
zD)qBWk}U#kQYZNshOqII*pC)Zvv(OQD=+Wiz@-qGo+zI3r9XP?vBL&R2+(C1q#b&2
zO;i~OoTn8g4K>sZ7$Yd?61q4yVT9^=k3w1OacjDolqMYR_r^yKrH=KC^?L*3-ssrK
z(Sg4HKCiuJ9RBt$@7O@<@W_#rhZJKy!>LETkwae3@T1;C1H*k?UjHMbWBudf-pJUx
zwF5(=g9H5t92o8$JkmEX{D5~589_O{!2uvUN==P;Db#4n8t6yHL*7vTSnpvJ-E(kY
za3J+)*SfWb22#T;_0Y(e*W-=$jHL#8j|}#Vd80?hMn}f`(SSabJ3KIaXbjc#5A_eH
zlBgD8UjI>eyz#?5gM+MV-P)cb=)f48==F|_J~}q=z~Pj4cx14zAAtw^(YBt0gZ;WL
z^s9HUXJDwy>+2cnd4TO3^F~k%y2o_7f!?vh{S0G`J@~(u3j=%7J2IRagSQJE9ZR{q
z#|FmxyS$#UfpNm*(AdZj`bd}}3(7|+$T-}uiy^!PNN!k=gg`rje%sb~ef>RyC=6pX
ztb1uwC%tXm9N4)s5l$}z_eH(Q(AEi2-QA%@9#e-xcJD|5^GpuWlMleS1ZVQPwVP*4
z>8Y8t2eKd}!zSK&*S$L%mZLWruB6x+bsCYMj+Sz#z^pe;uUptNS1GzpwH&?|OO5kE
ztwdxZ{)n(z>AaIqlpAM^G_Y8~1oyLza|{$P;maUKAeUgsFd&2?5AfuGBp~l$K4jh6
zv+LG+UN?ZWw>&p9lP=Bgw*do?14Cj3vQ?Q*gT8=%h}6JRF(f_-dH^1e6dF`zE}tc(
z*qhm3fJ}OCcV<5b)QnFVMj1G$t0@<;IQXK~0$Npyq{c!}GHNg=2(chLl`@Dhj!Hm4
zOgp=)8@ks&lg4R>zke^eM%Vu1PCWh?Rvw{|DXasXTRp7HY$4E57ioV_6?0h+tGP+$
zK;xm~C2!W-=jj-iQAd;!F`G;kwUEDa>wSQycTA)09&fY=f=Vp!FpLsq^X`Dkap4`d
z5$Eg&0Lbp_?Y7-x#Bs-F$vIXh!#vLgnmuq*ONP9sYGWJ%#zvZhckih><=Qi&xXf}Y
z7#Gohiuh5l$lg5G`?zeFJAP>rXM1sxayXgGxZZ4iOrQfUOrH;@^PTOn9Cf=+Da&^T
zt8+RJ9L`7^9KJYkdV2x@dS^Wh4QpAE*^(}RO+JO4PISgB1I>r^agRgtL&YL+JcPU@
zIjqtsUk5cg9XiN(K^Di^Y`gg?GKyTQoP5QR(TwVHP!V`ctV-kTB&S)$^8Cz1F|V02
z2!_iZj)8EtnFYTNHgmw7tdU5i%id{lto#K-8OoPRmrq)(Ad^0resW&V8!iys&+FA9
zX0af%Z}l-i(EXV%yM(aN<d|Q^j*x~w!G2b-DbO<vYlC5zSyl2V1=cQ+mkG}wV(-!?
zjv)a;^f_maht5tP8^l8T#(d>I^7e(PH%?XV^CpVLljW_pd7=5mg({ty_By#rFco=Z
zojN=XJuf%ubxQoEeKH%jMrLCm{^rgqoj;wP2V_fg*sp1bkkWgga6IQu%HbRihJ7g!
ztIv^HfUk4w*3jbQX0QABSGlIF1Z@wE?W<GDm)RRPr9d`Ta5A|Cb)}|LFcU)4kCS8h
z?AEJuN_35v!!L18xglQ8tcuxTm`w$C4NxTWCgz~=1nH0vVGn{PE#csyj)yZ*szHeG
zqO)kWS6e--*CH`0y9I%Qk6Ekt*agW-4zwD3CCZ|>x1r2LO4QXrq5_$b-pWj2krd<8
zOvUY$(GpC0EHskMqGpg8i@@G8#9@$gPws2ig(nY5Rh<L+7n@1Z(&#c(>ut*M$F{b2
z@86!!oz!)>p6p%p)@NKGrYq5VfzEd9!nlz#3^18kS^97&d0KQdI&~u;;F5G*8fah|
zWbbtRF@m-X#?pf{QDjdXB<}mSb(1sJYn1(Ut7?Q34eDw?l!h=byT?1b<6L{yjtPeo
z+kJCb=Ejq2YAnEA)t9rjaaA>A8@JoA+bQ-gX18T!0&!XGj%g(pvD#6Mopo%b&__Ka
zdj<JDN&@z^YY@6^6Hjj1Wyh@3j^n%qI`?4d^fYnvhP)V9E>X(i(I&7&W@v!!6*;P=
z^tN_RX}HP0sauLPRT>toLBBeWyX}oZ&zK$vQ|9{Yc^dB{ox~PBCZq65p551BckL>(
z`$J86;tD+L;kflajrqVqi};HXqZlF=0GIrpop&Cns@&PY5J0-_;XC6`7xQ4*kqQA|
zwy{i?^fe#QNJ|y1+}BY++a$~!(l1f|;xy7ji=cV#5uk%?6E2#*$m||0PElpA^Eiet
zUg7gGGh|ngqS!{XLu`XnLyd!=DiA1^i1Du|adETJieVfIuF&j{2eYd-Dz0dRlz<j+
zf@tRyBW1rQ(;sNzQCS`-u+F-b5;A_A766U(N{G`K++M(;gr*!kYSHq+GH04#TtF@C
z=B`^ZX&S&ATW)FNddkCI5+9syu=dkAee!^zl=F_<3`&vX3kv&q%7BxHsVRu`c;Mhl
zS~L;|qESlqNC`zyYC9C-Fby#<QX(!DIPP*iH-v}Ojem}E2(+Mo2B~@K1Z3{xDG;HZ
zLUO9o3TI)tobeP-8C-_;o$aA4cCoXKbk&)*QH}-axE_+Kh8>qJv}HLu+atYB$_mJC
zfj_W)VB=@z4{@ABZE$tEHGT;t8;XD*auT)Oi59NKtwDdPdPGWJ?CjoFDM1tgEjfb~
zB%9m1x8B!{vk40cj*RIFMxjoYaB|x;<a3|y<8oLHM-$?>g+`o&jDyJ0!GeU3`_Una
z308u}L{KD_t`J5tHQY$W=c!$|Ip3;RYR)<|pHYHgi&%>#%VjLSKx!Z7)p>K6F^R*>
zGFC-%d>YUorYoIF2ZE)6N(aR<I9=8BNGF~5W{lQ_2R*CplB-Om6)?uOh=4c=tV9lH
z;~*%SqnZtQF0Sr!&RWL4W9bB~YCg)PmQE;K?A)<KG!QDf%Mp&8A}MqdqP2bdyzM)M
zy$|iCV`WkufLYmh2Ns-K$)s!UD>`3fPBflKMA#Uf-Lmw7fZs0KmScY<rdsskQF#5X
zooD6@i~6%5odNUdCZav$Du$wU@6DPd*cYv`GhYOvQViVX$!DclJx8TNu+K<i=BVeT
ziX=M4>N(q>p1Dv(`uj@NBozrx*XfBgUJ@+1{Yx#@(KFfmR3BW%$UjIHLexng5osDV
zfj62?dr$hMqMK`r=5+$nfX#K}Y7<qHUL_2?j%H4J7A6cUvCvizL+o0sIV4Wu#iGyT
zTtT#)0<lT3K~Gx_6ZZ&s7gHo>kfcExhMN?&7IWs}_NGpg2UkXMMaZ)?B)QA>%+YjL
zNU6gz{f<B8-qRIFIYLM(W_1UR(2hsr?nE3iu**Pn!)QvyH7lNka>b*tTd0KP$SYKa
z#mVexuP3feo$bh4Ew6$Mw1B7Tpk!9eV}GI#%`7~Z#xpuJu-G_i1(z#<bPaE%h8Lfu
zBc2flVMl59i{OzUY6x#6v47L5A`Ua4DPj<y*Iwu0U50#e0CJexTVmk4_iLU?v;uev
zDSKF5w*@$wV0Vld6_ld4vD8Efjt_;7Q%jMz&l+J_$)~WU?37BlPc?lFW)>MgvpZ&^
z00GLHxS__xvZt73+=-(Rimb;&cDZ9t#XK{&p-WhCmF&d?oq;yXehmh5NzTY;w-O9{
zyCsRs9-l$b+=|a3RbF(?v#E2P0qb}65U2`*&Ml>Yt|Ku;Ah2;E<9@|%f!GE}&OPe4
zK_)MPxY!1CM`71pJ!Ey%gCd)H>?48y@n{!5!!?<pxPe8}w$zM|sJZC-;ic+0VKhi%
z?1*VuV=a!mn~;wMDQ8PY0n9s<D^+k8QVZ_mki%g(cS&UC!I-=KYfz#gjwjr{E7~;E
zOOr}(xPr_cc~2B=)Jk+At`aiaP^)1Ai2<LeSuHQx-u6M<9A(i$O}E(tyvSifgRO*Z
zPQO<Dl2vNT8Z5w03=`j2j#=U)Yqz7?9*b=86OB4$q9b54FD38MA@7XMe<r!85<=5N
z=7^DObZJm`c?)!+dd4(TFsoUSG=H=b%aKM1+hH?{+yrnHhZiOe`&l|})3eAzs1c=c
zbjdk`LxC_)VeL&-?=jqhEz<^yfp!6>zk0tBR#A*jx@a-s?H(8ki~Kcb4f;O$gedsT
z`h_e^9nIsgpmN&BF{WLj3$xIO=Q;(=cziZm{mSz=B|Q_YOSAjA6o}{**WaMZFP|w<
z;?-&Q?CqWn(nl$?%$-AhDUP1!?b)->gJ%rv*75uF$79=K*EN$$XBm*@>)AFU9l-YA
zd{AL$pC>6|3(|Q_-4O`y@s5fR<T&)nSwWqan0j<ej>nbo*f#Gh?|eK}t`ujV;<oE4
z5F;LwJ|#7ri*1;$+%$}`JoHI~Echjh%AaDzd_nbT!smB&QPF8Vz^fIGS((PM95w)G
z2b8>W0z-$HxDo*^+XC<ttcyAMF5*4{RgU*gl)Bw`9uYNf@Fnb_aATd=1HjLI(%+45
z(_MP)VLMndHl-|Ul06(S=%|s<V)P76muQA9)c9=CFBdw<VbkgcK!M}>e14ucL3nkI
zM-tf47~>OC)oiIq+h|*(?bl#N8$%!@ONkN+C{7gSGJ?!(q;6R-fy>dt9xcv^78;n%
zKm>@gV{QUHCa_|eq9?{FfObO*u0&SVj!N2I&bGjiBNgdj+2>%9cQmJcxNo0>QeCki
zng|pdVgQS>?(v?@q&e(kW?V7>0!cBHFN*TDpS5+cQMTW5!CWYVRUL#znQk)RNsAs}
z!_W#yn_-qPXiOURI_9By?)il&D5OQf1QA6Q5izSxdH>!R@=&e94K)qgGV5{2RGsa5
z?h^$=stksDiku^5Z~@0cda>$sTRaSe)~SwVpQGC2F;&Y9)Ko@h&mIQwzyzFKiQ2pv
zO>qBO1sNMbQcnx%D1Cpx7v{(6T$F*=oE<jtWwx^{4Kq9m=}9f5pof_E2Fk<ez(}co
zX0|f#+9GO+daX-FQ!Jx>fJ~2FdgMG&Phy}e=^Dh(oac@d2_7TDT^B%hE>$2Z4KkQW
z3%r;a*x4@CY1HB8eJnB@rhT<=vO-nda4Tc7%ygOnT<}gW101n!)6L>Iby13d^MpON
zdH0z?l+_><i!Ri*RmL<`njcLA5#4Zt&S1eG)mQLz@e4IvuO#l~f!_XBUp;UPnw3@P
z$%eZ}SLkpGN0e2>z%`*UkJun%?J*VRuhb$i5=I3mnk#qOa9nca@rU6jiF1ylTvk0@
zR29zi%sgzW!PIVeT~Yg$R$)738)BGAQFjSvMUtw*YS_+Udu1R>lx+LKDo+{RmPr4k
zp@*Uk4W(VIup4?UnDkmjpz0D)AXCX}8+DDPGj`)rwQA5lPF?$UoxxctcfI=Hocn))
zwUz@KMjt-_X8X*%5kIs?Z}!+~s5h^3SPFERLO)EPmDpd|_K``iF@)B5#x%@|z0@8(
zCj^vwV--P^#@Pjp>_x9?M7IqE;BAs>^fgE;#kk}F$}ScS8{TRY4HR;>WR2yv1k^cB
z`GNjFSV%5P(8QwV0XVBHcsU;WcI?~NLC&sY6Mw-$(PR}BZL;K)#g<jMQ{)2{TLPpP
z!KJw>p7ajmq!~O#S6soE;ilpoZdTipgkvNRj^#mkPB3NWA^{p=SbCdy(T7LerJ}Dr
zakjXem`cwhSZOoHx-?d3>87nwr#Vvg%i(l37MJOG|4!?CglD6^WtU0s1Wr&gA7sW|
zyCAnswrineJo9F0?iL1ATD!fGhr&g9nks|V8fFf3mkr?0t}q!0XL8fNf#Aw5s<NwE
za7`U(bP2Av-qpErOw3GWC1^;4p^sgB17bi*S|gx1HVr|9-CaOFj`IOIcJtik2cll5
zgPOJ^KrWaP<!MYnrlwvuEbs;I$w*e0Mj5<2ci^ndX{$rFl8i155iB*((^&l6uqHe$
zA?jp7#mUYAcch`W2jfPy&JeZmB|AMWT>1uZjZP=<lEw&{Y(ShZD8N(({T6%nLJAc$
zEjUfFkkoA%ns<eP)Q8dsDqEi0qS!^4MJ$NVg0c-+#CiaAz1q4GcwJqKE4}f$ST|dU
zk4#Z{al)8Au3p}30iaPfN%is;$Q#W<Ck48Um$A?pW=kJXa;v4+8{h^l#ZsW+C#RA)
zW#GiKrqm!ED&U+K0v2rZy?hSlKuYsl-3AT!x#bYf6l#?=am0mNaU!pf>)YGirSFhY
zpQINNFNGy4p)=wE(>|;l&ZIidhV_aXT(Afw(;E^o<cNe8wLuC%&c#Zo@8QyeZg<*B
zT?h|DnHq^iEw)#&oM>FgxSv}U%?RpQ#c>3SjU^qx^v=4~!Zt9HcwFDcrvXbFA7W6C
zN9*ehb>6`UcUDDFbzxiAdO~T|*EAwjJw@lPt?t7%szFOa!;m}%_29tb8|0ax7KhUr
zt7<`uOnXFKPL6#CjG?_yqn1aCR*1`vj>IDANS#btP!-yAhM*LTtAP<j)xpe&gJG~1
zyB%u1dP60-dPp6dr+POWj=Mq)33bV=>oTLOTN2mwqyte!*bAL;CE-!8&oM2PY0EJM
z@Etrs|HB|OYXB7Mg7zXwsP)yWgqas{y^mHDtQVZJ4iiEd!*zzggPe`GRNxV-?wdQJ
z3h6WXp+LY#aWr;9PT(g8w?<1un;+2fcPcrC)&Qd>LJCUK?+A1aKMC&A5_D7zjxg(I
ziduB;BGB0BH1={5&qE2`SqfE#aSG9LH>1P)NHz6Vb(5^7u(z(Gq6R`xger>m$}Oq(
zDt+i61bu+y4i<04Zk2$vcKLQtiE^)t<H<mhT~9R<z(NfV)I&x#;?h9Y2nb?9TM4eL
zuI2j1Tv^?0SDc>KuQ@cof=M5q^3`-K99NIJSh^M`d|Xsh!qdAFkeceY#5La5jcFCi
z7(-JR)sT3Y)a!SoN=?SD3{|b-X53AfIDj-Jvx_JxJQ(5c#nN59rR2o{P3Ul;S=;!?
z1spxF3JPap{)6`kahbV7MmJ{r9HL8gN077^lr5BW5wx<38p&EL46<3maFz`lBmpbq
zP=f<)hS{v=SQ53ZU{WG6Rzps5v23nU=#K~{MtJZGWiFW3Hb4=pfWa!lJwut6^hLnO
z6Y<C;Y}_mmLttpx&baae6>mEzSZu779lb=%h*9L)a&`aqdAs7u)sui-iK&4SL|o+p
z<IhWSyt%8FClQn2X(n`qy%Hf2hSTXI$^xQg3@Kjyn~~Fa%3S8eLR%!4TiI||xX9Ca
zeJNF|IATF9&t;}V1y+M?(W3^)-Wdmh#rL}gwBfoNJne&;?~$6Z=^?x$h+_kSNqCwd
z!`uoX3|Ig_MdqL#exRGo!*Aso7$Qcob5NUyH-n-*_3&TYu@f#&<a6YJj2OZR?E=P3
z6ciSiZ??ob69e-BXG=KVhh;iWYkRLMG%6eqh#souan%mB4%YS8k_z@K)CF+2RJh=|
zfu7kBjF7tHst&qk0Q!_?f;a??Ffb+ecrDjU4CH1R%3pG8mcTamIjQ1u3TuRL*m!12
z29QXtFNB+82y?0dEm~Z*MPJY#$aqj{=z_oW!p?wbZ;n?l9%0!;2(d&WV0vg+N5_9^
z({|3QbKy||=mms^7ss4(5G!j5&()W2`E9i3<*mjHXKi|`Ur3<1JyuMzK)9z*3_&f#
zWW3?P99IMRs|dY$+$&$z`@j0CgEs48*@zVEeQ6egx-l5=h&E5GiU!)m%eFo+3M%w?
z5G$*R8mB@|p1yJhB7iu&s9k}^klUXa)ik$)5WCXYsp<EqC~%Bep0p97ZY7#pAgZ>S
z>>LerSq&5ajGvj~s%Mc?j0Hyu2<7EnD)KY#2B$Vv39~cMHo@T@bojXJgNn=T0bEUo
zSr|?TTR9Ae<9yE6C3Kx20Ijixj*XTZItcYF-b{n!5Aa|r*H#Ukt&PD08LWQ)2&c_8
z5YkMYNkGzv8I40}))F*8*B3fa$1sFAIdgy^uN{!jlNnEq6qXXuHxcm4AXL&P!Fu|W
zle9i;6nSiI`aK_VA1Efzhi}j*+;cUj{vByPHQ50W*asn9=c}lRBLa13!isJ`4PT&j
z_+8C)NI?xZpcw=M#t7^KXoMJoM$j-86gVv$T}M_z+yWe+jhn6lN8J)5mJ#lQXJ>X6
zbO(r3C5P!qLuD<~VQ@yDh8eb}YOyq|d-w=m5C_{q(Hy}AfTf#8p$QZ*DnorwnSe9w
zF!|5o1nha7(t5_Cf~c?V1P690AgL4OxNAir+ru<)AR08~WaPCx9+THWJ{jRU@_v?~
zuTC1H`}3W=M|p-*v*OK*joCBaHg8_&HcPTpLJqlyxL(*0&vRw$+;vBncjvAugGggq
z@AZ_ZD1)7!MdcP&LRl)pW7_h-EZ9+7gauiL=XRMLl0jGraZUu#;b{~|4Qgf)0|M6G
z>1Y_$Uk8FPX$z*;3|<AY9CxEZt-d0pyG%t7D=ak3X=DasgoU)1aC3!=LTDIZ7i&hW
z>1(_sMX&g7Vc>cqsEjqBOzZH@^1N0VL1%I$9Jz^hvvjfQWX!w<jO7;9hG=#Kz#!Tr
z*5M{wL=d7qjVI-pKZ}JM6kCqv9Aj=~o?F7gjUf%^av0(ItU>EMOyc7~Em@oO$vw@-
z7>hrw4!JaQ0RB?!(i?-`kOcFz(Kb<^ryZ|-U$K*>eT1Aw+EgjxY7<l-&Dl>-PUQaA
zg0jz*L-yL1yO|Y6efHOId4`BK$Qa92HcDT^UPj)@s%A1D3_qfU9b6>w;u+3pF^gt9
ztIQ8!(?nf<V2sb}lYEu{A^OR@?RfXW*a63qC3gP=bM2C*O%6fD2^nY7c;9<Fo>hdg
zA{<y_pA)PS$0z~QQ^YBh3-cM6$|Ye57*h~iTZf2Hu>a`m*E;l?W!6_IAP^O!gZi!~
zri&}V-SET>H&&ic&rD>~A;yRNr+c9(MAT;?CIZjIYa2MzFy-s&ae%CCv^nZ%5@+&7
zD3ZIY+#Z;7;X5<wS-!a9CVPlKC4^}TrIh31%F1}DRx7oEg@nQk!l>Ygamm2Qt)7Vv
zCR);z2~~-7X<6c?{q$^T2+K)t1O=B)=ge%jcKr!0Z6iOph`UX|%oH?42e29PBzIH*
zFx_f<6<uFl5va$&OdF(QH?&1vtpV-sAj2we0neC3+Ny_<XGeNRf+Q5EWg6OKsa{hC
z3SRGK@80AN&8HD>PRs2YY+A7lXv<Xut0>oDxe&MqsNi}hOHl_`lpJ|8K0}i8oy|5B
zAWI9$EU=YhSJrRGqhPa>1AJ*_(%l}(<J$8aOhMu@gnsIIxzzJ=xo@o(VOZvI>0zIN
z`67v@aG>T%zvVNZfV~K&L=wvx>E<tCDw{th;PMI?U%b91Q&RAQ+g_M9G~R5NHVhG%
zVi}qc|AK;8CNJ<1IV1E4gE={u*Qn3MmB*F38SR|A(8g)kS3NzSC31IVD-(H*-nX*d
zm^~%5o042^lcoL9RuTnu^Uw_4z8S+tp)Qy-$jMuFS}g9&a^hlp2x+Pfo7A~-t%tix
z*ZTOV#!mYH=VRW$!L)r%(K|5_eDC9J<{Ez5sEwi&-uN1Z2%{7CxFN_Wz=?3#p4V3#
z2TZU46s9^Eji;*M;+(Uh-yJckCD6}Jq76~yQt+jbzi+o!miGvY<3_e4)Z`9rB+rh`
zveiOaK#UEeMOZ(oJ=Aq`HtsZ6vqLNVR)f|?jjOKufO$nW9Z__Or6Wt*f22d?fKwG-
z*g7QTmKUjH1;>t^7CL2?U_rsNHU0$GH9kAyCdw%DWwf?L36BX=qg-<H>2jE$p_v0d
z%ZrIhYfDl_sR+)y^DTjN=$k1ZVF%pPC9#ktrbgM@_&l+kZJnf+sl`ii;rbr~v_$qt
zMM}n0v$s^4WH>lmn`H4!t;@Vvezr$zN#d{&q8Y=lL{B=1J5HPf572`oORvi!%rSD+
zko=PTA6||r&XFNb8OyL8NzF`hv8QHcq)rrU#k08L4PH?%9D%3}A_|;Mly#A;){-Z-
z4=)_SBoH}gY&MYx%L&>irl@g<Xf4xX?P3aW8hAL#W{)DKZ0xIfBHIBApNV1?mK!l4
z3`ffK2Uh?>Z6iY}&!|y-8k{P+M!7hRnwXiQ*`qBTt{o|Wgd7_<9<I6Ii(Dr5Fv||{
zd@=xlUT6tT*F&b%n*uD*w%d*?a7(+H%vi5++yoUzK$|0$wMP-Qqd`uY?>M5t-F(`p
zpi$q`c#v}z1%Pf;P8Yq7Egf<s8*E$b1~|0ykG}a%d2N7NpiFirKEtQs&F;u5>ICKP
zjqH_W#H_f>4l1}<*)N%JEe-C#Hme_-0V^q#O@ale17UF1Bv?p%kwwx04-8XC6iH?X
zG675qydh2!2z$l30(6bZMzcoVod#qmS&^d=aym4Q%(NMt9J)cD!1myz+jvze>guUv
zhg8mv*m1KLXX&tUD;pToNnZC&(YObW5`Yy0{Z0(;V!Si7i*7;YQF<FZ2y%wwT9p8c
zd3W~~Z%hAh-<BYoliRruBiyh_mUwqWFXk(;rbdAlOZXR9QhW67-ryyN3_>Czr--`4
zD=gXQu#Brt-R=9$1h)7F^5gprMY+|~$m*MOqH81C!5lJYG<b#)dtXD%u`fWdh@&Tk
zhKA;OFu}>t^NgE)3y7{ALSRsqmNU$BN6NaBI@#||M>xMRF(BZm%p2NosFSqFG*C}Y
z&_%HGwmu7;C=0XbCTN*(wE7en9|tkr&oV>>1zaGowPFIucE>>loQ>H5HRvvgCPfY5
z+RTQ8rb!)rUj)_aVf%<=r0J1Cg$)c%_Z?vL;4Y3_ba_J@*G>%<@D=T}BY<}+gP~%e
z+1wvBQk7vSNOo?my{^4XViP>3RG;XuS)@C4(k_quG=gdsI0JNB9AF#KVQypfmQ4t-
z(ZIk-%W*vzQP@w-<+1=ahQy!~1_X9(9kYlFgiI0vUH7&vK(vy#<;<3WEC+ClNwuZj
zyWKkze%jAsxQF2IZc?)1*oN<6>cf{6BI2WV*^b*kX$s%)I#uuG=tT&CW63>xY508M
z8^RI*-T(R&UxX0$qL4k6HtRr$JS>gyvq8FIM|9%ZI?!ZcYK5NY98t~{x7cuWde9%n
zC0eHdYPK37T&p_i4C;?udaMTvt#VW3+vg=%X>zhVO}&HZgVQW<uRLeB=q&QkYF;&q
z*C2}X=4?N{Q#A~^PG)p5YU;&8C0uQpSCGMYi)w_hs3A5PrX{F9DOL`Hu2eVKY`WM0
zEgTEF4PIGn(uf^K8cfN+qEDeXSI%Rd!$M>ESoW9N8r!*%Bd478&scG%gG^AYQB7lw
z=!9WAz5V;V9a5?YOj%@rDm3Bf3W^1>#T#D7_U(FPK^<l-oca$>f*loF3c3u~&4E5+
zs2Q8fW08!mMi^n;D@D->&y_gX2x(TMiqJ1>7MbO<3M5X;ZNWpk9&DOtCV+S`cNP|u
ziCsBr*;Ae!I+f`G?>~eSV!t$KPOlE}P^z*J88thQMHUjeZ)!*rcrMgZEd$YYV@=c1
zHS&+)cuOEATa4`EU;p4>MTKf~{%FoWeVHS)a7nKYQ-A8<SkJ)l_=%q3;gM7iA1@y_
ztgzj4hu`20(0>KSr$G^~4$QI!=CTP}!vbC%lw}Rd!epqi0*an<3zzch@GNV18c?Cd
zj7x*;-?Ra#+43=<XM8j{+@JD>j`jvJm!yXchLu&xk=d->(AH=%M>uk_r!1BHXF-=K
zhG%OF)B+qW{F1u0fm+cOL2evWWajeWBl6Hy964rVz2;aw$xD4&;vI;Mz&d#u9EtNp
zPDJ&P*;^fpO3^kHW|z!;-edkm5{zK-aAhW+?1d(Fwnv)OZ;xuq$uZu~DCyu%raKJf
zJeqpKSt;aJVje0a@Cxs)7i7RNk@bz-IQCde4B}!IWSmz*kZhKB0msr9;%JuG<HOj(
zYv+hlu^BZI)oPx(2b;n98Eisr0<h0DnQA!cx-x^>;$qn9CC#Yl^$4P`IcRiqD?Dwm
zA91MkZydW{NgM`PmxjUN42|$NlFpueJNab|BL1tx-sP<az-UWp&3ZtRT`KZXnuXVd
zAgxmt{MWcH{5x46AhR|jyQCQwldZu#i%b1)aJDUJt<bx(I3~!>y~ex{EnHSHK>?VP
z<YYTFgLCB(`4wCWG}K~Ib)i%7IxO>&1`Qg)KIHSFOIo|WulT@?$aC;3!=bJmODXS0
zr2vDK9EN4DRA$R!VGp*95=R);^G`3lR)tf?tX5*nQQPaY<zM+-jNY$cXTg5d>|;#$
zzrT%)={O173gVqPInCzEP=}xv1C-gI{>E#~T9u2tT;dW`$DnhDWB#-;V~vffy+r9)
z{^;T6g1bjC&>6Qs)H}zOo3Z0~4-I!Q$^>42sZ=bjAOx!}5Qf!@0(k=93-vK5{uuQ>
z2eDcrDu>g{76J6>ibB)Igy57#=+-MbabCfN(R4Fq0DR>Jc!4c24|b`MbfpKHhrQ5@
z48%M-;NSW%>t24-NBK$&Z?CnmEb}x;Ypgx)rcOC|WZ*Ck`5j(&8s7Ix3-MmTDSRbH
zb$M)+Wp)Ar&Lxn#-7t#??Zbr`D@k7~7Jj7}s_GrlN;1^-7w)*2R}hc5%DZ_IKK5AV
z4(ZC^kg(g14_G4S1ZJ?l-2D{>wWC}A3SiD@xyZ@uCcCh|ZgT{6<}@Bnx*`MjEpcGN
z+2e+Eg{RUn-mi{7p_41Eh_siMoI#5!15KirJL}LtpwM`^jklplbL$L_oO{?yCskK9
z^zoQzPq*XJXsrUZPB11fW_g1}JU(Y1z0#&4j+F8dJzqlZV0sFtZ?v?5SRCxYO`3B!
zU5PyI5q=EBn4K5{7g@qCELF70aYfWT1upxSJsysLXeR@^8hHpjExotvR<%Cx5-)Q!
zq%QWLYUOrkx{u?yjGX~Xy8snga8tRc#@Ah;R-#utW9`?^kGsy4-B)-);&>U&W}~3g
zIB%?od4Vi7a%Rqu>Vq62J`j{qi$83XOflz+mp4SO$m&Iyxe+-^TSv~f@c6i-(TugH
zze|O59R<mHaE?949ZNq+$&4kVSnL)d;6)Ct*&t`Ro-U)T#Xydju8^n#8VjSa2T@L%
z4_<yXItt^uMkBXh8L@BNOkX+il0eEd+o44mgBD;pgTdh=A$knDs2~DGTfC}-aiRG?
zZJ0*haJ4tHytJ4Cwgg{pJq8zWw26tdn9H-~7HOvwcUhs&t#wdvm?>+98{gW+*{nu#
zlTy}ahb0cDj4eK$658@X8*J047K3AEF_L;FlZO?mQ?%vC!yRb|?rP#DYiBP{SEWJP
z$T8*X$s@C34}!ZGG=3|tY1&fFWmpuP?Vi)A^gVXGKPk6y;|m-^dSJw2Dzh~a?L#+3
z;<8eAJ#-P4-O|IOPfBb_Q667rZ&;U~0s7Tr;c?s)*7qKGj{vGbV$lmg2h>8aHshji
zIMlxg?uNR-wYDf<WL70@Ok-$X3mXCu0MiF^<q9vS0%0LO=G|4>U0K-JDPD4^8r}M%
zqZeSE%_{BctM{;+aVS?RS9BwScXrJZs<U|3v>9QTbgOXZj<A6Z#~!&4-9bJF7t?yv
z47sQa-mq@X=;|?;5ds*%o@tbgnQtVq1zvRV4Bpxrd8dds!!mfGQ$Ide3-GE(=rqv1
zGf0nIv@H^WkQ<6+8v}7PO<$uZC+lxf&|@@56RnuL5|(X@qsN*@meq76_^)bYbDani
zW<p&s)Lo&en=KYuL$fa+Sk-J*b&(`$daP<_os5;cmQuG-QbKI8F^m|eMC67T127OY
z&;(jxF1%Vw+<?eA2pUSK@AO*4)$dzTuhEk5V4bjwm$$^lC`v1j#~bpGgF|RsBi%QM
z>&$)5fvl75p@}Qlh0xPAW_64<#Pf!E0S%YCLf5_=h)o@GPmL>+SmWl(Ra>su{dJ8o
zt;khs%*V@uRma}Hsg6sbF$Gz-8`0`;ms}pV#Ewf0T-4MG2{fS6e+IN2)XdN_^g8x=
zfKW543+Ad=#cfo>GrGj1j`+Ht^6uowf?!*$b5OSd0Walj)tRMmMw@!2tN^#UaIWmE
zra&(^3A0$RY0Nj?16B#TUfv{Z1mgahEnY4jTILD-k3J$rnPr%9C{N~S=bg6*&E0bh
zg-!#5STu_s%I>5W`b^bU5So9ybSBLN=yfTP8JKq5y~`W1nY-WyOb^*g&|240U8HMf
znA9$s2iB-!dK|hiX?WJ^!2`Uhj%o(CQugz(pEfOa?<Sm~8G`~7^I;~gUaLvo0^l0s
z3I*_>MVJP$h7mEDE!c(AvxC8fT>|XjHRApj93m&iSkY0c#i#H<v0KqK(E+M9V`pug
zEKUyb>8o83&FH$7FyybZ1lFm?wMdFu?CLijJop+NCf*&0j-Ej*H|%zvJ1Sx3cL*7(
zjjaO~AfR*D>;?uYVT#roj+fnb84CB<IDt~aT0E&x$ZC3&SQ<W*c5L_}O%;V87(xRW
zUHm|SP=_Nt!88es`U$Nr&G|Kp!NB;5)Yy^!x{KZ-%a#We;tD_1GdRxDcg8NMi!7b5
zP-}iBu8QF!gM)RKuf^5?Y3l;DjHFJOn(mOkT-IteS8y{4n?0(I;8E=Y_R*TFa4VF<
zCbzqT4ghd0&1Vy6e+$gb9NrSBDG#^bPa|E%pXE~Kaz}CQ_z3OA!HTmsjm)Pn#IkvC
zGTqs+Z@=Je2Mk}(cqtekBR*e{$gSKulycAke>;W4vCCbFXfiXjx{zHK8{C5f!w;J^
zr%T(ZoGq3sJQn67Fqq1C0FeAer`RzI3^*mVeA^vsBq44o`CGY$kuPxv{bI%z+E%&3
zB;t*=hT{Wb6t<#Y3wSJ?oZP#c*G_F6E2c9jTj8by>z|lteUds}p+1mv#Gut1eFw!l
zPa>?`01oB~Po(Aw1wW9>hL@%#@DLcekl`!<2c%LPHx2+Ln{Z5MJ7OW48;CiBF`;nB
zsE`R(bc`19VirvmyXQP?NgR7XKh%PbkWO74I190kKbZ;}?n!aV4ouQmh_eSqsk6Z{
z;@x9j$~#5dj$*B_u$6X6VD%yeSc;6pE>^P4vs&JKvp3G<-DHu>38B+ZlQ<Zmber4B
zO>BUZjfDCgl?oZd7{U;+-cSQV-RcxsBTTbVbdHzJwRdY+cta5h1s*yI_HrnJf?S0C
z7%svXu1H`or6wQ>j2$1_XxEG7z3IE699m=$-K<;w3=rB?wX$_-3$al-GKmjlW>dJf
z$W_C>k%FlrrqAmj?1Gft0Vhu0^NJktjQLak8JOh6x>;BbKnIQCYR=|dfwomaMZ|LE
zWGOu*RslgcY4|aAi(#`5NLI`p*9LAf%>2y5b`}iY)B$ps-_ynTSz9b|*s$uo<Ku>N
zI-D9BG@lRl9PJtJ9UB-;*?{qpVe|RO&>*ufk2`T|O<iol!2~!8vAP%Srk!`>im~W0
zNi{ryi`HPcvB!gbH5V`9b7+HH4GzGx4iFoVR!6jsTd2Ch>a4CHNhm{h277uWKJBz7
zdKQ+y#^)wY$T=`Qc3d-MxzAaSqjn^>Cfc&$A}}&g;dIP{q7m&-r!~cgr{yg8R*GzW
z(`IH3wB6JQ)4LFcn~6|AU(Im28Ef-YZ$zud7h{?;liX&h-i`_;+1t^2AIaxO@(a?5
znjMMhPfZ(Y0>ZmmWB#eO0cMhk(5oHvJL5yT+wQt!anjP7D1YC`0GYvd(}>`irNy-b
zMymP9#!58AOtPAWG@@W={3$u`B<0X=|9N9R?7ZSZ3lN@3oJjIzBYmBP+#CXD#3sKx
zvoUmC_@+kGrTP#rg&MK$lyYF8$RX-?g1BN}82~xMB?=hY=aKu&1df49L95K78)nDD
zg;%e-oFXx%+I#b0dE;jH0FF^hlWAChi#ebfm&2R57_yfuA%z+)ngNKN?+bAFkMs-;
z22hQvHJB$AWCrjV65I?NfQEKiTlM3sTb7J+7X+KnR!C^ynY5%elvg&o#XK`_C(iJF
zO75g!`+={<$U7xDY*%nX&#G7_HMW_}KU0x9W#eTOwCdnb^@uvi*z-0S%as8BBk7sE
z0L667)fA?=mo)}XH32IZ?PwAt2dTYRn4*qH93=6=g#|tMP#no<IT$kO5(ru@gmysd
z4NC1UnkFA@(Pp%Sg=joVviA{8o6rr{ccA#Zu*D!O#C66})`#$PWq>>OG&m=(Yp^AF
z;S^K?`+Gk8$Ud6pg~uqsqCoM%5GC_wScEC6JH%13wgpR631C4?v^`l#WHjh8G8yjm
zD}$NimHGS<aI(zgW~#r6kUToIX!LIiAq6Y0EIL9w)#uOBCQl(VZzmN5sIr8N6#Qf9
zd71Zy0`Y`e#CMGq&tr*?7A@9rA|VCr+Lm;bz23nwqrXJiqA^@UOP>1aSFo5+L#oI^
zSeqYm$1)y7n<Z7lp(q5;VuzJaafRq+m`pB6d0r850g0!Ez9C9DhxQN7_i1(im8MAz
zksx)t_$m@uthJ2LD~1Fb`pZCq1@TyTxhrZCRt$g{CW+^Pv1m)eZA3?ABMM_yb#vBg
zHCNJ8)JTR+bf>S(x**FDODg=X-fgFJ7HqOjX0x~UL{AP!SyBL%*jy3SyYHYf(`d8p
zwM7S8W%du-TGu#}UaAPe2D~obE{MJyyx<TzdL6TYTDh|jj0g7;g7UFL81U)ef-R1}
z>adI^uLP$_J8hyMVn^<B;d$jJv3a5}>ssnQ!dtc2SzJGB?KDnX3R^u{gX&<4&L&#y
zbPO_I&h%QLk>`|eIPSGTls6uGp}be|K`TIGM4{EaLskw^4NYeP5*k}urxd6a;A0D{
zhJK^EK_Kh5EEpjt+Fy`3P>Y1d&9B^|Bo`zGaBaF&;i6HVh4d-Fkv+kafwe=?Fl#M7
z^Te<kn5+_u4x<9_=y9uAAIy3pS(byluM{k6&XXEb1r5cTOttW<V6ueQbOR6J;R%##
z*vfi}1s&N8Y&}m_ECY0xaj}q>0t3i59;TqCAjh{H-uKBRPKXYdg7LMPYR5|74n@J$
zgW@s+>s_M74nUCkTNtLiJ4SILv1K8!IxTvc1hF}5Var!PShxYL<CV!&+KK^Po6O<9
z9Lq70SrL{~r&HXRMK42>F4Bm`&0q`IQ!@)~LE&cVnH9q#n<?*l8Abz^TmP*ZS7SJf
zIi6W?6<RsQSzxZs8{-Q5Oz6-(OeDWatf(|@FpZ<1z>3Q<o3+9aJBA1B{XzVLE`lyP
zc4jRBZdam#E7LNkn6eT)rVOb-PdCmLz5x70T4zskIUN~L|Ng?lIu~9ESQR`e8j`eC
z*RnS(dSkU!LPF@8;?&mT6Y@Cd!fA4Gs*rmUMdQpu?#xWeQ^C-I!Gqt$(#dj1RozZc
zAvh<)TT7Mn8E-l_HJ!&lDu?irE99CqvH|T<oQqQB>r`$^lqb7TZ-J~E59{<ZPUgIp
zdXZHJ+Tr7vaNiarJ8rPz&OC*`h>NiMi4{Q~4%+2dJ*tl~WXq`JXbj0|^l*4YK?{gI
zlyEAd6GS}FuY@5Bl)<2X0<A$gsTN_ezJLtT@OTAg8RurT;;Rf1j^^~fhJ|s_q43(G
zbX3<sus%FQ46qMl+Sj*G9zk5hSs+(o5ry-TvAi@HYB#j5D$rNNVLe|3tLmQ|;p-Cd
z!YX0mx(OM!JO?LuZqZzCtBhJNx)}gU^aJL~SWt<1(OG7DDQ9t}Hgigh#G(kzgFPd-
z=OJdqW>*+cM0BlviM|Gd!=@NuNG}*Qna^c#Q%Ne-FbFC3BLjPDTeid_z7;!c9wTDA
z`4!+tELmh@&(f*Skpv|OTN7q7V!0AxjvT#OG_u=vtHh$Kvz*#Ot*vDegCSyrs=4s0
z=89a?OgAPE_;I&mk*QHls_V~gho5zo@Cx3Mu>nIVFJm>(N9Vk8upDA-(GW8yXbrU6
zgwcSa8?<hws1Spd!YIJQQ{6Y-(&QwN_h@a2OGIpkig6VE6(RLUO1W{-$zEs*7@Vw4
zA8Y$1CYfR4rz@4&qkd_k8aEG{aBN|0k^mpgEUf#ygv%VBfsM4V_Hs1ca)84$yhd#A
zPXVLKe!2wJ3TS!2Ix6@V7>9rw7Fi7d3zD}vz6Lq26OlxY;ps`LFKLFt;q^ep-kJb&
zO>9Cn(D*N8as6cyH(jA*QO4umWGi%jd9_;>2|;X891>azeLlBrRBJ$o+F{GF`dUJ=
zjnXEcu0K<mL&MQ`Gz=|~O7Xfg^oRm(g-^#z|0J1ciGiw6F;*{W;aA&bvC`|Y=#J>c
z&}kmB4*SNbT@rAcAcI$uy-uv_fUF4KpjVGqkNH3i7%txYC?O5n)FrKi`Cl9~L%?_v
zQxmFjn45T8JiFC_d6cj>-OvEni!jFwTKPHAiDGY@2Ax~Ad4$cE(u3~zgrOqG-|U%I
z8f1aE*MhF>@wSA3ccK95xn;Y#(@-5Dx9MO(tTd4WYl91$0R&wa_I5`QGK!u(r_#B+
z?nJ1k!K1>WmdoXJUps&Zwzb`1_FlkvX8#r%o!H`S59`r`0WiZVsEyovyCtzBWkFUh
zTg<+w3|f0olFPfUxa*+x2}5v+uj&aPXAa`I5;L>Ok)eP$?EnE}<girXyP%7)z+#oQ
z!0>=Vd#>y-3@lC>J?<^G>7kao5$eRb9d{czA;;X^;>z1n-AGmpe5OUYxB~Tr(34tN
zL|al8y)jp-$}lk$Hx|J)h}yyAlkCKS_?!XCF=yZlH$FZmrnI0A%ZI)t6+?kK_VQ`D
zZYI`4stN5Ro49V;om;$QGU;tW!ks%7ov~b*sl+sm7n8L4fcW~DEmVg~UO%;M9&<+8
zw?JM}g|oqH$z5Itxh$c0N!V-Qtm-(&rUc`v+2@#a4aPReeKscJ3REEdU~B95Ag1C5
zEhe7UZR^sCazk)-XV)(2>rOIeyTD@BLxXP8%YZ^QqP)SJ=K@a3lFpZl+&OVgLKr5)
z=|XrCxh018@^H9h_FbS|SPSi5(oP7i#p$Q1#Tig`0i2^dA_g%uEekd65HAw5D9KO}
zyjVI*Q@HgB(^}`YBt5y*>2)x=g9JeHI`ncjOGiR2aGTN&78S}sZ(IhilPyC@k+pJ<
z7e@Y+v}(XU>n!iQnIXb4pJ{BDHzbS2)}RTlA8d#7=({MqWLn+$q}Lw@P`N0H0fD5Z
zg54;G%@qcr&$OR8iFqiOlgqgAP8%C6U9{Y?KG>gpz$1)S4HnpqdESyQ&g5{dOE^5>
z2sjvAjE2`eO%VdIoc(Dwkb^)qyEqVHhrydYet8M2J?Q~2^HU$LOol2#U_xy`qU03F
zE|Z43boShqE*+@zgH$0XmOKK20iuP5CydUv7No7I#Ew9NDrBt!pd8*1eFINScO<r^
zsvu8;%1GgP)?x4xRR!NMH8FWjMJ`8!dSo7f$KiB}DVO0j<ub5Ak%r0I9>Z<f5zt9$
z3f95#MK6I?26H{!rC^dR5rZ}Er3fL8f?SS~tF<C&)r`dxbKuFaORzF7&{LXQ5U>hu
z!<I{PTAkR*RpxCu;P;I1E1uDkJ&LKCdJ;YeztkPB3S3}lj)m3eCA)5gsa<SL@Va2V
zYNK%OH*{zTAlotnvq-b0h1d-+rZA-;>!e=9%Hd8#P*IxBy+P1Rx46|?qc!`Y`)~8(
zmRTGxySVmVxt4;qhdZXF%x0YpJ&uYk4@~08GG2`1-xk}u%j$^b?dw#=esKE6jLNsd
zct>%&;Z9fPTnG!7MLlHtze+RqE-V(9nJ2){(urm5W3Wen8yX>65yN>0=|l|r2@y1B
z0*j;yjaw9%M$2jo<~{Ra)mZT<@0z~YRxU8F+0Mn!Yw1;Z>0Pm*S;7P!h~><dHI2D|
zi>-vEtp7M8^v_Vz$|GUn-z|17jRSC%_d@Gt9L8;7R__!!kw)x*upGJt*kEOlQ|#jE
ztesqV8?gYgNOBOD(%*m2{7!&tXM<1Bv6=bgwhkOXVXMAqvKH(P>dc@ZLD}mR2{cI_
z$9OCE2r!BWubh3N{tmNkYb?#FvzpJ$2fc~y7!hf)<T_~fV)_+D+5G%UcTHL&6ivn`
zLME?ROKZTq1ada+c7mZLZ&}m~l7QSyIzN)hh)^6SKbYnwFeF!Xn@0#}+}>sT&IRel
zO!`br@jCGGijP8Tc{`JF?32$rSa!jpo~LDXb`RpZT?b2RWO_A&st+q>U<o{2RRqHp
zsw67mcB#vC>f+{~xO@dV6NPz0j=9GO&ccAZk_c)gHdh8emtJ}lRLet8!0@bvV+?ZI
zMukBf;;jsB+En$>WoABHO$0G*);bkL@zN%)R}bQ2vC`(NQ^|t8hyuDHPHGWuxT0lY
zWVV<Y$S#E$g8e8DnMCTXkCJmx)6+s}BOVKgsul=cVUdi6#994=HG61JMAIrcP^Qhi
zky1aUQ&le~>Z2h`Ax-;3%TNdfJ0j(DSP`J<dPk(r4%z<Hp=r7nS)spmo4N!{JR}l)
z>I@XH8Ng1J;&3Q-k~NppR<5!<v)G&VrV+5Oow_IGJ>A`>PoGY{*{@7c7qOg#I%xN_
zpP%i9NFCY<(D~^ec3Pn)aI|wmD`A{C5$K25AjTT~kmNL;_i5iBr13(!8z#-(?lf#Y
zgP+Xg!xz(|>H!U{YHMW~r_YnZ%=atyg#;ZLb4#>0xK&m+jny%&n?O{S>c;ld@KN1Z
zX9TEj3Q+^B8>^vOH=4$R<WLEQiKt3Egvau7Kn3U<2}7+j4isRP61+qf!LotS>a<4r
zsqCloh{}i^qh6?<HPn%1iAI0W<d4l_pKC+m&^fXtR;x#8M6t1GT{L;Ms#J~Mo>t`!
zVV=%!%_-U~v(c;2+DAtFNsc0Qeq*)}(+Lme@U4chN|BYQT#m|+86h^G2c{u%=L9xd
zLDor~mZacgEQCd-kGmLC)`tvLkPbA;G=|iH>=0GCpo|N8f}?=bR0Yy1Dphbkjf1|)
z;#?scg6gB;vt^vx(Fli7B`6E%LZOnS_mb`Ul|2~vYqw9j@Oyl23NKfYy6tJ_@pn6)
z<i)2xJ(Na?eQ|BC&}fGNg&^9_RpvuYA%<F<FmgMqV*{lM3<q>zp!ArXqq3W;6bGj6
zXd_hE;o@o5Ag7!)r9eVbfT3Fy##cYA#*DF&>Y9dL<3I;TtJJCq6&+RJt{y@=7-sCE
z%C&U{uy$(LCR=b}Xem{~6p2g^tAxa}_C>dFOe_4k`Mi{tn!H%$qbO(QuH0n6yBhh4
zrM6}{VC$Kkg)*N2M6a;g!sa9nzjdgA;^Lqqmy)q)V|Mqj2;0>Ncnrk`$!j<xq4I#0
zy#yts8EDU$)))oLz|fk|k;R~vG;N5|#Bp7qTQJ?->qX&N&s?R5N^=>|F{lD+?KDab
zMK6f&FhfZpxFd-pH(KJG!9!|+4jZ*MpocbrOEAzKfG#BHNgA)Yqy_BcXWV3^)*Ob2
zuYiQ06;A-Ai{sPOS}BY<U9@-=_4>1jf-o~xK<l8lY7A79^*ZT?iOoSrG@^PV)mf;n
z!ujuYkUZHrb7CBwr^sDCG>hF4T0gXb=?<H!LlzYAnzZ1ud{M)2$C^<5E@pC|aWtsw
zY{805maCEB!O=N=bRa5<X={l<)I1jpYP;41uzARzuZFZ};X{jAsgZMEZ+dV4`>%V~
z@2vlbI(PNeZ~uYYx9<7umQxS?eD34F^o#%R$KHJZPyE{N{L*;qXATei!<%3D)a-Tl
zpSk;|p1kzB%+~x@@BB}{|6uOze=+l4r#3wLU620F@89?9-}dp3RDSeB?H7OV&UYMm
z><yLV*!O<*`&WJJU7z@#Kkt7_-<?nY?5C!hvTy$2(XYSmzI)&L$WK1@>AzW}@b~v;
z&c010hDMw4ox;C+-kMPNq!WyiKYne%zYhF+H{#wMierNHbV7e)<9*|=`^Im4sr#$D
z4*pQrQ-Am!smT<}=swY1E@irl6L0Pw!$Uw|)w>JeUn>}abSb+lxug3*zj9FX<$<;T
za&k61p~esQ?7C~WGR2NvZ;EBp4~+fk55D(D-t%2QclUp}`G2B$_U|A0?O9*+_b)@~
zlseH{EcNB{L)a0hnQ{g?$bK@L&+9DTcmoQl?qamGSAG8uAdnCwc&$?Bqn%ACqTG&{
zaHczqe~&zqP`g{`(|@~L?!?DYHLgy;?N?*?8c-u@82$mchw#n6kF@;#*A+@{R(~P$
z)c)Qtu}ZN>&e`x^A3O<^npP#0lT&%+qs)SuR7FX)MRKJOn?`6E@o7~-x*~k~@0VNN
z+srb?5ne(}K~2S!RFX71&~N?Uooa$GQ14auBX2Lto56oRa#v7`Qa$)8AhZ&yuZ)RR
zL6|4CWKf%r*fLxXzZv{DC+*Crd4v?v2M@6F(Xwg8m((o2O9;y#E-Sg%+OmXaP_ph7
zYgg({wbr$37P$*3a~QvDBmW-6Z$%xH`gAD@wRp&5q1cBMWz<EO%(~L!^TxL`9tKM7
zR#%1UIf{Bps3{neU4ZN^wNoY49r(9H{;}Sax(WF>x)sS&K!5U~Ue)#~S!W!QfL|W}
zDb)##N~l4UHzlQUBxeCD_Ie5ssUVGqe{YhyM@%>&#=0%6=+ls0P8&wmYte>Ll+AIS
z1I#O-Ud4@-2bc_FCZ*7}QM7|NO^h$06-s><pjTa9HI%Did^<3P7`+?+Wl<L0-Rf?P
z!aex!ZnS9!zIUm+@b4a^-HqQnB!@3w_aOZp2xpodNOdpTv0uiy2V+YdpF!%pVD>_7
d;h4Yr_f3Gn`GmR})vkUO{NDuNfAg#S{{ikv9{vCT

