From 59fa4079408e1d611592d41896bef45709b5e2e5 Mon Sep 17 00:00:00 2001
From: Damian Bregier <dambre@st.amu.edu.pl>
Date: Mon, 31 May 2021 20:09:39 +0200
Subject: [PATCH] ADD: Extra wykresy

---
 Bayes.ipynb | 497 +++++++++++++++++++++++++++++++---------------------
 1 file changed, 298 insertions(+), 199 deletions(-)

diff --git a/Bayes.ipynb b/Bayes.ipynb
index a5bf3ac..81f4260 100644
--- a/Bayes.ipynb
+++ b/Bayes.ipynb
@@ -4,97 +4,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Klasyfikacja za pomocą naiwnej metody bayesowskiej (rozkłady ciągłe)\n",
-    "Zasady zaliczenia: 40 punktów podzielone następująco:\n",
-    "- 10 pkt - prezentacja projektu\n",
-    "- 15 pkt - implementacja, w tym:\n",
-    "- 5 pkt - zgodność z tematem,\n",
-    "- 5 pkt - jakość kodu,\n",
-    "- 5 pkt - poprawność implementacji\n",
-    "- 10 pkt - efekt \"wow\"\n",
-    "- 5 pkt - aktywność wszystkich członków grupy\n",
-    "\n",
-    "Klasyfikacja za pomocą naiwnej metody bayesowskiej (rozkłady ciągłe). Implementacja powinna założyć, że cechy są ciągłe (do wyboru rozkład normalny i jądrowe wygładzenie). Na wejściu oczekiwany jest zbiór, który zawiera p-cech ciągłych, wektor etykiet oraz wektor prawdopodobieństw a priori dla klas. Na wyjściu otrzymujemy prognozowane etykiety oraz prawdopodobieństwa a posteriori. Dodatkową wartością może być wizualizacja obszarów decyzyjnych w przypadku dwóch cech.\n",
-    "\n",
-    "```Termin oddania na Moodle: do 31 maja. Prezentacja projektów 1 czerwca na ćwiczeniach.```"
+    "# Klasyfikacja za pomocą naiwnej metody bayesowskiej (rozkłady ciągłe)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Skład grupy:\n",
+    "- Nowak Ania,\n",
+    "- Łaźna Patrycja,\n",
+    "- Bregier Damian"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting pandas==1.2.4\n",
-      "  Using cached pandas-1.2.4-cp38-cp38-win_amd64.whl (9.3 MB)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas==1.2.4) (2.8.1)\n",
-      "Requirement already satisfied: numpy>=1.16.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas==1.2.4) (1.19.2)\n",
-      "Requirement already satisfied: pytz>=2017.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas==1.2.4) (2020.1)\n",
-      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas==1.2.4) (1.15.0)\n",
-      "Installing collected packages: pandas\n",
-      "  Attempting uninstall: pandas\n",
-      "    Found existing installation: pandas 1.1.3\n",
-      "    Uninstalling pandas-1.1.3:\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ERROR: Could not install packages due to an EnvironmentError: [WinError 5] Odmowa dostępu: 'c:\\\\programdata\\\\anaconda3\\\\lib\\\\site-packages\\\\pandas-1.1.3.dist-info\\\\direct_url.json'\n",
-      "Consider using the `--user` option or check the permissions.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting numpy==1.20.3\n",
-      "  Using cached numpy-1.20.3-cp38-cp38-win_amd64.whl (13.7 MB)\n",
-      "Installing collected packages: numpy\n",
-      "  Attempting uninstall: numpy\n",
-      "    Found existing installation: numpy 1.19.2\n",
-      "    Uninstalling numpy-1.19.2:\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ERROR: Could not install packages due to an EnvironmentError: [WinError 5] Odmowa dostępu: 'c:\\\\programdata\\\\anaconda3\\\\lib\\\\site-packages\\\\numpy-1.19.2.dist-info\\\\direct_url.json'\n",
-      "Consider using the `--user` option or check the permissions.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: sklearn==0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n",
-      "Requirement already satisfied: scikit-learn in c:\\users\\ania\\appdata\\roaming\\python\\python38\\site-packages (from sklearn==0.0) (0.24.2)\n",
-      "Requirement already satisfied: numpy>=1.13.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn==0.0) (1.19.2)\n",
-      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn==0.0) (2.1.0)\n",
-      "Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn==0.0) (0.17.0)\n",
-      "Requirement already satisfied: scipy>=0.19.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn==0.0) (1.5.2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install pandas==1.2.4\n",
-    "!pip install numpy==1.20.3\n",
-    "!pip install sklearn==0.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
    "outputs": [],
    "source": [
+    "#!pip install pandas==1.2.4\n",
+    "#!pip install numpy==1.20.3\n",
+    "#!pip install sklearn==0.0\n",
+    "\n",
     "from sklearn.model_selection import train_test_split\n",
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -110,16 +42,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Wczytywanie i normalizacja danych"
+    "# 0. Podstawowe informacje o zbiorze danych"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "W projekcie wykorzystany został GTZAN Dataset poruszający problem wieloklasowej klasyfikacji danych na przykładzie gatunków muzycznych. Zbiór ten składa się z 10 gatunków obejmujacych: blues, muzykę klasyczną, country, disco, hip-hop, jazz, pop, reggae oraz rock. Każdy ze wspomnianych gatunków jest reprezentowany przez 100 plików audio o długości 30 sekund, a same próbki były zbierane w latach 2000-2001 ze zdyfersyfikowanych źródeł obejmujących: stacje radiowe, prywatne płyty CD oraz nagrania własne.\n",
+    "\n",
+    "Zbiór danych jest niezwykle bogaty i rozbudowany, ponieważ do każdego utworu zostało przypisanych 60 unikalnych parametrów. Parametry te obejmują takie dane jak: długość utworu, etykietę z nazwą gatunku, tempo, harmoniczność, variancję czy częstotliwość melodyczną (MFCC).\n",
+    "\n",
+    "Dokładne dane na temat tego zbioru danych można znaleźć pod adresem: https://www.kaggle.com/andradaolteanu/gtzan-dataset-music-genre-classification\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Wczytywanie i normalizacja danych"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Stałe\n",
+    "# Słownik zawierający 10 gatunków muzycznych, które zostały sparowane z\n",
+    "# odpowiadającymi im wartościami numerycznymi\n",
     "genre_dict = {\n",
     "    \"blues\" : 1,\n",
     "    \"classical\" : 2,\n",
@@ -132,20 +84,21 @@
     "    \"reggae\" : 9,\n",
     "    \"rock\" : 10\n",
     "}\n",
+    "# nazwa pliku w którym umieszczane są parametry po wstępnym przetworzeniu\n",
     "filename = 'music_genre.csv'\n",
     "model_path = 'model.model'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loading prepared data...\n"
+      "Preparing data...\n"
      ]
     },
     {
@@ -180,7 +133,6 @@
        "      <th>spectral_bandwidth_var</th>\n",
        "      <th>rolloff_mean</th>\n",
        "      <th>...</th>\n",
-       "      <th>mfcc16_mean</th>\n",
        "      <th>mfcc16_var</th>\n",
        "      <th>mfcc17_mean</th>\n",
        "      <th>mfcc17_var</th>\n",
@@ -190,6 +142,7 @@
        "      <th>mfcc19_var</th>\n",
        "      <th>mfcc20_mean</th>\n",
        "      <th>mfcc20_var</th>\n",
+       "      <th>label</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -206,7 +159,6 @@
        "      <td>85882.761315</td>\n",
        "      <td>3805.839606</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.752740</td>\n",
        "      <td>52.420910</td>\n",
        "      <td>-1.690215</td>\n",
        "      <td>36.524071</td>\n",
@@ -216,6 +168,7 @@
        "      <td>55.062923</td>\n",
        "      <td>1.221291</td>\n",
        "      <td>46.936035</td>\n",
+       "      <td>blues</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -230,7 +183,6 @@
        "      <td>213843.755497</td>\n",
        "      <td>3550.522098</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.927998</td>\n",
        "      <td>55.356403</td>\n",
        "      <td>-0.731125</td>\n",
        "      <td>60.314529</td>\n",
@@ -240,6 +192,7 @@
        "      <td>51.106190</td>\n",
        "      <td>0.531217</td>\n",
        "      <td>45.786282</td>\n",
+       "      <td>blues</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -254,7 +207,6 @@
        "      <td>76254.192257</td>\n",
        "      <td>3042.260232</td>\n",
        "      <td>...</td>\n",
-       "      <td>2.451690</td>\n",
        "      <td>40.598766</td>\n",
        "      <td>-7.729093</td>\n",
        "      <td>47.639427</td>\n",
@@ -264,6 +216,7 @@
        "      <td>46.639660</td>\n",
        "      <td>-2.231258</td>\n",
        "      <td>30.573025</td>\n",
+       "      <td>blues</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -278,7 +231,6 @@
        "      <td>166441.494769</td>\n",
        "      <td>2184.745799</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.780874</td>\n",
        "      <td>44.427753</td>\n",
        "      <td>-3.319597</td>\n",
        "      <td>50.206673</td>\n",
@@ -288,6 +240,7 @@
        "      <td>37.259739</td>\n",
        "      <td>-3.407448</td>\n",
        "      <td>31.949339</td>\n",
+       "      <td>blues</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -302,7 +255,6 @@
        "      <td>88445.209036</td>\n",
        "      <td>3579.757627</td>\n",
        "      <td>...</td>\n",
-       "      <td>-4.520576</td>\n",
        "      <td>86.099236</td>\n",
        "      <td>-5.454034</td>\n",
        "      <td>75.269707</td>\n",
@@ -312,6 +264,7 @@
        "      <td>62.910812</td>\n",
        "      <td>-11.703234</td>\n",
        "      <td>55.195160</td>\n",
+       "      <td>blues</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -326,7 +279,6 @@
        "      <td>201910.508633</td>\n",
        "      <td>3481.517592</td>\n",
        "      <td>...</td>\n",
-       "      <td>-5.576589</td>\n",
        "      <td>72.549225</td>\n",
        "      <td>-1.838263</td>\n",
        "      <td>68.702026</td>\n",
@@ -336,6 +288,7 @@
        "      <td>39.808784</td>\n",
        "      <td>-8.109991</td>\n",
        "      <td>46.311005</td>\n",
+       "      <td>blues</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -350,7 +303,6 @@
        "      <td>185023.239545</td>\n",
        "      <td>2795.610963</td>\n",
        "      <td>...</td>\n",
-       "      <td>-10.068051</td>\n",
        "      <td>83.248245</td>\n",
        "      <td>-10.913176</td>\n",
        "      <td>56.902153</td>\n",
@@ -360,6 +312,7 @@
        "      <td>48.235741</td>\n",
        "      <td>-6.483466</td>\n",
        "      <td>70.170364</td>\n",
+       "      <td>blues</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -374,7 +327,6 @@
        "      <td>168211.938804</td>\n",
        "      <td>2954.836760</td>\n",
        "      <td>...</td>\n",
-       "      <td>-8.426083</td>\n",
        "      <td>70.438438</td>\n",
        "      <td>-10.568935</td>\n",
        "      <td>52.090893</td>\n",
@@ -384,6 +336,7 @@
        "      <td>65.547516</td>\n",
        "      <td>-8.630722</td>\n",
        "      <td>56.401436</td>\n",
+       "      <td>blues</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -398,7 +351,6 @@
        "      <td>105542.718193</td>\n",
        "      <td>3782.316288</td>\n",
        "      <td>...</td>\n",
-       "      <td>-1.452559</td>\n",
        "      <td>50.563751</td>\n",
        "      <td>-7.041824</td>\n",
        "      <td>28.894934</td>\n",
@@ -408,6 +360,7 @@
        "      <td>33.698597</td>\n",
        "      <td>-2.715692</td>\n",
        "      <td>36.418430</td>\n",
+       "      <td>blues</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -422,7 +375,6 @@
        "      <td>114070.112591</td>\n",
        "      <td>3943.490565</td>\n",
        "      <td>...</td>\n",
-       "      <td>-1.179920</td>\n",
        "      <td>59.314602</td>\n",
        "      <td>-1.916804</td>\n",
        "      <td>58.418438</td>\n",
@@ -432,10 +384,11 @@
        "      <td>77.082222</td>\n",
        "      <td>-4.235203</td>\n",
        "      <td>91.468811</td>\n",
+       "      <td>blues</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>10 rows × 58 columns</p>\n",
+       "<p>10 rows × 59 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -463,50 +416,80 @@
        "8             1719.368948           1.632828e+05              2031.740381   \n",
        "9             1817.150863           2.982361e+05              1973.773306   \n",
        "\n",
-       "   spectral_bandwidth_var  rolloff_mean  ...  mfcc16_mean  mfcc16_var  \\\n",
-       "0            85882.761315   3805.839606  ...     0.752740   52.420910   \n",
-       "1           213843.755497   3550.522098  ...     0.927998   55.356403   \n",
-       "2            76254.192257   3042.260232  ...     2.451690   40.598766   \n",
-       "3           166441.494769   2184.745799  ...     0.780874   44.427753   \n",
-       "4            88445.209036   3579.757627  ...    -4.520576   86.099236   \n",
-       "5           201910.508633   3481.517592  ...    -5.576589   72.549225   \n",
-       "6           185023.239545   2795.610963  ...   -10.068051   83.248245   \n",
-       "7           168211.938804   2954.836760  ...    -8.426083   70.438438   \n",
-       "8           105542.718193   3782.316288  ...    -1.452559   50.563751   \n",
-       "9           114070.112591   3943.490565  ...    -1.179920   59.314602   \n",
+       "   spectral_bandwidth_var  rolloff_mean  ...  mfcc16_var  mfcc17_mean  \\\n",
+       "0            85882.761315   3805.839606  ...   52.420910    -1.690215   \n",
+       "1           213843.755497   3550.522098  ...   55.356403    -0.731125   \n",
+       "2            76254.192257   3042.260232  ...   40.598766    -7.729093   \n",
+       "3           166441.494769   2184.745799  ...   44.427753    -3.319597   \n",
+       "4            88445.209036   3579.757627  ...   86.099236    -5.454034   \n",
+       "5           201910.508633   3481.517592  ...   72.549225    -1.838263   \n",
+       "6           185023.239545   2795.610963  ...   83.248245   -10.913176   \n",
+       "7           168211.938804   2954.836760  ...   70.438438   -10.568935   \n",
+       "8           105542.718193   3782.316288  ...   50.563751    -7.041824   \n",
+       "9           114070.112591   3943.490565  ...   59.314602    -1.916804   \n",
        "\n",
-       "   mfcc17_mean  mfcc17_var  mfcc18_mean  mfcc18_var  mfcc19_mean  mfcc19_var  \\\n",
-       "0    -1.690215   36.524071    -0.408979   41.597103    -2.303523   55.062923   \n",