literal 0
HcmV?d00001

diff --git a/legacy/Bot/bin/Debug/net8.0/Newtonsoft.Json.dll b/legacy/Bot/bin/Debug/net8.0/Newtonsoft.Json.dll
new file mode 100755
index 0000000000000000000000000000000000000000..1ffeabe658acc1d8a23ac7545ae329814a7e0f2f
GIT binary patch
literal 695336
zcmb@v37i~7*+1Uf-P3dIk)7S0nN2pyB!O&-nc2;;1~v&t2!va1FdJ?Vln7~<C4kxK
z5KvJuAP_-85d`CbB6#1ZC?0s>t*?S8iV7mAD2lJk@B4k8?wOvwfcO93&-Of3Pd)Y2
zQ%_w_RaaHNeB-s2V_BAq=lSO?>ob7-TPwda|7=HaNAYtV)+f^6UHF-nBfh)v_*b7h
zy5NGU|ElU4=Px+xjLn<<ix!-D_JZn`%?r-myx_p2j$3fP|H`um+uJj{hpdlYZ&^pQ
zIM&yG``-Ja)E==GEog07W?2_yEGwnhBd>*f0o=C%T2`mHZIzn{$}fM9ARPGbKafGb
zPJaVQ`QLz{SE;RkzcphWMK}0<v(5zmZ5hkV2mErzY6DmRy5j*~^xuNI=n^Fb{~@u+
zgBP8B=|%9*9S!rvyh85y-vY}zeXu%OJqt*Yjd}Orxfn3^w-&`USUvkZAB2>ZG7GQT
zB)WC6XyP28UhzlSY%5`PUf^2S&iAaeU>iTTU6Zl4H@wrj=G*42mX#?*@Kp8Ng{z;i
ztd_-j=UqVyN~^Rw#5)tcEYo`I1{LZk(PJTOPNYGG5)?9lgXUU@vb~46tL$PiD8OZ#
z+n_|-9;2tQ;p%LQWe0hZSl!E9>w4r20%j8f-nQ$Qk;FE}-;nXVd1pHA&NDV-+{A`f
z#B48j2rf-r8vb;0#a}$Hm_2I*#Kl$tef-t5PHVHrm`>{EZns>?x?>w%%k_q4!!-hz
z<0ecuB&(!CL<#UD1a!Muci_REk|pxp0{Nh#tTWJBg`5|RLg36+$KMH%@gku;6}mOw
zR_;nBA$ZDv8wk$W(3&c2XiF*iir*)bl(rQB(gHx*RuwWC%6xNsxj!wECWrEA6?Be&
z19(-E>Mf;3%J%fY&uk}Yvd98N6<JxQP+nO#WR`K@q6)m5N59-|d0|>!@^%zQ4)``m
z7toQ?7(<?Iy1+0g375K;Io5gLiNeh)qm0t^ZY*qQO@@WpmTxZ)d+D3I6XiX!36=e{
zZV%2-n~7A`yE&i-j%?!gtlOPHdH+bt(7Wc`75CXGsS$QkY7EkKtwE%lV7j8|#Gsuo
z<um0T+Y`k)uD4+)ab5v6MQK(%T404;0(nb|Y$p&0n#Eq!5Na%8ghsd-CO&F5h-jjS
z;s9cg2q6w2pl4+*4j{@ALL5L;B7``ASV6?TNR|$}#~@owbZjWX(-FfD5KrmFvBIt)
zf@^L>`Iq*zozb_$5fD{E(9+34L@@|`xoh4L#kz^Hh+@!k2HhM5y^}#i)iqIX+hze}
zowMy_8oFIzzn!0L_GSRer*B!pIsgz|WY%`gn_SDw6xW&u5~ym);v)ZDV4n1x9)m`d
zF}EVPj`iOSRNKRuWW`JReQ^IORkB(zZrB(+mh5F^z}8x1=gq#cwAKKZL|S1Q881u&
z#TusZUZhdSN*b*XXS|B%X&T8Wjg+-ysiv{7jW)a|v$$;~e^=P%R!Td<bXV9_^vf6c
zw*gaHJ<AGHc~7(V_Ga%L&EEGmdv`T^-`DJYKfUMJ=gqRJ?MPS^_W>Zyk(B2e`@HGG
z*qt3tnsK)<*lC*&BGN#{Hg^)*PRZ>Z>g;eU-ZnG?w^T*9aY1R8V?KmnCxqt1@RZK9
zn-aeZI8FRYdzz3&(qVFYgvl>-ii7?~z#uo2cZ%ynXRA{@Tb+ZD%c;(0M(<|Q=hzYb
zplz4zSV%+n_mHsR{V2Uh)=TDN@S3BjhJL%a$Q(}>`$oq?qo!$-Mdnzfs@ac&JTt3B
zJ)eN5HR&Z)4HExE#y@tJHJ(Rj(jxWAF&l|I6K(dBh!821scm4up0@uf;6pOuA`$v+
zCvQ$7#q~~c(O}LgE*b2s79mRY68wyQ8j-fzPQf*+(0n*HI@XUc;6T_ctEd#4?ZaHm
zm{+l(dA%d+nJJW{^&dR*Lbyi=QJV$7e+XY5LR4(QpB=&rLl}hc%^|!YgsAL7|D_P3
zLWuv{A$&B1PlfPjfath|=Cgpq|HO}d(UVZ1HY$4;NL$QW>cZaj9Nqh=`(Jb)qwc@b
zjn0KMf1~?Abw5ovtSaK4p!;Zb|DEpj>VAgqgVg;Gx(`t|Bxx~+s{2{Gk5KnQy3x6i
z=VH2#r8`vluHA^)M??GRK2F>hLHRA_6^du|Fej*cce)RzyZV->riBIfH5BbAl#<!n
zZ};?=Gf9Ms(PPe{OUir!w3R`rIu+NsX29tww~hY1#qt(A{_Ox6yT{XL93vsO(7F}Z
zjnldjt>c+jY8pL`Mw2wq&04KU!!?%+PmrNDc%_ov<G30_=n5fSLg_r$e--HMh#dXD
zYoS<Z17{b|iW~hLt0#N=(a+;2<BdLQb1ZQEa}d$r<yfAZv>pE%_~L!PCrzuQ{r_C6
zFr<C6D9Nv9Aap@p;#!0zh&0O_xnQA{F>JbuIMsd-t)S0*0WvsdGZ^fmztoH;6UDQw
zEkHh~_vv(rOiQYLpk<!hL2FkJYC%`>6iPKzrRUd>wSlBp@W-gC(JxXLom7ICbo@61
zH(}7zJP%l{!%?1*KeS2qTTBa8(dvkm)dY^O2S>NE#PxpyN2OTKC4JO*uWzs|Sv{7*
z?G60aq~8KC@Mv7;;IC<STQ56U)8=?Ps!5~#J*yXK&qCUxd%<|jpmVx<4w~}fymz}f
zon)jz57wYr?E3ksNm*DIGmi9wm(W$R9XD?-jirYs*x;4%9KQ?9Q0saK5`Hg${U9u>
z7Ae`7G%Vta7=SvEH(TrJj4AV%C?|TtP|j_1shpQ7E78k~l5=o6IT^sV-eRGXkiY7m
zP*U4hVqmWRovuCUdY-Pm=t|pg4bwG`u2pm`rE4`^2hatH+)}2%f3${sQRz^;NW-<*
z?eota$Uev_C8{%TL6t41-7UoQj5`Cx>fvA`EjLCb-PJHF5Q=ISX%jw%fsnUnq4VQZ
zKbz<oZ^2w>NkaI6+4o+EnKzJ12*Yx^t7SY#)=4iv-qPXvUq%8uh$tP@5XJGo!gP_h
zD_IUOpkaf`#1kDeCz~=qMa#JlWR=$H`A#%bgDEYfu)#^=*=`_F!0M|ZODNf+&0U`n
ze2oD_+)oE;tZTkrcYTAdgNXYm;&s~Y=nbJrFjdud7YAg-u%-xcoEXi_X?=Tk!N5F@
zPe8QT5N&M~O&l=U_Xom!lj(8}@GUq3qG-)ygmldVQP8&;L==NCNV?`bQP6i8L==@M
zIj&EM%<UAUu7Incvco`#{NIg$05!<9bHU*RJ;4!VfpYI&W?O6rS^V2$q|bPT!4#~L
z!k(SwdG;6yW$xCup=6IS_9k;Qs$K7fQuWO&p6@}Tt>BZgpbx97&Ic3A!DNzr&P7DB
z$#~liCAaS*Ms9G}sqRF4|3Ucr-J=Imt~2@rxQ}s<_KHjL+n#B$+<Z^M3?nh8dQ+XW
z<oG`XS$=t%X6cU@+wqi3$tj)`VpLTB#}ZsTMX{wpyLT{Q&mM0n6_9z3d!M8ZAag!r
zSN6zbg<+0oXVDEKUU#A^VeW$JHe|9`x8Qn$?f(=3D}DuA^Jj2Wx;?ml-c0Ms=-JxS
z*3)iRhHd{B4ED;oF3<cAok^@LblF{w`3P78`AZ-Ne*17omp7hY4xx046Qz)gCD4eL
z)PWYVYu|smwde{CFD%c5brT)<bIq^7dyfAQQ>eK9!+@yh#X<8E=yagnt=<Z)ip>sm
z^^Umb{q)>|2##U*5fa&zmA0RU+?4^h`o)OE_6tDmcpB2!J_Z|gn)@5^no}yBS?s#{
zDNrwd@JOsCm|r8A?W`H>5Jm_&a~1~>8zY1`fH)~ahy#d|BZN56WR3)4LTe(5t?4KR
zJ%R+K4gLm>fG7qX&7gNgK~Q2QAc{f9FzB~Y(4!0@ia{9IC(gr(Vh{|>iF1CU7=#&(
zYkn6o{5=^G#h?=y)ZE^QqHazhq;A9k+E<XaS!ANfW&?xJA~mD$WC}zv=tKtH7zMqZ
zK}4}fc`LDvr2~lJl{PxYfnBzEpsn8)1H{b5#0Bk#eFwTks|W3;%QBCV(Q8nkU04}B
zPoNW=FEAHu5?Iq<<APt8B|h^<CRyi|i}AWJ<W&uMjfT7~P+l5r63UA{<pp`Yy1W?-
zdnO4lvyrN!6GW$phWt@JP_dbh$C;0dB@H{cM4%H~Dlivp5m?h;lK}_hgFf?T<^zee
z))VPqB5h0r==M6glV~(nLC>nA+lcNYdWG$$=*fmO=u<=O+eD+glBiI7h<1sF=8&5x
zFKCg>%ahDYO=Px%K%f(B6POFe1lBazAot7*edeipnaqWLdcn&UdlYLhuGcq|!kB_a
zfVtHX=%iskYCV`8OY?H!Z3kBfbb{9j%mv#8)->2aN~TGlrin^^hE~O~*ATL@ivt1Y
zMF?>Kae9Oh2N16$VzZN9;X1RIw~AG-!xOkj7-(MApy{1RBVnL<b%Ulbkw(Hmb8eKl
zIDo*^OV+||YX~}<16??od@IU;%{)17!z_Xaon+>;N@w*cbe<~Ur-6ZC7D#;XIhA|>
zPa`}`;o<?Ozo%qABgG<R%rsE*F=lT9-QoeaKVMFmpMoHxMP&|@KHjdKiEtU%2W*e)
ztXfCVcUpan9}5px?zIT|27A>}Ob>Rb3*#m-QEtf(w9?vJ1`f$G7NeQM=wBsik;XIk
zuyU-k$IcSjynY!;q%eso-KkPn`W$u0sJgUWJ&p{%Fd4|U^st)2c0OReuxCp-+J%5c
z1td$uF0LK@JyI79!X86E#5s*?K?)1?H*1?YXdv;Nw!{AVFvtQ)L(?5-wMYL(HdLsh
zR0jX=z{VrkHI|hWW#w4g{}&h)HfRONgylVM*UNgDJ$4Sr2Mna9!>$(HaBS5$J6;Aw
z>L`oJgtt{>e;;I@S1;en%VUblFW*lE%V7DxA*?8|Fe2KMd`%Vj46;)0j-pH1kLMO+
zHW<%SN%N%Ap{S(>YXxozD{vCJXJ$O9SsYaA)vP3T)gK}Me9`?9<O%)8fH!YseG1sl
z@OeET#wS^1B8=R_H^9@C7|&5H*#w07CluV8-10OmhDt|r7hzjUioBHEvOBSz$z6o)
zY4Z<cunqlSQmULmR4h;Tb$fR8vPkPm(RJ1{%c*U9UP>&Nyy=BEWUFb)Vpq2dd(b!t
z!%F7;5f5+2GAJ!pXtcvD>r$)ZN@O3Ru>PU0b6%Zw%vaGCD?N6xwMUa-^I`UqC1;*(
zo<sgnU7m%km7G0K%_k-FG@EuYjb%p)Xjc~|nk_S_Gw2gs>)l9iK}_ex^fD+u>#+rw
zd+};*FyR$DhB|seol5>no>@=nj)?SJS(n_hk0`U!neI{PSjVB(xydbtdE=Y4l-i)>
zB&_96F%&YULM1{|TOuhZX^P{mYhgOdbuY9{tsjteuD9Mn4nB)#2_C4!qUh%hqte3F
zy5{`?JJ#GPFuMjOnl=10{Me?4s_{9hx8c>7cu*6(V*B7iyZZe|7VQ})7M)$pHUyW-
zCW>iK%d}@8ZF(_EVP-HdjrqW9Vfs{B;rKW53}h<J;)4rZvRDUU8D-s4q++~3^S1Ou
zXO&{s+eHW4lf+FjMly_%jEZD0)QXaAJg|Wl>yl5>B6YYNSziPlj{XM{WYjhrK+80F
zDgb5=K;@5~|Gx-sMQUMUczdE{fX=gNZ0}o^W_9Ts94Zfa^9H+7SqA&d#;z{$Q01ha
zIa*7!as3y3{c-%{F`ddo-1A9y3lLK)nlj&yG<S=Yd!-ch3owwl8fT$zwJyP9hcI%N
zw{*GVxmD~G+)!KS6rQDS(J2t3Q+OVUN1Z~p-YH;BGjm>Bxh?Dzu!x50IzP38k-{o_
zo<_A}E#IWI5WW)SYEmIULgoo$k%jJOns!c!(asnAfq9DBIt)3s_4!^*1}_vL*4F!y
z)3x<)f*bnndi!bU5eBF1W_Sf4Y!lJrRzN<~Y5CGFXFP|h*q08~$o8!LXm{3?j<hY6
z-^4yI(^`h*$NpDK9EK)qQYN_rl|EEmQr>2knl#rcnv;m~W*{V~o*(6Bk}9dkQy?AW
znnsV57<xoUiDsUu<ROL^6T=JBh-vRzQEK5FR(ndYL0Z2<TIgjO`kQimx)D?fJcN;Z
z_&Ru46<32(yegtNq$(1kDxwM3t72n(LYs__Pq6c4d`j5Wd8x3h!|^E*j!&qk<QUdd
zW!t3ecCDsoMe!%;cB--IT4Vx!`82v_)}ZU6U$3xe3t@$A2;-?Rj*m%JCa;?#P&}9_
zd4(aV+>>bI1KF1%f#b!)dT<+L+oN7Kcm<7dELrIV92_a1i%l0;6-U>E?H8ScZRHJJ
z_IPd$1Wtw3xjnrFJxohwZd%^?Wb9%mq(YU<?7)Z<DpG6pE+R%T^U5^W-&v~C?(_@v
zX9??0$cp}S8a+}h=+QP$pb6=mp{~&zIg;##of-_&m+p|%i^=GzWQdN|<}KKcq>vF5
zcjiUKv#aMu^2ar-eHO>ia$U!G1uwP&jD+;yN#*ltE3~ouPkN%`li6kLjm$K48B|_n
zF1idl&@FT(M=|Bi?4b6wx`tXNViY=smK)=1o1HS)M8sjPuIEWrSNhktLDtH&I;L3E
zvCPQo+I<JLi#`GhZHVKk5EB&INrg^B+Eibs(&oDQuxq-JnXNE|s|z!zHtcLrF<779
z0QvbBStjgHD<`QcWFfD5He)~;naY7pM0g8LVkNo|M00!?H<=1nYShSSgkD`|(GoWt
z3KOjz$ieygo`4CKzs&3B3?(%mSmB}FB$LNVug(K98dOQc!s1+K#Z!Ej(o+yyhfX(i
zlJEV@DE=^^<wO?C^rrzhg^~4G&EUI5vHy#JhvC_T=d2l?r3f3wvww`Hgh}8TWL+?@
zJy3o6)Y}_+i|KHx82Q^xpH#DAN933lI(n5-<}R)neY!4gC(LX{d7o_{GG21n2BWuE
zN!#-!Mj5Sk9s-;S9GqL0wuTd+mqV~hr+R9K!?XCX!iM9e9wg))Ahdun2WARi#F_bs
z>kTfltM6p#(Q<O6yu2N-Cv06ol~HSgAgNFgSc;S*tY;@dOUj>8XT0?;5Pf(!>}T*`
zA;~H)u;)l7O7`3k7Bitknc3AuYbZ@9hpYgL9LL(KiUGIShJ^r)_n1>`9}(M~2{BQR
z33-K5Z)r9v;{y>p&0K8OwAwEC3m~h_#*@K|-7%KzJXvROPJ?#+h0NYYd!D3(NvBk*
z9;sBN6T`OG7B)F~M+zo*r?Wev4K(9f?>!3(V%7lE<W|BOUvdVVeA#oxj<orPVm<YQ
zh9M>t41eo2cmg~lc&@~Q6;kU=JQv{^-iaUEET)XM&&9d^VbUl{n^LiDXqjDYYm3U4
zdjUMhyxqfQfPvQj2Qj(P_n<MgfEiZ<`^$UlN??C^NGCV_Wdw0av%kF1_F;{|fmX_e
z#{Tjw+ecRhhc}Ap5Z08WDd)!>yylX1TV&bvg95g?^a1D?<MuyIJ$b*3{dgJ|v&TPU
zI`BulEMeQ8U#AzcbU4#!Q1=b~PG0Jrk3!@U*5(lU3Jp4PeL;mOzPy^1W$4iLcH*wE
zosI<8rul-8(d<M(Zgz(o2sgaN>k-4KbgaE42UXiXW>X%Dd<Am`ITO~)Am<?4S^edn
z&Au!TI<!rBS&`8f6|H4Ak3~Ay@sf$rrDPDTfkXNWLg_CIVO8Nv%>|<&7v#oV2sg>a
zK1^?7F7}mNu&(}}^Rb33xEBI8l!YD?YlPP!@eI~b9Fs%MxCqU(ug$Izn@XD5Xsa17
zIrM9EBS?4Zc!inbL1{ag=w@f2Qx-L=8&Tg!F&CDV&DS9v<=tYfKpI#dflQYJyX7F%
ze$+CoOooVeqexqmm1J`4>u8Uq>TP6%&N#F6&N}mh+Z)Wcpl#ZV2a^5H==>BG^vIP}
zPhdlyc$7NLWu)Z_vp5iGdxQ`N5Tiusg7Tb+3&9cw8gv8^&7{R*344(=TN->OEnZ6)
z_*~qenY4H<VW7FBK{H7n2?Nb*XP}WV&}?ncOp-^!Kyzt>W|BM-2AZ)3%_Mmw3^cU{
z%_Ke&w#gntLAzX|MXmJ$dKAUN-p1&7m%n%bOU=(=H7gJdLaty4i&z|3rhGjR9J2Mb
z7z~3<KonOnVWzoeZWM%ZH4_lUKBa~0`CM;7y+sG_a+!BN2nOugB{+*wB#dR5fCOlN
z8VB_qao!ll!Dxm!1;$wv#o3K<NI)6h49LMt#lygo35a6Qn;3-dTZ6D_mI;Vr&{Yh=
zo+%C5gF!@b1s=l{-VS*E)$7n`WCt@bSc=93Pzc@<5^Q@f5mXg1)anjG_WP@mcjW&i
zzRzHEq;oa7K*nnTC%kkdsvcCL_-dP?$CSVN(m$&<#(vgb09H@{)<FPPGXS;_09Ggf
zwoL$59edk5@hjzTZ!4ltk>+npb|tHrGSq9#oYA1(|Jq_MwIbhU8<5x^kc6Fb1)N^#
zRI>`TkZ)aj$_c*=(amKVk^1U_^b&9T#CXh~YyA=ZFHDTA<G%+42C6L&3}RROT-!f`
z>=p0%74Y>3@FTn973&Yezv8D(?Ox={V93f;+OQLE7vlMN8Rt2*FEIeMPXpf1fYT-5
zlMKL`w+4KO0Y^)~rx}3ZQv>c`z_AkWc?Jwc0dHr(@e=Sk2D~f^xQPKLO2B6surdmG
zCj(BFfX^^s&nVy)25gXkPcdMxDBxBGY?Od6GGI6gxQhX&O28Kwuqq1p5(CbbfO{Cg
zdPJ3s(HF3aIsO+BENY^|-;c!p-4Tus|180Mk~o+RJ{|sQaPrqlz^55NJ=5VIBDjx6
zI6C~p1o!y}M~8n9akURnsx^^R^huB7)IP`n*sLlpebV1JwL2NW?nUxJpY$nC?L!P;
zA0h$tNyp)geF9FHIL_F|iIWztNNMIy?Zb??PZW_p={ua-T@2Va3ZPH=8>jXW20&Aq
z6n)aAIAeFiDaq3z4agb$7;#Mgqr^$WSERQ7)p&W<iM^Aqc|9~D8?pR1(1muweM2;v
zhu9vfzeXpe`!K6dMot?`eqAlxG1KK@-Tf_yX!{4_x6+}|e-&)$IZI(zV^6BN4k38m
zGkRDO3-M^8cE98Lhl>YqU?k$4tmpT`Sm^OzjhNU6n@pv%ZZ=_dP$;p@-b`0RZt-Gb
z(b$h_h|+DRh~WBHF@oeLZNc`Kn4kY2GGY5$k%oWdOUR1I<5&swH?WB@4!wm1wTD8-
ze-3?^zZPXYkVhk-?-(qjkjDQHJY~o2ImWJWIMdpApyNLQybghUk5NQiywz@z;L`?n
zPbk4Chr!GDgOTZk(Mjn4nLLwtvBwa*#vxBD^LodBQuq?p)@94s2)?!ipEj_#1LBcl
zlw;-PYn8FY=P2s`g^W`jSdwK>IEYH62Ir@QGY8Rj?78C53eJwyd2)uxq!{H;d-?v#
znZ!crKdqc0GlT50s}WK;(%~Cn53hV=h{QHA-O7W?`8&+B!Cv1D!;kpzpEl6ffgXpE
zE=M`=tvB0<|BQ0?^j)Amb}hMMd)r@u*OA-o53wqR4(vH5lEw2@#{3&&x*f@$W1KAm
zFo0n~_j`U8ZEPSkhqho4wfXj`(vBY=Ghff|Lb%pZ+qwgFV<qYY=E11FEN+}0kv^xL
z=Sb=WHY+S6U|lH}(e|u_b{fDY8k!3AlX5yd$y=Bs_>&@WUDk@qTG?GqH<1-ae@@X!
zk#;DF_9+sx8FK%*Yp!J@<APJdzn)HP2Gx*+xh4wg(IDDi1>|v0_A8@{(b_YP%!hf1
z2z?F)aT$xBK-<_$`kwzp9Zr`up4&$%k{FB9b?@i+@5QcndyJ^=R98xDNECHSy7tos
z=66|2!lk7Q>b=CJp()$t`NDT!;VD+8^!niWKH*7Jce*Pr#wU2DWmF)~7A4`j40%di
zTBts@=XqTB)~3{GqTu-f;Yn0?rYjS*(M;Gz=XYTgBZ*-nZEBw%Bu}jKt`j-M+>{{$
zJns^oM0K}zwTcxAIa_5&p`4DA@Leu>N?e+%{zu3Y4f6ouc@-GwpaPzE3QwZC+q&As
zd<D-o2`5jqd6F2Kv*0OlY2*4I0=B0+5%y+FqTcMIKx=Q-<M{M+C!*f$V-f?UCoOCV
z27e&pEetV;>h_`rb`J@r8D%_YbZ=Ck?xa1sm7Q@?2bqLFCceffll1qgk20|Mr6RTS
z1y-zDr~v?Mi<Ev?uu)4y7bq=Ji$+Iq&^l5Ap>kEB!0Hr0)KhPHX!^`bHc?4A*@RC;
zlrWd#jGS|+b?6oXdK85{kuuG4%nfW#v}N}|8ye==QP~|9Y60gqX_aCn!ACSq+>_DK
zO|eTK=_wtls}0l%S}N_Q3~hWdCEtPWQ1)Hk1V=y=gT$EpW)$=-1`)*|F(-c;1wG0j
zq8KCw<pWXBw;4nfgT$nKBntWsgNWjs=F5bf<cI^bV*4IIA-N~}KsW-T$VM#en<F;w
zBpad_BsTW?C>HFzOh6P;Ftn?gg848VpcS(l_KKze+c6Um#UL?p4~>FgLS_P@DCk!R
zDX2JrxGzG8<1pLMM%8^`VyTvN?WT<KT7~i&{WcG9cuw#J?5(rCF#<V;<c)jZLD1;o
zNX>kTGJ^S6Bj(}&;%gB?9N@|Rl)P87E|||F-hhow4&vJKZXJS>-lRpFBW(vYPnKrN
zEj{K7VS4vR?8Slfz8)dO5vDgEa1GNNqsh-XH?4jgqC;7@oI_*6qgs7ARYxLDd1(VT
zPkD*KRFqaS*O+Op4y3|eV_0~i)e7|?kN?-XE;%3pGG@7c0C2)015ph6AcNS2OMPR%
z9uQSuFIX=5y%Q+rmjm*YYYZf)$5|n9J`~2`{9wv81`<#Nn*S3P5{P2ZT@0d)B+}6)
z3W#EmObD9S7>J_6ITN758%qepFw#AM6DIvcl{Q|+A{@^_ay_-V{k=q^R-=Vw?9n5U
zvbmj=<xG3*8wkQH7{~ksJr4R5^FIub@^URG!rEKv<Pb~*eSgrJT;C3~jb~j5Gk|=E
zi|}0&{HWSic`f)qLjHh%5SVw&ui(gdW-;F>!nQdI(pBE3dtYs=<^M9Ie@I|8+Yzqy
z+q4J09bLF7A-1@$_3U6I1|y@sNB51gFV+s;EYJyX2pjvTG3gSR3$7GcQ!>*BNwII1
zzSv$}^4vGZ^J<A>2iFL6f@=k0ekd>(>=0N}GBa0s(ih_i1A}|K>vFzL;@H6r0-XSF
zu_0$J*eS54!DgQFqEC52oq25Zr-C&9M}hBJ<%>m#OmMTrw}W>Ibb@yYbc0(2CW3bh
z%mp_ItSLD!8lfKOGgz{S?IVZ1UYGej62}g17w80c2+Rey39M<bDJn1eV)>^|QHjXw
zF^&KqlsI;9r$8t8us}EXkicB<0f99oGYga_ef53ruB@jW0G_rz_C3^X+}%K67x22`
z_pzGRg~66p&E!$Dx1o!}wkOX#+R!1(=D?V2pOv)j;PV2V;Bx|V!Dj^4G}tUundqy_
zr2DtmE3e~N9`{NdJNS}7jF$p)!50PAG}tUsUi6t0sQ=GtmK<z{{Vwvf)C*h5*bm^t
zA#02}$OTWDKi0?x>%u{r_LJ1{S0xQQ_?kc`xL=?fd|hB7_=dn-aG$`MlAGOB7W!i4
zM;)OI>viGV62}g{BhU%HD=-&4Ah4#vW_RU9UyK*)!XYtz|4`!C!H)zw!A}Ib!H)&z
zf*%O1DVbTUJn5UD_d{bme<gA3;30ud@UXyK@JoR;4L1GCi@q2y>hZ7`uir`>JNTVI
zC-}WUH+W27B6w6_F8GbWnv$C(%A39zZ|42*m>&NmaqQr6flly*KsWfaz+CW0fi)#F
zMtRaVL7zv&cs?a@?BHpEPViTOZtypOx!^AXYf5JJP@eR~cy554FOTWq9}>q7{wdH2
zo)zc@{}z}Co)d`oa{m%XE_g;@P2(B#R_tKtQ)QfJ%l#6?OhNXZIp#^$V_-4Sbc>mM
z9J|hzv*+0L8<Igb+8C^pN5*9QuOw*);{u)Fd4aj$e+AYw*ueUtDD+Jz7nD2eH&$RX
zK{BD|Z_)ytASKWZG6HjfC$OeurlLIQGgyud`%jkj(J`JK62}fY1v<ejfo_l$m<!qk
z)|AW)C{Ow(@H{5QGbeHEpdio*@&etUS70va5m-|)Ubn)$(>H<Vu`xZ)l{j`VPoNVN
z1-ij}fw`bhU`@%)QsqgX>J!B^%tCC7iYqFs<1`KFn|2o-cCc8W6Z8wr1-l8XX|P$Q
zyy=V8O_uxdF?q`p#||n2aaWAMTu>5N(_jOOka?#sR_^3=LX6jPiDL&V1UkWxz+8aW
z$QYk%8f;*1kr#avc%2x_<6aWS4u%Cf0dBI8F@8^hH4QdH%8R}Ud3;4ouDvCW9qc2}
z3HBA33)Tp%X|Tbn80DgGf?R0N^|5!I#Ib_|1UkWi0&~Is0&5y<Rw^(0V!T+VN0gVe
zlS3qq9ULmq2@V(N28RjE1qTbPDVagzrhN2GsDB${Jdcq$c5tjfCpcc98yqJv7aT3H
zrer+4OP=&8&$y}k^U&0Vv8wZMmiI}T)(G4Ai14z5jRKwEB!RhLgTR^w^CUFWpwFDd
z_8m{-7hxK9JdKkzjmJ3-pCP>L;7ox|aF)PaaJs;n2J=lA)1Xh!z}5S>Q<Rt3O`9Z+
z9lS=M6Pzb77o01wrojd#IC;^hWfzzCm!Z5^&Ti=BR88Y#>ckgbc5s0}C%8~xF4!!v
zrop@*glW)cPMxTeUxjHT<7u3xX`IG1E)rgLfb$J#6Haikz+5mYu%^MhS&3=TXYfj|
zS?>>pY2ZG9Mm|p0G|priuN7W)uvMTFj0wyImkO+DFt0LU8uXc0PE6zBFpbuD8fR!4
zXETj$!pjaW6X*on1?GZ4U`>O0YZKF;&%A148ov(HXpg6Hrl#>~rtv!AWe2Yph@}XD
zx!?+cH4WyaF-(I#vuR=)kA!K=il=dwrtuo4@h0Jgd6PgVxJqCyc%#6Y2Aj2-27Tsy
zrtzF@{tMQ;4UX@e^foxGwtO1{04DMe64-Ly?m>)aw%)dI9~;Ft5X2=DOh$EqMjSx=
zn23$!i1nZbM?qh2*M5j#a}Q72<YD&{yMSBeMnB4z&kQWx>r{&N*mvMmBYI~sGt>dv
z_#8ofra*OUL>L9d{6XY(g71^f^%J6)J`iIz`!WHpna#LpqY3M^Etd64=n^|AXTyqd
zu}L$HZU*aG@_HZv_hm8v9dPqu`0%^ouxmd;g650J!9Yq*$+VU?#*oF`hjv%*c>dKO
zmu;*tR9?$@G?zF24TZLrmxNJ14?dyu3k@f^o6RV**a?=>B(Aa3M<~V9sRupkS$VBH
zcgQWYmuI>2hTPtEyd#t&pzZ0x&<cBu83=c0o4=w$g!_kGa|31E2m6P!?v906rO^G%
zV5%`<v<dTe(q>7Uh;245VejxHIm2zyp@@gsPVYuH(Yvw3?cHd6R!o-5>!4d$JXrT)
zWw_>C-h{|XFmj%E-flPF#J|t(w{dyOTk!nz&+oTlqt3NLn$~1~`RsT#Qg=RS=CA_T
zW2_`0tyCE7lOS_k+V@=v(F#a+{uaIr&71jBV`#f3EG$P`XNzp4a52wQ84Dx(B@yIQ
z&WbR-8#7*hh2xbI6!S6_(%n$lU@nl-ls1qwFj#PAOSt4y`6#jqD<q0CFlap7FUZtT
z3M#57r9DliQN$GoY2y;4Fz<HL`Q<qc*L>rgR(hHQvaY0nvtCncF^o687Sp0deYXTX
zh2{7INL}H!N=)%_`3uTZwWb*vDU4+7G3j~)uUOB&Q1qm}<OX4!#QCm5Kz31vw4jou
z&6>Ap=Di}#v=*@Bc+~~U&gD9pb&ix>T^^}dQJfK6!kBNhT`xHzW!{#mq>?Gz5b3RW
zJn1ck`~|6>e*>Rak&kJtxnbvQJJ9Sit_oorARpo)eD?(ZeE5gofFIkeqpY^sM?l`}
z4OqFOP_pxRgKaxrWyCh?0c~AMh;2R%b;ioDv$%99jqVJ$;9&Q$UEQy~6c;Dj@f4RG
zzs8OQtJ*jMswd`ng6##??)C)lGI;NT!P<v4(z&LeKA05r%@LoTs-(V8vn>n#UZ^MB
zFfw)qWDHj)JCW&spv6Qmaz}n@jPA&fd8Re|G=3n0=!VPh@m#f~ic41RpDMPmT%4+G
zNfpy0IM-=es6^H|cwQeq8v%ze!%wQXU=8dBz(s>6BktfUhB0cxd(z5zsbXc#6NsED
zE?x68fhVo`5a2L|HXu(~1KSaB^O}zd92@k9@mdIlxB}`}bvD|&oez(?Ldw)^(+hlf
z_5*frU%OXX6CGjr2fE!CdrmX0{U=VLXl<`Ao#~NBIR;wD4F7H%0yI<fh7=yB>$brx
z+=FC!sVT#sAiXI8H~hiLQKi)84t8nr^$n)gF@Fdri#miY^Js8+nN+0%$>vPqh&!FU
z;a5!JIDFtFRLWG**jJYs5V11L5gn{ws8UMXwjDX`H&vTt4Z0asF@ljh^2ci$!>-3O
zd;@-_t7$lqb?7=)ha@VSNv>iI!Qm{eZ=Hz94JW&W&%aSGhB1PGV!_DzIg|yMyYOJ@
zB#`dtnF@!O&~i}Z7SJz;oAAXvkaA5H^uxE|Cs{mYWiDAfat%i*c-HhY<Vx|Jv*t#y
zOrkooXG<0@T5~1f@VN*KYxI9vF0mSojnq=39kT>wRI&>N?W3?ogdKZS^>!8ymAf)d
z5F*DM45+Q3<jfhQ9mq~As>I2#3e9<ObdYl}Da%gfgse&NK-E!5VW0<$8)EDX+2VM%
zDWLY_n4Du?5lS>7a6)sK#oTIha3|GNt^6<49HPABjWmZZ{+Ar3Ied)Hri>5YIti83
z=5QqgB33WDIiNk*);mywo6z?<v$a~H0?Lb@3jt~5?t`c7hlQb_EwyDK28K#UY8N3+
zml8}q7`Q{a`+A(+!Z?JjNihJM<Dd?DJ23Y4m8##tc%(OyRPE}+(ct7v7?7gznwLPo
zrapeC;=^&a7vjGId5U1<j{I!XbVvR-fp&N&exf#=;f@q6Bje~?Q)R&@vTEH;h?!dT
zCH_Xu+CkiZp+VqXggk$S8zYFeXl%x@cHub+F$m+&RJZ}qaAv^=uPf{B2sa&+mYgb{
zvogmqh%FeNHJ=3+X~`In1`ki6`M#f+ix3<(-#PICDVA{_`)8A!WTx}9Xf#a3=<~6b
z*`3UWmrvxvgXTO&9Uhx*pL}e(9p{zPvGdAlJ+BOL$6Mqc?WVSj9gMi3Vy4!d!DQn(
zR5@wW*+h8yIULaTXtdz@FQEl@MFUza&(UZUw&d?OTJqN>HQV8jPC|tP+dCK#v67Y?
zOX7tFxPLbH7vb<bShS06I;s~d(#J()BQl}cYV$q2zHfA`3sG;+o4PG=9K>3TjE{to
zJHp4uM~1cF8Fuj#wWW!c7{*6!L&;UFoAsfQCK%`3Zx5gI0qdh84!~Kg(fcC?Vf>j2
z_bJewi|1W<?hGN_FZ2wv!C@d=ISb?C;IVKGt{-j}+$n2r0|ShMgXQ7>z!kP0PT!^;
z{3=bOhhua!iAI-~J}6>fiVcyGb#_~$GT-nLDs$XAi^h<!5+5lXWjH!u635|XlThJs
zRA4~F>ctO7G9Fz3ee%yK3}&3Z!QGtsgL{`>F-6bgaI}!=#T0=kCvV4U4kc3rrWv?u
z!Z@?1Jx}K-G63yq_v!pac<gD<SD!@EIpRqwChB%*((D@_9ts<F7PsK6usK#rUWf7C
z@cnfY;Yeh5==4>W81#rV^pTKLzf7t=y*s99dWT^*Zo<g4PWv|ANAi-Ub*1}M1Qm9u
z3)N~aq#r1R6KTv7o6M1X>2^(fpooC_LY=H7$l`#8=CL{g`I(nz(HET0zD3MAO!H{f
z;a)5P9JAx%Uf$fz^$W?9w;1#6o!pxX^SKv0bO+(etl0)GM6E$9vdTY(bJ3bNC~nD`
znmSG%+^39J?40v^g^{7V@smL>B>B1B_EgK&<7Y*OO(z%@TO*RZ7Vf}Ydj?`K$CX?(
zB)y&8NRQWG7EfL|h~FhE3;12M=2~cg-ZgK8V;FB3=vz8i!|%z1=MQ5?8xiNM#ML;(
zGY6N^J$T?SPLvS6SrFBc^;ia#d)?UuauMMGGV6e6es^LRr%bG38jdm@xRi5v5gl!C
zVA0Ylw!_g$$H{QexaVgnhR4Ao%B&eiadU_@KSCK)+7|rxd6ZjX_zutj`9mOAy&dj6
zc69cJwZ5hq*D~B$JbCCfafCL54cSC@Dy%zE8)!~zU~b4iN;FmIPL`7@01lPsAefy^
z;SQla>Cy@lg(tGf)#tM7)ml{^jPg^xCo13SU*N*NPt3(Kazkf)Q6uD@2)TQ0!vP0j
zl&V&ff|%wZ_u(^|!o*aMf(~Mk)`P;xPG{|&A!#yYRzhs9b$RlA5$Moz6GH{)gtZ2r
zW*Hg17upTDW?DX2^Xnv*V~al9gf=dvO*n}W^9eSQaHagSaOXq!N8oNS!u{3S;glXo
zxKr_R6K=>WmW*RQ4lXJyhfNIXxSJhIr;Uk{=6B8pEKG0Zif+y`qnMP3B|Ilg)UN(5
zQhz-1oKe`DnYjL|>pIa4Wr1m5+n%Ep%j_i@W540ZK7h@TBUH4q^LDl-2l}G2)~1e?
zhOm+U65HRCo>>hpl@{50TmB|^xz<0aeEd1Z4ePcGERU?83&#MSr6HW?rhle~@uqvG
ziw6M)P1!ibGe*{Xh)?Tjs>dl_I>Z%GS8Agp6TX4jPO&oB;}nCzSx)g3jFiPa1``f?
zf1H`Ls~@34G4sbI2~W}TZLHv()6~n#Yl-n3UpZjC(zV`+UcnbVHwt?vt~}Xe<$mmf
z7kib{aqo%F6Ca1T7)Y(2@cA$N!?WP=uw3>KJczdD1|ng6!?Sc{&ckBaUFuo0lOd0Y
z=aj(_4;k5e7>lh|(-4#ohoIH3Ve>Aw1!tf&KowdK!rDE2Bm<jJcClUT-H9VsWVKJm
z&x+_BT-+#YUC!!%!it$hDW>uy5xz}>C$yQqM7K`ixm@N+fmOPjB^<s<sQv)W@TC2}
z0C{-^?QpKH_GiNa^d=Udw1;0qQB#$Z6wy$<h%N!yd3df0pVI+l4H-o=h^uRs4jw|q
zm&+G5X}vWX_-~t-&+;{qW}9;ZE!CHjLoJ-aLQyT8IH`Pw4*+T!#TbdWZ`fZZtktMe
zunEI=JX*}GlZ$vq-;yDk9<j9{eDX<O<0a)aUU`3P1l*5(KquDgHN>oipT^PMk&@%x
z)6R(sw+&AV@wyqp@U`3YZ{Yk}QIu_|f&NKTWqc`XGA^>^V(qQCdJw`TyTTeUvv5tB
zx98}>72Z&xsPVdP4J0m<PISx`bh!ig4$WzUo>SaiZuO=;hUR2V>Q_S*qjQBt`&}7+
zs9@Yv6?UyZx2x}>boxpl?2I**7aPZ<|J|Rudw*pAor?7Mhv|zJN^{}mOJVPZo^18d
zTm(`r;UZyth(vv>dfn6YaXMyv0)`;guqM@;BiFqd5}*aFT;{F%x;VogZ82iZ!2>Hu
zo(@2EW_S-}4dZ2obYiP%ZQd)<mgrmH>)`n-D(T&L2>Ebb2V2s`jV;>NFjs**EVI(?
z^02sywS3&jD4ugx&hd&Tt-%IXgq*SlJ1qdu!Ha^=i0h)2vpiUUulBHtdED@O1-E65
zEpSZW@LS<&DyojC-+Ugu%)~}K{5ZQFPgiuHNpp<U65AZFaJu;nChJ?UNGmg%@}9}!
z+4LNQIORRUk_u~1xQyCdHFZXV<nl84?H3mpSLuq9#4Y-bhCti#Pz>mK!&TvaypPsZ
zCDKRlt;}&Mb8}jSs55Uy>zfTkZ$^i&CV&rDNhVX&M~AR;`v46)?c8pmvx(vGRX|PC
zx%m=wF<T?LumN^qgLn^D&zPsW+uY1C^yy}XizkK^a0n4vkj>9%Xtb9uLhYJje5l~2
zvdrKxUm}&ttD0&I)hv^v?@Uln`SS?j(scP_n&a}F96eHD20NRjDPJw`J-L2UW!tM>
z#8%4HxmA2+7WH=QPcZvyl=V)0-EW^Jf#SVQjZCz+k-omQIniN`Z)xe{FqSrzMfv7*
zv3;mhajmE~l9N-?*f26HLP|N74^8VhU5L=ld`jt}K|yKqxPPGfVATDjCk+^3kC=>i
zhA*z?H|+WAoSPIhMJlmjPrG`1w)X$%9ar~6Dx+OjhJr>}9y$WX6<?o(n^h<!9`QDZ
zz`?tT6V?jYrKn5Mz8C10MTP@~Oga(k>s@mR>NM~3;tWPMx)kIB>^8T!Y-m#)z5L|R
zH(?BAa~~YsZ~DNqas8LFpCab^=*I1KN4Dm?8gB%4A=iX!VO7(x{t#*)-F&WPpS}hT
zWf|LcgIy4nby>IiCTOrvFOh4*9K6$9iX8TrasGY2Iv*av(aP#IR5VR(+<i<l8}ryh
zp}(zJcL5jIB?#ZHK!2UV#nuh-N$E=L9*tZ~w`SeU@cS{}PgQ0?g(GtFa%T8RuuNA%
z4aSAW8hcFQflM|z-(Uqb19@@zqd1ktLwAA&X5-c0)&&o*(M)ODQ|maya*q*?%HkS-
zuREQ^&Y(;-qkPLJWYfJv$l-7<RLG1ujHN##-MrS}_n-(<m3gk&iu@FpjgA4B8h+%q
z&&)6f<8<ZJNo=}Xv#rfKY1NX0PFlnAG(nxu!>{eeHE6d-(q~(>INM&VR@%esJTuvL
z6^tfko!unq;>A6`b@&UYkEu$*J#RPU27iaF9gz!fGsB;TZ_4a-WILL(*HO=2N0hy7
z%-*w@0%jhrcF(e_|Eg!NL$fz4p1oLwidEiDsl1g;wo^GVUv)k&l&{~R&7~^c?&jjM
zO(Q#=p(H5M?*W^xoIWXXcQ%`C&QrFYr)-!fW%BUc?ygvC%4Rh?U78&vfjy7Wwz~=4
zg{CTfR9BC|XDht!?re8qXjZnTE+F5#WxIQaHcS$6vD|gN0QJvW4n-W1e=)UPdI577
z`UU3xL6oR|I-xLhMkvh;GUlXWsf?LyF05SJUTo!>l!I8&=A)v`H;T62p-qa|TxH8>
zE$?FPbzqjc23A~j-wEuHmbJ*H^;fIE&@y)!yx0=iGVoyJ{<OXHF+0TH=q$b83Ei9A
z(%W2d9|?0T+!@0STFWsr=2=T#AC%d#I~a<sld#?o&RaM}H4WHyK{rG3YQskwQpQKV
zVrd9>+Ld8;#Ox5Pptyw$t9Ydlm25ox1%~0!Jc-A^d*aN>&QzrcxCY~<HT)QUu={Au
z9Rf>(hYe%xyxD+X63w+fA(u18a^PTFCP!a$1V`!SOqcP+QV9%KM!eNkPNu^(Y~5`X
z7p=H-?J*>Xcos&4YcrFQSD96(2vb!%YPg59_{pJa7(dHdMw2+Q+h~)6ZN1^{53WDz
zY9O~;;J`7C!DC%0Qy9@TK*!*!NqsNraQ`hwaeSAWODUX}#$T17AS>~L#MhEt{c<wC
z4XaXkx-J=hLRml6wc^`hpn8>}P#Q-)dpVLG;JRD5t&S&!K^&_q=4g^MPcX51BzylH
z*;hdu94m7x_i&<BGc2`nr<-ik*%Z1R*wbfm0q>CVsaV2W*~Ya!#9NAw5xktMdzcOf
zLkA%Fs8Um`m?SWrFq<e1;!D6wC4D_l3tK-Ey3#`(1<0k->k4whcQ>*ZMvQZmVRTMQ
z-jc(Bypq*ln)X$CI7<oB_%wseu0ZnTILR>UBn^Gs{Tj}rK=MqT1j*rSNJzru^Z?l8
z<Zuw)-iEhsN@`AI4d9&SY-o#rZskOtl%F%W%$Yy5pZ22zd)f2Df%EC-r7IJ@%_wd&
zR>rJt;(~-LdZT>W)${6Z5dts!LLoL#2&DZ|<hX3I9EuV-gty2+7v#9Jo;FOQx?AMH
zE}Bq|jW1n}e@)g$h!Q!3x5z;k<d{FZp^qc#ZppiC9m4{^pWHVTzNEu-R+Dxg_vq_C
z^Ls31V86HQ*yRljxB_lFy6^Z~4f9F9ZO9b(NyFcP9Ie}&ifrQ24Y`G;j~_kP?fbZ9
zN_sclQ#`^WI=_L?a^n3M-q(Rvdpb3Qk4VkKurA->8haEZ{^dxuiT?PI{!G$ior_e(
zienZc9`F4S#<DZ`Sf#|#5mi3xz*(vWyhDgx=bHs`0iN<e$S%N*Kx7wsPigk<5qYcI
zpypXE<9Updy7OP&Q1zHOh`cqP$)iJ|&!HsInY)Y8$KXW_29->3wVdU^Id*|g@K%91
zzb>$*!DfHRh;t<L)z1#q75R!VjSYnH#E%OD&Q&#_EhOZ7h=Uw^&20#^#~_5e5q-|n
zplS7rZObs5re^K2KjSx_^fowVH&F>b4r?|EC+6cQs11wN^?Y0}^4h^pflhFPKsR`s
zz(jDPz+7;hz?zbqb($~w>gU;9xtHZq%IM(y0i>*7PoSdVfb}Ak2(I($c~hBQ8<XiK
zNek!h1v<ga0^Q(U0u#Y40&~GT1lE+CA9SX!>6@(U6GD}4X0lNwTrgE7JW(lKa{)=w
z4yaXFMIw5<luij*R+VM3RSwoV<~5QtmKjQ4$I)VX(>ULDxOh&(`w49W%QiV1evk+&
zE>g%-Wl<?U&QkOeT87|5e*J)=-+9gY&B)>PQKpNlAE4YVA_C&r)&|7c8q@7<qBA?V
zU7!=ZN1z+rAutiVS0K(F?-EBYxK&_H<Cz0h$@Hm`x#m$o8e>Sq$!{9RmA?48^x2&)
zSl@6BZuOPRv2f3VIGK;MyjMpQ*kPhudkpVpxc2-f0DR^hYOw7wDj;F7ZZM&fI5jGt
zt)ZS>=_|F~x#n~#QLn|oC+c|z^O)!bmni*_shCUQjVdM<rYJ#ib=X0mT}A$VnJ{FG
z+Dt8`!w-l`aNmMJC-|U1+_xYw5qwx+E_lDdnv$FKsyg~qb=9!%)8acV%vZt$6gWyZ
zh^33|8aUr)2lohc0vywV-wp5<888uiOkgg+yJ`4qO3sg!vt7_Pxm}#0shZ0)O_$$|
z&^p%Ji_dyDlnJ{<b#qeKXn4uUcE(a29gKL}VzPcpWUzzJ2y}u^3v>g#Lx*g+0Amql
zR5E_joHEikS;kj|GF~xB#sU^(r;>3jCgbaxWxOmVBTjTd20Orh06-_ek_(_4d`Vy~
zz)X=cDj7fYP8sP_8JotgbHd^?*d7zt$@W-!_X$5c_^LqMAtBHWu$YB(a=}*w)|8AN
zlV^JLP1eZ`p-$eYI&tKcxwL^|8!mE>l^c!-uujV{awqD59fQrc_4>`SyF4c6H$@IR
zctD^Nd`qAkd|O~H_=doml9|I)PWq<M%Z-s<u3~~G!fxom7@#smy=s#p{V2i?Mpy^)
z6>qR$t+?8;Hql-X(OyH^EAUy`608qYRk&`%DD4_yf^WA=CynDsGWd$IaiPZD?3#-i
zxruKz;(Hx~t^~I$$hVVxUk6tdDPnwMobf0lJFv}>J?w5qpX~ZFR!Cn$U0)H?_4h=V
zcJO_HPVfVPZt$SMT<~3iH6=5LtFGyrLf02XV&A}Ip}MqV)=s0kEfMY8NlVq?O+u);
zH^7C{hAO+Nu1P%?M{#c=Nu&M0E|#w!i`;hb6M;_fGl6dKQ-Qev+n(6Al*}BV`J!)%
zd|eWy_s&SxE9<hpQDhZCqS9!R_0lNrEzPpNJ|^q`h%9#SOMy=CkU%&1mB3u^3xPEy
zGcQ+J>6=2<*GB2x8p-;mx~y*&Sw)a3W!)OZeGlXAYtMJgvC|aaSVVe9l+0E2WZojl
z2#+Y4rkY>VxEPanF>)2@*hX$sG!FscGKa7{#Ey9&Ub6#6L`~U&1TYl{t2G<N)xZ=&
zeobj@tEcsWsnbI5F=bkpg=t+gDJ{H$Wm>|osi?PWTG)EVq|nYdhO)iAAy(FpNLkv!
zZv;BQqXOOFw*qs)uLahW%p9p@P2Ut{eR-7NT}+T|=vv68-OqJ!g_2BJ2v=y_O;dG0
zuZ#HJL%!^Oo<?V;-47;inJ7}kxJe7Ik0MX**4_{WeLRvEha^IIcZ$3cCt`b|Jr7Cq
zg^PKcgsNpry}G<}h<7Ao#>p<VvX|4S5YCp}v1VB5bmX&bFv47e9Ri#)`kx`QS787^
z--xfL6X%*M>1e7oZ`7bDpH2CDQxxqpk#cTeCZun^5w0jwl#3?D_&7Y%jA)t|UllR_
ze8l+eb;j?Qf^k#iw?vV@$jDOnT$!`!n4ET-#^j<?fzgLbOk=bZ4$ymvc^b};pq6`u
zQ)CCeF;<5klR9b#e-h{fe-P*fe-xMq9v7GkelM`5<OVZ8+7t9mQI~f_8ThhhAZj~r
zilzIc@Uw%z2y}v{1-ijg0&~Hi1=f_z9IffmH${3^N9o<i^w@T8qPAt|#$mjSYebMK
zOYs_w8;!zE1@YF1@BQS<wu7B^VfA<yTv4QmaZ}`LqsW&qa#PTCQP4Ldd2gx9`)-j}
z;zVpe67M&!XIy+D=98%X17a6EKqgTKD(#8yji;<DSP_<QnbvCy+s?NcU-~!B=dsJC
zKA-=)lz|=mL!c8pBhU^0DKHT{D=-)QO<+yQ4OW|2H|W!{YHBq*HSdw?n)LFvNH5=`
zNYwYOb$#QE<IMU#D~kMJJwNQPuZrd8Ig!&2{v*%{{wvT8{#Rfw__x5Cl5x0XKIqeY
zoD-G<uWH;VsbN|(LPtF0Bng>4Maan#f@y}tY0CN85`@C|35Ac|{Yx2|Paq6yv#vQ3
zj%c>YlJ|9afo1s9dY)N^Z;9m@pQ?gwWCylDCukAq29Cf);0nwI&uiY5+#IKQr%&^a
z`r%mI2et#eo#Jjw-QsSTm&?pL;|=hr=M(62963d>xGi`bo>a-Z=3HlmeEX&m*y>hN
zUeY!1L4j%{PwzfRN~9z{ku-*Z$;Ar2c4nhX(8mWvX#g)LA-gH=A`vu#xu8`#)yXey
zt-cSdbfcd|W8o*r1&{DQF#H+^n-M!591L%*J{PBAsBQj2m4X(Fp>F9+O!?`+m%?$)
z9K(A8u8om^O966|tvINslb|R5+2-RYGTxYS)126iDV##t#YKUnQwSK%E-p~Ey&|Kg
z9$nC~l$^f6(e6I{1RNuA_o&Ro{M(^Lyk9e)WhF6>QRc_ARJg!PeW*`W+#@Qs&AUL2
zQBGLMMaKRe<g<}Gy4vbfs^XacE+hzaZ*LrZlA<55XU~qIt9K)h{(GfVnL{V$E*gyj
zolrlgQ#tr+T8HffVm=;x0Zim71{x>yozmpnBlOL@2zwP8YKGYf7i`SGp`Vc7>QExe
zb2f#+Ur6p^<cP;8@2iE(C2=V`sXr255m8oIlG73u!xV9avQ(`zQM|G|CEU!OSR)F!
zXoUfv=M7!~d8S>XjpPWIXYt|&I~Ow>{P*FvJSQHC?^|l1guyeiIFn!Yklx?H{bpCA
zJ!=FXuVgbBPhREWa1VL2ju2jRZm})7L3akFDhu=N<=I}^e;X=?hNpK9y7@MIE4r|u
zJ)InSG=*FGp=dUgV%z8kz*pAB<vXg<Zj#YrXtAQ@5U!0D7pNEabT{gdyhj>>th5nB
zgK)RVAe?8~S;AOKuN#-ewD~(50*Vti5b+)%y!;ZX@VY_`2D&Xo4~w9RKGHU=^(LSt
zi<cbWNe<3HM|9_5ltw{nRp|kI;*V`m<(_I8Qd?t<J);@sir~IDx+Y>drhurJWJmtZ
zriX|af%qE)d6Of-RXE_L9O9F%JeN_t`6}RVF^bB(2cHA{8RJAzfamNd9<dOQI2~_J
z=wiHO{x$_~&f=PR$0DAJm6#~&T#tcIN-UOP(a1zU;%PacXxB1cSke>eCi+i|GOd%~
zP<*sA*V>MHm0%mdJ{2ip+sM}ga6hq!0nYH+eEq!rTy_ueq0VbBz>>^+(y+olON%|$
zGt3G(*oGJOn-4-Mv2HO&jymqi_cO1dEK-&7NQg<uaXKZvCB)!1{qfxCJlu{_yi+vI
zFD)2c_)GC7#?o~amv>&upMy(w!pcz0YB@~m*v!w-gq*x{&AI*_Y!2f0D|Akz6?+By
zMw|3FQFHD8-!z9!fYWJC`^$5p^j}DKwxOyjX=v`lU|OaMTj2n>3t;pk_~})mnf2*d
zSXJfcGEsN5R?cdvrg7>)d!mn{cdS<x4)w8W;Z0Ksz>nk6`bm6yl;_=$?WOX(Ih-u;
zd!X6@)Z38G1+@SAo^58;3%B~&>BsWwm!dFy+p%c{Rx*VpAgO<{-GlZ=aW_3#%1T+%
z{IM?&-|?*I#U6`I%ff|a>^8$|EFA*$1(59xI8NDi&i1s&lQxSkL+Tn|fib$g4?C?g
z1rsg_Oz|pA+pO^7ID0L}mE7i)m`9(F`{PhOO4<Y+b2K{YjEhUraXM5pmM;$6;^9n2
zX4A=S4mJmE+Q?I+%w4iDvbYslNN@PTEve2HXW)NiUggLUGtKXcJmg&#4!Ff-vCQIS
zS1hl+ltJX%r#Y`-Rwr{{PAPf4dQhUMcLERgbq4|B*hARAIGbn6on~%g-s)L<Nc$B1
zmge>3IhMfH*RgMKeiZf;4hIv9Z*U$D!*0?yI48!#hK9^V)5U|ui`{6hd@q{juXz52
zL7?vWciaP0$}2U(aB!pHd6u4I?Xec{bNqXdDy(`Jp3<ij>3@{C(ETyE`DF>%M2z5n
z9Db#S6pZB`+yQ>jZ69>}X6y*vDf(d0K(2802cU?>xcChR-`(3sKY`e#SUlF+Ium}E
zjo*LpKpGJp3g+RbjxkR0`^Of#E4f;0_@8jZqDN71d>k7ws`qo!QNgB}t)rg;UmT=b
zj0(a}yk**kKL$Q2A7@oE?P<R!jUI84!jK<%bh-X#L4ZrrvabI*xYB-}lqV?kKMz!Q
zqP!FzUiku@7#bui64x_d1kT)xpGtcYd#S$!mv8|&cF$x6zWHz)zT1+7w-4T5B{lbR
z)Bb!W(v$Xk3EPtDkf<q%xtizD9?qmHUMh@Qk{sfEb3BWmsb{e}l}-6`!ENAWaCE`3
z>Ydc3x+UHGzHcTb8RGK(mm_h@qam(xSW|@MYvSH8>-$4(g^}e8>WrH34W@{?fVnHc
zF^7&0i9u&Pqx($AXf|2W%2C~|&$^(x7k*@92vcEQ*>OMWgzWho{R*;x_w`nb0m^#Z
z+PlA{4PLl!wZ3*%OGe#Sv^;QxEpFN%BS6Bi&*GC?@<CyCzh$R-ew30YpBI+R<DTD(
zh9pk?lK0&x##TFtuh6k6Dy0&QK8g<o86&-#jsbI!&J9c_>^IQG;`=m5GXKZnr{rwC
zKD@%T@zFfAQnq?~^gd)PU}NO`+a|n9w;feltG7Z@{h%($8HTAkn;uCSyR<Fb24#K^
zAvwb#R%gKrxt~${%EKTZUi)F+VhaX?rR-TT$M`A|uB>$Yeuy~wHF$u#AHY0|{8ezi
z<k`BOc5Ft6AmxBdv^3*f2i*?7Z<l;CS=lRv&tHzLZv`G*hkV3iPr&%o1^;Y33-Bz#
zgL$D<ep#_oBq$XZD4e69S3yC+d<FOx45cm6ZlqjEe2PSSP-qCo-6{$gq0nVdM72#u
zr6!{?lTqjoCi3c-jOuKl-W|$?($#&ODkl5yJ~<^VvKC5xu#>*#;F2iOB@1|9@6jO?
z3x-jx6Mh*!Df*Bld6)w+pby}V416gZc^*9sEynBySB$1Ty`CCJXGtr=T89U3`q5@^
z2?KDbHWntu)-XEX*W-B*56)m&T?l(MAYO=A<Ds9p;W_xR&HhY861B&@Lzwp0S#eH4
zQJNF+gJVv~m^j*#nbjaf{cDPR@Z`vCc5iCt$g_K<3y-A~>pr{PGA^}{0Rrb`a6D!`
zQUyYK?#CX&HfCHzfzI329#}<;B-MP1@amL7C(cr?e_Gqb^54+oWR_SZVL{ErD`{tw
zW?d66Sn~fjUMXBxVfJg{b;kddSK7{|&Av^%a{pIe89SRX`!w<T7V2P=zFxQ>TWy?=
zSrg}iA-Jg&cSm49HcZPn8Y;bRHN*?B&Ca%&y<aM;I{SK=mPT<CL1t^I<(2IJPHb6t
zNi;@%uw~%`k^3Jl3-fk3cI|I3JTr1%Z7=*t<o<=d@VUrcbms5lg!G%7`EOD;&&JI{
z#k~oN3yryNA@R)cZ%{1g$sd5HHR*E{?H%e)`Uk*K0fK{ie%vm%<Z(j0<ZPRTW%0!W
zh5l_FaEyCLB3K&p;_VDEm{OEiVa>%#`s)!iuprjAm;=!NtiF(eqo=lFusaA8N~ctL
zAH7<L@wq*7^?@LcahS7asLt9y87$h9J;EhbxghBu2zF`nkqlbQ28dBP!mciv8yX3k
zk%AsdlaVPYrt7xO=KLJh$PYuz2)if_VIh0}Pz+PUccJ<@+p^73k8VU|h^pdsq>Ori
zGwYoS?7rR6LsyOt9iiA8k=nrCV%3a(4+YZgrL&%V1=E~_Apd*Fn_cyzOyL87?}j*%
z9rRKZGwP*oXs+BSV06`))!jJZ@Nfq{f|<VnBcXO*C2Rke>G*pIl<Py_#{i`Q!Hp^-
z?q`6brvre0+(-Elz?)H@#7<E2_Yf!{qgU>D{tuAkwytI^9YZbs5CMy~GsK+Cl6sA|
zt-G1*Sd#rn$QWWy0ok^mW}4$j^JAf5h`}owj3m{MN9w5-=2;gc%}+rw(H2QqpM>ni
zX4zTQ@gdO;6ih2V+4wY6TUm#1yG97ShRKZEVaREfC3bZl5|GkTOHfJWf-f-&)%bDz
zwuu}ggm84@9A7t)BS|N6#48zEW=?UfITU3sZZilM-usAW{zN?IX9+pp58GeM%PCAC
z3ftK~VJS$pf+}#EW?U7rJl?=g(8nUX`uDo@N;Oe%#~+8S7*lS@F~UyJ2RI%+Khm(q
zoy@TlGFA?Ad_w<cDC@SeN%=pAqaD2gr(grUcJ;10pLDEfbV@bwV0=CW8@Q#DRsGl2
z%er-*z`D-M0r5sC)ZLuvbqBS1q1w1UT-h=gRfrh5zRkP?y;goXayF~j$XO#8TN$w*
zlr7ouGtAkZlcNtH_E|3&+npo?F)<;Qg3aGzY#PCFAyRcA8sW?Xg*bXaBMXhtcx*c+
z#3~Njp6+QG^<+;=yW>X`zksfqef$()W`rN$%XGV4?rw-JfBa%!2CH9{)^vCyXIuH;
zbaUL%hd^T<CgCOn0~wWKcTd+5e+}Px4Qj5{pJEP#!od=kqN7#+x}GiePACY<sETXk
z4b?=}JU#`8w=Wn|VeY%D?u0isA?;m@@is&(ma!BQo(=vw^fWPhv$cF?Qmjp+zd<MK
zm7{f%JaCB4(<^wh#RP;$VI($rU(s(@_gWYhM2tx1rq~kerNRMB%-0(q&Lk@s&1Bi3
zUf1`rHh)N_U(&B*@xt1@Ue|l4uj{H&Ojj}!YW=Dm$aYxo+1#mR={ngYr0e`?B(ld%
zi%m_Wq2#a)uoI5jlyKr_$?Kf@7e%#jMoz4t6ov63@gkfsWj|bJ`q7C@r>@`9Iw)Qf
zrzY`Qcbg#I)SRMrKnY~ji4c^{9Dw>Mm2?30^TvrBr>viQ;%MSY{M=>6**L<lZ?YkU
zjp&)Z$8|m0<vCPY$Ja4j=H(m#v}9o$a?M>T9!GZ1|1B`}WjjF?{70FFEL;sN#AyX{
z5QN1IjMc{*a<Up~x2D|iF=w#THe_3;K?WEySw|P+Iu*M9@0b`){yDnzwnm}<d!VJK
z&y;&S>FHbDq%O9(g|>&W@GCX_cMszT1w@vbo-d_JVZXlIT5yck^?e;VA&J9`QpHPk
zf2aDNbNv6scqPjRt}Js5>|s}L**z>!4c!4_(01iFHnQ+@3TR6$a!E60&*&eh+buZ|
zL#(7OFyTz&v@|@(smn+&M8lP+(x9vYZkN?lj#vMNPH&TRv100Gw&4~Z*SXny8?MAj
z4=uN=Pe!?DI>trI@OXUERP9PO#PCcP?q?z&d#nw=g`ujqK7|A>#zO;fEuMZn89ZD*
zh#+<kxV|14a{c2_ElRi^6&qg7QFJ3radU9RQy-3!#zT%5>N)t1OGMQ-*zd(I{AL<V
zJA#buODz4ICW5%;v>)Jmpa)pMkAXN?2uqQBf))vMg53n>f&~I=8f;J_x&Mhi-2+s`
zD>9hHnF<bp74fd9Wc%E9qDf5=_?dd(#j~(CMPj;POvnFWJ#>nwKbk4<$1?@~q#ig`
zqQ9tzPLb$+^+3^^o?XUzJKmRk8bPv5hozjl##zecUTmp58KE#meccr1__76TlCr9=
zS&YjpQMoXzOg=Ek`pR+icBD61TjoIuF#IN_ZktcRW4?zUypmvzEDZIb+<7jbEhQiE
z#VJhLW7R386xLUqZzOT=;O^>}Gssx1eG_73C05e)kBx<8z|l2b5@ddW0KAqS$>jGr
zA1l<;)_^a9ZrZ>v%@p|gnF9G5Q`t^c#$T?7Zk^?X)h-cLJJ<h8J^02+dahvw({%z2
z#;!d_H{6w`s3>2kv(WZ+6pH1JSo`Xic4P-j1Ui8c=msT$iC_<bxnQxtnv$E-wY|}&
z?QN>ewV(hr>-}~g4%$PK(o}T+Qx|cn(0l8lQ{?V*^+42V>=ZqhX`JfBm*L$)A>p4#
zJV<(Qnubp5!m&<7fAn6~;R&jDWKEUpsa)yq#)C1*!lavDGnE@G&j7>-Xknza=HXd`
z=Kws1<2eq`NqEk}gO}jeFl<{(^<XFbS220OGYDPzJek2hVh`0D3^NJ;oaqAe0HAz0
z-iwH3BWa9_tLA#z)h+1dZuH%<*N9eh!hgLa?(IOUZb|r8PDsh!vRL-7ySoVSawOqz
z6_PD@35k8}?k+-n*&^ZB6c5Wo@PzGlI^pZFhcHdrnGxv}8I`T$G1((gp;JWjMbw0z
zAcDSb2>sfBCBptIq5W5ki@OCjQ%j}G-9^~#+F3W@>tUEstUm~UJ<HRq&9fy}4N=`K
z7$k)kp)4)qbh!xPUbGi#MZzDYuliB+md=i4=<upvgwvdk0>#TLj%R0}mh6hPw*hHK
zb}%T=36=_UgCT*50I#($-r*c7F2<rG5iA!+E~p5sDFXw(9P#K2cfD$Q2P3`r#nKy=
zSaz^VpcAYX=mu*9=7PNh)|AYgsa)x+@1@nVB=3*$+)v`z!8(CXuvVZO>@P4E>?^RQ
zWacd8N#FE+#c!d<>AT6l&lvjHjG@oY7&<;<=<jC?{pXCKf0{A$xfw$rpE2}5Glu?g
z#?XJy82XzTL;p5o=p!?R{&mLCCuR)&-x))HHDl<LGlo7qW9U;ehH|^+^fmSmGsgYb
zjG>Rt82ZeNp}(9l^v^Sf{&&XEhh_}@%Z#DFo-y?48AG3+G4yvchW?`-I)&}<^LpS^
zW`|ub-ffdiFFodfO-y3au-|p9U!mXS{<=xF1m2f(9}K5HT%*t9S%BvNJcr{sW|{}D
z7A*9fa=CIuQyztplaQ0&uw{DduGyi>-SQ;HDI9U=tUiHZNWXphX;>Bb)<8mT2!Q2b
zUWt5{LVF~<umacqB=n?*4Xz_3u)$OuGPvn?l4vtkUCY8Qp{&z8vL0JC<gGI;Bw9!7
z@KELp@m!DRHaz#?c@WRv@L)!5Ey9CowskC?)A3w^=UP12xnzAA4;Cz}r}1<_VGHoA
z!*etq9Avh(<GCHrJ$QbI=Mg-%gOA1FS%GIg9$0!7tQPA{cy7cqd=q|bch^%$J>g#@
zS#vAxiWvPPY=Lr`I}M=kJ{UY(lwj9$Las)LkP{)_^#Qr9Uu%LLbU{e|FCl+pgF6wU
ze^Tzwc<xkihj|2{pjmg<Uxi!`<q3BpMjt16rpq1AVLIDKyXC5o9?FC8I~(kY7=1$N
z6XUtFNS{dfZwu-1Dg^XbH0X&K{XJ>|_<Q3y6joo>OZcw~>7Nk#3mf!AjQ&~qC&zOm
zq=$B@A^qQl{+b3o5u;BkeQG>+2I--mw}$l32)$aLl3yZ5|DyEi@f_Y3^<|--H-z;6
z5c+Ey{D~NSO6fD>xid)*Bk{VBp7v+m_+;S{F^X!${I-tgP9lA4!oMt}ryUzwrnbmK
z#OU*?pSJNF)}#8eZ3#aJ=@$xpXfl#N5u;dIA^-OA+#1riC;Ur7`rU*+v^q&o#ON%g
z?-<YRP5O?6|JsnA_I7A&t2{)EVr_{0XN~7plYUmh9}DSej@B(v@+V^SuS(xJp2ISD
zU$!&hUmnt9BM#{GsH5bU2-!Uh{#o6goyDt7J)EeMhxjBxh2pGX^lzF-*Ldy{CepQo
zAe>9s?nUSlkka*<u6%tn;ftY<u)Ty*EM^$=r;x_oasnb?fo+yIs|Ee6hhBI1J^y+B
zd0x2`8UL<H0=-r3+imDZK8XHj8}{_z!X((18FA)sZWTE1=C%iEi0dJwaYP)u50veB
zYzSC`n^kY@PI&&`k%?^L+-1Athu?mgH`2Ff3Lkmt94V|!W1}8ko?^2#R^92y;-i54
zoLKsrb64E!`Tqb)=8v9rFZ(q7&A<A!$lBt3F>^H+;}$=7q6^u6jp}s!;zAvXPpd@8
z-a7K&26A>Cc~k>Ar;a?Kf$Xazk7*$1PKBITM{cN7ZWqnn4E1m4A|v;jwG%GF9yD<P
zfqhSk5C`WmsPXLUu`ZfM*x~`VKVMFmpCXSLU2>|(S7~I8nB&B&L4T*vb0G(tLb>k8
zm(d&qz3@o^@?jVsj4c-WbgSIK_1NCh?9!Z4UuiDx3_GFUy1=fHReZtoZbS<gJSii}
z-TDyIl(mtu!@#hTQh~8i^*Rt^*D1xv%2p1<eV)08Yq5;G5pglgkH>AqVY+h$anlqX
zb8)avSEr&2fd}xOLK!Bx^Og%Uqv+AO6wlPcRG?Na#+Eia`4z5HSRQ_x{Cgnec|QpQ
z4SI=bX(S9Zoei4V6ZuFOXtJcaQqt<3NW(DO|1evioEz+F@S8*{Vc^$I8rBhUth2{@
zfLJ`>^~(Z-gb-7@(>7=qxa?aOp!1S3dv-v%aOLDJNEF~!roA;RD6FdR_?DDf4Mvf>
z_^`r;<MCbiSUF-Lkh;Wb;V1^bOhAvKRN+2`pbU0pei#KBumno%FB1I5vivEP#;6>m
z&}a3{FCWJ4=>|JN7DoJ^FcmVIDiGsW2Dg)neB(Lv*0$6(Z0~M}j3ZIz-N<GIuMWS<
z?Dj`T7NM+yNuz55Dm<<TRBwa|k17H+TT#%uoDHOseJ&Z|6s0yg^BrZ3%>iGdYvaVF
zFaH}vLj!D+yP<7CKUrPh`f-a|Z)}jX;L3Ooz*7VML_k=x7RFr*-G#LnPX*5qp4E8v
z$8!jtx8fPbzH2NJe~t;+{uc;h>oMWFmVfUwc*%|(Ux)FzZWw0~E^76Ji(0m>U-_JX
z!I=7goSg}rTt&72d+xn`dzmHEGn4Mg+95FE(#s^7377z~2?!C{j5+}kR8Wb?#cra$
z3^N8)+yEDRE>TdUxZv`9?kkGohTFJ5H(XKNeLlrK{=dId_jdOr;pzL&eEL?^sZ*y;
zo!U=rUHw+9>D=Og>PNZVI#p=JnjF*VsJm~JoIcA`M6qVRcxc=p51FR#i8Zb5#3GHB
zLtV_VW?j7zr(8*u75~sEB<ERT%{ma%2c7X@XM99PkI)h&7L04)fW|GBMxeunG+43p
z3qZ8nKT!w9-#UPd#sx~hOcZpnCfWqDt~lN&$H8@1dTV|x`h*ZD0h-^SMRe`IAAJUJ
zG27Va+m7I6(~)ln9n>d`asA@W)QuLJvn!TI_rj<y1d2XOka~3}XtM54nF?3%g0r)^
zuoe|ti+y{h#`)Oxv!-9J_f@v<3xW%|kuxR(!satLKN3cthGb*VYdwval0Lxk>SYjG
z%IlrRYdE!(1|}O$4<w{dMnT77o~`+LT07UeAM2Uojro(ev=+MExA4nQo;?68YRY1L
z#li8lTdtW)(2`)=!S^$(pwFsf$sftEW__aY3T!=hw=a63%Sv#ByA@M^KJ!K&Q{7>K
z-!Q-W(t{Qp&sJ-+E(l`>uGVa=^Um?dkb}EIvAp5GYk%pbPOomAaGiGyYSp1ybqW3L
zCy?rWQdED*6h-^Vxbx7h_munVXZ9}l47fhBGEL-K14&mRIq9uVbbOL~)m*m<!dHZl
zw`DfZ7Ho1Ad+GjdT9fr{pFdrFiBpE%=GC`bl&4*y#<%z92I^~bd7W=kTVAmC+Zttm
zI?RvH6zlnXW&1$By#8BZlVbDdZn5dKkB=lxC8a4@L$k;5mIG8K6#0$ttFNIm6X!K=
z#eSyi@{nHj@c(7?Ont8YmEYOS9gw`0IFkimCzKa+*KT3@$opFLJ#Zk@yYg#dU8X0}
z#ic!wF3YNEy>*GOzuJ=<r#K|bC9OuLVGehe<&$*$7I<>0v#gvP@Rv^~10Z@fmvx~N
z#JcqGGR;SFUYXIQ9Z}|Bb9y;M^mRq#$KS*dqC1aB-{wvf|L+!eh8Ud0LU?yN_aMRk
zciLS7?l7%b>Dm&ex+He7M*3SyLH9T+fFA>hrGTu-`xUfyi@bI3O8ni_`$hYE-zabE
zvlRNK{k?U&O8mVv^bPxa)Abfz@1)Sz?eDE^D6XrLcWr;~mxy!gndJS_{k?VXPb@v8
zB}_M)EK1m`<^AK-`x*OtKU3a!r`~9I7KeViyrr}^;=09X5%HJETl!P;e(wI>&y%<H
z<Kq0<{k@+hZ|fW7{p|g{pCfPUOy&K8{k>l(Z|fxG{qp_2_sIK~V(rWW&n4F$dtawl
zYwzh|CwBtqUKX3xQW4%qUbxGlqO8U=R2dy_r9`_UtK2_Oi31=U^n?K}F}FIo+NK}=
zg{p<`7f;q!o*o>ipHs6n#qW52YPT=rcOAc%^ZNk5Pw~5lpGJFS26Kn<JDJ}({962;
z#*Zw|yp`V<_^~r7^EZClV_D;OKYla(F5!1AzZ>~+^Ht_{em~^*8-BxdW^4JK#_ys0
z9?cK+T4pm^r&K@Z%8XMsNME<VdM>_K$wYtl7d(@3m8mo2-d{NvYtcx^blZKdJUPm8
z@2{GRrI8o16mhQLJ7l>JP|U^B!3&$2NS~_*4qNU6)N--3(88wq=yR=w!<PF1rCh8P
z3t>wI=W2<=miqveT&&eCVM_t$ni_|FP=#D9ExoX%ese94!#=1wF4p>ouqhUOt|4;R
zavz|Ki+`J7Yv`Pgn(tE;7i&F8v^5e>N7TZW`v65;td%EWA0q5z;45so4^YF!e@U<p
z6?QUk7Pj06DB<G2CfJ%PxOT;*Qtks(aPglLYz?T>(X?pGeSiWk*2<Ommb%Tg8;<rt
z)o=0N6KqCteWra&r#Om0z=JE_QY_QG#s6?bO!XGqykyaEKRH)_P`%q{Oz+k?df!pI
z(XIq`pWxeVY;-9tQPB_MOE4?E(q)#qS3P`N5A0$*>%|4FN0=tAKj5)$?L>%g;B!vx
z$zJPu>GcCi3#dJZZcNP3$l9_yFqwsxG+Jd`X2Y<4%rSl%Q>#5Yl^^<pgZ>^0`~rTL
z^!V4Gz#58O1*9dE)Z#7&Vge|J559onX5dNFoD6Dcpmh(2UW>Eod^(3uC+<CKCu+>U
zsJuDXjbYQBJ2o0(H2AP&eB`^~Q4qab$VV@N(!(^CGXK&9f+6fFj55=i&srW6*SN~&
zCo`n?KapFyhwg#49`DVaZl*tXnwi1e8D?hZ9%Lqa4z`+Q>D;MiUgg3?h)-J$<Z=u6
z8viSfcl5+@mId+V&N0)UJKM})Zkw6expU1Vj)$0K>D-xSUgg3?k9Bd#)s5pU;&^9I
z91pi3-rROG{ka)4gSqp}%+8%}=F+)`nR%5%j~?ep%eCL0hIjSQex!x*<}NhTpL>*<
z!Q4e=E}gr;%&Qz)gmzORk;~C!oXmMSDN~~tYnrI+{&!iJNC&!;H?h4`wv%*GykI=I
zYoU(z;=N=Zm@8DA&Kx5&8OWtBz8AEY33i-^I`Tn^V*U($w=Q9Wq{b)S7$+HRi{oO@
zrLb7rGk*yz+WD`GvFr;U&o<un9|<?HTRvX<Pa07mXXR;x`EFz~&+*3MM044?`M+S<
z7ygxRn+~5q@+pR1`|p^wM<*+z=;}G`AxN@ia*@YBdOR*)5;(3=)v>-sjV{)Pl0f_U
z^(B5glnyi|9_|%9J@VXg^aMb=C5wu)N+9LP2VwVvC7V57^^EH^fS5ZQr_)FJt(&MW
zZmX3)$H)c+q2`t+kY@eq=Ce10(S8x_)2=|*vAthTQ_p7Q{6yEuO*6rEl&W12^)8!v
z7Igjt`6+EtM6P+W4-sn{HvnN_xDJSyqP^L31zqONioJ3a!1w?<NOR?*rUDv%4O@_%
zDOR`i*PB6ZCfpc=!S;SX;DMfQ(7`ll!GeM}>;>V}7nQ!OwcC^Ml-;`Qf+8nbj>=}Y
zhTFF}9(cNF+4UM&%o#`h^)Uy}k$|l;xtSNfGMR|q!yDL2ifD$`?q-!vOP<~0rSNw7
zj~`4_VBm$XB%aFlZQ-)@Sh&S2yR|upqmU5N^DxFAOKr)zf)myHL^_UkZ#s3!g{3-l
zA}TwMj`L=fn<`GPS4o3j+CIGp9@94PQcAn_5$`b5pL>Ft!Q5qLX6K%0ChgPZW?4G-
zcr&kZ;Ub32s_=5T3XcjoiI(zf4^FuG{Mku*m_Dd_cK07AMPcLrl0KKBPBQ&yJp~|H
zsz23>vXql3)F;QM#=|W^>q`_$`$T!cK9DHi{WfEB)Yp7yK>B4LkUcv$D1&4UVW#~o
z{c`5LmY)iMmMZ4tX|b`9JK?XphGmg`WqsolNKbPmt3`NPU+!NA1$S>ia&6(PV&Py$
zJnRVcIh1{5b`Jr?Z3G;7JM5PdFb;y4hmcu4F=~FyUVx%5VN3kPoSgFIW=Taw^T^Gz
z@hDb~1xc^Bg7)k<()B*2i@JSil-6yXoZ!R)sKxqF7%#=bUdOnMnVdxUdGARCICtmE
zmSvVtn-<8**XhvKpVyj(vb8G<bYgC_<Hfhris&eWxA5>37`=dg+rem?9byM20a<-u
zkeYcvJTGibaVpoZ#yU~<_r?`5ts@)9s9+fW)!;LSD=j?azVOHTz3NyzPS61^!#g`)
z*rV0Vco~4vEQ`LpM_jB-Zz^TW@d~Ul_XBW{l~qx@!m*Jp1^x9kXe(C%Zw!`DHe2F+
z;C6>zo^{b%bM|oBYEYsls=?;M`yjd;TSIT_a1aC^a)nYrn|mha2jMwr<{|~*`7Cni
z{+%51lH6IE<W7KAmG@_hjo#xTKkCgSJ1&ysQ#EtQ2`--$*Cyin5OGyF@}fNNuX9*h
zd5%^+FkU&hQOvm`D0lZBwhC)5-wT;)l+yDuEcItT3_Wy_3BB=zp3ey|V=^4PYq{=V
zN{D{Hm*~=3AwAbp$Q;&8%Tr+YE~!oBm9aH}f87yC3eE5#5Hx$dR-`FVBFu!JrJUl_
zH>?eH_ZlT(mme5(noCW4Lw$FtD{Db!GikXkNs9<pw)fZ9C9w{0HAT+ij}ODk%jM0q
zbb%M<roL`tP08aU&}dJ9RrK(P*I|nf=M%;eUw?9?gEhu-<(PyOcd|c0XZM19fBlPA
zjsv|7kohQdxWBQ;`K%)dIK70?Pjsp}!MKLI#-yW;*`GkK6d8*x$F-;rQt|nOI&XNq
zUUO>|_hP;f6kdF@71VI*ciIIon&;H@&7Pl+(C=b(tNRJ<sUIR7jSHsXhjPx{GHA9Z
z{M?HNa`gI@?bCVo2PhG>#5697)=z>k^|eF~b0pzErtm>@GSO%|LAggL!PIZ5#xWHn
zYFd(V(G_xtrCe?K{G`&Weyk3ikBg42s`{<Zrmc;?)2EEEbs|rfx?bq4%@Ux<4Zr*2
z;$s+ly;+$oo0#dx8!g<dg1MGDUQ5!}>0VnyM2jyop6cSojLM6(oA;+Q-Ni^K(UKKw
zFG}#x7>f|i^`;)gASQXm17bU}i(=V(0K!o{2z0o`+OPL#2bGi-`+E{5b|!S=X%CD6
zuR1}=S@TKeoA`^*V)C+=aHA)YG4ZTJHQaG<DQP{pxpj_ie2CC4_w~{-4fQP$UlAWI
zdwdMv%61MXM8{$y3liV^<6<!M<s=3Dt*@ydFQc#Bm7uckz<4%l*5$@%1B$h!9l7+k
z?sr5QC_#=Vg!0J_)mL;70>_-A8T1fcrx5$+e6OfcTX4hf{s}ma1awPGf9tQEm{L}(
zx^!9TBHSk{$W2|mf8`4iU=SZK85PU5YrWYMuyQBPRWjQ&UhMxkQ4BaA4S*!x=0xj)
zT5{~qTTm{z{XkkL>Dd;Ee0Iu{Q<}}SJ!o7cA2vpN0K9HyH~ds;r#gvFG}A2C{+Vkv
zG{!{v9B3vlzNm8oGsQEg%2<JNLpdtx2H3|_(TUpnaQey2CxOqSv=8u%Pa+A8tY1DC
z)k1W)m|5ai&b7zUou(MDSZZ*a^2y-#=N#7I3)THc0XaRDS#(1OXqcY42IWfasCm{D
z*aq@6nB7x5bpdTj@92fS3~p)t6vDf4zx2kRg3or$ERx~bsfya0eIUj_z7^IpSEOT=
z+}`*!pcBpVx_oQ(fgXbedxn}GZH$%HKj*jzByLg7=AOy`JKPsup=s(?MXa_M^={_V
zl3Q}R#WQVK5#gA=ra#BUYmQem)l_@8j6dYrGX6lm{U8zY+^RoN=@<tKcH|r=^@KAl
zo^27(z^HwO!&VO>xJ-?c+7EW{-zRXH+B?=h)4`>26~4?--pX#D<=}rv;4*c$Tl;JW
zzaQmJ4_r6$r9%50!CFTlC`*Uu)&!&xYoCkPE^v{<7m&kekb}P<rn}c{HwL^YN6N1g
zxaKb?fjz#1UX;Q2GJ7o&Z)s>3dq0FYo^YMuc#F{<Z)k7B`#OQ~m8!kRcgRZ)>|ZC3
z8vE`ZDr5=AcUkf<+{L@b`&+xx)cNi{m-fT)*`-RLaTe#QGRxkT8Q*|Y`o^aNxl?)W
zGi{5WL~ZHM(K$LLpv*P=H2A1LluZnGrnH^p0z293Mgy%{Bay0mJ-zih-0k;fh`aTH
z)N4^NYkOON6Pj7=-T*~sbystHvD4%c-@BG%$IdAi@5--pCRN&{g>{s2Cco0s5@z1w
zWip>tS{UVSZbH`S7+e)*6sjuB7}zqs$kqnN4OIWEcMd~mR}yv*VdJzQGV_GM)%J!f
z{*6+WT6{BMDiNzNMTLrXDnLIT(RK&QdR%y~ypZ=!x~aZK<e37!KUkn|?9J3A*<5=_
z<w<bES7Bwqc1W;5C$Shc-{#5C;&p->sw|DeJh$Z}SWw8G^3tCj@uNS(tKmx9syesj
z6G-;}+QUpe+UNHW4;JXKdt)B}quhRkOI5Hy|J3WN@X-?x#C7r@!h>)F1_f8#1PhN@
z9K2i#M)J{>5@eVy<v}5nm(><lr)bGUNK90s3sNTNntPd8lJ%k|iU+|h(XuqgTpKo;
zrDb8F1Ok3x^hl_vQE4pGJnKSSjV0MqIL1IYz9@;oA<@D##!H+_f0U4hvZWkI@uMA%
zH~$zc)60VBdFmwwvSXZwZM#TFL<K$F_wi^B2NCc8giXpIZ>WTnxv_J`T{2Y%?@z9L
zj&d6;s3iB~qUY7TCQp?ZbtxdNl3pQE=>-KZdIr>7jINIhGLM7S=T!$9>pKNKnI(I*
zc^0!#ZM83k>zGwUI<&?J3lWdS3a@E2t#he7)~fin?a8-NuRVyZH{z4jdS=hXUDCfk
zPNCKBK8(2OdrL`WdK`Qf!<E}lSpuA@+1YlgX666lt}`Fg#9eWGm9VtW%%ZmiO>Y?@
zxv|>Zr>)JyoOuS|qZaE6D%*z<)=Ym;Ep8a%Gkt^*H*6o@xW*r-m%Y3kgN5=`wool>
zBv`&OGmy_s{bao6Gj}<k<BY;khg6;lOIe3hEoQf#SITZz(aCPzSa*|}`pSc2r&FHX
z)c45jQn;-Pcr|4ryLAm|**)#yncaHHwu;G+yvuyi(zoNG|5^e^|FtAtaBwEMtT=lH
zY1-iub;kcSQ4j4V>gKRHmOX<e&;F8xopH%SO&>A3A)k2;8NZ;k*;dZJf0{fruw=?$
zel#eLX3IN9Lw>pPXudpJ7~yonc{F)CbwEqFV)MuQC{wu`M+Zs+)yc^c-%}<dzL!j{
z-Mklua=r=&)5pBbLVnV@1~)T!Og#$;N`EdvTA$_qW;QO0Zh3SNa`nk2e4EqvuZ#|A
zE?VDY-^n!U4HTAv>K2u#DqG*r!?9ue%;-=lTN+v)l!iv3$d;@8)E7#FTl$H*l)Yp$
zyP5rV8KHJV!|w(`ro}S>CfBA>Odhj=^Y!>$a?Huja>_BsU|Cix<*TcXrGw%U+v>{c
z9i{%!B_m7Ro|J7P{iX1b@BRGVM?UUp|MSEnr+OUIYfHH`FMn-y>5e0prnc!k^pE6v
zyJESXtJDK+4vbz(g@@+w$gn+rSkFQHCBT}K#5cOW#KwMZz82Hlcg&22^(CcTFMw-w
zQ32%Z<0D0T3$RHha$n#*bbuxF2BQU+(BqHdE;htfm{H~9IKXz*LL%6p7CL%7jm^~>
zf7P3*vr;aXg|Pk0?Mv~EcJSf$M`1=!NYSas$7Lnud?^bT;IG<9&69ZoV+Qh}vhn0>
z_DLdb1hbW1dKRVgUGsAOI-h0bvWxXHVro2&x@;sm{)X8>dZLg{DiudZM@F3x21dq`
zb3$YF0f5@mW1AI5OVuMsM@CAv`KdI*EkPyQ7<FO88Vrl2T5QjPA6=fNax+`QGmBEu
zBNI!*=HDr?|33`qX7vsa6`I5!PjME?_=w_^0mOXl3#<ep=IY9s@`g&5-J0`PfKbnx
zVQf@tMqte%UN1?Q?IfpuTH?P3lEZ5r<xL#7=8<1GYfUP1<+RJ0-i*7Xx-!kW4kg|$
zPkDV(8i=WxXP5K)Q45&U^FET8zNF$%-sTtU=Xl|b+zhj2!$6M8nd7s4pniC+da3{?
z3UEIF<y`f#0z689hXcqGnqf?KYo3lM+`jz0YW2cL&ula8U@-M&HHca>V(@5x?KaU*
zEc_<z{Kw-f4WI_jMgz+dfX4m?Se^jX^ci490{Byku`&UW9*40i0sME0u{r@<m||25
z34|izqNx@W2tzhUrP`lBe%rwrNFbX!ID-k~7ag3T1hTz@vm}9N8fPh}4kwUDb#O`v
zM6(=+Gm=0q=-`Ydkj`uh9lVp?qdPcD6G&%5RxKxx^E)_|1oFpDTxl+B>ENW<a7YIy
z%?Bn(iG0$G=nVC%X-*v5!AY}1ZH-G;niofRaH<JE8s<BkH3<Z5Y!cTY38XU~udYoX
z7j<wBO&}T#J35CYkf(HTCK5<z)L&hfKs0f1p$<<Vop}cDH#s?TSSG<a!XUe)U8#;<
z|3EtAiE?*HTW&v=0U;-~x}Zt|8t;OtM<lRi-6`<$1hl-TiX@;F3*8c*fL1Q_l)D7B
zYN1=~6VU2~o?@QBs)dD~a-9N;3q6H21@<rWl*$x1(4Ev*$5P;6R}oN6Wl$aJDi5mT
zDdv){zM;B01r9It6t@&u>I$qwa-_QpsCu|V+%3)1*uL;G1mE6Co6>$VpWT`TxcP#g
z`Ic*fGOJ9LKe2FHdVX#9Yrtx~M>ByN>Ew6smGh$==WFHsT*vu3Ilt3!-XiC(JI>e3
z8BC_6-XQ17j`NLjuI)JABxkMTyj9MlI?gxCd2+}37C9f*alTc~^E%GA;jG<@hLD}L
zXb7`2b8N4~63uH0wbq*#W5G;yTl5S8O5VI!3udCH$!W6=>BVkIWW)<#SFw#Yh_1nV
z3o6qLDq{ie|4i(jKQ9^0RGW_4l>j8A8Sg5#+6K|nMe{;J$hF~N&a4h>I0HP1uM_YX
z01_Yp*)W_0Tt`5;Hmq`{i`Wr~o<gqf^RyycX5SM_;=2`>_P$<?C7C;A?NrfY>DxSi
zt9KP!j~(eP&ldBua#zPU%;Pz)9GR&e?b30XAj%Z@d3hSB0snGTk6jP{Tj!Naoe(IL
zx6Ui02*Fm~Bn6x-lY<c4+2~@MX7jwLu(8Gbw20{wR=GN}4Jy@1$HP3w+}QRVuxeNL
zW|f=%R?W})bLFY^+}yT-$?&WYs>SniGlr_p7FwFtZG*freti(Gn)+zCYW)g#jl2so
zuY$fR-2US8EL?{-6tAKvp6IYu4*rgyV&um|a3Xi?Pfc*f&5fKSIBzl=R(DO8yviJV
z$~k(-M0E4V!STmu5(XYb)~bvm7&fgZf;q~Ym81HY4ULcqMkQo^!7(*QWL{+$LiJ&e
zLm1f<l5ny9VlKQ*vTB3H$tQ{Kbd^{(yeAQ{mM;epZGa_c9??Zzy%?2KBMEBOP?N|g
zVWda0n8*AN$zoX=+2u*bC|{7BiKQ}Qxs<rZ&Ba=m`7JZoU^(2diHXDd_2t}#IunNV
z>&p2JYk5Rx{i<?d!?FUFu>b_}l}}!u!VHt;Ktn9Y%c<}(DPB4WN2D%W4&;US#ueWv
z@hwq&XabFTO7oGaU{gkK46I_^dq`&YyZ`uyr@bn_`|XnAVA5i4?X;Nh?FB!)1AZUi
z+LYenWqIn~_|`t=*_k5^|3ZG!h1A0`2XQO4)}v{?yz4HZ<8580hU_9#JiA;vw}PiX
zzoVcXz*Xiq#jif$Ma;0n;S7t*(Q}<`<&q2=*me<VIhy5L?{D1-)npfl$<MQQCBBUO
zz@#3tDAzd=H4I&?!qvwUD&uWYso}axCi<y=iN;7Tjf$h;iOQU#GM_|Una(E>NxO9|
zX^5^<3#RmY8Ad*tuXOpWOfL84=OGY1jSo9D=Xa-$Mu$v7jyCaXJhvCaL;i96{eHhQ
zy0gBq*s<2oEUf2oDfWjRMWd-GqZcS%9Uy(aEZi(zNJ!N=AMm^m#&O=fO2hUw!;Hdq
zoX@J1vz=$)nIx^^Kgh5^CH)n9rS>g-5ZOMWy!jyci}4|6d>A8|gL<>T{S@l?rLKH2
zfc|h+)qk*%99YXDjAi^@$8YmmD39oRRpoTWa(XkG<;+MBTH6v@Z7xUG;GJNN1SS!h
zN0Z@bb|h<|H<a^^X@e-2`>he<!6xO$XvniY66FB_H!fLBXker-Kkit0Iy{c#?wggI
z%N$ry4t7|X8a;rO`Y{LNV^OH}OBPe<^1=R=NN@OEExB5o9;iHJzV<KY30I70V836U
zNVVQ`jBpT=6a4C1cvECY^mLL`KYGM-$-l&9#U(B;>T6SYZw}Q5tg*;hV*&e?FC(54
z5=zS*Qv-!g+1*&^Fy-g+?Pn0%)#B08m$Ul;zE1i&-P%B<xFl1aQwLmUN6rWj_G$>@
zMiS}RLcXqRb{%u{OvnmVJ>rh&T3r2%mqz-CNq@gRK6;j5IdquW%<VuZo@!52p3BGG
z3Lq)tL>@*T09su?J(VC-3saBxTEA9ncRN0f^S##J<oK8zY`<gY-yX89@qmh&43Vsk
zP92q6qYd?xX7m)AuLf-%E&7a$W!p4603h%p5nx0Y*u#y@6-&5u^YPR{Q*E=>1j;KC
zl%Y<|gKeefdzkw3>{|BnajXqyyt2J@N-DNGaJ6v!9>Fag%i|F^+439%C@st5?=pyP
z%zF0TY1sv7-$qh8sk@L}Y!(5Ddq`r7Hk_VB<dSv_$M9Ud+osohEpI(ix(UK-b`ujx
zcs7~PC=Ufg!IM<R7Zvv;<-B8iU#V3a^#KSi$~(0$2g$n)srU<17x(7f!+Y~?E7u^s
zQc^v!H|I8`L8A>vEY7**i*v4@;5R0j;=6oXwtv1wHzf!tm0i_5c%I!!GP-$oS|`tt
z-r_vF&Hz1mHh*BAC8@f&C&y+wId<lv96O;W$2RSsW7+6A%CMCOi($WnQ;Jy-JutB<
zpBjrP&$5+mUbVg|5%22l(MKs?#c=8Ls8?+s2%cP#9pC1qozF}V-Ha%kE9eIw$}-*j
zb7AdjsTLA?<J+mb)?HD-tiPIJamSCILa2%j3{`%ZUu3?a_J%XqJV_e8Tr_+8vKImv
z&{gZ>la=~m0nM8Fm~!+yMW;erFQ-7~%msO~1P3Y8F0Y%!`Cp1N$XZ2UA=@g#2zP6%
zy)O<0X`8=a-45(-ux}>(_OWJa{8oJ34kcz-x4ce_rLDek?~icE=6b-of8QGCT>FL;
z%i7(nosDO1t#$R3C(G+XHcXo5Zh@8Dt|krN27gJ$cMX>owGw$Vt{q2|PnMu3B}?T>
zxmF%Xa>n1jV&m!*cNw_e0k}c#vgrA+n7=G~0Y>yfu`4pHS_ZU^sT1xc)6m`<{SN$O
z-9xvH>Iv6NKgrg1Qtxgu$}_0acrmayQTx5HoJ$_QUDBualsTh&pViUKX8sCon!%D^
z9LiQscnhwqNsuXEQEQ_bU$Ck`YY#womtU{4(mIw_uIz=Yq8s5dTs2c9eQNZg7vrWo
z^w`uQ6-8|R&OSq)H*!Oqe?Ou=6}VC2zk}YG2;3;q9OGV?8_GtnRT<$%nXaRChKqBV
zi*p&r+8^Mt()t=<4h5CT2BfDqaNXN|C!j-^z4+|aFILaH$@VM;8OmGcF62-h)gJB<
zk79QVggrmEJlboP<tPJg^86JujG!E3m4mFFDWnj(xau~Ls{J#Z4|xfR>-Y>Z6_?MD
zd|uk?Go1L8<nywQ4-SnS*X(}D%Q8DIxKsn0#!;(y$8nHGMe8q2tj`y;TD+j~ODK=d
zR3{a~*i0#)jEF~8Nh>qVxx<;-E-u9Nvopf3Zs6rpg&y{@+zvhLH}7VQ5M<Cx+nmkw
zb&f%IQfW}hFg&O>IpkKq3CPQVM0@zK$t!e;v&m?-3BZDpOz>Wj;`JlK9z36dr?R|_
zxah;vvV2UpEC>JO&ENYf+9)@YqOJB<YqsBqK2=asJS6kcOKTb<oX-~9HUD|EmpPy9
zx@I|#3CNna+<@0WemSd+17Y5r27fng`1w4}vPKqzJh2am#J+smUp5-}xiv2Ua$dfA
z@p&_a<>w*$87}Z)Z|xdk5~LI?n;t4u$GM7O8BenZ72c;}^+gj?R$nv`<yx+lk703+
z6>Qerlxd~##p{0;usoeLM`inqx9e7*&hFN28f4hj$^P6cN#4PIZn{sOo8`l%J;V4N
zx(?h8uBVXcwy?V63GsUBFndXb*I*y3O!l#sFly~r@!gdMMr+g!98bGoibfl5W%}`}
z70QcVV~)LYkRN(e-~pKWOZX@|Gd@!7$&(nfM{PcEVdNM!%Zy*kC-E?n@#~DD=A_Ky
z>@T4g9l%;+bP?zlLTWcd5<}=xmU8;~gsyM(LxdB$sBZR4vURH0pu?xHUgWufq}(Ep
ztMy@s;?lA>rofJ#SpB$9nH(jVTq&9CJcvx*AelH1CzCgJWOAYtNy<B#%O2i4?4^u#
z^6X6_kV@iKInoS2SZUeVlkNMb?ajst`R!#+Sj<a8$u^Vw*S#D8#cLt7E@dXAWTnp@
z0QF<GmFdKfC4w!0X2x$dW)C9T_-z)s;y$<>dgEqTp(4Mi>_$&EX(wg(?Jg;{H%{Zc
zAJEPutelsyg7SwWHXFkDKfz_sgjz;D$Mp`Jw(S`EJ7rgSXuk{F>y+^15YDIXYKA*m
z)#;mP`s=t>Xa&^1b01~q-GrsgxbqrgnPbnkn_9Rdbg^vK2`wRkysQen$6&6cyjS*H
z_Y3sCo<PjgTser}FQ{bs^u_sjkqgZT<O5*1&Dv@R8r-y^!nVjmMGn9Rg((?CAHvq)
zT;KtiY3;m^S`&YmfKC^D!D4=nbi^VS)#~%Nfn{ewKZ0dn_))&?+XdS#pXjOJ#2>@9
zzl<Er$;j-Sa{ajQ(t1FvXf-KF+e#}qX_<8;#&SHcymoD;+PwqXDJq>&iY1$r4=Sn%
zCL88e9Zg(moixg^Dy9P;$klz<$zHCkcB(BtZLti~s-^OrvPAnVmP}mjI}|k|zZGfe
zkdS(yi|d^ABO^&a;&|T+eo7gip2WKTB9V#pC~VL-3OZM1n0BPhFyc+(@Gl7h)lrDn
zC;YSC!v$AoQ~Q?70d-j}Csg**s1A~)vHFH}2gIsY_n)=TE><<?wY!p7yCtdKBkje}
zUU@U34q~hHNn6ut{u&Bp2L(O>13D-mbMZj|L255aBIGEol9?tDr=*``WNmr$Z))_G
z4+ZS=t(DmxSxz0rvQ8u%%qM|41Q&)1bDKEZ!8*$)p&z)Dlb^!Mnaocs#+ElL)Asu=
zyxLj=Lio{Jm@6ma#d9f_Kct;)e+EQONzL7f8G3V{#qe4?G#>gKRy5Axth}Pn^Wi!w
z`6Q4p0AYXq7ctrke8!?L2}gA?mw6)ee<b=}1}6Gn!G!)-Wdw6y!|+<q=tMJ0G=?!N
zpXlp+h)n(tUb&uDhl7iu`R|RsAu{{I|H81iqi-q%acV4l4(5*)_qPBl?r#g*oBIxi
z*SfhAsnRB|=(~J~PCf}FN!wj`IQricN<v?<yIb_X4^Z@ffC>E{VtB3F7SWei^dmmd
zmrporkc9q^@z`H>{d$Ky^Apj(8*kD72`2P^DkGS?2g7T9wG)kGXBe~ciGIeXhHQS0
zX|#D4Q^<>ueql6RW-4s^mwX~|#jC2n!hpc91y{yKzu_}xU;3%g?!fq|`1>tz@%P`D
z@b^0z!QB5~c&*<r;?FQ<<rDp$55katINJjkOT_vIjKw0cJiP>&+@m<}#b0s$QRv>>
zpJW7cf5z}y?D$WLtz=>tv+{}l!iPBJzX+bh`B%ITh%=YDoH&1`IRA#f;`}=%aqg25
z%>4tyYc&?dX&AHei88*$DgSU*@NOBFf2R!hfwwqv$yjCV=Zf3ITXFlC#2sLGt#dn(
zstn63%JLzJ@<|{`84K|^kiO)}9+FZ!hqvhGF`-|O5zG}ayw;OD(MX<#F)N>_pAV7A
ze-S*9=K$V|qqIDv3hM>&QhN}8#W{pYoJ(W`bHg&Sb0rL~bwelWWkhX=vxYD$|7e5{
z3<&EWzN4bFkFD+r8%gURxn2wpzY-5i@e~hb;d*lw8Nu8*hSz$1Cvv6RFlOZwE#pJ9
z@?Qi`(!CsSOLt_=#qx+nO)k#x=2zfhqotKt_Jym2w_32>@`+}JrnW1aIRyTG4S$vE
zxu1&$ue%Axvqmqf5@0XqmB17J>6j?3xNmX|9>wwN8F}4ju!#!CfEA$*r8WJqJ{)%G
zM{7md(e*NGp!<I4CL8QIavJ@}G_BFkFdF~u+4J!lw}!Sl`&)47*{tnAa}i`ibVF)4
zEM57`U|%Nl-{5rP8;cGT$L$He{W&iZ|2@a##7(@8PcG0KmWe$ZU4^VTsvw`x`oDIB
zbs&h3!01wsYB(FKI7f}?JS~CwxDLRD8Ok;aL)pCXJW#owTu=|Qx01-vb)r#2bYI==
zM4PyCdUChuhz|w5ajXZvH)8WZ(Tg@3j^c}__<-248v{<(`3!4UdefjQziu*|korwX
z1Nr+F5}L)1kn&DQ`FuQ0td;G#jxF8<AUaYE9VM?M$+>?e$$3jMlDB+XiL7kXT(p0J
zIae+EN^qJ;>B62*8vmy^jsHLS{!eoxx*yRdERLsp_KZcB8wW6Vn-^^+2%B@FEizHN
zDt(@wfG_IFc&fWW#`NgB-Iu44l?&wt-1irJD@*w*C^6Nq7#}AP<Foj9EPi|f-{?d>
z?9#do{glghU%J7J>Ww!ms2|NywBwV&E4J^Ive85xKLCK^+sRXOPX^}4Y+)!yr}BaB
zM>=#vJ8$mj3hJl*PO$f%6aRuAKM;a3SL+q4$4;;G<I}PE@m9X`ABCEwGI|is8oLP2
zkja4b!7?XpXuMlkoYhVbuHKH5gR38tk)7iRL!c`|?humNFSQH5HyNA>jbf<hA!!b7
zxb%8j>AnG3{(yx$_p_UCM_##7c8}dCQQaJ$4eDsFfO?NX%Q-lre;~G3m2wNuN{eSj
zkNFiNv<mh4TR9JEjqsbW><eF~bD<BxcVf4EBF?VXquqQa#`bYOMyi5RIGSf0eHpFl
z)p#tOT-CZmU4u09C2lKwm}u)9ushtxwtH{(;lP?fDPL!Mk?d^758{5F65T7^vG)6u
zrsI#~FhjVoDrK`A&wyHBZTnB8z?F|6b1&&7@uh_$($=SVK$vE^wPs_&QrL6*iPjJm
z%$@mr0A;q{v)dm`Mgw7_=L`nh+lkhWp75Z7J#J9SzGO7y{elZiA%`Zy#%grmp&i)F
zMdyKR=l*Un%4LYAX>x(Nv*7)tb`+21{T*ojd;*!&AAx0Gcmdz`BL&+np9oz=y;0(L
z98ZIYYRTQ6QVQ7Q=i80c+4)aXShCUIOfuS+rLhsRDb0UMr737%2%-2QK1^{`0@y~P
zQrBGT-fPH3C|2rR*E=<k4QEyI@_RV9W)rY%n2Y{SkaMzUlk3)3`S#w-AFWIZx#*c-
zR3^FfxBY118LXhFX64|7d^4NNzoc<2yAob<A;0OGY}<!fQ){wnW3SN1k0HdmFu#YQ
zS|)ks<noB*3gHj=jqDi8>O)G|=F;2^96v6<q{#&jl=AwZJn{uwzt?%J+E$n3v2K!G
zS@AL-LneO`&z9k6!*nRT81k~tC_bp2GY{oAl9*m%Ruhv|GV?z^F^h?Lzr;K<F&~Io
ze~4%IVD6}ScCqB;n14}0zL<2%K{wwgPr9@|xf!0AN%L<eO_TYnUgJ&e@2j<TP>7Qw
zvgiTB%n`8oXQik00u^uR=6fzRcPH6-n`kedhT%@(I`Q~j@l4FB#VJl$w)a~`4&;Jz
zV2f$XBM-8JEKR{ukptIxte23Amqd@{TU)A{k*}|yHrs}TsHH``u*6}l`mj6cQ_4G1
zQ{OHHOQ-u=-&ZmiH*xJ36@iJI-HT%z8=3<g2Wd{~pk00X!R`S5Xd#I20%_PAW1Xy2
zaBF@KqyCH*@AB8(#VMA?+H9lT+BTKcad+^z{W$pB<&}zdB)6t>W2Im;xA_O;Pf%@8
z${r8GXn(1H>2$)O9HRrQiEso-XUa?c>nj0>;?X7SZafEA3FCNKDYub&kE6%?M*?pj
z9SlN+9$X&v^$4%Y^0R?gECwIn5%!9IBJ9@9`z6t&SPDCNauI3lcf329A2&iH>TzDm
zGK-YE^U}Y_l>V0SpNUib%LH%+D<wJ~wv1$ri6V>V%JJr)-wRFJ-7s@BVW=6pKZh%9
z_E=fTPFJv&-M*4b5iWJq&M+nS;n4JbRWEmJq7BQ{u4O~|pQ(WD9bmSfz-PDGs`l>@
z*={vwT+8wq88$CEpZKUjCBJo#WT3L%9RU^>K9IKlt$XFIQ8SBx;aD!pfjO}7!PGzB
z9neLWffYZI&z16a@jQWe{(7JBgsoo<rsNM6=Kyi$@)qad!iUm03*((Z<U}REw<lhq
z=<cj+=9~2Ke^b16QB*O!p;@w-QGfIdHO!lDfv=E`UQ?I;_T@0mNyO}CJh@Xx-F+!^
zH%aMWBt<p`qd{TRlUWP^Dgd+Y$$JgKMhio#Nl%isNEM;5=GitL4I(TmLB4S)2j^Py
z9mtpZDb%4}-YmA?$ap}HdU0THw4Y%{slU@>!`$&L3Y)Jty*NhgUwIy^=AtL#8mv$G
zdaTL7k23I64ZOn3`g)a#eIJV2ztVZ`lBd5@4qJkrrk$7tw$L4~+SFyhD{!BmLJroK
zam9s_7G0&l^<vj6uXm+*cVxCbk2yMMqw3M2yq>2jjxMPmU0R}7MvJ4vrQzBCV3^Zf
zUK+ysHmn$L)xgK*1Da2j1`{7W_EaR~7FCiD-!)KL(#YXlf%7}1p@L1mKpu*&0dJ67
zs7TB(>v-Jo{J{734dAr3oa-_FPQJIF4w~lL&%g|4_3hqt=G^Yg_#l(^XX0xYMAkCh
zaSuJZLPgi|;j*oq6U@NEY+4NS5f#4PQ~KmHc-QbcC_XEtICxj4!z&v~GY^=}4AVBy
zPK4YTk=8+m&q*EZ47+-2V~dO1k~-*D^axvVBDsE~ztzCYHU6$Z6vzDNIpUbd7Pxk-
zrz|)yPfMo>xucVigVters}5%~SHR~!tHYNc$fw<V=BCxY9(*e{#KQBi^uq#IVfC_L
zU@zBAo8zTFmvRv9jjlzgddbVp^FKn||5zNiBkA-Bys2@6;v9%hRe6sYgx71`JnUhv
z8>f39*P68bIF`^3!tsD^50effsT=u%mEX}|;ghKdOF`Nu=_FRJw@ng`YYV?iS~7iV
zb36Inla@{zI5*OphA!j9o`T@60W!H$IDI`;m$FA0c>ylf^iDCkrc+F)`qpv`a0EDd
zq42WxLlzemcHJiGh{E1TZO3S~C#Bgmr8!c%sP%Ozb^7oRE2&DW2@W+^#AvbDq~|m}
zP=64{w*zQ+K3hix+CtW4xODGD#Mhr~Y!9Me0BxMJK<>L~f9nns5x5D^e-JjktfwoW
zz0qk5V+QhL(Tz~yuHhhhF_v83x0Koxvfm%;28a&DJNkD%TyiT+H_IfPR6&|$UI7!#
zGKVmBTkqMu9_daxQ)Zc;RiApMH!IVd70{1gs?gLO?x5(SjY1CT+%#GWCBJniNr_(u
z)<kQ3Q|5~5cyt7C=J>ixD73qTq6&~!)b;ImzB%W+%;=nqx6z>xv|fxOssWEy@B#42
z4q%l7{H_D2TJY$|Bsh2M*eyn_s|0yDjK$AU<94XRC0^Gq_L}JNx?!=`Aw6EV%1iny
z$M)eC?`;Aq`SyBIYv`y1XYLCUqTgCBfc)paDCsVr|GW$~yDZ4$a*R2%0Qgc0FbjY$
zrvS46_(}>e3xKbt0J8x2S_&`=fUl<jvjF%;3NQ<R|4IR70l;{}F=iI3JZr<O3}$@t
z-neg0&VLRZuHy@tDeWO`4$OmKBgka8QfN0VhS4kV{0<$Q>))1SR@S@sEJn}_RG!$v
z^GfiF+&O+ag}yNsRM%WsT{Fp$3EM@9C;E;cb2ec(qhD&jh2aL9%aXzt+8Cg98M5YZ
zuZZ<e^pwB3|D>aK09WMZ-T`1=_*UsKZUWnMDQi=HW|ehmmE^9^o5fs0ZU+gheR*L!
zRlyW^tzE<!sZfkxznXOFa?ptix*`Sg#^P6DbjFZb+iM?+|5uBqH>)y{2JE)=IsyBc
zv^{nghNbK*+}VY{1{(eD&aPZ6#^&|{q04?PM*DSqQl*lz%XH0Xg)%wGJo*9nTfT*}
z{Q;!Fv7=jz8v8Rb=iZNe=6^*fsC`gUdIQktxzwY9{Fc{=Nc3X}<m30?X~lamg}g6?
zj0TLmY%um@HQ#2tj5hmCkedIXa->;VH~#_4iQ5#%t$0lAmQVCNVv@2aY5tB33aJ2+
zyI0Rab<z>I344@>bye{hkV+Q1T4(GW(^9~$#wh3{^nzNNc9vH&?T-@g{%QZP(*8d#
z?YE1s-n75<|C;uvb<(~wP5Xxyr~M<{w99Azv>%sclCY>~Rj4GSe?O=S+-S(A+QmXZ
z0&dCN#y^jv4cA}$&60I%4~4gNuRdqrkHObF;Y4o{;$y&r`n(>@R<N1RMSOb081SGz
z?-P1?3!fIB-Y^C{sL!23KkR5e&*0M==KBKL=j3A+`xyE`!@Lg6mVGkE_J-N5LY_!t
z2A|eP^;viSUYv>6vvIr6U-t7c^n--itpbEzn`g*uS2XJ0_^n8`Ht98T*xx35tLx2H
zWG<ExG)1(z-%;dOfik;hH<@-lv)sPjzlU&-^AC%i_bNI6p)BMW$lJ{vOw2-JJ|Zy(
zoVnTz-K{zh=DrmFCt>WGmFk+Q--H!op7&G_zbU$oIvKwc?}7S~eEcpfgY#mhP<dtE
z&2@fuS25q9QLvd#H|*L)VbKOD>}W7A))}Csp?|t4^a>aA!pZ3&$=>)=8rgZJm!{yL
zG}Q0V$#|I?GArKX`W=s(>{k6WwGZ{Js`nLDA1?RU5NpJ0eswKY@m90s(QbN~rf?X$
zdt6amL$SfCs_Az6brtRUte<;f&yB!0GNPNhE4fEH%sd-iT#-D?S{A<&hdZfq^yBg1
zY!7OcqiL{o3WFXmejQq}%@4*aY)lR1&Ti!S+;C~QerPB+6oxpWO={l!GS+fvJK8;G
z<*0fw9N7Fx$_zIkOEWddWFG$b7GAi8jrf^D^$|xNURb^Tl#m2zqsJlql(?(;ox+bf
ztB@vV>bnYSm=tCvA6{4!t-+irtT{sF_WDJw#~+uuVl>$N9{gLEJNvD&U+L^O%f8Fm
zUnKi(XTMhVYt3GtYhC9oSGM*z%M)5}be2aO*Hc-J08BrmP(6HlXec{AS!lh-c|V{)
z4ke%uInV|KkO*p}6V%F%pbD~m7Lm0+=8*j{%_p{K%32Bx=$=O$&E~_6PsoScQ6=lG
z!<94rsf~8E6G3i$G=$oF3W_KKvyFbdO1YKMYmoks<<B%;L#Xr4@#QR`*$cOK@+omD
zuej`p6##as(=jO5IbQ&Nd8HkZu-nFbQ=T0^$$dx88tOdz#-4JKbIHvSFew*`lVcV)
z(n%OOcjBU3_-ZS-XX^wK->)+1M@PYJ(Q5|PVI5ml*^TZ5)IT;rI~9c4F&6bU1IU>k
zeX#cm?Lu-d_lnH2P3gGKvpsvND^!y$i>gUZ)nqNtYjO))Sj)NMHJo$BE1$FCB_F#4
z#~qfMzHKS>FK=rWu$)<vcX#>chw|>FjVzm|eGan?WhHx{$HHpORj@`0_bT*=x|eSL
z!K%?Z=+{9#UZ@%y6f);S8~qvQgNAa^A*TC}CNP?#dH}+7pQ0b#ze~M&C<ho)YLRHM
z{H8Emf1gsR@vaD=j<bl=j$B1N1IQf}>Q9jL+(+o}^%lZM&GhF!W@a#VyP51qGm{m+
zk2_0t?ha=O=RV;qxw%g|OMdQCW?4FSo0(TR+R^ni+q{<`*W&kF27CDUtOfDrK4+#s
z_jxmexi6TRo%^DhOXu!1^D2iP-QY;e)p^e)Z*P}mk)=+fB@NpogM8l4aMFrSkT}SR
zFj|YatbJ`G7fvI0<nm>U$D8|xnf~0@%?#$gYG!usn`R=HZ#heL?rY8x&V9vMa&!M>
zmZfuFGV>}&GGZlN;*hJub0M>owvk(EN*t7>MeJkb=<(!2bP}943S`!csGiLwL2$NR
zarj9Z5?vrU(&tdrDKHZf(v}aBlJ>jl;3f$69?bZ?=IHA>1~|^s6VSJSjU`gmcLLPE
zosZu~z*Vdyv<8m1`10i)MlA7}`i-A2>%F<+rTL08xw~%MY>i@Kxs+?!^2-ve<%zi>
zF;_aX{Q+p$0lGQ|@*LNZj<FdmIq&6Xc961h<?Fts7cdEtjNB`*g`o9p$DwD>wpN!+
zYkRiYS52-BT0h3uIaxc{SKdIMY>i1I-3Nl?x=a=%LtD#g{~9S?tq3ZNm8yf=$B%?q
zxyH**sr6J(uf*qgB$7ws&2zcl$<f9D)iKQxhsn$Kr!}}oZ~A@mI-J$FyFw5;z)oqR
z=e}nM`ZlCP{fq|WHhsczXUCD!E{XOB5t(Rz2s513cM)v?9PJMqg1$!kHZ1Lr@To!j
zqaBCVWgiCLBcgpfVA1{<CTp;Kd-c6?o0`e!eH@q5_mU!cRifuoXy@+0N(`Uqc<*h0
z68nTd_bC~(`gV19`@&BP@iTmCd)s#k!=L-Cj9GnK9cAgh@N>9R#Lqj#FBqb}y@+2F
z;sPHien}Yq+?Qp{>f0OjS8zMTu9E6Z(x4ZP(md7NO@H1YnIHFWV{nw&^eB$a?%1S)
zR=pT=yymUB$LE5DO<b&)+_138TaNYB*Y*j@mhd$voU}w%p0Ri5*fx2qux5Vnhb++5
zvfjDpm>rw^YJs~MYeaU>lTWKCGLF#6Ieg1k?{UrnIbYp6bG%@_gL0m*YxsXCPaqG;
zx%-o^o4=lCFMK`YORI8w{JU!pm2=pf&%NNOr#z+)XUUuwzu}$l`Tiiz5p&-2oZ!$;
zJs0O_tvbeHJ{Q=8o=(b<+~W4?qp;k@VY1&+ftn~DIlJJjc4fwofdY{caZQlBE%0B&
zzhV*ob+{7x&T90p2MT=|(N~pwC^p?0jLk@Alrrpbm!?i=d>a(szB7m7#ou8kq_(#o
zdmfV=9<aD1w~37e`#fs7>yQ1+>%koB(rNg<+E}+bpFC1mBjUzfa=-H7zPo3+>bY$e
z%=3%1HGbC>eGUBN>hb2VyO^APOFJiM02-ToyOO*)?AwLq&Ajj6D-&|4e@Vu9P>5uW
z9;zM;e%PFZ)7qVE<}i4sci}wncMwObeFBQ1o3Ob{wxhRTEqY=5c7;%VXIs7D^PHGn
znhSHkf*Ibt<Gx*n(992C1&z?Y7F>E*--hTkoF(suOZ!Lqn1ATGIT{RI_*))eYLb6G
zTT}b|{0pOR;4xTecm;QPEg`0UkjzW`j24>mqGNAiUqX`97x%*Z=nPW0d!t1Cx>r3q
zewz{#E}b0WYHK%A;}*l|oIiF>;GIKz&cx)uh#MD!IlV8;LxXBRHRYhMWo*cN^8}tX
z(JW;kUwz_{d@h|F#Wp!sKQv!$%l_2KrS(c!xu{q#h2wnt!|E_b5H7#S%~4&{Q}0dI
zo_%{`unc{FCpuqU`CAWdJ;5z{mRqk)GbP#Vvy9Bx#US+sj0$T0&Swya3y<?K_Ci5g
zn@(JmiS19o?k>_GIUS9;C@!A=h@X7`AzR03J^5gSXzy-QKBIKyc{itYH4A{hrU0`5
z;MF&WVV0htf0qXc`(!3(GQIY<kolZIXqXM-(O&d#<Z+;<D2s6$tM6`gCP<IRPI+YY
zII6bgnUC*+JP98j(PK&MX|f@MAVX)JsUUAtIu*xQk%+!a$Ohe)Y2o_ohkNDd+2A);
zx4%hOI>C!MObP3AKclzZ7k(@4UHfzGU0@9aY*h<3bNyVVPzv^pW<B<MjOl^Y;shIr
zCM&J?rGjKv*&T#%D~6i4)JMbiQ-Cll3!}Sa;ZFbMlS8G#>dBK!h3fDLRIf~lMZ;I(
z+2HP3=?W=;_4rBvc$A}?ux$CY(9HAS<@sb_o4;j2uY42*L|4P=S=HheN&~mWy7!xk
zrQ!|ElW`qvHM-IMHVM@p61Km%=M{cV`n8Q@U-%u#U@Ox#W24@N(502>%MK*<G0I9~
zTl>2r#V(T$sb4r!1N*|eMCu2yW=z+k*4Rl&`AtM}9dha;)ei&5ZM}2*L?p78lFqor
zCm*XkXkVaS_T|Jd+eVl%7g)xB95c~;ZF2rcz<2*c1<sAR9)KiFm*4H!c_~@zyVC00
z5*E8Ke~jDCNV#RbgL%<9sh!Yz!#|<NUfR2%$GFhe2({sGtmZ(DC(+Pafv2~77xAIY
zGd}bfG210m4s({R@+tG?hI`NPeX5|w(;Dchedlt(YmraUZ-hRVEz}EP^eo);U2VFo
z;x5&Tjo_+n^&5x#*1pD%4VglRjX~Sg7TU<So^7?1KN<zow=sHAS7LEphRKeGlI@qH
ztt{&Wr8B*PwU^3_YGZN*-}BiJ6;v;nK2#`J<F7z#9gl=H2P>4d)ID4oKylwJbRhN$
zQ__0ADRjNf4PNU?v113h-3U{CxNif>vFK-FZ!NW1<4eb<pV1CC+R##U3~_L`-I|N&
z;_QX9`Cm~gO~e07tLZ;b(fm0yCU(n5*Fw|29vCHC+4Y_Z_nf_Lk*C%x8r+JbWs2_G
zDz+3$Zk_5vHFX`nJ>oz^=-T|er{{hd#NcB)UU;<I7C+p8Lgai$MQwVK^iU%hztHu)
z&?QuGX|$HBEOkfXe(R~Jw0yh6zrot<4++Aa(zm76&h`E7l?l&bt#Z*fmb$cX8-hjO
z;Drd*EIhqBT_iKPLpZ-@uwbk<Dg{e7+a?)ZSTm3f3She{>FL$KVyEI<zqBX~H+Iu-
z3Te>aqBP_mkmR@@{X`P(rr{|0bjL4A8ng~AlAlacG0NOcGEBL$G}u{(;f&`NeVSi&
z%t?+<BYu-_SB{?E!sd0xBzL^-^La@YG!73<uI%G`cFL3I+J5VfLp_gOEbpl;Tu9?)
zt8(e9e`B?S2ceF+`w3TAp5BoPq34ajH1Z9d$bWd>k@x$#@}6^oTxHKWwFBNBv{BL6
zIknb73Ap;My=5znm(?eCs-XI?Vz_(*`%ov32$xR|){hBSPu3X(O%ByJcvot)qQ|~z
zJg(Gm$3{BXt8CrZv)+QM!?G@^)@nexCGu{wr^(=W;$5Y9-ChQbvB%2MYZWmAv^8OF
zk8OyJwJ6y71Yw=3R86EU60o&v>RaX7d0uP%DJkFa??AqxZ2N)rR?ahtWA);+`EIkE
zYiW{XPUe)bCG}%m!hGA3&NDWaG=EHw$QS$Xy8l4yM8%|n>A7x9qkw$-_jvDWU4Ib2
zo;2swZn=GqM`eaj&8(vLw>NGsIZ*!h&Es%=Rl;MS7v)p&`<&moSI$=7sRwe{!v%4r
zx&*Qc*-HZ4_A!~uBZ1}hV;yx`RKy(WVR`GV$QzSIe=hzLAhlnH!QLo!w5}7QEfnp?
z5ywGjZ)Eqe6&pI^KO0%5TyEP@mbVrXf(MQAFZ)ILg5D@?zuNs6w5=gM?Z4tRLBH_^
zlBI{&&GDv=L$9kB+xz$w=YPGpllY-JiGL`>Zu#gvfqL8*knx-EAVs;@!#<FYUIx4}
z&F<Fd<-*7U-TYb*Eb76090FjM@y_<$8Y|f&0dWr4+<!y0U<@>uaPnbRZVO&NJ#qQb
ztKgIeT@p`?lv+<whNAZ=Hr5hqU-%R8nTKGshxpm6o??p=dfhFQ&gM1j{9{=Ur&|vz
zm@M2=lt*u5KV+{eogTH1&2(PoIhkwv-5x-XJ>_3d)~D27DNhgSlov{hYO9q96wg<~
zv@$TXO=`SCrv6fZoHftdNM!v6kmTfrZ=>qL!VSVIl=rytACIS7$*S1JmaLJ~tvr!}
z%v#0mM=W5~Cz}21bVcP6sOiF*DkBH*#G~rhp~CBKX7F8e67IYkWF}W$t`<i0r}gMn
z7R<u2uPAev!nLlJsI6frhC5eNJsG!5hdWt(<fX9Ync+@m54QEQd0x1a@?kH6HFtzN
z&zGH1d~?@k=585gBGM2v3vA%-CsFl$o_!|g0*YR($X}z#Q>0u$&(9ZY1$#xF=PlE~
zC2qbNl23_ejiV*9b^UlQXxRL+_>~s>u}f^k{PdLgsBGSq40-xALvnoOf_MygJ<k)*
z@ltG8vLe9tI^+>#9!v@*EU#}NAJZMzjg4NJe(A@QuITH)`x#oAi(74Nr6_T3uWUE}
zfqLMvj+4)|%i;i<Y8%eLTnBBNgHLzo;El=gJOfG^p$jJS^|DrBIN?~?UaU(ck!zEG
zMc3*l1IIf?Pz7&-=>laWmyr(taOnEho>hmZ&+?PfX+6UHZx+|}AE^#i;A>*He4@R|
z6)LfA*|LVcdhD2+ymLHhT>PVMQEg1<9~<);dog;Q^6@2}y(Q+#jUO+^qs{y6*6_R$
zK}EL+%hK=ktPy-_mr>&^*1e)zCFA-u505J3{BIXC`eQM=Qq*?KCqg#`kGbd#n7$2l
zCWiZxT>X&cpP!*dWR6h&E#ix{C=RajDJEmVb=~3KQVsXQf*%Vh<`sD@50Z6suy?Zt
z6}=fA^=9gE8pE%_Tg{qPcF%3CsU}Y5#jgj~M6aV>96*X$(dta&!kAT;;?(;>ra1nE
z{-g%R{-YAUA3!~<!S`mEXA7e-!$@E=Z6DK@Jp=8ZB8<U8e(rZjqA;tkYkHoRIG#?_
z&9l71zVIH2?NFkM-iV+yXIaX{*A6W{4EScIRvE2*vS?O}6!2)N{eUD$>x9IFH6wCA
zTSRW@^n5R=`W5Y;<1djE=6(-TOkT6IvN#s6f%rr-SU1M)HUCBtnrM!!>qRi&-2Z@&
zzF}YZi~Zsn0otWz%%`KD<A0*H*~O*c=#K+)?cpTc*O6!Ymypy6=|2z$s{MNvRdrK*
zI7viJ|0_j!tJ<bFfp2-?ss3jgDnrTGjK{s~%}6_zRE6*mzo+rbC1&dWSIwnKxYS&g
zxTC~8DKX>3)MQWbUBoY}9yWPK{i$X5*kIYt4{(s?y5G~r)z*TLagP`tDy#n7lZW=a
z%XJe%(s7SNu;i}Ox27ut55tnT=h<p%TGrt#T&HhMmn}kygJIXnKoaRoWL^{@L+QWz
zyhK4}7OoMYH|1%Az8aU!2ew`xy@PVV1LgjlytpvlFxB2p!C_YOKBMA--f-zO_v2Th
z_YlZIxItbaiZqQDGz@OCyvk-yv@}vu4Pl7e+bwn~Z^7=C6^(%7jTUc$6CDBL2}wdY
zW^Qme4dzvb9BDIwH@1jj;!<CGJZ6*f0j9VLX2#F6RI;s5(KHZ=On8^K?N3-lG&G_}
zhZ}SZ<9(9O(Jqu9w6Bz+Iw6AIZ`Ol~f1@C>$@CL5ka-?rl?VHX1?RM?fgkPA9kMqo
zn4@g{oQj&{o?cCfscWu-nQU|`H9>n~yrs4D0@CgDGeJhh@_W*)SeHkL4-<l(v6c|g
z%w@UnRLfy5p*VA+hD%m!UAU?5YGG6K9s<2d$??AEB_xuVW*pvj&C$n_eKuh}=OXCY
zwK4Uh3NngnDxF&?N7pmFV|!?@H~O^XjJU=-povO00blO<%NBtr#)r}%*LeQ&1N|nJ
z4W)t4^;);2!zVr1U02-J=O2^ES?^xH6O?2&Zz<OJkG&dWbh}K4lGAL!dbVLGEvI_z
z56D|Hul5J|AU>0y@<V@qx%!Bk{7QA_P5!u79e7N2$%WB5B=shLnYX&fwcK0Y<67Z$
z@GL)kJEO5)ZDs@ZwGP_Er0JZ*So}JDI4dub^xkfgXie7xY?HCY=*^TN#=>vGsBFmP
zqPMDb^+4IU2*trw7M@NzHdvecaX4@CrmB<4l;SZJ5<9tyDq#=FRi=w!?#4)7jR#S(
zm<#U<IDfj&%U{@-2!S}y<}zoW7UEizfCT!2<WgVS<48M{sW*h}O<>YYnW&>9aiB|~
z239YOzNDgqt?6vY-@Q4pvBPr%F1uR8&0iuxp4$voT{AtH+wvg*%lbFJy9;+I*vUI~
zr}#isRJ_EjpGbjrqrH6rXKqPbDY&pv_W(OpB>k*~Z}QxiKgo5vi3T#C+LFk-X<?A#
zaK`)VQ;DxXJ&@uf+LgIwyzQP)V)MVn<ib??t%f<4jz3h#z@74vns$_=hIv1k=Q)(}
z#{MJ=eS4wd$tie*M_w6PaKD35cfPJX&yjfR3z}hjGqiU3<@Noouc(N9fe^a_IltF&
z@&X}aWR^8VZfbRp598HNg=(zgir(QqKb-nmoeSN2Jn|Z1hyMDXvqi?|UQJs#vrcLY
zk<*$Ut@OZ?@>*l}DdqL7Jee0vJPEHAc83+?n{vH>pVs$Q#K$P?tb(#eIPs)wgrrZ3
zB)cI!sx!ZvhOWQ!H+VD(zK$KQD02Nf)g?Ms9Q@kFX;JwWy}LQ<L)x;x(#2&__!g~M
z?HWB)63KlAs8kSJGD@GjzgTP^$9L{{MZm!mne<WeiBAx0wHKe5m?tIX0}_*sq2_-|
zVxF3q4@}I{67%%L<Pi?Te^6qck(duo%rg`7ESZuu6Uxjc$+}qQCPZn#{6zbe&1!pj
zdZkM;^#4vkz36CfmouDqvz1O5sYb2zYE1uqw=-RBPCxNDowMFo{-tWTow>LO_>nID
zvqB>rpAB#Q^>w-U94rIqYClGqYmP9v*POC32;|p<Zv>UWyTCFv#FrPT8Io&g5}x14
z%s<(~H=LO|$?f5r)UIULLDZ(^&m|@=eu#wfNRH*cjTW@M4Ojc2e0IxM&#aGdLj#kP
zP(~0HF={7i)b%^K;fnUW08~w()mHn_=atB7yr{^ch-b%oCn~W5V<>P`z(Zwz1@gHX
z^C*?gM1|3Bx?{&6d*>pp(D>2C6vO!vqYG3F{kd=i<siH@Pp1+d0kMJkCHQG^v7p<d
zkye7-%(Z!krC1Mh?HSAoR=>7m?iQ)L`cdG71$T-iFMixPmIr2<S6a4Zmd_7^S9_Sd
zyF*d58GVPl4Z3`OM4ro|KXNRA<j=Bk-!0*q1;BU;FbjZXDZnfMmZt!-09cU%%mQF#
z3NQ<RRVlzMKk<yCxurmplUtR@m)xsk(chIl-o(C~MASZybhppvGsoQ&LG96gkCJ{?
zAU}QtzLJCV2*+V+{o6~|+LwjV%T@X`b}(IZ^fH`!bIMCs)|cc=Vcqf27)*Kmt-eeK
z{ULK54^Ffu@^fU??^lnFZidD@a~!vr^baOZ`@(xQUy}Z9Vz+#>f>s}J%D>$BF%N{8
zAKe217EHY8ZxApl6yE`*(-e0K61|-d+kTzCFx-4SDPp!PP2ni(Q2Pkj9OFKe3K<{K
zaTY6f(Ma?0wl;bJXpLflRZ?mF>JQ;u^nXIH2Xq3UcPs`qDN&<<-?<oE&W%rx0G1|r
z`E))d3Nxz1M?X&5LXKLaO$?nj(Qo~ornJnn*lI8u;{|VDICNm*1{XG@y!-L(kZSBK
z%=ax8<{!Jll#dD1Br{k)Efr1u@Ki3Wsas}_vf$0lBSkwX+l1qSi6P;*xv-dB9UON0
z&AH60SEc&Rm-TJ>oU^MuX?nR?^*k+4$=(gO1hzWa$f3n1w;b{C2c}9{3LFJVZRxbf
z7Ix8S|54?LzH0@hD^>mMkYisxm&U7X#XrC!jPH@RgNHF&1jBeepXgm=&)9&2M~s!~
z?4mg<EX<z7vUGg6LKbEpAp1|9{Y2S+guM`>6J^QU+T&I2zaiU+LiB4QoC^`|zVPpo
z_9H1U=rxB__3lR~2cYReJph;I!sQlJ>6wJHXcVwJ4y9Gt25VAw$R}%iEMnIGNeLS&
zLi7|aNpTsMrDF4cAy${P@##aNKf#IZTL>8i{|Th_h42!;9@}Qxb}(<j+CWGA2AQX&
z94y5p?W$V`-lB1G!h}0+6|<<@VIq-hmx=nr5>ftKh4u7~z$N`?Or+T^??|&_$0g1k
zX<L*ymi!f>OE=}NBob(t#-fW<Df4K(q7h*8+@(XSrM^3Q54pIMr_<us;z3g|jM07;
zpZL*)32T?PKBxkv=}q!Fxot0@M25R;>~%J2)*t=cA)@Uqu10@S#y$p86T9UTy_fjr
z{lz$c?p8JVsOyq`y^vvtPUdLJZF?UWj0WfOcyl+PkQf^<iD~xL61Vng4CEIco>pwO
z4Qr)(T_ITqrHeCLh3zvvXwmZ}`$HGbO~a+<$4UX`4NF;f&X5~({5)zy%L5mo#;WzX
z9IWKxSCH*nn0#m2|A0~DS~xd=4?9_Mv$7b=6}OQ_FT<CU%<Ou`|1NR`3*<aQw<D+s
zC~r$G)v>Ye@jlpd`rkx>7irIi_TVI^MQeqBJxtC;m~6r6Hy-wdJYEyFpHG&K7Cf3U
zw9y=PcsVI;T-@eqlTb}vI5!0L!oT-1;80I+P~pXoCHo_0$#u5+xD%tLeygK%gNlm_
zoIJ&Kqv9IyN&_+LPpY&rs>ftcMK;5vHFJ#GF!Y0r3ySjrVQnXc>-_$rLiBTlFtJ-c
z5p(f+{5WD}QBS=^V`UgW9%p~z>~9rLWA1|3!(F+Wzwnr}0Js&Q6HzK?orTL1f1CQ~
z0BVn8yw|a;#}VrYYHiH5y)F1YR(zWn7IE~5MI1FBoU=Vs1Nj>oC*?IJt4}4ny-?i7
zm%=*+!<k}Kk)&vL8U;F*rL<iQZ;Z8=&W5C^PvsQ7jYpOe|NTvFlotYXQwsa20`7t`
z6zd<fhIX9s$N-Cr*`y3^9|v9Aa&ek%xiAJAn?0(LSSj>yLK8z*=MXAVRLA3DkV&xG
zoNRmolsZHFcaWp%*VYo}@x;jj>sSq)`iY7zYz&1l1&~9l)>Yu80-$viHri&`R$6M`
z-w*?fKc;epVbC6DbR1Jt=(K{s7zyGXaA_$ia|mG?hQe|@CiF^N#_+e0D{7o6K5YL}
znxYfYVzA5AK=s5cW+)Y6r>LSV&#m*#7E|u&lAcLJ+8p!EY-qB+3~;>MLd-q`&jF4J
zJps~CHFv!)KPNZ@t6=GxC>9nhQn~AWg?NpHjt{}GH&Phno4){cEex!62!{$mkIyke
zGz0-a$i{~`gb5+&)j6iHh9Ce4+;iy=4i`dhBo{HCG6VrY$i<OEI6??{MpG!B3_$=8
z@^MWFnzQ@@3vV>4<Mpk<BD=^hKl5cw<XQ4<bfMM(y=7y_4`(MeDtHA_uOFItYEa4q
zWh@t8hO8O`ex?0Vs;nXTo<oY45hA{ck3T=A{>#y&=?UXk32Nhm#x_5G8Lo=$uH-Hz
zbGdC>TOj3}%SB3^yWivA98Db0p+^{tm?ji?1SEbVXr7<Mwh~8D9xo()Cy_pn>8X2*
zh8ff<{COD2x85|2K1`x>a_$A5GiP4T*Wk>XvmocIaTd(UFc_9!hnueYHbT3*j=0<@
z!x|JlxsGn@;PQ~nkh3PP*NvZu+=`o@1s;>-%W;gc!a2rOZ^77Tb|P8>#nEsgs>;k&
zkFph((R_Kdu=zB?aGhXbkJ|G%Cg{=Rc4ka%wcEqV2)lU{R~`*4u!ikM{JD|-s0ppc
z>g4cG`w9Y{6P!)^{vW{OC*dp=AweisuwAHpCsa0TM}}Oe?0%sJEEMZJE>s~6bqEKg
z+)*PJ4#(o?21~i-F&3DGTo;%-025Bva=@)ORve2%Gg1<_^yRKED`mSunM62-TS_Jl
zg1%kpY+E{M>exD{jkTpb+d<#TLKoR^C4NUg5&K<3I^WaRhi+*F#riw76IVKAVwEzq
z2VuXT9}{HzQ`@*5+w;kmWH;GaZZnw;*ot9mmVV2&T{Jg^t#5Z6hlj0WPEUQ7hpm@(
z9D`x2Z);~(plzIYdeLfSpLQ~uKBo1=6oK1H9psrQ#O?h2{|I93{4u2EWVPEn5l7MQ
zuX9W^elqr)DP6S9)SCK{o=bxa()>mk^F$hBQ0NpgXCR|f)Sk3C$k&;}_|ZqGC5!_Z
zR8hlvW*O_+K7)$ELe5PymM|s=SuhPW($GXBeg|aO9IeU5W~^OcI(*jjLOVn>x!CeW
zl9B9b=4P9B;+61ChxM+Gt2wan*mSf|Kfb$XFTIpufzh%nnCVE#(PCub$hhHVGU(*>
zs2kXwT8_B@A*>&di=KN0v+SU}$8~?VYhY8LM>C97t(%l*8mou#O9_J(`V%x}Zjj>!
zuX#5);(iG`*O0Y49#1v7J_T53ZkWtWBm-rU$$-Hhn~gL?7zpDh;FIXf2D)1e>#Gk;
zw8M328qB*C?Ibi$k}5&V3i$()W8N$GPfp>0)FiW^Hq>0Z>}7^w5>*G~<gY-}^tkhs
z@}s@O?|b|C<uVuJce?rA)$8Yal{1_;s+-2rd|O~Iv&%`13a2`bvG}PJu2f`=QR^8U
zo4i>Sf7dwGoct-ox7Fg~q-uxH|E)Y{DY|TZaOHVe<vG7EWDzhax*vzk0p(dhE@I;A
z`Q`2;-s+T-)q{+OyXk=!%c??sqt3B!<pzond#P#=;S7i$ABF`r@AVF>j=ev(^H5qe
z^Iam}+|F|9KGeAxOFRFy7J2~{t!rizWo_7n#Q%l_jn;&V38lV^*}`@UTXMscc(Pc+
zs6hY=87ZU}WiXm+7v7#`PZDA{jQ&7^4Rf6^7k2N5A)1HFCyeSCV1_Q;z%7N=wvCa8
z1#737XlV4xs>QxO_nf@i@dx6szlDdU<lPqBCI_;#ox4%*@v#lsP8&7W6^0AoNLrkm
zp!Z<%MVp*{szj&_U(i&=W?+dpdmv7nU}+CznAuCd>dy<@{lHr1OfQr<w6*O)Ndw|#
zd2*YEFPbFzLG-CkGeWPMw0sk-Z>HD)gC2aZ2j6q&8`icSe@05W2Q!F1*`e$m^pMCQ
zt-p5yb~*BTnu4I)XS=yu+ix7h0kHAuBmJah=~htJwXF0jt_5A78YbmXz0j6xkWA|-
z4^BDliLgteCrno?expj1v}X4aWux9Y?#%y{Y>y;{f`C<#FL-X_JlBYS4<YINn^ZQn
z)~~vRc2QN54ZmvX8JlWvF(b#!nJObpR>(f~CAR)fn6_zcyqHpeGTA10{qZam2I^-q
zPq_-)Ah$rqQ}|17)Y!rVqs&Az%OpaJ1EoOQtTbVYX-Y$z8YLRdtXmGyzD`Z|AU6lO
z<{KINc>BshXY7-k&n}*~|5fw$d2lCo%P0CM?MXzxru{xnH~PR#^|5v=j|JExGLLws
z8!DmFm-3!DNc(&kqHO0fx03F&NOv+v^xfu7n<KhOZHx{oq$!LyDh=q7!qD%XJTf1!
zy>yoEJTYnWM&80)8~QmrALV9{J}S2gf$d<F%_bSuRC;HZUKA2Tb5P4an-o_AbT0}7
z<XKCo9f{TTS=r1<ekOA^d~;2arb2=3<}5Zw=wMBA$61=60q?JSWBq}t(+2`mq30W8
z`RKFK$qeLUnYu)pm13q+(QahQ#m})!181{RDiDW;Oen!@%#@c#E*;A(&Bt=(<M;E8
zenpfhzK-BB4n|j8>d`yA;t~++h;)~<7v)KeDI`YxQi#&R?k2b5QRh0`LVT1&pouhR
z=ur5wWiOZC@-A}A&(G2}1~w<gzD-j@)Hfmavt-v)wlbQ-d8VA2`eHv@c1>cjpCh~G
ztJqQE1vWLs&i)m41UmS`WWS%;W0n}waSN$pDl0%fez#KmOHkq?U6_X}%rVYBBl{NY
zn}05zM~eHFHmPY$kc^>`etTJV1zIg*YGh#F(rNh*IL1Z>_bp{h&x+EZV{T-~c5$sJ
z4LJsT+{WaX<DrMF@zFy@yyO}ev7P5?)iT}7QfYslai0@pF1`jZ7w3#Daa&PKOX#Bw
zX!}*2h;#ANt%!^a?_0_MW<`msPAcsmKqVDBm(q@R$toYv4U7}F9k;1Bhj|*CvZN<y
ze-9KUM)O=xGr6KX%KY^Xymae*$!C69x+oqI3I!zJL@i2>2D-sm5S13eR4PIA2^CIe
zQcQ%=Iz9sqO`kFpmd|yIR(1>CbU=kWx0Gv`$wfF0K${`mgY;SFs2yOR6D^(9=GLRm
z%3R^9w;e5;{|D#v;?o$+YOtmo_L`NP>w*5#=}bFHsAL$|g1NbphF<%^yhdKngg?zs
zeA7>!t2hgaGjJ`4n@L?w%oZoA%WDas2%ja<uf9*TvuSNpH2AWHY+*mXP<<zCy;y7^
z-oS^iV#-%cix}pzPV3xYASWeF&J@k@6xJGNUsZAcA8T&{CTCIY|99TE_gRvjnaoTk
zAq!-}%XD{ugk_Rp35XydN)V7u0?59-(32oAbO;ED5J1^f6ciNq-K&DQAc~6js&QY5
zxZ!eL(Cc<x;{WqKRd4s(lSS_H|IPDESG}jIPHm^EPMtb+iZ?^eg+bJl0%M`>f>{7Y
zBjtv1w}W3c9o{22)2YU{tTs9i?UBvgOdsfPQm5G)bmTk7USAj;Y}`eAz%@5MVeW9_
zCvrFFP+~#(mJt@@(ZR`O-8^nEIyAY`+~LW6d-OeLa;ANcOzvdv+{s<coi|xEcc;lC
z&7D8lZ|;K9<ni)ski=-L9JWr|MJF@Ng8hr=vvA$3$rXL>`QbX+GC&qpD#SmejksOi
z=s!A<6X)aSs@<%=`>5N0iT4Ym{iz{Rp!i1mbcvhO!_0n!9}8|Dj;dDQjQEwrsPBq)
z<9y14x}==oD*ZZ5pqoS;d34}!##4~Gi$k6>fazao4`Z@{cD}-@nz8Jc$F796<U8Qx
z^AO54c?%ztxAN0ms5Y>3<vcv#ajN&!Z3L9VsaN52<yZVSxbw1c!ZF-HbUSYC1$s!n
z9N)>0d(vTVy<6<eXWs=d?<5feNEPo^#MiW7H6F<AuJ$I6IcWE~>@x}4!Nx0SRku}j
zm)49oDHfd7L`)>IK`7TwE-X0BZq*Mit74-!dM-8U*}Sm7(0Dvaj^$pJ!}!5UVUlCc
z{FO=adE1WW_@L5QA$lz-#b>BdGw;cCJ(O9v2cM$fM}Ztq8*znm!O2zzY)l=9&Kw$j
z1=B|A&K6sxHRh3S$(WeKS_;O==|p1vzJMMZM*m;J<Yzign&7xpfY^;&1p{pDb9w=Q
zIPhBh=BjmEJs+K`@4|}8{E4kBnGT5F0+)xR4vp+`bSSN1qLk#>fJ$N6y;^-=r=E~K
zM1&{%1XJqxFuq51mC~<Wd$7b~Cjoz+BC82kV_IAc=4$L?J_pnr7KmWrvlhu^bT`fO
zu@IHGEL9Eomz(Fm$bYpoJT@{me+)|=jRmU{y)eDIa1Ws`7QJy?H~D(9Dnr=MCyTD=
z4a)sqe!8ot6k`TWdF_MrKo9Rz0Ib(a)|QCza>jv{@{KB}3p}>)M#48E4bfd9$9zr;
z)a&YH6jkE;alI|RH*6j#+M}(;t+ez8L<1(1Tag)gQNnBPlsWhM%m78tBxCFL#K>=R
zF-?LJ+Nd1tE$t#V*Ic58qQQ2Ss|GU<0EH_+$>b`X+$6!SNdof_5cvqaSb-i*pm)M)
zv!=jgYtXY@VZz3dG)SU7Hm3MC#^HG~CushessXDH!hAwH!IWaS!$!efD910dY_K^h
zH%=EaJMDEb9Xl*#A{$|OH^5qB23_lVCeoOZz6v~3<_XP-Q`N2Wv_(pA7wXI`kY`up
zeLK$64{9b>H%{Am(ol5}swJ0t7gY!Q-pjjt%T-sH=gYy=z5wcrgQ<OR{n<KgqovWo
z+`*(`xwt|;E+kTrmnRsCE`f9fUm>`Vn`eCxttFs7+h@W9rR_|liov?USYXJUsD25f
z8;BhYH(w~EJSxwZUHYxp0(Z7ym%Gs+lzN8USF_>Sm~jkUq-%H8toXjBsvJZYk-T;e
zcqI6X?xL@oYMWrFd9<8Od(%fwTgRVkaxg_qCzfmOPMjeYjdm$`xx`S#Z-(@SwgjAo
zG}XxCcd}X8bV`EFmyQivc?EhtsQRn4vIOjM3$7AsJBsIScQB<de<E+?t5yIiNvg;W
zMJo%p6wC@Xp=8Vx#3IGlQF2#)FN+xRSR61~i8!XI#`oY&s5yA~$n9zwHOWwU{0XM8
z9TeC!Lbg%n=}An7pw1(}R`nxVS($3V;(ycht}ZTVH959$S_veDz21JWxuh=J=@q9l
zhngA$<3DAw_eH~wmcz!*R>~18KBgWkr2Auf%+9)T@y0h}QnyyaCL-=MS-Ao}3)1t1
zH@y-GX<Vu9_JkZ=#z%R)w_PiCdY!fU<<_+_Qf`Z;TkX5UQL&@;?I@ZfU>mNyXDuqQ
z;hM_NxS2UY!3G*vY-~&pS_ci4tB3p>8gZ$71GsJtTsu@tgGooD)unmKwV@vt%hSZs
zhk?Clce)+DWUdG+ZmDS#U4q$7Y;K-{x6Pbn>&KqdXXRVfrkkG2&-Nz~B%Vx~8sY6O
zk2K!uOky_hrKUE$RTi};i*3<RxyqBrN{r&RGDzYubD5>@#Ag2a8rLdnFs0w*VoCTw
z{1zs#wTDo0_~)FxA$+pcYZ=k%#|F`bw1n12ccP)5(#w7Ns;1t4EVt{FDuS4-mf8N>
zwG9*2U`oI7gUVRb81Il<ch#4>4QOK`nZj#}LNgg2MHD}X`t4U)$5quwy`zO@rQD}k
zXykF9wVfeBecDM+^mC${K*s@_6H37iDRaS)xcdpNCCfYn?4JP41K@xJU>*PmCIIs!
zI^d_cHS8UfH55tcbpP;tJPJS9nv>mUKHfDEu0&T8o%6Nb;T)rDoFC>P8@=55IaJ9;
z*W*{EPgK6RC&;xu*YhAk{e|v@kKd`a!luG$3pv&d&DANuQ)-u4KJ*uSnBZZsd5n;P
z{3N+*5Ay&xIRTglz$pp9Jm&{fF9J}<s{bW)Z&M!*#M_Kh$EJhy;Xn#HE*+!~2U5`S
z=^%YLkb++9gLLlP)Q1D9<S*gNN^TwirzQaNXb*D}ZG|Y0b7XA|!^UwUJKt(*U3a!K
zYX-cCE)F&^x1^SoBa<gOqNs;ql@2}xI&W%X3;e`4>K5UA()zZ9qjHmP9_f%6;Ag`6
zd;tcVFTjBZl6?9A%JmW(gQ{(fIa6-@Rw`Mpa#$X{4IfPDjZgw#Z|AH2OLvOi)siHG
z%jumDWajSfWWt@e)yTpv_y09+^{sHr{eO*HBqiK(|6kzF+MUQ#t-VD;HMWFu<9Aql
z&!mOlY3-ehH@XIYmm2&RuED!g4W6|3yggidpE22muxT`C_9J^jja-r^>5h(_G1=n+
zP`DzXR{=NsVZDZ|jc&~Y-JT`@?&;n=BZ*)h0?rWt*8O~3vF_*MPG{W(bF8~!xwt}l
z9$%K5c>r9H0L%l={s`r&&-U2i+1uDX&tA;D4L)&=o9~QgpX+#b$F#BOaqLHM)l0$c
z7^}BK;lY%?J#$q~eGT2y<=L!OTE~!#23zZ)y61*$H*ae;CT>#f#x5y#!|e`QY9H7R
z8fvQP{D|Getys^zwRqt14_Y}jRyUouje6A9q>4Rw&$cVkO6V%FOVG|KIW3=F3D3+`
zp+x0vrWWsJuJFy$(xGUv{yiP6zh7Ziyq3~?{gD+LRTH;=*@l({qQrvR>a;b4w^cYU
zh~=B2fFSyW=wgL$Uu}aM*S9occ6Q+DOE=Qhnr1waU|SKyZ>qFK;tbFCNxmtvhanWz
z2hoSf%rBd(f~c)fQd{P7axq+p@o{i6c{WrnKF+A#@o`!!^JY`~G#m40kwg8&U=t@O
zj(JNe?vT<n4zJ$|o~Z&&={MnX91S>rtL*qKr{3Na>R?L0)A_BV9KV&f_wH>CnmU_k
z_2agta90c&Pieo~)GE|8u#;*fWiW<IXahs;7%mZyM*fAs<z6ILE4+EG4K{IN<d1yc
z#M$H8ONl@Ed-^?kH`5;XuTl~2SA=zn@IQ+19z}r8!~@Zq_7|Wu|5<u(^1TF3zK@^g
z#VQft#|3`Bz#kAe7TE9l=Ow#-?bhuiwrS;cxW~bKI(-*5CR;1^l(1&w4}uBv#~;E$
zy!anDT?KnXd1)?J;LzAE&}r?5mCBqk9fyjLjX&&ge8h0%;*a7Q3a7e&qL<?qeyEol
z#VuqTceIKTHZM^%vdv57<eCkfvBy#jS-AG|v~T?8F5a8mjpg2L*d<YFzw+zaFlX!1
zORbD(Ai2E4bui!(#m{OyNT$Z<Im=wZlyc6~*Ov8^a*i)-$>W9d%;~sP6mW@$;f4c6
z@^bcvsIq7x`W%d_d5GyeUr0PpB%UuO9u8Pt9QH=e!;zcwd^z!aCGl)ZJYP*bUrRh+
zPdwj{2NIoBBZ@@LBdNbFGdmfP*eQV6>|nn8Cc)Yyfq4L|O918pa9IK{4}g~?0P_I2
zT!3qXsSUVwq%gKccBCkf3sC9SCn?PX;OYcm9spM)0P_I2G69$ez%>HAlN!nscxykk
zzD??EqY!rwOG2ir6qO`X2@dnXHwGD+J98F&99-2T&@`u4egIf7X!^oUL4)Y0v=w)N
z2cw7S<ys|h%ET5p{&K~RenE`*V}y29PcOue;Nkdu9;8%1q!@SVRd@I0{pn~P&NLT2
z1tAZwu~X8XVf3>xatNf7-7*8OG_lukoR-RQGfK@Xh*5t{`ogKNz}*ChZXI8BMCPK8
zy1S;=CKNEV^)zAK_A$G$RG-`Ux(22b*J>ekM=5u)-MZ4RP#+hi)0)>Nt!W<e)Eu@y
zZrz<>8G+4l*@b3MCHk9a&MhhHU#^z*YjykywQC{BCoDa<5NaZjIh($j{3M~9_Dx1U
zS4Zc>pAv|}CC7erjTVL+==TYzdlXy!7VDV=)H7Q0Ahwh)A@8SGvU6R$)>$a(%^1Ti
zIp}X%QS+mf{D=cCY5@zQJW1k!yR?AC(MoYA2mEsjSQ^DXt^@w11uTzN${zT~7O-oy
z(lu-`H>i=+);A|@Z5{wO3UC9|FAwaP9D7zL+L!5-9clBGib2ecay1i}2f!@}z&rqM
zO#tQra9aW}4}e!C0P_I2Nr20o%Y6e_8E7<Y3e3Lj6$&NRtCM8r;mAWb9&B#ZT?fUu
zJ&9o+0Cyw+^8mOr0hkBCT?xQEhX(G(1lKx(sT&k@00eeD9*Se%TU;p<vuui|;QGQ~
zO69B%(|Rg*^d9k9N&*6fcznx(tFBWpGjy4wuduDXfL$jSr0W=}&E3SarZx|N*CYV*
z0C;TzFb{y&B>?jPxF-Ra2f*tSfO!DCApw{Nz`Y5;JOJ)X0OkSk#spxVgM&y4SN2%T
zt5qvvyeWxc9su_z0P_HNAOV;Mz?&0*dHncah<lsK2b2Zey~q$V2dEtL+5(yY`S?+}
z$@$kOgXmFOFZwh;>1)to^DQJ~33-fwwR7Orn-40#`R*zWa<{B(tC?;VN&?C(`V3K5
zz}9EN7=?;_ZHDv+EabC<l(Ez6;4_hHJ`Q9F_O>Lh4r8w4CL4fv0LZ5S+7Y-I4s6Pg
z^@Z-kTbfOWJHr6deD!&_m@bM>>^@v#Y_BPM4>GF)6nsk6_jwp>z=1lW6>5tbSG~y3
z*RHNEn$u-;%6YIPRF*|S^Yak3(H$u)+(geITUZ$kC(ZT#v5V2OByv4xmSo)rQj;$j
z0;NBk^yiR1JF<MH+3r19Esa6^fiVn<u!&JG72+pI(2dO=wSG4GGmBU)D0-WA7A5{7
zF_vu_z@nVz4PSh$=hqmxZ&%-kF-q6@<~!8w9}A}5hAS=C|0Llgc8v0ki%8LUoS(`6
z0@hRn7tQKABXb00Jy&J5n@_UsXBVHAh4n5O!D5-3Ta+Z~X$pz)4j0-TOey!NE07!N
zt(h-sfRNPOr-P}tsu;7}TSb#EBxO?&2ak{8yEnVEt8vNklnScF=Dnq$VO`3u#;@8z
z1D4ig$ShKs^Q>REQ^JEihpBIZDOG;OeS|r0d=4259e4|Xe0((imDkEU6tBkb3^Z)I
zBjJA{I3c!&jk8~zxdaQ?x8ihRtg6u9)k@yP)ixn9)kY3wd-$NM(D)$nxPkB`n$&@M
z^Xs@(f<Yj?Dr8shv4?0S8{(m~_+0R0YlC4;HUO;cVwh%_<M$GPfkA$`0Ac|lrVsdx
z@3wG-*kW)9b*0Oo&wS<@hVS|6v$o;dtXamTF|1$K(=Yk>G}3m3F*88E-W@g`qRbJO
z@A6lM_889a^5++(UP#*wFRvBWZlLtMbsJx-q7<_xKd;JM<>df+N?~zeAUdBiEEx+M
zPYaW{ANOuS8tKp~IC`dnoymt=P`<7Raj+VEkjr;u`FLnA?3k^b#sMd^?D#Hp>0D^K
z*=aSy=*l~H`SQZr8!4|D<c~k<ORNoOwY!qb#-9QrMT3whJ5f?N$BOOJ4lVBwvSDE@
z)DbAG;j45grP#VkjW;D=SM-g0kj$7pSLYWbRzS3}hS5>#qa#v%5LzGD8)rYHSuUW@
z7*`GL@Vw2dZ1j8*F%b^XzQ^IE!~O)d*2lwq0WH3Gv3jqXuPx4-e7HU@Y#eoBQdV~c
zp_}2ZJOe$OuPW4L7vk$cQS0ILX#{pLSWyUKG%AyU=oo$HY745fT~>3KLL8{jwTOco
zQ~CN&iane6paTc;1L3;aR6C6S7o;@V;iLz5$O8pe;3ZFvZJvj3%Ls-QoUXuN<~8rh
z>_KbIU`)SLvq5(dOg=@VroJ4EcFEb^;zG5HwTj#K)=qFEZxb70FEY?Dtrf!PjxtT`
z%Dtd#`5r!=_M2|QYr}bt2lJ5z(@B%3vKi%>#F&?gkQ$xBS2?{w$kq<d*A8^a^Um1x
zr!_&;!p6hxR+n0aN&Q?z^V(wKdcnr2+gn_inRS)hTd1zDga$6^tg97>YeOHSJg!<J
z%lC8xV6C`n$blPq`P%*gy31>6KWL#~+yUnPhX!L_PRdt@la8F3k9i7;2}Kp_-V7gL
zefid~@%)pL{>|42xBGA@8#0y5g-usZy>E9V^UKAQGgs@&*$gp%IqP38I-2}_<98R)
zMygPs9RFG`uj2n({QsT*$N694|8);desKu;SDfTOiR0{orC+u?UOBn-qZdiz$?ns&
ziw#q(-M++?hRs9XnyQ>_Z+~oBuNCqU`<3!oHkXfHL~LEx#;(;i5ZF}Ir8d^ImLSh=
zn%R=w8Ug0lzmx+RaO}7wleZg{Hr_;|yRPBF@${F*l37zdwDN7T+nMSl&GSL#AlgcQ
zsQWU+Yx{Z=_O+$KCeenYc-vRZ3)(U~x6{C0cOkSXg9|9&5WXXEW6_^=DuyFZM6Z%D
z!hezfUjC(brSIgwI^6gNwHRw0a<YCex8Gmu*F7&`crPabFEydFm#t;9<?2PfSyNp{
z=Y20Vd$~6o%Y#`O(`o0L`Ff`4S}7OHEm!voYb(O|4F+>=xf=Q9(B`=KPFe}|&}^Yx
zof*dWDJG*>F1mL8dz3RAN@p&m3|W?4t%0$vIm@}$YfDji{1Mfv9W=Cx6(kd``z%mQ
zIiJA>jWHAN%+Fc}3uA~UA4AYGgv*{eAOFGmX?#?MoS*G=KK`}y|4x3G6vh9!{J)Uj
z3Q*#-uhchVX6wbrKAG2gv08`CHG@i}gjY<*$PwcN#b6y9!*10n@^eEfT8(GSjZB%I
z@ZG49cpUw9l6RlRt@6E!|M8>w;o5P|7QX&+Zvh_1w8?o$kmowvQ(cXRCHuRu3Kv%B
zHr{ycD@B)*ANP_=6r^h`sB~FS-qy~s+}c}dLF`Agn|65O$4nr8He2;A^J|p4_5PM}
zvfi(F#DY|F7kOy;lCUxBlw{EK_n>+|s3q4@nku&&H4M~BH}|mzc+OJGnQK1EVq5`l
z)2{p{m8S~5eU<1;m3~&GV#gLJVT6sV$ifZu=?iGiZji@chOZmklbA1l$@XKu1>%dp
zlEhqg>h@xC-N4Q?K=-HZMhYskC~a>x@3@sprM3&{9wv*?dT|NfhwvuC{>m(;T`^Xf
zweBdjeqW`pR;={t)G`mSe<mk9q%qE@ueY%7Y-WoZPtUHEr`khC1;-@ySNc=g_Rp-$
zq!C%B0=+a^flbdx7a6gwy;(rsV67Acyg<Ol1#77D5123;H=X8v(Iy%MoA{bW3()Mb
zM6{)mjgIR(I!0@ZY|Jzmue~L1)E5U<dkR?fBcq8UH4NEyB1LW-_)-WaUc!{xRan93
z;x6pTd`L?-{trC!m+tO;3(89u%hq!I2;j!yq_c7u2IJZWBFt@IcO-g72;dM`yvgu=
zlusF%D8vsdfO`_P#WEt}5Iw$KM7>2%_IGIXqKkbxy|mi#{k2u8?Z}*E94Kj@GKZx@
zd{pDUGkxi3<>Jvc{u~SO0YV3C!w|-^L5pd>##xM+c#NWT7i$+5UFnNOo2;bL>?V&&
zDS6YhQjAWd-SZ8NHA`39#|;rbD7h|Y9JsBU7R$XSz@5Y-p0cifF>ma-p}^);skc<J
z%va|dzoq;Jw;O9q**|LPG-C!D(6mgYoIN^sG;>Iyw{)~E;dHXAk;}xdOGSIPYjP`E
z7+Lk5&Zz@7iG5pn@PJ2wBP&6(wY*^Qst-<&<tWBB)GK~W|0Y!uU%jBTbksG%g07`=
z!)*~svM_7PN^e527@?XKSG>Y-3fstXP;7%d9EwirD0UFXAsxiQd8{Lj6sO&$5S|y_
zHh~O{4Rwvbh0-_*@wOdnNqt*$F3fsD8YQC)7h|ZadcNympOTibzGL~CUU|AsK_uDN
z^}SEBtAmrlw7yQZY`w*bE>#Y)c+-H^XcI4h+cCqS%mZw5Gi%lN==R76cZ9&9P-T*d
zWb{Px2+U%X>uY**P9UN1BH_8{I>wqL5*EHsZ=P_>bSu0>cs?2@z3jkPI0^=~GoFN-
zs?Gs=55!ZQMRM~L$VZ+P=(=WDR7~C9?_Q^_Ax$M~cQ$Zd#RM0hMc)0n&GWcx(AbiU
zWYTFX#^Yre{>fj-=F_pE!(rp3v;1iHar`o7y<R&oMGGU#_H<t(>0MXjp>}$2TtL?m
z{XEgS8GLZ|=uI0s8N=N%){(Q@&Gp4$<Cz2vFg@^<J=&bq-i1phtM$yC?PXWPIJ*7Z
zotd3Kl0p4McQo?GVMu<Ns}F^Zv(NTbbEhD#X1R5gBN(mk)A*c%ZH@TSIo@Y$1hNCI
z5u9bV*N8H_xd|x9SNUlCuY)!c-lx3@5kW^M(ZOB4xs|9ivDOzj2zsfyykwI)Z$Yx)
z9(}$qT<+hk^rIJSKW67jpSf^*F)^?KUDOvBVrhkItNxW8#5MiLSwS&`*dbRFuCPr{
zzR#sDOG>p1FwO(A+6Z@aq@b8HQ#z&{9v#MV-(7u)%dWP_MJUEg*rP;Wg-o%LjLnL;
zN;_Aw-POGcaRUr)p}Q1c!cP=EJ5gMBh*o)4P`HU)e7LxYV!3*JA&!Zy`mDI9Rd?yl
zr$iyBGn?qB(wkd%is(dkvUu&G+Ra!sA(EEWR9)fQe;aCDl8i02dwGn~))%-f%KW8+
zKD<rV3P(do`{^U;_{%hRBqJ@zj%B0k7=ug?b~R`>Vlc7vW}R{!`@anSBUy-7e4lx<
zH*4H0jPBI)fz=Ml+M}neva<o{WN)B27_rCA_dNL&@p-3+!frg2R&p=Ap68=wqZ4WL
zo+q`*@SshLS|d?Kt{vcT+j6;#2V7$99NfOb?KC*$<SG~1CD^XEz=(@&`He!;v-zy-
zy2W^EL)i!Ac0n^v9qD4?^lUzq`^EO1v(x^b&3kLYcQMdmB*tP*RWD1rFB_fiT1mIp
zlL;$}`CUKW>zTif&e++F_inwRn|YbWVK_4Ytz@q$<4bEGROlt^?A4$`mv!Q&dZ|wD
zp`s@9Q*i+oLFoCkUl-5NQ$^VuE0}bOi&yi41XR>J_NG%i!!h^$Iib%LCfH~&&U5Q-
z!C{6vLztThX6W@iaC606;4|GMig8DqVofAGvTVXnt&pfBTF|AjV*i=6iaQ+erw3}8
z)FkT=bNkian6P_0aAwl^DQ2FeeJ&Rr!pHU&29GhGwv5{~c#J=0!43r3Y56OM@joeO
zy-#Mb)CML2jJH_fcjcQk%`aB$tFzJ97|6p*Pep!;m5Zg=eX4=eTJ?xtY^J5!b#-rf
z=ool~+Md0opv5ytC`qZ{!KT;*X6t?U++-;8mrgAgj523eKcTm9Sp7xYXuCN{+hN|n
zqwV&**iQ&MYC9SVVclThx-N_kC9k37`QEOfV=$tD6%oE!seP@lRIR^Gt%su138)>i
z{TRa*qrP)KdM<UTjnKqmT*7!R38OWF`CMoYrU4imwf*Yq9$U9i>1Y?Ybbza6`p`Kn
zq-auEo`r|_=F-<3dHDNX;@D~jcM#ZKp1?ZS?xcybxoxuf=`93N&ZK~w=WxzfjD6E)
zZJPC5h05X9WOO6fWaem$&JVL|(1bt^!l4D>S3rxodJlqb-_-E+z7b)lw$seKzhfP-
zjTpX}XEA8o4QqP^qZ)ie<ek-9Taq2J7f9BNV_S7_7U0^V>a49rwf(cPE^pP!xuIjY
zm5q1Ig+$*(dSQ16xYVtcrB>ufLx&ON+~6^Jr{AA7lCI5bCGKj)Em+*H0_Yx8>ix3L
zhHS5|{uES(yupQ<23vP4ZPC(plTXp-V+(snA<aOMmc+NWtEiPMwIqg(mtT@};dJ(c
zc_W+A62yr)?WK)=t(uLc6zj8j6z>>CaDnaSus!^kueobBi~6kVNqc@&*{D5rQsUZE
zk^$OEl8STzvd^lXKi^S33mNzU`|}gaL8&n_XBl^8w%+@keCakCF2(Wkc#X01*V2pe
zyVX*%L-K#m=eGY#KJQUJngjk4WHM9Gd1%rXSwTb5tzgoZa&NB2GYP#&oKIu^2qbzw
z3r{xwD-%<9F8(k`>V3H&XU5Fj-2V|^xp=A-B0W?Wa+QUw=XbPTSwbR38c1fP9*1t7
zN#NkxWRL=TA;zTW{w}#{#gU9~Wa(g~ICo+;C9f10^h`+1GOnMj&6r4bu$t?{Y&vCF
z`D=n8%mpJS4(-!}I}dmDI7~lpQCqaEu_#%Si)Kwy+E+IIg-b`E^5Qks*4I?xdP-L|
zhNizT{?0V6-&0JFrAxQNb#-Y+Si0=trT#+o9M=Rj-xS8i?J(_LE`hN|o``f>x~>63
zkl6H<@daN^$NlveHZSS#&Nd!zlZ)0FUo+#Oy4|La30z%NY8~B?s(toSs?Ei=1}SU(
z82mwerQUQbP^mfsu^6P?%kHqQzl78cDbJp;k(-b2?+NlehMl!#IO`7OdfcHLjMY%l
z4&|!BmXXcXA*{qK$u_>x&{4KgLN7K?wkc7?)+BSMoke{9pa1$7pS_}8SN8J~N<JG8
zJkLRU*>KK0g(Fpx-|)W#zt$p!JjMTA_|`#fY(5OGakG@~tF$)RY-oF!)_4h%@e<m2
zfz-9zu~ESDb}f?cPG0$&{Dy6C#7KV=UuYJS+$dwCk&V9vFu!UhrN|bFNpI9H^6h}o
zZ0^#tFaywwVZUtrZ6Yth{f=<vk}Rr+6l-&fNq-gT9Tmy<SFt)<{UwN%&UY1zDz*l8
zrl*!vb5C-3n|X4WpZ>Cj*FQ8EIc3UKx~7h#VD?T~M@N=jM;=Q@R<K=QeaXJ|8EqhT
zFY(U0ONeB=JNhZWN)HN<-4>1lWVW$%GII$Q3QYch$rl$p`WZ1r5l_N7GvL+BD|8mw
zZj9ljJ!+9l)FMlFt{y!3U9|*8B#<IoWw1WcYYkoLg<F4Ks?F5UJZ#fyy=p>sH&7Oz
z!%Pg~Y=`VtJP%=zC%3SXS<Afd$t{dTZXpbh<99C3T=$2}&7!fn%=ODC{|TCd;m}NG
zP!7<?Ni$ygWKbk0)ix;fC}_ezl}gDS`eNqw^itF2^h1~lT64Pgz%3eeb2=94)E%fs
zNSMhn<yLcidnRwuaIBeptNB|G$uX9&+mp*cOdT^v?PTA0Y@6pU<vj;zH8-TjpFWGE
zs50+n5ka<^MNo&}e2b!sG7+R^o|GW7zlb>vBTVdLVNZ2CgjsA8CiXHMU1l9!?or7i
z_H_ySyI2K`FuNek@-)IMdcw^5*e$}`%$%^|Zz6=8pLf!PkT*hL-f{hQMdoGCWL_aU
z(3<~c`fWY^=8odaPC<8h;}{Xr52$S8>+8JF#7^3N+uGz9HKde$8~r?c=49dJJ7!L7
zc7rM=pP(BizsLCCbv7salvISLvn%<zOXEs@LC?|yt7yfv1hxHe*czR6M20scxv<r3
zv&}5;{-pBz;H{1gbyqnv(+ux3z^?SRhgz|YE`d$(m-2pxo_h$hh4%dZY#2SvRZt@d
z{l1eFF3s<{;N%^-g6@*ExwMT8*cndF)YF@VM`GZX+?*fc5syTHO4ikSpF{iVBcRW#
z>02K0Nwj1V)`z<2yaD}jiKRPqUW#7lrKdVGfXCI+;U?!E;ng~{f^}$vXy#ckx1O!u
zZJXPN{?zti?FK4XuCgQ8J~y0H+%t!0<XFW+V-DLy0*#I=?S<>=nc%!CGtP9*Ax4Wz
zIMX&Fp&wdAQcBTC)(nr9%(S}+^VYE9YNu8^9*?v>>qp@+DFja&$aHz996}I<;%F1>
zHIq0JWIdogk<y+jBv-iOoa&{H^lT4~C0@0IsV1N{7^B6_c^bCOtq1!#6+)3LXM1Y1
zHa17b#>5cK;;{qR9KX7ju}>kUN0I%?TE>nrmfyZWxt44`mSr@bEv2XXN>^*X$8eW4
zlWe}<dW3XiCvKaIQG0}}Wh!zbB+UV-yF3SkjrDmDCw~v|AJRWM%Z9u>uT#o=FQ1W~
z9_JeEXU5>QHGT%tk`A}G#-0e<GVLF|)?n?5j!n`9jiH^in*Nd)>#<1hUSPn?9=AM)
zbj$Mx%m$+y6cT+4rYqvq9ZqIjTdNme*)e-<eVo9|g&xc$G+=jrg=B8oxOH&@hVtCW
zelnCWm*qrvIGR|{hr5}Q<EZPo#Y$mIen91Vk_geaDN$_?fG&>oT_^hp<ywG2npp17
zVtnj}!p41&Z2S~H(63+uuiw`Bc#ms@_(#OJg+7?U1M5G=-(MVmKV5*){GaHnl&`_e
zObK!AoJ#2`VU2!DNc}{>JMo1=Lsy*zwY$2a(q&@b{+>!tZ8x9FhD74G!O&wmMD=i3
z_V&7~;V|r2snXppHL($oXfZ6;5AsQC<G$0dSeWj#;dZAfB*`d8t4H6VxrD@?Hi6_d
zVpo2hj>xZg7r(?+ZTe>p_Rs2@8U2h(Rc5i;&b0cisV{0p7}{op+IisggTKrv?N)=o
z?C#=J`Y<SDyX=-k&Jk-Zs&EoRLK6n*6vrb>aA~X8R2#dx1Abb4(p#$ZQZ@xVfZ_J$
zd_45m&k0+32qTV*&&4f+%;(--I}3yffH0n!tHCz+Vu3qZG4N^0X!ZTMOzBDAqYW`7
z+$&!M5Wk3E#bIr~YtVfhU`I{4vag+}zaLCSHv>ThE@HsRpLeSCn+O*Ec)$eEl|EOo
zap}DfsCjqg7v&#V@fn=do|90`yQU-BKf5y9DdVnUv}7u?wVVX~gOx#(rLADO;*E3@
zcdg{ORG20Ch3%0<y?>}OwBjYgUKw&SMoE3!DC}gWR2gzwy*-sdqDhq2Kc_OMf4DMi
z9PP?^mWMmWDOCpAkuk3ABgc#A?f#K!e`UmshV{>_3|Hp*Ey9Q!uRncKv|1T)_P*v-
z=3yypp3^w=b)35jQI6_p>N-1Bc5?N4gMgj&nqQf};(CF1a>^{F%6yZ5p~JFMg*!ni
z)}qRy{srv_1MLU{tq3vPN4bAtb$(@G`~gmC`gg7@=-WB^AYPmg;n?n)P@8hTyScxz
z(8@nr4`G*m>3!EGG`o?F@^qTb#=oUZwM!GitiB+j%3UhE7**Ey!?Po*tIu^vAgNuZ
z=0LkRBE0xjVD@>}-!VPoeC>=@o(Cp*F0L$gQ(&D3OhUHG5<+=cWwDzSZT?tnMdL6a
zdKrY1Wb)ATOyJNMcv=I|Sz`@+Hxx(jY~p_%|GX36jJV+!93AzHXughaUN7JJ6Pyiu
zhKpdCzv9jokUzmOZEo@co_@~s8`QziCOx+iGg$iR!1FJ@PbMDv@*QyO(%ajlkVgxy
zw+tRkaJqB%wCsM)GB3NIlirUvK9ek#Fy^fE&KO(zR7l5cLG(geB)*om^_z%ifqB7{
zUxv4XS-gbG{9@{TEquh}FPJ8I>en42iLH}JFE@8$4iUJC*E8`nEj|7-e%v95N$I+a
zZlMj0b0`{1*wi958rq`nA(|oe60VkAw}|PG*e##hF`2<C<{DdxG|%AoD+m@5F*{8C
zoxoDZSc2G!nKkwiJF~|T-p}sR!+G$`E4B~i+AM4_#lmuS%tb=S8P4M!EK93g9Ditw
z%lHBcQVN7sAqjHm+;NO9(%)#uoadLfli4;pZ6qOw5qI3>PYRn!r*ZHmW^HSYtseum
zw|(}K)=Hvv@-mpHs$Ts00yH$Ve15L+cncG{v9fZI(!mJb9HK=QEr-jfn^mQ{3@)C{
z4x7HN>XMvo`aCu8R?xOt+Cm+Iu}$A+nXC$I`f}kwd(&s<m-!6)WIKmIe>eUW6{zi-
zVzLJJ9F)ypmBoc}wtg{X;;=w*qwg|i-8oH_vkE(Fsrk*)pkM6ltYs+zlV6jdPK6cA
z&Roh>Z1D<OOJ$<(B$Ir7)w9{9g^l`E9c*oimSn=%$?YTNk(b+kl1;SO2S$oYBrA3;
zM#mp^VWj$Slw=5A&+%lMb(<sB?U#&{(G3b&kFD%<@Q8wcWx)!GzDN7CBuGxm>BN-m
z_nd^Mr!=y3pfq=)pOIWjWb;o!+)g%c1DTr`ntDw(|K9{b(<&Q8e-QQ1bg=TnKs_lx
z$GQI48=E#dVHxR|+1}ms?NveWv@KfB#YW3|$ZA`(ocFZ63sUifmJ?Ld3EGJq>$xZQ
zq~y{dpekXsj)Fg_G0Zm8Hl^KEk&c-JUT42;d)(ZUrpZe{KDwNMj@08fQDwhZ<!RJ^
zzmrX*2mJ*Qx;d(S$!0j)o{MPew#-Gv^0~7CE~xyW+X44<6le2s>~9h7X44IgE~Z_W
zk!*hYwzNYuU+ODudknKJ^^*B7F^07*WrLO)!?mMOK!@J?5eFf1`;EL^yrJ}4dAjlZ
zP9D<%L~FXzGvwBIgmb{_Ut*0fZyDs0nCH5M-In-pERpknmS&bbA03yLJRfi7JN)d6
zJ2SPuYt1?j7-JsdtgA24eQ>^9jJ*$x(7NUKc^yQ$(i7=`*N^=}LZ9y816lx7c~icy
z7aLNGFteD}%D~oqyq?Fl8vsMg_sg0NtFxE9Yt|_P%Zz7zBx{PbE~F=GDy>crmg!wH
zt={gCebvLk`2_Xx6SIu@x_KF04zYVE#Bi1>_H6Uwu3Y1c^?qpiMM9KRaH3B)gR8tw
zmRZcqTb!A{2cMF6Eds9d;BZRD7`Pr`cM>qo738?Mm8@$hP7zOXEZx(&PBI1#bD5)8
zUBq_=9S9puW4$?h`c5j67pSLa<>H@GM5#oQNx51kz&tYs%V6jM!^B5GF#bRMNYx}i
zynLr@<I72HK&+j6YGCvhR(!sl<H|>z-!TTgIqBC-V+?>u*Mr+IsR`F~J^apciWnxT
zYT(fZC)8PLd!vb8=y445ONe2+uIj<Au#2zuO;3zzh0V`VZC<tg2%Gn0l12H*rR*5P
z{?Z#0)DXuFE!mO;K8YNthssaT{(9GqgF2G~Ngv~d_^+prvb7^~wdXlWSyury<WgZl
z7l*35R+;Kl8OIx?^s-ap1|9^*TxBrK%_J_N(3xYmx+{0t8k*e36hN|?Fu6r;Onz>U
z-|>XK9pg0L12J)H#N8m(*~tx*=?2QAyNG{p-$l$d-=&zrloG^P-YH`${oMj(qb4Qf
zT$|f~1JSkkCBVgGAiiohKHV&gHt@B1k1kyqWE69O7OJ<okGO-;8x^;8Q*d(;BZ<oH
z2ehGVK&!<@k8|fS0sf<!b|)#5zCZXQP^9k<?i1<}ORBb>0IcV`MO??Zj$3!PFEuyh
zZJ(fA-0Ap&X!&!3A%?BpBYp$PYuJd?B|Ip=sj9pRU}(DW?p8i<DgR}o97jT--Lo6(
zfJqmuka;ux&wGHpugU$SZ-a2GeHlu^)Y1A8OVCx;%N{6w>Q>ZfeS8Vp%?5E7qekmX
zmgMX@)M$O)5?3vMW_l-mEZ#il?>^Od!0dX4YmJNMc{U@u7JVm3lj}jOv|JA=QgO`<
zgZzsi&*XoS|NHsZsQo7Yv}4v?5Av;Yv!O8|0*_c+NZm2l7G~G30awAtxl3^bd`Sv;
zw+FhbK|bcyE#O^D(9w+!xdZ^BpLJ)Go@-b=Fp0LQhoRw7&Z19zZ<5UhqPYtyKIEPx
z<oy<MGx&VS>lBh*`xubq%Ju0qUi5wOt6%0Wh5BoTtr<7cUpy5!dkNEOw#G96h%!y}
zd`!y(7mb)+v++J@U>W=8xp)JOA{BLuM}Oa=Z|S1s%m$?JLZUqMWTKrpOH0l(vRn#T
zIdnE*bFWFG-QPtRUOpE+El<zupy%P}cA`bE(Y!sUGiazCL`m)@hH`XiW4o6U#xJE@
zj5EzghtSp14-2IqmP_e}xv6;@+FwJ_tI4}I3u5ok{*umDUVGEbD~;Zz;+XXnl}4?u
zmQ&NAF*PkmrgN>yMtyacyQa03D_+?NME#gq_*yllZv*;03*%!mQ#Sr1Q&W}0CCyWb
zlD=Z-z^}UE57e;nN9OsiTy0*Vv225164EQe`)DSv@t>50x$E<8X|u4Fn%rf=`zJE&
zE<P7F$L_lh;2zEYxDHU6>K@Qk=kFGI=Eh(5@f`T#HgjXsVq?8BRz@i{MxAClh5sh)
z_qLIin;_P|MYH>M8$1MqD!M_<>XGj6Tr1F=WxKntIewmQEWXA!63fsw<s9yUvN2D7
zI?Tu4r`moZ6hD&7Owb%RK?`Z(%f@G@A7PAqLp3%ed8Hb?;4WrCq>e8_E;Ku{*zrYS
zW+`}Y7R^H7KNl@_W%EJ&XF7$OCBeG+41KPblrgEHOAWxxFo)<p%ga^CyB*x>eI<$g
zb_Z0i;A;uCR#XqB{cLm-0GiR}@fYLj&o|$G9nH6NTJwFQT_xK}+1>#eXL3QKLtX8i
zHW;;5{Gam3UhBKaofA1)-CiE=vOL5iewy*QQGKEcs{Y9!`U}kmktA#2Ed=o-E=^G$
z?5>{ewwJ{i5gm_{S+o*YR&&ut;?;xA2f=6TCv4nJ<>hIxxaEI0P6@NQk}+St!%;6!
zxPZLl0;HUjv6rb`Hg=e^Y-28dhGIJ<Ea>n~KDhBG;Y1<+U&?{fnD)3R(yV0Uu-8IP
z`8VLU{PS8mpMTe<c$I$Y=7TFGG-U1*3|Yt@L0ZpNx~eFQ$<NzS{}Ae~B=tb0tjZXV
zGmHj}-L*GdhTAwERAqf$KK6BB=eo)C*p;&BEr~Cp(2^{#Jdm?@&a(>0#0yTU>_(95
z75D2{uM^{cQm_uXbbQi&ncBIT-841=ZrXq0mKoimQ_&N$jm$0dHIt`F>a-ugFJ<u+
ztbHtmy%52h^ohL(|9QVYpW|X0>pmaWz0FCHUfij%Ql_MiD!F6L-O&TD^75^OQ>&Sd
zG0@ywING{Em$WQkwLnhyoAvJH%1<W*f{I@`y4BtTx&z>-N=Ve!Qd~~QQ%h@o1GaJ_
zNE}+^E1i3t`MM2;`5I;pnXk#4ak|#dr{H%!qr=M=C$rK%$?&4kt(}9P)919KJ+Xsm
ztbaTWjlBYCi)(GXngSXR;5f@<sCBIPt;1j~^KLCiQs@@)+ODu3+(dE8`Q2=&pv+ro
z0of6A3}H69jqho!ECbfdbi~A}EJ21zGIOiuP`f^XMI#z9Z&G3ZvY-q@`85P)&cAh(
z>sUj^Q<LDxGQV7nBx}gUFK?|OTggRj3*NDCxOujixzy*CtmA8G22Xz1Xz9J4&2zGb
z?KuC>?kIoVpzX{*QJSCbQXcKa#YO0kt+!_=ZA!2!Xd+DLvK3-PDSK36NOG~wtUEel
zNLnm^oo_LBF<{$EZ#K;FMkG1Lw8xGQ7!tgvJ{!E-n{ntE?Gg88Mz{@n$=aPT>BD8K
z=s24`997S!){?xdaj4r8wgGu9rA6WC<HjFoNzns{7?{$mog0Tom(XrMeIbEuGIfmB
zOG^;)yO|qpk^F#(+1xD5$uB`R@h+6thuNIVu7_uuoO-BjKlfx=&|!h8R{F7>h~St^
zOYH>+B~hMy%Km_&*vx#MW@heQxnoNdM46&X$rSwwDp9O?ZfAQfXU&rE5HliC&zj8Y
z_h-i3T)ieU^}S3%&>*uqbKmdW(U)jV+cEutV25k8+SRSaZl#Vcf!~c@1N2!G&9gV^
z+dq@ntF%t!Z#@qyASc}Nj5sKk{jLIXq~e|T+1i|6_IuZd$Q-sXIrNObmvn`E>Au3P
z=VjyFI5gcdV})c1Fkb)CA`0!dd(+7bWTGjHXtp)St2e#iK)soSwR#g-YirTmyr1JH
zIy%#H6U=cud3Zv*KfSpz0eQENHsNXbo)Tq;WiKD_TzWT*)s4`%<1trfRzHxbsc$q_
ze}#Iynl?!;xY@o;D~9duI%a9N8Do|XY`et+4D+@#Pgk=;m{u(Z!^y~a1Lf+<7fu^Z
z#0^_b<pr5m@-u;E@6g1OZt8L>o%ONw`s`GYJIyt_#abGtP9|{(WBGRK=9D8R8V_lH
zfUlaa9NFd6_EUU$!nC(`H~)r0#YMWMXL1J3Zpx1+ZutrQT1X`hfal|o-|p(w#W;(n
zM=zx0@VE&en|xxRt3@|Eg~Z7dCt=8Z3Y!=9g&}j_K8cajqHQOE=7d$#MKLabaph%3
z9I~9WgQ49TKgG;zXis+5|0kZht9lXlu<4S5@zf|T4pD&Gv2HvU`@+6#!dtIGmgjlv
zY^j)(b@OhFrEJcb3ruFV+%{QjG{?J;PAE6WT3?T<E>f%b+ibe~^W(3B);V3}dNn+C
z4&AN3V*Gg`uh>8U8hWl9ICd>Yucwf(`{y%Mivtzo^Q~<;i+qd}u+jyw((+5eU9&)0
zszI_}sWg>RtFbqa!ewVUKuHSk7512x)B6Ii_O<e4$0=jy2gqEA^#{+IQ+9LjA;@_g
zpDY2_$$QKgb@T8?Gw*(B!ZU}NgQ&9pJc(Wbi4HB#nbal`z+5Sq6M_t5!4C@_wndgH
zRCmV^R!ZA%_=eEdO@wqPG8+TRDj)`wH+*TxmbHS@%$h;UT0ySUTEV`^47;TquH+St
zj-l7<XkrBC+cakoCgu#hT>~+=_t0e6mBVD3=nOGTkRLJsiaRN4t~TN?3>%B?M4o0!
z>I#W*YK!4IrH)>k<D&V`R6K3HrzFAk_Vc;b)sh<rQxY35XPa!ts_SV$7uV@J<s42{
zUb{qY*-}7%$Fh+59OH}gCU_57w`uVnvTg&h?3nj(GmvTEPn#$IgX(N=p7i&!rlrv9
z0NYBT`&w=V<^)%oMLpx2Xw#BYEkf4LSe9y$!^#1w?LKq4_yZ(l$mBF+<fjE}$mB>M
zB_u{;Y6UI0afr@rVW493fU%&nwdKy6rC3H-!Er`4d3`JmP{!=4&UlR9)>JaeZW%5q
zmyE<xYZ@uG7gXwFjM1nDVqa-1b*O(k9P^1l#qC>vmAT}kWDLGD^W%*(`e-xTCml?G
zV+W+-#iXkS_;$i$RtgE-IWQS?QtUfOqJPA4_KyaTiR2{K)c^Rq`N=-Gh*dp|-_9gt
zm*^4U4jyA_|C8JoD33D1^5Q0a_&)pnfa1MT6Xbn#Cgk4{Is4!)hHa}9JaFYxWFPzs
zQP682Jf<&fa1`VW4@K`0y6l7STQl?mIHB28=-XXpL&xNu^?`xB-Ac<2AZCFqoZT;P
zIsZo#dkh|0GY<qM(N&FEfw!1-z@WRcR`mPau~4Wty7F_EbMIb95K_c`%xWrTJiEv5
znXEd`X{$yuI;WNIS)S8Ml-bz(24C4{eNLM`AKtx{uYCWPeDD9i<cocs_cI}d@qwHG
z=xJ8>wMt(|TlEzm)DZ+pwu5A1x1EX;zIM>z9YIH=gOIqi<DJ+Mj|bk`L1%OXy)+$k
zTu0FH>7W;P1aT9pjqv1-pi|O8FX{+7HXXFOBMA9W8{v^1LC;SIt?39lEgf`JN6-t>
zK`-nGIyxPMc3G;ec|ECJ41`pvAeh>A5SoXnphMF^XLbagl@2<)BM6JlZG`7`1f7=-
zI=>_6f^-muOjE@`{MJTzNk`D7>7a`{g5q>gqa$cC9kjM1Xk9w!vW_4uceIOfc}EZj
zLG2*42~vfh($`7B)O#5lJUU=53;z4~lsW1q!C@o!3I@M_*h~?F&=&lr22?xNuBli`
zKP{HaVy4R?#9qw{m<q_s#%MO?a4A1_yDtbwK+1;=(vcmJu2ZDDUGX>ywPGT)j;A_e
zvFFX(vmSqEO$kPlXFZ}Kn}CFW)3NBou*YE#oyv;5Iy|~|R_M%cu^6!ilpBXrA7}om
zeuM|Tz75*fgMQNnEzWN`gC!tkud^>ax^2QUUPDIWek7;cnf%#bRDyD~KOdDS8AlsI
zfj8%WB1xFHz+V3;mTWBkg~WfY;zyeaA6Z@qqOz(J$c*0%tSspZ7V7YPbPW+|2j$}{
zs6=<QR?J6l1!D1fc)Gibh_#s|7p+io3As)|obE!s#1rCT)aqX-aPBw5?z+6r@<k@E
zM+gh~_<jUbV?6Ugql@7T>%3a@C;J#S=1%p4UCP{J>7I1ymaKn-9?i$MkT~vk_3Z=M
zt@`fad+i8JAg0q#wTOYm!8%7PWE*fcIKkNWg18P2-hGcb1ft<M3dX`Z3Ih%h$S_hq
z`V9q)krQ*Gr05A997H)^PdO*&qR&^(2hCBG`SSTGIfK!SG>T5_XW0wJ4jSK1g_=tg
zZA*Thv;cc4<JhwG9~aHXGxec9b8lh3L2WQF6ulWtsP$G)$QSNmQ>gWwN4Sm7j=C|S
zv)VrEKLL{ZoYK%OzoA=42OEzQ_&~It)Eg9J;YEYSQvlZ@@15an$s^=h_JGL6=;d78
zd#(MSq2gU^?2Y~ID-1=iw+`7zW6hPZdLCS!4vBvw(i!Zj4i`)JY!z$*mm<0SSX&pN
zK}%eH{!a3|Pk9>YMI9OM0>O$5_455`Y#(cV#jOx;t0W>~U-rWsUMrj=jn&<GjQt*m
zR+^C>aX)XnPp^@a0djyvslTWvXROruv8BOaw3+5WQQ7=h?>4_{L$&1_RUbQgG}@1z
zA_ixX(16Y&3x()M25>o!-E=j#4&2K*^)B~n&IUmo_-;Dg_0NpVIJn;g?yK2A_ymih
zJ7^eYpIML)!#1wqB3H7<cQz=Vi)?5{?7i`^kMIuG@95ykKH^Sty|}zV!wl%}1sxD{
zwxA+Ty>#AJ)P_CE%gIJ%oUwzEO+<euI`-F!0`MM%qVc{&w23a$lNgUGxgJh2&+YD=
zf?y;<c_$xC4w6IjlSBz7KS>>%pHgh-f-wN|o4KEQPM$$Wjz&YoWP9BBC1h=kdh`xB
z-k5DwdC905<M;k>BeTqm#-6Vv-kS*BMwG0B8An_Wrj#i(i-^UZvJa+Ipm>-J7jj&j
zS%&FagUkW4#QF2_2p^lvrCJ^l2nGDXm}G_lnvHwi>+EDxccnr<Fvz@&x;!9yu)G+}
zdF{VRW|d3u&jn>qb!Un(4<EMK;;&Ku!95Gbcqe6z2`yJ}V$26n|0b%Ln2lc6Qq3$n
zRp6XiEYG`wVH()<rvh$N?0$^9?hs^3nbXg2mGjdor~1e|6#TISU>*RUNdV?KUe|M@
z5<OTC!ZFMO^{Ug42%^H)pxiW&%`r#P9#gEL?y-e<0_t7Jxn$;~AX0p3%q*aSrzb$+
z-3qAuTR^=jvre;mQ~~V9N|1Rj`RLC{Ya-2^UN&P>ebr?b#>#a0POkh|%+l9PSHUn=
zHkeKtY(2IDk)uCR9y>dEbK{yr+f2@9)ljIa${h%8@=iV(E1JvO>5g38PWQOdLHAi3
z-*D^DieMyqJ&86)<;LlXelIz@5YwSfT{_86u7G>F(&$iio;fqIU69RO!1tS5IM=<B
z0m7uTOFq608jNFpYJ-h8uk!fwraF*3ykVt??x7G}o1^Z=`*vK4qANwAt*3F&L9QGt
z{m>7apYv@b7e;L%Jq^<}<vw%qB7N^*!dXOzY-7UtirSWXO2&4a8i2nb`lr_Iu=#ni
z3flYo<`ep!(r;sLO%#%Ko4QT>7G@RXdJ3BSNq{=x7c8oNhxB;3vBL^=`{^TsDd9HN
z${^yXM&rPN0=i=wD7!T%w>7AHYfy1(P<d-m@7ADMZP4+-l&gyhJUBRHPXlOoYZdsb
zSexYIf72ho)CXH)MGNC!rGx&I{y>P)#(=n^{jrEb7*9A%pS)4y-jJDr8pjw8D*E8|
z<G91<A;l7JxlKGS4nc|&8c*S9;6wBpH1I{rpQeHT()W~p8!y==4JgRbz<*j){W=;5
zrsNM}$%Wz%3cUwN1Qfq51(5tGet!xekyHGR6yQAqyq6!fzOr<Lb!~!@B?$B?JU6oB
z4<k8nuRhQJO+}D@=8Whjh!_<Bd}u|LUHl;Wj*_+T{y3G){9z=gjrV_25#$HkSPQRM
z2^W7l-ltQ^%+J$69eDqeiXcCDuWjLdxyoztr{n!uDw+AiNKU)Fe@;b^AH3}Pd^=n(
zycXZ%eP_~qTT;p74|xmLH5Dx_K)cC)nTl%uFp|?Q!rxL6<fjOzQTrmG^jmiEr&sM)
zsbuC4BROrne@{h_AH1lIc)aX?%P#(Oygy1MGk+M#Y2*DvDuVprMHS5BWkD{x_#W?N
zuE}E2k)ufGb*+_zN}lLL%{z_ko@xb$vH5wZ5m38KzfVPwpEBLrDiaC^Wfy;XUH?0k
z%=}>_r;Ya?sR;6em+ii<D+jw}7k@h5AEc6*KaAwG@v`G-jZgW(`wWA_4Zojim{|Pj
zcsHk#nLmu=wDJBf6+wRRazyL#a=2J_@jYI*?|6|WJpMvw3wB{b;~9j-$sA(-ouVE<
zW7`W)je!Lv7qvBS{5(}I^M{d~cESIeiXcA)=d8vT94UU;#h>0FKS?Dse;CPW<NaeQ
zg8bk`4&w15;V8TK)A4>kmCXEMB&R*9f1Qe8{xFi$&iJ3H2=bFLf(@TBqKdMMKRx3o
zQ_0L9MsnJCf18RRKX?%+c)V=?%P#(Oygy7OGk+M#Y2$q+6+wRRvZ42Q*=Cnre2@3B
zgb4nXN+v%~S0TC4gQBhJP~djM{WO)p{9z=gU64PeBFIlc*cJPNu!qGGl;TgXV349#
z^M{d~cE&SO5zHS(a`cp@)@9Qx@>9^qRfI^tt<X;hWDBaPd+?yRLR<9x$yRU}n?H=?
zw32_S6&{;6(r-KYqpjf3pK(V5josv%8QVMU82%%4QIg`@EfZHj^G5n@<^M=4xm_^{
zA^~{B)N=#hYXyg~`4@Cg8^GU9#V~&u$!TTqa4S4EZ=_#cp0T!ls2wV=WRlqo%KI4@
z=gQ<llZ6d6Im||ii<oPX#9YUe5Tm^e-Z(#{0GDQ8(mbd%J6oE`oniZ%V@bMQI?`qH
zQwmv)Ir?3Qh8cpfC3>t_IK}g8kNrH{w`z~m*gx|7s<3g57(0_cn>DD(-6(RI!>%eW
zQfrimNAU4NBphn}ubW{;lee$@etzf7-2Geo<S#*7wof(>+u|=L0P_I&N&+wsfK3U&
zJOI9$0L%m6YYD(S0KT38%md&X3BWu6zL@~b1K?W;z&rrHodC=OfSZ`E=;i_N-2`AB
z0N+ah<^k~i1YjNjKS%)P0q|r3Fb{yu3BWu6ewYBv1K_CyU>*QJN&w~o@Z$tv9sumZ
zT+z(~;HL?|JOF-{0L%m6=>%XN06$Lv<^k}F1YjNjzf1t;0r0B?U>*SfodC=O;MWPj
zJOF-^0L%m6w+X;J0DdO`?lVb%c>w%A0hmYo7i7(uw~3yk#gIILj>c^w=1~n3q4C@4
zq3Au@4<jemSXdTJK39Y7Gy1qSXg`@3HttA<LOA(^Qp8_MCwN>RHjb3<+ie^@n4}0M
zKfyvUIg9NugU66}rN;`peIyDd{~F4a7rF7GM}Dm=pBL)e!^XY4`mxBf^$F%MJ9!x8
z4dTQ3)w~d9M!|(>uhQPFkJ=^-Jw)4o^Cax&!02{Ki9B&~iJIjPYL>}e<^7|)Ve@~~
ztef%tN#9e75XKP^f++>X&mr3cpd?)J19uqjMwBsD#&$B7I36Ql=)|}x6b=;q7&)%t
zSH~)WJEu`S-bp>2H9iz=)_n|tMDx!?pbDGF2D7EnS8$gU#_kTIIw%|OqhT5!<|kgN
z&xiH-spM1rlgr#2WHRp(-uM%w^)L0x%S%X~A?irn$I6O`89mqN%YwVQ=Ho-DY<F?x
zcPLn4#eeYYPz>GjJ9=+)aKSG3#=EO}$@lK}4#i|hdKJ1y27`0e3$Z$ryGN$p9QOV~
z$sSo6THaMc!Rxc!o9n3_;9$c`OU^TAc}+TG(Vk$dEvOU+myJ}4rRXQL-0rhVm7?iy
z`n16S4+&vd6x~}y^OY=u_l+egQ?fSeF7mkg=c%TVhpT`SSKY<N|H|Slv^qXTrBQbo
z3y0!I@rLn71X#)IX4NI`LzXiPiURL4%4FRTFUN=3sS{*(&IMa{FZ)kkobwpU!k~`|
zdPd$e9RrtQWt__ew=ld(7|f)<d@jTL@rCg_6>^+&Wg4>@??pj)VlCoo{@(JUDy>M|
zQrU;k0GH><eWAzk5pM-f{VT|WyS5BYwB*5Pv3TTiw~ZWQfeP&F@yL6gJ@U5;xz$NN
zSEqO2lSx)DbN<ncy$6F{K9P+d5bQna{5s<j8NOR|Y~zCa2D_p+XYX>&y{Xl`dcJGp
z;}YcAjj7kFkKKb`eKX>f1lIZ{KTVFK?!~V7llaFxnm1zWJ~ftJ0oK&#&3vwZHPf1n
zG#(`T4D!Qzx5X%$D6rdxllKQxnjSbJh*#3mwV*T>xQvVOBdYrs`C+_$RZjGuD#cvp
zvbUkl{T>^u&!Md4ajv#7b6rNR()61E6B&Q1happ1xbTLJ*B$&6jZ#{8+wv<`?tNit
zVb7M{*?TW3E&RcSn=2C)#(inw6@P!Gf1<{zgbSV62k&?dw55gTO~iZcJ-4**M|-?s
z-b9bZ7HHx#2-e3-7Uik)$M>B;Uf}bX_?m(+P{8-ikNxFL<#p8WcU?V!st2HJ{``lN
zCk{453Y|C|?>MI93Cb)!>`LYJrO|w2VlRs#&;%1xX8c|k<^304Gr`dzu~oH+k12>N
zn|wd|(#zkZ`u%0+Ywl2<yUp1D6%#D81Z_Tf;*N=%&8yIf_bS!%E!A=F-1vIsS^Dr<
zubud=HIzUTC^KZnce5xj_}2CJPQa%6JSOUR$9pa4jJN*zt9u_%T6oO$_dYBvf0;Ps
z{Syz9)PW|BSJa>6^!=CLb$xkaPs5^A6Spbo3l?<A2ao)3rT%nr`PU}U4k5Nc6JMY>
z;}2St6~7<;^TcKgTE6e!|1ojCigy!1#l~_mrWI!Am8*qvodItXwHr{@GsaId;9V`?
zX#yT;!24Uka|C2A0O!Lk;Kc$WJO}ud7Vt6wIW!0O{T6UiKrW#G%-x*ieu;oTHsELr
zc#(iSVol7$TflV!K4id?Tfl1se6s;BY5{K)@KyshTfn;nyuyIDw15u?c%}j0*8;v*
zz!w_ut1aNi1YBvrpS6IT9c0Ew4fww;APjY8e3k*bHYU}CGtG?u9kMJoc4`4dd*etQ
z0q)%b%838?R}FYr3-~Vq*-jGk#Vuf-?{TEQ0MBXx`vkn*fDh_`$hvs0fK3CwtQB*C
zfae<U6)oT{0=~$AuWkW%67T>6-roWa3CPBVN%Frm{V=oa$IKFahmZIb{ET<E0+TR3
zZx~;pFc?suup?|rVc*zx*!2o~s1?@A@LRy+w<+-T3QX{QTaZ_#A>R<>WogJa1vzUw
zDBq>P=d}V8yx*V>cETMu6=Bz{B3LEgr?9TA!hTEQ3n%}j`Ty05@k>UjYrh12D#jg(
z@%gP{v<q;TBCz?KhN2zeHHv@?dRl}hRfV6a5Vt78nLCJZvm!ia2N7Pc2s`f}!s`@)
zjqbEEr0RK_VxY<3W3+nEDCt8Ae5@7dn<+j3Bg^rJ753KYVNtvO*C@jEJBV;K5vu2O
z_JH-@!wPu8HUeI$fW2E~{JK7uv_8M4&!Ns_Do*?$0nTph%En*OTTXBJ%~lJfVwbS6
zT#?~6j@!%t(|Ud`E-O;Swi|GG{{hBfJ;!~zmh|dCAwCUHcRnZ@vuZmHcdxhp)6Me=
zHSTuUMFmb4v;lz8<+hZ?${V9NxM#O3az6ej;a&zYm|3`wD~9-g&doFLGoHe7U)o}u
z9@dPY^I=!6>1>ljJh?wpL;L}dyF!<%J8^YarFY@+`w1$zUOgPJH?jNu&USKpy~8rQ
zUE<31Ntfk<(sHgmX%Y&p?_+IE5%1Zw<f<HtKOEa$eA~lz#utwaFO|zYK)v6uw%7j7
z*Kw#_`|2$_tbNi&N4YLq`v^qd&Gnv>L=y*qu{O8yt^|bxr?wS7Vo<b)WnJHDi|G6I
zUFjF%C6IFMvw!BqyW9Py4c7-KzpI{6P<*eN%BYCl?b19?crB@3;3C-O!qbV{v05_@
z#&2x5>5zRSRh#drFP(mCI{e<|J==7caNIq&?y&JZ9iEUGyS81QA5)(Xw&<{{Fub(8
zFn<CLJv06me)=+l(Pv?GT<N;27Z#1oP+b$*&R@DKTSMdaFW7P3`F?D^YbJ=kOT~G6
zgSXB>%k3AGu`ZShS-v_G;Nt)*xzi?<XSrw>6Y3i`m${Q!bYqTvK^u=n8%wJHr5zU`
z>CYvZ%kOjbmz-i(D)}Mmzn=A%3u&47KnS8f*f{7m-<EFAGHLr#FWab*=08Nki%sSk
zG*PC{8UjS6qY`<p0!hS*P}K(EU_Lm@&SqlL>2LBmKt>x)vKWdFG5^i@7tHb>uj6CH
z``>|o*!%xAgGj6V$43d6@BN=w>V^K}>-xCbJ~pUQl4lHicaC=vmkJ(nMJO*(Xr$^%
z7@O5qNAG)$eAgzhAlFnX%<eRWjZ5my1RLy-6YZJj_+V-tfO<aqn{+wcm&scNR%zgr
zvSXiQ=e;X!)znuoHJ{%welCp>Ou-)|G9AURaihJc+VEK14{Q8c3ilfyN;1SbhgoW2
z>V!Y91&^MlPgpB>Tc@4*7$&N$NEm!GS=V6nTa8XsqEG=fZxWJqpC1X`CO{(Tlo%|?
zHdS=4F4cIUX~yti^atUg+%qz}P;ON8kt2QFiXg30>=i7cjoqFVRakwX089KJvwYj}
zLDP<}4~E0fK`8sh17q>w)YPRvjPxJbI==<!^!ga%Jx5e!qn}dJt^%_cGeD`zg_5_T
zPBdn^3T0QOT;_c+;U5*<?`Cu#HkU&8`6>Oj+aqv*!RYtG=)Z>StGi%TU3m*-bG=+J
zX_mc}Ih7q=`W#f_ZXnr$RncE?>dPzHQ3Ehx<hC_o<ZQxNu*yvs#wO=#dZhX)lJ^&e
z`imH2m6AiHh~@NxX+Er}^U&2$o8?4#)sYK>cT9u#2pTc^1&zvL@_h!At;Viasl_CS
zo+b|mw(D-piElvP+$`#v)h=Im6EI9hV2*xCiee4s;1}{3=bo#vl;|^6A_wrvqu&u2
zi;?dHKO?Ys_!|6?5G=UbrbD+^_y_z?$xJipv3GA8jQwNgDE|MOfAPV8<^O&np2EM}
z@%QuNb6GGe8~qPh+c-P2cin3>@Z~J6rCZShC(Zc!6rP387LRXX^pvXhWCY^7cn%aQ
zGyG*Acozds2K}8z@QT4!KJA6r71OP|m99!TJrr#w8GnjucT^@D{dF5P^@KNqaeYT-
zW_{n0;hGg3@nQ-^q#yl^LajhU$A53yiIv9g01U+^Fo;%O(#o&Rw)v#ox6k)i2K)P}
z<CV(PV>2)U-gkF@zYDGOM`x2hTC4}x2YkK6*kOWROdTfZPw2-8J}FsCC1HJJJWF0B
zXXo{1iiLvbkZXT?%)sH`EdwpDj!?d?v1s%YY~AIq9DO}~z0uuZtMvGG=MEZ)ME{i@
zqkU%%Y}E~IWR>1QH~kr_SdF)vhYxUBZHugKR$hN5FZ8arBJ}EDW9_T`5Or^(xRpAH
zenkqK<HwdVv*bc1iLnrsX7_}q$>9Ho;y$Fc#LyZ?Y8&6wLl|Y-54i_0u#RWuc33pA
zRI~_%>HaJaHNmxfQN594hRH_z(6VdlRh~(f1<Zb#)qRT4q3Dm4r`}(%i+6QU=^`9z
z=k!ZAk-PXR#Pj$q$~wE&jh;e`Rrq|ZQgR-n1KXpy@|wICiww*;B|GWx`=J-=9%pJl
z!~!8o@UzaK<K{hYJ1_IlHykgtk3%}-gKZ5$KQ@DGt%3@wF>AV9+#U9?Iq<w2>i@>O
zWvjga#k-Z(7;TlqE|h--D9|b{`5L<`GP3-DK;{;<i(%Mf7~T#ByFZi7%!bgBMOFsO
z^zPg+GHUN%u&NwJ8&%yRhA_Eq!$CrH4uh<#@LX{a%op}Q6IUy8mv{UBf4ZnN8otD3
zduZQ@o4{Fd4W773zWPr7k#lCMi}FLK@tQ6)o1f3WK1TQ-_I|z_?@#Eyu<=l86`C;~
zNBu_xlgJv&O=E>3spO08S_p4qe3*FvEJ^_80kCrdFb@F4S1yTp04z=b<^eF80L%km
zNdhnrfL#-Sc>qKSz&rqudbwQ80|0rm1DFQ@2mKCU9stPB9l$&Q>IuL+0G1{I^8naA
z0hk8>+`q$N9sqkL0P_G?mH^BHV0i*C4}iTAfO!DyodC=O0MWk7#XPtB^WFtlNB?Au
zbKtu`2d6{PA~}Q6-vQcPgXK`cBjU9p0?I-BeEPGi+FOW^!o!v~8UyY(uD_bLadrFF
zp3KOu?S0Na8808#To=TD(@ZogG#*Ks_u`M3hGOMS@IP=uTl}<nv2mJRa5ZL7X4rsU
z6I;Q9c#FlmiQni&jJWt;E;)A+NWM|vV+5X|iUo0C;9CVgR^ZTqvj%=u3!HP{yn%0T
zfeQ{?#8JubBp_XK9{5upWgmR^wBRlm+>H~)uN6`&aE}A`8u%W8t-v!Kcpgrj3*`3^
zpw`*Rf#>5exy0`_@L+7+7sLxa_yG^L?hN9cJ(9P0B)jk(nsBBz7Q~Ac+7&piV3ZFB
zauK5UYPb9==7J?uZHXoPAZXMnnpjVZcQx3Jz-psGJj)}K0!~~pWCDe;B1Eu!)g@z}
z3nmW+*4!7G3<54T)bzDNUz0D;gfVUbQ-Vax2y<uE<Iv70pq;Q*597T7XX6(V+YV>#
zd+3%oGXsqdW*>-tO-9iw;*_hiQecAwQ_46WAFaf^nc%)w^Ti#)Vs>(Wl2r@bB{&cd
z4`eC45Ys7GB=)P_K7>Sdet_sOwGi){K(fa3)yA-IG#nH#FslRJqC3{zok(Wv$U)t4
z?)S3so78wc0j6N(i}oa1Hhy~xC||UP0qyX4udE$6+hFXe_LXcfVoe$wQ`VGS&Xhv^
zX}eP7tx6TV+@*KM{Mw8%!y4Wro7o@tlh&py!WeQa^Q2rpW@sS7%hJU<BF3>3;-!?p
zOYnY>UfH#xxv^f(M9Y$~p388NGV>|n&LYn^4n#{rlofJT#Q1o=NhsOJ(ZqXH@#1lE
zh+j-db`SQY98?RCjZXlA%7aVEQ+%mtoHy&M5T7V~K0zTqNngk4$@>+;;Idqa*8wNm
z!2JYtZE+>^^=ZlxOo{%m{u!SP8r@BF<QDBlT;u{4nvYK*FpNbB@k{V_Raeqbr{e3*
z#{)p?UHM7ggfGXl`EVc@4o>7aa}T1^agDa)Gw_k}r8xCZaOOT^%i!wNnY##US_VD&
zcrcy8kYykccez{!;j_=cTJubD*HuPX$Ft<k<>Nk|1>5&>+;3S3#H)O++^{`mqMxmD
ze}Qsmw+wQ_-<$P!4PWcyf`N5`OEQP_VXW47A@aG9x%ku%Ga_+D`(_SXpWH2bZRW5a
zCjP%=_S<KMo&R5!o<dTSMvKc>Nf(OSnDFj?-re83oO@aLfpT?Q%{(Mnl>p2m-GUcD
zpZZI5r2H%sJ}`L>b#E$%VDemfm5KK%pJ4JlL6ubyM4GW|N@`l!MEm*7jSh8!+Ywr7
z!m@enH%w-!yd2R@p5MwbwAsf2EMGScCNF5^?mIyvfDW-{7vXPRDq3Rl075jTCFeTP
zm#=aB(#$2ndaSv*$4A&U@D^ZQa*7MdWpsl=qTf=`^?P>4S*kdvawl(egF>Q5@kYPX
z^!zOdh@DWpm-0gjcrl37We3q-)+OdS!Z*8Zt)oL)WeYP0Q#SUhmC4=70#&l+GVlkH
zw6Kt&?4f*PP)dOwQ*-s<R?wc)f{-p6-K4b2Ac*Iy+p^80R2tidob1~oj4#*Jd}J`C
zF!yBwq#70@mCD)QG_RR3cB;Gf?PGr*Jy9R`5xj`_BB!&aIe7~C7W_^nt#E2tc?Gje
zs8pR<O1XFFoVD|IxEs<2a5+Xt(JJVeDM~~70AYNkR+ThZ8UbN^-E^>y>K&K1aS0P;
zM-m-Shw*xnY#pW(?J(Z3&9MCK3cI!=%rSf7r^M{l76c~Db46~4iRXx(Ph}kMQ{Tnc
zN%{yK?JUkH)*YnkVodY!QJc3_R2L9E&(MiyYN3-RLkBvAw$RynouF$+4c+oBbV*Z7
zp~9&|MMtE-(uoL-b8z4=Ix-b1m4`4EH@b1f^cI)qLl}!C-8xJP5RNO|x;LZ;5yoOk
zw+@r$Ll}#N-niHF{G=)o8XvuNnDihVC%bi+6eq%1?CjQIQk4i}G2vU+LD~)6LnW+(
z(4^lGIv!Fa>zUV6eUD1fA=O*n6sTlW{w#ISoo2b{5d+!T<o6qwAE!4iE{-YlbC?E-
z+7h3O^foLP2cM2CjKvqH=~vnxE>=4%MZe<ZR!CNrQV$&5^Kze?^fY{KifFvz(DDJM
zZ+#9rhEk+fJ=CS}wYJFGrgc|)i^duDpzWxL{v3;KTKgQ!C;h~<`N-?r9TDz)VOZ9*
z7i3mtM^4I`U;6RK0TbVttF4Anew5_yJ&T-YA{TikGW0y8>z#y?RB1dX`Rz0(>1=#y
z?Q#6%P^Nx}<0o}q{t$2^hVY4Esq1J}YEx97f!y4tHR&n&qFBHx7qPY|r93&fXFn~d
z^r;r~2vXCwdvBwoU6rP`hexIM<)NIswXZa9Q#GP}+lv3G_VxVtjhT@L5+2#r36iL6
zdXM-E&Sc8Djpn_S9BKosE^1p|PWT?#o9XzmT=aV_v%KZumg41jw&eHHn${rE=mv#E
ze-MpNs+g{!YsY$A`FJhRE)QMi?zdzM?F`r%DMlOMd+O&|XV5eqckGB<tv4ZnT&&w*
zx{>|>Ykb%kVUkbG^&G>xqqW^!uBOV3H&Z9wQ?(ED!pdCsY2UX9)}zu^9<2{8S$`Rx
zE%}%6J9)Wa8x#^DH?2lzLrT+<U7;vSb|s!I`6+&VGKFk6*;PrhrX|DHNs>$<+f8<L
zl5B$|yQZB?A=^!MK+=8tTlYOj-FK}}ZBR(`N7e05{KynR^ygMhcMb-klT~G%mi|TI
zf|v&m>aPOa9W)N*gx0nEy2qd^j0!du(ixKvLtj@$|EpLhE!Z;oa^)8^uagrtug9S`
zqQA8=>+Fpi6iv<g3Orl#H}dP7RUxjTe;4WlK>bQZ4w`5xx-(}T2z(AY4>s<`3R7pp
zvt{-Z_zGhVvQ8hh!&4s*3R2#rUmF*jzLcD2I1@5ErGnVSrp+D6nvCVM?(@_hCGWAu
zsiH-xv$y~=e6wk{1h<n}9)qCv%3}OxopYcp?uvv!-Acl1?(Gb9Qt0Hnb6xy&g$R<!
zR~9#ficjSmp%Pj)Ok#Eap?1=F>`u&SoPkvWds=ASOW8m%d#D1Fh?2`2&jjFj@xY$a
z`2um_ke&u(b2Ws*!y(OM%?+E!n44|B$lP4>ICEz=k2d!z7hN_Cs(l&z(&=KE<8~x4
z7l@`Pi_Uy?X8VN8XCq~3o@nWV=1Jy;%~Q<HHcvKpcJl;tuX53%Ee^lFqFdGdjQW!_
z`pd%@GcX}jQ{O@KF6GIqQBopupfGxOLfe<hb9Oq=;xl<ap%9gGfO3A3a)#?ZLO0WF
zx})$Rh046fWb9B4MC;_Sx@Ar03U4Ne8C-fB-Cfq|4jQ+LLg<nFm58};mbfUxOBV%=
z@3pAMl5N}r1Wt$C5=>r94x@otAPD0<==4#_`OPH%m-+m)Zc1n4Kh@uzmta&xret?<
ztk9iaL5hCe=*|o*bn6CVbOQy9uJCvzLm3Az#Dk6CXczKl(s%k?3mezm;VIvN+KOCm
zHps74<C%?veZaxlEbHsXS#t*M%^UR;hCS#ZX8A883dDXl=Bcbl<5vM*_I+>X`nO?&
zG47_Fa2<QD8QiV-Ws<qcBfKdPn}ve#LB)wbtxEi1TWN2)(-(YuX|wTTO3X~?ut;yj
zfsVjL%WPJ4l|GFArNKts)J}*OGLOq<L>N6n;(C`O9#ew|?X4;94!}|9;Vo)w$UmW6
zmP!jF{v_Td2K<zO!Z{<OQ=I=&+s99kL=51oMfWO~(dzsl{)}P^@VGoy(9g+N9yI8q
z@-r=bN3M;*7lQLk;7rE)={ClK=1a{Dn`fAtZJudvu6dTZvzw=xdzFhGWoFn|*H>z+
z-)gzE$NHCDKDTQao@eQU=K1D^%?r)VHZL%DcJo|wuX52M=v8xxzM?x-XU6&|8tX|1
z=%dvEN&A0={K9n~wJ}Ru=i}Qc5C?<K6WzgAtNlTEvJldCP4krJv}O?@_p``)KT#OS
z%_20m@VZoL@#wXx_W|_$>y$&*b<UP_N}FQzOx>cpSS>NGRlYKe@4%_gYE%I^I3+J%
znUu%qD#+}Lo32-Ft}D2&lhn1RO;2~LG)9vLkanrYolD~G30phb2$Q)sUY}Cc3h9w(
zX1DXpe#R9ghO5zHm^lz!HioAUMqcW(0avchZNyZ?>2^v=*fA<G^xG=es%L|XW1H>b
z@}u#5S1-M^GsxrSvd`p5{dBr@Ai7@}O4c+<e)`*bZvM71*?MlCqq%wa*Z2asWYgzn
z9&TcyUyBHB#-#dv+tYMw%xy}Ixl4`KgXUUu!)C+WY;&Etx#py~vzwQgdzFhG1#V2}
z%Z<4f?yshBUukKA=2hl~O)N*UmS>whCWM=7t~Ymf^9pmXa?zvE;ntVKeFk!dm)IEN
zAiBJkBi0#BeUG&RhPDnkwVmQqN}+E{;Q}m0XTX|vibs_~-<HA!Sc-N4Gug*AdD54X
zXGwm_-5~TEwBm=2U#rKYvo?tQ{B#CWS9NxxZHnGO^d({~(K_R|=RfJ#o-=+AWrYK|
z+ar!-7EQQy@%CWqbjpRg)L&aPVOF}Fy3`yR;>xiRV>e(src~L{day($$57}g+a0UL
zl=bT>t2HY;#hhTMDXfy4qI^=`Tnq*ckl1LeWXf|hMUt6Lx6*&Z(yL{rrGL$i(;pDT
z@1*MSYl$|v#jVL|yKjOo4D_^}i$JR>9{hr07VH-bf;w@U%O)T8t&EX3Iq`?+23az4
zFKk%MWj}Clw*_`SgAA5+OXy%3EaN@U!((%-<bF<>PZ_>FJziH~&|(&(@Tj=U9Gc13
zQKsZ0g6-E1Wia{}Ip7&`9&J9(VaLM`ftMM19lnW$>Fn5j;kq?+Yi*}+oyKc@c4*s|
zdQT{8;B!$^(Ov!jkF_^}ldGuqfIIizzPGoTOixd`CzGX<z=TV0S%zg25W<qkzJm!U
z`x=&u-5HlmCI<BBQxX)^_Y@Z-5L6HW6%Y_yaRKoKcSIt)f&zjliW0y7|D3wrw|gcD
ze&3g0dT#A?>eQ)Ir%s)!%9nm=!>aSMBm!6s;$&uUGn+ZZwrY@FZiAb~Y`5ZT4`GY+
zD{#R6g1pv=;nZyG*R8hTIW{(WK~McooF}fI`U$XoYypA2`!@k%K3Vt;eDT;vCGFa#
zspUz1X_K**gx-VAM;M-K?F)X0)VO`~7SsuDeHT8woe>vlGXVE(;*+RwSCCOI<dvn!
zti*;5z=GYl3g85{u<lV(5H5bnJ9Y!Nw!zLGq1ty&?E7fP!;yC6=b0wl)og+RaiNKL
zrWv7aGk~W%T9z@{3M*=C@$#^BT?9KR4Zt~rWJ254CoT2*LLb|Qf|o@b)jv_A3SOj9
zLT5^kGIv$*@Ml1&E9K@F;_CQ-!WJ*=-$Q9kQI0ty9>ZGp|1};$40w+J15{9f*k^h@
zZ+hKAAF@Z^D2~FOSL|p<OV76u*XsF$fFxj!q|%PSCOHg@$J}JA-vR1A9nypHSUa`~
zc=!$JL0b@xXyvX0%3TMwGJHsUebSeP#cqkRST{H%+JE^e<A^e24KzN}1VQ<5Uu^E;
zmR^fIL;DhHIu1{(2K1U<g><KKI_#OHpQmh{#F33WoG&iNm-7V$F`AUCAm)(7@s`NN
z^&{{{M=4ZVp9L5T@q$FU{yqll+QR<}J5A!86${e!e>J1>uwfA<+dl8u3CCR?T#d4}
zHSYj8ZRhE++3yY8srM&ijgGrne<1Q~B@d`|rB%dxMI}J71pEZF`y0ES{FIsa^pwWH
z(Cm5_gQy?SyBGBeOI1fmQ*d>m0hoLW?;6YGSUP5c(2k@9lN2*UGn?Y~WZ?G^!tBTz
zsItF4vTLVk`K^=j-f%!5PsAsfjI*A+&4A-ZF!+OISqc-1<NpL0=Y!UGLq}FgO?(w%
zvxj3@R0;k&DVD=m)~UkTLsiGe$jh;0e;dDxb-4u6Xj?r&gzpP}iduB}BrcZe#LhsO
zLTmCAH83g5=-GDtO(Am4&7ddHj8%?)t1O8%!7qhSpf_{f1<3K<?dQ-rWb33@?6;)P
z`lHPb)!PU0c*w}zpo2`J(x<0;DjCljnpE;+48U}OT8CUBn^@WlpwB}=3l(^2L=}GM
z>tYAvdo{k%9~oT^;m6=R8$S+`Fn;(zM2HfiD~X{+1qAL<Aoh~!;e!wtg<*J4CeC9>
zQi~NPhpjr(XPxvggt_(%E&}wSaJeQ!#AqtWnt9`zH=Z7T1W>hAc0U3%Z-bqjbF*$U
zn=9FdKaV8NqHFj!_#c2(Sn}~^CaKW>^u#&K7mE-uL7C+f{HwQ-XK%b7?)kq$TXT35
zvx4`5(q-Q~YCfy}c-a5iQ?12>-e0PuwYaeL-~R@EllB6D`l#?K9JRnfwX4v8RD2aL
z=g^ghv>@#b1alDsx`0LH5GAbsp@LOE^wVbadl9nq8Y__$Z;7cYDN#-0fm3!)nG^7X
zh)e3-D%97IPj>LM9wSRb6r2SloED1Fe`w?J&GFeCn_g0WHXt_`;BKbi2dHG^d*FEH
zJjY6maxTGzSx>AAVXQ12MY+*b%(M;R9VkG^AEu*C<PT8b^p#ZnQzlXWEpVmm{X`3E
zfM<XZ!HOl!zOVxPL)9#co6(|dX?aDDvkcF3JCZxp=0FF7|NAs^Q5h4_@epWf7&Hea
zr%qndP|@NT(N{!Cmi2yX^i;@h)n022zj7IMpT8dzz;iXGBx@t;L(V~aalAj<_#D_f
zY@C9dQ9G`-hY#wrp3fNiF!KU=mpK9^NrZ{Qw4qqPC``GT3V%_U>#7H12^vjLQ00|U
z1@nKPaW%Ew<^z~rtc)ax=;)JFf)~KYy*J!F6Z;+DbgHCtsI-Nem%>tktT%QbexJne
zY5a!YF8pFPXr`+?B`Gg2t|C;J9t@i?_GwQ(dIFN_Q$J)VczX+vese~##O$!Kkw~#+
zTIwnnqeZ!3B*C?ahE@eG;dOdU@8L(m{wFTf-V1tjs28Y>3rlSS5N|;$rkViWh71z{
zm<KP{7M`nM<r1D1Q0$m@sO7b8pc%oYr6uP`GFe<r;{-oPlJfj)qEf+gXEJHxb{A3B
z7E?7C3`$mn+y?}g(YA#49G+M+Fnwgp&JzJte*qLwk9{^2jX1usnQz7B#dbbcRON85
zj@7;px;MvmekF8gC$0LgNmQrDj<D-zql|Fd?mA$&1pN)ae41+QCOFn(-kKJSk|$uL
ze?61jph>FfwV!8#v$<=bL3k$*Yd~2QYVaW@T*~yn0V)Kj;H6e<D!z|#?2oX5%4B;J
zzZew$7g$H0?}HE!E@;&yx}?(nK4z$L;|4^nBYaw(8#@c-J<jr+;2dbk@HhqTbil1F
zmYc_89saSva0$9KJWhTlMyVIvh`fBo;|qug6L(uK^lZ)KqI1*q!IzMH;dfCuG;<V6
zu#3{eBBd=@6ULrE-O%6OgmhyYM2};QT`pgH>?87Z#y%|HDPwr&8hW2Kn#}i9&qJSo
zGg|~TC}C86A-1hzPoivV^elv>aHzocMg4O>`aEN;VQ9kh6DTC*dPBcceAuyx@`>i=
z4di=D>ccspXnhZ8eGEl?$%*Rg%qR72D<8Ip)&PTJY-($Js1+319-2x_V|%C-*|t3-
zeZV!y)9nB7P9UKh7VZi*gUeY+sH$eT9c*CG=~zxUGA5@DF%P?+ntvdYb3X3*i9N$|
z#|iYc9Siif92QSOOXY|Qi-i+WCrnoHSFZKoI#l4(n8{Y~Aw*SX*Vk}ha;=~d<JSs+
zi@bU>zQ2#Qn>u!08I&Hr22oz{4wR9h5i0Kt0!JSpN56*kQ%LDbR`X#b((H{i{vHS~
zpJRNLlaq3CU3nXX$jTJ(b?!Hr1mdpk8fGpcrjl_gyEy*Ua2AGUCc}hpWx12Hj`1sd
z)HUhi<KzHQ>v*}@2kVV}^}Y$#UxtN>mMmP00a4t6t(wRFXm7a`2;e_V>O^>OApV&3
zcFr0w63)2w?i8!vrZF)kh{i2Ii0ERtHLs;CMJZgr6!^s)3!4U#lb^zn;BsX^5F3ue
zlTiO3*gvx8D=BGgEWPZxLKP)vptcCSX%{U>d6Ql<pw|gt>GC(MD=St)UtQ63%99@G
z!)0^+&VXetuANXLk&nT#)qOXu3~mMdgX<XMrvaZT?7}_74|5+Px_3ZD9Ja)!)yr2K
z>Knfu=lUf!oMkLR+qPZC&$m;C<Cj^;TD#u(Typ|a#HkeOpTu~S&w9%EJ_{U%xP_3I
zz`}y`L^T6Dgj+tOTX@;ave!_hgJZb4*U-AuaQwEz1_Gi>#psrBf!hv`Fph(9;%x_6
zU`@Q&z&($7gd^M!eva*E-w!67|M7nC0v2lUGx2_Kp*E}j{%Z{2+u9H2+C}=3>k9~X
z(5c?l+z%G+X=cjkelWw8`mi4?CapsDyAh<bmC(^0WG~Uq+&|7vVxpttNAd9+D1@BG
z1@Md1Q#w6L3_4JPbu7{hu;4O!NDujO^q?2`{}>4h!nf@hB+Cw(sm7hYqw^)~aFe$1
z#OOfdD;OQCSMsuZGDm#K{~{nk;(17?0-;0d`-Kio1|`_=y?d5wihwSNx-C#l?2j_`
zcdXmON!p1OTX+w9oK{P)6MPlGrpr(_oC?ZReu4Gwa^)GSk!XAkX|8({B&&$3>nG8*
zA!$f;U2l!;ai#KFtO_o|Ksp=6iuh%k8{CG%<`yUi%HH+jd}Foh_3?N(cZftpF}o~{
z+mSf91AiDnsUeK`IwS661O`WNJ}My$oXZZw_fH%jvdY^4X?ha&Lt)7c%x20137N~f
zTn`5y*k6seBYY#JG-K=t#b(Rc(QVwtI<#$5Vsp*D(ZJff8O&8rA1L4(Ef4)`@1fs(
z7TP1Q(X%QRUkKa7wi>I*QSW66m0?c<yw3yPaNqt@Km%=bJ|kay40iy@J|CWFm3_Xe
z<%`QKKBtcO*frvqGIo`G*JuHL2Amr5A$?|7k@dxe557zHj?V=`odCO9q40`R*OXjN
z07_ucK<DvvBA*po4me+kz`03kv&L}8AmFmcu9vSf_67OI$G#|Ez<Gl>ri@)D-!+=g
z?^5vSQ}D=R!?nC6oEu{z0bv=tlP<KPM$A^RdAWcka8Yi4QQ`U~usU{=RAY^OS-$qz
zE%J57ZkF$qu`kJYjVANE6%6|PZ?jV%H$(In%R{1ve1rbcYmq|c0jl3bA1Qisd$8)x
z6{S)tk`QO(^4Jkm+YdN(`45wWE7194ED!n!8ZH@eU`(po->%@{E8R=dnTvdi`+5A=
z5gFNmpt4-_M`V;`+_o?q0oVlcsZ+81NHn)D{aM7_+WMbEuD<zh&}ZlRe@F4549vFJ
z!Fn{Sw6#M9mOiW$>x(y<>0`o*0B2rBWq`(3+u(boH`?8ZUPtt!GzI7@#|>Rnuz;uZ
zhy}2R#h%RJJA#;H?t(JOdu=Hb5&&#2h<X)|RhDiy340rnl^VeNj*-4IyV5lI2$#Lm
zftbJ?pK}p;5&LdGeEIJUWVVcM9mfe}kq-g8L5ntR9LivMbMY0NDA;Nu9pVHVi)wI%
z?ag0t%&ax3)O>=t4Gk(p%O~Q^#R6@cd^66@Z4EncyihYhq2?!H=T0otL~SXkqnud2
z0p9TVp^iQwWU9nY*F~64{?Nc6T7^d8N4hrsv-Bm!5A@|s$C$2qa~)9~r*SNm*>H6M
zTZuAjo0(Q<UA<s9Y6i;r2)Z)vL(6tQH&k#Q(DE-uR%>)6$=nIP&p^``OYo)KijjN?
z(qQN5J=cc%Mufc=VX3v}k+_`T2gp~Q;z08R+omzZ;5ACn?FZn3^3?w!qO}g0-uxf2
zn*ZTu)+E@+>1LM3^YE#_65P);e2|=UF&nVF1YBsOOt%IC4Z(A7!2`^2wn@=r322QU
zBXEobmKN$VQ&uN{3Dn3II07xn`ax!AT_OLQ5WE2c9u_C8PYjrT!kn-*FtE-E@n-q|
zhZx<NRdgAY0DGI62No9uO>koS4<Tr-DTC`F7*KFOO7M(RX&q&yrG2=Xh^WU!Gg4Az
zwtmqg?8$O3jmFp=*l~wNKD&cW{V6NIe!})O(f>21Y5W|2S6`%s8p%*SpcTRQAyEPf
ze!)!bLBBLX1b@{&5V4xJZ<J+zhIYQpezj$lDve0l@=3eZxW{#>TLppt3fZ|I>Q%9V
z4L$18NLqWi!LN}lQ$8DO=ZE3y3T2>Pg|-^c=e#XvKro^NE3zPTtg3XtmPuSh-fA~^
zFL_Dd)=5acy9qXdYCa<6OhYZdK{?ecaMIJ-n!gy03pf9=xUpSjs>5TAZ|Pahnw;}n
z#}Om^e_yoyFK9dDqDN57*ljXzTVuD&*B-k=zEj4&Cf_w0?N8SEoId|iWaA!$|9&j#
zc_~)0%l|JrAtzdLj`<aoFH16cbv@<EMek{-WxoslsaL!=u0&fiTl!jBh1OAx1bG49
zj<;~#+!S+XfrYy{<}Oef7z_L>kiAq74}y+efabv*=zRTL$i_nAptp39onPqK1#CVq
zgE4;NCy;4rGbnLL!<^enBXLM0<!L$no#jYe_z2#>*u|#mi(17_?aU%^C^l%N83&%k
zA&r!$RZr_IC#>u3EC(GjCJtn(hXLXM3%-|vyjfBKx15)H@%^ief!^5GDT{${3|}f-
zRT8|BF3#<g1EJs`y=)=9+$(fpjeS$Tc>hzr&e*r*J7w%1`L5Aqevi@(eg0#l8_~|*
zq}6~YNHr&~Z2;hEs&NRYXdT@Wz#d!`wnQAEo&KwYTLdu3vPlagkir;-C_mMg_<prr
z>#-^@sYyT-r0o^>Bo{}c?+6PN2LeXI0C6bazXol_U09}$s;k>^jhBJNRA1W4lrKz(
zoWy<G{uPBi%2{V%=%|DY?Jl<$&&_@bn?1eQEu?B|3`-|mt{?mb*|8$5?2=g3gEf^G
zV6P^XbCIs!q|drzhqlrtuykUBaBSiuqL3`Rz24R-^N}~}`M013&aTq6rcXJCP*0$E
z!#wv_PMu<Ir$Lq)^QZ@_PCFl{8H3UUivCaXH*?dj?At^#?aIVKyRuK|`t!%=d)Spd
zfR7wVxmJT83jH^#|CjV%RNv`3Bh^MX+N1>+)z8^xoV4GPb<RJ8rsdHA2rr)N_ksjV
zJmtbeCf*LJ*mm^;vY`UX65DdwLGfV*P?pmn+b#9|+vyJ7;@nOOJNO=z9D4w<n4Qc1
zW}=yM7<EFgp{*0}Sz8~}fU%pL6OfJXj*|=+?w~>P)S}sQ=GY45cnhQ>%<@W5=xQsJ
z>hS_GXQ-Z-+Sus&l{EKH;=F$AU?{hb&mhAEH<fJ7--l`iqSEcQCBZ({X5}eE(;dhY
z;qoJGTxm?cv^Oh=j0B}e>|Zi29|9cc-fnm8yD)z9{?FitYIIYJLVk)6-FUr|jZg)2
zCZR8=<r15qFT#V(JlTZ@56PJ{g`ktxk`zcOm#smauXohBE?Vc)JE*fOIf9#|CRLKj
zP5h=kQ<GR$!VUpX1yFm*_tx8iz|KLcfrYfnhmeV5uy?`vq!gz8DOj>hOJHf&j)=k=
zMFfB%0xJE<afpzM5Dkpcx;pWr=>$ioP0%mzj^b!&Q*%^rw_m!O{c=3FBcoMf&%3!s
zxD55-?$Zz#dyT^dJE)0^U<Z|l9n@z!2xnuVj!!k9=?OtlJZL>pprnf=o#5dnz=c62
zaM@d@><zf^phTpP+Thv|;h@rHAzCTt8zm;})EH`NKr{=%3`q(b+D(#*#%pMkt~3fD
zlg>Yc?<4~|=nDct2Ys<an>f97$2b-G$~EcBBTBJ79Kxz7)oP+B#b&8Vr*N^atrlF^
z*4Q3gFTFvy(AS0m;ABopZ;WrFd95c37>pTGa<jbd{Ssex3$7E#!S$XfzUE!O{Q(e;
z#}>Yx0y}76bE+L*T1^zJr<>^%UppL&=j{NLkVm%0B6(!nSo|9U4BhOozD@seeb0>J
zgbaA2^*#O{*GE*hweJr*sE=LSoO5>6wd3(Qu0HnR8*X3m^V=t-@%261*}m8Rlm1)r
zAJ_L>XMKN+w2$M)X^k6L#%ymq?}%8(j~fze<LMT~DxcA6qT_}%tm#_(FW!Rdl<mW{
zBcdJ;7m2!o4aSht6fUhM3fJ$O=~{5PEx2G}yFGmEi1^3DC42@OVeN2fHBq=YCK{5i
z1((-?YsL2A+7Z!?hl@npz=l&hTv|;OF4D55Yr&Oh!FBTX;o1>#kB5uI-GH*Y9WJdV
z3KwZ!(<xl1gYJ`rt3w=U@{t{hDHzuxM@~-Y%x}b$Hpa!N9gHNL&r>Ztylwk<*pYxX
z9uE}I8rZmM$AeZA#RKIwO{Z{iKF7N)q3ni5&GzW|<2ztJAzZEb{8+$seghipcEGfr
zC}4kL2T8g&Cd2MX<QdO*De|;+4`rK>&yx>kT6n-Tx;;Gn><!|fB|kjR=3xUggh2U?
zRue_T3(a&by1^@BVgG<^w+C1D3)`QJ!v1N&bsXsi8;k95X*E%}UL;(St~Gu-*M4nl
zj2(%7VgF$KL`A>04x)@_#t+BqcI(>_X^yW?NVBaz(x9ntJ?iTgyvz0bt##dKRE|7y
z2l%ETlVVX0>fyOa4^QZ^77Y#i!;BYRy!;t3Q2%4C4$%OZYwEglhw|NyNHfGkOP(ZY
zwxNME7><h_*0&>)8($wuuB|@OR#@LA;C=E$c)uhn<Zaji-eu)g!HzV|+*%mq6)s`b
zA?hcar3d);OD#l5cR?EfGqU=&D+8?L?eE67hcHLxk~L9qf3_oVd5Tkrqj6#jIe-vq
z1<_RSn<<ZLPvaz}7X3WoxE^re{laj)`&Ja%zTPEJ%{AkWfC7iYdRGf+r{RnzU^cK9
z7h+SE!UAmyOJN1K(20Hlw>&AK85MVsY+}6~xEqV&uIr22XKA1>=*fVk#3p{z<FVwQ
z&)b)<7egHVH~Y-s2<z%o$MI8E-o(+4DpxM9g)~RBf|E4hjKhL}Q=a5Cz8sF}^8izy
zz|_HO)<$7DatDZr@ERBuK(SRFPcJW%!hs3JJ@=l@?9ipHj^h3UJE+pXkeC&`o`ABZ
zbo3+3YNB#SWdW5?3cnNm5o+Y5^aeHtX|B@vFt@buy`0}U)<umE(6P{3PZ?A;!V$W%
z8A=IILy-jZXgnw&%t!T6GX-@+O%JsoP0ymHr&)Zn_GSp`;F3l&p^gm(Hp*Ykk|^^u
zQ~b9{!4*d{1;zZPhq6J_!wI>W_cG>1E$hwqBm6djLd1tmM0kAgO5^`1zkv1BXlYlN
zK#ugViU5^!zS-^_F5iPnmtY*m0;mHAs$v(Ao+9?&^#@5q;PANX6zS47wjDo$OM1NE
zt?;Iz=5?SX(5>jfr^m8Y-CJB}?)2g1J!qre;sdq+W^Ny-InOSL+C(q&)0WV)V=+8E
zlJx<QyZ8kQnjvuab3v;quQH~p?3qIcqp)?nE@)Te&pgUk7`tqFEClvga#K}#lAUwv
ze{2ncKcmI4=$7M>u$*v{jaP7T689p}N@x1eoN?K$RX>2b%$HdR?~a07_ODn9YLp`f
zGw|j4PizszcE@i&{N9e=S@?YrzcKu-!SCbvfym`)m^<;MTJs_N?!)g{{2s**l2Qyy
z?bram&{4+@#cyByU`7*r3w}fR?SkK>_`L(aYw-Ixes|(`6MmQD7vMMiE&Q?kU!pxS
zYM?ItIo+5zVSVramToG&;C_tm7d89|y8o{3-_y<EhcwU74cO^^obEqrny2U{=_7m#
z-7jhQ({ytbApCc9V~>yZK1nx67~GH2{bvpT6Wyn2_)BzSQ;+GNrJI8f?qAXUiiST=
zH^&mfU#1&4WBTXl=J<sBMYzvj4}An99$^H?h!HQqeZl%yHR3mnAfrIUAL$;{@L$tS
z)`IXq&^=qjAEujJ2I0S;dnXMiNg?R34B`lc`xUx5g~I)JjeeCur)u=y=;j=Q=-22b
z&xHFQbW@au2|$amDIIW3@%@V41+hKnBf-g$(PInw>+s<i#yE-<Fm{~Qz||3+a&R>4
zgBUN{dXv64mY$(wmWJGAj#re$z3l@)Q~jJ9#@Uol$Br<r4*}%Z8J6S8FzgEkQA?^^
zR9mZQLw$Ay&vG#lHkVDLRlrs=E+;GB0PX@dg`<3O(ij_6=u+&_;z=crEevXbsTbb?
z{Pw`_eEjBg{Embl^dfPLkESV~2ZV}&Jic;g8ot+X8xUWyEK~b0VV;AYo=(M;Bp@L*
zu8?!O%?L50Z25Q|x9PL$?_`5M1E43iG$U%OL~UpzKb3fGw~?<Q{tes6lch>uv1H~f
zeZ{GyoZu5ccL7%uNC)N75?=mg2L-t7pdWwz1xze<>7T%svA-|9AVVCuD$`;eNaXyH
z!*_r%N`!0ykKWC_Wf)3rEB+fAFZObKvREn0mi^>uq)pyRmT7z*APyFS>+wlhw(Cn$
z^8W*nemc_Gjb9>nUkYYk!LxA5tk%3!$HmnMF9O?oZZtd$+;*o9YS#HEADg|W(zXD#
zSb?HEIURLv2PR3mj{*{cA@<RKfh5*=h2j``AlQoq!g_$uyy7cYvk*UV*$0}zwqOsq
zdSlB>?wydWs?#m89-LOS?Q*KH0kBLO%-b8s7>Tn|{P2huZceNYSp6nm!#55V!h|?Z
z<LI4Pm!CH;?s`{MU-I0`&l`e`iPwLkpGSvUu9WF|_C|CD?NGqrV>8*WQVZ|=j@D*^
zM>s1hZ$TxqQ8boRvl6Y~uH<?q;Y05s1PdR=h52F}fQR3-E->s^kvM?)Bt{1MZOCuO
zMCHKt`f=rrz!Urp7_Q*3kYEHb<Q0Y~c0!}VV?t=}!l(MgAmUYI!TmdbXW!6n;%+sF
zcoIYjyNM@An}COW?LvHKwQz7|2Moq54Lk^M7#bMlHX`pTToW;n6iieTZep;oJd+gr
ze4>8g?-G^CWfR=DG2tzIg^wUg0jBQvn!2}x6Iek;`y}6nlY_(Z@@W7x9cTeS)9j?6
zWdew{YzHFJ1Luv|z=ad7v(klOiIa_G1>M%jNIUG<7Ubi<=^G%<Sc97nb6}eEJM{88
zS`v&8rH*NaNqcBBEms=Fv<W_-8BATxG!uGkGnA{~0TX&~GnDJTF!Y#aC|6)%3Co(H
z+;9rh9^MS)ZgQCRxMnEz7Nt3`t-2k23ihWoTeal^0uK0l(NcUz|BI<`LGbJ3uMyJ?
zd+bui@Nzzi*|>K9z4Yt2I1hMJ+pDLLt+J;UBX+S;pqxVl%f)DXA9A@h8hsx_7K7W-
zSfhWWc*JduvL-p!sm?K`2ooNMgaxI0OD0NVpDlSj%Rh<**})y;#eBoUu65(tSDH%&
zKg6G*>E<q~Fe!K*BmH9q0C$4A_6LCq1bK2G7t;mCoGpB34tasd+_;ryg`BnToaf7P
zid~I=a0nN>&3CeXxypnDj8$g+gEsF@r|iC-#8*E6{FTpyxT_Vvq-klV-1`6~3hT8H
zVxJfRTNDuOG2*w09|{>ekuTW((MZLMFJB`cnt}Ig{eQwGErNkyTV|w|?oi$WTfo@P
z?d_yI6UVO$zbW|f=l~>5IXVFuSgtyvdgIgh-Gblu@cSozTo25_Z*TlA#BZ3302nY4
zttG49e5cy0?m)2I4aqT!sDX&Fv<aGK%Zf(zYMN@s_@J5NgNn%s!cr52r6&l>j0=kd
z;A)xKnD{DFy-oaP5)jBKpZqu0dm??O@p7VP;GZ7JXo8B_3Br<AH1K%zqNa|7c3H(_
za)hcDD!(9_CVi$W<FTROb`0yV_-z$6j{HWO<VP-nTRzk-i}axPAzw}Pp3kw~8sj2S
z1r*RIQN5A06ye@zS_*n$T02I5^I?JE9|(snKY0-z+)#cI+>L=G>`5c^+1$nY94;)+
zB{km6JIVh!hFE2?2Dtvsz!i<uCBA+&#-vO8OWe<1y4>bvD;5|S&P8?OfC224*B*|y
zG$D$1$9>rPQd%1QG{E2;2%p5474Zx4=TrRP1(UBLnO*-Pu}eJ!#vK#ja}^VS2{Qri
z`GD(Ax(>1YyYRK-sH%TD`n2>FYm|x6$(p&G(EvMmfQOH?D0~DGk&l)FSNsR$FsH;A
zw{SkGT;uSfholisG%!SbGD|BNA^L_ubP<or2t>z#UEIqsTdp0_K(yeD2)J^*UrtNm
zB7{Zg9SE;-hmNF10pDVCA;Qbb#qrN$UcgK~d!|4S_yv~AzWTL;`vF1a@DN1uc5Pa;
z1+_4mEMBo}wN8+Yv0spaA4S-uwpzdSSTp=Cj9SZ{;h#%b0HWNSzdu&-Y-?XJtw18+
z*BX$~fD@4PVMWK4sC0TObdnS(OR#Agx(sO_k+j96*2CPtl@L)ZnA#cosD@Il5DDF)
zp;RhFLVu^BR4GJ4AJfpkA#`S|w#PN})mA8Mr2Qu}^fiPQQ?0~LYA|&qk%FJnP-;j_
zDEn<D`c1|tbWuov<EUIr`d{OKvfOBcpV9o_m0^B1`1cx0T~4H~KWOM6LQNyCMA~N&
zYFqvn@DXm?Gs&`r;}&}qc(&;|%}RAg7|agXD#68UE8(9sAvGHzn9To24W>RM3}*g+
zYkt##q}jMkc!QiXg=?hve5-Rx{FH9zlttG7Ev_E<N}B7$dHKVkpfXu(;WaysxymyA
z>SZ?}8Ba{*hlJ5hDz`wMq+i^e;+auFPE(zw#EyrSJQ2y1|9Em6gNz0@z^a+#PA=z@
z%R%?*#9o6`GZ*cNSN4lvUU`!>`dNawdt4oIRlg4<9wn|>cxxIhtn{NpB$;S{f=du)
z80P3_keL_mOk1&(5(FiQgYknVEw*ZsY5<|=5-sHQ@E75J0$&Ou2LKQ(sAI!N<BzGp
z995J#$_dUO%oE0(B{AlFD-H~OoPLEdhZ}z6Li9>Do03bIp&oVu$dqj<ILssk9U0t6
z7(m);OTi&0Dd_03lTPrFjubmHML%kxBj?~YK2eo9!TXso#F-2gT1bqqf}~%`p#VB^
zaXTA#g4G?Ba5PIAI&!X^#SxA*9cjo1Bn=%o&(5O3YsbOBjc=28LZn_cE(cAdodCyF
z+7QE0C$-U$OW3?<3G1qk7Sl|{32*|V8NO;ytje7LXFr-j)l6?%w*#~3GKLFjaT7I`
zPJks&G+9Qs)v}ppvQs75hDfqil$)I3N(PA_WC?w$9DdJWD2Jbbi4W`?Gb^jZ>W4%u
z2kx)M$W_^04m(+333B@}W;0RtPS-O~^s>ihw7f6EJG12_$wU&f>mrHSOU8>em@>DF
zW~N`bTEs7$JE`$=E!$MXl)}FzYs|@(jb;14un?#^#Z(#MX{$tGe_8dD2NW<2cagOO
z&jr1cw-ea@=g@dfBC^H+(r_QoHut+wI&}43WY_;ZC(@LW4ei)_kk&_elVi5*ulcV7
z3@{Kr<~SlmKGzl+Pp`cg8y2ugfW^XAu36TAO8kG*6?3t!vPXz2Y?X`%Nmp#0Xh^wT
zK#Ts%IlWFY>Ei;{)CwP0Yo`23%nK=l{Cbv+jS%u{S#~-+<_EzS?-LjnwHH`V90h#~
zKe|`o_hoP({#L`yFzi8duSZzaCHM}15r2$4k+pTW<3W1I|6k(4Q9+~E(TGeoV@t$Y
zXbuUX+VAMMN0EVJ%8Ha(rg=0O%60X2nYT;m4bed~Fu#0}U(w^p7g}z||0E$W<&eqb
zx_iOxn>@$26s^LlLeM8?Cr=D|Pqyd(H_+QbdmNw{u;|2vU$y|Ikdn^;n*uf6nchhW
z-%-eC0|S*s_9lM2jAeXI4dZjYy}e?_pUaj*<dN;<vb}aT>&K;lO~R#eCTGgz>?`;7
z_L&-)g_jwmdUM%6-(@aGOf_T)magAJ4e6bbbyn-m*mz^+bcp;&#PJ6e>OWfv#7ZF-
zS8D}41Ru}3zNaP1q7JuU_W8G=Ea)3||5CGD;as_dpFmPvLibrDz}<NK1WC=!A&jan
zY~>MCfLzkiZ>l$$P5CJ%uHTGAmU6xC!YO!Fzg~PP+6d~`Di{Me--Pv_pq|4FPpLQE
zY24j;JeO4%`cT{IFrz&935>B4$C%z64tjo`y|k-UiCe{ehi1oZed0ssadA81mUS6!
ze$la1P3zEEv=MOF72B!C<9&=3CIWi75ONO|+NJrktzu?=msQyt-`Y9D1=Md92Wp2S
zt~NZJ5bu8TC!_M(RI9jO?ZDwzFk-FZ(pt$X9$cF;{4~7KLi=eTNG54iLw}~j-__yo
zj`+=Jo@PdK&WvU>f-G(kmY3~~5Ik})XAGgyjRpR=<{TujHk-3ZyzR|_1R8A)nNvKG
z^v?%3m8po$fuHO&>c6NXer3e3JhqDF2y;Pmgt?$O{4TnF!sd`fa*k<_c+le=4LWTY
zGm7-j!R((shR+pmv_bm}A17f;YjfE@x#6Yo;=FV=zFhV~*&O;-wE0eK^PLp>Y<>Uc
ze0zi|s_Gf`2xYqJN%jaAH`Qb95iVz{huR}tfK(5#N4Pwx?&WB{AxHC-9nH6sqxpas
z$p_XU`G6SaTXfZgh!Nh)t2!}eeerli%rp{i%BsvK;T~T;ja0jYSgDqe2cgc-kYsZt
z+2Qhmq}QE@kDl@{7)RDyE>ede1HKh)z7yMgCpCS%VhQ#Wa&CK$HLW41w&z$g)iJlP
zm}<Jx>e972+9>}pbc>jX*Pa1V84{-sZYWu=k-0{(G;FXw-(cA{*qs&5d8RioX9*TD
zr4xG<YdQx1w5ChuB?%MaLuw&Lq!zKknt{}_nc6O9;3O|M&ON!sV;8#-N&yMZB%I{P
za88Dk{1(n0ILSQW%$Dl^2>GGj03JC!NPb8?MOow+e=-Js)tXtF9P@kNOu5tW`oPRu
z+%0wuB}`yxCKW{@J>pAY(&&?yp&U#YP?0k*ynp5EAPvJ9<)I8rf+bjb;6$Vr(#WDB
zmBU0NH3F)X>BJtxkgotXr?R(mIp4lz(Utj*I@;E4)6BXJs|Ag1t=q6jk|i6Ii;*SU
zQoxJekt?#P#|3U<`{4IUxViR!2;V31n+F(R-yR#@8-FHPLj=@|gbSbOS}ypFi0OK_
ziXBkR5-!*H_8XRqlPW!U`;87P*8=KIq6uWR){6vm$Ra4%(vgIcR)tCEkTp<1-AS|*
zA+`dN$a;+n7nut5A;l0?%>g{F2BAnoNe7mU0Tme0@=3x10v)-eolOQ*$wZTox+Mu6
zxs;8&XQ|5xan_o9HRnV)rk1HaJw+yYEjL<|3>8942bQk^btdikDEI`_w}ip6LJPJ=
zLbka;3;xxfYU1@-@OmVb2sr_DEZbY1aU5dk${S5ey}Sh3tPSM~+LY2TP8$2Y7%}c?
zrSIb^fU<_#Lf=EUoQQO;1=uoH{7-;=>60b}?3RnR+t!r~>+D0FXGYdLa{tN~$noSV
zT#hzXkkh4C;jT3BnY#njCvuLwBY7e(!2dE!kP8vSrMz|_>Z)QaPn3V;P3SMIo#aX)
zFy)l#R8uZ)LAwuo$Bi6XF6X6>C$1=q$x25SYuFE;r_Uh*ZA8P&p>A~~6gvs^m0k6x
z%1PU#<ZE+mpeNYEQ*kV{qY#7QW+Cd=d=XR&-<l*wG2k0vTOmqn$`Jt0cH*8HEFV*H
zbAy<LQDsguFn(zQ#xKph_@!AFzcl0Gmu6f1(oBorUzsn>uwVnBW><8GnH5vetcqWn
zQHkjv4gC->dIMU`fH85r1AFw}*FE|_a*y5_dyb#@*z??~NB$R?4OfEji%d5%H#2S4
zOn=o(uV|)MHPh&q!aBr(N^c4S#DRb>hXLYXfxpoLw`zg7x`$9$_>}gDSK_v_)hXeR
zH33XFhcG><MRYT-GuEX=^stCl18xZu|5+0cXyU1wcsdgUJZx$>xPIEa-+szXSX`Lh
zkFOxcYV?6B$CwCRK?H#|sc(1?UkowOz}FC$g2^ATHp)Em;G2fy<pvE?Z}2gYMRDh$
z{UZqr9cl1l4pXJQVyzbUiYE<C!CcBZ3d_g%2`hwo9BOp){;%<=9;m^Y5Drb*gl68(
z^%1m8t4&A+UAWliMpPv07q#sP+XfLlwg7F*<EF%XCf_yuf0!u8wSyO~7kAx#2L8_4
zYZK4`hhOs<xED0*v0+@rz_3~vc~UJ6@7ro4!xbryFggpc8&5DzyPOrANpZbM)~)}D
zeUSx2uh@MuLAw6KXlpP9J%X3NEIc6L|E3i%5t@3e0$MOU8TA#qhOt;dGdfVSH)J_R
zQdrQ)ZNY?vZaYlWO18nI`3uV<-8&CPH9|<H50wOJLp=qUTebCTyHBEetbDqVDIXiJ
z5Up>j&BWimYyJ2;y9T+SxKAyEziVo^jj1?y$SHJ{eXmleKM2J2#yfkrqq<H!%yW~A
z`tdG>f)Q!KR_K8>&{?qGM4jdL!*V6aqgVJaNjr)tkwj2b?n^-b?8HxnuAum3=n6g#
z#rtX)FfFsIp4E5=y@`iw<C$=r*|7lKf*&{g*l}hM*u|4-PH|VL1mBjj>Sv)O{}0F;
zQhy8Kitb&8@CcOS|6YoZbce?7tFezttm{9*FUGhXI{=IDg{?78i-_GIqW57)(DsV%
z-njX?`W=25V<+0<QUzZMDb5<|@13Nr@Sn!OQ(%X`jSN}OUIKM%%*!QvlRhNI6ig_j
zy=4ypPa(ivkxL{h!`TEhCf77Er!~3IuW;0;s8wJ`q!JLr`r%46zR&g~^9zp8Ch`k#
ze<hJKL5_5?FqDRJCoRC0lfABSHa4SxK|Z`7-+1tVeBIzd7W_s5$~5kiY?%hz9(-5w
zdd13;OLo}qu54FZqZ3uP0p$U1AUAF4Ql8PB$ac3-Agynwnvgt5NOFpTJvh7sTmjuv
z8ak>Y^$zLEu3@a9FM7Ssu_lczq144oHteu{&|T&iED0Mkxw9*KlGSvtlkL%d={MbE
zyzC}*CJW2EIvQZ+q_*akkHaNQP-dj6GXP$7n=ODON;)JrnXrW&RdsTcegFQifB#z$
zs6+gj@=n@7C2Jjcvxa8xpqPdC>zriA9tIkRI2POZKy>Vn8`pkqR{?%p#T2tUroN71
z{l`I{oecU7(8C+A(5ZTEV=G62+lV#!ofCT(+R1<8fQH!6XNxp4xb{aNV3`Jwo(Qc=
zhLW1_=yKeq?xdq}_E83hB07K*FSt46#Cl&5?YYQv>*pb293V8)YR8`TktKI6@5Vk-
z!b7?(XkaC0{8EqbX)t+o!4r(^9~bHA;TlN9h+j=Ud<jPA?kKkpx0nsTg_F4d7~(R0
zxPP&e)$a=R=(xZ&wJGBR=MKfE=A80moW4L|nB%0-uB?+tHeO{2_K(MvJ~YLvO!k7m
zARbqvO$CDm3ye=t<=mmet@@9EKmTc@#pI^V@hVAg^t<T3nYCFn8-HarxNfq`^QA;r
zO3cD4y=r-|w2o9W9+ovjM_Tp&o1li2)=*5=W*3uMhu5mZ(>lBf>*$b;_WdXXf(Utl
zE-3pRh%VSe=EV;Dq}<{zLy*k;C($tI_*~^Q3Ab2XatksyPRA^j#`5TAU@ddo$+En9
zToX~;EUKq=9Xd#N4l;?gx03Y#580>GrX^<m9)$|ahcIh_so6=@pq3IdG}!S6&_~?H
zeg$i@qe~Olt>Z!K7wLW9i}7|u`DC}aimz<p+63*!%3*dvj`*-`#j_cQJ1I@vd^_KY
zyJ)D_s{gwg*N1V)Eb|0APbqd}Y?8it2x(8?Q_$1Fb%r4Tk7ov09lUkP5779K8%AK?
z`(}oR5oXQM(NWC5`wXwR%kCMkIHz_vT(yJBhj_(#wfzuP+q=AvS3H}Mr`P)7s?9D}
zz2Zf+DPFN&8}N#&YHoS@%uIP+F_|fsinv8|u$ak|XBE5fgshh-7wW$NVUB0Bcv5Vl
zuAh(kJJ&gSB&36P;W(V;A2;7TOiJy#C1hkmr_TWq7g2VU)SZYGG<A+}B4%hXL|z1|
z^Lh+Q`*j&sZU9+Br?%s{?2n^i+|@P>6o|WK!$KpgxJda6?^6%fkQ{`ru7f(7uW^xF
z`|brgtsmZMw#coAq20AsyJ5~hFS<0H2Tc;`!IfDUs>zRGI1RbH&A$v?h8!@^SIZav
z5SF_Hhx39ZPYa^xiHL4tbQZDdmjNPSd{9xROIH1(O+R%n^-ngv)W%!&t0Vpl{GSi~
z)h?@kQ|PH!{fPT&%Zt-o^*dYM0eJ6ed1t`;-IjNHss01_R2)&A)gx%ju*@sNUuCm|
zZQ*SIS;|Ld`%WUUhjyX`krzxy&&ovpGmL0V<Q^w-yavl<>I8E_WvGMaX;m)d**Rie
zpe2^KAw}@kgZ7CREVGki?@5B-f=C;R+lV_-EX3=tvg(4dyr>X(um5udSk8p*3q)YI
zE?d~}9bjZI?`*^gs~Ko#y4-{DJd@nijR${128hsp5&r7$aiuS=NW@N^xzZP9>3N{u
z*o=9xZWI|idFqB2!GQ-iAJw$@Co32Gt$Pu_nDfcV?Whw9v_-DGgyzim?aEHJzX^&h
zJjxxv6+}f{wj<Vuy3rv`!Q~N7FeWF>M(#inxFTR5%$^A7DB}3-6ya&Has?-jbJa|}
zbL+RZXR>5tqII}cvDpV9h+||a8;gH-Wxx(@BjCNZn8o*4<(k^6_E=7(*Rg8dcx|C+
z`Cog|sXz(BryZT(4&*~BD!_o76m?qsLMcblfp-_<rRbO0XHZ~$k3rP(iQ@<u`1~0$
zR$~^r#(#y0c-HHG0MlN?u}B{&?E84Ia;z;WuffY0NEFPLKAwU;ox?uH`wFa}>f|$9
zPC?*K?C2-7qsN*ZjlsOV+!d#g*)n^Zx$`A`-+&i0=)K7^V{?`$y;!lg#^$V5H_aQR
zP0rYTXsi_<`##$&JboV9g{>RxxR72!JL!;e-HoOA2+Uu2VMNLU#+1tasnAv^t$z)9
z-_*p()S*7$)f#0L{@>AdABTj?#C0T0j>e^2c`2~6QsM}Uq=$h$jvh=Kta@|7^pQ8?
z2B)Iix)PELvgQC*(F}oTnasxH{%feE5-;T3Emg$8sxVc3bV~;NVYH3Mj$-P!T2uA*
zEod38pq8+FDeIOLU+2bl`7l-w_-Dly#8z$6vv-s!eu=Ps(6-RlX%V*SaLNrb14hRp
zun}-^5|f+&WeFUm*hGr0#P1mVPR4IBe%$yQH9x2-<dHohz5H(1i~Ij%3j&BXp8qd8
z&lKnDbgpQIR*z_gRu7;vxD^TV3-)Ze7nWP@dBv7{ey#vDdI%~mE#4gy2#vI87xg}X
zu`l~iEW&4E*`p5LZf?ubi&Zq@7%a{U20^~5!2*uK$nrXk$u58$H0&6#9RwZDspz<D
zoX?nDi?K&@EFU|Csj9bjE^<N(<M>y>-(|Im^%DNsup~+PyC4O|7~WEWsJ4Xg*a3H0
zDxB*CqUj-MGitpWGGl0G1$H|2btH|JtYA0ybtY~QI5H38WD>>9ilr`)5XbL-@jC;*
zci>0F@H>1Z{uGz^_XAD?I<>YVvXM4mHqvrVGVA2~RC972&2sy3e3*8Fn`Q%hVa(Nt
zNy?5*ddrg5)6E7UdjfxQVNV(>Qpv1I%Tr7OZe7c9kF>}b?tnZ$7S0p@6FcmQ7VW+T
zQ1jpaN91XCMIlMTbW3E102ahf=}PQKyrw$@bGbEOkNLV$FsK^^C2kb7b`^dY=5c!X
zfW_EC&9j^4S)h6LV4iSGr517|915*9EXWA!xPXt8c|W~8L)oom?xTghiKVn^ejrR$
z&{S{MRLhu3<{sKN?g#pVNMhdnz>5ee)Q2fsw?I|y;Sk@Syg_HO>oE@M&vKFzW&3Pn
zN03IwwLd|fc75tsViyOaAR^gKh4sN^^d{fe<d^yYpF^O&E0PHrMJ*AAVR*gzIVq^R
z(6R~}W+KmEezQ=_H^AdI2G;<9!Uh?td8uoo{~lDfj+JAm%UJ&_zA&!_%y7BI^w4a_
zzYK}01-t%%uwF-v!S-gAJQu{I{X;2CA6>*kc^{KIIh2dMM=iG<rOJ4{`+=~a&exlL
zDbn%Rvqsw?pEwX2$g~%9Aa54zu|m8B??USEw6VJ}z}Uj2GMkArpAGjhW2Gt?x3Md*
zx1PZzyMFRlLm?;97QulNtK*AmvyFcuW2$_DLYela)eiM$)I6`)T^lGDVeb1kv|GpX
z>qr@F#h<e82LHkrRooj^F>x!+^XD7wIvCr<+VYp}&W3`w*&R)(_S&1*V!n+uPM9^u
z!TSM_(r6iNSV|gQh4s3XvPZBtLwT(WEAwStkVEdlpAzU*JhE^9g6Xv^9-r&^T9d@s
zxsms^h0e6;LZyv2v!G@6X4caJiK9}WsnricS&b5KzgqBX$M(i|S(L_j&5#q6ktF4=
z2PIB}9{K_1lFGUrTSJ5Qf#B3xTDF<#q&__ROoYRxIII)pQx;o~@0*E(<~Vg${S@6F
z4^BrxBj{!;)z}GSgg1ikf^9)i0R%x6e=_BT34IUwA*9Et;VNGAt|0=lhB|^pE;8`I
zl4mF5a<IX?WcK|neF@rt$}zl_gkfIWQ+a(Tw$I!3O<%rs`BA_LL%!CSqj0*Jkhbty
zg_Wug!j=Y<<BfSlRtWqYG^c#9!f(2UqdY*_sZ0XLPCR=v8zRLeaB%HX^yw;wWU3IJ
z*z82G0DtX(MJXK&)V_><ZGmCX+=gR21~Y@$!5uhe!BHIB!8eHAg{VNVn;!lwa(Rki
z=ow%UBlu4QLrk(;n69gQym?Cc=SY-u5|Gw*M`SJ@51^jJ1EpO!i?Sda7mKqR?1HP9
zVuL-9q};`eap-Dw6c0(rT|ef5=4-d~nFpG&3yO|1VxM-DdE4|YeA~3o)Bfy8b`Q-_
zR6aaDcG~UX8_m_&EsutM>%=Jg9@5db@eyJO=XVF4R<{V3YW6Y{pqKYV1JEfPf&*9#
zNBeddQPJ)KtESDo^5Pnp>`U7k!pdqiZ+Wuq!svSz20>e4-9x|E_Zq1aK2+cb!Q;r5
z5XkUR2#q{4g<DRPZrF8Ruo!>wD#bKBiH35!ykui9hS<`x7+3Hlf;ZuBN9%E_&rBt_
zbcF><`$c!0h2bcH)NoB!B#@Ng_d!v^cL32^YVZQ)xyou&MRWdBi@7s8t9TfLDi<&3
zvT<`m%Bu+IsOCvi@Wm*YFLfY={}A&*Ii1Ceb6~c3z!?NYNXqlIAjq^dge>PB%Vg(m
zHGH^E!gC&Sc#<Pp+rVDeLBr+-`=TUUhKw65fm`QiC$<UA?5EC2FqPu{O+5(e!;h<o
z=#Q(2=r0LBe@qj8!}#*B6}-yHV>20t@CnG&>;tU`dzP)v!eW#i8|+88tony;Gjl~`
znot^B*2>e#J-CKH0)X(Jy_o`Vgx6t$+biVmI(y4BNO8J76=srcPlJ$WTse^bW8llr
zK}ZPt@u#nO@yN#Df6<|Q`fQp%9qY)HCYpsrZg4jGZWU!H&Znu^Yk>1Gi~~(FFi|<n
zf$qlsz)52^1V>ZYHfyB4gs{QHRb5)>&L}h~^@=g020~$F0%eV*fpZz?{{o9cZ2<=9
zG>w@a1uSVANWe6D=C-06a{zJ<?`<CeDHlWP05l_kJ3tcN<>&3Y0!$EMh2V2GM*iWz
zLt`hlu{E<s_<efqPa#k`D)df<!Y#A*Ap|6X15s#~n;bnHb%gSovoXj5C~5K7afnC?
z2<}#QXH?L@vMc4!K;PhuoXionSM};Ifa1z&{kHf!b{|HMLJ0xQ9N|71?#kPZ<MPTm
za2+$gX6b$@_3x%sej}yI<dwtYT5dVd;eJ)l!%X7IOp?X=mNqom_~qv<d;;%Iqh>SF
zgI>6HejfNbg7_LFzKnzgR^3<XOS^qz#{-}~a7K=_;oosAw61C55>lEvO`njyP?HHV
z9YLm<5M&7`MJet^Pe7@I)&99I&^R|l<6zDmV+4+s%mhMlAL9jf-Mu{=yQ2gpA`7E#
zBEr<YYjk$#uVw7|a^@6q-S$gZ#ICv>RF*0C+x4r$GPChWdC>v}_L>5c@~YG9x^;&E
zKw26}?@9k%e%^-Z7=vBLSDA@v#jYRM%tR0)nG%(iVZMauC2P~|`VC=fx3i6Sc&$7y
zfxe_pGnde;S?{V`GMV+V2^isi7z_gP60hXjn1#lk7A9Kq!XJbroZOgmywPKcT1SXk
zd9^F*oFmcqn@<TDmvTWKim?kp8Z$^f#p`ZrUg9blE?r5L|4;imnkB2m$uLX7`0#!F
zDWuBHLAmh9a_NE&>wI)r+dAqufn!Z-O2oJKZFP46&u%Oue|j_itmBwO=v;>OVU#we
z;Lkb^U4`F{{R8xP6yR@-avjwEeB_A?_|#gQhIgF$eSlh*ubf2ECiKBD6e0oc$#N57
z!QN$tGq!9cUpeh+{-#`w4HFJz2z8#E$YhES;x>lJ&`e5!inCT?R>;IsakIE&p9%mo
z6G<qUKHx!b#QjRi1}P{Zrrc$WD4y((j^W6AqLB%4fchk^|Dv|)wvLu+zWy*fkUDf*
zs8bg*AzgeCc*V(O90nsI1x>PsRgTW(Yswa6_%+0vQBM>rij+k}>kZa`0BpfdB+K}8
z{wGj&+7`@25()&0FRZmZxm8b#lm@${>m@~XHty^)+5>hquI!SMHu`H({W<~&<LLU7
zJ7X8;{pS(Xq&_v5-5rgQeTbE4KV=wO9&4|HnH-Khp;$^`7x(DYT(`fLD)&ICuqQL(
zy9d9@Lj9#EzXJ6Oy8vtLYgvHcTjZkFdR0p$FScUbb2)~*m@;3gyptA{NldHtC$OgH
z{WydXXV-#*FhX$47;2jY&RqRP6hxF|$IeDsxUf6hI2b8Y<wLZruH-;1ot#$ND_QKS
z?N;6`!p*V9l%m>NYii86U@C{SSNY{=l~`Wm(!z-F6J70MZvrY--J1}{{?;fCO<)Fq
zsCflw5KnsAdix3PWt-GD#>f&9VR%U*pXTPWwZRa|J9g~5Xy0+9Q9VO*kRwV;n0m8@
zV$UhXo*Jw|#Ixj-;*y~Rb|1#5s-f*&SbP17E*Q4>`vR%{7opZomCrYaDN!D!IhdAh
zvpmZ`6d1u~+{ESGDCJG7r|&X7@A&TnO+<zplpACqY>yNBFy>*rNq{j&-BN2Jp1wWJ
zR+|pW%-uah=XsmkCD97VgmftS-fCVwf-iZWOHO5*B<9n4scDLNsflL7X*5ZWbhi?7
zFwnrnuHPQEQ{m{OaIG}T6!35up5b-fQz{TP#({i*VpYjvCafUYXWCWTC;nl;zJiV$
zjM#-H{P1S@4vRQ~Nh#Zx+KV`{8GeX0YaJ+Uj8tIxgB;*GR?kJ~2`&1|8~QU;=3fpi
zd8)j?P}599P1v|4%@CJg0xnIebltx@)Fs(<ob$RkxK}0?Ii_O=-z1SxRwK>dBz#HG
z{>F8mQ-_M<@h>&0`n|e~T|X5tbaE)o#7B=^!~J?=XyJbF;~=}IVdJ!+ozw@-CXXO%
zUzrPDp`}q|at*W$?K+8Pb%Pf%&<FFuQ9x3vAfL1arp{%~VBQ5o_f$)~4L(n8vGh+o
zc0I~`3;U<3TU)yYeQ2cqqgg>jLNE$85wkgUP<R(<p*cOrZ8su77+GKp^#!;J*p-Yr
zuX`Qyx@2t17-rUQWen~X762X)k7d9EmIG!plG8(YtX(wC;K|RjdyGPx_HR1tj>COM
zTA2vgz%B@ervtd8Hbv?@6<WcZ(~J<fS`qOwWO#B4k=Vufo>(w4_B4ni`CuoMh*=nt
z$^TNrNSy&x3NX`WS*?`uR?2uQWtsvE?`~<6wrd|sihL0+d`(A-o}BX{6#_hSXdAt|
zjZ#GKj$?+;p$jTAUH@@@ae@0LP|)V1KrgL@IQ9ucrQpdBDBp}h+&BjKX&j3`>qr$s
zGY)NKVz_lWLNm^)U!tg%b~<Un(Emts0bHOO_m84$CN9l!@XjOof-M(Oe;Bah7F8!W
z4iyyqBajN}{rbM&Ko{PO`7Vs=Z$=%^8HFn@!fan%7G`tnZ|+Pt&`fq{GYXf+VBwC<
z>3ga9Mw<xNh4|vF1Zamy5~^E{h`2{0H)T?OBLb|J)Dm4!G0FICCBso2Y3h1R=96UI
z*)E?##bxBFP<G02(#T@_rq)-+ZaXx5$0}0&WBA3MO+7#rMmXa9xB_M1^#+U$(x>Pe
z&_ZaYQ~3qMP;q?>E*N4}VOuAgylF4LV3u3(%to(^wUKP}%3f_I(KsGmgIexv)uMR1
z7xlaq_4J8h_=ljl82$u;TH;f8a@t<lIEYb#I<x$q$aujrIh3F#-xC6#5R{s6(H(`p
zd_ba3JeoLI)j)+%#LCw{0kD+^b)b}I;NE7tetfe)JlgKR1qn<Gq{_u_!*bQGw`#51
zVwY0Cnb~BW#;X{QCvNlH3n5iU21qzOmRbG^q#3ywOJ$x2ER=o%`;|yw1?QM}3$N>+
zDE$>lj+j0Tg}jHep(^i*0|B_HO9RBQ*eZ<CVYXoEZC<;Qj@Hp0iB_;?>}S4}09@6c
zvwk<*`y$}8aA6G2vl%$Ilgx=EyM5cGMUq)@t()}X#?ApLysd+0z&Zokb6GH>iNq-S
z>|c-ws>7Cl61-Kv&ki<%@z)<Gz+4x&b*MZq4n7B`h=_eYo_%*$V`RyVZf3DbdzE+$
z3^aDKjD?mQAoIkw_dtzUIg!Aa99p7eIk9l&^>sbU$rP5$YzYJ<wg87seV7}#U5|bU
zaXLC?428{uQlr^0EjywEF#&9}=huOit}O>|nG5|{{hb#YWSAtUp#n#w4~WHY5_HrD
z?`@`G<2%L7B>5fpuwW4q+wkMq-1+dHJZ|EUXA8uZaUD{g!v52B#Ln$<+}bBWrT%gZ
zHyYioz;|V;ytk3+c2%j4(j28aO}idz4FAOT3{e5k&99u7u8*J<y5`0r05g!TJF%S`
zJC(4=j&`ak25ff?5aQnO7CA<q4)=tM$)U0$Q#dBb@Faj%9k%N`f79SGs#CM;7lpxE
zY$tlX{K|z(rJ-oGcY*&_HZ)39ow8pxvGzm2NaKC@V}y0Hf~db5RuzRHlnYOs^%W1t
z@e28TzR-9ZYFWpgDBaK!=bVu<(6zWRIWk9;>Nk8VL@aR0u>w{W`%zS8#$v-DHaWB)
zf}6(4s3o;!Dbx?opb#SpbFvZI^+EzHeY+XiBtS#2EjbzcZbF0kSZ>$YOwu5jF5#Vk
z>EgC4e?5j2Ecc#b8xOHY`L%;n08oQ5tssbRok6FK4?2B(&>793V@&Zd>^qZz>sY}F
z&A`6kEQY?Hp+|(F!P(91P3X>H(3<vS=QhL64jXY^m~#*UBU7(z7P_(-1qqQ+muggS
zJ|cLbYs9NHMNsuLQH8#xd|1TyH1fk$FJj_KS)+_}gAFKi)gwT9Z~+ROi-YSDVFwq&
zi)UTL7hFV7q~K>*Fe3R40+Uia7t@16=&^&hN2BU(Q4RVcMdFS~DU#n{mB}Zbk*Mb#
zQO`T+8K~b0YNP27#1fdVjNw`aIjk&S0LgFgE|X6@@1_U%;ClyG;(I#qVvXV^1ZVL&
zcukhoNClhfwSPwV7$eJPLyaK?j3O#ngFlTi{E^6L%vuDdAe89lI5rk1LFd8}JpW4I
zJ9rP`jj`e*Te7ZfOykf#4E`}Irhcnw0ifC$939(rwY=$q^OagXAq)F;e*lavN~{an
zMC#liH>PSux0tPUmv^>S{R-tMWmv0zO}7xUwd!HIg@p0w4=GFW@gbCVrr_f_09+ll
z{Es2Bi3bL@@URY52AANE;(?I{M2WeS5H;~YDgz#v=#AomX`6UBIJRqD@j!Y8zlfbB
zaI!~6*h;-k;Jv66qeA+LQ1GtWd`Y0p<D5M2J*G(}wg%DXl2-up&_vDjK4dCGefVF5
z9twIGOiQ<s;HA{}vj)&OJ$CQ``gDx#igMe=7*n{K>A#T-_dqDE^o_A)cC*9}vJ6Oo
zxR;doviJFa0nS0n;_t@AHF&Y;9#YPSm@xEQM$aA^V+ZS_Q5)K#E~k%U%@m7UQDv<0
z8+=&vjf&?ZQO^}o&z1C;u?7HM0W_<y7Mw(tqyId7f&*J@y3ZkQ{TC33gS!t9?vEmO
zWgZSu@Y{o>$j9LbzKST2X~E4$k~QvShW|ml%ggIugink^TLtkYkcD;wOY2mFEu!ZW
zQO_r%p0=2lXECg{8&@$Q>nrUq*Q7Q+#en-5u!rq5K26^r=v#(s(dhXs|F~Q-LS8yk
zM6avc!w=DvPUCY7|2H$A9!(mLL|kf`eGQTdFV{BGtCfJBNb7_6o(r7H-Ac6E#wkIf
z6$W7V8*D^+(XGGC+QQD+5_XQF2$y9r?X^ftnxV%IuA>i7NYo~H6pE(Te<@lbwl=l9
z!xEohiPxJF>9K>)n-V3eRbtcIUgBe&CH|fzet~768RGe(DV`oXxWPmT`90p28#p3A
zzX;#+WNbeV>aLyweHn5#`4<DrhggjpQP3DYmj5T#`6c*+oA4)9?stPP!vR*scuydX
zvh2sA_T`$W$)gs<c7B`M=I&z(z8OW4V1Z?+sD+cJS8)Qk$(uffFRp3B`3{aQVQGyY
z1HzIu@+1QEK}VrLYlH~Eyx}DpG61cS#}I%gH(;zIRBMfp2&kv7!0f8AMT=oKa$6c<
z@y3%H_7uZt8kNYpO0?F<qYT4VDZ)G*6P6CQmD&_ogb{PMpn>3P#FZVHg<-!Vv#@**
zu}1kdtoT;sD5d&J12VOS9|SP0@fF0bHXj6p<V*;K*M!8km6d;$MS+~@f$=hZSf5MO
zZSVxQj|;hjA?OTA)9Q?-x1B(V@)n2VSe%9aIiF*36)Z#{NBMDZ12-T+hNyso){%20
zw;}BXLW>Mqd@a11KKMGIfNhl(ypDW+5=AsAlu65@hC=BvS?*+e?!upRx!()!h65Wf
zj890135BY`TNM=QRWO68G$D21o^GiIXX_+WzWyVT(NT=s;z+`FH33N4$R9_>c5Wz5
z^DQqP{<@IumtBX&T=m*k@DDU2TpHiaChZ4h_M#86!L~mc&6CuX(Y!&JAfR&2_@*r}
zO-lsL{!>UrK{Lk$vlF$%o`leVtqL5g5I(u|^(YE3iOWF<m8VXK9kwwXqqa|;3GyW(
zNo(xQ;2S8OR7DT$LFfZjNz}dYG^wf;B2;yV$>D#KiN3{bVTs?S2NV3?Sq;x0i30)u
z2m{1{fY-tRaUkHIVSqS9pK=`VdJ+1dwa|mEX8^#a8s9-hjql=5+QKoDX$Zq3S@>H;
zL+-*F8Y)>RDHuZS#GJNx(Xx}q7oz-teLPYH5eN^`VPTJmFe@((Q<5#!e*r40<MNt|
z8+XLSIG8l@6Xd@b`TJ6Kx;)jBdA=~z!?SzjyD+me)OrWWCS85hUG!OGaf9z6)(!5%
z-%R-(y_WxdxbQ*){sy-rsl;@Z4}da(F=T83sjWWwJ6LZDh3tZ$uQ5o-n^aioV={)}
z^L3W;L*zvfKf<T_HJsA2x8^?r9i|Q!)G85|Ai|19kjKM}#fqH}JL>vaH{gYP@7R04
zsc!0zspq0z8=76zQ&|X()C5JK!|}Fuc&peYkf7jxK!bf<XzRJ*S1q{pL(wQL!G8de
z{+EDYEX!G>1n8cq4?Wl0*;j&PdAlYHFy4$GPrA_5a4-CBHNOVF`x!Siv|;#-a9<tq
ze-iFt)CjeeKzw8q!`b?^Ex@R$FV#Pf037e~r-PhCd0(pEh@evaNBsK+N(eKT>W_>s
z>O1?4EB$v;`m^$P>UUYwp!|Ik{wgA{g}pWZ8+4hd`Sso=F+`_~a2DEC7o0VzDpg*L
zqZy<soKuiq`wYrbnwkZk#C~kypxYBl@_&r<nJv?hQ)uQM&`c~!GkJYSS@}*T_U2=i
zR;EEGdO!ND0T`U%Hk2)bamrnE2SQ;IJs0~T3|2Wg?#}>X)0Q3QDM*HU{w#!)`f#g5
zKIaTShxTX$ilbzWGjlvL$80$_Q5{WIF|T(Ge*@{VFgheHE=JeI8V|AoS*NgI5#$4|
zWpiFWm%!OO+#t`2a9Iw^li`|$Eg>&0$~p@lOTe}&_z7alxdd*;Z^0^os0@235`7rl
zy~*JJkhGeKgzuJ3j$DR8$Lsp5GleY-W|OO4!Jv}LcqxyIYk4*!W0v;1Tr&gE<s9@{
zq2O#^2U`6^wKsyCq#`E~JcKN{boub!wEt5&GrbxAXK-l6xGASHMY3hP5b<+FNXGIE
z2{dU1qA+?k@QK2Hfuz+N(AVj$`74`@qbUQPHtEE>CkcPxe&tG_83Qqk?L;nqzy_^u
z><|*G{!Elto>U<8VHnOAlPQ-Vo#8baIh&xC_xTi#has6OS)IHj<1ut>G)l<}A6L`G
z(le+)ipE=($gs^4B~r^VIjIZ8GBCDE=<=)R|K^*Q<S8WC@P}LcWBk#7JAULf--aPF
zzhStLxJifWXkx<`!Goa=GWLsXBxWt5pn~H`JMlX;(y{pkAb2O9*OYOY1f1wBaQ1d)
zfkO<(Z_NTQ<h}yDstjUpaZpIXIwer_#c`D{V#uq;8R9W4!J`w!6`d1Bw^Ql1Ent_U
zOgajKmi)vYLO^w&NLva+^V`M<F*_B{m$e1T)bt3Z=;2F&!$|JxJkxH{Pj!y7>MtB5
zUqKms=aiOi<zsDWo!Y@I)0v)jk$B5d^J27;;^GA%o6ra{QtrZE5ZeX8HW)SvjR(I(
zj=807XVD|*U=$<}$`!BUrrn&zMp)z|9L?_Fnn={aStp_NPArBM5fvq8)k|nBZb^ya
zS=;9ZpC__)wzTW7qVIILXoTPbYsqMu<o8->7-7=f5KS{9l7<nwud5aa&Z?IIO?o0|
zyx>dGQg&~aq7hPx7koLIX1_=pMwm1=MbjJ|Ny7+}=H_Uc(;{gY5rw#Z2_hX44RVRz
z#Kcpg=;5u{=VFbV@-5Dh2Z0Djzy~a0?jm_Xm1pKi=|>zHB^V-OIr(&J8vIS?G+4m%
z@ZlU-+9Q;g+}Pcqt9Qse%w2-6WTBQz@(2vKwjT%Jy0zVsX<^dQm)T62RJI5lIWtyi
zIGZ02gVa3qIA=2#&$^FLg0Vwoe`oWP?IH7>%}=z4%ye2sYdP3cKKH&R7{9`BkfnIq
z%WS3`90lS8XLBVC(#)uUO34TisGymxS;4N~Z&vVYR4~{VJj{A70%}o@SFsb-nRflh
zNQLFIGq<lCZ%H(YAIr~z;pjlXOyXhpkpZqh7k|q=YDI!@80{`VNE8#_0ZXhDa4?f(
z!`1vMVrek=4M4!@wHEx6FcV2w@+3WFP*=`XfLyS+EuezM_ruxk)G3&Ws8OxJM}bg%
zL-0{tQ{Q6vD9EXAzw&Y2aR={5qnzMVgz7H*0TXHletM$7&rB5f*+`(H=$XoCR;(cQ
z9qIzZ>*iEcLtWrQ_5L3;?F=~e;4Qd$jM0s-eq5o8Foeea5`#_dxL_G`e{OyvwgB|}
zPS7(7C0nY1eo83Rm?VLE4T4@Eu57=8a%lXF2jEay6+W1$`)43$y4%4?^q;egVz*P!
z)AtF9!d@PLPISkUzE3!ia%V}o=<|y97tRckN!0GMJoFt4-@a#rzNPS;l?r`(!ndL6
z+qI16t+dh}G+3CrtWRV{CJtcWvx`=)$zUxNNrsn<2xAt3I6WXxn$}jZ3Y~*JY9J2@
zTIB$8${sL}jlGLJ2U1J73!Ka+PU40Lx;;@SK=ytF<e4s?jh))x!o^+ZNAdNy;7?cR
zAsQ(wYESeyNkse(34-fD7Ufw^62LjZV+d)Q*oG-KAw@1u5%w0iP|M>;RiyxcH3i<r
zGAl(T3~I<`tXP_6mv;P4fuBq1ndVD(n5H~miCmO!N4q%C(N69%pF;qAf*?PMKNtcc
z0}0*Xp=@9VAu>gG$H%8Ox|_jR;}apjbz-LjfOiAGIw4@+8+UTuLjt9pLG+vl$dJf{
zvW5dO-EGu2I2!LM{<1i681Hrke_lX@WmGOn2+m|YII#=RpR3uQV$Yop@l*g9N8v|V
zaU*_wOSGrs_jQE7j^8lsY0W7`Y7hHL!u@cpjhoJygg(8~m)Nv3_!bY>vnBq=u)Z-#
zG=L8>_)igw&5VP{Q0Y!=;<qd5Kiw9I!f_#eViUhYM9;LMwL};(rTpJ_hB^%WLn{;j
z?qrJMms(f<*;cd`B~R8s0PS`C8<_tCF!%7EYo(GC+Hzk+3ERE(IX^NTrdaK?KH%E&
zD?r>R@E#YAk+R}WIGDg#JY}gku>!MvjKR3)52BNKP)u#JaHK-|3r91z;K&)`a2Gl*
zYAQ>rp_5G5v0h6u=qlsp9$7u}Ee(q(tML973J7uG?66nj@lTeqWooE=d7x9K2HmKf
zurjvj+K_JJvBS_F{<EF><!HSyB)Mo#C{HNP9@&bDE55DV>Q+>YLq<;hI;N+3NW)2~
zN}<$q9gQ;e3<Kqllsk3B(#Z(o7<D4QaLh)((RjMUI9}Lc-t2XFW;nJ6{m_TIp@(rv
zbi_ig4;FHK1rHW-d+~VL@SpHUO(cZEjp6s=2N9gmIg3~=?p!+pf0rz|72(vU{SkD6
z^F%sB7=uw6v|wP+?+1TEyp;fHEd=Hl-Gp3VH3;X@X85x#yxy8H_Dn?Pi&7#oUCc;C
zW{aLgYqD6hGmC6Z5oCvpKLCU@frHd3xBucwEJ&boKW|~V+|TONH+?X6aW=t=e7ELz
z!yM2OZG^K_AKXp~oV~5zh2F&#t<*v(2yCu{tEZ`|kv2w}SMdtzVd}X$&?Uq!XHlwi
z?fMT<q~YwT-qhAS_L;2~SUQzA+ol~BmCHD9c%r&Xh{DcvL3xity1Wp-8h$1G-e65$
z)f_ol{O#An^P5f#T9O#fX+X}XQxhfwY|(cSNTcVz%<xCxoB%Y?k1Uf^)S3w&7ewkS
z!pEhL`ev1@QCdos_ltVblBhR>Lpb%n3EIe%qu7w1QJfjxf+J%bYsIc5CqZ%J@XXBB
z7?WM`q>=O>V<|NoA@CZ+;;sPTQ6)j@RGvor*2=hIbNjl6VemgugORJwm6$fF0^fd3
zUll&Ct~9O&-)T+Xyz<`B2BykKMZM{EY~Y%SXl&R2J8bM@u#pTt#|AqTPPD;j8g<Ku
z;G2LRNkvGUERY$RM&8ep1;karg#a0sp;Pvt7NYQgI~7G1O+a(B5k;4P^Wa)KD08{7
zyD)y=16*pqWD?+kHmZUJdyKsBXtIc6*NVacFo}*0pNu~!m<kMt4}FfT94O~rc0W+5
zCrMkz@t8;`?P<~dOJHIa%Xi7N6X%5moOXB<^Z02eSy+Pm#CYJDTno3JKn!zmOZ!<6
z>WoNNO@;DX8LA4lG)GMsHIcGdh4y0!?wY1XSLY;XE-N+YGdU+=W~2_ZG829(DR7)t
z{M{BFvy@5d(W?L~@{3;gjz`yvFRI$52pcw{430t47e0~3JI;s${-uGYaYq=spFH$L
zd4Gtvnn4T>gR2O$bar_ctbAX1pvi%RF;2vRwA96K#x(=YIrFEnqvo{qjt2l@{<B2~
zHW2i(z4P^|%4tJMM?^;4vuaP%1<~RmSeH)M`00A_7h)Y=i7fl-Jj<gxb8gS1%KNv}
z<?U*os&CZgoOKngy{ax3uB_vYcilDB=C^g~3#c|%Ilzk@d4kFTciHvBejFZ3gg}C0
zexMDYYfXFT9t?&3_U5LVz}#A}thM;=xW}L->gRQT=0+Pf+&o;mD0_6`IKkMJ75v30
z89R4zM#B_{-{1w{qUm{&p8WyYfk-(Q-+c)S@W!>x9T-*XWSp%;sO;19BI-S$?d$NP
zEX=Kcpg494lHP&u&k_Cy{OJD`{EP98{&q(k{U_o(ygMBBsyHtQQrNuULR1v|89;eK
z9nMVoxJ2+0yfAbk@X6o`CLlP$%Luk`Qkw3y_#=dA{FlNXUg3|yw*+1!WnO8ZxiROu
zEvcvl8!N~Me<4QfRW6|74bcn)BX7c$Uy0>UJa{LH!f-ttO3GJ|A%}x(k}?s24HArq
z_J2hJjMk&T2s&ZPCIlWF=U>V5{AMrw2HX*d{YrRsH(ZKtLWWl^tX$I6mz1V!Js8d@
z!5zStM6j3?pi+@UTvh~s(>6*XgZgYbl87sX;8jhOVs#lSn`t2#ZXy^TqGe!(T=_3Z
zh7lWUwAaqT->s8^zeg(S0_wW6T~bUA&vvR|_FmZ@IkA|-J>82?8XqZ`EGIg1lgn7$
zGOT|ni7-f|sT}Xp_dkr)My|WOlPOG2bm9TctQ@|>2O?w2-yn$$2D-y0V`*mPdb7RS
zm_0$~xtvLpyZk)Cp7Hhu??=@_4q<}eAFbv@3g|QOePKM#ds(?DR(4A8K^D;@&M;si
z>O|aSorxu2g2bizs{OG#*a$8nJ#P!qt;d?w5}W46w}F7wxO-3ben(@cKBBe*R&45|
z6C0B<7_+hOOvas?Aec(8LJ#j#A^l)3@b?2S8e<3#d8jvAiSK^o36-;*Suu>_`OB<P
z7rkBWvBbaOk5~|mF=VYnQoym)kD_2rln-753g>FUCddi?NwjL};9qbXx^)$wy>?-F
zspM4cLV40Od%4H}4&RlFT{50GJrD3+#E-n`0{jjFga_hxA%6Sg$2V)}zZkz;I)2Y0
zJ;Ts|*zk>T7~V7hB_f$oS9!OjFr%DP;IbI2L*YKcJC}zc!eq!z5rWms@{GCl>3dsJ
zty>kja)lUq5t1cBX5$WEBXcEoD_!L-$@1>F2tv3EURKpBATZYpAMy8>3cw;PJfW(!
z;o=B#5#AoL2ner!2V4YpRqzTIF-4Hcuv*l@!CM;Oi+}(@hB+PxZc%G4lTBwce90^$
zeS_itIyxtKFLPsQoK_}~4&H|V{{Z~ag40?styfquN8Dg!v~&$l%XVFU-f~H-N%>CV
zDj780d5&-^+btu<G|Yr>G&DoHvy-*9ZY|;sjR+kN7sQr)DI5=v#P*+|<6&XGu@$Xb
zN1Ek~n&9_4I+5C9Eh6WS?90!?tC&6YmofGtHYAv1B?B_%nUUr);y}QZFhCp#D1-sx
zfL1o&?;nN6mDG7OozP-Hm4v$$Di30i>&d6azX9M_H*#Bzm<Q)%I-N0=i#v0rwEM$+
zzrzXm@S&^iXF0;M{IgN#STD=*-^S`W|KsdB2EwHnW7cR6QE6{!5&;-@F=`IOUmx6^
zzcQ}idJD=)m4__<1aXus|9ElCw(5)7CQgl7@cJzw<MQY8Fc7vit-POy6L%B$K<diO
zZTVxkOqg;O|6OuPEb|<=&05?GMGY-i=x(LQ8fAkAtJd67FbQ~q{j}Qmytmnw6lSfZ
z<PrQ0;5UrlyYc%N-JioBYoLBM!7Q-gX=6kHFGD^PZNU4lNy|SOE{&buj0FdE`EcDp
zy&mR8rh>G^njy)iHg>_qIBCoCSD*mcHL=98({YIlizm_sD@OT<XFEM>rsTqtz+GBO
z-c+VHE0ZTXF$=s32XU~@A^^CXt2W1-TI1WF7Z2a3Z4oWHw530URywsGd;4vz0TdE3
z6%QN~vv>m5KR0YBqU!6=E^7_3u{F<bi>Mi-?=*8iF8d!)y^Pd3wXM#nLnybSId7n3
zty`KXmNHl!jH_PPoreP-|JB4tCb6^&NMKJNfZtO5j>XSGqVUUby1@k7e#8GpppM{*
z-4C+|h@l*96?>pOX{wc!E|Ct!^CFNl*`-;Gp-7*~>nt2u{z-@=cKhIgjeZKo!sxB)
z=kP}TsPzAev^#nR2*I0Ibw^KwV*qIMk3lwGD<3TbM>iQP<cGVIHP_I=mVY8bD;cZ4
zh2*Cl;x9*-)`}Rd%0CBTTE|qpv(=4vwz|arMjFI^_J0x<poGRNnT6Iuy{0uP+(-Tf
z7DgEk<l6XXxP;)1tG+R|maiO)PwkL}N%S!qZ3l4{U~AwJ7&in{M|0pJDue-dq{iNl
z{<RA$mU2PDu3duPk@&5`kA`61#P5grJ&NCJ`0=&oBk)^`-v<1aAneola-RRD`3*k^
zhvlDvTG0-y9-YF_3~Qz<Z;q&p4Ut)2s}|gsnvo=P4ALlH2nW87_#9_UB@k?>H3_6-
z^$X4il!_>4Ya+b#7fFN-owow2j5jdUm1y9+cWV7yFuwAn1Rm$kCJitctmK-2Lq{!$
zvWVB%Oso)x?*WhK>LTou<TaBp@+=hZUqP7}eDpAQax^ybvR(Z+l2gECgZ^cdzrFHg
zENes*_sUSq=I~^6Pu5#M8_<MMqzx1@m0)G0-(>wZ(VXfh>2JneIJp#11_4H~XZU0x
z<aiSy(;6_bGZa57*T2%L8DVTjB&a^*W1e0IVxEkBFbc>mhNV(6b?Rl%0%!>QlPKr=
zHXKLC*l+G1nyeE#-}jL?m?V=Q>K-yUQ0xQ3XHG{LHRkzDqN|LDOM5`|$wBCevd*WI
zv(};fd6-2xEeRnH=A8Wehm(mFxwr=rF>R9U=~j316C49l0B(J=MzhG1|9@C}6Zp8Q
z^8bH&C&?sfT1cBF-5_OYxn`13sX*EQr64LO2rk&lBA_Cmb|7g{xihsOq9~w%8;GDR
zinxG^0-~r0D58SkE(xm}u7IKl)c^bQoO|b<+hi7h-~YT`bI-YFeV*q$=Q+=L&T>l~
zYPGfiEimpA7R<i)4e;YY(9yRpJO&Wv7BbmMo++T6k#&i5_VKSH=k(oFS4?}Ulk%5w
z^;m1)x?Oi>A5mdl-#WHx3k~UwF+A<z^Juh@1=#&!h_|hR6o1Zz%LV^~fyc?=owIa>
zcGjV}q&<8c(0T+}#u$xq18DD6zaC0HPaCF$%@@`gA=}q2Z^d6M#BYdX({Bt|Zp)o1
zQeie`Z`6&n`*;`cr*T{7+L_%g?Y0#!zoM8S6X(A?Dkn7Opn)SUJNRPiws9p`-q9{o
z09)EJMHH%(EyR>%RDiNO@O}6;kFu6eH12hjWg(`kkY7Cq`o`pRbld|vvN@zX4~G?Z
zM3^d1+xg+K5RvQj__E77ZfGm?4a-E+S!`+R-SyXneA~JeL4H8Gob;bVDMP>frcN^V
zoxfZ+TW;bixR&1Yck6OKh0Z6ms~RewLKhG^s~RewLKmXqtN-jxxz>LSFiiwE6XGU<
z0@V>`Xt5E^;!3k<754YrH$y6-K8I4b_vQU;5)ih~58K9tWzucy!ZKq;x55}yG`K9a
z!7;zKqLG!ryjNhpG)F0ITssbk^_y>CFYW7p=Ovp(srbEj$*n|Qvb1l>19CTuaMh>>
z<0wr$FWD@bRil_<TP~y&Q6iis7xU$6j$j@F79;`Y+1^b%2EH6b3yB!Nkak|0?;U?P
zZs~)1X(?SG|6IUt2}*ClKhU{=4gZ$D)I;z&A7{~@x<budIcPjSkHCtG3&N6upwA-S
z@}*uPI1b?oS*Su3Ug}dJpNI>Qxl4u2S0Rdy3*iK-3Imhv2^4DP9@pPFej4GM7ZbYJ
zdACynER%8ieihPESrTW{tFqhssvvJ;)AnIS^&Z_jzFCFsK$<@1m3c|YcakeYnuma$
z6@Wc+zGbBX3m{@?lF~e+SQMuS-=jK6u`Eep9s+hr0?cz(0=jF5@O@+pTH=>;RK{#R
zg+#V6$z~n`5Sk81^CXSCTM(@#q9g+s2RG~`Uy{Lg;JhIG3Ofn2lfMO!eteYZin0xy
zmZbT_E@*1;1Tl?HO5aQ6T?*H8zaj(s6}$%FC-Dz49;u)p+E-BQqjY-&V{`7#o!k?`
zCT*wjwl?$j=~{0GasRrCAAbj>gqN!*g6!zV_=akb4s+_>a$0LYVX!F(Z!(}R;i*}h
zz_D3dxVMT>`m6AeevQ%}gy55>^kxEG5RAeWaMhrM$fB0q>2$QSH6~lyK?tin8)anX
zP{=$avntP*PFBf14icBfOuqTjMwR3&Y72(BWI?v(g~K}rV~>LVwC1*$UNM|FqNvkV
z^SKqk!=VMvrR<EN!q<+jlG_<<*Auow<$xuL$GQKqcl^E7<In)_98sd7cFG2u)Qemc
zL3{|7AoV5}w29=}cN*jeJMw)dBZ{qF7JXdjev7xSDschPmBR3g`6Uc9@7#kAaGiKh
zwarO8;nF?{lu23@q021jmeRKR=eQzn$?rfB`dKkM|HLom0hgYI(`x#|Dds^JjH!A?
zpDRV7+$N*K|CNTGN+>tU5XvrSb$oDAr*}9DAnuJ&9`@R1;N*%01wY{h+xQ37?VJla
zxkUhFZJQZRqrYCNP|)Tmmln@u04fJtV5WFZ_bd3{Ua%10(6oY=5*(v-0ihh9Z$p{9
z8Yg@&ExPRt((ItD9S(Ql!KrFx-N_5yq>m-rE@>=6;TB8aGst>EIcD7!fWy;(ztCfz
zvxs9qyyCX>?n0ra@cP(Y!xcJTCD))=$A7JkDM{~`V)=qy9`M?^zbj_r{B{nb_i@|H
zIf+0@V_U}0t+KcDksvondvJqDcQClilG{JA_;jf@J%RgHUE8rsF67q9*P_y_oe7i|
z0_|>(aB{19U&1<A0pb2yIjxl=aMa&)j!SbqW-9dmlL$qVehbdbw!zo5^^N|NGzDAB
zU%2AUg&=w>QKg?T3GXc}M7rw2mqobRoVOTvXm!xLw6mgeg7(UKIpsI=<Yvfl3HLco
z{Fd^9ND*B@S<aSumoZ=l^Wt2TvpwZ3b9II<WC3X9yOgI;f`Qs9C9*UD#$G{RJuoJZ
zZ32ctCM8y#)x6u;lYAOA=-=XyGpv{=_5<wX+-z{Tgg}V9hP4F!bVy__G{)d&pNd)z
zi|yg)k`H%qY1Izbk<PWl$zVx4a3he~;e{YPj<+7&_cFRGA9UX!r+f-e(Vc3GldlaT
zg+g>W9?_L0Ci+mJ5CUi0X$huQoRyQoG!1r`I@{vx+{Sn17R0!zZV$H>vnOyL5InY#
zC~$il4!FHCurIe%-oYzqz8%Ly(>^*S{t};g{G~F2j>>6r%BS+2gUnzLLUH25&@hM;
zF)Jv`@laJR$`OBosxy~Tr>YJM;Jq?JF1vU8E(|kGYyq52nNMVQYuDrw=94gh)!t(u
zyV_Z)+=)Ub7fl=69`5YgIHA*Zx8#<@bQ0|OaH)$I4}|c@xkjgmDCktdfli0f7jFVL
z;N3)sPoq|LOo&ECEF;nyV+Iv~g{|^Vx#coXzOWav<D)onyHQCIuyk3jM<(Ut+GUZ6
zL*{RZYYZQ*cKW$fYuv~BTkRSr4-cYds_TOFjK)@!0S$3?0hGxe5jPEP4?|Hcq3NIh
zQ{_Ej%B~{CI{Bt)$azN7%|t=dGjO2k0igCSygo67d4}O!$5KWZ?o2u5(|L;Fc2>uF
za}X&MZj<AYQCSjh(-#AWok-J~*Wg**t&4_F$4nn%YsFi+8En(w7+VC#wjhH<IqO4J
zd<9O7Z=xupLBE^O82^5F>H~)VSp<Ur**M^TAUyRI;{--pEG81@wR)U#LV)+nDZhuO
z2(VPVbeol+2xuV3qk(e8G+<j^*Tk8mh3M}iIQ%u8s#cv&eAldV@|8i<utE_RqurK@
z6=N{7A1Gy|G&mt{Lfc0kuhLeSdc4$<dsRM{`=HVC9HOA*c{nrMY%`VYruRZ?)>w0e
z=!e)s3(=2xUO20x|9QKwW)JA@OFIUS@96Jv3n_mhtu}(N$It#-cpqV7JiA4gjIvUI
z`@B(Ia5uuuKfLU^`@D|+!BK8!*2^eTDK~PU#T+&I6YB2huZ(^kcZ3g)1Hg}}0PL#~
zw9p$w9}wXw8Nv^!vDJ}_NvjXy8FHdSxJh)JFSmRyPl=9YYHlw2MZhXTqC@VMd6y?6
zxbo3~hA3&3Y+8l>uFA21(c3lADVQ8R`qoxNmXTj7MJA{auW`UCu*X*E`IeWnwRcT?
zbDGF)lE_agGKfA1EF6B1k^X}rJuF-c?DyJL9&N>5?vR$Y;E-;sTtLPcX(oQV2<c-f
zGKds`bSlf;GVfqIqp2;1G+Xs4$<?~*zii?J1x&PCqo8k>utYcUtQsVP`p6H+RBfY&
z>w`PLKDgQ#-VC@ShpWzT|A5noFU8vtj$s=eoVyid%Po~wUIy9XB{gh_>k1tvle-tj
zmUYLoypD4)=ge^T8`??=mTWqp5!SefrTDZ1ronLs^!26r^s{CB7l2AUR*DV1zSx?3
z2Kt_Feept~ApVDO;=Xtg`E(BY6>5WCSpOr$g}*hj){}E82|4#sIpq)Wl$>KwE>3*V
zct8=zIXRx3Q?6LftpstgpVjxrv>C;0#&zm*9@klg-K~|6QAAv0E9Ws(<MvjgA`oO{
zxgOV9m955gg^1qI0n1pWfxYel^Na-II@s|-UksJk7p8+s%vA@f_fmEcHHM1O?PZ0r
z)w5&X$bhpopHv}7X0tl7#no^0XWw_qG6t3fCo<dT8LdfaIm+W1wP1#477>4e@yta;
z!80Gnnc24TeMT{(V*vs_qSqk0gvcQJ1kZ3gI!PhA%Veq(?M}YRf8L2s+G3{;qDz%}
zXQr+-W$Zqto~eGx*vsUWFXky3J3zxvmV-#460mYT0jo^0fSpAz6$l+`E9Rim_Em&H
zLTQEeexjqQWLSk(5lY4iq?J>t(%|;+1%xJh1!m_S_)B#h6nJdKv}oJs(BGNMU1)Ut
zBvH`uavb`e)RP0D@h|xwjL5&0|4RDt5BaZd$>kp8|C-iZ?jHVY+j2RsyvXfW$mMS2
ze_cD9j@U1eAdrlmhwOJYr7|e)zPw}Q-R6x)&`*=&<NR-m|I2VScwUM-@_BpNb@x>r
z{X34n0C+n3cUfoqF;Mn%W5nJ{rE>NE3jQqsC;uq`HS$(G9sLK6GKs)FWb_#@7+Gt9
z$M3#fNB<F{uOv%H|52lFGWYn=Z&2(ApE)IPgNEw$qy1pd%jl3|7F*25(f3h$M}IWR
zM!(!hfk1z#UJ+>wxB_A}Gfs>4;-M?$mOsT)JXDnMIGuunND*QqxgHxSUouXg$0!WP
z^I_GkN2CoJpF0w89!q6etr66L(^k2P;$v_aK?eoS6VND#6rmARt|!Y395;e8Y^o*b
zdq#u5)~{5es!pYq<}2ODtMqU6D_u~(QWhhd#gtv=xk4Xe*>Lp+CpLt;DV>he#8bM3
z)oR--pH2zPS~1*JoH(Ddf=Cezx@EcE_+$ibw<+C9(@H$;v~TT&x}Br{jl*T08~2Sx
zpMmuE`jl8y1!>wg-$Ea6rp-q#a?;kSe7t^@i|bd(=Wm;;viLltr>CKZA>#eCtlkqF
zL*U3AD7D%xpD+OTKor_dBYVm-lj}l8@Sr|#R|Rlp<uhqBv4LS&&GZ=3B#0Ej?uN45
zE%UBQ)Msm^nc+%GT1K_kkhWUwll80JzJ9gi|3kGaRBdxyxgUEGsufSmmj6L&HpP}_
zi3hwUfi3$FL}^LH&p+zn4;-CUIHslhn8DEm-HRtztK{GwgLsWO169tfRyi&E{}<uk
z4Exj4-zKTlGwT7KdNx(iQf*iLYS|6YY_*y^CX)842FT8;U+bLaYrT)baI2o?wy{8n
z8>Ys&%|67jI=gkgbVj&Ik@038(w3%4Tb&@fIYCVAx-%`PJ-a^Mc7CaeacyLme^sYi
zi{;Hu{yGS%G5h-KQ;Is3iP=zRNB_`pYe)a^ic^|n($zu#Hg(~jJgPYko!p_oMFf(O
zbJ;z$#SOZqaCFb|^m7}FEqFTp{1J4!$N78uxm1>ne$H9z+=ooByqYNVN`#>FN`iBL
z;V&lGg}(eV{!AcqHpVzwR=z;eAohRJ^Iwa<P`+2{aCj{X3Qo)fk<z0a>AES)+ijMb
zS*UJ<gB|^yqrX7xW3(AvgIoN8v`D_NLdY(o51V`B=<PsMyw2%wuDrf=D?aI$yv|Bi
z-~Jr6`8u7ZUS@mIgwV6S2yd?Ss(e{)`LjIdFd^JkgU64kHi#6V387q1r&B&kGTX~S
z4B2#SA!w$S!q}^}iRWQK^m#(3Ou+oay$Q6QMj}PTjLZ^Au|^`JiG#LK`3i-`(6aA~
zIdE~J-=I|lbGouzPa`pC3(gk5LqqUb#uhGp>nhjb`0aNgTduZl$M*gscOPu;-(~lC
z?X=kyv=J|(I(9YE1KYG?J3G$;j~p(K1Qv4q?%TBYA2&SI-hce?AtQf9n6>w>AKtUQ
ze{9_WaGC9OzK76m{738kF3G#?D_;VfktgKu?{F`_wU<u!@=JT^axa#pf0lc>#bS<h
zFJID2yYpX#KhtzE=R9iic~zoqjLO5w<!~>jc1RO;ZP8h|9-!j3=nTT=t1T`B%OFw&
z`v=Q%x6HdbQ9G<HI>WtHWJl2L5D6mT7T!mHVWYv4=h&a-0Q@dJFpj)et8-t`dvE2d
zq%O>Fl5?a&*WnzaVbqv&R%%7fR~jztKXTt$TZKuH*tjq2<T<!VI90wTP@SBZ?@W;p
z+`4B2B1;9YL*!vK<Nqs>hXy^O4)(+p7pQ!RMZ_)6^uh|^JlP|H$Pz@7a6zQL5$-{F
zA_Ns&%_h`?NF}X0n2BAIMxCd~rg>jjT$c!ovA^PDb?|enCc8u4x6$Nomvj{bSMb&#
z#04sN<o*AtiX*zPru9{U=ncQK=FD;yGF{*oTgWuSa1`&V+#m#;KxC)yZenX$_<~3g
z9JedW^=6gE)?Lmn>x~d2&pkZ_8Z&|V6dC!Y5}7)hh@K0b<lOfbKYI|_Kw}LM?hffO
zV9nM5X+RsW-Lt(#;xss3B=+rBTuz<i=3jq&l=%17QS-TLz}T(j+(;DT<IOm%<*W{(
zuj8w|prU1YWx=@l4LRkTc*?lB0zyt=w^AsUO*x*NR+iYZ$?2=G*P&?ZDpr~m!@5ek
z4xR=l>)^?{N~uz{C#iA|ua?{m;Pqh}_rFOLczp{;YgC1W$dqsM;u~+LTYwaT$}4-w
zcjT6D;VF9vwx8nUkwK(T8AIfHC@K@i5L@p=%h?kYt2Q$$&<et4wi*HHY-VY2d$=#5
zsm&}pyNaW#&xdO)+H7W6&T}6z9B(BG9B;z`M|S?(I#CeaPQcp4mbkX^eO~FAci;q(
zUV`X*Ji`ZUEU2-9<E)FSL6o>tZuz@BMG1})7bo8qL<$up<a#QEG9}i<jv*~t?upD)
z#U1Mm828e<_nXi!($!0=%j(FiUZ%mZGMMneH-1p1kpT1bzkKd-WavkYMn5158vPIl
z{ZE7N7x}M4y5G%T%VZhJ&l;O8ud&P)8wc+p+3oxviT{V;j<EQJ2DfBXqnH-a?x0Mq
zn|%s*<U0gTHOBo22z<q@)}omJ7Ri4sw|o~*k$exysqJlKP=u&1*Q2`fCC0etL0F7&
zQy0HRwvkE~FOOv>4@`Hi5#aO_1GVn_ag8n1J5bvycT;r?Du%i}1?o6O1(70TsFUl7
zR0FkYsFO`q^AM}m6Xa88t!o15Cv|Fl4)Qls>qxt7l5WN7IW0@Mo|CL7K301X>u#lv
zVf~l&A6o{RYH8aikapr=ZLi#uQV2^MR|^*>Ull|Om6=VhCrOMv&deqW!by!St{~$E
z4sE0>8CJdpCo`q&exa|zG2i9y&6UJaUSDa=$+q$_>nlGYioS9mPTW^s13^DV`|yp9
z+`gpC5~|Qgv;CCRo=~l^l}kmVoBmQx`CgvdQnXT>vdMHxMKJct@hG8O$*!WV!q|ho
z%s!YDU}E!3JQwy4tmr^>{5gq>`(<NxC8p2CgoUxezNS`@GR%++=f9xzOxbq9Qs?e-
zxV&m9mn?OT$_z4c3J>09jb0Ab@C=Nir{qRBH<S?x`(8z;x|&fBX_74SOgEj=1j*>f
zbnaJx5fhN<+$s@pZ_4IFy+mj_C)bk-M!|SGw>N!1h(xBLEHQ)VXDT*wI7+b!U&in?
zA<>s@@sX$1ak2!m5?4K_2u=AK^Mg8ZZGKo!r9sq?A6AxBVTKKM-S-HEO~r;&B*G^P
z_XHbNsYv7)+-{mojs&cJrS0SiCrxB+9ad5*)-u)MT{{&jzZO9qqcf2mRXe#oWs1;5
zR<0)!t(^*{WsZCp850AxuohsGd&c7H1PP=}gN(eIAZB1l4THv@;G|lrTcyE?ZsqHb
z-=ahQh`%Q@)L}h6E1&x`jdYR8jNcH2%$UGo{qs!7#de@}F^=Che0PLdbf;}L;ew#i
zOGr1uiMdRtSaGU`fk{g8$4L!pGH^(+n0>?T4OP7;p%2I@-_KJ@=w70;W^O^G2=q`n
zUUyI~$}#JPu9TXv_RW%Se+`idAYPMCx5%xoX^_x0AEfLU6uKrKS1eAPVJxNybxpZm
zlN&5<>3!sGF|HerZo}K&zht<*efn(_R}!DBZ(G%dJ?q<P`=-IuweOpu&pY^g?JN3a
z+IJGbFSho5h$!0kQ5<y46NB<2_(IlNJF{HJ=Ofg`_yipBL1d{OGPx$pw;E5~MH_sr
zx48XqDx6W__j1Y)^VF!Y50smHeGn;>AwiBuHD#d?ZIh{1_B+DD1ym1RY;UcVYXxR@
zoTkAET@qdLiZ4}lNrB<fq9u1HwD`Eu;txbYi^p)F#jC0RPk3#Ubl*Be-=BH&hQet#
zIBI^Oa-`z@MQ-_zJf-5T7T(A4i6Gvk-^umHLS;(l7q05?5Rk6UGOSoDE~7xF!Ksdr
z;C=4Lt9T0xPcLZ69Sx7TIpSZ50`I@$2yZ(0ZO{btA15S;p5Pg>3ym?X-kunhLXUk?
zPWf*<)noTnH!-V9qzHPf91m^fif4{P+ESqzp6$f3T0PUEh?CiJx!QEp+O?ieNkJ2}
z>h`G@!tDM!-=WZbXig@KO=28VS7X?;*6KAhGus8z;A&&|vUf*lj)LR&O;IY-E*iHk
zF@F39QMAibIJC>bG|0d3^4%DoAy-7k4N^lTQ*JunGjhuR<f+cLM(x3_e<-5}`kx%H
zJ(Md}$p(9<@oC=q4h>H`OI#c*j4irXHx($tyAZLoKRtGAB~f<Mp;P0M{%W8Y44M9#
z25;S8Z@9h6w}Ra3udTV=nB;xJ=<_U5(1-mP(8n&Fco~@8!v7d(JcQo#66v&K8|DIV
z^k0%SrkcuxYMi=+YR~Z^s_iGLebW?KML;z<9@Ug9rrOFQRA*eBx3f$0LDMM2$llH_
zs}Nz)lGcc@*k3ii)M`|O<b_;MP+FBO8N1aCQe=2m_X)4M4tn_0)qOWB0C8pn@pMz^
zJhs#zohM#ukS^ySEL2)k2+?_7C<s4mx~U?h^T_oEV}r2Z2IDts&>bt&5RNkvwL1{V
z+3|TB+71D?ak@Q*+fxi5L7%mvk|`iT#v9G2BexW|I^@2eii4;zxr-+QLgxz3)R;Y{
zVmR2~UOXJY#{~G|Ok@VZ;`7NKa+ishy|`Ur>>d&46PQrD+or6$xK<4!wQvxrLg!yM
zehZe?%6+KxL7^;XOYaF_Vdsr1c@eicPL$2M4r#sv%QQop4EObi!%WJmXl3H2!BF#(
zc!pjGN2P2m2qK|if`=1@@)w!etm;X)2>D`qut9FUHKPZo!HInFW%0l0U9HFxPft>R
z_hd~z$C!}2)MQN?l_P69aFE4oF?hA(t<2<6*fc8oN1gaR0aH^wP*GA3y5yD%Jf$8S
zK!1{Ypip&pxt@ekrmA`nM5?zVWZIdk2Ue^V2NNEEG<HVFG`Kx{5uw$y5Ffm>Dq{sh
zK76ra-C2kf?^#5F_qI4O-rL}<%;8bkw9@eI#@`IyDoS|Im0O<8Q+RW4nBlEZ;Vsw0
zTbVq(Rc}YQ*2mk5wc;{(r@^)H77R7;?jZ`i`*32sd+}Bl@F;9rWq8lW-wfU=N_a1n
zTb{>Lc)vKtTcN^Ru7|fWd3dYdj*wmTX?t6-R$K<}G`Ke2g2BVPCHE+N?&brFhyw5J
zaKQUSr}iVNJng8mgvdmCvFS-NW_z6Q-;L%2DxAS}M>*xiJT<t!M1(joh!o1mD#sh<
zlqH=Huvkn-v-%^|9KHmKBwB`*ZpCJ_j5OFufkZd{><v}^7bsr8Z_Pc;xOSQK`<;k_
z9!qi1F%TI#bDYx{wGC-9biuZ~<8V6*|NDN3B-v37#dsHrsLrDF!ohf+VLFR4iLteg
zpb3S55C=U^Zh2>(;vhEA7AIa|Bd{XGL~=diDIZLv$<&C<;;dV&hG2sg%xM{L43MX*
z%{MI|nhVzf<W3ts8v{~gOt>~V3Xq}SlrbUf7O)3$qnA8xd<$=8z>B@#NWI_0Te=M6
z#L}TT%chQmA%rOQO*2F)0`@ih8z^g1h3erCeYuf<^6EE42Ic-$8X^mG;*mcSH-#J-
zEuyfH`w*zi21hVZb5@Q)F1fLjiAsD7(LnC0D6L~1q=gxn?HJ;e*y^+;b1wB-xa^z!
zmw;q^&O%)1&0F$zcJ-6iX9kF(&kW*VGulj^L|P0J<d6&^`7i8hUuAph-K$&lwf*WY
zV8wbVszao#Ycg`y231Qx+f`1v$Ws&0gCt!x1(8CTeai7vRAou*MQ=d2oLs{Pa}2U@
zm3S|hT<%_(^l^pH+KPZ4Sw%{B{?Mwlx}a)mph<&c#Uqh9zx-WQ<_N%y%-M+!^eLks
z7X*ubyWt>%-<A;X1-xfC1mb-O;`++{ii!xff|PX$=4@=L2ZHS`r~G`LBG^kMRW4SW
zMT&rCay*(TS3<M5np%H}_}AI(lg4$hN~H@T`8`E-a!52*V90f+Z7a2ymCULQA_FC}
zSeyoLMbAGxRi&rk_vo3=ar``YxzTeEqM+vp4)lCghF)fqTq$6@nUI8L#wO_)OyxkY
zRdULE@)W&ZCI%}7kwPJs9FJJa5)*4^L45LpzFEwU4lJR@W(BLl%FdV-(%`gzCOmb~
zXR0(1OkSUCvCCMmFdB>!1r7Gbfd+?S<Dqew2Er}-5ayY+YM2vL0Au%RIpw{0YV1B(
z%re^~zakjE<#=k0awQYv&ls<!iiB;sS#dJL%OLoB<Kf3(U>w|+Qez+(2M-YtI{U$m
zD?~{A%Js%UgTjr2lZVEnPxkcqJ*scrh1D(IIv3;Qkvr|~I=n$(=w~{78tfR<pJ%X|
zZr?KU<{2WN$E&TmCrRVlX$?`d)BZTIzHuO!-OT^xq%q6b3yEvoUMh(8IzUc&Kb~qY
z%79eXUW%Z-<aq6+TuFOzB147@tFFP=DBZv+q;XzE5pj()&Y`MNhY5m65o#Q{UgKDm
zu5sccYF+U`uL(^<{Pww!18TOX)nM{gu&0~tYrL)l)4Pme8p9-g<$pS!Mpw48=~lN<
zKEo<$a4gGwUbzuM-NE1E6*Zm5D||<Wy|GtXpMEh>@XCQW@X9O@`Y8XojML0nb4yTG
zZ{~kEb6h4mxi9k{WZ%%I_;cG-?pOTZuj4!+pW|e=>#;EWtPU%tLNRY4DsdBk@=i$>
zm%KkqQ6q0sS%{N^04Kzjl9-FtHnt~JQZ?QaDy3G?OR0UC-119!N~vZ0MRDRJiV7k{
zh+pJ-EUJ9*%xJLC(>3ut)$7ksWcBJla6j3-WGmWzpu275gYG5X^T8dzIi0K87FYkX
z@Q=*IL;5A_4p>OyvkAJ0zoevG|36*WccIDQwA@>v{2}<|HuD##quer-8+kvq@OISh
z+zH7+vNbfDUPrH{w2}2L^7zr8QZB*kM?dc3H;n4++{hus(^rl5|EW)V_0>AG=OYG9
z(SGGKz*Xq&ara)(4&tjqH`hSDlm?1H@lJzHx1y>p>Od(}AX>r(SQpuRUz~Wofuaam
z7v*|l!`Q&BsDila40nN#yCxP04vlL@3~vQtI!2U9unsXE0>`EpajDON?oi5{1guvZ
zSZ9b_8RB>cN2Vi=OmcOIgX99VCWzxcjy4fkdeLd&a9>vapw;4Gk4c45tpRA-Z&?R4
zHp%`^puNe`*9R?t={F1)HgRL4Zmil}qw~Qa14-q?aHoqR(^%iu1UeR0r?sD?8#dG0
zLp{(h)ngK!fjepq7H6+CZ`Z{8DKp#bjP-~{I~k^(*>*Ba`-{AjVGhQQ%FBhB6OCNi
z`bzbQmm7?V!1P&`>zxc!K1$Nbu&#;jDK$S)zh)f{t5fsOkTXHlOwH`fs|urMtWG-}
zHI>OZIHQAMuF`2mz%f>7V=hPj?4Vj%*Ea_Cbc1QD)j6;_)uQe+#la(7x5fZEKo1|`
zLbI)mK5k51Jj_C(2G>Qy9nQ1;ifxixDuDY$>PmZHf`GS_F)ac*RaOUqut@}pBW_zl
zad}oG6qjV*kq}zf80x4(ia}l-Z{+lUv$7LY{n>hr%_$?DNvjR?<f+Zbv!1TWRfVzc
z>V*N}qgZ{;nZqrF{*4kdwLQkFvhKqL(Wyl|4UR3O**$f$(XSTs_jE8RCmDO`r_ec^
z4t6L}=wPqF@pQ1KAiwT+nL!^c^M8u}Taa?B+~&p*#82`sF;1}#n0uIi4IS*eL`lbz
zoA{G=N~r2!iqkRkkz0632YV&pG^2wFdg)-VmRmlIr*yDaNzi<UqJl^f(!u0<I+*hL
zI@o5_>x)Fw!B+04eL!emcbb#zxYe+(L;a`5|3^Rt=^iUykMq_-j5h7d_5ag_VVTW+
z0aEV@X|Llix0(OQe_geQn7(zO9l{5SLq;#5?M5DUkw=ZTI?wviZ54bMZ!o)&w+S_?
z4tb9QwPwh>@+xf|^3<$(WHdGFRkT42N)u|91Eo+oT)A9Jqh`HYEv*A=L8J(&S#mu!
z%UZgsW~t6_(Da#eJyO&a@@s6TY@PaF(`5a~N|szie-HHrW15sGS9M2)dJ@zbO;Og1
z6ZgX`L8J(crgFW})KE`G(@RO2t+~NCE*+^(&Brxe^UkVS`mAwX1ASH+j;nRrPR;$5
z&2ini^gh2sJC*jdECsy7*0nU5Iutm@U}=W-JCZV6a|8WUnvAP?T896fDHTmHe5^WS
zMf_jtkF}aQ0KD;k1Rzmuj02icZR*tRRGY@gj|}QKmQ!tZkT<*hU>eQ;tlAg?&8RkY
z5OAtZ69~LnN3h(c%VXrtATV<9l%QD0S+znj)hSWj@0w9<Y6C6RrZL3-gKA?{nHbNg
zHfeBGwaKjGyb--+oWG~rXwc8-Hm_zZ=Xfc1aIPf^-R8A8=r*WCW?zC5`j$X$F1_*t
z{3GNOi$S@^_<vG^(^PxXyU6+;q7papC-3C6xa55fX-CdbS%|$OsJ<CpM$k%^IZ|$U
z9Z%^puaTI4H$?@JBBaa6^>i8KOLQ4N7{6uSGWI}B?4#P7)o8jwpo9Mo@&99xB-Lmx
z<Lz4hw}Pcy|36*WPxD-O1d;WZkWBoI+>vWswa1%AbJXZv<OPc3NBR19Zsc4Sxqfsh
z#S*+>^bHC=m0)D65xEJCrVf#pfP6DVUb&O1Of;G*!GEMszJ?OR;;A&69NVh6CPMMf
zIa!VzEbW!o(SUJ-bNFVh8vH{P7DS5B`5U>ON@ES)?zR|DSt<@^GKcA!SnZL;({82%
zMJA*=pma0|N|g||(QKN>T9bGK-E?(Jy{S|8xfInD8El}_{XQFaX3{daJ>R~&O1{cb
zqHUyS?@3{v86e-O;dVc!z40eFTP&==S8odTWr6q_+`>4fXp9j5g2&8svt>r*Xi9<A
z<Z$)4XUK_hZD72SLgjFswvT^GH8s)4YZ-44bb>7XbdP(F@z|BptBvA~z|%E~bg(*&
z@^AWIGmSFx4Duo#fd4LUX8fvcTbP&F7OEV@%sp#iFxK*h;pwo@4MlaZ*sVz{{tpI*
zve<6RJn`YkDei2z0qzaOQvu&6kn&d>aNU~MSS}=_YueOc722S~czhuCH@EpN4Q>w)
za@*@N<1wF}$gvs2ACEO~<-?b{#QIhs2EyE@Z9G1PDA@Ek9L8g=*2`VW|J$_0i(pS(
zJaG{nK1ASL!ha6(_+I{JY9zBUnO#3`Ag^N`Z>i^KQpl_zH}NO$6i_uYDDJNWjI2|U
z49%~nv}T580j{C>jdIJ!^3>4GA>HD{krWj~iqOz3*BhFZFBzJ()U!zSP8pPU+5J$)
zGq=ujHsJCSuk)~XJjXGMoF?)5m+_C}@Q`H5C?`+k*>Ut2F4HdVEFZ^s6nw<)OLz{B
zKI=x&T}R*PJV%V`Q1QsuU8<u-`EG%duOEGkU_8<2oixbE#l$1*j6zL}t92;Ej)JBr
zwDQHOG8tE)mM~{TWf!eM;f<(MEsE~wt%IUac@uPx+X*AVYt>HIQ&<owLPi3)-iT`L
z<ctK|z^}n!!=5NnmE%=TB%WJ&H8f7>0ll$+*RTmYb3q+Q2AYJ#U8LPVYnY2;?ru$X
zSlLGk&S4LeZ!Y8l_aG9mZuD~gu8Ggnv|CACcLh~~NFGZ_9Yi!b*Fof1if@Xfeb#M9
zbVzqAaNMm|2{^wty1oq2VRyb9ERQ7QA$YiRP6Ma|r=xO$TE_K6Y#~Reh3}<!zRgF3
zY$0;D%==t&`HQu1NBD9|>YCu@rD|Jx%F%ROrLnLM368cp8*>%htV=RA0vj)TBk**!
zYV4|0E4iB@{K!AWv~CLgn7mDnz`k|=1nNpBlL`FZ*gGBk8p7(pA2td9|6%OyvA#R<
z0XOU#&AnlFD$E%7wUSdCXw86|Y3HU#T&v1Bm=Sj;_|lEIX>jsE1%H19-^0%F#Xo<%
z)oA2>TcIU46Gn4;p-&`=@%GI)jJNdG+>81Dod5A^Rla~iOqfcuDW@RaHaiu=PMo*O
zDWAksJ8_Owm%G9mQW5OLk>kl!<w`a_n@C;B1)e!4UF~?s*c&?$YTfAHE9W)~j@JHb
zgVXarfg|0+2j>*PSy*Brrqe#f(iC|#jjIjMnbg3yWSS$7qsh4mP1w50o#R!x-?z2M
zN84b9$m+H>$!r-<gQwY}a}QKvub|IEYE*BJj{8Q-XG~{XPZT_KG7kIhUQ}2dM5o{n
zpG((brQ&E6SnOkqz;3spa<bcy<s#Uo#8bNskAiI``6i-5*=;Dtvsfz&g=qcvD$b-V
zs%|T^vaArc6&_aYENO6i_zFU+XG1oeQQcFhn)4y6OUc;~?W?)ka2qEI+&1DEZrcUX
zsrZ|~O*z5sZF0&RcnUWTq#AAt1vfb!ZpxD2wwI5am1Tv18^)_x5TwEF;o&uKQ_cDC
zHCfy^(}p(icA~)TG#tZiQ4mG=!z~TlO*z4>ET{Yqp2F>QF>VS4H#r_|%97y5*^;!~
ztSl=8+}2fbOM~0P*Ve#IHRr=4vbbGixK)S(w|C+gZrcaZX8cXyrkvn*x}5SRp2Cg8
zmey_x1vfb!ZpxD2w$jJV%CbVh?WihlX>faZbPe28b3S}s7PrqCZfvs=ZfD{cZp>2N
zjlT)ploQ;}l2d*cPvLfKjGIEiO^%0~vLv{@z{kzXvO>V^*eY&maC>-M4ct_7K74%^
zx6d1H?;#4@&c-p^*sb$E{7v*X<pj6)%PGH?r*Jzi#!aE%Cdb1~Sz_Ea{DmoaRhC#;
zRtUJgv5H$7+#ViZ12@&oXF^llxF}zXxAeDjhyu6saKLR{!F@_`Ls0$zA$A`@@l6aV
zdMmw#Z>l-9d&-FGM5hX4#QmV0^0_=U;=W$!9>cdccxSkk;|;gUk_@+2ckl8K7D85v
zs>h40P%AAnR;0mO?M?U2*9in~d}zyUWc>J|;e9?);C&$uc&}$)_Tw5?qeoczE<}Ik
ziO%>TV)<?bPwuY0iV>C(;3K?+m{My|!s#9=9!h*vPWb|!qQo1Z#DsL)AW{UxkmC_U
zxng3Byn8Efrh%q0Fz!n|HG$FRks3n6kxNH`b$sNxB5!v~@7~9Hrw6Qj!xGL+Ph+IP
zaSLVld#s`@UXmTpMJTVm^11DZcYOLWqG+#=<Ir9&p}pL<kuD~n22Z+{P)1tfQaR;|
zc&a7dsFwJywS*#Q2{~R%C|AOk#uI#Pf*`&&!86>kcJn)m*Q|8%Fg`&+5p`=!4%S&I
z26Kk<)8HyylQRhq|Gla|2vv`kExGNhw7iTcXn8qKOiMl)2~&QGfV#9)MriqIIpt6C
z6fM!olg~^k0?g!iv{bHymR#%18(%)K=T$tz@#<uL8mS`1xn`{*X2U8IVp=H%bcUE|
za6-(4mfm-!gldMC=T~X@8KR)&H8}BHWev2vfqy?~g6OkE)TOBkfTmxNQ@)y~X!<76
zbXO}t5zthQM^oiWY5F<d3eg`BTHJz(Kg3j4TRw@VwQIe}SfO#P)+_o~l~Bw&YaqCn
z-78nKDm7ojdXna|Qj9*C=1YT<=F8Z5wKE*4>(3cP;!ImEV1nsn%omBGEw96&Et#_B
z4(6}1l0(C}Z}4ZwzV58i9P~@1;<jg=A&Z5zsjV&dDwsCDUQYR1o@!$(6vYV+yTT-j
zppE5tZLC~z8wb%>co~f1dXu)c+8efY?b@}!p^4f@7{VDE#b1M^WQhDdZ}l2~wKab2
zx*1NJsoPbTA$J|=yV~|V)vk1Tvz7j@xXR>MyIR+$8n&{-wNamvIk9F{>QfEtN&1wP
zV*HrtQ)%$DeQFY>`7?iSFN0Xx>r?sM0_g1e)K`h3Pu++Uk2TktfVqKy8e+qZ+sa6v
z`MR9)*LbSWuxGb8@hvMr5%d{3UY}8}L@qiBbwm)|L}0i>qcOW?g`#Q0+7g;tDF$zb
zrfG1MrpY)n`RD4GEi`M;@n)i+<1IKDI)0OYx^z@V==g0p<!|s59ZyQ>s0ipN$D^Zi
zW$74k=`Vqe=~%Nu(XnA|2_3BzgEvFRG`Joeg{DWxmfW`RtK-M-5Ct8-i-U}3Nh5b4
z|8Mfw%Id}Z3kz7exlO1Z*GR6bFlc*+obs(aMcX%vwwIfrQ3SM=<Iz^RGPE7LcA+jx
zvO4PuH;vr2>*NbINYp0l<Thi#Xr@xx3Ts#DL|c=U_J{RGjJ-^F*Q`qH)3Ba|eXJB~
zk_`K#!H#`=8}-j1-y{4zxuz!d_$Z%SOuXZx?-2zb{QxKKkM6UMcM?#yKPn?U^Ful1
z@ADMTyhS{7tI0J*z%z0@o>8t0&ukhoZSO9^8&5}S)+w4ctT3Uem0}=gXqpB)nkI7X
zp}$q-n$Yy<*sAYn$8`J=QPA;j9Qq?muGSxK#N7j0OG)4k3celF&9%P5^`njYqzZ(-
z_sS{%n5XFbR?(N64Cx<=fWC4(`YKn3zJ;i<f}`$OySi(Dy_xF5U!trB(NBN?lY^Y|
zxRpi7mWFGOSx&vGUAqnu{|~iWb!>X5t;cG+Rqg5|K%1#9wy!jz?OuTts+!e_Z5y^@
z!nRh5aYBY|)8Kd{@nr$`5azT->d6AJQig|rPQ0@v-A5EW{4*Snhgpu#G0x`x&i_c#
z7W%j4_F2+2=3x~G5C2k5`KLU^!(8B7oY3AuZt*1u9+u<ruyVybjLqg3ycNQ$=`P`W
zY1fK-zgF)Ier+q?4|q%`6dZOEI+q!wGk&q;+S1$2*kHicHFc>6)CTqj<H3f&j$rJq
zTHYFQhU5F%b?)C>o$<7yoKmF_eo3_XEYsN-^SVQ^x<hR!Tz6=Qc|!3<NW1P(vpRK$
zhIP>$>;g?I#h5B%tV@Fv#meswn)B+;L9aiEEi?V$Tg1Em@GGJq;%{)g{_p?{$5G1M
zulOH8TI&z@i~SnPbQMT{n3PlgHBa@2lKR8J6vW-e1koSlc>O`S5}6)OVf+bV!&%ca
zzCqQH@oPin7{4J@{|}5`yH3aW4eLx8U+5Q%Dly};<%Bjf%o{R(Z73Y$Hwi`KG1f7D
z&FaMX4eLr6-%2s=$uNEzJk2<8ChT<qe~<6QejeXrS0&!*gbxq}4Ijes<`&Xr_JZfH
z=3mfR<%fxH%=0P?o_|zM`9YrI`IE)-*HI8RM-v3k%kg+#xe}gla;$at`PQzEt81ID
zj(e<uV2m>h?oQ1RD?KDJl<TsUW*N0%aBTJe8_I5`QL{R+Rl~Xx^}<TA2Fj=xY4CLN
zRP&6-`Fnh&CeHBHO5z<~{gx<*_(z<0uA$E?bCWj#b>|w&2oL>9PWg8{#Y3lvha`--
z>zW{VNRG!t%9ZF5(I0pjoQI+y4~x2w;h1^3*y}{eCUzX6D_D4)7Iy0*H31UI8$y$i
z+)6QgG9*uf6O!kWxyY8T84K8ZIAae-h76sTRr~Ls!3{e9702tpw?L|WXbW{a>%V^!
z2^x-BDh%2`E~oq#o}xWF!iy6(QV@5169nz$c(hlpY5H#vS*3MsSgse>uG9?=O;#F|
z{|+2Ib}MdptqlxTf1UPqdRguI-C|pl^}B(gG19Id*Q``b)v(5dsjL)hs0>r3!3k6O
zdeNU~%BT5z{a8(%;jeVv?FpjbuYcmi{n)L${eytI{a6{{rGLvQKgm<PG%jAc&HAw-
z;3YX8FDX~TOWbD8Tj^^^V><D_@Yh=-a-Fqit)gqgDigX|DF$?gu4!;W*QBp{>uy5T
zqh&s~q}o@XCJI_UgA@1F7efl&*Cn~Bxi>QFS>gExbiBIMRZ&oPi=6U*c#66kMBVEt
zhI`Hlg1T}%>MB=4T^nh~ZZa#L)mgWrXwp=>&VB!vI)msrz*suewrtb+HqbV%T?1|H
z+HW;}Y8K;fHHES3r8VmmH#Mv<;U+7^8Y#m~X|UrazmNVFf^ufu@1ttu3}4MB-i@{Y
zB?`U@+Nyo@i$;<e1k~jsWrT}bNf(rJJjF%qHz`hN00|;Rz(sOAE>f<9i@0@LnImpq
zXek|Dn`>R~tXZGv*|4&No>q#1o1teK?C6>F&51u%ZF)j6rXl;-I(Qq{G00cULqKN|
zU>*WyDgd`Dc4s9C%tJ&Wj%bhFHo1^)?(){0Y^(YOA;j#Y8bt;z73B$9f-P+?ot<kN
zKY<NS`V@09d>fBV9J-}z^ex-;20UBZ2DT;1(Ji^orJEJA>G*7PQPC$+!qUx(3Exf;
z6~U69zhY&}CT>~t3ma5wZj2+fV#3$(rQyLjd=pD(2IG6T<~l2eso>A&Uhlxq<%jY;
z#kPFd)4_Kx+QN5G$3VWMw@oKxD;QD*Z#Hlg6P_lFPRynmR+_h18pVW>iW?q37+P$e
z4IGf$=ruqcJ5xu`mi)l>`O=Pf{h;1F-`E%!-eyMlR?3opNBryY4=e(9^$qT!?dJYx
z%A{}Xh{O1G2zzkUH;}VC5mI_pZH{#-ddf}@*;!6`2cGI7WniAr6e)-lK@XAR^$_Js
zdx+Ka%Gx!#zEQI#^^JzrBz?n5F+ekYBMq+EH-ub9zG%tP(QqkI&~O(VXn1HLydGpA
zVMqYi81oDdsfV7n6Q~RbS(H;=##4l>KuCQrT%k}=jz>jhNvSx%TliI~Nl>*Ct*8vD
zX>dG9CvxD-3#xKJ^?SIsm~H<$;}>o{6t2TKjL~ef$X&*NZ}{mC`~$GwRe;<HAe9&3
z3!>#bS6*Y>peQ>uv|!(h(g2-tS~}FStf$!OHlA->#_sp<DSG1I#<;*BdcKNXUz?1M
z9|ay{++9w2h^NT7N&I-1z>5?CCFOXORIZqkBMz|L2pOV#R3Tn8x*3RLXbguqQqafH
zy}2HA(hzDxr}GF+K*wFb0M^I1V-2u>TisO{!~HP=59NFLw$8dOP<{nzU#D;H4Q_N9
z9GoR?^F@2I?+@uiU3Q_QF7%5pl$17Mkn*9fJt!h3Bp>Q}rwDn5wVWdKp)R@JDMO=V
zt9{v36|b@{MkbXD9*05reMmJpt1$LXnz|=kh%x&#__m;P1Xi`RUFJhwX>iQ9zCJLG
zZC{P=@o+xJAd>r%@$jBR!NV(YxKo>LA;!0>cy)Zc7d|c_=INMq#cuIiJx-ksZ&(pr
z70^~(9KXe!Vr40JcssE{q#D$U7VcWHT3xmk`5N4MmoA7#1(DZyI_!}7jFa23EA-f)
zytkb42v2QL-Yik_4lXU`oej!zyk=7tHYn?Zyj+BxV;`u4F~R8vK)vgf<}%x})n?UX
zOmJy%++3MGT%)wbf&9HWmH^GP7oQ=_ec9S;AEIcl{cvKL^D^2@d+~n7pPPz_2%<GS
zJ?d!Vp@^f;s2O8B0im)G5HFNdUd>Yi;&k!#16GzI5D;=a`Y0FWh(1G*;k~P?T<dDL
zX5Ma?4>~<clovLaHuApgWMA}-`JMF~0dD7!uKEXtjPQJ|>FiHf^C(HG)JCc6|B6!o
zHp1yZeWc0(%T8xjm1^Pyzc~1<F;0bRoPIrb>IVV$vAtvlJ96d#VeQ%jIdg`X^%X&+
zPzf2ip18Kwa9;yVgoc%4`7=Tz4R&1U%M7hS>ma1ZgW`t_4=Ub`@h>6@9y|~yo|~?L
z2k+poHEmXy;KP>?S$966LSVjw<&<B{Q_S}+G2cyAfFfW%IUc&om5lQdP1@48qFjh(
z;+a1@?mP>JPlEIZk+4)pk#hE=+CaRZNg%j+m73!EUQuo2DO-J={it?*&d|{q>F_7i
z0e&y5)*8;Oy{zq~rZsDoy4bMFM2E6ctfewKR2n?ZSoA1Ve1gBnS8D1EUwwpl$5&i7
zEWSDnCzfq%;3<8IU}w?<(V;}t<tY^aPrX`B`Q<#tQ)h~&TyrV{o|5D7lyW6J#f^)+
zaW@msQr+g1w5eU6YtF{?CC&M&YOVE~Q~F5FTGgBlt4x~HN--j3nllZaZmi-!caA-Z
zetU|{nf8=D(6#5`MA4qy>OgzinFePM<dVkv?Wl~jBX>B^j<4aVc6_(m(a}{Aw4)ra
z9hEC-$Aqr0C5&rec-E(@TC--YqHDt{6S`U{26Tq5X>dZ<WUk@a1BGgamVbhlU$Jrj
zD59X{>u}<}@h18O=XmU!^droUP}~aP*N_h3k9ky%Bc&6FeJdmokKw&^R6PNush6sO
z@~@XuKANW}f0ii!desmq0?NzrD6d=z<?Bji1EQf+t_{Q__%;Ismze-kjC7rD>~`?i
ztZ(v^W-2pY3!-DGk<-83D6<v$b&BXj_t9Nb4y_iWX+}9rgQt<7T9dhjzb8M{C^3%=
zkH1N#kT(!gHHEZcASt1mZ9bVtHFWNBCXmpM`&_!`<hIPi?zE^)4{T3dGMu~Kc<clU
zg2zt6VJ@J3%$PtfqI%fp%>;5V-JWmd8FC#Am;1Bfp`~}9&yX~mrfg}7q#10r1))Op
z3ME}ohz^w-wgjl7Tzp_}e79x2%H)34L`jl9ugkeKklNi`PGuE{=<}z@DWAww`uuyu
zpE^3v8EAq~>E(F*sa%v3fBe@&#evDqA8lx$DROHoC2|D)D=R1Cj&LP6MZ0JPa7s?`
z&!0;sOK5THEOC}nO6_Ajw>3ezyR`|NowcVX&XY`K8zNjq%pk8gEZkGDuinWWbhA%O
zSWQM&t|?3px~0L1?DBQTzd`utvOKDWiFtIl-gCugn@{oC@o3ILlccBb+{NhnOMCN|
zmsoG?95^ID_B(y)mBqL=jG$x6vl}ZxxL2J-$^r55Hnhf9#joK8!aL{WLS{xBBA-4E
z>1lC6j)UvDuNj}K+|tH4^QpM!d`<U}M49lnDV&Wii^ktBpH<J?*V3%SNDE-@g>pOT
zbnhV5X7&iBo$fTOi~{W{5dgnHx#|r3<8(KW4f3Va$doTdJi|32z{2s%h&nB?YaYS|
zT;7A|E}r>PnRL}IeS8qzXK{SFffU@LPKt`3g51Bcxce<`(^hd0S=_@G_f9{Kk~yDB
znZLK^A1nnoF?S9y4N**<)pDGk(&<}ed)zWSVb3R(uyn>&DY&<dg8yw#K88n%cWss8
zIg8t3&;MGAGyN1Gm4{NSBJj+Y-tET$c)P`Q*ppp<q&Uk@u_=gVTiiDG+}564XivKL
z_~|IA*W&u@Ip0#eS1E?a&&Jujn2>5y$Oazdc7ebC#&+I(X$g^?ZKWM>LiP=`4Fzoj
zZTE=PLvEVx47qpKc(Nt;2J+~)>1w*e(2--0!Tr7AQJ~!u-b&KJg<}uh7jHGONV|)C
zd;!w@w7zq>y=_K#sfK_n$v)rSm-a2V)P-F|Sa=z+eVbJ}eWxuqMqM}12gdIBDW!6q
zWM}8t&wdtrXSN0Bof!Uvz^>j|#TT`W{q^U$ljoniy>-#T<xBhS(Rpn{;quZ;1mpa;
z|Ms3EP%bZB24Ztt+PhmiN|&)h(9yScRtIJ7c6~ctK=tda*^Np4eTHb~*t1mQ{4)(l
z=kL1bl}T-VF$`^&m-?u7R{L1TFR_YF582ZB_XuJK=F$Zn4%E3V;nRg&TX#zd5)?Oo
z8iKTsZTCNy(&|cSb)|epWc_*g323}YCH5@L(qm~~aQV{t`OB9s*rWz>LT<z9K$qlF
z33YO*3H@Bd<5#G)KTB|x`B?CHpJUATi!slUKan2`^Svjr+%Z;NR{8=tSyuX@oYu;<
zemO4+qI1csaotI*fvF)$xpa>&kvHFBVw+Qfyo_%Pr&nvHowcsDKkiMmcfR*xgmrGw
zrt_%6)~r5&XG_}$d5)j2u+54I-^)l&-@X8UGB(iYR{|0p??OU1D_^yiS0Git4oBeJ
zj5GcrMOLv5zY4K}mhc8St>H~L<cz*1thJP99s+Ji0?b3ejY)uc5~L5xm#$Yy(M?3#
z{W!q%O+q$*UFnl#XXLfE**W%nn9EWPcI8WK&F?(##6H@hmwp)7xk~^?RwxGPvsISe
z+DN@(u&wvv(qa;XY`JS2+%9=7T}1_H_b^F8!3hLaK)+c9bP+1lR~VzAQa6XJ2=8<Y
z7HeoADb)uN$>}8`+yq;eLaARf=gjccFXaphl{@8>zr}OTj37K)>epH;KoK*8vK&wS
zQZCAo`V~aq;l*}nuhL!ML3AtcE|gPC=D*FJx7+g$dw!2+xEdPN1Wxm}+Q4bsL=ZIr
zoCD_jROcP!nL-OUYp7Y><jb3=YqD>a4E$DzQL@8RxYE$A<^=Si90uAP{Uq0iKi>1D
zmdMN-dx&>!{R5&p{R;(tNI*?8Y6_?vwDCQ1%6IWp8^2F&{2e2sB4}edUK=Y{wT*wo
zOA!6oo_F)~4%JOVOtoFjN=3|uHO9oWLJVY|m}#gZrki&cau5G8*B8@ISbFqhoZ_I)
zH>|Jxgedxo4(@z`KXcdIB>&g6&}Rrl2_wNECm-bMV9QT&cb$*c{WCo7b#dtz9D%7Q
zg$r2uVWZ^F3E)VK!f}+bbRoT+LkpJD3upg3L5;8F5VTC+C*+jx<*DiW`_*=Qvm9U*
z!Sr2@*LKQ9f4BbGTlo!de1zqGIS#7IB>s^rL7S5U3XT*|`Vb0=7Pf-uSG=Bg;;yru
zue;^^m2V_*cT4|~yAO7^Y`+ehbuM=b|L5~x!(W_qEdRs!E7@=~@-_1=c{k2*rMqQG
zxFnYwJ>3O9PkzOWe2_;lcEUcnlMTR;V20k;SKpto_zl(fhwc5e>ifO+zPb92l12Wr
zs_$R6_j9W6m)rZr)%OeR{p#xb8N8<y*sUvwehn@9_SH@NAhyd28df3mhi3(2UrVwT
z4<T;HOx!oBam*_mFD>3x6HKwu;@*^lRGZ>Mc(jcT8P;6ZAg;0Yaa&*2LN0)%HKnn&
z(8X(K`fsc!#B~*_brmw)>Y7`u=~8yL7P?#4O<_|P+zgkhv)ATQK1ALWmpTsQySWZB
z!|!cLzcJ2r9j5L+HQ7>YgL0QRt8s@ZGNtgkE7!0!?x{LkQ@dj9B+XRJrMqd<^JU2H
zHpt;x-96QGr3>k!t6%i2)-bE@F<Z<mto)p~c`65Z+?I6slmmJTQ(C_l+fO5`-ARX<
zSWmvL30m{%X<HSyQep{ZQZ8BNTBsp)O6)XaC|6zOKFEIw1c=7~Ny~T)NEE3(g6IK-
zmOd=Ka#avL$Xid|ra4t@i$otGy2T}8diqh`^5<yI-;zy<KK(J`JPrGxAi9W<9?NTH
zf@-l8baAzyp3;+=GA3}5?c>$B(qVoS*)FL@v1ISZF?;_+8rM^L%8x|q{80>Zm2_>d
zqQ{IZEVLJ$i6)6Uj{WS4O4Rgtp0W&`_(AE!e<re8&OT1h4bNdD(*7&vEK+#oO1+hP
zDUemuADu6~gLrl~3nY1+j+<0Yyz&Rp6N;DD>Em}yA3oTZlxOMF@=~1{K9FAia$NnA
za{fX2TU9c+JRhDebv(?6XW%BgbyVuop~AAHswV~7cj$heX1*mU_n$<%$-zL9{wW0w
zP>7)YEpAK#3%@m64)d?e)RLu7^5kA4o{Mac(7-e?Zubx|;VqPswXj_-s;}xDzXEq@
zP`OgC5cw*(!{h%{L;e@IstqF=^+KPK8O&Waaw+zVPOKY!18w0&(#~nx!ySra2WoC@
zFe&tX<IjjZ`Tjjh+reNV2IHc=vD?MH$$r;j_5r>J-<=O9DX+7wf7{`<wuQq%??C>Z
zCCDn9)%TXxQrM6J`@%~|H?(Z*b((E`Lijm$8E(t9IY`3WNimMX)plAw{3!l=*vsh5
z1L&*g6EL_(q4YOO?7i5zeHVA7mJVye!JhVeXaZNg3jyh|e>YR<LHT>o)Lqs-gWPN(
zGjjSGEH)wLfO#wvvSa=o_=kdd19%@*gD32ch-oWG4$W%!rr1fW>?&`gEUeVb?Yc-9
z6)g4fnp(CEA$GW$wq<DP*j>Mhi4-SmC)o9#+e9N8we2)^wklL&0M1gzd|T;$sKX9d
zUfL3qPYd>-R%e@7Rt41N{JT|QnN5Z)EJ4pe5X~Yu5gYOBegg-p7FCM@-)&TJ7GFZQ
zof>*{5Opgwtcs|s{v!iRLY;$xXpZF)HL_8s2GLwaC9t%d1L(08E5+%oAnH-7D&j7c
zVJ~~vvVq=wi^;U`({cAvKV!JC{)U}l0ohY{@)4WO(55i2lTDptc)WKTI@%IH84{6>
zhrJ?pJM#mbp0v`w)$c;*ThvzuPtI=&$!vBNZvAI3a6bi(e@2~pvtq(?5xn6QbUK=G
zDFJPzPXYSOLQnWd+`%3*YZXc-!3KFVw{%UOICjmitL&mY&h%&<DTtO4FtliF{%`6h
zF+S>=+-}y`+ows{G5!&CON@hVPR6$nugq<y@pyQAdyz!QR4I3OerV>{l@#O5pZOBI
zYSTWlBl^(u{NyzJ0PiiO!`Kd7y4r7={je8(mH;-Bl0t14M1wAGG$i+niL6`^f}d6+
zTE<R4JND9|N{kuJ(pDKQUs?vtYzHOM4OheCgTewkN{R&y`VIAB8Z^myNWS!Tf`Vu_
z62{V+u&-|&HaxyU#jWs*)86Jh9CQunZ2p{&<Zey}DSfMx+J?tJuLNJ1F2T25f-fq;
zwbLcIQTwTf$FEa@FHM)=CYNA8LASRL9mwvi(&MoQ%6*yqLHHG(mFra$JiLf8<tSX#
zG31vLAorhHXWxawlAA*vwttA5{F5-n59HeVpJkYsKO9Uh%1zD>!mm=s{XzIOIk`!`
zG;o7_s%r)X1iAfk!3%RTrQqKqH}9p1|CPDm)y^-NWG`bqCx&Te!7kHLt}_37`E!X~
zuD5f7zLg8+In1IPscCHQgln)B^1kXZ)r46Qe<^}#D__SK^gm~LMr2mt0wzVCMr2pJ
z>5dz}N9e4M!JUJS@H~2N<!0sSvn+j`6WX+%lu%qBxICphA+~m;X;|y5e8Xya*0L0r
z2a(=O$4~{+K9-<AP%U+SQYvE{rMet(dM$Mgeb><xtALg1fK|+@{k1*PC{3hrD4seE
zrq4JS)_7<dl8!#5+XOZrS07x$EXIRtK@8XAP|)v+hzl^FzU<mHuD7>S4gF0ZxSxe>
ztxsa`u%FIK*?Fn>eSNjD1n(ddVx#>c(7|>bw~E7Fkw!I(td|i9q6SJ9@X)69dd?Xd
z)~Hu{)Bd>MuJTj=mKF24%8W$T8&=Fjep%<}EEamlZy~%BiSRAliueZqx8)Z<$%-9*
z2M?X{R-Db>l|PXtFN*uUc;Ort#TR+K?=}*~z3+DAvAzdxao<xkeeb#9mbShXoz`>i
zAc&qL`3&f}U~bw!E+yZ03?Jp2Ss0vI=o>zk4)Go$ps`gJeUCV=Lj)G!dcpUJ61|mB
z{Hb@>1!8`7tZ=82^;xREPO*Z^5vNc1Q-Hz>D#NkD5AcZ<40iFthd=CKS2H@;lvqt{
z6<ZV2jH=FSM!mXbR2%L|Ik#4Ec5Nt1rfp~?TN~a*xZ3T9xD}DX+U-YpME91Q_QrkJ
z$~qL8$zb#<9B)tWBz19~7TNF$BG{6~v&6SOVk7u8I}ByLRasa7xtSab;9)#v0sJ6c
zeS$MXl%)tPfO0$wpmL=aKrSwC8TlC17oykZRkZ?2A3&z~u&~!m!B@j)H8<tS+EW{%
z-%>?05Lx|hYt7n<U~v#R=t|7vJ6FHLwrP8W0`OX3IrDu@u(aE&A(hzeWt)1e6r*!y
zQ%@S~j1T^PiO<pwx<F$A*vGa9wUcLmZprNx<Z`zeZ>%LMUaL8h3}T9}TSLB9#Q!Jy
z(<+1vWYjx5-4u!{4DJizG%?8{+^sf#t5BHaXgTF|JjEp3`C6Q~!^&0!Od`kI_^n(p
z$@Y||;~=VV?7R!mirAE)iGv5h!}TB#Q`Cmwh0Q^*%++TUm_J;M!TGho(KOJD-TDg+
z!T5UgmC8|o&ll+IDabd@gNp3)!&_QDQ|ee6=YnJEUQTYu@N;!gM5|;r<}Mdq+)i%8
zj_*>vPNh2n^IfV7An@b~?BOF+AElD(^+6+VOT0>^x@ThVvTZq5T#tj;N@J9~o7yH9
z$=)?3wh-P!<Uqo6Qdypcs0i@f5}cUl)a?22CrT+Dti{fd-fld13{g<<^@71I6COc6
z7XNMSiM@416rv*#4c^JzHxO7lzILy3`?pjyoOisO^07R{c^9VQNfB_K9FH{0m5Qe~
z@fM4xBTYOhAQn#ojW0vl^Nk8_D4uFV<it~Bh$7XWh$s2^A`Q<%B<Otclmb_Rc=g3o
zO|T@M8p4!_Co9G1oDolH@K(0b*|hPE@QTEMC!f?%8Tq91D0djIoIuo8^63X;W)~q5
zzI@U<o8uJPP(G<pxMaPY@`*gfB_9%({M^b`1Y9D=lTXU!$tP7f_KT0C^2vjsp?s<h
z!K<2s0Qpp<zyir9183KdR^?MQc8Yw8bLxnceIa4|BFU?_f({?8v8VFJ^6D*kV|kUv
zv02g58MT{`SB5R}s-#?w;K-{F$MQ;{l2>xQPHE&$<dy2K$}20b$3bkRA+PSM%Bv}{
zh480a%c~CfYul{65*uLUN#)g-X^8I`51vdE@@j)%XdB$Xrq$WjNMi(yJunNB+lE5h
zuh&x25wpsvM7j4M8pq$5CsheN`8GM_Q+SFeIlEAta90s30-luP@uYGkJPAt}M0<Zc
zW`aT}6Gm!*a|0=hDN4roZ|o`P!Yk^HIcoxqs0JTx_;%%T0MdpZ6@a=5j~$mps2SyY
z&1euN&8UivKzwlr@ADIo-&G%E#ElJ927}d|**}^F$Kob){!Mx*d;5KPBl2eC%_~vh
zzi(~*4x(u5G7fu`U%)am&8<({GImD<TDPdY6CZmRHp_|h+;}r7I6t^cd+7)i*1>)k
zTv*XM@hg{EsY=%p7w1x8Ccoykcefuf2NM1mhFph>p~4QOXh1y_!0qBVA>jRW8P-qv
zYmdwB{Qt>6i66O=jHZOJJ1#O%ql#*CYKH*nDBVemv(Z^EBNvfAh4*>W!8@{1W%dHT
zTall(RCd2vv2cl%yCzfaPxR)hN$D`Moux5iNFdpvWD`hpTeXFG`g&U9dRkpQzk(1c
z{3lLZ&sQx?wVv@0p@PXKP^*eIt73%X%oHu^tsJj<OCNy_efu7lEz&>_FKMpaW#kPn
zZ7wbL=3IDLb7_A}TF0mk>I6NtNB9fcd~Tt;FgPpdcFa4XNw!Nrr*q4^Rm^6)J4-*O
zAz}@5=A4;M1J(B!mm?hc9;2p#>Lx)qUsRG?K8@!bzQ_16NrO%!iz4(rM!B9eP(Dg}
zJ|z{Gjy;rgdk2Dbhjh4dcGv`)P2}{X=-gRzX1UYUU30qJq0@a#PC3ZBDrb;BhO8?H
zFVa{u9z+VAix`#biBSWq%cdTyimYZ&?lO0_bYCm1$94A}*KrNm?%8fP)eiL%O{4of
zOW*25*6ae=V?9~4i)B%2#(bB6cV^6Y<K-TSAg6T?XTwx1c9dt=_3eRKa$Vo<+r=iz
z1(+zibCa{VL>pTvCd!lK$VP#^D#-1S3wFv$UB^E!#~}^#t87h*?lk$QvN2h_7pE&V
zNfyZy@&6HjEHHg);&~N=GCGT-gNqru&cuTuMWI*_pHww*n)cE)uwLm{kguGrRM=RQ
zY~<5Q#FwG)o8_?bKE?8RC>&Sb{G_}qlr+xg5^Q92lL_;vpra%kvMc}nDghg#$}fgN
zq<3ePv`_gOkPG5M(RJgLaXPd1+~vrJwa3cpaX>jBm0;CDW|S0dNh8>uMd$<(yUpfB
ziEU74uk|-VgMe&9j%vy#+mNAdwjr|(x=7WlNQ2!XF&qi<#Bh|nCU!q|@iSvqW@j@J
z)1)>dE3~sXw|D$afGC@hnzy0{{`bhQMl}0Ti4+6-wX*egH7hrz0VLgFee5aa?kM2r
zkXCo=##^B~3x9=HlcJrZeIaSvN=1pqfiF6v{H9WJ*~)r6TiQl+*~-u9_e(b`CgiI4
zqKt~akjAC?p>%%EA%Mp@02Ax{Z=uq^@^?D_c|>>$SEP4;4xo3wwWZL8a{yHW(_SE_
zd@fJvTOXGIb2bV^ASmQ`f<n0xeG5nppmRT&&H+3K9yTeu5cH<8l1QVf4S};!G=u={
zsY-!9%>fLY-v}IMqo~HFLNzv~*ic?MA8dliJ(V`r`9Fv^Ru<AYK0s!vjk-<f{DvKx
zz=g`?2#iR(L~<A#g+iq)%Jqf^BX6QCs_v@JZ^iXEh^;g<z+XXIXQP-BTL^zGzVdAp
z)9Cza_I&sor5qlAJDk7yLxfaK-7GIjne&!fKcZ;ZQ8JT$8OvjZZe`c5NES?I*UslY
zNxmN%pDQ1|{9`y>&b)mj!u1T|5fh3G+M_pUj{g-cw_KzE1fU{{qqeih3APP4OAaCg
zUkk7u2J$%HFzKv(R5|)ALt@#sQne|lrDU@1MlQYqROFb#oE(b;dz*bzQ*!L1A9dnO
zvMbFl@npp$;z^Yy;)&%6y`IBWHH)lo--`_Vk>PwX;po?&!12yt|B!l)A&u=Ws(*G@
z1<-FVms9>YPxaeRsNbG!1t@}kE5~Cj<%%slv1vzy<y&Hemp*5f=gaU0(I@TsDFrt)
z?bHUvnRXh2;_6RKJA%c96kM}^KD9ijz?HE2)Hm(a1WTr!hA<_j9V^8MoH6aB!PCvP
zm=4-E2Nuwtv3{c_iuD^d0<B~eU?b2NBY?WuyI?gtD{%#>@bpy7Gr(ugl#QkHgPeD;
zBM)s9c)3oAS?r3AIDH>mpkO%#YS&Jmwlc7r!x9a%-=%k8I6qWiL0uuvA6K+rez0x)
ze)>P=Ws7`K8W1N>*1gPzsk7%y(~$gEQWSjr8Jx`7^G_2{bGbsAvogZJpOaI*il_MZ
zQt|I?>iCf&;9of&|0-9^zs%pS=8aFI+4CBn;o<cB8nwDJo;7O~Pc*C&o<Q_$aj+ZE
znX~6sxCJLXF=JbB$<5ArXU~OdX3nkg{%)h?=ZTuu#`i@c{PA7yL3FJ`8;<WP6gq!d
zPWcNwMd!;zXJ_M61ay|;jql3kjqj>(tbBPozIzZf9N%k0;B0&iAz*y3QedIRcLV3g
z0>_Q-)z~THdz>@1@tp*RRK5f{9Idja^2X!+b$H|PK8@o$ZkEod-Nblr*fQRKMY$Zo
z8Sg(CkM|1IcrVvuYa@3u-mC8FcyGn^IEbw@#{0?Yct0h!5I)evcrP}{hYu>H#&GwY
zt$U0IuP2IgEZZ`cpyH4Jm=lm%7}o*wZYCu1jz6MP7B{&-gphn3_p;{=JWFq_Et%cw
zgerk2zb>cDE)wI(Pl+elM@`>X1UxCnBa(8(ecu7%j3gJ(qBAECZ0lufy}_a(Sum9}
zB;W7C1-HWGx^UN%4H5%^p8T-r+M^Dh1{cCd9GT)hEz5K7zd1Kwy;_>jjrTdfW;Tze
zcUlUieE6s`9$wv^b~Ay<UiLX?9@~;zic*0s2pRfKg6Q|McYT;Y=SWys%jM4I&pBJ@
z@huYg`o7+sJa9Y9zs)<w3~gO#F{8{Nq);Lbd?`pmqDVzv&_h+N+!oaq0jt#W#kfPR
zkpvZ%v0LSoZ{evc04`S}aU&i;DS|5i<amNZxhQ9Ql@r%ER=g$^90I|*8%H>*XvoIZ
zx9)&$kQWio<e!HKXYk({BAmv5##aABdDr0&#U=kFjTz|@Y(*1ChHzUO9exLBa|?3{
z;qT?qB~gD6u+F+`NO!kYzN=gg>$V`gLRj;CM6gx_M%c34E%WZvdG>jXW`=fK_(w{b
zIkC!8I%0d(znQstZ|O1BDl5{T<Y}MN9{$-?uVWIf`gZM`NUCoS!Yft%(-4EtY^nP8
zpe%RGJS4bReS7#9tNvz7Ii>pZtopyIR#~(DCQs*_&hYQ9dL5Q<)pu5IPpfAi3m;D^
zPH=B1)hl9dXHb^AWghlMulmmLajRb6D42%$YmN9%s8*S*o|H!>tADua^_?D9eOKj<
zw0cZdpH}tHz!H46OVuM7<ZhXFf2R7b@Sj%wH!Y>HmZZK-&6jfVh4l?terHZxoK2GK
zEXM}eX?DwO^2zmPpxi2OomnEc3Vfl%d+cpucsDdvd3@`k$r;uRW#cnUX-|QGlRs&2
zA^aDinrr!UxWCf_|Ha?aFV#cy;lGtQwV!=Ylyvs9JMj)^pN?!L8!7KzlRZj-{`Ny0
z^tZOQzTrK42bkelPOe2De5jode9(Hu8HLJ^crOky5XhrlK$=)7ZJ(D(an7_YpG!M*
z7fPyLwYye3fvS_F<TbJ3aN7ccLuEP}jsQ<>I0l#5a6XRC9Pj&SsR({-ICtSx1hd(2
zJ|(R>CB}~lkBM;)UJ*leZm7~i382MKaAI2QVYE=VBZ1WFpb*{wH7fTKYNU{-ayJi1
zp`=C%tBkKp@m{JXckf0DMHnfRF_|wy3VSuUv<HeiC!mGOa<mZKDJ`rHqs0|5Ed+W+
z55xbnS^V!KJjVZLcoY23sN$~#;Qw=+82?ejU*Q@2#SNwJi3q<S%7`FO<)=I#g3=oi
ztVYgl@$QHqpL;J1(<k*hbSsP@k5NM<c+{|0;8Qz^9^AeGJyfithww=0VbvKuJ{QwN
zs8sYYdVDEMkEiJ?F+KjHZe#qA&pl2(e3OfMlmL1>gA>ou5$txi!}oEGG)hv4-o_kS
ztl@l^AU1v-y{hsokz5|nbFjp=!{_i6U*!V0aGke{?V{Afx?46%53_?w8VYUC=HPrb
zk5;y*h`x%dF^U?@mNz+yrgB_qu4Jx(xFmrjEjHfmYW&L;^~y6pTdI*?5efO3N$S;r
zk^D*8fHSr+ZCo3Oc4-?JfYt{8C0zYe<g4gmZIElv2&hTgCT@cV@g{BX9QE-zJL*va
z@IwiMCl5g6p?BqS@8the{?-OoM)VLV;>$c{j%h#n_yx%G)3$D$uAtpD4%!*4sORA8
zX`J8UDZa!71d-k@#-#pYzQTjIqr_JaI531d^Z>6ki0ZTkag{55V!n{ymEcOFL9PZ)
zE671(r8!Yztm6uupDondL$xLCfpD<bL{9#yN*~t_qF>q$2A8$N&2e842`hTgCTO}X
z6=7y=@*VXH+P5WlEHuRi#RBFG+UcI}L2Ww<Wc%!g`r_bQGr@zct$9r2`O*~(u9(BO
zY3(`8B@Exsj{bqxHX91#pb|3)Qd5uWV@Fc%*Wl5*QB}<>4CPykZ3S0SODO<`uJ-<&
zmcO#Sf8O%F+E=b7d3%4?x&u1Nue}F{;65Ms99*fw@+W_-(fpbJi1rP%_b(isVeU@D
zM~o~q-|FS_+WQAaA0thB|LW0)l~z08+xypyqA}whFsee@`wtwwU6GIQn9XsS>ydm8
z-L~*!AkE>Db6A|50bbKb=j%rY^`l!FmfuppfWRxjQ8k8sHp>sdKR9c4%j}jzO;s`+
zdJYr!h52k>MR890DjIXu{+q7HDM58TuFqSK(~$wU9`^%SjrF)Y<-i2>*W>cJi00sK
zCFFq0X8wlKto1m_0j(vyi~q0qV=jILNzq#jNX}T2&aH#}6`#z-iWq5CqDVodV~`HB
z+QVDH-)2C6=Y8<SY!pBpZ}Gw(l~(u{r9v{VG}{z%`S&Wd&kALYkA-4ele0QL#Y@M=
zuMslT-m&opTw9qtmU1d6-L@#FghdP)dN)W+n!8fxC#7z-QlE7wxuj;R{(caOj0~H~
zUVMQASz2swu{FF3<{Vry)^>lag0dz^&FSVBcSJ8Hg34R<Coa!(O+t~PjWd6LPi=%;
zklx8u)qZ2pPk(<M&nRv*)dbsUYqoG($8(W_Q;~*lVkj30J=tcjl5IL0dVjUeooo}o
zrLxTmHQ9CtFlvkuTU7MG&jhQbqV~uC7x~qoCg*-kYEK_z;*;?66L=+)w4Q;zHeSzA
zf|#GRQ$JZ<?yGY1Per02yid-6<K_Nbu!AE{kei#E7dpReEdQn43Bya}LJj6CTJM-$
z=o|e2aqzHq9mB&0SP(r)yyIcCdgEb5jGV7T-0h{<W5iSpxmZ)8&uN9Eg)UoRL^du{
z^5SZ~MS~l*5`&4r5=T!F(&4yTSG6zVHpL3FH?g!bu}X5dR)h@@m%WYbL)QnPP<AD5
znK!8kSdXjU!DIaNI$IVNQHT_nj-P%aD#1^(iHe_|ddks8>nT=|6(Ke;)Qn9|QqN7H
z*wU*i&3>a<gdp}f$X=bEA|9Ho(%SKm+BoGQE7W-C9^euWsW~co;Ac|WQW5>}|4V)m
z%y{URr1p3Sy(Zxy6tOA~0es9uO0d<M*n|i%-I`cyu7Y5Dz~ELsNU;Sc=8+XBKJW<3
z9^w(jw}(GOF3svieqn8b6}mZOhgYKPb%Qg<Rv`o8?jA&_V=*@&){UD~#K><cm8;$e
za&kyVmP~k6U?ubzJy)KoL~(6#`9t}>Vj-@!0&fA#_XSK6%{6}t?Onc&E2H!z=7+v@
z#m?SpHe_CV_ya%Ff+SOZUa`l^SIts^4U?KOXo~_4ZLWwB%vN7_+uFHpaUC_1>js%>
zdd~lnb*<H+Em01G!V+3@b8$wmKAVWeD2>?)H`=ROl&mJCrF0cJ&$3#%amdz5lQ^q#
zOHe5YQPrgEeAI?#csH~fJdv|om3Cf>`^KPi8y~Nw3Ix`aL!DSn%ecgW0UMX}N@68*
zNUgOJn1Q*Vt?C&1^a$!0Y=IKU*7I~+S$Q47V{b@?maUq+Fm~q`awiuTr@`V$8(d`z
z3E#(A=dBt%&64oJ)GXe?@~IFF?l5+10#0}9^g~@w%*w{(CZT3Nrx8#65DBGVC!v0$
zj){bFwid}K{Wj3y^!LX+BdGNn$tx>38P?PSo=~z^n>$G+Jy`AAP9{laq%z4$HJS8l
zAdyT`_t$Jqa)_BzOGUjHKbmn%MXh5p=^;{kGO2^`cuqAFZ!)f>dYTe6(9?b|QUu}e
z<TTXNT5_L((H=6~R5m1;jpONQZwGg*agtw~#FO(X1|;X>N$1A6ZhVsViWrd&&w+DA
z9s}#lvI%vd+49Q!D5gE)GrTQ>Cv>+spKZ^Pem1z1Ce58Hs?SQI6U>S`fff8qa&zjP
z1R1{m4#8tB6S2zV3iZ!nGa=ev=8icknSoJd=_sAvOQ(v<+X4*9dug|U)jmI|T_byv
zBTjEh97jDxWB5;H6!)JB)ZngTYqJpP6o~aPU#Cm2U5E*09R4lS>C}AGAUZk@e^l+c
zl}^W+YfgWkzpZtQ{^D<I2jg$k>0G41R-}<2VSeH6F~7YgIvqElqQ@(<<9+dF%KKKR
z@%|&gDBc$fRrJ8mg$ykf@e2NJ<yQ+D@BfL^Y+Cs%&gQuUYTj}S4Knbev5TQz-k#y-
z6_Q#4=aDe(A$`iTmENc5x_aL~M8+U|QceTCuPrBYJS%44u5zK+LY#O^zZ&kKPp}6}
zeZs&BqWQ$TKCvA>^$A6c9IQl<f?S_)R;Hk`y%I%wuDn^y)*f9aO^^|zv(m3P_*DfI
z%Rx|9K#0)qtSnKKW#fZf_+1~8Hn_dC!Cpo8Sw{NM(=ittgwZ16{O&WjDD6A0Fu(8g
zxT;*$G;`;w4W=N9hz_7F^Y{+RqT&g`)RJE{c4^WF#`?C)o$Qd?)MF6vNj5e4mbSl9
zRQyRc#e|5XV#wl1XN`{16BEsdY5nC2>o00h`ip{He|cOjyj6esGVP$h-(MVEluup4
zinT6b`m046`dfO1XjOj|jlJGtuc`j}Xq9JNe-Wpq{lyBk{_=NVRDTg$RrJ8WKz{KN
z{>AdE$*jNpo77%U*n#l4C+vhb87r34-mJvIvr166FKiKsg77&x4f;Y$?lH<m<BZFN
zRLdwn?h9uTM_;&8tTwM=fbo$v;$2@T;!|HxMDd+mDWRBSG&r#1>sZ{%prv@$r9hzS
z-BNH>ZuC9+1fNSINEf?3Pf<@uowBLVj>_3*8JYSVq#k4SyCO_pUl)YJ6z;HUT=m8}
zrx-U2e<B5yW1VM25GfPMeCMHu9Ls3F<4RXz@r$97%Gb1NMH&`~d=;P`$Jndn>k$x*
z8>3^oI(|`mru<@s8o&Gpz{D>ieMJxaq5L8a{-yGZ?)V4f7qN_Y0yZsqyfZ|2%sac{
zO?W5OGn8OjJtLh#w^JnCQ;;KC)!m=nlG~Sl$8Fi*sj{KYZaAKv!4jTrlZB7sz1apN
zAA}>F<B~n`iH{U9@&hqjq#(yi9k!R+tn!<Xf+gL!;j)cbwr4F{@kK$TcgI|<?GaxV
zgQ->!q_Y~kE6SJ)N!nT&R?a@lh$P*t8sRU4%avmM^@Si5Enp#)Tzr9FC`PHY%!L@C
zt;qRtk(0|4bYe`5E0-BpimntX1RYnliqP4;*?U4${e5}npi?%npwTDg&lGe;8X*$?
z6kr~I+H1m}f2g*b<4^HS%AZ!K@n;Jlia*t~6+Q63Kz`8||MTTnd*a_ie$mGGvx}S_
zf374v=Fd^Q34d-!3pm-P1n}o`Tx!y{ZvO7>CWdZNNNLq?Wy1;PInrp)#(L=VN5eyT
z!G0ec_)l{M(eHKq@8>T&BRseUhu&_0@NqNJI%ZmpPmQLCk$;e$$(h28p(^|0YmfN+
zS<C;&+joFhRXp*|o^x|=LJcKA=pjgvUZhv)RYGr41O=pqUQ%w_4FU=n1f(}90#a3~
z^w1GR1?eEtMHCSMh4-7?J?9=G`q%e;@8$bo&Tn>hc6WAmww!WH6+!~T=xX>6PQ)4D
zL^nB$&TmQ7Wr=b8zj_})^Wi(pD|Q#bF4`|s67PUW*{#qd{k;<0ogbW4C9997t>H1i
zkN&FibLsLg46q6>@vg3D5{hGeN-)$CtY-;U>B;_>UDY7FY@%+m^wvM*^vkA)3|m5Z
zYi8~Xj-k#o$TwkB{^#^pX<=zXc1dYU%O%-z4wsZr?#JrEh_~#%R6fam;`d3ZRr+Km
zRD^w!yE~fy!*xx*=026a7Xk@~@4X7X_PzSJw0zG$ud={D&Z~L2?MAWOO#bJ*Iso>#
zE!)C+VS+{=dR}E`fiE^wz9=h(UpYg%^2Nru;$Gr}Do>R!GEezp6HdiXkby)u<?C_$
zzwKenhlepwd6-|O>Jp_(%Cnb6H<S7K|B|s-rQ$aW^(wVFCm<$r6-qQPq8Q6XDsHKb
zV|nSAct9%)mUQ@1k7i28tjBqC%B+~W`&nwVk{~{{#8;-+n2M#=n(#8#TcwopN?Ggd
z(Xz4QWaFh*{vy4SdjRsxR$iHlyX5&8+6%_ze@>s2mX_MsC#6>DlX6Wc6J<lwtO>b2
z4tJGMtqIv*@7R5;{FQyn@2^s+^w*rIBKs@5K{Wq|>xO*I{Vx5r03;j_r?mhd52v*R
zd3nA4$l7a)+lom*bOe)3uISd121K_3wYOWf%yCkBTw8GEr@quStf@IpW*^rMeD(E6
z{}<bOvWC*bilZ*x-cD`$Gi>@dhAZJaVmr|FgCFx3z4%9kw*xs|M@WZmmhXnaz&f@w
zOU8EpH+Is4^}=&kla?XhkJ7fB58br{_p32}_)|N*FnHc!Uk>4|eL8sVA|wvR=V|cM
z%*?>x;e-;e%Hxc5{53r+N^u~$1tRkHIj;G;`SSZV!!jY6_pf|kpYvI-BuM3aAd*hK
zlCaJw#x;iPF;BT(Fs?WtT5HOA^OVc+n$n-2{Y~nzVj!1hITdc}Gn`ZmI}zBvGTVNT
z)Ao4VgXaN<d53T^hhZ5v>x15vwqic+%?}09&x;h1H{_Y`k<XFieK>Uy9SI&9%t`XT
zIbX<I+?-(54QKii(yMKnzAE@$EdDa!-^hxk%a*QCwn90KOYY$T`2S8-lfq+&I#x63
z9$FLLX86#4gZd0(iN?KLbm=@%c)K=KtZo2dDyQ&9?V51is{rF*g~DGQF!&7?#-Bk#
zbeR8HiJ<%dk3|Lk_$HU$4*&g#e`q4rf|8i5M1_EZfh*xqJQlokkb^zg?+9$-`!hR%
zTVPS}8DuZvAJj>GxFi%!Gw_ek%nE?w3%PJVPQl}9v^jyO<Q2Z^pJZK$Wa}zC4rN_J
zKhQMD@SmF|LM{*9qjgiWL`!aKJnrLf<kXLlBcs%}+-qGGz*cKMZkN01TgZJlo3D<8
zr*dS57Io#ad8KXM5!6qm-n6eSpazP!8KtSKYj{LMT?z1p$Qm?84OH7ophc<%<{#G8
zO{6N`!By7P47bp{t|&vL#*MSClF%E9$Ni$NmJhM6GM*sH52tn01COQU?qS>)y2QNS
zmsoOHYgt#dR$Eumi><49un=3^P4C-Q`@AdB2prwGX<vGwuWp)xF6?C9YVg!m`)ou@
zmoo3!R!d)x9eBEE1M?<ehoY`Z;Bh=v`wj=NzsbRU8IzqTz9i8pYRC?QKR1Q+wB(vW
z%Py%UKgy_fk5h|%LbPcLUmb_Vm7Uk4l`8c^J8zyyEA>3A#&XZ7{Bf&{I;erlI|==(
zTHXs~C|(q<R88;V4eKipxU`lBtrq?A22qhG%o~?#*?zO#3j=kF+*ArSRa(MOE7n{=
z_-l{@WpK;g6lUj`lUwns3%@n;<gw)Dp&cqmamXpn2I``&CSV+>tCL!bn|9e{)V8la
zf=!jjzGGkY3=z7O<MUfSbOHKQ%YW)&U2QmPT^;(u(lY5J(LUMx2iI6NIM>DUocrx8
zUg2=7j5GEqtByWbWn{HmS@k7L?hg8>t+db;*j`;NwR!h!-UR!~IAqDqwRv0Z9HVSr
z7F%w$%{yhwJ%x{}(kiaQZ`{e=-?G-aI%MaVX7k)Q^jA4vMWj}}kQ!uJGRYog>FhEN
z;7a-0g^QNvYPP;lZ0j!DYUwMm-MX3y`>7FCG|K9QFYUhUW&1`>yY>SL3f<>J?^rp?
z+Hx7~5ioI!RmKjqC`Pt?sI9rl4J+@rsFm`erqFVCCwuWNj0MHJ4WHCk`z?LP?a1{6
zIi>{5IBwf&Bch&aQKH?p+88~`uO8ZYZ`&jO3Ow%aB`$Z+SnFzRN9$^7H|xrSC!3U9
zOT1UCdd9?9P`1CF&*DvwwRrIbE#BI%ty(pLKdZbyp;l@Z8i00i)NAG#t9vlcRXcW^
zwXTZ5pB1l_Jw7t&(RkAEkMow`sVM5jXjCmPh8C);a-UgO+wIf_Xpu?{#^X2Y>PZ!%
zCpFkEt&Ui{pY5xXxKgQSeza0I<4W;**>UI|d{XI)`qaA0Y1bvEU6(CbrzpABQ?0AM
zc4>+B)vWH8{l4#ET}`ojweu)z1Y|~x$P6!YQ!sM0EWuZ|F*mBK?xEIIBrK_T^{-ho
z#IQU<du#@Kwi>_QibFZ!C2B6)HO#u|1ix3j29Q&8*~Kx|)qTuQir3nn=|;iMidPVR
zrLKx%mQ+{g@F@&+wI1H2uI6JVR%KkDV$FG9+jE5vV@A#0N0wVx54KoWv+UKwLexO{
z-_d!N{VqaI@j~`kR|RrdSAP_;t}fbFcS0=Qk^<IMq<yvW4U5;oz8YcIV6PpALRVXI
zk+$6YeO8^n_}v;u-R#tr*{$05vU{s{aZ8J@nl+!Nv&)zQtEskqxWKx43eVw4(@ac(
zPx7Ce8vSX_`@8jsa?`dRR*qk7ebnCKJ-lf7X)b*8b|1#Cn^r8dQVZ>{uEK2Vwy-td
zv*WXAN7hz0?`8XHxqbDdrls!}c&wU9uGpz>+E?XKhDyz5YyJ{7RqLF6sFjMRp_sSS
zRThjbbv5C<RmPxrE9Ug_T31OnZ;H*^ijk*cb{{+1W*%VayAJEBh}qSS2P5pOY<a9G
z)%=Q8#zm}=RBs*l+Tx8tX^J-)F<V`&_|3YigmRVbKeeq}eVD}?w$X}6L3S_vWXG#O
zJL<VFSviJ4j#oy_L=VI!{&UkITl1@Ek%|^AFkh;xmXK3dU)ia@1X%O_bUT7H!s=Cx
z-)*Rsx+>n$y6S0P?L}lzsS^>O)zuhiR#)|QT34MB(^O;#>SbLG?O<K??{3NEwy%5`
zJxa@xQ&#H1DON05rY*#6E?tcO=Rdip#O67pmna$b>S=Sf`PBGijsd}7s;uW!=9rT6
ziY;UCZVxiAAX4^`dhp7l<ScP;Jd#t$=C~+GavC}~=}@a+Y6ovrdR&wqdr_h_Gl|A;
zBN~X3GD^-nA6lG1&6zjN;-uG{btx7nGjpK3DD1_0##44J6EoeC$ps#lb=}sSPjj+;
zV9A7Oj(5JrDGbg+XnqNP#G3glK8o`VR%aF`2Q&xM3RsLeJs=Y*IrXrjVNOpfM)@fY
zZT}fQZ*dCX%a_442R+N2NUBIxrA!3IkK)t-=3GDN()&^kN|u`Q*qUqLb6^58&){dA
z%T2YgE9d&$wChug-X~Eom}(;yvdk{XOq00|$6EENMIXvsLFgTp=}C2Ho-DnzwrV6e
z^QFuoU3w32U>#WX)zK=~K&nrxC=jJD-mExBsR=k-p8}Xil;)PSL27;_RdFJz8+{{d
z*h1GZ3cgTGaIW%RE7uV0oVXQ*ZJ8ld5nUQgM^0OMhEZ+qWGanF!DU6F<ujzrC|%YZ
z+D~|w4?@L<DVc%vEp<0o&!?D^IoAQY%pAzH|HP8{k#d_XvudB>%t5<zn{g=1t8+z?
zN3=A#hADOpLqv8L>uHL(!I}qB4v|-KI?lE@xizPmJqq$`PC?sy!!+lb%_*!o`D}kK
zra9lwur!y_oP_EYr<~>#u}4!S&3S#6B~wjvzIor`)YP0*tkqR3>Ve}$@4WRhJmq_~
zb2D&wbiaz(NpV_=qLOntx5a5I%E?^E>|7m1W!D1O@K4x>bK!(bG?BR$!snHqj-siH
z`zwDJ#TiIlL~kkc3RY}N#>X7UoWy>MIRo))hJ9pN6@IZ~-UWwcK8&>VOc7D8KyX%k
zsW?aJ18}_HMA|i6E0SDX!)@V8Gk$wiES05?x7)Q@ER&q1>{eOZ#X*!6O!YM9yx1;I
z$@+A%HGd)Q$y{6as9gKR7vg8h`AT!X5j(_v$vJgV$pjfY#RJLdu)`{Am-t<3{=n9}
zPdt(`KNVJWo+J(mR{-};`_HXhheWXCT(EN;5m^JcPk*yH=S3OGxn*;H73IOfd7^FA
zU&Sj@X1*=+NYs;>kJzO@5)GxyL|f*uXcfSH(O%o|fp{$1OHNL0G3IoZxq|Jo9*b@P
zUbNzo)*OlB%RaKKkF~{^6Dc`cF^?<X_*0CQb^gLG{h9CvaE#iceHF`i;}glb*h$&v
zDDF~jk)`K*U|B4{*eN-&ww?fEkIc1Ox8f*HuusX-tJuDg!8j{(RkrnHGR{lRC$^qU
z#!r&-3_gE<1CORm#y!+Om_92YB&WBL+2EB)Fx|oY&oT!@X5%->$%g2sa%D9hOO8>{
z%9Y=!?&f}X>s*nP->8YbR4_F<sB(ph0!BT_*?Zj56J|7(oTpg#D9we8*CeMB=6~jV
zXcRL#O3s`6tz0h~UEB*XQdT0;vJ6ovqnqTc)ul71hvcmP)-TgjatvJ`mU&ZhX6X7b
zr;p^6*JUxMpDcYA;x?D%GD;c!CC8&Vc@6xwnwx#B+Za{WK&oRTyEz)KSgbe?L{rV#
z=ip3~GOz0zvdm=3dGw7xSBg6hHrx+o%05jEKD`Ylk7?DXyRlAkUPl#F4F?$bnOex~
z{aWdXq#?#ZDYMd+8D^Z5oa<V1USqg%QF7|rEgf!LlAOmnS6(CDcqBQ)i(2(bH2##F
z@t>)(O4AafmWRhq`6#ROrAB=Z*Jsy0#i?(sG@43I?h}5_SjkDnJqK>ZD*D7&gcIgq
zYOHHm#aL(j;NiHj273@?jdjK~DdV%f_Y32R<easA><i<Whiw>C(5mw;1A_^@Qw9-$
z^*j)Jjcnkc&Pj^X6W_1TiGUqUruKwLaB@jbw9VOP-~lkm?D@*7PpXk$%FMItv)>4l
zGG(=|VpU+^Ss>&Zr`vvj4jDB9<4}5ajNE}Q^Qb&(43~P&16iEWGFRyX7Uvh^U?5wg
z#vc~vp>Z})6bz;ZSP^lqD9UA?lX@axF{LMuc~$C3UTSd)nz#Xo`n+jtu56aTk~)}5
zb+u%wnH43c_@|0fiRzklF#`tEe0Z<YT-U4@#8&-$sNxKySItH;S0wsE$utLtWgbtp
zWLlanrJh0<?=177(V97s*@HbNa~_B`W^2jWI14#<f{P!rZ7(_bQ!Gw<a4Z|LOeA#%
zXCY$4s;o+e!09W?YLHh*J(rBmW<SZ<1^aNWOGX!SQV@Gf2tqY;0?pUWML|KZ^J=Z<
zD7|62arP2S7cmA^uHI%Q$r)V4;`A}wN>0&BO3!7ZpBb5str`p)a;`zt&y12XNjMu|
zPF}OWIYn~PV^ztVNa}BXAUSz$&LDG+<h*WkBF)9=xXw#fTe*gr%cV?RtT&XNc(XBf
znZa}cV~{y>DBf(Ap6y(8l;R8|pV?W;^nz!y%mMmPbJip)PM|s0Oq97w&#-dMH+M_U
zJ-c@nY0mr)ESY8CaN8GPq?}(sRNwqqbCPY&N;6f;JV5D6W|fJTUy$pj%~@lfkQ}$2
zYpr=wa^^uR=Zd6t<~QlR$W>R%MAE0`9c=&bg<<qE%M1kPo|GxG&dRk194;%zTE%H@
z?ltdAPBAUh+}vkAl$>>T>HE#6k~6^O;Gwk)tS8Fm95fBdiL^O~%mB&BZF3Hro($YO
zv%ewQ`Ix9L9WjF>C;dr_^Q9S_fqVIzY<^C5DbvT!^`)6xa>_e$<(IkoM)`9UmNH?O
z9aLFgnnffh+2))yOG!>nTk~nNg5)&Nn!V;}v%2JTwmIj_`jT_j)^pBmBsr68Jr~Uu
zl9SPvxomcjoRPMk@6ER)r=~4))qGo8Y{xNG=WXUybCBdrvN=DPQIZpCbFP_Q$=Q3<
z%5~lJNzQo9iKH9mqzrHg+6&KTYv73=bC%=`U9WO6XPM;CSU+c#<fM1xS}Qr@yZU7|
z$g%=-eeRjJ&Fzx2MwfoiykqW@oKLjO9rH)?u;hGY=lan+FFErZoGX%3#=*HRIb9r_
zJCZX{b0X=kd0%qw+ok_xK9!s(Epx~G$qdNIei&x^&pk7XJo3mpj;P-@9y@=SRWq`O
zAIGR<P9%P&uByy+tCGbDbTyQm0+{t!W)20p8fWBD+58hrCYS4NspkS#k}MPK%Iz8=
zITP|(oII`(l2Zb4RmtRajg_3v?<hUNt^%%T$+@{oaSFP^Twcj>&9yk;u6W4_4Oe;!
zx?XbmB&QF~@s;Kxu6Gm%Ya8Z7QZd(L+4c_+A(=CfUUp5B?HZ!net>F&!?C4uG4>tK
zRnN5}qluQ*dkJjG>Ezm(5sMNki!%`Z8%SMUyE2-XQ{K#?I7jJi&3P}>;tX-^kuo_8
zS)38BeUg(e#Nv!|?U$V0`7Msubx?9vU|+_6k(A^*EID7~Q=FsZa~+kO76mQNB-fXc
z^CZmTq_|E<&fu3U&WEm(lGCY(#aZC`MshOgwjZVCu2Yh;34X<YM`@GmJIT3|-QsL_
zot2zbcAa;-&P&eM1udCFu8Weh)^7U=*Ja6RUD%R2>-t`D66_jYcKskZ@7XoH;kqt4
zJya&t=dSCf<eaf<_^a!-<lHG}Y5v3YqvX^Jw>a*ApCo61U3$iVpCxCLU3&I_`;xQI
zE<I1c1Ieifzv9266dv$Uaz3?t@a2F<l5-=EB~v!wiR5?+Se!}$e@ae}-8<C-o=MI)
zyM}cGL?-sAX?6`81(=d^$*y6mfB?zC7GM4yrH%m}$$2%0#pw|cBsuBwTAY3X=_RM2
zUHYJajFR)EUDmLG%t}vDE7$0NERxgGZhLe<HpwY#YmN^Hk(`>g=H!5!lGD((;iQ02
z$qBG6HZ>rR<czZYXJ$Y?$vI@(aDG4m$@$3Evn(J?a(=P<{gZ%j$th^t`O|=xBxkMN
zuB`z@B`3e#u3Z7eCFdpEV*3M1NKR9`T}J~-NzPz9*U5k~lC#2=`7WTG<osfnbuplV
z<ZQP4^oM{~Bq!Kz#hrl4k~7%m{1Q-Aa=O^ocoa}wa<mA%?pZ+1OdOFK4pqGqNp5%T
zOlBBPU`kk=Oz!5H5QrTyD#YDZ>M3BysDhd^No9iOm)z~8%u1V6(%n&VvfBM#+1*)k
zrrAAM%iUFSl3!8Tj#5K+H_7?j=CpM8kenZEPJ8zok`s(shX0Pz>+arZTG8A6mgJ1c
zs$?SREq6be%WL~aU-y7a2^c98vsp4j-GgM?U&or6{|3@9_h6X|UpkO~ku(~d^vJa`
zx8fY7G49ATrH^%wMlPI;FHtfNM3j4+)N=tVJ^tGVPORkcDH;DoQj|MRa(3997<Yo?
zyy~`O;@nA+^PbH~a3@PnX}g9=?sp`|U+3}eNs`kX`#b)Nq>1kLB&VfnC^+xAr%28l
zh9xuA{l4UQO^cJ_o+dfbE{ikWJza7_?K;nL&rD;Dx$aq+l3=kGSY7a6B+YlvO(V0y
zy-0Ef*!{KA{gL$53)s1+T&vy7Wm!LJKa8Zc?iG@A)vn<>_bSObr)SSd+Ti{~YA&Gr
zE0Q+4*Jk2p7R&6gG=JvaAaj+mTd~=_B~1;txVK4}uWgy_?wyj8VtdpV?%k3z-5!I7
z+<PTwfPemSr%H~$-!HlkNX~M*U6<X5C1;1t`QCj@a=x`WKe$gwj^7&B+$SZ+W!vz&
z`y0v0V{>k}Pf3oymv6eilbi>7oJP_u_gTrwo71w-ZTI;!*0|%oC}r?vUHKPDKf8aB
zoRPMDesN!y9Ff_Qx$nLuIk#+?2ktxI5G{;RG6U(M`>vFkVz=}UaJau>>>hmT{y9y3
zp1FUOxsKVM;P(8HrYw)=spLf1^$GGklbl$)rC6S2=GpVHttYd`l$<~95f|(Ukep-o
z*vaDYNKOSiS5{Au<XpFNW%Hz$9DIvSTP(XLV`h%V9c>Q}@np`-v3GT6%Nik`teLs(
zop3V1e~}al4%cv(t*3w|Tju9uctKByl$mbV=Os^`G%`g!`J~J<yA{Pf1tq7yo$F;!
zA<0Rx$9oA+VOi(3IFC{FDd{PaMy9l<c$%`xcuGhae;h0CDU(K~f~UOXl(&8L70)Y@
zQ{CoN_EeFa`ZlMkr@G{{usPK|wIs(sqt*1(m8Jh`$AsFR`clSadsJi3Yf>hY&1vds
zEjbJ9bwVpo7s-jSSLPi(ucwje;pv`6rmv@`<RsgAhIo2OPG#-km_a;mO3t)!%c=>U
zKGM$Xi>dB;izay@(`cUL8Ih)?(>$Z3%x=B9h@|PBv69o>o;_!I;-uz_cwRvD;9^gL
zlxc0(XNl*1$*EaT)#oU!@=TMQ5q2v!dZtS~i#I5lNZRU|DP@k^`-|<K4<+Z1mn@n6
zp4pPqt*FI0=2<K`PZR?le$um2a@yN{dfKx=a{7c>xh{CN%F^d+`@BcjJbRQ(K}+VE
zCslG*+x>peb6j$2g;+9AJZI9#1O<Md#;Vx@e@r725_m5SCoJ%B8cwM|S1^xLf9X{N
zGo|6Y8kiHe#DZzV8mm4n0>dQdaFoS)EwGTx<)6*l28K(He>QIy_!2mH58jTV9RiC6
zb8K0U`zibvNj<^I2Jn^kv7^*Gu$bg130_B8oY(Bx%Hq^h)p5w&vgbHUX14BSzn*S?
zDQov%^whFf-IkuN_KKQ0&qp@N;pk`g>Dz&2(#Q-7tR(F-!k$5f1Xjh;`O<B?K{&Js
zeLMwk6AcZc*7b?r0v#-=+uZ~)tz)P5-jz5B?l-i(_vr=f_J{iUGoN8a2>wz@{rqW>
zurK)hY&=tb9zN2MFLeo?p8>xM#vs!^)f5eE&$Jx<cACo-bSkTmwBz?gV}=U)9QitS
z@|V->b-WRQ{L98EIvKsq*e9u9j-S;_06%59qJ<|i^{4Z5cF9QRXY`XDd~U}?@UK^A
zDyaH>wvV7sYvBjt;9j@uDEeJvMRUU`Du)Sb6M^TZx(fQaHqqpvCiyUXO#>aF=`NfQ
z&j#M~KG72ByERtj8~K>?Wucs}Va6L;i3GM2t;|Yv4ZhEG2<p!?BkIHS8O~9dey?e6
z^j9l0j5dvCePL8u*CP`3<$8C}wA)6$7hzJ*nYgFU6#Djo8q@JB3u)Sa3hn3nYE1c=
zbf$3^@ty$sDg7h7eTsbihQuk*uQzl5m~?h9*TbYGxdqBKsl3!{QVQrL;77Xse*)hC
zz9#M9x6`mgL_dQ+N!RP|?3UKvJ+%F!bi41?<$4IJGm7YUwEuhb`}Gi$T4K=s3H`-l
zG5ChziEPw605m`BoDMWKh5b-ag~ePCL4Ph{`<b+M8vC<JMLu9Z5p-y-(q9gKlNtFI
zwo!I=Ng75qo3K5?Xe<1KsjHFdpBM9$9YTkh6g3M^T7Xvh5O?`O*<Qs!xxY$*vfavq
zvRx~KvRz|QuRTcb2O2q>s0R4CpWsba&=RMK8i4+YyG>0&-^2RqHPAN>;%Rx%Ay`Xw
zLj4Pz!kt>siSrQ;K<A+R{-A7+VW8apaiCAYPXJ{*Oa!eCehR2%Kg(`eG49##y5T*_
zX%2hweNpDm`3~ow!`gVy$@aDJ)|c&T;|-PVYU3Rx+ttQ<5wtwya!7l$@h+F`Y~wu!
zzX=6DLbkU8m67de;~j(gPaN9D8xQ$`pdZWrY~!6F`>%~RgY3UH-b>sLq!$2xDfG0L
zcCSF!$FU!@@zz1T-$MF&+0Sjf;qV9cZ)G1-+sD-QQT{Ow_Eh#!elihQ*~irOF||ER
z?O!G-JDJ*_(7)0?$`4i|zcp^yA2tG8<H;I7);PAt75C?M$gw}9f?DH?{o)w<X$Qu~
zDbNDV*)If*Zp!{3=nnk#66h@8rl2`N+k*Cm+?-)ybhQ-wcZBz{96u3W55`5uP7&S*
z3zdJTq!QhN{Gx{(7X*D$o%w?9{-)?dO@H01@cK8@csY*o{uA>3)QRX<&|Qdgk3gp(
zzS3}ioOV^lI33>J8=6DWc?A@Ga8uDY&`+HH3&tbUMVNn>{scc{8iDedW?RkF-=6L7
zsrp|&fqoy3n2P#j2K`316VC>2Q1w-DLr{}`s@^^C5M_tlhmhy?TJ`qH_WSG2^T_Xr
zo1M|GOcM}CnMPeye2$llKg2x~rZdogOrIQ9{JJ<sXUy{$Q-1QBDbEK?c|Kqo=TdTS
z;4UuXViy$O9iyo07Sk}|e#(XRbKJ^@`d2K0d!3+Xm*SaJ&=EuNRa;QL&s=GEIqHLz
zadprb=xs2(0u6({21vK!o)teio;C-c#|O`s+hrUSv;c8gj(0g8{qdE@#ZvUM950Z!
z;;D)wR=o7vu@2q@_#Evzwu<Mk_TFxnnflXtol;|1dv6f@gek8JY7FzIGcE-_$DL9R
zKCdGxYx&9!e)*jociMXwMl0I915>{~?$>anKY5~PuRj$nu#Bl+j>{{i^)nTd8T-?@
z-VyN2Zs<>@Z$l5$z7jt#w-ov7Ar3JekMyMuJ^SI``N3a;^7A|RT+dMOxt>h9o=mx(
zOu3z*4tcI`dz8Pqx}rS)Gv@i9DbN2*-^E^o>G5n#)jFjMD6d<(z)x1dzj_Ui@J7n<
z72(~3amw@Hb&Sh%@I!07T?4;6`hP)IUhjobwf6AG>|vA<{apj}#OrF_+^Fe~@W;<W
z!symUtdrO`QU3K1zn%IOLCiwExZX@d_TauTXj~6PdHgW$p)tQ*!T42;`JD~MKXg}=
z-`rq)Rbzg;gYkZi`TY>a^>luIzk_kAPUp8g7=NoVzw5#H8;$vm55|RbeqN6<&ZaT1
zqZyaenAg*cKhc=it&Ce~%x{=7c5BS<nKB-tF~60{c#6jS_9<hIFHAX3Fy;3#`VL2&
zcvY<@$JSx&w=?_IK*-&PUojmWDip5CxExj9i8q-+<F4QxHqi8tOLXMtb^mPS<9z_r
zdYV?0)J>tg*k9Zfzev%_h(C-wUQ)Ov?9Mm>{=#%B{DEm(o!(TZe};O_MqC<zy&uPE
zUptm7hv&hG7NB02;m3?SzpVJriYr=6<1$qgE~fEKonA%bl`B<xgvOttoviN-jc-W(
z5#H>7V7-rVvHT~zjg4`^`=?fBgf|HD1<#|QnikhIt89OSw+!074E5|7rt14~I8*G?
z@C;^)&@fsNF64Nr3)~ugpvFTOok6+2++no+AfC6=G;^K`R0OB_*F%cX0nA@-VgI-5
zHqpn!i_n_uM4y0WlQhEn(QaNRM|fM#<$9K*S%|yqhi9Q<c!nz#^=Z%vzZ{BsFujiP
z7=DE4IQWC%kJm%8Nd9-Q`ziRr1<<>r*pCG5Z;PEdaGu62FK9(eMLRW7G(l5O3xy}b
z|878TPF+Q_HBhv_OmFY)-k0mw-kYpxL?30>x^F3Z>`g@<Xqs2^XG!@8@6p~WJyPSf
z8qe2q<uy*#xRu5wwEQ5A8)`g7;&SxzZ`ePepZn;1S9E%IU5}j_-`4W&rF;b{fPTpi
zKXGYVP0|Q&Jvj~{yaxQ3`-AQN1a^4}`><WxHdA()3Q8lwD7HEForS`vXDj|fcqNLu
zfin&G$c)2ky>fmt`(2n`uZ2-X-0^4JPL|W&I}Co$cI9}=w9j_-hxXp7;4}TH1k)n4
zBs=EQ97U)~9;_R3gwZGCh?*1&qv2<XDi$k8)w;o7LAxzszbHric1`vXS!f3MxkjjV
zG#G|->38Mm5%$3)Q2!B9E=<am9f9>J{G<lx610cc6MJhby04z1V>SPjrpw@GlOY!*
z=amXn1NN#5y=72tL+Clv6!RG9!CIU@Os;dgAiYy-<<Bp{581EPzD-b=EH8}4==3vf
z@TLjmqj%s7LD1v-R@whOP`3XW*)L)AChEcZM{ECYJ6iSEnKRr!7~7z4L;gK2*9P_C
z_T8_q@&#SM_`w)2qUC#QIurfNa!ZAvp(sB^(^t^{qkucr;d2K;QyTJlgP@fdH%lDr
zC63?YMyPp`DbMdri%n8|o(~xJhR-k^TT<a4dMcU@@oC%$lNR^jdYBXoKGTh>6s<Rs
z+i6nRFkWwCzd1(9FMC&|Pp_uv=jdl{$L+g#lLPf`i}=WNlg0%!P5_+%eiZca`NkIQ
z_ce4oAHr_j?yS1~*I}QxfhWoS6;u~^8thyX{=)ei>HN9TpF_d_yZ(uQA9MexehDM1
zKdgSR`bG6e7_CBFnu~HDp&ndb%O^yOK)Zu(2Ickl2~aQ2CcXt7ycfSZ0Qp^9KlsD+
z#=Nc-v=#lf4YV!%ZXf8c=)Ys|BTN4Z=)Z(~13-TOJtoUD>4VkEo^e-I|EI0zokyyE
z`-Uq&91HvPMci()6Z<#xr*-aPPE>lzfnE<0v~GgJHwJTDz#C{o6&^iK>8UL3i27Ll
zg8tz1lPJV(K8HDq@$?(&`7!7dP^-U&%5jCYJ?g=Ja6;2x^tiVA_k?Ue<_*~866E@6
zzpe{<1Na^3--6a)+|Wot$2Gl-{%0&u4x!w%<3o)LYn+791lgW2;&o;q^8JQ*+XMN;
zk3?BVhSSB<c=sIi@^RiLyJ$0TLC8gqRd)Dssfw#%OYqj(NW_y=)!&OxAWj0`lKHdH
z7wfryve1eeOxt^#4N&p6*g!=;)Y#B?_8`Tdj5y8l_=cuW<vdc326n^#WMnxS3tDev
z7Mg%tXDv})sqcx}fR06dxF4;!cQ!)Vy9VMa+r5vZVN_thihC-q6_H=u=z{#s(GK1>
z?nOI#058B=<4w@%z;A;-&;RQ=X*JlN<I!!zIi@E3oaqVp4O8nJmDiht+3rYZzp2_t
z`B6trizEMNq>ln-x?a;MpseSxrW;;U`A%qB1vD1&Euoj&I{=vXz3+lDeoa#zU++Nf
z04S%2!Y+*O0<+v?V6NAXTn_lZV;<o8ZbCa6zKS>uIWETlX1R*89f((z6b^-dGk>D=
zd$kXbh5aJo7xO?@UsC0TmQdvv16@24=L6aw1`p!=?Y*ACiXPUuvrg};aU)HAn$91n
z<hN-$UDFMkUe|J@^=8nxwN9_1>2Xa@YPn=x?ys63s`)iEZKdf-O)qP?=^9ti<yii|
zLE}HP-WVxgi8jn8S`PcX`218*6^#E4u)8|%#61UXPd=yE1iocY_CxMR_OspK{}=z_
zeC%IL)B0B<tULBYANyH#_znBfmt1a3McJSDe1-jvspW^3U#0a!r+;xhLokldfad=h
zzuExW0Q=!Pu!p)I5k?iD|7Tz>_X#MM%arTm9;N*G4)SsNOq)Y4cI5N>x!v$Hp7)vZ
zxf4@9hhob6W~RKaX3FaYrhE>?^fu<M-6)Uu^&AhB{h!yr0dgsCaX+>9PVdDujMgEJ
z1dsB|@i|S%DC~bLGxhWN{3SQ|oX(WbDRPerqXW0`Wh=Drx54b+7B;D4RlH$}-%7ie
zOHgUh?7$Tur}jq}@3?RB7Vfj~`gU+l%<EXcL{tx>9M|yGF39bId`-|!Q}K2!%9)Gv
z;6|XToN_edOPqHho%dmFLHEw$`U<K&1Lql_HBz{Kh;#7U-k_g-fb)4!57yCxKw}U$
zhJjvb$o`Z?p36r8hv6CTIHrH9c=A?GA>+!92OO8nQQ;dzzES0<ou)lCeSBT<_iI{C
z^WB=(*7Sr<kCZr!n&A9|<JPVPY(G^#<79jtrs1o8`}2Bn670_X&y@S0DW3;1<?|q>
ze2&SK{e~&~5mWXnrtC*d*>9Ng`6W|6r)0|KpG^52f+?S8Fy(U!rhJ|;3HC|1hLoMO
zz4-plRPa^3G0%@sa_YX1za8v9-Jys5tGmO$$~@zKYVYN{&rI3hm=^EG^!fb#G@tu3
zE9N7<Uv(UQI32hT`jyu`YY=CcX2yDl(|?x!Y07=}JoI0_N9r7`_B!RaOCxZehx|hB
z!~Es3-PfYLoIfji@1CL$aGt>Yvako!9hzTQ<CPj0Z>Q2TW4+FD0b0*_oqkJFlSZHC
zbU|5K5q&zU2!#ZybAnEin#5mW+JSMTXeGIy+6%1Oh4~(SHE&decU@m@PlWfU{!Fvb
z5xEYD@O~`gd4#v1jPK>B1?+kdcIXINZ)Ak`JmLc9+av81q31j7cLzSNXDIrK{j?3n
z={KnFc#YM$afDasa^C32a^>jZD8x<FtJd2}{{l&|??S)v{_p_e<Xb~4(9`+YC+EU>
zBmDOo#(53U+n_VBPP+$M6zjVOpd5D}que0G<7c40n@q7UY>fP)m0YILR(cq{j`n8*
zR&o{S(G`}D@E(x%iSV}8Gz{^O+y8^6XEZIX>)mUyYWHrvp3YHU%|kP>&fxhdAcCp#
zn_gH?<_i~;Yjk^W*UzzT11*ek#5Av_IW?_QNbyf%KbC8BIpQzm<U>0+ju%3`mmX5*
zv%N6C76rdL%I%JGp|9Rk=UlV0GcHG^^C<dDK1JUt$W-ljN+KW6!{wmwbFI&NSe-*0
z9)tN3{QEIPbwF$7!rTVh74e1JwME8h#G?1Pe4GQIy-h(UqaJ)-Q2^uhHRxyBX|%de
z-V~JMN>eTu^L%ws6$jM%zB<zRzIrcEKCkWteI+q&Szqo(%ASe!*dA&f+84Am#syOY
z_Gij=XgXTidm!wZZy%SV;v7?slT0~IGUfhY%JpJ;y{4iZ4;gd(W6E)nDSt_c>GaMD
zpX#D0KgYoQ>O&RfILr9mor*qyA2YtM%gu}PI&MGr=MdDJ<2~Dt=NYDIKUYDVFM{pS
z=nCTF0{D$<P`uDVv7mj5<J=asZ(fD(hGTsSyd3=ZKu;IL{a?^6ITZhBQKFf^x$~>^
zG2qVy?w3*J4@JI3z~jOc9uIle(<VffV?aNb*B*Mg{PZs=ngV~~a%M<=IGvx!;~|_5
z!7hAX_DT+nY26?E+zQ{9Al%;=8B3D#{KR;N=5zlrKCChK6Jx&b!E_MxGhLz6d7fj;
z>kg)mu|B{9(Nqcg`P@0`0N$w_?WWi7!f%GVsoEz*>tNq^vf#^{pl{|<a{o^LdA(n}
zK<^po+i&ZCUjEA$$g6t%d%4?af44O4W4+rkj=P|J+|E<L&$si%`u%cl8jX32$2HgI
z9P0l~DA85uaoWYIzdxVfjxUnGsoTr)voYTx4pI#Ik^9A2zjObx9Dlt8T}3?O{u@^i
z^LU#4R)72J>84fihd<H2N3y>KjYWRG4<3U2O!tQ=`f6b%=d=Tt!{t2!Z4SR@JC(<{
zWPMzIT03$1&-aIAr|0b{#{BpG8nIc~&$6dw$85S?s@~6+^LPFBS9?C6??rZ;it)qk
z_rO1xT6R=^to(-cvb|npmlye`zaD=rU$xI~C)JN`-1Fu0OJ&!z<v82R<E54PyuW>x
z#$W!cywfhv>&J%`FyH+SKX3SsI@eKl`D?pgWC!-MB;AjFwSV3&pw<^^d?~x89WO!f
zBV`Z2oh-lDgmr*rr?l<<|DDfiH>+Q*emsPB^LTRl(?9jA)xIq{UqRT5*Pm?fwE6y8
zu0IaEXuP~g-s+bZ$vgeWs@IF;UZj`hxZhtCKmV&d_viC|W%-2_e{fF{_F_9Z{V8qy
z{5N|3DSwu~)WctXB+Ab>2J1%j7srnmwcDCk{qY<7b>w4t%U&6gpPx@~)}s*0b<S6R
zXTP-mQQ~jvo7OH>|D{}`e<|1QU&{6TmvRIDrJQA#9wVQRi)roRlxu|bFwfh2v#R_3
zPCtK9KA!)2>GcSg^L(8A`*E5!-z~)N5onhi`vG1*{MT|H%6;qq&d1}1>-oIAo62H6
zZ~5K7Y3F~f-~W=g>ZRnY`u%(R{a5|mPdu)8oV}>MY3sxCLAm~${Ai5-|Dr$oALK26
zvHa`#`u(?Zrv5`ata2><TwXl<^1s?Q8fE;`Je9Vb>f8Uiez`h2Og=w73FjLpE@5AZ
z`#<JYJpT+kWx&3g^*7S{=7u;=;rrY_y{7JqeBKmam>yG!4q|`z0XBhavAvoFdJ6kM
zrcbdiWQx}s=_AlenehAxXy=?n+sF9Nzjz<l9M1>w{*5W`-<b0LjVbTnnDYLODeu>q
z@_vme@8_8EzO6ap#Eatg+y62?v0Qq+?y|;7)ua6U`#<+T=bM(Q_JwDno$<v@$#OsB
zrYz9Q<G(KEAKoV@zg6>2+VzwbFP(ZV|8VAiQ9i34R{hz}-X1Ba63Vs4*FUWjoPNXQ
z^DhCc!#dZpOHa8Uw(9>^d#BBB<+Jqs*ZfM}s>k1#tJ-PFxydSL4BE}_2Pk_he?0;}
zEbPX8TkXGXv*n;6z|FG#wLPpiGyH1iVUA~RYOl_3>2%iT=dT;^Ywp)JSWo!-SKWV@
z?s$HYpQrnF49=gID|!R_55_0pAKxMlOkBp#MTODH_E^WZSM_}ungc(@i8vbSrph>9
zTajC>&prWVoH>slb3Dlo%>3>SKI3U=@Ky)zh<5JO`SRrT>vQsJ{|&#Fga3HGzkHvA
z&*d+5;M2GL<sEtf-`S35e@Rz%DVZ+^)$`!~P(C~ttj~pK_u;q|;jN_U$C@6JGz-na
zS62A>wN081T*mg!LQ$H&g6DPlz7fZ@v+$E0E<BH*{iHJ<4xJQ$`$z@c@_gzx`s<_a
zI6nrhi1R64S4<qo&k0!lh+pgI#`@j#3eNwg6;gW7cgR6~GUMwz9Rzi*tKw`fO{+?3
z&Cg2y0qQY36V9bUb0XhklxM9k(#AV${C)mZ#rrEK__=pCy>W-1J9ATwY4W*ZT8-xf
zd7Ux~{c;ZVw&F#f<NkAt7tPz2eoOD`uduw8-x_Cs9Y<C<z4W<*ResufTJ?CozJf~W
zbAk#OU;cSdP)2#~0)It4E`+;jJ;u#<plPwyFQ4vEa_2w;5Ck@Tfw=9U7Ju4B#LW@@
zepKU9^+&+i98}eST|qwr4FL_u_$&yDUnHfHV{twO{cj@wA4PC>5B<N6P=0X$`{>F@
z=X&ya!W_l7-^b(mu*O(Chj$v!!-2+Q{$Y9m@->ie;d!F3i@K>h#yQ^)<v8(WM^&%7
zDBp*9xFP6j%)?9@u)l&HhyO6%hB(LdCeSyaFIPdFwWI%D$NBXQ_{WgFs@|i43H7fH
z%63?Y^4O2L{sWP32I@7fkej;ZQP|RB(N1za;a7n$kF(z0u=52!wcE@0zlNfHd@jzE
z&wYoEEu!x`xrx^WXHovgMHT%N=jEeNKFi^`aN3CZpU2&FDW~k<Mf;LHL`k5ZATBdq
zg>}dj&@r&XT;#X>p||X3mG5JucR~6Zq(7*s^bSG1-j8v@@_hf>dS1GAEq;E((xdWk
zM*a@4>ldICFixkT-V0D)evgmi$wBb5p<c(vD!jT{4!R06&#9*H_rOh1PYeGF9E|v5
z;ehHY9$EN1=;L^FVJznNST*mA#C_dzh(+E?3SUL}5!KyvQBvG@!TkcZTL($;TnqYh
z80M|!=*PrPsvf;jPET3xzt^ku{B_)OoZxkYHLiI+y$Ac|fn7_Cu;K-t=aB27=kq^+
z+_Y5ZdApnD{)qc)XzzXW>pf7O*Z*lfW0lY4y2knI@o&lxg8bi={{-c`#{KW|)8^-T
z#Eo*(k>6B1+RAqSdp%ZuWe2N0f)+zhHq>Vw;v+v_@cjDn`F`a4|CWEW#{EDZKP@y>
z`juZK!Y`&kAIppU>>tnfPtJd^&ur9ZG1i^W_mA?guyHx4H~hW`>g)8|3ed;pb3XQ`
zwB_--*qVQooSR}7v!A=^jP^5YotgPL|1#zC1bE)zFZ%<hzFH{X@)yfrpZAmJ^Vj=_
z_OD0#YyA)ZXz`c)f*NC<dlT)S3A^{u_PaVdoNgj6ckC2SP4xWfocB98^rZC*r~P}P
zK75|uA9Nh%%@Lqg3*r77sIwpbmw#}20o0S}i}R^|<aG9f|693IIV?Z*>$?tp{Qh+h
zw1d~#&hq*BN6W9R`0hj8=Jgo+3)kbH@^iYCe<|YVJFshlrkp<oc4GU^1b?QamY-Vj
z^ZEMxFMV_WLEj3;xLfqM{iE9TG18x}@88!m0D3uYmXhlxyfYJ~>{zC-+Mo8peW|o|
z{a@`^^AGLdd1d1`HSYIf-1B@k4RPPuZ~sgG!GF-t@rmOF$ERbE+lKb=`tZN#{}%b0
zV%^2@p@F1-Z$C9p{x|(+{~znW1pO*r;+YW3Z_vMN*Z*RNAO1nl9oUuQS$)KdW8;d@
z#C>?49CXJGyx$9&fcc|iCu`kSgt}eEeNONzX*oWxxrK4FQOdcga6h&F?TK~svvL0O
zAlFCyoJf1`K=cFC2a^^40P7jXQ5GNT8OE(~uF7<tOUc)4$GC|6Zb5a-7cCcYz1;fy
z1<DW8MdhHySf@=J0g826X5bGnpYwTDH>}Tc0dE7pc+~UuE8k6xx0@Z6y|Z;w`l>;m
z{knD<dG;rc8;wS_LT}*ty<F4I6Tw%hV;bDUf$ucKTiXsCk0_Yzz??qafmz=?2Mz&V
zt#Dv(+&%0}27T&C=ij8+o(Au8;1HyLt@B@z`As?}@zh+iQ2+T+Cf#tz<DLp-fOp`1
zbh?h!{$ixBlGsNjs$iFEdZ;732I~^aho^m3Mg`I;jc12)?bA`=a892}kMH5MDJmTU
z{G|r)H=m90ObP-I-i}kOaCD9QOAQb^xP8GE&iKP#qOPVNH^6MkSbdwt7Z`G1$-gDb
z^92?I4nh9G3hVyKLZciwgtCy&frBY4O?F^B|3xzt4y+3=^%&V`zQ&D#gOR>m<701d
zdUjf?@oC^-{Nl}Kjk9gz^bp#maX2u(%1wtfo-`V(PFD{7T^nEEBH+^aMcA`CeZnK6
zEm1k?dySU@?}*ApcQoEMmh*?wuNt2OE{$K)aTR70`T}b_=KQ%SgT|eJcSPl(yb>n_
z{?ZX|t3>6c!Ww7ngtr8NOKJJso!P$msG7zv1BU=N*0_aC&rj_%?kUsrQ!kB^X5gDW
zQ3Ys-#*2YZ1IK9Wdmm3nM-`-r8qWv*0r*3S6LtTE(Lx8tcYR5H+j4r~5X{<lq6*RH
zGJSerBJjhgaN40THJD3eMi-_d4xBFfB`S$o5zl|h^a9O_Q56Rci7rl495{Eg612d9
zcbFw<wFBpmE`_Jw{Q3$<m!^XbTs*oAop#{T(Pinn16PQ~S9BcM)vP=P6k&Z+>C=z!
zbaQkC$}X|0Z|&%c6z;&m&0e9J5~s-a)Q_%2J$3$L&+uEr(bZ_7Lq4QgZQA0H?+{&w
z&T09o9k5D`u1|TfyXEp353JOXDoL!$e=E8XHE`hm(T%CC0|zy0LVX>0NOV&gF0rcL
zsOV<2+>ss|-GbIT(tXh_X}=@=z35hS$&vm+bOhaXqz|mrhJJV8xzTOOD8}_y^<5m@
zo{Bl}s_0JSd0C~0;%U!S(VZ!a1FwtjLU|o{Q}pZ9*nxLOcc&2!d?@-2@;dNW(Qncm
z2R;|wkCr>|wdjF#MB=GbU@6fr(L<?Z300rssLvnKBdC%Ci<nVV$AN=l#!^!U4vvYY
zHX1h=LzFWnhPpU#!I(Je?T{}SlSEMtTq$NeO>^KnF%xK~12>I%j|!Gl^-G8PwT+od
zB^|hPObX4>cxqSN-Hn+}%N_Xbm>G1$frrI>NDnn$1o`NgIe67d>1Ui6Gmp|a@T8ap
zl*@rXh*?bCHO>h6`7z6=uLCcQSx&<pcuma5G+*Pqkl!4$nwC58&X_f{-hmIqtfQR{
zd?IEe1(xReS@Y#42KY-2Shx+R$}xUC6Zl%pR+WtOsoC+SnQJF?k@-`D{>1#znRZb(
zjpxTI+*4vJ-tD3R5?>>wf0wO~aTnKKI-t{4dHd~j#sghPs6-iNT_aWgS5#ACOaDn~
zAh9oSEan@R>m;?%c&MCjPEtFCW&ABoU(;v@z8~`qjdx(YGfq>|;F%5_jP!*LEa)_?
zaNuCz^$tAI^&M?<;1t(cN|iW;ipF5K7;~OZtMtI7@YBw8i7sip5jX_+rpCi$`epiA
zW3NoVOpg`T^UD<qF3a|^)^k@F;4d{mt$(giE}3rG?-~_U@_|`4!hW&WsF=pZfztt(
z(>Qo0QI^>2R7>NRfpY>k*Z9~6`1(ie4eF%v4dBASeKf9J9j9usH)*)Wt$@n`$1AMs
ze~TtKaJtxA^q~U>(`{Phz*S=J&;f}P=#7QEp8Ao3%W-=XC<W`W+Oa><XA-B-g53CO
zLF_%+tMTdu_{tD|&}n9QmQSI_uj97~WA9VV3XHAs@f)?5*cYhQH@{Igjn(?*H|nFY
zTHidR!5XXe&F?f?W3|3{L|%>6`sNY6qp@1wJf^7{tM$zjnx%0yY2QC+nZ~W8egB|O
zHEu8+Zv)2uNxL=f20R@2xW*B3{qmH~YusP1U!Kx!jaNW^Z0s}oUE{sLUSM}cwudjU
zt6blZ$fj|mT;Grg(^##qgea}CT3-oKL*wp;xPJ`MMB{$I0c3~{8t0V$VT#@w7nc5E
zilG`8AHeHRml&&YHQ-=!i3E++`Yb?9(O9j|0>nIx)%wgWR%)!)XKt}sW3~SCh*XW$
z`p+Z2R#^LIpg8Bi!T7zy+Y+aW%a{j}V}pe26=^Rj4?jzZO($AOoInwfONmV{W=otZ
zuETHV#AXodH7*N3Tos#99MSUcLVi_jW|6ZJ>q`|i5hpjtW)Y<|W<NO`n^iQ>n8*G3
z*leP!#y=zO{}h{D^wW41>h~-*MDQsR>pP0Lk=dI=%#=8R3ZT5?*j(bO#ziC!6&WhC
zd>lPRdC9T4MF)u!16_#sdAxZ<cZq$0<FOa)OnF2fjb{Ld01wf4A5N<}Q(iGz<4eFH
zz;P0%29@7U6z0t*-jR4;P#xgaW<K%0mhX~+x0=2A#Vn171HTOXnZ^;*@U3)j0r7>#
zeSlv9KBMs-;OgFj;)=#+fa?H1)p#)0*B&EGxT|n`eSwL<Sf7i`8h^hCrxxBqBA3SZ
zfLj80kQntJMbyC?F1l;HJsEF3P`K!$aolj8pTb3y#_u72S2J8}(|E}OE<ap^S5@Wj
z7)SIv^i|aOFmNy6ni40_&ds=c;VmrsN^HgRm&5{z<H!enRbpQfU8}LaIGV8&UlH^c
z5t%Tq7<-o}%!ua){>oWrDkAdA^i&a=gWFp~6s*DN33PBa?rs53kvLVnA?;OE)Tk-*
z3;r6~Y<E%N#oZ^CUz`^wtiU@Zj-vsQh$r5nqGD|&&tFBG;4LNs>nQm;bBNvtj*>W4
z@Ym92dy9)!byfN=NM8)RP2yC+Ut3${eOYXKRiz(A`e(o$>oHCh{1vuc-V(x9U!_MN
z{V?!0iBkoCo$Xt1Nzu1~O23cxTmlYksBm4ZcW@RdLN%_u1;0DvEhUO+ywRo7w@K`y
z@jLnaq?C9naUzZX1W)ZlKBy7vQ}}mpX_4K5DXy&eLE;2Df%QNLe&;lEW0sGjC-5J<
z!!62c+^QYxt0<;OoJid<e(<iLc&O7KqTd;3Xrl7pg#CizUJ(r?zAQ>db9yDwPU5|y
zE^r9&K#5aDF_~UjBubn>jb!^Pi&+}C1I`*(S!~pJ|7g5r8dp`UZp!5)(0IJ1+L@}0
zof=Qp_@2aZbQSjK;H@rlH)DBUpgIq!E(&R^&O@q;mo--7p}Huqu#UggMKuS;FSLnz
z4m{CSL$q+<6jv?L!GR0K)e)m5R_m`)aScSg16Ph~BxX8r{kW#$v?D*>ofelA*7a{G
zZfLCP-%{MuSk=F!_&rU2YA)L^+Y=0ZK@L38^_s}$z$vZ>k<Wn(#kCQ&Bv$p0i0ddC
zJ8+k{&Z36{_l<j9ERZ-gDD!u?s}k2!oYpv0;x|Ob7F?gypnc!M>*9Ke5RK0QCj(d1
zc*+61Efv>W4Apox@HF7L8s|KS@f!E0_(<b2zzczQX}sh(-lC6tOB~dABk(7{UujHV
z;_c74KH`kVA;4RKzt>o;Kl+N>8msk3U*Tz~>VN4O?t;hl6PY!B0(=0tgvP<M@H9f)
z+oFQT6@b41eof=kGx4-ZTz}C)<A=akfQM<U)=vY(IE~f%X@Hoav05Jw6!SG!>*Il9
ztHx^mHc0HzSgqd%iHjPm_1j=^U1PO=8!XJ%RQuHWY=}s&v09%E5k)l~jr!h<ixg!v
zo&tOqxS7T|5x;+p8!FmpToL#&@L-M4K1O_qA0{Sfy!#X$!~lL@<5R#HfR|`IG$&3M
z<A;k?8pi;K0)MJ;m-$2m<41_y8ov$v67X@2+kS}sK>SE?UgQ42Wr6Ql`Q>~+N@Q)N
z+OOvOQKF#6YQ7&W8fvWO`_ZDU#%jJFBYJAA=KC>Xu*PdqUZwc4B1+=}z}0~#X#69}
zuNyy3%+T14^+F@yk2F^6g(&fv#%jF~B~mq3^LMo1uR`(oQuBATxTLXKPsE5D8msk0
zjNq>)vwS)kKVwC8jb8!|p;*yGVyxFNe|09W=qRzZ-}8!ZC7v$#mo4MH;*tZm1%9gW
zTX>qRWqh2-)tbwjPK?_ESCM!sHTV(hkN9{|67vq?HSt%j`@|=T>JB_O-X|JMyvJ;a
z^S#0G<HZ@B9x)l~h4=~LssqQwPZIYXI5~c@NZ(%RXZb1dQ$#L__m~TzZ%X|8V!6a|
zR3{a`MHxR$Z0pGKanv7pSNv?zzmv+(U$^`|evugKz_;U<h$IJo5Wh@(;K0w~KNbrl
zj-!{M&y%o9?3H+rIT_{Oj$bXdc4hqu^fTgNT-;jmt;WkwVttsfR$P@hg&5~dST90f
zSMtNYgTE(yDoRQ03smvrGf_=r6+b=`jWt&B<8#qYV--I>7riuA<A0MFqOltPn?#Jp
zD!yzM6E#-xWwZEDV-;Vvh@~2<__9Ut*DZNI=J8)LVXN4t@h#v=!22{-@o}5@N@Eot
zw~0#{tN6TK{HU>t&)dZljaB^lLIieG<*E4dg~*|?ir+g#evMW9-XV%=tm5}hQBh+R
zzjumPHCFL&mv~KM75{dL*ELr0Y`1t*V-?SKi$NNz`Dc$9sj-@W_J~-G)%>_uOw(A+
zk9);ojo0i)yhzw5PHKD<xH<4Sjn({@Dz0j*=D$>NPh&N|?iWS7EBmPVb-$>tv6^2G
zh<X~U`SpNku5l6gTgQZhqOHbdfk(t05<N6l@%pfsps|YAhs8{dRXjT)R%oo^*%7f>
zV->HCimx?R@#?6^-9yz^#rtETsKzSZ9}}-=tm6Hb;#G}Ry#G?P(pc^9j*F2Rt9XB0
zBx<bU{R#20#wy;Q5Suhs@%$^XPh%C&zY-@kR`bD0aY<t}ADk3-HCFNdYw<*574N?m
z{B;%{A1a=IBXVo3;`uisTw@ilzZGRQR`L2<QA=X$Jn58Zr_%L$_LS%=aVn|vgHs|=
z;sm-_lK0!EMGfqeINmYtmGGUIqVa2G(7y?1#Z`?xrC1)n+ulpbpMZRP!Ua*ofhQ+i
z5;Ha4Tbkv+7gseNR1QxcCR`O&daL~DfR`m)6J7^imvBQI(Kr?5ZBMu<a=xkLTjBiT
zV8SiYSmU}9|0tGd+^zzbcTe2Y_zU1=2|tVaZ>jtnpf4r%zL=u%7PRkF!mr|v#@T?c
zCOi~%`Y8GBsNdCuM`D`BG0^vO!V~ek#(d7368lVK>Z{}<(B70-G74+lvn*p_RMGee
z^rgfaMxw^sVZUbyrg2*1%M}?17$y5DeLE%g7+#HENB&HSLB>gq^U3z4H?CUv6>fh9
zqwL!%|9DwmMk7jN1L-NTnT!J(H$!@IY-S^Se_elB-(aJM#t%@xl-MlBDvh6_ea{lI
z8Z<!3hok+^5<-kN665|i^3RFQWekuwfqsxU)c9PdtMjMa#&Mlq8vVB_Hm{L)pwd?f
z_DzY+Z}gEkmDG7u0mCbC0#!%(n_~+ahjqF-p9(Xs>h#ITe>k>~QDzY9OO@x;=VQZ-
z#u6t`Ce-JAY+)lsr>pa;myA_9{ijlVo>jznsMFPXR#78(FzZX8k4vJxvBiuo5~q?n
z-zshl*Xf60kIdc@#tw<qdMS5eDdVZaflUzC!V*gx?jfwt7uX%RC~#(tKU$9a(TQb@
zoEmQct^{0K;y8M(2=B+s8f`QVjO6`wStCK?r=S5;&X}d~Xq-pXPAqTi)Hs906^#Z%
zx%{b=@(}CI#7ag-iQ|aRmGC_c<GRLkOW<qX^{N}cYWyy6>%<yHL7X#jyx{bXiM5QW
z8aIQ!Zi#h_wHnuz_*LU~iBn0P|I{~r8^QTgNuB@HH(VnXR_7`8jjS50^Pl=gL5<b<
zPkn>W$+`R~<MH*P#QMgZaSAVxxPeh8O5q_}@w8uJL!*bpsie+(8W{s(ReBB7AMbb>
z{5Cx6+xZUbYh;v+S2%eu<Hp8viBn0P=QJ@s(^#DkH8J*Ttj?R77+>rBYnQS9CPs|}
zrSDc%g;#3aX*ZvTH8IX<Job=E4@^|@H4pRoWD_Gpvci>t%jIfnBx;-mcxYk^W0S^3
zu?`xO*vcpcJ8*l_0f%`bj5-?khdjQ=ZcNg+HE>K~gz+BQ&GNP9ae5nLxyJn_;#4WI
zjnN8})33OAKhV~wF-hV4MHP;JSK+B>&pU~2jhPy!N`Gx<1iq)zo55dW65AQWHExc2
zbfykQ#i=TNg7m)*Mk|ebY-j)OU<}c?a!0OTN8_HxuU$~so1*g5KAayUb~H9=JPQ5y
zL1HJP3ijb#{{*a8<|TGDnga*I|C>bPydbfg(N(9vg|8xoP!A(B#tF-J0bZNf(|B3q
zJE-sHxqBP+ojCDLW245=XF0u(@vFvjf!|5&Ys{Rh^!*Gw;@h6aE{(TJ|LA92)%a#L
zmF_`5aQWXvDO^_L(|CTUcVa)Iz5{PheB1a`<6mX^0ON=QZ%-U()SR#M1-;DvJlJUK
zz}pjt7zZRyC3QYG)OcwD%U=`f`J$mlF@=NFdD>8;na1ioYN*j!;#4xra{L}@43jv4
zgv7&)2@>z6CFMAMxUpELFOqnKu|cPgMEu>CIMO(-)A_#izQoZ+?S+KDRO(fl^N%$;
zN}ND^A2=m;oUvG^bN-arC}W$%dug?tPoj;}T7IR(F-E~ftS?TcpGb@~qBO3G`kqUS
zH#R!(&BP=l^J13YOFpT8yirWz1WJ_n9ixiGsWhM>z5<&#!Dyq?-<Eii@rF(xD(&%}
zF;1rsk$AE(N#Z!lg8EU?6yu!6RWP4WQi>70gv*a3#_5x$8^a||kT^7HmNCPD!;|J1
zOSJqV$d^i*XPlNel?uaNza=g-e%11YBwlQ^{YdF&d^7PQV~xa0zEsk3<A}rw#ObAy
zRvJZ@vV1D>eXf+)PmDSeC(sIs*BGN6xJuGm<FJ<REA7ADxT@3Nl6ZrW7vq5ESH@M6
zHX4H^M!YW1_5IA4q|>uVyvewz(<?~(Z!vz?>E$HeYE)d#`r?Rjm85M(vc#!05BRsl
zFN{SJC(s;;cN&{?`dzedS;B7Pq)xvh@m}MqBfWl7s`1Q`UO(x8QE&y9mr9(!b<!cD
zh68s^I%0H?IDuGS*Q8^{7)ScsNym)^Iz0~l@OIKy#(hWn+=Q=<Odqp;m49x+w?+Yp
zRe2+lP8-!6=_8WP7+oCc2}$RS(T?<lqzlGjNBZQXOU5-v`sAc5hG(U}J@b>U8igfR
z<<C#LW{h&AuS~jOyzfX~nRLt8;7H$^bjLX6NZ*=t*SO$F-=B2Pc;rampY)57dzHWZ
zQ%S!Xl_XA(_Pw0+o6*LRekbXX;d9`}Nl%PL4tbaFPh*E8J+qI@UmQ4}&oF~m`|X?0
z=Q4Xqtn6Fd=Qi;O4E|L6i~9o2`Hu9ezI5hR2d?MKV1Db6ujk8T{_aQ*O$s)%ed2Fl
zgfFXET4JR?!k68AO=I?_?!FvmUx$2uUoLZ-1CQ|KHcvZntS_&5Ut*;%)|cN5S>rG7
zU0*@7oCDAB6*8MktmJ3-3Y&u*>AxiwF(*0Fe@iT8el2mF^yiz2FPml7`t^V0D`hs9
zSn2!7SH_(0NMGkGXKrz%uk%$f&pOg~`Cc&}I`Cm%Wi#tK*01y(_Ek0cD~;B83s0(U
zzUIK+`D&VPJLJFf)ix(O(y#mKnwxa`%(8fk&R5U8=fL-U4a{ci{rZjM#%3P}&XC;H
zjF(u|Cqr^`bBRvBgZTAZVoP(UPQN8_D>K^$rH`L0NQrH2meTl2ImT_wMj97_d`fIv
zv!}*ymSKJE%qWQysF%bY%#9MqNqjT0lNr2`^(RpBA>1!Z?rM&eI4-aja9DCTbGF33
zK()W<W-iuP?Jv5STQu$qy<N@j=5CEg0|!ud^Nhyo{&^4cipJ{xc@NY5DVOgH{2c4Q
zFmF#Ylg6pQtIeKfQH@LW<MrMfW*LpE1BU=tQCOZ=6ix1B&X+iqQWo&~r?<IEW3@i%
zZEle`j^dB2^j~#)J2&r-dYkD#<NBmh48EGcxRA!9F7o~M-eyOM_fqe6e82NebFWT6
zJ%H2SGH*DrFMl7i)90*jk1<QGPy3lYC61%Jx8?fGjFNaSEyVNMSl^kKbo!`=Y|nmX
zk4>C^FSU{N?Pm_wxMv5IUp*2RM^Tt(ODFd;C&~0w>OLO(De7+?*7!W;Ieg>TyrOYn
zb%h^k{64;l5sdVVo4GudKbQuZWhIWIyjYj|@((ikEo3V_fQFdE9C&n&NHfuahvgV*
zPIKVs9K+0y9Jo)8;pPDcu9Q5&6kGiIdt@1D7Ixs;$)n8I9XNzWn<E^!aq<{5$$?uW
zk2ODV;ML|hbF~BCX%=N}ci`*IqRpcYJRnDmdDVf>d16i87xMbxDaJ>sdS3Im#L8ar
zIpWNc+x+!!og8njl32-Cte0RG-0n~Bl$>aecHkb#N#;QZ?vv~@?`ixF_7lOBY!?1P
z<qz%1{x;sMDsh|~zj*J;?5Oeackp&<j(5zN4m>D%f_YWr@WXiiDtVIGcn9l?BbE=K
z_sm2G9-BPbJgzazdy}V{g?F;NFL2=z{GXg+cGY;B#M8{F5~l{L=Sw~?=V`2-C;7l!
zsqvBp{5;7A<|d6*eE7iJC-J^O6(2q@_gm?s`2PHK^J|UO^D5KL%Mz>SN#0GKZvLk6
z4A5!GGt9!f2!C;bUtGdlD!>&ro~H4ydt^E_{0?7RL3-D{3a`WctkuaMn(-2=^O%jv
zv&>}*2PVFZ`@hMv%{3ZN2HpkymBuCZ;r?#&9P^CC*8XUY`G>@D^g|824TSWzkYoGL
z9*X<D$#cz15?kjRbIto2tMiMwraP78Q%Rj4%r!$bR_6zE&5|0c^NP7<ZH?7=$Xv6v
z#_GIeuKA|M>U?6ZIa*_N-ZIx*AaNXR$GQOTc9~zp@43DifG?uHg^w!ywhO<skvz}5
zq;YdRUvn&ZzPa$2O5cb0b1Zp*xkcmeu#P&GywLnw<3z-lW66umV8l7rzZL#=EP1h6
z>??(vP2lpEn15(o73f&<5_8|zoIaHbqFxmXe`KE2_%8hRgt^STAu;qJJ|9b7ZVvcH
z=^IuJ_s^17m^U=;kNVzEUTOZW@m;j%e)1|a@LMIHJQnw(lUJLKCB7`a$2jUtYs>)(
z2kl3{b~V?SQ#3A$xW;&+#Mb%x8gr+_3AC&<?@!m52Tm#dj8kIQnb}S=P8Djuy520S
zvDzPRFk48xSE&8&Mzg=hYQOrKIbLJ6U;f<uNMp7C++^;SI8~_q=4SJf#%lk&)g+7q
zUJrzq<@#(hhiKfb6rK+ozuhc<M%7QPM|PO6X{^>GJIw(atM$k(^Bs-V{&lyxRAaTD
z++*%_;8IEZ%<m;mAQSfZE%AUEgmVzKhuSY5GK)!^KvgRu{*6CuCg^mvemZ6@)am?O
zX-e#IbEQsK>!B0oL5<b^@T7TNW3|8h#&n(I@_h6d?JqF?TQjo*ml%KAEb74f63?0y
z9k|N)^XAM8oPUqW`5TPCWCmVjjP(`PUj@ehV0M)_js~H91;$@D*GQZy)c*0NdDMaH
zC*3x0J8<iyAI+dktWU{zP5Q|!>cDR&{cJXH;JFF+&0Y>XBI$t{=fDX`56$@wJUQu+
zx!r;1Cp|IGI`GP*Kg~xDyfx{Wne#H?Pt|9C5*{yg;8RJatBnKSNeXa9I`HEpk8836
zXZ8iTRy%M$UwYSJ2QKc*=(_2^RehOVo-6+H>-n;{UUJ~jq-?Hw4jkbNarJcI{=S^9
zSO*^A3w6zP;8<TC*H#C9*O$-rodeJC6>vRt;NKF%T>NVkmOVc5g}W*^@H*d1t_TO-
z<tyqM?7)Y8#a-_@aClM)*GdQe&R5EH(1EY}%DApOu#sHO6>!yGeum@<u5gXjdB-cR
zIvT6<j7qL<5+~5t@W*|LRa{<)6KNW7mGRYFeSctmDRTeZc6?3Ojhp|6tNV_RqIeqs
zK9}s??%tLQKM6I`n}C3HkP@U<>0qc*Bw*+*^n^fqyIdMcNKZ%srKvQfg$U9W5a|d6
zkX{8rc%PYh4%RpS@cHs=yWQDoJ3Gt02=dEGp<RS|e^I;gw=O~_sMpf^!Fo2{wu{gM
zy^D>v?IL`RezTP3pPvaM&=r=^`uu0Yc;?k|>K>YZb`@eVzI8bvexY3j;V#{Nko+o>
z)*reG-ps*rE;=90pZ9hZzCrWny<LT0(ENFCH{n+_f8N_oxbIruNyqbc7oMZ}^WN^l
z|L)QK2h03<Y!9Idnm>>2A#`Bg@6PM{J%vsf=g)t83O&&L`EO5Q0-8VH?j=k^^XJ>W
zgt=(`e7Cni-@Bvx=g)V03sKCj@wI(~ZOlP9KC_STJF{!NZ6Bf1eY(DDylr109L>ku
z_7!Lp(y!>uq~&d2A&JFZ<1hOP>(IQt)K}PqUJB(UU493ex1ah7CFn7bUTFL%nzyI=
z3csUkL%s=f0SH&nygk)VxNUN9wf@3mG;M#uw@L)^fF5r!tB-6A`CPD>JSTLZP{-sK
z0fU7hkKQl;C}5~Co_W7Jt<NkD9VTdxX*`guSOL#3Lcb6?pp&x+i3%Mp_@NIlj}g*b
zhV;8NWUO!yO^>hbig7}<CwzMfYk!RwK0*KH5B1Z~@xm<hUAQ0GuJ}^8fF23^UmH3>
z==PMa?+Lb$iNZI`B^KWPoG5HW^Y-H;VKbVy7bgi@(Y(DlN!Z35MCQQu9tBJiLZ8v?
zms)uHaIz4G=Iz7DLK>R44<`$`X#WV--W7JDX?xRyOc4&4ED~Sgn92SXrV78CJh#F$
z;fl%2Dts;6HhD?~KjE>-<I7DKUYYDkW(eYQe*AR*y+URRhROXyW(gHd-rzo4sA=-u
zzH@|zCg1EkS7>3fUxj%>2a_*I^MxKJ4-Q!%3^e(RkcEPe$>T%(g-Iq42v{TpnVcKC
zSjaYcL+BD=ugTwsE)}kr+&W~L@YdwcA<Knof4~3zyF*t99ZfzM8X$}|`DAFIu*BqZ
zp(}+Hldpva3ENG+8@ft3Yx1+u)xzH<ldxc+!i)F&SHnVtRwh>n3l)Z%Tr(_8m}~ON
z3gJSC$x}iige;TCl1Sk=nzw(Wgx}G;{TnS@VRnrlj}aa)yT*^l2=YsMd`{LM(l{(u
z82>M|gVcre(k9F)Y(ekYK}ff-MBx<kDzYESn->AeLhc*7d=MEujNYFq!Ui-Qhu}d{
zg?;F7=s$sPu?i*VNSJM5Kv<e^4m|?)_Xy@%xQ#A1lCGa2xWDDw4}|u;g=7jEb0G0|
zgXiO6nZhizYEb72r_jwS!q+y!3WR2`7R|4I_adZSaIpYS&cLqnaDx!RjpKPX2u_nd
z$Oa+J<Oty#A>U-Duu<4(^8B#P!d_-RzI%DtcftviBf_=`*U=53e=;#_r|^_Hk@WnS
zkj$`Mf&^dVq~kfM3&VaCs-R2T)A(+oo5_V?djx;<_9pOrG;FWnFu5eGL?|-(WZ2I_
zDVmN~x*B#w_!GUn27D<p?4)py*;QZpRq$|Q=L`D}e-+v?2a?ufpuN-YH(?czlTOTM
zgi0_&3O(PyK|SbI*dKx)ddXQ>KKz2PjX4<mA1@34VtfSLH)+~s!2`-kT3(%n->--N
zDa>OIBwsd%{@3s_Ar#}hz4w=Jog1&;9pSmjcR#oz(D#I4do2FugL^^;Zqf$E-^P}E
zB-}uILH(<%{fY1t-FrB^uhRIL;12zs^m?>{=k4{w{}#%lJ;uUU!NOk%v(T-V!234g
z{|E=s-(Q09gW<1)7bXu6e<Q4h{nG8NhyR}t?j|}-o)s>L>(L#dJwMe$5-Z7k`E9WL
z>TsLri@p~F?;D5f;vV!rP;PArw~MdQ$G(7G(eVF?En!?R-Cx2onExQWyy(yDAa*Dx
zE`@uFd(h24gs&EbR}=?AKP$ceN5cO94zD7{GA9ymh?58}@g#auJ-8b^s*A8N{FUmN
z-_iG_s*7h?oS$zkqPl2@`_^^-KZvLye#BgAq4Rglu+$U>quut=^1GHe7fpZ9JWDMx
z6`lDL)ITh>#Xa20Tj+lp?Os>BicYUZ$4k@|-Qepqw7gy%Mc1z@dZRzS2=AL&>WO~L
zS1ngBKz>H*iLvO*ZQ!ds;PvS3u>a3VeX#_65YBfW(m;HPCXmmF<Rh_)PWN}!QWM^<
z8A=+8{kRo9n%3VNi_<awHj?%?Hx^f6yloMT2P7YhdFVsnp`?lUJ1!pxzt@*E6U#9E
zT}QgT=HhL%9rDjvVAY`ecMyF(9FN>wY>$qB`{0si3(=3cOySq3rMR3qSPq**-)Czn
z2BU>h@cwT^OEH?8-7j^(xy*^A8^k}3Xf5tCxm83Pv9+D<Kan)w4CA39+KJuJ_6hWU
zXfF;&x7z^qkBCpjNz4x7e;UqjL<jK#vy<FC46{Z?bQG_{dr@?I7r|2_I*BLXI#U<J
z`JEHtEslrTap~_Z056T`DyE_P<P#Da(Ni1>Gj-AB4fuCXL|-wJ*+GtthyRWET>Jp;
za~fX__e0x?LqvMtQ@@1$UGf|z!Yh@`UqCz~Vz@XS-ix5|Dd6G=AF%_JW7PA(KSYcb
zPeQ$&nskEpVZ;RS3VIIQ2gf2NiT&t(^RE5pE1szNUw;qjmgaXcu)VVpzT#8nK(Y>e
zCE{x_uQrXt_@EKge&Q~)HVft_iI^_FuEXP-X4Ci#QHJ}HF8>Ddfjbd1#d!2%=GkHy
zx*FVXk0a)aZ<t;A{d}=QeY$=kDTI9JMZ|ouH~J-9e@o;7aVfKtw1EBFA{UCMm>pyX
z*fVmGSg8SB-$7o_q+Trgp!Y*Nqgvz=ajD65BbSQTnVsYs#Op>b7ayBkEiyoSWpdNV
zK(YKseEY&fsGmdziFKHrBoOxBA##=20sU+@d>t-wwRoZ-UEWEQJuoX&WT?2Y5nrE<
z^BWZzF4k$x<DY>iMMj8EO`abaD_;4S$4lURtcpw!3!1#QGjfd>-;{6f8C<W-$hBgP
zX57_fC{H4@#CY@?DCg!!=87lK?I1odvQTXB319w4h&v;T#gXXmVSk$<zZGvWJISOp
z7_S?-S?ti9@1Oc;<PT!E7TlX)|JNda6hCUow^tFK&)tjMBlco;lJl^==aD7iR<xE*
zh+EV_v3x7Oe4RV+mEx$Q@A7jA($y?-QoP6FJU_1<byAdC)8)AzHuzPnX>z@&-^5Ru
zgJdV9(<zltiCvk?SbiT{?vxmSR$!LUd6qNc9&Y8UH}HO!<#*Ae4c&ej=HI`IzGy!v
zkGk5=il?|05svo{%Q>-5TeiHi2=dSJp6A5bXj?J7e^TzexRIOfzeUtV@im&4H<!f5
z?fCv^dE*^*NnDDi<xT&n%VMSWeEYn-xhnQY^YZ4Z=+DiL*E{N}D1XYgN6VY$RsIxb
zGrP*0YvLmGYbbA4k!#{MZp8<VKb+hU?H%a)SCu>9Rph4FkK1Cv_F~9wF%x~Bl~1?D
zd$@dSR^I$28XfuiJ>h&NkiW$K%=~<g1+T(**<Un%N8HbC;pNR8@fF5-d2>(vUnjmj
zUf$djTQZj^Jbl~~eV9uv%WlGW$!hn+FVTIsK>t+KebE<9^K0Lz$Knk14^Y3K8}&?_
zkFHzALIR>*ivH-7{ZQSAdMz$NABK2*)EjXnx^fA;pBUw44Mjg23*()mL~9Is60E;H
zO0g!o;;?>clx|&v?g8un8dc7khSuSDFGQ8MW;65jZ6Ot{Ma)60JS^%|(RzS6kSu}n
z?1w5<tk?Pa<R$EHSCy*PZk_4*Dkb$``;}q3^F8Q!5T8`Fnze>Ek57a1v8HNuYXG_v
z#7nEzu-53p<56&aYIxPM#-sPZ@9XAO$0~ou<JIAKeZA^gSD~*#`5WQYz<M3M0e)|>
z*GJZoUHS6AgO7VPvR*)2z`}=3to^#txI@mr2jy3lX4WCxWD~e<)n?YE=-XZv@>|tU
ztm{p_?A6>VbZ6@mCzN-)K5S`i#tiQZL%s51l~&fF=*!@HRoh!<^`OfKlDbeXx2)RP
zI<zPE2eAJ1s$H!Q&{d!vfBeIq*6O`zyp+)NwX9lCYa8Z7=9^JHt$mpJ^?DN3%j%2E
zOK|=>jp%L7HpTym>TBJD@s<$pGoqjMEprK}mk0T4wa=|Bdei;6uKz&mr_8SMaiF!Q
zIUYUG>c``xDIA{`J=7Z7hpwMUvLJu*iXLVS@5}w!TA1}OdW1F8<QCDRtfU`}L-`5y
z$<EQ^tm({&q!(<jXY>T?R*cWA4)vwziPly9`SxPhLwzxNlJ&^veEqnoFrF#e*GdL*
zU;Y~A2Z)|w?ZJ)n>&><LGCSn}NMC``bFH(OOUW_FUqZlV(Y@9|sTn=bI&cu(9*mC!
zXGbrv_8LN6N~&Lm_D1w#>r?a*tA*@|USdrf%H!0>qnBAL4dZ?Sz7)N}dfnuE(Sg<z
z!}<1)ZY1P&bdYt;2x`~)54L78yUu^Gb%QA`#{^sVbCVZvf27q4wVq`TWcO?Nm@sS2
zFX;9X+3&3y6K-w594xo20ke-c2)8z44wU<X%R{^!#<RYK@is9L)-D*|0B#f$Y3<{R
z-=gs->tKv;0XK??wvNL1r~@<}W1Wa`e{iFiSgRk#@36STIuGL_JV$O66K7q9asNwn
zeW!Ib#*@H}V&bjQ7~cr>{njxF)<lf|3ibwPV0_p#8c(#Y!}tuaH+Un)N5s;2l64!#
zXM(-Kdof=3D&2pw^$^Cr!Hr_pSWhuS{kJ20)gmUv+Q5gNUk7=Z1LHGeQmv!WwIRKX
zjY+p|V-6-q_rUuCF<I6!^f>T>m~5-pD7t<y`30V92F2uBW0_soJI|WN>?G^mEF>x>
z&zd}%F7G7OpuUt5Q)o>a!}gEq`x~o$9CadzJPheG<{N7*^rK&3_Vk#I)+XrnP+lI2
z*=+5{?AqV=)@|eY`UCF5S2JS1x1K_Wro;36m~B>%FL`_rxGZLeHFqNS4Di1(`>bsG
zX*eI%pnWC9mRg5S;_=H(Vg9AqL)NX#4l=trv`1rqwR%maaqiDz&slSs6G;c?9~u*T
z%}T!F@hn(=dhAVWOY|x5s@VJ1t<2Cq+6C!9_PO=!6u!J?IP@RIzO)YTrFN2^puE}^
z``YS*e)<jdzt+j<ip+1U3%N0Wcxw${cI6LmtryTde;`uZsdW3U{J>30LG%2;UD|K*
z@n})Hi{|-(BzaEb>r>y0R-{hMuKQn=dN8}Lk17piP9*KJVgATiRT}X%T_5Iot85`j
znkM<7KZE<VBvzNw(76yViM2~dOg<A^PI`?l2hTZf#nL(SVV*|Je=-zLbg6~rFV<QW
z-}yH9er)A;{vP}~_QU_Te)V^8y8c~K^PTDXg5$mE`i7&n<n70Q&qSynRB+Ulx}oDA
z!uX8J^`#eRI*!ZB@sZSEI$xg7FS&kMV`)4(7W$*=IzE;P(1)r*`P#avv<JNd%8Ti(
zKatL&FTaNNy`#D0HG^)?Np?Z`+uG4eTFM+u==JRAXf54B)9cmG(U$!lxc(wf5AENX
zmnR+GnU@zG-<hY6PXF=oXzzFC>EpBi_+E53smV-we6HttJ*3vm!Lr?-_Gk2vI-#q9
zn~)w-PxQ~w-_Sayr}R1cSFm?XFKHyY(HUqDI(kdv(XGLg9et#!+^jr-=X26*jBkW-
zAF~{NrA6pM=)YX#=qCj-^YUjkIF-lAOt>Fo9Q~!OXn$~mW1w^l9l8kG$F+t?SDE?y
z|7nh)(p{7D9mA!v+4T3j?uRd=r_4|vg8q*!jxVHcb9j2#unp#0b@)i3=)Tb3yTvhD
z`UO1`_P5_LMmmeG?hE;$W2_|1W!oohCc}6u$Cpw!bVIm5rnjCb?P5+OZK3?X<d`H~
zK@R}maC{}*HTj-niqv!-Kc1ZNv_EL7)DQi7F7-6YAFa-V{;LXKOX29rP@ahu{G?p;
z2k?7>gz3^QbQk8C(kYXlJ7!A(^ZEYjv-mtI+2rSr1=4M12l*op#?LqwNnQ*1^1m&h
z`&%j%pfBHq{*JiiQp<%rJ_h1?T%fcbeU=^nO6hO(g?bh^zd@3RKVRM%2K^s#tE8>y
zJn)Bc!BT@oJRSh97Z)b2LT7`U#6?Ii&=s#k|3+N2G;#@D9-hZQ`=DoBtaO+;k^H?H
zzP=F`C!I2RWL&)T8ts1@=1+}Fl3Fh1`=g#3w?^u=jC&AV-??#VQYN#51Xr<;<#FlK
z3yjMPp*|RwAuU|ammhT%=68zAk}{bc!~)Ka%aMc?JT8|(`ije!HlRbGKCwTpK-$IZ
zAf4g&9FHrKv_QVRFO)}D;x<UV(AVI8NU6V3TC|eJgXJ@D{YJZQmIBe&z#`c!g`>N|
z`Su}Oqy%nuf8LJUBIPpk`|n2m@1(6fPU43V@+9ti>8Z*8#%+_j1hM@Q+8@w*)h@}G
zIgkWE{or=okJ1+AQZ}Eh<hEPdhs)Fac2J$&(n)Ul(l7A7wzOCJ15NuQ#=Gy8uA$pN
z{;*QoC*48YtJ41KebN*3hgtOZ?vq||v)_}Z?UVkuiXNY9{|BVT%!!2d|J$4gr19vl
zpgb(^JS1&Ik6}J6*;mu``SXkQ^^Zyw(X%1$<vb=eG`X(xq}0Xarq0vS7?ayO&q>Rf
zp}qp^_i$d7;xRsS1e8DZuS@6A(n;uVbKa01qAQMp@u|)-$vc=I&vCdOW1Y98UFh$Y
z(){I))Hj629VF%~^xr%0N~4$)NeYYKlP05=GvAkH;qu|&waXqzi$eMKhc1Wya_x~6
zh@Qm!SPDba^R>wNM9M_d^R>wNOxlCaFNUvIIRBP<htciv`bccKf27gOrQ~Pm4;}6P
zTH1=P2>IjZ<h693xy(ZSVS|6AisA3q|GnZHsRK8g&%C_nTWKIO)Zd1~UYtZ8iLT`j
z<%QECk4Jy<lIE9gvM;&{obMjt?(!`32P{7lWPkKhxL<mNi*f+EGs|DCawysl+B?Zk
zNsdKth4y5Ra9K`9A9zKr$eHLwc>dTU+$I;Gqut@F2H~o_5&is28rS4)=%=v19^tyY
z2VF0c+K>;TYfgdwQm0*R7QwF<b+PjUxfi+~{JtQef?R^`PyqRhvyyxQP4mB!mR017
z%&zj#OMb@8uh({`m#jv<KmWU()nqS|Eu^Mghq;85pAOq|uPHbFPrQ~qkQv^0w?O}q
zvzGjjdB0nSaZn#~)|UTfcJ05mtVhxP@%Hs^5HHUR&zm>X=Qp+Gs^~~)U;F{_+UO$i
zWpHEk1IRCYNFBK)+VcgJC*Y3gGmu{3d6(QB{T%EG9)Rv0NaOY7;pnMgPw+VOcWnFh
z<tgZ2!JeePJPSP-_UA(y$o^<LF2NHVh#p_v0`s+hB!{`;;KSfJZmcggl#ei%TKM^E
zDBogska*Z$nX{4HGMb*R63eIHd(Osk+gR#AG6ec({&9XRFJ&&VG<AdL|M5-aopCho
zAcLU(rNlRt&oT#+TmI0#ivL7z>E!Xl&>pWG-(2=i;M*I2iICdyE#(*JHsD6_t!4j2
z9=~^y#@oqh%q5n$aKE*V|5Wx!;_)x)!`tlf9c6Fk5{u;oAw%N3$Y+yjyu|Y66pU90
z>MCDhhWEYCLw`(scljZ@2Be>9@jc`dYxwrQy$by^@jc}ZDcn}5k1dYxEjLc1c97xU
z(fj3d*?}J0k$Rw9g5I7%JxG4R97t-w`Wxa0%ie40`X!e8aK5(350R78xr4!b<A=(=
z8Qgyo3po@&OkRaP1;=+Xez=^8o(=Io;z!6HnSA-4;H&W?<#==pSpIhW7xG5*0f?76
zedJSFba^N7hWcBN@KN$pG?_p>TJDw2<6cmH_6Q#%hcd%_3DAG=D1NNmI+w>EPlft?
z!bI7Rxx~^0>JPSrN%GJ<8sG2E@4v6)(aa^5gr8x2K*Cq@By?lAUbPdZ$X@w$`9LB-
z{ikWdRN1ePyQimxbWWHlUqK&v2K^Zcv*n~B8ZWW%{9vBE5#4Aa{IC0b`5?MBr1ui{
z1#%g4Fq!E`;|t}7%&z>vUk)i|+r#|DUk+z>wQm>6@yvnb8QlNF6BfxQn9D3benHy{
zi{*!Crw<`tCM=cPuBY28vy6dwf@8Vd3q80kq+icKc@nd0`$4kb|8pdBAlX(8p3fu%
z$s0HD^I?VjcW%NedFeOYC!zmvN<xSn&Rj~)FNEhytwQBB=swfnt1GR-<&Dg)-y0?G
zVlGqq{YuEvgebX$8Rj1buLK`uhWWo1!~6RQ(ei7)yk+r7s9z?;$a}t}`*$6GoP3a(
z_rI@Ah?DPe%N^kN`ar+lGjva|C-@CtU#9aZtxHIftsB|)<WEKVJS#~y&{d#5O<e(<
zn@PvZCdt*%8BpH&K)fEh80-mdiY8XNy=1u!dVfWDA0{DL?##{dzb)WlChts0k!LZN
zS!nxj6-kw^qiOpvoUE1M$HQN#NbBqS5;FedqY0Vs%=6RicjoCO=bd?a$$Mv>KGwZ6
zPd|n4%&+hIcjnjk8<%x{JvX^5^6T~9{P%2q7w5m{dwJw0e*Sks`$-VC$>Y%@Kcw~d
zZL%+OkUR&{BlRrw`J?pwZ<GDeZJ@qO9l#tY_W^r?!!W)JuJ<g*cKJtU_}<7l>K*a{
z^g!@x$4>b)`aUf0Lw3m*(Kd*Cg3Fj;{4}%=eaMfpy!m~4Yy#f+JvGd~1IKqR;U_ut
z2ksf)!&AG2-LhpHH9Q}xV}bsM-Et@9AbAwzmp2pk$UV@0;A!!D<N)+C7T+s}G6&1I
ze}nQcVXqvEeg%FEUW2CdU8e@@le5rtzU$qg`{W`tolpBk!hU%(n$D-~mbhQuiKg>u
z>xm`uel(p=yCV1qGr!-OkOS;`zV8n!Mf3i!T8RhWnf7NjO#J1YX@6GR#AE+&{p0WA
zv_GtK;>mZW>-R}K_0D|#KmOnP=ibHX`U#E;?@ZSpm3aA``TE!Y-}*P+#p(LKi8tSw
zuJ50C`<?0fL5X+e7TekNmz%Nldtd$(JsRrUf^c8%hV}zDA@}8EG@Va9HQ<4qiEhZQ
z&jYy-{Q&aAdkGKaP3V8YkHI_8KeFrpNZ!XBB>%>)|0DS@#*g2J-=FwcE<?9@z-`&V
zk2f9TUT6X1ZO}8onXvv~^y_m_{|5V@?NEQ&n)pPXh!z{u`{AklHM%C)6FiUEm0q67
z+D?8yBtiY8R^oHn7hMJFFSQb1$R+6GEdGyNV;5h(J&V7Vm!hY`{n<J3jeM5bNphfm
z@Kd5id4%2#{yEW2vHi$@Z^yIn71>00<$vfA;B$$BQWc%Pm|ibYsg3>+>L=8Vn1keF
zP(QkvASx};m%!8FMWq9p=SNnh8=B`gR>hy$mETB80Gj7FvJ!^o`HiAD&^*6UloV!H
zeq&R1@i;3#nh={ZYY#s^G{3o#s46ee!ytdTo2V&s_VVAq<Qk+O@M83g-|6|#l$GcZ
z@VP`?iC~8DVrL+KNHmo7=-FU3$*!E>%j5pbDUJ8>{nO{|)sz0G%tF)W=N~0`DjS%2
zdEGpzl5&L^o)?C}^-HR%l;2O+FSF3`TRoDzl<_9_PpYmYqLmG_{ajPYKqrR~GAyZ<
zQh*NM2k9@Vwz3JGGz;q2Np+MR=v%q)mBFOC%6@b&sBcbBs;3-5|5FRbCnVKZPNU0J
zVB`6eOX$SvFyCp?N6Jm~T-g88q=w2vbRldnFsYI95>5Mma#}Q2+)DWGy9nD4PWo6;
z(SLcu^Z2ADN_jMWejA(ARQZrOSe_5}+bl;jr7k)eyxQ@J(u5n&e{-eNf80VDfXnmm
zeY8}DqxttfS}NnvbUxCAq*lrlG@Xw$13U{&zvoeu)LQXJ)9-m~1qY&!B|v$S)J6$I
z)9-;C0>`0wdTXnspwGOd>94J_6V1!tw#t4qzaQEuN0@_Te*d&rPN7f3{l41qsd5pQ
z?*i%ZR8j|+7m26uFTm%(_t6(mLwTFjQF)Gj2EGY?i;jW*2SMnhNC)`(Mc^i+lhPdB
zoZTOtmG<a??Edgpx-kdIUqE}qhjdYzm9q8mdUR0+F$a+$0<HgjrUY=yeEj5RN+`PA
zD0n`U^qCU>pY^*cSD0P(<!;K|pXv64<nHb?|LLYYMf-rK#dlNwWe%44_cOXH;z9m<
z`1doqEA`R-^Xc#Hp)^H@gW>sy(iTm>-_awWr{ayK-|rXz?v3_?_Lk(<OBsY-0rnuh
z6d!aKXkR@`>a9#b4*|ade~tbLo?p8q_fh7ePl09dQuOd8^!WNJtI$)xO-NrQ8_mnZ
zeo7IVmxuk7E$DI3zP^{xU)hPC3w{hPK_^1`^&tb4Bj{|fC-^ivU>uEqu3SV%fjz-x
z=yqSy_(0`8x(C=3{5N|2DjFZ8yhZN^dxGUd{CdPd{e2c0tW-f~g2#dzG6#{vEIve;
zhH?JAlp)GOH2+@85M?#my*=IEP$dq{>k~tj6g00-3{`T_?GxzzI!q}>)A@rBfw!W&
zC&K)R$-|XhXgYszCGY_>o!|IH!U*Lkn$B<RmN-H=gQoKr*Ge9#Ttd_NiyMP)qUrp>
zdg2$#12mmKxFYxk`Wp17w@LO<$S?eS{SEd`9;GN~73%YCl1D4$&^5u{$zzl%=&Eo&
z+9Z!vYN4Bey_3f&jnG}dqa5Rv7U-ei$&N3T4$Q9p^9f3SjPv^S1Z5bS*RLliW6`{R
zJyH1z&Fj|_m6>Q>f1ac)MDzOdBqadN>)(@=P&BW9PgdfYg9v?puTSz<${rqP`3tnj
zm4hZX0l#1_A>*MxdQh^jLJqU@2j9(u+4_>FDlLz2e-8Z}<C3Q-BbecP9gyC>O7>Ic
zVf-Y-XC}{34l(oRr;CzjD-W21<g?KK5Rg1asd1EUZx#6p(oZ;e0dpWJgZ6D=@;qfD
z#!rFaYof{#^bc^pvy=UmgU8tMu-~&NdAV|mo6SEybL9%<%M&ynNG6SeR<1KpnUDU;
zjrJd{R6?1937t=TQ*w~9>Lg$Od#KOtOb%8u(V<XZ*b9#QmB;^p{PJjWsM7K`ZeM7>
zok<Q;cAcVjkeln^`FnD#qMfD=A}w~o^S@-LGKo2m_?(9E8_Ds?E%d*Tzn@7?R+9hV
z+Z+E4r0+EuO73~?O;F$ZXib5Vd6E13ui$<5HJg;7m$~P_?-{dZo3aa?1pQ54ui2?&
zUghz=&|X-&X0LJqT@%Wupf&rHZr6DH*9hoETl2Fr={mPJJWt49b5t2v#!cJrrEAVA
zx6te0dE4<d=aq-(Be4GOYc48&H~I1>VgG-wxuT@q;{Ldfg*;qyO<8rD`*a2&Z`PD4
zchTNZAAhsvwlefD9=}rCLX?!dO5{E6XI>UkCFPNF_5t_M9nhbe@?2@~kUJapS0&{i
z<;o-O{4e4DO?j*AdO{sUK7sVyJ;mKt{wa@7g!aVH6xrtgoVzd7Cnlt5Hth|!@0T!M
zD5acj4|?t$=ub+iV5?(sgY5;Ac~*Fyky6E`xlzOXoFa^WOsQd;Cvw+?^t2<TuI(<`
zZ#s;3Ole^2X65mHjbJ==N<-Uv^hv1yT}WwayMX=)?w{K!Eo=cYU%to(#(Si+x1C3~
zhx$vo)Q+~d%)CClDB0UqUZKke6FR^Bot0f|CCq_@r{`|AD`<K@R7ve-yN{;#LzUF-
zHrdAaXOD#OSgAd1v(U3b;rmFb{cJTf9&ZoF(>ryr%^zI=>0?alaGQtD<DbIu&qy6*
z8;^#<g)B-PYkO$$_&2cp+m(}Ses*r!e-xGam92!?Nt!}^HzC#6Mm%_YeHzU7nfkS@
zEqX1apTg7`wls7NNI!+Cvu&gtU!H#7a%<{5+g+IAm|mZaaQzOZF19T#&%NUV`1(z1
zpiTDVzIPm+hopwsTB7ND47XFmY?){ce$SKCNZW@M`0_mcN89R}{5mz(_OZ#<v^ZN^
z^bkl-9%=EmwiWsM-$Ht=ot9$Dt;GGohwugav`kz1%J2PoTE4A86>j?e_Jp+cHlZr_
z4w^pGHrs}JanFYI9GSMwR-+pCA~^of$xpUf=s|G5XQ%D9ZLQAZPvHLgF0I6tTa&x-
zOqg#u?HAkQTHG{!olQGt^Qz7L3d+wbX(w#)=uz#df3qodXuMSB--|h8tB9@)>(3AT
z-BuUP=fgj1YmVmg;h(j6qjTZ;MnT{ow*F{7-~Au9QD{Ej{yCd3n$Nd?&b9!}zejT3
z7KEntkC`jZ+hWoD`vvE1>1aN`{{>qyn$Pcl!L}Vu+cV`>T(p&<`S%qr+D@Tq{ioWB
zOSY?M{{4hYwg+fhe_6cZvh6jRe}Cb!%~qG6FaCXpE4GSg{(Xllwz_Ek{e-Kw=IFoS
zc>WB$YV$_(?<f3e>yPH&Px#X|3eCTtaLwk6=HE}aW?O*f-%q%13qtenCtSBV(ER%e
zH*D!>{{4g-w)JTK{e&{xc62Np-~GTc+s`f^q~HI!X*-4H-zT_f`xDK-PjJij0L{No
zaLe{Dnty-bw#`<LpD+IXf!nr9Xg;6BU$(kvKA*&2wiakUpTr%TH=55UamO~mwZ0R^
zs|DP(jY9MJB<|X#q6fv(@59}*EkI8Jw*s$1^Z6t0+Z^bFYv}i2?%OiZ{QCh9Z0phd
z`vDJZJJ5W-iiftJ(R{v&hqlw`nUMcUZjWq#qWSz2k8BUoeEx~Ywtvxl{)xvnwLU*z
z*RpB*>xr!rnty-ciLD-*e}CYqtp%EYf8eRD3v&=FFYcs0vyEit<;x@Ra+6=AJ-6jD
z?|0+hU-{ei3z~o5<)!TbntvbVmCdsOJwCp@CghE+E^{KO9ze$zy|pz%=cPmWkY-Ug
zqBp_wcV(@+dIcQ_?V)mOMRnvybbE=U%T_4Q)=FwDdcY}oKDAa+kD(8Zg!<E3RV_m|
zfbysLT3vmEz6sCQI;^#;Z5z_<Imn}P@czTva_VaIjX89@)(2`l`l}#nPt~IlU!IMR
zT3b<FW%3tmE354r^W!@S`Sm5ws%j7PHD)h$D4LJ2`cMt$X3y^jd|X}Kh+f?R+Rxyp
z=x?At6&PJzeZ}l*-&a>Z`k3yIpWp4y>S||`cROpU15LJ&I_gMf*LZ|F>Ud_?^R&9E
zFJB&?r`1(w|7ZPrYBFCQpQqJRM>k>nCtgrrT~_TQbrQ3yJ@k<(G-Yvl<{R4H{7BW%
zVPH?NCwdl)@A4rH)eq6pU{7#8bSaBBQk$T!vUnr44Rfi5mS+>zHdgziyG^9eOPi?U
z(QVoD)TSz&Ech!C`SaIi>O3CD@iw2RhtYhz%_r&w=2e8+cWra^<7V{txW<RJVB35D
z{K94a{35MZD~y*~X#fArwXNTo_Wv(g+fHr8w#Tm@%-^qeG<hs|1T#F(W6$e4s9%|U
zG@+w9-?aSBgwATLD-P|mRm5Apj^@wHK2!ZZd7ob51G}k9n9D5u`CNB39L=B4^;A<#
z%Wrq~Q8${r+u2XuYckY8)I-d!^!2%V$`s!kGEi;RoTnH1-66?skop<3>-z$O)M04;
zeStx$FM0yY9|x&R(0o3=L25Lb&&M}N%|Z{FK))Y3NZpF&^YIN*e@659_Xerw(0u;A
zLFzr`VDc-p$6&sF)x8Bh-cp&*pEyYMK<8%i<NpxN=T97@He`0qpEyWu>xwU=^Cu2g
zyQBI1jDyv|=)ifj|7x&0jvME5AFTQ@yXLzatj2QVe0PJ@t!O^q-C*@BGfz)_k_W5N
zE&2HlVCQ>?n##=Q%k---MBQcbv(TaH6O%p3FtvOu_WRlV9lJw^t9?zr+joTOXY#es
zk!p^~8{EH8OH4i&>Z86gd1ZxBYQ5I)w|_wzt@bpTgpE;`n>;vVteRo68a7TnWAgZN
z<JG@S?i})^+N2HNKA%r#g4z$w=i8Z}PD9iAYo=6~phlS7I%J}{&*U#cCaKp<t`Ig^
zt<v`W@r&du^;44v1WZxqm^`<Fuj({;L+DiXh{<EgG__H?_uISK_iMGE$#+Bj)F6|4
zg-lm-O+FYpLp^V@2brn9HhFx=EcL_o@3&twY_>YoWd90t)cGdohR#*jo4l;TJoTu_
z--pgug-_paZ%W7lwUx>JLl&yTO+FdwuSTKy{4tBvZ_s>xnZ@dHG@rj}iFyys=dW6#
zsvY?L`21B%)W&E&f7Md851P+kwN(Au<lt({)MzxHKWl}$9?j>^TA}`e=JPA9Q17Do
z{7V6<-jVOW!7`dZ1geeDPRI|ayD=w{I~}3_U~Qo4Z*t_?Aay6Y+Hx3Ax;9unX!5$X
zp=#q!bo;?_m)10YiBMajdx1SjgxV2(Yyyo(s@>6N!5$=19e}3&zo`LH>Tope|J@xL
zrH(@<)PV2r21Kh<(AnVKq0#Cr^kC@!N)3on{n2B<yF+8tK=f7{T|ZV0L+=Cc4vkgg
z&>ukmm*nP9Q_!`+9>k&Mpy~V&r$ge@VsspM>)JSVE86=<y1rB0h3*ITAWroFIwOnL
zcjMKg=uO}O;4|oxES{iVLSJL?1obBRZB+}D4~gmnv>p1JI)@~xFVO!K)AA!pC7t>8
zu&#&ajUh>@g6;qg4@g$ap?k4-vRVcGKX7Wm8nqU>K6rQN8nqF6D2u14Eznb0JVot*
z?)!xPzErgvdNg<dxIcP2i>Il>(92moO&yCK{22DPcCGrAYklx9;F;(fn`nJ2U0sNF
z-%RUU>1qJl0n6_U$xuVldEmWZ2l^V=hh(a2(67Os;B5447SB?P(19$TrEWpf=X+<?
zW~)2V;ov{PCFpJNd~9b(j(P-r61*3D8Xa|muAi%3MCXA21ec+!fqh7xdLP{Z><RuG
z9de7NuYC0_Iv(sn@>SWJU+?8`JszxGr+T16!2f_NqXR2jApI7oHPO-Ft!oR^hUfu`
zg;>)I)#hj)um=7V?RS-4pCYv@+8_K2xF5RpTY7xO>QHnSum>qt$1n%UhvE83ZtK;_
z=(AuCvR<8mE@t`126X{?8nk~Tw+-rYbO0FUpHf56KR|oKhkT>PppSt)!O6_7`sugo
zN#;aC=O_Lk{af{t$zJIjRktqm{3WvaMjE7VRy|B^n!ZIH%?$H1!TsDP`8#!*$?ej=
zS7Vu7?T2mZULI%t{aw?ysb$Q3KE=Hu+tq5H(f#rH6nBQ~P(SCErA9RU>`+HC2eISr
zlfFX@!{rm<_m{ZuR5zl>{t4r4(|4)<-TC%%8^ZYe^dHsHJ-F9E`kb8pliGLy_bRwv
zTKaBvBYGdC4=sI<+Us*3cZ2k%rSDb!(MKTtYw7#c0`xBs_e<Ze`VQpFr@;NIrI)B}
z2XVJs1nq<L18UpB)J{?c^~cckQq|AonDn33TxM5$@Ss|K2wmRw{p^EkeKh~x&q1{*
zIyaN1hl6SxZhSs;Q0;882RW$rGC4vxqz*RODI8WuGxPB|iRnkxWjrpQb-?@y>A$M0
z(a*u_z|m;lANQM@i{|}tzo{FT1M&KuQorLS!En6aq@Pl6p_BH&_n^~HtG$M@-%FCA
zJlzQ%Ve<a;GwKABkENegXPf+c`Z+a|8Roy40OL>7FRCZdeW1L$mVQa~7)G}rNV4F5
zx}APmorE5dN65qUt7>&09?xC?{q^bB)kbLEUtFfPMBjq+Bxl@GyN%+@M?r0&T*hDO
zP-Z8g%U8*`t3Jf|);aL~wv78~xiNHk*ZucUt-~$T^4*6#R2!pd`R)mBg{I|ei;PEV
zM>H*8+ktzadHMNR9f0QL=VNsQnwOtX)NyEDem+rs(Y*Y8s?I|5^7E;>2+hmSXKEmt
zm!Hqna5OJJpQ~|bUVc7TQ_-~i^dWz%IcQpbdV<%ZY5D0xUZ`8qwEXl0|A^-0=S%ef
znwOt1)njO0e*UALLG$wSAN4Z&)N?|-GhV4T(O1E}!4J{2d`$^?t-e5y244nS#<J@r
z)ADUd#=ok9rsdl>@c+=X{2G$+My-OT<<~fHZ8R<a{4(CEjnK6GI~MX*ZHcDkvtI_$
zI-qI!d@O`$-O;>!w`l#*ynMH4!_mBabkoM7dHLw3O+oYW-CdiB=H<J)=8xv(yPyT2
zdHF7AVQ5}{ikbt>%TG~DaV-z!Ws3}}mW`(6XFG5)H|9r@c7mC|uR13~(rS#O=hs0#
zm<`X9Gi2?fFWLDfwTD4{EW@UaMyJ62=FCvFjT3nMAe{e<3|-SEa?|JA-(}dfmgo<l
zzE_&@KP?vh7o6{t8J=1mdOMuolNlAYUCdD4<P&l}qq62VnQae`w~7|djrI2`S{!=P
z75E-$MinguP1DB%a5kEz2kIg;O<%9To6$5qSTn0=JJ2*eXyE;5njWl~Rkg!tnjSRp
zDQ<RrdqcdmW?#|c<?k<^%=l0%VGd^J^MlOl+6iWO{}b{%ugn@+t0{DOK0cs9W=*Xl
zvnxNUrFG|)XTk5=y|$LtpLvzM68sBz_<xqKt@$yRTK<LW(=@ZTw!;)}mtIG^z#K?Q
zA-*@Hp4QEmU$2wQ^|f1m?D&bb0N$U;Y@m&v&ixhq-n}7>v}QB7M?il{&&<Z!Q*>Qe
zesE?}O`1vLyu8{F(p;;_94z154&ysATWBAldHL8vYmJ^ahL)EtG+3FK`uotlzwgV;
zR_{#v`=(~Ld1pRe_*1Pb+g>6`eg)$JGdpOD(FWxI3o|=vo6yZ(Q+LvCp{uiaXRZ1y
zdi;q*h5pa*OmA&CdhZ=*PiA(}CYih`v#U1U<dV#8+5(f$X7<!ppf5(i^W)6kT8POn
zGy7;8n4N@f-<s7=E5Ue$Z|U)UuAMZwa@Igio=uO>$>!6moHbahWOAddp<07E{P<su
zhw&Gg!?b25x5ygK&KKN?`l2Gp-`qxMqglL^)cpjW=Vy)7e!%7LG=}H-S);TPlP715
z(JIcR+b@w1LVfDN+VNU-=2A8v;i9bZS}kT*`W>&eGI<5Wdz-v_>6hAIlgE+?njiCi
zx6Fr7DrZg9BGH$ieQZsiq{TBQk`JJM9F;Xm%QQJLYqC~;9{>GxKECX%DOwGa3$uJR
zU$ieg&)$+XP4hQ-N7mQcE@t??64akdvZiZqxMh02j%Cf%+RvxkFO}*1WT&%cYYUmn
z*!*P|v*v06Xd1tsHBSp?-tRUPd^c->mWi$pev-9NJH?l`(D#AwW-Zb#qT83Z!1{}|
z+vv*BJ`u@M?J4@>i;#b1Ez|4^=<)FR*|hX!S|zmGKH6WhT&sbm^P>f4E!RFm)A`Y?
z*(<ab+zOu$El_KXaXSBIa8{t!nR&n4v{CeZ*_B!^W><PyspT+3e+;A-d-iH=qscY0
zLp1k=^!QxqC0tXPUFRcQtAXa{BSIU>4D&%ur1?RVHX2<C>_MWm$;__w7Ona6IF=95
z+7gpnW=CtOCV!e8qve@A_Y;TqoypMup}k;srN4Nskv~2D{cb${C1@>~UFjo13*g4|
zk)$0#^YpPstF(x(&(B|~)`FR*-_NpBwGJlt%}&#HF$a+!n?ZkecDhz+F<l?NU&NfH
z&1EiA>bHaOVcEG_B)ScAo|eZPEFY)@^Mz#RYd@iXV_v8Ij>~Ur3-4!V7ibUB$CwK-
ze<;!U{avIfOX&W2er_Q}T6vR~WEW`x+$0{xQ^1HFEsuG>`}Xf>{!px~XD+c^hyAb2
zF4p#-`@r#rWv|!BQo8*T%T3sSZ1x7to4HhW-eK)!Z6I?H`5Nj2N!j0M>oNWhjQ6mn
zf2*BBd!gTO6PkafXMd}y%jot?S^ip>y-};g9875bzcqW4RufJ0f9i(JLF8kI?*?~4
zzw)5tKQ?Pq(JAo!;&ApBJYV}Iet&<bWwQ8w_XxPZPiKFx`7WpX-|t?F-M`zl4lB5O
zLp(TZhc*Gd1bi`Tr#1^61iqfNOY;xl%f~=|Yau^rav*nAh+oS7No$Ut!M3+Y>xNE+
z{L7c@)gCfC$w@fhx3c$Z)mHNL-#~tPDf@snjG52pJC^*cea#$18o>4VoE+2w&~0;R
z`}vTT$Q(?B3Kke2`-_%~@iuudo;UljRwIaR-$@pfvyhkB$F$DO@V%K6@ZUMdwVvoV
za6QM8<Jxd^Wf<RQAt&CQPo5u~(8gl?BPj33l3z7nlb>Y$rp;pxB1hr(TXRlp*U{&h
zf7c$arpMzT8=yYwk@JV<7fc;Q7PI{9qPB<mswMSn_`Q~k+Rtbos4tGNT+%M1SF-&1
zigpWq0meIB&$_BTMQ?vY^XF^Yzud~BLV7)~Yt|6DzpKhhC_k=e-OxO^ElE&*Y3Vn$
z%IJ)}^!MM=YNGvEetk=8h;GgD>)TpOZl!rG`o7&AtqsPvM$+_jNAqS5BppL(d39Iw
z#W<fI`M$OSeVgs?ffj=1^Cdsf6489V<cC@wn$M5?Nc({s=SO~`?Le<)$MZz{5tn}p
z$1|2Z(T<||e9KR@Gq^mTKlzz<8O`TUey%;h<@tP;FSM7;WjJ5-Kbkd^pHDu&<v*H&
z=JQ+rqc!5T9D?@FO3Oc5GjuBCKeNayEeDt1E7SA$O3O#rhvlgY(F36Tqh62eUxo5G
z%<@Xxf?fyhA*bbEZ3iyz)qyVmR{Ig-ovYCOztwi5x4`-q7=l`g%b))q`mZfSKZKsp
zz(TUXN6=xL==O+y9G&6d{uNy{gZnJAlY9gDQ_UQrmxR;v!|Puk*xmKB%w-l@Uub9%
z^e4=&`Ah}<6_2y}i=UgI*NdR*^Z75rJBoS-=2G^2{&Qm0zeMx-GbG)g+0{QL>5&-c
z^+`$pmzmcOn&jAYIg)OlyK|1Fk79P!5A6CR<}zh6<X1y-?0On{5wnMW3jGrDYoDBQ
zx)#NbS25`M$@!n&h1pgA{6HUS^5panbYGKqFD<XnHhC=Z)FYW)<$neJdo<7AE9!^P
zJb$mGUq|!&w5q-;njX)7H=ciesLzU_c96G_zkQigUC(3=BC+s$d~<5(kI?nt_<eI~
z>Tj7{^^ID(6ie54)pu&?Hs)Z~KAD+QORtEIg7mW(T*u_VoH}|0dixz(9@f`)F_&U}
zrGZWyeEYn<(opZt%<HoeIgNE+G>ymQG|@AddHp3Nr<q=0a!$@Cdi^-MJ>Eb4ZB7fl
znaSVhwAB64wEn#(r?nnx^3OSK^dro!`c*sKaMJC;`}yJUK32}BdL@%j=5)}Tqv?40
z^EsXLuFSkX9GBBYAB?8+75`ABtNu0fRpsu#@V-$_H{BoOw7&a8m2P?(nr`o5PIrAj
zE+6(6Aun@!>c4PXDnb4&<o42UpsTU`w732ceGAI#a=E?r8u9e_uUfW3d0Z{GkA8t$
zDX2;Fn|}Ibj5mk;vRrOI{S~vTzTIDUPoV4b`}gDA{`&uzODqMDemuwky%D-Iq#qCR
zx!#TWs^tQdUym&V^?@e80B=M0gYhmFw?X=D^!jPEzCB3ahaL;%A@u=tlhg2iA@~S-
zKa}s~z{k-SVLWe^Wsv?Wdd+2ce+1&E(S=Z-`p|8Ne!=8=ZbS8J=&ncMeH8FR<`U9g
zfVUIehU+Ae{{BEx9{%Z+J3^nu>}r3E)(bFB^P54r<8&>FFHgTW*F9^Z-jbQ;HxqNe
z(!EWdm+PxLnO*tM*Lo_mtNrn{o`a_2Nv!E#>l@KOgEjDdZr1+r&GFNXWV-+TZoED>
zU9W-W^|=}PVl=P+&C*lQynZ-a-+|`&%^dwGn&&rjb$t!rKCkaB&?}*_J*oFY^ZMc<
z-4D&%(~I;BG;hx=*4Ht+@|VSW5i_svdyvKYab}*Meww{RzhLs*PnPM8Q|R%y?$;Ii
zSY}uH4b+!2^ZX(-H&72X*^#?ams08aeES|`m0ph7L2isBkUy-}D>IjpK)4@2C&9WG
znvb^%(OaQcL3}I;)7zr`!K+BPJ{>&`>O-HCNPQN!+<!404-};@Mf3J=l)eJZ-*=1B
zBbZC%FIassN_R55%9kiTlR1!Vg7!~bPL#flc@?<<UYi@E3u*NCl@NYC9J&`X)Q6#d
z_d^wj-i;Z)mk9Z5a8{f?8+{zclf#VidIXxjKa`j2)RUP@2u%;yv*PuQ%)x}`Uy1rd
zjMMZbk|e#_TDt#0avqMygCy%MnM(*yk8AW)=3rJHMC7FE2e{e$M-Ow-^s5-B^Sj^7
zN!OpE#XB@VOxLA!cKyh)FKGWoy6%NuvjpZ7%Y_c%3>tUU4>I*i%!wov?$0f`nfgR#
zem+Zbvvog{$C4b~AD6Gc9NJsCx%w4mSNWKy-!d(4A$hu($&N=(OM&-KbMtf^y%Bs4
z{1MtS33BD!eEk!2L-0*-fAotu7=N9+P9KJ@;w0oLcs4ioHx%dr7(Wf$f0bLHhobL-
zEqMicE;=9qo=@c!>g&;|U;}&*eR3B(U&$-dPq@|xR|VfikB0VEt-NCWDS8gLW>~TQ
zFWNJc)(6+?Viy1X4Z)sZgBy=`gI<BT)WYj$8}u4H&ielv=WWzGF(;C5o5B1Kd7JfK
zCil+Us;8o9e^1-IAM`wvd*^M}zvY(c`vqh2cIiJe!}C%VzU~G-&Kx8^1TV?nrJu$4
zrT@|P{Es>$7WmsQ@%-t%dH&^csZO82Ez0@{moL?Mf5A_>C7b^KL_(LpmHm@mi8)w4
z0_V$`zDKXY93-Cx!}DbQBaBN>AF!tH)jz>_c`!U*);nN4H<Ol!`}Es9j^}fq{*=dw
zH`GVRl6`v19CrRmbLRc}EHv$p-kM#aFGAD)XzG>BK_mg<yTNNP-j&4<=!NK3=<Up<
ztiFCM>wtb5&FgPJ>rc5Yw7<D?(jna|mmW{4!uv~p(c7YVf5|WUP;LwFFZo3u#T?A~
zPp0M_*5_cH_D>gPAJL;RPV*ahV^80R?l*#-ucNN(A@csyWBRXreVl*sgnk8=r|;j-
z%R8aB%%jKWVC~gqdB5tuXgZ&2aNa3BA5H7mQF*8JQ_L_QIJDos%sH#~%4gS`c}?CQ
z`U#V>^Umph>)yvV<ek?qn7lRbf<CX{eSB};MZH<!dmqcYq#rc-eBNb!e9`-OS>6@>
zg~^ZduIj$U@8d7>{?yN!{5J2J-fR8)xHbQ}zRhGk|AyXR!~1yo{4zb+<f{2M^=99^
zkJrn;r5`l;ll<HI_;26GJLLbRzc9IH{vADb<NNsF{JT2Y^xmWL@9D`Vf0ch<Z?^e;
ze0Kf=J=5gH`49EBTi(Z4=ReX<m>iq`SohxgKAx2SMBiv~aNbkB*LUyZ>G{v}Z6@dC
zKi3<4|31Dx|8M<*$y@VZ=#zeUAOA7`rCw><d;gOEj~;6BZ~3ouukG*SSMp!$@yrhP
z{i56X|LRxJ(c_{1oc~7uYzJR{9;COo`ET_;CR^7LW3b8gbrxfk$raYQ8NMdhT<31g
zHo3t%!B}o`i*=$AVREN+R>Ns>_jQtyY4YH8va!MBaqARghsl2HY{t(fFI=Y@Crn<x
zPBZ>6dG$Ko_|xRbb%yc4<fL_W<8PBQ*LfJi&iCi1cwIT8oXOv>`=3$S<lXB&FzT87
z%ewML3zJW;^EA9ozOt@@(ck1d>na+<On$tsk}<~Qm+LAUlT9WCRg4)XO9fSp1tuE>
zUdD2hD-?Wa1e;vFpqdeFa)W~EMykoJ3ThZRCU+{RX>4S6^{>=2wxRo8pwDY+8Iak*
z-+qZd52$7Q#^XvM^cVWM)iN$H2a^E@;Qh*w+QxNeCusxyrF{!(8y$AB^G#^``DJ!p
zqqoU@3+fqznS<d=GZr$epn>6Maz(cWM(l37K97$oXlN9eJgK0O@sK%@Y=QdY^nxbF
zYm*lgG&TC}<@=}4*Iuss#29Mw%7W%b8k#<zTTsx_$TN9*L2Kh1<{+Z)hW9HA+8F0e
zPAF(+{Kf1VkN>Ihf*Z%<e`-|PNB4hK;p6c?HQF!-lcmrf5B&p1H<MEfIvRc^=M;Dw
zN6_b?eZHaKGvlnu+Y7oH&~#(xhsH|_x*HzMB^KTu?P*kFzG|W0IrMkyWz<LSoK5GC
z=xsE`^?w6f(|a3j(Ft2<{h+sT0$mIqR?x@jP{Q{|UD2(tQH0*|jFz|kjU6VRF6eKZ
zLa&ARm4X4rzyo}J>Lz4>afLaEd<(vm{kc)8l*V24??Hy!&(yqs??%BOLq*g6yt`RL
zj7rR|_RUbE2DiK(&aX9nsL{X_*TA2dyf$~3F_hW$zR3vV2y-AwJVV<zBaBuD>HY)B
z{9&}dI@0KcJ_P0agMyL9R`f@E=<}X0jJxO+uzXUskJ0Q9Uq2L%M@t`Nd~%rE4)uwg
z7NZSsW>@+dWB74n`Wa&+qj~xnW0WwL%DjFz#%OnhZg0OE|GxbgV+=EV@0<Pp3C3h}
zF&wWoeS)!+o7D%GWKS^GnhZ5wBaa!{$C<RgG|^axZUFZW^|xp~UVfsnk2#RMV)f&R
z#zVBI(e~gZqsvixe5Hhs-=Aa*Mf36clZ>g%C4_%Jd9pDZ<9xjSS4KK>AbAY^7q1JZ
z7#o>`n1#ZrMvY^9`}BQ{vE*x`3!1;r;b#m+^Y=acjEQJIe&5engy!!XOgCcC{QJo>
zj4YEyGSm3MWd91YjH4#ctuWiTX7aKMbBvcJPpL51upfVayyMHwGwPb`N#+|JOzsu3
zz!+>Yygy<1n!Lf?--t8$Zr?>lfyp=fE;fEL*{{M9<CMu4q@~77lLv<^GdxbbKfaX}
zmK(K99v`y8XlL>lApyoflc$6P8k3m!yJ?XA7Kg4hTAt+F<L$R#qx~sr-d|%Bh8Y8y
z`Tbj=Fv9RNd2WSBV~NRr6{3uAlQ+0W8*5BnRw2eHFnN5rSmOtir&Mqlr6ymH;*2vU
z4-Rn}WhQ%)c;mUr9wfmar|I$Y<NYEe(eN;NN=TB?z~u2E$wpg~$C5S1Xl7S`eTuP?
z+4cN5)d*#Fki&<e|FJODD1V0T&q-+iMBT!*hW8)T4$>Izt|o<<hCg~J<i{-wvkk9{
zH16te$T#XRCldOd)6RwY#>Z%98nm|x*BPzRze0b1kHP}O8~vg^cRyz6tep?vmxcJi
zOML$qVLX*jVUZEc?COszHlmq>W&S=}v5}0X{f}cwv2m9#kK<L=8)vWb<K^{>Zw&2E
zzWoyq;r*+^Z;VdNJpZ3vxY6jx?0UYl$?!w-=R2E?AT({SNp72sbmm~zo-1+RY^-N4
zv(WfG_btXYlMlIXHJ&rO`g6ZGglqJ8u38rSNat_+!I;ckW(foP7j83hG2RTyqfEE$
z#;@pS>tX&B@HyA|`=I=G+hP2P9tY=RtJ_ZFFZ3$#PPbjgV{}<9n2*)%N8=y#A@Cu$
zpA5I_{CNKLg!%T|b{jVI8Sw9JdyM~~S5}Ae)NQX(1w9IU!)>2Y3!N|l%1^ibMk91f
z@DsNZqb2$jJU{=(?SRn%Jz*A%b8{~>x}o<hg!0V&XQMxJu>2i7kL(ffi!ltn8$19!
zmbsLC{430FUwGJ9%H!A{am-j{a&qA@!}*`(j~knKT>b){U-t<3)!2@n2p$05hyEDK
z+a3YG8NZ-gfd_zpLr>j9<EM=C=sDm4;Ooq;<2!A*-Js_;kiD;!U3k{;V0P6n&Ks3X
z_8{kt`pmBNFBsiT@xsE(#?b%7uNZT9TwebV^d}ZxGZvec_aN7d0_H%{<6BzZUN=^i
z@#Fa+6Xv5Vyl%X@MeQJ;!udQ>c+>E>&ErXDq5rV(wh@WW1z#w<W1PRs<87dS8fI`b
zD&6B=TgL*+-!;g6ZugBazP<3iu?jtFCp=Fod|>QC-|?aK#Ro?D2Yh)eEdLDRxevK}
zL;3p({PYobQ^;Q=w}(c*C)_3J+})mX`@-+}p~^!e4L$iH9AD8R!|xf7H|a!w&m&{V
zbM9eT^m)+}V<vMD>9PX0SM<~fV0JQBE&AKI!0f6&zB1rs!C$G!)88xODT~LG8SuQj
zP0<@;&);<W@uYqjyzgIRv6p*6?RuYEuvcd0*MCTnU~j-p24=$jR3O@6VfZT*seOth
zdkY?y$3BDntH@^m6g>kx2i)yH+f(f$nM>LBe2O%CrI&R7e1FS}?Dl5NfuuRSPZv`3
zKf52ZYk5z5uYdUY+4Y3(zmmQ2D{2Q39}%*qsH%Mzvy&`^^faZgnqByp#+_s&9PgCE
z8uls7e1G{xwd@O+UFo^DJ@r5FdiJ9K#OvFSGxPDo7ShoE`+wpM?bn!H*W+XR17^NI
z5Aw17&3~3}V)uOW{`VIaeqyh~>^h$2_73PQ=x;v1yp??tb0FCP@9S+SYHL5o>>zXL
zc*&x6_DXN*_DV@IEMK;~z1<5&>(cu>43^(s)Y+cGTuK};-eH;GZU3E_AD@MEv0uSB
z-(MGdMGIZuwY_fkA!xpSFZ*O>*ZRHf(=pE1?`>cDpY{8_OF#5^fp7Og_LBc>Z?OFg
zx10g#-G>aZ|H14^pF`}|ae2P|A@)c&^YIP0H*)9Z$2ZE}k=b>8qwT#g&etDp_Z7_R
zkFz%rsU7fbU6{|mXp()KmHRj7&puW(#XeOsZ{OFxgxR(IsrFU>iBGj(Vs;(>H2W=P
z2l-(Solj$$y_w9np8)CgOwrf&whG@qKR!RZuZ^2u&zbf~ntA{8?01-5`=4)r!t6?a
z^X+wX^Y-T38ynOP;sxUoE*1IPmomGq?_&EWb{^;HX@z|hvn#y>*rzhP_7`ZMjd6aw
zf%ds*e!Q#fOPS&O5m4SQTp4D6ie3og0T-@}uzP#Z{RNU<0(GQ)GqY=dQT7sUOb^la
zpP60zkG3D@mOqB^;AKV8_TSOo;Je@}=zec$ejH<egdPX>ATf5a9NmAg%;%ShwO2qN
zhWh8kfLMDC=2F6cpTqtT&3|9KJ>mnlK9=um?3v82<=5EDm#1;p`AxH5V|E>Xmi;cX
zgKUEP_eoKf-P@Bd&-a&Ye~RY&%d=}0d7P*3BKu%w*Y=9-KFqG`Uu>U;%k%9O+rMFk
z@1Ms)`Bn6deH(f?<QHP`M*F}@bo=nW7ua6B*?xrCb^PDiJu34!E#DTd++p`eM_Vn#
zt9YmVvnuA}`_VoQEW`F@!~4d|grDrQS=@EJyX}j(Wlxyz(1+}{N1*G2J;A%t{Cg;S
z>_4OD!g!IxOZV7Mpqs<<4<EAEeiq#u><PZg?Rp+$e`vBN*=N_Q(&OdF)1Y|2y`jl4
zKf8U9$xoJ-+Wk##R(#N2!0fu-hwWZo{P*+gb<95SL-YCg&F+il*W;A^DVi_;yZu$Q
z_s3%)7wqoUsa@Ceg1ri}YkwE)z0iF9%l1=fo<6SI%a~o~`-c6#$?b}7*xxXhS!jF7
zySU6=t_Iy-ndQ}J0={XlgD%`keaqekO`v||U3}Z#2i=|dFZ&qu%FXcpN%0-~9CW$G
z@V%<y2lh~O!4Gh~il5rk(EmbtKcV=!{acf#7XNMEkN$8aUH+y0qRBmrU)f)9D-GcI
zXBWS*SFZW~{4E4GW-hbzgWt2f_^o{ex&WSsmADg+$=nM6-kHT?j%oeX#TJj17(W2{
zv*hOH5raMh_YZX%`Wo1SxOo(zFGByK<mT@21Ntu5gSdMfVCL5&vRLpqjd4DnUiA1A
zoeKTClAGx90R2xR?$_x4l7+>UTJ-mp%4t)1+=H2K-&rhrRA(+zI>7IrMXVn6(Oc)j
zd_29Z9xeI$3SZyq(GBBg|AhDZime_`nM;(LU>{gts7?3hB)egIc@Q7UT&CQ(MVFU6
zUNZ;E?P2_q50O0D)?w*MIS9*t1M#_N+Fq92B#!_zZ7+Mk^0C|&V;Hm-i)D`k7$2NN
zmsdPs=kQl5^7Q(D?0pG*6-DxY&%AkgFC@bB5WqmVqkw=C4Jr^hB#J;3QIx|Vpix01
zqDB|ipdt}PQDMOYK>@{UNx&l>Xi(62hg}d9(5RqzAVEP<L8Jbwx~eks9toiCZ~y!M
z{WpHb;#*bSUEN)M_ssJ#`D?uzIr(c+4JLn0uEFH5hty#5*ZMV>{MD<$<gZO?F!^h0
z3?8?nX$>ZSO^d<(mb4Ij2>EO48chD>WAZN_lYjY`{Hsk(c=9hFlRvesk&{39nEc7d
z<WDs?Ozp+T)LznS;-mI*SPdqB@-g|7kIA2WO#b3y@(&-AfB2aEqXvh`KMt=+pZud;
z4JLnY=e}e7XKM5e=#SQ(($0O~U}_(3|90+I2EP_1``f!;v%gV&w0D1IO!d*;jUGn+
zO!d*;ZOoYJql4R)G1W&0H^X2`Z^e=$+&(O)`swJN#+d4-qk9fxs-KSTSjJR89o?%8
zmiFG!Ei`ya^jYZt%9nI>ryES^y#;s<>r?r6az8V;JWB1kllz;&(*JjIlMgrf<Nm+1
zo64Bl(~<7sjHx{x<sQSB+EW*|H)Co~8SWs))SkM!qZw0s>h6wbOzkPly*_})Ejhux
zhcUIMUha|r?zg0mTTYn!|7`b5gI`nRAK7kdJIv3N=w7%_vUbTy?%@Vg`D_4ul)<l=
z{kw>LvYW-2{Ox3S0Aupce(og(Q+yvS>E})~nBv<Ccna&2|DNLB&Y0pm#huTX;_L6O
zFnCJzb{G%0FX`{TWiZ9J3-CJDr}i|!-OQNcAK>m_Oz{tJe>QkZwBu^{-uRM%ZnQn-
zpW^!!aEigNMads>+=T{9|DNMM&T_IZ$6aqQ_4nc89QT?I7@w5ysqS3{Q~dRdPjeqK
zSjL+{ZVBlpQu`X@u4GL48RWjgnCuzkzQvgOhe7UI#$?YRcRgdu{~&h*W6J*^_ankQ
zew^-p#&YT(2D@JxJSDmU)|ZWn2fN=JO!hYe{443RKb+wv9bxhtrTEWqQyG)}XSy93
zQ~YPTCom>^&vef<cuMp$nBO@4&vY*_n9^%ge5PAqu*^?}xC=Qv<^L?Vpri3$_4f6+
ze{`0c-6>f9XS+iPv%N#zF^tLHq3&gj$=+e^6vkxlFn6}WpIg+w4|5+T%=4k)?o$R&
zi|#P@kB7T2FiyS%$Lry4Ipfo&;dnp7tzdj1;6{MoW!wtZ2P4!u?uU$z0o(}i=ZshH
z!SK26*NnFTZUlIb!7|?+=??2m<#GL^@O{hT(e6ErcW#02mllt4XC6uNFxcC@_<Ywn
ziZFg3v3v0a?(tm+Uk&#yvx_fs%NRcm^P7RiW8K9^ll(!z!;ACXrpNgB(&EeAA&f`D
zd}d(r)o$)_D4(Sc&x7xc7ys2=X|Sx9u5oYfM&Yl6{lclm*Sj;j6W#^>FsJxNcTEq%
z*#A9Le2d%V1d2~VeZN>d%U#IWh5fp<#rL>}_9FcYtKoaa#rL{jGJY7!dt31Ww|8%n
zw_OSQ+r^9Ag^W)J{hx{-aQB=@@(ZB9saNulyQeSV9dMuP(2~d8s%*k&&tWCSZuUup
zFM<3XQ}Vc*){pQrU|-*oQa7LRZkYcKD0#-+X|Sx{pLJWFLhHXTLb(2W&dp%_G{iUA
zS?=})Ed7(Eo_7ZtEbE=;-Jv9pru+?No%Nr0M=|aU`0SGB-LZl2BTHUzuO-aqxn=IH
zjOjeL%)N&(p4a7-l)1$Q!~6CRAztCm$f5i#oUha+B`>-w4W6d{4)fWoOIEqZo=Wmt
zZ-@Jnjb3qkFun=)YYR)t-BSYc8%kbt=MO@C$<OQVV+PZC(Y%D$-6sf}_2~_bUw1!Y
z`NYGpy}aRm#dt2@Mu5L#d<6LK2=%7>GvmI18v%AsH|5FY^_H8=n9A!dw-I5JUca2T
z-BtlSy`;kJ7{D`2R=ej1@Vt^W?p+2?QRu%Bd!1Wsu-qqD=MEc;>A%L`Z(Qe2G<b?S
z?h0(*>)at{Q27ml`^bp%&Lpg${1LAlLiiE$`v>dXL(d{S5Aee!>)g(auYvVKQO<fd
zn{fwN&y<v`cjpWxeFyGmmzKQiRxs93pAq{#w`v&4%NVB(C%o-Gc%Lx+JvVa%;Rjb@
z{cdn4GJYM-19H+gy6YJ~0R0pEf~VX39MbP_9{fH)zfEoi<L?^b``DY@lNe8I3GK1T
zCU>~#gS{(CHo2EFp0*tCgKu(gV7wgs?dzQP-Fplc|M{S1ev9{OSCo9<CgfuJbbt31
z!1Wp9{oOpkO&CwP4ehCPTQZ&lIIJq&_6E!T<cDq-lB<r#!}(juhwey&>AZ7A$;a+!
zgXw*jwSccMSngAO?EaO)^M1?6?rnrke0iY%fWZ>qX7?e3DZa{*&F-@XOMIK%mnb~P
zx7mHiVA_8#0sSooOMF}0Z3atuZE@|9rhH8JFG{w!hZ5%V+E3k9221!)-S!4k{EgJ7
z?r{c7d|TZ?96lB9L+6IJx<eVa0~}Uc-O-G@LVqwqeddlOY|{Ux<TG~?%YTLaknc;r
zaHlXn1oj($27F6E-(LEqJCEh~J;0=;U%3x5#_s`Yz{QMb{ebfAZYksY0BgW22%Geq
zsw($mgC)OTyPvXtgYz-`*Y0-4hXU4scQeNM)+;67xYdktevt<_e6C-<@QWAjA%waA
z+37ZAO#RPJw|zjrU(PN!CxAzmeCu9fu-wP_&Yftm+?V;zUC45}zp~q1%9!rgeD9Vq
zru!~GxNkA0?^*unZe&dNbN0B~8PolqJ?^iJ=|0XLxBe*1pY&&Y-6IXA`#*Uld)=Ol
z>Hg18?qJ4vf9H#mpWM*~PgPHsV0l%$I}N7q&z`G(agQ2J@zedCU)>WK)BT-a-Ln|e
z{hVLjs{%NzesgC8@WjSSKNrAPHn#LX19);{TmKfo7c~y)Mq?;_jDNItbjJX0q{6y?
z0H1PGf*u{fr`{CN69c#;6xELgaMi#>{ZasbF|eL~FM#tKC+VsHu8bsWeV%X688;oG
z+XwIsjqB@E0(kgM4Rl@rkG{#((*pRSo3wty;AyJzSoj`kV^4p|I2ZP#o2rI7dOoH%
zTNNyX`Onfuy0^h}U#I!f#(K2DTda>@{b;FFy)h5<w^&EQeqJWvFBy}(sV==BC~vCE
z8I!!3uDmcPZ>FmllRQm#85@+R>E4V<-dwjD7nC>GM;c7)2}`xm#~M6U4X+2Q-YG3~
zZ`P;qEp*|<!SF5g48|mHsiWhA@|OBg#w2g0vnB-Pt#l4!lDF1Hmj~so^&G||Z=)-(
z3Ci2(D#j#ltE+Da%G>JXsf0<MuGcfB@=w=WW8{bF=Zb>i57TcjrtpXB${U07!*vy7
zlDE^*=|OoreJEp+x7S&>1?BB^4r7vc&}nxB<sEb<#w0&Nx1Sl5AEA$DO!AJp)vTbr
zqwd0(<el_$e+$Yx={FdYytCdoJ1Fn0zhq4EBX#yYLHUt-2xF2TrL*P+<wxlp#w72e
zGw%(`yXb7jB+t;D<^|;$I*T#MkJef9gYu(w4r7uZqbDo~%8$|4GbVYaetcn2o~c(d
zCV5w_9tg_2YL_v|kJZkDLHV&dg)zyG)5VK}^5b+FW0D`QH$EJcAFsb;O!96z_wk^-
zo6cuU^6vWh<w1FOeF|fe_t0;w49a`xjf_d2rH_9jD9_TTFedp4y6~-_`~*FNG0A)C
zVQ&ZJJ@r_|B=4m&D}wS~I-4=cduw-fP~Ka&VodTrx++HAM_0$lPt<SxBN+Zfy^%46
z@2eO8Gbrz?OBs_qThCt;lxOQ=#w0&U=e!e?pQLjcll)|T&$^)eWc?^(lK0bF*9YbO
z^lru^KSe+OUQm9DUdfo`{k8LcP~KmsFedo`o%2CZK0xO(Ciy@;r!puXs24LPd5*69
zC@9a-Rg6h~s!rb=l%J|I8I$}p{pBY?`DuC&W0DWjdp--w2kGeNgh_t7-uY!ve!5oM
zg7U%ol<h(JU_Fd6g+D_-UKNy|p;s~{`I*}NCMZ8sw_;54Av$+wP(DQGGbZ_2y7b$i
z{48D0nB-^cJ>Lc8XX}LBh^Iv_g#Dq2Jyh3cd@W#C4b@E;Uu@?4!*ol=lg)g8m~PMb
zb+}(MLJikl7(W8{lfJocxIUioach-2eCcrAoAIfDI|1&`IBzq4PjiGG%=o=eVZUYR
z2tAzf(SO1BDM#o$!e%~y0?5<9NBgI!9iX4HG*{0ySk@~e^#X%selSu$V(?V;yansg
zn?~v<8E-iR@wxhW#?dB-N9k7?<28VyqS5+o#=YUD*|4Q!^ajRxpnv|-^YmteWxjO2
zPWch-lljv5I-N1qN1j%Dg7Q4=GA8*2y8Nf0`~tn6G088~1=T_Mg}R6_$uH7-ehtbm
z($U`tlYFc`-Eso<jn$(WlYAT;hX>{3bQi`XzgXuxLHWhHkTJ<G(K8c*@=Nsn221_q
z>%SW;^^>okG<d4&oPhfa`MNYte`TEh8>~MG^s7LBv%ymS`8qQ(n0~%K(O^mcQa#9E
zN&iwk)ZnSA7t9}TQJ3n=0`kF4$LpyEf1cP7_M?`kUZ!U<ehl`XCaBAFF=JdWB7TGM
znehD^#0~19J)fKT^a#LdjBkhYZ1@FD-N9h7?=s!hV6pEqeS*PLRdN)5Keg#)8X{Bv
z_b4YYrteW!G`*q*<M$}XFTJt`o8O;VdbQ4__)L5HyxBxO!Qj_aq#V9yF?5ptOHBIL
z=<7KBCQv>VO|Q|jW8{T;9?Lr#d7*wfMm|}uV7W5#$$D*!{93(<<&k8J|608xMt+_C
zp5@yTQGT6nkc8zqRdt7*p^B#0>vV&qyr$?*EPn*btD@-?eR7Qa27MaKR~q>ZdTfk*
zs-D2|$4z=u^(`^-BK<d(-)-bY`r#P)je04|2buibsNaZ@Pt$8yex8v}(>r72H|aer
z?`GsTX)hV`EB<k_Zeg(G_h#MR;Hm1=2>gCo^P6=p>8mdA{rLrj({%;o_3-<HMN6ma
zoI^}}s`t0>ecZxZblduf?^0>y;Qvc+(Zd<{H+Y7=!(d7Oc0JEvN&j~Jputnsrx4BT
zrMK%PG5UAtXJhp5&@Zw6qhaVjTi&5x3&`(XI#Yid!0^2Zy`A+}f&Wwi{)O??Q2&S<
zG(h{sp4qyI!BbTZ)X(rXvvo#{{7#+4@~a@e`EBmhw;C+@yG!34qkorP!1|XrfcN)@
z-la<e@(rilt=AHcZoLS{(|h$M#(M#~>R$Z`<GU_E`8@p<<G%xT)ja(j<MU1U`TA$Z
ze=*_bYsU@d?>?Pu@Kku;1MZ_Ny-&9^SnR)FcZ|`$UmwHz55f3Vy!3uOgmG)gPw~<P
zx+F$_p?;d>&ztxc>NgCQ{&kUF?4dm}zAe(FjH&;BKyPgrls}+%GbZ_i`peXy{6YPL
z!BT&V^{)m`Rr5{#EY`_QQGco$4D~&~%|p7G!J_}LZp-qEpuRtC`>@Vnj5j$xZTpx$
zDMr6opUQIbr(!*dG5J%mz9LTluPpxp%A;O-vA#V<zEs~8BVVc?i;+LBpJ4fq5dN_A
z$Mvf*@+bA`82OX>lNfoa{)*)vLw_~DO{xAdM*g(^jpdCYzqhETwbu;GOUmb2-PB+x
zzf_pdsAu&t28+Ke*S%u&m+OA4-`9rs7nUy9Lm4jv{QT1A^(e-j!Jbu1U(gpZ#@7bk
zTw10tGg#7Jq5s12JHfu6X0On9#K>RNce8vm)c2aDFY0BCp9K9iOJCAkV)R$)DwdxM
z`B}4crS6v&%<n2a$l$5!TJVSBrK|KEG4hx7-7KF5_7_+$>n9k05AC7AdPTn-(BH7M
zTz_UT?bq)f_Nv}(u<X~rrmGE>@_bE4n+NTAO}hre_tjwj`P1yz^wEr;h56@CvtQSz
zFdhW`;kKo3=zPZSLwo&c_M5tI3-bThV7~Lz(zo<5#`OUIu=H(xt-(^>6?z)Whk?J9
zhbr`(82M^FKMpTueFge=&RVV8wG7(7Mt3oIs(J_NW9(gP^!OP0JNjyt&ok-0ql;qX
z>+}qkXBqiAeQ%6>y?%h@sW9FRQtS2eG4gly%PemN;RmUA^}8|h_w<J>uY&q)bJu(N
z=NS1$9co48S0DVL*IgU+VFqup2JeL5k8JdT?#TF7c)x3a`cNNZu$1RVx<>%J>LWcM
zfGzd0KEq&mUlq#dm!%);3k(*2*sL#)(ci2mvHq+G{{H%AJ%#aeu$~xv&lWwM@iQ=O
zcb0vsXE2@${&2{$t@;jw#lFw<JuJV~^p~IMiLEielK$tq$Y9a`T+c9gs=5H;YrO1p
z{XmTT3;lPNPlWOuv+xW3RE&I^ej!G_P5&cCzFoh^^0g2?ZP|AH1<B3#xE-fTf5W)w
z2>iX4D*a<1e1~OKx^0`F|9!1H8a!2Xh59>s+1I+K!IHmk^ne)sZ}b_gKL*-Iw`Jex
z5sc?R`z{ad)FWf`cj*gQJ{Iily=<4B#P~DN@4f6>eVf6O-gkO#O!)8gBGylX`s}yt
zJN-zE{%-wLjQ(!@0_)??o<Gxlw|*l=|9ibYM*n+V$@*u2KRna@d%Z12{|EhjjQ$V$
z7uG)&it(B5Kj?aGgZ}WNZfvml!;iX!!Bf>6M*m0MF-CunJ}yRokM70#cS3)4+Oj>m
zUqBvKdv%_{Fh7`#>$#uw9>zHSj{v;>Ftlfi`Y46uI~iZmjPN0clRUWv;SL6GNz89x
zse%SS=?ucwWoG^Kvp$~XnP$E9vp$984J+Y$>%*({-Hf*Y&Q`zZ1qMrf{;HQ4Eama5
ze#&6@zOyNhU-hd2Ih;S~%?3;Uly_)5w3p^baAfYSH(2tgyqzRx`<1tc^~rwA`;Fyf
zzvU&jr})Tz+Z)7~><@Xv7}I>p@$!x!eL5d-yaL7~4|^Ls1?6GyOU5Km@SLN9@&qr1
zG07v|%^5*?#Jh(v$)jGuF+q9MD`HIYL{A+blqY)i4W{xAt9o90ksE(W@`@N^_z^0}
z+u04nOL-=F$=wN)za)7b4BnFX0Qg73{YhR1VfK$C?|7C|`6hY2Sx(<SN%rnwOyzrs
zcelZ>snl|~Kd`L6_bB6&0gqj#z4eSIf1%Ws%Them1MLz2Y2-x>miclcukZwv%Y3<!
zcZ<Q|e~rDn43_YXz55NGs>VbAaNV-T-opWTST*sUGg$oZP;Vz=Dz8Jm;+~kEl-Hr&
z8wPJlJhr~2ZeDh%w}vp6PpY?p<y1bY-d2`V{WbMA_YT_M)ce|Cnh*WFsG0W*%c=aD
zdEq{&FW>)f=GA9xzl`lU&1=G#zW<-*wP8%(|8MSfWsKkRnzpE=H-IpIZ@HB>lreoD
zv6VN1_38HtT6tp`)9)3u_6iu&?-jK5uIKRh`w1T|O7~_l#_yGFU38fDputif?Yv@x
zr9RtvU!E8&k9OW3##CPIz5J7c^7dXKW0H69Dozf{J9zIKOzGYE=n>vdmXm%bulK;9
zekboVgGoQEI(uV9Zrab0UOr<CKSCYp?I6tU<4Es&mQ(+8q*u*ys{bQBCx_Cb_HmSV
zCS$7KF5Wo?OZpk!RV=6e{%CI^V@m&MZx3Nk|7cI)zy$4q(m&dZ5Kg4@kM>-aQ~Jkv
zXELVrGre;Rmh`)NSFxPZKh~Sbn9@JiOE}G>$LSyI)hEp9AL}(@Ii-KB*NWwo{&C*<
zj4A!&y-N(1^t*XCvYgWI?%m9o((mrwIEd0e7W$_u)!n;|FsI+$o5ONSzq_}X<sW>2
z-^cFZeZiR0&+@)8SkgbiOFW&*lhW_$B{Qb<dwPuqQ~KXR`V&-7uQ_2(zo(bZa!S9a
z*NNr#!FV}mSugJz#*}_<ugKun)T8h|?S0Goc)J-7d<wn~v@F}3eI}+y_F3u_Z(ab0
z0Y7K3^k4nGlp$n4+1uZH?kvR8fA#m)Gp7E&zqiX^>F@h{dkJ%X26*ah$}i<-fEOif
zQGE{Z&Sy;d8Q@)Fu$1Qj?`D=$c@Fe8GbZ~6deuXN_6_t79Y&b!8|WQlu-G@y>p__9
z8|a<La<Xrrm&0<hFUNa?G1+&jx6EL%?=-K1<z(L=&lzs=%lpfNyyD!TeS^F=3>JF^
zd20x>J*RscSWfnw?rml{*>k$rV<g2-_MGnZHCXI9-5bqvvS+YY#+b@$us35=(4N8G
zql~G%279j>EcOldRug9X&hXZ=oa{Tpt7JLZccypT=%9T=yj}*&_<EK%&|vY$v%E76
zo~m%0sXTO+cR@fNR%d(H8!Y}g)Z56I@-x&cJ1>|YkTa(I4E4S<Sn@N}`<XE3XQ*eN
zZ|vp%Zm5?`nEi8@cM)SM@8RBM21|L5@Mf@_%KIE|D`T?n9Iq-bXx}+r^a8?U-#Ol4
z28(^?c%2Eeedl<YET{55$ID_lm3OZ9AY-y`q_@OivF}{(4VIIAqrCJBO@7%wM|m?Y
z3fecydz3NRH_Cg}V6ktMx0*29H_BVja<XrfSIKg+Z?xBUETu>GjqwH=EcTt}jb%C6
zcfPlnF_qu>Ue?7y`_A`<87%gk?~NwR_T+gNv7GG5^CqyI?76^ui80x8q4&DMV$Vh1
zR+f`JW4)|Pg5@>VTazEOXRKGnn96Ic7rE5v^Y}K_Ye1Oo8|yV@IoUVXYsGT1Z=81-
zW3umJ?=J?Eea|er#JeqkUtM;o_j~|b>I&~=gQu!Zus(lx*%jV9G4d<D_gVgykzeWU
z2*^uH3cPBArM+I|MaN@)D1A#^<+%n=RVA>VFArViH53f%^Jkl0?KNh+z?|Ry#cRQM
zEUd>Xmre9KF@6p7w=J9Gbv0PZ|F2%Q!P4HZ@d_D}KV0MWzARY&*Lb-GZ%JGM^XVU!
zUE}2u=JGD|E@3&9ccC}YV98&hS7fl{uh5%e@KkjaCi8fqHzyzutI6Jj28(^ydXF+D
z`>yr&5a#;7)>9LVKd?R5dJ)3h-+&xu;RVwnEuh~UivPwxxZex!n8PWfdJn$6gQtvV
zKM>?ws>f!;wps^!WvKrOtl<$4g|$2C4>I8#<t;)Ca;CS#St;V}w-P@K?g}7I?MYPl
zwNuo=KR-O_5qDpHyxzW;(mhakTO9^zV!kS8qi%dWB9G_)tFf)B=VJO$zOaUZT|xEg
zXrz*kttP{7$)KF{9M%iR>G}Ex7azR8V&au>2ckBo8?X2K`4_E;%^%^A%9<18@0-qy
z1wpy3n*AJ$NgiT8%J5I(vDJM$h(rJP`L|w!oKT%|iT6O^NzdVU_Kp98jkTpOJYRqR
z>4@IG;r`*EpU9u+*^2me=P$k-_n*F$uOEMG`T6{lVGl1@{)EL2_#W6uv{k6Bq<)Dn
zIHXR7ckBE=iC?I#I$lEYP`@EKq^_N|Kfa^FdrAJEV#g<I(H@)o(U966=Pz?!Bl+Y>
zNJHwQn~^%K2kqJLEmeCwb@4F{{Db%R_8UKG?N4vDu_qj7j~{ONL`sLKtzPExmvJHr
zcUmxhqP80I0^w~hA+^<CxxY$fe8?i?!~1v~E_QrD8UEEh?#1ehKlyYNd?N(Ii+$wh
z@LijZ$hWziL+YNlNl)ZA!MiFbU%j08`?nW$@Xt^8&yIfw%2!?TZI{8F1=wfrzSO5^
zS1cxLMUN2KAJh|hT~xw}T+*qH)@ASS<)82%*(u?k{gtrPpWp*gYZ-+jD(w~Sdl|mq
zkoq3JxrO-=`fdq@OXBb!-A{N5yi1DyvTJ?qSp54y?Ly?zF1|0v_#Ab%@xyRUdy;l8
z@y6niDuj1Q(C*s()~S(8cuBXe_A2s!mt9i7|G!Zhw_)GT%yXbWG2@5Kd;I=g#$~DZ
z+A;bL{z*7OK8ATIk2e&L=*9D6u?>E{0p?pa^xL<=yxRu<oDB0m8{RhDWoUx(pq<((
zqUPQgOrOGGe~;nNuQ43H^@-u&>)0k7+`_5}_iD^|YN=aeaHz)qSUsN~t1tPhD;~4~
z|3Gf$lY|h{{K_wfEAJq^|4je%?f!SzS6lzTVlmoan{fxrZ(?;U#&HJu^Wd9GK|R6O
z_CS89CcNkuUst=n$SGYK?=b$WOuuP!I(~e0@g<&F#y;PE;Y<2QUm0s3m9wv37pCzA
z=EG)P@9dL5Nhh9CdVc)B7stnUa2WH5f0l|(kGS#tef@y+e7+wpn7%KMpQrpWEUEbM
z3di>g>F?s(NBvh&7v7uxk!WmvTPhxR>56)`T42^m;9u|!N$l60DAKUn2=8QIeB{?c
zZAJdQFOK!gc>k4p@aagCzK^M%#7;|n!}HkLrX7UT`G#7mz1gR*8Hd#4%s-Fwy@%x#
zUfQoezZE&fEBT1G14af@o*^ZAIFE2$yJr58{wfp?lwZq2q?V#~C)DO}A+_^4l1qJ&
zoib1J?W(;zV4mNN!b|yxKktw4`;YH8LBB<NX&&ge%OH*qACED-#8+<o#8&;x{L=5I
zBtFWw&zJr+o-gqU?XiN&JBX#ft<9dV%>JLP5@uj|`RUcg7duEE;&6VyM)vvTBk_nm
zBByrcr}yJ!v34UCyRqCPUE;@vL%lBWEehAIoiF*SE&mcP+IJv1+Oa=74<`JuUt;r#
zIG9f?A8ar2<rT!gSN?nT|EKNw-w;3T_xk=;TfNm*?te7gzq9^pvpbmnf$A?ld_4ZW
z;SZ!AluLW2erEsm6AV{d{T)dDpPH}z*@fo^K{=f#P=6!+S3JdjZ-3)TuzyB9>2Hxw
z=T)|P<VEEB<2Kb#-I&&Q)Zbz_*?*z_U-;DT`|;W}dJ;d`6Q7RF^SXHc|2tUDfoS}Q
zw_C<@;m6~;_;u-tzNMzZIE&>i`$JvLzN)1X%ZML8&ldSRn@KMC_tIN=-T3Gxibv`<
zHXd8yekj@(%ZKl*7&~n>3ch)S@(#<8`r|D2Z?*Zo%*UkvtBVHPhs1;Fh(7Hv?7y7_
z!__s<sB4}Rv>($IySuH%eER)heES#sg7#zhVERG3uzrL62i3Q&G?Zs>{)OoW^~4_P
zXMKBu;nB|bC!@WNy2<Pxglo!A^u*qHO!4~l`ClAc{{JnHKVBX||Bs))i(iXh3dZsd
zwmUiBJN+EulRSRi<oB~A|Km7zoZNT3r1m5Gqtt$b{f4AZ`H9zyZ>RgppW}NfvFV7O
z?6=lUC0**LY5sxs)jpp8@p6)U{%@!8^94D_s@*Rw)%-jh9c_jFiu?XjKB6x;*555f
z<Bj-R{5T}oQZjFm`T7{M&l}=#%TeulzxemcB^<?P*Th5f6@T7xu;*T)C;6s${PQ+C
zAG3Kr<nyI`s9(S~kAMDrK>7=z5<i9W$H#-^)BP}q_aFBiH|e|-?vI&s!hQ9qoaMfO
z#pmNT>xXLgH7TAy&wtSDJ05I2em_}zIA1R1Iq{!Z-nKfzP}t9m!6Eg|NR&J3X+z;x
zwBX$g)E{;>@(H8=3r32V^x!)V&yZdPpL+<7KSvt(7u17!>D5T(&r-4ckm}0w(yz^a
zvcJz6i$iK5=VvRt*oN_nzX)IS-`tLJU!UxQ_0hG+x0LAB75=UzK|M=Re6}j({cXVo
z@C^%0C;SQN3qKxHxrEd_Q(lt3PbEE}bU)En1NmHA_|mT8Dfu<5H;ulOi$5;ZC67-h
zK3<u()3|M^>-jzlQuK!xiYec>jUg)XhmAk^`V#MT=01b1W=<mgAI<$6*$)rxmyhSJ
z5>IX6d^yIAf6x!t8b3)<PVw35F5{nm`e;Aq?-KaNvae?;X@9}{sD8SCUXJ%TFx~0}
zWWVfBNP73fIxv`?$h(?)_wUD&zQy-uYp0mbTU>vrACEDm%NLRzYoYyPIkaIa<@V_1
z#Fu&z`osj{6SY-+b3SCL+Of|+gU@%?e?#HZ;hlI)SMvF~Ie!nSH_iPz;-lSypX2_4
z?!(v$^|5`veIHWCoV(z;z3Bg5J&Gs9@i_a$Q<og=M>{hiKRBPj^1ynO^4wqkFvNlK
zA@NJTB)+=rIS0Pwjq#%2h&@=&@#8JsH+EE|DZg;dxmA2Spn7+>p2M841ir7lZ@UmX
z>x$=av;N&T9Qlz`6Ys&=y9WFg%RdU|e3;*xVSbL~-vhoGhxwk6UOSch=i=9d<$fMf
zjJI~V&zJsNXnem*eDqQLllUb(aeaQgT(D0ie(Cp59!~KRwbgNNAr7gZpr65VkbW*G
z$8xHTkKu-WU3WN?Z#3gC+><rwN_z2_^!_K(KREmR@g&wxf4(2&2g{@P6IgyyKOyx&
zd-9(rxqqbch2MKM<t%!F<0;IupgggDMgKq;>&@5q!%4YIJcRxH#PVSs!s*7+eeGRw
zKBg!2NcE5X>7g*6aow<bAI`bap1Sn*!neQhe!zkBFuwi8Bl3CX-mj%ze3SI)KA5eB
z^FC}mpW0`rMlScK*HmG67(aNv_zb*5kNIkCsI({K;h)SG;49BOua$UL6;gYUeTR7Y
zp{=ODCppE2@%4vq_+dzCXB2MVbTB-&D@xB%jm$j)(X-W+#@-Oy?Wj!ntzj(hD;8l|
z398Eqq!#N*{f1c2VZCsT9{Dkb!9PohUlX<YzF0`z{y54Vb+w_gpMgC5gI`a4iT*|Z
zx72gy97K5gV$z4-LFD#@^F-WNy>%Xj@%xYT3c}*I=e5H8N_n6R|Nd22;)$n}ik}Yr
zej?i;^JZ!fA$6VcGhaVe?$47;%{s_d$6h76Na1&wuSQDiDC~zO!+k95H`~1*E3Y<s
zA+;1J%1JL=qj&G46wl(@DV$(h*CG%9EG7I2cOdrfi_`lq{`)gBe>%_P&!0aKKcwhA
z8Ar|g3F8SX(o5j?W+I$URNZU(Nhx2O!@+vVgmY@%vx!a5fB#120SB9IiJ8|^x~`j8
zv(H{vJY;v6?MP4*9_%-EM{B~%ehtMFQe=<A`Pn!BK|7T4?TC+u?)muZ{CGRbJ}IX#
z+n2!hMQZGe4@dW-9IlrG=MVQYalV7|c=W>^yni745VXr6U-mKMX<hvbwF~qW{QKkm
z2dy*Vy+ZEyo=U;^;J#5aiuXYHZr1O}`uCglhF#MS%6KFFPb?0p24);{6otby@z1L1
z=i}oeeY;NmM7R%z=Si}UH=+Z^2j_W)!tV}UOxQ8^EcboCWDe)2y~z*i;-96KnQ=Xq
zV*PI6b;0Z7NN<o?FPK0k+_iE2dOVIDFaCAo0*!yZ9S6c!eIU9>Vf--n7Hu`5yf!T3
zE35}tPWu^fFOTN~f+=3O=g0i`aZbjuyLetWHL!1j_0||{!G4wIW77Z2{K`_7UO@Iq
zdM9)WVqd>@zQ}zl<IPCESGYfn?YE^FN2K0t^-^3o#6dkeS0+85k2d3<#AB&`*I+o<
zM}u(=^=O>)%bV=7l$0CQgU$6OdV=HY%T`hz5{__4m9(Mxf#kc_X0nU?)>ed(hkur8
z^*a5F{x8^8J<L3|ZhU0{h6u5q#NpSc)GO_O!M#}1jx65y5<4u_WDACseX<bSx33=A
zZL4K7Q6GMvYisS{q<-d`c}0l(G1SFBzkaAa`Q!gwo+rrrMlvsm*OUEq+J}<;b%{qP
zoJTC8^k{zy_P=2rj_FH(E&BoS`&lyX(>MtG#b&<?-V@^RBBy;1i^pAfe}UVb*h~8v
z@SgM=m~Kctl}zc^&Ue%j({IA>@PUn3AF<&?9#*~L`qhKePf*p(YfoRs70Q>T&NAx+
zOHuyqn)ml)AAHe=7*9xzx|QOSaHKEg5l?OPnCTxSUY|<5Qf~2B%3Jm&r5--uapp2p
z9+sM7_Iu&n3+4q_&(v?qx(a>|_H5!yJCOGO@1eHZdm*MDQfr~T;e2`gLb6BtiJ;tX
zC$@Unybs~eSN~?lUkr<XetUWl_H!}j>d%QEi$kiI`Lb@5d`r3iUMk^b{F3*bq<jT;
zT}gJy`>7I6-s_|AsDpp<o-QFD+iLk|!VBtI>b8#&?<;R&@@t#>HCXO7>9(Wxb$8sn
zLhQwL45mx=3AJm^BQPEuSC%Y58d9B4K<cQ^3=XS71}CU%Hc>osP7zPVo|<%F{psgd
z%HhDYu6SkrJkzXeLW;%<M{PFk1m1Tv<A?7*WDm^qO34nXR~g@gPvfP{<5q~v*HPMx
z<6#pXZnvq`KVv-dC*!G%w=ypZv7S|PpKLhK6Z@O*ark`kTk#{Jwpus>)3EscXW4J?
zG0FY$Tf&LI|I>V*!?&}UnTOiys#fSHmLfTPy)Pei;JXSuPn~Yo#kS$29|&LK-SaT&
zTZ*X6r~Py5)57G3RK6i~$RgBt)C24{5|2=i*9A#D&PY3xc_!&$9QY^mPq@dy^9ZVc
ze_kQ}C*^UVauhpa_5E@^*z&2XJPwqP*!)p0F`r_WP};xn&pQQ+{6JLn<JV2`{a7rH
zUx(Gb{)!JL^;tXL;q_gZ*J}rkm(C6RdXacz`3D=XU(OT{-1l6GJe%zb)wzz`e>wZ{
z#_LJ>OFHCle!71Q!+jTSH!qugK}!*>%MRfSt=rG&JZE3M`2APuza)ROuT0|;_8(FX
zK|YRK!l(OPHjhi8nsp<|<I|UYQ?Z-w7uD8YB>w+d8gHNMGx~J@>BonU$CA%@y04y;
zzoZwBh5w&T<MZ`<aeO)Li)sJPpC8D1B8~rkdU6jO`xl%S#h)*d+*S{ma|}plBZh^0
zZ#Ip8vi_Fwt*-G<<TNjXePXlzaQHh6VIDsdYTkd8bfn$T_~PeB#y^qMJPg(~n#O(M
z%RG><+^dxL)kEAK9d3_d&R2rk_6p`R!r!5hd7R&VW!)t46NUNAdJGrhcpP<)nXmrw
zbR{2>KT2o+_QGsMO@s2aVV_vO@A7{uo+93#sy+P!mkWIdD4`}FQr;3DwR5^>f%7kt
zhimQ!$$WRoyS3Fzn7<2@P_u9Jd-U-;4rOp}2+R8nF7M0WdnY#hU{o)IlhmQ+UXh<q
zst<o$Ap8A&8A+e=;p<6$k&oj|8(xne41a&)ko1!@4%%EEe!C$54cC;v+=rud>(&d}
zh3QlKcWU;f|7bkazd*my6YEp{<i4zoo4#G7@0Y)nYivCG$|>It??Xub{q$BCfAY(V
z+NV>~Ur;)*|6<yIh|3ku*?65O<ulauqap5Zq`#o_a2+P~T*T{#_;ROwh<$!O$dBNA
z3T7YM=ig-ZAN+MOh4<|vd6?&ketsoCvHI}4Ty@w*_u9j}KbxRlH0!*G`tBBt)3-<L
zbj*IF&!1z~Q6VMw<7GUE{cef>JrnU`p>lpjINl%peqQ?Lc>VQYBlag!zBJw?)b!_4
z9%6r~MfwsSr3>S!+1C##iI4QcHSv_1a~PY)i%`vecDy|lkKez=+Y_trpRY=Jh&?!N
zV*erIS8O;bCqKNj7l}vo>*AB1#N(GA<=ZbmiKi}o%rE-?9w=ArXQ`ZgyXp!TpH6H&
z`Qd5);P<a$kIXA5e!pKn5SH-NFG#$8eZ|`)`f{Iy>^yLJ{j2n;T_x1qM?R1}`}em$
zU*GZNBlZ0^b6(-kyQn=n++O^4bRhfV;}?Be*OC3W4~Xv#V*ey^>3;-EdzOB+E<X9o
zzVq_`?fen1QTZ)4-=p&T^Z0R3$}he@zZTB>u>5IVhyTVuIY*=UKdw{mT}Kl7zpM*{
zXVsjSQM$I8K1F0m{rKd2jdG5%-J~z~>?91l4|AjZo5E8+A-sLlKagDZ|LB|u&W+4D
zlOLb(W6uHLo@>lG!M^7L;$L;`(}_HuQagbAWxRhY`yFzRRdD<{P5eHalq1mt?=#W)
zY2EvrQcmPAvOnyXllam3Z&JM+>^^2}IUcxNPi4QBa=V-TTkwA=mH&atLEdMR^H%Yz
zSn7YTK;B~%D(8#x-g*$@ye8{YvLndH_8yDjy-Yr5JdnNrzsqU8;rHWL@cxg~2T@xI
zpT<Lf{1m=WDNl)4&Ml-q<^I<$^W9Kc*ZqDf^Ejb$ABt$*-`|au`{z3Jy;1m;jpeob
z`3px7U(zG$my6)<Mg{dm9#XM<8Bc`Tsx0naN=6Ti2fi0+sINzSnRoc}S4p4dnZCYY
zs+WEBW8;%}|KFjXcck*JH0OEn9t&fk*W{wy=Sx2!<BE(sv=0jRNjUrl4oB}z?8}eW
z6D;@1r5y+rxuh>xsN_TTaV4CzADM?rJCkriMK1ZE^L79GAF|I&dj5HPd^ky0zM~sI
z{!cga7`SI{?gLvY7T0~wM){V0`asm?-+K@}tV8@0EOO%deDPbKPx<k&l)ub-2*=Cg
zvEM(%=R@L=a_qho!}{Nu$9^#Q{SWFF>ASWu-wEOI>VDqe7ApQE{b?{f<~zQhmhb1r
zp9j+S`~7~KzU%7eQ}RJn=3(+4sZgIUeoA)w-{}>;ynh;t;eNpqOw*_Gep3AVt$(&W
zC7pQt#J}Yp63t8CzAcw~?HJDMN0QxQ*TI%wUG*mQSn&hB*Fb*bkHc7xLH`iD50*;)
zq@Bi7`aXfwzfen^!S@HHJg6LPb=qRezm#ii{f27f@}60&p6@@xKLhULV86VE->VgV
zyg%T+JL=(i2l|biJ5zY8rryL}nO_JNI|=)CiaeJ3eq0wnSZ`?0Ih)ZgKU^#h@%b0L
z-<eG3R)6^!_2th}g#Gp)_DXq3dB)Ox+o9;kmml%{^bX`dl775iZ2EqEik_q^dbE$?
z-zRGGEZX2>>2E|&#$l-^>Bs7gp>U!vel6=%X^(%j-xd4oqLN;$KluHDl)IE0Vc*Yc
z_gCLv#9zNYocx2RUtiQ8#ba1Mtq@+2`u8bg`*G3n?`z3?K=NHzKlSIvmwnTd_s=AK
z#KHR&!WX*;$Hs&ELnQa}CG{Xw{4sW1@%7{5y=j}skiz#{`2Jlye>vY%ZNG-}<oz7F
zw+!n}vu_~ZZ3(LeroH>)ZoHpLc=BWU-icH5`#}FH{}w-%_8IH1@%>LcmU{!fUrRYt
zyYT&4#>x2gzO<kC@m2CCzvEMvA4+`m9<HT6NW%6ae}dzw_=(S#b|Ll?wN)d&&nW#&
ztlaPKgfDthp0QZUE4DrQe(S%FA@zd!4$c#W|7IEHQRW3Qzy0G>$~)c<g&(Ykpgl6~
z#m`IP?XF8t+Ozn_#uUt#t@4{u{zNWR{6#3uOHdd8V#^idk#JNGq_yw={c?-XFa3U#
z{4SS-^!25mmj1kq-?M#nH0t_#V#j~!f6L|1_=WT*;%~BkBka$E<A0Y;&S7fj+e+f0
zb}Idf-+$LN-Ur)tus@RD*^&7g?W4i_JZ2vaeE|O~zW+tY$MBxh!l2x@gYH{PxVrUZ
z92WY_Q<6rI%6{0sROX$suK&fnPh_jl&AEQyy_IETC-npH`(FzQ%l&?Nzb2kazajmB
zl%qEB#eYB0|6ZAVKTfEOe-fWyiZ}jyzwy}LCy;%4vFAUP?!TPso=-`6(0(ZF+x%ZT
z2as{@aUSQgd7Pv79BoDK?ZSP=4&+Di@9~~%{5Vz*&#&eC5b^c)F#Psea2zD;k4y36
zUHto5Qckk2qW9J;^|N{Z(SPqvu(UVP7b@qP{~n5EhkuqD$lu|RbftU-nBRSc_q0s;
z_`lyM-$$uSF5%;8UHUSgE;QfU2&rpUl3mhH>k3ET8L?Dda{B%Sd>`W{jL%jrO#c*8
zkIHw7%=^i<T3<}*T?pSbLOsEU7!3VrfNw*2{bM0&`TimIg@Srmu5zv-_ECT5m!sH4
z@({m=<^PVTgo`hKibwi;`5vRCJhM+?^Y=65o(=TFA5#7#KIwO3v2T~uTkYX|J(An1
zX%yQJv`0hz`jGs}xLa3xC!2jNTMahUkDtDO2loxkdfn%feYRS~`&#i>>Rr-{FTVrH
z>k9X0{ZaZ6vKQ)y&kLozsl2eR@Xw!L(7a^d-~E(%h|F8+ieJWqy5iY?c(UeT?2&kc
z)^5+i$0zT#|JisX|Ma~yzucw#BptC^+K13To4&+bdpv$U)4s~Vmyg6NwD$N9Hoka0
zvCF5Dj`(Xl{d4L2^OD&15pS3OenR~3;QqVn3(p;J1d#6qNWLeT@x{*vVez+v&xiP>
z_^tF4QvO2Y%U^Ie^PLe(;rFg^A7b=&OdG~4zCR}Sxc*|sMd90uaHvLK^7TjSL+t#s
z@%-8FV#hmX-^)^4I#PMad_Eo%-><I^%zDFCJI(n{Nd0`d$S58f7vNm=Si~W<&QM2<
z=KJNcPLgy^zJ$WjcVlc-@L+5_Qjf86@FO1YPT+iWy@|p}_<NotEP6uQbG^&FTByjI
zo<-rLJcNq9g8$RYMe-vwzI=o)`8`m1iND0=e_#LkPcIkAujE6@As&Co=S(7(^|Sbk
z#B;8BzuTYx3SY`w!q?@eqAz;lzk=(cwf6(Q9b~7io_d@7`QWhMUz0w3&%}gx)K~cw
zkEBcd5c3`N_9rM0t4Ga#rXNqd9>u$_UR~)CALGV98Gq{@_hg(PfA#IB`9xTKV(#Pk
z@rWMr9sWK+xTf6V;}bpVw?r>Ntuy0YM7i5z?W`;QA>&Xlfzyeo>r8m$;h)$i)K(G?
z<-_58Ncxr{{}Me3N3r?w`TkH>zT`Zsb~&6^UV?E-I?%rxyPZ1AOZ-R59rKOn7x+Ce
zv`hB6WZoH1#sA6g{PPZ(-^h1k&M@Cm52=SZ-dH~DbMklAiQ4J{{tl7EBk9yd*FB2)
zmU6WeVOxzf-<1fdet!$f_vN3)@1^5@60Vm!0>${`J2&!u?cE)TFTZ;#G~O;5XJsEx
zsO(pfzP#t=_Y1M{!1wDGQF`%wu`iYn-*+|ZPg~KvudaBde54=n?>$LBE9=MDePaK2
zE~LMW&xhnQ-md?29A9p-Pa^qScMjGAeBYAit3A!U%2NNO<e&fB%b_m+?Zf+#?`)=W
zkb9cqKk-z)3wQ(fH$wl_<tO`Cav$vUkz`-|Jx}?5GSLIS=ON!Oly!oHmvsK8seA{U
z-k+2H$mZ`3?TdGCe=X%M?RlxWzYX^=_;)KLKf}JJauO=_Q-3V-ZS|sg4<7E-UPO9z
z`GtJvtb+Gzg$lo}`WAbMVjB48p9B6rK85zf`|(J5ke}E}Fy-41N97{lF|y6~<1s#&
z7f8O|UWsAgyLEi8FrF`Rq1#>}{rL7l{)007vy{Xm^c23YBUtQ^cqKl`7oD%ldX!=f
zmWSvIm39+LegBep>H9Y!HHq7CUFV51j*5LmeLI9N`4LLr3$xVwJg<@a*W#D)Sp1ac
z7ydo?+^$rPl0W&5gkYgk|5JH=Ul$esJ@z`u!~Ur6CkLxvSG%Z-zyEl_Z-=9O^qp0_
ze<tT@K|YS_(vIcck)$W}Q+qo8_%HW`gw}p9t*-l4a_=af`uAPLo_HF24)5DjyS-9Z
zmioUf50?M_>Lvd7Dq`*az4<thzF$7_9qHKgL@x9|@%>r(!KZWQtEg+MSHDDRsZ!Gq
z``=x>U<>jcb)wM=^Y<G4_9^|StWU)6g-SmmSZHkdSgJODJN2RJtcIuzc#ejDXRFJt
zY&8j<$<|4#zB);H@Zi5)crJ%$l69(@3lGxC)@ktd<kM7Bcv`@NI1`?^@JzBUQEk)}
zs>&)*JFTnKZtH6J(!eBDZQY_u?0ewf^VOHueSq%=*+NMFX-Mm7h~pV`qIyQ149@^~
zCR@(|eh%<+fS)t?L;E?k4W3CLyBwbH?d9qWwH(4P2l;Y!wt8NTfahFz&Vy&N^}K2w
zdR}RG4hy{uR=o^yy$t-9A>Ef%2JkHV6?KCu2mHF42G95KbPc_(ZUK6hwOy?YZC7hT
zRjLA>HK84<9G*3yuhkpytO<Ps&rW!D!SgLV-@)@e=zI_K2hjNe=#LP`k3g&8`4!^%
z6=>M{n+jW#tpw{Xl>oG%wO6IU(+Hl%@HBxZ6`rQ>G=nD%p62khfTtxqt>9@5PaAmJ
z!jlfqVelLdPdj+p!_xttBjD)>PbYXf!*e7&N5Rtto(y=7hUXY~GU4e8&#~|v2hZ{F
zbc3fmJU!sag69NydcxBSp5E~Ef#*bc`ofb9&q?r{3{O9JPJyRCJOkhv2u}_?r^0g@
zJcHml9iGAPoCVL>@C=1#7(Bz_83E5Z@Z`cX5}wiU<XMZXJZlR4w^!W&&s2Esv<`=7
zGDwbAPg_0VnQT36bq6{H=!fcA;5`exXMy)D@SX+Ua}aJhJkP`P0z75#tbpf5YXCfx
ztrx+AUj*G3L3btaU$!oWXNvVQ;8y^bTPMPEGCTv|nQXlZyw~7)9iBJgc?<Y&0sk$C
z=Pd~L7KD2ncy9ymZQ#8Pytjc@0lW&}RRFI7coo1~ZF%rawpK%nSq;3^!25@FEj*L0
ze^`G7ItA#5Y7K;41H3g5b`9{>0B<eu)&g%W@YVuvE%4p}-aEj12YBxQ?;YT+1Kv8|
ztpnaV;H?ARyTE%Fc<%!5UEsY7y!U|j9`N1+-h04%4|p4Zw*hz?fVTm78-TYFcpHJY
z5qKMcw-I=ofVT;Fn}D|oc$<K?-8urEDb{xQ_YUCifPe3Re<!LraJ#@#+EDn`4&&B_
z)`R|u<lPK)RUfAP4Q-?bGak-(4C8T3FK0T5=@h2Zna*N5m+2y=j~UujJ;8Xnq42F_
zLtCkBjQd+;-(aT8tv-`6Kbs6bQth%ng4K)#^hns9$W+=U{o#hXYOmcH@}I4?*d32o
z&^K+df7-3oAfP+oEkDGmzu_M77Q1_j1%1>O`z+Xn8v*pMw<+l7w%C*YuGINJ>n%|r
z-(@d^uQ-kcy8Z;z+hrdKw{Rw?ZXrz9g&q&nDFTY&ZUKtn5ThM;0HyRZLX=KM=tB4^
z!kxemZ$~=Gp?It?(j%1>!u*YZl+T1)K2?Bkhf^!~FNAo48fWw@m8Y8Qg0axbQ?J9W
zO+BlC^$QiIdxW(-Ty_cM8|XEkDd-J~6z0DkP|Wujpi~Y;3hf>X80{Wo%}|)1@jz|3
zWn-xs3hgfdjOFlGp!dVuy(55+<yBy9u}N=>P5iats#FVll(pd(Vbw4}tqpI5TPP!d
z4gfyV%OM<62YwZEI_P}{r(Lsw_R1pud2s6p!@mc1f`5d6eMYHyfNz9bjEjI4KS6pg
z&m!7$0n+)L|2drhQibLGPped6Id8JcIA3L)uQJY88Rx5v^Hs+AYMl`MT&Zs@qI-Z&
zgtsazyLH0Gi{V>)KyQKaMLObLrILVt18?1FpozxL)(NkEtW*=g?^Y}L0u0f^fnK&+
zsZREKws)&3|9snWx;yZTruLYG4dB-kpysLm(>Xu88CNqNmoR+=`txIUHq`$@`!PGQ
zM5%{?-U9Vj0`y8ajVuLv56~BY{?taPmw|o=^i80;u~KV*e$_~+4M6W|snkb6F9Z4+
z(8tbGstV{SK)(a}CA>BF6VSUSz*-{onEf&A3MGeDal0K8$%WnR6GP)7<KXR|(?gd>
z$gjsmn#1mHF7S{0QK`{D(QhvZWm-k>wcsikCsA*_mBl!VaW>;@#{KPwuY-JrR#`v5
zuJx0lE$XEEu>2=QkiHb!WN>+CiotJ&rZb(zbS~3HOj}zR?#<9+jGth-JmP}=YeUN;
zhaRDTQv4BnT;!<VlzI>N6mDDu+tY`TALM@$8qWMxOgnJ=8B8gj9icwfx$xEd-9SH{
zkM_T5%BiW^qHdl9b61D33!aYU5?0#K7S1j;73!gjvxUob3zvJYwWv&~bDUi39-x<k
zUKzCG$v`oksZKKMO)>Gn-7`a7rnfqTXFGXZ-g!no-pXg3&$xhb0pls$Z;gv=h5q1W
z2keD*{DwpJ{u40OJMrIR`G-!SMg2{o^&7O8+)$C#<rZweMI3*jh5eTso?(&y&oKS{
z2sI}1E%;sA@VLmx2he}UMIMHHM(k~oP4H`n?ZR_7{2aD#KI8d}7c*YWIMHr*tx~6l
z6YVw7pPvnMA^3M*c#9Q<TW{Bfi!GGj50vD^7Rr}~OPOEF{8HvGuE_`abD>GcQiT@v
zr-c^vx2>(~p+7u5VQ&Q6-_V39kr|Vr|4rCd<1cqZe&&IE(>!R`3FRCQQ215e7l4oL
zc@^*}-U`kS#rrYHvAuqtu-wG^V?t}|V(^2M$leI{4{4FLhTl1|J|^6mAjkHd8|h%7
zJy%A?MOwhA(hZSRt1*=O!bm0SCmQ)vz{h-6Mz%6P)$n&hIP|;UfL>_oDKR<{+DSM{
z{sQB;(-8W%E>Rs#Jy)r$=q3lpkuFi{FPf^<D8}o;YKQuVDvp0Q<K2v_xxA~HU(I~S
zCVz5l3h$VC!FVf~aWdl+#wm=`7^gAL<oMI=Jz!@Q^w{LL$vl2$+T_34te3^<W;4xU
zn#J-wrt_KRGo8b92<zuE&Es^dSiYNS3d@t3rZL@O<^zM&xCqwwZ;=#*cK<Cghx3)g
z`O0FP#W<UBHsc|ThcM1FbU|V+^K+S>Zz%j0mHmS0#|uq+x<6dV?WxeTr={T{<`*%)
zi1{;^KZE%*m_LX4bC^Ge`SY1SpZW8dznJ-pnZKC%#mp~eelhb)nP1BMQs$R2zl`~1
z%r9qtIrGbzzc;cA+GFG7*3qA!|7)4tA-WjmH^(OTv7O_<uahT5h75&%EqQMQ$K$(`
z$C-Y6KJe>7d5r*?47(Ian;N_~g5&SEppX5+p5zWz0rV5U0;O@FgLQ8yw4+0)oob-i
z&JR7LwRH!y*XD=xvD*WM-(IvsU9cakV85u~dRxzUJ>yFDr%G-=TN!U<yq@uT#ubb!
z7*}$9l^owz##<RzF|J}v<L!>nZszZ1el_E2#!iUaZ;0A&GUH^%DU4GXr!h`ryv_a%
z>SIFv{`TyJR31_;IDc?ehv<pR6x0*VC*}jiaRtYBEaw~QCvrcWXnlqA()y{4Q?0h9
zAB27n=HqkgJ7)er-s;Bk^w2a&Z@iTrx_c$|7wIADAJRkDL%$0BhtY4?Ad~roEtO@~
z2}fAjW`19#azgnq9`*%2>feX3JeTEpp~s+Ku2N4VoDS*z7Rd{(fbpQ9LB3gkjJ2qr
zZ3^jQexQAF`4q6d1zb*r1}{j=h%SQp)XoMO(YIhe?z-tFy%XIcPA?<69m?}8HzSJc
zfN?<6VSQ5M(l|fY-Dcx_Zz1T@x@Cr0*SzG;;rQn>U0jpiLkX7J>yC@$Lp`+A#hkxV
zPOrkk{8T|bHG=W?Y~3OH{$`}h&Ajm(-P*K=(Lk$VUVVY4c5^xCC2m5!y=MM(vz`%3
zgtuT}-D1{bGj$H*9JBuVC#>hkW#PPIZv^MFTlISG*LE|lW}0T~9&c4JPG(%iIE8T~
z<E>1~m|xEIUC#Ac9-?ua=D{1iCyf4r#4RRYM>Sj@nvkH>K%j57gY_rSNr0<VrHOAr
z;#Na1ZP?#lexy>@Fuj54^oD)xEuFw`8*Z_(eo<;M&|WZ~_<O^>kudnp6Ai0Er$K+S
zrr~a;)h52GhD3jD=$Lt3T1s`O``JphOG&Q5hd{k&0e|gCm^X#0L#2jxQsl2`4$)3#
zoi$UZIq1JLb-F|Cpp!%E(oBcfHAX((+NFMjuUHnP>{2)W4C|MaK29^Z1^akP7Kh7r
zXx!aokDZ9~<Fz)<FWyYaaVQ@{99jqGIyl~?G|FTB4mR@J0;cry99rMzoBY830gvAW
zY)1j>ZL@F0c~EGZJrUaB*^Rc@N5MR1WTT8I*4x#9pC1PHHY#+`@9%0<<a`DFatY8K
z(0-n2l*a4KG+t+>@j5e&*O_UG>`CMGW*V<IGono({HqYpAy?sisfg_=V!Mi*TVZ|p
z5y)vhUS#G?Fe`UxU0&q$fO+f}jc8u=RiimvADh%wS3`V_2S<r^Gvh^a<N40w6s0-?
zJ-(Sz8I5<Tc~IY78z+Xae0w!sY|3dtBDJ@R8WYWD8dij_WIU1SWTrPX9?tVPnwQ=I
ze4O{*#q_?$dn2o_1pjPI^SxW0O$x`ce>UEvwuLRVp>c+34>vG{eJX=L0y}Y?^m*fP
zQCxTLXsknztibh-4*ebI_kg#X^^XpH4b*NjDf;29N*&T<zRAx_UF>`T{ZC4x#q2Ms
zA)N0X--O27=|HjHoC$O}^iSbKI#}O5OSCo2pXUM}%LnO3=oe9b65RfM5b(~Ilqv?=
z8gBhQ*d)VR2497HqRAZgw>jJ%O1WH%4c*Wr(JBQ0IyQMq^kwk7txcvxAA|AbE8ySx
z4XnF?qCGzXU1`RjM5}`79gwfYLlccX^??qaj<_3#>t@1b0LJiFHimHv=qaFwbU4rj
z5dZl=&-q2Ei-DrOmmivH=oAQt`I!a$#^4tZ0w3oiuL8z){SUwx&j!#dhIYFh=>4!i
zf)vZ^TcAfm|2?<<xCr`Zvs7Al?+7KD^$6NK5c>68z`dZqM|u>blN;)0Jpl3LhN$1~
zoZ8L$8^oI%DswJNx74YrWzJ)5VZE19=JWx4M(T9qXO=2=sJ}0BW`q2q)J}?Uxr6&a
zR|2N}po|dh^L65OSMK2c&<!A`eWG&Hf21_(WcFo{V!ts{XL7sF2+@95Cbw@I7w<~V
z<aXXC)DZk*dFo&@PESyS&G<Y)Rj~hMazC)1+jS=Q2N@ySKg;BPp~9hYzQUpLo#yxd
zfOKfzt<tfh;4i6_4vrH)0!;>NH{HtqmBsr+Wv1Ufx@nc+4{ln<{lu0K_Fv<hE;s%E
zl})Ri_uE@)TGM3SUrY|;EttmukA&MIOF@1O`2Vw@zxNfT$^bvSL#bCm-lU#V6-`r2
zxCM#T&Q=&-K5UxC@uZn}8Z=Aec+$e#!EaNVEe{=Du2gEXYUc@v=P;0WH1SkBhe1D>
z*({yIr<?Gj0FxcdLj{n29$@(40!X*oX$b4}OPh6K{Z2-I4q(y081@n80*)l3ekSW>
z8od{qWwKso7;hb{1iN}d{I3CjTO!534aU!P&9Y3m&zq%$ah$<;y1OL*81lQLSvJR;
zZQ^Z~md){I*Tj1pj2o?h{|MAmMbjMC&oTPF(sEcor$&Dzq>~N&l}3LE>kl#d=cf%}
z{UKpIJ;w5E`4sFU0RM4gcP{Ja8vSW$xvalD^eOaDw*tP~=;g6qp3!?aEsyo`!uVDx
zmPZNf$1VZ>AX9$%te<c6-%878{e0HXuc_Y+5bmF8)ee^5$Dntf317hB3rzUm(h4|y
zK}~uVgman~gu5C2!f@XR%oCdzhSASE0X-FP*XBhgo&|}kLhW(=)Ex2!{q=<At3uOZ
zKH)g4LQA0DuLe9D{9_8x7HgDR)O-e~KPOD{>uLw(We^_wvsat14L$J}48Kd^I(n+J
zJoNqdFfMUC?>47#vJ&vLN0j;$=x@;9{Rp%G`tN9qYNzQBsJ|uD0qU=5i#cH$@8^eU
z9<wUc1MEDZ#r!bMM^>43IJCub?kAS>e0_Onp4kuF66y=_k8H6e)CuU=7EXBb7fM|L
z`ba0Ym>z0yKCWM;haN7(`RsI)k7+H|h8{Qz*8eTE(-+ETL5s&&?=jYUjP+8T%@7XD
ztrW)NM_Z&iJ)!+gXrAi)4DA&8n7^eE&*2X#wE`%Xlcg4iY5uTDEdV<{ZLv#*A-%6!
zw017L1<yfdIfp~Py}Ly**HcEc0PJYka+^7aY1ML;Lv}9?)BIzW^Ko~m-<EkC|0*j9
z?0Pa(8lDkWYH-U^Q~x7?J_`8!mSr68Vnb)@@-U6-+w3T;i|%N-$c#^TVu0rzkG7<J
zvu9f_=Jf^5Gv004!KCNHz6tK<R<;}$nR|^=pR}yt_{$C5&}3EU!XuPw-0IEfOR(-f
ztW}la!#q;iGqK!Z-__LDs!(sZ{e41<RiTGQDRob)O?DS^p0k_%t&j8JN2tFlbSCT<
zJl3j@b6+RI=ugkIn&nKqiu@PxE3FdQAIZPgwc5@8RL$u-2{f-xPM~>pN&@*c>4#dU
zB~X2(CE%@x;{dOMezS_x?*aH~=*Oy5f9D<;fBUyy75W6m-_u*SuCXf$^*SE-nE$`F
z?v#M}uTrg@H(~xW0>YgQ@j6a_=OGS{cCQEhy-*HUH%|;<JL=VVRcIuP_uGMg<-_Rz
z&_2N*erTPQfc<Ggn=uZ~>sqwgrPj8yRC=3T(G~B&z97*4pcjr(yE~yxmdVe8#Kh1v
zNbmGEeat*=c$;j_=PvaPl<%#8yI!l*9H3W0e&@HT<oevD<~&F3`?GP_ZgM%kjOb!W
zr^wx<UV~dCueZr#em=*W&*fT>FdzC0yKO<jWh-F5)wYo3g)A>(nh|{p+IdFX8C*Ye
zxPIodew9t@-1)3OKY{y+1nMVfp7u=J#jL+L0e@w46JWf>@)6LBp#T1~?N-j$;)J$P
zkDs<JX8mHW?_#d+;)JJRKKBvGslH2DuQUOF)jOPCn(##uoDZg_8~%dCGM1MmJoh5(
zGo_cYyqx*v%r8&)5yBmop2^`VSYE;LR<NA*$1>A9Shy}bDSb)=&#y){8g6J#`nbsY
z`=OmP9h$zL<Eb>|k<w_Z@spXlkAwd7Wcn_Z2J?rq^o-~d@W+NN`#AkfJKD;2R3^~8
zZI>$iQmNJHeVj?)cOz6{s08Z&opkbdr0BQrKzxO!A6;u>{BNdIaXi#-p<g@({)z4U
z6Y!7lVY{FYM*rWX?yN@K+PMe%PsiCC?L3j%LkF{t?Qi<S;ce(#=T@h+gX_v7Xg5fY
zfN*4Yf9IEDG5*%hNMmPzXTf!dcXK)HPH@kKd^Xt4enR}Ps;;rKC5*Gv4s#;hA4I4>
z$cV0mbGg-r(Y{T?mb=;CQdlpI_0rA$*Mh`d>h*tOy3`&TLO+fD_z{P%Qb@Zp?a8!1
z(=(ZlVmglLRZOot{0WYS=<XI-CcT+DyCz+1_p1-5di@mWIZ)2K4ySxJY>-o<hv%{x
z?S>fKu)&ZBmCKL_wVNRk8drxzs6FL!xV#AUi}|dV&wA9}uWhFjab16JJK8^N3H!BO
zVSZfP4$eUotmE1hn0R1EDY6L0zx9CeSE)}A6-1`N{H(yrHRH&F#Dd884G>R?T)!I1
z5%zcQ`v58JFMif;Qsj<}&<_F~0{s)p^9-f)4>&K1(70L{p>c7GIp5C>6`K98=Ix6j
z1rT3m`x$IcQDiCfzx@HXJRbIo+HZ2MG5!qWEBIkv`(3K@UaZeus_{^w4Zu$kPkfGO
zrvmB^+qJUP#i0LqG4WUZO!RLsE~EY}us%fk?HIy4Aspg1Gmy?<f11z!v^dfo{B>db
z;s}k)rCblihHhw57NPy3GR_Clo$bp^y})?R`W38S!SU=)p#6{aEU#ob%k&HPbfEdg
z9WA$VeOIwOBl-=DS1)wPh~CBhJnlEuKVmoAUCnlHvT0tj$!-ekfJcug;C=5cPSX*X
zPbZ4)%MB+-seScvsC-j+9hwp)KTBhr#yB}j`=bu?9p*dHxs!1|MdQ{@9e1g#U><_!
zx09j1BKB@Gez@UuPA{F~>15);{o8iX?zeW_#`CFd&SgE&&TY=9w{YFO%}E14g?cjU
z$O$Tw!_hc9xzj8M_cQM4kQGgXc}m{~S<(LBKM!`IbMzgdtmtvDA2CyBn0-o1Wpnr(
zLpL<ZVt>tIe;wnjg>m)cPGd~GpLd!Y9s%RvPM`xmRq6-e)3^)tVx@lR)IX8RoAz}=
zouPcpIcH9k{H8U}b5g^P?@_8p=fR0s{@JQS_{@=*uMXjd;T*Y7=hX16Fz-99bBFM3
zpq9$uaJ$s)FfY#yRho4h%mX4g->6b4T#vb2uDM*UY0S@Kejf7+qIe$sUgtulMO+Ru
zxE$s%p3is&+g}jv1?AVd@!}|^n}6hd<`*)**zo5ZN&8_-j)WaO@XyFmrN)jUtTMLa
z^29S?|L@**<xy%k<t822_lVLsHz_d*=I19Lm21xX(^5*KXMC$v4q$38xxAlI8m0Y%
zQue=G)y3?Wtmk+uqSTL8M5+I*jM6@9mPzl|hEuEpDEElHmHXGNtVj1Cns=f7(L1`(
z`FrH3Ih>y=wzn!e4eDpSRmI_|qI6zT6{T~MA*?su%-=80m~MT06YSq)>^6MZUtpRM
zZ3*+tvs@<;=eKaan@H`hl;ce{e&U8x5~-a!iF~e<NasqWQMwP6l1S|`*~A0Cy=~}%
z#B`<^(N?g}wEE~yCcdO&GEIDKkI71;{h6#p+Mmhfcrs0W7FeAUsa}S#Jwwd-Ndez;
zi5#Uvhu;tF?3f{352al0IlOO^&GBb*{OgV1rZgH7rF#V1>`S4Yj)eVaTyGtoxy>GB
z@D$F+l<>Naa1P*1H}@${&YT{`a>&V?6-Ij}s99lZchkd^|D41hZ&a#0a|qL1ru|L7
z`fX-jBDKf-MD*j7M)_Pm1<aot?)VF~$GPFVvXB<CJkQV#O^RytvA#AmnPIS{60M=I
z4l3?CBa!@VM&bgnuS(5Gd;}=mLo?<6a@RRrkBh9z2}-R4Om?r0;QhDUP=@`%Uts>*
zHNzeN{mNHe=X3mvIo=GbK~GqZA4~Uf%0tC0FJ)TB{!?z~OkKv|DiW!@Y21K$rnPq-
z+_O1$TqFnj=PQ6NfPMERK-)mOed1WU&+)>s6^Y!Aj311#*0VoWaz3_l{ch#@EoXnJ
z<b0JUQai6=y(-q*&Gwcj(*9;O>pAtXTyZ?W`EZe&T#x)S#qeRryB@X=IF~VWyp>ju
z$~(Ot*_mFC^0_FnVHvjXP8?q*htH}<^UCac)ShyfKcpV@FZtZh<a0lh%jx8CxO@&*
zP>=FeP><S2A=^>Nb`)~BLL(nz<*<GZ>lc~$l8%{S;%j@%9JXUV=XZWRYL7X5?l+?z
z`AMM(=Y|)v9gCSi#DuTxR>biXG9|pTJ>iB8iVa`J+dbX#`Ce5i^UD&c95yN3$GD_>
z8S~3I{}r6>`g&U}SdVsJUk~@QN`USF{7m;s*56vMe+cDUjr>!fX8?YtdsRKxL4K9t
z?*@7*;Agt;uJ@AR?>78!kKOg&F#Kwc&q+eR2rDP)W0=1T?%^a2g>%s_8YL%<G2xPv
z(2l$w$w`-(a4D>x!ush3H*C-;iS9#nN}~Hvos#H2)Nc0k-P~R>lgLl9SU-#Pvsizx
z8HbO{+GXfTS(aJXotZU*_rWtcy+pIFxjReK`i5y4(@k01Y`R}Y=X9I1Ht~4BDSRQE
z?@UnHCZ4lS$WB7PFR*fw5@FstxJOPB?t5Q$LJr%RS%Yz1ddmqJQIy{aaxAxo4JwU4
zkF_e<z9F2iA)K!voG-G|>zQl9!M-H7(@O5os|=p0OL_nPO@m!E*yMk7&$Wz)8$7n>
zn<hVZ^z4v;_T1f5oBcZYmbbz4d*+#R{@yd6?a60*^4XqMCj1vYH}U*xQ{r&bPgWYg
zt?X8i#O*JM&QB}V9p9Pz0!ehfQIJIaOF<I1SCd{^N~OAW3EmT~lyeA0^NdQ>ANXNa
zki_Q`YO2vINW%UlrO_5HkAfsThjDuqB;k3>OKw3D^+N@wK9Y_pO2Yo7El_H=GdO=U
zIDa!Ze{+~Whxv1upAkJ5_Rn5zMEBjQdZn84&gx!t{&-Pux{ouyH{q)R<Gt9)K=EGe
zjm*D|=^WtWJ=%Fd@jh(hJ`}EVpS=;hH=79*@5>HE4CBHmkkkD=I<Iuqd{b_)U&{5h
z%^nZ)`!*-;QbWMbL8{pB!$9%8;^Y%c4IXcmCeeJQG>PUbeayMp?oewB_f6KGNcSV)
z+|k^R!1G7sZ#;3i*-!cS#6^ZS?+f<~U|!g!?^=V~!)7q*b?%#LXlCCGLwocsPr`X#
zf1tSkd1l{khJQ}q*5*9#0=RF1_m7a`xhGOQAH6`Av7KdXXBpc+g~#Oz&i8slEw$dX
z$MM#<$fai~7&qy@Q#3oD&$}yGzR28DJT-e#q|JLUpGcn1<MVpsCvJGF@sldGmHniW
z<F90S70atwPWM<o$sQNk2Ic=@=!t}Wu-=(<DBYvcCrwGfabsS>6w}|_(3s8@T~%%T
zZ0t$1657DN!<3UO)1POn=?V4naGacF^x;N}=?7<@wAa{q?@7}W&iDn+%TAh`kT44N
z%>lzJOzIWD*dE?IX;DH;n5V$LJ%^v3a45v*LcEv5eCV5#v>6Y-2R-bEssUp^7CL!R
z!fWPyW3JiXx-w&K!VEZ<g?W^b*E@Nan)z2;&pXK&4o*^%X<nR?O!K!i!-x4T(-cED
zH0i{8sb<`O6S!pRA5zVE^M;dCttVmq|0U2nCc`@J<V1r*{Spn<{ouwM<ZG;z&FSQD
z{Mj7;6A80^fb~zmCro~>?>EHo$6G^^$-fhgf4$NV&XKpk`ANT2gV*&VKllpLnGJkA
zr^bBk>X&NzLl=6YE8+b8*M4KHpL3!AIc1FX82D*2P&EwtZ=fduZ3^^;CzWaqbO-F?
z9Rc*wXQBN9{RQ@OvzYb;+WZD+&rHt%+8*K`amw<99EktIQ-)iQ!o06Mv^=3N^vkf1
zpYSTQgNVH<;XOD9K>8_A7x*7uPPhuF3-4jkeEZG+L)p8BIaytO|NG8ihB-0=qJU@<
zCZi&tD4?Ps36qh7peQJypotTzD6~+tYK=DO1S^%cj#AN<wuuv3QF>6c>VP(Jf{LP|
zAmE`k>13+3*m`=bwzj_Cwe~m9<B#ilujlvv!T#jC*4k?y?tSn3&Yfhadge5-@2onX
zoP+m=)viU3eE#xv<ll(*&!XS?liWuyat?sW85i(AX|c|m7k``6z7Jl^<F?4@$8*m6
zW<(hmrT>YlGx6L`@;rzBKQ`m_s-tu~i}Z64Y3Et2d+A>z&c`Rw{@+GP`z4O6#Br54
zt`f&ps<PucSY^j`kmEYYaUHC(<67lhh<Sfy#^QRr&oAaWSzK>_hw_=OhbHp@xlGqz
zlPRg*KjWbC?HP_f#~E%`)ynq}{ybxt>i1@>s+D#9@eIG}&u6T1-p2Dz&&*ZM6==6<
z<_CISSGzu_vg2Il^ke*<nV-@A8SPH>S$Z6t>gZ@0zf*lR*ktT{<NT@~i02orGh_Ak
z{$_c-y$`Ob{u}0X+RPe#-gKE+#^Y$e*Wws|wd^bSok+E9-(M~B)iHBbtv$}GYM+Cy
zb`7hx$7xvgO*me+%)FNE#M#a;<{8F3!<gqrosS1*4y%@R`4DK=Ws>LdD4m!5%*FNg
zIn!dz+vH6#@2Ki4u%9?)RQ2tsKis71?R9rl^>%!IQ*d|HevJODIIXKzzCXU@_;tGe
z@Vg*gcXu?etGD+D>$qR6)BJVg*K7Wk@f+*y{rJXu`+j8of9GG0b}zxNPVGJw{cooC
z8}rjy&DC-rf$JvoZDGDgn5V1u3+>k<%s;C7Nj)#O{CEBrp{re^s_)Tpb=Au6HazEA
z){sr)5OO&A74o=qvm9@h<L#<_0O#k5(<W8lrO%lrRiA+U5cg%(^8He;+0J>j`kiwv
z51m_}-TvO>J-4;5KGHJMY&nE^@V%#M=P&rZ4eNVmTU`&f%kOMvXY}|EF(Ey^(`WBu
zKSS)#E}n0@>h1fwT|Do0alZ&v--LCUnr-iseu=!Jjx(kQ{MtY8x&Lf@ev19zh1sEM
z`9AZXW?!J~;P-CTccA?xLqgS~a6k0&^icIjL*)H;sQL!n2Yqx@sQM!0{R`?nfpz@1
z*?ZaEUaq^n>}Q$wGVNvBCviO2x|ZTRpET#=`Wd^iAJ6%`e*amrUTR!9a540$*tZWs
zKei3ehvw9{CPU*qb=ltqZgFhf7RUOxIO6}BvxUDKe1!f-=zoO%N9gx7*!JriZ2Lzv
z*!GWTu<e!{@h?0t>JtCu=Ph$t|1y{O@f@H1jI)1n?cdj&IQut(`2);9hWW=Z{{;Fc
z(;uckOn-{`Q_P=Y{uJ}S;>?~b>vCE3chH||*E+_pWBfYCuVegr`q$IHp8oap&t`w-
zvp<X3pT+FYM%LfR`Wsn)BkO1B&(fczKTH1>`nS-(h5jw{KSKW_^glxXBlK^le>?r#
z>EBL&f&K#h1^Ns0#~NfGlK1yHTu(YLT26f}87ET>c0Wrs*!^ss+OZ!t$ouH|gV#66
z`?b4=Y;4$bF`jQ<ys<&96D!Te2K&9Rtm;E%3+r!Tz6<nzCS~5@_aE-%_ciYQ@9%X~
z7mPW3!DjaaaMpqw-D`S9N8R7V_Z!Y$aIgD(=tbZQcs{pi!7_Im_dSaibWtyIkKZcw
z9&&$z>*Co9lI|XS&t}nrjJpgjTd>OAtUAv3(UbJ^f@)WR<16U+F8Ru?2KzmNy$x3m
z#C_0$y=wmhxC;8+1#fYl$_@7O_r0utNO?!|EBd>ZjUzv9u%Bap++aV~{+xL~Z?K<J
zdm8QM()Eq@^XDVT5w!a~v#{S@Vgil!^SCjM_PJ!B(S9yBp;6}l<%N?Q<+ysy<VJaa
zb#UQ?M!9}}yzrpDKWSJLR{zgyvV4D0UwM9{c04g{w9jegQ_ol3cjjX1#i~C(Ge#X#
z?LBLGqx{u~g^QxqOL3n!&Aq%a0WMs$yzv`H%XzuH@$Dll>yNdR|Fz=+)RVvF^Ujdv
zjgP~>8u8!6`Z?vu<&CpZF9m&)UT>CbdtT(R&vlkJ+UGjU8wbCI`@lulvOm{qe`06F
z*`GN35oi1HM%!+j?Zw$%obANfPMqz;nSV^3-CxJl+5L4)o!wu@)Y<)aGX0b3pG^N`
z`cs<s=QX?d9Bn@1=QDmj<L5JeGw)}XGj2KKmNRZS<JQr?j{bG@ucLo`#ouW6_w|i-
zf8W?>_wS93c7M;(zlHus=zoO%?erJu-$nl}`uEcR7X4-V%k-~j|JJjA>)F3UJWhvr
zoDS)_J!jgX#&?Dq^RLB+8Y9QZIy$7sY3z}Qczg~uzWFBJ*Emsr|80@`HJs=7A3K8g
zRm=5zpiSP78|{7T$Bp(rZzG>ee@^@7v`=O~CUc!k*7Y&?GS5I;Kg{P@leHhGUN)KQ
zWq=uK-n(po8DTy|8PL}!=<9L%dV;<_!~6r@pjyIenW2{1=5yF%YQIYD%hkS8?Kh}B
zuJ(l5Z&S-UwXE0I_vq^*9O=VQNBS_rk+yt}WS-#?`)n=eYB^uag<3AwGNNTn%d50p
zspYj=-r$lvahGIHs3oQ4ZCb8F=`!nG&gG5f9+&&_0cN9X?&WUdb6<ql0rx_bC%BiO
zT<n&fMBI`m=9V5_<(ACL-B-Z7(tRb$Yu(FH-r!z^GVcB+%7ptSl()GPC?D4h+cg9J
z9t!FfG{bY6VV7putr_-eh69@6EzMALuR(^g`+F$g)oh2{Kg8?z-Rn_)?7kP}r|zGj
z{M_Ar`5@EkdGE48W|WqvXgNvCb}eUWIY-M2w7du<#;C8C=<A3_N9vJ&E?3J+wOp%~
zxLOiwxlLcM)7R_u^*#D}qef=ca=%)(sO7gFnYYK)zFqA(wLhnpU255_mc1JJmfDMI
zFRT5KTHaU7r~3MHeQmtbTaQ=r5Ae!yY4Xarj_}G@hN^djdVT5*c;%>_;FY6xhWCDy
zlfAz}xl(IgtF>;>T5+wF&{`?2b(_{&=apl9hgXjEdaoSod%SX_HhSeqrM+_8vtBt;
z_j~0?ZSl&H`mI-v)Fa+*l#hEKM!DVl7|NV?JIaDLkMcS1Gbnd?UqHFryBp<R?`tRz
zc;7(zmiLb+i{3KIviBg$cfEf@dC2<_%J;pWqWswVZ<L>UT@kDbZ#7C&RgcnB)r@jL
z)sZOctByw5ROLf?L{%%wK$Xnc2~{#<W2$66$5qLEhSfep?X%TB7xpT%z3RkBqbXF4
zMcwDBzKU{J)io%0SFJ?3x9Yk`tvOKjt%w`HY3@Yi(CS+wgUpiZ_bzKRS5@B*%kt{`
zP_C@rhVt6#S5e+jJvQ2iZ`OPdWup2Wl&R|A6^-V$>J2E@Rey-`j_T<hjrbNz8s$CJ
z|3JC1`odTve)+r^WwzS0w9(vO-GOpT^)o1cTkZd9qj|hKfpUBGJ1BG2Gp<C>tAB;^
zx$5CpVOFb`V;?A1-+omizHjgYltVFTZZl%QT~}dG7;rbrz<_(N8f2ma<osHp<;_~I
z(enFR{z%K8YPlID=DbGA0W~tK^))i9O*JyBN7TqF9a<yna72x)Ltl-os6fp(aTZ^P
zT7%3^Ee~q>ftF)y#Xd>PwOVf0@(C?>qHHv~YB!?)yKB2q?ycQ|@<8olDBr3rp#Oi+
zm}2c7Sjx2@qI|d3yKIm-s2TpPWka2`a*UQ^wVbGByOtMexm3&Rv|OX*kF?yR<)d2e
z(DF5uXua-h%P_-r*P|@gtwZ^4-3Dn_y@%?42h00)&%*M7T0XA(3oM`34ZgY&Pj&++
zP5pS3p89E54>I0*$vL3D1D5*w6f6yDX{!GbEJxJ;0_D*9M^TQbe-7Sb)a$E%6P7@I
z8RZG}|Alf)y}4$P8LQrL^+#RPh^MImSSG6FjQa7gOs+o%Ww3q;%5eSF@V2XWM*U5&
z%&z|)EOXQ{xBiE)%&-3i%7yh0!M+rBtQIY=({hcL-`Db|T0WuWPAy;8vZUn)T7ITw
zO@pkWdM%q8WGx-hAZux8gRG(v4YCXQ8f0$?G<+&o0gXJNVJ<9V8n&Y+W7RSl<sdUf
zUk5c$7<mSnnQEV-_PP4{0)0InF@ww^eZ5%Ah}Mc}>mwSaSH4E+PoPoyb3&u6?J<qg
zt8tC8wkI^|9@{8u8+RZmgN?FVh8tzKoY5$|<?Ke;E$24MZi%~hlnWbWw_MyPBZ@T2
zZkf=wQrgx!eSL?%Ua#%mqwS{Eo`oIf4qoGLN~z_5mTzf%QRB-R|1SE86{y}r=<Oi$
zp1yux`~0!?`BUxl=jihw^KbQ<fot&E8K`IX!0+L;S6>epn852AydGqlwLC`4Q79YD
zxPf`BiU|YXM>%=m7dRqQ)E*o-U^(VrU(X(hHxp*=z}c`|pqBXq=i&85`g-BO#dy6$
zUq=SI(bh73y;9>>>Fa9|gZ*>hm597qEs25O#Ouv?J;*$y<r7-w)ms?22Jz1gyaO>i
z)v{|~2A16epMvETwd@`E8Y~9}dR7cFudC&)fem<F!fVW)mLH(>nvVxMmwU~p10(Q$
zrk2kKUa<l{0%(fC;xtK&r|GM(3~0IvmKwFxH(ia_&3HY?4Aat&5-X}{Iqaj<a*CFd
zv<zuEQ_BmqT%=`G%Vk=w((*<vlUlCTvP;W*wajR_8KuW;X^O1ynBO*CffgT9%OmKs
z$2^YS;xFu}Wjp%pF}bEyu;kTJXj%=+b4}O5vQsU)ny!aschilqyrP!9O*i57Yx?>?
z)6MX{)%0yx{-Bm(QxcYPQzy!Io7SK_)bw4H?>DVQ`Ek?jC_iob{tDb-Hp`sc&@AU#
zyjjk*M6;Z0sb)FXZfllvZC$gRYj-ruxwgJp&b51*<y_m?EazIfS<ba=vz%)Shf21^
zLnT{esAP)`m26iHm2At0O170lCEK+_CEE={C0l%`oCW<_clR*Kuy>ebI512yyfy4|
z+))o1E_c-N;Xhn~EBNr!zlJOL@UdUV6@2(vD{%!MK5rHF&!eSoQOoktlHuK>CBvbk
zr4I{7Xx~O?-$uypbgx<>ezC{=V!z5S@yq=ZztS&#zSb{&zQHejj{Bw03BO!PSDhm3
z>Sis6ohq+ewM=UHeJ#gM5c?!8e>zcKZ`N|nY4Z9-EnnBNq~&`kU8Z?zWJRlyC-HOR
zCxRo*hJ7c2b$DMR9BgiUM0m9Mj<UsMl%we%Zyr!R-W>5ei5qWjeoS~4@(J6>bIrSt
zi=Jm@sr^zjNPbX`zkD`Rd5u}5Ty4Ipyvh7f*=hDDZ#VtQyUg$&srM6eg0kDpP(EmG
z?HLdIaMRT@K{+el25v<^g|%S2+ULgSDZ6^`1r7Cge2Ef=4@NZ)J}}k!zz1cjlZ@;9
z5+z>rHuInTYcy_d{4QlzPdDR)5+~edS`TcczgP9_1AD0ZRDbmV9>i;VACY)`qy5AO
zqNtxZ-~~sTi{p6Es&V*2n6gFvcOSqPY}G%W_BOR|Inb_pZhWTtH|(27|5DmlQzt-~
zw>7k{1!Y~_MgJz+_fYqNC*rsYWgW>kjwJpg`rR&k5{-O=K{-CH;7AjVkEd=|y&^tS
ziGI<(l=jusouIU{Mm6S{_D$5i;5PI0fgRL4LFq@I+TS`*rtYWzBV|{Q+bwx5rT?NO
zj|Y_cUge7TU{L%mp!7p1x|JMHw$U#Xe;AZJ?bP#>T|FJ3<Pl0-43xN~)SdJT#V_1u
zJ~?og>aLzN{oSCfhfQjq8{bNQFR1fD`%X~u@1eg4%5m-k_4uiMZoHrTNZHl%v2s=%
zf9lq*LvWjEdff|3ziL3qJ6LsBj}Mf*Euh4;s(o&JJpDoX!=TO!{qyLL(BA=WGbg?t
z19e{1KR3Qw+0~N(b)BmJ;`m*(r)lp7Wn4n(=O%Kiva2TtO8s6?j=xaigxgH`^&Ozh
z>mIdt^%Oy!FHqw9)Q<TACGI2DT|IdGt8otL2BjZD$?pXvez5AU9zUq_1?qgM9rHzh
zi2gQE@(3kQJ1BYPsm6W=N}di-;)D{n6qLBts=IoUsyFNtN_;0M;}J^y8uep7Rb!oj
z5|<&nL7i9iV_wOvO3W)L@jGbWLwg@6^@P$+Klu^;-YRP!tnBJ(0o~5Q7h7o`PkS4v
z^FVt$83kpXbkHx<@vHsfc#?jh^luIAn?Q-%q550#e&wxkQ*GPvf+NkHaUUr4T0q&)
zT4@i`-VREhnaWx5rPRx)*HC9b>8DWgZK6F--3v<o9iSYyo$BxEDbe3gyBqKBWgdjm
zu28PqUh2V0%p)jqLWyf7N6|l?Yy)*(>2D`z(%(V9Q1S?+y`|L4K(}+n8wqe8{A)nz
zf10`*l>9=ezlq$e?CRM8>UgR9K#3Phd_RdlI;;7;pxA3bx3lq$7Es4Ydn+jU+NnF}
z7fQXQWKxOsPrp$7LK)v0>P_TUCH4zY+7n8=aJ6&U>pQ6TD6vm~Iu6?VNq4Qa3#GnC
z+12Bv9!%{6Wj<R#9UuLorGKr|<CR@KZJ@*nC2u=9kNyt&h2oFVzLa`3btfo!gc6sc
zeGT<p)ZL)O2_<e5xt0E2`i0^z(7uCu4|N|Xd4v+zPkuzdyH5PLPJ`n2l7p4lM?pP5
zLCMoXe}Mj0>haVeP|ic4)N2E!o>20Km0dmU)bpsLpu~58(jTG3$LL>5y_z}+N;^WS
z*9l5HLaDcgOoK1Lp8+K<tM;y*P4sW2&Vdrw3rZfL<SBrAVBbN#M~UkeDDh?Le)>P6
zHucu;1trg5C9dP3j87=_1EAy|Pk)epq4?WWWB$~S`BTn{&r^2wMAUC7@g1shol^hY
zc!GYR_&ceE(ywmSSZCB(<*fK7>dn+!mADRplBXBcc~U#hQ?L&99n^c2m?u!;`#>4D
zP}=P$KT_iQ-eBY0pyU-woNy583uPbh(mq&;c?5MnL20K&HP)&6aejl+uYl@V@mAVL
zfwI4iSKZYU1f_l(DD4ZSeWBEEr=F+8e1j4n1!Y}zfKpE=^@P&zrPQmHxW59WUJ{gg
zouJecO1(AYT}s?1sNYiRWuT>Ax7u;sG!DlRlzwJa<M`3ORf*#UO8q=2^>@&}6LdQt
zys<|$ju$BNS)vw7e81|s@sDUXjW(YHir))L{|76vZ_wTX%J_uhZzV^8a$Os*x~nJj
zB}%?FXgv;U$GWG#oq8TM9_%A;R}UWNYkoW$)_q|$btkCvOTC7?i|nRfDE-SSu|H66
zR?doVrOtseKRc-RC^2ue_bIX8(qE>(pZX(egKt_$9=8(f9hCOH<Y2Oeexc4EIi75z
zKdc)2D(&;g4*Hi;uO>TbUqgKtnO1vOPewJ~Z>yda&#K0{QsVs(nNvTm+saw-g4!`Z
z^zT&8itnK=g1Wv`V?R(mE8b84N2Jpv<Hh`dI!~%GKV%L4gUJ?9;)F6!0oq%sM^TR_
z+i8zdFQr~ht|9NDKTB?+eKYk|>K)`B+WSCRcS7k;8Qdf15%ou;yP4wyCC;Oq757pP
zCR;!~kEmP8@k-pE&@YrcVcOfN=aEav)%35SzKiSzWgdl+CkslyH&N$6IX`<r>5ow2
z3d&jW9n^csk`l)ml)OTT?<d`ZY#yOJzwm<M_p9EpPblrRfI5EqTgf)sg;KAb#0QER
z-|;1ie<}T)v<t<)hRiB){!{mYI!@{W{X3}3)cw@%!5j}LdA+0$)N#<?LVG}o=Qq?r
zP~zIDBh($>HgnbMOKDHg-U&+ogwkG0+10a#x|@EX_&1Tgv<t;vP~!Me7wPY(cH^5&
z(vDE#JxZ)o>K6Ki;%_BKDX~65xjqTy{bxJ<9n>+^cz;LxG9}(u)7}Y6y*2c&rM;W>
zO|<7}@1=bQ?S0f`)m=UPw7c=mSIO%EWxw$%ao<3DD|H)nST*)p+9ROMO9%Z+Y44<7
zL){I^`%j^?msLB~8T~m>&W~RDchJ65iG76jGIc-w?jvnJFDUcgLLE?z`yJZb!I5S~
zJOax4N|2qPt{+h9i<aY*QjO<=puXP(rCv8E@j|&D+XPCz%}U&7fD+dWN}N#qLVX`c
zT?8er50p5e#0e#?pV}N{<J_Rc2_?=;`si<=UnqW|+<yrrUqJ0R&*%?=(taB#d4*C>
zDDmyo9rO#uA5-FfgSwM`q4?L3-Lwm3{)Ccu6aBsP3#DE`iR&hHKk48bW5dxuq4d|Q
z8t>1+k!IIEKkcowkD@(9dmE_ZqhGZ2Bdi+hnD&{V<cosRP6sIUgwnsI<T6m=lAy$O
zf)Xdx@sn#oiOYZzmsLB~JN<d}Z`dake=n%(pZ*<WpW3lLsLM*6ztrAgw%r<Vq&cv!
zmAVa-^DV3z<D@+TN_$H|dH%FaS>BhRJqgM_&<X0eRAXPIeGT<maHJ{k+eE!t^@e@D
zppJv~9b}){F%Igo65{}6eYl6)_JlHzUQp~UYR7#I?X9%8Q+KEz$B8<oemw7{PSBrJ
zjd4*6^>~qK+B2$g{|CxEb<-}C_)TPvc442{(9=sTl(+)82laPQ7wH%FnP+?YsD%>W
zPvS!{op<+_D1I-rum*HHKYO!9{g@ZpTgf2UXR5ZfsUPPx?O`R>0d<7_jxSO2FQq?0
zdnf32Zh3tT?P=P(zeK6GiT)hzy<ejEchJ96iT4Zi_kD>H*Uvcb2s>UMDC<zzXJ%|`
zp%zMfD;cD{?MoDYSp66`<7QGv7$@vAYqxb!3#Hyta+wm>VX_m{^Md|0WE#}<LVq{y
zo5&pfz4QyEogL&(CFTW`Jbj?FBb2y)(%_r3;&+4M7mD9Y`juE0^b5t`N`~lfqhBcg
zb}~wT2mM0vFC~-ochWBu{~EHJcA?m_O1y8P?xkNS{vG5_P@ZS?sUP>rwD*$->38Fs
z<}&UUvW@H@W1#L|s&OAryHMKOME24y)P9hCv<t;v2Bly9)NcGjNcK0O_&uQbz0^MS
zqkl>q2ijZ7ApLFh3nfn&lspmoJHABm$LL=M>iE=;{Y>@T_*&Y#X%|YpUe&k{q3)v=
zioc)q`mAmx+dw(*+o@-Q(!UPcm(sq5T&w<v_hmp?pPOjUfpWZdP#3BDsl9&Nu3w4!
z6Y5c*wAW5OlX@xj8ZrY)ds+3@y|Ib*JncKEcdGy4eSM&etE{^2jehzMs@|~AJ<{g$
zf-*lXWGmSQo(Ow8bwu^7cvLl>+k(=crSvCh@1%VV?Q4~@;u-pdQm>obq;}j_DslX1
zFMuOW-5WdT->Dks4gLMJAEe#g!tsDQ9_m)=HtKfj4(g@U%fON5;eDO7uc5u0dXxHB
z#CxgxNOyqkg4!;1D|H)nJ9P(jC)rK*l6~Ys(Cy56%e313Zqi4#kU>z+Q=!aj8`(j2
zlHFu4*+;sMv-MiYHnM|Ekey_j>?SvZvVL=*^g}59=p}d3-$#9r+I_r@s{!Tys)f3Z
z>>xYIZnBr$3CjLZr0ydR(r!+$`Q2m<DDgf}`q@Hzkh+b!gX|<zpw#Q8?j?($<msa}
zqpWs=GJYR*3v~dLJVDyqXb;ogLA?x==hdCm-DDP&`n}XUL3ys)N9{h*w%bC6K#A|5
z?gZt0?xyY~`^YjV@y=*VHz?!qgK~UZs6+I(QFoA?WQzW7>MV6Hbsy;-WAl4JX}1NG
z{slm3uZ=oP-2qB}Izc(lgz~<xoAzE%>i5xqP>Ji;SX-|K)OM)b$PTiT>?V83KGJ=X
z&C^1*ksV|w*$qnnda3(J_sQ1Y0?Ihr$PTiT>;|<ybss7JQ;^j2fI1H90CgL61eE9h
z9n=ZxPU>zlM|+;Smn_m=qV6NjDRzAeCBGY#{`hDYioJ#QAnk3`VNm)Vp}m9l1a&8M
z3Y55R>Ky&O)Fp7Fxo=+|bs3cR&F)h<KcMIq>L4iRX&ZHz{tmTc9nqemy_@!4+WScN
zc$>e4Y$H3!7%1&_Qg@TRWFP6Cz<C4Ze!)lG0?Kg@(k|3>q8gtwDRDl75*JhAz6g}K
zlxjS$q3$L7NcTh==K*#7P`8mW+B>Pc$*kIOU8XLQeWW?f=5>=EQ1bhzTgV{UMutI&
z>!6N-a^81Rr^#+INA{9MvX3;Uvmc}%)NxXWK)JrRQAfxq?H$w!G6_okPU^MPY3gn=
zNA{9^puW#P!}g~J)cFAQexCLqbsP0eQ0IfXQ#IBnD9>@aY2U1NTnDIkQkTGy=Ba&s
zw7Vxsd$_LyWqkxd-B+o@)KTTGeI2xSk{M9?(@ot=_JJ}!=S<Ew=?C>W6?F?4qFt!#
znT&ukA5r>+;_o06^e5>TiocUw3rhbo^b5t`P3Fm7vPAZg_>!&eR}Luk+@v3r=SxDd
zw~%e1oOcnh&%Cm&gL;_~pZBW0dV41*_14m!R(t#QZrXQ(a$j3i`<m@$vgCVmyHEL#
z?Gfc!PbQSfCv(cDo-8UqebP*k_z6$>l&hW!Du44-8z}8YRR8s<4(f#J<9^>somTzL
z-*;2{&X#-+|30Yv==TxjskwymmfTuU<~gnU`CL(1l{ZtRUN~O^ir=StZGIGWQ1#3C
z5UBl9UH^0xl>0rQ*q4D)KcW8VPbcXY>O9h)R)6a0jQa8YFZ#3eZzgkKpD8?@r{4rc
zdwNHK(oRryxHqEwPH$SdtGB3ZDELAWKff>vl=z71^@X%@PazM=`Y5Vy-r<`j>lot)
zrN4e~q<Mc|kanTi!)mX4BSQN!P{&Q3rGGQ%b~@kMNq-5H`c9i=4Jh^e)FCnq%DReD
zC&?7;8Bpp8HD6fb|Fh2ziXH`uKSUiR6QGPQNxx9y)3j%37m9te>RIs|{dwAp)FtYJ
zpg!+zx9vKh)c2DCP{tDhrTwVt^1c}D3EGpiuLX5{pybJu{&OS`zNbV+LGdTa3|XT8
zAgKMCZtK;6qWz%OBQs>4ERj2B*nZ5MX}L@}E54H)HOuCUg3_-fnIZFJi987E@jlnK
zA0nfm*oD%sWvXYzle7!PzLxe3DD4Two>hBSPoBD@{)hJo#qZ3vtO0fW)FCnhO1>P}
zXXft6Q<vyJ2ulBI=CJ>u#E+s5f_>(@J3_QaK^+JE3HA5xNYb7G<@zm@xRUDn{iDv4
z^Tqpz5UBG3N;^?d;*w+<#Pi~3GSqppL^|i&aSElLpA3-^5YJ_wiGtD(p~R)tzV4a4
z+BfVgk!G&M?|vo(N?b(spl8#{i=H(Xi2tY0hC%H&sQo2Vv}dUEWQlYxWIxFe83Fsu
z+s{U+lVpa>lO@ubXY=^LKI41NPaPtoWD=Bl73#RbKC|Sx4DCX(=c$FFORCHJgrc4K
zmVPosM#(%V^HBn2p8XeD9U>!OpZW1~QR+NdB6Anp?|T->=mN=e%>JbEz`hJ9`Ey{O
z+5enbC_G}P14{q=WQdG_^8L{$b%Hua7RksW$#cQZ1Ss)oG6U+okR{SwBJt~YI-vMH
zp!Ca69U`M-lFX1fu+Qw>nWrw1&ZRb=50rh=PaUKVQAf!nnIZFJiF6ioUdRv`C6i=^
z%#$V3S;G2ch>Vg+GDGG`=Q7qK6QGQzNSe#7j(|GfWFo@jPe!6P&Rk*XBb^SL-vjEr
zQwPZq86l&j6Jx)~5E&(tWQNR>1yI*DwX>A%gW5iIh>Vgk`jgZdGEbIB=d0`&86u-(
zlFX2KGIXV_7bTNqhRl;C(z%N5kO5HoUsz^Y1pCa8=RH?joh8d;_!?`Ek{L2jmPlv0
zjSGT(X3p~=P~LZBXcy{vMV+S(t+4w>0_-#Eo=+=#pU)}1FZjP^>xIZDDE&{888T0n
z$mmMOkr}cG%K9^_EOTU$G~cjx9~mJN6|T1ZNs~D;4@$p^)S>HaT$0R?d9nz~`C-0k
z83kp20@t(OH?SR0;_{&6&);ZSAOqjBx<vYJvhhJO1WLUKb(A^{$~+XPi`4$OjTcIP
zLSzDz_#)}tEOEHc0_8drBBP+x%TVV)saK*lw^$tjrC(`k^KEN)K(PxYkDm;YQ8EYW
z{z8^Ow=-tHNm#oB;`5aKKI#%E`JAMs`Hrl&b6yaN-KYBQ7lfjNs>?43MMqShyh|uL
z0ZRKq(PdEk-D$O-On~yeoir%%LeV*Dq3Ap)d5Y8~W$ivP080KKb%cz9+8^p9b(%Uu
zohM7Avxfa7b6}rI>?(uOj`^-_KS8F+99bmIZR{r*BqJ63S{e7Ocw(JxFHI)CZ|5Tk
z%KP*@8Tx_MNit8CNdFJ5KSU<U3|S)mcQB4jk{L1w%Dk7Toh}>iC!=JB%vbz(vVUZT
zERmtRtUpSYNdJ2FhfI<=Q06C3T>@p_4E>1lcQYTEBvZ<+o-!HQVB^9{yw9Lcl37sB
z(>!&F^xwn$WRlD(aX)e|{bZ8Nlg^LnC!L>I?I$y2iA-*!|EHEAGD>F15@~*BkCOw+
zI`NZHGEbJs;Ll|~=f*>z#7D`P68k50f!awkj?9oH(x0*ZB$+3j`>fpu>T@yb1SscY
zn#|Ciqb`yq+D*5O_mM#|1nToE>I9i4GxX=Ei=@di9~mSgWP(hBl0QwIq0UhkNoNzs
zPo_cH$4b<}U)b@7K*^UOOQiXwjdwuthsYEt-z!doeI~anLwip3aW9sr&HduP=EW!|
z^<zpr7ojdtmq}-{jq{UXWmiv(OwyjB&QO;?Iq%A}dmfNHSpQ1&n@rIzJQL?_hV~*T
z>!1Wm9{+<JFBt*FpQO&I#`_v@oAdUIMQZ;R)*~}yo^*aC`Cr-}1!X=;r1NX5eW0wr
zAQ=KBK0=)!lc3a3Q)j4i)Fn{rncr|dk$zB*BbfvxE>B$|eZRHmtsfMBkU9!V{RAlM
zC`p|MCC+)s`u$`S6nj=R&R^QY4@(^OIpy4V4wQTa+Cy7yp0E;e)Jf9+i1mj^|D#q1
zLAfr3sH3FwJL~t8K~Roog3PKN_kYwS)ws`m%*HvOj5|Z-N%OdE&qoHy2q@!;f;wNc
zr^yWMIqD)P@j~gpd4lr~>hU6DO5C^5U!vXL!*)QapC&W3=cohQBo6&jV*P*;p9ST7
z&QTYri_~S+{rk;!8}B3ip!6>QO8p@1Vb%TnBh(2pO=dyqM~=Ekmeh`Uf6}(&fRf*%
z#B*QT{j>+EBV>Y1lQ}XEN}eJp$K5<-833gpNhQ9IN}Z-ZLwla|{NBb#l{gPTy<U(R
z+H=%J(&Ts^l73L<O^N$L+C#KQsH4;g>Xd5Shf?RrB5Cq=9SNmh9#F>Tqg^O=KN%vU
zWRgsQQZGYYc-rbv!S=gE=66t&o@ea$Q}du6w`XNt9QlSH)bXnJzY(I2fO0%C)CKyT
z=WKk441<y<NnM~eJMDNKQ1bi9ASmOEkZDkl`}5ZBfD-Sc_EQI`BcPm*Ldlb$7K%=R
zBh9VxG<8<>k#FRvizGhu(mX<`=YZNSwVyhy?CKH9bty|-_!7llQjPBq(w^L9^JU1a
z66X~t{m;{0B113Q_y{QdiBik|$SFEa=Ex#xUb6lWI2`>*P^Uood?rm@1a+QYwsgRe
z=Jov^P}d>tezo`S4^oHJkNZ6`rv3x_lGGV8OM4EK{^n^fQkSU9p!Cb^ws8)q^G@xj
z4uBFDq&-A?ggQ!{piWY!sWa3$P{y67E`fSKqTIF5yu$ear5_$}5Bl$?4uKLMrH-lI
zwJ!lm-X!fQ+B3?F<5}uF{RQe0b(z|{%JxBRAJor}sRPs@>M(VbI!2uUwLi3{XwN7w
zj^}AFf^vLH)Xp9|K93Tg_kz-o0Cf<Q_z>+8>L_&rl=Uo>xFjg^m8M-N_6#U_bF>S^
zo~OM4%D9X43pL+fsn^w$QqGE}LCr^djyg|Wq%KjHzeG7t&1<&42bA}1Lb3Zn>0gM9
zl1Vb9L_a`@&w^54D7rwuv(M)7fznSuDCb#_I;8r;{b6u8>P2Xes{P`4f;y>w+y_u+
zsB_eLvPhOlXFtaW%DnlN7#A5<qFqq>7o|Nxoutl?IZ)PBLG`S7k=h*Kx&>w4e4vh(
zI!GO&4lA*5(;lTgL*~ik>o#A8jJ`ol=E)N2f7ALCptPT)PE+SV>3@+d(Qn?e@%T@{
zZ2zhK)IsVHb%Z)fouE!qr>Qg4IqE!hk-9`}{$Tqhlzuy)&Li!9P|l-}^455iOo9@Z
zA#<SA&r=tvOVs9V<_9%DwVygfM#%*IN$NCphB{9cL77+c$N%A9@wclz(6a7?XUe(&
zrGG&(M!!(}NpPh3Ry<9AM*Y|)sPpRY-(RBLDcW&(K)F8nK*{f?JxCp*j!;LbW1!Sa
z(4M3{O`W06f)babJx_a)x}^SF<7FkTXC=-vsPjzi2jzYwpv3iz_K<2^SLjcIQZG$^
zhW;FNp8gW){E6*=I(|^v2~vktN8=IdC@A?8)Jf_zb&f2OcsQVWJ>VY9uTZ{+;0I;j
z3Xoyiv(!aU`cqPk_X}mt3#ju#?WYb>hd@~e5$Y)YF(vM6Xiw6frp{1j!Amf{9PN2f
z&YvQ636%CtKl=k}f2f0GgiMf0G6l-IPE%*d99bmGpv0L!+je|pkc@zG-o!wOOVFMs
zGit{;sPohXP}XCS_7d&p9gYW-@pwQzFKGAE9;6OYhe3&p&>p2d0ZRXp)EP2Q7C@=z
zyleaK0Y!(D7stcYQR*Zp{YufEq0ZA^BFoD1KIb6E0m}EIe4yN4_-PN&9wwt|$8#vv
z8}=nYiBC~yl(-)QWxjLt=V>pI<}bEhkPLy+@30crE!vZ`r>Qfbtg|dA=ShzCB3TA?
zJ^a<?_ka@TqxOT+PKXRE(H{K?GD&}$Izyc&i?n+V+4h2<%tJ^u#zA|OIs;0-{qM1T
zGD&90e8vAa8y8bzzCpR~Nz$GnOQh%TwjU8t`Vm!)=Zv%`sZ-QxP~tMQ7nFEkMQz@<
zeg~BK@=*uL5bY7_1nGQW<2_2OBT%oav`48k)OphR(AM*lAu_DQa|!Acb%weG%0BFT
z#D0QuUG#&}ZdmO&zMzaZK_=CX^8_4eKHQ(7E|LC^ZT%>y`vxfW(`1g!s~^u(s7uu5
zzc_!O&L4FMlzwGs&y&t4HqN8Oyite944EgLe=uH&c<MY^BAridT!@T<(teW6kok)J
zGaH{Fv*4LH{+@q|9qlM_{*W=+Q)Hg@5?Q9*^DkRJK!%n09<CDWlsXAY|5DWc&uxE#
zpv-@WI!eYsIlqN+y-a`-msE}SMXIr`LAj3l|84V!$UIphqyORjkQq>pU!E+2vc5{x
z&KEY`4@z8!Oe*odMg4f52<m+?sMl+?UmTbJ*GIJce_%51D4C``M;1xr*mxfqBtxK#
zFA3`UBJ*m;^L5g3NgU2+Q0B=GN_<FpXFRNaJbwXYzlo~GeGI7ADQd@U>v=#~7s699
z4nOT7wPPQoKT4e>GoZAar!G+&kL{NON<R~-F)mQYMds-*k&c(+0(D%}LFy276qGzk
z(y6lAPliBgH%cbSloI<TwfqlY(oUYbK!1_;5-9CB)s_KJ<|(Aacxca%S^D!z9533P
z0k%JWGN8oufXsl>P7c)mP?yv{H}2qn>=nNs)b&Y*)sF8gQpc#1N}PYxS+YQ8@CS?~
zPo6A-(ryC(W2P`rZ^s!XBcRxeWVAv06OCt-E8=-j@|DQ4+BfVojkcbT41p4t0OdKo
zQ2x$Inp!A2M=caxq!x-c19|?CK{7%n$TXQFi=@H-v?b@Oj|`F#G6u@^Bte}5<+!J*
zb7YY$gA#9=ZC(#3@jmJx8CE-<BTy&EG?^odq{08dsQn}ZpyUlwhp8jfG3o?$n#_?!
z(hO!lLG34Xkc^NCG6m}Rsk78M>LO|IKh$Y|$sida6J(mqkwwxBVSO@4M#u!20<}NX
zIkKR3Jhz}W_@AG(KV*=MkTFp5B&gG5mi8QVfx1X-j$&Ri0LpW%Aa$5JLLH+{Q|HJc
ziT}V#`x(an2rc`63jaf&us|m8KeWm?(qxV-fYSf6a#q|k!qy8YF)mQ+k!5hC8S_Tq
z7`6k74j*g#A0vbKpXsDtgiMfWGDj9k{HI$Q?<0d`gp7gmecS|fiaJf5BMWNZu&+p6
zrZ#??-vi1#`>2CtgiMerP})mVXQ^}4MY62+NpF~u>^~VKBV>Y1fx7Ofb7YY$({Eas
zj|`GwQ0hmh6J(0^G<A+Fk|w}>WRQ%I2{KLQ$Rg=E&dytaOn|yB$Q)TD&GFXn0d>Bq
z1Jpt4Fm;4FMxCH8tA2Q&=LCB^BA~RJAaf_$>p+n-qpkLlK{5<VKO)pI>I8L)I!&FW
z&QTYri=bY|XfM-l#@Kc}puT?wd!5)zKH3Ac2dN`u0_=6}eMzXFQ-hK}O`Ri)q;IT)
zM?^2b6b5x2sCK`cr54KlT7f!zl8sA|*@`|{?6@x_i=ea{8)xkWGH{C32@t>gdO4^3
z+RNrt8y_UYpyZ2Cd&XN`CW8~C{-&2B%D$I96RkgR8uNoXZn8+0=?|VR^VG6C0%|`&
zX)jHkBh4A?7a0a6E=8tk_e>HyuCJiv3s48C!_*1tG?`U9p2MAK^M=VHD0#AHS$lbk
z&F?weGDT)d&s1yAk_FNiWPd>EUzm)M<%&II{V}pYnrSl6m+kg|Qa?ZjLFu1R_hsq`
zb%I)`=QVYjItxm^G8t*J`4VIb6n~a1kg>3}r^r}4^N@jatd4-vzXX|@ZqL`i4Dn-K
zfxXThyL~e`KcKW1qz+TZ$OP>v)mSIg;aSWF;_pN4j(|E|)y=P%bHzU6l_2<nT))UH
zDE%mqMNs-{W?Kfy2$>*#b8Mb4DE$?li9F@=toEF586dM{`Ad{|&s-aqBC}+HER*I!
z>3{r{ASnIw%wv8sOBTp78JTbAAqGl66Vxf{G<B9bM;*Ax#z(+D^Q%{iptLKL`76_J
zE@po~$sZuIWRCs<b&<MEZ5G)2J~BwgK*^UP)3j%)bJU)N5{KWrgT2n5UI{9XdNrb)
z`DzZ-ev@TT@|s2LH>mw4gKEd`ap})pV&e*+TxZIpxs?4TgJgtEkVR0x$G_MzNJc>E
zSAtA`i4vcqE`Et(_bsvYf@I=L6n~mpsO?_HeuAQdWQ6uKStQG#%%{2B){B9ni=fmm
ztH%6Atd4;)e_66fyNOzV0Mz3M%KR0`BK>9h%@sD@M+U%=X2u&q>KLf)kU83mWSRCr
z2jj?G%#On>we*0J$9&c5EVu{jSt!q!3S^P~z?Bk@>%vvGUJ&ec;;)9OBdWK)nx!r)
zv9B$&`O{Zh2CorfzaoRnsmUVg`<k7H2x-1fUExaBQ(~W7W&P$GY==yc_>sS^vmj}%
zwK_s3NOK+IZs2u<On}mFVXyP<t3~CwJ?2Im=Ocq;giMfWG6zZ?VXu?e6a1Fd!d_?R
zo`kY~ZyJ<+I~ce9N!)DXBDYw3f-HVp>MhvoO9+3wHwS8d)%dnTQtU0SB~sR&{;tiJ
zBg41Z<Cy~GxR=SmT6?_1O6)(Nt_y0<_pA<+DNy&H+iiXysQsr7QinlVXA$ZInFHO<
ziq{IDtjoYU8&?2Tf8VkIO8qi*_y^XW0A(LZlb#=1dju4F>JD4a(?vU|`;HR#Pqdpm
z#g6L{DD4EPBV>X0GN|XzT{b=piq4Wnwc~z*+N`(jg+YnSQI~%t^LTOGb2sM`JQMB5
zK#5C(dOXSC2CLJc%x~%*8&@EM_u6&}pwtii*fIg?JW!{p3)G&UFkXrC7Swp^v}&wd
zvY>XHcN=YgB4m*?KehH0DE%-$v-yfj?5jVw{sfsLi)1)$>t#WipFqYQj|iy8jZBkS
zQ0JA}cb|<D$~*<Bg`y*5f=q#OywlV<vPhb4&NnFS6v%Ma>J(W9^>}Ww_86HW%VhW$
z)}JMPzhpnjFev@;+;8KAvM$2ZIZ(z?pcab149a=x*(}8O1IV!QqBm01S?a(8>_1s}
z(6$@iVj1|A^+!NyFF~fs9O?ND<3Nc^fztm1?VjIS9R$T6A!DG-XM#FM7RfUGzK3kR
zAQ>SOWRWxv+c+N?B(qy>J3^VK$fNdpktWNa^f&N3_7l|m2r>&wKf{k%1|N6KzYw1w
zv!Jd&YV(BkdqA-Vl(XVN+QXo%lNgy&*1eIVzYI!$OpnbI07VC>!=TO^b&kZ36SQAJ
zGC}4@vz>l2LFP#FB>iN9%#r3P`pE>DBhByWClh3jG&%ao1eqgEo_;bx=1B82{bYj7
zk+|6D_yeHKPmnr6W@*n+n*#HZ2{K2T9oFvwH6L|YHTG%R6SU_@^9<{OT8}zLouJNA
z=cvuI><<|O^*B>!sdLmtP|hdwoTUdGj`i)M4w4ZvL8i$ZSpcP-@J`DZnSS2(r%3u<
zusTAf$s+07ML(IY=oe)_#(g;`=Z$&E?$5!Otv#W{b@vs!&J%lR-)mU_rGI5I_?opx
z$SkPm$3E5rWnNOC)Js$6$RcU>bH5-Xp!73G7D?X$wg*c8(`1n>gL<4^w{dAQ3yQr+
zZQii{04Vk#b%M;1W%|vVHogE#yzecmgJc*KdxSbc=EySr<`1@>kBpFMvH;5ak|MQv
zo9%$TPWiQnYFz(mFMv8OYV$|tCxc`d)c#RtsS8THpD2nSEZMjM8TgacK~V005=wlJ
zf%YPGncDQReKJUfL1{lr7HQ9xtvv_IIw_KYerr#Wo<CEQ1+oarItjjG83v`DG<BBR
zyledtG6CxPA%h344ud*R)Cn?0dzv~&mTC9>h5Y~}Z-h*Nk}pSHpe|D5!+nkSkpWQR
zgVYH!OM8yGOl=O?`av=bO8o?Njx3VqJsY1O(_|LZeo`07GVSJXwq6+2IO+nm@9!Kx
zDB}-<5*MM)Qs=0Pq<NqHC&QrD1ACnd_NA!HWZ(nq4}ZuwP})mTr>To%nfBmE>=&r{
zsS{+1_8fJQEYog2w)K5vkc^NqQ1T|Iv(!22B5D51=J9|M@1qWq5i&uhK#9vy7fJI8
z`$GoF2$>+Wpw_1@lI9=m2dMp^4w4ZvLFULJX+CB9WRT2}MKbc4jZ2UzP})mV=g0!>
zMQZa;<|hN7)}zi+=ctQhnRfFp)+d8x7?eB_>MV7Rx=5C3H=i>P86?A?=AllI1=`D`
z`M0g_0VUo?9iR?Uhp8jfG1b^_sf(cOb7k7if7mZFO=dx9Cr4c*%@>R(gJc-gaZqQe
zi`2%r?EXXs$q1Ps)1=R_@c~fs2C2i;5$YIqf;vT=rp{95s0*s`+>biwvh9RHxo$<M
z6J(mqkwwzDZ9NYt{Yy}%$t>+T>LO`8tOsg6Q14I3l-Js`WPvP#vOkrnO%?Nik|&@<
z|7lN<X);T{skZI;K*<**W1#kjI!)$C&j9O>k%c<yftCfb+-$XHkju#Die*spdInpD
zkFfT_5UaCC{txAST%biXzJEo=$P}3+3uKw}1Z=$k875<7ip-J)vP^nfS)UA(F)~GF
z$pTp>J;$*=875<7ip-J)vP^o8XMHkE#>f<zB@1Mk^qj!@WSESRDKbkI$TH~}#rkBJ
zjFBlaOBTp7={b@0$uJosQ)HGbRQzLXy%d=x3uKw}jAec@OvcC*SpY|x{{7*TBp#m+
zlLaN--<@pjDKbkI$TH~}XX657n2eDrGD{Z7G8s6<rSD&2r?Ndzp5tW6zyz^los%&#
z1!^2wP&>|_iPjzfbzIab>H--!jear(YCdYu>Fm!LV(;IdCCihn-E*ec@qL-eqVah<
zDDNY}Q<+zZ`*t!*dV<y;P-0wUj7*VPvOtzePe|(H_k?7OOp#gAGtIV>RpR@-ZPGtH
z7Xx)Z$#U3gPdoFHDY8J8NzXZ~PsYd;Ss=@#XFB7_7?~mqWSR8Lu<<b^)+H$WU71>_
z``=87!#Y)Bp2;j(rav&t#)Xym9x|CC%cSRA+irl2kts4umPyZS#*;B6o~Ki1$pYz_
z!+0`G#?F`H5{(x?IWCT=GR71g$JBx)unGJVcm&u74h75L2(TXvfPV&00N(+}fbW76
zz=O^??s@JN?(5uZ-QDiT-S4{p<F4@x^bGNw;hE++*K?)kQO`C{*)z*~wf7G1U%dbJ
zx~m3O4XqkgbxPIbs>`dctLm)UT(z_6Z&jY^K=p*`uU3Dz`WMwtSNB!d4rm%MWWf9Z
z*9^F6z~2Wn)l958yCz(-uIApFO*IeJJXy25=B=7{Yd)^2t{q(amD&?(C)7@_omYE#
z?KQR6*LK$au=XdlzpUL_`%LYtwQtw{rS_9rS6yS>@VevbPOUq;ZdP5iZbjXVb$8Z1
zSXZcfv+lz>Z~d71aQ&?Mx%HRVUs=DZK2?8veYSpc{jch`)<0hV`}#ut@eN}eCN;D-
zoZGOVp`+o-hSd$<Zn(W+Lqm7NqYckC>}lw4_@be?@tDRbjpsLB+IVH-O^vrT{=Bif
z@qxyN8h15*XW(rEe=smT@K*yzHvO{c#O4{zH#XnX+}(VC^Fz%~Hb2|Er}=Nq|7@-r
zG-%MRgB~07<e+B<l?J^#=>0+e81%)Uy1_>cZW%mj@b<yekLWn!+efTB;>{!8KjPm<
zj2bd-$ebZp47qy9Pljw8a$v}ZLykQ1q9boR^4CW`dgP#^jyx)K)VW7pbkw$^zBtM|
zbm-7ChF&=I?xDXK`smO<4(%WMpP_Zb0>e%ncE+&Z4%;yN-^1q|{hg!lJGy4X*bx_u
zxOT+)5s!~}Z^XP~u0G~_$J}vD%ds<$edgHlU%B=x_kQKKU)lZ@$2ZzH%{R=y)W6A}
z^B4UCM~)tO`pD@cBO~t{`S!@Bmc=bswXAOWcFXN88(Q)$&$j%v<;=j;z~VqGur_cY
z@Tb71fx6bgt;1S<t*5r0-TGgx?&Ag=H{`e<9+x?8%W=-}!;TLhf5Y*&9iKja)A8Gm
zfBX24j;}qT`Gl1x+<3zF69$jEWK?8S->4xcPCW6#6BnJh;>6V_{`$miCw_S1l+iOs
zFB!dj^fyM|F#5-%H;sO9^uwbcA6+%3VN7hywPU_J=AJRnkEt2kI`+b`3&*Y)d)?Uh
z*yPwBjomypH}<@ft~=@HCv7_E?UP2IyzJy}oqXrX_n!Rglee8*IQgZMUqAVeC;$26
z)^Y9Q=8U^x+&$wS829M7=f=G<?)7p18F$7h3s1T6lr^VhPr2aK)u)~{e&P7&_^Zd?
zGXB=_>&O3O{QcwqJpP~K&4i;SjG1uSgbOFEobaOwTP8d|;mrx3PjF2fJ8{ay851v>
zc<sda#O{d?Pkepi#}ofKvGKGcPV=2M>a^HtH=Op#Y3|cUoj&dKMW?SkJ$d>Ar}v!h
zI^&o#TF+Q{#+_&U_Kc^`=s&|uYMr!X(uzsnp7fP7Pd)SeGnbrs<5{<zb>CUPKCAbv
zooBsv*1@y>>#W+zO_N7VK4J3I$#W)OI{E99zcu;R$@fowX7ZmWe>}N)%CS?9pOTo8
zp7P5n+o!xVr8wo!Q~o~1clOk?+t0q>?3HKtoc+w%ub%z)vumb~n7Ux<@~JmWy=CfM
zQ}3OcpSpAEo~eJD`oYwy;E>>P!83w0gA0S*!H0wS;O^ibgMSNt8mtZ-8=4%N7g`!x
z6}mZeN9f0)2Sbm9@}ZYPuZ7+Yy&w7{^m%B+v@z38pLXuFdD9k7TQTkWX=|qaVA|Gc
zJEoPVUD0-3+f8lhw%@cp(e_;1%WZGAHHD7~w}jimmxQkfuL!RV-x+=&{CN0p;eUpY
zX&=*mO8e>UXSZM2erfyH+rQhMY2V$xuf6%4G3P8k=jL;|&e?d*edqk{oEOje!#T%J
zA3uHi^p5G@n0~|b+oo@t{^0cAP5<Nc$c*b|+%e<s8TZfl^^9j`)XqF==8T!kW`2F<
zPi8(o^SPO?%>36(&#b|-md^UltRKwE&e}Tb-C6I?8h38nxgF=OJolz^e{}Ak*$ZZ0
zKKrWKt7hLed&}(CW)Gcn?3|W4x6auzXZM^><_tUUYv-lUE1Wmw{ND55Ilpf1#dFhh
zU!D8cxkq0x<$~{Cu<L?%E~vZkqzf;&aKnXF^QO&vbl%(Zs^^cJzhM3?^E2~%=9e$3
zy7-KXFTMDIi+5ezuwd|l_=4LOY+i8o!uEx+h3-Xz7G1jN?nSvp&o0`t=)Fb9UUJ4I
z%P#rxC4HBCaLIs6CtVu8^s-B@zx4h~^Ox>gyl3(2i~qRzy~STFu30j8$*?6YOC~Lu
zy5zzo3zuBIWaX0YExBXKrX>$9d1A@$m+W4$Z%KK{UzU8b<ntwGT{hY{<`m;H>y6ty
zZak*PRAbNIh+lfkwWD{JF$LHE=-HEu`3(BcLml&P&;{+djmd+fz(rlgjCEHdXDxCL
zLCzzM7gw7qGYtPD%5XCnSDYcnXO1#0upEc~NbPtt7FV6gcwaWvj5gD7<%z(OFejT7
z`t?2hUgT~w-gKL=$(nYv$xJs}%pCJ;a~}R!(_FLNEHF=+Mfd|XmzkX=W}Y`o&5Pzr
z^Rl_hylTE-_Tf)X{?S}-islCMXZ$(Fcaiy^NtzGMcg?5fHvEuet!Z#>Hv^sTn<nQ6
z<{0OP=2)l8OmWtmvz@!mV&@)nxpObRsFF4vPPe(n$(lQyUzjfEm-tZnezU=O!2H_T
zVjgvVYkucEiVv=ShYy_|!v{H!<4M>P=77^<UU#<PTD%?C+b3~N{k{3Clf#3#y!pU+
z+I-{`jN^L7cwNt$YS#;<*7c&PbL}>buDxcU>kp>M)n}SrWi!b2t{LL`s~PG#WQMuk
zGs9hfGsn38ZbrJ^H)C8Mn6a)8%{bRbW`gTuGtu>5W{T?*GtKo6Gu`#6IoI`>InVV^
zbAjt$X1?olv(WW#6LI~=EOmWhu5mfe*Ih1WrOWNCa(SF<U0&yUSCw;vtJ=BIHNg3n
ztJaCT>YQ6#_0G3l4bFF51D%xXLT8OD=6v6Ev(x2z&{^+#)VbUBm~)ToDd)$or=5+i
zg7Y)i^G?R~g46BV<!o}j=={?4lC#;h+j-FSit{VitIls+dz^<{uQ^*?`<zEz`<=&J
z2b?Edm?_s^ohM!IIlsr+x@xU4)xX7BBM%nvSB{`(K4Ccqzrqv$KTk7eAlQF}<(%!7
zzqrAe!LUE@nK47b{ok^B34U!R`lLn190Pvq5}Ri!`O6K)w7}kdC)PB08-8thGU%Lc
z<GR0X`2h3(n)x3eZS6lLXFY;n#i8E0_ZTC85$w>9j5!CKf0i+`!T+s)zPoMwE#QTS
z?_<CI47~vQHTJL4{}s>MdFf>SH@|M@;qdWP#(6Zxu>^U3a-^g4D8Dk1`3yg8Ob7VT
zUB+Aq{%pN5%fZ3u|0?i_pW3`9b{q3e=+mw@<`(b|w;PiJM_*^>`E>Z@cn!$d_#^Q<
zw%ec&KaLgu|IOa8-<Thu-Y|@BJ$T+uV}1;lb{mrhZ+;nb30CIg3;b$s3-pp78}l%D
z%)Q1u4p!!`ay$;7-^#rH2FL3u#4r2__CIhSer3259Dcx<m%yvA_w5A}KR4zLa4qux
z5&XZ+*E{f+;Xe-Zco3}2Uu8Zj^EkcP&i~>4tn};f{z||8j(nBzRmNZG_u=zU8CPW-
zm3gbo?<>z@Z%4k$ydFN^a!v3&^grXTjHA;3|7|`h^H5nwmHDX5LuLFOmt)S+&SUo*
zGXy+*T};C|IOa?K_*T2lD(m)nuHUK4?D<$(mxr&%N_=JgR@Uv)MV$Z0e-Akp=krO>
z>z*-Y0{AoTL!Uox+m&DO$aypTX4~G+bJqUMEja%X|3%ua|H?Z5zs*x+J}UE6ncvF%
zRO)9RwddI@gYc^Ww0qx3+y0Z^x9wlK8T$>|{q7X2-$9(5mz8?So9y{H{ZeDv5my(m
z{tYdTIT!j7{AzUpD8FKtzew2ipfT5ghqt!|?R*3FKe65C@ax#?p*#L!d3gJkb{@cf
zdNblCFTyy;?}6w09CHWw&~J>n8+;6ZeLxuBVOfLu7yXmBE%#xcNkf0*YRlDkScdV}
zI3%u;f8-5zyhrb|{%?a1z`y)Wj1~Oxr^Y-9?tQ`@x9P{(`TP^s*-qGH{4aqU|6%p0
zqwTz(cb|=ycJ{!3|HF2^e~n-9zYaYNdG-2qvE_=S<v!#Q`}OqKVBB&YioA+_1oh56
z!}{fX>xZ7|wa4cwtUEc5m38pcnKsT-W&Qs-$}xXMd?nAmZ`yG$KE{sco5x!I@kLwj
zydPRNzhUQLKG_IL|GwB{c`u#=e1N=3@{2vj`~zC9BcFqEeeq!Ia=mN>Z(zGCSKIdG
z`Xq5%U$gc0v;H3ZRf3_2KkM(7_fZd^{s!0r|1=z@lfYIyl#qU1dcc^|q3`>a&F|;+
z=1(r$&&v4zw{g|suXTiw=XP9QB;TDFx2%URU$>{jF4yY|K)HTj3Rdd9@tLh(nYT)N
zOR&C{BJMMc_Zslm*V=y0XFE&Eb{v)S?=rXTcjbER=KRU^T&}O3*vI7lCWHNX74kKJ
z*MryGWb;<$^^U!Eet&`f$~<*p9~I7uT0Wby4BT$lQRP0N`b=xD94EQX$T;P`<7QB<
zM{<1SI&>>2*BLp^|N4P3cYv)}N5WH)N4OA_^Xu?+Qdu9B_3{?ZlY0?gx&BnHJC*BF
z<+@wB|EXNJ4!?d^u8)=L=Kt&U@bK&0|KIC-WuHF$dUp7}UAaD1u5YqWbmQN~$(EnV
z{)_ploVVY{{wv4rPOML1Kl#61zbe<c%607U>(t@*$#VSlyu|fGD1T*F_)M+UmHTM9
zUi=dMo%^I+hmYcZPS)i+<lqsGc?kXqTd;S7-y}zal5Yhme_ccBeTV!V_ypps<sa-`
zXt~ZBQ1qG)E#-VGAf_FE#Yg<V1VslyiMt2a2{}J@qMhBa-#N_D{ytdam^YzsFWGjE
z#k*38dkZ;)zd6k^h5cLft2iE#uWqz$=kWWE%D(&0F5Dv_Z)H7J;$=RizRcG<@c+Ne
zlhl)W5*|KJ{ht`~9`eb2$$g2;$0yJ-4?>w2*H$}!!peMB=I8MF{PH|WKP&5EC+>F!
zpuXfE3`%~XwEGoM+Bp%F<0QNl@pAmW*k;T)=*s-yt+>voTsKdHeHY^8xX8RrgBJS?
z@_evz|5Azj;On;j;qev!hQHc*3gEs=j_<@FmI2(4NWO{w|L^@!in?-NRJo5i{QhM9
z09)^0xWAX<@D<+IywBtM!0(K?`2X<s-tkdY`TzetH*M0<1Xv42K><a`^n_5HOcD$b
zT0lV17?J@ZNhZ!DK&)$9*R^6<bY1&ev5R6a*aa)AU|)6BUCXMg?poIM`#k5|duJvy
zVf}sn`0?Pp=5^lZea}7j)b}a(hWI^Wg|UoZX)8&+qo@z^_ivZ$-(T|F%enq&?4@>o
z3$T}B{N9EgAmg#0)6NIkuH|#u`7!Sp-S6A^W{(4hFQx3bUfy5EFYP!PzqI3I{G0wj
zf6ng&`twux9ex3G7k<A)elEXPFuq+R{mh>)<#!GFdlkPEuwP}mWWT%-|DHv(cYY5p
z)A??v%PyDqt#bJl&nMGu*9*Jc`<L5au=8a->EB=NxK&@@Uv9@|hiBiW_3ZoG>9PC6
zx!7y6-+77js{Uu8?uSl!RR7-o1GXmjU;nLPY~k0Q-`l^nw1?-B_x-HvrEQNNh`lBA
z#kSuX$!F;wU&v`n^ldxxS<)x_mygfWzh54$f9>+Ug7T8#jpOz2ygc1cJ!Fmh2QSm(
z^%HUIO5(NU|9u~O5&x(B%KIMsckQ3buk>%@x=#p~`5@z8$$VV?+Ufhhrptb=fzo5A
zW1#!n&vPbq<4e+K$Mb(pkDVXSknjH@zW(`QKW_`=^`E5tu+t~y=B7kG+4841Q+JWu
z@!R=05&O9WdH?&^*V)f!KbL*It^dFB=fKkqqdX}+_Vsr8+4u4BynCW&$7iRnzrI~w
zgI4SEKk7HSegv5}$a?a*H4ky0Zx|!3`|i*0W&7#y>DIi#4wvogK*D7|;O6&Y`bpXE
z*z*3rc007?hgkF0D(nY&|B=KOBpzvBOyTzl#yk0I>&tc`{W2bTKRX>KBbW90BrCqH
z*da2$jbH2WX5Pz&5k_B*YjWKD@&ncm@yq#s4fP<*^EKgb8lmS;b~?LQ2b1%xpH}Ml
zZ;j~RV=cc-mt0r0*9Dr2g|UQqN=x*0{r#sAF8e_{d>r|9M3TNe9@+i5>?h?o*8jTy
z<==<<9F-g|e)$~segD_>|9^+yOg==D(wlmJmoZJ4>{}lUnug2B!tI%rjbty-G>VKI
zBv<F<BDn@M*;B|v@~3<kl50VeeTRG`*X0)=xgIpxb11|L+!J&eH{=%~zY#Qz5k@ic
zk)X@iy`Thn8E6{&7^TSf1zkpY!64*=L6hB!-H?w4O=CZHH3WMZW$-cVZpdBq!SHdU
zNS^;23O|rL%U$(7;0GCd!6$$wyBx#d6`;v(#|XFwG}-ak2krw+!_VD>5dcl2io3~o
zaQ1_r%H2%kH11;>XLASBIET1R;}T*O#vee_xRh9h@kh`!t|C@pTn(D+r}*K20!{W*
zg7E7=lRscm1-}6_IoVwe-vpY*-NqF7J)p@Sd6)*j7c`Cgj1c^O&@>(}!te({lRcR_
z_`{%SJi?xhFdhX><1wQFz6CUm$BmirCqUD9l06+^JO!G@)5aY5GoZ<y&OG>YpvfEK
zP4I1?$sW%__zR%PUe6-<OQ31I%wCW%UI9(xuf|gFL*sBHAAzRvvC$0w8)&i%)B^tm
zG>uP<R`_S2X?)HOk}&=On#LFGBnjh7&@{d>+Ts5MP2+20HT)aU<P(0K;J=J0_;3Eu
zn#sOW9Q@ws1%EKsqWL3e8e>HtI8Gc39w3efed0vWFHQmj;uJ8%pQ;i@4QLu+aXP#f
zG>tm;sf1Avn#OeYtAud~Xc`UdTM1(ZXc{xwyAsAM&@>v^!xF}9&@|?V3*d7>)0ihN
zg3kv{qe*OlF91zrA$wfHI21IEMdDKUV$kFT_sigifu^xkTmfGOn#SScD)<qg$^O_i
z@FPLfXyH%jn8tE;#{~Oj*TdVyjqnI)^1Ad!_)5?;R*75S?VxEKC2oVS22G<w+yU<d
zO`}WP1&@NJ(ar9fFpdUIBgPJ!;HBiv@E&&A1TP-n2VcWZn=sabrm>FQHevLErg4mT
z1b!@NvSarc{9=%nA|8kT0i>mfC*gktO<uoz8h$xw@~Y*t@GC)Dkk|^p8l(k@=iz?>
zX+h#e_;nyHNW2Wc0W^&p#di2jplNIruflHzP2(1^1AZ%L8n=np;kSdPaff&lekVxl
z6K}&efwVsHF8m&l)+gSB-wV?E#0T*EL0X^q5dI)Y>k}Wt9|mcC;_vWBL0X^q6ut$d
z^@-2nPk^*O@df-Tkk%)@f<FV&`o!1p=RjJY_!hnmr1kM(Nm?IB>l5F@Ujk`;;z#%^
zAm{PKPw+p3rtzxy8U7k*vYRP{U_VpPua~jw$eARN8fE5yN3ic`8avH=@OAbbh4BVx
z8gH`qD2%s2(|DUbNMXDKn#Q~4Zt%Z=rtzLx27ezkjlHu5gTu0hg2S`+07qo)Md(P7
zUOj6VxNp`7aKEg5!2PpEfd^#mht7eZX^hVr1J2AE2hPem5d34-L4;lgn#Sc>6X92Y
zrg3GK8@wvZ3tpY&2d~Krf`7`YLg!l0G_K34hF=ew#tm6h;5UM%aZ}bb_(qUgkrjg9
z3Q{Yw!tmQcYDHEZ{7%p`%Co0~4cQIg-0Yd)yzEBsu<SYDvg~<i9uCqfvzx#pvloJ`
z*^9uo>?L3%dntHb_Tk`7+0EcB*)8Dh*{$FmeA<9}fz-6@mEgVE?chV%tC2qpQj4-X
z!7bTQ@QLiB!Kbq0;M3W?;4|54!4I?h!0eo3!Lpp=!Sb9FiD58k8bfkUf)525qjOGy
z?*VdlE$1}&ULYlyb2@w&ND1bg0UrUH#>kvA;roD;V9wd_QJ`sz&N&ypA4qBDtcQ;Q
zO`|^N0`TaZi;%=X(}?G6fcJo=(VHVzx7UF57&(`MeL0tbx8_`d{5FuB$+-%=FXtNY
z{+w&U2Xd|lAI!NCd?;rl_(RSu;Ey@Cf&a<5gKK{RP2;~gcfo%KO~c5&8!kXBn%vFs
zERbBvy$_xPnnrH!1MoZ$n<w`ncs^(vGjbn+&jcy=+{fVaK+~9?`#9K?`y{v^_i1op
z?z7;bxm(d$1e(U;+~>h1xi5k%b6*Bm=WYi(a$f~Ib9aDUxvzuK+&8(Z8zjea-v(p3
z?;?qV^t8F}!Fxe^+T0JoV{<=5avW$H$LD?wo{;-@BqxHV@$1}A;U|HnadPhG@KZqS
zmfSDkr-AgwxnF@7=YEak63{gMkoztCQjivx`)~MVAT2KUd-xR~EiU&*@T%OOz^ikA
z2CvB#CM_;E3%oWr$D}s~>5X&q;5UHO?c99$oglqyZXviSw-~%Tw-ot3Aay=>H}Kxv
zGVs3K!N~6iDe>H)@CQLkJa-T9vE04DExE&xKMtD4Te&0PZ-b`sm)w2e?}4WAe(osv
z2Ou^{?tbtOLDTpscMSYv&@}#*I}ZGN?t$PZxd$Qt6f}*`awoz+2TkK2xo-FuplN)W
z>xF*>n#MnK{qV0r)A%Mg2>%wuCdsV=zt62k@&jlZKjlt={}(ikpL3_djl5}akr#rS
zplM{~h2hztY2@V9!E-^=$jh4!cY&snpVt5{08OJXZzjA5G>ziCMtBKm8l`!2;DbQ=
z{JeSa-9dVTye4=#$oZhWh43LD)_dL}_^&{$^t>hTJwf_`yruAAApJnz;oyk8W+Wp)
zdh)y$_`V=Ed0s1gG)S+W7lH2&Vw2~sgpUQWzVh1P2Y}e*d7OSR#)H`8d7bbHAU1hk
z6kY*hljj``_kdVyd2zTO#9GVig$F^?sLWdnuL3z$oYx1h2C>=ljs^decRZ47L3-W1
z6XDl`i~@Nl!EXfVhx1MWH|Cv&<YtgQIq!7%tsrAT-Wl-QLF#bcneaP7>TurK@J%3f
zIPYBeJs@>BZ$128kUE@q0sMZDI-GYA{6Ua9oVNk~Fo=bacM1GakXoI0DSQivg^+g{
z{0R^XA@2(KQy~3(-c|5tKrDp3Yv9j;SO|I7!nc7~2zl4TUjVTX@@|B`1Y#lNZG^u9
zVj<++0{=6Jg^+g}{58-tcI4dw-w9#^<lO~-1H=Z%yBq!%hz*dp8U7B4)tz@A_+8!u
z;P-hCfj{RxLa5<-1fJu144w;8hh2}uT_APX^(4Fiq?BDxgC5tjpx3n(xevrLcRddV
zTrYw_*UR7}*LJYl^(wf|wFAvQ5DU}wI{aABG>&t<2|pgBKXknfKM|xqbiE5d38X)C
zy$3%9q(5|h06z^hjo-LFgr5$Y#&2C8!_NRs<9DvV!_Ndw<1E*w@UuZ?e6G*o=Ypnj
zp6d(vdeAh^cYOuF0OVw!>udN$plSTx^(}k@NU!VqH~bQiUf1<K{8Er!*YzX(GLT-^
z^%MLGkY3mIGyE!$Ue_hqwY&zThP$%h*Mih=R}TDokQ(mFgWm{J!(I9Cji71V>?(xc
z0y56Jis83`jI*v%_#L2W-09j4eiukD?J9%c4VuP1uEFrlAicC}DEvN<aoM#8`~i@0
z*|it=kZTx{he2w%YXtmJ&@{HW_5t5=jRN0x?FYW&8Uy~-H4dE*L3(D_f$)z(dS=%_
z;18~eNPYwv-(7C_PaxyF%M1S*WPH!}!v)Cro*#r~fsF6@Rqz~;@jbs9o(D3%=TCv>
zgQiiCKMh_8va*pMf)|6PQIa2qmx8Q?<k!J>15IQ1{ORyA5X&mR0X`VSvdW(c9|~ew
z<u}6j0I{s{=fL*@=~46N!H0qLsQFFs5uj;|%wGuK2gI_<Uj!coVp-)cf$s-mS>-Q<
zj{)fq^A898`ORP;zXc5Dw}O@V5wI$MC7P2!+Dm>rd@{(aHh(p|7G!3c-wDpmkAm~^
zj|Nxe$H9*LUa&KNEqH8xANt3E*ckc8!cPEA<Ba^{!E^IZ1kcYu3Hb#ewLSk7@Ur~V
zkX#N@_wr8%@60~~+?0PNcz6EU;5+%}qVq0D>F2KpKg+)W{5=06@XP!Sgnk7w-_O4U
z{3icW@Z0>$!0+;}0Kd<_3j87e8t|w5YeA#ndQcSH2$}^O!K{K?!0dwC!2E(cz}*V&
z0!J0x4UR3?%w5KT%svY4gC7WzhXoIS)ddfM2Nyg7E-82nJgnexaB0DlXf6XyqrKp1
za9zQ(U|+#j@R)+<!D9<v1kWgV89cLKJ9t*XtKjboc5v+mkRG7mb@(M9JwU;m@Jm5*
zui$O?<skFlf_LFpf>>My@4>GIvA7C8fd2`^;wtzMejP}QF8CO{t>EuSZU-5&3O)rl
z6?_ifUGN3Cx!@~8?*$p53ciNl4>Cd(d<#BN@NXnff{Zl<--FK-{0Qz-_!H<Y{265J
zJDb^AVHUWkFb7;*m<KK?%m<ei7J|zPi^1O)mV#Fo?gm~{Se8wiK`ghz!SKgH;x8Nu
ze-<>2=L+|LZw2Wq3ipCP4>Cd(4g<d~9D(E;koHxy4_H++3al>L5BX%!G^P}dflmcZ
zV_MNT_`x9QEIJUr7$luV2f+^mNo~<Y_%e`ED{_M;7J0#wiu~ZoML|MO0U0TZs^F)A
zrg3^vHTc`2Dc~7J)4<;qg}^h5!r<9Ob>KNg)4_9#8o=|4W`gUB8o>*S=71L#%>yqg
zY65>>w2+uLfUJrXEdsAAS^{2Qv=qFd=x{=B1Zho0&G3yNJ$X?JcuP?$cxzDvysc;@
zcxO>NcvsPCa8pqycz010yr<}BaC1={ytk;Ac<uu!_oB7ngGGJdLq*4ej}#pbK3a4l
z_*l_NXl?<qNs3N^KLIl9EIJK*y6AN9nW8hmXN%4RpDQ{W+*))l_<YfN@P(oaz&DF7
z0^cs$0KQvv3Gw^|WZj_XQt<tv%fP=DU4i^VkWrxMD)8f?Yrwx3T?>9vbUmS;g7g(d
zH-i5t+6aD8bPM=p(QV*2MR$PT7TpE@tLSd<-$k3j?~3jNzb|?K{GsR};`tGz-za(n
z{BO}?pi%rdaskqN6h8@O6+aDT7e5Q;6mJDx#m|F<#V?{+1Y&g;zYLZXZwChzzY6YF
zyo1o)LG10~*Wu+L_IB}`@F5`fcJbTrUxC!&;&<VbK<3NE?}3LDe}JR`WbRq~A$%st
zN=Wg?@J5hTyW+ottBXHH(gD(A7k>`#0+~M+e*vy3{t7&{_-pWl;%~tdi~kLtT>L$n
zr-0Pz;vd1^6#s<ebddU7{4@M_AoaOe<Ot&|kosJl1)f`+gXBDr`dpj`KOdw%7w5w-
z0%><8h46zx)>KQ1;WZ$mPf00QTe2HiS5k(&9%N0mWH5M0$xyJNWDhV_vKJUH83y*0
zi~xH}_5nAQjN+=hLF#kKe(=p8^|@pW{63ITvUD6cru0B?M(IJ|tkQ|#p`~tcaj6&V
zDD{I~r9tq7(kk%RrPbi?N~eHlmQDlDEe#Rpc_5ZpX&Ah+v<|$kbUJuLX#;p`=}hqU
z(nj!}(mCM0rSrgtOPjz)OBaG$OBaF9mo5QcEnN!kC_Nl}r?eUTOKA)Ex6)SdlhO$I
zZRtwz-=*!~&!wwDF{l&F9~1=(2OSOWJ}3^B59$T?9<&x5KBy1$4muX}4>}&4Jm^Gl
z>Y$Ty#u!u0AiEG3=8gw{pF07(IJbh`j$BvJsKZtagEzVAz?)su!CPGo;O(xN;GM2U
zaFc5ec#mryc(1Doyx+ADe9*NBeAu-FeAKm+H$&FshrxCE&EPTlE#Ptat>6jy5%Aaf
zE5VcV+rd-wSA)OJ?*xCBzX&`le+hU_{!-&gBQ|I}*fVGXxMolVdmeX|od#|yI~}~I
z><sYUvNOT^%gzQLEISu`xNJT6XxRne77mBqYdlf50eq_L67ZR_OTp*LE(2dE<4HJ|
za0U2E*>v#FWewnKWsTs@vN_-zWmkc3mCXa+DVquYrK|~jzic7+*Rn<6-^!MNpOh^H
zKP$Ti{72cf;Fo3Bga0hM5&WiXBlxefTfpziZUcWPy94|WM||!z{#$l8Xq0aT&GP%e
z?D7Y|-13J&SNS9C^b9Hwv(wXE{v^1j{AqAq`Lp0L<y*ny%AW^MD1Q<Bb@|KS$>rO@
zv&vru&ne#lo>%@lcz*et;DzOHgTF6-7reOqJ@60ZAAo->{}8;q{A2LS^1p*umw(ER
z(c~dvaO#l9!I~jofNev@gDZwi09OsE0M`xq3Or`W*X%0YOz0EFt%N>d+)n7z#*ssR
z0^5fE46Ya|SlwSWGz&axXb#vhG!N_=nh$mlEd*mji@~0urQn*OyMgP5mVw6%9Sj~f
zbSQYj&^^Fk58VqqdFU{@s*m;z8@b|3uuPo1S2cL*UQ>*6@f&!#_$_>}_#J$(I14^R
zoC6;s&VvsX=fj7J3*mc+Kfw18e}wNTE{E?au7vL;u7>X={siA!TnFD<+yEaYZh{XJ
zH^YaETj9gS?eG!ePWTA12|iNX10N~wh3_NohwmdEgzqaJhVLsLg^v<j;G@J7@X_Ka
z_-OGAd_VCVd_S=bzQ1?@zQ1@0K1RF(A0z$@A1hvij}<%N<HQ^AapEob0pcC_0pc(4
z1I7FB1I1tA<Hbkt@#1grgTyEBgT!a>3F05{3F1rmMDb7fMDY#0Li`I}A-;pV#Sd_|
z_z&D8{tNf;GV>I}E6lyC!ECrs<idTz1^0^rxL*{(1EK^T5QE@Bu{%5{%Hfq_2)t7K
z3SK4lgjb2Z;giI0_#`nBUM=>8SBuf`$zp%_WHA;#MH~R1BF4j~iV5(kq5?imc;M57
z4}P!+zz-Ic@Q|1U4~fa}8Zi}KBMyd#MGZVGYT>n_9$qUBf!B!{@H#OIUN2_D>&0C7
zbTJ=3T`YhfA`XQgA{N6N#9{CTu?#*#908vpj)cz?%i%Lc8+?{n0iPvS!5hU<@J7)A
zpDnuJvqd+2j)=kMh#vS{u?9X@tb@-J$H3=_<KPR$4e*8HCitP^X857vR`?=uJA9G2
z6TVn%f-e^Lz?X=7;Y-B*@WaG|@WaHz@TKBW_)@V2zDzs;UnZV{A1<DOA1<DQA1QXi
zj}&jfPZFmNs|J5FY>IKR_$~Zo@jLh_;w<<n;vD!z;-g_<<0>(H`0?P#;U}_}_PgOh
z@T}o+@WSCIfxjQV7QA?PANYsir+|MP9%iTQIy5(n8_?V=ZboynxE0OK;&wDQi#yTW
zEH<HeuefJ;HF)pvDeTGJ55G@52)|!E48LDI3V%Rsfj=OgfIldnf<Gvpfj=akgFhs;
z!5<bcamR<nE8Ov6v2*xi;2YfWVeuAsd|13Q{BiIv-0@NI30FNTKI5t_;$K{~MSRCq
zTf~2ce-8eatF|z)?_;0O98nEskC?)4pKC;z-M-OCo)r5dc}k3hKP3);KP|??pB59~
z&k7IxS>b~}Cn`q-!AT?H;N%gHfm27U1rH|v=S0nj$H7`+-YO13bE}wv=2kHa&8=cK
znp?$OG`EWRXl@k?(A*{#qq$8ShUPZ049#ug2sF2eBhlO@mZP~%w4wRDScT^E;wUtq
z7ab$M1-sCEUUZ}RyoinXH`s&b3t}CbFNkB%d_f#H;(PD}G+z+EM)L)6@`xY7Q_*}$
zoP*{|;yg5862Ir}FNurMd`bKP&6mU<x%*4vax`BSSEKo|_!F8hi<{7VS=@}~%i>lv
zUlzBc`LehZ%~!-dXucxuMe`N$Aeyg;htYgRJc{NkVhftDh$qn8E}lViyLb-G?cyag
zw~JTM+%Eo%=63NKn%l)rG+!0(qxq`%E1Iv0Ptbf-e1_($;vZ<fD!xSXRq;<WUladA
z^EL4uny-ofqWKz2<ZD55WFMG4@^LVCWZ2jt3PuLOqLFcM_mPi*<!J5@L(tqIeud@^
zu_u~4#c(utijipU6r<7HDfUNmrx=UoPH_O5JH>c3Ul$c<zAijyzAh@!d|gaJ^K~&9
z&DX_LG+!47qxq>g63tJ=ax^~`D@OK$tI+&Z9EIlRqKDAW#Tr6C7wd@Ub8!rzpNr!N
z-Jg>vJ;rgIX}OkDDdZ+^_I}0Lia}yev9IunDWYCnFK!q2iYLVf!fV!;GtI-yR`VqD
z4D&p5gE=^Bc-EM#ima-vv$HP9x-{#c?0MM>vM<ZNHv5+BpR-*#d*--vDsv9b`F+lp
zIoIWG%6&a|X5Phlf6hDH)#mDPo$5N%b-wEo*Hy0DT$^2wxSn>s=-T0W$Muox?)+!-
zf5@Lya9_dP!V?QGF1(}gxx)7fzbm}0__5-5i@z%VzBs3(uw-0`yChiBQ1W<5LFw?)
zF{KryRi(2^mzEw?+E;pU=|`or25la+b<p-fpAMR|TWGfvcE4fwJ9iJ49bOhGd#3D-
zviHk&FZYzsE}vJvv^-kAwtRE>qvhMn|5jc&c(1|J2G<WhXYjRyKN$SO;88;whwMA_
zv7zBT4&URfJs#QPjXieTGrVW(o+s>i?w(ie`Rbl|do9}QtiA5q>z%#I_73j7bng@P
zzI^X3d;fdyp~I#QyJ^^6!+gVc4mU?Mj94||xDo#tQ8co2WYx%XM_xJd-I0GA`PIlD
zMo!#k**+Wg*}Ttx_lfS?x9@TLZr=BWeZSuKKl_dvb-<|EqZW?3ZPd%7z8&?`s6nHL
zjUGR`bM)HLCyoBw==gre?RVRL5AXN-e(&%1>3+NIf9C#|?0@(ErDLkc96DyjnCO^o
zV~nu}jGZ?2w`0#6d-2$t#@;pdov}ZSb&We?-1>1>kJ~tI)B)oU*z3Si2OfB!@4%N2
zeB;2*@#l@ddHe(8R~&TKL02Ah??F!;^ua;j98@r2<b>damI=R|P%?4o#E}yZo!C4v
zKC!%FqI-yEj3?-s<~hW3x92g>&z_;)iQad-5Bgs5{oPmYAMN-0Pxhbh|D%7KpN*Zs
z#6V@BCNMLwFz`a)?Z9V&5y82^+k?*rKMZ~m{66TaY^l7ja(m@lmETmBRqb6ht!j2v
zYt@>nja7G7JyP{-)%L27tHw@RH|gX_XHL3s(&Llfob=hGpC*-5Pp>|#I$C{x^&hG?
zRsW^>$Ld*AR!+HP$^lcan!0o9cT>%2jnfXFcIvc(nzEXyHIbT!YHGsQg&zq|s{M8C
zRkbtfj;tGAKc+rZKdb&X^|#eOS%2X4$<r54-!T33Ln<1o8qR1qzu}(^jWZU`IDE!S
zGj`7S%Zztsel>IUtZ!!hG%L5Uw6V5vVPkOi)Y)~j_ntFq&H;1Io^#=xOXr;4bZ*mz
zrp|>IExcyoBMaYJ_{5<fANs|iBNk0qRJo{n(ItznT6F)SpB5dk*thtE#lKm6&f?!M
zzI^d@i#IKPaPgCi-(5Un$%G|=B~zEwFPXjM<|X$m`S)RCm${csS~hRlVar;V9kc9|
zWoIqBX4%cl?q2rcvYCfpe)y;(7MxdLgtJctYqKu|>#`pN>$Cq1PS5@icu4l%>kByD
zFdm$d-3HFgJ{6pm{YS7d`(|)<_MgEy+5Z9OW@n#YzzK-);QZ{#U{m%2a6xtlxG?)d
z@X+iB!A05MfQz%Uf6XF`q5rW%WXwF`x14sD|5$x-{?#1u;Gwo>H&JTrF2-QTkHLQD
zd>?KIZYb_of)jn>AZ&Ba^x^i#4Z{t`jlhk>?StDFHwrfzw;yhQ+!)+g+&J6;xC3$H
zaR=ch;3ncKaBiFj=f(MOep~<-#8u*|aFcM=xXHLFxT&~lVj{NYL~P87*p?HqDJNn}
zPQ-?si0wELn{gtx;zVr3iP(k{u?Z()3r@raoQUl=5u0x!w%$Z+yosC_b8}kE%~>%I
zcAf|O&Vya&!JhMA$9b^dJlJg>>@^Q|nul{?2XiXyVC=LIC&I$Ua?!wPum;Y8&E_Q7
zY|eqr<rLUl&VbG31lU~8f6e9e*Idqi&E@3RT+V&X<<!?)&V0?~#MfNTd(Gvv*Idqe
z&E=%mJkEI?$|<iyIpej66JCor-?hXz7Iz%(cyX9<0`5fIuW=`dW!Qzwum_hJr-~zt
z({R7RosRo0?hM@TaA%5$aTe}u+&N-}aW3vW+<M&kxC_KleDUEZEXtVid)x-x#kfmw
zf52Ue`y=i$+~v3{IJb8t?kdjUT`kTxuEG5YcP;KZzASM)?gnu+XT+}NgxH@rA9g*b
z!>;FSnEs-~ZQ@o=g>B+Y*d|VdZQ?xGCQgHG!ZzK+Nw7`Q@|1R`^Tmk=I1Tq8U!8ae
z_b~1ezC7_L?lIgJ+~c?>I2rdO?kU{U;$cpCZNbLdg7vlqyKD;<*A{H4Em%ogIM?+w
zHqchAn{C)F&ts9iz-g`*u{U1Cl6VapVF#oC>x}Df@LA^f_|)=y##^|zaqoyM@h<K!
zVvu+b_df0e++T4Y;y%KCjQbnz@3>FIeDSHcNPH$95ub}EWW(ePt?vNc1_KT|)%cos
zePl3aU#%AUJ16vy#-BK;E<^ul+`}1q`R{Tk^h$?4s+iP!)VPax#N@v%PUv=rz3Q;n
zjGO2*q~>eJk3-Z2#ttWRr*q$(&V65ZLf>@QI}Y;+{cN1qwis{dIP89hJ#5-}u*G4|
znD$dXW6GzL{nj()4?KC6V%fjb>3h!6drs*|={6^p7aaDI!(MUNpB?s^!*)9C4Trtu
zuy-8x7l*y?u)jL&BZvLXV!tx};mH2!uzxwJ|Cf{c?;P28j;tWd&Y>cQl{jpW!*+An
z?hY$=*x0Nchv{59AWP<?Ea?NX?3_H((Od4YHixZn*inkfYmdqbSar9<k##t-E=Sg7
z$+C?<II+Cuq~J9t1yfx1Q-&NCb=c7k>vPz#4m;juKii3p>?DVs;;_>kcDlpXTkKct
zdfF){F0k2d4l8rmV22HL*d7jBT42}Rr3H2iUGIcm;INAvw!vYSIP6k~z38x)9k$(J
zI~?}9!`^h*+YWozVWC3%*}@L1bJ!(?c1~VYZ0kK=YO@a=_OZi0ci1hv+j_S-><)+B
z<*)~Kw`<Nr4tvC5k10J_!ya>Fk2|u*9odskd`~-}PdlN{I<jXS*;Yrk)semE#1bvD
zQ-Ahgn_c9v4GwD@Vu#MNShjJJ!%lP98H&AOjLm)pH`!I6^P)Hc*PS!jwI*k~Si|p`
z{GOS!L)-wr7k)3lTln3=?>2s=dky{$zwhw-5x*bhye<BX%g;T}wSn_<4X!TNO|G}a
zYx!@B_i_Ko=d@D6+oHJOMX?8NLcxt@2!A7P3H}J~Xx#Dm&%j*-zY2Ft!ENTFxNW$d
z`2R}Sm;C+*=PG<#lo!4zM&ia7-ey+fY76f+mlp02E0M(uA281<e9XMI@M-hm!spFb
z3%8q}6~1o%OqjR$MKKLG4|h1O6W5134YwY58SX~h1GwjLuj4+&4J~<5jKYO+3vn$a
z2bm9*oSRiJXgzKN?vkt%23^YUajp{vU6FN@YeQCj&be9d4ceGhzT1mpGwylZH#lSW
z7e&$T>$9%nH>d1Hu^Vm}Zd}>HuCdt%yRIzTE@b&^DLV%LakwSfSC*ZJ{OIiMgI_6V
z7_vTV`{1LqkKbc`*718Z=lp4pqqG0K$HuHtd!9#`oafqY*v70G!@BuB&vn-DqqF}r
zd}CJ8h#PZ1%Grq9kkvin@!aka=ef=pc|}%upFQ%<+UKo;vDxq7b`tlzyvO#Pm$!4@
zNZyQ5<PGjL-1<>F34a6k8aj{UJw5sbeqX}v$ouDL;kseJJlE5s^Kl!po*rFOI5xW$
zH<+{^oh{QZe=GK1g4>w&D8J9_e`D^`qmR!1asP9(e%!yC>wfJza?FOTo%?RgoiXa%
ztg+d7Tzg~g*zC_rw+}v-IM?GoD?Mb~XQi`o3&wT3jvu$J^ptUr^UTk>-W<1;I1Vm!
z9gy$(=jd~@{yBPE={*N@yFS8wcED%wpItv6NPcnc{PCZax{xjA_lWTq7T4!oSiB+k
z!r~dDE-b!h!iB{T;<n(P!d+Q*VexbPzJPlL_Yu0U@%sj@d&Cu44MR3&jr2T}@Ah0=
zc>K8YT;xao*PbQWH@QxRZ!0bJ*A%Y8b@~5VIyU<w+>(ML0(sPt^;ug2rxd&%TvD)G
z<-vtRD>r2AR=KTob>#_!t>c*@c}E1+XB`pPnDtfFn+1DL+JXO@{Me*(vtpB;b#2Fu
zsD5GhvDI64pMVQiKZ<*9_i5En?Y@1|GyJ}=`w4?y6gO1wRrpZ#sl@rB_@a7DVfN$~
z#Sq-Slb=C<%kJx@G!<?ie0bqKQw}np!M!ns@~YmL6`1;>coes7>Pdy~O}(#h#k5BX
z&z|-h*8|g@<GRNw+s9pRP5bqbPePv-HVj#k-7w^etQn)u<30^V^4U%pl+W{@QgCSf
zj=V$b2W4F`y}D#&V|B?fjddkgHa3<#f%^nkJiD=E7A}H26SrY@q~u<Hf5hd@X)GCp
zbI++R>BXIdyBc>ZZu6YuOWq{xXPkd-V@U`%V{UcHiMTUy8*mTdp2fXSSk}D8lG1tA
zC2rgbTnu;YyfaF!z-`2B#{W1v@9_IGZr}NhC3W+wOFD6VxRY_`%s->#3EWG#*Kr@>
zzQX-5e|^b8P3udln(9hoxD#+!G*y?}+;mmRD@`|+{Md9y$>;_5mCRi5SV?Tbvn3ZT
zs4lq!w-xti+}pU1a9`lQ!-<8}B?Y)L+}^nTa1(Gr+`+g8+<e?pT-(BDUB@llUh+}S
zj{M6OHWhwEpL5s3jae@e_6_bQ+^9pVOUC2o;Euul26ry*V%$}@2XOBkx-o0;V&cUu
z#q}<3D7tm=#;mbRp4<I6+&N3COa8j#veNv+E-M{;Sar$7!>%n|fIAX?8g4!A1>BCq
zHfD*Xca)CCRV=M8S%~Xe`atOgxGR=EUV87+t)+V}+gcjNb>J?-J-lpV)}E7w7%jX#
z=*rpy=izO$8h*pLTHZCO!_~tN!Og%eBRr0N53U!t4%de}26rs(ICPH3oxpV`;!c5|
zhC3biTiow(XW`Dlt;aFEH#ax+wDh#MhGManzJ{*$o&|l~k*4-zB2(NI#@y)INQ~df
zwb4bf_MXW6mbHhrbo53-@xHFsscp^8ZcoK7VjysGV@pr#Ds|DK_MTM>VlC|*?OiLI
zBHb;qmY!(LiAm}<boE4HT`e8zUL-5llM<}N3!<wdU5?nhtK0ij!ska?+CuS~XtX2J
zlDJTY)kIcAW09sv#|rr<evSxuytRRF$m{X=++nY;s?O(ehbr6wU%kicbJuw)!eM_+
zz+dYRRro@o3XeNnUmtYW20Y%%D!)HSlv7)qn`_(S-5o7`;f|Jg++EQurB%(%Ud86d
zqTP{LPoK(=_|)aLTx$4~#`H)}QzX{j($RiQ%kqv$W2AF=B&IZVxYYD3%|v#{c(knl
z{VSdOD-!Bzjl{`y6`v03m*C`>buO=LZVs(&Y45R;-X4h`=S1pu%f}<hwUHGqy&XN_
zXji-^*4xT0WSVqv|1@c7N?F%+QADyz^}AW$aaI&E1MPd&w?{hK;-Rj#M2b{Ks__0d
z(^skNwXYAaYVY9M_BAa%5gnJj+@~*>xvLl@)ZT^$Nt?6$s7Py%%H)jqsB|t>#K~lm
zs4ADt2~`dH<#lGECzGm2OJyxlY;Lr>w}aFrlBVVTA3w9ypYa|6=N^jGt&6nw${gyT
zQObu`Qd!o0T`isMt-5B;Zt0Au+N45jBk|TqS6fS0PeLa1-tCnSr7}Ji>5`FYN&n<#
zmU`6H{dKFfRMrd`dD5+u@?8bVUQIQqh;DX-JIT%%bw;7vm$6{9R2Gb4<WJqYo=8``
zJ=#^<($k`IsXiK$-Kwf*8Kcn`t7<Cml3KIvi0eCAR>l)ON9Kq!Ceoit>eEt^9#kwG
zi%_FdZ>>XApChBFk3~DfEp#Fskv7#HGR5me>j}tUa-=~iYp;w;$@|Bpg#8Oy3%SG-
zlyOb4dlv0g`K1E5NvNkM*1nt?+piPyDTV$K`!h-dS}L<d-cI+hR>5Vwz|nVCTF)Aa
ztyFbi3uFpCcF_FD(Y@`lNWXqd2TMh-Qq;wu8}`CDW2F*AV{-VM%b?jAp*^ZwX#M_A
zmRagoarf8NLYc-YEBSR@y`2#?GOH%9#42sJg09dFT`Qup0PR}s)r$Q(IOl3z4Vqe4
zMLJt#y!abrO>S2e10M}!W&0ZWuq|;$WSUP;`x^S5E?c1ns&l4+Ax2g~D;^op(6t7u
zz!EgJbla&SVttJjq&?2?+Cv)+w{&zYZ)sgUutr@h7LD~&n;z+snG~@Hv6QRpn^v{N
zy6bzpTIE$KL5-1~RnfL2aYI~-=B$atV(o2hx^pDm^05|nwZ!`7U@u^wMOCk91rV4a
zL>f9X;*O%Kyh;8<M(HB75+=p9wC5hn)JPK3sM1uM`pI;bQfhMS=OL`If`rB+J<anY
zD<X9Gt&#bWc(j9HJN90NU<yiIt!)La;C`tB9l2BKF-qI|HEyobN|_B1E7GI^Kec-!
zoCiwjuw`DXq-a$O)8P&ArtU~<`-=8RTc}kQg&k(yT91dMtS8o@?F1)Tx*7KiS`h22
zkr~j?+1cA8jXY|<GQa!jS<$zvds5*2f-^-^JCL%DiyTg=k376xBC>ty5=_-t747Z7
zjL{ZpWC7-5(iOVmG<3B^)+I@~OMekJN*3wVI95E_B`dEo?Paf$9AH_wNurk6N-m%v
zv1Q}o7TQhAN-9`SZ!BTnyWQ5zL)ijJ(+K3&T*j&iebN+gV)qiBu&bLeNoB(5mr^vS
zk#T#V>`Mu$jY|Dcyf)fPxjI39w-NMtt3tkTRh_pY6!v*)m`eCVweFCoCg2X%d8+Dy
zZckNBptd#?4pxOLD+5(NUyVCh>8|nwJmIk0SU}R$fLGBBBaiJuNuMTInoJ2&9!D0D
zmh~S8u(Hv#doz^*X(qWWzpfr>klWEKHjtfcDPg$6Qd_o`sb^848I4){dY`;LQ&mrE
zF&?F=qE(EWrnKx#rkK1-zd;LA%)YmOpnGR-W*()V7N3?f$odsiO+U5o(*ZK&*ht)5
zHXz$%veOcj0m=4{9Z+dqF`#v!BTNjw_;)?rqM#e7tT2uzb?ic%X+UwbchzY-N|`%t
zg2{jZW_da=c?y@>qZ7W%9^JY3FEOxC4)yj#!_m%eIY2ti8M$ocvOlItr1V$>_3yC|
zHLY&%PQF@J&4i&P*HNg4z51tLb(GVNBV}+u`2b>!?@Zd*(V4`UubIS{g_*>dbGFza
zZd>B9iC2<PS65UnE6K4^K3=8I4t7WYi8PY<ncdsbA$u`sgUoEKT+3(WlI5F`>YWp7
z!@6io71kZy%d|^hKmo02U)hV!f|mGdwTfYd%thAT$}rC@r8(Bb;udsXZ=_fLvi8)v
znqPStKMks{nadIqp1O72F$NwUO@@S{9UUwVSUy=R=5%#1QYT4O!ONI*3sUVr5|c5l
zq4pfo($&_Xy`9}XeNL~|(vFFVQX;BFv6gO(L48-5Nt(+C?r87fo{lWs%OX#wYQrSV
z8rGGzv<H*@;TCN2HfQQYy~Kc%ca1R@jH-JjDk^fvp0SsCkXh>iw8M3MJYtL1k;$Yb
z@^eT_e3fPmv)bcQ(qQM4<xh@*;1;*hMj1qop5M~7GU7JWloxSqrQ2wC8*5rRWP1oU
zH?Ji}+y+S@!A+wlPpxcj?ufRwbj0084|-Cov$>gB1SQ;uPmS9PyV_f$ZAp#UV>MYl
zz$Ka~a+yvnh{oue$O5_n)!b7QQWr_of~ZmBu65UhYu&XKff`@PAMnbNyv`dA1Z(~7
z`hd5V)x=<hr_Ntf7YtO?1}m$A;Q*#Uz26-UxIK1qxSgHJEF>h->$TD~yH~CZGG<3>
zdRJg(JLy89sW09W>6}3AR5p1#&0)fV=t7hUln<p@X0?P?D6Y9}GA+aeSV6tC7VCIi
zABnMTo|-<aW+xSd47{}L_ZUHMAQ%ex-Ie}2UwzOQ423*Z-heMu<F2Zzm20j6R$ePY
z0S{(TC{Ppf)Or1NRi3Kapr^{mlB<FBR1*w`D&2vq3co)Ps`0RJTj35>`l>3uRrMjS
zKi~;e_-bl=fhupMCmgH|kk_FaANEwGx6WfMAnkI@#U!%xTv?!&nIuE|^|{zSNXe7L
zDs`Hfgh1LzcHD{$WFuv`1&cD)r7Us4l1F?ifC)ZKkI=tl6w~?)H@>NUEDU3Ei}l-T
z{kF$-*V1OonZL$ubHb8E)-eU@Vbw=RAy-E2tM%$yZKS(rl_g?=9%D&3A!tkD?UquH
zT*yz2V{TN=`<cP&n?$W&HVl|)uduz{miXx2D2qL$)$*@sk1-2P_?N3@VFk7%4M;ln
zRy4Vx6)qQAIwJB;^rM~h%a**gH^v0k_O-|B;;k*+5vJy<6Il>hX9vaCT2~}YC$gFj
zbzobC@L5qgTd+1$@XNLtiCNuFC?01*ogAFj-RN5+x@bArB9pd+{OMPJu`?@<M6Nni
zJuthaU+THNl35*w8nb<NklzneLp6=27gGrDf>kk++SIZl(!@9+B@NSK(cW&d5uUIW
z@#r}-WM-sKPT-xP(PJcr?u12c4|7UirF3pfdyLByDv9Ss+@v-f8rgkl=LQHG5{me0
z+S^!j(^Ke<ln|cPD%}*ZGdQJ4TRcV^_p-($&ZWw2FpnIQJVv`mj!7~ftue_X$0Sd`
zG09`}peMBwV-mi)uGG=UYmG)$MVryGrlqMh*52L2bCS7o6_8nXdnX1<EYaJ#?aO56
zv@#M?$xGB6Lanh+H5z*bGHLFLhBYp*(<kkWHo1tVyB8-OA6j;AuSNzZ#BbkU-FSAi
zOPX}aq29{MimIBbs-VwZRT&7n-M)${ue)A$=5Bwzx4t&$udNSMh8Qg?>%6|oK&Z}J
z<FEAleO}rBs~aoBI`zq9K$<Zvg}d&g=d%vk-bHrGt{QFCBl1hPW>eKUBI>qw?Bp_m
zY~hI5<dA)UN{J<Ek1vSz+Tk6Mu9dnkL|Jur@*?)DlDan;qC3uhQq{{5dK8wLdSKPP
zAy6F+ztZs29TFb9G^`#3i7dsG83{7k9e8yM_BwUgD=V)Iv?{HniJIv(0u}y{x4z07
zstbmyf^`)YVYfd}Ulk12Rrx#>o?u<b@AcGq0^X3n#^<XDu~%AERq3m*te`Y&D}2sE
zi`AhzwSls?>bS~5yQC7~cChTrQ>60Qq%f)8qnl!#$w#O9(YEE(Ecxnztq~8A$f#wV
zO}VF+0Bf~K??%f#awkqg2aeO{GcsFO(g+LqY$FVoSuLTZj4<@vRUvkuL;he*MbKO4
zt*fl9_4&hr`asB6SB2SB5e(FYymdA8_4S_0I$x-c=2PP%vxA;+eN`2Y(weX+?b?iV
zjqJ%}HHNFo$}d~jJ*x60S5D+umEGZJudaUhQ-&}6s*~ZGp43)KNZQDSOjb*uu9jqj
z)wLqPu=BVpE8P4gPVD5Gnkqk?Lq*u@2?Z+swIO#!jXxap)YZryrM4=>)_ZM@JIv$z
zYAd~Nzm<%BeU~GrYS?zQQ<NSq(i5?ie#?v4Zeh{}U(lYkROm|z)P1uPo!@WNw)Djp
zL>sAwtGod;(<a1zqpGTcj?e9>41~kpDo<t5<M&ksYdk@Um2(9(;b6Fm##H0uNj*V#
z!0q<?Dr&HW>jMG5uZBpwtUd{<`y^FM$$wd#2$=<T>t$E%xBD_H*zPKrAhpNkG|)Cg
zxKxIzxhNGeDlL2EN!F01=QkSLP`0}e|1KJYJ76_>7JX#T7wPER#Sj<m#ePg5<%mO7
zv9vBFU}SDavbAyN%z-*A%t?+Q!%xubuL{(7nKsna(!cq=-ujBVknGC*-U@m)cfBWA
z;if;Vl54mXwN%Q=px0w3Nl(XSx6F3(h@gS{2QSyD{O_OlGJp{+X**jQV@OkK6iQad
zl-km&c6CqQZXoTxdVjdmCl9j(y}n?bn^_irk+?2gMF(Hy^Vfwq))EX?R?xcS%&gwW
zkR7b5@m4WDGy148Yz<alMA_kp*2<GXvfq@|3V&To&!Sa`;@vH+iD|1lXqBDLDBZEF
zk#@bT4T)aWgPzP7-K0{V&P!Nwf84K$>tI!L<92hMvtmV@y<xj%`w}svMjZ?q^?|zD
zusi7W)>H&4>wSJ^<=#5lUrmM2T|*B`!wc2=!?nRc&|6DLC8kWEvesK)6RvSHzr>7S
z(Q8#Cc1ZimRZYwrqH-TeW(q;7XXRI>j<mHnDMF^IbiXddUAc?O<Ec!^%h__nitW<$
zyhKmlP-l7S1A7Uho;inAWG(X{%UrE0jg@TYEnHBK#;X30r>+WjMB|ClMx>r9^`1x*
zHr(uJPfBO&uBb{rE#_p98?lurYpFMN%f>^vx|e=R=Q`vSfvQT6r?RHLvZ^W^stZwy
zes7(>E)WjVqU$Psfv}I}5WvQ)@K@Aw$g8%tzQP--4F)~+Zg1kos$b1CaA+N?%CSe-
z*^;n&eOw<}fthZfFq49e+3T+|LX`n;ptgb$+e3#_Sy#{bvtWg{w$>Z0VJ)PxK3wMy
zxV@Du9<g~;$(9g9M>vEeT;r{&B<87AhM(kDy8WSWg)dMStn~#j#X|nd0P9h;@+_OX
zGF0oUudAwLJmpXuV+w;zEdx$PU7g<(CKCB1x+bcAKXqY=@WyCcZ%1S*Ij+x7HOQ55
zIbBw3y|t|VwRgm)8nr%|sJfaEk5a{>Ft+(Z6}4VB&lhmBC{w}k=4Z(B`6%|f+5nE!
z5D=`Z3%f%`eVB!_u-{kd<<Wx`jCWWumEp=d#(1U(9(Rqep48X)c_w#VnEpCc5%%~!
zwSEur1wsZ>2cq(Yd03ilO${qXHQq{pnA=tQ>F;YRD*T@MdT*7V<ak4&+Ca!%8>*@G
zRr%}5rjS7usHzD1D=L|qFhbPT`YL>3ilMTCj(|Wc-`YTsWu~w<Kxue9@(z_jcSRr=
z_Egq}jOg;C6x0~g!-kh}rJ^3=MW&(7jiL{8E*z*NivktlTEDN3l*<0Z@A1_5>*#Ae
zZ2tMZ!H~PYuCC7T)zz{9<_pyG!1T-1)_Py9FXRb&t7_`1Dr)M;aCeaU<E^Qx_p$QG
z0%Jum$Y|xkV5+Y-W*u|LA*vBe(@%BH3+Fc=?c-l_W22GOgc!t(hPXs~+?<n8Y;J2?
zz~4fU(KRPY<qWtt(kw*;bapyn2;eQ9%iH)kQe<rp)>m{z&jh&;JwfkDsZ?2%zPj0>
zp|&=QsTFgJ%UP*KdlJ++XD}zGIv7hs-o&Ezi48quIKm!_$=)rjmwGHov$Zy0dt=e4
zt=t?}2U!fdBe?~w`Z#HkwAlG2J9uMpSpCSD6_JnTNbF(2kW$CPu0%JpJH4LE9-R{)
zBT*0L#FX$g_1oq&rbl}8Q!3>)nHHU3$by#TMtnJ)MXSp7x$IK6YijSs#ni$-Qz$D;
zCEGgP**~ny(UJ*lh+CIfwZ>Z2*Lw*O+h^}uCj@$}I<YPkpP&lMN*9MO)cIt?HpJ<e
zm<h-My<eTxGCR+-&%Q`%JChYP`ARW*wS=i-s#LX!)J1QSp|ZZ8R3D0KSRq$UIU-@%
z(gtZwUn;hvu$PW)zutdna;zp>-p`ma((Nmq5y=}HtgJc9N=9>>WxCY-NIIX8n#i;v
z)=ovH0GZdEbx58W>7>-N2jko^VHp@I>ypM%d8tmAUNzO68m$y%!CS#;+psz+t*?qH
zVQHj3P?9`t>{Z3y-_SG?C)RcJ@+tdUQ2O?=cvBYJ^wXzKfb9%SIXkZv<(iSchk9PM
z`_M#Hm3L%uDk;dCDkT|-I)-F{UDuOkD&7I+SvvaKXnjX-e3k97iZscT(T_@HwJUQ{
zvQ*}#%KJ^Dbut}8&(4g5K&4Q{le}uEZ)%AXcB0cxbsA)1Qx6R(1u9s|zC1V8kCprq
zfWCveT1RH}<{1tY*{WL4*~`~rXTeC_T0U1ok~F3!60D1dOiSLkvo6lCqi5%s$_r;s
ztfNcR)Pdiv(5a;OlA4|M!15GRn$&6}87<yKMmk%4I>VhcIr^PA`|UCd^dem%lal=d
z)W{5r+g9@Rifg9D@br}eeaZiEnZdfaePw%3N`mdT021XWOEB|Nxvuj`lT*74Ri-A(
zRRJa)QFAib?o*SduXZF!+tm6-$%`SWeP2q5Rj?_t|Fv|gN;l&Mc<PPRgC(*i(+-xM
ze7ZrcsJkX!{BRak_1&abwT_fUC;M`Jr^F>mO)TRy*zSL1%gVUspcNK&W!M<eLH59>
zQyq^<LxM8~bu5w2S+TIk@-AZ3*WOmW=gxLJVUtr&1ND2o#d?=!Y8!fXFY08ATC(VO
zfqoz67wGFsq7Li*0&RK+wP*bcR8x0OxIkHcZOV>q&^~Pi;^|h_r6skj>&BKYPHVR5
z?L&elPwi-KmMcKKpy3D|a~=h(S!wfIvDmqUm#egKWd&)g6Hi<jln|Aby1C)2VKU%&
zh}F6b%S{dry6&x#OxlTtWy5B$O692vdkb^&F4WkJRDHJGy|ndo-#@<pJZM&X*U=MZ
zsIh43)Co*{e(CLZ712QPs6#jd1|~K=l?wSZCngib2~7Fp%omvks-owGzSf8?ev3A6
zbig|G&2y}3U(uuIGYu<dM<Q+LrjD>Yd!(KKdp70x=XB}Z(qn}(E3k<eSvV8aX0)`m
zH7Cl!mc}BT9M#DXFozu>yU=Xq23DZjS0tU)vICl1({$zS<Y6US#kx_lXiitMuel>h
zTV8E<BmH9NFSaXqfBBrQ{t_$sX=1h=I9Rc;tJTV>PWb{RIVNklJ-uPKg#(s))nB(@
z=e&n=)LqGCJ4u{=SFOCCXeBd0V(YKfPqeTrZXcm%X~9mj9-~Q{dTNett%l18i7nE?
zJ4VW*ElOR-Y~B!8uVvU?x$9@Ymt-vD)P#yzMjW?U>Ris6lsdCG-B%>MG7<I(7;BEM
z-`&yc98Qo*EJv*zL0qOjS<uW%cp_LooTE({B~IEH%`xi*A)N;+yEs^3^{f_eRu}Mq
zv?96Q+GeOjjpQyfJXy!}K~*~w6Mj7&pzm~8o!!wc*6mo(i*!tAkY{;mU9tvGAoHWV
z$MK7Ft-{j!^xx674J&j(@OC06dE}F-`LI>21BcaPIrlLkl9OjkIYrdGqQ8RE%}{sM
z+a)@$GKlGi)w`TDcwWxsFd(lrCq16sF~ldR%8xQHnouX@OGWfT_PY;l2CGR4j~&iV
zO+v^^f63k^{q_W>e^bQTNN&)hp1PS+EQz!v4KuPxm==<~UB5)zDwO0a=CsSUpz=XJ
zn9L93&?rE^&J;ktxHG*MF9VADT_`;g=5#P}>er26su9hT=dS2Vcng=4&HA0L)y>T{
z9GRsGS#sIiV8pgeO$$+@a^@h~s(On!mbwO&9tWr6)8f^`Pr7`1O6Qawp4wBThg*G2
zdVt-E(?gOwfDEC!F{cNl*W7d+Oto}jgNo6voAQXW>QGlOG^Z=G{!1}2wz9ag5=%R-
z-=<5;PYQ!~(jsk6s?wuM8tO9y((mn3L7H`8x}Wq*?eRe_RkLErhC^EFWc|t<BI{S?
zkOl2MnWB<+Nxy+!kW#0hGAmf;xaCu1R-j#x$*szK&8#8En^;F5#gRFjs>`Atr&(I!
za&s@5Su3e)na?71o!*eitec#(8J=0MfbUvKK5+V_3A<4ly#pvkT02_gDYneP{U5h>
z*XoJf%N$+8;*;$$vyOfzD07&+%IX^<`t|L!T9|lhto_WfP!37WEwhg9U^9m$uY5AX
z%TYf)_tQH7=SY9LGUxI2WA8#ob<%ofZogKY9+4{Pbg_OFZ-78X5Skkkw*kU=8KYHw
z*`Q5+gCsq2Yecn2+<_F5+C;hvM#wJ1``5z-Ei2O_aco6B0yea%ae|^wSG5;&W@2Ow
ztR;(IHrjzT>}tGgEx81#GI!Sz{Q@;>IQ}1z={dFPF6pWUzNs^g%!)?42UgQ#r!|4x
zwQ6HKui<p9)RlSQ7}ZQ+(W*$-?5J9AOD~|gEgaWOYupW8tsT8>ky-jQXIgM0^QX?<
z&H;m4*6lJ_U!8u}x^>(&&S?%S{&bB-9dmj>;?e;%x_Ud)uRBD)c%Jrz4RJO4q+cKx
zz|=YYv`h37Ta*`WWI1Ysa{yJ{yAKek%ha)GGT%%t71%dSPb5uTotu{L6QrlJMZV~i
zAwZpLN>kU#NsmURKyMADhq0L}=U;q~LggZzh_qRF1wJkIewqWRIo&nm^P2XIF{!3H
zaIn(^tqD_XVAYhYm$So630E)Rr9Y4EqqPBLo#szh>i^nWx`s07)j*UX&#dJ{S<41e
znH5<R>99Uula}Ysz@C0zdkJP&D#@lndJGI8)UI@S%InAjDKQ`U-xvQPB{@#mC7qs_
zWGioAE!&74SR=&-8dy#B!s)3{wS+G~q!kcWieoFtN$-G)yk(g9T-JbEsZ-1J+x2UM
zJa)Xt`jAyx()5TQPme^d97oy)4DN4N=vSQ6qfynQEkpcPk+E%%ei5s)>fNc9j$LZW
zwU1w>%AhE_$X&%om^=WJQmtjB%aA<l11Aw@d2s+eYf_ydhUDDK5FC<ECY!FkA)i6V
zssy?>mrIPX)>Z1GO6l2YsmgYSiIE{LHNK@^M_<p1*8qWuj2}oLwRi^5vMhsvLiON3
zK&VciKG`it==3K@yx*K5a89=ziZcXQwIxG{mD}kz(vy~?h2sG=?G~;E)OOyMwoh}l
z4S0z)BTD^x$$;AWw2Za-#|piiw#$z!r8RwLOmO<93`#QVe~}_>AZc#6t0?s_mf>E`
zeKLeu%`>Hg&Y;FYGxiA5pOq;sv8yGF^gydy*oDTN7@O!Af_i?C9)mT44-`CyH{9fM
z#z5g#15J+?vw_b?$ycP(N{<}QC~xZR?q&&Fy+g=(R{7K!_2hdH`t_HzsGSxyK)Bks
zPEXN%x~p|+RwCam;VeVCikk8_bYVyi5H_c`Cu5)*an-Z%l4@Fl?S=645V=>dE?r#9
z_qpQoP4b$)^guOLpyN~7kSSQ@Z5z+1zKq8yX}N`;NpXOH)TWYtq5kMrm>m=O)@<kU
zjtr4Y!&-iwA?<307QJj8;>AZ!%E<3U8C{XJny7_7gHsS{TY?2xwIL*b<?7|4rdB@b
zsX&Ub!0pdQrXG=yfeT{oo$^;EWI<Fi#uxe190A(MU_P<Ko{ma|A>YEpCyno4vTehD
zUYjxShh0?S<q8BZlIS;?lxJbAUH#7It*$oSFzr@8Jxcc}zFJ<hpefQC<#(mp?NhW_
zy>GoBYK5?oKsMI&S>CwTmxDHs5v!5W%f0+r@{6+CC*#sD3UlOcb;LUKX-QD!f5JmX
zS)NYy4K<@%mQaseyl!iWsf~+8QGR=5X6pT;ffvQ~X@y;c%cM2Pj~_&0auMBdj=3j9
zdJl(5tUU8Sw?k^pkMP}BsU25GHq}W^y|LIplj`cxg2l@6k*7RWG7~<mgM^237VKmv
z{A30u>WcPtb5<mTERU@2?N0b*vaNnGA*9ob>o?cB+74sSkBXc)QEKgjCJF4>kD65_
z$rpEaB>7U#pjweMwXf{rBWbOC{WD3E>oNzZ>76bqYY#$Ibv<pgw<QctlB)0X^%Ki&
zs~EdPEQ{;?3o8vN-#1BwaaPCi$V?)4k_KKUC_ZqIzS{uTaTLW$g>2c*EZd+&6CSH@
zQukC;O{B>BNf&mpc)>fyiL3RTS`|t{Xw9|~0$I<fAqjy#LXq${Gppgr6N2!nNb72P
zjc|KRuDf#N+**3mC6hSIq1#N7-@19y7LUFJ<0n}r^Hdr{$uhMko+?O+n5v#gMa)SL
zJ%DX=&=TVEtAa+iP1LtX8rYs?43Ys0nxefiekV`$HaEvLR}G<wE;gy(mOZ#iwp@Kx
z9+_Up!&@$xAuPr@5^JM2<;yB0*a`2k<FTq4%enDYDq^&1BN0|UEx%=D#2rC~Jz%R(
zoY2sJwZuM9WLZa?nCC-6R>Z4pLesPRdfS3l%WZPc-;f=*HE)(Cy^`6I$l}>jC25gd
z3li^BsBrm+)(J@Er-Ym}DobE}Hn4wm${$(BX#*;w@-&dx)N5=(aw;sHhPWLlZ?7(s
zY0>7L@^&jrPf_bV4o7Pioq_V`CM`eVp}eYkFg<G3xg%!*A-Mpm{1lelBe%VYQC@{M
zcgbt4^<t~xBtDQKhdo)?a#A5bn4uIJnKyF~PKG(TuD;%x6p-}fDM8I`e1t=Ny-E+Z
z%Fwf~O9a-p^F5T5FnXD2%vRK9zwPU^(j&VpdBTb|qCO|Zn(`{;Z;Zr{HmmwAKN@Ao
zD(@7x`i_)A_Q=gj8LFsM(PNYrsvMUeQ<WOnY0=hpx^O0OJ#6Oov`BS6=qLj@f~7M|
zd(~K>K9{Ba*i8EPg&Hy}Y7Z5bcZD@_*oqNmBNk`$uvoh~<e_^2xoIqoqZYO0Y{)9c
zT88#mqpEBG2_a>d@Tdxy^sNYeo$O8QUMr#7+~q_pFO!3Yy1CSLJPjEBT@fWeluc7b
z*4RWECvwQ)*rZIge9hVGky|>BH{eV0x&w|k@g|cal4m{~kA64A@ix&!J6Pqm;<kGf
zN6;kuUdLw{M2_D&a_{)%GzU9KeIG=nRkpK4Xtbt%rL;g;#BqeYiq`+;`dG9h?y|$R
zQKo7pTSF_m)E(%n?N7=X4R!2*@IhUfX`y)1_vEAr?u76KnRs_JuG5$K8*-}3$l+Ki
z$%1n{PJ6aG4W*IPmXpN|@nj#BUiLETf(G3_wa2n`?3k5;?TMr;fMR^|9)%sIV`8N%
zu|jHfPlxKc9O}tK8D1+ra-wHg=}}zPOI<VU$w=~rd|k{6lzR5}PmTF}DuM!6lPk4B
zhmovBvWzSV%}Dy>89qyrX^>^SQWcSNzj2!XoE0pI=w3?xHnq!>Z@L}JQy$7%QRh8m
z537lOcU4Z2Snj~Gw<n5CJ$+oD%yJchY^UmaMbuk-idhCv+G?k~L%NV*MbC9*kD{(;
zTuxd2R5!B|F;9NPZv|f%R;rxv%N(G-_2;PRsgSxt_9K$S<-|fsGFsg#gf_}}rKn!`
zQdGvEIxXdsH9FyAakYuaRfzM|;(798iI#;T3otdbNq$0c;81m9ut`qx_{?E@Yx?!l
zoY_^lJgPY0#cB~@z)*d>F|jN*pyI$P6uCN`=oFjt08I?d1BIz0jOmv+h3Z5i%R;w#
z=Kzwbk^N<@F*yfH=wvJ#XU^(G@2Djg(yr8;a@vJbQi1G3D=kaYb*cR6DOHut5nD~s
z@uxnJBd(+O>FHw~Q!o6=5_7_2SvVewGpQ2Dp2!KJ9a;m)f~X@5t53X#Vk;Rkx|mw7
zNCfGHDC;BiPQ&VVSWFdWf7x>RO>lkENL{CQU3YO$wRO=iTwjzLqH2?6PdVzcfz#J3
ztJD$F;in477$-21$#&yS53*XV6QFa0cI-%;SY&;1q*^aU<S44f>}c|Ud<xz1I$Hf3
zb-G5P>N#O#fHghH5ZGh)X{{W;b4pU3epNo1g9%@=dVxp<FuLf!M4^0YPu{saacIqn
zqn}8v20QxdT_fewU3J1w(Ih+_@|7f|Cift%KNTV0sc|Bbm0x+dF5IEcvpFKGIXQl2
zPc5C!m7JsN?NnwjGeUWQPfa3Ku(j)>xU6{#)DcBBuyGq}I*>TKsH%O&fKK&ij#NDA
z=JJG?@>v^BiaQhJ)U)gQYqzpYF|yQ=k7!Izh}9opQWxS;zbh%RBtxv<X-Sp3T?Dr!
zPImXdlD`=sI|6kRtWAgie@!ulsydawtH+fO+9q3yd`9`Jl+`p=8ZHQ>MveMNiXj(;
z)uNE)w^~nBwJCKWy`xUH?$WHqb0um?_Q<?Tp2?`(N)a?TpA}SB+sC6*bV!|5*;192
zg_fczA0kB{55cAg>`8cvPz@*cpAbt4St~zNlp@h}OSR7wxs}3xvK1+QYY$eR{z?r=
zKKPXq5|(RiDH5H6SuM*W_O87yjKoPUeM~InqC}v+Q)(cym=u9N+?67-n+cQk6nW~7
zzg>akAKOY%Q?_nDSz>3~Qss<7%0;Ph_Ybvq{ZlScbySs3YE*P9tn#V-?Noi|1VnmR
z@|JnZMb=@u^egl<Aw`$Uo8q??1yW?JROsEu6sc@H)_!M-Tn)nMhpze5seVf(MLB5$
zE=5iYPVvhbnB3+{5jJ-xtxXte0m5in+s><D>P1yKE!S^%C??ZuPxMu9&bzFR-`e@G
zc9G;8Bx>o*3D-ud<IOyobAlOy<(xP1YWITn*44bj<|tSbO8a(gHnCH$20@i-o}`Pl
z+8BAK{CXcvp3!4HNxo^NsC+hFb5VRwm(4IyY)S{dTwc=<lCR)mBJ-zlSd@|?v{L->
z<UpCw{_hd9C4{lmu%ec3U2XfRN^Kp{r>{zUT2E^#Jtpv0VZ`Kf$W0rg$0jPTmB`6h
zd!1;4S}|gf<|B<#rb>?VvO}i4vbH6A6CurUUQtS}FLi4Bf#JjYq%ymeTB7$Q)T4x%
zMC#p7Lmzoko1jUH>)Jn;uS-}g+O0|A+d6EW@^Ko8vRGuFuf`QcrZa@=wjtMN^)2jx
z#7eZ1SoKw{Aj%O?n=DizM}irjVYM-N<I#zzA)YiBa5iye=_-|&P2||i>Xm#QRRv)C
z&W_S2P*Jo%rYdR1rk*U>-<&jkBhXqaPh6{(O;ptF_D-aeNSP(dW37ZMu2Z9EL%gPs
zU6fhvtF;=hz9p|1S~~U$n)2#)ZGDzat{f#v6_w9Z!zipg{QU$z{Oov_+w!E1Dy7F3
z%^GK!SV<i7*ojni@{TWMEhP~oYwrL-%_)m5iA$&ndIKyWlFy)CgzMFhfl4CqP|9G1
zerwxFO=4n@&}&{JKX#>58sf4lrtA~RH8pELaLx*)<m9X>lSI_X@3*Mhq3vtdxs;Ax
z-bt0o^poZk(>hf{=F|Vs=v^-3x7&N_<!vd6Qp;-6oVC*=@1K~ksAO8-ZILymUkJ(2
zGw*bHKS?hem-<a86(v@W`XGSp59GGEHCNX!-^#ICY00#tzvN@Jf9tPuILfWgJKc_G
zb)?VXPEzG$-d+GO<eIR$m3}$jxvP4`TKSrL<lEMYGI^GR4_1}(CeG1Wq4ukt`nr{R
z;VY4T)pMvLoJv7{uE4rbu3op>Uir}a4I35e3<7!>wf@qBjzLYx6t$KkjOI@L$MNXD
zS6CUOcM#|5bqCc8>mpOv$-0!}mG@5mqa{j5)_(?ZzWm2Zo{}_M&t{$dRx)`hLl-j?
zW_FIm8vK(avOrR$G7afcxj`iD2J2GUJ7ck_h$%{SW=a)Ok}#$7&`*^V%8N(ppaGjv
z{E5DJa)|sxzK684Yrt{QK0AQcBhS97J0?vULtW9XzRqZ`4zTmaehpC8O`YNDx~TfY
zvUZ}?FMm0KtWs2F*(|yL?D!aAEYrchCfRF`s>!m&wb53!Mx`#WEVE>h+;`2O(_h-$
zmV6O;Z=a5~*T9@K%FPyRk1m~RYjwp=Zekx#Nt)$Wlw$Jl$|)jG0yzslsyZe*U<_TQ
zL%UVKGJ}|E(lyb`W2LRsVp(A_h^5WjUscACSWr@3aFSOx!6Yw6p|$cv51&YtN=lpa
zclVr>+1;xW$TX!09D7^^t78g1vbS?SwX8R-A_gxm#jr)yVI{RKYGtYVvkNMN!u;t2
zzPY5$3yaH9x1CyOhgnk<+bf@&9Z<QO*oBd$ua`p0;Ic(jDIKO;g&ge-WqR4hO3a>b
zt03DW4X;GUrh;s9-f7!Px*$3;vb<%voZ71c33POnBYz-M`@5(NimUXhEU<#YbR->$
zw>r!j6ZJ$!eS=PAP{Rt%lV7D&J%kEPmRc`Zs;tZ;w|m>f{@?^nDL7~2Qd#KIZuENW
zxsmh*e2$M1SDqSk{Ma38*sx|&9D0hgOd<ypIcewxRh%*pt?yIm7v&q`YMP{F<gDE&
zm7-ejkSr-cf2_}$vNg81#-j3boazGV4AofPgU>K@DT2wPPBU7ktN1*?N*ONy3NN1`
z(;juQvy~J(k_LLqq%R=TS~&IRd=S}CGbr80BkbDA2zB-5WSae_0O=_8UsyG)zZ9=4
zg}PLgn%r&WBh>QZrsU5BN+0ipX`@bu%VsRche%BOo0s<!BvpK^v9P<1h^@9@UF0-7
zdCSRD-5e`YcdwJSpp>aP%1VX$yW;YXiqUqEtGAGt&&u{J|A?HCT(8I)^&o1f;{1rM
zBL^e-C&^T#ysoO047I{+EOxAEU48AvX4NoL#<+wcvmbRPmS(I@S|o+EwIw`td?ky&
zL7OC*&R8lBWF(1X`?uHj5|_%yOF5aJ2ul8hMnamjOPw71oUWw7H0iGLghJwC`E^=r
zuQMTJbd={b+gsb!lBn7VO@umZp*(lv_^8so3?3bF%}<^fi_c<0*PEhI8(H4Fa-}qH
zldi%dO_e0NsN~)D#1(P`(A7M1IGaoyUd|jw=34vpiTLayRV$~-;(luui6D$9de^9K
z1=fm2B2*Tu)eM~)rR{};&Kz2f`a_70hBdCJsZm0&HX30nOcy5qCTqirL_ne{FcK$=
z6NR1<ptD{!+(@_lx=ioNRf(&d9Ej1jJ6mF_6G01<6;&_I&9%|w01iy`(sT65b68;r
z{ar`mGI~n;C8~t5v88XhI^U8IS##usfc^34S~*Keh_Ij%9-0xU)e8`I8<YdToM<Gp
z?5EXBwh6g)JNXK8Lg=*ggvibic{0KpQqlwE{6-#tV4Bj>F`yPXq5g8B(o2sdX$3hE
z`2W~@``A3M>rU{YXql8`(k4}{n^tm|IvF=@EZcE1juSf#Eh#b`OR`NWcGfXiihLz8
zqDY!AX^BlTC1nZIU}{W_#+V7F#-^AS(_k~$7MsDg*e<rkc7P5ryO?5IY>VvxEw%-^
zKo@9%4z>gIkNy45@1FbdJkR@*l$A6CY#i~u-yi3md%o_u_i^su4Y)e9lI0n8gU!rI
z@J=}ZO<OYkbm~R9#jwfi$=q2j$pJO%rClU9btMgB-Zhw%6iR{(W8<XcbIayaml8rb
zXTB-JPGM72B+?-IQfF!Ix`nza-;fB5o|<ob3W=NmazH8-;DrSO*_uxdmqz}jTcpB{
z5fD8WHuAzm$he8U)QOeVO<dZ!oMoMOewxExqMVp2xY>w<=v>b-xI)IOjJ>A{T%xx0
zYvkg!vVg$c)mj>C0EBWal^(=0b*G-vT%a{CZ>9{^PeOZ25B^A;B^KnuB0r^>a#_pe
z_LMpoLB|Q>Yyp?NE81j3ki#;!xE`)j%3~>t`#lO-)=4@vZ*=gis(zm{N?Xn7hrKIA
z04u~+BW>W$O34ZVmI_G9F^en@jB!+GUx87k*ik4M4#cqB+zhlasF3Vh3cE0?n9~AV
zbS2eZ?oeW)`=Ln#o%yJ%fJVhsJ8EBmrUwq8JP5|8f)BA0E<-;q65PdD*=P6?o`S)^
zqzJYVyss1vbCiK+1w>rQ*e_>+>C?E}5EHD7V(T;lDF_r$fMTaYT&C<%lxofRqVgcz
zadM(kN<v?G6eXt1l&f}FuEMN**1iO)xB;Bd_iL~wB-);eudY<9ZirEx84_E`1|h-j
z@&I{q-9cHL3y7^E-2b4XA%=YtpTn=7o4W+NxNTU>CvR3^OewTM004azWAnL85v-#2
zESO5nVv$`rSSX63pMa>1^r9R(E8=Vs#yS%miwq9t6znfW;_QNSuQ1*fcoHrLO+iUP
zQBVY5!3i_N65#yWH4~|9{v<f>nyEaFBNc#+LjjDo*v^TDDIauUEfn<~7EdWvLe}bP
z;|&_E!yz9Y9+$#$k#CTrf9Q2MI)nBUP9dU2Tg9l?=q7|ytEAt@_xiu<FQe*G)NV(B
z^St6~6FBjUudE*L-IU%(t>>36C$I{|=Ms%=@4h&ZDzCzLR2J>gBN=jBe^gRpb?4ST
zyLcUs4B$<KHb7|fQ{DcB5)$EQ!Tv?f(}aWEsbJP-z4)SD(y=kq?(+{n#?x1eiiKD(
zLcwCB&~s3NF-@55va)(j))MI10{az?cC$omAxi;g?OG|0#<xbmZUb>WEtSi-QdTlg
zWmypBJP8}Mp2T~_YcLXniNTP9nSeg=2Y22#F2Iwj+dX%&Z4r&WEzTde#eAwS9-DJd
zdn*AIbR`U*VBsA&yB>yNgiBrsg#B$NQ@G>`3Jw*q3sVfgT7<LY?mRA`Li&qIkRP7u
zK!<>?iUNqmZybf&R!k;*PS=j9@B)rOvn^fwhXQj&Gbput^92X5w?wcU`@Lu-P%2pB
zbjt>%Dt?>FT;v#G_c6kSiIEP!i@_pu^*4E$xTg1mUE;IzRHnN))5tg%rNdMf>KaZF
z8%4v2DQKE~yyfN9a1R~+`aEi2$GfP+xi%{@m1k^r8m+VQ^hB90L+@PHCG)5A0j~i2
zN?5hljM;^@;%tXnZoZn<MkA3WVV;Oyw?&Vw8Fq!R(N5shjdZ)VR1CxB=+~%xt%?#v
zo0EM@q^nk~4dh^?a!^dl!B}Lt)xI?|%&j#q!oO(FeMVx(9FYWQd2_@J&b2ioblRF1
z?vmD;@>!d_Q*^+Qm#O)D%baYoWroePOk$6pU0Ru2FOxZ^mSpd<B`b8=k~4d;**?3a
zB5XodG0|qK%cad6wqjT7npvDckE~gU>zB+ZRrb!-xLw*CYtONr)T|JI(ae}#ZcLip
zhVtcjY!7Afas-pFZ(Cj=VaFQmL*@;$O2T{<v)D_Itk6%7+}Ra7fNeC$z9^y4R#x=V
zNW?SmwPLIr`D^}up;5l}9M<9DwC8eqy>r9+;w=v*f_a<X)ZQu{8*i(_p|rlMXv1&M
z3H`KZ&R%VMrO`Mv)W>dkWUGL3TYBiDEsL%IjdnPX7a)6e*4{|B=yBJ2c>yaXIiyc^
z=&er)oZt3roPCqn!Bqf;)}qgr<j_}3j?ubWBW&9UR4tuI(v}`tZ_5%<tF^=Soqf73
ztyY9x?NB82x<jeRm3wr=u|Qm+`%wYJdKwU*EX0aNInDl1)NM%?F)fR#1s#?-bFa12
zBQs3ht^AG3=&eUhSbA5kz04<IGRZHPCyxh9J#%Q2Ox}=i?OA0i4M7l7AOEO$b;Y9v
z=~^LSW~GSw6BEF@xlqFRC)){evn7eWdlGYVL!5Q1o1dW)OAwbSJm&4x(yXtL51wJC
z%ps~%r@U?mN8?0@vnx5^X9?kKJ*03Q{UNHXP`H&GQfR3QL7ofYrl}bjafVCBCYn5B
z60DppNimy-v~Xa}b2geP4MLOnh@nL|p0v|LA~#ec5%k48I2suZdJ$U5s6|M*X!IvU
zPP;FpmMpB0E>edgw%)!@MNXV^bQ%d&PAoxHd8q>(F?d?auoaLdIdzBP?nb=O$dNLx
zdW}ph1W5^%QM^zD{WOdxv`Rd%hqOW*%5_)ZUa))I*>>8M_63jaT~^_8M%^n^bDOiV
zIFU|k&M36*9<y~P%qeA7>t%)YJV9|>ntgr3Mi)OV;oE|#UN0FYn3{i;g7N29@CQev
ze5K*~snwPF%X~)x&P`H0r9Shmi0sv5<NtQ@l_%PZ5_!T@*R1T7Jy_D^xdInueqE_x
zhv<uofD;p~7Bu_I6@A35S2{=7_Xy#cy?(iR4%bGMVF9}_X(1yY7V?irS)_BFY?;0^
z7Hzkj@JPp-1tQ6D(Nb2K4Al9ksEUm!1oaqKmR`ILT?GqQ$d^$oM50+M#NVJ9Hlk>~
z5G_`OT1Y2?;Y1<5Bpt-CJO$>lEZ=J6SCCYrNO_*e<1(3re!?{v?mX?WaD+fhn!SY>
z%7Kc65DJ74kcjumc>^t^%r4oR**LXYFchH6&)T|z+7X;0-rCrNr}P;t!R6|z{pH|K
zc2*IOP&1+w{A0{1Soz@qr)Oa^xhXe5L=o;nOjZ$D{^#wpR58&BZ%lBG9&26o(1HzB
zL<{~blim5nMMD}I63qVu&#M`~E)tgWT&N&;i2_a=n6Wva!a8USF~$Qw43>F2HSU*Z
zdo&gZrx7uZ1%zk<4_B@L#;lLE`O_)Yybr=(Pr=7y{YU_dIDZ9k`=Q+rHbPz-nd761
zcDJpvYb05mRGV9jIAnfAxU6%fdIq;u(UARpq0_lyAj7LOSLhyVa+pQdn8L-Ind@FL
zqCwx21&hIv8ENbM!xDA}2%FFl#EoY4_bKFb7x-}y6D)R?G<eAx!i(0l)%xo<9;HVE
z-|%to5PHx70&W(p+-)r;_z6`^&?1_49McT7%Ot1eP(9&vz>y?Mp3+?2h=uY^66#GY
z!*LWJu?)6kAhCf5PCni;0SD@f^#eYl7ATLuvK-6A@koP?>69*ONl7${rPu?U{}$1>
z!4y5ikZi2Mpbbo4T;thmCexZ(v@%N1J1&YwGpPH}caN+0T6S()g|j>AFJY<hB8GY4
z;R9RnH&$?okAq|N?ZrNv#}saPjLRa;kd{t2)ISTyNbV&KP&>)uDwHL3%cp0p5FF#f
z(XOH(Mmp)W=ser4v#bTA@m_Xy@)??9DpNwt@}rYHOEh``iOwP#i3JTUl(^D?bK9w_
zt8jfX(FixS*___ik<cccA|2yQT?~##%63gAB~OO;54;$}wB;3U6N=2%JisTvsCc8%
zlEZT1bRA94t@2ky^x1U;r#G)&SXrtrY+Qta;)N9iiAP2kEJhNZVzYC6Nz^hhv(DUA
zGI0K^60aNZgazmI<5StgKRX<+z$)YPr1iv;$78qGL^z)5(E*+ynl~S<waw!+{=C<T
z*jDV(*B*|W`Zc2o^(~Q2s$aQ142Z~J@M$3t8c^R7*??j>5_Uo=5C;pJ*}y{DUd5YB
zp)?=tv|u=9v+(K8I<751=uu^fn_bVc-)VfHjQ!!#81{_?7a1pnG%L`s#jE)C=CjYv
zUgfxO*9G(!n-_)?IO@<5)e@LvqEd{J`GV$JJgKu5W%EI_%!NwpHeE57Y$@Tt_GJ<)
z2`P!fXD(#cTF45dW<w&!K%H6bGhl+fB^hJnsiVnGxS(XItbva|vMAqyVvX<!J;S(9
zKedbtrE|;iA>N?s+vi_B9B`#3Ab2jJIN9~s%IfTC&=at*nagPQd8ii_uz{M91Ab<7
z;Gk_7@~_Qa9-KC-5QJW2Vpn(vA4VQ5UltKs>6%$pIDFuSp{=PktPn6%0YV}KPs2~d
z!#J^3zFh-W5c!nGfQjjHR0NWWBq;)8iw+;5r8yi8!{?baNhttwEroUz<<Uf?5P^xA
zo7Z{ZAh`UmJRBf*@Od=)PSI^&0c=ABq(lMCz+a_s?Sre05}fZ9i8hNO1yc|EK-F;k
zPJKiXtegV6$yZ&YOQJpejE;G49FOuaw9a8Z(}DWT4zQ{CfFU+tg*_y!Jp)%LzTJFs
z<tjtjXhX_#D@cZYg?D4{qDoCi#<y+BqygU`aoBwpYbJb%$MDI+z)L*{fb#4r7znGg
zD+U#DVQRvbn=T{55#Z{Dg}E%jJ@>LCGm}8%g@2tZ)RNk>IC^k#I7>|uP?5;bZ)XY6
zKa<><rR(+^)?5;4mJ^h)ECu~T6<LONR2-sE2G@e}L`YOw5yVV($db;}@{z^4O=J$q
z5(7DSk=|BWGHxEn-XjOfrO@)zs%|SXXMv763h>4aS7{fuxy*f9U^W#YYmVG3MwsIt
zN}5|DhsnOoWLVWZ7kFj>(%027(9<F$nIC!e3bZMI7s${pk8>n>+-@9Z<wf}*1LlQr
z23Tux6I%1)Jl`A$x)p&oyU?1<`JTh8;{pYpwQLG0$@Se@GIl~Og>{7s&C2z3)UGGt
zVS_LT4vBIB%uR89@;J;`Dbti|h|(%b#zJUMQo2@>3EBz<yg9wJa)CZZMWP=bxE7~O
zY=OcruFT~?rIeKET*kI^phHl_%qz95gu@Q*{4&wd9%gjc<+K?VCQaVOxdm(mTw5R>
ztYl_7U{q*guQB2hhm`=%6!8EGoOg!1%@sJj_fq+-<Sc0_yk49oJ?Cl;q&BF-sd_qf
zp<0?{kf7U3iCLOGqZv#u=hkp-2FH+;(y}5Ld~_(!l1=K#;#`h&S3b)K@CfWF%Sa3+
zOOz4}U4_@`vUJW$tO5c~mTtGab9;LPqr;~)lYPImaxH5JV9a6~^bD^3t>89wp2LB7
z(^-Z2v;eyxkbDE~<l<UDk6lzCbjwQA=gO3Y*dS2dA9jF4hncmBnMfwf$di(>zz|i_
zW?g<#igjMWT<I(gaUxIVc-nK+M0*}ksXb%0wG(VC5Ey%D0Cujx#QD~1NQ>{EB%2X*
zlOEQj^l5~qr3tLS9``hR5Wl04HOQI9jtIUi4*(4sU^9bfm|4-#gFW?DBqxSf@Hf2Z
zfIFs;Y`6$@LO4YvbZUE?fn65mWQyWTd0qH<uA5LATU*2RAG%w|gfmbbV<ea{W8Aie
zkJ-f2nU$CcSjDhE0FV+^y&6Xd?F;s5PMj^u+LO{imE%_~I=DgNWsx3ajyzT9h%@Bt
ztJrPGS<b51ci?;EfgYg6&+>C1sSma0<|KU22A?qCyE>@^S>^S%3?E2WpYYMkJE@+x
zNwZT*#c0o6l(i@&@@Nz1(2>ul90ONwelN4i7!@AMx(>g^W$b|z_3=93;sHx4!tM-T
zvT8atNoA&aE5N((Nv0Ite~fapeUm8VhM7uE_C`9fIpT^_cVdeKxcD(m(mP1Nw(NN^
zB<E2(8Jxz&Sy0Nh%|>NWhD8h2_M#a%2nZFi5acWN0acv#3lsnKg+9w&o4aPe96hKZ
z1g~{_!H%koo=*`WXwQnzG0@nMRRNNKL}v-}sktw4c-9^6gTYv@_Cpv)iIx@!^9fif
z7w0a4N$^Un3;PgHI5<iM#}$P0fo6<fUdJ<8o#D(C!D}Zr`MtY=!7*Vtk+r&5&_06N
zqLQSh8yLNt%A_`c#X6kVkG+URS-8R**h@HnL5ODa3pkmQ=4nX<A>V<_;{=4H+v;Ig
z#V|>@CDO)2w_3G)_3DN_$)12u9%Xsq(F_aIGG&26^tBWT(H0Eiv4!JiwHUGDDi38W
zmf^a^!K)=oz^q6Rn6zdg$9#)`{o6LM$RZ0)b^q(m+6qsv&>a|Q@B+a*5{)$Ze=tX`
z8Ki4q<STYiVjq8?E(Y%-|4_n$9oHbvk^~C#MD7^kM&UJX+a(qdb9w`Q07&IYVkMuU
zxvQ%@P_r5{_{FB@<prLCkXqv56K~fnB<xBbnFzh%BQVwM%0>PJJcWQ{xaV;!^6L$>
zu*7p~b}IcsXYC7cZ1M7GvD@*y&vpR6Gm-v<xy3dAXp4x-NBZrKPvb4WJq(~<Q@vY~
zeOw)FvN!6W*P#dSBl85c>{=Ewxx+$SNOJ+R7@0$g=@DehAQq78S|l_K-nwk@0I-??
z*0RicHAP*R&*86G8h2B4tFjahf--?_{}@hX(V$FQ$>5z3jQR8>wB(?iS=c^gW}(Y?
z!Vhi?7G@XPtI1N^Sq_3}$e?*ZE;k{;4GTpgwp$TrEk2flcO^|djvu)y5>cy&C$%=z
zI=f<m7Pbc)?BK&L)U{y%v}Dk-A{jJ8o-UynGf}lRf|Md|n-xZbwZbU{g;S(<uj-UN
z!XByyiYLSpirApxu7S(POptfsS-aBO*rNquHwk@L1-X>i-Z&q%&dg~$gpCe{Fb_km
z0NlpFRCsb}(PjOx6^;-vHa-->#)(2$<Ss;5{)HYN0S~f>i5iVBgu|~Pd@CG|Gn79<
zgOqP<dq8<85}nDAT~O-ab0#)UogDNYPsr&uM;dY{n}wj}W`+a~B&1^5H&#O!+YYj>
zM_}{7Lk4gv@DwP>yrHXsp#?e&g(#2_<^Zp`rZT~{2bN}EJI8yhe(gRc5yZ_@PN$hV
zrFX)c8EIoR^JeE7eNKs<`=~gCGKJljDG--~TUwc(dM?ug*R=gy<_HJl?L9olg0uDr
z4)%;T@n9H^W&rLoH+)ehmcD4NhcP;JQnpl{*2tn4vsQhwTzDyt;o}8zU7|~l#4k@4
z-g+!mGk*<Fe(KZfv$GfU)dOq@(lBHCqFc5_LQnv4<4Z^sP4yDku+uoO2+`7v8xrvY
zEQ<<WNKHq%)Kfwt;q1H0tfnEI5N=C{?O_g2rL|>62dccCoF9ayb7CKoPCLmB@#$Jz
z8_w(3moKD4c%(mumomOHMMfV|&`BOeA9>U+4uIOcfU_&?R-SBenJ(yuV3Jq4U9d#P
z>;=2u>robf6BRNDIfZ6dEJ24g@<N2CV0UA!V_I)WnKg;SBRE}v$0DxudcuVic3h*o
z2;I|2F+(wS?<6rKNdpTJ>(F)>yyMN^MA{bxfHFm_Af_dLrQRtS#}52O2DZ|gktN#0
z)UoFBdI(}$*mNMd+VMBP>T_mp85-nTbv68A+~zjV2{Juskm(^h-2sU2(&2@@EKi>|
zWxd5ve`OsuK5}Mh9@wfTf#HKo_K`G77QEP+gkj8&j~|=%A-|-xDti>UNCz!3U8Jo}
zk%z@AD$e1aZkQ>yXOR_d>-NZfL*S`bqUjUg9PC^xnkww$)pOW05|*l0Qjo}$V)L;`
zl>W<ezh0lE@Y$Zb24)G-(!P&tN)+^|@MLN=4<3lqS4bW>K!SO|(~I{!V`BM)rzdl~
zlEp+mJv}`j7gMsMx0qHO$i-BQzG=7iTu~H{CD6qb@s$`orO#%>_|>I)3UHbtdo1Bk
zmBxfL^_WDt!Xw(9VP^o^=6i@2eqsuN)JP<)YDvVBFEo-RVl|Q@8@3$zJm&qH&e5nf
z+p%v!W#gMS3lR{-b#)V1*Q$k}x`(I{I=8^bEsWPhF^&|4t;0}ZWi6!%HykNzmbFxD
z)yV>n4D4wzdE!o9n*-Nfqmu5yL{!1j_-Y8U7h)*M0#*)Wi`u5h9-Mtb;*@R35aHzV
zIF~1npM+&?8853W&Uf=<s2nT!QV`ugLqoIcS;RMmjZYC_ODJpujWR5WECuUqNWsTs
z3diXol>=+03PVB>3e+KJBV#Wi{-qQYV-M+3fat7+6jl<J+u&!{4dX4$_OT5i=XmXF
zbZ7}G3tom}*gPPn@!$=<%x4Lv&)7qrc%w3;@vWm;(y8lsJ`d(sRSKAcI};&+>tx=w
zAx?1QadVzvvmu{f(Fs8tk@sfuN1l-<SR)}5swHOAB6LkI%&jsDSSgz12^=C2m;w>9
zZM|ZQpc8?b!ojbXAqTbtUr)eB^z&X%3h%Jee3dGuTTH%nQyz>G*PE%dWTi6eMGP~s
zD>UF;sR(D|h?!D3t7fW|1*+D{6)D1ha-CEP@7v<i74DdT;J}Rn>@?*CpQDt3Q4%*(
z0(+I}pVBzKdYX&`FqYLbu9^~*D*I<Kd0Oq@XO}u4!J?+*-1L@GVd6=Vpd%rHl)+t>
zff>-)a8-Ck=#a|qk=3V)M4D-HLkp;<&BG&@RLJhIrKq-Xim$o|6(>F)LxeMc??@ej
zOg)u<hcYYDmhOX+DisRDfnN=?HO#^L2*E}lS&^cTkY4r?66qMvc0uawmzDrJM`kCL
zuFh6dk+sGoj-i=~wpvn8XI~eO4FvVA*+#l0AIml-OaVRSveRPPMK0?)t1U8Et!D;E
zP;8$jQ^VDm!ZsU;Y_E|RAI;8t;%+D#48Ull$8RzhT_+D}?6;W1{@Zf^QQ0MUt>NP0
z(h^0E=Q|hIaRj~w73KMrYxsrW!OzB-Ia(3p5T1fXbPDIx-;pJ9?27y}%gxv*Zc*r4
z={RLxSh-3YRRu+NWzAj%vI|uH6p$je=DZMTEJJD1cocqVZV_)0T7hu*O`#pvbbK_*
zlhz*|5XG&`tcGk~N}&Dg1yBaXI@avM;v0)y^lz(8<sgKkgqGdju|c+Gur}u2n#U#U
zl{dTU;$PkkTMI7CcuZGgVk`BuSuX7wF1C9iR!n9W`bA~6TQl5wtHpK5>zY*wCruvr
zDnS+S<kO(S8T=gCvjNnS=9$$ok9*vgt7W3KT8eiYN1C>Q_p9&zU;&MzLw((_E1!@D
z@%kt}X3!zDxxjE#j^S<$`8TXt!V%1DOFZWbDx>9X!rkjCPEcD8X_IF8)Xb;(A;G=Z
zJAa$?`kqzY)<^<Z7PH>Ps2qSicHgnX{31?SILpbPM`1)JQadWLb;N$_c4T+NnwbO}
zV>F9cA^JbQVWH!EU%xs&Z$B_&;JPrZb{1pn6cfRzJOy;R8(vWf2cYHl&ayH7o_Vz`
zp4Bo%0xmOoM`$U2c{NYBndS*77_I7g`X!Kn+Tlo^O)}CaJMfu=#R<vtR@jNTVnzUA
zIC%lxPxD)_zLT@L+m@xn8Lhi{O;E~~R5ag`y3nj{Ftu9<d86}L&wCU#Pr-Q)NQ2AN
z_8SfXvx5iTU`2s&3PK!mZyi=MDLYFr<5(7tZn0S!fNFMNkSs2-v8HG7h`zmqYytK%
z)wK+535EHITD)M1&kEocuQ97Kh07MyXbsGZ#;?$LLRQj~X1k%?$qQXSzlZS_jCelA
zGlKzDM7!+~KHOlMaM!OAhZws-IcYzzZ<El5EFx-;EF_0Q7wN2`xtnZ=Fzb03;uvje
z9)*Px9?q~esm2VPzLz{9AU^MlCZCrsC!zBfHq5sJO**2>rR8NYzO;Z%3Y%KAD|5CB
zumwc$xZAi@!#Dfjyxf*e8yY|P5c8uB7E{7-qg<W3@Xsya`c`;YmdHYvl5VAU@PSr5
zE$Yf5e;SYf<GZ(1#nFh1RYmHhyvQZu5S^n{gHdeREXD4^W^vv0QR&%b>jqE-Pln+g
zi!%p5wo3M+7&XZ(#!x!EV}uJ%)hr;q#z39jonPk@<ILfhf+WtUNS;~A84XAww9bmX
z1mTcJbPLcZvt%Gm7Gu-?jgBlCLu^TfME?2)oSgFpP*S)HlU&w-$YGX%a{2DbaySG)
z-1|F+55}ZdoU?L#gJH))?h-2v*nvk=KF{pt{NuR}#?}lZwx(k&+<mQioP_a+)?BVw
z@7x{)oM;|D-%Q(K1`BqFhe)6ayDz-YsF^CmNLD$_BTMNqf*lSf*rmHeI;8D>gD}?3
zR@3X7O95+ZN%1ktP((*(A!1X^=pApU(Kuq%$oD{`hGp(*&w6atwT@}Hrt;uQ_eQPB
z9$BuI)M^Bx73!}^T@A4*Juv|-$i((|J(U!g(nu}!nI$+TF7euhdBkaw9h6fDmExg0
zJx!FVm5n7rrSyeyEYC7b4qbi?anafZk;mt8h*=<>;b1^9_7E?UyFpA@GWIEqXG%h%
zbOx@-@#)u>lo;;5DdJaN6|;j<>sOpwsao++Ii5d;INxPf&`7X?T);pDL<wVrIhaM{
zy~-|A@bxLoe42jAxZvVQ!vl4Gu1a_trO+;Ky>4`}I4Xu+LoX)!)^ur<$V#BQn)iqr
z1%b;-?Qy?lHIti(+Qpqx*x9>wfa_rs8PBg@`~;7|yXIx75IvjtY|zxQ;`U`YT2zSk
zO2UIYd6YZ^H=L~<Of6LFqb?>K+Jg_{acu?{!jg#IJ)WsfeM$viyS(&Xy1xX>+1(Jb
zl5_6w!)+I814P_aA;!W+^(h|tR~CeoEy;8ABP&Y=`r;@D4jT*uSqDI@)Hz?i_9!Gj
zeOJt@hhlo<zl)1Hw3(SmXTNm8q%F$YsKSTM;FoX;m*jC9oGn*+zb|@m=2_F&3`?EX
zEWXvj?VlG1r0PlL;E<Q$feczz*B9~#AseIwQ4^RyD9oxTxoz)UyiRX(Sq#5fTT%Li
zafnYPN?i6ZKZ&YT>ef_5oCcWj*&co<Qljt)!B@nBm%f)kI^5qarkgFmRQH&ajVq>g
z)68O;eGw8FMvY8VJiCH>9yWIS?dObP1?rv{mgrcMyJ0jl03=fYEe8ljHxu`xur#ww
zrXrM%l23(|nB!QV`t-+UXV)(;MzKFrykM7F=uAR(+w3Ageu=P5CbZ5aBQ8D+DZK|Q
zFthDu$*OP?o`zvDvOH92&nSDrOj^4s((C;cnMFrMj;a~-iau&7EH<+^f2GUk^%V$$
zNBvNdWs1+<Ght&Hcgxn`!m6sUFd%9qjLdsxB{iY~;H_EtX8xD(D%`Zayr}Z)HnhO)
zVDAPu!7Pu{3ox#9PVFNdulqYVHFwLM<p=&8!u*(h2xiZ4oLdWN{v1b45IGFF6mjO)
ziwL$&i1PqF1nuB0gt3>`R<4JLDT3y+NQTP?UV@4T*K9ZlEp^j7b-uGaBM5d>_ppq7
z;7vht5-x*c62cVbi-!tPl2pj-eSCRB(X<y84`x8@O~n(`71upfh2>(QaAv!U#$n~)
z5odeVW4VK`$0{kIi{b&FLd2}&BG*J|$uHDv#1a)U*+oI(HW2RhjZP|*$C;nBtyHF-
zhIl;*OHgWZsYm0?_41^<tr<23f4V9gLYXv+>FRZ>)DMI;K8KU)6bWoMJeZS}&|5f4
zu*(<<hS?~Rpr&&$gRdPQ*yAOFx@qDt?S!Q4d6oX#GGUEx$dr;UytwwT_nR#8l-u<f
z>J}4T%sDP68eEI%(^$CA(yv4kWK&CoEhdZ?S&X}$#O5>|0s{`-_raq&Y8Rln(0Q_G
zpoKQlWOLQ3<Z)tL9(NiFQiLxOUBU-rvsY3g$%*eifZX67TzUrrY%sk9f#Jd^-;K(w
z-cx9Ux``8zLXA3tc69mrD@k~fcG^CtmC6|65&ZO&Xdxhw&CE>0_7a$PS-~$V3uJi4
z$%aL6d01;Pjn8zgdy<Xt67B_u;w#~o3mOb2B0@1@!YHms<MBx<O?(B2G4u|-d9#aa
zUynWdC~tnn_{ZvT<r>aF_>~?;nYSB!m=!jHr(kE9pDk4wBUXS*1xa0(kuV&<KSu0%
zOfIyF+L#5GAQjvCP1zSUv-S2&0<C8z#~lS#H{gbYs!sR{onW?eDNM!-QN!^5*@gPA
zT?Fz1X@ETn$h*sn=YHY>10`F&ECAqd;BpZ@71UY6DO%WW{J>AgcEOzKC%{(bu~&Je
zH}h~^qP31WzjGBr8=RUGpdi$|;0NQ_o5l(y%bf30?qdQTQN~NFS+URvMKWC7NYxE)
zpzG+Mk!K+5kvHE`-uh7QSr2~hoF!?@=n}*LPg7)$p+*a{lBb2S*Rv${0yP;k#pZ-q
zbBVV~p*BZ&_spaLtG(K4Y5{r~?u0}iQZ27=ql^2$bX;tj*CX^)Jc|;BS(NSq*n?S0
z;f#enIq!gtG}I49BHEKo9goKztOJ>V*H8d08(vJ94heQ<(5+|_oSdh$f{$(48B|&V
z>3IGnF5B!pO_Mc9q3{&oY*(|y87Cf@=vER){5VtX!^z-E*qb3?dc!)dgsBit-Q=NH
z1E7q}UH4abPA*=;8?x1celRO&Q2s%%%sC5Cw70mrW|oQ#25;_=%6B?r3SLgbC&WO3
z=-%B8udaCv{e<lWT@q(^EU`!9nklCS$rtgyY_tahLD80dB(%#Hh9KK-ENuviIB&KW
z_k$HWMEIc(B@^B8Y5SF{E0;*qRS7&~9YN|oA;@BQ1IKhXG8K8-;%Dg^V{sl)@P-cp
zh#5ckme{5XV&ZyzuL>WBVAk4f)?Cjr@3CvnbvNC|ats8)Hci%A;P!0KS(R8{E8h=}
zWDxxyWev*QdfReHjEp4|pcp>6PUm+#ux5Kw+uo2R1UWfZ@3P!lQm|pwQsbdXs2E&|
zLoAv{Sj$TUk94zpg*5TX>|-xBQVOdyXS(hh3KQZzvOrAW5wOH}ORr+O7xhDV0Y}Hy
ze0(}mG4YY0^8!dFBpQLJr>SJ=<mKfJ8!^N7T<XH@IgyL0+c#+6kAqNVJZ6sRY_6Fa
zjI*&+I`D}}YEU~fCYq_w)P<NtbRgQq1OmKBU<uqLo3khRVzF7s(e)&-l+@0A^yrFp
z*nNy>fK*0e67!3MtL|5wR*Hpjy{|(TYNgIMQq;6KM=Zw1Ye_5mu$DJ;8gj3Pg`<Tw
z5k#jljjfg`xMglmeo^@VdX^;!P*9WD7a^X<HT*<LVnKG+lHAr~OEGo}+NBF_KF>hV
z%0$qzG9fzEN~rI`Yeld}*I-uZjgC{-A%w<PI?9;S61~}>JWcgEtqVK}UkM?($Am~c
zee^58P7gx{SW^8~DduU{vFSNSKZMZIOE3b4ATnUki3g>g9Bfy()aEIsB&8@lwe%78
zrA}yyHsY73@%T%}{$zdONM%Kyop$aSa^{v-mN&10H<PY~G=pLY`xrtr$L9Ekf5>wg
z&zs3i$DssQ-M=jql2+CrC}H!4=<M~Y9^`>yNYw^NraLbT!P&ezIQA$)HHsyI)X1W<
zdU*LTw3)87z)I}h=T{pH>w>RU$e-U>qkB+@*(7m?i+6QG5)u1cnFJr|yo4>RY1dgR
zFzbYvz@FmEF^5=W#@JAD#mEQ@8=Mx6;kb>06A^{&iLjWk0Om}MhoX1YbsqrVl}yAg
znVZJ1iZRsqWRfLb4WIX51UPxQGk+yn4oNWp9p(`<5}EN}1Fk14#hj*q0{p@Yk`@D(
z4$&5=#l2CXe8C39I&4^5x$3UXhY)6Pe%&J=4z}SgUS#<?sR%V}6-v?|@w{70T<GG#
zHEU@_KRi6Hcx2pYItn6<9JP@0Us}9$dA-7Zz+V7OR|f5hAmwg8HMcmwxXv&PVhV#4
z(2db)5UD}0FXfuqcDIEI&s4$a!ADK8(anO7DeU)L;HgAy2TQMCGda<X%UBJ!AI&xR
zyQnu2EBiPIbqcLqknc<|^srFQV>(og9#K^PK$tc<DTVJHrPyZ|*YfBLums+yh9)66
zHOT{p5U~k{u<=XU<w2^$M_)pYf8ZrVNuNS66+gQYlI(mcgfZF>!X^kI;z|hGiia@P
zou&-W(XfH7&vvf!^krrRn+1f)u6*Eb1fFTbd(OlbQzn6|h#3~kR#wd~Ei#GgIkPqg
zBQ0at0a<KKU-Y2PSiKUoO{?Hx?i&wlSv<yvi9O6N?1FZl;Fpg*ZMGw)97E{k`L&LE
z4N5KSfpD2N{Z@G95%uaZrx!L)PsB((cxw5L#kG}XHSHs?8O4absJt?7iZW=Lhvt3%
zSZQE|Ln{P9)IvxqPKY=U3sL%ovtkS7ippc~!v+O&z4gu>Ac!M<mxdr#^N}z=#XgOL
zhP`!)TNHb&f%_ACtN|KSJlt%@4e?-PZ{sbe+ixT}f7;`K```?ua-(^BK274z{O%5;
z2SVIN$3TkPs0GBhjaq$?<2Kr>A;=Cb<VaE!<yR*ml<|?6g>%cka8G^uqqF{6Exbe?
zPmvas0Y8`z$Zg&@f!gren1nH-Z{d+zzeDa><PJTu@JSA^(M2z5!)_J!jK#Fef$&w=
z)sXgzIJ;?+9)k!<p6k7nEx{9v4*}1tc%mtXB);Hw_<GYD44#WmfQ8pk5?L(8rCx5n
zVZ>_9CXL5<A=5y4(8E#oTCBm8j)961j9JVK9VvcVK7=ZIAy#7J^cYDEN`x|72K!{I
zwInnJ^E1y}RIBt{RN6v3p;bI!M>tT^Z07BUBcSGzV3e$``75K-l%94`j_t*=vnF$~
zmSON3Lr~uF0bUrHg_9OC6toaDHi1}0GVRu~3sFEtW^g;g3&%4WC78e<TBQVoq=H>*
zuVpVqmc6ry6t8VTc58(kD<2Q}L|i@b2K1$c5p|db+l(o)F_CA?Kx_(x@xeono4li}
z^wO=F54{AWMi0hmmG~acux)G-X!wqM8h`K7a^GX&(8akmo(0(wQWB?=PY|H>dXyzX
zYqAnEI7oM$^76{Y68r~)@0sP8HM!E4*X(8z2WSwoz!ZUG_W}F~AWN2vXhHwBphaTa
zs+`T4ax`03O>hH?#d}*8!>Kn)%$Po5V^;`*paQreJ&$`wGq~IYje{y4w&OFKie0oc
z?f`taXankx{l(ckBe)Sood|DK;RCO9Z(2d3?LN53UN2I)9ZbhBQd!nsf|**zw_!zb
z`7n&4i<TJ6pvR(6h%=whcUo-=kepf|;0(2n>J|IT!WNKkDxbFCA#lu#m*p*mgDz9%
zVNhQJny3a4*vbVL1cgnRw&3hCJGOUP3@X60bswmQVNk|r>v(J&GI>=ihF4ZS$lqwD
zWh0!490)^2b0R8IPQx{x*T}sM=e<Q@Cof@m$-}<*!DSn;v%Aofx8v<Dckt~8P0jES
z=gFDD(_k<k@`-8I<drw7=U$q-1{A`HDGVn@*O;)2eGbe8rrWK3nrO@i)o<>ngmcR&
zJUbW5?WTS6@Inu}D9dFqD+5Qw$k~-^W^=+}VAz!mA(ub&&A$luF)Ilp7AOoNM6(#d
z<+f{h5#t)qV+oy^73j^lSa#j&klS6i3JUFFgn)v|Zmcfwl*L*g3E@o%kNXR@c^U{o
z9({wxie2{_&d;nf;GsFEsd5)DIV*&8Kb(r^{mLPEgfqM%94m?{;scEdB3G&8biHIu
zK*<<(6jr@USO8<8iU!F7sxZY^e4Ni_!B60NI2eVv7IQq{R*6L`Crl;*w6D(@(Sun@
zziXBruN&a-`QqaB@YZ&u@*x#o1#<dd(1(>nvR=Io5r83tf>k2KRzt(lB8rEy*wgvt
z3oF7TeMSu8^fQ|Q0rAwtqT+VJ66yAGBYw>R_=V0I4z}?G*(Su_CH#HPpb(D)Vfavj
z0U*ZQ_^v#&OaZ9t9mOd#P@+@}PZh>6b1W5mAWO!CQyH$CeHjgYrD`GB$rVR&I1?!B
zEY;-yC{4~SDK|pDJ)53cl-!@2!?z8<E89zHK<#-~sdhBFrLA5An1QE1P>EkGT0b!j
zQFzteAb9aO<bhQ5%99Atqauf{Cp#%*dx!NDPSQwDD#RIaDJUnK>OR?3ZH%Nn11d#J
zaY05^4MA*YDL!ZOHdX7T4zwK@`R0LiFcuii<1dI9e@^S8JDd$k1dq3wyq<+cc$kVw
z;Gs$$y)p)uT>i`?tCf`jI|5tnG7L6m1?R5w2j)vYq?^wDn27$%goXT?oiu_gD972B
zZcvg=jMIbV1OG2=R-fx!n7cq>qE>Wz8O}Q`=}{D^m4~}gkV<M97%myJ$qluvz|w1}
z%jEz$4X4mHCwZ2ifDcbhCLXjTU+fZti;{D}y@1CnFjCrQz2TJvZ>Wp7t|Xul)y^#=
z;^<|O$mNnA7gd^MbNsGYAxXHykajr=u=?Kiy8g|{2c>_R@72V)_In>m-^uUXqySJW
zho=LldFBw7p%V^{3^P=eRNtcX?IM}Q#Vp89V9)0+PA+eTlSI6vyIDmyTT{j$THwvZ
z3U^*ZC_SWDWsX#a`)T9*JFUgF(?|$d_*9Q9V0Eu7&LpgD)|0<0lqLD+>#`)BEoTYN
z>awKB-?B9JnLaa@Wz4K(k*Hf{X-4D;@+?i|X8bd6S<dW2f{xu&6~Ul(8We`9i)TBe
z1Y8ZsU0qy0#3wf^@OAv<FkwUqhuoA&dBOLy@QlDqpZKyV42}G<RPQ3m`M!4<c5({5
z$9Rnb4l-SXv%EAZ6(P3*%f7JD-NYwp@Ln@sd$rTe@S>{@i8>x*Cv-YF+gX47<3c%-
zLUeF}??Z^}^(`>_rGC{7bh?MFFL&bG)Y8!dSd|CwcRZnx<?&f)Q{}N2bRif|k8I!x
zQEV9WXLbybZC)$IohyD%-WFx5tFZ7-SB-ep$_f~7S1{Tmf~0E#K=3hfRZ&NNSRdKS
zru+d4a$-D;78FVf0x@i}J-__=#tLrQ$5hS*J3mXA=ZhdQzq@gPvO|p9V2TuLH|7hi
zV^rSjRw<^P2A`$bqN~?BV>)3MBV5)PfqxxFg||-3AuA?RJ2q||Q^3DrU6hly!vL~1
z^0{4NF^nLFBcCd5UprWaV|9W%8uemok4CARVH(`Y$7y)mOL=aFqRy&M@Hvw-6M|fa
zvLTyinFfgZ9<}cwpd8<@z|ZqPwTb`XXZELls>1(<u2h|Bv#JJ9;@?Y%KUy8H9<8eV
z_V?wJ$X%)yD|}wOT1R-Tnm~A?>LNB#T|ml2HHUw=VO`<tUnt9z6)U%Z-wl*e+Up3f
zSR2Qy>hMCN-D>MM&Z7xj*Q{3YADdu@3utZ~16)P;lL&tqgZnCmI8mKPS$4KwUAEun
z5MMzH(}>{}cMOXI|0#=2RP%_<p+whetl;k@<Zu#Re+5JR64K_8@{pB#4sD&Q&Z5j3
z(pT*-`^49;(H2a8*5C1J;Lsb^+XnKgt#>9d=mlhY0S<)SbgJw4J5gOkwGA}FsS#=u
zRqBXv<=_cRLiVE7Bh)Wi>LLbqIp!0J98f-8ubWQ?5I?|nZGeX^+F)6)I<SniCHz+%
z)`&Wx9|FD_o5DD}UoJv*_~ELaHZXJ+?VZKMw@(;6-mn2VvdkkB--AaEr+&?b$~>YB
z*K{6}VHaBI1V;jltK^{Jifio&G_E4ogh;h$p#|VXwe{}KLtwQzgS|J+VR5xCqftkO
zw!v}z*frNx>yw7NtKt3dd;>r;0kx|GF|_|xpx?m$hpN}Afk8}aaH6V?ynr!lgbxKU
zpv+?*!H9|E%b1gchiTkC+>wrf%zF&mrh4S_XlKdrpt+CpT3kf*Jv$RNmqU%Xrp<8%
zEnTljA6CQ2@D;i^iV4)?b`y=@mYG1w>sam%{v+OU!3Iuoy@-L_3yvfulpGN_OCk=p
z?OH(!s#i}TWeqKHpZG|9jmmmlCz2VCMd-&iMPybg(!%Ak?djc&u-vsm+_K-CbX8yI
zH{QIhJ&*R~el1@Bu5q^AKtA^Xg;VV?BeV#+YlbCU<2v;7KC9{WRtPUaes?YH9+o)=
z+A}O)yxo=0do9M=7f&w$3M<$Nq)y9wU!mK^)3V=UynEQ4+|eZESB<_Dt@NFBS#Qs4
zblbBo`z^+MzTX~N!2Gy}_B_(_w~I|DLyt2C-BJju=I&Ih=RmtJf-+DfDxh(qeb4T^
z^PRhbiyZnOT9pV_{qQtGq;VbK%JJ%%K$Bhtx2r}zSS5~HExJHAlhKD#1aOX_T8l=D
zC|!{cP~a066t7OLX(Y}QDK`8sFN1(B+UBENLLek2?;?SdwvIK=DF_wF;^xrZ6?7`n
zKM$5hhD0Dy>d8eh3Uwru8~EE~Ld`+qG!G*p^IS$B5*!IUw!V-pRW*4Qx$KSNcn%N;
z1Vx0@zLVUv*wvZII5KVz34V3v=+c*izTTV5=hdl~tPPEnV`u*yHKp_=j8Jea<wGeH
zE|hX<e))s}%)4`;ecGxgAwP-Z&tM_RmDD^NWEa#}H~;~Es-YzGs_)>J@c#zd<`Uq-
zD)81(1;d=`JszS*KB65ad}b3Z^S_6xhw<+W{!@rL#Y5E${ymHT9>UT;gdKD>gs9jY
zuRdG+_ql3dgaD_sOK4OhL|Hsci`Y73$}ibc;FHowbM3BrxP8ATbWaGw<@qN&DLd_W
z+gr5}zWJ~TI4VL;Ot`J7<8cS@Z!wrLuN6h<jrPgRn{bNP?a`X{Qm_3bfy61ps)5f{
zUEgI?s)}*E%3jqj)#hc?VvK@PUAQ~d5JS0CrNUjb{q!?vjV&dJa7PiTD9#EcsTB$C
zwUG^ev8v8ZVMIB7;Aj^OuI!tT*r~lQqW!qfue8IM{kv$`3x0d+8R&Mcy{|j9o}@ch
zv0b@Y3aD@W<4KT1N0mAnBoaulJB`@@0Gb58ZKzIT+P6wEkeBO=N4m6POHXqNX%e}h
zHis+L;)c}6a+ELH-TL`@!)*i2%@K7VGzUVwzlxzyRBGb62pEau1_n-R47pc6sDZ`y
z)!q$e5%|gAL_F*_zUpdy_RnGL+#}Q27bK2UaogIsEnp83F|Jj;)S=zXPgGFUWW%T!
zg&ypX9x!WLO)71@c4m9~`RY_>+E=Hf&;9AA(=d#C|D&LU)il>(HDE+eD<{C_L~$O%
zI>`RxB0<`I5&-jE3=_loJJIGM#>MR~;{#fSaO$?fGSAI0K1xc2v}4iwDAw!d(Jpoe
zjWIM1y3p$43443I`Y2cQj*OoqA*p8={~dvJuIQr8bMFNvlAN8YnDOaEDf&5gr=cj(
z>6{Cb^vb>~5g*X|TsxNTc8^iO5-~+vh1teO%GIZYLcGsQH)$Ss&yQ6!&xzJKrl92~
zPXRaFfK_j~RM7MJqUPiMYPuX<tMhcfI;5P$mR$Omv_R&@vG!V(rfInBcc5>-JJx=p
zSHIlfxskf;wfa=Q+3ryH;a+{p<X!9Ov3@<Z)_92h=dxJ4ekQu;Y(1$edH+pw?j>Lp
zg?|@YsK<)F@Q4St@@D64Os^!viK!Ch>oNsd{pXFSi4^xQ&8<qJNSTRbor5E*{t*ms
z;0TA!^O8b4!FdH)CoJOoAHim-_WxM*NL9Ud0t6u`+%MQxqBty>h@j^-PBv+09TfGr
zy<L+dX;zHenJoZNhm-vwDi$AaCo3dkZpn0uM`D`QV-wZ$U|yVqZ~lJPdi4EriGYGQ
z2@YGJn9idwvXPR}CX^Dglu8uZeZ*(t<8XGxb1OWYWNc{#kfz##@~6)lWF7{bsVqpr
z(CSg9l-$V`yNZ#pPqrsQavfn;76wiTJ0vG7)qD0esS_WzBlV0#A7hz+3H@*ce(+Nl
z%y^iT;}(@L>c?z}r23-a2S=J4X3{#LkNsSisxxU=Ri7VmgK*$1eGbG`Kigp6Toaxa
z@vM|R<#qg=geNlSYi0MnNbsSJ*bg#&OH0w*Revu}W8~?KF&C_Mngx3%40RG!&SPM%
zce}eE_wDvTQADfmc*GT0w;dw06ZZ^pk?fV`JyHi^7U$2EcnE7Dv50vR^mf0#+Hu0`
z5-!!&TT_NCZH*F*x!KxKrdN}RP-?k&oJZuQMXiZ=0=*yp0^MHgHJaToQm>Jr_1#aF
zyD{NwuO@ezY}ra<vZgP$t*1&lu5=7|%UB3fGSWa=`Uq6_Uf?+vu_5ePF;9}JMBe(b
zmpV~%R_C_ml9V@*IT2(6q|LoR2E3~nG*;o<XEBu(L-ad?!e47|@b3YAztA?4JA=Br
znq-M^-zBu2?mfxdN7-Mi%SEM9O-@2G<Q*j0=(~@+rEy05n+hdKl|U?c(q*dS80RT$
zz48R#d2wk{%69EnlO&h61s9aER65SlE~iA5d=n_8(lu!b<s{pPGAW#oF)6{hbb(FT
z;|$&BBneK^sJqE<Y?N4-s>7b@P?9lw+3HgX9!N!1cySUfxlzugF3zizrP5h`{UEGd
zCDOyxT7n;D`u_U5rCdr`)d4@#>;)6eH-`wvNy6t4NpnPhkDtX#QO44VAK75aPsSC8
zg^l4us*{&vIBx|le~5Da75t+tOgu>nn&4_}y?hRN;vF)ok>e1jC~N58tEnq-uhEw)
z;TU%u4+aT$le$M74=AftBgAW-(pu~gAt44-9ZJ$=_2NmCTE%E+(x;@uHk~;TXP3~H
zV96CE10(gsB~irO43%U;?81B5lYEI-o1SEvdo{wNJZfH?WA)ReuEMc#Cb@4K$3j>x
z<7|tEq&ykpC}{$tCg{3^C=q36?l<mzN(#gZ@?_Sp*TFasaL$}bHAkJ12O^{o)F`K_
z6R1T~8_i(u74;NO(>{&dCc7=25Lw@0&2w_##r&P_;U$dhD#l15ZW1FzAG3G556(g>
zV(s5?C?$;Ks$&LQ|7yk*)Y49okjse>QG#Sug0rJnE}Dzjaw&-}$)B*?G9t#@{IqNn
zp((~w-6j&wTkR$q0Cn!CvOkMuz8bd<5xxrPVl~=QmR_?=a{G}{v7cNi6hYyE^d&=~
zB!~hy=d@iC!>zZH)YP^X+}FTW>uPqV_L4Qw86_7?WAshwW#*#b>mu*K<_sY3hsTMr
z<cu^IACgsGN>N%>xo0rndu4CjxPCk(B1|GcX_ls-)borJpUIu(o8ag3)m(i>)u-Bx
zIQcp_xpB`05hSeM`v|*46hDs<%B`~M;poHJ2*-6CS7OYLV;%W2`eW})bmKhWP6E{-
z4?PS*ox*e*7#J(#C~>5oLQP`e{<hQ^xS-O}pc>-ePr`{uzqD~zBPooWu{_2#s|L~6
z(8KV({wa{<D}WQpXZ3VDyj>V@2dB@4@;TK2#7|72m0ZwrS1jV9*n>1M_Qky_(MdIe
z?Rw-9IIM~({wRz*AExo}$@baT=hWg{aqkyVKWlI3!xdAiBV<>J%@B9tI{m&G`#lXR
zv5wUtxhM4kV-Gpg&7AkE&Pn8ub&-A)HIFEt%q`KMD%29ZWRNUsJ?OP02(VR_C4Q2&
z8Z(E{lcUbts6M1LQX?{8=ASbb%YExiwF<3st$UWaUo8n1>>~&=AzhHj(vPWp4u>ok
z4u;$tG_xgvH0Ltho4th|!Y&yYbFKs0GnLv-6wK*~c#Lx|QZ%yH#K|tA-#O5J)5#m6
zBsr5t?JWb#$2>LCxu-qkW=@(W2rqKO=2Z;N#m9{Hx@XXbC{OOvPSG#lOI%S2e=?g%
z$VviX{Y;AG#b+hxt^>YIyF|7>#kEPij!;SYrP5i4An98&I}c1zETVcsQIaik&S^)N
zZMhqTuLtL|fsvS_`3S{F$^|QkR|mvTK&jqWS86zR)~v=jpJGo;HWlcm=twnMmN?=A
z<qtPD0y2kn(}C{pv7g&$Rai!Nn3%PHhT}bR#;6WY5h<3_U4=Aj8gu4sR}mvLiIcey
zT$XWMtL~jbE8N4vgzAU2S5pePFUJ8Gp1Tu0e;(}x4*D43+%b`z@)-R?I@wq#hg`C`
zP$4u5`s4j;aNe=($NQHREfvNl&x&gLcIViHSX}u7XE7?`Wi_NZRX<D$r<+Q?pW=!f
z)(U(UD?)CtuuI5uK6S;1^yqoC!&#m|{sLf3tf&rAy&?5|y{cZ^dB(lk&av(c=D1q@
zBzzgF5zhP#^v5Hz>iy54SNfkUU<@pC!d9J}gc4%)YUwE4t1rszq=MvKGS(i)-fE`$
ze18%q-0BPOrR{ouMa$>E8!cC_y_Z&-uy_&h>exSn#(RQv9U8T^aoCi*d+%)(d$)b3
zAu?_FLm%!t50P5icN8M2?e~V$O&lQ=SQj*A@8lXji+xJ7SJwU=#lN!sYW_}+ecC9K
z<dAIqcN9L&KHeK8tnZ;o&{mn*4Abu@Hm0_#OYa5lO>8^~PEpsWjHh(oOFwnk^z3Es
zyWLCm`km|{$Dy8cy86BBvD*JdP!s-Lw!h%-2w$|n7wj)Q(?{r6{wV(YMD_6y`zT@`
zMO=A`J!$26MfQtu6{-e5fTI=H!f838K1a!h*PV$ovNur*cKei!&6ki*>5ICGX=q=j
ztVV-)d8{NYO}e_<D5XE-_4^%GBuhp6=%5+coq=ZG936F@bSOi~I6ny4McZ3%wNH$o
zac`yf(m~r<97R16N-98Wh0|m8<CC^5a(pP&IalGLQF9Ory!B5hHBdq&BwV|rUp67Z
zPD(v1X|3MOgk3YE7QhS2thbK#<o=PgNn!`r)ekAC$Cn2I;aFz!y^g2aMGIXUC1*!_
zp&!injCkd_j;FFeECNFQ3<I&-txUur+AH%*?rqi**gRfqN0+j-b;xzxC#4SV<1EV-
zE;a+zASl7di5ST;@946Ind-=lno#oU3|i<Ha;r|=cF?r;6BdG?x5~qwX1kd4!{vBR
z0NY3}@ZHmv^3{ub8;3MHxoq|5|5L8#^|Pmwden6}(2iqm_C{~7+EvSKEudwynm7hk
zTmS3~fZ<Ut8JD|hgsLCoB$d_c(7Uc~Ov&?3nF)o9=cy#T+z1?jn{hm^S}a-FOW543
zyJ+P7cr+yFvcsB*o@?lVA_WZ?R2cEiUm&zd1Qj~c#Ywaw!<9S=ptO#yVifs(o9c7z
zFv$_bS<fOy#pQP|G>T!|C_>S0H2`B>8+`M&N~GTz(hj#?&>q;>xzD<h@ecHQV9I8V
zql5=Z!b>P?tZwFns4yb3BGY6w2$RzfC$ddJ+)Y|$I5<S+PeKkdGr}AaMB+?!Z(<r&
zvStR55HSaQI0sXLKLtbl@SH>zgg2)Go{<>lb?Rz|*T21%g_UFkoC|T*M0dM@<3<lZ
z4kX*CpSTn61QWl8s})Rm1p@dw4aGWbr}qE}d%LhgHrZq)?iWGU*c88-I|;;Pmuiz*
zEU#2+Pjfc8-*?UQaL3zE%JyE21iK$UuXbM2+SVu<+o%(M;^pq!?x_Y`DQRb~o^tG#
z$&kBF_$-{`j+6netqp_N{kdU~`!*ODiuBB2xb<%|!KO6taz;YooDGrS$_7tqQ7*rA
zV(WylDK+V=BPY!|=^&w^hk<+qNT|6T<v7D}L?_~{H_4eqq2#yLq;bd5)H4{I1hRY@
z?i!k!Ol}Ri$n{b4S=N!KCd(R+yC<_8aq~gYVoxOU*9tfU5`Sl>lH}CD&RvoW@Mh9(
zCIIrX_4A(x=tz@Y?r(O@hrM_Yq|RS*NX%mrb@vbQg*D^YQk7iHWj%*d+X@V95(jf?
z$x?pwRR@>AqeX59kB>j`OH%Ay)mTC6b{w_!Z~Jw93WM_7V0U5|X+Nxkbobj4)Q?KZ
zJ^@ggXo+P1y5BVJ?9g|&QQL4wQved)>*|v>Yc+q?cpkma<^h&Eg>_8qHn7CK!bt*L
z9_EI~*E=YS@8<!WtFLuvL83f79n1}WTN}rd50x}|3bF-d5MnBKdX#%V)K-qtHu-lf
z^+;PO@&k_64J111Ct0n!GZ-=Xzf>Q-ok6+aC8Wvs(4`Qj@Tjr=m<QqJ{*Y}BT9S9E
zd=lcDm|OKU34L9~mkT@hv{W&{T7H&9L+v-L>lfN;OL=7N5}|#UQYz}zawO%42uZ>m
zevj`zi{EQ#O%e_<jYq65`BX=8Y>3c5lW%5X5~W@CO~2kG#k%~K<|QcRR0lJCF>j43
zYp>RlL(vW`K3QT1bst`xKMp4XGCIQU1*5MVwNppq=I_h7d2tAKjQSTfoi$ru&2|PY
z(O^)$e*qzx9;kser2R4OMKV^hY~pA2@aGLz<VaR(K%@%K!H?4|S-_>Oe?{QaazkfX
zcEJ-<Hb_Z;gSv_oYU3jH6r?7y(??$%G8(<p!xT-PD62OmiC8&k6OwUT(Pu#$jAY@W
zp*G2ktdf*x$P@o)5=lAMv`N-&MjGPn&Mqw+o?~+F=Q8V>;UorE>SK<Zbq5jxNCE(X
znw)PiwIg+!$r9DeoG(oc?#1lOlJA51%v^h>ph2oMg%)zqqqpsE^=ep!y@`6$Bu*TH
zlL7i^(k8_YT*AzUme!jXLoz93<m8-RQy8IljR+sb6?f=n8gqnK`Q-qWcDy70q&gHP
zO0|rekVr$!;>o>x3dpuq2YfEluxHMVqvEVJM(&a8Vb)E>u1>OYMIfEnYbQNx?I!O#
zd7Mz4luZvywOL-(rQcP8oo{@aNVr?`JVCk*c}S4Odb#Te7cv;mGt~!K_;MSQpk<C$
zWTI~>?W!-^;YhgJr}~UjW2<8q&ke}n(Dy@mZ4$=%DrxPj2lH}0%HUyMT%maQtj#)I
zHc7A(r6c}|45R<rHDW&WNxvGd3+I@>CPY~1d{7KX&`s1W-va|f9|Z0|U;zUrM!9Xi
zU;8EAi_mlZdi1dM-?kg0OOh-K<!2dbi21=uIVj(3`Eaz%b+dW`abA|dA<`sp0lBa2
zeo;gQh@@>3r+&fh?ErODat#}OZM{wLtd1kX`!wX;G)~MRs~4qk(Kb;6?t$_|tZda;
z%s`LlJxO6X(R9Srdj}A#$@B)y?Qt$ftn}s9l^dC?cH6RD+c}-!l=nJO{U{)N9K3)C
z@nxJbIt%_0_(A_jCbazs6xI*p`cY~pp+niqt6;<*fin6LDCn=TPB+zA1tO86OTv&U
zpL3mjG_r&Ud1*w?&n6i!-wN>(q(bQP!PIBoPA-O4Jzn#w!&_XlAGG_=%HY5CM|HA6
z)+dw}7#J^g4wprk!(-5-X^=J4T?7aK?bEhgl6ke`3?oU+lq<RWcvFef<N=Zxu`>sO
zFdZ>-O0Hi*v@54Zj>}b;fGXE~p*#_zWt8}SpW1Ktn_AAm2)(xHP=qG*{@zw+qntq-
z8j~qD7ecs{3rtiZ_{6;A+B*K_I8kjM+g-OV=$6LqoJaSgRy)xvQY@LTdUJM3hQwLP
zq1<#5z7ypqa7rzQTSf|pZ_-|XCC8zZA@hSw00TGv`xzut@TNGbF2w~qR#>p_AXkGS
z_buN;@j)gsrzz$GQ!DND+J{^6Xxck8HnK|sQh&*<on}zM<aVC`133%9amH%6?KUUp
z&fi(HjbJ8lbI*|B`O@W{6?rQJEeTET;?41T@2MTB06D`FY@SC?+?~1|Nn{~hHCv(t
z_p}s;-ta2vXj+dPamifWc5*Ib`jhCdu77&JgsN6RBOnLhD6~etGK5l^v2Z}@9M8^t
z&6M6g2Vx-=I$kN^+Vq-1b>M@B#&Bf%aw<c_u5&GM2EW1~GX%%jer&-$OBSuQaXBz&
z*$|Sp{*Ormgd_-(U7=rZoj_=mPpoaw_2rr@K(SK|P9d2-rA^3qM|2W{=R|vf1`|8?
z5<r<uj9lh?Od`>BQ0L;m7FkpYLi4@(1JgK0ZahuyeK|)?Xp|d2Xiwr;SSp8M>KT2#
zHWN49-s)0PPQGd+FNCy@j6I|ECz_B+V@>1WM&P!H5ZJi=QI#smPFe%U5H$nbO>LTY
z%UR`HwmE)nLc(A<9C{}6AfU0m|4y;ioF3+s`qi2M?{ru+$Ej;Ra8ju6H4ELIvDN<m
zW}(8!UhIb}oiI7Ep7wKBmTqTx+?y2ZlSf&pk+nYwK#_DR-i;yiOANDQj2z{iVv7ms
za;7+mvC9(k{ml~d(N|mld$|)S)$CTMOAWc!#=51J$K@z>2T%Dv)#iHP`S;7z{?~H7
zNU@Tl+SKO0&!@9k&Zd`i*95-gt9H%KF|5$$aAHYVxa?h87b#_Wt(=^>eW04oAuBPB
z0ulWqT=d9IKrSMQbSCmxFxk!&c1H6wIUQwiFXq0B`6-ITGZz#ny76q0V;6_hx5L0{
zw{FoO>xM`#&<oJ{B-E4}Rs3n2)Y9&UhhdmxNYbzyiLicTlt2>YqR-_E;^^T^u@{*W
zW5Y*ja@5F;Ov&uKM}(n4sVPD=o0eQc3dtcz->LgVI2Q5<%Ow{HciP=;tW*1gizU58
zC$prv?kJt6MjDcKhBP&{>7yc!Q=gP5Tg|s26nCP`#?IoKUWJZ)Zlv%dyR<)ird4<6
zK8V6QTlx0T=i}413f$9D7uq(7qnJioef+aFZ>|OnE=2EL)Y=|tM-EEMPFCs)WiF~}
zXSSP2X>_2Pnt+-Uo&4OKM30N*NX0Rw{z=>%cqPHhm2EfS2+15cPK0EGYK|!1pV)t2
zXg@MyEBTqh&Nl44U9(u*bCN<$>e?dneBXT)bK^oWjYdE%T{0qY_I^T4sgbBn20=Fv
z=((GBYQ*WZn5)TCVs0Cq&dMf`ge;rUi|sw{?aU^%iMee5v?D;tU)lxuwlDGc5tP5<
z*G5m;c7=dWYLE~|DATVOmgR!wib5~lt!|Rl8)IBZj?ukiNQ3j!J9q!kHX<h_dTX>o
z1u7lWHirC*Mh*wfp9j=Dh~x7PL_Gf0co@?)F#4_!-1yuK3UODxS3@eXfNuE2+~t5x
zNhlCu?>#x{QtgJ;USQKccaf@niAh7Ws7{gCj|_pkHz<}A#anW<qkYtFtgJ7TC>xg}
zcg7O+OIC`Pke_Tgi55EZBITs-2_H8<iQmMEc5W%1)lQiQ3z>DNCqhlNAiPHJ^qHfk
zCk1wMhht%G?k4No%f-@s%k@WO;*n^3IygfwyQV^QC~E}zAZd~{u?e=n+HW9f!E-ja
z(_=2`fDR8bI<!rN?2ns|EUrA_AXTWlj`!d9{UskaK2XvlTO-Apt}RreNnCWwM@dSf
zbRt2QN6%zLj%e<F({zMqAEi-OL?CJTq@@7=mA#@(l4K=mCX{X)g)}jm7lAx@KEUt$
z>{epR{p`3>buq=&g6g%~k;iu3`e{O(r^>`ACwmevm{NKM=9tP6yRHUkUwUut<YEp<
zVABa)hFol_oyBN@v%bfjvN*NtEvir8+Fhwuln7TI0G1~S<QR!=_S&T67X%>LX0OpE
z^rLK3-V}E((QkY9x{~0$%XtYGu1Q9?N$ZDTK=jtnvF;UJ23DrCQ<=7HO1O|Pr00%(
zwRW$eYlG79e&EU}BZS)4b^4uXwiH(hITVhP+?s@&q_?*baBPJ0<g)2hnwzNqnA4_n
zZ#f@_ALo&n?X}h1W6BSP^O?F}>)!$LV?FdWe<%8K8;iGF9v2QgO*XM)+nnp1pPxBr
zr|W&YR#QEcR-qqk2OiAHV2|~BmG<swurGdLZ8fkViMDO>2C=7o+#>u5#@H=WYP(%>
z*;7le@*!TGt;%h`B$#y=a{oxa+=bn#Fa)|y%vWLOetc&p<^UYRg^!XUa<*;o3`U2?
znoMqo8e4Cl0EtRA9v?WTE<skVuQ}rF4sEz-FJQ2xosu9<+q$`>FzKy-`Ta1Y9d_RN
zy<^fYaA>mLYT9k?xmc>=jx{dk4A>`O$mv`y?Z5u{&K3uCD--c72__Xj)<9g&%~^M-
z<?S!eMB8OT{x%t#*wRlIaAGVi+Zlr7+WKJse+z?I7;tAeId)9fh|3)oALgeZh_sz*
zkS5$A5#<BTbzUnUm~1WK+m1+Iy;$C(qF_gDPl6`3qWso$GgpFOzK!<Q;!A)=&dBwA
zW~tZIZ`{9Zo0D4aH9(RNfl^4*CuAfT>OxF<|6m#={JsB{5ntO24E#-m$;d~Nw#w0*
z!Hmep+_L5SeG;kET={JtJq=ntSTCF2Y4BRrX@BN<@88wtm!Td4bot-F;3Zt=XcwC)
zZo1fL#?=R#d!tbX-%y~riFX_4@R-$EyfGroPWLLS17E`W5R<C?{|J64)gT_3M(9z5
z{t^B?h!3Z7HR_sV*$&03)YDF`QxtnoH5~s-fh2dlmzJI^uVr6(J*r<W_(}kQur~NA
z{=J4-(YnHKoKW1N7h`oll{&V@73Y0K8MCV+rSPe5##Hz!23Z(!xcab+f)d;aJsQAE
z*&@_>e_P3^)Y5~x9Kn|PXjE$(wrWX6!7OSi7}345w9pR3r^6V}zY<q{fQkc8)MQX2
zH8_L$l7WUAttE;!4y(=QQJ2Ch70wE2Y^6vWQ@L!GNMWfg1+cGw35&1fRAWrfzY<v8
z%T-FMcQwMRaq`Us*2WLBa~0?USlwGfv^v1i3MR5XF>X)X-oJs6e{nDy+v`p2oW$z*
z)g=nx`C(754;_2Ixun`H-YbhZ`qRxG&t-nA68d-3XSp}p@VIr4%$0b@(*%tzeRzx*
z&RtJ@W<I4D&QOO_G8IUM;Yybj0lV<E<_y!(j+;H4!j-j|n=bCOkH6V^ueOo^D?<`T
z%lp!4Ra<-LHhb9p_;0h%PfcNLxn+2#ImtA_Tj>0LjyM@-SB>Nltn8~hq&Rv(uNu+*
z;EF=pxVOCMQyoh4tcIwWVUKP9Gg|9PCP4PX`_TMk57`B)wX*`(5fWIEm`x>3pw`jX
zBBmkFUXcMVmsCL_nKYQxUU_<>4<7jfQ+agTSiZR)iK&wqv2XIGmCTWpHy4O3BywaK
z6z=KP#7&W2dSjletZAMaZ+@oXRy)EopWy-o)5KC)?^*PdpY1m7PhhNlzdJ&NaOVmi
zQ-(6<IfhPyF?&f_G&#n0jW3nsYDWLu!|GKwHxdlK)yZBe*ou<KmlMg{ab3!i)@~*e
zUJ#T=N}ZgmK$K5}i))nWi+#Oto`YpLt$jLMcYJvXvnJu=20`CjzkJGy%kbz_(GhKL
zjnT~^e`zWqY2GQdPe~sPQSWqZl1M?zhz~zo&`UX*H%(un)($384Q{@2^qdpH%3kF0
zQ%4Yj<G-0iCbRkSftoFwN!BwCJz2ond`{rV;<%4e`7|94ZVKh~_4<6uva51+c)6!-
zfbURRCUSgV4GUkK4v+NXqfeAyM6=WJC0AJ`xcRsSzUtZ*lt%TA23O&KXj_F|4JQb{
zw-w>O$<HuImFPx5bd@;0^Xk}3KU$st0*3o?|GnR920Ig#|M9!i@OvjPz0R`I-mDI#
z$2uw;8Rn>3dOWyTS-vje=UvU~Wo2#um@W^3t1plm_^ziCBl@qc;WUO6hQggB1Bx~j
z`YHaCpX)eMdCwbRwU&Q8Z&~D)3Zq_GWm_V;_O1IDn`LECmQR$(s~E*4>=p76DSH}~
zS|ZYLT-N)MM9fp$)yLaXof-5T-L-zT_C8LBBl~j|(BYVR!rE*fnh7A!WlJy0qZZOG
zi341Ua+zq6a9a>*cFD)IAiaU<gXBf9IkZes)cu`!U5X+Qg_yJ>)6f&Zr4c05;aDl+
zmPkYNBieEN5(i1gbLIqK4C3XhAg<(mn>uVyaphD$V&I!7&7g;%(!*V`oXb4sMAXx2
zkUdkw<_gi%oS%B8PA)AeYOI6M6c&Ar+j^(Gku14s-rAlHB|+IWTZgTC4#(cM{%KP|
z^&Os;C^5^VPjD$p#g*~_QAn>VmWWq(!b=KQu0TP~etfdQ_v&I95x0<7U(+rxT{4J3
zq6adO5a{l!m3Xuxql`%&OS8m?e)=q0qK8)!6h(Vng05d@<+3;FZJmy?RKFKI;u4Kf
z@=IQ7FiCaV^$Bi!ed{sbCT-u>ORsu(9Y;f77i_L0L|eU<Pwhq<z1sTMVnPY;9CW2k
zB)4?9BrM;bwzI+}%F$BA!Lc6?^+z~ieu++aSFBBTE6HBMhW941m8~|xktWh6Nxz#U
z>9V2ki{Ujsxpp?JN>#j-*VNd`x$H<+x4zYnJl7f4PR+e{35_ggB{Py`lG$Q8HSW^<
zyuOzbSk~gcBgLe&(o+qbD&;=ka7jt_j}RvsMNCf6iL_)=p}NwOJhDio%9qP>^aE8W
z9y4q%caxi>QM55Lw7B)(^<VP6H~HR|y=el<79Q8JCD^uK5qE1s(=#<O$XqTHd;1*F
zXA}Px%%ii;J^-eNy;q&uxJ;qiWN~^Nk<tW8*FJVk$;{0NTzttPl#<f&648n75m{=t
z<-~?|UHd2Md_vNYpO$(*&T^kY?_`@4<Ci&uGkdGAlgSPQ1D?BdAldTdPh&=FpW0h%
zQy9Z~^$)9$aU_#_t6LulTG%5jPGbBt_|gz+Ptk>yNIbVcEKdTG{7yTeiQb>w+xSla
zJ1Do`F4P{!lUcW0_c6d6b@w>m^TB)-YVUErJRRen-TRoQ?fVm<?jFZHjrHYT4QsT=
z+CJj|>)Wgy@OzK7rvWW8x<F~*;3`$yEol95lh0`qV!4P4$nSB@P6NMrR*9N>+?`Gz
z=$#TM%@cc@!FiiKdfwyA+dc@7R*roMREm_B@?R-HKobPz;9f3On>vJ(y5iI{I*ILI
zoKI?0E-v(7)Ytm+>AkeYmu=gRMEnFe<F`-0fR&={L5E)cHoF9@rocjNLwYYgQYsjI
z+`60W2-Oqq{XZA)Q_#M_5$e*9^k%g7aIbo4_A|h6z7>ShhqY1^@4U6@=~4i!tA%w<
z-S^9@c!CfuKDoEr**<w<Z#5&9PwuT=%l<l?WDJK*_3?H%rjo>D!7;;xPTOv1Q}kVp
zErs{gX7X$GUR)j_#z;M*aGM^Su6jI_DKn}c{|R4mJCCHb<YU7)ahbEK27j#QHLA9D
zc^pg0kiK7{zmg*f0X_!2rdCh2!`5w6yBQsz@Ts)xR8#vcY3lO!rNqdwq%oi_z82@F
zW_fPw5t7{`uPeloGAV^nawWguKHSa7^Zp`4p4k0d=ncer_QL&aJ0#oIW_M#tt=szV
z{|D7Sz@hC|s;^X{NLK(!t}dZU?3>5@NlV(`!4~iV#-G?}ZsN{qwWw}p{5zwr?dx2x
z&wat;`0J!cLr+)yuiEc_;oCO!fsn@EYX4K!r|mDogHIv!X{HV$4RS}5x4B9=VZP&9
z3GQOOzQ(;nwHG~~n~GTHdza<tLcrZdeB^%beLRt$SfagG-+OzUPxGu1Z9mc0w#!SR
zc^q-lz``xL+E*al`%)hQ*Ngv@Dri_D##Z-|3#GkR_4p`Y8<o}WA_g!-{E+RP9y}Ma
zJ~C=t(=o;Cl72_yA*ofVV{-j`y#Sf5x7)WEi@EWdBy-IcRi^TMILFc0DWD?JBi-T8
zMN|Tn#Di&UXFUT@t3y(cO}~;^fpSK|jlU<b^|&{i&p5T|+kuO$rI7gCHBv8Cy?UYv
z0at2h2Xbfmh6Cr;Ha#Rr4`_-$0pREmO~=c^W?xTH;PT{w*e>@RnZntYbBT1)!JWwT
zT(;G8u>K!9YxqzCH{B~`Ys3=sifS^6#O8MgJ>Nhxzyw#1d*RQnaHR+Hdhg@Pw%Gxy
zCj?+M!kZa9;xi}97X`WvEQ0$3Bw>c40&_@I63cdSm@Lsrb-+o-B=@4XMaM($tL`@>
zd>3ONoXDn)VP0zA%}rZ)>Q|yz8msT7I_9&4#)grGx#z7k`$+Rnb@Std1S7O~UeLUq
z7E4GKt#19BnZSYCPHR)eoJMlXHf@J;ilgL_xLRAAB^cx@-Si}-R^gvXk<~9v9Ke`A
z15k4p(&2;}36Ib7FUROBc3dKbc#x&@U~&z%cm^nut_Mz{1xX#z|9?mp&+5?UQIg)%
zK=4mB30c}o<;*z#V$4sJ_ZWPu^P4!d<&mFlR_gN%`tmP8RXv>Zh~nBjy%17k<tE^2
z<ysObR*#L)PNquE>7>4Q`x$ZA`d0gn%UOKlChZ+9U;$*rYNLe$WHPKeKdq<d2G>@;
z`g;3FbCA?IegBc=T)wr(7QW~$au&(;Ip2O{H~)C&p-QM|2Xnt^%%EclHfa?(__Pa@
zP6@C&FUyomiskZ=l=YUdgz2eOY6)~9#`Fdk@eel{1~>2z?xOcHkNcB6dKvL^*tx!s
zaojw$Gc*#4)yeitXf<jJcGX)kp0MP-)>#boA0CNO+jXfzRI7_LGDPl6v+(Rf-xb5^
zJGL*J!tDLIr`|HlgxxXo8BO4=aLTjwx#UcsI+S2geMqcVdt3;t&36gZX$Jo&$W|w&
zFn6v8`=Rif<C!1f)!ic8G7w~@b33n^x0F_o^T<VQ!i_Ef+!W#9zZWv5os|!k5(g(p
zwOq0&ubgo6P(9-i_1;kj6JNn{lfB6V_9(7OeG>otFH6rrNM$~il*jPzIke3Ia?oT5
zk5x}rPoM^dTRStYZyhdx#wo?C9CN9n?1CE9;pV_-S5NAzn-mn+%Uj9W*01#^2jv|8
zKHyH#q<~!uL;t*?G*xu1V=p>foya$29l%mcQY%zq9C(SaR$FiFybJb1$;)oJx_hGM
z_nw5DZ~}F{fct2taA)p1_SO<m5mffc&jXDm2Q_J-MD14~RXvYBbC3bmKXn<CU&jXe
zWc3LCyJr6#$KS`X5jeX`Nd2g7rAHA;ed0#q;3shpv8?|kjDshrq^)w8;-W5tYl&(d
z$2O(D_y~F=y-eTSegt8iWmJP7MBAUi>BfU7t=%w(ud<Mmr|)eHj4_vOu|HD0YVaqj
z>cCHgF;<T}h#I=jsxNKQ9>#JHV|LUqNh6cz;lQB>Erp-|s_uWVwM^ARy?+H`STbr(
z+V7M&w~CM!CpRpe#mdU$>J$;LIy4EWNFl1uoQ<#~0?WZeT#v}=vT(lM$rXdHyJ<^f
zoyR_dIQfC2eRcRVh?7gM0LEIK>i*9lUk)XNJNYM9u{!h_{Ay*Zdq0D<(+M%<e8#&V
zpsYv9xO)F*tTmSi*vhn(=lTJ*w+%zqZ`uJ8EM?ZHwr-Wnal$5{$!kzFh-y=CdNM)~
z4C(STxx+NJ+gX4yaUmKiF4^SBjgp4wZ6%qIEbo8bc^IjC-fO4E!yP9V$bM49D|j%D
z3{MVr#A(v}S3nhbBJBG?X_|h1illVP9>PG;uvh9@$ks1@Hz&Xi(>@92loKJ*q99X_
z2QdCzF9I!5DBpnP`U>6IB8j4Hy|E8qJOzQ>db{kfHzM^CYZ4OGk%g<IJ0eYQA!4+9
zEs7;-n3(YPd}@;9lm7iBNl#Zp`>DJ+R&!`FwaO4zLP{n%4MTX|Btc|llla$bMA~f*
zq?}0c+9D70>k57yC^AK`A1wlj7?GD)kvmdt+U}&SlLYW2O4Nx)2O_xOR^pfwTNp5|
zl#-H2H(Ojro6bganIR{&`B6BbCUuSQcAP8>4@aEoeKNBwS4XZJLm_MbWMYdWd1~*&
zWHHg1*tcQ#*3Y+Zl_ruT+;i@*fr*Q7?!`Xq+m(x;H>1hdTr;#mZO`o<o0~`V<<Jv`
zxY4W^Hl{=K921F0q6#FTb^IfiGFOzyF;BZ9H#iIL#CW9}IKI%#lN0Rk4KDXO#BnE$
zge5tx<oxRU#wjLuG4pK!Ye!5Xm-zux$yBP^=gYmkew`szyHGxp(pf)ldM5B~jxU^W
zJUMAAmmArpFo#@l66Jdgf-X-y{FKD6Yx7_Og3XV&Zy^ax+#p$>hdzka7Nn|?4+5Hu
znR?#iSS_`ZuSDJG;InJH!EGzj!`#BHtRq6+t;$t^G~b+xfz`ZB!vnpAxflEvZ`ls<
znc^x_$%n)_a#16tbs(18J*pngGtU4<6o-=VoWxg~nO&OklNZ+&9X^}|U0~S)M@*TG
zySN;$r;2uKRWZ}xisk&tF^csQed64bs=<9u8PL}Qtpu0Tom*)h^0u@V-0Ip|BN^q5
zk~Fr)Shn8G)nA{GZ(+CGBmk2b`Bd_4A*7^4+7x&5?c@Sl(p~_Nt>5@OM(RqbEI6c?
z)baHW0XeI$B#$#>=^>RR_jct?Ooz^jT;)`XJ5DC9a`>dQq+e1D*k&nQ>*%6vJ%PfE
zf_HZ4^pyOb4gFIhcI6fqD@u&72e~_zV@=&?Qr%l45L^GEogQ+HUFu16+IOlWp&PlJ
zLTFE>OV(Ud9b)8X^QUZopFj`IT|~9UPpPXxD6L!nsqG}{1Q-Mv{~E{|KUXg4Mt<M&
zhY))|jvc|lD#pflYK>(+57bLK_S5DAPLrbkvBU}V$V(C=ogH|yP`{?%3Ago<>2WV0
z&I4v@*RsSXHvKem9))e@V)b$SdjkI+!+#5iJ&O8|<6pJ)e-S=)w@-FP3cF-xfL`+G
z1dL0pOCq4J+H-iN4xkH?+@Wl<e1gn(^G={Tjv-6uAhwXWP{X@oA?|Qma`~W*gi!(_
zjhH)hV0&ceF8Zx)js>Jz`DOVjbW1bye?e=$egAhK9G}IC%>ht)ENQ3yzE3v8NODX8
zMc6*H)3EzfgdA&f>hNDAXbzvo_T?*u*?IQi>1NvfIS5|@3ecGdz&t`n7+o1%2Thed
z72Y=7KH{fhJCgh;iIL}$a&vD}6(kL5)BRN@*ON(n(=c^-ueWS+ai!GfI7mO31jX3;
zz3=tFlFFYrFZs+WHd3<E@%faKM(bTSCR8`^HD0kq@}Nsbi}H)hpC%F9`j4fhf;t=J
zog~drFr#cv8ry#zXf$-UhSE8L#}qD2Q-<%gl9Z51({m%epWLOxW}*)zbjfk0Wk_O)
zW`XHlrA8=Dv<?nHlfC@dB$~)XCc}h2$<1~-il%H(`U&X_v7F_=ta@v<%^B&9iC%3}
zM4<diUdb`Ew*TX~aFuKZKBpYp2j<Fk<(M=A?wAD2=2)Hox~9`UVhHDtPoW)-n{V^{
zn6)ZfV!m^=52Y@k&WERM_4(zawsqr2(P?u*$=AqIDbUbv!XtNbaLW2Ropl97c&Y0q
zZPzoX{V3g^eKqNPvRCWnp?05{Out|wm0K{kX^snR4pf8_Tj%Z)RLa}Lz}CtBZMr7c
zHYz#n4h%ljXvJNy9AZ1SQvH*!{mNGkE^bf$%OCrN|M$}W_J2N54Ng=8cTEfo4DZLE
zu_FwQ^3T5g6Z-~6M=iB8bYy(%e;OFS%-Hzm-Tb@tpNEf(Z;uU(Z~w{uiE4a%8<mES
z;?KULtT}uX4ew(^OglO}gtYPPjbWr7MQhugfx{Jl&)}Ekk6JNQMN_|Me>V>x`?Y<C
ztHT4MR^iCl_GFBn8OWnII*fkXa__NLE%)eAMxItt_D~(J(CUrf*f%<U<EgumH#cw;
zBV#>`VqiGS{uPey>4C!oVUz<0CMu;=ocM<a#@`%fpyPov3=B;Sj2;`m@uvqS2F7pv
zIYW077}omX%3y3^^hk&tLz+R8H2|O+e>!&KuaPl~5MhBBVR7S6+0)b30fztkfEgxc
zlfLn1hzL@n=wE{*aAPQk9*bCme?A-rILGLlN3bkdYQX+$7Jnaraddo>h2A`NY+Qwo
zVWdYf7OqABKilKbp2E=og6RR)A)Pp51u$d;4owW$=n*{1rnmp&`1YR$FjriSrw5Mm
z=M3-#KXa^u;f&q*HT1l1{Kg!92K957e)j9<Zv71D2f$;=1Nu3rpF{e&M?WL@!3f5-
z-toXK3+%(B88G0EzsX^|`6Py6<G~~qz)Iiz7)Qv!eS=jsd|+t*F#b1w^W?s}M~8>*
zQW!uT2J#OQ`S*>E-FV;7&>+hUA08Sy#$Wq`0K<br*n&sKzB;gPh=0en{v!PT?SY}e
zv8`L<TVJz%Ik>;7#<%|T?xB5_QVom_qL0y0^f3yop1Z3W*f(-_H8_g@A!8WjkuiGL
z5dH!xEOMaQhswW*|L-3f7~B3q?2GYD1jo02)&72a*cu<-`b|U1t>0s_2L~Rg_B~Jy
zRUiOkZ~i|R)cDr7$G5(Nt&PSI`UTc~^Y4J!Si>b?;Gu~@H2J%SG2CzOAL1svcVa(d
zzc9XexMC$>*}n13Ar5B~zt{nz<6FP2PXF!L*1ro7_`~q~$Km%cIqa>U0|-X(!+m>f
z5K|l9`aRB&zlw##;U5~p(qI-_UpqE9Jht`qBV${Cga*Ej|6>)m{(#}o^w+s<n2n9;
z#jwDm02#^G*qi_PZd;wtgz|r4OEx@s5cCp*y7@d-XKZ`_(4zwb1NS{p?LTN~fbZ5{
zTmB1{^1{$T&?GGNJ@*Y@OKkl)VEaG?5FgubrDo9j_6TAJAV)I<n!%Q4Y*V8+wzpaU
zV#Lr}G4xJoe<b{Je52O#D;Pf7+h@(a!lbR=vWzcU$``Si9N6&ieF_+~h>c$#8nPh|
z9~{~j5{bsBeiR^md(>)S@$Rk$7{{6pgI3I;J-`tS-c#L$)w&5fF?Zka$k2XP#A5#^
zAmQO5)cCEjt#2ILKY9!q-#B_~KL)%XXbE^!0K@3MWBbvst=0Zx`+;9rwEZ9rpr7bz
zKYO}YZH}OGb~s`SgN-o26cic>!x^#Md^jVjifLKReMgCqh#cf^sAW*ucQ+D7@7woZ
z4c*P*gL;H2q$69u9Z=$(;R^6#7LW!3wr~%D_6~c-&u9h;L^;jD`-bis+j<MFs?06$
zNGp7c^l$5y)xQ;ztUekYd-ME#$Q!eXBVcQQR=n<8+zej%EmeN2to#;;1Ly*1#=ucS
zhF=Gr7{-Pf1a&jsUjIFWjeFy&{txUOe4qlF^S^^Y$WVtAVf%gKH?C{1Jg=WehlN37
zpHTc09{-r);pbV!P6hz{6?f=1NfWlm_+@enuwMHG=f$sq>G#9$--O>kQzIV<(SH{j
z+5W4Ep&<}YtMm!``!W0bEdLIJHg0{81-{dxfR!5G{#_$^-$4K{L!Hw@hE!wETeU~C
z$|o(n^;hT<jo-9b?@}O=!{e9l1{pkx4UeX8{-lXUKWN=tKM>$Yy2uSMe)*8?i9a9M
zH+B<XBOMVHTCmN$z!klD*}|7Cyky}e3$Fr1pajE~v}#G~7GAgTbqinT8l`r>Vu@d|
z0$Zv-Xh~a^bko8&E&MYU{uvAZtc8Ep!arx>pR@4SE&O#0|8oofa|{21g~?CGZhq6k
z-?Z>AS@@SM{447Is3rZ1C4Jk%U^S@rs}?3Z8@u@(3xCJLzi#1QxA1RT_%|*5TNV~6
z{2dGbj-~&ug}KgSH~+nb|GkC(gN6Ttg@50|zi;6`JZMnmLW{=!p(X#Z75QTe|A~eF
z#KM1ePb`j&Y8iiK8Gn&yyk!}GVHtm!XS`z>e`y(im3khr<iE1yze&kj>c6q%zZLjK
zt>oX{M}kK3JN7XWl&!ahtHERY!7M?x_nX*G(OpswQjbxlfQU0R!SL|#euy>v3m83n
zVCXJlBdSvn8HB{c*r;R}5~p!c`<u4k_6?HC?i)P{M$STqh7O>a;laVdeS@e>v1xFC
z?UN=ALOvQqM}vbDWd;ZDrjP~E2jSs|hwcY4{w+{4P~j2&8QY$~ZpJUDZI*Tr@ez#u
z;OH=CFggtOfTTeXHT>xRN;-%Ej2tuq{x(G>^o%js6#vd(_IC{5GPaDrl%0&h-@1#r
z_{&@a_$^9ywk>bnV|n*j-hCFn&%!@o;UBQ@4_cTze(aVZH~um_Vd)db)l5jZ^+8Md
zprzQzZvCjG{HT@xh=o65x$5yzOL;Vq#BW&6L6+Tm+roF*-@C2$$E@tfEc^)ze<IX-
z*Ge6*zlSXUDa(J#Qid$$GnVoh3!k*`$>E{FWA@*{3PR!b2Js0?I6Ax!68=7{HQ2ie
z3S(RElI3l`V~p=zz;4u*`p_Z!IdI?*1kW44qyM9A1Sp9^NWb;$(?bWw-uwY^h#TL;
zFEpkb-_-vthRrrf)$f}rVC=*SNfbopL9k5}r)J8g!DeoJlL1Kk3RttQb0KMhvh96R
zQ=?*Z<C}#(bmN-`VyEEvhX>dqIN6PFg6faYP&&g@oW$Swj=+86JI47X72NpFCx)Q7
z{E}DnUrG(dT+wLl{~YX#VBFu__rCiEAV*;?4^W#8KQBVCC%Hm_F-fF<kn-O@gt2U&
zB7pJ#ebCAB-nPDG5;O8mVph4s=p^gKn*RX^3!Y|u&!G$U`Q|XPFo4={Op=Epci%vD
z?1AcDa=PuQeWPk(lyU%Lhp3d_JO=nnQGapYzI`Ledl3Q~*LvSR6N)WZq>uj4eLh4j
zwhe$IePH0=L9ab}kjj&dZ1i5BE9ttX4`oWp{CRp{6e8_W=8ll2jgWebK>OOqKLaC1
zU*1Lr{tuz@AVtDa5KXJ9r9$#3lc@x8G6zirL&Kx{?gDDeK;#&^^#cBHyK2UE)eNXA
zMQ5iyuUP6UM$OUHJ0tg)O!*BXW4GA4RsNzC`=W(^(!xJ!;ROpXTp2n%zI~o^88kM=
zKX>sDSjJaj4Z$DVsUif7g2jAw;1K_SIROOx13sdkAb1Ah_Zv!lS3hqDqVo0+0mmrP
z9K!_TzeB@^FuA*-6(JQE9P;mC)CT;5Z<npF$2n`pBlm!VVdbE6les`Prs_>t85IHR
zqKFD*-`crs6TfWXB?~WEc-6wI7GAgTy6L9dUl`l^zu7k>Q>es8#&3NEzrz&4wz&@w
zK#Cc-yoBj^BUs!}1}&J9`qxrY|9bSm1QhDOHl8~H=^N#Zw?Yvz8_3NMfH9%K@WE>T
z@G)2$P|^mrWdqy7AZ~sLT~cFID5MN<?+@Ds8ME$TS2@hhhM$4M6L%5&vy{<MmY=fn
zfe<qHAkkdLxR9SEA_yp>wF-Zjje>|^03e<ZR3@cjk5Efs8i@{nnL^x#;*USa#y4Rf
zfgNCb@fiLM;~&a^LT+Cj--J1hiDNfDemArwBysD2Fe_k%faF-xMf_hAf1tW2Y$5gw
zqY9^Ki6kK!%?N~u7%@YZkt!@q7(Y-BL<;o%D1;Os&;u2=Eoyvt0ONZd|L2nb!2lN=
z!vR`xVIaep41bKqAEMeI3Jwno3|4bv1XLDO4Yddi44o`-^Vl#(dFv*@ayK+v!!R@}
zNd5qRLqCU+k0zB*HpS*bTjSebg`y<&0YS)MM+ec?zPq8-4j#Sl=+UD<eb{`)HxHu5
z6bvtj5Vc@{F*@-s#hrVG?i#=KvrHS`zD36MZL`n}-hH^jKbS%>;9D?svI>LvKM>Un
zyDtJ(V?&U%-X6UNs`op?2M-*AFbFbukMvO}yaNLV`R4%t9NLdR(CLSuYMc20MC)w~
z2z#GDZ0G<+F2fv4jotd0_YEDuxVLVZwE#Atw_&%m|G&jaj&F`qQ^zlDy92N~JccM!
zhcHi++`MPvuA}2OpM+M1LI)>MMG=7Y=F<>4j$*CGHvu0`<_La&l?*{J2&u!E_~?D(
z+ix8JJSnzPJPx6Qj}IMD+Mz?hDo{gFQiza@$d<rRmKj9RL&FpMkvzN)rXLz1D3rQA
z;hno6rN0AUfUrUF{|3cw@DLkNeR%f(sFp!q0-MzM%}KL&z6+?qEP0TCIY=oB^56D5
znDRk3eoLJ~1Gx2r5Yx9mf&fbWD1<VE?C-mGLny}pX*02Wq_PMJqsm~7NO2EL+|6?M
zAC{f%=x5~KYCpyg91G@)TKp}J<6Yn%N2dmjdvZ)+yj!=>Iq<^9WQ7buu%6tqIdY(y
zA7n|&w{MyBE(pIXW{kT0KS%`VsF=~aXcZjd-Gjl7^6oH*qtW2up}RE`Y$xhD`%vfL
z5R8iWA9gz_-Y1M;a$A1GNaZhEoH+`aoZ<wOG7C07wlQ`pHdBa#bovPgHndA8AYO-P
z>kDTeW?T8ivF&e}l>9BoCO4;2F3mYdO>Nlz7COK6bsNSw$<vG2_1}ukaF8K)6jpg}
z9Yw|(Wk(MdRqwO$217s53;h1p&)vlk|Bd~B?7a(oomG|h|D2O+b8B*Xp+Hg&(n1Tp
zoZQ+}0=Be;7TR)Yi+~`UB&SU@mo`a(%)pp)(j=h8p|3Cl+K$CRtphmdfP#Zs6h=^(
zDf5C;$1(#7zGW<mieni;-tTYi=Q)=&r5$D7|9?L3+ve<NU)NrH?X}ll`|da|2oF!n
zE2&At7lD33gQFR~h|L7XVv?C)YsMlmt*^<p;a$$hzDoMI#Zv+#N4O$bLwLN<Q>XA^
z;C^0W^bTU36fIzpp(2ue#RyImKfwRET<-RB(O0=VC*%22$++in34-UuD*(vn`DxL7
zc^(ed!Q1V_(^sIr9^m!$$?1ebHwTG({Y3gI(U*cmU(!r$WcYsk{RAqVoQn2zL7^}z
z^O1d8juNI!eM}W~2T>(?iG1Q=^dPzq)#3gCk(Eea!GAD-{GcGaI1l-8^i`ZEgE&uS
z;ymKV(N}Tq3&?q28q6L)ioS~SgP_DeaM?WOhwH2GM}ur0&1Cb0A4gxsd1fM7lxpo6
z#o@2q3uCidOK1F)pGaRNdN=_5uo9t8kw7%qOL&=UItJ`Tai94brZDjHrPf&d&=SWm
zEX^4GC(`F^U2WY6Bgf>6h$5M+1tec&SP|KJF&5U?uqI!oAF}Z0E2S;G3Ro#M%EH25
zrT0QpLDj%B(0yJ+?2!~Ul<>9Kwpy6{h5B<~QtRrb>9ZqS*IW27fDM^Sj}^3X^LtU|
zK$S4M+z)<LG_ykff>vrMLCiE(Lcq|o$>6yTO1U&@n8}4$mvfbhnFIvz=~<k$8m-c@
z0k2|T^s)3vKW!t>3@<;g+6X8ux>XIGRY6g*(Dp-9<yBG$YAX0&%Ct@-RaK#BFYB;V
zZBV18LOLV^;3|d$jdTt*5ZsM;Yb2yKHm<2Cqz3tDQ6{aMl~#uGi&523@1@nD7KEo1
z6-zTJjU?AuaS1AQqk#RSGZA%h7(B!+`^96(s!|w=TuQ^JNu{O{t}88$;Fr-XQf&a7
zfZ%u8RTLGgI9j+0m)i0kwqhnjq`igdi3-Bgid14EIb9+TWdoguP6>5~@}XHVXH{J4
z6*ALR!RRM-ymGXmu}>OuWS}W6?^S9Es(^E<Jyb0^Z%xQwP3KiHZK?}x*^jBnVuC@2
z%hl%=RS~C)9}-QqKl}n#7!MU0)tzcL4wD&hQ1Dd(CR0c-{DRuu@C!bZ55G_(7KZJ8
zW*>f`ObjaZUKyEVJ$cA6F^j+D7FTY)+-2*-OTH>NaK7c>^7J8tl*M=9hN88QDMk6z
z-%He}k->&-u9oGOSEzMMX6QR;xRMyJy2ci^%)69%YNc|}fWnqYjlx->Rm`*?*tc$0
z4vx%j3B^*BTS$<eB_0vOKp5z`fKJU8=t)Ijk`;(5?g+3R>R|V)7vc|_(AlE&4)v?}
zlEWYxLvhqbY&F#t%87Kt0OdcrIg&?3OU=~0Yqb>XP<5r|Gl`@og@B<+nOdTB)+DJG
zHi-aN0M&aPw0RXOPfCe%gs0oa1m?>ksYMoSNvYm}BA*M%9SD;d6%HQ)o8i|DDMQb1
zw8O7kBOZR;4GS5nSX)jGJ6STUv?Hp(?6lBMf^_)xf+>-ULK=Su+c5Y>lsR1NPtGvk
zoQDVT^<glUH_fWe#%*|H&7;w>EzWB9IV;43wlu`N3R-ktNl|`{R_tr?Y$0|eU&D4x
z^wSZWC&rZB=7>kij)VIcAlS?b12yt@R)<STNtE3J?w@L6q|&;~k;(89Rcc>)w$#b$
z-Z~I>tS8k}yExPQIKj6Xx8lwewXO3XiTYkuf;9t&hO?7w-_FP^oM@TlOm7uo3g^Mw
zA)&@92)bfcWl_vD@?{?clmDsZh#<O#)J%l0s)t#-Nb*+Mt}+{Wz{%CsvzXgVZgb)%
zlDw@3kwp_CDoj<CT1~_5ij2e}BMlm(P&EEZkmJM=|EUe8Rx)fB&(fZT*^wd1=}+3H
z8UxPgaGcRmM!4o2Oq7#EY^On=08pmXY6LACUl`O;hSkiSU98mzakNc9S5YAR8^kSQ
zrqoIbgw7jDt=9xIuCyg9n1IMgv(JC2l|D|C;N)O3($Y<b#u?snbCT9Kt@P(2)6o4X
z<{&dVE1RAaS4#LG$ea|?jCCfvup~JZLIW55n1dm@6_<`Kj~~s35?@Y)RIPAMT?O^w
zkDBVRR$=2P9?*tBM2fRm2XEGuh`>B&L-<o!X1^-!dsW6a*%kO&N1;@Z+d4BkJnO(K
zDrh*gA=kqhA5lY^x}eKesv>D!e1+G*l&Bb4{1Dliw7RUR+B#ew1+uZpYC<MZ3|KMX
znJV-a>^`xKL!}H<g^%;6Bmqc{unF6W;Nh7rJ(b@W)Z@&mBKR&f(;|#4E{=>W3u<<z
z(=leMGPI6h7P?4Uo0{zk11A1%)GB^*nhkiFy1i3_R!>{7s2n!8*f6y?pV=ICqU34x
zTv1bP1A~!^GR&gpD(3`=s*4%=p)F{9%T_+7Sn`TI*3FpnD5J|5e5Pt-RWS=~ZgNKh
zHyB=@QmrxvTj^-mX@|;mzeTYTuHf5jU#cVR>02kd-o!_O`3H2j)M`^AYFP+!?XE)F
z1W{m3_|WF(U{~vr<V9JM8a!B8?w0@)4bv7TW+r(j7cmE&nU-0ZnWTxN;NR-%um-ca
z-u}dho67Y02sW>l^?uqv)`p@|q}g6Pto;NHJd)ZRSP8NgrM5u|8c3A{(BK_r-fhBY
zi)uJEvl?YF)j%Ieg)r?gxJ}V*niA6@YW)m<6<ARuP?Di)C*^=_T7-C~Y@)>o1{A54
zCj~-dI&vrIi6kwYE2O3&>}DWYN3Mws4vNtJ{}Gf;w!BS_jI;}zl$ITd;kwZd+5|8t
zq9N)_66RzBPv#H<D^Vnx^)r2sZIw3s>5yWQTAbw{Ni`cunj28oQ_VIDRyBA~E!(&6
zm%VDSnS%%1=LPpUWZbh-BKjICLaqIp)2V(ZeAx_XQuat+Ew(~HGb`z6rf7#3lyI6(
zV1^M@rnRE{2ye<LsIa1oPVrqXfu+*zzfc@@w5hyNu!CkJol1}9BsoiOWF)eVC6a0~
zBE*<Sj-=)gge*tlj%a2;^V<k6#Erz3G`G|Y0FGz{B9dB110^G^)i8-U4b7$*0z2Hq
zg8Vf{nl;6!!ezvLbFo|M+PX6%DX6`yZl#%}SY%#vcv(@z;B0}FD~!@@>T3sF9k}Wl
zTQLf=`g7B0i%sGUZWXP!-enXYQ2#&D4{hP?)Q#E<jQe__i2ms0wW$cpBB_hSSj8-b
z*>*3pG0nsJYN!%%W@!8U0@TIn51(jR1Cxu2HLqB!%1hPK>S`l4pNvJ@_sEI9tX2cl
z)YHc2I8YU~eilIsuoH_)k@3LsYu!{gKd&heJGH$k#UyhwlDeHgRJrGEjQfQ$CTt0$
z)47@b2p;RG)DPPb*asoDaMi0u{T%b-R)fh7SCf+Mk!wmMZ>`44f~qUa_*Djel_oDU
z5Jjhzs7=NV9f*e1PH7)^!84y_+p{LD@IjwV6;uX+Uw|74v>9zgP2laPar4|`eWx1D
z)4l-+dEcJ_5qL)=IC3zNU=6UMA}3j25z*S2ix#A0CM6ZvnIN2$r`~0f5tB{85NfEJ
ztphAAq`pM&2Vc=unt3~VcR9a8ekJ_!`IYf2;x}QI)~>Y}c@TUhvS~8S=CjzBGfvtp
zMuR0o^)cGBoRW^_;;Ub<Rg%GnPADo9{m1)ntnhU=lRFzofrMyQrzt%*)r@8%xcp!S
zmg*{wbE;0&px>^7kC-F5zd}P7^+qN@HUlBb+_JP*yxLg~8GKZ2W~ewaG)<s~rfJVL
z$`Xs7+Dy3znpLH&VYf%x=N3(dvx&h1kc3KV4q>#223$=<26{E%$+&@!3a=igvd9A*
z|4yQ9qz*(<2Q5Fc)SvRBL)*b$Q4w1-D=Ml_56FGUQT?zFowb@O$WJZO{!nD&W-{Fp
zNwSo(nZIJj;y4G2tJLH`ZF@yE1!DK}qa{U!%xa{nwI@?MqW8EwTnDi@*i5*XK?g<I
zTs@;`BHso!)M>RV)x^S)?b&2?;1;0qhdWkONsT~I!BVl=M`C!edsSpnB(@7{r3Qa|
z(s52IDg!*JT~dRd9lgpXi3T4JR|gdW_p75w-Di_s3?pcb<P7<$r)ytF2(hO;;n8+i
z^X;l-yfzojM3y+YG_tLxx>}OYar4NnPI!!Li}06fuC5xniJ3z2{K!rGnQgHjru<T#
zsAGSue-A$I*L);-7ZuO+zoL$83-s4!Hz(F?i^Ax|Bil^8kD#r%EPO^E*+!lcVk6s5
zE+Vn|N#a&ufRsvQWU3n6hkZ$r@TXA`GICQfy4D^w5Q8|lPZUw1r&OiB?ngliFY&D|
zjX&*btvPK6*38O0t<J7wZehAK7=Lu`x;oru373|rC1xN6m}regnVJ@ymA(1<%P&l|
zrE#g&##_^0#(Wl20ve}k0FI%RLifQ(r69D6<*g-GrQoPL{Y}*j$ZUkerhi!A01`mE
z*3?^QwNhj0>c*mS@t(iEh#4Czg2@5A{nZvkyU<e`*B=X>zI&2{kK5{+##ll%)iAY)
z-6+XQmMiJoES=iM^X!+fg>2tPa|1OR3$rRk#fMociwr(atvz0qdXk(>J9``xXH8KV
zz(n8-cE|?dL!oA9tKngCK_WWvB%(u#wwu&SS4G&zVxp4?HcTon(-2W(1kFbva{bOV
zwOM3(graTsN4Y7)i9hm^_ZFuMXecKN3~pnGRb&%sE@t|(T8iZ+DcTwf1fGIup-M1v
zhZ=_s6$Uh^!)TbH@BoroGld!|@+~T|X~Wb?8#<;|f}`}I$IX1wsR5~#Rs&CGsr8nL
z-}dNdq%J+9d6ayK!0Ir)4Bv@Gh1IAZ&%mM2u|0^5BVya<AORb6<fY0Oe8&2lnPNO<
zZ;<`kV1oRqtf;iTW+Uu7qcC?n&BQh{CG3nqWYBiLvb?O^?iBkX<5Sy=SdaMaJ!KL?
zQ#mPGzUwW_%lbO=(kOSE%1aDj&}F2yF@V*HGWvjux*~Qq;YEO^dYCeD62ur*U=O#z
zgcUV;=!{Hxv#a!JrXpE_FAi2ml?cIt=t(DKB^RNTcDnUd#nb)<Iy7})sck1}rZ;Fp
z>ec)NgiZ*O3>8naSXL^FtrA(G(t{=Q@Ch_jJk>UeIC`WLqz<x};`S&azb!$aqNE<k
z^*3>dXUc6xPdz{d;1G8J6dN<Ek&)dFSDcQG(oUqDUvNt#v7kgsPPJk4t8L_7<AZzU
zq;NQ?GG}gm#)ex}sYlMFrH$M#?V6TI&Cp!aXqh|+M=pcM@T95nVX1-L@#~69DWcEL
zBlp_W(PbwGLr0Uq;s66TxIfT?7T0K0Z#iT#$=D=Up-OU^Pw0_*wLF#H?MZiI+^3Pd
zS=3E#(<HRSmJe55hJ^lZZM{Qv!KUI{RF^EJ5tP!KHn{tEe>|M#q_jmnS!T<p_%Fjq
zuFa%&`&Fh~s50fM>})8x`wWd5cUfm6Gy=HU78@HVkzaRaQ$M8yX}`?&Qh>Yh#BP88
zmK0LI79fR9ByQ7;BVFaz@y3LBv+XIAUqU+-#v>0wi!=a^$Jo@gs>EnUd&$r)-56-u
zscnHvHI{ln?UnT@7z~GlhlK&FNQlkB!6I(kgIupfI3aj#w;B~X#KM+^uq^AZA>zlW
zGK=L;d*H*o2hx`>kskw$rSeykN1g~g{H5cOrveWx2QyS6Lqvn&gu+{_CBXw&Op=+?
zTCL_*hLH6BSG(Ruo^`7jLRy<U<h9SdG+&0*pD!EPZ=MxJg=i@n*I*2|$$5@R+62D;
zt>SPeJqPW6M9X6`Wq*kZ=Hx~swa+|tU;w+Or6s8M@n_>l5`!XfoyC$KuoMT(elP&D
z-#iZ*nEe*?n)$qD_Sen+dS<(-D?iFCvfx!q^ad*oxQ4VZJu(#XT@z3`AO%9jIK;jT
zP#F0+zhFO{kfaR<-Z=RajH<0aC@!Ycs@nQ|HD}Evqz#CRfjNN<ItoM?vIZJtWulc4
zodTt7SL?u;({E6CIef|l)VfIO1r2CKFY0$c^?(z7jjo|8^%QN3u!GiMUnR+mB80{e
zL<iZbp&6P708fJ^fC463`%(Ojmf`}C*8WKpF*~UXFFFg1!{D4UX!vhIv{nR^v$E-#
zgi>EM8f4d}s$!I3rK@<W#!)s_GVYu0{-`A^<})`kq(AeSYZY|B%Ao?iM*~Q4x34kg
zbaz(f7s?$~-6%mipGrnhplE9lXw5<v1Aj2!JqURqhfz`o9W!iw+5f&WT`E&>p4k_7
zSv_u%W~vEiMQUh@j2cOW#W45*wstT$d(lYR3`vb7n$xQqEi?OMv)eI>QMw05DmC1y
z(P_$_{fat|sQPx@lJu7$Ne8M(3+r;Z4duVK#u_V9<}Oyo;zVy2&ZIKR9Sox_m|Qlx
zRE=_K*dcMqkg%1b!J9c&8%Z4!#)GdI-ZT7Yq!O+(@PwaER_1gP3_z8E<$;EPljzcy
zZ&PfY%XE@R6=lFpX2Z%uMvtM%R)dUYw^yTng*8P0uQLd)&>XYAT21gh{R5DkMs#sp
zPQz^IEbtRKpIcaba8WD;Ykw^CW_gt7I`gX8gTYx*ZGO+AvBYsQ43M)>PQF@jk~c#t
zZTsXjq(bxMn*+Hx7jj{)vnbbDkn7Ax%EHYmd~QHVuCq)hKpEbT$$ElHF~K1=;jqwB
zB`TG-M)pITVI^iYLle%lIv?CM1vc(?p?5NmA+p*oR!7%6(-`hdQ`p}tztg5oky3+q
zX~Z(bP)}U2oq>logh<h6#GsH$V&-wC1YzbkOyeKeB{CWnb2)P;pY9pHg0oFv%AzCg
z5@Tv{Vq%6w#oCe*s$^9V)+xYUt<5l6Si(vdJKBJhQcGpIHy=OlfM5ZjTIW_ci(@$M
z@h9Q-n6MMpdvpSLXfidyv4p|9)GCK60imUe_Tm!l{e`F8`30i+#BraU0hFQ14(`z8
zQro$CyONY6_u0j{-F<9Hx}39190VaRqcnpWvgFTg0ky1@n|2!8pJs^MA0zML5>~qz
zpU4_^K!@Gu7WcUm`LXql5L3*EY%Iunw%uql(S`5Sfj1wr!?46oT@6ikg(YWYMk%e>
z<z=~rRecTqI;aw{hoVyb3P`ReK#JthGM#H+J1}SHH~|w5>-hhiqAC*=E{-$-|Kx_V
z<++dMxfy-Iyc!I}je}MJhxro+nbA1nXH{hAB4%HQRz!x@VdXE#dKF9Uh@NLE%1g3O
zRkJ}>zIp6?pl=6MMMa+LrcIS|I_l6ekdT6l1s+gmyq^w)Y<%xk!FtBfs(@P^R<na~
zgfXxKY2#4i^?;{gsjwEsgUKN4A4NTK;1oCY3KRHj<$p?bI-3E~azfD2untW|uu5i#
zvF+^L&=Sa0!v=zYOtf{M$%mGxAX~ZSp_%BmpJJ$=FU&xHKVUm~o`?+H5RnG491X(O
zAl@>>R?2^9IZ-j)RR%-Lwf2yF!lJJ>^I}?zr5a|h1uDdHnZpxEMZqo>TMR8{_jY7x
zHT~Je49I{X`ZLY5#U^ToR$C4ilY=f?V5bh7Lx(PQpVgXT9&K0Sj*NEtbv)XsPJ1RR
zERoSo!e)Rd1S?j0;dHd!A0v!*+9cs<XSAp!GTKi(Dz-*@fv6U=Yr?^fK655B8<H^T
z1$}`<$dCi`MFY%JAl8^!ak?Sg$MGFNB-o#3+92cL>I(THhBPQv>(ERIbp=f@fu*Sr
z)m^!#1NIuiYU6pC#{{zVd^jv<I45X0h^Kw%29W5=1r^hlvy!)gWV+}{&L0&q+r^6F
zZ8k=(X8)m;RX!%&E{7q6RBE|3+$U%q6%3P?n_|M361jcnSkufzWcqj|2}iov31Ov?
zq__R?!i!Dpx3H<qEI75dKfS?SLX6%%>`E|PXwxMnW$CR$Y^N6^3|H9(ui+`~bG-Yo
z8&Y8>v-(Dt8Mk>Ry0rurT{RLJo@v*KM24<1Cm%~N)a9IAn&*KzuQGmoL|eOty7&_b
zhC0OU@c-a_0VmH6gvnmwXCjcqXLZwKhprMSNo8=b2&{nIXTu5;40|ox%{rxEnC9zK
zLB|~#UL*|lMb^nBOyzWq;ugd7S=S1fJCw*!zj!l$B}}nN9mfiy8H<HmhBtG^NM!Kg
z$j~j3q2b6-GBQNFyD>7fJu<XK5r!8T_}36_ZjQi$wr2ou4*;7*fVUGjKmst*iXy{j
zsSFi-jj8rVhE#?4FE`3O2q~h$s`wD0dwnsQ{^Au#+`S}$)clcA5RdTiq9P{bC?<+;
zbp<VTFAhiB;_ATW6kRvnT#@!w3sEYKc9@^gSMyGsIhqg|dMq;Z1es;!#PovIHNzh3
z1NMeZ)w?}1w1-^P+O2G>5yK9!s*<T>3Ad+Q7PJ}Vz(3*Xivm#4_93BQ+lUlF?M-p)
z&Jy~0yS~Gnj~v#tWC`RY{q3@s$tah>c1QW%f~L%e11Q^VzG&#FG!ems)iEDEMcSjT
zH`CSP!G0SbKk{c4E5P>nq7qvhq8D-#l>`Hh6f26RR&8yHY}Hw2SYc}ee+DwLb(->$
zU#0y;BU|0yEG%m1Qf?z#=Wu$zAhI<krZNDiFQI%H1%X%PglK^}e$EowIeSj76|gm`
zpkjVerOP~ssod$$rWNOAJqyKFSx7T-Eo<nrBoU{K-T=A7RioF3#bcw_;~~q|75uTm
zQcgKo78<=l6pMNV(~By~tG06C3+k{`HU31j(Hj&cscH!ql*2~sfd)}2En0IOy0e5*
z_{Smb1m!dAcPcJZV^l=_qjVO7T=K1iQgDT(?r<{3v&)>!Cs=OZTB+u`buxP#ClpW7
z<iX$$I8BN~)!<uPTEucBQ&6(&Y@PFT2cbji>|NZFSQyP&H>8A0?MN}{xgyB8dhpRK
zuZuYOs4Qx<pDCRQ<jsk-jxA}5r8SsLtlT)4`H*s=h|oEZu9WT*8GIloR&JEG8g64y
z6hs4=iBfN&SSK5+lD4k`627c+kLK;l%S^ljNaeOuNMf?{jI;cWOcyBS)^?iiVfm(E
z2FamOXON=PmH7c_uNgG5P$jQT3lLaGKsB3Et|Z7TN}^6Q$PW<xL4N&#CrI=obtoiz
z;w^<MWOWdsng|PvCMx4RsnQwj4$o8K^{r6eVI?K!>jQXX$bC7=kX^(U-DQ{;L}1C|
z`T))BR5$n}qO&+6TMYVfq)lfK&85NC*+n{SFm8<>0W$dC6rgldfF}jHX;x8*KxHQz
zpO3N<Z42x`-4;OI=5x$DrzXX%L5f>-jE}t2#0~;(3uy2Tbh;zJfkNzX?LMavSrjA2
zu5pU#vduv^H__bzK6eLsf&JY+Zq@IA+wLGfIp3RQzdr!@0DC4}1s+!RhXNkSu72WX
zw}D4|p>QPJL32-z-0)>WE|kYkbVy`ZeHxuCte*(5ej>~BslfB8IV>Vmi8Qhm*^vAu
zWL}3iWC*i2fUq|J3y8Fx2+sx)p3REz?5Qq0^|8ds&C*0yvNl^{5NBTiXWx96mbry7
z30<)bR|$3`z*vdCKLD~H&54#@Q-IoH@uWHFSag{$QUyI7`-N&JzTJp*_z2XX;4kGw
z<R%7S9!@qQ9>|HvT@6Qyc<_Ww)@-9ZQnc5yqB)T>Znm%c{3I>z2$&sm&H0fu4+SK9
zlPe!!5kEk@?1v09QDt>DoF7;;Uo8N`Wr3wKuuKjt(*jF%V2K8n8G&V1V3`wGVu7V0
zurvjh=D@Nzuq^Wjz_wbsnki>3F72vtSFYG>KkAw${iR2<L62cH%49fuWtaGwm0cWT
z!YUlUpzsQ?uYPN{2K0GiED49#1&Cedq^uuftPBcbT^Y0(ZX)qDh$qIz&5Cl(bnEP&
zu(~B4e2rqaCyhl`)V3gtw52`p=?pBJTz&gEX4iK=<g)8FO#UVFeJv$B%AlfbMKjAf
ztIWydi5ge<D<SMmv|E&tUvN9%xoQ?4H&~+x&gr%TD1cDRQbxjd&J?qPfpTES5}ZV}
zV~h&0v%gr_8vMNbW7<;d<;=?zGux|kT<*VZNEXIAJ*e+Xd@F6`%s(#WFC`AYX6qEw
zR1E*d8-=iKWRV5fDQ_2NvQ|ueZ&w^59(ROvll=>Da)2?r!_h52XCIQ=>)pk4girKL
zGaLz8-w3OFsNt^OQO>iXlu<YiD_J2&rupA$|Emczh0pN6v-oNb+csu%&cU4suwxEd
zYF%fjS&jM&@;<7g?pk15gmsZBY&f6ZGo?Az)N0Y5QARN{*GzU%DIJpd8Uu0GfW6x#
zGKJ3`AD_&MvcpsB$3o;;LnK^SOrNWXV4JFUYp|O2Vt(Cm9jUa4T+Ja0=RuHLPIiYd
z0!VIGscs?`^z3D16_AN|Wg?!9u(B*KYbdFGj^Nqke!=?Y)V`b+8EEvG=EwrG6PF_|
zIZ*o#2X(-K+J7`q2Ln(CU4j>mmf*D@!E5zWu8*)tD})ALVStehE{m~V&nnI9tTpID
zWtMwifW#WujU0lMnu<&vbVB$b+rQDBSQ%AE6bP%sNy8GXBh4sRXygm(0bn>P9XO0i
zqh%&}S&IuG1*s>y><%6+yP#7Vo#r4NIt)_v6(SRSwJryi_D@n0D-nV>C>ay6CW!58
z;nbjoyPBjhzWX55%n)&uHA*Qcz@a;L0A0V1Pr0w7U}g0(h`29^D2rvaFB5TJR>b{5
zL|Gh8nTY$dB5K2eWo<hbRJenghzE(tq`XL>8Xlcx<{UG*^G?fDNPktbwv1HkJW*bu
ze<sqxbcHivY$mWYO-Kc|P;eGA@Pa#sP&Wx<t=M8MMuaOYrlcCS{eTmY%6=JrkpkSb
zjY6IO3dZO1Z^8$98AwfFVX<_qau~p7g$E#Q!`|pRKj<<SWYKlkm%v>qzchP~Ug<||
zbM42XUgM+Mj-LbAHdQQ)*l<!2;=AkBb9zPONp>HIQ{^P4ZbTyM(aPed{&>>cPtSe+
z;=x_s4A09S^t{_V&npaL;J#$-l3Fj{^FsXpb^KpXyP>mm<&7=3);&=9^gqn%UVB5q
zc{6_V_O7W*m;d`2XSYRX|I_ZWZ(qFq=Jj>k&fovh{F_es`K|Z=L({+Ac>h1m{_Zco
zQvdC*eWUpP+Rz=379W56qqoj}WaGu3=wEZs&c5XjeXjSigU3A+`TMW`?Uz53Tyf*y
z|J!eO4*k_9Ce~HH?;AC<tIqkr<Nb@n-vJE}49O67`5e~(t5pMB@vr}<@%Hm+JSDQ)
zx9KUP(|F$EG;am}PvZf<)9jHqXVa4l^SpC&{$JqbF9v4#t+(@UAe;QR-+udj`N}n)
z*(z2^WfaLMmcf#tmtQ8ML`JEMav2peCd#OkQ6*!Qj72gcGA7BGEMtm{sWOg}F-^wt
zGER_jqKs-8H8Q5lI7voS#>q0?BjXeqRF{`OQ^u(>PLnZ9#%v63PihJ%HP@&#7y7h8
zA5rJMMw$0QlX0nX@5h1irT2!t{0E^#eiy_k_V#p~{-glU@$&T^(A0Ci%-dV;rI~AZ
zy=q#v_bUIL1qI~9U(It8o4jjy2JVWrRHfcx(2FJW>_ynI?~Vw?m4@&Z;svh;%I|RB
z>>RIrcR^pwQ(Ai6D*@iM-n$H3uW{&E@DCgC%idkgGqrQ+9|Bk}CT7<_=H-q*s1lD1
zR&-wXZekp8#~(!BvG@i+h3&ga-weaOtMtu)eb+J0aPhl}aR#g~{@}aG5o>t?Mo(hu
ztsmD+TK;Rns|4>VaaJHV`gl*tVgIfYCl|CZ-|&YJ=WuB~-cwoX6t4M0vKiYx$IGPc
zzd8ezqVwI%B%4vkE77L^lbNY>;ghBQaHRy7c41EN3TM#Uw|T`gXi$1Wt7Hc4%Dt0b
zHp9CXHg(U(R%AQ+yy?;+f+sG;#Jzw~T=xuEWG#w<ULotSSGm+q+mEfNOYL!|+-HrQ
zg3%4Q?8lDBqo;~k9N<5ByywYF+#`8iw7%(jTDc7rGq1jNqMSITrq8PsK{k_Rhmoeu
zdygLb3ZAp;_HrMaa?eD~8V}@n5yt@U^D4T?r<<R8>n=It_TZIsM)mrYo^am+-W^m+
z0_89}{<smcAyhi}S;ccXWIQTzix+X>tH?@EzUetLyp(6QSFu8cuoXz9*V|m3a8QY;
z+K)BiP1P%BdVgC?vI$T324R_dq)qwsdll{lw>ZG`G;74Y#_Ap{R380;qLS+kunv2i
zHNo?+174XPrBgjBR;A+f86$Rr$#q^u=E=P_0`e7gs@|LJu&PaD)ptpT*-P}yrk=v<
z^zuJ~_B=0_cb6H)aTHu>5#2xvUaE~#!~w5DkHu#6tYSSS*9Ww+bBQjXLS?~`U@o`P
zigp8@dwVwf;l)DdRlJT+HL;RQt+Y~bD=sp;+;e>0Ud4r3wC?jtP648$M&&8gMfO|T
z)$#})gq&-*wi4S_)6`R-qlm2JeZZ8)oABnHf};b@5H0oeo_mn4pYh#z(Tm>)2X|5S
zOWFa{5199Q^ZEZF!0F*b4Hxuy-*tqEg+2^K@!dQh)%m2*({>*sj&SR<w0cgr+Y2$B
z6v_+bhYCW2`GeF~s4&FcO`!=PE`ke{g~~$}p^2f&5Ra{d-VS|a@N;TA;>I4p=si5W
zik|(j=>I=nP91#xXn4N!Q>od{qyG1KNH<ybhm|_J9@Pdnns{;|WF^e<-|Jc7<kbIq
zYW#P6IyYJJ?$IT?o&0yC1#_$g;X?kxd4*MT_{I1&@N3e=wOf~ATg*@275rA|Cc3sl
zW*AwB*F>WZ3mgb6hqO?_<wy}8gW~we-oSrfU^y6A_AC5WGS6e{pnxn6LSAsdixpu{
zxG<0J))jKxM%IHbS`K#TMt+B6ye{J<83$zSlktKK%j8*Zg6t=0@}LY7M3OqUl(bY&
z%J!O%!Ycn8^S@30S9iz>#by3?MS$vSwk~9^a4Eaj0{_<o%S(afNk8eFz~fmgwpqD7
zAX)M?;lhQ0RmmMPc0mW-eu(ejO#=Ur!e`3Jhi+l{?=S?o5@Q$&ahy}eE)3^<%tF}^
zodQ;k9%TTG8H3QGYVb{v(wc#R`i1hGCL>xZ)a*)?<TSgzxO%F5G8dt`bN8IZC-y9G
z;oL=GxUd9UDZh$Ixv}lqRFF#Uk#VIJTvuAk)h1&YgZqG#O4qpuSPxS}k>vIAPwD<a
zyVQ(L<dGzN!_`WPpL=zfZWnAf?EQ<vKrq!PD9_rIdDdM-3Q@j;k17~OiKJE<Ik?$N
zj%u=KSEORAA2uVV%F?z<A6E{Y5OuLn9cJNkF4j5Dks<9JT%MyeR$X&sYnCxt#<UFG
zU0uJ4Z}Nb!(w$7{+_?;jlIsd4!UykF(u)jrP7uhk%1-5GUyopva~C2A<ZT9$dkf?^
z9%Jwk88^##0^s6x4g;H|JgAM-wlw`YYb*@xFehnJv~^ZWf?$%0ZjS_UM!*n1<w+8@
z@$ywI8l$G}wfmccv)%%<TM6CqZi}naea>@GwXj=p?hwc)WIUzV3K%L@z#B5&!Wat4
zCJq{!En_amC~Glf9`}(DV5O=WiprQFW0qhZ@6%v#r&5+-q+XyMaLhzeR98dn#<6U<
zc;3DhX*8B8&Wf~im13K5O@=W?+ZAbj#v^$f(FVVyxL3)D%Sg!YzBHu0^4lz9ivk`5
z#8ywvJ7s{XHsNBsD@ZYVgQ7HHj9yQGoa{&rLbo=gZIue3_>rx%!iD+zVpNSHNUBD+
z%Ge=en+))*8of!z-7;>Kp~@H?RI+8*k_8xpuRs@Ds1+$W3eCPLM<{*eVKPTdp;)Sf
zWTPJ<lFCyg*4H`+QUu>>$R-G4Q=o8i%kt1(tny3<yOVQVx8%5P&w^2&Wp{a_@rMgb
z1=`*^5PdlTC)R;wg!s@jjNw(ro?4^cWmicfytwW{(K2rv<vP{ihE*5I%Yi-&9V5g0
z(okxwJ-(>mR~7PR5W;1-xJR1O?v8L^<<d;!BCybPc4_Y_OR<<-M#Ow0m!(5ihW$hP
zc6$mZCQ>WYVa-%TWaJv9y2)<?BSLcIwoH_Y;KKD(Gr}w5Ac<nS9?3Rj$oW7z;Wm|S
z<PqPEKrjsVRylehKcJhiIY_kBK6uj|%-tvFn=<yN{q`f9*QH$t2pr8%Bce0UWMYR7
ze4%hK9XDf>reZm8&{#HGiBH>H{_=CV+EO;5?qrFmG&DyB?S!ReF{;*;<!F%6l)XUf
zgiK`gB84>jaYc-w%Vb<BqfJITM)DfvB6&92sjfFQGaw*mvIrmDZG5P$XMQ#MC4~pa
zdMJ<4`-Ajsq$tn|FV6)s#`Q`<o)%c;Q5y2{%!<7%q?KU#X9eU$B2^|54w7=8Kpq#q
z=y(F>IEssPK2J_oFl#uvL(8-SJgGL@SD$Jt(9y30a_}+7A?G;6Dl4To{jn4j0=qlo
z!S&eg@K~&>iCf5Y>Z2-F!10er7u$j=bopvUp^PFK6I`P0vX_EVMHxCQQ<eo4<rT8Y
zt6UaNkK(0h1u|Fy)`5~cIfA55o^$SW&Yk8&8pz2fPl+q?c*+VB1yBGugG|?4wMtQ{
z(MNP0EKY3ZgKBI(Y+&+jRjaUZs8J{C%$^Rv7kHec^SGjnD8S`Az<X<KK&&X{&@Ug}
z9<euGu?U{?RIbkE@=!8|iEQ|RHi&{Yra^{?9M-Zz-B-0r2;i<-rPDR^OF}`P3R2h?
zI_5izoxh<sa%mq7(qeIGA9iUU_S5opX=Mr9J0pWnfWp1@;19QB49H=}z6LmVNOm{y
z^qx+tF`95pqVr=Y`OdM=9Q&PPzc~&%hyS^iyM-yjGnME!mE6<IDHX;Y5lU*!j8TQD
zJDk#2naK*Kty)|l-ozxM7aX`3>}hl<%XDsbs_hq8CTh<fp4J!Pwp&kd>lpJZyt9%H
zwDU4!d^r6##^?4iAj`+NIi~zdfCtR^iV);k&Zw6steXt$CRrQI+92x+v#uy&M;#p|
z*LBdWWU<2S!DLLOBDxRsa3Qx6(c*C{l@8}3k_vnn<$h2($MXzm*!SS-W9Ol>+AcY^
zjOTFLb2#I9OWN}mJCHcI%MpB!n1B%=H{Wx?2w&ww)nPUE;F>|cbiUQFQ2}QNZQmlt
zz#@aY7#LM>fYi64<8$m;4yqT2#o3*XW40j9Za)rGrt;ha%c4>8HKS4THFF&A9LJl3
zo9&@q@^y2VAY+tijwvo=3XZB|jU0UB7_>Be%<+<iFn}dwj@1?>k?f=+AN(Wz_%MQX
z01sO5lP=klO18?9t&%ln)|jlz%!&yx!=F!qM}^;8tY*!N@Q5M~Seng<nmpMa<bpPO
znuMmfqnNAd8I^LTHYTwX_^73x#yM5ow5%5^#VS=z?$h8tE9_HTS#h$9M>H49Oxt!<
zwZgHO%X?<^1Z%6v1D*oWRU%xDW^c|jEP)N^jR(mO#B}{4OHh$^s)$htE=QOb+)hk+
z!Bwh2XZ1Uk<ZG^4)xjiRb0<4(@Xl>8)>3_wmc@fJ&PUBXaOfh$SCA(b!n>%rxa%x3
z_zOh8Z&M61!OtZRTB~9NDZ>V*jLpcF@*#qP9Mt6y4`%%ZHl2WcrNYMqyJBiT^B|%}
zc30?=#TtV*nPc!KHA&4HKrYIUMYJXNhj^7xPxboUG;=p}*6wXrJ|q@=J~Wd&9nH1w
zoA*G8#rwUSAPk3P5T;)HA=fA$%HxI~H8?dZznems<N;ma(T2%gfO!Sfp}hyRk@^%t
zZ#`0OSt+T??HsP?J~`#>qU*wMpEATSm!myLD*edZN#RGXaMhqJ4Lk{D7qcnEC6tKH
z_{+s>1;jGhoe5CQ1=w@u5s@E&axy&#lQho`lRF4=ay^qyZij><yepudCC$P|0WJzh
zVErg%xlt4l<iK_GIl0U2l8ell(&V~Cy8r?wZv7PEI+vCM`B`b@a{e0NWnjt`K;g7|
z4jyv5G+fJ)l_uAvnsX4)sjzFKzEz!bIsn{GlLbJoLM|FKtrO9hZ~UK}DRx)WG5)8%
z%L0qN^I8ZGhl(5z>vj-W`A+xY)jaiqcfq!yQgMB;nV|`0pP&|k{CUhi!z<L}hF3TV
zf{01&vTXUfXm>bZI}|Oo&G~J!W-95W_(4aRdcf~#0w{Lggh={QZhaUl1>~jo^NB+d
ztusv1Z3jU?3)&$@Nw+Q4X9e$yiJ}}$RoF!Jhd7^L%1^=Uc(nOJaMZLQG*DFC-sO_e
z+~M9G40Ab3j0K`P1mp&ffXEX9OG#kixkRUMsQWgJqA;)&1r{f)sH}p(C*Q=k`V`Y7
zEFH})<14Mf_vL_bVBKkNSG!Wt|0CtsS5m^tYU}c!Qa1ukebgtKn1u`!Kw>E%p4@17
zx}CtZ0$5Qg<p4YmUx{$a9q*TT5J}3Qd*c)BZ#qPL#q<we)|x2+l6FX-@t&fHpPVV?
z#U1f<OFn0H>1TZ}sj0#YEI2w{sY}+l%>)Z-GX6SFEvU}npahqw=snknE(EgPFL-J|
zH?VTihUmcU_Z*}sr6|ruS4wocJ332U927hQ?Ob^4QC1$MwyO^0iI;zEuR`>aG+UGd
zFJ0gpu5_`{83mB}cI7}M-$SU1dI~P{T+S4JGY?^@&ba${3y(H3+QD8UPdwy3Aof!P
zt8ra3!g&)oma8Vkv3e)9$V12C=~N18U#`uidXhj|4^4GpOvDpqG|$Y)**zF)A=?>k
zsIS_t#Cpwi@I_H6A1q|6wEIbhZwg!tP3$@mMPbTZ^~Ob~8GDKDEb;weVqkL<D$<65
zlL@U#oq9K$x>b71P?%X|8a!asb#hgZ;S1NxlHAVhUWu*|p${6?_?N=S3hug8fb!}?
z0qKTi3+EG{D-*DD>knuBle<vR<N_5OSa0#6B<ED}<flb|%U@Ka3cUeZ)s%7OE8KV9
zf;!Ex*G0XBhw2Po3F&sy+-|4B<WVd)ybk$i0<kKl1XFnG3|iqfEs)z$lDAjIAh_?#
zs9ahKZ}GAN+am6)ilu=t-WL$ESFtJ~m1riGxYK_%#J%Ndj$pGq4xNE>JLl1+LYc=d
zxl1lT%=w?>eZ0F{LVd{orOIj=mRTR<37540!;D9%7)AMd5aH462#;qHJ}Lho($jJT
zIX%M_#wfQ2=D9TN9}vO#k~4>qRLGU-arb$y1Th@0Db+)A6UEFrN8-N@r-$(1X4h23
zT8y8=l@$<AHWuo5lt6>}<hoK?1|K^?Rmw&E+}p;T%WY^%x(k8vpgmNFSV<iZxy`3W
zV=CMlG9<T|CTkC2q)x72EI_vQxLhQ4awT$zH_K9t65KmyWfqQ_P2(T%BTF!V(ac<D
zm2<MbW1(Z5GfPnffXv^3R7j4-Dpw9RsM6h25y>{E^vm0q3$i*-HFMunR+@v7nMC0U
z5$=#`bn-_ja1)iK2R-(|1H{NBXGI{y!bm;2s*JLT1OA*3A_!XRf5wj{R>LSkYB*M4
zVynrc6$ZX=Hc@EJuQ4NmWT**G^@^#VnH9(cd0P_ZE2vXumgJ*0LOg8KJTBQ#{}DuH
z{f6t72VXhPqi=Qr7BTQY!6g_kC{P5HN+70sMLvie2pP!5IsOtXr~{H1l@&ZmXbv$?
zNo57Z2HQQzAzq0`%8SV|hVR2^Pyadka^|G;XqkBF4LybyNxsEkltXp6C!lmE-w1qQ
z$2T;C#WQB~4XGtuf&-Uxg$Vs3JqY(_Tkg8EDVN(;=5pIgV-mNv2t!eyT4pTP2ugKr
z7fsqyRr1yeE*xKkGuQiTki*SZh+US`m|U~z2H9u@T+Bq#b$OPW9B=r{r1PaY!gkEQ
zG3sZsfa=#wT=I2QzaCdoR61t-?aTH`i0PP$bR-vdF&Y-62uqb7{ZeWreHLMStMMSq
zm2`Kc;~VC2Q|n@JkSVWfxd_<Y1Y&g&eKL)X4hQKX3mnG%oA5rFqgx?kxZi~`Sfy3K
z$Oa0-K-*+}m4U7V36nxWUYQ6pErRa9N;SH_tjI3VFs7tT3HeP$gG$$E@{plH^Whn1
z`UE-_x(Ah3{Sj&Dhf;AWf_5ScqOHKUL2z#JF;C=yACm_(A>rW7#SmVNatf+Swn`&0
z#U!8?%G2C#xPtac7Ia9eAnLLUi_W5|3M5Ndb1{*?e7tH$mBy)XU{^Dn1&cCpU^pCR
zu8P>umU)>-vq@B&#i2IfELDoJ%t|+bsC7<ijUdt7k~RoBP^$)?G&jN2DDfKn7~&oc
zU`?q@Ev}UO()0(*G<c;JOO$HKW-)w+C^b7lT_*Do)!-&nK$2cgpU6NR4}_bXLsMh<
z8-aH;OQoCdAUYLlct-s%9f7%<{BO+v&hfu9{4YHe`Y^@N!=bEbVS%tTAkb!boh}pY
zXfLCMNb*g}ACzY$o)d*M&umyz+gnMup@z-+Iq~S?6^4yDL$RzD(bb|ciN}hV<9aAS
zHL8O_yG-deNvcQ>XYzy)7J=b|D)Oj?NQeV$HoP-4xnBb)<kx;jP_k8C)I~gO-HKUh
zW(4!F1NkG+Hp@&^i#^&=2ARPMhKPd)Nbiqz4x~K~WjqgAb})vMonjG5O3OU?hLCk$
z`z!Uh^c%iIQFs|i9;^Z2u(@nnwdqQ{K}13YD#&KCGeLM~hQ4JPYo&=!@zMlgtc4r|
zlvsyaGz)LY3<o_k4+mN-ba8hxU9!QE;!Y#QVpG&}HdOE)vFdWCj9@bbQbPg{p%33W
z8^uyJuiy<GD!UL}1nE{Nu`M3s!wW?y-31Uk#J#uFDwlHE=XQCO%P5plA|qc$nT#SC
z6J(T{dO^>{Iz*-(_N6Q5V8-)c+H-%#bH9~vvei&(vN@(XhkY2i$S?V(`6b^p#{uU!
zAQ~&@#YSV_=DY@9$Yk+?>&CQi#Hg$mU=RkM-~(?ro69~p><O=mN^HnpxzYPv^m|=>
zsin}&*i&U9rx|?~26wDq5q0nWR5N*QZ6Z%I$1u2+>>{;J4!`0f*`l2q*r!CfPgs#8
zY1AZn?x9&yTD=|*kvJq_%6vV^qE~n<#);7hx`-B$0IpdHE2}FjEKKv!<Afa-%vI-x
zRg_CAYF68Ft&DQnaRw>F0&0Lof&BTss9Xmk6j(_6NGa%~XsNs^v;8W=phW_{%znU=
zEK8u$&~+`YT1@jY7^#M8A;Iug1oA4w=HdceT+vE@i41#nHO-gS=!cM3h4b(e%7aQ#
zCW=hS@~te^2r4S%g7DB*jUpX5L&+w&;<<`-x{pA)r;kuvrbM~fU7}xa1<~I$D-RLD
zWnex+CtLxbj34crA`+E~RjILp2jjs2Mul2xb=n!Bj{MZcq+vrrc(S7!E(EAH3E6u@
z&eJBNRMbh5_*z9wH4(w_NyZQFw4XhT0F;a$Bn&$jrdUuoV^x`e0nUQkY)KSMIbd;C
zBVbe*p;Be^GhgRhb5U}{QPQbVU5zPla`9)ZLGzU2CNCtJKtXWpfn`%5TO0XvwnOZA
zgzgrt9Y{VfUR5=(<c;=v5x@m6?Ykx0H%OlJeR)JEQ&75~F^N-;I*!Q&nW7SBq~k^)
zq7V~0W0J$JJf;-*qRkogh*_!CJ0V@{xa0{UnfOmFbAoAw`>+-*1(}ZH<|1_^ng!Aq
z>{k|RK4h=t3_k7D2`fVA;z$uF+9sPNX^UKAukF#%F!-hqV9>0ADT=SYyz)b(vg60J
ztu`BSzTt)+c+ukCFcfrre;dlH$uex#qwCp16KFhG<57DSiD}}r98Nuu=^izNrt31L
z47=vhY#-b<f9}FL((!fEeGs3!2j~#EZ2%uWMuBa#ClqV2vNp!{0LZdU80o{VJXprK
zB6YtdOWiNwYq5O`@25B)A23e?n5N<oAs=a@>S1^*hA2{eAJEuFkJH$iE5aVt7}VJA
zr#$>mgx6_6cZ79(%h2>vGDi4-gJG=UJd)S>(N9Rdql?B&2k>MUc6&>P$4LeclG4UL
z4S{7&IjrI1XBa8223StBe!yY$fOUir9ZkU}C&DdF{9W0^-(?-vu5wXwS5Q+BR8EjJ
zYRO{y=5F>PVxIz9>6#2IyDMqSX$O_D&4fd0x0uN4#nJ`<cUz^qJ&z7Ix9QQmP3PWi
znC*5Ix!cky^HM5<Y_xAm%M4_sC^+aFOqeJ%;44PPv^Qwv`XX*{HlTN?A<}a9`067z
z>3qx~Qv%*Oq3SIQnu@7k_;3e_%k%(uYvx2Ea&<ad=HhQn68k`0N=35_z&-OWjAqa?
zuCmH7&#we|ANG`Pbrs<*G>BAWDVTl_a8Hlyf)%AFnB!n4Or;Hubz90l8V`PxJh!Ur
zJlJuDAJk#Drz813K5F^a@|4C}o-D+*mbcHfynWPx@8RUoKDsn?C~J!3tm>igj^@_x
z1XWJEO+?Ep?rjyiYtP97u7@QGF>3V9puT@B>hf@?JEZs1qIlc@!AL<BXh{390*d6M
zc*7-p%RWPH9x`P+U?Qnkoo9jjOyEOHM&m^Hbay9}#L{n~IK*`wvxHbs<t!+f^yJES
z>FO$}ycCN++L<59keSyVZT}FY5|a)-;V39%nQw*xT~Y0k7?ZqQtwQfN<YmZnfG9aN
zD~~Ml49#{WnM?I4V{@fY-6b1rCn7%Nq<IN_oJLGUEo#%0d}a_-qpJ_znBjn9h68D4
zIN+G!K(%8AK>;2zkXiQB@s$E*`SQXWHO*21J`R(F$YVk(*wTo@O=t^;%!IaKRB2GM
zo{ph!)`T8wNoxDX{8i}G=9Tuz5KVN=A(~HU{EUm_w9@+iB89MkRLY+Zw0b=Q?~sG%
zXnV*&$V*Kt^@M!+SUZcFV<>7NPq~n%EM$fYnPHAu&N0iP*$4&lrH5EMC74yTC$if(
z+?87>^KhJ?nV6w6pox{mzv5*c(x5A{EqlUvNSZU%ssffe1webjwX&qEoT(_}BHg4h
z5vwlvNZ(M$$+M_g4#iD}aEEq~usmbcQ$A#6vVen#&Q;_-ei#puC<<4-2k8~t$3ZE4
zt5W$^CGo9FW{3+j-%l9(*0LZt3q5WI4}$ev9C-_2K8nmo;d~TU<gs4K*K)WMOS-sn
znD5SPUpeq)-8~5A9`#HjIe?5QV)KFC&Rqb$xJBdw=fe#>flrpz7b2{H1%cE_wu7~-
zOb9D%fur0(Ep(qE_YoQVTn%j@#ClrbSCX-o1^8xxEX)KKWvml2)>4;?pLLmYN#P<-
zZD-|U%Q!s9$V9iC5H84QXLdn$2EKP@`?B~b9+eC#fY)L3m}$W52V+_U^~`Myv*w{j
z9<&peOtOr4A<5h~HMeQq7LnM|Nwee2e(2JSw6Ab;UDgTk_OQ*}IS27jkWI>xYd)xi
zfMEGi`Gi>0*+Y}GZjwa=lm2=Z$oBs6Hi+w%IflW!KWnyhaV#qG>`FCLuiOx(IoYbA
zvlug18{L?URYR{vV<eWrCo;6jxF{qT(A29DF|zTYVO3AlelPV0P$Q$zP=pDlPNJP5
z_2{qEl}Tg5gFzvEi1fChWsK(0e4M?QX?`Ft<JDIYjJ?O?p|2#mKf5}#OjCe2NFoib
zLN8_t!35umNjZ2JHe4O0C)W(OAP<Ko=+P^t?Zqt31P&7|61+nz7=43=Jgi0noL5na
zt{|WwU6H6vnvKmL@+r9}Msi9*QG_uU#4{d4RWFhq(QExPU9TQfxwi<uh0%8C%L^p~
zP=w~c!*=)%trrvy#wbvWDdGxNariR^*xDQ!y4)uB7#j~=4m6uu!m>k<#ha-%H`Bt{
zPs9cEm-s|@d4PiGqgPgufwHI{H=D(H$~oEHVYIg{h|@R*WXQH2b1cWX687Oui*z;b
zwf44`J=2!hW~S|R2bW-rd<P$P$sV>S11@C195*`0jpj%?N75WN_Z%6r*;4=xKImXP
zXn}e{iL&`0?PsDm)vm4ZPQmTiXp^!Lq@tqX+lbKo!c6-(*X;p^LY0i4NJHaMI%Y{!
zNs*yFz!J!8kG*)@KF#0bsTk&=fd_*awp-XHQA-MVr3WYjksTQrK9Uacr-*9k4(@RC
z{RTyu-|o<NXzWG2XoTatG<F%{O~4@@svf$XS;KS?9T~MJm5O1XzmwU#;52()1)}XV
zLT;_pAkDqa&7c+?PbLkUP+nK4v`9mD*+_2av#dX{$ysCmyJWS%(Hr!s+S<ebt)_rS
z8f<&YRyJmPq!)u$@O65fJ^GNbS}6BC6PMALW==`%ph+76oP)+0Ez4@_tbC-VxEeG$
zd1VaYCXsn=-n2j+R+4%s8Ruplb_L=YS-uMq?5mMl4Dp}oV1Yuj{&xHATh$t8>MC4T
zTlKnhG14o=W31v`Ak<j#lP|L7FB0)$%j@<@ifOeF<)Yd873(-%=9{W=pP8&FxGuof
z8+0qGB)`+V-P#vp<z|b_+vOmVYUU3M^MY6qG)$CbRAC#|A^@0U&`6j!QSapgmd@oq
zL@G~ba*#7eQo#8{lI-1}G|v8<)`AQZk{?jk2uNO6gs6cE?v(Yjv_|Y?V6bZ>f-tn*
zBqDqyd5sgxqn*qkYl{VI!Fcj#D2{e!@TWp>__ct;bXs?bqZQ*|-Yj0`o5h`&v%HG@
zSxjGHaIOX$2Wn=q<LrQ$hf0bi$CL2?{pPXP%zb9=H}fDVs}`H}1(}>6n8gVKY)vv6
zWUMHfWort>7Htm^EUUp6akF>Y%;U{`-ONK~PQla`cTD!S%X~?|=9u+atn6b~`h({2
zq{3Foh{;%Hp07<HW8>EL64_J|_G+7_?bQ|lwmHj#T@?l{-v!$iN_p_CuOjWhWH)2}
zELNdpO-?H{FjukphO2tbt}SF~Td0s5jPXzkD{gY2u-IcA^2LH;u-;~-h|S8NOEk!8
zlCx=hiP_v90s*%@1b&V>v`isuATygn43H5a)hNtT!ve}0B4$#R!~`j^IWa_XjVuJ&
zl%fM^+o4RM%QDu=Vr6JcaF(;y)(qa_bi#dU+rhNW<vH16PBSdtH1mMKDdSA?7c2)m
zkZd_<VMx1K0$7~%b!o~oSZxakulAVsW>S+fn#sf=cC}%H$z)wtid~9^yi$-qM`p47
zIWpO$U!`_iJZIifI*TUkX0NIR8jW$58gjM!7(=9fVJTj8UKK8HM&J%Z8+a=zBUAy1
zn*AxW&oFbA`5YgVd~m1gmC>ZoeZhb@y)R>3W$D%{#*l;5kqJ~wrDrm8v&_MV%|2k}
zjb<jzG<F%<ZuVO+>3_sr1+vnEkd)_fWcSQ2JUO!}Jlzpig(bg067I3aGmvWXu+|_C
zHo}CwVmk{sUFQbZg{Qv1H@<02PxtccTN9i5J9@g;wfFX1*B2t59VPUV?IADOvL<m|
ze@}N`&xZba=lAt=&s*Etp6H6tiC(m&rL&{;g2cdCu~=P0O#d31?6b(ZVzC9WI`i<u
z8|xbD8*1xo71R7><F}!4L2cWHx`hi{7qrG&8Wt^VTTr(sR$tfBP#bG)Yu!-Sv>?$~
zzpypd*s!p!VZ*`=8{)Cr1r7BLjT;u!wbUmTG}OiF7PT#mH^o|8>l<qq)iy0^Y^<+q
zYHf(OwKm1UtEsL&v0+iHCDyv3z9|u3*wzwjTUfWKHNK&)WkG#?Ykf<+B@t_CiGf}1
zqNdi`SX1qyMUAbsi-6qHx?mwt>lZdOB;qY(M9wWu@dXR(8k-WWwF}#7YwKDQEp06=
zEp_p_rrL(4g>9{gM0^8ro9g57MfI@_^>wwe`VB3K+P1nywY3d(jV-N>z=L$H3q3En
z5lY9$QhHsYufOmA5&B&vbO(fP9ZTqC1Kshij@EaR)U7RTO>K?!;+D1q^<TT8wr*k5
zh6VKt60Hpj>RLA}YOQN*ShT1Xc1SE(kZ5VCZG?fF8W+?z#T#oEHZ?7TFI$@6#nzVA
z4Y9Vy*2WDR7Pcgs8siJ#)kTSg3mV&E@z#Z{ZSe1crnWd-npj9<XsnMVn(FFcxZ2v-
zq7Ai;4e^%RhWMg}+SZ0eq^NCdY;CJ;YuM1*5|3?&x6tBfPAv@y8d~F``j%L2V{5zx
z+rqYZeQoW+x(#sdf`x6dMTxeyctfnAr3vPTbL%(67SzQP3v1hAEp>^8hWe(Kg$=E(
zwY6=nT$?;Q<jtUg)i*Z9<8$k47q-l8SX9?Iw<+EhpW6_xTa;MTP#>>fQ15vQLf-N7
z)+}GQwm;t87VmAFb7`Wt4|#OfwRQ8DOjuVj`P|-kSK_*!-VZP9=-bp8A6SFan;7y+
zGBs((n2Rn+bSC0`3D27q@=jUWvuU8WV`F=N^jlwuo==j#sEa@Mf{yNuQDjq-=PeC+
zXPaCDfoMlxG#=g5vuR6byf@lT(z%-wy&HOZyW-ugiRk%jFI*Gd5TFvpKPt36uPWq~
z)z;68&8wYP*GT1gUPH*6?|jaPCl+mhIEmVNWYD787C57}2@2Q7Ya3#V7Hw#WBiZU|
zH+Wt-0y;p&^G*tRCj>6bCmPTv3RyhwtdLiJNn%q^Uq^pW@4))r&ePlb`#1HycmDj1
z9sTWFTIRL(bj`O?ueo4;hE68L?YwwFqI>T8wRV6Uc|SkoojxX(MZF{uZ%g!^(Y*G<
z9h;W)4Rp7j-FEfWwVro&$XoPI@m)HZOS;>Z^z|ir`_swmJa0+JYd%`?CB41zftB6;
zJ?9McC(MzC(CB%KL*AK3h0xu$y64)&x}K#y-TjH~3<kBJa9+qe=bZrLq&&B`r;EPw
zFgOjK*BJ6*<H1>%xV}H2-m)H}L_j~7p6Y8lTB?iUy?u#`diwe=jdyNIqzg};?+bbF
zJz8qzD74S*=<euiPf!Vd?LoYSA+PRO(l3km$J6QH>qQ~2;b`d$>s*Zec6PLs!gy_e
zFOAb`eanUoiQaVXh?M0aZ}HJ!T-4jsn&|6W8t?7xh;K|>lIY*k+nokd3+Fb6yoE=F
zBFeAt?r804%g|j+b1Vc<F_yLiA(Jgtc4o+1a7@`=*z%!7YZj|gUB`^_(w?ra96n!g
zY@D_{-v9pgj{Zd7rg&>sfvNFhM%z_frpVO!dqZC1(Tcn}-rw4u%PqB3^@Snt{9{l0
zek9(y-grkRtg|+;Dc&1LCZ<uTLs}k7F$@)?)!Iac`gJhOkt9ua12?8eI`sATZfVWQ
zoa#H0tUL_Bnk`)|iQYBwH5uHgzzag&xksag<?oUsFgp4>u1&1$-ayOmAIQL>I{%Zf
z)+9E@$A?Ar9t%yg@XFvp{T{pc>+PWr&MHMcHL@(^z3*rgQ7d!=8Aq7Z)0B@%nm9SI
zc}q`!q7Bh91{LJ^u{2GGN@u5pLYfNo$b@5QAS+OcIumQQbarNv9-~N5zK2QwdN}G>
zyARi<b6aOUTy(7I9sS3ohjWf4eb8l|)6;`aml5F&PWL%#=b28A@{p0l4OINfkhknu
zxJP`WG4iHF+q!{Gi7|4yfyzG?$q?W{SDIMY(Up;|4b<YfA#cgiP#Fv0!VMexvLsZ)
zG1f;KOro`;E1qGd23q5>2&i=W9tQrIQZ&*WkEJmO$suZeU7C{{A>w}~^D&)FBP3+>
zP<td2zQ30~4dr!N&(hAGz6{bw5ne$u`>yP6%jr>JpmRdrSx3v#(i-Pe;zVzE7R766
z`A5=!)g_F{zCW=s(VN?b>L8m2E=NOR8T<<c0>PzWQ=0U3)cYR_L$df-O5)?e0ESV3
zwE8p_l7h!V&TI%{8lnW!k#zZNa%O`VucxkuAn(+mgKF{NtcNe?V6*gU$(CcGMus&Q
zQRE1AHHUZVP{yE;%M^{^|9dCaIQ;icypS6By%R5@Kl=R>(~^&ceX>d3pF?t{NZz?W
z8bkBLG;Foz{{-7y62C6D+G?eq9%(!lWWHoeKV#UgO`Qow!ofI>#&;yy8KetjkGWm*
z3sNiFBgxJnxqa95d^nNme(OngBql#R*;<C2(n8S7(`ZX*9Eo5#5V9JnI?oIM+S!rD
z2LXWQoRTls6of{<8?U~4$#wCL{y-w$P+NO-?CdtrTOIONj3-Tx5uWQL1g%!k&JTIZ
z{{U#9D^PU;by3K>;12+G33K&~0=hQ}V7&m2r+cmMXXw=5k?5Ou4zp(q8kTo!;7iBp
zENRNuA|(<b@0zg)WzwG?zc#+MwYOtazx?{nXf^^a?`w^tMP&kAKdxchvP5S`mwM@p
zj;373)9{a$OOAT2X4xC^y8g4ME|X9PpU#1IK)Pp3|9E&K$+(BHVA=9>)}QBje-!ez
z*p#7LF^OIq@9bz}G}Y4`ZIAawTM~)xXcx0nZPEUoXh(lvwCB3+DC6%BGi@<1x;BxB
z1`~SMU3cBQ?#wcbCiXtWQcRz%#MnG!S5I#u+Lq{N2;4W%O+St;_^OWX&GVLD&or3k
zR;d{iLwz=#S9=XjYP@-@!xGUZuVpx9sT!23KIF|CH>>llqxTtLT~C^akRMFyp7D-p
zR`oD#m=;vy&$<|D=V!@?akCmv!i|%1Y=+}$wCQ9}xm%4xg3W}ZX4%r{Qoiw;*tn^|
zUel5}>5-1(4GRxTe_jF}>}yRhW!~Ly)u@qOZOEH5K53oSbVl>oq8^z-K?-tO-o2$O
zVY4c#c_|Cy4Wb;5K{D6$gPI>Bd66|Y#GL3oG6I2=KD=fj(vcZ2lPssK2!tau9^LX8
z%}aX|+5HSPsd@bI$!Qhucp#_xAk9$|LC_^EDD*K;WEIzd7IWl^^K)9V0U^0I(YZl2
zpg~(*$ea6)xSf+=i6RjsX{0zuCUuZWcXv;}$tWinni{Tw)JHAQlCjAcmyM^AI|e~?
zT@>$cpJxI>J?Hi3c68b}ZTa<y)-C-B=d^N;-_wr+XZ4oO{ton`cZP?+S`qS=j!(86
zyw<X~?IS`rI2JEW1&^n9<|1}(N1~Gnm?M-85aa0y<A4at_D-OVj?Kke61@ZO1Y*3&
zh+J$8#M;()_d9_ZPdm&7b2JD?mMuXQ3Bs{Z<=T+9+36aRV1DP7=$+NVq|$6H_w}^&
zF}>7JhwB<Y>wm4Ti%axIuR}j!)lCZ$xyczwYo@s$iI~_pFemDRWA>LWf&RBM<ZT?w
zV`~#ENOX4mk+rUQs2OoMRvkcZyp?q#(4b>I!xyy7uq^9nm97x)9dL6{z1P&8(R`RF
zhKa_T$8_{g3&QgQ+p3Pf{&Aqt#{CPV!`u_i!m6IkWtWI<x=3Fr9S#x38YNq=6{eW}
zRns~4P<9IJeu^;AveO&1O1CA25dH~ZMaV0(+p2NlyqLFw*R@kk{mj2*w5GPirrty=
zv~P=cbVvK!6VY?DG~2g{sjUqi1VFcW(e-`!cZh(*3Tj5<hCc}6`Yj#GHGt=n&C3+p
z6UqE5wMD`-l{^*j)R4FJ!@R)D{+se8ot-_`1ze2Bgq7XxFbTy{v{jRj9_{#$w`aC$
zV=S}~A=>S8c!)P8<PG|1vNDwK1Q$QoH-M7m3+(*-abXh@HSQRTk4MN3URoXpA3_E}
zcKndCBjyueJBErAz-?uJqRUe`7yGzm;Xe2=vKuybp|jVxG|K#X^g75c`|qQeZ`qjW
zpV!m7alWs+_Z_A&oP*dJPfhokM=d6>f^pCivHEPJwv*j9tf>SnW(_59&@7#{4C6C^
z%aw3y5IIsPh?M5$bs_IUO?A78!oEaTOXtA3+WydgIHd98*=mI-nQ0<}_dxQ`UXoxj
zH-Xj@h`Q(|e>vrF&}z|##Yco)+o3Y0(=lvfBHY^l%EO|+S7EBtzIlg*r9qt)oi#gp
z*4fcsU;KLB;9r64-M(~Q4FMRKu1&;yTieg=jBo7A#dTc|1Gu)14FiTlmy1olPSBfl
zvIfnjk)E#T5k$W6AHzV_tJ@L=2qB-YCOQHii)dG(zr810_B4gOdKZ}P*mChxg=Yn&
zt9g(H_s8S(Yx9;eLEO2NDFDrz=E7o8biA%`UT#<Dy390~F@YJLxFqDQ*5;NqiT?UV
zC!{sKh2(D0OvC_iIy)I6Q!{RejGT{0H*8@sKM{pi8p1|<HbjFBGW5Cg@4bHJw?6ub
z2lkKbzjk2u?8|FjydiYU@4j-&7oYq5?jO&&^h5WZ{l`~-^8?<}?`$f#>!zBkcYb%q
z!9`E~=3_5?xaQ-R^gq3<>B(tbWvATptG{~clq+YmE$Z!M9hbkf;j5QyJaNW(pZNKu
zfBxI<*k`xDKJnLA{%rEMc3<$(ZNKP<kA{}q_m8D-oR<3Oie%BB(OkV(x2#Wjq16}B
z2)z&T>;KaduYYrig6toym@S!}r+41}k+6Tv3R8qi8Cm}>S-WiQ*Ds&^Z+Es7o%gk8
zH~wk!%zusx#{8@2ukCFeD`)2Sw0vm3zXNK1ccQ<~-%rJMp|MftZE9=r)~;Am*SNqN
zn*oIgpY{*m{CGv<oZ(0Q^vt}IZ_O0oOxdGZ7-m`LX2tWotC#llF6-=E%~;v%>T6{n
zl9<=l*%`!tdnRBi%W$f{m%5?7sZuTIB|Lf}uY%u1epUPey1WO<HG5}!r{br7qO*RA
zuV3H}zJdREc@W*Bcd=g(FK`Fn!2fOji3n}{4)8)oB^9`9gbwB$VVH}%@YTPQ_?^k`
z3Vzq~`*VH|@_UxwulTWPOyf~+4Zkja`Zpe)!a=mqzaxc<j@k5^=S88^eD6Kp2fVAi
zS>9}@Qvzknh;^km?9GbKjt1}qGbpF**}uSlp0}KrHr+p=D*8eOqn+wZ^`~DDKN}a-
zUA9{k3(BK!c9`<kKjk4<`Uds@_V4orjMYao-w*H`;CBbVukd@0-|PG$WW*p`Gs|AL
zGWGwdoI#!{>v-i2;{M<B4OyF<Mu(YOW^FBpugCFMycPT|+g0LyNDa*Y_n~|yX1q(i
zwcgd3%e_mmt@JMR*5F==c`kPS`+EM*f9JTN+`F$;xkys55k!If6G-u8_P=E~LO|6^
zyn1d|?+*vhek$G!OSO*BIFEt$5w6#V-AlI)&i(n{g#kj~q&JA&E3}70`kcjyQEvCY
zf%k%;f%j)%$8-bF%IZaP_v~mN{^#3Mv)w#2yM_|4^P)iR!;JIWMC?tT9zGV<QBRNU
zZp566uU^*9ypyaquoJ{MAM8Z0C}Apza=H#Ey}X+%KiQ(`yi~3Q9yg8nc<Bk>Zd$||
zKc&Lg7zFqBZty6s{6FZONV=f>%ZPROL(YnKc=<9V6(yCUUKsAhjPgqNJfjv9pVVmv
z{-wb0C5KFX)Oz(6cOLK9&m&zU|7$%~ANjY!n@n2OZt&)@ULenuYP>Sd%fhsc*y^4>
z<edT>J!`!Y3_FcVo1l+sU-i&W9Mx#9U)1{oB{|=xq3SX_zq#H8q}B_@Dy?{g89)A=
zPEOf*IZo8;)p`e8^(Ss||1PrfUFhQ>Xu>Y23%OM9<JaAI<*Y-hh5UER+u%37qnx4E
z;dLl1wu1TCG-GQiyN=EPtAw`*9!z)*{7+C@^-!SRd~0bXt^C^XZK5XiW^}Fn8pvmn
zrE9^xkdPRuYJnpj5#CFnj9$_1BE23MkJ8F>xg~=~EX`jk!~gsDdr|=PPI|^g5^VqP
z-~V+Km^GauGq)8>O`ldc;q;LeBflvR6@_=Drcc1DfN6-@iLvs+3D=CQ2qp6J!=VB%
zc4cA78HGH>o>~|V<?mV(TNOJl%V%P2LSD$*mH(OWpvO6|+_d@8pY55mWN6vf?)&r~
zl|;)wf7*q=`^YDEk393}1v~$4<=*elx$T@?sq$aNQhATZQsMh{<>_wT%J98cp8BEE
z#h?1q(t%%o@!Xl$38s%}r~rIAYfEDj3iH<I7fuSVUt2pRHc5_>NoC7AHg+s!T{F6}
zyS4Usu*AD`Ql%DN+X!kUNarbP&xp-dP~N2Xq*JWmm`IcZ4qJNdkjU~&qnGsb^hcL2
ziA_JQJhq^gLpbi=6~~oxoF?P^Ka5Q(^qy>TP(696&_t>;R1!`xzw~!cz1;Vc-#+`d
zFVCG@_ql(4Ir+XPpPsq$E1OUM_H(y2Z2U|~_5D|Od~WfV9{cK-|8(yicYk5Y<?sEU
z`)~Vx^AG!*?mqvi)iYDSIsdb3BTYa1)yl5T+yA8XEBAcv>Tln@_0WYa7k}f>;|G7(
z^gn)F_;T^~vd3mES^wMbU2*f2KiPbs@tdoAC!F<-_cmShf$M5dyyb7cGh_KDKYwqR
zH~EVEuYPUPPcJUHW66^DtVzU2lS?l8+`yGL|I6{`zyG;;Ke?)U?VLZk<LMI$r{3M(
zx3?jG(v^2@{-0Oeu>OZ1Y<qFxXzbGbm#ZF_JsA1Lz@nQz6Rx`9v<+Xq>8scO^1CIw
zo9-!T`_?x%Ty^`_NAH?Bee|jmE-H;io?7y^-K7^dJ#@kM!ti3~FI!{rSOryFGcgo;
zJ3k+`8i`Gmdy?D*u{=Jp8hKRYPtBkFrL(`?k$32u=Y^iV<J1$1!#{6rJLB82lN5YP
z{<PTC!SN&HawXLe@~TfL{PX?)wByswH*F{^e*eioJo&ssr*HhLZ%(Vbc5&tVAN}aB
z{^$o)iH|q_$6vg;{K3EY!K&H|9@>6j>70-M&Gmo(!ym65*!=yMPrfbm&+A7&Iiv00
zKf2+mf7!9{>D>)q>iE<tPyEI8?>S@B(jTusuQq=C&o6s?c>bE$zwfG@ddd_3>qm*H
z_22vE2`BD4>(F;U__5lLT~mDX+_J(eFZt?YZ<O5e%#^D?9r?x$3tF4LzU=Ogymaar
zC6E1iNBJw;ezd3Z{SQ5HZDjlUU#{rb_sHqt4@_M4Y_#@=uYYaRsaM^4&0jon!EqDj
zKk&WjOWU6;I_=Sayz)2ycJjmRksZxDzWEn#4qo}G)~64i_g6Dd+qi4tue{Wa>;CBr
zhhCXC=`XK4aMJm)RQ`4v^MK!wi|$`?%V(!g-d2C&BmeT%NkgY(b8hMX7Y(_#Db`T8
zu(qzDws8>+xh~royCPStQ1Ng8Vr8O^heglr*}@vV)+A{A^A(aeY1WvKC{vO;_0h+C
z9kYOIJ2q<1j)ch3n(d<{fBoWT8j5du;HsaTzIwri?{4c{SzKLQb?eT7pWXPkL-Q&_
z2R<~ZVfuF-TCsZN{F5HM`fK0)>%za^+jmX4|F-b+g^gdn`#azJ@y?I@YOvzvJMJvn
z^2f27FaBM@?#)+y@YaRhYrp@K=PH^%-gf=U%S+$0_ZxTReRJ~Qf)yw3Tz~ugYd)Wv
zIq%|+?fcaGzx>ZX`skVYx861Nls6wf>96;{yzu#br@mAfzv9F3A8q~RJzak@;a9(D
z_}Q+4cyaT(feU`LW8l}5S~~vyFTXs0<juZQza75!nP2xz+jQnBGY>UXZ)v~%>>n+e
zRsZqdRhFDmdG3$q-ScE&sQvTtio(Ih=x08$Jes(t{ujkFe|e+#^N;L2`IMjC@xIcx
zZ{F!Q+f?X%Q0mOAmifIaKk>zzKJ~k$J(pf|Nx19!H}8G%OIIAdWsYt7>K|%m=9k4v
z3XAE<3JQwy@?sHrnmj0u6(SE<*NHU*;h|9etk|is8G$<%8kwejJe<qxY3<vT)1AaF
z5Tujxm&MMBE#7tZuI7>V`cYeZJL_|z&JX$<bvNnWX8EP+YnCpVNBr1X0v63*z~Sn;
z!hEb=?DW{I0FQ8JYIXL0qU>&M{?d!qj%i^gl>X-2$L{{szm<HgcWuG{y!bCm`!}5S
z?~8ZcxBh33%-H?PwEeff{M(=Y`nc3RQ{wd(eEY-=-+JHYhrjyl%IOc@dthPpSDv5z
z`cr4lKlPFP(*8er^RcULo&L|O|JR(B*6Fe9=U(#gb&oCm>eQe7uca$XsxJIa{*32`
zYpy@xvHZ=CRZiM{#+1vi`LnOyf9T488BWZ5er9Fl(_Q~D^9xu1^3&gle*Zg5e*NL5
z&(_{J@aJ{w9{b`;rHelOXAdvhc1mj7U){CnnVat_x%Vrb6Mwtr->!W1hORw-Q#rr?
zwYNXfvZDVV_D%WXWxxIE%&(uZ{~y-<^78v1xT5gWd$)b3=%=4NtM#&_-+%eODTSwY
zo;%~hOTPY<m%sC1VeNA#T>aMGM;af#{-dwNQUwU5{6Hv`Y`*LMZ~fV?zjJHiyprbV
z+CLdXvj0*sfG$znR2!>nOzRSLND~eZ+X?$CV##f_lZ`R+3nrB=h}B2yn-)c53z~8z
zldt_0PYPcbudTF>F286Jr)Jx_db-<cPl=rj4<WcR2T#Wes@XxLymyLpRH^mQzx=Fb
zVAD_cue|BZ?r(g5>m6O+_`t`m{MJ1?=C1hd<8Kdr;LcBc^fM>?=%w2>e&7rF%TB%f
z2S2X);Ac+%Rejk*Pkp(5*+|}L|0DIg1DkqA=bd}zd%keu>%VEZ;r*{XSib+Z$G%gz
zZsKcSxU%=;f4%%Sv(Jb(9{>I)KUwqJJ1%`_{&g4a=xcoZ)$<;@<Ghb-{z<C#g=Eb=
zt9n29uNU@S^2N74xc&9BAOHB}n}7Y8&#syH;*S3)n|0M2?|Znm?sx0v?v8wO^WUF(
z&L@9<?vK89&znE(-jsjgc?}aUdFjEYCqKDh!^<bfn=kmp6?c5+)2IINkJr7je*UgA
zPk-u>KRLdC&;R(~J>OsU7iHg@vFK+fUG>ugB};$uY}Ms8|MEy({68M}*tWS&*+_+_
zqr9GwBbLUMTN#DpgR$jLURUy!pUwWG50rj%((Hln|Kx|KB7A81RrzycXY86CwD;n?
zT{W>2%Z^{acJBL^tX;8k&3WrCTr)R|Gji3OqCwMz50iDtJDrG1ZjU8D9$UU^>8>Rs
z?>nN9qPxrVc0re>9?kF2d`Wc}n1)<gcZ^_GIf1u4wlucTjXOr(>dXuMr=(tSVd2JP
z8AFs<a%(_3MELcwYmZTcuL)Ac!XxJdgy9fnZ-3wKQK<e{nF?i{OPG10EKL!$$At2(
zM?bZH?K$18cU^wljDm;yzWdkL)NPr2)f>^%&v<Ck^Og5q*ZdDNPP}1#`&I2rXH9?Y
z#QV>?VDfJ+`^|xy=AZM6YiAU#J@c_wp8jp>SFb#DM%gPno_g!-iaVE7&pLHy+?(^I
z)WC7A&wu?>zkB}ptFGO-{eel>UD3Mi#=l7|Tesxn|MAR$gCG3-->rP&*7+~KdGo#N
zFIn)uA7Ak08BeD6&X{%1&zHqtS@Op-X7yJ6*~mYA`suTlhqit6k-z=q|1R##!=c*$
zIL;W`m@yM0>sSUwp)=GZaxK}0D~3{8ZYDb^VeFA<tXT?OqMM<@rII}&l1fyl$0()9
z+EuuhDJk4DGMeJ|RDb-QJO6#pneUwQobx^J@B8&WXFi8isNEvwa4y?w_i${X0FC1r
z?5qh)GOFl$!_7DkUJ<TdJa6C$ltHu%v8~-9xx5eRyO0{`*R40DnMiF_4dAAdjjb;v
zH%2!YZb{6DQ|Ri~P{SnMiuwmfL1qFJWIBYC6FBt$)LaN^HNj`YTUt~FAifNk{9HIV
z8{9$xDYljXP7pk^Utm}SG=eNL<%i<1P}yW{x|D9G1mcRj%wTrTu<rRG%~mTbf(b>(
z7V_9BE*~&u>kh|h0h%<SV<@)#axqEH#UA_KQ^T_Cg9@ZTD20<_HzPjWUOL_IfJacH
zE#{MbvByP&wOU;!K6A%6xY`rZt<<(APqi8wNsb4MZ3%t#B`*_`CM)*+>N44@f9vm>
zeNM3COzr$}_mud&o&`L)o%0;u@szRI`r<ycN>)*R_4Nj*oX%f~8Sd(r9_()x_SBTB
zF>Q||i$u2&$U3*yq?Y=>n;)?I_!PbHq+{Unqm#R@b$h`NubaGIjd_>gqhg+m3e_(>
z7-E*w`6?72nZpGYyC)j?@!GW{>1UZ#hw=25wF>8X!c_ccJ>C+JaWusYyorFTR>3Vg
zjr&53d@whMj%B&fE_XFGF5Firjtw_<mn@E<#LwMZ(AH#Zm@P%;QV@m!1);Zmh`G2p
zaTEjqE>l@PPt2?XfdLnZgKrvInRNv52rNYn;JezPG!H0Pv;!QY%fiIVxk|=ic%`Mj
z1649)W;k%-L*w?f!bPZ{a=%;%zj+<wk)-Vz2#OF>%B5G>Byd2B1344^C|nE)7P|p!
z$$)h^06;?nZ%3oRwt^;Q9;Bz?C3->_nDyPzid%5flY5BfnCxXg<4k(;``Q)!d<^o1
zV_3FSmU%Pza|{AL_dqa|W&YI>yaDawyTmb|fnio8Vb=R$eip+JumRQ)S!@EPU+<GB
zAOZ>@Xhj5BTTo$UGio?lK$^LNOIVb(;>)JMk{;kTzDG<=!z8Nv?F0sb?}AaQvp2C@
zYmGrYj7+vjuYg-su0zdZS`MG`hW|by;b1ypIMk3$dLVM@fZeqPcY(9cb9k?)S>>7D
zjlR(@AYxO&K6OVw-1Pxt46nf0*9rAAR7dH+dxLiZ1GQ{AJ0IKOQ|_Ga-z2G3O``k1
z+3BN^5bkKCYF=tJtgp4zHs*PneA0U>#Zi~x-p47M_Eg5ojtM5KbjkNjTLc>qa2>}f
zn>oO731*3Lx8DPHAJ8Qt`w#9Q@XxK2w|ST3JStIaUy1>mvOOsk&pz4is*}l$Exr+I
z50l+w&VTQ8K~mW)-TB1Mi+)n$IiZ~XGvf4l1c6!)zVw+HbWk|2fk&!hg$ww4{$YYF
zMv)nPup<y3pwC<`3k6jOz_X8VWnevPJ_Lb7a%n#NHbz_I4fd2|x~g9mGp~H*(IDw!
zS)O89i|Cd^Y%GU2j7h#wd)4hmosufX5Z^oC6<P-zVeWzq)C=$c)M)@sEk=bk`>tRo
ze-DznDv24<xw@!=^D>i`R`mJCgRIsEtc18kfuKbQ0fK(eB3QC|ffiv0YbRH;2&@s!
zyp|Rj&((!s$bKO$`lo<zF6Tv8U@G;z@^zMc-H619tYEUc@tlDlF#slSa=tkAkz%v&
zz8h%T(E6y07HAQ1-jK;o=&hKi+V%5>5I2jro)?hsuKBhf7-bO6f<y43>#L_mtaH)|
z-tIUTHd4{6kB$Ern`g|m^V9TvwM&rmetB!p<CIwmo3|(LC*_LzBc`WZxA~e+ou!9{
z^}y?N)H@{G7^F{RK90b;D8ZS=euwtvHGU(KE`%r8EV76JLseZfs%P3MueQSaqeCA_
zLD918<vry>{~g4hdS|+fefE1bNlRhOdo#q5h>YrKUFz7)f%Ami*@p!Uw(Yh10!i`v
zBMK^#-Ge{iGyB_=dhBEK6E=aU244A4@g)U><tozY>XH%qgWNs%j3$w94jJJ3wc7l=
z0xAI5&oU&*I%(|U$Cd!&@Sw_p!O^11F%w|6=6@o@-;>As4)t;K2Pu)gf7VjY+}^fJ
zX-}2BS+gLokp>ZUwBy533y+AAD%x~XdE8(|gR|Vp%m(4QJ&$koNAS_T&>WiY)ugq@
z@8`c$-#5SCRktsDY{1xrM`Cw`!1{>BJO-`r1y)yZA>#DMsq?kiU&Jo08Js-B<)a-P
z&i{Dau_a4j*FYiv5%0csEDA|(KjOC&cKM~AmP0kMKQ{z@$3j6KaIAZ?-O20HCf@sV
zlYf~!jkEe~&h3ybX}xkhl{icpF}9jZ-=I=3NdBcZ6G@HC!J+AwyZ&)$3zO#Vpo;s6
z{w>Ngq4R)lF~MUnmuIuVG8)s2*<RA#a|y-hy>#id?Lrz$9v_18*vckCt7;Uxh#8IL
zl}x9$QE;ln69olwe@ywbAXm+bejsGx1He+1p93dQYJ33iAC#IMtX`nhZ2b|bS(cgQ
zASnQdiux?Ap+GR8Mbo6=V>YvX%J1MeGRYeKD}U$U%D(?2(ps&r)#nL@0GI%pJTRu|
z2HB1S0{tnt6JVd3RzayFr-fb<V{JVjO!W4I7deNP2e<QV)uT?BQyW^_xgc}53vLO@
z8mN>6O>LBc%h_$H=H86)x^iY_eV$tbf05J6zzbSw$eFUvC*k4;Mt`FyN;lU^g{jEB
zqT8#Wx9os=lszLu`uy%@(zm+|$(W+V_Qz|IEDjhe#5yOQvQ{Zkjvoq^p$nid9_tn|
z@hvBJrA4PjRHeFqv?0Y8GQ59poZjn+2yfvmtx+OZpK*@HCX!l1(j)eLN<<DEuH;--
zb@1YNEKzQ7uTyp{Ty>*QUUS6tX2p#r5WaB272&G>a03ISJ7Tu&`kSlV9IFDt7`7VN
zO;~)Hd7jznCye}LYa9h)0R|8RCQ3!c{1<8jL9OOhzhbORKw{Z~gN%jv%4UO*wTw~R
zIOL+o{9=uPjci|rz;RN51OSHfP;`GcrLh0hjS278wWlhJD$?^6vZq!MnLK4@p0H^^
zBCNof_-<_5sGIcdyGZ)Wt6E_*siNyTD%t0hC5{ryb@%NSI)SRKCmIEgHk}GHygWT=
zjZPja58%+ddnkL`k%AGLrQOuo8J((f0kk81&6}ViY&qYP+zVyci404zy+&|L7}Q|G
zyQu4}TP6%n**RL?Kee|Y2SGjkP;BcnAyP~VtmpaF;auf6>5@iLHi#?N?Sl6P|LK#{
z)^syIv4%(JRqNO~)4Wgs>C~3;_E++2<4R={iy56=E!=TiW?k=EUhTeJ8m)mkDxH|s
z7OA`8j)@{NaBO&Ow-U#zqH-?({yn8s$4qX$q&T$~g3U9k+o?7DHuCR<Ob@N`^!<ar
m_HtUn^b>POwNwIk$8(&&s!|*8ao5JqtFW5T1O?_z@qYo?gj|LI