-       "1    -0.731125   60.314529     0.295073   48.120598    -0.283518   51.106190   \n",
-       "2    -7.729093   47.639427    -1.816407   52.382141    -3.439720   46.639660   \n",
-       "3    -3.319597   50.206673     0.636965   37.319130    -0.619121   37.259739   \n",
-       "4    -5.454034   75.269707    -0.916874   53.613918    -4.404827   62.910812   \n",
-       "5    -1.838263   68.702026    -2.783800   42.447453    -3.047909   39.808784   \n",
-       "6   -10.913176   56.902153    -6.971336   38.231800    -3.436505   48.235741   \n",
-       "7   -10.568935   52.090893   -10.784515   60.461330    -4.690678   65.547516   \n",
-       "8    -7.041824   28.894934     2.695248   36.889568     3.412305   33.698597   \n",
-       "9    -1.916804   58.418438    -2.292661   83.205231     2.881967   77.082222   \n",
+       "   mfcc17_var  mfcc18_mean  mfcc18_var  mfcc19_mean  mfcc19_var  mfcc20_mean  \\\n",
+       "0   36.524071    -0.408979   41.597103    -2.303523   55.062923     1.221291   \n",
+       "1   60.314529     0.295073   48.120598    -0.283518   51.106190     0.531217   \n",
+       "2   47.639427    -1.816407   52.382141    -3.439720   46.639660    -2.231258   \n",
+       "3   50.206673     0.636965   37.319130    -0.619121   37.259739    -3.407448   \n",
+       "4   75.269707    -0.916874   53.613918    -4.404827   62.910812   -11.703234   \n",
+       "5   68.702026    -2.783800   42.447453    -3.047909   39.808784    -8.109991   \n",
+       "6   56.902153    -6.971336   38.231800    -3.436505   48.235741    -6.483466   \n",
+       "7   52.090893   -10.784515   60.461330    -4.690678   65.547516    -8.630722   \n",
+       "8   28.894934     2.695248   36.889568     3.412305   33.698597    -2.715692   \n",
+       "9   58.418438    -2.292661   83.205231     2.881967   77.082222    -4.235203   \n",
        "\n",
-       "   mfcc20_mean  mfcc20_var  \n",
-       "0     1.221291   46.936035  \n",
-       "1     0.531217   45.786282  \n",
-       "2    -2.231258   30.573025  \n",
-       "3    -3.407448   31.949339  \n",
-       "4   -11.703234   55.195160  \n",
-       "5    -8.109991   46.311005  \n",
-       "6    -6.483466   70.170364  \n",
-       "7    -8.630722   56.401436  \n",
-       "8    -2.715692   36.418430  \n",
-       "9    -4.235203   91.468811  \n",
+       "   mfcc20_var  label  \n",
+       "0   46.936035  blues  \n",
+       "1   45.786282  blues  \n",
+       "2   30.573025  blues  \n",
+       "3   31.949339  blues  \n",
+       "4   55.195160  blues  \n",
+       "5   46.311005  blues  \n",
+       "6   70.170364  blues  \n",
+       "7   56.401436  blues  \n",
+       "8   36.418430  blues  \n",
+       "9   91.468811  blues  \n",
        "\n",
-       "[10 rows x 58 columns]"
+       "[10 rows x 59 columns]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Index(['genre', 'chroma_stft_mean', 'chroma_stft_var', 'rms_mean', 'rms_var',\n",
+       "       'spectral_centroid_mean', 'spectral_centroid_var',\n",
+       "       'spectral_bandwidth_mean', 'spectral_bandwidth_var', 'rolloff_mean',\n",
+       "       'rolloff_var', 'zero_crossing_rate_mean', 'zero_crossing_rate_var',\n",
+       "       'harmony_mean', 'harmony_var', 'perceptr_mean', 'perceptr_var', 'tempo',\n",
+       "       'mfcc1_mean', 'mfcc1_var', 'mfcc2_mean', 'mfcc2_var', 'mfcc3_mean',\n",
+       "       'mfcc3_var', 'mfcc4_mean', 'mfcc4_var', 'mfcc5_mean', 'mfcc5_var',\n",
+       "       'mfcc6_mean', 'mfcc6_var', 'mfcc7_mean', 'mfcc7_var', 'mfcc8_mean',\n",
+       "       'mfcc8_var', 'mfcc9_mean', 'mfcc9_var', 'mfcc10_mean', 'mfcc10_var',\n",
+       "       'mfcc11_mean', 'mfcc11_var', 'mfcc12_mean', 'mfcc12_var', 'mfcc13_mean',\n",
+       "       'mfcc13_var', 'mfcc14_mean', 'mfcc14_var', 'mfcc15_mean', 'mfcc15_var',\n",
+       "       'mfcc16_mean', 'mfcc16_var', 'mfcc17_mean', 'mfcc17_var', 'mfcc18_mean',\n",
+       "       'mfcc18_var', 'mfcc19_mean', 'mfcc19_var', 'mfcc20_mean', 'mfcc20_var',\n",
+       "       'label'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
+    "# skrypt ten realizuje dwie podstawowe funkcje\n",
+    "# 1) sprawdza czy plik music_genre.csv istnieje i jeżeli tak to wczytuje go\n",
+    "# 2) w przeciwnym przypadku dokonuje preprocessingu danych w ramach którego\n",
+    "#    gatunki zamieniane są na wartości licznowe, a wartości takie jak nazwa \n",
+    "#    pliku, etykieta czy długość są usuwane\n",
+    " \n",
     "if os.path.isfile(filename):\n",
     "    print(\"Loading prepared data...\")\n",
     "    data = pd.read_csv(filename)\n",
@@ -515,21 +498,23 @@
     "    data = pd.read_csv('music_genre_raw.csv')\n",
     "    column = data[\"label\"].apply(lambda x: genre_dict[x])\n",
     "    data.insert(0, 'genre', column, 'int')\n",
-    "    data = data.drop(columns=['filename', 'label', 'length'])\n",
+    "    data = data.drop(columns=['filename', 'length'])\n",
     "    data.to_csv(filename, index=False)\n",
-    "display(data.head(10))"
+    "display(data.head(10))\n",
+    "\n",
+    "data.columns"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Podział danych na zbiory train i test"
+    "# 2. Podział danych na zbiory: uczący i testowy"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "scrolled": true
    },
@@ -566,7 +551,6 @@
        "      <th>rolloff_mean</th>\n",
        "      <th>rolloff_var</th>\n",
        "      <th>...</th>\n",
-       "      <th>mfcc16_mean</th>\n",
        "      <th>mfcc16_var</th>\n",
        "      <th>mfcc17_mean</th>\n",
        "      <th>mfcc17_var</th>\n",
@@ -576,6 +560,7 @@
        "      <th>mfcc19_var</th>\n",
        "      <th>mfcc20_mean</th>\n",
        "      <th>mfcc20_var</th>\n",
+       "      <th>label</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -592,7 +577,6 @@
        "      <td>6670.863091</td>\n",
        "      <td>3.556853e+05</td>\n",
        "      <td>...</td>\n",
-       "      <td>8.023183</td>\n",
        "      <td>37.339474</td>\n",
        "      <td>-8.121326</td>\n",
        "      <td>33.968277</td>\n",
@@ -602,6 +586,7 @@
        "      <td>35.162354</td>\n",
        "      <td>3.192656</td>\n",
        "      <td>36.478157</td>\n",
+       "      <td>metal</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>500</th>\n",
@@ -616,7 +601,6 @@
        "      <td>2799.283099</td>\n",
        "      <td>2.685679e+06</td>\n",
        "      <td>...</td>\n",
-       "      <td>-1.957420</td>\n",
        "      <td>50.311016</td>\n",
        "      <td>-1.503434</td>\n",
        "      <td>41.141155</td>\n",
@@ -626,6 +610,7 @@
        "      <td>50.006485</td>\n",
        "      <td>-3.353825</td>\n",
        "      <td>49.906403</td>\n",
+       "      <td>jazz</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>332</th>\n",
@@ -640,7 +625,6 @@
        "      <td>4958.057490</td>\n",
        "      <td>2.650020e+06</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.122951</td>\n",
        "      <td>78.892769</td>\n",
        "      <td>-1.054999</td>\n",
        "      <td>79.877068</td>\n",
@@ -650,6 +634,7 @@
        "      <td>75.059898</td>\n",
        "      <td>-5.256925</td>\n",
        "      <td>120.275269</td>\n",
+       "      <td>disco</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>979</th>\n",
@@ -664,7 +649,6 @@
        "      <td>4479.264304</td>\n",
        "      <td>9.787046e+05</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.621152</td>\n",
        "      <td>37.060532</td>\n",
        "      <td>-13.479134</td>\n",
        "      <td>50.848667</td>\n",
@@ -674,6 +658,7 @@
        "      <td>56.781952</td>\n",
        "      <td>1.085497</td>\n",
        "      <td>54.243389</td>\n",
+       "      <td>rock</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>817</th>\n",
@@ -688,7 +673,6 @@
        "      <td>3777.969679</td>\n",
        "      <td>2.632339e+06</td>\n",
        "      <td>...</td>\n",
-       "      <td>3.633915</td>\n",
        "      <td>64.068756</td>\n",
        "      <td>-2.219202</td>\n",
        "      <td>99.249870</td>\n",
@@ -698,6 +682,7 @@
        "      <td>62.661850</td>\n",
        "      <td>-2.923168</td>\n",
        "      <td>67.490440</td>\n",
+       "      <td>reggae</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>620</th>\n",
@@ -712,7 +697,6 @@
        "      <td>5358.261979</td>\n",
        "      <td>5.918222e+05</td>\n",
        "      <td>...</td>\n",
-       "      <td>5.089191</td>\n",
        "      <td>27.937113</td>\n",
        "      <td>-10.676390</td>\n",
        "      <td>26.519361</td>\n",
@@ -722,6 +706,7 @@
        "      <td>24.334734</td>\n",
        "      <td>3.255899</td>\n",
        "      <td>25.199259</td>\n",
+       "      <td>metal</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>814</th>\n",
@@ -736,7 +721,6 @@
        "      <td>3790.901258</td>\n",
        "      <td>4.734865e+06</td>\n",
        "      <td>...</td>\n",
-       "      <td>3.066329</td>\n",
        "      <td>66.090370</td>\n",
        "      <td>-4.590122</td>\n",
        "      <td>72.595345</td>\n",
@@ -746,6 +730,7 @@
        "      <td>50.693245</td>\n",
        "      <td>-3.665569</td>\n",
        "      <td>89.750290</td>\n",
+       "      <td>reggae</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>516</th>\n",
@@ -760,7 +745,6 @@
        "      <td>2822.406728</td>\n",
        "      <td>7.392007e+05</td>\n",
        "      <td>...</td>\n",
-       "      <td>2.887793</td>\n",
        "      <td>109.811813</td>\n",
        "      <td>-0.027696</td>\n",
        "      <td>113.660950</td>\n",
@@ -770,6 +754,7 @@
        "      <td>136.810165</td>\n",
        "      <td>2.935807</td>\n",
        "      <td>95.914490</td>\n",
+       "      <td>jazz</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>518</th>\n",
@@ -784,7 +769,6 @@
        "      <td>4248.194549</td>\n",
        "      <td>3.987029e+05</td>\n",
        "      <td>...</td>\n",
-       "      <td>12.366530</td>\n",
        "      <td>57.230133</td>\n",
        "      <td>-1.110214</td>\n",
        "      <td>48.080849</td>\n",
@@ -794,6 +778,7 @@
        "      <td>55.737625</td>\n",
        "      <td>0.350456</td>\n",
        "      <td>64.126846</td>\n",
+       "      <td>jazz</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>940</th>\n",
@@ -808,7 +793,6 @@
        "      <td>6131.200719</td>\n",
        "      <td>1.788624e+06</td>\n",
        "      <td>...</td>\n",
-       "      <td>-5.717880</td>\n",
        "      <td>42.315434</td>\n",
        "      <td>-3.953057</td>\n",
        "      <td>48.761936</td>\n",
@@ -818,10 +802,11 @@
        "      <td>58.219994</td>\n",
        "      <td>-0.909785</td>\n",
        "      <td>63.111858</td>\n",
+       "      <td>rock</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>10 rows × 57 columns</p>\n",
+       "<p>10 rows × 58 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -849,43 +834,43 @@
        "518             1993.352766           64753.479332              2127.165109   \n",
        "940             3009.958707          435134.775688              2778.049758   \n",
        "\n",
-       "     spectral_bandwidth_var  rolloff_mean   rolloff_var  ...  mfcc16_mean  \\\n",
-       "687            45771.294278   6670.863091  3.556853e+05  ...     8.023183   \n",
-       "500           283554.933422   2799.283099  2.685679e+06  ...    -1.957420   \n",
-       "332           215375.540632   4958.057490  2.650020e+06  ...     0.122951   \n",
-       "979            72155.551685   4479.264304  9.787046e+05  ...    -0.621152   \n",
-       "817           201432.199120   3777.969679  2.632339e+06  ...     3.633915   \n",
-       "620            32730.579626   5358.261979  5.918222e+05  ...     5.089191   \n",
-       "814           358557.016423   3790.901258  4.734865e+06  ...     3.066329   \n",
-       "516            58868.399307   2822.406728  7.392007e+05  ...     2.887793   \n",
-       "518            36027.039069   4248.194549  3.987029e+05  ...    12.366530   \n",
-       "940           135548.871316   6131.200719  1.788624e+06  ...    -5.717880   \n",
+       "     spectral_bandwidth_var  rolloff_mean   rolloff_var  ...  mfcc16_var  \\\n",
+       "687            45771.294278   6670.863091  3.556853e+05  ...   37.339474   \n",
+       "500           283554.933422   2799.283099  2.685679e+06  ...   50.311016   \n",
+       "332           215375.540632   4958.057490  2.650020e+06  ...   78.892769   \n",
+       "979            72155.551685   4479.264304  9.787046e+05  ...   37.060532   \n",
+       "817           201432.199120   3777.969679  2.632339e+06  ...   64.068756   \n",
+       "620            32730.579626   5358.261979  5.918222e+05  ...   27.937113   \n",
+       "814           358557.016423   3790.901258  4.734865e+06  ...   66.090370   \n",
+       "516            58868.399307   2822.406728  7.392007e+05  ...  109.811813   \n",
+       "518            36027.039069   4248.194549  3.987029e+05  ...   57.230133   \n",
+       "940           135548.871316   6131.200719  1.788624e+06  ...   42.315434   \n",
        "\n",
-       "     mfcc16_var  mfcc17_mean  mfcc17_var  mfcc18_mean  mfcc18_var  \\\n",
-       "687   37.339474    -8.121326   33.968277     4.910113   42.063385   \n",
-       "500   50.311016    -1.503434   41.141155     0.221949   55.707256   \n",
-       "332   78.892769    -1.054999   79.877068     4.496278  112.834435   \n",
-       "979   37.060532   -13.479134   50.848667     3.308529   47.726006   \n",
-       "817   64.068756    -2.219202   99.249870     5.304260   64.088127   \n",
-       "620   27.937113   -10.676390   26.519361     3.875155   25.613684   \n",
-       "814   66.090370    -4.590122   72.595345     4.261040   63.185764   \n",
-       "516  109.811813    -0.027696  113.660950     2.098475  160.025497   \n",
-       "518   57.230133    -1.110214   48.080849    -0.784249   57.033504   \n",
-       "940   42.315434    -3.953057   48.761936    -3.092345   49.514446   \n",
+       "     mfcc17_mean  mfcc17_var  mfcc18_mean  mfcc18_var  mfcc19_mean  \\\n",
+       "687    -8.121326   33.968277     4.910113   42.063385    -2.474697   \n",
+       "500    -1.503434   41.141155     0.221949   55.707256    -1.991485   \n",
+       "332    -1.054999   79.877068     4.496278  112.834435    -0.978958   \n",
+       "979   -13.479134   50.848667     3.308529   47.726006    -3.704957   \n",