literal 0
HcmV?d00001

diff --git a/legacy/Bot/bin/Debug/net8.0/SoUnBot b/legacy/Bot/bin/Debug/net8.0/SoUnBot
new file mode 100755
index 0000000000000000000000000000000000000000..7a3aa7b5cd318c248ce760603cd35bc84cb23e39
GIT binary patch
literal 77288
zcmeFadwi6|^*_D=0uhCcSMW+yKoErl2oNEV1s0yw4W@#E7mN@BL?O^*7x01vlC?bD
zZW~)|sntqtRkYPoEf}oeBA7(0HriGxSV^tbS=S4q<*MxO{XX;T?z5Y$Rr~q=^LsVE
zWOvTYoH=vOnKNh3Ty~Y)JEcojmLvYx)iK_|RNg2JDX}sZ4osFQag1^Fc9b~Iah&bw
zj{I);KVB~VGq)=;@fZKh(MbovtXF2`r+=Phm5YDITjwY1W%|n<sB_|<j$EB)y=;EX
z*Xi`nzdo$t@lSg_X|LU?m-g9rw1&q&?e)l;yfZZAc^9UBe$vybcf9_L*Xx2L>#eZr
zt+49FKl51tfAP;aKS?A1Uuy9){WA}F{L7Pn9gg&RjaI$%&&gK#_$S-oUl07x@0t3?
zGW?6T6aSq4FW)@YOQ$cM&SNF~<;lOv`ejzVzkI4Q(m%ZxVe!v6o!>$|^7E_yS?c1r
z!fG#5|Jp^F9FF*>mafAwY*E$RQ6q;fns@P{s%rm=i&u;pb@8Z?Lu;1|9WEr0F1Eqy
zQ>R_&=u+nh6Be(}@2B8@ii7#L4Q<@CWY8f0IXgz|EI)Vd$91u*m&I|g4$JV5F#aF^
zV_y9KIP9x@E`p#=>ZIUO2)r}+MCfs6@FB-J94B^yXHy0|H$tyF!}CZ6dOim3&dNWU
z0X`PY=&XD*=<E#sQigV)%~1ZM4CN~zl+N&P0G{(Y;ZKhY_)pCMpPqsLw`HKGUxxA{
zGr*^0;OD#y@cInxzMX;o`!ke(GlM>SCj*{OGw4-WhVp(Gh0gTz$qe)i&w%IR41AlI
zfu7zO@VuHqPex@Zzd8f_sSI#`2K?`3=<l8kc%Fpe>P(;il%f3K400Tsp}d)a9$yCd
zaT)kOH3OZw8Sos-0B_F#=LFapA6zipox$(Pfafn6_;6PSc^%16K9T|M&j7zI1OEOQ
z;EoJ@t_J?j#?i41a{omJdd6nJzaHbXGk*Rw1D-E3$bD{x^3DwH9-pDzB^l)VhYa#P
zH3L174D-?a40v{Afd4at9uCjY?!XNFotA-~SOz=q&p^+%41A-{lZXG)|7K>uGbMw(
zwq)qnof*mx%|K66208Z0z@LQ~+6CJ>?bk&a;BRK2^WF^O<*ym|b7lrSKhGdXM|t^z
z#Y?KoYkhNSeC6eia?gy?@_Cgtl?$qBeU&vcN+&H^Qe8P?&fG<nI<NDbYo_~#=a-lJ
z7S=3TUcRWZdVz0Yd1Xz_lA5y1Djen4Oq=c-U0z<XVnsng!SK0rYO5;HU`<u^0#C(s
zUqL~|!Z|hNzM45zzFJR(d-@#w<1Q*MEuT|cTeYCteZ^(dM}o3%jAiN4%IbM;P_h_E
zru&M}uf<i>b9|M{G}0nXQPmQ(-jN)4Va1}#IW=zN3@`UAspbDZRMvIJpjJ8RS56-8
z7Ic?={jL^_o?lfxuY7)0O|7rIdWo-m$$X)f<WBo0_=XF<MRS18j_T`EHoB&=wz6hf
zrTd$bGOE@;7m{`_76f472+iyHRf{U;`sZij_mbMmZ_LfY8us38348d+xK>tGFPpQd
zYF_!Ang#yFmDN5=Grs|Utrn<I>D@wKE9Wfkv|E!3beF!q6bkY&7HTS&E}ByTNi0q%
z^5_MXKCC7yJ8Mo4qAQXd$8Z+XW|Ys!FLzHLU4BL8L5bluy|8>nLAl#)jl6HbrI86S
zVBpUwp9_mpU0yNASFsRkJ#SGNM+?Xs6_?N4IrETR>$3*s*R>aOrn@gsn2E2VW+Rut
zEO4BWM>4Q42Vn7%c^ug=X9e+*U5=%D&77K>%d4uZNL~4Y6)Vb@R@T%msfI%NZZ2Pz
zZw=JK3g693Ar3bXmX<HCnpasZmA<jyM<xYdKF7ZT!e2~tTD{!TyRXY68r2G@fv?hi
z1$9>>Q&TBsTlYFKT$cE0Jj!e4R4=G3pTC&H<ra4H>#HvtUF@gHOe*h;8A;8Y|0RP)
z6P1`*>_mYe(({cqlf&p6vT+Q0R9OR~|F!Jee=$Rhu2`~o>6{u!Ejbk?bo@)$H(6|s
zNEuULV)TLcMe$*`XvqT1#{b=1jfoL@wWJ!9_$z#F>gbi#%Q>fAQCSOP>zEHqRqLp&
z^euzYcg*L=8#U4~Z_%82^J*OPs%qxXgDzrLt+=VYV&P5Y^XF78a?G!(taLzE=OG`3
zp(S(hpH!<@gbBhizk0D)6G@>E3olwa#|QgTj`rqNE~;AWm_L7!zjmQx@!~m4QMJ6h
zs&+}m@Db&;wH0%!=Q~hr>2iP7JO?O;*Wy^P*jEK|7vDs5$-=%G-;zZrGY|U5qVuH`
zzQA5pRxDmhJQ$vsUr-6wg5C|?sl{`usvWfz3oGZ9-&|F>h|R#Ysv(N%c-47}YHwcb
zSXNuT6#kG8%wa!4Dq38!yb|15L~a2ujVMeiOwKKpfQrP3sbZlZ)akhvHI>!N9P>4s
z7FR9?<I{TM2Y;9PeHD&n^W*IzS?ge1?7MG?A6`=pJ4W`*si>%|t#trZ#nPJztXM8Q
zV{7G9p6Vs#i<ZE+S1mz@**j!_5$JElkRgn6j$(3g;@g4>@QA4b!Yed<>ZD2KBZlTX
zrg}XSC(W9L?<0qf7@m?Go|;=w;6OpwL{E9a&=K~6_U}W77wBa9@Sz2x5?@Cqz7-_D
zj<9_llPF-JM*R`VufvBHI{IIJg=eZ~+OTSHWJyisyvn7u@b9}IO4|khb;Ezz_`fUu
z?}F%k7X$}#B!{`j$v5UQ#nSx$IQhQ|meo4Va)iZ8u=MegI>D-`vFkQ_2o8<0tF*wH
zy3JlvzPD8?N1(cHS44!Jh(31(wOu8jkSx?BrXKj0G2<+U4{M<KdaZ2W&Df!EI=%-O
z|FY1(YpwjHNKZWU6Rcc%JMN0ZB;CvLODjD`(kD2cu+o42?M+xo<v6xmY2V-<VujPq
z@sgGPwv_Mc*o8FzvQT!7mB;jt@BA@Vbh(cAEZ8YkRlOYG)P5^{m*n?!d~T(GFX`hP
zJ<tpGsY%j394A@nlcj!l$Jti;Ny*Q4TwtaBXH;RO)x|L!Y5v9caX_^E<y!kcgRFg;
zI6Q3apXH~)XZ=&x8<PgV&VrYu!RK0VBMrXLf|sSieHMIH8vHg3enT3(!GbSLga7p(
zn$D$Z@b6mXbMm?JP43Uc(@n!P972LrlYe0woN|c&wc6nQ;y`@1+2DCL_%0iKk`3N&
zgI{BVD;vDR2H$6c&$GeDSp8xzX4v52i*&ybZh=lF|5|Nu41~mAn+@JI0d+Wb+29xm
ziNAImoV<(wDI1(^$N%=(;BlQ}-XR+tiktkqI7vT-M&d8W2DemP@^WqP6B4Bzj=nZH
z243PX&j#<EfI1w5Y;dOyo^ONO<A-BxaC_XZ#0EdvhR3kMxo;N#E3?5*jRWyH%LYHq
z2EV}u?_+~6w88t@;7e`r({1n-HuxDfc%2P?rVYN<29NJ`GrQ3SKigJ*oeh4D4Zhw6
zKi3A|V1u7$gKxCK&$q#wZ1Dazc-RIXV1u{X-~(;&HXHl`8+?}yKF9`dx4{S7;K~NS
z&<5XUgAcL6582?1!6p8NCiVZ~1Q7Pi1|MpJ=i1=IZ1BD|c)ksuXM-2m;Dc=N;Wl`_
z4L-sKA7g`$w82Yk@Io8hu)(b<M{>(-aBGSZ_$(WIj1A8XHh7T@zR(80#0Fn#gO9br
zSJ>d=Z16f8yx0a`YlDxs!5eMx2{!mT8@$8@UvGoEZ14>>_(U6gqYXaU25+*#-8OjG
z2A^Vsx7y%SZSXc5+_1rS+29@<yxj)B)CN~J_+>WuJ{#O?gCDZNOKot6HQ#X#nP!9M
z*x+S0c&-ipEgQVA4St0Uo@awkx4{S5;QFM3&dj&Lud<aNV}sAM!AorLSvI&~gI{ff
zm)YRg+TgQn@at^w8*K2|Huypt{CXRFsSRFkgRii`Z?M7ZZ16cY_*xr$t_|L3gIC(%
z>um7(Hu!oQe1Q$V!3JMwgKxCKt8DQ9ef=K?{>Op;ao~R(_#X%U$ASNG;C~$W|2GFd
zb)Iv?sQ)0x2xk8cXMK!7*w-c2X4LP@*&!W|6@K?~ha)!Nd-&JcuLR$i7EdzL9>d>&
zHB57H9ch#FEle{M770tbhG~YtA{!-rBhw5GM%GJuF4GK!MH(f24bu#PMd~Dd1=C!%
zM3zeWQl@j5zCqFxnP#XfQYPt3m}ZD8QX=W$Of$3<$(QtnOf#ev$&>VXOf!@f$(8gO
zOf!TPaY*_krWv}5>^la)fSyb<gc)g<bQh)>!iuy>`p8kF8M=yuCB2_%hO8nRCH+3r
z3{^$eOZsi58KR0bN_scb3{6GqB>fuG3`s?nO8P~n8H$SBAnE6rW(X=$Ch4b{X6PwW
zBIzfXK9lKuNk7W;Sxo0i`T?dHVv6KS`W~hkT8cO%{X?c1nvLxHg8lz4(|Js{OL`5{
z=Q7<U>06j)s3{VbbPdxCF-0~?`bMT1T8gZf^jxMHQi?Q6`WmJgN{ZA;`U<8mV0x*f
zFJ*cV(>F+ZBGZGJE|c^nOfyszDUtMWrWqoN<V*TOrWqQF<VpHGrWq27<VyMsrWp!~
zI3#@%(+mMc_I)n$XPTj(NV}xFFwKxpq)pOCjv&oYPb4hq{Y*2&6WJ)~_nBsBC$e7B
zZ!^u1PNY%NyP0MvCsHTr*O+DqC$d!1FEY*0P2>hiKgTpfHjy$(Kh5+dOqWRd38u#~
zoiFJ}nI6Y<o}?dOnjx7;uB7i_nxUA8L()HFnjx6TzM~?4rc0P^m-HH@8FGoVN%|J1
z8ET1yC0)bxB&Ih?`bMT1a*3>$^jxMHYKb&T`WmJgVu{pA`U<9}GQCvNmojZIeS@SY
zGVNiyOwyMy%@9hYMAE~VX6Pi6FX;=JX2>LxC+YK;W~d~RE9o<sW{4!>kn~ARU(WQt
zBO-sM87hgiOS%iw43R|IBz@#C(hQA6!jj(4G(#ehjgo$!=^0F~m-O3AGc*!ul=N<<
z84`)qN%}RW848IkmGp~DGXxU3LDJ7LeKpf%l75=$YnU#P^b<^9%XGe^A7%PFrt>8I
z0MiV4L~<p457P{FL>x$Sekl0RW4?O5VZLqDzq7AwhPxnK@PZL4UcDX@!>RQs{@MJY
z&VCK3f(ay4y!{wIvY+Nh(IMZdm|B*h5T};oV(rd;bu72T`b7CVrF`Lc`7!vIVeT`w
ze>B0^eyFRF)oi?a%y$Y9xGe-ZvG)1Se$bwHebQG~T#C95|A;G%`r`Kw;j3Z(%h$^Y
z77s+V*r~e!QWcP0b9R3e%$mI;N$-yN$?u%sh%d4LWu5(GzPl2r(Eh)GVm8`z==205
zRJeKzK#^y_$s~sPjmi9nHZcF*jQL%kX8vs%^H)jz>pIDI&2(Mmy3#dcIwtPEhI!Oz
z;q(ZePesbzYM4`VjFzdKq8!ar`#QX#<%5tn26=7B8-zTXFXW9u-YUZzS~AOPE-?V7
zS_3`{|1Sg+4|>f5Uh{}L_#KDC^-O)8qpl(Bo9;DZhWVE20?OP4?=*z{E>Exz<hbWy
z(olE;=q?DWu9(sRv{L`>Mht6!0kE+sfr8_7!QIH3Y*s9S&(Zf<*LAMhuIpXpvv+vR
zou1$XZ_&s8Gd;oa#@Ll@{yZaiYYx7(`UiN;_tc#*PwJV&v6z+bYgdmW6))u5qwYtN
zB^RKirEiqa^fEFnIKc?SeA((Aj1AN=jL@W6Mo|kO3pExnZPFMGxhjsgen(&WMn=(I
zUyo~NN5%tw?d%<f*`ny-So)zJJ_yQ&;~%XLuB#}ivUx_Z+kinR20<uq$iK^L`df__
zcbhQXy^HLcB|P-Es?We(Fnf{_T-mBF0EvO{?W5H9Q54XNjnK*s>RT{6M$o?j)$&md
zJf8r!3l#uyH)+D}J4A-8Z1ol$M8|%BRFaCZZsjjTZ_2K9T|awAv`Z9(?MN1$+o5od
zVeVCD!YHcE$kXGH{Iu(%k;D&mLodVV<wo?fN%u1Bm>evA7$(ylh&cm&;Wuu~x+D7`
z0MM^t1$&}r&F5djo-lVUa$`G??>x9820r`WSDV22=sz(qPx&{y49c@PZ-5n-Jj4(G
zM)d@khB}4l(MD)nA>gVv*#SE=06m57LT&{Io~Ex_@Paz;-^er1J{f4DImnE5b^Sie
z8(O^HYc6g?2iEBhtY;(dKPZhfs!}v!Zf}ALI~rc_9f!6W)y-D}mc4@(3rT1&IDaaX
zp*edSU=-O#cf&irlYr+BzyrCx0X7iqEjr)>lMkwQ55!_*&|P7$^8~FlV@uooS3z8c
zZIGZF(X;u_iCT14YY(G(b9R|-Z6(lB_&)?NDV(wHHowU8GH_Dbv&j?sy7~jErD1Lw
z1Y2mBchh$^%+MHoc+CJkYp)qH@PYAPm_!TBt}kwZvFy6?`Xm&Z<_x?=e5-d7ttgyx
zaDWcGb8zSe`M7(je9W$M4z6r;4qm+8IoQ9^Id~;mkQ_Jl#Z|<9+XEcJp#WWY45xNz
zd@eqB8BICBpD(1l+o(L3p*YlFpu6Be^u+m%1dRd66>A1G*LAR2G)k!oNGSRbZ?Nzr
z<QkzX+q~vMnz=n-+p}Cwz!s|8g|P<@BAKL$y=yoRgsA(WLP^z?c`7kJV69hwkV`>>
zaC6fx_|{-+yL^NcKET#}@)0_O4-hv79O4oL9OBBvIrwh&);ah-%ECGL!4mm+OfzxQ
z4U*bQCMHQEM?!%%X#~bUR0<}Qwi;WKOezh7Z5y3~++pWn_j-H>>)mxI87_zw95Bq?
z>d{YuGVEOI1?}F7Lr_Z}%x_H9d<M&FHaC+qM~v8uzLUtY7)F$uiYn2r;5hKq0qQj|
zt?yPh08IWj9T1U>a2Ld)=g4qRGL8A`knf7MX!*2Q^4S62M*lI-8|*&-+}G^KXy$Ns
zHmni(9E`Qq7hj6X4qvf1SnR<!BbdE^A1E9<#^2i$YUnRD>c=>IXF<dm*^t3o>JWNj
ziTIfM84}Ierw~hN0K*aFtC=7?$<(p#UCzLFkPmI9Ksg>|%0im~H0BA8W~C=k310=V
zasz99@#8R74rhR(ivBDGq5l$CgSUPG6Se0PD#niH>^oUFLBe15r;(sXZwOpbgV0jk
z|DwLt7rUjQyTh}0i2nVT<N4rcILq_@Y&`#z*uVOCz7@sO#`8jeI*jKZfhjVcCnDE2
zo{tASF`gd>SDUjhK#5Mr^PeD#q-rL|vyGn|%m3L#`UZb9Yxk8bT-B$-k^Z!BPL7{h
z0CeC-J-RCVc<w{0=y<Y(%6}An^aMv~HT^jf&DniWKb0T;6YQ2-J++StSBFyC9}X4I
zeuNX*?8x6?YU~`7eEL8C&3v+aKThENKhwX{iT$hV-_cJx=$`_p1HVrOD>c87Ytz3V
zc$d(>p-41mKa3Ko{I=$k%Rf%#Ce}S1&+uRCi*IBlUkT;C5MMDQ&V~2XTjY;)n!gl4
zL-d!5M<P$n1m5QCd}4rsd^O6CG0Fufk`L%4-CTIM7p0J({aLYGw50P4bKC%a;nJu9
z*ff2K{MkEXJ!JO}u?qRK$2^cUe&QuA4#UaX=!L%@TmXw(xxs5HPyOe)HD|)ph4<#)
zQ1F`Cjy_<5+GqqzH((kImj>SPorO|a{v4NcvvX}uv^N|&%r#!~Z8h>kcE*i@aD{`V
z8=JdjLF-2mom$I&^UH)92+nLa0(+cyhbK4e^PND?kzQ`)2dqxliQd~xAWKqA*XfeU
zSf<wR9FyvcC2I`mP(z%px4=zxGxFeUCHa-YpJY2W&KQ9M&W18^9zOMOGCioKM>+kr
zpnsZKu~cT=MPa5eE9N`XQy<In_i%1L-r^O;KbLno(%DpA34i{Cu!zgtne5gylq?P2
zo<sjNk9>OheL5}&3%E=)%)fceu)6f4SS)e}SX>HE;VMtCyQ}WD9<gD*X{DhBS)R~U
zxmy@aHHzAN-8{i*xt^kzmi2Wtp>Oab=qVJu250==K!U0ho4bt}R%*`Ak?)hoWI5P6
zT?=$>ueuzq8(W}%{F~q%eAp=3UNaSF&@sb&PCXk*m)qhrp(VLZsa}pr_>cEufHVVf
zYSu_(&12SBwkhi*OF1rqAX3YUs6U02)%THSH-{zK9%B8@72%j<HMp?e&`S86E5k;x
zpEtN1qrl(fH4k~}k7>b%p_)w?1%Ciu2o}Xkn=lI6=pjI`$2tmjzZynbgBSpe3It2T
z90XVjJ^WMA|FBwx04LTRKwbO`z_1c|8D20%A6EUBU=hRBVAxy4Rq~89j<ov=&0U(A
z;`?ZpScNa>P*_;f4((C5A}`63Mk6#aW{jPhQ#*i`4Pm1pRD@9WNPINB;B~U26I`)2
zczJihohb<0VmFM}NUe(3W=AR%=cHu%Q?!G$VIDGqtFT~yqqZw5vpBF1lty|ns@w2F
zsT17kI>s3MhAZ}})lSOzoE_;-IVR@+*^!5!SUN5Er(yeqZT!jJ#&gg~Z>ZNF@zL;_
zv;p(OX8vSrrqOIOf>-4jp-aJq%X4Z6Nq=akdT~VoQirg97>wAS&WHp(Vm~Arl#j)Q
zU8#0?LzS(tr%hPDhS6C^v%3`<Fcr&y9^T+6F9s7-ZX@hMZq4bI?SqcZM0X9dEA6IX
zUY5&&3_}asK||X+VQ3{b^vCx^$53z*xNy9vSa4;Nx9B}+%{dTCk{&qDe%ODiO@6{@
z){0}5{|LxZ%ZuVX3|Xp)z-7f#82^&~q93XKHR^BAaX1@-=&A>1AXNC#j~$LJT;bE#
zZ=p<LsxPF6@Dd=~80U|JUqvfe&|pT1`R0}ueYR}25t@V+x|Rk<mqNbaR9k{mZLmM!
z)WxWyIR$`l%GtoBfpBY~#jTqyJVwhTF1#GglX4x=T2JtD$lYoHfv!)y&WWF3)T0dy
zc}l}-;{}kL(#8Sb1v21Iwx+$)W!j@OqQ}K&!2N3YyCN7MNeCvDwYgdpxd~CIy{M8L
zo2mL`i#I0GFruhA{wO<beg=-;b~nDYJWozrjDIiA;roU0%?Qb*Fn$)nv1U)u+aKb7
z(K~p*bIoIducdVG!BXeMFO2#_F|Tv-p*X*~j_mI(Is%3rflyi#LTS}PSqDwg3<E%`
zZ-EHpY)c^LcP0?TQQ$o2c;`F9I@Iozb#4A4%{qI$GRYLqBz<90FNNxXAHTte*W3@|
zng{JxxBkn{<D`G|CHjop^5Cp_a2xGaoR{ablVFSQCU0nIR%!6I{yCna7UvzmV;M`W
zz&vuQ6TS6w)XE5t>#eU;SX5-O2oY{k%@mz2iX#q=>@O|GQP(~geJ07irhf|iGHc!?
z>6&L}-Y!3!zT#fcA~0_WKEOP$c^Ld_2kX>W$R??yN&Z>-IV;T~An1W91ddSB+%<~6
z#68PKjN%0xD%x3U)Sl&+7`)wYOYcxH=~*JqxzU;rg8n9WYreBF?{2KSEk8EQf07Y<
z<!Q^mQ6GRe(cSc}Qlxn$DS$Mc4Rw&87qP%Vc*WUfxY1QNuKO_m$p&I=h;$fc>#7fp
zmM{kwVlkdr3#WGpQWhP;a5%(RC4{n8{AbH0#2o8ii(ak<`#^Lf%=%jNay`ttv*Cwm
zP6U6u7JOti0L|{kVWh!JLms***J}>3>wzb<Jd5sE9Yo=IhP2>sg4b*}hD^%w79Db~
z;er|^tl>1K9n=<8<TxANBs}DA6m}5B*~p6gKuX64-!1<T{(?mJI*{6^XB7WB@T_qQ
z*;w$JtBE5)InW^(Y$J4Qu3?Tfg8A@=J@qZQ-lAjh<joPDU=NJo+~^4&PO-7)R6n5h
z1gGS1T$h^TJuOqj9=n>S<YEOBkJVNQ0bF)Kb7VtcCLnedZF2_h6Ph`d&9bclQVRKR
zgC6ID!Kw#(NkSzOt&yG|y;MUQquu4pSZ;Onw-xaZPQ^BqL8bekKB^VyYz2L9sLg<+
zl=i)*PGUE(F>xb?dUC*M|DuNh4^wMruef%qZ=r(T%NUI+w)H*zi|J+NpIzobEIyz~
zPZ2e3&k(nBTP!7B?+Nx^dsY@moy=v*bLx9mQUAMORSp8b+}lZ#(_#Gfb(ycY%s0f~
zrA+OxCI`*e(fB36=rNzSOp!-S(L5Nx`Y&P#`<a!P4zO@M*D&eVVuMBI>3|GcK*Z(&
z$Z6pn-%MFPPSH-yrHRV@utH7N^`I^^Ikl>@;WlW1RKH1AUm^zZZT0k9qQ7V(F?o<n
z7C(((*C}g94gytRkk@=)-6Xp4JlW}gJ4J^6u^nd_tBtEVwvkfR_q;lJZ^9pL>H$)@
zM7QDxMnwz!%i*tMEC#!6w~NEtzr^NkNY_YNuYl?lkzmQ<$C3qHZGl^fzh>-ZLFZSa
zGT0YZ!zS{Jj84QS&Va4L0%4yI{eo`^*8zhQCZ;udiP1C%_nR+XF=`}Xv^5-<&6E0a
zSfb8B-}&mbzr%8##qyreOla5vRR)OH#Fn7dyqyK@{!yBHgDjmh;;R|%h1`Lfsikvh
zjV3@AYB%zd3M%6TsvIi*_4l}6d!4J}ey?85<tGth7bL^}-U=Y0W~ozv2Ru0s{cBZI
zalC@$L0dz`)hL3=ZW27p`lDI?<1q_1f}u}l!MO_+exJE}P;9X-<`pk5zQOnSo2mW6
z24CFW<`y4pGM9ALZs6(;g@p;1KylPxk(baHdwgOAHZ2yy+<QzL1K|c@%FAW|GDp3S
z5Fmppq1hO1vs1SIJoT&Sor}H442Z?P`h2&H76f(tP1+!x2!q7=QRWEs3<xoTTiK+S
zKKR&GxxOhLtYAD`!Bk?3T8w4#V$A7ZXr~#|Ukp~_E{qTq;RNC_FUMg2&^H2Qvi$up
z&z;a8Ync8B$_WXPzSxayIN%>0`3I&K8DZ~oWPYMaL;b0UKRER~6d)x=eMwFpsyG{d
ziyF?E*gFX<CA`I5$8lhUWFJPw+?>ldF{=T)6qQ6D46I;DQ+5G7Ma|BJX#|I6uaBOB
z^{S`7InO&*@8&e{E*p5*SQ;G2xCbY8D0RYpiBIesps{HayKjI@>L<oKds;l+6z|><
zb&~ELFxuu>%&}0<jq3S%R8ME%F7_tQeuU=6F39y=>IqHm5B_)S59T*wK&~TUnku;V
zs84U>WDvzN^fo-Ex4*0+hX_@DKvk&&A~?%yz6&Wds`)tI79AqAeZ+{=vcI86K{qN+
zhGh?)&_A8|(BejH!aEyYg0`urP(1P+zFEvRg7=Zho}#z>vk)JlD(a1#?+ac`?byH1
zR&_`v2QA!oX8`-L4op-lY;(G7P$l-GAs>XS-twJ{5IVopHbvDaV2ZwDwQjZO|9<o?
z^hmwG7kSy3<D=*CpA)TVUz6~~E_Bq=mIC3m-S60o+!O-+(1C+$-5&IL1e&zs9eRG2
zxDiH3(NW)EOw-ZcGJcKv9iZ{3?_?u5mfzWIRNVo@(RVz-a}fH(Mzpy%IXOpeBuSmk
z)v5MC#~ZlFPMs8X8J1UN1iN!@u+sp9ze$}uP7GikhI&)M0X0u2PjhX~f&yVk$J9Sw
z2a#vfsPyGlV(`i)n7zBOx<+&l2J$?*vx!K<@83ve6(S8fM4KVf(9og07{6i+3bhM)
z@$sVV|9)8Bv1oY<H-cwJ-Vol`;d^3!wBiv-X>se7=FV3GX9Cs|7!D&UTChiOV1WD(
zNMzQZUK*UBcK!t^bI0UhUJKE3lEBm&dzuZZJ`jnwXl0vkZt(cP3%-*2!{_<OC!1ni
zWfFL^4HT-IP_|%CYUV(h1d<E=$>#Ykv-^`-DW!b7)n0Hs1?K+%XcrLeU4O-NG}hto
zin=J0IFqo;{{od9hlf+=3&uAGrKv`{QGscp*Or%QQ$}vb6d9&EzaZW1^=<&c=B5*n
z0I=WyIOITAI9k54e?I|4eu;k{hDYC~J^BDj+I!lL4On4Pn!Av!F}*_#QJ51zqk0&5
zY5sC&`rr*M$n}~RNHBj3`bjU7hXD?%JO0d`GMJBe%AwpE>`up{KYxTWB3NZ}nHY{c
zM+|9C7}D;pwcYwhd&BTrtF23@N!q)L2jslL+8oA-2YG{Iak9mJzGjkh+qetVLuh2u
z+HMzkVmnKV-u1t4XU#4@3^NxxN&QL9#->T}-4LE?L@(6O(0tN;>fqlS9`oO@PDu>x
zvb97<j1nFoA{=hM&ggh{1^!XTgS55o@Ux?sI0gk>>)!q>qr>VGNjwLQ`gJ`A%*u^g
zeMEP5QWX&63$0v_*$HdjeEL3O0N=vVK+_LUSMYm%G}jZHfw)ubIog~rUZVzK(&4P=
zG0*DnX+Z+5Ib6+{^yrS-)`b%&Hp~N|DX^<8z9IBByhVrMpaiF2g6!?gb+2<dPsD-~
zBEx6^9cU3D<LF#S2t^MebZ>UYceI64L1y+Ky@>oru|*4X<{^XzeZyh?))|4<e5V=p
zM-Yc~Zg#I<+arqU;F$7&<~Ry0zw}gzWq$m~5=R@?whZHkq<`bR=0|Gykyy;!zA>xe
z1^+-<AcuM_#W@OB)A8u73_-ODvPAz_x*JMEXo+#(hSf=jvq-7jITvju<;<v<jF{a(
zs{F`HolmUz%^*D}lJ%e;-q0$Hn+Z6dD3WN#(MC$!Fke?&v85m*p1UETkvT?vb9Z|3
z|Dt4`fe*mH3GAK~p1{77b4^$HRoo0j!F&H?A;77yZ-IMtx8-23YATdN22H3sOSY>h
zs;+PXJw-3acdJkvBTja!rpl1(iruRGVP5kl2om2X<0TOo{qgX2Nv(t~;=#KgO9hZz
zwB2w{-0mGxf#BWFnra>b<7|wDb5{^%G|~LyFKl$hVzU0wamy|P%D>h*95OMm1AAVf
z^zB;tNLw1;oq_KHN5Z}(j#ws{OyN{v7_V84adkCW!e|9T5;5Ny(Q(>iiR-<;pU(03
zPpv<gww&Wf4FUmH?Kjn#>C_-xu^g~;dzqB~>uJ*l<i7;+M?`A8H#i@Hgx7Cv58L)E
zeu<X#5*-}WtNV<b!+pqG#OJWsjGM^P;P}$uvK+W8n3F0~S1qAsh`(XGxbam~YtlLu
zn_vt<<gI95O)c|e*LlC1_A*PHOp%Gu5FxBTbkedi2yMtJtp`stR}El6Eo^m|ArjeZ
z=9Wnm@=Rsyt^Y}K*FbWuK(Fox)+nYGi8Nsc#u63cN~st{R9B!kYNcooV^6*})zb8)
zA*z=EOPX`r{>y(WuTQBY5{>!K@>(F=`Wo`Wab(D=U53z?$?NCXoim!S_~0J~naJxj
zk=GAaTJmD9^0S~NFOL;|(_36)L0c+!zmmNA-VUrE1M63o*8}K{dif>NVaY3uL`u_;
z*I6LvOXX#lfApBI!*BPPpC>DemBk4R^J6T@xB{E^IqhO%YQy{k%OnBOX5Uq%=D%d?
z-W-J)t|>9YVW<9cOmdKCX*dd7hTTJ{vE6qrYnZ(OYVw%eE#<L8rxd4DTcRa~*<4!m
zZ)d|~E_vzdW3D?%<~p2L=#Hr_Tg?HtfQPjZ^yY+_nC;MJ{Dg52nWHi9TYJ$zqvT|S
zVrBWZ8ux`T(BSR{rtAyYFUE`AMi3Bl1UuwB5oQ51Hckn%Gw!fV*C>YRTKi$xQK#kL
z&H|@v@GTa_G>I?~x>K}EZ$M_~#yu~R0%%&&_=x>Z?SSSSPowWfc}`YA7@^`5Z<XNC
zKzN-=;n8}ZhuIjuYr?i$j4}^K-p0BZK4$di$^lkzJ)`4QOVi9OLsqS{;g2As#LsW-
zm1C#_68^Uv;VJ(v0}(vLIvK&+g+3sH=WmPN1Qu^VZmYV90jUrI6(Royb-g6~YdKL}
zp(9nuK)gZ1>JH(A{kZx^FOWAY(GGB<OrClZkm%`b14l=))NCZZW-UX0JV=~&YAWz+
zfV8z-Drby!yer<MzW}&505|5{7QbO@$Un!<P350Yf(tOb)ODJocrD+HQD_N+XbDJ@
z8k0u#lhKnBp|*HW?pG(FR7xwpdsH=G$<Z1=&tQ+Y&C-K4O;&B5XXqj0FYMWxFmST3
z?*_AbfIS-rAQwW|h=tU5I0llo)_59E4Ew7q7EUnCva_AJ*-?1f&J#;=@wH99f{B;q
zG<VD486IZ?&h|N6brTS|yPr6N*yPD-H|oPZFuC<~t{DeXFu^^8nhCGO-Ap;f)MN4)
zF@{`*;}P4PYjRnZj+1N-^g}M!CCgoP;~l<lCo$&&^U2ONZ)4*gm=_Z>r%g&F`n=Z8
z>8J}o37!CVo#58h4vaj(@(J=z6eM@t2jJ7Eptd(uI2xFw`2*^gz>_T76=l0S*NoN;
z4q$`btp;zg7d+I}x#lEY=qJlih*BU`SK14Oy8v4^=N)|GALPHa90jwjf)|nePHshD
zY+nk56qq`6A&J!s7sQCq#7Kkj+2>mfTZDL9B@VRF@?h<<#2z?zucyl|fphl;Z3T-C
zIvWg7q3)&}f|ZEW3A?;S?X{Ea%81a{UZJ*l6{uBLkxDe=3C-w_P&-6<K)p?vX-84B
z71py+`1!V)zEg&-wayqo>2MCdcPu(pzp}~UpT>1fi+epI{Uo#-IMVF?6$o{2Y;gz_
z>mYP1ab6EOov&hex`47X>f1l}gi60Kidvlw_mIP(!etZ-aZ@bYcCg@;P2M3l!NLA(
z&CT&S{6Wd!`95w3EC;?cZoDcO()agV&yc-ICOYenz`91Bz&~mA?-Xi^3qXy^0c%pS
z{mUTk)0p)k=t#}F7FkFA$a)P~(E(VV$^ks`)t?*f8$DU4h4P+iD^N$f3TP~DMy*9>
z9_`Q?T;}&wMGLIiG30e_Z{$vtS1+`1cWlhi{Gsz(rk*katMREhqP(HvMb9}L+e*+e
z^$-ry@E%PGea%o|FVLIV(!CUUoIajlnJo$9q%8m<8?tQT3g=uFkjO%OUl3h)u<LIi
zC%FR2R&Rh6M0r27%1*wupC$HEpPxwb67jdp@s`u1fllKs*P;#bQzp1JPH_Jr-tr>5
z@ipTuuR{+1RlH?@4{XXq%W4RCljMoSTb!G-v$s1O<0}2#XGeaGvu-$<9ltLr^S|x>
zAmT9x?M!Pkf}@RKIacbsjKTYiE%0^uw@QZC6j(lw`B&V{%!Y--8G!9Z{i`rl-C!f0
zGq%5fiSe{99_xw&YtI>3+i@o0IXG;d!Jm1yf1X_$fZ5^-ms<wz^*%(cT8!=QWvhYc
zNwiBr*t7ke>`OzZ<V_2W%wu$&)?k{6??E@lvwK5na50p5F}8TI+k;a)+}?e|Fs}?_
zl6wF=#aKrRUEp7~!rg=lao=aK?E9Pm8EtTGcDUAdkM>~|C={&02JZCq*ltw~!o`7)
zdZ{`2+#HYjk`e65XbOBOO!ydaF<)(IXi@)MZ)kaLbezO5Jf?pi4ySrdH;$&xKU8W~
z;{Rp)aJ1kM1`47^-La+fe4ZyXMLJYkbPQDa@=DFcsD^Sy2mH^lqGf46R=0-1iYC<A
zCvCzABYf{{XaX_-^Lf4@>PiY+x8FmzzZ`A*@&78cKh<i#ht>X6wBLfQi@k_5`ugei
zm!tiIY=1f0zY0S9SbhF1n%@#{9$v5!oV^b2*Lql%GLYsq&u4_gv;E!d(k!eLYIBp?
zBReRoL1<cJhzzh{@^6lANvY(=`ndR>Z$qQs+D4PmqUQ4xv3>P-kds=rzj}nyGIyu~
zC{1b}w05t$g`?tBkV-_0F`65b-0EcgT5A3&-MKPHn=`waJe|jbl9-P|Q}b|fHn%jG
zR~lR>3J7pmlrWD<2>xKl;vkgpY&0$rJ>0SqUF=B>JX<vIxcFwAKBg{vj3MbLw9cH=
z*9cygOLg;xDoVU&MSf{;n04$B`i`?adEQWMzBDIIJpndYO?k`{`om_?Q1z3*@bPHM
z4OLG>hOzcJqVkfBjX`7iY%G<5jOp1|5#s7{LLP(3B@7iP1&Y)v!t?H+Mg0PKI^HX%
zoB#8ExHnXa^};a`wY4YxA-q{Teg&UhPK#puTKnBr!JQon3Lz^Ychwh!*!OQ_e+>H(
zDR)!b;f1xOA6|ghg|*yDI#L@re4E`HAr72c2$il^+hyqpw@f0blRyAeR1PO(tHLyL
z+WlASL0~%^&5Hg!tB!wwy_@a+3#q!#Lfh5*2wTPx?@@0el~U68x*CJV{_pz-rp01n
zzh%-^uOR{eI;`%_uu+w*rleE#jk?>0Ce_hD*t`27Qt91g92bY{8YmFiZ&N++#X6`R
zpU=48gtb!2#gg`5_o5!o01i)J@;K%SPU%)OwVN}5z>WinoT4c?ejkpn_Qk^H$AF<}
zj3}mkHzJOlD-%$#w<ma-bF(YUx%rkX*R$}E9VKH9`3Je0Fc&)JH*#N}8(8Cpmg0Qs
zMCayiS@>sM7f&pVa=v!>@1Ai({g?egYBt1t&gcZ^{bA?kaOf;I8%*h~GcW<=#3#bB
zGSQuaJwlT6?j6y;l+?wB`gk}3lh+aT305fhzTVpRc^k<V_r0Kn&@c?ez3TdZBF4YN
z+6Z7-K-VdQ(7)wvuREOO?*pH^V9!|hz4d>N)&DUodO^XSNEOJ+*-?kXCq12;cg6ll
zxYNS9UiCx-R93)1#%Kp)6r-Rw)LB5<0C6@91PYZ0Xyhz>i(-qJYvy>quU~YEhQx8m
z$+viR>dCiu|4@#NUqTDiD^N{4o;TDpfTXYxC*S&_-4uO!<BM1f`VzknsalZUuKMBR
zlh`Zul+M53x-Y37*6E+e(?8MapIT}A`g~0K<N*g~!{p$2xA}$Z;NRU&<73w|6uqnd
zi=H*pT+eW2(sXs7r(T_GM!4RuKU}eF=<37oVIjHfqSc3UFK{?&E-+s-oWE^@Lv^vM
zerLDUhwr~5%VEB{{Le^z5j_t79>eAI*h{YMANQLS8ad98qq^?(^jXclPhX5C)C};&
z+jT6OomaD%HLy)qhh?pcE4s;81y+Uo1)|?FwyOa*?VC5*{CMy|=jM?&J2&5UbCzrE
zxIUB3w_R&T_F+xe>Mxui%Vj>ld^;BWH3Llu>I8?0H$YCk7$}{ay(gm1<rpH}CPsS#
zIZD;;?v{@g<ObJToWa3f%B?x2I5n!T{)~m|$CEkhjo@*fqE}KjRP^<q1ENpdP#H5+
z!acH~g6ltSoOABhDQ~Yx=dHFE|2O=V<u;<3IF%&aJ@6E{iw#Gzx7uRwxmY<j{vY%A
zWh@fEl)uUFk8m1G-9$jdxn>k26;{;x$=}P+u|l#Ow5vB0^0j4ijf}wzvdM9qpSupe
z3B7;MwN2~&=RIq#a&5Ee{Xgvc&;L<A%_;idEc)Lp`u~ch|JWx@m(Nw`nwHP!qW;ZP
z|J7JpLOxeP{oB*k|Gf_CzhA3=Gt~cct^PZgKbNllLxHk`c-j-{@7L;|DwHsWXIv=f
zJee+(4<IINSN3K6yKhpKHJ4+x0B02OuP3Cy7#27+9@i*9Za#BgMy}l)BCfNO?a7of
z#ugt=D<pwE?Eh85bX*Oh(z5G_BUD_$>`T(K{gucvnAMNnf!BSI!xx_)Tc7XYGqKMh
z(!#9KQ#sIA1~0hP2>+5xj1M~?hr4}HJp_3w7m6p(6X^B6+$Q}j?LYy-(g)>&iR!%x
zgz%v9_3_Ik#^Bw~HIJe@SO$3p@5Q<d0haEbu4maUl(dPvEjMtqS3ecaYgfAs+ZPfL
zm>_=k5Pg?&xM25~e*(suGCNM|>P797(HFrG>%QeW2#FB`+ZKkM-@E)H;X7tJZP6FZ
z-d90!6kaiplHHB7P|aYCIv2c4@<;aPR)4^Cqs#oO?DwRey1+pYVG>p??@?}Y`(Oyh
zDQ4!$=41Jox4q`pJbbhS#^GNe;u05Iq&%LmvJM;8UgxCt(jYAKgEvTN+*XvM*P)el
zMo7*u0(no$z{HypL3!u{n?sE}dW|&^ZtikTBu4@Dnh493x7E$RBYWh0B<iijL{Z0;
z3al@&k`#5vZk{pS30I&_d*ho>2bWTv4GS1xu0PtP28aH;*Nw?{He3k^E-_$rtCfNa
z{sWYNba*s!TLNQj{qq#P>Ad4vqA>%50EeQK4*nQg+sIzv%$zqXS~~cH(xT5xos)3r
zM*7}a$4`V%I_WqrYog=nw{YK9Yc_saw@IP;!QU*`3yc6c;(Gl7)f)CV8>$(bsgHH3
zfy3H;V%@kw==wE)wFE|5o%05_vOlGvCFBJUb@T&1&4#70)pc7bbG%P+KBo;6FEFv=
z*b%RbA1-<y_i)rbq%*XMSQ%G5imfP16iK26qKt@R7x1Q2wG%K3V+a(A&w^Azc4&*y
zq``k9Ifip`t2bon6gZ4iQoGgpK*{#@Ktt(t|C3lk^7@I_e5?f27%c&V9l&3+JT1eP
zV?HkxNBhV|3)ZRN;~T$a7q&{JkiSm-gk6FzB|A`=ETO8b7MG#Lpy*%lZi*uqA`b+6
zNVL@(3K6AR_c6h<q46?&FzVHn!qUC!@6aneD-_|$UQh6Pp4N2UQ72ug#HgyQ<2i(^
zkG+GVhIyYJRM=@b%KHo{gNpi|rdvG9c^1lcxDHS54S1YA%rvIh4>cSIt;9>mkb2M3
z6<FkJQL_PuM4?r@$lb-^8!p=A9|8knH(Qu$u0uySxvt(xvtyfWI%6C7g9+=peIzWw
zCw=01ui6BDfltw&^C6~F@DIw6xB5PQ%VScB{4qlM<TV!^)$GQvkr$bVZ)voJK@m*n
zrtA4p*r%L22z~wstmj$?JFPUYMWOU}bm<5_P#PE9QTiH`PCS;CAgZStj)tRfz_HNT
zYNITACZ8qX>W6&w=S9!AQkJs$d&eJdvP{|gze%G;ADj9mnpNY`Mlza}*e|?lx;{09
zMJ?Zc3*rbA{OeV@MrcwVt;9$=515vaX!cD<=qJxW;7;5Ob9!HVB0ZfUI^TsJ^L9*#
z)AFM8WQSksY=>UqNDTme@rm-&P~M5z3l2$3bS$>+aU$B;@DX?xcU{u9?jQO!ot7!6
zo(yw&f@rB_N?+7+G;`PfRs^>umBi`I2favWdP_iWK5%2THGK>|k)B=>r#Bz;mPBs=
zSymlQFUn6t`O8p#8p@MixJWJ0(VAWyV<*0j#|(}<Dzu_1UVpQqWS})4w2q0>YVNYO
zmk&mYsD#*Fx85aWJVN$T!2d*l@ctJVfF5FBb4MN~ckEZ{2jsp$V|YL=Dbsj7BmTZ$
z${PVV94Z$mj$)i)-Dw0di#X~JUF<uq{?HI-APTVQ{*`MhJ`?*f+7ZfzQfW)ir+N`9
z#4H}B{-B2loV9ufE9aU<47yVD-O|CBh7kwrIs}`J2;lESlni0=<WmG#L&$?1`5cBR
zZ%p9cqufmHIIZtj4?IE^q`fl1ok3^APe3oSHaa)wG`!$!SixkdEX#Z)dSXf4v7+U-
zI5!W+0nL(z1HOv^aNTw~{=K#)Qc~9(z_)j6_eN)0=gE*9w(3Rvd$I4u=)}5}zZ&AZ
zsP1sS|AM*|_+h(qWlaK8x>23~n&yb_?3DX!Jb$iZ{n%|gd48juJlC=RY;`%hAlE)t
zgF+r@i9HV+CLE?$v7VX(uY`_8;Fl8DybRk2uYjS>H6y@xGCj`P3_N|4iju}t+?v4i
z4#`^+Pi94)2LXuGY$K<5Z5a~VuSzy#<Zyrm;~d6DvgR)O2Tm0&zwtlwcWP(+9WMOs
zNB*86{5^%N6&|Y{Z5DrfJ(=KWuV^mrE?HYC*~$TWQ|zvP?w7Aq{1ai5Kb=MGD7^A&
zvB@yVFF-2s^A@s6ldT?lI32S@hsz-#bp&~-<85}N`7^*86ZTZz=UDy0TH$W$mA8VJ
zIvohC`#VNROuc&dA%H@s?gMGwklgp#2HzZW25v`eSL=}L#UXaDITjH0(+8<0%|Nk`
zwu9%r&x1oH3AwDe3mp=Ns4J#y4dyQ70bhSngcF>bWzNHbAAvR9ob1u>SK~=Xq7G&P
zd5^))<90uu3fzSmnw_&kjOsm9v!BQ0d~yuWO>7IJ(9`{)xnOe_P-X6bZu^eIynPp$
zj;R1IRTRf&QX;4JM(<IsCl*WfM`MV#iH%IMI&t1twu<FE?Cc27`zMQ}@6W9MU>)*Z
z=AT^VUphND9-M%~-eI)>ibTRjW1bCdqMv}p=K}y@i+YkbG$0T2cWH2xD*q+Cwp=_O
zqa9k*oDI(}>V;FUCP^N8vwb$-HHCzE$)uS1BMg0qi(5I}u;Jz?h#@>S**xqrce(Lc
zjUfVU;kfq=0dH2IWjoK?{O7`ag6Hgv%_{<BThLf!4hU8iKw{nJky9n|cEaq)2QVX@
z@<-OcnEFx{b+~+^&q!lxXofWKgrB+v9iz(uUR=amWc0dD0}QHYf41jGR2^j91Ue#*
zflyWO0Jt><OA2SheN5kpbmBS>!l`mDNEEq6J&!z5S)5cc+u?WXM{BTkCI^tZ%aGA0
zJFoz3<xW+`7e#I+!LU^@gJJH*%HogL>O;kwuM`z~SiITzBO_Z_UY>9Q(-Q~OcVSo2
z{?3S9AY5UGivKYKoq7|5YlqBwg5)M(OD!uL>ksy*XOWi_Mna#H@!-`T@MU=H9OKlM
zVSbu^NfPUo^;jEVz0!yetXH^F0Bf;cA>5&E{{^YpZfuW?cLwUYa5XorVA0T4*07FE
zsr7&a1+p!hl9~A{04R|<q_jl1=%}-V5(xWGSM}^@sQ5v28hJQ>0qu6uD+q1cD5zs9
zAFT(%F6Z6wrMZs)OTxyzW<D;|u~2Ey$D0v*)$5=DV~F~*-+K0GI%tRl;6cCuhNYI^
zl>=mdAsDB@?kC|;MLx&C_p`9B!7a?KL~SX`-`Nv0AP{+dCPZ9Z+6<PZ&8dhN@TIRa
zX-G+LL{4MLvtL~26qA9XJAo!v42FnGYml_#+}ux}`Kd?Ax}(F;Q%=n0gG*VwC9oDB
z=sJ$Q1nS~nL-z?0rtrXM8n(1%NyV_*O3*3rrpiV0hv9dpYXNA{SlG4`SegTWA`(nj
zzr~B<<lOhrEPaHa?ls)NY;_21K}f{ea3V;-vLGxDK>R$YI}DgKjdvd4dTk|%kQ&L8
z^LALor~%{~{f>s$L~vH2!k=N51YaQQuJ_YMX8E^qvznBg1ORsY?K1oUbOfN3mnr_h
z(M8J^fMQXIyMHc)^oe&)Z-=Q_(snrv8Q17jDd(Wjpw1MDz$*|<eT=C<*cu4V2HBen
zhD68BEZ0-K{I?NRDGk+*mZInLmC$@*{R9NZu|AI$Xvg({GT>4O-y|aPh%VO%6neS1
z4gmX*#!8jPvnGp8=76=?WLo|tHvbdRO(`|~Jf3x38OGcQ5SN-&f~*j;){^eg(eBZ1
zSSJn>>mq~I2+1(#3TK?iBc;A?v??d%vQ~_D2m8W$peO74ccxV1{ky#?0KTe?XtzUg
z->cCdC2f|(2QfL+jTt8IY3X<&_Up}0)wfWc#zV}-4B`qEFGGJ4=As=|9OG7|b^HFB
z?5~&U5F@<?Xt6`>(0$<@Y#=;~)2~D0%mz*g>d*weiMdi6Z!U{0ha`<&HT6E3%KNBo
z_yVd<7pj{1;N-nngQTyY@TN2G&CHJU16Pu`QtWl-_Y+fQPk8HX@YcgP4A2U6@z)K)
zm!o%AvMZQTr^M&KpOLVYVctFSnxF6;yoqE${mNFXI&_X=oCo{CCmH(5wY5M54t!U9
zHda+jPz6XASOxCT1*%xUq5-xojENlv<yXigUiI{E*>vH$c=}PMvDXTG{WQKlvWUSm
z4G8dSl&1kD-I|r*ZP+z-r~~K@8je%;V_j+=TmgL_7Zz!+`WE|$D%SajdsFov4=(5n
zw>UpyJM>_cKD9$>nMYnhyQ&I#Df|0cpP-VdSBX_S3X<u(N6j5YtbCOB9>Cx=^W7$k
z@h?zG>TktM19yk*N|hr2#QIu4YLXmMxCsFr2J@cyDKU%Rba~uwdJC{Lzv)`G2JVay
z;`opxa3#DDm>MUIdg@b=>h*P&Yvp^%-4OG)aC8hO?vQ>76TNgc{0Nc}p9<%pkjGgg
z{@7#M%a{)f4e*%0%FCE%kYnlV@ZgoOIK_`&AtMt?>4xZO2Vp>+4Fh73(sWddoQZEq
zR`K2e9jSGfL4|%~u}fFNtactMq?Q$t?*X6GGUP$zlau$SzxjAPtrV?4&raFKV<8xv
zHXfg#`lgJ>eP750hq%_i>6P(#gwvTGKYLLHNatJQahEReHx}qH9#280jK>SbsVaOg
zo<4(Vjqe8<-)UR~%W!Pd7{3FI(a9_%`=n}tE*B^4ZCz>(+r`mwYTNTaiN(0C7A!|~
zzRMz9kI!F%19tYuA8hP&crNQuSY$N}bf_1Rmo%ahpOW=q!5$2f$3E9A#8shfJe8w<
z13^G}@7iI7$P)3a8JJ`6OBMFz2~=k`NLM*jowRZ7`!Qgt<N6a8<G99cSmr4Ho(=3N
zeqdy*ZhV+-yy5qt#0ZW6{w}~D;bgBW?v@ow3%=V|oa!<(1UoX43WJHNud9ASH(vx*
z-yv7`U@R0-I#-L$S&`=rR$xamrv3tS7JYAHSA+)tTQUMj*iViMVJ9XC`!TZ6!G4It
z<^gpp28^J73lkI>Wj!E{H(owTvN~ma==<adV5Wp4cBy-hu_<T6`QkbjJ_@3#9}i-A
zu9<XvIxRglEtitGFlwpO(G?I^q)P76R_R24A!iKX@k>WEeoQ)Gsm13OFpCR?yP6}9
z1CBW&@{r`8sTKi8$|CVM$gn2K^~n2@MdCjw0_*tye_SNaApbio66J}AUxT}P^}b5{
z(D*v>PE<j^?1N&Le|q{W5vkcUZ(F8-Nxui}+BLJ*iIadWwX6)8&oMC7PmpJer|9F?
z4PZpl-o@vig)VsnAc;wC;>mqitTZZp6~pE&@fkRL+A_6|ue#F55p+E8G*3Mf=BBfy
zLh(nJqW`={vhP(<!^ES;jX#PZaB)x_+JwI7M@J9~S6djV3-#J9)#TwCSJR1TLhjMu
zhT%cgx2~heBWpW7*q-(lz3aoqhQHAh#3L^zE~7XRP><iE--M<R@CQrW>q^amX)mqG
zr4g0Ipg`hg`lqO2!12cngMQWN&CRz2Xm{>k0wl>SIdS|)(5eqOEVMcD)H#jZKQ0w7
zenrFP;V*$!W0v##Qn31O<^ppVI<KzjNZ7Nyg#q*_)IO^6)2Mxr_rNqAaev--r|K!C
z<O=8a)E7TY>yGa`>S`3|;16kiS^Ys@j95>n@1r$$$9ymWp5WvX=xcwrLNDTEseoG=
z0sUJO@@o*yl7N=nt$!P0J6_ZDoyz;|mVs`@etR$^nJoeSRt0$M5a3n;%0jCI#6yn=
z*ycbpj6vVVaRO`N#|ADllIN46r!&5ag<PU+nN`-q)m?ofU8{&^2+m#erWiLdB&zu?
z?~45U2h^wKxR!aq_YB_k<5N*R{ZGW-uA@c%A*hiDrhFBY-Htoqygrrk#7Ud8;R;}l
z{1N}C>A;9%&A~Gf;F1$8eGFUN@A;pjc5}D>spq4e4JWaF5dY}urD>|v!6b1CEIFI?
zqS+KpayGmL)<tHr&CAgy_(DM-;vkHqh(1ICstKl{mFy??!UL^RNQcrcw76`9HW7?N
z6&znEBq)L*0u3`nyk8nw>jxeiK|zhm@)IOl$T~oxl_6B@L9es{9S;WpYR+c#ZRb*8
zz!Ev7hyL63N}8%^8-S^0ct^dz3p_ab{WJ#op|izTGOG-oCli`v?&m&gvf;lVFBPMh
z+Yp4V27#y!>(S2MpUJSI`#uaj9V_+hOJXoDu`PP81Gz94)-q;V8WrvF`6KRCsqby1
zI|1&giI6JaLDQA}JE9OUw}CBGT|1zX4m|_?Q8STen?Gbdg#N6+lE{@B@NG|BA{kf^
zPR3omA0H;w;vBx=7O(a*ND|#g+%5HVdvjnFEH1jw!?~OI9S?G?R{`xcEC5^9)1SC9
zM1=IHx`A2H_g?{D-BziKqsi#jdhK>zWbq3&oF{>?4mcxC;O)-9AZUv0^sKPfiWVmS
zP0IUQ%uz-A{BuB2oe1R&Y}&x8m7yPmsTXBZ_an2oRQIhfwNxLENOWK){1(l96pWvh
z`2l*t{ks5HG_n&#s-1x;NJjWiv|P(5UdMm2)ieJUl$_Nm|4h3kw)?Lmhq^)u)lgxR
z=EW<~b1Z?!dGSIVBD{KBIdthbTHhn;qwl3^dAsjd>Qa<SI)!n6y3_Si2K~Mn>nvgA
z-WNLP_iaH6Hm;P_>FxWMr7r5#ZypoAs$b!ga>_G;({p6J^csm~wCA8*M;8Pi{iWVO
zrPBM?M}L_z1RNb&lOeDL_0$aHrR>KczI4DmsxFfDTZ-^Xu(%tcURx){o0XNhdJ+6`
z;c2+L(iZ<|{?`oa%mnuC?dMUqorhry1N#@WFJaG_I6k0fq~9UOdG~hb=2y`W9j82$
z!B@nxRN;5!Yc!lFU7uchKR9ib?x9O>yFj<K&}z$%w~XYmOl+%_hHi(8b<Bs|I=o}c
zlNQ(l#Ob?eZ~iN{#Z;M~sp$BWt!+txQSuzG;D!qKf=@c^zfO3Y#t-YfAb+VhecJ;1
zTZj2#DFz6_;w$h0x)Bbm16qC5#oSuPOzp5Psl6DbZxF!6HI<7h=SZx4fv!FaA5K;e
zEWtN8nd)YcgK04EQ4i92AAhx#1L`&QF7)6mCWD)ZA8x|aIjE|>V-gmUl3lM>SgT;=
z);(C8SN}>kR-H<Q1_P2GG*taFy>0*&oNOC_CUB^6DHe_fe&PzNjO4=+?l4-%nRJ{_
zY;mvmOfJSAfO{R-0dR;v9g7(@j~^|8LBu9~Ae{O9=~(1D__5_G_Z8DUmrsK&5qL|W
z55VQXh^jgGD*k)mOnhV2eIy2xH$Pee3LhFZ;czoblo7l)JD)Q=_P8339*2W^x8et^
zx;r;dniM^a-JK=uz|o_<+ZgwZmE}9PwLvt}e=Y=`g6iQ6mDvB#V+3PVM)4+O2~ICh
z!V_SKbK>ETW9s{24J^i$r~?hzH=uAAXW#?u;J6O#%);B;yz(6^#RX3KAzwg9;%0K0
zw=v6gpgGGK*Z}hmkqI+`A@*1oZnFvpW&<1U72<K6qdlE>EJc^#p0)`e0%3f3gLsN0
zbnCw%suf7eb|QTs=3$m13~m38=&%PvAhr<rVTJ@t{n6u{4ZKN%n}^4vkrQyH5h?4e
zX=H%VC(U<p-mx7j)oJrze0lT3zohxDzH`wu9u+v+t&=0JN7Rq+Ak6_ybD&lCfE}?M
zF@ZEe<@tw~hD77!>8e^%8{DKhu{968Rex*XK*7|mMQa2tK53519L)A`oX!$ZLpij`
z@-HY<XP_0*i?raM_+gSG>eIlNkyyW#BszTaw)A4HxM&KmA1~H(^IBBKmXM7s|70V0
zcZYr{cAZah;jIbA5t3bNM~*sHk16DM8<x&Gw*{19mg*1f+nMF-#vde24F0syv$eY(
zJ<dQkaE@c=ERlqL>_k2!O`)wcJgO7~(6|h+V+@TOWUOxCFbe)&1_;cK`tx_dViGVC
z{iK1gzZ9k4d}Tjh0|n?L1&20hQq}2bg9FFk+nw{Sd|4Rj!V!{GfFeQQY`BZHLxt~8
z6rQuTekJ$yH9!}<Tez95H6+>WwYpg{HQww4sDr)QjR0Ync|CK2_YtKxxOD|T^x(sW
zPh$K@p~UzrK#wv0bj|qqBRZ}@GX2)jy<})Y^EXJVA)*k?kL!_;qsk7kY|0`VlC^Z-
z((YzoC9DyqCpmTF%$339JW=-?bl@1D9uC?xMEx)oVYX@+oFY6<1hc_@r&YEr3r-BW
zFxkhIx8r%i7lrqF5SzN)py0E1ou#!F$;VL9M;$wk(9Z<RR&qk`PybT`+BL2F_#s32
zj~Xt<H58C}r>F%YTgsYxx`fmHpf07=EFiO&)D8)pkNmpVYkaqANFqRyt*Qia_7kh1
zWbzCioDXYCxU57bB=dXO)P6j{VIZpvU%7&Rbu~XXSvfo=stmKCm!xqt8>QHPsGkAz
zqej$`-p3kAw)t2c`xjTg2etY=$eOqUBWLSr{-qGk_W%s@{{+k)TGutTFJOj<j<*Xk
z9kM?gG{%Rr8CWZ=hKNF|pY+A*ZHpvo7Gih5B`^bR4uwM?-N=@1=-3<HtemrGX;q7R
zM^$y@P<RaT*m|?O1s$CYXJR&;37i+WX?~0QS2wf3It)cPPY>Z`=m$vvx|i*e-EsWV
z$!9#~l?SD_`l@-mU!4lN>EpQSk7fIop-K;zguLTb&P`O|o2vWOzrbCT@7aWfV9b@(
z?DL>bcbDw;S#_KZPq80XQ~V{c{pxY4rhnZ4O?7t$xCtds;Y9Q04V&m-*q<9=<t=$U
z4UftpXSviT)Ek}@m<u-($2C#Vl`N>syc{nx9^+8hkGqb8pL4GH8|Gx5@i-Fmp8^4-
zNru-?HA~=jKLR2>c&WsLCn19(e{YfU^^w=U15^M!*o{}IHW5ekWb2E5-{2#4)+#87
z$)DHKI)@V<D26`3A!S`KL|&H07@C8kNZCfl<pQz%Dy=Irszz%#Wu{LY4Ag+K7%=F&
z0eZ0Y)^J)Ij6f^MdHD9!MIZJI^Wk>tBV+W(GX+(gfgI$j)4>n>f)w@lar(eM!!Ujn
zUYHLViX$rr*nirwd{~!Xr^_F~$J2CD^?~=Qj^%k$82G27pt^8nhvvVnvqv)fc%6NU
z^&`etg1Reo_Jz8&siWBt&jL6)Hgth5`aXWANo~Ikt8~WE7$6JAO{Fu?2cXDA{&NsC
zNA}|%DIs$7-(nM?e--M9JqpF8=M2=bqk0r@w=)pX=~US{1B=iAirGZx4161~G|uyq
z2&0joLDH%p`fh99w_5cQNE!{kI`wb#%|=ASt<VtFu7PQkG%Q12<nN#|S{l79dOl8>
zxTBLaPr}f^#LU*>%UAaSMHJ70lVRz2h7!-+z=Q1QVn(W2s$HYl#}8bp2t2CU;rK!@
zTl0-G@LLVe(_m|YaR&6xTVj532DV5!`ylEGk_^2#QE%Oh?;R<-ixb4#-~saN3~%@W
zGhRSOD%TsnOZd4!tbU_wrIFk4E%LKb=cUoy;ANgq@{)u%OeOSs4UL-sXJ9;e#((Vb
zM(ex)M9ZB|ToO-)YZCYI^ql}a@--3;@&R=vI?j+UBIRRvlmUSOwVKRG-SRw!E-~wM
z(1Bn!<E+V3GSBt{Z`IGxEDtRKQ#-tO;69A$Q{(pkD*95Amanp(h(Wy@y)*CxyE+&2
zAcO|Q=i`z8rufe1ZlJfwjPHET1etF9L}lvPhh6?&pf*3TyQ%7KvECm{{C*quGxzB&
zP3y#jS-OjDGYb3w+UcbEYdbc-3*7k%&1a!`kw^M|Tzc9tU!_R#qk_G`VLJm$lJC9S
z?pNYTP4n(?U|afIO6nZy4@3OC^Cky4TGS=%0AXAvOW6OuL8u$`K-u&01&8^0lChS>
z5W^6(wx=$ouu(rLCr{)_$4f1b3*6Z-ok)WdN<-Br1Cd@Gw4&p12L6Ke@062XZLL3)
zy}Z>3^(rocMXTtx+Q@2+wG&Sr@W^EX><h779BrNrW5X$ZT`({UkS&gV4#&Bzbz9k6
z{D^k3qXaRr0P4k}{Vb?cj;u#>vmN1B>=DOCjFrGuD1Z<H=AvPvptDawv&ex73Gl)K
zfY)1KvV*TLd&k!1;`w~Ou5AX$8r;DJ7Bve*F_5T#haN6RZp}c95<~`s>etjy(BWj2
zBo}ifgz^xK1rL+rS!8?zQ}DJ_Fhu-ovmz!ltzWKsUmXKCBR>U*?D-wYOR+onWv>o5
z-PF&(jHJpW&ezp{0A2qiS=~(jL<~H`T^BOg)P~kT$^?vnRyeqK(zfC;OuZiXGdi8D
zKGvP_Wt>Ml6qKlH1*liYke4Jb9$(e^F1j*1!mw1b$Ty~6Yu+oL+q;n4fwByb`@H8*
z@2hlSJ%)rDo6?2<IG&B*%p7CvRXGS;Vd-`{!{-JcQ=OomLnD^G#t(yT%u4<~lHGvE
z<L&YNdLw;|<GnQfqs8r+-b;J^YrmKFg=g@;J=?#q-b;fu<RdTcb;<YA4)`$O<h`_i
z;k~r823;CDVUSV39pi+nlF|*AnkNjxtu5^K98?$9YU^O)l_?o2R4*Fv*VGc|syuWj
zuT^a@%$_j3Sb_MDj}C<9ZQx==U+6mCkjq03Tsgr*YDBzWy$ZwtxALJYyqI=j{JIHW
zh?C#ti216$p&N6JP<d{&*orsty)vvS`Cb`@xV%?pnAh&ZPd*)j@;jQlGG>=&@ZusK
z>&Nw240QcmnGvkrXB2(v-^zON=gLY!L-c7>0n(LCxRdQ{*akEJ$V+KB>%2ZX$sXUg
zy_e<*mLKw%v%mDcw9=yY;1;F5m&S@lXu7<Y_EHT9h`*PHSBt@s+3T=)=&zX*hRG^5
zt8<L)|I9YBaHabCulQaXDYvk7JcL|^Y5K<gu$Yg3n*wZ6qxgwk9Q7E;v0I5Ye}8o|
zlm%Q=S!tundXN87fYrZHkBEw4CBEdnwACLda#zkDq+LDHcN2Zf6Gs@E7FEq%xTF@R
z=ctrT__CsAXf0k2c0bQ$GUJBL?w7HQ5H;n4Ms0`<$rdS(9fF7L=oPLW_xIpQ!cg&u
zA+qs!Ev8ghV{W>Hm%@DG?9ni+ZLr#g=Fd(E=gaRR;<ur==tgU3ZavEJ^@4ZRy!&J;
ztS`gap5Q6ChEV+8h0?$gOde6Zse?O$zx@Xb;l>mbYu;hP*%0B9gpaC(ia*pfIKS#{
zyr(V#O`xR?*SGNvHzqE^FJ;c_4+rnTGNf@%MZy^u7}oilknC(=EdG-UV{NhoL%M8f
z-Iu?Va5)<b%3XI<g>^(K8F~s8)??vkHAG&hA29e9D*QFbw!rE(0g0Eb^8YP78HX^E
z1Gqr)KF)?g>xMO+wH}Wd3M~<gb+v9#_J&wKGK#tK)+^r50LPZLqqd}vlXS^Hp=meW
zNg8A^g0=jIyJ4Jyry+!ghi!2y#Oisd_;{hQ@Y%CPCZpiwf+|k$pemmoO+MOyJD>ot
zN6b*){Y;3H??cfTmo=fSxyX_OO+R~xsIq6|kq5=4x|vV#f!}IA9V!OPCcif7DvNDm
zC3Oj<ivv$aP>^7Gl?Sk1&yU9*7T|14pO3g$1Ik#!zVJU4aPGNWWwA}$ulIOP;JelA
z)TTq?c!<)VZ)B}j**bPi7>@m!x4>c9)z%RCjpe6NA2ci#qIjL;5dt_I<oFW@(odua
z7GZMT#6&8^MmPh0;N?c|>YW_lws-?~Z$rfs2Z<E&VH~25&}B_$g?~_z3$n;gWq@8}
z%UV+%ugQ(G?9Wkb>=L1L5gn>kk;Q0ck%q>L{f3KJ7E49!9}=AuU71>Rwb}p#9g6zS
zP>n=<a+L3af;VDh7Dngj+DkS2{AZDCM##<RsGld~G0tJWkQfsktELI&L;>GkKF>>J
z@hZNT)rGoDydK!!7myT&>!0y@6*{37Aus7}b-e$`S<x^zDbbFUv!88zsWnvmq?XNO
zxJCMh=w!^c1RRzI!7COB_B!$pfG_5Kat;i~B~<vg94XcyzmE#Yw@(_nK^eSyV;2G1
zT;PeGmN<`wqknl)>Man)AKwHC(=`d(F_9qjLF+mYTLbjeuV)JceMEnDr<j+S2HFNh
zeftSn;5CH7;X04g{d~~KG4a#1L39{K1%?{0_Qi*r)4fl%-H1Wd0EL85B!}bPV9sXb
zK;n`{Xh&qpvnM~lN#HVIL$ZX><Ms{>zkvm94@mSwTHT1e$f=Se177{~ahBp~6m1Z0
zMyEJUb!=`}hUWSJj)^P}rc{_8%;$<dRD8(=GOl)kaT2yNw4K6SQm20OJCd%26_keI
zv#FP12=zIf@w%=TlRI{CsJ<{F(jnW#t1Y)}?hT+R+RlaekQq1tgtVQys=1fDNKgbd
z8B2PX1cZoFFG0$L|M*E5^g|R9KdKZe{@MA`i5>LBFrD&O3YrJfxE6NlD>;Yhf23Os
z2q#N~mKb1wd|btn$-$JZ{w|RF#5e@SMtb1_PU3|?SrTkTe#+2t1{nX-2huopY_?3+
z%i62_H0}5BmH|!l9k5%5N??!wm(fSK!qhK$u=hjM6)e*~Iv&0Oa<rsWuWbO%L{Qw^
z5xtuK%+S?1YT~L3+mVmUHpo0J3f0~by?|vb1TSqB=A>#OSn&=dM5R-|W&u3|C}(3@
zZ}rn4YoT81e2{1RJwSb4oQGzT?BP$+g=WL|pN(M71ifmh#30iy34ah9cWnLqLF}uj
z$oeQY@_AqP9p2Z)yG1!zFY^K>j{f4=fn$8H>4gRC0?t^`&x_yJ4Hb5WRUk@rI)fmg
z!ou^c^l_3#M)(6U2<KyecLUC)tpgJLGQ%!x17NY^4ORjhU&eKBh~ll81Ms;(Uhyie
zZoi`6?Pj6)8}*z^$Ac8SqO+b|gx42_0>JBQB^@}ha6tY1h?pGc*gAb&ZKcqTe#^|b
z1gRvu__KP8^zo;%L=a1xd)D@2I7>zUh}1e`xa#^0;!yo;*!SRPsAZ~<wq90U)7$d3
zs&c@RlR+|GXT9%o1l@o96d77`@qz13YUK{vB3y#NcH3#lzD<~1xE9$6sK+oSt$P^u
zcqPvhX1Gt_#Ti>~sQAD<qK~PM=1~uDr9pc|yyb8c6f=InpGE$NBJ7yl>BVu?W2ra4
zPXHf~*966Y(WWLH@SQKu7##21-0S;j2g-z33<~S<Rdi~<T2Uzqh4MCq;!hsbA1bNg
zLA<6<olAP)|I2gdJAJ)=3TuPU>|e;|p6&CUZ?vvHO#cbn_VwX*RLgrh51N3V+QKTO
zB@eA_LRl3?JBV&hfI-Dwno#l2!8~y7u=bX$bN<(&P^x^ra&$2n*F<%kB@7k!)YW^!
z7Dt~IGZ4Rd(5C|qF<xyDx4H>=Ntx?-i@ZP4$?qrPH|*85IItj*dX86I^22Ct{TW4)
zz4}zDM)55O3!{f!K}}q0c(w@cEDpF<!{yMvY~nB@Y|RRY(B8#%|7aA7A8FM+31gDB
z4s?1t6qnv~VQ=n3Ua~iKdt}8+dGxd6G1fNK@Dcl9-F{2C`izCdQ;LSd*&xmkW`6{^
zN49FvzbIF_Yy4sRlyzR_Gi%l}SlUl$k-#RfU;~g=7}&(gl=qKa&)i+-sAJP@UD%G~
z$n|Kj{dc1ITwG|63J0yKS5b2l6BrI`2zjz#GJ>0=9Dnkcw`MRRK()US#|}7|I#0xp
zU+~vf>X~P1oMwm<QiL0|u4e)u1x65#WpR0)XzKlztErdEznhyXM(niR-97#w7x`|R
z3$aerL~GJ;3Ie}uRGLDIX<Zo5FFaX(K_~Aey*P(`)(!#eHkWeN$p>knMRL#q>*NE0
zSjx$aF6);gQeT$A(C9|>KHLQrm=B~Fo4M%hV^C-e%ek17qVO(OHbVS`&07551>U3p
z8r6s5AL+%AwZU3!F`gGzZpVyLXC%&wXuj)(GtP>POt4+J5(yHycG>WQDZcU6@;o6h
zsfhKcbjf@#dcEtJd$f!iLH22y?4uw%@<U+aZyjsrsuLzs^%6|%u#(5F9JmIzjRXUg
zjg>~ok|5pP-x5n)9mOY9FTuP_4nD{|E;lCe8CJIXj0{d+P65RtAtZ%?*=nW$ZEF3U
zFCZfqppljQ_6A0RY-yas(X9zkuUW%`f&SqfJ;8u@MUZL_e9xhWWCoh34)|djdKlgC
zr4bT>;SYoXSF4CuUxk1W2eC$|_+uRsn9hkskA^<yh=RBTvPlq!iciwOi*zvx#Dbhb
zNT~Q^<O69d+JSOq3sBgsirA{Y*1}W1*L39=!hzqnAU=|Xu|uj$&31$<Sl<<U11})f
zZQX_M4rc@R3(&zfez5c0QYeVU+9Gv?Sls!nf@XZD6qrn|b;=!LkN2EEjsxCx1B2jd
z3FB<MLXXyI#$2+Hb;L{@J4+ZdNFZ^>9M-^-bulu=f@Be5kyj|>{eSI!dw5jUx%b+0
zNrq%H1OWw!b+?C`HW_9zlgUL;LN0)WixBWaG)!jpkdaAdI+qaIeDzwqfS0z4H*C=&
zVx?Xx-VUi+<xup{ii%1-oO94xOf|LmX*t+Z_xF3(UTbD&4Cwbf{o{L{k9nTtx7S+l
z`>ywW*SoH>_Z}MCxEXN65;S4H8%VvDbzGHteII-<@DEHdxcw{h4ZJmX1NuT{Ea{A1
z{3{1<J&cj3V`|R&ste&`_SL0Gb6?R7H_~!Ewr=SBC^W3^C%I;UFCRr5PR!Bsw?2-q
z@4uGQ&Dd`6roj8BFX;P-n`XRcWR=p4^)Su$!s;t9f^vNgL-oBd=})hKM+nQt;B$t6
zGfC#pNhXd?EeM;B!gO^M&im++k1oQ@DGW_7%o&+(AOk;|L6pBEO3z2`kIGH7|6-y&
zjnm>LaC{$1wDd*^%nr1Dpdn$Pf-;N6t0s6sexJ@@{4;lFyhAHDL1ktmK?fn3OUw&^
zy_t5}Y<k*J!}2zmH$+1Exq)V$nH><)&kp#F9Db;>9p4c^MRCNkWndi2hKNt!H;_i2
z{Qe2QM`yghaNrx5&{}Tr?P_qafnU<(hTy?dw8eAqtJfpo1pPG>DdGhbSJFtB|NYF=
z;T9LG(XIpUp|P+~PGSamY`}g8S;ZfEubMz(R#hti1Hm!s{JB#fjEI*k=ie@W|JzMB
ze1@g*HMm>3$@^!yo<4t_|067e!TBOM@j|%ywS$=t5o1n6-ZtbK(KPT0j$dwgX`n8a
z=Z(w^zM#Z<EBotx!hLg$qbDfw<|X+|e9d+9r7-L)D9An?`HjEf5h45})=tMu<d~D6
zlO8zffs-CM>4B3TIO&0t9ypE%6lP)kTl(H)drC{UYsq9h*}gg%O>4<)L02%^&00G(
z6<_HmgUL1Oq?Eckm{KG0On10k?Z~9nSUebxcCS>UX*H6JcM(M-7>#LRCT|1a-~KaI
ztx)H7r_#Y#jL6*!np)LFQcG&z$wX7AG^KV$S9YdV+;*Q(lUgbs>(SJ7XEX(>ct=d@
zazg|@lTxfRold0crcGNJcXvfY$#^OrNxMVwu4$3gkiBZ!dGtRTo>A!u_<YqK*06AK
z)0~A%TH9L}*Eck=)?_e5Drnt7$PKGAN#;<R=D96RESQ(sj>r3EEnG6U^<uV^v#RO1
z8c%55>X(^kkQ?GjmHdgiSSZ-t9Z##Fq!vtTYAPMBtLxE1X;8$1$(33XmIk}ku3&F_
zD*9b*>RIHxP&}!HVsz^SYv|NMtGJgVY6G$xS{AE`V7gP)dZVdyYR2+OsY}_kHr5;P
zv&Mz3@NN6zr3+f;&TqoghcajW(w0`(wXCV7y=CFTc}o`^w?Kv05ffw|Tr)otOQS)1
zv=|yYzABTTV&q4Hv6RLdqhXayi$uG_D(RtyqtF|st>H=L@mBi$Re|c7T3O%Xg$rBT
z%?aiXYl)Qm@>IN=xs#dhbhJwg#k(WXmBZ$RwH_mXu|cXDw4_!jvXWd63P3hcf~$h=
zt`xmc1us&mAiq`w6Y{5!StJ?k(pJZlt7fpt+3WTmEt!hOyJxVjXetHQw+p>yq>~wq
zO{Sl%1$`1>#wN#8GZ_7Fs*kDoJ=-q&Lqp(yPk_N?-7AS2If+Cl;E!o68BK+HkWTq(
zJ-w{ACcskZcqYa=f~jbzJ(W(PzPdW7sC9?gq*R^S-kyl3)TygGqoGdj{qJVZQqSex
zqvBj6V)9Cd$|4UzY$lY3Nk%{I&ctFQirTIx7>kC7a@3_V9pGR_l-AX?&u>TU#$xeM
zd$c>HCDUwDNS$;EqxhdXDRq|U^lG%5LIc)>I@=?N8nzOzvRNITDhcYSxS0<S6karX
zAg5gA!w)Z9gzk=BNxdhCkP4gQOAbS+HC-L?SRP|eYNngZ71U<o)KlAMEpDoBY*@T>
z{;VZzF^u9pDZj)B-o|V!Q`D#(@Ig3~%yf06j990^kUW@5XrX8%3caB=A06SDG$SSx
z4-AjRI)b59ratl|xxw^M(T22H*_ttWkSe^ZiT0~+XsJNetD_hZlB+BDxYfR-Y4NhT
z4RaUF&a*L_Kdgml+MXyM9`p4W#zfLR%Wz!dvJ`5*NW-)|A0MAwn2Gryp(SH$*i!13
z2yJ-dOH6FeLcAoRQHumIdSRGMW6<ZpKXi0bHTX<Rs^Pen>YhS`7*DAUg=d4IkcRG$
zd4dNdHNrGLN4iZx9M&*rcHuEFj5}Yd4?QMG6}#1@?w)8e-rc2jQ;$nVY4%a4Oe968
z$oe1va>63=<v5zu67ggj*&S<C^ryt?Oq6;Q4}t%O4KS&i)Y6$G8Ma_)ON*PiF%Fv?
zD5k|>yf?J9+%$N|cVm%=b}`wtHeq(3yI@IceG5%)4fU<`82+OA);SEL6|FhiWjkQv
zF^?$0?ll;*I0*(Lqp4^Rn=&ai#m$#K`2sN0q1~1f0p>cy4IcoEz^PDEtD=bn!t!hI
z8p|ZPh@i30J=CDtEemJCpo`lVH_e{Aq_t^rQ=?&13#MTTeLhBFYQgd;1pjIkqK}58
zNm;86rL%VB+r-Qg^9%EMn`!Nb71~Pl6fEDuvaX!_4BriDo5rT*dMrP(ZN!^qS|*j8
zmg)>9H8C|CZHw1Ph?hq&`9cMHDMM}@;r`YXhMvgBZPVP7H?`o!%v^IfU!$4mjB%1r
zIO?RZDGYNQ5d=i~|Bj^#+{fpTuZ|WkjAoP0PE_Deopko{isb6vRN_)x@?@Q-qBd9&
zp)}2mMT{Oqbu)U_6wjG5dZKiVo0;Tky;|PXttIi|4UGDH4UL(!H_3+zx7#g-Ol`Q4
zXp*svR57E8rB1d*CEv<eyd#KJgVA63u$HAUMx$Ywn`moMht*1MfD|Hf++u;;p{enX
zG<F?Y*v+6g?}2=eK#?Y=f2{CAST7k1Pq6TL#H}vTXq^Q*V<L=WUglFfjgwde3}>`a
zBnO@^X=N0r@FXxoG8^z>xtSd<h>D@9is};y3c{j#Q=c?45($+yTdG)9@=U%3l0tY+
zQ41`<HUKkPU6I`73|YKR!=kVv7R+>qIxCFvqo}AczS>ytkb}8U(SCJBlTwqxKs9IA
zvlu}yj%U=a478E<awe?Cz-Mf^z!Zy$d5bNe)jf53CK{6L%^UfcF)Cm~*AtCrQn58?
z*<g5RlM$Pn79)JW80nR}Jj}jwm1S&X8n9T6hBaCR^Wc?BaV`KsJggdM6p9<noNJ_)
zE4r~z<3kjNNJL8{9)r{rRs}R(l5*IL!41?wj#CG@^y`HmoM|kmT4~RUP8rIi`3x^s
z5*>Wlgp-5V=5+^GqWZ>`78?BxTFtGtqOuNbqsVS7$uk;O1$BHT;@!Z^mX*eL$U|xq
z9A9c=ENU`|Fl|3i+}yckwA_V9Xsf_`Qar+YaVV2a(kjMOFYg0c$1Z^hF|DGRW=g~o
z4hp)%*b9(>rqD7OpBGMs4Q@8KyGP7)rfA{eCyy@ZgreOLkBw6>na*IGjKtX->TRTR
zozW_deD7z@sfS)Bf>~Tebq>N4>%oPQ2sXgP8w+2`^&Tz?va^3Glb{uvN()lh%~uF8
z6chT_w7{%7dFgA_hFKD0Fpf#c#bMD#C#<u_`_;C{jzpoa^<rN`Jq4|e{d^)G?M}N{
zMFkz@pk+{ip?JAzP`ebtjgBxw{T_o-)JW`q83#}vkH_n&^!Pk}Pn9R&srJ-(YP}w>
z*IViJdHvohZ@^pat?|}YdMdq@m6g6qe`Qr=pt8EMrn1)O@p*lfKA+F;tMUbW)xH{E
zt>5GK`YZiDzu#Zw5BRJ7HU8QvPnEZ-vdUNGud1pFR8?2iRMiGN0dJr(;0yQzRe?aD
zI#3g+t@c!Vt1GL0)&A<L>OggMbxn0`ji<(2Q(5Dy@z+$<1Zt{lYHDh0p<*qnuZ8GZ
zu+<`qtl2|1q2SN`8v{oIDbIY=R_pOshr?hE1Uh`7n%a;r*ijRSczxBC9<2tu$h>qu
zy;&ArSNpZ9+R9L6Bna!n{-6(*ReSxRpce6j{E<qcQ6^Z=nPbD9b+~SsuIqicZkdHI
z5D6P}eKW8PxD(h1d>^<GXv2#w`hgRH2Y^=s=|{@91N(rD!1X{6-uVz~*7Y{v^f|gd
zNcnT|;1JTs=IQzv+;!x{9VdQZ+kDUgV+-IT;CkR@;DLqk6_qd2^~1nEU@7?511I9$
z1KTdp^^1TTm+1O>O1DB^ln(;mr+R?&&LHPf&;wh5jlecw7#IUy3)}|W3>*aZL(YL^
zkb`u~MW~m`FV=MxU+E6w3)VrDmtBUm6Xn1b<ZlC}fll1)vKd$g+zFfxd>_~XoI~`u
zi^*1mdV#%^##hdpfrGgJW+L+2BD%hx@^Q~WE71WRxC6Tn-%8gI;s%s7^4H_0=uV`!
z;alR3gt$FnFR(2E`FI@JiThqRfvya9N!1|TpMkx=ay+GSCvXtB7v;xrlkG90>(lk!
z$S=DB{wzg3SHhn_=T*A?3~&%Q06cIt=!qXV4sZW;{y^8uMuHDG9as)*15O9_0b76@
zfo;G!pg(XU{EPIlbtp%A@Q09#bl;EBu8`jkd<^;J_~N|=>FL1tk)D1h%1=Q%1O32$
zU@PHWXg}Zq;Fwe4FWlqU2rSzKzaqaJxDyxy4gmK6OGl%AU=6VBKC}bq`|pR{l>V8n
zZ$*0BW?er3Y}<nPM*e!>X5gL&b=`Ry(htFZNN)sAM|#i0x}E?Ydjxcp#?AlxfCnB&
zd*J@|J$Q)WFv))cdg89z^6jt#`LUODeJ0ZDfh&MzJK=ZWM&L7){x{eUEC<?f<K}eW
zL|_}R5x5>>Nhjo#??zl8-2%J~=|MuIj{&y>oi9Tl;B??;L<f8UbS-<(j^knfYbXaE
zdlT_?I`n-TbSN*|5517?|2_IGaQ!==18#c{b^`~24&1^375PW`?qa6&mMRmRg^pWs
zzY^u+CKz^bhOSR0ipD~;Dq|Dz=f~gfYK(*E*UVMg>>79e$l}$GE7^IcpL4cvB5q7I
zc-w%R&NUuHVD+xjwU&mGf;p%_l=lH|MLE|&me0$TZ~Th#{;w!MaDsBUh#kY9XQr;B
zTj2gnp&u^GD2<Qu-9;_jD~0!b5@2kC^o<8)<cC)1UJGB@9!0}Q{`uDwt}nRBezWZs
ztL1IjQtvDv$C1x2!rzMX@Ovge+d31^tZS^6*`RF#?W`OR$ap=Q>He`<xJwC`t>^q}
zY&Tmi%Oy4Gya(k6o6t{)QR*Z7N%{_=d@ssRB!}q6;8w7s&AL9~%k}-$T;Fui9h|M}
z=pMQH&IbjOB&SW3pQt_+bk~B;F&F&+*IB*ECs9Kd*|rrlVbD;!Woee=%6E%$3`x0i
zq03Zc?Sn1UUhm-Ab|xZD`e!!x<Gs=@YFm6|#vEv;SHNQ{Hqn~e)*4eJ3nLyscs{E~
zp9b1Kg-4|P1=kd;=h0yOeU8J3u|C{+co6ewCDl1k#NP^t-DQ+-L^;F!is>OIuQ6wH
zKgth-|9IsGg#2paUnJy*a`KM}`Its>^6PW*%WQ`H<CRZGIq82c@wW>3k(~TCl#~AF
zQ27Nz>!&uMbxSGM3qf<7&CB|$Mf$58v|B;jiZ#VjGp)HjPLtG(k?j4TzZPqaDl>gn
zw&e)4<UMmDXlY$^6zh$pxX!j)^EE~v7BNIRl!MNLb;hM;nPwf%$mu{a+Xng-SdU=Y
zVUnBkZy(ARp&SvGD_@u^-zdt5w6AIW>__>`3-kCze<ytpp!@~gJQg&|mv+hVM%YLC
zIx(If#(jI3PICGzGV~#zmeY8Tdj{=h`*S{hQ~H$Zz6i8otbt0=AF@6*`|426S0wv-
z(2v0y=&NK?du<2pQP2+2vjw%&_}O6SOMP-T>|cTLj$R;TTa8gs_DPc-mi<y|v@Lj!
zfu{vy-+RP!;qW}xuAJ^<s|WhvhY`5$wsTB8b43ijlB<L0SAc%%`MUlwu5D+U=ySe&
zTyl_3x01c+CzH`%ZS->8VeGK>StJV|dv}88Gpw=CqB@FAb+l#480j(yx?@;_A0fI=
zO|qr#J96~YPGw?^{vSmDhKb%7M>k1o(t8eQH=)1WOL{+VqBZ&7dYJTH$R|nCrw=@R
zShEk1{8vppIo~`i>md4Vpl`$4zL?tU(P8NyFw-9Z{dDw+&q@A0CVFY_ze;K&9-I!?
zjJq%=(71kuiQa6({#-ne-7~@CKtK7MbXtBKp38^wq`|Xq1=gU%bIx&iCJyD<3Z6wB
zf@jKC^Gt<q2f%Yc)Ab)wJ)=!LM*F=dttUGi*#9g-AMU_dYAZI;nr&Zil`K4-rh{ia
z9*KCI{A<=pjyqRp$$5W^(H^Ty*FPoy79OXLj$CZhIJF%U(*D%&>l`ZIhw=gJn-KN6
zzG7Neen$K#AF}S73%x*2{9`a+?>+&)ALY-Spu833TaQytZIDL!IPBlfL>pxLaxT9A
zAU#B5-6qf;!yfKNGi?K{S*#fu4Mevabm41s9qZJbOst_gAxthS$+p9w8(gdF!_Dc`
z=Cr5m@5bw_DLx*=%ro3rQzmOR=2{PA4&cFn>%nhZXW}tpie@xE*HV9N18ob&?)^l&
z%0w&s-)c#X`35v}HgF8|9}xY#Vd&QmrKdB1J>S7Pk>csw!_dExqbGamOu&h~_7xcS
zZD*S3%`yIzEz3kabbio_efWFCQ*Gi|lw12-uNunJ3Le|lczBM+!sR9&b3I?=;+*1y
z&IcC#z!<Bt?a(6Tlx&nhAJFaw?L@Te1-Q=A@;I^Z$fmX*1kDQ23|ViO)(cK_2rB=F
zS%%3b^L~cpO$WW_y5Z$fc^k?TH{m$|vplihP?6V1^`X4RT+Ve)z~9CmViRckKy#wC
z8IOO^Z3o?Cvn<*jQSjGualCII23<esa5RzglUP%coKh&;c8jiGVlJ0smsp#rD60Y8
zz8~SN%uL61ryghY3p!6Z2pX@s@6OJyZ@(#f5&7zR&~CpSd-5Uj482w4ZKe8tqU*EG
z<wCDMkWsws0S)^pWDHxM<$aLxen|Bl1?@zfbzm2o^Mh%vH?Gtuzsg)L{XleFl3`~f
zXs?H?E6udI@obf(CLI%?JpkG(hNrz;(vprFLF;*Jc%M*tKg!h;lpjEODavttl(SKc
z^E9>`6Xlc4YgALbTZ?o3k?Jc)&^(6opdmh^btlTGyanZpa85K_8&Y`;<ugxEz8>Ws
zlz$tv+5OrDkiW~2zfJIC$(FN6#5UPO=Y20=4!8>KZ<~OLRgT@+wy>ThW6lG5Iu}$i
zzC2(9J!viJ%|6&Mbbc>`4jXY6cn8%n+Ej-azpZVu24nq1K8dY2<}cf)raDaHo+Tw)
z-5B$)1kY~t(JfTR8zvrE$EsW%WH+5VK65MHUP$y$nCNqM2PFsDeGv2)p<mL=XKi<w
z=uJ9VFO(dH9b*tgt+(m=R2sk6nRv{;dLS1sq}LqqEW$bH%eb~(Vd9Z<-FJrC-3$7|
zpf4c3+J~Vx?NvzrR?zRo`RO3Zzi=3OlYFv!Kgq{AD~(;YY7@QLhP#WiN=*filg>WT
zUw=e(JZj=G=JtyvG11bw>|>z4ooH|Q5^Z+pLAr!NdoB7VU-!jLwB|N2t@~8)Y{SI0
z_c@$L<2oCw#vC^jda!4Z-4D6t=tE(en`fKknryVpce6{}3^yn(aI^2Z*+p))$jy+s
zl;<RPCT{Jy(9M>(nFW6+o{KM}-E4)MWq9r!$}F`2F5m*d+UaJO@#JDICMI0d01>FC
z15htXyW9*nK7g(jFCBN2IpmXG*mv-0U7tby@dIpBhU<^b)E}Gqeu;R<Cxbh5{nr%x
zBTe?^Vt-LCl8v<jog;VRe7Tk?mUVDHQotB}kj|{P{|aqMa^E)Tl9SsY<<5i{Kj<eu
zuj?=2+P1?)FJc`|oJT$`g}*Noo`F|~+7B6zK}H#Lu&p!6FtwfaYFVq{vu)rx@Ect(
zBt1_v*>8#?%T7@tbfb^1D@ChT{YhbODIuP|r$n)hvM*I2gQs<6bb0A76xQf^SYgXX
zJfyJp@1X#E<8D?;ud%XEm5J9|*$#{A3k!SMLgOvUMzIgkaX+-!pR}>RSZMQS@RYJ!
zaD9tX{-MHlC~boFPUPLGjCj(@?o)nE6A;fYWhCq><=guewoSR1=M~L!p>;2&mO%QR
z8H$fRi0cQHlAjbPn=B(Ava_R>c{}awCF>N_Z#{K~on2|W7bN%EoydO3j_+nUGv|k`
zV~f6NvbD%t`m)06pETs_%9!%fM_~K-Z3^pg;p$rDLaMi33D61lMb6y?$bYUtc|@tj
z<p;_F#-6k|cNMU^tRsJ0z_wbO8T;7UhhM_@(Dno@lT~xSH=e&kVK*y$c?F-HdV}J`
zvGncAJcK^K98~aJJ-6G%aBx{Yez(FFp#_Y*qK?x)w6eaRVie{yrPa<mtZd0n%VsOP
z!`i{~%7lAb->@)UalB?>|7LOG`ZddvuT_2qgBzzf@E*X6N<OeKt>h&OOFBPR*!OSC
zwo?mx8Y(@lJf&=Mu&b>dPdeC9Yw4{H_Jz&)BL}<DK9#Xw+KX}d@AeTlJJ@pt756*X
zo<b)GKPYtK`f7(0tT#JaxlTprU2>Pg?o!5ayY8t_zQs@T_b8{_Z&&_Hc^~$^Xt)2y
z&R((GKe8*Y+ObwO`*jrSN3vfz<q-?}o$^DY2>x}Hk66yC47-bFmHa?q3vb|sC@Y#(
zcALU3%cjqOx1fwN@<A)R-BR*^l|5{k_pFs2vD`1@-7{CI|EW^)c#-m^W$f!k?3mT{
zP?7Sa-La#HePXAajfJlJij-Fzj%SP5KOC1M=WipND80SNiJbe2E|Y4b{>AJa$bCmC
zeX^K+sC<Cy*Q}*KD`xLnp(Z<I8~Nj6_E%dcWB+M)Tv^P1TyW}#MeN@ToCBz=;Iu<U
zcv&f}5FT-q-d@a}b07kYm^g#|H`?jHEv{z^*q<%0<NA5K>*)gaTRU7VXiC}Nas7A2
zwa>zCu=MgasBa&ScKL-e=57c3n=*Q%gZ<ia%1<2Z4a>cdao9HDHMFCBG+J$MLFt_i
z_6cvt^$sU!?sVLgm0i62eFY=I^A_|LiuqD@H~8<ijQURtJlf9likE+;d~RW{TF|u&
zn#Vx%m}S&@D{3|L`PK^cFIIM?a@r?|ZRL#JR<>P1OI~N8(7eTRCm6tc&m`pxb|0?q
zvy>dRE00(Zt?U!qsH1jvwVnKv)vL&R=|<{%{5T$ZeUstf{#@6utZd~?%BxoPxl;16
zg{`#&4qI5ig}(eC`G^zm_n%kTqsk1Ceo(nZ8TrBlj87HUma!MCXWdZ7uC=+=m9d}O
z$M#QP4;74kZUQ16qt1>(=k5vYH-)c4*oG0#*T%DBBTfb39Yr4~;QG09?4#q^^G=K)
zH<gUuKAvqVxdC(EL!-w25dub`r?ahNN4_<lJw0|Eta|Y@JDA=%?TlR$*p1`P*g1jy
z>p16&6WH&^Igx*K+&EDFY`k;Nc=qV{8!-PvcZa`NW9J$1@9?`i8J-1z&!~UB0iE7Z
zT<Bl#D(`+(|5_Ftxo-qZtiS?93Ex-5_FKl@KLXJ;pMH5^gYw=8w%0cL^%02Iu{Vx@
zAI9Ncf*0+hKX<S<?Nu}&6s%$Fn!=HPppK6&!ftgS@(lkjV;A7-k&*p{Y(?qQh3s*~
zb+C{<uN41RA$vt}{JxOAu5|vfkgc;ipDe^6SGu_ngS_khLiP`9>83(<k8S*~3)vCd
zsW%p~N9|K_{kHv(lwHQIgPGSU8oo;!RL({#Seyqf?0(Bnk<DZCE|A`3nLl7*hb+^0
zUJ0wgTF#9V!pNnhBK*~_;J2mNOvYxG{m)4DOU3z-i@m3m{>g>T;X=;47W(e-7dH2w
zUF;9G(hpqhf9x(?|DYg-U@RE@M;Cj)(7xBj{&%7Cbr<V*#P_+_!y}xVUF_)*Bky;y
zT_e7Etb|=xWIJ4<+*;(^;$j0uvw!Dej}|-sR>F1^pK`Q>{kC}SRu_B1X**P+yyTn+
zIl6N)C?6=9{&5Mr&1JjWrM%#>?Q$tQU6?+HjE^pM9%JW?eM@1jPoU`J=cETtdVoAo
z%D)`OxdA^o#`5kDhF{tQb_nbgm=@S4@LGXyzH9K^DAKZ%E$0W#xz2cbUYhQSqK~A%
zTBOe~ve~&fpQVq}0AtaQYaC_r&+fMk952%9*@mPEB8?pZ|KOGv{+TMu={$x$viz@K
z=y6K#x6*&cFn#dPfT)O{xgh7LC>Lt7#X>&;Y@Fc3wq(dhu(7S>hJJj22E~9-0NXhJ
z!LEmYP7^p)I6~%ogke(eqr!1GiQykvKi+=CKa%e6x?vy4dDa&qT`C--i?r19ut?Vl
z$9^u-*>(`}|5?y0+9h9JfHt}KGz(lNFe0!=;97wj1a1-dl)zmA-x7F8;1PiZUZb8<
z1x^+i5ZEkmnZSrZ+-l1|YXxo)xJBSo0(S{~OW+}aM+6pBiuwgk78nrNEO41X<4#b<
zdPI7yzzqVo2z*N5E`e_eJS6alzyhDBU*Kec0fEf|mkEpr>=C$D;0A$P1U@Bjm%z6K
z9ujy&V1ZxMFL1IzJU+`m%>tJRj0o%zxK`i>fm;MVC2*I(w*(#%ctl`9m8f6fWPt&J
z%>tJRj0o%zxK`i>fm;MVC2*I(w*(#%ctl`<7=KR{XlV1#`|t3GfkKE}cZ%iN%Y$l7
z@_RqFjCp%g<><dYd+0BZ_{M#FPQ~vgK5_XYvgVmn|K+S@#xv3?fA+c3@Al%3Y@a%_
zA>P#$#m)1z5r2m_QWXva{J}^#5~}fN-YQQ`u%lWFdunULkw~T26X^(Zk^gLYVP8!s
zR8v`9q51rg3fv^_t3ZwZ3a?gO8_;Sa-mpi^aE3)o;!C?{#nW!wM=wo0i6=d9(gXj$
zcwlxzL!CNx_JXBnsa~JE((P68D2fk{sd!l^7NkeA#AxTO#;YDM8oN`SctZjn@?vg0
z7NaE-c&s^|*4!(*Gj8J<_4FE3R!0WUpjF^K3kFsFthp8G;7Z2HJA<iCyaZ!SH(qRD
zU^;1(h)Rbf+EGT&{t$z>O2pEnMHK(jc-Wn}@fdj&WpO<46=ZG=FJqv$T(ozF!7Eb`
z8Va>*y&)};Zs!lHfx9D>G9<MJlgS_qAW8C?q*2un%+(Cy{VR|XM}<a3oExuVp@-c6
zIe|m#XPTkpx_FUTh)R^}by?fc*^IH#D4;d6<WC5GiK=82DA&a@donJ}{I?2zi8yZ^
z^1(6HkWVdAX8!GhU!ppM8sAmqrDr3J_XozM{&F88QQ9x}A^G}W0DkhJ<d^#tiHF4o
zfpjF_Nq)IsxEOh)vgDWh9*J_F0}}qp_kR$%w1<%Va^E0vpWu`FOFD_&D5pJ%Ov`<V
zL{(Hs`jehge<7AXenvi({BoZpQSOsu{gPkWe;s&<f0hs^_gxa@K8^TkOO((5L*$W)
zQa=Ae4{3>VKT28j$#4Jl$TRb+VqYRLCz!LdQUkJhM?Sw;N2&rJyiYLX@yXnK^Z4by
zTB6)<OZt5Me<t{)|Jgx7DeyJwpr()1UuHdw3$kDG@B7p!k=T+~VP29wo5!ynHTWfd
zcB+w<|7H8h^h>xf*FR<?rEo8xKwBO|e*JHPmPo9kK%PUWryFwe*t79Nb<pQO@aMuG
z#WsDuF29uf0eJAWewJUJb4>k;`XxWPn>3a6d&GX+Blzc_%>2o3KVqiXm;7=+-z)e%
zqG753(nr=S<%|Hoxqf+0vbBtIaUX>g%u_jjDaVz^&*XkbHTXx#10BIH>lH~h24o~l
zRx^%2IKbGbNd~_v2?fe}W%hVczei>Wl>GVmhE&65BBivfTd;Dd7xjNjGKj*wB>O+_
C*-o1P

literal 0
HcmV?d00001

diff --git a/legacy/Bot/bin/Debug/net8.0/SoUnBot.deps.json b/legacy/Bot/bin/Debug/net8.0/SoUnBot.deps.json
new file mode 100644
index 0000000..7582c96
--- /dev/null
+++ b/legacy/Bot/bin/Debug/net8.0/SoUnBot.deps.json
@@ -0,0 +1,104 @@
+{
+  "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==",
+      "path": "jetbrains.annotations/2021.3.0",
+      "hashPath": "jetbrains.annotations.2021.3.0.nupkg.sha512"
+    },
+    "Newtonsoft.Json/13.0.1": {
+      "type": "package",
+      "serviceable": true,
+      "sha512": "sha512-ppPFpBcvxdsfUonNcvITKqLl3bqxWbDCZIzDWHzjpdAHRFfZe0Dw9HmA0+za13IdyrgJwpkDTDA9fHaxOrt20A==",
+      "path": "newtonsoft.json/13.0.1",
+      "hashPath": "newtonsoft.json.13.0.1.nupkg.sha512"
+    },
+    "System.Threading.Channels/6.0.0": {
+      "type": "package",
+      "serviceable": true,
+      "sha512": "sha512-TY8/9+tI0mNaUMgntOxxaq2ndTkdXqLSxvPmas7XEqOlv9lQtB7wLjYGd756lOaO7Dvb5r/WXhluM+0Xe87v5Q==",
+      "path": "system.threading.channels/6.0.0",
+      "hashPath": "system.threading.channels.6.0.0.nupkg.sha512"
+    },
+    "Telegram.Bot/17.0.0": {
+      "type": "package",
+      "serviceable": true,
+      "sha512": "sha512-YvQ9lqEt1bTafu6BJPTbYWDHxyHP+TK8PtjTjNV/6VQw3XxVcZnGwYkJ1CdYW3lJHmHjYxzhBlhhOGNtqJ3U7g==",
+      "path": "telegram.bot/17.0.0",
+      "hashPath": "telegram.bot.17.0.0.nupkg.sha512"
+    },
+    "Telegram.Bot.Extensions.Polling/1.0.0": {
+      "type": "package",
+      "serviceable": true,
+      "sha512": "sha512-OsUbHdHIMmldevoRYzArh5uJDVs1fzlpj+T3mddeP/ELhhhHLmcjon0ZEypgf1KFEj6QWbuZHkijauIW1LZlqg==",
+      "path": "telegram.bot.extensions.polling/1.0.0",
+      "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
new file mode 100644
index 0000000000000000000000000000000000000000..0b65b967c809e9b672c89650adbf62045519e93a
GIT binary patch
literal 50688
zcmeHwdtlsEmH+*I=b1@nl9@>+ueO=Kr%jW-OH$j?^g$bGX=zd@0=Cm++J+{XbY=pr
z>0n|3ML>(FC=aC|D2nnB1q6i3;>!Ad?GKbzx}XTF>tkhI7A?Qe=iKji9!bg~vcLW3
z7nu7!_uO;OJ@?#m&V79EOgFykuyQD+ocO%{x>66~$-kA7zHu^);y}aK0_y&f?@oTu
z*zn!SoqKxH(ScNQcPic&?TYvJCo|EViD+uDKibnDU9+hp+L!E3H2eL&X%_3vYn9qy
zI8=7YogcSbdrnPJrN%s^9s$Oyu^;&vo>6?B!-vvE@i4XAfJA;iRn!nXL5JFLxl$GK
zfBtD@Nr1nV;Jb;yb4p$2A~5>TP-Q^vc}A(u(M4~RkLE-dDlhWu3+S6OiTxSmKk+pr
zD3iXTKZSo$rFJx@(y1;WMYiVvxbgWqJ_Ua((T(O*qBjXb%Bqy5gMW}rE?KG6s$9kb
zGSn;nD4VHV>Yn|ufgQquT-e&*^jAO6qb`OXQ8$=EG7zg)$~V2qJtI|xoG&%K2zMHC
zr6~+R0bL(*WvSbWaQ}#$-<|p?ku_i#cBXzwxE8P~3Ati*03s)ius7(P<5#JmVHeK!
z1YPNM=t<a}Ud@LGN$NtDlToam+)5&0U&~|YXs{&cyCGbvf~Bc6NRks+S{5vubDByG
z0TC>XO=K;pDvbv{LAQksmPUfk>NG@gC)XfRs&O|M-I!zK5BlFcquL@ylT_+<-ToxB
zpLz&ztbvk<5D?r~y)Enu`hq1N>N$PM>u}}5PDa^{KqOq=xGES3mfsMru)0$bBfHVv
z(b!-Gx>HjqMX(%=ln2X$fe-a8_^LYI5DuzTi3zm?laq`29LqSUPEWFp+L*%jLoKU-
z3x#4+nG35>IEE<T%V0pvaAg{X;R{!(U}ZAKDl?d^Zg~Jc5vq>OWUfY~LnsQ@25W;g
z$wj0pMWQ4NOL8a6bvAV#WJ(4lQ<H4PGX|bwAN9fdISGa8#HP+C-7NB+5S%b)wo1<?
zJTW-YB1kqeKM4&?ip>G2d8)=KY}1)au<KR>G8pKe+oE9`6fxDqHk!z%A=uD*C%wro
z$~PYkM&~TGnx7n;Y!TEYn^CX97fH^=Gjf9#XA0V$QY6lTB5~O2=$hJA3d9qgk<x0a
z!Iwn9g%L@UTCXZSpJLZV!c$ue__9iuph9>Wsw`p6smTQ>FX-U(;Plp8tqzXT?~LG#
zIc-)4X9i~$v&i>Q%dPtv3r-8hbU(dl4&KzNfXb=8K!(u_3c9PS2B&G802eZvPDVw&
zNJ@!KW>u;SPD?IC7cpW}Vfj;oQ>{uIS>H)#cvf)M(qEl?{dH)wF6k%lU{$a-xmfDJ
zY$2j=>GHq6{`&FqQ1!_O+Ne*$P<`szU{!Uj1yDHRPNiWX+6AFB-wcg_cPvFNbrlQW
z0Vo)WEyF`RXgPqT_x(lqhE7J{BR1dJu;8{090R4U$LCCp+rQug^HlH-1Egv-l7GU-
zk2npS{1IP)WGieWQGlOf##XRZuvOuCh6>{evY;|!rvvYE%K()&D$nd3=_C`OQaOQe
zWCZ{Mbu_{PEUqX)gStcjAe=Tr03aZarVs!K=usmqAkPfy(Io;v*7|uabs3&u9<;im
zOlKA`$7#B<7-y#$aAslGPBXm{9m*o2I88@oWx5TySS80ay^1~M?BOdlW6*%xJn62+
zBfSPFz?co(>9v48?&KMG#MU8!h9azeywp7hcs=a14J=1YwL&f3uh0^8YAOT=u3v)b
zb3hM`u1D=G!j02yW>MyJS7i~0obH3fg;WeWZPepos*R`%wFJ`_=(_J@UDyfhuH?g!
zg|Rr@&Mbz}>2_r?YGeqD0z(plA;zRpXJRlNUm<U3IG8@KfHz#8ykXho4euatxC42^
z?#UZb1H4HH-cUSvGgHJHDpeSSV0wE&pEt3hneE`ik=@LPGkZ24uIv^*s<LPCagc=I
z4W&^L)Zt<9!)WHm*pV#*5ym*chcgR9m!XFK@pNT_e5Rp3U)C=mIQz080m0sv#W;fi
zLin;+xd4P9zAOyF8F1H}G<@zkB_<aGZq-;DaMzwRN<GA2J_W{O#wHM$P1(R%{&Mzb
z$@bP-w>h)l4)uF*hV{Z9huF~=)a3J3mR6T3w?7hgwtR)<LFWx&mkPRc41{`pVRz6y
z$5~z0XhzC_zdkodd4irfAuO3{WFCklVIk<wUeMf_qv0AWPg8BttW(}5x2N)?qtS?j
zOM)eHBFdBcG%5vM65NuoL$I1m60TI{_{|||-jnQvK#(pCn{>k|u!h<eb_ShX$+Tf5
zQ$%Jmw2NLnY6OnpE9m=H_8lxDWi98S$m6}iuS!z4F@VZ?4eo`7BPNjil+5m~2ogt0
zljnk>dRs}&HLw6<dZc0Q&C{zJF1R_l4fq(`tTFYF%`N#Z;nq0ozoQO1Wj12N(!;Q;
zL>8Twf`M~-Q}v8X;a<*@?r!ueeLk2@A7)7mp4S+Ii!_EYtGL5nD7EpTpf^}@Lm1lf
zr5>ZeFfzS9H5=Ch_v0LnOHH$JQP3BYUYDZqSgtS-49xKdUBOa&>`Ixj1{-T&=pO?Z
z^kS9wA+7zOOOKNBR&OiwdN4Ad!n9`(25gLWJ_}EdpKXh58Q~0W*;vX^FZ0eRGd(ew
zYoqua<4pZLr)tMw!C8Y!90Gw;>_S$6r7f?4C}hY2unfBJdSz8rt(PeXIBumedH|cy
zq`#!;8JK?KdKjBmuT-4&Q&uci<sG0!WkfP}(5aU)Vg%$7b_HEc-^iJwrm|;!_)gPp
zR;yfT#^S6&Q>s>OuU1s*6&BR839MTR>f4)?>3Pz14mDu0I{;8GUUXG<9Rm86z6y0?
z5n&W9B229*cZ`h`>$MG*fv~O+uT%i~oq`WSp!|jBm8x5ihxY~5r4blg#8P<|nyNl8
z2~+g>8tYG8W(|=$nE<6;=H?Vp>S_9>Im9^WNbW*0I*fr2S*Y}G*4TptJTy@cF&7~z
z5aOg0)%+!`H&fsu!o95Obi{h`3^^NGt7ClvrM^Pd^|LhOEZ`J_MK%JJs&?kfV*{+?
zI$4EyUmtW;hTJEs5bx`Q?gGejvI+v$2R(UQWypK7Y8qlg&>Pza7Bf_16srMkuUdIn
z6*Bm_-PPNCh_1!mbSG1&#pu`ct-@ZplW7XB8ELJCv8D=^V*b$Q4+!pDbIpXY`Wls6
z<`vGN7}H?-5V6*};nPq&=Fr>taAa@c!<jw8hbwz4A63~;@o|uZm_y-s_8dxnm>z@a
zYyrOmBsQ}^k>7jxaAt@2aAgnjQI*}#$3YT;AB@W8_fGJ`G#gB#R!%;sjr=a=!;ytL
z$Pc|IzYHH(jt%lzmBk=|FDrmA6rlM!)O*2K>m${OK~2p?l0;pf%wwt6Ty`}}FU)24
zGW-5q_IhUT&Sf8F_NTe*^UVG^mwlbtDwoxe41vTv>I_B{Ob-|I@#7TS%-+C<BYQm`
z&g>`naAiNqM^*M?d>kYp`i4Q&eJdR6Az+=>SZh2il(7{W*7)W$6y+PH1JA~)xSCI{
zxdi~C#zqJL1Y(M&5CDie8({(YwLatWi--~MfZUoYjX^^|PhLV*H&7QCc}%!T482dK
zmII?BUdyLY6L^ffuhf%-wfM@s(Qg5lgvlW2rasKtuqnc~0Z#p#h)Xr%7o!oiZcua}
z(G;E}EWnq9#(kBs5u}XPb3)1+J&bmB6UXWSI%J1lK%RUbsC9gW&&YuQ7ecK1(R|hC
zAn52ysq@*qOt%d5Rgk-jQe|^esm3*9DqWka#5Gf{(kBZll^2P9eD@p4d^%+Id$BIM
zD0vw`OkS7c5q4spDv1D{yn<MD+%*G<I_?iT=Xf!-okr}l$4k9)JYFlKs>3BM@4}o?
zus2a{i2M9I0#(3o)qQwk(L-O8eIxNOC56K$&K>e+B3+BmA>h7+52CI75s!N6d%!(g
zfQQl5+eRk<5YVa^@I<>n02E*?t?gBSkL1_SF`4+y^!w4XEUTLl?xwLW{2GGf5<rgR
z2iR-V+2l22P=LG9uM1=nuRhNrKS!BnY!T`85>fclnHub-1vn?yboxrLjlt_>4eC(M
z=wp@mWvmUzO4%5cnFVB~6gZMA*h=z)l!^^Zu^SLT*78(8oH^%jaOEib=8T=e_8h6Z
zVK-V%%X+cp2q-gs71b~qq>VERiOvDqXS!pE>5V*r&C_vTVz4cRSizXW%E_zQH4J>C
z4sR0Qrh`VuTA_(uBRq#FS6&>qKAD&e+A#Lp>+#e&kvfj#hgiuyr%JcuNPZZ3XYw%8
z8I5%oGJP$w(=BZBBgkMR+bunt3PULSJ4@D6@w`rWIwPfx*r`kHYB`&bzRVg=t|mvM
z5sMQlz!vPIU}C8N^-9&}P!Dz=RF34wSfr`l(yfB(ZmVCcUh2V`$64)f+~;!3QYG~S
z#?G1gGe;06<x5@W0rYV|>Fbe%oo`_^5_FDUjzs*8ssv+Os{R0(5#@u*Wyav2T8NO*
z&{VJ2OGfH03eb<XvBuOLi(w+eQprd?SXA^0(8NBe2~*!ID!QSt=to6GHx?E>T~Oq-
zdC4LyRpBkvgD+(2MVYrU<j`xfl04wFR%Ff`%1yP&g3P5CWV4&OWp@ux#dzjVq!!3T
zpnZJy5{K*+^tLQdlRLf=8$J{IlfT$a=ty<)X7=A4f{8na5M8mt6cFn%fG{i@5d)a~
z6o`|bMlwORDV!7$E^-ajW8eNKFvp}DG=~~d1I}SKnvI*$LKCub#zal9XWrN?WOoEf
zDRu+5;@Py?j7;~*p^)jx&SUF-Q=bkQAqQ(Ws$(!!uw!AVd618jRWRauv&yasW}gAG
z>|EA2hxo`MoH@-(L8YP?8FPr3qzoo%Tdy0APYyfHhLc)=N?l2s$6oh#P^shf;}*y6
zV4<9W@$~1O2h?ONatwD~HP{c9s&5*QQ*<*Qjf-wvE#Kg1KuiQ{=ODUcWDydeb*9)`
zmU>#B6pEpRf?k4j$b%%AvwlsR{48Xk{lM7kODaQ93-nbFU&=$UsY>Epd7Nw!#L(Pg
zXh{SGUs=eE-HB(@8W|5tYkIRw`3QSVeFA2NJUm6jfc#Mggr#NXK|W4a!2<Mvuqv2+
z0nBC~ps&I++d@8a>X>aMIK)2b<3|At&mb)XhDaYonnSt@c@!K;9$)W{%&jzsh{j%A
zp4N&W?B$S27b>-CE42ibx^?Ldwmo-2G<AH|n;?#Tk)&{wHjJ5x&O0!!XXDw4CtM=;
zhhb8?fa}8tRw(Dxqiy0H*r#Bkhrhf7visciOXx7Co9Pa8Iok%DIi%B-fV0{~HDWmt
z+EFzEGqE#EM@7}OZZt^YC<NM&g%2l27@%sL)bekJQZ4s`pk+SDTNb*ITnO5hmwl+0
z?qus2Ewrvvwvc6LfbS8b!-PbIFF_r3JSJ@22rgFn7^=HLmAr=u=bEezEu%OI&Ap^y
zkda6(Z<yxH7@ysJq}l*?mUmq^pDXjW`b^3&K7EdLy~MBl+m+at!Reyx8SL<XQ#G{>
z?M0IJqeAr!*>$Wjw2}&e@&F2pA)=G-gbs_eHB%_Y>+@IGkq?1!p=0K~6}x!LMZXHd
z<O4|jh=&j2X}Ktk$1KTi;Dau@3_!>M7p)Q(eY%9>!cxRjK2BD_BMga|tO{lif!R3H
zvOjvFzg5B}dnQY@P>FTQ@OQ?Vus7t@!R8FJ8X;VI67*&_qM>|Oyyyz77tzKqU12Fa
z`yihM;YRx6)#2vYx4gD778vU&6FW)cX+qcE(8Rc=u}zGJ9Ief7#>g6W==Ym2llAOO
zp_z<l1auB_NR#HvX*9{R{zg-Bm;4Q;6fO;wYFj$PtjUc)=_q3=%^A~G9^070n1RQ$
zrqWYcQ@&dgIf`R~nAsb6-`7Mx<FUy3b!r|uUa<P~k87ntKTp0ak9rtfWzq|mh01J0
ztj(#RY?L9E<qYvML|Ai33=#9*c!pSJ8RF?e`No3h3^B|@Y_~e*L!}~r6pb;~R-ZI@
z*oylKb6m%sN5~zcGg+rSk|(VQR)F-u9O?KK9;GI}j%57cEKM(5J$_wjY~k22om(-c
z-ieVBPvdKpU@+La7&8z=FNmF^G$eH&gFLBoP`9vM@d;?$O+AEyA$0ENn8ikuSb0u0
zI)wGpE#9uU6~>$FJ0V@u8D?G1T*9NwC7d&tts<b9OL%;932SrFQlE5hMgP)|qk;5M
zB(ZNGQOEw|DDGz7DBk+We1<*~2YZf8Ww3J2Bzss&C2YyCP>IPWK!jzS9<$Mf|A4}9
zRj_K###2!|35rvU5my#N?2I{dI$MHOzz5u??rlRMBhf;k(X%I!V5HR}Ha0$~EHQY~
zMXa)E!l|hmqo*#@?_9F&BD&aeiGV~Tze$x;2dn42=M*ZjR9R5vA5kUwEhKi0QDuda
z&<zUnWAw)jhr+gp!eCvK6EFD`8n_ezP`{_+p7NyoY}(sDp<wzjOL)Jn@zUwVG+RjX
z9id?f7Xpph)qrg7USiJyL3RzX1xv*OVP}!37ewX|sopk^tO?cF9$BCB$eK|eS(Ecf
zSTmF?9$7Q4N1EWrol6!gIrfl`>{`N=f}(2a9C(NhDs`tTh35FIaIG1v&7KJYmAX$N
z7~$k(aMXugwSeS6b23P=Ns*;brC#*Q3NBb{ZA)a^Ii}d&h;SdmzfJgD2Dkh%KG3o3
zH;DJ(^Jm~r;)B-5-G}f#8jdHi55Zak`;h7-Uc8Q}X?8Yx8ke`MLn)MH4q;bfZ9mt;
z6tdVR;D|DdP~~j76vhS$K@Ab}@hl9%39>n4Ro(1q^tE6TQ7wL&W$I<B<RD36-z!#$
zIkXvAb4bdxoxt$0kNlgCP%1|qp~xi9lZHFdnzd|ZUvbiKE+1jHsqbGHAvaGpu-3%Q
zL|6z*vC!e8V5!NfVD<$to6*o6`#zqsFb{iRV6rmT>se_c=*ey?Y6I`yc(oI4v`HH*
z<+`2Eg0;Cc;?b)qb-ZGRO8I;U*0$kL-_&&VHW>n)(L;bY*-5_1uGC{>X%s$gO@&eL
zc{>!EU%s!F#iQ=7ca%!Kwn~q0)%xVxeLRI)Yxm=I;Szd;aFbC9mRKh+mBo1L=tcJ8
zv{4wSdM8wRGd>l71K8#ePahb86@LI9DBkjc=5!bAbND={C4W}aEU%7}Kfs92UQ%wv
ze#oJn1G?Fh9LXOM`z6Pv7otkRC5JsIPX8EX(}!8Yix7>xDbqN63OM|Pm3)rbD;)Ap
zInppG`BS2BVz&X~V4c|I+G-xvR(Ek*$$VQZ`TN?MH>$0@#cf@jZ;K^wsjY(Z!BX`@
zjK(C1GXvm<Gq8MY_y~rjWjja997>_k9Llgs27uI-x5->b`9EcV|6jrf{eK^T5d9ya
z5WYDR!k9xWh5z$W;Qy>@`~O&Ze^<&CYZ^3>hGjC?snRl{27%I30K)m-dMZ3_NRxmg
zF>SSMprua;IK`b}z>!YT16jmqGhUZGI;6n^<={-XiC%C<b~{8?$HfElc-sSU9tMNw
zAAJp%!_}?$<b5q?267hqeK=-=*uj1X<8!YqK_$*I{Ez`ZW%vZ~iC7<a70<3FU{YyW
zfc;U+4y-p?{sUE8R$?Y?N&7*7n3x4ibLc&i4k3+2VaC}@0bw}#5I0QzGrH3dJ85DU
zffr75RjN&fC`XV=Gk=hL^c2M*vx0Bq;xXirhL|4dH>jqLT?-M*aGH9BmWJYKfrrrB
zf>YfT^01e@Fn3Fy)e7wA?>_IHam)dwSrSx<Fw*4zL07rE;!9yHjj7S0d~tdK6q<aR
z=3teDr7BgGd<JOwz5>?hQZHX-_2*}-*f{Z2#XlFma;Y!-Uh+*p3ru#HU?e+&2M1Zi
zTtYdHvGn~wH};w4K?1SkM16o2hM4M0RI`AbGgV^>@#g|B2ZCY|)${P$Oe)EBI#y~l
z@LCy{Up!{DG>=(Tc<Z))0g9$8j#uLdI;5LF8JUcOR_g%TLRAq}D7Mvu)7S&3)0cf8
z#f@aI#G~aVSP4|izH$_Jt?*p4{+c$-X47%0iCVdJdbK%3V%%!u3C?riaqILNxyg6y
z^x6UiX)<Z#*6DRJ<=h%O!6rq!L!#KxtYZHHg5>i^C<xoZX%BAJZ4&`Crl1XZ{fs+8
z_)f{v)x6wj3ftrhY~~PZn>E)=fd(i&vyi^}nilC!>H%1`G<WN*>0g3=dUg6&Z2Uzw
zp1q8ft(F$ik1r=e4}UB2xvxOrYGCVM4Jxj1R(TNlmaB;_s8jF-2^_byKE_JskoHA!
zcTPG5^s!$fFK_Nb=tI$oXa1BHvjZ$0Z(IuBsl3FFd=5#tJXCJ)PWY0GVb2VfXRqTk
zTw&^aM&%*-jtb_UdrK%1OT*>nK|Tv6A65oCS$wJ%EHB0tUz`u|Q-8CdusaP9DE2aw
ze?{_lb}SeQ+8yHouI^ZHRL6q3jy>(obu2i3$AVVJ)bV+YlpjP7pbGZJ9wTi694T{%
z#r(nz<^y=W)k_*m!F5=i5w$Mj%)%`VR~)sn8+ihV7S2+3HQG~wZG?m2)_-CxyWIlY
zpa^5Wjq)~_NoA<g)+P^QwKgk9X|pn?%@1Opw9KS(d~H_RX7Y<u^klS|jN7xf)nckb
zRdyeF(yIGdHL8zQxjx>3p|$#0HGUtfiY?|Uj16j>%3@fYvlxUHZB0yzzbzz@1{P7*
zY&N=D9voMFsV8JwX64v)S`QcWIzUTazT#pIF^Gk$Ytp|FcgsbJ{OcZWRx24j3f|2I
zs|(Jlu{5;;NhB1pwa0@;t-Z)7?L~6h+vCb<FEXz7Oz>kQ$zB5?p$<N>R}-!j6puNW
z)!z6aKyRR17Uy*<6Ii$=Skt=2NjHjQKaBF0smw&*4aY0kH`j)0?R_&(Z*;e6vmZr;
ze0;1$w_u8hkJo~SImA*No*d+}VBgHjVD}Ju1)|(a!5m`q`bu>Eu6$jvu636V4ih9e
zOqAg8W60R@jZ+5)eB-Va3mHl={=~txNkNPGw~ab%yd87OgwO=r6ei?!Her-0OvstS
zg^rvlOc>V`z>bzML$&<w6g?XqEXLFAn4Z3^!DC`*qTT0-xjs)E)#r)1K4Y9QLNbp|
z9KX*KbHU?6PB({PBB+rM9;t8VSK_0_G}2J1m;67?M}c=hf%fKuymH>4T57a4jWU<i
zFQhU}vtTKgdi9j8>WiL?Hgb;m_C}XUp-HxSC*{;TX_R^=<<#p#mn`*88dtq0_(^p6
z2>OTU!bcXHAVwF$j4sTA3pHRbG6`!;f&bczOnl*V=_9Y<yTHfW%Xl0lv7%Boc;NP4
zR;|?Ag-X4uuGCA=O1+q@)JsTNaoL;NwSOlcdl%ex7aa$;b@9PoO}&B-x+mvNj4>OL
zz-QFA@vO$v{^RSRe}XO>FxSlkvfMhi8bJO8@MmMyREWO~dFuX)SSA+YukoQGcz>u&
zsS6AEV40v=vFBciPrHR5eHUc`>P2Nw{xPV~x}WrQG{WuH4L}WH^@~xt9%=HoXg_-+
z^X3pAazbW-%`nYv77XFs+p^ftpyDSaWpz2#X(-EWXmT8Q_2@aU#{MAnIy?Y7ngafN
z%^`Byd;S#M%3bv?UV`{HA+AkT`$>#jm%@q?<XP3H8l)5(*<9%<SxvN9;V!EXvjcfn
z4JE0C!m9yJo-02kvxZX5O}1N{?V&;0Az-=>>*+oZ;ia@J0p#+ZF0BmV+Zxj_q(M*q
zdRFcbt4*$Faj<ZRbu%A4SN%8u9AbH67|TX=e5X0YBAj6HQLu?%J^KU;H{ayIRd`kE
zaTP|nb60r<F{aoy`=YZS-Yt3spUJQs+VN;R`%Fa2@UG!g5CLaDIDyEW`@n8<XFm0S
zqxc}y$rp9!;<*SPEYtB-Q}nK_<y_co%g-^dwLAw;Y567Gq2*KH(XtOrTXt0-xhRNa
z0u<j;4yCpPq2rb(nH;VH518)}NTKgGJ_cjb_me&YoH=wG(joA0;(TZgB9!F89Yw{J
zbWsdF#*}{x2$CBCV(X6UvaVwvWkW4LHrQMtk9;FiGr2+6!S{pi_-|?730iPtTLnDx
zw*niQ>(YJbPS_Q472b(;jTT+{Yh96b6<(1YO9}tAZ_K(1Z_JLRM6}3L7F?hmO9>y!
zQ=SRevhMD>^mm!y6XG&%@dR-SykN1M$HRNV;xf1&WaBFNK2cnzB=0h~hwQYcHSWG?
zcxKR<{TiQPpQ*n!<P7;Z5LjJ0!POjMDQ<)w<g>tWSQ+f}N(p2~kWYRI{jhFklm<&N
z=%0qLg}QzuPrx7Ea2>mJ6v3xKkh&ZikQdTn9%sA=>sB~)`W#v)<V8|Btg1U{%7u|B
zqf7M-H}fEC#(oF#88CD)U)ZAa(({oNyuL`ln9{!o)%0PO#1LK^FTJ3cW*2GxC^Rg&
z^xeh9yIK72Qp^(m7)E3EPC!`ne;yAy8nek)Ky&GKKn3mhu=0OMWtMQs)R_EF<g-w+
zlP?ds1M7lQwD83aSP`Fml@xc8!W?27<`BDU-;HT`pC%O{hDetDFSfk{P(j-lvF+ES
zZI*D?)_7^Wm}W0&z9Td&(eJ&#2*`Foda|C=Ce`HK?!yJ<Y>EWdxAt7AM~jNc%DO)O
zHrss#R%%cHZ>jKbM%#VGD7&x7*?k#erDgXxk{ZwME41Bfg@=P?Fo+{TJ;0S&tr8iV
zzo5~j9sG7@t-hN}Ebrz@DNa4{)g7$Tm^+q1h#hUfsqg1Jfu}dNC&0~4?Fl%qnR7}{
zV9jwnVJFZ9*3iq5)}Fu;?Fn7QGzhD<C$QvFcuygVeimy_V2SpGy8*!y?!klR%dGYU
zxK&~00an(Yz>)$_xR+INd^Oq=h}WKQA1TZswqXvjyYK|w=W*GdAVLh0OnU-+wy^De
zY+HK*OSC8KDW*x0MtcHFv?qKS&{$8HSl|iI6q!3EIE5#W)&Gnq6dXG7U`=~M)hJJ>
z%6Wnr$aw+|oyPM7>(EIn-1Y=Lz?E685*eE@o<J-;K}vI;z}&Hm{Ns3nD`3wFywhZR
z!u_Bh?KHVLftQX>=?Sbkjwc`p7g|G_BmH}cc`VVMfM8ijlOfF?g@z@U!g~sf2U+~@
zQp^(V310yOPxvYx^aN(LC%~-=D__FO+7nn(;0X_~DvoeRdjj#=6CNamIm9;1A$Av@
zfK#ZvCx{S3B-5S%pDk>AKik%xz!L2V$zqxVq|u(h672~O0eWLkD92#hOB_=qIE5#W
z)&Gnq@TAR}6L|cpJ)w4#C)DOV;Xi#jPrymrc%D#e&k0nx?Fo8-E3;Z9GB#s8fmnKi
zl%8hK3CtbaA*~((nE%>Q>K*ii4$y^pX!juIcZ{;EYAQi!4sjZ`znmZD0o|jZ0Ld-6
zQkkSvYr%(St08W|{@$f!!66*aW3@Ubj8f->oI39;$*B{EaO0`dI)poRp-_(Q9-Psa
z+)G`J?bo5VvtI=VZ9I_G{hBzcUlViv`k^=1FC4Uu*Dvd!?bzi)Dg6rP21Z^Rw;q<^
zDjsz`!txUV$-koCa#yvfXLRh7ufmO?t;tnr8B^hlbVaP^e7yWw+-B-wyNvICCj}?9
z!klx)2D2#&y^n#1Y@?DV0rB_H<}_J$c}i0K3jKA^tH#DRM~s_H9CRwKP~KeYwmsYm
z0>)b0psmBX4}$DyT^rz(y`6kg-$h5wA;NrLjbC>N^L^fPkSpachkhCWW$zf(<fmC&
z#o|+_PhRP=jOxRWz_m^@MypSJ^|v6I&R=Ll!^!y+*%G!&S`@KN2s@hQ7AelHy3HX<
z__lfU<j`b0K=N2!TmIxxmOnXX`Txh2v;4{9TmED_K<crh+~W*ZANf>eKP#oh_Glg?
zRwixU2bdC^(wfm49i@p>Ek@;SQ^eHJR9g`|YuAdHI!X~!bBg$$GpC5D<11pSt%%WL
zZ+tt&o)(&Bi_J4^E%vlgVo%G7jbBiK*)d{;r;RW6G%fbAZ#zI~4sk51&$9+f57&4e
zCJu$s;?D=_o9IKMyM5!^+3gvj8M@o+%!ZO&zh{i<_l#V>8`vA&?-}Fwdxkw+(mfr^
zXuNXzJ6(=^;kQk1<hOIQZwrlTd3b!IJ^FU4VP<G%P7S`C8fK1C!_1r-YRsG(W{$6h
znYJ264d2@wc4DDePVmy4;IUDH$8v)6uB0`7vGE0u*@BNhe$9Gp=DyMR71CJ2S+BE-
z0@w_)ke?|jc%wTjG)uqHg*}$#`ZFv0Ff1s4=r#-eL3JFueOR$SEDg^x5As>?N|#Oy
zeppTB=fLsf=<!&1Hs9um+gIvou2N57a?BT;ZCzxX@<vqF$u@sG1ook3Ax1m~eg7IU
zx)yo<5x>x*@qfgcrVzgz%U9C>r8JLc9gwRn5v9(<CkD7&&c~Hn)$z_%1}1p^mEm*o
zqUL$c3+FAI-%5;2VgICBrYbe{Ql&nF=Y$D>J2I)B{@vK9DfRg2kPXbHZtYN?+obP4
zPhGdQeGSSUM4o5rQ&;sScaj)?kli@9;nva;$f^FzSjan;Y<(9#><6^3F2`pdK3Ik-
zOu|xLh391C*$=Q3n9texus*sg@?%8t7Y-9g+i>8AeBG*F(r=ajIa01>R};Uj0@JqI
z8eCoJR^O>$dZnayNIF&UB?A9z1vz}NoasHahw<xi*H<3KPsTms{IPGf`UT`iDPW)4
z;P|m`lVAd>t`6r<YMHeAtkk_6t-IB~Aq}akgD+M_)P}li>)h%h|IuKb`asPKLAUx%
zfc&G<;yWrx|61rjsw3)5P)5|Q^7|meSHnj^d4rRb54f0qBtY3_IDDX79wE(LRmAKA
zhlu*JpO`<Ghr<zdt-rFyt)4(>L|s$)1;Fbfgcny$g0x29CVczv`Y?GqfuE#~IQLdX
z)Vom{QC>;kDU^%Q(<y3R<aq5AwV|HrgmU)x-Qk;{hgB$bt7IMd%ncn5&rp8p*Bm!7
zFW0k`kB6B4KxjTuL;Ump!{LznPTh-@A+<8}B1WXT>L&bl=Cr!$Rc`e$)U8uXYN)Xf
zR2>daQcwH$0{<8D$G%C*=b)UomEMQGoF_C54(d%!xUYJO+FD5o-H_9*o`F28RSMg-
z2-ZB4YMa%u(xbtM`c<H^CZhhip1L~jpss!<n8UUARX3}L>PYh+{#__-M<3nlcaRqH
zM+ULb#|?j4GwhI7SD`M(?xIz426E-@&(|^Mz^6ieB%*UJ$^D^@IR`=u>wT&&5pIQr
zhox+8;6QyVY`RWzu5#w8VejPf?g(@BYN6zgSL)m%bp&Nqf$On|ZBQ$u?9)}(SG6M7
zCb<`@8qq=&zcA#OT%o>?W?R)%e1FGbRH&CBL#vvBZ!$2q7LMAAZ}W6YZcr#2)j5*;
zIl9@Z=Bx80*D3Yhp)QczqYjpxrM4HeaGu&JWv@%U0reiqeM{=?S6Ru`Bfhn&kEvnF
zy#Uv5Ri9FaAoVB8j^l;%GD#zT!cUc*K>9CbFXiyBRQ(3<?ID)lRQ-FTua&)ubVvPP
zk?PWLt-I7vR}0Oq04a}!0>FGeLiow*AmBgM)FJ(0%~YhjYUh>)Rez`zaEG*|Tf9i%
z5lJb}WXQ7uG==iqQ$`u?k+uTiwJ4ohwxx85iukscMpV>159yyAJ4@m5)xAjHT{nQV
zuY3^c=S#OB?QpyY=}qM?!Pb6Obtzy{8tOLl5YnriOkXxXP(bq$z?#pI%Ig7tD|}<F
z)?MZ;pdWJHj`WD@PO1CFaqDUs3hO>=z6bP|J0C#$As2Nu;AYzDc^H^iJX?@H;3Xzg
z!gPm^X`f>0L&o(;Up5~{dY!YdMfONbtNWX&KaQU2K0a?g3Hqy@9Nz;j@_*U<qHuUz
zbodmsr;P6+oof6u((}z<A)Vv;3({Jrzbv8_xThn1&E1Uj0nbk0_jyUVq=b|um>nak
z&PU1v1}VE7q<qRr%CEafd6S!zbzV|-dr0}Lmy{nVA?2Xr*cFbB?n|Zgq5)%#*c{|2
z{HD64Ov_(PuW58GTic}kk+}?MpOZSg%(W8m6>eJTHV-kcd)k5dhIbRv_lmZ+`KaMO
z)d|cG4AR{1px#0*;!jHYteazTw~W`jyu?o^A<fTA2!BOJs>UQuUe9k%+HacYquq05
zq*%AOmhN4#MX`^cG&!bSPWJR7v8W+8<vG*C)a%_2{M}x{SC#aT#z(kav3K7v2;b%)
zO^J)J$4#2w35U;!mb#?w^b*qiNP7A&iZuT&b${$2pL|a@M-hl{SH@76fF>#nh%0Jd
z#@uVFIg<OhT2_7)a%UElIaH_4c~g=55y3C8a@j})atSMUqyK&!2=`gJ+x->DU95A`
zf=j*EDmzwpKUQ1sw{kDmRUmiR%B`u*mbuj@tlZ|>E08;4<-T5frr}X{TDgC!g+A1M
zR*tQE)z_>XTlcCbbWU0?QQxu3=GGl9D^Wj@+=zF1-N%r7E=PG|nNJPRq?Sh11%5Za
z+xCc+3zUAoETC$*phVf+(ueRKFJtAt5%|ZlO7#ztqdtF7R;_l-A|-P_FPo?yk=%%S
z0_%`T>cQEX%L@+X)?@!ho*zV1n52fS+`kCrpRlRpEvKJUo+z87Zkfy6uZ$-2HmV+!
z+zDe<`O9Td<;4jPxg4$hbJ<k2&B}c}V)|#Odn9+QY6xG2S#{ZbO?g4>pUWE6)sp*_
zu{v-+ev$4olDh&&|9*e7dPH*9*8DyaLGD|+%=?GHnMSkvft7nLV4&VJR*qvdSDmnO
z9HY7FWh?it(3!?O^+zk$9WrpM>NP9(Jj&)P#{x=rh4;58o3F}rPR4J6s>$Ul)B-iI
zNK?|57OMSLj<&Q=jaa!gwRQeQ>Pjm|$rh<=B{$-wWQ)`dRvE2lvAWeNqxCFSU(jV}
zy+SQfKhQb#fyfm9Y3g+=Hy@O;d4MqAba7OcQY`#^(?xyebK5aSOI6ikmR;e!tUBgj
zt|m%u#Cy1U0dg~Rne=vrTBdVqVqm#{g}T_Pcc}FD&ePSUR=p3G?nmwexq7SpE7gBm
z^;Xoj`&X$6OW59s`ZHFTtJG%6U7;3Mo`tf{SUG29r+<z5wUw(v?hF;hF(s|8s`6d_
z_39^9ZhB?Hf2R6j3(K@koTZLQPREe5)UPCWNX<3}{Aa0uw{j;P_0CPo5JhRqvz4zX
zS1GweDsH&(D^Gii$}*BWVN9-P*=3^A6UOa<8OU8BIn8s6I&77Za*O)JGRmnbx2PK=
zrztzsXRI<(cBp83p0Yztk({RNRCBB{Qg*6o8}gK$YKG*rCbp^tlGF9Js&`245bh5i
zGPkOAR<6ky^q-@)TDgQd;y+hiXyv9luk&wHS6aCxuFv|<Qy;N%%iQ<--=#ig<y@YJ
z{O7CNt=v&hz4HR~Wh*!29rV9j9kp^ROGf<L)l*h(n(sRQ4)uzat5l!$$JI$IcbRd&
zf2T4wvVVtEzvCf)mkL_BpE&EC-KxRLJ>?qo?@~=x?z8R@|8BL|$|XJ5`S+-}m7DJU
ztiMNHWaVD;-tWIi^;x-_OCIv?Rs7umwl}QmoxSQR$!QJttB*=fk9)s5%>A`Bn*Hh{
zlGBt)b(2-bF-odw+w+u3HA8ZmazM?q%1Ak&u8!v^2h@iorz!WTPgrH7+^6o^pQqdh
z&`>X8XN~m)j_V9{SMbfIKf`XxRR2-Me^cT{)OD(xh0nX|i0>;W{#&3iuwEqQNabkE
z=dt;Bs7yU+$j4Nf05O!rsUG(89ODa-iVEx&g%4@4hKP`oJZ;W}Z8enHrNymRq7{c?
zsS_(a);(0qbS(X;H1JilJDz^X8M_ye>Ys(D#*Ed2=FDGkw)tF!o|@`L=)socv(i&7
z)mV6yv_iZ??GX)(#r#~x@!Qz>nd-YpF|yL)*pxKb1Bf<sdWrPq73rzwGblPQ<n}*J
zKUU7K1UzaEPLFQJFHGNzUG~kY5}yb@)%eulQ;ScDdO2c%iYvfcq(1C|Ip58fv{lkJ
zN!ukoThj9+jUzQw4^p?f7ipDxNYbZ})~O#Ntx&HbjVi++O{+xkR*BrCY*Ob5-Y}2)
zuTmxE<+X?L(-<EJd|cI<t>rhW$>xRCw*voU=yPh8`5aa*v&_>1n(~V%ZAIw;bvNeR
zGWBfaarAGf?#K9@zgvU9Qq#cSjs8Cyc@?w#P+f^pVg@DzkT%tPPTeV`cdEO6VPn2I
zDO3ym_s~j-xg^v8_?6meNFNF|8Owxnjk+{^I^Y*d*BWco`|Hj$rU|@AO|IW$w3+kE
zaZ+M_thU3LY`#!`zA-`FThA6Ruia^U(m1{TBBN3$_X)R88sDwH*w`TOGf1y8P6+&x
zq@76bFfNevgt{yIsIkvHGxUUUwvj1+1?gjTuNemb`^^Iu{O3@OIY;VtOWG@GM$&_l
ze$u#IH0ej*=ZNH!&Go>1(pXqeeLfUCWU|*EFh|U%>pl$l$+}z2%TV_YW5m3%{!75$
zS^qt=-Mqc_1;BOne=wnk@FYix`KS8njuG>|x<<zw(aaHJdVLRIYV4qTs6Oeq+LGjj
z(jmt|;12;q9bT>8hcm5DnlIIU&~Ya$;aa2$|G3zlMtRxwfG?@KUmY=D^*sdorW*30
z{!7fA{(9#Tv7BeD^k>!YbUtLhP+Fm$flhGlW%So?cAhZG%g=GXWV{bQh4PZ|g~|$~
z*Hm2q{D0N$bRIR^!h4)gLH>TE>CpYq#$av0c*&@7??<i9aMt+;<Fn<5oNGk>HPU*S
zz-54k9CrzPmpWMcWhY1f3FlqX-@Bxb_k$+sxZk27{r&2`ATjJqyU~x8+%x7QzCSsy
zMK4}+wj0~9US!`Z@NZh23Ao+pMS8;gQRx)d3A4o?bJZGm`<q;GV|G0?^KZU0T#u-Z
z$PU*(sMkw3!8U$fng;w@Z3*Dv%Bx&w8!l*LpK3zyz9W=B5y}HX`4gc$Ae0A$vex(^
zeip6P_)+}=_aDqpg<Fua#Xp#L`0iJ)n#;;pxM?S=f$uM+3|mV#xJ!(V$Zq$Q24%R?
zcn#-{abvLbOyh{Tvh;oKIC$QR^y=CN+$9e7I&P42&_U}uVZI!B)P2I_sBIAaY!Dr8
z5FM5YTqf`a(RrC@`&np}mia*Kcib;n@KE_H?pl*QEirDZ-{7Ggo@x9>qQRfUAO9kh
zzp;FCN$9Yr#QYj|9T{mQBm8>>?uBg+IeG=-6dPM3k)+IEBq>82+hn||+k%gGdXE|}
z)xOK?G-#hr<IeiH_bJrs_1=UQ*BVb5?f$H{*6~v9`@KiZXz7Q%pET|*eOfTT@P1Ti
zx}}e^9F+f6M8EHsOaKS=@w)n7mP`|Pj)i%${&e3WgI2f9!u%-sC3B7OY~<s<4I<$p
zgI?AtrSlz>w#2*$JNItsUAOeE+v+bVGt#;jmhy~mvg(p_NZnB}U8U8&vYG09^|gvw
zSfxEt(TsNw|B9VpSp5lUy$T1Kai%%}X`^}<(uHa_(s!s7($(s{NY4~}2huj-+@{ER
zwZLnIe>*sTzhZ~ltG*nF3qCG12`TMC>CY-g1V1A9Lqd5-N-vl6O3?hW;ufJfVx?^5
zHle&tXl|GEPEh`_;&GulYNe!nLMWdQnkOZFN~C&DXr7nS=LP>uNtHn@xeV%Ox}+-w
z(`Kd8M}s|DEu}jI-zAt~fe#6MNJ_6X*vDH0f1BWM6a1aP|FYt7!9OACQ^5SO;yHnz
zm-HoIzF(nC%HxnUD(O^7S4+A>(k@AdB|RkRZIV82UZoxiJZ@eG4o?W?IbdE4JSUhF
zf;nNbN6!na99O9+<;p>N!@+h92TL6SM^Sn~c~nX#3udyEP8GP#N=Y9VI4-3l0*?rM
zMBpO=9~Jnhz$XMgA@EA)RqCVVE1i^~%}E*BoaDI!_}`ZA5PaOp(HRD&xnfu_BZ4_1
z>EponS3EBGqk=ghso`R+l`i2a@D728CA~$`BZ7Zi;G+URC-4b@m0NV;{(`!@A}a6>
zx7fB@<Ppq>z_$qexTHq~^PIpZ1Xdp5<6*6+z>@`DDR7&I{ErCcsHBEhD7~yTS>Tld
zw+S4VbVSly1b;-}#|1tiuu(z|lO=7FG%o3p61HXdguibu^&@GUVB(UFNP5CYN>%!0
z%1}!BHc58~CNA)Zq(>xuT<}K)J|U@5CashS2Z7rpjY~RQ#@0s!zD3|80zWSBQGuTm
z_=La;d(3@hhF|(6=}N)03A{t#xWK~#j|hB=z()jrT;QVuKPT`BfujM@gQRT%^4}qF
zT;O4WZ;|wfU>+CvsK6%zqNQ?jh)NnSCx_v3_WFpxM<snuFed~yDp+e}1!>wUNFNu>
zh`_f9{)oUwB|RafMv#<klEx(+k@Se9M<q2vq?s&fo22oOjCfe;N*b5+NLcWdLN94t
z(j$@{)%Yrw#w9%>=?O_EM+7hF5lK%-I=Na}sV3)zYQQ*X{NC`Ilgya8z&zjVHJ>#-
zjx~-~9e;Ir@Xw%4b~ZW}Ij?el*!d;r_wi5kEp%P$dfMgpeBARR&+ol6N}Sl8xs(I%
zsXaIW_Tp`08UCxlzd3>}oBCMfG3=1<^fUca9n;R*Cy;)x_B%OzXWb6~my{Eu@q0pq
z{~)Pd>!-O=&F!Pr$AG!6^hH1aC1k$;#QS*cCOydEoFAzPSv-JCk(!XH1Tg-MIluwn
z4BS#E1x!h~Bl80e17~1gQx14Ga0X^s{DT`<!}G0a6H*f=uT_BOA~kWES`BzUQWLwL
zTEL5unmE0zM|v85d(coVNKKqsHUM6V)WqA;$$(cOHSw-=D&RAadej=6$^_My)J&xJ
zU|$$g_X7^8uL2IMuK^C@AD*5C`5p&cslEXi?~MUR)PDl5R(}Cptxf{2!QF<LNS($k
zNaO`vi<7Zg_!q;!jUN>GSM^7fFECn-L&isqyNuJ!)6I3}X7dhn&~dBd3y%97A9a4q
zd9Lds*F&yvxc<TY8~6F1Zco~ChW9M*Ztp*rgncpJ0pI(4ANJki`>fA7%pH2+-{-OG
z_ZA?&?mK|p{b-ziyIn~4kG^u$%>R2egn!_B5oa>`UIw4~?^>ZNZuJEk+^BZ<;$)y0
z_m95mI5{ZBeck6CN51J}>VDn#HC_dQ{kUNxZrBd~wLg49_=NGP#HR`)iGNHH<KTh*
zJ<z*HU8fx8^(u_dRP#F3WPVxQWIm*xG9SkCQT4CpW6E?qiO(<8rySSepTW6KmEjY?
zXNL1pb&K<1^_cS)_^dN7b!|1ijZdF@t1;-l-gwb{qj8hD&N$t(&gk%5ZyfSG3_k0O
zCz1cQcb)NTd}>S98Q&;*-thQdGP-@gGd}3M-nhrN&iE=$R`9oMW4xz-#Wb~SMeFwM
z^INyipQn~}B)9gjN@kijCc6iF6C0B8?nJ7&UFVuNr;=TXbb9MRcRZ6=u`^exFTH!k
zV&qVNeZ0TBH=!xo(g*sxR&?uv6>00K+gk8e+G3@2qCc|{Ld16`*rxDF^mlJTyUD(@
z2NUT`PqLqlvV@JwvupeIH9hHp-uQvlz43H<{=Drhr6`LCv7dI`^f?QpYf@lo1|)y8
zVo+zSGN3cNVd#7QTVTsd(sKg+gMB*_DbYQ0@&2?8>Xz50Qpr?)6hP9GN%V=@0I!Z`
zy7p|2r_#7|C6uBy6o}UNq<-dY2Q+R+bkT~js<o@4hi|2WYS~~<w{%1;>(gCD-v)ZN
zB=&K5*Y59149FnU<X{k)ct-bIE!&sQSQS^aNTFCAdNRn|S%ba3@twVi9rIQDTBt6O
zvU8hKF!Mxr5wbI$-U~`T+AfJhajAlOnIv=T<LNyei433(J!w8xcT8KxmV1+3@m~D1
z+&Rx$@KzLoN`1+6=G;`WU;7sjJ<>n2gVA>;*It}R9Z2>k#LGypJ#oPT)!v_3xIm3E
z?A(}w^i*StBFpO8%HJ9eP$!ej#Cyfa)UvKYu})xNoR=h0GEm5M=^+C|b@4$@#RCJi
zK%Ftz-?al>SeHr;4!}AApP4vtPP}(8u{qw8LUv707fm3ZI)IEV@BD>tL10$u5~Tvr
z)<!naZig5aBMA_eN?7!_sswc|;Sg@rV}tBqT01Sky*ouEfG&>r_H^q&Yok($u0#(;
zc$3(>g%#z1-jZnkqPHLtE9t%h5Fc5j+WU5QCRZ(4q|%AZ_SMP$3<B+jME~y09^hb2
zNbNS?N|UJ5m&}M?103k-+DoMaL{#p{wCT{bNDn=*cJCIymENJ!sA*z8P<HGiY_WeU
z+{ZEpCLf5A=}M+j2iQBnc0ZQ9m42X-J+%UqapF_Ypl^jE>R7QA&@ORJ01Ps#A#<Wj
zq9-u6)Tg}_sZmMW7J$57<|AjZeJfm1N4ylzfkaB?Fo3DVK<@#BhMmcHs(WKRwRdoU
zdPj-fgO<0_160y+766sAsyE&*<~kn;i}PDyj5@aP0A)6#xMYg($A=zT+>-v=!wf6w
zKn6e$K%Pxg;d-vgTF*5VujjZ>LyZ;kA-p|b|IM!lQAZlb)3RMaw(9|0m%#Fk(-mMX
z)QaL+>YW{%&Qcu*(paf9w{KFr5slWwGjX*hc}agS)@hPCw<oh_b-cHCXS{2#Aa+9V
zF1y<5WN$Bw852ahd0nDEk?QGE(ypw9luVt8{q{0aBgagKLbiENGLtMM$S<kX+U_2k
zMJKpvDg-i2bGB$$Q>G5+0`{Z5Te@Q{U#(nQcVACGV1`{Aj$63Zd*T@sb?L{Zfkgk7
zM7&#B+UeYrLJsp|vxbEE>OnYif5yVJb$2USy=?7I$9E+*^{-C$4fG~52`x!`I-hSF
z0Q>GUlBvFUhUEwpYMCX_ioM&nuY#)4))_sCUKF(VaRELC-GSIQ4q7i^#!zd&8iUq4
z9LwCE98XapJ+_7&)yCAo7H4&D5A-y)j_d=*pjY=nk)k0PsP;6~u^a1?em!zq66v1z
zB<xV6txat~c#3x=)arPDR|0EKT4ZN(Z=$~=IhX>TL(WykAcmnkp|}=Lppyw1NYSuv
zjJhtg9EJ7e-UJ(2+kbHn))sx}$vN><57)CQzlv4u-1H>Vg6_cJp|rh!U@)_(zqhA9
zaYhgFmMvkzPbXQdl`Jl4fqk+SR8ZNvc%QV&if37{JZan;;^|B-b4E|f%5Co_Aqlo(
z)gu|J$t{EZnI4p#n?iWl0C`lqxC%9r*ck8HLll<RMfr4bVc{BB8tFVRkWiL`39PrG
z0#XPuP_1~Hs@jyvBsGqu7{vi7BB9o2G6P)GGkD~XTA2yenK~dtrQZBfPz}nSS*?c0
zW)iAx_wH0;H=#9&orAk~CsM0Y$xCFMq_+rW^S5u8zH0R-`YE)Yl5mEK-EA3sTW05A
zjs?d!kF&QrdCZuG@<nawbfRx(?}5&qOd*dkgORUcGwaAC1`uhIgS+?S3p?W}=<<vd
z#{H6HYHz;4uC>0WyF1aJ$J_1Gtj@t60bVk;u)TkmgeI=~drwIPGv3v+doU%gmS?gh
zv8w~&FLPiE4mHwwyw<7^Z%OpV_X|`|ZcB!VvU{*Aldov&SJugh{kg%gYmjri|3JRL
zvUb5|dUp0;(OXdZ&UCULjbL?9h^0x&u18^WFql9*=}TTr7w%5%-?Yni-P|;wWE;Ec
zz?ww53!xnTt~wLFiQO29W}fM+-4A=FcGJz9F+;Mv*cF>Cy-LmGs(3oFWRd<-mg-De
zK*!+Dw0uuXSyP3u$;4BcEwWo-mU2tT7e~|3_LNo7wiAH{cDp&A!Tq(aUU_bp#Slf4
zEO!dq2S~6T;2sMEv4+TBZ%q*Fy3<??DELh(qrt7I9>p#AUJ*nl84*N7tjtZl70x$s
zXCLoX+rd5O(A~YgYBLs>@l;}crZ10g>*Dzr2R)VS)j=)Qt7x~-P9okXs7-?zgjfq<
zhX{Khz&6PN5z2O~add<ypvOY#lPoDeBbDq^Tro;cW{$CQiRv0XyQmE37)}p%pr(eB
zgE%^|3_xL1Nkf1|T@2dIXrd=Ah}C-%U3&}iZRx_?x>USBQ&4J~Phyv~-YN>5(BQ6~
z{rSZhH}M5Ig!bH74hCb)`Ho81Cp|h|%S1`rPW8xsSQco-=YDE4_LZYhXC*GlaB|y~
zX{MK{j`+oi&ZL0tG7Z|3_Nqb51^qZ$;6^B!I>3lyJIGmyOf%;M_#S6YyNcd&$UHx4
z8=~4d`5&+dEInb8+n4jCw*Kzby@_~AVUd@@KkDAS9*em|DrbXw_SGk_s*Ojf=Ol4J
zkibZGuNv&>C9R-o%*E(sD>B=+@3d*eW;e$Bv4}{~%xS`Qc1>bee6Tlz?R_FOki-fl
zfnyJT3sWzLA&p*$+x@raM8(Nm@f@S@fQ%+*os}tBId>#durzdeLlUcEv0}ZrlM%H*
zblrk5R2CGMWwhdfE$VIW>D$d{grQM8t>>82%iNhB#!fGd<b;p8W{xLPd00JK(-Ys_
zkG)+_SK98R6{4H%xt7xRKwH)%4Xt2IkVP+TBk9pui2$OhwFnIb$aHZ8<}xJREK4v}
zNsC7GK&M3|HjJ>x^Y=7NE8gAK+sozAc)Zm1G?y_sx>W}<7FX?j$Y3^3V?D8G`}WM9
zo?KsZlSVVmC#}}*8^np_c+zq?na28~KNIii&r{@9(AHFvi!I}j6qH%o&xZkuUUbTZ
zNKOOn;IWW(@Kr2UE@-5SH;!t%m`na3AFkftlfklFmh_xlGzqxpF&NBzCO-`oH>t<1
zz@yXR0DAni$?Ea5;oSM5%CY3a4tm~?Ri&<nAR>D0if6<QxY<KY!z#WPGH=>>5yB8m
z4?b3e>A;kZg58+LtnH0hjt#>c7{6?rhU2Q_!1i|R`g*#0fTmh$!C2M8@zGum%6s~`
ziJf|PB;niw*<fzarOAA*-Jag^sGar>OLfUc3o~_YtD=S18&#_#*b5Y?JeL7SJ0wDR
zJo;DKc4fjxJcfQUis2k4ieQsb+=vKQ8CQ{1LkKS#U{|cS2}Ki@jYGV`D5;HDY@L<Z
zubFX+Z+oM_vTcEDN~UnXV66$&c7{h;EU+q6ZWq{V0?XN(xd(xR5AIiT;JxaAy^zD`
zBLE_Nwxv>dQfwGa&KC)HCi6$(T9wwR2zr_Vsq8oGXmx<5AfArJr@c!;#A2>J%{A?&
z)Y`rQ<WzVK-d1<wvt4Z#Xg$8%zY2H$VUjA26qojcZE7{{5*AQY7gNkdO;V_dop|59
z0iSjFwn&@Wh&rA4e(;&VoTb#_jM{_S&1t-6PlIy`aKGA(oW}J_t4(OJ38hKg5KExk
zm^zkc8)VyvZ}jnd!tH>Se;(e{+yANZxxn^;&u(x?<2M@4#j_Xp&XT|;R8+V}RR<)C
z;=7prNOwxxla)HNQ)sc1g?w?)N70XNJSl5bT8oOjO#9Wvpy>tFjj|+aXntw%$S74c
z7vHBD1kB#{puC^bcrt1~OB*%-6Gu(0g(%ucA~y(rlu?yj1Zvh%;ST7c2j8w3$kD1z
zQPfR?jyfg%E|lq-3E@xOrGX;{TVk!@DDZ>AjS^>2yBq%qW2f**39m~m-tk;r)?h2W
zqhz*QFO+>L=x|-2WDj^}(2jD<MQ?i0zujs%em;Dzj0AO;$h8lO(q@(Gv10{lU5pQ2
zU_*=ha$0N#N70_EnXQD!DJf}2o3txc%@I%GzXV#$;9iGS_iWv-1SA>&?f^LD`Pdq>
zb+8LOXd~UYBSOuM9*G`sQ1i9+I0{MVh+4WNH&&y0i+<h_#oZO`i)0KrE*wve*d?Ns
z^c%GnU-5MMnEi>u^Ek%ZUNexj7o6T>jeHO8phVSrV0!SR$EDHdBvN`9+oS~z;zK*&
z_%b!ZQS_JILYcaOn~br*7raVlfFgt2sDyTFsMf|&+oa8v_?O8@6NmAvx9Rlw7g3fk
z!+jgOK4`0L+5s889LLgSke=fSnq0ecEnJQY?b<ld;rAo7%scavd6vQBQC4M_%|$*}
zzl8Ore4=f`OM#h-yFyEG+lO&xK62{p%@{#ON^Bn>Q_8Yv+uIlysAqbj?c>@TIRf;&
zXpUPmtg~4>oe`HKLf>ZORHwB;GhN_<l?NiD9&JWXT3QpLlWlKJpyvqhmevf%Z2Snq
zo3FbbEif9fA8CBE@XgoUh&qe|9DUUEtV6W#M4Yp{u-2BCddNWHPI%k^>KI|h1RbS0
zUgEQ!ivYoQs|w4AV?<WrxuEYtj|*bOLe4!K;Z0{j0>(Oe6|KD&ccn;e>&~|K9m*KK
zY6n<EG35=SlI<E@sM>>uv=ZpVbP$GiJxOpn&=Y|kSWXZed>u47WZGR=kHI(!vFVN)
zuN{oRfbB7~)9I2*&0PMp`&bkcW)DQQB_K_)@J6ZbsDzIy{9FvWN5|D_6}NRDB)Xz%
zC5*-=POn|w=4w0r_=+K4quez!ipiTj<6s$$9Z2KI(^$tdXa+n^duX#XUr#M&$oZRF
z$3!<+G+>jJI-e406M8ctPHUAqp>8-5Z_rzh)~K3QsC#OOvL_JCcOm5{4FV%eq1w@u
zY+?Dla{N<_b1|j^pzA~U+KpC|2q9b?H0SY)+acG*7-wx+{UT*QdaN-F5>e!+QQC(U
z6#R3+h0e(dQ=PUJrF4F3ou<wJ&5@z|<d*}S;JZ;u`w)w1;7GDvTS_~)Ygts)dMVu{
zGLxU!W+S!V2Hw=CmQ>q(@zlX-#&G%Iglgo*4KiSiBV8gi8{jI(UNQ9mOM@bYOsVX`
z*@k%vM^Q=hYkc%tp+{CZ<f^-(-I6V7h0qD85Iu8g2kwUgv@5bza-^cP^Rp)BY^qkf
zG3R(vaF8g$Tu|uso9zUQM69Lp+B{gsz|af@&@GE4(z2)2bEp-!%#!`12G|#_N$G5q
zo-4=Q$kA}@UdBWk+j;5Cq$GV8C^-uxQAf+mo=^ktk=hJX>@izA9d|~JdXCKo<i={;
zSgbWoP15A!(wh#{exngE=G$hK*BT9yW6mCoZAW>u>8OQgLl=c>V|(7Dw7nP=jtF(b
z0NjMx8sQ0beR!(U&7fi5;+SWaKmEODQC4z3Lg<AoEy>Z>md`9dsY7di-9xDi*HRj5
zwe4;kO?wR#1JCKu7Rs>>(qqZ7pw>}_I@CH{)pjt!x7s0>n&^=gm_34QQBMzgs?d{z
z-g98qFX0L|jnPz1bT@{WytHhU9H~=UC2d2?Vg%_I9DnAeKNhD;u;<}OkL8q>pv3S*
zd$IUfwRWP`@~2PyV!V1|du*T<{R!*)<!tLLODA2he|ju+khg1Uin?UGdJ3h_a9wH4
zVxPH&;2t6+a*d{>HG&d>Fz<CLXy;hUc%n|%(rf!Br@R)~x(=-NNNagG(b<+t>uQ~W
zQ0~B<YheeZ;+W`hV_dLxz?BZU#nE>iCNTv(-E`d8xV4eiW=9CtWN6o8t~Jmi8elNp
zZ>{jVq|Y2#Mw@(~H>QuZ1uZkJUVA8XGJWhg+db3E6VaSnzxu)7e)WkvSKspfUqA4h
zn_u{naz+iqi}ixx0uT%`8(`v+e4J_MfSc*?&CE=6o$F}~@Xr@o6q;vF40r>kq@m?i
z!L?=}G^Z-q&PQlMRcNCTsti;G&o)B%amY}mASz5Vv~{9Or`}L5>%<So1#p)itN|JU
z{BVvLsPMUzF%g)kVFKe|!;m)MsS35R5noiX)vyX19;<mlwc98W#TiApTZ^L~F)u5g
zZQw4q7LO$OE<Rd8FDJSnh!L3RLbCy{H;Vo_f+G`=^E!0>3KzMNHmtlz@qk!Vq=~N}
z1SSMWX5okMLL*K1uDFvzvuEs|$JES`x;<uSUuaOe;tS33Mjb{VG%`OBtO|`Rc7}$p
z3=Lls9KJR*e7P%X8WU<Na`@#oS^=1&To{}ybA@)L9Kxaw4T9L|F+ywb8)?DeJ3aFZ
z!>E{qf58YS;x`EXQRX*b^@w(fUC;b_LYoO~c6v<Hv}BH2&zP6Pff0`S2q4*Lcq=@m
z69b``Wg(~(@sv*t&|ZL@7!W*6$D~#RurQQ4$|?+Fl7i%G@o@O4U=tP4%<#tm)WKL#
z*q*16f=OzU;q;;0kLnY1^*tU(U?K!?3)6thXCPOB48_Y)u~V^#r$nqHX4|Uig=D51
zdiRDEh7ML)Q(>@iN>==s<|!j}aQLTH!QrnM!QqGGX-eva(qZZ`bj0hWrNA)7cDx=i
zr`@9KVcH?hWuuQn?e)6nVQfSLUa!p*h6?Vcet1t;rI2DPM*dz}%E(5znbkOD42_(p
z$qQ1=$qWr2gG>$&{2YVY&Hi^2x`5CHgtigdM%Badkl(@l4zEWP7m8JRe4*jrhKBzT
z8h&M>D>VEnRb|p*MlKY)`Gcgduq&{5;t@xXCh(0M6A%24K{wD>7}={H*!@J8*9(bE
z7+1hV$Haz)|7ryMJ~W!kL70iYQh07?q(>YaCgO9#QzyVHNgnl-_#g|qnA3(RO>!Ix
zI4FJz3hL>j$?@6Xaf?PyHF$7}(8xZ@D_w-TJuYhU2vmPNIEL1Qmg7sJ)?nO@!2lBe
zAY*eomLNilC}G#}%`;8<UBx7$%ts>;PVh>~vwWtf#A_q(Md5h%6j~ozZnto+&G+6s
zvwK79J3Mu;$>8vl!Xj^qp$%4pIo=i#g@7eJC1YvRFk{1^<ruEu$e@hm0U5g6p?YsX
z!eap8u}*mqGoXNgwnbAvsCHHze$wM(W1$U&l5S{LaJ@o9_MXC$A)Li&WJH9xT#w+D
zR&)qh(E$z$o3Z>9VZ)9N0jO;Flbk`S;mHL~3?yjq-+YfRr%ln@SXT~h);iVdT3*<b
z&9tlG%e_`FH-lBX#KZvFea6gX%Oz&ng`t@6(3BD;FyS=A2yDHdN|0?fL8wTIZD~)`
zYjQ3g*K|0d3|6_y7uZl9XJhOq+=JtGY64CNnCXX4xEMb5)QQQO7`p#NOso?DSHQq!
zU?c&egb49Zami}}%SH#a(8x8Xum-6|B_Sm0h*mfj7YM^^175oNH4HM^^e%`M46(ON
zs*f|sJBoF>WB3M7W1-eg9qL2f)NoF{g_R1>c>+ivrc_vH<LqaSn~kuabf%)g@78N3
zco64H+r+$+^|ZMb+J_-dDxAP9|0rc3(l+SDDvgzkY}OkY2Z`Bq8orv&_za%;xzXY&
zHQ-ier4KZP9^geySuqgM-s|;Z0>`{3rr~AO3=O};Sy7&t1SwM}hDn^MHJaVtdrsf}
z-r!GtOTFJ23cvo{rekM&=Y4V0ukO3!OTTp*3cony4tT8fn(4;UEwqR&$ifksKApkR
z%Ob*3!L)Hv8;F%k6}_e@5ts`dCuWTwx`lb5%%<QJWV(Ie-NNb+IS^nK9d>}OVYMrn
zT!Y*)ryJ`q=57a-H4zZ^C<>PoxR!-Sk=uvQV2<cs<W{oiUJ^a&a8G2Lopzhva@}}t
zNUN-T@hDUjQ-xyH<B@T!G~Vl#zQ{PBxe544k#>DIU7Rxg5?WyxKyj26+DsW{@_5Pc
zx;QRaQDLEmnFGU&+7%KA1i~^r)F(4xUm)tldUz3RSq3Z+b?DheuZsP-ESI-jJ43O)
zfG_GcP$z)Jpm14)*(nsmdgaP8O`>c2kRcLPeP{*{wlm9Zd1CnSVQvB`99N(iRIx~F
z$H6EjptLYY2z(HHqAcEgtYK0T2LjXW0g5F}3y5_@AW(`GuRS(F!bRf}#!?uItRh@l
z6pK{53V6s$63b{uOa;6#ON+>OoiXUoqcMRJKDD*Sl+VlWO7m}$fwyv<X5`#deBi8P
zf9`q(FLqv%Hb5u;+JjndsG8=p)^_5iK>}By=UA5omtWk{JP!;4mANaQ)>kcg8`#Sl
zQBahDjrHOizj=>uj>~OO{JJ{u^YP>G_z$~l{Jy1R8c$oWYe`r4!g<~ES`+h^w07^>
z8DD(b;`yyz^A<1OwR6{!#a;7Gi?=Ss*$6BMHq>OOnEhVezKqap1Gq!bSop;QwbW3H
z-<W#L*8$`kb`Y@@KW6!6s^-5aAT{UXgw{|C-ngcGF=6y~`qVZ<b=dXBTp8fSPF~Ay
zw7)R&=D+EoRvPLZZ-z1Mk`~{oV~_B)T6H?U*#0IMjJc{M^+##sjd}(F<o1lzTZnIP
zOVEJn!j4RuF!I33H?$Rg1K4d{9}_$4k=yeE?&J4IwaIA;ymqJk*>`5hN~t-2{OuL^
zdB)A;d?7xMJcG}}EYW{#JdDpwd}@D&&m$Udrxi9}J-2kM>3BT!Si=pqkF2}xyoRRA
zJ4XJKJm&66_9f=_#Zz6$xm$X0oex(-dFLlRH=S%6>~F#inz<O=xyhXu%@t>v+n>n5
zljhngAL!nxI@Y%>SiD48{AWII@kbxrzg{_f*%jxOKKaPz>oa>Gr~UUn!cjX{{LB>4
zDa8v$Yxm<Weow#ZlZ$4FW?qfu-|N#=bfrN5Kly*H0seM{4B>DMo|}~#E|x$dBHkg7
z#glFnF)*5b&zK@wsZxhA{T+S|0G_!&$0C5|s1Dq;IS21`w*YF#duDy}<}CcK#U>$r
z)cL!U;+}L-`P0wSt(+52yTn5A-VSi*$m#7FIRE6?XFE<=dGgP{X^OQvaX!j78fldC
z+*9Ar5CV0Na|fIkVH0?s_wvooSWe4?#=adi55MEF6L0YO@6ThKAg>mA4}@(TK)>|+
z81nCc99x0acb)L>m<YYie+{JN`7Yb#Et0%kqwDE*?0V;*o_-I-cL@BOk2fvzav!!+
z2DzvSp3kT7<ol4kWX1fO@n(QG02JO$n5ee_b#_Y)zBC;`pD5vOyfs2ZR8f3Z3ja+O
zUcXVm<#teU1lStix{U6Bz8&44&8V;MEAehkUb@lslzJb|wGCKrA^m7A<aN-9UsYKE
z8(0F}cB_TZOgBEQkZ3+|t;p??cNmL7w-~rq(9Hw1Sn#a3STLudAFZg5l>`3t*tKEA
z_=c?y9D3#6&#C3$m?(A2^Gf}qz;J)}@MFtP-{038wJz2#pMqt~lxU*AE6ML+ET6Kq
z^NgmJDbaKWzf%$K#W(hsPdSiCPdR;s&$ldYeg7s32L0*fQwCH0OVeF@5`FP>Q(sS4
zD#^?1O<l>prSWuM^TqS0MEh{Dbr<F`dkH43t?)&oIbQAfh7Ira6$!*e)|4o}gSC9h
z#sgTx;ZCT0<)t}3Ffe7VFvz3^)3^`5EBPj+Sulmw&~iF~FZ-iKodqJLf3G3Yy*br$
zF>X8WPNd%?&xICG;hSTFnW$@!U!mKOxH!=p?Pa=rN<7`(&+jm#rbGvO^!GECPuYdL
z%n98g;W0PIBrl64_S{0bm(9&}e}!+^Tzg<}Bj|to?*~|^K!%Sl{ofYk|F{3Yp@IJk
DE>q|K

literal 0
HcmV?d00001

diff --git a/legacy/Bot/bin/Debug/net8.0/SoUnBot.pdb b/legacy/Bot/bin/Debug/net8.0/SoUnBot.pdb
new file mode 100644
index 0000000000000000000000000000000000000000..8281c26c9ed2d8dfad7c429ba1d78ba9ab0889e2
GIT binary patch
literal 23980
zcmd74cUTn36ENH}EX$HX0YycZ6-AINpooE-lYnAETwsA!*u~vNT;q~OKrqKM=Zu&m
zdg^(1p5aWV-kHOl;ZDzdr`}sNGm9(W@B5zjua}4Jp6co9s;;iCs_xmDproiEickdq
zOdutM1PRA`x_K}agij{JMLwji37P3w_Y{(Y8NgEsu`nkj0#LgqsT5LKwi53TwIGBC
zW!J$;WF%ZS;&u4o$Pg$ySrSqg18`8T96M`lbF|B1+e6{2hjnqaT37VW<?m<Z&>cUe
z%PM0-Bf@Z%1?_Ah9fVW{X&fX&NHjD=uB{=nhtdENbkAI4=?n?)8$-fzb%%t1G+PZ<
ze=#s2Cm1~J8-_%BLqd82NMVq|p_f5`H5JMVC>x<X1La334Jn$0Qxq8tWf_!npgacU
zIVk^w(v+r2Utlx>%0ej1plpEhFqDs>d<CUYk0Q=`G|7Nc4&^~8e}uA{N0FsGntY>A
zk*)ePF(u({zz$F@^1{-Xob)m#XCPgKbe*X)A$Pn?$nTK;g7gN`M@YP0Cd3$0n_h@#
zMr?bT5g{Z;2F5z=pFUaZtxq&Q`eYZRGGBe7=&et_fi$*{KC$rAw!>1N`1NJW!F}~f
zHY9al1EPUc0cj?rd61SuS`BFvq<W^kfb8pQNdEOVBy>MRVhG6sQaebUA$5b~0!amF
zMn7G8JhG%8kF0{U5z^&;6nO*bBP3pbkQ^ipq(zX9Li!6589<SK1GIgZk+=b7WQYzt
zin%r;DxJE0I@jI<KjY2~)Lvt$Pf~;QNhYLRNGeE^A(cX^hBOz_Qb=ncZGp54(jiDE
zAf1793DU2S?n8PC=@q08ka)pt-zH4SUJuu~&I!jl{L?4LnQIEPLA!Rx>!V`Rur?^t
z0B!a|Lf8oMc_0iFCc?&$-vzh}2b%zF4T~a3kG*dSaA$!1IM@Q<2!Qu83x|fa0_y>;
zw{dV=fa~G92iKo9z#f3xhlASzd>UX8&_w#}AwLiBzubLWfGxpbps@A8a&!b3|Jp)=
zA(2iTjO&}P1LOL{I^FwNr-ktvOh^-qZP6C!-bcHjh4H?r4vhF<=DxTO=B^3G`(WOh
zV8lneqwNpx;~2Cs-nY_$@xC>TKzqMb2gbH&cWvN)XKwti0JqbDV*zg847SyQgW!G#
z7_k<=4&aVDa2&v$bl~S4Y!CE0!S!}Xc5q+B(Jz8&LAWcx?cm;E4t4;zBfx_=*b(3-
z0B3UeJple2u2Ug(27D)g@y`iT7l3<ma3oYC>;f>_U(^qTT>*}U>vTv$fZe$EE1<nS
z!0sH3b1Bk+ar|AI!45ib6x{Eo15X3EyAF)_jyf>*-=i7qqywXFq0iCsgLBwZ2gdn!
z(Sh+k&Z)LN-bVwkh4H?-4vhCbbYQ&isRQHvUc|f!{}ISX3uD{c9P9z|@&$Mqz}`@L
zbMnJBJ~}Y=hwXIlqi@uOagA$XY>y5?3uAw1@U<}B_lFgsg&hFyrvu~I`|H5g00%<S
zw#WW(PP8!g7Yr*z3uF5b9T@uy)oG9T=+Cw75kDL>O$(#GYGG`P`WgZEeK`5p034|U
zcLO*IU|;Tj1i;ZcFv==M2S$C5)qytv90zb84*vzfgJAt?Vbr%o9T;uXU>z9OM2Zgl
z3gF>7@F|$zR2|qKIvlM7qdm_A{ngS#{gvpzNH0qVMww*mz^L0fIxxypssls!WQ-1s
zwsWiwjQ!^}gY$G?q$ksXk)B)!Mtb==Fwz^R10y|!4vh4a&0v)djPwe0V5B!*2S$2@
zIxy0kpaUa4wGNE*Ch5QzU_Pemz~{^eDbj(xpu<ufcnvBX{IPx%R{~8`kn2GkjIbji
z*JmsWALaplVGZQQj1972EVLzY0h(<=$I$Z#AxGbgUIl$id(r~{TVe}&2Vw!a9TQh~
zCcEISJ##H$uDgOBK+I2mgxry-@4?iAeZlSs1gOyO6}%+dAW>u!<TQp~z#*;^Lf%5s
zBO@W_F_C~i(}qtDK#dVehTNF^3_0ufFtWz$hmdH7PaeYqA>~+2RzjkPJG6ml7_Rlm
zV#s*}LT>oO(P?wE3KZxW{!!$jCq>qKLGS_B6hyjgL_v{YADms>Rba$GkrTLBm}med
z3E-jq(9@%LZ^OVx`&mKB_8s0Iy<vMuDInS5aFq(u9}SY71TvcnKBx%%N-6jzA?ztW
zc{+kj91R>HmiZoX17d148tNOpGAJJDX7Gm6BTw~gXgzY6=L`8+{S~x;xO+E3?hCmg
z@%O$B*8z|l5;^2XWVAQY6OdBK4M~l+5UyuKZb%k+2f(%3M+145?+VBRd#}(lB4c|u
z!u7n~w;@mLLwEx6rjHPEkzWAh-5@t4n;<tL*?yy;<`U$Fq-|df<eq(3FfAJ)kM7$D
zH3@xhGc`mXdl=0Zkl*|@0s(p1?>6Mp{=^XLg^<th9{~Bwexo6O++V}guYlZjz-=P|
z*#WsRSp>NWSwA4aSU?;H1{i~`LvBLM2Cjgb!GSBF=2*ZAsQC~;On`?VVq#3%KyE_3
zf<~JQNKEi(b7SHY+z8jU;f-)@AJ%AYLJGr#76P(6TxelT=0R>k)`V-IMi`@knuk#u
zsF6j(G|D7}A!3!7WDr$$U}m0F7MV#3q?sg7o>`D9A=%<Q3CWepB%qO8gBZCin`8n`
zf>@P9#2L9vDa{jSOO$Mtj4j6%NR%q}CN?8EVr7Cvkq7-G%g0J&WQ<ZSBMON!pU4XG
z(j^LxW{g~%2|#*5Ru&YwQl&~FgNuv;1Y{B=Nmr{NSLQ&2TuEjiOHGL*fomd$q>yAt
zq~j%u_<WUA4zN@<UYsk<9E1!p6%1?4l|rs087d`VYP6jcG8as9_68ZJR1wJpXgPr}
zBMnq2#A=eSkc8&ttJEMz_%lE6G<~0C+n|c7LbI|3TWsE?4;pyN;G$(+5^0QIO|R^j
zGQ+I9R2H^t>x>;?majh@Qn}69cw<6HO_;gY`U4FYhv-|LnYE%%?DD7GHZS(+Fh>9B
zr`_kPOQPmq4@)mS82yVNJa($gaAeH3>pxl?7^5=XkeF6*a@LX1UHgRd#yv<pob%I!
zs+(@cK?{xV3@zQaX>|+<UKc>iBiEOBT#1iV-#qF3F}d3x9Rij*zmIQcp?KGM-NwA{
z-l-yvq~{zO;&E#n;pNElB>H(`MTXooQ3{igppcJ|fOHI%a@PWxYr0&e7bI7i24-YP
zl*(YaOr?<LTCh;8SOyB9aLZ5{LQKau>EO`ASGakb(CgL3;cv|km6b?py3JpEZ2uic
zy`#I7D;8YydLa4PI5H1(A;{Mo&;%TsLTF+7tLv41ise3cc4)i}4F^o`Jx+RcQqt4l
zOG^{?P1xXW63gf&t0zX0ECf9mb3JG_Xm$%1tv}qU^3|%PkN<P~-L$s<{R53R%Vzd}
z-qmSQ^2duSPK-C#?c_`3XToolR|9?!Kdl@s-e+L_V_?TNqrP8XSZV(x|Kg$2B`%kK
zF<_CEhVd$uJVq)jm|z5Q35T@;J(+TB6gd}p74;J?=<~iN_4Uts8`fN)`S+KZU)y!>
z&zQ<lr!y8%3g-oz7MWomi4xFOWee=_LW}zqX~TXUG}uS_(&=6=Gs<Cik2;xZ`i%tt
zW5T%Vv*jj;os=P0w7}gZ-0Rn^`@xxW&$g|RmONGYKYvz~IPa}w*14@$EqN#O{&{Ye
zkRu0Vi2`Uh=li+vm;e0bJI~1*=p`kWuD-M%oHyHJsXV9Z`}O5p&K!2D`^Q+jnwp^&
zy9v*qG!(kO+tSJD&#G_d2d)lpSaa-$;gL2=a<&WCjOlscf@z8*z4Z|nONGlfMNf#c
zt@*d}jJgnBwO&Rai-(GutZ5t8i{~3(+SVn`B#v3<g<t4e@j787W!=@G_r}wXJDz$w
zUH^614a<HL8q505_}91HhP}s3dKv>B!!q!zRng*`LQ}7w&&51Tt2a1k+-~&MQQIS5
z?3$s+e>gol-mzgu?xR#ASOeJ#ah?uViM4RKx0T_PZF9~h{Wsv%(6*m^B;)@3X%^kf
zgjb&Yszb~bGeNQ>SAu9}+6&MR=t8TaCDA3x!XLkjRIW9Dzme~-<lKWfN8?B49ev>9
z^kMl4gPy&oMmRX=$<xQ^he*;3viUNJs*jt8ZIXPj3_A*k14VJ}U?rYHx@AMZ=A6!z
z)RoLFSzNMGxb=qB!0O=rX&v|WH90U@?Nu~qWYoShn-9O)5j((Kv&<~N3j_}rucqvX
zbaDOuYHa*Z(XLT0k+B!{-itc-eSFlJK|Ng0o{3J;<Zszh+_Ne=QMvU+<YT`_kM6JO
zz994ABkL#oOH9thoZ5cuVwa<b;<HBCPqm)9vD?JX-MZ<&8(fxYwYK{@(rtS3x^}aN
zE=|q-NuRj6#f2sZgPsQF=erC6We4lqe>@DzEl{bH<fZ4T166RWnqC05y~Th^Q-#|C
zf9RY&SQgC}{o-sdS`pt);bLNYap#MFf7rKb(~CSe%_%g&?(V{V;sTXC8(oH2CCL=J
z4=^e~ISG^0;7IcPzku8lCE!9b&?opCjSzE;^c7|^^6lG~#c@k4kg24wwmG(;H@9T5
zB3q&ggBg|-$`xaqc<&+l+Biin!lvdSQe}RwSRDu0!u~=AMF^r0Ii(u4)6TJ0ERnJ-
zc?*G*l?gNYM)u1cz2=4eWvP$e(sSdk?h?HXJ3B49=T+bMe}DeNzi99|2>rIsz7_qg
zhsAfd{!QsLDKRte(eJ003T|ZHEfLBdm%Pi5b;}dRIz-xi{44gGjx$%9?D;Y2$bx@=
ztsOaA6+dRs>_(p}H&w<zXI^M~RJ!}h{I#k^6UU(|OSX3Tn{RQ*)7i|ucWrjJ$HgPh
zAKN=F$hp|4<LIIO^u|T?^A}bujruV66g|eQ=f>oO0NY1_GfdAc*cI0)VE&#(_x>6)
z=c6LN-<%EO6Wi^`S-O7AA!XI_hz;k<y(e}GmH*yp@cuE|-u(9B{&&55T{d`}b@%W*
z^|L>=G<ckwIA-#r4<8eXZFX)5J{b7%<o>mX+|1UU`L^~@UBSWXQA_tc8vVmzn^Qw>
z&0F!0UZLk9E4@?yI>m=y>R7$+v}ebx!<NwvG56E!>Q&QM5Ag5vWVO$stWVQz-PWe4
zLOvXwGxqJ@vcq-!KF(#0$<bvgJ^TN3-1(fVjkVN#<neFQheumq@%x9S^fW6bi8AFG
z1$h#gN-63)N#u@h)17gf?q9e~_a?XL?kwsn%2BEEm3`ga72-m-Y^f@zAiY2-0beVF
z00)eNJkMR6pD%^!R*Lhb64^wt4t{U1EZ>aGULKhqeiBb#zs#(3u}^OwPrnQgAD^uB
zEMK1tkKSUxUKv1W%9P?D^d_Lx#EU?;`O;j8!c|-4?FO3W+S|>`+rt<0o?gDae0qC&
zdiBc6=#`n_E%xozN8-^NU~h4TAN1|#>F3$Y&)19Oie=da;N(egSE7^`C^96jS<tm>
zh8)%h@$60H1*-f4mFrlkER#TVpp@t0YR{Esh;fAxIYeW5(upjPiFVC3Kes?hU@54w
z<PaDJ%GAO61w_Fvw<eC!Nc8IIm*t)AndOt|>+3Df%FN2><00|%@#rH??=1nLfJCyq
zJUz0~Gn?3K!uRxo=R2~3++1-wM6{tYrpT0JVN{!(7#JMt5)v9TINSvc6sA6D9#{m&
zCx!+lB;ch_nnzlEVp=#<B!?zqjc<#Z-YsgNpJrHGm!4n0;n_Nd*Viz-(!Pq})p}RG
zzSdQ*FS;UY`b0@oK?<={rgRIG$>b_#+LUgYxv;&Y_6>~H;2GQdfB$)P^4i4Uiqc^t
z_l|hIHCCDd0ku3!<rbWjBUa>d&4*7ry^d($1mIwS%biVa0yftUll)*hZ^-B7L!`<A
zac+=UDa~+0FTo)en*=>1LW)e@Z+|y_#kPp`ca}udK94`%IwH$k|M6q5BeANBFQZ59
zu867jU)=TDs&4dWoD`|7mzP_D0<=*D+XU^P{3ky@fQW+~2JpNikH^k_<kw+(zDbAX
zh>4PH*pw*L9FG?>jy@$T;CSg=bXNbmhUncUWp#&t@=VQBLRgk7P3M~VMyY?op`3Gi
z@ch(kztHkr=iU4Q<t=dSN|jiaDOO~1Ei1pZI!PMgT)Ew}WTDfks5;MX7ot7#iy(ke
z;i>}ZM#0vJYklJGn&afQ5vi)&Fxg026u8=X>D%nHH#&2^3l<;->XHd6u6+m3Z!2(=
zgOa-cqN?(ZTpBi7HmzNwjB6jnY`e4~+4<?adTe~~mP`<$7ZiSIne#u-%io!Cjf3U6
zocmP5tjS=Dra)81&cop+@f%PsB?>zy+xl@ab3`Ysgi>X8E4VDe&VREv;`zgB;Yj*J
z$J~Ie8du|6&MA{yL5R%DD^TG&=4id#U3Qm@rbwUd^*18dOo%NRWwxZk(ILDQ)Ht~;
z9HRqJG?r}2qQD~LQz12bd+qnp;opo+S!cETa%C&HN%@itDeNRCO0;v8;-<d|kt=>{
z_wld9xdE#>ud5;9{Vp~k=jF>~AQRXyNOD_QNU86s$E1;vd*lB8X7=o);M$Hm%|<O>
za<X-N+^M%*jBog}@8BdA)xGMQIHo$L{Lqb&JHvBCt@ptW`up?K9wE-Tx7HKa{rq?E
z$~D9IBNuPn+&WI^co+gKPS|ZL)iSN%z8}_j3H$gjU+<dz*^>CuomHowKU<aGdLJ#8
z+vXw8TTpav*CR(PTTDmP?J<)(xlYV!9X(l{F98(;FQ$UXpB==dp@05|R_JwF;M%Q6
z-$l%-KJo6nAlIX*2U(Uhy8z-q@ID+$!00^;&+jixxZ-Y{7F!zSzq0<_{)*2i=(V`U
zcU<~hLk?Y2oI9(x%!ybjtypt=Lt}1J<0d;b)aMwZL){OJw!NeNF?7L|%6~RVZk*#<
z;=I8E$;)fvLwvIGHCaKEr&;xXeVcG5a_;`E#wRb-Z_!0StR#slbOOW&=*_i@^7y3_
z$5HMLqn{>!EFTa$J#}Gm`95lfE-ove9NB}lH~_O}rO$?b^EiA}=d1n~^@1F=Ev4dY
znH-KpGMMG7Qix%%&yD@#zN5b*k>ew8-Z$QLH)!kh66pZ@Q~$Ms%4|_MG~cd^o+4UU
zhmBp^H=c~D-ZrRY-3N~=t)VqhPhx@zu7_Eb2d|RbguL8rZWAJy8Ce^-wfnRCNjqBU
zAq=7qE+%K9WOmSt=Fa~XSy(*DHv56)(%8cnito+YcGjSk4q$JGCm9^&h%WMfa4jA>
zM~1$gw=uMOWwLYZ&eN+}K}=GKGsY%^ytJy)Y0_`cQPOL>kKL&*O^x0DY{^e^YR7hJ
z1veRNaf(6;YRq&H!yRyOOt_f)Tt)7$3%oDfT<aUUc<PY6%dz{lMlhsFXiB$axja|P
zdt!2%21K)4Hbw8@xS*JgY1J#<yvj0fjXtvdg~%Ps`#nMC-*?<_ymXawz<#S~0}EZd
zEd2^H?tfTOKlx!`10vQKJ011C=@?n{d{oHp@uJflBHQSmcWgD9^*ws2c24^bL>$qi
zJ{x&FuW3XO4u9^^^UReh9LLPKHK`}NbP3q#@q0y)s;FsQaE)6Slf(TlFT%O|?MaYB
z`_!1g8Ri*B|8y=Is2ze@Ce8zkuieS9oNWw^JA=f|S{}{axoln7F2Cfs<nxtUBNzrT
zsTdYSpi<_^4fxIDPPcKu`gbxeuBzP{zvRuFE;eV<ia9b`4A>oV2=8uh{ToU2ecV;q
z?)!1^TW==Ek7<(@!y&L6c`hb)3l`@}Fc8pAYO&<`Qbdjn*(s>&)i-k1KZc`*6~z6~
zDl$8yUY$n1M3m>s4$JQG{t7Aae_HgRsPmXsQCe7*4Of3>lyA>c1OAhz3nKSUb=~sv
z*n5h@j*Lem28W(XG)Hbh3b`s*$_;ec!Nny=c-{1kANI(PM6D07tDW$8z}VJNS!Nv?
zK5R#vPiGx76O)wjyCP4IHT}2xSnD{r+POO{dScbSnTRfYX>)M%`m;gn?~H0ZKhffT
z>*&L!`Pc);ZsOqZXNVitt)SzC$5&zw4f#WOcY6ZwGcHR*vNmiXwlj*X4_fp~x2Snk
z`ycMld9m|zI~}&ZI2Yvz9axaZi1da)KH#SByV#l}%H>qUo3vItU}?7fr$a5$+}6eA
zT8~NnBg&{f@{RP_d99*^C?JB>v3`<r%OXU+Vfl2bc=oB721(VK)wjF%Xcf7|GP-CW
z-i+8At_>Zv!0S~+srl>f!pt<S#(@Ip3^zrr%E)PQ#%C9N%V^$<>93Y_H*XBwVRq{H
z&8PQt2uFfz#WR^Cm0SVaNKQbz2ipFJOKQWJPY=D5r-vUI@oi6e?4{YC8IG0Y$rb8Y
z*c?kTk-T<!EWCAk2QusRC_eAH|B;Yo1#@>+?-)4$GiHJ`A6mCIEiW$&S&g`6uhd&-
zWrReQy}slUHnED|9G9Ju%9mv)kw=%IzPFb4*&0$;GGy+DnhWWlTMw4yF$dt8EVJ+n
zK?$50ve@rxmwbn`Zpyc8lTZ6CcA?$31w+4c-oRnQ`SCce<@qOf8_|mOYMA!A(D7#I
zc1d~0%2?-G-Ik#fv<pxxhb69m9wGr0`Sg1qkDpyLBKBL@UKp3(PpXTRAXbPWR?p=K
z`HEcEBO!&yUjeU7&xS0^S!#cD4?Rm41rAFUYMmHcV3IZmaa8YLsz#IQh(rC`EL%Tv
zd?Sa$4xH=%8ohis+Wem;%)ViDV0p~Gfj@m9qFs7i+c=4KlhEmeSsBV^tKmy4GvBLG
zTdGn8Hy^sb&}kR}$0l62KkScKgnKC4et{nzG%gF9x!FzGP`2l3^VT}sqs$RqYH;cb
z?Dw`kINmU9-^T#G`R_WrH$!RWh3}u3Fc-0|b!&gbw%_`&9e>WPn3}X&YqpX9Sg}+q
z&Gh8n=TS2Eb~{8J+d$&#?wj0eOmT0}p@Bzzi4s@<1v<g#f`jREaqvAy3>$ai#krWR
zwvpS~RbF4FiziXx`64cGbVi>8+5E9jULj3ou3`H(>dleGksr68_|!2{2RGrfTV4E9
zVJT8_TG~-zOqPXiTDZ+-dEtaC-PX;w!-@Cg%+_S^GOPKYZmkSGwD!gE>+xHBbZ`b0
zH0`4NAMe<Q5_@?#=?Bjq262n|cZcxv#>VQjPLzzp2XI=o{pfz;C5~Xk%ctXwTrxtJ
z5A1b1WQs}~VWNzaB(SBFs<b=pJ5M|9M6DXQ()ID)lYOJA?iRmjI5Ig-hXy{)kY-2{
z<kF^nXt0Ubc^rFkpy!Q?d#;4dcwckz`kEMLT`YOVSlEVhTJ|7v+hrtk*xIc$@_AWE
z@v~E9wmX*u=^(%ZjNn{2qtmMKwBPbq;^_ZQ7=8A3d~9IF-P{#h`gc$M5+y={Pc}bq
znmKwImC9Sl;D|LIQNt(Bi><2lxNg%vY~7d05UWnm3c^9S=^bLYor-wh|B`$B-f=c<
z-d>*M@FhkPH>xi<wH@{P3ZgE2b8g>ezIE9A=pXFbp6#q%`6zW5oEDWeU{1dFOnkWO
zJI990J4xj9@r7O1w=<rmB?m{Lu$|V5FXnFYFG!*DI%}Wtu|d&`DxW>MW%tj)=B<ao
z!MnUE)_L9I?RE_NiCNa60igpzOW(~qaMUd_g~N(Z#|M$@E}CUZnZEiyioC8dZCS{q
z(Lvuh?UVWJ$XUT5d_LKC!_#(jRk{*%ntq3IlQ-YB9~knJMP%jJV?z_x<xX#j%~Bg<
zTyp{kB{MmE^L$66s4XEs6iBFVU0Sveg+nE7V)s3{wG+oAIn*cPcd{o^Gg}qD_C!$k
zuOLOnFb5O5t2|jDKZ#_2+LYX`S8`(Hj+qOuznA{`<SRYINV9WPg%Zp;7Dg{GIgA~=
zfAqtCzbC6=G{=@*T$7b@p_LArZ!-sNIkgv2mxpgWzsYe)^s>cW*9@(FxW6T8tVAWw
z#4Ro>tLyc@y&yuGOnxBj^18n&yf)xQKaa%8k6R+Ph(DsIc36#*dVIid--lU-g={I<
zW}7+j#K@MYcplIqeszDSV#L+6`(*iP*~r-Xx+9vB`!;u)(Ai^aB|P`RmB{hiCwkH*
z9OyM~x2jO*ThX(gtP!@OAC71Ri^bPI>t=^*z28WK=zinnzBj9G$1M0kxM=(C=Pik{
zn?8<c)~)Av5liX5YT@2-^CK4Zkte@6+oncKlzZfa@iv4!3eK)6-3H5);w)|KL<j!H
zXz@VDzEvlyyM*j;eSF!#Z?p0LS10UgYcR*k{Q1L~$ZfD<(uk@3PDSj!H*3I<hNXgL
zy|ANo@1A*$+%_TIZcg=k<FGs8NS^iaz>AI#n>B7RH7l=pzs2$1d3DO@Tu5N-!g=2x
z_|R|ay=Hh3a1#1C!u#*RlyXEm>iBX1uXk1as&%SWdnf;`H9XqgTqK-tD&+Z1GGaOP
zbGL#mAw{NHT7365Tt9Bsb4B!<Gaa;hwXY#ZiN}kRG8EE$?Gk!0-sk}8<iN+7&lAVo
zik;WD$E5iKPHOieU+agfZ|S*5d^hVk4(fL4?Kc)}4MX;o&$QpWzyH-{l7K<MTPAQG
zuC=S{>K4H?@yMT(C+t36l@(F)!=isK?(NjH9yyI+e-^<_ZO@>gl}O8T`Nl2AJ@mup
z_D!rI{hjVMqZRk11;5qsR~33@>(YY*_r}%6ADv?H_xCo>>YHJKyqUvLU4zx$axY^i
zm+B9zjjmZ7Feh))gDGdEbN(kPyKRO!)DE@h=7*P2<eR<`O$q;07*nBF_PXNXijV)Z
z7oDK5w1?|P9QNM9X8Qki(~E7m?)dN1J}avJCw>cKWH(H+2775=YU<Fq@AtU<FGxVj
zGgs}|tajqR6vNOwZB!WXq5WdC{vLYM?@v$54w#jy9CWT-+B=Skrr4&n?Mb`eql$3`
zDjU1cx);+XP_r`l-Sla>Lz?3T=Zd9yTB*&vVYCvFZrGQ<J?vr=d?b%|CwP~Y_FS-u
zV6Z}+50A(d;`|)7TVQUs973#|re2<vE&qt4z8D;QU3%oNkcwGWoi|6XBrQnm_7dE5
zB-9Z4lu-cRqf7kdzfqwT-I6;l-hHygsJ~z$SgDQhRpPhIkql+9Y2>ka7GXOR=5?^`
zmK^;*sk7@n`-<gh5<ruS8-4ebmk$b_6HwrB<)ing|A`tZ%aCV&j+0IL!}$&je)+7C
z{D9R9;to&VT3v5`SG&eq7h!z9M3$t~PWx~5oz`O)YK_q;y6>I9`S-H!ZT0unZlPQ6
zqJ{27=3M&~d&vrZvt?5E^nhZ!j>~pjKCRu;wcg869}mA^I3Sm1;Um|keezhd35=Im
zKd<+C+uQd77FQT0nN6OxvAJMaF-G#;9w22>^TJ;9&-2Iy^DdvYyO6FuRBxgTEXy*a
z`C{$3Wxn3bVO4yeX(PuO*@kR!u~{=I`P%d5R6gUuW4KItp;mhr{M)zy*ZjuXLxs!M
zd4;aJwah!Z=3DJ^tR}k5g@k<tkF#>6)#LUf%e@SztlCwT9en7)&lbDy-8<hzn%NVu
z!6O8>9M+6q`oBP|pSK)M)ELKwm5uwfCGEg>PG4euc|`0orV~SHkN&YMX|w+cDvG+1
z^(aNPmczlLUAHEc3WjGs3HYl>j-YB|S7y8CW7*C2$7iQPwl8vR^DyRb?aB6+$n0b^
zq~|aw_j^`P{gBonNb}bx&4QOb`n5{0h0k52?tBPG=$HClb-J!Tuww0<=b6td$F+v7
z#IT=Z^l)#{uQ(O@PF40~+31)VogZ%>xT^9mT|6CY+t@v8JwALZo%m__woMrkvju%u
z=Eu8y!6ZC0EyxusS{`0J4PU<ojn;*~tYbXw@5anMZ!<s?xo&}$Rt_AbFbgy|M=X;`
znhw145`R95WBYMRx6F(E1A;5n;k7>wtTJtZ#|m=DBf$wIvp8Y*!b2zaL>+OuX}zRw
z_P`d+<KYQYE_2kx3B&dDcg!KB(aJOGG^0_GGc8CV|GoL{7MRIm<yfT~KD5col^1f@
zF}xk`k?s!$Pu>)t=^nnSqxHrm)2`Zo1)F7d#3--XIJWg2yA+-C*c-go*>#FpseblX
zkXp>UV8IRMaMNJs<frK+=i|1Q%1aiQ<yw3NzuDQ)>=)T}NIjhFAGUeCMU+M{^H09{
zmR&8enYrNx`W@N57j-6cy&#>ka0}nxc9iL3i}&KMU?sq_3l8%Qb%Y5&|9H{!w5d8L
zyeh%Qeo5JyCiQ93fMoa$mNsVZ(Do@a{T4}!F1xsOkJxrg@iN5y{Uj|p1^f&co@VJh
zHLGran|b1Cf7QH``@rs@^XHaanEd*h)-J=dn9mq7@{A_Cc6-AwKjOgn&b>~(?NAVV
zVEcvIC5a=oC+b7QxdoEsg8ZhF<-0M*ncq5?PgTgM=xsrpL?>N#SV<RhoPAzpLH6^R
zCv4AtJbCEI?@Pn#C!Vizb9t#Xc-Rn+z6PtLxu1_fQjYNM<K(oP_M^RghCF)ano_%t
zNzt#gy?<d+`~K0T0vkV<#LW<k76l%-l)C3oUUzG*@#j!Q<9L`kz8Ljv&w*zZgTpF%
zl@(e4?X2}S*ih$`^1>+rb6!z6H&D#G)i$pF!M_u7_0zQnD%hIcUwyIRxgJOF$2S<(
zFSdKhA2>E(SLm#VD^`D}-S8mRmyr~^7S8s$a0ADX{{7jsPcCi|Yp#qqR^VlGKxYg?
zL%3gKaSGMvQu1kBbDq?qPOn#6N3QR)y1G~4z8`f_@^YIY{rbx!#<CmcI5lKUc^pzP
z{a2Awbz#nzNLmh?^zh~DsmJlGs82${u=vB4!FzX~IW%SJ-Ev(t{0ThA+OsXo8J}~r
zYTA$~>z9S?u5)bAqx1b+G;cnp|6;aZKruYnIpWhV=~e;rcc^RzzIxxN{duVy{w$Q0
zSo!+9%kfo|s^LqQt@pYbxuvvAXXj#n;TKJHY(@h7h~skvWbFFKe<-}@T@~j%|FeqP
z^MbZtd*YX$IA@JN0^sP(yj{RN+FCi~g!Q9u?1OjjnRWTW#H^RaE8(Z5@rINYzBp?|
zvM@`*%n4uX?#Y99U)=flx(U3&f-m&?(*k2+fLC68wGrt~!)qcGA3y_IK*39e&hX}m
zKD;y{g=$((5gS?n@2POe(X^l)k<j>FDXj*&G-*d0akmTMRZ6^_qkEeIL?WgI90H5Q
zC$!iBm=L8>@RlJGq;&`agYC%B&z(SPwS$0AMuYW8M;e+k!-&FGFbsWSzz0sOh#Rn>
z53krr;2mY;+aCGuilbu&+m`fT8W@nVT!YTEzyLcmCtWbJLUDD&SCF-X?LrIS9b0bX
zOluI91#!pL+Tk<f8$b($Yz<RwPs4jrC{+}4%T9u5cv+`qC&Ot0ygi9hg$54n6|iGK
z3~3|yWvM>YfQ0RkX<(zn{~!=f!<$sDEx1gj1-;pB+u~5a7y)o;L)tV|!z<hHW;gPp
zmlVf`caf6d)g*YE*h6dt`k=i>8;U@r<|K4tni26v$wkueRxN`^A628g&541gdKjNw
zZymU0_xVIt>Bjvw$dJ1g8LvG|K?A6LoQ|iYXq#JjR2vR0`Lol)sKAJ;20Zg-zv43B
ze(!7D^t;~-otG=Dw6*LD2khRy>1W_<rP8hGEd&c~bM0Dprgl80cD$hKO{jVss$N9Z
zdr|elRQ+(OUP9FuQ1w%&`Z-kn2CDwJrijusP(^xF(O|0R5LIkN6_25cKTsv^baA0U
zNvdH<Dpit7mu#g=w;PlP8kPrA@Lv(cuh>W7|6kN}YpQM&f4U!q|0=5b7F|i`%6?R3
zDOI_Zp0S0Vai6Mkp{fQ`HEpPxc)F&5t~o%>G@@qqrfUr8*#q&#d`h5Cfv$j3IWuqf
z(`sFz$3h6uzV$KfT)?B+*nr`H6(Z=w7WPbG)R8TOOku!61}tRQovAeJ$rQ#N*rE$l
zn6fQR*_LhGu+pSWuLw|nm9^pL<zxUh2b2+%SU^1RlLKJr%!wP1@b!3n!smquz={b>
zm<6bYA;MdC@IoH+rJW@--tBY&x6lb0687DtF3swjehb8z?KWTB$5T;d`<7L(DJjTP
ze%Tk(7Cd!+E<9URj4jAly0$J^R!JK2sfH<3!#t{CH`Q>OYPiFvG#{y=g*>XQU3>U?
z2Wi)(3kCnRqp8|cRGl|fB%_M1QKbi{nk#m7K~&udGh>RTy3F`pueOq^T}N4gJd3B$
zB|Uj%8T|52f*H<6b%6p35Z$SwOzbg>Z(wd{PMI5n#5L#XqA|SUPTBD8VnOFGqz?uF
zUM$4S45z>rGZDHO7n}?q%z8InS;Dm7E0k@Bg8`a(OFEl@tvJ|%PG$T|o0hi0t`)gP
z%HS8z%tL7&=02@UaJMP5-k8OPa%3d9TL?ll+&w_^6Q3@6LKoZj0{!ag@x{<Y$aibQ
zu;8)kq}gEz+SCtTAnikgih<7nPm3AjWAur$KIc5ap`sJA;+&@iE-z;IVj3h?2&Rz-
zFZ_GMo1X}$Ft8&BQ@mit(8Ft@obzdqp2!gCSn$*!KOXoyktraifnP*3%^GvYuNo06
zQK~6&g@RRgrrJ`el-fWu-%vB3(#)rEW?ku`GP;<iiyJftaCr=Y0Y&0~m|++aZypZA
zl%yej7)Bs1XhZCH_{j<$yr<o-AF>@m$2*a(yh1}Vj0bMq0NR3g^kAmhm}D^1N)Zt!
z0Yef-gZJ`hx-caDXjrkKhQxtpJGCWI@HQs+H98Q!A%ZkPZ|HH(0%Fw$q{EJnN9Ytc
zLt^EU#0<@eYHJ`dlu!~%vxO>pPZc}Sntu(7>kNzQDEKc)r;BgXCH8d5O1?QoinA#Z
zkf0PWR$m-zf0Phd5tuSPcs;p?2~Zhi0OA@#YnEynK>Cy#+PcsJJ}Va^Yy-_9gg~()
zk2FLWaczl+_(RyB$GK{ZP{38gO;>uTJ@{fzcu5*1iT-pF>ra^|!%83qzsk*V2O8dJ
z9e@hR4i;r$1p0xup*Q9>L`Coglcou$(^v=G*C7o)ghH#^Q*HSXR0ORlr!)`gB6nKz
z8(p*>{E4O#;y+3=3x?xiBmi!JMVu}e8+;)J`|CalzssRVoQ?3iBYN<$h#_rAZ(dIk
z&I1$3Ks?n(&x9tLN^ly0fqh`u2ouZ<0wx%=2Kc8#Q2-zvPc^71@ZZe{;-L;a=om=Z
zQbqmgqVaUG9$h>JRv@61!W3Pwp)5f)D9ujb(UKNmjHae-s6d#HxrWr--qiebsw|Hx
z`$U!3@XOaw@L%CiP5+y!ETLwY@n>|S@c#f+Eu*UcqiSaGYgSV5KhrnJiX!DbsPbs4
zJcTNkP~~^1iZrUChN|35Ri2?Lf1xTLQZq(VGk&G2jHnt%swRS}Nv3MjshVG@nM0|W
z<&+ahuV^M+v6ENj&Y!tiQ0Hbe%h6<BxY_*E77J3^ESzAqsLEz>+xAOb2LQVz=ctlT
zRH+wLrcaf3q{<Vh3TLY59KSSyg8#DqROL>p`Vv)h)(!~NT2QsKs5ySr+*G@{W9`7Y
zOy|+lukmKQ;aA-g)Z8$ty=GGP!fbXsi@9T9ae~P$N@E07Y;8yxgwcY@gtAbBQ=s~y
z30JfCTtJAXoDeM`7Nv?kp{5O20|taRcQV9+XUw{8W7c(>bY?0|x-x|+d(V`;XU0Ni
z_C-c8t0H5q;p~CyqX$NRmbjo84TeR+=!ORd77*$h5GNjdCkw(^6VeNtGSQ+hUUlMO
zu*|IiPh6K^=k&=yzPejewR=;wCtnR}%C!dVh3^HS_JZ1Z(nj1JSS%3Ge7?hIy0&MU
zB=FVt$cZuO#skmA+;yM@OhBv$ltGLXF~N)neh@^ChJd+X0|{CJw;cf!1B<T&NA<()
zC{T0}RWydty4Q5^1-hhyE*(Xe8PS@1ta!Pw$AN*&9ht(CnE?|^b_UuqO-$N4;P;g5
znt~uRG!=F@1Xz!Z2hc{%c&OR2b*FeJ2Am>nwFqSgx&V&J6r(35T*vPZP+g(c9Q_PP
zlwn2R7Yh@8e?+o2(9SnV9WABWx>YiE(3mo134na`;2RRoCSW5{n0{&6oax$x9hZqi
z5PXNi%S_ufFlB(d5CMVjRe+M|0UeM%Oh2<@rAdFD+LBD9nNLk<UZLANs&C&a9~b~|
zuK$icRm7*`E#ONER43r9BR5EEv^Vxp0oq~$+hX{z0@@s^Z8sc)BisO+3oBm_Sn&lN
zrNLYd?f~nmknuj?nh52l)2bcU%9I|=V_U)OpsK2Qw7CssfxY@d`v6+d9cx|SvksI$
zRKU74Ah2n3!07~q(*u?3z~h<F*?I5*2eu0{Sm9o@rq~8l5&kT}hEw5&6wjo+K=T*u
zjn&|GiGil3Kb9t%n&CKURK6tz(J_oJ1Z0Oa9bn%Gs}cSg*1OXJ(5zhI2d)`E%K<H5
zRHpl&Q2?Y-kW!$6kXMilI#DA05=A(EkO}&L9irwNVBe1xKn>?5V3R4sPsu<}6oC-l
zSP#B3XN)m<81o$qU?1L{HDbs>H_WF#E<!mRdnWi<ndYTQCyF)~WwSSms$h{Lao}nI
zd}Ra3<Kl$xs=)yR56&-K`1nN<ju{x9Gm+9Hsi`KJ7Wm`oh!@6Vc6uf}G7PxYgKs?)
zFdw`TfB`7rsgoHAq3*%f-W|8Kf#~{%LmPc)tJ%RcHi5={c<P>vK_Ut~L1_nuk2en7
zDGvn-K_e~D2mMlungAhB?M$ZdP`0Q>Kq842gfO>>W`jOGly`KP9J=S(@L@~_M#vCf
z4Xrt&2L}M$S&Ehh(+M1hgkzMVPZIg+zD<2+(}EoQ;2KDm!v}Tn!V`udwl#1oAR~|+
zFCJ{|g?#)Rqa3Yw7hH*W3IHtj$0>#>WeuWMPk;=3B?%NZ9a5?(+;oMMS_@&oc1Wr7
zA+p*7DfJ{w*aJwZDG-1aLP{NqsQ4X}8Yn}c=4)jv%&rl1uPt{11vh|)gQqm$v<3UR
z2z@v~IRLaJh1OgX6;H)#QHcx07wtr4nWAz#LRTE8r-#wgSJIUty7Gu<Mo+rx0$sg~
zt`XBSqeOK<WD3k0sux%*O+6?Vb0%g(T%cMXKH-v$Rt)1l_=nHkpu+VA(V&ixM6I_G
zU^JV;ib@|P$B0je28<COvn-$uozcAm9VQ$BSVQm&ITRmPM3+h!+26ncEL$=fzkybO
z7qEEY2n{ZnHG;#^{(;_;d}v|`OH*@!sTCEC6&2gT3V@H^h#^jfGZ@XU1F_WC{F;ts
zXU(rN4xWr9rMVS{WqZwC#(J1(?v6#+RP&O7^))X?A>3K>atxLLPr#DWyotfGz2?0?
zmS&pwQY=l2j99!PBNng7h{Y>1V)2ShS-c_(7O%)c+M_cd0x}4-*wB!I#U>bm3zH`x
z@aZ&GL;@6%&1x9#3~0O&(xg!UOu}Re0>E!15h5xYO^Q#Vzl83h2vw91(+C#cj*Upb
z_=6NV#}Yvc-~(}70Ko(T!)QS-wg#&CtS`bTgc%lSN{9j4hi-f|*gGDmTTCb7$n#;=
zmK{j|9f6ZQ+TV^?@j%HTx-lj;xNl@ORmP+T_&N-Q##+K`7Dizg7DRIb*fb#Yw4*7&
zKeT|+K%m4=gKs49jNl%3?guU(G@J(?6ziNqI?)*-mP<W#TpK)|1G8>G98AQP20R{E
zdfPS-SwV~gPJlBq*e|ECaMlN|kgcb|n~NEZ#Y0pihGs%R3nn#HGJ4B6Wk#0)!2xNb
z2m!?&-KH)P8+KWIL1ZK?$ZhIROGL8@L|Wcd{Z%raJR)Ri;TT{Y0f+iH9c7H6GJ!K}
z<|v55PdT{|9&ks7>k(7l(Mg~$-0%(QHlJk)C)%+6O2K1PQBw=-yfxls7-Hj+))kOu
z7to*IRNajC(X?QYj)=Gc>rVlk6&%mpwq38k&h$}X!+-}^=aGmEyblfpWAYI!sT~;6
zsaGI-gBN&^5&~%|1c9Unq^V(G46`6jwS$nWKcuOHKv~8?ni>i=Rsw128i-_0K$?m?
zz>A2`_5W@88W3l(=u93DJm<lcIoRg!p~T?M1Ij_b8<yLk{0OD?Z!55h_0OP8fZ@#q
z{!#&c0cCp-K^)ldT&6Ky{E#jQrb~<HvZ;hF^QJ2z>59Fg>H0)8{W?8kAU$KFXvSMn
z)qPRTuY|50DXP6r*Xh%Bb47K3i)ObM%^geEPInPPUm!2=WyKio*`f#ah4o7poAF2y
zEJSek;L{irZ1x5iXn=GMlBUQ4*??e_`NI;T5z#~zgja1v_HYSZPa%|MFLVui<c`D$
zw>nxA15ZSJ5HKK`g}9WDilJY2nL+q1=++dO^rQtM^dm(*8MVS4HnYIGTe15iZ6v{1
z8wTjf!@Urv;1IbR;L{E;1U&c>VuT1{48IA;3uk|vi8J_XAP!o=Jl??#61Xx8S^$H@
zQ#z48JUmKEqsQ=wW)(64#<vYJAs}>~1MKmEG~h#j?NjwhPi80fRqE<5srUH*Lp>9z
z=lnlT-3G?)s5^E?Cj8~h=}=lQ8bxQo2O)Qc38KMyf3DG7;;S{=nkEetz!7EdMbcr`
zX*fG|>PaGbg|2LDW{+fIX*kUe#5vVYH5>*y;3F#sJ+Sp+q~>b`A3tS+KQr72c;W*l
zt~DLUazy9wb@(vism)*p38){(IBZIQ&7q`&fH<I0XKXyoHs=wz+jI{;QXER!V>9G|
zRZ@7^)EOTPfmt1jjt|K9U^Rzm%7=qjn+8i~IV4SKA(*fQkTj)5U=~(F(v((#PHu*z
zDZLIW=O0L#(w$(deukte-2(b~29l<9H}roQlBP5l)Ug_prZf|FW~GocrDI{oSOrN_
z>HvN(1d^uo735YxUsH-PJ0aTd9=29?2lMp^cx+xG9{%}(srm*)5(Q;7lpbK_uzmBA
zZ4Hqz7zS*g3?;UQ4^uPko0r=5v0%8d{U|80y$sCY06Lr2yb~4GvW?IZmY$)@y3l2n
zbcL~~qJhxU*U*(_^o$vzDi69^NLSCGXYxfex6rjq=-NA?I(N~m9`vls^z760+?k?z
f5%heuX#V%21w-kD?xKZi(V}Y6Vk^-SH$wgozdRtg

literal 0
HcmV?d00001

diff --git a/legacy/Bot/bin/Debug/net8.0/SoUnBot.runtimeconfig.json b/legacy/Bot/bin/Debug/net8.0/SoUnBot.runtimeconfig.json
new file mode 100644
index 0000000..becfaea
--- /dev/null
+++ b/legacy/Bot/bin/Debug/net8.0/SoUnBot.runtimeconfig.json
@@ -0,0 +1,12 @@
+{
+  "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
new file mode 100755
index 0000000000000000000000000000000000000000..67e11a1d25c8df33c5e25b2c064e19e8141908e0
GIT binary patch
literal 26624
zcmeHve|%Hdk?-8At9xZh{viq5{0Kx2F%cMyje!6q1cOa*_zmVqk`g0Z!YIg=kz|4+
zD>X?;!Zu<1yV)ddA*B1#Zni%@?d~Sq<TY86?xx#pH}9q0bQ{>vblXigecf!kpQgXy
zeP_;<bY&abZuj&4c`LYc=FFKhXJ*cvx#ylMdDG4BCl?VJxGr8KdJ-jnRtvm57(;W_
zm48`9U#fVi;Yn@dOAXxz5}EK&I@O<!4ut!ngM+DTxHle74-bYDgW--XJHi90SiHHi
z(tEWndTS@qM$JXO1#e<>^?9AH3j4GcqFYh-DB7RrQHF7i;UcOLTvvKCf%TWqvw)!U
z$3^>&F)RNsm3Ea$_{1Q03nODhKP?Jjg8ey3<OiA@CF(BFew#8}R9(0|sIPbQYtF`x
zWKn;5F96!aR$Fh><P#>^*PPCz`%n|z#(<c(_Th4TR%5!F)A3{qjI1l2#Kp4q<8pjf
z6RmMn1$~hHiVy3ilbfcr6YY5yktWpN9n8`rQ;EFuZdIjT|G;$+)>!9yBoC3sycIUx
zHNFacjd`>59D>ns^j@WUKbjYMskc<YjwzFFZZ7XA3THCLR=tf!^U}PlyxCCH&3Xiy
zHQf)1Gh9Zb8ZMr<PInoZMqutLn-Xd<2yS7&1Tyo{;x%1)SZbI?ei{#EUN9GcoS6>m
zfj2Wg19-U@3bnZCCflu<g<xvb^XOtUy7J&Q8jbuFT)6WyxyW(T0$&=V4rS2by`wS5
zY#`MZNf%5k6*Gel-pDL~IrAVt)S}Z3DB;#XkRn%to(}^ANzXS(>B?UvrIDX4Wo`ay
zDMuAs1U;LAB(=yKw9FHkdms~@D$2YD+OW)sl&<_-DUCdI0NtH$lCsLk!)GAW<{=8@
zsOlGiC?14sY7o|&$G`*@$AFt;j73tq@-P>5Bi|~eJKrWH##k&!Z640&F;u?@MDZA0
zD~+)QV<0GsV_Yj;^!zd@UHKJK8u{f?y7QQOh^);om2y<EM<9Vkb4`gxL|UlKyGnQE
z1pxsWOc4YD0ZZ6WbL66S=z)5mT8qF5-ubma;7hO9tY73duZ*=C^}Z&(&S%Z9OT*qF
z*Zq((BYhpuK78%H0|SXX$gL<0BnVa_2Jz1OtSWU^;{yL2M<nHaVAqYjmkW0uiNlz8
zLLaR}Szi~yym{-}eCg9X5ax~t%B+O0mzGzh<W(zq$hpS68}(ceW#a<ZoVs}t*lN!E
zpZaJ3q{)ZC7;C_k=T55YyuT|`zZCWP5c5DZjQV+TE##+ws(h<UUdJR9=NNG%afJ1U
z(v`niN+W-Zl<xeyrL4{GmU2|FN8nwaW3D;Y0mlz!q9}Wt^wIO%rF7+YNNMD8&%jvj
z{7xxr^IN4HRqPRH$FjMmWPh;C+S0Pm*MS9-RgYp`VQpiYi{tl*Ts<F^(v{yQrIGKI
zvNnIKl%t9*0?m0`uBmaGBM{a!2KT~yEdP4ghsa?0SWVviko4E{hop4n(^A&vQ&Ntq
z<_LPQT&_i0&<zP)1>tr*Hx)#e;ZC)J9$CbVAWpN1Si&~3&;#SM9#~q0KrPxiEaU0<
z!&27faSw%mM-@2&P9C3Yh4ok*AE|NT_*dG*()bu}26FvpP-000^c<23R^$AwdTt3T
z31ZXzSR^y_9CCqI&p|_P1i@&gmI3nxwFq*Ccfm@%uF=aG$*WtOO}zRja)>vm=@Im9
zI=4m7G1aG!a&e&+Nm$cs?Jf|474AJ(@7J%qpoB<oPh=I7L*>*6CJX7Av>GT)79DvH
z)z3tAsT$H1S*_aHt}hhrngi2e*KR$Be1p|CS0KcwJD;9I3IG{v-?*fCECc4@#%Jg%
zrNcbr0WpuMFpmp|dEBMkJP{(jo#vIO1@nsbz2}~yeaM+cnSJMAAN-4X<C}ojTz(XU
z3}VjoS$YmJY2-N&fP-e_dH(erqS3G}m?+f!8#n^OasSqHsDlyH>2QWWs5|GH2h=@z
zL<yX+5HP6gyqyqdFx9V*a^Y|WcYzQ!*Off0B1C#uIOe)UEjXhv*H3)0=nEdw;R_f2
zNpY_Eu7$bYrsoi&umcflWH9Zf1R)4~dy60fCJkcljaf$ahhYeJ%%2`XtchVv#Vm8-
zFpRsD&$0-S-p*MrQEkHtc8!b`?cx!OcJcn{3D{M|b1X?Kk2{7@NSk@M5_vL67iy7O
zG?VW+-aHnKq3by=QsDOHv0x0{mGh$D8%R*QP>Yl}!)kIz>fr=l#PE(c-*IWa84kY8
zT4u&jC85TgDG&x7%=~K5=l#1r%7aEA(F;}3!KmMp=R#HV9Gh_>vB<CUyqlR$FwU#b
ztm6Xq7IGotiF4r%JYWtzc<(hTWo`ZrDMuAK62Jr@7jiAq#Zp$NG1PbuL1?H&qcCJ+
zDzRR~J2!&SMmGRuKVr>jChyEQLlW*i2EB|v%Ks4J#RddQPINj{WT$hXoW7n3t{g15
zywkT_ns1`hp>Rp4!|AZt&@&sMUt@i0lPX|<Hxh(f@}L{~1rt7=Y~Q>M_VGv0!3V~C
z<O(kD&_~$<HG?^pklM=Tk3zbULLcw^kUq*zifl(^jy-oecd(}uG7IyF*(uKB-RQ&f
z_+BY%^LI%(s>qysc^<i@=CLRPrWEgs_u4Y<vt`_)WGHfEiju*#!hJEvYV*C`83Qs|
zDh32Cj{&}a{3+WY@dD!w#0qRReCGU(Rt|$8n0E;57Pu2I1%g*@jpNcK_eZ+i#f91r
zAzju&QNA@Y73q=-M^@o3<>^v{NN;DlEKv*T(!O_8QTDE4Ucr==@=CdFyU_&OZbG4K
zgUPV%W`VZ|d^cbUW=?7w_lIqJxlsE^*j8uT##GqGg~K-PQf`|Fk={<*O4Q5Q#=F&G
z&ScvT8AO;Mtn*vZ1lyu0d}b%e^c7qOHKK%$qX-wr{XR67J!9%tt{$|;1&$@w2SSmK
zB&w?<>OtP)b;{E%Ni=q8KFBQb#hLF;M1Yas4^SAfa!wF_LafgS7){>DOkPb0EN^Ad
z6+wJ>mtPN+fx|K0<!!*poZo5X;BCxUFCah5m??O~8#04t1e5Ar&@Q_(c7+}=^Zi_^
zy|JnD9iZuKhxmaTLZ&{-<%L@KZ-#8qxE~}T9K0d79&qPb^|OBg_su!`1wb`=3W4Gc
zd4iq@V#S*XS;&TLSpEQjUYEZO1)25l`VdskAR4@ldX9Z1cas#xU;;~4gdQdYELFh-
zaVt+j3Nz^qRj4ro70Tw+R2Wgm2SVPEKj@D@t~cZh`XZ3W*4JaBsjT<q4?>7-y-(*`
zR@2#aFhb99LkRbFE-%!=Ee`8_5t!l)dG&x->^WCA=UgaMsRt_a36@tc=ADBSHZ$Z7
zxa(Jc5QgeGZq8yBwNMqAQ=75>gfW<Hp0Gfbtv;f_tV_Y|-cU`jCIXLnL)F3R2s*R+
zQ;X_X=acAXt6!~0rnCC@BAE3YH-xJ7Q7$jkUW*Y+lUE&qTHbT6InZ#5(pb;&yyOR1
zoSK|v&$GgMPDo<ML-|28lR2&V7N^>Jj>)#_h?2{xKJD_Ve_ed^n^lKeJZ1`W<HbgT
z!=zx1=%W0N7Iwd?C__#puAh7H;zd|3W=&DiI4TPT!(uu8`r^fN*MtJR8<h~5lIJi(
z2M%84=|DhjP8%-ARLoE9ABIu*d=FO+R}ZGgM0qu)1O>ex@M|b<M$}>1O`z`u9SN8?
zXi(4z)Q_U92D6jD6O{)+KRb?I1=@6jh);s90nT#pY=i~n%#kjd0_g}m<j547@a(|C
z^xPp-)LM22-D)lS)q1UWEY(`aD)l%PDd)l_S)-Lo6jo@OB|2896|~d}<@_Wobd}^M
zR)h0XhD-H~jr?>41fCnzb4=yr#HC|-awmw9<!J<r6Z3-sqwz$d{-%fQVDX)sS?4=9
zE#%?JlOaTOz!O2_c+X8KCtDG)c;^-N<^S}3nr8cN2m`qyfV@HFdrSuY!zzaVvS`9R
z<}eD}KdX@BKF_YK%O3#+`FV>aC!3ZWW?DH|gaiPwlRaErFRoyTxTB76p`P|}M@5gO
zvm6t8jvH`O<-&1O<z9uG>XbR>0+o72{&p5oFPd<AVvR!ZQ~m0@WVX3kr6)I%bMyLw
zjz}y<z^$}Qae5lRr{R=(NiEOq{cTjV(-EtH`7wDSM4}iRFdYwsC9=KB+GEfjpD-@;
zr;p)su8jiyA<(h+EUu*&fg`|a1-$6w=QXnov~{>}ODL?FVW?KNuc$<)dYSV((1bZ3
zMS*<<%UXf!2e=Mmy#`%Yt`kjG?m9sG$;+5ihs^?J(TqR`Zv`(5?+RA2f}`8J{11U5
zTyC^``9y#`M>X*b>;X@R7v)Vr?0Y(^!MEf*m+D!_t@F8f)7cpG&~w~?yY?uTj(a-y
zgQS~Tq+taNdG?c(dd_dwEwrrqJnR3OReI>O>f`}#*`LEli0wPk%=h#E0i7KRt5jj-
zI?(LM5e^QOTq=SUD!Dk4hm~tbFSQr2a;q7SLP0CXK5%9bZ_ukU2yZtegQ$DF@`fir
z27cQc9-ZF?G@aeWTIaX{nP8MlM-mZlcvKRxtbipC+2>ZxxiEbg)1~n~u&!w~LNs8?
zV#&V;{cV%{K&M>L>iECwLSPOS8@q-V8<(=!+U|5P>p9i?dbFkxVP5;r&l?xH^Yg~V
zapzY?`SM4>ZySY;OPGxU3Bzy$Z(O)^-1)^QpWOLzH&bJ-0n3Tyt1eu;Xj$?IUY05)
z2<rV<jB+?514~Q=DkDg4cCsvyJrS%ud0ubFI^YlXfP_e+S8)~A7s62JccDCs3l1xO
zJQ~qC)V`0hzz>uP^y%pAMg2`C;=}9oWzhZ!mt&pcHj8zN+@^A~i@plJsj^NnDzDS`
zqCjrmV3i(at=xJLQzPgls|t2uEd-4Sq<I&pTN3xyu{w@%sUA^r_|gF1pG)XDriMW0
z@<J`{d}_zYogfA-@m5+D`Aw)=&L?Gduw*-tE>e;0N)9fOiw;)zqbmA?2aETkyCK$L
zvq_J^f$$0LNB5u!Hs6QB&P~_BAAgH0s`Fx9hlbL@giAecR5+hD_-SSr^j|&&LV@pQ
z+`}^@=}81&F0cmJhmJ>%ap{RQ;q-Kz%h7uQgGf(TaqPYWRrDM;aC+j>k)F6;S$f(6
z!r6P#WzN~7+_5hIK2S>er>IdO|8R%0{8Qu@_n=aif4b01a!#X_yOHr*VYnSAh^N9c
z1}87vfxIE!$~f|mGS|xAkFK`4mL8eS=2nQg+<+&eQ7#?%N6fWU{xJjQiTO8M0keE*
zvOOw_(4L53k$LCX!HhJ6N0WdV^3{cmruu*{U_2D^2fP7)3aj2b2Y35DoQ48E9Jrl#
zJi7b=t649e<aEUzReOVaEH`Yqxn1SE2Y7T9H#I;PT!B*}rsX$C!wrBb_{+QONrVVL
z=iKbzR;;!c9SM)0a3tYX1*@D1PkFH_e*#i%FIMT1tJ#ZZAYIRKL#Rq0<<gPx#EVsu
z@Xob`YOHifqoJt}%;XGSr<8cKu|81qXl4wmj#vF076w~0zfN^jwan21D{?4XjDp=_
zsgu6D(7HUuj}X(;BZTNKfnIoMkhd2lvf98D@+HEHpp4`|6D5q4O!pS>CBPaGHN5J^
z)2{|S1UlP>g6=~7y(mpEw>juoqm%&cc3h5p$qTW#uUWI>#x<G}Bf0tT;^vm-wwAWm
z<>-vJ#!0}xnNBntakdyqZVw`0_Ks{iG1!k?3(@^ecpnKZX7AiVznZVUZ!-Iaon0Mh
z^Dn^nU7*D5HOW*jGvfnQwcS@f>Z_QA$}hAw-bAwzn{b73VO9zGM!wE51?641Fg$$=
z7u&#o^5Lq+g*8doPD_6aCGYiYe@RL7T7@_Wy&kn5^qaKU!|?9}{=m%oL-eGdWnORX
zsxax#Je^*X-mw@C2wpGn&xG>71a=FhQt%s1<~(e&%<p>eRS5D|GyH~$w`lZP^S;U|
z`T#~T>6Em#p*2XKtA4B6r0L#99#LmXgZbCs`AWrCDoy%<mnC$AZqgLUG3jd299@9F
z;^whF=X=m!N4vpUN3FoCC}r^|7Z8v5u7FulWm3I%i_4@<BIl0<f5grF18yF-L*uc!
z1pcLr`?kjN@6%ZRJiu?zr{HFjEYasrG46MSQb)I;bvkX;z6A+)3;t2yc*k5b6H2}%
zmJU_0w7q_Yr$oZfpqoi);IrrkgLU`|q2E|T-s$wYs&7?JrzZveZ3W{OAagq1ETdFF
z{~$f3-4DA?pckf@w(~?|<}tsxJ;uz(D?5v|Ut&hM#<5mI_hGg>fg4iWW8DW%7k?iD
zZTCvez}xMksBNlxt9mbN_R}fI-sxLf!5)|*weQ-sI?}-T*Hud^n6n<E)R5)pniuiQ
zwSNcaUhGw_l3GS;A!?M`Ea9xB2#rC?P}NmcdlAJ=<U)-jnrri=hBc4cbXq7iUW|Kb
z7PU(4yQm$*Xp5!xLzsDt=Fn29`QVph)J)5yHdks(@pR&XX6q2^d+9n_>8P#4w=#J2
zPl%MQ_-+)}nx(dzZj{>lq;@N9lp3}w7$HTQrRIlQk5QJkO6@^uyMwk%jn6WUVFh<f
z?P;mqL%X2sP-V(Jhr$&ZcLUYB4+DPDxWmRZx~n?x)~MOd@b8Udfd3|~|4{W9HPByG
zp9K7v?|q>Es_Ka%MYWdb8g27G2$}c!9s&HA?=$XJ>Z^PbxRSv9CLQ-Z<2LC*-;01}
zAekxO1pcbD&i6h(j%TU)1Sso$4+ARx5~oJrq0^d46M2q92Oi}!Ws)c3pQz7oBVX|-
z%~@w9xq%k@zYTk>>a&2WKySbc<72>;hu79T2_G)4c>?@fs~KMO@+>U&fA=!I&UwF&
z*8g4cL-c*4`X_)pDhm?IZNIbfXGO2Rjb0{|bh|WX#Qe!+IF*feR{pZc^G%H-WSY)8
z3<~@Qo$dM#T?G8K;ImwosnJRo^ZZcr0$-<30ldM*oEq_ng;|?s7G}1ftrwI+E6@9#
zl?~|oHH~Ff$%y6i-U$9XD;J<u>F_m;$8FTVkFiRlmt-pb2Sw-B31cZb+u|Hi$+=5p
zvMpP~SbB8N)P6tPv!__w04;`!wXLYVr&!~*6RPI<m#ST;eYDtC11X;^)^0I%ddB8_
z+w)`Oj~5*MT=Y_rGm1^fTg6(x=_VaPNAyv;y<*U`sIFKWF}=hOQ>yJ@vzmsAwWrJ}
z^r~I^r0WF}S@9~x`AOHy<_vm&vG!9lOg}BwLRKTQ;qz7Z1Fo<&S5;7Z!qsG9dm^8&
zx<Bb!YAvKKv$=YYXGQh(Rx9ncYn|0sxmszTUHeD>dTS9Kuxmf^Z$a&lT^mHdHX5~S
zN6@d0UbJhoD}Rjq{IXqpS7ocdnEp}KWR8~5cZ;>%))K1XgpViTRsP$oWwc0YJe#|%
z<#em6d1@*jbFHL94$hTys>pfRx{hA8+t~K&X|Xut`x@K6hW=1$Ec1g_Cw<9oV^7>b
z)*K%F15*e54_RF_MQR_SmtCJgtw(AqvNlk^)b6Gzg{=)VYS+F>P1a`mfP-@zJyPU+
zmbTG9m9_oAZrcng+vrzz?Te7oO;fI6UCwI@YBcNwU$bjJa9@MkTaLCp^mIgVa@!vI
zx?SUbH__k7)SuV5?IwEFu5sJVbaGy)-_7)Ssj+>3V%<Uqu%G35xZwJtwU-{3+EIGO
z^)zb#s@f|4q51{Xer0oxxxR*4M1t!cnWMebqH2)&_h_rZKDZ#WhlZqflnz5?4}Hn5
zJ>z<odgy10Q%2|^+{UFJkI+N&rKbGSL+{u%wy%dg3z<{3?W0%h8n^AE?>X9{^v+_%
z$!$^cEGhNtrDaP?ZN0SKuCdShX#TQNTOX~oYupy2SKd`>i_!P&8n?yiZ&sGt;`FLr
z<F@@|w3piU14TCVG5l#2Lk+tx-KHp9(a%)Z)abZB*VMRhKVi-@)y#PueO<W6*3=l%
z>i(TtSsKeUioO2djKZ4p*DlL#c^h^WD92%uZfi9iKGA3*bozunpiN?hlg_@<;j5DO
zF0VsDKJz>EQFJv6KbKiha;}$oQ50p1lX7{UpSgLIiExRZbyj0J@rj(5rucC8_T#Fg
zFm~uy<0<+$_V>r}ChRyKM2}-X@5S{6?83dczJMpr<Jf~Ary01ez%>)sEL>OO3gfCE
zzTXA_-QZ=s0nm$mdcVJhmJ4hb*d=hAz?%d{1ttU@5;y`_OGg1U`Xj&@bV~3~3;cpW
zP3!kJP(ZR{K(bpaD3}##{kgWzy$W{q`)|c_1Vc?{O%@A&o_^^ap}E>Z^8}s&cn`Z$
z_OL5ux4RO)eH;|t>CDxBUHv)0PgXoj9aLHIW5DCpKf&>0zyBS2O2(R|A9G*C83oIq
zt8cAo)Hc#W^EyE8yOBOwu~F-$kGP}Sd3wC!p!ROTV<JB$<Hls%n2dX#zU=$BHch+D
z_%SjH@26s-;aqKp@i^!YS8LGcW$#zCx!R`$=M0w6cDjF}CA1pvueA<nrRy)!9fpSI
zk$!)*&QRA!MXOQKYE-mJXwTzZAfZjej;9s+YqVSs`&#wo`pbar`nASV{k>G{+pOPD
zO~yWbn*LICzdj%OpU|%s_+gR!Ve!m|#WRmU|6l2k5$pdLJq%wxN3}ImU7yzO_0Mvh
zmpMAG@gCv4#ygDjT0;%PANl9Io}(YRmSJz&Q+<o;OOTUtJuPyc7CGlZ|3lXov@iQU
z>Uv82^OShw+jO<@nCnIQ7)IPk?4^yw{@F;Ms@C<5#Qu36dbSwP)AQOgK##uC7!}U1
z!-^Pi<{uUQQQ^NIccl}?E70Ll<2BLYHPK<3-exqo-%DThMcm(`c5{V00l#&*--P^a
z?u5oV;2eW)a|iLH%W#Rn<pR3|ZW4Htz`X+F0uus91m*=kAn*eMKQ8c;z~=;hTi{uN
z=LNng@J9mQ7Kk{44h;h50ybh5ufXqAeDn*e8LR#x;0*G3nrSv&1K30@fNiuN@LhBS
za4j7J+(4fJ+(G{ecniG)h&y3LGaVF|p=}jyu)EV-i+BCMu+}1L{KD#>hXFff#2wNa
zmDWBRGEX1fO|MkOrS+)L@3tYg-i_8@RvweqlR`fw@HEa8zU(_K^wZ$Es_Hbo5B#U;
zLx7(W`WfLlW8=(oMtIH$&-0?&3(|UCWWFK%ZwTcL@y|aCP8wUJ33O>ZuAv2Kq2CSI
zX@-T~AaJ&{HfrqQcHvnI%0mBI;p`OHCp>+E$AxoD<GDBr{BxCeOY7azdQ9L+X+16U
z(?WR+aH0QGf<G&*XQcIvP+k!Dy3o%H=Np3mv(QODjSNIOkErQ9qDyD%!l3_qMOf&w
zh0>_Ab*n*N;ax5Cc2Ho8v~~#IDLnhYbC-9Y@bpP*T<CWTeoXMwf}auiy!3rt@biMd
zAw1*~Kf9*Wmn&U3<$`a8(jb)Cf?o~#LjP*P+l6PX;8DT*1V1YH-GZMK_^iO^M9vw(
zpBFjjh4O|_Tn3Ncj&Edkno)r<z=i&2jnnis@9Th9_`W9;4eJTIxlhx8ufaW?{<^YV
z;669U#y-%W17r_Jg_BH<0Wwe162E4$bqxaBO&LqzN%PC#e;)Wk|LcN(4?Lf%)GQgx
z;?@Si+XZ$2F7)pcddy<Zlh#$x>RGISkNGu^w0f8_<~dD2_n!pZQu(~l!xbW}LU;s^
z3Vu@XQ-Z%Pc-YIF?Kny8H1`P}75u2clR|l2V1ti&+66`hYJR3P2<!m-vM(z5b2u?w
zVV)OU!@1@PvjHcj@VUTKLU~T`=c<@~USPDE@uLDy2|O=QtC79}j|x0h!!k8|Ll&b8
zY!`S`;3<I{aTfOieS^MDZ;+)0v>95fmeY=DFKPdxP1l?B0sVIUZvE5x<F40SKXnZo
z&l+y*!%V^vGR{u@I3umceusBPL~m8SY7za1=PaPY_f($;{<i;zfTMz6U&*Z<m2U%n
zr}{t2aQ^kB3z9u3bx5@US-J-KSg6=b`G8Lc)M*BGYZ`W6mB44>lvKkGtQz=i?7R54
zJ0ZY!?BH}-2N@wcKuv(R(E{x3lEA0XAn;lm0$xjL;C0v^Gyxt4K8@ZFd>VZa_;mU(
z@aZ_KSb$L<1U`cv0)7QO0{ja41n`-368KDd6!<LM>6-xm82FX+IPfd+Tbc#f89o6#
zOrHhbjC0v8Jpab<&65w)ztB=`mo}uG&>qt8l_b4G-=qH@{XcZmwcWMfHR6gGdyI#S
zCyYgIV~lsf&JUkP8?X|`sPft4egQjWrpZaSP5ZaXiL`srQ@tG#CIN3@w?4k_SEMf<
zMv9-P`&*O9YZQ4n_r?rZxLa88w*^02@GnkyaMfexrXpshVb-SOnt|Q+6_~Y|=rIee
zSE4nHi{BSbqZpn;YOs4y<s6i4)Il5QFK~Sg*DJWr;qq%6s18?HyM^X!J#+)`F|Cb0
zqK(iOalL@+Ra~!YAD}t<C&BXpO6waauaD3l;W~w@()9vubdAtmxE^ucLNB_$L*D@Y
zmg`MgZM;bva1G%4k}*P-dxS!`R^#j~48MmFvtjnl%Hg3{G#g*Fq^GB~g;uWW>*?u8
zWQLN_k+sQaCbOt#5@OqA#HCE!9`B1M4#(RwBZGabdV83(BO6U;6&)|yY-0C;bn5mk
z!`ZFz!5FAJMTd;)7L#tar134StrG_UY2qLtt{R3-<Jcj5@xE4CH$2#fV)Jk^8SPER
zfp&Ec4iCiBg_@{RMp~O1+@I(lPRC>2(agd2+oOrB&Acm`9F8*)V%8qOujk{*61F`a
zjm6Wbi$vzy6we+=#nucblB%KDU1SQcEoFB)k(GLvQ=M#z#dU3u9~zElvW#Zq18nZK
zJw3OlqeDQW$z<yGc+7Sl&+Q`Rjzy$K8y<qui;CJV5*f^6%K(~83?#DLwT)KBkJ!li
z=wK`vS8|K91qOC?oAPQKwfAKcslk09v++f?*2T1PAfCxY`{P^o@6W_VIA}^Zkh0+6
ziK3Niyv}qwl`hIzti~yIm=GgNAar0TJL0e(r19cG8dh<~#x0%508+spleAu2YIUH?
zXyv9<Y&aQTMLR|^n3(3Zsbn%P9?3M{5Fd=E6Ma<l3-MY@Wz>S93El-`X+7D^C1(^`
zo%YjZRu*lF9*o;zBi^J<sl)Nj_=5(jwsMkX)Uql@{qby1SB$#TBdj;I$6~ZL9Y@mG
zIoO*T9*o6f)HR4e#UC;_2wcK==U^tfKfYyfZE66amIYa)WJ+~Wv}^y`=wM$QtB^y+
z)~&=mX&WXoJDeVrKwfpQr)N#H?;vu>x<ot~qm}LDp+BKrhAmG!6NPMw5A?><nTZW{
z;EtE7+|f0d$+E__cQz&l;g0Uq!T8{g)Nq>V4tvVza8fiK?>y3n?9m^GHxHv1sa3Bc
zgi{S&A_Wy9ZTs+GHZf4>hm@F&Z;JLENDRhlM?70rhoYrM6+hi0LvcGhFlGlPsYX&O
z#kLHQ-`dk7GB_$EO(DN!;I-rk(pwaFlxTYv->m2z&c>+|&Ws^SN)(QQL^7V1^#DyA
zO&#&x;r{-3dUI-UXC|Iri@!Tk>IV-chhy<|>C^x>tVQ^ykxohsMQkUhnPQEURCZ#C
zR!IIOO2LHgMApeDtHbHOQjhLv8gac2e>Nq4dn$df)Sx10b1KWDl*k3^)+gYQ!4kcU
zefI%pQ|FOvd@zI6QQ|J>xeh+ug*ctGv1@RDDm@_UDw@0`S0SW?Z{i3Y@l0PjF~p0r
z)Tn09Ua`sO5y3L0_JVp_)5wH<*@+EnQ$r)^ME?OtQ(-co5C4UgQlG6+IJZ5Wj*gUQ
zg#lQR=%Ay)UI9YSCVCUeMAp%|W(2{=X~>a4C|yb>(zZUDk<3De`L0gulF|Oc`a<eJ
z{zIyv?sziJe=DbXO)A@5oS;neR^)naw^P5ZPP6TOX_2oR5u(YlDJt-HB%=L;sZ2J}
zmuZ$r5si4yAPAc!xl}0XM$T<2trTwN)WcZ>dywCxWpgZ<v`40H<c%Hhr5cb^*2H8*
z_`^WOR4wU?da}Y=FgL#6zSGf`AuLRe8l_u2hLszaFrxN!KQ>x}S?vC{?BCA$&e7K2
zpN{t<Avj1jI|oRKiVPV|CSt{5N-fBArAiOBdC_46T8R`dDuUUe9+lTJa#t9n0m=K~
z1isb9(}ZnG&kjsn5;smEwDZ<>!Zr>WG27GAtM+u%rIMz)ou{e0yxI}pA019+%Zauk
zg)TjD-trV`g+=PvdP21u;#qsgC<?31qC;VuU@K$qB+|5PI6hq18WxH8R-4>Jl~P4=
znW{x<Pg?CtlnU$*d!vk<DfPtFKALgf2p*0y*4(F{6n3~cqTLR=Y&5we-j^DTWeO{z
zu!2;|iKlnO(}xp%@r<3&F)`R}p<1>e`A(J*k>#5K@y$3=Px)3^dX?`Ely3aIGLFun
zG0AR%dWOm#YH<52ZAFVu9Gq7&h?>PcJz4zmt%AJ{pJ3yNTi;+bIg&y9rbJ&ll}YW-
zmXmE)7Bf>bM`lr4iEKYm6kD9*bf;3u$voK1vpTL}Vy<(fzv3()E3Qo@u*U6tSn4&A
zWs)|@s`g$m)7;*h$)=-f15L#pr)|&{$tjt_PB>kt7ell_;Iwb=>g-jXp`OGbZc&IP
zpncg46>l)=u0GE6f<m;V_crY3B`nl-P~yr)d&-`0N;@`LsnTa7KUYZ?h4sjT7O!T>
z=v&hEqGQVTcm|n*cW*Y<RS5K=i>ar#;B735zI2>(s|>VuIGtv^r_V+Tec8W+Ug|Ey
z<EUkyu}xGFk2^wYSSiYP2gVDJ3x%}Vg}wigLPhS;D%km4A=FDLoK3C8xm5!1&If_h
zjA5Ky#qh2^3(9(wgQz8e<M%_p!>A`|7&VGtgSS8l8o-ssFaN@LQ^CApTr4w8z2FK<
z&kSXNWpPrbI9gFlL0*{p0pp<ZSX_7Vv``CaJ8wWs7Uyjl8iC|K>I59dsLap(l$-(J
zBO-MWBQmB&XPuIwL(FdNLr<E<z0=ZnoQ1b^(h-ajhYzx_EF~7*4k>Iw!7`RQ4BZca
zhezV^SaY`bQj+=W6C1vC?>Aa6PPy^V-_{#G_57<Zkt<A^5!N)18z4~2Xq7vxYgK?C
zbTJI(@%BRtjvcpZgJXCfF~H@YqIBb}`z-wQLYqZ8N=pl__SXgzMlc#22*iVjSkM(y
z3#4Htq3cn%7CeN*&fp;%_gGqOU?YU!a4#_S5mQwR6S&I^MsY=4W+1Mc9;-#ecTs1N
z$7}a6!W2A&fdgBi1pu&a#=05X!`L4DF&K{ynPVT<Jg`<rmk{BVgI0A#l|?R3jmP6*
z?gPwyfUy{3F~*XNB~6$)Q|t^5Fm;Hfo!2}#6BH!GE(i=&gb99mMhlU*HaOx^PN={@
zAqr_B-M|k^4i#G93zn4Sj#-c8@&v{n2o8(oA`K_JA##V|jtAHe4|rR2oei&<rTOrq
z?6C*fN6dkF8McHH*y^<mPjJ0RvPs=`xyNhifw=Mk4x58xe-?;C1IUlF2>1hSPieXr
z725_@?}#!~XDyGoK^S|E14OT^5w?HElm(AYtH3nkEz&@6Ks<1Ct`^dGn&%=y5IA}b
z^dt>_W|5;TYgA;7xq*(2iPnonYj4;Uaw%`wipT8o0A7T6g3FWz0(AvFC|=Y&@ZUFs
zV{gw44xs+BrplMN3=Ri}17l|cW8W74A_@=;JWU?fmOCr!A;jxtp+Evjas4+DE|nGR
z5(yrkIOpgBD18clS3?&mvoPmJTj2<J(#zv{gbW0}M8B6HUS}mm88zg~pfO4G^vj@v
zBGSST{%u`GKP#r64J4t3Cu~(9OqGQ&wa~dBH#jW5JmnNNRBc(FV23!?!(OmmnQgPG
zJi&LfsW0fd=mwLoP%cw?$N-{n!E=_)jTeYeKBT$K5aH5FE<55J`-l%-__Pb2$CCGi
z^$?;NLBVl)HgIGrE4B|21EFssNKib5A~*~W3m$?uc|@ivm%WKmnZoQ?05%pFUB_Xu
z4^>R7f_6YmZVt5i5hS3IhbtI|fr~RgHdg7Fkg+km4B`wSk&V8rg_41F1EW03q_Ht}
zOw~+;7?;6e&*!i2c~9V*-W8s&=Ry}>T5xt7mOsAWWX^Q!rW-|-Zt4~<FJvXRITIn}
z=ddjJ3^{LKB?zTlTC9mr4HGu_z);9DMI6oO9L6=@FtN~qwBQ={uz0*29zcb!#kiJ<
z2AF-_yrP1Yk?H4zb5>fA<G|Hn<2vwnLyVdQul))}!%t>#;l&D;hnus>Og9qCOu2tB
z)HS_|y`s;o^44fB4S6vVh&P~DFzPhW#>G75m}zqqEVOOqcC`8kFXyAdu;36IJf!4a
zMQ%nfCm8n`#WJ~MKYoeF!bCJOl2mXzE1*yS5e;gX5l$hPLK7Ba4lOnxCHyaTI%bq(
zTirDS@fmQ(r8ML~VpCjr8RjRIWIVwr76doUFN}#~&4Y6y<2xGe#eB1PPMBJd+9Q|&
zR|LTuDQE}^Gy~0pM%>7F((toW56>OV;;&G5>vg;FVgN^K#phsoaLH)k(}fSe(VE)4
zxwCt1Djjbh8k%n(SzLFxt+^Efs-_grxa_w>n{niY?{1O5u&czT62GFt7m%#&I30;*
z;`p(bMpqWkdcuG8>2QHJ%*^3MEsI)-R%`SwY{L&H@EjMOxW5g@3+G>%Fb=fBL+Xen
zJgkoHvG?I2Tk%UT{KId?@M8<9G%Z`UytO6PyZGAH-o>qLean|#8;$n0?Z5Wg{e4TK
ztu4!!EROXpUA_q4{$w=^nqBHdhw!uO%I$HS)hF;IJF+vKTzep!9m=d&xUfHwJuuwc
z+?N_ySdhZz6)Tqz*^GXK-z`x`p_ecgDiCprXQ-wLZ!-9e7TzGznnLIQv?P3o_P<09
zzHj?qBIjD7Z5nMXv#a#PSU7LPVcEnZl_vY;k$Sd~W1CfYse~UDZq;bR?<2AF28IRV
zOURovTK_u<EF7gwGL@2bv4jUSI`ErhIy_NeErw00t!bq!_RGC(<xGz}5Y_121wp@c
z2#tSZjF-brmwJ;lafFhZ#mSufEUKw=##Hp0{n%QZTf7{mo%sDvnGKVSFxe5<IQix8
zE%{I1-27i4RbJ*z&>D-*r{R4(z7wor=53r9M{bk-1)h%Zi*b$Kdyv04+^tv2G1ev=
z4kU0S3_?ZM-~qf^<QHvV+c@R7VtbZ{EblyCG=`PWvunQiand$z<+}xbvQ5v9;<{C}
zF=W0Fu4`~z2ZrnLm4D|4Kea9UY~Rtb<6BFgez>J+bi*_M_FVfbH>6uw#=@S3_`~VH
zDR(YR_1?D7zRfHgjA#3B*NP4e;a>HddNvQmdTGb{_C-sUl0E+4=I!xe<IGdvJ@atm
zYj=L)PhNcM?EP%0{rO|Y!-ZOTS@>J*irRx)Xh$-+DVo4}a)vdF%Sk)8UTlPn@vbNr
z_MfWpZ#CsN#AD^%`Q}eJ>!1mA=39?z4_Y3S5Mv7C$Y=On;|}~1V;4?Dwgc<JUu52l
zdKch2^;h@l8RN(Ji7rd$i$AKoUR5M&)y~2upy&{;T{z)LLry}@PP%aNu^(q4{JC0s
zb<63?AWlV+;4GY!2m^iIc!#%7&?yV91p4+*BIy<3;WHlfZ!ykM-~)U+Y9;!v#b^U^
z%EM<;{KN09a$a*7@?tn+;nSEd{LV8j`td1J6x?gjtNb*J&tr~&#%Eg@`#gvHvNe3(
zG=!4Rhm@9Koa&U$zB2N2m=*GEFs08BMj6Cu&t@47dnbG**MT)AoCt+sOTmjBqIaM8
zq;$H(eB)&_+m<wAZYOHr3|~04=GgH##@U5&@J__GmgTp?l9uAddR+~Eo>lft8YQ2S
zm8|;x^l6s!!X(D+#rLj|=P*XUjKE$Tg8w)w)ae<?OqbKb^lA8QybfX8Cp>$LV9g>}
z#h)RuWs5D7=aFqG_?&BeR`~n+xMb5lnNt;wF~mzBd|2|_?>i3v{egw>S-Jf2Gg_L@
zCj9Pt8GiTC3XjC#@5T6=-mOx$QX6zzj&B2A3m-&*<J;!IuLbO*C7^Jh<-iuBXCL^M
w!>95yD?pAXK6A~$cLR{8&Qve02glcce<pZf2%7^CpS*5D%YPI8Kk&f+1#0}(X#fBK

literal 0
HcmV?d00001

diff --git a/legacy/Bot/bin/Debug/net8.0/Telegram.Bot.dll b/legacy/Bot/bin/Debug/net8.0/Telegram.Bot.dll
new file mode 100755
index 0000000000000000000000000000000000000000..3e19fe1fc62c785a6587b1c5812a4b1c1d785b49
GIT binary patch
literal 305152
zcmb5X2b^40`Tsw;({^V!n`D#Oma?HFTy{61LkPY1-jUuxV8RRmOy-28_YNW*X@VW3
zDI#D&ML|SGML<MFL`6jG82_Ko=Q+>pP0Uw*|GZ$H_xYT2p7We?&)m6p=I$;BUYX11
za=8Nj|IdGNxkq97Z*#r=@0U?zcMX22EB9dgvnxG1Zs%uL+WWZU8#5QwYsc0PKWXNX
zho5|M?bMk^96htX@Z_1tpFDHx-S(P!Qthat=Z+s=Sv9)eeVbfv=W+Sm`JZ_GHgD~<
z+{!aM#;uvl-HNE%OdWb0JQMsRBh5_|Cl)sasf1iHmxIoK`P^aWu_*r=voT2t|6crZ
zA-5X=e!n~Bgo^!pt&r;k6;N((*ZyDZ%vR_s-G=n`nR;_iJ^GAOkzVtb*e~`q#_h#W
z<*>Q+M*T<#ij93QA#wY%fo_iJnp;2m#2N}R)*ND%uY3Nh+?LGF{;P0Y@sF`37`wJj
z$K~D+`P?|E|NmcRB#UR~a+M+2)oAxk(iAqy2g*qyNX_LYubIzn0rzNUQhwpwTyAP5
zDa~ECG_ppzIt$86Nw2jb2qV4euyoch$#-o8IMg%LJM_ZL*;mXMKXdhc_PP7OO!e9v
z6xct?W3_8&t+GFyPF}N|J3{@=O`1|iBn$d3n~acV^;q+j?%b?X)<S5P@?}n8_LSj{
zr9I@@8gOyPtX}J6NB6i{r>xC#eDQK*E0<y^b3{^9tqdJvHtD_WD7BTp8N;0`49$;v
ze)%LOsKD{W!Y*WLBXUHVzavN=tF0u8W1*;8nRZdjIc2nMwF`FyWn-2Y8)iw_1ykW<
zJS(rAEXJ}b)X<~K(E66h*VeI4E$(8Gl~Gl|dBp^BPnnb~o`AecsiT}v*G1`5F~n0l
zlP1-+a*EV&rBq($=a>quhZWUZ|NpTb1_|xqK+m?9SbJ2_9#a3^-tzU--v6vu$c-Xi
zjPj(Uv1fa(q<-5f&(G<oZ-Hgh*Z@&4O7bbjs1!JcNpZ^3eCQx8=IfkteHRoQE)`QO
zr4rVD{Y<3u={(d76_zX2FB((YsT5|-Da~$d$P%27p~-6|x$O`O=4?`TE}hSejquW)
zOTxyo`LKy>A#5sJ4V%d}tyqd#8t%?1l_K!U5auDtp-c|Tf<Cu|7`lqN0)0-9&4-Dy
z)i7SRY1t`8lD?Qqw{H2`4Zg62)7LiiRaKoN^vUK!k8C0I$`(VvY&A@gZCddZqfOsT
zrRcHZ%;ye-Zw$<&1jByI*s%AaEo^6%k%3Ze2iSB+yv!M2s_o>7k%{%spuAPu8R{Y{
zS)Vg5w;-0|MDVENT+ng`XgPaWuw3Wt8?q-QDdw_LDGkm~F%`od<=(Z6<-v(-Rl4&_
zt5{G|+lNY%3+0{zCKtO4J(G$lDptCaba$9JnL=%iJ)qbea9+_A$8ArDVJ}v}9N;|&
zHXrtpErfk#t6@LcrWH#udmJyO(gW}^<`WC2<#U$CI0>_5^I<jFLcnlizN=wX*`^gs
zF^lxcRL1Awh!YE(J~17YFswRB7?I6~IkJT?SGE||knJjjH4TbkEkQL5$u`YFis5E!
zOr-~@2ORSx*FbyNXj<!OUDZm$db0VjzHA|EAX^OcWYOM+2E{PnpcFPTD2I&&)v%6i
z(_E&QSw_HAYt7m5HX+mgW~!5f&1Lgp3)w>0Qnng4m2FzF6f;VnOl5rXc*7pWJ{`<4
z+(vbhu&r!9Y$sa{Tgx^rJH<@W7gOn>tvRNzN#^S?`r1)-lCYC(KI|-84Lis-Ejz^w
z(ic<d5zF}M&h&LR)k(tcviY!wY&GmE+qCQyvqN7@E!W4%8DD#=P7?N!&4+zut6?wM
zre&uXY5HO+<Lh$taf<oE8BZk~pgKu7P&OYvBwGjv$ri)GvRDI$7!<>y2BmPAK{*_5
z&=!s`Xb(pkRKih$YS>@4X|1K0FowoddbEa2>yyVrH51#hs+5G|Wb@&8*+Mu$wirGv
zi`Y&yD29^+)o_e#(;TFjQ#QwxH>dWlM|(Zi-Z31D1*(;VQ)Kg@E?Wo<*<v_V7VRxG
zD2CGvO5t>aa`=crTR6j@JuEV)1e}CmYv}3-O+hu(WSiD(iq*m>nM#jqts~A?Z)Qx+
zRGlP@%I3pavW0NAY%!c8ix|%}D2DS4O5uEia=5^tEnH~O9xgJdgo_2$utc_Lt)*Ce
z42>xtK<+0GAhy0tY?rB05-yj`hbv?Y;Y!(JxJnkWU2RYd*BF$-wFc#Iok3gps6l(U
z-k=gbX3!CCFz5_78jKG&8FYo41=Vn=Y||o2vEUg$Q`z`$NBsSn_-|9CB-}2W4<DB;
zgga!5;Z9k^f0scq+-*<__ZXDJCk)!cy$0>!K7&g5q(MjcltE|sw4fSpm2F!4DGmWf
z%aldSv3?A(4rF5eoT?<@^RoGHzic5qAX^MykVUKy8Wh7r2Bq+@K{-5P&=wvwXb)dB
zsDv*WbcDwYI>VO@#)roZy24isCWI#h)$m!_riGT`$YM8`ii7TJW@@G%UsatXd`&hV
zzAjq`-;gbar)ANPZyFTCw+u?*+Xm(Ej6qxYjzN3)u0bU{YtRv%Gw2N8GZ-J9H|PrA
z7gWPjvQ3L9#X-mLnTp}J_Kazn_<yKMN%)a$KD;Pf2tSrBhM&kH{+}8Y!%GIG@H2yQ
zc-f#WykgKEUNxwMpBr?9*96t@1KFlEo8stWkW6L9n0v<bOpL!&oh1B9HXnX1TL^E+
z7Q=635#yT%#qe8$Qh3Xt9DZle7JhHg9{ylZ32zIk;dR-jwU*+*VrWcd$L|EhHp5~&
zo_p?}R4EDX%I3qLWeedw*<$#MEMohsK{5QzpcMWtsD^iBo8~FSLBe*KN<TdI-om|p
zFf%6qQl%ukFPjhlmMw&(vc>QpS+t8!2;8-cVVprJBnIV>H)sn5gZ5A~sDzS1M<@%b
z;h*x?v}RKr0SuC<%)Fh47-wc;tf+1hI%M;qQ??Ms%N9eIEMlBsPz)0dN@0>gIdmJe
zg~<l(VTzy{+GU&8NQyPhh?w%}(t2+qrj;xveFo`OEqn%%&4+&3LKu)OhN-fMX_`SX
zOgAWn83yGrXwVjB3aX(;wrRdoEM11el%2S6-`F2<tehE}Ra7MjtIFoXEZIVsEn5t$
z$s!JXtk4Ph%Cb$XmtqOB4W_KSY=fUkR>|~rSXFR7E{pSVS)7l{;(S~dXWz0-E1qJN
z(KS<<^De$uu9}H|ZPiJ_I<on&u52N!CtD2b%Od^_3~)AXPzoCwl*4?3wy=>wd)U~Z
z5;igD2%8#ohRqDdhs_PT!WM#RSWC8P5v4ONe5QOMaBR4z%*w>SwJIfH8`*r=R<;nf
zlP!krWfA`l2F0+WK`HEHP!2mAw1r&^+QY5}m9U$j8n%*cT5Bm5B8Qf#Y-}$gw%M83
z_Ee=L>?NBId&?HWKC;EIuPkEQ&!8CgHz<Vz49ej^gSPM?gZ6NcK_wh)&=C$X=nRJ%
zj1PwybcMqWCWIplCWa#oCWWI6y2H_eYS=@zX;G(G<m@9;**@~K+iICU9;Zr4I9@g%
zPLM5x56c$AiL&V9Ne0DmvOy`-49a1Fpc;;qZJNszM*st0DhA-kMr!SAA84pf5>A!P
zhlR3*aGGo}oGx1pb=jsBPjLj$H&dBogU9yj=KDmxCoEE(B;Ya}4&8ic$`%4HupwOx
zOJvcvGX>S~5!t5IOR-GZ2vh0Fd<S6sye5E45B!{1V;g6yN)pbI&4+Vk3*kK3YB)=_
zX~j~kF1lnYJw<OSJ|@E%pBJi55-yU>hl^zk;ZoURxJ0%ZE|6_n@f5R4-%MqE^Rv)M
zX1uRbog`c>n-AB>7Q(f%#c-W0#^j?0#c;i#8m^RWngd=?W@}8v8L(>uY;R7cy&F|4
z2{*~+!_BgVaEoj)+$xLqZZjx`+XdBdgKX0r@QO0qV``c9IOlUS?cJ$bNw`ZkAMTbd
zgnML*;S;iG?_NPQ+#%bvdMT!s%`j!naDLdQH8O2{N>!5ZY1w@EjBFu%R<;;ECyO>d
zZ%_>P3##FhvQ2Zqi|K5Psb%`aJ$}thdk?Bs5+0Jxhlgbg;St$lcvKecebJy8zGP4e
zj~SH1mkrv&;|A^FD+ZPDgh5Am(x5XuWiUQ`)u1bUO;8PAkZoE-DHawxz?64@<Hb2y
zD--|Is+5Fp%I3qjWDDWjvc>R>EaLx;pc=j*+q8NqmJ?fHYHUvU9=mp?jptM)3Ez{=
zhv#Jr;rp`1@PceLJS*F@;we@RT{9Is;K%qn=DW^g{70&jgcoJ=;m5Lt@Dtf;_@Qjm
zit$=9eKM7v+B(L0zPhgYTu7fkQ=KHdESnFn$QHt@vc>RoS&YeR2F36TgHm{1Pz^81
zHq8_7Z?IXW($m$f&bzqZua{}}*Q%6+H)QkSH?lZom&F;oEZTj`pcsB<Pzt{{D2G1?
zs^M3%O>@bc7Yu-@7=Z6>>u1`3M|G0$C)s>>SGExTEL#lk$yUQ3Wt&z!#Z1#TQ_K0@
zz<lc(`QKD034fQ(hkwWx!arq;;a{@V@K@QU73ci{`etgmYv%JZzL%;_68<Bb54bpw
z_mDyuCtD1OY&HDbd|PqeaiMRfmh+7}2(9a<Mb%A0Nj4wKvW3tlTMX^8)liUaTJaRK
zO5aQ^w|3`ee0Qo&62{Bo29YdI^<}G}L$+zfQp^;6GL`YkV+}VeTJd(PP7)@|=ED@(
zLa53XLyv4VOp<L{@f0&f-%Ks%d*h7ne$`3BfNVZYmBq&ZS$qnRt%g3?rWNNM8~SD{
z<D2`?CK=x|RVN86$>zh#vW2jUY%#1Vi?u&XPz{5!O{<q;%Gd}~)`+if+}CN14KDj*
zT_j<3*?bt1Erem&Vi=LFhSg-7R-Ctu=$ffa{5;pdwd9uXHB=`FxMiyInYCmKVQtxJ
zm@C_~Vku^fKABq1=jIuo>#0r>)|btP4P*;po@_O&E8DbUDQ1d3nOe^07UuH|?rj^X
z4(@-+;{KN`?tjTv!+hDM6-zNg^vP7ZsI_0R?>MO0_r>(Nh3X_>OWAzbO12QTmaT@(
zWt&zk#f;D=Q)yVn=T_!(34Ly-I!V}GHXnA7ErcCqt6^K&rWH#u6ZFYcI=YNc+-YN+
z^0|xZBw<(CeArF45O$ZXhMi@bRxHI#&?i$FpFG}ha!j9RG0weI2lt(1ao<T6_nl;m
zVL#bw*i*J?#d$B0zL`qTX~j$5+nR5ke;=qiN%)X#J{%-l2nWj+!y&TOaDZ&niu0Bv
zeKWP(I>5ZO*6ZP_lY}E=^WjL@LO4pc8V-|fTCo%}L!V4#eDZyB`;5<HRR`DdWpOQE
z7T5A+aV=jK$MA^;xR!5F3MUJ2QD3%cp13WuS*BvMdf(*Uu|uZaQ&cGlb=iDq$QHt>
zvc<4a7VVxUsD=fyO{<q;tl0`v){5^PJ7(JWh^i!Ek!(IJmMw&)Y%$;)0XXif;SAZP
z6;Cmnbj?&|+&IrWneTJChDKE<31`XX!`ZThaE@#-oGV)mXUaCMIQ`K#Q|bAwHN<N<
zJDcw#>H7lJNy3G)`EZeJAzUnr&nvPRn@bIf;WC3#xZI!|t}ti|R|=}(eA%Y?=DlPF
z!jyfO<oNO2W|z$PU85>VxK=hFu9L+_8CiUikwqLIGbn}|3`*ffgL1e@Pz_hhHqB*<
zmB8ki%8W7F-!(H1x2jGOZj;T2+hq&k<FdtYhb-E^)1VmcGAM<+4a(sjK{ebW+ccNF
zd(QTm%6yjQv4MGR9dGxkP7*#Tn-8CoErd_Y7Q<&`(f(%*@JYp>6h1GghI?h3<|)Mj
zVzW%eZtK|K=b_y*{r-X~CE-EYe0WH<5FVB-hDT)4?xP09@I^s2JRsXN2PxJRn`6o^
zS+tJDJu?0LvT7yaaoK$Mifkb~AzKVj%A&oe1l91EY}4xTivnzhDeD#25|8&iGkyBH
zswCkXvib0|EN&mj;_iVg+W5Ah8onmmw0bF)1>0b1Y`yZB+$+<M@2W}?o|Vmq=VS}v
zd$PswylgdmN49Cj`NajgW-7B58UNnq`vM-5KTw?{{7^O@ek5B6FUl6fk7W`6PXyKQ
zf^5_3@oN!mgsJo*9{9dr?URZBXR3nFFtYdzBa6>4viJ-miw`QYO)H*aCh3}~O#D2S
z_RWm{>#CE4U&`jguVf41*RsX%hAiU$jX^QIX;2EkH7JL-1l8~h*`~SVS2@@|Q<-Cl
z=NtQ3`xkTk{-8QZcw06f{wP}r@5ole?`4}-EXAzSCsXOA94@U}&Nt?T$7hW`|ExMm
zcuzJT{vwN860*1>AzKaa$~LWdidm&^rZU%cxCbAQnTvm_P7?kln-A~H7Q(+}i(#oO
z#_K-@#elB_;oYRG6vi2pLt@Yt@`7skhsrk1cZ#WJAWV%NH;m)JOdKUuO+r~VAKGLK
zp<T8ZDzb>9!=M;C4e%W;gL3FHXbTer)ligen(q|LgmEx676<pj4`t%$Ruz0kk;P{e
zS$sy3#b*>*#L;U|41ETr&@aHp7ulwHO0hWDDpPUUYL40NL78@^sZtWA%jUxj*+LkU
zEryx0Xm=%pVp!Rr6jl*b!&KR(c}lT>*e+AqcDc3=&a^vQm6EWUY(C(tLYUn`SY5Ul
zhGeT@mTc3C^E*Uz&Qx^H<DAFBAsOFuR3`~@W%FSTS$us+wi-rcn^ufpL84ElGCsLK
z9h&jEj_M>~UD<qCPqq-&mo0`3WHC<j1l6#%Y}4wcm})k{lr_S*xfT!0w6T$@;B$y9
zK8MKSbBJs)Y$l5~Ha94SEeuLwOM`OQ%AhT5Ex?Bt*{1pC*P<8)Q)6+gHZI3&hCJ?d
zezcvc;B$s7K4-|{bB1g&>?Dggb~Y%6T?|TLSA%lc&7dvpZqOd~FsOt*4LZVJ2AyGV
zgYjV>gRZcz!Gy4%!Njn?!K842L3cRNU~>47!IW^2K{Xs~&=U?Z=naP&^o7F=`orM{
z1K|jRso_WgK1azmZ5a6VDh?S_nf;gR`H0Ni9iuu)I94_vj*~5f<7JEC1lejhTDED$
zQ><G0W-7Dx=o=H!I!`)Db&_zhY(CUv3t@q5F`Ob>4JXPrtvJ8mMc+(ieDhd2D&zZ9
z)k(ra*?c%nwh&I2t%ind(~6~7t@O!M#wX)FI^%PZ>fk<xEbe2-;y#8f?qkT}_JwTI
zil<nC^vzVpH{WNEG2dF7XQ@sS&X&!Gb7b+gA6b0sM;2@Ie1l@Rz@QW^G$@CQ1l2Gq
z+ccN_4jbENDl^Y)|JY3Xm#R(@E|blN%Vi7U3fW?~QWouBWl#)P8<fH|2IX+Apc*cb
zZJJ9SdTgJmO#9rgkIRhh^{SJEkICl44YGxBqiiwUB#ZWMHYkQ$3`*fvK{b3-wrQSH
zECx2qR2=r!IqdP)?qxg|__!)1;SSk+xKkEi36jM(f@IO|J%VbuUAAfU*deyURC)zZ
zIQ;w!{o^`U8~3RSJ`c#^^MEWq56I&4fGke>Wt&z!#p<AIrpz_Ia65@#xaIfD1rWMj
z5I~shf&ju47X%QhE(jp>xFEn+^M5)nSK`-uuVjbVngGI8E(jo8?ScTpH7*DsT<e11
zY5xU8dL8tn*!U<+dOf+TkbX?InBG8JNN<Ee7%6UX>D$NCJWc_8Pj^88VTKC=2!k#N
zAk1_@0AVE;1ewmS>=^=-T*U=}$2HLSHokN|G|>2tnP<&>k7oVG4`5~`wHGY)V@tir
z)Siu3EcFsizV<RjcUtKsev?wAZw&j#a9|9l0vE69!3kQGr!jAq=LuSQ)0sEh^8^r9
zb3x#RVSN^0Z3zlcV|5n<5Qbb3Kp1vG0Aa)h0fad&2q55wkhLNhO48q9Fz~YhcH1La
z!^;RzX-yXd5Y}=*0AXzx1Q6D7K>%T07X%R2b3p)MeHR1}HgG`zVV(;D2)JTxy%9i|
z?}7jVE)rV_0fdcR5J1?(1p$OjT@XOn%mqPapf+a)3T#2Us~>{;f_+e4&c4IH5A27r
zZ&?ZcfMY%U2Ecy6k+0o^!?1U)o_u{fh<&x2ksN8m<{+<m>~*(L8K0Cgj_W(o!L7(x
zq&5<MzchCu+Q=`QiXT~3N+bBWM>+!=PRDT4(^2Mjqa=P?qaO_Q)ow#=`y7W^%$=n8
zCZocvIjhZ1(ryrb1TZVT9mVG4XHOYQKMumrkkUI~(>qyvM$(vKds?GPyLK`snKEPz
z3~if?bSAYwVtrYmDcHmFxrfo#$!sgXPSQJ+jO6okk{(o1dp##~pucRVymTChZ67+a
zP0Y=!P-iTG-!`yds~plSzI5E7Bi%`j9m(r8y$dRS!DUW5%?W=Q!P?OmoLKGHCY;P!
zfs@{$xp5SGmX71ZZ|N&T0AVW^1Q51%K>%SJ7X%QtbwL1OI~N2Hws%1QVFwok5O#Dy
zkXfNSd4>QbcXmMlVHXz!5O8J3ju-)i-CPi4obT=#0+igt1wp3do}MA_4DH5?_wqae
zyzK3Q0EM^Z($apt7)O!?CqUtST@XOn&jkU5{ap}1z-L19A%Jk83jzoqazOy$AQuD>
z4t7BR;Sd)D5Ds-g0O2qf1P~5)LD1@_-i)lvdY3uEiwRKgNEZYUj&eZ&;b<2G5RP#{
z0O42{1Q3pMK>*=+7X%Paa6te8JDQE7pcTK~K`nm0gJM&%VgfIQ`X_mY00OoJD=C0b
zb3p)MfeQi%r??=1P<KIq)>0n*$Y^+m0K%y*2p}wUK>*=27X%PacR>K*3>O3tKH`D^
z0=8%ClOPk%V$TquWYYx!gy4bz!V(t*8DD35h5#i;T@XMx%LM_1vt1BCIL8G+#`(FP
zAwbFVTo6Dw-vt4L3tSLDxX=Yb#`#5_AwbECT@YkSUg8-7l)Tgh0fft35TL4`ZeQ*h
zf=tF0o*{sXD_sykxXJ}VCY-B1Lx7UkxFCRVtqTGO*SR2o@KF~85UzJYkZJ8>o*_WV
z8(a`TxX}ecrs_?eAwbEST@XOH#RUO`TU`)9xXlFtgxg&Zcx$thJnnE8uvznDV=-av
zWTc!l@(FOtP=#CD;*Wb>0fakT5J0%o1p$P+TyRKNqQ?rin#Ff}rU1e{E(joe!UX|@
zdtGoaf5^FyIk2Df5&{UHazOy$(=G_`%jgsv4bIx+p`xAdGhRf1I-hmHA@-wJ17>~V
z$CAaL^MV2hpLan3;eHnc5FT(r0O1QRIJ7$Xpyvos^dT1n5FU0x0O1iA1P~r|K>*>4
zE(m-{AmdA(A%O6h3jzpVc0mB)aTf#`UtjSI0ZKmMf&jvkE(jnz<$@sN>#Lq2K*_JU
zAb{|77X%Q#;er6d(=G@geA5L1gm1YZfbeY>1Q4EaK>*=9E(joe*98HDXI&6Lc+Ld@
zgzvc^fbhHv0tnxCK>*<e7X%P~;DX?eBwdJuZUiHEI_xZcBuJK)zKui!hh8N;7S~m9
zl%wVky`})dk6aKyc+mv`gde*gfbbI+1Q34ef&juxE(jp}%mo32mt7D*c*O+)x^!{u
z(xurhJ+p!?z3MduaP@N+1Q1?xK>*<wE(jpJ?t%cqFI^Bo_>~I+2)}kg0O1W61Q34X
zf&jvsE(jp})&&8Cw_FfF_?-&^2)}nh0O1cV2q3)ef&juFT@XNc#{~g|Ke-@)@U9C2
z2!D1#0O36s1Q7n>f&ju_T@XO{n+pO6e|JFu;U6vtcwO=GB$uo4GigV8!?VHV8khR~
z$Hx&ziC+{pi4O_SCEhQ*koXzl65@SA_$kf5TX+L-WMa8?J?w@zK$>@xbf)KDDZGMs
zvG5w=xx({_ON3t_o*{gQ$R~-EYmX356y5~vY#Zrpt9=3oW6vfZl6ngZ>?6F5xSR0f
z#2tk96SorH2^`;@Pwz$j`M5v<|93;0T<9*CHe1>!s1>`5ru9m@k6Nj_WLlfF&rmCO
zm($zn4`U5~pQF~+-DVn=AGGVB&BuBNUPZ(K2K*Eehd%HzA~q%9<3#L&z$b{<c7ab3
zpA>$Lh&P(tJihc+<~;<{SDE)&nEuK<3{7sJGVgYnsg-#*z)Y*myBcPCW!@z)Gb;1W
zg&C|=hR+;quM965?5GSc9PF$NFBlwO8UFBKS7rE^!3mY&!v`l;h7TH?R2klHu)8w6
z=iubZ@GgT>D#P0kRx86>4fa%qHy!M)3~xBtR~cS+u)i|A#^69@cxZ5HWq8)$w94?z
z!ReLZse>~r!##t8mElQ)Gb_WLgDX{r%Y!RdaK)xFcG2<(+j;Y~=h3-*?FGEn?!%Z4
zA%pwZe`jEk|I-(_AoI5OFV7I5<ohlNApF|}0feP42q65&1px#+VaGn@2r{jW^9(^I
zBk>FYT;yF4proI@7d%6Nl0_E;5K1lxAe3DYK){N$_yiEzT@XO1xFCSg;er4{rwalI
z<6RIy=yE{-VS)<+2oqfpWQJpsX9!Rdm%}V#0R&voGC`23iVJX-AwWr7i8DcvDcR#0
z0+htTYb6B``dkoXs`h(^03`=p5M)YD^$Y<@PIEy3VY&+f2)IgVVF(}$x*&is(**&9
zm0S>@d0#CndxiidS8+iAVO19d5N5d`fPj6@;t@c={caNkna<+zLY5&w$<<vDKp1jC
z0Abh#0fZ431Q6!9Ab>E}1p$OLTo6E5(**&9wOkNDSla~wgmqjHKv>rW0R+4SSmy)~
z)^|aGeR49I`!ei;_}=+>aJjZ_K3AG^Lb<j+?J?!r#<WM2Yn##@Qm$=IdtkXXk9Oa3
zZ9eUu<=U3CyOwKP)9zTVZA-gdxwbv+*5%rcv|E&GJJW7juI);@(eS3&W;e#RI*&Uo
zHrh3@&yHY+O|ikQiv4vaH}a|6(tEg@PvUOg$=$rn-F%sy7Jb~n`zU}g&jkU54P6jG
znD2rB!bUC#AZ+Y{0Kz6N2q0|gf&jv1E(kQ%MSizV|LQN`>VJ|RhyfVs)@=hzq@Tpw
z!tg3dx-W8)bbq|gvL}W0)Zajr^i#-Jp(NcCnU{VTkM~MH&G(YbqTbw_79f@_To7bR
zZs{2Ul-$Y%0fenx5J1?*1p$O@T@XOn&IJL4?OhOLn%}`Q1Sq+q3jzo`xgdbBvkL+U
zySN~Lu&WCKwB|d@Zk{1P$=zKLK-j|tL8j`So*_WVy<8AvO786$f=plc@eDyGV_(k@
zjAgVgeD3E31ZaGJ7X%Ova6tg!Ko<lMKIDP`!a*(wARO$10Ky?I2s|#k<bSB=36S>>
zEJl8CJj^o$5Ds@i0O1H11Q3pNK>*<>7X%QFc0mB)7#9Q(j&(uMx|WkZ#IWWkjj!`S
z%GVxQHu=SDGO0b5P3D(RKE7=36HFfBp7qEzocES$Pf_1B>b;H6;LxmnjhZf`rQam#
zBld9&d0RYV??Bw`;}iPQ=b1-kcbwcx`976aZ-T1KO^?IJ^oFwR;@tPlr|#CJ&mz%?
zie;<hlz1UeZK(a@eaHkD!xLN(K=`l=0thF%AZW#v{)7Rb&p(Hq75n_s3bdE09TUqn
z-eBo`?bk51SDAx*k@Uc2bdt9u@U@4GlRZNKq2_`B!U7ir5KeJH!0|oUNBkW&M`aHm
z@jp>P&$ym(3G_`SPe?|pUdckdqoJBph7QCX(%#GVRPE%B;c-*a_gLer$;fcB<bHfV
zW!9YD*-JhLSXw%_f|>ao*2An(=A_4S8r2>ytm<nGeh}3?VnL?&4bKpuZ>PE-fUwX7
z0ff_B5I{KH1wqF78J;0P$&a`oXg#7cE#bN_zJ$`o%fl$P$cqUeEOtQvq3MDELU2Kl
ziD8Ln2vG7&7X%PST@XMx%LM_1vt1BCIL8G6gmYaGWST$EGXyAkz6$~f7q}pRaG?tV
z2p72^fN-%3f=p|dc!mHaFLglx;W8HlnW~q2h5#k6a6tg!N*4qWu5v+;@pZLl2vG7G
z7X%QlbwL2(Iu`^GKI(!1!u2i)AbiXPL8kc|JVStzH@YBzaFYvyOx2q`Lx7UExFCRV
zs|x}Mx49sIaJvfv2p@Mr0O1Z71Q70YK>*<{7X%RQc0rJd_(9JQpyWd?2r?xf_6z|s
zerjLSSo{bxQ1j7fzvu-8txY+oT)lNygT-;sF66P>YnkgO_?F6)#_2qc^_K7@y3#$M
zbExT@HJ@7v_7r{J1K+JU6B+5*cq!={E~<fj@+_9WC+SyKlAn{GGF-~1_%u<OHK%iS
z{VjgiWdo!Z1=WZx>rRuuGwE*A6g0~3m{iebJKJnMrL&mt-Lrov-?OJ~uRP|Rc1TZS
z1d3h#*)-{s<gvvT)h}=HTvk!Xzd?5x)2H37U~DXvZ)ZN=j77f{?YF&(06v~^K>*=9
zE(joe*98HDXI&6Lc+LgEf#}2~h!u}TX6p%l+Qa;czFhD<BC0$e?e}T<%|rn*UT{GG
z;Rh}VApFn;0fZm9Ab{|q3xZ5MY#s5aaf|22EQ%^W@hSocSicsS0K!Wy2q65-1;M6c
zy_V0)47z=pWnf>4_EoPVK*rBq5I}g%1p$O#xFCS=x(k9#@7M@>r#9`@=Py|lRet4F
z1Q34hf&juBE(jp}#svX{H(d~9x~oR)*643p24!&o+29Ev{LTddgx|X$fba(w1Q6bK
zL6B)rjo7zy{>U;Y`;M0tK=_jj0toNAAb{{^7X%RAb3p*%FD?i&0jSlk!SQVQuPg-n
zH!m#66jlMdJ^S}q_8-yy)9VP}<X<ibAiVE_0K&gr5I|V!f*`XP*ccXr+UgqYV2uA^
zSy((0U3MHTk39i05*Gvza39@D2p|+(5M-L8Z!{;@6@4MK$fBrHini=k1;}V~K>(rM
z1p$PL3jzooE(joWx**8J!`2ax8n;7nJd2`Amsb%$nBal{!bBGY5GKW{-O*0=0s<76
z;(`D|)dc~B9v1`<dR-7K8rwbC7$Rr5_$qZ!n_YvQ484zaGHXj|JJS1^g(3sAoKpdW
zsV)d0Omjg1VS21O!!rcP7<54ZVWtZL2rIcDfUvR)0tl<PAjnJ*14cL1c-LS%V_%hJ
zVP{1<JKEJ~*(U)C;GyRx2q3KPf&jvh3jzqkE(jouxFCQq#{~g|xh@DYGsHL%I>Y1)
zDNNh{)?gJ>#VtMyPyk^q7X%R2c0mAPomh2U&k!JEJr@KJ)^|YwVFMQg5azicfUuzp
zg3K&1V01-|+m<??MNws=XgBt%0%UCBf&jv%E(joO=7Ipi<}L^zY~g|c!j>)wAZ+D=
zAQLg;L&OTNYf#^`+lysUXYXkDiFV&;_oL<d5uo7yE(jnT;DP|cfi4Ije8>d>go9iV
zKseY10fa+b5ZpL+gfm`rf#GufFx&)>u~fMBPH`x!!o^|H9v<xx(H=?59tlwJC>I0}
zj&?x+;TRVL5RP?00O2?n1Q3pQK>*<d7X<H)^@#DJ3nS$^-d-0B7we@Yw;(Wj^pnA}
zp9~&_(g%w;kBx6hABM(`o*3;(wCtDw87I3SfKYQm0AYa(0tlzLAjm8oHime-tvbu0
zd?VUZy`BIW3tbT4g2F!#Ibvf)oZNc7(_;P8qdg<qk3_pD+Qrc}Y1u~s+6gWQAS`h~
z0O3p*1Q14D5I{J~1p$P!T@XMx#{~g|b6pTXIL`$Eg!5exK)ApK0fY-(5J0%d1;Gog
zG2<X%%s57v0uB+E1c!)ApF@=3htobp7bDH7xg^?4X*py9WL)Ng0K(-i2q0YHf?#a&
z=o*1U&#jBrSF$*&UFFpT5UzGX0O1-J1ibcUtHM288$DeY?MI`%KH87bvM&NSyuk$l
zgd1HDK)A^T!PTuM=^A~ZXACME=Ae4RGxFA@L0UE_fV-J42q3KFf&jwGE(mr_>H|2Q
z)BEv>7;fC}Dqcu{TC2JsSUah2>9shrvpiP-VYUkb6vy7RAbkLhu=r}sg~ea<u;>L4
zR(C;w*l-B6V)I%<EQGw9nfC>YTN)e;N6#bfNPvtvE(lQj)2#iV)mAM8`64xZ$)kcR
z)b-bycsM5BZYAJrZfs<YXmOFix*<S;wOkOKkksF2^*F?=jfwTjYey&R(4uHxeyk|-
zP*hoPi+iCOw)JQ^=>lZn=BNn*2phN{fPmYhRziU8<9_IZ^bza6``wU*P#E`670QB4
zD9l4B%0iKiVo}_VRh3O>8Ik}6aDUbW0ffz55J144TPq<z&u`6yME_{c-EYCNh#L1!
z7Nn0_)Y9MwcX}1VR<v;QcE%0!;6_;}x^;BZR~Q>(X~^3q=FQ9GNkbk6OU)m_yf0ew
z(vY`Z%)6X<_?{4tB5BCO&1QwR11&;(kYygTGR#A0%0f}xk5&`UXWU3b-cB*^@65w3
zayB6idANVACU%Jy_pxnM1t_qa3jzeuR)m+h7FioXc;($$7Q;L{Q$!m4?7=)(-0GFx
zi<UDkfZzF<N-PY&UUu(TXP;<sN7?EM;B-G11Q7OjK>*<Z7X%P+zuJ5VAbiLL0fd8G
z5I{KC1p$OZTo6Dw)CB>A!(0%cm-}aW$!?*S-mAk|7Ilt@_Q+_DqUA^l&QI#ij1N|V
z57xmp?^TYDRga1GSX$J)BU6)QP?JSaQzg*&ak0knv}{y>ImL70Ths1^PKbp+Ov|NF
z8Y{y*xK|eZo*0Xs6pPMf(Z_8qG7m+SW!FrHam}>OYo;ll4O^<!amBEAoheCud(<*5
z{R-;hYZFQO1VMLiPL6HVVjFvB!eJiTP!__$)}*mMB^Es+Q<Qlqsw@<($D)mBPo?F^
z2+(Uh+_yC{ER4Edb|K4R@E*t%kp{n~MSrKeKLHBkA*C&UUg(Ti_#@FSigvMA7Qk!M
z1p$QMf&ju27X%Q_bU^@N)CB>Avs@5h9pMq13(_ZT_81zv%*e2gJg~D_1y#?9_S|UC
zqvhxc;Hsw#S5KKMR)Q<mfh(_aeyn;yv=>Hu5iMN_(ALE+2q0YIf&jv$E(p;2x@<je
zW^4zovl+DRtz5<`sCs#{S44Ydv{%tG3IPgU?ScTpH7*DsT<e11o)|?O1Ga}K*c_r@
zYdmlB)~;h!xcF$a*GKy?T1Fv2fg4;9K)BHb0fd`e5J0%u1p$OxTo6FG)dc~D@2Je=
zF*F3u$Pl;(b{ngp>g~~fJlZ>=y))XoXxRY)D%|aY0Kz>k2q1jI1p$P6T@XOH&jkU5
zPr4w0@F^DrPbc*ynI18A^oXIOM~s|P!N~c!#3TPS((K`9XxRe+j$=I3uC=zj&}U=e
z&qe!ruO#4Rl4+lX(Y}|xKh}95+Anxr0U~(N1p$PITo6Eb*aZQEM_dp<c+>>}gfF@v
zfbb<31P~r`K>*>)E(jnz?t%cl{3?6-RXga}E%Z{on!IK)hw~#oYY=<?K-u^HxdTbM
z5>osES<i4=syBqC<2JkyWs|<kj?f>5tm8>&3-Bi@>v*Eqf;&?DaHi6cb+XP%c(;(x
z2HsCpI;KxWI^7t5*>HuejIbVCuc;or$8@V+S?@$&M~n7O^c8j(9e*O)Cw%|}$au;H
z0feu*Ab{{S7X%Q#?t%cqH(U@vc-jR4gm1bafbcCB1Q5RMf&juZE(kDHc$8Uds@N?|
zm3oDHo7uU-MjGFzIagbJ;9TL!UjIE;V@@<zN@~nmI!Tpojrj^&(Oj|h>|80`s<-@H
zeTO|p&%aBH4$nYa{+7TzbXZv^@@y>nT(sYd_IX+!`T`XEz6$~fFSsCp@B<eF5Ps-_
z0K$)45MZ<2n1RK22}Xjz7znl|kK;vFLDe67RRM&bxFCS=Qx^mfUUESI;b$%gu$}Fl
zY2F)pnT1gJm1tk}N&;m3+yw!I*IW=l_=O7s2(P;!fbdHf1Q34Zf&jv=T@XNc!vz5*
z70;+@tzrg^u~W?0KbBp`CoQv%PyWDljHfXD_c|VPqIImK)-g*bsnV_Wn4~LcMeCS)
zb{#9-s<-?){tf$#zP}mmZ+#2|$au>I0fgVVAiyZ$VNVNE{G5;<5a|x1BySfd=?VCT
zSBhV_RAwdVIf#pYrD-@lkuRHI<7C6Xr#sj`MEkb87a-%0E(jpJ<AMOfpIi_?c-I91
zgg?6=fbgCR0(9c@>_j|F7!o?+5&eZ_QRlDG{>|$OknwjH1Q7n=f&julT@XO{mkR<2
z@4Fy?@NX9ci11sCFuwaSBt)o)mjCQ?YilizefFW2ew%MJ{OmLKj<b}Fpt1i%n=4p)
z94)sd0SY882q5HL5I`unAb?PGK>z{Ynlq;Y2xS)p5b%XMD<Oc;?t%b9#RUO`4i^Lv
zI$aPz81I4rLYE5yY>_W#=AI*hk>!A3i}dlBz$&OZG1^J)LV%2J7X%O{yC8rt#RUO`
zstW=LJuV0!^tvFx5_mha1Q-$mRYd=_AK|IAnfFyZSM~$Wfhz4Q*pJ4XXg^X?`w>eg
zsnV@|B}rG%iuNPw+5JfAR=wr-BU~iYNcTrO;A0@bpiFf^0AZR70tnMx5I~sWf&jvx
z3jzo;T@XN6$pry~m0b`(Sj7bagjHP-K$zu%0Mj=U<K{;&`-$mOH<x`c!9#B|bB71z
ze!$$VyP~-pbE3IZQgg@BNvd>f4w7^Qt!VD3XXj4oR=wrtZZ<oNj;|Ii{?xS{&H`kt
z?t%cqkP89`!!8IQjJP0xFvkS}gt;yVAgtkn0OP)SX51Mg#$6%udv;^jjBv`Dxn{Jt
z#);a0?VH#a(`T@k+L!IVLt0uGX)9=;=1i;~hN|^b@zTK0Jn`qv{g24w3+~TCm}Ld?
zg<5MM@twb^hy`~Qa;M@mOyfBetCaHLd$9FK8U6F1OHbiXUA_<M#G{^Gpn|tk`|%5(
zCdbb$KZL3M2rs3jCHyYE()cl6(v$FpyX2=JeHr^Dz|s<aU|wmwOl_1o{Oei6wO2sn
zho`mI@J;>-{uKA5LJhwSufV(pQ~L#8+H0@lb<WGLR@z5O?KS-DyYg~*C^v2*K2zn+
z%%z{;pZdnnQ1O)o&I9aPv}-46Pa6hvAbyE+ZHm}?pJwmy(^;N8uma|W<+1mw1gfvY
zlCbO2Vm7lin1>q5LXq`Y1T9U^wzPh%zX2`Z(*!7uYwjipAZ+M@0K$A11Zev$MjOu(
z*cjS&_ZzV+>TDeCCSF&7j7?n-K-i1|qFo~!?dBBk!M&SIOLUEv=o#a}YP_PLl5n#H
zYrt+9?N+qxrvMpSyC8tD4F!z7+K892=ml-i3EEHzl-$;<3UGU>r-A{{?J4AL=eYt1
z+q)ouu!9Q%2s^qUfWx)v5Kjis&9Cj5z*7NqyKW~IMU|bsiU7hcE(jp(>Vg2mZY~HQ
z?Cyd9A4`s5OYuUo8ah8ocfdU0xoh<%@+_sx#dyFVFBUVDJ?I8@&uI5@w*qAB?ScTp
zJ}w9#?CXL6!hS9Yu*R-sXmM89AZA7Ftzg^PHc9cwgo++mm%bT;+@B8N_<(2+q=nx{
zGk#eHUxHQ<EQJ5c>QNS2R%JN*P;_=sv<F9f2rWFkk#WwlNl#!=c=$g{^m+;Rbtrw|
zc)%lm$M%WCC?bHvqdg+pBcnYk+M}aAhL)>efQrYuAb@b33jzqoyC8sYf(rr&A9g_i
z;Y1e%5KeMI0O4d81Q2R22p}wQK>*<t7X%RME(jnrTo6Dw)dc~Bg)RsnoaTZ6!s#vu
zAe`ZXfX|ZHf*UygCH1Zjti`zBae6SHoDl2~oDc3ooDW{t<b3e^7Mzcys~mhjej8Wi
zTeR!ro%45qRBxKC-_rzU!nMjCn54gFkw4(2J{#>f-Ue|A;Hez;$XUTMYyX{Cq>0v9
zG(@{3+B2garRC}nV4K~Z9gO<`D<cZlMF&}x9aPmDlk`5UO1;B#i#QI|Oj>@6IEyVH
zg0rJNC)#tPJ&%?V2$qY0l@S5!A_7)r1geS%@SCaSBcQZ=1n08_L~ud07e;$gv=>Kv
z2`wWLpu(jt2%g5*ATonPuLzA!5gL6mH2UQ1%O^tol7~1a-eMc_v0{!zik2=zHHLV3
zv{yuXWwcjCdo?XX6rjR2E(n$jkzNrZogzf~WQg?15asjMB>e_M!~s?6ojVrey_!#E
zwPL&$^>`ERNe)8Xj-?@t>tfzp%=;789zv9cypJ*u_WEc)M$0}3ka2?x0th#{Ab@a_
z3j%E2rB1XHKl;-@dMWql&I`+Qho9^*<TFs-vUKvwaBikg*js4faxPuQZZQuom4za=
z#-g`Hdpj+AAVA$iST`OtER4D;%k$~ov2QHYb@<m28GrlIoh+>$gcIDh;fk)nH{L~%
zd<N^EVMk^F@MN5dZq(XEa#qwdEO(>!<1wB)Xc5M_4C61Ti_n;dFqEa=&kQ3DyBGYM
z)(G)>h|M&$6p!1n>mhi|PP_ttXKdoG*u=e=QDPpNP!>k%?pX95T8@qY8J}=L0O4L2
z1Q70XL4Y;(^-MGF_meDy!k>!v(_Tq{?!T3(#KMTp%YG)-`K;FwprOw3W3%psJ{Jpr
zKHB@ek^m7r;DP|c7hDiPc+dp_Vw<07hyD?pyMKsfQRm@kAMv^ZWIXDE0Kykt5J32n
z3j#E!@r!R$^pED;{bMYPI$w_V@o2w7%V`swABQs@W2^)ptb?6IRrncw6|AY#w6;&+
z6-?eLs+4*!TUS4(P0~JG(Y!;~HFaV0iP+MU(LP1X76ph}lM$n4VYKLFzZ&a&E!wX~
z`wd$95TIL6yC8t@O&0_ZzU6`d9o5{#X4wv!Wi#lgxAJXPLDgrX{SGZ#6(I5hIlXum
z6^}d?hKpF%zWw-?-o2CBKTu@J!~P@Shx8-h%lIQ;JkbZAJDJ13x-*<Ez)J&9^Wih{
z3w*Y6?sSZEaZbK4F+bIB)SZY{aigwq$E?2kmE!*59kT}NpC+g8qjtIxrl68<{G0A;
zc+^j&{&{59pTSEF5BhQOeWXhgI+AjsSj&w=%e;<S$SpzJXQ1tTrKe-~lv4RdJdFy!
zjPWZ2)03EN;2k95slX|^5!kkHJ*<WC?IY{9*T&(oM$6rJ9GRY&uJee9W3|h@`*)N}
zmmk0OMT#ZKZH8Drf>`jY!}$n4j6<0GyOoJ-KICQbH%Dcgmd(FE#J>%|RJsMK4dL2?
z{k@n)T)X(yVy%e0Ie32b((0kyn1_-^2}-5ZD8qE5ZFnu$uzL=7zB;8mQYzc0{L)CN
zWYmVw`_uS&f0bO<TK0KA$xX`*Ze+}7s!T@gi`ibKbSWE|Q_kD(A4@}v3L{<lq*g&I
zg~tB84G>d}{o*}=7vsYFaa?%mKFr|HR0vpYDxlvw?#rAllYB8Hc^1E-ohgd7Emub*
zdHgAXOhM1UV*iORash;&x*&k?k_&>m|FAUOMGdDr(G=6&7|;><+K#aK+Rl326|ar$
znUVQ@>(9J%9T*6^mefhw6R}fD3XSnFLFM*k51GA;iZSNCv<YkzV_itQP-4o`ag}^+
z0%-a=z4_WiKu38YuM~~6mG74A#8Xa-7>P;98!GYRBY$?P_0RXsKwl$0_}fHFs~e+U
z=~BM($>n&ed%lN{B*$3>T%=P-=u?$e@4K9L97B_qyo~PQ$?vruWbyIn_%$uh4|<Vb
zWd18LznA%?Mjy-~jZc5{RmkCnh5j{g(Wo-3zG_!)L2qrUcwKN`<o&_@k<S1RM7|e1
zHSz=CX^~$7PZy6e$i_5!?5V#Cxi+2LtEP<m>TJrmzs{zN2kLCfcxs(Z8BeRTDdXvN
zHYFZqcWsRGIyTPa7`z(CU<O)U(HQ+a<_|JIJ4Q1ha6Ah+jJ%D}N{qaoOyC%;ES?DN
zi#!DGkGwT_Ao3yLsgchBPm6ptczWaqz%wF02OeyXiFcW(C^5>eG*)4|aU@qI_bQ;8
zxUbHDjQi^h$atX6fQ+Zs8IbX`Is-DEUS~kYGwKY;c(A?Bh+?8rXGBVja_ELGF6i<3
zBja(g6^pi1n}wi;kib$d)n+rXZ9ZKMHf``72!p95xH@wxJ$xv|wo6{)B&0)-YQxlb
zEa)n0o_{!exHduwGr+aYNBNCnUi0Z3sFhN^XClIx%bGYP9_}dj9<WUbWfJ^90smtQ
z&8?3A*T?@`;s4=XpPz}`6x%1R!XEVeN@I!Lo?m$^vE}otjwRN6e%4qbeIDm1?qF(t
zRkWXpB3%oP8*AfbQG1lvL1K2gE?zsk3Z>e5fVSHDeC1)i0bpp~=Lg-VMq!i#qQ8T{
zWBn|y<6m%yaIDWm`4z3>*JJ*M%+Idl`D{Sz7#~ISudxxNS@kmzd2elF@rB^N$ajJJ
zBR>Ehi2P;n)X2|(r-?@ySYs1<iK}u`a<AOi#C>(TH}0>~z41Vu?v1C`>E3u+o$ke>
z?3T|x$Kq^`#V=!DH$zJ+8jD}W{LPtP(yoKO5re%21RIOXd@QzPwDs4}U~g?J@jKwY
z$nS&uBX>^XSXh~r!BfSf45qO)Yxc@bZ5wi5oo<Z#>vUs0P^TN?sdc&$k22(JKhI%5
ze;xa=Ej+HMpKrwc?U<kK=k^e+pLcsdcVJZYHPK*iZAbC?;J(NkgZm?I2_A^NEqH3=
zox#(@qYSLE6TS4xWo>72U!5+E`|EURJW!`g<EeGJG@e$cOYtbXlI{Pw?Ei0K|8{}*
z74`qkn7=FYv;E%<g7tqt-@A8bg!MhqU~g>?@u}dx$mfImBi{uci2NjYYUE#mr$v4b
zJUwz(H}`JyIReJsZHZl(sKmq~COTr`UM4zY;;T%IS7MYsZtThMdKGJJFLGa<u^RW+
z8LRO?ov|8Etut2RX?4bGJiX3Xjc3#ut1-591{)KVI)jagjyi*liOxEMjfwGf2CKv<
z2Y+aOJ}K1pMvS)Y?}+oN)>dEJ2Re_seQB`{xqtH*K96(z+c<ZapcT&TTQPrs=4a>j
z0D0s4^1;5g4}>(U{v1N<t$j%RGPp1DTljHfzj&05HV$HmUKOt$Ozx|*xN*N8BO)GU
z2-&#LXWYMwaUFu1D_WDkkNJl(KO6U95Nu7>d`%wC*0nwL)Q%9pu09y^EA@R*e;0ax
z<bQ$(BJ)=B)X4lQ-n7VFles?455L+pBk~OBSf7@$CKHvI;LTX9Ps`buIh|ey7eTN-
znKR1XH;!beakD>)+*@bdHF2MQ3Sit{XWYgEb;fNxwa&PWr_~v^@$@?5Hl9&u+{Rd|
z3_K<(bp{?29d!mC6P<Mi9uwnriB5@8PRh`Mc82jhJ`LO3ckO5dj|t}W_#?5FF2MX2
zPF)AtH9X1zk4eV4L>@ZNB`WwI=L)&u_489|$FN>uAwC82FDR{=FV#lz`P2@9!a{t|
z#uK7(LR7AOq$St4MP!X*QE`!u16<I^;XAT8k7*q*mC~HfCC8yed|FPa7uW5!N7Hl4
zrN+7NG*oIVrt!0eZJ)<kjddlT9`AiA<hF+Ii_{n9SL|~&&Wz?xEcYGIyWUFrSaB&o
zD&^}<gjKS;<ex-S)`$9)tzdb~ZU}$;Jh5!7_(W4ub^BXX3^I?;<3s8~35)CmMAS$q
zA>hl|9Q^nma54NX`9nNbK8%tpI+os!`6n_zdn}!#&T<cEy^o&^X_nSrZw&_+m}{&r
z@~il+Y=2~~w}Hr9V^hVW45qPwHG6FdpF+lG6Rt0DzrLAlJYdUK`czx0<WYv4?dK)z
z=O1G~>L{_Ie!dg)8_du4^Hd17XW-;k{~8M+&8k0%3F@t#CVm0j7x`syf8;m81L9Ez
z&^Vo?dsVe|2Dz`!s>c0wRy7`|v#NNMp=JAXDf{!M81F~mWkvmYH|8&5ezrf0Ay|K|
z_dT%5Nb2vR!QNUBCsWuT<8E+&WbWewk@+?>HS!S3OpClOczWb5!80Q7491c)r+YI|
ziHYNw=!l8<zHG(LX3qGSb2SrPF>wzQ6O<U`STvR}@VFzMNyb;*IfUZAI(uZ?UuTbu
z2kfmzWv1F2t9V+SJ+d;>>+F&7j5>Q{j2)3(iit{{U5bg0I=d7T@!eZ~2gldhrI^!I
zXP07PLY-YwVw97|IkNX^9AA@`{5j6&D7v<y`F=0vpT+#_XOFWX*nFSIjTHYH=WuxH
zUqy4hwR6SxK4;8hvp?!Q@CG7tlb;%y$LzGoybCkk86!R;GT%oq-{v&F&sl=+5SVXE
z@LP6xpR>ew5fkRy5-&5+6%$Dni3u?=n~8}ru`?5sV&Y^bx?|#6CML(kmzbCm6F*_1
z8WT&I=!uDe9wdBNHfN$Q<{ZX^56tCE_{coWgb&S=O!(M5!-Nk`S1%GiI$JU^7+s&p
z#LSqunu(QS;z1@>R$`P(pm84OD=zc%$-SD$nmE4ki?PfxNv03j4k~@By<3Z?*{&&`
zZtvCN8TMW;#xmC#fhFP_za={Cz1|X?b<U)<hh@&0jESx~XEG)x)H#zeF|p2>jEPBg
z&SXq<*Ey3hF}co}jEO0A&SXqf>zv7$=&5riW1_dtnT(0PI%hH_`s<v@m>8&YCSzi1
zoiiB|)9Rebn3!JYOvVIG;V~;QF<9qJ#>C7zXEG*Ms&gh|V&yt#Qi)L>s@eVgavtA*
ziO25+n9~&<-+zty7cxJ)pI-#Qj_=mF_{Cfo_4m+VZ|xFssgL`)@kDUHc$AGcE@g>c
z6|Y@J?yIx7aetk~#iI-%8}}8A`)@I>%TaShasNH$U%~ur+*d-dxVN!wDc87)t=DIx
z!QR@{;`PCO;!!r#xQ1%4iqx(p<MS@#BagDFY<yQTzJJ8_uS1Cy#rMyc|54^=<HNgS
zj1TA9`q%gvq*?XN72gfw-4&nl3E=*k@wMQA$Pa+0Mt&YVE%IC7>5;qpxo=wCHNn_7
zEupQY5)=C}rz0j#W}-7DcmWpsChLr{$Bi2qUfd>bBKIopnz*mdxQ+YkjN5pi&bW=I
z))}|)v^wK9o?d6%#xv@S+Zfvf1CNP{9Z70!u%phvV@_wCfycynyK>B8qnrqwr7r$g
zwC~g6yE8W<6ZhXJ<F<!Xl=^opwKSGWapH_plu=6R+{P{Rdn;ae_RA7Od)lpv5!^Z8
zjSJjE80sB5CSSV^g+?aiYq!H*x)$!^SPlT^w{r`*+*MdhdSqy&q6dXmI!XiU@25wE
zR`5q+FGZj1zE62^1%Gm*elrBit=$Em={&SPK8ecRP}&>!;B{71J^`-Xi<fX8UUH>v
zaIUX=bhu}qn}zk~|CblzifUoQ>mg^{<6b)*d=f8>`_V6arB&td#a7%|XV$0j!aw`c
zlYWN8Klbuj0{=S9=KyoINP1@BPk{rINyYGa+UEUu?Jg!MZUzs(GK{ka{B<)(lZ#1{
zc4>8O+%HM0u^v2q0cv@1CnB_ntnm~#Ifr{{53*{dyOKUcXzy-M9|oY|-d4lyd|s(H
z+@7SkLpl7)1NiKR-(sp#(xhEly%RGt)^L0JD0ADANp0bapj@fXJ~i@_$y|)yGx)!}
zcp^vco(~wg4%~=`i%A`g2RV?PR;@g#GY;gJ*wX(V$i7y8RydHaBbfh}fwVw$|8VBF
z5cNhkD|AM;<T1SHOU_)~OG{tIYvXZhqs&QhHx&c^72xCv-4oI$2wmM>>5~8qcz-Lv
zuJ9D3IMZE8id(0{uWX?JnK8L5Y0@sO;?MD92D~f%Ds#t|(^+^F`lRu-y)agbEZ`+y
zLsllXuOr#`2DMS<q_|s(*q#PXp42@l{U%{z_r&yD0K_)Xifv-}Hl!Ha#3XGpw!1Y3
z%$PheY0@sOerlQ6CZ?F}ijL&YSNALRtTEZ*8i_L-Z}Lj#QA3^9^vT`H;8Hsbrs!Bq
zrf5l77?Zlw@1W?bEB~)TZRvMW25&@L?2L&<yf`e;k6i9S96^0Z^B?sS@c$9e@B_-0
z&b%)CkKYjH@B_ZwYGB-KAGf{*kWZgA-~1l^i^#iL`_lMgu7G`M0!&9~Hoo4wW4W;w
zo(+Lt@<2*)qZ32^9BS$>M12pyzaI5G?3|5t+|xnQ9wyN=yYSkbPg3099Den5Jj4;{
z@kx_rY4x){*sKC4FTly{-p*GyTKFII%DxFOp|&N~!%V!L=a!D^S(;xuX?9QNtNqwF
zFt%z+ZA>brKS1qQrz|VCr9Xs<@#DSsak)vkg?HwPxbBC4UAY_Y&GpCeV3_&zM=U$H
z@f9>U+70D@Z}s~9!6O*_Ycy_CPyyrC2h&m75M9I9<qqiG>iYovP6=xIBHYjEvV{Kp
zci$Ql>$hWbuYa9eK4uudd|2FzqIeg0G4n3aKK3pkM|=aoX2QL(Em_=8-{?mvZj0Q8
zwJq{5c*Sjz`!NGdCKbX|+GZ86-GwB@P1WI7-qTUajLFEN&1{RT2T$A<$75axkmNT3
z&n)$A^gVTsO6@1eo^QJf(@Q4fMMo|Na%t%ajA`6en3LUA_=mx?tMH8^?ke2IE#7jN
zhVn69eEV>e@ouennK8MXG-)%tisCKXu2PD-O38N>#rjiDL{I%~y{}5CK+#tz7Gb}i
zyEmTT37<8Vvk9ra1njB(46l0Id6~esomT+ZX}C>WyzOE7SiE@KIg9aLt8rq+<o2XV
zyR?c)wD&v3+n&A(Pp=M4?x@cj;M;vGEWCU03m=PKooD+GGq}&Fz%Q{9UY^{^MX%<k
zXnJh4CUvGim(LaErmrDyNc#$c)4LkK$Fn`w^8hT53+^j_#QzI1+ITA1f9)$LL&MYa
zax-Ij>?>SV`ScfZt>fd@$V2aOzJs?a+~4JfvKM}7jD#MDjMra-Yj5Bsu3s)Tu3v5|
zTE3fUTVZ`Hzu(X_-^44om9sgzxVMBuj2YNjXflhJwcv+~xAWCGJlkM%>05XiNtO=O
zPs3nODmM82mPM0G^&6QiS+cDI+f12*r>52Bq;mQ@RJCnpOfILthl*_mKP|!w&KjEM
z=VsswNEkWrUAYMyPn<X8KL1E-&tV_(=^v~j&kf%|O&hCKF^o1=Sc-VY6Hb9v!)q@d
z#0FD)8+HEoV9@~vYjv9DEWC2C&KVmlW?-;rGK0lh%MKQs`#%SZ3-!MTOHHfIWd>_Z
zZVi@?Rk&bmtis)6V|D+dt+8SkmKm$xVyv#yx*9@OrDPANSUiG6{UyYeVo?k?w&nL}
z@%uNZwatv@lD9BkgS?e7z7tfaZ3D)SuZZWNeoyU>7-_D<cVPJovOmGbw>h?(w1u^4
zn!|YQPLdRFEyJ&3eN*jBnlwwR5BS?0tH8;h;e>0ez_lees*mp?VIPibYfLVue}SsC
zg_BOMp$8vpjSJh&r+>8qTtk0HiH~Yr)<G7=<wgkjl@}6Y<AUeVA>G)^dcFa;wxjVp
z^3KK^l6N(pPu|^lBl4bL{BVni+bkHVzaa*W(BEPCP33>U#*tzqNjYpn(_9y?-Q$w<
zLXOn#c#tj99Z8dBY4sr=DOQ1#f5OS^S9`~EpyWl>+ihQ)ysT;)8h=yAF?#yhTrZC~
z>`81fvnLgDPqfBpQ)J<e&rx{B9cXNT8k<;Gw}4v1(;(2*Er}o4RXRjhx1woohF5kK
z_iofxs^}_BrmL*7Tvz2q)w5k4Q{&$4eZB13R$tj<rmy+j!{{qM;9>vW8d>P;{`iF&
zDAU*N)W7W@)-Kjm{tKhdMgKl5r}W=rQ%NUS^xM-k@xfAy9`{7F=&54S(=4q%>Ql)o
zaPl8GX)SuWQGG0WoWI6JKPHz`y!&v`dmqobu(jsdY(B+}DCYCrm3{n}`nUtKtdBc^
zGJUiY*qtHPE>$0S0?R(~{FQyooWIfu`nU^Cb4R@Lz{aOP^^q$2NRv5#WfeH#`D?3>
za-;g_W1-c@F}a);|Ia?M*?fxU_j(_{&|0&*BFp-?J1En~b##)x2gDCvr*wjKx+hI@
zH@tG4;@Y9sDOIdfn#?+7mF3o{yr_D1osOx?ty9dQACqiynRPnWzrB!U{o4nW>EGJw
z-@Xt(xPNql{_RK8+#9d#AFeT~e^k*wnoR##Wx4*zi>hb)H>UpI{bQ4v{uOflc=x|S
zYjl5PVPD4afqnTvB*wak-wYy+=ON;?aWj9#jz=MaYgZT_LcYrQQ1Ug#hmo%{KAe2L
z@e$-3jE^MW1jb{kiMXJNUAv4G#>1cumWM$*Y}~aOCk}(7X_^P%m4^W?UFk5Oio<|r
zY4tHb)L8{iI^d*r7|4z4<1pw%j-5M=$>nrB)a?7)uYT7$*4b`8?Xm)VfBPYZ&BpN<
zWMQ40hF>RwvV(X$()dN9UO$Z2#&cLX7|IjDwP&@8P9lEb5U>dh!O1kuWAVx%z^7yl
z0aXkEO=bvKW%(hH8`WQC2*%_%1ishZc2BGSY;>9a4`LnREgk!L4Ov)6U(q@`1&Qns
z)RD$-AN6`FUK`)CAy^2mJ!X6w`EldZ$xj%cLH?TYN61eZFCu@#crp2#U_1wrh>yTn
zO%pIrTul>Uxtb=y#?{1VF_eL(xd5*mN}TL!D5+v7X_i*eA-g)nDsVCxPFkx;Zd4yb
zIR!bkn#SaET7_!At><~bTYqYeC0os>J<8U#*s)`B39?$p<R}u^u{;ZD{5TP>jnCMz
zbPl+7yYac?JB-gG-(`G0`5xm7$oCpwNdBbpMdVL|@kB-92QEm)iBY_Srg<h_If^(9
z)+kcNDAHsWB&#gHAmv8&msyZw^8Y+0+3qrHa);L5b}6zjj&EvBUXDa|9Irqce_{-;
zjh(FjSAlEm8(&SHXM7ELzVWr>jg7A(Z)*He^5(|ZleaYf7<p^s8_3%l-$>rx_$Km>
zVEo39_<`fjeqr2ip=n-*SB^VQvo-EiG43>(ac7m~$6anzf0=O~lgGw=3tX?_zcT)x
zg7KY-|NFu5oR%>xGY4z6=HOOjVGf>0VLS{HiR>JF9BJHhz-wdF#^O$Jt!8`|`4r>3
z$qnOs$P0}>K|bC1Uh+qb?;|fZ{v<gVe~Nr2I9dE@;s=g3<HuNkhNgKNUOCn{h1FP7
z#aPp1#+p@@A8WZ${bj~_OdcC+-*?~7kMrw+Y<HRQ-KRCapG6kN_dSj8=aI;c@BK*Q
z*ME3z{LA|P1#s<G#t)L;Fn);qrt!n%w~QYl|K9jf^4rE=B)?<)B{F{a$v3sf$nP0{
znfzDd$H{*;{tEe@U_2+0_<?i4K4K1@q-lN*ubcy%VrvelVh(6BbHFOg&w<>i{xWkg
zCXdZQcD>KUSg*G1dS?vF%*WWC^AxgLd(PL8$j-;tk;d~I@!D9+#^PykZJO~n$uo?<
zMV@K=ZSu;-&yZI&{tkJz@ps9o@w4P1<LAgD#@{2)HGZDFCK%5wB!1u=uwR&i7igMa
z#Vh9kr#_kks+a?s%p9=F@^c_Js=v$}jL9pWgB9#KjA5BM;C)0p7yALSFb7}PG4UfL
zvUBhv(s)uLUK<naWA#tKwVd%!$%*kx<bv_f$R*>K$!*53kSoTok~@umPVNFHi(eyt
z;P^6TjPEaKnm@!V#}}to8eggyUz*JLvdZ$~D>tgY%=nJUW8>@l%sYQ;os+QLWyW;u
zoaA+6VNC6F!mp6Xj_I$F#={r!+W5Jxuit=cFBrc`{-N=2$uAneMgEEL@5nD1|DOD^
z@gK<edL_p3ZQ=)xBjdz4{*k8nOT2O%Z;Inc72`;g8An!GejMdS^_LmPG5Np8G4nZr
z?JhHpygrB^aGv}QvM`Rg-h}tVcag}B<DZenlMV6OxY+vt7jW%7<G+$GF#a3)B8|)6
ziMZ8)Gu~dDP4bMl50+=V{jl+jm+fL){z23H6J9wkx5ROwigBS?T7AmTcv%Hbrou_<
zj8|?{ALB9&Id;Z7CYRIcP_v((GS>&$Za$r11^D@ipC9a){U@?8F82B1eI&Bu@^7T^
zXYKU*AG|jHVdwq161x6-<8kD-jT7=a#(DC)#s%_w#zpd9jZ5UegYiT|;s>rWMvKvG
zL*b<PFC;jcx5m+=iqWLWtTI+vewE3M>Myg(#$;b*eynCbKd{xz8Y||;wZ^g?En_Ti
z*Ba|UB0H9yNaK-;dhOEd1iUtWV<RyUTzk!U68UxGZt|~;CzIa*<B^EO4;(GVgVCze
zG%I-JXx$b^iz-HoCNo;BvixYtjp{EmT4S=0*4TBr@BW~*x3S&KcxA6m_aF=7W!I+r
zkjRc#Khn6prPrx=ZM<ZoFb!P$f$?<mkBn!Ke{4KR{wWyGCnSF01zSdf3q>o@G<)&N
z3q_bmT_~c83q>@U3q`E5+=U`}QT6PFqA~UVyf)1im$^>#9F~wBpDQB^*Eeu0a8*!t
zfMy|$+d_D4^x5XL8o1W0%_${*-~yp@ERfY{nycWI3*_T*fl$Q)p~)-|R#|?5$c^eR
zvp~k=)&lY4>4w`|>x0cMvp&9ozT3TvA!MQNC*WCzplsjgAl;a3y`BrMb*tBF5I=CT
z=@yf{CQWk~ubk{V;$&0BWYc6On^l&ZY<W@j>|~Fr@v7k1F>%d}t?Qm_G&A3&+y<>Z
z1ed6JOuVQ)1lMwx>Grxv<1UU~*VpR?dYy;Y#snLV4Z$^hVC1KQ`Q*fSBXYrbV{*xO
z6LOpJrsRt8X5>!e&B<M0JTs5@feVYhz{1*+ruqM|b{_Ci6z~6^+qskjkrYC22>}9u
zG>Y^VI!UOZcQAxNxWEAca`e!PfD~zB0R==*DT08~n-m2^1O!CDhKdD6<ct5$^UU1c
z<|x6xKVJIG^Pc`ZGdnxGx7o!VTb@|2>xo6Nm{=5;CKeU(o>*KBm(MY=JkhR+Wxj4O
z;7c3+F9H|p>tyOdjyV?W_IEsz;BWjmhkXJ5#yE$+n}YGOK9<cP9lex4T0pmVP<l(!
zJ1V^u>7A9HKzdiDCz9S>=}DybgpQZ(k(#S7sfX}o8w#9_A?YApt$j(c@FfK%Us4fo
zUvf2EK8G(o(Jo)Qj?v3E+{Y-jn!|q%xBuEA3H}R0gpt)AU_MOoK^XWinPo>v$4TXb
zPSEX}mEM{3txE4g`uj@nO8O3^cO!kL(z}zsTj@PW|48XQN#CpVUZn3=dT-JXD!mWs
zhn3!!^rK4eNBYM~?@#&(==g3Uskx31c>v>+LV>d#B#jRqD&s2%ipBU)U>YAP;ypfG
z4VTX`KAvdT_?YK<J-EFI{x{8a>OqdN`WsVQol6EF$$h>Z2r!RT8rU&N`Ct%qdtapw
zCcVGX(@9TJ`Vi7nm2M|J4LaV<M{2G<qn^NLLn&~kLXyw$93}gVV&O9iOg^I`-ag}M
zxO@(ud7@oDbIob|9_+feL#>+pBu&2u4?_~pJN@yNKxk$sjR51#fRK*XY;Gph+|M{v
zA<j6XC~!I;>5TIxnTwykDHdlO3QT7lD&lp<;aa%3`HbU<_4*yk^m{NhX*%Di-%oJq
zg6pEuNK(I_WI;3A>I6Gt)P`y-bURMEbW#~dI?l8h?D3?og&hU$rEqaUmq4Wv(j`zR
zLVXEDO<=GmP~aQ`NrSyk4>rYOuqk*Df;$K5rau)yC1p^F`x1z&;qn;laHOb9AWt+N
z@<MDy*I2Le{C+}>%7}85iWcx&@h%(_xCn#&6Ojb_d*aJU(9HH{gB>`Z!_FyC?d#dj
z@~DLDj6g_sRzRrjq^e-&R0^DvAjwWVNy?L&Vqqr*4?+&R?4%;7BodXl?c{2>JnXE5
z6lJF;+ApFqV%>I5o9mv_)ToSzqExceD*9t>z~vF_oQ5RWIncLI?eF<qHv<u7b7z9J
zxwD{}%;hIM$zHOX?NyIrQI&YQXf6fL>5%kv(FWN2<!x?(67X~p1tycJ6zU<9pMkgL
z0P&?#mZaP96FfXVn<Ka*q5MoEKXZqNq$Bv5J9@a0pSi2Yk9u=Y@QnEjR7!OP&zQeW
zu203+M-fYpd{Jn6;Ep@u!w=lyy^2W7d%3iF(2J`ppt{Dj;SnYth+_1t$wR4nJk0BI
zDEuCQ|4YLWdJjSl$}u*dBTemd=OGDW<HWbDpqU5dMKHc!1?j-TN=M%!==OA_FDBj2
zGjs{5x$b<aA*@3$QQ({pN$U`vQRQ`rVzCZUU|NT$h}SyAwQzCsI^>D<T8GwbaPNDm
z$sFsD#|KN1q<pX(n%M^{z<B#2q@$Ly`(@~M?0m@|t4OcOK6r)HTzx<d!3VEW;9Let
zKG>vvK(X)v1tuR*5icKbEnM8}15fPZeLzj-@WIFKdGH#N-~(Lit%hdy!5T2W$OY+m
zN7?-*bo(Z@do8KC+D(<i?sXJ6Uxy^S-_dqcEbOMhWH%M@vYTt+;%2)&v0irLI?wg2
z2Q`_)Zh9nI&HwdCg5CHHe*-kL-EV`9tc@&Ttkr=jPooYe;86#*{#{aYwVs-Q_3u&O
zd<&ASe^*;iv9O*3ll4@@%X+Sbi<_<Y#A@q3*L<tqaNAFf=CHph)(2((W+Y)>rhYHo
z3NX818yJ790_ngZM#gW4ZpYc0^c|$*>`eLxq~nxK`cBeu${>9g={UQQzMFKM&Pe}|
zbgX8ie?&S~Vbb@Ij-{9My`*DtBz+(0SVXZr?<X}^r;}Z9`T+`@TOi5lxYFX~hGOA#
z3QSI?B3@4CTDZ8`>7H1Z)6MIz>vxMnp7od7&*6X1eTIWb!u-R1hQrXz&OZXiN4r=a
zgLL5Nh4~*t&E0&e7UmzPz<CIg%*PW>Y(B-pd<snFQxPxoxfU*NHs2HLGT;4-oasIT
zHEOciyvIF(B-q^F$T|hhZ1X2z{5l2cz&Z$PPeaY!TB;D%o}s{b5|XUlqOGM^SWAJ)
zS}NjYE!V=u&DMHiz1A1g9+#TTVJq$NaI+J8pR-7Ut=Qw8gJ!n%bFiZmTX`O;eJlD~
z&>pS#I(UHy@9|<0>OCG+g<Z=R6gWSFq&?m?vJ#K`QY`j(6g&ty=Gx;?5$y4*pc3~U
zkE`MG*yB}2irV9OqWvPOA@)&wJZe-%RHsz6$D?QSUtv2hAPMUT9^k{Z_$7eZ)Gxt~
zW-t{;@@44u21>s|dLyM@CB3oIzaqUUbUd?9>ig`P=g=-~18X3p?Qczl+BMWVT=O*r
z&Wn)b8vJg>uAx}ChJptnCtR+fBB-P`Dsj7ptKsr+O&z2t*Lb4+BI+X6^K8!td)@nA
zYE?$m<7D3d-f{cpI+EZUJZl8s+yt0?^9>ju@`7|EE9<|5ZckGB_oTN``VXYHQ~E8^
zJ3z-LxJb>_L(~U&=qC!CHz3JFJG6%=79OI&<RL2J?IEs)%jfWrC)(v9&v|IiVYiQ{
zU6YTb*yr}q&qz{h!Y|Ow4*C_0Z-GHNy0XE)L(Sb_st^X>p}=_?k_`Sp8%(h<m;#f*
zRK(k0u7=C!FxV6AHrRFThjqbqolng^Wbdndy!?SAW$#^RW_$kv<J)79j=F5`-%w3!
zARPkqh&vqurbpbVLfCts0_UHQWG|kJ<U@dBVJ`)yHIRy+5_-hleF$(hTpsqGL5eyA
zJkfsczh?`;;NfPk$zEz!{|y_h4{Cgsz5hd!viBcoW_wWu?6p8TTC%;kY5WxSQiZTr
zQfcP{0NIOmm+hrk*h_)QUMk{kFIU6mbJ**Nev-Y^Yz})p&-LSOx3(Aex^vj;2gXO!
zARVjOUK>=?_|mvO;!hZ;LY(7phaC3$K+-vGH`$B(I24O>90ewOsfgD(j%(rK=5w4U
z_VMR9YBI+;4vUB0L*_-2I>+UMW*%GIx5wMj*h>6F@Dx^3Rj@LE0w;a}U@P$hF<VKo
zu#y6kl~lybO0I>Ao2~T3KD2MaeGJ#N0yUb$PS2PIB1z3*{QQ%{&H`Y38xGQey*3u*
zf>7<bZc?d2Sc{*{U@d+|Vr$VJTT8L9mI9NtRK&|#u7!)6t@Xq{eoU!JldV?a*_YyH
zJ+c-1(xTAJV_FRCco|mWWR9nptoD^`aS2j$wV0}f#dzWg78ik}S-eMEOtG+-0+Yp5
z#LHr?g^Qal_QXCkzNURCHJZa_&oyu<B*A7}1BXI0+l(i%Fur9V9hu7VaOn1tY<XEy
zbG4i*hvnrcaN;R1wtTO)oMK@)1t!a>h?nJD3l}$A?uqppYtuC_HJQV1&sdj7k{as@
z(9CvM1Ut4VizA`ix3a}}xa=t`rfOkvWeS{lPK_<b8J}l2#lm6=OcqlSFN?VrE^fBi
z6Z@pGrbbOROa9)py6(j@abz?0<#-}4ht08I2d>9)6vsi$ePpRB*ja@FXEY?u>HXSH
ziiMpNnCzq?UUqUVT-<D@C-xybO=}i4`WQQ_B1zd<9h!M;p94FFv7I%b=58ld1v_g}
z;H(BocH*`<k0-^#P6|wRQV}mZxfU*Nw$l^)Bs;0m9Cp%m7Jfa)byh7TDLd;xGuv4g
z>=?*))`M!tyvKD`eQeZeC)EHUopBo?)E5&}6?PW&DR9<?q@BefvJ=-?6pNh&1rI_#
zaa~MM5mXY7O58gsu7=BFXVDlbYA5B1_KRqO*hgJwQKK@VDW$4?6xoTNuwiEdBq=)^
zK{MMK4|eosI~zl_A7(q7p%Stae^t$Pwm_)uq^e+N6AGLSA<51o+D?jvofJF>IpeaE
zioniRsKjk2SHtCDX97}`ot|jFh(yFb%1&xjMkG<H$4>mb3_F`5N!i&Pn%T}4V8;Nq
zvn5pfQMR)+Dj_@DAf)kZi%{E1Rl&|y6gZnflAX9j=JBLh*h#^IkaI3OsR$}*k4oHj
zay48Yc6LCDveOgo7m<wEN7+e@%7~7X>TPENl9ZiE(9Cw?M?R~gFWcD$YVLMYRj{)y
z1<piBvJ+=Fwv%FECj}-usff3oTn(4cVW%hh@pe+9IqanCyEQzX?T`dJ`{QeS(9Cuw
zgYktuNXPSRZ6~Pq<2<UJ;4&K3&IoB#yCBq~N)^J|&J;M?Lz1<)x@T)C7S>YmAmj_z
zs8SJB(hZfkN0qDL^02l$Qq-t=qWvOzAQt2Hi0eCQRz~zx>2$s0*(-KIlCrlOG_$>U
z76bP7fOO#QInFXYq2|8&QiWK3dr{!*3Q4Okp4H;jmtwK{QeawrsfgF=%e8QE^Xlt~
z_1Zs}?r~9*Io4j!{=YYp)Evg|_Bm|r2X^#iEBiyu-AbwoRz6RGvkxR$c}iPJv9OW?
zla*A&%Sx_=i<_<V#6Ia9Lyem3w2E7p!)nivf+Y7nu2g_|SO<a~g<&eT#c9y(fl41l
zda%+5lOCe<bkYk!$5#hQ&2<Awt;02T3Y-HV$u&3w@dTh)xP}6gYp95qYq%CJZg!0)
zR=LJC*KvQ#bzMX)=J1Q>H<Sz{Vf^tM$}nhVr#Qfl2xaha==QQoA3=I~Hhv_jxf)MR
z!1zoGoI@eWc=Ujcr&t(IfysC(;$=M7!o|(Tdtx7&7ppwKp-`ha?Dt$Zj6xFZ$92OP
zXde3=C6u*Uh_x40x|8(cZ24GHbG4i*hvnlaaE^u~%TH^|DHfJfV6vQwcv;T1aB;Kc
zo>;Hr-gMnSP3Ev0YmUI<GB`(%N0PF8A~ciTVanD?h_#ne`ef2W+3svov8BK+tQVF}
zTHkvkr1iZILcP9I<yhaRP~e;ZN$dMrvKzZFipBa)!Gn-XuJxUYppt&5#J#?AHC!I+
zJ6<89*LP2}U&Ql>HLdTk-F5$tnw1eLDxIEb@I22n6-ihN)bqpB0Uq=5NC6msP|0!@
zq@yxk?t*n;HgtPArOzQfLg{l!uc-7FNUsDPf2c_6Ja*I*{z{s90!PiKz&Q<)9QB!Y
z6ve_(6quf8q9Slqj=z%TYPdWc<@qaVPxKR>XQEb3&of!YukJN-0g~MJz83<_ep&>^
zw-+HDc-ty@XbE(?RQgM#`zU=W>3&LIM!F3;zPU(hu7?oy0f*2E3Y;%O(jkPs9xomg
zi$jP4(;-Afybd9*g^QaHAy2Gp@o=r3dk(wTPHHp9F+{%&EahWpC6e6!Sp_ir=M^ws
z(+KGZQMSJZ-Tti7Unf04>8nW(Qu-Rw3qZ%O_N3-IgQyRfL2pvvd>N8v5FU@<LyclF
zgD5b~AS&WLgSZ+lpJN7jqFpn{vzLO8T*n%<n_~`n&bw=o<Ua4N2bg{I7TEEJ`U|WL
z(Ct4f{cX~JQTj&Ge^dG<((gdWFZrbAI(w*f%$|2CaIS--*>hgc9*V{6p};hIsEF6>
z;aa%3dG>f>AA8=V7IVxUtbuy&yoV%p-rWMt?3Arwe5{e>`;d-eYHDnUZZFDHV+X0Z
zP7P`TQ{w{)oSPwOYG6anQ-fkLH7GDm4JzV2HMklspJQrxqV?2p9XmMhy5<Ennqyvg
z&bvF2q|QIPp_%Rf5R3;4ART|JVc!GYevgNJFR8f>J5`Qh-$#LS7bFe)1wHH(i(#k0
zH0)HwYuLFKE^Z!nPweB*yVPWkai{aHS~K<|N!fi6n%V9{U_40x=?GOLegwL`G>`aE
zQga<~svIMJi~{EYNE-2rdc-LfBTj*7#Hon)h;ubuKF5fAqTP#uYs^jOU1~PRn0xM3
zevBm8j{DLlpqXty33mLWM*9?W`vV^BPe{#mw5eK*_NNp$k3-UEV`IUm2#UpMQ(zix
zD&jTTTniUBkG3b)b&7CbOPcOWQ=<=!_nUl<IE^GV-e;kiZT<|5Ckr4QacaCjhi;GM
z@jg%Lm$0FreE_yFw9iUKNc*gT2=zXTD#v(#L4orOB#rlFJ>C?H@uuKG$Q9Q<i;AF<
zL8!#N&*EyhJjQ!4Qq*;{C)zI}9kGwzXHl~<0x$D(-5>E>M_)h^Z10bU3ZR*7{}PPH
z2_PM}c%-jDwdXq0R3S$CDh1AqkTlXTjYpbdG13&6Mw*IvjWpN7#myt_iS;_JP1n)X
zWR7w6>;=9;l6x=kHNb4{buivk2<f<}CizY1_OnX=hV*kv|CaRgO8<`Z3($?M?@7(|
z@TWdtI{ZL^^BN>ghpT!zP%Nec1*XHFig+LXTn(4cark?p-G{&XSTOAcs9n?Xub!F4
zv);JoxP>Il2i&*+37XkKKZ6~gu&KA9+P~tNZ^xvfnV*4>X8urwdgfDAF!dJ-oIgU6
zsn@ir6bn-+co2d}^=vp5K_w1U;-2|j4VQ<h!;zw9z9-r*VgzCzbzhJgl@TK;6)ouB
z%hj{tzamM^``@9N?Ysjvvi^W{e9PAU3Dxw-G+FzIC&Z~jSbLWO=Wmc??bq5`iiNcl
zm>!p=BCwVomv&pr)o^)O%MT{=`))na`iW%E9?$fA05xlR4%ydqZ{;r}x%YT~1I#Ah
z2jeB_EFZA^hb8`i-|F~S&2$TN`w^w%kNK_kW9%GBYOad{wT?vrfA|mQ+yl^}aGjik
zAJ-`sivk6vMS+TVEec!<7dI~op4i9k@u)@9nqc;e4_bBm#SdWi3tmV7zt~vfg#^&^
zvcwAstd2d(7x|#ucPkw)C&1q<v0v~Wgs1QewGO`oP~gO?5ZEs_v|lI|exbnR7b@cA
z7p{eioBiU6eXL)oMU!8A1TISSy|_Rm;rPIF>%q{>PALG!Z}BV(vMdDYz;~?a0<$o5
z`xbUV5mIxV8q^S6P?Q2EzCpq+xT#%0v2XzeCKpf<FBfnvT-@vePpsGZ!t~raHTf7H
z6ho5oK?!JPACv?eS*2K(W*G|Ucw6}(47z;-`=AV|x%z+_f)Bzea2AK8!{Zz61B!(Y
zC@}eeig@{eYvJN%A9!LP?*nSm<OB14_OeJ)>tuOoW*<a=jjRfgjw@_yMX0&kN)^J^
zND7?gAjwud9?eSu#llt!Otw-HFI%}5E^fBf6YFKGX@5XX=CIXspS=>2U@OjjQP9k`
zMuQzE*~%EGxm!t9!OB<)oRuNTO1!axt)y63NrA~qD&l1&*TTikR(fK!m7a5f={IX?
z)MTesc&_W>kOVt%KeZ|}vz^tzcy%nK<GPy5&q23;&6d|7HP^XJmBaFy6gaCulI7oP
z%PAI?Q(&^3ig;PhwQzB><(^ot@x`@`>pnU)nZxb}?lr9zlCajlf(X3W7GO3V?}LTu
z^&lO6l<D=M+dC`00qI?pj#mrgFMgHYi1eOH$7_bI_TJF(CRS2&brtmou4+Povo<8T
z>IdyAiiN8vFu96~c)5ye;o@djd176Y#C08r_cyqFMQxgV<u49fT-U%&kpy3*Ai~IM
z4lw(w1=z@H$+8v81eS>`lUTNfbiAxQ(+0YIw9?y>o~875q>oj4d(y`%y#whJm7Yxc
zWTkf`eTvdMkv>i77$p3KgVMW@K1=CcNuQ(iZlu4UbPOKevZ?eQq`#<i3?%;aMCrXq
zU!rsjD&DfGbW9GsWmD;WNnZ)w$m&OGuDgHoDmIDzDR4G}q)j4j#PRJOip3_80@LoF
zig@q-xf(8?WB2cgcJ2Pne>Z_oB#EW?zX+y(H7pr;Z!+~D$8~_`y6|}<sdaw<H1pu6
zf{m<!kPbVWo(47d$xanwvJaxbnF2|Z{YO37DHfBR0@GxtB3_f7YvJPN$?l2uS{F>~
zJ~e5Y>*jOZU?eG9hd?vi3Ikzl2BZT?80Mi+bGMZ$gssCUaHd0&tv_j7DHgU;V6v5p
zc-hLeaB;J(p4i9RN=@dl)pL$>AW5C$MnE&$Iuh)_;KIsGsJUB7Rl&+p6gY=Nl9l+2
z1wO}7EUcu!WF-~xvXX1z;$|y7v99IIeI08$$5Ep>?DWiEtTuW~vDW0U(+M`Ru<GEc
zCAM}P)ZDG53Sli)C|ElNlB~V0t)*C4OM%H+D&l1=*TTik)_P(eKc>{A$yTfI+}D_Z
zB&^ly?;j=s%)^SM6Xs?^I<RA-E&3Gb_E4oyCB2N&v6$iqWTj6hy*zaM*hXruD;xCz
zE89#8oLIVfW&4HPgP#*A7AqSCrj?C~c&}_+4VTZcvU#FiE1PG}W4f<F?V8pzA3^U3
zQ}=XnMj#(e=W{<!2|0W;7mU9pW;u@~P7b)`r+l#hy1kOpUnD(>{j!kMT>V1r!Y??f
zz%R2QX~oCSckCC6g<mKz`Gtyj`-Q9F@;UtCiGJ8Grgwx<yC%O_#Z|aLJ#(=bN$|@g
z_RC8E^W0eqHnNtn#3>Jdq^rEJ0=gZ$M4C-F{o$>y>=B&ppW;BG7I7f0qQHq0EFVa}
z=>v&kaUfA(I*_P{*MY>faB=g2<cU=Wl55YiajSdJLoMbwj_^dDz#9v2@9PyLVQs{{
z@7JK29q~FCf4R$Y4Wy$88~g@T`|s#_L3<`{;?SL;Q3&Zi*l2|MJ{VPtDexu*&Q~F6
z3gGDFDL}E90u($5!Ii(d4@N~$NtXG}5Ld(HF$M5Z7JVPg6YUo<7P0R8V7T|~`i+Mg
zl@a48)pH+A*}N7>>fE;;n%U;J!1$|ONJnwD_HC%STT2zf+Km)A*FloCe`sqd7S>W=
zvX+W?Tg%mO`5e}IqP4Z2b-;8VjGEQ<MmZ+q18);HA;$f!r)6^7J7E5H5hXnEoq{}$
z@$gUC36v7?C;D5lq;y=7>RZ?m_{a;sE|`TUrSL7l3CI|6mxdi51dMV_0#3j$SDx<%
z4pVugeKL~l(MWP+Lvu`lK!MrRqpjv1m6Q=vDUI$?;$cp)p6BB<L}6^|<Gl{kXdhl+
z>~zFnMTX=r7rDQ1#4(;P93kgS%2{yiECj;096rgDWGZK$jp%~*IgtEGzqzE)M|oa=
zf^Qwsx1^|X`j*r@3Z3&I>045N!RjwJ@<jn9(6^*0d=P>wJT-Nx9BNvCfW9TAdQ39n
z9?Cv*IV70QDd1s0@yBH%8sf*4G5-7+#mYgZqyj3%$a)c({p|}OD?KV5iz||hLTu~h
zXYiVbKtDGBGOk>x;YveQXkQGVPde$Z8q&uFVG6zkh|g2$uNu-<sg&jZW0z31%5r}r
z0*|Idez}F0NlL)y1DzC_zG}#p`}0=~amu4Set~|K3I+O&pzU_q$}-SzOvFoww=ab-
zvS@6A^9JTERK<pOKLGMxuHzaxC-H-jSn{eGcD%rXKLW+Q(=&lPhmSz<SLW0UEU0F4
zs-Nrga_;XyCBlYT_#Z#9ixB)zokJj6;(zSN`F&;2APm;=e$eTAbb<K4G<3?3YX_PU
z?(fSrYTvSvMO7oH?8vH7F;P`ws>Id=2p=&RvML@zDl-m$&x-KCav{pJA3kj0kO2<L
z(f__78W%%ca(l71kQ4!kE7L5wMPr29!HrYEjRxEMQfB-^S1g@MZOdyb3AkseA_}U`
zP6cMenmztPypTtT76=a`4h|RiTRt(YC?8u?9-lZ9d>+hzf(JvyaWF2v*$T2D4gZsW
zF!0*4i~7^9zT#l_OZYpL*L#p0(39khZe22i#r;gY&_s0N_`E%%QhY^N_d<PqMN-cz
zctXBd50bZ8)?#_SJH^~#`8mfN>qarRx{@s1jcWdCYE()d(WGma%sgTfTFE08bnk-r
z`CR{m$<)fwZc!=EiZ<O|apn<KkjGcl;?#m&DYZtw;IY2qt(19qdcDOAiaFbt<Zvs=
zg()0kr95>xXQ7nX{6XT~8M}t~ipHHN^(*elhg|9+N(G4yt#=Ix66M=ao<@F@=jDzh
zMKbl-r|qT8!(!fW^2RdjrOZ=e4C`+Uib^RczDJC&__58U%qtvU5R1X3%!1-x8uekk
zH7&qbZ0JC>1$2)}DJotcXd7Hq?C(Kx^U^5;i;5{dNgq{;<gVVf!M>t)_g9=K3t3dm
z<eUyjUoi*0Eh>tP=svNiSkF0=IOhwnI!Hv$CM$~0BKb~BvZbX@T0oGvhnS+`z2({V
zqT+rx+u$IP7dcCcxS_H6i;9**ZG*$a2Z?`7Eh_3mFDfpNB)M%kwb;ThEug5V<wG$|
z5fA^QUqPRDP0hCZiV@DJlyagFTJaV2`jCzHhLT*)<5dddRZ*-cNh4JbzVa0-Tat7@
zj}n!}k-pSMveICZt<p0F#))lhMxo{wODSe+n-$}H#mP|812Re9+lD+)c`o(j)!vqu
zg2bW;&rb*vGayTfBv=G*bDmQvlqUdfl@smQiq$FPfw*zh^SfhdPWEq0wbdEKc4d+e
z!yM$jgK6ZS*tyhhr}UjT4)B(o{3Ll@z(%YRj<G~H`F7jm>>;E;B+^!5ei7kt1%={>
z>Ve}E2QVlV<JALeNM5G(OuqQGBfXnd`fQ&w0;Ph)t7z%SjO}v-s32Mi5Jy3YpfE8J
zwFHbK3KZ2jt#E3itnHwMOoyzEvJyoh)IsI`OsBNs;%%k{Eh!D7EhZwZYcf$do<pac
zzfW(J)c|yasqth*$C&zQI?nX>M3r`uX~q;qpD=yFX=U-<6RNXn2Bnq5>P$4a9kmxB
zE;HGdE4sqea4JzWzTZbVFBK<hAcEkQ0AcG#)JT+O+BKi3m8imGA3)Sb)U!-P&P`}9
zQM42BoHoMID9b6@i)Ks}Gl`N#BGX|zQC~5TX;o=OgP5jGBN_}(Q|qCn6isGY4N4U0
zVj5GAaYPQWkZIl!qAan7Y3K~1@nR#>7Pc>2>|pXsptKp{0Mm?_G)A+;QKrLfiROw=
z*rErWDCY~}45uw&4=xZFm<Ds&OX3@*{mUrla`BUeW>2^Ljj{yhz~4;oqfQUC)@k=S
zt$Bb-v*2}<0iqAm_8{J1dKNS5I%@G@+JU)r9e<&b-ymQA%&o5zML4ZGY`BhD7RFQv
zp13YHi3p~*1}mz_bd*PIi>S_2?|JI=2O^%ynMU-XNMMR<L$psMGi8<}x+gwndVy!p
z-{K_G?!uI2v0P-@)r&l6SS~YF>rZJ`%hyb2MiK>D9x#2t?G>>2NV2^*k8MFq9;SMj
z$%*(5QXo?h=F)b2)uJF%Y0MBDN0zcoK3$0-EOAT=kTVhcj&@8P#}hTMq%d_w&oGlM
zgP1IQY__nBVLFdmwu{!5X-tDLL$-@{mbpx&+4c^W7nx#1sg`8RVx}vc*3q(*X#l5n
zvaDcAM+=FflVv4Sm_2oTwxyS4qof&9cW~->A$nWh<FrIjB4`WK@gYR1mXn<G4oKig
z`JB^IIj4(0=tw!!E!R2iE9A_!46%H}<m^Q>%yLK4eDUi{Ifq&P<g`f%A5X@SW4XsP
zlWCO2$4cWcE1lBDSn@E%EGHUg31-?iiD-%?jOnd3qFI(`rW?zM=31&V?VU-qz*3*7
z<=C2Y6LH5liRsP^q7{~8D-In|v{TKwv6eS1X`EI*fYP>TTHm(j-0fnU#m;HJKOX>p
zzHb@EG`(#=)&a{0meEWvfp%GTS|(U0V1@2Ga3xybWtnCr+h3YNbPz=LwQ9R^@^Q;y
z%WS5b=|rb2E0~TleQw#n)OjYQU9!B-6xo~TYs*2VUwRRJXF1JOWeCyFmMcs<h7tW?
z`HAW9V4}O0-<aYOi2kuye5fyJQ;B?xd`zRby?jOqrm5{Ht*{Zt)RbE<VpL;t4xqGf
zqZLyir$rmRnU-;1Y8fMa$h+1~D<{WV8XGe?tp)eGy{6mTvtGt4oYswdHptk<G<nv_
z$uY(-;{a0?rjf?SOaqu^8bA4vcRv6nih0IgoOX@V<{9^xl9(16hA;J{VaHu#V=XI;
z{7m}>60O$saW>IAnr?H>_cguTm(q4?`Zk&9fTl@IM>J*3rnHlqp6f((M$=Kw`L(9W
zNtEU%RbP&9dwDgz$n-1`>}#|nHakd^)YN?;Q5ji;(}sfzin6jK(+o~4FUv43d?7Zw
zsHi9_FqK>sn_W^w%1EZ2b7Ql^M3jtWDn2hZyBsKv>G)zrRhWKyk=l!rRhhQVCyJA`
znO2}&MNw7OV|tm>YRcwJ+c~YaY{hgM6eSwUj!Y9ct&!}^6vHhvmfe_2qV*urMD}2+
zg<i*rrm`2)5~NiZ&14^@s<5_}XfFFPy~}AWWeQU&%GDFCWGd5nZXrRYF?B*(kZ2=^
zGSz~Yf<#;CVETx2c95f(in2fZ$oWjKbI#}G5~dRis9dUC&Sd2})8s0qW~il+7%X37
zs>K!!k#8^^<61K0Iwr|243is}US=90-(_mUIY-LPOsSxzVw8NJDL<&Cm?V!f`EXyR
z%2P}&KrO`_d6DTjMx~`#B)?^<2hX<@tK>bVt2|;GWPl&dvx;19qb$Ib3Q7|1$|6k9
z!lEScp{&9*mfQPSCNgEh_Lky=Y{PT}Gpi-O;oh5RGHg#0m*ilkb*QDC_(o1*y3aXp
z$=OV$xP?1%B~u9Z`hncUQ~|wC68WvCn7-kj6}EoGbRM-NiE`He`O#Xi#TuL4QN&mS
z{mG&);Lon05T*~9s#uFMU1kqfvzB1;XTQ|8R$%&xJ>Sq;MG?}<iKf=tOuwR*o}!tx
z9@Aa)rLSmZZN?<g>qemFOwaOh+t!-Q)X#@{*4f&ZsRyTZwm#2Po~fHPjp;k~OD}7N
zKUuUL7A1<&)>WK#3BGP5oYps)4l+%&ZeseIX{z;orc`*o5v~~bG0j9;kXUFv!PFjU
zX=0J}6jKXWlq42gKV^D@bFQ#{$@D7v(n!2)y~>mYzYGztT5mFyWiP#M{hsMXo)c@V
zKQawMxgfFL`X|!>jBSv3+j^hr0v`{Xtn~8p0I?b6T8gdKyi7BAG<RAHGR;D{mf|C8
z38p_$uBF&#4Pz<`Uk?*Utx>iKsHN|~(c==uacdPD?QOnnGaC11&RMH6b@owIjp>9=
zt7W6}blkXo6B3cLHrKggQ0ei{SkGA-F@<(6jUCH*YfGCz3u_%j7p-l%mL(toYR@@W
zq1<Pn4xBS+MCQb@mP^(yOhZ5;#U*QZrnr%r6UT_l*4|8)hG$M3C$3uiGj%}P2+LLL
z0H##XB=MDX2-CZ$WvckvI*h3d(q@Vq){#utkTzF*V;#eusOXb9ae?^GI*!x2BW*Eg
zGShz0GVz0T8q+^$eT3yl>nx@tpa#|-tuHV|Bj?NFN9&7BlhEF4;%Dm;CV$Wy;uq_3
zre?^wUi@ZV#dHg`Y!r8_uQRPe+I!+p>zhooK-<J$*0<Paot)*I6D@yRH!;-%C5r!9
z-?Pb3+%LmjtVHpTb*oKcjPihX3&Upz6aE|ml+R};=j?^di6Y2nH>drMOYYsGfX^PL
z#$2wX&pxIlg(xlD=KvGl<OZtfbBO6{NmRw>2-7p%LOq{jOt`&|w8lQinLdRjyG0A1
zlT4rCqJFnX^7(}6EH3JIi}pUJnXcfr+iua>=Pc77`G`_{&M^hzUczo+_c_mmjR|O+
z&jqHo+_Tv}mzeNjH>ADnbD8M{?%774t4tTTXM26FG5yXxJL7Ym=^O6ZHJ_VIH@IiF
zeZFP7$345}^F33sXQ>67?=2=c2~^PcC#C`1moVSkO!&MT(klA?%7i~01I7CO&XkW^
zkN5q9DG#^a*7q)xg<Busdyk23sqPlTeD5;_vtLI0K47ZPeVOQso3R-8+T53Ez6KL+
zFr(Z&Un>(nu?1S{>&t|{-~hem>(5k{`?B6Q4^t`b%Vytam<n=V4*EXJRE+y_!Z(1a
zD);5AZxB;^?#l(=0!%%*FV}qwGQGil`PsKH6F$3y*8lJ=%Jc*G<$u1#nHqCntbQe#
z>T+N5`juvC#C-|$3uCIqeJSi0&Xml3Ddks=X*l<#yk7*<Snf-dUqz<F+?T3;m6!_P
zrsZx?%P)$_#xtaWUkp<Z?n_g@IHoS#mju76Ox?II?fj~%H110mzZy&#+?U>dwV38}
zUsC+)FfHZ2Wcbx%`jY!H%C7+v{*)Vi8SmGKDU$m#)vqzrSnkULzoty1xGyXHnlp{y
zzO40Y$&|@`dCxC_X+HO5r(Y7&yWE%ker=dGb6-C8YsYkt`*PZ^0~6jGiN0L$>&VoM
z`|_1vXC_*?c8gnnU740}Uw-xL&a{yG@|RyvMcfx3|K3a+xi5kKeVNX3UyAwnXFA7y
zDeIrYl%Lm^O8%)#c*Pmoi}O!o>cxGj<v*BdhZ-aQAxvAjFVFjDFm2(!4E7(!w2S-V
z@E^`}jQcX$e<agg?#l%KQB3%$3hmAEAH#$n<v@%5olIHWmskA9G2uB9q^<RzkcU=b
zwYF~ZpTueJVwp-5@A*$PrET_~ZlbOJvp8osue<O2&&?Am&V-({go<mSr!D0~VduD#
z<wUpMlS+n)<qeJ^^+??J{z`8<A>jERp`Y$g@+|A`jHJ|EGj_{R@oBj!B`u<GojH){
z^A<pE4P6d-w#w(YlXJQ;$rqj@xixGx;%7(EKIFL=lGCfw4rNCT(uYRgh8*1RF66u>
zCvj1zN_|tCQa`9n(p*;6Hmd@~s~SSftwa50X)E_OVvdJVo`Kchf!?LkR>-1dDCTBG
zl1(_(h$j7b80Ehi{Q;zEu_MRVW*N#dz2-+qZCI1?t9DmZC4Csz{A2wCNcC+&wp?RN
z3pGB9n40XRtJOb+hkMta0;y_NWw+L$zJ1t?yfLQ*$vfpqHsBhD$9{%XM-!^^Zc~y)
z<F6n_SyVpuYv`&!E2@4A{c81FkY^fFuMDpFQ}*7uMpS3X3M9=j9pWjbT0QD#0hW{M
zQcNP}|FZ$<TUh$lBt4-a$u)H;|5c938BeJVxrSmaPt~WGpIHBm>wJqXQu1M23fB4s
zH7B!F7B%Fyim^OZpW6M2_1_wi?pKp!LPL_OS3SbX_VltO8?m0i`upW5M)^&7NzH_{
zEhv6LEt2N3w(+R_&U0)ikMg^5kDkddSD~`M^1O=TwhC3Dky5ir&6D(+chJM`%_--X
z_5Oxl<2egHqnBEP>Qp1OK8AGjj8%PW+n7qti}68z)$^@k`JtcXR!%pjwqAISq>5L%
z=hz7qQJnu3KGt?|>t9C}Kn*H??Hc60$JP0K6-o`SR1*2?vn<;p6#A|T)M5hn;hjdL
zr&XhV4r3cL_;}gG<I=xr1>~R7I0|wl%W0KK|0j&>RU@*HXSJHoYF3QrW7fg(waZZb
zdwJb>gO7{4Jc~Z%<8F8}I)ZL+%n$7UuQ{jk_E?j*JJyUvyT_YUgH$8ptX&KG?HctV
z7soY*Tv4$Fq}f-O<C73GwPJh7XQDbomZ;VP@<v==NHrp#Hy8lDS}p3yPkh9!;StZ~
z`MD#4e0Y)NB9@2Brz8Jjo?{M{Y7N<3aTwxnHpzq>9!LIM66S>7E@C2NK*UtYffZ&!
zzEOT2WbF!zAotf?2Dvtx{CP8a74){e_I9aCF=|}Y982cD9cRy*S7|lt&hZSX*kCnk
z_`E5V&5EGoSFIj5c}%DC3UI6}jY#|FxQFcF^E@Bs@hHsWQBW<Otg{}qsj=9}b)KkA
zUTVhjctvVO`C)7ws?*#ngV!sy+N)#mD3AW68XHlkS|Qa@rgFZ>t4*<nn~|zYDNEJL
zKc8odY))r^(kwq}u>(2RHu(^;WXt`K|Fk#^S-0hJNHxk!>wF4*c&&4g_iB>7*6bpr
zI$zA@b=$mdsI|R)BzeZ!fX4kGuU8#;bzQ|C=-=Q9YEUyr^<S;n0qnO7p1oK3+;pFP
zZuZIJ&Zlau&8bRPXIrILYe=*7A3kfSS@9gs^%!ODRa_(L^C2F={5((cG@ua)s(%xu
zO7po`jiV}iz1l6rcjJCu;#KsWc&Z^R>KDZS()?)2P|>vF9q6jmpYi04d(}x+sb(!r
zbCvSvt!RUuQjy}zvQ%x|kE0m#D%ztC<!`}l)oeyH=Pa+xp{%R<pjPr9IQ~|{!l>E2
zqMFN2El;(rWuKR;Spqo^@u>CS<E~drO8q^QWFXtPfm2m&s#mgZm{<L(PSx&{+nUVh
ziw!K*K4dzdM{4qFe=|A)HLG~DMSs_;jQARnRUnJ8v{z>RIf|zme0}TY|J>*}A!Tet
z>5db6%q4j(fn?EEB$GMj_9W78^&|Q0Op-TdknEpIax}|)LrLF@Jk*mKknh#62iX<#
zCsdS(i7y>0Y)xbGtG!vMUT5e$7>K+7!6gF4_h}?2Cy{((49SzMuVZ<;55+t)o#dqc
z!6icVNYQE*%Htj?!kRWmi)y}Yu151golnQ}x#kBx`+ScxV5lfuo%X0|T-55V&T;B|
zr_P4f8Z@7&&QP(0_XZE=RH;-?sJP8}REv+xS)TJeu3c5zXFSSJ&a=f-&y!pHlyj<@
z$3S;k{p3=4OgSHC3(d699J?U3|Dm=l;$fdZ+M-aAz+<6S9@VSI)j)f1j608}UsW0%
zKi=ye!G0!Gx7e1~!$-H}8co`ngz_%M<&DSfQXbcnHr$8pJfpVqo+{&C_92d~R$i*h
zw$x`^RL`F@I<8}O+TcVSaoyXJd}|2FPQ6Knv9zR<{wK>VQ%RpOljP9>B)j$^IXHpj
zl6EA69Eq6c-k!nLA733j?p*UY|2%+yLiyTFjf>0A56`7gwmpn(zrp)b&m0I9OSzuM
zjpjPePkjjGJ^914QMDV&>p-YT#~L0gR<P%3y$uzzZad7?j?HK;&1yt@kI}q()96@4
zERU~wbUH@Ss<NpTNrP9Ie0*hbhhsir`Eq4Cmw7yi^VB2vz@g#=-u<eTN9{b1@p;fw
z*46(fje=`Fl$p^T<Jf*O$zd$>Od`GERFe5v-;~k4v@3sXpFW5$KZNA<!6bLGY@I-Q
zIhHMElHPX!$<tj)?qhj=SRZ#i{+TI=ugY?B2I=#skz7=g<hs%+?wkX<eozu;89T`-
z{Ya*>-gnpsC0)JpOBjSwk=Y~<_ak|lWlS>ZpY|T)uB}i?JYrUsAbIuqcz66;LoyI!
zNheu2mE@nS&zPCv&a<IJg;E_S1UpI2>`U^WlnSL>eRw@>6=G6*lU&e}<kja%?(Ii%
z1jqCXAiZo7$)rvs%W&!{E}L&C#q{PH>Lin%+Gmx!{%M0ppjY!=BKg)3l7FU?w6GkU
zK>CcCB!e6zj}9QYWh%+8y+|%;NAi4El9M?9Ud}(aG{xL4L2}wOlF=neHeeZ7iu5g<
z=OF8+S#D$5&rYdrSyt;$x*yk^$z^+Uo-3>m2&L50tj9uk`EA_bF^F&4pX5^CG4A+}
z?Bfx$Yx#J0%(TJTh>2@Ma{n@tLuZh*E$8^wB#$p4d5Gl=&J)9_)%sKXnUN&l&LFvo
z<*xbJSO+3_pQiSZuQ#LPRP6`mMAI>TDtZQvceUU4=%M1fI&%<jj-j0ib}W2fs4cJX
z-LXc6@?9fWyt*&Z7dv)yywY7)9Oip6AEG}G=fsM{`_G5(;h6K&-71ySRfCFoSW=%K
zl}Fj~aGzBT>iT*o_S>$qD*riN-#YMaJm;Ms@@)*q|H)S_TUdJEV=>njD%4#s>O;<W
zy33?u)Sk_iO8H%Sdd<ZcU)5W>W9HK7?BLQ>pR3}k*%d?i>F$AN>^iY0mAyI7N6Rq>
z$O9J9hwmZuYeD+{nrqzcn#(F*(S4yut@h@79cqv2@$<jQ?>QGd>MqKYd+RCtH0P1W
z+pg|?d0V0GB)I%s<GJ<lp1PZ4zSpGg6#ZL2cdSXZJzUEBZk6j!)RX;CrbZw42tIDt
z`lK3g&$;TRT<6B|Ce-K0*{klGJ*gGC^S0v=t++g_)-=~{f#xdh79KxpYQCjE#*<I(
z3EkTX6%`xoM=NRtxEcL1^yz$$&U`=T0pA;3Qjhv$zN?dkV=%YAEspyPrPP>q;eDX;
z-jn-wyT-qpY3iQjlj~ej@k#UTab;aAEv>h-o~ZLeDF13eqek)S_ku^&K-aCR26Zpj
z8c80D<=vwyYp(O*JnE>7<Gb9RGn7TdH>WdQQ2h&-^XlAJG;T5ctnS#j?$TVK7?cea
z-{M|(s2JAhI%3pWa(h+k$-u_62j0MY)LOWg5-M8o{+WJ@2^GIoq8L9@Onif%(4vZ0
z{Z}h9?dNmfd*4Ot>W<C-%?|w0bB}z!y26IMp(2Ct4O(N#7BvcV{tXrH)~2&eR(-Oa
z?i}V!T~vGh6H=AE<{Dn)(RsKYmD60CIj0kM&qDc*rR&&tqTNSZMCT@zpMD*1l~U)y
ztzpz#W!)mS%X|(}^XK6;R;}~uycQKELS5^;`7EGPAGabse5QC@E9U-~+w#P_TKB|+
z>N|uFuY?b`OEI|njk`$}afKz_U$Tf=g-KSildQ?I^Z?S=aQxp(NpBxOcbSGpl2_HQ
zjq1vjV$|=Cp<;HVPiU-|mXaT`MxUarmt{Q$a%RWc9MgbfTCv`d^**c*W<8VjNvzLd
z{Uz34XMH2<J6J!!`YF~guzrK}+pPc1x^EcuBoK01N^#c9vmVEKUDlhk-abqn1)-u@
z)1J`Zsg&>xer>GZFRZ%Amw!n>K|C3`I<TNv?fXBx+P-_&&kbJ@I&3#&){<k8+0H|d
zyHn1<8~1$oNsCzSpYV)D{3W9Tan0*D9rB3(SCH3it%E!@q=w}z`<Kk%7RQ7Y#M6nZ
zO>K=w&e!_jE~V(48iT(IGPiifSG7g0l=Y)lj`(LIzpI~{CHjWnoqa{O7x$6Q{rpQ#
zH`VXSv%W92@~AvBQ0K)N3n3>=yNx{8rjxwAgk)CMh0>l<FPU1Wo~u&VpQoDt>arL)
zf0{)4XX89&w`UzK>8auMN7nH8Ja4&MarJzwsfLdpO3g5(dV1(8`{>pmm3q`&^Vh}K
zq5q3sBD>}!6~MdSza6t2mY#fx?5a9|<hH_OZ-Hf`H?glVdF+g-Ki+k|!!;i+OtVha
z<LZgI)R$g8F_-!^&C{!G+$+@+RokQb^UV|b^X`!K==sBS{udknm;UHFy9^|c4RTQb
zquP<|5|AIh{V#1Tm`QD^dLFhSHtoNTmmk}rdfvS+^?w2%DUWN>r_X<FF)WoWVqZN?
zi~q%Y*$Gb?ssB>*wXUDhxUdzY*l+)Stbg%33NrKWK%Ecw%5?<Yf5JTfJgFpR+Gdtl
zThcgA?@e-iHpz$UeEK6=9n0qW$1V*;Tju(k*q0&3)lc)0?pd!|7N#TO-_&DX*}T^o
z)vKp(H)qZ#)okveI*RY|`oFj+^*^g4$-9L}?noy2Uwm6+DAf?vfuxOPfmZ*uPBEBj
zs4;#&=F*mII$B$F`WX8DiKI^~M)M&dmGX?@m46Jcv9?weU!u!r$g{lEe?7)m_ucf!
znf&-!=b0z!+;n0f?o_*HlzF`T%DB$aM~l6U-kQ(vt{E7JJgyx@F?arS9zj>0vbp3@
zyBF+Fe51?I?x-weSwHh$#k<d{Kk@9tS)<5@uC~nm|Gz9%fJf@-TS*^x0KHN@eEL#q
z7G+H#i*Ai3xrOB+wlQWh#k8D2^7?p^vFRiOdA>dPmwpg@-h7(ADO(<uv#NW<)m(Dr
zsbd=P#$I_w^3nH4XPW=#_zc)@K3U-^oA`vXh5OOb^xtcER9m#`Ry$H<i<)DuoaTA;
z^f|p};JZU;m;0zXy>mWI9<^pZP0kC0$Zt=R^HKXrv+dsfJj{F7r>Xh>H%~{e`Qxgm
zmua4)J|@pFQy%AI@?`(ZJhQ#($vICJnQHU))yDQuI|?ovAMN>+5>$`6rU;zNs{vm_
zz1D%wEKe@GD02>GU|RAF$kfE#`;%MFm)+iYRL->~Z#;Tm@wh9aoV$o8?Q@>42D2w0
z-IIs+{7>J9+{)(OdS-rsZ-G61i;v2mdn-?0f6kodn%{GexLPM4wLjct8h7ubK;?hb
zEcG6jq7J&I_&2-0L+-w*bA+pvPfY#s>{8T`duNz=O*ZdUJUv%ARh??py)%sZw$w>S
zkk42;24|#gH0{jI9(z(f`5n~yf2qgZlYiR>bL&r2wkTh5FHWO-4ldt5+ESJC$$6AN
zm2bNQkUyWCzcjCV`zK#VPPNwc?nj;<F^xuHEZ<`Z8UDC6%{;qkH><``of&U&%|E12
z+4W0FZpmziR#wlU6}3?IUFeBivrhn}F6Q<2P%lbt%h#mCc_(wE9redHl;m#i+lP}#
zzsfQ5ST<-&F^`(nxnJ2e;(i8<|0!2rshnT5ew;Tvb8-&X`DD+lJddx<+qYlwneN}%
z=&3=?hcTUKj(uI6MoO(1)<p7Kfi~=0eXsJ-=LK^O>RR|wWzF;Uf_vRiWzFrnJafae
zhIr>!t$1?M{o&hX3W^r;4rDYQ_{V(9I+};#YvnB{X3L1Yqz8r<6v38)NOfI9(cR<c
zJ10DYH+Z#bi+ja4ed&qNY(Ki+e#)QjtAA&szv%f()<H~Jzo@`ArSYdvVygd&cFk-S
z(OJ`#nXB?zL~lif6EZ{cS;Q!&L!#H%^Up|eP}7LSA~vhIuc=O-V8bfXLIwU!ML7%J
z<21}oG2uR9rkkpPmb!`3)++MfFupeE9d}v)YB}Pj2C(;Yo%3GLc$=TNVk+k+zH#Sl
z8tx~2!(4qy2*)3e;h*H5jke)W#h4CR=C>&rh(8X~G`mgvaC#pd<ve7W)TUoJy%moB
z9m;1dF$g|a{|@Clza-r7(Dfx1P4tmtcsTu~DE&K>@2q2tiCWK_7+zQ<AuR<|#7zrB
zXNTjZb^Py8zKx(_F7gqDmV_5~Q@x<K!s&H*^zV>QX5hB)QldCpd(%e!3={Yd|8Cl-
zpJ5_a6ZJDp)YnA)EF+p}qJEYUZ8T9o!^Lz>)X%bFk0$D8IdNAL^|QQ)DMRhixbF*(
z5WAReTBz5NA|RamVxeAF7Tq;bucO3vrupJZ|Bu6CMai<1bF%;5=Yx$nQBKpo{zOrl
zR*sCf#fchBFN!3~C*g5oA(7=pG1zh;yo&f%(;>@G;nhW*a#ZJH5gGDNculcE(}AE0
zW$TMd<yG3dK?!A>i3yta1a~jnQXJQ`J77@R1W`Cb<qRw|rfibvugMxTrfeJWj;3aT
z*=5^_gbFI>9|3#I_P}4~DC!<~v1~ulyP~46gG0*M#eGd@gIkvyBX&iqv^516mYXS3
zD=B(6`15j0L`-ExNx_xMzbS5OIv0>tew$brrP7KA?JU1f#6&BqR^YetC-9djij3e=
z_}*(mjH1}!E)iG7RX0t-Hycv%`bqLqf#B5<zlq~++JTp!M#QPK=%B+9_r<rGR)^e)
z$ZN^2qSDR=m8wwCl3G>Kk$_nhDp<bN)F*gH1-#=1FO#Ge+J_veP|dPd(<_13E7Y>w
z*OVUkdxg4|&ec`U-Ua+BHnbekG#u2-QsX(5HmyKd#YD>rO)CmitJu!+ObwORqCjHB
z&X(hv-VR(*aeyVJrb??FvcBSA%WO?cf_7BQuw2#je9)1K4ol}+D(Cp1A1jWroY&MU
zC^&MGr7Hc=HEh^AKRj}(WuB&&K{GA4H2D>%8ada}ypGEGYjAwz0?P_brvqC@F0$O!
zG$*)q<WftAx+-Uh!0wSNE!#E42HGQEvlOVO(lSTKN4{Z6)igaMK60I9m!>5l6C*cR
z0_v-r=^^tYH(B~?`ljG(k?&cyYdQtmYI&xC%DJV$yOG;1shav0Y#q7FazIn_g7J}i
zEQK4YoP7)Ji`;LSr^ym>G4hz@cTMLD{2Y18(x#Eh`BQ=Wk!LI$G?ggyY^BdFRpV7!
zi9)q1UA1h{G$OcHrEe`&8>_St!BZ;TwrtQeC17=>KP|<ZsI(~oyDI(9GC@<30;?ko
z<GP!6MEDsEnyQ@Ff=*P*Ypm9kC-7RO07EoWX@?8^T&aLDLDT4hg)0{~&T5(w7*V;D
z5z}1doL#VX<ub;0P2~$FSFU6<Xra>XgxD)rGq!8WQ*aVq`yA6!rJWC1R5{+5t?9#%
zS1UI&u4-Byu%U8Gqj4*hGp^9i%B_v#nr;MqS-G=;KOV-v#iB)_u2I8{?rutt8f9$P
zlvU`}%1+~%B$YF(P<qsOBURJ&z|m2Yjc+ym8n`NImXX?8<@`19!>AXHtC|Xgd>*yT
zXxv7n6$tr0YL&5yX{!IxK1FP=8*gel)5k}=ZoH%E+O#6J)y8&BKTh)ztBt*yHm4S`
ztuc;j+LP)d))=QXRSmcw^@edlQ@sEq`VHf2O_uh-#+$|unx1VRAl@{7*K{vA*jQ`a
z*W}j`?-((B+ETC8?qZ#hPm%wjzVWtoMhMeH{}X*f#5$v-PWxa`u(94Kr_+uN3J~j!
zXq`4O(H8xd@tjV3A<=4k%V?m}N>2=m-e5G>Y0(p(wQVrk>a;2|n%Ukqy6CieGZx5=
zMqibN^IzfUO~yi%#=D>`#tL1IytKtwt%<y}#dup&Ye%rL)!3@3ha*62H9pjIylt?t
z%{Zj#T-yM#%{ZlrJovuxxhC@9`^HsG<iYL6x0=X<+l^l|(WvY&{?bIFvcoXidA$39
zkw=mLn3loD2S$*lsV(tFF{79!^6pNfj3)B#PNR|{wtbgDHPS99BRM>Jmr<3|7K<`7
zDuQZjYCky|)JW5?$yGruH07C86Vygi!>RQ^JvHT<)FgVhF<8^4jD+Y9jfqUz-1;74
z2Gb#bYJHC}n@P={J;uTuX-jgXt;&)1T8^~!Inv(Fk+wBQ+IE%ZU$=M%w7y4E^Wt4V
zM@{AS8lRfV?KRGt%I!5Snab@oZkWpLGk!Fc+h^Q1mD^|BHI>_ESlZJ#O!ePf?2A16
z4V$J9i}i}$Zv-h~pB*rY=Aba0^H8B+<A707(}_X>;($>_Q?-=-(Fcv%n(C#bff{SN
znlv=}kddJ2R#GMiFMj5}gcS}p4jVl*RWBSM4jU<&_9O%wM~oqwK1~P^M~qBOWAg_a
zM~(5Crsl`{UybRScH?+BX3W!c3dh4SW2vSd%i?Vx8><zuwI3TBblRapU*!4N*rMq~
zp<dA+8@n}SB#(<eZXDD!IyoD3QWG7UCyaBN=-51AT+vjcL$Gnu_(oHc4gq){=WR_v
zNIPZR)l?d3rwmI6<(Hpf!zYGK(|y?Ri4mkp4w)7GsZmr@z9I8LWi%~H4>nF4k(ySf
z2Z+-~RZVoHo-yiZq9gT;(L~b^eS?j&Mxv&BeFMZ<qoby~7{AYqo|^Ks&IAq6RCnA<
z(dP`iro?eCgGOm8v($#af}WtM+ET0Sb7O|4>9Fs-F<;Yi*mvGorpYpGP4pMWYnlp9
z+X#9~Q~TnB!Y>$`HT5r^obQ6MOH=a!+oLZU2Q(!Q*bO?Nh)4R8@tIDWHf&J%CF4s?
zG)9+<8}6LFqAwZu-4r0cG<qk~sHkh-%SJZSO-rw_`=hTIb95SA312l<Xre3OtHu{h
zi~Sc*KN@}2_{vSEqQ5e}bJJ(h*Ni{hbTRsC!@nc7x7dHc@@vu8jboY|pqs`yrm6lk
zV&58<HPMKDYkZ^W?#TYp-x)t^vSy}%?rN$wKi>AeAv#eDQ~eX>hluYDo2JS*I({$$
zHPv1gB7QK6YFb+|-ge6f)3mK*h`41$YFg1Q*!a<?qG?UL0P&+yN7MPv-$(ysG}iP@
z=R2T8P1~m1qJK7$H65C6wf$`L)Kp-OE&8^RqA6yM)ppykE8@}o#mLlYg{D|yelf;t
zDm}#)G+h&|OTQZPG|{^BtFctm?9R``{ARqWX;J4O(0Ui)IQrdqPZJ$SzZ*L>{m{2a
z%pGIDrh9!$f{rWVp8a8*)oINKgvb10T+)<0pd#porld*1#-GNInz~NHo2ZRH6mczg
z-RI3c*|9NqjR!jC6i5H)yM}LPHGXp)X`uW}>Wuf7QHbe~Kb8B-D5`T_#VYle5vJ)j
zR;j;?NKFf~<8AkhDw<YjhlqPd9Yx%izm3LBs?NWSW;*As(($&xjn<m(mktqs8=W;#
zd-sjrny9_|#z0Nf-v5lDny9`18Dlhcni6k&U`*09a7u`HV9e5VVDy^ke~cG3ogcjs
zv_jKtqzSoN(<?|5@@-AaM#bAKa;v6yMuiBA{7{pa9B(t^Ax(uRhX_NS(o}pxyiLl_
zHC34qBBZ>kY5Vwin^k_R>E!qje1qf{P5IK}Z9eiZO{LO9gpV}3s1f@Cb^6LYnjWA|
zUm2|F{HQh2ezKS==ctXKa78>0{<0F28V7$Fog?kJ9BH*wn*Vuwyv<)W(saWfBK&15
zP1H}DY_Ey>X_MVGQ9tv@{+g(td1Sh#^YCY0Il`3_{>&@KX{w8~XXI2(ZISkjd_hxQ
zq~()KG=(EApL|8r$g%OZXXRQ=)5eB~XXU$^nv9OO<(E4&^&A}{^2>diE@3VO$YYxR
zz+4KDXEc@X@_lrmyr`*Kmph>AirAk)@)ncwXOR3kN7|n`((b7=f2uP`8eP?BQk_9E
zuOhB9SO)7fT9<-lF-^2C1<P<(Ijl<sWF<|sE)|f~G|{>gBI|0RbtyzP)fABNTueck
zq$w<;4yco+;Yceaduf`Av_djf6RlZ=WrikNvkJ@6in!NB<V2lT7jv+PoS~^L=3o)I
zKvQ0%6_v|0g(Iz~d|lJXv1_7>$+t938@my-MN<<To5kfWO+9gJ7MBM#UCLe)T|%DF
z^hfqa&^b-{($_?nl$SM?O5X_jM$-=y)<l<*KWln0VI%0SCOVEvOVLdqap-4hX;Z}3
zhRQ&l_I{CIBUBdAbhrrKW-r4u{a7^E2$K~x$zlN_Ojgmf9OcT$+M3p(Tp8I|(?OIA
zmkFA_C>kKbWwIh}y{zn^)4nerY?PJHYx=Wz0NztPL=&C4%E^(MXoi%N<2BKFrM#S`
ziOwtK<vdNaqmGa-X`&r<gnU&~i)Fz^1-VXB`(**5f_zUA_pGA)K&S1^2sSFpeVRVa
zz`Jkd$C_y08Y$0cqJ3+myrhZtt(D|;O|);VB!ASjx!9oa%JPn;4~r$|t1KUAD&H#D
zh?2hD)riHm3c&l6^DE+>Max1uZ65YL(Xy1LW!U#b%Lq-c^ekeFkujRq_w*4lvZf-g
zB~~`nX>{!yD_dxyYu{MeP7_^E$H}gm=z2O%_R~by(^cdkO>{k7MGjZQEmW0Goi-gi
zz^XD^)1p53)`Og5%2`b=(rG;~m#WE?no=>Bs>wGry^gt5U2fF01#_vod|%VNUbg7x
z<VTuT_OjZZlSi1;PN0VTM5oaeN)35l6J4RykY8z{tAd*HJ56*|P*eV@Ne*cgQ%l~{
zly682knEwxDBnxLMs1l_Q^A+;HcDAQ)1fhKW9rD_nm!-XIi{|}jd1*%>i_G2-ZAxL
zWlbXWc~Esl?7{l7o=!_lD`KlJn`-Ks<|FFM)|zM+)IfI9M7y8{vbUzror>5R%2Z8#
zJNe+P+Cvp_3ytJxokmx*jpRg4bVb`p&eTN5U%XtPiH^T`xm*(+M~&s{n&>!cEH`Ls
zI4#&{BDZKtm==IHb??@6rQ@KOrt+YsTOCJ&PHLiKx|uwuiH_-J@`@tvb#qDnqx&eI
zB~Oa+P)71}6E(>{*F+c6f(=j3ib)GiX>&0~p0ug~b7NY_Z`|#z0{!A9ypdntW1?%B
zH)9f{Ur+7}?y5|WNs@V(ZrZ4pBpJjs)xTcAMo=+LNBh{KTgx(<&h)X`TFXkBu1&K=
zw~<vf{W#5PYa{Dw+MH^OZY!H;+LLOvwUtSV;Fpat?PMn=y2CR)roHUSr25ic_SNO6
zFYV<ZO;l$G>Ci-Vc92d@bQdmJ;sw_DH`Sl+!X?W&n&>#{C>Lp><EW#2SrPZ8lY5M)
zFP-I^oOaVjqtaP!(nO=uS-!7pxtSIqI?FvejYg%5JfhQH!;#ZfeyY=G{JP06bXtw1
zt)Od6bi8kj=`Md^Ql97`f7j(`RC>t!x*U0;hxF;C?E7Wln&_S~pQgaHji7>xxXxa(
z6qBm6w+u6-^_G#Qv_3M5NqN4HtgdqMEa)TaYob}uS2oi`v!JhRqlsogKiOFm&4PZi
zk0u(i{&Juu8nOOzn5Hj!uZezMj?wgU?~S0zir9t}Ia{YKN!lATKrYm@Cg~t(rKa6T
zOO<Oh9Yb2G+^DJb(ql0L<u*+zOV5Bl(zLIoEjmpe*7Rvht1V4_qA8=dEqahVugTfl
zY8xcK(v+BOiykb$)0C2JwGEcPDnhUEPCR)}r}e>apXt)*twwBM{{W=r)l>?r@DLfS
zDH^Nr5LsMPJDjKOGF($%oTu%wvZm6L)<kE>YMP##v=LNK5w|{6Hq~j}XMY(pR3>Rk
znf*1WlcvSfzK<Cudue)g+E1WVO&gHrkQtiZN18*9*7Q=F?_-9`iJD$(^Al*MB5r+z
zT%gl@3m37Ckjpd`DC{Ff$k#Q|3O!Q3rHNMPk#dWsgV=j#%3YeyV(*<P4{D<Q^(c8l
z6YZ}@$#a@$&pujS)<k>u(efKjZP4Br`Lm|pXm5<Xt7#?5Wl7OT;NMjLcTp}&+B9`7
zY>RfvKuwOqR-026)ij|~urXGKX<F1NK#Y}<n!>RU87He~s)c>XI9W&2%ebpDUN+XW
z5qDL_%S271S`Uq$Ad@xC!u`4lvZtnlbNWY5lqs4%nUe;xYZ{6xn@KWL(>Pq&Op+5c
z%`X;gOqSC%y@tK;WI12cdf1RHmumVDHe}1!G|?_$id?UWb_rADW=%0z>!!+`n(AV$
zn<fuvI-B)d%yjvqCfYO1kaslEo?(W3plMC|{g|23x3BX2rgTf}OqpL(@tJ<Hvt%Jn
z6=voGmDWUi8$3y)iS{;gWUQuFhZTsOD{E?cdstCWBTY;2XQeO5mad$G%Y)i$dZTMp
z>^#{`)0VDPLH#uaCDe|cF9&NXp3o39LKB@47Ra%h=!~#HPSr$bgcs#pO>{<BD3@ru
z0WU3*do;D1+B|l#Jff-R)FjZSn(nnqj$I<Z(1eQ<&^1j}+x3loNq(=XZo5>_Z<_A*
zv&Sx#e`~Vz9|5xVQ@*a&%Ne^&KBK9AuSuW~O|+X?E=y>l-OO@XPSYRbXT+|MQJQS{
z)X7TuoTj9V{?RYX`kMM=q=A}idIonHSIIV-O5iTzD%nNTD2(PSvX7=27|mB@nx-~G
z)<nN1hiU3RWFsg`Q&8zO(XY$Nn#z~n2%4>_4$@Z3g_=4dZM9sf>3XR((QD)yO@Ei#
z2->KrZONBn-;mohIZD0^`bg7G+`oQP9@caU_pjfSpJ>WP+FE&D(=w#3m0xM1d+_Vz
zcbe!P{5tuoreO(dV%N)in#Lu(1(N;M7`-|3z1X*8UQN4aZU+_6<m?)3Y>>q@&F>l@
zHpsG?f&xB@eOp%6R3_jcsJf;GrH;pLl=U=qFLfH!Ow)mqU&L;btu<XNc?Hy2Q&Q;7
z*mq=aP5nY|fd*=_mHaLCT{%=!@sfXm#%RKyO~<__Cuyo2>I<5s$*|{(+bmzy6l4zu
zt<aQ_Tr_TrtUiD}!RM7NvL4e!vBXjuX`M9v3fd|&R8Ie`9g5ht$x)h4b?_0}<U~bW
z%lmSMPRmFxANRhTuW590WzaHB4e?v~cKMp71pHRMUB0EMZ{H%e9dfg#^u9h~huo!!
z&h{V31Dfb;|A9QAiLQEf%Fi^>RnJa&SyQ7n!Nx9mQ&a0U0b-Z@SrPYoxBOG59V%4B
zwp;$A=|mwPv0M75dc5?Z3{b?+0(>Y7YoccXK9r%F=vjb|WCcz1EWk%HP7$}aN7mA5
zbd|M7Hqt~_S$kwFO*e7%xmUK=^c$`|_sZ^?u8*r4w@>!hbbDNFP`ajDvy0gF%MqHy
z93Qb?j?*-;RJ`qgoT_PVsSt5MzMzTjJRg)xG|`>sgYp$k8MwATB-d&hog5$z$#*rq
zFfrbCSnklYZeoZyEca>ZQp^^8L>|+WUd(DcBF|{5l#1Ue<wZ?(Qt>;bysoJwR`z4^
zmZrW~**}(dH2r{G<#G8xP4}>?JT83)s$<%M^YjV%tfu@pPoI#56tU+|%2GP52+rCk
zWqD2EIBTDjF`8&Dosu;)(Ofzu8)_Py86WqFY@umPW^+(GMcl%tva3#`^ZTc=uO>Rb
ze<}xQI*BnlEghP^ob4k{OQ#~P<&4bMY1Pn|Gjg`3M(E2Kxk%H%Vf~}e%9WZ%3`+yO
zp=n7%5!+{Sqo&mfKH@X^z9Menocu_q(Ykw19@a$b?m791rtZV>JEc6YY0z-|PAR`q
z#I>B4-{~}ZHs-wiMH3wl=jA<3bUb_^jWjhzbUb_^^J>b#?<5ywu%_|&o#cWnu8HnM
zU6kRP=uXr{Sy>a^>$)VXX`*{wmt;Ln8Q2|uDVu6?Vt4qZY^`ZPT2kC)*-6u=v<{%&
zirAtnGF7M1FLzgDh9>&u?us0(iLQ39%88ojYWJ#~sfjH5N-oeu7JVg`Yx>F=Z@VU6
z*Yuk+L|l^_G_9S`E$(Z%Mbp*^eL=f5kteRpgPO<_*X2n?-0K_ioKB+^?1sFoiB_;1
z@*7R`w82gJvnG1l;HJE*sW^7V-$*e?jZqZNqTfiHrrAq|Mt>^<HLX~Z2`Z{-1Jb^e
zVVd?K?K>H%DScU5-1oAIrm@R1Ky@^AoRJy#gKX@|Ib$p+QPXeBvg2;aWKBNHXMlQY
zI<@SDxF2PTrmvPQ0@*bcTD~IgCz+|K((>0p6Ex9o?Pob%6YbW1mh(038M!X*wp^;|
z)W}Vs*EGG9u`TWwxn9${j9sA3ntru!i~CjX)MP>0ukwJVk@NS*{U(oVnmPXn=)9(k
z<dbo~%U1?_&MUvmwTk@dNsHg*JDTW8i{IrAO&90H+wRD{nr_Sq5qIP<O?2n^4|!S>
z-Ff~)UerW)p8u3zYoa^Pf67~$=okLG@^?-26vqE!@7?2ctiJ#M>%Q-OxaP!+L(Zp+
zO37em7z{b)G~PKDCFgULW6n7XA<8i-%2Z0C5JiZ@J93CON=Yb+qNbu$-}PF1&D^)}
ze%I&s`+Pp%KfWH1z4rCoYwf+)UVC4M`?{{VTnzmtbZ1j~E*jE(-I}<kDLoer<r11a
zIy32)&<#RQjb1@iLMV=&Y5W?xS*R>M)A%)1LFh8+E{D>DZWz9Ts2<aH4{rwi7JA<4
zcwPS%dPOW((h>8w&>KRV=!p4SXoFC#(FZF19@-|<Wb{#@-Hy1GKSBpZhimUYLZ1rZ
z+WU{tmqOU5J@u^+_GwT3EQBZ00riIvo=69j|DcP@Ai577RPjO+={|H&6%uNFcivz~
zl@jVn_ryc$R-prQ?pLak(3f=XSE{<uEIQw6RaYpJ&bL}+IN}i*)k<_Y?i$rj2*+Kc
zx(VU9YgJz%9CxjHSO{<1`RWlNylv;JM}=^-iBgXX;b;@3<_qBoK(tycgeL&eYMBt8
z0K}+OLU;lYquz4FUdO6;MTe`rSoKdKT;;{8kA&b^occrvp2ew?Lhvk?Ix7Uva;YC3
zaSyrGZ=yphxs`s%%@JD3t#S#$&v<o%5d4f+B^+@p3F>Cip_K%cB!pHHR9b{(9#u<p
zhsI2)m`61fI!?db@~Y+$mibg$(LL5Swn9GDNoYaaJVd=CEc2@YSI}^=#MN(pHBJau
zzxmZ;LhrY&8+C(vQt0!x`Mn#|BB61#=M+#&g%;4BQ$W2cw4c^>LA55k9<A$w>K&n`
zbS+g#Z5Qf5*HVSl9--In${Q@K4he0$E8Z)tKF`)Y5|va$ofdlc5fxQLT@Y$L;#8%g
z>XJ~;5od{l{ldq=Vk+8^|G|Q~QN>i8Q27CQgT+*Sp@swEy<)0(M7`n)ZQuz2uDZg6
ztFGeeCbs-C3c3=iybyFHR269x(JiU&5JGfIs)j-(yJb}>rJ4!d-t8BnwnC57+jcjq
zPC|?6ZMz#)Z=vb*ETps=AoM&v3n{He2%VTnPeRprAvKAfgsN#m_p}Hkmr=8YdbTh`
zPYdN;kePIo$`mTHU<J_%N3?e1y_?lWrn#QqEjIZUwM(d4w|vRBs!xR8%UwLVoccP4
zVv}!ImxR_o6`P!-{QijNT(8We*yM_;fY6;S$|NVNB%y^<i_?FQP}dPX{V1MXSv6yt
z<d14nKDml&<A^+~Nz^-s>L;hEkvY^nxvF}MX|A_?Vdvy(YMvu%vnSEg9O|E(uGZ$z
z(BztGYYsh{Tubd`ax+~=9Tvh&*HOorCi&whWhT{CXN2ZXT0!)EHucC%s;7Ptdb!66
zqR;?DVv---byjkH6(dx*>pY@-jyUpns?tnzy=9Z0Np7I32))zendFA5iO{WGGm{&s
zZjNYt&m=cdeVHcFQ<}WN3^iB?zp^saBaZmE)KpCpUG*lfCO1_xgc>&4NHkCA^(L7~
z|4`2gz1L&~(Mv+DNq3i8Db$N}cd7M4*vFcwEkfAGnyG&}qTWADZmy2z(81&u>f0RJ
znA}qR#)M~J$CF#Bf&+Q=5S>nLt&(!+eDd9@Bhw`R*(P<P?onMF@%Zjh4~VW~hs>mV
z)IgyJJFFlY$>g5jwowyAhqHDYHBAU-?KWzT5T4(*RZk1y`E6VEg3!%_@&@l!D}<^I
ziudkSZwk#^kT-ar+9dS!f_U#fwL_@$^g>bX)Lx+h^y|8vI^u}OaKAb(x(B-C4c@QL
z2o3HM@7=Gy7s{Yk+N)oL?x9xNtI!}`XVl82<PNHkBl_#ZyunVYln{P}bW*np;Z20j
zs*(`iMCh!l3r(lqyQsQC^QiYODnqFIxIdD+s#Ze7$LW-=>V8K&hHk2x=x~3co9Zis
z`xD*N!$Nq*&|N(ugl7!h)uTd)U=Q`U5F*$^%@;xhd#c4kh+t2(OlW(tyun^-mC%u5
z@m??WmQWJ?V(+cq6{<(S*n6uFgogEvO?g0lB=l(CJVZww@re4UlcJ0MN8Vr`byleG
zKjOVU>PMl9bl0!1`c0_%z4SCmnZa)FZ@rM7CaGLP?HAJ1Bvn9YxTdE`s)W!~9q&D)
zZV~EW^9K8=3PKOrc(0$TDs-N%uluXoLch`Vb$`{^5qmvAwGbWVVSu_<2=g#NbrHfI
zFi<@pggs!O8YJ}0kU}Yg)JUOaLrM}&bi_RjR?|g?9tNv9Lg-<zdPaz!CaD*M_-T@Q
zExR7g*bw!m5N2$M+AM^1HdO5p!a5tO_BrC-hp8i?!+IH}jtgPE3{zhVVZ97j-wR>A
z3|GGjVZDq{p@-cZq4yCgRtW25q{=6R^)gZw6T*5KrOF6ly^K=jh4vMsXG^NG(7A&2
zY)RcA6h-f&j8+YV%Fz2Lqg69UJfbnGjp*<e{utF!2yfw!QN4s7rr$YZRezz;^#03O
zHC*U6dPicM8Yfhl-jNum9&^M!j8{*Ju3zh0QpT%=Lc>~@Ct50m-Y2N#Lg;;hS|fy`
z#zeJI2uF>HYP%4Q8k5wALO5znQip^t4^K^bR2>sCBhphQtJ6ZaHm#R3MV%K)YucFT
zl2A11rmDaYH%Em@H&sOo^=Vcw<uR2<Xh^fhL`8)5_ef2drb-Ka(IY)&x+*7x2+mL`
zLWtlDRl^ZS>2XzGbfa=NPkCH56`GX04bj~~w~p$VGE=n|sy3=SQ4dGl$}IJe=-#2{
zqO;Txp%3V}=qxoR!tx0<nd!?Yyn*tBnkkmYruI#FLd_S#Z>J~JVj=u?dO|H@!dY!#
z%9CoH(-A#eVYWKRgmdcfl)37J)WenPeDzI)<$QH9hvL1bRGFdKXOi(L3)NjrXk}{3
zB6WWbJ(=>f>cce4`=a$zDbK3uuAYy#u9m1dOyhjKb+tr2!{q#YPQB<X+0W<IsvL^<
zGSwC)^!I$q3u;deEl+tV=PJEl>o-zfR!3buy5~WEg{Dpl;km`j>Z}l+TfD4(WJ2%p
z-YZHC!}zw-_0k(D%avuC<l{KFT;&#e=CSppD<FiJEmtLkaCN_2-6HgI*UY3>RRy87
zT~`oQ6~Yz73RPPOR}d>yW1;@k=4+~j&?D65YwA9sPw8Ebm8y%-S$daarRpPek>2H4
zr3MN4^k)AmHOdi<Z&S+aYBrM_-x@VfEW6WReXmi^3SqQs)QduRO1eh9CWNP?Yt%X+
zyc@GtZ5G12F>BRMAv{5SQ|%MN6V!F;V<EilvtE56gtvXxt8au-dqpL^rG5}<&`U+V
zr7jC0@^33O-0kmcAKO6`=ZMC)CuM_5WOC!%q;3|=A86HWQb|G>(I!<z2qW60Y6)RP
zn^i+0jA*lJE`$+nQEi1VqIXqiA$Yb`^%jC>Th%~E<XK^Fn;OsLJln1&i6vsaUCj_`
zLic;NtGPm0A=}k6LRcZ&)r&$H!~5zrA&lXDwN40Q*r7HHVGKLeP9cn8r`jikG5k|~
zEQB$9puP~o7(P(nIO6E;Qo#}7y<?BkO!TXVqPs`M31LKgRDK~`Gwe~th2ZsGb(0Xh
z-m8*?;PpOLMF?K+Q?-QP^?ub*2ws1rnhU|}1FEeMygr~hJL1S6R0Eh?<PWLAVp*HK
zKBPto!Rte6q7b}3q-F@g>%(fU5WGIDo)v=EN7Rc#@cM{aDFm-SR_lb|^(ShJ5WGIB
zb_&7kqiVk+_WD!x1(Wmom^vkvxoL$QQ|E->^)dCM5WGI7eiy>(`COTiZePLb`CR1|
z!ibKm0zw$kaaB?XBl<$!B7_lrp(+YtL?={LA&lsxsw0FEol=d3Frrhcr6V5Em#Pbs
z8_{XiLo9Ej5uH{K389tKYKRb8IjzPx;#SV6nM|&gZ`6}wd5C878?{gfars6)FN8>Z
zqh1k0B)(Crg%F9eYJ(6WaaO%2gh-rIyM+*mbLwA0h{U(*Ga*FcJN1<i#&BMJCxkJa
zR~H@881|%GP`O8i_oFP8muZqekj9XuiU?s0S*o-U{LE6f3Bk`Sl_CT`e^lv0@bgDi
zUkHBwq?!uB&!5%ZLh$pVYA*yoFRC7n*w0Jq5hfSw%WAAx;wta5nj(a!1DDk-Av_Pb
ztQH92dBA1$oDkkN|4qFtgtyIqQ*Q{tm*3UfLh$8xwM__T!#~t6A)F2WPzQx@HuUtT
zLO2_G`b#044FmdHA)E~Z`e#QxzM#(aNO&H!PGE9pd#wuzA@W+65<=v)zEud3H@cD#
zB5!ncAw=Hlx<ZJ&uQP-)>3kcdTM6Nwag=T+gw-0Qy9!~oM(Msnh*Gp3EQBaU>qmqT
zr5HU)2vLgBj|(A6v3i~mq7<ta3n5Bz`XwPmDNe6)L{a)zN-n*Z$*qDs`hZyC?|t*=
zqdAs^y*&DCgk?Vcoml=vp5@a&3;jyI<kNo$`JGi%KJAZoN2!97qmuILc%hrfm;Abr
z&;@!I^#)x^C_vW*H|Sf1F!lnvl2AT+GEzWS6S|4+!57eVg|Jr@)J=qN#4M;=31OxS
z>2^Yx=|Z}j5N5it?kj|uF03CG!b}&@j|gF=i|R*(Fk{8^<3gCRVtT$KUW3K;YfLV>
zrSuzOiT7el>9>XO25l+5RS07!r9TkDTe79}M?!c@wv;|9gtugG)F*}Tmh6rCoDklU
zEv<hP!dtSX_3uJ>OEyuPF>Y4ymTaQVErb!3(FKGsqB6Rq5a!_~eTxv*;7z)s5Z2($
zx~dS?;LW;@5Z2%=y0H+};4QkPBZ|^zDP{ElCKsjK^<c4VLjB#YM+v<`J>0G*3cW==
z+^%N`y-Pim*K>vT(_7Bv^|L~c)5=QHFA6Q`siKnfN+FD>f?g+tl~qA+7Q)J^pmz#k
zRx0X!LReW9^~XY3S(Wq`LReXq^fy9SS;_hbA*`%qeOU-AD@CiZ9{ro-V`Zi4I3dhK
zWu0FL^H5nAcf>2JicV#6F{`H2#BwAZd#dSLLKt5)-B2jGb5v3_-At$s?VHtfTcK98
zZ&ufxgu2okRoA_RF!ppkKnP<`*CT{*JgcF{3*mTHLr)VzTx#msLWoOE{j?C`a)-_o
zLR{|9D})f2T6(Pz;!<005<*<+==X&XmpXc{BaTa5{S}jo%bofgvBVyCr~W|*d)%G+
zS0Rk=P8}NO<^fM^@6<6uct(4t&MSmxw0G*FLU=~oKqm^}8Epf7yAYnyHq@y?ct+b$
z*A&7t+D7_LAv~jPr0){K_!{ebgfPCwx}y+gvx)90g#E9H?k|M>FGCL#!v2?`#|dHo
zYpSOTVgGBYpL9eq`!eMp`Yk3GvzB_JST>`z)Kb4Eg!5QSy;}&rwA2TM5W$xEQz6WI
zOMOZR^WIW_D};G(rGFB_ytmSS2w~n^Ydb!?KisY3h4Rzdy+;=mN~E=Wk1i#YLTk5;
zE-O@z#@9wy5=x-on{9P9q0)3-X{+lBVeQ_ln+Rd;-m6;)VIJ<&?SwE7_vvnqC}!WK
zwA0TqxtMj*FNo!Aic2TGLI`o`q}K`|E}ir{LWoNz{k{<5(pm2jLR>oQ!$OEl7yY>q
z_J^+ejL>d6x^>eRgpSeCt(*Qu2xI83gA-glF^29s#u4u;J@k#D!!=)TeX9_z`FiV0
zLb&GZt*Z&)ny<I6D}-yl2Xqr5T=PAkTREa03VVHYZ_yzV59$Fzi2Q?kcs9|!*a!7E
zp(EWvQ-yGU_d)%H&@)4RNqJB|B{Yt{X7HeXPUuPcn!$tm6``-j*RAxBenaSw@ePSK
z2%VsNoc;7Rp)9(`*-!5l!tt}eJ}5M$8{Jvgp9#H8e;YVJe<`$%{x)!c{!R$T)`9wG
zAskx=YHy;8JdUk{v@e8X>mZ#Vw4z03(qLUk=<OCOh;9@*-Y+xhVSTI6kNs8<B?~R-
zl9@C_R~LG%%L<};LNn;^P=@LZp%>`yP=@N(LQDH+CJoc~3%%Qa1yOgQj-(r|9~2r&
zy5V|=(2dPAlSb&#LKT~@Ae!umW?H9?)Eh;IBkef-o)C_-<MeJJT+NKr2ZeAoGfsad
zgsYkH`b!~P&5YOI3E^sHg8o?uS2Gi|H_1g8S2Gi}FNCX^i8{d%d3HnUBz-fJd;T|B
zCyC`9^h|%Ut|El}Z?e8a2>ah;-B1XBQ!zz16T;tAOx10Lo|>PT^qB4>w0iyuqTY_E
z_jqrb9ws`(Wx5_Kgt$!CQ-rX0OxLr7a0Z&L7YN}DG+i$d!Wn3~epv`-pc(pgA)J9`
z=(mM%26|j?6~Y<lalK0jXP}w-fDq0=GxevA$m^o1vvlxL7u^=LZ_d%tLU<2ij?N>5
zr{i;U5uqQaXC}?nrG-K>RuGjF!d=vPIz<R~QRnFzLX%oWCC%6Mg=V)>QS<db9B~8}
z=+2_Mvvp$XQ@XcMv({ya24?H%iP%CtLI`i1Ez}bnQ7Zv&k)D-pNv$l>3xv?hBK=&p
zj#_zIzbu4Sp4M+T;#Qu~n?*NyNK)!EdPjCWYV#SrR|suBqmN|ksLf~faUrz%tp3^&
zwOKWFvHo3jeD_?N$sBne`x0H4>B}g5U+Ou1qtp4d>5TrIzEvoLu6Li)$wJT3(dKzw
zU1&KSZJyWl98sGcl9uW=IYj?^obGl74Y-0vGkqC_FKuP&M}^=^rheR6^0yl@^*kYb
zyCG9A7V1Xd40u7mBs7%18SsK$<%oQ#m-?dK#pHTerVofEdRV59Mp!P>KSx-;qW=&}
zJmr2x+bM3f;wkqlI$j80$yu%o3gIg`%XKLsd?n{qT~-KR$$3>*5=xzwnY2Pz6Uvyi
zf~c+|^15x(Yx*80=k-e6K`i0*O5G#JvR>*+JubrXbv;!q@tvX9^%FuE!|VDfA&lX5
z{k#yy@P>Xx2xE9duNJ}>R_hHy7{hA)o)E^cM(-BF7}n^2IifK%NnNWiFu5_T)0c8|
zZIjmNxT%Pz)2-L}ncUZr*6U)9xRv!fEyD6`T`Nb|HtB8MDnhqGw~Nqi(ETEG8}-l#
z-9|kvLidiI9ie+izZ9X{q*n?d@|*O!9LsvCoAgH!mRs~EIl8t<Tl9Akx_9-@5xRGE
z%wyrPZ`FB)(8E?;glQ75`K`LN5Z3%wT}}vVew$7a!kXWvYdGRH|DJBc<f8k&?wF%%
zoAkaO6rtOpM>?I4zu4TNCkQEe*Jy{HCRCK(HQJ%)IHDd}r0&!!nOqMa=ygIE!v}h^
z)SFG;2>C$o5L!y#2>C$o6S`+&X3{QwM5x!q6+~YM;d?>5_18l9UeIp+gAl$K^r8M$
z2;U3(P^)Qf-+YSVvPZ`Xt)#f@(fNhSQh$4OF`-)2-(G!_P|-1&N&9qpp=A0d$v$00
zXdS&vykFlT^dY@Vyk9pI!go$S(#?eMos*ArTOoYs<bduZgzuaj(7lE5os)xlK(;0M
zc~FmV#PjzrJ(tNv_pp9O2+=*PUyyqEhTCDiLI~ftIjq+TVZ9vIn;cP_ZBviv<4ms2
zqxy^x+B~W+NIiW0>8QSxQ?E<vQC(zuc(oqWrI{xAxV}E7Zxg~Vn`1h~5&BE|TsMrU
zcU(6UOL%=;x5=@5AoaK&7-4xrj}XhygM(5}=<zw0!-*C%ZTHGfyDR#XJ`_>!ls+c)
z@Qu1t`m3CJO;S(k;EeDXzS7Z5?mq2TI*%jrx=HF+`t}IRGdfiWQ97e*NWJfiXC|G|
z^@T$8SA=KuKOFJ=eXV;*J)G6f>i#*pwnP&nbm#PRr=$HZD(Re_Bb1N++W4G)CZgWA
z`t^u<-|4r+axnGqoqkscd+~SrpF;RG_?_M_gkOW-=}&}kHF{p35W>~yd3{!B#_;i}
z7j%};bHit(ey@KM!ja(ztsi%L9F7b>=v+e6Xbf5U2B9T1hAdq|=wo^}^hbTO(9iU4
z=#RRBQ1GFrQh(BELU|rqOjO$u#q-6~pLG`|w|0Ng4~Qkk{)-+cgt7mkM+m{sU-Wn(
z`1y;T=7`(;Rj&{ozP$6hUMqxa=->4_Lb!(hU2hk{Z;RjcheG&m@w+}Ggx?Q;=wm|o
z{qToAErj0>o;jagkA6RR=8_QJC<vIqOczhQQ4lcEj>ylIsX<eY$&Fo`6tTo;wMiF(
z*V@z*g4fz)2*GP@S_#2xZSEI>*T!@cg4f18C<L#qc{sZsd2P*TA$aYZM}^?EZ)Q3o
zuis3KGH)?Cuj9<SVhKOv%s++TXPntDgkNWI=3^oJI*T(Wgy3f`^NkSv%w@8K;Ad`g
zSqQ(*avMF%#S^O_-oy!E6~vnxgs@r@OmQKs)&z625LRm*lO%-In#ZIGVYTKpwS=%*
z^O{DEXzZI)^O*;j+}I14fntfV7ce7)F!lmwybyk!6)=wp;n!IK^P~`dofR+(h4AaF
zfLSVpUuOl)av}UWD`?gT;n!Iqvr!1Y&I+0BLilx7*nB92UuT8QAtA)9h&d*Nm=!Un
zg%Go%=DZMMR@7V)Ld=Spz!NU=h*>ccErggAH+h5*v*M<RBZ}FM)Dott=&&~?n$|h>
z;wmMY`*Z5$CF-72FWxI-hBLWX-(<##CH9V+%v2$))|<=|LRhUgnT3w1_ZF!)o3%`?
zm9pj?vHZTr{?xK&yU-gwqms&+4~6h8ld|TJ5WbIct2riw@1xvmP7C4tD7TsOLij$)
zZRU~?zVTAd1fFy;!#7^anP?##y>B;pgmCn}-4qeR(Yw4UErg?Yc~edZNADz)B7~!N
z1ye%^b6e5W7sA|DH2)C7+*UGo3t?_6nGQmj+ho&22y>fk`UzofQ_N5y%x#Jp>xiOz
zI5pKQVsdBgDyGwHjMl}vis{8P$<OS&KedYKFNCpIF~fu~_9|wa5XPQnrV3%~Y34~G
zjJ>K^D1@<BGfRaqf7Q)$A<SQOvqlK-6Q!GtLU^Aj-E0@azFEV3D1>JdHOwI)%wJ7&
zObGK=)0`GUtnV=Ag%ImI%q1bjx|Rvdar2H?*D}#Uh;?m~M+mX5ZHfpX)^$v2A;h|l
zDd&h|UDq^Za<Q&&;^u~9UEkzqn&iLP>v(E?Q_K<7`-&(n!m@#>C6>5r+rTsw!d=@2
zrkN1#+BPt4gmBlkf$1cKyS5EYFCpBuZD<AvAuf%~a3RE{kr^+9xHLA82_Y_x&1@mW
zrHNT2gt#;@nL>z5hIv&8amg@i9ntvCr#3Z*ncS*tW<D3onVm1DHZ!M%p6VQ`+{|1M
zf}hRJB_a6P+yv*j8H1lKOtcXEY+>>W!Oxbah!Fg2X%dCtXDd@q2!6IQsgB6cwn?o`
zBPQqP-KM!%7M;33^={Kv2&?XH(@6-c?rzgt2z$;wW`Gd(oO{d&A?!JA%y=Q}Ic>}|
zA?!JA&1@m;Iro~Uh48hh`%I<~zBYBASs{cdwKHpl5T$lzlMteGzj<E>QM%vk6+)ES
zo5MngQhRe;2vO=_&Iln&9nANRc$IfF@$)ecZk2a72bo;VI-5_KCi(Sq=dRq@oDzbc
zoy|ER_}SU~Bm_UZnBRrqXBT4^xS58ZT}^Hw_}SGIbVR-suH4NeGdW*+n7f&rFFj0q
zsfW9oJxq5Y%tH_Jpb+MvhZ!PNf##v787)+U=AoCFEQCA3z0FJ^+zIY&76@T{511uF
z7~cctWg(2Ok9l1P<LhJI7Q*=Ynyo?@UthCJ2=nluIUt02c+h<6h{oO|^&xYS$&I$Z
z@t$%q3q80$wZHL&up;}Lcp<FF{-%%+A~C>}5<(;fm|KOgA_tmELRgW5Om!iQcCe`{
zgwYN*8A2HC!={xGM*Fb2UkIZeV!8=ov_s5;LKy8(^RN&`JJgJJ#H((YS;XXKdW89i
z$&G!4`9$jB%NQff389mZ{7_+p`9=u$Lq?b%gm6D(gt;t4Z_HO7Y1Bd&!C|eFiQ<GF
zADfvp%H$VXHg*M3aUp!g;t_L`5WZsZh)EK{S1d-GDnj^*#b{GY=uV3K7}HRw14Vv}
zX)e^5B0tu&73xfpA7?rX;R_ezO>ZH5;bOcQD1<LuOfVya@P&&BW`fXI`tr_1Gfij)
z{brtM<_KXnCz+>(Fq@Oi3qqL9N6iW$%;ux!O(D$YWV1;KvpL!95W;LuF?)qDn^VjY
zM>LyvRGw;nWpeX3-Gmmo$iHxJM&;=yMhMpo(@kC>Tr*5JMTI)k^SBu%QK&CHkDFm`
z7s3(xag!>9BlP2@rVvIu)7&Y9(atn?31PIe%soOF?JUz#2%~+%^c2EqpD_J}Fxn^0
zFd>ZgNi)t7jkZPQ*=8}5Tf1{jx2G{$H}<)vFViG{UZ4G`bIo8OjD4;dC4{lhHIsyJ
zy*tm$5W@BDJTp%S*Squ0vqHGuoo`+eLR=P@l|qQi0<&HS$BC!R79kubo-+Rw!f|4u
z*)N3S#6t6l5RMaz%n2bJC!RKEh478RXH1q5zA^ZW`ArD(@T}3#xOvAsJZo|ZVICHn
z8-y?qi%kh3%)=6Mvk>NCiK!rjd3esG31J?dGqoM@G30sEj>*kKrYZGocpfrMS*A&T
zo{asenWmx;;*x2q3L!3;rj8Ke@`7nBgt)w5S_&a9FPi&=5SN!sS0T*pGSf#0bGyt8
z7D8NJHlu_PmzT{XA;jesGeZb*dBw~VLR^-cXN3@#<>n<J#N}19QV4N*)vR~Kaamyw
zFuAy_G%<@27xy<6D^0!}nijpv6nEqme5`)-8>XYs&r{n+uQ3&tP&HcDxL>!{q%lpR
z|HTDVi|Ke&G~G2?YnqA<zZBP+){e-R`zo(BLzrAE>&<Af#4pqJ=FuF>&Xw1j=R}7d
z-Zn2YxgOp&Z;B4@IlgT+2_d>~oA-qf-M7tNAw+kBIV^<eZZOA%5Z#UDj1Z!`(R?q2
z-?#6WUxe`c_8k*?4w1nBnA5rPCR3crjcALx$?1H2ad(R;FLXX5Gii&dEc8pp3ZgrN
zzPu|l>0Q%6=(oF85H%CR*teQCOm6I3O*hdYx?4?ON90S-%3IAmCg;oh=2@|XFYlWd
zh2YEk<~1Ss^1fLo1YdTT%|h^HhuJBFIofIVIimjhRo-cS5Z%-f^u)pZDzsz-J#jGV
z`RphKyxk_j5kLFfZ3+qD+2?L^qY$2berRqL!n4m0O|sD8c~MDwOm(4?^HkIxQ%|V%
zh#{5tnhc?yBgPT6c0_&#ynUwg74(o;;<@%dGeqdcwu4glnbAUTww+OVpP4L#?<MRv
zGlf2<?<MRv3xr;y?;d<)mI&dy2OpW2h2Yr%^STf`J7C@xLd*`DtwM;|L9@#dc^&Wl
z%Y4q{_S-`yxRm4R=b~r&hfK84tnqUyA2NA_@HGCADJ+Di@rO)lp*r-=$zgMwP;+|c
z<giH*x`+Py@Q6tl>P~-sc*N8fx{t1}KQ>K;`p{bgADg>{&d_iBPfUBEKj^ppC#HuZ
z8pGnsN6ok#iuXP<kLA#`=woIMlN;Z0^P<!F57YRLo7aT!znC32Zwftg7j&B(ag>gm
zk8`N7_l3DEmgT9HlSXB_n4y)ECRXV3Bd=CIY4Qo7%~PhRBWkm-cgobvp>>tNG9ASd
zd*d0?lgaIkXUt&H;ZDXGGfD_gaL$;C*+jmaG1G<chSV7|M+k37oiR@f;SH%XCQ}G+
zNS!gS3gHc@GiI&OSL5HS{Iz*U=#TLq61^{kzj6P@>=DA>xPM~~3*qglv*vT5EP8wD
ztT`iux2MjT3qp8%>YVvS2yah)Yl1Jhc;fA;Z%vF4-k$o-<Q2l(Q{S1QLak_@K5r6*
z@b=VsbGs1Up1NRCh4A*&1yfT9Z%=)1?i9k?Q{S7rgz)y%59S^rygl`U=_rJ^r?N~>
zA-p}6W%>)@?WrHlFd@7>^`jXlgtw=DGE;@{_S8@2Ng=#F^|M(hgtw=DHcN$Y&bero
z3*nq|(X0`|Ip>nuD1>v)C9_=!=bT^6he9~#{9+CX;hgiUIVOa2&adXQBU+z_D_=IL
zFJd0PjLK{rNC?>)Li^}VgpjSz<j(IQ+cJlyMJwBjX?p<A_>3JR^{Td^|9{0!7P_kq
z{r@X=7SnkDb86Gt=S7EezqPM0;eSYm?hPSt;Gk4%-xiuN61uHI`1+c)9|+;=Yt|lc
zL?imTvTuK9a>s&LTjnJ<KJ*uBZ)bA-#oAP%F9+;TjkPs|&|j>rFNFSLZBrri7i;f!
zMEzw|j<aK!Tz?7nMJ9LNOt7y>J&Z5GzA1z~Ji%@f!e|rh`$FhF!R`?{J~A^Y!5$L&
zVdM&;&xPPw9(!5{p5?I@9Fb?gSI%ooFT>cKX9aCzCg)i}+k(kOqM*H32%Z(RorMsW
zg0{C1JS%7i2*I<0cDN8cD`>|H!Lvg4F(G(X$j){|o<&tDY_~Hx&q~;D#d662`nrz&
zDaVq&u4AKL#u%KRC2bx@)CzrF$KJ%`ejk;x<;8Ldd0om@7Q$Xt%GMOZURBCA5W-${
zqrEG;9_>{(+BQPit4iCBLfETH+g?JrPENG_g>apmXoou@&km=Sv2&Q*_-?iv#S-JY
z*}f-)|AFCVyE~`erR1CKY0+W5l(pxX+>DjA7e$v6PtQB7_lk>4&G>v(%GxML)O(RC
zx7tJ|*LyiThRL;3&Q6wkUk(_QTF%bQsaL8>IlDZfUU_>$>fz~ldHapj!?j|0`$JB>
z7OCZJuI0`bT-Q~wHwfVuQ3YFE2)~Fb*qep$i>RVa62dQ{iZ)FMzlbW?T0;0mlx!Ob
z;dqu}n+xH1mSXR9#A_neJ}5f)QrSK%R6lpwDwXXcLTIJ3og{=-D%-~$Q7cJRs@RuB
zhgPcE*CXmxwQoh#t7^CA)Qk73*+UWas@r2ixUX2<ekFvlSGV5@VeHlIMIqc*Ot;>v
zE}porSi?pM;l5%`n;?YyiZyLvA&lq_d!rCWbcemo5sj#@SIgeX<o1<1_8(%2*{oyl
z7Q&3xvF(L0V|8o~A$VQaJ|qOM>)N40n6Y|xj1Xq5zMUcj&+fFdgy7ko_9;i?S&P&L
zb`6vBtdZR)mhh~ReNPDU*U0V`LYs~3K_Rr+$bROC+N@HgvGrCUf^H9JYLl2;4^3?q
zrb+xRRa1M15ZY{N8wjDzrnZ?Q_VXY10VdZ<GdoZ$;b${DLI^Y6%#IgA%$nK9gb=f4
zcD4{^y188>gqdz(GlekIE$yp9nCX^wtq}ZdW#18kpRMfsj>ym2Ra)CKOwP|X_JUZ#
z&o=gw5L#(t1FyMthF03xXh%HSwzdq@SfYS;pM@R8GY-dsFyWdZOt@wU6Rrfpge!p@
z(zs@5m(wQB%<b&$=r1tKi_i5zVmsTD=}FI@)~L$;_N371Zmp|yvi(-V@=5Q_8#-3$
zVmC0&@>KVSt8}yFRykd@uA{4Tw@sL4c@rO)T&1V&4f3`}jcr;e>H#}|X_Alshxh?I
z+!2kI{&K;N&!J1nee9Dtv@rHT`*aS?snXA`$f0Md47Bg%(2G?bwx2oTcd&-rFPX;r
zcn52^{Z4f7WrY1%bdNXH-U#cx4nLh|BdyOg*2n*lJklmO9r^i2l~J}l)0crsZ8lUH
zV;kh?0^Zo{=xXT3+Pj?2U)%J(D&y=uIhOQo7~3(27RHXZy>n=9m5FwA4jr!YsGZNW
z-7C~*TkKSOQs~A$pT<6Ji@d>J^Lly0CNgdF&b2>L<q2C)XjA*It2}9YL|D$Y{g`GI
zY%%0QmD#r8YP5oSKN9s6+SLAXl{xk?CilM!&$Y9eT)nw=%^K9(?%miYls4BM7An*y
zciIB`HIu9NjQ!r}{D<zX8}*F+QD|{7&_$_t<BWvd&)Dd-*=>Fr`>ZV~gt0$ozh`o7
zzF>c0a=yG^<KIL*jG<)O3$}vLruH|dy=Yr9xq2_zc1*6`OSb5`?0V(XUb5*zo7$(Q
zEwde%T)mfVPbOFIWvkX_*Go@(*(M5YYF{_)72ANx)mv_xF}Zrnt$HiFUgNaoHc@C(
z`{rq{+6GLn-U{1{$<<roR#sq}KG*)WTw&VOep#;9>>F>RP0T}^w3YT<p+bGSroCYg
zGP%FLSZ6<HLX-xlt+U<+=gX${Bh%K~(oC-Rw`@5k*ZW&`zF3}XKR)d(yDq2R)U>zl
zClU2F*pm_UHrNjDh-Wmn8|=_*qP4rxo)*H~Znn2<f~D(yo2|e^BcgiSY`tuvdhglU
zLa4XH9*L;8%bsMy_-3W;vT8Hh+~#d+KR<1^y_w1N@S&~1<i_`*-Nl4@&!&B7&kAj7
z|3caxn|q5#|6IMjwjh(Mx7RLWLcLeh_Sy|Xo7%5V+h>nO)Z1@QN7UPIr)@=>sP}f-
ze!E;~Q~P(*KC*k5Tz?1bVJ6q#0o&*O?0P%X4%lfzo7(S9J7`~Ha`paY*D<+z|FVfY
zvg;j6`<JaJw5k1PX@_hNCRgvU?Z@Qm9k$zY>YYkEY)|IYJDYaI+MQ_A)%)1SGr4*n
z+tL4IosYBA$M#X7LUacD*go!vj#)pXeQYyDSL?1{(~jC#g<6gbR6T0f<XBpw513pJ
zpV>n>x`6kYJ;pTN8xVRe;WL{hw8F&XIcDQOfG@`b7v}}M&+Ys@j`FsyOZ1WubjR&W
zd&9cpb}f_Z?+g1blN;?9_Mc4Sy&crw7xr_hhgMG5^B;v<Ibpp6j-WefiyjQ?PTF!p
z(4Ddk{}tAqvTcN*`_gti6xMxd`w2n!m7Q=ntozE&7J}}y?RO-sJ8j1ZL3hTs`Z%mR
zW4j7L_q9#?B&_?|HWY&H8(Z*bSoe*+MF_gH_F{zYto1*2me8HEwLS~iJ7=2-LHDh#
zbu6s=);1G@?mIi}^RVtayGRJS^Y*~;u<pD)DFodG+vY@Acfs})g6?~}?qpc^z1<-M
z-4C|mmtoxxwv7;US$5Y~VO^FzDg@n+w&a<x?nhfe2)dta&#%L}pX^W}=zg|iz6tAo
zwljsGyJ&Zv4eKu2qe9SKvX{?=b(d`Hw~nCu#g_aotoy}Q5Q6SkTl9Qb_p2=@1l?sD
ze<7^9Y)cA3_nRH_eOULKohby}?{@PKVcqX`uMl*9*gijob${5ALeP2s?4QCq&tED8
zUBDmnb66MfX9__V^v7Hb>w^AFA?WD8{kRm?h5VsH&?#U28rCU4pAdA~_b-Qa+Ak;s
zo$+IT3+s$uR0uljtKY*q>*o`K&iB<HVV&>i6M`<vpWsmh=(vO<QItPh2)by0XdtYM
z_9qKL7vpyghIKLiAR*{t{ftmp7wfkZf-cVQslvKAf2a_2x%_E5tjpyu5`r$bKhlJC
zx&3KE(8c?GY*-iXj}(F~!Efh>bqRhSA?Wh>4Wq)kJboJ?=<@m%qQknpek~#B^7%z$
z!n%BZIU(rs`|+`1U4Fl$5Og>Aeq300gI`bxx&r>?2wed`HkY%6uAqNX2y;}>&x+6$
z@^|G9w^_(PDujB4{nhbdU15Kl5OhWSMG0YD5r4T5bVdCMdBVD){%j%Wiurx=hIPgK
zkwVZF_uJ(Q>x%n*grF<oH_RW_mGIjLL08hRa6?#E(yt{1T`9j|fv~QW-$n?!8~ug_
z!@3*&HbT&q_G=Xi>q`60grH0G(+h`niGGF<bY=XmMZ&r={vaXfZt^D-4eM_5XA41h
zv;R`Du<mAmoe*@l_?wG|b+`C?g`g|zZ!QtmmG$=uL3gXau4GtutG`1Cy4(EKrNX+~
z{B1(emGfV^F{~@+uM>jqc7JK<u<mw$wGedW{h5hjU3q`85OhiY>M~(nlD|y|x(fcH
zo5H#Z{&FGcD*DrI4(lrVi-e%7<S)G?tgGa&7J@F>-%vKJOZImOL6_ohzBQ~%@%IWr
zm+Ei3Ev!rR4+ue5*<V>MtgGyA7J{ydKmYcyu8RMX5Oit&%<^Gfn!i{Gx~l$$q_D24
zze@<ZYW{|bVO=$Umk@N-{g*0*b=CcKLeQoAvy;QRbbqN3bT#~uDPdg=f0__<HT|Bc
zVO>pss1S5__|qzfb$9rSgrKYCFRc>R)$&&hL08*fnHJX7_BRVbSI1vkHLR=SuNH!?
zu0Ok4SXbA7j>%oc*7H{|xvSWE{#vH-Ue!QC)q4J?LM=nZtJe2VMOfbHpA%ia;CIn?
z`l0IR!L`}I&&}l8Y~UAQ8t=^s)s1Q3r$ksb^lL>}HuM{cWzj&Hstx^SOztYZk$<s9
zcnpnvy7A;CxAMH8moW|7NgZ%j#lL{}RzJx5$3R9?c~DB+wktn93FU7$hRr8auswQY
ze0<2;*gdku^IVDY@V^LZ548f`%Vh6*{7-Vky!iMawHZ%Gd)M-FozPdCp5eaW`G0EX
z6170>OhK)0yTbmjwCpS|@rX9^81H*Bdqm_9m%L2l3XdASjCmBjd@(sbUU}!qf4UPk
zB0k<+5t)<SU~{Q0Y`z@_n}d(Q2A0-aN7B!+N1G_QvX|^QyZB&4pU~(KtE;yWu@gn%
z=bZWIar!^d<LTk(@k1)HUP=%2xVB(?e3X~(srdM4ul_^v@i95Ag<Duj?I`bkveaDb
z%5k|~B5l6!jb6~B_1034KG%xkzM{FW815_f8hx#L5Z<B%%`F&i;op19OPHe(_W8HI
z*KVh6FZlDoK#aP43yhj3ghq{M#IlER>|rkUF!wb)d}1nEMlH>LUOCc=<ardg7R{~2
zaBH#LTHG~Sv+O6lqA%%pM%>2GDze<Z&+SKX9H=#lWQ?af!B2P_$L;6h_H%Rl@z-eo
zM%ru0+rQlYUys9tjDHtlwDZ?y{EK_T9uDOQAh!R0#<Pz<ZtV@pjTX=&{eO(KN)5-v
zULzWZ{(+VJ2JZ`>H-_cpj_7Ojguk~+-!F)E(3<Afj5lfl+CghRw-&`S5`Bdajj2|M
zYi0X@T37mjeK6{ZT5gQk`xLiH|0iR_@wPFQhqzo_QSNGGj~9NHZ{c~yyGTbK>&-0;
zKS$h+zAhHPUa-FP-`fic^+yECw?fRLNK(w3{JmN8`k;lh(P&{c#ou_1?m-J%$HKR<
zMc`Wot=?#FD9u-lH=NdgtQSSS#CgSNiRAJYmWYqf&GR15^Pa%-o`<86_fOG4l$O%!
zy4E^D%)`gt@G)dJ?KM|Mbtich;-fvC-Dp3(axVH(RKrJXw9~gRW*ld(E8B6igVvN+
zm&T&K$A%&Tku&>uZSqkxuJk9n1;n{8ooh7rb^Ts23v}k12v5!q!(6N;>3iGBw<xc|
zc+5rPF_?>!G;6V5MLKrHc?mRMxxC-W=2}O~okh^QM@QOg`3A549GO4I@I+CRJWOj$
zc|Wy5JO84{8?VbOczAWh3U+}d>^0jPwzq75tyy;VSGVJ4=dYK$GuM^&SC0kj0q31-
zjr!vUt}chY+xOk^?dtZO_UiKgsS<d2y;!YidG(0?9Fy+FP~xsq5@ul5aHV9t=V%76
zoZmS^VA-3FRLbkwG;&4ZDhTBvE)U1{dgbAL+#Si$d-nAPu8}NXBl&zJx$<c2j^Wq4
zrn=sd#PxFZRn67*u6<Z$U$f!7Z`hM7qv7^$*D}t4nr*J!yIpJU{D{7;wGaN+&Meof
zmF?Bl^Nw{=n6A72+7TH0@0G_=97#gnj|2YPvMYa>e%H|Xh|Y1EY;dMqK)+3_r$^xk
zHifUsBKL@^YvmsJ_iCXR&Ak|I|H|2kY#%LO-?A`iL*87T@kkF5t1BabF=p>;bkg|K
zY?dqr?N&NUE8cgs*O88d#vA-7;xLL<xbNj3bnUAP#7%i0KL9_^Qai@`hORNJhrjOg
zz16heMR`l7<M>~I&Ji(QQ)(yH)8s>(H<$L-T;BE$IQ}=MGi<y!h>p(*-VQp-<?*8W
z$H&vTm^{hny+hX?`MuAn<OXjh$pT)j=JD|b`3zo&&)|jm3|@p+a?vY}?R9u`@pRq^
z@7>soUHOr_{#-ttk1CPzoX#y=ZRu9*aXwOAeGPf_9_nUx_&tai>S{hd8&A;@$$I&k
z{rOrV(jHf9nl0&ii7c<RX3%E#udPV`T{~zYI|6^@SMarMvVSo-ZMiOpBz~cXyr-MG
zl>k4RkNwZTvafC*{?qfj92umu!+)int6Tn`DVa<Ab-)`y`&z(zmwxHf`zTxzS?)%6
z=Ok#K(um$@`{(Wd8E^kzFaLK%;L1JgzwTRhJFVzWUC=Ai5~~ZXWtac^mWSldw!hY0
zvat4At%bGObzRBT<yZUgS8T4X^?yrAWHchTv*Q3iuU8&v<F+F$Be$=%j4ZjH<@L%V
z>;Cs_<8P(|o|FGOGxfhW_Wzyn{`bt||J+#qx94lle5_c=ApMpMZ#UrWReWr8+JDJ5
zeka!Eb&g+lS8flZYh%}1z;ksw)151NqlSB#*~E<&B}1q@d+bw5o4p-rgYE0jZ`kwf
z9-Tjp=xQ^&<loODe4w{J=(;`N9p=7Vn++zQWYs9hf8WB@+R=P{xN7=;rS8?O{oh`a
zJ!{wY^WST)jO~Tih*+y>SW!5GXV)#x_iw}H*|pqSg*MwV+lL^n<Y#&Hx{^P)9N;^R
z*|j2Dz#Yi!_V<<wZ)dlB?Gji9`PX#F`|z<VZL)j0UJI`7K=KxMy8d%rXCI32Gu-mE
z`f~Q|`OfN}+jk?qeyv%BuJq?W**CwY{eP-eB4RAg+Zquie`R!EQNMpDdhR~+wWE<e
z`{8~Ap7RR#@TnL6r4!a(D|%t=m94pU*5u4J-Os-p5q9OrbFNyitaa_yuGOz=C&2AL
z#?NrCUHAI!yB6>qBt%bY@Z0vUM+tph->Wv{PssbLUR}SPx&Ob9PSaDKE7$q8dcR&I
zUBvFs@d^>c-GIM-9B?fx;rLt~ZLB&vw)|<;U4I6z=jZjx(PNPBWrg@&mg0L^n(t*9
zx|eljWUlNN>m<nQ#FbwwZg=r{|KInH_=lhL{ktB!(_N`6eYjrBbJ+99<GHiE`tAzu
zPGsM8iQK+k%YXe|jcctST@PG&hv&~Fky^JM=})#c(w|5_oj*?N%5j(buSF@c&8xQ~
zdx>n{-CM)|U>E6ah@M-8`*mKq79uTIJs7UFf{%F4CUWIR_UqaS_Z~T}Ypu8IjRJ36
z;MvfX9tORu%dc($&jo_~B-$MnBYn7X+j)*&{@QxJx-Zv4_y~Mu`&ZjvUGnD^vhVXp
z9_28i?3TkmXO~>vX1L_<#Qesd;a<?fU#lDL>q>u|pTG4Dub;!cu5ABG`^a`&-D{u0
zuT=~7SC{`C|6PwyOube+*?Ud+{?FfU+5JulkMXay;JkACV7TtpBXuQzt<9_3alPP;
zg%G{5aP0_OUCWiYx?kM;*J}M84_(Wa*H5_jzu&@y4EXjFAL}5q-|=fkN3)>!ZgXhM
zkATE+GW)$UcjR#;k$Yd*lk8b=eqMbw=<4E0N{F8q;Hk%-qZDcL?|Zqr->Xa9FRZJ}
zBTMicElAJNt{oFRMGAP3LGQ?2F4ic)b0}Amy^a4NG&=&3?f-XgyJ+L=6XL5N#aBU^
zuYwF;1<`vQd@XVHj7R$BBJZ9#;t8grXPw~@MQZUR^h&L3C(;J~C@=d-Xt<s0S!O?7
zg(aSl2I!5BKi7)%){WX(LI&xHYS<oiv#&s$S7<-`sP*Sncdb1ra_wO|=!K7U0WYk*
zR$b?(tMyloa&9b<UWH3SSM(b$|4+xauqEEL2uCJde&t%NI}KyR(}(PLZ*T{L-izk)
zK78M-TN~IM=etbqo(4u5^g6Sp(^jLWoFQ-J1E^cAS+)%Fb4FJZ^zO;gy7JI9O5C$Z
zw2*Cs5+#<AE5U8!IcD~+09W^R@|OO7fMuj7k-hJvTG?J<+j){br)~L{%O(1S8}g2|
zNAKqzhW(a7=nMUZq~vOUT*=jzSGV~gwXCHb^mVQFudd}hxn942rTp*ucD*+LuFrqh
z{(AlXe<}Zoe&Gf^{C_+FuO}VVgWdyttQ)|q#mUlqH_yqdOCq)ZZ~Om6;IBu(-I<?1
zA8YST{w?X`oB}8*-8$M!^on@7yh`3)uL}JcbQ-4}l<)PbQmRH=&D%j~uUDP&>XfHb
zSvqZ})Aqey*T8*Vzrg*JccT0uZ<n{k%L-+BXDMZcUZDI%N-t4bM(JfruTWY}=~YTA
zD7{8$C8w;=D#~A{^aiJ_&}z!pP+H3=EA%GiZ&JJKC|^hUddlCT^fsjplr~a&hf`K)
z6XlyJZK3oorEQenqqLpU4oW*I{gcuMly*_tP3c2QdnoOtw2#t$N*_@=K<OZ*L)7OX
z$`4b1gz}Fm|CsVmC_hR0DX+WtC2fC2=`^J?l+IH5x0HWJ={#-!Na-g^KU2C$=@O+t
zz*j{BTfAbFN>D0EsT8FfDV3&_NU02^n<(8(=@v?5DcwrxHcI6v-A<`Ir6fufC{?6X
ziBdA96iTU-DpRULDUDK9O4TS;r<6{q2Bn&m?x0kQQf*3gDAlD@k5YX~cT#FVsUf9C
zlp0fNLMek%Q%e7!bQh&&l$uj&L8&FBR+L&(x|`BHl-f{gOX*%p_fcv`>3&M>DRrRK
zky0m0ohfyp)Rj^<O5G{-pwyF6FG{^BJwT}srM{FNr1TJ_ew6xC8bE0vr9qSiQ+k-v
z5K2QS4Wl%i(g;c;DUG7^2&K`KCQza`#D~z|$dc4vA>J-UGTN(1GM@I1j3N2G+PvL}
zWpkEoS$1OCi)DY7!&r`GIfZ0lZx+dbw~S;-ZxwIvBzcp!kGGGoJkGLFAY(|nH<rJS
zFqZ!o`3#lBdzVNy_M(DlrxeSwEGx3CN;2JROER8qoPEGsz}i89k4k+Q7!>%T)P8!P
zhZaT#PES4)7#Zj=>W9Dt-k!kQBLjn*hJq7VF7VPj(O39FO(qo%uIE}+1Bd4&2CD{6
z&bu{OHgvI7qhQ5QyJoFvdrF5!!Kxwjo)C(k_ypOYmqmfx_r4Nb8YnejBgqLPx04((
z@1G<$cK?WEyL%4>7X`kX{xQj<E@y+w*?u{fEa#G)RFXTilS_6|Ns&<VK<vFYlN>?+
zzehr7>B3~%9=kAauo3lM*lQkox_h%w^U#?N_l7nEVl#$^+J?rrnG{-0{yY)d#_jJS
zf8Ge~3T&LRk>q_(zKd=0XBYdkjr-chy&quv16*=|OOA5MQ7$>kB`3M$B$u4zlCxZL
zmP^iZNfwu6aY+`JT;`I?TymL9R1mRJK}16Z5sg?biRF@5F3HCw^f!NGlaEV^a!FAx
zDas{@T$0EoiCj{SOUiLcIW9@zk`yjU;gWPNN#~MuE~&>Q^|+)Smt=5B2A5=TNh>aC
z#U-t{q#c*E<C1n<(lt0a<!kyI`Soe%NcO+&Jjp(_FOr;D|HDAn;O@@(y*{)`p9pmg
zKHgMCbq(U%P+f!g(o@%9%_&cW5<;Cj7m7*<jqY453bR=z3UiH>Hn?e}sMUcRI@Bdu
zXi9^qUZMT>WROjIrxs-2V_~nTLDcfFsQ#hvCXJ37#_fy@!agBXzta(t@e@yzEHdL;
zl1(OMMU7-nhJ}_dRMCsLZu7wF^uLoP()y40%CSsgna;8v%M6ySShi!?m1Q56gIJDa
zIYA`82cF3PGvG8Xnay&MNPHiBHfxu1$x;s`{X^Yn^d{M0!6VU&_>1DpSqnKW8hc-1
zZ)p&g%gOVAw>(&W#Lnm`p$|&!jy@hZIsYS)hejTW?oV1eJ_HX;`ZW4d@WSwuWRJfi
zUCra|A8K)r7qcNaEp!XXxZKr9;%`T{v36Im=#WQZ4g^1%+92v^@YvL6V~z$FwP+C4
zKbTc)Y0Rw9#d&q37KBE$33#j7w+$?}iJ$ly+HxM(a&Kwb<uS{>e&x^k!-C`It%_L^
z+BWIUm}Q}JaT{W$P+#woR#p7kU+&?XH3ztblT`jh=p@hJ$slI@D3>4QXryq<C%I%<
z=z|gGW6rYA2^5)7Eao~sb_tc-5Zfy_YD}@%^`Wd{g*|v)IyNhaV^mhKTbm5pKHj7`
zNgSOj2JlU+Wn60&$$_yugE3Qvlib^3Ol+sXi%sXmF5}}f<ic1*eLWG{8G7mNRk2%w
z`|f^=<l^?5V;cqWb)*Gehf#-P_XStC*IuI_z8Kbs+NAG9gi3d~7@Lpf5o&?H+(M(E
z?+@^HQSvi?Tq18L@^(44OktVDmg%fbXKg*+uE*QxE8c@_9d{<wpnHe73!$<ddd6Mm
zT9-n1^nEz)La<-=(WHI6&$PHp!8OH}k}T8xRVq0)X?0xfz{AZq$6@c-85gD8%9+w(
zPh5gpm2rS<%JlyvZfEe8dymJR;jv_}SFKnoJ`1#Cf7-D>U3t4JZ}(xFK`dk0awKa<
zvUUP*PvGroY%`l>KDJ!M+C{8g%G*nMdpX;zW?7UiH?VdCYq#<CHs0P9!neA1h4B5Z
zM7BJ@wT`kZ$0aA({v_L<<?XY)oy9hnS*Eb1qR7zqOcZ7+mbYVhyLRAchlJdSe`0RT
zY&>15)$Up$ckMurg{ir56l$0|pTg`HWtq;cC2|Xi+(J3tF2~y`&W2?@wyek6daTXh
z?F`;-#Ww9&X0T;f)^=rWAKvc6+k@C<B+FK8If1nkSUZikr}6e|wpqlo9a}DC?NZh*
z=k4XZy_#({u<Xi~+gQ7ewYzwG7jGY6o1-lIu;oeCo@DJ=-agCQ0gitbmt?89-1FkQ
z2Jsd0uEAtF4qR5)TQ4hIV_a6a#@NNjja?qT&e)4uwh6oV_|Plx&D7ipm(?%k-B7Q<
zv8i`OUsgE#$9l7x-H>p>dvtQCgeien2Hlb{D{!=AU26GKs65%@_@MNAC96~UgekPo
z(*OV6DIuSJWzgV+qS{%Ov6E?g0{gSj&xp;(ZDy&Sp`{5~3g5y?)HolN<GLwaH(l!P
z@Q){?bKNYp)ci*F_*zyyw$EVuR&3u+?7xV*D^EMNSGxNH{qrb2gU*#o<I5;r*}f0k
z4`Tb}0eC)$?Ux5&zdQi@<$Q&i5c;Y~GF@fCrc>Z>)4EZeI5NG0nR#!>TQ>0M<Pv$;
zQ%r8kyM<%2g(I+&+u2F&(EWUFXQaj~jMTWY8mVz*McWkHLh6n9j(JDwOQFdLBQ>tR
zwsBvVyrHG)Ms4GEwy{6kxSgtjHTlQooxr1*#-o_cqd35|X7eZxaIFJe>j2kU#P&<s
zez_YpTdwBv4P3sB%a3w9+gN*)+d0bZ9OZUSvi2mGpXBnBT)vCjIl%24<#x`p<xy_u
zEZd)D`?Ibcwm-}ES!|!h_E~J7#r9cjpT+i<*<Kl}s93`*itS?!uPC;^%=VYrKA*t~
zEXwwYY_Ect!9=!KL0%O>UKK&CigIk9!uIKGAItXXY#+<^v1}j9_Vw64gY8?feLl8t
z#rFBwJ|ElXWBYb&-<9qAuzgXs@5A;**}f>-7iIfFY(J9iC$N1Y+tdHUM0-#o+b6Pp
zBHK@6``K*2i0#X<{UWw6$M)sez8u>xW&7o9znbk+*nTzJr?7nr+o!Po2DabE_Pf|V
zo$YtAeLCBxvwb?-A7J~VY=4sN>#_Ywwy($b_1L~1+n;6oEVjSQ_8Dw{ne8*!K7;Ku
z*j`!8Z>+_7$Y-%0TCsgTi}<u+`&Mk<itUTCeInbJWBYb&UykkDv3)zXZ^!m27RRDl
zbp1yE=UM3BsP~I*;4}Cvx?(w46xU$!-cgeI<I1Xa4}DTJ-D2-fw_lnHxzjDKl+x`4
zo3~iH-Qyn$_6nZQb3?Iu>_Z0o(29LXx0gccWP{^pEA}Ma;`*wcoz<)pm7j_FFi<g&
zJ5NvAE@R6lR8(KxGrU+u6}N!CgXNX<o+*~ctKr2L&*KedIgI5*mYFP<v0TM+4a+So
z&#*kl@&d~rSzcnP186gfWgN={mibv0Vp)o18J4%ROl4VvWqp>dS+-?)Kg&)myRq!W
zvM<a2EGM#@!t!aBOIT*IT*mTMmaAB<VY!~=PL{h_?qhk7<q?*jvOLc66w6C2e`6U4
zV!WDV6w5f42`uxoEX1-H%Tg@MuuNrHm1PZ<wOQ6@*@$IRmd#nVX4#fyFP42-_GdYm
z<uI0`SdL{mk>wPY(^<}9Ifvx}mdjYa%5oLUH7wV&+{khZ%lBCBWVxH=K9&bro?>~1
z<vErYSpLZJ63gFM20|P+mibv0Vp)u3DVAkemSuT6%Ze;(v#igu5zD45JF)D>vKPy~
zEc>$@%yJmZQ7q@MT)^^amP=S>vRuz{Bg-u;-($Ix<q?*jvOL4`9Lp$$amBGrV40s~
zA(q8hmSS0kWm%RrSk`7)pJgMKO<6W)*_vf9mVH_FXE~VVFqWfOj%7KK<rJ3FS<YfP
zhvfp6PqSRY@>Q0rSgv8Yp5;cCTUhR6`6<ieEKjk#z%rmQ_5_ysSr%ehmgVg%E3&N3
zvOdeUEbnL8mt}vJgINw^If~_2mJ?Y{VL6@UES7UvE@1g8%Z)5|vOLK02+L1d9%p%q
z<&P{cvHXo?!0`HHnV)4DmZ>c3vuw_?6U)9V>HoN-2#jSpk>xCwb4dE$BD1HM?=3b5
z=-9i2OO~1=RPvJfjO222g34dz@|ET^m8>@BNUk&AQ~7!>-(Y^GlFjBa$!#V;eZ9x!
zJB*?BcbOO}*+(ULyiZx4WBD7)D2wv^EDNzL#<CR4GAt{yOl4V>Wo?%ASvF$XoMmg4
zZCUnZ*`MWLmcv+%VmY1VES7UvE@1gI%T+Abuw2h_Bg@?^_pv<4@(9awEHAJO_&i4}
z>AQ$Dx<V|=uq?~6D$5!yo3gy0Wha*1SoUJsm*p^)Q&?uQT*mTMmOEMQX1R~$8J6c*
z>L~aSMbh{3u`C$1hvu#jZx@X^K(kgd3NxM<^%<3y;qqIePEbj?D9m_;D9m_D6lOe?
zwP{g5({@$fPLIO8*NO^I`}LwQ;|-%Q<Bdq0$7{;6Im^~8+p@f$<ye*zSx#X&o#iZ+
zb6755`83NVEHhayWBDq}RV>%AT+ebN%PlP5W4V*%ZkC_2Jje19%cy9cDVD`pmSS0k
zWm%TBS=MLSh~@n(JF)CW()R{MV{aN7jlF3YmyC=?Y{x`nZ<-K|s7{VPP1-4}ofeJQ
z&gNS4sTS>zY`>W8m$Lm!WKVk{Tds`$p0-y<V~<-GjmU3^M&!3~3p==lUDQGzZ#TEU
zm)kkO?HuNIK4;65Y<ZgdI?Fca+2$f^FSFK*f#)g)p8GNIJT?ZN<cq-=3&z0PqHL4M
zCAV-%IWDQdwNkiNI%{jOwjOI6vbGtQx8m|PT;7h$J94eA+*i*S#HUXTM%^z4qaGB4
zQ4fv5s7J<Ny^V>9Avqx?Kgr24g-Ong!CcJd_UCi^i@5#8-2PH-|0QmJIk&%(+h5J?
zujBSNaQmCN{cYU-4sL%Jx4$>01hs!4CXwXfn6e~~#*`=dc}z0NlQC%|Psh|Cc{ZjF
z$@4J{NM^-kkh~bvoaE)0yGeSn_mNbw9ZCAJ-AKm9_9hu0`w+=|v4cn!j2%X@WGrHy
z7>k(S5{r4Mz$GbMk{*lk*5Z<ST+)zjGGZ}z&0;Zktzt2EZDJA6cCm<O$5_O(Yb@f~
zGZyje6N`BEi$y#K#pW-b%i9v~(Utj5l5`A@$39Xv0kU--$ZjlWu^gKpC1Z<0E@RoZ
zM0|1jlOXR&uit%TD6iv{NW7Ku#@?{|%2OWW?J0&luh%#oc@eKA=cT;?oZse+kbFMp
z$y6V9)xEXMb-lBkH}RrtpuDA5fb;vjbk4hY&vX8Ow~F(D-j|$@kbJy%u|{pG2mk6)
z{uudFpYq0DnMC?rA^X{p@`Y4RFR!y6c{O5sO^o&Q(in}aPR%it!;W46WIN=rL;k$C
z=sx;GI<iZzIg#@ciBl;@92QfKdP^ur|IbI{s1JVL+j?In<%r)KoY$$jnR3+ILOJSf
zrMv~Tj~sT$pZC7L5BV#ie}nRE#BWpnp4ffFd7YZaDMvdeC`UWU(GGI7gB<N3M?0rP
zuLI5wAH+o*@L^Td$HzOtb%l|my?T_R|CXZfNIClLLOJRohaK`5Dj&#Pr{;LpyBvD(
z2`{1DldLb1xP)@F^Ssz0haGa*Z5I6&$+uG8g8V}cJLItYT=d5&$NV5iyC*2;xKIvz
zFznAz{*Cu*r$ErzBZqy6a@Zq>J#yG9=Jc9Tl%rl=<~lV?QI2}0DTiGmm#5b(!+D9s
zvXsL<nYm8Q+LXh-F6FSV&*dc&8&Up^_ekfCl%xMnl*6tI<*@5UIsEBGIqb%Ye!S?X
zNcoeLW4$gB{qvNgJ>;m59PMQaFQXj(y}?|k<`&A)?^eoD4>{@~N4=etqyOWS!|nv-
zutN?z<ghy>e1>w^2SUz1L^<?|a?F#a9QC3ohg~Vk(QawVG0(`MM~-@ll%rl5DKE=;
zdQIf0Uz>8&uS+@VBS(GYs9%qA)UPk)jVQ<XJ5r8%ow%IhFWiN4w1XV&Add-bXxEi;
zw9`$>dr^-5$54)T#!5b(a>NHY>LZ6A6NRTxj(UqJN4+JIKTkQ&8|A16M!ihoWt5}d
zX39}-i{x7==Xs+X^}wk2p72h}QSUhAsCR;Lo<GrpVUIi}aHicUDL+Fw>IIam7or?-
zL5_Zqqn;Lyq8xE8MLGH@O*zk}=)tfrBV3kp*w>;Q_O&IiOF8W7Q;vQbQ4YIKqVFR5
zZldoc`mv%PFZv0TW4<O*j&`O{j&>GPj{cTN{ygRAZz<)l&!imo%P5EaX3Al|Me?nb
zbG#{s9T@euQI7iWN%>C7QU7zwQUAE)Cn$&gNy=e=igL6=FNLyQfO725$WbpuIr>8m
zJLIUZnA2-&DUYHY{gtE~^-75zIk+_CXa_mkL5_CH2$!WC^=nZM``VJ%r5yE;qaJe9
zt1sM$a@6ZcIp(_)<*46<na&fGqaJe9>n7Zba?~3``BtxC&9Pjb&N=FjCq{e7Q6D+#
zPZXX)IqEN_e5*I9<`T+L?|I5m4>{@~N4-qpWt5}dX3DpE@6_BvIqGetoadEt)B~g5
zd%`<8Pp^p_^^a4IdM7AHJ>;l|9Q95KpW!^cCUVpZ7*{VuIqD%tJ>;nOfB3rdcuCT_
z&i7B8bE><OZWh^B&9thOR7aFWZNMU;PE|uTG&EbY4Ri#j3QZ7PZ~+&Lw5ruq!EIa+
z&<Lv<NSATNQ5-eWtaeCoM?^%fkyf{3x}vzF<9xrr{M@;HU*6aKC*Sq`{hsHEcp@V6
zELAke$OGy3-$?p6$EC+xkGCFwBJ!z|*Ha%$&nu+oouNJnpM28#JWBc%o{PS|-b7!Y
zYjTx7cG7yC8J?FukK3isV=jHXMy}Gco?<<|#f#GS;|}TZom{0Ke2TqR<CfCnSJLZR
zkF(ci4}a;M<hAs9_i~l~<SEwkH`4R3NFT>~zK#1p-b&BcfA&oCKP9)GpGnW(m!5Au
z-^TqQ52W`wl&kc`x%K>!^!(#8?vM0*XV1T$tp8Wcn4im4`nlYCejz>oP<s3s>G>1s
z<CSui{wcQ}UrCQYCp~T|J+79k^f`s~xS900^U~|yE<JuO<9^9i`ii2F9^XojzbHNa
zlJxh5cZB*pl%ChgRq6`sc}wYeE9vWJeH`m`?j*0J=l61zt~q5re<MBriuCxqrRQ&@
z$Ll{(r|~zRvL2sFkKdPZzof@Id;T%<KzjaAdYoOQZ$D+d&PaNl<I?j^NbmRhP~XQ%
z&(Gy5z5kT;yh3{3q4c`e$G2YRbmUVf&rtV&m)+}>a+Mx=XuVD)z0Q&JymQj)Or^)y
za+S(M>+v(`@#m$-UywfE+e6(~>3NM@rFR}$&ugXUU6g)4t&d~9&K=~1^!!e)(q9}}
z&tFQ<U&(lXO3!!p{5#2O>G{1}rN^JPp1+B{er}Oo=Zf??*6UcWb2nN4=u`8*=Ct+v
zOnTnF^gQc%*7J^$hjNuJpSGSilAd>5dffHW^TyJj1G!wK2cNMXUr3KXlyP6B$2)ue
z8S+HN=ciny&p%U2&#$ECA4#95^?d93=g3p(`L$f7cb~DIKa-w+Ui$df^R4IKPM%B8
zZ{#Xnccb<ER(k$L>G{_4t>-VK=XG+GZn@EV-ctHGvYx+^ai62E`z$?w9r@JBUar!2
z-)KF5L*07*73t$!AK!ZZ-Q=yjHhb8S_4AVM<@G*zqxE{3^g8>}>sYU2z0NW6KzjaA
z>V5T8>-i(;`NyT_ThF(ipUYJ`_f+fgh4lDC>G9U%t;bJf+;6!`KmF8FdR`?x??`&w
zIq7**8TVJN(my=4mL5No9)DhX{O!`?=hEY?uS+9W>8Ve%o@YOlzU^tP^!$s`^Y4)U
z{l`N3{@Hl{N}cD^I_ddK>3J*Zd6#8;pDKO)b$IHejrXryrGI@|FFk)FJ^zY~`zk%p
z+3Vd+-b(L7lQjRviS_(Udj7tQ`zbx&+4GN)2h#J0a+SVwVm*H(J^#4$b9ue={IUFV
z)pNN@e>AZkUr3KXlyN_$$2<FYXUG%j<CSuiZn(*MekDEsNXGkEdcL#gpN)L#<T>iK
z)c2n^S<jzI&%d4iT>82;QeQ`IvL4q;kGm*+9P8s)ALkD8Li#*Axk@LVZase~J%1&=
zPwV;C^Y0}0Qs1vU-Fn_edfpZ3<66(No_9BSE8~7%=lR{!ll1&ddj7uj_+!%Z2QuD|
zQlCrD7)p;HNsm7+J^p&>@nh-rbGb?{e1`S-LVEn6^!Ztjx1N87Jdtt#<SPBZGfL_C
zmGt~08TU_mzO&~~rO&gLtF(QF^}Lz%yz|o6(|VruyxYlh8TV7J(r4V<NY8Jj=U<d@
zKc(k8d;T5dg^c?t^>yy%PI~@Qdj3j!zV&?T`FE1n((`+{O22Zm_56+W{43J)t>;_M
zzbo>olXp|!O0Sdd?R7rxnbzxM((CL?uVcNA^*YDM1L^rgxk@j2ruF=h^!($}^R4Gw
z&p#pk`Exz>vGh8*)O~!W^*V+0I)~EhSg&Kf&KdGVdVVQaY4c3$`IYqiBkB8TJ>Po%
zIr3D-^B`C0GoMvU&$k~+U;C_?^g8EV&z}C$+ojiO<SI4KvL4?`kGm*+zSiTck9R4u
zzMrPv$yNHxXIal*O3z<OkH1rT{#tr}y<DXy++sa`BR&3#^!}{JThF)GW^a1w-PE_z
z>*y~Dw9f1n>vb~eb@rv#v0le|9qV<DQ6EUJGnA|JTen!RGm>8Cxb!;K>sYU2z0UR2
z$I|QMa+N;*+1Be6(bw-yrPn)6J`?KaBI$XhT%}h$+j?FlJ^o1g`dc60`uOL_Q|bA&
zT&16Qw)Om(^!)SE=WjjVdj19J=k|8$bLn*&xk~^1Z0mJe>2)qjuVcNA^*VQu7t-@P
zxk`u6v7Wz_p1+cwZ$007{+;Bt^!#3~(i@*+J%1xT|BCc{>-pC6?<Q}hpL=_4_V%Ns
z|IPO*{lRmr*U6;U*_U3&dL8R^4kDjAd5ro{uF@wz*Lwa)dj4_g`PTET=U-3G<tly2
zbFJqU((?|b=ULCQo_9L(lTV(ZK9N3u>;05+m44y5*6UT$>mA8>E~M9U_IhU{Kl$W2
z>Qfodhg_xmJg=5sXC}SQdFk`AUdMW!3z7AE5$bap&xc&4+n(1*uhUAeb5VL7>vgQx
zxfJ=yC-0!Xkp8~V#`7Ze`;+H&u4|p8^m;4l^{m&kUhhuwTE_DtSLx55*GtdeNYB3_
zJ>Po1_58cZTj}{pf5WJ+d(XF?pGnW(m%hH%^R4F}NWX86Q6Ge-PFmlup<JccJ>Po0
z5q;}*k4vAI^}5#UUQZrNuaist{P%q8b&BZgb3}TbL+N#_*Rfvb40$5s`H`!1|65Dx
zbt>ukN7D1H=UdM|N1jU0ucdx2yw!UCOnUx#>G{_4t>@oPo=eYf<SPB-t=98f>G>C(
zvtNAa9n$j`(&Ib1N(V2n9>0vfex8$_w~{`t^*rn2-br3duhYv_y7dLt>ujXwUy;7A
z*7L3B-%Z|1&)5G<Q}ge5f%W`M`sWhsb@rvt!+IU-b&in-((4T6D*f#Xtk)Sy&p$3b
z-+I3F{OieM>E|<-t90^(*7FPL`G?Z`u%2%{{|tE|J-?Kzbm4{8^DF83N7CcZNzb23
zkFVt_{mu)m$Iqn4pO?q8e|hQc(&OjS;~Tk3!xvePZ>7gyls-S}@z%$?6j|RlP+v%|
z(@A|_|03&kmeT92WZZA*b)3D<<;bT_-bsBey-qJz>6c$*z0O8@oh!~+`?9;G*V%@r
zPTIKN_xHSGFV;Udh<TaxynW~FLoYj!@xGUyKM3`{mvNuvDm~-HL+Rs=q~{-Z&aS)d
zdg*y%>GRB`elC2m_4q=1{GoGpc-tB2@e}FsrPR-bFSZ_ENsm8r&c5WfbJF9d(&KCT
zGdwS^&A$J(+oi|N!&4`%&%cpgucdCCFUo7PU%%}V^*chnU!~`D(&LuW*T*`q<h9ve
zd8hR8)}ekbm!8*4kK0hU&R68M*%QmVqptfTy&wH!jp%2%FSYOTnDqJs>2X8)BRnpz
z%`TPKOOG4NKTrKpPQSoId2RN)<r(R56Y29T=~s9pug&(WbJF9c(&K9SGdwT-+**&b
zer|6k&!y)z()(`lqVzoLc{biRk@dczzL4H;Cp~|OD;e(>c~g3P<s9!9>Cdk_L){PQ
z`Mr$$fLEmFTOZ$g{@vuQ^m%6w+^uIle_zJeOX=U^ID6hfWPQDqUT+}dK1t6T;c@AC
z*7L5H9zT{IpG%J~@KAcZ_4qT=<0sPNOX=|y9!ZZoCp~T&o;qpc{z;FY;qB4a`$YP=
zwsF7cw|G&;=bZGq&OXki$hu$D7i8;oI_cvraV5Qu^*Yw;+(}-`xL?xqHh4vPp7r&x
zo_DwOI$P;^=|Q`B8ScxtU((~8J?|KKAU$s=J<j^LBRo#s#(k5Xf4%g2W9j+1^nC02
zMfCN34Ed10jr%FR&Kc=-CerJa((6>x_rrR<BkI=kt>>SUo<EhIU(5YW_g8w}dFl16
z$63$2ojjMG*GP|Vqkih7b-pM)-+I3F{7aE_->ENT+;8c5OI%6MxA8ueo_|@!`!v-1
zlx)3DFTKu&x^=!Hy`J^?Sg&_CIX!qcF2jB4@z&SPdi+6Ty<er*8A`7|!sF8OtozpU
z?1xfbT~9rio>$<Z^gJ8)S$f_X>2)UIsgtGjyb6z`=UI=lA4;#PtmmJjUQ5rL;d$wK
z*7K~#Ux=)~XQDorex4fX`7K_Qo^L(hdj1{cPI~+jSJLCH$6JrTlf0IGo_gtd8@wVt
z&w8Hqyt~O;>HD7a-`DZH4ELqyS<kbccZ@uco;Q-7cU*eB^?2*?CnD?b38{~z=jGD#
zil{&Nq;)=&UdMVJ>vhhMC(`pv>G2gFNzb#MXFcz%^z}bSeJbPqDm{OO=cVUc&$pg`
zJ9#dB+(vp{ix;KmS<kbccL#YPJ#Q&JZzVn6dc5`cJIQP5@f+#!SER>VkGCFwH+d^P
zK6~hH{J!*f>+#m(4<hUL57Y<J*I^`moa548m#yDF*7L5HUS}*lzK|Y&D196o?>Fi3
zXQanZ!c!-$&##i6cO*T}dYtvVbL6S?{j#1nlb&~8dY<+1tmoY>z0O>Ed@DWvqV#y{
z@z&$-kRHE~-p?}flTTXDTgmwTMtYvJ=iMniZyoCUOzGooq~~3cKA!bB>v?xY*6)$2
zZ>8sD`fu8M-oEra>*HC^8_2jn(&LWHxIfb4oIP$V<Gx6bJCq)0J<fXEMEY~z`uLUf
z_#^4#Tc5Y}__LAq{fqSYne_Pc((7BVZ#{l4J+74=cTvXmmmcTraSQ2jOX+bd8P{8S
zoU_NRrN?cg$6b+eeWkyiIeXkzdjFaJKLtF_|IZ0=J*C%k_PBxcxS{m85gwOuU8To6
zd;D1X___4>0uQCvw;pdjej+`-in_jkke+uWy}tE$>+w_R@iXah=Ve@1>2b~;H<upQ
zNRMmrqV#y{@z&!P(*GXLdcU3Yyd|!r&)<5U^}Mz8aW+xczYmt4cO|l}xAeHJ^tep_
z?OXTvWn6FR`{L|z1L<)?>2V`GE<N7*_}1gc(&w8?k1z00dYtvRiS)RVeuYQU>)LpZ
zrN>XD$Jf&9&hR|_IsI1pbH{qzMH$yO>W{CS{ric9^m<F_ajWR-I!Yh!vh@3GExrF<
z`n;^iZ}3Xg^>dJn&kO1KTj_a8|AlJLv!0jXe$@4QA?bP6^9IuAW&Qilq4a#~`6E0|
z-NyBoo<Ej;ZmieIrRQ7EFYu7M_3^FePo&SglpbH<k@Pw??u+#Jsr2|-di+fK`djDo
z((|m((|X=qdR`+vuEmSe<E@WlJ$@lQzLOrm#Fg}T>+#m(*V5yA`quL{(&uNLuh6%i
zZ#{o2J>U91q(|+JV?8g!ed^Zpt>+&^*6)3!*BMIB8{u*3^R}L6J#Q>MK9?R};Gy(*
z>+#m(C(`4s&$E=CSK*QLJnQRkJ#Q*Kua+J^!}HSPZQL*E@pI|%t@QYd(&Mbp$9mjC
zdfZZa+)DcWXFbk(+*<lRSdZ_e$8Ye8^!nE4VLfjf`SmYJAHN%);lA{E>*H9DA4so1
zlwN;?$EDB9dc5`c6Om7y981s3rRNoRD1993dDinL(&J0%@f99PkGCFgJ$@>^f9rX*
z^t>6Km!4-m&wAcmdVC{2zQv0&KIf#bm$Sz&q{nyC<CnOSUf+7W_4vy&KKG>O_0sb;
zctv`ijr$@!Z!0}MeZp>hhWpavt;buBA4rcMO0Pe{<I>};$6JpdM}GZFtmoy@^9npI
z<NG4%>snH;@JM>ysr3As`V7z0pVM!sx459*k(aoVUf23Q*=u!PYw6?kGOiz9ksfb7
zek(mL4Y_W(FFnqBoQ>-yJ$@+Ty5U%Q-`4${dVz=1>z$TyoutQ=^ea4)9%p^rv!T9!
zlpbG8A9sf5rN>*3zYyy8#nR_vJ+G0T*WyL#dDinTh5Gkg(&Ibn@k?AukGCGbmLAvB
z-{2MLan{ezR(f0-?bge1UwWMNdIRZkL;52;E<MhA+=)=157Ogv>GLe`P<p)e_=)tm
zl759p(&MbhO{K@x^k;ZpdYtvRx%9Y(ej9cDzKMKMdc5`i7t-T9`pc;6_wVGD^myy_
z*V5yA`Ww6=J<fXEU7`M7M0$MsM6NIHOOLl6e-P^Hq4fBX^mRKfJ<fXEn0`*bz(eVA
z*5fA9>y-2>Jdz$~J#H#JZbtt+ee3>(P|vIMxQ2c!eSR0o*5en_<CgSS^sW1A`WyOJ
z=v()*<GXeDWn3TW`at^owITJ9jO#-_E<J86<NG@5h4i>Xvh}!$^th6GB|Yv4r}Ss!
z^Ei*XzRr^`;)42;yu!;ezFyGprT4X=e+9SE|FW~`#O^#Y+{Xjy&x?VK>p*>k$8k(Q
zCl`2#6Z$2&!Xup0ugNn!k8}DBxy6gPpx=>~xWYC4p1hI1KGxU&3f@J1E4|-zJ=X>I
zaUkP*kVkkN$MkdQum9G^Dew>{GTtxJ<4Wok9^q8_e67dV)Mt1e=Q2LuWxQ{ww|Eg3
z^gHqrSGcBc{rziCeS=r<F6r~y%D7&i#Pz~`9LTs{<PjdnG5vykh!g51xxyoy(yz%g
zJdbnw4Y|dOxS-#Wm$<?;{hqwRE4YomK4(9fed9h3r0>H}`u<u!2P5g92d(pQ97|uP
zT*h-ly}(19NRKOJJU7%UJi@8;xLSIg_3>tS9_P~M*^)2ff_g_@;tJRFd-4Xa;Ff-R
zG}j0BaUkQlAdm1kj_DWD*US3+4{<`jqHo<l!YTclJd<9>dfa)OOP@zWZt)^6q@Q!^
z>$If4!nJ%vcK>r5@)g`lk56OvkNY@~@qCa+cpS&{b8>-)IH6yXD?Gv}{TcZ@&Z)QL
zi@2cPk(ao_HT|Bv!7I3>pFV~C;yw;!?3X;k<2a_DlM6h=3H_2>;So;h*W?+V$2t9m
z+)965T0ajLvBM><a4r3u^yH27^JG2$3T~yxrBCHL;64td=h=AvsgLkDj-#*dXUPQ~
z;)H%luJ8z_^lS1A&*PkaLvHaRF6ejUC9ZHyzb9|-3T~sX>+oq@2kCuh<b52-xDMnI
z9>=ltJnL~e^#TuZB0bJ}TuHscBb-W)vmRGdpW%6&M_=E6l3Top3;G>-i7Q;w@5vjy
zf?N98f7)H2eH=)i*NA)^$J7h*Ax@}Q<RhF$UEeQ|XVRZzHa`DwF8#i2$Sq#P1^te^
z#1*ciulEIcgI91%KYcpS0q)~K#^(ZgBz>ON=Xo5*GVVXQz(brwUq644D?Gw!^mV_<
zGdz!T8Q(w2xE|D7yoi@%d_9mppM~`INge$qu5c~=yjhR$sc-NKZt17TaGh`;2h#J0
z<PjdnarE_hK`!tRC(`F>y}y!rg-1B0Uz2Be9_REMa*G%7l8oykeVmT^5?8p6zODy(
zgI91Hef@pNXY8)WK92A>j%9p4NME0V`XNrDuD?H$J}>L%>IkRwYw}EboQ?Mh&Sg9o
z<Q5mym*h3MCvR{|J$)wk7YEYo4#^`NM_r$H<N_zuOLB$NsO$Ysp5dH&LvC>)<37>v
zs4sC%y(e#Q8+CoIKbC#qK*l~~e7{S5gk$<See3I6;6(a-tk<ciPb2GnOP=98>iXOw
zx6<>i*Ih(ke@{nV(zouf>G$LfZmFlou|FKhc>j?{IHsPH3!G3d$rYZJKF=xrn)(dq
zQP;l6EiR~c<Rz}D_v8(3si()Y59!ahj69IBAMyyt)N^uy6Y3?o!YTEc^!nD<cTT+_
zx6<!Z8}}!&zOR<P-yQWeb?bUheH&TN-4k~62Qr>F@;I{IFXRciBu~jTc}`wnM}1B1
z$y;*zERHMVx{=4^T*l9B)F;$S>el<7Qm?67*Jn7V-_W=2x459+k(aop-jg@Dm44o=
zuUGcj>_fUfkZ~QUk8m7yT~Bg>6Y3?o!YTEdJSR8O*Tu$tr`}PwuCJ;0<ZWdA{Pa1T
zuZ;7Q@%ci1Og*P=z1~Fnd9<E4#hU({+>l$mBz;{L^gHTH>Nd_-di}NZ`aSiH^mA@K
z?yktX{?x6XpX`R+IO}l(8P64Ygk$PCxxfi^8-LG8y}}x2ctLt!4Y|cj)I0JL*V6mi
zL|^-%zNMa?xI6DmdcW5DIEbw0O2&SukMIQjG5v!2gu3-{OX`*Mb+%rA8hw2|AkXlE
z^#11IlS3cBq2J;q#w{3UJ+7m_#C6nlU&$L8_m#Y*pPs~Zz=8DnkH}+kPA;VPW4)gV
z{gPbalzL5`Ngv;O-UaD(<}&t4y_FtkJ#Im^u}|ttyc~V~yNXcXr%I3O>2Giwb-f>-
zynFvyub<&Sdf!9(*8P$6d0OW&*?N3Vy}$|el3Ype$9n#hel7huU|pZ#1sT^xdi_Sm
z=MViB7tz<hHz8k^ety=Wt^@s^{ziIT>*H;suj}!-yPV-b#&sZ%q|e{R{-xI$%h*5l
z0w>f<a)oE5&u<!iJ^$2aIFG)bd-|>PajcJfiM)_L9~;jD{Uu(GzOF;`_54%cL|vZ)
z<SqSllKtaA#{S789Lv}@{hWG%r=|Bdp<hz3a2j>(n>>?V&w76s$aCra*?3>jZ}C#}
z_4yusecwTSiEHXTc_V#ZHtsw9^c3C)()CRGxYl_feSS8shm5aV^hYwD3-VZcTtS{h
z)_-q~d{)N&5B2*C`ZN0T$of65jQdV~LA{ec{*wBd`bPRZZ0t{Z{+520voGoXL1cZt
zOP}wE`Z%(F{vuDL*R{S6mGrvS>rA7rpI4;MYes!ey_KGCJ^oT;-8cFj^<~uc?@Y;S
z`aOArTN(SJpNieO84hIJC-MkSNMHAH^!5D^^#Ui<OLB!%8TTvt+9&lH&ZDlcPvlm{
ze#r~6jrXba-{URmUyi=sr=dQtq{nUOZzJpT=@k2v-hW0O$k;D=Bz=7A>piBQlMDLR
z{fYE?*6WniE1XiV$upcsUHc}txR5@+jr~$z;+np7zo)*zZPc~j!`*etq|b98<2jYF
zKk8%Y^SAEj)C)W<eg7u(OX=s-dff`A(bwk^ee3=V=hDZuKJSKlOWk_>f^1{|)R(x9
zx;~G|8{AS)r+4dQIFPY#@|avm@7wyg6Y8am{ZX%QO24LWV}Cf0zV=0KaUtV=((kA*
zsavnJj=uIK<L{}dZ>eWzcH;)p=WV^Oq4f9>{jv1%tgl}|{dClIAL(27OBwr>K935g
z(bxNxzK#9joPI-YaY4N!FL6!1CvT+B(|X-4{dD6lXE=~P&mnn)W9m7%zzOw|TuC3t
z`Z#AJ>-z`l*6Y{wXE=|#zHgGgzAg0y^^Uy6HT9mn!L9W1t&fwQ%KoJ9V@4jxxS!+^
zj;ZJ5LV7*x{hW@h`zk%Zq+Uf``zBB6*W?+_rO(59enY**1@(@+#C6p5`9t2|mU?;`
z`^15a`$!()2^n8kLOr+A;|dw?Pw9P5qOSc(-w*40g;V-9ee3?5dP822JMx;`legqF
z+07rwxG&@}IVVpdf2^8H-xnLt6a9H)U03oY>HRLKchuM9p1hUu+(cc^%}tz-jPoIn
z$vJsKF3A;6sn5t4q|ak6<Nnic=(o|=b5Fh$S?4W%{EqrE>iT+2z8qO!kEO3ePkn>i
z=<EGJKRtc7uM7v$`?m4CP#@tq>iRlGE^tD<Bv&}4UXy1yr`||EPu9;%iwpW4ee3=b
z*Ytbx2Dj4J)A~B4XRtpU$hd#x5uT9oK8U`)zoK5?Y5EiTCH0EB_4%D8Ph~uZ)Mt2s
z{yfyzW9e}X{Z{(?t=C&f-w*3?9rY!yqpq)8<PC1Ar<>V74rJ_~Jd)nG_3_5^b8<o7
zx<8>`lB?+JbB;WvUz2C_t>@3_H{=!<)I0JL*HPDVOx{ReAM5qD^wTqUIm3aB=a)Rf
zG4-5WNMB#;^-f3D=ehL!l6n<&?UOu>tgmCz*R!TR!#VYa+~Pv|{H^D8)R(xX-jg@D
zrJkO}K5-zu&WJpg@f=gnsTVk*Z#}Q1Ug0$A`g%;B;avJU*?3;5w^7&McaRs6^}Nb>
zUa2pmuAlqJ>*(vbC2#24xUclnE$kNu($~-W{D;&>IHsPH3!G3d$rVne_ho&)HT4<J
z>D$;h^%fUV*L@@}aZSA^Z*UuReLZ<L`@(_r`a|*vPsq5R^mFP3PN<jU3a8X-@(kzH
z8*+<_sO#Sqk=N44wZ6_h^$l+6Tle)L9``N7f%JSE_lf!l$JBFjA$?!1@Bf5;Nv?28
zy(Z6aPQ4+wxS-yVm(us!`Z#O)J$Xal#&bwt|0gZG{oz2yzQ`jSQ_sl-PN<jU3a3%m
zb4Q+&8}cHuo-gv6+>^KD^t|2q4P=}bc^p|^&&U({C3z}+-L2=<)aT@eyoju?KjbyJ
zCvT;%tMzl7p3nJ6*9S75Z|dX7`aXg@A(!MSxhBua4f&G%sP5lFdVTBlJNlR9$9#XI
zuIHEfmYi<o{A64&@|c{HC*+cRR{FV~Qm?7c$qjiy?#OHD^RzzCp87Vj-ajwkd}N#t
zc^p~yk31ol<Y{ExH}ag^kQb44UC3*4Pu@!Zy^Zy8(+fEd8RtPBN7i|eC*+bmCD-J+
z^mVdcr=h-(KAv^GqrN6@aquGdKUNLpbM)`lt>2Gh>N#~ApBL0i@|0YY=Q7SG>Th`Y
zLdJQJI~kt`<et1Grx$ZwWSkdyOwP#@a!H<&Yx11jkQd~Rye9YLEjhh}<I6Zcc}$+%
zL;wG?l=P?6Yx11Dkg@;Bx)1c%<et2ZtoQSO=J+y>Pacyep}wD>KBZoh=j4XGh^*t1
z*W{kOC8w8iTp7<Jc}&j96LLwOl56su+>jUKj=U!K<SjY9jN{ALH+f9X$rEx(o=RU|
z>+`Ou&m-&med*u(SU-0Q>K%0(&m;Aoyd|gGI4>DrXJuR;>f^|Izmg~9k~}5X<T-hP
z9rZQ2CvVBA<oGhK3wcb=$rEx(o<@Gd%jeV^>I-s5UPspFHhC-K>r2%2JXRcE#@83=
z^B7}Je-c^GC3#A&$#dzif7aKpp}rt@<Tbe`Z^?s~bKRxy*Eq7SJ9$Db$y0Joo=4W_
z5%rGxn%tAO<aEToW!!J_n4FU*<dQs<@pX%OO?@6&&m(z3?#N5&>uckAq~DV_xTT)X
z?#5*}ka3>m5ss<n<N_zuOLB!%8Rt)bMtvSx=TB~NLA@g{aZSA^Z>0BUy}xbr^>y$Z
z`@n&WeUL{urk;}voKP>x6;7$w<QdMXH{=!<()+ePen)+Y>*(wIis<Y63hB=k>-}tS
zOFzA0*SGFxIFNq-jL2i@>tbEcsTVk*Z{07cS2&HjKKIBooKtVet@Qq^&u2luBQJ4H
zy(e#QOFexa`@w;X`$Zn%n0ihwa6-K#S2&HjzCS0=@Pdrb4e9frOCPVH-%_{U*Cpxk
z3+e0CQD5SkdQaZqR(gHw_0#9GPaMej{F7dHNPUE3`Zn$(^#UhR*XJX-!YTEdJi|Hl
zhTP(UdPiR3ntD&(;FfxtvL8H<zD|RC826F>2*=X(oLu09dP%NuO1&n}@Pd3m_U+Z2
zenY**h4l5cao?yfaZTU4-&5b<HtPEKZm-<!b0B@*Bl1{!zc%(my}$|el3d}GdQG0;
zoO(lUaY4P4{(Y<UbGgJd{hq#ce}h~4=~dh>9LTso<Pn~bu`l{L^#V`RpG04uAJi+H
zQm@G~oJ-#i>+9W6Z*f82#{Htc#5H{z_lx=lw^7&o><ic@4rJ_;Ji;;coLu09dP%Nu
zO1+l84>Rg>>MeN@S)UW+C9b2c?~BMA+)_`k=Dcwr<Gjfu>CY4E`#YvzkSFx5=an-4
zzK4E=Q|bF)eI0A*an|e3a4zG1$+%zA;~M%cE~2lmZ}e?EpSY&q)3@<_;+B4T&2D|`
zeue`XpDQx12lWw-spsSZC)7)Fg;VM^d4_Z94Y|by^^Uy6b=38_LEhk&dip~4A$=V)
z@<4hYBkI=s9aGQA1x~1!<O-)z*Zm{Ua8A7;x6;?o`Z$Z|>wb}!xRxGgeSSUl4Q{EY
zFJfOfka1tgBOFuD$pucRm*fhk($BZ`dNuVK&ZDo-kLc_3gL;b#`qt0ulKN#C`--~u
zMSX*J(cjWfU(EjSK*sZT4`aXdM>v+Q=i~w>)Jt-OQ|dK&CcS^_`*cD2xO3@sTk4mn
zFQTr`8R|=1Q}4+e+)__p!v1g|V?X2(j;R;q3ArR!cvkv6r}S&;Ga36KUy!jM>T?<U
zqJN40f_g`NiEH}S^LpwV+)~e8yW8JD`sa8X`=dU>G4-5W;DmZbo|0$e3o`aWy^-;`
zNWaBP(&HA=*TMQcI_dG&_jyU*I<F(^=K>jD=csR}TaVk4t;eM=-PJQZkUsuE#{Q{~
z@C5xa{hWFs{dr}5oC*DsT+z4gPwCg>8P2IU<Q5myJMt3O)O+#<x6<chW1nBfK5-!3
zx9$(Ak8m7yy)VfHPN<jU3a8X-@(kzHTj~30z5fOEC3#Km$s62C-w*5ik-nULNUxWX
z2h#mf)b;O^sE?@^)NR}!>J@oPu4Q~(r#{0u{f55vIxQ}wuc!6(?5HnsP2alTQ{Uh=
z>e}B|us<Bgcn--U>FZ&A{4v>jTu!~f3H6d(;got!p5dH&LvC?Fy(2HB_iKF~Yx+HT
zL*Kf;jlSM*uVa5Wkn#MHM>v*VKPMN``?g+hB0aApS2(3!%eap+{yWz6=ky!;E%gQU
zj{1^%Pu`HX(#K7&->siXpU*(XeWE_1KBk^iFQ}iE-p_=7Nx!0hmi|=wIk#TFmfqJ)
z`ndC`f2?Yyua|XwLA@g{>09^L(bxaZgt~QoOFjF_T^`7|KhoE6M13rMyn;NDaX+Mg
zA5&4EQm?7csL!c4)LZI{sO$5d`jYyZdQaZqHtM<`U$r~m3<omq2YG~J>N&Z<3H6d(
zNndyC>oTQZlV{P_eIU>2H!^<DO}&+IU&srx_4Vqc$6Fs~iR<X=>$UWC-%#ID&%T=b
zBfZa&jL!w?W9kKYLaxYD>GQHakDB@n=k%@n4fPfmQP<y}lh@>)yumH?^flZc9LTso
z<Pnak=i~w>QP=yOT;Y^@O`hRg`u<rTr;%}A=(o6tzU~8k8}|X%^f%Ji*T(yVyp6to
z-|+_ShxBm=GWI9qeM5g7b^U%(`g{uNCAlI`W!x9)GwF4$k3Xm1MqTfF>I>>i>el1e
z)O+#<x75?u?#5*}kUsuM#&bn|9Q8N6JSP`8kv>1`eV5cLoYJ@M*VJb?r*GqaQEzcU
z-@4yXU*ej2Pu}2`daBtU4y4x^l1DhEo|6llP%p_9PN~nxb8<s&rLT+ieOS=%$V*&P
z@5vh(&l`D5Kh4+&4rJ^@#&bq}Og$$TIH6vWE1XK7r}cU@^%>6Pr|NU4A-A}o-jSEm
z>)3ei==bDJ^!4`-<ZWdAzE?gX`+;-m>)4<4=Tk-=NI#E5@(9P&3-W2{>o%cYQm^nV
z{VDyL`V8mN=Wl%-8tN@BqOQ*i@)Fn7d-4Xi)YEzPC1YRYfsB2TNA#_)-#D`VcQDfD
zlT$Bn5_Nqpk}I52ugNo<Q*UH^9#U^{LBFGKeI83()9>k9_cyqWzP^vWuv_1{pGn^r
z>pYOY&er=HQ6ERv=Oekm3H6d(;got!p5dH&LvC?Fy(2HB?}znyt)s86yW|ah>;5*f
zo>%GfPPel^9LTtz<Pnak=j1~AdRVVJp<j|KoKmmJGn_|V&nLOX1@$F)P43AX+)7`6
z8~gZr_JIQ#`yh{SOg$$TIFY`N*7Hj06;7$w<QZO&@qUZGeqTd<5n1mu@)Fn7d-4Xi
z)YCVx4;;wY2YG~J>GQSj=hTa+>+3T4w0uDJ)@mYsol5EzPN~=A8D5YN$lh7arSG%#
z{Dyvu3+hYqW%+>YXRCG8^>ZTi4c;YhO21ZZ>8EdGzjz=$e<0)iC4Jl>{Sls!KCf}~
zwSVdbPNe7C*f;eGr%~7UjpP~5sW&n{U#PdZh`zpl)3<*9m$;TbFYDv>((71{+u)Xd
zn)5j$-Oq3!WB)StPkn^r=<EJRU;C$C;OXdV|MW}hRn+zGqRG?f>+2<XhI8sIc_F<o
z>+|WTFL6!1CvR{Yb^UzvP3#W`GM;PlNcwrUz7AvhIk}*3-Je9({$$)=>J?6-{;_IC
zo|7ALi<e~VOZs{*WbBXr5-&?Xx9jNZxu(9sEp?sT?mpr`#y-g-97kPW56Ok}@onr+
z`n)DG_D8+KDfOB>!@2Z2*7F+bt@L`<>o20O&nNOydY*NEO}{5^a4S8|dj0e*yYU$g
zafCS*SYm}W&alB2J6z&g`uM%{?-8uecSGGeZ>6t$dgE@Kbv=`wZ=DCy<3`l2>tnL@
zK6B~?b?b2@R#@W<8*FijYZ?0>Z!o=y^TZM6SYU}2);PlkTU=r<ecd+X>|41W()$_W
z2y^N21-T?w<eEGqx46Wf{)U{sjs3~E59A?^u)tFK_!YS(&&aLxI@agE#0}mh{oJQ-
z=eT$vT^~w+K3mTpVNTt;USNeY>El}WFG#P~NT08Dy~RuPJNnlBCHA<%^c}l?hC>`-
zjs=!jNuQVX`Zdn5!4@w`kMGD!yeuD({eIPxH<-SY^THvH@PzbwIk~`6#(kt;QJ-N$
z-^O*rOVa<|qoZ%*y2<#ym)zq9(|_SSafl<#vA_~5tZ{}Hr1#a3+sOL81-Zi|UY2qF
z$Qw-G#d%3TFB$njJ|MfR8j?qtV}YfN>qwqSpO^J@Y3SRyj@aQ+dc1YN#|@_M=KOGo
zBh0aoK7Z?VO02NPnT+!zH`qp9*N@yu?`uik;9b(kN#C>UTOTjO1L^*dzIA^@-8$!3
zV2KshIKu{8>~JaL`jLCwVESIp4~ICCo@aeNC#26iCl`2{dP%OZmcBkSa*G{x>vfmX
z>ssd?H<;ec`AMHoMjqk_Pslhwa)GC*m*h%%9qZ%O*kFe}rtjO08%pozwDfUGvh_M=
zsn_HNJ6vLq8|m|~o}b>bGs7W{FvmiAp7p%bGR}uw;aTc6xxr3)AL*_Ck6ve}Y;!E}
ztn~Ud`2x8ici3Zk+ism9=2&8ljr4h3mR`T7eiu1?zx$_7+BiS*kX%RBzXu{WWb5%A
zUY4;w`7Uz$0oKJFORTWQne=tHUcbQ>J6vLq8|nXFgw*Wzn@RuwV~oNJFUhG}uUnF>
zb0s~$CeO$Xxg~ex9yiqQlHPx6ck5-+>m5kfhtzW{qJI5}l3bB%8Sm%F7hcklt<Q5Q
zy>3t4y1t>FesGtEIFcTplM8Z5uB5M1O>VHmrHtc|H{^7YeM<L-<Qxm?CApG5&e>4U
zxr}{Mzd*gCZoRJ_H`2#RmpHDB<B~_@oLpi>y(Tx($F*L+rQcDvah<96<PAC9vFi_U
zBz+z^xx`A|l)mjHHF+jIt|52WV|qLLlCdx8^DM|E{R`61Q%n64xsx7m<NWFO<PAB!
zgX73J4mrnydP%Oak#XI~t@Qkk++%tt>r3|!WIX@WPmptRiIw#Dn%rPZy(2HlJvl8n
zo{asFa~ao<dWALp23zW#jO$0e$Mi$2FJpajjs^9STw{YR{Y%o<p`(78+><xb>!%;y
z9dC#u>3U8s$R)WV*W?+wA$Pcxe*g8-`#ks&udAPLq_g#NlVgdM^zqM1pMOpL0=Xf#
z(&IXEj~nXgM|a1uK29b*en=jXb8<m0$u&0E;Zk~?o}B(G>&sZ5JR;}hf?Sd-a!qce
zuS-k4CtIKYM*4cDA7fwA^&#e1VkLb(HMzl-dPnYYLp}XC`;oC9@`#+13vx-W$Thja
zR{Fd<a*ydJIDhGVkED-xLi)a+#!`Bp75$prNYAtGxAZ&e*7YUzp1dKapWO8`8T%xU
z$T_*hN_rnPxxrR?en(!Cd-8^yerk8TA?8?OCB05fZm^}^k$dTVZ>VQK&G}33cSO#~
z1-T?w<eEGqH!^<DOuZx9xQ;U3&*bzj_JcW=SjpHAxxtotNA7V$J^jpX-Au-Pk@0&G
z>Lcnob?fshsF&2O>owWNKBU*}qpsiEQQuHc|7~}Dt;Y{>B)$KfTw;w4cDR&r9CG^E
z-FlgfeUWo4sF%|Bw<6bM>+_jWZ>U?>Tk0LT$MkdT14q)w&!vx3kW2bC&ZPI-kUQ)#
zy_@~W*bh0!f_f=^|E$lmCfnGL^!$dpjr~yXs9V>U)O+gI^$qp(^Sk3&*E1RCN6xX3
z-d{<svB3^|+(@sJ{`+p-A&#W$Il07&dQF~@8*)qT$V+lhPQSo$a3te6<PvLa>38HF
zH`LQFvL244zh68N>gRvzh4gVwQ!lAk)NArA`g%Ud9rif-CC*p+K9pEdugNoVLvG0(
zxyRJ)*2%HNTKYM*K2Afn&K)kL_tlfra@WtK>qBym1@)3#k!$jd+>krz^XTuPkDq>d
zcbp;SSYjo8{F>ZgOT8oam{z;vWHQcM`ZyzUPA;*=4tv~4&rg>*KO9NdOX>YsuU}EG
z$un|8Zpj_F#|`y#=We~BjPsRo9CD2_>J7OicjP6xC*LLg{X+IDyY&vFb3v}KmOk!`
z+>kr$aYH}-kKOo8#&sr-$R}hRhkTk`%D7(CYx0cTk$dUSMeFhDJ-ck3Ga1Jv=U7lL
z$u-WXH{_Pwk(cBRrZvZraUAl9TwqDP#zw~X3FMA!ecnsy^?GvpRgNR$IOH4)>Ls}%
z*W?+w#g6(?`tv)zcenl!PssTG5liZo^zj>V8-4wJLLUCwZk-ZqoJr5?$V+nib@nCQ
zKaf7|h<t*aOJA3QdP%Oa!47-e$hcm=v70}V&L#P*^zmx)jQ$1c4fU3KM^3-VdCORz
zoMS<~Bv<5`++c@2rr%;+8S9cyNS|j;F6f`8UQ(~9*W?+wA$Pc>-jmaBb6gq6C6CBC
zxgeM18fVlSa!c;WOL9*h{?6`rITkYR3%MfK<OX|8@8f)BoG-b=ih3>mKC^y)X4Ef8
zU(bfVb^j9eo}7MnS070qCnuLUquxj#rzLmvd%R2fcn9y_Ig~!$NXEM48fVlSa!c;W
zJ#MHEe{Z*5A>%rdYizKke@Xtlj=v=LGQRFgpI7?8E?egz=2&8l4R+XL`hDhOjwRMO
zlRoc;++mL!`ssta^U9>h52ep9$AWrEu5m`aA@{hUp8jCBP9|er@`#+1ORT8Z<OW;n
z9eGLa$s2O|(C&G$aec`{@<_&gBv<5`JR>*cmfVq-<er@VkmF#EC05eMtH~E+ygy`o
zoub}KkL##gpZ}8j2GfVRo-(c{c|^|1CDzzrhfC@CJvseftSe((@`#+13vx-W$Thja
zmU>6-rT1&SuMPe5N8B&zdM17R5$4g?|2~L*i8aoo$2ZdNGwXFavW@E~W547LIrSV@
z#=glpmRQlR$qjb6q~DV_<g{UZ8S9gCEU1^{8XIiscjP6xm)>vsW7e0kzVvk-QqRfO
z*P)UgSCePthTM`n>2<8<^<?Y3k#W2$yYm=I*K@LUE~Lkm<ceIAXXJ+5;gWh!PIv9r
z8{$ZM-JD#IOLC13cDR(D*OSxTtcxQV>yis{Nv^TMmVQU>G5yJI-AsDDq4fP4kxxs1
z4wTfbuUAF?Ed83kb-%$5mooN4-jLJ(WM4Rvu`hCo74@1tBVUm5IYqsteu;WVy~hpx
z^rsvbN76sX=j1~AJW6s!uE`f<9GBeEzeK&G-s6UT+U|~<$++I+5jiK9()XdF-cWDJ
z9eGLKVEVJ&JnQ=~BwOc^^l?u}pMOr@x__E_Nv@>F*W?+wA-Cj?+@t=HU96YM*e5y1
z5-a*Oc}8x?Ex9B2(${4}J^jV*I0rKRUWA-u5q13@M*91ml6pnGCO6np@5q;>|2vjF
zIsGO3lCdutpJUWV)N|_A*R`O2I@I^0(bw-==+Edk)UD@r*yBd}xaqHU{UPR9NcT%}
zCH?bVO`g$j$SwI&sLxUAJ#Ofyzuq11K*n_@=U7Bt$0b+fnmi*n<PMkAd-7e8_3s)!
zvb)cj^!suoy{}xx^(2?%id>T$Y^itT9yip}-|W`QWZX~kh<rNKzlR~$^c!rYuWv{0
zaU<hC{Vmr`#&si)B5Ob75^J2%Z^$jVBQME4IenCUNT1J;oJ&8SHm;kD=Yx7leMY^B
zy1qZ5-jS{6FQw=A<PACf9mkh(9mzQs)Jt-WGwJhf$gTAJj@;vhdiwj_afX;<A!A?U
zid>Ut<VO1QV<~;yp8ke@`Ulp>k@UJbxx|WkO`ee(a!c;WJ#MI{f8=_}xL)L3#{D7}
z^h@g2_o2oHTN&4lyd?MJ4LSW2$CYtha*hS{l3e4AdP8o>9eGLa$?2atj*R1wb1bNr
z<XU>)*86M7*145lXNi6E^?PFa8|vv_cK6qM-VjI9^K){E74@1tBRAxh+>v|SP}diS
z-EnXv<2d9J&qiJQBhMo1=K*p{?#R9Lb9$G&DSf2M{*~(}T^~yCFDF~)Li+e6xgyu(
z8Mz_1<c{29`ZxB4BN_W5msnA+$qlyDJMxm;lQ-n_zc>z#WE_WFVnw|s&&UnA!zJ~e
zoc?{cekQ%{kep*FeLXAs4cW$XN4=wNU0+h~$s2M?S@bg*$0O%hP%p_f&Zsx!4tw0t
zPuXt$A&z8RFLFUH$rZUK&&UnA!zJ}z`g&x0yLBsUWLyt&M_!V9>3JKn{>Q(&>mcL&
z$OXA1H`r3|$s2MyU>zCPlRP5l<bqt1Yn)MU$er}(jP-tcvUT1_A16D;@ucfHmRQlR
z$qjb6q~DV_<aE{UIt``Q9Z4UrB-c2j-jG{zNA7V$Jzc$9Z-^rq>ym4nQE#!MzLfFx
zkesezJse4|my-)}Nv_B>xxtqD2Gd}-f9w5c((4Y%ITqAQaz(DmGjc<2$sKt~?#UbJ
z{iS=cFC59(7r7vpGCmi`Gi<0^pMOWT&OPcsS`~SSIhI&sgB|vmu4BIRc@)y;Sx45t
zvn4k&&I5b;*7c3_yzJgPN7C0Jm+qHX(XYvk^!vnm+>-tV(|vaJOvd#i7vz#$V}mXI
zj=UuI<aA%wm0ov9&SiXGL@vqJ=U2(NKGbXKGwKa>>+vo1j=FWdCtK%@^t$POyW?Be
zhh*zKl5ri#1-T^GIHTT>JM1xi+-|*0#{S7U7Su~}jWg;Exg~ex9@G6ff6TGO8XIh-
z&!Z#vsQ+<G>^qZjp5z=$tmxM=zQ3d1kZoLd+(@sN9=NNIq<{V^r0b=Ob?7(PQSZqc
z8S6ZV{o+W*?|I0DjCG~gEveUJ>+_pQkL#!}$vyoIIX!qcK9e3lB#+2BxsZMzmNJe@
zy^@|+Q@3$^>MeHkm*if0TzUw{mvMaZh@6uP8OJAA^lS2r+>krz^RzzBl73I!y1t=4
zeCTd`E?qCN#s)j=F+FTIekgrB>-jm^I@jce+~e@!jK>mdY_P)~^&k6)b%!#JOU}tQ
zj!SOH9i~Tmo_^0QJ#L6OmRL)V>&QJ0AGJGfE<L}*ChGcmoZMlL>Ek&b=2&8l4R+GU
z@5$*Cm@i}B<eY3{-`G&Mu6JbXoQAB2IhI&sgB|vmM!WN{UT;XY&Shl%dm3_$4R+XL
z`b5^j980XRk#S$hJ*MN#mvMc_IobNW8tkyAZ{xb0U|-TX#}aF7u)`kH^_&OhSW2Il
z^}03LI(OJ(`XtUnx<4e_$e+x5@>;Kl4R&&sKJ_Km{a*U`);T?TXD+>7DdYUe`ItJ^
z*kC6;-g<sdw$8&(abLeTmC=8~&h%NkdDitD%l|unc9!0vdX2-++2s-&>~VMleQdDD
z;S=d&gFOzPL?0XMark8V*kF&t&!vwI_BcFA9~<m(_!RosV2{I`J~r6nu%M3(_BcF6
z9~<m(ct{@`>~VOSJ~r6n@C<!yu*czz^s&Jnhfk%C4fZ&E8hvcA$KixNHrV6vCi>W5
zkHe?a#|C>GK7&3s*yHeK`q*HP!)MaR274Sni#|5k<M5VUzn1?0cWSW5{MoyDFa5sx
zy=<`enR{Qb_olr!@BPExV1Km#Is4zc|J(Z?-v8+SzwcjrFg$p|L2+>N!OIU`b8!CP
zTMyoH@Q#C@I{2l7_a1!U;4csU`QX^Gk306LW8-5_I943H`Phq&ojdlG$G-L0TaLZs
z*iRk%rDN|s_JLzpj{Wtqy{jI4)hAx{=~q4RsvEC*&Q&kF>Xlc0`Bh(c)tj#RzN_AT
z)w{3y&8z<Ss=vBw@9O(p{qU<l>FUQ`ee&w(UVYouue$mxu0DVDw_g30tKV_;PhI_<
ztAF?E{_4NKI=klH*F5Z+>#zCDYd-gy$u-Zvrn=_U*S!9k+pqcdYu<Lv;+l6|v%Ka5
z*F0$OX@eUEFB-IiKN>vgUQfB#^Y8W6d%gGCKf88t-KSmm#Ouyo_onN9<htLv?oY4#
z<a>Y5y|?#%$bHV-=e75F*L|+s=b`r<-}fo^z4^Xhb>GE(KXBh;_j}y^s{1wfd)@t)
z_kY3z+6VmO1D^E23lF^fz(+jjS041X2mi@~AM=o3c*x&A<mQKd@SzWX*wMq@`LOFB
z{(BF<;Ssk!vVY_tW$9jNKc)2QZ@Wsb?;73f|MllR^{-O@>U59>`qRDidR@A2x_5d|
zx=(tz{xnSYOP`oNE*($zPbbm?()H<q>66s`<n-V)P7g_+njV%uT_Zm$Jt93dJu*$w
zqja}FKD{7)LVA&Aza)*)%e2B5XoXj6h1Y3?*K38Z(h9Tm=rq?VZ%m((zC$a0Px`d<
z=JcP^Teafb(qmGSJ|n$7eP&vu$E6=jk550Io{-*^K0EzP`W*dD+YPBpPfT~FC#CnK
zC#QA#-1M92WO{#kO8Q{R(}z=$9-5s>56ceICuFD7FguezF}pE6CVOgnZ1%MD_-v9s
zJG&{}kUc%+*)!6O+0E(J?3wAd>{+SGZb_e?Jv+T7drtb|?78W++4ItuX3tMwmc1Z-
zdG^BehU`VD&R(2m*-O$qdujTn>}Baq*=^}tvod{WR;B-vy*zzac9gy^JDc8`ol8yj
ziu8``^U{xIpPzm_o2H-6UYUMAdsX_y?A7T#*=y3TW?z`zn|)FGjqHolZ)UGezm<Jy
zdSCWs=?}9nPj_cuk^Ve;UHWMD`t&c^SEg+5tI}ZatJ8IRUy~lV_lER{y{}Ct_UiN*
zd$aV|y{}6r_s*xA_AaDb_r5+=d*6^=v-geZtM=yU{N6XEZ`k|h^p?Fhrgra5>5jd(
zrXSsVTl&ep?@vFy_XFu?_L}r_du@96-W}-|_THYBd+$iAy?3Vf>@Cu-?)^~ut-T*k
zzrFV(>3w@Yntpfhzoz%^{aE_Fy&q2>*!zj}!M&eMf3Wvc=|g)zo&IR=U8&#unRI3E
zzon1t{cQTXy`M|}xcBb#&wD?gKDPJY)4%QgLi+c;UrgElFQtS1E*;xnrmOdVIepyz
zDm`fba(d+co$18>ucS}k{~zf|`|nAgyT4BP{;#H+_urddy#H(IKkxr~I=BBD=~er`
znZA7gx6)Vb|8}~t|2yf8`|nHNzyG`G9sBQ3@7n)B`lbEfPpkb8raSllApP3@htm7@
z|1kaj{)f{C_x~t;c)w3q_WwBj+5VOEm-~06zuCV#U3c&&>3#?QGd<|wPt(H>wrO<m
zXX()gf1bt%e~~`r;4jl>9Q;*!to~~92?rlZpMCH*>4t;9O(zdNnu>$JONR%4pC$+Y
zke+q$kLkGw|CDY$_~-P}gO8>1;9pa9@Nent!M~?-2Pyl!gZ=E42iIh;I(S(2>Vt=8
zUwDwx<DT~^H>C88Z+VPfpI<*-um9_1H~gPJU-PKX)t~?Ldv4V0@4xmLdi{@;JI_5w
z^<O#b*Y?Xj_tux+s@K<lqhD{C`t`Z@d$C^c|6GrG%&mU??U#GxZ@kKX{?40Az1}#R
z>h<E>=d+ttz1H1FKgX~4oV#1?yKZuQx5DWce4+mQ|Nr`jkNmR#Gv+`3`IjH@mFm9j
zQC{<_U-1ULKK7OidVS(UU3vdA{Q8*t`SpFVi|YG){%?DfXPth*H|ceEp3~=f%!znS
z@%qEB`8HMBSG&GD+j}4R&-Zzj*RS62-Fm&@&AtLZ5Gz0Y8{VovfBqwV<=3BghhD$=
zOZ?~E%DcJOe1*^OmT&htJSEQL-5>8iKQ4~==P&#r{rOkF(Q7{bjeae@{#|<A?du0$
z^ltrmcRu$%>h$OJ=iRB-k9?ia>irkqtJm*%t=HfE`R|_X`a3Up{aq!E{O5btUwykP
zfB1Tz`K5UM>DRgP$n$>v?Aout^R<3G{%XJO&HVb;uk-lbn!DHS7yidFt~@_hdvv^h
z^VhiY^4I(IU*fE8c#-#e&vkZ{|G4W<`l8>~$Zv|*-QJ%c=kVxw{pPRn`Mf;N|6eZn
z&&OZw*S(ovcW3+Fhxuw;{RqGQX#DxZ5BHxx_DH|(uJG=9?w*_{y~Xu=f3aWh`BeP!
zBYaPv@O0O||K)xi-}e9gPI+_X?knOb*Tr$$+k8)V&)e>a`r+67o+{t=y&n0Yul%rH
zANu)s>Gh4T^qRZ7`K2%LpYQpE@6PS@-{4svxpk}8-I@RC^Z!nNKK+7!(d!rAS6}4P
zpS{v!&Oh@i{rT;&{&Vl=`fkkb&$qtZXZ8Ac>Ta3(&-d)^D))Rx|Ht)rEANhS&$$nJ
zrLX_4a?kpIJmN=7k6b+S<8*Gn5wEX&^Mmx~_uu66sY}1U@`7Lc_|$q_T(MWgyXHw>
z=eb|~Eg!G(cYMPMz5aZ>zWo;e`4=AgzghbiFgc3q{~xdJxh_NQgb+dqh{!#Vgb)x2
z=0;eOB_W#-F5)uTo!t!C-Pz2{CM*F1A|hf$Km<fUK;&*jL~bG?A|fIpA|N6ncMt&)
z5E1yj&Z+L6p6Q-^{QjT+K2N{*-RD$QS65e`I(6%j(zDO^iPPn6vhR_tV_j-7Zc+K#
ztt9=6mP?7v)HnKv`aYhm@B1G%$eZ;At+C(Jr}=XYX`kIQTDEVl+nFocC-`396!@86
z*$1>wd35CfxYIWqeB$Usz}|(2f%X~MGJHi-_|cb^BF`*58oXf1ap3f>Zt&=iUeG@4
zVW*Vgg#pRc_G)Q2XkA*_FQu{Dc7jJMemqI|+HHpj|EOB0OBJmh`@+KY$nW{e>EQWC
z%f0V3@+|mqlg<I3+4cgkXZuUQ?Y6xVoU;A3;C`iz;N0c6fR8Sf5}v#KPWaFn!dGiM
zVxN?)!|jVD<x3`tHr&eCTK%53CR?q)hZK?<B+R*!#7bD(HCM~|MzuVvC&)AU%t}eE
zTg&_-ts!Idx$LaZ#a{9AYkx&*_UW`}ng^?G94bqRU9(WGVoRe`l{CY-I&C|-x^1oO
zJARAShm($fh%~LOwr{Zw`QAwq{`ONIMQdZ(JGCv*RINY1xj<TtUn<`Ftv?ZR^_hPK
zTP}VPeBoQKfqPy27HFUKqif!S-#G9gxKZ&Z=Op%*a?XOb{d#4CTsLf;Y+G|oo0QL`
z>*cxcp;mG06NTUVRk`c@`r)KtYmvPR+k%{_ma11<`B%qD3fC?Z43|tZufRU>u-0<x
zGg!X$sZwJv(N@gXjpvV&{9mHEdR|+xPJNEQS|r!~<>%$O^sd{I+&+Afgj`q_xqYS<
zZ8|dx6XlLZi1w~;3GVVG!QLf1AfI%R-215uc81$nH-1SZKkpHo+_f)qTgM(=FjLZ5
zAo4y%d&Mgk$kodoA`e^dnN_*U<rfLA(K<Gy9>R6XZ9TCq$UQ3m`4<i&{jenu$9-CT
zm_xJ<pVhaNklP+6DIcM4<%)}ie=Q|g$p|{CwW--$-MIGXhV*Tk_PU$C)?4Z??V>HC
z^_Nc5_UqHy%FJD!CuJ)S-_g$NdXOK`)-SnLT(?;Gi#;N@DeO723U5Do9r*B(;=|Z>
zYT;K;MPmI!`y6dsId}OP$nAakc19%j9`8771Kftcqf4HMZArhNe(alhsWY2vTV~Ta
zUfT;BH`*H7c5Q*SdNySn>pd-pX!vj#o8}^YYiu4i9sUeWvsq5UDbM?#Lypm_+cf`|
zJnX6A!`FH$bglm-g|(sZo4((u?_|9ud-Z0|t6BM6tIyHe6PwRXr(?Z+`!xTfbz^h&
zb9U9bk<uq_^RTO0yk^%8mzDKQte0ZLH}g_9owr4rpPy=7{o@%@W3O5%`2U)p4O%8P
z*1>vrEA{UF_xcdMqfNI3o4&85p=V@s)vQ!^*4!SVx!p;hMfjQ8r*@&1QnM7E(-O1!
zX;#z1<?y^(*QdM1a#~Ax<J$AEBo}BuEAd6~7XGa*h%MEhsIML_)w`F7wRwL)@a9gz
z>(ul7<!a$4DNa9Jc(_!X)sQ*r!CIf$dXKkV*cjL5f74Io2)!@cu4TR^DOlTh;Zmtl
zUCS=z9<4tR=3iZVCGxPu+Fsk69@0AbXMH-h$8F`dcXpEYxx#YGqkY)L=SY5{A#GoH
z)8TC_8`AP-X!91;Hp{aOY5N7+sSjW!HvBc(f9yM5bFRLV-!B%)T$Rr_MfmU4zl!!W
z!>M_r1nSj<<+cxQCAQ{UZo^xCegBO-Bg^dwgXOkXSZ?#2Q4ilrEZ(Jk8OyKI@e_N$
z^%nbP(zhjWm4^R7J*E9hKMk%Mj%!2O-f^^shx=%@)%J5DKIi`)J{&UI``dJG!*TzY
zcc)ou_8y~m5teMV@)wkn?Hz~vv$jwD|60!P^+{^cSkd0MO}|;rZJ9q({B`gIo7u)q
zr*P?t-;u(m!<_jQ$#b*&wSE6hr?7g&Q?XaEcVTUsZy}|wS_`@9G_UWEFUd{E@_V4t
zf43H3?(!G8;*C0j6z+9)>R3wmiISgi-}7932RD6{CysuLSoWE&))w%8d76&ajsLx-
z*|C&O=cIRGd@0$hG>hw7S{Xb2-2@qxwN}N-?OXHbZE9NUgT<vLqX#yY9o_n9p;%OB
zv0B|w3gPslA?+2fS|_o>ZPv>dNZZ%D?mbG|wuY}CCAQS|G5vm}Xl>ll+J@eFntOmT
zw%uH&?fdcC_ZiTZI-z~N6E&nA1OJWoQm2L_X8kZyu<~#@{J;1iTakk8SN{LY8);Vl
zKRR@KuJxLZ9km}RJ>2J&+vj3i=VV37?c9^ZYqFysVePABsYT7S_O8Pzymyj3sc1Sj
zWH_~O$k7$Ky3LQZ45KS@-MIUbmdl2>Bd9i(&2#%miDmh#<yzZn>zJ)eQ44HszSXWC
zu@fa0mV|jTdlhaqnzh2wFyU*3TheIA|NXk*xY4I?OXj>bX|L^^j`d8!k~`Y=pad#M
zOKth?L_u4BcKhnS$d6n!6I_1M+yk=A>blGTZv)C)al&Mo-%TRf78F?u$xh51k7O=4
z&3VpbknF-Sl3Cp>lHtrLyUZl#z%7|sc9}IEL0VgZF0;p@;9G+(v&3V-9hhBqm?<6$
z-;uBmv&G}!dlA-UHdt~rm0Y;y8_Y2~=6ujK-(;TIVFr17_=Tk5Fq6Ck{9B|U>kD>-
zf18{-%q;H&zmz;W<}%PVmoxY5m@7cnT*>^iW4;Tz<|^i(9cG~SgkQ~-9dixn>U^|g
zu47i(H8(OR?U?U_E_2i~;6DIebCcN*{zK3;H#0}=m>+>I^VbK$e+;_jR_3Z5a~tTI
zJD9I_%$=ZXeq!dp?*d(Ox0wh2in(>y{F>Qy*ZjsT0Do)Rz~7lg;3MX6!v7w0%^yrV
z_?TIS<d2|ho?tHCWlsDkB!2>3^OQLT{%6oN&zNK3e+6Chtm%ON4Rp<OCJ%oebj=H<
z6aFITnwLxg{xayAS4<E5RnRrBnIin}ple<?tH3vy?|00bpljYTCHOx<*Su|3!~X@k
z<{eXkzYDtNJu?XZH|U!8&06?>K-YZ0dI87$7j(^stQ&C5N1$syHYdZ4b28j<*27)U
zH3{cbcoL*`I;X+Ypv(4!)8SdrH7(8=@XbJKtaB!ObI>)zoU`D=L29hC0X`CR&1mOr
zkY5!5w{y-zzCB2db<T(H0J`Rr&IRxtL6;?P7r}P|U6!j|4BrKG&92TR@ZCVy?Cx9!
z-ve~bWao1Do*?zyxe`7Fbj>v9D)`=@Yo<F_!}kGQRxVr%-xqXQ!EinN)1YhicW#6q
z0J>(Tvk`s}=(57$CipDSHFKPs!MV;YNalgAneW^R9^%}Nq!o0{q0XJ~1)yu%oV&oo
zoV$@M0$sD%xd(nY=$a+Yz3_I>HA|iQ;LAXlwG{WmmxHc3(s=-W6zH0xod@B^fUfzh
z^AP-4&^4cP9)@>-t~t(m1fB<7v%+~4-U+&<%Xti50A17VJPz*xT~l<P1XnpvfxXVt
z$ooK-^&8K?OQ358oM+*yLD!U>=in94WnIS$@IlaJZO2RSwV-QGa9)8Afv!2xc@4e}
zbj?Z5>+q97mvta-z}JH=YeC)uPjlV|zv#S!{B+PYXE^V{zYMyp6?q^270@+jIUm5k
z3c6;4^CA3epvxMPkKtbjU2~4ZpE(!6);S6I`5?B=Nx?4wv2{)cei4YRb6VgRgV;JJ
z2fqZw);YuAmw~SNjxz#&Ip~@zoKf&AL2Mn%?6Gwqw$2#~zXrtCIpg5hfv&mU84te!
zbj|mj3Gj`e%bJ#n@S8x_{Lq;MzZrB{=dwNg7SJ_6c6NZ@3cBWYXGi!QpvyX#o!~zK
zU2~VS3;d@bcF)-j{xi@u_c(jNe-66l7tWsW`#{(H(wPFkA9T$F&NTS1L6@~O)8W4X
zUGtDL1O8hOYw7F<e*|<{U$Z~_4<OdkIS~Fw&^3=c2f?2JUGpbr7W^sDWi8Ge_|u?k
z{^HDoKLfhvug-k<v!HAK=Cs0}16}jHvjF}Ah|P4`;4g#NOlJ}NRS=u$91j0G=$d~x
z?eI51*SzU0gTDp3=AX`T_}ids{^cA6e+P8UyUsE2_dwVD+c_5gK8Uq+I>7%rdGJH0
z6a2_2fFC<OpmB?!<E{czZXcL-OT^89uF1Nq;VqzRHghZR9O$yvXb?UO#45UL!IAC|
zl2IVG(Om~01G=n8IvG9|q^)<?!^eTN^6shN*6wNG1ow30+kmc_=$--J7Ie)d_e}V9
zAa>O~3*5oofaH@Po`8Ec{8Jzn)jbE?)jbc~%{?Fa?x1V-a4&#Q23@nKdl7su5TC)l
z7(NxmqPmyB_Xb_Fk9!$>2I!i7-OJ(ofv)+qdnJ5-&@~6RSHTYiv8e9V@Pj}ss(USb
z7KlZ4uZPbCT{F+U5q>a;U3E9Y4*^}%>fQuD6m-o(_hxt-h+TDWfiD8FtM0Av!$It-
zdpo=xbj>pNPWTa^YnHor!H)!8bCi2G{AkcM$GG>vKMT6%SodD|=RntVxc9-216`AM
z?}x7dUDN440Pg}_Q*a-IcZ06!aUX)O1hK2`!|>xltgHJ7yccv$pZh4hA9PL0eGEPT
zx@NWeIJ^wHrs6&cuY#@_bf1E+0bR4!eHwlO=$aw-8Sq5+StRQ~tgHJR{A3X8>b?M9
z54z?Q_a*qLpliP1z5+iD#L~L2!A}QW^CkCn_!*#UzU;mMKNED#SKPPYXMwKys{1y4
z1L&Hsx$l5yyYC_SI*7e>--n+Iy5>Ch1Nb*U*PQQu2>&MNnhV^I;TM9gxyW@~$9xNP
zSyz{Ue;ahoC2k6SDd?KZ+zk9XAbzIX0>1*p&vbL}?}FG~cNqM8Ahy>X0lx;s_PV3s
z*MZnxcMSXn5TDZ>3;#Zd&*_eX{{Y12bjQPg2;y_P6W~7r@j2az@E?QtobDv}Z6J2o
z-5!1ih#hu!fd2%<4!b+Te+puU-JRe+1F^&IF7Tg&E^7mKgZ~0_&3*13@Lz(ix!>Ir
ze2|sASYQxO)tv@^7{mg*)8UVRuKB$?1O6z8*Xr&Ee++b4Rk%O=aS-3tJrMpRh}Y^K
z1phOL1$Jk_p8>JJ?i~2DAYQ9G5B@xe*XquPzX)Q1-B$R^AQsqN0Dl$40=sSSzk_(K
z?jra<KzvvCaQK@bzN^~~|0js=>Mn!-3&eMIm&4x$@m<}c;Qt2kUEO2g{{gYa?y>Oy
zg4kiV1O73H9Zuxo4oDv=(Fsq0*yKb3o(Az(6Fu-Oh&4_W;W-d%oLB`P2GX}m^ub4f
zE-NTY@X;WiYGO5f3lKY;sKCd8uGuOv2p<n(jT39(6F`?$mqYN0plh~Gtb<PiU9(-{
zWcc<VHaW2#z5|FgPMiw=6zH0r5~sm;23@mD;&k|~AeK3C27GrA%bYk9J{iO^C(eTJ
z1=5d8Y=BP#U9)%MZ1{8#%bYj|J_E!uC(eWK2V$8M=fn30vCN4J;0J<O=EOztgFu(n
zq8Gzwfv%aIxCA~2#N$m|2A>DI=HSHT@cAHqZ{kXLD~R8lxC*`i#7ZZwhPQ!O>BP10
zMIcr>aXq*saU+s;5WhFE5xxw>?@inUUk>8;CT@lw1>*N6Zh;>I;`b(Qg&zyz_a<(K
zcYyf4i96wW5WhEZ7rYb1*G=3F?*U!2GI0;Q2x5~H_riNYY;xj0ct40uPTUV40I|u5
z2jFE8o1AzMUInqqiHG28Kx}g2VfYCk);RG9oK>~(lM;`@PX@8ZiO1mUL9B7&armhq
z);RGb{4@}2oOlX;I*8w!cpCm?5WhF^4E!q~esAJg_*X$JaN;@m*`RB_o_GO%4v6hd
zyafLSh_9P?1%3gDy-mCZ{}zaiO}q}j6vW0R-hh7x#KtDxf?ok*V-s(~zYAhx6Ys#k
z2V!Fr@4>GDv9XEw;n#uK*u)3$8$kTq#E0O<#K%Z}0OHvuaK6pWAf9a^0lx*rvrVMn
zw}P&@Es=rW4$`Afw7~BK>Cq>0@Vh|Q{4_BPemCfvpCv}X?*U!&^Ta6ly&yI-F$R1*
zF&4=aApQ5mIQXAHEMa0i{Li4vTILDxzkv9hiHYzRKrCKj68vQli^pCHEFOr(OY8vu
zJBY<g><Iq{h&4;>1iqKp1<Aib*Ze238~g(he=@NL{6i2sme>>iF^C;YPJufhb}Ts!
zo&d39$?5Pkh#gDLfM-GMSaLsj4#bWn_lFMyv17>t;UhqN%j7}uQ6P3KISal8=&~|<
z4tz_H5v1fi_*NjkWpX}zYY^Wu*$UqV#J5Z?fNu-pTPEA!+kx1z<RbVEAbs}a;qV<n
z`s~Se_)Z`mW^x&P7Z5))xg5S5NS{4<6nqa5JC-~Kz88pxnLHLg6~x0#cEI-rv17?R
zd<KXeOLoHd1F>Vt0(^fEJC^K$9|*eUpkxvL8PH{A`6~Eq5Dzoi2cHY#VJ1uPgF!sZ
z<ZAdKARcD20zVXV&4T10d?ASKO0I=30<m4mA^71S7A(0A-VWknCQpVR0pejM*Tatl
z@i3F8!jA^=Fq5ajKMUevCQpZd4#dMuo&i4&#KTOU310!?VJ6RlcY%1A$qn#s5DznX
zHhd-Mn&Xq_z*m9v+>__Q`#>yM@_hIJNN+uP0a!_11Xhz5BOe6u9+Q`Vrz9^!aw>?;
zNnQ?qIe8^`X7VcJUjgy@l2?NplGlP?OI{D2oxBn4*Fn6#<VN_pAeJY26Z{(>USINN
z_%}hkzT_?N3qh<<@>ck_KziB9+rdkccY>ED?*cDN-i`J<AhsxZ5Bv%cTa>&P{#_7T
zl)MlAJrG-zydQoIh%HJ!0KX1&%?-&1!5fngf!|L)jC><VUpx5-{3Z~KlzbF^Gl)e>
zJ_f%9#K%iM4&Ii061+Y66nIDSX|#8O*rMb!@Vh{4QSw>%-5|Co`5gQn5L=Xd0e&xt
zcb9w#{AKbL@c!g$$bSXW<4(Q~|22remwW^M8;~A%@-6spLDxK-d>j5d5PvWE4)|#D
zJ@607_mMva;_oFtfIkl6?<GHkKMB(NPJRr33Z(a)bdvPGK}IZ-3GlDU6!>g1gZyux
zYo1HCz@G>4{gOHOiy)RTISl?Xh$T#pfWHc236rDXe+TJ(C&z$qB*%hpCdVOv3&h_`
zjtBphoB+O)oCv;~oP_p0kbZY^d-(ex)-t&R`~whcncNZnA&9k1?galBq~D#|1$0xp
zfr-=}U^2BQ+7w7nJT(QL0qKdSromf4Y-VaYJO?s9nVJC(PwfYeNbL`fOdW`J6i81z
zbr5_ENKZU93qBUa15C|<j|1@lQ}f{CK|H|JeE0+qYnf^Vw@oboC#BkuZwI<&`_v-%
zCqUQikUAXxNe~Y()ee3twG7-TwH*1*ARb`qDEO`*9$@Mi`0gMcVCq=-WDv`l>VWSB
z;sK`e;IvdHI6YMW_eu4DGg3vu><co&nOX(^G{^{Nst<ku=$ZplC2(eHHIjos*L)^b
zfzJZ*{ZfPQIUv4YYAt*oi0_vgg3kx>{Zi}TtspitbuzdxwH|CsoeCb7It^TuI-M|!
zLDw9fIs?80bWMBeO!!g|`<Xfmegug9Ol^Q431UA}XTy&Mv7f1P;GYGtpQ-cUp9Asw
zQs;yD)CJ&*)J4cUK}I`M7sCr69$@Mccn^pNn7RxsrY=WvJcu8dx)R<C;s>U#g7<@1
z(bU!O0T4ehbuGLMVoOuk!>b@;o~awbwW*EZ38|aFq14T2PXt}FE_Dn1B+xY{r*4IR
z9(2w6)a~$7Kt?`Ocf!8_GX9ym3;spWHK(WUhJOjfGN<l=e;K4dp1K!2D|H|E)ztmq
zhSUS#*HRCHXQv(_<kvwgbLwIExgeG~^$7eMAeK4xDEylsmO1qp{6Y}RoO&GoEs*|r
z>Ph&wLF{wtDfp!z<DaRg!OK(6fLElR1+Pp!2Yxs80(e#GB|?4=#4@K|fnNh+nNzR9
zuLJQCQ?J8s0I|=hH{jm~@fA~V!G8eK6HmPj{~<_EJoOIzM<Bjp>OJt*)cfFVsSm*0
zQy-$e1Ede0`WXHbkUn^d;a&4nkUn@S0sk3DA3T+U{~Tm2G?fAGOSOQ%Oy!W@4>DVn
z8U}v=#4@KwfWKjHES5Pn2K;SmEckG09QeD`c<_<b1j7Fw#6Gi!mT^&#anaNy_#Z)h
z(bV?vCqR7B)DG}Jf%u}S9pQfl@kLWR!T$ncrP+zgyb_3&PVEN&8;F%o?E!xt#7d|3
zgue)4rBhSjFN0X=)HL|3AXb|FyjW=vE1jAFzLDAwd^5E_^0z?7NK*&G-v+VOse|C}
zfY@qw0%NN|>~m@k_(5tOlK+B?j;7|rKLQyYO|`;Jx)tuG7l4U$8<<Ql0#oV3!F0Nv
zFc}d0oL&ZR0kO~N<?tMcPn$jpJ`BV@r;h<grjJE33dBCAJK$qL>~lH~9}8li)1B~f
zAl_`c0N)zKn@#t?w*j%t=^}g*=$h@)tKi#%*yMB{_{nq$$&Mg4IlUUb6NoiVSHRuU
zgW&GzwaE7XnQuxD!S@8|cc<5Z)6*w|`=r++p8?{prcZ_M2V!;8r-28gPX}kF&j1ff
zpNaM}Af9UaEck2?Pc^*(J{QDOO`i=v7{t=1&jDN0=Yb2-=YtE=7l3W)iwJWVh^0+m
z3||alY15a$mw;H>^kwj+ApPs~<=~O&E5W1ESAoZ*uLh4zUki4muLtw#8^O->MzAY=
z6WE=;8C;pZ1w1}|E4V6sJJ_4Pla%{FW{c8y!Al_4GJQ8#N#6rj)Axdd>HE;G0kM<m
z`@x~~1K^422a&G>=`W`rf}af1Urs*^Uk~D=rXPWy3esOrKMFq$q`#bg41PMu=w<qG
z_!%G`Z2C#~nIP6O{S^Ex5Nnx!8omL<TBe_YpABLs)6atErk?}POTPeqBmENE^Fb_R
z`W5iP^lRWn>DR%F({F&^PQOK%OF+EV^xNQf((iy*q~8OtOur9)FZ}^{b^1f_+VscZ
zb!jJ!cbZOsH>OkI_tTj)-YJOPOSgbGr*q(s(!-G70%GyfBf#6zqrltKW57GoW6|CT
z;*qAu!S4d`NYmrtcY}DO=?U<AKs?g)MEJcRb}u~%ejkY4OK%UqAH*X~?*RTfy(9Qw
zdMEHV>0Qu11Y+^hyMez;?*TrN-V^+NdJ5V{K|Ip*H27m69%*_y{BaPEG(7|UB#5m`
z?+5-ly+8PL`atk6>4U&$(z6KjR}fp5o&*0Ih^<S{gFg>q>(cY#FM_UlIo%4rl3oD5
znr;JMOD{tEcaUCr`f&I^Kx|#Q9sVZBIAnSm{GT9xX?i*QUm$*I`Y8CjApQ6BG4Owb
zSiJPH;0Ngr@W1Ii_+h#e{3u-jKTh`$(qwwzPNoQVLF`^;6+8)I_cDF(G)ON#Q-Wtf
zX3sOL;hTZXo@XlX%|X1=%piO?h~3Mq1xICukc<Ygc$szJ*v!e`mYMa)$AMV9%&Fkk
znbW`tnbVPP1LBcp&VX+V;*n;~gl`Apk!H?<e*(n%Wj4S+3F47v&W3*q#3Rj|1K%0M
z24>EK?+RiGGv~v12k}TV7r-Zjc%+$&;Cq4iqM3`qy)&19(=(SL-v`7a&0G%O7sMmY
zTnYa)h;7VV1wR19Bh6e5p9x|iGuOgD17aaF*TZLnSjfzc@VOusGP4nWFi2lMa})d!
z5IdQ<8Ga~;h0NRn9+tTkT%5TbJUnwJxFmBI*q*tYkV`?l(9AvXBS5^+%)Rg<LF{Da
zKKRif)-rQH{Iei7GxGrab0A)5=0Px@c?ii05c`>V7~Tcaf6qJu?*{3=XC8&G1o1*M
zkHL=zv89>E!M@CsV1MQ*<Ry@q_srAq)gXQO%ro!`h+WM*3m*jWLNm|7*MeBr%nR@#
z5bK(G3BC@*x@KO1pA2GMGq1tdgILte>)>gbH^47u-a>vlh(*o34L<|KqGsNKp9x|`
zGw*>LGVg<5%Y1<RY>*Mi%!lxEKx}E|WB7R>wlu@!x;Y<YG%}NbUjSlJGb#8*AT#fo
z4E$mcZ!^;ZUYf~)mt}?_{|<=HnHd4U0>rvzM!~-e;&W!kz`qA#X)|Nt*MQjD%sBXU
zAU<biJp2X_KQl7{{6S_ScvEH)_`}ThXm19wx|to|w}5z?nH}M`g4o{7PVn17Y;R^4
z_?;lOH?tf3E)d(B*#mwzi0#en3BL!#_GYHQ?**~FnQ8F*Kx}VjI{bbRpEEN9{s4#t
z&g=(&5X1s!_J=<NVu3RUg1^fg1U{0Ph5Yv*K4)eQ{0|^LXJ#J!k03s0W<LB05T7&C
z3jY&`9nLI({~5#%XWHO@0kOTAMd05uhl9`Y<rZu&i0#cRgTDykXJ(eeUk0(jnWNyZ
zf>_|pG4Q{GSm4aD@PB|<V7~u?1qQLenLPZTAQm{&3I7*}1<n-U?}Aw1Ob`6uATvRk
zBK$ufwwJHSV0$xt;K!K~a+58=o$PA33u1e-6?hWF_GSm+X%O3+T?@~G*xu|Ad@~T+
zn_UOr9K`l!PlgW%v9a0p;Fj4_!ExEsz^$^UgIj0M04HS61Se+C0=La>04HV72Di(e
zLkio2jFD!~gYN+1TV~IP?+D^sW-oy61Y%dS7lFHGF9vtZUV?mg5WAYa3_cmeu4XR>
z_sU)gPRU*cPRm{m?w!3BoSwa&ko$mG*X)h(eL<{ib|bid_9i3;fcTu*o8bq6^zpN|
zz-NK<@w2zW=YaI_v$w<Nfq0wQJHgiMT}TcE@j0`1!xw^B)a*U*!$5q_?7i^CAa*r-
zAAAXjEzRB!9+7<jT%LUp`H>*DH2V<zXb@YPeHi{(5L=pk1pYY?FEslo{5TNHnSBiG
z&OQ$IWS<0&&prjN%03PDW}gB3vd@A8+2_F3*%!ca_9d{AeFdy$UjqlTuY+r{Z-8sF
zZ-FOd-{vYqAa*kQ4tyPmoy@)mKN-YMX5WXe2k}C)AHYuqv6<Nq;irMv%<RYT(?M)z
z*2!WsL2PC=0Y4MO3(cnBXMxzyYzDpo#C~R5;Aex_&uk8U4v78a+ep|?5c`=O0Y4wa
zer89(F95Nh*)i~oK<sCBEc{{+zcf1zehG;E<O@sqrP&GKce4|bUj^cqW+#DHXSWBh
z$?gDN%a@q&PP03K*JpPj<P9L!GP@i2gX|vQP1!xcAM(W}eAMhT!u$xtJIzi9Z_CaA
zZ_n-r-jUrOypu0GVIi{z5%Mk&3z?k-zZ=9VX6JzSX6J#w$j%4v<2z7T#q0v`{%ji|
ze+A-~W*5PK4blhC9u7X7Z3iFWt5JBN+2!D)*`o;a2M`;WJqG?q5F4017XAc?4a|1H
z{{&+9_}&!$XSNgkYqkJBo9zMrmMs$IInXsPW><kP@m(tX%xnpKExQ_gJzD|)ksT!D
z8z8;u>{|F+AiZh6VnuHnq&J;i2Y&~|vSm*O|D9bAzMnl6`F}us%j{|3hkW^p-ZaRp
zP|F#h({d*0ww#4L0n(dp*#J+0^rl<RhG#%}(=F$~TR?1D%X#4DE$4&7S}s679Hckh
zauIwah*#NiF?=-03{lG^@GU?rUdv_RxR%Soty-=G$G2RCc54ue*K#$uZOgUbq?YTE
zZwF%QT5be)XxRvUvgIal$CjJXehS2sY`F!#Gl<1&xfQ-Eh{bEU9lkqAZ@T49aL<;z
zz`a`TMm`0^%C+1BPH(vv+^6L}a7N4hX!iw~18R8yoZa#uIH%<y@X(fr(Jlb#9k)CJ
zZv*lDS{{Wj0-0-Wc?|4nc^q8X@+7#i<tgw7El-0#Y<UK}x#ijH<7V>6m7||D2Y`Pv
z$AQnAzk%C0<uTLDHcoZSbTiRe1E1)e0H5TX0-xl30luB{Mfi5km*CqwUxsh*d<Fgq
z=d18fIA4SB;CvmvgL5wYlg>BbpLD(n-_f}czN7Ok_@|t2!$0L*3g5~34tyu)3i!^>
zci}rb--GYsTm#?5xemUoa|3)==lk&8oFBk<bAAZl-T4uGcjw3OJ)GO%dpLK%Cp$lZ
zPj-F^-_!XSd{5`+@V%U0!1r=~37_Kp3O>d8HGHb`8~9Y`xA1As@8Hv%-^2HI{s7<G
z`6GO~^8|dl^C$Q|&Y$7?IDdi9aQ+IP;rtE0uk$>7U*|>me$LDA{hU|fpLYHZ|FrWD
z`2NnD@co^C!Vhr%1wX)f7k;4gZ}@@Ef8aBn|H5ZFAHn%(;uh0DcZ=!fGfooz87B>&
z<z(TroXy~~oz3C1o#F5~&PeziXEc1Svju#vvn70<vlV=vvo-u+XB+sz&bILR&UWzm
z&L`l9IG=<c;(Q9;>g)_}b#{dx>g*0b)R_!l;Oqro;7o-tboPcXboPO_Is3xfoKM3K
za}IzX=FEgIay|oJ<jjUIcILttI|suLcMgFc?i>nV;w*$OaSnsGJB#7%&Jy@iXDNKC
za|C>ub0mD3b2R)2=d<u5oX^3RJIBG7J1gKvI$iK1oo@J1&Pw=E&hhZ0onH9SPCxt@
zX8?YTQ-*)mslq?&tbrfvoB%)8IT8Lj=Op;&oX^8MoKxT(&KKavIbVby=X?pCcfJhI
zJ70mXaJ~v(;d~9=>3kjD>6{Dia=roYa=r;KI2XbT&bQ#*&bQ&+&ZY1k=R5Eo=L+~r
z=ezKg&iCL&=Nfp?xek83a|8T%=lk$g&JW<LoFBq_ogcw_ogc&doZH}i&K>Z6=O^%f
z=cn+J^D}tK`8j;R`2~Ey`6Ya{^DFpj=hyJE^BZ{C`7ONS{0?4meh;rYe}Gq=Kf(u{
zC*Xt5pWthpKf~8Je}S)c{t92~{0)AB^E~_n=SBFC^D=zMc@=)5^LO}(&OhMmoHya?
zoPWYka{dKB$$1xkvh#2F$<BY^pLhNX|Ge`Nd_9Aq(?ECZbk<LA1OKA4Eqr&r`*%3I
z7LH?QL78>`U*y}X=dr8fO7>s;kX;k^uq)zG_A<O^{=wb>m-WtLoNZWHJed{0pK%Uh
zZSB!cr_<|Hos*p}IcGZ;I+r`wIX62$aqe@Tbv|%LvU2W9_ZIiRZf~NJxFT_V;<?1A
zBx?bZ)#TI3RjE@_@1(Nn8`3{ZKbHPidT!?S%!`@(v(IHe%$8by-||7r=*=c<w(Vx$
z*zBIo-ra1w+~K)_+?R9T%srcXH#c|lfz7Yn{2!Y;!*atW4%=hcjA65eEg1I3u=j^0
zh7TJ)arjQdrwreJ_=4ffhIb4v4j&qR+VBm-FBpFL@H>a!H~it@PY!=&_}jxj9G)4`
zKH}IBJtJ0+ICaEXBhDXj*@%rJZXa>)h=)czJ>sPiZ;kk1L~i8Rk&{O5GIGC>b4Ip}
zTt2dA<m!>@MxH+M?2+$}oHpvnQN5$qkNWYbUygcs)Dxq|j^1tbjL~yOUpV^m(btVW
za?G#C{9(*r#th$L>=xT>am*H7Tl8)5{1&fo@$MG;kDWWVZS1RK|26ibv3G6x%Pk+?
zGCgj@xUI%LGVY0S&x{+s)s|aryVW~eeYjO>{HMpy8NYD+y76Baf9CjKjQ`E}Ka8KZ
z^<i5t-}>FHKi)b!;iw5KCajup;x=F0=4;#hc$?hB?I-R!aoWTqCnirZ?Dz@)-L`?n
zB&>PId(gkrCcd&%2%pUh$rk0KvI|ePvT)p=^hd*u(QuoK!u$K`#Fk#6_1FE2mtOd~
zAFi|E!m@9FJ}UeAaaM-+BZR;6C(gfE{?}jQgp$Z6IGfQDZ$=Benc0?O632EN+jD%v
z$<eyzXjOBxra4;C9Ia=LRx?LynWL4=(K_a66?3$PIa<LStzV8-FGp*aqm|3iy5(rq
za<pbSTCp6hSB_RIM{AX%mCDgN<!F_1v_?5vp&YGGj#ei}Ym=ju$<eyxXjO8wCOKM>
z9IZ!=RwGAik)xH!(K_U46>_u&Ia+}n)<1{U&tdIzSos{*J%?4#Va;<`@f_AWht<wu
zt#erE9M(C9RnB3Jb6DXV);EXM&0%eGSlJxbHHTHrVNG*b(Hzz@ht<qsEpu4O9M&<1
zRm@=xb6CL~)-Q+E%VF(uSh*b5Er(UhVa;+_u^iSbht<kqt#VkY9M&m^Rmx$Fa#*1p
z)+fhzJv-RT@kjFoj?<ivutNOI4}VE>QAwU@(p=56V|>%>dy>evHoKiE{|_?bj+FmL
zdH;*v{~H`D|G8ts{n|VKxA%W*_tb8Qw~uSX?d$#jjQ8J7Sb0+aCs_}={|L#&$)oN6
z_4=RhBJqEfS>di?P4ubeZ+!VPk=Vc&H7{Ys;7#U!j>i+Xvf^uld7mSdyo(i6_p*j*
z1FMiWu#V^fR`xu=s+@<-$^1W)<9v?GId0^*o#Q@^NBD8+m-zoS$H&CWr5<2i!3I_a
zJix5}1I*4pVCHkQBkM>##T@gq=1h+BIlr9aMvmJ#?&Elb<7tjpQZF$h_c}9WuQS*6
zy4g8>26IwpFt@aUIid~B^=x1kXM-8wIEmxS9N*yhPWo-;7~W^p|6|7FKW0=t<@^_U
zHnV}T<_(M%Z(yW%10%LMr!DgUqo5lY$9#a1$PJ7la*zB!pZ}Nh|HjNH=jZ(YE$4sA
zjC0<|JZnDSJd@pE#&AsH*o|Wb#~hAD9LHoIphv%fUit=l*c<2_Z=h%UfVq-mBmB<n
zMCTEXr?YL&w_2#<o3%NUH#^qp-t27Wgw4+3xXIZ%*XFF?_$YUdvodk2IcD=Q?wOk(
z?sgOIOT#+cJBAe!2aj55+D8=<=Z-pQ^w0SJ8vj2rx{&zn=p)U!qpwWdJbGi|KOEUH
z8xy;ac{DM=ao(6yM*nooOmpv;7ZNY?f3qz%CU)T1bBmc~*%lur*6{z_(V66>$Zp`c
zjq|^3F*f-z$FQ+8&GfN5CXX09Hrd5d<~W7pYaHL^xQ^pC!aO&2dNRA^*yK2l9k<+=
zn8yEu_}|9=qd0mv202bccFvYF&8=I`OFqGWGj3UOdyXX>$Bo;VC~^#PoXW9*<06i$
zIBps@({yiENWRGbmaPVppW>Lt@tLizOyoKGw%V8&;y9h-9F9vkuI0Fe;~tKOw&Euq
zw>mvJZ2U~K_xSUZ%lN-`{8h=Xb9@_q_xM|qe;&Uv@e0Q~9M0Ao6T>*hZ#~l-wDo<-
z<F+1~?C1O>{(qVO-{k)l{Qm*}f5QJ?6YkBeA4`s!urV=d!r0_)6K0zB2`?n86P6{v
zfb8oW-{!c6<3}7n<M<876CBSI{!RXW#Ie~n8xvb{d}5oi$;liCzz^MKrm1Z6PVz#I
ztG8)$ezr{_MOh>cp7{CEQWyFrZjO}~yI~G!FZ6dUURkP^W>tpzJ7;!vbWGXH95An|
zSZym*D*2wm!s433!cu3xS}gSkVWtvhexcgh-(8y3-!-?iw!gQO?>e~HTL^+o^OBjf
zGEcZQ#VRTFuL{(Aqh8iOQ0$-8+bco5t5qZi)6umTs%=AaN_~C#{;o<8YacIBFZ7ba
zK<`jnzPxI1AP6$UMsBYbJ69FTi%XTF<h-??1cNa9+A#C;eTDYUQn{At{ZP!@ucKqH
z4!KZ&XQ`{OtlGWr%oUzUbW^8@vNX`q+BeW!>?~GAYlV|LtEKYHo{kPFo34Y)rM{)5
zRfYbOthOz`s<5Q6dazLOaza2U{;=$%sZ%BOo<g-_UVpV%9XcZ4O9Z*ZjA^2o+1b%C
zw^$kI%@57#%~vWaj6MLHhb4u|K&iiC@9(6kd;3Yx$qz`OG+trKl<5*;exbim&Q}X<
zgT2+_K)zf(xK!?&o3G~Ql=`cMe!X7}R$VsGMY!7IK`J*iYp|;rs!vmpR!ak$tWShl
zzyq^2p>IW@96YOOsFoDgP>#)N5&;$#@@rHdM4OIuZlSlpZNyX~l#->zrQY5k#*83_
zS9j$3)zun_Rj9_)8Wdj5fJJMDub$cgi_`+CJ*tr;rb&?h<pF3wz3AQ&C0YX{Syhqc
z<%Jb1OQqV&Cm9;#O}Eu)>eS<4k$mwYTFu--rBE*Bdy6L)yc!Uc*))k6joPeY&+H=n
zVS~NB`4zndXzRTG!9MDmf3|v%T4qnUQg^Xuuw3X`ny;*ywKiY$HDYDFS7vs;BFW4e
zDEc*>GYQYXC53#~qW<2Y_JMrAgc0*5s<uLPWvOfSV6nHWQ1%t`)=;kx!PNAsY#tfu
z<>kZvQ2(unoRX(Cg>qHT53Q8?!{`33U;s$8)yp7salVY1AyO~)v?i7}yuDg36snlT
z!W2_!I4`X@o}!hvX>O^LLh7$suPIaihZl-4v-ACN?-QC?0|VufePIqQ75lwhiRs-N
zEyhdY1rK?;mBqbBXuOxqHWopg8EB|@-kStlx^l2@MWEi-CMmDdA;q4RYJ_WMWWT9K
zjCvQ%$`J(AT0XS{7OA85-9gh$rI<OL8fy+%S*;HE)xi^WTEivfo$sp)rq<Y#*gI)_
zS0n?xEAp?qqhnToseh=iG+0?WG*H;<xT)sg!TwJE@xX0$n_KJ@)0!_2A+3MNQyU)g
zRKr7-bZ2yQtS#pUpz^$rD0US>){~(>l@|I3Ywgrjq)Vk887Ky2Dzat$E8-tIy19kU
zUaFcr+Qx^D)_>?-eZ~G_rHcJ8m1|FAIw3+n!P3GBwd%1CI?18;tb_c_Kt8L#vbIoe
z?UxH5K3FIZ1=@Y>HJjA{w2jZvM(`ewdLF*Vf<_*Z{PT(uDp{J<U+k-QT&C<DXDh|j
zp^zxN)75-u?e0WQZK{@eu5oXZMpI3_7qvGEuT0wt(x6r5lm<gD@pSY}OLTA3+F#wr
zrcir@g0h>T7wzxDttu5eYqeru8>6izK_Dd~|Gbj%q8?EfaSEf%a`ScV;Z%f+X#z`C
ztuyv6f;>z^YhM^Cm4<sqS9k9Xw~jh=>z*?bq`VL6Wdq_*`t5Mot(<|@)~fcE#R2@s
zo`!mbeqpI+(O~TnB9WWZ_v$e2Cb-g+eXNFewkDjaLTdrTFT<R{N)>;uSPlEQwXpSy
zy;0PaKyc0JXf}28sOMm%s)fS}YvUe0`c2-x#P2P6E}vL*1akLng|1?Lez_F#l&8$F
zDmC_MwSrtuRh6yQg>ql9Qo*Lx%)zvp8t0(1TvT^5h`o1B9dirQ(S}MuGZsYM2W4>o
z_Vq3J^?+6+2seXpwj36hsh)+-Ri(jdLru_J^mmD|@_f%)bbIHKe$AA9^|>v>;i2kF
zqoOstcIQ(GFz<wcQd!F;re8CS0E>%W!>b*r;G%n@s=YFCB_oN~dR`4BgK}1vUlvPC
zb)_LXHMPx4V_$8JmMuM)usHN<^JVHpeFpb6wJuLxPGb9jQ})xcs_XPkMP?tLRI0e*
zA%J%KtSzm-KEGe$-Nu}JZ|{nH=c@WAIvsteeBye4`$!U9p5YH_PD5!CMqUYNdxD3v
zFT$A|b4mk4jpc&4t<*J$hhf@>D%C>Y<km%IsaYdkvyKieSAABdwY7g>kjloZW?E<U
zt{ChK35qpcpN8nc<&$oex&=x^;@^6psS{MC<)xMK%0~k&FIHF92|B}CNt3#~2Ct$V
zq**5z2x~Ps{Yt6gwcufagivoz^KDt^#;u?#?j9xDX<Dne(w%<6&7sFtP+L=(tlb<`
zp+{P)@}`ORh@5s^T7EO1Ap-jVRTx-tJ*zcvdKzvjO-%JSQ8$)PG)7)y6I*xn3XNvQ
zG}a`^Z?gdt6W`!Tl;Hj1D*tz@<OixXN1C~ys*7GuYQr9;)?64FSn%5pjaT1hT2bJL
zny?2-f2&C!-83}_`s8B_g=uPGy~h;x(dCXB&O?rjsq5xzr*Kxq_5*AslIm2~3}}pI
zV-?}L8ZnAmJoPT8iW=J2a2jFXM{Y!dHnyl*k(SyXfu>jMAV~dabck&(f)N-!t+{7j
z0m^kkLhs5UN$8zyG_#tbzNj^K!eCHh97Pi|remVrQr@a)w}odF>vhXLOZG$lsOHFP
zPwi9n-sbv|ABow}i6v%4M5vfvi9`+$cS%ew#Rm6C!^OG}e#U2oy-Mp@nELUnAf%c(
zsiEOsiD<)j>D_pEv`RAPiK7FcCeDixVS~F>Yc#z#7>~3e2$u=xM9QPVt?`SgQOrFw
ziUPN<Q6U9THwZ!{jvbNm%gt7eTBoC49_olO%uqihYeHRz@Ov6`c#U<l8hf;!INBxg
z6n^tu^JhFotiQ&PT68?G*@*7UE_Fa!yR<pxUk~l`{8Fh0-?ca2S@0C~lAb!f#PBSh
z7rJ&PI(xS@StUkR)INSKjKuTWS*e*dg+z{3NDQ?#OH1{kBf_!_`-emISx45p`K1i|
z_AuzSI6oxgX4J}Zk6#SUbaHXuDT<5NOegW$1`0iUOEb;&`$Alcz>4*zEfuLcCteIW
zpI_|u&uR?$>|&26mjr5O5~sZnFE6AGpX)Vb7|GL%^XGHX=>WZ_%S%(?anWfb;GGZ1
z#861*y_ue*Vy9R_djBo;L|xv<rFYt0D}aMbgXIc?tY!bX)Cs!7TFH|nu&lqhdN2qc
z7Dx!MZ0Z6v$|PJ+N_8QlQY{I5RquU}ff28E`S185?elv2h*09sDV59kx^)2?Wz0U+
zg^_r*w$)3$P9Pzqf9mNxI%p++^`1PtRGsV%>sBVuS|I~^p4Z(fR(h$t5;~whSx!mo
z|A&}*k2bbcPcI>nGNiq#(B%n4S0|7VViQ6U+KRle)j~)rv7*xM*l0^jp-Xv@0$Hcc
zp<EP?f+;Pp;DQs;P+W+Gg)BP~Yt__hdwTy#`?5-S6v_)zXYG;s{V_j|T&L0S6KtSi
zBqK<T0s4HG^jZolg)C%fO!JDNl3_A0-#QlPi=_CxK94UN@Iuql8!0buSd!C%O+u}b
zXy)~cNRns}iBfwOkp3RRh(Ha7FOXaFtoN+JYRURxON&hM_#yPcdIJyfA;f7UZH7RZ
zG$|?9C&TQCbTPb!E;tiikk9tcl?CakH)Ovime4XS)Y^?7+V6#{6O_VQxj|b{UU2W`
z=o?C8NsXrAgkm4FMYEVZsxB=TdwL2?TWR?<)5)t@ltA{zv<v3nmv>UEc%+sE>wjvg
zbM;>9Fm)Mq!~K)?!GS`#N5=VliCjVoUt(}KzF3|^j95b31RKIegc34zwT9sF8hg9G
z7im3~N^nY7hS_?VWbDUoQaZI;_d5fgQ0ttnTwO*d2bc9%^4*0+%*in;&vaW*v~}lm
z^8K?5(zijc1rR!yyan}$Svy)cX21KZ6+4X8P^01k)TPi~B?<2`L9)KkmIJxzEHD`=
z6Gon~CPKceiy9i5(_B*MEU*Zp%ajX!%-S_k;+@YQEOwa#4x@WNv)DnSS?J|dx(+gF
zUz14p*PC{nxvHaMHsfpzqkF+?%BfMMHbhaej1xtrz7>qu`VLhSZD_zLnz9ZtMa8XM
zQCXea8&wC6W>nSS^hN{3IkC}Djm~Q{gx~UFr^D`QG@xH(|7XCELmExRI%83}*3o9l
zz(I}13%t^(DsZdfo{WxSG`n8NL5(IG@=&A7Iu|vnuJ=%*`mi4wqYb&8QEh|284VDW
zhwpqwgIGs1D)-$E-<gbtXz(nf0m5EnRBQdns9a2N+~W&-bkSHfM=q+1cy&#K_&!`T
zfbYCTHR{Pl#dU67RP7h9=b=Rd_{OYhfT%wgO~muWqKbM?EvgUN5B1EV`b~OV(crDV
z?-W%9R|-6)Xqd3K6xB9yd7=SZJqIVMiMl1xFpcg=G(@Zi5)IZG_#aVS$X$pkgQALY
z1vU}da{;0uyv2@Dg<Y`}mCFG1Vpe9AV%oh$1BL!(x+6N7`->*GpfI$e6suX(Ul13*
zc>w!%HP`dHvUseyUZ!rM8PWodioKU5s$lk_Djro-qeiU1nU;n;E)u;BR&5m889Rxl
zh=J>`#;X?6H5$zK;Ks&9oL4TF%JHg0Shz&(s17nh85fnI+-kf=hdRrQcE?7`a~I5C
z#@q$-EWP=D^*^b^%|b0K_4gF3gK-HgByPOMGfB~svpuJ#YU`XgRa296Me8O4gvSP>
zm)G)d8hc^BQr&b2Dw(WM49$;3ui@8#sHVNp+g*%#5bX>R#E4rf2lp0ED001MQrZuX
zibKm(qDuM*@+vfmDzhHZIDX3)uhq^*q!V#iq0kktv9>H;C(d9Kja{!G69I*Ce314+
zwTZ%xQa07=Z0Jg6yW^uvjfmGATr4+Ji8Iw7Tbj5h&6RWVj9!SPjXnjh#@3IiM1wJ{
zI}j6F7Ar;rhx$hG0px+iYud<Uyhi(G&DAykIzGBq&)D4RjCJ%Tyk1y)u?IWGa^YwQ
z%8bXmi2$t?Zw}QrF3~jTrN*S}UAu{r!i(2nbs77K*Xfu~ylNTy0ea(Af!T^zi?NE=
zs526;n!{jLvAf8|kC;0@Bww+HW|N_$F^h>DjPH2kJ<;M0mL0^XWYJAjyrjUx@7K)|
zQRNY`WFacX7|T+WH8Cow`R%wY(Ma0)j8`4P;4A|v(KvXbvYf_ScMuzd0$mx6DA`z?
zuf}M!i@&_s6{l+=);F_>j{bcUjXsJdIxUMPx`l=Qp191->nj~!jNa*@RZ*E8cax50
zjIM%r%M$#2Z&bH1zoHN$p4XQz#)ze@YNC?Pcr<x$q%K~m&t+LTCR(gepGmAno!?l^
zAvmP53LavtoH^}cf2>9yZmdQJU}81;&|@|J*h;KYMg*c|85%>2D*f>;-9r^sd!868
zdaapa5H%XeU)mGZc#pApfTiVpf5i^YMq}GC>)1<%oa$&itsip-ne2*<+gzjdp}Cr8
z-(0DWxw%@O^(NFhaTvW7ul&97WogdmGVb0au)ho?noJvea++(s1ya#yp;b~*C8IP<
zPRG>;EsAIaI{Gr`z%l`LG)C2n%J`k}s@cVu@LHnLWc1aEitRjxcs@-6cy4<Wt&T9;
zO_0&FTPt?<Dyos|HPbEX)ydmt>V<{w&=Ra@(q4X>MQ07xCa%q0_7@Ig^=P3SmvTXN
zt2PfHT?Dn}OH01_h^8WeLaA&jsIK4DsS#aFv$7<+#-b@M&sTBXV-&4j%)MZr;&k3n
zbu_ib{XI=}OM7r5bZcug9u{j2K21zEXe_(g#2KfO?npGLc_(xhbb&)f2L@t7w6864
z&x=`Q<Lz&YCZgWuCiFOM><H>+nl~C(npeBlDyq{dOg7p^`knshcqOXxw-QG+i`m-4
z4%<j#*aWF4I&AKBzcv+0m$JtMu#?uCOu$>MXeY&Ek{~CHnrt!&Ss^g1w^v6+HyKcF
zX;GEBy2((X;f*HwVJxb73ckZz8+9+nmGC^-;T*524NNr;6yC)gn~JR3YaU&k>zJFP
z6iaIhUEW&HXtkJIX5TP{h#TKDj85LJmPSZ7md+~o#9T-{;TX9zsQF@lMH<F9HOmIX
zrsR9$l(x>w%M+u;3iw-jqW9I7Kf&vm(Qw8@w(nMq!k;^78bI5vn21zs*@&ohHb!lm
zj~Io#4tSI7<Z2qEt_6xug3i8f>T8;YXJ%t!i^+@;lP}w4X`&A-VN6`Dbrrkgo(*QY
zmnSBe=d>;_vw~q(CuS=q3=4Y7u?qh|H4PwLq5m^b>p;`^6hIrp$vuT&Y9uCmlsk*C
zVifIzESzC?P1n-mz$~`dvhpHYOY3KsmeAoIj0q)kI^I6rXcFX-J7DZ#F*#-0ZcVYU
zHYN@6QDP%E+~Ov}#3vEz^TY=b6Rqo|Vy<sn-I`q;nls1(LS5t<6IxA4XN<zDM@{vV
z5d)^)8vu*mWP72r94BAY-Cc>3ABHV%raO{K*Bx_>qngOI6Lm;|jpH$qn#r}R-AqaM
zshLW949%2k+L|dB%Brwv$ytNbL}}lSXrwtS*_VTP>188zvoL=DI~u!vP;Eu`V6V4!
zBpO7j0`(}W@OH4Nd($+KOkFiqF2=~mBwW{s>I_!&;!8{H9&4gIf(C$@H0(r^c$*5~
zT|BfAzDY{f5;eI(GbL?lV8o(Pk0|Du28haAE3xACm8G@1v@jaOuH~zQ?jj}(cI^KO
zAZr6<b}<^;PRC)Vng@{y3pU?I<BIzgm3rncUg^1%@k+nP5wG=zBkJvX^zys_QDHkS
zpUjZrh{vf9DV2*SidWW4o6FcuXG{{-4##P<X&0N0$0%DhDJ$)=N3+pdp)b<Mrz4^&
zU3U@{hZ=&YGSXJW1PQNRj;j5+zb032T=No>0Lx+{(%E#Pb>XYmC$lRt?Wdj$uYfOJ
zRxU0m_S>L#-q47h@o1lZ$3@erErigdLZ`jK5(y1hTNR-JL#Ire_1pClX3ZQHK^WU{
z=Myk;q6<4^)r3aV^%E-dS2U<NIFZl|OD62)>X%Vy3ibg8QHyHegHuUqi7xtV5QUdh
zXxg+1p%oTpmA96|uo6LTrXnBRSyW!W62FlT;&c}D;<D^QV=HNAo353s`=$!Z?B-O<
zLp9E}Lpo{MWtFT{EtNeU321}p%2)f{3cZ52n#1UzJ%c;a8TT?*(bXJY2E7w&@mjsL
z92!uZs6c@4VK*Z9A?B8;0_BCI%E}N8j{)*-+p=yww?!SS_|U6{R)E-;{-O`n21`Ix
z=g;}e8Vps}30P$jtO=3bXfoZSA?32ck`RqoJ1h80Mf3tPnPyjp=xP085wo&x?TChG
zt$59TLri}eiC!%@k&I{)XqS<wGJGz29SRaVX`}ipM)Yc-QSbU-v5G>ie_sndP?un^
z>O?PAJL~mVqv&P*1t>O<w;ILBsJ$#p(I8%%q9J^)ccV-AeTh_@x}_=hu5_t*IE-x&
zeTk&QYY<+NqBg2gR&EGm<AqnMX!hzC3)>t>B?w<GnCtb0lJkgA>y95nt`=RlqF0O&
zcSTmXc(<ej8h)C6cC^ZS2)1^655YUZjPpbbj3nyFx`wptUo@OY=h@HLtSXq<1*WOQ
z9Ka>Vx+ca3mDMr9cf-P84KuCWz(7ft<d|0Jch`0{nTDNC?4$0F`=nS%uJgxr-y74M
z)Ry6Bc0#AcV1W+XRm3;fmeLx=S+awGd@sR6^_EK<qF)#j10d5SG9~6o)X8RDB`L_T
z6p8U234dX@N)~%7;;q^nmL_CTLaI>8Ojq`3CM^?LXho1}!snfX!AEWMPQwd?G;p1O
zmx?P&nzCp{^-BB?P{>XK73gL2_7fgKP~VwQ0Z&vv@S!J>q6-p{l{YpYLL^y#X_1#Z
zyUxh;dkc;<NNtUgp5hT6!pHAIQtLWs+LEM{UT~q{q}!Kjkj4c_dU?;4wf%KXK_X$$
z$Si#mmGBCL&LpjNyHZJ0@VUzC7AjeFgQ)&gB1uWnL9nZrG)}#wr%o?1`~^)KI5_F`
zmN{vO1uL60Ozo`OU-6`&<ZbIGc?}et$R$FHoKzV;AM|7CEi-B2J=b9Elg6l>b^2?f
zj4V@;zJ~XV?Wbevc93aQ&p_3$lrr9TTfBFXSH5or?;0OzkuCPpKo;7#kcfCBb?22j
zWpAnK2V5qjH1fu3hIHkXX27zVAM0skWtN7NS6>4v(KlJ=81z)1X>D_M5Vm2hmI-a(
z)-c{(2Wdz())PA?8=Oi74FdVgv~0O~Hp=EvKHpNOu3Oh-`uxBRw;3c|zs^f9&>-p#
zHu-8K`IMs)JE*SInqw)h`Fwmqi+hMpl<Itkc=lkmTI!bpZxu`V>3V_SOx!~~k#;gT
z;mdnWRjWK=eN@Vqkjg^?j7Iu~cU~`5wckeZN_(EnUkqvRs^pUXkXg>uN>C$Ua#d;o
zOkT|3V~3`qGRdxQMf??PUSa7Mnu1kp`cxWLvT49ZnWRV~n&*R^j0!T+%9=PMGbQrB
zR@tPujnoPyp($iNU+ViW;ZJLteC;!vnw`kHIn7X1D!yrupzjI!-<mN?%R^X9aw9pH
zcU)6Z5-OcQ;gwcR&pp(X{)xoI@${D2X^Np!yiZ>v0fRMn8m@NMDc?~GE)*=k(@?cD
zY29^Mp58#<hy{77muSYk<#;w*{)uQXJo?ooui6A>n#<aHJe$wZx#;7T<Y~ZIMQ13s
zedFXo*xr@VX^2i*1euuZkw>z)G%$!$?Nw~Ch?=w-O1{RiLWb_kv&*Hm>WNwL0W+s`
zbg1>@#VD@vV>7g@(-&G?xMX&#r?(W)-dUqiZcm3{<?DSKq;}@@O<5CFQmxko4bImD
zY*wYhhZy-@G#lT$*s(aP%KXoYK~kK@VguO)U6Y9KN?j`ZU*Zj^{JTA@)Gv*m^x|sq
zaCUp?cZg>viaBi8EHlec3lOkn@@?(l%qv4#SzeR*AIhys{A&j#T~qmYr+!~p+|a(&
zRPqJe)}DS$Q%$yLMWrOWXKNDcPaiB!%-T|URZY>BA6mgW4QA<sa^wMsptaj0HH{tf
zj)-+=tUe9@7Fp%SG-s0(FtQ=lyb~C?mJbxxLi?p57VZcp>tdC%pr>rV51Q|78p`iA
z=!&iA6+-KfYD(R@&f3Jv5ygtE-skI0K_S(BI5;9^PI6E_B3@J2s;F0A!tmq?$|^Oz
zq_;?0vzpAmeP+wLNG__7Z<Dc6ePu{BXJNZss#n^LS_Hoi>fk<~o(pb6z1&&la(<{L
z^Disu<oiPk&+SrqwYZ`v-&U?glRF@%1DK?cboP*EZqy5~Z@sMjHEKPqaK89zWn2P#
zx-wsxcLJM$0s(s`LT7}Q?@imVVpBqkhS^&kEFf0Pgw?bGF^fdBe2r#6qUw@jn<-gb
z9PRzt01`A<W~{+$XWHocZj2q}(<@7zl?BEcJUEe*bhar_)y@XwGc76)eHe*BzxKfu
zojCTDMcw4Dt8jw;Z|J4k;X9pCV-hTSAI&v3W^5!89^9Mn!SSf9tn#1I)c<l9-ZE*u
zJ)g^WEqH3ao4#}<pFmZCevnc$QW5nbXpdPWa|_*h{F{2=?9y6puty8hUxgxD+VgzY
zxW_(##>LpC5|PyR7AAYPK~liklYdf=_&%1_Jwq|`+KSkVH@BF_u2S<lE0fip840<H
z0c*a?(6iF7Un^}9P&C1Fm&M+}bJvq@YJ(CfI(-<r;93KPPDNW&SSGUCqHm26Iu$L?
zsTL_X5sfymHC^abv|9CQ+R&+1MXOmWtJC$)>{@WmfJb|EbvVU>zf+On1JlN|hz+gl
zVl=R2jDH_rG*S*s2xxkI&!*H%!!74H@+w=jN~7>kyLA3wL-6et4P=*REM!Hw1`eIt
zL1h0|Di~D>sLVELjM6GiLxzWpRyt);9L(Ezi_vYewbNpaN<bat)-aa!%1U+l%7x6X
zY3jOmQA1cJv1G`jCa;~zpof*?v{SoQFs8y+VoH|yqeuEFpe{7a@Kdq7vx{(ji1eBC
zO44~!NoZxeD#GXTkizG@qNTO8^t_e~5BAvvv_i7{+kOU1h+IhiOE<_r*GlLMgOjRs
zIsG6yS0E>A<U7^A)LZ$kiTPYEAbl1wloGCEMd$?2K)!n<deMo>Pm9{F)c%vZlT!)l
z7n~o`Up}vw4NfFRaMJ6ou-DtwcSRzr>*=TBFQ_+p`QDZ<)i*~@q<n%CeThU`toQ-_
zxqUhB@;<BO>m<|OqY;soK<IoB84F&t1@Eu6b_ZjJ)-HxWrY9}2_^zG?tA^p!n`-5&
zx1M>^a|xxHRwAnpmB`cZdT6x}cI-^gS(wA8q}i&4Q>22WVoB9J(yuS+iHz~ck2dfk
z>%T*_KTlIq`&f#VbVekSf2-39?tRzRN(SoAs`9%mym`fby&Z2D90xVnOQ3fbC-=t3
z>%z+A{G9||J+Z%-Lm#_8_`ayT>D3@uWZWUpNNU~#3}jM48fBq(G6*MeLpv8VGvRa5
z2b&jETRW=_57cCN{}cLJPIRJ~%2F3p<A%@aRR_8*7IGE6aQ*<w62|wbQ8x0@LLXbA
znCh;RO6Jst%=LCi=tb%!5~fZaoQOsZM<9_xtCvU^Uj4cd;d4zxx{P%p^py@I5~gm?
zge^vSuY)jM{tfsquDxx~L73lL!dMqiv_i(%8N#ZRx~r3=0#9!Der?5SZP%$@ra91^
z7FwldS=veM@~HjTo9~Y-DRg@uT@F`v&mhR~Rj_eG@5HP0v3jra$Lb_k%qQyaEonl*
ziD<(6JnUl)ZTv8O;S(lwY$DS3wlU~N_s#eV6YVlnMkJWf))neHJEdKhf&}!0a!GH2
zuwKpQo%W}X^jBi6Nud$-7rPr?YR_v7OIg1=C}V%p*ww<yv#NV6HI>XPmTRZ&bUcDf
z_=(jdcGSgtxohIkBH|!&Az*TUp}wbmaE0Jo{DwEs|1Q5ibS9>FB`HUFCxR25(i3Sz
z9pvM?`7XIZ+uKq`qkxaAeYkQ_RIPsJ#g4&xeTN2Nv2I0!7m`sIYJBh`FMR)ZRX;!W
zVlTF|q#4$CPIGHn%{bZf(7;c$*JAr7HehH(C>qcPStD<}UX>~#?wH*O<W-!ogf=In
zpKmAfWFWzKW39@AhsdimAX`IJ${3gY3X^hqe8H|Fy-w{+bZje9sr;97wKpoUtX3NS
zd2GL~iPDPmVzD%J^*l6n%WA3Hb6eKFF7`@RDOqjU&ZNmU$|RgOwP+(st*jG7T1Ojh
zK)SkoDX}!*8@Iy>)yc9LqM`cP4N=r%CfWTe>B;!59)#YB<RpA983~``&2-{j)gsu8
zg(c;X*88}TdPBOf-S`r^i@~S){JhhzSl#&QO;(}(ireuW9ZMN#seQ!F7wDIf%G_NK
z!?I;<1Z+EDQ6H+m^-CW>a3c3BZip99ZZ~u)+PckO8o6H5Fy+$_yMAw&MsE;>o_#12
z-op>&KfFy$)2S2m>5uC8wb$Pers1e5!MVLy-KH@OBfY3_IO$7;&#7lY8oqtbGv6n!
zDn7B8M^3~phNv`G-YJ13fLvathqaql+h?X(h?hsh`e}r+7n4#mZIhjDrj?4#*eBy|
zV*JWYejklraHw{4tgt_pW^0vQZ@{MoLmT4se$;3*Zk023a$KW&o8&aG&r7lu$tE1^
zlrv<AHeWW)*#flF-nP&@9x)Hr;<tNhmP<2t<DW?h*7nqyfna-`CSTi8XR5*GIzwYA
zzaOd2g*V;DZ~?srn<rdq-D;<q3QqJw{#H9xF)$#5%*0}N(_^(aX3<*w1kHp@C1?aC
ztw95#Q)6ZNXgb!_D3<F5GH<7zrV<fqC0g6CV^4$nt7>&XSC%c9N+g`dma&>pAgS36
zqWV*bRNLXF*9x8DK{hVN;nFo}Fv6l=_e3?HZ_UfE07B)p(^25ESt~nX%GiGYcUMS(
zITw*hF}F&$*E`pul+|lBkymAVLn1!&DjiV1(09UIrmTcFgbya`Yx2<Sdrc@K65?Ff
zBn_6cURj&>u0`;twQCZ&srq=4H=$m=sd?;LvXKexno0-ZYNwI`+sUZO<ayN2YSlYV
zCUb+bjM0B8;ckI$6sW1B0!hENmvY0=<}sR%-uiP1B86l(Olchpol3rZ51q)oV)j=o
z?JK8dy=31ypND?MQ7W8_N61g15~_Arb-&+Wq%4@*B!vF>D=5=9eghvG3D(!#c1~Km
zDw@^WncN4Sb)Wp$t?BfM-kt7FwRdLefc@7nMjgEIQc{eWRs1Y?b1##{ai>O>1HGMT
z!=;yZqN3Tu`xSd#x8+qY9jnkx`!VeH*ZRPoOv2Ugg4JLRqOyM6*{g^_cC6zVNY!1*
z_cus!${QqJ)<U~x{hWnG#0#H#k)#D@VzO>4tsgs97b=Ikowa@>DbjjH+z%i74<Nkt
z)=!~MMy~75{d*1V$o1pV0msqv9)sL%_*~-oQzSJb!bVz+9j}o%y6VLb7M!T2HUbpL
z<%xwxi2`|Ov?!3-JJC*kAhS=rUgq`TgD?_Le6K(tk?I5mS*as=_U0LdN-VL__I@q%
z$9{FtTnuSta?nOpOhJ3!UJ^sHysu)SF*ISF*$#FQYl$|97_1NNGuBAat;HHKM%)$I
zge>MtrcCX9X-Rm$9qm=DS`qqk&w@w?O7@*lKx>FTZwT<GePt4_;T~l(vstPb`bKrI
z&)MF+tQ69*g_>Mf4>igptpjH6-O*89S@d!)vpp2G{TDN#rVB2ZTOX<rtgYBtF6qcw
zomeXb{+nk;!W*jXt5GNEtUL2A8B9S=mT4X@q1x7Kn@s3DXlH3k{QcQRe@B=~=gkf=
z%*H2(ytG0)wQXWyLCk$GEtQB`rJ7e{Fuvib*t#n1RYjaA-9X;h0?hN*NmTHcs+8M+
z{aQ;%?$>7VRC<fNH6a~cvr>5|B#=_(*8DBydKckyX)?p-1EH|~g_{3Rg`fM-FUD!z
zs^6!s7i|#r)$L^0NVcU7x4QNz)DJ46_x3~i;p?&)G_e&^(znT`LC>pNvPpYXe0Z!x
zH^#-2)TLveRcHhus1dd8@P3sDNu;=K_c#d8MMB%){WuL$vHSJmB&E9kw#|KmNJ7oY
z)4yfVo*%#UFYPtKjbg6SD6(Djz!C8WY9`C1KW~IS7@x}sPMAv_ruz9J$&1&Uvw00p
z>Pq&ILNArfrYsM^hPBMQ9Xsb+Q*wu45w{UOl}JIK$9u;9g6;~#FLvigD8xWbOL5i>
z>j#c)!>b#T%jLA0X#5g@KH7MVwj+`74P6cO+UtIIEVR*17buf>Od!dSXUVn0WxqL~
zxrq^L_U+dPY=%89a{o>9J|3X@Mwx_@ftg0RRJk~Le@s|OBltvtUZr-%cLls0<wH56
z1~hExw+}i_PW6a{Z76CA(p&B*<yna-XZ0+tGFSw)G%r8MV{0zeO1_W~<67X1Gz`90
zSh&<Qw71#YrW*5k1^W=9pIu-@OypAxEWc-|Mtz>VD&VzB`aWZ6LO+ILzt5nTiB$~5
zh1Mrn`+$RehN1Ifh)Y=8qYQP>Jb~zVhCK56J;A}#jnVKfGH<JQA`eKft;9>L;o)0-
zq)yoQzPOf9og}DAs&A-7!8IABlV9qUx$+>LMwu1@eGC?(`gyjaZb2%Moxq{5M(90k
zGJs4oZ!$oqT$Wrho@j4f9FR`0^gJG^xnkiZkmTO0NWMtk_XbgDs@o5Bs9p8Q2&31n
z_s*rPJi4^xk&-TuB&f`T`kn<1lK-UI!ymiwpK86(mU1WS1Z|1w_b)WQ{j7ybJt|>q
zAG}aa=(NA!e+t8t^lKTRx+`PlnyCFIhDP<MBx=8ip;C{ER42&oS142!J{NsR<x!F9
z%A1fXd@g#X#Y5WKndo%dETj#ei(cKHkUo4qApVl4Td5!Ms15t+2p`^6N2{zv?JU}|
z&~IhvomnPVu<l4r6F#?&ht>5;$3{!k8$)e0$i7&IfS3u_fmZ2F=?LVavM-T65twC7
z%A>ZPs<d^MY$OT=iGHv{Zy-j@rM!4lIFCi%7SGJvxo-8$O^58OuwJ@p_1&PraW^Y`
z7ali7KV8tnul~p^w*1I<?A8|fW!6<<JZ&&3Wq<X8<~=y6jkX3NJ3|<pM)Gfm=bP&T
zO_#U0$;cb;HDFrik#Cr2o}yC8wrAcno|p@{Yk62c_m}nC?=8`=iSc5%H}41F$0d2z
z{QOf?r8h5A0SvNvV=I0vRq4XtIEBANCr&3RU<?~naw}e0>lluI9l4b_dC`u7*M-$^
zG8WR1wm+M13x}rAPHg#?kV}hC?w>C$h0nC+dF9&Y7nC2g(^E(iI-;vZZ&_rtS07RI
zOE5MHZ5qRqc3@ohqHE~dH(@kX=v1y)`{;{mL#LwEz#*BGWTQ;;Z7&`AZi>CWgm3cc
z7|omgAdFtwqrp`}D!pPzAO2j7#;p_3oee%FWAgf8tU5PLFKoXlV{chMFQZcZqKpbH
z<K-?}3l@A-Mzx_c|6?-@5zv9E%Cb8itW2Tkw2RMudlMQc^VfOF;DYQ7<y%AcXXIrS
zI97*^PSz}VHBm>Iy=FfATf24g_He_jEtDT)zWn;Te<J>le8eJrt_#1y0`H{_i@dD(
zBG0pGkk*YY`#~8v7Wb+lXyebr`=LX>1@DWj#q|2IGWzH%xUujlB_J27n{^0n;r3&B
z!F2miP1fHfyAEsT-V@M0!m<G|q>0FMBU~*&f0s7$tdI)DLRet?gCB}+6<<E#N0oVP
zYs;5vzgrj4*f{{XKilhO?ZYZRG20-=>!dO@(VQTkT94P+2k9-t@h?NcMI`nI<bx1Z
zzWB%_lYcI+w9Q=j%S-wq#fUpu3c#>z$du@-DLD+wDw1e}Y;K`bx_gb{Sp)Q&)<nee
z4)|@1pVfA<UiZ?;w{aQ+Mc&M~K$4PI+2hob#wOH~w_r_akU4~MZ!(1z`~YIqPIxQ#
z{8*tYHE6t7AhasZk0Y1Y+UpB=$3m;Ie5vMA7Da^x>bm*@TZ1Iibz<+zbuwuq{b-RA
z4h{Z=!vxkOSZ?HNC2MLJb@0HK(y5m5Kgo~ve|?4gglbfZe-{-l>t7Kk#u3$Fzi0%?
zE-IA#dA7{YgJ(ARRU$jD>C1`FN}dKGB^VXPO_POEu@$>+TQxsyrc(^!zc*D_haw2-
zKfK!LbSRO?P;N*krBG*<gCrLgSREqjhS2`0l$N+lxO~3ImX_Zf@Lb83uY-CDe?@I8
z^R$6loi|;r!Wy$Wv3=zjP_v6!>eSX$F-X}ED3n62wtESLbWpj$sDhG@3WJM9RZ<qd
z_wV0uT@_Q$D^|lY0(M{fDt2Qml25DI3J`ha#0L&5y>~4vwyOy0`YtvxtMG><4<9VZ
zy7jQi-)<D6YWzr=AF=Eu&?7D-^Rj-c;D2b(wkLLLP@tE)(jV5Y3l)`0qOx>_O;wtX
z&#RuSjrK!EB>bTeDFw}dl8VIkCI4T0-vcA%aphTG&%d5#rkU;;0)!d6iN`SxV;Eq-
zLjp0F!5N&v9>dsSPeemQH}r&_KVf=o*nqZsb|&ZC63NjK3Ez^Hqvh^M#9r_okyvRW
z&EBz+S0X1{vUTL-D30WuvQk!O>%2Oj#BJ{Pd#~#IzV4a9wAaz@opzw>d-dwot5>gH
zy?S4LUw7G<$S(-z*-S`}W>lXw`*n5KXmHf8T#m5s8A++W5-ecBHEmx+FW<hC{w!W%
zUrw)}r;yJUjy*O0f<4aLy$*T+n3ML0(g@2}nnVk@@04bEW%aV(2b7X>48GsXZ^8=g
z!XFEi3X7oevn>@rD<}-~=Jl9L;Af7>@4Nzga4dZ?i-?*(S%WYOw`~Y7PH5X08~d`V
zaFPcGoL`{h?X>&lK@n)l!ZaUDFIJ11@PDFEcmz3?iSY|wA*H7q4&{RlY~>Kji>AdU
zLQ_zv7VJLC?!ZE<a0vgTq1G<Lkfv<93QakNG?nvK$QB^KPG8_=5!PTlA=N>|H|>-$
z0c>dP{rVKX@uBd(G0c6<u_gTp50=@vw3lxyoH5w7;KluT-`>#)<etQuk7rzfo|zi?
zeZ#-UzG1#$Qsya>HlrpBXeU13Fx}>y=>oLJjGHl2KnYV<x(THze9jrOtN@CWQf@Zo
zKvOUiX3~7#Y{b7b;1RPy>gPdo!km(JHlPmPqcWy(qd5w=U`*>qa~!m!@^Q<Lf*OzW
z&9k7+8}kSIAjR{*;md6%0||}`pHW~6XeDKK;}b4-^;2d_IHXW>9RKhQPEdZvIC)iC
zmK_uBIY4+YfnM}N6TM~=Xj8^CD4j`6iBzW`jcU!*>@ub}`)mc;2{VN*q)<k-QxJio
z9~Im_z^Yn&Nfn*SLd+DXm@ed200(xJyb6F$A!SG4n*ikiL5x-%yp&moVdOwPY)lP>
zR~4964(4glZ<OwjQn9Vii$b$#c_YkV!t@yPP#WEv1aHc;N7RBZLE?H0>U#$LApVRo
zYuN)18~N@Bh9)zLf2@z+yfkLb2`OE?jKf=4yxwxH_XK1kfAg^Bon8jf{<v>6gQ8s;
zBU)VMT12g7$55MCz|DkCn5Q8<Ol}Q(gYWTKt?mcKsEo@gV;-UDPDl&S1Mg^_MEw!e
zIRM<(MF$)S^YAfHX2Fd*aI|Wy9GR>ckXk3fm)fF4<Py*>!S}%|G)NA2<@qqja{`jl
z9_~&_DW3-=C+uA*?+=fvqYQLx9$u{cAle@h`=D(cL>th}dL0YLV>fy<D!m`Um=r)q
ztx<N2+k@q_;Sr%#O+jZIx237fLra(8-IjodwJj%=={RX6cbIV7rj_=WhUNmTnGU+P
za_hmvK~PZw^MKmUC|V5LL>boyIU1@1YAqPMGOEXPhB&#F*54lJhuk>(h9Fx(XU&7<
z_4}kv+Up2xhT}yCZz`w;L3K*TiG5NUjL+(FULku}HsQ}d^FTS(qE=DCDJ1Xx9Ob*r
z%!gU7y5Ef+;v4Xx`5TM?q+J_OE>2@s9z*XZ;IpXlpswlAh2;f|<|)`~Md?O2E6eem
z<Ft?g@Z>V$>sI<oU$~&8m6~G7b8@9mH5+vdCn{Q9lKUFRV>dXBV9Y`-x6;2=;ILrl
zU@7!;#&3T&F!VIz@R#WT?r^-F{&tI<TANuJlxmQ`wE^=p;qZOnc#M7WHP9!zVe7i;
znM9@%<F`reQl_-|5=Mcuf5I5Q&2rrbJqg*QFUKHi(^8DDtLMO%9&8+ekIqoDB3J{5
zp*4&3H5Ak^%L2-v-RDrl_7>$n+)5uY0on148|AW&?OBL(Yd&Y~L$JgY^j02t(Oag#
zi}6F=c+I-X>k4lkP_OqT=!%-{M@uI#$_IsudYNEU9F?2RE+0^vHDT}m6>vN%vzzON
zS)pw`i(X~K);M!ahhocV_d#CHB(s7RQ84ADoPG2%oF|NZbk5Ls(rR7gK^|_k3`HGS
zp93030_vH$ww^?b)OU!wES5QlzR>rn4>=L0^R0`mhDLF%Mgx>1JhL~NuR%`npZBMs
zjSMu--cSbiYzSosrRQcP?vOz{j8B-tJQR)`o&xO)6+B%12}pEeVg0goxV-fzWY*yK
z5mW-ZYL?SD?vtRQA1W_3%PXXXZ{j$XQ*m~2jd1>sy0d)ks!~p`WkZ1%dVn*Vkr{PZ
zPV3K^GK;L3`+k&Ej%UmoTH)3%?zLG*y^MKeaf$<=pnO~<xE`ETPjvs{)I*?Vbj|t3
zwRpn#-sb^#yYdKHq?b;cFUXwMS-TTA8Qz-D2#)iCzQWyLu*}rpvzjqXa6|S8Z0ro$
z$Qp0cQZ~xfh%rdPJR;V|xxyB`hbno|Ur&Nt`FMKE=?#vf4t)=LhWSY!Gz>~c5xn6d
z|6#PF_b2cNm=8+BTR!#OW#t=PxvMh_E_aCx8yy{IjJEO-HQ-$w9etu7*)L~Qa1x(!
z%r!(l%>~im2w>cht`6HGd&qk>j-y#kz0oT(A{a)m-3n4!CtOll$6psJ+6nm@#6Q7o
z7)DzhztD=ghPJ`EIt&e0L>doNw&!FFt!QPlE!>X~O*3tmqsoZV9DEk-1sdbp<nBYm
znss-jTC4otm-lHYtZce!J!HJ)f#0J>&6=f0-!z9?F^!xT$03_(b}>6=qFKSdQ*PR%
zT0F4J%Waf*hRz-hWMDtW@Q-(}T&1XcdhQSvbfLbHcvH9I_MiX4L;530=YQeV{N%ID
zUz7ZQ{DrqxIq14TE9V$;_N5^geyazbVFECtP0lo~f2JdYt45Fbni{Cs1GXyB(;6K`
z&U$*;GoauqL_0JO&=xrRbTdL{Wd--;x;CkP&^~OBoY;zTJ1RM7phM&ClW4(N0A~&*
zfs|{>fspo!cRIv`DArf@Y*;8xfWMARC^mhBQgg3?I$&?uFY;TIdbQ3(juh=w<5wAx
zW#po|HDb5qLH}mzs5gV&YmC9WZbTOi<bNEoFWY8oJ>Y0@u!!<VcEJooY)2hPq~3T!
z+5yk4Zk<Z`{x~IlX2eGS!MQ=(aw9Gli|Cx&WsI70cEikUT#VCQcqDX9$$|rWjs7-f
zp-G9!mhtYBW1Rz*ZO~TCayOnfieJt-z`ZF-0uQ)SH{@tz;e8y_NIdthOf&6<Hb5&S
z=W$@ES<W?F{T(K?Wcm)XnxoBiaoD#f*FJ4I)P5`dGx>4Lj}n6;;&z?ajH}omox_ZA
zO{3#%mXkW<NfCWaK|;<U&bH&ox%qL%m%=>29j>D&XRmY>qYbcK--|rxJf2GL))K~Z
zI=5sz*Dg*)Em)5g;-hn!C2n2h`XhWoRKfbQfU1Mz7~HdRtz!QvH^yiQ9@Hz_WKThb
zn9zAc^4FPT%S3yX-f#ic{n}^eLl=}k!o420qA>zjEv}~YFN^rvhZa&F7YRpEBaqcJ
zavsSYT_a;R@sfKg^}%>W+Y0xZ?emV9_O(E#TWG=Kuv1bv??_80HEma|5Wf&97}4Rw
zfyFs~q{KIIVV$I))#;clMakJj{zA2OaVmNp_5-8#*y0rQz0T9|{)Q_X_lJkk6V3~@
zZ`4n?IF_-6#6XSxpp>296_sVrna%JS>X+FQwqY8%_c>zl1VOw>V1;Hi+j8@E*zS?a
zJ~8%4bz7Z1QJDfd@~1^eeAz*dz|nQP&1HN#r~}s*osZ%6EA5e%LhoT7DzCRB9rho}
z?+?pbe^7m#j&%5Lvy5t`vrqbm;6Bx0&g$yDcw|Z~gm#r{He!9>Cu67HRv&rjok2=Q
zSbRIy0L2ZWHNmW;rLfl=jnJCA?||Sb9i`$^3EBW@#}J)yoeEY!)=i20XF_<50KA=h
zF-th_f;%ko;xh);JAoQbDs6*y=I-&#1C%4^X-XvJoMaDm%`%UDbPHVf83Ss2720dn
zb!-n><#QE@U=F%=M<A!RrZZms9Q8$A1gkbzS6z>|o>L#Jr;(d#&=~Va|Kif0{QJ%S
zb>G#Q`|tbQ^cPZm+~U=yJTF>@B(Z{EoJpiE74hP6!9F8tx?x2!<t1k__L{2)S+e+j
zz5FFFiQ@l8VQH<G%-63-{^12L5|v9wa)$!R;&ls@dI`*U$x;tVXS@h%qDFE;YsOv8
zvzm|E;<NUeBl?hQAX#d1d<}s^;A<KM=m4a3S_N~#>qr*oSw^VT?Zh}tqW8R3sSi0y
zU6xTGavLx+Jzip(y%As+DwV#D?m9%D1Nt2>V1d##rIiMx@}MK}`3^bEAqO0Gz!3*z
z9FTJW8;?d>O{-T^-)ivjVhx@{vC&!dsuA}L3N@r6B&JeW;$d*3t){*~I3R&V#3QX<
zqp67}OW%%nwAZCfqWEnc-*2yMsIg{HepzNB$<lZAe^ZNBbDwFs&+uPk46=yb_*HX?
za2KB<SJU9d>(MU$8qtNs%!GFz<V+NQhAVqOh>~kbwB90W>XYk`(ebOPPn?U?M;p35
z&w~u)uH=m=sG#^AFsN(r5;Fy@9jS{kBdD`=SaFKK63kh2J6ZfL@X6v2mYJGJJW=|7
z3*cJgMdB+PSo_bsnrIByZ+T3aYcyz$P`tT?#z~l3sEJ{4l+spQmWoIz>>&!}X{M$n
z#<_;!lI|B#mppz38D3?tiD*>0fvK%e7Qe^j$J$AjN<U_#2Qvb{*eDEW>Vkbyu;&Fn
zFYrqOza;Pl+g$0eATJ2=qC@rw@}eLw34BT5R|I}V;LDEK2=cNZuLyia;H!?HNsw0s
zc}?JJ0$&&Sy1+LCz9I0Oz;go63p_9IO@VI;{F=b834F`%xr%a2u&)cn>jJ+i@S6hP
z7WlTnZwvgk!0!nBj==8<{I0;i7Wmf!|3={72>hPF?+N^Yz#j<wq3d?9AU_o39g8e#
zpYISkE7Bq*TuWl4grfpS1s2`SHVND!_!fcN1a1>}mB6b6ZWp*+;FKCrTu>=Nb?PKb
zP6)13aO(tKCvca*T>^It+%0gAz&!#>zh-*{-XZuM0`C%dm%x1j_X*rDaKFF<0uKm0
zDDa@bLjn&8e8|d}6Wk%e!98+zbR^FT@~|L}X#Z4QM+B9T;*7vK%SWr^1ecfcyuhl1
z34sfOF9>{E;L`$Y-LnE0oebLPq9A94ct+qC1%6TB^8%k2_$7f~68M6^7X-d2@I`?y
z34BT5R|I}VV1Jgi3G%WauLyia;Hv^(75JJny)Hps6XbP)uM2!b;2Q$Z2|Oq8yukAU
z-xT<!z^@7Xn!vXNz9sPM0>3Wsn*zTn@NI!_3;edgZwvg6!0!nBuE6gK{A+=KE%0vy
z{*A!z3H+YG9|-(`z#qDqUQ~%c6zm<LxPx_}2Oc$2ABoh*#chAfi^6M`I%y|>8{u&k
zl2E49iDf8;RWQnx5?=q+dbsB}Jaa=NnU8=b3S46YJvJOaDI>KhET~vKlBFM`oa^Rt
ztdm-<mpbx@BkU;t1s2LE0;`DEzUtF0aQw;Q-+K39k>rId)L%rfVtDm2q3dpFOiUj^
z3<C$=BdoODg{fVLv{MY<;?ZcdA(EKR)Abv)&PL-=U18hX5jCL2WGPqQ;+c;7Onr>;
z5Tgn~7a|?;<swZ-+@YFcK)MXH6oH<TrLB!Ah`JrUp5vNX+KN_@0@Z+ZzqGw0j@6Ir
zq3dI^^p_Z#Wbt+4(6VsELeGdZS=x!f1OZ&D>1?Q5k^E*YATUW3-$%T%TjClJ?PYw!
zD~N@proJQ2)w}dpvWCBpQHj+XG=Hu=ChI=Cx(|Z`(e}Av*cVxiRoXTmTsQy>3qVQs
zQAm)070Jl$a}6`zkfP?4FBB@YRMItb9Q|6&(u(6D5eYigYJd-Q4r8Fbqg{E_3-lSM
z&F&N<+ld$?-Y(!<(Nqn!_EvmFLk+0JL=w}3?#ikEErhItGF00UU3ApN(JOSOuBJAI
zgaJoK4dOp=d8Q7GJK{1AiZ9D({)`Q<`FL|fEkftlqxd9?Z%Tyn%@&v}{Ha_SiM)#F
zguF4|Bqou97$ti)S^7Pw6?TU9lBHjY^w}t)wEHl=>Dm~s5Q4!H1J2S{)f|d%*&dX>
zTH6qbv_~<f#qZ<45d95?xxG16+YS!E;gaRlFTRgkm<R@*l8-p-^bU-BX%tf$0X(L5
zt7$=)D=1(%H%sqJAzNlRtr(V(>GUqYx`luWY6<RrOgG@1Ly1w^(%(TiTDmoKAOw9z
z@10;BXT;W~VlvJnn1oYJ4Yjb`#B^Ufh=lqam_T!=9Ixy4s3Zgi(|s%}em^no$pmj_
zw=usxR+G_7tMR1Ur3Za}4=e-@K@SAS9`tE{`+aH)C8qlWYQd4ZU#SOtY6~T%2Lfur
zk$OO=OV=E=gA%1{KDFXN4IE4rH%oA?aI2EG8f|aTahSFcY~1IdIJP&a)NMYsg%Z<k
z0kz;r-A2=oKzYk(vNw~Zd2rzEaJ$S81plz{mmA^BL4*JFbj90B6OW3Hhy0c-l$ah0
zS`r*v8dA9q`P3FlOdkrU1xM;bN`2U;woqdFa6m0MQXf|8BR;i-u#g1Qf+O`2rOx=&
z7D`NK0&2mLI-}G%pV~r+>0Cf9I8x`7I`30k2#ZocEjUu=Wwi4cZ93N(0*4w}qses`
zbL=}LihrFrlPLZbybPS$8!&ucf66OLlQX&G4DS$8AXgOFyaoV5t6;MD7MvFDE@@Hl
zK09@m>{Q2B+6u>!IFsyz+dl_;K$BQbabx(`we?6ed~7wb?ns0;m@W5tSQk-XIl`04
zJwEO<Az2Ko1BHY`q$3k)>Gmln$!tgDrcSz)h;eDRy%P~*>Qd1g1|Jbzp-wJFK+to{
zfDP|!1&}6)v5IhvXN=F7S`o(0%Gkz7jo__dld!kwDDU4n1D8o74rV(6{Dy^_7=5@*
zo9&5`**4JwsO7EI4fPmT+fxZ_pqWw+O2ilT*dCPtNRJeNR!TisP_=WV9^AM0HZ+4#
zd{LWNU=4}lFB8R|qY?b~*%V(*%$)Ue(9_J>C>(9^yO`-R`ajpv|7Db;{7V#nrXh+&
z@YTiIEdGshr+F2jwB@+Vm$4!Fj}zzoL7?plMN0}BDrzfOm=cL|z9<xIroEvqiWQ+-
zx@>`;%ElEMf~h4tHE!#sO?*cR7z`^3p*I8<w@M?2#LSDyhFY}{s`97wcyA#_i_jjv
z!;2twEF;To)ktFId<cGNH6%-x4oeey<q#eQD;YxWT(Mgvxkr;CYm}wKeiuuJ6Ehc-
z5rbP9Uuj%zA)v!hUko*VDFnZw_3dDncx7wYyiq~i>?p~kkRm6D2^zq#UoOjCQMOH1
zEM8f+$+2y6{R3c&9NVUVQBz{(YLG)C*UEC&%W^l$a&u+5`Lf*2vfOLdNK^$FV$}fw
zNUQcCK11vS5ang5ZyzRP9}x0Z8T;4Ea&LxIf4i*Y?NG@(WhL*H<$hh3`%PKyy%3KN
zbOd_s2=K}g=yfB&9b~aE0Jg}DKySdbH!<^}Fe+YGMsfwC;&sbNHh9U1fG|=3)>sS%
zsCYdwb4O=BDlXvVyB5D?RG2l^XUv+QU$eC#I2wYRLU2n6ZVSPyLU4O4W?)=8#q_7R
z4ZvuNPU1R+<?@CXca`Fa+0>^vZ6{O}yg(BZs7F^a!TSQ}0awNI3N1`veX~$vwlkbr
z7tVB19wiLiifcNOnC%Y1Jt4R^1n&sJyFzeZ2<{KT10i@Y1P@6!b1F42=w{A#6B|I%
zP1y({-gYzRcP5vZJw%(qzLIdzE9cBvwHW|vd;*ZW1?X-4#ROnt_As@gcB$5drGw6v
zAr6#{3#-fJ4bL}+Xkzw=>ki<IbVr2|nIJYr;i&P62uBT9>84yUYG?iKK#H7WhH=P;
z;E51i2*IaA@L5&G4yy`Y+20*b6+4_N0N5h!FKFCs0GOC9x*S|jVs<9f#EWGmp=D<F
zrLvL>p*k1KN<u5btX&b>MBrqJSLmzMrUC<Sjst)#Qh`wvFl|fBUJf;PB`AULSJgLl
z*(P{p6J4%}F4qJATjZMP3K(@IW`p&B{^EM5!5blXE(Fhq;F}@%wGezO1iv1F-wfJD
zCvJyILe6{koe=)r5M1WCXUiP-?0X@)4|F{$&Wm>VxJ`6G0Q!;jNS?sKYKe2}kwTn7
z#ra@8D$XZnKP=DQ(fAkp6$R{)@xq5KZXZYX50a(7!yZXZ-RBS<@?jTZIT1#$7^>pS
z5T)JSQgBXU`e3($8-;eq2y?3VvK>bPGL8yhdf_%u10+B}REJiC39HF22qMT<FvUhr
z;tVFF+$Jam?%bzWquuDW$A<qP@mB2f(e6Kc`oI7Azxxk=8@=_7*5Cg0k)J=)?Y{JP
zuU}HE_3(_hHo~NaNi7rXw3ym@CJjuYOz<$x)W(=BW75Q=nF&6oHqK-@lNKfkCP^l(
zOxl>NVDc#@E19ffvYN@KnXF;b&ZL9MeN66Wl47!!$!CyY)2=R3UuQREW4O7ViPA43
z{GQ1;Ajf^L7Chp^-YZE;JxtIeY&`%fzQW{^<q-khX&D%7dKKSC4%HQyAl(PRoUN-z
z@Y5n`6!~sPul#QN*-WIq+45~-bB7}JO`z{3qiaY?>zM3f(#J%^xdNzomB}1&ZA^AZ
zTOxNY^Di>F%;Y+g6q6w)hnNg78Dx@YtB0j>J9HHVu6ReX>j>Utav2h3NK8GHE-+a|
z{6*jk_4OULtGK%+_CsKgAS~b>uO!&<WtG$AxB_t(nY<*;orfT$YXaUN_J)9030|R$
zly<tCP#d(T8_29Py@LrfgiR$T15D6U>^d-k+}I;vl80QoIO5!UMkVYe`;4N@PB5AG
zhi1T)7GJ{%u~ytKlMdaq1Yqq*OSgQ8=E6#3U3}NoEPa)?+IEM~g53QJu)BQhF4>Mf
z6BPCNMSY<P{XVuogdOm)0|B;l&0(?aWkK8#4mvEhvI1<Ik8KNKhkWc%2z$uK9tvR(
z``E)F>=7S(B!tcQ*h~nU^Rc-QHt%EeqN1%(G2R)Wul$)85mm!`$|RmSjiE38ipeig
zOP}PGx?u{PlI2oR>Xxc+*s6FEUOMbEQHZ!6!4cBu<7~ozQeJBEYcvJz;Q4?o334N(
zt>fmxr&Nef-mg1c@jA`ry6uZTOG0!lu^R{{1VGCQ0F!f=oD~ldp=Ach@`>6G((}oq
z{<E%@((>`<4!@Q{>?miN-ZCphT$hjQ@;h3b_d8mgM+d&ij&pV5!r7u=@KlEcO%Q+t
zcs9WG#Kb(qCB-1az0f*&Vo(QPjz=!YGmUWHnJFF`rRtK!lJ1@*i?i)`2!zeFVks)S
zUU*{0Ew`DdYy{0ju|Eb!Bo03SbFJO-SmryjXO6dW?!!(TchVT>MiS?irE0K0gWH{#
z6X*2#NAY`ctoL}pChuRMk$5X!LJ@k|srjDlzO<MyDxNs^uwhRbhLpOKolqwp2bQID
z5^y1jbDuGY5x$%3gwp1Cw?#r*C^<;A+)ygZ2Kl(Ldq3C|=?rIkn8hZtP$$mquv=@e
z*koriuiW7rg+HDZD*sSwF>s2BbE|C+Y-PTQMB&i<cEf$V@4zaW$QC7c;~eI>y<8yp
z-FvOsXZXmFZAw>`tQn1C@NO^BrN{X+tc(e0?<N87Hr>GCaSF6DWcVB|EJuh$yNcng
zlIc?mI#)H*u$CY5$LAw-wS<};tsJGBVR4pL?|Q!Z1>Ml^(J+v~np=ErVKZSH64K`8
za1$YT;RviVvG52)ZB=fgxuL@DAtu&Z)2I_`Ns2au=fJ^WYPDuWq;@`n$s^@M)7;Eg
z$l_`<3uSw_TyuA!JZW0PP3yDoFk6H#VZAMGr8??-f-O@sl!oo$kzk`zhK00<n3kZ9
z+x{o>h_4do*7!Q=#o{S-ly_A8l6C?+UY&4yNks3WPsxiD{j|Zt2G^C$Yh7PfQp25x
zir`sR2GoeWr7=`UoWleuCk@H2qfkYPjqsplNF8(-Z8EE_JWin#=Q^!XV=oJNI}BFg
zR>_HT4>}Di(GpZGPCZIR{i(hpqMtHJN^@9@tlOs;P5!8eo}K1=2@-y}4pC@ua@rBM
zL?J#<gzIS3mD|CS;#lI`D$}47!J4IBFzk4?B?TC3#?;AO5XqO183-eGz^$B4DPC`P
ziLGZ?YQd0s+vyfWtH#RpN{D=otE*YaP^j<<LAVQ{rB8}sZfdWV2_kmEx7ZG1DN#h=
zDB7qjbrgP=^U%pUX952D6f)89WW-u-+w596XJUu}xbmvwieW8tD<qcmI~651TF{cD
z?P*M^2yqxShpwRhGUwYMam-qm%m5wGINF~NIZhqm(k(S9?GSug;@nx&9w-iC;?L5a
zx3n}3ti{%5LJX`fl-WI}z6LQB;AM1BZIhQm{eo;|mJzeI8xjEPIGFJDSKsx=qOmfu
z0^5&S!?Ny0oR-v+EvL>{42le_9lKDSF^Ir8wA3PTXsN|Tr+5z@*v@6CIu9?1L@`dC
z!0<-am}DQ5ekKD<2Kj=~5Z-mi<xn!;fQareJSYO=WS)U>GS6^X;$6JuPDH7v4uvKA
z4vf6$GTqM->}9Z|_==DfUy;lu&0KP_MevAL-&ErZXUWsj22;G3RtFjKF-h?r6dnbL
zRC#P}^JN7@e|!r{mXzZA%N!ecZ8FLUBcyo7{k}+}_zA%yg<v13EFX{v_wqpD1!?r?
zY&~uRphiUbj3}T^X7TErWTPbP7Q${{fa(4~0O>Yva)s@Xv~)Wl%*b>3;>&!ch2fgY
za642zgBO6BspV#;q$XcUO~Uk$bZGhz2%AXQL_*p<zUE+(y&@Y@bn%+9zs5{yon%Vu
zB(qC1yCl=6nLf$L;9=*7nc@{?a78j#HFH%mbDGij<Jy!#n=sg+8C>KIKdU!2lUgmS
z6~Y%a^P*%fYv!_Ku50GHWKx<*NoGhhqSulr0$WT{GN2^`k{Q$t2LZEAGcaHb^buq*
z1)`EU%o6NJsi?fK<A~#(Bhvu_WJm@FCJcCCkJeeTby9Xy%Wg_WJ5jvMamdIxAcX`v
z2TC#rN*AO?>4KE3(#$HUeo=W{Wc7knM+%wq4QQCAh^P3T0F*@V$m1%tKh)YEvi1?F
zeT4J3l;b_5T(Mb3vjZk-5l5C=)3Gisr{d(q9%Yp+<yz3F6g!$aL#6UPtrA|u%n8eU
zJTCbTM}*r}6hr8o^z*bbJ}tzBxCmB=<IY+w*VZajwpGw-g^)Z2VR05L(7@RabVaUu
z%Z@jesBcwd>7ry_QXaT)^v*ZvAfHDWHUL>B7na?yWjCbW4XuX@%dXn8t5T-(qI88B
zd8YzaG$@kH**v~hA^CacDdIep4yt%dL<(-Yf}7ZEz#zAg*L1hs{Ko-E>%8D)$auUV
zRm+QBaWfEwH;?nCAVvUxs6j8IV|KnJRWDe7b>x0VbF*@3hI=tdOno@JUHTOghEuv(
zA(2%*-`m5smt15Q1hGdW-#yI75UVyQ1R+QUgy>|>W=nH{Akomk>CjB5Rwv{gOy0sv
z2b<ySYQYOPRm!}%xe*;p=GjHzKJU2Ci?()Y7Iz9tPes_(V0s|rO$U5gimx?`xT>P!
zYXQAlyWX7Snxt>`wn$pO=~~8Auff~V=tl&*G1Mj3Z$U<-c`6^X(eE>0?QKXSx0+Df
z4a+TGy3(aEUe-r)(>J*VN~G`(H%Ho4KHo?Q;LGOldZt2n$ONrP_%;`S^Sa8EpgA-h
z;|j!hkDV*N%N)Z^9bIf80K;*P#u2l@Y`4Qqp<KfkcDEz=czuPwU(AgxzAnUFHM|5H
zmA64b3qam@jW;38o58y!uY$Y2(#uCuV%`X25stv130S1WC45Wi5AWcuFMn|COS5=+
zmao3z)%CA}9!ljVHZFYe6_15H!R)e_UD!;6v?DOhQnn>k$7`BzH|F65vqIWuN&6(%
zZ*%>U8?ZSpC~ys$D_yfp`RX>A@=B(7y5^V;+KPk1Mqa#xd~K2&vSnPq`0^tzC_5y%
z!#2m&h%YZ{85b(tH`rW8aygsJNiJ`5SRUjx^Q|(XuQHv<;0<;J7<`SeNp=}A!%GEu
z4;oi~AF`&db{!rgGVze$mG*TQ#&tT0Vs-2I4LPLP*F*seqs(CqvLu@ABq2gc6y-s~
zdJGAf8Y${0jOdT)C092>`qMGXi7^}UInpaQA><zrfyfMHXX_y$V==iziR$*NBjO_A
zprk`m^>$F0fmqG1;U=Lu1XL4IoLF%(>y=v9m?A)8x=YeNNwp7G2p6wPIw$xxNq4AF
z+UZ(By(sBrNv}(ql2n94yut<sB&E<;Cy`?0B|WU{+94Lg4>oy6G3z9AQ_{;6Iztda
z1=9<Xu9CuwEGz(Iga9s1aSRktQv8tRN0!_E+a)6>3{F>I$py=)q%Wx$=aJ!xDI;-1
zN^WT8s${M(#hdnQzKt*zCfgKNUpBp~(dBnYjk%y)I$V<Bhr1$+UBO5W_;m2zU|~JB
z@VM#9&`Ad-OSb}0Y)>4i(C=hgeAj1F`l_I}3i?&1cwFllK3O*S>Ixnd3`IWml~d`7
zr^d(p!$jp*rzbto$-@NG?wOV4r>o$X_$N2uKzjbljAvFYQpgimP8hQlr5m2wJG2KU
zd*G0WuIF$9CQeq^`TUj*o6vT=?TJ(PG3FP>Po2c?XHVqQa)hz)Jb)ik&m0&(Ix^NZ
zI6jq6XS#4m`>`y3Yoy6D4F_;+8h&uvm`=~!&!bFEjhx68Qvc}(DXEg$|DWgbI9WWk
zxqH(#V~%?!J(SC)a1>Q)_u#(N$Yd%#mg*lG8hqp^xMWfzJRjzG`dBulXJcei$Hz~l
zGTG;|dH%HVWa?x#JC+(BOC6mW$@90XQ#i|NQi%M%<0nj@*iDaa=;l9TdOWjHbL+Qd
zdXD#Q-MnpUcJuZv+tXXqJ?UecvgxD8dX8_|*8Aw@E!~^9Y}&FVV@$#`O+H76Ys?<c
zG~;ZB@yQV!;(q4YQ~7Ql<1qR8jT=wk_p_&tZa6kRy3tiZjmeD#=;j2}x&bn701<w(
zh6BwM<GIr_y^dUu%CY3At4DXO-+@C>+yN?bh}ai1!^4|5AK7Hgqn_FPQMHF)9XM(w
zsJX?M9iDmgqw4jIzc7{`PiJ`4Oi;N8KNaPfr#`CkLYYqt5A!F(?T_0F!?T+=4|jht
z!;T#A%>Iw$xlkf7{gNDsHZhLFh%D0{GL@0~;@w&9^^ZGVzXNRSsSkc%VKf-+DtO?;
z1AAtO&jVe1!ZW+?T@F1_p^^=Lp5sfNdHmklpgWewzOm!uzBm~AFM4KsRebgbqmRLV
z%rl?A_v(7$g5UhsYMYl6hWzSq9sQo!bMMX5;CNtw{nd1z(WBL>(;jJ1$*0$tKF>UM
z?*fHRdGdwXUcH&&^dTOR=(E`LF-#T5?eHL*fJfD)O6|IHIRZ8?)oN#rJ$fhL0W+=6
z+$oBGdQiXz_WCho;kkVQ57=z=viJvK1$;i1@dgL_1dL$3)wvWZ$DQaDuz~G9<C%eb
zw_TngVkbcOe5ZgT4EdAgxEW?$9mgfkpz>wh3?u)9Iet8Q=b31MRIvBGp4oNpW5FS{
z#{&giVC|24W@mL=cx+j~0>)lF3m&T%u=rSZE(ayaAzuL_*!fdlaBk`1!BOQBjuQ!S
z{KavEEaD+219q_Y-JaQT@7lo<S|0kqz6U+?<*GQy>Pt7dNG!Dl=3U)-R(@!g-?uHW
z>Id-ERvbl@dOUnomrx*@o-Ab3h={85V+%Pk?%V=<-{+aWd)Gq6@nM0~u=)YdeCggf
zEj)27U<Tv=vS*&ScV-JtMhkdh7W^LWhVGr0J?g6LsISloi~(m%j_SH>IS&R$j|I%C
zo*;V{Ir%VPhY3>MeA6SlICX=g83T5hE!B0E>&p_%dN5O}lWnnMBYoL=Fkh-Omc<WL
z<b3GCe5j7s;%7DrFU*K)mv&KEc{JFAxlr8{4UTcE;Dnj*u>_JsMgx6+T(b`y)EF>>
z*;nT^%MW4=7{TD5^vvFSpXQZkw+5_W?A2*}v4aa~0$Zz&<o%NbeQCDBysKX7WCi94
z<st=)`f1NRdGD&hVksOlIB{$Q=R&5jI}$9uTVdGyJ=0$`KRvTjPw@--2m4NWW(}7+
z+pf~dVShqat?yZajp(B48-Oxqfz+4;)j105`-1}!1720T{$PeL#tt)~y5Z5|4)@?7
zxTEP~C*|<AKsZc>>LLwvG&tJR=d%q{p}P60om`9+W<+%&VGI}JQ+4nYx@qD@M9?|R
zi0VAXlAJIbs$XdBjhsCnHxLWP{>d?e#aB0gp+3r;Zy*;8{*z(_W3MiZ4@ng|l{b*B
z>idF^VE8BuzskEXd;YX9)1y@zyq!CGJZHcM24DT1sdX^{7Z`ih({B+!^=Q>W{(>Xa
z1DRmy)%m<dPN)sI!PKAi%;3E*a6Y$%hwBDhVfEEHiHd`M15Pmd>h?XrsX{V28#v$y
ztFMkDyu2OLvNMW(j=fa}`D(M4=@WV)a=;E#pgLC;2nc8>H#K@RU{-aMzu=_cfEQ*%
z^>YfhSY`W?y_gHt+3zAp6bCX@Jsa-I4YQ#-7pvSV&rS`*!$kOG8Dc(E>0+_t#)E1D
znJ@vWzBeL=gU9&r)rH#IVfCK`8(4dF(Ip}rO4E-AwIWdvt#7Z|*zdv%#=hVYNq;ua
zr??x!n|HX?%bps+k+;8}<;EsXw)IuCg%1ev^jrU8`WQTd{4&q*%i4UH>ch-@)-y9F
z4W6zJMVjRh<NZ_l!pKBE3q<3-u^bx08QCNoXuBt}c{D(7p~{u4z2Xn#sV>?d8cdd-
zygYPfB3t}dI7q#Kr<CKz3mcxC93R_o(DpZ7z!UflOB~wVm8z)Lm2%JQH*MBO+g+(W
z=-$++?9Q?56z;t8U8%vTqxq3zIK_Hs{A6})=a%j?o_J@s9o?MXmd<7~U7mTUY^8$6
zpT~*8924Hf?c?6EB8=(w%=)_#yIZe&s<3ZtVhRrtG9&3w3kxQqUj`EyJ9g^K1V(Ex
zJvliseyXrHpB;s9P^CT@&QtWv=av#u+T>1di2e(n*|s!&eq>A!q{fCLR9n$j@!!gO
z8voP>dAnzNmg*wkEf^miO^;<T^(V3w9sH;f-z^*UA#HB+%%-K9qCKgJ94w^q%1Ef%
z2G2Zvw`vt)t@q4lmm=RgI{vkhdt^;hs9?=^LzZG4m~2I*1@B(??N4slpPoGFqMX5T
zyw2j6E%MHWUxs=bf9vty742>EOxIFE+Ikf=?lvn1)2Al0`^PikS#h@k;d7!2_II-r
zeV3x5g72^4qd(`F2N&PKv2Tb3k1ZbbOlrZSjRjO#7wVo*=cn)z656RhI#qzT#n~mE
zi7CwcZ=74?nfQntDG&E}bUb|u&qb_U5l`2YdhB0i(>Pm-eE2A7A872d!=<KJ5(n_?
zXoYLuK$%8P!N2}4oOaZ4=SyJ4msNx_l_K<IkEh|Xo}(iAF~8x;PW(>9X<Mu%(as-v
zI3>MOu8@u6J+;$)=|XmBWHd0o--(RI+|>!D$0)+0;y}ayS~dK9Wlx@(%IAOg>P;+Y
zMs)kf41R|GF)eQ>-h?M-2;U0wZeVOwJ=X9@rig)WoSMjBXpuu0^p7cK@fFP7(QF|%
zE|UBcoF|yY{CoN5Km1?D+ds%z{S|x;&DEMie^SdL`Qxy+;`0zbKSJ4$LS-aqVG{Z~
zaIo*-nQzVg>Hl2+)|day|GEAzfA#oD7!4w%;f<50j+L!)8^@1+ZKHj2bK_XH!26Q)
z!~|XiEhF17kvVD(_V3=jb(?XV|C{9u>0kKmn-f2*z0*JUt8d<{|9eR2{#N}2aLVP%
zuQ8VSf5UhkwJ)FFpB@=AqmvXPyCIXuQHApN+Xum=vYUS0|33gvI=BjMr@TM(K?uPY
z!IQ2ZpF{JtW-O%4v07{lnCHwv9KZFP*=r5}+Gn1|5!l1HK7|9SpJr9_liL6B_j>1)
zkUZXXmFr_RN0$1!u|?q0hmQxYJRZy)VYm+mCdu*7DC;z=HH1<<5Q_(i@wg=(M5TY%
zYX1wrM**4ikTo9aw2;#eg{B+*)_*-XBn&z*Z{v#h|G;ll4tLX|&)6alVN03EQNrV`
zcH=nSeYomz+LR>)T@B&iAdbg93aNN3Rz@hug9pFy=vy9j#lx|9{F6J%jz?Va@Jb$V
zmBj&TJX((j{<6*_usp1g2T$=xE2c-~&@>*Q=#H$?BdSR2I1LFG9!p70jhYSU+X1uM
z@0%X$b_S!RhXzx32f$wssUpO~JjYCz96PKBwq=mhLs{7i)i-_*1U#L7`+(<xbv(Q)
zIM9o{7s{iWWZ7mM9~AI-4m{ZJU`#ieM<IW=|6!fSVM};(2U4hRc|2J@)SIQ++W@V}
zfq&p}1i#dd`*XC%132?C>JvCvjneZ-NQ82W;8NxX&~u=n6pnMgBRna03ccwzn+&$6
z%#$MH)0Q8#$yS`MnB!4pOY0^q>*qz!gW$zOGI<<jA=KNYd4Ro{fkfCiE~{Iq<*<&|
zL;4JkGd*s4%~teh8`3PU+szhy(#WMzN?YCpd>WQ=OsKbjvKM2u8Fbx}ZUVFgybO*d
y<rweAxN=_b;7cBaox)5nAMLOl9P@wv`v+*?TCL%a2X^iJ2k`jks{hYw;Qs<62S--`

literal 0
HcmV?d00001

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
new file mode 100644
index 0000000..2217181
--- /dev/null
+++ b/legacy/Bot/obj/Debug/net8.0/.NETCoreApp,Version=v8.0.AssemblyAttributes.cs
@@ -0,0 +1,4 @@
+// <autogenerated />
+using System;
+using System.Reflection;
+[assembly: global::System.Runtime.Versioning.TargetFrameworkAttribute(".NETCoreApp,Version=v8.0", FrameworkDisplayName = ".NET 8.0")]
diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfo.cs b/legacy/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfo.cs
new file mode 100644
index 0000000..7ef4872
--- /dev/null
+++ b/legacy/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfo.cs
@@ -0,0 +1,22 @@
+//------------------------------------------------------------------------------
+// <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;
+
+[assembly: System.Reflection.AssemblyCompanyAttribute("SoUnBot")]
+[assembly: System.Reflection.AssemblyConfigurationAttribute("Debug")]
+[assembly: System.Reflection.AssemblyFileVersionAttribute("1.0.0.0")]
+[assembly: System.Reflection.AssemblyInformationalVersionAttribute("1.0.0+72f6cd30d09e169dfba57519c055fbf65c07a93c")]
+[assembly: System.Reflection.AssemblyProductAttribute("SoUnBot")]
+[assembly: System.Reflection.AssemblyTitleAttribute("SoUnBot")]
+[assembly: System.Reflection.AssemblyVersionAttribute("1.0.0.0")]
+
+// Generato dalla classe WriteCodeFragment di MSBuild.
+
diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfoInputs.cache b/legacy/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfoInputs.cache
new file mode 100644
index 0000000..2cfa414
--- /dev/null
+++ b/legacy/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfoInputs.cache
@@ -0,0 +1 @@
+3680e5812d66c6777eb08b0e2862f4230324a1c5273b0801b0700e9dd5595131
diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.GeneratedMSBuildEditorConfig.editorconfig b/legacy/Bot/obj/Debug/net8.0/SoUnBot.GeneratedMSBuildEditorConfig.editorconfig
new file mode 100644
index 0000000..fe34a67
--- /dev/null
+++ b/legacy/Bot/obj/Debug/net8.0/SoUnBot.GeneratedMSBuildEditorConfig.editorconfig
@@ -0,0 +1,13 @@
+is_global = true
+build_property.TargetFramework = net8.0
+build_property.TargetPlatformMinVersion = 
+build_property.UsingMicrosoftNETSdkWeb = 
+build_property.ProjectTypeGuids = 
+build_property.InvariantGlobalization = 
+build_property.PlatformNeutralAssembly = 
+build_property.EnforceExtendedAnalyzerRules = 
+build_property._SupportedPlatformList = Linux,macOS,Windows
+build_property.RootNamespace = SoUnBot
+build_property.ProjectDir = /home/marco/RiderProjects/so-un-bot/Bot/
+build_property.EnableComHosting = 
+build_property.EnableGeneratedComInterfaceComImportInterop = 
diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.GlobalUsings.g.cs b/legacy/Bot/obj/Debug/net8.0/SoUnBot.GlobalUsings.g.cs
new file mode 100644
index 0000000..8578f3d
--- /dev/null
+++ b/legacy/Bot/obj/Debug/net8.0/SoUnBot.GlobalUsings.g.cs
@@ -0,0 +1,8 @@
+// <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;
diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.assets.cache b/legacy/Bot/obj/Debug/net8.0/SoUnBot.assets.cache
new file mode 100644
index 0000000000000000000000000000000000000000..b28c592465963fb86a6fd1ce631858229da9a456
GIT binary patch
literal 3539
zcmc&%Pfrs;6i00Nqx_Q!ih?Kz@j!NIi5e4)DWIfiNCS<D$7y$H7j|biJ5!-&ZzlR3
zNQ{XGFCIMk0sIc$jK-t!8~9##OCM!gFrl!?k7nP_oA;YHznS;QK3hrmb#!zXd%qrk
zc>Q(nXXo2Lf8Txl^x)0V-1ejShhMz>x9^{S{C<A{21QRCev4+wbGhlVpx~K?C>Oah
zeOB0JMJ~-PuJQr1g*2EDo?^=Ogfvt0sid)B%$ts#Hw9NxF=4U5N`YyWtHsbGUH337
z$2lLxJO{s>0DO-Dki*MX90cD{5Y^jM#+LMinS{tjQWtxzK5`XC(x_b!1*3KYFlrCL
zY86#hq~fljN&#n<Es92=1S#N-H19$9{iVZ;xe*S{?GIgwhEf8(5D*Dq_L0Cvz<MhJ
ziaWd*FxSX?%1qv;K?@#`^8E$KLasJy(Ni=WDgndw12EhGVCT5u48E(lkhs@|?>P=E
z-%K8dz>~UPHuR&M27v=|8Ui4vO8|V(cG9WvtvpVP4^73Hq=0atDG1qxfAJ8EXpatl
zA8Zgd+Qyoy4!`zMU7%r-8jQpHgk$hTjy8mfP8;$Y2H-JD0Pq-H28;kk0apN50b_s!
z4HYT3=8t=U{wPbG|244BP=5{8P>Qt~F{3ymib^-*b+F@=Gu}%4@V$BuoyuLf>pC@Q
z(z~!tcOZ#I@4)uoeIziw`%e0xlQ3s~&<U5ugN|}N6?H+S#eK$ystfO1<S-7H0Nemf
z0&W6s0otTK!iK63*<c0Mv6%vU`_)N^QFS6Qtjs!M(_lYo^%5FXy+{KqwvNVau%Ag8
zBV4Jvkt<em9oHGK(`Oc52G+_eTxG)!Gh^wiUV;vK!2|3?dTMQFf0n6IMnqH0{ZA9x
zSV_CWW^z_DM?)dRMy7eqjaQq(JJB>pHiZYub642hO-0Tvn!BTlc)Z%A-92KW!^I!h
z7F%O3?~YQP$jFTAJ9fcVPGy;I+Jam0r_7POXPryWdBv?*E-3RD?nJ6pWfk?7oHp;R
zTvV;4tv9~u<Bo6Ix%Q@|<SJM3d3R72%6570lIK^Rdj;mi6f9+UnM-Ah>VG&J<vcEI
dOxuuQvJx6`Of+rC&|~O8Qv-(S0|xz``~#YK?Oy-@

literal 0
HcmV?d00001

diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.csproj.AssemblyReference.cache b/legacy/Bot/obj/Debug/net8.0/SoUnBot.csproj.AssemblyReference.cache
new file mode 100644
index 0000000000000000000000000000000000000000..72ef4e65c29bafede7467955f5942c686fe891c8
GIT binary patch
literal 1446
zcmZQ$WMN=nU`*G~$j?pH&rK{!&ezw=D@{)=(Jx3$&Q45EE!NLUElDa$%*-p+OU%p5
zFG(!P%+D*<H!?6X)HBvI(9g+C($7mRDK1IOOGzwBF#-yCrIt8BbT}e(=%wW3Ffsyd
zW7wkn;{VK>9E^;NqF7ZjK4oWMU<Rp#0<bP1o3S29urvS}K$?MZ0f<zAh)e-8*_a^w
zpkPNs2B5zffw29X^|Ixihxruw>Q1&Rd)@qJyz9GJ<>Xx(Hw#_+uCr*`PPwbcBBuHO
zo>yA%TEZ)+Cga%NU5@9U)%N_g?`Sen$o@Qub#bb$)n}LJZQn)zt_jxDnE%RAY4#fP
zGVS!&Vqg2;71pYx{#p4>{6pWx^rD4-f({-mP<r^`=H3e|L0)NArx-baZW0h!aO0W<
zvnpdWR{!cKgryc014Gpo7N~j#IttFEIVGh<skV8kr6om)IXVgfrAaxN$=<1zA^F*<
zdA5cIh6afSDHbVa#uf&~My5b}z<@ClUu5Q`mX`pdFF&nBFRM5|Pu~z2g?fhABGC__
z(hH~(5q)>|?XQ1%0~CGyXlfXFVUB>g1xAw|abFYftJmakd)6jRFxnz;_Pj=3a1E=w
z%J=^Z#GGBfY1^eJXkK5zb2xBfXQ)w+*PSkw4(0a~Hk`M3RlDK3=BZEHj1TYH$FaeX
zamGOo$*Bh?Xzoc5oLu6!dTz1nj%7s=Z^SPOo!h&6)1pR?+H<dd&uv~cncqmjexIzS
zpi}hG5(lQ^;D|F=>;G)ivUP76gVB77h%#^h8W0m<#s-NgCTXc=Nk)lgiK(e6u&`nb
z!WUsBsX3|XMTxn3N%<xEhUUQ7f@itp{G!yvf&yb;{Dr_&IpvoiVrrhC?BkW-jK+hk
zfKd=;H5!eYzo5Cy1z9VSzrju+#@~6RIXN(k7|V(Et6pkFNopP_R}||N<mcpM=B4W!
x661SUguVcXK16`<r7igg2@rW?U+H1$fs|A*XCu*A!zhe^X-L5a@&c)$1^|a5#4rE=

literal 0
HcmV?d00001

diff --git a/Data/Questions/ingsw/0120_22/wrong1.txt b/legacy/Bot/obj/Debug/net8.0/SoUnBot.csproj.CopyComplete
similarity index 100%
rename from Data/Questions/ingsw/0120_22/wrong1.txt
rename to legacy/Bot/obj/Debug/net8.0/SoUnBot.csproj.CopyComplete
diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.csproj.CoreCompileInputs.cache b/legacy/Bot/obj/Debug/net8.0/SoUnBot.csproj.CoreCompileInputs.cache
new file mode 100644
index 0000000..d8d035c
--- /dev/null
+++ b/legacy/Bot/obj/Debug/net8.0/SoUnBot.csproj.CoreCompileInputs.cache
@@ -0,0 +1 @@
+982238a87ff881b98e1723d06960a74addf8ab5fd20a247d9811e87171da4117
diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.csproj.FileListAbsolute.txt b/legacy/Bot/obj/Debug/net8.0/SoUnBot.csproj.FileListAbsolute.txt
new file mode 100644
index 0000000..1c743c7
--- /dev/null
+++ b/legacy/Bot/obj/Debug/net8.0/SoUnBot.csproj.FileListAbsolute.txt
@@ -0,0 +1,21 @@
+/home/marco/RiderProjects/so-un-bot/Bot/bin/Debug/net8.0/SoUnBot
+/home/marco/RiderProjects/so-un-bot/Bot/bin/Debug/net8.0/SoUnBot.deps.json
+/home/marco/RiderProjects/so-un-bot/Bot/bin/Debug/net8.0/SoUnBot.runtimeconfig.json
+/home/marco/RiderProjects/so-un-bot/Bot/bin/Debug/net8.0/SoUnBot.dll
+/home/marco/RiderProjects/so-un-bot/Bot/bin/Debug/net8.0/SoUnBot.pdb
+/home/marco/RiderProjects/so-un-bot/Bot/bin/Debug/net8.0/JetBrains.Annotations.dll
+/home/marco/RiderProjects/so-un-bot/Bot/bin/Debug/net8.0/Newtonsoft.Json.dll
+/home/marco/RiderProjects/so-un-bot/Bot/bin/Debug/net8.0/Telegram.Bot.dll
+/home/marco/RiderProjects/so-un-bot/Bot/bin/Debug/net8.0/Telegram.Bot.Extensions.Polling.dll
+/home/marco/RiderProjects/so-un-bot/Bot/obj/Debug/net8.0/SoUnBot.csproj.AssemblyReference.cache
+/home/marco/RiderProjects/so-un-bot/Bot/obj/Debug/net8.0/SoUnBot.GeneratedMSBuildEditorConfig.editorconfig
+/home/marco/RiderProjects/so-un-bot/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfoInputs.cache
+/home/marco/RiderProjects/so-un-bot/Bot/obj/Debug/net8.0/SoUnBot.AssemblyInfo.cs
+/home/marco/RiderProjects/so-un-bot/Bot/obj/Debug/net8.0/SoUnBot.csproj.CoreCompileInputs.cache
+/home/marco/RiderProjects/so-un-bot/Bot/obj/Debug/net8.0/SoUnBot.sourcelink.json
+/home/marco/RiderProjects/so-un-bot/Bot/obj/Debug/net8.0/SoUnBot.csproj.CopyComplete
+/home/marco/RiderProjects/so-un-bot/Bot/obj/Debug/net8.0/SoUnBot.dll
+/home/marco/RiderProjects/so-un-bot/Bot/obj/Debug/net8.0/refint/SoUnBot.dll
+/home/marco/RiderProjects/so-un-bot/Bot/obj/Debug/net8.0/SoUnBot.pdb
+/home/marco/RiderProjects/so-un-bot/Bot/obj/Debug/net8.0/SoUnBot.genruntimeconfig.cache
+/home/marco/RiderProjects/so-un-bot/Bot/obj/Debug/net8.0/ref/SoUnBot.dll
diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.dll b/legacy/Bot/obj/Debug/net8.0/SoUnBot.dll
new file mode 100644
index 0000000000000000000000000000000000000000..0b65b967c809e9b672c89650adbf62045519e93a
GIT binary patch
literal 50688
zcmeHwdtlsEmH+*I=b1@nl9@>+ueO=Kr%jW-OH$j?^g$bGX=zd@0=Cm++J+{XbY=pr
z>0n|3ML>(FC=aC|D2nnB1q6i3;>!Ad?GKbzx}XTF>tkhI7A?Qe=iKji9!bg~vcLW3
z7nu7!_uO;OJ@?#m&V79EOgFykuyQD+ocO%{x>66~$-kA7zHu^);y}aK0_y&f?@oTu
z*zn!SoqKxH(ScNQcPic&?TYvJCo|EViD+uDKibnDU9+hp+L!E3H2eL&X%_3vYn9qy
zI8=7YogcSbdrnPJrN%s^9s$Oyu^;&vo>6?B!-vvE@i4XAfJA;iRn!nXL5JFLxl$GK
zfBtD@Nr1nV;Jb;yb4p$2A~5>TP-Q^vc}A(u(M4~RkLE-dDlhWu3+S6OiTxSmKk+pr
zD3iXTKZSo$rFJx@(y1;WMYiVvxbgWqJ_Ua((T(O*qBjXb%Bqy5gMW}rE?KG6s$9kb
zGSn;nD4VHV>Yn|ufgQquT-e&*^jAO6qb`OXQ8$=EG7zg)$~V2qJtI|xoG&%K2zMHC
zr6~+R0bL(*WvSbWaQ}#$-<|p?ku_i#cBXzwxE8P~3Ati*03s)ius7(P<5#JmVHeK!
z1YPNM=t<a}Ud@LGN$NtDlToam+)5&0U&~|YXs{&cyCGbvf~Bc6NRks+S{5vubDByG
z0TC>XO=K;pDvbv{LAQksmPUfk>NG@gC)XfRs&O|M-I!zK5BlFcquL@ylT_+<-ToxB
zpLz&ztbvk<5D?r~y)Enu`hq1N>N$PM>u}}5PDa^{KqOq=xGES3mfsMru)0$bBfHVv
z(b!-Gx>HjqMX(%=ln2X$fe-a8_^LYI5DuzTi3zm?laq`29LqSUPEWFp+L*%jLoKU-
z3x#4+nG35>IEE<T%V0pvaAg{X;R{!(U}ZAKDl?d^Zg~Jc5vq>OWUfY~LnsQ@25W;g
z$wj0pMWQ4NOL8a6bvAV#WJ(4lQ<H4PGX|bwAN9fdISGa8#HP+C-7NB+5S%b)wo1<?
zJTW-YB1kqeKM4&?ip>G2d8)=KY}1)au<KR>G8pKe+oE9`6fxDqHk!z%A=uD*C%wro
z$~PYkM&~TGnx7n;Y!TEYn^CX97fH^=Gjf9#XA0V$QY6lTB5~O2=$hJA3d9qgk<x0a
z!Iwn9g%L@UTCXZSpJLZV!c$ue__9iuph9>Wsw`p6smTQ>FX-U(;Plp8tqzXT?~LG#
zIc-)4X9i~$v&i>Q%dPtv3r-8hbU(dl4&KzNfXb=8K!(u_3c9PS2B&G802eZvPDVw&
zNJ@!KW>u;SPD?IC7cpW}Vfj;oQ>{uIS>H)#cvf)M(qEl?{dH)wF6k%lU{$a-xmfDJ
zY$2j=>GHq6{`&FqQ1!_O+Ne*$P<`szU{!Uj1yDHRPNiWX+6AFB-wcg_cPvFNbrlQW
z0Vo)WEyF`RXgPqT_x(lqhE7J{BR1dJu;8{090R4U$LCCp+rQug^HlH-1Egv-l7GU-
zk2npS{1IP)WGieWQGlOf##XRZuvOuCh6>{evY;|!rvvYE%K()&D$nd3=_C`OQaOQe
zWCZ{Mbu_{PEUqX)gStcjAe=Tr03aZarVs!K=usmqAkPfy(Io;v*7|uabs3&u9<;im
zOlKA`$7#B<7-y#$aAslGPBXm{9m*o2I88@oWx5TySS80ay^1~M?BOdlW6*%xJn62+
zBfSPFz?co(>9v48?&KMG#MU8!h9azeywp7hcs=a14J=1YwL&f3uh0^8YAOT=u3v)b
zb3hM`u1D=G!j02yW>MyJS7i~0obH3fg;WeWZPepos*R`%wFJ`_=(_J@UDyfhuH?g!
zg|Rr@&Mbz}>2_r?YGeqD0z(plA;zRpXJRlNUm<U3IG8@KfHz#8ykXho4euatxC42^
z?#UZb1H4HH-cUSvGgHJHDpeSSV0wE&pEt3hneE`ik=@LPGkZ24uIv^*s<LPCagc=I
z4W&^L)Zt<9!)WHm*pV#*5ym*chcgR9m!XFK@pNT_e5Rp3U)C=mIQz080m0sv#W;fi
zLin;+xd4P9zAOyF8F1H}G<@zkB_<aGZq-;DaMzwRN<GA2J_W{O#wHM$P1(R%{&Mzb
z$@bP-w>h)l4)uF*hV{Z9huF~=)a3J3mR6T3w?7hgwtR)<LFWx&mkPRc41{`pVRz6y
z$5~z0XhzC_zdkodd4irfAuO3{WFCklVIk<wUeMf_qv0AWPg8BttW(}5x2N)?qtS?j
zOM)eHBFdBcG%5vM65NuoL$I1m60TI{_{|||-jnQvK#(pCn{>k|u!h<eb_ShX$+Tf5
zQ$%Jmw2NLnY6OnpE9m=H_8lxDWi98S$m6}iuS!z4F@VZ?4eo`7BPNjil+5m~2ogt0
zljnk>dRs}&HLw6<dZc0Q&C{zJF1R_l4fq(`tTFYF%`N#Z;nq0ozoQO1Wj12N(!;Q;
zL>8Twf`M~-Q}v8X;a<*@?r!ueeLk2@A7)7mp4S+Ii!_EYtGL5nD7EpTpf^}@Lm1lf
zr5>ZeFfzS9H5=Ch_v0LnOHH$JQP3BYUYDZqSgtS-49xKdUBOa&>`Ixj1{-T&=pO?Z
z^kS9wA+7zOOOKNBR&OiwdN4Ad!n9`(25gLWJ_}EdpKXh58Q~0W*;vX^FZ0eRGd(ew
zYoqua<4pZLr)tMw!C8Y!90Gw;>_S$6r7f?4C}hY2unfBJdSz8rt(PeXIBumedH|cy
zq`#!;8JK?KdKjBmuT-4&Q&uci<sG0!WkfP}(5aU)Vg%$7b_HEc-^iJwrm|;!_)gPp
zR;yfT#^S6&Q>s>OuU1s*6&BR839MTR>f4)?>3Pz14mDu0I{;8GUUXG<9Rm86z6y0?
z5n&W9B229*cZ`h`>$MG*fv~O+uT%i~oq`WSp!|jBm8x5ihxY~5r4blg#8P<|nyNl8
z2~+g>8tYG8W(|=$nE<6;=H?Vp>S_9>Im9^WNbW*0I*fr2S*Y}G*4TptJTy@cF&7~z
z5aOg0)%+!`H&fsu!o95Obi{h`3^^NGt7ClvrM^Pd^|LhOEZ`J_MK%JJs&?kfV*{+?
zI$4EyUmtW;hTJEs5bx`Q?gGejvI+v$2R(UQWypK7Y8qlg&>Pza7Bf_16srMkuUdIn
z6*Bm_-PPNCh_1!mbSG1&#pu`ct-@ZplW7XB8ELJCv8D=^V*b$Q4+!pDbIpXY`Wls6
z<`vGN7}H?-5V6*};nPq&=Fr>taAa@c!<jw8hbwz4A63~;@o|uZm_y-s_8dxnm>z@a
zYyrOmBsQ}^k>7jxaAt@2aAgnjQI*}#$3YT;AB@W8_fGJ`G#gB#R!%;sjr=a=!;ytL
z$Pc|IzYHH(jt%lzmBk=|FDrmA6rlM!)O*2K>m${OK~2p?l0;pf%wwt6Ty`}}FU)24
zGW-5q_IhUT&Sf8F_NTe*^UVG^mwlbtDwoxe41vTv>I_B{Ob-|I@#7TS%-+C<BYQm`
z&g>`naAiNqM^*M?d>kYp`i4Q&eJdR6Az+=>SZh2il(7{W*7)W$6y+PH1JA~)xSCI{
zxdi~C#zqJL1Y(M&5CDie8({(YwLatWi--~MfZUoYjX^^|PhLV*H&7QCc}%!T482dK
zmII?BUdyLY6L^ffuhf%-wfM@s(Qg5lgvlW2rasKtuqnc~0Z#p#h)Xr%7o!oiZcua}
z(G;E}EWnq9#(kBs5u}XPb3)1+J&bmB6UXWSI%J1lK%RUbsC9gW&&YuQ7ecK1(R|hC
zAn52ysq@*qOt%d5Rgk-jQe|^esm3*9DqWka#5Gf{(kBZll^2P9eD@p4d^%+Id$BIM
zD0vw`OkS7c5q4spDv1D{yn<MD+%*G<I_?iT=Xf!-okr}l$4k9)JYFlKs>3BM@4}o?
zus2a{i2M9I0#(3o)qQwk(L-O8eIxNOC56K$&K>e+B3+BmA>h7+52CI75s!N6d%!(g
zfQQl5+eRk<5YVa^@I<>n02E*?t?gBSkL1_SF`4+y^!w4XEUTLl?xwLW{2GGf5<rgR
z2iR-V+2l22P=LG9uM1=nuRhNrKS!BnY!T`85>fclnHub-1vn?yboxrLjlt_>4eC(M
z=wp@mWvmUzO4%5cnFVB~6gZMA*h=z)l!^^Zu^SLT*78(8oH^%jaOEib=8T=e_8h6Z
zVK-V%%X+cp2q-gs71b~qq>VERiOvDqXS!pE>5V*r&C_vTVz4cRSizXW%E_zQH4J>C
z4sR0Qrh`VuTA_(uBRq#FS6&>qKAD&e+A#Lp>+#e&kvfj#hgiuyr%JcuNPZZ3XYw%8
z8I5%oGJP$w(=BZBBgkMR+bunt3PULSJ4@D6@w`rWIwPfx*r`kHYB`&bzRVg=t|mvM
z5sMQlz!vPIU}C8N^-9&}P!Dz=RF34wSfr`l(yfB(ZmVCcUh2V`$64)f+~;!3QYG~S
z#?G1gGe;06<x5@W0rYV|>Fbe%oo`_^5_FDUjzs*8ssv+Os{R0(5#@u*Wyav2T8NO*
z&{VJ2OGfH03eb<XvBuOLi(w+eQprd?SXA^0(8NBe2~*!ID!QSt=to6GHx?E>T~Oq-
zdC4LyRpBkvgD+(2MVYrU<j`xfl04wFR%Ff`%1yP&g3P5CWV4&OWp@ux#dzjVq!!3T
zpnZJy5{K*+^tLQdlRLf=8$J{IlfT$a=ty<)X7=A4f{8na5M8mt6cFn%fG{i@5d)a~
z6o`|bMlwORDV!7$E^-ajW8eNKFvp}DG=~~d1I}SKnvI*$LKCub#zal9XWrN?WOoEf
zDRu+5;@Py?j7;~*p^)jx&SUF-Q=bkQAqQ(Ws$(!!uw!AVd618jRWRauv&yasW}gAG
z>|EA2hxo`MoH@-(L8YP?8FPr3qzoo%Tdy0APYyfHhLc)=N?l2s$6oh#P^shf;}*y6
zV4<9W@$~1O2h?ONatwD~HP{c9s&5*QQ*<*Qjf-wvE#Kg1KuiQ{=ODUcWDydeb*9)`
zmU>#B6pEpRf?k4j$b%%AvwlsR{48Xk{lM7kODaQ93-nbFU&=$UsY>Epd7Nw!#L(Pg
zXh{SGUs=eE-HB(@8W|5tYkIRw`3QSVeFA2NJUm6jfc#Mggr#NXK|W4a!2<Mvuqv2+
z0nBC~ps&I++d@8a>X>aMIK)2b<3|At&mb)XhDaYonnSt@c@!K;9$)W{%&jzsh{j%A
zp4N&W?B$S27b>-CE42ibx^?Ldwmo-2G<AH|n;?#Tk)&{wHjJ5x&O0!!XXDw4CtM=;
zhhb8?fa}8tRw(Dxqiy0H*r#Bkhrhf7visciOXx7Co9Pa8Iok%DIi%B-fV0{~HDWmt
z+EFzEGqE#EM@7}OZZt^YC<NM&g%2l27@%sL)bekJQZ4s`pk+SDTNb*ITnO5hmwl+0
z?qus2Ewrvvwvc6LfbS8b!-PbIFF_r3JSJ@22rgFn7^=HLmAr=u=bEezEu%OI&Ap^y
zkda6(Z<yxH7@ysJq}l*?mUmq^pDXjW`b^3&K7EdLy~MBl+m+at!Reyx8SL<XQ#G{>
z?M0IJqeAr!*>$Wjw2}&e@&F2pA)=G-gbs_eHB%_Y>+@IGkq?1!p=0K~6}x!LMZXHd
z<O4|jh=&j2X}Ktk$1KTi;Dau@3_!>M7p)Q(eY%9>!cxRjK2BD_BMga|tO{lif!R3H
zvOjvFzg5B}dnQY@P>FTQ@OQ?Vus7t@!R8FJ8X;VI67*&_qM>|Oyyyz77tzKqU12Fa
z`yihM;YRx6)#2vYx4gD778vU&6FW)cX+qcE(8Rc=u}zGJ9Ief7#>g6W==Ym2llAOO
zp_z<l1auB_NR#HvX*9{R{zg-Bm;4Q;6fO;wYFj$PtjUc)=_q3=%^A~G9^070n1RQ$
zrqWYcQ@&dgIf`R~nAsb6-`7Mx<FUy3b!r|uUa<P~k87ntKTp0ak9rtfWzq|mh01J0
ztj(#RY?L9E<qYvML|Ai33=#9*c!pSJ8RF?e`No3h3^B|@Y_~e*L!}~r6pb;~R-ZI@
z*oylKb6m%sN5~zcGg+rSk|(VQR)F-u9O?KK9;GI}j%57cEKM(5J$_wjY~k22om(-c
z-ieVBPvdKpU@+La7&8z=FNmF^G$eH&gFLBoP`9vM@d;?$O+AEyA$0ENn8ikuSb0u0
zI)wGpE#9uU6~>$FJ0V@u8D?G1T*9NwC7d&tts<b9OL%;932SrFQlE5hMgP)|qk;5M
zB(ZNGQOEw|DDGz7DBk+We1<*~2YZf8Ww3J2Bzss&C2YyCP>IPWK!jzS9<$Mf|A4}9
zRj_K###2!|35rvU5my#N?2I{dI$MHOzz5u??rlRMBhf;k(X%I!V5HR}Ha0$~EHQY~
zMXa)E!l|hmqo*#@?_9F&BD&aeiGV~Tze$x;2dn42=M*ZjR9R5vA5kUwEhKi0QDuda
z&<zUnWAw)jhr+gp!eCvK6EFD`8n_ezP`{_+p7NyoY}(sDp<wzjOL)Jn@zUwVG+RjX
z9id?f7Xpph)qrg7USiJyL3RzX1xv*OVP}!37ewX|sopk^tO?cF9$BCB$eK|eS(Ecf
zSTmF?9$7Q4N1EWrol6!gIrfl`>{`N=f}(2a9C(NhDs`tTh35FIaIG1v&7KJYmAX$N
z7~$k(aMXugwSeS6b23P=Ns*;brC#*Q3NBb{ZA)a^Ii}d&h;SdmzfJgD2Dkh%KG3o3
zH;DJ(^Jm~r;)B-5-G}f#8jdHi55Zak`;h7-Uc8Q}X?8Yx8ke`MLn)MH4q;bfZ9mt;
z6tdVR;D|DdP~~j76vhS$K@Ab}@hl9%39>n4Ro(1q^tE6TQ7wL&W$I<B<RD36-z!#$
zIkXvAb4bdxoxt$0kNlgCP%1|qp~xi9lZHFdnzd|ZUvbiKE+1jHsqbGHAvaGpu-3%Q
zL|6z*vC!e8V5!NfVD<$to6*o6`#zqsFb{iRV6rmT>se_c=*ey?Y6I`yc(oI4v`HH*
z<+`2Eg0;Cc;?b)qb-ZGRO8I;U*0$kL-_&&VHW>n)(L;bY*-5_1uGC{>X%s$gO@&eL
zc{>!EU%s!F#iQ=7ca%!Kwn~q0)%xVxeLRI)Yxm=I;Szd;aFbC9mRKh+mBo1L=tcJ8
zv{4wSdM8wRGd>l71K8#ePahb86@LI9DBkjc=5!bAbND={C4W}aEU%7}Kfs92UQ%wv
ze#oJn1G?Fh9LXOM`z6Pv7otkRC5JsIPX8EX(}!8Yix7>xDbqN63OM|Pm3)rbD;)Ap
zInppG`BS2BVz&X~V4c|I+G-xvR(Ek*$$VQZ`TN?MH>$0@#cf@jZ;K^wsjY(Z!BX`@
zjK(C1GXvm<Gq8MY_y~rjWjja997>_k9Llgs27uI-x5->b`9EcV|6jrf{eK^T5d9ya
z5WYDR!k9xWh5z$W;Qy>@`~O&Ze^<&CYZ^3>hGjC?snRl{27%I30K)m-dMZ3_NRxmg
zF>SSMprua;IK`b}z>!YT16jmqGhUZGI;6n^<={-XiC%C<b~{8?$HfElc-sSU9tMNw
zAAJp%!_}?$<b5q?267hqeK=-=*uj1X<8!YqK_$*I{Ez`ZW%vZ~iC7<a70<3FU{YyW
zfc;U+4y-p?{sUE8R$?Y?N&7*7n3x4ibLc&i4k3+2VaC}@0bw}#5I0QzGrH3dJ85DU
zffr75RjN&fC`XV=Gk=hL^c2M*vx0Bq;xXirhL|4dH>jqLT?-M*aGH9BmWJYKfrrrB
zf>YfT^01e@Fn3Fy)e7wA?>_IHam)dwSrSx<Fw*4zL07rE;!9yHjj7S0d~tdK6q<aR
z=3teDr7BgGd<JOwz5>?hQZHX-_2*}-*f{Z2#XlFma;Y!-Uh+*p3ru#HU?e+&2M1Zi
zTtYdHvGn~wH};w4K?1SkM16o2hM4M0RI`AbGgV^>@#g|B2ZCY|)${P$Oe)EBI#y~l
z@LCy{Up!{DG>=(Tc<Z))0g9$8j#uLdI;5LF8JUcOR_g%TLRAq}D7Mvu)7S&3)0cf8
z#f@aI#G~aVSP4|izH$_Jt?*p4{+c$-X47%0iCVdJdbK%3V%%!u3C?riaqILNxyg6y
z^x6UiX)<Z#*6DRJ<=h%O!6rq!L!#KxtYZHHg5>i^C<xoZX%BAJZ4&`Crl1XZ{fs+8
z_)f{v)x6wj3ftrhY~~PZn>E)=fd(i&vyi^}nilC!>H%1`G<WN*>0g3=dUg6&Z2Uzw
zp1q8ft(F$ik1r=e4}UB2xvxOrYGCVM4Jxj1R(TNlmaB;_s8jF-2^_byKE_JskoHA!
zcTPG5^s!$fFK_Nb=tI$oXa1BHvjZ$0Z(IuBsl3FFd=5#tJXCJ)PWY0GVb2VfXRqTk
zTw&^aM&%*-jtb_UdrK%1OT*>nK|Tv6A65oCS$wJ%EHB0tUz`u|Q-8CdusaP9DE2aw
ze?{_lb}SeQ+8yHouI^ZHRL6q3jy>(obu2i3$AVVJ)bV+YlpjP7pbGZJ9wTi694T{%
z#r(nz<^y=W)k_*m!F5=i5w$Mj%)%`VR~)sn8+ihV7S2+3HQG~wZG?m2)_-CxyWIlY
zpa^5Wjq)~_NoA<g)+P^QwKgk9X|pn?%@1Opw9KS(d~H_RX7Y<u^klS|jN7xf)nckb
zRdyeF(yIGdHL8zQxjx>3p|$#0HGUtfiY?|Uj16j>%3@fYvlxUHZB0yzzbzz@1{P7*
zY&N=D9voMFsV8JwX64v)S`QcWIzUTazT#pIF^Gk$Ytp|FcgsbJ{OcZWRx24j3f|2I
zs|(Jlu{5;;NhB1pwa0@;t-Z)7?L~6h+vCb<FEXz7Oz>kQ$zB5?p$<N>R}-!j6puNW
z)!z6aKyRR17Uy*<6Ii$=Skt=2NjHjQKaBF0smw&*4aY0kH`j)0?R_&(Z*;e6vmZr;
ze0;1$w_u8hkJo~SImA*No*d+}VBgHjVD}Ju1)|(a!5m`q`bu>Eu6$jvu636V4ih9e
zOqAg8W60R@jZ+5)eB-Va3mHl={=~txNkNPGw~ab%yd87OgwO=r6ei?!Her-0OvstS
zg^rvlOc>V`z>bzML$&<w6g?XqEXLFAn4Z3^!DC`*qTT0-xjs)E)#r)1K4Y9QLNbp|
z9KX*KbHU?6PB({PBB+rM9;t8VSK_0_G}2J1m;67?M}c=hf%fKuymH>4T57a4jWU<i
zFQhU}vtTKgdi9j8>WiL?Hgb;m_C}XUp-HxSC*{;TX_R^=<<#p#mn`*88dtq0_(^p6
z2>OTU!bcXHAVwF$j4sTA3pHRbG6`!;f&bczOnl*V=_9Y<yTHfW%Xl0lv7%Boc;NP4
zR;|?Ag-X4uuGCA=O1+q@)JsTNaoL;NwSOlcdl%ex7aa$;b@9PoO}&B-x+mvNj4>OL
zz-QFA@vO$v{^RSRe}XO>FxSlkvfMhi8bJO8@MmMyREWO~dFuX)SSA+YukoQGcz>u&
zsS6AEV40v=vFBciPrHR5eHUc`>P2Nw{xPV~x}WrQG{WuH4L}WH^@~xt9%=HoXg_-+
z^X3pAazbW-%`nYv77XFs+p^ftpyDSaWpz2#X(-EWXmT8Q_2@aU#{MAnIy?Y7ngafN
z%^`Byd;S#M%3bv?UV`{HA+AkT`$>#jm%@q?<XP3H8l)5(*<9%<SxvN9;V!EXvjcfn
z4JE0C!m9yJo-02kvxZX5O}1N{?V&;0Az-=>>*+oZ;ia@J0p#+ZF0BmV+Zxj_q(M*q
zdRFcbt4*$Faj<ZRbu%A4SN%8u9AbH67|TX=e5X0YBAj6HQLu?%J^KU;H{ayIRd`kE
zaTP|nb60r<F{aoy`=YZS-Yt3spUJQs+VN;R`%Fa2@UG!g5CLaDIDyEW`@n8<XFm0S
zqxc}y$rp9!;<*SPEYtB-Q}nK_<y_co%g-^dwLAw;Y567Gq2*KH(XtOrTXt0-xhRNa
z0u<j;4yCpPq2rb(nH;VH518)}NTKgGJ_cjb_me&YoH=wG(joA0;(TZgB9!F89Yw{J
zbWsdF#*}{x2$CBCV(X6UvaVwvWkW4LHrQMtk9;FiGr2+6!S{pi_-|?730iPtTLnDx
zw*niQ>(YJbPS_Q472b(;jTT+{Yh96b6<(1YO9}tAZ_K(1Z_JLRM6}3L7F?hmO9>y!
zQ=SRevhMD>^mm!y6XG&%@dR-SykN1M$HRNV;xf1&WaBFNK2cnzB=0h~hwQYcHSWG?
zcxKR<{TiQPpQ*n!<P7;Z5LjJ0!POjMDQ<)w<g>tWSQ+f}N(p2~kWYRI{jhFklm<&N
z=%0qLg}QzuPrx7Ea2>mJ6v3xKkh&ZikQdTn9%sA=>sB~)`W#v)<V8|Btg1U{%7u|B
zqf7M-H}fEC#(oF#88CD)U)ZAa(({oNyuL`ln9{!o)%0PO#1LK^FTJ3cW*2GxC^Rg&
z^xeh9yIK72Qp^(m7)E3EPC!`ne;yAy8nek)Ky&GKKn3mhu=0OMWtMQs)R_EF<g-w+
zlP?ds1M7lQwD83aSP`Fml@xc8!W?27<`BDU-;HT`pC%O{hDetDFSfk{P(j-lvF+ES
zZI*D?)_7^Wm}W0&z9Td&(eJ&#2*`Foda|C=Ce`HK?!yJ<Y>EWdxAt7AM~jNc%DO)O
zHrss#R%%cHZ>jKbM%#VGD7&x7*?k#erDgXxk{ZwME41Bfg@=P?Fo+{TJ;0S&tr8iV
zzo5~j9sG7@t-hN}Ebrz@DNa4{)g7$Tm^+q1h#hUfsqg1Jfu}dNC&0~4?Fl%qnR7}{
zV9jwnVJFZ9*3iq5)}Fu;?Fn7QGzhD<C$QvFcuygVeimy_V2SpGy8*!y?!klR%dGYU
zxK&~00an(Yz>)$_xR+INd^Oq=h}WKQA1TZswqXvjyYK|w=W*GdAVLh0OnU-+wy^De
zY+HK*OSC8KDW*x0MtcHFv?qKS&{$8HSl|iI6q!3EIE5#W)&Gnq6dXG7U`=~M)hJJ>
z%6Wnr$aw+|oyPM7>(EIn-1Y=Lz?E685*eE@o<J-;K}vI;z}&Hm{Ns3nD`3wFywhZR
z!u_Bh?KHVLftQX>=?Sbkjwc`p7g|G_BmH}cc`VVMfM8ijlOfF?g@z@U!g~sf2U+~@
zQp^(V310yOPxvYx^aN(LC%~-=D__FO+7nn(;0X_~DvoeRdjj#=6CNamIm9;1A$Av@
zfK#ZvCx{S3B-5S%pDk>AKik%xz!L2V$zqxVq|u(h672~O0eWLkD92#hOB_=qIE5#W
z)&Gnq@TAR}6L|cpJ)w4#C)DOV;Xi#jPrymrc%D#e&k0nx?Fo8-E3;Z9GB#s8fmnKi
zl%8hK3CtbaA*~((nE%>Q>K*ii4$y^pX!juIcZ{;EYAQi!4sjZ`znmZD0o|jZ0Ld-6
zQkkSvYr%(St08W|{@$f!!66*aW3@Ubj8f->oI39;$*B{EaO0`dI)poRp-_(Q9-Psa
z+)G`J?bo5VvtI=VZ9I_G{hBzcUlViv`k^=1FC4Uu*Dvd!?bzi)Dg6rP21Z^Rw;q<^
zDjsz`!txUV$-koCa#yvfXLRh7ufmO?t;tnr8B^hlbVaP^e7yWw+-B-wyNvICCj}?9
z!klx)2D2#&y^n#1Y@?DV0rB_H<}_J$c}i0K3jKA^tH#DRM~s_H9CRwKP~KeYwmsYm
z0>)b0psmBX4}$DyT^rz(y`6kg-$h5wA;NrLjbC>N^L^fPkSpachkhCWW$zf(<fmC&
z#o|+_PhRP=jOxRWz_m^@MypSJ^|v6I&R=Ll!^!y+*%G!&S`@KN2s@hQ7AelHy3HX<
z__lfU<j`b0K=N2!TmIxxmOnXX`Txh2v;4{9TmED_K<crh+~W*ZANf>eKP#oh_Glg?
zRwixU2bdC^(wfm49i@p>Ek@;SQ^eHJR9g`|YuAdHI!X~!bBg$$GpC5D<11pSt%%WL
zZ+tt&o)(&Bi_J4^E%vlgVo%G7jbBiK*)d{;r;RW6G%fbAZ#zI~4sk51&$9+f57&4e
zCJu$s;?D=_o9IKMyM5!^+3gvj8M@o+%!ZO&zh{i<_l#V>8`vA&?-}Fwdxkw+(mfr^
zXuNXzJ6(=^;kQk1<hOIQZwrlTd3b!IJ^FU4VP<G%P7S`C8fK1C!_1r-YRsG(W{$6h
znYJ264d2@wc4DDePVmy4;IUDH$8v)6uB0`7vGE0u*@BNhe$9Gp=DyMR71CJ2S+BE-
z0@w_)ke?|jc%wTjG)uqHg*}$#`ZFv0Ff1s4=r#-eL3JFueOR$SEDg^x5As>?N|#Oy
zeppTB=fLsf=<!&1Hs9um+gIvou2N57a?BT;ZCzxX@<vqF$u@sG1ook3Ax1m~eg7IU
zx)yo<5x>x*@qfgcrVzgz%U9C>r8JLc9gwRn5v9(<CkD7&&c~Hn)$z_%1}1p^mEm*o
zqUL$c3+FAI-%5;2VgICBrYbe{Ql&nF=Y$D>J2I)B{@vK9DfRg2kPXbHZtYN?+obP4
zPhGdQeGSSUM4o5rQ&;sScaj)?kli@9;nva;$f^FzSjan;Y<(9#><6^3F2`pdK3Ik-
zOu|xLh391C*$=Q3n9texus*sg@?%8t7Y-9g+i>8AeBG*F(r=ajIa01>R};Uj0@JqI
z8eCoJR^O>$dZnayNIF&UB?A9z1vz}NoasHahw<xi*H<3KPsTms{IPGf`UT`iDPW)4
z;P|m`lVAd>t`6r<YMHeAtkk_6t-IB~Aq}akgD+M_)P}li>)h%h|IuKb`asPKLAUx%
zfc&G<;yWrx|61rjsw3)5P)5|Q^7|meSHnj^d4rRb54f0qBtY3_IDDX79wE(LRmAKA
zhlu*JpO`<Ghr<zdt-rFyt)4(>L|s$)1;Fbfgcny$g0x29CVczv`Y?GqfuE#~IQLdX
z)Vom{QC>;kDU^%Q(<y3R<aq5AwV|HrgmU)x-Qk;{hgB$bt7IMd%ncn5&rp8p*Bm!7
zFW0k`kB6B4KxjTuL;Ump!{LznPTh-@A+<8}B1WXT>L&bl=Cr!$Rc`e$)U8uXYN)Xf
zR2>daQcwH$0{<8D$G%C*=b)UomEMQGoF_C54(d%!xUYJO+FD5o-H_9*o`F28RSMg-
z2-ZB4YMa%u(xbtM`c<H^CZhhip1L~jpss!<n8UUARX3}L>PYh+{#__-M<3nlcaRqH
zM+ULb#|?j4GwhI7SD`M(?xIz426E-@&(|^Mz^6ieB%*UJ$^D^@IR`=u>wT&&5pIQr
zhox+8;6QyVY`RWzu5#w8VejPf?g(@BYN6zgSL)m%bp&Nqf$On|ZBQ$u?9)}(SG6M7
zCb<`@8qq=&zcA#OT%o>?W?R)%e1FGbRH&CBL#vvBZ!$2q7LMAAZ}W6YZcr#2)j5*;
zIl9@Z=Bx80*D3Yhp)QczqYjpxrM4HeaGu&JWv@%U0reiqeM{=?S6Ru`Bfhn&kEvnF
zy#Uv5Ri9FaAoVB8j^l;%GD#zT!cUc*K>9CbFXiyBRQ(3<?ID)lRQ-FTua&)ubVvPP
zk?PWLt-I7vR}0Oq04a}!0>FGeLiow*AmBgM)FJ(0%~YhjYUh>)Rez`zaEG*|Tf9i%
z5lJb}WXQ7uG==iqQ$`u?k+uTiwJ4ohwxx85iukscMpV>159yyAJ4@m5)xAjHT{nQV
zuY3^c=S#OB?QpyY=}qM?!Pb6Obtzy{8tOLl5YnriOkXxXP(bq$z?#pI%Ig7tD|}<F
z)?MZ;pdWJHj`WD@PO1CFaqDUs3hO>=z6bP|J0C#$As2Nu;AYzDc^H^iJX?@H;3Xzg
z!gPm^X`f>0L&o(;Up5~{dY!YdMfONbtNWX&KaQU2K0a?g3Hqy@9Nz;j@_*U<qHuUz
zbodmsr;P6+oof6u((}z<A)Vv;3({Jrzbv8_xThn1&E1Uj0nbk0_jyUVq=b|um>nak
z&PU1v1}VE7q<qRr%CEafd6S!zbzV|-dr0}Lmy{nVA?2Xr*cFbB?n|Zgq5)%#*c{|2
z{HD64Ov_(PuW58GTic}kk+}?MpOZSg%(W8m6>eJTHV-kcd)k5dhIbRv_lmZ+`KaMO
z)d|cG4AR{1px#0*;!jHYteazTw~W`jyu?o^A<fTA2!BOJs>UQuUe9k%+HacYquq05
zq*%AOmhN4#MX`^cG&!bSPWJR7v8W+8<vG*C)a%_2{M}x{SC#aT#z(kav3K7v2;b%)
zO^J)J$4#2w35U;!mb#?w^b*qiNP7A&iZuT&b${$2pL|a@M-hl{SH@76fF>#nh%0Jd
z#@uVFIg<OhT2_7)a%UElIaH_4c~g=55y3C8a@j})atSMUqyK&!2=`gJ+x->DU95A`
zf=j*EDmzwpKUQ1sw{kDmRUmiR%B`u*mbuj@tlZ|>E08;4<-T5frr}X{TDgC!g+A1M
zR*tQE)z_>XTlcCbbWU0?QQxu3=GGl9D^Wj@+=zF1-N%r7E=PG|nNJPRq?Sh11%5Za
z+xCc+3zUAoETC$*phVf+(ueRKFJtAt5%|ZlO7#ztqdtF7R;_l-A|-P_FPo?yk=%%S
z0_%`T>cQEX%L@+X)?@!ho*zV1n52fS+`kCrpRlRpEvKJUo+z87Zkfy6uZ$-2HmV+!
z+zDe<`O9Td<;4jPxg4$hbJ<k2&B}c}V)|#Odn9+QY6xG2S#{ZbO?g4>pUWE6)sp*_
zu{v-+ev$4olDh&&|9*e7dPH*9*8DyaLGD|+%=?GHnMSkvft7nLV4&VJR*qvdSDmnO
z9HY7FWh?it(3!?O^+zk$9WrpM>NP9(Jj&)P#{x=rh4;58o3F}rPR4J6s>$Ul)B-iI
zNK?|57OMSLj<&Q=jaa!gwRQeQ>Pjm|$rh<=B{$-wWQ)`dRvE2lvAWeNqxCFSU(jV}
zy+SQfKhQb#fyfm9Y3g+=Hy@O;d4MqAba7OcQY`#^(?xyebK5aSOI6ikmR;e!tUBgj
zt|m%u#Cy1U0dg~Rne=vrTBdVqVqm#{g}T_Pcc}FD&ePSUR=p3G?nmwexq7SpE7gBm
z^;Xoj`&X$6OW59s`ZHFTtJG%6U7;3Mo`tf{SUG29r+<z5wUw(v?hF;hF(s|8s`6d_
z_39^9ZhB?Hf2R6j3(K@koTZLQPREe5)UPCWNX<3}{Aa0uw{j;P_0CPo5JhRqvz4zX
zS1GweDsH&(D^Gii$}*BWVN9-P*=3^A6UOa<8OU8BIn8s6I&77Za*O)JGRmnbx2PK=
zrztzsXRI<(cBp83p0Yztk({RNRCBB{Qg*6o8}gK$YKG*rCbp^tlGF9Js&`245bh5i
zGPkOAR<6ky^q-@)TDgQd;y+hiXyv9luk&wHS6aCxuFv|<Qy;N%%iQ<--=#ig<y@YJ
z{O7CNt=v&hz4HR~Wh*!29rV9j9kp^ROGf<L)l*h(n(sRQ4)uzat5l!$$JI$IcbRd&
zf2T4wvVVtEzvCf)mkL_BpE&EC-KxRLJ>?qo?@~=x?z8R@|8BL|$|XJ5`S+-}m7DJU
ztiMNHWaVD;-tWIi^;x-_OCIv?Rs7umwl}QmoxSQR$!QJttB*=fk9)s5%>A`Bn*Hh{
zlGBt)b(2-bF-odw+w+u3HA8ZmazM?q%1Ak&u8!v^2h@iorz!WTPgrH7+^6o^pQqdh
z&`>X8XN~m)j_V9{SMbfIKf`XxRR2-Me^cT{)OD(xh0nX|i0>;W{#&3iuwEqQNabkE
z=dt;Bs7yU+$j4Nf05O!rsUG(89ODa-iVEx&g%4@4hKP`oJZ;W}Z8enHrNymRq7{c?
zsS_(a);(0qbS(X;H1JilJDz^X8M_ye>Ys(D#*Ed2=FDGkw)tF!o|@`L=)socv(i&7
z)mV6yv_iZ??GX)(#r#~x@!Qz>nd-YpF|yL)*pxKb1Bf<sdWrPq73rzwGblPQ<n}*J
zKUU7K1UzaEPLFQJFHGNzUG~kY5}yb@)%eulQ;ScDdO2c%iYvfcq(1C|Ip58fv{lkJ
zN!ukoThj9+jUzQw4^p?f7ipDxNYbZ})~O#Ntx&HbjVi++O{+xkR*BrCY*Ob5-Y}2)
zuTmxE<+X?L(-<EJd|cI<t>rhW$>xRCw*voU=yPh8`5aa*v&_>1n(~V%ZAIw;bvNeR
zGWBfaarAGf?#K9@zgvU9Qq#cSjs8Cyc@?w#P+f^pVg@DzkT%tPPTeV`cdEO6VPn2I
zDO3ym_s~j-xg^v8_?6meNFNF|8Owxnjk+{^I^Y*d*BWco`|Hj$rU|@AO|IW$w3+kE
zaZ+M_thU3LY`#!`zA-`FThA6Ruia^U(m1{TBBN3$_X)R88sDwH*w`TOGf1y8P6+&x
zq@76bFfNevgt{yIsIkvHGxUUUwvj1+1?gjTuNemb`^^Iu{O3@OIY;VtOWG@GM$&_l
ze$u#IH0ej*=ZNH!&Go>1(pXqeeLfUCWU|*EFh|U%>pl$l$+}z2%TV_YW5m3%{!75$
zS^qt=-Mqc_1;BOne=wnk@FYix`KS8njuG>|x<<zw(aaHJdVLRIYV4qTs6Oeq+LGjj
z(jmt|;12;q9bT>8hcm5DnlIIU&~Ya$;aa2$|G3zlMtRxwfG?@KUmY=D^*sdorW*30
z{!7fA{(9#Tv7BeD^k>!YbUtLhP+Fm$flhGlW%So?cAhZG%g=GXWV{bQh4PZ|g~|$~
z*Hm2q{D0N$bRIR^!h4)gLH>TE>CpYq#$av0c*&@7??<i9aMt+;<Fn<5oNGk>HPU*S
zz-54k9CrzPmpWMcWhY1f3FlqX-@Bxb_k$+sxZk27{r&2`ATjJqyU~x8+%x7QzCSsy
zMK4}+wj0~9US!`Z@NZh23Ao+pMS8;gQRx)d3A4o?bJZGm`<q;GV|G0?^KZU0T#u-Z
z$PU*(sMkw3!8U$fng;w@Z3*Dv%Bx&w8!l*LpK3zyz9W=B5y}HX`4gc$Ae0A$vex(^
zeip6P_)+}=_aDqpg<Fua#Xp#L`0iJ)n#;;pxM?S=f$uM+3|mV#xJ!(V$Zq$Q24%R?
zcn#-{abvLbOyh{Tvh;oKIC$QR^y=CN+$9e7I&P42&_U}uVZI!B)P2I_sBIAaY!Dr8
z5FM5YTqf`a(RrC@`&np}mia*Kcib;n@KE_H?pl*QEirDZ-{7Ggo@x9>qQRfUAO9kh
zzp;FCN$9Yr#QYj|9T{mQBm8>>?uBg+IeG=-6dPM3k)+IEBq>82+hn||+k%gGdXE|}
z)xOK?G-#hr<IeiH_bJrs_1=UQ*BVb5?f$H{*6~v9`@KiZXz7Q%pET|*eOfTT@P1Ti
zx}}e^9F+f6M8EHsOaKS=@w)n7mP`|Pj)i%${&e3WgI2f9!u%-sC3B7OY~<s<4I<$p
zgI?AtrSlz>w#2*$JNItsUAOeE+v+bVGt#;jmhy~mvg(p_NZnB}U8U8&vYG09^|gvw
zSfxEt(TsNw|B9VpSp5lUy$T1Kai%%}X`^}<(uHa_(s!s7($(s{NY4~}2huj-+@{ER
zwZLnIe>*sTzhZ~ltG*nF3qCG12`TMC>CY-g1V1A9Lqd5-N-vl6O3?hW;ufJfVx?^5
zHle&tXl|GEPEh`_;&GulYNe!nLMWdQnkOZFN~C&DXr7nS=LP>uNtHn@xeV%Ox}+-w
z(`Kd8M}s|DEu}jI-zAt~fe#6MNJ_6X*vDH0f1BWM6a1aP|FYt7!9OACQ^5SO;yHnz
zm-HoIzF(nC%HxnUD(O^7S4+A>(k@AdB|RkRZIV82UZoxiJZ@eG4o?W?IbdE4JSUhF
zf;nNbN6!na99O9+<;p>N!@+h92TL6SM^Sn~c~nX#3udyEP8GP#N=Y9VI4-3l0*?rM
zMBpO=9~Jnhz$XMgA@EA)RqCVVE1i^~%}E*BoaDI!_}`ZA5PaOp(HRD&xnfu_BZ4_1
z>EponS3EBGqk=ghso`R+l`i2a@D728CA~$`BZ7Zi;G+URC-4b@m0NV;{(`!@A}a6>
zx7fB@<Ppq>z_$qexTHq~^PIpZ1Xdp5<6*6+z>@`DDR7&I{ErCcsHBEhD7~yTS>Tld
zw+S4VbVSly1b;-}#|1tiuu(z|lO=7FG%o3p61HXdguibu^&@GUVB(UFNP5CYN>%!0
z%1}!BHc58~CNA)Zq(>xuT<}K)J|U@5CashS2Z7rpjY~RQ#@0s!zD3|80zWSBQGuTm
z_=La;d(3@hhF|(6=}N)03A{t#xWK~#j|hB=z()jrT;QVuKPT`BfujM@gQRT%^4}qF
zT;O4WZ;|wfU>+CvsK6%zqNQ?jh)NnSCx_v3_WFpxM<snuFed~yDp+e}1!>wUNFNu>
zh`_f9{)oUwB|RafMv#<klEx(+k@Se9M<q2vq?s&fo22oOjCfe;N*b5+NLcWdLN94t
z(j$@{)%Yrw#w9%>=?O_EM+7hF5lK%-I=Na}sV3)zYQQ*X{NC`Ilgya8z&zjVHJ>#-
zjx~-~9e;Ir@Xw%4b~ZW}Ij?el*!d;r_wi5kEp%P$dfMgpeBARR&+ol6N}Sl8xs(I%
zsXaIW_Tp`08UCxlzd3>}oBCMfG3=1<^fUca9n;R*Cy;)x_B%OzXWb6~my{Eu@q0pq
z{~)Pd>!-O=&F!Pr$AG!6^hH1aC1k$;#QS*cCOydEoFAzPSv-JCk(!XH1Tg-MIluwn
z4BS#E1x!h~Bl80e17~1gQx14Ga0X^s{DT`<!}G0a6H*f=uT_BOA~kWES`BzUQWLwL
zTEL5unmE0zM|v85d(coVNKKqsHUM6V)WqA;$$(cOHSw-=D&RAadej=6$^_My)J&xJ
zU|$$g_X7^8uL2IMuK^C@AD*5C`5p&cslEXi?~MUR)PDl5R(}Cptxf{2!QF<LNS($k
zNaO`vi<7Zg_!q;!jUN>GSM^7fFECn-L&isqyNuJ!)6I3}X7dhn&~dBd3y%97A9a4q
zd9Lds*F&yvxc<TY8~6F1Zco~ChW9M*Ztp*rgncpJ0pI(4ANJki`>fA7%pH2+-{-OG
z_ZA?&?mK|p{b-ziyIn~4kG^u$%>R2egn!_B5oa>`UIw4~?^>ZNZuJEk+^BZ<;$)y0
z_m95mI5{ZBeck6CN51J}>VDn#HC_dQ{kUNxZrBd~wLg49_=NGP#HR`)iGNHH<KTh*
zJ<z*HU8fx8^(u_dRP#F3WPVxQWIm*xG9SkCQT4CpW6E?qiO(<8rySSepTW6KmEjY?
zXNL1pb&K<1^_cS)_^dN7b!|1ijZdF@t1;-l-gwb{qj8hD&N$t(&gk%5ZyfSG3_k0O
zCz1cQcb)NTd}>S98Q&;*-thQdGP-@gGd}3M-nhrN&iE=$R`9oMW4xz-#Wb~SMeFwM
z^INyipQn~}B)9gjN@kijCc6iF6C0B8?nJ7&UFVuNr;=TXbb9MRcRZ6=u`^exFTH!k
zV&qVNeZ0TBH=!xo(g*sxR&?uv6>00K+gk8e+G3@2qCc|{Ld16`*rxDF^mlJTyUD(@
z2NUT`PqLqlvV@JwvupeIH9hHp-uQvlz43H<{=Drhr6`LCv7dI`^f?QpYf@lo1|)y8
zVo+zSGN3cNVd#7QTVTsd(sKg+gMB*_DbYQ0@&2?8>Xz50Qpr?)6hP9GN%V=@0I!Z`
zy7p|2r_#7|C6uBy6o}UNq<-dY2Q+R+bkT~js<o@4hi|2WYS~~<w{%1;>(gCD-v)ZN
zB=&K5*Y59149FnU<X{k)ct-bIE!&sQSQS^aNTFCAdNRn|S%ba3@twVi9rIQDTBt6O
zvU8hKF!Mxr5wbI$-U~`T+AfJhajAlOnIv=T<LNyei433(J!w8xcT8KxmV1+3@m~D1
z+&Rx$@KzLoN`1+6=G;`WU;7sjJ<>n2gVA>;*It}R9Z2>k#LGypJ#oPT)!v_3xIm3E
z?A(}w^i*StBFpO8%HJ9eP$!ej#Cyfa)UvKYu})xNoR=h0GEm5M=^+C|b@4$@#RCJi
zK%Ftz-?al>SeHr;4!}AApP4vtPP}(8u{qw8LUv707fm3ZI)IEV@BD>tL10$u5~Tvr
z)<!naZig5aBMA_eN?7!_sswc|;Sg@rV}tBqT01Sky*ouEfG&>r_H^q&Yok($u0#(;
zc$3(>g%#z1-jZnkqPHLtE9t%h5Fc5j+WU5QCRZ(4q|%AZ_SMP$3<B+jME~y09^hb2
zNbNS?N|UJ5m&}M?103k-+DoMaL{#p{wCT{bNDn=*cJCIymENJ!sA*z8P<HGiY_WeU
z+{ZEpCLf5A=}M+j2iQBnc0ZQ9m42X-J+%UqapF_Ypl^jE>R7QA&@ORJ01Ps#A#<Wj
zq9-u6)Tg}_sZmMW7J$57<|AjZeJfm1N4ylzfkaB?Fo3DVK<@#BhMmcHs(WKRwRdoU
zdPj-fgO<0_160y+766sAsyE&*<~kn;i}PDyj5@aP0A)6#xMYg($A=zT+>-v=!wf6w
zKn6e$K%Pxg;d-vgTF*5VujjZ>LyZ;kA-p|b|IM!lQAZlb)3RMaw(9|0m%#Fk(-mMX
z)QaL+>YW{%&Qcu*(paf9w{KFr5slWwGjX*hc}agS)@hPCw<oh_b-cHCXS{2#Aa+9V
zF1y<5WN$Bw852ahd0nDEk?QGE(ypw9luVt8{q{0aBgagKLbiENGLtMM$S<kX+U_2k
zMJKpvDg-i2bGB$$Q>G5+0`{Z5Te@Q{U#(nQcVACGV1`{Aj$63Zd*T@sb?L{Zfkgk7
zM7&#B+UeYrLJsp|vxbEE>OnYif5yVJb$2USy=?7I$9E+*^{-C$4fG~52`x!`I-hSF
z0Q>GUlBvFUhUEwpYMCX_ioM&nuY#)4))_sCUKF(VaRELC-GSIQ4q7i^#!zd&8iUq4
z9LwCE98XapJ+_7&)yCAo7H4&D5A-y)j_d=*pjY=nk)k0PsP;6~u^a1?em!zq66v1z
zB<xV6txat~c#3x=)arPDR|0EKT4ZN(Z=$~=IhX>TL(WykAcmnkp|}=Lppyw1NYSuv
zjJhtg9EJ7e-UJ(2+kbHn))sx}$vN><57)CQzlv4u-1H>Vg6_cJp|rh!U@)_(zqhA9
zaYhgFmMvkzPbXQdl`Jl4fqk+SR8ZNvc%QV&if37{JZan;;^|B-b4E|f%5Co_Aqlo(
z)gu|J$t{EZnI4p#n?iWl0C`lqxC%9r*ck8HLll<RMfr4bVc{BB8tFVRkWiL`39PrG
z0#XPuP_1~Hs@jyvBsGqu7{vi7BB9o2G6P)GGkD~XTA2yenK~dtrQZBfPz}nSS*?c0
zW)iAx_wH0;H=#9&orAk~CsM0Y$xCFMq_+rW^S5u8zH0R-`YE)Yl5mEK-EA3sTW05A
zjs?d!kF&QrdCZuG@<nawbfRx(?}5&qOd*dkgORUcGwaAC1`uhIgS+?S3p?W}=<<vd
z#{H6HYHz;4uC>0WyF1aJ$J_1Gtj@t60bVk;u)TkmgeI=~drwIPGv3v+doU%gmS?gh
zv8w~&FLPiE4mHwwyw<7^Z%OpV_X|`|ZcB!VvU{*Aldov&SJugh{kg%gYmjri|3JRL
zvUb5|dUp0;(OXdZ&UCULjbL?9h^0x&u18^WFql9*=}TTr7w%5%-?Yni-P|;wWE;Ec
zz?ww53!xnTt~wLFiQO29W}fM+-4A=FcGJz9F+;Mv*cF>Cy-LmGs(3oFWRd<-mg-De
zK*!+Dw0uuXSyP3u$;4BcEwWo-mU2tT7e~|3_LNo7wiAH{cDp&A!Tq(aUU_bp#Slf4
zEO!dq2S~6T;2sMEv4+TBZ%q*Fy3<??DELh(qrt7I9>p#AUJ*nl84*N7tjtZl70x$s
zXCLoX+rd5O(A~YgYBLs>@l;}crZ10g>*Dzr2R)VS)j=)Qt7x~-P9okXs7-?zgjfq<
zhX{Khz&6PN5z2O~add<ypvOY#lPoDeBbDq^Tro;cW{$CQiRv0XyQmE37)}p%pr(eB
zgE%^|3_xL1Nkf1|T@2dIXrd=Ah}C-%U3&}iZRx_?x>USBQ&4J~Phyv~-YN>5(BQ6~
z{rSZhH}M5Ig!bH74hCb)`Ho81Cp|h|%S1`rPW8xsSQco-=YDE4_LZYhXC*GlaB|y~
zX{MK{j`+oi&ZL0tG7Z|3_Nqb51^qZ$;6^B!I>3lyJIGmyOf%;M_#S6YyNcd&$UHx4
z8=~4d`5&+dEInb8+n4jCw*Kzby@_~AVUd@@KkDAS9*em|DrbXw_SGk_s*Ojf=Ol4J
zkibZGuNv&>C9R-o%*E(sD>B=+@3d*eW;e$Bv4}{~%xS`Qc1>bee6Tlz?R_FOki-fl
zfnyJT3sWzLA&p*$+x@raM8(Nm@f@S@fQ%+*os}tBId>#durzdeLlUcEv0}ZrlM%H*
zblrk5R2CGMWwhdfE$VIW>D$d{grQM8t>>82%iNhB#!fGd<b;p8W{xLPd00JK(-Ys_
zkG)+_SK98R6{4H%xt7xRKwH)%4Xt2IkVP+TBk9pui2$OhwFnIb$aHZ8<}xJREK4v}
zNsC7GK&M3|HjJ>x^Y=7NE8gAK+sozAc)Zm1G?y_sx>W}<7FX?j$Y3^3V?D8G`}WM9
zo?KsZlSVVmC#}}*8^np_c+zq?na28~KNIii&r{@9(AHFvi!I}j6qH%o&xZkuUUbTZ
zNKOOn;IWW(@Kr2UE@-5SH;!t%m`na3AFkftlfklFmh_xlGzqxpF&NBzCO-`oH>t<1
zz@yXR0DAni$?Ea5;oSM5%CY3a4tm~?Ri&<nAR>D0if6<QxY<KY!z#WPGH=>>5yB8m
z4?b3e>A;kZg58+LtnH0hjt#>c7{6?rhU2Q_!1i|R`g*#0fTmh$!C2M8@zGum%6s~`
ziJf|PB;niw*<fzarOAA*-Jag^sGar>OLfUc3o~_YtD=S18&#_#*b5Y?JeL7SJ0wDR
zJo;DKc4fjxJcfQUis2k4ieQsb+=vKQ8CQ{1LkKS#U{|cS2}Ki@jYGV`D5;HDY@L<Z
zubFX+Z+oM_vTcEDN~UnXV66$&c7{h;EU+q6ZWq{V0?XN(xd(xR5AIiT;JxaAy^zD`
zBLE_Nwxv>dQfwGa&KC)HCi6$(T9wwR2zr_Vsq8oGXmx<5AfArJr@c!;#A2>J%{A?&
z)Y`rQ<WzVK-d1<wvt4Z#Xg$8%zY2H$VUjA26qojcZE7{{5*AQY7gNkdO;V_dop|59
z0iSjFwn&@Wh&rA4e(;&VoTb#_jM{_S&1t-6PlIy`aKGA(oW}J_t4(OJ38hKg5KExk
zm^zkc8)VyvZ}jnd!tH>Se;(e{+yANZxxn^;&u(x?<2M@4#j_Xp&XT|;R8+V}RR<)C
z;=7prNOwxxla)HNQ)sc1g?w?)N70XNJSl5bT8oOjO#9Wvpy>tFjj|+aXntw%$S74c
z7vHBD1kB#{puC^bcrt1~OB*%-6Gu(0g(%ucA~y(rlu?yj1Zvh%;ST7c2j8w3$kD1z
zQPfR?jyfg%E|lq-3E@xOrGX;{TVk!@DDZ>AjS^>2yBq%qW2f**39m~m-tk;r)?h2W
zqhz*QFO+>L=x|-2WDj^}(2jD<MQ?i0zujs%em;Dzj0AO;$h8lO(q@(Gv10{lU5pQ2
zU_*=ha$0N#N70_EnXQD!DJf}2o3txc%@I%GzXV#$;9iGS_iWv-1SA>&?f^LD`Pdq>
zb+8LOXd~UYBSOuM9*G`sQ1i9+I0{MVh+4WNH&&y0i+<h_#oZO`i)0KrE*wve*d?Ns
z^c%GnU-5MMnEi>u^Ek%ZUNexj7o6T>jeHO8phVSrV0!SR$EDHdBvN`9+oS~z;zK*&
z_%b!ZQS_JILYcaOn~br*7raVlfFgt2sDyTFsMf|&+oa8v_?O8@6NmAvx9Rlw7g3fk
z!+jgOK4`0L+5s889LLgSke=fSnq0ecEnJQY?b<ld;rAo7%scavd6vQBQC4M_%|$*}
zzl8Ore4=f`OM#h-yFyEG+lO&xK62{p%@{#ON^Bn>Q_8Yv+uIlysAqbj?c>@TIRf;&
zXpUPmtg~4>oe`HKLf>ZORHwB;GhN_<l?NiD9&JWXT3QpLlWlKJpyvqhmevf%Z2Snq
zo3FbbEif9fA8CBE@XgoUh&qe|9DUUEtV6W#M4Yp{u-2BCddNWHPI%k^>KI|h1RbS0
zUgEQ!ivYoQs|w4AV?<WrxuEYtj|*bOLe4!K;Z0{j0>(Oe6|KD&ccn;e>&~|K9m*KK
zY6n<EG35=SlI<E@sM>>uv=ZpVbP$GiJxOpn&=Y|kSWXZed>u47WZGR=kHI(!vFVN)
zuN{oRfbB7~)9I2*&0PMp`&bkcW)DQQB_K_)@J6ZbsDzIy{9FvWN5|D_6}NRDB)Xz%
zC5*-=POn|w=4w0r_=+K4quez!ipiTj<6s$$9Z2KI(^$tdXa+n^duX#XUr#M&$oZRF
z$3!<+G+>jJI-e406M8ctPHUAqp>8-5Z_rzh)~K3QsC#OOvL_JCcOm5{4FV%eq1w@u
zY+?Dla{N<_b1|j^pzA~U+KpC|2q9b?H0SY)+acG*7-wx+{UT*QdaN-F5>e!+QQC(U
z6#R3+h0e(dQ=PUJrF4F3ou<wJ&5@z|<d*}S;JZ;u`w)w1;7GDvTS_~)Ygts)dMVu{
zGLxU!W+S!V2Hw=CmQ>q(@zlX-#&G%Iglgo*4KiSiBV8gi8{jI(UNQ9mOM@bYOsVX`
z*@k%vM^Q=hYkc%tp+{CZ<f^-(-I6V7h0qD85Iu8g2kwUgv@5bza-^cP^Rp)BY^qkf
zG3R(vaF8g$Tu|uso9zUQM69Lp+B{gsz|af@&@GE4(z2)2bEp-!%#!`12G|#_N$G5q
zo-4=Q$kA}@UdBWk+j;5Cq$GV8C^-uxQAf+mo=^ktk=hJX>@izA9d|~JdXCKo<i={;
zSgbWoP15A!(wh#{exngE=G$hK*BT9yW6mCoZAW>u>8OQgLl=c>V|(7Dw7nP=jtF(b
z0NjMx8sQ0beR!(U&7fi5;+SWaKmEODQC4z3Lg<AoEy>Z>md`9dsY7di-9xDi*HRj5
zwe4;kO?wR#1JCKu7Rs>>(qqZ7pw>}_I@CH{)pjt!x7s0>n&^=gm_34QQBMzgs?d{z
z-g98qFX0L|jnPz1bT@{WytHhU9H~=UC2d2?Vg%_I9DnAeKNhD;u;<}OkL8q>pv3S*
zd$IUfwRWP`@~2PyV!V1|du*T<{R!*)<!tLLODA2he|ju+khg1Uin?UGdJ3h_a9wH4
zVxPH&;2t6+a*d{>HG&d>Fz<CLXy;hUc%n|%(rf!Br@R)~x(=-NNNagG(b<+t>uQ~W
zQ0~B<YheeZ;+W`hV_dLxz?BZU#nE>iCNTv(-E`d8xV4eiW=9CtWN6o8t~Jmi8elNp
zZ>{jVq|Y2#Mw@(~H>QuZ1uZkJUVA8XGJWhg+db3E6VaSnzxu)7e)WkvSKspfUqA4h
zn_u{naz+iqi}ixx0uT%`8(`v+e4J_MfSc*?&CE=6o$F}~@Xr@o6q;vF40r>kq@m?i
z!L?=}G^Z-q&PQlMRcNCTsti;G&o)B%amY}mASz5Vv~{9Or`}L5>%<So1#p)itN|JU
z{BVvLsPMUzF%g)kVFKe|!;m)MsS35R5noiX)vyX19;<mlwc98W#TiApTZ^L~F)u5g
zZQw4q7LO$OE<Rd8FDJSnh!L3RLbCy{H;Vo_f+G`=^E!0>3KzMNHmtlz@qk!Vq=~N}
z1SSMWX5okMLL*K1uDFvzvuEs|$JES`x;<uSUuaOe;tS33Mjb{VG%`OBtO|`Rc7}$p
z3=Lls9KJR*e7P%X8WU<Na`@#oS^=1&To{}ybA@)L9Kxaw4T9L|F+ywb8)?DeJ3aFZ
z!>E{qf58YS;x`EXQRX*b^@w(fUC;b_LYoO~c6v<Hv}BH2&zP6Pff0`S2q4*Lcq=@m
z69b``Wg(~(@sv*t&|ZL@7!W*6$D~#RurQQ4$|?+Fl7i%G@o@O4U=tP4%<#tm)WKL#
z*q*16f=OzU;q;;0kLnY1^*tU(U?K!?3)6thXCPOB48_Y)u~V^#r$nqHX4|Uig=D51
zdiRDEh7ML)Q(>@iN>==s<|!j}aQLTH!QrnM!QqGGX-eva(qZZ`bj0hWrNA)7cDx=i
zr`@9KVcH?hWuuQn?e)6nVQfSLUa!p*h6?Vcet1t;rI2DPM*dz}%E(5znbkOD42_(p
z$qQ1=$qWr2gG>$&{2YVY&Hi^2x`5CHgtigdM%Badkl(@l4zEWP7m8JRe4*jrhKBzT
z8h&M>D>VEnRb|p*MlKY)`Gcgduq&{5;t@xXCh(0M6A%24K{wD>7}={H*!@J8*9(bE
z7+1hV$Haz)|7ryMJ~W!kL70iYQh07?q(>YaCgO9#QzyVHNgnl-_#g|qnA3(RO>!Ix
zI4FJz3hL>j$?@6Xaf?PyHF$7}(8xZ@D_w-TJuYhU2vmPNIEL1Qmg7sJ)?nO@!2lBe
zAY*eomLNilC}G#}%`;8<UBx7$%ts>;PVh>~vwWtf#A_q(Md5h%6j~ozZnto+&G+6s
zvwK79J3Mu;$>8vl!Xj^qp$%4pIo=i#g@7eJC1YvRFk{1^<ruEu$e@hm0U5g6p?YsX
z!eap8u}*mqGoXNgwnbAvsCHHze$wM(W1$U&l5S{LaJ@o9_MXC$A)Li&WJH9xT#w+D
zR&)qh(E$z$o3Z>9VZ)9N0jO;Flbk`S;mHL~3?yjq-+YfRr%ln@SXT~h);iVdT3*<b
z&9tlG%e_`FH-lBX#KZvFea6gX%Oz&ng`t@6(3BD;FyS=A2yDHdN|0?fL8wTIZD~)`
zYjQ3g*K|0d3|6_y7uZl9XJhOq+=JtGY64CNnCXX4xEMb5)QQQO7`p#NOso?DSHQq!
zU?c&egb49Zami}}%SH#a(8x8Xum-6|B_Sm0h*mfj7YM^^175oNH4HM^^e%`M46(ON
zs*f|sJBoF>WB3M7W1-eg9qL2f)NoF{g_R1>c>+ivrc_vH<LqaSn~kuabf%)g@78N3
zco64H+r+$+^|ZMb+J_-dDxAP9|0rc3(l+SDDvgzkY}OkY2Z`Bq8orv&_za%;xzXY&
zHQ-ier4KZP9^geySuqgM-s|;Z0>`{3rr~AO3=O};Sy7&t1SwM}hDn^MHJaVtdrsf}
z-r!GtOTFJ23cvo{rekM&=Y4V0ukO3!OTTp*3cony4tT8fn(4;UEwqR&$ifksKApkR
z%Ob*3!L)Hv8;F%k6}_e@5ts`dCuWTwx`lb5%%<QJWV(Ie-NNb+IS^nK9d>}OVYMrn
zT!Y*)ryJ`q=57a-H4zZ^C<>PoxR!-Sk=uvQV2<cs<W{oiUJ^a&a8G2Lopzhva@}}t
zNUN-T@hDUjQ-xyH<B@T!G~Vl#zQ{PBxe544k#>DIU7Rxg5?WyxKyj26+DsW{@_5Pc
zx;QRaQDLEmnFGU&+7%KA1i~^r)F(4xUm)tldUz3RSq3Z+b?DheuZsP-ESI-jJ43O)
zfG_GcP$z)Jpm14)*(nsmdgaP8O`>c2kRcLPeP{*{wlm9Zd1CnSVQvB`99N(iRIx~F
z$H6EjptLYY2z(HHqAcEgtYK0T2LjXW0g5F}3y5_@AW(`GuRS(F!bRf}#!?uItRh@l
z6pK{53V6s$63b{uOa;6#ON+>OoiXUoqcMRJKDD*Sl+VlWO7m}$fwyv<X5`#deBi8P
zf9`q(FLqv%Hb5u;+JjndsG8=p)^_5iK>}By=UA5omtWk{JP!;4mANaQ)>kcg8`#Sl
zQBahDjrHOizj=>uj>~OO{JJ{u^YP>G_z$~l{Jy1R8c$oWYe`r4!g<~ES`+h^w07^>
z8DD(b;`yyz^A<1OwR6{!#a;7Gi?=Ss*$6BMHq>OOnEhVezKqap1Gq!bSop;QwbW3H
z-<W#L*8$`kb`Y@@KW6!6s^-5aAT{UXgw{|C-ngcGF=6y~`qVZ<b=dXBTp8fSPF~Ay
zw7)R&=D+EoRvPLZZ-z1Mk`~{oV~_B)T6H?U*#0IMjJc{M^+##sjd}(F<o1lzTZnIP
zOVEJn!j4RuF!I33H?$Rg1K4d{9}_$4k=yeE?&J4IwaIA;ymqJk*>`5hN~t-2{OuL^
zdB)A;d?7xMJcG}}EYW{#JdDpwd}@D&&m$Udrxi9}J-2kM>3BT!Si=pqkF2}xyoRRA
zJ4XJKJm&66_9f=_#Zz6$xm$X0oex(-dFLlRH=S%6>~F#inz<O=xyhXu%@t>v+n>n5
zljhngAL!nxI@Y%>SiD48{AWII@kbxrzg{_f*%jxOKKaPz>oa>Gr~UUn!cjX{{LB>4
zDa8v$Yxm<Weow#ZlZ$4FW?qfu-|N#=bfrN5Kly*H0seM{4B>DMo|}~#E|x$dBHkg7
z#glFnF)*5b&zK@wsZxhA{T+S|0G_!&$0C5|s1Dq;IS21`w*YF#duDy}<}CcK#U>$r
z)cL!U;+}L-`P0wSt(+52yTn5A-VSi*$m#7FIRE6?XFE<=dGgP{X^OQvaX!j78fldC
z+*9Ar5CV0Na|fIkVH0?s_wvooSWe4?#=adi55MEF6L0YO@6ThKAg>mA4}@(TK)>|+
z81nCc99x0acb)L>m<YYie+{JN`7Yb#Et0%kqwDE*?0V;*o_-I-cL@BOk2fvzav!!+
z2DzvSp3kT7<ol4kWX1fO@n(QG02JO$n5ee_b#_Y)zBC;`pD5vOyfs2ZR8f3Z3ja+O
zUcXVm<#teU1lStix{U6Bz8&44&8V;MEAehkUb@lslzJb|wGCKrA^m7A<aN-9UsYKE
z8(0F}cB_TZOgBEQkZ3+|t;p??cNmL7w-~rq(9Hw1Sn#a3STLudAFZg5l>`3t*tKEA
z_=c?y9D3#6&#C3$m?(A2^Gf}qz;J)}@MFtP-{038wJz2#pMqt~lxU*AE6ML+ET6Kq
z^NgmJDbaKWzf%$K#W(hsPdSiCPdR;s&$ldYeg7s32L0*fQwCH0OVeF@5`FP>Q(sS4
zD#^?1O<l>prSWuM^TqS0MEh{Dbr<F`dkH43t?)&oIbQAfh7Ira6$!*e)|4o}gSC9h
z#sgTx;ZCT0<)t}3Ffe7VFvz3^)3^`5EBPj+Sulmw&~iF~FZ-iKodqJLf3G3Yy*br$
zF>X8WPNd%?&xICG;hSTFnW$@!U!mKOxH!=p?Pa=rN<7`(&+jm#rbGvO^!GECPuYdL
z%n98g;W0PIBrl64_S{0bm(9&}e}!+^Tzg<}Bj|to?*~|^K!%Sl{ofYk|F{3Yp@IJk
DE>q|K