+       "817    -2.219202   99.249870     5.304260   64.088127    -6.597187   \n",
+       "620   -10.676390   26.519361     3.875155   25.613684    -4.943561   \n",
+       "814    -4.590122   72.595345     4.261040   63.185764    -2.127876   \n",
+       "516    -0.027696  113.660950     2.098475  160.025497     1.109709   \n",
+       "518    -1.110214   48.080849    -0.784249   57.033504    -2.984207   \n",
+       "940    -3.953057   48.761936    -3.092345   49.514446    -2.731183   \n",
        "\n",
-       "     mfcc19_mean  mfcc19_var  mfcc20_mean  mfcc20_var  \n",
-       "687    -2.474697   35.162354     3.192656   36.478157  \n",
-       "500    -1.991485   50.006485    -3.353825   49.906403  \n",
-       "332    -0.978958   75.059898    -5.256925  120.275269  \n",
-       "979    -3.704957   56.781952     1.085497   54.243389  \n",
-       "817    -6.597187   62.661850    -2.923168   67.490440  \n",
-       "620    -4.943561   24.334734     3.255899   25.199259  \n",
-       "814    -2.127876   50.693245    -3.665569   89.750290  \n",
-       "516     1.109709  136.810165     2.935807   95.914490  \n",
-       "518    -2.984207   55.737625     0.350456   64.126846  \n",
-       "940    -2.731183   58.219994    -0.909785   63.111858  \n",
+       "     mfcc19_var  mfcc20_mean  mfcc20_var   label  \n",
+       "687   35.162354     3.192656   36.478157   metal  \n",
+       "500   50.006485    -3.353825   49.906403    jazz  \n",
+       "332   75.059898    -5.256925  120.275269   disco  \n",
+       "979   56.781952     1.085497   54.243389    rock  \n",
+       "817   62.661850    -2.923168   67.490440  reggae  \n",
+       "620   24.334734     3.255899   25.199259   metal  \n",
+       "814   50.693245    -3.665569   89.750290  reggae  \n",
+       "516  136.810165     2.935807   95.914490    jazz  \n",
+       "518   55.737625     0.350456   64.126846    jazz  \n",
+       "940   58.219994    -0.909785   63.111858    rock  \n",
        "\n",
-       "[10 rows x 57 columns]"
+       "[10 rows x 58 columns]"
       ]
      },
      "metadata": {},
@@ -893,7 +878,12 @@
     }
    ],
    "source": [
+    "# Podział ten jest dokonywany w proporcji 80:20, gdzie 80% danych trafia do zbioru uczącego, a 20%\n",
+    "# do zbioru testowego, podejście to jest standardową praktyką w dziedzinie uczenia maszynwego\n",
+    "\n",
+    "# wartość X reprezentuje 57 parametrów opisujących poszczególne utwory\n",
     "X = data.drop([\"genre\"], axis=1)\n",
+    "# wartość Y zawiera kolumnę gatunków wyrażonych przy pomocy wartości liczbowych od 1 do 10\n",
     "Y = data[\"genre\"]\n",
     "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.20, random_state = False)\n",
     "display(X_train.head(10))"
@@ -903,12 +893,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Ilość krotek dla poszczególnych gatunków z podziałem na test/train"
+    "### Ilość krotek dla poszczególnych gatunków z podziałem na zbiory: uczący i testowy"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -929,6 +919,9 @@
     }
    ],
    "source": [
+    "# skrypt odpowiadający za przeiterowanie po słowniku i zliczenie liczebności poszczególnych gatunków\n",
+    "# w ramach podziału na zbiory: uczący i testowy\n",
+    "\n",
     "for key in genre_dict.keys():\n",
     "    count = len(data[data[\"genre\"]==genre_dict[key]])\n",
     "    count_train = len(X_train[Y_train==genre_dict[key]])\n",
@@ -940,7 +933,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Wizualizacja danych"
+    "# 3. Wizualizacja danych"
    ]
   },
   {
@@ -950,14 +943,25 @@
     "### Boxploty dla tempa gatunków"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jedną z najciekawszych i najbardziej intuicyjnych wartości mierzalnych dla poszczególnych utworów jest tempo. Parametr ten został przedstawiony przy pomocy wykresu pudełkowego w odniesieniu do wspomnianych wcześniej 10 gatunków muzycznych.\n",
+    "\n",
+    "Ze zgromadzonych danych jednoznacznie wynika, że najwyższą średnią wartość dla tempa mają utwory z gatunku Reggee, zaś na drugim i trzecim miejscu znajdują się odpowiednio muzyka klasyczna oraz blues. Podczas gdy najniższe wartości mają gatunki hip-hop oraz pop. \n",
+    "\n",
+    "Z kolei największe rozbieżności pomiędzy wartościami zauważalne są w przypadku muzyki klasycznej, country i metalu, chociaż najwięcej obserwacji odstających pojawia się w przypadku hiphopu oraz popu."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1152x648 with 1 Axes>"
       ]
@@ -968,13 +972,41 @@
    ],
    "source": [
     "f, ax = plt.subplots(figsize=(16, 9));\n",
-    "sns.boxplot(x = \"genre\", y = \"tempo\", data = data[[\"genre\", \"tempo\"]], palette = 'pastel');\n",
     "\n",
-    "plt.title('Tempo boxplot for Genres', fontsize = 25)\n",
+    "sns.boxplot(x = \"label\", y = \"mfcc4_mean\", data = data[[\"label\", \"mfcc4_mean\"]], palette = 'pastel');\n",
+    "\n",
+    "plt.title('mfcc_mean4 boxplot for genres', fontsize = 25)\n",
     "plt.xticks(fontsize = 14)\n",
     "plt.yticks(fontsize = 10);\n",
     "plt.xlabel(\"Genre\", fontsize = 15)\n",
-    "plt.ylabel(\"Tempo\", fontsize = 15);"
+    "plt.ylabel(\"mfcc4_mean4\", fontsize = 15);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax = plt.subplots(figsize=(16, 9));\n",
+    "sns.boxplot(x = \"label\", y = \"mfcc4_mean\", data = data[[\"label\", \"mfcc4_mean\"]], palette = 'pastel');\n",
+    "\n",
+    "plt.title('mfcc_mean4 boxplot for genres', fontsize = 25)\n",
+    "plt.xticks(fontsize = 14)\n",
+    "plt.yticks(fontsize = 10);\n",
+    "plt.xlabel(\"Genre\", fontsize = 15)\n",
+    "plt.ylabel(\"mfcc4_mean4\", fontsize = 15);"
    ]
   },
   {
@@ -984,6 +1016,13 @@
     "### Korelacja między cechami średnimi"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "W procesie badania zależności pomiędzy dostępnymi cechami wykorzystana została mapa ciepła, która jednak pokazała, że w wielu przypadkach korelacje nie zachodzą, co jest szczególnie widoczne w przypadku średniej częstotliwości melodycznej cepstrum2 (mfcc2_mean), a jeżeli takowe korelacje zachodza to mają stosunkowo niewielkie wartości.Występowanie zależności widać w górnej oraz środkowej częsci mapy."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
@@ -1024,10 +1063,21 @@
     "### Korelacja między cechami wariancji"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Odwrotna sytuacja ma miejsce w przypadku mapy ciepła dla cech wariancji, w przypadku ktorych korelacja nie zachodzi wyłącznie dla dwóch parametrów czyli harmony i perceptr w środkowej cześci wykresu. Z kolei stosunkow wysokie wartości korelacji można zaobserwować dla parametrów \"skrajnych\", czyli pierwszych i ostatnich na liście parametrów.\n",
+    "\n",
+    "Należy jednak podkreślić, ze takie wartości nie są dobre, ponieważ niewielkie zrożnicowanie odchyleń może mieć niewielki wpływ na znajdowanie korelacji, a co za tym idzie skuteczność modelu!"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -1057,6 +1107,48 @@
     "plt.yticks(fontsize = 10);"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Wykres punktowy przedstawiający zależność pomiędzy krótkotrwałą transformatą Fouriera a częstotliwością melodyczną cepstrum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-10-3d72438c776e>:3: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
+      "  ax1 = fig.add_subplot()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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q9qgzzYrO4mlonz0DncXTKGmcGJeQwCIIIq5IGYfHuqE4Ui9ROB9atEK0uHwWFm0oQ0qhBeAAU4YJWpMW793+NjaVbER9VZ2Cu4kMY7cNqD8OrdsNDsPt0dHwoBFEokItwgSHxq8QY53pa28Ttb6AsWUolvJFReIlUtL+i2VlmxACOlIrCpNb2wGvV7RtpKMWCCLRoQpWAiOMXxHylYTxKx81147uhRFEBOSXLcfsH/wIptw8gONgys3D7B/8aEz4XwRh5GhpBnjeL4yaqrfBmWZFb34uPDodeAAenQ69+blMgaGk/afGyraRWlGYCFELBJHoUAUrgaHxK8R4YawaisMZ9JVmVQW3+aacU4qzrvomkjIy4a0/gr6cLFVWtrHM7nLboyXaUT4EMZGgfw0JDI1fISYKoz2bTgq1DPqB7b8p55Ti/NXfhc5oBCCOd4hViHJaDryHZ25Xk76cLKQ2t4rahCMdtUAQiQ61CBMYqTErNH6FGE8IeVKJaJhWy6Af2P4766pv+sWVgOBfihWWuJLbbu/ch5P7n0HDZz/Dyf3PwN65T9F5nGlWoHiKovYoQUxUSGAlMCunLYdeoxdto/ErxHhDLk9qtFEr8TvQh5aUkcncJ9i/FE1KfEohO/uKtd3euQ9dDVvgcfmO63HZ0NWwRbHIQm4mRS0QhAwksBIYGr9CTAQS2TCtpkE/v2w5LnrtDXj1eub3A/1L0Vb1IsnE6ml8Dzwvfo953o2exvcU3hFBEHKQByvBofErxHhHzjCtHYXrCUZtg76SeIdoU+KFKIaayt2wn+xFSoEFpRULmRENbg/b+C61nSCIyCCBRRDEqCInOFJH8brihZJRMbFU9YRMrHDokQQX+pnbCYKIHRJYBEGMKokym24kCRfvMBIxCPmGs3Bi8N/g4fFv46BFvuEs1c6hBom6wpQgwkECiyCIUUdpntRIU19VJ9tuq3vqSTRuecMXV6DRoHDF1Zh17/0xnzeSlHg5gsWJMyUJRns/NG43eN0UcB4vmjx7MYh+GJCEfN18GArOhTOCc4R7j2JB8KIJ74PW7YblVDNSWlphz81JyJ8ZghAggUUQBMEg3NiZuqeeROObrw+/wOv1fx2ryFKjqscSJ+buHgiJWJzHi0z9FGTop4Lj+ajOEe/RPEwv2tC1C9lhJLKIRIUEFkEQ4xKpGYJKkRs7U1w+y1e5YtC45Q1VqlhKq3pSLTQpcSL6mge8Oi06iqdFdY3h3qNYkfOc0exDItEhgUUQxLhDyXDlcNhPSoydEbYHDTv2I7U9DjBbaEOVHaUxF7HEYYR9j2JEyosmkAhRHgQhBeVgEQQx7lAyXDkcKQUSoZ3Cdo3Er0+p7XFALs5BqSE+FuN82PcoRvpyssBz0mN+aPYhkciQwCIIIiaiSRyPN2rMEAwX2lm44mrm66S2xwO5OIe+nCywB+QME+v8wEiCTaPBmWZFb34uvBwXci80+5BIdEhgEQQRNYk6R1CNGYLF5bOwaEOZb8wM5xs3s2hDmd9bNOve+1F41TXDFSuNBoVXXaOK/0opUhUcr04HZ5pVsvqj1vzAcO+RGjjTrOiYVYzeSXk0+5AYU3A8z4f7I0cWu92O66+/Hs8//zwKCwvx4x//GJ9++inMZjMA4M4770RZWZni43V02OH1xnRJCUl2tgVtbZSQnIjQs4mejPojTI+MR6dDZ5TG6UCifTbBHizAN0Mw2jE38SBWEz4Q6sECfJUdQXyE+34s0L+bxIWezcig0XDIzEyR/H5MDewvvvgCDz30EI4dO+bftn//fvz5z39GTk5OLIcmCGIMkKhzBAWhEquAiQRj02YkH34EGkcjvKZC9E1fD2f+Kua+apjwgfBxDhMxxHWsQUGq45eYBNbmzZuxfv163H+/ryQ+MDCAU6dO4cEHH0RLSwvKyspw5513QjOCpk+CIEaOkUgcjxa1ZwjKYWzaDMvBu8B5BwAAWscJWA7eBQBMkSVnwo/0muXiHNghoBPnwzvRxYvcKtBEuk4iOmJSPpWVlTjnnHP8X7e3t6O0tBSPPvooNm/ejE8++QR//etfY75IgiASE9Yqr4loPk4+/IhfXAlw3gEkH36Eub8aJvxwCCGg9sZegB8OAa2vqlPtHIlMovoDA5FbBUqMfVT9M3Py5Mn4zW9+4//6O9/5Dt544w2sWsUuk7OQ62eOdbKz1Vm6TKgPPZsoybYAqWbg6EnAOQgYDeBOK0BqbqZ6p1Dx2bR+8E80/OkVONs7YMzKRNF3ViPnkotjP7CjkblZ62hkXn9SXh76m5qY29W631ce38MMAf348T248LbzIjtYS4foGeO0AgAJ/u/myFGAIV5SOzqBMwpH6aKCOMhupWvd7pjf24R+NhMEVQXWoUOHcOzYMVx22WUAAJ7noYuwVUAmd2KkoWcTIxoDMO008TaV3k81n01PzR60bHoZ/OAgAMDZ1o7Dv/4tensGkFq6IKZjZ5gKoXWcCNnuMRWik3H9p3/3VqYJ//Tv3qra/doa2JUaW4MtonOEGOWdg+APHQMHoE1jiPk6o23jhZuBmOUcDEmuBwDeOYj2BPn3niHRYvfodMyfG6XQ77SRIZzJXVVzFM/zePTRR2Gz2eByufDaa69FtIKQIAgiXrS/XuUXVwL84CDaX6+K+dh909eD15jFx9aY0Td9PXP//LLlmP2DH8GUmwdwHEy5eaqvcFQrBFSqjYWjJ6O+NoFo23hK2p9yERaJAqvFPuhwY9sz+yZMK3c8o+pP2syZM3HLLbfgW9/6FtxuN5YtW4Yrr7xSzVMQBEFEhbuzI6LtkSAY2ZWuIgSUmfDDVWnkKK1YKBrEDEQXAiq5ItQ5yN4eAXIeJLkqlpIZiH05WcyIikTyBwr3aDjWBKMOsLUOYPvGQ9i//RR0WxsAqDM0mxgdVBFYO3bs8P//1atXY/Xq1WocliAIFYnlw3o8oMvIZIopXYY6fjFn/iq/oGqq3obD9zwPR+tTEUVEBD4jU7oJzl4neJdPIAhVGkDZh66wj9wzV9Kek5wHaIy9PRhtzIeSGYhjJaLCmWbFxu9u9lXjAlBzaDYxOiROrZQgiLghtFSEv/oj/bCWo6dmD9pfr4K7swO6jExkXVMes6cpHmRdUy7yYAEAZzAg65pyVc8TbcZV8DNydDpC9on0Q7e4fJbkvkojAqQqQdyQ0T0Woo35SCmwhAgSYXsgchEWiUS8h2YTowMFVBHEBECupRILgnFcqAy5OzvQsull9NTsiem48SC1dAFy19zor1jpMjKRu+ZGRWIwcN6i8J/U3MVoB02znhELtT50lUYECPMAg8fUQIWVotHGfMR7BuJIE++h2cToQBUsgpgAxOsvZDnjeCJWsVJLF0R8XaxxM4B0xSfajCulz0KtD91I2nPxqgRF28ZT0v4caWIZfaSWX45ILEhgEcQEQGlLJVLiYRwPHDmDpCIYT/+JrFk83rAqPQIsQ7YpJxeOluaQfcMNmpZ6RoGo+aGbKCn80Yo3ufbnSBPr6KNEFIxE7FCLkCAmAPFqqUgZxKM1jgsjZ7SOE+DAA/3HYTl4F4xNm2O5zJgIZ7gO/v70tbdBYzSJ9zGaMH3tbbLHYT0jABDCnFIKLVi0oUy1D11We87l9OCNhz/GppKNFBMQAdG2hQMpLp+FNbU34/aWdVhTezOJq3EAVbAIYgIQr7+Q1TaOy42cGa0qluQquoDvBxLtoGnhWeyueF9scOeHxXBx+SzJlX+RBnaK2nMuF2ytDmx/4Uvs334KAFRbBCFHrLMCO6vrkGcYRGqmET0dTjQPGpBRNvLCZCRGHxFjD47nJWrfowQluRMjDT2b2AhcRZg7uwtT5h+Ajm9TlAUVTFa11Ve5CoIHh/aykZkhFxxnceUTC3FGPphD671eL+yFkxSLAiUrLjeVbGS3cwstuHnHKuaKvgGrBWZbb8j23vxcRdcmd841tTczXxPu303yqSaYu3v8Xw+kpaJvUr7/a5a3LZJr7qyuw2mZPAwmrX/boMODox3ciIusXdddzW4L5+bhotfeGNFrAeh32kgxoknuBEFMPFJLF+D0J3+Fef/zdUw7swZ6vhUceGgdJyJu73lN7BlxUtvVhpUQ/te11fjg5X+gr6MdPM/D6/GA53n0dbTjk7+9FpG4UrLiUm5BgtTKP3N3T0xDg9VeBCGIKw7w/2fu7kHyqeH5i7EOOs4zDIrEFQAYTFrkGWIPQI2UaNvCxPiGWoQEQaiCGu29vunrYTl4l+g4ciNn1EYqzmLP//Wj6ZP7Ql/AcTjtJw8rOrbSFZcss/vcpZNw6W2zwvrBgtG43YracGovghDEVSCCyBKqWNGGjAqkZholtyuTaOoRbVuYGN+QwCIIQhU0jsaItrMIHjnDJRWhN06rCFnJ9lIVG7cribk93MpA0TEUrrgMXrI/d+kkrPjh/JBqjRJ4jlMUJlpasRDbv7/NnxoPAJyei1tMgNysQaWrGHs6nLBmm5jbRwMlo4+IiQUJLCIs9fV1qKnZDbu9FykpFpSWLkRxMa1wIcR4TYXQOk4wt0dC4MiZ7GwLnHHwkkgl25vSTcwEdXOWHhqjSbRSTKoFJFUxUjqqJ3hBwqW3zZIVV3IeLGg4cB6vaH+pWX8cx4EP8L9xQysMYzWiB+P3Xknci9JZgc2DBpgdnhAPVvOgARlRXx1BqAcJLEKW+vo67NxZDfdQ2d5u78XOnUOri0hkEQGMdnsvEqRagVqTFjqzLiTw8cL/KYMltRiOHQ9jzte+QlKqCy4uC465PQisl8iNn4lkxWVgxlPqwUPMe+ABkeBxJyWFCCHLqVDjNRDahqup3A3voFiIeQe96N5xCJbZWub9IFu6fTiQlhrSJuSHtkvlivGAYoM7AGSUzcLR6jrkGRyjvoqQIFiQwCJkqanZ7RdXAm63GzU1u0lgESKC23vRrCJUAyWJ2lKtQGe3E5c+dzkzzsLYtA8WzVFwXhcAwIB26A/e5XudcO8yxm3nkM9KWEWoPzsPugsy0M29h979H8M6aQlSMuaFXJNcIGhn8bTha2cEdia3tisKE5V6Py4snyJtRD9DujIp+KxYqwjNEoJRuIdIyCibhUHA77miyhWRSJDAImSx2yVWF0lsJxKPwGT0eIuewPZeJKg1MFpporacqVsqIVyJiV/OuJ1RfwTGmXOQ+uQC2Dv3oathK3jed50elw1dDVt91xAksqSGLStppSl9rdT7Yc01S95P2HNPyhfFMgiomSCvdvuSINSEYhoIWVJSJIaQSmwnEovgZPRoohPijZoDo5UmakeTbK/ExC8lEjgMt9eM3TbYTu0Az7tE+/C8C7ZTO0JeKzVsWYmQUPpaqffDKb7EsPephGgHPAcjtGO1bnfI+0sQiQAJLEKW0tKF0AX9MtXpdCgtpSGkYwG5qotSqqp0KClJRm5uCkpKklFVpV7h2965D13ud2FYWwDDdyZDc0YygOH4gkhRmqhdXD4LizaUIaXQAnDKxtAoyehiiYdAhPaax8UWAVLbnWlWdBZPQ/vsGegsnhZRlUbJa6Xej8Gp+aqIoeDriVYwBhJrjhZBxBtqERKyCD4rWkU4Nok1OuH9bQcwOWU7Xt/YjZa2NPz6pcuxbt3ZABwoL48skykYoU3GJfv+zuNS9dAvzoYLgPervqgGRkcyaDnSYcFKTPzONCt0/f3MHCgBjdsNrd7KFFNa/ei1t1jvh2DgV7sNF+2A50BizdEiiHhDAosIS3HxLBJUY5RYohPsnftQkLYVJqOvT5Sf242H7vkrfv40UFl5VswCi9Um4/Qa6EozMPhVX1QDo6evvU3kwQIiT9QO9PU4wWP7f77EJw3/QUqKBVfO/RGm970Y4mcL9JCdc8ud4GRa6F6dDpM4N6wtL8Dg7cGgJhUnkxai23wmrJOWRHzP8UYNMRQP1PRyRQorQ42GMxPBkMAiJiwTId8rlugE26kdfnElYDa5cOdNb+Oxu05i13VPx5RaLdUO4yy6qAdGx5qoHRyzYAKHsilnwOFwYH/rKfz1Ex1uWvhX5EMDDQBLF8A3fy6KXzAkS88m4zkOTs0uZB9/0v9MjN4eTLVXoz3rXHCMVYQEm1jM/7EglaEGxHcwNjH2IIFFTEgmSr5XLNEJUgIoL7sbN096GI4W+ZV64ZBqk6GfR+6aG6NaRShcQ7SJ2ixfj0Grw9LTZ2B/6ylcOnUG8nkOgbakVIMJUy+8BEff9/38OHt7YEoNrfgIOU/J+34R4ovTwIXM5j+g8wzGOB6CiVBVG+lVhFIZajWVu0lgESJIYBETkomU7xVtdAI8BkAbOjh3sNsBA8deqReJsDH0F6Kf6wanG1YrHKdHxuwrmXlQ8cbeuQ9Nfe/AhX4YkIRJ2vnI1E0BAFiNvriCcyYV+RPOh6+ZQ96ZJX6BdXzXTkxfdjm0eoN/H57j/EZui5Qvrv8ENpVsHJF2k5KsMDWId4zCaLQv1R6MTYxfSGARExLK9wpP29ZDyLx8MjSG4V8T3kE32v9ex9xfagUfi56aPeje9A64KXroSjPAWXSA3QOzaU7U4iqWLC1/LhV8LdFB9OO452MAQKZuCmxOX8VJI7FCMFB0tR86CACYeskSGFIsIcJCyhfXbzePSLtJaVZYrMil2ieip0spag/GJsYvFNNATEgo3ys8nR98iebX9sHV2Q+e5+Hq7Efza/tgr21i7h/J4OP216vADw7C+1UfBv90As7njsK5qQG9f408+wqIPUuLmUsFD0559mLQ48b2//jSx72MES8AwAdt7zh6GI3eQWY0Qt/09eChDzmG0TyIuQv2+ttN8UJpVlisjNcYhWgy1IiJCVWwiAlJaelCkQcLoHyvYEw5ueitbUJvkKDSp1rhcTpjWqknFcEQuL2nZg+O174Ffo4enEUHDcxIn3o5s8IlCDYAyJoxG1MuWgSjJRXO3h7YPvkEmnPOkb0eKb/ZIN+Pf+z/FPu72pGSYkGLBsgHQmbs9bqc/mHO4apnzvxVSDl0PzhXp2i7Tu/B0lXbsX/P/Li2mxytLZhyTinOuuqbSMrIRH9nBz5/8//h+Kf/VvU84zVGIXgYN60iJKQggUVMSCjfKzxSkQcz7roXQPQr9QD4xQhrO+ATV20fvgbdRenQ6H2Fdh4OtH/1V3Q7vkDhJd8GEOAlammGVqfDzEvKRP4nU6oVepcL7WFElpTh3tXVj/4Xfovb3/+Xf9vAqSbRjD2e45BqNOOctbcr9hhxri7mdmum7xri2W6avuQylFx5DXRGIwAgOTML56/+LvTWNFXPM5oxCrESzjsWaYYaMTFJ/J90gogTQr5XdrYFbW3jw3ul5tzBcJEHsfh1sq4pF0UbABBFM7S/XgXtFang9GIXg8aghbPvAJqqtwGASAB63G5MX3SpyFwOAFq9HmlOB3ogjXXSErQf/is0eq1/m3fQjba/HwppfQoz9gSPkSYKj5GUD8vWYY17u+msq66FTiduUeqMRpx11bVQc8jMaMUoREugqAKGq5TjxTtGjDwksAhinCDMHRQiAIS5gwBiElnxWF0mtM+kTOnuzg4YLanM1+rSTDj8hM8vFOwlSmKEk3a4j+Okfi9cn/0DWr0V1klL/G3GwMDIqStScMb1PTBk6ODuGkDb3w/B/lkzsk+bjp6aPSEtPzmPUbgPYlY+2aBTjw/fuyLsyJ5Y0etC/V9y26NltGIUoiHYkB+M0udKEIGQwCKIcYLc3MFwAmsklu2zznH6k79i7qvLyATf6waXGvqh7+4a8PmIZnfjrGtbkGR1o9+mw+fv56C/swPJmcMVkg73cRz3fAweHgA+r1VXw1YAwKn3daLAyGNvaXFiWwbypuxFkr4OWp0OGdl5MGu0aH7xBbT83yvI/dZqv9CKxWPEyidzzF2P866MTghHwki27hI1BT4YllgOZqx7x4iRhwQWQYwyaiXKRzt3MJ7L9quqdKisNKKo9++4edLjMHAOWErykX3FDAym/xsNn3yOjNOvCDGuZ11TjrYPXwMWpkFjCG3bTS/lUXJRE3R6LwAgOc2N869owpF/P45pl1ZCZzACfW/D0vMrnO21+cfRdJlmg+ddsJ3agZrKpJDASM8gj45TszBraeiHLd/Xh5ZNLwPwVeBiFSqR5pPFEkMRyFhr3Y0ESsTTWPCOEYkFxTQQxCgiJMoL+VtConx9PTtrSg6p+YLh5g7Ga9l+VZUO995jQGOjBquyn/GLq7zr5kGfkeTLjtIOoqthK+yd+0SvTS1dgOwLr4NjZxtcnQOimIi+A104a0mrX1wJ6Aw8CqfuwbHDh+Dq/Bt426MweG3g4BtHM8X+LtIdvowqj8smuVLP0adlbgcAfnAQ7a9XAfAJFT4oFyteQiXWGIpAnGlW9ObnwqPTgQfg0en8IagTlXDiKZ4C1NhtQ0b9EWQdPISM+iMwdqvphCNGExJYBDGKyCXKR0rf9PXgNWbRNiVzB6UCQiMJDmXxPz8FHE6fWMnSNwMAsq+YIQouBeCvKAWTWroAF/3yT0jyXoKGJz7Df37+AVwnecz+wY+gBzvmIcnqhvXab0LD/wkcLxaNWrhR0O97X7V6q+RKPVOyR/a+BJEzkkIlMIZCIFDsRYozzYrO4mnMnK6JCFMsD/0Xz+cqeL+0bjc4DBvqSWSND6jmSRBxRq4FqGaifLRzB005uXC0NDO3x0JTm8n//9tdecg2NEGXbmbuK5VDBQBT5/Zgzp1fDd1TD/qm98B7mL0KT6jWSbVFDd4ecJwe1klLUFoh9mABvsDIr906DVzD4RBB498nwEg/Uh4jJblhSon3+JqxyGgZ8mNZKEEkPiSwCCJONFVvw8dvVuFk8Rngh1oQwUOlU1IsTDEVbaJ8NHMHWXlXHMchNdnCXD0XiFwsRI65DS0DOQCAza33YO2k9XB3DUCfkRRyHK1e4sPk6CtIOXAHNLzTt5/jBJK+uBVd2qXI1LSLTP1e6NGR911wAPp7DUi2OEMON6ixgNP4noVcYGRPTTZaX/0LvHa76PWBURJKqa+qw4c/qcZAuws6fT/ypjfg3IfKVc0NU8pIjK8JXJk5lkI4R8OQP17DWAkfJLAIIg4IxvHmS5f6xZVA4FDpREiUFz7o63/zDAa7u6DV6ZCWkQ2zRisydQcTLhbie+e8hcf2fBtOjwl7eq4EANzx1ss44wbffMN0x0EU9O+GwdsDtyEXA035OLY/VbTScMXNn0PPi4WSTutB0sA/cTzleuRrXofB2+M3sXf3DSK9cx/qt2fh/K+fgs4wXB3wQIeTSRfB6xnwryQsLp/H/PBPLV2A1NIFMRvL66vq8P7d2+AZ5AFwcLuScfLLM+B+8E+4AMoXEYTLDVNKvCsm9VV1oqqg3GxFe+c+2E7tgMdlC4nPmCiM5TBWIjz0FAkiDgjGcXdSaLUGGG4BJkqifH7ZcgxUV4dUSQSfD0tUhIuF+PYPJ4P/+UY8v+86tA5k4YjrPNQfOYqzOAuSXH/HZPu70ML34aIfbIF2/+2wb82HoyUZgG81o47vDDkvAJjNDrRoLOiw3iL+xpCfq6V9Bv79dx5nLW5FUppbtIoQGPZ9CR/oUkJK+C9aaip3D4mrgEvkdWg/OROHX3xescAKlxumlHhXTGoqd/vF1dylk7D05hmw5pjR0+EE123zizj/cO2h+Y+B8RkTSWTRis7xDQksgogDgkFc198Pd3JyyPcDW4BCony8CVcxiNTnEy4WIrV0Ab7zEHD56w+JRcF5C5Cx66d+ceV/HQZx5sKTOPZFsX/boMYCoze0hTqosUCbagjZDvg+rIW25/GDaSjecLlvxSJjP2B4hZ5QHRJW6An3EAtSKxXd7qSIFxFEK/YC27jQ5gGW7wHJl4v2UatiItzv3KWTsOKH82Aw+Y5rzTaBD2hFModrB4neicBYCmMlIocEFkHEAcE4nrl3L1rPPVfUJgxsAY5EwCcgrhikOw6ioHM3DE0/8bXmiivhzF8l6fPhGAIRkB73wuvT/f9fShRIibMkq1h0NXjPx+nYKRJjHujQ4D0fnp5B6KzGkGNo9VbRmB93lwP6jFBzveD7kluhF6vASimwwN4YKrJ0un7JRQRqepiC27jwNIHvftQ3BmZIZKlZMRHud+nNM/ziSiCwFSm1qEFuscN4ZayEsRKRQzENBBEHpq+9DRqjCaknTiDn44+h6+sDeB5mnQ6LFpWhuHiW36flaGkGeN4f8CnM2VMToWKQ7jiIKfZ3YfT2gIOvNWc5eBeMTZt9fh5taAYU73Ay85b6pq8Hz4VWkThXL4xNm/1fV1XpUFKSjNzcFJSUJKOqSieZzdVvE38of/k2j2OmS+HUpIIH4NSk4pjpUhx6Tw8DNwscJ056F1YIAr6250WvvYHcs2+Q3c/d2QHNGckwfGcyjLefBsN3JkNzRnJUK/SCKa1YCK1BXD3jODeyCr7E9LW3+bcZmzYjY9ccZL1rxZzBZZha9CHAD3uY6quU56IF5ipZ6n4S2saFA57u34DnedUjCEorFkJn1sGaw14tKrQipRY1SC52IIgxCAksgogD+WXLMfsHP4IpNw+pjY2Y9cmn+EbxHNx0y/f97cB4BXyyECoDBf27Q1pzgm8qtXQBNGbGB6PHzcxbcuavAq9NCdnOwYXkw48A8ImrdetMaGzUgOc5NDZqsG6dCe93PhKS2eWFAV/sLhBt6z/YhR7vSuy33olPM+/DZ/gOvtqejKJLb0LhJd9GetGV/g9lrd6K9KIrQ1pMKRnzZPfTn50H/eJsaFL14DgOmlQ99IuzoT87T/L9BHyVpk0lG/Fc7gZsKtnIFEHF5bOw+JnlMGfpAPDQ6ftQMPMrXPDod/xVNqHKpHWcAMfxSMuyYcXaLZi7YC8AwD3gRk2lsly04FwlzhMavwEAGm8LNnxnl+oZWMXls7BoQxl6OkJXcALDrUjrpCWyopcgxgPUIiSIOBFuUHK8Aj5ZaPW+tozB28P8vtCyC44lEJCq5nDuLtnjVVYaMTAgruAMDHC46ZH/woG/e0MiHlKQClPzcMv07HX3IuX8S8QHXzrkK9o1B1kK8r6MTZuRERwlESDCdBdkgOfEQpfTa6C7IIN5PCCy1XLF5bNkW3ysxQIGowtLV23H/j3zfceX8HKFHCt4laA2F2CILFuHVfExJWnpQMbhEyHeoeLyWeC6beBlzNuCuJ3oqwiJ8Q0JLIIYJSIJ+Iw1LsA6aQm6GrZiUJMKI0NkCS27SPOWpHxYwvFOngw1lwvbWZld+fni6ILsbAva2sRCIFw8RKT7BosrAantgHi1nIBQaYrULyXlR7NmDvuRpFLnQ44VvBrQcjtgexQISLUfdOqxffNSxcdkYey2Ac2t0Hp944qC87SUmLdTMuaRoCLGNdQiJIhRQvBpBaIxmkTeHECdOXRCm6zJsgyeoL+rAsfpZF1TDs4g9lXJ5S2FG89TUBA6NBkAckxt+M/990U1S08uHiKafaPxA0lVf6KpCkn50WwdvvPrzDqUVijLRQtZDZh8OWB9EB7kgvcC3W1WbHlxBb78rCTkmPbOfTi5/xk0fPYznNz/TMh8SNFhW9sBr3gWpGBiF6BxPMREhypYBDFKBK50k1tFqNYqt5SMecB5f0Bf03LJ9PXgvKX3ui7HC1+uxqJTr+OJG+aiIO0EBh3JOPb5THTbz/YJr9nPSh6vosKJdetMojahUevArbNeiToOIVw8RKT7CtW9wNgADloUemch8+AhDKSlom9Svuj1UqsDz7n2dGTUH4loyX3f9PXilX4AXINDVabCyFYRMnOVUr6Ow92XYeuPh1cmLtogPmakuVSUQE4oYaKPZSKBRRCjSDifFhA+nyrSROxw43SEaIWqKh2eWGfC1Wf/BS+svQXJxn4AgNFsx/RzP8Phj4GWTb3AmhvhvOgA81jl5W4ADlRWGnGykUOOuQ23znoFywp9pu1ohGK4tmSk+wb7gfRIQoF2PjJ1UwAA5m5fSzVQZJVWLAyZYzj/8kIs/95MfzK30jE0rBmSA3PX4+I3VuFiyVdJHEuiNZcxy4o1ZdIiLdJcKkogJ8IxEmOZEh3610AQCUhVlc4nSk5yyE16AbfM2OQXJQK6jMy4JWIbmzbjSs/PcMvGE/B6NdBpPaLva3UeTJl/AO0NRWEFUnm5G+XlbtSvvZH5/UjjEFgVn8C2ZDT7Cn6grIOHEOwa4+ATWYECizXH8PJ750OrCYpkUDiGJpoZkpLHiiBXKVCcs5Da3peThdTmVlGbkBLIfdAIIB80yJoEFkEkHEK0gdBWa+7LxBNf3A4AfpEl+KLikYgtGMNT032iRBMkrvz7Jfm+r1QgqTWwWBAi5gMPQudthbPfjMb/lIJLLkRqPntfqRZmLASvDjQePMTcL9K2WfLhl2FueMy3+k+bh4GiH6Nv+o0A1A2mDRbnLKR8aM40K5BqhoexinAiQyOAhqE2Mgksgkg4WNEGTo8RLxxag2WFu0WrCLs/e495jFgSsVnGcBbOfp+5XalAUmNgceBqSkBs0uYOvwwg1M+lZnVIDjXaZsmHX4b52P3ghFV/niaYj90PADh8NA8Hf/m4PztNCKYFlA+NDoQlzgMJm0uVm4lODXtc0XglXMo+jQAahtrItIqQIBIOqWiDlv4MFL/4Mk5/8ld+ERGPRGwpY3ggHrcWx/fOQXXzIlzz92dEKe1SpJYuQO6aG/2CTJeRidw1Nyr2XwWvpgxG8HPFwkCaLzFedNyh7eHoy8kCHzTzMNK2mbnhsWFxNQTHO2BueEz1YFo5ES4V2sqiqXobdl13NaoXX4Bd110dl0kEiYCQfWZv7JVM2acRQMOo8e9hrDNxpCRBKCDWvCk1KCjg0dgYKrJYkQfMFXAxJmJLGcPdHi20Gq9/FeGrX9yKJ764CQ6nb7xOYyOHdetMABxD5vZQoh1YDLBXU4ZcY4zjbVpM7ehxVcPttUOPJEzSzkdS1ryQVYQspAzmHd4G2PYr9ORIJK/D0wxHKzv4NNpgWiF8lrW9YO7dio4hjHtSq6qWyCjJPpN7TycaNMiaBNa44aPmWrx1ZBu6nN1IN6Zh5bTlOC+vZLQva0whVEiED/FoYwRihRVtYDbzqKgIHT8Sj0RsKWN4/9xn/a22jJXAiyXJcDjFRfCBAQ6VlUZJgRULSsRTpH6uQIL9MymOT2DpfxqGll4YjyjzbgUbzKU8OU77CTh6vgp9Zto8wNMUemBtXkTBtEpQQ5zLVdXGm8BSkn0Wjz94xjITfZA1Caw4UF9fh5qa3bDbe5GSYkFp6UJkZ58Xt/N91FyLv3xZBZfX94+6y9mNv3zpa5WQyFKOWnlTsSKKNjjJoaDAJ66kRIvaidhKjeFyKe2qcfQVZHz2Y2gcjUhd6auctTcUMXeN1M8VTKB/RhiKLcxtlEuLV3pMAZ53oa/jE//XgUbo5KIfiz1YAHjOhIGiH2P6WrEHC2AH0ypFDXE+kuOeRhup7LPARHwaAUQEQgJLZerr67BzZzXcQ+Y+u70XO3dWIzXVjLy8qXE551tHtvnFlYDL68JbR7aRwIqAcHlTI4kQbTBaKDGGR9LKjAZj02ag7vvQesT5Wzmz58E68zZwHAee59H8RS1OfFEbczs3sLUjNxQ7EoGl1HvjN0IPteZYqwjzp/v2VWsVIRC7OFe7qpbIsLLPWCn7NAKIECCBpTI1Nbv94krA7XZj+/btWL36v+Nyzi5nd0TbCTZqxQiMJoH5WULlq6zgn3HxlUXSyoyG5MOPAEPiSkCr8yAt421wGl9sBcdxyD/7HKQtXqLIJyVHoH8m3FDsaI4ZDmG/vuk3+mMZglESTDuSTF97m6pVtUSGlX0WSco+MfEggaUydju7T2+zxW8VSboxjSmm0o1pcTvneESNGIHRILAlbbNZkZ6+FI2N89DYyOHeewx44Mx6lOWJ5xgCsfvKpFqZNyz4C5J3Kc+dMjZtZrYjpcQM5xG3n4KDQKPNigr0z4Qbiq0UlidHirFohFY67mm8EJx9RhBykMBSmZQUC1NkWa3x++W5ctpykQcLAPQaPVZOG5+/5OJF8By+0VpFGAnBLWmr1YYVK7YAAPbtmweHU4vn912HsrydyCpqwJT5B2BMGsBg61Y4m56OOR8quJUphJQKBvlw3iW5/aVWM0Ir3X6KZVVboH/mZNJCTLVXQ4Phf1NSafFKjyl4ckypZ6C/84txY4ROtKoaQSQKHM/zMRkm7HY7rr/+ejz//PMoLCzEnj178Nhjj8HpdOLyyy/HvffeG9HxOjrs8HrV8XCMBsEfeACg0+mwcuXKuHmwAFpFGAvZ2Ra0tbErj4nOpk0bmYK+u9uKp5++BwDAwYu6O8/H9HM/g1Y3nMrOa8zonf2sqiGcGbvmMEWRxzQZnYx5hXL7901fj9S674vahDxnAmd9EEi+XLQ/D6B99gzsuu5qpidIn2rForfeiehepCprapDI41SUDugdy/9uxjv0bEYGjYZDZmaK5PdjqmB98cUXeOihh3Ds2DEAgMPhwIMPPog//elPyM/Px6233ooPPvgAl1xySSynGVMUFw/16YNWEc6bNy+uP/Dn5ZWQoBpFgr1PZWVuVFfrFK0CjAWplrTVOtySzjG3Y8r8AyJxBYhN2/xXv0Jaw9PQe21waazoLroH3Bn3RXw9Um29aLY781cBFhM8Q6sIvaZCuDPvhgGX+OYF9r0N9D4H3tMCXp8PY/rPJFevuXpsaKreFlGlJZ4J8IIROvlUk2+YdDOA5kMYSEtV7CWLh0gzdtuQ3Njkn6modbuR3OiLjZjIy+0JIhpiElibN2/G+vXrcf/9vlEOe/fuxZQpUzB58mQAwIoVK7Bt27YJJbAAn8gShBYx/gmeHdjYyOGll/QAhr8OF8AZLVItaUevAU9PvxRZ+ma4Ncn+uYHBaByN4L/6FTKPVfpXzRm8NmQeq0QHELHIkmrrSXmXwu5/2mp0pqwUfS/5VBPMJ18DbI+C4x3gAHCuU7AcvAvTS0/D4X+xry3RspkEcRW4DtPc7fN9yYkse+c+2Br+AQ8/vJhArZl3hmNN0BrEK0O1Gg6GY01wnkUCiyAiISaBVVlZKfq6tbUV2dnZ/q9zcnLQ0hJZHopcuW2sk51tCb8TMSrE8mwefxwYCNEv4g+pgQEOjz9uxm0qL65atqwMW7Zsgcs17OfxuDkU7P8XMg2+yoOet6O/R49ka6jRmksqQtqJp0MiCbRww9rwNGbc8DAaGoCiIqCyEli9OswFnf0Y8NEt4tV/2iRoz34M2dkWdJyqxanDb2PQ0Q2DKQ2m026Fpf5Ryf0BxrPJtgBHfw8Ej5TxDuDspW04/K9ssHC0tsT93+C+V/Zhe8V22BpssBZZsbRyKeatlhA8jOHQHICk7h4knVnMfEnHqVp0NWwBz4cKdZ53wd7yPk6bEYFnsKUDOHoScA4CRgN4PXs3ox4wMd47+p2WuNCzGX1UNbl7vV5wAbOHeJ4Xfa2Ese7BkoJ64olLrM+moSEFwYKKvR+Ptja7qq2dvLypuOSSS0Ut6bSPPoK58Yhov893ZOP8K5uh03v923iNGb2n/wSW/WuZxzZ4bbhXfymyZjaj3ZWHF++5G729l/qrcOyxQithnOUI9S6lrIT90B7RirpBRze+cupROPmHyGz+Q8j+aOuVfDZZ/Q3Md1zHt0OXOg3untAVgKacXMnnHG6Ir4C/pTdEYEtPmFUn5CTZjtvw1s1voad3gHmsLLB/angA7RLXefLQP5jiSmBwoFvxz7Kx2wZLUws4wYbrHPSdnHFRtpYBuIKOS7/TEhd6NiNDXD1YweTl5aGtrc3/dVtbG3JyctQ8BUEkHFKBm6z9pEanANG3doJb0tUv/SFkn+MH0wCOQ+n1mhDTtungfTB4Q2NEnEhB9lAVLNvQhDVZD+P1J4Dy8kVhxgqxvUtSqeannEngGAZ4OeRaizPvWhdRNlOwMBKG+GalAtOnGPxmb7deB8OAQ7Klp2RWnXC+msrduOflhdDqxKOGwhEuU8vdozyDLLm1fVhcDcFpOPBeHpxm+C4HHW58WHUc5y05O6JrJYiJTmT/usNw5pln4ujRozh+/Dg8Hg+2bt2Kiy++WM1TEETCUVHhhNkcXHUN+uDifCLsy0/fZ4oM26kdql2PKScXPZMn4+gVV+Crb34TR6+4Aj2TJ6OlfQY6LzqA9jIbOi864BdB3UX3wBP0t5YHOhz4XDySxqhx4FLdMwDkxwpJISUOlAZxBnLScTXcLvGvLyFGIb9sOWb/4Ecw5eYBHAdTbh5m/+BHkv4rljCauSAHp2fx0Lrd4OAzeweLK2A4gyvr4CH89y/Pwdylk0KOHzirThBz9sZefPJWA4IXcfPwVcWkkMvK4qBF65Y6ye8Ho3FLV8K6m/vBe3l0N/fj788cQNqSGYqPSxCED1UrWEajEY8//jjuuusuOJ1OXHLJJVi+PHFMpQQRD1iBm8IqwsZGDhwH8Lzvozkro5t5jGhEBsBObp+86nq0nmwAr/P983YnJ6P13HMxuUBiht8Z96EDEK0i3PdpIQ79I7RVn6X3RSBEM1ZIKtU80oBNX9bVh5g8LQ9nLW5FktWN/h49OqxrYc5fBWPTZswxPYJ5a4VK3b1w5kv/HmIN8V168wzojVrRNqkapbA9LS8JK37oq0Lu337K//3AWXWBYm7b//qqduesLIJGywEcF3YVoXXSEnQd3wI+yDOngR76egNcjV6JV4bi1emgZYgspxv4/Q8+obRygogRVQTWjh3Df31fcMEFeOutt9Q4LEGMGVizA594womSkmQ0Ng5XWlra0pCf2x3y+mhSvFmrF9etM+HHP7b7xZUAr9Phy75eSI0c5864D7aAFYN1T18NDULzpPhUX8hnNGOFrJOWoO3oVmg1wwOVC/p3w+DthbfrBcU5U4dffB5epwPHD6b5Wp9DmHIP4NK5kQWdAuwhvtYcc9jrYGEw6bD05hl+gRU8qy5YzG373wM+ocUBt7esE31PLpHeduxteDgH9EjCJG4uOvYcxmdv/hWzf/Ajxdfal5Ml9mAB4DkOg1PzsKb25ojvPRzB+VrOlCQY7f1h87bUOp/axyeIcKjaIiSIsUx9fR02bdqI557bgE2bNqK+Xnm7RYqTJ8V1j1+/dDkGHOKlWtGmeFdWGkVzAAHfakWPhz1HTyozi8X8798GXmcSbeN1Jsz/vs/HlHVNOTiDQfT9cGOF3nn/bPz8qXI0taQhbeAginqrYfT2gAPvF0LGps1hr00q68rR2oLkw4/4xZX/uobyvqQorVgInVksSG1tDua+SpbfWHPMAAekFFqwaEOZqPoTWM0KJHi7kEjvaGkGeN6fSN9UvQ0pGfNQUHI/9O3n4fDju/HuvY/gwAfvybZBWTjTrOjNz4VHpwMPwKPToTc/Ny4iRDDUB7Zczd09oq8tTS0wdqszUox1PjWPTxBKoFE5BIHQBH67vRc7d1YDQEyZZsEG+Hfe94XB3r32beRkdce0ijBYvAnYbFakpYV+kJyTVY+MXXMUJZMHzpgbaGlBtzcPfzl+NxoeuAIVPU6Ul0c+Vqiy0ojGxq9hy7tfw9Gnp0KXLfaiBQafymHKyWWmtZtycqFxHGS+Rm5IM2uIb4vbCCvHhVZ3TEYYBobFF+sJeA36kGqUQGnFQpGhHgitcgHDVTrRcZ0OOGpqkDHlDGjcbmRMOQNTf/fHmASRM806IlUdpqE+aB+O55Hc2q7K9TDPp+LxCUIJJLAIAr7kfXeQH8XtdqOmZndMAquiwilq4wHAP2vOxtWrZuGcslD/SyTjWaRWL9bWLsaSJVuBAJ/O3JT9WJ6xFVqH70NbSessv2w59vRcKb5+GwJCUxcgtXSBuBVTfwR9OVk49vG/QtpbJ09e6z92UVYD85xyQkhg+trbJFcJek1fMVcX9vcaZJPcWUN8e8O0mEJiDuATYX05WZLXzhJzLI8Tq0o35ZxSlFx5jd83JVRlgMRPWZcz1EezX7THUev4BKEEElgEAen2mdT2+vq6kHFILCHGMsBLjc2JdFAyS7yZzTymTZuFt97icPHFO2C12mCzWXFBzofQMoI5w1WMpNqQlZVGlJe7Q0SG1u1GSuMp2Hds91eZhPbWFVNM2HpsBQCgob0IU7OPh5xPKvE9kMDqWrA/qa+pR/QeAoB7kMPn72XixBFlQ58FhOqOICAtp5qR3NruF1qCqInU58MSc8GwqnRnXfVN6IxG0baxUpWRMtSz9ovn+dQ6PkEogX7aCALSI2dSUkI9M5G2E1kGeBZy/iGWCJISb75W3HzU1s737/urP7NbVuEqRlJtSGE7qxWj0Whw5tevxrF/7fJv8zoduC73aWxvuRIDAxwe3FyJjWtvQbIxYJDzUMyCEvLLljOFkvA+af91B5JSnOi36fD5+zlDZnhHxONyWAIysGoUrxYbq0qXJLGAIN5VGTXM4kxDPcRtwnDVv5jPp+LxCUIJJLCIhOaj5lq8dWQbupzdSDemYeW05REPtVbSdistXSgSTQCg0+lQWrow+HBxaydGOhAZYIu32283hezX2pmFvMy2kO0uSK/6A6TbkAUFvg8uqQ93lhjQ2FuwYYNPEL76rxuQkcHjseseRIomfDs0Epz5q1D97FMAH2pJd7S2hAiGJnM72m01zGT90fLyTD33Asz8xa+h1+rQ39mBAzur4fK4YdCFzrKJZ1VGTmAiglEsrGpf4CpC5yCP7Rvr8Mlft6oSDRFtdZEg1IQEFpGwfNRci798WQWX12eG7nJ24y9f+oIslYospW03QRgpaftF2k5USqSDkqVgiaLP3s9G2dUd0OmGc5Lcbg0+/2cOTiuTPpZUG7KiwpcYLtWK6WdEOJhyclEmEoRXwYGrwF6vFxtSRvjpSy4TCYZuxxG09n0MHh4Aw8n6zR+dxJ4HT2DdposATajAjGfVyC9qhsRUcmYWzr32BgxYLdDbeke0KmNqOOG/DgFBYOKMyH4uWdW+Pkgn6QOIWWSRoCJGExJYRMLy1pFtfnEl4PK68NaRbZICK3g+XsmyN8HxytpuwSNnpIiknRgJfdPXh/iHlLbNAu977WmX4/G2m+BwDgdlupIm4XhKEQoH98Dg7cGgJhWNKQvQMujGaTLHDech68vJgu5oC5KMwx/6Aw4vPn7jDdFx5EbVxAMpI/xZV10rEiinPHv94kqA513wcB/D3pgMW+sA0vKSQo4vVI2kqqOxzJuUqpoZ7f3ozc8dsapMU/U2ZE2ayvyemgJT6YghghhrkMAiEpYuZ3dE21nz8XTeVuZaeiWr1aSIpJ0YCYLgY31gsxLbA4cuB973pelvgz9zABuPrkVTuwkFBTyyV85Cd4oR3ZgrOmfOivCz6+Q8ZM40K773CxMq155CUc4gGloNeHBjAY5/fCVW5RxGtrE5JCRzJBDO9eWzG/yDn7VGI/Ra8a+8QfSHvBYATFk+gbN94yGs+OE8GEzDrxOqRlLVUYf9BLr6BqOeNym3Am4kqzKHX3we0+78IZIzQytkXp0OWsZrooGVpC+3nSDGCiSwiIQl3ZjGFFPpxjTm/qz5eM5+M0zJAyH7Rtp2C141OGPGbBw/fjRsOzFSnPmhg5KlEtt9UQlu5n2X5e3E5bP34fQnfwUAaKgVrz4T0KUa0fDZz8JWWeQE3j+/zMBp1wd/CF+JhtSvo3ZPX0T3H1wRwtmPASkrIzpGIF7n8Pvi6rGhv6sTyQH+MAOSmCJroM33XguJ7EtvngFrjhleg95fNcrYx16UkNbwNE5kiJPQhXmTSgSW3Ao4YVD0SIyxcbS24PM3/x/OX/1d0epFt9OJ/kl5kJ6YGBmsJH1hO0GMZSjJnUhYVk5bDr1G7P/Qa/RYOY1dCWGNbjm+dw48bvHf2pGsVgOGVw0KbUG7vReHDh1EaelC3H77OqxZc7Mq4koKuagEQNlcQKdbouoxdFihymLv3BeyiyDwGhs14HkOjY0arFtnQlWV7+8z1rDrQJ+WsWkzMnbNQVa1FRm75kimtQsVIa3jhD/dHR/doijdnQUrrPPzNzbD7Ryu2k3SzgcXVItxO4AvXxpOqd+//RSeuf59bFizC53F0/wVJKkqqN4b21Drvpws8Jz4efMch8PHB/2DosH7vErv3f42nsvZgE0lG1FfFfvkgUBMObk4/kkN/v3KH9DX0Q6e59HX0Y7ara+rWkVjJemzwlcJYqxBAotIWM7LK8ENM8v9Fat0YxpumFku6b9izcFrbyjC0bqL4TFNBg8OHtNk9M5+NqLVanKrBmOlp2YP/nP/fahfeyP+c/996KnZE7JPuKgEqfl/wvaqKh0e/9/QET3B8LwLBz56HyUlyX7xBIQXeOXlbmzY4EBhoRccx6Ow0IsNG3zVNZZoChyJEyi+LPtvDakIwdMvO+ZGDlZYp18wdHaA53mYbMlw7rbB1TUA8IDb5sTxPzagaaf4fjmNJ+QDX6oK6tKwxYfSeZNSI2zeuOv9EK+SMLtHMIarKbKmr70NGqMJxz+pwZs/uQ//d8eN2PLzCphKS1U7B+Azsi/aUIaUQovkiCGCGItQi5BIaM7LK1G8YjDrmnKRFwnwzcfjFjyATpkRLuGI16pBlmesZdPLACAaORMuKkHqvoW5gMKImkEXhztvehu52d3gOIBj6Lbc7G5/hUpoQYYTeIBPZM3DPn/7qq/SgnosRGmO/GxAsalfbDYXiNYvJ7WSsOXEMRwZ6MHhZ58cDiqdeRvyS5ajevEFAM8jJ9+Gjtb5cLuToNP1IzN3H4rLfyg6jtSihO6ie8AFeLCAyOdNBnut6qvq4OiUX2+ptjFcLtBVbZSErxLEWIMEFhEVauRTqY0gSiKZjyeH4LuSItZVgyzvFD84iPbXq0TXHC4qIdx9C0LonfdL/LMQt2yqRH5ud8g19Tu1mDdvH/btm+dPaw8n8ADppfYLNjZKLjJgBauyiNQvJyA3UkcqqFQQZalpJ5CaNhyZYcrNC9lXalECl78K6TGsImRRU6msWqq2MVzqfSIIIjwksIiIUSOfKl6kli6IWlAFEpzWHowaqwaVeKcAZeN25O6bJZB+/dLl+Mm9f4XJOFxlcXuA+hM6rFixBQCwf79vxWE4gQcA3X9/Bnc89ndYs2ywtVuxffNS7N8zHz3dabCmd4Vck9dUqKwypU2KyC8XSDQVGDlRxoK1KAHwrRaMRVAFo1Q4kTGcIBIHElhExESTTzXWYPmuBNRaNajLyGSKLJanSum4HRZSA6dPdg+iIP1vMOq9GHBy+LLBgFMdehgMLixduh1dXXP855YTeMamzSi75v/BMCTW0rJtWLHWJ9Le+8tiXHPX28xsr+TDjzCDVfmhkpfXVAjt2Y/BGcMqwuAKTFP1Nuy67mpJwRXvtlhwTpvSCqvUSrtAdGYdppSdhk0lG0dklSFBEPKQwCIiJtJ8qrGInL9qzZqbJb8XCeG8U2ohJZAWL5+D5557h/kaq9UmqlBJCTx75z5YD97nF1cCBqMLV971Hj7x3oa2ovuR2fwH