literal 0
HcmV?d00001

diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.genruntimeconfig.cache b/legacy/Bot/obj/Debug/net8.0/SoUnBot.genruntimeconfig.cache
new file mode 100644
index 0000000..c5c3993
--- /dev/null
+++ b/legacy/Bot/obj/Debug/net8.0/SoUnBot.genruntimeconfig.cache
@@ -0,0 +1 @@
+7855311d295825590d1f2d744602bb1f5d081ad75841f435f06048bdf5846452
diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.pdb b/legacy/Bot/obj/Debug/net8.0/SoUnBot.pdb
new file mode 100644
index 0000000000000000000000000000000000000000..8281c26c9ed2d8dfad7c429ba1d78ba9ab0889e2
GIT binary patch
literal 23980
zcmd74cUTn36ENH}EX$HX0YycZ6-AINpooE-lYnAETwsA!*u~vNT;q~OKrqKM=Zu&m
zdg^(1p5aWV-kHOl;ZDzdr`}sNGm9(W@B5zjua}4Jp6co9s;;iCs_xmDproiEickdq
zOdutM1PRA`x_K}agij{JMLwji37P3w_Y{(Y8NgEsu`nkj0#LgqsT5LKwi53TwIGBC
zW!J$;WF%ZS;&u4o$Pg$ySrSqg18`8T96M`lbF|B1+e6{2hjnqaT37VW<?m<Z&>cUe
z%PM0-Bf@Z%1?_Ah9fVW{X&fX&NHjD=uB{=nhtdENbkAI4=?n?)8$-fzb%%t1G+PZ<
ze=#s2Cm1~J8-_%BLqd82NMVq|p_f5`H5JMVC>x<X1La334Jn$0Qxq8tWf_!npgacU
zIVk^w(v+r2Utlx>%0ej1plpEhFqDs>d<CUYk0Q=`G|7Nc4&^~8e}uA{N0FsGntY>A
zk*)ePF(u({zz$F@^1{-Xob)m#XCPgKbe*X)A$Pn?$nTK;g7gN`M@YP0Cd3$0n_h@#
zMr?bT5g{Z;2F5z=pFUaZtxq&Q`eYZRGGBe7=&et_fi$*{KC$rAw!>1N`1NJW!F}~f
zHY9al1EPUc0cj?rd61SuS`BFvq<W^kfb8pQNdEOVBy>MRVhG6sQaebUA$5b~0!amF
zMn7G8JhG%8kF0{U5z^&;6nO*bBP3pbkQ^ipq(zX9Li!6589<SK1GIgZk+=b7WQYzt
zin%r;DxJE0I@jI<KjY2~)Lvt$Pf~;QNhYLRNGeE^A(cX^hBOz_Qb=ncZGp54(jiDE
zAf1793DU2S?n8PC=@q08ka)pt-zH4SUJuu~&I!jl{L?4LnQIEPLA!Rx>!V`Rur?^t
z0B!a|Lf8oMc_0iFCc?&$-vzh}2b%zF4T~a3kG*dSaA$!1IM@Q<2!Qu83x|fa0_y>;
zw{dV=fa~G92iKo9z#f3xhlASzd>UX8&_w#}AwLiBzubLWfGxpbps@A8a&!b3|Jp)=
zA(2iTjO&}P1LOL{I^FwNr-ktvOh^-qZP6C!-bcHjh4H?r4vhF<=DxTO=B^3G`(WOh
zV8lneqwNpx;~2Cs-nY_$@xC>TKzqMb2gbH&cWvN)XKwti0JqbDV*zg847SyQgW!G#
z7_k<=4&aVDa2&v$bl~S4Y!CE0!S!}Xc5q+B(Jz8&LAWcx?cm;E4t4;zBfx_=*b(3-
z0B3UeJple2u2Ug(27D)g@y`iT7l3<ma3oYC>;f>_U(^qTT>*}U>vTv$fZe$EE1<nS
z!0sH3b1Bk+ar|AI!45ib6x{Eo15X3EyAF)_jyf>*-=i7qqywXFq0iCsgLBwZ2gdn!
z(Sh+k&Z)LN-bVwkh4H?-4vhCbbYQ&isRQHvUc|f!{}ISX3uD{c9P9z|@&$Mqz}`@L
zbMnJBJ~}Y=hwXIlqi@uOagA$XY>y5?3uAw1@U<}B_lFgsg&hFyrvu~I`|H5g00%<S
zw#WW(PP8!g7Yr*z3uF5b9T@uy)oG9T=+Cw75kDL>O$(#GYGG`P`WgZEeK`5p034|U
zcLO*IU|;Tj1i;ZcFv==M2S$C5)qytv90zb84*vzfgJAt?Vbr%o9T;uXU>z9OM2Zgl
z3gF>7@F|$zR2|qKIvlM7qdm_A{ngS#{gvpzNH0qVMww*mz^L0fIxxypssls!WQ-1s
zwsWiwjQ!^}gY$G?q$ksXk)B)!Mtb==Fwz^R10y|!4vh4a&0v)djPwe0V5B!*2S$2@
zIxy0kpaUa4wGNE*Ch5QzU_Pemz~{^eDbj(xpu<ufcnvBX{IPx%R{~8`kn2GkjIbji
z*JmsWALaplVGZQQj1972EVLzY0h(<=$I$Z#AxGbgUIl$id(r~{TVe}&2Vw!a9TQh~
zCcEISJ##H$uDgOBK+I2mgxry-@4?iAeZlSs1gOyO6}%+dAW>u!<TQp~z#*;^Lf%5s
zBO@W_F_C~i(}qtDK#dVehTNF^3_0ufFtWz$hmdH7PaeYqA>~+2RzjkPJG6ml7_Rlm
zV#s*}LT>oO(P?wE3KZxW{!!$jCq>qKLGS_B6hyjgL_v{YADms>Rba$GkrTLBm}med
z3E-jq(9@%LZ^OVx`&mKB_8s0Iy<vMuDInS5aFq(u9}SY71TvcnKBx%%N-6jzA?ztW
zc{+kj91R>HmiZoX17d148tNOpGAJJDX7Gm6BTw~gXgzY6=L`8+{S~x;xO+E3?hCmg
z@%O$B*8z|l5;^2XWVAQY6OdBK4M~l+5UyuKZb%k+2f(%3M+145?+VBRd#}(lB4c|u
z!u7n~w;@mLLwEx6rjHPEkzWAh-5@t4n;<tL*?yy;<`U$Fq-|df<eq(3FfAJ)kM7$D
zH3@xhGc`mXdl=0Zkl*|@0s(p1?>6Mp{=^XLg^<th9{~Bwexo6O++V}guYlZjz-=P|
z*#WsRSp>NWSwA4aSU?;H1{i~`LvBLM2Cjgb!GSBF=2*ZAsQC~;On`?VVq#3%KyE_3
zf<~JQNKEi(b7SHY+z8jU;f-)@AJ%AYLJGr#76P(6TxelT=0R>k)`V-IMi`@knuk#u
zsF6j(G|D7}A!3!7WDr$$U}m0F7MV#3q?sg7o>`D9A=%<Q3CWepB%qO8gBZCin`8n`
zf>@P9#2L9vDa{jSOO$Mtj4j6%NR%q}CN?8EVr7Cvkq7-G%g0J&WQ<ZSBMON!pU4XG
z(j^LxW{g~%2|#*5Ru&YwQl&~FgNuv;1Y{B=Nmr{NSLQ&2TuEjiOHGL*fomd$q>yAt
zq~j%u_<WUA4zN@<UYsk<9E1!p6%1?4l|rs087d`VYP6jcG8as9_68ZJR1wJpXgPr}
zBMnq2#A=eSkc8&ttJEMz_%lE6G<~0C+n|c7LbI|3TWsE?4;pyN;G$(+5^0QIO|R^j
zGQ+I9R2H^t>x>;?majh@Qn}69cw<6HO_;gY`U4FYhv-|LnYE%%?DD7GHZS(+Fh>9B
zr`_kPOQPmq4@)mS82yVNJa($gaAeH3>pxl?7^5=XkeF6*a@LX1UHgRd#yv<pob%I!
zs+(@cK?{xV3@zQaX>|+<UKc>iBiEOBT#1iV-#qF3F}d3x9Rij*zmIQcp?KGM-NwA{
z-l-yvq~{zO;&E#n;pNElB>H(`MTXooQ3{igppcJ|fOHI%a@PWxYr0&e7bI7i24-YP
zl*(YaOr?<LTCh;8SOyB9aLZ5{LQKau>EO`ASGakb(CgL3;cv|km6b?py3JpEZ2uic
zy`#I7D;8YydLa4PI5H1(A;{Mo&;%TsLTF+7tLv41ise3cc4)i}4F^o`Jx+RcQqt4l
zOG^{?P1xXW63gf&t0zX0ECf9mb3JG_Xm$%1tv}qU^3|%PkN<P~-L$s<{R53R%Vzd}
z-qmSQ^2duSPK-C#?c_`3XToolR|9?!Kdl@s-e+L_V_?TNqrP8XSZV(x|Kg$2B`%kK
zF<_CEhVd$uJVq)jm|z5Q35T@;J(+TB6gd}p74;J?=<~iN_4Uts8`fN)`S+KZU)y!>
z&zQ<lr!y8%3g-oz7MWomi4xFOWee=_LW}zqX~TXUG}uS_(&=6=Gs<Cik2;xZ`i%tt
zW5T%Vv*jj;os=P0w7}gZ-0Rn^`@xxW&$g|RmONGYKYvz~IPa}w*14@$EqN#O{&{Ye
zkRu0Vi2`Uh=li+vm;e0bJI~1*=p`kWuD-M%oHyHJsXV9Z`}O5p&K!2D`^Q+jnwp^&
zy9v*qG!(kO+tSJD&#G_d2d)lpSaa-$;gL2=a<&WCjOlscf@z8*z4Z|nONGlfMNf#c
zt@*d}jJgnBwO&Rai-(GutZ5t8i{~3(+SVn`B#v3<g<t4e@j787W!=@G_r}wXJDz$w
zUH^614a<HL8q505_}91HhP}s3dKv>B!!q!zRng*`LQ}7w&&51Tt2a1k+-~&MQQIS5
z?3$s+e>gol-mzgu?xR#ASOeJ#ah?uViM4RKx0T_PZF9~h{Wsv%(6*m^B;)@3X%^kf
zgjb&Yszb~bGeNQ>SAu9}+6&MR=t8TaCDA3x!XLkjRIW9Dzme~-<lKWfN8?B49ev>9
z^kMl4gPy&oMmRX=$<xQ^he*;3viUNJs*jt8ZIXPj3_A*k14VJ}U?rYHx@AMZ=A6!z
z)RoLFSzNMGxb=qB!0O=rX&v|WH90U@?Nu~qWYoShn-9O)5j((Kv&<~N3j_}rucqvX
zbaDOuYHa*Z(XLT0k+B!{-itc-eSFlJK|Ng0o{3J;<Zszh+_Ne=QMvU+<YT`_kM6JO
zz994ABkL#oOH9thoZ5cuVwa<b;<HBCPqm)9vD?JX-MZ<&8(fxYwYK{@(rtS3x^}aN
zE=|q-NuRj6#f2sZgPsQF=erC6We4lqe>@DzEl{bH<fZ4T166RWnqC05y~Th^Q-#|C
zf9RY&SQgC}{o-sdS`pt);bLNYap#MFf7rKb(~CSe%_%g&?(V{V;sTXC8(oH2CCL=J
z4=^e~ISG^0;7IcPzku8lCE!9b&?opCjSzE;^c7|^^6lG~#c@k4kg24wwmG(;H@9T5
zB3q&ggBg|-$`xaqc<&+l+Biin!lvdSQe}RwSRDu0!u~=AMF^r0Ii(u4)6TJ0ERnJ-
zc?*G*l?gNYM)u1cz2=4eWvP$e(sSdk?h?HXJ3B49=T+bMe}DeNzi99|2>rIsz7_qg
zhsAfd{!QsLDKRte(eJ003T|ZHEfLBdm%Pi5b;}dRIz-xi{44gGjx$%9?D;Y2$bx@=
ztsOaA6+dRs>_(p}H&w<zXI^M~RJ!}h{I#k^6UU(|OSX3Tn{RQ*)7i|ucWrjJ$HgPh
zAKN=F$hp|4<LIIO^u|T?^A}bujruV66g|eQ=f>oO0NY1_GfdAc*cI0)VE&#(_x>6)
z=c6LN-<%EO6Wi^`S-O7AA!XI_hz;k<y(e}GmH*yp@cuE|-u(9B{&&55T{d`}b@%W*
z^|L>=G<ckwIA-#r4<8eXZFX)5J{b7%<o>mX+|1UU`L^~@UBSWXQA_tc8vVmzn^Qw>
z&0F!0UZLk9E4@?yI>m=y>R7$+v}ebx!<NwvG56E!>Q&QM5Ag5vWVO$stWVQz-PWe4
zLOvXwGxqJ@vcq-!KF(#0$<bvgJ^TN3-1(fVjkVN#<neFQheumq@%x9S^fW6bi8AFG
z1$h#gN-63)N#u@h)17gf?q9e~_a?XL?kwsn%2BEEm3`ga72-m-Y^f@zAiY2-0beVF
z00)eNJkMR6pD%^!R*Lhb64^wt4t{U1EZ>aGULKhqeiBb#zs#(3u}^OwPrnQgAD^uB
zEMK1tkKSUxUKv1W%9P?D^d_Lx#EU?;`O;j8!c|-4?FO3W+S|>`+rt<0o?gDae0qC&
zdiBc6=#`n_E%xozN8-^NU~h4TAN1|#>F3$Y&)19Oie=da;N(egSE7^`C^96jS<tm>
zh8)%h@$60H1*-f4mFrlkER#TVpp@t0YR{Esh;fAxIYeW5(upjPiFVC3Kes?hU@54w
z<PaDJ%GAO61w_Fvw<eC!Nc8IIm*t)AndOt|>+3Df%FN2><00|%@#rH??=1nLfJCyq
zJUz0~Gn?3K!uRxo=R2~3++1-wM6{tYrpT0JVN{!(7#JMt5)v9TINSvc6sA6D9#{m&
zCx!+lB;ch_nnzlEVp=#<B!?zqjc<#Z-YsgNpJrHGm!4n0;n_Nd*Viz-(!Pq})p}RG
zzSdQ*FS;UY`b0@oK?<={rgRIG$>b_#+LUgYxv;&Y_6>~H;2GQdfB$)P^4i4Uiqc^t
z_l|hIHCCDd0ku3!<rbWjBUa>d&4*7ry^d($1mIwS%biVa0yftUll)*hZ^-B7L!`<A
zac+=UDa~+0FTo)en*=>1LW)e@Z+|y_#kPp`ca}udK94`%IwH$k|M6q5BeANBFQZ59
zu867jU)=TDs&4dWoD`|7mzP_D0<=*D+XU^P{3ky@fQW+~2JpNikH^k_<kw+(zDbAX
zh>4PH*pw*L9FG?>jy@$T;CSg=bXNbmhUncUWp#&t@=VQBLRgk7P3M~VMyY?op`3Gi
z@ch(kztHkr=iU4Q<t=dSN|jiaDOO~1Ei1pZI!PMgT)Ew}WTDfks5;MX7ot7#iy(ke
z;i>}ZM#0vJYklJGn&afQ5vi)&Fxg026u8=X>D%nHH#&2^3l<;->XHd6u6+m3Z!2(=
zgOa-cqN?(ZTpBi7HmzNwjB6jnY`e4~+4<?adTe~~mP`<$7ZiSIne#u-%io!Cjf3U6
zocmP5tjS=Dra)81&cop+@f%PsB?>zy+xl@ab3`Ysgi>X8E4VDe&VREv;`zgB;Yj*J
z$J~Ie8du|6&MA{yL5R%DD^TG&=4id#U3Qm@rbwUd^*18dOo%NRWwxZk(ILDQ)Ht~;
z9HRqJG?r}2qQD~LQz12bd+qnp;opo+S!cETa%C&HN%@itDeNRCO0;v8;-<d|kt=>{
z_wld9xdE#>ud5;9{Vp~k=jF>~AQRXyNOD_QNU86s$E1;vd*lB8X7=o);M$Hm%|<O>
za<X-N+^M%*jBog}@8BdA)xGMQIHo$L{Lqb&JHvBCt@ptW`up?K9wE-Tx7HKa{rq?E
z$~D9IBNuPn+&WI^co+gKPS|ZL)iSN%z8}_j3H$gjU+<dz*^>CuomHowKU<aGdLJ#8
z+vXw8TTpav*CR(PTTDmP?J<)(xlYV!9X(l{F98(;FQ$UXpB==dp@05|R_JwF;M%Q6
z-$l%-KJo6nAlIX*2U(Uhy8z-q@ID+$!00^;&+jixxZ-Y{7F!zSzq0<_{)*2i=(V`U
zcU<~hLk?Y2oI9(x%!ybjtypt=Lt}1J<0d;b)aMwZL){OJw!NeNF?7L|%6~RVZk*#<
z;=I8E$;)fvLwvIGHCaKEr&;xXeVcG5a_;`E#wRb-Z_!0StR#slbOOW&=*_i@^7y3_
z$5HMLqn{>!EFTa$J#}Gm`95lfE-ove9NB}lH~_O}rO$?b^EiA}=d1n~^@1F=Ev4dY
znH-KpGMMG7Qix%%&yD@#zN5b*k>ew8-Z$QLH)!kh66pZ@Q~$Ms%4|_MG~cd^o+4UU
zhmBp^H=c~D-ZrRY-3N~=t)VqhPhx@zu7_Eb2d|RbguL8rZWAJy8Ce^-wfnRCNjqBU
zAq=7qE+%K9WOmSt=Fa~XSy(*DHv56)(%8cnito+YcGjSk4q$JGCm9^&h%WMfa4jA>
zM~1$gw=uMOWwLYZ&eN+}K}=GKGsY%^ytJy)Y0_`cQPOL>kKL&*O^x0DY{^e^YR7hJ
z1veRNaf(6;YRq&H!yRyOOt_f)Tt)7$3%oDfT<aUUc<PY6%dz{lMlhsFXiB$axja|P
zdt!2%21K)4Hbw8@xS*JgY1J#<yvj0fjXtvdg~%Ps`#nMC-*?<_ymXawz<#S~0}EZd
zEd2^H?tfTOKlx!`10vQKJ011C=@?n{d{oHp@uJflBHQSmcWgD9^*ws2c24^bL>$qi
zJ{x&FuW3XO4u9^^^UReh9LLPKHK`}NbP3q#@q0y)s;FsQaE)6Slf(TlFT%O|?MaYB
z`_!1g8Ri*B|8y=Is2ze@Ce8zkuieS9oNWw^JA=f|S{}{axoln7F2Cfs<nxtUBNzrT
zsTdYSpi<_^4fxIDPPcKu`gbxeuBzP{zvRuFE;eV<ia9b`4A>oV2=8uh{ToU2ecV;q
z?)!1^TW==Ek7<(@!y&L6c`hb)3l`@}Fc8pAYO&<`Qbdjn*(s>&)i-k1KZc`*6~z6~
zDl$8yUY$n1M3m>s4$JQG{t7Aae_HgRsPmXsQCe7*4Of3>lyA>c1OAhz3nKSUb=~sv
z*n5h@j*Lem28W(XG)Hbh3b`s*$_;ec!Nny=c-{1kANI(PM6D07tDW$8z}VJNS!Nv?
zK5R#vPiGx76O)wjyCP4IHT}2xSnD{r+POO{dScbSnTRfYX>)M%`m;gn?~H0ZKhffT
z>*&L!`Pc);ZsOqZXNVitt)SzC$5&zw4f#WOcY6ZwGcHR*vNmiXwlj*X4_fp~x2Snk
z`ycMld9m|zI~}&ZI2Yvz9axaZi1da)KH#SByV#l}%H>qUo3vItU}?7fr$a5$+}6eA
zT8~NnBg&{f@{RP_d99*^C?JB>v3`<r%OXU+Vfl2bc=oB721(VK)wjF%Xcf7|GP-CW
z-i+8At_>Zv!0S~+srl>f!pt<S#(@Ip3^zrr%E)PQ#%C9N%V^$<>93Y_H*XBwVRq{H
z&8PQt2uFfz#WR^Cm0SVaNKQbz2ipFJOKQWJPY=D5r-vUI@oi6e?4{YC8IG0Y$rb8Y
z*c?kTk-T<!EWCAk2QusRC_eAH|B;Yo1#@>+?-)4$GiHJ`A6mCIEiW$&S&g`6uhd&-
zWrReQy}slUHnED|9G9Ju%9mv)kw=%IzPFb4*&0$;GGy+DnhWWlTMw4yF$dt8EVJ+n
zK?$50ve@rxmwbn`Zpyc8lTZ6CcA?$31w+4c-oRnQ`SCce<@qOf8_|mOYMA!A(D7#I
zc1d~0%2?-G-Ik#fv<pxxhb69m9wGr0`Sg1qkDpyLBKBL@UKp3(PpXTRAXbPWR?p=K
z`HEcEBO!&yUjeU7&xS0^S!#cD4?Rm41rAFUYMmHcV3IZmaa8YLsz#IQh(rC`EL%Tv
zd?Sa$4xH=%8ohis+Wem;%)ViDV0p~Gfj@m9qFs7i+c=4KlhEmeSsBV^tKmy4GvBLG
zTdGn8Hy^sb&}kR}$0l62KkScKgnKC4et{nzG%gF9x!FzGP`2l3^VT}sqs$RqYH;cb
z?Dw`kINmU9-^T#G`R_WrH$!RWh3}u3Fc-0|b!&gbw%_`&9e>WPn3}X&YqpX9Sg}+q
z&Gh8n=TS2Eb~{8J+d$&#?wj0eOmT0}p@Bzzi4s@<1v<g#f`jREaqvAy3>$ai#krWR
zwvpS~RbF4FiziXx`64cGbVi>8+5E9jULj3ou3`H(>dleGksr68_|!2{2RGrfTV4E9
zVJT8_TG~-zOqPXiTDZ+-dEtaC-PX;w!-@Cg%+_S^GOPKYZmkSGwD!gE>+xHBbZ`b0
zH0`4NAMe<Q5_@?#=?Bjq262n|cZcxv#>VQjPLzzp2XI=o{pfz;C5~Xk%ctXwTrxtJ
z5A1b1WQs}~VWNzaB(SBFs<b=pJ5M|9M6DXQ()ID)lYOJA?iRmjI5Ig-hXy{)kY-2{
z<kF^nXt0Ubc^rFkpy!Q?d#;4dcwckz`kEMLT`YOVSlEVhTJ|7v+hrtk*xIc$@_AWE
z@v~E9wmX*u=^(%ZjNn{2qtmMKwBPbq;^_ZQ7=8A3d~9IF-P{#h`gc$M5+y={Pc}bq
znmKwImC9Sl;D|LIQNt(Bi><2lxNg%vY~7d05UWnm3c^9S=^bLYor-wh|B`$B-f=c<
z-d>*M@FhkPH>xi<wH@{P3ZgE2b8g>ezIE9A=pXFbp6#q%`6zW5oEDWeU{1dFOnkWO
zJI990J4xj9@r7O1w=<rmB?m{Lu$|V5FXnFYFG!*DI%}Wtu|d&`DxW>MW%tj)=B<ao
z!MnUE)_L9I?RE_NiCNa60igpzOW(~qaMUd_g~N(Z#|M$@E}CUZnZEiyioC8dZCS{q
z(Lvuh?UVWJ$XUT5d_LKC!_#(jRk{*%ntq3IlQ-YB9~knJMP%jJV?z_x<xX#j%~Bg<
zTyp{kB{MmE^L$66s4XEs6iBFVU0Sveg+nE7V)s3{wG+oAIn*cPcd{o^Gg}qD_C!$k
zuOLOnFb5O5t2|jDKZ#_2+LYX`S8`(Hj+qOuznA{`<SRYINV9WPg%Zp;7Dg{GIgA~=
zfAqtCzbC6=G{=@*T$7b@p_LArZ!-sNIkgv2mxpgWzsYe)^s>cW*9@(FxW6T8tVAWw
z#4Ro>tLyc@y&yuGOnxBj^18n&yf)xQKaa%8k6R+Ph(DsIc36#*dVIid--lU-g={I<
zW}7+j#K@MYcplIqeszDSV#L+6`(*iP*~r-Xx+9vB`!;u)(Ai^aB|P`RmB{hiCwkH*
z9OyM~x2jO*ThX(gtP!@OAC71Ri^bPI>t=^*z28WK=zinnzBj9G$1M0kxM=(C=Pik{
zn?8<c)~)Av5liX5YT@2-^CK4Zkte@6+oncKlzZfa@iv4!3eK)6-3H5);w)|KL<j!H
zXz@VDzEvlyyM*j;eSF!#Z?p0LS10UgYcR*k{Q1L~$ZfD<(uk@3PDSj!H*3I<hNXgL
zy|ANo@1A*$+%_TIZcg=k<FGs8NS^iaz>AI#n>B7RH7l=pzs2$1d3DO@Tu5N-!g=2x
z_|R|ay=Hh3a1#1C!u#*RlyXEm>iBX1uXk1as&%SWdnf;`H9XqgTqK-tD&+Z1GGaOP
zbGL#mAw{NHT7365Tt9Bsb4B!<Gaa;hwXY#ZiN}kRG8EE$?Gk!0-sk}8<iN+7&lAVo
zik;WD$E5iKPHOieU+agfZ|S*5d^hVk4(fL4?Kc)}4MX;o&$QpWzyH-{l7K<MTPAQG
zuC=S{>K4H?@yMT(C+t36l@(F)!=isK?(NjH9yyI+e-^<_ZO@>gl}O8T`Nl2AJ@mup
z_D!rI{hjVMqZRk11;5qsR~33@>(YY*_r}%6ADv?H_xCo>>YHJKyqUvLU4zx$axY^i
zm+B9zjjmZ7Feh))gDGdEbN(kPyKRO!)DE@h=7*P2<eR<`O$q;07*nBF_PXNXijV)Z
z7oDK5w1?|P9QNM9X8Qki(~E7m?)dN1J}avJCw>cKWH(H+2775=YU<Fq@AtU<FGxVj
zGgs}|tajqR6vNOwZB!WXq5WdC{vLYM?@v$54w#jy9CWT-+B=Skrr4&n?Mb`eql$3`
zDjU1cx);+XP_r`l-Sla>Lz?3T=Zd9yTB*&vVYCvFZrGQ<J?vr=d?b%|CwP~Y_FS-u
zV6Z}+50A(d;`|)7TVQUs973#|re2<vE&qt4z8D;QU3%oNkcwGWoi|6XBrQnm_7dE5
zB-9Z4lu-cRqf7kdzfqwT-I6;l-hHygsJ~z$SgDQhRpPhIkql+9Y2>ka7GXOR=5?^`
zmK^;*sk7@n`-<gh5<ruS8-4ebmk$b_6HwrB<)ing|A`tZ%aCV&j+0IL!}$&je)+7C
z{D9R9;to&VT3v5`SG&eq7h!z9M3$t~PWx~5oz`O)YK_q;y6>I9`S-H!ZT0unZlPQ6
zqJ{27=3M&~d&vrZvt?5E^nhZ!j>~pjKCRu;wcg869}mA^I3Sm1;Um|keezhd35=Im
zKd<+C+uQd77FQT0nN6OxvAJMaF-G#;9w22>^TJ;9&-2Iy^DdvYyO6FuRBxgTEXy*a
z`C{$3Wxn3bVO4yeX(PuO*@kR!u~{=I`P%d5R6gUuW4KItp;mhr{M)zy*ZjuXLxs!M
zd4;aJwah!Z=3DJ^tR}k5g@k<tkF#>6)#LUf%e@SztlCwT9en7)&lbDy-8<hzn%NVu
z!6O8>9M+6q`oBP|pSK)M)ELKwm5uwfCGEg>PG4euc|`0orV~SHkN&YMX|w+cDvG+1
z^(aNPmczlLUAHEc3WjGs3HYl>j-YB|S7y8CW7*C2$7iQPwl8vR^DyRb?aB6+$n0b^
zq~|aw_j^`P{gBonNb}bx&4QOb`n5{0h0k52?tBPG=$HClb-J!Tuww0<=b6td$F+v7
z#IT=Z^l)#{uQ(O@PF40~+31)VogZ%>xT^9mT|6CY+t@v8JwALZo%m__woMrkvju%u
z=Eu8y!6ZC0EyxusS{`0J4PU<ojn;*~tYbXw@5anMZ!<s?xo&}$Rt_AbFbgy|M=X;`
znhw145`R95WBYMRx6F(E1A;5n;k7>wtTJtZ#|m=DBf$wIvp8Y*!b2zaL>+OuX}zRw
z_P`d+<KYQYE_2kx3B&dDcg!KB(aJOGG^0_GGc8CV|GoL{7MRIm<yfT~KD5col^1f@
zF}xk`k?s!$Pu>)t=^nnSqxHrm)2`Zo1)F7d#3--XIJWg2yA+-C*c-go*>#FpseblX
zkXp>UV8IRMaMNJs<frK+=i|1Q%1aiQ<yw3NzuDQ)>=)T}NIjhFAGUeCMU+M{^H09{
zmR&8enYrNx`W@N57j-6cy&#>ka0}nxc9iL3i}&KMU?sq_3l8%Qb%Y5&|9H{!w5d8L
zyeh%Qeo5JyCiQ93fMoa$mNsVZ(Do@a{T4}!F1xsOkJxrg@iN5y{Uj|p1^f&co@VJh
zHLGran|b1Cf7QH``@rs@^XHaanEd*h)-J=dn9mq7@{A_Cc6-AwKjOgn&b>~(?NAVV
zVEcvIC5a=oC+b7QxdoEsg8ZhF<-0M*ncq5?PgTgM=xsrpL?>N#SV<RhoPAzpLH6^R
zCv4AtJbCEI?@Pn#C!Vizb9t#Xc-Rn+z6PtLxu1_fQjYNM<K(oP_M^RghCF)ano_%t
zNzt#gy?<d+`~K0T0vkV<#LW<k76l%-l)C3oUUzG*@#j!Q<9L`kz8Ljv&w*zZgTpF%
zl@(e4?X2}S*ih$`^1>+rb6!z6H&D#G)i$pF!M_u7_0zQnD%hIcUwyIRxgJOF$2S<(
zFSdKhA2>E(SLm#VD^`D}-S8mRmyr~^7S8s$a0ADX{{7jsPcCi|Yp#qqR^VlGKxYg?
zL%3gKaSGMvQu1kBbDq?qPOn#6N3QR)y1G~4z8`f_@^YIY{rbx!#<CmcI5lKUc^pzP
z{a2Awbz#nzNLmh?^zh~DsmJlGs82${u=vB4!FzX~IW%SJ-Ev(t{0ThA+OsXo8J}~r
zYTA$~>z9S?u5)bAqx1b+G;cnp|6;aZKruYnIpWhV=~e;rcc^RzzIxxN{duVy{w$Q0
zSo!+9%kfo|s^LqQt@pYbxuvvAXXj#n;TKJHY(@h7h~skvWbFFKe<-}@T@~j%|FeqP
z^MbZtd*YX$IA@JN0^sP(yj{RN+FCi~g!Q9u?1OjjnRWTW#H^RaE8(Z5@rINYzBp?|
zvM@`*%n4uX?#Y99U)=flx(U3&f-m&?(*k2+fLC68wGrt~!)qcGA3y_IK*39e&hX}m
zKD;y{g=$((5gS?n@2POe(X^l)k<j>FDXj*&G-*d0akmTMRZ6^_qkEeIL?WgI90H5Q
zC$!iBm=L8>@RlJGq;&`agYC%B&z(SPwS$0AMuYW8M;e+k!-&FGFbsWSzz0sOh#Rn>
z53krr;2mY;+aCGuilbu&+m`fT8W@nVT!YTEzyLcmCtWbJLUDD&SCF-X?LrIS9b0bX
zOluI91#!pL+Tk<f8$b($Yz<RwPs4jrC{+}4%T9u5cv+`qC&Ot0ygi9hg$54n6|iGK
z3~3|yWvM>YfQ0RkX<(zn{~!=f!<$sDEx1gj1-;pB+u~5a7y)o;L)tV|!z<hHW;gPp
zmlVf`caf6d)g*YE*h6dt`k=i>8;U@r<|K4tni26v$wkueRxN`^A628g&541gdKjNw
zZymU0_xVIt>Bjvw$dJ1g8LvG|K?A6LoQ|iYXq#JjR2vR0`Lol)sKAJ;20Zg-zv43B
ze(!7D^t;~-otG=Dw6*LD2khRy>1W_<rP8hGEd&c~bM0Dprgl80cD$hKO{jVss$N9Z
zdr|elRQ+(OUP9FuQ1w%&`Z-kn2CDwJrijusP(^xF(O|0R5LIkN6_25cKTsv^baA0U
zNvdH<Dpit7mu#g=w;PlP8kPrA@Lv(cuh>W7|6kN}YpQM&f4U!q|0=5b7F|i`%6?R3
zDOI_Zp0S0Vai6Mkp{fQ`HEpPxc)F&5t~o%>G@@qqrfUr8*#q&#d`h5Cfv$j3IWuqf
z(`sFz$3h6uzV$KfT)?B+*nr`H6(Z=w7WPbG)R8TOOku!61}tRQovAeJ$rQ#N*rE$l
zn6fQR*_LhGu+pSWuLw|nm9^pL<zxUh2b2+%SU^1RlLKJr%!wP1@b!3n!smquz={b>
zm<6bYA;MdC@IoH+rJW@--tBY&x6lb0687DtF3swjehb8z?KWTB$5T;d`<7L(DJjTP
ze%Tk(7Cd!+E<9URj4jAly0$J^R!JK2sfH<3!#t{CH`Q>OYPiFvG#{y=g*>XQU3>U?
z2Wi)(3kCnRqp8|cRGl|fB%_M1QKbi{nk#m7K~&udGh>RTy3F`pueOq^T}N4gJd3B$
zB|Uj%8T|52f*H<6b%6p35Z$SwOzbg>Z(wd{PMI5n#5L#XqA|SUPTBD8VnOFGqz?uF
zUM$4S45z>rGZDHO7n}?q%z8InS;Dm7E0k@Bg8`a(OFEl@tvJ|%PG$T|o0hi0t`)gP
z%HS8z%tL7&=02@UaJMP5-k8OPa%3d9TL?ll+&w_^6Q3@6LKoZj0{!ag@x{<Y$aibQ
zu;8)kq}gEz+SCtTAnikgih<7nPm3AjWAur$KIc5ap`sJA;+&@iE-z;IVj3h?2&Rz-
zFZ_GMo1X}$Ft8&BQ@mit(8Ft@obzdqp2!gCSn$*!KOXoyktraifnP*3%^GvYuNo06
zQK~6&g@RRgrrJ`el-fWu-%vB3(#)rEW?ku`GP;<iiyJftaCr=Y0Y&0~m|++aZypZA
zl%yej7)Bs1XhZCH_{j<$yr<o-AF>@m$2*a(yh1}Vj0bMq0NR3g^kAmhm}D^1N)Zt!
z0Yef-gZJ`hx-caDXjrkKhQxtpJGCWI@HQs+H98Q!A%ZkPZ|HH(0%Fw$q{EJnN9Ytc
zLt^EU#0<@eYHJ`dlu!~%vxO>pPZc}Sntu(7>kNzQDEKc)r;BgXCH8d5O1?QoinA#Z
zkf0PWR$m-zf0Phd5tuSPcs;p?2~Zhi0OA@#YnEynK>Cy#+PcsJJ}Va^Yy-_9gg~()
zk2FLWaczl+_(RyB$GK{ZP{38gO;>uTJ@{fzcu5*1iT-pF>ra^|!%83qzsk*V2O8dJ
z9e@hR4i;r$1p0xup*Q9>L`Coglcou$(^v=G*C7o)ghH#^Q*HSXR0ORlr!)`gB6nKz
z8(p*>{E4O#;y+3=3x?xiBmi!JMVu}e8+;)J`|CalzssRVoQ?3iBYN<$h#_rAZ(dIk
z&I1$3Ks?n(&x9tLN^ly0fqh`u2ouZ<0wx%=2Kc8#Q2-zvPc^71@ZZe{;-L;a=om=Z
zQbqmgqVaUG9$h>JRv@61!W3Pwp)5f)D9ujb(UKNmjHae-s6d#HxrWr--qiebsw|Hx
z`$U!3@XOaw@L%CiP5+y!ETLwY@n>|S@c#f+Eu*UcqiSaGYgSV5KhrnJiX!DbsPbs4
zJcTNkP~~^1iZrUChN|35Ri2?Lf1xTLQZq(VGk&G2jHnt%swRS}Nv3MjshVG@nM0|W
z<&+ahuV^M+v6ENj&Y!tiQ0Hbe%h6<BxY_*E77J3^ESzAqsLEz>+xAOb2LQVz=ctlT
zRH+wLrcaf3q{<Vh3TLY59KSSyg8#DqROL>p`Vv)h)(!~NT2QsKs5ySr+*G@{W9`7Y
zOy|+lukmKQ;aA-g)Z8$ty=GGP!fbXsi@9T9ae~P$N@E07Y;8yxgwcY@gtAbBQ=s~y
z30JfCTtJAXoDeM`7Nv?kp{5O20|taRcQV9+XUw{8W7c(>bY?0|x-x|+d(V`;XU0Ni
z_C-c8t0H5q;p~CyqX$NRmbjo84TeR+=!ORd77*$h5GNjdCkw(^6VeNtGSQ+hUUlMO
zu*|IiPh6K^=k&=yzPejewR=;wCtnR}%C!dVh3^HS_JZ1Z(nj1JSS%3Ge7?hIy0&MU
zB=FVt$cZuO#skmA+;yM@OhBv$ltGLXF~N)neh@^ChJd+X0|{CJw;cf!1B<T&NA<()
zC{T0}RWydty4Q5^1-hhyE*(Xe8PS@1ta!Pw$AN*&9ht(CnE?|^b_UuqO-$N4;P;g5
znt~uRG!=F@1Xz!Z2hc{%c&OR2b*FeJ2Am>nwFqSgx&V&J6r(35T*vPZP+g(c9Q_PP
zlwn2R7Yh@8e?+o2(9SnV9WABWx>YiE(3mo134na`;2RRoCSW5{n0{&6oax$x9hZqi
z5PXNi%S_ufFlB(d5CMVjRe+M|0UeM%Oh2<@rAdFD+LBD9nNLk<UZLANs&C&a9~b~|
zuK$icRm7*`E#ONER43r9BR5EEv^Vxp0oq~$+hX{z0@@s^Z8sc)BisO+3oBm_Sn&lN
zrNLYd?f~nmknuj?nh52l)2bcU%9I|=V_U)OpsK2Qw7CssfxY@d`v6+d9cx|SvksI$
zRKU74Ah2n3!07~q(*u?3z~h<F*?I5*2eu0{Sm9o@rq~8l5&kT}hEw5&6wjo+K=T*u
zjn&|GiGil3Kb9t%n&CKURK6tz(J_oJ1Z0Oa9bn%Gs}cSg*1OXJ(5zhI2d)`E%K<H5
zRHpl&Q2?Y-kW!$6kXMilI#DA05=A(EkO}&L9irwNVBe1xKn>?5V3R4sPsu<}6oC-l
zSP#B3XN)m<81o$qU?1L{HDbs>H_WF#E<!mRdnWi<ndYTQCyF)~WwSSms$h{Lao}nI
zd}Ra3<Kl$xs=)yR56&-K`1nN<ju{x9Gm+9Hsi`KJ7Wm`oh!@6Vc6uf}G7PxYgKs?)
zFdw`TfB`7rsgoHAq3*%f-W|8Kf#~{%LmPc)tJ%RcHi5={c<P>vK_Ut~L1_nuk2en7
zDGvn-K_e~D2mMlungAhB?M$ZdP`0Q>Kq842gfO>>W`jOGly`KP9J=S(@L@~_M#vCf
z4Xrt&2L}M$S&Ehh(+M1hgkzMVPZIg+zD<2+(}EoQ;2KDm!v}Tn!V`udwl#1oAR~|+
zFCJ{|g?#)Rqa3Yw7hH*W3IHtj$0>#>WeuWMPk;=3B?%NZ9a5?(+;oMMS_@&oc1Wr7
zA+p*7DfJ{w*aJwZDG-1aLP{NqsQ4X}8Yn}c=4)jv%&rl1uPt{11vh|)gQqm$v<3UR
z2z@v~IRLaJh1OgX6;H)#QHcx07wtr4nWAz#LRTE8r-#wgSJIUty7Gu<Mo+rx0$sg~
zt`XBSqeOK<WD3k0sux%*O+6?Vb0%g(T%cMXKH-v$Rt)1l_=nHkpu+VA(V&ixM6I_G
zU^JV;ib@|P$B0je28<COvn-$uozcAm9VQ$BSVQm&ITRmPM3+h!+26ncEL$=fzkybO
z7qEEY2n{ZnHG;#^{(;_;d}v|`OH*@!sTCEC6&2gT3V@H^h#^jfGZ@XU1F_WC{F;ts
zXU(rN4xWr9rMVS{WqZwC#(J1(?v6#+RP&O7^))X?A>3K>atxLLPr#DWyotfGz2?0?
zmS&pwQY=l2j99!PBNng7h{Y>1V)2ShS-c_(7O%)c+M_cd0x}4-*wB!I#U>bm3zH`x
z@aZ&GL;@6%&1x9#3~0O&(xg!UOu}Re0>E!15h5xYO^Q#Vzl83h2vw91(+C#cj*Upb
z_=6NV#}Yvc-~(}70Ko(T!)QS-wg#&CtS`bTgc%lSN{9j4hi-f|*gGDmTTCb7$n#;=
zmK{j|9f6ZQ+TV^?@j%HTx-lj;xNl@ORmP+T_&N-Q##+K`7Dizg7DRIb*fb#Yw4*7&
zKeT|+K%m4=gKs49jNl%3?guU(G@J(?6ziNqI?)*-mP<W#TpK)|1G8>G98AQP20R{E
zdfPS-SwV~gPJlBq*e|ECaMlN|kgcb|n~NEZ#Y0pihGs%R3nn#HGJ4B6Wk#0)!2xNb
z2m!?&-KH)P8+KWIL1ZK?$ZhIROGL8@L|Wcd{Z%raJR)Ri;TT{Y0f+iH9c7H6GJ!K}
z<|v55PdT{|9&ks7>k(7l(Mg~$-0%(QHlJk)C)%+6O2K1PQBw=-yfxls7-Hj+))kOu
z7to*IRNajC(X?QYj)=Gc>rVlk6&%mpwq38k&h$}X!+-}^=aGmEyblfpWAYI!sT~;6
zsaGI-gBN&^5&~%|1c9Unq^V(G46`6jwS$nWKcuOHKv~8?ni>i=Rsw128i-_0K$?m?
zz>A2`_5W@88W3l(=u93DJm<lcIoRg!p~T?M1Ij_b8<yLk{0OD?Z!55h_0OP8fZ@#q
z{!#&c0cCp-K^)ldT&6Ky{E#jQrb~<HvZ;hF^QJ2z>59Fg>H0)8{W?8kAU$KFXvSMn
z)qPRTuY|50DXP6r*Xh%Bb47K3i)ObM%^geEPInPPUm!2=WyKio*`f#ah4o7poAF2y
zEJSek;L{irZ1x5iXn=GMlBUQ4*??e_`NI;T5z#~zgja1v_HYSZPa%|MFLVui<c`D$
zw>nxA15ZSJ5HKK`g}9WDilJY2nL+q1=++dO^rQtM^dm(*8MVS4HnYIGTe15iZ6v{1
z8wTjf!@Urv;1IbR;L{E;1U&c>VuT1{48IA;3uk|vi8J_XAP!o=Jl??#61Xx8S^$H@
zQ#z48JUmKEqsQ=wW)(64#<vYJAs}>~1MKmEG~h#j?NjwhPi80fRqE<5srUH*Lp>9z
z=lnlT-3G?)s5^E?Cj8~h=}=lQ8bxQo2O)Qc38KMyf3DG7;;S{=nkEetz!7EdMbcr`
zX*fG|>PaGbg|2LDW{+fIX*kUe#5vVYH5>*y;3F#sJ+Sp+q~>b`A3tS+KQr72c;W*l
zt~DLUazy9wb@(vism)*p38){(IBZIQ&7q`&fH<I0XKXyoHs=wz+jI{;QXER!V>9G|
zRZ@7^)EOTPfmt1jjt|K9U^Rzm%7=qjn+8i~IV4SKA(*fQkTj)5U=~(F(v((#PHu*z
zDZLIW=O0L#(w$(deukte-2(b~29l<9H}roQlBP5l)Ug_prZf|FW~GocrDI{oSOrN_
z>HvN(1d^uo735YxUsH-PJ0aTd9=29?2lMp^cx+xG9{%}(srm*)5(Q;7lpbK_uzmBA
zZ4Hqz7zS*g3?;UQ4^uPko0r=5v0%8d{U|80y$sCY06Lr2yb~4GvW?IZmY$)@y3l2n
zbcL~~qJhxU*U*(_^o$vzDi69^NLSCGXYxfex6rjq=-NA?I(N~m9`vls^z760+?k?z
f5%heuX#V%21w-kD?xKZi(V}Y6Vk^-SH$wgozdRtg

literal 0
HcmV?d00001

diff --git a/legacy/Bot/obj/Debug/net8.0/SoUnBot.sourcelink.json b/legacy/Bot/obj/Debug/net8.0/SoUnBot.sourcelink.json
new file mode 100644
index 0000000..75ffa21
--- /dev/null
+++ b/legacy/Bot/obj/Debug/net8.0/SoUnBot.sourcelink.json
@@ -0,0 +1 @@
+{"documents":{"/home/marco/RiderProjects/so-un-bot/*":"https://raw.githubusercontent.com/appinfosapienza/so-un-bot/72f6cd30d09e169dfba57519c055fbf65c07a93c/*"}}
\ No newline at end of file
diff --git a/legacy/Bot/obj/Debug/net8.0/apphost b/legacy/Bot/obj/Debug/net8.0/apphost
new file mode 100755
index 0000000000000000000000000000000000000000..7a3aa7b5cd318c248ce760603cd35bc84cb23e39
GIT binary patch
literal 77288
zcmeFadwi6|^*_D=0uhCcSMW+yKoErl2oNEV1s0yw4W@#E7mN@BL?O^*7x01vlC?bD
zZW~)|sntqtRkYPoEf}oeBA7(0HriGxSV^tbS=S4q<*MxO{XX;T?z5Y$Rr~q=^LsVE
zWOvTYoH=vOnKNh3Ty~Y)JEcojmLvYx)iK_|RNg2JDX}sZ4osFQag1^Fc9b~Iah&bw
zj{I);KVB~VGq)=;@fZKh(MbovtXF2`r+=Phm5YDITjwY1W%|n<sB_|<j$EB)y=;EX
z*Xi`nzdo$t@lSg_X|LU?m-g9rw1&q&?e)l;yfZZAc^9UBe$vybcf9_L*Xx2L>#eZr
zt+49FKl51tfAP;aKS?A1Uuy9){WA}F{L7Pn9gg&RjaI$%&&gK#_$S-oUl07x@0t3?
zGW?6T6aSq4FW)@YOQ$cM&SNF~<;lOv`ejzVzkI4Q(m%ZxVe!v6o!>$|^7E_yS?c1r
z!fG#5|Jp^F9FF*>mafAwY*E$RQ6q;fns@P{s%rm=i&u;pb@8Z?Lu;1|9WEr0F1Eqy
zQ>R_&=u+nh6Be(}@2B8@ii7#L4Q<@CWY8f0IXgz|EI)Vd$91u*m&I|g4$JV5F#aF^
zV_y9KIP9x@E`p#=>ZIUO2)r}+MCfs6@FB-J94B^yXHy0|H$tyF!}CZ6dOim3&dNWU
z0X`PY=&XD*=<E#sQigV)%~1ZM4CN~zl+N&P0G{(Y;ZKhY_)pCMpPqsLw`HKGUxxA{
zGr*^0;OD#y@cInxzMX;o`!ke(GlM>SCj*{OGw4-WhVp(Gh0gTz$qe)i&w%IR41AlI
zfu7zO@VuHqPex@Zzd8f_sSI#`2K?`3=<l8kc%Fpe>P(;il%f3K400Tsp}d)a9$yCd
zaT)kOH3OZw8Sos-0B_F#=LFapA6zipox$(Pfafn6_;6PSc^%16K9T|M&j7zI1OEOQ
z;EoJ@t_J?j#?i41a{omJdd6nJzaHbXGk*Rw1D-E3$bD{x^3DwH9-pDzB^l)VhYa#P
zH3L174D-?a40v{Afd4at9uCjY?!XNFotA-~SOz=q&p^+%41A-{lZXG)|7K>uGbMw(
zwq)qnof*mx%|K66208Z0z@LQ~+6CJ>?bk&a;BRK2^WF^O<*ym|b7lrSKhGdXM|t^z
z#Y?KoYkhNSeC6eia?gy?@_Cgtl?$qBeU&vcN+&H^Qe8P?&fG<nI<NDbYo_~#=a-lJ
z7S=3TUcRWZdVz0Yd1Xz_lA5y1Djen4Oq=c-U0z<XVnsng!SK0rYO5;HU`<u^0#C(s
zUqL~|!Z|hNzM45zzFJR(d-@#w<1Q*MEuT|cTeYCteZ^(dM}o3%jAiN4%IbM;P_h_E
zru&M}uf<i>b9|M{G}0nXQPmQ(-jN)4Va1}#IW=zN3@`UAspbDZRMvIJpjJ8RS56-8
z7Ic?={jL^_o?lfxuY7)0O|7rIdWo-m$$X)f<WBo0_=XF<MRS18j_T`EHoB&=wz6hf
zrTd$bGOE@;7m{`_76f472+iyHRf{U;`sZij_mbMmZ_LfY8us38348d+xK>tGFPpQd
zYF_!Ang#yFmDN5=Grs|Utrn<I>D@wKE9Wfkv|E!3beF!q6bkY&7HTS&E}ByTNi0q%
z^5_MXKCC7yJ8Mo4qAQXd$8Z+XW|Ys!FLzHLU4BL8L5bluy|8>nLAl#)jl6HbrI86S
zVBpUwp9_mpU0yNASFsRkJ#SGNM+?Xs6_?N4IrETR>$3*s*R>aOrn@gsn2E2VW+Rut
zEO4BWM>4Q42Vn7%c^ug=X9e+*U5=%D&77K>%d4uZNL~4Y6)Vb@R@T%msfI%NZZ2Pz
zZw=JK3g693Ar3bXmX<HCnpasZmA<jyM<xYdKF7ZT!e2~tTD{!TyRXY68r2G@fv?hi
z1$9>>Q&TBsTlYFKT$cE0Jj!e4R4=G3pTC&H<ra4H>#HvtUF@gHOe*h;8A;8Y|0RP)
z6P1`*>_mYe(({cqlf&p6vT+Q0R9OR~|F!Jee=$Rhu2`~o>6{u!Ejbk?bo@)$H(6|s
zNEuULV)TLcMe$*`XvqT1#{b=1jfoL@wWJ!9_$z#F>gbi#%Q>fAQCSOP>zEHqRqLp&
z^euzYcg*L=8#U4~Z_%82^J*OPs%qxXgDzrLt+=VYV&P5Y^XF78a?G!(taLzE=OG`3
zp(S(hpH!<@gbBhizk0D)6G@>E3olwa#|QgTj`rqNE~;AWm_L7!zjmQx@!~m4QMJ6h
zs&+}m@Db&;wH0%!=Q~hr>2iP7JO?O;*Wy^P*jEK|7vDs5$-=%G-;zZrGY|U5qVuH`
zzQA5pRxDmhJQ$vsUr-6wg5C|?sl{`usvWfz3oGZ9-&|F>h|R#Ysv(N%c-47}YHwcb
zSXNuT6#kG8%wa!4Dq38!yb|15L~a2ujVMeiOwKKpfQrP3sbZlZ)akhvHI>!N9P>4s
z7FR9?<I{TM2Y;9PeHD&n^W*IzS?ge1?7MG?A6`=pJ4W`*si>%|t#trZ#nPJztXM8Q
zV{7G9p6Vs#i<ZE+S1mz@**j!_5$JElkRgn6j$(3g;@g4>@QA4b!Yed<>ZD2KBZlTX
zrg}XSC(W9L?<0qf7@m?Go|;=w;6OpwL{E9a&=K~6_U}W77wBa9@Sz2x5?@Cqz7-_D
zj<9_llPF-JM*R`VufvBHI{IIJg=eZ~+OTSHWJyisyvn7u@b9}IO4|khb;Ezz_`fUu
z?}F%k7X$}#B!{`j$v5UQ#nSx$IQhQ|meo4Va)iZ8u=MegI>D-`vFkQ_2o8<0tF*wH
zy3JlvzPD8?N1(cHS44!Jh(31(wOu8jkSx?BrXKj0G2<+U4{M<KdaZ2W&Df!EI=%-O
z|FY1(YpwjHNKZWU6Rcc%JMN0ZB;CvLODjD`(kD2cu+o42?M+xo<v6xmY2V-<VujPq
z@sgGPwv_Mc*o8FzvQT!7mB;jt@BA@Vbh(cAEZ8YkRlOYG)P5^{m*n?!d~T(GFX`hP
zJ<tpGsY%j394A@nlcj!l$Jti;Ny*Q4TwtaBXH;RO)x|L!Y5v9caX_^E<y!kcgRFg;
zI6Q3apXH~)XZ=&x8<PgV&VrYu!RK0VBMrXLf|sSieHMIH8vHg3enT3(!GbSLga7p(
zn$D$Z@b6mXbMm?JP43Uc(@n!P972LrlYe0woN|c&wc6nQ;y`@1+2DCL_%0iKk`3N&
zgI{BVD;vDR2H$6c&$GeDSp8xzX4v52i*&ybZh=lF|5|Nu41~mAn+@JI0d+Wb+29xm
ziNAImoV<(wDI1(^$N%=(;BlQ}-XR+tiktkqI7vT-M&d8W2DemP@^WqP6B4Bzj=nZH
z243PX&j#<EfI1w5Y;dOyo^ONO<A-BxaC_XZ#0EdvhR3kMxo;N#E3?5*jRWyH%LYHq
z2EV}u?_+~6w88t@;7e`r({1n-HuxDfc%2P?rVYN<29NJ`GrQ3SKigJ*oeh4D4Zhw6
zKi3A|V1u7$gKxCK&$q#wZ1Dazc-RIXV1u{X-~(;&HXHl`8+?}yKF9`dx4{S7;K~NS
z&<5XUgAcL6582?1!6p8NCiVZ~1Q7Pi1|MpJ=i1=IZ1BD|c)ksuXM-2m;Dc=N;Wl`_
z4L-sKA7g`$w82Yk@Io8hu)(b<M{>(-aBGSZ_$(WIj1A8XHh7T@zR(80#0Fn#gO9br
zSJ>d=Z16f8yx0a`YlDxs!5eMx2{!mT8@$8@UvGoEZ14>>_(U6gqYXaU25+*#-8OjG
z2A^Vsx7y%SZSXc5+_1rS+29@<yxj)B)CN~J_+>WuJ{#O?gCDZNOKot6HQ#X#nP!9M
z*x+S0c&-ipEgQVA4St0Uo@awkx4{S5;QFM3&dj&Lud<aNV}sAM!AorLSvI&~gI{ff
zm)YRg+TgQn@at^w8*K2|Huypt{CXRFsSRFkgRii`Z?M7ZZ16cY_*xr$t_|L3gIC(%
z>um7(Hu!oQe1Q$V!3JMwgKxCKt8DQ9ef=K?{>Op;ao~R(_#X%U$ASNG;C~$W|2GFd
zb)Iv?sQ)0x2xk8cXMK!7*w-c2X4LP@*&!W|6@K?~ha)!Nd-&JcuLR$i7EdzL9>d>&
zHB57H9ch#FEle{M770tbhG~YtA{!-rBhw5GM%GJuF4GK!MH(f24bu#PMd~Dd1=C!%
zM3zeWQl@j5zCqFxnP#XfQYPt3m}ZD8QX=W$Of$3<$(QtnOf#ev$&>VXOf!@f$(8gO
zOf!TPaY*_krWv}5>^la)fSyb<gc)g<bQh)>!iuy>`p8kF8M=yuCB2_%hO8nRCH+3r
z3{^$eOZsi58KR0bN_scb3{6GqB>fuG3`s?nO8P~n8H$SBAnE6rW(X=$Ch4b{X6PwW
zBIzfXK9lKuNk7W;Sxo0i`T?dHVv6KS`W~hkT8cO%{X?c1nvLxHg8lz4(|Js{OL`5{
z=Q7<U>06j)s3{VbbPdxCF-0~?`bMT1T8gZf^jxMHQi?Q6`WmJgN{ZA;`U<8mV0x*f
zFJ*cV(>F+ZBGZGJE|c^nOfyszDUtMWrWqoN<V*TOrWqQF<VpHGrWq27<VyMsrWp!~
zI3#@%(+mMc_I)n$XPTj(NV}xFFwKxpq)pOCjv&oYPb4hq{Y*2&6WJ)~_nBsBC$e7B
zZ!^u1PNY%NyP0MvCsHTr*O+DqC$d!1FEY*0P2>hiKgTpfHjy$(Kh5+dOqWRd38u#~
zoiFJ}nI6Y<o}?dOnjx7;uB7i_nxUA8L()HFnjx6TzM~?4rc0P^m-HH@8FGoVN%|J1
z8ET1yC0)bxB&Ih?`bMT1a*3>$^jxMHYKb&T`WmJgVu{pA`U<9}GQCvNmojZIeS@SY
zGVNiyOwyMy%@9hYMAE~VX6Pi6FX;=JX2>LxC+YK;W~d~RE9o<sW{4!>kn~ARU(WQt
zBO-sM87hgiOS%iw43R|IBz@#C(hQA6!jj(4G(#ehjgo$!=^0F~m-O3AGc*!ul=N<<
z84`)qN%}RW848IkmGp~DGXxU3LDJ7LeKpf%l75=$YnU#P^b<^9%XGe^A7%PFrt>8I
z0MiV4L~<p457P{FL>x$Sekl0RW4?O5VZLqDzq7AwhPxnK@PZL4UcDX@!>RQs{@MJY
z&VCK3f(ay4y!{wIvY+Nh(IMZdm|B*h5T};oV(rd;bu72T`b7CVrF`Lc`7!vIVeT`w
ze>B0^eyFRF)oi?a%y$Y9xGe-ZvG)1Se$bwHebQG~T#C95|A;G%`r`Kw;j3Z(%h$^Y
z77s+V*r~e!QWcP0b9R3e%$mI;N$-yN$?u%sh%d4LWu5(GzPl2r(Eh)GVm8`z==205
zRJeKzK#^y_$s~sPjmi9nHZcF*jQL%kX8vs%^H)jz>pIDI&2(Mmy3#dcIwtPEhI!Oz
z;q(ZePesbzYM4`VjFzdKq8!ar`#QX#<%5tn26=7B8-zTXFXW9u-YUZzS~AOPE-?V7
zS_3`{|1Sg+4|>f5Uh{}L_#KDC^-O)8qpl(Bo9;DZhWVE20?OP4?=*z{E>Exz<hbWy
z(olE;=q?DWu9(sRv{L`>Mht6!0kE+sfr8_7!QIH3Y*s9S&(Zf<*LAMhuIpXpvv+vR
zou1$XZ_&s8Gd;oa#@Ll@{yZaiYYx7(`UiN;_tc#*PwJV&v6z+bYgdmW6))u5qwYtN
zB^RKirEiqa^fEFnIKc?SeA((Aj1AN=jL@W6Mo|kO3pExnZPFMGxhjsgen(&WMn=(I
zUyo~NN5%tw?d%<f*`ny-So)zJJ_yQ&;~%XLuB#}ivUx_Z+kinR20<uq$iK^L`df__
zcbhQXy^HLcB|P-Es?We(Fnf{_T-mBF0EvO{?W5H9Q54XNjnK*s>RT{6M$o?j)$&md
zJf8r!3l#uyH)+D}J4A-8Z1ol$M8|%BRFaCZZsjjTZ_2K9T|awAv`Z9(?MN1$+o5od
zVeVCD!YHcE$kXGH{Iu(%k;D&mLodVV<wo?fN%u1Bm>evA7$(ylh&cm&;Wuu~x+D7`
z0MM^t1$&}r&F5djo-lVUa$`G??>x9820r`WSDV22=sz(qPx&{y49c@PZ-5n-Jj4(G
zM)d@khB}4l(MD)nA>gVv*#SE=06m57LT&{Io~Ex_@Paz;-^er1J{f4DImnE5b^Sie
z8(O^HYc6g?2iEBhtY;(dKPZhfs!}v!Zf}ALI~rc_9f!6W)y-D}mc4@(3rT1&IDaaX
zp*edSU=-O#cf&irlYr+BzyrCx0X7iqEjr)>lMkwQ55!_*&|P7$^8~FlV@uooS3z8c
zZIGZF(X;u_iCT14YY(G(b9R|-Z6(lB_&)?NDV(wHHowU8GH_Dbv&j?sy7~jErD1Lw
z1Y2mBchh$^%+MHoc+CJkYp)qH@PYAPm_!TBt}kwZvFy6?`Xm&Z<_x?=e5-d7ttgyx
zaDWcGb8zSe`M7(je9W$M4z6r;4qm+8IoQ9^Id~;mkQ_Jl#Z|<9+XEcJp#WWY45xNz
zd@eqB8BICBpD(1l+o(L3p*YlFpu6Be^u+m%1dRd66>A1G*LAR2G)k!oNGSRbZ?Nzr
z<QkzX+q~vMnz=n-+p}Cwz!s|8g|P<@BAKL$y=yoRgsA(WLP^z?c`7kJV69hwkV`>>
zaC6fx_|{-+yL^NcKET#}@)0_O4-hv79O4oL9OBBvIrwh&);ah-%ECGL!4mm+OfzxQ
z4U*bQCMHQEM?!%%X#~bUR0<}Qwi;WKOezh7Z5y3~++pWn_j-H>>)mxI87_zw95Bq?
z>d{YuGVEOI1?}F7Lr_Z}%x_H9d<M&FHaC+qM~v8uzLUtY7)F$uiYn2r;5hKq0qQj|
zt?yPh08IWj9T1U>a2Ld)=g4qRGL8A`knf7MX!*2Q^4S62M*lI-8|*&-+}G^KXy$Ns
zHmni(9E`Qq7hj6X4qvf1SnR<!BbdE^A1E9<#^2i$YUnRD>c=>IXF<dm*^t3o>JWNj
ziTIfM84}Ierw~hN0K*aFtC=7?$<(p#UCzLFkPmI9Ksg>|%0im~H0BA8W~C=k310=V
zasz99@#8R74rhR(ivBDGq5l$CgSUPG6Se0PD#niH>^oUFLBe15r;(sXZwOpbgV0jk
z|DwLt7rUjQyTh}0i2nVT<N4rcILq_@Y&`#z*uVOCz7@sO#`8jeI*jKZfhjVcCnDE2
zo{tASF`gd>SDUjhK#5Mr^PeD#q-rL|vyGn|%m3L#`UZb9Yxk8bT-B$-k^Z!BPL7{h
z0CeC-J-RCVc<w{0=y<Y(%6}An^aMv~HT^jf&DniWKb0T;6YQ2-J++StSBFyC9}X4I
zeuNX*?8x6?YU~`7eEL8C&3v+aKThENKhwX{iT$hV-_cJx=$`_p1HVrOD>c87Ytz3V
zc$d(>p-41mKa3Ko{I=$k%Rf%#Ce}S1&+uRCi*IBlUkT;C5MMDQ&V~2XTjY;)n!gl4
zL-d!5M<P$n1m5QCd}4rsd^O6CG0Fufk`L%4-CTIM7p0J({aLYGw50P4bKC%a;nJu9
z*ff2K{MkEXJ!JO}u?qRK$2^cUe&QuA4#UaX=!L%@TmXw(xxs5HPyOe)HD|)ph4<#)
zQ1F`Cjy_<5+GqqzH((kImj>SPorO|a{v4NcvvX}uv^N|&%r#!~Z8h>kcE*i@aD{`V
z8=JdjLF-2mom$I&^UH)92+nLa0(+cyhbK4e^PND?kzQ`)2dqxliQd~xAWKqA*XfeU
zSf<wR9FyvcC2I`mP(z%px4=zxGxFeUCHa-YpJY2W&KQ9M&W18^9zOMOGCioKM>+kr
zpnsZKu~cT=MPa5eE9N`XQy<In_i%1L-r^O;KbLno(%DpA34i{Cu!zgtne5gylq?P2
zo<sjNk9>OheL5}&3%E=)%)fceu)6f4SS)e}SX>HE;VMtCyQ}WD9<gD*X{DhBS)R~U
zxmy@aHHzAN-8{i*xt^kzmi2Wtp>Oab=qVJu250==K!U0ho4bt}R%*`Ak?)hoWI5P6
zT?=$>ueuzq8(W}%{F~q%eAp=3UNaSF&@sb&PCXk*m)qhrp(VLZsa}pr_>cEufHVVf
zYSu_(&12SBwkhi*OF1rqAX3YUs6U02)%THSH-{zK9%B8@72%j<HMp?e&`S86E5k;x
zpEtN1qrl(fH4k~}k7>b%p_)w?1%Ciu2o}Xkn=lI6=pjI`$2tmjzZynbgBSpe3It2T
z90XVjJ^WMA|FBwx04LTRKwbO`z_1c|8D20%A6EUBU=hRBVAxy4Rq~89j<ov=&0U(A
z;`?ZpScNa>P*_;f4((C5A}`63Mk6#aW{jPhQ#*i`4Pm1pRD@9WNPINB;B~U26I`)2
zczJihohb<0VmFM}NUe(3W=AR%=cHu%Q?!G$VIDGqtFT~yqqZw5vpBF1lty|ns@w2F
zsT17kI>s3MhAZ}})lSOzoE_;-IVR@+*^!5!SUN5Er(yeqZT!jJ#&gg~Z>ZNF@zL;_
zv;p(OX8vSrrqOIOf>-4jp-aJq%X4Z6Nq=akdT~VoQirg97>wAS&WHp(Vm~Arl#j)Q
zU8#0?LzS(tr%hPDhS6C^v%3`<Fcr&y9^T+6F9s7-ZX@hMZq4bI?SqcZM0X9dEA6IX
zUY5&&3_}asK||X+VQ3{b^vCx^$53z*xNy9vSa4;Nx9B}+%{dTCk{&qDe%ODiO@6{@
z){0}5{|LxZ%ZuVX3|Xp)z-7f#82^&~q93XKHR^BAaX1@-=&A>1AXNC#j~$LJT;bE#
zZ=p<LsxPF6@Dd=~80U|JUqvfe&|pT1`R0}ueYR}25t@V+x|Rk<mqNbaR9k{mZLmM!
z)WxWyIR$`l%GtoBfpBY~#jTqyJVwhTF1#GglX4x=T2JtD$lYoHfv!)y&WWF3)T0dy
zc}l}-;{}kL(#8Sb1v21Iwx+$)W!j@OqQ}K&!2N3YyCN7MNeCvDwYgdpxd~CIy{M8L
zo2mL`i#I0GFruhA{wO<beg=-;b~nDYJWozrjDIiA;roU0%?Qb*Fn$)nv1U)u+aKb7
z(K~p*bIoIducdVG!BXeMFO2#_F|Tv-p*X*~j_mI(Is%3rflyi#LTS}PSqDwg3<E%`
zZ-EHpY)c^LcP0?TQQ$o2c;`F9I@Iozb#4A4%{qI$GRYLqBz<90FNNxXAHTte*W3@|
zng{JxxBkn{<D`G|CHjop^5Cp_a2xGaoR{ablVFSQCU0nIR%!6I{yCna7UvzmV;M`W
zz&vuQ6TS6w)XE5t>#eU;SX5-O2oY{k%@mz2iX#q=>@O|GQP(~geJ07irhf|iGHc!?
z>6&L}-Y!3!zT#fcA~0_WKEOP$c^Ld_2kX>W$R??yN&Z>-IV;T~An1W91ddSB+%<~6
z#68PKjN%0xD%x3U)Sl&+7`)wYOYcxH=~*JqxzU;rg8n9WYreBF?{2KSEk8EQf07Y<
z<!Q^mQ6GRe(cSc}Qlxn$DS$Mc4Rw&87qP%Vc*WUfxY1QNuKO_m$p&I=h;$fc>#7fp
zmM{kwVlkdr3#WGpQWhP;a5%(RC4{n8{AbH0#2o8ii(ak<`#^Lf%=%jNay`ttv*Cwm
zP6U6u7JOti0L|{kVWh!JLms***J}>3>wzb<Jd5sE9Yo=IhP2>sg4b*}hD^%w79Db~
z;er|^tl>1K9n=<8<TxANBs}DA6m}5B*~p6gKuX64-!1<T{(?mJI*{6^XB7WB@T_qQ
z*;w$JtBE5)InW^(Y$J4Qu3?Tfg8A@=J@qZQ-lAjh<joPDU=NJo+~^4&PO-7)R6n5h
z1gGS1T$h^TJuOqj9=n>S<YEOBkJVNQ0bF)Kb7VtcCLnedZF2_h6Ph`d&9bclQVRKR
zgC6ID!Kw#(NkSzOt&yG|y;MUQquu4pSZ;Onw-xaZPQ^BqL8bekKB^VyYz2L9sLg<+
zl=i)*PGUE(F>xb?dUC*M|DuNh4^wMruef%qZ=r(T%NUI+w)H*zi|J+NpIzobEIyz~
zPZ2e3&k(nBTP!7B?+Nx^dsY@moy=v*bLx9mQUAMORSp8b+}lZ#(_#Gfb(ycY%s0f~
zrA+OxCI`*e(fB36=rNzSOp!-S(L5Nx`Y&P#`<a!P4zO@M*D&eVVuMBI>3|GcK*Z(&
z$Z6pn-%MFPPSH-yrHRV@utH7N^`I^^Ikl>@;WlW1RKH1AUm^zZZT0k9qQ7V(F?o<n
z7C(((*C}g94gytRkk@=)-6Xp4JlW}gJ4J^6u^nd_tBtEVwvkfR_q;lJZ^9pL>H$)@
zM7QDxMnwz!%i*tMEC#!6w~NEtzr^NkNY_YNuYl?lkzmQ<$C3qHZGl^fzh>-ZLFZSa
zGT0YZ!zS{Jj84QS&Va4L0%4yI{eo`^*8zhQCZ;udiP1C%_nR+XF=`}Xv^5-<&6E0a
zSfb8B-}&mbzr%8##qyreOla5vRR)OH#Fn7dyqyK@{!yBHgDjmh;;R|%h1`Lfsikvh
zjV3@AYB%zd3M%6TsvIi*_4l}6d!4J}ey?85<tGth7bL^}-U=Y0W~ozv2Ru0s{cBZI
zalC@$L0dz`)hL3=ZW27p`lDI?<1q_1f}u}l!MO_+exJE}P;9X-<`pk5zQOnSo2mW6
z24CFW<`y4pGM9ALZs6(;g@p;1KylPxk(baHdwgOAHZ2yy+<QzL1K|c@%FAW|GDp3S
z5Fmppq1hO1vs1SIJoT&Sor}H442Z?P`h2&H76f(tP1+!x2!q7=QRWEs3<xoTTiK+S
zKKR&GxxOhLtYAD`!Bk?3T8w4#V$A7ZXr~#|Ukp~_E{qTq;RNC_FUMg2&^H2Qvi$up
z&z;a8Ync8B$_WXPzSxayIN%>0`3I&K8DZ~oWPYMaL;b0UKRER~6d)x=eMwFpsyG{d
ziyF?E*gFX<CA`I5$8lhUWFJPw+?>ldF{=T)6qQ6D46I;DQ+5G7Ma|BJX#|I6uaBOB
z^{S`7InO&*@8&e{E*p5*SQ;G2xCbY8D0RYpiBIesps{HayKjI@>L<oKds;l+6z|><
zb&~ELFxuu>%&}0<jq3S%R8ME%F7_tQeuU=6F39y=>IqHm5B_)S59T*wK&~TUnku;V
zs84U>WDvzN^fo-Ex4*0+hX_@DKvk&&A~?%yz6&Wds`)tI79AqAeZ+{=vcI86K{qN+
zhGh?)&_A8|(BejH!aEyYg0`urP(1P+zFEvRg7=Zho}#z>vk)JlD(a1#?+ac`?byH1
zR&_`v2QA!oX8`-L4op-lY;(G7P$l-GAs>XS-twJ{5IVopHbvDaV2ZwDwQjZO|9<o?
z^hmwG7kSy3<D=*CpA)TVUz6~~E_Bq=mIC3m-S60o+!O-+(1C+$-5&IL1e&zs9eRG2
zxDiH3(NW)EOw-ZcGJcKv9iZ{3?_?u5mfzWIRNVo@(RVz-a}fH(Mzpy%IXOpeBuSmk
z)v5MC#~ZlFPMs8X8J1UN1iN!@u+sp9ze$}uP7GikhI&)M0X0u2PjhX~f&yVk$J9Sw
z2a#vfsPyGlV(`i)n7zBOx<+&l2J$?*vx!K<@83ve6(S8fM4KVf(9og07{6i+3bhM)
z@$sVV|9)8Bv1oY<H-cwJ-Vol`;d^3!wBiv-X>se7=FV3GX9Cs|7!D&UTChiOV1WD(
zNMzQZUK*UBcK!t^bI0UhUJKE3lEBm&dzuZZJ`jnwXl0vkZt(cP3%-*2!{_<OC!1ni
zWfFL^4HT-IP_|%CYUV(h1d<E=$>#Ykv-^`-DW!b7)n0Hs1?K+%XcrLeU4O-NG}hto
zin=J0IFqo;{{od9hlf+=3&uAGrKv`{QGscp*Or%QQ$}vb6d9&EzaZW1^=<&c=B5*n
z0I=WyIOITAI9k54e?I|4eu;k{hDYC~J^BDj+I!lL4On4Pn!Av!F}*_#QJ51zqk0&5
zY5sC&`rr*M$n}~RNHBj3`bjU7hXD?%JO0d`GMJBe%AwpE>`up{KYxTWB3NZ}nHY{c
zM+|9C7}D;pwcYwhd&BTrtF23@N!q)L2jslL+8oA-2YG{Iak9mJzGjkh+qetVLuh2u
z+HMzkVmnKV-u1t4XU#4@3^NxxN&QL9#->T}-4LE?L@(6O(0tN;>fqlS9`oO@PDu>x
zvb97<j1nFoA{=hM&ggh{1^!XTgS55o@Ux?sI0gk>>)!q>qr>VGNjwLQ`gJ`A%*u^g
zeMEP5QWX&63$0v_*$HdjeEL3O0N=vVK+_LUSMYm%G}jZHfw)ubIog~rUZVzK(&4P=
zG0*DnX+Z+5Ib6+{^yrS-)`b%&Hp~N|DX^<8z9IBByhVrMpaiF2g6!?gb+2<dPsD-~
zBEx6^9cU3D<LF#S2t^MebZ>UYceI64L1y+Ky@>oru|*4X<{^XzeZyh?))|4<e5V=p
zM-Yc~Zg#I<+arqU;F$7&<~Ry0zw}gzWq$m~5=R@?whZHkq<`bR=0|Gykyy;!zA>xe
z1^+-<AcuM_#W@OB)A8u73_-ODvPAz_x*JMEXo+#(hSf=jvq-7jITvju<;<v<jF{a(
zs{F`HolmUz%^*D}lJ%e;-q0$Hn+Z6dD3WN#(MC$!Fke?&v85m*p1UETkvT?vb9Z|3
z|Dt4`fe*mH3GAK~p1{77b4^$HRoo0j!F&H?A;77yZ-IMtx8-23YATdN22H3sOSY>h
zs;+PXJw-3acdJkvBTja!rpl1(iruRGVP5kl2om2X<0TOo{qgX2Nv(t~;=#KgO9hZz
zwB2w{-0mGxf#BWFnra>b<7|wDb5{^%G|~LyFKl$hVzU0wamy|P%D>h*95OMm1AAVf
z^zB;tNLw1;oq_KHN5Z}(j#ws{OyN{v7_V84adkCW!e|9T5;5Ny(Q(>iiR-<;pU(03
zPpv<gww&Wf4FUmH?Kjn#>C_-xu^g~;dzqB~>uJ*l<i7;+M?`A8H#i@Hgx7Cv58L)E
zeu<X#5*-}WtNV<b!+pqG#OJWsjGM^P;P}$uvK+W8n3F0~S1qAsh`(XGxbam~YtlLu
zn_vt<<gI95O)c|e*LlC1_A*PHOp%Gu5FxBTbkedi2yMtJtp`stR}El6Eo^m|ArjeZ
z=9Wnm@=Rsyt^Y}K*FbWuK(Fox)+nYGi8Nsc#u63cN~st{R9B!kYNcooV^6*})zb8)
zA*z=EOPX`r{>y(WuTQBY5{>!K@>(F=`Wo`Wab(D=U53z?$?NCXoim!S_~0J~naJxj
zk=GAaTJmD9^0S~NFOL;|(_36)L0c+!zmmNA-VUrE1M63o*8}K{dif>NVaY3uL`u_;
z*I6LvOXX#lfApBI!*BPPpC>DemBk4R^J6T@xB{E^IqhO%YQy{k%OnBOX5Uq%=D%d?
z-W-J)t|>9YVW<9cOmdKCX*dd7hTTJ{vE6qrYnZ(OYVw%eE#<L8rxd4DTcRa~*<4!m
zZ)d|~E_vzdW3D?%<~p2L=#Hr_Tg?HtfQPjZ^yY+_nC;MJ{Dg52nWHi9TYJ$zqvT|S
zVrBWZ8ux`T(BSR{rtAyYFUE`AMi3Bl1UuwB5oQ51Hckn%Gw!fV*C>YRTKi$xQK#kL
z&H|@v@GTa_G>I?~x>K}EZ$M_~#yu~R0%%&&_=x>Z?SSSSPowWfc}`YA7@^`5Z<XNC
zKzN-=;n8}ZhuIjuYr?i$j4}^K-p0BZK4$di$^lkzJ)`4QOVi9OLsqS{;g2As#LsW-
zm1C#_68^Uv;VJ(v0}(vLIvK&+g+3sH=WmPN1Qu^VZmYV90jUrI6(Royb-g6~YdKL}
zp(9nuK)gZ1>JH(A{kZx^FOWAY(GGB<OrClZkm%`b14l=))NCZZW-UX0JV=~&YAWz+
zfV8z-Drby!yer<MzW}&505|5{7QbO@$Un!<P350Yf(tOb)ODJocrD+HQD_N+XbDJ@
z8k0u#lhKnBp|*HW?pG(FR7xwpdsH=G$<Z1=&tQ+Y&C-K4O;&B5XXqj0FYMWxFmST3
z?*_AbfIS-rAQwW|h=tU5I0llo)_59E4Ew7q7EUnCva_AJ*-?1f&J#;=@wH99f{B;q
zG<VD486IZ?&h|N6brTS|yPr6N*yPD-H|oPZFuC<~t{DeXFu^^8nhCGO-Ap;f)MN4)
zF@{`*;}P4PYjRnZj+1N-^g}M!CCgoP;~l<lCo$&&^U2ONZ)4*gm=_Z>r%g&F`n=Z8
z>8J}o37!CVo#58h4vaj(@(J=z6eM@t2jJ7Eptd(uI2xFw`2*^gz>_T76=l0S*NoN;
z4q$`btp;zg7d+I}x#lEY=qJlih*BU`SK14Oy8v4^=N)|GALPHa90jwjf)|nePHshD
zY+nk56qq`6A&J!s7sQCq#7Kkj+2>mfTZDL9B@VRF@?h<<#2z?zucyl|fphl;Z3T-C
zIvWg7q3)&}f|ZEW3A?;S?X{Ea%81a{UZJ*l6{uBLkxDe=3C-w_P&-6<K)p?vX-84B
z71py+`1!V)zEg&-wayqo>2MCdcPu(pzp}~UpT>1fi+epI{Uo#-IMVF?6$o{2Y;gz_
z>mYP1ab6EOov&hex`47X>f1l}gi60Kidvlw_mIP(!etZ-aZ@bYcCg@;P2M3l!NLA(
z&CT&S{6Wd!`95w3EC;?cZoDcO()agV&yc-ICOYenz`91Bz&~mA?-Xi^3qXy^0c%pS
z{mUTk)0p)k=t#}F7FkFA$a)P~(E(VV$^ks`)t?*f8$DU4h4P+iD^N$f3TP~DMy*9>
z9_`Q?T;}&wMGLIiG30e_Z{$vtS1+`1cWlhi{Gsz(rk*katMREhqP(HvMb9}L+e*+e
z^$-ry@E%PGea%o|FVLIV(!CUUoIajlnJo$9q%8m<8?tQT3g=uFkjO%OUl3h)u<LIi
zC%FR2R&Rh6M0r27%1*wupC$HEpPxwb67jdp@s`u1fllKs*P;#bQzp1JPH_Jr-tr>5
z@ipTuuR{+1RlH?@4{XXq%W4RCljMoSTb!G-v$s1O<0}2#XGeaGvu-$<9ltLr^S|x>
zAmT9x?M!Pkf}@RKIacbsjKTYiE%0^uw@QZC6j(lw`B&V{%!Y--8G!9Z{i`rl-C!f0
zGq%5fiSe{99_xw&YtI>3+i@o0IXG;d!Jm1yf1X_$fZ5^-ms<wz^*%(cT8!=QWvhYc
zNwiBr*t7ke>`OzZ<V_2W%wu$&)?k{6??E@lvwK5na50p5F}8TI+k;a)+}?e|Fs}?_
zl6wF=#aKrRUEp7~!rg=lao=aK?E9Pm8EtTGcDUAdkM>~|C={&02JZCq*ltw~!o`7)
zdZ{`2+#HYjk`e65XbOBOO!ydaF<)(IXi@)MZ)kaLbezO5Jf?pi4ySrdH;$&xKU8W~
z;{Rp)aJ1kM1`47^-La+fe4ZyXMLJYkbPQDa@=DFcsD^Sy2mH^lqGf46R=0-1iYC<A
zCvCzABYf{{XaX_-^Lf4@>PiY+x8FmzzZ`A*@&78cKh<i#ht>X6wBLfQi@k_5`ugei
zm!tiIY=1f0zY0S9SbhF1n%@#{9$v5!oV^b2*Lql%GLYsq&u4_gv;E!d(k!eLYIBp?
zBReRoL1<cJhzzh{@^6lANvY(=`ndR>Z$qQs+D4PmqUQ4xv3>P-kds=rzj}nyGIyu~
zC{1b}w05t$g`?tBkV-_0F`65b-0EcgT5A3&-MKPHn=`waJe|jbl9-P|Q}b|fHn%jG
zR~lR>3J7pmlrWD<2>xKl;vkgpY&0$rJ>0SqUF=B>JX<vIxcFwAKBg{vj3MbLw9cH=
z*9cygOLg;xDoVU&MSf{;n04$B`i`?adEQWMzBDIIJpndYO?k`{`om_?Q1z3*@bPHM
z4OLG>hOzcJqVkfBjX`7iY%G<5jOp1|5#s7{LLP(3B@7iP1&Y)v!t?H+Mg0PKI^HX%
zoB#8ExHnXa^};a`wY4YxA-q{Teg&UhPK#puTKnBr!JQon3Lz^Ychwh!*!OQ_e+>H(
zDR)!b;f1xOA6|ghg|*yDI#L@re4E`HAr72c2$il^+hyqpw@f0blRyAeR1PO(tHLyL
z+WlASL0~%^&5Hg!tB!wwy_@a+3#q!#Lfh5*2wTPx?@@0el~U68x*CJV{_pz-rp01n
zzh%-^uOR{eI;`%_uu+w*rleE#jk?>0Ce_hD*t`27Qt91g92bY{8YmFiZ&N++#X6`R
zpU=48gtb!2#gg`5_o5!o01i)J@;K%SPU%)OwVN}5z>WinoT4c?ejkpn_Qk^H$AF<}
zj3}mkHzJOlD-%$#w<ma-bF(YUx%rkX*R$}E9VKH9`3Je0Fc&)JH*#N}8(8Cpmg0Qs
zMCayiS@>sM7f&pVa=v!>@1Ai({g?egYBt1t&gcZ^{bA?kaOf;I8%*h~GcW<=#3#bB
zGSQuaJwlT6?j6y;l+?wB`gk}3lh+aT305fhzTVpRc^k<V_r0Kn&@c?ez3TdZBF4YN
z+6Z7-K-VdQ(7)wvuREOO?*pH^V9!|hz4d>N)&DUodO^XSNEOJ+*-?kXCq12;cg6ll
zxYNS9UiCx-R93)1#%Kp)6r-Rw)LB5<0C6@91PYZ0Xyhz>i(-qJYvy>quU~YEhQx8m
z$+viR>dCiu|4@#NUqTDiD^N{4o;TDpfTXYxC*S&_-4uO!<BM1f`VzknsalZUuKMBR
zlh`Zul+M53x-Y37*6E+e(?8MapIT}A`g~0K<N*g~!{p$2xA}$Z;NRU&<73w|6uqnd
zi=H*pT+eW2(sXs7r(T_GM!4RuKU}eF=<37oVIjHfqSc3UFK{?&E-+s-oWE^@Lv^vM
zerLDUhwr~5%VEB{{Le^z5j_t79>eAI*h{YMANQLS8ad98qq^?(^jXclPhX5C)C};&
z+jT6OomaD%HLy)qhh?pcE4s;81y+Uo1)|?FwyOa*?VC5*{CMy|=jM?&J2&5UbCzrE
zxIUB3w_R&T_F+xe>Mxui%Vj>ld^;BWH3Llu>I8?0H$YCk7$}{ay(gm1<rpH}CPsS#
zIZD;;?v{@g<ObJToWa3f%B?x2I5n!T{)~m|$CEkhjo@*fqE}KjRP^<q1ENpdP#H5+
z!acH~g6ltSoOABhDQ~Yx=dHFE|2O=V<u;<3IF%&aJ@6E{iw#Gzx7uRwxmY<j{vY%A
zWh@fEl)uUFk8m1G-9$jdxn>k26;{;x$=}P+u|l#Ow5vB0^0j4ijf}wzvdM9qpSupe
z3B7;MwN2~&=RIq#a&5Ee{Xgvc&;L<A%_;idEc)Lp`u~ch|JWx@m(Nw`nwHP!qW;ZP
z|J7JpLOxeP{oB*k|Gf_CzhA3=Gt~cct^PZgKbNllLxHk`c-j-{@7L;|DwHsWXIv=f
zJee+(4<IINSN3K6yKhpKHJ4+x0B02OuP3Cy7#27+9@i*9Za#BgMy}l)BCfNO?a7of
z#ugt=D<pwE?Eh85bX*Oh(z5G_BUD_$>`T(K{gucvnAMNnf!BSI!xx_)Tc7XYGqKMh
z(!#9KQ#sIA1~0hP2>+5xj1M~?hr4}HJp_3w7m6p(6X^B6+$Q}j?LYy-(g)>&iR!%x
zgz%v9_3_Ik#^Bw~HIJe@SO$3p@5Q<d0haEbu4maUl(dPvEjMtqS3ecaYgfAs+ZPfL
zm>_=k5Pg?&xM25~e*(suGCNM|>P797(HFrG>%QeW2#FB`+ZKkM-@E)H;X7tJZP6FZ
z-d90!6kaiplHHB7P|aYCIv2c4@<;aPR)4^Cqs#oO?DwRey1+pYVG>p??@?}Y`(Oyh
zDQ4!$=41Jox4q`pJbbhS#^GNe;u05Iq&%LmvJM;8UgxCt(jYAKgEvTN+*XvM*P)el
zMo7*u0(no$z{HypL3!u{n?sE}dW|&^ZtikTBu4@Dnh493x7E$RBYWh0B<iijL{Z0;
z3al@&k`#5vZk{pS30I&_d*ho>2bWTv4GS1xu0PtP28aH;*Nw?{He3k^E-_$rtCfNa
z{sWYNba*s!TLNQj{qq#P>Ad4vqA>%50EeQK4*nQg+sIzv%$zqXS~~cH(xT5xos)3r
zM*7}a$4`V%I_WqrYog=nw{YK9Yc_saw@IP;!QU*`3yc6c;(Gl7)f)CV8>$(bsgHH3
zfy3H;V%@kw==wE)wFE|5o%05_vOlGvCFBJUb@T&1&4#70)pc7bbG%P+KBo;6FEFv=
z*b%RbA1-<y_i)rbq%*XMSQ%G5imfP16iK26qKt@R7x1Q2wG%K3V+a(A&w^Azc4&*y
zq``k9Ifip`t2bon6gZ4iQoGgpK*{#@Ktt(t|C3lk^7@I_e5?f27%c&V9l&3+JT1eP
zV?HkxNBhV|3)ZRN;~T$a7q&{JkiSm-gk6FzB|A`=ETO8b7MG#Lpy*%lZi*uqA`b+6
zNVL@(3K6AR_c6h<q46?&FzVHn!qUC!@6aneD-_|$UQh6Pp4N2UQ72ug#HgyQ<2i(^
zkG+GVhIyYJRM=@b%KHo{gNpi|rdvG9c^1lcxDHS54S1YA%rvIh4>cSIt;9>mkb2M3
z6<FkJQL_PuM4?r@$lb-^8!p=A9|8knH(Qu$u0uySxvt(xvtyfWI%6C7g9+=peIzWw
zCw=01ui6BDfltw&^C6~F@DIw6xB5PQ%VScB{4qlM<TV!^)$GQvkr$bVZ)voJK@m*n
zrtA4p*r%L22z~wstmj$?JFPUYMWOU}bm<5_P#PE9QTiH`PCS;CAgZStj)tRfz_HNT
zYNITACZ8qX>W6&w=S9!AQkJs$d&eJdvP{|gze%G;ADj9mnpNY`Mlza}*e|?lx;{09
zMJ?Zc3*rbA{OeV@MrcwVt;9$=515vaX!cD<=qJxW;7;5Ob9!HVB0ZfUI^TsJ^L9*#
z)AFM8WQSksY=>UqNDTme@rm-&P~M5z3l2$3bS$>+aU$B;@DX?xcU{u9?jQO!ot7!6
zo(yw&f@rB_N?+7+G;`PfRs^>umBi`I2favWdP_iWK5%2THGK>|k)B=>r#Bz;mPBs=
zSymlQFUn6t`O8p#8p@MixJWJ0(VAWyV<*0j#|(}<Dzu_1UVpQqWS})4w2q0>YVNYO
zmk&mYsD#*Fx85aWJVN$T!2d*l@ctJVfF5FBb4MN~ckEZ{2jsp$V|YL=Dbsj7BmTZ$
z${PVV94Z$mj$)i)-Dw0di#X~JUF<uq{?HI-APTVQ{*`MhJ`?*f+7ZfzQfW)ir+N`9
z#4H}B{-B2loV9ufE9aU<47yVD-O|CBh7kwrIs}`J2;lESlni0=<WmG#L&$?1`5cBR
zZ%p9cqufmHIIZtj4?IE^q`fl1ok3^APe3oSHaa)wG`!$!SixkdEX#Z)dSXf4v7+U-
zI5!W+0nL(z1HOv^aNTw~{=K#)Qc~9(z_)j6_eN)0=gE*9w(3Rvd$I4u=)}5}zZ&AZ
zsP1sS|AM*|_+h(qWlaK8x>23~n&yb_?3DX!Jb$iZ{n%|gd48juJlC=RY;`%hAlE)t
zgF+r@i9HV+CLE?$v7VX(uY`_8;Fl8DybRk2uYjS>H6y@xGCj`P3_N|4iju}t+?v4i
z4#`^+Pi94)2LXuGY$K<5Z5a~VuSzy#<Zyrm;~d6DvgR)O2Tm0&zwtlwcWP(+9WMOs
zNB*86{5^%N6&|Y{Z5DrfJ(=KWuV^mrE?HYC*~$TWQ|zvP?w7Aq{1ai5Kb=MGD7^A&
zvB@yVFF-2s^A@s6ldT?lI32S@hsz-#bp&~-<85}N`7^*86ZTZz=UDy0TH$W$mA8VJ
zIvohC`#VNROuc&dA%H@s?gMGwklgp#2HzZW25v`eSL=}L#UXaDITjH0(+8<0%|Nk`
zwu9%r&x1oH3AwDe3mp=Ns4J#y4dyQ70bhSngcF>bWzNHbAAvR9ob1u>SK~=Xq7G&P
zd5^))<90uu3fzSmnw_&kjOsm9v!BQ0d~yuWO>7IJ(9`{)xnOe_P-X6bZu^eIynPp$
zj;R1IRTRf&QX;4JM(<IsCl*WfM`MV#iH%IMI&t1twu<FE?Cc27`zMQ}@6W9MU>)*Z
z=AT^VUphND9-M%~-eI)>ibTRjW1bCdqMv}p=K}y@i+YkbG$0T2cWH2xD*q+Cwp=_O
zqa9k*oDI(}>V;FUCP^N8vwb$-HHCzE$)uS1BMg0qi(5I}u;Jz?h#@>S**xqrce(Lc
zjUfVU;kfq=0dH2IWjoK?{O7`ag6Hgv%_{<BThLf!4hU8iKw{nJky9n|cEaq)2QVX@
z@<-OcnEFx{b+~+^&q!lxXofWKgrB+v9iz(uUR=amWc0dD0}QHYf41jGR2^j91Ue#*
zflyWO0Jt><OA2SheN5kpbmBS>!l`mDNEEq6J&!z5S)5cc+u?WXM{BTkCI^tZ%aGA0
zJFoz3<xW+`7e#I+!LU^@gJJH*%HogL>O;kwuM`z~SiITzBO_Z_UY>9Q(-Q~OcVSo2
z{?3S9AY5UGivKYKoq7|5YlqBwg5)M(OD!uL>ksy*XOWi_Mna#H@!-`T@MU=H9OKlM
zVSbu^NfPUo^;jEVz0!yetXH^F0Bf;cA>5&E{{^YpZfuW?cLwUYa5XorVA0T4*07FE
zsr7&a1+p!hl9~A{04R|<q_jl1=%}-V5(xWGSM}^@sQ5v28hJQ>0qu6uD+q1cD5zs9
zAFT(%F6Z6wrMZs)OTxyzW<D;|u~2Ey$D0v*)$5=DV~F~*-+K0GI%tRl;6cCuhNYI^
zl>=mdAsDB@?kC|;MLx&C_p`9B!7a?KL~SX`-`Nv0AP{+dCPZ9Z+6<PZ&8dhN@TIRa
zX-G+LL{4MLvtL~26qA9XJAo!v42FnGYml_#+}ux}`Kd?Ax}(F;Q%=n0gG*VwC9oDB
z=sJ$Q1nS~nL-z?0rtrXM8n(1%NyV_*O3*3rrpiV0hv9dpYXNA{SlG4`SegTWA`(nj
zzr~B<<lOhrEPaHa?ls)NY;_21K}f{ea3V;-vLGxDK>R$YI}DgKjdvd4dTk|%kQ&L8
z^LALor~%{~{f>s$L~vH2!k=N51YaQQuJ_YMX8E^qvznBg1ORsY?K1oUbOfN3mnr_h
z(M8J^fMQXIyMHc)^oe&)Z-=Q_(snrv8Q17jDd(Wjpw1MDz$*|<eT=C<*cu4V2HBen
zhD68BEZ0-K{I?NRDGk+*mZInLmC$@*{R9NZu|AI$Xvg({GT>4O-y|aPh%VO%6neS1
z4gmX*#!8jPvnGp8=76=?WLo|tHvbdRO(`|~Jf3x38OGcQ5SN-&f~*j;){^eg(eBZ1
zSSJn>>mq~I2+1(#3TK?iBc;A?v??d%vQ~_D2m8W$peO74ccxV1{ky#?0KTe?XtzUg
z->cCdC2f|(2QfL+jTt8IY3X<&_Up}0)wfWc#zV}-4B`qEFGGJ4=As=|9OG7|b^HFB
z?5~&U5F@<?Xt6`>(0$<@Y#=;~)2~D0%mz*g>d*weiMdi6Z!U{0ha`<&HT6E3%KNBo
z_yVd<7pj{1;N-nngQTyY@TN2G&CHJU16Pu`QtWl-_Y+fQPk8HX@YcgP4A2U6@z)K)
zm!o%AvMZQTr^M&KpOLVYVctFSnxF6;yoqE${mNFXI&_X=oCo{CCmH(5wY5M54t!U9
zHda+jPz6XASOxCT1*%xUq5-xojENlv<yXigUiI{E*>vH$c=}PMvDXTG{WQKlvWUSm
z4G8dSl&1kD-I|r*ZP+z-r~~K@8je%;V_j+=TmgL_7Zz!+`WE|$D%SajdsFov4=(5n
zw>UpyJM>_cKD9$>nMYnhyQ&I#Df|0cpP-VdSBX_S3X<u(N6j5YtbCOB9>Cx=^W7$k
z@h?zG>TktM19yk*N|hr2#QIu4YLXmMxCsFr2J@cyDKU%Rba~uwdJC{Lzv)`G2JVay
z;`opxa3#DDm>MUIdg@b=>h*P&Yvp^%-4OG)aC8hO?vQ>76TNgc{0Nc}p9<%pkjGgg
z{@7#M%a{)f4e*%0%FCE%kYnlV@ZgoOIK_`&AtMt?>4xZO2Vp>+4Fh73(sWddoQZEq
zR`K2e9jSGfL4|%~u}fFNtactMq?Q$t?*X6GGUP$zlau$SzxjAPtrV?4&raFKV<8xv
zHXfg#`lgJ>eP750hq%_i>6P(#gwvTGKYLLHNatJQahEReHx}qH9#280jK>SbsVaOg
zo<4(Vjqe8<-)UR~%W!Pd7{3FI(a9_%`=n}tE*B^4ZCz>(+r`mwYTNTaiN(0C7A!|~
zzRMz9kI!F%19tYuA8hP&crNQuSY$N}bf_1Rmo%ahpOW=q!5$2f$3E9A#8shfJe8w<
z13^G}@7iI7$P)3a8JJ`6OBMFz2~=k`NLM*jowRZ7`!Qgt<N6a8<G99cSmr4Ho(=3N
zeqdy*ZhV+-yy5qt#0ZW6{w}~D;bgBW?v@ow3%=V|oa!<(1UoX43WJHNud9ASH(vx*
z-yv7`U@R0-I#-L$S&`=rR$xamrv3tS7JYAHSA+)tTQUMj*iViMVJ9XC`!TZ6!G4It
z<^gpp28^J73lkI>Wj!E{H(owTvN~ma==<adV5Wp4cBy-hu_<T6`QkbjJ_@3#9}i-A
zu9<XvIxRglEtitGFlwpO(G?I^q)P76R_R24A!iKX@k>WEeoQ)Gsm13OFpCR?yP6}9
z1CBW&@{r`8sTKi8$|CVM$gn2K^~n2@MdCjw0_*tye_SNaApbio66J}AUxT}P^}b5{
z(D*v>PE<j^?1N&Le|q{W5vkcUZ(F8-Nxui}+BLJ*iIadWwX6)8&oMC7PmpJer|9F?
z4PZpl-o@vig)VsnAc;wC;>mqitTZZp6~pE&@fkRL+A_6|ue#F55p+E8G*3Mf=BBfy
zLh(nJqW`={vhP(<!^ES;jX#PZaB)x_+JwI7M@J9~S6djV3-#J9)#TwCSJR1TLhjMu
zhT%cgx2~heBWpW7*q-(lz3aoqhQHAh#3L^zE~7XRP><iE--M<R@CQrW>q^amX)mqG
zr4g0Ipg`hg`lqO2!12cngMQWN&CRz2Xm{>k0wl>SIdS|)(5eqOEVMcD)H#jZKQ0w7
zenrFP;V*$!W0v##Qn31O<^ppVI<KzjNZ7Nyg#q*_)IO^6)2Mxr_rNqAaev--r|K!C
z<O=8a)E7TY>yGa`>S`3|;16kiS^Ys@j95>n@1r$$$9ymWp5WvX=xcwrLNDTEseoG=
z0sUJO@@o*yl7N=nt$!P0J6_ZDoyz;|mVs`@etR$^nJoeSRt0$M5a3n;%0jCI#6yn=
z*ycbpj6vVVaRO`N#|ADllIN46r!&5ag<PU+nN`-q)m?ofU8{&^2+m#erWiLdB&zu?
z?~45U2h^wKxR!aq_YB_k<5N*R{ZGW-uA@c%A*hiDrhFBY-Htoqygrrk#7Ud8;R;}l
z{1N}C>A;9%&A~Gf;F1$8eGFUN@A;pjc5}D>spq4e4JWaF5dY}urD>|v!6b1CEIFI?
zqS+KpayGmL)<tHr&CAgy_(DM-;vkHqh(1ICstKl{mFy??!UL^RNQcrcw76`9HW7?N
z6&znEBq)L*0u3`nyk8nw>jxeiK|zhm@)IOl$T~oxl_6B@L9es{9S;WpYR+c#ZRb*8
zz!Ev7hyL63N}8%^8-S^0ct^dz3p_ab{WJ#op|izTGOG-oCli`v?&m&gvf;lVFBPMh
z+Yp4V27#y!>(S2MpUJSI`#uaj9V_+hOJXoDu`PP81Gz94)-q;V8WrvF`6KRCsqby1
zI|1&giI6JaLDQA}JE9OUw}CBGT|1zX4m|_?Q8STen?Gbdg#N6+lE{@B@NG|BA{kf^
zPR3omA0H;w;vBx=7O(a*ND|#g+%5HVdvjnFEH1jw!?~OI9S?G?R{`xcEC5^9)1SC9
zM1=IHx`A2H_g?{D-BziKqsi#jdhK>zWbq3&oF{>?4mcxC;O)-9AZUv0^sKPfiWVmS
zP0IUQ%uz-A{BuB2oe1R&Y}&x8m7yPmsTXBZ_an2oRQIhfwNxLENOWK){1(l96pWvh
z`2l*t{ks5HG_n&#s-1x;NJjWiv|P(5UdMm2)ieJUl$_Nm|4h3kw)?Lmhq^)u)lgxR
z=EW<~b1Z?!dGSIVBD{KBIdthbTHhn;qwl3^dAsjd>Qa<SI)!n6y3_Si2K~Mn>nvgA
z-WNLP_iaH6Hm;P_>FxWMr7r5#ZypoAs$b!ga>_G;({p6J^csm~wCA8*M;8Pi{iWVO
zrPBM?M}L_z1RNb&lOeDL_0$aHrR>KczI4DmsxFfDTZ-^Xu(%tcURx){o0XNhdJ+6`
z;c2+L(iZ<|{?`oa%mnuC?dMUqorhry1N#@WFJaG_I6k0fq~9UOdG~hb=2y`W9j82$
z!B@nxRN;5!Yc!lFU7uchKR9ib?x9O>yFj<K&}z$%w~XYmOl+%_hHi(8b<Bs|I=o}c
zlNQ(l#Ob?eZ~iN{#Z;M~sp$BWt!+txQSuzG;D!qKf=@c^zfO3Y#t-YfAb+VhecJ;1
zTZj2#DFz6_;w$h0x)Bbm16qC5#oSuPOzp5Psl6DbZxF!6HI<7h=SZx4fv!FaA5K;e
zEWtN8nd)YcgK04EQ4i92AAhx#1L`&QF7)6mCWD)ZA8x|aIjE|>V-gmUl3lM>SgT;=
z);(C8SN}>kR-H<Q1_P2GG*taFy>0*&oNOC_CUB^6DHe_fe&PzNjO4=+?l4-%nRJ{_
zY;mvmOfJSAfO{R-0dR;v9g7(@j~^|8LBu9~Ae{O9=~(1D__5_G_Z8DUmrsK&5qL|W
z55VQXh^jgGD*k)mOnhV2eIy2xH$Pee3LhFZ;czoblo7l)JD)Q=_P8339*2W^x8et^
zx;r;dniM^a-JK=uz|o_<+ZgwZmE}9PwLvt}e=Y=`g6iQ6mDvB#V+3PVM)4+O2~ICh
z!V_SKbK>ETW9s{24J^i$r~?hzH=uAAXW#?u;J6O#%);B;yz(6^#RX3KAzwg9;%0K0
zw=v6gpgGGK*Z}hmkqI+`A@*1oZnFvpW&<1U72<K6qdlE>EJc^#p0)`e0%3f3gLsN0
zbnCw%suf7eb|QTs=3$m13~m38=&%PvAhr<rVTJ@t{n6u{4ZKN%n}^4vkrQyH5h?4e
zX=H%VC(U<p-mx7j)oJrze0lT3zohxDzH`wu9u+v+t&=0JN7Rq+Ak6_ybD&lCfE}?M
zF@ZEe<@tw~hD77!>8e^%8{DKhu{968Rex*XK*7|mMQa2tK53519L)A`oX!$ZLpij`
z@-HY<XP_0*i?raM_+gSG>eIlNkyyW#BszTaw)A4HxM&KmA1~H(^IBBKmXM7s|70V0
zcZYr{cAZah;jIbA5t3bNM~*sHk16DM8<x&Gw*{19mg*1f+nMF-#vde24F0syv$eY(
zJ<dQkaE@c=ERlqL>_k2!O`)wcJgO7~(6|h+V+@TOWUOxCFbe)&1_;cK`tx_dViGVC
z{iK1gzZ9k4d}Tjh0|n?L1&20hQq}2bg9FFk+nw{Sd|4Rj!V!{GfFeQQY`BZHLxt~8
z6rQuTekJ$yH9!}<Tez95H6+>WwYpg{HQww4sDr)QjR0Ync|CK2_YtKxxOD|T^x(sW
zPh$K@p~UzrK#wv0bj|qqBRZ}@GX2)jy<})Y^EXJVA)*k?kL!_;qsk7kY|0`VlC^Z-
z((YzoC9DyqCpmTF%$339JW=-?bl@1D9uC?xMEx)oVYX@+oFY6<1hc_@r&YEr3r-BW
zFxkhIx8r%i7lrqF5SzN)py0E1ou#!F$;VL9M;$wk(9Z<RR&qk`PybT`+BL2F_#s32
zj~Xt<H58C}r>F%YTgsYxx`fmHpf07=EFiO&)D8)pkNmpVYkaqANFqRyt*Qia_7kh1
zWbzCioDXYCxU57bB=dXO)P6j{VIZpvU%7&Rbu~XXSvfo=stmKCm!xqt8>QHPsGkAz
zqej$`-p3kAw)t2c`xjTg2etY=$eOqUBWLSr{-qGk_W%s@{{+k)TGutTFJOj<j<*Xk
z9kM?gG{%Rr8CWZ=hKNF|pY+A*ZHpvo7Gih5B`^bR4uwM?-N=@1=-3<HtemrGX;q7R
zM^$y@P<RaT*m|?O1s$CYXJR&;37i+WX?~0QS2wf3It)cPPY>Z`=m$vvx|i*e-EsWV
z$!9#~l?SD_`l@-mU!4lN>EpQSk7fIop-K;zguLTb&P`O|o2vWOzrbCT@7aWfV9b@(
z?DL>bcbDw;S#_KZPq80XQ~V{c{pxY4rhnZ4O?7t$xCtds;Y9Q04V&m-*q<9=<t=$U
z4UftpXSviT)Ek}@m<u-($2C#Vl`N>syc{nx9^+8hkGqb8pL4GH8|Gx5@i-Fmp8^4-
zNru-?HA~=jKLR2>c&WsLCn19(e{YfU^^w=U15^M!*o{}IHW5ekWb2E5-{2#4)+#87
z$)DHKI)@V<D26`3A!S`KL|&H07@C8kNZCfl<pQz%Dy=Irszz%#Wu{LY4Ag+K7%=F&
z0eZ0Y)^J)Ij6f^MdHD9!MIZJI^Wk>tBV+W(GX+(gfgI$j)4>n>f)w@lar(eM!!Ujn
zUYHLViX$rr*nirwd{~!Xr^_F~$J2CD^?~=Qj^%k$82G27pt^8nhvvVnvqv)fc%6NU
z^&`etg1Reo_Jz8&siWBt&jL6)Hgth5`aXWANo~Ikt8~WE7$6JAO{Fu?2cXDA{&NsC
zNA}|%DIs$7-(nM?e--M9JqpF8=M2=bqk0r@w=)pX=~US{1B=iAirGZx4161~G|uyq
z2&0joLDH%p`fh99w_5cQNE!{kI`wb#%|=ASt<VtFu7PQkG%Q12<nN#|S{l79dOl8>
zxTBLaPr}f^#LU*>%UAaSMHJ70lVRz2h7!-+z=Q1QVn(W2s$HYl#}8bp2t2CU;rK!@
zTl0-G@LLVe(_m|YaR&6xTVj532DV5!`ylEGk_^2#QE%Oh?;R<-ixb4#-~saN3~%@W
zGhRSOD%TsnOZd4!tbU_wrIFk4E%LKb=cUoy;ANgq@{)u%OeOSs4UL-sXJ9;e#((Vb
zM(ex)M9ZB|ToO-)YZCYI^ql}a@--3;@&R=vI?j+UBIRRvlmUSOwVKRG-SRw!E-~wM
z(1Bn!<E+V3GSBt{Z`IGxEDtRKQ#-tO;69A$Q{(pkD*95Amanp(h(Wy@y)*CxyE+&2
zAcO|Q=i`z8rufe1ZlJfwjPHET1etF9L}lvPhh6?&pf*3TyQ%7KvECm{{C*quGxzB&
zP3y#jS-OjDGYb3w+UcbEYdbc-3*7k%&1a!`kw^M|Tzc9tU!_R#qk_G`VLJm$lJC9S
z?pNYTP4n(?U|afIO6nZy4@3OC^Cky4TGS=%0AXAvOW6OuL8u$`K-u&01&8^0lChS>
z5W^6(wx=$ouu(rLCr{)_$4f1b3*6Z-ok)WdN<-Br1Cd@Gw4&p12L6Ke@062XZLL3)
zy}Z>3^(rocMXTtx+Q@2+wG&Sr@W^EX><h779BrNrW5X$ZT`({UkS&gV4#&Bzbz9k6
z{D^k3qXaRr0P4k}{Vb?cj;u#>vmN1B>=DOCjFrGuD1Z<H=AvPvptDawv&ex73Gl)K
zfY)1KvV*TLd&k!1;`w~Ou5AX$8r;DJ7Bve*F_5T#haN6RZp}c95<~`s>etjy(BWj2
zBo}ifgz^xK1rL+rS!8?zQ}DJ_Fhu-ovmz!ltzWKsUmXKCBR>U*?D-wYOR+onWv>o5
z-PF&(jHJpW&ezp{0A2qiS=~(jL<~H`T^BOg)P~kT$^?vnRyeqK(zfC;OuZiXGdi8D
zKGvP_Wt>Ml6qKlH1*liYke4Jb9$(e^F1j*1!mw1b$Ty~6Yu+oL+q;n4fwByb`@H8*
z@2hlSJ%)rDo6?2<IG&B*%p7CvRXGS;Vd-`{!{-JcQ=OomLnD^G#t(yT%u4<~lHGvE
z<L&YNdLw;|<GnQfqs8r+-b;J^YrmKFg=g@;J=?#q-b;fu<RdTcb;<YA4)`$O<h`_i
z;k~r823;CDVUSV39pi+nlF|*AnkNjxtu5^K98?$9YU^O)l_?o2R4*Fv*VGc|syuWj
zuT^a@%$_j3Sb_MDj}C<9ZQx==U+6mCkjq03Tsgr*YDBzWy$ZwtxALJYyqI=j{JIHW
zh?C#ti216$p&N6JP<d{&*orsty)vvS`Cb`@xV%?pnAh&ZPd*)j@;jQlGG>=&@ZusK
z>&Nw240QcmnGvkrXB2(v-^zON=gLY!L-c7>0n(LCxRdQ{*akEJ$V+KB>%2ZX$sXUg
zy_e<*mLKw%v%mDcw9=yY;1;F5m&S@lXu7<Y_EHT9h`*PHSBt@s+3T=)=&zX*hRG^5
zt8<L)|I9YBaHabCulQaXDYvk7JcL|^Y5K<gu$Yg3n*wZ6qxgwk9Q7E;v0I5Ye}8o|
zlm%Q=S!tundXN87fYrZHkBEw4CBEdnwACLda#zkDq+LDHcN2Zf6Gs@E7FEq%xTF@R
z=ctrT__CsAXf0k2c0bQ$GUJBL?w7HQ5H;n4Ms0`<$rdS(9fF7L=oPLW_xIpQ!cg&u
zA+qs!Ev8ghV{W>Hm%@DG?9ni+ZLr#g=Fd(E=gaRR;<ur==tgU3ZavEJ^@4ZRy!&J;
ztS`gap5Q6ChEV+8h0?$gOde6Zse?O$zx@Xb;l>mbYu;hP*%0B9gpaC(ia*pfIKS#{
zyr(V#O`xR?*SGNvHzqE^FJ;c_4+rnTGNf@%MZy^u7}oilknC(=EdG-UV{NhoL%M8f
z-Iu?Va5)<b%3XI<g>^(K8F~s8)??vkHAG&hA29e9D*QFbw!rE(0g0Eb^8YP78HX^E
z1Gqr)KF)?g>xMO+wH}Wd3M~<gb+v9#_J&wKGK#tK)+^r50LPZLqqd}vlXS^Hp=meW
zNg8A^g0=jIyJ4Jyry+!ghi!2y#Oisd_;{hQ@Y%CPCZpiwf+|k$pemmoO+MOyJD>ot
zN6b*){Y;3H??cfTmo=fSxyX_OO+R~xsIq6|kq5=4x|vV#f!}IA9V!OPCcif7DvNDm
zC3Oj<ivv$aP>^7Gl?Sk1&yU9*7T|14pO3g$1Ik#!zVJU4aPGNWWwA}$ulIOP;JelA
z)TTq?c!<)VZ)B}j**bPi7>@m!x4>c9)z%RCjpe6NA2ci#qIjL;5dt_I<oFW@(odua
z7GZMT#6&8^MmPh0;N?c|>YW_lws-?~Z$rfs2Z<E&VH~25&}B_$g?~_z3$n;gWq@8}
z%UV+%ugQ(G?9Wkb>=L1L5gn>kk;Q0ck%q>L{f3KJ7E49!9}=AuU71>Rwb}p#9g6zS
zP>n=<a+L3af;VDh7Dngj+DkS2{AZDCM##<RsGld~G0tJWkQfsktELI&L;>GkKF>>J
z@hZNT)rGoDydK!!7myT&>!0y@6*{37Aus7}b-e$`S<x^zDbbFUv!88zsWnvmq?XNO
zxJCMh=w!^c1RRzI!7COB_B!$pfG_5Kat;i~B~<vg94XcyzmE#Yw@(_nK^eSyV;2G1
zT;PeGmN<`wqknl)>Man)AKwHC(=`d(F_9qjLF+mYTLbjeuV)JceMEnDr<j+S2HFNh
zeftSn;5CH7;X04g{d~~KG4a#1L39{K1%?{0_Qi*r)4fl%-H1Wd0EL85B!}bPV9sXb
zK;n`{Xh&qpvnM~lN#HVIL$ZX><Ms{>zkvm94@mSwTHT1e$f=Se177{~ahBp~6m1Z0
zMyEJUb!=`}hUWSJj)^P}rc{_8%;$<dRD8(=GOl)kaT2yNw4K6SQm20OJCd%26_keI
zv#FP12=zIf@w%=TlRI{CsJ<{F(jnW#t1Y)}?hT+R+RlaekQq1tgtVQys=1fDNKgbd
z8B2PX1cZoFFG0$L|M*E5^g|R9KdKZe{@MA`i5>LBFrD&O3YrJfxE6NlD>;Yhf23Os
z2q#N~mKb1wd|btn$-$JZ{w|RF#5e@SMtb1_PU3|?SrTkTe#+2t1{nX-2huopY_?3+
z%i62_H0}5BmH|!l9k5%5N??!wm(fSK!qhK$u=hjM6)e*~Iv&0Oa<rsWuWbO%L{Qw^
z5xtuK%+S?1YT~L3+mVmUHpo0J3f0~by?|vb1TSqB=A>#OSn&=dM5R-|W&u3|C}(3@
zZ}rn4YoT81e2{1RJwSb4oQGzT?BP$+g=WL|pN(M71ifmh#30iy34ah9cWnLqLF}uj
z$oeQY@_AqP9p2Z)yG1!zFY^K>j{f4=fn$8H>4gRC0?t^`&x_yJ4Hb5WRUk@rI)fmg
z!ou^c^l_3#M)(6U2<KyecLUC)tpgJLGQ%!x17NY^4ORjhU&eKBh~ll81Ms;(Uhyie
zZoi`6?Pj6)8}*z^$Ac8SqO+b|gx42_0>JBQB^@}ha6tY1h?pGc*gAb&ZKcqTe#^|b
z1gRvu__KP8^zo;%L=a1xd)D@2I7>zUh}1e`xa#^0;!yo;*!SRPsAZ~<wq90U)7$d3
zs&c@RlR+|GXT9%o1l@o96d77`@qz13YUK{vB3y#NcH3#lzD<~1xE9$6sK+oSt$P^u
zcqPvhX1Gt_#Ti>~sQAD<qK~PM=1~uDr9pc|yyb8c6f=InpGE$NBJ7yl>BVu?W2ra4
zPXHf~*966Y(WWLH@SQKu7##21-0S;j2g-z33<~S<Rdi~<T2Uzqh4MCq;!hsbA1bNg
zLA<6<olAP)|I2gdJAJ)=3TuPU>|e;|p6&CUZ?vvHO#cbn_VwX*RLgrh51N3V+QKTO
zB@eA_LRl3?JBV&hfI-Dwno#l2!8~y7u=bX$bN<(&P^x^ra&$2n*F<%kB@7k!)YW^!
z7Dt~IGZ4Rd(5C|qF<xyDx4H>=Ntx?-i@ZP4$?qrPH|*85IItj*dX86I^22Ct{TW4)
zz4}zDM)55O3!{f!K}}q0c(w@cEDpF<!{yMvY~nB@Y|RRY(B8#%|7aA7A8FM+31gDB
z4s?1t6qnv~VQ=n3Ua~iKdt}8+dGxd6G1fNK@Dcl9-F{2C`izCdQ;LSd*&xmkW`6{^
zN49FvzbIF_Yy4sRlyzR_Gi%l}SlUl$k-#RfU;~g=7}&(gl=qKa&)i+-sAJP@UD%G~
z$n|Kj{dc1ITwG|63J0yKS5b2l6BrI`2zjz#GJ>0=9Dnkcw`MRRK()US#|}7|I#0xp
zU+~vf>X~P1oMwm<QiL0|u4e)u1x65#WpR0)XzKlztErdEznhyXM(niR-97#w7x`|R
z3$aerL~GJ;3Ie}uRGLDIX<Zo5FFaX(K_~Aey*P(`)(!#eHkWeN$p>knMRL#q>*NE0
zSjx$aF6);gQeT$A(C9|>KHLQrm=B~Fo4M%hV^C-e%ek17qVO(OHbVS`&07551>U3p
z8r6s5AL+%AwZU3!F`gGzZpVyLXC%&wXuj)(GtP>POt4+J5(yHycG>WQDZcU6@;o6h
zsfhKcbjf@#dcEtJd$f!iLH22y?4uw%@<U+aZyjsrsuLzs^%6|%u#(5F9JmIzjRXUg
zjg>~ok|5pP-x5n)9mOY9FTuP_4nD{|E;lCe8CJIXj0{d+P65RtAtZ%?*=nW$ZEF3U
zFCZfqppljQ_6A0RY-yas(X9zkuUW%`f&SqfJ;8u@MUZL_e9xhWWCoh34)|djdKlgC
zr4bT>;SYoXSF4CuUxk1W2eC$|_+uRsn9hkskA^<yh=RBTvPlq!iciwOi*zvx#Dbhb
zNT~Q^<O69d+JSOq3sBgsirA{Y*1}W1*L39=!hzqnAU=|Xu|uj$&31$<Sl<<U11})f
zZQX_M4rc@R3(&zfez5c0QYeVU+9Gv?Sls!nf@XZD6qrn|b;=!LkN2EEjsxCx1B2jd
z3FB<MLXXyI#$2+Hb;L{@J4+ZdNFZ^>9M-^-bulu=f@Be5kyj|>{eSI!dw5jUx%b+0
zNrq%H1OWw!b+?C`HW_9zlgUL;LN0)WixBWaG)!jpkdaAdI+qaIeDzwqfS0z4H*C=&
zVx?Xx-VUi+<xup{ii%1-oO94xOf|LmX*t+Z_xF3(UTbD&4Cwbf{o{L{k9nTtx7S+l
z`>ywW*SoH>_Z}MCxEXN65;S4H8%VvDbzGHteII-<@DEHdxcw{h4ZJmX1NuT{Ea{A1
z{3{1<J&cj3V`|R&ste&`_SL0Gb6?R7H_~!Ewr=SBC^W3^C%I;UFCRr5PR!Bsw?2-q
z@4uGQ&Dd`6roj8BFX;P-n`XRcWR=p4^)Su$!s;t9f^vNgL-oBd=})hKM+nQt;B$t6
zGfC#pNhXd?EeM;B!gO^M&im++k1oQ@DGW_7%o&+(AOk;|L6pBEO3z2`kIGH7|6-y&
zjnm>LaC{$1wDd*^%nr1Dpdn$Pf-;N6t0s6sexJ@@{4;lFyhAHDL1ktmK?fn3OUw&^
zy_t5}Y<k*J!}2zmH$+1Exq)V$nH><)&kp#F9Db;>9p4c^MRCNkWndi2hKNt!H;_i2
z{Qe2QM`yghaNrx5&{}Tr?P_qafnU<(hTy?dw8eAqtJfpo1pPG>DdGhbSJFtB|NYF=
z;T9LG(XIpUp|P+~PGSamY`}g8S;ZfEubMz(R#hti1Hm!s{JB#fjEI*k=ie@W|JzMB
ze1@g*HMm>3$@^!yo<4t_|067e!TBOM@j|%ywS$=t5o1n6-ZtbK(KPT0j$dwgX`n8a
z=Z(w^zM#Z<EBotx!hLg$qbDfw<|X+|e9d+9r7-L)D9An?`HjEf5h45})=tMu<d~D6
zlO8zffs-CM>4B3TIO&0t9ypE%6lP)kTl(H)drC{UYsq9h*}gg%O>4<)L02%^&00G(
z6<_HmgUL1Oq?Eckm{KG0On10k?Z~9nSUebxcCS>UX*H6JcM(M-7>#LRCT|1a-~KaI
ztx)H7r_#Y#jL6*!np)LFQcG&z$wX7AG^KV$S9YdV+;*Q(lUgbs>(SJ7XEX(>ct=d@
zazg|@lTxfRold0crcGNJcXvfY$#^OrNxMVwu4$3gkiBZ!dGtRTo>A!u_<YqK*06AK
z)0~A%TH9L}*Eck=)?_e5Drnt7$PKGAN#;<R=D96RESQ(sj>r3EEnG6U^<uV^v#RO1
z8c%55>X(^kkQ?GjmHdgiSSZ-t9Z##Fq!vtTYAPMBtLxE1X;8$1$(33XmIk}ku3&F_
zD*9b*>RIHxP&}!HVsz^SYv|NMtGJgVY6G$xS{AE`V7gP)dZVdyYR2+OsY}_kHr5;P
zv&Mz3@NN6zr3+f;&TqoghcajW(w0`(wXCV7y=CFTc}o`^w?Kv05ffw|Tr)otOQS)1
zv=|yYzABTTV&q4Hv6RLdqhXayi$uG_D(RtyqtF|st>H=L@mBi$Re|c7T3O%Xg$rBT
z%?aiXYl)Qm@>IN=xs#dhbhJwg#k(WXmBZ$RwH_mXu|cXDw4_!jvXWd63P3hcf~$h=
zt`xmc1us&mAiq`w6Y{5!StJ?k(pJZlt7fpt+3WTmEt!hOyJxVjXetHQw+p>yq>~wq
zO{Sl%1$`1>#wN#8GZ_7Fs*kDoJ=-q&Lqp(yPk_N?-7AS2If+Cl;E!o68BK+HkWTq(
zJ-w{ACcskZcqYa=f~jbzJ(W(PzPdW7sC9?gq*R^S-kyl3)TygGqoGdj{qJVZQqSex
zqvBj6V)9Cd$|4UzY$lY3Nk%{I&ctFQirTIx7>kC7a@3_V9pGR_l-AX?&u>TU#$xeM
zd$c>HCDUwDNS$;EqxhdXDRq|U^lG%5LIc)>I@=?N8nzOzvRNITDhcYSxS0<S6karX
zAg5gA!w)Z9gzk=BNxdhCkP4gQOAbS+HC-L?SRP|eYNngZ71U<o)KlAMEpDoBY*@T>
z{;VZzF^u9pDZj)B-o|V!Q`D#(@Ig3~%yf06j990^kUW@5XrX8%3caB=A06SDG$SSx
z4-AjRI)b59ratl|xxw^M(T22H*_ttWkSe^ZiT0~+XsJNetD_hZlB+BDxYfR-Y4NhT
z4RaUF&a*L_Kdgml+MXyM9`p4W#zfLR%Wz!dvJ`5*NW-)|A0MAwn2Gryp(SH$*i!13
z2yJ-dOH6FeLcAoRQHumIdSRGMW6<ZpKXi0bHTX<Rs^Pen>YhS`7*DAUg=d4IkcRG$
zd4dNdHNrGLN4iZx9M&*rcHuEFj5}Yd4?QMG6}#1@?w)8e-rc2jQ;$nVY4%a4Oe968
z$oe1va>63=<v5zu67ggj*&S<C^ryt?Oq6;Q4}t%O4KS&i)Y6$G8Ma_)ON*PiF%Fv?
zD5k|>yf?J9+%$N|cVm%=b}`wtHeq(3yI@IceG5%)4fU<`82+OA);SEL6|FhiWjkQv
zF^?$0?ll;*I0*(Lqp4^Rn=&ai#m$#K`2sN0q1~1f0p>cy4IcoEz^PDEtD=bn!t!hI
z8p|ZPh@i30J=CDtEemJCpo`lVH_e{Aq_t^rQ=?&13#MTTeLhBFYQgd;1pjIkqK}58
zNm;86rL%VB+r-Qg^9%EMn`!Nb71~Pl6fEDuvaX!_4BriDo5rT*dMrP(ZN!^qS|*j8
zmg)>9H8C|CZHw1Ph?hq&`9cMHDMM}@;r`YXhMvgBZPVP7H?`o!%v^IfU!$4mjB%1r
zIO?RZDGYNQ5d=i~|Bj^#+{fpTuZ|WkjAoP0PE_Deopko{isb6vRN_)x@?@Q-qBd9&
zp)}2mMT{Oqbu)U_6wjG5dZKiVo0;Tky;|PXttIi|4UGDH4UL(!H_3+zx7#g-Ol`Q4
zXp*svR57E8rB1d*CEv<eyd#KJgVA63u$HAUMx$Ywn`moMht*1MfD|Hf++u;;p{enX
zG<F?Y*v+6g?}2=eK#?Y=f2{CAST7k1Pq6TL#H}vTXq^Q*V<L=WUglFfjgwde3}>`a
zBnO@^X=N0r@FXxoG8^z>xtSd<h>D@9is};y3c{j#Q=c?45($+yTdG)9@=U%3l0tY+
zQ41`<HUKkPU6I`73|YKR!=kVv7R+>qIxCFvqo}AczS>ytkb}8U(SCJBlTwqxKs9IA
zvlu}yj%U=a478E<awe?Cz-Mf^z!Zy$d5bNe)jf53CK{6L%^UfcF)Cm~*AtCrQn58?
z*<g5RlM$Pn79)JW80nR}Jj}jwm1S&X8n9T6hBaCR^Wc?BaV`KsJggdM6p9<noNJ_)
zE4r~z<3kjNNJL8{9)r{rRs}R(l5*IL!41?wj#CG@^y`HmoM|kmT4~RUP8rIi`3x^s
z5*>Wlgp-5V=5+^GqWZ>`78?BxTFtGtqOuNbqsVS7$uk;O1$BHT;@!Z^mX*eL$U|xq
z9A9c=ENU`|Fl|3i+}yckwA_V9Xsf_`Qar+YaVV2a(kjMOFYg0c$1Z^hF|DGRW=g~o
z4hp)%*b9(>rqD7OpBGMs4Q@8KyGP7)rfA{eCyy@ZgreOLkBw6>na*IGjKtX->TRTR
zozW_deD7z@sfS)Bf>~Tebq>N4>%oPQ2sXgP8w+2`^&Tz?va^3Glb{uvN()lh%~uF8
z6chT_w7{%7dFgA_hFKD0Fpf#c#bMD#C#<u_`_;C{jzpoa^<rN`Jq4|e{d^)G?M}N{
zMFkz@pk+{ip?JAzP`ebtjgBxw{T_o-)JW`q83#}vkH_n&^!Pk}Pn9R&srJ-(YP}w>
z*IViJdHvohZ@^pat?|}YdMdq@m6g6qe`Qr=pt8EMrn1)O@p*lfKA+F;tMUbW)xH{E
zt>5GK`YZiDzu#Zw5BRJ7HU8QvPnEZ-vdUNGud1pFR8?2iRMiGN0dJr(;0yQzRe?aD
zI#3g+t@c!Vt1GL0)&A<L>OggMbxn0`ji<(2Q(5Dy@z+$<1Zt{lYHDh0p<*qnuZ8GZ
zu+<`qtl2|1q2SN`8v{oIDbIY=R_pOshr?hE1Uh`7n%a;r*ijRSczxBC9<2tu$h>qu
zy;&ArSNpZ9+R9L6Bna!n{-6(*ReSxRpce6j{E<qcQ6^Z=nPbD9b+~SsuIqicZkdHI
z5D6P}eKW8PxD(h1d>^<GXv2#w`hgRH2Y^=s=|{@91N(rD!1X{6-uVz~*7Y{v^f|gd
zNcnT|;1JTs=IQzv+;!x{9VdQZ+kDUgV+-IT;CkR@;DLqk6_qd2^~1nEU@7?511I9$
z1KTdp^^1TTm+1O>O1DB^ln(;mr+R?&&LHPf&;wh5jlecw7#IUy3)}|W3>*aZL(YL^
zkb`u~MW~m`FV=MxU+E6w3)VrDmtBUm6Xn1b<ZlC}fll1)vKd$g+zFfxd>_~XoI~`u
zi^*1mdV#%^##hdpfrGgJW+L+2BD%hx@^Q~WE71WRxC6Tn-%8gI;s%s7^4H_0=uV`!
z;alR3gt$FnFR(2E`FI@JiThqRfvya9N!1|TpMkx=ay+GSCvXtB7v;xrlkG90>(lk!
z$S=DB{wzg3SHhn_=T*A?3~&%Q06cIt=!qXV4sZW;{y^8uMuHDG9as)*15O9_0b76@
zfo;G!pg(XU{EPIlbtp%A@Q09#bl;EBu8`jkd<^;J_~N|=>FL1tk)D1h%1=Q%1O32$
zU@PHWXg}Zq;Fwe4FWlqU2rSzKzaqaJxDyxy4gmK6OGl%AU=6VBKC}bq`|pR{l>V8n
zZ$*0BW?er3Y}<nPM*e!>X5gL&b=`Ry(htFZNN)sAM|#i0x}E?Ydjxcp#?AlxfCnB&
zd*J@|J$Q)WFv))cdg89z^6jt#`LUODeJ0ZDfh&MzJK=ZWM&L7){x{eUEC<?f<K}eW
zL|_}R5x5>>Nhjo#??zl8-2%J~=|MuIj{&y>oi9Tl;B??;L<f8UbS-<(j^knfYbXaE
zdlT_?I`n-TbSN*|5517?|2_IGaQ!==18#c{b^`~24&1^375PW`?qa6&mMRmRg^pWs
zzY^u+CKz^bhOSR0ipD~;Dq|Dz=f~gfYK(*E*UVMg>>79e$l}$GE7^IcpL4cvB5q7I
zc-w%R&NUuHVD+xjwU&mGf;p%_l=lH|MLE|&me0$TZ~Th#{;w!MaDsBUh#kY9XQr;B
zTj2gnp&u^GD2<Qu-9;_jD~0!b5@2kC^o<8)<cC)1UJGB@9!0}Q{`uDwt}nRBezWZs
ztL1IjQtvDv$C1x2!rzMX@Ovge+d31^tZS^6*`RF#?W`OR$ap=Q>He`<xJwC`t>^q}
zY&Tmi%Oy4Gya(k6o6t{)QR*Z7N%{_=d@ssRB!}q6;8w7s&AL9~%k}-$T;Fui9h|M}
z=pMQH&IbjOB&SW3pQt_+bk~B;F&F&+*IB*ECs9Kd*|rrlVbD;!Woee=%6E%$3`x0i
zq03Zc?Sn1UUhm-Ab|xZD`e!!x<Gs=@YFm6|#vEv;SHNQ{Hqn~e)*4eJ3nLyscs{E~
zp9b1Kg-4|P1=kd;=h0yOeU8J3u|C{+co6ewCDl1k#NP^t-DQ+-L^;F!is>OIuQ6wH
zKgth-|9IsGg#2paUnJy*a`KM}`Its>^6PW*%WQ`H<CRZGIq82c@wW>3k(~TCl#~AF
zQ27Nz>!&uMbxSGM3qf<7&CB|$Mf$58v|B;jiZ#VjGp)HjPLtG(k?j4TzZPqaDl>gn
zw&e)4<UMmDXlY$^6zh$pxX!j)^EE~v7BNIRl!MNLb;hM;nPwf%$mu{a+Xng-SdU=Y
zVUnBkZy(ARp&SvGD_@u^-zdt5w6AIW>__>`3-kCze<ytpp!@~gJQg&|mv+hVM%YLC
zIx(If#(jI3PICGzGV~#zmeY8Tdj{=h`*S{hQ~H$Zz6i8otbt0=AF@6*`|426S0wv-
z(2v0y=&NK?du<2pQP2+2vjw%&_}O6SOMP-T>|cTLj$R;TTa8gs_DPc-mi<y|v@Lj!
zfu{vy-+RP!;qW}xuAJ^<s|WhvhY`5$wsTB8b43ijlB<L0SAc%%`MUlwu5D+U=ySe&
zTyl_3x01c+CzH`%ZS->8VeGK>StJV|dv}88Gpw=CqB@FAb+l#480j(yx?@;_A0fI=
zO|qr#J96~YPGw?^{vSmDhKb%7M>k1o(t8eQH=)1WOL{+VqBZ&7dYJTH$R|nCrw=@R
zShEk1{8vppIo~`i>md4Vpl`$4zL?tU(P8NyFw-9Z{dDw+&q@A0CVFY_ze;K&9-I!?
zjJq%=(71kuiQa6({#-ne-7~@CKtK7MbXtBKp38^wq`|Xq1=gU%bIx&iCJyD<3Z6wB
zf@jKC^Gt<q2f%Yc)Ab)wJ)=!LM*F=dttUGi*#9g-AMU_dYAZI;nr&Zil`K4-rh{ia
z9*KCI{A<=pjyqRp$$5W^(H^Ty*FPoy79OXLj$CZhIJF%U(*D%&>l`ZIhw=gJn-KN6
zzG7Neen$K#AF}S73%x*2{9`a+?>+&)ALY-Spu833TaQytZIDL!IPBlfL>pxLaxT9A
zAU#B5-6qf;!yfKNGi?K{S*#fu4Mevabm41s9qZJbOst_gAxthS$+p9w8(gdF!_Dc`
z=Cr5m@5bw_DLx*=%ro3rQzmOR=2{PA4&cFn>%nhZXW}tpie@xE*HV9N18ob&?)^l&
z%0w&s-)c#X`35v}HgF8|9}xY#Vd&QmrKdB1J>S7Pk>csw!_dExqbGamOu&h~_7xcS
zZD*S3%`yIzEz3kabbio_efWFCQ*Gi|lw12-uNunJ3Le|lczBM+!sR9&b3I?=;+*1y
z&IcC#z!<Bt?a(6Tlx&nhAJFaw?L@Te1-Q=A@;I^Z$fmX*1kDQ23|ViO)(cK_2rB=F
zS%%3b^L~cpO$WW_y5Z$fc^k?TH{m$|vplihP?6V1^`X4RT+Ve)z~9CmViRckKy#wC
z8IOO^Z3o?Cvn<*jQSjGualCII23<esa5RzglUP%coKh&;c8jiGVlJ0smsp#rD60Y8
zz8~SN%uL61ryghY3p!6Z2pX@s@6OJyZ@(#f5&7zR&~CpSd-5Uj482w4ZKe8tqU*EG
z<wCDMkWsws0S)^pWDHxM<$aLxen|Bl1?@zfbzm2o^Mh%vH?Gtuzsg)L{XleFl3`~f
zXs?H?E6udI@obf(CLI%?JpkG(hNrz;(vprFLF;*Jc%M*tKg!h;lpjEODavttl(SKc
z^E9>`6Xlc4YgALbTZ?o3k?Jc)&^(6opdmh^btlTGyanZpa85K_8&Y`;<ugxEz8>Ws
zlz$tv+5OrDkiW~2zfJIC$(FN6#5UPO=Y20=4!8>KZ<~OLRgT@+wy>ThW6lG5Iu}$i
zzC2(9J!viJ%|6&Mbbc>`4jXY6cn8%n+Ej-azpZVu24nq1K8dY2<}cf)raDaHo+Tw)
z-5B$)1kY~t(JfTR8zvrE$EsW%WH+5VK65MHUP$y$nCNqM2PFsDeGv2)p<mL=XKi<w
z=uJ9VFO(dH9b*tgt+(m=R2sk6nRv{;dLS1sq}LqqEW$bH%eb~(Vd9Z<-FJrC-3$7|
zpf4c3+J~Vx?NvzrR?zRo`RO3Zzi=3OlYFv!Kgq{AD~(;YY7@QLhP#WiN=*filg>WT
zUw=e(JZj=G=JtyvG11bw>|>z4ooH|Q5^Z+pLAr!NdoB7VU-!jLwB|N2t@~8)Y{SI0
z_c@$L<2oCw#vC^jda!4Z-4D6t=tE(en`fKknryVpce6{}3^yn(aI^2Z*+p))$jy+s
zl;<RPCT{Jy(9M>(nFW6+o{KM}-E4)MWq9r!$}F`2F5m*d+UaJO@#JDICMI0d01>FC
z15htXyW9*nK7g(jFCBN2IpmXG*mv-0U7tby@dIpBhU<^b)E}Gqeu;R<Cxbh5{nr%x
zBTe?^Vt-LCl8v<jog;VRe7Tk?mUVDHQotB}kj|{P{|aqMa^E)Tl9SsY<<5i{Kj<eu
zuj?=2+P1?)FJc`|oJT$`g}*Noo`F|~+7B6zK}H#Lu&p!6FtwfaYFVq{vu)rx@Ect(
zBt1_v*>8#?%T7@tbfb^1D@ChT{YhbODIuP|r$n)hvM*I2gQs<6bb0A76xQf^SYgXX
zJfyJp@1X#E<8D?;ud%XEm5J9|*$#{A3k!SMLgOvUMzIgkaX+-!pR}>RSZMQS@RYJ!
zaD9tX{-MHlC~boFPUPLGjCj(@?o)nE6A;fYWhCq><=guewoSR1=M~L!p>;2&mO%QR
z8H$fRi0cQHlAjbPn=B(Ava_R>c{}awCF>N_Z#{K~on2|W7bN%EoydO3j_+nUGv|k`
zV~f6NvbD%t`m)06pETs_%9!%fM_~K-Z3^pg;p$rDLaMi33D61lMb6y?$bYUtc|@tj
z<p;_F#-6k|cNMU^tRsJ0z_wbO8T;7UhhM_@(Dno@lT~xSH=e&kVK*y$c?F-HdV}J`
zvGncAJcK^K98~aJJ-6G%aBx{Yez(FFp#_Y*qK?x)w6eaRVie{yrPa<mtZd0n%VsOP
z!`i{~%7lAb->@)UalB?>|7LOG`ZddvuT_2qgBzzf@E*X6N<OeKt>h&OOFBPR*!OSC
zwo?mx8Y(@lJf&=Mu&b>dPdeC9Yw4{H_Jz&)BL}<DK9#Xw+KX}d@AeTlJJ@pt756*X
zo<b)GKPYtK`f7(0tT#JaxlTprU2>Pg?o!5ayY8t_zQs@T_b8{_Z&&_Hc^~$^Xt)2y
z&R((GKe8*Y+ObwO`*jrSN3vfz<q-?}o$^DY2>x}Hk66yC47-bFmHa?q3vb|sC@Y#(
zcALU3%cjqOx1fwN@<A)R-BR*^l|5{k_pFs2vD`1@-7{CI|EW^)c#-m^W$f!k?3mT{
zP?7Sa-La#HePXAajfJlJij-Fzj%SP5KOC1M=WipND80SNiJbe2E|Y4b{>AJa$bCmC
zeX^K+sC<Cy*Q}*KD`xLnp(Z<I8~Nj6_E%dcWB+M)Tv^P1TyW}#MeN@ToCBz=;Iu<U
zcv&f}5FT-q-d@a}b07kYm^g#|H`?jHEv{z^*q<%0<NA5K>*)gaTRU7VXiC}Nas7A2
zwa>zCu=MgasBa&ScKL-e=57c3n=*Q%gZ<ia%1<2Z4a>cdao9HDHMFCBG+J$MLFt_i
z_6cvt^$sU!?sVLgm0i62eFY=I^A_|LiuqD@H~8<ijQURtJlf9likE+;d~RW{TF|u&
zn#Vx%m}S&@D{3|L`PK^cFIIM?a@r?|ZRL#JR<>P1OI~N8(7eTRCm6tc&m`pxb|0?q
zvy>dRE00(Zt?U!qsH1jvwVnKv)vL&R=|<{%{5T$ZeUstf{#@6utZd~?%BxoPxl;16
zg{`#&4qI5ig}(eC`G^zm_n%kTqsk1Ceo(nZ8TrBlj87HUma!MCXWdZ7uC=+=m9d}O
z$M#QP4;74kZUQ16qt1>(=k5vYH-)c4*oG0#*T%DBBTfb39Yr4~;QG09?4#q^^G=K)
zH<gUuKAvqVxdC(EL!-w25dub`r?ahNN4_<lJw0|Eta|Y@JDA=%?TlR$*p1`P*g1jy
z>p16&6WH&^Igx*K+&EDFY`k;Nc=qV{8!-PvcZa`NW9J$1@9?`i8J-1z&!~UB0iE7Z
zT<Bl#D(`+(|5_Ftxo-qZtiS?93Ex-5_FKl@KLXJ;pMH5^gYw=8w%0cL^%02Iu{Vx@
zAI9Ncf*0+hKX<S<?Nu}&6s%$Fn!=HPppK6&!ftgS@(lkjV;A7-k&*p{Y(?qQh3s*~
zb+C{<uN41RA$vt}{JxOAu5|vfkgc;ipDe^6SGu_ngS_khLiP`9>83(<k8S*~3)vCd
zsW%p~N9|K_{kHv(lwHQIgPGSU8oo;!RL({#Seyqf?0(Bnk<DZCE|A`3nLl7*hb+^0
zUJ0wgTF#9V!pNnhBK*~_;J2mNOvYxG{m)4DOU3z-i@m3m{>g>T;X=;47W(e-7dH2w
zUF;9G(hpqhf9x(?|DYg-U@RE@M;Cj)(7xBj{&%7Cbr<V*#P_+_!y}xVUF_)*Bky;y
zT_e7Etb|=xWIJ4<+*;(^;$j0uvw!Dej}|-sR>F1^pK`Q>{kC}SRu_B1X**P+yyTn+
zIl6N)C?6=9{&5Mr&1JjWrM%#>?Q$tQU6?+HjE^pM9%JW?eM@1jPoU`J=cETtdVoAo
z%D)`OxdA^o#`5kDhF{tQb_nbgm=@S4@LGXyzH9K^DAKZ%E$0W#xz2cbUYhQSqK~A%
zTBOe~ve~&fpQVq}0AtaQYaC_r&+fMk952%9*@mPEB8?pZ|KOGv{+TMu={$x$viz@K
z=y6K#x6*&cFn#dPfT)O{xgh7LC>Lt7#X>&;Y@Fc3wq(dhu(7S>hJJj22E~9-0NXhJ
z!LEmYP7^p)I6~%ogke(eqr!1GiQykvKi+=CKa%e6x?vy4dDa&qT`C--i?r19ut?Vl
z$9^u-*>(`}|5?y0+9h9JfHt}KGz(lNFe0!=;97wj1a1-dl)zmA-x7F8;1PiZUZb8<
z1x^+i5ZEkmnZSrZ+-l1|YXxo)xJBSo0(S{~OW+}aM+6pBiuwgk78nrNEO41X<4#b<
zdPI7yzzqVo2z*N5E`e_eJS6alzyhDBU*Kec0fEf|mkEpr>=C$D;0A$P1U@Bjm%z6K
z9ujy&V1ZxMFL1IzJU+`m%>tJRj0o%zxK`i>fm;MVC2*I(w*(#%ctl`9m8f6fWPt&J
z%>tJRj0o%zxK`i>fm;MVC2*I(w*(#%ctl`<7=KR{XlV1#`|t3GfkKE}cZ%iN%Y$l7
z@_RqFjCp%g<><dYd+0BZ_{M#FPQ~vgK5_XYvgVmn|K+S@#xv3?fA+c3@Al%3Y@a%_
zA>P#$#m)1z5r2m_QWXva{J}^#5~}fN-YQQ`u%lWFdunULkw~T26X^(Zk^gLYVP8!s
zR8v`9q51rg3fv^_t3ZwZ3a?gO8_;Sa-mpi^aE3)o;!C?{#nW!wM=wo0i6=d9(gXj$
zcwlxzL!CNx_JXBnsa~JE((P68D2fk{sd!l^7NkeA#AxTO#;YDM8oN`SctZjn@?vg0
z7NaE-c&s^|*4!(*Gj8J<_4FE3R!0WUpjF^K3kFsFthp8G;7Z2HJA<iCyaZ!SH(qRD
zU^;1(h)Rbf+EGT&{t$z>O2pEnMHK(jc-Wn}@fdj&WpO<46=ZG=FJqv$T(ozF!7Eb`
z8Va>*y&)};Zs!lHfx9D>G9<MJlgS_qAW8C?q*2un%+(Cy{VR|XM}<a3oExuVp@-c6
zIe|m#XPTkpx_FUTh)R^}by?fc*^IH#D4;d6<WC5GiK=82DA&a@donJ}{I?2zi8yZ^
z^1(6HkWVdAX8!GhU!ppM8sAmqrDr3J_XozM{&F88QQ9x}A^G}W0DkhJ<d^#tiHF4o
zfpjF_Nq)IsxEOh)vgDWh9*J_F0}}qp_kR$%w1<%Va^E0vpWu`FOFD_&D5pJ%Ov`<V
zL{(Hs`jehge<7AXenvi({BoZpQSOsu{gPkWe;s&<f0hs^_gxa@K8^TkOO((5L*$W)
zQa=Ae4{3>VKT28j$#4Jl$TRb+VqYRLCz!LdQUkJhM?Sw;N2&rJyiYLX@yXnK^Z4by
zTB6)<OZt5Me<t{)|Jgx7DeyJwpr()1UuHdw3$kDG@B7p!k=T+~VP29wo5!ynHTWfd
zcB+w<|7H8h^h>xf*FR<?rEo8xKwBO|e*JHPmPo9kK%PUWryFwe*t79Nb<pQO@aMuG
z#WsDuF29uf0eJAWewJUJb4>k;`XxWPn>3a6d&GX+Blzc_%>2o3KVqiXm;7=+-z)e%
zqG753(nr=S<%|Hoxqf+0vbBtIaUX>g%u_jjDaVz^&*XkbHTXx#10BIH>lH~h24o~l
zRx^%2IKbGbNd~_v2?fe}W%hVczei>Wl>GVmhE&65BBivfTd;Dd7xjNjGKj*wB>O+_
C*-o1P

literal 0
HcmV?d00001

diff --git a/legacy/Bot/obj/Debug/net8.0/ref/SoUnBot.dll b/legacy/Bot/obj/Debug/net8.0/ref/SoUnBot.dll
new file mode 100644
index 0000000000000000000000000000000000000000..e575ec3e00be44dde179ba3708f923642c3ef04d
GIT binary patch
literal 11776
zcmeHN4R9Rgd46~AbhkR}LpuM-fZ-%#;{ZPEEXlS&lJ#e7fn^IzHjq$GtGku7`0iHh
z?ink8$T858UnexsCQT}jDV>y*0t3?wDT5j6bO@zW+A^7TG6j_hleRQT(;qS=1AX3i
zf9?*Olw_upPRdH(yU+Xcz2E!2`|bDbw|k=>JV6m6ilV&zHqke5=A&Qe&n62{Cog$2
zNsl+a(E1H!?+dLHv!;`tv#l9hFQxN(xoo-VDI;xH%4xHl9^OBmE?EU5vwV5tQs4F1
zE~33klzubU_GZx9E7V5kDeXjQND`j(;ahO-M@geF;Irz*4J7gNnHEvFGdLnNc^6TW
z{8!ID>zUBa@VlS!B+;D>zOeSRK(q{eKkP25mH$t;pGd2*(ZKJi;WMQpW&u2+3;Pnp
z9hmhK4Q5>9s0+d47lS|`{f^;WbEFY{#&+yHWYUL80JhnPQgigfL&i3W77V#hw19#f
z(v>JRM?c!FVc&<p!Wnp|>w+^2pKKld^dBAgoj>`}{kdP^uD;*3b7bHI&#O3iZc?d%
zCi;n9Uq_^{@E14|D?dzw<JS%<z6YLXj&x?)Gac<6*=}~&Kt<3WVd7i=aiW`>iG~}2
z$6ebj&p2$cc!4*CuRl0W4*>HVxqkP-kzt%42fqUg*80JsHO0m_Foh{MT(THx<hks>
zlnz?$#lHdUT9i?gLnv3Hpeb5~!u`7sC60m+WNzm(%Xt})`H}v)!OKI8z7_rZc#QTo
zFnuuceEfWR3sqy(B9aTE_cz3-qv2F6MxTo^-7C}-`kK%`kFfk<@$;z&`}wH05t6q>
z@+2e)y0ziEu_XN+-KeJMG3_NSNqdwV)fjD5*zzNwDS8z06xoqhEk^&LvY+K~rk&B}
zK`#~jM2zjX#_mQ`8zPKfl@{MZ<T2`o&(*Y9c^%#g@m5);uH)RH_uStmx)`fRuW$;u
z&nQ38m{U=j=u2^riwc)$^f=CI6a9m>JjRwO(Y>f%*2vsS;rb#TccF0QxW`>8++W8$
zZUdc!mp@j2g7y{KEINw+M0~ajw<rEa1N-ctNNW>KAZq5Wriju+*CK{qETTQaJt<rq
z64vb$%SP%G?her{qXFUgTK6L6Vc~u%Tnp{4Y2jk{W-s@Pu8Xb{u3fku+9%v3QrC-x
z^LpXl7H$ts&^F4deHfCDK)0Gw%C#6Zo!U6)pK3RN&PP53`s->Abcv=w7b4T3SI13I
z&oYpdAn|HF9lH&9I9B-?HlF8m*|2BNkJoNn<Gk;Oqx~r2oW#SyYPywv1N1NHw?J=G
z9tOQgc^dRS<vXAcDKCSbMvqq0OA1^5lfsq?v1OOamM*d7zY<#>6w6PD<)T=ATYNqt
zK3^7}|1Lf^Am8<Q@S<HIaeh{Eb!P)-pi}$)J1jk|XCL(a7qRg?)aUT~G}e!cPItGQ
z!F{Co+<mks^5c;6bZTI!eK`IG?i|0Kc2~TqhWm>82A)y_-S4YSRZd!nhPwaM-oVHX
zEUB@nhAU`qV$KS5b6QlXa>uo#5({;o(-dSYu;kX`A;+zUoV2cmx>H(82|4K2)Nvb?
zWq~DhStS*4^jvJavcl(HQ@()qR{7kLvM;hz3AopkzlE+PtarUOqJ*4wc_dlGbw-q`
zrN;eR8`RUde^u_$hNN;qsQV`7@+$Wo=q?O(OVEX!^!=hh2d)XLLy$AR4(mdWuS1n9
zXa|&wLmfv}<v7<NCq6@tPQ*W`ToTly`*0mvd641wRs-j(&11lEma5!gWUwu$r!Che
zl??$WeF!;vTD`TZ(;Rh9*;r%gRyn#w`>?Vp(9ylIJ8HN>>@MZ<P*(wWWexW!B@=RY
z#lEOy15RS;{JFU$Wo;wwZ@=D>qD3wUIqx3p3UzE*<=8UdJj-o$mQ{`|1J1MDUT0b5
z*fQWe%N@RDJ?BLo=)9-{&a>>UYoW?<3jya@UR7sV<=8UdJj<)=EUO$_@>7pOe}#t{
zg%;ieJ%qJar9*hSiO>tMkJ4H^At)kIX$k9SM7%}mQQ!u8EZ)=<qnl;L=2{9p1gc_v
zVa!y;8ppMU#pc~&cuMMe+7)=5Q0M{i^JVelNzV56ZN#9^8N{j59fCg#s?ug@>)o&^
zp4lcse<A+Qmb4&d%rx(b#<7KNCKJyPCapkOiLx4H4N42jT9igQ6IWoxE5};U1bsko
z7PJXhp6P(l5uw)$y-BDp)C5)NHqaOy16@V;2>m>06TL`$7raP(&6mWcQ8^P&Q!{7`
z?;>`gjaK>yy-pj1UPgKPvHAfzPH(CiI**>m{kwwx2J}MuH_-L4&(Ib0Q{WD|3N}~M
zZqOk*0D2weK*vS?A<%wu=w5X|JP*-f)ZHa`1pea@U9ch4gd`PN5SwEnIWBdN3%*O}
zJ+N69SrnU-A~_{Crv(3=(67Pf^2lkic}679ip{fv&j@`^T7N}sUKQ=DBLALHMd55T
z2u&*-^9I5Ff`=6D(U3?cMWPGNi+n*O#|0l3`8^8vYf<E<M1D%-UxR#I<h0125y`WH
zpM(7J$SZ=EME<Jae}p_9A(i8a2yIn4&h>)(g$@bL3q2;<<AP5LJ|*~Bp-UopRWL<P
z(Qm61d5Aqn*#CN=0}=K=DR@EXF_A0^J}LOL;AaHCB6vwKMcI!MWe;h=t%Can4+z$U
zE<`z>#{@5m_N3s`f}aumir^)|)F7T4#IxX5!2?2dp~r-t6#9(NC7~3Px-qWXDs({T
zq{wx_$Am5kRpM+pAXFE+B$Tu-qj#+uYbOPt6nt9nlHhbB+YB_ajV^RiBqs$^g0+)E
zPYdOJQv>Fm7@oE?{3Wp0T!vlGa@mJ|CH}kEIXt8?UEavl)lPxlt9?C`KdC(eJQ!h#
zCx5zu@%MxVwVnvIp0{T0DM)Tt|4dOar$_uKRK(E$90ye~N5z2?pemgQPC;aiz{|lY
zlmu1jd~m!ETn?N9r_zOpT){I^Gw?dhAqt*!Rsdgur#*$zpekmbHNb74D(0WHz#BnT
zx(u~d+Jt&4=EF-s+hMC>##xUj+UXW}{Wx$l_9Gk6-krcHx*K=}eG+&DeHwTrod8}*
zUjSZ>-O75<F9ENiuL7^3Mc@{C0Jw!71YS#j0KAsI4jf(JdA|1e0agV3hsR~~8rBqC
zk5EF0?7LV!-f7n-)%ZO^^>|5S_5NO$x=G~ze3T}XW|S1l3Y3*7tI+$^C~Ht!P}ZWz
z(bLzR%VoQB*>>vLqn8Us<KSFDcZ~sOzMStXKnI0yvlVdMC>IXE&MIABF&x*l%521%
zzI-k>Y&vsAeSWB@J5IJe7gX&GQFjf>W`ibo*|ud@TZDW_ck{Djy6xa4j%cM7C?u5j
z9HX<_3-ss??KK^DGD}0_m-h7S$mQ^sS}!^^23x(pOjx^)81}qXHl(wV`Yi;gJGYgt
zsg(1RTWMssZB^zF5%6`!{2{$qF~)S$20Lu#ITyM;4<^W0w!>><+|^x2Isk#6%MQ<J
zRgmqZk<!eBHMp%aG^(O(S17Ea?6y!?McM74u!^!fLSYr*{aMerb+9~Wxtm9=LZxWz
zwe*5vZyxcu&11HeHyp<s5Rx9$=#wI2257h8j!Nc$y;^-R$<z3};~J&RkX0-ik{Ktn
z+bA2hnWq`U%?%VvW_hGQx_{2k>Mqy{zF`cN3N$b`hhZj`BPD&tAP03kgzI?NAU{Il
zfW2m^Bt0N>X18842!ZY6bD4=B&4EhUHNlR^P~)p&jOzJWvkVKvt>c|q<;47)A;FC2
zE!zvmL;GFV@(>SIc=58C@j~*lnek)I)Mb+@a+e`j*<4N<qamv_XBG`x@`TU|11{cS
zPE}lkhK;Gp%#1!&G^(<Fm1418GT=ByX{tCsVY;=3XLYvf!}Dv{<E}9`F^j=HGh0<o
z=r%6ZHTd*q+-}*2tBRo39<xv|%2j#L{x#^uAp^Irbq?i7dD^l|y4)<qceNU_%G2gd
z#TL723kQtpaedlw=MP}8I#s!sIx#+A6!oKmYV<*5xCDht-mO*)l8E;B!&p^;mJHh{
z=P?g@Nv)~^2{)>j=c@{Tc!}IKr_7@1)@ZMFtTNi4GwfRF5uQVW@rg@d=mpF#fx!2e
z@$<wQzI$%4Eg-%pJf!D~W4b$wTfi;k<`}WnU{Nn07Ap@0gR@X9h68mV&o?;t-nOdJ
z38QH6Cs#tea`x^YLhL0P(vcMmJ-&hy)?uSe_%Qnpi7Rr<t2_qifN=!;7%H2N2!>{j
z{NWmYz^UbS+j`lp(FQlKG3`$!wCiZznB(@z2+^3CKU}{q(LUpL*D5>Kw434N2T9#)
zmTwCaP<yA6+$o1FvoLD7vsPiSViqwSdg2M)Ib1L9>2q_rslY}OIjWa2TiK-7aR(iU
zDon;^Ele1Ojm*d;8Ynxr1M-#h;Xt9_BSh~b-Lv9_6~<<1W>9yGVbi85|D4IYmK}6&
z*wkmr7FOoG6NrLg6wELjfr$(6&np;KNCQ|NHC{p-e>!z27z-IptQnpN9n4vsxtxnn
z^MU`GS)S+J;K9Hl?z!jI>xxs;RfqOZ-HNqAS{<@%n=xl1z?f@ACS%!hhl-|Ac4;IC
z2=Vx$sSxEoo4|x)4m-fuzXha8iBcN$5c2JT+xWSLMy(^pKK@oNAIyV$$IniNCw`2h
z%F*Dt?5yve(X@hBOleXY0GgYbO)_a<K4r$3E*#Sy*_pee`K82FTKgaFf9<gcAO4pp
zKRZTZsU7jys?<$tGO6MGYmsC!31JJ0j$){>1Wr9_OpC;tODGkv^EiJ@jj`c`Ut2q0
zysT<o_>7v`luB!>JU+GXc~z@Qo9eAno7SAmY+Y)T=T_4=sGH!C+q%g6sD*t9VoXy~
z!xMUHR7o{8qK_VeSc|49b$v1&Rg$UB<f_y`HJQ3Hk&Y?gl4??uv~<SPaGFTzjp>Mz
z(o;tB#{zGg@aln`B^%PJl2jX+6CYCXO0<v=O#<z#6PRFAHIalbHMQ`?r0-QwWE0}t
z<i`m{i*7=6aZi$L<g+-3DuH*v-qZHQ{5fkvU44VC&+W6y;Vi@}=k1OHJG@T8(TmqS
znSHw^Fpv#A$z18r-n~b5WZK~%xiVZ+{l{f~P|>(X8WzjoCK#ubz}`-lTYN!;Jc|#X
z_#^62C?m`j+qX_{%NIJ@3+>%Tc3XF0dP?uw-j(gnw|8|-Pfc&@%C~RVyF2oDwI*NV
zZd7ReT}LzDF#Pm%#n1m-Na+4~ayOiZZvy_5CGnf`1SinDwWKSa1-{R6zk*v`>W75C
z)7Bn$`rnpW_EpCHjLtrSZ_u8G=aXGW@t|jxspOy~!^rRw$j93osJ~y(`{aKk11k2S
zgdICAwBS63Gfp*!_kDQBIpl1#P-E#q&wlnSmABtMfp@DXCSi$lctkn@579W~KzGpr
zpb`8+YajR#&}+P3Ytd8DAHL=7+dW^w9)73qD2j8S349NW>=5Z>!;gvIAz*7kWtt{S
zOfDs^H36*-sRO-^@0TXDW#7dk(Mgs4jzf->9rBrTc<K?GcDy6-{yOnh7jpoLhPp%W
zSE4y-V;()y!5QLz9GZjhIfyg==4*%82ayiL%E3EZZg;LOu6Ne++6n3%LOndP)bt~Z
z_qy$2;dYiGF3yB2wq?Xrtck4Ne})Rs^8L68?`ag;i#jt>gL^hd^Ah0<%}O8Au<IB9
z`+YfQjr|AlG3RCA-P-Z`Glu#W+O8l6ZcTKAt5Kh~PtX>`Rd4^!EL3xF1-|QS#TeKI
zzXiN^Y6oq{J3E82kadHb#`V+jMQJ-q7Lq*lUHD@;*Xt6=cGz^IK4xwl-nAP*-W_-<
z!Lo?Vq%o%JuSPWvz9vMAsQz-z!2RJjpYGXtv{X#{59hsYSVr2?*y&ij@#t+kIC0J9
z9c^iBNy`Pjh}R;$ZS#iHwzDsh=+XVXP#O-(PH$VqE??#3XN{8XY%ZC3+u~>K&3UVI
zmF|=>N3w0{l3q5a@i-MMfzn!EBApI>jbIDuVxv?S5HE6VY2Lc`wvEnX4K12^*>Gg^
zxw*D2;=r{lj?25r_ZZFAHm-)29V1_{(W1vfU>mY78HF+1Jc7OJjN!aTpB=tW@f*6q
zYLl<<_GGVd#3-hVOncjOXQa&gAiFJHF};0PZ`(B9Nf};;#K)G<Ni`Nf>@Br%_iPEf
i-<Rmw5?q)*87se3f=Ql#|MJNHflz*_dH8?Y_<sYc%Hbja

literal 0
HcmV?d00001

diff --git a/legacy/Bot/obj/Debug/net8.0/refint/SoUnBot.dll b/legacy/Bot/obj/Debug/net8.0/refint/SoUnBot.dll
new file mode 100644
index 0000000000000000000000000000000000000000..e575ec3e00be44dde179ba3708f923642c3ef04d
GIT binary patch
literal 11776
zcmeHN4R9Rgd46~AbhkR}LpuM-fZ-%#;{ZPEEXlS&lJ#e7fn^IzHjq$GtGku7`0iHh
z?ink8$T858UnexsCQT}jDV>y*0t3?wDT5j6bO@zW+A^7TG6j_hleRQT(;qS=1AX3i
zf9?*Olw_upPRdH(yU+Xcz2E!2`|bDbw|k=>JV6m6ilV&zHqke5=A&Qe&n62{Cog$2
zNsl+a(E1H!?+dLHv!;`tv#l9hFQxN(xoo-VDI;xH%4xHl9^OBmE?EU5vwV5tQs4F1
zE~33klzubU_GZx9E7V5kDeXjQND`j(;ahO-M@geF;Irz*4J7gNnHEvFGdLnNc^6TW
z{8!ID>zUBa@VlS!B+;D>zOeSRK(q{eKkP25mH$t;pGd2*(ZKJi;WMQpW&u2+3;Pnp
z9hmhK4Q5>9s0+d47lS|`{f^;WbEFY{#&+yHWYUL80JhnPQgigfL&i3W77V#hw19#f
z(v>JRM?c!FVc&<p!Wnp|>w+^2pKKld^dBAgoj>`}{kdP^uD;*3b7bHI&#O3iZc?d%
zCi;n9Uq_^{@E14|D?dzw<JS%<z6YLXj&x?)Gac<6*=}~&Kt<3WVd7i=aiW`>iG~}2
z$6ebj&p2$cc!4*CuRl0W4*>HVxqkP-kzt%42fqUg*80JsHO0m_Foh{MT(THx<hks>
zlnz?$#lHdUT9i?gLnv3Hpeb5~!u`7sC60m+WNzm(%Xt})`H}v)!OKI8z7_rZc#QTo
zFnuuceEfWR3sqy(B9aTE_cz3-qv2F6MxTo^-7C}-`kK%`kFfk<@$;z&`}wH05t6q>
z@+2e)y0ziEu_XN+-KeJMG3_NSNqdwV)fjD5*zzNwDS8z06xoqhEk^&LvY+K~rk&B}
zK`#~jM2zjX#_mQ`8zPKfl@{MZ<T2`o&(*Y9c^%#g@m5);uH)RH_uStmx)`fRuW$;u
z&nQ38m{U=j=u2^riwc)$^f=CI6a9m>JjRwO(Y>f%*2vsS;rb#TccF0QxW`>8++W8$
zZUdc!mp@j2g7y{KEINw+M0~ajw<rEa1N-ctNNW>KAZq5Wriju+*CK{qETTQaJt<rq
z64vb$%SP%G?her{qXFUgTK6L6Vc~u%Tnp{4Y2jk{W-s@Pu8Xb{u3fku+9%v3QrC-x
z^LpXl7H$ts&^F4deHfCDK)0Gw%C#6Zo!U6)pK3RN&PP53`s->Abcv=w7b4T3SI13I
z&oYpdAn|HF9lH&9I9B-?HlF8m*|2BNkJoNn<Gk;Oqx~r2oW#SyYPywv1N1NHw?J=G
z9tOQgc^dRS<vXAcDKCSbMvqq0OA1^5lfsq?v1OOamM*d7zY<#>6w6PD<)T=ATYNqt
zK3^7}|1Lf^Am8<Q@S<HIaeh{Eb!P)-pi}$)J1jk|XCL(a7qRg?)aUT~G}e!cPItGQ
z!F{Co+<mks^5c;6bZTI!eK`IG?i|0Kc2~TqhWm>82A)y_-S4YSRZd!nhPwaM-oVHX
zEUB@nhAU`qV$KS5b6QlXa>uo#5({;o(-dSYu;kX`A;+zUoV2cmx>H(82|4K2)Nvb?
zWq~DhStS*4^jvJavcl(HQ@()qR{7kLvM;hz3AopkzlE+PtarUOqJ*4wc_dlGbw-q`
zrN;eR8`RUde^u_$hNN;qsQV`7@+$Wo=q?O(OVEX!^!=hh2d)XLLy$AR4(mdWuS1n9
zXa|&wLmfv}<v7<NCq6@tPQ*W`ToTly`*0mvd641wRs-j(&11lEma5!gWUwu$r!Che
zl??$WeF!;vTD`TZ(;Rh9*;r%gRyn#w`>?Vp(9ylIJ8HN>>@MZ<P*(wWWexW!B@=RY
z#lEOy15RS;{JFU$Wo;wwZ@=D>qD3wUIqx3p3UzE*<=8UdJj-o$mQ{`|1J1MDUT0b5
z*fQWe%N@RDJ?BLo=)9-{&a>>UYoW?<3jya@UR7sV<=8UdJj<)=EUO$_@>7pOe}#t{
zg%;ieJ%qJar9*hSiO>tMkJ4H^At)kIX$k9SM7%}mQQ!u8EZ)=<qnl;L=2{9p1gc_v
zVa!y;8ppMU#pc~&cuMMe+7)=5Q0M{i^JVelNzV56ZN#9^8N{j59fCg#s?ug@>)o&^
zp4lcse<A+Qmb4&d%rx(b#<7KNCKJyPCapkOiLx4H4N42jT9igQ6IWoxE5};U1bsko
z7PJXhp6P(l5uw)$y-BDp)C5)NHqaOy16@V;2>m>06TL`$7raP(&6mWcQ8^P&Q!{7`
z?;>`gjaK>yy-pj1UPgKPvHAfzPH(CiI**>m{kwwx2J}MuH_-L4&(Ib0Q{WD|3N}~M
zZqOk*0D2weK*vS?A<%wu=w5X|JP*-f)ZHa`1pea@U9ch4gd`PN5SwEnIWBdN3%*O}
zJ+N69SrnU-A~_{Crv(3=(67Pf^2lkic}679ip{fv&j@`^T7N}sUKQ=DBLALHMd55T
z2u&*-^9I5Ff`=6D(U3?cMWPGNi+n*O#|0l3`8^8vYf<E<M1D%-UxR#I<h0125y`WH
zpM(7J$SZ=EME<Jae}p_9A(i8a2yIn4&h>)(g$@bL3q2;<<AP5LJ|*~Bp-UopRWL<P
z(Qm61d5Aqn*#CN=0}=K=DR@EXF_A0^J}LOL;AaHCB6vwKMcI!MWe;h=t%Can4+z$U
zE<`z>#{@5m_N3s`f}aumir^)|)F7T4#IxX5!2?2dp~r-t6#9(NC7~3Px-qWXDs({T
zq{wx_$Am5kRpM+pAXFE+B$Tu-qj#+uYbOPt6nt9nlHhbB+YB_ajV^RiBqs$^g0+)E
zPYdOJQv>Fm7@oE?{3Wp0T!vlGa@mJ|CH}kEIXt8?UEavl)lPxlt9?C`KdC(eJQ!h#
zCx5zu@%MxVwVnvIp0{T0DM)Tt|4dOar$_uKRK(E$90ye~N5z2?pemgQPC;aiz{|lY
zlmu1jd~m!ETn?N9r_zOpT){I^Gw?dhAqt*!Rsdgur#*$zpekmbHNb74D(0WHz#BnT
zx(u~d+Jt&4=EF-s+hMC>##xUj+UXW}{Wx$l_9Gk6-krcHx*K=}eG+&DeHwTrod8}*
zUjSZ>-O75<F9ENiuL7^3Mc@{C0Jw!71YS#j0KAsI4jf(JdA|1e0agV3hsR~~8rBqC
zk5EF0?7LV!-f7n-)%ZO^^>|5S_5NO$x=G~ze3T}XW|S1l3Y3*7tI+$^C~Ht!P}ZWz
z(bLzR%VoQB*>>vLqn8Us<KSFDcZ~sOzMStXKnI0yvlVdMC>IXE&MIABF&x*l%521%
zzI-k>Y&vsAeSWB@J5IJe7gX&GQFjf>W`ibo*|ud@TZDW_ck{Djy6xa4j%cM7C?u5j
z9HX<_3-ss??KK^DGD}0_m-h7S$mQ^sS}!^^23x(pOjx^)81}qXHl(wV`Yi;gJGYgt
zsg(1RTWMssZB^zF5%6`!{2{$qF~)S$20Lu#ITyM;4<^W0w!>><+|^x2Isk#6%MQ<J
zRgmqZk<!eBHMp%aG^(O(S17Ea?6y!?McM74u!^!fLSYr*{aMerb+9~Wxtm9=LZxWz
zwe*5vZyxcu&11HeHyp<s5Rx9$=#wI2257h8j!Nc$y;^-R$<z3};~J&RkX0-ik{Ktn
z+bA2hnWq`U%?%VvW_hGQx_{2k>Mqy{zF`cN3N$b`hhZj`BPD&tAP03kgzI?NAU{Il
zfW2m^Bt0N>X18842!ZY6bD4=B&4EhUHNlR^P~)p&jOzJWvkVKvt>c|q<;47)A;FC2
zE!zvmL;GFV@(>SIc=58C@j~*lnek)I)Mb+@a+e`j*<4N<qamv_XBG`x@`TU|11{cS
zPE}lkhK;Gp%#1!&G^(<Fm1418GT=ByX{tCsVY;=3XLYvf!}Dv{<E}9`F^j=HGh0<o
z=r%6ZHTd*q+-}*2tBRo39<xv|%2j#L{x#^uAp^Irbq?i7dD^l|y4)<qceNU_%G2gd
z#TL723kQtpaedlw=MP}8I#s!sIx#+A6!oKmYV<*5xCDht-mO*)l8E;B!&p^;mJHh{
z=P?g@Nv)~^2{)>j=c@{Tc!}IKr_7@1)@ZMFtTNi4GwfRF5uQVW@rg@d=mpF#fx!2e
z@$<wQzI$%4Eg-%pJf!D~W4b$wTfi;k<`}WnU{Nn07Ap@0gR@X9h68mV&o?;t-nOdJ
z38QH6Cs#tea`x^YLhL0P(vcMmJ-&hy)?uSe_%Qnpi7Rr<t2_qifN=!;7%H2N2!>{j
z{NWmYz^UbS+j`lp(FQlKG3`$!wCiZznB(@z2+^3CKU}{q(LUpL*D5>Kw434N2T9#)
zmTwCaP<yA6+$o1FvoLD7vsPiSViqwSdg2M)Ib1L9>2q_rslY}OIjWa2TiK-7aR(iU
zDon;^Ele1Ojm*d;8Ynxr1M-#h;Xt9_BSh~b-Lv9_6~<<1W>9yGVbi85|D4IYmK}6&
z*wkmr7FOoG6NrLg6wELjfr$(6&np;KNCQ|NHC{p-e>!z27z-IptQnpN9n4vsxtxnn
z^MU`GS)S+J;K9Hl?z!jI>xxs;RfqOZ-HNqAS{<@%n=xl1z?f@ACS%!hhl-|Ac4;IC
z2=Vx$sSxEoo4|x)4m-fuzXha8iBcN$5c2JT+xWSLMy(^pKK@oNAIyV$$IniNCw`2h
z%F*Dt?5yve(X@hBOleXY0GgYbO)_a<K4r$3E*#Sy*_pee`K82FTKgaFf9<gcAO4pp
zKRZTZsU7jys?<$tGO6MGYmsC!31JJ0j$){>1Wr9_OpC;tODGkv^EiJ@jj`c`Ut2q0
zysT<o_>7v`luB!>JU+GXc~z@Qo9eAno7SAmY+Y)T=T_4=sGH!C+q%g6sD*t9VoXy~
z!xMUHR7o{8qK_VeSc|49b$v1&Rg$UB<f_y`HJQ3Hk&Y?gl4??uv~<SPaGFTzjp>Mz
z(o;tB#{zGg@aln`B^%PJl2jX+6CYCXO0<v=O#<z#6PRFAHIalbHMQ`?r0-QwWE0}t
z<i`m{i*7=6aZi$L<g+-3DuH*v-qZHQ{5fkvU44VC&+W6y;Vi@}=k1OHJG@T8(TmqS
znSHw^Fpv#A$z18r-n~b5WZK~%xiVZ+{l{f~P|>(X8WzjoCK#ubz}`-lTYN!;Jc|#X
z_#^62C?m`j+qX_{%NIJ@3+>%Tc3XF0dP?uw-j(gnw|8|-Pfc&@%C~RVyF2oDwI*NV
zZd7ReT}LzDF#Pm%#n1m-Na+4~ayOiZZvy_5CGnf`1SinDwWKSa1-{R6zk*v`>W75C
z)7Bn$`rnpW_EpCHjLtrSZ_u8G=aXGW@t|jxspOy~!^rRw$j93osJ~y(`{aKk11k2S
zgdICAwBS63Gfp*!_kDQBIpl1#P-E#q&wlnSmABtMfp@DXCSi$lctkn@579W~KzGpr
zpb`8+YajR#&}+P3Ytd8DAHL=7+dW^w9)73qD2j8S349NW>=5Z>!;gvIAz*7kWtt{S
zOfDs^H36*-sRO-^@0TXDW#7dk(Mgs4jzf->9rBrTc<K?GcDy6-{yOnh7jpoLhPp%W
zSE4y-V;()y!5QLz9GZjhIfyg==4*%82ayiL%E3EZZg;LOu6Ne++6n3%LOndP)bt~Z
z_qy$2;dYiGF3yB2wq?Xrtck4Ne})Rs^8L68?`ag;i#jt>gL^hd^Ah0<%}O8Au<IB9
z`+YfQjr|AlG3RCA-P-Z`Glu#W+O8l6ZcTKAt5Kh~PtX>`Rd4^!EL3xF1-|QS#TeKI
zzXiN^Y6oq{J3E82kadHb#`V+jMQJ-q7Lq*lUHD@;*Xt6=cGz^IK4xwl-nAP*-W_-<
z!Lo?Vq%o%JuSPWvz9vMAsQz-z!2RJjpYGXtv{X#{59hsYSVr2?*y&ij@#t+kIC0J9
z9c^iBNy`Pjh}R;$ZS#iHwzDsh=+XVXP#O-(PH$VqE??#3XN{8XY%ZC3+u~>K&3UVI
zmF|=>N3w0{l3q5a@i-MMfzn!EBApI>jbIDuVxv?S5HE6VY2Lc`wvEnX4K12^*>Gg^
zxw*D2;=r{lj?25r_ZZFAHm-)29V1_{(W1vfU>mY78HF+1Jc7OJjN!aTpB=tW@f*6q
zYLl<<_GGVd#3-hVOncjOXQa&gAiFJHF};0PZ`(B9Nf};;#K)G<Ni`Nf>@Br%_iPEf
i-<Rmw5?q)*87se3f=Ql#|MJNHflz*_dH8?Y_<sYc%Hbja

literal 0
HcmV?d00001

diff --git a/legacy/Bot/obj/SoUnBot.csproj.nuget.dgspec.json b/legacy/Bot/obj/SoUnBot.csproj.nuget.dgspec.json
new file mode 100644
index 0000000..244f051
--- /dev/null
+++ b/legacy/Bot/obj/SoUnBot.csproj.nuget.dgspec.json
@@ -0,0 +1,81 @@
+{
+  "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": [
+            {
+              "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
new file mode 100644
index 0000000..311e8bb
--- /dev/null
+++ b/legacy/Bot/obj/SoUnBot.csproj.nuget.g.props
@@ -0,0 +1,15 @@
+<?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
new file mode 100644
index 0000000..3dc06ef
--- /dev/null
+++ b/legacy/Bot/obj/SoUnBot.csproj.nuget.g.targets
@@ -0,0 +1,2 @@
+<?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
new file mode 100644
index 0000000..c6e325b
--- /dev/null
+++ b/legacy/Bot/obj/project.assets.json
@@ -0,0 +1,280 @@
+{
+  "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": {
+    "JetBrains.Annotations/2021.3.0": {
+      "sha512": "Ddxjs5RRjf+c8m9m++WvhW1lz1bqNhsTjWvCLbQN9bvKbkJeR9MhtfNwKgBRRdG2yLHcXFr5Lf7fsvvkiPaDRg==",
+      "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"
+      ]
+    },
+    "Newtonsoft.Json/13.0.1": {
+      "sha512": "ppPFpBcvxdsfUonNcvITKqLl3bqxWbDCZIzDWHzjpdAHRFfZe0Dw9HmA0+za13IdyrgJwpkDTDA9fHaxOrt20A==",
+      "type": "package",
+      "path": "newtonsoft.json/13.0.1",
+      "files": [
+        ".nupkg.metadata",
+        ".signature.p7s",
+        "LICENSE.md",
+        "lib/net20/Newtonsoft.Json.dll",
+        "lib/net20/Newtonsoft.Json.xml",
+        "lib/net35/Newtonsoft.Json.dll",
+        "lib/net35/Newtonsoft.Json.xml",
+        "lib/net40/Newtonsoft.Json.dll",
+        "lib/net40/Newtonsoft.Json.xml",
+        "lib/net45/Newtonsoft.Json.dll",
+        "lib/net45/Newtonsoft.Json.xml",
+        "lib/netstandard1.0/Newtonsoft.Json.dll",
+        "lib/netstandard1.0/Newtonsoft.Json.xml",
+        "lib/netstandard1.3/Newtonsoft.Json.dll",
+        "lib/netstandard1.3/Newtonsoft.Json.xml",
+        "lib/netstandard2.0/Newtonsoft.Json.dll",
+        "lib/netstandard2.0/Newtonsoft.Json.xml",
+        "newtonsoft.json.13.0.1.nupkg.sha512",
+        "newtonsoft.json.nuspec",
+        "packageIcon.png"
+      ]
+    },
+    "System.Threading.Channels/6.0.0": {
+      "sha512": "TY8/9+tI0mNaUMgntOxxaq2ndTkdXqLSxvPmas7XEqOlv9lQtB7wLjYGd756lOaO7Dvb5r/WXhluM+0Xe87v5Q==",
+      "type": "package",
+      "path": "system.threading.channels/6.0.0",
+      "files": [
+        ".nupkg.metadata",
+        ".signature.p7s",
+        "Icon.png",
+        "LICENSE.TXT",
+        "THIRD-PARTY-NOTICES.TXT",
+        "buildTransitive/netcoreapp2.0/System.Threading.Channels.targets",
+        "buildTransitive/netcoreapp3.1/_._",
+        "lib/net461/System.Threading.Channels.dll",
+        "lib/net461/System.Threading.Channels.xml",
+        "lib/net6.0/System.Threading.Channels.dll",
+        "lib/net6.0/System.Threading.Channels.xml",
+        "lib/netcoreapp3.1/System.Threading.Channels.dll",
+        "lib/netcoreapp3.1/System.Threading.Channels.xml",
+        "lib/netstandard2.0/System.Threading.Channels.dll",
+        "lib/netstandard2.0/System.Threading.Channels.xml",
+        "lib/netstandard2.1/System.Threading.Channels.dll",
+        "lib/netstandard2.1/System.Threading.Channels.xml",
+        "system.threading.channels.6.0.0.nupkg.sha512",
+        "system.threading.channels.nuspec",
+        "useSharedDesignerContext.txt"
+      ]
+    },
+    "Telegram.Bot/17.0.0": {
+      "sha512": "YvQ9lqEt1bTafu6BJPTbYWDHxyHP+TK8PtjTjNV/6VQw3XxVcZnGwYkJ1CdYW3lJHmHjYxzhBlhhOGNtqJ3U7g==",
+      "type": "package",
+      "path": "telegram.bot/17.0.0",
+      "files": [
+        ".nupkg.metadata",
+        ".signature.p7s",
+        "lib/netcoreapp3.1/Telegram.Bot.dll",
+        "lib/netcoreapp3.1/Telegram.Bot.pdb",
+        "lib/netcoreapp3.1/Telegram.Bot.xml",
+        "lib/netstandard2.0/Telegram.Bot.dll",
+        "lib/netstandard2.0/Telegram.Bot.pdb",
+        "lib/netstandard2.0/Telegram.Bot.xml",
+        "package-icon.png",
+        "telegram.bot.17.0.0.nupkg.sha512",
+        "telegram.bot.nuspec"
+      ]
+    },
+    "Telegram.Bot.Extensions.Polling/1.0.0": {
+      "sha512": "OsUbHdHIMmldevoRYzArh5uJDVs1fzlpj+T3mddeP/ELhhhHLmcjon0ZEypgf1KFEj6QWbuZHkijauIW1LZlqg==",
+      "type": "package",
+      "path": "telegram.bot.extensions.polling/1.0.0",
+      "files": [
+        ".nupkg.metadata",
+        ".signature.p7s",
+        "lib/netcoreapp3.1/Telegram.Bot.Extensions.Polling.dll",
+        "lib/netcoreapp3.1/Telegram.Bot.Extensions.Polling.pdb",
+        "lib/netcoreapp3.1/Telegram.Bot.Extensions.Polling.xml",
+        "lib/netstandard2.0/Telegram.Bot.Extensions.Polling.dll",
+        "lib/netstandard2.0/Telegram.Bot.Extensions.Polling.pdb",
+        "lib/netstandard2.0/Telegram.Bot.Extensions.Polling.xml",
+        "package-icon.png",
+        "telegram.bot.extensions.polling.1.0.0.nupkg.sha512",
+        "telegram.bot.extensions.polling.nuspec"
+      ]
+    }
+  },
+  "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
new file mode 100644
index 0000000..5f88de5
--- /dev/null
+++ b/legacy/Bot/obj/project.nuget.cache
@@ -0,0 +1,15 @@
+{
+  "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
new file mode 100644
index 0000000..fe4ca64
--- /dev/null
+++ b/legacy/Bot/obj/project.packagespec.json
@@ -0,0 +1 @@
+"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
new file mode 100644
index 0000000..d8bcafa
--- /dev/null
+++ b/legacy/Bot/obj/rider.project.model.nuget.info
@@ -0,0 +1 @@
+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
new file mode 100644
index 0000000..d8bcafa
--- /dev/null
+++ b/legacy/Bot/obj/rider.project.restore.info
@@ -0,0 +1 @@
+17116412128528733
\ No newline at end of file
diff --git a/Bot/run.sh b/legacy/Bot/run.sh
similarity index 100%
rename from Bot/run.sh
rename to legacy/Bot/run.sh
diff --git a/Data/Images/25.png b/legacy/Data/Images/25.png
similarity index 100%
rename from Data/Images/25.png
rename to legacy/Data/Images/25.png
diff --git a/Data/Images/26.png b/legacy/Data/Images/26.png
similarity index 100%
rename from Data/Images/26.png
rename to legacy/Data/Images/26.png
diff --git a/Data/Images/27.png b/legacy/Data/Images/27.png
similarity index 100%
rename from Data/Images/27.png
rename to legacy/Data/Images/27.png
diff --git a/Data/Images/35.png b/legacy/Data/Images/35.png
similarity index 100%
rename from Data/Images/35.png
rename to legacy/Data/Images/35.png
diff --git a/Data/Images/36.png b/legacy/Data/Images/36.png
similarity index 100%
rename from Data/Images/36.png
rename to legacy/Data/Images/36.png
diff --git a/Data/Images/37.png b/legacy/Data/Images/37.png
similarity index 100%
rename from Data/Images/37.png
rename to legacy/Data/Images/37.png
diff --git a/Data/Images/38.png b/legacy/Data/Images/38.png
similarity index 100%
rename from Data/Images/38.png
rename to legacy/Data/Images/38.png
diff --git a/Data/Images/39.png b/legacy/Data/Images/39.png
similarity index 100%
rename from Data/Images/39.png
rename to legacy/Data/Images/39.png
diff --git a/Data/Images/40.png b/legacy/Data/Images/40.png
similarity index 100%
rename from Data/Images/40.png
rename to legacy/Data/Images/40.png
diff --git a/Data/Images/56.png b/legacy/Data/Images/56.png
similarity index 100%
rename from Data/Images/56.png
rename to legacy/Data/Images/56.png
diff --git a/Data/Images/57.png b/legacy/Data/Images/57.png
similarity index 100%
rename from Data/Images/57.png
rename to legacy/Data/Images/57.png
diff --git a/Data/Images/58.png b/legacy/Data/Images/58.png
similarity index 100%
rename from Data/Images/58.png
rename to legacy/Data/Images/58.png
diff --git a/Data/Images/59.png b/legacy/Data/Images/59.png
similarity index 100%
rename from Data/Images/59.png
rename to legacy/Data/Images/59.png
diff --git a/Data/Images/60.png b/legacy/Data/Images/60.png
similarity index 100%
rename from Data/Images/60.png
rename to legacy/Data/Images/60.png
diff --git a/Data/Images/61.png b/legacy/Data/Images/61.png
similarity index 100%
rename from Data/Images/61.png
rename to legacy/Data/Images/61.png
diff --git a/Data/Images/62.png b/legacy/Data/Images/62.png
similarity index 100%
rename from Data/Images/62.png
rename to legacy/Data/Images/62.png
diff --git a/Data/Images/FDS/1positive0negative.png b/legacy/Data/Images/FDS/1positive0negative.png
similarity index 100%
rename from Data/Images/FDS/1positive0negative.png
rename to legacy/Data/Images/FDS/1positive0negative.png
diff --git a/Data/Images/FDS/accuracy80.png b/legacy/Data/Images/FDS/accuracy80.png
similarity index 100%
rename from Data/Images/FDS/accuracy80.png
rename to legacy/Data/Images/FDS/accuracy80.png
diff --git a/Data/Images/FDS/matrixwhatcanwesay.png b/legacy/Data/Images/FDS/matrixwhatcanwesay.png
similarity index 100%
rename from Data/Images/FDS/matrixwhatcanwesay.png
rename to legacy/Data/Images/FDS/matrixwhatcanwesay.png
diff --git a/Data/Questions/Domande Sicurezza.old b/legacy/Data/Questions/Domande Sicurezza.old
similarity index 100%
rename from Data/Questions/Domande Sicurezza.old
rename to legacy/Data/Questions/Domande Sicurezza.old
diff --git a/Data/Questions/diritto_unive_inf.txt b/legacy/Data/Questions/diritto_unive_inf.txt
similarity index 100%
rename from Data/Questions/diritto_unive_inf.txt
rename to legacy/Data/Questions/diritto_unive_inf.txt
diff --git a/Data/Questions/ingsw/0000_102/correct.txt b/legacy/Data/Questions/ingsw/0000_102/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_102/correct.txt
rename to legacy/Data/Questions/ingsw/0000_102/correct.txt
diff --git a/Data/Questions/ingsw/0000_102/quest.txt b/legacy/Data/Questions/ingsw/0000_102/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_102/quest.txt
rename to legacy/Data/Questions/ingsw/0000_102/quest.txt
diff --git a/Data/Questions/ingsw/0000_102/wrong1.txt b/legacy/Data/Questions/ingsw/0000_102/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_102/wrong1.txt
rename to legacy/Data/Questions/ingsw/0000_102/wrong1.txt
diff --git a/Data/Questions/ingsw/0000_102/wrong2.txt b/legacy/Data/Questions/ingsw/0000_102/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_102/wrong2.txt
rename to legacy/Data/Questions/ingsw/0000_102/wrong2.txt
diff --git a/Data/Questions/ingsw/0000_2/correct.txt b/legacy/Data/Questions/ingsw/0000_2/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_2/correct.txt
rename to legacy/Data/Questions/ingsw/0000_2/correct.txt
diff --git a/Data/Questions/ingsw/0000_2/quest.txt b/legacy/Data/Questions/ingsw/0000_2/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_2/quest.txt
rename to legacy/Data/Questions/ingsw/0000_2/quest.txt
diff --git a/Data/Questions/ingsw/0000_2/wrong1.txt b/legacy/Data/Questions/ingsw/0000_2/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_2/wrong1.txt
rename to legacy/Data/Questions/ingsw/0000_2/wrong1.txt
diff --git a/Data/Questions/ingsw/0000_2/wrong2.txt b/legacy/Data/Questions/ingsw/0000_2/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_2/wrong2.txt
rename to legacy/Data/Questions/ingsw/0000_2/wrong2.txt
diff --git a/Data/Questions/ingsw/0000_3/correct.txt b/legacy/Data/Questions/ingsw/0000_3/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_3/correct.txt
rename to legacy/Data/Questions/ingsw/0000_3/correct.txt
diff --git a/Data/Questions/ingsw/0000_3/quest.txt b/legacy/Data/Questions/ingsw/0000_3/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_3/quest.txt
rename to legacy/Data/Questions/ingsw/0000_3/quest.txt
diff --git a/Data/Questions/ingsw/0000_3/wrong1.txt b/legacy/Data/Questions/ingsw/0000_3/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_3/wrong1.txt
rename to legacy/Data/Questions/ingsw/0000_3/wrong1.txt
diff --git a/Data/Questions/ingsw/0000_3/wrong2.txt b/legacy/Data/Questions/ingsw/0000_3/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_3/wrong2.txt
rename to legacy/Data/Questions/ingsw/0000_3/wrong2.txt
diff --git a/Data/Questions/ingsw/0000_32/correct.txt b/legacy/Data/Questions/ingsw/0000_32/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_32/correct.txt
rename to legacy/Data/Questions/ingsw/0000_32/correct.txt
diff --git a/Data/Questions/ingsw/0000_32/quest.txt b/legacy/Data/Questions/ingsw/0000_32/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_32/quest.txt
rename to legacy/Data/Questions/ingsw/0000_32/quest.txt
diff --git a/Data/Questions/ingsw/0000_32/wrong1.txt b/legacy/Data/Questions/ingsw/0000_32/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_32/wrong1.txt
rename to legacy/Data/Questions/ingsw/0000_32/wrong1.txt
diff --git a/Data/Questions/ingsw/0000_32/wrong2.txt b/legacy/Data/Questions/ingsw/0000_32/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_32/wrong2.txt
rename to legacy/Data/Questions/ingsw/0000_32/wrong2.txt
diff --git a/Data/Questions/ingsw/0000_4/correct.txt b/legacy/Data/Questions/ingsw/0000_4/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_4/correct.txt
rename to legacy/Data/Questions/ingsw/0000_4/correct.txt
diff --git a/Data/Questions/ingsw/0000_4/quest.txt b/legacy/Data/Questions/ingsw/0000_4/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_4/quest.txt
rename to legacy/Data/Questions/ingsw/0000_4/quest.txt
diff --git a/Data/Questions/ingsw/0000_4/wrong1.txt b/legacy/Data/Questions/ingsw/0000_4/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_4/wrong1.txt
rename to legacy/Data/Questions/ingsw/0000_4/wrong1.txt
diff --git a/Data/Questions/ingsw/0000_4/wrong2.txt b/legacy/Data/Questions/ingsw/0000_4/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_4/wrong2.txt
rename to legacy/Data/Questions/ingsw/0000_4/wrong2.txt
diff --git a/Data/Questions/ingsw/0000_7/correct.txt b/legacy/Data/Questions/ingsw/0000_7/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_7/correct.txt
rename to legacy/Data/Questions/ingsw/0000_7/correct.txt
diff --git a/Data/Questions/ingsw/0000_7/quest.txt b/legacy/Data/Questions/ingsw/0000_7/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_7/quest.txt
rename to legacy/Data/Questions/ingsw/0000_7/quest.txt
diff --git a/Data/Questions/ingsw/0000_7/wrong1.txt b/legacy/Data/Questions/ingsw/0000_7/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_7/wrong1.txt
rename to legacy/Data/Questions/ingsw/0000_7/wrong1.txt
diff --git a/Data/Questions/ingsw/0000_7/wrong2.txt b/legacy/Data/Questions/ingsw/0000_7/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_7/wrong2.txt
rename to legacy/Data/Questions/ingsw/0000_7/wrong2.txt
diff --git a/Data/Questions/ingsw/0000_8/correct.txt b/legacy/Data/Questions/ingsw/0000_8/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_8/correct.txt
rename to legacy/Data/Questions/ingsw/0000_8/correct.txt
diff --git a/Data/Questions/ingsw/0000_8/quest.txt b/legacy/Data/Questions/ingsw/0000_8/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_8/quest.txt
rename to legacy/Data/Questions/ingsw/0000_8/quest.txt
diff --git a/Data/Questions/ingsw/0000_8/wrong1.txt b/legacy/Data/Questions/ingsw/0000_8/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_8/wrong1.txt
rename to legacy/Data/Questions/ingsw/0000_8/wrong1.txt
diff --git a/Data/Questions/ingsw/0000_8/wrong2.txt b/legacy/Data/Questions/ingsw/0000_8/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0000_8/wrong2.txt
rename to legacy/Data/Questions/ingsw/0000_8/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_0/correct.txt b/legacy/Data/Questions/ingsw/0120_0/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_0/correct.txt
rename to legacy/Data/Questions/ingsw/0120_0/correct.txt
diff --git a/Data/Questions/ingsw/0120_0/quest.txt b/legacy/Data/Questions/ingsw/0120_0/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_0/quest.txt
rename to legacy/Data/Questions/ingsw/0120_0/quest.txt
diff --git a/Data/Questions/ingsw/0120_0/wrong1.txt b/legacy/Data/Questions/ingsw/0120_0/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_0/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_0/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_0/wrong2.txt b/legacy/Data/Questions/ingsw/0120_0/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_0/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_0/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_1/correct.txt b/legacy/Data/Questions/ingsw/0120_1/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_1/correct.txt
rename to legacy/Data/Questions/ingsw/0120_1/correct.txt
diff --git a/Data/Questions/ingsw/0120_1/quest.txt b/legacy/Data/Questions/ingsw/0120_1/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_1/quest.txt
rename to legacy/Data/Questions/ingsw/0120_1/quest.txt
diff --git a/Data/Questions/ingsw/0120_1/wrong1.txt b/legacy/Data/Questions/ingsw/0120_1/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_1/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_1/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_1/wrong2.txt b/legacy/Data/Questions/ingsw/0120_1/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_1/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_1/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_10/correct.txt b/legacy/Data/Questions/ingsw/0120_10/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_10/correct.txt
rename to legacy/Data/Questions/ingsw/0120_10/correct.txt
diff --git a/Data/Questions/ingsw/0120_10/quest.txt b/legacy/Data/Questions/ingsw/0120_10/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_10/quest.txt
rename to legacy/Data/Questions/ingsw/0120_10/quest.txt
diff --git a/Data/Questions/ingsw/0120_10/wrong1.txt b/legacy/Data/Questions/ingsw/0120_10/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_10/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_10/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_10/wrong2.txt b/legacy/Data/Questions/ingsw/0120_10/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_10/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_10/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_11/correct.txt b/legacy/Data/Questions/ingsw/0120_11/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_11/correct.txt
rename to legacy/Data/Questions/ingsw/0120_11/correct.txt
diff --git a/Data/Questions/ingsw/0120_11/quest.txt b/legacy/Data/Questions/ingsw/0120_11/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_11/quest.txt
rename to legacy/Data/Questions/ingsw/0120_11/quest.txt
diff --git a/Data/Questions/ingsw/0120_11/wrong1.txt b/legacy/Data/Questions/ingsw/0120_11/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_11/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_11/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_11/wrong2.txt b/legacy/Data/Questions/ingsw/0120_11/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_11/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_11/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_12/correct.txt b/legacy/Data/Questions/ingsw/0120_12/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_12/correct.txt
rename to legacy/Data/Questions/ingsw/0120_12/correct.txt
diff --git a/Data/Questions/ingsw/0120_12/quest.txt b/legacy/Data/Questions/ingsw/0120_12/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_12/quest.txt
rename to legacy/Data/Questions/ingsw/0120_12/quest.txt
diff --git a/Data/Questions/ingsw/0120_12/wrong1.txt b/legacy/Data/Questions/ingsw/0120_12/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_12/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_12/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_12/wrong2.txt b/legacy/Data/Questions/ingsw/0120_12/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_12/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_12/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_13/correct.txt b/legacy/Data/Questions/ingsw/0120_13/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_13/correct.txt
rename to legacy/Data/Questions/ingsw/0120_13/correct.txt
diff --git a/Data/Questions/ingsw/0120_13/quest.txt b/legacy/Data/Questions/ingsw/0120_13/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_13/quest.txt
rename to legacy/Data/Questions/ingsw/0120_13/quest.txt
diff --git a/Data/Questions/ingsw/0120_13/wrong1.txt b/legacy/Data/Questions/ingsw/0120_13/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_13/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_13/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_13/wrong2.txt b/legacy/Data/Questions/ingsw/0120_13/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_13/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_13/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_14/correct.txt b/legacy/Data/Questions/ingsw/0120_14/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_14/correct.txt
rename to legacy/Data/Questions/ingsw/0120_14/correct.txt
diff --git a/Data/Questions/ingsw/0120_14/quest.txt b/legacy/Data/Questions/ingsw/0120_14/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_14/quest.txt
rename to legacy/Data/Questions/ingsw/0120_14/quest.txt
diff --git a/Data/Questions/ingsw/0120_14/wrong1.txt b/legacy/Data/Questions/ingsw/0120_14/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_14/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_14/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_14/wrong2.txt b/legacy/Data/Questions/ingsw/0120_14/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_14/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_14/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_15/correct.txt b/legacy/Data/Questions/ingsw/0120_15/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_15/correct.txt
rename to legacy/Data/Questions/ingsw/0120_15/correct.txt
diff --git a/Data/Questions/ingsw/0120_15/quest.txt b/legacy/Data/Questions/ingsw/0120_15/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_15/quest.txt
rename to legacy/Data/Questions/ingsw/0120_15/quest.txt
diff --git a/Data/Questions/ingsw/0120_15/wrong1.txt b/legacy/Data/Questions/ingsw/0120_15/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_15/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_15/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_15/wrong2.txt b/legacy/Data/Questions/ingsw/0120_15/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_15/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_15/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_16/correct.txt b/legacy/Data/Questions/ingsw/0120_16/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_16/correct.txt
rename to legacy/Data/Questions/ingsw/0120_16/correct.txt
diff --git a/Data/Questions/ingsw/0120_16/quest.txt b/legacy/Data/Questions/ingsw/0120_16/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_16/quest.txt
rename to legacy/Data/Questions/ingsw/0120_16/quest.txt
diff --git a/Data/Questions/ingsw/0120_16/wrong1.txt b/legacy/Data/Questions/ingsw/0120_16/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_16/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_16/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_16/wrong2.txt b/legacy/Data/Questions/ingsw/0120_16/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_16/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_16/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_17/correct.txt b/legacy/Data/Questions/ingsw/0120_17/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_17/correct.txt
rename to legacy/Data/Questions/ingsw/0120_17/correct.txt
diff --git a/Data/Questions/ingsw/0120_17/quest.txt b/legacy/Data/Questions/ingsw/0120_17/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_17/quest.txt
rename to legacy/Data/Questions/ingsw/0120_17/quest.txt
diff --git a/Data/Questions/ingsw/0120_17/wrong1.txt b/legacy/Data/Questions/ingsw/0120_17/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_17/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_17/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_17/wrong2.txt b/legacy/Data/Questions/ingsw/0120_17/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_17/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_17/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_18/correct.txt b/legacy/Data/Questions/ingsw/0120_18/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_18/correct.txt
rename to legacy/Data/Questions/ingsw/0120_18/correct.txt
diff --git a/Data/Questions/ingsw/0120_18/quest.txt b/legacy/Data/Questions/ingsw/0120_18/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_18/quest.txt
rename to legacy/Data/Questions/ingsw/0120_18/quest.txt
diff --git a/Data/Questions/ingsw/0120_18/wrong1.txt b/legacy/Data/Questions/ingsw/0120_18/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_18/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_18/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_18/wrong2.txt b/legacy/Data/Questions/ingsw/0120_18/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_18/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_18/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_19/correct.txt b/legacy/Data/Questions/ingsw/0120_19/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_19/correct.txt
rename to legacy/Data/Questions/ingsw/0120_19/correct.txt
diff --git a/Data/Questions/ingsw/0120_19/quest.txt b/legacy/Data/Questions/ingsw/0120_19/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_19/quest.txt
rename to legacy/Data/Questions/ingsw/0120_19/quest.txt
diff --git a/Data/Questions/ingsw/0120_19/wrong1.txt b/legacy/Data/Questions/ingsw/0120_19/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_19/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_19/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_19/wrong2.txt b/legacy/Data/Questions/ingsw/0120_19/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_19/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_19/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_2/correct.txt b/legacy/Data/Questions/ingsw/0120_2/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_2/correct.txt
rename to legacy/Data/Questions/ingsw/0120_2/correct.txt
diff --git a/Data/Questions/ingsw/0120_2/quest.txt b/legacy/Data/Questions/ingsw/0120_2/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_2/quest.txt
rename to legacy/Data/Questions/ingsw/0120_2/quest.txt
diff --git a/Data/Questions/ingsw/0120_2/wrong1.txt b/legacy/Data/Questions/ingsw/0120_2/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_2/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_2/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_2/wrong2.txt b/legacy/Data/Questions/ingsw/0120_2/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_2/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_2/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_20/correct.txt b/legacy/Data/Questions/ingsw/0120_20/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_20/correct.txt
rename to legacy/Data/Questions/ingsw/0120_20/correct.txt
diff --git a/Data/Questions/ingsw/0120_20/quest.txt b/legacy/Data/Questions/ingsw/0120_20/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_20/quest.txt
rename to legacy/Data/Questions/ingsw/0120_20/quest.txt
diff --git a/Data/Questions/ingsw/0120_20/wrong1.txt b/legacy/Data/Questions/ingsw/0120_20/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_20/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_20/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_20/wrong2.txt b/legacy/Data/Questions/ingsw/0120_20/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_20/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_20/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_21/correct.txt b/legacy/Data/Questions/ingsw/0120_21/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_21/correct.txt
rename to legacy/Data/Questions/ingsw/0120_21/correct.txt
diff --git a/Data/Questions/ingsw/0120_21/quest.txt b/legacy/Data/Questions/ingsw/0120_21/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_21/quest.txt
rename to legacy/Data/Questions/ingsw/0120_21/quest.txt
diff --git a/Data/Questions/ingsw/0120_21/wrong1.txt b/legacy/Data/Questions/ingsw/0120_21/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_21/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_21/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_21/wrong2.txt b/legacy/Data/Questions/ingsw/0120_21/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_21/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_21/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_22/quest.txt b/legacy/Data/Questions/ingsw/0120_22/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_22/quest.txt
rename to legacy/Data/Questions/ingsw/0120_22/quest.txt
diff --git a/Data/Questions/ingsw/0120_25/wrong1.txt b/legacy/Data/Questions/ingsw/0120_22/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_25/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_22/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_22/wrong2.txt b/legacy/Data/Questions/ingsw/0120_22/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_22/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_22/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_22/wrong3.txt b/legacy/Data/Questions/ingsw/0120_22/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_22/wrong3.txt
rename to legacy/Data/Questions/ingsw/0120_22/wrong3.txt
diff --git a/Data/Questions/ingsw/0120_23/correct.txt b/legacy/Data/Questions/ingsw/0120_23/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_23/correct.txt
rename to legacy/Data/Questions/ingsw/0120_23/correct.txt
diff --git a/Data/Questions/ingsw/0120_23/quest.txt b/legacy/Data/Questions/ingsw/0120_23/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_23/quest.txt
rename to legacy/Data/Questions/ingsw/0120_23/quest.txt
diff --git a/Data/Questions/ingsw/0120_23/wrong1.txt b/legacy/Data/Questions/ingsw/0120_23/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_23/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_23/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_23/wrong2.txt b/legacy/Data/Questions/ingsw/0120_23/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_23/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_23/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_24/correct.txt b/legacy/Data/Questions/ingsw/0120_24/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_24/correct.txt
rename to legacy/Data/Questions/ingsw/0120_24/correct.txt
diff --git a/Data/Questions/ingsw/0120_24/quest.txt b/legacy/Data/Questions/ingsw/0120_24/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_24/quest.txt
rename to legacy/Data/Questions/ingsw/0120_24/quest.txt
diff --git a/Data/Questions/ingsw/0120_24/wrong1.txt b/legacy/Data/Questions/ingsw/0120_24/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_24/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_24/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_24/wrong2.txt b/legacy/Data/Questions/ingsw/0120_24/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_24/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_24/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_25/quest.txt b/legacy/Data/Questions/ingsw/0120_25/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_25/quest.txt
rename to legacy/Data/Questions/ingsw/0120_25/quest.txt
diff --git a/Data/Questions/ingsw/0120_39/wrong1.txt b/legacy/Data/Questions/ingsw/0120_25/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_39/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_25/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_25/wrong2.txt b/legacy/Data/Questions/ingsw/0120_25/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_25/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_25/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_25/wrong3.txt b/legacy/Data/Questions/ingsw/0120_25/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_25/wrong3.txt
rename to legacy/Data/Questions/ingsw/0120_25/wrong3.txt
diff --git a/Data/Questions/ingsw/0120_26/correct.txt b/legacy/Data/Questions/ingsw/0120_26/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_26/correct.txt
rename to legacy/Data/Questions/ingsw/0120_26/correct.txt
diff --git a/Data/Questions/ingsw/0120_26/quest.txt b/legacy/Data/Questions/ingsw/0120_26/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_26/quest.txt
rename to legacy/Data/Questions/ingsw/0120_26/quest.txt
diff --git a/Data/Questions/ingsw/0120_26/wrong1.txt b/legacy/Data/Questions/ingsw/0120_26/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_26/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_26/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_26/wrong2.txt b/legacy/Data/Questions/ingsw/0120_26/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_26/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_26/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_27/correct.txt b/legacy/Data/Questions/ingsw/0120_27/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_27/correct.txt
rename to legacy/Data/Questions/ingsw/0120_27/correct.txt
diff --git a/Data/Questions/ingsw/0120_27/quest.txt b/legacy/Data/Questions/ingsw/0120_27/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_27/quest.txt
rename to legacy/Data/Questions/ingsw/0120_27/quest.txt
diff --git a/Data/Questions/ingsw/0120_27/wrong1.txt b/legacy/Data/Questions/ingsw/0120_27/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_27/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_27/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_27/wrong2.txt b/legacy/Data/Questions/ingsw/0120_27/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_27/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_27/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_28/quest.txt b/legacy/Data/Questions/ingsw/0120_28/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_28/quest.txt
rename to legacy/Data/Questions/ingsw/0120_28/quest.txt
diff --git a/Data/Questions/ingsw/0120_28/wrong1.txt b/legacy/Data/Questions/ingsw/0120_28/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_28/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_28/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_28/wrong2.txt b/legacy/Data/Questions/ingsw/0120_28/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_28/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_28/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_28/wrong3.txt b/legacy/Data/Questions/ingsw/0120_28/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_28/wrong3.txt
rename to legacy/Data/Questions/ingsw/0120_28/wrong3.txt
diff --git a/Data/Questions/ingsw/0120_29/correct.txt b/legacy/Data/Questions/ingsw/0120_29/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_29/correct.txt
rename to legacy/Data/Questions/ingsw/0120_29/correct.txt
diff --git a/Data/Questions/ingsw/0120_29/quest.txt b/legacy/Data/Questions/ingsw/0120_29/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_29/quest.txt
rename to legacy/Data/Questions/ingsw/0120_29/quest.txt
diff --git a/Data/Questions/ingsw/0120_29/wrong1.txt b/legacy/Data/Questions/ingsw/0120_29/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_29/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_29/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_29/wrong2.txt b/legacy/Data/Questions/ingsw/0120_29/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_29/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_29/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_3/correct.txt b/legacy/Data/Questions/ingsw/0120_3/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_3/correct.txt
rename to legacy/Data/Questions/ingsw/0120_3/correct.txt
diff --git a/Data/Questions/ingsw/0120_3/quest.txt b/legacy/Data/Questions/ingsw/0120_3/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_3/quest.txt
rename to legacy/Data/Questions/ingsw/0120_3/quest.txt
diff --git a/Data/Questions/ingsw/0120_3/wrong1.txt b/legacy/Data/Questions/ingsw/0120_3/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_3/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_3/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_3/wrong2.txt b/legacy/Data/Questions/ingsw/0120_3/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_3/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_3/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_30/correct.txt b/legacy/Data/Questions/ingsw/0120_30/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_30/correct.txt
rename to legacy/Data/Questions/ingsw/0120_30/correct.txt
diff --git a/Data/Questions/ingsw/0120_30/quest.txt b/legacy/Data/Questions/ingsw/0120_30/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_30/quest.txt
rename to legacy/Data/Questions/ingsw/0120_30/quest.txt
diff --git a/Data/Questions/ingsw/0120_30/wrong1.txt b/legacy/Data/Questions/ingsw/0120_30/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_30/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_30/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_30/wrong2.txt b/legacy/Data/Questions/ingsw/0120_30/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_30/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_30/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_31/correct.txt b/legacy/Data/Questions/ingsw/0120_31/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_31/correct.txt
rename to legacy/Data/Questions/ingsw/0120_31/correct.txt
diff --git a/Data/Questions/ingsw/0120_31/quest.txt b/legacy/Data/Questions/ingsw/0120_31/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_31/quest.txt
rename to legacy/Data/Questions/ingsw/0120_31/quest.txt
diff --git a/Data/Questions/ingsw/0120_31/wrong1.txt b/legacy/Data/Questions/ingsw/0120_31/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_31/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_31/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_31/wrong2.txt b/legacy/Data/Questions/ingsw/0120_31/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_31/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_31/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_32/correct.txt b/legacy/Data/Questions/ingsw/0120_32/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_32/correct.txt
rename to legacy/Data/Questions/ingsw/0120_32/correct.txt
diff --git a/Data/Questions/ingsw/0120_32/quest.txt b/legacy/Data/Questions/ingsw/0120_32/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_32/quest.txt
rename to legacy/Data/Questions/ingsw/0120_32/quest.txt
diff --git a/Data/Questions/ingsw/0120_32/wrong1.txt b/legacy/Data/Questions/ingsw/0120_32/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_32/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_32/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_32/wrong2.txt b/legacy/Data/Questions/ingsw/0120_32/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_32/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_32/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_33/correct.txt b/legacy/Data/Questions/ingsw/0120_33/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_33/correct.txt
rename to legacy/Data/Questions/ingsw/0120_33/correct.txt
diff --git a/Data/Questions/ingsw/0120_33/quest.txt b/legacy/Data/Questions/ingsw/0120_33/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_33/quest.txt
rename to legacy/Data/Questions/ingsw/0120_33/quest.txt
diff --git a/Data/Questions/ingsw/0120_33/wrong1.txt b/legacy/Data/Questions/ingsw/0120_33/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_33/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_33/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_33/wrong2.txt b/legacy/Data/Questions/ingsw/0120_33/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_33/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_33/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_34/quest.txt b/legacy/Data/Questions/ingsw/0120_34/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_34/quest.txt
rename to legacy/Data/Questions/ingsw/0120_34/quest.txt
diff --git a/Data/Questions/ingsw/0120_34/wrong1.txt b/legacy/Data/Questions/ingsw/0120_34/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_34/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_34/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_34/wrong2.txt b/legacy/Data/Questions/ingsw/0120_34/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_34/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_34/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_34/wrong3.txt b/legacy/Data/Questions/ingsw/0120_34/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_34/wrong3.txt
rename to legacy/Data/Questions/ingsw/0120_34/wrong3.txt
diff --git a/Data/Questions/ingsw/0120_35/quest.txt b/legacy/Data/Questions/ingsw/0120_35/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_35/quest.txt
rename to legacy/Data/Questions/ingsw/0120_35/quest.txt
diff --git a/Data/Questions/ingsw/0120_35/wrong1.txt b/legacy/Data/Questions/ingsw/0120_35/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_35/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_35/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_35/wrong2.txt b/legacy/Data/Questions/ingsw/0120_35/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_35/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_35/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_35/wrong3.txt b/legacy/Data/Questions/ingsw/0120_35/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_35/wrong3.txt
rename to legacy/Data/Questions/ingsw/0120_35/wrong3.txt
diff --git a/Data/Questions/ingsw/0120_36/correct.txt b/legacy/Data/Questions/ingsw/0120_36/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_36/correct.txt
rename to legacy/Data/Questions/ingsw/0120_36/correct.txt
diff --git a/Data/Questions/ingsw/0120_36/quest.txt b/legacy/Data/Questions/ingsw/0120_36/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_36/quest.txt
rename to legacy/Data/Questions/ingsw/0120_36/quest.txt
diff --git a/Data/Questions/ingsw/0120_36/wrong1.txt b/legacy/Data/Questions/ingsw/0120_36/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_36/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_36/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_36/wrong2.txt b/legacy/Data/Questions/ingsw/0120_36/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_36/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_36/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_37/correct.txt b/legacy/Data/Questions/ingsw/0120_37/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_37/correct.txt
rename to legacy/Data/Questions/ingsw/0120_37/correct.txt
diff --git a/Data/Questions/ingsw/0120_37/quest.txt b/legacy/Data/Questions/ingsw/0120_37/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_37/quest.txt
rename to legacy/Data/Questions/ingsw/0120_37/quest.txt
diff --git a/Data/Questions/ingsw/0120_37/wrong1.txt b/legacy/Data/Questions/ingsw/0120_37/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_37/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_37/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_37/wrong2.txt b/legacy/Data/Questions/ingsw/0120_37/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_37/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_37/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_38/correct.txt b/legacy/Data/Questions/ingsw/0120_38/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_38/correct.txt
rename to legacy/Data/Questions/ingsw/0120_38/correct.txt
diff --git a/Data/Questions/ingsw/0120_38/quest.txt b/legacy/Data/Questions/ingsw/0120_38/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_38/quest.txt
rename to legacy/Data/Questions/ingsw/0120_38/quest.txt
diff --git a/Data/Questions/ingsw/0120_38/wrong1.txt b/legacy/Data/Questions/ingsw/0120_38/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_38/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_38/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_38/wrong2.txt b/legacy/Data/Questions/ingsw/0120_38/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_38/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_38/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_39/quest.txt b/legacy/Data/Questions/ingsw/0120_39/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_39/quest.txt
rename to legacy/Data/Questions/ingsw/0120_39/quest.txt
diff --git a/Data/Questions/ingsw/0120_45/wrong1.txt b/legacy/Data/Questions/ingsw/0120_39/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_45/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_39/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_39/wrong2.txt b/legacy/Data/Questions/ingsw/0120_39/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_39/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_39/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_39/wrong3.txt b/legacy/Data/Questions/ingsw/0120_39/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_39/wrong3.txt
rename to legacy/Data/Questions/ingsw/0120_39/wrong3.txt
diff --git a/Data/Questions/ingsw/0120_4/correct.txt b/legacy/Data/Questions/ingsw/0120_4/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_4/correct.txt
rename to legacy/Data/Questions/ingsw/0120_4/correct.txt
diff --git a/Data/Questions/ingsw/0120_4/quest.txt b/legacy/Data/Questions/ingsw/0120_4/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_4/quest.txt
rename to legacy/Data/Questions/ingsw/0120_4/quest.txt
diff --git a/Data/Questions/ingsw/0120_4/wrong1.txt b/legacy/Data/Questions/ingsw/0120_4/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_4/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_4/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_4/wrong2.txt b/legacy/Data/Questions/ingsw/0120_4/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_4/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_4/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_40/correct.txt b/legacy/Data/Questions/ingsw/0120_40/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_40/correct.txt
rename to legacy/Data/Questions/ingsw/0120_40/correct.txt
diff --git a/Data/Questions/ingsw/0120_40/quest.txt b/legacy/Data/Questions/ingsw/0120_40/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_40/quest.txt
rename to legacy/Data/Questions/ingsw/0120_40/quest.txt
diff --git a/Data/Questions/ingsw/0120_40/wrong1.txt b/legacy/Data/Questions/ingsw/0120_40/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_40/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_40/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_40/wrong2.txt b/legacy/Data/Questions/ingsw/0120_40/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_40/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_40/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_41/correct.txt b/legacy/Data/Questions/ingsw/0120_41/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_41/correct.txt
rename to legacy/Data/Questions/ingsw/0120_41/correct.txt
diff --git a/Data/Questions/ingsw/0120_41/quest.txt b/legacy/Data/Questions/ingsw/0120_41/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_41/quest.txt
rename to legacy/Data/Questions/ingsw/0120_41/quest.txt
diff --git a/Data/Questions/ingsw/0120_41/wrong1.txt b/legacy/Data/Questions/ingsw/0120_41/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_41/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_41/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_41/wrong2.txt b/legacy/Data/Questions/ingsw/0120_41/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_41/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_41/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_42/correct.txt b/legacy/Data/Questions/ingsw/0120_42/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_42/correct.txt
rename to legacy/Data/Questions/ingsw/0120_42/correct.txt
diff --git a/Data/Questions/ingsw/0120_42/quest.txt b/legacy/Data/Questions/ingsw/0120_42/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_42/quest.txt
rename to legacy/Data/Questions/ingsw/0120_42/quest.txt
diff --git a/Data/Questions/ingsw/0120_42/wrong1.txt b/legacy/Data/Questions/ingsw/0120_42/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_42/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_42/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_42/wrong2.txt b/legacy/Data/Questions/ingsw/0120_42/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_42/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_42/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_43/quest.txt b/legacy/Data/Questions/ingsw/0120_43/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_43/quest.txt
rename to legacy/Data/Questions/ingsw/0120_43/quest.txt
diff --git a/Data/Questions/ingsw/0120_43/wrong1.txt b/legacy/Data/Questions/ingsw/0120_43/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_43/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_43/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_43/wrong2.txt b/legacy/Data/Questions/ingsw/0120_43/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_43/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_43/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_43/wrong3.txt b/legacy/Data/Questions/ingsw/0120_43/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_43/wrong3.txt
rename to legacy/Data/Questions/ingsw/0120_43/wrong3.txt
diff --git a/Data/Questions/ingsw/0120_44/correct.txt b/legacy/Data/Questions/ingsw/0120_44/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_44/correct.txt
rename to legacy/Data/Questions/ingsw/0120_44/correct.txt
diff --git a/Data/Questions/ingsw/0120_44/quest.txt b/legacy/Data/Questions/ingsw/0120_44/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_44/quest.txt
rename to legacy/Data/Questions/ingsw/0120_44/quest.txt
diff --git a/Data/Questions/ingsw/0120_44/wrong1.txt b/legacy/Data/Questions/ingsw/0120_44/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_44/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_44/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_44/wrong2.txt b/legacy/Data/Questions/ingsw/0120_44/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_44/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_44/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_45/quest.txt b/legacy/Data/Questions/ingsw/0120_45/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_45/quest.txt
rename to legacy/Data/Questions/ingsw/0120_45/quest.txt
diff --git a/Data/Questions/ingsw/0210_2/wrong1.txt b/legacy/Data/Questions/ingsw/0120_45/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_2/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_45/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_45/wrong2.txt b/legacy/Data/Questions/ingsw/0120_45/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_45/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_45/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_45/wrong3.txt b/legacy/Data/Questions/ingsw/0120_45/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_45/wrong3.txt
rename to legacy/Data/Questions/ingsw/0120_45/wrong3.txt
diff --git a/Data/Questions/ingsw/0120_46/correct.txt b/legacy/Data/Questions/ingsw/0120_46/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_46/correct.txt
rename to legacy/Data/Questions/ingsw/0120_46/correct.txt
diff --git a/Data/Questions/ingsw/0120_46/quest.txt b/legacy/Data/Questions/ingsw/0120_46/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_46/quest.txt
rename to legacy/Data/Questions/ingsw/0120_46/quest.txt
diff --git a/Data/Questions/ingsw/0120_46/wrong1.txt b/legacy/Data/Questions/ingsw/0120_46/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_46/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_46/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_46/wrong2.txt b/legacy/Data/Questions/ingsw/0120_46/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_46/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_46/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_47/correct.txt b/legacy/Data/Questions/ingsw/0120_47/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_47/correct.txt
rename to legacy/Data/Questions/ingsw/0120_47/correct.txt
diff --git a/Data/Questions/ingsw/0120_47/quest.txt b/legacy/Data/Questions/ingsw/0120_47/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_47/quest.txt
rename to legacy/Data/Questions/ingsw/0120_47/quest.txt
diff --git a/Data/Questions/ingsw/0120_47/wrong1.txt b/legacy/Data/Questions/ingsw/0120_47/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_47/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_47/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_47/wrong2.txt b/legacy/Data/Questions/ingsw/0120_47/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_47/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_47/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_48/correct.txt b/legacy/Data/Questions/ingsw/0120_48/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_48/correct.txt
rename to legacy/Data/Questions/ingsw/0120_48/correct.txt
diff --git a/Data/Questions/ingsw/0120_48/quest.txt b/legacy/Data/Questions/ingsw/0120_48/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_48/quest.txt
rename to legacy/Data/Questions/ingsw/0120_48/quest.txt
diff --git a/Data/Questions/ingsw/0120_48/wrong1.txt b/legacy/Data/Questions/ingsw/0120_48/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_48/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_48/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_48/wrong2.txt b/legacy/Data/Questions/ingsw/0120_48/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_48/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_48/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_49/correct.txt b/legacy/Data/Questions/ingsw/0120_49/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_49/correct.txt
rename to legacy/Data/Questions/ingsw/0120_49/correct.txt
diff --git a/Data/Questions/ingsw/0120_49/quest.txt b/legacy/Data/Questions/ingsw/0120_49/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_49/quest.txt
rename to legacy/Data/Questions/ingsw/0120_49/quest.txt
diff --git a/Data/Questions/ingsw/0120_49/wrong1.txt b/legacy/Data/Questions/ingsw/0120_49/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_49/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_49/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_49/wrong2.txt b/legacy/Data/Questions/ingsw/0120_49/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_49/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_49/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_5/quest.txt b/legacy/Data/Questions/ingsw/0120_5/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_5/quest.txt
rename to legacy/Data/Questions/ingsw/0120_5/quest.txt
diff --git a/Data/Questions/ingsw/0120_5/wrong1.txt b/legacy/Data/Questions/ingsw/0120_5/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_5/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_5/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_5/wrong2.txt b/legacy/Data/Questions/ingsw/0120_5/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_5/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_5/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_5/wrong3.txt b/legacy/Data/Questions/ingsw/0120_5/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_5/wrong3.txt
rename to legacy/Data/Questions/ingsw/0120_5/wrong3.txt
diff --git a/Data/Questions/ingsw/0120_6/correct.txt b/legacy/Data/Questions/ingsw/0120_6/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_6/correct.txt
rename to legacy/Data/Questions/ingsw/0120_6/correct.txt
diff --git a/Data/Questions/ingsw/0120_6/quest.txt b/legacy/Data/Questions/ingsw/0120_6/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_6/quest.txt
rename to legacy/Data/Questions/ingsw/0120_6/quest.txt
diff --git a/Data/Questions/ingsw/0120_6/wrong1.txt b/legacy/Data/Questions/ingsw/0120_6/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_6/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_6/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_6/wrong2.txt b/legacy/Data/Questions/ingsw/0120_6/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_6/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_6/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_7/correct.txt b/legacy/Data/Questions/ingsw/0120_7/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_7/correct.txt
rename to legacy/Data/Questions/ingsw/0120_7/correct.txt
diff --git a/Data/Questions/ingsw/0120_7/quest.txt b/legacy/Data/Questions/ingsw/0120_7/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_7/quest.txt
rename to legacy/Data/Questions/ingsw/0120_7/quest.txt
diff --git a/Data/Questions/ingsw/0120_7/wrong1.txt b/legacy/Data/Questions/ingsw/0120_7/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_7/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_7/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_7/wrong2.txt b/legacy/Data/Questions/ingsw/0120_7/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_7/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_7/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_8/correct.txt b/legacy/Data/Questions/ingsw/0120_8/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_8/correct.txt
rename to legacy/Data/Questions/ingsw/0120_8/correct.txt
diff --git a/Data/Questions/ingsw/0120_8/quest.txt b/legacy/Data/Questions/ingsw/0120_8/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_8/quest.txt
rename to legacy/Data/Questions/ingsw/0120_8/quest.txt
diff --git a/Data/Questions/ingsw/0120_8/wrong1.txt b/legacy/Data/Questions/ingsw/0120_8/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_8/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_8/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_8/wrong2.txt b/legacy/Data/Questions/ingsw/0120_8/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_8/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_8/wrong2.txt
diff --git a/Data/Questions/ingsw/0120_9/correct.txt b/legacy/Data/Questions/ingsw/0120_9/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_9/correct.txt
rename to legacy/Data/Questions/ingsw/0120_9/correct.txt
diff --git a/Data/Questions/ingsw/0120_9/quest.txt b/legacy/Data/Questions/ingsw/0120_9/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_9/quest.txt
rename to legacy/Data/Questions/ingsw/0120_9/quest.txt
diff --git a/Data/Questions/ingsw/0120_9/wrong1.txt b/legacy/Data/Questions/ingsw/0120_9/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_9/wrong1.txt
rename to legacy/Data/Questions/ingsw/0120_9/wrong1.txt
diff --git a/Data/Questions/ingsw/0120_9/wrong2.txt b/legacy/Data/Questions/ingsw/0120_9/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0120_9/wrong2.txt
rename to legacy/Data/Questions/ingsw/0120_9/wrong2.txt
diff --git a/Data/Questions/ingsw/0121_34/correct.txt b/legacy/Data/Questions/ingsw/0121_34/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0121_34/correct.txt
rename to legacy/Data/Questions/ingsw/0121_34/correct.txt
diff --git a/Data/Questions/ingsw/0121_34/quest.txt b/legacy/Data/Questions/ingsw/0121_34/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0121_34/quest.txt
rename to legacy/Data/Questions/ingsw/0121_34/quest.txt
diff --git a/Data/Questions/ingsw/0121_34/wrong1.txt b/legacy/Data/Questions/ingsw/0121_34/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0121_34/wrong1.txt
rename to legacy/Data/Questions/ingsw/0121_34/wrong1.txt
diff --git a/Data/Questions/ingsw/0121_34/wrong2.txt b/legacy/Data/Questions/ingsw/0121_34/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0121_34/wrong2.txt
rename to legacy/Data/Questions/ingsw/0121_34/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_0/correct.txt b/legacy/Data/Questions/ingsw/0210_0/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_0/correct.txt
rename to legacy/Data/Questions/ingsw/0210_0/correct.txt
diff --git a/Data/Questions/ingsw/0210_0/quest.txt b/legacy/Data/Questions/ingsw/0210_0/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_0/quest.txt
rename to legacy/Data/Questions/ingsw/0210_0/quest.txt
diff --git a/Data/Questions/ingsw/0210_0/wrong1.txt b/legacy/Data/Questions/ingsw/0210_0/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_0/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_0/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_0/wrong2.txt b/legacy/Data/Questions/ingsw/0210_0/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_0/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_0/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_1/quest.txt b/legacy/Data/Questions/ingsw/0210_1/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_1/quest.txt
rename to legacy/Data/Questions/ingsw/0210_1/quest.txt
diff --git a/Data/Questions/ingsw/0210_1/wrong1.txt b/legacy/Data/Questions/ingsw/0210_1/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_1/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_1/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_1/wrong2.txt b/legacy/Data/Questions/ingsw/0210_1/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_1/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_1/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_1/wrong3.txt b/legacy/Data/Questions/ingsw/0210_1/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_1/wrong3.txt
rename to legacy/Data/Questions/ingsw/0210_1/wrong3.txt
diff --git a/Data/Questions/ingsw/0210_10/correct.txt b/legacy/Data/Questions/ingsw/0210_10/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_10/correct.txt
rename to legacy/Data/Questions/ingsw/0210_10/correct.txt
diff --git a/Data/Questions/ingsw/0210_10/quest.txt b/legacy/Data/Questions/ingsw/0210_10/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_10/quest.txt
rename to legacy/Data/Questions/ingsw/0210_10/quest.txt
diff --git a/Data/Questions/ingsw/0210_10/wrong1.txt b/legacy/Data/Questions/ingsw/0210_10/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_10/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_10/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_10/wrong2.txt b/legacy/Data/Questions/ingsw/0210_10/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_10/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_10/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_11/quest.txt b/legacy/Data/Questions/ingsw/0210_11/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_11/quest.txt
rename to legacy/Data/Questions/ingsw/0210_11/quest.txt
diff --git a/Data/Questions/ingsw/0210_11/wrong1.txt b/legacy/Data/Questions/ingsw/0210_11/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_11/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_11/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_11/wrong2.txt b/legacy/Data/Questions/ingsw/0210_11/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_11/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_11/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_11/wrong3.txt b/legacy/Data/Questions/ingsw/0210_11/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_11/wrong3.txt
rename to legacy/Data/Questions/ingsw/0210_11/wrong3.txt
diff --git a/Data/Questions/ingsw/0210_12/quest.txt b/legacy/Data/Questions/ingsw/0210_12/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_12/quest.txt
rename to legacy/Data/Questions/ingsw/0210_12/quest.txt
diff --git a/Data/Questions/ingsw/0210_12/wrong1.txt b/legacy/Data/Questions/ingsw/0210_12/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_12/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_12/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_12/wrong2.txt b/legacy/Data/Questions/ingsw/0210_12/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_12/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_12/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_12/wrong3.txt b/legacy/Data/Questions/ingsw/0210_12/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_12/wrong3.txt
rename to legacy/Data/Questions/ingsw/0210_12/wrong3.txt
diff --git a/Data/Questions/ingsw/0210_13/correct.txt b/legacy/Data/Questions/ingsw/0210_13/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_13/correct.txt
rename to legacy/Data/Questions/ingsw/0210_13/correct.txt
diff --git a/Data/Questions/ingsw/0210_13/quest.txt b/legacy/Data/Questions/ingsw/0210_13/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_13/quest.txt
rename to legacy/Data/Questions/ingsw/0210_13/quest.txt
diff --git a/Data/Questions/ingsw/0210_13/wrong1.txt b/legacy/Data/Questions/ingsw/0210_13/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_13/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_13/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_13/wrong2.txt b/legacy/Data/Questions/ingsw/0210_13/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_13/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_13/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_14/correct.txt b/legacy/Data/Questions/ingsw/0210_14/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_14/correct.txt
rename to legacy/Data/Questions/ingsw/0210_14/correct.txt
diff --git a/Data/Questions/ingsw/0210_14/quest.txt b/legacy/Data/Questions/ingsw/0210_14/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_14/quest.txt
rename to legacy/Data/Questions/ingsw/0210_14/quest.txt
diff --git a/Data/Questions/ingsw/0210_14/wrong1.txt b/legacy/Data/Questions/ingsw/0210_14/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_14/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_14/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_14/wrong2.txt b/legacy/Data/Questions/ingsw/0210_14/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_14/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_14/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_15/correct.txt b/legacy/Data/Questions/ingsw/0210_15/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_15/correct.txt
rename to legacy/Data/Questions/ingsw/0210_15/correct.txt
diff --git a/Data/Questions/ingsw/0210_15/quest.txt b/legacy/Data/Questions/ingsw/0210_15/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_15/quest.txt
rename to legacy/Data/Questions/ingsw/0210_15/quest.txt
diff --git a/Data/Questions/ingsw/0210_15/wrong1.txt b/legacy/Data/Questions/ingsw/0210_15/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_15/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_15/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_15/wrong2.txt b/legacy/Data/Questions/ingsw/0210_15/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_15/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_15/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_16/correct.txt b/legacy/Data/Questions/ingsw/0210_16/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_16/correct.txt
rename to legacy/Data/Questions/ingsw/0210_16/correct.txt
diff --git a/Data/Questions/ingsw/0210_16/quest.txt b/legacy/Data/Questions/ingsw/0210_16/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_16/quest.txt
rename to legacy/Data/Questions/ingsw/0210_16/quest.txt
diff --git a/Data/Questions/ingsw/0210_16/wrong1.txt b/legacy/Data/Questions/ingsw/0210_16/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_16/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_16/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_16/wrong2.txt b/legacy/Data/Questions/ingsw/0210_16/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_16/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_16/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_17/correct.txt b/legacy/Data/Questions/ingsw/0210_17/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_17/correct.txt
rename to legacy/Data/Questions/ingsw/0210_17/correct.txt
diff --git a/Data/Questions/ingsw/0210_17/quest.txt b/legacy/Data/Questions/ingsw/0210_17/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_17/quest.txt
rename to legacy/Data/Questions/ingsw/0210_17/quest.txt
diff --git a/Data/Questions/ingsw/0210_17/wrong1.txt b/legacy/Data/Questions/ingsw/0210_17/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_17/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_17/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_17/wrong2.txt b/legacy/Data/Questions/ingsw/0210_17/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_17/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_17/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_18/correct.txt b/legacy/Data/Questions/ingsw/0210_18/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_18/correct.txt
rename to legacy/Data/Questions/ingsw/0210_18/correct.txt
diff --git a/Data/Questions/ingsw/0210_18/quest.txt b/legacy/Data/Questions/ingsw/0210_18/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_18/quest.txt
rename to legacy/Data/Questions/ingsw/0210_18/quest.txt
diff --git a/Data/Questions/ingsw/0210_18/wrong1.txt b/legacy/Data/Questions/ingsw/0210_18/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_18/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_18/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_18/wrong2.txt b/legacy/Data/Questions/ingsw/0210_18/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_18/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_18/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_19/correct.txt b/legacy/Data/Questions/ingsw/0210_19/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_19/correct.txt
rename to legacy/Data/Questions/ingsw/0210_19/correct.txt
diff --git a/Data/Questions/ingsw/0210_19/quest.txt b/legacy/Data/Questions/ingsw/0210_19/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_19/quest.txt
rename to legacy/Data/Questions/ingsw/0210_19/quest.txt
diff --git a/Data/Questions/ingsw/0210_19/wrong1.txt b/legacy/Data/Questions/ingsw/0210_19/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_19/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_19/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_19/wrong2.txt b/legacy/Data/Questions/ingsw/0210_19/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_19/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_19/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_2/quest.txt b/legacy/Data/Questions/ingsw/0210_2/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_2/quest.txt
rename to legacy/Data/Questions/ingsw/0210_2/quest.txt
diff --git a/Data/Questions/ingsw/0210_28/wrong1.txt b/legacy/Data/Questions/ingsw/0210_2/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_28/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_2/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_2/wrong2.txt b/legacy/Data/Questions/ingsw/0210_2/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_2/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_2/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_2/wrong3.txt b/legacy/Data/Questions/ingsw/0210_2/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_2/wrong3.txt
rename to legacy/Data/Questions/ingsw/0210_2/wrong3.txt
diff --git a/Data/Questions/ingsw/0210_20/correct.txt b/legacy/Data/Questions/ingsw/0210_20/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_20/correct.txt
rename to legacy/Data/Questions/ingsw/0210_20/correct.txt
diff --git a/Data/Questions/ingsw/0210_20/quest.txt b/legacy/Data/Questions/ingsw/0210_20/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_20/quest.txt
rename to legacy/Data/Questions/ingsw/0210_20/quest.txt
diff --git a/Data/Questions/ingsw/0210_20/wrong1.txt b/legacy/Data/Questions/ingsw/0210_20/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_20/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_20/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_20/wrong2.txt b/legacy/Data/Questions/ingsw/0210_20/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_20/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_20/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_21/correct.txt b/legacy/Data/Questions/ingsw/0210_21/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_21/correct.txt
rename to legacy/Data/Questions/ingsw/0210_21/correct.txt
diff --git a/Data/Questions/ingsw/0210_21/quest.txt b/legacy/Data/Questions/ingsw/0210_21/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_21/quest.txt
rename to legacy/Data/Questions/ingsw/0210_21/quest.txt
diff --git a/Data/Questions/ingsw/0210_21/wrong1.txt b/legacy/Data/Questions/ingsw/0210_21/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_21/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_21/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_21/wrong2.txt b/legacy/Data/Questions/ingsw/0210_21/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_21/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_21/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_22/correct.txt b/legacy/Data/Questions/ingsw/0210_22/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_22/correct.txt
rename to legacy/Data/Questions/ingsw/0210_22/correct.txt
diff --git a/Data/Questions/ingsw/0210_22/quest.txt b/legacy/Data/Questions/ingsw/0210_22/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_22/quest.txt
rename to legacy/Data/Questions/ingsw/0210_22/quest.txt
diff --git a/Data/Questions/ingsw/0210_22/wrong1.txt b/legacy/Data/Questions/ingsw/0210_22/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_22/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_22/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_22/wrong2.txt b/legacy/Data/Questions/ingsw/0210_22/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_22/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_22/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_23/correct.txt b/legacy/Data/Questions/ingsw/0210_23/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_23/correct.txt
rename to legacy/Data/Questions/ingsw/0210_23/correct.txt
diff --git a/Data/Questions/ingsw/0210_23/quest.txt b/legacy/Data/Questions/ingsw/0210_23/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_23/quest.txt
rename to legacy/Data/Questions/ingsw/0210_23/quest.txt
diff --git a/Data/Questions/ingsw/0210_23/wrong1.txt b/legacy/Data/Questions/ingsw/0210_23/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_23/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_23/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_23/wrong2.txt b/legacy/Data/Questions/ingsw/0210_23/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_23/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_23/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_24/correct.txt b/legacy/Data/Questions/ingsw/0210_24/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_24/correct.txt
rename to legacy/Data/Questions/ingsw/0210_24/correct.txt
diff --git a/Data/Questions/ingsw/0210_24/quest.txt b/legacy/Data/Questions/ingsw/0210_24/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_24/quest.txt
rename to legacy/Data/Questions/ingsw/0210_24/quest.txt
diff --git a/Data/Questions/ingsw/0210_24/wrong1.txt b/legacy/Data/Questions/ingsw/0210_24/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_24/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_24/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_24/wrong2.txt b/legacy/Data/Questions/ingsw/0210_24/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_24/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_24/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_25/correct.txt b/legacy/Data/Questions/ingsw/0210_25/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_25/correct.txt
rename to legacy/Data/Questions/ingsw/0210_25/correct.txt
diff --git a/Data/Questions/ingsw/0210_25/quest.txt b/legacy/Data/Questions/ingsw/0210_25/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_25/quest.txt
rename to legacy/Data/Questions/ingsw/0210_25/quest.txt
diff --git a/Data/Questions/ingsw/0210_25/wrong1.txt b/legacy/Data/Questions/ingsw/0210_25/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_25/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_25/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_25/wrong2.txt b/legacy/Data/Questions/ingsw/0210_25/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_25/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_25/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_26/correct.txt b/legacy/Data/Questions/ingsw/0210_26/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_26/correct.txt
rename to legacy/Data/Questions/ingsw/0210_26/correct.txt
diff --git a/Data/Questions/ingsw/0210_26/quest.txt b/legacy/Data/Questions/ingsw/0210_26/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_26/quest.txt
rename to legacy/Data/Questions/ingsw/0210_26/quest.txt
diff --git a/Data/Questions/ingsw/0210_26/wrong1.txt b/legacy/Data/Questions/ingsw/0210_26/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_26/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_26/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_26/wrong2.txt b/legacy/Data/Questions/ingsw/0210_26/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_26/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_26/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_27/quest.txt b/legacy/Data/Questions/ingsw/0210_27/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_27/quest.txt
rename to legacy/Data/Questions/ingsw/0210_27/quest.txt
diff --git a/Data/Questions/ingsw/0210_27/wrong1.txt b/legacy/Data/Questions/ingsw/0210_27/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_27/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_27/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_27/wrong2.txt b/legacy/Data/Questions/ingsw/0210_27/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_27/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_27/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_27/wrong3.txt b/legacy/Data/Questions/ingsw/0210_27/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_27/wrong3.txt
rename to legacy/Data/Questions/ingsw/0210_27/wrong3.txt
diff --git a/Data/Questions/ingsw/0210_28/quest.txt b/legacy/Data/Questions/ingsw/0210_28/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_28/quest.txt
rename to legacy/Data/Questions/ingsw/0210_28/quest.txt
diff --git a/Data/Questions/ingsw/0210_34/wrong1.txt b/legacy/Data/Questions/ingsw/0210_28/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_34/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_28/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_28/wrong2.txt b/legacy/Data/Questions/ingsw/0210_28/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_28/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_28/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_28/wrong3.txt b/legacy/Data/Questions/ingsw/0210_28/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_28/wrong3.txt
rename to legacy/Data/Questions/ingsw/0210_28/wrong3.txt
diff --git a/Data/Questions/ingsw/0210_29/correct.txt b/legacy/Data/Questions/ingsw/0210_29/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_29/correct.txt
rename to legacy/Data/Questions/ingsw/0210_29/correct.txt
diff --git a/Data/Questions/ingsw/0210_29/quest.txt b/legacy/Data/Questions/ingsw/0210_29/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_29/quest.txt
rename to legacy/Data/Questions/ingsw/0210_29/quest.txt
diff --git a/Data/Questions/ingsw/0210_29/wrong1.txt b/legacy/Data/Questions/ingsw/0210_29/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_29/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_29/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_29/wrong2.txt b/legacy/Data/Questions/ingsw/0210_29/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_29/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_29/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_3/quest.txt b/legacy/Data/Questions/ingsw/0210_3/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_3/quest.txt
rename to legacy/Data/Questions/ingsw/0210_3/quest.txt
diff --git a/Data/Questions/ingsw/0210_3/wrong1.txt b/legacy/Data/Questions/ingsw/0210_3/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_3/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_3/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_3/wrong2.txt b/legacy/Data/Questions/ingsw/0210_3/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_3/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_3/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_3/wrong3.txt b/legacy/Data/Questions/ingsw/0210_3/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_3/wrong3.txt
rename to legacy/Data/Questions/ingsw/0210_3/wrong3.txt
diff --git a/Data/Questions/ingsw/0210_30/correct.txt b/legacy/Data/Questions/ingsw/0210_30/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_30/correct.txt
rename to legacy/Data/Questions/ingsw/0210_30/correct.txt
diff --git a/Data/Questions/ingsw/0210_30/quest.txt b/legacy/Data/Questions/ingsw/0210_30/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_30/quest.txt
rename to legacy/Data/Questions/ingsw/0210_30/quest.txt
diff --git a/Data/Questions/ingsw/0210_30/wrong1.txt b/legacy/Data/Questions/ingsw/0210_30/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_30/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_30/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_30/wrong2.txt b/legacy/Data/Questions/ingsw/0210_30/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_30/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_30/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_31/correct.txt b/legacy/Data/Questions/ingsw/0210_31/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_31/correct.txt
rename to legacy/Data/Questions/ingsw/0210_31/correct.txt
diff --git a/Data/Questions/ingsw/0210_31/quest.txt b/legacy/Data/Questions/ingsw/0210_31/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_31/quest.txt
rename to legacy/Data/Questions/ingsw/0210_31/quest.txt
diff --git a/Data/Questions/ingsw/0210_31/wrong1.txt b/legacy/Data/Questions/ingsw/0210_31/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_31/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_31/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_31/wrong2.txt b/legacy/Data/Questions/ingsw/0210_31/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_31/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_31/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_32/correct.txt b/legacy/Data/Questions/ingsw/0210_32/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_32/correct.txt
rename to legacy/Data/Questions/ingsw/0210_32/correct.txt
diff --git a/Data/Questions/ingsw/0210_32/quest.txt b/legacy/Data/Questions/ingsw/0210_32/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_32/quest.txt
rename to legacy/Data/Questions/ingsw/0210_32/quest.txt
diff --git a/Data/Questions/ingsw/0210_32/wrong1.txt b/legacy/Data/Questions/ingsw/0210_32/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_32/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_32/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_32/wrong2.txt b/legacy/Data/Questions/ingsw/0210_32/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_32/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_32/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_33/correct.txt b/legacy/Data/Questions/ingsw/0210_33/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_33/correct.txt
rename to legacy/Data/Questions/ingsw/0210_33/correct.txt
diff --git a/Data/Questions/ingsw/0210_33/quest.txt b/legacy/Data/Questions/ingsw/0210_33/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_33/quest.txt
rename to legacy/Data/Questions/ingsw/0210_33/quest.txt
diff --git a/Data/Questions/ingsw/0210_33/wrong1.txt b/legacy/Data/Questions/ingsw/0210_33/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_33/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_33/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_33/wrong2.txt b/legacy/Data/Questions/ingsw/0210_33/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_33/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_33/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_34/quest.txt b/legacy/Data/Questions/ingsw/0210_34/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_34/quest.txt
rename to legacy/Data/Questions/ingsw/0210_34/quest.txt
diff --git a/Data/Questions/ingsw/0210_37/wrong1.txt b/legacy/Data/Questions/ingsw/0210_34/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_37/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_34/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_34/wrong2.txt b/legacy/Data/Questions/ingsw/0210_34/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_34/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_34/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_34/wrong3.txt b/legacy/Data/Questions/ingsw/0210_34/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_34/wrong3.txt
rename to legacy/Data/Questions/ingsw/0210_34/wrong3.txt
diff --git a/Data/Questions/ingsw/0210_35/correct.txt b/legacy/Data/Questions/ingsw/0210_35/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_35/correct.txt
rename to legacy/Data/Questions/ingsw/0210_35/correct.txt
diff --git a/Data/Questions/ingsw/0210_35/quest.txt b/legacy/Data/Questions/ingsw/0210_35/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_35/quest.txt
rename to legacy/Data/Questions/ingsw/0210_35/quest.txt
diff --git a/Data/Questions/ingsw/0210_35/wrong1.txt b/legacy/Data/Questions/ingsw/0210_35/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_35/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_35/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_35/wrong2.txt b/legacy/Data/Questions/ingsw/0210_35/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_35/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_35/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_36/correct.txt b/legacy/Data/Questions/ingsw/0210_36/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_36/correct.txt
rename to legacy/Data/Questions/ingsw/0210_36/correct.txt
diff --git a/Data/Questions/ingsw/0210_36/quest.txt b/legacy/Data/Questions/ingsw/0210_36/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_36/quest.txt
rename to legacy/Data/Questions/ingsw/0210_36/quest.txt
diff --git a/Data/Questions/ingsw/0210_36/wrong1.txt b/legacy/Data/Questions/ingsw/0210_36/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_36/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_36/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_36/wrong2.txt b/legacy/Data/Questions/ingsw/0210_36/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_36/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_36/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_37/quest.txt b/legacy/Data/Questions/ingsw/0210_37/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_37/quest.txt
rename to legacy/Data/Questions/ingsw/0210_37/quest.txt
diff --git a/Data/Questions/ingsw/0324_26/wrong1.txt b/legacy/Data/Questions/ingsw/0210_37/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_26/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_37/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_37/wrong2.txt b/legacy/Data/Questions/ingsw/0210_37/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_37/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_37/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_37/wrong3.txt b/legacy/Data/Questions/ingsw/0210_37/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_37/wrong3.txt
rename to legacy/Data/Questions/ingsw/0210_37/wrong3.txt
diff --git a/Data/Questions/ingsw/0210_38/correct.txt b/legacy/Data/Questions/ingsw/0210_38/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_38/correct.txt
rename to legacy/Data/Questions/ingsw/0210_38/correct.txt
diff --git a/Data/Questions/ingsw/0210_38/quest.txt b/legacy/Data/Questions/ingsw/0210_38/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_38/quest.txt
rename to legacy/Data/Questions/ingsw/0210_38/quest.txt
diff --git a/Data/Questions/ingsw/0210_38/wrong1.txt b/legacy/Data/Questions/ingsw/0210_38/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_38/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_38/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_38/wrong2.txt b/legacy/Data/Questions/ingsw/0210_38/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_38/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_38/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_39/correct.txt b/legacy/Data/Questions/ingsw/0210_39/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_39/correct.txt
rename to legacy/Data/Questions/ingsw/0210_39/correct.txt
diff --git a/Data/Questions/ingsw/0210_39/quest.txt b/legacy/Data/Questions/ingsw/0210_39/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_39/quest.txt
rename to legacy/Data/Questions/ingsw/0210_39/quest.txt
diff --git a/Data/Questions/ingsw/0210_39/wrong1.txt b/legacy/Data/Questions/ingsw/0210_39/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_39/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_39/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_39/wrong2.txt b/legacy/Data/Questions/ingsw/0210_39/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_39/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_39/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_4/correct.txt b/legacy/Data/Questions/ingsw/0210_4/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_4/correct.txt
rename to legacy/Data/Questions/ingsw/0210_4/correct.txt
diff --git a/Data/Questions/ingsw/0210_4/quest.txt b/legacy/Data/Questions/ingsw/0210_4/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_4/quest.txt
rename to legacy/Data/Questions/ingsw/0210_4/quest.txt
diff --git a/Data/Questions/ingsw/0210_4/wrong1.txt b/legacy/Data/Questions/ingsw/0210_4/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_4/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_4/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_4/wrong2.txt b/legacy/Data/Questions/ingsw/0210_4/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_4/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_4/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_40/correct.txt b/legacy/Data/Questions/ingsw/0210_40/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_40/correct.txt
rename to legacy/Data/Questions/ingsw/0210_40/correct.txt
diff --git a/Data/Questions/ingsw/0210_40/quest.txt b/legacy/Data/Questions/ingsw/0210_40/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_40/quest.txt
rename to legacy/Data/Questions/ingsw/0210_40/quest.txt
diff --git a/Data/Questions/ingsw/0210_40/wrong1.txt b/legacy/Data/Questions/ingsw/0210_40/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_40/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_40/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_40/wrong2.txt b/legacy/Data/Questions/ingsw/0210_40/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_40/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_40/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_41/correct.txt b/legacy/Data/Questions/ingsw/0210_41/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_41/correct.txt
rename to legacy/Data/Questions/ingsw/0210_41/correct.txt
diff --git a/Data/Questions/ingsw/0210_41/quest.txt b/legacy/Data/Questions/ingsw/0210_41/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_41/quest.txt
rename to legacy/Data/Questions/ingsw/0210_41/quest.txt
diff --git a/Data/Questions/ingsw/0210_41/wrong1.txt b/legacy/Data/Questions/ingsw/0210_41/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_41/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_41/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_41/wrong2.txt b/legacy/Data/Questions/ingsw/0210_41/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_41/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_41/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_42/quest.txt b/legacy/Data/Questions/ingsw/0210_42/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_42/quest.txt
rename to legacy/Data/Questions/ingsw/0210_42/quest.txt
diff --git a/Data/Questions/ingsw/0210_42/wrong1.txt b/legacy/Data/Questions/ingsw/0210_42/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_42/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_42/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_42/wrong2.txt b/legacy/Data/Questions/ingsw/0210_42/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_42/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_42/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_42/wrong3.txt b/legacy/Data/Questions/ingsw/0210_42/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_42/wrong3.txt
rename to legacy/Data/Questions/ingsw/0210_42/wrong3.txt
diff --git a/Data/Questions/ingsw/0210_43/correct.txt b/legacy/Data/Questions/ingsw/0210_43/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_43/correct.txt
rename to legacy/Data/Questions/ingsw/0210_43/correct.txt
diff --git a/Data/Questions/ingsw/0210_43/quest.txt b/legacy/Data/Questions/ingsw/0210_43/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_43/quest.txt
rename to legacy/Data/Questions/ingsw/0210_43/quest.txt
diff --git a/Data/Questions/ingsw/0210_43/wrong1.txt b/legacy/Data/Questions/ingsw/0210_43/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_43/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_43/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_43/wrong2.txt b/legacy/Data/Questions/ingsw/0210_43/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_43/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_43/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_44/quest.txt b/legacy/Data/Questions/ingsw/0210_44/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_44/quest.txt
rename to legacy/Data/Questions/ingsw/0210_44/quest.txt
diff --git a/Data/Questions/ingsw/0210_44/wrong1.txt b/legacy/Data/Questions/ingsw/0210_44/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_44/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_44/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_44/wrong2.txt b/legacy/Data/Questions/ingsw/0210_44/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_44/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_44/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_44/wrong3.txt b/legacy/Data/Questions/ingsw/0210_44/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_44/wrong3.txt
rename to legacy/Data/Questions/ingsw/0210_44/wrong3.txt
diff --git a/Data/Questions/ingsw/0210_45/correct.txt b/legacy/Data/Questions/ingsw/0210_45/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_45/correct.txt
rename to legacy/Data/Questions/ingsw/0210_45/correct.txt
diff --git a/Data/Questions/ingsw/0210_45/quest.txt b/legacy/Data/Questions/ingsw/0210_45/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_45/quest.txt
rename to legacy/Data/Questions/ingsw/0210_45/quest.txt
diff --git a/Data/Questions/ingsw/0210_45/wrong1.txt b/legacy/Data/Questions/ingsw/0210_45/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_45/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_45/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_45/wrong2.txt b/legacy/Data/Questions/ingsw/0210_45/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_45/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_45/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_46/correct.txt b/legacy/Data/Questions/ingsw/0210_46/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_46/correct.txt
rename to legacy/Data/Questions/ingsw/0210_46/correct.txt
diff --git a/Data/Questions/ingsw/0210_46/quest.txt b/legacy/Data/Questions/ingsw/0210_46/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_46/quest.txt
rename to legacy/Data/Questions/ingsw/0210_46/quest.txt
diff --git a/Data/Questions/ingsw/0210_46/wrong1.txt b/legacy/Data/Questions/ingsw/0210_46/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_46/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_46/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_46/wrong2.txt b/legacy/Data/Questions/ingsw/0210_46/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_46/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_46/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_47/correct.txt b/legacy/Data/Questions/ingsw/0210_47/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_47/correct.txt
rename to legacy/Data/Questions/ingsw/0210_47/correct.txt
diff --git a/Data/Questions/ingsw/0210_47/quest.txt b/legacy/Data/Questions/ingsw/0210_47/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_47/quest.txt
rename to legacy/Data/Questions/ingsw/0210_47/quest.txt
diff --git a/Data/Questions/ingsw/0210_47/wrong1.txt b/legacy/Data/Questions/ingsw/0210_47/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_47/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_47/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_47/wrong2.txt b/legacy/Data/Questions/ingsw/0210_47/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_47/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_47/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_48/correct.txt b/legacy/Data/Questions/ingsw/0210_48/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_48/correct.txt
rename to legacy/Data/Questions/ingsw/0210_48/correct.txt
diff --git a/Data/Questions/ingsw/0210_48/quest.txt b/legacy/Data/Questions/ingsw/0210_48/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_48/quest.txt
rename to legacy/Data/Questions/ingsw/0210_48/quest.txt
diff --git a/Data/Questions/ingsw/0210_48/wrong1.txt b/legacy/Data/Questions/ingsw/0210_48/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_48/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_48/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_48/wrong2.txt b/legacy/Data/Questions/ingsw/0210_48/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_48/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_48/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_49/correct.txt b/legacy/Data/Questions/ingsw/0210_49/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_49/correct.txt
rename to legacy/Data/Questions/ingsw/0210_49/correct.txt
diff --git a/Data/Questions/ingsw/0210_49/quest.txt b/legacy/Data/Questions/ingsw/0210_49/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_49/quest.txt
rename to legacy/Data/Questions/ingsw/0210_49/quest.txt
diff --git a/Data/Questions/ingsw/0210_49/wrong1.txt b/legacy/Data/Questions/ingsw/0210_49/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_49/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_49/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_49/wrong2.txt b/legacy/Data/Questions/ingsw/0210_49/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_49/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_49/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_5/correct.txt b/legacy/Data/Questions/ingsw/0210_5/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_5/correct.txt
rename to legacy/Data/Questions/ingsw/0210_5/correct.txt
diff --git a/Data/Questions/ingsw/0210_5/quest.txt b/legacy/Data/Questions/ingsw/0210_5/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_5/quest.txt
rename to legacy/Data/Questions/ingsw/0210_5/quest.txt
diff --git a/Data/Questions/ingsw/0210_5/wrong1.txt b/legacy/Data/Questions/ingsw/0210_5/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_5/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_5/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_5/wrong2.txt b/legacy/Data/Questions/ingsw/0210_5/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_5/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_5/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_6/correct.txt b/legacy/Data/Questions/ingsw/0210_6/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_6/correct.txt
rename to legacy/Data/Questions/ingsw/0210_6/correct.txt
diff --git a/Data/Questions/ingsw/0210_6/quest.txt b/legacy/Data/Questions/ingsw/0210_6/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_6/quest.txt
rename to legacy/Data/Questions/ingsw/0210_6/quest.txt
diff --git a/Data/Questions/ingsw/0210_6/wrong1.txt b/legacy/Data/Questions/ingsw/0210_6/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_6/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_6/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_6/wrong2.txt b/legacy/Data/Questions/ingsw/0210_6/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_6/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_6/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_7/correct.txt b/legacy/Data/Questions/ingsw/0210_7/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_7/correct.txt
rename to legacy/Data/Questions/ingsw/0210_7/correct.txt
diff --git a/Data/Questions/ingsw/0210_7/quest.txt b/legacy/Data/Questions/ingsw/0210_7/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_7/quest.txt
rename to legacy/Data/Questions/ingsw/0210_7/quest.txt
diff --git a/Data/Questions/ingsw/0210_7/wrong1.txt b/legacy/Data/Questions/ingsw/0210_7/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_7/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_7/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_7/wrong2.txt b/legacy/Data/Questions/ingsw/0210_7/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_7/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_7/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_8/correct.txt b/legacy/Data/Questions/ingsw/0210_8/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_8/correct.txt
rename to legacy/Data/Questions/ingsw/0210_8/correct.txt
diff --git a/Data/Questions/ingsw/0210_8/quest.txt b/legacy/Data/Questions/ingsw/0210_8/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_8/quest.txt
rename to legacy/Data/Questions/ingsw/0210_8/quest.txt
diff --git a/Data/Questions/ingsw/0210_8/wrong1.txt b/legacy/Data/Questions/ingsw/0210_8/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_8/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_8/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_8/wrong2.txt b/legacy/Data/Questions/ingsw/0210_8/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_8/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_8/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_9/quest.txt b/legacy/Data/Questions/ingsw/0210_9/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_9/quest.txt
rename to legacy/Data/Questions/ingsw/0210_9/quest.txt
diff --git a/Data/Questions/ingsw/0210_9/wrong1.txt b/legacy/Data/Questions/ingsw/0210_9/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_9/wrong1.txt
rename to legacy/Data/Questions/ingsw/0210_9/wrong1.txt
diff --git a/Data/Questions/ingsw/0210_9/wrong2.txt b/legacy/Data/Questions/ingsw/0210_9/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_9/wrong2.txt
rename to legacy/Data/Questions/ingsw/0210_9/wrong2.txt
diff --git a/Data/Questions/ingsw/0210_9/wrong3.txt b/legacy/Data/Questions/ingsw/0210_9/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0210_9/wrong3.txt
rename to legacy/Data/Questions/ingsw/0210_9/wrong3.txt
diff --git a/Data/Questions/ingsw/0221_18/correct.txt b/legacy/Data/Questions/ingsw/0221_18/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0221_18/correct.txt
rename to legacy/Data/Questions/ingsw/0221_18/correct.txt
diff --git a/Data/Questions/ingsw/0221_18/quest.txt b/legacy/Data/Questions/ingsw/0221_18/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0221_18/quest.txt
rename to legacy/Data/Questions/ingsw/0221_18/quest.txt
diff --git a/Data/Questions/ingsw/0221_18/wrong1.txt b/legacy/Data/Questions/ingsw/0221_18/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0221_18/wrong1.txt
rename to legacy/Data/Questions/ingsw/0221_18/wrong1.txt
diff --git a/Data/Questions/ingsw/0221_18/wrong2.txt b/legacy/Data/Questions/ingsw/0221_18/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0221_18/wrong2.txt
rename to legacy/Data/Questions/ingsw/0221_18/wrong2.txt
diff --git a/Data/Questions/ingsw/0221_28/correct.txt b/legacy/Data/Questions/ingsw/0221_28/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0221_28/correct.txt
rename to legacy/Data/Questions/ingsw/0221_28/correct.txt
diff --git a/Data/Questions/ingsw/0221_28/quest.txt b/legacy/Data/Questions/ingsw/0221_28/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0221_28/quest.txt
rename to legacy/Data/Questions/ingsw/0221_28/quest.txt
diff --git a/Data/Questions/ingsw/0221_28/wrong1.txt b/legacy/Data/Questions/ingsw/0221_28/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0221_28/wrong1.txt
rename to legacy/Data/Questions/ingsw/0221_28/wrong1.txt
diff --git a/Data/Questions/ingsw/0221_28/wrong2.txt b/legacy/Data/Questions/ingsw/0221_28/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0221_28/wrong2.txt
rename to legacy/Data/Questions/ingsw/0221_28/wrong2.txt
diff --git a/Data/Questions/ingsw/0221_32/correct.txt b/legacy/Data/Questions/ingsw/0221_32/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0221_32/correct.txt
rename to legacy/Data/Questions/ingsw/0221_32/correct.txt
diff --git a/Data/Questions/ingsw/0221_32/quest.txt b/legacy/Data/Questions/ingsw/0221_32/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0221_32/quest.txt
rename to legacy/Data/Questions/ingsw/0221_32/quest.txt
diff --git a/Data/Questions/ingsw/0221_32/wrong1.txt b/legacy/Data/Questions/ingsw/0221_32/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0221_32/wrong1.txt
rename to legacy/Data/Questions/ingsw/0221_32/wrong1.txt
diff --git a/Data/Questions/ingsw/0221_32/wrong2.txt b/legacy/Data/Questions/ingsw/0221_32/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0221_32/wrong2.txt
rename to legacy/Data/Questions/ingsw/0221_32/wrong2.txt
diff --git a/Data/Questions/ingsw/0222_24/correct.txt b/legacy/Data/Questions/ingsw/0222_24/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_24/correct.txt
rename to legacy/Data/Questions/ingsw/0222_24/correct.txt
diff --git a/Data/Questions/ingsw/0222_24/quest.txt b/legacy/Data/Questions/ingsw/0222_24/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_24/quest.txt
rename to legacy/Data/Questions/ingsw/0222_24/quest.txt
diff --git a/Data/Questions/ingsw/0222_24/wrong1.txt b/legacy/Data/Questions/ingsw/0222_24/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_24/wrong1.txt
rename to legacy/Data/Questions/ingsw/0222_24/wrong1.txt
diff --git a/Data/Questions/ingsw/0222_24/wrong2.txt b/legacy/Data/Questions/ingsw/0222_24/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_24/wrong2.txt
rename to legacy/Data/Questions/ingsw/0222_24/wrong2.txt
diff --git a/Data/Questions/ingsw/0222_27/correct.txt b/legacy/Data/Questions/ingsw/0222_27/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_27/correct.txt
rename to legacy/Data/Questions/ingsw/0222_27/correct.txt
diff --git a/Data/Questions/ingsw/0222_27/quest.txt b/legacy/Data/Questions/ingsw/0222_27/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_27/quest.txt
rename to legacy/Data/Questions/ingsw/0222_27/quest.txt
diff --git a/Data/Questions/ingsw/0222_27/wrong1.txt b/legacy/Data/Questions/ingsw/0222_27/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_27/wrong1.txt
rename to legacy/Data/Questions/ingsw/0222_27/wrong1.txt
diff --git a/Data/Questions/ingsw/0222_27/wrong2.txt b/legacy/Data/Questions/ingsw/0222_27/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_27/wrong2.txt
rename to legacy/Data/Questions/ingsw/0222_27/wrong2.txt
diff --git a/Data/Questions/ingsw/0222_33/correct.txt b/legacy/Data/Questions/ingsw/0222_33/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_33/correct.txt
rename to legacy/Data/Questions/ingsw/0222_33/correct.txt
diff --git a/Data/Questions/ingsw/0222_33/quest.txt b/legacy/Data/Questions/ingsw/0222_33/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_33/quest.txt
rename to legacy/Data/Questions/ingsw/0222_33/quest.txt
diff --git a/Data/Questions/ingsw/0222_33/wrong1.txt b/legacy/Data/Questions/ingsw/0222_33/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_33/wrong1.txt
rename to legacy/Data/Questions/ingsw/0222_33/wrong1.txt
diff --git a/Data/Questions/ingsw/0222_33/wrong2.txt b/legacy/Data/Questions/ingsw/0222_33/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_33/wrong2.txt
rename to legacy/Data/Questions/ingsw/0222_33/wrong2.txt
diff --git a/Data/Questions/ingsw/0222_35/correct.txt b/legacy/Data/Questions/ingsw/0222_35/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_35/correct.txt
rename to legacy/Data/Questions/ingsw/0222_35/correct.txt
diff --git a/Data/Questions/ingsw/0222_35/quest.txt b/legacy/Data/Questions/ingsw/0222_35/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_35/quest.txt
rename to legacy/Data/Questions/ingsw/0222_35/quest.txt
diff --git a/Data/Questions/ingsw/0222_35/wrong1.txt b/legacy/Data/Questions/ingsw/0222_35/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_35/wrong1.txt
rename to legacy/Data/Questions/ingsw/0222_35/wrong1.txt
diff --git a/Data/Questions/ingsw/0222_35/wrong2.txt b/legacy/Data/Questions/ingsw/0222_35/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_35/wrong2.txt
rename to legacy/Data/Questions/ingsw/0222_35/wrong2.txt
diff --git a/Data/Questions/ingsw/0222_39/correct.txt b/legacy/Data/Questions/ingsw/0222_39/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_39/correct.txt
rename to legacy/Data/Questions/ingsw/0222_39/correct.txt
diff --git a/Data/Questions/ingsw/0222_39/quest.txt b/legacy/Data/Questions/ingsw/0222_39/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_39/quest.txt
rename to legacy/Data/Questions/ingsw/0222_39/quest.txt
diff --git a/Data/Questions/ingsw/0222_39/wrong1.txt b/legacy/Data/Questions/ingsw/0222_39/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_39/wrong1.txt
rename to legacy/Data/Questions/ingsw/0222_39/wrong1.txt
diff --git a/Data/Questions/ingsw/0222_39/wrong2.txt b/legacy/Data/Questions/ingsw/0222_39/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_39/wrong2.txt
rename to legacy/Data/Questions/ingsw/0222_39/wrong2.txt
diff --git a/Data/Questions/ingsw/0222_41/correct.txt b/legacy/Data/Questions/ingsw/0222_41/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_41/correct.txt
rename to legacy/Data/Questions/ingsw/0222_41/correct.txt
diff --git a/Data/Questions/ingsw/0222_41/quest.txt b/legacy/Data/Questions/ingsw/0222_41/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_41/quest.txt
rename to legacy/Data/Questions/ingsw/0222_41/quest.txt
diff --git a/Data/Questions/ingsw/0222_41/wrong1.txt b/legacy/Data/Questions/ingsw/0222_41/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_41/wrong1.txt
rename to legacy/Data/Questions/ingsw/0222_41/wrong1.txt
diff --git a/Data/Questions/ingsw/0222_41/wrong2.txt b/legacy/Data/Questions/ingsw/0222_41/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_41/wrong2.txt
rename to legacy/Data/Questions/ingsw/0222_41/wrong2.txt
diff --git a/Data/Questions/ingsw/0222_5/correct.txt b/legacy/Data/Questions/ingsw/0222_5/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_5/correct.txt
rename to legacy/Data/Questions/ingsw/0222_5/correct.txt
diff --git a/Data/Questions/ingsw/0222_5/quest.txt b/legacy/Data/Questions/ingsw/0222_5/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_5/quest.txt
rename to legacy/Data/Questions/ingsw/0222_5/quest.txt
diff --git a/Data/Questions/ingsw/0222_5/wrong1.txt b/legacy/Data/Questions/ingsw/0222_5/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_5/wrong1.txt
rename to legacy/Data/Questions/ingsw/0222_5/wrong1.txt
diff --git a/Data/Questions/ingsw/0222_5/wrong2.txt b/legacy/Data/Questions/ingsw/0222_5/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_5/wrong2.txt
rename to legacy/Data/Questions/ingsw/0222_5/wrong2.txt
diff --git a/Data/Questions/ingsw/0222_50/correct.txt b/legacy/Data/Questions/ingsw/0222_50/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_50/correct.txt
rename to legacy/Data/Questions/ingsw/0222_50/correct.txt
diff --git a/Data/Questions/ingsw/0222_50/quest.txt b/legacy/Data/Questions/ingsw/0222_50/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_50/quest.txt
rename to legacy/Data/Questions/ingsw/0222_50/quest.txt
diff --git a/Data/Questions/ingsw/0222_50/wrong1.txt b/legacy/Data/Questions/ingsw/0222_50/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_50/wrong1.txt
rename to legacy/Data/Questions/ingsw/0222_50/wrong1.txt
diff --git a/Data/Questions/ingsw/0222_50/wrong2.txt b/legacy/Data/Questions/ingsw/0222_50/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_50/wrong2.txt
rename to legacy/Data/Questions/ingsw/0222_50/wrong2.txt
diff --git a/Data/Questions/ingsw/0222_7/correct.txt b/legacy/Data/Questions/ingsw/0222_7/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_7/correct.txt
rename to legacy/Data/Questions/ingsw/0222_7/correct.txt
diff --git a/Data/Questions/ingsw/0222_7/quest.txt b/legacy/Data/Questions/ingsw/0222_7/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_7/quest.txt
rename to legacy/Data/Questions/ingsw/0222_7/quest.txt
diff --git a/Data/Questions/ingsw/0222_7/wrong1.txt b/legacy/Data/Questions/ingsw/0222_7/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_7/wrong1.txt
rename to legacy/Data/Questions/ingsw/0222_7/wrong1.txt
diff --git a/Data/Questions/ingsw/0222_7/wrong2.txt b/legacy/Data/Questions/ingsw/0222_7/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0222_7/wrong2.txt
rename to legacy/Data/Questions/ingsw/0222_7/wrong2.txt
diff --git a/Data/Questions/ingsw/0321_1/correct.txt b/legacy/Data/Questions/ingsw/0321_1/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_1/correct.txt
rename to legacy/Data/Questions/ingsw/0321_1/correct.txt
diff --git a/Data/Questions/ingsw/0321_1/quest.txt b/legacy/Data/Questions/ingsw/0321_1/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_1/quest.txt
rename to legacy/Data/Questions/ingsw/0321_1/quest.txt
diff --git a/Data/Questions/ingsw/0321_1/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_1/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_1/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_1/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_1/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_1/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_1/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_1/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_10/correct.txt b/legacy/Data/Questions/ingsw/0321_10/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_10/correct.txt
rename to legacy/Data/Questions/ingsw/0321_10/correct.txt
diff --git a/Data/Questions/ingsw/0321_10/quest.txt b/legacy/Data/Questions/ingsw/0321_10/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_10/quest.txt
rename to legacy/Data/Questions/ingsw/0321_10/quest.txt
diff --git a/Data/Questions/ingsw/0321_10/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_10/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_10/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_10/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_10/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_10/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_10/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_10/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_11/correct.txt b/legacy/Data/Questions/ingsw/0321_11/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_11/correct.txt
rename to legacy/Data/Questions/ingsw/0321_11/correct.txt
diff --git a/Data/Questions/ingsw/0321_11/quest.txt b/legacy/Data/Questions/ingsw/0321_11/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_11/quest.txt
rename to legacy/Data/Questions/ingsw/0321_11/quest.txt
diff --git a/Data/Questions/ingsw/0321_11/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_11/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_11/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_11/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_11/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_11/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_11/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_11/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_12/correct.txt b/legacy/Data/Questions/ingsw/0321_12/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_12/correct.txt
rename to legacy/Data/Questions/ingsw/0321_12/correct.txt
diff --git a/Data/Questions/ingsw/0321_12/quest.txt b/legacy/Data/Questions/ingsw/0321_12/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_12/quest.txt
rename to legacy/Data/Questions/ingsw/0321_12/quest.txt
diff --git a/Data/Questions/ingsw/0321_12/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_12/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_12/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_12/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_12/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_12/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_12/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_12/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_13/correct.txt b/legacy/Data/Questions/ingsw/0321_13/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_13/correct.txt
rename to legacy/Data/Questions/ingsw/0321_13/correct.txt
diff --git a/Data/Questions/ingsw/0321_13/quest.txt b/legacy/Data/Questions/ingsw/0321_13/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_13/quest.txt
rename to legacy/Data/Questions/ingsw/0321_13/quest.txt
diff --git a/Data/Questions/ingsw/0321_13/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_13/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_13/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_13/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_13/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_13/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_13/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_13/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_14/correct.txt b/legacy/Data/Questions/ingsw/0321_14/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_14/correct.txt
rename to legacy/Data/Questions/ingsw/0321_14/correct.txt
diff --git a/Data/Questions/ingsw/0321_14/quest.txt b/legacy/Data/Questions/ingsw/0321_14/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_14/quest.txt
rename to legacy/Data/Questions/ingsw/0321_14/quest.txt
diff --git a/Data/Questions/ingsw/0321_14/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_14/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_14/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_14/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_14/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_14/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_14/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_14/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_15/correct.txt b/legacy/Data/Questions/ingsw/0321_15/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_15/correct.txt
rename to legacy/Data/Questions/ingsw/0321_15/correct.txt
diff --git a/Data/Questions/ingsw/0321_15/quest.txt b/legacy/Data/Questions/ingsw/0321_15/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_15/quest.txt
rename to legacy/Data/Questions/ingsw/0321_15/quest.txt
diff --git a/Data/Questions/ingsw/0321_15/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_15/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_15/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_15/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_15/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_15/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_15/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_15/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_16/correct.txt b/legacy/Data/Questions/ingsw/0321_16/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_16/correct.txt
rename to legacy/Data/Questions/ingsw/0321_16/correct.txt
diff --git a/Data/Questions/ingsw/0321_16/quest.txt b/legacy/Data/Questions/ingsw/0321_16/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_16/quest.txt
rename to legacy/Data/Questions/ingsw/0321_16/quest.txt
diff --git a/Data/Questions/ingsw/0321_16/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_16/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_16/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_16/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_16/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_16/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_16/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_16/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_17/correct.txt b/legacy/Data/Questions/ingsw/0321_17/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_17/correct.txt
rename to legacy/Data/Questions/ingsw/0321_17/correct.txt
diff --git a/Data/Questions/ingsw/0321_17/quest.txt b/legacy/Data/Questions/ingsw/0321_17/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_17/quest.txt
rename to legacy/Data/Questions/ingsw/0321_17/quest.txt
diff --git a/Data/Questions/ingsw/0321_17/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_17/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_17/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_17/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_17/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_17/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_17/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_17/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_18/correct.txt b/legacy/Data/Questions/ingsw/0321_18/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_18/correct.txt
rename to legacy/Data/Questions/ingsw/0321_18/correct.txt
diff --git a/Data/Questions/ingsw/0321_18/quest.txt b/legacy/Data/Questions/ingsw/0321_18/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_18/quest.txt
rename to legacy/Data/Questions/ingsw/0321_18/quest.txt
diff --git a/Data/Questions/ingsw/0321_18/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_18/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_18/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_18/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_18/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_18/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_18/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_18/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_19/correct.txt b/legacy/Data/Questions/ingsw/0321_19/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_19/correct.txt
rename to legacy/Data/Questions/ingsw/0321_19/correct.txt
diff --git a/Data/Questions/ingsw/0321_19/quest.txt b/legacy/Data/Questions/ingsw/0321_19/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_19/quest.txt
rename to legacy/Data/Questions/ingsw/0321_19/quest.txt
diff --git a/Data/Questions/ingsw/0321_19/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_19/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_19/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_19/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_19/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_19/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_19/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_19/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_2/correct.txt b/legacy/Data/Questions/ingsw/0321_2/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_2/correct.txt
rename to legacy/Data/Questions/ingsw/0321_2/correct.txt
diff --git a/Data/Questions/ingsw/0321_2/quest.txt b/legacy/Data/Questions/ingsw/0321_2/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_2/quest.txt
rename to legacy/Data/Questions/ingsw/0321_2/quest.txt
diff --git a/Data/Questions/ingsw/0321_2/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_2/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_2/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_2/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_2/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_2/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_2/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_2/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_20/correct.txt b/legacy/Data/Questions/ingsw/0321_20/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_20/correct.txt
rename to legacy/Data/Questions/ingsw/0321_20/correct.txt
diff --git a/Data/Questions/ingsw/0321_20/quest.txt b/legacy/Data/Questions/ingsw/0321_20/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_20/quest.txt
rename to legacy/Data/Questions/ingsw/0321_20/quest.txt
diff --git a/Data/Questions/ingsw/0321_20/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_20/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_20/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_20/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_20/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_20/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_20/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_20/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_21/correct.txt b/legacy/Data/Questions/ingsw/0321_21/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_21/correct.txt
rename to legacy/Data/Questions/ingsw/0321_21/correct.txt
diff --git a/Data/Questions/ingsw/0321_21/quest.txt b/legacy/Data/Questions/ingsw/0321_21/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_21/quest.txt
rename to legacy/Data/Questions/ingsw/0321_21/quest.txt
diff --git a/Data/Questions/ingsw/0321_21/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_21/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_21/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_21/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_21/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_21/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_21/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_21/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_23/correct.txt b/legacy/Data/Questions/ingsw/0321_23/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_23/correct.txt
rename to legacy/Data/Questions/ingsw/0321_23/correct.txt
diff --git a/Data/Questions/ingsw/0321_23/quest.txt b/legacy/Data/Questions/ingsw/0321_23/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_23/quest.txt
rename to legacy/Data/Questions/ingsw/0321_23/quest.txt
diff --git a/Data/Questions/ingsw/0321_23/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_23/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_23/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_23/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_23/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_23/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_23/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_23/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_24/correct.txt b/legacy/Data/Questions/ingsw/0321_24/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_24/correct.txt
rename to legacy/Data/Questions/ingsw/0321_24/correct.txt
diff --git a/Data/Questions/ingsw/0321_24/quest.txt b/legacy/Data/Questions/ingsw/0321_24/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_24/quest.txt
rename to legacy/Data/Questions/ingsw/0321_24/quest.txt
diff --git a/Data/Questions/ingsw/0321_24/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_24/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_24/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_24/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_24/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_24/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_24/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_24/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_27/correct.txt b/legacy/Data/Questions/ingsw/0321_27/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_27/correct.txt
rename to legacy/Data/Questions/ingsw/0321_27/correct.txt
diff --git a/Data/Questions/ingsw/0321_27/quest.txt b/legacy/Data/Questions/ingsw/0321_27/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_27/quest.txt
rename to legacy/Data/Questions/ingsw/0321_27/quest.txt
diff --git a/Data/Questions/ingsw/0321_27/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_27/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_27/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_27/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_27/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_27/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_27/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_27/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_28/correct.txt b/legacy/Data/Questions/ingsw/0321_28/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_28/correct.txt
rename to legacy/Data/Questions/ingsw/0321_28/correct.txt
diff --git a/Data/Questions/ingsw/0321_28/quest.txt b/legacy/Data/Questions/ingsw/0321_28/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_28/quest.txt
rename to legacy/Data/Questions/ingsw/0321_28/quest.txt
diff --git a/Data/Questions/ingsw/0321_28/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_28/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_28/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_28/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_28/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_28/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_28/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_28/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_29/correct.txt b/legacy/Data/Questions/ingsw/0321_29/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_29/correct.txt
rename to legacy/Data/Questions/ingsw/0321_29/correct.txt
diff --git a/Data/Questions/ingsw/0321_29/quest.txt b/legacy/Data/Questions/ingsw/0321_29/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_29/quest.txt
rename to legacy/Data/Questions/ingsw/0321_29/quest.txt
diff --git a/Data/Questions/ingsw/0321_29/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_29/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_29/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_29/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_29/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_29/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_29/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_29/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_30/correct.txt b/legacy/Data/Questions/ingsw/0321_30/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_30/correct.txt
rename to legacy/Data/Questions/ingsw/0321_30/correct.txt
diff --git a/Data/Questions/ingsw/0321_30/quest.txt b/legacy/Data/Questions/ingsw/0321_30/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_30/quest.txt
rename to legacy/Data/Questions/ingsw/0321_30/quest.txt
diff --git a/Data/Questions/ingsw/0321_30/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_30/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_30/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_30/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_30/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_30/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_30/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_30/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_31/correct.txt b/legacy/Data/Questions/ingsw/0321_31/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_31/correct.txt
rename to legacy/Data/Questions/ingsw/0321_31/correct.txt
diff --git a/Data/Questions/ingsw/0321_31/quest.txt b/legacy/Data/Questions/ingsw/0321_31/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_31/quest.txt
rename to legacy/Data/Questions/ingsw/0321_31/quest.txt
diff --git a/Data/Questions/ingsw/0321_31/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_31/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_31/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_31/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_31/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_31/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_31/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_31/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_32/correct.txt b/legacy/Data/Questions/ingsw/0321_32/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_32/correct.txt
rename to legacy/Data/Questions/ingsw/0321_32/correct.txt
diff --git a/Data/Questions/ingsw/0321_32/quest.txt b/legacy/Data/Questions/ingsw/0321_32/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_32/quest.txt
rename to legacy/Data/Questions/ingsw/0321_32/quest.txt
diff --git a/Data/Questions/ingsw/0321_32/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_32/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_32/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_32/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_32/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_32/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_32/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_32/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_36/correct.txt b/legacy/Data/Questions/ingsw/0321_36/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_36/correct.txt
rename to legacy/Data/Questions/ingsw/0321_36/correct.txt
diff --git a/Data/Questions/ingsw/0321_36/quest.txt b/legacy/Data/Questions/ingsw/0321_36/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_36/quest.txt
rename to legacy/Data/Questions/ingsw/0321_36/quest.txt
diff --git a/Data/Questions/ingsw/0321_36/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_36/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_36/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_36/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_36/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_36/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_36/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_36/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_37/correct.txt b/legacy/Data/Questions/ingsw/0321_37/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_37/correct.txt
rename to legacy/Data/Questions/ingsw/0321_37/correct.txt
diff --git a/Data/Questions/ingsw/0321_37/quest.txt b/legacy/Data/Questions/ingsw/0321_37/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_37/quest.txt
rename to legacy/Data/Questions/ingsw/0321_37/quest.txt
diff --git a/Data/Questions/ingsw/0321_37/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_37/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_37/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_37/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_37/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_37/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_37/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_37/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_38/correct.txt b/legacy/Data/Questions/ingsw/0321_38/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_38/correct.txt
rename to legacy/Data/Questions/ingsw/0321_38/correct.txt
diff --git a/Data/Questions/ingsw/0321_38/quest.txt b/legacy/Data/Questions/ingsw/0321_38/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_38/quest.txt
rename to legacy/Data/Questions/ingsw/0321_38/quest.txt
diff --git a/Data/Questions/ingsw/0321_38/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_38/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_38/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_38/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_38/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_38/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_38/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_38/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_4/correct.txt b/legacy/Data/Questions/ingsw/0321_4/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_4/correct.txt
rename to legacy/Data/Questions/ingsw/0321_4/correct.txt
diff --git a/Data/Questions/ingsw/0321_4/quest.txt b/legacy/Data/Questions/ingsw/0321_4/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_4/quest.txt
rename to legacy/Data/Questions/ingsw/0321_4/quest.txt
diff --git a/Data/Questions/ingsw/0321_4/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_4/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_4/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_4/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_4/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_4/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_4/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_4/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_40/correct.txt b/legacy/Data/Questions/ingsw/0321_40/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_40/correct.txt
rename to legacy/Data/Questions/ingsw/0321_40/correct.txt
diff --git a/Data/Questions/ingsw/0321_40/quest.txt b/legacy/Data/Questions/ingsw/0321_40/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_40/quest.txt
rename to legacy/Data/Questions/ingsw/0321_40/quest.txt
diff --git a/Data/Questions/ingsw/0321_40/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_40/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_40/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_40/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_40/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_40/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_40/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_40/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_8/correct.txt b/legacy/Data/Questions/ingsw/0321_8/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_8/correct.txt
rename to legacy/Data/Questions/ingsw/0321_8/correct.txt
diff --git a/Data/Questions/ingsw/0321_8/quest.txt b/legacy/Data/Questions/ingsw/0321_8/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_8/quest.txt
rename to legacy/Data/Questions/ingsw/0321_8/quest.txt
diff --git a/Data/Questions/ingsw/0321_8/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_8/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_8/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_8/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_8/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_8/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_8/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_8/wrong 2.txt
diff --git a/Data/Questions/ingsw/0321_9/correct.txt b/legacy/Data/Questions/ingsw/0321_9/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_9/correct.txt
rename to legacy/Data/Questions/ingsw/0321_9/correct.txt
diff --git a/Data/Questions/ingsw/0321_9/quest.txt b/legacy/Data/Questions/ingsw/0321_9/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_9/quest.txt
rename to legacy/Data/Questions/ingsw/0321_9/quest.txt
diff --git a/Data/Questions/ingsw/0321_9/wrong 1.txt b/legacy/Data/Questions/ingsw/0321_9/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_9/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0321_9/wrong 1.txt
diff --git a/Data/Questions/ingsw/0321_9/wrong 2.txt b/legacy/Data/Questions/ingsw/0321_9/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0321_9/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0321_9/wrong 2.txt
diff --git a/Data/Questions/ingsw/0324_0/correct.txt b/legacy/Data/Questions/ingsw/0324_0/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_0/correct.txt
rename to legacy/Data/Questions/ingsw/0324_0/correct.txt
diff --git a/Data/Questions/ingsw/0324_0/quest.txt b/legacy/Data/Questions/ingsw/0324_0/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_0/quest.txt
rename to legacy/Data/Questions/ingsw/0324_0/quest.txt
diff --git a/Data/Questions/ingsw/0324_0/wrong1.txt b/legacy/Data/Questions/ingsw/0324_0/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_0/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_0/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_0/wrong2.txt b/legacy/Data/Questions/ingsw/0324_0/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_0/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_0/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_1/quest.txt b/legacy/Data/Questions/ingsw/0324_1/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_1/quest.txt
rename to legacy/Data/Questions/ingsw/0324_1/quest.txt
diff --git a/Data/Questions/ingsw/0324_1/wrong1.txt b/legacy/Data/Questions/ingsw/0324_1/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_1/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_1/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_1/wrong2.txt b/legacy/Data/Questions/ingsw/0324_1/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_1/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_1/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_1/wrong3.txt b/legacy/Data/Questions/ingsw/0324_1/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_1/wrong3.txt
rename to legacy/Data/Questions/ingsw/0324_1/wrong3.txt
diff --git a/Data/Questions/ingsw/0324_10/correct.txt b/legacy/Data/Questions/ingsw/0324_10/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_10/correct.txt
rename to legacy/Data/Questions/ingsw/0324_10/correct.txt
diff --git a/Data/Questions/ingsw/0324_10/quest.txt b/legacy/Data/Questions/ingsw/0324_10/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_10/quest.txt
rename to legacy/Data/Questions/ingsw/0324_10/quest.txt
diff --git a/Data/Questions/ingsw/0324_10/wrong1.txt b/legacy/Data/Questions/ingsw/0324_10/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_10/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_10/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_10/wrong2.txt b/legacy/Data/Questions/ingsw/0324_10/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_10/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_10/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_11/quest.txt b/legacy/Data/Questions/ingsw/0324_11/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_11/quest.txt
rename to legacy/Data/Questions/ingsw/0324_11/quest.txt
diff --git a/Data/Questions/ingsw/0324_11/wrong1.txt b/legacy/Data/Questions/ingsw/0324_11/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_11/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_11/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_11/wrong2.txt b/legacy/Data/Questions/ingsw/0324_11/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_11/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_11/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_11/wrong3.txt b/legacy/Data/Questions/ingsw/0324_11/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_11/wrong3.txt
rename to legacy/Data/Questions/ingsw/0324_11/wrong3.txt
diff --git a/Data/Questions/ingsw/0324_12/correct.txt b/legacy/Data/Questions/ingsw/0324_12/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_12/correct.txt
rename to legacy/Data/Questions/ingsw/0324_12/correct.txt
diff --git a/Data/Questions/ingsw/0324_12/quest.txt b/legacy/Data/Questions/ingsw/0324_12/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_12/quest.txt
rename to legacy/Data/Questions/ingsw/0324_12/quest.txt
diff --git a/Data/Questions/ingsw/0324_12/wrong1.txt b/legacy/Data/Questions/ingsw/0324_12/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_12/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_12/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_12/wrong2.txt b/legacy/Data/Questions/ingsw/0324_12/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_12/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_12/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_13/correct.txt b/legacy/Data/Questions/ingsw/0324_13/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_13/correct.txt
rename to legacy/Data/Questions/ingsw/0324_13/correct.txt
diff --git a/Data/Questions/ingsw/0324_13/quest.txt b/legacy/Data/Questions/ingsw/0324_13/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_13/quest.txt
rename to legacy/Data/Questions/ingsw/0324_13/quest.txt
diff --git a/Data/Questions/ingsw/0324_13/wrong1.txt b/legacy/Data/Questions/ingsw/0324_13/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_13/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_13/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_13/wrong2.txt b/legacy/Data/Questions/ingsw/0324_13/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_13/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_13/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_14/correct.txt b/legacy/Data/Questions/ingsw/0324_14/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_14/correct.txt
rename to legacy/Data/Questions/ingsw/0324_14/correct.txt
diff --git a/Data/Questions/ingsw/0324_14/quest.txt b/legacy/Data/Questions/ingsw/0324_14/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_14/quest.txt
rename to legacy/Data/Questions/ingsw/0324_14/quest.txt
diff --git a/Data/Questions/ingsw/0324_14/wrong1.txt b/legacy/Data/Questions/ingsw/0324_14/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_14/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_14/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_14/wrong2.txt b/legacy/Data/Questions/ingsw/0324_14/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_14/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_14/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_15/correct.txt b/legacy/Data/Questions/ingsw/0324_15/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_15/correct.txt
rename to legacy/Data/Questions/ingsw/0324_15/correct.txt
diff --git a/Data/Questions/ingsw/0324_15/quest.txt b/legacy/Data/Questions/ingsw/0324_15/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_15/quest.txt
rename to legacy/Data/Questions/ingsw/0324_15/quest.txt
diff --git a/Data/Questions/ingsw/0324_15/wrong1.txt b/legacy/Data/Questions/ingsw/0324_15/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_15/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_15/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_15/wrong2.txt b/legacy/Data/Questions/ingsw/0324_15/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_15/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_15/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_16/correct.txt b/legacy/Data/Questions/ingsw/0324_16/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_16/correct.txt
rename to legacy/Data/Questions/ingsw/0324_16/correct.txt
diff --git a/Data/Questions/ingsw/0324_16/quest.txt b/legacy/Data/Questions/ingsw/0324_16/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_16/quest.txt
rename to legacy/Data/Questions/ingsw/0324_16/quest.txt
diff --git a/Data/Questions/ingsw/0324_16/wrong1.txt b/legacy/Data/Questions/ingsw/0324_16/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_16/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_16/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_16/wrong2.txt b/legacy/Data/Questions/ingsw/0324_16/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_16/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_16/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_17/correct.txt b/legacy/Data/Questions/ingsw/0324_17/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_17/correct.txt
rename to legacy/Data/Questions/ingsw/0324_17/correct.txt
diff --git a/Data/Questions/ingsw/0324_17/quest.txt b/legacy/Data/Questions/ingsw/0324_17/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_17/quest.txt
rename to legacy/Data/Questions/ingsw/0324_17/quest.txt
diff --git a/Data/Questions/ingsw/0324_17/wrong1.txt b/legacy/Data/Questions/ingsw/0324_17/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_17/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_17/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_17/wrong2.txt b/legacy/Data/Questions/ingsw/0324_17/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_17/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_17/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_18/correct.txt b/legacy/Data/Questions/ingsw/0324_18/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_18/correct.txt
rename to legacy/Data/Questions/ingsw/0324_18/correct.txt
diff --git a/Data/Questions/ingsw/0324_18/quest.txt b/legacy/Data/Questions/ingsw/0324_18/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_18/quest.txt
rename to legacy/Data/Questions/ingsw/0324_18/quest.txt
diff --git a/Data/Questions/ingsw/0324_18/wrong1.txt b/legacy/Data/Questions/ingsw/0324_18/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_18/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_18/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_18/wrong2.txt b/legacy/Data/Questions/ingsw/0324_18/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_18/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_18/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_19/correct.txt b/legacy/Data/Questions/ingsw/0324_19/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_19/correct.txt
rename to legacy/Data/Questions/ingsw/0324_19/correct.txt
diff --git a/Data/Questions/ingsw/0324_19/quest.txt b/legacy/Data/Questions/ingsw/0324_19/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_19/quest.txt
rename to legacy/Data/Questions/ingsw/0324_19/quest.txt
diff --git a/Data/Questions/ingsw/0324_19/wrong1.txt b/legacy/Data/Questions/ingsw/0324_19/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_19/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_19/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_19/wrong2.txt b/legacy/Data/Questions/ingsw/0324_19/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_19/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_19/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_2/correct.txt b/legacy/Data/Questions/ingsw/0324_2/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_2/correct.txt
rename to legacy/Data/Questions/ingsw/0324_2/correct.txt
diff --git a/Data/Questions/ingsw/0324_2/quest.txt b/legacy/Data/Questions/ingsw/0324_2/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_2/quest.txt
rename to legacy/Data/Questions/ingsw/0324_2/quest.txt
diff --git a/Data/Questions/ingsw/0324_2/wrong1.txt b/legacy/Data/Questions/ingsw/0324_2/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_2/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_2/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_2/wrong2.txt b/legacy/Data/Questions/ingsw/0324_2/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_2/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_2/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_20/correct.txt b/legacy/Data/Questions/ingsw/0324_20/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_20/correct.txt
rename to legacy/Data/Questions/ingsw/0324_20/correct.txt
diff --git a/Data/Questions/ingsw/0324_20/quest.txt b/legacy/Data/Questions/ingsw/0324_20/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_20/quest.txt
rename to legacy/Data/Questions/ingsw/0324_20/quest.txt
diff --git a/Data/Questions/ingsw/0324_20/wrong1.txt b/legacy/Data/Questions/ingsw/0324_20/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_20/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_20/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_20/wrong2.txt b/legacy/Data/Questions/ingsw/0324_20/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_20/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_20/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_21/correct.txt b/legacy/Data/Questions/ingsw/0324_21/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_21/correct.txt
rename to legacy/Data/Questions/ingsw/0324_21/correct.txt
diff --git a/Data/Questions/ingsw/0324_21/quest.txt b/legacy/Data/Questions/ingsw/0324_21/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_21/quest.txt
rename to legacy/Data/Questions/ingsw/0324_21/quest.txt
diff --git a/Data/Questions/ingsw/0324_21/wrong1.txt b/legacy/Data/Questions/ingsw/0324_21/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_21/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_21/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_21/wrong2.txt b/legacy/Data/Questions/ingsw/0324_21/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_21/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_21/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_22/correct.txt b/legacy/Data/Questions/ingsw/0324_22/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_22/correct.txt
rename to legacy/Data/Questions/ingsw/0324_22/correct.txt
diff --git a/Data/Questions/ingsw/0324_22/quest.txt b/legacy/Data/Questions/ingsw/0324_22/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_22/quest.txt
rename to legacy/Data/Questions/ingsw/0324_22/quest.txt
diff --git a/Data/Questions/ingsw/0324_22/wrong1.txt b/legacy/Data/Questions/ingsw/0324_22/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_22/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_22/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_22/wrong2.txt b/legacy/Data/Questions/ingsw/0324_22/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_22/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_22/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_23/correct.txt b/legacy/Data/Questions/ingsw/0324_23/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_23/correct.txt
rename to legacy/Data/Questions/ingsw/0324_23/correct.txt
diff --git a/Data/Questions/ingsw/0324_23/quest.txt b/legacy/Data/Questions/ingsw/0324_23/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_23/quest.txt
rename to legacy/Data/Questions/ingsw/0324_23/quest.txt
diff --git a/Data/Questions/ingsw/0324_23/wrong1.txt b/legacy/Data/Questions/ingsw/0324_23/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_23/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_23/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_23/wrong2.txt b/legacy/Data/Questions/ingsw/0324_23/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_23/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_23/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_24/correct.txt b/legacy/Data/Questions/ingsw/0324_24/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_24/correct.txt
rename to legacy/Data/Questions/ingsw/0324_24/correct.txt
diff --git a/Data/Questions/ingsw/0324_24/quest.txt b/legacy/Data/Questions/ingsw/0324_24/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_24/quest.txt
rename to legacy/Data/Questions/ingsw/0324_24/quest.txt
diff --git a/Data/Questions/ingsw/0324_24/wrong1.txt b/legacy/Data/Questions/ingsw/0324_24/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_24/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_24/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_24/wrong2.txt b/legacy/Data/Questions/ingsw/0324_24/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_24/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_24/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_25/correct.txt b/legacy/Data/Questions/ingsw/0324_25/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_25/correct.txt
rename to legacy/Data/Questions/ingsw/0324_25/correct.txt
diff --git a/Data/Questions/ingsw/0324_25/quest.txt b/legacy/Data/Questions/ingsw/0324_25/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_25/quest.txt
rename to legacy/Data/Questions/ingsw/0324_25/quest.txt
diff --git a/Data/Questions/ingsw/0324_25/wrong1.txt b/legacy/Data/Questions/ingsw/0324_25/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_25/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_25/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_25/wrong2.txt b/legacy/Data/Questions/ingsw/0324_25/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_25/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_25/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_26/quest.txt b/legacy/Data/Questions/ingsw/0324_26/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_26/quest.txt
rename to legacy/Data/Questions/ingsw/0324_26/quest.txt
diff --git a/Data/Questions/ingsw/0324_35/wrong1.txt b/legacy/Data/Questions/ingsw/0324_26/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_35/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_26/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_26/wrong2.txt b/legacy/Data/Questions/ingsw/0324_26/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_26/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_26/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_26/wrong3.txt b/legacy/Data/Questions/ingsw/0324_26/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_26/wrong3.txt
rename to legacy/Data/Questions/ingsw/0324_26/wrong3.txt
diff --git a/Data/Questions/ingsw/0324_27/correct.txt b/legacy/Data/Questions/ingsw/0324_27/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_27/correct.txt
rename to legacy/Data/Questions/ingsw/0324_27/correct.txt
diff --git a/Data/Questions/ingsw/0324_27/quest.txt b/legacy/Data/Questions/ingsw/0324_27/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_27/quest.txt
rename to legacy/Data/Questions/ingsw/0324_27/quest.txt
diff --git a/Data/Questions/ingsw/0324_27/wrong1.txt b/legacy/Data/Questions/ingsw/0324_27/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_27/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_27/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_27/wrong2.txt b/legacy/Data/Questions/ingsw/0324_27/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_27/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_27/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_28/correct.txt b/legacy/Data/Questions/ingsw/0324_28/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_28/correct.txt
rename to legacy/Data/Questions/ingsw/0324_28/correct.txt
diff --git a/Data/Questions/ingsw/0324_28/quest.txt b/legacy/Data/Questions/ingsw/0324_28/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_28/quest.txt
rename to legacy/Data/Questions/ingsw/0324_28/quest.txt
diff --git a/Data/Questions/ingsw/0324_28/wrong1.txt b/legacy/Data/Questions/ingsw/0324_28/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_28/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_28/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_28/wrong2.txt b/legacy/Data/Questions/ingsw/0324_28/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_28/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_28/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_29/correct.txt b/legacy/Data/Questions/ingsw/0324_29/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_29/correct.txt
rename to legacy/Data/Questions/ingsw/0324_29/correct.txt
diff --git a/Data/Questions/ingsw/0324_29/quest.txt b/legacy/Data/Questions/ingsw/0324_29/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_29/quest.txt
rename to legacy/Data/Questions/ingsw/0324_29/quest.txt
diff --git a/Data/Questions/ingsw/0324_29/wrong1.txt b/legacy/Data/Questions/ingsw/0324_29/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_29/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_29/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_29/wrong2.txt b/legacy/Data/Questions/ingsw/0324_29/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_29/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_29/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_3/correct.txt b/legacy/Data/Questions/ingsw/0324_3/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_3/correct.txt
rename to legacy/Data/Questions/ingsw/0324_3/correct.txt
diff --git a/Data/Questions/ingsw/0324_3/quest.txt b/legacy/Data/Questions/ingsw/0324_3/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_3/quest.txt
rename to legacy/Data/Questions/ingsw/0324_3/quest.txt
diff --git a/Data/Questions/ingsw/0324_3/wrong1.txt b/legacy/Data/Questions/ingsw/0324_3/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_3/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_3/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_3/wrong2.txt b/legacy/Data/Questions/ingsw/0324_3/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_3/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_3/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_30/quest.txt b/legacy/Data/Questions/ingsw/0324_30/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_30/quest.txt
rename to legacy/Data/Questions/ingsw/0324_30/quest.txt
diff --git a/Data/Questions/ingsw/0324_30/wrong1.txt b/legacy/Data/Questions/ingsw/0324_30/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_30/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_30/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_30/wrong2.txt b/legacy/Data/Questions/ingsw/0324_30/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_30/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_30/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_30/wrong3.txt b/legacy/Data/Questions/ingsw/0324_30/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_30/wrong3.txt
rename to legacy/Data/Questions/ingsw/0324_30/wrong3.txt
diff --git a/Data/Questions/ingsw/0324_31/correct.txt b/legacy/Data/Questions/ingsw/0324_31/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_31/correct.txt
rename to legacy/Data/Questions/ingsw/0324_31/correct.txt
diff --git a/Data/Questions/ingsw/0324_31/quest.txt b/legacy/Data/Questions/ingsw/0324_31/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_31/quest.txt
rename to legacy/Data/Questions/ingsw/0324_31/quest.txt
diff --git a/Data/Questions/ingsw/0324_31/wrong1.txt b/legacy/Data/Questions/ingsw/0324_31/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_31/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_31/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_31/wrong2.txt b/legacy/Data/Questions/ingsw/0324_31/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_31/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_31/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_32/correct.txt b/legacy/Data/Questions/ingsw/0324_32/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_32/correct.txt
rename to legacy/Data/Questions/ingsw/0324_32/correct.txt
diff --git a/Data/Questions/ingsw/0324_32/quest.txt b/legacy/Data/Questions/ingsw/0324_32/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_32/quest.txt
rename to legacy/Data/Questions/ingsw/0324_32/quest.txt
diff --git a/Data/Questions/ingsw/0324_32/wrong1.txt b/legacy/Data/Questions/ingsw/0324_32/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_32/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_32/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_32/wrong2.txt b/legacy/Data/Questions/ingsw/0324_32/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_32/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_32/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_33/correct.txt b/legacy/Data/Questions/ingsw/0324_33/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_33/correct.txt
rename to legacy/Data/Questions/ingsw/0324_33/correct.txt
diff --git a/Data/Questions/ingsw/0324_33/quest.txt b/legacy/Data/Questions/ingsw/0324_33/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_33/quest.txt
rename to legacy/Data/Questions/ingsw/0324_33/quest.txt
diff --git a/Data/Questions/ingsw/0324_33/wrong1.txt b/legacy/Data/Questions/ingsw/0324_33/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_33/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_33/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_33/wrong2.txt b/legacy/Data/Questions/ingsw/0324_33/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_33/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_33/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_34/correct.txt b/legacy/Data/Questions/ingsw/0324_34/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_34/correct.txt
rename to legacy/Data/Questions/ingsw/0324_34/correct.txt
diff --git a/Data/Questions/ingsw/0324_34/quest.txt b/legacy/Data/Questions/ingsw/0324_34/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_34/quest.txt
rename to legacy/Data/Questions/ingsw/0324_34/quest.txt
diff --git a/Data/Questions/ingsw/0324_34/wrong1.txt b/legacy/Data/Questions/ingsw/0324_34/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_34/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_34/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_34/wrong2.txt b/legacy/Data/Questions/ingsw/0324_34/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_34/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_34/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_35/quest.txt b/legacy/Data/Questions/ingsw/0324_35/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_35/quest.txt
rename to legacy/Data/Questions/ingsw/0324_35/quest.txt
diff --git a/Data/Questions/ingsw/0324_42/wrong1.txt b/legacy/Data/Questions/ingsw/0324_35/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_42/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_35/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_35/wrong2.txt b/legacy/Data/Questions/ingsw/0324_35/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_35/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_35/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_35/wrong3.txt b/legacy/Data/Questions/ingsw/0324_35/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_35/wrong3.txt
rename to legacy/Data/Questions/ingsw/0324_35/wrong3.txt
diff --git a/Data/Questions/ingsw/0324_36/correct.txt b/legacy/Data/Questions/ingsw/0324_36/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_36/correct.txt
rename to legacy/Data/Questions/ingsw/0324_36/correct.txt
diff --git a/Data/Questions/ingsw/0324_36/quest.txt b/legacy/Data/Questions/ingsw/0324_36/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_36/quest.txt
rename to legacy/Data/Questions/ingsw/0324_36/quest.txt
diff --git a/Data/Questions/ingsw/0324_36/wrong1.txt b/legacy/Data/Questions/ingsw/0324_36/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_36/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_36/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_36/wrong2.txt b/legacy/Data/Questions/ingsw/0324_36/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_36/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_36/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_37/correct.txt b/legacy/Data/Questions/ingsw/0324_37/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_37/correct.txt
rename to legacy/Data/Questions/ingsw/0324_37/correct.txt
diff --git a/Data/Questions/ingsw/0324_37/quest.txt b/legacy/Data/Questions/ingsw/0324_37/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_37/quest.txt
rename to legacy/Data/Questions/ingsw/0324_37/quest.txt
diff --git a/Data/Questions/ingsw/0324_37/wrong1.txt b/legacy/Data/Questions/ingsw/0324_37/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_37/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_37/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_37/wrong2.txt b/legacy/Data/Questions/ingsw/0324_37/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_37/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_37/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_38/correct.txt b/legacy/Data/Questions/ingsw/0324_38/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_38/correct.txt
rename to legacy/Data/Questions/ingsw/0324_38/correct.txt
diff --git a/Data/Questions/ingsw/0324_38/quest.txt b/legacy/Data/Questions/ingsw/0324_38/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_38/quest.txt
rename to legacy/Data/Questions/ingsw/0324_38/quest.txt
diff --git a/Data/Questions/ingsw/0324_38/wrong1.txt b/legacy/Data/Questions/ingsw/0324_38/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_38/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_38/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_38/wrong2.txt b/legacy/Data/Questions/ingsw/0324_38/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_38/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_38/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_39/correct.txt b/legacy/Data/Questions/ingsw/0324_39/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_39/correct.txt
rename to legacy/Data/Questions/ingsw/0324_39/correct.txt
diff --git a/Data/Questions/ingsw/0324_39/quest.txt b/legacy/Data/Questions/ingsw/0324_39/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_39/quest.txt
rename to legacy/Data/Questions/ingsw/0324_39/quest.txt
diff --git a/Data/Questions/ingsw/0324_39/wrong1.txt b/legacy/Data/Questions/ingsw/0324_39/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_39/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_39/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_39/wrong2.txt b/legacy/Data/Questions/ingsw/0324_39/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_39/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_39/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_4/correct.txt b/legacy/Data/Questions/ingsw/0324_4/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_4/correct.txt
rename to legacy/Data/Questions/ingsw/0324_4/correct.txt
diff --git a/Data/Questions/ingsw/0324_4/quest.txt b/legacy/Data/Questions/ingsw/0324_4/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_4/quest.txt
rename to legacy/Data/Questions/ingsw/0324_4/quest.txt
diff --git a/Data/Questions/ingsw/0324_4/wrong1.txt b/legacy/Data/Questions/ingsw/0324_4/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_4/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_4/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_4/wrong2.txt b/legacy/Data/Questions/ingsw/0324_4/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_4/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_4/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_40/correct.txt b/legacy/Data/Questions/ingsw/0324_40/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_40/correct.txt
rename to legacy/Data/Questions/ingsw/0324_40/correct.txt
diff --git a/Data/Questions/ingsw/0324_40/quest.txt b/legacy/Data/Questions/ingsw/0324_40/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_40/quest.txt
rename to legacy/Data/Questions/ingsw/0324_40/quest.txt
diff --git a/Data/Questions/ingsw/0324_40/wrong1.txt b/legacy/Data/Questions/ingsw/0324_40/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_40/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_40/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_40/wrong2.txt b/legacy/Data/Questions/ingsw/0324_40/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_40/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_40/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_41/quest.txt b/legacy/Data/Questions/ingsw/0324_41/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_41/quest.txt
rename to legacy/Data/Questions/ingsw/0324_41/quest.txt
diff --git a/Data/Questions/ingsw/0324_41/wrong1.txt b/legacy/Data/Questions/ingsw/0324_41/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_41/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_41/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_41/wrong2.txt b/legacy/Data/Questions/ingsw/0324_41/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_41/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_41/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_41/wrong3.txt b/legacy/Data/Questions/ingsw/0324_41/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_41/wrong3.txt
rename to legacy/Data/Questions/ingsw/0324_41/wrong3.txt
diff --git a/Data/Questions/ingsw/0324_42/quest.txt b/legacy/Data/Questions/ingsw/0324_42/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_42/quest.txt
rename to legacy/Data/Questions/ingsw/0324_42/quest.txt
diff --git a/Data/Questions/ingsw/0324_47/wrong1.txt b/legacy/Data/Questions/ingsw/0324_42/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_47/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_42/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_42/wrong2.txt b/legacy/Data/Questions/ingsw/0324_42/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_42/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_42/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_42/wrong3.txt b/legacy/Data/Questions/ingsw/0324_42/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_42/wrong3.txt
rename to legacy/Data/Questions/ingsw/0324_42/wrong3.txt
diff --git a/Data/Questions/ingsw/0324_43/correct.txt b/legacy/Data/Questions/ingsw/0324_43/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_43/correct.txt
rename to legacy/Data/Questions/ingsw/0324_43/correct.txt
diff --git a/Data/Questions/ingsw/0324_43/quest.txt b/legacy/Data/Questions/ingsw/0324_43/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_43/quest.txt
rename to legacy/Data/Questions/ingsw/0324_43/quest.txt
diff --git a/Data/Questions/ingsw/0324_43/wrong1.txt b/legacy/Data/Questions/ingsw/0324_43/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_43/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_43/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_43/wrong2.txt b/legacy/Data/Questions/ingsw/0324_43/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_43/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_43/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_44/correct.txt b/legacy/Data/Questions/ingsw/0324_44/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_44/correct.txt
rename to legacy/Data/Questions/ingsw/0324_44/correct.txt
diff --git a/Data/Questions/ingsw/0324_44/quest.txt b/legacy/Data/Questions/ingsw/0324_44/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_44/quest.txt
rename to legacy/Data/Questions/ingsw/0324_44/quest.txt
diff --git a/Data/Questions/ingsw/0324_44/wrong1.txt b/legacy/Data/Questions/ingsw/0324_44/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_44/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_44/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_44/wrong2.txt b/legacy/Data/Questions/ingsw/0324_44/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_44/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_44/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_45/correct.txt b/legacy/Data/Questions/ingsw/0324_45/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_45/correct.txt
rename to legacy/Data/Questions/ingsw/0324_45/correct.txt
diff --git a/Data/Questions/ingsw/0324_45/quest.txt b/legacy/Data/Questions/ingsw/0324_45/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_45/quest.txt
rename to legacy/Data/Questions/ingsw/0324_45/quest.txt
diff --git a/Data/Questions/ingsw/0324_45/wrong1.txt b/legacy/Data/Questions/ingsw/0324_45/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_45/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_45/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_45/wrong2.txt b/legacy/Data/Questions/ingsw/0324_45/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_45/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_45/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_46/correct.txt b/legacy/Data/Questions/ingsw/0324_46/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_46/correct.txt
rename to legacy/Data/Questions/ingsw/0324_46/correct.txt
diff --git a/Data/Questions/ingsw/0324_46/quest.txt b/legacy/Data/Questions/ingsw/0324_46/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_46/quest.txt
rename to legacy/Data/Questions/ingsw/0324_46/quest.txt
diff --git a/Data/Questions/ingsw/0324_46/wrong1.txt b/legacy/Data/Questions/ingsw/0324_46/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_46/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_46/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_46/wrong2.txt b/legacy/Data/Questions/ingsw/0324_46/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_46/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_46/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_47/quest.txt b/legacy/Data/Questions/ingsw/0324_47/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_47/quest.txt
rename to legacy/Data/Questions/ingsw/0324_47/quest.txt
diff --git a/Data/Questions/ingsw/0613_0/wrong1.txt b/legacy/Data/Questions/ingsw/0324_47/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_0/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_47/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_47/wrong2.txt b/legacy/Data/Questions/ingsw/0324_47/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_47/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_47/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_47/wrong3.txt b/legacy/Data/Questions/ingsw/0324_47/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_47/wrong3.txt
rename to legacy/Data/Questions/ingsw/0324_47/wrong3.txt
diff --git a/Data/Questions/ingsw/0324_48/quest.txt b/legacy/Data/Questions/ingsw/0324_48/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_48/quest.txt
rename to legacy/Data/Questions/ingsw/0324_48/quest.txt
diff --git a/Data/Questions/ingsw/0324_48/wrong1.txt b/legacy/Data/Questions/ingsw/0324_48/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_48/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_48/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_48/wrong2.txt b/legacy/Data/Questions/ingsw/0324_48/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_48/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_48/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_48/wrong3.txt b/legacy/Data/Questions/ingsw/0324_48/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_48/wrong3.txt
rename to legacy/Data/Questions/ingsw/0324_48/wrong3.txt
diff --git a/Data/Questions/ingsw/0324_49/correct.txt b/legacy/Data/Questions/ingsw/0324_49/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_49/correct.txt
rename to legacy/Data/Questions/ingsw/0324_49/correct.txt
diff --git a/Data/Questions/ingsw/0324_49/quest.txt b/legacy/Data/Questions/ingsw/0324_49/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_49/quest.txt
rename to legacy/Data/Questions/ingsw/0324_49/quest.txt
diff --git a/Data/Questions/ingsw/0324_49/wrong1.txt b/legacy/Data/Questions/ingsw/0324_49/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_49/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_49/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_49/wrong2.txt b/legacy/Data/Questions/ingsw/0324_49/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_49/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_49/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_5/correct.txt b/legacy/Data/Questions/ingsw/0324_5/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_5/correct.txt
rename to legacy/Data/Questions/ingsw/0324_5/correct.txt
diff --git a/Data/Questions/ingsw/0324_5/quest.txt b/legacy/Data/Questions/ingsw/0324_5/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_5/quest.txt
rename to legacy/Data/Questions/ingsw/0324_5/quest.txt
diff --git a/Data/Questions/ingsw/0324_5/wrong1.txt b/legacy/Data/Questions/ingsw/0324_5/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_5/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_5/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_5/wrong2.txt b/legacy/Data/Questions/ingsw/0324_5/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_5/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_5/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_6/correct.txt b/legacy/Data/Questions/ingsw/0324_6/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_6/correct.txt
rename to legacy/Data/Questions/ingsw/0324_6/correct.txt
diff --git a/Data/Questions/ingsw/0324_6/quest.txt b/legacy/Data/Questions/ingsw/0324_6/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_6/quest.txt
rename to legacy/Data/Questions/ingsw/0324_6/quest.txt
diff --git a/Data/Questions/ingsw/0324_6/wrong1.txt b/legacy/Data/Questions/ingsw/0324_6/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_6/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_6/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_6/wrong2.txt b/legacy/Data/Questions/ingsw/0324_6/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_6/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_6/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_7/correct.txt b/legacy/Data/Questions/ingsw/0324_7/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_7/correct.txt
rename to legacy/Data/Questions/ingsw/0324_7/correct.txt
diff --git a/Data/Questions/ingsw/0324_7/quest.txt b/legacy/Data/Questions/ingsw/0324_7/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_7/quest.txt
rename to legacy/Data/Questions/ingsw/0324_7/quest.txt
diff --git a/Data/Questions/ingsw/0324_7/wrong1.txt b/legacy/Data/Questions/ingsw/0324_7/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_7/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_7/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_7/wrong2.txt b/legacy/Data/Questions/ingsw/0324_7/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_7/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_7/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_8/correct.txt b/legacy/Data/Questions/ingsw/0324_8/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_8/correct.txt
rename to legacy/Data/Questions/ingsw/0324_8/correct.txt
diff --git a/Data/Questions/ingsw/0324_8/quest.txt b/legacy/Data/Questions/ingsw/0324_8/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_8/quest.txt
rename to legacy/Data/Questions/ingsw/0324_8/quest.txt
diff --git a/Data/Questions/ingsw/0324_8/wrong1.txt b/legacy/Data/Questions/ingsw/0324_8/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_8/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_8/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_8/wrong2.txt b/legacy/Data/Questions/ingsw/0324_8/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_8/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_8/wrong2.txt
diff --git a/Data/Questions/ingsw/0324_9/correct.txt b/legacy/Data/Questions/ingsw/0324_9/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_9/correct.txt
rename to legacy/Data/Questions/ingsw/0324_9/correct.txt
diff --git a/Data/Questions/ingsw/0324_9/quest.txt b/legacy/Data/Questions/ingsw/0324_9/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_9/quest.txt
rename to legacy/Data/Questions/ingsw/0324_9/quest.txt
diff --git a/Data/Questions/ingsw/0324_9/wrong1.txt b/legacy/Data/Questions/ingsw/0324_9/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_9/wrong1.txt
rename to legacy/Data/Questions/ingsw/0324_9/wrong1.txt
diff --git a/Data/Questions/ingsw/0324_9/wrong2.txt b/legacy/Data/Questions/ingsw/0324_9/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0324_9/wrong2.txt
rename to legacy/Data/Questions/ingsw/0324_9/wrong2.txt
diff --git a/Data/Questions/ingsw/0422-16/correct.txt b/legacy/Data/Questions/ingsw/0422-16/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0422-16/correct.txt
rename to legacy/Data/Questions/ingsw/0422-16/correct.txt
diff --git a/Data/Questions/ingsw/0422-16/quest.txt b/legacy/Data/Questions/ingsw/0422-16/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0422-16/quest.txt
rename to legacy/Data/Questions/ingsw/0422-16/quest.txt
diff --git a/Data/Questions/ingsw/0422-16/wrong1.txt b/legacy/Data/Questions/ingsw/0422-16/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0422-16/wrong1.txt
rename to legacy/Data/Questions/ingsw/0422-16/wrong1.txt
diff --git a/Data/Questions/ingsw/0422-16/wrong2.txt b/legacy/Data/Questions/ingsw/0422-16/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0422-16/wrong2.txt
rename to legacy/Data/Questions/ingsw/0422-16/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_0/quest.txt b/legacy/Data/Questions/ingsw/0613_0/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_0/quest.txt
rename to legacy/Data/Questions/ingsw/0613_0/quest.txt
diff --git a/Data/Questions/ingsw/0613_14/wrong1.txt b/legacy/Data/Questions/ingsw/0613_0/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_14/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_0/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_0/wrong2.txt b/legacy/Data/Questions/ingsw/0613_0/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_0/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_0/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_0/wrong3.txt b/legacy/Data/Questions/ingsw/0613_0/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_0/wrong3.txt
rename to legacy/Data/Questions/ingsw/0613_0/wrong3.txt
diff --git a/Data/Questions/ingsw/0613_1/correct.txt b/legacy/Data/Questions/ingsw/0613_1/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_1/correct.txt
rename to legacy/Data/Questions/ingsw/0613_1/correct.txt
diff --git a/Data/Questions/ingsw/0613_1/quest.txt b/legacy/Data/Questions/ingsw/0613_1/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_1/quest.txt
rename to legacy/Data/Questions/ingsw/0613_1/quest.txt
diff --git a/Data/Questions/ingsw/0613_1/wrong1.txt b/legacy/Data/Questions/ingsw/0613_1/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_1/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_1/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_1/wrong2.txt b/legacy/Data/Questions/ingsw/0613_1/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_1/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_1/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_10/correct.txt b/legacy/Data/Questions/ingsw/0613_10/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_10/correct.txt
rename to legacy/Data/Questions/ingsw/0613_10/correct.txt
diff --git a/Data/Questions/ingsw/0613_10/quest.txt b/legacy/Data/Questions/ingsw/0613_10/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_10/quest.txt
rename to legacy/Data/Questions/ingsw/0613_10/quest.txt
diff --git a/Data/Questions/ingsw/0613_10/wrong1.txt b/legacy/Data/Questions/ingsw/0613_10/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_10/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_10/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_10/wrong2.txt b/legacy/Data/Questions/ingsw/0613_10/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_10/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_10/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_11/correct.txt b/legacy/Data/Questions/ingsw/0613_11/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_11/correct.txt
rename to legacy/Data/Questions/ingsw/0613_11/correct.txt
diff --git a/Data/Questions/ingsw/0613_11/quest.txt b/legacy/Data/Questions/ingsw/0613_11/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_11/quest.txt
rename to legacy/Data/Questions/ingsw/0613_11/quest.txt
diff --git a/Data/Questions/ingsw/0613_11/wrong1.txt b/legacy/Data/Questions/ingsw/0613_11/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_11/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_11/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_11/wrong2.txt b/legacy/Data/Questions/ingsw/0613_11/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_11/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_11/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_12/correct.txt b/legacy/Data/Questions/ingsw/0613_12/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_12/correct.txt
rename to legacy/Data/Questions/ingsw/0613_12/correct.txt
diff --git a/Data/Questions/ingsw/0613_12/quest.txt b/legacy/Data/Questions/ingsw/0613_12/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_12/quest.txt
rename to legacy/Data/Questions/ingsw/0613_12/quest.txt
diff --git a/Data/Questions/ingsw/0613_12/wrong1.txt b/legacy/Data/Questions/ingsw/0613_12/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_12/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_12/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_12/wrong2.txt b/legacy/Data/Questions/ingsw/0613_12/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_12/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_12/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_13/correct.txt b/legacy/Data/Questions/ingsw/0613_13/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_13/correct.txt
rename to legacy/Data/Questions/ingsw/0613_13/correct.txt
diff --git a/Data/Questions/ingsw/0613_13/quest.txt b/legacy/Data/Questions/ingsw/0613_13/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_13/quest.txt
rename to legacy/Data/Questions/ingsw/0613_13/quest.txt
diff --git a/Data/Questions/ingsw/0613_13/wrong1.txt b/legacy/Data/Questions/ingsw/0613_13/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_13/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_13/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_13/wrong2.txt b/legacy/Data/Questions/ingsw/0613_13/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_13/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_13/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_14/quest.txt b/legacy/Data/Questions/ingsw/0613_14/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_14/quest.txt
rename to legacy/Data/Questions/ingsw/0613_14/quest.txt
diff --git a/Data/Questions/ingsw/0613_19/wrong1.txt b/legacy/Data/Questions/ingsw/0613_14/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_19/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_14/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_14/wrong2.txt b/legacy/Data/Questions/ingsw/0613_14/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_14/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_14/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_14/wrong3.txt b/legacy/Data/Questions/ingsw/0613_14/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_14/wrong3.txt
rename to legacy/Data/Questions/ingsw/0613_14/wrong3.txt
diff --git a/Data/Questions/ingsw/0613_15/correct.txt b/legacy/Data/Questions/ingsw/0613_15/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_15/correct.txt
rename to legacy/Data/Questions/ingsw/0613_15/correct.txt
diff --git a/Data/Questions/ingsw/0613_15/quest.txt b/legacy/Data/Questions/ingsw/0613_15/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_15/quest.txt
rename to legacy/Data/Questions/ingsw/0613_15/quest.txt
diff --git a/Data/Questions/ingsw/0613_15/wrong1.txt b/legacy/Data/Questions/ingsw/0613_15/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_15/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_15/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_15/wrong2.txt b/legacy/Data/Questions/ingsw/0613_15/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_15/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_15/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_16/correct.txt b/legacy/Data/Questions/ingsw/0613_16/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_16/correct.txt
rename to legacy/Data/Questions/ingsw/0613_16/correct.txt
diff --git a/Data/Questions/ingsw/0613_16/quest.txt b/legacy/Data/Questions/ingsw/0613_16/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_16/quest.txt
rename to legacy/Data/Questions/ingsw/0613_16/quest.txt
diff --git a/Data/Questions/ingsw/0613_16/wrong1.txt b/legacy/Data/Questions/ingsw/0613_16/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_16/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_16/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_16/wrong2.txt b/legacy/Data/Questions/ingsw/0613_16/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_16/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_16/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_17/correct.txt b/legacy/Data/Questions/ingsw/0613_17/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_17/correct.txt
rename to legacy/Data/Questions/ingsw/0613_17/correct.txt
diff --git a/Data/Questions/ingsw/0613_17/quest.txt b/legacy/Data/Questions/ingsw/0613_17/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_17/quest.txt
rename to legacy/Data/Questions/ingsw/0613_17/quest.txt
diff --git a/Data/Questions/ingsw/0613_17/wrong1.txt b/legacy/Data/Questions/ingsw/0613_17/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_17/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_17/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_17/wrong2.txt b/legacy/Data/Questions/ingsw/0613_17/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_17/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_17/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_18/correct.txt b/legacy/Data/Questions/ingsw/0613_18/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_18/correct.txt
rename to legacy/Data/Questions/ingsw/0613_18/correct.txt
diff --git a/Data/Questions/ingsw/0613_18/quest.txt b/legacy/Data/Questions/ingsw/0613_18/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_18/quest.txt
rename to legacy/Data/Questions/ingsw/0613_18/quest.txt
diff --git a/Data/Questions/ingsw/0613_18/wrong1.txt b/legacy/Data/Questions/ingsw/0613_18/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_18/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_18/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_18/wrong2.txt b/legacy/Data/Questions/ingsw/0613_18/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_18/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_18/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_19/quest.txt b/legacy/Data/Questions/ingsw/0613_19/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_19/quest.txt
rename to legacy/Data/Questions/ingsw/0613_19/quest.txt
diff --git a/Data/Questions/ingsw/0613_2/wrong1.txt b/legacy/Data/Questions/ingsw/0613_19/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_2/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_19/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_19/wrong2.txt b/legacy/Data/Questions/ingsw/0613_19/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_19/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_19/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_19/wrong3.txt b/legacy/Data/Questions/ingsw/0613_19/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_19/wrong3.txt
rename to legacy/Data/Questions/ingsw/0613_19/wrong3.txt
diff --git a/Data/Questions/ingsw/0613_2/quest.txt b/legacy/Data/Questions/ingsw/0613_2/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_2/quest.txt
rename to legacy/Data/Questions/ingsw/0613_2/quest.txt
diff --git a/Data/Questions/ingsw/0613_2/wrong2.txt b/legacy/Data/Questions/ingsw/0613_2/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_2/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_2/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_2/wrong3.txt b/legacy/Data/Questions/ingsw/0613_2/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_2/wrong3.txt
rename to legacy/Data/Questions/ingsw/0613_2/wrong2.txt
diff --git a/legacy/Data/Questions/ingsw/0613_2/wrong3.txt b/legacy/Data/Questions/ingsw/0613_2/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/Data/Questions/ingsw/0613_20/correct.txt b/legacy/Data/Questions/ingsw/0613_20/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_20/correct.txt
rename to legacy/Data/Questions/ingsw/0613_20/correct.txt
diff --git a/Data/Questions/ingsw/0613_20/quest.txt b/legacy/Data/Questions/ingsw/0613_20/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_20/quest.txt
rename to legacy/Data/Questions/ingsw/0613_20/quest.txt
diff --git a/Data/Questions/ingsw/0613_20/wrong1.txt b/legacy/Data/Questions/ingsw/0613_20/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_20/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_20/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_20/wrong2.txt b/legacy/Data/Questions/ingsw/0613_20/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_20/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_20/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_21/correct.txt b/legacy/Data/Questions/ingsw/0613_21/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_21/correct.txt
rename to legacy/Data/Questions/ingsw/0613_21/correct.txt
diff --git a/Data/Questions/ingsw/0613_21/quest.txt b/legacy/Data/Questions/ingsw/0613_21/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_21/quest.txt
rename to legacy/Data/Questions/ingsw/0613_21/quest.txt
diff --git a/Data/Questions/ingsw/0613_21/wrong1.txt b/legacy/Data/Questions/ingsw/0613_21/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_21/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_21/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_21/wrong2.txt b/legacy/Data/Questions/ingsw/0613_21/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_21/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_21/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_22/correct.txt b/legacy/Data/Questions/ingsw/0613_22/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_22/correct.txt
rename to legacy/Data/Questions/ingsw/0613_22/correct.txt
diff --git a/Data/Questions/ingsw/0613_22/quest.txt b/legacy/Data/Questions/ingsw/0613_22/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_22/quest.txt
rename to legacy/Data/Questions/ingsw/0613_22/quest.txt
diff --git a/Data/Questions/ingsw/0613_22/wrong1.txt b/legacy/Data/Questions/ingsw/0613_22/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_22/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_22/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_22/wrong2.txt b/legacy/Data/Questions/ingsw/0613_22/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_22/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_22/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_23/correct.txt b/legacy/Data/Questions/ingsw/0613_23/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_23/correct.txt
rename to legacy/Data/Questions/ingsw/0613_23/correct.txt
diff --git a/Data/Questions/ingsw/0613_23/quest.txt b/legacy/Data/Questions/ingsw/0613_23/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_23/quest.txt
rename to legacy/Data/Questions/ingsw/0613_23/quest.txt
diff --git a/Data/Questions/ingsw/0613_23/wrong1.txt b/legacy/Data/Questions/ingsw/0613_23/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_23/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_23/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_23/wrong2.txt b/legacy/Data/Questions/ingsw/0613_23/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_23/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_23/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_24/correct.txt b/legacy/Data/Questions/ingsw/0613_24/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_24/correct.txt
rename to legacy/Data/Questions/ingsw/0613_24/correct.txt
diff --git a/Data/Questions/ingsw/0613_24/quest.txt b/legacy/Data/Questions/ingsw/0613_24/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_24/quest.txt
rename to legacy/Data/Questions/ingsw/0613_24/quest.txt
diff --git a/Data/Questions/ingsw/0613_24/wrong1.txt b/legacy/Data/Questions/ingsw/0613_24/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_24/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_24/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_24/wrong2.txt b/legacy/Data/Questions/ingsw/0613_24/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_24/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_24/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_25/correct.txt b/legacy/Data/Questions/ingsw/0613_25/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_25/correct.txt
rename to legacy/Data/Questions/ingsw/0613_25/correct.txt
diff --git a/Data/Questions/ingsw/0613_25/quest.txt b/legacy/Data/Questions/ingsw/0613_25/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_25/quest.txt
rename to legacy/Data/Questions/ingsw/0613_25/quest.txt
diff --git a/Data/Questions/ingsw/0613_25/wrong1.txt b/legacy/Data/Questions/ingsw/0613_25/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_25/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_25/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_25/wrong2.txt b/legacy/Data/Questions/ingsw/0613_25/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_25/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_25/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_26/correct.txt b/legacy/Data/Questions/ingsw/0613_26/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_26/correct.txt
rename to legacy/Data/Questions/ingsw/0613_26/correct.txt
diff --git a/Data/Questions/ingsw/0613_26/quest.txt b/legacy/Data/Questions/ingsw/0613_26/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_26/quest.txt
rename to legacy/Data/Questions/ingsw/0613_26/quest.txt
diff --git a/Data/Questions/ingsw/0613_26/wrong1.txt b/legacy/Data/Questions/ingsw/0613_26/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_26/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_26/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_26/wrong2.txt b/legacy/Data/Questions/ingsw/0613_26/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_26/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_26/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_27/quest.txt b/legacy/Data/Questions/ingsw/0613_27/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_27/quest.txt
rename to legacy/Data/Questions/ingsw/0613_27/quest.txt
diff --git a/Data/Questions/ingsw/0613_27/wrong1.txt b/legacy/Data/Questions/ingsw/0613_27/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_27/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_27/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_27/wrong2.txt b/legacy/Data/Questions/ingsw/0613_27/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_27/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_27/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_27/wrong3.txt b/legacy/Data/Questions/ingsw/0613_27/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_27/wrong3.txt
rename to legacy/Data/Questions/ingsw/0613_27/wrong3.txt
diff --git a/Data/Questions/ingsw/0613_28/correct.txt b/legacy/Data/Questions/ingsw/0613_28/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_28/correct.txt
rename to legacy/Data/Questions/ingsw/0613_28/correct.txt
diff --git a/Data/Questions/ingsw/0613_28/quest.txt b/legacy/Data/Questions/ingsw/0613_28/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_28/quest.txt
rename to legacy/Data/Questions/ingsw/0613_28/quest.txt
diff --git a/Data/Questions/ingsw/0613_28/wrong1.txt b/legacy/Data/Questions/ingsw/0613_28/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_28/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_28/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_28/wrong2.txt b/legacy/Data/Questions/ingsw/0613_28/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_28/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_28/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_29/correct.txt b/legacy/Data/Questions/ingsw/0613_29/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_29/correct.txt
rename to legacy/Data/Questions/ingsw/0613_29/correct.txt
diff --git a/Data/Questions/ingsw/0613_29/quest.txt b/legacy/Data/Questions/ingsw/0613_29/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_29/quest.txt
rename to legacy/Data/Questions/ingsw/0613_29/quest.txt
diff --git a/Data/Questions/ingsw/0613_29/wrong1.txt b/legacy/Data/Questions/ingsw/0613_29/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_29/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_29/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_29/wrong2.txt b/legacy/Data/Questions/ingsw/0613_29/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_29/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_29/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_3/correct.txt b/legacy/Data/Questions/ingsw/0613_3/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_3/correct.txt
rename to legacy/Data/Questions/ingsw/0613_3/correct.txt
diff --git a/Data/Questions/ingsw/0613_3/quest.txt b/legacy/Data/Questions/ingsw/0613_3/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_3/quest.txt
rename to legacy/Data/Questions/ingsw/0613_3/quest.txt
diff --git a/Data/Questions/ingsw/0613_3/wrong1.txt b/legacy/Data/Questions/ingsw/0613_3/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_3/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_3/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_3/wrong2.txt b/legacy/Data/Questions/ingsw/0613_3/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_3/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_3/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_30/correct.txt b/legacy/Data/Questions/ingsw/0613_30/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_30/correct.txt
rename to legacy/Data/Questions/ingsw/0613_30/correct.txt
diff --git a/Data/Questions/ingsw/0613_30/quest.txt b/legacy/Data/Questions/ingsw/0613_30/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_30/quest.txt
rename to legacy/Data/Questions/ingsw/0613_30/quest.txt
diff --git a/Data/Questions/ingsw/0613_30/wrong1.txt b/legacy/Data/Questions/ingsw/0613_30/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_30/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_30/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_30/wrong2.txt b/legacy/Data/Questions/ingsw/0613_30/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_30/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_30/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_31/correct.txt b/legacy/Data/Questions/ingsw/0613_31/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_31/correct.txt
rename to legacy/Data/Questions/ingsw/0613_31/correct.txt
diff --git a/Data/Questions/ingsw/0613_31/quest.txt b/legacy/Data/Questions/ingsw/0613_31/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_31/quest.txt
rename to legacy/Data/Questions/ingsw/0613_31/quest.txt
diff --git a/Data/Questions/ingsw/0613_31/wrong1.txt b/legacy/Data/Questions/ingsw/0613_31/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_31/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_31/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_31/wrong2.txt b/legacy/Data/Questions/ingsw/0613_31/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_31/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_31/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_32/correct.txt b/legacy/Data/Questions/ingsw/0613_32/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_32/correct.txt
rename to legacy/Data/Questions/ingsw/0613_32/correct.txt
diff --git a/Data/Questions/ingsw/0613_32/quest.txt b/legacy/Data/Questions/ingsw/0613_32/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_32/quest.txt
rename to legacy/Data/Questions/ingsw/0613_32/quest.txt
diff --git a/Data/Questions/ingsw/0613_32/wrong1.txt b/legacy/Data/Questions/ingsw/0613_32/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_32/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_32/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_32/wrong2.txt b/legacy/Data/Questions/ingsw/0613_32/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_32/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_32/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_33/correct.txt b/legacy/Data/Questions/ingsw/0613_33/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_33/correct.txt
rename to legacy/Data/Questions/ingsw/0613_33/correct.txt
diff --git a/Data/Questions/ingsw/0613_33/quest.txt b/legacy/Data/Questions/ingsw/0613_33/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_33/quest.txt
rename to legacy/Data/Questions/ingsw/0613_33/quest.txt
diff --git a/Data/Questions/ingsw/0613_33/wrong1.txt b/legacy/Data/Questions/ingsw/0613_33/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_33/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_33/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_33/wrong2.txt b/legacy/Data/Questions/ingsw/0613_33/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_33/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_33/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_34/correct.txt b/legacy/Data/Questions/ingsw/0613_34/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_34/correct.txt
rename to legacy/Data/Questions/ingsw/0613_34/correct.txt
diff --git a/Data/Questions/ingsw/0613_34/quest.txt b/legacy/Data/Questions/ingsw/0613_34/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_34/quest.txt
rename to legacy/Data/Questions/ingsw/0613_34/quest.txt
diff --git a/Data/Questions/ingsw/0613_34/wrong1.txt b/legacy/Data/Questions/ingsw/0613_34/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_34/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_34/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_34/wrong2.txt b/legacy/Data/Questions/ingsw/0613_34/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_34/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_34/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_35/correct.txt b/legacy/Data/Questions/ingsw/0613_35/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_35/correct.txt
rename to legacy/Data/Questions/ingsw/0613_35/correct.txt
diff --git a/Data/Questions/ingsw/0613_35/quest.txt b/legacy/Data/Questions/ingsw/0613_35/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_35/quest.txt
rename to legacy/Data/Questions/ingsw/0613_35/quest.txt
diff --git a/Data/Questions/ingsw/0613_35/wrong1.txt b/legacy/Data/Questions/ingsw/0613_35/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_35/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_35/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_35/wrong2.txt b/legacy/Data/Questions/ingsw/0613_35/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_35/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_35/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_36/correct.txt b/legacy/Data/Questions/ingsw/0613_36/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_36/correct.txt
rename to legacy/Data/Questions/ingsw/0613_36/correct.txt
diff --git a/Data/Questions/ingsw/0613_36/quest.txt b/legacy/Data/Questions/ingsw/0613_36/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_36/quest.txt
rename to legacy/Data/Questions/ingsw/0613_36/quest.txt
diff --git a/Data/Questions/ingsw/0613_36/wrong1.txt b/legacy/Data/Questions/ingsw/0613_36/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_36/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_36/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_36/wrong2.txt b/legacy/Data/Questions/ingsw/0613_36/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_36/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_36/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_37/correct.txt b/legacy/Data/Questions/ingsw/0613_37/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_37/correct.txt
rename to legacy/Data/Questions/ingsw/0613_37/correct.txt
diff --git a/Data/Questions/ingsw/0613_37/quest.txt b/legacy/Data/Questions/ingsw/0613_37/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_37/quest.txt
rename to legacy/Data/Questions/ingsw/0613_37/quest.txt
diff --git a/Data/Questions/ingsw/0613_37/wrong1.txt b/legacy/Data/Questions/ingsw/0613_37/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_37/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_37/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_37/wrong2.txt b/legacy/Data/Questions/ingsw/0613_37/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_37/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_37/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_38/quest.txt b/legacy/Data/Questions/ingsw/0613_38/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_38/quest.txt
rename to legacy/Data/Questions/ingsw/0613_38/quest.txt
diff --git a/Data/Questions/ingsw/0613_38/wrong1.txt b/legacy/Data/Questions/ingsw/0613_38/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_38/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_38/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_38/wrong2.txt b/legacy/Data/Questions/ingsw/0613_38/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_38/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_38/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_38/wrong3.txt b/legacy/Data/Questions/ingsw/0613_38/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_38/wrong3.txt
rename to legacy/Data/Questions/ingsw/0613_38/wrong3.txt
diff --git a/Data/Questions/ingsw/0613_39/correct.txt b/legacy/Data/Questions/ingsw/0613_39/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_39/correct.txt
rename to legacy/Data/Questions/ingsw/0613_39/correct.txt
diff --git a/Data/Questions/ingsw/0613_39/quest.txt b/legacy/Data/Questions/ingsw/0613_39/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_39/quest.txt
rename to legacy/Data/Questions/ingsw/0613_39/quest.txt
diff --git a/Data/Questions/ingsw/0613_39/wrong1.txt b/legacy/Data/Questions/ingsw/0613_39/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_39/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_39/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_39/wrong2.txt b/legacy/Data/Questions/ingsw/0613_39/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_39/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_39/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_4/correct.txt b/legacy/Data/Questions/ingsw/0613_4/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_4/correct.txt
rename to legacy/Data/Questions/ingsw/0613_4/correct.txt
diff --git a/Data/Questions/ingsw/0613_4/quest.txt b/legacy/Data/Questions/ingsw/0613_4/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_4/quest.txt
rename to legacy/Data/Questions/ingsw/0613_4/quest.txt
diff --git a/Data/Questions/ingsw/0613_4/wrong1.txt b/legacy/Data/Questions/ingsw/0613_4/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_4/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_4/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_4/wrong2.txt b/legacy/Data/Questions/ingsw/0613_4/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_4/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_4/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_40/quest.txt b/legacy/Data/Questions/ingsw/0613_40/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_40/quest.txt
rename to legacy/Data/Questions/ingsw/0613_40/quest.txt
diff --git a/Data/Questions/ingsw/0613_40/wrong1.txt b/legacy/Data/Questions/ingsw/0613_40/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_40/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_40/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_40/wrong2.txt b/legacy/Data/Questions/ingsw/0613_40/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_40/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_40/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_40/wrong3.txt b/legacy/Data/Questions/ingsw/0613_40/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_40/wrong3.txt
rename to legacy/Data/Questions/ingsw/0613_40/wrong3.txt
diff --git a/Data/Questions/ingsw/0613_41/quest.txt b/legacy/Data/Questions/ingsw/0613_41/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_41/quest.txt
rename to legacy/Data/Questions/ingsw/0613_41/quest.txt
diff --git a/Data/Questions/ingsw/0613_41/wrong1.txt b/legacy/Data/Questions/ingsw/0613_41/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_41/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_41/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_41/wrong2.txt b/legacy/Data/Questions/ingsw/0613_41/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_41/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_41/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_41/wrong3.txt b/legacy/Data/Questions/ingsw/0613_41/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_41/wrong3.txt
rename to legacy/Data/Questions/ingsw/0613_41/wrong3.txt
diff --git a/Data/Questions/ingsw/0613_42/correct.txt b/legacy/Data/Questions/ingsw/0613_42/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_42/correct.txt
rename to legacy/Data/Questions/ingsw/0613_42/correct.txt
diff --git a/Data/Questions/ingsw/0613_42/quest.txt b/legacy/Data/Questions/ingsw/0613_42/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_42/quest.txt
rename to legacy/Data/Questions/ingsw/0613_42/quest.txt
diff --git a/Data/Questions/ingsw/0613_42/wrong1.txt b/legacy/Data/Questions/ingsw/0613_42/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_42/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_42/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_42/wrong2.txt b/legacy/Data/Questions/ingsw/0613_42/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_42/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_42/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_43/correct.txt b/legacy/Data/Questions/ingsw/0613_43/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_43/correct.txt
rename to legacy/Data/Questions/ingsw/0613_43/correct.txt
diff --git a/Data/Questions/ingsw/0613_43/quest.txt b/legacy/Data/Questions/ingsw/0613_43/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_43/quest.txt
rename to legacy/Data/Questions/ingsw/0613_43/quest.txt
diff --git a/Data/Questions/ingsw/0613_43/wrong1.txt b/legacy/Data/Questions/ingsw/0613_43/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_43/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_43/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_43/wrong2.txt b/legacy/Data/Questions/ingsw/0613_43/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_43/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_43/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_44/correct.txt b/legacy/Data/Questions/ingsw/0613_44/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_44/correct.txt
rename to legacy/Data/Questions/ingsw/0613_44/correct.txt
diff --git a/Data/Questions/ingsw/0613_44/quest.txt b/legacy/Data/Questions/ingsw/0613_44/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_44/quest.txt
rename to legacy/Data/Questions/ingsw/0613_44/quest.txt
diff --git a/Data/Questions/ingsw/0613_44/wrong1.txt b/legacy/Data/Questions/ingsw/0613_44/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_44/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_44/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_44/wrong2.txt b/legacy/Data/Questions/ingsw/0613_44/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_44/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_44/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_45/correct.txt b/legacy/Data/Questions/ingsw/0613_45/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_45/correct.txt
rename to legacy/Data/Questions/ingsw/0613_45/correct.txt
diff --git a/Data/Questions/ingsw/0613_45/quest.txt b/legacy/Data/Questions/ingsw/0613_45/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_45/quest.txt
rename to legacy/Data/Questions/ingsw/0613_45/quest.txt
diff --git a/Data/Questions/ingsw/0613_45/wrong1.txt b/legacy/Data/Questions/ingsw/0613_45/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_45/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_45/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_45/wrong2.txt b/legacy/Data/Questions/ingsw/0613_45/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_45/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_45/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_46/correct.txt b/legacy/Data/Questions/ingsw/0613_46/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_46/correct.txt
rename to legacy/Data/Questions/ingsw/0613_46/correct.txt
diff --git a/Data/Questions/ingsw/0613_46/quest.txt b/legacy/Data/Questions/ingsw/0613_46/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_46/quest.txt
rename to legacy/Data/Questions/ingsw/0613_46/quest.txt
diff --git a/Data/Questions/ingsw/0613_46/wrong1.txt b/legacy/Data/Questions/ingsw/0613_46/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_46/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_46/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_46/wrong2.txt b/legacy/Data/Questions/ingsw/0613_46/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_46/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_46/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_47/correct.txt b/legacy/Data/Questions/ingsw/0613_47/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_47/correct.txt
rename to legacy/Data/Questions/ingsw/0613_47/correct.txt
diff --git a/Data/Questions/ingsw/0613_47/quest.txt b/legacy/Data/Questions/ingsw/0613_47/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_47/quest.txt
rename to legacy/Data/Questions/ingsw/0613_47/quest.txt
diff --git a/Data/Questions/ingsw/0613_47/wrong1.txt b/legacy/Data/Questions/ingsw/0613_47/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_47/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_47/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_47/wrong2.txt b/legacy/Data/Questions/ingsw/0613_47/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_47/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_47/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_48/correct.txt b/legacy/Data/Questions/ingsw/0613_48/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_48/correct.txt
rename to legacy/Data/Questions/ingsw/0613_48/correct.txt
diff --git a/Data/Questions/ingsw/0613_48/quest.txt b/legacy/Data/Questions/ingsw/0613_48/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_48/quest.txt
rename to legacy/Data/Questions/ingsw/0613_48/quest.txt
diff --git a/Data/Questions/ingsw/0613_48/wrong1.txt b/legacy/Data/Questions/ingsw/0613_48/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_48/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_48/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_48/wrong2.txt b/legacy/Data/Questions/ingsw/0613_48/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_48/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_48/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_49/correct.txt b/legacy/Data/Questions/ingsw/0613_49/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_49/correct.txt
rename to legacy/Data/Questions/ingsw/0613_49/correct.txt
diff --git a/Data/Questions/ingsw/0613_49/quest.txt b/legacy/Data/Questions/ingsw/0613_49/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_49/quest.txt
rename to legacy/Data/Questions/ingsw/0613_49/quest.txt
diff --git a/Data/Questions/ingsw/0613_49/wrong1.txt b/legacy/Data/Questions/ingsw/0613_49/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_49/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_49/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_49/wrong2.txt b/legacy/Data/Questions/ingsw/0613_49/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_49/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_49/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_5/correct.txt b/legacy/Data/Questions/ingsw/0613_5/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_5/correct.txt
rename to legacy/Data/Questions/ingsw/0613_5/correct.txt
diff --git a/Data/Questions/ingsw/0613_5/quest.txt b/legacy/Data/Questions/ingsw/0613_5/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_5/quest.txt
rename to legacy/Data/Questions/ingsw/0613_5/quest.txt
diff --git a/Data/Questions/ingsw/0613_5/wrong1.txt b/legacy/Data/Questions/ingsw/0613_5/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_5/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_5/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_5/wrong2.txt b/legacy/Data/Questions/ingsw/0613_5/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_5/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_5/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_6/correct.txt b/legacy/Data/Questions/ingsw/0613_6/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_6/correct.txt
rename to legacy/Data/Questions/ingsw/0613_6/correct.txt
diff --git a/Data/Questions/ingsw/0613_6/quest.txt b/legacy/Data/Questions/ingsw/0613_6/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_6/quest.txt
rename to legacy/Data/Questions/ingsw/0613_6/quest.txt
diff --git a/Data/Questions/ingsw/0613_6/wrong1.txt b/legacy/Data/Questions/ingsw/0613_6/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_6/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_6/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_6/wrong2.txt b/legacy/Data/Questions/ingsw/0613_6/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_6/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_6/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_7/correct.txt b/legacy/Data/Questions/ingsw/0613_7/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_7/correct.txt
rename to legacy/Data/Questions/ingsw/0613_7/correct.txt
diff --git a/Data/Questions/ingsw/0613_7/quest.txt b/legacy/Data/Questions/ingsw/0613_7/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_7/quest.txt
rename to legacy/Data/Questions/ingsw/0613_7/quest.txt
diff --git a/Data/Questions/ingsw/0613_7/wrong1.txt b/legacy/Data/Questions/ingsw/0613_7/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_7/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_7/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_7/wrong2.txt b/legacy/Data/Questions/ingsw/0613_7/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_7/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_7/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_8/correct.txt b/legacy/Data/Questions/ingsw/0613_8/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_8/correct.txt
rename to legacy/Data/Questions/ingsw/0613_8/correct.txt
diff --git a/Data/Questions/ingsw/0613_8/quest.txt b/legacy/Data/Questions/ingsw/0613_8/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_8/quest.txt
rename to legacy/Data/Questions/ingsw/0613_8/quest.txt
diff --git a/Data/Questions/ingsw/0613_8/wrong1.txt b/legacy/Data/Questions/ingsw/0613_8/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_8/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_8/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_8/wrong2.txt b/legacy/Data/Questions/ingsw/0613_8/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_8/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_8/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_9/quest.txt b/legacy/Data/Questions/ingsw/0613_9/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_9/quest.txt
rename to legacy/Data/Questions/ingsw/0613_9/quest.txt
diff --git a/Data/Questions/ingsw/0613_9/wrong1.txt b/legacy/Data/Questions/ingsw/0613_9/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_9/wrong1.txt
rename to legacy/Data/Questions/ingsw/0613_9/wrong1.txt
diff --git a/Data/Questions/ingsw/0613_9/wrong2.txt b/legacy/Data/Questions/ingsw/0613_9/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_9/wrong2.txt
rename to legacy/Data/Questions/ingsw/0613_9/wrong2.txt
diff --git a/Data/Questions/ingsw/0613_9/wrong3.txt b/legacy/Data/Questions/ingsw/0613_9/wrong3.txt
similarity index 100%
rename from Data/Questions/ingsw/0613_9/wrong3.txt
rename to legacy/Data/Questions/ingsw/0613_9/wrong3.txt
diff --git a/Data/Questions/ingsw/0621_0/correct.txt b/legacy/Data/Questions/ingsw/0621_0/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_0/correct.txt
rename to legacy/Data/Questions/ingsw/0621_0/correct.txt
diff --git a/Data/Questions/ingsw/0621_0/quest.txt b/legacy/Data/Questions/ingsw/0621_0/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_0/quest.txt
rename to legacy/Data/Questions/ingsw/0621_0/quest.txt
diff --git a/Data/Questions/ingsw/0621_0/wrong0.txt b/legacy/Data/Questions/ingsw/0621_0/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_0/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_0/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_0/wrong1.txt b/legacy/Data/Questions/ingsw/0621_0/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_0/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_0/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_1/correct.txt b/legacy/Data/Questions/ingsw/0621_1/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_1/correct.txt
rename to legacy/Data/Questions/ingsw/0621_1/correct.txt
diff --git a/Data/Questions/ingsw/0621_1/quest.txt b/legacy/Data/Questions/ingsw/0621_1/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_1/quest.txt
rename to legacy/Data/Questions/ingsw/0621_1/quest.txt
diff --git a/Data/Questions/ingsw/0621_1/wrong1.txt b/legacy/Data/Questions/ingsw/0621_1/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_1/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_1/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_1/wrong2.txt b/legacy/Data/Questions/ingsw/0621_1/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_1/wrong2.txt
rename to legacy/Data/Questions/ingsw/0621_1/wrong2.txt
diff --git a/Data/Questions/ingsw/0621_10/correct.txt b/legacy/Data/Questions/ingsw/0621_10/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_10/correct.txt
rename to legacy/Data/Questions/ingsw/0621_10/correct.txt
diff --git a/Data/Questions/ingsw/0621_10/quest.txt b/legacy/Data/Questions/ingsw/0621_10/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_10/quest.txt
rename to legacy/Data/Questions/ingsw/0621_10/quest.txt
diff --git a/Data/Questions/ingsw/0621_10/wrong0.txt b/legacy/Data/Questions/ingsw/0621_10/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_10/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_10/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_10/wrong1.txt b/legacy/Data/Questions/ingsw/0621_10/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_10/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_10/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_13/correct.txt b/legacy/Data/Questions/ingsw/0621_13/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_13/correct.txt
rename to legacy/Data/Questions/ingsw/0621_13/correct.txt
diff --git a/Data/Questions/ingsw/0621_13/quest.txt b/legacy/Data/Questions/ingsw/0621_13/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_13/quest.txt
rename to legacy/Data/Questions/ingsw/0621_13/quest.txt
diff --git a/Data/Questions/ingsw/0621_13/wrong0.txt b/legacy/Data/Questions/ingsw/0621_13/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_13/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_13/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_13/wrong1.txt b/legacy/Data/Questions/ingsw/0621_13/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_13/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_13/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_14/correct.txt b/legacy/Data/Questions/ingsw/0621_14/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_14/correct.txt
rename to legacy/Data/Questions/ingsw/0621_14/correct.txt
diff --git a/Data/Questions/ingsw/0621_14/quest.txt b/legacy/Data/Questions/ingsw/0621_14/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_14/quest.txt
rename to legacy/Data/Questions/ingsw/0621_14/quest.txt
diff --git a/Data/Questions/ingsw/0621_14/wrong0.txt b/legacy/Data/Questions/ingsw/0621_14/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_14/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_14/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_14/wrong1.txt b/legacy/Data/Questions/ingsw/0621_14/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_14/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_14/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_17/correct.txt b/legacy/Data/Questions/ingsw/0621_17/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_17/correct.txt
rename to legacy/Data/Questions/ingsw/0621_17/correct.txt
diff --git a/Data/Questions/ingsw/0621_17/quest.txt b/legacy/Data/Questions/ingsw/0621_17/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_17/quest.txt
rename to legacy/Data/Questions/ingsw/0621_17/quest.txt
diff --git a/Data/Questions/ingsw/0621_17/wrong0.txt b/legacy/Data/Questions/ingsw/0621_17/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_17/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_17/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_17/wrong1.txt b/legacy/Data/Questions/ingsw/0621_17/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_17/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_17/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_19/correct.txt b/legacy/Data/Questions/ingsw/0621_19/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_19/correct.txt
rename to legacy/Data/Questions/ingsw/0621_19/correct.txt
diff --git a/Data/Questions/ingsw/0621_19/quest.txt b/legacy/Data/Questions/ingsw/0621_19/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_19/quest.txt
rename to legacy/Data/Questions/ingsw/0621_19/quest.txt
diff --git a/Data/Questions/ingsw/0621_19/wrong0.txt b/legacy/Data/Questions/ingsw/0621_19/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_19/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_19/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_19/wrong1.txt b/legacy/Data/Questions/ingsw/0621_19/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_19/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_19/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_2/correct.txt b/legacy/Data/Questions/ingsw/0621_2/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_2/correct.txt
rename to legacy/Data/Questions/ingsw/0621_2/correct.txt
diff --git a/Data/Questions/ingsw/0621_2/quest.txt b/legacy/Data/Questions/ingsw/0621_2/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_2/quest.txt
rename to legacy/Data/Questions/ingsw/0621_2/quest.txt
diff --git a/Data/Questions/ingsw/0621_2/wrong0.txt b/legacy/Data/Questions/ingsw/0621_2/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_2/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_2/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_2/wrong1.txt b/legacy/Data/Questions/ingsw/0621_2/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_2/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_2/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_21/correct.txt b/legacy/Data/Questions/ingsw/0621_21/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_21/correct.txt
rename to legacy/Data/Questions/ingsw/0621_21/correct.txt
diff --git a/Data/Questions/ingsw/0621_21/quest.txt b/legacy/Data/Questions/ingsw/0621_21/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_21/quest.txt
rename to legacy/Data/Questions/ingsw/0621_21/quest.txt
diff --git a/Data/Questions/ingsw/0621_21/wrong0.txt b/legacy/Data/Questions/ingsw/0621_21/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_21/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_21/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_21/wrong1.txt b/legacy/Data/Questions/ingsw/0621_21/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_21/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_21/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_22/correct.txt b/legacy/Data/Questions/ingsw/0621_22/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_22/correct.txt
rename to legacy/Data/Questions/ingsw/0621_22/correct.txt
diff --git a/Data/Questions/ingsw/0621_22/quest.txt b/legacy/Data/Questions/ingsw/0621_22/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_22/quest.txt
rename to legacy/Data/Questions/ingsw/0621_22/quest.txt
diff --git a/Data/Questions/ingsw/0621_22/wrong0.txt b/legacy/Data/Questions/ingsw/0621_22/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_22/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_22/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_22/wrong1.txt b/legacy/Data/Questions/ingsw/0621_22/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_22/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_22/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_24/correct.txt b/legacy/Data/Questions/ingsw/0621_24/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_24/correct.txt
rename to legacy/Data/Questions/ingsw/0621_24/correct.txt
diff --git a/Data/Questions/ingsw/0621_24/quest.txt b/legacy/Data/Questions/ingsw/0621_24/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_24/quest.txt
rename to legacy/Data/Questions/ingsw/0621_24/quest.txt
diff --git a/Data/Questions/ingsw/0621_24/wrong0.txt b/legacy/Data/Questions/ingsw/0621_24/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_24/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_24/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_24/wrong1.txt b/legacy/Data/Questions/ingsw/0621_24/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_24/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_24/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_3/correct.txt b/legacy/Data/Questions/ingsw/0621_3/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_3/correct.txt
rename to legacy/Data/Questions/ingsw/0621_3/correct.txt
diff --git a/Data/Questions/ingsw/0621_3/quest.txt b/legacy/Data/Questions/ingsw/0621_3/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_3/quest.txt
rename to legacy/Data/Questions/ingsw/0621_3/quest.txt
diff --git a/Data/Questions/ingsw/0621_3/wrong0.txt b/legacy/Data/Questions/ingsw/0621_3/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_3/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_3/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_3/wrong1.txt b/legacy/Data/Questions/ingsw/0621_3/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_3/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_3/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_32/correct.txt b/legacy/Data/Questions/ingsw/0621_32/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_32/correct.txt
rename to legacy/Data/Questions/ingsw/0621_32/correct.txt
diff --git a/Data/Questions/ingsw/0621_32/quest.txt b/legacy/Data/Questions/ingsw/0621_32/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_32/quest.txt
rename to legacy/Data/Questions/ingsw/0621_32/quest.txt
diff --git a/Data/Questions/ingsw/0621_32/wrong0.txt b/legacy/Data/Questions/ingsw/0621_32/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_32/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_32/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_32/wrong1.txt b/legacy/Data/Questions/ingsw/0621_32/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_32/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_32/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_35/correct.txt b/legacy/Data/Questions/ingsw/0621_35/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_35/correct.txt
rename to legacy/Data/Questions/ingsw/0621_35/correct.txt
diff --git a/Data/Questions/ingsw/0621_35/quest.txt b/legacy/Data/Questions/ingsw/0621_35/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_35/quest.txt
rename to legacy/Data/Questions/ingsw/0621_35/quest.txt
diff --git a/Data/Questions/ingsw/0621_35/wrong0.txt b/legacy/Data/Questions/ingsw/0621_35/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_35/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_35/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_35/wrong1.txt b/legacy/Data/Questions/ingsw/0621_35/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_35/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_35/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_36/correct.txt b/legacy/Data/Questions/ingsw/0621_36/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_36/correct.txt
rename to legacy/Data/Questions/ingsw/0621_36/correct.txt
diff --git a/Data/Questions/ingsw/0621_36/quest.txt b/legacy/Data/Questions/ingsw/0621_36/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_36/quest.txt
rename to legacy/Data/Questions/ingsw/0621_36/quest.txt
diff --git a/Data/Questions/ingsw/0621_36/wrong0.txt b/legacy/Data/Questions/ingsw/0621_36/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_36/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_36/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_36/wrong1.txt b/legacy/Data/Questions/ingsw/0621_36/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_36/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_36/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_39/correct.txt b/legacy/Data/Questions/ingsw/0621_39/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_39/correct.txt
rename to legacy/Data/Questions/ingsw/0621_39/correct.txt
diff --git a/Data/Questions/ingsw/0621_39/quest.txt b/legacy/Data/Questions/ingsw/0621_39/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_39/quest.txt
rename to legacy/Data/Questions/ingsw/0621_39/quest.txt
diff --git a/Data/Questions/ingsw/0621_39/wrong0.txt b/legacy/Data/Questions/ingsw/0621_39/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_39/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_39/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_39/wrong1.txt b/legacy/Data/Questions/ingsw/0621_39/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_39/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_39/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_6/correct.txt b/legacy/Data/Questions/ingsw/0621_6/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_6/correct.txt
rename to legacy/Data/Questions/ingsw/0621_6/correct.txt
diff --git a/Data/Questions/ingsw/0621_6/quest.txt b/legacy/Data/Questions/ingsw/0621_6/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_6/quest.txt
rename to legacy/Data/Questions/ingsw/0621_6/quest.txt
diff --git a/Data/Questions/ingsw/0621_6/wrong0.txt b/legacy/Data/Questions/ingsw/0621_6/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_6/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_6/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_6/wrong1.txt b/legacy/Data/Questions/ingsw/0621_6/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_6/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_6/wrong1.txt
diff --git a/Data/Questions/ingsw/0621_6/wrong2.txt b/legacy/Data/Questions/ingsw/0621_6/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_6/wrong2.txt
rename to legacy/Data/Questions/ingsw/0621_6/wrong2.txt
diff --git a/Data/Questions/ingsw/0621_9/correct.txt b/legacy/Data/Questions/ingsw/0621_9/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_9/correct.txt
rename to legacy/Data/Questions/ingsw/0621_9/correct.txt
diff --git a/Data/Questions/ingsw/0621_9/quest.txt b/legacy/Data/Questions/ingsw/0621_9/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_9/quest.txt
rename to legacy/Data/Questions/ingsw/0621_9/quest.txt
diff --git a/Data/Questions/ingsw/0621_9/wrong0.txt b/legacy/Data/Questions/ingsw/0621_9/wrong0.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_9/wrong0.txt
rename to legacy/Data/Questions/ingsw/0621_9/wrong0.txt
diff --git a/Data/Questions/ingsw/0621_9/wrong1.txt b/legacy/Data/Questions/ingsw/0621_9/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0621_9/wrong1.txt
rename to legacy/Data/Questions/ingsw/0621_9/wrong1.txt
diff --git a/Data/Questions/ingsw/0622_1/correct.txt b/legacy/Data/Questions/ingsw/0622_1/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_1/correct.txt
rename to legacy/Data/Questions/ingsw/0622_1/correct.txt
diff --git a/Data/Questions/ingsw/0622_1/quest.txt b/legacy/Data/Questions/ingsw/0622_1/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_1/quest.txt
rename to legacy/Data/Questions/ingsw/0622_1/quest.txt
diff --git a/Data/Questions/ingsw/0622_1/wrong 1.txt b/legacy/Data/Questions/ingsw/0622_1/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_1/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0622_1/wrong 1.txt
diff --git a/Data/Questions/ingsw/0622_1/wrong 2.txt b/legacy/Data/Questions/ingsw/0622_1/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_1/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0622_1/wrong 2.txt
diff --git a/Data/Questions/ingsw/0622_2/correct.txt b/legacy/Data/Questions/ingsw/0622_2/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_2/correct.txt
rename to legacy/Data/Questions/ingsw/0622_2/correct.txt
diff --git a/Data/Questions/ingsw/0622_2/quest.txt b/legacy/Data/Questions/ingsw/0622_2/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_2/quest.txt
rename to legacy/Data/Questions/ingsw/0622_2/quest.txt
diff --git a/Data/Questions/ingsw/0622_2/wrong 1.txt b/legacy/Data/Questions/ingsw/0622_2/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_2/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0622_2/wrong 1.txt
diff --git a/Data/Questions/ingsw/0622_2/wrong 2.txt b/legacy/Data/Questions/ingsw/0622_2/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_2/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0622_2/wrong 2.txt
diff --git a/Data/Questions/ingsw/0622_3/correct.txt b/legacy/Data/Questions/ingsw/0622_3/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_3/correct.txt
rename to legacy/Data/Questions/ingsw/0622_3/correct.txt
diff --git a/Data/Questions/ingsw/0622_3/quest.txt b/legacy/Data/Questions/ingsw/0622_3/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_3/quest.txt
rename to legacy/Data/Questions/ingsw/0622_3/quest.txt
diff --git a/Data/Questions/ingsw/0622_3/wrong 1.txt b/legacy/Data/Questions/ingsw/0622_3/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_3/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0622_3/wrong 1.txt
diff --git a/Data/Questions/ingsw/0622_3/wrong 2.txt b/legacy/Data/Questions/ingsw/0622_3/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_3/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0622_3/wrong 2.txt
diff --git a/Data/Questions/ingsw/0622_4/correct.txt b/legacy/Data/Questions/ingsw/0622_4/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_4/correct.txt
rename to legacy/Data/Questions/ingsw/0622_4/correct.txt
diff --git a/Data/Questions/ingsw/0622_4/quest.txt b/legacy/Data/Questions/ingsw/0622_4/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_4/quest.txt
rename to legacy/Data/Questions/ingsw/0622_4/quest.txt
diff --git a/Data/Questions/ingsw/0622_4/wrong 1.txt b/legacy/Data/Questions/ingsw/0622_4/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_4/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0622_4/wrong 1.txt
diff --git a/Data/Questions/ingsw/0622_4/wrong 2.txt b/legacy/Data/Questions/ingsw/0622_4/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_4/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0622_4/wrong 2.txt
diff --git a/Data/Questions/ingsw/0622_5/correct.txt b/legacy/Data/Questions/ingsw/0622_5/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_5/correct.txt
rename to legacy/Data/Questions/ingsw/0622_5/correct.txt
diff --git a/Data/Questions/ingsw/0622_5/quest.txt b/legacy/Data/Questions/ingsw/0622_5/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_5/quest.txt
rename to legacy/Data/Questions/ingsw/0622_5/quest.txt
diff --git a/Data/Questions/ingsw/0622_5/wrong 1.txt b/legacy/Data/Questions/ingsw/0622_5/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_5/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0622_5/wrong 1.txt
diff --git a/Data/Questions/ingsw/0622_5/wrong 2.txt b/legacy/Data/Questions/ingsw/0622_5/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_5/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0622_5/wrong 2.txt
diff --git a/Data/Questions/ingsw/0622_6/correct.txt b/legacy/Data/Questions/ingsw/0622_6/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_6/correct.txt
rename to legacy/Data/Questions/ingsw/0622_6/correct.txt
diff --git a/Data/Questions/ingsw/0622_6/quest.txt b/legacy/Data/Questions/ingsw/0622_6/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_6/quest.txt
rename to legacy/Data/Questions/ingsw/0622_6/quest.txt
diff --git a/Data/Questions/ingsw/0622_6/wrong 1.txt b/legacy/Data/Questions/ingsw/0622_6/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_6/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0622_6/wrong 1.txt
diff --git a/Data/Questions/ingsw/0622_6/wrong 2.txt b/legacy/Data/Questions/ingsw/0622_6/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_6/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0622_6/wrong 2.txt
diff --git a/Data/Questions/ingsw/0622_7/correct.txt b/legacy/Data/Questions/ingsw/0622_7/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_7/correct.txt
rename to legacy/Data/Questions/ingsw/0622_7/correct.txt
diff --git a/Data/Questions/ingsw/0622_7/quest.txt b/legacy/Data/Questions/ingsw/0622_7/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_7/quest.txt
rename to legacy/Data/Questions/ingsw/0622_7/quest.txt
diff --git a/Data/Questions/ingsw/0622_7/wrong 1.txt b/legacy/Data/Questions/ingsw/0622_7/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_7/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0622_7/wrong 1.txt
diff --git a/Data/Questions/ingsw/0622_7/wrong 2.txt b/legacy/Data/Questions/ingsw/0622_7/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_7/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0622_7/wrong 2.txt
diff --git a/Data/Questions/ingsw/0622_8/correct.txt b/legacy/Data/Questions/ingsw/0622_8/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_8/correct.txt
rename to legacy/Data/Questions/ingsw/0622_8/correct.txt
diff --git a/Data/Questions/ingsw/0622_8/quest.txt b/legacy/Data/Questions/ingsw/0622_8/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_8/quest.txt
rename to legacy/Data/Questions/ingsw/0622_8/quest.txt
diff --git a/Data/Questions/ingsw/0622_8/wrong 1.txt b/legacy/Data/Questions/ingsw/0622_8/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_8/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0622_8/wrong 1.txt
diff --git a/Data/Questions/ingsw/0622_8/wrong 2.txt b/legacy/Data/Questions/ingsw/0622_8/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_8/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0622_8/wrong 2.txt
diff --git a/Data/Questions/ingsw/0622_9/correct.txt b/legacy/Data/Questions/ingsw/0622_9/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_9/correct.txt
rename to legacy/Data/Questions/ingsw/0622_9/correct.txt
diff --git a/Data/Questions/ingsw/0622_9/quest.txt b/legacy/Data/Questions/ingsw/0622_9/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_9/quest.txt
rename to legacy/Data/Questions/ingsw/0622_9/quest.txt
diff --git a/Data/Questions/ingsw/0622_9/wrong 1.txt b/legacy/Data/Questions/ingsw/0622_9/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_9/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0622_9/wrong 1.txt
diff --git a/Data/Questions/ingsw/0622_9/wrong 2.txt b/legacy/Data/Questions/ingsw/0622_9/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0622_9/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0622_9/wrong 2.txt
diff --git a/Data/Questions/ingsw/0721_1/correct.txt b/legacy/Data/Questions/ingsw/0721_1/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_1/correct.txt
rename to legacy/Data/Questions/ingsw/0721_1/correct.txt
diff --git a/Data/Questions/ingsw/0721_1/quest.txt b/legacy/Data/Questions/ingsw/0721_1/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_1/quest.txt
rename to legacy/Data/Questions/ingsw/0721_1/quest.txt
diff --git a/Data/Questions/ingsw/0721_1/wrong1.txt b/legacy/Data/Questions/ingsw/0721_1/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_1/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_1/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_1/wrong2.txt b/legacy/Data/Questions/ingsw/0721_1/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_1/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_1/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_10/correct.txt b/legacy/Data/Questions/ingsw/0721_10/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_10/correct.txt
rename to legacy/Data/Questions/ingsw/0721_10/correct.txt
diff --git a/Data/Questions/ingsw/0721_10/quest.txt b/legacy/Data/Questions/ingsw/0721_10/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_10/quest.txt
rename to legacy/Data/Questions/ingsw/0721_10/quest.txt
diff --git a/Data/Questions/ingsw/0721_10/wrong1.txt b/legacy/Data/Questions/ingsw/0721_10/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_10/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_10/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_10/wrong2.txt b/legacy/Data/Questions/ingsw/0721_10/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_10/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_10/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_13/correct.txt b/legacy/Data/Questions/ingsw/0721_13/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_13/correct.txt
rename to legacy/Data/Questions/ingsw/0721_13/correct.txt
diff --git a/Data/Questions/ingsw/0721_13/quest.txt b/legacy/Data/Questions/ingsw/0721_13/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_13/quest.txt
rename to legacy/Data/Questions/ingsw/0721_13/quest.txt
diff --git a/Data/Questions/ingsw/0721_13/wrong1.txt b/legacy/Data/Questions/ingsw/0721_13/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_13/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_13/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_13/wrong2.txt b/legacy/Data/Questions/ingsw/0721_13/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_13/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_13/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_15/correct.txt b/legacy/Data/Questions/ingsw/0721_15/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_15/correct.txt
rename to legacy/Data/Questions/ingsw/0721_15/correct.txt
diff --git a/Data/Questions/ingsw/0721_15/quest.txt b/legacy/Data/Questions/ingsw/0721_15/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_15/quest.txt
rename to legacy/Data/Questions/ingsw/0721_15/quest.txt
diff --git a/Data/Questions/ingsw/0721_15/wrong1.txt b/legacy/Data/Questions/ingsw/0721_15/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_15/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_15/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_15/wrong2.txt b/legacy/Data/Questions/ingsw/0721_15/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_15/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_15/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_17/correct.txt b/legacy/Data/Questions/ingsw/0721_17/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_17/correct.txt
rename to legacy/Data/Questions/ingsw/0721_17/correct.txt
diff --git a/Data/Questions/ingsw/0721_17/quest.txt b/legacy/Data/Questions/ingsw/0721_17/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_17/quest.txt
rename to legacy/Data/Questions/ingsw/0721_17/quest.txt
diff --git a/Data/Questions/ingsw/0721_17/wrong1.txt b/legacy/Data/Questions/ingsw/0721_17/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_17/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_17/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_17/wrong2.txt b/legacy/Data/Questions/ingsw/0721_17/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_17/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_17/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_18/correct.txt b/legacy/Data/Questions/ingsw/0721_18/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_18/correct.txt
rename to legacy/Data/Questions/ingsw/0721_18/correct.txt
diff --git a/Data/Questions/ingsw/0721_18/quest.txt b/legacy/Data/Questions/ingsw/0721_18/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_18/quest.txt
rename to legacy/Data/Questions/ingsw/0721_18/quest.txt
diff --git a/Data/Questions/ingsw/0721_18/wrong1.txt b/legacy/Data/Questions/ingsw/0721_18/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_18/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_18/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_18/wrong2.txt b/legacy/Data/Questions/ingsw/0721_18/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_18/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_18/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_19/correct.txt b/legacy/Data/Questions/ingsw/0721_19/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_19/correct.txt
rename to legacy/Data/Questions/ingsw/0721_19/correct.txt
diff --git a/Data/Questions/ingsw/0721_19/quest.txt b/legacy/Data/Questions/ingsw/0721_19/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_19/quest.txt
rename to legacy/Data/Questions/ingsw/0721_19/quest.txt
diff --git a/Data/Questions/ingsw/0721_19/wrong1.txt b/legacy/Data/Questions/ingsw/0721_19/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_19/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_19/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_19/wrong2.txt b/legacy/Data/Questions/ingsw/0721_19/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_19/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_19/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_21/correct.txt b/legacy/Data/Questions/ingsw/0721_21/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_21/correct.txt
rename to legacy/Data/Questions/ingsw/0721_21/correct.txt
diff --git a/Data/Questions/ingsw/0721_21/quest.txt b/legacy/Data/Questions/ingsw/0721_21/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_21/quest.txt
rename to legacy/Data/Questions/ingsw/0721_21/quest.txt
diff --git a/Data/Questions/ingsw/0721_21/wrong1.txt b/legacy/Data/Questions/ingsw/0721_21/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_21/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_21/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_21/wrong2.txt b/legacy/Data/Questions/ingsw/0721_21/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_21/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_21/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_28/correct.txt b/legacy/Data/Questions/ingsw/0721_28/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_28/correct.txt
rename to legacy/Data/Questions/ingsw/0721_28/correct.txt
diff --git a/Data/Questions/ingsw/0721_28/quest.txt b/legacy/Data/Questions/ingsw/0721_28/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_28/quest.txt
rename to legacy/Data/Questions/ingsw/0721_28/quest.txt
diff --git a/Data/Questions/ingsw/0721_28/wrong1.txt b/legacy/Data/Questions/ingsw/0721_28/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_28/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_28/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_28/wrong2.txt b/legacy/Data/Questions/ingsw/0721_28/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_28/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_28/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_29/correct.txt b/legacy/Data/Questions/ingsw/0721_29/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_29/correct.txt
rename to legacy/Data/Questions/ingsw/0721_29/correct.txt
diff --git a/Data/Questions/ingsw/0721_29/quest.txt b/legacy/Data/Questions/ingsw/0721_29/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_29/quest.txt
rename to legacy/Data/Questions/ingsw/0721_29/quest.txt
diff --git a/Data/Questions/ingsw/0721_29/wrong1.txt b/legacy/Data/Questions/ingsw/0721_29/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_29/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_29/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_29/wrong2.txt b/legacy/Data/Questions/ingsw/0721_29/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_29/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_29/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_32/correct.txt b/legacy/Data/Questions/ingsw/0721_32/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_32/correct.txt
rename to legacy/Data/Questions/ingsw/0721_32/correct.txt
diff --git a/Data/Questions/ingsw/0721_32/quest.txt b/legacy/Data/Questions/ingsw/0721_32/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_32/quest.txt
rename to legacy/Data/Questions/ingsw/0721_32/quest.txt
diff --git a/Data/Questions/ingsw/0721_32/wrong1.txt b/legacy/Data/Questions/ingsw/0721_32/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_32/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_32/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_32/wrong2.txt b/legacy/Data/Questions/ingsw/0721_32/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_32/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_32/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_33/correct.txt b/legacy/Data/Questions/ingsw/0721_33/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_33/correct.txt
rename to legacy/Data/Questions/ingsw/0721_33/correct.txt
diff --git a/Data/Questions/ingsw/0721_33/quest.txt b/legacy/Data/Questions/ingsw/0721_33/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_33/quest.txt
rename to legacy/Data/Questions/ingsw/0721_33/quest.txt
diff --git a/Data/Questions/ingsw/0721_33/wrong1.txt b/legacy/Data/Questions/ingsw/0721_33/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_33/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_33/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_33/wrong2.txt b/legacy/Data/Questions/ingsw/0721_33/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_33/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_33/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_34/correct.txt b/legacy/Data/Questions/ingsw/0721_34/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_34/correct.txt
rename to legacy/Data/Questions/ingsw/0721_34/correct.txt
diff --git a/Data/Questions/ingsw/0721_34/quest.txt b/legacy/Data/Questions/ingsw/0721_34/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_34/quest.txt
rename to legacy/Data/Questions/ingsw/0721_34/quest.txt
diff --git a/Data/Questions/ingsw/0721_34/wrong1.txt b/legacy/Data/Questions/ingsw/0721_34/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_34/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_34/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_34/wrong2.txt b/legacy/Data/Questions/ingsw/0721_34/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_34/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_34/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_36/correct.txt b/legacy/Data/Questions/ingsw/0721_36/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_36/correct.txt
rename to legacy/Data/Questions/ingsw/0721_36/correct.txt
diff --git a/Data/Questions/ingsw/0721_36/quest.txt b/legacy/Data/Questions/ingsw/0721_36/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_36/quest.txt
rename to legacy/Data/Questions/ingsw/0721_36/quest.txt
diff --git a/Data/Questions/ingsw/0721_36/wrong1.txt b/legacy/Data/Questions/ingsw/0721_36/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_36/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_36/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_36/wrong2.txt b/legacy/Data/Questions/ingsw/0721_36/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_36/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_36/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_4/correct.txt b/legacy/Data/Questions/ingsw/0721_4/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_4/correct.txt
rename to legacy/Data/Questions/ingsw/0721_4/correct.txt
diff --git a/Data/Questions/ingsw/0721_4/quest.txt b/legacy/Data/Questions/ingsw/0721_4/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_4/quest.txt
rename to legacy/Data/Questions/ingsw/0721_4/quest.txt
diff --git a/Data/Questions/ingsw/0721_4/wrong1.txt b/legacy/Data/Questions/ingsw/0721_4/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_4/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_4/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_4/wrong2.txt b/legacy/Data/Questions/ingsw/0721_4/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_4/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_4/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_5/correct.txt b/legacy/Data/Questions/ingsw/0721_5/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_5/correct.txt
rename to legacy/Data/Questions/ingsw/0721_5/correct.txt
diff --git a/Data/Questions/ingsw/0721_5/quest.txt b/legacy/Data/Questions/ingsw/0721_5/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_5/quest.txt
rename to legacy/Data/Questions/ingsw/0721_5/quest.txt
diff --git a/Data/Questions/ingsw/0721_5/wrong1.txt b/legacy/Data/Questions/ingsw/0721_5/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_5/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_5/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_5/wrong2.txt b/legacy/Data/Questions/ingsw/0721_5/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_5/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_5/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_6/correct.txt b/legacy/Data/Questions/ingsw/0721_6/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_6/correct.txt
rename to legacy/Data/Questions/ingsw/0721_6/correct.txt
diff --git a/Data/Questions/ingsw/0721_6/quest.txt b/legacy/Data/Questions/ingsw/0721_6/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_6/quest.txt
rename to legacy/Data/Questions/ingsw/0721_6/quest.txt
diff --git a/Data/Questions/ingsw/0721_6/wrong1.txt b/legacy/Data/Questions/ingsw/0721_6/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_6/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_6/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_6/wrong2.txt b/legacy/Data/Questions/ingsw/0721_6/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_6/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_6/wrong2.txt
diff --git a/Data/Questions/ingsw/0721_8/correct.txt b/legacy/Data/Questions/ingsw/0721_8/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_8/correct.txt
rename to legacy/Data/Questions/ingsw/0721_8/correct.txt
diff --git a/Data/Questions/ingsw/0721_8/quest.txt b/legacy/Data/Questions/ingsw/0721_8/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_8/quest.txt
rename to legacy/Data/Questions/ingsw/0721_8/quest.txt
diff --git a/Data/Questions/ingsw/0721_8/wrong1.txt b/legacy/Data/Questions/ingsw/0721_8/wrong1.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_8/wrong1.txt
rename to legacy/Data/Questions/ingsw/0721_8/wrong1.txt
diff --git a/Data/Questions/ingsw/0721_8/wrong2.txt b/legacy/Data/Questions/ingsw/0721_8/wrong2.txt
similarity index 100%
rename from Data/Questions/ingsw/0721_8/wrong2.txt
rename to legacy/Data/Questions/ingsw/0721_8/wrong2.txt
diff --git a/Data/Questions/ingsw/0722_1/correct.txt b/legacy/Data/Questions/ingsw/0722_1/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_1/correct.txt
rename to legacy/Data/Questions/ingsw/0722_1/correct.txt
diff --git a/Data/Questions/ingsw/0722_1/quest.txt b/legacy/Data/Questions/ingsw/0722_1/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_1/quest.txt
rename to legacy/Data/Questions/ingsw/0722_1/quest.txt
diff --git a/Data/Questions/ingsw/0722_1/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_1/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_1/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_1/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_1/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_1/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_1/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_1/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_10/correct.txt b/legacy/Data/Questions/ingsw/0722_10/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_10/correct.txt
rename to legacy/Data/Questions/ingsw/0722_10/correct.txt
diff --git a/Data/Questions/ingsw/0722_10/quest.txt b/legacy/Data/Questions/ingsw/0722_10/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_10/quest.txt
rename to legacy/Data/Questions/ingsw/0722_10/quest.txt
diff --git a/Data/Questions/ingsw/0722_10/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_10/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_10/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_10/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_10/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_10/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_10/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_10/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_11/correct.txt b/legacy/Data/Questions/ingsw/0722_11/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_11/correct.txt
rename to legacy/Data/Questions/ingsw/0722_11/correct.txt
diff --git a/Data/Questions/ingsw/0722_11/quest.txt b/legacy/Data/Questions/ingsw/0722_11/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_11/quest.txt
rename to legacy/Data/Questions/ingsw/0722_11/quest.txt
diff --git a/Data/Questions/ingsw/0722_11/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_11/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_11/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_11/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_11/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_11/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_11/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_11/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_12/correct.txt b/legacy/Data/Questions/ingsw/0722_12/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_12/correct.txt
rename to legacy/Data/Questions/ingsw/0722_12/correct.txt
diff --git a/Data/Questions/ingsw/0722_12/quest.txt b/legacy/Data/Questions/ingsw/0722_12/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_12/quest.txt
rename to legacy/Data/Questions/ingsw/0722_12/quest.txt
diff --git a/Data/Questions/ingsw/0722_12/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_12/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_12/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_12/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_12/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_12/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_12/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_12/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_13/correct.txt b/legacy/Data/Questions/ingsw/0722_13/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_13/correct.txt
rename to legacy/Data/Questions/ingsw/0722_13/correct.txt
diff --git a/Data/Questions/ingsw/0722_13/quest.txt b/legacy/Data/Questions/ingsw/0722_13/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_13/quest.txt
rename to legacy/Data/Questions/ingsw/0722_13/quest.txt
diff --git a/Data/Questions/ingsw/0722_13/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_13/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_13/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_13/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_13/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_13/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_13/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_13/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_14/correct.txt b/legacy/Data/Questions/ingsw/0722_14/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_14/correct.txt
rename to legacy/Data/Questions/ingsw/0722_14/correct.txt
diff --git a/Data/Questions/ingsw/0722_14/quest.txt b/legacy/Data/Questions/ingsw/0722_14/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_14/quest.txt
rename to legacy/Data/Questions/ingsw/0722_14/quest.txt
diff --git a/Data/Questions/ingsw/0722_14/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_14/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_14/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_14/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_14/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_14/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_14/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_14/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_15/correct.txt b/legacy/Data/Questions/ingsw/0722_15/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_15/correct.txt
rename to legacy/Data/Questions/ingsw/0722_15/correct.txt
diff --git a/Data/Questions/ingsw/0722_15/quest.txt b/legacy/Data/Questions/ingsw/0722_15/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_15/quest.txt
rename to legacy/Data/Questions/ingsw/0722_15/quest.txt
diff --git a/Data/Questions/ingsw/0722_15/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_15/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_15/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_15/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_15/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_15/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_15/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_15/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_16/correct.txt b/legacy/Data/Questions/ingsw/0722_16/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_16/correct.txt
rename to legacy/Data/Questions/ingsw/0722_16/correct.txt
diff --git a/Data/Questions/ingsw/0722_16/quest.txt b/legacy/Data/Questions/ingsw/0722_16/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_16/quest.txt
rename to legacy/Data/Questions/ingsw/0722_16/quest.txt
diff --git a/Data/Questions/ingsw/0722_16/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_16/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_16/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_16/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_16/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_16/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_16/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_16/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_17/correct.txt b/legacy/Data/Questions/ingsw/0722_17/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_17/correct.txt
rename to legacy/Data/Questions/ingsw/0722_17/correct.txt
diff --git a/Data/Questions/ingsw/0722_17/quest.txt b/legacy/Data/Questions/ingsw/0722_17/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_17/quest.txt
rename to legacy/Data/Questions/ingsw/0722_17/quest.txt
diff --git a/Data/Questions/ingsw/0722_17/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_17/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_17/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_17/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_17/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_17/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_17/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_17/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_18/correct.txt b/legacy/Data/Questions/ingsw/0722_18/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_18/correct.txt
rename to legacy/Data/Questions/ingsw/0722_18/correct.txt
diff --git a/Data/Questions/ingsw/0722_18/quest.txt b/legacy/Data/Questions/ingsw/0722_18/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_18/quest.txt
rename to legacy/Data/Questions/ingsw/0722_18/quest.txt
diff --git a/Data/Questions/ingsw/0722_18/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_18/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_18/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_18/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_18/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_18/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_18/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_18/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_19/correct.txt b/legacy/Data/Questions/ingsw/0722_19/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_19/correct.txt
rename to legacy/Data/Questions/ingsw/0722_19/correct.txt
diff --git a/Data/Questions/ingsw/0722_19/quest.txt b/legacy/Data/Questions/ingsw/0722_19/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_19/quest.txt
rename to legacy/Data/Questions/ingsw/0722_19/quest.txt
diff --git a/Data/Questions/ingsw/0722_19/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_19/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_19/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_19/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_19/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_19/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_19/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_19/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_2/correct.txt b/legacy/Data/Questions/ingsw/0722_2/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_2/correct.txt
rename to legacy/Data/Questions/ingsw/0722_2/correct.txt
diff --git a/Data/Questions/ingsw/0722_2/quest.txt b/legacy/Data/Questions/ingsw/0722_2/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_2/quest.txt
rename to legacy/Data/Questions/ingsw/0722_2/quest.txt
diff --git a/Data/Questions/ingsw/0722_2/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_2/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_2/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_2/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_2/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_2/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_2/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_2/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_20/correct.txt b/legacy/Data/Questions/ingsw/0722_20/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_20/correct.txt
rename to legacy/Data/Questions/ingsw/0722_20/correct.txt
diff --git a/Data/Questions/ingsw/0722_20/quest.txt b/legacy/Data/Questions/ingsw/0722_20/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_20/quest.txt
rename to legacy/Data/Questions/ingsw/0722_20/quest.txt
diff --git a/Data/Questions/ingsw/0722_20/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_20/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_20/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_20/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_20/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_20/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_20/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_20/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_21/correct.txt b/legacy/Data/Questions/ingsw/0722_21/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_21/correct.txt
rename to legacy/Data/Questions/ingsw/0722_21/correct.txt
diff --git a/Data/Questions/ingsw/0722_21/quest.txt b/legacy/Data/Questions/ingsw/0722_21/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_21/quest.txt
rename to legacy/Data/Questions/ingsw/0722_21/quest.txt
diff --git a/Data/Questions/ingsw/0722_21/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_21/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_21/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_21/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_21/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_21/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_21/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_21/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_22/correct.txt b/legacy/Data/Questions/ingsw/0722_22/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_22/correct.txt
rename to legacy/Data/Questions/ingsw/0722_22/correct.txt
diff --git a/Data/Questions/ingsw/0722_22/quest.txt b/legacy/Data/Questions/ingsw/0722_22/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_22/quest.txt
rename to legacy/Data/Questions/ingsw/0722_22/quest.txt
diff --git a/Data/Questions/ingsw/0722_22/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_22/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_22/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_22/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_22/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_22/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_22/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_22/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_23/correct.txt b/legacy/Data/Questions/ingsw/0722_23/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_23/correct.txt
rename to legacy/Data/Questions/ingsw/0722_23/correct.txt
diff --git a/Data/Questions/ingsw/0722_23/quest.txt b/legacy/Data/Questions/ingsw/0722_23/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_23/quest.txt
rename to legacy/Data/Questions/ingsw/0722_23/quest.txt
diff --git a/Data/Questions/ingsw/0722_23/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_23/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_23/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_23/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_23/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_23/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_23/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_23/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_24/correct.txt b/legacy/Data/Questions/ingsw/0722_24/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_24/correct.txt
rename to legacy/Data/Questions/ingsw/0722_24/correct.txt
diff --git a/Data/Questions/ingsw/0722_24/quest.txt b/legacy/Data/Questions/ingsw/0722_24/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_24/quest.txt
rename to legacy/Data/Questions/ingsw/0722_24/quest.txt
diff --git a/Data/Questions/ingsw/0722_24/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_24/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_24/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_24/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_24/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_24/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_24/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_24/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_25/correct.txt b/legacy/Data/Questions/ingsw/0722_25/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_25/correct.txt
rename to legacy/Data/Questions/ingsw/0722_25/correct.txt
diff --git a/Data/Questions/ingsw/0722_25/quest.txt b/legacy/Data/Questions/ingsw/0722_25/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_25/quest.txt
rename to legacy/Data/Questions/ingsw/0722_25/quest.txt
diff --git a/Data/Questions/ingsw/0722_25/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_25/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_25/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_25/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_25/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_25/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_25/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_25/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_26/correct.txt b/legacy/Data/Questions/ingsw/0722_26/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_26/correct.txt
rename to legacy/Data/Questions/ingsw/0722_26/correct.txt
diff --git a/Data/Questions/ingsw/0722_26/quest.txt b/legacy/Data/Questions/ingsw/0722_26/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_26/quest.txt
rename to legacy/Data/Questions/ingsw/0722_26/quest.txt
diff --git a/Data/Questions/ingsw/0722_26/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_26/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_26/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_26/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_26/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_26/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_26/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_26/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_3/correct.txt b/legacy/Data/Questions/ingsw/0722_3/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_3/correct.txt
rename to legacy/Data/Questions/ingsw/0722_3/correct.txt
diff --git a/Data/Questions/ingsw/0722_3/quest.txt b/legacy/Data/Questions/ingsw/0722_3/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_3/quest.txt
rename to legacy/Data/Questions/ingsw/0722_3/quest.txt
diff --git a/Data/Questions/ingsw/0722_3/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_3/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_3/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_3/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_3/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_3/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_3/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_3/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_4/correct.txt b/legacy/Data/Questions/ingsw/0722_4/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_4/correct.txt
rename to legacy/Data/Questions/ingsw/0722_4/correct.txt
diff --git a/Data/Questions/ingsw/0722_4/quest.txt b/legacy/Data/Questions/ingsw/0722_4/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_4/quest.txt
rename to legacy/Data/Questions/ingsw/0722_4/quest.txt
diff --git a/Data/Questions/ingsw/0722_4/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_4/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_4/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_4/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_4/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_4/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_4/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_4/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_5/correct.txt b/legacy/Data/Questions/ingsw/0722_5/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_5/correct.txt
rename to legacy/Data/Questions/ingsw/0722_5/correct.txt
diff --git a/Data/Questions/ingsw/0722_5/quest.txt b/legacy/Data/Questions/ingsw/0722_5/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_5/quest.txt
rename to legacy/Data/Questions/ingsw/0722_5/quest.txt
diff --git a/Data/Questions/ingsw/0722_5/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_5/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_5/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_5/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_5/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_5/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_5/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_5/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_6/correct.txt b/legacy/Data/Questions/ingsw/0722_6/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_6/correct.txt
rename to legacy/Data/Questions/ingsw/0722_6/correct.txt
diff --git a/Data/Questions/ingsw/0722_6/quest.txt b/legacy/Data/Questions/ingsw/0722_6/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_6/quest.txt
rename to legacy/Data/Questions/ingsw/0722_6/quest.txt
diff --git a/Data/Questions/ingsw/0722_6/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_6/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_6/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_6/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_6/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_6/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_6/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_6/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_7/correct.txt b/legacy/Data/Questions/ingsw/0722_7/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_7/correct.txt
rename to legacy/Data/Questions/ingsw/0722_7/correct.txt
diff --git a/Data/Questions/ingsw/0722_7/quest.txt b/legacy/Data/Questions/ingsw/0722_7/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_7/quest.txt
rename to legacy/Data/Questions/ingsw/0722_7/quest.txt
diff --git a/Data/Questions/ingsw/0722_7/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_7/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_7/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_7/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_7/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_7/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_7/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_7/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_8/correct.txt b/legacy/Data/Questions/ingsw/0722_8/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_8/correct.txt
rename to legacy/Data/Questions/ingsw/0722_8/correct.txt
diff --git a/Data/Questions/ingsw/0722_8/quest.txt b/legacy/Data/Questions/ingsw/0722_8/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_8/quest.txt
rename to legacy/Data/Questions/ingsw/0722_8/quest.txt
diff --git a/Data/Questions/ingsw/0722_8/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_8/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_8/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_8/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_8/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_8/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_8/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_8/wrong 2.txt
diff --git a/Data/Questions/ingsw/0722_9/correct.txt b/legacy/Data/Questions/ingsw/0722_9/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_9/correct.txt
rename to legacy/Data/Questions/ingsw/0722_9/correct.txt
diff --git a/Data/Questions/ingsw/0722_9/quest.txt b/legacy/Data/Questions/ingsw/0722_9/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_9/quest.txt
rename to legacy/Data/Questions/ingsw/0722_9/quest.txt
diff --git a/Data/Questions/ingsw/0722_9/wrong 1.txt b/legacy/Data/Questions/ingsw/0722_9/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_9/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0722_9/wrong 1.txt
diff --git a/Data/Questions/ingsw/0722_9/wrong 2.txt b/legacy/Data/Questions/ingsw/0722_9/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0722_9/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0722_9/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_10/correct.txt b/legacy/Data/Questions/ingsw/0922_10/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_10/correct.txt
rename to legacy/Data/Questions/ingsw/0922_10/correct.txt
diff --git a/Data/Questions/ingsw/0922_10/quest.txt b/legacy/Data/Questions/ingsw/0922_10/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_10/quest.txt
rename to legacy/Data/Questions/ingsw/0922_10/quest.txt
diff --git a/Data/Questions/ingsw/0922_10/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_10/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_10/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_10/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_10/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_10/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_10/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_10/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_11/correct.txt b/legacy/Data/Questions/ingsw/0922_11/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_11/correct.txt
rename to legacy/Data/Questions/ingsw/0922_11/correct.txt
diff --git a/Data/Questions/ingsw/0922_11/quest.txt b/legacy/Data/Questions/ingsw/0922_11/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_11/quest.txt
rename to legacy/Data/Questions/ingsw/0922_11/quest.txt
diff --git a/Data/Questions/ingsw/0922_11/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_11/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_11/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_11/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_11/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_11/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_11/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_11/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_12/correct.txt b/legacy/Data/Questions/ingsw/0922_12/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_12/correct.txt
rename to legacy/Data/Questions/ingsw/0922_12/correct.txt
diff --git a/Data/Questions/ingsw/0922_12/quest.txt b/legacy/Data/Questions/ingsw/0922_12/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_12/quest.txt
rename to legacy/Data/Questions/ingsw/0922_12/quest.txt
diff --git a/Data/Questions/ingsw/0922_12/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_12/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_12/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_12/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_12/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_12/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_12/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_12/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_13/correct.txt b/legacy/Data/Questions/ingsw/0922_13/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_13/correct.txt
rename to legacy/Data/Questions/ingsw/0922_13/correct.txt
diff --git a/Data/Questions/ingsw/0922_13/quest.txt b/legacy/Data/Questions/ingsw/0922_13/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_13/quest.txt
rename to legacy/Data/Questions/ingsw/0922_13/quest.txt
diff --git a/Data/Questions/ingsw/0922_13/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_13/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_13/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_13/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_13/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_13/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_13/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_13/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_14/correct.txt b/legacy/Data/Questions/ingsw/0922_14/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_14/correct.txt
rename to legacy/Data/Questions/ingsw/0922_14/correct.txt
diff --git a/Data/Questions/ingsw/0922_14/quest.txt b/legacy/Data/Questions/ingsw/0922_14/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_14/quest.txt
rename to legacy/Data/Questions/ingsw/0922_14/quest.txt
diff --git a/Data/Questions/ingsw/0922_14/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_14/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_14/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_14/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_14/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_14/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_14/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_14/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_15/correct.txt b/legacy/Data/Questions/ingsw/0922_15/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_15/correct.txt
rename to legacy/Data/Questions/ingsw/0922_15/correct.txt
diff --git a/Data/Questions/ingsw/0922_15/quest.txt b/legacy/Data/Questions/ingsw/0922_15/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_15/quest.txt
rename to legacy/Data/Questions/ingsw/0922_15/quest.txt
diff --git a/Data/Questions/ingsw/0922_15/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_15/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_15/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_15/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_15/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_15/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_15/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_15/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_16/correct.txt b/legacy/Data/Questions/ingsw/0922_16/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_16/correct.txt
rename to legacy/Data/Questions/ingsw/0922_16/correct.txt
diff --git a/Data/Questions/ingsw/0922_16/quest.txt b/legacy/Data/Questions/ingsw/0922_16/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_16/quest.txt
rename to legacy/Data/Questions/ingsw/0922_16/quest.txt
diff --git a/Data/Questions/ingsw/0922_16/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_16/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_16/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_16/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_16/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_16/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_16/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_16/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_17/correct.txt b/legacy/Data/Questions/ingsw/0922_17/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_17/correct.txt
rename to legacy/Data/Questions/ingsw/0922_17/correct.txt
diff --git a/Data/Questions/ingsw/0922_17/quest.txt b/legacy/Data/Questions/ingsw/0922_17/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_17/quest.txt
rename to legacy/Data/Questions/ingsw/0922_17/quest.txt
diff --git a/Data/Questions/ingsw/0922_17/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_17/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_17/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_17/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_17/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_17/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_17/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_17/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_18/correct.txt b/legacy/Data/Questions/ingsw/0922_18/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_18/correct.txt
rename to legacy/Data/Questions/ingsw/0922_18/correct.txt
diff --git a/Data/Questions/ingsw/0922_18/quest.txt b/legacy/Data/Questions/ingsw/0922_18/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_18/quest.txt
rename to legacy/Data/Questions/ingsw/0922_18/quest.txt
diff --git a/Data/Questions/ingsw/0922_18/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_18/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_18/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_18/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_18/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_18/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_18/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_18/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_3/correct.txt b/legacy/Data/Questions/ingsw/0922_3/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_3/correct.txt
rename to legacy/Data/Questions/ingsw/0922_3/correct.txt
diff --git a/Data/Questions/ingsw/0922_3/quest.txt b/legacy/Data/Questions/ingsw/0922_3/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_3/quest.txt
rename to legacy/Data/Questions/ingsw/0922_3/quest.txt
diff --git a/Data/Questions/ingsw/0922_3/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_3/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_3/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_3/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_3/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_3/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_3/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_3/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_4/correct.txt b/legacy/Data/Questions/ingsw/0922_4/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_4/correct.txt
rename to legacy/Data/Questions/ingsw/0922_4/correct.txt
diff --git a/Data/Questions/ingsw/0922_4/quest.txt b/legacy/Data/Questions/ingsw/0922_4/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_4/quest.txt
rename to legacy/Data/Questions/ingsw/0922_4/quest.txt
diff --git a/Data/Questions/ingsw/0922_4/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_4/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_4/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_4/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_4/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_4/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_4/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_4/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_5/correct.txt b/legacy/Data/Questions/ingsw/0922_5/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_5/correct.txt
rename to legacy/Data/Questions/ingsw/0922_5/correct.txt
diff --git a/Data/Questions/ingsw/0922_5/quest.txt b/legacy/Data/Questions/ingsw/0922_5/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_5/quest.txt
rename to legacy/Data/Questions/ingsw/0922_5/quest.txt
diff --git a/Data/Questions/ingsw/0922_5/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_5/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_5/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_5/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_5/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_5/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_5/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_5/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_6/correct.txt b/legacy/Data/Questions/ingsw/0922_6/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_6/correct.txt
rename to legacy/Data/Questions/ingsw/0922_6/correct.txt
diff --git a/Data/Questions/ingsw/0922_6/quest.txt b/legacy/Data/Questions/ingsw/0922_6/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_6/quest.txt
rename to legacy/Data/Questions/ingsw/0922_6/quest.txt
diff --git a/Data/Questions/ingsw/0922_6/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_6/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_6/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_6/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_6/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_6/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_6/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_6/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_7/correct.txt b/legacy/Data/Questions/ingsw/0922_7/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_7/correct.txt
rename to legacy/Data/Questions/ingsw/0922_7/correct.txt
diff --git a/Data/Questions/ingsw/0922_7/quest.txt b/legacy/Data/Questions/ingsw/0922_7/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_7/quest.txt
rename to legacy/Data/Questions/ingsw/0922_7/quest.txt
diff --git a/Data/Questions/ingsw/0922_7/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_7/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_7/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_7/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_7/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_7/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_7/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_7/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_8/correct.txt b/legacy/Data/Questions/ingsw/0922_8/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_8/correct.txt
rename to legacy/Data/Questions/ingsw/0922_8/correct.txt
diff --git a/Data/Questions/ingsw/0922_8/quest.txt b/legacy/Data/Questions/ingsw/0922_8/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_8/quest.txt
rename to legacy/Data/Questions/ingsw/0922_8/quest.txt
diff --git a/Data/Questions/ingsw/0922_8/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_8/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_8/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_8/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_8/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_8/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_8/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_8/wrong 2.txt
diff --git a/Data/Questions/ingsw/0922_9/correct.txt b/legacy/Data/Questions/ingsw/0922_9/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_9/correct.txt
rename to legacy/Data/Questions/ingsw/0922_9/correct.txt
diff --git a/Data/Questions/ingsw/0922_9/quest.txt b/legacy/Data/Questions/ingsw/0922_9/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_9/quest.txt
rename to legacy/Data/Questions/ingsw/0922_9/quest.txt
diff --git a/Data/Questions/ingsw/0922_9/wrong 1.txt b/legacy/Data/Questions/ingsw/0922_9/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_9/wrong 1.txt
rename to legacy/Data/Questions/ingsw/0922_9/wrong 1.txt
diff --git a/Data/Questions/ingsw/0922_9/wrong 2.txt b/legacy/Data/Questions/ingsw/0922_9/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/0922_9/wrong 2.txt
rename to legacy/Data/Questions/ingsw/0922_9/wrong 2.txt
diff --git a/Data/Questions/ingsw/10/correct.txt b/legacy/Data/Questions/ingsw/10/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/10/correct.txt
rename to legacy/Data/Questions/ingsw/10/correct.txt
diff --git a/Data/Questions/ingsw/10/quest.txt b/legacy/Data/Questions/ingsw/10/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/10/quest.txt
rename to legacy/Data/Questions/ingsw/10/quest.txt
diff --git a/Data/Questions/ingsw/10/wrong 2.txt b/legacy/Data/Questions/ingsw/10/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/10/wrong 2.txt
rename to legacy/Data/Questions/ingsw/10/wrong 2.txt
diff --git a/Data/Questions/ingsw/10/wrong.txt b/legacy/Data/Questions/ingsw/10/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/10/wrong.txt
rename to legacy/Data/Questions/ingsw/10/wrong.txt
diff --git a/Data/Questions/ingsw/11/correct.txt b/legacy/Data/Questions/ingsw/11/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/11/correct.txt
rename to legacy/Data/Questions/ingsw/11/correct.txt
diff --git a/Data/Questions/ingsw/11/quest.txt b/legacy/Data/Questions/ingsw/11/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/11/quest.txt
rename to legacy/Data/Questions/ingsw/11/quest.txt
diff --git a/Data/Questions/ingsw/11/wrong 2.txt b/legacy/Data/Questions/ingsw/11/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/11/wrong 2.txt
rename to legacy/Data/Questions/ingsw/11/wrong 2.txt
diff --git a/Data/Questions/ingsw/11/wrong.txt b/legacy/Data/Questions/ingsw/11/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/11/wrong.txt
rename to legacy/Data/Questions/ingsw/11/wrong.txt
diff --git a/Data/Questions/ingsw/1122_1/correct.txt b/legacy/Data/Questions/ingsw/1122_1/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_1/correct.txt
rename to legacy/Data/Questions/ingsw/1122_1/correct.txt
diff --git a/Data/Questions/ingsw/1122_1/quest.txt b/legacy/Data/Questions/ingsw/1122_1/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_1/quest.txt
rename to legacy/Data/Questions/ingsw/1122_1/quest.txt
diff --git a/Data/Questions/ingsw/1122_1/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_1/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_1/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_1/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_1/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_1/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_1/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_1/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_10/correct.txt b/legacy/Data/Questions/ingsw/1122_10/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_10/correct.txt
rename to legacy/Data/Questions/ingsw/1122_10/correct.txt
diff --git a/Data/Questions/ingsw/1122_10/quest.txt b/legacy/Data/Questions/ingsw/1122_10/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_10/quest.txt
rename to legacy/Data/Questions/ingsw/1122_10/quest.txt
diff --git a/Data/Questions/ingsw/1122_10/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_10/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_10/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_10/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_10/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_10/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_10/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_10/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_11/correct.txt b/legacy/Data/Questions/ingsw/1122_11/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_11/correct.txt
rename to legacy/Data/Questions/ingsw/1122_11/correct.txt
diff --git a/Data/Questions/ingsw/1122_11/quest.txt b/legacy/Data/Questions/ingsw/1122_11/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_11/quest.txt
rename to legacy/Data/Questions/ingsw/1122_11/quest.txt
diff --git a/Data/Questions/ingsw/1122_11/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_11/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_11/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_11/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_11/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_11/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_11/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_11/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_12/correct.txt b/legacy/Data/Questions/ingsw/1122_12/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_12/correct.txt
rename to legacy/Data/Questions/ingsw/1122_12/correct.txt
diff --git a/Data/Questions/ingsw/1122_12/quest.txt b/legacy/Data/Questions/ingsw/1122_12/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_12/quest.txt
rename to legacy/Data/Questions/ingsw/1122_12/quest.txt
diff --git a/Data/Questions/ingsw/1122_12/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_12/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_12/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_12/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_12/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_12/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_12/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_12/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_13/correct.txt b/legacy/Data/Questions/ingsw/1122_13/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_13/correct.txt
rename to legacy/Data/Questions/ingsw/1122_13/correct.txt
diff --git a/Data/Questions/ingsw/1122_13/quest.txt b/legacy/Data/Questions/ingsw/1122_13/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_13/quest.txt
rename to legacy/Data/Questions/ingsw/1122_13/quest.txt
diff --git a/Data/Questions/ingsw/1122_13/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_13/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_13/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_13/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_13/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_13/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_13/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_13/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_14/correct.txt b/legacy/Data/Questions/ingsw/1122_14/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_14/correct.txt
rename to legacy/Data/Questions/ingsw/1122_14/correct.txt
diff --git a/Data/Questions/ingsw/1122_14/quest.txt b/legacy/Data/Questions/ingsw/1122_14/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_14/quest.txt
rename to legacy/Data/Questions/ingsw/1122_14/quest.txt
diff --git a/Data/Questions/ingsw/1122_14/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_14/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_14/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_14/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_14/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_14/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_14/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_14/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_15/correct.txt b/legacy/Data/Questions/ingsw/1122_15/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_15/correct.txt
rename to legacy/Data/Questions/ingsw/1122_15/correct.txt
diff --git a/Data/Questions/ingsw/1122_15/quest.txt b/legacy/Data/Questions/ingsw/1122_15/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_15/quest.txt
rename to legacy/Data/Questions/ingsw/1122_15/quest.txt
diff --git a/Data/Questions/ingsw/1122_15/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_15/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_15/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_15/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_15/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_15/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_15/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_15/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_16/correct.txt b/legacy/Data/Questions/ingsw/1122_16/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_16/correct.txt
rename to legacy/Data/Questions/ingsw/1122_16/correct.txt
diff --git a/Data/Questions/ingsw/1122_16/quest.txt b/legacy/Data/Questions/ingsw/1122_16/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_16/quest.txt
rename to legacy/Data/Questions/ingsw/1122_16/quest.txt
diff --git a/Data/Questions/ingsw/1122_16/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_16/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_16/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_16/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_16/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_16/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_16/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_16/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_19/correct.txt b/legacy/Data/Questions/ingsw/1122_19/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_19/correct.txt
rename to legacy/Data/Questions/ingsw/1122_19/correct.txt
diff --git a/Data/Questions/ingsw/1122_19/quest.txt b/legacy/Data/Questions/ingsw/1122_19/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_19/quest.txt
rename to legacy/Data/Questions/ingsw/1122_19/quest.txt
diff --git a/Data/Questions/ingsw/1122_19/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_19/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_19/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_19/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_19/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_19/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_19/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_19/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_2/correct.txt b/legacy/Data/Questions/ingsw/1122_2/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_2/correct.txt
rename to legacy/Data/Questions/ingsw/1122_2/correct.txt
diff --git a/Data/Questions/ingsw/1122_2/quest.txt b/legacy/Data/Questions/ingsw/1122_2/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_2/quest.txt
rename to legacy/Data/Questions/ingsw/1122_2/quest.txt
diff --git a/Data/Questions/ingsw/1122_2/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_2/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_2/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_2/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_2/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_2/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_2/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_2/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_20/correct.txt b/legacy/Data/Questions/ingsw/1122_20/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_20/correct.txt
rename to legacy/Data/Questions/ingsw/1122_20/correct.txt
diff --git a/Data/Questions/ingsw/1122_20/quest.txt b/legacy/Data/Questions/ingsw/1122_20/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_20/quest.txt
rename to legacy/Data/Questions/ingsw/1122_20/quest.txt
diff --git a/Data/Questions/ingsw/1122_20/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_20/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_20/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_20/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_20/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_20/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_20/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_20/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_21/correct.txt b/legacy/Data/Questions/ingsw/1122_21/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_21/correct.txt
rename to legacy/Data/Questions/ingsw/1122_21/correct.txt
diff --git a/Data/Questions/ingsw/1122_21/quest.txt b/legacy/Data/Questions/ingsw/1122_21/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_21/quest.txt
rename to legacy/Data/Questions/ingsw/1122_21/quest.txt
diff --git a/Data/Questions/ingsw/1122_21/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_21/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_21/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_21/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_21/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_21/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_21/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_21/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_22/correct.txt b/legacy/Data/Questions/ingsw/1122_22/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_22/correct.txt
rename to legacy/Data/Questions/ingsw/1122_22/correct.txt
diff --git a/Data/Questions/ingsw/1122_22/quest.txt b/legacy/Data/Questions/ingsw/1122_22/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_22/quest.txt
rename to legacy/Data/Questions/ingsw/1122_22/quest.txt
diff --git a/Data/Questions/ingsw/1122_22/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_22/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_22/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_22/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_22/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_22/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_22/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_22/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_23/correct.txt b/legacy/Data/Questions/ingsw/1122_23/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_23/correct.txt
rename to legacy/Data/Questions/ingsw/1122_23/correct.txt
diff --git a/Data/Questions/ingsw/1122_23/quest.txt b/legacy/Data/Questions/ingsw/1122_23/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_23/quest.txt
rename to legacy/Data/Questions/ingsw/1122_23/quest.txt
diff --git a/Data/Questions/ingsw/1122_23/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_23/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_23/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_23/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_23/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_23/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_23/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_23/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_24/correct.txt b/legacy/Data/Questions/ingsw/1122_24/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_24/correct.txt
rename to legacy/Data/Questions/ingsw/1122_24/correct.txt
diff --git a/Data/Questions/ingsw/1122_24/quest.txt b/legacy/Data/Questions/ingsw/1122_24/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_24/quest.txt
rename to legacy/Data/Questions/ingsw/1122_24/quest.txt
diff --git a/Data/Questions/ingsw/1122_24/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_24/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_24/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_24/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_24/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_24/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_24/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_24/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_25/correct.txt b/legacy/Data/Questions/ingsw/1122_25/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_25/correct.txt
rename to legacy/Data/Questions/ingsw/1122_25/correct.txt
diff --git a/Data/Questions/ingsw/1122_25/quest.txt b/legacy/Data/Questions/ingsw/1122_25/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_25/quest.txt
rename to legacy/Data/Questions/ingsw/1122_25/quest.txt
diff --git a/Data/Questions/ingsw/1122_25/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_25/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_25/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_25/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_25/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_25/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_25/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_25/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_26/correct.txt b/legacy/Data/Questions/ingsw/1122_26/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_26/correct.txt
rename to legacy/Data/Questions/ingsw/1122_26/correct.txt
diff --git a/Data/Questions/ingsw/1122_26/quest.txt b/legacy/Data/Questions/ingsw/1122_26/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_26/quest.txt
rename to legacy/Data/Questions/ingsw/1122_26/quest.txt
diff --git a/Data/Questions/ingsw/1122_26/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_26/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_26/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_26/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_26/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_26/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_26/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_26/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_27/correct.txt b/legacy/Data/Questions/ingsw/1122_27/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_27/correct.txt
rename to legacy/Data/Questions/ingsw/1122_27/correct.txt
diff --git a/Data/Questions/ingsw/1122_27/quest.txt b/legacy/Data/Questions/ingsw/1122_27/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_27/quest.txt
rename to legacy/Data/Questions/ingsw/1122_27/quest.txt
diff --git a/Data/Questions/ingsw/1122_27/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_27/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_27/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_27/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_27/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_27/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_27/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_27/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_28/correct.txt b/legacy/Data/Questions/ingsw/1122_28/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_28/correct.txt
rename to legacy/Data/Questions/ingsw/1122_28/correct.txt
diff --git a/Data/Questions/ingsw/1122_28/quest.txt b/legacy/Data/Questions/ingsw/1122_28/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_28/quest.txt
rename to legacy/Data/Questions/ingsw/1122_28/quest.txt
diff --git a/Data/Questions/ingsw/1122_28/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_28/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_28/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_28/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_28/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_28/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_28/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_28/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_29/correct.txt b/legacy/Data/Questions/ingsw/1122_29/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_29/correct.txt
rename to legacy/Data/Questions/ingsw/1122_29/correct.txt
diff --git a/Data/Questions/ingsw/1122_29/quest.txt b/legacy/Data/Questions/ingsw/1122_29/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_29/quest.txt
rename to legacy/Data/Questions/ingsw/1122_29/quest.txt
diff --git a/Data/Questions/ingsw/1122_29/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_29/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_29/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_29/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_29/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_29/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_29/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_29/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_3/correct.txt b/legacy/Data/Questions/ingsw/1122_3/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_3/correct.txt
rename to legacy/Data/Questions/ingsw/1122_3/correct.txt
diff --git a/Data/Questions/ingsw/1122_3/quest.txt b/legacy/Data/Questions/ingsw/1122_3/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_3/quest.txt
rename to legacy/Data/Questions/ingsw/1122_3/quest.txt
diff --git a/Data/Questions/ingsw/1122_3/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_3/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_3/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_3/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_3/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_3/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_3/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_3/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_30/correct.txt b/legacy/Data/Questions/ingsw/1122_30/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_30/correct.txt
rename to legacy/Data/Questions/ingsw/1122_30/correct.txt
diff --git a/Data/Questions/ingsw/1122_30/quest.txt b/legacy/Data/Questions/ingsw/1122_30/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_30/quest.txt
rename to legacy/Data/Questions/ingsw/1122_30/quest.txt
diff --git a/Data/Questions/ingsw/1122_30/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_30/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_30/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_30/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_30/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_30/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_30/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_30/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_31/correct.txt b/legacy/Data/Questions/ingsw/1122_31/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_31/correct.txt
rename to legacy/Data/Questions/ingsw/1122_31/correct.txt
diff --git a/Data/Questions/ingsw/1122_31/quest.txt b/legacy/Data/Questions/ingsw/1122_31/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_31/quest.txt
rename to legacy/Data/Questions/ingsw/1122_31/quest.txt
diff --git a/Data/Questions/ingsw/1122_31/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_31/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_31/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_31/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_31/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_31/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_31/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_31/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_33/correct.txt b/legacy/Data/Questions/ingsw/1122_33/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_33/correct.txt
rename to legacy/Data/Questions/ingsw/1122_33/correct.txt
diff --git a/Data/Questions/ingsw/1122_33/quest.txt b/legacy/Data/Questions/ingsw/1122_33/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_33/quest.txt
rename to legacy/Data/Questions/ingsw/1122_33/quest.txt
diff --git a/Data/Questions/ingsw/1122_33/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_33/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_33/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_33/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_33/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_33/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_33/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_33/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_34/correct.txt b/legacy/Data/Questions/ingsw/1122_34/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_34/correct.txt
rename to legacy/Data/Questions/ingsw/1122_34/correct.txt
diff --git a/Data/Questions/ingsw/1122_34/quest.txt b/legacy/Data/Questions/ingsw/1122_34/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_34/quest.txt
rename to legacy/Data/Questions/ingsw/1122_34/quest.txt
diff --git a/Data/Questions/ingsw/1122_34/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_34/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_34/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_34/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_34/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_34/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_34/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_34/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_35/correct.txt b/legacy/Data/Questions/ingsw/1122_35/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_35/correct.txt
rename to legacy/Data/Questions/ingsw/1122_35/correct.txt
diff --git a/Data/Questions/ingsw/1122_35/quest.txt b/legacy/Data/Questions/ingsw/1122_35/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_35/quest.txt
rename to legacy/Data/Questions/ingsw/1122_35/quest.txt
diff --git a/Data/Questions/ingsw/1122_35/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_35/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_35/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_35/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_35/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_35/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_35/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_35/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_36/correct.txt b/legacy/Data/Questions/ingsw/1122_36/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_36/correct.txt
rename to legacy/Data/Questions/ingsw/1122_36/correct.txt
diff --git a/Data/Questions/ingsw/1122_36/quest.txt b/legacy/Data/Questions/ingsw/1122_36/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_36/quest.txt
rename to legacy/Data/Questions/ingsw/1122_36/quest.txt
diff --git a/Data/Questions/ingsw/1122_36/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_36/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_36/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_36/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_36/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_36/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_36/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_36/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_37/correct.txt b/legacy/Data/Questions/ingsw/1122_37/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_37/correct.txt
rename to legacy/Data/Questions/ingsw/1122_37/correct.txt
diff --git a/Data/Questions/ingsw/1122_37/quest.txt b/legacy/Data/Questions/ingsw/1122_37/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_37/quest.txt
rename to legacy/Data/Questions/ingsw/1122_37/quest.txt
diff --git a/Data/Questions/ingsw/1122_37/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_37/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_37/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_37/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_37/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_37/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_37/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_37/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_38/correct.txt b/legacy/Data/Questions/ingsw/1122_38/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_38/correct.txt
rename to legacy/Data/Questions/ingsw/1122_38/correct.txt
diff --git a/Data/Questions/ingsw/1122_38/quest.txt b/legacy/Data/Questions/ingsw/1122_38/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_38/quest.txt
rename to legacy/Data/Questions/ingsw/1122_38/quest.txt
diff --git a/Data/Questions/ingsw/1122_38/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_38/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_38/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_38/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_38/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_38/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_38/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_38/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_39/correct.txt b/legacy/Data/Questions/ingsw/1122_39/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_39/correct.txt
rename to legacy/Data/Questions/ingsw/1122_39/correct.txt
diff --git a/Data/Questions/ingsw/1122_39/quest.txt b/legacy/Data/Questions/ingsw/1122_39/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_39/quest.txt
rename to legacy/Data/Questions/ingsw/1122_39/quest.txt
diff --git a/Data/Questions/ingsw/1122_39/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_39/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_39/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_39/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_39/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_39/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_39/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_39/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_4/correct.txt b/legacy/Data/Questions/ingsw/1122_4/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_4/correct.txt
rename to legacy/Data/Questions/ingsw/1122_4/correct.txt
diff --git a/Data/Questions/ingsw/1122_4/quest.txt b/legacy/Data/Questions/ingsw/1122_4/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_4/quest.txt
rename to legacy/Data/Questions/ingsw/1122_4/quest.txt
diff --git a/Data/Questions/ingsw/1122_4/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_4/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_4/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_4/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_4/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_4/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_4/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_4/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_40/correct.txt b/legacy/Data/Questions/ingsw/1122_40/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_40/correct.txt
rename to legacy/Data/Questions/ingsw/1122_40/correct.txt
diff --git a/Data/Questions/ingsw/1122_40/quest.txt b/legacy/Data/Questions/ingsw/1122_40/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_40/quest.txt
rename to legacy/Data/Questions/ingsw/1122_40/quest.txt
diff --git a/Data/Questions/ingsw/1122_40/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_40/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_40/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_40/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_40/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_40/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_40/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_40/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_42/correct.txt b/legacy/Data/Questions/ingsw/1122_42/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_42/correct.txt
rename to legacy/Data/Questions/ingsw/1122_42/correct.txt
diff --git a/Data/Questions/ingsw/1122_42/quest.txt b/legacy/Data/Questions/ingsw/1122_42/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_42/quest.txt
rename to legacy/Data/Questions/ingsw/1122_42/quest.txt
diff --git a/Data/Questions/ingsw/1122_42/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_42/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_42/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_42/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_42/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_42/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_42/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_42/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_43/correct.txt b/legacy/Data/Questions/ingsw/1122_43/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_43/correct.txt
rename to legacy/Data/Questions/ingsw/1122_43/correct.txt
diff --git a/Data/Questions/ingsw/1122_43/quest.txt b/legacy/Data/Questions/ingsw/1122_43/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_43/quest.txt
rename to legacy/Data/Questions/ingsw/1122_43/quest.txt
diff --git a/Data/Questions/ingsw/1122_43/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_43/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_43/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_43/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_43/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_43/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_43/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_43/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_44/correct.txt b/legacy/Data/Questions/ingsw/1122_44/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_44/correct.txt
rename to legacy/Data/Questions/ingsw/1122_44/correct.txt
diff --git a/Data/Questions/ingsw/1122_44/quest.txt b/legacy/Data/Questions/ingsw/1122_44/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_44/quest.txt
rename to legacy/Data/Questions/ingsw/1122_44/quest.txt
diff --git a/Data/Questions/ingsw/1122_44/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_44/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_44/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_44/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_44/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_44/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_44/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_44/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_45/correct.txt b/legacy/Data/Questions/ingsw/1122_45/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_45/correct.txt
rename to legacy/Data/Questions/ingsw/1122_45/correct.txt
diff --git a/Data/Questions/ingsw/1122_45/quest.txt b/legacy/Data/Questions/ingsw/1122_45/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_45/quest.txt
rename to legacy/Data/Questions/ingsw/1122_45/quest.txt
diff --git a/Data/Questions/ingsw/1122_45/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_45/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_45/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_45/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_45/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_45/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_45/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_45/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_46/correct.txt b/legacy/Data/Questions/ingsw/1122_46/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_46/correct.txt
rename to legacy/Data/Questions/ingsw/1122_46/correct.txt
diff --git a/Data/Questions/ingsw/1122_46/quest.txt b/legacy/Data/Questions/ingsw/1122_46/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_46/quest.txt
rename to legacy/Data/Questions/ingsw/1122_46/quest.txt
diff --git a/Data/Questions/ingsw/1122_46/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_46/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_46/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_46/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_46/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_46/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_46/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_46/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_47/correct.txt b/legacy/Data/Questions/ingsw/1122_47/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_47/correct.txt
rename to legacy/Data/Questions/ingsw/1122_47/correct.txt
diff --git a/Data/Questions/ingsw/1122_47/quest.txt b/legacy/Data/Questions/ingsw/1122_47/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_47/quest.txt
rename to legacy/Data/Questions/ingsw/1122_47/quest.txt
diff --git a/Data/Questions/ingsw/1122_47/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_47/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_47/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_47/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_47/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_47/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_47/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_47/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_48/correct.txt b/legacy/Data/Questions/ingsw/1122_48/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_48/correct.txt
rename to legacy/Data/Questions/ingsw/1122_48/correct.txt
diff --git a/Data/Questions/ingsw/1122_48/quest.txt b/legacy/Data/Questions/ingsw/1122_48/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_48/quest.txt
rename to legacy/Data/Questions/ingsw/1122_48/quest.txt
diff --git a/Data/Questions/ingsw/1122_48/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_48/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_48/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_48/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_48/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_48/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_48/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_48/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_49/correct.txt b/legacy/Data/Questions/ingsw/1122_49/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_49/correct.txt
rename to legacy/Data/Questions/ingsw/1122_49/correct.txt
diff --git a/Data/Questions/ingsw/1122_49/quest.txt b/legacy/Data/Questions/ingsw/1122_49/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_49/quest.txt
rename to legacy/Data/Questions/ingsw/1122_49/quest.txt
diff --git a/Data/Questions/ingsw/1122_49/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_49/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_49/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_49/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_49/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_49/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_49/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_49/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_5/correct.txt b/legacy/Data/Questions/ingsw/1122_5/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_5/correct.txt
rename to legacy/Data/Questions/ingsw/1122_5/correct.txt
diff --git a/Data/Questions/ingsw/1122_5/quest.txt b/legacy/Data/Questions/ingsw/1122_5/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_5/quest.txt
rename to legacy/Data/Questions/ingsw/1122_5/quest.txt
diff --git a/Data/Questions/ingsw/1122_5/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_5/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_5/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_5/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_5/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_5/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_5/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_5/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_50/correct.txt b/legacy/Data/Questions/ingsw/1122_50/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_50/correct.txt
rename to legacy/Data/Questions/ingsw/1122_50/correct.txt
diff --git a/Data/Questions/ingsw/1122_50/quest.txt b/legacy/Data/Questions/ingsw/1122_50/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_50/quest.txt
rename to legacy/Data/Questions/ingsw/1122_50/quest.txt
diff --git a/Data/Questions/ingsw/1122_50/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_50/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_50/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_50/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_50/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_50/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_50/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_50/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_6/correct.txt b/legacy/Data/Questions/ingsw/1122_6/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_6/correct.txt
rename to legacy/Data/Questions/ingsw/1122_6/correct.txt
diff --git a/Data/Questions/ingsw/1122_6/quest.txt b/legacy/Data/Questions/ingsw/1122_6/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_6/quest.txt
rename to legacy/Data/Questions/ingsw/1122_6/quest.txt
diff --git a/Data/Questions/ingsw/1122_6/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_6/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_6/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_6/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_6/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_6/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_6/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_6/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_7/correct.txt b/legacy/Data/Questions/ingsw/1122_7/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_7/correct.txt
rename to legacy/Data/Questions/ingsw/1122_7/correct.txt
diff --git a/Data/Questions/ingsw/1122_7/quest.txt b/legacy/Data/Questions/ingsw/1122_7/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_7/quest.txt
rename to legacy/Data/Questions/ingsw/1122_7/quest.txt
diff --git a/Data/Questions/ingsw/1122_7/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_7/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_7/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_7/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_7/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_7/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_7/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_7/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_8/correct.txt b/legacy/Data/Questions/ingsw/1122_8/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_8/correct.txt
rename to legacy/Data/Questions/ingsw/1122_8/correct.txt
diff --git a/Data/Questions/ingsw/1122_8/quest.txt b/legacy/Data/Questions/ingsw/1122_8/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_8/quest.txt
rename to legacy/Data/Questions/ingsw/1122_8/quest.txt
diff --git a/Data/Questions/ingsw/1122_8/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_8/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_8/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_8/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_8/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_8/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_8/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_8/wrong 2.txt
diff --git a/Data/Questions/ingsw/1122_9/correct.txt b/legacy/Data/Questions/ingsw/1122_9/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_9/correct.txt
rename to legacy/Data/Questions/ingsw/1122_9/correct.txt
diff --git a/Data/Questions/ingsw/1122_9/quest.txt b/legacy/Data/Questions/ingsw/1122_9/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_9/quest.txt
rename to legacy/Data/Questions/ingsw/1122_9/quest.txt
diff --git a/Data/Questions/ingsw/1122_9/wrong 1.txt b/legacy/Data/Questions/ingsw/1122_9/wrong 1.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_9/wrong 1.txt
rename to legacy/Data/Questions/ingsw/1122_9/wrong 1.txt
diff --git a/Data/Questions/ingsw/1122_9/wrong 2.txt b/legacy/Data/Questions/ingsw/1122_9/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/1122_9/wrong 2.txt
rename to legacy/Data/Questions/ingsw/1122_9/wrong 2.txt
diff --git a/Data/Questions/ingsw/12/correct.txt b/legacy/Data/Questions/ingsw/12/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/12/correct.txt
rename to legacy/Data/Questions/ingsw/12/correct.txt
diff --git a/Data/Questions/ingsw/12/quest.txt b/legacy/Data/Questions/ingsw/12/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/12/quest.txt
rename to legacy/Data/Questions/ingsw/12/quest.txt
diff --git a/Data/Questions/ingsw/12/wrong 2.txt b/legacy/Data/Questions/ingsw/12/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/12/wrong 2.txt
rename to legacy/Data/Questions/ingsw/12/wrong 2.txt
diff --git a/Data/Questions/ingsw/12/wrong.txt b/legacy/Data/Questions/ingsw/12/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/12/wrong.txt
rename to legacy/Data/Questions/ingsw/12/wrong.txt
diff --git a/Data/Questions/ingsw/16/correct.txt b/legacy/Data/Questions/ingsw/16/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/16/correct.txt
rename to legacy/Data/Questions/ingsw/16/correct.txt
diff --git a/Data/Questions/ingsw/16/quest.txt b/legacy/Data/Questions/ingsw/16/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/16/quest.txt
rename to legacy/Data/Questions/ingsw/16/quest.txt
diff --git a/Data/Questions/ingsw/16/wrong 2.txt b/legacy/Data/Questions/ingsw/16/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/16/wrong 2.txt
rename to legacy/Data/Questions/ingsw/16/wrong 2.txt
diff --git a/Data/Questions/ingsw/16/wrong.txt b/legacy/Data/Questions/ingsw/16/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/16/wrong.txt
rename to legacy/Data/Questions/ingsw/16/wrong.txt
diff --git a/Data/Questions/ingsw/17/correct.txt b/legacy/Data/Questions/ingsw/17/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/17/correct.txt
rename to legacy/Data/Questions/ingsw/17/correct.txt
diff --git a/Data/Questions/ingsw/17/quest.txt b/legacy/Data/Questions/ingsw/17/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/17/quest.txt
rename to legacy/Data/Questions/ingsw/17/quest.txt
diff --git a/Data/Questions/ingsw/17/wrong 2.txt b/legacy/Data/Questions/ingsw/17/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/17/wrong 2.txt
rename to legacy/Data/Questions/ingsw/17/wrong 2.txt
diff --git a/Data/Questions/ingsw/17/wrong.txt b/legacy/Data/Questions/ingsw/17/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/17/wrong.txt
rename to legacy/Data/Questions/ingsw/17/wrong.txt
diff --git a/Data/Questions/ingsw/19/correct.txt b/legacy/Data/Questions/ingsw/19/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/19/correct.txt
rename to legacy/Data/Questions/ingsw/19/correct.txt
diff --git a/Data/Questions/ingsw/19/quest.txt b/legacy/Data/Questions/ingsw/19/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/19/quest.txt
rename to legacy/Data/Questions/ingsw/19/quest.txt
diff --git a/Data/Questions/ingsw/19/wrong 2.txt b/legacy/Data/Questions/ingsw/19/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/19/wrong 2.txt
rename to legacy/Data/Questions/ingsw/19/wrong 2.txt
diff --git a/Data/Questions/ingsw/19/wrong.txt b/legacy/Data/Questions/ingsw/19/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/19/wrong.txt
rename to legacy/Data/Questions/ingsw/19/wrong.txt
diff --git a/Data/Questions/ingsw/2/correct.txt b/legacy/Data/Questions/ingsw/2/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/2/correct.txt
rename to legacy/Data/Questions/ingsw/2/correct.txt
diff --git a/Data/Questions/ingsw/2/quest.txt b/legacy/Data/Questions/ingsw/2/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/2/quest.txt
rename to legacy/Data/Questions/ingsw/2/quest.txt
diff --git a/Data/Questions/ingsw/2/wrong 2.txt b/legacy/Data/Questions/ingsw/2/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/2/wrong 2.txt
rename to legacy/Data/Questions/ingsw/2/wrong 2.txt
diff --git a/Data/Questions/ingsw/2/wrong.txt b/legacy/Data/Questions/ingsw/2/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/2/wrong.txt
rename to legacy/Data/Questions/ingsw/2/wrong.txt
diff --git a/Data/Questions/ingsw/20/correct.txt b/legacy/Data/Questions/ingsw/20/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/20/correct.txt
rename to legacy/Data/Questions/ingsw/20/correct.txt
diff --git a/Data/Questions/ingsw/20/quest.txt b/legacy/Data/Questions/ingsw/20/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/20/quest.txt
rename to legacy/Data/Questions/ingsw/20/quest.txt
diff --git a/Data/Questions/ingsw/20/wrong 2.txt b/legacy/Data/Questions/ingsw/20/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/20/wrong 2.txt
rename to legacy/Data/Questions/ingsw/20/wrong 2.txt
diff --git a/Data/Questions/ingsw/20/wrong.txt b/legacy/Data/Questions/ingsw/20/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/20/wrong.txt
rename to legacy/Data/Questions/ingsw/20/wrong.txt
diff --git a/Data/Questions/ingsw/21/correct.txt b/legacy/Data/Questions/ingsw/21/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/21/correct.txt
rename to legacy/Data/Questions/ingsw/21/correct.txt
diff --git a/Data/Questions/ingsw/21/quest.txt b/legacy/Data/Questions/ingsw/21/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/21/quest.txt
rename to legacy/Data/Questions/ingsw/21/quest.txt
diff --git a/Data/Questions/ingsw/21/wrong 2.txt b/legacy/Data/Questions/ingsw/21/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/21/wrong 2.txt
rename to legacy/Data/Questions/ingsw/21/wrong 2.txt
diff --git a/Data/Questions/ingsw/21/wrong.txt b/legacy/Data/Questions/ingsw/21/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/21/wrong.txt
rename to legacy/Data/Questions/ingsw/21/wrong.txt
diff --git a/Data/Questions/ingsw/22/correct.txt b/legacy/Data/Questions/ingsw/22/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/22/correct.txt
rename to legacy/Data/Questions/ingsw/22/correct.txt
diff --git a/Data/Questions/ingsw/22/quest.txt b/legacy/Data/Questions/ingsw/22/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/22/quest.txt
rename to legacy/Data/Questions/ingsw/22/quest.txt
diff --git a/Data/Questions/ingsw/22/wrong 2.txt b/legacy/Data/Questions/ingsw/22/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/22/wrong 2.txt
rename to legacy/Data/Questions/ingsw/22/wrong 2.txt
diff --git a/Data/Questions/ingsw/22/wrong.txt b/legacy/Data/Questions/ingsw/22/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/22/wrong.txt
rename to legacy/Data/Questions/ingsw/22/wrong.txt
diff --git a/Data/Questions/ingsw/24/correct.txt b/legacy/Data/Questions/ingsw/24/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/24/correct.txt
rename to legacy/Data/Questions/ingsw/24/correct.txt
diff --git a/Data/Questions/ingsw/24/quest.txt b/legacy/Data/Questions/ingsw/24/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/24/quest.txt
rename to legacy/Data/Questions/ingsw/24/quest.txt
diff --git a/Data/Questions/ingsw/24/wrong 2.txt b/legacy/Data/Questions/ingsw/24/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/24/wrong 2.txt
rename to legacy/Data/Questions/ingsw/24/wrong 2.txt
diff --git a/Data/Questions/ingsw/24/wrong.txt b/legacy/Data/Questions/ingsw/24/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/24/wrong.txt
rename to legacy/Data/Questions/ingsw/24/wrong.txt
diff --git a/Data/Questions/ingsw/25/correct.txt b/legacy/Data/Questions/ingsw/25/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/25/correct.txt
rename to legacy/Data/Questions/ingsw/25/correct.txt
diff --git a/Data/Questions/ingsw/25/quest.txt b/legacy/Data/Questions/ingsw/25/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/25/quest.txt
rename to legacy/Data/Questions/ingsw/25/quest.txt
diff --git a/Data/Questions/ingsw/25/wrong 2.txt b/legacy/Data/Questions/ingsw/25/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/25/wrong 2.txt
rename to legacy/Data/Questions/ingsw/25/wrong 2.txt
diff --git a/Data/Questions/ingsw/25/wrong.txt b/legacy/Data/Questions/ingsw/25/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/25/wrong.txt
rename to legacy/Data/Questions/ingsw/25/wrong.txt
diff --git a/Data/Questions/ingsw/26/correct.txt b/legacy/Data/Questions/ingsw/26/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/26/correct.txt
rename to legacy/Data/Questions/ingsw/26/correct.txt
diff --git a/Data/Questions/ingsw/26/quest.txt b/legacy/Data/Questions/ingsw/26/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/26/quest.txt
rename to legacy/Data/Questions/ingsw/26/quest.txt
diff --git a/Data/Questions/ingsw/26/wrong 2.txt b/legacy/Data/Questions/ingsw/26/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/26/wrong 2.txt
rename to legacy/Data/Questions/ingsw/26/wrong 2.txt
diff --git a/Data/Questions/ingsw/26/wrong.txt b/legacy/Data/Questions/ingsw/26/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/26/wrong.txt
rename to legacy/Data/Questions/ingsw/26/wrong.txt
diff --git a/Data/Questions/ingsw/32/correct.txt b/legacy/Data/Questions/ingsw/32/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/32/correct.txt
rename to legacy/Data/Questions/ingsw/32/correct.txt
diff --git a/Data/Questions/ingsw/32/quest.txt b/legacy/Data/Questions/ingsw/32/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/32/quest.txt
rename to legacy/Data/Questions/ingsw/32/quest.txt
diff --git a/Data/Questions/ingsw/32/wrong 2.txt b/legacy/Data/Questions/ingsw/32/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/32/wrong 2.txt
rename to legacy/Data/Questions/ingsw/32/wrong 2.txt
diff --git a/Data/Questions/ingsw/32/wrong.txt b/legacy/Data/Questions/ingsw/32/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/32/wrong.txt
rename to legacy/Data/Questions/ingsw/32/wrong.txt
diff --git a/Data/Questions/ingsw/33/correct.txt b/legacy/Data/Questions/ingsw/33/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/33/correct.txt
rename to legacy/Data/Questions/ingsw/33/correct.txt
diff --git a/Data/Questions/ingsw/33/quest.txt b/legacy/Data/Questions/ingsw/33/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/33/quest.txt
rename to legacy/Data/Questions/ingsw/33/quest.txt
diff --git a/Data/Questions/ingsw/33/wrong 2.txt b/legacy/Data/Questions/ingsw/33/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/33/wrong 2.txt
rename to legacy/Data/Questions/ingsw/33/wrong 2.txt
diff --git a/Data/Questions/ingsw/33/wrong.txt b/legacy/Data/Questions/ingsw/33/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/33/wrong.txt
rename to legacy/Data/Questions/ingsw/33/wrong.txt
diff --git a/Data/Questions/ingsw/34/correct.txt b/legacy/Data/Questions/ingsw/34/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/34/correct.txt
rename to legacy/Data/Questions/ingsw/34/correct.txt
diff --git a/Data/Questions/ingsw/34/quest.txt b/legacy/Data/Questions/ingsw/34/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/34/quest.txt
rename to legacy/Data/Questions/ingsw/34/quest.txt
diff --git a/Data/Questions/ingsw/34/wrong 2.txt b/legacy/Data/Questions/ingsw/34/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/34/wrong 2.txt
rename to legacy/Data/Questions/ingsw/34/wrong 2.txt
diff --git a/Data/Questions/ingsw/34/wrong.txt b/legacy/Data/Questions/ingsw/34/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/34/wrong.txt
rename to legacy/Data/Questions/ingsw/34/wrong.txt
diff --git a/Data/Questions/ingsw/35/correct.txt b/legacy/Data/Questions/ingsw/35/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/35/correct.txt
rename to legacy/Data/Questions/ingsw/35/correct.txt
diff --git a/Data/Questions/ingsw/35/quest.txt b/legacy/Data/Questions/ingsw/35/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/35/quest.txt
rename to legacy/Data/Questions/ingsw/35/quest.txt
diff --git a/Data/Questions/ingsw/35/wrong 2.txt b/legacy/Data/Questions/ingsw/35/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/35/wrong 2.txt
rename to legacy/Data/Questions/ingsw/35/wrong 2.txt
diff --git a/Data/Questions/ingsw/35/wrong.txt b/legacy/Data/Questions/ingsw/35/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/35/wrong.txt
rename to legacy/Data/Questions/ingsw/35/wrong.txt
diff --git a/Data/Questions/ingsw/39/correct.txt b/legacy/Data/Questions/ingsw/39/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/39/correct.txt
rename to legacy/Data/Questions/ingsw/39/correct.txt
diff --git a/Data/Questions/ingsw/39/quest.txt b/legacy/Data/Questions/ingsw/39/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/39/quest.txt
rename to legacy/Data/Questions/ingsw/39/quest.txt
diff --git a/Data/Questions/ingsw/39/wrong 2.txt b/legacy/Data/Questions/ingsw/39/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/39/wrong 2.txt
rename to legacy/Data/Questions/ingsw/39/wrong 2.txt
diff --git a/Data/Questions/ingsw/39/wrong.txt b/legacy/Data/Questions/ingsw/39/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/39/wrong.txt
rename to legacy/Data/Questions/ingsw/39/wrong.txt
diff --git a/Data/Questions/ingsw/4/correct.txt b/legacy/Data/Questions/ingsw/4/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/4/correct.txt
rename to legacy/Data/Questions/ingsw/4/correct.txt
diff --git a/Data/Questions/ingsw/4/quest.txt b/legacy/Data/Questions/ingsw/4/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/4/quest.txt
rename to legacy/Data/Questions/ingsw/4/quest.txt
diff --git a/Data/Questions/ingsw/4/wrong 2.txt b/legacy/Data/Questions/ingsw/4/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/4/wrong 2.txt
rename to legacy/Data/Questions/ingsw/4/wrong 2.txt
diff --git a/Data/Questions/ingsw/4/wrong.txt b/legacy/Data/Questions/ingsw/4/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/4/wrong.txt
rename to legacy/Data/Questions/ingsw/4/wrong.txt
diff --git a/Data/Questions/ingsw/43/correct.txt b/legacy/Data/Questions/ingsw/43/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/43/correct.txt
rename to legacy/Data/Questions/ingsw/43/correct.txt
diff --git a/Data/Questions/ingsw/43/quest.txt b/legacy/Data/Questions/ingsw/43/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/43/quest.txt
rename to legacy/Data/Questions/ingsw/43/quest.txt
diff --git a/Data/Questions/ingsw/43/wrong 2.txt b/legacy/Data/Questions/ingsw/43/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/43/wrong 2.txt
rename to legacy/Data/Questions/ingsw/43/wrong 2.txt
diff --git a/Data/Questions/ingsw/43/wrong.txt b/legacy/Data/Questions/ingsw/43/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/43/wrong.txt
rename to legacy/Data/Questions/ingsw/43/wrong.txt
diff --git a/Data/Questions/ingsw/44/correct.txt b/legacy/Data/Questions/ingsw/44/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/44/correct.txt
rename to legacy/Data/Questions/ingsw/44/correct.txt
diff --git a/Data/Questions/ingsw/44/quest.txt b/legacy/Data/Questions/ingsw/44/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/44/quest.txt
rename to legacy/Data/Questions/ingsw/44/quest.txt
diff --git a/Data/Questions/ingsw/44/wrong 2.txt b/legacy/Data/Questions/ingsw/44/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/44/wrong 2.txt
rename to legacy/Data/Questions/ingsw/44/wrong 2.txt
diff --git a/Data/Questions/ingsw/44/wrong.txt b/legacy/Data/Questions/ingsw/44/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/44/wrong.txt
rename to legacy/Data/Questions/ingsw/44/wrong.txt
diff --git a/Data/Questions/ingsw/45/correct.txt b/legacy/Data/Questions/ingsw/45/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/45/correct.txt
rename to legacy/Data/Questions/ingsw/45/correct.txt
diff --git a/Data/Questions/ingsw/45/quest.txt b/legacy/Data/Questions/ingsw/45/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/45/quest.txt
rename to legacy/Data/Questions/ingsw/45/quest.txt
diff --git a/Data/Questions/ingsw/45/wrong 2.txt b/legacy/Data/Questions/ingsw/45/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/45/wrong 2.txt
rename to legacy/Data/Questions/ingsw/45/wrong 2.txt
diff --git a/Data/Questions/ingsw/45/wrong.txt b/legacy/Data/Questions/ingsw/45/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/45/wrong.txt
rename to legacy/Data/Questions/ingsw/45/wrong.txt
diff --git a/Data/Questions/ingsw/46/correct.txt b/legacy/Data/Questions/ingsw/46/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/46/correct.txt
rename to legacy/Data/Questions/ingsw/46/correct.txt
diff --git a/Data/Questions/ingsw/46/quest.txt b/legacy/Data/Questions/ingsw/46/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/46/quest.txt
rename to legacy/Data/Questions/ingsw/46/quest.txt
diff --git a/Data/Questions/ingsw/46/wrong 2.txt b/legacy/Data/Questions/ingsw/46/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/46/wrong 2.txt
rename to legacy/Data/Questions/ingsw/46/wrong 2.txt
diff --git a/Data/Questions/ingsw/46/wrong.txt b/legacy/Data/Questions/ingsw/46/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/46/wrong.txt
rename to legacy/Data/Questions/ingsw/46/wrong.txt
diff --git a/Data/Questions/ingsw/47/correct.txt b/legacy/Data/Questions/ingsw/47/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/47/correct.txt
rename to legacy/Data/Questions/ingsw/47/correct.txt
diff --git a/Data/Questions/ingsw/47/quest.txt b/legacy/Data/Questions/ingsw/47/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/47/quest.txt
rename to legacy/Data/Questions/ingsw/47/quest.txt
diff --git a/Data/Questions/ingsw/47/wrong 2.txt b/legacy/Data/Questions/ingsw/47/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/47/wrong 2.txt
rename to legacy/Data/Questions/ingsw/47/wrong 2.txt
diff --git a/Data/Questions/ingsw/47/wrong.txt b/legacy/Data/Questions/ingsw/47/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/47/wrong.txt
rename to legacy/Data/Questions/ingsw/47/wrong.txt
diff --git a/Data/Questions/ingsw/48/correct.txt b/legacy/Data/Questions/ingsw/48/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/48/correct.txt
rename to legacy/Data/Questions/ingsw/48/correct.txt
diff --git a/Data/Questions/ingsw/48/quest.txt b/legacy/Data/Questions/ingsw/48/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/48/quest.txt
rename to legacy/Data/Questions/ingsw/48/quest.txt
diff --git a/Data/Questions/ingsw/48/wrong 2.txt b/legacy/Data/Questions/ingsw/48/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/48/wrong 2.txt
rename to legacy/Data/Questions/ingsw/48/wrong 2.txt
diff --git a/Data/Questions/ingsw/48/wrong.txt b/legacy/Data/Questions/ingsw/48/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/48/wrong.txt
rename to legacy/Data/Questions/ingsw/48/wrong.txt
diff --git a/Data/Questions/ingsw/49/correct.txt b/legacy/Data/Questions/ingsw/49/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/49/correct.txt
rename to legacy/Data/Questions/ingsw/49/correct.txt
diff --git a/Data/Questions/ingsw/49/quest.txt b/legacy/Data/Questions/ingsw/49/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/49/quest.txt
rename to legacy/Data/Questions/ingsw/49/quest.txt
diff --git a/Data/Questions/ingsw/49/wrong 2.txt b/legacy/Data/Questions/ingsw/49/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/49/wrong 2.txt
rename to legacy/Data/Questions/ingsw/49/wrong 2.txt
diff --git a/Data/Questions/ingsw/49/wrong.txt b/legacy/Data/Questions/ingsw/49/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/49/wrong.txt
rename to legacy/Data/Questions/ingsw/49/wrong.txt
diff --git a/Data/Questions/ingsw/5/correct.txt b/legacy/Data/Questions/ingsw/5/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/5/correct.txt
rename to legacy/Data/Questions/ingsw/5/correct.txt
diff --git a/Data/Questions/ingsw/5/quest.txt b/legacy/Data/Questions/ingsw/5/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/5/quest.txt
rename to legacy/Data/Questions/ingsw/5/quest.txt
diff --git a/Data/Questions/ingsw/5/wrong 2.txt b/legacy/Data/Questions/ingsw/5/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/5/wrong 2.txt
rename to legacy/Data/Questions/ingsw/5/wrong 2.txt
diff --git a/Data/Questions/ingsw/5/wrong.txt b/legacy/Data/Questions/ingsw/5/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/5/wrong.txt
rename to legacy/Data/Questions/ingsw/5/wrong.txt
diff --git a/Data/Questions/ingsw/50/correct.txt b/legacy/Data/Questions/ingsw/50/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/50/correct.txt
rename to legacy/Data/Questions/ingsw/50/correct.txt
diff --git a/Data/Questions/ingsw/50/quest.txt b/legacy/Data/Questions/ingsw/50/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/50/quest.txt
rename to legacy/Data/Questions/ingsw/50/quest.txt
diff --git a/Data/Questions/ingsw/50/wrong 2.txt b/legacy/Data/Questions/ingsw/50/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/50/wrong 2.txt
rename to legacy/Data/Questions/ingsw/50/wrong 2.txt
diff --git a/Data/Questions/ingsw/50/wrong.txt b/legacy/Data/Questions/ingsw/50/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/50/wrong.txt
rename to legacy/Data/Questions/ingsw/50/wrong.txt
diff --git a/Data/Questions/ingsw/69420/correct.txt b/legacy/Data/Questions/ingsw/69420/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/69420/correct.txt
rename to legacy/Data/Questions/ingsw/69420/correct.txt
diff --git a/Data/Questions/ingsw/69420/quest.txt b/legacy/Data/Questions/ingsw/69420/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/69420/quest.txt
rename to legacy/Data/Questions/ingsw/69420/quest.txt
diff --git a/Data/Questions/ingsw/69420/wrong 2.txt b/legacy/Data/Questions/ingsw/69420/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/69420/wrong 2.txt
rename to legacy/Data/Questions/ingsw/69420/wrong 2.txt
diff --git a/Data/Questions/ingsw/69420/wrong 3.txt b/legacy/Data/Questions/ingsw/69420/wrong 3.txt
similarity index 100%
rename from Data/Questions/ingsw/69420/wrong 3.txt
rename to legacy/Data/Questions/ingsw/69420/wrong 3.txt
diff --git a/Data/Questions/ingsw/69420/wrong.txt b/legacy/Data/Questions/ingsw/69420/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/69420/wrong.txt
rename to legacy/Data/Questions/ingsw/69420/wrong.txt
diff --git a/Data/Questions/ingsw/8/correct.txt b/legacy/Data/Questions/ingsw/8/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/8/correct.txt
rename to legacy/Data/Questions/ingsw/8/correct.txt
diff --git a/Data/Questions/ingsw/8/quest.txt b/legacy/Data/Questions/ingsw/8/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/8/quest.txt
rename to legacy/Data/Questions/ingsw/8/quest.txt
diff --git a/Data/Questions/ingsw/8/wrong 2.txt b/legacy/Data/Questions/ingsw/8/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/8/wrong 2.txt
rename to legacy/Data/Questions/ingsw/8/wrong 2.txt
diff --git a/Data/Questions/ingsw/8/wrong.txt b/legacy/Data/Questions/ingsw/8/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/8/wrong.txt
rename to legacy/Data/Questions/ingsw/8/wrong.txt
diff --git a/Data/Questions/ingsw/9/correct.txt b/legacy/Data/Questions/ingsw/9/correct.txt
similarity index 100%
rename from Data/Questions/ingsw/9/correct.txt
rename to legacy/Data/Questions/ingsw/9/correct.txt
diff --git a/Data/Questions/ingsw/9/quest.txt b/legacy/Data/Questions/ingsw/9/quest.txt
similarity index 100%
rename from Data/Questions/ingsw/9/quest.txt
rename to legacy/Data/Questions/ingsw/9/quest.txt
diff --git a/Data/Questions/ingsw/9/wrong 2.txt b/legacy/Data/Questions/ingsw/9/wrong 2.txt
similarity index 100%
rename from Data/Questions/ingsw/9/wrong 2.txt
rename to legacy/Data/Questions/ingsw/9/wrong 2.txt
diff --git a/Data/Questions/ingsw/9/wrong.txt b/legacy/Data/Questions/ingsw/9/wrong.txt
similarity index 100%
rename from Data/Questions/ingsw/9/wrong.txt
rename to legacy/Data/Questions/ingsw/9/wrong.txt
diff --git a/Data/Questions/ium_unive.txt b/legacy/Data/Questions/ium_unive.txt
similarity index 100%
rename from Data/Questions/ium_unive.txt
rename to legacy/Data/Questions/ium_unive.txt
diff --git a/Data/Questions/ogas.txt b/legacy/Data/Questions/ogas.txt
similarity index 100%
rename from Data/Questions/ogas.txt
rename to legacy/Data/Questions/ogas.txt
diff --git a/Data/Questions/sicurezza.txt b/legacy/Data/Questions/sicurezza.txt
similarity index 100%
rename from Data/Questions/sicurezza.txt
rename to legacy/Data/Questions/sicurezza.txt
diff --git a/Data/Questions/sicurezza_appello1.txt b/legacy/Data/Questions/sicurezza_appello1.txt
similarity index 100%
rename from Data/Questions/sicurezza_appello1.txt
rename to legacy/Data/Questions/sicurezza_appello1.txt
diff --git a/Data/Questions/so1.txt b/legacy/Data/Questions/so1.txt
similarity index 100%
rename from Data/Questions/so1.txt
rename to legacy/Data/Questions/so1.txt
diff --git a/Data/Questions/so1_new.json b/legacy/Data/Questions/so1_new.json
similarity index 100%
rename from Data/Questions/so1_new.json
rename to legacy/Data/Questions/so1_new.json
diff --git a/Data/Questions/so1_unive.txt b/legacy/Data/Questions/so1_unive.txt
similarity index 100%
rename from Data/Questions/so1_unive.txt
rename to legacy/Data/Questions/so1_unive.txt
diff --git a/Data/Questions/so2.txt b/legacy/Data/Questions/so2.txt
similarity index 100%
rename from Data/Questions/so2.txt
rename to legacy/Data/Questions/so2.txt
diff --git a/legacy/Data/ingsw/0000_102/correct.txt b/legacy/Data/ingsw/0000_102/correct.txt
new file mode 100644
index 0000000..6613ca3
--- /dev/null
+++ b/legacy/Data/ingsw/0000_102/correct.txt
@@ -0,0 +1 @@
+(a=100, b=false, c=true), (a=90, b=true, c=false)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_102/quest.txt b/legacy/Data/ingsw/0000_102/quest.txt
new file mode 100644
index 0000000..e13f059
--- /dev/null
+++ b/legacy/Data/ingsw/0000_102/quest.txt
@@ -0,0 +1,20 @@
+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 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 in 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, bool b, bool c)
+{    if ( (a == 100) && (b || c) )
+        { return (1); }
+      else { return (2);}
+}
+Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_102/wrong1.txt b/legacy/Data/ingsw/0000_102/wrong1.txt
new file mode 100644
index 0000000..9e9ca26
--- /dev/null
+++ b/legacy/Data/ingsw/0000_102/wrong1.txt
@@ -0,0 +1 @@
+(a=100, b=false, c=false), (a=90, b=true, c=true)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_102/wrong2.txt b/legacy/Data/ingsw/0000_102/wrong2.txt
new file mode 100644
index 0000000..2a2414b
--- /dev/null
+++ b/legacy/Data/ingsw/0000_102/wrong2.txt
@@ -0,0 +1 @@
+(a=100, b=false, c=true), (a=90, b=false, c=true)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_2/correct.txt b/legacy/Data/ingsw/0000_2/correct.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0000_2/correct.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_2/quest.txt b/legacy/Data/ingsw/0000_2/quest.txt
new file mode 100644
index 0000000..c5fe9fb
--- /dev/null
+++ b/legacy/Data/ingsw/0000_2/quest.txt
@@ -0,0 +1,57 @@
+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 il seguente programma C:
+
+-----------
+
+#include <stdio.h>
+
+#include <stdlib.h>
+
+#include <assert.h>
+
+#define N  4    /* number of test cases */
+
+
+
+int f(int x1, int x2)
+
+{
+
+  if (x1 + x2 <= 2)
+
+  return (1);
+
+  else return (2);		    
+
+}
+
+
+
+int main() {    int  i, y;   int x1[N], x2[N];
+
+  // define test cases
+
+    x1[0] = 5; x2[0] = -2;    x1[1] = 6; x2[1] = -3;    x1[2] = 7; x2[2] = -4;    x1[3] = 8; x2[3] = -5; 
+
+    // testing
+
+  for (i = 0; i < N; i++)  {
+
+      y = f(x1[i], x2[i]);       // function under testing
+
+      assert(y ==(x1[i], x2[i] <= 2) ? 1 : 2);   // oracle
+
+    }
+
+   printf("All %d test cases passed\n", N);
+
+    return (0);   
+
+}
+
+-----------
+
+Il programma main() sopra realizza il nostro testing per la funzione f1(). I test cases sono i valori in x1[i] ed x2[i].
+
+Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_2/wrong1.txt b/legacy/Data/ingsw/0000_2/wrong1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0000_2/wrong1.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_2/wrong2.txt b/legacy/Data/ingsw/0000_2/wrong2.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0000_2/wrong2.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_3/correct.txt b/legacy/Data/ingsw/0000_3/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0000_3/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_3/quest.txt b/legacy/Data/ingsw/0000_3/quest.txt
new file mode 100644
index 0000000..ba81b1a
--- /dev/null
+++ b/legacy/Data/ingsw/0000_3/quest.txt
@@ -0,0 +1,45 @@
+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 il seguente programma C:
+
+-----------
+
+#include <stdio.h>
+
+#include <stdlib.h>
+
+#include <assert.h>
+
+#define N  5    /* number of test cases */
+
+int f1(int x)  { return (2*x); }
+
+int main() {  int  i, y;    int x[N];
+
+ // define test cases
+
+  x[0] = 0; x[1] = 1; x[2] = -1; x[3] = 10; x[4] = -10;
+
+// testing
+
+for (i = 0; i < N; i++)   {
+
+      y = f1(x[i]);       // function under testing
+
+      assert(y == 2*x[i]);   // oracle
+
+ }
+
+   printf("All %d test cases passed\n", N);
+
+    return (0); 
+
+}
+
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+
+{0, {-1}, {1}, {tutti glli interi negativi diversi da -1}, {tutti glli interi positivi diversi da 1}}
+
+Il programma main() sopra realizza il nostro testing per la funzione f1(). I test cases sono i valori in x[i].
+
+Quale delle seguenti è la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_3/wrong1.txt b/legacy/Data/ingsw/0000_3/wrong1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0000_3/wrong1.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_3/wrong2.txt b/legacy/Data/ingsw/0000_3/wrong2.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0000_3/wrong2.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_32/correct.txt b/legacy/Data/ingsw/0000_32/correct.txt
new file mode 100644
index 0000000..1ef5b94
--- /dev/null
+++ b/legacy/Data/ingsw/0000_32/correct.txt
@@ -0,0 +1 @@
+(a=100, b=true, c=false), (a=90, b=false, c=true), (a=90, b=false, c=false)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_32/quest.txt b/legacy/Data/ingsw/0000_32/quest.txt
new file mode 100644
index 0000000..f07b439
--- /dev/null
+++ b/legacy/Data/ingsw/0000_32/quest.txt
@@ -0,0 +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 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 in 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, bool b, bool c)
+{    if ( (a == 100) && b )
+          return (1);    // punto di uscita 1
+      else if (b || c)
+           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/0000_32/wrong1.txt b/legacy/Data/ingsw/0000_32/wrong1.txt
new file mode 100644
index 0000000..6946352
--- /dev/null
+++ b/legacy/Data/ingsw/0000_32/wrong1.txt
@@ -0,0 +1 @@
+(a=100, b=true, c=false), (a=90, b=false, c=true), (a=100, b=true, c=true)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_32/wrong2.txt b/legacy/Data/ingsw/0000_32/wrong2.txt
new file mode 100644
index 0000000..f9b6750
--- /dev/null
+++ b/legacy/Data/ingsw/0000_32/wrong2.txt
@@ -0,0 +1 @@
+(a=100, b=true, c=false), (a=90, b=false, c=false), (a=100, b=false, c=false)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_4/correct.txt b/legacy/Data/ingsw/0000_4/correct.txt
new file mode 100644
index 0000000..998dfca
--- /dev/null
+++ b/legacy/Data/ingsw/0000_4/correct.txt
@@ -0,0 +1 @@
+Customers should be closely involved throughout the development process.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_4/quest.txt b/legacy/Data/ingsw/0000_4/quest.txt
new file mode 100644
index 0000000..7d22084
--- /dev/null
+++ b/legacy/Data/ingsw/0000_4/quest.txt
@@ -0,0 +1 @@
+Which of the following is an agile principle?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_4/wrong1.txt b/legacy/Data/ingsw/0000_4/wrong1.txt
new file mode 100644
index 0000000..b34acfc
--- /dev/null
+++ b/legacy/Data/ingsw/0000_4/wrong1.txt
@@ -0,0 +1 @@
+Customers should just provide requirements and verify them when the project is completed.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_4/wrong2.txt b/legacy/Data/ingsw/0000_4/wrong2.txt
new file mode 100644
index 0000000..9cfa092
--- /dev/null
+++ b/legacy/Data/ingsw/0000_4/wrong2.txt
@@ -0,0 +1 @@
+Customers should not interfere with the software development.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_7/correct.txt b/legacy/Data/ingsw/0000_7/correct.txt
new file mode 100644
index 0000000..ae41872
--- /dev/null
+++ b/legacy/Data/ingsw/0000_7/correct.txt
@@ -0,0 +1 @@
+Testing interfaces for each component (i.e., integration of several units).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_7/quest.txt b/legacy/Data/ingsw/0000_7/quest.txt
new file mode 100644
index 0000000..8632d4d
--- /dev/null
+++ b/legacy/Data/ingsw/0000_7/quest.txt
@@ -0,0 +1 @@
+Component testing focuses on:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_7/wrong1.txt b/legacy/Data/ingsw/0000_7/wrong1.txt
new file mode 100644
index 0000000..c2fa097
--- /dev/null
+++ b/legacy/Data/ingsw/0000_7/wrong1.txt
@@ -0,0 +1 @@
+Testing interactions among components (i.e., integration of several units).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_7/wrong2.txt b/legacy/Data/ingsw/0000_7/wrong2.txt
new file mode 100644
index 0000000..85de863
--- /dev/null
+++ b/legacy/Data/ingsw/0000_7/wrong2.txt
@@ -0,0 +1 @@
+Testing functionalities of individual program units, object classes or methods.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_8/correct.txt b/legacy/Data/ingsw/0000_8/correct.txt
new file mode 100644
index 0000000..aef914a
--- /dev/null
+++ b/legacy/Data/ingsw/0000_8/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che un sistema che soddisfa i requisiti risolve il problema del "customer".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_8/quest.txt b/legacy/Data/ingsw/0000_8/quest.txt
new file mode 100644
index 0000000..e821a05
--- /dev/null
+++ b/legacy/Data/ingsw/0000_8/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "validity check" che è parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_8/wrong1.txt b/legacy/Data/ingsw/0000_8/wrong1.txt
new file mode 100644
index 0000000..32c628c
--- /dev/null
+++ b/legacy/Data/ingsw/0000_8/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisiti funzionali descrivano tutte le funzionalità del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0000_8/wrong2.txt b/legacy/Data/ingsw/0000_8/wrong2.txt
new file mode 100644
index 0000000..eb23d05
--- /dev/null
+++ b/legacy/Data/ingsw/0000_8/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che non ci siano requisiti in conflitto con altri requisiti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_0/correct.txt b/legacy/Data/ingsw/0120_0/correct.txt
new file mode 100644
index 0000000..b110af1
--- /dev/null
+++ b/legacy/Data/ingsw/0120_0/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_0/quest.txt b/legacy/Data/ingsw/0120_0/quest.txt
new file mode 100644
index 0000000..1b78d21
--- /dev/null
+++ b/legacy/Data/ingsw/0120_0/quest.txt
@@ -0,0 +1,11 @@
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_0.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0120_0/wrong1.txt
new file mode 100644
index 0000000..5464d05
--- /dev/null
+++ b/legacy/Data/ingsw/0120_0/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 30%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_0/wrong2.txt b/legacy/Data/ingsw/0120_0/wrong2.txt
new file mode 100644
index 0000000..a29d476
--- /dev/null
+++ b/legacy/Data/ingsw/0120_0/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_1/correct.txt b/legacy/Data/ingsw/0120_1/correct.txt
new file mode 100644
index 0000000..279908a
--- /dev/null
+++ b/legacy/Data/ingsw/0120_1/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 <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/ingsw/0120_1/quest.txt b/legacy/Data/ingsw/0120_1/quest.txt
new file mode 100644
index 0000000..5922b9f
--- /dev/null
+++ b/legacy/Data/ingsw/0120_1/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 10 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�: se la variabile x � nell'intervallo [10, 20]  allora la variabile y � 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/legacy/Data/ingsw/0120_1/wrong1.txt
new file mode 100644
index 0000000..867889a
--- /dev/null
+++ b/legacy/Data/ingsw/0120_1/wrong1.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) 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/legacy/Data/ingsw/0120_1/wrong2.txt
new file mode 100644
index 0000000..a159504
--- /dev/null
+++ b/legacy/Data/ingsw/0120_1/wrong2.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) 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/legacy/Data/ingsw/0120_10/correct.txt
new file mode 100644
index 0000000..fffebc7
--- /dev/null
+++ b/legacy/Data/ingsw/0120_10/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>
diff --git a/legacy/Data/ingsw/0120_10/quest.txt b/legacy/Data/ingsw/0120_10/quest.txt
new file mode 100644
index 0000000..e11a044
--- /dev/null
+++ b/legacy/Data/ingsw/0120_10/quest.txt
@@ -0,0 +1,4 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 50 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se la variabile x � minore del 60% della variabile y allora la somma di x ed y � 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/legacy/Data/ingsw/0120_10/wrong1.txt
new file mode 100644
index 0000000..c5ef6d8
--- /dev/null
+++ b/legacy/Data/ingsw/0120_10/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>
diff --git a/legacy/Data/ingsw/0120_10/wrong2.txt b/legacy/Data/ingsw/0120_10/wrong2.txt
new file mode 100644
index 0000000..06e9d5a
--- /dev/null
+++ b/legacy/Data/ingsw/0120_10/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>
diff --git a/legacy/Data/ingsw/0120_11/correct.txt b/legacy/Data/ingsw/0120_11/correct.txt
new file mode 100644
index 0000000..1a8a50a
--- /dev/null
+++ b/legacy/Data/ingsw/0120_11/correct.txt
@@ -0,0 +1 @@
+Per ciascun requisito, dovremmo essere in grado di scrivere un inseme di test che può dimostrare che il sistema sviluppato soddisfa il requisito considerato.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_11/quest.txt b/legacy/Data/ingsw/0120_11/quest.txt
new file mode 100644
index 0000000..793b220
--- /dev/null
+++ b/legacy/Data/ingsw/0120_11/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive il criterio di "requirements verifiability" che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_11/wrong1.txt b/legacy/Data/ingsw/0120_11/wrong1.txt
new file mode 100644
index 0000000..fac8307
--- /dev/null
+++ b/legacy/Data/ingsw/0120_11/wrong1.txt
@@ -0,0 +1 @@
+Per ciascuna coppia di componenti, dovremmo essere in grado di scrivere un insieme di test che può dimostrare che l'interazione tra le componenti soddisfa tutti i requisiti di interfaccia.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_11/wrong2.txt b/legacy/Data/ingsw/0120_11/wrong2.txt
new file mode 100644
index 0000000..3fdb31e
--- /dev/null
+++ b/legacy/Data/ingsw/0120_11/wrong2.txt
@@ -0,0 +1 @@
+Per ciascuna componente del sistema, dovremmo essere in grado di scrivere un insieme di test che può dimostrare che essa soddisfa tutti i requisiti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_12/correct.txt b/legacy/Data/ingsw/0120_12/correct.txt
new file mode 100644
index 0000000..b9f32a6
--- /dev/null
+++ b/legacy/Data/ingsw/0120_12/correct.txt
@@ -0,0 +1 @@
+c(0)/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_12/quest.txt b/legacy/Data/ingsw/0120_12/quest.txt
new file mode 100644
index 0000000..aed3c79
--- /dev/null
+++ b/legacy/Data/ingsw/0120_12/quest.txt
@@ -0,0 +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 � in (0, 1).  Il costo dello stato (fase) x � c(x). La fase 0 � la fase di design, che ha probabilit� 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 � 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), .... � 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)  (poich� 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/legacy/Data/ingsw/0120_12/wrong1.txt
new file mode 100644
index 0000000..70022eb
--- /dev/null
+++ b/legacy/Data/ingsw/0120_12/wrong1.txt
@@ -0,0 +1 @@
+c(0)*(1 - p)/p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_12/wrong2.txt b/legacy/Data/ingsw/0120_12/wrong2.txt
new file mode 100644
index 0000000..3143da9
--- /dev/null
+++ b/legacy/Data/ingsw/0120_12/wrong2.txt
@@ -0,0 +1 @@
+c(0)/(p*(1 - p))
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_13/correct.txt b/legacy/Data/ingsw/0120_13/correct.txt
new file mode 100644
index 0000000..e74b1fc
--- /dev/null
+++ b/legacy/Data/ingsw/0120_13/correct.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x > y) then (z == x) else (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_13/quest.txt b/legacy/Data/ingsw/0120_13/quest.txt
new file mode 100644
index 0000000..c1cd6d0
--- /dev/null
+++ b/legacy/Data/ingsw/0120_13/quest.txt
@@ -0,0 +1,9 @@
+Un test oracle per un programma P � 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) � 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) � un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_13/wrong1.txt b/legacy/Data/ingsw/0120_13/wrong1.txt
new file mode 100644
index 0000000..d63544a
--- /dev/null
+++ b/legacy/Data/ingsw/0120_13/wrong1.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x > y) then (z == x + 1) else (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_13/wrong2.txt b/legacy/Data/ingsw/0120_13/wrong2.txt
new file mode 100644
index 0000000..1753a91
--- /dev/null
+++ b/legacy/Data/ingsw/0120_13/wrong2.txt
@@ -0,0 +1 @@
+F(x, y, z) = (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_14/correct.txt b/legacy/Data/ingsw/0120_14/correct.txt
new file mode 100644
index 0000000..475d1ef
--- /dev/null
+++ b/legacy/Data/ingsw/0120_14/correct.txt
@@ -0,0 +1 @@
+{x = -150, x = -40,  x = 0, x = 200, x = 600}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_14/quest.txt b/legacy/Data/ingsw/0120_14/quest.txt
new file mode 100644
index 0000000..36947c2
--- /dev/null
+++ b/legacy/Data/ingsw/0120_14/quest.txt
@@ -0,0 +1,6 @@
+Il partition coverage di un insieme di test cases � la percentuale di elementi della partition inclusi nei test cases. La partition � 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/legacy/Data/ingsw/0120_14/wrong1.txt
new file mode 100644
index 0000000..a6df32d
--- /dev/null
+++ b/legacy/Data/ingsw/0120_14/wrong1.txt
@@ -0,0 +1 @@
+{x = -200, x = -150,  x = 0, x = 100, x = 700}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_14/wrong2.txt b/legacy/Data/ingsw/0120_14/wrong2.txt
new file mode 100644
index 0000000..0aaedb8
--- /dev/null
+++ b/legacy/Data/ingsw/0120_14/wrong2.txt
@@ -0,0 +1 @@
+{x = -200, x = -50,  x = 0, x = 100, x = 500}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_15/correct.txt b/legacy/Data/ingsw/0120_15/correct.txt
new file mode 100644
index 0000000..aef914a
--- /dev/null
+++ b/legacy/Data/ingsw/0120_15/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che un sistema che soddisfa i requisiti risolve il problema del "customer".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_15/quest.txt b/legacy/Data/ingsw/0120_15/quest.txt
new file mode 100644
index 0000000..9af4805
--- /dev/null
+++ b/legacy/Data/ingsw/0120_15/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "validity check" che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_15/wrong1.txt b/legacy/Data/ingsw/0120_15/wrong1.txt
new file mode 100644
index 0000000..32c628c
--- /dev/null
+++ b/legacy/Data/ingsw/0120_15/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisiti funzionali descrivano tutte le funzionalità del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_15/wrong2.txt b/legacy/Data/ingsw/0120_15/wrong2.txt
new file mode 100644
index 0000000..eb23d05
--- /dev/null
+++ b/legacy/Data/ingsw/0120_15/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che non ci siano requisiti in conflitto con altri requisiti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_16/correct.txt b/legacy/Data/ingsw/0120_16/correct.txt
new file mode 100644
index 0000000..0902686
--- /dev/null
+++ b/legacy/Data/ingsw/0120_16/correct.txt
@@ -0,0 +1 @@
+Requisito funzionale.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_16/quest.txt b/legacy/Data/ingsw/0120_16/quest.txt
new file mode 100644
index 0000000..f6839df
--- /dev/null
+++ b/legacy/Data/ingsw/0120_16/quest.txt
@@ -0,0 +1,2 @@
+"Ogni giorno, per ciascuna clinica, il sistema generer� una lista dei pazienti che hanno un appuntamento quel giorno."
+La frase precedente � un esempio di:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_16/wrong1.txt b/legacy/Data/ingsw/0120_16/wrong1.txt
new file mode 100644
index 0000000..6084c49
--- /dev/null
+++ b/legacy/Data/ingsw/0120_16/wrong1.txt
@@ -0,0 +1 @@
+Requisito non-funzionale.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_16/wrong2.txt b/legacy/Data/ingsw/0120_16/wrong2.txt
new file mode 100644
index 0000000..396c8d3
--- /dev/null
+++ b/legacy/Data/ingsw/0120_16/wrong2.txt
@@ -0,0 +1 @@
+Requisito di performance.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_17/correct.txt b/legacy/Data/ingsw/0120_17/correct.txt
new file mode 100644
index 0000000..f2bb2d0
--- /dev/null
+++ b/legacy/Data/ingsw/0120_17/correct.txt
@@ -0,0 +1 @@
+0.12
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_17/quest.txt b/legacy/Data/ingsw/0120_17/quest.txt
new file mode 100644
index 0000000..fc7cc95
--- /dev/null
+++ b/legacy/Data/ingsw/0120_17/quest.txt
@@ -0,0 +1,9 @@
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_17.png
+Un processo software pu� 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 probabilit� 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 probabilit� 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario � una sequenza di stati.
+Qual'e' la probabilit� dello scenario: 1, 3, 4? In altri terminti, qual'� la probabilit� 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/legacy/Data/ingsw/0120_17/wrong1.txt
new file mode 100644
index 0000000..2a47a95
--- /dev/null
+++ b/legacy/Data/ingsw/0120_17/wrong1.txt
@@ -0,0 +1 @@
+0.08
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_17/wrong2.txt b/legacy/Data/ingsw/0120_17/wrong2.txt
new file mode 100644
index 0000000..b7bbee2
--- /dev/null
+++ b/legacy/Data/ingsw/0120_17/wrong2.txt
@@ -0,0 +1 @@
+0.32
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_18/correct.txt b/legacy/Data/ingsw/0120_18/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0120_18/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_18/quest.txt b/legacy/Data/ingsw/0120_18/quest.txt
new file mode 100644
index 0000000..fd2ddc5
--- /dev/null
+++ b/legacy/Data/ingsw/0120_18/quest.txt
@@ -0,0 +1,9 @@
+Il branch coverage di un insieme di test cases � 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 � la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_18/wrong1.txt b/legacy/Data/ingsw/0120_18/wrong1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0120_18/wrong1.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_18/wrong2.txt b/legacy/Data/ingsw/0120_18/wrong2.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0120_18/wrong2.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_19/correct.txt b/legacy/Data/ingsw/0120_19/correct.txt
new file mode 100644
index 0000000..e13eda2
--- /dev/null
+++ b/legacy/Data/ingsw/0120_19/correct.txt
@@ -0,0 +1 @@
+Accertarsi che i requisiti definiscano un sistema che risolve il problema che l'utente pianifica di risolvere.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_19/quest.txt b/legacy/Data/ingsw/0120_19/quest.txt
new file mode 100644
index 0000000..b59a64d
--- /dev/null
+++ b/legacy/Data/ingsw/0120_19/quest.txt
@@ -0,0 +1 @@
+Quali delle seguenti attivit� � parte del processo di validazione dei requisiti ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_19/wrong1.txt b/legacy/Data/ingsw/0120_19/wrong1.txt
new file mode 100644
index 0000000..b24f900
--- /dev/null
+++ b/legacy/Data/ingsw/0120_19/wrong1.txt
@@ -0,0 +1 @@
+Accertarsi che il sistema soddisfi i requisiti dati.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_19/wrong2.txt b/legacy/Data/ingsw/0120_19/wrong2.txt
new file mode 100644
index 0000000..884d6b1
--- /dev/null
+++ b/legacy/Data/ingsw/0120_19/wrong2.txt
@@ -0,0 +1 @@
+Accertarsi che l'architettura del sistema soddisfi i requisiti dati.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_2/correct.txt b/legacy/Data/ingsw/0120_2/correct.txt
new file mode 100644
index 0000000..2ca9276
--- /dev/null
+++ b/legacy/Data/ingsw/0120_2/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 35%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_2/quest.txt b/legacy/Data/ingsw/0120_2/quest.txt
new file mode 100644
index 0000000..7cf45ee
--- /dev/null
+++ b/legacy/Data/ingsw/0120_2/quest.txt
@@ -0,0 +1,11 @@
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_2.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0120_2/wrong1.txt
new file mode 100644
index 0000000..2d5aeb0
--- /dev/null
+++ b/legacy/Data/ingsw/0120_2/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_2/wrong2.txt b/legacy/Data/ingsw/0120_2/wrong2.txt
new file mode 100644
index 0000000..a29d476
--- /dev/null
+++ b/legacy/Data/ingsw/0120_2/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_20/correct.txt b/legacy/Data/ingsw/0120_20/correct.txt
new file mode 100644
index 0000000..7311d41
--- /dev/null
+++ b/legacy/Data/ingsw/0120_20/correct.txt
@@ -0,0 +1 @@
+Test set: {x=1, y=1}, {x=0, y=0}, {x=2, y=1}, {x=2, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_20/quest.txt b/legacy/Data/ingsw/0120_20/quest.txt
new file mode 100644
index 0000000..173901c
--- /dev/null
+++ b/legacy/Data/ingsw/0120_20/quest.txt
@@ -0,0 +1,8 @@
+Il branch coverage di un insieme di test cases � 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/legacy/Data/ingsw/0120_20/wrong1.txt
new file mode 100644
index 0000000..3e327ab
--- /dev/null
+++ b/legacy/Data/ingsw/0120_20/wrong1.txt
@@ -0,0 +1 @@
+Test set: {x=1, y=1}, {x=2, y=2}, {x=2, y=1}, {x=2, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_20/wrong2.txt b/legacy/Data/ingsw/0120_20/wrong2.txt
new file mode 100644
index 0000000..7e48e4f
--- /dev/null
+++ b/legacy/Data/ingsw/0120_20/wrong2.txt
@@ -0,0 +1 @@
+Test set: {x=1, y=1}, {x=0, y=0}, {x=2, y=1}, {x=2, y=3}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_21/correct.txt b/legacy/Data/ingsw/0120_21/correct.txt
new file mode 100644
index 0000000..98939be
--- /dev/null
+++ b/legacy/Data/ingsw/0120_21/correct.txt
@@ -0,0 +1 @@
+1/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_21/quest.txt b/legacy/Data/ingsw/0120_21/quest.txt
new file mode 100644
index 0000000..5e04a05
--- /dev/null
+++ b/legacy/Data/ingsw/0120_21/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0120_21/wrong1.txt
new file mode 100644
index 0000000..db2276d
--- /dev/null
+++ b/legacy/Data/ingsw/0120_21/wrong1.txt
@@ -0,0 +1 @@
+(1 - p)/p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_21/wrong2.txt b/legacy/Data/ingsw/0120_21/wrong2.txt
new file mode 100644
index 0000000..56ea6ac
--- /dev/null
+++ b/legacy/Data/ingsw/0120_21/wrong2.txt
@@ -0,0 +1 @@
+1/(p*(1 - p))
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_22/quest.txt b/legacy/Data/ingsw/0120_22/quest.txt
new file mode 100644
index 0000000..306d75a
--- /dev/null
+++ b/legacy/Data/ingsw/0120_22/quest.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_22/wrong1.txt b/legacy/Data/ingsw/0120_22/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0120_22/wrong2.txt b/legacy/Data/ingsw/0120_22/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0120_22/wrong3.txt b/legacy/Data/ingsw/0120_22/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0120_23/correct.txt b/legacy/Data/ingsw/0120_23/correct.txt
new file mode 100644
index 0000000..8b0c318
--- /dev/null
+++ b/legacy/Data/ingsw/0120_23/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_23/quest.txt b/legacy/Data/ingsw/0120_23/quest.txt
new file mode 100644
index 0000000..6f49368
--- /dev/null
+++ b/legacy/Data/ingsw/0120_23/quest.txt
@@ -0,0 +1,11 @@
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_23.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0120_23/wrong1.txt
new file mode 100644
index 0000000..a29d476
--- /dev/null
+++ b/legacy/Data/ingsw/0120_23/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_23/wrong2.txt b/legacy/Data/ingsw/0120_23/wrong2.txt
new file mode 100644
index 0000000..5464d05
--- /dev/null
+++ b/legacy/Data/ingsw/0120_23/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 30%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_24/correct.txt b/legacy/Data/ingsw/0120_24/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0120_24/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_24/quest.txt b/legacy/Data/ingsw/0120_24/quest.txt
new file mode 100644
index 0000000..702f202
--- /dev/null
+++ b/legacy/Data/ingsw/0120_24/quest.txt
@@ -0,0 +1,9 @@
+Il branch coverage di un insieme di test cases � 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 � la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_24/wrong1.txt b/legacy/Data/ingsw/0120_24/wrong1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0120_24/wrong1.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_24/wrong2.txt b/legacy/Data/ingsw/0120_24/wrong2.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0120_24/wrong2.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_25/quest.txt b/legacy/Data/ingsw/0120_25/quest.txt
new file mode 100644
index 0000000..5e6a3b5
--- /dev/null
+++ b/legacy/Data/ingsw/0120_25/quest.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_25/wrong1.txt b/legacy/Data/ingsw/0120_25/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0120_25/wrong2.txt b/legacy/Data/ingsw/0120_25/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0120_25/wrong3.txt b/legacy/Data/ingsw/0120_25/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0120_26/correct.txt b/legacy/Data/ingsw/0120_26/correct.txt
new file mode 100644
index 0000000..1c7da8c
--- /dev/null
+++ b/legacy/Data/ingsw/0120_26/correct.txt
@@ -0,0 +1 @@
+0.03
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_26/quest.txt b/legacy/Data/ingsw/0120_26/quest.txt
new file mode 100644
index 0000000..458f85c
--- /dev/null
+++ b/legacy/Data/ingsw/0120_26/quest.txt
@@ -0,0 +1,9 @@
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_26.png
+Un processo software pu� 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 probabilit� 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 probabilit� 0.1 di dover essere ripetuta (a causa di errori).
+Uno scenario � una sequenza di stati.
+Qual'e' la probabilit� dello scenario: 1, 2, 3, 4 ? In altri terminti, qual'� la probabilit� 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/legacy/Data/ingsw/0120_26/wrong1.txt
new file mode 100644
index 0000000..7eb6830
--- /dev/null
+++ b/legacy/Data/ingsw/0120_26/wrong1.txt
@@ -0,0 +1 @@
+0.27
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_26/wrong2.txt b/legacy/Data/ingsw/0120_26/wrong2.txt
new file mode 100644
index 0000000..8a346b7
--- /dev/null
+++ b/legacy/Data/ingsw/0120_26/wrong2.txt
@@ -0,0 +1 @@
+0.07
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_27/correct.txt b/legacy/Data/ingsw/0120_27/correct.txt
new file mode 100644
index 0000000..5f37ecc
--- /dev/null
+++ b/legacy/Data/ingsw/0120_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>
diff --git a/legacy/Data/ingsw/0120_27/quest.txt b/legacy/Data/ingsw/0120_27/quest.txt
new file mode 100644
index 0000000..864cc93
--- /dev/null
+++ b/legacy/Data/ingsw/0120_27/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Durante l'esecuzione del programma (cio� per tutti gli istanti di tempo positivi) la variabile x � 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/legacy/Data/ingsw/0120_27/wrong1.txt
new file mode 100644
index 0000000..8856598
--- /dev/null
+++ b/legacy/Data/ingsw/0120_27/wrong1.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 >= 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/legacy/Data/ingsw/0120_27/wrong2.txt
new file mode 100644
index 0000000..2057e11
--- /dev/null
+++ b/legacy/Data/ingsw/0120_27/wrong2.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 >= 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/legacy/Data/ingsw/0120_28/quest.txt
new file mode 100644
index 0000000..5826ea3
--- /dev/null
+++ b/legacy/Data/ingsw/0120_28/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0120_28/wrong1.txt
new file mode 100644
index 0000000..bfb3c5d
--- /dev/null
+++ b/legacy/Data/ingsw/0120_28/wrong1.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_28/wrong2.txt b/legacy/Data/ingsw/0120_28/wrong2.txt
new file mode 100644
index 0000000..c5d8c6b
--- /dev/null
+++ b/legacy/Data/ingsw/0120_28/wrong2.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_28/wrong3.txt b/legacy/Data/ingsw/0120_28/wrong3.txt
new file mode 100644
index 0000000..40db007
--- /dev/null
+++ b/legacy/Data/ingsw/0120_28/wrong3.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_29/correct.txt b/legacy/Data/ingsw/0120_29/correct.txt
new file mode 100644
index 0000000..080c618
--- /dev/null
+++ b/legacy/Data/ingsw/0120_29/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>
diff --git a/legacy/Data/ingsw/0120_29/quest.txt b/legacy/Data/ingsw/0120_29/quest.txt
new file mode 100644
index 0000000..576af1a
--- /dev/null
+++ b/legacy/Data/ingsw/0120_29/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se 10 unit� di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora � 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/legacy/Data/ingsw/0120_29/wrong1.txt
new file mode 100644
index 0000000..5ea42fe
--- /dev/null
+++ b/legacy/Data/ingsw/0120_29/wrong1.txt
@@ -0,0 +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>
diff --git a/legacy/Data/ingsw/0120_29/wrong2.txt b/legacy/Data/ingsw/0120_29/wrong2.txt
new file mode 100644
index 0000000..a55c0a4
--- /dev/null
+++ b/legacy/Data/ingsw/0120_29/wrong2.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>
diff --git a/legacy/Data/ingsw/0120_3/correct.txt b/legacy/Data/ingsw/0120_3/correct.txt
new file mode 100644
index 0000000..e940faa
--- /dev/null
+++ b/legacy/Data/ingsw/0120_3/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/ingsw/0120_3/quest.txt b/legacy/Data/ingsw/0120_3/quest.txt
new file mode 100644
index 0000000..2758118
--- /dev/null
+++ b/legacy/Data/ingsw/0120_3/quest.txt
@@ -0,0 +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 (cio� non � 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 � maggiore di 3 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_3/wrong1.txt b/legacy/Data/ingsw/0120_3/wrong1.txt
new file mode 100644
index 0000000..ad32d88
--- /dev/null
+++ b/legacy/Data/ingsw/0120_3/wrong1.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/ingsw/0120_3/wrong2.txt b/legacy/Data/ingsw/0120_3/wrong2.txt
new file mode 100644
index 0000000..642ec6b
--- /dev/null
+++ b/legacy/Data/ingsw/0120_3/wrong2.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/ingsw/0120_30/correct.txt b/legacy/Data/ingsw/0120_30/correct.txt
new file mode 100644
index 0000000..a40ea7d
--- /dev/null
+++ b/legacy/Data/ingsw/0120_30/correct.txt
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 5, c = 0), (a=50, b = 3, c = 0).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_30/quest.txt b/legacy/Data/ingsw/0120_30/quest.txt
new file mode 100644
index 0000000..dbd72c0
--- /dev/null
+++ b/legacy/Data/ingsw/0120_30/quest.txt
@@ -0,0 +1,22 @@
+Una Condition � una proposizione booleana, cio� una espressione con valore booleano che non pu� essere decomposta 
+in espressioni boolean pi� semplici. Ad esempio, (x + y <= 3) � una condition.
+
+Una Decision � una espressione booleana composta da conditions e zero o pi� 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 � 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 � true ed un test in T in cui c � false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d � true ed un test in T in cui d � 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/legacy/Data/ingsw/0120_30/wrong1.txt
new file mode 100644
index 0000000..5b77112
--- /dev/null
+++ b/legacy/Data/ingsw/0120_30/wrong1.txt
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 5, c = 0), (a=50, b = 0, c = 5).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_30/wrong2.txt b/legacy/Data/ingsw/0120_30/wrong2.txt
new file mode 100644
index 0000000..abe0eaa
--- /dev/null
+++ b/legacy/Data/ingsw/0120_30/wrong2.txt
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 4, c = 0), (a=200, b = 4, c = 0)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_31/correct.txt b/legacy/Data/ingsw/0120_31/correct.txt
new file mode 100644
index 0000000..3bb4f54
--- /dev/null
+++ b/legacy/Data/ingsw/0120_31/correct.txt
@@ -0,0 +1 @@
+State coverage: 25%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_31/quest.txt b/legacy/Data/ingsw/0120_31/quest.txt
new file mode 100644
index 0000000..6c8f77e
--- /dev/null
+++ b/legacy/Data/ingsw/0120_31/quest.txt
@@ -0,0 +1,11 @@
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_31.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0120_31/wrong1.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0120_31/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_31/wrong2.txt b/legacy/Data/ingsw/0120_31/wrong2.txt
new file mode 100644
index 0000000..a8aead7
--- /dev/null
+++ b/legacy/Data/ingsw/0120_31/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_32/correct.txt b/legacy/Data/ingsw/0120_32/correct.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0120_32/correct.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_32/quest.txt b/legacy/Data/ingsw/0120_32/quest.txt
new file mode 100644
index 0000000..dcec721
--- /dev/null
+++ b/legacy/Data/ingsw/0120_32/quest.txt
@@ -0,0 +1,8 @@
+Il partition coverage di un insieme di test cases � la percentuale di elementi della partition inclusi nei test cases. La partition � 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 � la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_32/wrong1.txt b/legacy/Data/ingsw/0120_32/wrong1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0120_32/wrong1.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_32/wrong2.txt b/legacy/Data/ingsw/0120_32/wrong2.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0120_32/wrong2.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_33/correct.txt b/legacy/Data/ingsw/0120_33/correct.txt
new file mode 100644
index 0000000..a7029bc
--- /dev/null
+++ b/legacy/Data/ingsw/0120_33/correct.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [1, 4] oppure nell'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_33/quest.txt b/legacy/Data/ingsw/0120_33/quest.txt
new file mode 100644
index 0000000..e5fbc81
--- /dev/null
+++ b/legacy/Data/ingsw/0120_33/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/0120_33/wrong1.txt b/legacy/Data/ingsw/0120_33/wrong1.txt
new file mode 100644
index 0000000..a82929b
--- /dev/null
+++ b/legacy/Data/ingsw/0120_33/wrong1.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [1, 4] e fuori dall'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_33/wrong2.txt b/legacy/Data/ingsw/0120_33/wrong2.txt
new file mode 100644
index 0000000..710b111
--- /dev/null
+++ b/legacy/Data/ingsw/0120_33/wrong2.txt
@@ -0,0 +1 @@
+La variabile x è fuori dall'intervallo [1, 4] e fuori dall'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_34/quest.txt b/legacy/Data/ingsw/0120_34/quest.txt
new file mode 100644
index 0000000..29d0647
--- /dev/null
+++ b/legacy/Data/ingsw/0120_34/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0120_34/wrong1.txt
new file mode 100644
index 0000000..160702f
--- /dev/null
+++ b/legacy/Data/ingsw/0120_34/wrong1.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_34/wrong2.txt b/legacy/Data/ingsw/0120_34/wrong2.txt
new file mode 100644
index 0000000..3c05a37
--- /dev/null
+++ b/legacy/Data/ingsw/0120_34/wrong2.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_34/wrong3.txt b/legacy/Data/ingsw/0120_34/wrong3.txt
new file mode 100644
index 0000000..bdfb644
--- /dev/null
+++ b/legacy/Data/ingsw/0120_34/wrong3.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_35/quest.txt b/legacy/Data/ingsw/0120_35/quest.txt
new file mode 100644
index 0000000..99379e6
--- /dev/null
+++ b/legacy/Data/ingsw/0120_35/quest.txt
@@ -0,0 +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 (cio� non � 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/legacy/Data/ingsw/0120_35/wrong1.txt
new file mode 100644
index 0000000..cbf1814
--- /dev/null
+++ b/legacy/Data/ingsw/0120_35/wrong1.txt
@@ -0,0 +1,6 @@
+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/legacy/Data/ingsw/0120_35/wrong2.txt
new file mode 100644
index 0000000..03b9f52
--- /dev/null
+++ b/legacy/Data/ingsw/0120_35/wrong2.txt
@@ -0,0 +1,6 @@
+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/legacy/Data/ingsw/0120_35/wrong3.txt
new file mode 100644
index 0000000..6fcb8b5
--- /dev/null
+++ b/legacy/Data/ingsw/0120_35/wrong3.txt
@@ -0,0 +1,6 @@
+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/legacy/Data/ingsw/0120_36/correct.txt
new file mode 100644
index 0000000..b110af1
--- /dev/null
+++ b/legacy/Data/ingsw/0120_36/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_36/quest.txt b/legacy/Data/ingsw/0120_36/quest.txt
new file mode 100644
index 0000000..6f256c3
--- /dev/null
+++ b/legacy/Data/ingsw/0120_36/quest.txt
@@ -0,0 +1,12 @@
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_36.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0120_36/wrong1.txt
new file mode 100644
index 0000000..5464d05
--- /dev/null
+++ b/legacy/Data/ingsw/0120_36/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 30%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_36/wrong2.txt b/legacy/Data/ingsw/0120_36/wrong2.txt
new file mode 100644
index 0000000..cf27703
--- /dev/null
+++ b/legacy/Data/ingsw/0120_36/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 70%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_37/correct.txt b/legacy/Data/ingsw/0120_37/correct.txt
new file mode 100644
index 0000000..bc5692f
--- /dev/null
+++ b/legacy/Data/ingsw/0120_37/correct.txt
@@ -0,0 +1 @@
+State coverage: 87%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_37/quest.txt b/legacy/Data/ingsw/0120_37/quest.txt
new file mode 100644
index 0000000..cdbc688
--- /dev/null
+++ b/legacy/Data/ingsw/0120_37/quest.txt
@@ -0,0 +1,13 @@
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_37.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0120_37/wrong1.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0120_37/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_37/wrong2.txt b/legacy/Data/ingsw/0120_37/wrong2.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/0120_37/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_38/correct.txt b/legacy/Data/ingsw/0120_38/correct.txt
new file mode 100644
index 0000000..25ac15c
--- /dev/null
+++ b/legacy/Data/ingsw/0120_38/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>
diff --git a/legacy/Data/ingsw/0120_38/quest.txt b/legacy/Data/ingsw/0120_38/quest.txt
new file mode 100644
index 0000000..b420aaf
--- /dev/null
+++ b/legacy/Data/ingsw/0120_38/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Dopo 20 unit� di tempo dall'inizio dell'esecuzione la variabile x � 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/legacy/Data/ingsw/0120_38/wrong1.txt
new file mode 100644
index 0000000..157567e
--- /dev/null
+++ b/legacy/Data/ingsw/0120_38/wrong1.txt
@@ -0,0 +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>
diff --git a/legacy/Data/ingsw/0120_38/wrong2.txt b/legacy/Data/ingsw/0120_38/wrong2.txt
new file mode 100644
index 0000000..d021c3b
--- /dev/null
+++ b/legacy/Data/ingsw/0120_38/wrong2.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 >= 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/legacy/Data/ingsw/0120_39/quest.txt
new file mode 100644
index 0000000..4777dbc
--- /dev/null
+++ b/legacy/Data/ingsw/0120_39/quest.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_39/wrong1.txt b/legacy/Data/ingsw/0120_39/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0120_39/wrong2.txt b/legacy/Data/ingsw/0120_39/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0120_39/wrong3.txt b/legacy/Data/ingsw/0120_39/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0120_4/correct.txt b/legacy/Data/ingsw/0120_4/correct.txt
new file mode 100644
index 0000000..ddb0d65
--- /dev/null
+++ b/legacy/Data/ingsw/0120_4/correct.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [0, 5].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_4/quest.txt b/legacy/Data/ingsw/0120_4/quest.txt
new file mode 100644
index 0000000..d1cf8cb
--- /dev/null
+++ b/legacy/Data/ingsw/0120_4/quest.txt
@@ -0,0 +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>
+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/legacy/Data/ingsw/0120_4/wrong1.txt
new file mode 100644
index 0000000..3e05ae7
--- /dev/null
+++ b/legacy/Data/ingsw/0120_4/wrong1.txt
@@ -0,0 +1 @@
+La variabile x è fuori dall'intervallo [0, 5].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_4/wrong2.txt b/legacy/Data/ingsw/0120_4/wrong2.txt
new file mode 100644
index 0000000..7c7a691
--- /dev/null
+++ b/legacy/Data/ingsw/0120_4/wrong2.txt
@@ -0,0 +1 @@
+La variable x è minore di 0.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_40/correct.txt b/legacy/Data/ingsw/0120_40/correct.txt
new file mode 100644
index 0000000..31a01d5
--- /dev/null
+++ b/legacy/Data/ingsw/0120_40/correct.txt
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=0, y=0}, {x=15, y=0}, {x=9, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_40/quest.txt b/legacy/Data/ingsw/0120_40/quest.txt
new file mode 100644
index 0000000..74a4d32
--- /dev/null
+++ b/legacy/Data/ingsw/0120_40/quest.txt
@@ -0,0 +1,8 @@
+Il branch coverage di un insieme di test cases � 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/legacy/Data/ingsw/0120_40/wrong1.txt
new file mode 100644
index 0000000..549dba8
--- /dev/null
+++ b/legacy/Data/ingsw/0120_40/wrong1.txt
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=0, y=0}, {x=15, y=0}, {x=10, y=3}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_40/wrong2.txt b/legacy/Data/ingsw/0120_40/wrong2.txt
new file mode 100644
index 0000000..0c564f7
--- /dev/null
+++ b/legacy/Data/ingsw/0120_40/wrong2.txt
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=2, y=1}, {x=15, y=0}, {x=9, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_41/correct.txt b/legacy/Data/ingsw/0120_41/correct.txt
new file mode 100644
index 0000000..7a6c6b9
--- /dev/null
+++ b/legacy/Data/ingsw/0120_41/correct.txt
@@ -0,0 +1 @@
+300000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_41/quest.txt b/legacy/Data/ingsw/0120_41/quest.txt
new file mode 100644
index 0000000..47201e7
--- /dev/null
+++ b/legacy/Data/ingsw/0120_41/quest.txt
@@ -0,0 +1,4 @@
+Il rischio R pu� essere calcolato come R = P*C, dove P � la probabilit� dell'evento avverso (software failure nel nostro contesto) e C � il costo dell'occorrenza dell'evento avverso. 
+Assumiamo che la probabilit� P sia legata al costo di sviluppo S dalla formula 
+P = 10^{(-b*S)}    (cio� 10 elevato alla (-b*S))
+dove b � una opportuna costante note da dati storici aziendali. Si assuma che b = 0.0001,  C = 1000000, ed il rischio ammesso � 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/legacy/Data/ingsw/0120_41/wrong1.txt
new file mode 100644
index 0000000..997967b
--- /dev/null
+++ b/legacy/Data/ingsw/0120_41/wrong1.txt
@@ -0,0 +1 @@
+700000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_41/wrong2.txt b/legacy/Data/ingsw/0120_41/wrong2.txt
new file mode 100644
index 0000000..2df501e
--- /dev/null
+++ b/legacy/Data/ingsw/0120_41/wrong2.txt
@@ -0,0 +1 @@
+500000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_42/correct.txt b/legacy/Data/ingsw/0120_42/correct.txt
new file mode 100644
index 0000000..f6a4b07
--- /dev/null
+++ b/legacy/Data/ingsw/0120_42/correct.txt
@@ -0,0 +1 @@
+State coverage: 90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_42/quest.txt b/legacy/Data/ingsw/0120_42/quest.txt
new file mode 100644
index 0000000..4650bbb
--- /dev/null
+++ b/legacy/Data/ingsw/0120_42/quest.txt
@@ -0,0 +1,11 @@
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_42.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0120_42/wrong1.txt
new file mode 100644
index 0000000..1c07658
--- /dev/null
+++ b/legacy/Data/ingsw/0120_42/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 70%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_42/wrong2.txt b/legacy/Data/ingsw/0120_42/wrong2.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0120_42/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_43/quest.txt b/legacy/Data/ingsw/0120_43/quest.txt
new file mode 100644
index 0000000..8636c5a
--- /dev/null
+++ b/legacy/Data/ingsw/0120_43/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0120_43/wrong1.txt
new file mode 100644
index 0000000..0ca6415
--- /dev/null
+++ b/legacy/Data/ingsw/0120_43/wrong1.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_43/wrong2.txt b/legacy/Data/ingsw/0120_43/wrong2.txt
new file mode 100644
index 0000000..a5879aa
--- /dev/null
+++ b/legacy/Data/ingsw/0120_43/wrong2.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_43/wrong3.txt b/legacy/Data/ingsw/0120_43/wrong3.txt
new file mode 100644
index 0000000..b4f56fb
--- /dev/null
+++ b/legacy/Data/ingsw/0120_43/wrong3.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_44/correct.txt b/legacy/Data/ingsw/0120_44/correct.txt
new file mode 100644
index 0000000..232aedf
--- /dev/null
+++ b/legacy/Data/ingsw/0120_44/correct.txt
@@ -0,0 +1 @@
+(a = 6, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 0).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_44/quest.txt b/legacy/Data/ingsw/0120_44/quest.txt
new file mode 100644
index 0000000..e44e320
--- /dev/null
+++ b/legacy/Data/ingsw/0120_44/quest.txt
@@ -0,0 +1,21 @@
+Una Condition � una proposizione booleana, cio� una espressione con valore booleano che non pu� essere decomposta 
+in espressioni boolean pi� semplici. Ad esempio, (x + y <= 3) � una condition.
+
+Una Decision � una espressione booleana composta da conditions e zero o pi� 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 � 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 � true ed un test in T in cui c � false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d � true ed un test in T in cui d � 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/legacy/Data/ingsw/0120_44/wrong1.txt
new file mode 100644
index 0000000..5d5c9a4
--- /dev/null
+++ b/legacy/Data/ingsw/0120_44/wrong1.txt
@@ -0,0 +1 @@
+(a = 6, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 2).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_44/wrong2.txt b/legacy/Data/ingsw/0120_44/wrong2.txt
new file mode 100644
index 0000000..2b6c292
--- /dev/null
+++ b/legacy/Data/ingsw/0120_44/wrong2.txt
@@ -0,0 +1 @@
+(a = 5, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 0).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_45/quest.txt b/legacy/Data/ingsw/0120_45/quest.txt
new file mode 100644
index 0000000..4818a62
--- /dev/null
+++ b/legacy/Data/ingsw/0120_45/quest.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_45/wrong1.txt b/legacy/Data/ingsw/0120_45/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0120_45/wrong2.txt b/legacy/Data/ingsw/0120_45/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0120_45/wrong3.txt b/legacy/Data/ingsw/0120_45/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0120_46/correct.txt b/legacy/Data/ingsw/0120_46/correct.txt
new file mode 100644
index 0000000..3fb437d
--- /dev/null
+++ b/legacy/Data/ingsw/0120_46/correct.txt
@@ -0,0 +1 @@
+0.56
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_46/quest.txt b/legacy/Data/ingsw/0120_46/quest.txt
new file mode 100644
index 0000000..6205846
--- /dev/null
+++ b/legacy/Data/ingsw/0120_46/quest.txt
@@ -0,0 +1,9 @@
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_46.png
+Un processo software pu� 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 probabilit� 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 probabilit� 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario � una sequenza di stati.
+Qual'e' la probabilit� dello scenario: 1, 3 ? In altri terminti, qual'� la probabilit� che non sia necessario ripetere nessuna fase?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_46/wrong1.txt b/legacy/Data/ingsw/0120_46/wrong1.txt
new file mode 100644
index 0000000..c64601b
--- /dev/null
+++ b/legacy/Data/ingsw/0120_46/wrong1.txt
@@ -0,0 +1 @@
+0.14
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_46/wrong2.txt b/legacy/Data/ingsw/0120_46/wrong2.txt
new file mode 100644
index 0000000..fc54e00
--- /dev/null
+++ b/legacy/Data/ingsw/0120_46/wrong2.txt
@@ -0,0 +1 @@
+0.24
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_47/correct.txt b/legacy/Data/ingsw/0120_47/correct.txt
new file mode 100644
index 0000000..eb23d05
--- /dev/null
+++ b/legacy/Data/ingsw/0120_47/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che non ci siano requisiti in conflitto con altri requisiti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_47/quest.txt b/legacy/Data/ingsw/0120_47/quest.txt
new file mode 100644
index 0000000..7710e8f
--- /dev/null
+++ b/legacy/Data/ingsw/0120_47/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "check di consistenza" che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_47/wrong1.txt b/legacy/Data/ingsw/0120_47/wrong1.txt
new file mode 100644
index 0000000..9e12d11
--- /dev/null
+++ b/legacy/Data/ingsw/0120_47/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che per ogni requisito esista un insieme di test che lo possa verificare.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_47/wrong2.txt b/legacy/Data/ingsw/0120_47/wrong2.txt
new file mode 100644
index 0000000..32c628c
--- /dev/null
+++ b/legacy/Data/ingsw/0120_47/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisiti funzionali descrivano tutte le funzionalità del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_48/correct.txt b/legacy/Data/ingsw/0120_48/correct.txt
new file mode 100644
index 0000000..519c7fd
--- /dev/null
+++ b/legacy/Data/ingsw/0120_48/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));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0120_48/quest.txt b/legacy/Data/ingsw/0120_48/quest.txt
new file mode 100644
index 0000000..22c683f
--- /dev/null
+++ b/legacy/Data/ingsw/0120_48/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Durante l'esecuzione del programma (cio� per tutti gli istanti di tempo positivi) la variabile x � 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/legacy/Data/ingsw/0120_48/wrong1.txt
new file mode 100644
index 0000000..5229f7e
--- /dev/null
+++ b/legacy/Data/ingsw/0120_48/wrong1.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 > 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/legacy/Data/ingsw/0120_48/wrong2.txt
new file mode 100644
index 0000000..c2e617d
--- /dev/null
+++ b/legacy/Data/ingsw/0120_48/wrong2.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 > 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/legacy/Data/ingsw/0120_49/correct.txt
new file mode 100644
index 0000000..2a2ecea
--- /dev/null
+++ b/legacy/Data/ingsw/0120_49/correct.txt
@@ -0,0 +1 @@
+time(0)/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_49/quest.txt b/legacy/Data/ingsw/0120_49/quest.txt
new file mode 100644
index 0000000..2d6940a
--- /dev/null
+++ b/legacy/Data/ingsw/0120_49/quest.txt
@@ -0,0 +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 � in (0, 1).  Il tempo necessario per completare la fase x � time(x). La fase 0 � la fase di design, che ha probabilit� 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 � 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), .... � 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)  (poich� 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/legacy/Data/ingsw/0120_49/wrong1.txt
new file mode 100644
index 0000000..d68fd15
--- /dev/null
+++ b/legacy/Data/ingsw/0120_49/wrong1.txt
@@ -0,0 +1 @@
+time(0)*(1 - p)/p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_49/wrong2.txt b/legacy/Data/ingsw/0120_49/wrong2.txt
new file mode 100644
index 0000000..9927a93
--- /dev/null
+++ b/legacy/Data/ingsw/0120_49/wrong2.txt
@@ -0,0 +1 @@
+time(0)/(p*(1 - p))
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_5/quest.txt b/legacy/Data/ingsw/0120_5/quest.txt
new file mode 100644
index 0000000..3e68301
--- /dev/null
+++ b/legacy/Data/ingsw/0120_5/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0120_5/wrong1.txt
new file mode 100644
index 0000000..6c46c45
--- /dev/null
+++ b/legacy/Data/ingsw/0120_5/wrong1.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_5/wrong2.txt b/legacy/Data/ingsw/0120_5/wrong2.txt
new file mode 100644
index 0000000..39e7bfc
--- /dev/null
+++ b/legacy/Data/ingsw/0120_5/wrong2.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_5/wrong3.txt b/legacy/Data/ingsw/0120_5/wrong3.txt
new file mode 100644
index 0000000..93e29a3
--- /dev/null
+++ b/legacy/Data/ingsw/0120_5/wrong3.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_6/correct.txt b/legacy/Data/ingsw/0120_6/correct.txt
new file mode 100644
index 0000000..7c149d8
--- /dev/null
+++ b/legacy/Data/ingsw/0120_6/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisisti descrivano tutte le funzionalità e vincoli (e.g., security, performance) del sistema desiderato dal customer.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_6/quest.txt b/legacy/Data/ingsw/0120_6/quest.txt
new file mode 100644
index 0000000..8bba4b8
--- /dev/null
+++ b/legacy/Data/ingsw/0120_6/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "check di completezza" che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_6/wrong1.txt b/legacy/Data/ingsw/0120_6/wrong1.txt
new file mode 100644
index 0000000..32c628c
--- /dev/null
+++ b/legacy/Data/ingsw/0120_6/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisiti funzionali descrivano tutte le funzionalità del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_6/wrong2.txt b/legacy/Data/ingsw/0120_6/wrong2.txt
new file mode 100644
index 0000000..3461684
--- /dev/null
+++ b/legacy/Data/ingsw/0120_6/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che per ogni requisito sia stato implementato nel sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_7/correct.txt b/legacy/Data/ingsw/0120_7/correct.txt
new file mode 100644
index 0000000..b559d4a
--- /dev/null
+++ b/legacy/Data/ingsw/0120_7/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>
diff --git a/legacy/Data/ingsw/0120_7/quest.txt b/legacy/Data/ingsw/0120_7/quest.txt
new file mode 100644
index 0000000..031c331
--- /dev/null
+++ b/legacy/Data/ingsw/0120_7/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 40 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se 10 unit� di tempo nel passato x era maggiore di 1 allora ora y � 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/legacy/Data/ingsw/0120_7/wrong1.txt
new file mode 100644
index 0000000..4b8db59
--- /dev/null
+++ b/legacy/Data/ingsw/0120_7/wrong1.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>
diff --git a/legacy/Data/ingsw/0120_7/wrong2.txt b/legacy/Data/ingsw/0120_7/wrong2.txt
new file mode 100644
index 0000000..05ce544
--- /dev/null
+++ b/legacy/Data/ingsw/0120_7/wrong2.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>
diff --git a/legacy/Data/ingsw/0120_8/correct.txt b/legacy/Data/ingsw/0120_8/correct.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/0120_8/correct.txt
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_8/quest.txt b/legacy/Data/ingsw/0120_8/quest.txt
new file mode 100644
index 0000000..2809138
--- /dev/null
+++ b/legacy/Data/ingsw/0120_8/quest.txt
@@ -0,0 +1,11 @@
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_8.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0120_8/wrong1.txt
new file mode 100644
index 0000000..1a8a508
--- /dev/null
+++ b/legacy/Data/ingsw/0120_8/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_8/wrong2.txt b/legacy/Data/ingsw/0120_8/wrong2.txt
new file mode 100644
index 0000000..a8aead7
--- /dev/null
+++ b/legacy/Data/ingsw/0120_8/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_9/correct.txt b/legacy/Data/ingsw/0120_9/correct.txt
new file mode 100644
index 0000000..ce9968f
--- /dev/null
+++ b/legacy/Data/ingsw/0120_9/correct.txt
@@ -0,0 +1 @@
+0.28
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_9/quest.txt b/legacy/Data/ingsw/0120_9/quest.txt
new file mode 100644
index 0000000..4962ecf
--- /dev/null
+++ b/legacy/Data/ingsw/0120_9/quest.txt
@@ -0,0 +1,9 @@
+img=https://unspectacular-subdi.000webhostapp.com/0120_domanda_9.png
+Un processo software pu� 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 probabilit� 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 probabilit� 0.3 di dover essere ripetuta (a causa di errori).
+Uno scenario � una sequenza di stati.
+Qual'e' la probabilit� dello scenario: 1, 2, 3? In altri terminti, qual'� la probabilit� 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/legacy/Data/ingsw/0120_9/wrong1.txt
new file mode 100644
index 0000000..e8f9017
--- /dev/null
+++ b/legacy/Data/ingsw/0120_9/wrong1.txt
@@ -0,0 +1 @@
+0.42
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0120_9/wrong2.txt b/legacy/Data/ingsw/0120_9/wrong2.txt
new file mode 100644
index 0000000..f2bb2d0
--- /dev/null
+++ b/legacy/Data/ingsw/0120_9/wrong2.txt
@@ -0,0 +1 @@
+0.12
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0121_34/correct.txt b/legacy/Data/ingsw/0121_34/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0121_34/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0121_34/quest.txt b/legacy/Data/ingsw/0121_34/quest.txt
new file mode 100644
index 0000000..6dbca93
--- /dev/null
+++ b/legacy/Data/ingsw/0121_34/quest.txt
@@ -0,0 +1,53 @@
+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 il seguente programma C:
+
+-----------
+
+#include <stdio.h>
+
+#include <stdlib.h>
+
+#include <assert.h>
+
+#define N  4    /* number of test cases */
+
+
+int f(int x1, int x2)
+
+{
+  if (x1 + x2 <= 2)
+
+  return (1);
+
+  else return (2);		    
+
+}
+
+
+int main() {    int  i, y;   int x1[N], x2[N];
+
+  // define test cases
+
+    x1[0] = 3; x2[0] = -2;    x1[1] = 4; x2[1] = -3;    x1[2] = 7; x2[2] = -4;    x1[3] = 8; x2[3] = -5; 
+
+    // testing
+
+  for (i = 0; i < N; i++)  {
+
+      y = f(x1[i], x2[i]);       // function under testing
+
+      assert(y ==(x1[i], x2[i] <= 2) ? 1 : 2);   // oracle
+
+    }
+
+   printf("All %d test cases passed\n", N);
+
+    return (0);   
+
+}
+-----------
+
+Il programma main() sopra realizza il nostro testing per la funzione f1(). I test cases sono i valori in x1[i] ed x2[i].
+
+Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0121_34/wrong1.txt b/legacy/Data/ingsw/0121_34/wrong1.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0121_34/wrong1.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0121_34/wrong2.txt b/legacy/Data/ingsw/0121_34/wrong2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0121_34/wrong2.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_0/correct.txt b/legacy/Data/ingsw/0210_0/correct.txt
new file mode 100644
index 0000000..a40ea7d
--- /dev/null
+++ b/legacy/Data/ingsw/0210_0/correct.txt
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 5, c = 0), (a=50, b = 3, c = 0).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_0/quest.txt b/legacy/Data/ingsw/0210_0/quest.txt
new file mode 100644
index 0000000..2d895ca
--- /dev/null
+++ b/legacy/Data/ingsw/0210_0/quest.txt
@@ -0,0 +1,22 @@
+Una Condition � una proposizione booleana, cio� una espressione con valore booleano che non pu� essere decomposta 
+in espressioni boolean pi� semplici. Ad esempio, (x + y <= 3) � una condition.
+
+Una Decision � una espressione booleana composta da conditions e zero o pi� 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 � 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 � true ed un test in T in cui c � false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d � true ed un test in T in cui d � 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/legacy/Data/ingsw/0210_0/wrong1.txt
new file mode 100644
index 0000000..abe0eaa
--- /dev/null
+++ b/legacy/Data/ingsw/0210_0/wrong1.txt
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 4, c = 0), (a=200, b = 4, c = 0)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_0/wrong2.txt b/legacy/Data/ingsw/0210_0/wrong2.txt
new file mode 100644
index 0000000..5b77112
--- /dev/null
+++ b/legacy/Data/ingsw/0210_0/wrong2.txt
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 5, c = 0), (a=50, b = 0, c = 5).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_1/quest.txt b/legacy/Data/ingsw/0210_1/quest.txt
new file mode 100644
index 0000000..89110fc
--- /dev/null
+++ b/legacy/Data/ingsw/0210_1/quest.txt
@@ -0,0 +1,5 @@
+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/legacy/Data/ingsw/0210_1/wrong1.txt
new file mode 100644
index 0000000..0487745
--- /dev/null
+++ b/legacy/Data/ingsw/0210_1/wrong1.txt
@@ -0,0 +1,6 @@
+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/legacy/Data/ingsw/0210_1/wrong2.txt
new file mode 100644
index 0000000..6b9f4b0
--- /dev/null
+++ b/legacy/Data/ingsw/0210_1/wrong2.txt
@@ -0,0 +1,3 @@
+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/legacy/Data/ingsw/0210_1/wrong3.txt
new file mode 100644
index 0000000..bf32c35
--- /dev/null
+++ b/legacy/Data/ingsw/0210_1/wrong3.txt
@@ -0,0 +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);
+end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_10/correct.txt b/legacy/Data/ingsw/0210_10/correct.txt
new file mode 100644
index 0000000..ddb0d65
--- /dev/null
+++ b/legacy/Data/ingsw/0210_10/correct.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [0, 5].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_10/quest.txt b/legacy/Data/ingsw/0210_10/quest.txt
new file mode 100644
index 0000000..d1cf8cb
--- /dev/null
+++ b/legacy/Data/ingsw/0210_10/quest.txt
@@ -0,0 +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>
+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/legacy/Data/ingsw/0210_10/wrong1.txt
new file mode 100644
index 0000000..7c7a691
--- /dev/null
+++ b/legacy/Data/ingsw/0210_10/wrong1.txt
@@ -0,0 +1 @@
+La variable x è minore di 0.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_10/wrong2.txt b/legacy/Data/ingsw/0210_10/wrong2.txt
new file mode 100644
index 0000000..3e05ae7
--- /dev/null
+++ b/legacy/Data/ingsw/0210_10/wrong2.txt
@@ -0,0 +1 @@
+La variabile x è fuori dall'intervallo [0, 5].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_11/quest.txt b/legacy/Data/ingsw/0210_11/quest.txt
new file mode 100644
index 0000000..57dc789
--- /dev/null
+++ b/legacy/Data/ingsw/0210_11/quest.txt
@@ -0,0 +1,4 @@
+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/legacy/Data/ingsw/0210_11/wrong1.txt
new file mode 100644
index 0000000..157d205
--- /dev/null
+++ b/legacy/Data/ingsw/0210_11/wrong1.txt
@@ -0,0 +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);
+end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_11/wrong2.txt b/legacy/Data/ingsw/0210_11/wrong2.txt
new file mode 100644
index 0000000..04886bb
--- /dev/null
+++ b/legacy/Data/ingsw/0210_11/wrong2.txt
@@ -0,0 +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);
+end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_11/wrong3.txt b/legacy/Data/ingsw/0210_11/wrong3.txt
new file mode 100644
index 0000000..903ba76
--- /dev/null
+++ b/legacy/Data/ingsw/0210_11/wrong3.txt
@@ -0,0 +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);
+end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_12/quest.txt b/legacy/Data/ingsw/0210_12/quest.txt
new file mode 100644
index 0000000..86ee3d4
--- /dev/null
+++ b/legacy/Data/ingsw/0210_12/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0210_12/wrong1.txt
new file mode 100644
index 0000000..c7f67fe
--- /dev/null
+++ b/legacy/Data/ingsw/0210_12/wrong1.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_12/wrong2.txt b/legacy/Data/ingsw/0210_12/wrong2.txt
new file mode 100644
index 0000000..b84dfd6
--- /dev/null
+++ b/legacy/Data/ingsw/0210_12/wrong2.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_12/wrong3.txt b/legacy/Data/ingsw/0210_12/wrong3.txt
new file mode 100644
index 0000000..162b572
--- /dev/null
+++ b/legacy/Data/ingsw/0210_12/wrong3.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_13/correct.txt b/legacy/Data/ingsw/0210_13/correct.txt
new file mode 100644
index 0000000..25ac15c
--- /dev/null
+++ b/legacy/Data/ingsw/0210_13/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>
diff --git a/legacy/Data/ingsw/0210_13/quest.txt b/legacy/Data/ingsw/0210_13/quest.txt
new file mode 100644
index 0000000..b420aaf
--- /dev/null
+++ b/legacy/Data/ingsw/0210_13/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Dopo 20 unit� di tempo dall'inizio dell'esecuzione la variabile x � 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/legacy/Data/ingsw/0210_13/wrong1.txt
new file mode 100644
index 0000000..d021c3b
--- /dev/null
+++ b/legacy/Data/ingsw/0210_13/wrong1.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 >= 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/legacy/Data/ingsw/0210_13/wrong2.txt
new file mode 100644
index 0000000..157567e
--- /dev/null
+++ b/legacy/Data/ingsw/0210_13/wrong2.txt
@@ -0,0 +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>
diff --git a/legacy/Data/ingsw/0210_14/correct.txt b/legacy/Data/ingsw/0210_14/correct.txt
new file mode 100644
index 0000000..e74b1fc
--- /dev/null
+++ b/legacy/Data/ingsw/0210_14/correct.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x > y) then (z == x) else (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_14/quest.txt b/legacy/Data/ingsw/0210_14/quest.txt
new file mode 100644
index 0000000..c1cd6d0
--- /dev/null
+++ b/legacy/Data/ingsw/0210_14/quest.txt
@@ -0,0 +1,9 @@
+Un test oracle per un programma P � 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) � 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) � un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_14/wrong1.txt b/legacy/Data/ingsw/0210_14/wrong1.txt
new file mode 100644
index 0000000..d63544a
--- /dev/null
+++ b/legacy/Data/ingsw/0210_14/wrong1.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x > y) then (z == x + 1) else (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_14/wrong2.txt b/legacy/Data/ingsw/0210_14/wrong2.txt
new file mode 100644
index 0000000..1753a91
--- /dev/null
+++ b/legacy/Data/ingsw/0210_14/wrong2.txt
@@ -0,0 +1 @@
+F(x, y, z) = (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_15/correct.txt b/legacy/Data/ingsw/0210_15/correct.txt
new file mode 100644
index 0000000..519c7fd
--- /dev/null
+++ b/legacy/Data/ingsw/0210_15/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));
+algorithm
+when edge(z) then
+y := true;
+end when;
+end Monitor;
+</pre>
diff --git a/legacy/Data/ingsw/0210_15/quest.txt b/legacy/Data/ingsw/0210_15/quest.txt
new file mode 100644
index 0000000..22c683f
--- /dev/null
+++ b/legacy/Data/ingsw/0210_15/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Durante l'esecuzione del programma (cio� per tutti gli istanti di tempo positivi) la variabile x � 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/legacy/Data/ingsw/0210_15/wrong1.txt
new file mode 100644
index 0000000..5229f7e
--- /dev/null
+++ b/legacy/Data/ingsw/0210_15/wrong1.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 > 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/legacy/Data/ingsw/0210_15/wrong2.txt
new file mode 100644
index 0000000..2029293
--- /dev/null
+++ b/legacy/Data/ingsw/0210_15/wrong2.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 > 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/0210_16/correct.txt b/legacy/Data/ingsw/0210_16/correct.txt
new file mode 100644
index 0000000..293ebbc
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0210_16/quest.txt b/legacy/Data/ingsw/0210_16/quest.txt
new file mode 100644
index 0000000..5922b9f
--- /dev/null
+++ b/legacy/Data/ingsw/0210_16/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 10 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�: se la variabile x � nell'intervallo [10, 20]  allora la variabile y � 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/legacy/Data/ingsw/0210_16/wrong1.txt
new file mode 100644
index 0000000..d7890b2
--- /dev/null
+++ b/legacy/Data/ingsw/0210_16/wrong1.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) 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/legacy/Data/ingsw/0210_16/wrong2.txt
new file mode 100644
index 0000000..d50b268
--- /dev/null
+++ b/legacy/Data/ingsw/0210_16/wrong2.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) 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/legacy/Data/ingsw/0210_17/correct.txt
new file mode 100644
index 0000000..2ca9276
--- /dev/null
+++ b/legacy/Data/ingsw/0210_17/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 35%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_17/quest.txt b/legacy/Data/ingsw/0210_17/quest.txt
new file mode 100644
index 0000000..5fa40ee
--- /dev/null
+++ b/legacy/Data/ingsw/0210_17/quest.txt
@@ -0,0 +1,13 @@
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_17.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0210_17/wrong1.txt
new file mode 100644
index 0000000..8b0c318
--- /dev/null
+++ b/legacy/Data/ingsw/0210_17/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_17/wrong2.txt b/legacy/Data/ingsw/0210_17/wrong2.txt
new file mode 100644
index 0000000..2d5aeb0
--- /dev/null
+++ b/legacy/Data/ingsw/0210_17/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_18/correct.txt b/legacy/Data/ingsw/0210_18/correct.txt
new file mode 100644
index 0000000..1a8a50a
--- /dev/null
+++ b/legacy/Data/ingsw/0210_18/correct.txt
@@ -0,0 +1 @@
+Per ciascun requisito, dovremmo essere in grado di scrivere un inseme di test che può dimostrare che il sistema sviluppato soddisfa il requisito considerato.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_18/quest.txt b/legacy/Data/ingsw/0210_18/quest.txt
new file mode 100644
index 0000000..793b220
--- /dev/null
+++ b/legacy/Data/ingsw/0210_18/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive il criterio di "requirements verifiability" che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_18/wrong1.txt b/legacy/Data/ingsw/0210_18/wrong1.txt
new file mode 100644
index 0000000..fac8307
--- /dev/null
+++ b/legacy/Data/ingsw/0210_18/wrong1.txt
@@ -0,0 +1 @@
+Per ciascuna coppia di componenti, dovremmo essere in grado di scrivere un insieme di test che può dimostrare che l'interazione tra le componenti soddisfa tutti i requisiti di interfaccia.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_18/wrong2.txt b/legacy/Data/ingsw/0210_18/wrong2.txt
new file mode 100644
index 0000000..3fdb31e
--- /dev/null
+++ b/legacy/Data/ingsw/0210_18/wrong2.txt
@@ -0,0 +1 @@
+Per ciascuna componente del sistema, dovremmo essere in grado di scrivere un insieme di test che può dimostrare che essa soddisfa tutti i requisiti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_19/correct.txt b/legacy/Data/ingsw/0210_19/correct.txt
new file mode 100644
index 0000000..f6a4b07
--- /dev/null
+++ b/legacy/Data/ingsw/0210_19/correct.txt
@@ -0,0 +1 @@
+State coverage: 90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_19/quest.txt b/legacy/Data/ingsw/0210_19/quest.txt
new file mode 100644
index 0000000..e786bcf
--- /dev/null
+++ b/legacy/Data/ingsw/0210_19/quest.txt
@@ -0,0 +1,12 @@
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_19.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0210_19/wrong1.txt
new file mode 100644
index 0000000..1c07658
--- /dev/null
+++ b/legacy/Data/ingsw/0210_19/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 70%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_19/wrong2.txt b/legacy/Data/ingsw/0210_19/wrong2.txt
new file mode 100644
index 0000000..a8aead7
--- /dev/null
+++ b/legacy/Data/ingsw/0210_19/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_2/quest.txt b/legacy/Data/ingsw/0210_2/quest.txt
new file mode 100644
index 0000000..f9f8976
--- /dev/null
+++ b/legacy/Data/ingsw/0210_2/quest.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_2/wrong1.txt b/legacy/Data/ingsw/0210_2/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0210_2/wrong2.txt b/legacy/Data/ingsw/0210_2/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0210_2/wrong3.txt b/legacy/Data/ingsw/0210_2/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0210_20/correct.txt b/legacy/Data/ingsw/0210_20/correct.txt
new file mode 100644
index 0000000..eb23d05
--- /dev/null
+++ b/legacy/Data/ingsw/0210_20/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che non ci siano requisiti in conflitto con altri requisiti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_20/quest.txt b/legacy/Data/ingsw/0210_20/quest.txt
new file mode 100644
index 0000000..7710e8f
--- /dev/null
+++ b/legacy/Data/ingsw/0210_20/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "check di consistenza" che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_20/wrong1.txt b/legacy/Data/ingsw/0210_20/wrong1.txt
new file mode 100644
index 0000000..9e12d11
--- /dev/null
+++ b/legacy/Data/ingsw/0210_20/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che per ogni requisito esista un insieme di test che lo possa verificare.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_20/wrong2.txt b/legacy/Data/ingsw/0210_20/wrong2.txt
new file mode 100644
index 0000000..32c628c
--- /dev/null
+++ b/legacy/Data/ingsw/0210_20/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisiti funzionali descrivano tutte le funzionalità del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_21/correct.txt b/legacy/Data/ingsw/0210_21/correct.txt
new file mode 100644
index 0000000..ad21063
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0210_21/quest.txt b/legacy/Data/ingsw/0210_21/quest.txt
new file mode 100644
index 0000000..031c331
--- /dev/null
+++ b/legacy/Data/ingsw/0210_21/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 40 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se 10 unit� di tempo nel passato x era maggiore di 1 allora ora y � 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/legacy/Data/ingsw/0210_21/wrong1.txt
new file mode 100644
index 0000000..b14ac60
--- /dev/null
+++ b/legacy/Data/ingsw/0210_21/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/legacy/Data/ingsw/0210_21/wrong2.txt b/legacy/Data/ingsw/0210_21/wrong2.txt
new file mode 100644
index 0000000..e4201ab
--- /dev/null
+++ b/legacy/Data/ingsw/0210_21/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/0210_22/correct.txt b/legacy/Data/ingsw/0210_22/correct.txt
new file mode 100644
index 0000000..a7029bc
--- /dev/null
+++ b/legacy/Data/ingsw/0210_22/correct.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [1, 4] oppure nell'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_22/quest.txt b/legacy/Data/ingsw/0210_22/quest.txt
new file mode 100644
index 0000000..e5fbc81
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0210_22/wrong1.txt
new file mode 100644
index 0000000..710b111
--- /dev/null
+++ b/legacy/Data/ingsw/0210_22/wrong1.txt
@@ -0,0 +1 @@
+La variabile x è fuori dall'intervallo [1, 4] e fuori dall'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_22/wrong2.txt b/legacy/Data/ingsw/0210_22/wrong2.txt
new file mode 100644
index 0000000..a82929b
--- /dev/null
+++ b/legacy/Data/ingsw/0210_22/wrong2.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [1, 4] e fuori dall'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_23/correct.txt b/legacy/Data/ingsw/0210_23/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0210_23/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_23/quest.txt b/legacy/Data/ingsw/0210_23/quest.txt
new file mode 100644
index 0000000..adede32
--- /dev/null
+++ b/legacy/Data/ingsw/0210_23/quest.txt
@@ -0,0 +1,9 @@
+Il branch coverage di un insieme di test cases � 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 � la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_23/wrong1.txt b/legacy/Data/ingsw/0210_23/wrong1.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0210_23/wrong1.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_23/wrong2.txt b/legacy/Data/ingsw/0210_23/wrong2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0210_23/wrong2.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_24/correct.txt b/legacy/Data/ingsw/0210_24/correct.txt
new file mode 100644
index 0000000..2a2ecea
--- /dev/null
+++ b/legacy/Data/ingsw/0210_24/correct.txt
@@ -0,0 +1 @@
+time(0)/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_24/quest.txt b/legacy/Data/ingsw/0210_24/quest.txt
new file mode 100644
index 0000000..d188da2
--- /dev/null
+++ b/legacy/Data/ingsw/0210_24/quest.txt
@@ -0,0 +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 � in (0, 1).  Il tempo necessario per completare la fase x � time(x). La fase 0 � la fase di design, che ha probabilit� 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 � 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), .... � 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)  (poich� 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/legacy/Data/ingsw/0210_24/wrong1.txt
new file mode 100644
index 0000000..9927a93
--- /dev/null
+++ b/legacy/Data/ingsw/0210_24/wrong1.txt
@@ -0,0 +1 @@
+time(0)/(p*(1 - p))
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_24/wrong2.txt b/legacy/Data/ingsw/0210_24/wrong2.txt
new file mode 100644
index 0000000..d68fd15
--- /dev/null
+++ b/legacy/Data/ingsw/0210_24/wrong2.txt
@@ -0,0 +1 @@
+time(0)*(1 - p)/p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_25/correct.txt b/legacy/Data/ingsw/0210_25/correct.txt
new file mode 100644
index 0000000..43dc0c9
--- /dev/null
+++ b/legacy/Data/ingsw/0210_25/correct.txt
@@ -0,0 +1,5 @@
+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/legacy/Data/ingsw/0210_25/quest.txt
new file mode 100644
index 0000000..f6744fd
--- /dev/null
+++ b/legacy/Data/ingsw/0210_25/quest.txt
@@ -0,0 +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 (cio� non � 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 � positivo ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_25/wrong1.txt b/legacy/Data/ingsw/0210_25/wrong1.txt
new file mode 100644
index 0000000..6a97baf
--- /dev/null
+++ b/legacy/Data/ingsw/0210_25/wrong1.txt
@@ -0,0 +1,5 @@
+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_25/wrong2.txt b/legacy/Data/ingsw/0210_25/wrong2.txt
new file mode 100644
index 0000000..3f63933
--- /dev/null
+++ b/legacy/Data/ingsw/0210_25/wrong2.txt
@@ -0,0 +1,5 @@
+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/legacy/Data/ingsw/0210_26/correct.txt
new file mode 100644
index 0000000..b9f32a6
--- /dev/null
+++ b/legacy/Data/ingsw/0210_26/correct.txt
@@ -0,0 +1 @@
+c(0)/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_26/quest.txt b/legacy/Data/ingsw/0210_26/quest.txt
new file mode 100644
index 0000000..d318528
--- /dev/null
+++ b/legacy/Data/ingsw/0210_26/quest.txt
@@ -0,0 +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 � in (0, 1).  Il costo dello stato (fase) x � c(x). La fase 0 � la fase di design, che ha probabilit� 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 � 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), .... � 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)  (poich� 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/legacy/Data/ingsw/0210_26/wrong1.txt
new file mode 100644
index 0000000..3143da9
--- /dev/null
+++ b/legacy/Data/ingsw/0210_26/wrong1.txt
@@ -0,0 +1 @@
+c(0)/(p*(1 - p))
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_26/wrong2.txt b/legacy/Data/ingsw/0210_26/wrong2.txt
new file mode 100644
index 0000000..70022eb
--- /dev/null
+++ b/legacy/Data/ingsw/0210_26/wrong2.txt
@@ -0,0 +1 @@
+c(0)*(1 - p)/p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_27/quest.txt b/legacy/Data/ingsw/0210_27/quest.txt
new file mode 100644
index 0000000..75e942b
--- /dev/null
+++ b/legacy/Data/ingsw/0210_27/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0210_27/wrong1.txt
new file mode 100644
index 0000000..c296b22
--- /dev/null
+++ b/legacy/Data/ingsw/0210_27/wrong1.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_27/wrong2.txt b/legacy/Data/ingsw/0210_27/wrong2.txt
new file mode 100644
index 0000000..d21df5d
--- /dev/null
+++ b/legacy/Data/ingsw/0210_27/wrong2.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_27/wrong3.txt b/legacy/Data/ingsw/0210_27/wrong3.txt
new file mode 100644
index 0000000..421d23f
--- /dev/null
+++ b/legacy/Data/ingsw/0210_27/wrong3.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_28/quest.txt b/legacy/Data/ingsw/0210_28/quest.txt
new file mode 100644
index 0000000..932f11d
--- /dev/null
+++ b/legacy/Data/ingsw/0210_28/quest.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_28/wrong1.txt b/legacy/Data/ingsw/0210_28/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0210_28/wrong2.txt b/legacy/Data/ingsw/0210_28/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0210_28/wrong3.txt b/legacy/Data/ingsw/0210_28/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0210_29/correct.txt b/legacy/Data/ingsw/0210_29/correct.txt
new file mode 100644
index 0000000..0902686
--- /dev/null
+++ b/legacy/Data/ingsw/0210_29/correct.txt
@@ -0,0 +1 @@
+Requisito funzionale.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_29/quest.txt b/legacy/Data/ingsw/0210_29/quest.txt
new file mode 100644
index 0000000..f6839df
--- /dev/null
+++ b/legacy/Data/ingsw/0210_29/quest.txt
@@ -0,0 +1,2 @@
+"Ogni giorno, per ciascuna clinica, il sistema generer� una lista dei pazienti che hanno un appuntamento quel giorno."
+La frase precedente � un esempio di:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_29/wrong1.txt b/legacy/Data/ingsw/0210_29/wrong1.txt
new file mode 100644
index 0000000..6084c49
--- /dev/null
+++ b/legacy/Data/ingsw/0210_29/wrong1.txt
@@ -0,0 +1 @@
+Requisito non-funzionale.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_29/wrong2.txt b/legacy/Data/ingsw/0210_29/wrong2.txt
new file mode 100644
index 0000000..396c8d3
--- /dev/null
+++ b/legacy/Data/ingsw/0210_29/wrong2.txt
@@ -0,0 +1 @@
+Requisito di performance.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_3/quest.txt b/legacy/Data/ingsw/0210_3/quest.txt
new file mode 100644
index 0000000..985c244
--- /dev/null
+++ b/legacy/Data/ingsw/0210_3/quest.txt
@@ -0,0 +1,4 @@
+Un test oracle per un programma P � 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) � 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 � un test oracle per la funzione f ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_3/wrong1.txt b/legacy/Data/ingsw/0210_3/wrong1.txt
new file mode 100644
index 0000000..ed5ad19
--- /dev/null
+++ b/legacy/Data/ingsw/0210_3/wrong1.txt
@@ -0,0 +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);
+}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_3/wrong2.txt b/legacy/Data/ingsw/0210_3/wrong2.txt
new file mode 100644
index 0000000..69b9722
--- /dev/null
+++ b/legacy/Data/ingsw/0210_3/wrong2.txt
@@ -0,0 +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);
+}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_3/wrong3.txt b/legacy/Data/ingsw/0210_3/wrong3.txt
new file mode 100644
index 0000000..a26ce6e
--- /dev/null
+++ b/legacy/Data/ingsw/0210_3/wrong3.txt
@@ -0,0 +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);
+}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_30/correct.txt b/legacy/Data/ingsw/0210_30/correct.txt
new file mode 100644
index 0000000..b110af1
--- /dev/null
+++ b/legacy/Data/ingsw/0210_30/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_30/quest.txt b/legacy/Data/ingsw/0210_30/quest.txt
new file mode 100644
index 0000000..a27fc55
--- /dev/null
+++ b/legacy/Data/ingsw/0210_30/quest.txt
@@ -0,0 +1,13 @@
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_30.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0210_30/wrong1.txt
new file mode 100644
index 0000000..2d5aeb0
--- /dev/null
+++ b/legacy/Data/ingsw/0210_30/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_30/wrong2.txt b/legacy/Data/ingsw/0210_30/wrong2.txt
new file mode 100644
index 0000000..a29d476
--- /dev/null
+++ b/legacy/Data/ingsw/0210_30/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_31/correct.txt b/legacy/Data/ingsw/0210_31/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0210_31/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_31/quest.txt b/legacy/Data/ingsw/0210_31/quest.txt
new file mode 100644
index 0000000..65cfd2d
--- /dev/null
+++ b/legacy/Data/ingsw/0210_31/quest.txt
@@ -0,0 +1,9 @@
+Il branch coverage di un insieme di test cases � 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 � la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_31/wrong1.txt b/legacy/Data/ingsw/0210_31/wrong1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0210_31/wrong1.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_31/wrong2.txt b/legacy/Data/ingsw/0210_31/wrong2.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0210_31/wrong2.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_32/correct.txt b/legacy/Data/ingsw/0210_32/correct.txt
new file mode 100644
index 0000000..973ef63
--- /dev/null
+++ b/legacy/Data/ingsw/0210_32/correct.txt
@@ -0,0 +1 @@
+State coverage: 75%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_32/quest.txt b/legacy/Data/ingsw/0210_32/quest.txt
new file mode 100644
index 0000000..cb591da
--- /dev/null
+++ b/legacy/Data/ingsw/0210_32/quest.txt
@@ -0,0 +1,13 @@
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_32.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0210_32/wrong1.txt
new file mode 100644
index 0000000..1c07658
--- /dev/null
+++ b/legacy/Data/ingsw/0210_32/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 70%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_32/wrong2.txt b/legacy/Data/ingsw/0210_32/wrong2.txt
new file mode 100644
index 0000000..a8aead7
--- /dev/null
+++ b/legacy/Data/ingsw/0210_32/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_33/correct.txt b/legacy/Data/ingsw/0210_33/correct.txt
new file mode 100644
index 0000000..1c7da8c
--- /dev/null
+++ b/legacy/Data/ingsw/0210_33/correct.txt
@@ -0,0 +1 @@
+0.03
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_33/quest.txt b/legacy/Data/ingsw/0210_33/quest.txt
new file mode 100644
index 0000000..cf9113a
--- /dev/null
+++ b/legacy/Data/ingsw/0210_33/quest.txt
@@ -0,0 +1,9 @@
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_33.png
+Un processo software pu� 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 probabilit� 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 probabilit� 0.1 di dover essere ripetuta (a causa di errori).
+Uno scenario � una sequenza di stati.
+Qual'e' la probabilit� dello scenario: 1, 2, 3, 4 ? In altri terminti, qual'� la probabilit� 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/legacy/Data/ingsw/0210_33/wrong1.txt
new file mode 100644
index 0000000..7eb6830
--- /dev/null
+++ b/legacy/Data/ingsw/0210_33/wrong1.txt
@@ -0,0 +1 @@
+0.27
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_33/wrong2.txt b/legacy/Data/ingsw/0210_33/wrong2.txt
new file mode 100644
index 0000000..8a346b7
--- /dev/null
+++ b/legacy/Data/ingsw/0210_33/wrong2.txt
@@ -0,0 +1 @@
+0.07
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_34/quest.txt b/legacy/Data/ingsw/0210_34/quest.txt
new file mode 100644
index 0000000..33e1f49
--- /dev/null
+++ b/legacy/Data/ingsw/0210_34/quest.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_34/wrong1.txt b/legacy/Data/ingsw/0210_34/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0210_34/wrong2.txt b/legacy/Data/ingsw/0210_34/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0210_34/wrong3.txt b/legacy/Data/ingsw/0210_34/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0210_35/correct.txt b/legacy/Data/ingsw/0210_35/correct.txt
new file mode 100644
index 0000000..7c149d8
--- /dev/null
+++ b/legacy/Data/ingsw/0210_35/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisisti descrivano tutte le funzionalità e vincoli (e.g., security, performance) del sistema desiderato dal customer.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_35/quest.txt b/legacy/Data/ingsw/0210_35/quest.txt
new file mode 100644
index 0000000..8bba4b8
--- /dev/null
+++ b/legacy/Data/ingsw/0210_35/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "check di completezza" che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_35/wrong1.txt b/legacy/Data/ingsw/0210_35/wrong1.txt
new file mode 100644
index 0000000..3461684
--- /dev/null
+++ b/legacy/Data/ingsw/0210_35/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che per ogni requisito sia stato implementato nel sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_35/wrong2.txt b/legacy/Data/ingsw/0210_35/wrong2.txt
new file mode 100644
index 0000000..32c628c
--- /dev/null
+++ b/legacy/Data/ingsw/0210_35/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisiti funzionali descrivano tutte le funzionalità del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_36/correct.txt b/legacy/Data/ingsw/0210_36/correct.txt
new file mode 100644
index 0000000..3f63933
--- /dev/null
+++ b/legacy/Data/ingsw/0210_36/correct.txt
@@ -0,0 +1,5 @@
+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/legacy/Data/ingsw/0210_36/quest.txt
new file mode 100644
index 0000000..595ab5d
--- /dev/null
+++ b/legacy/Data/ingsw/0210_36/quest.txt
@@ -0,0 +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 (cio� non � 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 � maggiore di 1 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_36/wrong1.txt b/legacy/Data/ingsw/0210_36/wrong1.txt
new file mode 100644
index 0000000..6a97baf
--- /dev/null
+++ b/legacy/Data/ingsw/0210_36/wrong1.txt
@@ -0,0 +1,5 @@
+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/legacy/Data/ingsw/0210_36/wrong2.txt
new file mode 100644
index 0000000..e607157
--- /dev/null
+++ b/legacy/Data/ingsw/0210_36/wrong2.txt
@@ -0,0 +1,5 @@
+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/legacy/Data/ingsw/0210_37/quest.txt
new file mode 100644
index 0000000..5743032
--- /dev/null
+++ b/legacy/Data/ingsw/0210_37/quest.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_37/wrong1.txt b/legacy/Data/ingsw/0210_37/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0210_37/wrong2.txt b/legacy/Data/ingsw/0210_37/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0210_37/wrong3.txt b/legacy/Data/ingsw/0210_37/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0210_38/correct.txt b/legacy/Data/ingsw/0210_38/correct.txt
new file mode 100644
index 0000000..232aedf
--- /dev/null
+++ b/legacy/Data/ingsw/0210_38/correct.txt
@@ -0,0 +1 @@
+(a = 6, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 0).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_38/quest.txt b/legacy/Data/ingsw/0210_38/quest.txt
new file mode 100644
index 0000000..b2bed72
--- /dev/null
+++ b/legacy/Data/ingsw/0210_38/quest.txt
@@ -0,0 +1,21 @@
+Una Condition � una proposizione booleana, cio� una espressione con valore booleano che non pu� essere decomposta 
+in espressioni boolean pi� semplici. Ad esempio, (x + y <= 3) � una condition.
+
+Una Decision � una espressione booleana composta da conditions e zero o pi� 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 � 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 � true ed un test in T in cui c � false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d � true ed un test in T in cui d � 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/legacy/Data/ingsw/0210_38/wrong1.txt
new file mode 100644
index 0000000..2b6c292
--- /dev/null
+++ b/legacy/Data/ingsw/0210_38/wrong1.txt
@@ -0,0 +1 @@
+(a = 5, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 0).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_38/wrong2.txt b/legacy/Data/ingsw/0210_38/wrong2.txt
new file mode 100644
index 0000000..5d5c9a4
--- /dev/null
+++ b/legacy/Data/ingsw/0210_38/wrong2.txt
@@ -0,0 +1 @@
+(a = 6, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 2).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_39/correct.txt b/legacy/Data/ingsw/0210_39/correct.txt
new file mode 100644
index 0000000..8785661
--- /dev/null
+++ b/legacy/Data/ingsw/0210_39/correct.txt
@@ -0,0 +1 @@
+{x = -200, x = -50,  x = 0, x = 100, x = 700}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_39/quest.txt b/legacy/Data/ingsw/0210_39/quest.txt
new file mode 100644
index 0000000..36947c2
--- /dev/null
+++ b/legacy/Data/ingsw/0210_39/quest.txt
@@ -0,0 +1,6 @@
+Il partition coverage di un insieme di test cases � la percentuale di elementi della partition inclusi nei test cases. La partition � 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/legacy/Data/ingsw/0210_39/wrong1.txt
new file mode 100644
index 0000000..0aaedb8
--- /dev/null
+++ b/legacy/Data/ingsw/0210_39/wrong1.txt
@@ -0,0 +1 @@
+{x = -200, x = -50,  x = 0, x = 100, x = 500}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_39/wrong2.txt b/legacy/Data/ingsw/0210_39/wrong2.txt
new file mode 100644
index 0000000..a6df32d
--- /dev/null
+++ b/legacy/Data/ingsw/0210_39/wrong2.txt
@@ -0,0 +1 @@
+{x = -200, x = -150,  x = 0, x = 100, x = 700}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_4/correct.txt b/legacy/Data/ingsw/0210_4/correct.txt
new file mode 100644
index 0000000..973ef63
--- /dev/null
+++ b/legacy/Data/ingsw/0210_4/correct.txt
@@ -0,0 +1 @@
+State coverage: 75%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_4/quest.txt b/legacy/Data/ingsw/0210_4/quest.txt
new file mode 100644
index 0000000..84d1f53
--- /dev/null
+++ b/legacy/Data/ingsw/0210_4/quest.txt
@@ -0,0 +1,12 @@
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_4.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0210_4/wrong1.txt
new file mode 100644
index 0000000..1a8a508
--- /dev/null
+++ b/legacy/Data/ingsw/0210_4/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_4/wrong2.txt b/legacy/Data/ingsw/0210_4/wrong2.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/0210_4/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_40/correct.txt b/legacy/Data/ingsw/0210_40/correct.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/0210_40/correct.txt
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_40/quest.txt b/legacy/Data/ingsw/0210_40/quest.txt
new file mode 100644
index 0000000..a550159
--- /dev/null
+++ b/legacy/Data/ingsw/0210_40/quest.txt
@@ -0,0 +1,9 @@
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_40.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0210_40/wrong1.txt
new file mode 100644
index 0000000..f6a4b07
--- /dev/null
+++ b/legacy/Data/ingsw/0210_40/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_40/wrong2.txt b/legacy/Data/ingsw/0210_40/wrong2.txt
new file mode 100644
index 0000000..a8aead7
--- /dev/null
+++ b/legacy/Data/ingsw/0210_40/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_41/correct.txt b/legacy/Data/ingsw/0210_41/correct.txt
new file mode 100644
index 0000000..5f76c88
--- /dev/null
+++ b/legacy/Data/ingsw/0210_41/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 45%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_41/quest.txt b/legacy/Data/ingsw/0210_41/quest.txt
new file mode 100644
index 0000000..cdbd481
--- /dev/null
+++ b/legacy/Data/ingsw/0210_41/quest.txt
@@ -0,0 +1,13 @@
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_41.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0210_41/wrong1.txt
new file mode 100644
index 0000000..2ca9276
--- /dev/null
+++ b/legacy/Data/ingsw/0210_41/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 35%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_41/wrong2.txt b/legacy/Data/ingsw/0210_41/wrong2.txt
new file mode 100644
index 0000000..c376ef7
--- /dev/null
+++ b/legacy/Data/ingsw/0210_41/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 55%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_42/quest.txt b/legacy/Data/ingsw/0210_42/quest.txt
new file mode 100644
index 0000000..8e91c31
--- /dev/null
+++ b/legacy/Data/ingsw/0210_42/quest.txt
@@ -0,0 +1,5 @@
+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/legacy/Data/ingsw/0210_42/wrong1.txt
new file mode 100644
index 0000000..512c141
--- /dev/null
+++ b/legacy/Data/ingsw/0210_42/wrong1.txt
@@ -0,0 +1,6 @@
+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/legacy/Data/ingsw/0210_42/wrong2.txt
new file mode 100644
index 0000000..77d39c1
--- /dev/null
+++ b/legacy/Data/ingsw/0210_42/wrong2.txt
@@ -0,0 +1,3 @@
+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/legacy/Data/ingsw/0210_42/wrong3.txt
new file mode 100644
index 0000000..b9a8baf
--- /dev/null
+++ b/legacy/Data/ingsw/0210_42/wrong3.txt
@@ -0,0 +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);
+end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_43/correct.txt b/legacy/Data/ingsw/0210_43/correct.txt
new file mode 100644
index 0000000..4c75070
--- /dev/null
+++ b/legacy/Data/ingsw/0210_43/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/legacy/Data/ingsw/0210_43/quest.txt b/legacy/Data/ingsw/0210_43/quest.txt
new file mode 100644
index 0000000..e11a044
--- /dev/null
+++ b/legacy/Data/ingsw/0210_43/quest.txt
@@ -0,0 +1,4 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 50 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se la variabile x � minore del 60% della variabile y allora la somma di x ed y � 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/legacy/Data/ingsw/0210_43/wrong1.txt
new file mode 100644
index 0000000..6dafe94
--- /dev/null
+++ b/legacy/Data/ingsw/0210_43/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/legacy/Data/ingsw/0210_43/wrong2.txt b/legacy/Data/ingsw/0210_43/wrong2.txt
new file mode 100644
index 0000000..a3d79a4
--- /dev/null
+++ b/legacy/Data/ingsw/0210_43/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/0210_44/quest.txt b/legacy/Data/ingsw/0210_44/quest.txt
new file mode 100644
index 0000000..5c4c81d
--- /dev/null
+++ b/legacy/Data/ingsw/0210_44/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0210_44/wrong1.txt
new file mode 100644
index 0000000..421b38f
--- /dev/null
+++ b/legacy/Data/ingsw/0210_44/wrong1.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_44/wrong2.txt b/legacy/Data/ingsw/0210_44/wrong2.txt
new file mode 100644
index 0000000..f385f1c
--- /dev/null
+++ b/legacy/Data/ingsw/0210_44/wrong2.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_44/wrong3.txt b/legacy/Data/ingsw/0210_44/wrong3.txt
new file mode 100644
index 0000000..1034e02
--- /dev/null
+++ b/legacy/Data/ingsw/0210_44/wrong3.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_45/correct.txt b/legacy/Data/ingsw/0210_45/correct.txt
new file mode 100644
index 0000000..4a8e634
--- /dev/null
+++ b/legacy/Data/ingsw/0210_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/legacy/Data/ingsw/0210_45/quest.txt b/legacy/Data/ingsw/0210_45/quest.txt
new file mode 100644
index 0000000..576af1a
--- /dev/null
+++ b/legacy/Data/ingsw/0210_45/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se 10 unit� di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora � 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/legacy/Data/ingsw/0210_45/wrong1.txt
new file mode 100644
index 0000000..68aa37a
--- /dev/null
+++ b/legacy/Data/ingsw/0210_45/wrong1.txt
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_45/wrong2.txt b/legacy/Data/ingsw/0210_45/wrong2.txt
new file mode 100644
index 0000000..a43796b
--- /dev/null
+++ b/legacy/Data/ingsw/0210_45/wrong2.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_46/correct.txt b/legacy/Data/ingsw/0210_46/correct.txt
new file mode 100644
index 0000000..001b1d9
--- /dev/null
+++ b/legacy/Data/ingsw/0210_46/correct.txt
@@ -0,0 +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);
+end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_46/quest.txt b/legacy/Data/ingsw/0210_46/quest.txt
new file mode 100644
index 0000000..9f5199d
--- /dev/null
+++ b/legacy/Data/ingsw/0210_46/quest.txt
@@ -0,0 +1,4 @@
+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/legacy/Data/ingsw/0210_46/wrong1.txt
new file mode 100644
index 0000000..fc95495
--- /dev/null
+++ b/legacy/Data/ingsw/0210_46/wrong1.txt
@@ -0,0 +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]);
+end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_46/wrong2.txt b/legacy/Data/ingsw/0210_46/wrong2.txt
new file mode 100644
index 0000000..eaf9272
--- /dev/null
+++ b/legacy/Data/ingsw/0210_46/wrong2.txt
@@ -0,0 +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]);
+end SysArch
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_47/correct.txt b/legacy/Data/ingsw/0210_47/correct.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0210_47/correct.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_47/quest.txt b/legacy/Data/ingsw/0210_47/quest.txt
new file mode 100644
index 0000000..4344b75
--- /dev/null
+++ b/legacy/Data/ingsw/0210_47/quest.txt
@@ -0,0 +1,8 @@
+Il partition coverage di un insieme di test cases � la percentuale di elementi della partition inclusi nei test cases. La partition � 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 � la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_47/wrong1.txt b/legacy/Data/ingsw/0210_47/wrong1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0210_47/wrong1.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_47/wrong2.txt b/legacy/Data/ingsw/0210_47/wrong2.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0210_47/wrong2.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_48/correct.txt b/legacy/Data/ingsw/0210_48/correct.txt
new file mode 100644
index 0000000..7311d41
--- /dev/null
+++ b/legacy/Data/ingsw/0210_48/correct.txt
@@ -0,0 +1 @@
+Test set: {x=1, y=1}, {x=0, y=0}, {x=2, y=1}, {x=2, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_48/quest.txt b/legacy/Data/ingsw/0210_48/quest.txt
new file mode 100644
index 0000000..d3a9fe2
--- /dev/null
+++ b/legacy/Data/ingsw/0210_48/quest.txt
@@ -0,0 +1,8 @@
+Il branch coverage di un insieme di test cases � 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/legacy/Data/ingsw/0210_48/wrong1.txt
new file mode 100644
index 0000000..7e48e4f
--- /dev/null
+++ b/legacy/Data/ingsw/0210_48/wrong1.txt
@@ -0,0 +1 @@
+Test set: {x=1, y=1}, {x=0, y=0}, {x=2, y=1}, {x=2, y=3}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_48/wrong2.txt b/legacy/Data/ingsw/0210_48/wrong2.txt
new file mode 100644
index 0000000..3e327ab
--- /dev/null
+++ b/legacy/Data/ingsw/0210_48/wrong2.txt
@@ -0,0 +1 @@
+Test set: {x=1, y=1}, {x=2, y=2}, {x=2, y=1}, {x=2, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_49/correct.txt b/legacy/Data/ingsw/0210_49/correct.txt
new file mode 100644
index 0000000..b110af1
--- /dev/null
+++ b/legacy/Data/ingsw/0210_49/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_49/quest.txt b/legacy/Data/ingsw/0210_49/quest.txt
new file mode 100644
index 0000000..8cb7d37
--- /dev/null
+++ b/legacy/Data/ingsw/0210_49/quest.txt
@@ -0,0 +1,12 @@
+img=https://unspectacular-subdi.000webhostapp.com/0210_domanda_49.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0210_49/wrong1.txt
new file mode 100644
index 0000000..2d5aeb0
--- /dev/null
+++ b/legacy/Data/ingsw/0210_49/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_49/wrong2.txt b/legacy/Data/ingsw/0210_49/wrong2.txt
new file mode 100644
index 0000000..a29d476
--- /dev/null
+++ b/legacy/Data/ingsw/0210_49/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_5/correct.txt b/legacy/Data/ingsw/0210_5/correct.txt
new file mode 100644
index 0000000..e582263
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0210_5/quest.txt b/legacy/Data/ingsw/0210_5/quest.txt
new file mode 100644
index 0000000..864cc93
--- /dev/null
+++ b/legacy/Data/ingsw/0210_5/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Durante l'esecuzione del programma (cio� per tutti gli istanti di tempo positivi) la variabile x � 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/legacy/Data/ingsw/0210_5/wrong1.txt
new file mode 100644
index 0000000..0f38391
--- /dev/null
+++ b/legacy/Data/ingsw/0210_5/wrong1.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 >= 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/legacy/Data/ingsw/0210_5/wrong2.txt
new file mode 100644
index 0000000..590f7e1
--- /dev/null
+++ b/legacy/Data/ingsw/0210_5/wrong2.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 >= 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/0210_6/correct.txt b/legacy/Data/ingsw/0210_6/correct.txt
new file mode 100644
index 0000000..c37d6ae
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0210_6/quest.txt
new file mode 100644
index 0000000..003d1dd
--- /dev/null
+++ b/legacy/Data/ingsw/0210_6/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se 10 unit� di tempo nel passato x era maggiore di 0 allora ora y � 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/legacy/Data/ingsw/0210_6/wrong1.txt
new file mode 100644
index 0000000..14bd900
--- /dev/null
+++ b/legacy/Data/ingsw/0210_6/wrong1.txt
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_6/wrong2.txt b/legacy/Data/ingsw/0210_6/wrong2.txt
new file mode 100644
index 0000000..edea147
--- /dev/null
+++ b/legacy/Data/ingsw/0210_6/wrong2.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_7/correct.txt b/legacy/Data/ingsw/0210_7/correct.txt
new file mode 100644
index 0000000..31a01d5
--- /dev/null
+++ b/legacy/Data/ingsw/0210_7/correct.txt
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=0, y=0}, {x=15, y=0}, {x=9, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_7/quest.txt b/legacy/Data/ingsw/0210_7/quest.txt
new file mode 100644
index 0000000..d649932
--- /dev/null
+++ b/legacy/Data/ingsw/0210_7/quest.txt
@@ -0,0 +1,8 @@
+Il branch coverage di un insieme di test cases � 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/legacy/Data/ingsw/0210_7/wrong1.txt
new file mode 100644
index 0000000..549dba8
--- /dev/null
+++ b/legacy/Data/ingsw/0210_7/wrong1.txt
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=0, y=0}, {x=15, y=0}, {x=10, y=3}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_7/wrong2.txt b/legacy/Data/ingsw/0210_7/wrong2.txt
new file mode 100644
index 0000000..0c564f7
--- /dev/null
+++ b/legacy/Data/ingsw/0210_7/wrong2.txt
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=2, y=1}, {x=15, y=0}, {x=9, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_8/correct.txt b/legacy/Data/ingsw/0210_8/correct.txt
new file mode 100644
index 0000000..81a4b93
--- /dev/null
+++ b/legacy/Data/ingsw/0210_8/correct.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x >= 0) then (z == pow(y, x)) else (z == 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_8/quest.txt b/legacy/Data/ingsw/0210_8/quest.txt
new file mode 100644
index 0000000..236ccc7
--- /dev/null
+++ b/legacy/Data/ingsw/0210_8/quest.txt
@@ -0,0 +1,10 @@
+Un test oracle per un programma P � 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) � 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) � un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_8/wrong1.txt b/legacy/Data/ingsw/0210_8/wrong1.txt
new file mode 100644
index 0000000..f52d5ae
--- /dev/null
+++ b/legacy/Data/ingsw/0210_8/wrong1.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x >= 0) then (z == pow(y, x)) else (z == y)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_8/wrong2.txt b/legacy/Data/ingsw/0210_8/wrong2.txt
new file mode 100644
index 0000000..d246b94
--- /dev/null
+++ b/legacy/Data/ingsw/0210_8/wrong2.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x >= 0) then (z == pow(y, x)) else (z == 0)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_9/quest.txt b/legacy/Data/ingsw/0210_9/quest.txt
new file mode 100644
index 0000000..fcfd787
--- /dev/null
+++ b/legacy/Data/ingsw/0210_9/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0210_9/wrong1.txt
new file mode 100644
index 0000000..acd5e00
--- /dev/null
+++ b/legacy/Data/ingsw/0210_9/wrong1.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_9/wrong2.txt b/legacy/Data/ingsw/0210_9/wrong2.txt
new file mode 100644
index 0000000..298890c
--- /dev/null
+++ b/legacy/Data/ingsw/0210_9/wrong2.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0210_9/wrong3.txt b/legacy/Data/ingsw/0210_9/wrong3.txt
new file mode 100644
index 0000000..3b3e08a
--- /dev/null
+++ b/legacy/Data/ingsw/0210_9/wrong3.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0221_18/correct.txt b/legacy/Data/ingsw/0221_18/correct.txt
new file mode 100644
index 0000000..eb23d05
--- /dev/null
+++ b/legacy/Data/ingsw/0221_18/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che non ci siano requisiti in conflitto con altri requisiti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0221_18/quest.txt b/legacy/Data/ingsw/0221_18/quest.txt
new file mode 100644
index 0000000..937eabd
--- /dev/null
+++ b/legacy/Data/ingsw/0221_18/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "check di consistenza" che è parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0221_18/wrong1.txt b/legacy/Data/ingsw/0221_18/wrong1.txt
new file mode 100644
index 0000000..32c628c
--- /dev/null
+++ b/legacy/Data/ingsw/0221_18/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisiti funzionali descrivano tutte le funzionalità del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0221_18/wrong2.txt b/legacy/Data/ingsw/0221_18/wrong2.txt
new file mode 100644
index 0000000..9e12d11
--- /dev/null
+++ b/legacy/Data/ingsw/0221_18/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che per ogni requisito esista un insieme di test che lo possa verificare.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0221_28/correct.txt b/legacy/Data/ingsw/0221_28/correct.txt
new file mode 100644
index 0000000..7c149d8
--- /dev/null
+++ b/legacy/Data/ingsw/0221_28/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisisti descrivano tutte le funzionalità e vincoli (e.g., security, performance) del sistema desiderato dal customer.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0221_28/quest.txt b/legacy/Data/ingsw/0221_28/quest.txt
new file mode 100644
index 0000000..c71c807
--- /dev/null
+++ b/legacy/Data/ingsw/0221_28/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "check di completezza" che è parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0221_28/wrong1.txt b/legacy/Data/ingsw/0221_28/wrong1.txt
new file mode 100644
index 0000000..3461684
--- /dev/null
+++ b/legacy/Data/ingsw/0221_28/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che per ogni requisito sia stato implementato nel sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0221_28/wrong2.txt b/legacy/Data/ingsw/0221_28/wrong2.txt
new file mode 100644
index 0000000..32c628c
--- /dev/null
+++ b/legacy/Data/ingsw/0221_28/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisiti funzionali descrivano tutte le funzionalità del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0221_32/correct.txt b/legacy/Data/ingsw/0221_32/correct.txt
new file mode 100644
index 0000000..e7c5bb8
--- /dev/null
+++ b/legacy/Data/ingsw/0221_32/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che, tenedo conto della tecnologia, budget e tempo disponibili,  sia possibile realizzare un sistema che soddisfa i requisisti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0221_32/quest.txt b/legacy/Data/ingsw/0221_32/quest.txt
new file mode 100644
index 0000000..5552f2f
--- /dev/null
+++ b/legacy/Data/ingsw/0221_32/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "check di realismo" (realizability) che è parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0221_32/wrong1.txt b/legacy/Data/ingsw/0221_32/wrong1.txt
new file mode 100644
index 0000000..bfb5124
--- /dev/null
+++ b/legacy/Data/ingsw/0221_32/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che le performance richieste al sistema siano necessarie per soddisfare le necessità del customer.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0221_32/wrong2.txt b/legacy/Data/ingsw/0221_32/wrong2.txt
new file mode 100644
index 0000000..2b6e242
--- /dev/null
+++ b/legacy/Data/ingsw/0221_32/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che le funzionalità richieste al sistema siano necessarie per soddisfare le necessità del customer.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_24/correct.txt b/legacy/Data/ingsw/0222_24/correct.txt
new file mode 100644
index 0000000..1e091a3
--- /dev/null
+++ b/legacy/Data/ingsw/0222_24/correct.txt
@@ -0,0 +1 @@
+90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_24/quest.txt b/legacy/Data/ingsw/0222_24/quest.txt
new file mode 100644
index 0000000..ce59bae
--- /dev/null
+++ b/legacy/Data/ingsw/0222_24/quest.txt
@@ -0,0 +1,12 @@
+img=https://i.imgur.com/6m6ALRb.png
+La state coverage di un insieme di test cases (cioè sequeze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) rggiunti almeno una volta.
+
+Si consideri lo state diagram in figura 
+ed il seguente insieme di test cases:
+
+1) Start PIN validation, card inserted, PIN Entered, Valid PIN, Cancel 3, End PIN Validation 2
+
+2) Start PIN validation, card inserted, PIN Entered, Cancel 2, End PIN Validation 2
+
+
+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/0222_24/wrong1.txt b/legacy/Data/ingsw/0222_24/wrong1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0222_24/wrong1.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_24/wrong2.txt b/legacy/Data/ingsw/0222_24/wrong2.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0222_24/wrong2.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_27/correct.txt b/legacy/Data/ingsw/0222_27/correct.txt
new file mode 100644
index 0000000..1e091a3
--- /dev/null
+++ b/legacy/Data/ingsw/0222_27/correct.txt
@@ -0,0 +1 @@
+90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_27/quest.txt b/legacy/Data/ingsw/0222_27/quest.txt
new file mode 100644
index 0000000..b1548b4
--- /dev/null
+++ b/legacy/Data/ingsw/0222_27/quest.txt
@@ -0,0 +1,13 @@
+img=https://i.imgur.com/6m6ALRb.png
+La state coverage di un insieme di test cases (cioè sequeze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) rggiunti almeno una volta.
+
+Si consideri lo state diagram in figura  ed il seguente insieme di test cases:
+
+1) Start PIN validation, card inserted, PIN Entered, Valid PIN, Cancel 3, End PIN Validation 2;
+
+2) Start PIN validation, card inserted, PIN Entered, Invalid PIN, PIN Entered, Valid PIN, Cancel 3, End PIN Validation 2;
+
+3) Start PIN validation, card inserted, PIN Entered, Invalid PIN, PIN Entered, Invalid PIN, PIN Entered, Valid PIN, Cancel 3, End PIN Validation 2.
+
+
+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/0222_27/wrong1.txt b/legacy/Data/ingsw/0222_27/wrong1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0222_27/wrong1.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_27/wrong2.txt b/legacy/Data/ingsw/0222_27/wrong2.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0222_27/wrong2.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_33/correct.txt b/legacy/Data/ingsw/0222_33/correct.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0222_33/correct.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_33/quest.txt b/legacy/Data/ingsw/0222_33/quest.txt
new file mode 100644
index 0000000..857057e
--- /dev/null
+++ b/legacy/Data/ingsw/0222_33/quest.txt
@@ -0,0 +1,45 @@
+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 il seguente programma C:
+
+-----------
+
+#include <stdio.h>
+
+#include <stdlib.h>
+
+#include <assert.h>
+
+#define N  5    /* number of test cases */
+
+int f1(int x)  { return (2*x); }
+
+int main() {  int  i, y;    int x[N];
+
+ // define test cases
+
+  x[0] = 0; x[1] = 1; x[2] = -1; x[3] = 10; x[4] = -10;
+
+// testing
+
+for (i = 0; i < N; i++)   {
+
+      y = f1(x[i]);       // function under testing
+
+      assert(y == 2*x[i]);   // oracle
+
+ }
+
+   printf("All %d test cases passed\n", N);
+
+    return (0); 
+
+}
+
+Si vuole testare la funzione f1(). A tal fine l'insieme degli interi viene partizionato come segue: 
+
+{(-inf, -21], [-20, -1], {0}, [1, 20], [21, +inf)}
+
+Il programma main() sopra realizza il nostro testing per la funzione f1(). I test cases sono i valori in x[i].
+
+Quale delle seguenti è la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_33/wrong1.txt b/legacy/Data/ingsw/0222_33/wrong1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0222_33/wrong1.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_33/wrong2.txt b/legacy/Data/ingsw/0222_33/wrong2.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0222_33/wrong2.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_35/correct.txt b/legacy/Data/ingsw/0222_35/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0222_35/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_35/quest.txt b/legacy/Data/ingsw/0222_35/quest.txt
new file mode 100644
index 0000000..216c715
--- /dev/null
+++ b/legacy/Data/ingsw/0222_35/quest.txt
@@ -0,0 +1,52 @@
+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 il seguente programma C:
+
+-----------
+
+
+#include <stdio.h>
+
+#include <stdlib.h>
+
+#include <assert.h>
+
+#define N  1   /* number of test cases */
+
+int f(int x)  {   int y = 0;
+
+  LOOP:  if (abs(x) - y <= 2)
+
+                      {return  ;}
+
+                else {y = y + 1; goto LOOP;}		    
+
+}  /* f()  */
+
+int main() {  int  i, y;   int x[N];
+
+// define test cases
+
+    x[0] = 3; 
+
+// testing
+
+  for (i = 0; i < N; i++)   {
+
+      y = f(x[i]);       // function under testing
+
+      assert(y == (abs(x[i]) <= 2) ? 0 : (abs(x[i]) - 2));   // oracle
+
+    }
+
+   printf("All %d test cases passed\n", N);
+
+  return (0); 
+
+}
+
+-----------
+
+Il programma main() sopra realizza il nostro testing per la funzione f(). I test cases sono i valori in x1[i] ed x2[i].
+
+Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_35/wrong1.txt b/legacy/Data/ingsw/0222_35/wrong1.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0222_35/wrong1.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_35/wrong2.txt b/legacy/Data/ingsw/0222_35/wrong2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0222_35/wrong2.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_39/correct.txt b/legacy/Data/ingsw/0222_39/correct.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0222_39/correct.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_39/quest.txt b/legacy/Data/ingsw/0222_39/quest.txt
new file mode 100644
index 0000000..0e6f9c0
--- /dev/null
+++ b/legacy/Data/ingsw/0222_39/quest.txt
@@ -0,0 +1,55 @@
+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 il seguente programma C:
+
+-----------
+
+#include <stdio.h>
+
+#include <stdlib.h>
+
+#include <assert.h>
+
+#define N  4    /* number of test cases */
+
+
+int f(int x1, int x2)
+
+{
+
+  if (x1 + x2 <= 2)
+
+  return (1);
+
+  else return (2);		    
+
+}
+
+
+int main() {    int  i, y;   int x1[N], x2[N];
+
+  // define test cases
+
+    x1[0] = 3; x2[0] = -2;    x1[1] = 4; x2[1] = -3;    x1[2] = 5; x2[2] = -4;    x1[3] = 6; x2[3] = -5; 
+
+    // testing
+
+  for (i = 0; i < N; i++)  {
+
+      y = f(x1[i], x2[i]);       // function under testing
+
+      assert(y ==(x1[i], x2[i] <= 2) ? 1 : 2);   // oracle
+
+    }
+
+   printf("All %d test cases passed\n", N);
+
+    return (0);   
+
+}
+
+-----------
+
+Il programma main() sopra realizza il nostro testing per la funzione f1(). I test cases sono i valori in x1[i] ed x2[i].
+
+Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_39/wrong1.txt b/legacy/Data/ingsw/0222_39/wrong1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0222_39/wrong1.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_39/wrong2.txt b/legacy/Data/ingsw/0222_39/wrong2.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0222_39/wrong2.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_41/correct.txt b/legacy/Data/ingsw/0222_41/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0222_41/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_41/quest.txt b/legacy/Data/ingsw/0222_41/quest.txt
new file mode 100644
index 0000000..77ee0c6
--- /dev/null
+++ b/legacy/Data/ingsw/0222_41/quest.txt
@@ -0,0 +1,55 @@
+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 il seguente programma C:
+
+-----------
+
+#include <stdio.h>
+
+#include <stdlib.h>
+
+#include <assert.h>
+
+#define N  4    /* number of test cases */
+
+
+int f(int x1, int x2)
+
+{
+
+  if (x1 + x2 <= 2)
+
+  return (1);
+
+  else return (2);		    
+
+}
+
+
+int main() {    int  i, y;   int x1[N], x2[N];
+
+  // define test cases
+
+    x1[0] = 3; x2[0] = -2;    x1[1] = 4; x2[1] = -3;    x1[2] = 7; x2[2] = -4;    x1[3] = 8; x2[3] = -5; 
+
+    // testing
+
+  for (i = 0; i < N; i++)  {
+
+      y = f(x1[i], x2[i]);       // function under testing
+
+      assert(y ==(x1[i], x2[i] <= 2) ? 1 : 2);   // oracle
+
+    }
+
+   printf("All %d test cases passed\n", N);
+
+    return (0);   
+
+}
+
+-----------
+
+Il programma main() sopra realizza il nostro testing per la funzione f1(). I test cases sono i valori in x1[i] ed x2[i].
+
+Quale delle seguenti è la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_41/wrong1.txt b/legacy/Data/ingsw/0222_41/wrong1.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0222_41/wrong1.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_41/wrong2.txt b/legacy/Data/ingsw/0222_41/wrong2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0222_41/wrong2.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_5/correct.txt b/legacy/Data/ingsw/0222_5/correct.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0222_5/correct.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_5/quest.txt b/legacy/Data/ingsw/0222_5/quest.txt
new file mode 100644
index 0000000..52b1367
--- /dev/null
+++ b/legacy/Data/ingsw/0222_5/quest.txt
@@ -0,0 +1,15 @@
+img=https://i.imgur.com/6m6ALRb.png
+La transition coverage di un insieme di test cases (cioè sequeze 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:
+
+1) Start PIN validation, card inserted, PIN Entered, Valid PIN, Cancel 3, End PIN Validation 2;
+
+2) Start PIN validation, card inserted, PIN Entered, Invalid PIN, PIN Entered, Valid PIN, Cancel 3, End PIN Validation 2;
+
+3) Start PIN validation, card inserted, PIN Entered, Invalid PIN, PIN Entered, Invalid PIN, PIN Entered, Valid PIN, Cancel 3, End PIN Validation 2.
+
+
+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/0222_5/wrong1.txt b/legacy/Data/ingsw/0222_5/wrong1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0222_5/wrong1.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_5/wrong2.txt b/legacy/Data/ingsw/0222_5/wrong2.txt
new file mode 100644
index 0000000..711ba55
--- /dev/null
+++ b/legacy/Data/ingsw/0222_5/wrong2.txt
@@ -0,0 +1 @@
+40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_50/correct.txt b/legacy/Data/ingsw/0222_50/correct.txt
new file mode 100644
index 0000000..1e091a3
--- /dev/null
+++ b/legacy/Data/ingsw/0222_50/correct.txt
@@ -0,0 +1 @@
+90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_50/quest.txt b/legacy/Data/ingsw/0222_50/quest.txt
new file mode 100644
index 0000000..a3effb0
--- /dev/null
+++ b/legacy/Data/ingsw/0222_50/quest.txt
@@ -0,0 +1,14 @@
+img=https://i.imgur.com/6m6ALRb.png
+La transition coverage di un insieme di test cases (cioè sequeze 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:
+
+
+1) Start PIN validation, card inserted, PIN Entered, Valid PIN, Cancel 3, End PIN Validation 2
+
+2) Start PIN validation, card inserted, PIN Entered, Valid PIN, Cancel 2, End PIN Validation 2
+
+3) Start PIN validation, card inserted, PIN Entered, Invalid PIN, PIN Entered, Invalid PIN, PIN Entered, Invalid PIN, PIN Entered, Invalid PIN, More than 3 failed..., END PIN validation 1;
+
+
+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/0222_50/wrong1.txt b/legacy/Data/ingsw/0222_50/wrong1.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0222_50/wrong1.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_50/wrong2.txt b/legacy/Data/ingsw/0222_50/wrong2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0222_50/wrong2.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_7/correct.txt b/legacy/Data/ingsw/0222_7/correct.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0222_7/correct.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_7/quest.txt b/legacy/Data/ingsw/0222_7/quest.txt
new file mode 100644
index 0000000..97e921b
--- /dev/null
+++ b/legacy/Data/ingsw/0222_7/quest.txt
@@ -0,0 +1,13 @@
+img=https://i.imgur.com/6m6ALRb.png
+La transition coverage di un insieme di test cases (cioè sequeze 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:
+
+1) Start PIN validation, card inserted, PIN Entered, Valid PIN, Cancel 3, End PIN Validation 2
+
+2) Start PIN validation, card inserted, PIN Entered, Cancel 2, End PIN Validation 2
+
+
+Quale delle seguenti è la migliore stima della transition coverage per i test cases di cui sopra
diff --git a/legacy/Data/ingsw/0222_7/wrong1.txt b/legacy/Data/ingsw/0222_7/wrong1.txt
new file mode 100644
index 0000000..711ba55
--- /dev/null
+++ b/legacy/Data/ingsw/0222_7/wrong1.txt
@@ -0,0 +1 @@
+40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0222_7/wrong2.txt b/legacy/Data/ingsw/0222_7/wrong2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0222_7/wrong2.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_1/correct.txt b/legacy/Data/ingsw/0321_1/correct.txt
new file mode 100644
index 0000000..f3da655
--- /dev/null
+++ b/legacy/Data/ingsw/0321_1/correct.txt
@@ -0,0 +1 @@
+3*(A + 2*B)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_1/quest.txt b/legacy/Data/ingsw/0321_1/quest.txt
new file mode 100644
index 0000000..5d8e650
--- /dev/null
+++ b/legacy/Data/ingsw/0321_1/quest.txt
@@ -0,0 +1 @@
+Il team di sviluppo di un azienda consiste di un senior software engineer e due sviluppatori junior. Usando un approccio agile, ogni iterazione impegna tutti e tre i membri del team per un mese ed occorrono tre iterazioni per completare lo sviluppo. Si assuma che non ci siano "change requests" e che il membro senior costi A Eur/mese ed i membri junior B Eur/mese. Qual'e' il costo dello sviluppo usando un approccio agile ?
diff --git a/legacy/Data/ingsw/0321_1/wrong 1.txt b/legacy/Data/ingsw/0321_1/wrong 1.txt
new file mode 100644
index 0000000..316107c
--- /dev/null
+++ b/legacy/Data/ingsw/0321_1/wrong 1.txt	
@@ -0,0 +1 @@
+A + 2*B
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_1/wrong 2.txt b/legacy/Data/ingsw/0321_1/wrong 2.txt
new file mode 100644
index 0000000..82fe5c7
--- /dev/null
+++ b/legacy/Data/ingsw/0321_1/wrong 2.txt	
@@ -0,0 +1 @@
+3*A + 2*B
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_10/correct.txt b/legacy/Data/ingsw/0321_10/correct.txt
new file mode 100644
index 0000000..466ac31
--- /dev/null
+++ b/legacy/Data/ingsw/0321_10/correct.txt
@@ -0,0 +1 @@
+Gli utenti del sistema lavorano insieme al team di sviluppo per testare il software nel sito di sviluppo.
diff --git a/legacy/Data/ingsw/0321_10/quest.txt b/legacy/Data/ingsw/0321_10/quest.txt
new file mode 100644
index 0000000..c35e04d
--- /dev/null
+++ b/legacy/Data/ingsw/0321_10/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti affermazioni è vera riguardo all'alpha testing ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_10/wrong 1.txt b/legacy/Data/ingsw/0321_10/wrong 1.txt
new file mode 100644
index 0000000..9a5ec0f
--- /dev/null
+++ b/legacy/Data/ingsw/0321_10/wrong 1.txt	
@@ -0,0 +1 @@
+Test automatizzati sono eseguiti su una versione preliminare del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_10/wrong 2.txt b/legacy/Data/ingsw/0321_10/wrong 2.txt
new file mode 100644
index 0000000..e43ca64
--- /dev/null
+++ b/legacy/Data/ingsw/0321_10/wrong 2.txt	
@@ -0,0 +1 @@
+Test automatizzati sono eseguiti sulla prima release del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_11/correct.txt b/legacy/Data/ingsw/0321_11/correct.txt
new file mode 100644
index 0000000..b1a56d9
--- /dev/null
+++ b/legacy/Data/ingsw/0321_11/correct.txt
@@ -0,0 +1 @@
+3*(1 + p)*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_11/quest.txt b/legacy/Data/ingsw/0321_11/quest.txt
new file mode 100644
index 0000000..e383a9d
--- /dev/null
+++ b/legacy/Data/ingsw/0321_11/quest.txt
@@ -0,0 +1 @@
+Un processo di sviluppo agile consiste di 3 iterazioni identiche di costo A. Alla fine di ogni iterazione vengono prese in considerazione le "change requests" e, se ve ne sono, l'iterazione viene ripetuta. Sia p la probabilità che ci siano "change requests" all fine di una iterazione. Il valore atteso del costo del progetto è:
diff --git a/legacy/Data/ingsw/0321_11/wrong 1.txt b/legacy/Data/ingsw/0321_11/wrong 1.txt
new file mode 100644
index 0000000..769cb45
--- /dev/null
+++ b/legacy/Data/ingsw/0321_11/wrong 1.txt	
@@ -0,0 +1 @@
+3*(A + p)
diff --git a/legacy/Data/ingsw/0321_11/wrong 2.txt b/legacy/Data/ingsw/0321_11/wrong 2.txt
new file mode 100644
index 0000000..1045d03
--- /dev/null
+++ b/legacy/Data/ingsw/0321_11/wrong 2.txt	
@@ -0,0 +1 @@
+3*p*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_12/correct.txt b/legacy/Data/ingsw/0321_12/correct.txt
new file mode 100644
index 0000000..04fb622
--- /dev/null
+++ b/legacy/Data/ingsw/0321_12/correct.txt
@@ -0,0 +1 @@
+P = 1/10
diff --git a/legacy/Data/ingsw/0321_12/quest.txt b/legacy/Data/ingsw/0321_12/quest.txt
new file mode 100644
index 0000000..98d8c9c
--- /dev/null
+++ b/legacy/Data/ingsw/0321_12/quest.txt
@@ -0,0 +1 @@
+Una azienda vende software utilizzando un contratto di Service Level Agreement (SLA) per cui l'utente paga 1000 Eur al mese di licenza e l'azienda garantisce che il software sia "up and running". Questo vuol dire che failures del software generano un costo (quello del repair). Sia C = 10000 Eur il costo del repair di una failure e R = P*C il valore atteso (rischio) del costo dovuto alle failures (dove P è la probabilità di una software failure). Ovviamente affinché il business sia profittevole deve essere che R sia al più 1000 Eur. Qual'e' il valore massimo di P che garantisce la validità del modello di business di cui sopra ?
diff --git a/legacy/Data/ingsw/0321_12/wrong 1.txt b/legacy/Data/ingsw/0321_12/wrong 1.txt
new file mode 100644
index 0000000..76d3cf5
--- /dev/null
+++ b/legacy/Data/ingsw/0321_12/wrong 1.txt	
@@ -0,0 +1 @@
+P = 1/1000
diff --git a/legacy/Data/ingsw/0321_12/wrong 2.txt b/legacy/Data/ingsw/0321_12/wrong 2.txt
new file mode 100644
index 0000000..79f61ef
--- /dev/null
+++ b/legacy/Data/ingsw/0321_12/wrong 2.txt	
@@ -0,0 +1 @@
+P=1/10000
diff --git a/legacy/Data/ingsw/0321_13/correct.txt b/legacy/Data/ingsw/0321_13/correct.txt
new file mode 100644
index 0000000..e639181
--- /dev/null
+++ b/legacy/Data/ingsw/0321_13/correct.txt
@@ -0,0 +1 @@
+S = (1/b)*ln(C/R)
diff --git a/legacy/Data/ingsw/0321_13/quest.txt b/legacy/Data/ingsw/0321_13/quest.txt
new file mode 100644
index 0000000..074190a
--- /dev/null
+++ b/legacy/Data/ingsw/0321_13/quest.txt
@@ -0,0 +1 @@
+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 = exp(-b*S), dove b è una opportuna costante note da dati storici aziendali. Quale sarà il costo dello sviluppo S di un software il cui costo della failure è C ed il rischio ammesso è R?
diff --git a/legacy/Data/ingsw/0321_13/wrong 1.txt b/legacy/Data/ingsw/0321_13/wrong 1.txt
new file mode 100644
index 0000000..587fc4b
--- /dev/null
+++ b/legacy/Data/ingsw/0321_13/wrong 1.txt	
@@ -0,0 +1 @@
+S = (1/b)*ln(R/C)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_13/wrong 2.txt b/legacy/Data/ingsw/0321_13/wrong 2.txt
new file mode 100644
index 0000000..7e82f01
--- /dev/null
+++ b/legacy/Data/ingsw/0321_13/wrong 2.txt	
@@ -0,0 +1 @@
+S = b*ln(R/C)
diff --git a/legacy/Data/ingsw/0321_14/correct.txt b/legacy/Data/ingsw/0321_14/correct.txt
new file mode 100644
index 0000000..b74296c
--- /dev/null
+++ b/legacy/Data/ingsw/0321_14/correct.txt
@@ -0,0 +1,68 @@
+<pre>
+model System
+
+parameter Integer F1 = 1;
+
+parameter Integer F2 = 2;
+
+parameter Integer F3 = 3;
+
+parameter Integer End = 4;
+
+parameter Real p = 0.3;
+
+parameter Real A[4, 4] =
+
+[
+
+p, 1-p, 0, 0;
+
+p, 0, 1-p, 0;
+
+p, 0, 0, 1-p;
+
+0, 0, 0, 1
+
+];
+
+Integer x;  Real r1024;
+
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+
+algorithm
+
+when initial() then
+
+   state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+
+   x := F1;
+
+   r1024 := 0;
+
+elsewhen sample(0,1) then
+
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+
+if (r1024 <= A[x, F1]) then
+
+ x := F1;
+
+ elseif (r1024 <= A[x, F1] + A[x, F2]) then
+
+ x := F2;
+
+ elseif (r1024 <= A[x, F1] + A[x, F2] + A[x, F3]) then
+
+ x := F3;
+
+ else
+
+ x := End;
+
+end if;
+
+end when;
+
+end System;
+
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_14/quest.txt b/legacy/Data/ingsw/0321_14/quest.txt
new file mode 100644
index 0000000..35d991e
--- /dev/null
+++ b/legacy/Data/ingsw/0321_14/quest.txt
@@ -0,0 +1,2 @@
+img=https://i.imgur.com/6cnLynh.png
+Si consideri la seguente Markov Chain, quale dei seguenti modelli Modelica fornisce un modello ragionevole per la Markov Chain di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_14/wrong 1.txt b/legacy/Data/ingsw/0321_14/wrong 1.txt
new file mode 100644
index 0000000..c7e45ef
--- /dev/null
+++ b/legacy/Data/ingsw/0321_14/wrong 1.txt	
@@ -0,0 +1,68 @@
+<pre>
+model System
+
+parameter Integer F1 = 1;
+
+parameter Integer F2 = 2;
+
+parameter Integer F3 = 3;
+
+parameter Integer End = 4;
+
+parameter Real p = 0.3;
+
+parameter Real A[4, 4] =
+
+[
+
+p, 0, 1-p, 0;
+
+0, p, 1-p, 0;
+
+p, 0, 0, 1-p;
+
+0, 0, 0, 1
+
+];
+
+Integer x;  Real r1024;
+
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+
+algorithm
+
+when initial() then
+
+state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+
+x := F1;
+
+r1024 := 0;
+
+elsewhen sample(0,1) then
+
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+
+if (r1024 <= A[x, F1]) then
+
+ x := F1;
+
+ elseif (r1024 <= A[x, F1] + A[x, F2]) then
+
+ x := F2;
+
+ elseif (r1024 <= A[x, F1] + A[x, F2] + A[x, F3]) then
+
+ x := F3;
+
+ else
+
+ x := End;
+
+end if;
+
+end when;
+
+end System;
+
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_14/wrong 2.txt b/legacy/Data/ingsw/0321_14/wrong 2.txt
new file mode 100644
index 0000000..099e40c
--- /dev/null
+++ b/legacy/Data/ingsw/0321_14/wrong 2.txt	
@@ -0,0 +1,67 @@
+<pre>
+model System
+
+parameter Integer F1 = 1;
+
+parameter Integer F2 = 2;
+
+parameter Integer F3 = 3;
+
+parameter Integer End = 4;
+
+parameter Real p = 0.3;
+
+parameter Real A[4, 4] =
+
+[
+
+p, 0 , 1-p, 0;
+
+p, 1-p, 0, 0;
+
+p, 0, 0, 1-p;
+
+0, 0, 0, 1
+
+];
+
+Integer x;  Real r1024;
+
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+
+algorithm
+
+when initial() then
+
+state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+
+x := F1;
+
+r1024 := 0;
+
+elsewhen sample(0,1) then
+
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+
+if (r1024 <= A[x, F1]) then
+
+ x := F1;
+
+ elseif (r1024 <= A[x, F1] + A[x, F2]) then
+
+ x := F2;
+
+ elseif (r1024 <= A[x, F1] + A[x, F2] + A[x, F3]) then
+
+ x := F3;
+
+ else
+
+ x := End;
+
+end if;
+
+end when;
+
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_15/correct.txt b/legacy/Data/ingsw/0321_15/correct.txt
new file mode 100644
index 0000000..2563af3
--- /dev/null
+++ b/legacy/Data/ingsw/0321_15/correct.txt
@@ -0,0 +1 @@
+Plan driven
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_15/quest.txt b/legacy/Data/ingsw/0321_15/quest.txt
new file mode 100644
index 0000000..9a415e5
--- /dev/null
+++ b/legacy/Data/ingsw/0321_15/quest.txt
@@ -0,0 +1 @@
+Un azienda ha un team di sviluppo in cui il 90% dei membri è junior (cioè con poca esperienza) ed il 10% è senior (cioè con molta esperienza).  Con l'obiettivo di massimizzare il numero di progetti completati nell'unità di tempo, quale dei seguenti modelli di sviluppo software appare più opportuno.
diff --git a/legacy/Data/ingsw/0321_15/wrong 1.txt b/legacy/Data/ingsw/0321_15/wrong 1.txt
new file mode 100644
index 0000000..feae3c0
--- /dev/null
+++ b/legacy/Data/ingsw/0321_15/wrong 1.txt	
@@ -0,0 +1 @@
+Basato sul riuso
diff --git a/legacy/Data/ingsw/0321_15/wrong 2.txt b/legacy/Data/ingsw/0321_15/wrong 2.txt
new file mode 100644
index 0000000..f28b849
--- /dev/null
+++ b/legacy/Data/ingsw/0321_15/wrong 2.txt	
@@ -0,0 +1 @@
+Iterativo
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_16/correct.txt b/legacy/Data/ingsw/0321_16/correct.txt
new file mode 100644
index 0000000..58e85d7
--- /dev/null
+++ b/legacy/Data/ingsw/0321_16/correct.txt
@@ -0,0 +1,40 @@
+<pre>
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+block Controller
+
+InputInteger x;
+
+OutputInteger Integer w;
+
+...
+
+end Controller;
+
+block Plant
+
+InputInteger u;
+
+OutputInteger y;
+
+...
+
+end Plant;
+
+class System
+
+Controller k;
+
+Plant p;
+
+equation
+
+connect(p.y, k.x);
+
+connect(k.w, p.u);
+
+end System;
+
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_16/quest.txt b/legacy/Data/ingsw/0321_16/quest.txt
new file mode 100644
index 0000000..ca5c33a
--- /dev/null
+++ b/legacy/Data/ingsw/0321_16/quest.txt
@@ -0,0 +1 @@
+Un sistema consiste di due sottosistemi: un controller ed un plant (sistema controllato). Il controllore misura l'output del plant e manda comandi al plant in accordo. Quale dei seguenti schemi Modelica modella l'architettura di sistema descritta sopra ?
diff --git a/legacy/Data/ingsw/0321_16/wrong 1.txt b/legacy/Data/ingsw/0321_16/wrong 1.txt
new file mode 100644
index 0000000..16efe9b
--- /dev/null
+++ b/legacy/Data/ingsw/0321_16/wrong 1.txt	
@@ -0,0 +1,40 @@
+<pre>
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+block Controller
+
+InputInteger x;
+
+OutputInteger Integer w;
+
+...
+
+end Controller;
+
+block Plant
+
+InputInteger u;
+
+OutputInteger y;
+
+...
+
+end Plant;
+
+class System
+
+Controller k;
+
+Plant p;
+
+equation
+
+connect(p.y, p.u);
+
+connect(k.w, k.u);
+
+end System;
+
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_16/wrong 2.txt b/legacy/Data/ingsw/0321_16/wrong 2.txt
new file mode 100644
index 0000000..6e931cd
--- /dev/null
+++ b/legacy/Data/ingsw/0321_16/wrong 2.txt	
@@ -0,0 +1,39 @@
+<pre>
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+block Controller
+
+InputInteger x;
+
+OutputInteger Integer w;
+
+...
+
+end Controller;
+
+block Plant
+
+InputInteger u;
+
+OutputInteger y;
+
+...
+
+end Plant;
+
+class System
+
+Controller k;
+
+Plant p;
+
+equation
+
+connect(p.y, k.w);
+
+connect(k.x, p.u);
+
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_17/correct.txt b/legacy/Data/ingsw/0321_17/correct.txt
new file mode 100644
index 0000000..3e05ae7
--- /dev/null
+++ b/legacy/Data/ingsw/0321_17/correct.txt
@@ -0,0 +1 @@
+La variabile x è fuori dall'intervallo [0, 5].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_17/quest.txt b/legacy/Data/ingsw/0321_17/quest.txt
new file mode 100644
index 0000000..fd92d29
--- /dev/null
+++ b/legacy/Data/ingsw/0321_17/quest.txt
@@ -0,0 +1,31 @@
+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?
+
diff --git a/legacy/Data/ingsw/0321_17/wrong 1.txt b/legacy/Data/ingsw/0321_17/wrong 1.txt
new file mode 100644
index 0000000..7e7f05d
--- /dev/null
+++ b/legacy/Data/ingsw/0321_17/wrong 1.txt	
@@ -0,0 +1 @@
+La variable x è minore di 0.
diff --git a/legacy/Data/ingsw/0321_17/wrong 2.txt b/legacy/Data/ingsw/0321_17/wrong 2.txt
new file mode 100644
index 0000000..750bfd2
--- /dev/null
+++ b/legacy/Data/ingsw/0321_17/wrong 2.txt	
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [0, 5].
diff --git a/legacy/Data/ingsw/0321_18/correct.txt b/legacy/Data/ingsw/0321_18/correct.txt
new file mode 100644
index 0000000..20bf664
--- /dev/null
+++ b/legacy/Data/ingsw/0321_18/correct.txt
@@ -0,0 +1 @@
+Sviluppo Plan-driven.
diff --git a/legacy/Data/ingsw/0321_18/quest.txt b/legacy/Data/ingsw/0321_18/quest.txt
new file mode 100644
index 0000000..367a9e2
--- /dev/null
+++ b/legacy/Data/ingsw/0321_18/quest.txt
@@ -0,0 +1 @@
+Si pianifica lo sviluppo di un sistema software per controllare il sistema di anti-lock braking in un automobile. Quale dei seguenti è il tipico processo software usato per questo tipo di sistema software ?
diff --git a/legacy/Data/ingsw/0321_18/wrong 1.txt b/legacy/Data/ingsw/0321_18/wrong 1.txt
new file mode 100644
index 0000000..61e542a
--- /dev/null
+++ b/legacy/Data/ingsw/0321_18/wrong 1.txt	
@@ -0,0 +1 @@
+Sviluppo Iterativo.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_18/wrong 2.txt b/legacy/Data/ingsw/0321_18/wrong 2.txt
new file mode 100644
index 0000000..04301d6
--- /dev/null
+++ b/legacy/Data/ingsw/0321_18/wrong 2.txt	
@@ -0,0 +1 @@
+Extreme programming.
diff --git a/legacy/Data/ingsw/0321_19/correct.txt b/legacy/Data/ingsw/0321_19/correct.txt
new file mode 100644
index 0000000..6bbf6f3
--- /dev/null
+++ b/legacy/Data/ingsw/0321_19/correct.txt
@@ -0,0 +1 @@
+Le attività di definizione dei requisiti e di sviluppo sono interleaved.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_19/quest.txt b/legacy/Data/ingsw/0321_19/quest.txt
new file mode 100644
index 0000000..d0df919
--- /dev/null
+++ b/legacy/Data/ingsw/0321_19/quest.txt
@@ -0,0 +1 @@
+Focalizzandosi sui metodi agile di sviluppo del software, quale delle seguenti affermazioni è vera?
diff --git a/legacy/Data/ingsw/0321_19/wrong 1.txt b/legacy/Data/ingsw/0321_19/wrong 1.txt
new file mode 100644
index 0000000..45da4a7
--- /dev/null
+++ b/legacy/Data/ingsw/0321_19/wrong 1.txt	
@@ -0,0 +1 @@
+Per evitare di sprecare tempo durante la fase di sviluppo del software, il customer non è mai coinvolto nel processo di sviluppo del software.
diff --git a/legacy/Data/ingsw/0321_19/wrong 2.txt b/legacy/Data/ingsw/0321_19/wrong 2.txt
new file mode 100644
index 0000000..ddbf5eb
--- /dev/null
+++ b/legacy/Data/ingsw/0321_19/wrong 2.txt	
@@ -0,0 +1 @@
+Per evitare di sprecare tempo durante la fase di sviluppo del software, questa inizia solo quando i requisiti sono stati completamente definiti. 
diff --git a/legacy/Data/ingsw/0321_2/correct.txt b/legacy/Data/ingsw/0321_2/correct.txt
new file mode 100644
index 0000000..cee9602
--- /dev/null
+++ b/legacy/Data/ingsw/0321_2/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/AFS4W2C.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_2/quest.txt b/legacy/Data/ingsw/0321_2/quest.txt
new file mode 100644
index 0000000..bdf9fb8
--- /dev/null
+++ b/legacy/Data/ingsw/0321_2/quest.txt
@@ -0,0 +1 @@
+Si consideri un software sviluppato seguendo un approccio plan-driven implementato con tre fasi: F1, F2, F3. Dopo ogni fase c'e' una probabilità p di dover ripeter la fase precedente ed una probabilità (1 - p) di passare alla fase successiva (sino ad arrivare al termine dello sviluppo). Quale delle seguenti catene di Markov modella il processo software descritto sopra?
diff --git a/legacy/Data/ingsw/0321_2/wrong 1.txt b/legacy/Data/ingsw/0321_2/wrong 1.txt
new file mode 100644
index 0000000..66185ec
--- /dev/null
+++ b/legacy/Data/ingsw/0321_2/wrong 1.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/Crqd1FF.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_2/wrong 2.txt b/legacy/Data/ingsw/0321_2/wrong 2.txt
new file mode 100644
index 0000000..2079027
--- /dev/null
+++ b/legacy/Data/ingsw/0321_2/wrong 2.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/fmFEpRh.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_20/correct.txt b/legacy/Data/ingsw/0321_20/correct.txt
new file mode 100644
index 0000000..f331550
--- /dev/null
+++ b/legacy/Data/ingsw/0321_20/correct.txt
@@ -0,0 +1,69 @@
+<pre>
+model System
+
+parameter Integer F1 = 1;
+
+parameter Integer F2 = 2;
+
+parameter Integer F3 = 3;
+
+parameter Integer End = 4;
+
+parameter Real p = 0.3;
+
+parameter Real A[4, 4] =
+
+[
+
+0, 1, 0, 0;
+
+p, 0, 1-p, 0;
+
+0, p, 0, 1-p;
+
+0, 0, 0, 1
+
+];
+
+Integer x;  Real r1024;
+
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+
+algorithm
+
+when initial() then
+
+   state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+
+   x := F1;
+
+   r1024 := 0;
+
+elsewhen sample(0,1) then
+
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+
+if (r1024 <= A[x, F1]) then
+
+ x := F1;
+
+ elseif (r1024 <= A[x, F1] + A[x, F2]) then
+
+ x := F2;
+
+ elseif (r1024 <= A[x, F1] + A[x, F2] + A[x, F3]) then
+
+ x := F3;
+
+ else
+
+ x := End;
+
+end if;
+
+end when;
+
+end System;
+
+
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_20/quest.txt b/legacy/Data/ingsw/0321_20/quest.txt
new file mode 100644
index 0000000..82d67f0
--- /dev/null
+++ b/legacy/Data/ingsw/0321_20/quest.txt
@@ -0,0 +1,2 @@
+img=https://i.imgur.com/l6Qc8kQ.png
+Si consideri la seguente Markov Chain, quale dei seguenti modelli Modelica fornisce un modello ragionevole per la Markov Chain?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_20/wrong 1.txt b/legacy/Data/ingsw/0321_20/wrong 1.txt
new file mode 100644
index 0000000..18b6dcd
--- /dev/null
+++ b/legacy/Data/ingsw/0321_20/wrong 1.txt	
@@ -0,0 +1,67 @@
+<pre>
+model System
+
+parameter Integer F1 = 1;
+
+parameter Integer F2 = 2;
+
+parameter Integer F3 = 3;
+
+parameter Integer End = 4;
+
+parameter Real p = 0.3;
+
+parameter Real A[4, 4] =
+
+[
+
+0, 1, 0, 0;
+
+p, 1-p, 0, 0;
+
+0, 0, p, 1-p;
+
+0, 0, 0, 1
+
+];
+
+Integer x;  Real r1024;
+
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+
+algorithm
+
+when initial() then
+
+state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+
+x := F1;
+
+r1024 := 0;
+
+elsewhen sample(0,1) then
+
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+
+if (r1024 <= A[x, F1]) then
+
+ x := F1;
+
+ elseif (r1024 <= A[x, F1] + A[x, F2]) then
+
+ x := F2;
+
+ elseif (r1024 <= A[x, F1] + A[x, F2] + A[x, F3]) then
+
+ x := F3;
+
+ else
+
+ x := End;
+
+end if;
+
+end when;
+
+end System;
+</pre>
diff --git a/legacy/Data/ingsw/0321_20/wrong 2.txt b/legacy/Data/ingsw/0321_20/wrong 2.txt
new file mode 100644
index 0000000..f66d694
--- /dev/null
+++ b/legacy/Data/ingsw/0321_20/wrong 2.txt	
@@ -0,0 +1,68 @@
+<pre>
+model System
+
+parameter Integer F1 = 1;
+
+parameter Integer F2 = 2;
+
+parameter Integer F3 = 3;
+
+parameter Integer End = 4;
+
+parameter Real p = 0.3;
+
+parameter Real A[4, 4] =
+
+[
+
+0, 1, 0, 0;
+
+p, 0, 0, 1-p;
+
+0, 0, p, 1-p;
+
+0, 0, 0, 1
+
+];
+
+Integer x;  Real r1024;
+
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+
+algorithm
+
+when initial() then
+
+state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+
+x := F1;
+
+r1024 := 0;
+
+elsewhen sample(0,1) then
+
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+
+if (r1024 <= A[x, F1]) then
+
+ x := F1;
+
+ elseif (r1024 <= A[x, F1] + A[x, F2]) then
+
+ x := F2;
+
+ elseif (r1024 <= A[x, F1] + A[x, F2] + A[x, F3]) then
+
+ x := F3;
+
+ else
+
+ x := End;
+
+end if;
+
+end when;
+
+end System;
+
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_21/correct.txt b/legacy/Data/ingsw/0321_21/correct.txt
new file mode 100644
index 0000000..37e1847
--- /dev/null
+++ b/legacy/Data/ingsw/0321_21/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/hrzgmMX.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_21/quest.txt b/legacy/Data/ingsw/0321_21/quest.txt
new file mode 100644
index 0000000..269050d
--- /dev/null
+++ b/legacy/Data/ingsw/0321_21/quest.txt
@@ -0,0 +1 @@
+Si consideri un software sviluppato seguendo un approccio plan-driven implementato con tre fasi: F1, F2, F3. Le "change requests"  arrivano con probabilità p dopo ciascuna fase e provocano la ripetizione (con relativo costo) di tutte le fasi che precedono. Quali delle seguenti catene di Markov modella lo sviluppo software descritto.
diff --git a/legacy/Data/ingsw/0321_21/wrong 1.txt b/legacy/Data/ingsw/0321_21/wrong 1.txt
new file mode 100644
index 0000000..eb880d8
--- /dev/null
+++ b/legacy/Data/ingsw/0321_21/wrong 1.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/FzqL7wa.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_21/wrong 2.txt b/legacy/Data/ingsw/0321_21/wrong 2.txt
new file mode 100644
index 0000000..1f25f6d
--- /dev/null
+++ b/legacy/Data/ingsw/0321_21/wrong 2.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/PHih8ak.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_23/correct.txt b/legacy/Data/ingsw/0321_23/correct.txt
new file mode 100644
index 0000000..986f4e1
--- /dev/null
+++ b/legacy/Data/ingsw/0321_23/correct.txt
@@ -0,0 +1 @@
+I metodi agile sono metodi di sviluppo incrementale. 
diff --git a/legacy/Data/ingsw/0321_23/quest.txt b/legacy/Data/ingsw/0321_23/quest.txt
new file mode 100644
index 0000000..fe96eab
--- /dev/null
+++ b/legacy/Data/ingsw/0321_23/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti affermazioni è vera riguardo ai metodi agile ?
diff --git a/legacy/Data/ingsw/0321_23/wrong 1.txt b/legacy/Data/ingsw/0321_23/wrong 1.txt
new file mode 100644
index 0000000..06e87ff
--- /dev/null
+++ b/legacy/Data/ingsw/0321_23/wrong 1.txt	
@@ -0,0 +1 @@
+I metodi agile sono metodi di sviluppo plan-driven. 
diff --git a/legacy/Data/ingsw/0321_23/wrong 2.txt b/legacy/Data/ingsw/0321_23/wrong 2.txt
new file mode 100644
index 0000000..d291b48
--- /dev/null
+++ b/legacy/Data/ingsw/0321_23/wrong 2.txt	
@@ -0,0 +1 @@
+I metodi agile sono metodi di sviluppo orientato al riuso. 
diff --git a/legacy/Data/ingsw/0321_24/correct.txt b/legacy/Data/ingsw/0321_24/correct.txt
new file mode 100644
index 0000000..d4074cf
--- /dev/null
+++ b/legacy/Data/ingsw/0321_24/correct.txt
@@ -0,0 +1 @@
+Testare funzionalità di unità software individuali, oggetti, classi o metodi.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_24/quest.txt b/legacy/Data/ingsw/0321_24/quest.txt
new file mode 100644
index 0000000..b8b36ab
--- /dev/null
+++ b/legacy/Data/ingsw/0321_24/quest.txt
@@ -0,0 +1 @@
+Unit testing si concentra su:
diff --git a/legacy/Data/ingsw/0321_24/wrong 1.txt b/legacy/Data/ingsw/0321_24/wrong 1.txt
new file mode 100644
index 0000000..bc8b2f6
--- /dev/null
+++ b/legacy/Data/ingsw/0321_24/wrong 1.txt	
@@ -0,0 +1 @@
+Testare l'interazione tra componenti.
diff --git a/legacy/Data/ingsw/0321_24/wrong 2.txt b/legacy/Data/ingsw/0321_24/wrong 2.txt
new file mode 100644
index 0000000..a801d80
--- /dev/null
+++ b/legacy/Data/ingsw/0321_24/wrong 2.txt	
@@ -0,0 +1 @@
+Testare le interfacce di ciascuna componente.
diff --git a/legacy/Data/ingsw/0321_27/correct.txt b/legacy/Data/ingsw/0321_27/correct.txt
new file mode 100644
index 0000000..35e7b12
--- /dev/null
+++ b/legacy/Data/ingsw/0321_27/correct.txt
@@ -0,0 +1 @@
+2*A*(p +1)
diff --git a/legacy/Data/ingsw/0321_27/quest.txt b/legacy/Data/ingsw/0321_27/quest.txt
new file mode 100644
index 0000000..67e890e
--- /dev/null
+++ b/legacy/Data/ingsw/0321_27/quest.txt
@@ -0,0 +1 @@
+Si consideri un software sviluppato seguendo un approccio iterativo implementato con due fasi: F1 seguita da F2. Ciascuna fase ha costo A e deve essere ripetuta una seconda volta con probabilità p. Qual'e' il costo atteso dello sviluppo dell'intero software?
diff --git a/legacy/Data/ingsw/0321_27/wrong 1.txt b/legacy/Data/ingsw/0321_27/wrong 1.txt
new file mode 100644
index 0000000..b84e570
--- /dev/null
+++ b/legacy/Data/ingsw/0321_27/wrong 1.txt	
@@ -0,0 +1 @@
+2*A*(p + 2)
diff --git a/legacy/Data/ingsw/0321_27/wrong 2.txt b/legacy/Data/ingsw/0321_27/wrong 2.txt
new file mode 100644
index 0000000..ebab514
--- /dev/null
+++ b/legacy/Data/ingsw/0321_27/wrong 2.txt	
@@ -0,0 +1 @@
+3*A*(p + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_28/correct.txt b/legacy/Data/ingsw/0321_28/correct.txt
new file mode 100644
index 0000000..489e74c
--- /dev/null
+++ b/legacy/Data/ingsw/0321_28/correct.txt
@@ -0,0 +1 @@
+5*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_28/quest.txt b/legacy/Data/ingsw/0321_28/quest.txt
new file mode 100644
index 0000000..7441816
--- /dev/null
+++ b/legacy/Data/ingsw/0321_28/quest.txt
@@ -0,0 +1 @@
+Un processo di sviluppo plan-driven consiste di 2 fasi F1, F2, ciascuna costo A. Alla fine di ogni fase vengono prese in considerazione le "change requests" e, se ve ne sono, lo sviluppo viene ripetuto a partire dalla prima iterazione.  Quindi con nessuna change request si hanno le fasi: F1, F2 e costo 2A. Con una "change request" dopo la prima fase si ha: F1, F1, F2 e costo 3A.  Con una change request dopo la fase 2 si ha: F1, F2, F1, F2 e costo 4A. Qual'è il costo nel caso in cui ci siano change requests sia dopo la fase 1 che dopo la fase 2.
diff --git a/legacy/Data/ingsw/0321_28/wrong 1.txt b/legacy/Data/ingsw/0321_28/wrong 1.txt
new file mode 100644
index 0000000..bf91afb
--- /dev/null
+++ b/legacy/Data/ingsw/0321_28/wrong 1.txt	
@@ -0,0 +1 @@
+7*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_28/wrong 2.txt b/legacy/Data/ingsw/0321_28/wrong 2.txt
new file mode 100644
index 0000000..23cbd0e
--- /dev/null
+++ b/legacy/Data/ingsw/0321_28/wrong 2.txt	
@@ -0,0 +1 @@
+6*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_29/correct.txt b/legacy/Data/ingsw/0321_29/correct.txt
new file mode 100644
index 0000000..aed001f
--- /dev/null
+++ b/legacy/Data/ingsw/0321_29/correct.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [1, 4] oppure nell'intervallo [15, 20].
diff --git a/legacy/Data/ingsw/0321_29/quest.txt b/legacy/Data/ingsw/0321_29/quest.txt
new file mode 100644
index 0000000..9eb2619
--- /dev/null
+++ b/legacy/Data/ingsw/0321_29/quest.txt
@@ -0,0 +1,31 @@
+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?
diff --git a/legacy/Data/ingsw/0321_29/wrong 1.txt b/legacy/Data/ingsw/0321_29/wrong 1.txt
new file mode 100644
index 0000000..bc08e8a
--- /dev/null
+++ b/legacy/Data/ingsw/0321_29/wrong 1.txt	
@@ -0,0 +1 @@
+La variabile x è fuori dall'intervallo [1, 4] e fuori dall'intervallo [15, 20].
diff --git a/legacy/Data/ingsw/0321_29/wrong 2.txt b/legacy/Data/ingsw/0321_29/wrong 2.txt
new file mode 100644
index 0000000..52ad14a
--- /dev/null
+++ b/legacy/Data/ingsw/0321_29/wrong 2.txt	
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [1, 4] e fuori dall'intervallo [15, 20].
diff --git a/legacy/Data/ingsw/0321_30/correct.txt b/legacy/Data/ingsw/0321_30/correct.txt
new file mode 100644
index 0000000..8cd4fca
--- /dev/null
+++ b/legacy/Data/ingsw/0321_30/correct.txt
@@ -0,0 +1,26 @@
+<pre>
+class System
+
+Real x; // MB in buffer
+
+Real u; // input pulse
+
+initial equation
+
+x = 3;
+
+u = 0;
+
+equation
+
+when sample(0, 1) then
+
+  u = 1 - pre(u);
+
+end when;
+
+der(x) = 2*u - 1.0;
+
+end System;
+
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_30/quest.txt b/legacy/Data/ingsw/0321_30/quest.txt
new file mode 100644
index 0000000..6b6eb9d
--- /dev/null
+++ b/legacy/Data/ingsw/0321_30/quest.txt
@@ -0,0 +1 @@
+Un I/O buffer è alimentato da una componente che fornisce un input periodico di periodo 2 secondi. Durante la prima metà del periodo, l'input rate è 2MB/s mentre durante la seconda metà del periodo l'input rate è 0. Quindi l'input rate medio è di 1MB/s. L' I/O buffer, a sua volta, alimenta una componente che richiede (in media) 1MB/s. Quale dei seguenti modelli Modelica  è un modello ragionevole per il sistema descritto sopra ?
diff --git a/legacy/Data/ingsw/0321_30/wrong 1.txt b/legacy/Data/ingsw/0321_30/wrong 1.txt
new file mode 100644
index 0000000..d9a0133
--- /dev/null
+++ b/legacy/Data/ingsw/0321_30/wrong 1.txt	
@@ -0,0 +1,26 @@
+<pre>
+class System
+
+Real x; // MB in buffer
+
+Real u; // input pulse
+
+initial equation
+
+x = 3;
+
+u = 0;
+
+equation
+
+when sample(0, 1) then
+
+  u = 1 - pre(u);
+
+end when;
+
+der(x) = 2*u - 2.0;
+
+end System;
+
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_30/wrong 2.txt b/legacy/Data/ingsw/0321_30/wrong 2.txt
new file mode 100644
index 0000000..e11b34d
--- /dev/null
+++ b/legacy/Data/ingsw/0321_30/wrong 2.txt	
@@ -0,0 +1,25 @@
+<pre>
+class System
+
+Real x; // MB in buffer
+
+Real u; // input pulse
+
+initial equation
+
+x = 3;
+
+u = 0;
+
+equation
+
+when sample(0, 1) then
+
+  u = 1 - pre(u);
+
+end when;
+
+der(x) = 2*u + 1.0;
+
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_31/correct.txt b/legacy/Data/ingsw/0321_31/correct.txt
new file mode 100644
index 0000000..07800da
--- /dev/null
+++ b/legacy/Data/ingsw/0321_31/correct.txt
@@ -0,0 +1 @@
+(1 + p)*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_31/quest.txt b/legacy/Data/ingsw/0321_31/quest.txt
new file mode 100644
index 0000000..6e4c617
--- /dev/null
+++ b/legacy/Data/ingsw/0321_31/quest.txt
@@ -0,0 +1 @@
+Un processo di sviluppo agile consiste di varie iterazioni. Alla fine di ogni iterazione vengono prese in considerazione le "change requests" e, se ve ne sono, l'iterazione viene ripetuta. Sia p la probabilità che ci siano "change requests" all fine di una iterazione e sia A il costo di una iterazione. Il valore atteso del costo per l'iterazione è:
diff --git a/legacy/Data/ingsw/0321_31/wrong 1.txt b/legacy/Data/ingsw/0321_31/wrong 1.txt
new file mode 100644
index 0000000..8c7e5a6
--- /dev/null
+++ b/legacy/Data/ingsw/0321_31/wrong 1.txt	
@@ -0,0 +1 @@
+A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_31/wrong 2.txt b/legacy/Data/ingsw/0321_31/wrong 2.txt
new file mode 100644
index 0000000..14dff62
--- /dev/null
+++ b/legacy/Data/ingsw/0321_31/wrong 2.txt	
@@ -0,0 +1 @@
+p*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_32/correct.txt b/legacy/Data/ingsw/0321_32/correct.txt
new file mode 100644
index 0000000..1c03108
--- /dev/null
+++ b/legacy/Data/ingsw/0321_32/correct.txt
@@ -0,0 +1 @@
+Costruire un prototipo, eseguirlo usando dati storici dai log di produzione e valutare la capacità del prototipo di ridurre gli scarti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_32/quest.txt b/legacy/Data/ingsw/0321_32/quest.txt
new file mode 100644
index 0000000..49d08f9
--- /dev/null
+++ b/legacy/Data/ingsw/0321_32/quest.txt
@@ -0,0 +1 @@
+Una azienda manifatturiera desidera costruire un sistema software per monitorare (attraverso sensori) la produzione al fine di ridurre gli scarti. Quali delle seguenti attività contribuisce a validare i requisiti del sistema.
diff --git a/legacy/Data/ingsw/0321_32/wrong 1.txt b/legacy/Data/ingsw/0321_32/wrong 1.txt
new file mode 100644
index 0000000..5187be2
--- /dev/null
+++ b/legacy/Data/ingsw/0321_32/wrong 1.txt	
@@ -0,0 +1 @@
+Costruire un prototipo, eseguirlo usando dati storici dai log di produzione e valutarne le performance.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_32/wrong 2.txt b/legacy/Data/ingsw/0321_32/wrong 2.txt
new file mode 100644
index 0000000..52330c1
--- /dev/null
+++ b/legacy/Data/ingsw/0321_32/wrong 2.txt	
@@ -0,0 +1 @@
+Costruire un prototipo, eseguirlo usando dati storici dai log di produzione ed identificare errori di implementazione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_36/correct.txt b/legacy/Data/ingsw/0321_36/correct.txt
new file mode 100644
index 0000000..f8c9568
--- /dev/null
+++ b/legacy/Data/ingsw/0321_36/correct.txt
@@ -0,0 +1 @@
+Per tutti gli istanti di tempo della forma 1 + 4*k (con k = 0, 1, 2, 3, ...) x vale 1.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_36/quest.txt b/legacy/Data/ingsw/0321_36/quest.txt
new file mode 100644
index 0000000..c00055b
--- /dev/null
+++ b/legacy/Data/ingsw/0321_36/quest.txt
@@ -0,0 +1,21 @@
+Si consideri il seguente modello Modelica:
+<pre>
+// class System
+
+Integer x;
+
+initial equation
+
+x = 0;
+
+equation
+
+when sample(0, 2) then
+
+    x = 1 - pre(x);
+
+end when;
+
+end System; //
+</pre>
+Quale delle seguenti affermazioni è vera per la variabile intera x?
diff --git a/legacy/Data/ingsw/0321_36/wrong 1.txt b/legacy/Data/ingsw/0321_36/wrong 1.txt
new file mode 100644
index 0000000..a7af2cb
--- /dev/null
+++ b/legacy/Data/ingsw/0321_36/wrong 1.txt	
@@ -0,0 +1 @@
+Per tutti gli istanti di tempo della forma 1 + 4*k (con k = 0, 1, 2, 3, ...) x vale 0.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_36/wrong 2.txt b/legacy/Data/ingsw/0321_36/wrong 2.txt
new file mode 100644
index 0000000..f485a50
--- /dev/null
+++ b/legacy/Data/ingsw/0321_36/wrong 2.txt	
@@ -0,0 +1 @@
+Per tutti gli istanti di tempo della forma  3 + 4*k (con k = 0, 1, 2, 3, ...) x vale 1.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_37/correct.txt b/legacy/Data/ingsw/0321_37/correct.txt
new file mode 100644
index 0000000..ee47430
--- /dev/null
+++ b/legacy/Data/ingsw/0321_37/correct.txt
@@ -0,0 +1,27 @@
+<pre>
+model System
+
+Integer y;  Real r1024;
+
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+
+equation
+
+y = if (r1024 <= 0.2) then -1 else if (r1024 <= 0.7) then 0 else 1;
+
+algorithm
+
+when initial() then
+
+state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+
+r1024     := 0;
+
+elsewhen sample(0,1) then
+
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+
+end when;
+
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_37/quest.txt b/legacy/Data/ingsw/0321_37/quest.txt
new file mode 100644
index 0000000..a90ebb5
--- /dev/null
+++ b/legacy/Data/ingsw/0321_37/quest.txt
@@ -0,0 +1 @@
+Si consideri l'ambiente (use case) consistente di un utente che ad ogni unità di tempo (ad esempio, un secondo) invia al nostro sistema input -1 con probabilità 0.2, input 0 con probabilità 0.5 ed input 1 con probabilità 0.3. Quale dei seguenti modelli Modelica rappresenta correttamente tale ambiente.
diff --git a/legacy/Data/ingsw/0321_37/wrong 1.txt b/legacy/Data/ingsw/0321_37/wrong 1.txt
new file mode 100644
index 0000000..98dc977
--- /dev/null
+++ b/legacy/Data/ingsw/0321_37/wrong 1.txt	
@@ -0,0 +1,28 @@
+<pre>
+model System
+
+Integer y;  Real r1024;
+
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+
+equation
+
+y = if (r1024 <= 0.3) then -1 else if (r1024 <= 0.7) then 0 else 1;
+
+algorithm
+
+when initial() then
+
+state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+
+r1024     := 0;
+
+elsewhen sample(0,1) then
+
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+
+end when;
+
+end System;
+
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_37/wrong 2.txt b/legacy/Data/ingsw/0321_37/wrong 2.txt
new file mode 100644
index 0000000..dda46fb
--- /dev/null
+++ b/legacy/Data/ingsw/0321_37/wrong 2.txt	
@@ -0,0 +1,27 @@
+<pre>
+model System
+
+Integer y;  Real r1024;
+
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+
+equation
+
+y = if (r1024 <= 0.2) then -1 else if (r1024 <= 0.5) then 0 else 1;
+
+algorithm
+
+when initial() then
+
+state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+
+r1024     := 0;
+
+elsewhen sample(0,1) then
+
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+
+end when;
+
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_38/correct.txt b/legacy/Data/ingsw/0321_38/correct.txt
new file mode 100644
index 0000000..1a8a50a
--- /dev/null
+++ b/legacy/Data/ingsw/0321_38/correct.txt
@@ -0,0 +1 @@
+Per ciascun requisito, dovremmo essere in grado di scrivere un inseme di test che può dimostrare che il sistema sviluppato soddisfa il requisito considerato.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_38/quest.txt b/legacy/Data/ingsw/0321_38/quest.txt
new file mode 100644
index 0000000..580fc18
--- /dev/null
+++ b/legacy/Data/ingsw/0321_38/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive il criterio di "requirements verifiability" che è parte della "requirements validation activity".
diff --git a/legacy/Data/ingsw/0321_38/wrong 1.txt b/legacy/Data/ingsw/0321_38/wrong 1.txt
new file mode 100644
index 0000000..3fdb31e
--- /dev/null
+++ b/legacy/Data/ingsw/0321_38/wrong 1.txt	
@@ -0,0 +1 @@
+Per ciascuna componente del sistema, dovremmo essere in grado di scrivere un insieme di test che può dimostrare che essa soddisfa tutti i requisiti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_38/wrong 2.txt b/legacy/Data/ingsw/0321_38/wrong 2.txt
new file mode 100644
index 0000000..fac8307
--- /dev/null
+++ b/legacy/Data/ingsw/0321_38/wrong 2.txt	
@@ -0,0 +1 @@
+Per ciascuna coppia di componenti, dovremmo essere in grado di scrivere un insieme di test che può dimostrare che l'interazione tra le componenti soddisfa tutti i requisiti di interfaccia.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_4/correct.txt b/legacy/Data/ingsw/0321_4/correct.txt
new file mode 100644
index 0000000..2736f39
--- /dev/null
+++ b/legacy/Data/ingsw/0321_4/correct.txt
@@ -0,0 +1 @@
+A*(2 + p +q)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_4/quest.txt b/legacy/Data/ingsw/0321_4/quest.txt
new file mode 100644
index 0000000..aec403c
--- /dev/null
+++ b/legacy/Data/ingsw/0321_4/quest.txt
@@ -0,0 +1 @@
+Si consideri un software sviluppato seguendo un approccio iterativo implementato con due fasi: F1 seguita da F2. Ciascuna fase ha costo A. Con probabilità p potrebbe essere necessario ripetere F1 una seconda volta.  Con probabilità q potrebbe essere necessario ripetere F2 una seconda volta. Qual'e' il costo atteso dello sviluppo dell'intero software?
diff --git a/legacy/Data/ingsw/0321_4/wrong 1.txt b/legacy/Data/ingsw/0321_4/wrong 1.txt
new file mode 100644
index 0000000..66061d9
--- /dev/null
+++ b/legacy/Data/ingsw/0321_4/wrong 1.txt	
@@ -0,0 +1 @@
+A*(1 + p +q)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_4/wrong 2.txt b/legacy/Data/ingsw/0321_4/wrong 2.txt
new file mode 100644
index 0000000..dd9b48a
--- /dev/null
+++ b/legacy/Data/ingsw/0321_4/wrong 2.txt	
@@ -0,0 +1 @@
+A*(3 + p +q)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_40/correct.txt b/legacy/Data/ingsw/0321_40/correct.txt
new file mode 100644
index 0000000..b126cfb
--- /dev/null
+++ b/legacy/Data/ingsw/0321_40/correct.txt
@@ -0,0 +1 @@
+Requisito funzionale. 
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_40/quest.txt b/legacy/Data/ingsw/0321_40/quest.txt
new file mode 100644
index 0000000..91423cc
--- /dev/null
+++ b/legacy/Data/ingsw/0321_40/quest.txt
@@ -0,0 +1 @@
+"Ogni giorno, per ciascuna clinica, il sistema genererà una lista dei pazienti che hanno un appuntamento quel giorno."
diff --git a/legacy/Data/ingsw/0321_40/wrong 1.txt b/legacy/Data/ingsw/0321_40/wrong 1.txt
new file mode 100644
index 0000000..c09e71c
--- /dev/null
+++ b/legacy/Data/ingsw/0321_40/wrong 1.txt	
@@ -0,0 +1 @@
+Requisito non-funzionale. 
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_40/wrong 2.txt b/legacy/Data/ingsw/0321_40/wrong 2.txt
new file mode 100644
index 0000000..4c69e5b
--- /dev/null
+++ b/legacy/Data/ingsw/0321_40/wrong 2.txt	
@@ -0,0 +1 @@
+Requisito di performance. 
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_8/correct.txt b/legacy/Data/ingsw/0321_8/correct.txt
new file mode 100644
index 0000000..0b6b40f
--- /dev/null
+++ b/legacy/Data/ingsw/0321_8/correct.txt
@@ -0,0 +1,53 @@
+<pre>
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+block C1
+
+InputInteger u;
+
+OutputInteger x;
+
+...
+
+end C1;
+
+block C2
+
+InputInteger u;
+
+OutputInteger x;
+
+...
+
+end C2;
+
+block C3
+
+InputInteger u;
+
+OutputInteger x;
+
+...
+
+end C3;
+
+class System
+
+C1 k1;
+
+C2 k2;
+
+C3 k3;
+
+equation
+
+connect(k1.x, k2.u);
+
+connect(k2.x, k3.u);
+
+connect(k3.x, k1.u);
+
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_8/quest.txt b/legacy/Data/ingsw/0321_8/quest.txt
new file mode 100644
index 0000000..01ba436
--- /dev/null
+++ b/legacy/Data/ingsw/0321_8/quest.txt
@@ -0,0 +1 @@
+Un sistema consiste di tre componenti C1, C2, C3 connesse in una architettura ad anello dove l'output della componente C1 (rispettivamente C2, C3) è mandato all'input della componente C2 (rispettivamente C3, C1). Quale dei seguenti schemi Modelica meglio rappresenta l'architettura descritta ?
diff --git a/legacy/Data/ingsw/0321_8/wrong 1.txt b/legacy/Data/ingsw/0321_8/wrong 1.txt
new file mode 100644
index 0000000..6a2cd60
--- /dev/null
+++ b/legacy/Data/ingsw/0321_8/wrong 1.txt	
@@ -0,0 +1,54 @@
+<pre>
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+block C1
+
+InputInteger u;
+
+OutputInteger x;
+
+...
+
+end C1;
+
+block C2
+
+InputInteger u;
+
+OutputInteger x;
+
+...
+
+end C2;
+
+block C3
+
+InputInteger u;
+
+OutputInteger x;
+
+...
+
+end C3;
+
+class System
+
+C1 k1;
+
+C2 k2;
+
+C3 k3;
+
+equation
+
+connect(k1.x, k1.u);
+
+connect(k2.x, k2.u);
+
+connect(k3.x, k3.u);
+
+end System;
+
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_8/wrong 2.txt b/legacy/Data/ingsw/0321_8/wrong 2.txt
new file mode 100644
index 0000000..34bb9bd
--- /dev/null
+++ b/legacy/Data/ingsw/0321_8/wrong 2.txt	
@@ -0,0 +1,53 @@
+<pre>
+connector InputInteger = input Integer;
+
+connector OutputInteger = output Integer;
+
+block C1
+
+InputInteger u;
+
+OutputInteger x;
+
+...
+
+end C1;
+
+block C2
+
+InputInteger u;
+
+OutputInteger x;
+
+...
+
+end C2;
+
+block C3
+
+InputInteger u;
+
+OutputInteger x;
+
+...
+
+end C3;
+
+class System
+
+C1 k1;
+
+C2 k2;
+
+C3 k3;
+
+equation
+
+connect(k1.x, k3.u);
+
+connect(k3.x, k2.u);
+
+connect(k2.x, k1.u);
+
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_9/correct.txt b/legacy/Data/ingsw/0321_9/correct.txt
new file mode 100644
index 0000000..936832d
--- /dev/null
+++ b/legacy/Data/ingsw/0321_9/correct.txt
@@ -0,0 +1 @@
+3*A + 6*B
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_9/quest.txt b/legacy/Data/ingsw/0321_9/quest.txt
new file mode 100644
index 0000000..9f5e001
--- /dev/null
+++ b/legacy/Data/ingsw/0321_9/quest.txt
@@ -0,0 +1 @@
+Il team di sviluppo di un azienda consiste di un senior software engineer e due sviluppatori junior. Usando un approccio plan-driven (ad esempio, water-fall) la fase di design impegna solo il membro senior per tre mesi e la fase di sviluppo e testing solo i due membri junior per tre mesi. Si assuma che non ci siano "change requests" e che il membro senior costi A Eur/mese ed i membri junior B Eur/mese. Qual'e' il costo dello sviluppo usando un approccio plan-driven come sopra ?
diff --git a/legacy/Data/ingsw/0321_9/wrong 1.txt b/legacy/Data/ingsw/0321_9/wrong 1.txt
new file mode 100644
index 0000000..316107c
--- /dev/null
+++ b/legacy/Data/ingsw/0321_9/wrong 1.txt	
@@ -0,0 +1 @@
+A + 2*B
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0321_9/wrong 2.txt b/legacy/Data/ingsw/0321_9/wrong 2.txt
new file mode 100644
index 0000000..68f09b9
--- /dev/null
+++ b/legacy/Data/ingsw/0321_9/wrong 2.txt	
@@ -0,0 +1 @@
+3*A + 3*B
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_0/correct.txt b/legacy/Data/ingsw/0324_0/correct.txt
new file mode 100644
index 0000000..3fb437d
--- /dev/null
+++ b/legacy/Data/ingsw/0324_0/correct.txt
@@ -0,0 +1 @@
+0.56
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_0/quest.txt b/legacy/Data/ingsw/0324_0/quest.txt
new file mode 100644
index 0000000..858d9c6
--- /dev/null
+++ b/legacy/Data/ingsw/0324_0/quest.txt
@@ -0,0 +1,9 @@
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_0.png
+Un processo software pu� 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 probabilit� 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 probabilit� 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario � una sequenza di stati.
+Qual'e' la probabilit� dello scenario: 1, 3 ? In altri terminti, qual'� la probabilit� che non sia necessario ripetere nessuna fase?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_0/wrong1.txt b/legacy/Data/ingsw/0324_0/wrong1.txt
new file mode 100644
index 0000000..c64601b
--- /dev/null
+++ b/legacy/Data/ingsw/0324_0/wrong1.txt
@@ -0,0 +1 @@
+0.14
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_0/wrong2.txt b/legacy/Data/ingsw/0324_0/wrong2.txt
new file mode 100644
index 0000000..fc54e00
--- /dev/null
+++ b/legacy/Data/ingsw/0324_0/wrong2.txt
@@ -0,0 +1 @@
+0.24
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_1/quest.txt b/legacy/Data/ingsw/0324_1/quest.txt
new file mode 100644
index 0000000..a4a7e01
--- /dev/null
+++ b/legacy/Data/ingsw/0324_1/quest.txt
@@ -0,0 +1,4 @@
+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/ingsw/0324_1/wrong1.txt b/legacy/Data/ingsw/0324_1/wrong1.txt
new file mode 100644
index 0000000..4bcd55f
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0324_1/wrong2.txt
new file mode 100644
index 0000000..a3caf2e
--- /dev/null
+++ b/legacy/Data/ingsw/0324_1/wrong2.txt
@@ -0,0 +1,2 @@
+input12)
+connect(sc2.output23, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_1/wrong3.txt b/legacy/Data/ingsw/0324_1/wrong3.txt
new file mode 100644
index 0000000..1d08fb4
--- /dev/null
+++ b/legacy/Data/ingsw/0324_1/wrong3.txt
@@ -0,0 +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)
+
+
+end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_10/correct.txt b/legacy/Data/ingsw/0324_10/correct.txt
new file mode 100644
index 0000000..aef914a
--- /dev/null
+++ b/legacy/Data/ingsw/0324_10/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che un sistema che soddisfa i requisiti risolve il problema del "customer".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_10/quest.txt b/legacy/Data/ingsw/0324_10/quest.txt
new file mode 100644
index 0000000..9af4805
--- /dev/null
+++ b/legacy/Data/ingsw/0324_10/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "validity check" che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_10/wrong1.txt b/legacy/Data/ingsw/0324_10/wrong1.txt
new file mode 100644
index 0000000..eb23d05
--- /dev/null
+++ b/legacy/Data/ingsw/0324_10/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che non ci siano requisiti in conflitto con altri requisiti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_10/wrong2.txt b/legacy/Data/ingsw/0324_10/wrong2.txt
new file mode 100644
index 0000000..32c628c
--- /dev/null
+++ b/legacy/Data/ingsw/0324_10/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisiti funzionali descrivano tutte le funzionalità del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_11/quest.txt b/legacy/Data/ingsw/0324_11/quest.txt
new file mode 100644
index 0000000..26df850
--- /dev/null
+++ b/legacy/Data/ingsw/0324_11/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0324_11/wrong1.txt
new file mode 100644
index 0000000..2f7168f
--- /dev/null
+++ b/legacy/Data/ingsw/0324_11/wrong1.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_11/wrong2.txt b/legacy/Data/ingsw/0324_11/wrong2.txt
new file mode 100644
index 0000000..c3b40d2
--- /dev/null
+++ b/legacy/Data/ingsw/0324_11/wrong2.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_11/wrong3.txt b/legacy/Data/ingsw/0324_11/wrong3.txt
new file mode 100644
index 0000000..9116c62
--- /dev/null
+++ b/legacy/Data/ingsw/0324_11/wrong3.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_12/correct.txt b/legacy/Data/ingsw/0324_12/correct.txt
new file mode 100644
index 0000000..e74b1fc
--- /dev/null
+++ b/legacy/Data/ingsw/0324_12/correct.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x > y) then (z == x) else (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_12/quest.txt b/legacy/Data/ingsw/0324_12/quest.txt
new file mode 100644
index 0000000..c1cd6d0
--- /dev/null
+++ b/legacy/Data/ingsw/0324_12/quest.txt
@@ -0,0 +1,9 @@
+Un test oracle per un programma P � 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) � 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) � un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_12/wrong1.txt b/legacy/Data/ingsw/0324_12/wrong1.txt
new file mode 100644
index 0000000..d63544a
--- /dev/null
+++ b/legacy/Data/ingsw/0324_12/wrong1.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x > y) then (z == x + 1) else (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_12/wrong2.txt b/legacy/Data/ingsw/0324_12/wrong2.txt
new file mode 100644
index 0000000..1753a91
--- /dev/null
+++ b/legacy/Data/ingsw/0324_12/wrong2.txt
@@ -0,0 +1 @@
+F(x, y, z) = (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_13/correct.txt b/legacy/Data/ingsw/0324_13/correct.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0324_13/correct.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_13/quest.txt b/legacy/Data/ingsw/0324_13/quest.txt
new file mode 100644
index 0000000..4344b75
--- /dev/null
+++ b/legacy/Data/ingsw/0324_13/quest.txt
@@ -0,0 +1,8 @@
+Il partition coverage di un insieme di test cases � la percentuale di elementi della partition inclusi nei test cases. La partition � 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 � la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_13/wrong1.txt b/legacy/Data/ingsw/0324_13/wrong1.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0324_13/wrong1.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_13/wrong2.txt b/legacy/Data/ingsw/0324_13/wrong2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0324_13/wrong2.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_14/correct.txt b/legacy/Data/ingsw/0324_14/correct.txt
new file mode 100644
index 0000000..1c7da8c
--- /dev/null
+++ b/legacy/Data/ingsw/0324_14/correct.txt
@@ -0,0 +1 @@
+0.03
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_14/quest.txt b/legacy/Data/ingsw/0324_14/quest.txt
new file mode 100644
index 0000000..b9ba678
--- /dev/null
+++ b/legacy/Data/ingsw/0324_14/quest.txt
@@ -0,0 +1,9 @@
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_14.png
+Un processo software pu� 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 probabilit� 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 probabilit� 0.1 di dover essere ripetuta (a causa di errori).
+Uno scenario � una sequenza di stati.
+Qual'e' la probabilit� dello scenario: 1, 2, 3, 4 ? In altri terminti, qual'� la probabilit� 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/legacy/Data/ingsw/0324_14/wrong1.txt
new file mode 100644
index 0000000..7eb6830
--- /dev/null
+++ b/legacy/Data/ingsw/0324_14/wrong1.txt
@@ -0,0 +1 @@
+0.27
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_14/wrong2.txt b/legacy/Data/ingsw/0324_14/wrong2.txt
new file mode 100644
index 0000000..8a346b7
--- /dev/null
+++ b/legacy/Data/ingsw/0324_14/wrong2.txt
@@ -0,0 +1 @@
+0.07
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_15/correct.txt b/legacy/Data/ingsw/0324_15/correct.txt
new file mode 100644
index 0000000..a40ea7d
--- /dev/null
+++ b/legacy/Data/ingsw/0324_15/correct.txt
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 5, c = 0), (a=50, b = 3, c = 0).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_15/quest.txt b/legacy/Data/ingsw/0324_15/quest.txt
new file mode 100644
index 0000000..2d895ca
--- /dev/null
+++ b/legacy/Data/ingsw/0324_15/quest.txt
@@ -0,0 +1,22 @@
+Una Condition � una proposizione booleana, cio� una espressione con valore booleano che non pu� essere decomposta 
+in espressioni boolean pi� semplici. Ad esempio, (x + y <= 3) � una condition.
+
+Una Decision � una espressione booleana composta da conditions e zero o pi� 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 � 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 � true ed un test in T in cui c � false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d � true ed un test in T in cui d � 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/legacy/Data/ingsw/0324_15/wrong1.txt
new file mode 100644
index 0000000..5b77112
--- /dev/null
+++ b/legacy/Data/ingsw/0324_15/wrong1.txt
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 5, c = 0), (a=50, b = 0, c = 5).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_15/wrong2.txt b/legacy/Data/ingsw/0324_15/wrong2.txt
new file mode 100644
index 0000000..abe0eaa
--- /dev/null
+++ b/legacy/Data/ingsw/0324_15/wrong2.txt
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 4, c = 0), (a=200, b = 4, c = 0)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_16/correct.txt b/legacy/Data/ingsw/0324_16/correct.txt
new file mode 100644
index 0000000..1a8a50a
--- /dev/null
+++ b/legacy/Data/ingsw/0324_16/correct.txt
@@ -0,0 +1 @@
+Per ciascun requisito, dovremmo essere in grado di scrivere un inseme di test che può dimostrare che il sistema sviluppato soddisfa il requisito considerato.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_16/quest.txt b/legacy/Data/ingsw/0324_16/quest.txt
new file mode 100644
index 0000000..793b220
--- /dev/null
+++ b/legacy/Data/ingsw/0324_16/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive il criterio di "requirements verifiability" che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_16/wrong1.txt b/legacy/Data/ingsw/0324_16/wrong1.txt
new file mode 100644
index 0000000..fac8307
--- /dev/null
+++ b/legacy/Data/ingsw/0324_16/wrong1.txt
@@ -0,0 +1 @@
+Per ciascuna coppia di componenti, dovremmo essere in grado di scrivere un insieme di test che può dimostrare che l'interazione tra le componenti soddisfa tutti i requisiti di interfaccia.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_16/wrong2.txt b/legacy/Data/ingsw/0324_16/wrong2.txt
new file mode 100644
index 0000000..3fdb31e
--- /dev/null
+++ b/legacy/Data/ingsw/0324_16/wrong2.txt
@@ -0,0 +1 @@
+Per ciascuna componente del sistema, dovremmo essere in grado di scrivere un insieme di test che può dimostrare che essa soddisfa tutti i requisiti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_17/correct.txt b/legacy/Data/ingsw/0324_17/correct.txt
new file mode 100644
index 0000000..b110af1
--- /dev/null
+++ b/legacy/Data/ingsw/0324_17/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_17/quest.txt b/legacy/Data/ingsw/0324_17/quest.txt
new file mode 100644
index 0000000..1f51ab1
--- /dev/null
+++ b/legacy/Data/ingsw/0324_17/quest.txt
@@ -0,0 +1,13 @@
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_17.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0324_17/wrong1.txt
new file mode 100644
index 0000000..a29d476
--- /dev/null
+++ b/legacy/Data/ingsw/0324_17/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_17/wrong2.txt b/legacy/Data/ingsw/0324_17/wrong2.txt
new file mode 100644
index 0000000..2d5aeb0
--- /dev/null
+++ b/legacy/Data/ingsw/0324_17/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_18/correct.txt b/legacy/Data/ingsw/0324_18/correct.txt
new file mode 100644
index 0000000..7311d41
--- /dev/null
+++ b/legacy/Data/ingsw/0324_18/correct.txt
@@ -0,0 +1 @@
+Test set: {x=1, y=1}, {x=0, y=0}, {x=2, y=1}, {x=2, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_18/quest.txt b/legacy/Data/ingsw/0324_18/quest.txt
new file mode 100644
index 0000000..d3a9fe2
--- /dev/null
+++ b/legacy/Data/ingsw/0324_18/quest.txt
@@ -0,0 +1,8 @@
+Il branch coverage di un insieme di test cases � 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/legacy/Data/ingsw/0324_18/wrong1.txt
new file mode 100644
index 0000000..3e327ab
--- /dev/null
+++ b/legacy/Data/ingsw/0324_18/wrong1.txt
@@ -0,0 +1 @@
+Test set: {x=1, y=1}, {x=2, y=2}, {x=2, y=1}, {x=2, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_18/wrong2.txt b/legacy/Data/ingsw/0324_18/wrong2.txt
new file mode 100644
index 0000000..7e48e4f
--- /dev/null
+++ b/legacy/Data/ingsw/0324_18/wrong2.txt
@@ -0,0 +1 @@
+Test set: {x=1, y=1}, {x=0, y=0}, {x=2, y=1}, {x=2, y=3}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_19/correct.txt b/legacy/Data/ingsw/0324_19/correct.txt
new file mode 100644
index 0000000..6b560cf
--- /dev/null
+++ b/legacy/Data/ingsw/0324_19/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 25%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_19/quest.txt b/legacy/Data/ingsw/0324_19/quest.txt
new file mode 100644
index 0000000..b7a608e
--- /dev/null
+++ b/legacy/Data/ingsw/0324_19/quest.txt
@@ -0,0 +1,12 @@
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_19.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0324_19/wrong1.txt
new file mode 100644
index 0000000..b110af1
--- /dev/null
+++ b/legacy/Data/ingsw/0324_19/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_19/wrong2.txt b/legacy/Data/ingsw/0324_19/wrong2.txt
new file mode 100644
index 0000000..2d5aeb0
--- /dev/null
+++ b/legacy/Data/ingsw/0324_19/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_2/correct.txt b/legacy/Data/ingsw/0324_2/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0324_2/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_2/quest.txt b/legacy/Data/ingsw/0324_2/quest.txt
new file mode 100644
index 0000000..adede32
--- /dev/null
+++ b/legacy/Data/ingsw/0324_2/quest.txt
@@ -0,0 +1,9 @@
+Il branch coverage di un insieme di test cases � 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 � la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_2/wrong1.txt b/legacy/Data/ingsw/0324_2/wrong1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0324_2/wrong1.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_2/wrong2.txt b/legacy/Data/ingsw/0324_2/wrong2.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0324_2/wrong2.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_20/correct.txt b/legacy/Data/ingsw/0324_20/correct.txt
new file mode 100644
index 0000000..90b2f35
--- /dev/null
+++ b/legacy/Data/ingsw/0324_20/correct.txt
@@ -0,0 +1 @@
+State coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_20/quest.txt b/legacy/Data/ingsw/0324_20/quest.txt
new file mode 100644
index 0000000..9d685ad
--- /dev/null
+++ b/legacy/Data/ingsw/0324_20/quest.txt
@@ -0,0 +1,13 @@
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_20.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0324_20/wrong1.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0324_20/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_20/wrong2.txt b/legacy/Data/ingsw/0324_20/wrong2.txt
new file mode 100644
index 0000000..a8aead7
--- /dev/null
+++ b/legacy/Data/ingsw/0324_20/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_21/correct.txt b/legacy/Data/ingsw/0324_21/correct.txt
new file mode 100644
index 0000000..31a01d5
--- /dev/null
+++ b/legacy/Data/ingsw/0324_21/correct.txt
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=0, y=0}, {x=15, y=0}, {x=9, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_21/quest.txt b/legacy/Data/ingsw/0324_21/quest.txt
new file mode 100644
index 0000000..d649932
--- /dev/null
+++ b/legacy/Data/ingsw/0324_21/quest.txt
@@ -0,0 +1,8 @@
+Il branch coverage di un insieme di test cases � 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/legacy/Data/ingsw/0324_21/wrong1.txt
new file mode 100644
index 0000000..0c564f7
--- /dev/null
+++ b/legacy/Data/ingsw/0324_21/wrong1.txt
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=2, y=1}, {x=15, y=0}, {x=9, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_21/wrong2.txt b/legacy/Data/ingsw/0324_21/wrong2.txt
new file mode 100644
index 0000000..549dba8
--- /dev/null
+++ b/legacy/Data/ingsw/0324_21/wrong2.txt
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=0, y=0}, {x=15, y=0}, {x=10, y=3}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_22/correct.txt b/legacy/Data/ingsw/0324_22/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0324_22/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_22/quest.txt b/legacy/Data/ingsw/0324_22/quest.txt
new file mode 100644
index 0000000..65cfd2d
--- /dev/null
+++ b/legacy/Data/ingsw/0324_22/quest.txt
@@ -0,0 +1,9 @@
+Il branch coverage di un insieme di test cases � 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 � la branch coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_22/wrong1.txt b/legacy/Data/ingsw/0324_22/wrong1.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0324_22/wrong1.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_22/wrong2.txt b/legacy/Data/ingsw/0324_22/wrong2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0324_22/wrong2.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_23/correct.txt b/legacy/Data/ingsw/0324_23/correct.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0324_23/correct.txt
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_23/quest.txt b/legacy/Data/ingsw/0324_23/quest.txt
new file mode 100644
index 0000000..c9ea208
--- /dev/null
+++ b/legacy/Data/ingsw/0324_23/quest.txt
@@ -0,0 +1,11 @@
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_23.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0324_23/wrong1.txt
new file mode 100644
index 0000000..a8aead7
--- /dev/null
+++ b/legacy/Data/ingsw/0324_23/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_23/wrong2.txt b/legacy/Data/ingsw/0324_23/wrong2.txt
new file mode 100644
index 0000000..1a8a508
--- /dev/null
+++ b/legacy/Data/ingsw/0324_23/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_24/correct.txt b/legacy/Data/ingsw/0324_24/correct.txt
new file mode 100644
index 0000000..e13eda2
--- /dev/null
+++ b/legacy/Data/ingsw/0324_24/correct.txt
@@ -0,0 +1 @@
+Accertarsi che i requisiti definiscano un sistema che risolve il problema che l'utente pianifica di risolvere.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_24/quest.txt b/legacy/Data/ingsw/0324_24/quest.txt
new file mode 100644
index 0000000..b59a64d
--- /dev/null
+++ b/legacy/Data/ingsw/0324_24/quest.txt
@@ -0,0 +1 @@
+Quali delle seguenti attivit� � parte del processo di validazione dei requisiti ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_24/wrong1.txt b/legacy/Data/ingsw/0324_24/wrong1.txt
new file mode 100644
index 0000000..b24f900
--- /dev/null
+++ b/legacy/Data/ingsw/0324_24/wrong1.txt
@@ -0,0 +1 @@
+Accertarsi che il sistema soddisfi i requisiti dati.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_24/wrong2.txt b/legacy/Data/ingsw/0324_24/wrong2.txt
new file mode 100644
index 0000000..884d6b1
--- /dev/null
+++ b/legacy/Data/ingsw/0324_24/wrong2.txt
@@ -0,0 +1 @@
+Accertarsi che l'architettura del sistema soddisfi i requisiti dati.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_25/correct.txt b/legacy/Data/ingsw/0324_25/correct.txt
new file mode 100644
index 0000000..7c149d8
--- /dev/null
+++ b/legacy/Data/ingsw/0324_25/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisisti descrivano tutte le funzionalità e vincoli (e.g., security, performance) del sistema desiderato dal customer.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_25/quest.txt b/legacy/Data/ingsw/0324_25/quest.txt
new file mode 100644
index 0000000..8bba4b8
--- /dev/null
+++ b/legacy/Data/ingsw/0324_25/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "check di completezza" che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_25/wrong1.txt b/legacy/Data/ingsw/0324_25/wrong1.txt
new file mode 100644
index 0000000..3461684
--- /dev/null
+++ b/legacy/Data/ingsw/0324_25/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che per ogni requisito sia stato implementato nel sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_25/wrong2.txt b/legacy/Data/ingsw/0324_25/wrong2.txt
new file mode 100644
index 0000000..32c628c
--- /dev/null
+++ b/legacy/Data/ingsw/0324_25/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisiti funzionali descrivano tutte le funzionalità del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_26/quest.txt b/legacy/Data/ingsw/0324_26/quest.txt
new file mode 100644
index 0000000..aef871e
--- /dev/null
+++ b/legacy/Data/ingsw/0324_26/quest.txt
@@ -0,0 +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)
+
+
+end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_26/wrong1.txt b/legacy/Data/ingsw/0324_26/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0324_26/wrong2.txt b/legacy/Data/ingsw/0324_26/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0324_26/wrong3.txt b/legacy/Data/ingsw/0324_26/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0324_27/correct.txt b/legacy/Data/ingsw/0324_27/correct.txt
new file mode 100644
index 0000000..e582263
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0324_27/quest.txt b/legacy/Data/ingsw/0324_27/quest.txt
new file mode 100644
index 0000000..864cc93
--- /dev/null
+++ b/legacy/Data/ingsw/0324_27/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Durante l'esecuzione del programma (cio� per tutti gli istanti di tempo positivi) la variabile x � 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/legacy/Data/ingsw/0324_27/wrong1.txt
new file mode 100644
index 0000000..590f7e1
--- /dev/null
+++ b/legacy/Data/ingsw/0324_27/wrong1.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 >= 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/legacy/Data/ingsw/0324_27/wrong2.txt
new file mode 100644
index 0000000..0f38391
--- /dev/null
+++ b/legacy/Data/ingsw/0324_27/wrong2.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 >= 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/0324_28/correct.txt b/legacy/Data/ingsw/0324_28/correct.txt
new file mode 100644
index 0000000..4c75070
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0324_28/quest.txt b/legacy/Data/ingsw/0324_28/quest.txt
new file mode 100644
index 0000000..e11a044
--- /dev/null
+++ b/legacy/Data/ingsw/0324_28/quest.txt
@@ -0,0 +1,4 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 50 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se la variabile x � minore del 60% della variabile y allora la somma di x ed y � 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
new file mode 100644
index 0000000..6dafe94
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0324_28/wrong2.txt b/legacy/Data/ingsw/0324_28/wrong2.txt
new file mode 100644
index 0000000..a3d79a4
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0324_29/correct.txt
new file mode 100644
index 0000000..e7c5bb8
--- /dev/null
+++ b/legacy/Data/ingsw/0324_29/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che, tenedo conto della tecnologia, budget e tempo disponibili,  sia possibile realizzare un sistema che soddisfa i requisisti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_29/quest.txt b/legacy/Data/ingsw/0324_29/quest.txt
new file mode 100644
index 0000000..296cdcb
--- /dev/null
+++ b/legacy/Data/ingsw/0324_29/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "check di realismo" (realizability) che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_29/wrong1.txt b/legacy/Data/ingsw/0324_29/wrong1.txt
new file mode 100644
index 0000000..2b6e242
--- /dev/null
+++ b/legacy/Data/ingsw/0324_29/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che le funzionalità richieste al sistema siano necessarie per soddisfare le necessità del customer.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_29/wrong2.txt b/legacy/Data/ingsw/0324_29/wrong2.txt
new file mode 100644
index 0000000..bfb5124
--- /dev/null
+++ b/legacy/Data/ingsw/0324_29/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che le performance richieste al sistema siano necessarie per soddisfare le necessità del customer.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_3/correct.txt b/legacy/Data/ingsw/0324_3/correct.txt
new file mode 100644
index 0000000..f6a4b07
--- /dev/null
+++ b/legacy/Data/ingsw/0324_3/correct.txt
@@ -0,0 +1 @@
+State coverage: 90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_3/quest.txt b/legacy/Data/ingsw/0324_3/quest.txt
new file mode 100644
index 0000000..b865ed9
--- /dev/null
+++ b/legacy/Data/ingsw/0324_3/quest.txt
@@ -0,0 +1,11 @@
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_3.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0324_3/wrong1.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/0324_3/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_3/wrong2.txt b/legacy/Data/ingsw/0324_3/wrong2.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0324_3/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_30/quest.txt b/legacy/Data/ingsw/0324_30/quest.txt
new file mode 100644
index 0000000..985c244
--- /dev/null
+++ b/legacy/Data/ingsw/0324_30/quest.txt
@@ -0,0 +1,4 @@
+Un test oracle per un programma P � 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) � 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 � un test oracle per la funzione f ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_30/wrong1.txt b/legacy/Data/ingsw/0324_30/wrong1.txt
new file mode 100644
index 0000000..69b9722
--- /dev/null
+++ b/legacy/Data/ingsw/0324_30/wrong1.txt
@@ -0,0 +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);
+}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_30/wrong2.txt b/legacy/Data/ingsw/0324_30/wrong2.txt
new file mode 100644
index 0000000..a26ce6e
--- /dev/null
+++ b/legacy/Data/ingsw/0324_30/wrong2.txt
@@ -0,0 +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);
+}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_30/wrong3.txt b/legacy/Data/ingsw/0324_30/wrong3.txt
new file mode 100644
index 0000000..ed5ad19
--- /dev/null
+++ b/legacy/Data/ingsw/0324_30/wrong3.txt
@@ -0,0 +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);
+}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_31/correct.txt b/legacy/Data/ingsw/0324_31/correct.txt
new file mode 100644
index 0000000..293ebbc
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0324_31/quest.txt b/legacy/Data/ingsw/0324_31/quest.txt
new file mode 100644
index 0000000..5922b9f
--- /dev/null
+++ b/legacy/Data/ingsw/0324_31/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 10 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�: se la variabile x � nell'intervallo [10, 20]  allora la variabile y � 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/legacy/Data/ingsw/0324_31/wrong1.txt
new file mode 100644
index 0000000..d50b268
--- /dev/null
+++ b/legacy/Data/ingsw/0324_31/wrong1.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) 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/legacy/Data/ingsw/0324_31/wrong2.txt
new file mode 100644
index 0000000..d7890b2
--- /dev/null
+++ b/legacy/Data/ingsw/0324_31/wrong2.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) 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/legacy/Data/ingsw/0324_32/correct.txt
new file mode 100644
index 0000000..2a2ecea
--- /dev/null
+++ b/legacy/Data/ingsw/0324_32/correct.txt
@@ -0,0 +1 @@
+time(0)/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_32/quest.txt b/legacy/Data/ingsw/0324_32/quest.txt
new file mode 100644
index 0000000..5d96d42
--- /dev/null
+++ b/legacy/Data/ingsw/0324_32/quest.txt
@@ -0,0 +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 � in (0, 1).  Il tempo necessario per completare la fase x � time(x). La fase 0 � la fase di design, che ha probabilit� 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 � 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), .... � 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)  (poich� 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/legacy/Data/ingsw/0324_32/wrong1.txt
new file mode 100644
index 0000000..9927a93
--- /dev/null
+++ b/legacy/Data/ingsw/0324_32/wrong1.txt
@@ -0,0 +1 @@
+time(0)/(p*(1 - p))
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_32/wrong2.txt b/legacy/Data/ingsw/0324_32/wrong2.txt
new file mode 100644
index 0000000..d68fd15
--- /dev/null
+++ b/legacy/Data/ingsw/0324_32/wrong2.txt
@@ -0,0 +1 @@
+time(0)*(1 - p)/p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_33/correct.txt b/legacy/Data/ingsw/0324_33/correct.txt
new file mode 100644
index 0000000..232aedf
--- /dev/null
+++ b/legacy/Data/ingsw/0324_33/correct.txt
@@ -0,0 +1 @@
+(a = 6, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 0).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_33/quest.txt b/legacy/Data/ingsw/0324_33/quest.txt
new file mode 100644
index 0000000..b2bed72
--- /dev/null
+++ b/legacy/Data/ingsw/0324_33/quest.txt
@@ -0,0 +1,21 @@
+Una Condition � una proposizione booleana, cio� una espressione con valore booleano che non pu� essere decomposta 
+in espressioni boolean pi� semplici. Ad esempio, (x + y <= 3) � una condition.
+
+Una Decision � una espressione booleana composta da conditions e zero o pi� 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 � 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 � true ed un test in T in cui c � false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d � true ed un test in T in cui d � 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/legacy/Data/ingsw/0324_33/wrong1.txt
new file mode 100644
index 0000000..5d5c9a4
--- /dev/null
+++ b/legacy/Data/ingsw/0324_33/wrong1.txt
@@ -0,0 +1 @@
+(a = 6, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 2).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_33/wrong2.txt b/legacy/Data/ingsw/0324_33/wrong2.txt
new file mode 100644
index 0000000..2b6c292
--- /dev/null
+++ b/legacy/Data/ingsw/0324_33/wrong2.txt
@@ -0,0 +1 @@
+(a = 5, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 0).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_34/correct.txt b/legacy/Data/ingsw/0324_34/correct.txt
new file mode 100644
index 0000000..ad21063
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0324_34/quest.txt b/legacy/Data/ingsw/0324_34/quest.txt
new file mode 100644
index 0000000..031c331
--- /dev/null
+++ b/legacy/Data/ingsw/0324_34/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 40 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se 10 unit� di tempo nel passato x era maggiore di 1 allora ora y � 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
new file mode 100644
index 0000000..b14ac60
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0324_34/wrong2.txt b/legacy/Data/ingsw/0324_34/wrong2.txt
new file mode 100644
index 0000000..e4201ab
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0324_35/quest.txt
new file mode 100644
index 0000000..627c57e
--- /dev/null
+++ b/legacy/Data/ingsw/0324_35/quest.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_35/wrong1.txt b/legacy/Data/ingsw/0324_35/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0324_35/wrong2.txt b/legacy/Data/ingsw/0324_35/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0324_35/wrong3.txt b/legacy/Data/ingsw/0324_35/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0324_36/correct.txt b/legacy/Data/ingsw/0324_36/correct.txt
new file mode 100644
index 0000000..b9f32a6
--- /dev/null
+++ b/legacy/Data/ingsw/0324_36/correct.txt
@@ -0,0 +1 @@
+c(0)/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_36/quest.txt b/legacy/Data/ingsw/0324_36/quest.txt
new file mode 100644
index 0000000..36471c2
--- /dev/null
+++ b/legacy/Data/ingsw/0324_36/quest.txt
@@ -0,0 +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 � in (0, 1).  Il costo dello stato (fase) x � c(x). La fase 0 � la fase di design, che ha probabilit� 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 � 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), .... � 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)  (poich� 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/legacy/Data/ingsw/0324_36/wrong1.txt
new file mode 100644
index 0000000..3143da9
--- /dev/null
+++ b/legacy/Data/ingsw/0324_36/wrong1.txt
@@ -0,0 +1 @@
+c(0)/(p*(1 - p))
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_36/wrong2.txt b/legacy/Data/ingsw/0324_36/wrong2.txt
new file mode 100644
index 0000000..70022eb
--- /dev/null
+++ b/legacy/Data/ingsw/0324_36/wrong2.txt
@@ -0,0 +1 @@
+c(0)*(1 - p)/p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_37/correct.txt b/legacy/Data/ingsw/0324_37/correct.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0324_37/correct.txt
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_37/quest.txt b/legacy/Data/ingsw/0324_37/quest.txt
new file mode 100644
index 0000000..fc6a5e1
--- /dev/null
+++ b/legacy/Data/ingsw/0324_37/quest.txt
@@ -0,0 +1,12 @@
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_37.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0324_37/wrong1.txt
new file mode 100644
index 0000000..90b2f35
--- /dev/null
+++ b/legacy/Data/ingsw/0324_37/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_37/wrong2.txt b/legacy/Data/ingsw/0324_37/wrong2.txt
new file mode 100644
index 0000000..a8aead7
--- /dev/null
+++ b/legacy/Data/ingsw/0324_37/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_38/correct.txt b/legacy/Data/ingsw/0324_38/correct.txt
new file mode 100644
index 0000000..98939be
--- /dev/null
+++ b/legacy/Data/ingsw/0324_38/correct.txt
@@ -0,0 +1 @@
+1/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_38/quest.txt b/legacy/Data/ingsw/0324_38/quest.txt
new file mode 100644
index 0000000..d24403f
--- /dev/null
+++ b/legacy/Data/ingsw/0324_38/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0324_38/wrong1.txt
new file mode 100644
index 0000000..56ea6ac
--- /dev/null
+++ b/legacy/Data/ingsw/0324_38/wrong1.txt
@@ -0,0 +1 @@
+1/(p*(1 - p))
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_38/wrong2.txt b/legacy/Data/ingsw/0324_38/wrong2.txt
new file mode 100644
index 0000000..db2276d
--- /dev/null
+++ b/legacy/Data/ingsw/0324_38/wrong2.txt
@@ -0,0 +1 @@
+(1 - p)/p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_39/correct.txt b/legacy/Data/ingsw/0324_39/correct.txt
new file mode 100644
index 0000000..4a8e634
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0324_39/quest.txt b/legacy/Data/ingsw/0324_39/quest.txt
new file mode 100644
index 0000000..576af1a
--- /dev/null
+++ b/legacy/Data/ingsw/0324_39/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se 10 unit� di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora � 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/legacy/Data/ingsw/0324_39/wrong1.txt
new file mode 100644
index 0000000..a43796b
--- /dev/null
+++ b/legacy/Data/ingsw/0324_39/wrong1.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/0324_39/wrong2.txt b/legacy/Data/ingsw/0324_39/wrong2.txt
new file mode 100644
index 0000000..68aa37a
--- /dev/null
+++ b/legacy/Data/ingsw/0324_39/wrong2.txt
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_4/correct.txt b/legacy/Data/ingsw/0324_4/correct.txt
new file mode 100644
index 0000000..f2bb2d0
--- /dev/null
+++ b/legacy/Data/ingsw/0324_4/correct.txt
@@ -0,0 +1 @@
+0.12
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_4/quest.txt b/legacy/Data/ingsw/0324_4/quest.txt
new file mode 100644
index 0000000..40b7789
--- /dev/null
+++ b/legacy/Data/ingsw/0324_4/quest.txt
@@ -0,0 +1,9 @@
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_4.png
+Un processo software pu� 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 probabilit� 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 probabilit� 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario � una sequenza di stati.
+Qual'e' la probabilit� dello scenario: 1, 3, 4? In altri terminti, qual'� la probabilit� 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/legacy/Data/ingsw/0324_4/wrong1.txt
new file mode 100644
index 0000000..2a47a95
--- /dev/null
+++ b/legacy/Data/ingsw/0324_4/wrong1.txt
@@ -0,0 +1 @@
+0.08
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_4/wrong2.txt b/legacy/Data/ingsw/0324_4/wrong2.txt
new file mode 100644
index 0000000..b7bbee2
--- /dev/null
+++ b/legacy/Data/ingsw/0324_4/wrong2.txt
@@ -0,0 +1 @@
+0.32
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_40/correct.txt b/legacy/Data/ingsw/0324_40/correct.txt
new file mode 100644
index 0000000..ce9968f
--- /dev/null
+++ b/legacy/Data/ingsw/0324_40/correct.txt
@@ -0,0 +1 @@
+0.28
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_40/quest.txt b/legacy/Data/ingsw/0324_40/quest.txt
new file mode 100644
index 0000000..fbee794
--- /dev/null
+++ b/legacy/Data/ingsw/0324_40/quest.txt
@@ -0,0 +1,9 @@
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_40.png
+Un processo software pu� 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 probabilit� 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 probabilit� 0.3 di dover essere ripetuta (a causa di errori).
+Uno scenario � una sequenza di stati.
+Qual'e' la probabilit� dello scenario: 1, 2, 3? In altri terminti, qual'� la probabilit� 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/legacy/Data/ingsw/0324_40/wrong1.txt
new file mode 100644
index 0000000..f2bb2d0
--- /dev/null
+++ b/legacy/Data/ingsw/0324_40/wrong1.txt
@@ -0,0 +1 @@
+0.12
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_40/wrong2.txt b/legacy/Data/ingsw/0324_40/wrong2.txt
new file mode 100644
index 0000000..e8f9017
--- /dev/null
+++ b/legacy/Data/ingsw/0324_40/wrong2.txt
@@ -0,0 +1 @@
+0.42
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_41/quest.txt b/legacy/Data/ingsw/0324_41/quest.txt
new file mode 100644
index 0000000..bfb2790
--- /dev/null
+++ b/legacy/Data/ingsw/0324_41/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0324_41/wrong1.txt
new file mode 100644
index 0000000..1fad89a
--- /dev/null
+++ b/legacy/Data/ingsw/0324_41/wrong1.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_41/wrong2.txt b/legacy/Data/ingsw/0324_41/wrong2.txt
new file mode 100644
index 0000000..882ae3e
--- /dev/null
+++ b/legacy/Data/ingsw/0324_41/wrong2.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_41/wrong3.txt b/legacy/Data/ingsw/0324_41/wrong3.txt
new file mode 100644
index 0000000..e5618fa
--- /dev/null
+++ b/legacy/Data/ingsw/0324_41/wrong3.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_42/quest.txt b/legacy/Data/ingsw/0324_42/quest.txt
new file mode 100644
index 0000000..071ac68
--- /dev/null
+++ b/legacy/Data/ingsw/0324_42/quest.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_42/wrong1.txt b/legacy/Data/ingsw/0324_42/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0324_42/wrong2.txt b/legacy/Data/ingsw/0324_42/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0324_42/wrong3.txt b/legacy/Data/ingsw/0324_42/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0324_43/correct.txt b/legacy/Data/ingsw/0324_43/correct.txt
new file mode 100644
index 0000000..5464d05
--- /dev/null
+++ b/legacy/Data/ingsw/0324_43/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 30%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_43/quest.txt b/legacy/Data/ingsw/0324_43/quest.txt
new file mode 100644
index 0000000..710edb6
--- /dev/null
+++ b/legacy/Data/ingsw/0324_43/quest.txt
@@ -0,0 +1,13 @@
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_43.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0324_43/wrong1.txt
new file mode 100644
index 0000000..a29d476
--- /dev/null
+++ b/legacy/Data/ingsw/0324_43/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_43/wrong2.txt b/legacy/Data/ingsw/0324_43/wrong2.txt
new file mode 100644
index 0000000..2d5aeb0
--- /dev/null
+++ b/legacy/Data/ingsw/0324_43/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_44/correct.txt b/legacy/Data/ingsw/0324_44/correct.txt
new file mode 100644
index 0000000..8785661
--- /dev/null
+++ b/legacy/Data/ingsw/0324_44/correct.txt
@@ -0,0 +1 @@
+{x = -200, x = -50,  x = 0, x = 100, x = 700}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_44/quest.txt b/legacy/Data/ingsw/0324_44/quest.txt
new file mode 100644
index 0000000..36947c2
--- /dev/null
+++ b/legacy/Data/ingsw/0324_44/quest.txt
@@ -0,0 +1,6 @@
+Il partition coverage di un insieme di test cases � la percentuale di elementi della partition inclusi nei test cases. La partition � 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/legacy/Data/ingsw/0324_44/wrong1.txt
new file mode 100644
index 0000000..0aaedb8
--- /dev/null
+++ b/legacy/Data/ingsw/0324_44/wrong1.txt
@@ -0,0 +1 @@
+{x = -200, x = -50,  x = 0, x = 100, x = 500}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_44/wrong2.txt b/legacy/Data/ingsw/0324_44/wrong2.txt
new file mode 100644
index 0000000..a6df32d
--- /dev/null
+++ b/legacy/Data/ingsw/0324_44/wrong2.txt
@@ -0,0 +1 @@
+{x = -200, x = -150,  x = 0, x = 100, x = 700}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_45/correct.txt b/legacy/Data/ingsw/0324_45/correct.txt
new file mode 100644
index 0000000..c37d6ae
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0324_45/quest.txt b/legacy/Data/ingsw/0324_45/quest.txt
new file mode 100644
index 0000000..003d1dd
--- /dev/null
+++ b/legacy/Data/ingsw/0324_45/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se 10 unit� di tempo nel passato x era maggiore di 0 allora ora y � 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/legacy/Data/ingsw/0324_45/wrong1.txt
new file mode 100644
index 0000000..edea147
--- /dev/null
+++ b/legacy/Data/ingsw/0324_45/wrong1.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/0324_45/wrong2.txt b/legacy/Data/ingsw/0324_45/wrong2.txt
new file mode 100644
index 0000000..14bd900
--- /dev/null
+++ b/legacy/Data/ingsw/0324_45/wrong2.txt
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_46/correct.txt b/legacy/Data/ingsw/0324_46/correct.txt
new file mode 100644
index 0000000..a98afd2
--- /dev/null
+++ b/legacy/Data/ingsw/0324_46/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/0324_46/quest.txt b/legacy/Data/ingsw/0324_46/quest.txt
new file mode 100644
index 0000000..b420aaf
--- /dev/null
+++ b/legacy/Data/ingsw/0324_46/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Dopo 20 unit� di tempo dall'inizio dell'esecuzione la variabile x � 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/legacy/Data/ingsw/0324_46/wrong1.txt
new file mode 100644
index 0000000..66064fe
--- /dev/null
+++ b/legacy/Data/ingsw/0324_46/wrong1.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 >= 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/legacy/Data/ingsw/0324_46/wrong2.txt
new file mode 100644
index 0000000..c71f1f5
--- /dev/null
+++ b/legacy/Data/ingsw/0324_46/wrong2.txt
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_47/quest.txt b/legacy/Data/ingsw/0324_47/quest.txt
new file mode 100644
index 0000000..0240bc8
--- /dev/null
+++ b/legacy/Data/ingsw/0324_47/quest.txt
@@ -0,0 +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)
+end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_47/wrong1.txt b/legacy/Data/ingsw/0324_47/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0324_47/wrong2.txt b/legacy/Data/ingsw/0324_47/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0324_47/wrong3.txt b/legacy/Data/ingsw/0324_47/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0324_48/quest.txt b/legacy/Data/ingsw/0324_48/quest.txt
new file mode 100644
index 0000000..1109458
--- /dev/null
+++ b/legacy/Data/ingsw/0324_48/quest.txt
@@ -0,0 +1,4 @@
+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/ingsw/0324_48/wrong1.txt b/legacy/Data/ingsw/0324_48/wrong1.txt
new file mode 100644
index 0000000..4bcd55f
--- /dev/null
+++ b/legacy/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/ingsw/0324_48/wrong2.txt b/legacy/Data/ingsw/0324_48/wrong2.txt
new file mode 100644
index 0000000..19be218
--- /dev/null
+++ b/legacy/Data/ingsw/0324_48/wrong2.txt
@@ -0,0 +1,2 @@
+input12)
+connect(sc1.output13, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_48/wrong3.txt b/legacy/Data/ingsw/0324_48/wrong3.txt
new file mode 100644
index 0000000..3387be9
--- /dev/null
+++ b/legacy/Data/ingsw/0324_48/wrong3.txt
@@ -0,0 +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)
+
+
+end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_49/correct.txt b/legacy/Data/ingsw/0324_49/correct.txt
new file mode 100644
index 0000000..eb23d05
--- /dev/null
+++ b/legacy/Data/ingsw/0324_49/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che non ci siano requisiti in conflitto con altri requisiti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_49/quest.txt b/legacy/Data/ingsw/0324_49/quest.txt
new file mode 100644
index 0000000..7710e8f
--- /dev/null
+++ b/legacy/Data/ingsw/0324_49/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "check di consistenza" che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_49/wrong1.txt b/legacy/Data/ingsw/0324_49/wrong1.txt
new file mode 100644
index 0000000..9e12d11
--- /dev/null
+++ b/legacy/Data/ingsw/0324_49/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che per ogni requisito esista un insieme di test che lo possa verificare.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_49/wrong2.txt b/legacy/Data/ingsw/0324_49/wrong2.txt
new file mode 100644
index 0000000..32c628c
--- /dev/null
+++ b/legacy/Data/ingsw/0324_49/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisiti funzionali descrivano tutte le funzionalità del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_5/correct.txt b/legacy/Data/ingsw/0324_5/correct.txt
new file mode 100644
index 0000000..81a4b93
--- /dev/null
+++ b/legacy/Data/ingsw/0324_5/correct.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x >= 0) then (z == pow(y, x)) else (z == 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_5/quest.txt b/legacy/Data/ingsw/0324_5/quest.txt
new file mode 100644
index 0000000..236ccc7
--- /dev/null
+++ b/legacy/Data/ingsw/0324_5/quest.txt
@@ -0,0 +1,10 @@
+Un test oracle per un programma P � 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) � 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) � un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_5/wrong1.txt b/legacy/Data/ingsw/0324_5/wrong1.txt
new file mode 100644
index 0000000..d246b94
--- /dev/null
+++ b/legacy/Data/ingsw/0324_5/wrong1.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x >= 0) then (z == pow(y, x)) else (z == 0)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_5/wrong2.txt b/legacy/Data/ingsw/0324_5/wrong2.txt
new file mode 100644
index 0000000..f52d5ae
--- /dev/null
+++ b/legacy/Data/ingsw/0324_5/wrong2.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x >= 0) then (z == pow(y, x)) else (z == y)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_6/correct.txt b/legacy/Data/ingsw/0324_6/correct.txt
new file mode 100644
index 0000000..b110af1
--- /dev/null
+++ b/legacy/Data/ingsw/0324_6/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_6/quest.txt b/legacy/Data/ingsw/0324_6/quest.txt
new file mode 100644
index 0000000..f6ffda4
--- /dev/null
+++ b/legacy/Data/ingsw/0324_6/quest.txt
@@ -0,0 +1,13 @@
+img=https://unspectacular-subdi.000webhostapp.com/0324_domanda_6.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0324_6/wrong1.txt
new file mode 100644
index 0000000..a29d476
--- /dev/null
+++ b/legacy/Data/ingsw/0324_6/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_6/wrong2.txt b/legacy/Data/ingsw/0324_6/wrong2.txt
new file mode 100644
index 0000000..2d5aeb0
--- /dev/null
+++ b/legacy/Data/ingsw/0324_6/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_7/correct.txt b/legacy/Data/ingsw/0324_7/correct.txt
new file mode 100644
index 0000000..43dc0c9
--- /dev/null
+++ b/legacy/Data/ingsw/0324_7/correct.txt
@@ -0,0 +1,5 @@
+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/legacy/Data/ingsw/0324_7/quest.txt
new file mode 100644
index 0000000..f6744fd
--- /dev/null
+++ b/legacy/Data/ingsw/0324_7/quest.txt
@@ -0,0 +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 (cio� non � 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 � positivo ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_7/wrong1.txt b/legacy/Data/ingsw/0324_7/wrong1.txt
new file mode 100644
index 0000000..3f63933
--- /dev/null
+++ b/legacy/Data/ingsw/0324_7/wrong1.txt
@@ -0,0 +1,5 @@
+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/legacy/Data/ingsw/0324_7/wrong2.txt
new file mode 100644
index 0000000..6a97baf
--- /dev/null
+++ b/legacy/Data/ingsw/0324_7/wrong2.txt
@@ -0,0 +1,5 @@
+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/legacy/Data/ingsw/0324_8/correct.txt
new file mode 100644
index 0000000..b8bf06e
--- /dev/null
+++ b/legacy/Data/ingsw/0324_8/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));
+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/quest.txt b/legacy/Data/ingsw/0324_8/quest.txt
new file mode 100644
index 0000000..22c683f
--- /dev/null
+++ b/legacy/Data/ingsw/0324_8/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Durante l'esecuzione del programma (cio� per tutti gli istanti di tempo positivi) la variabile x � 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/legacy/Data/ingsw/0324_8/wrong1.txt
new file mode 100644
index 0000000..2029293
--- /dev/null
+++ b/legacy/Data/ingsw/0324_8/wrong1.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 > 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/legacy/Data/ingsw/0324_8/wrong2.txt
new file mode 100644
index 0000000..bc8720d
--- /dev/null
+++ b/legacy/Data/ingsw/0324_8/wrong2.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 > 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/legacy/Data/ingsw/0324_9/correct.txt
new file mode 100644
index 0000000..7a6c6b9
--- /dev/null
+++ b/legacy/Data/ingsw/0324_9/correct.txt
@@ -0,0 +1 @@
+300000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_9/quest.txt b/legacy/Data/ingsw/0324_9/quest.txt
new file mode 100644
index 0000000..47201e7
--- /dev/null
+++ b/legacy/Data/ingsw/0324_9/quest.txt
@@ -0,0 +1,4 @@
+Il rischio R pu� essere calcolato come R = P*C, dove P � la probabilit� dell'evento avverso (software failure nel nostro contesto) e C � il costo dell'occorrenza dell'evento avverso. 
+Assumiamo che la probabilit� P sia legata al costo di sviluppo S dalla formula 
+P = 10^{(-b*S)}    (cio� 10 elevato alla (-b*S))
+dove b � una opportuna costante note da dati storici aziendali. Si assuma che b = 0.0001,  C = 1000000, ed il rischio ammesso � 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/legacy/Data/ingsw/0324_9/wrong1.txt
new file mode 100644
index 0000000..2df501e
--- /dev/null
+++ b/legacy/Data/ingsw/0324_9/wrong1.txt
@@ -0,0 +1 @@
+500000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0324_9/wrong2.txt b/legacy/Data/ingsw/0324_9/wrong2.txt
new file mode 100644
index 0000000..997967b
--- /dev/null
+++ b/legacy/Data/ingsw/0324_9/wrong2.txt
@@ -0,0 +1 @@
+700000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0422-16/correct.txt b/legacy/Data/ingsw/0422-16/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0422-16/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0422-16/quest.txt b/legacy/Data/ingsw/0422-16/quest.txt
new file mode 100644
index 0000000..1b18990
--- /dev/null
+++ b/legacy/Data/ingsw/0422-16/quest.txt
@@ -0,0 +1,20 @@
+img=https://i.imgur.com/6m6ALRb.png
+La state coverage di un insieme di test cases (cioè sequeze di inputs) per uno state diagram è la percentuale di stati (inclusi START ed END) rggiunti almeno una volta.
+
+Si consideri lo state diagram in figura 
+
+
+
+ed il seguente insieme di test cases:
+
+
+
+1) Start PIN validation, card inserted, PIN Entered, Valid PIN, Cancel 3, End PIN Validation 2
+
+2) Start PIN validation, card inserted, PIN Entered, Valid PIN, Cancel 2, End PIN Validation 2
+
+3) Start PIN validation, card inserted, PIN Entered, Invalid PIN, PIN Entered, Invalid PIN, PIN Entered, Invalid PIN, PIN Entered, Invalid PIN, More than 3 failed..., END PIN validation 1;
+
+
+
+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/0422-16/wrong1.txt b/legacy/Data/ingsw/0422-16/wrong1.txt
new file mode 100644
index 0000000..1e091a3
--- /dev/null
+++ b/legacy/Data/ingsw/0422-16/wrong1.txt
@@ -0,0 +1 @@
+90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0422-16/wrong2.txt b/legacy/Data/ingsw/0422-16/wrong2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0422-16/wrong2.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_0/quest.txt b/legacy/Data/ingsw/0613_0/quest.txt
new file mode 100644
index 0000000..1f3419c
--- /dev/null
+++ b/legacy/Data/ingsw/0613_0/quest.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_0/wrong1.txt b/legacy/Data/ingsw/0613_0/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0613_0/wrong2.txt b/legacy/Data/ingsw/0613_0/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0613_0/wrong3.txt b/legacy/Data/ingsw/0613_0/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0613_1/correct.txt b/legacy/Data/ingsw/0613_1/correct.txt
new file mode 100644
index 0000000..f2bb2d0
--- /dev/null
+++ b/legacy/Data/ingsw/0613_1/correct.txt
@@ -0,0 +1 @@
+0.12
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_1/quest.txt b/legacy/Data/ingsw/0613_1/quest.txt
new file mode 100644
index 0000000..654955e
--- /dev/null
+++ b/legacy/Data/ingsw/0613_1/quest.txt
@@ -0,0 +1,9 @@
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_1.png
+Un processo software pu� 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 probabilit� 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 probabilit� 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario � una sequenza di stati.
+Qual'e' la probabilit� dello scenario: 1, 3, 4? In altri terminti, qual'� la probabilit� 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/legacy/Data/ingsw/0613_1/wrong1.txt
new file mode 100644
index 0000000..2a47a95
--- /dev/null
+++ b/legacy/Data/ingsw/0613_1/wrong1.txt
@@ -0,0 +1 @@
+0.08
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_1/wrong2.txt b/legacy/Data/ingsw/0613_1/wrong2.txt
new file mode 100644
index 0000000..b7bbee2
--- /dev/null
+++ b/legacy/Data/ingsw/0613_1/wrong2.txt
@@ -0,0 +1 @@
+0.32
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_10/correct.txt b/legacy/Data/ingsw/0613_10/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0613_10/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_10/quest.txt b/legacy/Data/ingsw/0613_10/quest.txt
new file mode 100644
index 0000000..9e4d3a9
--- /dev/null
+++ b/legacy/Data/ingsw/0613_10/quest.txt
@@ -0,0 +1,31 @@
+Il branch coverage di un insieme di test cases � 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/legacy/Data/ingsw/0613_10/wrong1.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0613_10/wrong1.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_10/wrong2.txt b/legacy/Data/ingsw/0613_10/wrong2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0613_10/wrong2.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_11/correct.txt b/legacy/Data/ingsw/0613_11/correct.txt
new file mode 100644
index 0000000..aef914a
--- /dev/null
+++ b/legacy/Data/ingsw/0613_11/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che un sistema che soddisfa i requisiti risolve il problema del "customer".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_11/quest.txt b/legacy/Data/ingsw/0613_11/quest.txt
new file mode 100644
index 0000000..9af4805
--- /dev/null
+++ b/legacy/Data/ingsw/0613_11/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "validity check" che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_11/wrong1.txt b/legacy/Data/ingsw/0613_11/wrong1.txt
new file mode 100644
index 0000000..eb23d05
--- /dev/null
+++ b/legacy/Data/ingsw/0613_11/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che non ci siano requisiti in conflitto con altri requisiti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_11/wrong2.txt b/legacy/Data/ingsw/0613_11/wrong2.txt
new file mode 100644
index 0000000..32c628c
--- /dev/null
+++ b/legacy/Data/ingsw/0613_11/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che i requisiti funzionali descrivano tutte le funzionalità del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_12/correct.txt b/legacy/Data/ingsw/0613_12/correct.txt
new file mode 100644
index 0000000..475d1ef
--- /dev/null
+++ b/legacy/Data/ingsw/0613_12/correct.txt
@@ -0,0 +1 @@
+{x = -150, x = -40,  x = 0, x = 200, x = 600}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_12/quest.txt b/legacy/Data/ingsw/0613_12/quest.txt
new file mode 100644
index 0000000..36947c2
--- /dev/null
+++ b/legacy/Data/ingsw/0613_12/quest.txt
@@ -0,0 +1,6 @@
+Il partition coverage di un insieme di test cases � la percentuale di elementi della partition inclusi nei test cases. La partition � 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/legacy/Data/ingsw/0613_12/wrong1.txt
new file mode 100644
index 0000000..0aaedb8
--- /dev/null
+++ b/legacy/Data/ingsw/0613_12/wrong1.txt
@@ -0,0 +1 @@
+{x = -200, x = -50,  x = 0, x = 100, x = 500}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_12/wrong2.txt b/legacy/Data/ingsw/0613_12/wrong2.txt
new file mode 100644
index 0000000..a6df32d
--- /dev/null
+++ b/legacy/Data/ingsw/0613_12/wrong2.txt
@@ -0,0 +1 @@
+{x = -200, x = -150,  x = 0, x = 100, x = 700}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_13/correct.txt b/legacy/Data/ingsw/0613_13/correct.txt
new file mode 100644
index 0000000..12d93cc
--- /dev/null
+++ b/legacy/Data/ingsw/0613_13/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 20%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_13/quest.txt b/legacy/Data/ingsw/0613_13/quest.txt
new file mode 100644
index 0000000..6f20250
--- /dev/null
+++ b/legacy/Data/ingsw/0613_13/quest.txt
@@ -0,0 +1,13 @@
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_13.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0613_13/wrong1.txt
new file mode 100644
index 0000000..8b0c318
--- /dev/null
+++ b/legacy/Data/ingsw/0613_13/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_13/wrong2.txt b/legacy/Data/ingsw/0613_13/wrong2.txt
new file mode 100644
index 0000000..5464d05
--- /dev/null
+++ b/legacy/Data/ingsw/0613_13/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 30%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_14/quest.txt b/legacy/Data/ingsw/0613_14/quest.txt
new file mode 100644
index 0000000..b95c7d3
--- /dev/null
+++ b/legacy/Data/ingsw/0613_14/quest.txt
@@ -0,0 +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)
+end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_14/wrong1.txt b/legacy/Data/ingsw/0613_14/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0613_14/wrong2.txt b/legacy/Data/ingsw/0613_14/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0613_14/wrong3.txt b/legacy/Data/ingsw/0613_14/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0613_15/correct.txt b/legacy/Data/ingsw/0613_15/correct.txt
new file mode 100644
index 0000000..b8bf06e
--- /dev/null
+++ b/legacy/Data/ingsw/0613_15/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));
+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/quest.txt b/legacy/Data/ingsw/0613_15/quest.txt
new file mode 100644
index 0000000..22c683f
--- /dev/null
+++ b/legacy/Data/ingsw/0613_15/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Durante l'esecuzione del programma (cio� per tutti gli istanti di tempo positivi) la variabile x � 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/legacy/Data/ingsw/0613_15/wrong1.txt
new file mode 100644
index 0000000..bc8720d
--- /dev/null
+++ b/legacy/Data/ingsw/0613_15/wrong1.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 > 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/legacy/Data/ingsw/0613_15/wrong2.txt
new file mode 100644
index 0000000..2029293
--- /dev/null
+++ b/legacy/Data/ingsw/0613_15/wrong2.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 > 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/0613_16/correct.txt b/legacy/Data/ingsw/0613_16/correct.txt
new file mode 100644
index 0000000..e582263
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0613_16/quest.txt b/legacy/Data/ingsw/0613_16/quest.txt
new file mode 100644
index 0000000..864cc93
--- /dev/null
+++ b/legacy/Data/ingsw/0613_16/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Durante l'esecuzione del programma (cio� per tutti gli istanti di tempo positivi) la variabile x � 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/legacy/Data/ingsw/0613_16/wrong1.txt
new file mode 100644
index 0000000..590f7e1
--- /dev/null
+++ b/legacy/Data/ingsw/0613_16/wrong1.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 >= 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/legacy/Data/ingsw/0613_16/wrong2.txt
new file mode 100644
index 0000000..0f38391
--- /dev/null
+++ b/legacy/Data/ingsw/0613_16/wrong2.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 >= 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/0613_17/correct.txt b/legacy/Data/ingsw/0613_17/correct.txt
new file mode 100644
index 0000000..c37d6ae
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0613_17/quest.txt b/legacy/Data/ingsw/0613_17/quest.txt
new file mode 100644
index 0000000..003d1dd
--- /dev/null
+++ b/legacy/Data/ingsw/0613_17/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se 10 unit� di tempo nel passato x era maggiore di 0 allora ora y � 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/legacy/Data/ingsw/0613_17/wrong1.txt
new file mode 100644
index 0000000..14bd900
--- /dev/null
+++ b/legacy/Data/ingsw/0613_17/wrong1.txt
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_17/wrong2.txt b/legacy/Data/ingsw/0613_17/wrong2.txt
new file mode 100644
index 0000000..edea147
--- /dev/null
+++ b/legacy/Data/ingsw/0613_17/wrong2.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/0613_18/correct.txt b/legacy/Data/ingsw/0613_18/correct.txt
new file mode 100644
index 0000000..98939be
--- /dev/null
+++ b/legacy/Data/ingsw/0613_18/correct.txt
@@ -0,0 +1 @@
+1/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_18/quest.txt b/legacy/Data/ingsw/0613_18/quest.txt
new file mode 100644
index 0000000..91edad5
--- /dev/null
+++ b/legacy/Data/ingsw/0613_18/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0613_18/wrong1.txt
new file mode 100644
index 0000000..56ea6ac
--- /dev/null
+++ b/legacy/Data/ingsw/0613_18/wrong1.txt
@@ -0,0 +1 @@
+1/(p*(1 - p))
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_18/wrong2.txt b/legacy/Data/ingsw/0613_18/wrong2.txt
new file mode 100644
index 0000000..db2276d
--- /dev/null
+++ b/legacy/Data/ingsw/0613_18/wrong2.txt
@@ -0,0 +1 @@
+(1 - p)/p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_19/quest.txt b/legacy/Data/ingsw/0613_19/quest.txt
new file mode 100644
index 0000000..052028b
--- /dev/null
+++ b/legacy/Data/ingsw/0613_19/quest.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_19/wrong1.txt b/legacy/Data/ingsw/0613_19/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0613_19/wrong2.txt b/legacy/Data/ingsw/0613_19/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0613_19/wrong3.txt b/legacy/Data/ingsw/0613_19/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0613_2/quest.txt b/legacy/Data/ingsw/0613_2/quest.txt
new file mode 100644
index 0000000..fcb1323
--- /dev/null
+++ b/legacy/Data/ingsw/0613_2/quest.txt
@@ -0,0 +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)
+
+
+end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_2/wrong1.txt b/legacy/Data/ingsw/0613_2/wrong1.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0613_2/wrong2.txt b/legacy/Data/ingsw/0613_2/wrong2.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0613_2/wrong3.txt b/legacy/Data/ingsw/0613_2/wrong3.txt
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/Data/ingsw/0613_20/correct.txt b/legacy/Data/ingsw/0613_20/correct.txt
new file mode 100644
index 0000000..2a2ecea
--- /dev/null
+++ b/legacy/Data/ingsw/0613_20/correct.txt
@@ -0,0 +1 @@
+time(0)/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_20/quest.txt b/legacy/Data/ingsw/0613_20/quest.txt
new file mode 100644
index 0000000..79b69ac
--- /dev/null
+++ b/legacy/Data/ingsw/0613_20/quest.txt
@@ -0,0 +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 � in (0, 1).  Il tempo necessario per completare la fase x � time(x). La fase 0 � la fase di design, che ha probabilit� 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 � 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), .... � 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)  (poich� 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/legacy/Data/ingsw/0613_20/wrong1.txt
new file mode 100644
index 0000000..d68fd15
--- /dev/null
+++ b/legacy/Data/ingsw/0613_20/wrong1.txt
@@ -0,0 +1 @@
+time(0)*(1 - p)/p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_20/wrong2.txt b/legacy/Data/ingsw/0613_20/wrong2.txt
new file mode 100644
index 0000000..9927a93
--- /dev/null
+++ b/legacy/Data/ingsw/0613_20/wrong2.txt
@@ -0,0 +1 @@
+time(0)/(p*(1 - p))
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_21/correct.txt b/legacy/Data/ingsw/0613_21/correct.txt
new file mode 100644
index 0000000..936832d
--- /dev/null
+++ b/legacy/Data/ingsw/0613_21/correct.txt
@@ -0,0 +1 @@
+3*A + 6*B
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_21/quest.txt b/legacy/Data/ingsw/0613_21/quest.txt
new file mode 100644
index 0000000..07ce5c9
--- /dev/null
+++ b/legacy/Data/ingsw/0613_21/quest.txt
@@ -0,0 +1 @@
+Il team di sviluppo di un azienda consiste di un senior software engineer e due sviluppatori junior. Usando un approccio plan-driven (ad esempio, water-fall) la fase di design impegna solo il membro senior per tre mesi e la fase di sviluppo e testing solo i due membri junior per tre mesi. Si assuma che non ci siano "change requests" e che il membro senior costi A Eur/mese ed i membri junior B Eur/mese. Qual'e' il costo dello sviluppo usando un approccio plan-driven come sopra ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_21/wrong1.txt b/legacy/Data/ingsw/0613_21/wrong1.txt
new file mode 100644
index 0000000..316107c
--- /dev/null
+++ b/legacy/Data/ingsw/0613_21/wrong1.txt
@@ -0,0 +1 @@
+A + 2*B
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_21/wrong2.txt b/legacy/Data/ingsw/0613_21/wrong2.txt
new file mode 100644
index 0000000..68f09b9
--- /dev/null
+++ b/legacy/Data/ingsw/0613_21/wrong2.txt
@@ -0,0 +1 @@
+3*A + 3*B
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_22/correct.txt b/legacy/Data/ingsw/0613_22/correct.txt
new file mode 100644
index 0000000..2ca9276
--- /dev/null
+++ b/legacy/Data/ingsw/0613_22/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 35%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_22/quest.txt b/legacy/Data/ingsw/0613_22/quest.txt
new file mode 100644
index 0000000..aef94a6
--- /dev/null
+++ b/legacy/Data/ingsw/0613_22/quest.txt
@@ -0,0 +1,13 @@
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_22.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0613_22/wrong1.txt
new file mode 100644
index 0000000..5623b39
--- /dev/null
+++ b/legacy/Data/ingsw/0613_22/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 65%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_22/wrong2.txt b/legacy/Data/ingsw/0613_22/wrong2.txt
new file mode 100644
index 0000000..c376ef7
--- /dev/null
+++ b/legacy/Data/ingsw/0613_22/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 55%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_23/correct.txt b/legacy/Data/ingsw/0613_23/correct.txt
new file mode 100644
index 0000000..4a8e634
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0613_23/quest.txt b/legacy/Data/ingsw/0613_23/quest.txt
new file mode 100644
index 0000000..576af1a
--- /dev/null
+++ b/legacy/Data/ingsw/0613_23/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 60 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se 10 unit� di tempo nel passato era stata richiesta una risorsa (variabile x positiva) allora ora � 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/legacy/Data/ingsw/0613_23/wrong1.txt
new file mode 100644
index 0000000..68aa37a
--- /dev/null
+++ b/legacy/Data/ingsw/0613_23/wrong1.txt
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_23/wrong2.txt b/legacy/Data/ingsw/0613_23/wrong2.txt
new file mode 100644
index 0000000..a43796b
--- /dev/null
+++ b/legacy/Data/ingsw/0613_23/wrong2.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/0613_24/correct.txt b/legacy/Data/ingsw/0613_24/correct.txt
new file mode 100644
index 0000000..5464d05
--- /dev/null
+++ b/legacy/Data/ingsw/0613_24/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 30%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_24/quest.txt b/legacy/Data/ingsw/0613_24/quest.txt
new file mode 100644
index 0000000..9534ab3
--- /dev/null
+++ b/legacy/Data/ingsw/0613_24/quest.txt
@@ -0,0 +1,13 @@
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_24.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0613_24/wrong1.txt
new file mode 100644
index 0000000..8b0c318
--- /dev/null
+++ b/legacy/Data/ingsw/0613_24/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_24/wrong2.txt b/legacy/Data/ingsw/0613_24/wrong2.txt
new file mode 100644
index 0000000..cf27703
--- /dev/null
+++ b/legacy/Data/ingsw/0613_24/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 70%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_25/correct.txt b/legacy/Data/ingsw/0613_25/correct.txt
new file mode 100644
index 0000000..e74b1fc
--- /dev/null
+++ b/legacy/Data/ingsw/0613_25/correct.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x > y) then (z == x) else (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_25/quest.txt b/legacy/Data/ingsw/0613_25/quest.txt
new file mode 100644
index 0000000..c1cd6d0
--- /dev/null
+++ b/legacy/Data/ingsw/0613_25/quest.txt
@@ -0,0 +1,9 @@
+Un test oracle per un programma P � 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) � 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) � un test oracle per la funzione f.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_25/wrong1.txt b/legacy/Data/ingsw/0613_25/wrong1.txt
new file mode 100644
index 0000000..d63544a
--- /dev/null
+++ b/legacy/Data/ingsw/0613_25/wrong1.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x > y) then (z == x + 1) else (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_25/wrong2.txt b/legacy/Data/ingsw/0613_25/wrong2.txt
new file mode 100644
index 0000000..1753a91
--- /dev/null
+++ b/legacy/Data/ingsw/0613_25/wrong2.txt
@@ -0,0 +1 @@
+F(x, y, z) = (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_26/correct.txt b/legacy/Data/ingsw/0613_26/correct.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0613_26/correct.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_26/quest.txt b/legacy/Data/ingsw/0613_26/quest.txt
new file mode 100644
index 0000000..dcec721
--- /dev/null
+++ b/legacy/Data/ingsw/0613_26/quest.txt
@@ -0,0 +1,8 @@
+Il partition coverage di un insieme di test cases � la percentuale di elementi della partition inclusi nei test cases. La partition � 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 � la partition coverage conseguita?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_26/wrong1.txt b/legacy/Data/ingsw/0613_26/wrong1.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0613_26/wrong1.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_26/wrong2.txt b/legacy/Data/ingsw/0613_26/wrong2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0613_26/wrong2.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_27/quest.txt b/legacy/Data/ingsw/0613_27/quest.txt
new file mode 100644
index 0000000..35670bc
--- /dev/null
+++ b/legacy/Data/ingsw/0613_27/quest.txt
@@ -0,0 +1,4 @@
+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/ingsw/0613_27/wrong1.txt b/legacy/Data/ingsw/0613_27/wrong1.txt
new file mode 100644
index 0000000..4bcd55f
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0613_27/wrong2.txt
new file mode 100644
index 0000000..19be218
--- /dev/null
+++ b/legacy/Data/ingsw/0613_27/wrong2.txt
@@ -0,0 +1,2 @@
+input12)
+connect(sc1.output13, sc
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_27/wrong3.txt b/legacy/Data/ingsw/0613_27/wrong3.txt
new file mode 100644
index 0000000..29daf30
--- /dev/null
+++ b/legacy/Data/ingsw/0613_27/wrong3.txt
@@ -0,0 +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)
+
+
+end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_28/correct.txt b/legacy/Data/ingsw/0613_28/correct.txt
new file mode 100644
index 0000000..973ef63
--- /dev/null
+++ b/legacy/Data/ingsw/0613_28/correct.txt
@@ -0,0 +1 @@
+State coverage: 75%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_28/quest.txt b/legacy/Data/ingsw/0613_28/quest.txt
new file mode 100644
index 0000000..32aecd3
--- /dev/null
+++ b/legacy/Data/ingsw/0613_28/quest.txt
@@ -0,0 +1,11 @@
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_28.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0613_28/wrong1.txt
new file mode 100644
index 0000000..f6a4b07
--- /dev/null
+++ b/legacy/Data/ingsw/0613_28/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_28/wrong2.txt b/legacy/Data/ingsw/0613_28/wrong2.txt
new file mode 100644
index 0000000..90b2f35
--- /dev/null
+++ b/legacy/Data/ingsw/0613_28/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_29/correct.txt b/legacy/Data/ingsw/0613_29/correct.txt
new file mode 100644
index 0000000..7a6c6b9
--- /dev/null
+++ b/legacy/Data/ingsw/0613_29/correct.txt
@@ -0,0 +1 @@
+300000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_29/quest.txt b/legacy/Data/ingsw/0613_29/quest.txt
new file mode 100644
index 0000000..47201e7
--- /dev/null
+++ b/legacy/Data/ingsw/0613_29/quest.txt
@@ -0,0 +1,4 @@
+Il rischio R pu� essere calcolato come R = P*C, dove P � la probabilit� dell'evento avverso (software failure nel nostro contesto) e C � il costo dell'occorrenza dell'evento avverso. 
+Assumiamo che la probabilit� P sia legata al costo di sviluppo S dalla formula 
+P = 10^{(-b*S)}    (cio� 10 elevato alla (-b*S))
+dove b � una opportuna costante note da dati storici aziendali. Si assuma che b = 0.0001,  C = 1000000, ed il rischio ammesso � 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/legacy/Data/ingsw/0613_29/wrong1.txt
new file mode 100644
index 0000000..997967b
--- /dev/null
+++ b/legacy/Data/ingsw/0613_29/wrong1.txt
@@ -0,0 +1 @@
+700000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_29/wrong2.txt b/legacy/Data/ingsw/0613_29/wrong2.txt
new file mode 100644
index 0000000..2df501e
--- /dev/null
+++ b/legacy/Data/ingsw/0613_29/wrong2.txt
@@ -0,0 +1 @@
+500000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_3/correct.txt b/legacy/Data/ingsw/0613_3/correct.txt
new file mode 100644
index 0000000..3fb437d
--- /dev/null
+++ b/legacy/Data/ingsw/0613_3/correct.txt
@@ -0,0 +1 @@
+0.56
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_3/quest.txt b/legacy/Data/ingsw/0613_3/quest.txt
new file mode 100644
index 0000000..d8bc097
--- /dev/null
+++ b/legacy/Data/ingsw/0613_3/quest.txt
@@ -0,0 +1,9 @@
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_3.png
+Un processo software pu� 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 probabilit� 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 probabilit� 0.2 di dover essere ripetuta (a causa di errori).
+Uno scenario � una sequenza di stati.
+Qual'e' la probabilit� dello scenario: 1, 3 ? In altri terminti, qual'� la probabilit� che non sia necessario ripetere nessuna fase?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_3/wrong1.txt b/legacy/Data/ingsw/0613_3/wrong1.txt
new file mode 100644
index 0000000..fc54e00
--- /dev/null
+++ b/legacy/Data/ingsw/0613_3/wrong1.txt
@@ -0,0 +1 @@
+0.24
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_3/wrong2.txt b/legacy/Data/ingsw/0613_3/wrong2.txt
new file mode 100644
index 0000000..c64601b
--- /dev/null
+++ b/legacy/Data/ingsw/0613_3/wrong2.txt
@@ -0,0 +1 @@
+0.14
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_30/correct.txt b/legacy/Data/ingsw/0613_30/correct.txt
new file mode 100644
index 0000000..973ef63
--- /dev/null
+++ b/legacy/Data/ingsw/0613_30/correct.txt
@@ -0,0 +1 @@
+State coverage: 75%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_30/quest.txt b/legacy/Data/ingsw/0613_30/quest.txt
new file mode 100644
index 0000000..56ab57a
--- /dev/null
+++ b/legacy/Data/ingsw/0613_30/quest.txt
@@ -0,0 +1,11 @@
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_30.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0613_30/wrong1.txt
new file mode 100644
index 0000000..90b2f35
--- /dev/null
+++ b/legacy/Data/ingsw/0613_30/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_30/wrong2.txt b/legacy/Data/ingsw/0613_30/wrong2.txt
new file mode 100644
index 0000000..f6a4b07
--- /dev/null
+++ b/legacy/Data/ingsw/0613_30/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_31/correct.txt b/legacy/Data/ingsw/0613_31/correct.txt
new file mode 100644
index 0000000..f6a4b07
--- /dev/null
+++ b/legacy/Data/ingsw/0613_31/correct.txt
@@ -0,0 +1 @@
+State coverage: 90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_31/quest.txt b/legacy/Data/ingsw/0613_31/quest.txt
new file mode 100644
index 0000000..9f9ed74
--- /dev/null
+++ b/legacy/Data/ingsw/0613_31/quest.txt
@@ -0,0 +1,12 @@
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_31.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0613_31/wrong1.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0613_31/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_31/wrong2.txt b/legacy/Data/ingsw/0613_31/wrong2.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/0613_31/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_32/correct.txt b/legacy/Data/ingsw/0613_32/correct.txt
new file mode 100644
index 0000000..b110af1
--- /dev/null
+++ b/legacy/Data/ingsw/0613_32/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_32/quest.txt b/legacy/Data/ingsw/0613_32/quest.txt
new file mode 100644
index 0000000..1724f1c
--- /dev/null
+++ b/legacy/Data/ingsw/0613_32/quest.txt
@@ -0,0 +1,13 @@
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_32.png
+La transition coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0613_32/wrong1.txt
new file mode 100644
index 0000000..8b0c318
--- /dev/null
+++ b/legacy/Data/ingsw/0613_32/wrong1.txt
@@ -0,0 +1 @@
+Transition coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_32/wrong2.txt b/legacy/Data/ingsw/0613_32/wrong2.txt
new file mode 100644
index 0000000..cf27703
--- /dev/null
+++ b/legacy/Data/ingsw/0613_32/wrong2.txt
@@ -0,0 +1 @@
+Transition coverage: 70%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_33/correct.txt b/legacy/Data/ingsw/0613_33/correct.txt
new file mode 100644
index 0000000..e940faa
--- /dev/null
+++ b/legacy/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/ingsw/0613_33/quest.txt b/legacy/Data/ingsw/0613_33/quest.txt
new file mode 100644
index 0000000..2758118
--- /dev/null
+++ b/legacy/Data/ingsw/0613_33/quest.txt
@@ -0,0 +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 (cio� non � 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 � maggiore di 3 ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_33/wrong1.txt b/legacy/Data/ingsw/0613_33/wrong1.txt
new file mode 100644
index 0000000..ad32d88
--- /dev/null
+++ b/legacy/Data/ingsw/0613_33/wrong1.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/ingsw/0613_33/wrong2.txt b/legacy/Data/ingsw/0613_33/wrong2.txt
new file mode 100644
index 0000000..642ec6b
--- /dev/null
+++ b/legacy/Data/ingsw/0613_33/wrong2.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/ingsw/0613_34/correct.txt b/legacy/Data/ingsw/0613_34/correct.txt
new file mode 100644
index 0000000..e7c5bb8
--- /dev/null
+++ b/legacy/Data/ingsw/0613_34/correct.txt
@@ -0,0 +1 @@
+Assicurarsi che, tenedo conto della tecnologia, budget e tempo disponibili,  sia possibile realizzare un sistema che soddisfa i requisisti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_34/quest.txt b/legacy/Data/ingsw/0613_34/quest.txt
new file mode 100644
index 0000000..296cdcb
--- /dev/null
+++ b/legacy/Data/ingsw/0613_34/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive l'obiettivo del "check di realismo" (realizability) che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_34/wrong1.txt b/legacy/Data/ingsw/0613_34/wrong1.txt
new file mode 100644
index 0000000..bfb5124
--- /dev/null
+++ b/legacy/Data/ingsw/0613_34/wrong1.txt
@@ -0,0 +1 @@
+Assicurarsi che le performance richieste al sistema siano necessarie per soddisfare le necessità del customer.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_34/wrong2.txt b/legacy/Data/ingsw/0613_34/wrong2.txt
new file mode 100644
index 0000000..2b6e242
--- /dev/null
+++ b/legacy/Data/ingsw/0613_34/wrong2.txt
@@ -0,0 +1 @@
+Assicurarsi che le funzionalità richieste al sistema siano necessarie per soddisfare le necessità del customer.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_35/correct.txt b/legacy/Data/ingsw/0613_35/correct.txt
new file mode 100644
index 0000000..ad21063
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0613_35/quest.txt b/legacy/Data/ingsw/0613_35/quest.txt
new file mode 100644
index 0000000..031c331
--- /dev/null
+++ b/legacy/Data/ingsw/0613_35/quest.txt
@@ -0,0 +1,5 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 40 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se 10 unit� di tempo nel passato x era maggiore di 1 allora ora y � 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
new file mode 100644
index 0000000..b14ac60
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0613_35/wrong2.txt b/legacy/Data/ingsw/0613_35/wrong2.txt
new file mode 100644
index 0000000..e4201ab
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0613_36/correct.txt
new file mode 100644
index 0000000..1c7da8c
--- /dev/null
+++ b/legacy/Data/ingsw/0613_36/correct.txt
@@ -0,0 +1 @@
+0.03
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_36/quest.txt b/legacy/Data/ingsw/0613_36/quest.txt
new file mode 100644
index 0000000..58782d5
--- /dev/null
+++ b/legacy/Data/ingsw/0613_36/quest.txt
@@ -0,0 +1,9 @@
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_36.png
+Un processo software pu� 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 probabilit� 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 probabilit� 0.1 di dover essere ripetuta (a causa di errori).
+Uno scenario � una sequenza di stati.
+Qual'e' la probabilit� dello scenario: 1, 2, 3, 4 ? In altri terminti, qual'� la probabilit� 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/legacy/Data/ingsw/0613_36/wrong1.txt
new file mode 100644
index 0000000..8a346b7
--- /dev/null
+++ b/legacy/Data/ingsw/0613_36/wrong1.txt
@@ -0,0 +1 @@
+0.07
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_36/wrong2.txt b/legacy/Data/ingsw/0613_36/wrong2.txt
new file mode 100644
index 0000000..7eb6830
--- /dev/null
+++ b/legacy/Data/ingsw/0613_36/wrong2.txt
@@ -0,0 +1 @@
+0.27
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_37/correct.txt b/legacy/Data/ingsw/0613_37/correct.txt
new file mode 100644
index 0000000..a7029bc
--- /dev/null
+++ b/legacy/Data/ingsw/0613_37/correct.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [1, 4] oppure nell'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_37/quest.txt b/legacy/Data/ingsw/0613_37/quest.txt
new file mode 100644
index 0000000..e5fbc81
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0613_37/wrong1.txt
new file mode 100644
index 0000000..710b111
--- /dev/null
+++ b/legacy/Data/ingsw/0613_37/wrong1.txt
@@ -0,0 +1 @@
+La variabile x è fuori dall'intervallo [1, 4] e fuori dall'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_37/wrong2.txt b/legacy/Data/ingsw/0613_37/wrong2.txt
new file mode 100644
index 0000000..a82929b
--- /dev/null
+++ b/legacy/Data/ingsw/0613_37/wrong2.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [1, 4] e fuori dall'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_38/quest.txt b/legacy/Data/ingsw/0613_38/quest.txt
new file mode 100644
index 0000000..230115c
--- /dev/null
+++ b/legacy/Data/ingsw/0613_38/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0613_38/wrong1.txt
new file mode 100644
index 0000000..00b636b
--- /dev/null
+++ b/legacy/Data/ingsw/0613_38/wrong1.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_38/wrong2.txt b/legacy/Data/ingsw/0613_38/wrong2.txt
new file mode 100644
index 0000000..dc39134
--- /dev/null
+++ b/legacy/Data/ingsw/0613_38/wrong2.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_38/wrong3.txt b/legacy/Data/ingsw/0613_38/wrong3.txt
new file mode 100644
index 0000000..6a9ef82
--- /dev/null
+++ b/legacy/Data/ingsw/0613_38/wrong3.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_39/correct.txt b/legacy/Data/ingsw/0613_39/correct.txt
new file mode 100644
index 0000000..b9f32a6
--- /dev/null
+++ b/legacy/Data/ingsw/0613_39/correct.txt
@@ -0,0 +1 @@
+c(0)/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_39/quest.txt b/legacy/Data/ingsw/0613_39/quest.txt
new file mode 100644
index 0000000..24a64fe
--- /dev/null
+++ b/legacy/Data/ingsw/0613_39/quest.txt
@@ -0,0 +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 � in (0, 1).  Il costo dello stato (fase) x � c(x). La fase 0 � la fase di design, che ha probabilit� 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 � 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), .... � 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)  (poich� 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/legacy/Data/ingsw/0613_39/wrong1.txt
new file mode 100644
index 0000000..70022eb
--- /dev/null
+++ b/legacy/Data/ingsw/0613_39/wrong1.txt
@@ -0,0 +1 @@
+c(0)*(1 - p)/p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_39/wrong2.txt b/legacy/Data/ingsw/0613_39/wrong2.txt
new file mode 100644
index 0000000..3143da9
--- /dev/null
+++ b/legacy/Data/ingsw/0613_39/wrong2.txt
@@ -0,0 +1 @@
+c(0)/(p*(1 - p))
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_4/correct.txt b/legacy/Data/ingsw/0613_4/correct.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0613_4/correct.txt
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_4/quest.txt b/legacy/Data/ingsw/0613_4/quest.txt
new file mode 100644
index 0000000..5cf5cae
--- /dev/null
+++ b/legacy/Data/ingsw/0613_4/quest.txt
@@ -0,0 +1,12 @@
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_4.png
+La state coverage di un insieme di test cases (cio� sequenze di inputs) per uno state diagram � 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 � 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/legacy/Data/ingsw/0613_4/wrong1.txt
new file mode 100644
index 0000000..a8aead7
--- /dev/null
+++ b/legacy/Data/ingsw/0613_4/wrong1.txt
@@ -0,0 +1 @@
+State coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_4/wrong2.txt b/legacy/Data/ingsw/0613_4/wrong2.txt
new file mode 100644
index 0000000..90b2f35
--- /dev/null
+++ b/legacy/Data/ingsw/0613_4/wrong2.txt
@@ -0,0 +1 @@
+State coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_40/quest.txt b/legacy/Data/ingsw/0613_40/quest.txt
new file mode 100644
index 0000000..2959407
--- /dev/null
+++ b/legacy/Data/ingsw/0613_40/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/0613_40/wrong1.txt
new file mode 100644
index 0000000..f919b6b
--- /dev/null
+++ b/legacy/Data/ingsw/0613_40/wrong1.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_40/wrong2.txt b/legacy/Data/ingsw/0613_40/wrong2.txt
new file mode 100644
index 0000000..fc9e0aa
--- /dev/null
+++ b/legacy/Data/ingsw/0613_40/wrong2.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_40/wrong3.txt b/legacy/Data/ingsw/0613_40/wrong3.txt
new file mode 100644
index 0000000..e537817
--- /dev/null
+++ b/legacy/Data/ingsw/0613_40/wrong3.txt
@@ -0,0 +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;
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_41/quest.txt b/legacy/Data/ingsw/0613_41/quest.txt
new file mode 100644
index 0000000..99379e6
--- /dev/null
+++ b/legacy/Data/ingsw/0613_41/quest.txt
@@ -0,0 +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 (cio� non � 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/legacy/Data/ingsw/0613_41/wrong1.txt
new file mode 100644
index 0000000..cbf1814
--- /dev/null
+++ b/legacy/Data/ingsw/0613_41/wrong1.txt
@@ -0,0 +1,6 @@
+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/legacy/Data/ingsw/0613_41/wrong2.txt
new file mode 100644
index 0000000..6fcb8b5
--- /dev/null
+++ b/legacy/Data/ingsw/0613_41/wrong2.txt
@@ -0,0 +1,6 @@
+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/legacy/Data/ingsw/0613_41/wrong3.txt
new file mode 100644
index 0000000..03b9f52
--- /dev/null
+++ b/legacy/Data/ingsw/0613_41/wrong3.txt
@@ -0,0 +1,6 @@
+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/legacy/Data/ingsw/0613_42/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0613_42/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_42/quest.txt b/legacy/Data/ingsw/0613_42/quest.txt
new file mode 100644
index 0000000..2bda796
--- /dev/null
+++ b/legacy/Data/ingsw/0613_42/quest.txt
@@ -0,0 +1,29 @@
+Il branch coverage di un insieme di test cases � 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/legacy/Data/ingsw/0613_42/wrong1.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0613_42/wrong1.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_42/wrong2.txt b/legacy/Data/ingsw/0613_42/wrong2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0613_42/wrong2.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_43/correct.txt b/legacy/Data/ingsw/0613_43/correct.txt
new file mode 100644
index 0000000..293ebbc
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0613_43/quest.txt b/legacy/Data/ingsw/0613_43/quest.txt
new file mode 100644
index 0000000..5922b9f
--- /dev/null
+++ b/legacy/Data/ingsw/0613_43/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 10 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�: se la variabile x � nell'intervallo [10, 20]  allora la variabile y � 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/legacy/Data/ingsw/0613_43/wrong1.txt
new file mode 100644
index 0000000..d7890b2
--- /dev/null
+++ b/legacy/Data/ingsw/0613_43/wrong1.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) 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/legacy/Data/ingsw/0613_43/wrong2.txt
new file mode 100644
index 0000000..d50b268
--- /dev/null
+++ b/legacy/Data/ingsw/0613_43/wrong2.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) 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/legacy/Data/ingsw/0613_44/correct.txt
new file mode 100644
index 0000000..1a8a50a
--- /dev/null
+++ b/legacy/Data/ingsw/0613_44/correct.txt
@@ -0,0 +1 @@
+Per ciascun requisito, dovremmo essere in grado di scrivere un inseme di test che può dimostrare che il sistema sviluppato soddisfa il requisito considerato.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_44/quest.txt b/legacy/Data/ingsw/0613_44/quest.txt
new file mode 100644
index 0000000..793b220
--- /dev/null
+++ b/legacy/Data/ingsw/0613_44/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti frasi meglio descrive il criterio di "requirements verifiability" che � parte della "requirements validation activity".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_44/wrong1.txt b/legacy/Data/ingsw/0613_44/wrong1.txt
new file mode 100644
index 0000000..fac8307
--- /dev/null
+++ b/legacy/Data/ingsw/0613_44/wrong1.txt
@@ -0,0 +1 @@
+Per ciascuna coppia di componenti, dovremmo essere in grado di scrivere un insieme di test che può dimostrare che l'interazione tra le componenti soddisfa tutti i requisiti di interfaccia.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_44/wrong2.txt b/legacy/Data/ingsw/0613_44/wrong2.txt
new file mode 100644
index 0000000..3fdb31e
--- /dev/null
+++ b/legacy/Data/ingsw/0613_44/wrong2.txt
@@ -0,0 +1 @@
+Per ciascuna componente del sistema, dovremmo essere in grado di scrivere un insieme di test che può dimostrare che essa soddisfa tutti i requisiti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_45/correct.txt b/legacy/Data/ingsw/0613_45/correct.txt
new file mode 100644
index 0000000..232aedf
--- /dev/null
+++ b/legacy/Data/ingsw/0613_45/correct.txt
@@ -0,0 +1 @@
+(a = 6, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 0).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_45/quest.txt b/legacy/Data/ingsw/0613_45/quest.txt
new file mode 100644
index 0000000..e44e320
--- /dev/null
+++ b/legacy/Data/ingsw/0613_45/quest.txt
@@ -0,0 +1,21 @@
+Una Condition � una proposizione booleana, cio� una espressione con valore booleano che non pu� essere decomposta 
+in espressioni boolean pi� semplici. Ad esempio, (x + y <= 3) � una condition.
+
+Una Decision � una espressione booleana composta da conditions e zero o pi� 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 � 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 � true ed un test in T in cui c � false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d � true ed un test in T in cui d � 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/legacy/Data/ingsw/0613_45/wrong1.txt
new file mode 100644
index 0000000..5d5c9a4
--- /dev/null
+++ b/legacy/Data/ingsw/0613_45/wrong1.txt
@@ -0,0 +1 @@
+(a = 6, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 2).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_45/wrong2.txt b/legacy/Data/ingsw/0613_45/wrong2.txt
new file mode 100644
index 0000000..2b6c292
--- /dev/null
+++ b/legacy/Data/ingsw/0613_45/wrong2.txt
@@ -0,0 +1 @@
+(a = 5, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 0).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_46/correct.txt b/legacy/Data/ingsw/0613_46/correct.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0613_46/correct.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_46/quest.txt b/legacy/Data/ingsw/0613_46/quest.txt
new file mode 100644
index 0000000..03acbcc
--- /dev/null
+++ b/legacy/Data/ingsw/0613_46/quest.txt
@@ -0,0 +1,30 @@
+Il branch coverage di un insieme di test cases � 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/legacy/Data/ingsw/0613_46/wrong1.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0613_46/wrong1.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_46/wrong2.txt b/legacy/Data/ingsw/0613_46/wrong2.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0613_46/wrong2.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_47/correct.txt b/legacy/Data/ingsw/0613_47/correct.txt
new file mode 100644
index 0000000..f3da655
--- /dev/null
+++ b/legacy/Data/ingsw/0613_47/correct.txt
@@ -0,0 +1 @@
+3*(A + 2*B)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_47/quest.txt b/legacy/Data/ingsw/0613_47/quest.txt
new file mode 100644
index 0000000..6395b05
--- /dev/null
+++ b/legacy/Data/ingsw/0613_47/quest.txt
@@ -0,0 +1 @@
+Il team di sviluppo di un azienda consiste di un senior software engineer e due sviluppatori junior. Usando un approccio agile, ogni iterazione impegna tutti e tre i membri del team per un mese ed occorrono tre iterazioni per completare lo sviluppo. Si assuma che non ci siano "change requests" e che il membro senior costi A Eur/mese ed i membri junior B Eur/mese. Qual'e' il costo dello sviluppo usando un approccio agile ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_47/wrong1.txt b/legacy/Data/ingsw/0613_47/wrong1.txt
new file mode 100644
index 0000000..316107c
--- /dev/null
+++ b/legacy/Data/ingsw/0613_47/wrong1.txt
@@ -0,0 +1 @@
+A + 2*B
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_47/wrong2.txt b/legacy/Data/ingsw/0613_47/wrong2.txt
new file mode 100644
index 0000000..82fe5c7
--- /dev/null
+++ b/legacy/Data/ingsw/0613_47/wrong2.txt
@@ -0,0 +1 @@
+3*A + 2*B
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_48/correct.txt b/legacy/Data/ingsw/0613_48/correct.txt
new file mode 100644
index 0000000..ce9968f
--- /dev/null
+++ b/legacy/Data/ingsw/0613_48/correct.txt
@@ -0,0 +1 @@
+0.28
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_48/quest.txt b/legacy/Data/ingsw/0613_48/quest.txt
new file mode 100644
index 0000000..adccf3a
--- /dev/null
+++ b/legacy/Data/ingsw/0613_48/quest.txt
@@ -0,0 +1,9 @@
+img=https://unspectacular-subdi.000webhostapp.com/0613_domanda_48.png
+Un processo software pu� 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 probabilit� 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 probabilit� 0.3 di dover essere ripetuta (a causa di errori).
+Uno scenario � una sequenza di stati.
+Qual'e' la probabilit� dello scenario: 1, 2, 3? In altri terminti, qual'� la probabilit� 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/legacy/Data/ingsw/0613_48/wrong1.txt
new file mode 100644
index 0000000..f2bb2d0
--- /dev/null
+++ b/legacy/Data/ingsw/0613_48/wrong1.txt
@@ -0,0 +1 @@
+0.12
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_48/wrong2.txt b/legacy/Data/ingsw/0613_48/wrong2.txt
new file mode 100644
index 0000000..e8f9017
--- /dev/null
+++ b/legacy/Data/ingsw/0613_48/wrong2.txt
@@ -0,0 +1 @@
+0.42
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_49/correct.txt b/legacy/Data/ingsw/0613_49/correct.txt
new file mode 100644
index 0000000..4c75070
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0613_49/quest.txt b/legacy/Data/ingsw/0613_49/quest.txt
new file mode 100644
index 0000000..e11a044
--- /dev/null
+++ b/legacy/Data/ingsw/0613_49/quest.txt
@@ -0,0 +1,4 @@
+Si consideri il seguente requisito:
+RQ:  Dopo 50 unit� di tempo dall'inizio dell'esecuzione vale la seguente propriet�:
+se la variabile x � minore del 60% della variabile y allora la somma di x ed y � 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
new file mode 100644
index 0000000..6dafe94
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0613_49/wrong2.txt b/legacy/Data/ingsw/0613_49/wrong2.txt
new file mode 100644
index 0000000..a3d79a4
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0613_5/correct.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0613_5/correct.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_5/quest.txt b/legacy/Data/ingsw/0613_5/quest.txt
new file mode 100644
index 0000000..579b39b
--- /dev/null
+++ b/legacy/Data/ingsw/0613_5/quest.txt
@@ -0,0 +1,29 @@
+Il branch coverage di un insieme di test cases � 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/legacy/Data/ingsw/0613_5/wrong1.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0613_5/wrong1.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_5/wrong2.txt b/legacy/Data/ingsw/0613_5/wrong2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0613_5/wrong2.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_6/correct.txt b/legacy/Data/ingsw/0613_6/correct.txt
new file mode 100644
index 0000000..a98afd2
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0613_6/quest.txt b/legacy/Data/ingsw/0613_6/quest.txt
new file mode 100644
index 0000000..b420aaf
--- /dev/null
+++ b/legacy/Data/ingsw/0613_6/quest.txt
@@ -0,0 +1,3 @@
+Si consideri il seguente requisito:
+RQ1:  Dopo 20 unit� di tempo dall'inizio dell'esecuzione la variabile x � 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/legacy/Data/ingsw/0613_6/wrong1.txt
new file mode 100644
index 0000000..66064fe
--- /dev/null
+++ b/legacy/Data/ingsw/0613_6/wrong1.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 >= 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/legacy/Data/ingsw/0613_6/wrong2.txt
new file mode 100644
index 0000000..c71f1f5
--- /dev/null
+++ b/legacy/Data/ingsw/0613_6/wrong2.txt
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_7/correct.txt b/legacy/Data/ingsw/0613_7/correct.txt
new file mode 100644
index 0000000..a40ea7d
--- /dev/null
+++ b/legacy/Data/ingsw/0613_7/correct.txt
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 5, c = 0), (a=50, b = 3, c = 0).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_7/quest.txt b/legacy/Data/ingsw/0613_7/quest.txt
new file mode 100644
index 0000000..dbd72c0
--- /dev/null
+++ b/legacy/Data/ingsw/0613_7/quest.txt
@@ -0,0 +1,22 @@
+Una Condition � una proposizione booleana, cio� una espressione con valore booleano che non pu� essere decomposta 
+in espressioni boolean pi� semplici. Ad esempio, (x + y <= 3) � una condition.
+
+Una Decision � una espressione booleana composta da conditions e zero o pi� 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 � 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 � true ed un test in T in cui c � false.
+3) Per ogni decision d nel programma, esiste un test in T in cui d � true ed un test in T in cui d � 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/legacy/Data/ingsw/0613_7/wrong1.txt
new file mode 100644
index 0000000..abe0eaa
--- /dev/null
+++ b/legacy/Data/ingsw/0613_7/wrong1.txt
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 4, c = 0), (a=200, b = 4, c = 0)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_7/wrong2.txt b/legacy/Data/ingsw/0613_7/wrong2.txt
new file mode 100644
index 0000000..5b77112
--- /dev/null
+++ b/legacy/Data/ingsw/0613_7/wrong2.txt
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 5, c = 0), (a=50, b = 0, c = 5).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_8/correct.txt b/legacy/Data/ingsw/0613_8/correct.txt
new file mode 100644
index 0000000..489e74c
--- /dev/null
+++ b/legacy/Data/ingsw/0613_8/correct.txt
@@ -0,0 +1 @@
+5*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_8/quest.txt b/legacy/Data/ingsw/0613_8/quest.txt
new file mode 100644
index 0000000..570368e
--- /dev/null
+++ b/legacy/Data/ingsw/0613_8/quest.txt
@@ -0,0 +1 @@
+Un processo di sviluppo plan-driven consiste di 2 fasi F1, F2, ciascuna costo A. Alla fine di ogni fase vengono prese in considerazione le "change requests" e, se ve ne sono, lo sviluppo viene ripetuto a partire dalla prima iterazione.  Quindi con nessuna change request si hanno le fasi: F1, F2 e costo 2A. Con una "change request" dopo la prima fase si ha: F1, F1, F2 e costo 3A.  Con una change request dopo la fase 2 si ha: F1, F2, F1, F2 e costo 4A. Qual'� il costo nel caso in cui ci siano change requests sia dopo la fase 1 che dopo la fase 2.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_8/wrong1.txt b/legacy/Data/ingsw/0613_8/wrong1.txt
new file mode 100644
index 0000000..bf91afb
--- /dev/null
+++ b/legacy/Data/ingsw/0613_8/wrong1.txt
@@ -0,0 +1 @@
+7*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_8/wrong2.txt b/legacy/Data/ingsw/0613_8/wrong2.txt
new file mode 100644
index 0000000..23cbd0e
--- /dev/null
+++ b/legacy/Data/ingsw/0613_8/wrong2.txt
@@ -0,0 +1 @@
+6*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0613_9/quest.txt b/legacy/Data/ingsw/0613_9/quest.txt
new file mode 100644
index 0000000..89f55eb
--- /dev/null
+++ b/legacy/Data/ingsw/0613_9/quest.txt
@@ -0,0 +1,4 @@
+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/ingsw/0613_9/wrong1.txt b/legacy/Data/ingsw/0613_9/wrong1.txt
new file mode 100644
index 0000000..4bcd55f
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/0613_9/wrong2.txt
new file mode 100644
index 0000000..2c10a10
--- /dev/null
+++ b/legacy/Data/ingsw/0613_9/wrong2.txt
@@ -0,0 +1,4 @@
+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/legacy/Data/ingsw/0613_9/wrong3.txt
new file mode 100644
index 0000000..7ddc09e
--- /dev/null
+++ b/legacy/Data/ingsw/0613_9/wrong3.txt
@@ -0,0 +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)
+
+
+end SysArch;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_0/correct.txt b/legacy/Data/ingsw/0621_0/correct.txt
new file mode 100644
index 0000000..81ceb23
--- /dev/null
+++ b/legacy/Data/ingsw/0621_0/correct.txt
@@ -0,0 +1,14 @@
+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;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_0/quest.txt b/legacy/Data/ingsw/0621_0/quest.txt
new file mode 100644
index 0000000..2eb7f69
--- /dev/null
+++ b/legacy/Data/ingsw/0621_0/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/0621_0/wrong0.txt b/legacy/Data/ingsw/0621_0/wrong0.txt
new file mode 100644
index 0000000..e09501c
--- /dev/null
+++ b/legacy/Data/ingsw/0621_0/wrong0.txt
@@ -0,0 +1,14 @@
+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;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_0/wrong1.txt b/legacy/Data/ingsw/0621_0/wrong1.txt
new file mode 100644
index 0000000..f7ab72e
--- /dev/null
+++ b/legacy/Data/ingsw/0621_0/wrong1.txt
@@ -0,0 +1,14 @@
+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;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_1/correct.txt b/legacy/Data/ingsw/0621_1/correct.txt
new file mode 100644
index 0000000..b740a0a
--- /dev/null
+++ b/legacy/Data/ingsw/0621_1/correct.txt
@@ -0,0 +1,14 @@
+model System
+Integer y;
+Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+equation
+y = if (r1024 <= 0.3) then 1 else 0;
+algorithm
+when initial() then
+state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+r1024     := 0;
+elsewhen sample(0,1) then
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+end when;
+end System;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_1/quest.txt b/legacy/Data/ingsw/0621_1/quest.txt
new file mode 100644
index 0000000..5a1289f
--- /dev/null
+++ b/legacy/Data/ingsw/0621_1/quest.txt
@@ -0,0 +1 @@
+Si consideri l'ambiente (use case) che consiste di un utente che, ad ogni unità di tempo (ad esempio, un secondo) manda al nostro sistema input 1 (ad esempio, esegue una prenotazione) con probabilità 0.3 oppure input 0 con probabilità 0.7. Quale dei seguenti modelli Modelica rappresenta correttamente tale ambiente.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_1/wrong1.txt b/legacy/Data/ingsw/0621_1/wrong1.txt
new file mode 100644
index 0000000..57fc69d
--- /dev/null
+++ b/legacy/Data/ingsw/0621_1/wrong1.txt
@@ -0,0 +1,13 @@
+model System
+Integer y;  Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+equation
+y = if (r1024 <= 0.3) then 0 else 1;
+algorithm
+when initial() then
+state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+r1024     := 0;
+elsewhen sample(0,1) then
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+end when;
+end System;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_1/wrong2.txt b/legacy/Data/ingsw/0621_1/wrong2.txt
new file mode 100644
index 0000000..3390b13
--- /dev/null
+++ b/legacy/Data/ingsw/0621_1/wrong2.txt
@@ -0,0 +1,13 @@
+model System
+Integer y;  Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+equation
+y = if (r1024 >= 0.3) then 1 else 0;
+algorithm
+when initial() then
+state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+r1024     := 0;
+elsewhen sample(0,1) then
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+end when;
+end System;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_10/correct.txt b/legacy/Data/ingsw/0621_10/correct.txt
new file mode 100644
index 0000000..f8c9568
--- /dev/null
+++ b/legacy/Data/ingsw/0621_10/correct.txt
@@ -0,0 +1 @@
+Per tutti gli istanti di tempo della forma 1 + 4*k (con k = 0, 1, 2, 3, ...) x vale 1.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_10/quest.txt b/legacy/Data/ingsw/0621_10/quest.txt
new file mode 100644
index 0000000..ba1496d
--- /dev/null
+++ b/legacy/Data/ingsw/0621_10/quest.txt
@@ -0,0 +1,13 @@
+Si consideri il seguente modello Modelica:
+<html>
+class System
+Integer x;
+initial equation
+x = 0;
+equation
+when sample(0, 2) then
+    x = 1 - pre(x);
+end when;
+end System;
+</html>
+Quale delle seguenti affermazioni è vera per la variabile intera x?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_10/wrong0.txt b/legacy/Data/ingsw/0621_10/wrong0.txt
new file mode 100644
index 0000000..f485a50
--- /dev/null
+++ b/legacy/Data/ingsw/0621_10/wrong0.txt
@@ -0,0 +1 @@
+Per tutti gli istanti di tempo della forma  3 + 4*k (con k = 0, 1, 2, 3, ...) x vale 1.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_10/wrong1.txt b/legacy/Data/ingsw/0621_10/wrong1.txt
new file mode 100644
index 0000000..a7af2cb
--- /dev/null
+++ b/legacy/Data/ingsw/0621_10/wrong1.txt
@@ -0,0 +1 @@
+Per tutti gli istanti di tempo della forma 1 + 4*k (con k = 0, 1, 2, 3, ...) x vale 0.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_13/correct.txt b/legacy/Data/ingsw/0621_13/correct.txt
new file mode 100644
index 0000000..0c54a95
--- /dev/null
+++ b/legacy/Data/ingsw/0621_13/correct.txt
@@ -0,0 +1 @@
+Sviluppo plan-driven.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_13/quest.txt b/legacy/Data/ingsw/0621_13/quest.txt
new file mode 100644
index 0000000..3c60626
--- /dev/null
+++ b/legacy/Data/ingsw/0621_13/quest.txt
@@ -0,0 +1 @@
+Si pianifica di sviluppare un software gestionale per una università. Considerando che questo può essere considerato un sistema mission-critical, quali dei seguenti modelli di processi software generici è più adatto per lo sviluppo di tale software.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_13/wrong0.txt b/legacy/Data/ingsw/0621_13/wrong0.txt
new file mode 100644
index 0000000..9d2b250
--- /dev/null
+++ b/legacy/Data/ingsw/0621_13/wrong0.txt
@@ -0,0 +1 @@
+Sviluppo Iterativo
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_13/wrong1.txt b/legacy/Data/ingsw/0621_13/wrong1.txt
new file mode 100644
index 0000000..b37e1a6
--- /dev/null
+++ b/legacy/Data/ingsw/0621_13/wrong1.txt
@@ -0,0 +1 @@
+Sviluppo Agile.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_14/correct.txt b/legacy/Data/ingsw/0621_14/correct.txt
new file mode 100644
index 0000000..a4a8878
--- /dev/null
+++ b/legacy/Data/ingsw/0621_14/correct.txt
@@ -0,0 +1 @@
+Testare l'interazione tra  le componenti del sistema (cioè, integrazione di molte unità di sistema).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_14/quest.txt b/legacy/Data/ingsw/0621_14/quest.txt
new file mode 100644
index 0000000..8bbcdb8
--- /dev/null
+++ b/legacy/Data/ingsw/0621_14/quest.txt
@@ -0,0 +1 @@
+Il system testing si concentra su:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_14/wrong0.txt b/legacy/Data/ingsw/0621_14/wrong0.txt
new file mode 100644
index 0000000..3214f65
--- /dev/null
+++ b/legacy/Data/ingsw/0621_14/wrong0.txt
@@ -0,0 +1 @@
+Testare le interfacce per ciascuna componente.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_14/wrong1.txt b/legacy/Data/ingsw/0621_14/wrong1.txt
new file mode 100644
index 0000000..6a9cb98
--- /dev/null
+++ b/legacy/Data/ingsw/0621_14/wrong1.txt
@@ -0,0 +1 @@
+Testare le funzionalità di unità software individuali, oggetti, classi o metodi.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_17/correct.txt b/legacy/Data/ingsw/0621_17/correct.txt
new file mode 100644
index 0000000..3f5bba6
--- /dev/null
+++ b/legacy/Data/ingsw/0621_17/correct.txt
@@ -0,0 +1,13 @@
+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;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_17/quest.txt b/legacy/Data/ingsw/0621_17/quest.txt
new file mode 100644
index 0000000..de77723
--- /dev/null
+++ b/legacy/Data/ingsw/0621_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 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/0621_17/wrong0.txt b/legacy/Data/ingsw/0621_17/wrong0.txt
new file mode 100644
index 0000000..d23fe8e
--- /dev/null
+++ b/legacy/Data/ingsw/0621_17/wrong0.txt
@@ -0,0 +1,14 @@
+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;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_17/wrong1.txt b/legacy/Data/ingsw/0621_17/wrong1.txt
new file mode 100644
index 0000000..33310f9
--- /dev/null
+++ b/legacy/Data/ingsw/0621_17/wrong1.txt
@@ -0,0 +1,13 @@
+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;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_19/correct.txt b/legacy/Data/ingsw/0621_19/correct.txt
new file mode 100644
index 0000000..d3826b5
--- /dev/null
+++ b/legacy/Data/ingsw/0621_19/correct.txt
@@ -0,0 +1 @@
+Ad ogni istante di tempo della forma 1 + 4*k (k = 0, 1, 2, 3, ...), x vale "true".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_19/quest.txt b/legacy/Data/ingsw/0621_19/quest.txt
new file mode 100644
index 0000000..b3ee6d9
--- /dev/null
+++ b/legacy/Data/ingsw/0621_19/quest.txt
@@ -0,0 +1,13 @@
+Si consideri il seguente modello Modelica.
+<html>
+class System
+Boolean x;
+initial equation
+x = false;
+equation
+when sample(0, 2) then
+    x = not (pre(x));
+end when;
+end System;
+</html>
+Quale delle seguenti affermazioni vale per la variabile booleana x ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_19/wrong0.txt b/legacy/Data/ingsw/0621_19/wrong0.txt
new file mode 100644
index 0000000..6245a2f
--- /dev/null
+++ b/legacy/Data/ingsw/0621_19/wrong0.txt
@@ -0,0 +1 @@
+At time instants of form 1 + 4*k (with k = 0, 1, 2, 3, ...) x takes value "false".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_19/wrong1.txt b/legacy/Data/ingsw/0621_19/wrong1.txt
new file mode 100644
index 0000000..0ba96d3
--- /dev/null
+++ b/legacy/Data/ingsw/0621_19/wrong1.txt
@@ -0,0 +1 @@
+Ad ogni istante di tempo della forma 3 + 4*k (k = 0, 1, 2, 3, ...), x vale "true".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_2/correct.txt b/legacy/Data/ingsw/0621_2/correct.txt
new file mode 100644
index 0000000..23cbd0e
--- /dev/null
+++ b/legacy/Data/ingsw/0621_2/correct.txt
@@ -0,0 +1 @@
+6*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_2/quest.txt b/legacy/Data/ingsw/0621_2/quest.txt
new file mode 100644
index 0000000..c91abc9
--- /dev/null
+++ b/legacy/Data/ingsw/0621_2/quest.txt
@@ -0,0 +1 @@
+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'e' il costo totale per lo sviluppo del software in questione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_2/wrong0.txt b/legacy/Data/ingsw/0621_2/wrong0.txt
new file mode 100644
index 0000000..489e74c
--- /dev/null
+++ b/legacy/Data/ingsw/0621_2/wrong0.txt
@@ -0,0 +1 @@
+5*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_2/wrong1.txt b/legacy/Data/ingsw/0621_2/wrong1.txt
new file mode 100644
index 0000000..63ca2eb
--- /dev/null
+++ b/legacy/Data/ingsw/0621_2/wrong1.txt
@@ -0,0 +1 @@
+4*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_21/correct.txt b/legacy/Data/ingsw/0621_21/correct.txt
new file mode 100644
index 0000000..c24cae9
--- /dev/null
+++ b/legacy/Data/ingsw/0621_21/correct.txt
@@ -0,0 +1 @@
+A*(2 + p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_21/quest.txt b/legacy/Data/ingsw/0621_21/quest.txt
new file mode 100644
index 0000000..77c80a6
--- /dev/null
+++ b/legacy/Data/ingsw/0621_21/quest.txt
@@ -0,0 +1 @@
+Si consideri un software costituito da due fasi F1 ed F2 ciascuna di costo A. Con probabilità p la fase F1 deve essere ripetuta (a causa di change requests) e con probabilità (1 - p) si passa alla fase F2 e poi al completamento (End) dello sviluppo. Qual'eè il costo atteso per lo sviluppo del software seguendo il processo sopra descritto ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_21/wrong0.txt b/legacy/Data/ingsw/0621_21/wrong0.txt
new file mode 100644
index 0000000..a9b1c29
--- /dev/null
+++ b/legacy/Data/ingsw/0621_21/wrong0.txt
@@ -0,0 +1 @@
+3*A*p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_21/wrong1.txt b/legacy/Data/ingsw/0621_21/wrong1.txt
new file mode 100644
index 0000000..6e771e9
--- /dev/null
+++ b/legacy/Data/ingsw/0621_21/wrong1.txt
@@ -0,0 +1 @@
+A*(1 + p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_22/correct.txt b/legacy/Data/ingsw/0621_22/correct.txt
new file mode 100644
index 0000000..83f9204
--- /dev/null
+++ b/legacy/Data/ingsw/0621_22/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/LSxqSIl.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_22/quest.txt b/legacy/Data/ingsw/0621_22/quest.txt
new file mode 100644
index 0000000..5d926db
--- /dev/null
+++ b/legacy/Data/ingsw/0621_22/quest.txt
@@ -0,0 +1 @@
+Si consideri un software sviluppato seguendo un approccio plan-driven implementato con tre fasi: F1, F2, F3. Dopo ogni fase c'e' una probabilità p di dover ripeter la fase precedente ed una probabilità (1 - p) di passare alla fase successiva (sino ad arrivare al termine dello sviluppo). Quale delle seguenti catene di Markov modella il processo software descritto sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_22/wrong0.txt b/legacy/Data/ingsw/0621_22/wrong0.txt
new file mode 100644
index 0000000..d2eb66b
--- /dev/null
+++ b/legacy/Data/ingsw/0621_22/wrong0.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/yGc7Zf2.png
diff --git a/legacy/Data/ingsw/0621_22/wrong1.txt b/legacy/Data/ingsw/0621_22/wrong1.txt
new file mode 100644
index 0000000..dbdbad5
--- /dev/null
+++ b/legacy/Data/ingsw/0621_22/wrong1.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/3t92wEw.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_24/correct.txt b/legacy/Data/ingsw/0621_24/correct.txt
new file mode 100644
index 0000000..a7029bc
--- /dev/null
+++ b/legacy/Data/ingsw/0621_24/correct.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [1, 4] oppure nell'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_24/quest.txt b/legacy/Data/ingsw/0621_24/quest.txt
new file mode 100644
index 0000000..e943282
--- /dev/null
+++ b/legacy/Data/ingsw/0621_24/quest.txt
@@ -0,0 +1,17 @@
+Si consideri il monitor seguente che ritorna true appena il sistema viola il requisito monitorato.
+<html>
+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;
+</html>
+Quale delle seguenti affermazioni meglio descrive il requisito monitorato?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_24/wrong0.txt b/legacy/Data/ingsw/0621_24/wrong0.txt
new file mode 100644
index 0000000..710b111
--- /dev/null
+++ b/legacy/Data/ingsw/0621_24/wrong0.txt
@@ -0,0 +1 @@
+La variabile x è fuori dall'intervallo [1, 4] e fuori dall'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_24/wrong1.txt b/legacy/Data/ingsw/0621_24/wrong1.txt
new file mode 100644
index 0000000..a82929b
--- /dev/null
+++ b/legacy/Data/ingsw/0621_24/wrong1.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [1, 4] e fuori dall'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_3/correct.txt b/legacy/Data/ingsw/0621_3/correct.txt
new file mode 100644
index 0000000..68bfd31
--- /dev/null
+++ b/legacy/Data/ingsw/0621_3/correct.txt
@@ -0,0 +1 @@
+Una release del software è resa disponibile agli utenti (beta users) per permettergli di sperimentare e quindi segnalare eventuali problemi rilevati agli sviluppatori.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_3/quest.txt b/legacy/Data/ingsw/0621_3/quest.txt
new file mode 100644
index 0000000..4589c15
--- /dev/null
+++ b/legacy/Data/ingsw/0621_3/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti affermazione è vera riguardo al beta testing ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_3/wrong0.txt b/legacy/Data/ingsw/0621_3/wrong0.txt
new file mode 100644
index 0000000..ab58544
--- /dev/null
+++ b/legacy/Data/ingsw/0621_3/wrong0.txt
@@ -0,0 +1 @@
+Test automatizzato sono eseguiti sulla versione finale del sistema presso il sito di sviluppo del software.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_3/wrong1.txt b/legacy/Data/ingsw/0621_3/wrong1.txt
new file mode 100644
index 0000000..f021931
--- /dev/null
+++ b/legacy/Data/ingsw/0621_3/wrong1.txt
@@ -0,0 +1 @@
+Test automatizzato sono eseguiti sulla versione finale del sistema presso il cliente.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_32/correct.txt b/legacy/Data/ingsw/0621_32/correct.txt
new file mode 100644
index 0000000..ddb0d65
--- /dev/null
+++ b/legacy/Data/ingsw/0621_32/correct.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [0, 5].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_32/quest.txt b/legacy/Data/ingsw/0621_32/quest.txt
new file mode 100644
index 0000000..7004fa1
--- /dev/null
+++ b/legacy/Data/ingsw/0621_32/quest.txt
@@ -0,0 +1,17 @@
+Si consideri il monitor seguente che ritorna true appena i requisiti per il sistema monitorato sono violati.
+<html>
+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;
+</html>
+Quale delle seguenti affermazioni meglio descrive il requisito monitorato.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_32/wrong0.txt b/legacy/Data/ingsw/0621_32/wrong0.txt
new file mode 100644
index 0000000..3e05ae7
--- /dev/null
+++ b/legacy/Data/ingsw/0621_32/wrong0.txt
@@ -0,0 +1 @@
+La variabile x è fuori dall'intervallo [0, 5].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_32/wrong1.txt b/legacy/Data/ingsw/0621_32/wrong1.txt
new file mode 100644
index 0000000..7c7a691
--- /dev/null
+++ b/legacy/Data/ingsw/0621_32/wrong1.txt
@@ -0,0 +1 @@
+La variable x è minore di 0.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_35/correct.txt b/legacy/Data/ingsw/0621_35/correct.txt
new file mode 100644
index 0000000..0dcbeca
--- /dev/null
+++ b/legacy/Data/ingsw/0621_35/correct.txt
@@ -0,0 +1 @@
+Per ciascun incremento di funzionalità, scrivi test automatizzati, implementa la funzionalità, esegui i test e rivedi l'implementazione come necessario.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_35/quest.txt b/legacy/Data/ingsw/0621_35/quest.txt
new file mode 100644
index 0000000..f3019d0
--- /dev/null
+++ b/legacy/Data/ingsw/0621_35/quest.txt
@@ -0,0 +1 @@
+Si consideri il Test-Driven Development (TDD). Quale delle seguenti affermazioni è vera?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_35/wrong0.txt b/legacy/Data/ingsw/0621_35/wrong0.txt
new file mode 100644
index 0000000..2891ab7
--- /dev/null
+++ b/legacy/Data/ingsw/0621_35/wrong0.txt
@@ -0,0 +1 @@
+Scrivi test automatizzati per tutti i requisiti di sistema, esegui i test e rivedi l'implementazione come necessario.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_35/wrong1.txt b/legacy/Data/ingsw/0621_35/wrong1.txt
new file mode 100644
index 0000000..cf5eab4
--- /dev/null
+++ b/legacy/Data/ingsw/0621_35/wrong1.txt
@@ -0,0 +1 @@
+Per ciascun incremento di funzionalità, implementa la funzionalità, scrivi test automatizzati, esegui i test e rivedi l'implementazione come necessario.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_36/correct.txt b/legacy/Data/ingsw/0621_36/correct.txt
new file mode 100644
index 0000000..fc560a2
--- /dev/null
+++ b/legacy/Data/ingsw/0621_36/correct.txt
@@ -0,0 +1,15 @@
+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;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_36/quest.txt b/legacy/Data/ingsw/0621_36/quest.txt
new file mode 100644
index 0000000..6473814
--- /dev/null
+++ b/legacy/Data/ingsw/0621_36/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/0621_36/wrong0.txt b/legacy/Data/ingsw/0621_36/wrong0.txt
new file mode 100644
index 0000000..61fa628
--- /dev/null
+++ b/legacy/Data/ingsw/0621_36/wrong0.txt
@@ -0,0 +1,15 @@
+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;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_36/wrong1.txt b/legacy/Data/ingsw/0621_36/wrong1.txt
new file mode 100644
index 0000000..c8a2c3d
--- /dev/null
+++ b/legacy/Data/ingsw/0621_36/wrong1.txt
@@ -0,0 +1,15 @@
+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;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_39/correct.txt b/legacy/Data/ingsw/0621_39/correct.txt
new file mode 100644
index 0000000..91e6e0a
--- /dev/null
+++ b/legacy/Data/ingsw/0621_39/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/J4TFpmw.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_39/quest.txt b/legacy/Data/ingsw/0621_39/quest.txt
new file mode 100644
index 0000000..406c612
--- /dev/null
+++ b/legacy/Data/ingsw/0621_39/quest.txt
@@ -0,0 +1 @@
+Si consideri un software sviluppato seguendo un approccio plan-driven implementato con tre fasi: F1, F2, F3. Le "change requests"  arrivano con probabilità p dopo ciascuna fase e provocano la ripetizione (con relativo costo) di tutte le fasi che precedono. Quali delle seguenti catene di Markov modella lo sviluppo software descritto.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_39/wrong0.txt b/legacy/Data/ingsw/0621_39/wrong0.txt
new file mode 100644
index 0000000..0f68af0
--- /dev/null
+++ b/legacy/Data/ingsw/0621_39/wrong0.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/xVrmeoj.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_39/wrong1.txt b/legacy/Data/ingsw/0621_39/wrong1.txt
new file mode 100644
index 0000000..908366a
--- /dev/null
+++ b/legacy/Data/ingsw/0621_39/wrong1.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/4Ew3YtM.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_6/correct.txt b/legacy/Data/ingsw/0621_6/correct.txt
new file mode 100644
index 0000000..81653ea
--- /dev/null
+++ b/legacy/Data/ingsw/0621_6/correct.txt
@@ -0,0 +1,16 @@
+function next
+input Integer x;
+output Integer y;
+algorithm
+   y := 1 - x;
+end next;
+
+class System
+Integer x;
+initial equation
+x = 0;
+equation
+when sample(0, 1) then
+    x = next(pre(x));
+end when;
+end System;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_6/quest.txt b/legacy/Data/ingsw/0621_6/quest.txt
new file mode 100644
index 0000000..8bc0606
--- /dev/null
+++ b/legacy/Data/ingsw/0621_6/quest.txt
@@ -0,0 +1,3 @@
+img=https://i.imgur.com/F6JCFSU.png
+Si consideri l'automa segunete:
+Quale dei seguenti modelli Modelica fornisce un modello ragionevole per l'automa di cui sopra.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_6/wrong0.txt b/legacy/Data/ingsw/0621_6/wrong0.txt
new file mode 100644
index 0000000..4c7125e
--- /dev/null
+++ b/legacy/Data/ingsw/0621_6/wrong0.txt
@@ -0,0 +1,16 @@
+function next
+input Integer x;
+output Integer y;
+algorithm
+   y := x;
+end next;
+
+class System
+Integer x;
+initial equation
+x = 0;
+equation
+when sample(0, 1) then
+    x = next(pre(x));
+end when;
+end System;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_6/wrong1.txt b/legacy/Data/ingsw/0621_6/wrong1.txt
new file mode 100644
index 0000000..47cf8cd
--- /dev/null
+++ b/legacy/Data/ingsw/0621_6/wrong1.txt
@@ -0,0 +1,16 @@
+function next
+input Integer x;
+output Integer y;
+algorithm
+   y := 1 + x;
+end next;
+
+class System
+Integer x;
+initial equation
+x = 0;
+equation
+when sample(0, 1) then
+    x = next(pre(x));
+end when;
+end System;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_6/wrong2.txt b/legacy/Data/ingsw/0621_6/wrong2.txt
new file mode 100644
index 0000000..81653ea
--- /dev/null
+++ b/legacy/Data/ingsw/0621_6/wrong2.txt
@@ -0,0 +1,16 @@
+function next
+input Integer x;
+output Integer y;
+algorithm
+   y := 1 - x;
+end next;
+
+class System
+Integer x;
+initial equation
+x = 0;
+equation
+when sample(0, 1) then
+    x = next(pre(x));
+end when;
+end System;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_9/correct.txt b/legacy/Data/ingsw/0621_9/correct.txt
new file mode 100644
index 0000000..4bef521
--- /dev/null
+++ b/legacy/Data/ingsw/0621_9/correct.txt
@@ -0,0 +1 @@
+Requirements specification precedes the component analysis activity.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_9/quest.txt b/legacy/Data/ingsw/0621_9/quest.txt
new file mode 100644
index 0000000..47b8c7e
--- /dev/null
+++ b/legacy/Data/ingsw/0621_9/quest.txt
@@ -0,0 +1 @@
+Consider reuse-based software development. Which of the following is true?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_9/wrong0.txt b/legacy/Data/ingsw/0621_9/wrong0.txt
new file mode 100644
index 0000000..d37b8fe
--- /dev/null
+++ b/legacy/Data/ingsw/0621_9/wrong0.txt
@@ -0,0 +1 @@
+Requirements specification is not needed thanks to reuse.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0621_9/wrong1.txt b/legacy/Data/ingsw/0621_9/wrong1.txt
new file mode 100644
index 0000000..53c7eb8
--- /dev/null
+++ b/legacy/Data/ingsw/0621_9/wrong1.txt
@@ -0,0 +1 @@
+Development and integration are not needed thanks to reuse.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_1/correct.txt b/legacy/Data/ingsw/0622_1/correct.txt
new file mode 100644
index 0000000..8da85a2
--- /dev/null
+++ b/legacy/Data/ingsw/0622_1/correct.txt
@@ -0,0 +1 @@
+3000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_1/quest.txt b/legacy/Data/ingsw/0622_1/quest.txt
new file mode 100644
index 0000000..045f2d6
--- /dev/null
+++ b/legacy/Data/ingsw/0622_1/quest.txt
@@ -0,0 +1 @@
+Si consideri un software sviluppato seguendo un approccio iterativo implementato con due fasi: F1 seguita da F2. Ciascuna fase ha costo 1000 Eur e deve essere ripetuta una seconda volta con probabilità 0.5. Qual'e' il costo atteso dello sviluppo dell'intero software?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_1/wrong 1.txt b/legacy/Data/ingsw/0622_1/wrong 1.txt
new file mode 100644
index 0000000..0b3e0a6
--- /dev/null
+++ b/legacy/Data/ingsw/0622_1/wrong 1.txt	
@@ -0,0 +1 @@
+5000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_1/wrong 2.txt b/legacy/Data/ingsw/0622_1/wrong 2.txt
new file mode 100644
index 0000000..9463411
--- /dev/null
+++ b/legacy/Data/ingsw/0622_1/wrong 2.txt	
@@ -0,0 +1 @@
+2000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_2/correct.txt b/legacy/Data/ingsw/0622_2/correct.txt
new file mode 100644
index 0000000..f8ae137
--- /dev/null
+++ b/legacy/Data/ingsw/0622_2/correct.txt
@@ -0,0 +1 @@
+2700
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_2/quest.txt b/legacy/Data/ingsw/0622_2/quest.txt
new file mode 100644
index 0000000..7083f7d
--- /dev/null
+++ b/legacy/Data/ingsw/0622_2/quest.txt
@@ -0,0 +1 @@
+Si consideri un software sviluppato seguendo un approccio iterativo implementato con due fasi: F1 seguita da F2. Ciascuna fase ha costo 1000. Con probabilità 0.5 potrebbe essere necessario ripetere F1 una seconda volta.  Con probabilità 0.2 potrebbe essere necessario ripetere F2 una seconda volta. Qual'e' il costo atteso dello sviluppo dell'intero software?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_2/wrong 1.txt b/legacy/Data/ingsw/0622_2/wrong 1.txt
new file mode 100644
index 0000000..a211371
--- /dev/null
+++ b/legacy/Data/ingsw/0622_2/wrong 1.txt	
@@ -0,0 +1 @@
+4000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_2/wrong 2.txt b/legacy/Data/ingsw/0622_2/wrong 2.txt
new file mode 100644
index 0000000..0b3e0a6
--- /dev/null
+++ b/legacy/Data/ingsw/0622_2/wrong 2.txt	
@@ -0,0 +1 @@
+5000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_3/correct.txt b/legacy/Data/ingsw/0622_3/correct.txt
new file mode 100644
index 0000000..07c6432
--- /dev/null
+++ b/legacy/Data/ingsw/0622_3/correct.txt
@@ -0,0 +1 @@
+24000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_3/quest.txt b/legacy/Data/ingsw/0622_3/quest.txt
new file mode 100644
index 0000000..641cce2
--- /dev/null
+++ b/legacy/Data/ingsw/0622_3/quest.txt
@@ -0,0 +1 @@
+Si consideri un processo software costituito da due fasi F1 ed F2 ciascuna di costo 10000. Con probabilità p = 0.4 la fase F1 deve essere ripetuta (a causa di change requests) e con probabilità (1 - p) si passa alla fase F2 e poi al completamento (End) dello sviluppo. Qual'è il costo atteso per lo sviluppo del software seguendo il processo sopra descritto ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_3/wrong 1.txt b/legacy/Data/ingsw/0622_3/wrong 1.txt
new file mode 100644
index 0000000..45842b7
--- /dev/null
+++ b/legacy/Data/ingsw/0622_3/wrong 1.txt	
@@ -0,0 +1 @@
+35000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_3/wrong 2.txt b/legacy/Data/ingsw/0622_3/wrong 2.txt
new file mode 100644
index 0000000..137b176
--- /dev/null
+++ b/legacy/Data/ingsw/0622_3/wrong 2.txt	
@@ -0,0 +1 @@
+30000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_4/correct.txt b/legacy/Data/ingsw/0622_4/correct.txt
new file mode 100644
index 0000000..7c67f71
--- /dev/null
+++ b/legacy/Data/ingsw/0622_4/correct.txt
@@ -0,0 +1 @@
+950000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_4/quest.txt b/legacy/Data/ingsw/0622_4/quest.txt
new file mode 100644
index 0000000..0c283e9
--- /dev/null
+++ b/legacy/Data/ingsw/0622_4/quest.txt
@@ -0,0 +1 @@
+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 = exp(-b*S), dove b è una opportuna costante note da dati storici aziendali. Si assuma che b = 0.00001,  C = 1000000, ed il rischio ammesso è R = 100. Quale delle seguenti opzioni meglio approssima il costo S per lo sviluppo del software in questione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_4/wrong 1.txt b/legacy/Data/ingsw/0622_4/wrong 1.txt
new file mode 100644
index 0000000..7695ad8
--- /dev/null
+++ b/legacy/Data/ingsw/0622_4/wrong 1.txt	
@@ -0,0 +1 @@
+850000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_4/wrong 2.txt b/legacy/Data/ingsw/0622_4/wrong 2.txt
new file mode 100644
index 0000000..1acd587
--- /dev/null
+++ b/legacy/Data/ingsw/0622_4/wrong 2.txt	
@@ -0,0 +1 @@
+750000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_5/correct.txt b/legacy/Data/ingsw/0622_5/correct.txt
new file mode 100644
index 0000000..4d597fb
--- /dev/null
+++ b/legacy/Data/ingsw/0622_5/correct.txt
@@ -0,0 +1 @@
+22000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_5/quest.txt b/legacy/Data/ingsw/0622_5/quest.txt
new file mode 100644
index 0000000..5e83ec2
--- /dev/null
+++ b/legacy/Data/ingsw/0622_5/quest.txt
@@ -0,0 +1 @@
+Si consideri un software sviluppato seguendo un approccio iterativo implementato con due fasi: F1 seguita da F2. Ciascuna fase ha costo 10000 Eur e deve essere ripetuta una seconda volta con probabilità 0.1. Qual'e' il costo atteso dello sviluppo dell'intero software?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_5/wrong 1.txt b/legacy/Data/ingsw/0622_5/wrong 1.txt
new file mode 100644
index 0000000..137b176
--- /dev/null
+++ b/legacy/Data/ingsw/0622_5/wrong 1.txt	
@@ -0,0 +1 @@
+30000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_5/wrong 2.txt b/legacy/Data/ingsw/0622_5/wrong 2.txt
new file mode 100644
index 0000000..fcb0699
--- /dev/null
+++ b/legacy/Data/ingsw/0622_5/wrong 2.txt	
@@ -0,0 +1 @@
+25000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_6/correct.txt b/legacy/Data/ingsw/0622_6/correct.txt
new file mode 100644
index 0000000..ea557e9
--- /dev/null
+++ b/legacy/Data/ingsw/0622_6/correct.txt
@@ -0,0 +1 @@
+23000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_6/quest.txt b/legacy/Data/ingsw/0622_6/quest.txt
new file mode 100644
index 0000000..b5b9386
--- /dev/null
+++ b/legacy/Data/ingsw/0622_6/quest.txt
@@ -0,0 +1 @@
+Si consideri un software sviluppato seguendo un approccio iterativo implementato con due fasi: F1 seguita da F2. Ciascuna fase ha costo 10000. Con probabilità 0.1 potrebbe essere necessario ripetere F1 una seconda volta.  Con probabilità 0.2 potrebbe essere necessario ripetere F2 una seconda volta. Qual'e' il costo atteso dello sviluppo dell'intero software?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_6/wrong 1.txt b/legacy/Data/ingsw/0622_6/wrong 1.txt
new file mode 100644
index 0000000..137b176
--- /dev/null
+++ b/legacy/Data/ingsw/0622_6/wrong 1.txt	
@@ -0,0 +1 @@
+30000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_6/wrong 2.txt b/legacy/Data/ingsw/0622_6/wrong 2.txt
new file mode 100644
index 0000000..fcb0699
--- /dev/null
+++ b/legacy/Data/ingsw/0622_6/wrong 2.txt	
@@ -0,0 +1 @@
+25000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_7/correct.txt b/legacy/Data/ingsw/0622_7/correct.txt
new file mode 100644
index 0000000..8eb46f4
--- /dev/null
+++ b/legacy/Data/ingsw/0622_7/correct.txt
@@ -0,0 +1 @@
+21000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_7/quest.txt b/legacy/Data/ingsw/0622_7/quest.txt
new file mode 100644
index 0000000..3ab551d
--- /dev/null
+++ b/legacy/Data/ingsw/0622_7/quest.txt
@@ -0,0 +1 @@
+Si consideri un processo software costituito da due fasi F1 ed F2 ciascuna di costo 10000. Con probabilità p = 0.1 la fase F1 deve essere ripetuta (a causa di change requests) e con probabilità (1 - p) si passa alla fase F2 e poi al completamento (End) dello sviluppo. Qual'è il costo atteso per lo sviluppo del software seguendo il processo sopra descritto ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_7/wrong 1.txt b/legacy/Data/ingsw/0622_7/wrong 1.txt
new file mode 100644
index 0000000..fcb0699
--- /dev/null
+++ b/legacy/Data/ingsw/0622_7/wrong 1.txt	
@@ -0,0 +1 @@
+25000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_7/wrong 2.txt b/legacy/Data/ingsw/0622_7/wrong 2.txt
new file mode 100644
index 0000000..137b176
--- /dev/null
+++ b/legacy/Data/ingsw/0622_7/wrong 2.txt	
@@ -0,0 +1 @@
+30000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_8/correct.txt b/legacy/Data/ingsw/0622_8/correct.txt
new file mode 100644
index 0000000..60eaa92
--- /dev/null
+++ b/legacy/Data/ingsw/0622_8/correct.txt
@@ -0,0 +1 @@
+Una volta selezionato il piatto di mare da preparare, la preparazione del pesce e del contorno procedono in parallelo.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_8/quest.txt b/legacy/Data/ingsw/0622_8/quest.txt
new file mode 100644
index 0000000..31346ae
--- /dev/null
+++ b/legacy/Data/ingsw/0622_8/quest.txt
@@ -0,0 +1,2 @@
+img=https://i.imgur.com/7c1TI6f.png
+Quale delle seguenti frasi è corretta riguardo all'ctivity diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_8/wrong 1.txt b/legacy/Data/ingsw/0622_8/wrong 1.txt
new file mode 100644
index 0000000..3e13d27
--- /dev/null
+++ b/legacy/Data/ingsw/0622_8/wrong 1.txt	
@@ -0,0 +1 @@
+Una volta selezionato il piatto di mare da preparare, la stessa persona prepara prima il pesce e poi il contorno.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_8/wrong 2.txt b/legacy/Data/ingsw/0622_8/wrong 2.txt
new file mode 100644
index 0000000..06a3fbf
--- /dev/null
+++ b/legacy/Data/ingsw/0622_8/wrong 2.txt	
@@ -0,0 +1 @@
+Una volta selezionato il piatto di mare da preparare, la preparazione del pesce e del contorno procedono in sequenza.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_9/correct.txt b/legacy/Data/ingsw/0622_9/correct.txt
new file mode 100644
index 0000000..997967b
--- /dev/null
+++ b/legacy/Data/ingsw/0622_9/correct.txt
@@ -0,0 +1 @@
+700000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_9/quest.txt b/legacy/Data/ingsw/0622_9/quest.txt
new file mode 100644
index 0000000..da5f8a9
--- /dev/null
+++ b/legacy/Data/ingsw/0622_9/quest.txt
@@ -0,0 +1 @@
+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 = exp(-b*S), dove b è una opportuna costante note da dati storici aziendali. Si assuma che b = 0.00001,  C = 1000000, ed il rischio ammesso è R = 1000. Quale delle seguenti opzioni meglio approssima il costo S per lo sviluppo del software in questione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_9/wrong 1.txt b/legacy/Data/ingsw/0622_9/wrong 1.txt
new file mode 100644
index 0000000..2df501e
--- /dev/null
+++ b/legacy/Data/ingsw/0622_9/wrong 1.txt	
@@ -0,0 +1 @@
+500000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0622_9/wrong 2.txt b/legacy/Data/ingsw/0622_9/wrong 2.txt
new file mode 100644
index 0000000..7a6c6b9
--- /dev/null
+++ b/legacy/Data/ingsw/0622_9/wrong 2.txt	
@@ -0,0 +1 @@
+300000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_1/correct.txt b/legacy/Data/ingsw/0721_1/correct.txt
new file mode 100644
index 0000000..f8c9568
--- /dev/null
+++ b/legacy/Data/ingsw/0721_1/correct.txt
@@ -0,0 +1 @@
+Per tutti gli istanti di tempo della forma 1 + 4*k (con k = 0, 1, 2, 3, ...) x vale 1.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_1/quest.txt b/legacy/Data/ingsw/0721_1/quest.txt
new file mode 100644
index 0000000..c5af322
--- /dev/null
+++ b/legacy/Data/ingsw/0721_1/quest.txt
@@ -0,0 +1,13 @@
+Si consideri il seguente modello Modelica:
+<pre>
+class System
+Integer x;
+initial equation
+x = 0;
+equation
+when sample(0, 2) then
+    x = 1 - pre(x);
+end when;
+end System;
+</pre>
+Quale delle seguenti affermazioni è vera per la variabile intera x?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_1/wrong1.txt b/legacy/Data/ingsw/0721_1/wrong1.txt
new file mode 100644
index 0000000..f485a50
--- /dev/null
+++ b/legacy/Data/ingsw/0721_1/wrong1.txt
@@ -0,0 +1 @@
+Per tutti gli istanti di tempo della forma  3 + 4*k (con k = 0, 1, 2, 3, ...) x vale 1.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_1/wrong2.txt b/legacy/Data/ingsw/0721_1/wrong2.txt
new file mode 100644
index 0000000..a7af2cb
--- /dev/null
+++ b/legacy/Data/ingsw/0721_1/wrong2.txt
@@ -0,0 +1 @@
+Per tutti gli istanti di tempo della forma 1 + 4*k (con k = 0, 1, 2, 3, ...) x vale 0.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_10/correct.txt b/legacy/Data/ingsw/0721_10/correct.txt
new file mode 100644
index 0000000..f4e4c53
--- /dev/null
+++ b/legacy/Data/ingsw/0721_10/correct.txt
@@ -0,0 +1 @@
+Il performance testing è tipicamente eseguito una volta che il sistema è stato completamento integrato.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_10/quest.txt b/legacy/Data/ingsw/0721_10/quest.txt
new file mode 100644
index 0000000..4a711a4
--- /dev/null
+++ b/legacy/Data/ingsw/0721_10/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti affermazioni è vera riguardo al performance testing?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_10/wrong1.txt b/legacy/Data/ingsw/0721_10/wrong1.txt
new file mode 100644
index 0000000..4885062
--- /dev/null
+++ b/legacy/Data/ingsw/0721_10/wrong1.txt
@@ -0,0 +1 @@
+Il performance testing è tipicamente eseguito su un prototipo del sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_10/wrong2.txt b/legacy/Data/ingsw/0721_10/wrong2.txt
new file mode 100644
index 0000000..bd881bc
--- /dev/null
+++ b/legacy/Data/ingsw/0721_10/wrong2.txt
@@ -0,0 +1 @@
+Il performance testing è tipicamente eseguito solo sulle componenti del sistema prima dell'integrazione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_13/correct.txt b/legacy/Data/ingsw/0721_13/correct.txt
new file mode 100644
index 0000000..9b5317b
--- /dev/null
+++ b/legacy/Data/ingsw/0721_13/correct.txt
@@ -0,0 +1,18 @@
+<pre>
+function next
+input Integer x;
+output Integer y;
+algorithm
+   y := 1 - x;
+end next;
+
+class System
+Integer x;
+initial equation
+x = 0;
+equation
+when sample(0, 1) then
+    x = next(pre(x));
+end when;
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_13/quest.txt b/legacy/Data/ingsw/0721_13/quest.txt
new file mode 100644
index 0000000..c105449
--- /dev/null
+++ b/legacy/Data/ingsw/0721_13/quest.txt
@@ -0,0 +1,4 @@
+Si consideri l'automa seguente:
+0->1 e 1->0
+
+Quale dei seguenti modelli Modelica fornisce un modello ragionevole per l'automa di cui sopra.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_13/wrong1.txt b/legacy/Data/ingsw/0721_13/wrong1.txt
new file mode 100644
index 0000000..9b5317b
--- /dev/null
+++ b/legacy/Data/ingsw/0721_13/wrong1.txt
@@ -0,0 +1,18 @@
+<pre>
+function next
+input Integer x;
+output Integer y;
+algorithm
+   y := 1 - x;
+end next;
+
+class System
+Integer x;
+initial equation
+x = 0;
+equation
+when sample(0, 1) then
+    x = next(pre(x));
+end when;
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_13/wrong2.txt b/legacy/Data/ingsw/0721_13/wrong2.txt
new file mode 100644
index 0000000..78c7306
--- /dev/null
+++ b/legacy/Data/ingsw/0721_13/wrong2.txt
@@ -0,0 +1,18 @@
+<pre>
+function next
+input Integer x;
+output Integer y;
+algorithm
+   y := x;
+end next;
+
+class System
+Integer x;
+initial equation
+x = 0;
+equation
+when sample(0, 1) then
+    x = next(pre(x));
+end when;
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_15/correct.txt b/legacy/Data/ingsw/0721_15/correct.txt
new file mode 100644
index 0000000..355e195
--- /dev/null
+++ b/legacy/Data/ingsw/0721_15/correct.txt
@@ -0,0 +1 @@
+Costruire un prototipo, metterlo in esercizio ed accertarsi che i porti i benefici attesi.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_15/quest.txt b/legacy/Data/ingsw/0721_15/quest.txt
new file mode 100644
index 0000000..15dbdf2
--- /dev/null
+++ b/legacy/Data/ingsw/0721_15/quest.txt
@@ -0,0 +1 @@
+Quali delle seguenti attività può contribuire a validare i requisiti di un sistema ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_15/wrong1.txt b/legacy/Data/ingsw/0721_15/wrong1.txt
new file mode 100644
index 0000000..6806506
--- /dev/null
+++ b/legacy/Data/ingsw/0721_15/wrong1.txt
@@ -0,0 +1 @@
+Costruire un prototipo e valutarne attentamente le performance.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_15/wrong2.txt b/legacy/Data/ingsw/0721_15/wrong2.txt
new file mode 100644
index 0000000..586ebee
--- /dev/null
+++ b/legacy/Data/ingsw/0721_15/wrong2.txt
@@ -0,0 +1 @@
+Costruire un prototipo e testarlo a fondo per evidenziare subito errori di implementazione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_17/correct.txt b/legacy/Data/ingsw/0721_17/correct.txt
new file mode 100644
index 0000000..d3826b5
--- /dev/null
+++ b/legacy/Data/ingsw/0721_17/correct.txt
@@ -0,0 +1 @@
+Ad ogni istante di tempo della forma 1 + 4*k (k = 0, 1, 2, 3, ...), x vale "true".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_17/quest.txt b/legacy/Data/ingsw/0721_17/quest.txt
new file mode 100644
index 0000000..4e55a8a
--- /dev/null
+++ b/legacy/Data/ingsw/0721_17/quest.txt
@@ -0,0 +1,13 @@
+Si consideri il seguente modello Modelica.
+<pre>
+class System
+Boolean x;
+initial equation
+x = false;
+equation
+when sample(0, 2) then
+    x = not (pre(x));
+end when;
+end System;
+</pre>
+Quale delle seguenti affermazioni vale per la variabile booleana x ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_17/wrong1.txt b/legacy/Data/ingsw/0721_17/wrong1.txt
new file mode 100644
index 0000000..6245a2f
--- /dev/null
+++ b/legacy/Data/ingsw/0721_17/wrong1.txt
@@ -0,0 +1 @@
+At time instants of form 1 + 4*k (with k = 0, 1, 2, 3, ...) x takes value "false".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_17/wrong2.txt b/legacy/Data/ingsw/0721_17/wrong2.txt
new file mode 100644
index 0000000..0ba96d3
--- /dev/null
+++ b/legacy/Data/ingsw/0721_17/wrong2.txt
@@ -0,0 +1 @@
+Ad ogni istante di tempo della forma 3 + 4*k (k = 0, 1, 2, 3, ...), x vale "true".
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_18/correct.txt b/legacy/Data/ingsw/0721_18/correct.txt
new file mode 100644
index 0000000..eea60e9
--- /dev/null
+++ b/legacy/Data/ingsw/0721_18/correct.txt
@@ -0,0 +1,16 @@
+<pre>
+model Env
+Integer x;  // Pulsante premuto dall'utente
+Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+algorithm
+when initial() then
+   state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+   x := 0;
+   r1024 := 0;
+elsewhen sample(0,1) then
+  (r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+  if (r1024 <= 0.6) then x := 0; else x := 1;  end if;
+end when;
+end Env;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_18/quest.txt b/legacy/Data/ingsw/0721_18/quest.txt
new file mode 100644
index 0000000..c46480d
--- /dev/null
+++ b/legacy/Data/ingsw/0721_18/quest.txt
@@ -0,0 +1,3 @@
+L'input ad un sistema è costituito da un utente (umano) che preme due pulsanti etichettati con 0 ed 1.
+Con probabilità 0.6 l'utente preme il pulsante 0, con probabilità 0.4 l'utente preme il pulsante 1.
+Quale dei seguenti modelli Modelica fornisce un modello ragionevole per l'utente di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_18/wrong1.txt b/legacy/Data/ingsw/0721_18/wrong1.txt
new file mode 100644
index 0000000..f66dbc7
--- /dev/null
+++ b/legacy/Data/ingsw/0721_18/wrong1.txt
@@ -0,0 +1,16 @@
+<pre>
+model Env
+Integer x;  // Pulsante premuto dall'utente
+Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+algorithm
+when initial() then
+   state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+   x := 0;
+   r1024 := 0;
+elsewhen sample(0,1) then
+  (r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+  if (r1024 >= 0.6) then x := 0; else x := 1;  end if;
+end when;
+end Env;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_18/wrong2.txt b/legacy/Data/ingsw/0721_18/wrong2.txt
new file mode 100644
index 0000000..2192e79
--- /dev/null
+++ b/legacy/Data/ingsw/0721_18/wrong2.txt
@@ -0,0 +1,16 @@
+<pre>
+model Env
+Integer x;  // Pulsante premuto dall'utente
+Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+algorithm
+when initial() then
+   state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+   x := 0;
+   r1024 := 0;
+elsewhen sample(0,1) then
+  (r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+  if (r1024 <= 0.6) then x := 1; else x := 0;  end if;
+end when;
+end Env;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_19/correct.txt b/legacy/Data/ingsw/0721_19/correct.txt
new file mode 100644
index 0000000..44ac343
--- /dev/null
+++ b/legacy/Data/ingsw/0721_19/correct.txt
@@ -0,0 +1,35 @@
+<pre>
+model System
+parameter Integer F1 = 1;
+parameter Integer F2 = 2;
+parameter Integer F3 = 3;
+parameter Integer End = 4;
+parameter Real p = 0.3;
+parameter Real A[4, 4] =
+[
+0, 1, 0, 0;
+p, 0, 1-p, 0;
+0, p, 0, 1-p;
+0, 0, 0, 1
+];
+Integer x;  Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+algorithm
+when initial() then
+   state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+   x := F1;
+   r1024 := 0;
+elsewhen sample(0,1) then
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+if (r1024 <= A[x, F1]) then
+ x := F1;
+ elseif (r1024 <= A[x, F1] + A[x, F2]) then
+ x := F2;
+ elseif (r1024 <= A[x, F1] + A[x, F2] + A[x, F3]) then
+ x := F3;
+ else
+ x := End;
+end if;
+end when;
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_19/quest.txt b/legacy/Data/ingsw/0721_19/quest.txt
new file mode 100644
index 0000000..6229852
--- /dev/null
+++ b/legacy/Data/ingsw/0721_19/quest.txt
@@ -0,0 +1,4 @@
+img=https://i.imgur.com/c4UjAQc.png
+Si consideri la seguente Markov Chain:
+
+Quale dei seguenti modelli Modelica fornisce un modello ragionevole per la Markov Chain di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_19/wrong1.txt b/legacy/Data/ingsw/0721_19/wrong1.txt
new file mode 100644
index 0000000..45f3fbe
--- /dev/null
+++ b/legacy/Data/ingsw/0721_19/wrong1.txt
@@ -0,0 +1,35 @@
+<pre>
+model System
+parameter Integer F1 = 1;
+parameter Integer F2 = 2;
+parameter Integer F3 = 3;
+parameter Integer End = 4;
+parameter Real p = 0.3;
+parameter Real A[4, 4] =
+[
+0, 1, 0, 0;
+p, 1-p, 0, 0;
+0, 0, p, 1-p;
+0, 0, 0, 1
+];
+Integer x;  Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+algorithm
+when initial() then
+state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+x := F1;
+r1024 := 0;
+elsewhen sample(0,1) then
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+if (r1024 <= A[x, F1]) then
+ x := F1;
+ elseif (r1024 <= A[x, F1] + A[x, F2]) then
+ x := F2;
+ elseif (r1024 <= A[x, F1] + A[x, F2] + A[x, F3]) then
+ x := F3;
+ else
+ x := End;
+end if;
+end when;
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_19/wrong2.txt b/legacy/Data/ingsw/0721_19/wrong2.txt
new file mode 100644
index 0000000..f6b2fef
--- /dev/null
+++ b/legacy/Data/ingsw/0721_19/wrong2.txt
@@ -0,0 +1,35 @@
+<pre>
+model System
+parameter Integer F1 = 1;
+parameter Integer F2 = 2;
+parameter Integer F3 = 3;
+parameter Integer End = 4;
+parameter Real p = 0.3;
+parameter Real A[4, 4] =
+[
+0, 1, 0, 0;
+p, 0, 0, 1-p;
+0, 0, p, 1-p;
+0, 0, 0, 1
+];
+Integer x;  Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+algorithm
+when initial() then
+state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+x := F1;
+r1024 := 0;
+elsewhen sample(0,1) then
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+if (r1024 <= A[x, F1]) then
+ x := F1;
+ elseif (r1024 <= A[x, F1] + A[x, F2]) then
+ x := F2;
+ elseif (r1024 <= A[x, F1] + A[x, F2] + A[x, F3]) then
+ x := F3;
+ else
+ x := End;
+end if;
+end when;
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_21/correct.txt b/legacy/Data/ingsw/0721_21/correct.txt
new file mode 100644
index 0000000..67edba8
--- /dev/null
+++ b/legacy/Data/ingsw/0721_21/correct.txt
@@ -0,0 +1 @@
+Costruire un modello di simulazione per i principali aspetti dei processi di business dell'azienda e per il sistema software da realizzare e valutare le migliorie apportate dal sistema software ai processi di business dell'azienda mediante simulazione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_21/quest.txt b/legacy/Data/ingsw/0721_21/quest.txt
new file mode 100644
index 0000000..02d9102
--- /dev/null
+++ b/legacy/Data/ingsw/0721_21/quest.txt
@@ -0,0 +1 @@
+Una azienda finanziaria desidera costruire un sistema software per ottimizzare i processi di business. Quali delle seguenti attività può contribuire a validare i requisiti del sistema ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_21/wrong1.txt b/legacy/Data/ingsw/0721_21/wrong1.txt
new file mode 100644
index 0000000..2c917d7
--- /dev/null
+++ b/legacy/Data/ingsw/0721_21/wrong1.txt
@@ -0,0 +1 @@
+Costruire un prototipo del sistema e valutarne i requisiti non funzionali usando i dati storici dall'azienda.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_21/wrong2.txt b/legacy/Data/ingsw/0721_21/wrong2.txt
new file mode 100644
index 0000000..1aa1cd5
--- /dev/null
+++ b/legacy/Data/ingsw/0721_21/wrong2.txt
@@ -0,0 +1 @@
+Costruire un prototipo del sistema e testarlo rispetto ai requisiti funzionali usando i dati storici dall'azienda.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_28/correct.txt b/legacy/Data/ingsw/0721_28/correct.txt
new file mode 100644
index 0000000..c0acec0
--- /dev/null
+++ b/legacy/Data/ingsw/0721_28/correct.txt
@@ -0,0 +1,23 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Real x0 = 1;
+OutputReal x;
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+if (myrandom() <= 0.9)
+then
+    if (myrandom() <= 0.7)
+    then
+     x := 1.1*x;   
+    else
+     x := 0.9*x; 
+     end if;
+else
+   x := 0.73*x; 
+end if;
+end when;
+end MarkovChain;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_28/quest.txt b/legacy/Data/ingsw/0721_28/quest.txt
new file mode 100644
index 0000000..04a9c59
--- /dev/null
+++ b/legacy/Data/ingsw/0721_28/quest.txt
@@ -0,0 +1,2 @@
+L'input di un sistema software è costituito da un sensore che ogni unità di tempo (ad esempio, un secondo) invia un numero reale. Con probabilità 0.63 il valore inviato in una unità di tempo è maggiore del 10% rispetto quello inviato nell'unità di tempo precedente. Con probabilità 0.1 è inferiore del 27% rispetto al valore inviato nell'unità di tempo precedente. Con probabilità 0.27 è inferiore del 10% rispetto quello inviato nell'unità di tempo precedente.
+Quale dei seguenti modelli Modelica modella correttamente l'environment descritto sopra.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_28/wrong1.txt b/legacy/Data/ingsw/0721_28/wrong1.txt
new file mode 100644
index 0000000..af5ef9e
--- /dev/null
+++ b/legacy/Data/ingsw/0721_28/wrong1.txt
@@ -0,0 +1,23 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Real x0 = 1;
+OutputReal x;
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+if (myrandom() <= 0.9)
+then
+    if (myrandom() <= 0.7)
+    then
+     x := 0.9*x;   
+    else
+     x := 01.1*x; 
+     end if;
+else
+   x := 0.73*x; 
+end if;
+end when;
+end MarkovChain;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_28/wrong2.txt b/legacy/Data/ingsw/0721_28/wrong2.txt
new file mode 100644
index 0000000..7e94fc7
--- /dev/null
+++ b/legacy/Data/ingsw/0721_28/wrong2.txt
@@ -0,0 +1,23 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Real x0 = 1;
+OutputReal x;
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+if (myrandom() <= 0.7)
+then
+    if (myrandom() <= 0.9)
+    then
+     x := 1.1*x;   
+    else
+     x := 0.9*x; 
+     end if;
+else
+   x := 0.73*x; 
+end if;
+end when;
+end MarkovChain;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_29/correct.txt b/legacy/Data/ingsw/0721_29/correct.txt
new file mode 100644
index 0000000..cb4fc9a
--- /dev/null
+++ b/legacy/Data/ingsw/0721_29/correct.txt
@@ -0,0 +1,21 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Real x0 = 0;
+OutputReal x;
+Integer countdown;
+algorithm
+when initial() then
+  x := x0;
+  countdown := 0;
+elsewhen sample(0, 1) then
+  if (countdown <= 0)
+  then
+    countdown := 1 + integer(floor(10*myrandom()));
+    x := x + (-1 + 2*myrandom());
+  else
+    countdown := countdown - 1;
+  end if;
+end when;
+end MarkovChain;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_29/quest.txt b/legacy/Data/ingsw/0721_29/quest.txt
new file mode 100644
index 0000000..8f5424d
--- /dev/null
+++ b/legacy/Data/ingsw/0721_29/quest.txt
@@ -0,0 +1,2 @@
+L'input di un sistema software è costituito da una sequenza di valori reali. Ad ogni unità di tempo il valore di input può rimanere uguale al precedente oppure differire di un numero random in [-1, 1]. L'input resta costante per numero random di unità di tempo in [1, 10].
+Quale dei seguenti modelli Modelica modella meglio l'environment descritto sopra.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_29/wrong1.txt b/legacy/Data/ingsw/0721_29/wrong1.txt
new file mode 100644
index 0000000..f32ca15
--- /dev/null
+++ b/legacy/Data/ingsw/0721_29/wrong1.txt
@@ -0,0 +1,21 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Real x0 = 0;
+OutputReal x;
+Integer countdown;
+algorithm
+when initial() then
+  x := x0;
+  countdown := 0;
+elsewhen sample(0, 1) then
+  if (countdown <= 0)
+  then
+    countdown := 1 + integer(floor(10*myrandom()));
+    x := x + (-1 + 4*myrandom());
+  else
+    countdown := countdown - 1;
+  end if;
+end when;
+end MarkovChain;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_29/wrong2.txt b/legacy/Data/ingsw/0721_29/wrong2.txt
new file mode 100644
index 0000000..38e1c17
--- /dev/null
+++ b/legacy/Data/ingsw/0721_29/wrong2.txt
@@ -0,0 +1,21 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Real x0 = 0;
+OutputReal x;
+Integer countdown;
+algorithm
+when initial() then
+  x := x0;
+  countdown := 0;
+elsewhen sample(0, 1) then
+  if (countdown <= 0)
+  then
+    countdown := 1 + integer(floor(10*myrandom()));
+    x := x - myrandom();
+  else
+    countdown := countdown - 1;
+  end if;
+end when;
+end MarkovChain;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_32/correct.txt b/legacy/Data/ingsw/0721_32/correct.txt
new file mode 100644
index 0000000..e13eda2
--- /dev/null
+++ b/legacy/Data/ingsw/0721_32/correct.txt
@@ -0,0 +1 @@
+Accertarsi che i requisiti definiscano un sistema che risolve il problema che l'utente pianifica di risolvere.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_32/quest.txt b/legacy/Data/ingsw/0721_32/quest.txt
new file mode 100644
index 0000000..ea06339
--- /dev/null
+++ b/legacy/Data/ingsw/0721_32/quest.txt
@@ -0,0 +1 @@
+Quali delle seguenti attività è parte del processo di validazione dei requisiti ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_32/wrong1.txt b/legacy/Data/ingsw/0721_32/wrong1.txt
new file mode 100644
index 0000000..b24f900
--- /dev/null
+++ b/legacy/Data/ingsw/0721_32/wrong1.txt
@@ -0,0 +1 @@
+Accertarsi che il sistema soddisfi i requisiti dati.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_32/wrong2.txt b/legacy/Data/ingsw/0721_32/wrong2.txt
new file mode 100644
index 0000000..884d6b1
--- /dev/null
+++ b/legacy/Data/ingsw/0721_32/wrong2.txt
@@ -0,0 +1 @@
+Accertarsi che l'architettura del sistema soddisfi i requisiti dati.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_33/correct.txt b/legacy/Data/ingsw/0721_33/correct.txt
new file mode 100644
index 0000000..9f4a8bf
--- /dev/null
+++ b/legacy/Data/ingsw/0721_33/correct.txt
@@ -0,0 +1,23 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Integer x0 = 0;
+OutputInteger x;
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+     if (myrandom() <= 0.8)
+     then
+     if (myrandom() <= 0.7)
+            then
+            x := 0;   
+            else
+            x := 1; 
+            end if;
+     else
+     x := -1; 
+     end if;
+end when;
+end MarkovChain;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_33/quest.txt b/legacy/Data/ingsw/0721_33/quest.txt
new file mode 100644
index 0000000..496b6af
--- /dev/null
+++ b/legacy/Data/ingsw/0721_33/quest.txt
@@ -0,0 +1,2 @@
+L'environment di un sistema software è costituito da uno user che, ogni untià di tempo (ad esempio, un secondo) invia al sistema tre numeri: -1, 0, 1, con probabilità, rispettivamente, 0.2, 0.56, 0.24.
+Quale dei seguenti modelli Modelica modella correttamente l'environment descritto sopra.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_33/wrong1.txt b/legacy/Data/ingsw/0721_33/wrong1.txt
new file mode 100644
index 0000000..8e7ebc7
--- /dev/null
+++ b/legacy/Data/ingsw/0721_33/wrong1.txt
@@ -0,0 +1,23 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Integer x0 = 0;
+OutputInteger x;
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+     if (myrandom() <= 0.8)
+     then
+     if (myrandom() <= 0.7)
+            then
+            x := 1;   
+            else
+            x := 0; 
+            end if;
+     else
+     x := -1; 
+     end if;
+end when;
+end MarkovChain;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_33/wrong2.txt b/legacy/Data/ingsw/0721_33/wrong2.txt
new file mode 100644
index 0000000..2fd0f2e
--- /dev/null
+++ b/legacy/Data/ingsw/0721_33/wrong2.txt
@@ -0,0 +1,23 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Integer x0 = 0;
+OutputInteger x;
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+     if (myrandom() <= 0.7)
+     then
+     if (myrandom() <= 0.8)
+            then
+               x := 0;   
+            else
+               x := 1; 
+            end if;
+     else
+     x := -1; 
+     end if;
+end when;
+end MarkovChain;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_34/correct.txt b/legacy/Data/ingsw/0721_34/correct.txt
new file mode 100644
index 0000000..5bca5f8
--- /dev/null
+++ b/legacy/Data/ingsw/0721_34/correct.txt
@@ -0,0 +1 @@
+Testare le interfacce per ciascun componente.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_34/quest.txt b/legacy/Data/ingsw/0721_34/quest.txt
new file mode 100644
index 0000000..561755a
--- /dev/null
+++ b/legacy/Data/ingsw/0721_34/quest.txt
@@ -0,0 +1 @@
+Il component testing si concentra su:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_34/wrong1.txt b/legacy/Data/ingsw/0721_34/wrong1.txt
new file mode 100644
index 0000000..7a3fe03
--- /dev/null
+++ b/legacy/Data/ingsw/0721_34/wrong1.txt
@@ -0,0 +1 @@
+Testare l'interazione tra molte componenti (cioè integrazione di molte unità).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_34/wrong2.txt b/legacy/Data/ingsw/0721_34/wrong2.txt
new file mode 100644
index 0000000..d4074cf
--- /dev/null
+++ b/legacy/Data/ingsw/0721_34/wrong2.txt
@@ -0,0 +1 @@
+Testare funzionalità di unità software individuali, oggetti, classi o metodi.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_36/correct.txt b/legacy/Data/ingsw/0721_36/correct.txt
new file mode 100644
index 0000000..3a0f9a1
--- /dev/null
+++ b/legacy/Data/ingsw/0721_36/correct.txt
@@ -0,0 +1 @@
+Stiamo costruendo il sistema giusto ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_36/quest.txt b/legacy/Data/ingsw/0721_36/quest.txt
new file mode 100644
index 0000000..f7ef080
--- /dev/null
+++ b/legacy/Data/ingsw/0721_36/quest.txt
@@ -0,0 +1 @@
+La validazione risponde alla seguenete domanda:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_36/wrong1.txt b/legacy/Data/ingsw/0721_36/wrong1.txt
new file mode 100644
index 0000000..6633b8c
--- /dev/null
+++ b/legacy/Data/ingsw/0721_36/wrong1.txt
@@ -0,0 +1 @@
+Sono soddisfatti i requisti funzionali ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_36/wrong2.txt b/legacy/Data/ingsw/0721_36/wrong2.txt
new file mode 100644
index 0000000..7edd4bc
--- /dev/null
+++ b/legacy/Data/ingsw/0721_36/wrong2.txt
@@ -0,0 +1 @@
+Stiamo costruendo il sistema nel modo giusto ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_4/correct.txt b/legacy/Data/ingsw/0721_4/correct.txt
new file mode 100644
index 0000000..fe4a402
--- /dev/null
+++ b/legacy/Data/ingsw/0721_4/correct.txt
@@ -0,0 +1,21 @@
+<pre>
+model Env
+Integer x;  // Pulsante premuto dall'utente (0 nessun pulsante)
+Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+algorithm
+when initial() then
+   state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+   x := 0;
+   r1024 := 0;
+elsewhen sample(0,1) then
+  (r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+  if (r1024 <= 0.5)
+  then x := 0; 
+  else
+         (r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+         if   (r1024 <= 0.4)   then x := 1;   else x:= 0; end if;
+  end if;
+end when;
+end Env;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_4/quest.txt b/legacy/Data/ingsw/0721_4/quest.txt
new file mode 100644
index 0000000..f50c002
--- /dev/null
+++ b/legacy/Data/ingsw/0721_4/quest.txt
@@ -0,0 +1,4 @@
+L'input ad un sistema è costituito da un utente (umano) che preme due pulsanti etichettati, rispettivamente, con 1 ed 2.
+L'utente può anche decidere di non premere alcun pulsante.
+Con probabilità 0.2 l'utente preme il pulsante 1, con probabilità 0.3 l'utente preme il pulsante 2, con probabilità 0.5 non fa nulla (pulsante 0 per convenzione).
+Quale dei seguenti modelli Modelica fornisce un modello ragionevole per l'utente di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_4/wrong1.txt b/legacy/Data/ingsw/0721_4/wrong1.txt
new file mode 100644
index 0000000..ad42984
--- /dev/null
+++ b/legacy/Data/ingsw/0721_4/wrong1.txt
@@ -0,0 +1,21 @@
+<pre>
+model Env
+Integer x;  // Pulsante premuto dall'utente (0 nessun pulsante)
+Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+algorithm
+when initial() then
+   state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+   x := 0;
+   r1024 := 0;
+elsewhen sample(0,1) then
+  (r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+  if (r1024 <= 0.5)
+  then x := 0; 
+  else
+         (r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+         if   (r1024 <= 0.3)   then x := 0;   else x:= 1; end if;
+  end if;
+end when;
+end Env;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_4/wrong2.txt b/legacy/Data/ingsw/0721_4/wrong2.txt
new file mode 100644
index 0000000..bb62616
--- /dev/null
+++ b/legacy/Data/ingsw/0721_4/wrong2.txt
@@ -0,0 +1,21 @@
+<pre>
+model Env
+Integer x;  // Pulsante premuto dall'utente (0 nessun pulsante)
+Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+algorithm
+when initial() then
+   state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+   x := 0;
+   r1024 := 0;
+elsewhen sample(0,1) then
+  (r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+  if (r1024 <= 0.5)
+  then x := 0; 
+  else
+         (r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+         if   (r1024 <= 0.2)   then x := 1;   else x:= 0; end if;
+  end if;
+end when;
+end Env;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_5/correct.txt b/legacy/Data/ingsw/0721_5/correct.txt
new file mode 100644
index 0000000..0902686
--- /dev/null
+++ b/legacy/Data/ingsw/0721_5/correct.txt
@@ -0,0 +1 @@
+Requisito funzionale.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_5/quest.txt b/legacy/Data/ingsw/0721_5/quest.txt
new file mode 100644
index 0000000..c5dbb4e
--- /dev/null
+++ b/legacy/Data/ingsw/0721_5/quest.txt
@@ -0,0 +1,2 @@
+"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/0721_5/wrong1.txt b/legacy/Data/ingsw/0721_5/wrong1.txt
new file mode 100644
index 0000000..396c8d3
--- /dev/null
+++ b/legacy/Data/ingsw/0721_5/wrong1.txt
@@ -0,0 +1 @@
+Requisito di performance.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_5/wrong2.txt b/legacy/Data/ingsw/0721_5/wrong2.txt
new file mode 100644
index 0000000..6084c49
--- /dev/null
+++ b/legacy/Data/ingsw/0721_5/wrong2.txt
@@ -0,0 +1 @@
+Requisito non-funzionale.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_6/correct.txt b/legacy/Data/ingsw/0721_6/correct.txt
new file mode 100644
index 0000000..fc3d081
--- /dev/null
+++ b/legacy/Data/ingsw/0721_6/correct.txt
@@ -0,0 +1,14 @@
+<pre>
+class System
+Real x; // MB in buffer
+Real u; // input pulse
+initial equation
+x = 3;
+u = 0;
+equation
+when sample(0, 1) then
+  u = 1 - pre(u);
+end when;
+der(x) = 2*u - 1.0;
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_6/quest.txt b/legacy/Data/ingsw/0721_6/quest.txt
new file mode 100644
index 0000000..40a0c99
--- /dev/null
+++ b/legacy/Data/ingsw/0721_6/quest.txt
@@ -0,0 +1 @@
+Un I/O buffer è alimentato da una componente che fornisce un input periodico di periodo 2 secondi. Durante la prima met� del periodo, l'input rate è 2MB/s mentre durante la seconda metà del periodo l'input rate è 0. Quindi l'input rate medio è di 1MB/s. L' I/O buffer, a sua volta, alimenta una componente che richiede (in media) 1MB/s. Quale dei seguenti modelli Modelica è un modello ragionevole per il sistema descritto sopra ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_6/wrong1.txt b/legacy/Data/ingsw/0721_6/wrong1.txt
new file mode 100644
index 0000000..eeb1bba
--- /dev/null
+++ b/legacy/Data/ingsw/0721_6/wrong1.txt
@@ -0,0 +1,14 @@
+<pre>
+class System
+Real x; // MB in buffer
+Real u; // input pulse
+initial equation
+x = 3;
+u = 0;
+equation
+when sample(0, 1) then
+  u = 1 - pre(u);
+end when;
+der(x) = 2*u - 2.0;
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_6/wrong2.txt b/legacy/Data/ingsw/0721_6/wrong2.txt
new file mode 100644
index 0000000..eb68041
--- /dev/null
+++ b/legacy/Data/ingsw/0721_6/wrong2.txt
@@ -0,0 +1,14 @@
+<pre>
+class System
+Real x; // MB in buffer
+Real u; // input pulse
+initial equation
+x = 3;
+u = 0;
+equation
+when sample(0, 1) then
+  u = 1 - pre(u);
+end when;
+der(x) = 2*u + 1.0;
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_8/correct.txt b/legacy/Data/ingsw/0721_8/correct.txt
new file mode 100644
index 0000000..99b5226
--- /dev/null
+++ b/legacy/Data/ingsw/0721_8/correct.txt
@@ -0,0 +1,35 @@
+<pre>
+model System
+parameter Integer F1 = 1;
+parameter Integer F2 = 2;
+parameter Integer F3 = 3;
+parameter Integer End = 4;
+parameter Real p = 0.3;
+parameter Real A[4, 4] =
+[
+p, 1-p, 0, 0;
+p, 0, 1-p, 0;
+p, 0, 0, 1-p;
+0, 0, 0, 1
+];
+Integer x;  Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+algorithm
+when initial() then
+   state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+   x := F1;
+   r1024 := 0;
+elsewhen sample(0,1) then
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+if (r1024 <= A[x, F1]) then
+ x := F1;
+ elseif (r1024 <= A[x, F1] + A[x, F2]) then
+ x := F2;
+ elseif (r1024 <= A[x, F1] + A[x, F2] + A[x, F3]) then
+ x := F3;
+ else
+ x := End;
+end if;
+end when;
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_8/quest.txt b/legacy/Data/ingsw/0721_8/quest.txt
new file mode 100644
index 0000000..ebf5ec9
--- /dev/null
+++ b/legacy/Data/ingsw/0721_8/quest.txt
@@ -0,0 +1,4 @@
+img=https://i.imgur.com/rw4Tvcj.png
+
+Si consideri la seguente Markov Chain:
+Quale dei seguenti modelli Modelica fornisce un modello ragionevole per la Markov Chain di cui sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_8/wrong1.txt b/legacy/Data/ingsw/0721_8/wrong1.txt
new file mode 100644
index 0000000..75546bd
--- /dev/null
+++ b/legacy/Data/ingsw/0721_8/wrong1.txt
@@ -0,0 +1,35 @@
+<pre>
+model System
+parameter Integer F1 = 1;
+parameter Integer F2 = 2;
+parameter Integer F3 = 3;
+parameter Integer End = 4;
+parameter Real p = 0.3;
+parameter Real A[4, 4] =
+[
+p, 0 , 1-p, 0;
+p, 1-p, 0, 0;
+p, 0, 0, 1-p;
+0, 0, 0, 1
+];
+Integer x;  Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+algorithm
+when initial() then
+state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+x := F1;
+r1024 := 0;
+elsewhen sample(0,1) then
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+if (r1024 <= A[x, F1]) then
+ x := F1;
+ elseif (r1024 <= A[x, F1] + A[x, F2]) then
+ x := F2;
+ elseif (r1024 <= A[x, F1] + A[x, F2] + A[x, F3]) then
+ x := F3;
+ else
+ x := End;
+end if;
+end when;
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0721_8/wrong2.txt b/legacy/Data/ingsw/0721_8/wrong2.txt
new file mode 100644
index 0000000..ed6823c
--- /dev/null
+++ b/legacy/Data/ingsw/0721_8/wrong2.txt
@@ -0,0 +1,35 @@
+<pre>
+model System
+parameter Integer F1 = 1;
+parameter Integer F2 = 2;
+parameter Integer F3 = 3;
+parameter Integer End = 4;
+parameter Real p = 0.3;
+parameter Real A[4, 4] =
+[
+p, 0, 1-p, 0;
+0, p, 1-p, 0;
+p, 0, 0, 1-p;
+0, 0, 0, 1
+];
+Integer x;  Real r1024;
+Integer state1024[Modelica.Math.Random.Generators.Xorshift1024star.nState];
+algorithm
+when initial() then
+state1024 := Modelica.Math.Random.Generators.Xorshift1024star.initialState(614657, 30020);
+x := F1;
+r1024 := 0;
+elsewhen sample(0,1) then
+(r1024,state1024) := Modelica.Math.Random.Generators.Xorshift1024star.random(pre(state1024));
+if (r1024 <= A[x, F1]) then
+ x := F1;
+ elseif (r1024 <= A[x, F1] + A[x, F2]) then
+ x := F2;
+ elseif (r1024 <= A[x, F1] + A[x, F2] + A[x, F3]) then
+ x := F3;
+ else
+ x := End;
+end if;
+end when;
+end System;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_1/correct.txt b/legacy/Data/ingsw/0722_1/correct.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/0722_1/correct.txt
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_1/quest.txt b/legacy/Data/ingsw/0722_1/quest.txt
new file mode 100644
index 0000000..e6594c7
--- /dev/null
+++ b/legacy/Data/ingsw/0722_1/quest.txt
@@ -0,0 +1,19 @@
+img=https://i.imgur.com/HZd8X10.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 act2 act0 act0 act0 act2 act1 act2 act0 act2 act2 act2 act2 act0 act0 act1 act2 act2 act0 act2 act0 act2 act1 act0 act2 act1 act2 act2 act0 act2 
+
+Test case 2: act2 act2 act1 act0 act0 act0 act0 act2 act2 act1 act2 
+
+Test case 3: act2 act2 act2 act1 act0 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/0722_1/wrong 1.txt b/legacy/Data/ingsw/0722_1/wrong 1.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0722_1/wrong 1.txt	
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_1/wrong 2.txt b/legacy/Data/ingsw/0722_1/wrong 2.txt
new file mode 100644
index 0000000..a8aead7
--- /dev/null
+++ b/legacy/Data/ingsw/0722_1/wrong 2.txt	
@@ -0,0 +1 @@
+State coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_10/correct.txt b/legacy/Data/ingsw/0722_10/correct.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0722_10/correct.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_10/quest.txt b/legacy/Data/ingsw/0722_10/quest.txt
new file mode 100644
index 0000000..c18ff48
--- /dev/null
+++ b/legacy/Data/ingsw/0722_10/quest.txt
@@ -0,0 +1,20 @@
+img=https://i.imgur.com/pz1HiRX.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: act0 act1 act1 act1 act2 act2 act1 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/0722_10/wrong 1.txt b/legacy/Data/ingsw/0722_10/wrong 1.txt
new file mode 100644
index 0000000..52f25fe
--- /dev/null
+++ b/legacy/Data/ingsw/0722_10/wrong 1.txt	
@@ -0,0 +1 @@
+70%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_10/wrong 2.txt b/legacy/Data/ingsw/0722_10/wrong 2.txt
new file mode 100644
index 0000000..1e091a3
--- /dev/null
+++ b/legacy/Data/ingsw/0722_10/wrong 2.txt	
@@ -0,0 +1 @@
+90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_11/correct.txt b/legacy/Data/ingsw/0722_11/correct.txt
new file mode 100644
index 0000000..f293f3e
--- /dev/null
+++ b/legacy/Data/ingsw/0722_11/correct.txt
@@ -0,0 +1 @@
+(a = 6, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 0)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_11/quest.txt b/legacy/Data/ingsw/0722_11/quest.txt
new file mode 100644
index 0000000..709cf96
--- /dev/null
+++ b/legacy/Data/ingsw/0722_11/quest.txt
@@ -0,0 +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  + 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/0722_11/wrong 1.txt b/legacy/Data/ingsw/0722_11/wrong 1.txt
new file mode 100644
index 0000000..eafabb1
--- /dev/null
+++ b/legacy/Data/ingsw/0722_11/wrong 1.txt	
@@ -0,0 +1 @@
+(a = 6, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 2)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_11/wrong 2.txt b/legacy/Data/ingsw/0722_11/wrong 2.txt
new file mode 100644
index 0000000..fc010a3
--- /dev/null
+++ b/legacy/Data/ingsw/0722_11/wrong 2.txt	
@@ -0,0 +1 @@
+(a = 5, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 0)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_12/correct.txt b/legacy/Data/ingsw/0722_12/correct.txt
new file mode 100644
index 0000000..8785661
--- /dev/null
+++ b/legacy/Data/ingsw/0722_12/correct.txt
@@ -0,0 +1 @@
+{x = -200, x = -50,  x = 0, x = 100, x = 700}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_12/quest.txt b/legacy/Data/ingsw/0722_12/quest.txt
new file mode 100644
index 0000000..58ef38e
--- /dev/null
+++ b/legacy/Data/ingsw/0722_12/quest.txt
@@ -0,0 +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)}
+
+Quale dei seguenti test cases consegue una partition coverage del 100% ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_12/wrong 1.txt b/legacy/Data/ingsw/0722_12/wrong 1.txt
new file mode 100644
index 0000000..a6df32d
--- /dev/null
+++ b/legacy/Data/ingsw/0722_12/wrong 1.txt	
@@ -0,0 +1 @@
+{x = -200, x = -150,  x = 0, x = 100, x = 700}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_12/wrong 2.txt b/legacy/Data/ingsw/0722_12/wrong 2.txt
new file mode 100644
index 0000000..0aaedb8
--- /dev/null
+++ b/legacy/Data/ingsw/0722_12/wrong 2.txt	
@@ -0,0 +1 @@
+{x = -200, x = -50,  x = 0, x = 100, x = 500}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_13/correct.txt b/legacy/Data/ingsw/0722_13/correct.txt
new file mode 100644
index 0000000..b110af1
--- /dev/null
+++ b/legacy/Data/ingsw/0722_13/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_13/quest.txt b/legacy/Data/ingsw/0722_13/quest.txt
new file mode 100644
index 0000000..83987bd
--- /dev/null
+++ b/legacy/Data/ingsw/0722_13/quest.txt
@@ -0,0 +1,15 @@
+img=https://i.imgur.com/dMvnEEi.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 act2 act0 
+
+Test case 2: act0 act1 act2 act0 act2 
+
+Test case 3: act2 act2 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/0722_13/wrong 1.txt b/legacy/Data/ingsw/0722_13/wrong 1.txt
new file mode 100644
index 0000000..eb5e1cd
--- /dev/null
+++ b/legacy/Data/ingsw/0722_13/wrong 1.txt	
@@ -0,0 +1 @@
+Transition coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_13/wrong 2.txt b/legacy/Data/ingsw/0722_13/wrong 2.txt
new file mode 100644
index 0000000..cf27703
--- /dev/null
+++ b/legacy/Data/ingsw/0722_13/wrong 2.txt	
@@ -0,0 +1 @@
+Transition coverage: 70%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_14/correct.txt b/legacy/Data/ingsw/0722_14/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0722_14/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_14/quest.txt b/legacy/Data/ingsw/0722_14/quest.txt
new file mode 100644
index 0000000..f3d1bcd
--- /dev/null
+++ b/legacy/Data/ingsw/0722_14/quest.txt
@@ -0,0 +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:
+
+-----------
+
+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/0722_14/wrong 1.txt b/legacy/Data/ingsw/0722_14/wrong 1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0722_14/wrong 1.txt	
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_14/wrong 2.txt b/legacy/Data/ingsw/0722_14/wrong 2.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0722_14/wrong 2.txt	
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_15/correct.txt b/legacy/Data/ingsw/0722_15/correct.txt
new file mode 100644
index 0000000..973ef63
--- /dev/null
+++ b/legacy/Data/ingsw/0722_15/correct.txt
@@ -0,0 +1 @@
+State coverage: 75%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_15/quest.txt b/legacy/Data/ingsw/0722_15/quest.txt
new file mode 100644
index 0000000..035eb2b
--- /dev/null
+++ b/legacy/Data/ingsw/0722_15/quest.txt
@@ -0,0 +1,16 @@
+img=https://i.imgur.com/wYIAk1e.png
+La state coverage di un insieme di test cases (cioè sequeze 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 act2 act0 
+
+Test case 2: act0 act1 act2 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/0722_15/wrong 1.txt b/legacy/Data/ingsw/0722_15/wrong 1.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/0722_15/wrong 1.txt	
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_15/wrong 2.txt b/legacy/Data/ingsw/0722_15/wrong 2.txt
new file mode 100644
index 0000000..a8aead7
--- /dev/null
+++ b/legacy/Data/ingsw/0722_15/wrong 2.txt	
@@ -0,0 +1 @@
+State coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_16/correct.txt b/legacy/Data/ingsw/0722_16/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0722_16/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_16/quest.txt b/legacy/Data/ingsw/0722_16/quest.txt
new file mode 100644
index 0000000..12ae518
--- /dev/null
+++ b/legacy/Data/ingsw/0722_16/quest.txt
@@ -0,0 +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:
+
+-----------
+
+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/0722_16/wrong 1.txt b/legacy/Data/ingsw/0722_16/wrong 1.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0722_16/wrong 1.txt	
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_16/wrong 2.txt b/legacy/Data/ingsw/0722_16/wrong 2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0722_16/wrong 2.txt	
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_17/correct.txt b/legacy/Data/ingsw/0722_17/correct.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0722_17/correct.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_17/quest.txt b/legacy/Data/ingsw/0722_17/quest.txt
new file mode 100644
index 0000000..3150037
--- /dev/null
+++ b/legacy/Data/ingsw/0722_17/quest.txt
@@ -0,0 +1,16 @@
+img=https://i.imgur.com/ixzrFpG.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 act1 
+
+Test case 2: act1 act2 act1 act1 act0 act0 act0 act1 act2 act1 act2 act1 act2 act2 act0 act2 act0 act1 act2 act2 act0 act2 act2 act2 
+
+Test case 3: act0 act1 act1 act0 act2 act2 act0 act2 act0 act2 act0 act2 act0 act0 act0 act0 act0 act0 act1 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/0722_17/wrong 1.txt b/legacy/Data/ingsw/0722_17/wrong 1.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0722_17/wrong 1.txt	
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_17/wrong 2.txt b/legacy/Data/ingsw/0722_17/wrong 2.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0722_17/wrong 2.txt	
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_18/correct.txt b/legacy/Data/ingsw/0722_18/correct.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/0722_18/correct.txt
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_18/quest.txt b/legacy/Data/ingsw/0722_18/quest.txt
new file mode 100644
index 0000000..ca50f58
--- /dev/null
+++ b/legacy/Data/ingsw/0722_18/quest.txt
@@ -0,0 +1,16 @@
+img=https://i.imgur.com/a7JeI7m.png
+La state coverage di un insieme di test cases (cioè sequeze 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 act2 act0 act2 
+
+Test case 2: act2 act0 act0 act2 act0 
+
+Test case 3: act2 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/0722_18/wrong 1.txt b/legacy/Data/ingsw/0722_18/wrong 1.txt
new file mode 100644
index 0000000..f6a4b07
--- /dev/null
+++ b/legacy/Data/ingsw/0722_18/wrong 1.txt	
@@ -0,0 +1 @@
+State coverage: 90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_18/wrong 2.txt b/legacy/Data/ingsw/0722_18/wrong 2.txt
new file mode 100644
index 0000000..a8aead7
--- /dev/null
+++ b/legacy/Data/ingsw/0722_18/wrong 2.txt	
@@ -0,0 +1 @@
+State coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_19/correct.txt b/legacy/Data/ingsw/0722_19/correct.txt
new file mode 100644
index 0000000..8b0c318
--- /dev/null
+++ b/legacy/Data/ingsw/0722_19/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_19/quest.txt b/legacy/Data/ingsw/0722_19/quest.txt
new file mode 100644
index 0000000..a412231
--- /dev/null
+++ b/legacy/Data/ingsw/0722_19/quest.txt
@@ -0,0 +1,17 @@
+img=https://i.imgur.com/Rd4gO4k.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 act1 act2 
+
+Test case 2: act2 act2 act1 act2 act2 
+
+
+Test case 3: act2 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/0722_19/wrong 1.txt b/legacy/Data/ingsw/0722_19/wrong 1.txt
new file mode 100644
index 0000000..eb5e1cd
--- /dev/null
+++ b/legacy/Data/ingsw/0722_19/wrong 1.txt	
@@ -0,0 +1 @@
+Transition coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_19/wrong 2.txt b/legacy/Data/ingsw/0722_19/wrong 2.txt
new file mode 100644
index 0000000..a29d476
--- /dev/null
+++ b/legacy/Data/ingsw/0722_19/wrong 2.txt	
@@ -0,0 +1 @@
+Transition coverage: 80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_2/correct.txt b/legacy/Data/ingsw/0722_2/correct.txt
new file mode 100644
index 0000000..7d0c43c
--- /dev/null
+++ b/legacy/Data/ingsw/0722_2/correct.txt
@@ -0,0 +1,5 @@
+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_2/quest.txt b/legacy/Data/ingsw/0722_2/quest.txt
new file mode 100644
index 0000000..8210340
--- /dev/null
+++ b/legacy/Data/ingsw/0722_2/quest.txt
@@ -0,0 +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(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/legacy/Data/ingsw/0722_2/wrong 1.txt
new file mode 100644
index 0000000..392cc67
--- /dev/null
+++ b/legacy/Data/ingsw/0722_2/wrong 1.txt	
@@ -0,0 +1,5 @@
+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_2/wrong 2.txt b/legacy/Data/ingsw/0722_2/wrong 2.txt
new file mode 100644
index 0000000..2fde3f0
--- /dev/null
+++ b/legacy/Data/ingsw/0722_2/wrong 2.txt	
@@ -0,0 +1,5 @@
+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/legacy/Data/ingsw/0722_20/correct.txt
new file mode 100644
index 0000000..973ef63
--- /dev/null
+++ b/legacy/Data/ingsw/0722_20/correct.txt
@@ -0,0 +1 @@
+State coverage: 75%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_20/quest.txt b/legacy/Data/ingsw/0722_20/quest.txt
new file mode 100644
index 0000000..afddbb1
--- /dev/null
+++ b/legacy/Data/ingsw/0722_20/quest.txt
@@ -0,0 +1,15 @@
+img=https://i.imgur.com/dzwfqoB.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 act1 act2 act2 act2 act1 act1 act0 act0 act0 act0 act0 act1 
+
+Test case 2: act1 
+
+Test case 3: act0 act1 act2 act0 act2 act2 act2 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/0722_20/wrong 1.txt b/legacy/Data/ingsw/0722_20/wrong 1.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0722_20/wrong 1.txt	
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_20/wrong 2.txt b/legacy/Data/ingsw/0722_20/wrong 2.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/0722_20/wrong 2.txt	
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_21/correct.txt b/legacy/Data/ingsw/0722_21/correct.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0722_21/correct.txt
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_21/quest.txt b/legacy/Data/ingsw/0722_21/quest.txt
new file mode 100644
index 0000000..37d7e62
--- /dev/null
+++ b/legacy/Data/ingsw/0722_21/quest.txt
@@ -0,0 +1,20 @@
+img=https://i.imgur.com/wVYqOVj.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 act1 act2 act0 act1 
+
+Test case 2: act0 act2 act0 
+
+Test case 3: act1 act1 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/0722_21/wrong 1.txt b/legacy/Data/ingsw/0722_21/wrong 1.txt
new file mode 100644
index 0000000..f6a4b07
--- /dev/null
+++ b/legacy/Data/ingsw/0722_21/wrong 1.txt	
@@ -0,0 +1 @@
+State coverage: 90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_21/wrong 2.txt b/legacy/Data/ingsw/0722_21/wrong 2.txt
new file mode 100644
index 0000000..90b2f35
--- /dev/null
+++ b/legacy/Data/ingsw/0722_21/wrong 2.txt	
@@ -0,0 +1 @@
+State coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_22/correct.txt b/legacy/Data/ingsw/0722_22/correct.txt
new file mode 100644
index 0000000..f6a4b07
--- /dev/null
+++ b/legacy/Data/ingsw/0722_22/correct.txt
@@ -0,0 +1 @@
+State coverage: 90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_22/quest.txt b/legacy/Data/ingsw/0722_22/quest.txt
new file mode 100644
index 0000000..fdca1b9
--- /dev/null
+++ b/legacy/Data/ingsw/0722_22/quest.txt
@@ -0,0 +1,16 @@
+img=https://i.imgur.com/zkjv6a7.png
+La state coverage di un insieme di test cases (cioè sequeze 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 act1 act0 act1 act2 act2 
+
+Test case 2: act0 act0 act2 
+
+
+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/0722_22/wrong 1.txt b/legacy/Data/ingsw/0722_22/wrong 1.txt
new file mode 100644
index 0000000..1c07658
--- /dev/null
+++ b/legacy/Data/ingsw/0722_22/wrong 1.txt	
@@ -0,0 +1 @@
+State coverage: 70%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_22/wrong 2.txt b/legacy/Data/ingsw/0722_22/wrong 2.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/0722_22/wrong 2.txt	
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_23/correct.txt b/legacy/Data/ingsw/0722_23/correct.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0722_23/correct.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_23/quest.txt b/legacy/Data/ingsw/0722_23/quest.txt
new file mode 100644
index 0000000..2e81fc7
--- /dev/null
+++ b/legacy/Data/ingsw/0722_23/quest.txt
@@ -0,0 +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=-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/0722_23/wrong 1.txt b/legacy/Data/ingsw/0722_23/wrong 1.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0722_23/wrong 1.txt	
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_23/wrong 2.txt b/legacy/Data/ingsw/0722_23/wrong 2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0722_23/wrong 2.txt	
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_24/correct.txt b/legacy/Data/ingsw/0722_24/correct.txt
new file mode 100644
index 0000000..a40ea7d
--- /dev/null
+++ b/legacy/Data/ingsw/0722_24/correct.txt
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 5, c = 0), (a=50, b = 3, c = 0).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_24/quest.txt b/legacy/Data/ingsw/0722_24/quest.txt
new file mode 100644
index 0000000..5b1bcf2
--- /dev/null
+++ b/legacy/Data/ingsw/0722_24/quest.txt
@@ -0,0 +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
+}
+   Quale dei seguenti test set soddisfa il criterio della Condition/Decision coverage ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_24/wrong 1.txt b/legacy/Data/ingsw/0722_24/wrong 1.txt
new file mode 100644
index 0000000..5b77112
--- /dev/null
+++ b/legacy/Data/ingsw/0722_24/wrong 1.txt	
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 5, c = 0), (a=50, b = 0, c = 5).
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_24/wrong 2.txt b/legacy/Data/ingsw/0722_24/wrong 2.txt
new file mode 100644
index 0000000..abe0eaa
--- /dev/null
+++ b/legacy/Data/ingsw/0722_24/wrong 2.txt	
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 4, c = 0), (a=200, b = 4, c = 0)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_25/correct.txt b/legacy/Data/ingsw/0722_25/correct.txt
new file mode 100644
index 0000000..39d8c13
--- /dev/null
+++ b/legacy/Data/ingsw/0722_25/correct.txt
@@ -0,0 +1,9 @@
+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/0722_25/quest.txt b/legacy/Data/ingsw/0722_25/quest.txt
new file mode 100644
index 0000000..23565d6
--- /dev/null
+++ b/legacy/Data/ingsw/0722_25/quest.txt
@@ -0,0 +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(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/0722_25/wrong 1.txt b/legacy/Data/ingsw/0722_25/wrong 1.txt
new file mode 100644
index 0000000..c5c0179
--- /dev/null
+++ b/legacy/Data/ingsw/0722_25/wrong 1.txt	
@@ -0,0 +1,9 @@
+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_25/wrong 2.txt b/legacy/Data/ingsw/0722_25/wrong 2.txt
new file mode 100644
index 0000000..e4e10cc
--- /dev/null
+++ b/legacy/Data/ingsw/0722_25/wrong 2.txt	
@@ -0,0 +1,9 @@
+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_26/correct.txt b/legacy/Data/ingsw/0722_26/correct.txt
new file mode 100644
index 0000000..7311d41
--- /dev/null
+++ b/legacy/Data/ingsw/0722_26/correct.txt
@@ -0,0 +1 @@
+Test set: {x=1, y=1}, {x=0, y=0}, {x=2, y=1}, {x=2, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_26/quest.txt b/legacy/Data/ingsw/0722_26/quest.txt
new file mode 100644
index 0000000..78ad81f
--- /dev/null
+++ b/legacy/Data/ingsw/0722_26/quest.txt
@@ -0,0 +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:
+
+-----------
+
+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/0722_26/wrong 1.txt b/legacy/Data/ingsw/0722_26/wrong 1.txt
new file mode 100644
index 0000000..7e48e4f
--- /dev/null
+++ b/legacy/Data/ingsw/0722_26/wrong 1.txt	
@@ -0,0 +1 @@
+Test set: {x=1, y=1}, {x=0, y=0}, {x=2, y=1}, {x=2, y=3}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_26/wrong 2.txt b/legacy/Data/ingsw/0722_26/wrong 2.txt
new file mode 100644
index 0000000..3e327ab
--- /dev/null
+++ b/legacy/Data/ingsw/0722_26/wrong 2.txt	
@@ -0,0 +1 @@
+Test set: {x=1, y=1}, {x=2, y=2}, {x=2, y=1}, {x=2, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_3/correct.txt b/legacy/Data/ingsw/0722_3/correct.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0722_3/correct.txt
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_3/quest.txt b/legacy/Data/ingsw/0722_3/quest.txt
new file mode 100644
index 0000000..ac6007d
--- /dev/null
+++ b/legacy/Data/ingsw/0722_3/quest.txt
@@ -0,0 +1,15 @@
+img=https://i.imgur.com/K7pm0xk.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 act1 act2 act2 act2 act0 act1 act2 act2 act2 
+
+Test case 2: act0 act1 act2 act2 act1 act2 act0 act2 act2 act2 act0 
+
+Test case 3: act2 act2 act0 act2 act1 act0 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/0722_3/wrong 1.txt b/legacy/Data/ingsw/0722_3/wrong 1.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0722_3/wrong 1.txt	
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_3/wrong 2.txt b/legacy/Data/ingsw/0722_3/wrong 2.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0722_3/wrong 2.txt	
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_4/correct.txt b/legacy/Data/ingsw/0722_4/correct.txt
new file mode 100644
index 0000000..1e091a3
--- /dev/null
+++ b/legacy/Data/ingsw/0722_4/correct.txt
@@ -0,0 +1 @@
+90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_4/quest.txt b/legacy/Data/ingsw/0722_4/quest.txt
new file mode 100644
index 0000000..681243a
--- /dev/null
+++ b/legacy/Data/ingsw/0722_4/quest.txt
@@ -0,0 +1,15 @@
+img=https://i.imgur.com/IAPlGNV.png
+La state coverage di un insieme di test cases (cioè sequeze 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 act0 act1 act1 act2 act0 
+
+Test case 2: act2 act0 act0 
+
+Test case 3: act1 act1 act2 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/0722_4/wrong 1.txt b/legacy/Data/ingsw/0722_4/wrong 1.txt
new file mode 100644
index 0000000..52f25fe
--- /dev/null
+++ b/legacy/Data/ingsw/0722_4/wrong 1.txt	
@@ -0,0 +1 @@
+70%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_4/wrong 2.txt b/legacy/Data/ingsw/0722_4/wrong 2.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0722_4/wrong 2.txt	
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_5/correct.txt b/legacy/Data/ingsw/0722_5/correct.txt
new file mode 100644
index 0000000..cf27703
--- /dev/null
+++ b/legacy/Data/ingsw/0722_5/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 70%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_5/quest.txt b/legacy/Data/ingsw/0722_5/quest.txt
new file mode 100644
index 0000000..5201b57
--- /dev/null
+++ b/legacy/Data/ingsw/0722_5/quest.txt
@@ -0,0 +1,17 @@
+img=https://i.imgur.com/4nez8mZ.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 act2 act2 
+
+Test case 2: act0 act2 act2 act1 act2 act1 act1 act1 act2 act2 act2 act2 act2 
+
+Test case 3: act2 act2 act2 act0 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/0722_5/wrong 1.txt b/legacy/Data/ingsw/0722_5/wrong 1.txt
new file mode 100644
index 0000000..2d5aeb0
--- /dev/null
+++ b/legacy/Data/ingsw/0722_5/wrong 1.txt	
@@ -0,0 +1 @@
+Transition coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_5/wrong 2.txt b/legacy/Data/ingsw/0722_5/wrong 2.txt
new file mode 100644
index 0000000..eb5e1cd
--- /dev/null
+++ b/legacy/Data/ingsw/0722_5/wrong 2.txt	
@@ -0,0 +1 @@
+Transition coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_6/correct.txt b/legacy/Data/ingsw/0722_6/correct.txt
new file mode 100644
index 0000000..8d957c2
--- /dev/null
+++ b/legacy/Data/ingsw/0722_6/correct.txt
@@ -0,0 +1 @@
+45%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_6/quest.txt b/legacy/Data/ingsw/0722_6/quest.txt
new file mode 100644
index 0000000..363e53e
--- /dev/null
+++ b/legacy/Data/ingsw/0722_6/quest.txt
@@ -0,0 +1,15 @@
+img=https://i.imgur.com/gNFBVuc.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 act1 act0 act2 act2 act1 act2 act2 act2 act2 act2 act0 act0 
+
+Test case 2: act2 
+
+Test case 3: 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/0722_6/wrong 1.txt b/legacy/Data/ingsw/0722_6/wrong 1.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0722_6/wrong 1.txt	
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_6/wrong 2.txt b/legacy/Data/ingsw/0722_6/wrong 2.txt
new file mode 100644
index 0000000..1e091a3
--- /dev/null
+++ b/legacy/Data/ingsw/0722_6/wrong 2.txt	
@@ -0,0 +1 @@
+90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_7/correct.txt b/legacy/Data/ingsw/0722_7/correct.txt
new file mode 100644
index 0000000..711ba55
--- /dev/null
+++ b/legacy/Data/ingsw/0722_7/correct.txt
@@ -0,0 +1 @@
+40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_7/quest.txt b/legacy/Data/ingsw/0722_7/quest.txt
new file mode 100644
index 0000000..b33abf0
--- /dev/null
+++ b/legacy/Data/ingsw/0722_7/quest.txt
@@ -0,0 +1,14 @@
+img=https://i.imgur.com/uEiyXTN.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 act2 act2 act0 act1 act2 act0 
+
+Test case 2: act1 act2 
+
+Test case 3: 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/0722_7/wrong 1.txt b/legacy/Data/ingsw/0722_7/wrong 1.txt
new file mode 100644
index 0000000..1e091a3
--- /dev/null
+++ b/legacy/Data/ingsw/0722_7/wrong 1.txt	
@@ -0,0 +1 @@
+90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_7/wrong 2.txt b/legacy/Data/ingsw/0722_7/wrong 2.txt
new file mode 100644
index 0000000..52f25fe
--- /dev/null
+++ b/legacy/Data/ingsw/0722_7/wrong 2.txt	
@@ -0,0 +1 @@
+70%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_8/correct.txt b/legacy/Data/ingsw/0722_8/correct.txt
new file mode 100644
index 0000000..31a01d5
--- /dev/null
+++ b/legacy/Data/ingsw/0722_8/correct.txt
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=0, y=0}, {x=15, y=0}, {x=9, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_8/quest.txt b/legacy/Data/ingsw/0722_8/quest.txt
new file mode 100644
index 0000000..462c3bb
--- /dev/null
+++ b/legacy/Data/ingsw/0722_8/quest.txt
@@ -0,0 +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:
+
+-----------
+
+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/0722_8/wrong 1.txt b/legacy/Data/ingsw/0722_8/wrong 1.txt
new file mode 100644
index 0000000..549dba8
--- /dev/null
+++ b/legacy/Data/ingsw/0722_8/wrong 1.txt	
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=0, y=0}, {x=15, y=0}, {x=10, y=3}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_8/wrong 2.txt b/legacy/Data/ingsw/0722_8/wrong 2.txt
new file mode 100644
index 0000000..0c564f7
--- /dev/null
+++ b/legacy/Data/ingsw/0722_8/wrong 2.txt	
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=2, y=1}, {x=15, y=0}, {x=9, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_9/correct.txt b/legacy/Data/ingsw/0722_9/correct.txt
new file mode 100644
index 0000000..f6a4b07
--- /dev/null
+++ b/legacy/Data/ingsw/0722_9/correct.txt
@@ -0,0 +1 @@
+State coverage: 90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_9/quest.txt b/legacy/Data/ingsw/0722_9/quest.txt
new file mode 100644
index 0000000..2b0b595
--- /dev/null
+++ b/legacy/Data/ingsw/0722_9/quest.txt
@@ -0,0 +1,16 @@
+img=https://i.imgur.com/l0OUTrQ.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 act2 act2 act1 act2 act1 act2 act2 act1 
+
+Test case 2: act2 act2 act0 act1 act1 act2 act0 act0 act2 act0 act2 act2 act2 act0 act0 act0 act2 act2 act0 act2 act2 act2 act1 act2 act2 act1 
+
+Test case 3: act2 act0 act2 act1 act2 act1 act0 act2 act2 act0 act0 act2 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/0722_9/wrong 1.txt b/legacy/Data/ingsw/0722_9/wrong 1.txt
new file mode 100644
index 0000000..1c07658
--- /dev/null
+++ b/legacy/Data/ingsw/0722_9/wrong 1.txt	
@@ -0,0 +1 @@
+State coverage: 70%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0722_9/wrong 2.txt b/legacy/Data/ingsw/0722_9/wrong 2.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0722_9/wrong 2.txt	
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_10/correct.txt b/legacy/Data/ingsw/0922_10/correct.txt
new file mode 100644
index 0000000..cefc84a
--- /dev/null
+++ b/legacy/Data/ingsw/0922_10/correct.txt
@@ -0,0 +1,69 @@
+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 := 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) == 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 := 4;
+
+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 := 1;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_10/quest.txt b/legacy/Data/ingsw/0922_10/quest.txt
new file mode 100644
index 0000000..9dd6e3b
--- /dev/null
+++ b/legacy/Data/ingsw/0922_10/quest.txt
@@ -0,0 +1,2 @@
+img=https://i.imgur.com/okpLYQL.png
+Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_10/wrong 1.txt b/legacy/Data/ingsw/0922_10/wrong 1.txt
new file mode 100644
index 0000000..cc2b129
--- /dev/null
+++ b/legacy/Data/ingsw/0922_10/wrong 1.txt	
@@ -0,0 +1,67 @@
+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 := 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 := 4;
+
+elseif (pre(x) == 2) and (pre(u) == 2) then x := 3;
+
+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) == 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/0922_10/wrong 2.txt b/legacy/Data/ingsw/0922_10/wrong 2.txt
new file mode 100644
index 0000000..f0f54bf
--- /dev/null
+++ b/legacy/Data/ingsw/0922_10/wrong 2.txt	
@@ -0,0 +1,69 @@
+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) == 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 := 3;
+
+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 := 1;
+
+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/0922_11/correct.txt b/legacy/Data/ingsw/0922_11/correct.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0922_11/correct.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_11/quest.txt b/legacy/Data/ingsw/0922_11/quest.txt
new file mode 100644
index 0000000..55e0e6a
--- /dev/null
+++ b/legacy/Data/ingsw/0922_11/quest.txt
@@ -0,0 +1,19 @@
+img=https://i.imgur.com/im1GU0x.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 act2 
+
+Test case 2: act2 act2 act1 act2 act1 act2 act1 act2 act1 act2 act2 act1 act1 act2 act1 act2 act2 act2 
+
+Test case 3: 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/0922_11/wrong 1.txt b/legacy/Data/ingsw/0922_11/wrong 1.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0922_11/wrong 1.txt	
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_11/wrong 2.txt b/legacy/Data/ingsw/0922_11/wrong 2.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/0922_11/wrong 2.txt	
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_12/correct.txt b/legacy/Data/ingsw/0922_12/correct.txt
new file mode 100644
index 0000000..1e091a3
--- /dev/null
+++ b/legacy/Data/ingsw/0922_12/correct.txt
@@ -0,0 +1 @@
+90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_12/quest.txt b/legacy/Data/ingsw/0922_12/quest.txt
new file mode 100644
index 0000000..dd553a4
--- /dev/null
+++ b/legacy/Data/ingsw/0922_12/quest.txt
@@ -0,0 +1,17 @@
+img=https://i.imgur.com/rWKWcCt.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 act2 act0 act1 act1 act0 act0 act2 act0 act2 act2 act2 act1 act2 act2 act0 act0 act2 act1 act0 act0 act2 act2 act2 act0 act2 act2 act0 act2 act0 act1 act2 act1 act1 act1 act1 act0 act1 act0 act1 act2 act1 act2 act0 
+
+Test case 2: act0 
+
+Test case 3: act2 act0 act2 act2 act0 act2 act0 act2 act2 act2 act0 act0 act1 act2 act0 act2 act2 act0 act2 act2 act0 act2 act0 act2 act2 act2 act0 act1 act1 act1 act0 act0 act1 act1 act2 act0 act0 act2 act1 act0 act2 act2 act0 act2 act2 act0 act0 act2 act0 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/0922_12/wrong 1.txt b/legacy/Data/ingsw/0922_12/wrong 1.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0922_12/wrong 1.txt	
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_12/wrong 2.txt b/legacy/Data/ingsw/0922_12/wrong 2.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0922_12/wrong 2.txt	
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_13/correct.txt b/legacy/Data/ingsw/0922_13/correct.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/0922_13/correct.txt
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_13/quest.txt b/legacy/Data/ingsw/0922_13/quest.txt
new file mode 100644
index 0000000..7e33553
--- /dev/null
+++ b/legacy/Data/ingsw/0922_13/quest.txt
@@ -0,0 +1,15 @@
+img=https://i.imgur.com/em6ovKG.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 
+
+Test case 2: act2 act1 act2 act0 act0 act0 act1 act0 act0 act1 act0 
+
+Test case 3: 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/0922_13/wrong 1.txt b/legacy/Data/ingsw/0922_13/wrong 1.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0922_13/wrong 1.txt	
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_13/wrong 2.txt b/legacy/Data/ingsw/0922_13/wrong 2.txt
new file mode 100644
index 0000000..f91ad01
--- /dev/null
+++ b/legacy/Data/ingsw/0922_13/wrong 2.txt	
@@ -0,0 +1 @@
+35%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_14/correct.txt b/legacy/Data/ingsw/0922_14/correct.txt
new file mode 100644
index 0000000..7734d60
--- /dev/null
+++ b/legacy/Data/ingsw/0922_14/correct.txt
@@ -0,0 +1,71 @@
+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) == 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 := 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) == 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;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_14/quest.txt b/legacy/Data/ingsw/0922_14/quest.txt
new file mode 100644
index 0000000..6afe072
--- /dev/null
+++ b/legacy/Data/ingsw/0922_14/quest.txt
@@ -0,0 +1,2 @@
+img=https://i.imgur.com/512MuK3.png
+Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_14/wrong 1.txt b/legacy/Data/ingsw/0922_14/wrong 1.txt
new file mode 100644
index 0000000..fd1c3a4
--- /dev/null
+++ b/legacy/Data/ingsw/0922_14/wrong 1.txt	
@@ -0,0 +1,71 @@
+
+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) == 1) then x := 2;
+
+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 := 0;
+
+elseif (pre(x) == 3) and (pre(u) == 2) then x := 4;
+
+elseif (pre(x) == 4) and (pre(u) == 0) 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;
+
diff --git a/legacy/Data/ingsw/0922_14/wrong 2.txt b/legacy/Data/ingsw/0922_14/wrong 2.txt
new file mode 100644
index 0000000..763d3a6
--- /dev/null
+++ b/legacy/Data/ingsw/0922_14/wrong 2.txt	
@@ -0,0 +1,69 @@
+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 := 2;
+
+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 := 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 := 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 := 2;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_15/correct.txt b/legacy/Data/ingsw/0922_15/correct.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0922_15/correct.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_15/quest.txt b/legacy/Data/ingsw/0922_15/quest.txt
new file mode 100644
index 0000000..a64d8e6
--- /dev/null
+++ b/legacy/Data/ingsw/0922_15/quest.txt
@@ -0,0 +1,16 @@
+img=https://i.imgur.com/02dquYj.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: act1 act0 act2 act2 act2 act0 act2 act1 act2 act0 act1 act0 
+
+Test case 3: 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/0922_15/wrong 1.txt b/legacy/Data/ingsw/0922_15/wrong 1.txt
new file mode 100644
index 0000000..7b19605
--- /dev/null
+++ b/legacy/Data/ingsw/0922_15/wrong 1.txt	
@@ -0,0 +1 @@
+75%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_15/wrong 2.txt b/legacy/Data/ingsw/0922_15/wrong 2.txt
new file mode 100644
index 0000000..1e091a3
--- /dev/null
+++ b/legacy/Data/ingsw/0922_15/wrong 2.txt	
@@ -0,0 +1 @@
+90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_16/correct.txt b/legacy/Data/ingsw/0922_16/correct.txt
new file mode 100644
index 0000000..8dd7202
--- /dev/null
+++ b/legacy/Data/ingsw/0922_16/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/Zzrmwyx.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_16/quest.txt b/legacy/Data/ingsw/0922_16/quest.txt
new file mode 100644
index 0000000..df0415d
--- /dev/null
+++ b/legacy/Data/ingsw/0922_16/quest.txt
@@ -0,0 +1,75 @@
+Si consideri il seguente modello Modelica. Quale dei seguenti 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 := 4;
+
+elseif (pre(x) == 0) and (pre(u) == 2) then x := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 1) then x := 2;
+
+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 := 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) == 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 := 1;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_16/wrong 1.txt b/legacy/Data/ingsw/0922_16/wrong 1.txt
new file mode 100644
index 0000000..db7cce6
--- /dev/null
+++ b/legacy/Data/ingsw/0922_16/wrong 1.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/3ANMdkr.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_16/wrong 2.txt b/legacy/Data/ingsw/0922_16/wrong 2.txt
new file mode 100644
index 0000000..f0634e5
--- /dev/null
+++ b/legacy/Data/ingsw/0922_16/wrong 2.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/2RoLmLS.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_17/correct.txt b/legacy/Data/ingsw/0922_17/correct.txt
new file mode 100644
index 0000000..9a7cc7e
--- /dev/null
+++ b/legacy/Data/ingsw/0922_17/correct.txt
@@ -0,0 +1,69 @@
+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) == 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) == 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) == 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 := 2;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_17/quest.txt b/legacy/Data/ingsw/0922_17/quest.txt
new file mode 100644
index 0000000..5ebf9be
--- /dev/null
+++ b/legacy/Data/ingsw/0922_17/quest.txt
@@ -0,0 +1,2 @@
+img=https://i.imgur.com/WSvoelw.png
+Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_17/wrong 1.txt b/legacy/Data/ingsw/0922_17/wrong 1.txt
new file mode 100644
index 0000000..b635e9d
--- /dev/null
+++ b/legacy/Data/ingsw/0922_17/wrong 1.txt	
@@ -0,0 +1,74 @@
+
+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) == 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) == 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 := 1;
+
+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 := 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/0922_17/wrong 2.txt b/legacy/Data/ingsw/0922_17/wrong 2.txt
new file mode 100644
index 0000000..7006918
--- /dev/null
+++ b/legacy/Data/ingsw/0922_17/wrong 2.txt	
@@ -0,0 +1,69 @@
+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 := 0;
+
+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 := 3;
+
+elseif (pre(x) == 2) and (pre(u) == 1) 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 := 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/0922_18/correct.txt b/legacy/Data/ingsw/0922_18/correct.txt
new file mode 100644
index 0000000..9667516
--- /dev/null
+++ b/legacy/Data/ingsw/0922_18/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/WRn8QOi.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_18/quest.txt b/legacy/Data/ingsw/0922_18/quest.txt
new file mode 100644
index 0000000..3d86edf
--- /dev/null
+++ b/legacy/Data/ingsw/0922_18/quest.txt
@@ -0,0 +1,75 @@
+Si consideri il seguente modello Modelica. Quale dei seguenti 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 := 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 := 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) == 0) then x := 4;
+
+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 := 2;
+
+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/0922_18/wrong 1.txt b/legacy/Data/ingsw/0922_18/wrong 1.txt
new file mode 100644
index 0000000..a9214bc
--- /dev/null
+++ b/legacy/Data/ingsw/0922_18/wrong 1.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/oUj28ho.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_18/wrong 2.txt b/legacy/Data/ingsw/0922_18/wrong 2.txt
new file mode 100644
index 0000000..2a58fb7
--- /dev/null
+++ b/legacy/Data/ingsw/0922_18/wrong 2.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/eVnEYDY.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_3/correct.txt b/legacy/Data/ingsw/0922_3/correct.txt
new file mode 100644
index 0000000..faa122e
--- /dev/null
+++ b/legacy/Data/ingsw/0922_3/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/VgLa2I6.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_3/quest.txt b/legacy/Data/ingsw/0922_3/quest.txt
new file mode 100644
index 0000000..7159aee
--- /dev/null
+++ b/legacy/Data/ingsw/0922_3/quest.txt
@@ -0,0 +1,77 @@
+Si consideri il seguente modello Modelica. Quale dei seguenti 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) == 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 := 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 := 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 := 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 := 1;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_3/wrong 1.txt b/legacy/Data/ingsw/0922_3/wrong 1.txt
new file mode 100644
index 0000000..6e77050
--- /dev/null
+++ b/legacy/Data/ingsw/0922_3/wrong 1.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/5MjNRI5.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_3/wrong 2.txt b/legacy/Data/ingsw/0922_3/wrong 2.txt
new file mode 100644
index 0000000..c7e9639
--- /dev/null
+++ b/legacy/Data/ingsw/0922_3/wrong 2.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/ugOv25D.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_4/correct.txt b/legacy/Data/ingsw/0922_4/correct.txt
new file mode 100644
index 0000000..7b19605
--- /dev/null
+++ b/legacy/Data/ingsw/0922_4/correct.txt
@@ -0,0 +1 @@
+75%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_4/quest.txt b/legacy/Data/ingsw/0922_4/quest.txt
new file mode 100644
index 0000000..2eeb93f
--- /dev/null
+++ b/legacy/Data/ingsw/0922_4/quest.txt
@@ -0,0 +1,16 @@
+img=https://i.imgur.com/PkKCYTb.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 act1 act0 act2 act1 act2 act2 act1 act2 act0 act1 act2 act0 act2 act2 act0 act1 act1 act2 act2 act0 act0 act2 act2 act2 act0 act2 act0 act1 act1 act0 act2 act1 act2 act1 act0 act0 act0 act0 act2 act2 act1 act1 act1 act1 act0 
+
+Test case 2: act1 act2 act0 act2 act2 act1 act1 act0 act1 act2 act2 act0 
+
+Test case 3: act1 act1 act2 act0 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/0922_4/wrong 1.txt b/legacy/Data/ingsw/0922_4/wrong 1.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/0922_4/wrong 1.txt	
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_4/wrong 2.txt b/legacy/Data/ingsw/0922_4/wrong 2.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/0922_4/wrong 2.txt	
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_5/correct.txt b/legacy/Data/ingsw/0922_5/correct.txt
new file mode 100644
index 0000000..e0afa1b
--- /dev/null
+++ b/legacy/Data/ingsw/0922_5/correct.txt
@@ -0,0 +1,67 @@
+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) == 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 := 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) == 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/0922_5/quest.txt b/legacy/Data/ingsw/0922_5/quest.txt
new file mode 100644
index 0000000..6cbb6d3
--- /dev/null
+++ b/legacy/Data/ingsw/0922_5/quest.txt
@@ -0,0 +1,2 @@
+img=https://i.imgur.com/XthureL.png
+Quale dei seguenti modelli Modelica rappresenta lo state diagram in figura ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_5/wrong 1.txt b/legacy/Data/ingsw/0922_5/wrong 1.txt
new file mode 100644
index 0000000..53db382
--- /dev/null
+++ b/legacy/Data/ingsw/0922_5/wrong 1.txt	
@@ -0,0 +1,69 @@
+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 := 3;
+
+elseif (pre(x) == 1) and (pre(u) == 0) then x := 3;
+
+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 := 4;
+
+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 := 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/0922_5/wrong 2.txt b/legacy/Data/ingsw/0922_5/wrong 2.txt
new file mode 100644
index 0000000..11f8d0b
--- /dev/null
+++ b/legacy/Data/ingsw/0922_5/wrong 2.txt	
@@ -0,0 +1,71 @@
+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 := 2;
+
+elseif (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 := 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) == 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) == 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 := 0;
+
+else x := pre(x); // default
+
+end if;
+
+
+
+end when;
+
+end FSA;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_6/correct.txt b/legacy/Data/ingsw/0922_6/correct.txt
new file mode 100644
index 0000000..d494d0a
--- /dev/null
+++ b/legacy/Data/ingsw/0922_6/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/2GmgSsg.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_6/quest.txt b/legacy/Data/ingsw/0922_6/quest.txt
new file mode 100644
index 0000000..daf5598
--- /dev/null
+++ b/legacy/Data/ingsw/0922_6/quest.txt
@@ -0,0 +1,73 @@
+Si consideri il seguente modello Modelica. Quale dei seguenti 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 := 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) == 2) and (pre(u) == 0) then x := 4;
+
+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 := 0;
+
+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) == 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/0922_6/wrong 1.txt b/legacy/Data/ingsw/0922_6/wrong 1.txt
new file mode 100644
index 0000000..2a0dce8
--- /dev/null
+++ b/legacy/Data/ingsw/0922_6/wrong 1.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/vB4iDg8.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_6/wrong 2.txt b/legacy/Data/ingsw/0922_6/wrong 2.txt
new file mode 100644
index 0000000..e4e9137
--- /dev/null
+++ b/legacy/Data/ingsw/0922_6/wrong 2.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/5Mtuh64.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_7/correct.txt b/legacy/Data/ingsw/0922_7/correct.txt
new file mode 100644
index 0000000..fae4f5e
--- /dev/null
+++ b/legacy/Data/ingsw/0922_7/correct.txt
@@ -0,0 +1 @@
+State coverage: 85%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_7/quest.txt b/legacy/Data/ingsw/0922_7/quest.txt
new file mode 100644
index 0000000..d94d7c9
--- /dev/null
+++ b/legacy/Data/ingsw/0922_7/quest.txt
@@ -0,0 +1,15 @@
+img=https://i.imgur.com/YoZA1G0.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 act1 act2 act1 act1 act0 act1 act2 act0 act1 act2 act1 act2 act1 act0 act0 act2 act2 act0 act1 act1 act2 act2 act2 act0 act1 act2 act2 act1 
+
+Test case 2: act1 act2 act0 act0 act2 act2 act2 act2 act2 act1 act2 act0 act0 act2 act1 act2 act2 act2 act0 act0 act2 act1 act2 act2 act2 act0 act0 act1 
+
+Test case 3: 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/0922_7/wrong 1.txt b/legacy/Data/ingsw/0922_7/wrong 1.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0922_7/wrong 1.txt	
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_7/wrong 2.txt b/legacy/Data/ingsw/0922_7/wrong 2.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/0922_7/wrong 2.txt	
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_8/correct.txt b/legacy/Data/ingsw/0922_8/correct.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0922_8/correct.txt
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_8/quest.txt b/legacy/Data/ingsw/0922_8/quest.txt
new file mode 100644
index 0000000..983cffc
--- /dev/null
+++ b/legacy/Data/ingsw/0922_8/quest.txt
@@ -0,0 +1,18 @@
+img=https://i.imgur.com/PqUZdeV.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: act0 act0 act1 act1 act0 act1 act2 act0 act0 act1 act1 act2 act1 act2 act0 act0 act0 act2 
+
+
+Test case 3: act2 act0 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/0922_8/wrong 1.txt b/legacy/Data/ingsw/0922_8/wrong 1.txt
new file mode 100644
index 0000000..973ef63
--- /dev/null
+++ b/legacy/Data/ingsw/0922_8/wrong 1.txt	
@@ -0,0 +1 @@
+State coverage: 75%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_8/wrong 2.txt b/legacy/Data/ingsw/0922_8/wrong 2.txt
new file mode 100644
index 0000000..f6a4b07
--- /dev/null
+++ b/legacy/Data/ingsw/0922_8/wrong 2.txt	
@@ -0,0 +1 @@
+State coverage: 90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_9/correct.txt b/legacy/Data/ingsw/0922_9/correct.txt
new file mode 100644
index 0000000..973ef63
--- /dev/null
+++ b/legacy/Data/ingsw/0922_9/correct.txt
@@ -0,0 +1 @@
+State coverage: 75%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_9/quest.txt b/legacy/Data/ingsw/0922_9/quest.txt
new file mode 100644
index 0000000..13fde42
--- /dev/null
+++ b/legacy/Data/ingsw/0922_9/quest.txt
@@ -0,0 +1,17 @@
+img=https://i.imgur.com/dIi2Wn7.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 act2 act1 act2 act0 act2 act0 act0 act0 act0 act0 act2 
+
+Test case 2: act1 act2 act1 act2 act0 act2 act1 act2 act2 
+
+Test case 3: 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/0922_9/wrong 1.txt b/legacy/Data/ingsw/0922_9/wrong 1.txt
new file mode 100644
index 0000000..f6a4b07
--- /dev/null
+++ b/legacy/Data/ingsw/0922_9/wrong 1.txt	
@@ -0,0 +1 @@
+State coverage: 90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/0922_9/wrong 2.txt b/legacy/Data/ingsw/0922_9/wrong 2.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/0922_9/wrong 2.txt	
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/10/correct.txt b/legacy/Data/ingsw/10/correct.txt
new file mode 100644
index 0000000..00cf334
--- /dev/null
+++ b/legacy/Data/ingsw/10/correct.txt
@@ -0,0 +1 @@
+La risposta corretta è: La variabile x è nell'intervallo [1, 4] oppure nell'intervallo [15, 20]
\ No newline at end of file
diff --git a/legacy/Data/ingsw/10/quest.txt b/legacy/Data/ingsw/10/quest.txt
new file mode 100644
index 0000000..6befac6
--- /dev/null
+++ b/legacy/Data/ingsw/10/quest.txt
@@ -0,0 +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>
+
+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/legacy/Data/ingsw/10/wrong 2.txt
new file mode 100644
index 0000000..fe0ce72
--- /dev/null
+++ b/legacy/Data/ingsw/10/wrong 2.txt	
@@ -0,0 +1 @@
+La variabile x è fuori dall'intervallo [1, 4] e fuori dall'intervallo [15, 20]
\ No newline at end of file
diff --git a/legacy/Data/ingsw/10/wrong.txt b/legacy/Data/ingsw/10/wrong.txt
new file mode 100644
index 0000000..5303e44
--- /dev/null
+++ b/legacy/Data/ingsw/10/wrong.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [1, 4] e fuori dall'intervallo [15, 20]
\ No newline at end of file
diff --git a/legacy/Data/ingsw/11/correct.txt b/legacy/Data/ingsw/11/correct.txt
new file mode 100644
index 0000000..c24cae9
--- /dev/null
+++ b/legacy/Data/ingsw/11/correct.txt
@@ -0,0 +1 @@
+A*(2 + p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/11/quest.txt b/legacy/Data/ingsw/11/quest.txt
new file mode 100644
index 0000000..77a393f
--- /dev/null
+++ b/legacy/Data/ingsw/11/quest.txt
@@ -0,0 +1,4 @@
+Si consideri un software costituito da due fasi F1 ed F2 ciascuna di costo A. Con probabilità p la fase F1 deve essere ripetuta (a
+causa di change requests) e con probabilità (1 - p) si passa alla fase F2 e poi al completamento (End) dello sviluppo. Qual'eè il
+costo atteso per lo sviluppo del software seguendo il processo sopra descritto ?
+Scegli un'alternativa:
diff --git a/legacy/Data/ingsw/11/wrong 2.txt b/legacy/Data/ingsw/11/wrong 2.txt
new file mode 100644
index 0000000..6e771e9
--- /dev/null
+++ b/legacy/Data/ingsw/11/wrong 2.txt	
@@ -0,0 +1 @@
+A*(1 + p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/11/wrong.txt b/legacy/Data/ingsw/11/wrong.txt
new file mode 100644
index 0000000..a9b1c29
--- /dev/null
+++ b/legacy/Data/ingsw/11/wrong.txt
@@ -0,0 +1 @@
+3*A*p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_1/correct.txt b/legacy/Data/ingsw/1122_1/correct.txt
new file mode 100644
index 0000000..fd39f0f
--- /dev/null
+++ b/legacy/Data/ingsw/1122_1/correct.txt
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_1/quest.txt b/legacy/Data/ingsw/1122_1/quest.txt
new file mode 100644
index 0000000..df0e6be
--- /dev/null
+++ b/legacy/Data/ingsw/1122_1/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/1122_1/wrong 1.txt
new file mode 100644
index 0000000..febcca9
--- /dev/null
+++ b/legacy/Data/ingsw/1122_1/wrong 1.txt	
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_1/wrong 2.txt b/legacy/Data/ingsw/1122_1/wrong 2.txt
new file mode 100644
index 0000000..94279c9
--- /dev/null
+++ b/legacy/Data/ingsw/1122_1/wrong 2.txt	
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_10/correct.txt b/legacy/Data/ingsw/1122_10/correct.txt
new file mode 100644
index 0000000..e940faa
--- /dev/null
+++ b/legacy/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/ingsw/1122_10/quest.txt b/legacy/Data/ingsw/1122_10/quest.txt
new file mode 100644
index 0000000..c939cfb
--- /dev/null
+++ b/legacy/Data/ingsw/1122_10/quest.txt
@@ -0,0 +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) { ..... }
+
+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/1122_10/wrong 1.txt b/legacy/Data/ingsw/1122_10/wrong 1.txt
new file mode 100644
index 0000000..03abba5
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_10/wrong 2.txt b/legacy/Data/ingsw/1122_10/wrong 2.txt
new file mode 100644
index 0000000..a820d7a
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_11/correct.txt
new file mode 100644
index 0000000..e74b1fc
--- /dev/null
+++ b/legacy/Data/ingsw/1122_11/correct.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x > y) then (z == x) else (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_11/quest.txt b/legacy/Data/ingsw/1122_11/quest.txt
new file mode 100644
index 0000000..b46cb2b
--- /dev/null
+++ b/legacy/Data/ingsw/1122_11/quest.txt
@@ -0,0 +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);
+
+}
+
+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/legacy/Data/ingsw/1122_11/wrong 1.txt
new file mode 100644
index 0000000..d63544a
--- /dev/null
+++ b/legacy/Data/ingsw/1122_11/wrong 1.txt	
@@ -0,0 +1 @@
+F(x, y, z) = if (x > y) then (z == x + 1) else (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_11/wrong 2.txt b/legacy/Data/ingsw/1122_11/wrong 2.txt
new file mode 100644
index 0000000..1753a91
--- /dev/null
+++ b/legacy/Data/ingsw/1122_11/wrong 2.txt	
@@ -0,0 +1 @@
+F(x, y, z) = (z == y + 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_12/correct.txt b/legacy/Data/ingsw/1122_12/correct.txt
new file mode 100644
index 0000000..95722a4
--- /dev/null
+++ b/legacy/Data/ingsw/1122_12/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/toYPiWs.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_12/quest.txt b/legacy/Data/ingsw/1122_12/quest.txt
new file mode 100644
index 0000000..464b817
--- /dev/null
+++ b/legacy/Data/ingsw/1122_12/quest.txt
@@ -0,0 +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;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_12/wrong 1.txt b/legacy/Data/ingsw/1122_12/wrong 1.txt
new file mode 100644
index 0000000..c737a86
--- /dev/null
+++ b/legacy/Data/ingsw/1122_12/wrong 1.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/0yWuing.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_12/wrong 2.txt b/legacy/Data/ingsw/1122_12/wrong 2.txt
new file mode 100644
index 0000000..27b6d1e
--- /dev/null
+++ b/legacy/Data/ingsw/1122_12/wrong 2.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/AmIbYTU.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_13/correct.txt b/legacy/Data/ingsw/1122_13/correct.txt
new file mode 100644
index 0000000..f2bb2d0
--- /dev/null
+++ b/legacy/Data/ingsw/1122_13/correct.txt
@@ -0,0 +1 @@
+0.12
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_13/quest.txt b/legacy/Data/ingsw/1122_13/quest.txt
new file mode 100644
index 0000000..d57a552
--- /dev/null
+++ b/legacy/Data/ingsw/1122_13/quest.txt
@@ -0,0 +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.
+
+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/legacy/Data/ingsw/1122_13/wrong 1.txt
new file mode 100644
index 0000000..b7bbee2
--- /dev/null
+++ b/legacy/Data/ingsw/1122_13/wrong 1.txt	
@@ -0,0 +1 @@
+0.32
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_13/wrong 2.txt b/legacy/Data/ingsw/1122_13/wrong 2.txt
new file mode 100644
index 0000000..2a47a95
--- /dev/null
+++ b/legacy/Data/ingsw/1122_13/wrong 2.txt	
@@ -0,0 +1 @@
+0.08
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_14/correct.txt b/legacy/Data/ingsw/1122_14/correct.txt
new file mode 100644
index 0000000..97f2744
--- /dev/null
+++ b/legacy/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/ingsw/1122_14/quest.txt b/legacy/Data/ingsw/1122_14/quest.txt
new file mode 100644
index 0000000..bd20578
--- /dev/null
+++ b/legacy/Data/ingsw/1122_14/quest.txt
@@ -0,0 +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.
+
+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/legacy/Data/ingsw/1122_14/wrong 1.txt
new file mode 100644
index 0000000..189d31d
--- /dev/null
+++ b/legacy/Data/ingsw/1122_14/wrong 1.txt	
@@ -0,0 +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>
\ 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
new file mode 100644
index 0000000..4a9e2c8
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_15/correct.txt
new file mode 100644
index 0000000..1a8a508
--- /dev/null
+++ b/legacy/Data/ingsw/1122_15/correct.txt
@@ -0,0 +1 @@
+State coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_15/quest.txt b/legacy/Data/ingsw/1122_15/quest.txt
new file mode 100644
index 0000000..0d7fe08
--- /dev/null
+++ b/legacy/Data/ingsw/1122_15/quest.txt
@@ -0,0 +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
+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/legacy/Data/ingsw/1122_15/wrong 1.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/1122_15/wrong 1.txt	
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_15/wrong 2.txt b/legacy/Data/ingsw/1122_15/wrong 2.txt
new file mode 100644
index 0000000..973ef63
--- /dev/null
+++ b/legacy/Data/ingsw/1122_15/wrong 2.txt	
@@ -0,0 +1 @@
+State coverage: 75%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_16/correct.txt b/legacy/Data/ingsw/1122_16/correct.txt
new file mode 100644
index 0000000..7a6c6b9
--- /dev/null
+++ b/legacy/Data/ingsw/1122_16/correct.txt
@@ -0,0 +1 @@
+300000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_16/quest.txt b/legacy/Data/ingsw/1122_16/quest.txt
new file mode 100644
index 0000000..db10798
--- /dev/null
+++ b/legacy/Data/ingsw/1122_16/quest.txt
@@ -0,0 +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))
+
+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/legacy/Data/ingsw/1122_16/wrong 1.txt
new file mode 100644
index 0000000..2df501e
--- /dev/null
+++ b/legacy/Data/ingsw/1122_16/wrong 1.txt	
@@ -0,0 +1 @@
+500000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_16/wrong 2.txt b/legacy/Data/ingsw/1122_16/wrong 2.txt
new file mode 100644
index 0000000..997967b
--- /dev/null
+++ b/legacy/Data/ingsw/1122_16/wrong 2.txt	
@@ -0,0 +1 @@
+700000 EUR
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_19/correct.txt b/legacy/Data/ingsw/1122_19/correct.txt
new file mode 100644
index 0000000..e0bba82
--- /dev/null
+++ b/legacy/Data/ingsw/1122_19/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/EDqWXLf.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_19/quest.txt b/legacy/Data/ingsw/1122_19/quest.txt
new file mode 100644
index 0000000..28aa70c
--- /dev/null
+++ b/legacy/Data/ingsw/1122_19/quest.txt
@@ -0,0 +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;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_19/wrong 1.txt b/legacy/Data/ingsw/1122_19/wrong 1.txt
new file mode 100644
index 0000000..75dcbd7
--- /dev/null
+++ b/legacy/Data/ingsw/1122_19/wrong 1.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/u6No1XI.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_19/wrong 2.txt b/legacy/Data/ingsw/1122_19/wrong 2.txt
new file mode 100644
index 0000000..5e5c30f
--- /dev/null
+++ b/legacy/Data/ingsw/1122_19/wrong 2.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/SLOrqrl.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_2/correct.txt b/legacy/Data/ingsw/1122_2/correct.txt
new file mode 100644
index 0000000..ad21063
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_2/quest.txt
new file mode 100644
index 0000000..2ef9a23
--- /dev/null
+++ b/legacy/Data/ingsw/1122_2/quest.txt
@@ -0,0 +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.
+
+Quale dei seguenti monitor meglio descrive il requisito RQ?
\ 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
new file mode 100644
index 0000000..b0c70b4
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_2/wrong 2.txt b/legacy/Data/ingsw/1122_2/wrong 2.txt
new file mode 100644
index 0000000..50c4137
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_20/correct.txt
new file mode 100644
index 0000000..81a4b93
--- /dev/null
+++ b/legacy/Data/ingsw/1122_20/correct.txt
@@ -0,0 +1 @@
+F(x, y, z) = if (x >= 0) then (z == pow(y, x)) else (z == 1)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_20/quest.txt b/legacy/Data/ingsw/1122_20/quest.txt
new file mode 100644
index 0000000..139b0a2
--- /dev/null
+++ b/legacy/Data/ingsw/1122_20/quest.txt
@@ -0,0 +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>
+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/legacy/Data/ingsw/1122_20/wrong 1.txt
new file mode 100644
index 0000000..f52d5ae
--- /dev/null
+++ b/legacy/Data/ingsw/1122_20/wrong 1.txt	
@@ -0,0 +1 @@
+F(x, y, z) = if (x >= 0) then (z == pow(y, x)) else (z == y)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_20/wrong 2.txt b/legacy/Data/ingsw/1122_20/wrong 2.txt
new file mode 100644
index 0000000..d246b94
--- /dev/null
+++ b/legacy/Data/ingsw/1122_20/wrong 2.txt	
@@ -0,0 +1 @@
+F(x, y, z) = if (x >= 0) then (z == pow(y, x)) else (z == 0)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_21/correct.txt b/legacy/Data/ingsw/1122_21/correct.txt
new file mode 100644
index 0000000..e582263
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_21/quest.txt
new file mode 100644
index 0000000..9f10cbc
--- /dev/null
+++ b/legacy/Data/ingsw/1122_21/quest.txt
@@ -0,0 +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]
+
+Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ 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
new file mode 100644
index 0000000..93791b3
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_21/wrong 2.txt b/legacy/Data/ingsw/1122_21/wrong 2.txt
new file mode 100644
index 0000000..826c225
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_22/correct.txt
new file mode 100644
index 0000000..b110af1
--- /dev/null
+++ b/legacy/Data/ingsw/1122_22/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 40%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_22/quest.txt b/legacy/Data/ingsw/1122_22/quest.txt
new file mode 100644
index 0000000..a116140
--- /dev/null
+++ b/legacy/Data/ingsw/1122_22/quest.txt
@@ -0,0 +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 
+
+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/legacy/Data/ingsw/1122_22/wrong 1.txt
new file mode 100644
index 0000000..cf27703
--- /dev/null
+++ b/legacy/Data/ingsw/1122_22/wrong 1.txt	
@@ -0,0 +1 @@
+Transition coverage: 70%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_22/wrong 2.txt b/legacy/Data/ingsw/1122_22/wrong 2.txt
new file mode 100644
index 0000000..eb5e1cd
--- /dev/null
+++ b/legacy/Data/ingsw/1122_22/wrong 2.txt	
@@ -0,0 +1 @@
+Transition coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_23/correct.txt b/legacy/Data/ingsw/1122_23/correct.txt
new file mode 100644
index 0000000..37bad47
--- /dev/null
+++ b/legacy/Data/ingsw/1122_23/correct.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [0, 5]
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_23/quest.txt b/legacy/Data/ingsw/1122_23/quest.txt
new file mode 100644
index 0000000..63f2e9f
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_23/wrong 1.txt
new file mode 100644
index 0000000..6fa1af9
--- /dev/null
+++ b/legacy/Data/ingsw/1122_23/wrong 1.txt	
@@ -0,0 +1 @@
+La variabile x è fuori dall'intervallo [0, 5]
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_23/wrong 2.txt b/legacy/Data/ingsw/1122_23/wrong 2.txt
new file mode 100644
index 0000000..b383e07
--- /dev/null
+++ b/legacy/Data/ingsw/1122_23/wrong 2.txt	
@@ -0,0 +1 @@
+La variable x è minore di 0
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_24/correct.txt b/legacy/Data/ingsw/1122_24/correct.txt
new file mode 100644
index 0000000..293ebbc
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_24/quest.txt
new file mode 100644
index 0000000..0accc5f
--- /dev/null
+++ b/legacy/Data/ingsw/1122_24/quest.txt
@@ -0,0 +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.
+
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ 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
new file mode 100644
index 0000000..835a5ac
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_24/wrong 2.txt b/legacy/Data/ingsw/1122_24/wrong 2.txt
new file mode 100644
index 0000000..5a7d171
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_25/correct.txt
new file mode 100644
index 0000000..b9f32a6
--- /dev/null
+++ b/legacy/Data/ingsw/1122_25/correct.txt
@@ -0,0 +1 @@
+c(0)/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_25/quest.txt b/legacy/Data/ingsw/1122_25/quest.txt
new file mode 100644
index 0000000..4087608
--- /dev/null
+++ b/legacy/Data/ingsw/1122_25/quest.txt
@@ -0,0 +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).
+
+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/legacy/Data/ingsw/1122_25/wrong 1.txt
new file mode 100644
index 0000000..3143da9
--- /dev/null
+++ b/legacy/Data/ingsw/1122_25/wrong 1.txt	
@@ -0,0 +1 @@
+c(0)/(p*(1 - p))
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_25/wrong 2.txt b/legacy/Data/ingsw/1122_25/wrong 2.txt
new file mode 100644
index 0000000..b9f32a6
--- /dev/null
+++ b/legacy/Data/ingsw/1122_25/wrong 2.txt	
@@ -0,0 +1 @@
+c(0)/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_26/correct.txt b/legacy/Data/ingsw/1122_26/correct.txt
new file mode 100644
index 0000000..2f4c4c9
--- /dev/null
+++ b/legacy/Data/ingsw/1122_26/correct.txt
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=0, y=0}, {x=15, y=0}, {x=9, y=0}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_26/quest.txt b/legacy/Data/ingsw/1122_26/quest.txt
new file mode 100644
index 0000000..514a3fa
--- /dev/null
+++ b/legacy/Data/ingsw/1122_26/quest.txt
@@ -0,0 +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>
+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/legacy/Data/ingsw/1122_26/wrong 1.txt
new file mode 100644
index 0000000..a82e779
--- /dev/null
+++ b/legacy/Data/ingsw/1122_26/wrong 1.txt	
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=2, y=1}, {x=15, y=0}, {x=9, y=0}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_26/wrong 2.txt b/legacy/Data/ingsw/1122_26/wrong 2.txt
new file mode 100644
index 0000000..82d4c38
--- /dev/null
+++ b/legacy/Data/ingsw/1122_26/wrong 2.txt	
@@ -0,0 +1 @@
+Test set: {x=3, y=6}, {x=0, y=0}, {x=15, y=0}, {x=10, y=3}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_27/correct.txt b/legacy/Data/ingsw/1122_27/correct.txt
new file mode 100644
index 0000000..8b0c318
--- /dev/null
+++ b/legacy/Data/ingsw/1122_27/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_27/quest.txt b/legacy/Data/ingsw/1122_27/quest.txt
new file mode 100644
index 0000000..59f8742
--- /dev/null
+++ b/legacy/Data/ingsw/1122_27/quest.txt
@@ -0,0 +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 
+
+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/legacy/Data/ingsw/1122_27/wrong 1.txt
new file mode 100644
index 0000000..2ca9276
--- /dev/null
+++ b/legacy/Data/ingsw/1122_27/wrong 1.txt	
@@ -0,0 +1 @@
+Transition coverage: 35%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_27/wrong 2.txt b/legacy/Data/ingsw/1122_27/wrong 2.txt
new file mode 100644
index 0000000..6da4c51
--- /dev/null
+++ b/legacy/Data/ingsw/1122_27/wrong 2.txt	
@@ -0,0 +1 @@
+Transition coverage: 90%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_28/correct.txt b/legacy/Data/ingsw/1122_28/correct.txt
new file mode 100644
index 0000000..c3fc7c1
--- /dev/null
+++ b/legacy/Data/ingsw/1122_28/correct.txt
@@ -0,0 +1,9 @@
+<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/legacy/Data/ingsw/1122_28/quest.txt
new file mode 100644
index 0000000..733c0cb
--- /dev/null
+++ b/legacy/Data/ingsw/1122_28/quest.txt
@@ -0,0 +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) { ..... }
+
+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/legacy/Data/ingsw/1122_28/wrong 1.txt
new file mode 100644
index 0000000..1b8fa8b
--- /dev/null
+++ b/legacy/Data/ingsw/1122_28/wrong 1.txt	
@@ -0,0 +1,13 @@
+<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/legacy/Data/ingsw/1122_28/wrong 2.txt
new file mode 100644
index 0000000..b0705b4
--- /dev/null
+++ b/legacy/Data/ingsw/1122_28/wrong 2.txt	
@@ -0,0 +1,13 @@
+<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/legacy/Data/ingsw/1122_29/correct.txt
new file mode 100644
index 0000000..2d46409
--- /dev/null
+++ b/legacy/Data/ingsw/1122_29/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/SXM3yWp.png
diff --git a/legacy/Data/ingsw/1122_29/quest.txt b/legacy/Data/ingsw/1122_29/quest.txt
new file mode 100644
index 0000000..52863ce
--- /dev/null
+++ b/legacy/Data/ingsw/1122_29/quest.txt
@@ -0,0 +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;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_29/wrong 1.txt b/legacy/Data/ingsw/1122_29/wrong 1.txt
new file mode 100644
index 0000000..b008b75
--- /dev/null
+++ b/legacy/Data/ingsw/1122_29/wrong 1.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/CeDe2lF.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_29/wrong 2.txt b/legacy/Data/ingsw/1122_29/wrong 2.txt
new file mode 100644
index 0000000..861967c
--- /dev/null
+++ b/legacy/Data/ingsw/1122_29/wrong 2.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/HBR1EoE.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_3/correct.txt b/legacy/Data/ingsw/1122_3/correct.txt
new file mode 100644
index 0000000..6d02149
--- /dev/null
+++ b/legacy/Data/ingsw/1122_3/correct.txt
@@ -0,0 +1 @@
+Requisito funzionale
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_3/quest.txt b/legacy/Data/ingsw/1122_3/quest.txt
new file mode 100644
index 0000000..ddcf7c8
--- /dev/null
+++ b/legacy/Data/ingsw/1122_3/quest.txt
@@ -0,0 +1,3 @@
+"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/legacy/Data/ingsw/1122_3/wrong 1.txt
new file mode 100644
index 0000000..fb5bb3e
--- /dev/null
+++ b/legacy/Data/ingsw/1122_3/wrong 1.txt	
@@ -0,0 +1 @@
+Requisito non-funzionale
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_3/wrong 2.txt b/legacy/Data/ingsw/1122_3/wrong 2.txt
new file mode 100644
index 0000000..2c39a1a
--- /dev/null
+++ b/legacy/Data/ingsw/1122_3/wrong 2.txt	
@@ -0,0 +1 @@
+Requisito di performance
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_30/correct.txt b/legacy/Data/ingsw/1122_30/correct.txt
new file mode 100644
index 0000000..2a2ecea
--- /dev/null
+++ b/legacy/Data/ingsw/1122_30/correct.txt
@@ -0,0 +1 @@
+time(0)/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_30/quest.txt b/legacy/Data/ingsw/1122_30/quest.txt
new file mode 100644
index 0000000..8b8cea7
--- /dev/null
+++ b/legacy/Data/ingsw/1122_30/quest.txt
@@ -0,0 +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).
+
+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/legacy/Data/ingsw/1122_30/wrong 1.txt
new file mode 100644
index 0000000..9927a93
--- /dev/null
+++ b/legacy/Data/ingsw/1122_30/wrong 1.txt	
@@ -0,0 +1 @@
+time(0)/(p*(1 - p))
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_30/wrong 2.txt b/legacy/Data/ingsw/1122_30/wrong 2.txt
new file mode 100644
index 0000000..d68fd15
--- /dev/null
+++ b/legacy/Data/ingsw/1122_30/wrong 2.txt	
@@ -0,0 +1 @@
+time(0)*(1 - p)/p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_31/correct.txt b/legacy/Data/ingsw/1122_31/correct.txt
new file mode 100644
index 0000000..a98afd2
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_31/quest.txt
new file mode 100644
index 0000000..7314e2c
--- /dev/null
+++ b/legacy/Data/ingsw/1122_31/quest.txt
@@ -0,0 +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] .
+
+Quale dei seguenti monitor meglio descrive il requisito RQ1 ?
\ 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
new file mode 100644
index 0000000..8f1589e
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_31/wrong 2.txt b/legacy/Data/ingsw/1122_31/wrong 2.txt
new file mode 100644
index 0000000..8fd5deb
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_33/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/1122_33/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_33/quest.txt b/legacy/Data/ingsw/1122_33/quest.txt
new file mode 100644
index 0000000..6283906
--- /dev/null
+++ b/legacy/Data/ingsw/1122_33/quest.txt
@@ -0,0 +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}.
+
+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/legacy/Data/ingsw/1122_33/wrong 1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/1122_33/wrong 1.txt	
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_33/wrong 2.txt b/legacy/Data/ingsw/1122_33/wrong 2.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/1122_33/wrong 2.txt	
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_34/correct.txt b/legacy/Data/ingsw/1122_34/correct.txt
new file mode 100644
index 0000000..bc5692f
--- /dev/null
+++ b/legacy/Data/ingsw/1122_34/correct.txt
@@ -0,0 +1 @@
+State coverage: 87%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_34/quest.txt b/legacy/Data/ingsw/1122_34/quest.txt
new file mode 100644
index 0000000..09970ee
--- /dev/null
+++ b/legacy/Data/ingsw/1122_34/quest.txt
@@ -0,0 +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
+
+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/legacy/Data/ingsw/1122_34/wrong 1.txt
new file mode 100644
index 0000000..1a8a508
--- /dev/null
+++ b/legacy/Data/ingsw/1122_34/wrong 1.txt	
@@ -0,0 +1 @@
+State coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_34/wrong 2.txt b/legacy/Data/ingsw/1122_34/wrong 2.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/1122_34/wrong 2.txt	
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_35/correct.txt b/legacy/Data/ingsw/1122_35/correct.txt
new file mode 100644
index 0000000..98939be
--- /dev/null
+++ b/legacy/Data/ingsw/1122_35/correct.txt
@@ -0,0 +1 @@
+1/(1 - p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_35/quest.txt b/legacy/Data/ingsw/1122_35/quest.txt
new file mode 100644
index 0000000..215b21b
--- /dev/null
+++ b/legacy/Data/ingsw/1122_35/quest.txt
@@ -0,0 +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.
+
diff --git a/legacy/Data/ingsw/1122_35/wrong 1.txt b/legacy/Data/ingsw/1122_35/wrong 1.txt
new file mode 100644
index 0000000..56ea6ac
--- /dev/null
+++ b/legacy/Data/ingsw/1122_35/wrong 1.txt	
@@ -0,0 +1 @@
+1/(p*(1 - p))
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_35/wrong 2.txt b/legacy/Data/ingsw/1122_35/wrong 2.txt
new file mode 100644
index 0000000..db2276d
--- /dev/null
+++ b/legacy/Data/ingsw/1122_35/wrong 2.txt	
@@ -0,0 +1 @@
+(1 - p)/p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_36/correct.txt b/legacy/Data/ingsw/1122_36/correct.txt
new file mode 100644
index 0000000..a66c9ae
--- /dev/null
+++ b/legacy/Data/ingsw/1122_36/correct.txt
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 5, c = 0), (a=50, b = 3, c = 0)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_36/quest.txt b/legacy/Data/ingsw/1122_36/quest.txt
new file mode 100644
index 0000000..da5d010
--- /dev/null
+++ b/legacy/Data/ingsw/1122_36/quest.txt
@@ -0,0 +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>
+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/legacy/Data/ingsw/1122_36/wrong 1.txt
new file mode 100644
index 0000000..abe0eaa
--- /dev/null
+++ b/legacy/Data/ingsw/1122_36/wrong 1.txt	
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 4, c = 0), (a=200, b = 4, c = 0)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_36/wrong 2.txt b/legacy/Data/ingsw/1122_36/wrong 2.txt
new file mode 100644
index 0000000..ea25d73
--- /dev/null
+++ b/legacy/Data/ingsw/1122_36/wrong 2.txt	
@@ -0,0 +1 @@
+(a=200, b = 0, c = 1), (a=50, b = 5, c = 0), (a=50, b = 0, c = 5)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_37/correct.txt b/legacy/Data/ingsw/1122_37/correct.txt
new file mode 100644
index 0000000..deba1f5
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_37/quest.txt
new file mode 100644
index 0000000..843e4e9
--- /dev/null
+++ b/legacy/Data/ingsw/1122_37/quest.txt
@@ -0,0 +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.
+
+Quale dei seguenti monitor meglio descrive il requisito RQ ?
\ 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
new file mode 100644
index 0000000..6a0d3e9
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_37/wrong 2.txt b/legacy/Data/ingsw/1122_37/wrong 2.txt
new file mode 100644
index 0000000..f2a9214
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_38/correct.txt
new file mode 100644
index 0000000..a7029bc
--- /dev/null
+++ b/legacy/Data/ingsw/1122_38/correct.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [1, 4] oppure nell'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_38/quest.txt b/legacy/Data/ingsw/1122_38/quest.txt
new file mode 100644
index 0000000..24d3f68
--- /dev/null
+++ b/legacy/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/legacy/Data/ingsw/1122_38/wrong 1.txt
new file mode 100644
index 0000000..710b111
--- /dev/null
+++ b/legacy/Data/ingsw/1122_38/wrong 1.txt	
@@ -0,0 +1 @@
+La variabile x è fuori dall'intervallo [1, 4] e fuori dall'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_38/wrong 2.txt b/legacy/Data/ingsw/1122_38/wrong 2.txt
new file mode 100644
index 0000000..a82929b
--- /dev/null
+++ b/legacy/Data/ingsw/1122_38/wrong 2.txt	
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [1, 4] e fuori dall'intervallo [15, 20].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_39/correct.txt b/legacy/Data/ingsw/1122_39/correct.txt
new file mode 100644
index 0000000..8bec3c6
--- /dev/null
+++ b/legacy/Data/ingsw/1122_39/correct.txt
@@ -0,0 +1 @@
+{x=1, y=1}, {x=0, y=0}, {x=2, y=1}, {x=2, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_39/quest.txt b/legacy/Data/ingsw/1122_39/quest.txt
new file mode 100644
index 0000000..5826af4
--- /dev/null
+++ b/legacy/Data/ingsw/1122_39/quest.txt
@@ -0,0 +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>
+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/legacy/Data/ingsw/1122_39/wrong 1.txt
new file mode 100644
index 0000000..08bfca1
--- /dev/null
+++ b/legacy/Data/ingsw/1122_39/wrong 1.txt	
@@ -0,0 +1 @@
+{x=1, y=1}, {x=0, y=0}, {x=2, y=1}, {x=2, y=3}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_39/wrong 2.txt b/legacy/Data/ingsw/1122_39/wrong 2.txt
new file mode 100644
index 0000000..256a361
--- /dev/null
+++ b/legacy/Data/ingsw/1122_39/wrong 2.txt	
@@ -0,0 +1 @@
+{x=1, y=1}, {x=2, y=2}, {x=2, y=1}, {x=2, y=0}.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_4/correct.txt b/legacy/Data/ingsw/1122_4/correct.txt
new file mode 100644
index 0000000..9ddc3d7
--- /dev/null
+++ b/legacy/Data/ingsw/1122_4/correct.txt
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_4/quest.txt b/legacy/Data/ingsw/1122_4/quest.txt
new file mode 100644
index 0000000..4181719
--- /dev/null
+++ b/legacy/Data/ingsw/1122_4/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/1122_4/wrong 1.txt
new file mode 100644
index 0000000..c92e243
--- /dev/null
+++ b/legacy/Data/ingsw/1122_4/wrong 1.txt	
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_4/wrong 2.txt b/legacy/Data/ingsw/1122_4/wrong 2.txt
new file mode 100644
index 0000000..ef7bdb0
--- /dev/null
+++ b/legacy/Data/ingsw/1122_4/wrong 2.txt	
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_40/correct.txt b/legacy/Data/ingsw/1122_40/correct.txt
new file mode 100644
index 0000000..6b560cf
--- /dev/null
+++ b/legacy/Data/ingsw/1122_40/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 25%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_40/quest.txt b/legacy/Data/ingsw/1122_40/quest.txt
new file mode 100644
index 0000000..62c01e2
--- /dev/null
+++ b/legacy/Data/ingsw/1122_40/quest.txt
@@ -0,0 +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 
+
+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/legacy/Data/ingsw/1122_40/wrong 1.txt
new file mode 100644
index 0000000..d4b5815
--- /dev/null
+++ b/legacy/Data/ingsw/1122_40/wrong 1.txt	
@@ -0,0 +1 @@
+Transition coverage: 75%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_40/wrong 2.txt b/legacy/Data/ingsw/1122_40/wrong 2.txt
new file mode 100644
index 0000000..8b0c318
--- /dev/null
+++ b/legacy/Data/ingsw/1122_40/wrong 2.txt	
@@ -0,0 +1 @@
+Transition coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_42/correct.txt b/legacy/Data/ingsw/1122_42/correct.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/1122_42/correct.txt
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_42/quest.txt b/legacy/Data/ingsw/1122_42/quest.txt
new file mode 100644
index 0000000..67b07dc
--- /dev/null
+++ b/legacy/Data/ingsw/1122_42/quest.txt
@@ -0,0 +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}.
+
+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/legacy/Data/ingsw/1122_42/wrong 1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/1122_42/wrong 1.txt	
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_42/wrong 2.txt b/legacy/Data/ingsw/1122_42/wrong 2.txt
new file mode 100644
index 0000000..23e721f
--- /dev/null
+++ b/legacy/Data/ingsw/1122_42/wrong 2.txt	
@@ -0,0 +1 @@
+50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_43/correct.txt b/legacy/Data/ingsw/1122_43/correct.txt
new file mode 100644
index 0000000..bc5692f
--- /dev/null
+++ b/legacy/Data/ingsw/1122_43/correct.txt
@@ -0,0 +1 @@
+State coverage: 87%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_43/quest.txt b/legacy/Data/ingsw/1122_43/quest.txt
new file mode 100644
index 0000000..1045bb8
--- /dev/null
+++ b/legacy/Data/ingsw/1122_43/quest.txt
@@ -0,0 +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 
+
+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/legacy/Data/ingsw/1122_43/wrong 1.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/1122_43/wrong 1.txt	
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_43/wrong 2.txt b/legacy/Data/ingsw/1122_43/wrong 2.txt
new file mode 100644
index 0000000..1a8a508
--- /dev/null
+++ b/legacy/Data/ingsw/1122_43/wrong 2.txt	
@@ -0,0 +1 @@
+State coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_44/correct.txt b/legacy/Data/ingsw/1122_44/correct.txt
new file mode 100644
index 0000000..2fd674f
--- /dev/null
+++ b/legacy/Data/ingsw/1122_44/correct.txt
@@ -0,0 +1 @@
+60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_44/quest.txt b/legacy/Data/ingsw/1122_44/quest.txt
new file mode 100644
index 0000000..6428a0e
--- /dev/null
+++ b/legacy/Data/ingsw/1122_44/quest.txt
@@ -0,0 +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}
+
+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/legacy/Data/ingsw/1122_44/wrong 1.txt
new file mode 100644
index 0000000..a2507e5
--- /dev/null
+++ b/legacy/Data/ingsw/1122_44/wrong 1.txt	
@@ -0,0 +1 @@
+80%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_44/wrong 2.txt b/legacy/Data/ingsw/1122_44/wrong 2.txt
new file mode 100644
index 0000000..95bc750
--- /dev/null
+++ b/legacy/Data/ingsw/1122_44/wrong 2.txt	
@@ -0,0 +1 @@
+100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_45/correct.txt b/legacy/Data/ingsw/1122_45/correct.txt
new file mode 100644
index 0000000..3fb437d
--- /dev/null
+++ b/legacy/Data/ingsw/1122_45/correct.txt
@@ -0,0 +1 @@
+0.56
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_45/quest.txt b/legacy/Data/ingsw/1122_45/quest.txt
new file mode 100644
index 0000000..1454704
--- /dev/null
+++ b/legacy/Data/ingsw/1122_45/quest.txt
@@ -0,0 +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.
+
+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/legacy/Data/ingsw/1122_45/wrong 1.txt
new file mode 100644
index 0000000..fc54e00
--- /dev/null
+++ b/legacy/Data/ingsw/1122_45/wrong 1.txt	
@@ -0,0 +1 @@
+0.24
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_45/wrong 2.txt b/legacy/Data/ingsw/1122_45/wrong 2.txt
new file mode 100644
index 0000000..c64601b
--- /dev/null
+++ b/legacy/Data/ingsw/1122_45/wrong 2.txt	
@@ -0,0 +1 @@
+0.14
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_46/correct.txt b/legacy/Data/ingsw/1122_46/correct.txt
new file mode 100644
index 0000000..973ef63
--- /dev/null
+++ b/legacy/Data/ingsw/1122_46/correct.txt
@@ -0,0 +1 @@
+State coverage: 75%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_46/quest.txt b/legacy/Data/ingsw/1122_46/quest.txt
new file mode 100644
index 0000000..5bf1a08
--- /dev/null
+++ b/legacy/Data/ingsw/1122_46/quest.txt
@@ -0,0 +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 
+
+
+
+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/legacy/Data/ingsw/1122_46/wrong 1.txt
new file mode 100644
index 0000000..d4625fd
--- /dev/null
+++ b/legacy/Data/ingsw/1122_46/wrong 1.txt	
@@ -0,0 +1 @@
+State coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_46/wrong 2.txt b/legacy/Data/ingsw/1122_46/wrong 2.txt
new file mode 100644
index 0000000..4e45af2
--- /dev/null
+++ b/legacy/Data/ingsw/1122_46/wrong 2.txt	
@@ -0,0 +1 @@
+State coverage: 60%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_47/correct.txt b/legacy/Data/ingsw/1122_47/correct.txt
new file mode 100644
index 0000000..475d1ef
--- /dev/null
+++ b/legacy/Data/ingsw/1122_47/correct.txt
@@ -0,0 +1 @@
+{x = -150, x = -40,  x = 0, x = 200, x = 600}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_47/quest.txt b/legacy/Data/ingsw/1122_47/quest.txt
new file mode 100644
index 0000000..3631f63
--- /dev/null
+++ b/legacy/Data/ingsw/1122_47/quest.txt
@@ -0,0 +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)}
+
+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/legacy/Data/ingsw/1122_47/wrong 1.txt
new file mode 100644
index 0000000..0aaedb8
--- /dev/null
+++ b/legacy/Data/ingsw/1122_47/wrong 1.txt	
@@ -0,0 +1 @@
+{x = -200, x = -50,  x = 0, x = 100, x = 500}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_47/wrong 2.txt b/legacy/Data/ingsw/1122_47/wrong 2.txt
new file mode 100644
index 0000000..a6df32d
--- /dev/null
+++ b/legacy/Data/ingsw/1122_47/wrong 2.txt	
@@ -0,0 +1 @@
+{x = -200, x = -150,  x = 0, x = 100, x = 700}
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_48/correct.txt b/legacy/Data/ingsw/1122_48/correct.txt
new file mode 100644
index 0000000..f293f3e
--- /dev/null
+++ b/legacy/Data/ingsw/1122_48/correct.txt
@@ -0,0 +1 @@
+(a = 6, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 0)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_48/quest.txt b/legacy/Data/ingsw/1122_48/quest.txt
new file mode 100644
index 0000000..4fc3c18
--- /dev/null
+++ b/legacy/Data/ingsw/1122_48/quest.txt
@@ -0,0 +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 ?
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_48/wrong 1.txt b/legacy/Data/ingsw/1122_48/wrong 1.txt
new file mode 100644
index 0000000..fc010a3
--- /dev/null
+++ b/legacy/Data/ingsw/1122_48/wrong 1.txt	
@@ -0,0 +1 @@
+(a = 5, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 0)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_48/wrong 2.txt b/legacy/Data/ingsw/1122_48/wrong 2.txt
new file mode 100644
index 0000000..eafabb1
--- /dev/null
+++ b/legacy/Data/ingsw/1122_48/wrong 2.txt	
@@ -0,0 +1 @@
+(a = 6, b = 0, c = 1), (a = 0, b = 5, c = 0), (a = 0, b = 3, c = 2)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_49/correct.txt b/legacy/Data/ingsw/1122_49/correct.txt
new file mode 100644
index 0000000..d4b5815
--- /dev/null
+++ b/legacy/Data/ingsw/1122_49/correct.txt
@@ -0,0 +1 @@
+Transition coverage: 75%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_49/quest.txt b/legacy/Data/ingsw/1122_49/quest.txt
new file mode 100644
index 0000000..e591c8c
--- /dev/null
+++ b/legacy/Data/ingsw/1122_49/quest.txt
@@ -0,0 +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
+
+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/legacy/Data/ingsw/1122_49/wrong 1.txt
new file mode 100644
index 0000000..eb5e1cd
--- /dev/null
+++ b/legacy/Data/ingsw/1122_49/wrong 1.txt	
@@ -0,0 +1 @@
+Transition coverage: 100%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_49/wrong 2.txt b/legacy/Data/ingsw/1122_49/wrong 2.txt
new file mode 100644
index 0000000..8b0c318
--- /dev/null
+++ b/legacy/Data/ingsw/1122_49/wrong 2.txt	
@@ -0,0 +1 @@
+Transition coverage: 50%
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_5/correct.txt b/legacy/Data/ingsw/1122_5/correct.txt
new file mode 100644
index 0000000..f64e200
--- /dev/null
+++ b/legacy/Data/ingsw/1122_5/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/t6Yscfv.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_5/quest.txt b/legacy/Data/ingsw/1122_5/quest.txt
new file mode 100644
index 0000000..bcbb3d8
--- /dev/null
+++ b/legacy/Data/ingsw/1122_5/quest.txt
@@ -0,0 +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;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_5/wrong 1.txt b/legacy/Data/ingsw/1122_5/wrong 1.txt
new file mode 100644
index 0000000..03aeaee
--- /dev/null
+++ b/legacy/Data/ingsw/1122_5/wrong 1.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/AZ8nnvv.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_5/wrong 2.txt b/legacy/Data/ingsw/1122_5/wrong 2.txt
new file mode 100644
index 0000000..ade29f4
--- /dev/null
+++ b/legacy/Data/ingsw/1122_5/wrong 2.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/flqJ7iy.png
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_50/correct.txt b/legacy/Data/ingsw/1122_50/correct.txt
new file mode 100644
index 0000000..7470aaf
--- /dev/null
+++ b/legacy/Data/ingsw/1122_50/correct.txt
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_50/quest.txt b/legacy/Data/ingsw/1122_50/quest.txt
new file mode 100644
index 0000000..971e607
--- /dev/null
+++ b/legacy/Data/ingsw/1122_50/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/1122_50/wrong 1.txt
new file mode 100644
index 0000000..e77e043
--- /dev/null
+++ b/legacy/Data/ingsw/1122_50/wrong 1.txt	
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_50/wrong 2.txt b/legacy/Data/ingsw/1122_50/wrong 2.txt
new file mode 100644
index 0000000..03c4dea
--- /dev/null
+++ b/legacy/Data/ingsw/1122_50/wrong 2.txt	
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_6/correct.txt b/legacy/Data/ingsw/1122_6/correct.txt
new file mode 100644
index 0000000..cf8581f
--- /dev/null
+++ b/legacy/Data/ingsw/1122_6/correct.txt
@@ -0,0 +1 @@
+Costruire un prototipo, metterlo in esercizio ed accertarsi che i porti i benefici attesi
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_6/quest.txt b/legacy/Data/ingsw/1122_6/quest.txt
new file mode 100644
index 0000000..b17d629
--- /dev/null
+++ b/legacy/Data/ingsw/1122_6/quest.txt
@@ -0,0 +1 @@
+Quali delle seguenti attività può contribuire a validare i requisiti di un sistema?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_6/wrong 1.txt b/legacy/Data/ingsw/1122_6/wrong 1.txt
new file mode 100644
index 0000000..2cddbca
--- /dev/null
+++ b/legacy/Data/ingsw/1122_6/wrong 1.txt	
@@ -0,0 +1 @@
+Costruire un prototipo e valutarne attentamente le performance
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_6/wrong 2.txt b/legacy/Data/ingsw/1122_6/wrong 2.txt
new file mode 100644
index 0000000..04f8a5e
--- /dev/null
+++ b/legacy/Data/ingsw/1122_6/wrong 2.txt	
@@ -0,0 +1 @@
+Costruire un prototipo e testarlo a fondo per evidenziare subito errori di implementazione
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_7/correct.txt b/legacy/Data/ingsw/1122_7/correct.txt
new file mode 100644
index 0000000..ce9968f
--- /dev/null
+++ b/legacy/Data/ingsw/1122_7/correct.txt
@@ -0,0 +1 @@
+0.28
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_7/quest.txt b/legacy/Data/ingsw/1122_7/quest.txt
new file mode 100644
index 0000000..8db1ade
--- /dev/null
+++ b/legacy/Data/ingsw/1122_7/quest.txt
@@ -0,0 +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.
+
+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/legacy/Data/ingsw/1122_7/wrong 1.txt
new file mode 100644
index 0000000..e8f9017
--- /dev/null
+++ b/legacy/Data/ingsw/1122_7/wrong 1.txt	
@@ -0,0 +1 @@
+0.42
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_7/wrong 2.txt b/legacy/Data/ingsw/1122_7/wrong 2.txt
new file mode 100644
index 0000000..f2bb2d0
--- /dev/null
+++ b/legacy/Data/ingsw/1122_7/wrong 2.txt	
@@ -0,0 +1 @@
+0.12
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_8/correct.txt b/legacy/Data/ingsw/1122_8/correct.txt
new file mode 100644
index 0000000..1c7da8c
--- /dev/null
+++ b/legacy/Data/ingsw/1122_8/correct.txt
@@ -0,0 +1 @@
+0.03
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_8/quest.txt b/legacy/Data/ingsw/1122_8/quest.txt
new file mode 100644
index 0000000..1f66143
--- /dev/null
+++ b/legacy/Data/ingsw/1122_8/quest.txt
@@ -0,0 +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.
+
+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/legacy/Data/ingsw/1122_8/wrong 1.txt
new file mode 100644
index 0000000..7eb6830
--- /dev/null
+++ b/legacy/Data/ingsw/1122_8/wrong 1.txt	
@@ -0,0 +1 @@
+0.27
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_8/wrong 2.txt b/legacy/Data/ingsw/1122_8/wrong 2.txt
new file mode 100644
index 0000000..8a346b7
--- /dev/null
+++ b/legacy/Data/ingsw/1122_8/wrong 2.txt	
@@ -0,0 +1 @@
+0.07
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_9/correct.txt b/legacy/Data/ingsw/1122_9/correct.txt
new file mode 100644
index 0000000..a7a3133
--- /dev/null
+++ b/legacy/Data/ingsw/1122_9/correct.txt
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_9/quest.txt b/legacy/Data/ingsw/1122_9/quest.txt
new file mode 100644
index 0000000..0e4c593
--- /dev/null
+++ b/legacy/Data/ingsw/1122_9/quest.txt
@@ -0,0 +1,2 @@
+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/legacy/Data/ingsw/1122_9/wrong 1.txt
new file mode 100644
index 0000000..ea67dd7
--- /dev/null
+++ b/legacy/Data/ingsw/1122_9/wrong 1.txt	
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/1122_9/wrong 2.txt b/legacy/Data/ingsw/1122_9/wrong 2.txt
new file mode 100644
index 0000000..578bb6b
--- /dev/null
+++ b/legacy/Data/ingsw/1122_9/wrong 2.txt	
@@ -0,0 +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>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/12/correct.txt b/legacy/Data/ingsw/12/correct.txt
new file mode 100644
index 0000000..3769c66
--- /dev/null
+++ b/legacy/Data/ingsw/12/correct.txt
@@ -0,0 +1 @@
+Sviluppo plan-driven
\ No newline at end of file
diff --git a/legacy/Data/ingsw/12/quest.txt b/legacy/Data/ingsw/12/quest.txt
new file mode 100644
index 0000000..48d53db
--- /dev/null
+++ b/legacy/Data/ingsw/12/quest.txt
@@ -0,0 +1,2 @@
+Si pianifica di sviluppare un software gestionale per una università. Considerando che questo può essere considerato un
+sistema mission-critical, quali dei seguenti modelli di processi software generici è più adatto per lo sviluppo di tale software
\ No newline at end of file
diff --git a/legacy/Data/ingsw/12/wrong 2.txt b/legacy/Data/ingsw/12/wrong 2.txt
new file mode 100644
index 0000000..9d2b250
--- /dev/null
+++ b/legacy/Data/ingsw/12/wrong 2.txt	
@@ -0,0 +1 @@
+Sviluppo Iterativo
\ No newline at end of file
diff --git a/legacy/Data/ingsw/12/wrong.txt b/legacy/Data/ingsw/12/wrong.txt
new file mode 100644
index 0000000..541e265
--- /dev/null
+++ b/legacy/Data/ingsw/12/wrong.txt
@@ -0,0 +1 @@
+Sviluppo Agile
\ No newline at end of file
diff --git a/legacy/Data/ingsw/16/correct.txt b/legacy/Data/ingsw/16/correct.txt
new file mode 100644
index 0000000..445c2fd
--- /dev/null
+++ b/legacy/Data/ingsw/16/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/5gsmkFI.png
diff --git a/legacy/Data/ingsw/16/quest.txt b/legacy/Data/ingsw/16/quest.txt
new file mode 100644
index 0000000..ce9037d
--- /dev/null
+++ b/legacy/Data/ingsw/16/quest.txt
@@ -0,0 +1,3 @@
+img=https://i.imgur.com/sB0yXg9.png
+Lo State Diagram in figura descrive (in modo semplificato) una macchina distributrice di bevande. Quale dei
+seguenti Sequence Diagram è consistente con lo State Diagram in figura ?
diff --git a/legacy/Data/ingsw/16/wrong 2.txt b/legacy/Data/ingsw/16/wrong 2.txt
new file mode 100644
index 0000000..d880802
--- /dev/null
+++ b/legacy/Data/ingsw/16/wrong 2.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/oqO8kfc.png
diff --git a/legacy/Data/ingsw/16/wrong.txt b/legacy/Data/ingsw/16/wrong.txt
new file mode 100644
index 0000000..79ee317
--- /dev/null
+++ b/legacy/Data/ingsw/16/wrong.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/kAJWpZb.png
diff --git a/legacy/Data/ingsw/17/correct.txt b/legacy/Data/ingsw/17/correct.txt
new file mode 100644
index 0000000..5aeccb4
--- /dev/null
+++ b/legacy/Data/ingsw/17/correct.txt
@@ -0,0 +1,25 @@
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Integer x0 = 0;
+parameter Integer xmax = 100;
+OutputInteger x;
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+if (x < xmax)
+then
+if (myrandom() <= 0.9)
+then
+if (myrandom() <= 0.8)
+then
+x := x + 1;
+else
+x := max(0, x - 1);
+end if;
+else
+x := max(0, x - 1);
+end if;
+end if;
+end when;
+end MarkovChain;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/17/quest.txt b/legacy/Data/ingsw/17/quest.txt
new file mode 100644
index 0000000..ff93c6c
--- /dev/null
+++ b/legacy/Data/ingsw/17/quest.txt
@@ -0,0 +1,10 @@
+Un'azienda decide di organizzare il processo di sviluppo di un grosso software in 101 phasi sequenziali, numerate da 0 a 100. La
+phase 0 è quella iniziale. La phase 100 è quella finale in cui lo sviluppo è completato. Tutte le fasi hanno circa la stessa durata.
+Alla fine di ogni fase viene eseguita una batteria di tests. I risultati del testing possono essere:
+a) si può passare alla fase successiva;
+b) bisogna ripetere la fase corrente;
+c) bisogna rivedere il lavoro fatto nella fase precedente (reworking).
+Dai dati storici è noto che la probabilità del caso a) è 0.72, del caso b) è 0.18 e del caso c) è 0.1.
+Allo scopo di stimare attraverso una simulazione MonteCarlo il valore atteso del tempo di completamento del progetto viene
+realizzato un modello Modelica del processo di sviluppo descritto sopra.
+Quale dei seguenti modelli Modelica modella correttamente il processo di sviluppo descritto sopra?
diff --git a/legacy/Data/ingsw/17/wrong 2.txt b/legacy/Data/ingsw/17/wrong 2.txt
new file mode 100644
index 0000000..5ab3880
--- /dev/null
+++ b/legacy/Data/ingsw/17/wrong 2.txt	
@@ -0,0 +1,19 @@
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Integer x0 = 0;
+parameter Integer xmax = 100;
+OutputInteger x;
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+if (x < xmax)
+then
+if (myrandom() <= 0.8)
+then
+if (myrandom() <= 0.9)
+then
+x := x + 1;
+else
+x := max(0, x - 1);
+end if;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/17/wrong.txt b/legacy/Data/ingsw/17/wrong.txt
new file mode 100644
index 0000000..12836de
--- /dev/null
+++ b/legacy/Data/ingsw/17/wrong.txt
@@ -0,0 +1,25 @@
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Integer x0 = 0;
+parameter Integer xmax = 100;
+OutputInteger x;
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+if (x < xmax)
+then
+if (myrandom() <= 0.9)
+then
+if (myrandom() <= 0.72)
+then
+x := x + 1;
+else
+x := max(0, x - 1);
+end if;
+else
+x := max(0, x - 1);
+end if;
+end if;
+end when;
+end MarkovChain;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/19/correct.txt b/legacy/Data/ingsw/19/correct.txt
new file mode 100644
index 0000000..0465ee7
--- /dev/null
+++ b/legacy/Data/ingsw/19/correct.txt
@@ -0,0 +1 @@
+Costruire un modello di simulazione per i principali aspetti dei processi di business dell'azienda e per il sistema software da realizzare e valutare le migliorie apportate dal sistema software ai processi di business dell'azienda mediante simulazione
diff --git a/legacy/Data/ingsw/19/quest.txt b/legacy/Data/ingsw/19/quest.txt
new file mode 100644
index 0000000..b8d789e
--- /dev/null
+++ b/legacy/Data/ingsw/19/quest.txt
@@ -0,0 +1,2 @@
+Una azienda finanziaria desidera costruire un sistema software per ottimizzare i processi di business. Quali delle seguenti
+attività può contribuire a validare i requisiti del sistema ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/19/wrong 2.txt b/legacy/Data/ingsw/19/wrong 2.txt
new file mode 100644
index 0000000..43fd110
--- /dev/null
+++ b/legacy/Data/ingsw/19/wrong 2.txt	
@@ -0,0 +1 @@
+Costruire un prototipo del sistema e valutarne i requisiti non funzionali usando i dati storici dall'azienda
diff --git a/legacy/Data/ingsw/19/wrong.txt b/legacy/Data/ingsw/19/wrong.txt
new file mode 100644
index 0000000..1aa1cd5
--- /dev/null
+++ b/legacy/Data/ingsw/19/wrong.txt
@@ -0,0 +1 @@
+Costruire un prototipo del sistema e testarlo rispetto ai requisiti funzionali usando i dati storici dall'azienda.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/2/correct.txt b/legacy/Data/ingsw/2/correct.txt
new file mode 100644
index 0000000..23cbd0e
--- /dev/null
+++ b/legacy/Data/ingsw/2/correct.txt
@@ -0,0 +1 @@
+6*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/2/quest.txt b/legacy/Data/ingsw/2/quest.txt
new file mode 100644
index 0000000..78e700c
--- /dev/null
+++ b/legacy/Data/ingsw/2/quest.txt
@@ -0,0 +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.
+Scegli un'alternativa:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/2/wrong 2.txt b/legacy/Data/ingsw/2/wrong 2.txt
new file mode 100644
index 0000000..489e74c
--- /dev/null
+++ b/legacy/Data/ingsw/2/wrong 2.txt	
@@ -0,0 +1 @@
+5*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/2/wrong.txt b/legacy/Data/ingsw/2/wrong.txt
new file mode 100644
index 0000000..63ca2eb
--- /dev/null
+++ b/legacy/Data/ingsw/2/wrong.txt
@@ -0,0 +1 @@
+4*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/20/correct.txt b/legacy/Data/ingsw/20/correct.txt
new file mode 100644
index 0000000..375f7c5
--- /dev/null
+++ b/legacy/Data/ingsw/20/correct.txt
@@ -0,0 +1,47 @@
+: block CoffeeMachine
+parameter Real T = 1; // clock
+InputInteger Customer2Machine;
+OutputInteger Machine2Customer;
+/*
+0: nop
+1: enough coins inserted
+2: drink dispensed
+3: done
+*/
+Integer state;
+/*
+0: waiting for coins
+1: waiting for selection
+2: dispensing
+3: refund/change
+*/
+algorithm
+when initial() then
+state := 0;
+Machine2Customer := 0;
+elsewhen sample(0, T) then
+if (pre(state) == 0) and (Customer2Machine == 1)
+then // customer has inserted enough coins
+state := 1;
+Machine2Customer := 1;
+elseif (pre(state) == 1) and (Customer2Machine == 2) // drink selected
+then // drink selected
+state := 2; // dispensing drink
+Machine2Customer := 0;
+elseif (pre(state) == 1) and (Customer2Machine == 3) // cancel transaction
+then // refund
+state := 3; // refund/change
+Machine2Customer := 0;
+elseif (pre(state) == 2) // drink dispensed
+then // drink dispensed
+state := 3;
+Machine2Customer := 2;
+elseif (pre(state) == 3) // refund/change
+then // refund
+state := 0;
+Machine2Customer := 3; // done
+else state := pre(state);
+Machine2Customer := pre(Machine2Customer);
+end if;
+end when;
+end CoffeeMachine;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/20/quest.txt b/legacy/Data/ingsw/20/quest.txt
new file mode 100644
index 0000000..1fb3954
--- /dev/null
+++ b/legacy/Data/ingsw/20/quest.txt
@@ -0,0 +1,3 @@
+img=https://i.imgur.com/Wk63xgA.png
+Lo state diagram in figura descrive (in modo semplificato) una macchina distributrice di bevande. Quale dei seguenti
+modelli Modelica è plausibile per lo state diagram in figura?
diff --git a/legacy/Data/ingsw/20/wrong 2.txt b/legacy/Data/ingsw/20/wrong 2.txt
new file mode 100644
index 0000000..43c9f97
--- /dev/null
+++ b/legacy/Data/ingsw/20/wrong 2.txt	
@@ -0,0 +1,47 @@
+block CoffeeMachine
+parameter Real T = 1; // clock
+InputInteger Customer2Machine;
+OutputInteger Machine2Customer;
+/*
+0: nop
+1: enough coins inserted
+2: drink dispensed
+3: done
+*/
+Integer state;
+/*
+0: waiting for coins
+1: waiting for selection
+2: dispensing
+3: refund/change
+*/
+algorithm
+when initial() then
+state := 0;
+Machine2Customer := 0;
+elsewhen sample(0, T) then
+if (pre(state) == 0) and (Customer2Machine == 1)
+then // customer has inserted enough coins
+state := 1;
+Machine2Customer := 1;
+elseif (pre(state) == 1) and (Customer2Machine == 2) // drink selected
+then // drink selected
+state := 2; // dispensing drink
+Machine2Customer := 0;
+elseif (pre(state) == 1) and (Customer2Machine == 3) // cancel transaction
+then // refund
+state := 3; // refund/change
+Machine2Customer := 0;
+elseif (pre(state) == 2) // drink dispensed
+then // drink dispensed
+state := 0;
+Machine2Customer := 2;
+elseif (pre(state) == 3) // refund/change
+then // refund
+state := 0;
+Machine2Customer := 3; // done
+else state := pre(state);
+Machine2Customer := pre(Machine2Customer);
+end if;
+end when;
+end CoffeeMachine;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/20/wrong.txt b/legacy/Data/ingsw/20/wrong.txt
new file mode 100644
index 0000000..4e53f48
--- /dev/null
+++ b/legacy/Data/ingsw/20/wrong.txt
@@ -0,0 +1,47 @@
+block CoffeeMachine
+parameter Real T = 1; // clock
+InputInteger Customer2Machine;
+OutputInteger Machine2Customer;
+/*
+0: nop
+1: enough coins inserted
+2: drink dispensed
+3: done
+*/
+Integer state;
+/*
+0: waiting for coins
+1: waiting for selection
+2: dispensing
+3: refund/change
+*/
+algorithm
+when initial() then
+state := 0;
+Machine2Customer := 0;
+elsewhen sample(0, T) then
+if (pre(state) == 0) and (Customer2Machine == 1)
+then // customer has inserted enough coins
+state := 1;
+Machine2Customer := 1;
+elseif (pre(state) == 1) and (Customer2Machine == 2) // drink selected
+then // drink selected
+state := 2; // dispensing drink
+Machine2Customer := 0;
+elseif (pre(state) == 1) and (Customer2Machine == 3) // cancel transaction
+then // refund
+state := 0; // refund/change
+Machine2Customer := 0;
+elseif (pre(state) == 2) // drink dispensed
+then // drink dispensed
+state := 3;
+Machine2Customer := 2;
+elseif (pre(state) == 3) // refund/change
+then // refund
+state := 0;
+Machine2Customer := 3; // done
+else state := pre(state);
+Machine2Customer := pre(Machine2Customer);
+end if;
+end when;
+end CoffeeMachine;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/21/correct.txt b/legacy/Data/ingsw/21/correct.txt
new file mode 100644
index 0000000..60eaa92
--- /dev/null
+++ b/legacy/Data/ingsw/21/correct.txt
@@ -0,0 +1 @@
+Una volta selezionato il piatto di mare da preparare, la preparazione del pesce e del contorno procedono in parallelo.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/21/quest.txt b/legacy/Data/ingsw/21/quest.txt
new file mode 100644
index 0000000..7799f39
--- /dev/null
+++ b/legacy/Data/ingsw/21/quest.txt
@@ -0,0 +1,2 @@
+img=https://i.imgur.com/jHN6wRm.png
+Quale delle seguenti frasi è corretta riguardo all'activity diagram in figura ?
diff --git a/legacy/Data/ingsw/21/wrong 2.txt b/legacy/Data/ingsw/21/wrong 2.txt
new file mode 100644
index 0000000..06a3fbf
--- /dev/null
+++ b/legacy/Data/ingsw/21/wrong 2.txt	
@@ -0,0 +1 @@
+Una volta selezionato il piatto di mare da preparare, la preparazione del pesce e del contorno procedono in sequenza.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/21/wrong.txt b/legacy/Data/ingsw/21/wrong.txt
new file mode 100644
index 0000000..3e13d27
--- /dev/null
+++ b/legacy/Data/ingsw/21/wrong.txt
@@ -0,0 +1 @@
+Una volta selezionato il piatto di mare da preparare, la stessa persona prepara prima il pesce e poi il contorno.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/22/correct.txt b/legacy/Data/ingsw/22/correct.txt
new file mode 100644
index 0000000..2d1c2f0
--- /dev/null
+++ b/legacy/Data/ingsw/22/correct.txt
@@ -0,0 +1,23 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Real x0 = 1;
+OutputReal x;
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+if (myrandom() <= 0.9)
+then
+if (myrandom() <= 0.7)
+then
+x := 1.1*x;
+else
+x := 0.9*x;
+end if;
+else
+x := 0.73*x;
+end if;
+end when;
+end MarkovChain;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/22/quest.txt b/legacy/Data/ingsw/22/quest.txt
new file mode 100644
index 0000000..fcc9ac9
--- /dev/null
+++ b/legacy/Data/ingsw/22/quest.txt
@@ -0,0 +1,5 @@
+L'input di un sistema software è costituito da un sensore che ogni unità di tempo (ad esempio, un secondo) invia un numero
+reale. Con probabilità 0.63 il valore inviato in una unità di tempo è maggiore del 10% rispetto quello inviato nell'unità di tempo
+precedente. Con probabilità 0.1 è inferiore del 27% rispetto al valore inviato nell'unità di tempo precedente. Con probabilità 0.27
+è inferiore del 10% rispetto quello inviato nell'unità di tempo precedente.
+Quale dei seguenti modelli Modelica modella correttamente l'environment descritto sopra
\ No newline at end of file
diff --git a/legacy/Data/ingsw/22/wrong 2.txt b/legacy/Data/ingsw/22/wrong 2.txt
new file mode 100644
index 0000000..40720c0
--- /dev/null
+++ b/legacy/Data/ingsw/22/wrong 2.txt	
@@ -0,0 +1,21 @@
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Real x0 = 1;
+OutputReal x;
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+if (myrandom() <= 0.7)
+then
+if (myrandom() <= 0.9)
+then
+x := 1.1*x;
+else
+x := 0.9*x;
+end if;
+else
+x := 0.73*x;
+end if;
+end when;
+end MarkovChain;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/22/wrong.txt b/legacy/Data/ingsw/22/wrong.txt
new file mode 100644
index 0000000..eba6b6d
--- /dev/null
+++ b/legacy/Data/ingsw/22/wrong.txt
@@ -0,0 +1,23 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Real x0 = 1;
+OutputReal x;
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+if (myrandom() <= 0.9)
+then
+if (myrandom() <= 0.7)
+then
+x := 0.9*x;
+else
+x := 01.1*x;
+end if;
+else
+x := 0.73*x;
+end if;
+end when;
+end MarkovChain;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/24/correct.txt b/legacy/Data/ingsw/24/correct.txt
new file mode 100644
index 0000000..c7c83e5
--- /dev/null
+++ b/legacy/Data/ingsw/24/correct.txt
@@ -0,0 +1 @@
+3*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/24/quest.txt b/legacy/Data/ingsw/24/quest.txt
new file mode 100644
index 0000000..1e2f071
--- /dev/null
+++ b/legacy/Data/ingsw/24/quest.txt
@@ -0,0 +1,2 @@
+Si consideri un software sviluppato seguendo un approccio iterativo implementato con tre fasi: F1, F2, F3. Ciascuna fase ha
+costo A. Qual'e' il costo dello sviluppo dell'intero software?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/24/wrong 2.txt b/legacy/Data/ingsw/24/wrong 2.txt
new file mode 100644
index 0000000..ff38c25
--- /dev/null
+++ b/legacy/Data/ingsw/24/wrong 2.txt	
@@ -0,0 +1 @@
+2*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/24/wrong.txt b/legacy/Data/ingsw/24/wrong.txt
new file mode 100644
index 0000000..8c7e5a6
--- /dev/null
+++ b/legacy/Data/ingsw/24/wrong.txt
@@ -0,0 +1 @@
+A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/25/correct.txt b/legacy/Data/ingsw/25/correct.txt
new file mode 100644
index 0000000..1c03108
--- /dev/null
+++ b/legacy/Data/ingsw/25/correct.txt
@@ -0,0 +1 @@
+Costruire un prototipo, eseguirlo usando dati storici dai log di produzione e valutare la capacità del prototipo di ridurre gli scarti.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/25/quest.txt b/legacy/Data/ingsw/25/quest.txt
new file mode 100644
index 0000000..bf0f99b
--- /dev/null
+++ b/legacy/Data/ingsw/25/quest.txt
@@ -0,0 +1,3 @@
+Una azienda manifatturiera desidera costruire un sistema software per monitorare (attraverso sensori) la produzione al fine di
+ridurre gli scarti. Quali delle seguenti attività contribuisce a validare i requisiti del sistema.
+Scegli un'alternativa:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/25/wrong 2.txt b/legacy/Data/ingsw/25/wrong 2.txt
new file mode 100644
index 0000000..5187be2
--- /dev/null
+++ b/legacy/Data/ingsw/25/wrong 2.txt	
@@ -0,0 +1 @@
+Costruire un prototipo, eseguirlo usando dati storici dai log di produzione e valutarne le performance.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/25/wrong.txt b/legacy/Data/ingsw/25/wrong.txt
new file mode 100644
index 0000000..52330c1
--- /dev/null
+++ b/legacy/Data/ingsw/25/wrong.txt
@@ -0,0 +1 @@
+Costruire un prototipo, eseguirlo usando dati storici dai log di produzione ed identificare errori di implementazione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/26/correct.txt b/legacy/Data/ingsw/26/correct.txt
new file mode 100644
index 0000000..e13eda2
--- /dev/null
+++ b/legacy/Data/ingsw/26/correct.txt
@@ -0,0 +1 @@
+Accertarsi che i requisiti definiscano un sistema che risolve il problema che l'utente pianifica di risolvere.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/26/quest.txt b/legacy/Data/ingsw/26/quest.txt
new file mode 100644
index 0000000..3cb2d1f
--- /dev/null
+++ b/legacy/Data/ingsw/26/quest.txt
@@ -0,0 +1,2 @@
+Quali delle seguenti attività è parte del processo di validazione dei requisiti ?
+Scegli un'alternativa:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/26/wrong 2.txt b/legacy/Data/ingsw/26/wrong 2.txt
new file mode 100644
index 0000000..b24f900
--- /dev/null
+++ b/legacy/Data/ingsw/26/wrong 2.txt	
@@ -0,0 +1 @@
+Accertarsi che il sistema soddisfi i requisiti dati.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/26/wrong.txt b/legacy/Data/ingsw/26/wrong.txt
new file mode 100644
index 0000000..884d6b1
--- /dev/null
+++ b/legacy/Data/ingsw/26/wrong.txt
@@ -0,0 +1 @@
+Accertarsi che l'architettura del sistema soddisfi i requisiti dati.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/32/correct.txt b/legacy/Data/ingsw/32/correct.txt
new file mode 100644
index 0000000..90c1575
--- /dev/null
+++ b/legacy/Data/ingsw/32/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/qKyYHVj.png
diff --git a/legacy/Data/ingsw/32/quest.txt b/legacy/Data/ingsw/32/quest.txt
new file mode 100644
index 0000000..f0c9221
--- /dev/null
+++ b/legacy/Data/ingsw/32/quest.txt
@@ -0,0 +1,3 @@
+Si consideri un software sviluppato seguendo un approccio plan-driven implementato con tre fasi: F1, F2, F3. Dopo ogni fase
+c'e' una probabilità p di dover ripeter la fase precedente ed una probabilità (1 - p) di passare alla fase successiva (sino ad arrivare
+al termine dello sviluppo). Quale delle seguenti catene di Markov modella il processo software descritto sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/32/wrong 2.txt b/legacy/Data/ingsw/32/wrong 2.txt
new file mode 100644
index 0000000..54e368c
--- /dev/null
+++ b/legacy/Data/ingsw/32/wrong 2.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/5I3NjLb.png
diff --git a/legacy/Data/ingsw/32/wrong.txt b/legacy/Data/ingsw/32/wrong.txt
new file mode 100644
index 0000000..c3a4d99
--- /dev/null
+++ b/legacy/Data/ingsw/32/wrong.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/NDNLPgt.png
diff --git a/legacy/Data/ingsw/33/correct.txt b/legacy/Data/ingsw/33/correct.txt
new file mode 100644
index 0000000..ddb0d65
--- /dev/null
+++ b/legacy/Data/ingsw/33/correct.txt
@@ -0,0 +1 @@
+La variabile x è nell'intervallo [0, 5].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/33/quest.txt b/legacy/Data/ingsw/33/quest.txt
new file mode 100644
index 0000000..4ea55e0
--- /dev/null
+++ b/legacy/Data/ingsw/33/quest.txt
@@ -0,0 +1,17 @@
+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/33/wrong 2.txt b/legacy/Data/ingsw/33/wrong 2.txt
new file mode 100644
index 0000000..7c7a691
--- /dev/null
+++ b/legacy/Data/ingsw/33/wrong 2.txt	
@@ -0,0 +1 @@
+La variable x è minore di 0.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/33/wrong.txt b/legacy/Data/ingsw/33/wrong.txt
new file mode 100644
index 0000000..3e05ae7
--- /dev/null
+++ b/legacy/Data/ingsw/33/wrong.txt
@@ -0,0 +1 @@
+La variabile x è fuori dall'intervallo [0, 5].
\ No newline at end of file
diff --git a/legacy/Data/ingsw/34/correct.txt b/legacy/Data/ingsw/34/correct.txt
new file mode 100644
index 0000000..3f7adfb
--- /dev/null
+++ b/legacy/Data/ingsw/34/correct.txt
@@ -0,0 +1,21 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Real x0 = 0;
+OutputReal x;
+Integer countdown;
+algorithm
+when initial() then
+x := x0;
+countdown := 0;
+elsewhen sample(0, 1) then
+if (countdown <= 0)
+then
+countdown := 1 + integer(floor(10*myrandom()));
+x := 1 - pre(x);
+else
+countdown := countdown - 1;
+end if;
+end when;
+end MarkovChain;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/34/quest.txt b/legacy/Data/ingsw/34/quest.txt
new file mode 100644
index 0000000..0ba09fa
--- /dev/null
+++ b/legacy/Data/ingsw/34/quest.txt
@@ -0,0 +1,3 @@
+L'input di un sistema software è costituito da una sequenza di 0 (false) ed 1 (true). Ad uno 0 segue un 1 ed ad un 1 segue uno 0.
+Il tempo tra un valore di input e l'altro è un valore random compreso tra 1 e 10 unità di tempo.
+Quale dei seguenti modelli Modelica modella meglio l'environment descritto sopra.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/34/wrong 2.txt b/legacy/Data/ingsw/34/wrong 2.txt
new file mode 100644
index 0000000..25f1613
--- /dev/null
+++ b/legacy/Data/ingsw/34/wrong 2.txt	
@@ -0,0 +1,22 @@
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Real x0 = 0;
+OutputReal x;
+Integer countdown;
+algorithm
+when initial() then
+x := x0;
+countdown := 0;
+elsewhen sample(0, 10) then
+if (countdown <= 0)
+then
+countdown := 1 + integer(floor(myrandom()));
+x := 1 - pre(x);
+Domanda 35
+Risposta non data
+Punteggio max.: 1,00
+else
+countdown := countdown - 1;
+end if;
+end when;
+end MarkovChain;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/34/wrong.txt b/legacy/Data/ingsw/34/wrong.txt
new file mode 100644
index 0000000..4fb78cc
--- /dev/null
+++ b/legacy/Data/ingsw/34/wrong.txt
@@ -0,0 +1,19 @@
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Real x0 = 0;
+OutputReal x;
+Integer countdown;
+algorithm
+when initial() then
+x := x0;
+countdown := 0;
+elsewhen sample(0, 1) then
+if (countdown >= 0)
+then
+countdown := 1 + integer(floor(10*myrandom()));
+x := 1 - pre(x);
+else
+countdown := countdown - 1;
+end if;
+end when;
+end MarkovChain;
\ No newline at end of file
diff --git a/legacy/Data/ingsw/35/correct.txt b/legacy/Data/ingsw/35/correct.txt
new file mode 100644
index 0000000..3a0f9a1
--- /dev/null
+++ b/legacy/Data/ingsw/35/correct.txt
@@ -0,0 +1 @@
+Stiamo costruendo il sistema giusto ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/35/quest.txt b/legacy/Data/ingsw/35/quest.txt
new file mode 100644
index 0000000..9af583e
--- /dev/null
+++ b/legacy/Data/ingsw/35/quest.txt
@@ -0,0 +1 @@
+La validazione risponde alla seguente domanda:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/35/wrong 2.txt b/legacy/Data/ingsw/35/wrong 2.txt
new file mode 100644
index 0000000..6633b8c
--- /dev/null
+++ b/legacy/Data/ingsw/35/wrong 2.txt	
@@ -0,0 +1 @@
+Sono soddisfatti i requisti funzionali ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/35/wrong.txt b/legacy/Data/ingsw/35/wrong.txt
new file mode 100644
index 0000000..7edd4bc
--- /dev/null
+++ b/legacy/Data/ingsw/35/wrong.txt
@@ -0,0 +1 @@
+Stiamo costruendo il sistema nel modo giusto ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/39/correct.txt b/legacy/Data/ingsw/39/correct.txt
new file mode 100644
index 0000000..634f690
--- /dev/null
+++ b/legacy/Data/ingsw/39/correct.txt
@@ -0,0 +1 @@
+Il performance testing è tipicamente eseguito una volta che il sistema è stato completamento integrato
\ No newline at end of file
diff --git a/legacy/Data/ingsw/39/quest.txt b/legacy/Data/ingsw/39/quest.txt
new file mode 100644
index 0000000..4a711a4
--- /dev/null
+++ b/legacy/Data/ingsw/39/quest.txt
@@ -0,0 +1 @@
+Quale delle seguenti affermazioni è vera riguardo al performance testing?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/39/wrong 2.txt b/legacy/Data/ingsw/39/wrong 2.txt
new file mode 100644
index 0000000..74c1239
--- /dev/null
+++ b/legacy/Data/ingsw/39/wrong 2.txt	
@@ -0,0 +1 @@
+Il performance testing è tipicamente eseguito su un prototipo del sistema
\ No newline at end of file
diff --git a/legacy/Data/ingsw/39/wrong.txt b/legacy/Data/ingsw/39/wrong.txt
new file mode 100644
index 0000000..bd881bc
--- /dev/null
+++ b/legacy/Data/ingsw/39/wrong.txt
@@ -0,0 +1 @@
+Il performance testing è tipicamente eseguito solo sulle componenti del sistema prima dell'integrazione.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/4/correct.txt b/legacy/Data/ingsw/4/correct.txt
new file mode 100644
index 0000000..6e771e9
--- /dev/null
+++ b/legacy/Data/ingsw/4/correct.txt
@@ -0,0 +1 @@
+A*(1 + p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/4/quest.txt b/legacy/Data/ingsw/4/quest.txt
new file mode 100644
index 0000000..07df0c7
--- /dev/null
+++ b/legacy/Data/ingsw/4/quest.txt
@@ -0,0 +1 @@
+Si consideri un software costituito da due fasi F1 ed F2 ciascuna di costo A. Con probabilità p la fase F1 deve essere ripetuta (a causa di change requests) e con probabilità (1 - p) si passa alla fase F2 e poi al completamento (End) dello sviluppo. Qual'è il costo atteso per lo sviluppo del software seguendo il processo sopra descritto?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/4/wrong 2.txt b/legacy/Data/ingsw/4/wrong 2.txt
new file mode 100644
index 0000000..a9b1c29
--- /dev/null
+++ b/legacy/Data/ingsw/4/wrong 2.txt	
@@ -0,0 +1 @@
+3*A*p
\ No newline at end of file
diff --git a/legacy/Data/ingsw/4/wrong.txt b/legacy/Data/ingsw/4/wrong.txt
new file mode 100644
index 0000000..c24cae9
--- /dev/null
+++ b/legacy/Data/ingsw/4/wrong.txt
@@ -0,0 +1 @@
+A*(2 + p)
\ No newline at end of file
diff --git a/legacy/Data/ingsw/43/correct.txt b/legacy/Data/ingsw/43/correct.txt
new file mode 100644
index 0000000..c4cb236
--- /dev/null
+++ b/legacy/Data/ingsw/43/correct.txt
@@ -0,0 +1,15 @@
+<pre>
+lass 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/43/quest.txt b/legacy/Data/ingsw/43/quest.txt
new file mode 100644
index 0000000..71eee89
--- /dev/null
+++ b/legacy/Data/ingsw/43/quest.txt
@@ -0,0 +1,6 @@
+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/43/wrong 2.txt b/legacy/Data/ingsw/43/wrong 2.txt
new file mode 100644
index 0000000..98b6414
--- /dev/null
+++ b/legacy/Data/ingsw/43/wrong 2.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/43/wrong.txt b/legacy/Data/ingsw/43/wrong.txt
new file mode 100644
index 0000000..a4ee4fb
--- /dev/null
+++ b/legacy/Data/ingsw/43/wrong.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/44/correct.txt b/legacy/Data/ingsw/44/correct.txt
new file mode 100644
index 0000000..aa45c64
--- /dev/null
+++ b/legacy/Data/ingsw/44/correct.txt
@@ -0,0 +1,20 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Integer x0 = 0;
+parameter Integer xmax = 100;
+OutputInteger x; // Connector
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+if (x < xmax)
+then
+if (myrandom() <= 0.8)
+then
+x := x + 1;
+end if;
+end if;
+end when;
+end MarkovChain;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/44/quest.txt b/legacy/Data/ingsw/44/quest.txt
new file mode 100644
index 0000000..18bac37
--- /dev/null
+++ b/legacy/Data/ingsw/44/quest.txt
@@ -0,0 +1,8 @@
+Un'azienda decide di organizzare il processo di sviluppo di un grosso software in 101 phasi sequenziali, numerate da 0 a 100. La
+phase 0 è quella iniziale. La phase 100 è quella finale in cui lo sviluppo è completato. Tutte le fasi hanno circa la stessa durata.
+Si decide di realizzare un approccio incrementale in cui, alla fine di ogni fase, si passa alla fase successiva solo nel caso in cui
+tutti i test per la fase vengono superati. In caso contrario bisogna ripetere la phase. Dai dati storici è noto che la probabilità che
+il team di sviluppo passi da una fase a quella successiva è 0.8.
+Allo scopo di stimare attraverso una simulazione MonteCarlo il valore atteso del tempo di completamento del progetto viene
+realizzato un modello Modelica delo processo di sviluppo descritto sopra.
+Quale dei seguenti modelli Modelica modella correttamente il processo di sviluppo descritto sopra?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/44/wrong 2.txt b/legacy/Data/ingsw/44/wrong 2.txt
new file mode 100644
index 0000000..2e82c1c
--- /dev/null
+++ b/legacy/Data/ingsw/44/wrong 2.txt	
@@ -0,0 +1,20 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Integer x0 = 0;
+parameter Integer xmax = 100;
+OutputInteger x; // Connector
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+if (x < xmax)
+then
+if (myrandom() >= 0.8)
+then
+x := x + 1;
+end if;
+end if;
+end when;
+end MarkovChain;
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/44/wrong.txt b/legacy/Data/ingsw/44/wrong.txt
new file mode 100644
index 0000000..75b3383
--- /dev/null
+++ b/legacy/Data/ingsw/44/wrong.txt
@@ -0,0 +1,22 @@
+<pre>
+block MarkovChain
+//external function myrandom() returns a random real number in [0, 1]
+parameter Integer x0 = 0;
+parameter Integer xmax = 100;
+OutputInteger x; // Connector
+algorithm
+when initial() then
+x := x0;
+elsewhen sample(0, 1) then
+if (x < xmax)
+then
+if (myrandom() <= 0.8)
+then
+x := x + 1;
+else
+x := x - 1;
+end if;
+end if;
+end when;
+end MarkovChain
+</pre>
\ No newline at end of file
diff --git a/legacy/Data/ingsw/45/correct.txt b/legacy/Data/ingsw/45/correct.txt
new file mode 100644
index 0000000..19d3060
--- /dev/null
+++ b/legacy/Data/ingsw/45/correct.txt
@@ -0,0 +1 @@
+Layred architecture.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/45/quest.txt b/legacy/Data/ingsw/45/quest.txt
new file mode 100644
index 0000000..e43794a
--- /dev/null
+++ b/legacy/Data/ingsw/45/quest.txt
@@ -0,0 +1,2 @@
+img=https://i.imgur.com/7DG7vhi.png
+Quale pattern architetturale meglio descrive l'architettura in figura ?
diff --git a/legacy/Data/ingsw/45/wrong 2.txt b/legacy/Data/ingsw/45/wrong 2.txt
new file mode 100644
index 0000000..fd0a8b5
--- /dev/null
+++ b/legacy/Data/ingsw/45/wrong 2.txt	
@@ -0,0 +1 @@
+Model View Controller
\ No newline at end of file
diff --git a/legacy/Data/ingsw/45/wrong.txt b/legacy/Data/ingsw/45/wrong.txt
new file mode 100644
index 0000000..9266c1a
--- /dev/null
+++ b/legacy/Data/ingsw/45/wrong.txt
@@ -0,0 +1 @@
+Pipe and filter architecture.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/46/correct.txt b/legacy/Data/ingsw/46/correct.txt
new file mode 100644
index 0000000..4a45407
--- /dev/null
+++ b/legacy/Data/ingsw/46/correct.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/cMy78HJ.png
diff --git a/legacy/Data/ingsw/46/quest.txt b/legacy/Data/ingsw/46/quest.txt
new file mode 100644
index 0000000..20c9a97
--- /dev/null
+++ b/legacy/Data/ingsw/46/quest.txt
@@ -0,0 +1,3 @@
+Si consideri un software sviluppato seguendo un approccio plan-driven implementato con tre fasi: F1, F2, F3. Le "change
+requests" arrivano con probabilità p dopo ciascuna fase e provocano la ripetizione (con relativo costo) di tutte le fasi che
+precedono. Quali delle seguenti catene di Markov modella lo sviluppo software descritto.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/46/wrong 2.txt b/legacy/Data/ingsw/46/wrong 2.txt
new file mode 100644
index 0000000..5b7d09a
--- /dev/null
+++ b/legacy/Data/ingsw/46/wrong 2.txt	
@@ -0,0 +1 @@
+img=https://i.imgur.com/7lOYboM.png
diff --git a/legacy/Data/ingsw/46/wrong.txt b/legacy/Data/ingsw/46/wrong.txt
new file mode 100644
index 0000000..50bd343
--- /dev/null
+++ b/legacy/Data/ingsw/46/wrong.txt
@@ -0,0 +1 @@
+img=https://i.imgur.com/4gXreOh.png
diff --git a/legacy/Data/ingsw/47/correct.txt b/legacy/Data/ingsw/47/correct.txt
new file mode 100644
index 0000000..c8bbd53
--- /dev/null
+++ b/legacy/Data/ingsw/47/correct.txt
@@ -0,0 +1 @@
+Una volta selezionata la bevanda non è possibile cancellare l'operazione
\ No newline at end of file
diff --git a/legacy/Data/ingsw/47/quest.txt b/legacy/Data/ingsw/47/quest.txt
new file mode 100644
index 0000000..193a65f
--- /dev/null
+++ b/legacy/Data/ingsw/47/quest.txt
@@ -0,0 +1,3 @@
+img=https://i.imgur.com/qNh120A.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 ?
diff --git a/legacy/Data/ingsw/47/wrong 2.txt b/legacy/Data/ingsw/47/wrong 2.txt
new file mode 100644
index 0000000..bc8629f
--- /dev/null
+++ b/legacy/Data/ingsw/47/wrong 2.txt	
@@ -0,0 +1 @@
+La macchina non dà resto
\ No newline at end of file
diff --git a/legacy/Data/ingsw/47/wrong.txt b/legacy/Data/ingsw/47/wrong.txt
new file mode 100644
index 0000000..5d317c8
--- /dev/null
+++ b/legacy/Data/ingsw/47/wrong.txt
@@ -0,0 +1 @@
+Una volta inserite monete per due bevande è possibile ottenerle senza reinserire le monete.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/48/correct.txt b/legacy/Data/ingsw/48/correct.txt
new file mode 100644
index 0000000..455d534
--- /dev/null
+++ b/legacy/Data/ingsw/48/correct.txt
@@ -0,0 +1 @@
+Are we building the system right?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/48/quest.txt b/legacy/Data/ingsw/48/quest.txt
new file mode 100644
index 0000000..b7e0b09
--- /dev/null
+++ b/legacy/Data/ingsw/48/quest.txt
@@ -0,0 +1 @@
+Verification answers the following question:
\ No newline at end of file
diff --git a/legacy/Data/ingsw/48/wrong 2.txt b/legacy/Data/ingsw/48/wrong 2.txt
new file mode 100644
index 0000000..87e99c2
--- /dev/null
+++ b/legacy/Data/ingsw/48/wrong 2.txt	
@@ -0,0 +1 @@
+Are we building the right system?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/48/wrong.txt b/legacy/Data/ingsw/48/wrong.txt
new file mode 100644
index 0000000..ddc2301
--- /dev/null
+++ b/legacy/Data/ingsw/48/wrong.txt
@@ -0,0 +1 @@
+Is the system cost reasonable for the intended market ?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/49/correct.txt b/legacy/Data/ingsw/49/correct.txt
new file mode 100644
index 0000000..88f9125
--- /dev/null
+++ b/legacy/Data/ingsw/49/correct.txt
@@ -0,0 +1 @@
+Requisito utente.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/49/quest.txt b/legacy/Data/ingsw/49/quest.txt
new file mode 100644
index 0000000..e544e9e
--- /dev/null
+++ b/legacy/Data/ingsw/49/quest.txt
@@ -0,0 +1 @@
+Si consideri il seguente requisito: "Il sistema fornisce l'elenco dei clienti in ordine alfabetico". Di che tipo di requisito si tratta?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/49/wrong 2.txt b/legacy/Data/ingsw/49/wrong 2.txt
new file mode 100644
index 0000000..6084c49
--- /dev/null
+++ b/legacy/Data/ingsw/49/wrong 2.txt	
@@ -0,0 +1 @@
+Requisito non-funzionale.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/49/wrong.txt b/legacy/Data/ingsw/49/wrong.txt
new file mode 100644
index 0000000..4cae0da
--- /dev/null
+++ b/legacy/Data/ingsw/49/wrong.txt
@@ -0,0 +1 @@
+Requisito di sistema.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/5/correct.txt b/legacy/Data/ingsw/5/correct.txt
new file mode 100644
index 0000000..58964fc
--- /dev/null
+++ b/legacy/Data/ingsw/5/correct.txt
@@ -0,0 +1 @@
+Se ci sono abbastanza monete è sempre possibile ottenere la bevanda selezionata
\ No newline at end of file
diff --git a/legacy/Data/ingsw/5/quest.txt b/legacy/Data/ingsw/5/quest.txt
new file mode 100644
index 0000000..4ce9b89
--- /dev/null
+++ b/legacy/Data/ingsw/5/quest.txt
@@ -0,0 +1,2 @@
+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
diff --git a/legacy/Data/ingsw/5/wrong 2.txt b/legacy/Data/ingsw/5/wrong 2.txt
new file mode 100644
index 0000000..a75a40c
--- /dev/null
+++ b/legacy/Data/ingsw/5/wrong 2.txt	
@@ -0,0 +1 @@
+Una volta inserite le monete bisogna necessariamente consumare almeno una bevanda
\ No newline at end of file
diff --git a/legacy/Data/ingsw/5/wrong.txt b/legacy/Data/ingsw/5/wrong.txt
new file mode 100644
index 0000000..e47f380
--- /dev/null
+++ b/legacy/Data/ingsw/5/wrong.txt
@@ -0,0 +1 @@
+Anche se ci sono abbastanza monete potrebbe non essere possibile ottenere la bevanda selezionata
\ No newline at end of file
diff --git a/legacy/Data/ingsw/50/correct.txt b/legacy/Data/ingsw/50/correct.txt
new file mode 100644
index 0000000..bb086af
--- /dev/null
+++ b/legacy/Data/ingsw/50/correct.txt
@@ -0,0 +1 @@
+l paziente richiede al client una visita con uno specifico medico e, dopo una verifica sul database, riceve conferma dal client della disponibilità o meno del medico richiesto.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/50/quest.txt b/legacy/Data/ingsw/50/quest.txt
new file mode 100644
index 0000000..7816962
--- /dev/null
+++ b/legacy/Data/ingsw/50/quest.txt
@@ -0,0 +1,2 @@
+img=https://i.imgur.com/0OTH4Yw.png
+Quale delle seguenti frasi è corretta riguardo al Sequence Diagram in figura?
diff --git a/legacy/Data/ingsw/50/wrong 2.txt b/legacy/Data/ingsw/50/wrong 2.txt
new file mode 100644
index 0000000..d61601e
--- /dev/null
+++ b/legacy/Data/ingsw/50/wrong 2.txt	
@@ -0,0 +1 @@
+Periodicamente il client comunica ai pazienti le disponibilità dei medici.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/50/wrong.txt b/legacy/Data/ingsw/50/wrong.txt
new file mode 100644
index 0000000..dd9b316
--- /dev/null
+++ b/legacy/Data/ingsw/50/wrong.txt
@@ -0,0 +1 @@
+Il paziente richiede al server una visita con uno specifico medico e, dopo una verifica sul database, riceve conferma dal server della disponibilità o meno del medico richiesto.
\ No newline at end of file
diff --git a/legacy/Data/ingsw/69420/correct.txt b/legacy/Data/ingsw/69420/correct.txt
new file mode 100644
index 0000000..431a7c5
--- /dev/null
+++ b/legacy/Data/ingsw/69420/correct.txt
@@ -0,0 +1,2 @@
+img=https://i.imgur.com/a8kMXoW.png
+Serafina che tagga Sabrina
\ No newline at end of file
diff --git a/legacy/Data/ingsw/69420/quest.txt b/legacy/Data/ingsw/69420/quest.txt
new file mode 100644
index 0000000..8fa4d25
--- /dev/null
+++ b/legacy/Data/ingsw/69420/quest.txt
@@ -0,0 +1 @@
+Chi insegna questo corso?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/69420/wrong 2.txt b/legacy/Data/ingsw/69420/wrong 2.txt
new file mode 100644
index 0000000..670e7eb
--- /dev/null
+++ b/legacy/Data/ingsw/69420/wrong 2.txt	
@@ -0,0 +1,2 @@
+img=https://i.imgur.com/F4evurl.jpg
+Gioele che tagga Sabrina
\ No newline at end of file
diff --git a/legacy/Data/ingsw/69420/wrong 3.txt b/legacy/Data/ingsw/69420/wrong 3.txt
new file mode 100644
index 0000000..673514a
--- /dev/null
+++ b/legacy/Data/ingsw/69420/wrong 3.txt	
@@ -0,0 +1,2 @@
+img=https://i.imgur.com/qyKmPIA.png
+Deco che disegna un Hentai in aula studio
\ No newline at end of file
diff --git a/legacy/Data/ingsw/69420/wrong.txt b/legacy/Data/ingsw/69420/wrong.txt
new file mode 100644
index 0000000..6e6963e
--- /dev/null
+++ b/legacy/Data/ingsw/69420/wrong.txt
@@ -0,0 +1,2 @@
+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/ingsw/8/correct.txt b/legacy/Data/ingsw/8/correct.txt
new file mode 100644
index 0000000..b3843cf
--- /dev/null
+++ b/legacy/Data/ingsw/8/correct.txt
@@ -0,0 +1 @@
+1.5*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/8/quest.txt b/legacy/Data/ingsw/8/quest.txt
new file mode 100644
index 0000000..e4ebc4a
--- /dev/null
+++ b/legacy/Data/ingsw/8/quest.txt
@@ -0,0 +1 @@
+Si consideri un software sviluppato seguendo un approccio plan-driven implementato con due fasi: F1, F2. La fase F1 ha costo A e la fase F2 ha costo il 50% di A. Qual'e' il costo dello sviluppo del software?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/8/wrong 2.txt b/legacy/Data/ingsw/8/wrong 2.txt
new file mode 100644
index 0000000..8c7e5a6
--- /dev/null
+++ b/legacy/Data/ingsw/8/wrong 2.txt	
@@ -0,0 +1 @@
+A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/8/wrong.txt b/legacy/Data/ingsw/8/wrong.txt
new file mode 100644
index 0000000..54d2e91
--- /dev/null
+++ b/legacy/Data/ingsw/8/wrong.txt
@@ -0,0 +1 @@
+0.5*A
\ No newline at end of file
diff --git a/legacy/Data/ingsw/9/correct.txt b/legacy/Data/ingsw/9/correct.txt
new file mode 100644
index 0000000..e86ff88
--- /dev/null
+++ b/legacy/Data/ingsw/9/correct.txt
@@ -0,0 +1 @@
+1/1000
\ No newline at end of file
diff --git a/legacy/Data/ingsw/9/quest.txt b/legacy/Data/ingsw/9/quest.txt
new file mode 100644
index 0000000..7cae29d
--- /dev/null
+++ b/legacy/Data/ingsw/9/quest.txt
@@ -0,0 +1 @@
+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. Si consideri un software il cui costo per la failure è C = 1000000 EUR. Volendo un rischio non superiore a 1000 EUR quale è il valore massimo della probabilità di failure P accettabile?
\ No newline at end of file
diff --git a/legacy/Data/ingsw/9/wrong 2.txt b/legacy/Data/ingsw/9/wrong 2.txt
new file mode 100644
index 0000000..78abc32
--- /dev/null
+++ b/legacy/Data/ingsw/9/wrong 2.txt	
@@ -0,0 +1 @@
+1/100
\ No newline at end of file
diff --git a/legacy/Data/ingsw/9/wrong.txt b/legacy/Data/ingsw/9/wrong.txt
new file mode 100644
index 0000000..bb7060e
--- /dev/null
+++ b/legacy/Data/ingsw/9/wrong.txt
@@ -0,0 +1 @@
+1/10
\ No newline at end of file
diff --git a/legacy/Data/motd.txt b/legacy/Data/motd.txt
new file mode 100644
index 0000000..4451483
--- /dev/null
+++ b/legacy/Data/motd.txt
@@ -0,0 +1,36 @@
+"Benvenuto 👑
+Con questo bot puoi esercitarti con le domande di alcuni esami del corso di Informatica! 🤞.
+
+✅ 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 😢
+
+ℹ️ Sistemi Operativi I si riferisce al corso del prof. Melatti (canale I)
+
+ℹ️ Sistemi Operativi II si riferisce al corso del prof. Casalicchio (canale II)
+
+ℹ️ OGA si riferisce al corso della prof.ssa Castaldo
+
+ℹ️ Ingegneria del Software si riferisce al corso del prof. Tronci
+
+ℹ️ Sicurezza si riferisce al corso tenuto dal prof. Casalicchio. Le domande presenti sono prese dai test ufficiali forniti dagli autori del libro (versione inglese)
+
+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:
+Il bot, per garantire il corretto funzionamento, potrebbe memorizzare il vostro ID utente Telegram in modo permanente.
+Dati sulle risposte date NON vengono in alcun modo memorizzati in modo permanente e persistono in memoria RAM solo durante l'esecuzione di un quiz.
+
+👷‍♀️Per avviare un modulo puoi utilizzare i seguenti comandi:
+/so1 (SO Modulo I)
+/so2 (SO Modulo II)
+/ogas (quiz OGAS)
+/ingsw (Ingegneria del Software)
+/sicurezza (Sicurezza ⚠️)
+
+N.B. I corsi relativi all'Università di Venezia sono stati trasferiti al bot @so_1_unive_bot, mantenuto da @WAPEETY
+
+Per cambiare modulo puoi usare il comando /leave
diff --git a/Dockerfile b/legacy/Dockerfile
similarity index 100%
rename from Dockerfile
rename to legacy/Dockerfile
diff --git a/README.md b/legacy/README.md
similarity index 100%
rename from README.md
rename to legacy/README.md
diff --git a/Utils/check-ingsw-photos.sh b/legacy/Utils/check-ingsw-photos.sh
similarity index 100%
rename from Utils/check-ingsw-photos.sh
rename to legacy/Utils/check-ingsw-photos.sh
diff --git a/Utils/find_duplicates.py b/legacy/Utils/find_duplicates.py
similarity index 100%
rename from Utils/find_duplicates.py
rename to legacy/Utils/find_duplicates.py
diff --git a/Utils/make_questions.py b/legacy/Utils/make_questions.py
similarity index 100%
rename from Utils/make_questions.py
rename to legacy/Utils/make_questions.py
diff --git a/Utils/moodle-scraper/README.md b/legacy/Utils/moodle-scraper/README.md
similarity index 100%
rename from Utils/moodle-scraper/README.md
rename to legacy/Utils/moodle-scraper/README.md
diff --git a/Utils/moodle-scraper/scraper.py b/legacy/Utils/moodle-scraper/scraper.py
similarity index 100%
rename from Utils/moodle-scraper/scraper.py
rename to legacy/Utils/moodle-scraper/scraper.py
diff --git a/docker-compose.yml b/legacy/docker-compose.yml
similarity index 100%
rename from docker-compose.yml
rename to legacy/docker-compose.yml
diff --git a/scripts/docker-compose.yml b/scripts/docker-compose.yml
new file mode 100644
index 0000000..acc5bbf
--- /dev/null
+++ b/scripts/docker-compose.yml
@@ -0,0 +1,15 @@
+# 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"