5qiilP3fgwbDr/VAh+Mpy9BjuQAFc+9GdrYFaFOn7dVUvU1UnRKGUAMIEVnxaIsp9dyxKK1YKGrBAgCn52C0GOHociClwIIpZafh0KsHVU9EJwgiOkhgERETaT7VWCSatHa5rCgWanvG5JASSFL3qdWmSl57YEo5AEyViCUweHtgyvKisW8QfXNeDWmZOfNXof3Y6yjo3+1Plz+ZtBBdptmAwkiDSGDFNnidkQ+AjhalnjsWgkCSy8DaVLKREtEJIoEggUVEzMppy0UeLEA+n2osEmlae7gw0OB9h4VYGSoqLo66/RcrUve5aNGFzP3tnfvQ1bBVtEJuUJMKo7cnZN9BjS+KUi5ks8dygU9QBcFpzBHfSzhYsQ1y29VGqedOinAr7SgRnSASC8rBIiIm0nyqsUhx8SwsWlTmr1ilpFiwaFGZpO8qXFaUQLjQTqUoDe8Mh3CfyXoDwPMwOQcxu6kVeZ2hxnQAsJ3aIRJXAHAyaSE8QX+reaDDyaRhMSoVsumLLgj9NcTzg8zQ01gw5eRGtF1twuWVxYqUwZ2M7wQxOlAFi4iKSPKpoiV4PI1a42iUonT4MxA+DFRATogFVrHkzNBSM/AARBSgKpDX2YUFn+0Vta+kvEEsoSRUoJitviGkQjZTMuahu3EbvJ6guAbeA9upHcAM9dqlka4QVJtYPXfGbpvsoGeWT4sS0Qli9CCBRSQkwTEJdnsvdu4cMuyOoMhSipKsKECZEAtnhmblRwnhnZEILGHuX9bACRQsM+P43jlobygCIO0N0uqtkiKryzQbvNsLcACnHa5KCSGbxqbNMNdXQDfYAl84Fg+3IRe9hq8x24RKR8soZSSDM1nE4rkzdttgaWoBx/t+nrRuNyxNvtamILKU+LQIghg5SGARTEY7SFRuPE0iCiwlWVGAMiEWaIZuakzDkbo8OAb0ML29Gwt/mY4Fqez8qEgSz0VVMA4wJQ9g+rmf+c4/JLJY3iDrpCU4Vb9VlJ/l9fr+t609FemffwUA0JVmQJOq94dsZjrrkHLgDmh44f3w3a9+sAVTBn3COVhkKR0tI4WdEfY52sGZ0ea0Jbe2+8WVAMfzSG5tF1WxKBGdIBIHElgTDCVtt0iDRIVKCGsZfrSoMZ4m2syhaFASBgooE2KCsGlqTEPdF4XwenzVIEefDjvXVeNrL+TCpAkdAcNKPJda2ciqgml1HkyZf8AvsFjeoJSMeTjZrUEytx05Wd1oaUvDr1+6HDv/ORsPnPlbAGfjd3Wr0fqrbBQUDp1vrhvJu64PEFditHChoH+3SGBFOlommGAzvjDMWriHaI6nZjJ7pGgkMtmkthMEMfqQwJpAKG27RRIkqrYfSCCamIRAYskcihYlYaBKhJgQQHqkLs8vrgTcA268s+liXP6tv4mypwadehztX4uMgH3lVjbeKlEFMyb5nqOcN2jx8jmoqjoTt/3Ydw855vYhcQU88cX34PSYFJ9PwODpATwGQDsYkYCR+oOBZcaXW83YVL1NsnWotliLBq9OBy1DTHl19CucIBIVWkU4gZBruwUSSZConB8oFkpLF0IX9OERyXgaucyh0aa83I3a2j60tNhRW9sXIsqyrikHZzDAMaBnvv7z6tnY8uIKdLdZwXuB7jYrtry4AlsfSRHtJ2eol5rv5+w3Q5eRidw1N8oK0cB7+PDVWnx9fh1+V7faL66Unk+g36bD8V/UoOjsn6Jg7t2KxdXOndV+IS78wVBfXyfp32JtFwJIHS3NAM/7A0ibqrcBYK+cFMTaSNGXkwU+aDo3z3Hoy8kasWsgCCIy6M+fCYTStlskQaJSvp9I/EAshIpatKsIY80cGk0EYWN6ezccfex/ovv3zMf+PfPFGznxc5Qz1PdNXy+qPAK+ETaD5z+L06+OrPIo+Ipa/5jC/L5wPrEHaxi3W4PP38+JOI9K7g+GpSVsM36gr0toIR+r/Te8QccJDCD1DNqYA6s9g+qHoUoh+KzkVhFGQ1XVZlRWPoKTJxtRUFCIior1KC+Prb1PEIQPElgTCKVtt0iCRL2mQuY8uXAVCyVesEhiEoKJZM5fJESa1h4tqaULsPCX6cxl9x6NHnzfQMhrgvOO5Az1Qvs2Wu8cy3dXUHBT2PMZPr0bRmMfhFWEAwMmfLYtA8cPpsGUG1keldwfDHxHDrymTmgMWv9276AHmh7f8w9sIXskfEyC4HP3OKGzGkO+7+5he8rihTPNGrOgCqSqajPWrbsLAwO+n6XGxhNYt87X3ieRRRCxQwJrAqE0nVzwWSlZRShVCembvl7yOkYigiEec/4iSWsPhxIDPmvZvabsEvz5z3p8fd6b0C31AlYANgA7OZTeKn6O4Qz1zvxVUfnkpHx3L63XYOX3b5Q9X90//43uY/9BZ1sz+IBVcZxGE3EeldwfDEef+yv0BRyyr5gBXboZ7q4BtP39EFwnv0DhJd8WtZC1Oh1TZAkBpK1b6pC3ai40huFfl95BN1q31OH0RRFdckJRWfmIX1wJDAwMoLLyERJYBKECJLAmEJG03ZQGiUZTCRmJCIZ4zPlTGhIajkgM+MHL7ktKkpE+8wCwggMMQxvTAM8VGiDIthTOUB/tyjgp393ijPXYsOF62Qpf1jXl8Azda3dnGzxuN7Q6PaaWr5KNT6ivqgvJd5L7g+H4S3+Ao4VHb22T+EBDPqbA6mZaRnaI4AsMIHU1etH82r4Qsdb6aRY2lWwcs5lTJ0+y2/hS2wmCiAyO54PCVUaZjg47vN6EuiRVyM62oK2NZoIBwHPPbZD83u23rxvBK/Gh9Nnk5qaA50NbYBzHo6XFrvh8/7n/Psn25elP/irsNdx99zNISwv1/6SkWLBmzc2Srw1sy55eoMWsIjs4eALuQ4/0oivDiqysais4hP4b5cGhvSy8LymS+IzsbAs+fP4jZqv0/O9NQa/zY9SnpsBh0CPZYMQFlyxFcfEs7Lruap9pPQivJQ8/PlmNk40ccsxtuHXWK1hWuBv2Hptf8Jly80SrCAUTfGACfG/fNLQ1nQPPIC+6pkUbysaMyCopmYPGxtD2fmHhZNTWHgj7evqdlrjQsxkZNBoOmZls7ylAFSxiFIg1gmG0kPI0aTS+9qHSKlYsBvyCAh5WK1vEyGWEBbdlp+b0hIgkuRiDQKL13QkEh202VW/DF9ddLZmuXlO5WySuAF9cxafPf4mFlzYiZ2gbZzAgd56v6soai8PrTPj9kXvQ2O5bPN0ykIMnvvgeAGBZ4W5YsrKZqydZCfCN+86HZzD0mrbfuQ3v3f72mKhoVVSsF3mwAMBsNqOiQrq9TxCEckhgESOOUi9YrKgdNMryNAGAxxPqxZI7dywG/IoKJ+rrrUyRJSdQg9uyZiO7SqxkPE00vjspgqtDQkQCMCxs7CfZwtHRL/71FTjehyWKXjp6D3a2rxC9xukx4Xd1q/H1+XWyPx/BCfD7ctlVWN7je1/tjb3YuW7IV5igIqu8fBUykm2waA8jKyMJ7Z396PVMx+Ll5L8iCDUggUWoQiSjdWKNYFBCPIJGBU/TnXea4PFIe7HCnTsWA/4NC/4CJD8Bi6YZNrcV29uXYr99fliBGlzdGnBySDKFiiwl42liXYEYyOEXnxdVmabM7sZZi1uRxK+Cd9dk4OzHkFJggb0xVGSZzK6QbYHCNVgUXZvLLuW3OrLDtmaDkbom0bUMuFFTuTthBZa9cx+m57WD55MBADlZycjl2mHv3DeiKfWRwPLiJer7SxAUNErEjDBaR8jOEkbrfNRcK/ma4uJZWLPmZtx++zqsWXOz6vMF4xU0Wl7u9s/eC0bInZI69953/oFNmzbiz7U12HP2fLRO9rXUlAR7AkOr9/Z/D6naZnAckKa34arcNzBd9xVee20Frr/+HFRVsf9mCq5ufdlggNsj3kfJeJqqKh1KSpKRdtZ3MfXuY/hdTx86LzoQdWp/YPbVlNndOP+KJiSnucFxvtWJ+OgWXLneDp1ZfF8arRfTZoV6rOSqgMGDt8Ntl6O0YmHINbGQqr4lAokQoBoJ9VV12Lmu2ids+eEqYX1V3WhfGkEwIYFFxIzcaJ3RIp5Bo2lp7A9kYTvrHE3padifkeavJPW5BnEwPwe4/wGc/uSvFFXVUr68HxzE77NO48WyzG3Yt28+Ghs1WLfOxBRZwcn4pzr0OHDMDC+XBMBXuQpncBdiKhobNeB5TvZ8SjA2bcZVdx3Gtx48iKvuqMfXljVBZwh6bz39OD35N1i0oQwphRaAA1IKLbjg7jMw6fR+0a6cwYA9GXegpCQZubkpKClJFl1bRYUTZrP4+KyB3EooLp8luiZOyw51Dc4mSyQiSbtPBKS8eDWVuyVeQRCjC7UIiZiJZLTOSBGvoFHAv9Jfcjvr3Ecm5cGrDZorGGE0BefuZG7PsgyfSyo2gtWWnTF/IaZGUDlUK6YCCMjSsvjETXKaG1LrmXXeVuQVdGFNrXiFZE9NtsjntifjDlT8dr5kTpnSgdxKCYzQEKorwSsdSyvU9RWqiVYfPu0+nkTa7pOqBiZylZCY2JDAImImktE6I0U8gkYFurrYCkvYzjq3w8CeKyi38i9apEbkxJKMDwBzz/gMv310G3Kzu9HSloZfv3Q53nm/RPJ8cjCztCQO4+w3o/29qpAqX/BqxGtLksMKQCUDuaOBFQqb6P4g66QloiHWgLI2sRoEC1IliwKkfG+JXCUkJjYksIiYiWS0zkgRj6BRYWVgjunnaBnICfm+4OVhnTvZYESfazDkNZFEU3i1Vmg9oRWH9l5xVS4aT1E47J378NC9W2Ey+p5xfm43HrrnrwCAA1+dFfHxpGZV8rxYaHncWhzfO0dRazdY6C1I3YpVOU8jS9+MXdeFxj+oTXAo7GgQSVVIaAdHEzYbK3LtPqnrLa1YOOaqhMTEhgQWETORjNYZSYIrHLEQuDLw1lmv4Ikvvgenx+T/frCXJ/jc7qAcKiCyaAp75z7Yky7BlN6t0GDYZe/y6HH3n56RvA61sJ3a4RdX/nOZXLjru2/jhD30AzHcEGGpLC23ywiPSwNj0gCc/WYc3zsH7Q1Filq7gTllC1K3Yu2k9TBqpOMfxhtRVYUy5o3YisFA8cfIqQUg3+4bi1VCYmJDAotQBaWjdcYqgSsDlxX6TLW/q1uN1oFsFBSG9/LEGk1hO7UDHmMxeH45Cvp3w+DtwaAmFcfNy7Cr4VvgOD6uw6eljM+52d047zLx+ZQMEWZlaXmhR4v1v9Dwlx7wg4MB6epfwpCWDnP1NllxFJhTtirnab+48h/f6cDhF58ftwIrmqrQSMHyqLEI1+5LhCohQSiFBBZBKCC4RbWscLdfaBW/+LKiY8TigRIETpdpNrpMs0Xfq63ti+qYkSBliNYZQg3RSoYIO/NXwWE/AWvDU36xeDJpIbrNmUi78QKcev4N0XxA4+km9Gs/wPHaf4PvdYPf70Lm11aKqoSBJvYsfWiEAyCOhZBDyZxGNTOZop0LKTpGDCbweOdLscRfMNTuI8YbJLAIQgHxXJWohNFe8RWJIVrpEOFTTh1OZNwi3ol3YTCpET19vX5xZSnJR95186Ax+H5dcal68Odq0bbrNQAIEVnl5W7sui4X+gIuZECz66S8P62nZg86Pn0LmnNN4PS+VZ8elw1dDVsBDPuWomnHSWHv3Cd6b1nnU0K0JnA170UKWZHHgdp9xLiEcrAIQgFZ15SDMxhE29RalagE66QlyHDWY27nCyhp/yXmdr6ADGf9iKz4Anwf9IXJBszr3IiS9l9iXudGFCYbmAKgoIA9kzB4u1Tb0T3YLRrUnH3FDL+4EuD0GmjPSZUMjj3t9muRd9186DOSwHEc9BlJyLtuPk67/VrJexR8dtxcvV9cCQQHcKqZyaRW4Ccr/FRJVWgk8qWkRF5KoQW3t6zDmtqbSVwR4w4SWOOMj5pr8dCHj+KOHffjoQ8flU1TJ5STWroAuWtu9FeslKavq0Wmsw5T7O/A6O0BB8Do7cEU+zvIdI5MirWxaTOyG56EwWsDB8DgtSG74UkYmzaH7FtRsR5ms1m0jTVEWKr6xve6oQ0IRdWlm5n7cRad5OrCAXMHNAataJvGoMWAOXT/qqrNKCmZgzNWLsc1f38TnIVd2A8UhNG245qqt2HXdVejevEF2HXd1Wiq3qZa4Gdw+GlKoQWLNpSFFS4jkS8VrfgjiLEMtQjHEcLIGiEuQRhZA2BcG9BHCjVXJQZTVaWTDcBMPvwINLx4daCGdyLly/ujHlMDSA+lDvYEzen4dWhulXcAyYcfCTm/4LMKXEW4btW3cPa//436t//hP4+1OLTtyLu8cNd0Ii0j2+/BcncNQJ+RFHLtfK9bskWr17HFSfD2YEN+y8AAWlp7kZebGvLaQEEYTTtOarD1GZXLAW1ohEc07d9oTOAjkS9FKwCJiQjH81L5yaNDR4cdXm9CXZIqZGdb0NYW38Thhz58VDLw8+cXPhjXc49llDyb+vq6uA2nFkbQBIZkchwPngcKh1Yo3pqaDI6xtp0H0Dv3xahEVvBQasDX9ky78TI4DPUi4VPS/kuwckB5cGgvk6+0SJ0nd82N0BRb/EKO9/IA5xNO7ppO9Hx6Ct2dbUian4286+Yj03toeAUlZ0HD8bOhmfMgU/T+e9v/Ij+3O2R7U0sazl/+/eH7KpmDxkZxXMRli2fiJ/dcBpNpOByW4/SiUUJSye1yFaNd110tan1OOacUZ131TQxY7WjwfAIew8Mhg88XT6K5F2BkfqcR0UHPZmTQaDhkZrKHyANUwRpXJOLImliIp6iJ9DoCM6zs9l7s3DlkAlbhelgjaHhePO5l9QuFsGhCc6M4gFlFUoLUUOr+wX0hHqRBTSqM3p6QY3hNbL+VkvO0v16F05/8FQD4TN0an6DjUvXQL85GKoCUr6zQ6TIxJSMVmceqoR2axWjkezGt6N+wT2kEK/Xr2KdeXHr+CzDywysUT+FM/OXNy3B+QEoDy5D/zvtfggPws9svA2fRQQMz0qdcLhI7UhWZSYvdOLn/GeZqQNFg63NKcf7q70JnNCIZWeDA4aRnL1zoV7yK0NhtQ3JrOzRuN7w6HfpysuBMi67qxboXqi4RRGyQwBpHJOLImmiJt6iJhJqa3aKAUCDyOYJyhBs1MzDAoerTB/Ff536PWUWSSkUPh2Q6elLoWU4mLcQU+7vQYvh94DVm9E1fH7Kv0vMI21kmb06vga40A+4GN7KuKUdm881+cSWggZMpLo1Nm3HruY9Bz/vafkZvD4p6q/H6rgtw8aVz0FPzT39bNDcpGc199pBr+6zmOLxT+mXT/4PbceFWA5pycv0VrLOu+iZ0RqP/tZm6KcjUTYFHp0Nn8TTm+UT32G2DpakF3FADQut2w9LkE3DRiiwSVAShLmRyT3AiMa2vnLYceo145t1oj6yJFjlRM9JIzQtUa45guNE231ragW8uPxccx/7gVFJFYiEZMdEfej1dptk4pl0K50AKeHDwmCajd/aziipnUucRtkuZuTmLDtPuuA2ppQskRSRre/LhR6DnxH4xHefCrQtfQFnBP9Gy6WW/uLtlxkwYtWIzvNlsxsMbnsXpT/4qIs9duNWA09feBo3Rl/6fJPGeaNzKQmKTW9v94kqA43kkt7Yrvl4p6qvqsKlkI57L3YBNJRtRXzUyCykIYrxBAiuBEUzrQlVKMK1Liazz8kpww8xyf8Uq3ZiGG2aWj0mDe7xFTSRIzQuMZI6gHBUVTpjN0iLr0ZtPItnEA9b7AM4k+p7SKhILqeiJJMM88C6vaDvv8qL14xx88tYy9M7dCACw7L8ZGbvmhKwkNDZtRsauOciqtiJj1xxMvTJdNuJCysytM6Qh55KLAUiLSNZ2KTGWomkE/9nnKFmzFgvu/RG+tvZ23LB0OR4482zkJaeA4zgUFk7Ghg3Pisb6KCXcasD8suWY/YMfwZSbh36Jqp5Xp6ypICXElAo0KQQ/lr3RN85GyMSaaCLL2G1DRv0RZB08hIz6IzB2R7aikyAAahEmNG8d2SYaoAwALq8Lbx3ZJima4jmyZiQ9USkpFqaYUkvUREJp6cKY5giGQ0gg/5+fAqfajPA5q4bbdEU5Q/6l5Mt9/9v7HOBpAa/NRe+sn0e9ilByIPZ5C3D0Nx/78qAsOr/p3PtVH3Jnd4lG3GgdJ2A56BuD48xfBWPT5pDv52ieBa6/C8e2djEHbysJMWWN1pESl1JzDnnDJJy24CJo9T6xZ0q1Yvqyy3EDgGWFRYoT+aVQEgabX7Yc+WXL4em2gQ9o8QEAz3Hoy8lSdC6vTgft0M/jK9Vvo+LF59DQ2oJ0Syqg06Krq4s5AzIciTxuZ6RQu/1KTFxIYCUwiWRaH2lPVLxFTSTEOkdQCeXlbpz97/vg7uzAu40Lh+YcZiHH3I6ebgfSMoYqV8mX+4WWV6eDMz+8X0cOqeiJzK+tZK78mzL/gGxcQ/LhR5jfz+L+D5onDzCvQTBzy42KEURk8uFHoHE0wmsqRN/09UxxKSXGYLndL64EtHoDply0CN1tLTA2bZY9frjvR5J2L3xQR2tS78vJgqWpBX959x+45ZePon8o+qGzZ1jgsWZAhkNJJla8x+qMNnLtVxJYRCSQwEpgpEzrgK99OJKtv3gbvYOREzWjsbpQyRzBcFlW4RB8QYFzDgGg5V+zYVl2uUgceFyD6JuUF7drkapu6XpfY+4vtOUi8UoFkpIxL+yqOWf+KkXVOikxZuk6k7m/0ZKKqVemR1yZC/y+cA+AvFAUXWeaNeoPbOF1D774W7+4YhE8AzIc4TKxRmKszmgTr/YrMfGIi8D6zne+g87OTuiG/AQ/+9nPcOaZ7F9uhDQrpy3HHw++yvyeXJswHoyGJ4olahJpdWEgwVlWQrwC4FAsbKTmHbYfOggAmHLRIhgtqXD29qDxs09gPfP2uF2LsWkzproewellgdWaBfDuYrffBC+UVHvOhUxfDlRrC0w5uZi+9jbklw0vvlBj2HEgLDGW3HvE31YLxM17kcX9X1SVOe2/7kCTJdV/L0qEolo406w40coeah2I1GxIFqUVC5mZWELi+kRoIQa2X4O3E0QkqG5y53kex44dw5tvvun/j8RVdMgJqJFuE8bL6F1VpUNJSTJyc1NQUpKMqir5X2KJtLowEFaW1cAAh8pKo8QrQmGZzgXaDx3Epy8+hz1PPY7aTS+CO/usuF2LUK3ROk6AA++v1hibNqNv+npfuy2AQC8U6/teGPDJ1mRfRAHP+xPMm6q3ARiONxD8S0K8gb1zn6LrVUpfThZ4LihvjOMwUFgQtvIm9f2kFKfoXkYaYb7j/bcvQc0/1uHjbfeh5h/rcP/tS0L2UUK4cTsjMVZntJH6OVHqjyMIAdUl+X/+8x8AwHe/+110d3dj1apV+Pa3v632aSYMiZJtFQ9PlFSlZWCgFl7vB8zWYCKtLgxEKssqXMZVIFJtOdY2ufiAWK9FqlqTfPgRdF50wL8Py4vEas99+g8Ljn0hPofX6cDhF59Hftly+XiDGeqNJpLzPSVLVN7CVeb6bTrRvYw0FRXr0VC3Gdd8fR64IVGg03L45sqzAQDPvvSvkBmQ4ZDLxBqJsTqjTaz+OIIQUF1g9fT04IILLsBPfvITuFwurFmzBqeddhouvPBCtU81IVg5bbloviAwOtlW8TB6syot06fvR0fHO9DrffcrtACbmk7i0FCrjMVorC4MpKCAR2NjqIAJl3EVjJTpPHibnMcq9FpeAVABnm/AzJmFqKyUX1kWrpoTzgsV+H175z54B/+C4m+a4O4aQNvfD6G3tgnAcLK5WsOOlSDle5Iyxr/feRluKpmDi4tOYOPNHJIMw8/TPcjh8/dzAIhT2keS8vJVOF77JYIKLuA4DuVXnoXT5t0QVeSEFOFaiOOFWPxxBCGgusA6++yzcfbZZ/u/vvbaa/HBBx8oFlhyc33GOtnZkYuAK7IvQWqqGf+390109HciMykD35p/FS6acl4crlCe7OzzcOGF6p335MnQbUuXbveLKwG3242DB/dBamymXq/HsmVlUb2/ArG8FgAefxy45Ragv394G8cBK1ZoYj52MK+8Atx33/C5Ghs53HefGRYLsHp18LW8AuAWAL6dOztP4O67vw+LxYTVq1ezT5BUBPQfD9nMJRUx72Xfvn3Yvn07bDYbrFYrli5dinnz5qHjVC26TmyFPsPXMtRnJCHvOp8/qbe2CUl5ecjOtqDZlIZBR3fIcQ2mNACxPxtFZP83YDEBX1QA/Q1AUhE+7Ps6rrr7j+jv78dfGgEePB67DijK9FWuPn8/B8cP+q5RuJfRoEGiMKnVanDbbf+t6rmybzsPqRYztldsh63BBmuRFUsrl2Le6pHxnRHKGa2fR2IY1Yc9f/LJJ3C5XLjgggsAAC+99BLa2tpw//33K3o9DXseO9TX1+GfH+6Ac8ABl86Dvnwvys5dpth8X1KSjMZGsQ1w/fpHQv4aB3wfbhxzUAxw6aWXK6qkSa0+VOvZPPCAES+/rPfPEQQAs5nHhg3KzeVKYL1vAFBY6EVtbR8AX4XrzjtN8HhOAzAslr61AHh0FVCUBfDmycyog+AVc4CvmsNKbg9edAD4WseLFpUhefBdZhXK1dmPY0/WYPYPfoT8suUhI2aA4WHHp81YMGr/bliDoAEgS6/HM2ec4f9aYzRh9g9+hD09PaisfAQnTzYyM6jiFW/Q8Nn/AIxB4ACHorN/EvPxpRiPv9PGC/RsRoZww55VN7n39vbiySefhNPphN1ux9/+9jeUlZWpfRpilKmvr8OO99/B4IATHDgY3DpYG3XYUrNFdpxPIKwE856eyMryKSkWxeJq585qv1dLaD3W16uXUF1drROJKyByo7sSlHisysvd8HoBoMG/7VsLgI1rganZgIaDyLweiDN/FXpnPwuPaXLYsThyiw6kWny6dLNfXAG+lXfpRVf6Azm1eivSi65UbTVetKNfpFbfdbjcMOXmARwHU26eX1ytW3cXGhtPgOd5fwZVVdVm/zUoSUi3d+7Dyf3PoOGzn+Hk/mcUGf2TM78W0XaCIEYG1VuEixcvxhdffIGrr74aXq8XN9xwg6hlSIwPamp2w+sRj1PR8BpktpoVR0gICeaBXqLJky9Cf/820Ye2l/OiK3UA6T1maPjhvwkiMdnLCQG12p5qGN2VoNTv5duvCEIF69FVQHKQ1guMIghEaeaU3KIDqWRznSEtxBAer3iDWHKbCgoKmRWsgsJCXPTaG6JtlSVzMDAgXhggZFDNwzxsv3MbeI/4+QTHG4QbFi1FZtHXAQB9HZ/CV8nikJz5Nf/2YMZ7UChBJApxCfa45557cM8998Tj0MQI81FzLd46sg1dzm6kG9OwctpynJdXIvnBqndr0eVUPnC2vNwd1D4rRn29x9/KG9S50ZJtR4/Vgf4kF3LbUqB3a+HSeXDpImWtQWBkcrzUMrqHo6LCKVp9CfhakRUVzpD9vv/9SrhcPg9WkcQq83AhoHLIjTSyTioNaf3xXi32/QZ48/UNsh/uaomAWHKbKirWY926u0TCyWw2M1flSVW7GhtP4L3b32Z38CCON5BbTRlOfGYWfd0vqIRMsYbPfhaSKTYRgkIJIlGgYc+EJHLDpqVW7bl0npgjJIqLZ2Hmsq/h1JkOfDW9HT1WX1J1j9X39cGZLWif445oBWO8BzYD7JYnS/goJXhostDKKy93Y8MGBwoLveA4HoWFXqbPq7zcjf/932uQnv47AEVokNC9UoOUlVBautAfKCwgVBaDW39etx6uXi9mrbVhyR/7kHpGJ7NNxmqnbf/+Nvxh5nMRt/liyW0qL1+FDRueRWHh5LCDoKWypqywSoorQBxvoMZqynCZYnKCkyAIdSGBRUgiN2y6tHQhNFrxj4+X86IjZyDmCIlgYReM0piKqqrNKCmZg9xcK37xi19g//79ou+rPdtQqfBRglzYp3Cu2to+tLTYUVvbJ3mO8nI3Dh26Cq2t+5Gx+EXJkNBovD+ATwwvWlTmF6opKRYsWlTmF78pGfNQMPduZEz5BniPCwYrD04DJOXymH+PEzmlAyEf7iwRwLt4ODodsv4lFlL5TEpzm8rLV6G29gBaWmyorT0gGXlQUbEeZrP4vdVDj6VYKnns4HiDwKHQgUhtZyGbKYaJERRKEIkCZf8TkggCJ9VmErXmWrLtKL7Q9wEavIpwxbkrYh7hwxJ2AoFtSjmqqjaL2jstLc3YsmUr9HoDZswojtsMw9CWZ3TIhX0q8UaxkJrR12Gcpdj7I9W6C/c+2k7tgDbI/6UzATNvGsSO/xJ/uCv5sFfa5usps+OZl5+CjbfBCiuWYilKzCWq5zYJwquy8hE0Np7wn2s+5jP357ScKCEdiGxYtBRS1S6304ZNJRthSjf5hGoQSgRn8LNf9ngZ8pZNVXxtBDHRIIFFSJJuTIOn1YGC5lS/udzg1qGg2Yr6+jpFA5CjQW4M0M8vfFDRMSorHwkxHTudDuzatQvPPPN8VNdlbNosmWCuNtEOTQ4Hy7xu2/8Ms+rR3bhNNB9woHEGdq6rj8q/I/XBb87mQz7cpdLCgwknxKqqNuMXrz6OAd73c2CDDVu4LZh7/Zlx8RuVl69CefkqbCrZKHn9kxa7MOu7gzBl89AZ3oW90+0XsZEOi2YhtbBgoI2DvbEXnJ6DxqCBd3B4gYqSoFCWd2vLLVtwya8uJe8WQUhALUJCkpXTliOvzSJauQcAGp5TNPvvo+ZaPPTho7hjx/146MNHFcc3SHm4IvF2SZmOIxl8G0i4lp3aSPmiYvFLSSElfryeAZGXR5v2EXJKxaJVqX9Hqs3laOdCPtxLKxZCZw7/t1+4qgtLZLt4F/5Q/WLYY8cC8/o5n7g6814nzDk8OI49c1FoqRad/VMUzL1bsbgSoig+/h8HPEGWP7cD+PIl34xL3sVDn6yXnDUoBatt6+p3kXeLIGSgChYhyXl5JfjEvZP5vXCr7wQfldDqEwzywnHlUGM8kOQSe4WDb3tq9ojm/5UsexMcr27LTg6p0S3CUGU1kap6hOxn9LX0Tr2vF223N/bgP/ffJzsjkdX+8jgBLX8upgd9uAsf9kI7yphmhKvPFXHVRW2RrZTg6xdaqckz3oXHJVY/SlcJylFfVYft398G3sXD3uh7NjO/OwhzFo+BNg5fvmQQPTNntxP/feiOiM5B3i2CiBwSWBMUqVTzYOSW4cshZ5APJ7CE77PiIZSidIl9VZUOjz8ONDSk+Gf6lRX8Ey2bXgY/OAgAPpHlbQUrSD7Wlp0UUn6peIg5lviRwpwduiTOZHbB3dmBlk0vAwidmwiw218ZU6TbX8LAYSGRWsr7VVW1WTI9PVaRHQuBA5OFa79kow0co2cQ68zFXQ/uAO8afi6n3tf7BBUH5grGlAILmqq34fCLz8PR2gJTTi6Ms67Hl2/1SsZiTIQhzwShNiSwxghSeVTREDzeREg1BxAiskpLFzJHoYRbfSflo5LzVwHDlaO0zg58NyNTtioih/Ah+9OfVaCtuQVJmSk4/9uLMfnC6f59qqp0Q3lSAMChsZHDunUmPHDOCVyaPig6nrPfDFOyuIIFxKdl5z+nwrDPWGGJH69nELw39H4d7cMqcy/2Yju2o2fAhpxqM26dNQdff71K8nnFEiYaKFgEghcyCOnpgO/5R5JjFQtyf6wEepcG2jgk5YYqnkhWCbJwdknEgPC+Sl/wYOaZKy04+MvH4XX6zO6th/Ro3dkAnvd9HLC8dawhzwDg6htEfVUd+bAIggF5sMYAcnlU0SCXah5MuGX4UkTio6qq0qGkJBm5uSm48PoS/GOv79hCVaSnZo+Cuwpl8oXTUfbMKnzz/27DFb/+NrJKC0TvW2WlURTWCfhG2/z2k5Uhxzq+dw48bq1oW7xadqNBsPcnffJycJy4Fchxemj5c5FSaMFe7MUWvAUbbOABtAwM4IkvPsM/9n42YtfM8lgJ6elAZDlW0RJuBFOgd+nLlwxwBy3gi3SVYKQs2lAW4rdy1r3qF1cA0NE63y+uBIK9dcXls7BoQxlMGSbRfs4up+LIDIKYaFAFawwQS7stmI+aa9Fr72EOTpbyVUWzWlCpj2q4iuS7npb+bDzxxfcAAMsKd4MfHES7TFVEjnDvm9QIm9aB7JBt7Q1F0KZYcNq5h0dkFeFoUVWlGxpddAGuv8aAO2/aBqM+YEXbWfMwfTnwi6k/hatfLNKdHg9eOPQl7ozgfLGszJRLT8/NtfpbhrW1ByK4IjbBLbXpa29Dftly2T9WiotniTxKgg9q5k2DMGfz0BkjXyXIwpTBjl4wZZiYlb/jv24Jut4k5nGD/VXF5bN8omvoXJMWu/z34uysgr3zqriMOyKIsQoJrDFAtO22YIRK2FSdFQZ36KNXM9VcqY+KVUVyekz4Xd1qzJu3H0cm5cFh0CNl08aIc6vk3rf6+jqsW7cHKSk22GxWbN++FPv2+T4c8rMd4AwGvwcLADiDAdyCB9AZhdAbaYZFEuf3lSnJ5goWu8erm/HJ3p3I1DfDnJuL6WtnIKVsHnpq9qC1v495jOZ+O+7Ycb+iNrawMlMw8gsrMwEoEllSHisAooHLAGKqWjVVbxO11BwtzTj4y8cBhB/BFOxdEvxRKYUWrKm9OeprCmRh5WLsuPsd0SIAjUGDhZWLmfubcnLhaGn2f63T9cPtTg7Zj+WvEkTXpMUuzL/HCd1QQcuU5VU0N5EgJhLUIkxghJgDKSIdSSNUdFqy7fBy4kHNaqeaAz6R9fMLH8RvljyJn1/4IPPDVqqKlDO9CXVFhXAYDQDHhbRelCD1/kzqy8DOndWwWGzgOCAtzYYVK7Zg3rx9MJt5/ORnQO6aG6HLyAQA6DIykbvmRn8VLbClWVKSjKqqxPk7RRBJjY0a8Pxf0Nh4Or73vWTMnDkHVVXykRKBYndB6lasnbQeWfomcOD9oqKpehvaX69CTlBquUBSZgoAZW1suTDVQKRGBrHS04MJbBlGghB78FzuBmz9bi26W8RVTa/TgcMvPh92BBMrskHJCshIKC6fhSXPXCZqBS555jJJX9T0tbdBYxxu9WXm7AXHiQW41DUKomvmTYN+cSUQmBhPEARVsBKW4JiDYCKNLQCGKzrCbL9oByerSVaeE21NppDtyy59D96gUTyBrRcg/EpIqTZlbrsFg26xMdhgcGHZsu24/fbpQ9WeBcy2ZHCVRzDGA9IjcZSu2FSDYZH0CgDfkGcA6OwMX80JFLurcp6GUSNuOwmiIteShltnzcETX3wGp8fj/77WoMPc68/3fx2uja0oTPXoK5JVrsD09JMnG8Hz7KF/crEMgasTzZk6ZGbvg6vHhtamc8F7fZ47l9OE1qZzAQCpacMVM0drS9hFIFKRDWqbwlmtQCnyy3y/N4SWZ84MFyZfXSS7ilBAMLuzVpMCsa+IJIjxBAmsBEWNcTGs1wWKLEFopRvTRkVcfdRci+nXHkPn766HZ3B4jopB50RKqp35GqH1wloJ+d57b6Op6SQuueRSANJtyk8O7mQe22KxhW2lSRnjKyuNzNdGsmIz+HXRiLJhkVQBQVwNX6evmiMlsAoKeDQ2+l6fpW9m7uNobYFuyjQsG/r6d3UH0DowAHNWCuZefz6mLDxDtL9cG9trKoTWEdriE63M/KJCdmSQkJ4OACUlcyKKZQhOJx9od+NkxxngNG6/uBLgeR06WueLBJYpJ9f/TOSeVSTiJ1qkPGJS5JctD/n+eQ+HP49wH87OKpiyvCHfj3VFJEGMJ6hFmKCEGxcTTUTDymnLodeIV4ZFUwlTi7eObEPhhTX42i2vICmrA+B4JGV14MI7/waLRb71wjIXA8CBA3tFbURWmzK4rTMp04UlZ/fhilJ72EHHUi1Nqe2RrNgUCLcyTY6CAqGy0CBxndLVnIoKJ8xm3+vbXXnMfUw5uci6phycwYBlhUWoKrscu1Zeg+uf+FaIuALk29h909dLDp/208++D1b1i9UylItlYA6V5nXweozM/QPN4B69FrsWWPHQh4+iO3UAa9bcjNtvX4c1a24e8T9WBI+Yo6UZ4MXtXLUIHJx+feW1OOnJBacJXWWqxorIwHOVlIRvbRNEokIVrAQlsNoUvD1a1AjwVBPh/qYs/BhTFn4s+l5p4U2yrRe5JPnANiKLwLbOpEwX5k9zQjdUsJAadCwYxyW6UAHCRozUdfb29iI3N4VpQhdE2dyUvViatR1WnQ02txUffn4QKJafo1hR4RxqYRYBOM64TuncLt81OFBZacTmtrvxrdKNsM2fCXdSEnT9/ciq+xLTzi1F++tVvgUAGg3g9UKXkYnL087D3zx7I0rfVxSmmlQE9IfeByt/LLhlGBw8GkykKeR6oxPggP5UEw4smYKT83KBCCYUxIvDLz4vil0Ahtu5clUspbDyxm689SG8uukJFGXaop6bqPRcaixUIIjRgARWgqLGuBgW5+WVIK3H7G9pfHnkU6SVmkelRSgnIsO1Xnp7rbBY2H6PcGN8Ao89s6jZL64EgseXBPuugjGbfSKJhVQSvs1mBc9zTA+X3d6LuSl7sSJ3Cwwa3/NP09tQZv1/cDQtkV1hJ4ikBx/8Obq6bkVgm9BkMKDcYkH14gsk20jl5W6Ul7tRX5+L97efA8+QonQnJ6P1nK+hseEY8jo7fDt7veAMBmRdU47TSxfA3HxGxOI9bJjqmZXg/32z4pFBgS1DFoGxEPc8m4b3/rIY+/fMF+3DcU4AOlE2lM6swyUbvoFNeaHV5WCvmVy6fDxwtLZEtD1SpPLGvv+DX+Hjj6WrvWqeS661TRCJCgmsBCVe1aZoPUHxIJyIlMvfevfdJbjmmr+BY2geJXETwrEbPvsZ8/uBZl2W78oHj8JC+RgElgl6cFCP7duX+r8eGOBw553DIislxYKlWdv94krAoHFBq2D2oU8kXYWqKqf/gz4vMxPXJCfjPA6iNhIAZpWjpma3X1wJeAHsL5yEwznZmHaqGfld3aKcsvPyStSv4py2Gr29DlVGBgXHQljTu7Bi7RYA8IssjnMjJ98XltrWUuJvF2pNPhUeLjJFaQUmUITdfkU6HrsOSNF0RXV/wbELgdvVQKqt3NDAbt/G41zxnh9JEPGAPFgJjJKYg0iJxhMUL87LK8ENM8v9bc90YxpumFmu6D67uubio4/OCWnZuVz6iOImpEy5gdul/FUcB9TW9ska4wOT8Hke6O62YsuWFf7MLQGPx1fJqqrytUGtOnZ1TuNolIwtCKa8fBVqaw+gpcWGX8+bjwXJ4qwjoY3EQrIKyHFwGA2oKypEU3oaAF/ifjxx5q9C50UH0F5mQ+dFB0TiIxK/DisWwmB04dIb3gc4wJylQ8HMr5Ca3gi9NQ2cdnh1q5BYPqkmg3ls4Wc4XLq8cM3r1t2FxsYTuP4CHk9c0wmLphMceP8qyS/eXKf4voJjFwBAYzRh+trbJF8TCVJt5aKiItE9qeGbkjrXSMyPJAi1oQrWBCNcMCKLWGMG5IIvo616+LxGX0djYxGWLt0Oq9WGnh4rJk++CMXFxYqPY520BF0ntoIPqKIFm3XT03l0doaKLCnfVTBCtSw3NwU8zxZrwPBqxNraWRg4ZEGSKfSZuAcNUYVzRtpGkmptCni1GhyZlIf8rm5/XthIE6lfRyoWIjW9G7e3rBNt21SyEZ5B8f27B9zIfc2CtgW9klVXJRWYQBH26CogOchTz3kHcEbfi2gcekm4+wqOXVCyijASpGY6VlZWAlDXNzVS8yMJYiSgCtYE4qPmWrj1oUurAem2Wiwr2oDg4EsOjY0af6UmFsrL3diwwYGurjl45pm78fvf/wTFxbfi299WLq4An5F9yuxr/RUrrd6K9KIrRf6r3t5QUWQwSPuupBAE2bcWvIKjT0+F588aHH16Kr614BX/PkK17MRnM0JmH3rcWvBer6JwzmCk2kVS27NnFsLLyQtIh0Hv92BFglpBrUqqRYFIDeZmbZcywA82O2WrrkoqMIFiqyiLuTsmB2nWcIGp+WXLcdFrb6Ds/X/hotfeUE1cAdIzHVevXg0g8ucQzbnIf0WMRTheKp1vlOjosMPrTahLUoXsbAva2iJbtRSOj5prFXu0hOBSc5cWBc2p0PDD2lqn00kOcN60aSOzkpGSYsGaNeFHfZSUJKOxMVTHZ+c78I3nHkmI1Yxyz0bq+tPTvfj4j9Vof70K7s4O6DIykXVNuezMxKoqHWpeex2/WXMLko3D5vM+ZxJufvEF/N+e1Sgs9KK2tg//uf8+pKV8hinzD8CYNABnvxlfHDkP5837gOk748GhvUw65DF43AvgayPN/sGPQj6MA39WhDBa1uxKk9uNa89bGNGcyNAFA6+A4x4Ez59AYaHYEF5fX4ePP94Dm83GrJzm5lqZ4aIcx6GlJfS9CPZgAT7DfO/sZ+HMXyUKHeU0HHhP6LHDjbgJruYAvgpMoEgIzOs6+jQwNXT0JVp6dfg46SbkZqeipa0Hv35pF97deYh5X6OF8O8m0udAxJ94fN4QoWg0HDKHplewoBbhGCU46b0rzHJxIbjUZfXtL3xwevQ8Lr2ELa6A6FqKgUj5l9qajX5jcLhrH02krr+7m0PLppdxKjkJR+bMhMOgh6lmF77W0YYzr7iK+ZrycjdWp/wYyRpxAGiysR+PrqrAG5/d4K+KZV1TjpZNvWhv8PlcmtLTUDelEDPcnyNNH/qhZfemo6RkjuTKNSVtpJ6aPWh/vQqvXwS4krVwWV3osTqQajMxRfnCSy9HaphW8QMPrMPLL//e/wHMcSng+ecBrIaQNs/zvvcjsLU0b968sIsxpGYRSlWR5GIhgkNHWeJKyYgbJVERgW2wV/fl44dL2qDFsI/PAx1sOZciP9lXVc3PteKhey5Dxii1YsMR6XMgiIkCCawxCivpXWo0yUfNtaLVT4Ep7gDwfZkPSSkvjtLB0IHp4IEkZXYquvaRoPWDf+Loy3/2mbUDsp2yrilHQUEZ8/pzkzpxKjkJdUWF/pE+DoMe/zr6Fcz1dZKCNUXD9ugUZTVgw4bhqAahKiRUyI5MngSvRoPt7UtF8Q0A4OINuOv3vWhs9L2nUh4YVnq3QE/NHrRsehn84CB6k8QllcDRSga3LqSaJBVL8MAD6/DSSy+KjsXzdgA3Dn0lnTZ/zz33SC7GEM4bjV9HKhaCFToKAJyWA+/lIxpxEy4qIlCEzb1kOY6nnERB/24YvD0Y1KTiZNJC9Jpmi15jNulx500XhRwrsOomXOOkxW7YTu1QNZ9KDtZz0EOPi/ouQn1Vnex7NtKRFgQxkpDACiKStttoEm65uIBQ6ZIiSWvGQx8+Knm/paULsWPHO/B6h71bGo1G8Uq94eDLYZGiNTgx9/o3Fd9TPOmp2YPWP/0RXueQn2roPt2dHWjZ9DLWrcpDxW/ni67fbOZxy4xNODIpL2ReolejkQ06lRoPw5sLUb5M/AGfWjo8D/G95zYAAPbbfXECgQGkD/1Viz/uFAvWSLOD/OGhACz9XvQmi/1fPVYHtDkm/PzC+0Xb5QzOmza9JHE2N3ziSjptXknlNNJgUTmkPFe8lw8xwKuBIMIaPvsZumBFV5CgYmHUi38+gqtu9sZeHPrbVhinDoLT+GZESgXnqonwfj9c8RBaOpthhRVLsRQzuoqxc91Q1ZEhsuIVKsoSnfEeVUQQLMjkHoAgRoJbVx81147uhTGQSnQP3i4301ADDZzewbD3G+yviMS2J5jRCwu94DgehYVeXHz730KS21nXPhK0v141LK6C4AcHsaDzNyHXv2GDA1+fXweHQY+9e/fiqaeewsMPP4ynnnoKe/fulW2fKhoPwyCwYrjfPh/PHLsXPzv8MH7f/lM89/cu5msiyQ4KjFpY8LkdOrf4GQeH3ApG9e9972eSBmdPwCDoUBoAFDG/U1BQKFkhDd4eGEVRUbEelZWPSEYFyMVbpBRInE9iu1rotMqPHxwpwqq6nfFth19cCQjBufGkvHwVfpj0QzyMh3Ev7sV8+P4QcA+4UVPJjoBR0xwvIIhOe2MvwPtE58511aivUrYohyDUhARWAHJtt1ior6/D008/jeee24BNmzYqXoEnh9K5gnJVIbPOBA8v/mUcfL81NbuZAiuS3Kzycjdqa/vQ0mJHbW0f7vtu/qjNRPyouRYPffgo7thxPx768FHst7CHSgu4OztCrr+83I2sa8pR99nn2LJlC2w2nyfKZrNhy5YtyOx9R/KD3Jm/Cr2zn4XHNBk8OHhMk/0mazlKSxdCpxMXnIXRQWpkBwVGLcw87sTSf/fA0ucBeD5kpVzgylDAV427bPFMbNl0Mz56+z5s2XQz5p6RAq1WyzoVAECrnQygEhyXJNoutPjk7pdFYLYUz/P+aoggsgSDu9ZxQpQ3JTyb0oqF0JmDzqfAcxUrk7TzwCH4feLABf1qZs35Y1XdzNnsP34Cg3PjhVQVUGp7PEJFWaJTTuQRRDwhgRWA0rabUj5qrkXllsfx7va/+z+EI405kEJpSKdcpavP3c/8XuD9xmpyZxFLwGgssCqU289PxZdT2MN9AUhmPKWWLsD7H+6GyyUW5Nee68J35/5L8oMckA/OlCIwsBTwVXKElZ+RDjlmIQxwFph53In/frsXjyV/IyTkVpxsX4TLFs/EQ/dchvxcKzQazmfKvvcy/M9DtzDPpdVq8etf/xStrSvx3HP/y1ySL9yv1WoNuV8W4aohrJDRwHiL4vJZWLShDCmFFoDzrRZctKEs7q2lLG4ypmjPhQE+oWlAEqZqz0eR9jzJ6BABVnVtoI29KEMqUFdNIq0CxiNUNFKRRxDxhDxYAag5YFn4MJ/aZBWtvgJCzbrRoiSkU24cjeA1CybwfmM1uUsRl7EqYRAqlMd3f4X9r/4b/R12JGWm4NTV56IS6SH7h8t4auvsDNn26CogySDeJnyQRzPeJRCp0UFqeJGCTfVysRPilZWVuPOmf8JsElckTUYdLr8kE1/dtDZoFSEHj8fjFz5yhvDi4lm48MLzFC03D1cNkQoZDdxeXD5rxLw6TdXbcPjF53HZnT9EZuYUZOqmiL7v0enAFy+TPUZpxUKRBwsAvvqzCfPvHRS1CVnVr3jAuh65KmA8QkVTCiy+9iBjO0GMNFTBCkBp200Jwoe53s1uk8RSAZIiuP31UXOtbLVIyf2yWjX79+/HL37xC9mxGD01e/Cf++9D/dob8Z/770NPzR7V7zdSupzdOL77K3z6wgfob7cDPNDfbsfuTbvwXteQj0nj+yehy8hE7pobZTOeWH9pSwVHSn3Aq0WgF6m29kBUJuHU0gU4/clfofjFl3H6k7+SvHdxgv1q5GanMvdzOW2orv4dnnuuD7/97Yswm81+oRXcwouVcNUQF9iVSKnt8UTIJHO0NOPzN/8f3EEeQJ7j0JcT+oMUPI5mH/aFVN1mfONKZJ62Mmz1Kx5EWgWMR6joaLV6CYIFVbACUHPAslAZcuk8MLhD3+ZYK0DBhMvFYt2DkvsVKibCqJyDBw/izTff9LfGWCt/Apf8A8Mr8gBEFEqpNunGNPz91X/DMyj2aHgG3Xjx6GHcXnUgouOx/gJv7OBQlBXqg5FKER+LBK8MbWlLQ35ud8h+LW1p/uR+s5ndwvvxj3+IlpZGxdEP0tckXw35fEcOShZ2QGcYfjbuQQ6f787BaWUju/Ls8IvP+wNfj39SAwA466pvIikjE169Hn05WXCmiVt6UivuNmx4lhl8OhKCikWkVcBwkRbRnB8ArSIkEgJKco8TQvSBVEijnJ8klvMFk25Mw88vfFCVc9TX1+GKK5b5/WSBFBZORm2tT6D85/77mAOAdRmZOP3JX4m2CeGWStPQY+Gj5lpceeYigPHjFW3qdLAQeGn9ZVia8opkWngwsc55HC0C50tef82nuGft69AGZHMNOPT4+dPX4p33BbGuAfONB/Dwww8DGP53sW/fvrBp6CweeGAdNm16CR6PB1qtFmvW3IQnnvDFW1QvvgBTZnXhrMWtSLK60W/T4fP3c3C8Lh1T7vwDs7XFqr6okdtUvfgChEwpBwCOQ9n7/2K+JjD9PZDAf3cjDaWFJy70bEYGSnIfJQTvU2BIo96thdFswsUXLlH9Q1Rtgz6LmprdTHEFiD0wLHHF2h5tpUtueLQc5+WVIDsvF21NoQOOlRprQ3LSLlwe8gHX21TKTAsPRpjzKJdWHgvxDHEsL3ejrOCffnGMmjxwF2TAwzvQ0paGX790eYC4AoDJYOVeCSZ2YNib+PTTT0sa1qWuv6pqM1599RV/NITH48Grr76C884rRXn5KphycnH8II/jB9NErzPl5squPAsUWGrlNplycuFoaWZulyIeK+4Igogv5MGKE4Hepx6rA13zvTj3msVYe9MdcalQKM3FigW7vVf0gRhIoECRWnkXvD0w3FKAHxxE++vSwaixDo/+2U8rQ1bcJSUloaJifVjfmNKcNKWrBGtqdkumlcdKuNiCWBHEsSCaXZ81w/HiYVy95mdYsaYiSFwB6emh7zsAZGRkiL6223ujEhPhVhFOX3sbNEaT6PsaownT196meOVZrLlNgofq2vffx91ffYUPu7tDrkXqdRoN+1c1jaMhiMSFBFYcOS+vBD+/8EH8ZsmTeG5FZVxXzalp0JciJcWCpUuXQq8POo9eL1r5E7zkH2CvyFNa6QpEHBHgY2CAQ2WldNRCICxj7QsvvICygkKRYBCqaYEiS+2ctHhEYAjEI8QxEClxfMvMV2A2i9tfZjOPRx8tx7nnnh9ynKNHj2Lr1q3+r1NSLFEt3w8nyvLLlmP2D34EU24ewHEw5eb5B13LxQvYO/fh5P5n0PDZz/DbR5fjssUzFZ87EJHgBY92lwu/b27Ghzab6FqkXscKbQ30mAUb4NUS0gRBRA+1CMcJahr0pSgtXQiHw/ehvX37dthsNlitVtx99zpRi0Tpkn9dRqakV0sKqeHLUttZBBtrs7Mt+PdNt0hW06pPXozKSiMaTz6KpMxOzL3+TVESfbRtWLUiMOyd+0Jmz6nRUpLzx0mJ4EvT30buhtXMFu6dd+5ivubTTz/FlVde6Q8SraiwRrR8v6l6GzINBrQzEvkDRVnwLMYPHngPb6x5SnKw84JHJ6OrYSt43ieqhaHLAPDO+18yzxFIoCeMhdPrxVucBg+/9gbz+wBbKAO+LDHBkxavkTMEQcQGCaxxRCTZUtGYq4uLZ6Gp6SQ8Hg/mz58PjuMwe/Y8XHLJpSH7Bs7RkyLrmnKRBwsQV7pYcyELCi5iDl8WRwdEjpRg+MfeWfjFa8Mr5vrbM/HpC6sBwC+yom3DlpYuFHmwAPm0chb2zn0iESDMnrv+mgX4v6oPQ/YvKChU5M0K54+TE8fl5W6mJ05KaPA8L/oZFH4OlfjHhMiDVZmZeLGpCYMB5nE5UfbBA+/hwEt7md9LKbRgStlp8GprwPNe0feEocuCwJI6B2vQNYtwglfq+16vV5R/FqlnjSCI+EMtwgmIYK4WqidK0+Xr6+tw6NBBf5YRz/M4dOhg1Kn0qaULkLvmRn/FKjB7SsrvdN0dXzJbUBUV7HmCwQgz9HJzU1BSkuz3bklVzV44tCakJekZNGL/q1cBiK0NK5fOrhTbqR1+cSXA8y7cedNFzHT3srLLFHmzwvnjlLaBA5EaneNb8Xez6L6FXC+v1yub6yVEHlyYloa1+fnIGspsyzIaJVcd1lfVSYorTsuhtGIhDr16EMYML3MfIffLCiuuNn0D8xAaiSA96FpMOA+VknYpGeAJIjGhCtYERM5cLffhHu3r5JCqdEn5nXpn/gkbNvw0qlWEgkFeEEyNjRzWrTPBYgFKJappLf0ZzGP1d2So0oaVSmdXitSMOaPejQ0bng2pAklVOyoqHkFl5U3+9/S7ObOwrDDUbC9UrVJLF6D/8Ffo+ecHgNcLaDSwLLhQtmq5Zs1NzKrOmjU3RXLLIhytwytCL0xLw4Vpab4vOA5lEuJq57pqyePxHt6/qnCgjUNSbmhl1NmuwcN42PdFF/zHC1xxKD/o2oeS1HIlaecFBYXMCAcywBPE6EIVrAlItObqeJqyg5GLnWANX1aClEG+okK6mibVeiws4ENm9ClFzZR7qRlzTpeVme4uVdXo7GwUrcx88ovb8W5jaKtSeH96avagd8+HPnEFAF4vevd8KHsvTzyxATfdtNZfydJoNEhKSsbLL/8+xJgtmLY1Go2saVsq2kBqOyuSIRBOy/lXD375kgFuh/j7bgdQ9wdx5Y41TFhu0HUkqeVK0s7VmEVJEIT6UAVrAhKtuTpecwmDqarS4e2fPgp7e1qIqTyW2AkpI3zDUDzTl1NNeOuqTHQ5tb7q1FRTSGo5EFlLMpgv/v4mPv3qSzimTIIpPxvTTjXDE0PKvXXSErQd3RoS8vnkby7H17+hCxGfUtUOoAjz5u3D0qXbYbXaYLNZsX3XeViGYeHAGQzYk5GOa0vm4GTjCeSYzbh11hwsKywCMNxClLuPJ57YgCee2ICqqs24+47b0d/fB8BnzL77jtv9+yk1bU9fexsO/vJxfzI6IB95IBXJsBd7sR3b0ePpQaomDUv5xcD78wEAM28ahDmbh6MNqHvJgFPv60NeH3xcqWrdTTet9YefKiVc2rkasygJglAfSnIfIRIpWTc44BJQli4f7esiIbiNBwBagxNfu+UVTL/4c/8cReF6IjHql5Qko7ExtGg7ZQrwmy0fMIdi3zCzHCc+PC+qlmQw9fV12PHu3+ENyDTSeLyY1dCIyZw2JOVeKXd/7xBuuOp15GYPoqWtB79+qQ7vvH8bCgu/hdraPtG+wSvOfCShsPAnWLPGC4Nh+P4HB/U4x9aCnBON0GVkYk9GOip++6zotUatFg+cebZfZAFA8Ysvh73mmZNPQ6cz1CSfYcxEUnaSotRyY9Nmf6Brf68Bn2/PQkv7DExfexsz8gAANpVsDBkGvBd7sQVb4MLwveugx0qswHz4RBbHuZGT/zE6WufD7U4OOW5KoSVkZI1csny8iGe4bDCJ9DuNEEPPZmQIl+ROAmuESLQf+GhHtMR7tIuUCErJ7sKr2/eKxFWkYo8l3sxmHhs3cvhn8o/DjhqK9d43bdrIrACanINYeOBLRcKERU7OWwBuAdAfsDUJwAtobV0Zsn9V1Wbs//v9+MGlnSjKAho7M/FB93L8x3tGyL4pKRasWeMTDlLjWnLNZlSVXQ6APQ6Jfc3sAdGAr4XG+rUUOM7I2LQZloN3iUcSgQPAw2uaLJ2eP+TBCmwTPsU9BRsf6mWzIB3rcDf0uj5k5uxFatoJ9HRPRmvTueD54eK/C3pk3nQZvv1Ecdj7jics8axkxFC0JNrvNGIYejYjA43KIZhEa66O1ZQdDqk2Xl97msjvFI3h3ld1coRUo1avNuPN17qZrxFElxpjbaS8ag6DXjb7Kxxa7YPwePqDtvZDq30QQKjAumEBYEkbADdknyrK7MB16ZuxpWUF9tvnS16zlH+rdegDPdwqwkCssMKGUFFjhRWWgtSwpu3kw4+IxBUAcEOzDrWOE7Ac9LUUA0VWU/U2tGx+HpkZBnS2nwWX04SUQgt6GnuY19iLbqRP34UsfRO4oR/L1DTfdfkqWUkY4Iz4B38luqrn4NtP9DGPM1JQXANBJBYksIiEoqCAV5RzJWe4FypFrCqTVEZTujFNsoIFqLOCUsrD1ttjwZ6MO3C6oqOE4vGwPFXS21nixKBxYWnW9hCBFeivk/Jv5ZjNEQ/qXoqlIW05PfRYiqUoq/h62JVzGod8BAHnHUDy4Uf8AkvIy/I6HUi1Avv4vdjc1oaORjc0Wg1z1V+mTodsQ1PI9tS0E36h1TaYj32Hfwzu5OhX3SmugSASCxJYI8S+ffvw7rvVcWutjReUmsqlxAqAkHwvIHyVSRjOHezBEjKu1FhBGRgsunfvXn8avl6fDyAP5jOKo/J2FRayhU9hIXuZvsbBFl5W3XBFaVKmC7OmDMJktOPk/mdgnbREMjLg4Q3P4vShConSNurlF18B/BPYju2wwQYrrFiKpbj84itwVfk3Acibtr2mQmgl7mP4PoeFhZCXBQAfdneLQklZ4krPaXBdTrbs8QEgU+8b2hxr0K0aUFwDQSQWFNMwAtTX12HLli0RB3uONdSIHygvd2PDBgcKC73gOB6FhV5s2OAIER6lpQuh04X/+0Dp8OTA4dyAr3IVaKiXWikZyQpKIVj0o4++wpYtW2Cz+QSNy9UEl+sWPPig9JBrOSJZpm9s2gyA3YZ16vKQkmLBpEwXzpzuhNnoczV5XDZ0Hv8bzj39S7z3/+7Gt8ovZEYGRBJge9Vfv4nLL74C9+JePIyH8fsFS/Hy8x/gv29ZC+fmudj1JxOAY/jTn9hBo33T18PLyc+f9JqGhUVgXtbm1lZR4ruAVqsFx3HIyJiMtfn5w5laMnS48mJaVaomFNdAEIkFmdxHAClzc6CBeKwTPFoF8HlyhGT2eBBcLZGrJt1++zrJ7ykxhKq5gjInZy6ABsZ3itDauj+iYwkoXT2WsWsOs/LDg0Pv3I1w5q/Cyf3PSAaYCnAaM9InL0dKxnCKebQ/5yzDep8zCTe/+ALe/Hw1fvWrgRCBbe/cB9Q/gUl9O2Hw+jxUgbKR15jRO/tZf4tw13VXw9HiqzatPniQfU8BJvrA/aVwek143fEwrnjg0qgqj/GAVhESAD2bkYJM7gnASAZ0qgFrBmC4QE250SrxEljBhnu5D3g1znWk+xi++uIAtC4OHj2P0848I8o2r1RrS77lJUe4rCQBae8S7xcj4cQVAPDeAXQ1bAUAv8iK9uec5QlLNvbj0VUV+L89vsHRwQLGdmoHPMZidBp9K/fSHQdR0L8bBm8PcxXh9LW34dP/eRxGjQNZOh3a3aGCKLCVxsrXglYHfXIyXL09MOXkYu7a23Bl2SIAiSGuAOU/BwRBxB8SWCPASAV0qoEwA1DwIgkzAAHIiiypYclS2+OBGsOTpfiouRbv9O+Ga9qwR+s//V3IbM6NOM09I6MQnZ2hYiojI/5eGSnvkt07GSUlyTh5kkPtn49hluZdGLw9GNSk4mTSQnSZZoe8huddsJ3a4RdYwT/ngs+stCQf553RguzMZOgMVlgnLRFVvqREX1Gmr8rHWlkaLAK7TLP911h09k9D9s8vW47XK0y4VPcMrs2x4fdNTXAFDHIWWmmBVVFz+TXI3LsP5gP7YcrJlc3XIgiCCIY8WCNAaelC6PXi9Ge1PvjVRmoG4FtHtsm+TipmIJb4gUhRY3iyFNG+LywqK9fDYBB7ZQwGMyor4++V6Zu+HrxGfG4Xb8Zdv38MjY0aXH/BXzDTtRVGbw84AEZvD6bY30W6g91WCxQ6gb64vXv3YsuWLSgtycdD91yGnKxkcJxv/66Grb4W3xCBXqlAGjp84aUsA7nUiCCp7QBwxQOX4scnq/H8qUa4+E0ApgDwea42bHgW8+bNE3nIBtxuNM+fhym/+wMueu2NuIgrYSRQbq5VdiQQQRBjD6pgjQDFxbOQmmoeE6sI5WYAypF1TTk+f7MKh3Oy4TDoYRp0YXprG866SlkuklrEK6dLyfuidAVdtKNNmqq34fCLz8PR2hJ1RUVomwkJ6F5TIe596VH8cedqAMCjqypg0osH8GnhRkH/bmYVK1DQCPdaU7Mb27dvh8vlwp03XQSzSfzHRXDlq2/6+hAPlgc6dGaei5WX1eLyq0PfQ+ukJehq2AqeHxa9HKeHddIS/9f2zn2+VqLLBq3eissWL8GGDWcP5aDdgIKCb4lS+Tdt2hhzFEckzyg4GFQYCdT00UmkVqfAfrIXKQUWlFYsFA2SJghibEACa4SYN28e8vKmjvZlhCVcHpQUzRnpqCuaDM9Q28VhNKCuaDLyMtIhndkdGUoFTDzS5sO9L5EGkUbqlQnMcQIAR0szDv7ycQCISmQF+pOeu3bYpFmUxTLfw28kDyRY0ADDAveHP/wBACA3m/30AytfwrWY6yugG2zxtyV501Q8dO9fkTX1SgDzRK8XxFmggApsPdo794kEmFA5u2wxUF4uPpZArF7JSJ+RVDDoMy9vwL38vb5zN/Zi57qhnyMSWQQxpqAWISFi5bTl0GvEFYfAPKhAPmquxUMfPoo7dtyPbR/83S+uBDy8V1FEghKURgCE26+qSoeSkmTk5qagpCQZVVXK/sYI977IBZGqQWCOk4DX6cDhF5+P+diBLbiG9iLmPl7TZGRM+Ya/YqXVW5FedKXISyU+pq/t19LGTkkPbuU581fhQOadqM36AfZn3OKvlmk4X7UrmPqqOrx+aQ3eXOrBzv/OR9+hZaJrsZ3aIapuAcOVMylijeKI9BlJBYAGj+1xD7hRU6nOzxFBECMHVbAIEYJhO9wqwmAzvNbFzlZSa6Wk0iR1uf327ZsnCjFtbOSwbp0JFguwbJn8+cO9L/FeKRqY46RkeyQEhrs+uLkSG9fegmTj8OgdXmNG3/T1SMmYJymoQo/pCyX99Uu78NA9l4nahKzKFyC9ejF4e/A8QVaVR+mxApEKgs3NzUNysjVsxTHSZyQVDGpFqI/MfjIxVxwTBCFNXATWli1b8Nvf/hZutxv/9V//hdWrV8fjNEScOC+vJOzKuGDTt0vngcEd+uOUkmJBT80etL9eBXdnR8QjVQSUChi5/Z5+2ihKiAeAgQEOFRXhBRYg/75EslI0mhamKSeXmctkyskNf+FhCJzR+Oq/bkBGBo/HrnsQKRqfR0tqcLL8MYd9ZpVPv4vvr10kuYpQQKu3Sgoge+c+/2tqKneLhjUDw1UeQWBJHUvOBC88g9/85hls2bIFLpfv57ulpRnr1t0lui8WkT4jVjK+ntNjKb80ZN+UgsRbcUwQhDyqC6yWlhY89dRTeP3112EwGHD99dfj/PPPx/Tp09U+FTGKBPuRWrLtKGhOhYYf7jrrdDqcmZkjCiB1d3agZdPLABCRyFIqYAxmIwYHQlO1U1IskoOkG9i2o4hQGhER7dBoVi6TxmjC9LW3xX7xCJ7ReBUcuAoO2VcoOWZkPjPrpCXoPP435vda976KXtdJ5JctZ1Zz9mIvtjduxw9zf4CCgkL86tF7cHq2HZxmeAwO79UyK2eBFBfPwq5du/ziSiDc0GRj02ZcuXYv9Hw7+m06fP5+Do4fTJN9RqzFDt8tWwvDq1qRgNSZdSitSLwVxwRByKO6wNqzZw9KS0uRNjRm4rLLLsO2bdtw5513qn0qYhQJNn33WH0fx/ntqdC5NDCYjWjJ6sULpt2wXG7Bgs/tmHncJ3xOJSdh10e74aitUVzBYQkYL8ejznICD334qN8LdTy9HbmOZJHQ02g1KC1dKDlIuohtOwpBrvIUuIJOrjIV7dBowSQd6yrCRCYlY56kwNKmGnDwxz7DeEqBBfbGgLwt7B0eHM37VuOtWVuB+y++DmVrcmDO5jHQxuGrPxsw4xs6FIdZ2Brp0GR/Ej0GAA5I/v/t3Xl8VPW5P/DPmcxkMtn3xQRwoZGlYE0tzUWsgIWCAlrGi63ey08tIsWiiK2lxl6KbdDaNsVypVa8t1wqLtw7uFcUWSyLKbc33stuRCshIXsgeyaznN8fw5nMcs6sZ5Ykn/fr1VfNySxn5oTMk+f7fJ8n04qv39IIXUYmkmav9XmN5ILQ2mmnUF15kLsIiYY51QOslpYW5OUNDUnNz8/H0aNH1X4aCkMondo9yQ1H7s+yofwfHBmCoe8J6E5JwJ6vpwPoQkaXAafGlsCe4AiA/GVwXIOapKQkJCQkwGw2w6K1oSmv2xHYmQfw0mkTEjU69Kb3wSraUNCaCp01ARatDb1FdpSWTlQcJF1ZKZ/Z8jwPf5mnQFpEhFOrVTRnXlwHVKG2kXBtp+AYeOPd98p6od9ZMF5e8aRbDdYe7HEEVy7MgwP43QevI+GDh92OXzh5UDZYqTUNBTUZmkxctF3wuo3S0GS5TvTaRBFfvbkbHTfM82oXobREKik1TmRARTQCqB5g2e12CMLQB5Yoim5f++Nrrs9wl5cXWh3FgbNH8PLRN9De14Gc5Gx8d+qtuGHctNAf65OdGLQ5luwumC/i5U92Ij3dENRjpvcZkKRNhGXQ8cGWqkvBPV9dghvGTcPKtyq8mnJatQIOfyUVpWfynMGV83tWK/77vw/j+uvdn//YsWP48MMPnMs1AwMD0Ol06BxnRb2hze22FrvF+ZxdGQPOjBrg+MjOy0vDihVAWhpQUeFYFhw7FqisFOAoEfR9bbZvPyybeZI7b18yMjKcQ549j4f68xEL27dvR0VFBerq6jB27Fisvv12FO3bB9vAUIuCU795CmnpBlyxYIHi47Sfr8GFc29DdP68eAdX9kErWt/5xPG4Lc24fsU0pKcZsKdiDzrrOr123Uk64X28p6Hb630+tv0YPnzkA1j6HOcw2zZrKCN2SXJyMp566kn5a6TQiT5h4ByS6n+Hc+29ztdns3Tiwrm3kZ6WhJzLgvujJh4Np5/Z0YbXJvZUD7AKCwvxt7/9zfl1a2sr8vPzA77/SBz2DIQ+fNNzt15bXweeO/Iiurr6g846AcCLH7/mDK4kg7ZBvPjxa5iQ7P5Xs1Kmy/OcAMBsG0RXVz9aW7vR1tch+9zdyRoMJOpkv9fZ2en1/rz//m6vWhiLxQJDA4AgSvoy9ZnOx547V66g3f+1kQuKlM7bl699bbpsrdbXvjY9roezumZhzBYtXt32Z5w9exYAcPbsWaz97W/xvcJCXH+pNAAAbAMD+OgnP0F3V79iJqvhkz+7BFdDRLsICI7MVes7n6C7phGAo2C8tbUbhXMvx11zv4da0yls/MEzshknud14qcXe1/r9tbudwRUATMVUAMDehH3otF90NoKdO3eR7DXKVhg/BAApp9YjM3WOW5NW0W7BuU/+DLvuS7L3GS44UDh+8dpER9SHPU+fPh2bNm1CR0cHDAYD3n//ffz85z9X+2lGDV8jWkIJsALt1O5rJqG/c1JqypnWZ0eSzYYBrfxuQ09Ky2Y6a4Ls8eQEAyyi1e3clHp4BUuteZKB1mpFksmkvdTNXEBxsejWzRzwXuq7YuXtEDPOOftK6XVWPPrALAxaBvHevtMAALPdjh0tLW4BFgDAbsfJXz+Fdw4dwOY3X0dDQz2KcvOwfMJkfDMrC/qVVyhkuAW8t1CDnLzPkJ7pCK48C8aldg1yGSd9YhLWf/1q/D/jb5GR24nOtgzse30O8u/4kdczyRXNT8VUTLVPxcrmNX7fT7lO9BINLLJd8AMZqE1EoQl2WT5SVA+wCgoK8PDDD2Pp0qWwWCy4/fbbMXXqVLWfZtQIdXSNkkA7tfsKovydk1x9lk6jw+KvfQeZVxsCHsisFNToDUnQaXRej/+PV9/qPPdw6svkqDlIWqlWKxq/FH78Yz22btVBFN17gQEDMBqtst3IzbYT0Inu8wsNSTr84J4bnAEWALR5LKFKDjQ34d+eexZmu6MR7fnWFjzZ0Q77NddiQfcYCOneWc3+VgFWSzJamqYBgoD80kGvmi6pXYOUcdqDPehEJzITsvCfzxoxy/AiEgTH68jM68SiZW+jd8o8mOF476X3+5Z3e9DfIuD0HxNxft/QuVx3Sy2yD0x2jhRSalchHUs7vgxyoaJcF3yzRYuysslBjUoiIv+UpjgAiHqQFZE+WAsXLsTChQsj8dCjTqija5QoBT+eWR5fQZS/c/LZlLPQcVspg6O12SBYrPhg95/x0Yd78A833uQMPpSCmm9cPxtT0/sVAym5gCrc8TmRzjxF45eCyaR1C64k/f0CKiv1MBqtst3ItZlJso/nOQYnVye//LujpcUZXEnMNhv+cOoE5lVPhG5WHgTdUF2edQA4/cdEAI7WCr3CHNzw6n1ejytlno7iqDO4ykAGZttmYVb2e0gY8JipKAwg7fj9AIB2/UTn+y0IQHKBiKmrL+1y3afD1JnHMe87bzsfI2HgHNJOOnphKQVZKWfWyy4VDmoc71PWwEkU9x1Eor0LrT063DDWgpfrh2YQAr77bBGRf76mOIyIAIvUIxcQAY5AR2pPEEyGJtBO7b6CqECCNF9NOUtLJ6Kw4wL+9w0TTl5WBLvOseTXaxnEvj3vOW/jL6gJ9HWH2ntK7rwjtZQXjV8KlZV6r+BKIvUIk+s6br3QD112stdx1zE4BoMBa+5ZBvxlH2C349DFi9jR0qKY1QKAlv5+2D/thQWAtjwbQqoO/a3emaSe+i4cuOM2rwxWanEaDtcfclse7EQn3hLegqbfArl0kgAbUo79AI3CNyHmTnD7njYJmHDPILo+zcb87x10Zr+c97X3I+XMesWmq3JLhaLGgM6xDyPnwgmM6XkfCXC8H/mpFmxZ5rjNy4f999kiosCEMsUhUhhgxTnPgMiVa11UsEGWv9v7CqICDdIkcsXyuTtNOFPkvaNQml8YTOsDf0LtPRVN0filoNRoFRiaRyjXjbz1nU9Q+J2p0OiGat9sdgEvvXESgiC4LXE17t6F5x/7EV5obMSg6L1Z5bvTgQ1LgLG5QEO7gL5P69D26VjY25Nw8IMJbv2tJFptn+zg5K45PXjtj69B9Nh5aBEtaLiYgJIsm9djAY5M1uWaD3EcE7y+Z8gXsbTmPuh3/1D2vhqFHYPAUGYr5cx6t2VFoWgJxhyY7AyuJCl6x3vx8mHH10p9ttQk10rj8i93eZ1zsJ37ieJFKFMcIoUB1jAgBUSPH9rgFWQFU/AeTP8rf0FUIEGa9JyexfJ/OrkDiTMFXPmFTrZmRa35ff4eT+3nCYfSLwWx146u6sNBjxaSo9RoVRAche6AfMf4tr9dwEfmSVjw/5qg1znqw7Ivm41nfv9TPPN798cqmjMPOx9aqRhcbVnmCCwAYEyuCFvmxxC0WiTMXo7yr2e59bdynJsVOfmOPnpSH6yiOfNgMu3AU9srvYIryY9fsuHFVQbZwnMASLTJX/vW9l6MA2BX2BlY3yHgHdMOxUyTuWiJbHCiFJiNzRn6b6U+W3JCqdeTq6/reftHSEUjNHDsLPa3FEoU7zIum+1WbgEozz+NNAZYw0g4Be++dgW6BkpqNCF1JVcsb4cdA0kan/ML1aTWDsBIkvulIFrssBxqRfPZrQCCGy0kR67RqiCIuPtui3MXoZQdOvq75yB0NaPNUogdLatx+PQCbHxbRFXVgNuOQzlN7e2yxzcsGQquJAlaG66Y9jkulk+HVNFVXXkQPfVd0Gr7kJN/FOmZQ4GOtIRZWbkeg4Pu7UZc/aVuDLonrUPa8fshwDuT1dKjRX+axW0Idf+ABb97YT+2zXEs9xmOrYROGHqOXjPw45ftMP1tJQDf9VIm0w7HCJz6euToE3H6NxrkyGTU6i69VUmJiaioWKf4eK5CrdeTq6+7ZkYDNB6ZNX9LoUTxTPo3EA+7CDX+b0KhqK09hW3btmDz5ips27YFx44dC/sxlQrbAyl497UrUCIFYVLAJgVhR5pqQj1ln8Ffc14P7IJ78XOCoAlpZ54v5eUzoPVoDaG0A9Bk0qKsLAUFBakoK0uByRSdv0FSs6cga+wCiL12iKIIe5cFln2tsH/aC3FwEG07TWE/h9FoRVXVAEpK7BAEESUldmzePIBf/tJ9dmPRnHn4ScNu/NOp41h95gMc7nI0CpWK4f1RysSMzZW/vVZsdf53qXEiltbchymzDuCK0nfcgitgaHCyv+W0igrHMlf3l/8AUeO+A3LQrEPFNh1+sfE9NDZ3wm4X0djciV9sfA/HP+0B4MjePPxKKr5oBex24ItW4L4XHMt5g4ODqKxcr/jcJtMOrFmzCvX15yBCRJvZjIdfssE86J497DMDj+0AcrVafK+oCNPT0xUe0Z2vej1f5OrrkjPkg2VfS6FE8S41ewqKv/wQxl77Lyj+8kMxCa4AZrAiQq6o+q233sKNN34zrJqfQHcAyvGX/TrSVIM/nXwVds96ljB6bgHKxfLA0PxCaaxNqk7vtovQn0B3Bga6A9Bk0rpleDxbGERaavYUnN/6G9nvWTvks0KBcmZULrUF2LzZuy2A621EcQyADVh18wDu+Kej0GfrYO6w4NUXpwC4w+dzVVSsw5o1q9DfP7Q8ZzAY0CsakCZ4N6G1J3kHZP6GWxcXl6C+Xr65Z1ZWtvO1edZFme0FeHfbDFw4bMdevOXWZsJgMKCqapPz683vXMCzb8u/Rl8BXmXlerfXDgB/OgwYNEDVd7VIzrA6B0IvuJiJBaWO20jLn/6EWq8nV1/X16lFSqb3z7bcNSGi4DCDFQFyRdUWiwXV1QfDetxphWW4c4LRmbHK0mfizgnGgIIfX9kvKXPlGVxJXAOkYDM8i66aB51Gfvs+4AiyPh3fhtMTWnDkqjpsa30toIyZFMRKS3/SzsDa2lOyty8tnYilS+/DypVrsHTpfbKBWGWl3m35DPCftVE746XNzgnqeCBMph1YvfoHjoyKKKK+/hxWr/4BTKYdbrdxZl1EEUAd5s96CneuOI6knEQIgoCknET88/dPov7DF30+n9G4BFVVm1BSMgaCIKCkZAyqqjZBnPq0VzZJ1BjQO957aaxozjxM+uFaJBcVAYKApIJCTPrh0ODkiop1SExM9LqfTqfDhg1Pw2TagbKyySgoyMDkW9ZjS8s6tM3pRPe3apF/x48wveR6LMRCZCZkQcDQOboGnb5qonx9Tyn4ev6giDeeLcXLGybhjWdLcfZkptv35TJMcpSKdf0V8Y5ftgIavXvLjf87WAw73N9HpWtCRMERRFGmGjWGRsKonM2bqxS/t3Kl/87QgQi2VkpuvI1Oo8OdE4w+m4cCjiDsF9c/5pXhARzDkv3V5bieayCk8/L1erZt26JYV7V06VDPpECyXNJYiYKCVNk2BoIgorm5x+t4qO+HL13Vh9G8bStEl/oiITERBUvvDrkGa8qUUjR7ZC4AoKCgEMeO1QIAysome2WE3tp2H4oKvD+0rZ1mXDmzMqRz0TfuCGrHmnRtPDNwUr1SRcWj6OhwZMWysrKxYcPTACCbQfMMoPwxmXbgwQe/7zWuKTExEc88s1nxseTeS8CxFPhMaSmg0TjWHT0kFRTihldf93tenjVYgKOIN2vsgoAK3dXaRchxLPGL1yY6/I3KYYAVAYF++IfKV7DkL8iSC8oe2Puo4n1cH7esLAX19d5Jz5ISO2pqev2et9wuSCVZ+kwszfu2YnAUSBDruVQLOGqvZs6c4xZkSb+Mgn194b4fSrqqD6NtpwnWjnZos3OQu9gYVoF7fr5ybU9Li6OXVUFBBjx/FRx59xFoNN4BpyiKGFcWnQxHXs+baNv7ELKT2lHX5qhZevmwfMAkBWFKS4clJWNQU3MiqOc3mXbgsccexYULjiAuOzsblZVP+y1w9wzwEgUBy4qKcENBIS6bdzPO7/qz1/Kna4bOn3gYBcIP8fjFaxMdUZ9FSPIdyHU6nWrF26HOJ1RqraBUJ6WB4Ba0KfVR8tVfyZVS01Q5tpYB7D+h3BxUaWdgomFoOS/Y/ldyu+wMhqEWBp7CfT+UpJdPV6UtgyQjI0N2WHVGxlB2Sq6mqbm1SzaDZe6woKwsxWuGodr0jTtgPfYgcg19AIDL8+DSnNO9MadcUOMplD5TRuOSoJt/Srd33UW4JDcXN5Ve7WyWmvnlqV6ZpECDK8BRrxerwl0iCgxrsCKgtHQiZs6c42wDkJqahoULF6rW1FLt+YRydVI6jQ7/POkOt4BMakbpSem4J88askzBgCSz91IJABS1pSsGRwAwbtwVXvcRIaJZf9FZwxVs/yu5XXa+lvvCfT/CdaSpBo8f2oAH9j6Kxw9tUKxdu/nmW6DzGGOj0+lw8823OL+uqFgHQXDv1r75jx/BYnZ/7fZBG159cSrq6zVYsyYporssU86sh1bocz92qTkn4B4wyRWWewqmz1S4jMYlqKk5geaWTpw814r7n/4tAOD4hvU4cMdtAIAbXn0dc/Z9hBtefT2o4Cpe1ZpOYVvZFmwuqMK2si2oNcnXQxKNFsxgRYhnB3I1U7ZqzycMtDN7sBkepeeSHvfzRx/B8bQe7Pl6OqzaocfUWkVoLfKxf09PN2prT+Hs2b97fU+AgJQenTOTF0r/K6PRGnBWRun9WLPkGD5/9FnVlvjkKPU12/fObrz67Da3WqUHHngIFssgdu/ejc7OTmRkZGDOnDl44IGHXF73Ehw5osHWrT+DKJ4DMBbv7luLCWn9+Me7jkKXkXhpF+FUbPrz9wC4zzAMVePuXTh/6FVk3lAIbVYSBLse2VfegtTsKX6bc7oGTP6yUwaDARUV62RrkIIJbkK5v1yDT8+u9MNdremUW5PYnvpu7F9zKeNsjI9pCUTRxgBrGAqnXYOSQDqzOz5IB1BZqUdDg4DiYjGsZSJrRzsmdABAFw5/JRXdyRqk9dkx/X97cHFcqWKmyXP51ZXOmoAL5jYAysOi1VqqlXs/1iw5hq/XPQXrpSJ1e84ALgzuwsWaD5CQqF6tjNwy8Zm/nMSOLR/CeinrJA0RrqrahNWrf4Ty8n/wWez/y1/ejmnTvuP2ekrnmzF+1h2KGwDCWQ5t3L0LdR/8EQWLr4Ym8dKvooRBdHzxBgAgW6Gbel07IAjJbo05fbVtKCkZg4qKdZienh5WoBNqoCTX4NO1K/1IUF150K0DPwBY+62orjzIAItGLQZYAVK7w3k4gp0FKCfU1xNMhscfbXaOI8g6a8aEs2b34zLBkcRqtUIQBK+ibACwaG3OTF6g/a/C4fl+fP7os87gSvOlFOhm5UHQObJxgXbcDoRcBvP4K391BlcSaYhwTc2JgF630vVVGrMTznLomReew2XLJwwFVxLBjs7ze9E7fh2Sjz3otkw4YAFS9BrY/tQHu2E9ehsdva6Uem+5FsIfuOO2sAKdUAMlpfYLgbZlGA56GhSW4xWOE40GDLACEOiYmWgKdBagHLVeT7hBZ+5io1dLgqa8HPz9yivQ+8G70Ov1ipkqURShSdDAbhuq4bILdrTn92PRVQudx9QYFh0M14ag2vJsZ3AlkTpuhxtgyS0T97V7t5IA1BkiHM7ysNLPyUBLM7RZ18rex2bphLnoIaSnJaH70E+QItSjvTsL6ck9yEv3npvnVljusjzqWqAebqAT6v3lGnxKx0eK1OI02UHdqcXxM46KKNoYYAUg1F178UqN16NGkHb68iS88Y9FuGjvQ1qfHdd+noyLiTmwWRwfoGaz8oe3lI36y6G9MPcPwKK1obfIjoVfWxjTayJl5QBASJP/5+Wv43Yg5JaJk3PS0Nfm/SEXTnG3ax+xn/wkHR98MBsHDkwNeHnY189JUn4BrBf6octO9rqfs2nmFXdhIHURBgBkH5iMhAH3TvCuc/P87fgLN9AJ9P61plOOeYoN3UgtTsOERd/B4MXnFLvSjwTlFTO8BnVrDVqUV6g79opoOGGAFQC1d+3FmhqvJ9wgzfnBK1oAQUB3SgKaDOnQWeV3FbqS6qiCzU5FY5nXNSsndlshpHt3sffXcTsQcsvE33/kQWx+osprmSzU4m7PPmJ2exfmzn0bGzaYA37fff2cPLBsBere96jBAgBRg4zLZns9llLRe6Bz8/yN31Hj/nLF3v/7x3585Z4VMJ96JeTi+ngn1Vm5BpblFTNYf0WjGgOsAKi9ay/W1Hg94QZpch+8Wqty1xBpR2CodVRKmZTLLuxG3qEXkdtXF1QXayXSbsG2nSZYqzugm50PwWWHpCDoZIOHUHgtE18PjM+8wmuZLNTi7mD7iMnx9XMiPff5d+V3EXqyKxS9Bzo3T3q+UHcRBnJ/pWLv0292Y2nN6wE9z3BVapzIgIrIBQOsAERi114sqfF6wg3SPO87UafF7LJeJCcC/WYBp+sScb7dkf1RowO+XEA3zXoSk+o/BGCFAPeannCDLCnQinbHbbllslCLu4PtIybH389J0Zx5AQc4vePXIe3kKgj2oQxdsHPzgnm+UO7PYm8ikjDACoAau/biiRqvJ9wgzfWDd6JOi/kpeuguJXqSk0RMvcpRf9XSaVClrcIF80WcPfg1HH/lVvS1ZyM5pwPP/PJq6JPcsw2uNT1qiIeO26EWZ4fSR8yTmn+cSNcklLl54ZKbhWg0LvEKoK9cnIDPTTav+7PYm2j0YYAVoHB27cWjcF9PsEGaNF/vnGjDZ2Muw2XaJORp89CU140bx2igE9xbAGgTgAnjBpFl/qoquwDbqm/E/zz/bdgGHaN0+tpyUKDvkL2tZqBe9XmAkRDo4GR7agE03d7F2fZU38XdavQRc/05KbT3YFZyElI1gLbtAHoSdUEHn+aiJVEJqFx5juGR+otlp3RifGGbc+iyzdKJicsSYOlOxLn3E5z3Z7E30ejEAGuEc90FpnYfqECDtK7qw2jethXnU5JxamwJ7AmOWiudNQHFTRlIHyffXsCQKOKToydRVFQc9jkff/U2Z3AlqWsbi8vzznrd1irkubWPsHa0o3nbVgCImyBL37jDbbksYeAcUv73frS8vB3C9B+7neerzauxOOln0GuGlgnN9iTsbF6Nb/l4DqU+YpflWtFw/JmAlz2nFZZhUqIOF+redgtG1OoJFmlyY3j6+/uRlnAGopjidlzQ2DDlQQEXTqax2JtolGOANYJ57gLzHJgcLW07TRAHB/HZl650BlcSjSjAbElAUqL37sF+sxB0UbXiOTTpvY49tqMSW5YtR4p+qJGlzabF34+WuvXmAgBxcBBtO01RD7CUlqZSzqx3q0UCgAStDSVXVqPGIxh85+wCdKQJWJK/ETm6JrRbCrGjZTU+6r4FgHxwK/HcqdnTcSykQKnz/F7nfSSB9AT7+9tv4+Oq38Z0951SH7FcmfYSAKDR9WFpzQ9Dfr5AM5NEFN8YYI1gauwCU4PUF2og0btlAQCc/EKHr15td/sAttqA03WJAICe7i7ULrs7rKU6uU7kLx++C1lpg/jN7T+A3tAHc58BZ49ORltdvs/XES1KS1MAcH+6/Ie+PrnfKxgsLhZxuH4BDnctcLttSYn/lhieQg2UlHp/+eoJ1rh7F0795inYBmI7w09pDE9bRx/yc1O8jofThkMuM6nGxgsiij7lffE07KmxC0wN2uwcnB6nh0XnXfwLAF3mbGSNXYCBQQ1EEegbEHD0M71zF2HSoOMDXVqq66o+7Lxvbe0pbNu2BZs3V2Hbti2orT0l+xwVFWYYDO5jXQwGEfl9NvzPW/NweMdi/M/b89FWN9bn64gmpaWpysr1iq0J6trH4P36GW7BoNJrD2ZItySUQAlQDjp8BSNnXnjOGVxJpN2P0VRRsQ4Gg8HtmMFgQLdtPATB/Y+GcNtwyGUmpY0XRDS8MMAawZR2ewWzC0wNuYuNOPyVVDTl9cAuuGdN7IKI8vIZjuxHlhHv/S0Tez9OcQZXGpsdV50fKtCWsjPA0BKoFDBKS6ByQZbRaEVV1QBKSuwQBBElJXZUVQ3gm1nvBvQahMRE5C42hvT6Q6W0NNXQUI/e8evQN+i+RNVrTsZPXn0Sfzh1l1swqPTaQ5kpGUqgBAAZl80OOhiJlxl+RuMSVFVtQknJGAiCgJKSMaiq2oRZ8+5D1tgFzteeoMtA1tgFYdWUhdtMlYjiB5cIYyjSncXV2AWmhvTy6eje+zoARzaioDUVOmsCLFobmvN6nMuVnkXVSeZBXHW+CUUXLro9npSdCXYJVG6Q8ed/zZFd+hNSUpCgT4rYLkKl2ipXSktTxcUlMBctwfe3JKFySQXG5tShrn0sHttRiZcP3wUBdq9gMNgh3Up1QBmXzXarwQICy9pIQYfU0sBsycC//nEeXtn5VcWxO/E0w09pDI/abTjCbaZKRPGDAVaMhDLLL9iATGkXWCTrr5TOUep71ZUxgK6MoWUfz8akrkXVnz/6CKwewRUwtFSnxhJo7mIjWv70H7C7zD0UEhNR8N27kF4+3dmuoemF59G206RKoOWrtsr1Q7yiYp3b7YCh0TcA8Je67+KK1Xd5PX5Rnjmsc/RVB4RLdUChNE+VghGTSes2OLq+XsCaNUkA3LNq45etcKvBAkbeDD9PajRTJaL4IIiiKPq/WfS0t/fAbo+rU1JFXl4aWluHPvgfP7RBscP1L65/zOu4Z0AGOBo23jnBGDf9uXydI4Cgzr+r+jCaX94OsbfX7biQmIiCpXcjvXw6tm3botgIM5jO7+LJj/H3rS96Zauk9hLSjsLcsXUYd81J6JP7A9rdpZQFKiubLJuZKikZg5qaE27H5DJdwJ2orNSjvl6AIACiOFS8bzCIIS//SRxDlb3Pz5Y0Bh03nJC5R3DKylJQX+9dnVBSYkdNjfv17vnrhzHfRRhtw2UXoefvNIofvDbRodEIyMlJVfw+M1gxEuwsv3CHK0eDr3NcmvdtfPnvl8HcPwCL1obeIjvmfG2ucnDlEthINKmpyP/Onc7sjFpLoPk3fgPCpGu9jkvtJQBHcDX+ax8jQeso1Pe3u8tXFshXbZUnz6Upz+yPKAKCIEIUgZIS+aW2YEW6DqihQQj4+BULFiD16zeq8rzDhdrNVANZjiYi9bHIPUaUZvYpHQ93uHI0KJ2LrWUA+/fvxmC/GQIEJFq1yGtMRmaXQfb2roGNK02i3m3pq7R0ImbOnOMs2k9NTcPMmXO8lkB7Oo6h4fgzqPv4CTQcfwY9HccCej2utVnjpp5wBlcSX7u7fO0GKy6Wr6dROu6qslLvDK4koiigpERETU0vjEYrTKYdKCubjIKCDJSVTYbJtMPv47pSqveRjof6fkqKi+Uz1ErHIyHc92i4kJaj6+vPQRRF53L0SH29RPGEAVaMLLpqHnQa911Vvma0BRuQxYLSuRS1pSsWo8tR6jcld7y0dCKWLr0PK1euwdKl9zmDq67qw/j80Udw5pcPoP2znc42AlJjzECCAtedePrkftnbBJvt0QzUK277l2qrfPGX/VHjA7V3/DqIGvfzk+qApEajobyfErXaRtSaTmFb2RZsLqjCtrItqDXJt+jwFM57NNwCM1+tPogoshhgxci0wjLcOcGI5IShD7JEjXwjTiD4gCwWlM5Ra5H/MVMqRlfqNxVoHyppidHa0Q5teTYErWfGx9EY05/cxUYIiY5mp+Y++Wybv2yP3HHPbf9ZWdkwGAxYufI+vx/axcUipkw5htWrN2LduvVYvXojpkw55sz+hPOBajLtwFcmj0fmNcuw8g9WXOzWQ4QAW9IYdE/aBHPREp+NRgOlRtuIWtMp7F+zGz313YAI9NR3Y/+a3QEFWaG+R8MxGxTMcjQRqYsBVoxZxKEPlV5rH146bcKRphqv20kBmZQlytJnehWIH2mqweOHNuCBvY/i8UMbZB8nkpTOMdh+XK6BjSTQPlQmkxYzvnsdZvzXdhh3/x5CmnzQ6q8xJuBoL1Gw1NFB/uzRybDZ3EsWfe3u8pUFAhy1VTU1J7B58xYMDPSjo6MjoA/tNWs+xqJFbyEzsxOCAGRmdmLRorewZs3HAEL/QDWZduDh1Q/gfGsLRADP7beg8AEr1j/zdXyh2+KsCbIOyr9vSseVGI1W1NT0orm5x7m0GYzqyoOw9ntkRfutqK6Uz4q6CvU9Go7ZoHCWo4koPCxyj6FgC9d9DVcOpe1DJMidY2a5QbYYfdy4K5w7AV1bSEh1Vm07TY5lQY3GrcGoUguCoQJwR2DT3J+P5pYMFBZ4f/gHOs4kvXy68/l6g9jdJR33d3tfH9pyhch2+4fQ6dx/ZnQ6C+z2DwGU+uyd5Utl5XoMmN2X6Mw2G5479n+Y7zJ2p7U9E/m5F73u39qeiXE+n8FBrR1yPQ0KLToUjrsK9T0ajtkgf60+iChymMGKITUL130Fa7EmV4x+9dWT8MknJxW7sKeXTx/KZNkd3d/lRuW4kisA3/THmzEw4P53RKjjTMxFS9Bxwwm0zelExw0n/AYGLx0GLn8ISPgnx/+/JHPawX5o++v95au+q3H3Lhy44zbsnvUPOHDHbWjcPfSzofR8Lf39brVvv3thHvoH3LOC/QM6/O4F/0vV0s7KhIFzECA6d1bqG4NfYkstVsiKKhx3FWoN3HDMBil1oecuQqLIY4AVQ2oWrsf7LkPPYvSzZ//ut/BdbjehaybLk1wB+Hv7yvCLjUaI3VZAVGecSSACrdcJ9kPb33Kr9IGalZXt/J7BYMCFY/+Hk79+ytEZXRSdg5OlIEvp+QShBB9cmO/8+vin1+IXG29HY3Mm7HagsTkTv9h4O45/6t3mwpOac/bKK2ZAa3APnLUGLcor/LfoCDXo8BWY6Rt3IPvAZOTuzkD2gckhBY2RIi1HNzd3oqbmBIMroihhgBVDahaux/suQ8+hzIF0YQ9mNyGgvM3//f3X4sDhuzG27F9Q/OWHkJo9xa3VwLG/VAbdasCfQOt1AsmmuO5c+9WvfoXjx4+73V6u99fAwNBzd3R0YP1zz+KAx9gZ18HJFRXrkKTXe7yKZNjFp/DUkXtgMmkv3c6Mv1Rfi4VLKzBt/q+wcGkF/lJ9bUA7ANXsr1VqnIiZVXOQWpIGCEBqSRpmVs1BqdGxi9Tfbr9Qgg6lwOzO6VAtM0dEIwc7uUeJUmfdUOcRmkxaVFbqkZV1HHPn7kVqWiesWhua8rqdo2jipdO7NJTZM2Mlx7UL++ePPiIbTGmzc3Dl07/xOm4yafHw6kQMmBO8vpekt+G3GwdhNFqdrQY85+ldSJ+MHY3HA74W0jVoaBC85unl56fL3kcQBDQ3u9eE+WoE6TlWBwD0+iQsXrwYV19dKjv+SKlTfK5Wi2dKSz1PCHP2feR8rh88sB42ez2AsQAqAThG8bh2Wff1un0JpUN8KB2p5d4zg8EQsaWxSHe+j1fsFh6/eG2iw18ndwZYUaLmD7xUzD1+/HEsXPgWEhOHAgW7IKKhsBMJ+UmqD4/2VFt7KqA5h0oZKxEiBAwt62kSNJg961tuvaw8O7q7jsqRs9n4Kp44eA/soneQJQUJDcefkd1F2GUX8fvOoVEtvgJUz47qwNCYGuAlrFx5H+T+acmNw/ElmLE6koKCDNnnBoDtkya5fZ1UUIgbXn3d5b6pbqN3JIIgorm5J+DzluPZ3R5w7KyUWkDICeXfjWKAqddjz7//SfVRO7m7MyDA+/0WIaBtTnC7K4cTfojHL16b6PAXYHGJcBiSirlvummPW3AFABpRwMTuMfjF9Y9FPLjav3+3YpG6K1/Dlwe1VogQMai1ov2yAbcAzbVNAuDIXPkKrgDgm1nvygYIwFCNllKLhjSPu0mbBEwmLcrKUlBQkIqyshRnBsezoL6/X0BlpR4VFetlAxxBEILevRXKzjWleqpcnftytNzg5Eh2WTcXLUH3pE2wJY3x6q+lJqX3ps1sdqs7U0uwvdCIaHRgm4ZhSAoUMjLkAwVfAY1aqqsPKhape2axUlPTZM/JorXh0/FtPp/HtU1CILTZOcg3tKG5P9/re1KQkKDLUMxgefrfPV/Cn/5tKFNVXy9cylzJP399vQBA/gNeFMWgl6gKc3LQ2Ob9HhXmKDddVdqav+aeZUg6dcLn4OSKCrNsZi7YLutK1J6zJ0epDUOuVuusO1Mzi9U7fp1sZk6pRxoRjQ7MYA1DUqDQ2Snfy0lpp5maAilSl5SXz4BW6x7L2wU7mvPcl5zUKMjPXWzEiimvQp8w4HY8SW9zBgkZl82GILhncywi8GG/9/zDk69+WzZTleC9AgkAl46Plf1eScmYwF6EiyV5+UgU3J8/URCwJM87gJQoFWMv+1klbnj1dczZ9xFuePV12SBDjS7rsSa3cSBRELAk3/GeDbQ0q/p8XxxPx39/cAV6O3UQRWAQuRHJzBHR8MIM1jAkZRn27LnJqwZLbkdZJChlpeSCOymjJdVrJRr0OJvVhq70oSBIrbE/6eXT8c+PA8KvXsTv/7YILf15KMobwE+fgDNIkFo0dJ7fC5ulEwkwoO5UOz7NswEuY3V0Gh162zJln8dmc2R2PDM9jqRRJYDlAPqc3/vWrKn4l0duRt3HTyBBl4GMy2YH1CpimkaApagIO1pa0Ga1IlerxZL8fEzTyC+DSozGJSEXdBuN1mEVUHmSXvdPVz+ANrPZ+Z5dn5kJAEjKL1DtuRp378LJXz8Fuxk489GXADiWXif9MB1FRao9DRENQwywhiHHh98AKisn4623RMyduxdpaZ0+C83VVl4+Q7Y7u2tw57VDcu5Q0X2ouycDkV4+HStNwEoAgHxhdmr2FKRmT0FX9WG0/Ok/kG8246Zxehz+Siq6kzXI1CTj1gm34i/F4qVlP3clJY5lM8/ddJWVetTX33XpVhUA6vCtWdfjpw//A/Q6x3slDUiWzsOXpPwCXC+KzuDA9TgpMxqXYHp6+qXgZyiQl6s7C8eZF55ze3wAEVmGJKLhhwHWMDWUZbgCwPei/vyeWSnP4M7f6B5fY3/U4tpOICX3Iibd8Rq+ctOnbsFc204T7JdGxEw4a8aEs47/1mbn4MpZZT5rkpQyPY7b3wWpxcGqeyuRpL/odhtpQLK/AGv8shVhBQmNu3fhzAvP+ay7CpWv9hLxQHqdkXr9gPJyo9rLkEQ0/DDAopCVlk5UzJb5G90TqeyVxLONQk9rFv7n+bsAbEePZSjQ89fMdChbGFjfJ7nbF+RdlL1tIAOnD3d14YlzdWhqa0OOToe7xn8J9/5wbUBBwtDylSM4k7q3Awg7yPDsNSV1qgcQd0FWJDNJSfkFju74MseJaHRjkTup5khTDR4/tAEP7H3U5+iel06bnN+Xvj7SVKPquci1UbAN6nH8lVvdAj2pBYQn1+NGoxU1Nb1obu5BTU2v3/okz9trE+U3I/gbOC0FMY1tbRABtFkseP6Lv+NwV5fP+0l8LV+FK9BO9SPd+GUroNEnuR1TexmSiIYnBlikCmlJ0N/sQw2EqAyllptLCAB97Y4ZfdJ55i42QuMxIkZITETuYqNq5yK3azGQgdPhBjGhLF/5GgjtKpT+XCNR0Zx5mPTDtUgqKAQEAUkFhZgUYIaRiEY2BlikCrklQU86jQ52mY7XgDpDqbuqD+PzRx9B7bK7UZDcIXub5BzHcaklRHr5dFz1wIqgmpn649mY9L191yJr7AJnxirQgdPhBjFKy1RKx6UlRaWB0K6CHVI9khXNmee3/YUcf/MSiWh4Yw0WqcJfgCTVWkm1V3LfD4TS7kPPsTrLr96GX/7fSphtQ9mphEQzvvydN7xaQuTf+A0Ik64N6Pn9ndtv/r0Rf9n8bdgGHX+7SI1Jq6quhdHovy2DK6WGmYEGMcEWyPvbEee6aSAzcwMSE5djcNC9mWmwnepDFcni/WgYLjVsRBQ6ZrBIFUoBUpY+E8/Ofto5umfRVfOg07gvlwXaA8tzGdK1fqttp8ltZuHckoP48TWbUZjSDkEQkZp3AV9dvh1fuenTiAzAPtJUgy9ObMDWq36EwX834O8bL8d3p28HMDRCJ1hyDTODCWKCXb7ytaQobRqor9dAFAVcuPBPEMUtyM52b2YajeAgmExbuHo6jqHh+DOo+/gJNBx/Bj0dx1R5XNawEY18HPYcJSN9+KZnWwZAeVhyqD2wHj+0QTH7tfSPtYr3K31hq8/HVePavLH/LvyT5V0kYagAvtecjPteeB4vH74LgIjf/z74jujRbIVw4I7b5HfEFRTioU93o77e++8xaYB2pMhdG1/n6Tq4Olw9Hcdwoe5tiOLQz7Qg6AJa3vVHaSC3IAhobh4eA6JH+u+04YzXJjr8DXvmEiGpQgqQAgmcQu2B5WtnojY7R7blgtIuQQDQN+5Aypn1wEA9spNK0Dt+XcjjTW617HMLrgAgRd+HDUsqLgVYjqVCILggK5yO7MHytaTY8E++B2hHU7R6T3We3+sWXAGB9y/zJ9zlXyKKf1wiHCFcWyQ8fmiD6m0PAjGtsAy/uP4xtyVBNflahsxdbISQmOh23NduQH3jDqSdXIWEgXMARCQMnEPayVXQN4ZWaJyr0DF+bE6d87/9LRXGuujZ15KiNP/Sk9LxSAq2eD9USn3KrIMXw16ODHf5l4jin+oB1muvvYYZM2bg1ltvxa233orf/va3aj8FefBVmzSS+KrfSi+fjrTp1wOaSz/SGg3Spl+vuBsw5cx6CHb3GhjB3u/IaIWgXyf/4V7X7j74WSnjIxU919efgyiKzqLnWARZcjviKirMMBjcgympo73nrkmTKbKJ8Wj1nlLqU2a90B92zZfSQG4WuBONHKrXYP385z/HtddeiwULFoR0f9Zg+Sa3e+rZ5BrF2qRfXP9Y2M+ppq7qw2jbaYK1ox3a7BzkLjYG1RIh0F2EgCODpdRyIXd3BgSZlhEiBLTNCb4GRt+4A8knHoBWNDuPuddgOSjVLJWVTZZdMiopGYOamhNex2MxpsZ1F6HU0R6A7Cihqqrg683kSP9uak2nUF15ED0N3UgtTsOERWkwn3olorsI5Wqw7INWNL16DN01jYo1X8N9h2OgWOcTv3htosNfDZbqAdaSJUuQlpaGlpYWXH311fjpT3+KjAzfHatdMcBS5jn6BHD85f7Xm8ehYYp8BuXZ2U+H9ZxqCjYICsbnjz6iWIN15dO/8TqefWDypeVBd7akMei4wTugCYRU06UZqEePvQSr/u1J/Mf+oeDKV+ARTNGz5xZ/x2MbYpIBKStLiWjxe15eGg49dwT71+yGtd9lsLhBi5lVc1BqjOxg856OY2j++CVos5JgvdCP1nc+QXdNo+ObgoA5+z5yu73Sv9GR2HyUH+Lxi9cmOqIeYD3wwAO49957UVZWhqqqKpw/fx6/+Y33B1wsHDt2DHv27EFnZycyMjJw0003YcqU8IpVo+n1b34TfY2NXscHMg3Y9eDXvI7nJmdj88LKaJxaQP627H6YW9u8juvzcnHdC38I67EP3XY7IPejLAi4/vX/8j7+9+3AkeWArW/oWEIyMO154Iq7vG8fgu3bgYoKoK4OGDsWqKwE7lJ46Msvvxxnz571Oj5u3Dh88cUXId820jQaxbcddrs6z7Hx8o3oPOudVcwYl4HVX6xW50l8UPp3l1xUhNs++CDk2xLRyBZyscS7776LJ5980u3YlVdeia1btzq/XrZsGebMmRPU40Yqg1Vbewr79++G1er4K7izsxNvvvkmurr6FQcWq0mNvyj6mry3pgNAUmc/dBqdV4uEWy6fG1d/xcgFV9LxcM9Tm5Utn8HKypZ/7NRF0E8cQMqZ9UgYqIdN2kWYughQ6T2bO9fxP1etrfK3Xbv2p7JZqbVrf+p1/nV1dZ53dx6P9vUuLpbPYBUX29Haqk4Gq7NOfsm2s65T9vWqvUR35b33y2alrrz3fq/nV/o32tfUFFf/FtXALEn84rWJjoi1aZg/fz7mz5/vdqy7uxtbt27F3XffDQAQRREJCQmhPoWqqqsPOoMridVqRXX1wagEWGpIyi+Q7/+TX4g7JxhD6i0VTVIrhdPj9Dj8lVR0J2uQ1mfHDWcElIb52LmLjbLLj75mCpqLlsBctAR5eWnoUPmXUU/HMXSe3wubpRMJugxkXDbb59Z+aWkvkLqqeNriX1Fhlq3Bkuqz1JBanIaeeu/rk1qc5nXMc4lOakIKIOQgS7pfIEGb8r9RdXc4ElH8U3W7T3JyMl544QVce+21uOaaa/Diiy8GncGKlJ4e+Q9QpePxyFefoqIQe0uF2vQzFLmLjfhw33bs+WoyrFrHB3J3SgJ2X5OAvKaagJ9X9pwv1XCFU0Cv+NhBvh+exdE2Sycu1L0NAH6DrEBqqCoq1slmuzy3+HsWhpdXzAi6ZslfoOioJxvwKn5Xo8BdUl4xw6sGS9DYMGGRd4Dlb9xPqIrmzAvo/sGOJyKikUvVACshIQEbN27Ez372MwwMDODyyy/H00/HR5F1amqabDCVmur9Szpe+fpLOpRdZZ7d16X2DgAiEmSll09Hdf97sIru7REssOHNz3YF9Jw+z7l8esjF8rW1p/CXQ3th7h9ArlYLW14SLmSE9n5EskElEFi2q9Z0yi0o6anvxv41uwEg4CAr0EDRaLSqGlB5KjVOxMVj/4f/3XIGVosBWm0fcvKPovujd9C4OxNFc+YNLQvKZI8A9ZuQKgkm20VEI5vqDWuuu+46vPbaa2o/bNjKy2e41WABgFarRXn5jBieVfDk/pIOdXDsm5/tcqvbAgCL3RJwsBOKix7BlcTfsGhJJM752LFjzp8NAQISrVoUN6UDALoyBvCfn7wRVFZLqUGl0vFQ+Mt2VVcedMv4AIC134rqyoMBB1iRDhSDYT71Cq74knvwZDc7AhkAXlkjT9Fcogs020VEI9uo6eReWjoRM2fOcWasUlPTMHPmnGFTf+VLqINjfY2eiRRf3dgDEYlz3rNnj1d9nkbUoKDVUbzYZ+sPqomrUoNKpeOR0NOgsCSucFxONALFQPkajyO3LOiKS3REFAujahZhaenEERFQeWpoqA/quCRLn6nYoDRSFl01T3Yo9KKrAvuLPxLn3NkpHzDorPIbNPxlzDIumy07JDjjstkhn2OwgikMV5Kgy5ANpqIZKEp8FY/7Wv5LKijkEh0RxcSoyWCNZEq7x/ztKvM1eiZSphWW4c4JRmdAlKXPxJ0TjAEv70XinJUa4Vq0NsX7+MqYpWZPQdbYBc5AJEGXgayxC6K6rFZeMQNag/vfT1qDFuUVgS+JZ1w2G4Lg/l5HO1CU+BqPozib8FKndQZXRBQLoyqDNVIFuqvMkxTURLu9w7QQdzxK9wXUPeebbroJb775ptsyoV2wo7fIjhRtMnqtfV738ZcxS82eEvU6JVdSnVU4uwil8w+m3USk+Cse5849Ioo3qndyDxdH5YQmFrPpoi3cOYZK8vLScOjQEVRXH0RPTzdSU9NQXj4DpaUTvXYtSlK0ybi9dFHc9RobaZT+3Xj+vK9cdBuuPnWCO/eiiM0s4xevTXREfVROuBhgkZxIzjH0d22ONNXgPz95A302940EOo0uqOVNCp7ctYmnWYyjGX+nxS9em+jwF2CxBouGhbadJrfgCgDEwUG07TRF5Plqa09h27Yt2Ly5Cqff/x9kdhm8biMVu49UJtMOlJVNRkFBBsrKJsNk2hHrUwIQ+q5ZIqJoYg0WDQtycwZ9HQ+H59zKnp5uZPRq0VOYhK4M93YAkWxpEUuh9laLhlB3zRIRRRMzWDQsaLNzgjoeDrm5la59sVxFsqWFRN+4A9kHJiN3dwayD0yGvjHymaR4zhKFumuWiCiaGGDRsJC72AghMdHtmL9hzqFSmk/p2Rcr0i0tAEdwlXZyFRIGzkGAiISBc0g7uSriQVY8Z4kqKtbBYHBfsg1k1ywRUTRxiXAUqa09JbtTbjhIV2mYcyCU5lbqDUnORqfRammRcmY9BLt7Jkmw9yPlzHqYiyK3VFdcXIL6+nOyx2MtkFmMRESxxgBrlJCrK9q//9Lw32EUZEUioPKkNLfyG9fPxrIov1eaAfmMkdJxtYTaWy1a/M1iJCKKNQZYo4RcXZHVasWuD9/BxfT+uGs1EKmeV4GQAs54yPbZk0qQMOCdSbInRTaTxCwREVF4GGCNEkp1RQkWAS+ddrQ6iJcgy7PnlbWjHc3btgJAVIOsUAKqI001qnaZ7x2/DmknV7ktE4oaA3rHRz6TxCwREVHoWOQ+SqSmyg/5tWhtcdfPKdo9r9QidX2XWjdcMF/ES6dNONJUE/JjmouWoHvSJtiSxkCEAFvSGHRP2hTR+isiIgofM1ijhFxdkV2wozmvB0B89XOKZs8rNb352S6vkTpS8BpOFstctIQBFRHRMMMAa5SQlrt2ffgOEiwCLFobmvN6nI0zo9HPKVDa7BzZYCoSPa98CbYOzDVITe9MQkFrKnTWBFi0NtTmnQq7hqun41hcDF4mIiL/uEQ4ipSWTkT5wtn4dFIHPh3f5gyuotHPKRjR7HmlRKoDkwI9qQ6sq/qw4n2kIDW9MwnFTelItGohQECiVYv9+3ejtvZUyOfT03EMF+rehs3SCQCwWTpxoe5t9HQcC/kxiYgochhgjTLTCstw5wSjMxjI0mfG3cDi9PLpKFh6tzNjpc3OUWWoczBCqQNbdNU86DQ6FLSmQiO6/9OyWq2orj4Y8vl0nt8LUXRffhRFCzrP7w35MYmIKHK4RBgitXeLRdO0wrKQzzVarztaPa+UhFIHJr0P/31yn+z3lXZyBkLKXAV6PBIad+/CmReew0BLM5LyCzB+2QoUzYmfzCcRUTxhBisEkdgtNhyMptcd7OxDk0mLsrIULLzmG+jpzpS9jdJOzkAk6DKCOq62xt27cPLXT2GguQkQRQw0N+Hkr59C4+742X1KRBRPGGCFwNdusZFsJL/uI001ePzQBjyw91E8fmgDzi2cFnAdmMmkxZo1Saiv10AUBbz//k2wWHRut9FqtSgvnxHy+WVcNhuC4P6YNruAp5/dhYKCDJSVTYbJFLn5hGdeeA5284DbMbt5AGdeeC5iz0lENJwxwAqBUkuDeGp1EAkj9XUfOHvEKzP3mu0ozn/3poDqwCor9ejvF5xfHzs2BW++uRDd3Y7sUmpqGmbOnBPWLsLU7CnIGrvAmbEyW7T4xW/fw8umQxBFEfX157BmzaqIBVkDLc1BHSciGu1YgxUCaeCv3PGRbKS+7pePviGbmdut+wy/ePo3fu/f0CB4HTt2bAqOH/8ympt7VDvP1OwpzrYMZWWTvYYx9/f3o7JyfUS6ryflFziWB2WOExGRN2awQiDtFnMVb60OIkHN111bewrbtm3B5s1V2LZtS1gtDMLV3tchezzQzFxxsRjUcTU0NMgPe1Y6Hq7xy1ZAo09yO6bRJ2H8shUReT4iouGOGawQSLvFhusuwlCp9bpra0+5dZXv6enG/v27AcBrGS0auxZzkrPRJhNkBZqZq6gwY82aJLdlQoNBREWFWa1T9FJcXOKVwZKOR4K0W5C7CImIAiOIohi5P7ND0N7eA7s9rk5JFXl5aWhtDX2b/kiybdsW2ZYFgiDgppvmOYMsadei6/KdTqNTvW/X6b5TeO7Ii2E9j8mkRWWlHg0NAoqLHcGV0Wj1f8cQmUw7sGbNKvT3Dw2BNhgMqKraNKIGNPPfTfzitYlfvDbRodEIyMlJVfw+M1gUdUr9oERRdMtkqTXbz18W7IZx09DV1R9WpsxotKoWUNXWnkJ19UH09HQjNTUN5eUzvDJ7UhBVWbkeDQ31KC4uQUXFuhEVXBERDWcMsCjqUlPTFIMsqeN5aelEVXYtembBpN5dANwCqHCar6opmOVTo3EJAyoiojjFIneKuvLyGdBqlWN7KfhSqoEKZtficOvdVV190BlcScIds0NERNHHDBZFXFf1YbTtNMHa0Q5tdg4KFxsxc+Yc7NmzC3IlgFLH80VXzZOtwVp01byAi9+HW+8upcxeOGN2iIgo+pjBoojqqj6M5m1bnTP8rB3taN62FYUdF3DTTfO8MlmuHc+VBlMDCHhkjxpZsGhSGqcTzpgdIiKKPmawKKLadpogDg66HRMHB9G204TSS008fRV0y9VGPX5oQ8DF776yYPGovHyGWw0WEP6YHTVw0DMRUXAYYJEstfpPSZkrpeOlpRODHiETzLLfcOtZJr0X/nYRRpM06FmaRSgNegbAIIuISAEDrDjhb2t+NBpuuj5XIDvvAqHNzpENsqQZf6EIdmRPvOwQDFQoQWck+Rr0zACLiEgea7DigLQ1XypklrbmS+NjpIAnkJojNai58y53sRFCYqLbMSExEbmLjSGf32gdVRQrHPRMRBQ8ZrDigK+t+Wo23AyUmjvv0sunA4DbLsLcxUbncU+BZOqG27LfcMdBz0REwWOAFQf8bc2PdquBYJfg/Ekvn64YULkKdGkymsuloQqkG/twMX7ZCrcaLICDnomI/OESYRzwtzU/2q0GYrUEF8jSZLSXS0Phb8l3uCmaMw+TfrgWSQWFgCAgqaAQk364lvVXREQ+MIMVB/xtzY92q4FYLcEFkqmL9nJpKPwt+Q5HRXPmMaAiIgoCA6w44G9rfiwCnljsvAtkaXI4dGZnN3YiImKAFSf8bc2PdasBz3E3vgrVQxVIpk7t+rBIUBpmzW7sRESjB2uwyC+lcTdd1YdVfR6l0TiugeVwaNEgN8w6HrqxExFR9DCDRX75GnejdhbLX6ZuOLRoiMdu7EREFF0MsMgvf+Nuoi3Wy6WBiLdu7EREFF1cIiS/lMbahDPuhoiIaCRjgEV+RWLcDRER0UjGJULyK9hxN0RERKMdAywKSKDjbiJJahXx56Mf4/lPTqO5rxfFxSWoqFgHo3FJTM+NiIjIFZcIaViQWkX8+ejH+OX/fYym3h6Iooj6+nNYs2YVTKYdsT5FJ33jDmQfmIzc3RnIPjAZ+sb4OTciIooOBlg0LEitIv5w6gTMNpvb9/r7+1FZuT5GZ+ZO37gDaSdXIWHgHASISBg4h7STqxhkERGNMgywaFiQWkK09PfLfr+hoT6ap6Mo5cx6CHb3cxTs/Ug5Ex8BIBERRQdrsEjRkaaauGnoqc3OgbWjHfkGA5plgqzi4pIYnJU3zYB8oKd0nIiIRiZmsEjWkaYavHTa5Jz7d8F8ES+dNuFIU01MzkdqFXH/xMnQJyS4fc9gMKCiYl1MzsuTPUk+0FM6TkREI1PYAdbGjRuxadMm59ddXV1Yvnw55s+fj7vuugutra3hPgXFwJuf7XIbugwAFrsFb362Kybnk14+HQVL78bNU6/Fj6+5FoUpqRAEASUlY1BVtSludhH2jl8HUWNwO9Y3mIx9HVwiJCIaTUIOsLq7u/HYY4/hj3/8o9vxjRs34rrrrsO7776Lf/zHf0RlZWXYJ0nRJ2WuAj0eDenl03Hl07/BD3btxdG/n0dzcydqak7ETXAFAOaiJdjT8yzq2sbCbhfwRes4LNvyPBY9eDdMJq7IExGNFiH/xt+zZw8uv/xy3HPPPW7H9+/fj+3btwMAFixYgCeeeAIWiwU6nS68M6WoytJnygZTWfrMiD5vPNV9heqe9f8P9fX3eB2vrNTDaLTG4IyIiCjaQs5g3XbbbVi+fDkSPOphWlpakJeXBwDQarVITU1FR0dHeGdJUbfoqnnQadyDYp1Gh0VXzYvYc8Zb3VeoGhqEoI4PFyaTFmVlKSgoSEVZWQozckREPvj9Dfnuu+/iySefdDt25ZVXYuvWrQE9gSiK0GgCj+NyclIDvu1wk5eXFutTCNgteTciPd2Al4++gfa+DuQkZ+O7U2/FDeOmRew536l+X7bu650v3sctU26M2PMC6l6bsWOBs2fljgvD6mfA1fbtwCOPAH19jq/r6wU88ogBaWnAXXdF9rmH63s2GvDaxC9em9jzG2DNnz8f8+fPD/gB8/Pz0dbWhsLCQlitVvT29iIzMzPg+7e398BuFwO+/XCRl5eG1tbuWJ9GUCYkT8T68oluxyL5Gtr65DOdbX0dEX1eta/N2rVarFmThP7+oYyVwSBi7doBtLYOzyXCtWtT0Nfn/odSXx+wdq0dc+f2Rux5h+O/m9GC1yZ+8dpEh0Yj+EwKqd6m4cYbb8Trr78OAPjzn/+M6667jvVXFBCl+q5I132pzWi0oqpqACUldgiCiJISO6qqBoZ1/dVIXfYkIooU1YsoHnroIaxduxa33HIL0tLS8Otf/1rtp6ARatFV8/DSaZPbMmGk674ixWi0DuuAylNxsYj6eu9gqrh45GWbiYjUEHaAtWrVKrevMzMz8dxzz4X7sHFnJOxui5Su6sNo22mCtaMd2uwc5C42Ir18etCPI72ffJ/jT0WFWXbZs6LCHMOzIiKKX9wGFABpd5uUWZF2twEY9R/+XdWH0bxtK8TBQQCOmYHN27YCQMhB1mh/T+ORIxs3gMpKPRoaBBQXO4KrkZSlIyJSEwOsAPjqaj7ag4G2nSZncCURBwfRttOE9PLpqmW3KPZG2rInEVEkMcAKQDx2NY8X1o52xeNqZ7eIiIiGCw57DsBI2d0WCdrsHMXjvrJbREREIxkDrADEoqv5cJG72AghMdHtmJCYiNzFRp/ZLSIiopGMAVYAphWW4c4JRmfGKkufiTsnGEd9/RXgWOorWHq3M5Olzc5BwdK7kV4+3Wd2i4iIaCRjDVaAuLtNWXr5dNmaqtzFRrcaLGAou0VERDSSMcCiiJGCLu4iJCKi0YYBFkWEZ3uGwmXLGVjFEX3jDqScWQ/NQD3sSSXoHb8O5qIlsT4tIqIRgwEWqY7tGeKbvnEH0k6ugmDvBwAkDJxD2knHRAYGWURE6mCRO6mO7RniW8qZ9c7gSiLY+2GorYjRGRERjTwMsEh1bM8Q3zQD9bLHtYPN6Ok4FuWzISIamRhgkerYniG+2ZNKZI8PatLReX5vlM+GiGhkYoBFqvPVfJRir3f8Otg8yi9t0KIheQZsls4YnRUR0cjCAItU56v5KMWeuWgJzmXcBrMmHSIAsyYdZ1Pn4kLSJCToMmJ9ekREIwJ3EVJEKDUfVcORphq8+dkuXDBfRJY+E4uumscmsEESxz+ME3VfgihanMcEQYeMy2bH8KyIiEYOBlg0rBxpqsFLp02w2B2BwQXzRbx02rE7kUFW4FKzpwAAOs/vhc3SiQRdBjIum+08TkRE4WGANUp4Nv4crh3V3/xslzO4kljsFrz52S4GWEFKzZ7CgIqIKEIYYI0CI6nx5wXzxaCOExERxQKL3EeBkdT4M0ufGdRxIiKiWGCANQqMpMafi66aB51G53ZMp9Fh0VXzYnRGRERE3rhEOApos3Nkg6nh2PhTqrPiLkIiIopnDLBGgdzFRrcaLGB4N/6cVljGgIqIiOIaA6xRQCpkHwm7CImIiIYDBlijRCQbfxIREZE7FrkTERERqYwBFhEREZHKGGARERERqYwBFhEREZHKGGARERERqYy7CClkI2WANBERkdoYYFFIRtIAaSIiIrVxiZBCMpIGSBMREamNARaFZCQNkCYiIlIbAywKidKg6OE4QJqIiEhtDLAoJLmLjRASE92ODecB0kRERGpikTuFhAOkiYiIlDHAopBxgDQREZE8LhESERERqYwBFhEREZHKGGARERERqYwBFhEREZHKGGARERERqYwBFhEREZHKGGARERERqYwBFhEREZHKGGARERERqYwBFhEREZHKGGARERERqYwBFhEREZHKGGARERERqYwBFhEREZHKGGARERERqYwBFhEREZHKGGARERERqUwb7gNs3LgRCQkJWLVqFQDgyJEjWLVqFQoLCwEAkyZNwpNPPhnu0xARERENGyEHWN3d3XjyySfxzjvvYNmyZc7jx48fx7333ov7779flRMkIiIiGm5CXiLcs2cPLr/8ctxzzz1ux48dO4aDBw9i4cKFWLFiBRobG8M+SSIiIqLhRBBFUQznATZt2gQAziXCf/mXf8GMGTMwd+5cvPzyy3jjjTfwyiuvhH+mRERERMOE3wDr3Xff9aqhuvLKK7F161YA3gGWp+uuuw779u1DWlpaQCfU3t4Duz2smC8u5eWlobW1O9anQTJ4beIXr0384rWJX7w20aHRCMjJSVX8vt8arPnz52P+/PkBPZndbscf/vAHLF++HAkJCc7jrv9NRERENNKp2qZBo9Fg9+7deO+99wAAr7/+Oq655hokJyer+TREREREcS3sNg2efvnLX+KnP/0pnn32WWRnZ+Ppp59W+ymIiIiI4lrYRe5qYw0WRRuvTfzitYlfvDbxi9cmOvzVYLGTOxEREZHKGGARERERqYwBFsUtk2kHysomo6AgA2Vlk2Ey7Yj1KREREQVE9SJ3IjWYTDuwZs0q9Pf3AwDq689hzRpHrzWjcUksT42IiMgvZrAoLlVWrncGV5L+/n5UVq6P0RkREREFjgEWxaWGhvqgjhMREcUTBlgUl4qLS4I6TkREFE8YYFFcqqhYB4PB4HbMYDCgomJdjM6IiIgocAywKC4ZjUtQVbUJJSVjIAgCSkrGoKpqEwvciYhoWOAuQopbRuMSBlRERDQsMYNFREREpDIGWEREREQqY4BFREREpDIGWEREREQqY4BFREREpDIGWEREREQqY4BFREREpDIGWEREREQqY4BFREREpDIGWEREREQqY4BFREREpDIGWEREREQqY4BFREREpDIGWEREREQqY4BFREREpDIGWEREREQqY4BFREREpDIGWEREREQq08b6BDxpNEKsTyFiRvJrG+54beIXr0384rWJX7w2kefvPRZEURSjdC5EREREowKXCImIiIhUxgCLiIiISGUMsIiIiIhUxgCLiIiISGUMsIiIiIhUxgCLiIiISGUMsIiIiIhUxgCLiIiISGUMsIiIiIhUxgBLRW+99RZuvvlmzJ07F9u3b/f6/gcffIBbb70VixYtwsqVK9HZ2RmDsxy9/F2f3bt3Y+HChbjllluwdu1aDA4OxuAsRyd/10ayf/9+zJ49O4pnRv6uzb/+679i1qxZuPXWW3Hrrbf6vH6kLn/X5vPPP8c///M/Y9GiRfje977Hz5xoE0kVTU1N4qxZs8QLFy6Ivb294sKFC8VPP/3U+f3u7m7x+uuvF5uamkRRFMWNGzeKP//5z2N1uqOOv+vT29srzpgxQ2xtbRVFURRXr14tvvLKK7E63VHF37WRtLa2ivPmzRNnzZoVg7McnQK5Nvfff79YU1MTozMcvfxdG7vdLs6dO1f88MMPRVEUxV/96lfi008/HavTHZWYwVLJ4cOHUV5ejszMTCQnJ+Nb3/oWdu3a5fy+xWLBunXrUFBQAAC4+uqr0djYGKvTHXX8XZ/k5GTs3bsXubm56O/vR3t7O9LT02N4xqOHv2sjefzxx/GDH/wgBmc4egVybY4fP44//OEPWLhwIZ544gmYzeYYne3o4u/anDhxAsnJyfjGN74BAFixYgXuuuuuWJ3uqMQASyUtLS3Iy8tzfp2fn4/m5mbn11lZWZgzZw4AYGBgAM8//zy++c1vRv08Ryt/1wcAdDodPvzwQ8ycORMXLlzAjBkzon2ao1Ig12bbtm2YNGkSrrnmmmif3qjm79r09vZi4sSJ+NGPfoTXXnsNXV1d2Lx5cyxOddTxd23q6uqQm5uLxx57DN/+9rexbt06JCcnx+JURy0GWCqx2+0QBMH5tSiKbl9Luru7sXz5ckyYMAHf/va3o3mKo1qg1+fGG2/EX//6V8yaNQs/+9nPoniGo5e/a1NbW4v3338fK1eujMXpjWr+rk1KSgq2bNmCq666ClqtFvfeey8+/PDDWJzqqOPv2litVhw5cgTf/e538dprr2HMmDF46qmnYnGqoxYDLJUUFhaitbXV+XVrayvy8/PdbtPS0oI777wTV199NSorK6N9iqOav+tz8eJFHDx40Pn1woUL8cknn0T1HEcrf9dm165daG1thdFoxPLly53/jijy/F2b8+fP47/+67+cX4uiCK1WG9VzHK38XZu8vDyMGzcOU6ZMAQAsWLAAR48ejfp5jmYMsFQyffp0fPTRR+jo6EB/fz/ef/9959o3ANhsNqxYsQLz589HRUWFbPaEIsff9RFFET/60Y9w/vx5AI4P9bKyslid7qji79o8+OCDeO+99/DGG2/g+eefR35+Pl566aUYnvHo4e/aJCUl4Ve/+hXOnTsHURSxfft2ZykERZa/a3Pttdeio6MDp0+fBgDs3bsXkydPjtXpjkr8U0MlBQUFePjhh7F06VJYLBbcfvvtmDp1Ku677z48+OCDaGpqwsmTJ2Gz2fDee+8BAL785S8zkxUl/q7PlClT8POf/xz3338/BEHA+PHjsX79+lif9qgQyLWh2Ajk2jzxxBP4/ve/D4vFgrKyMtxzzz2xPu1RIZBr8+yzz+Lxxx9Hf38/CgsL8fTTT8f6tEcVQRRFMdYnQURERDSScImQiIiISGUMsIiIiIhUxgCLiIiISGUMsIiIiIhUxgCLiIiISGUMsIiIiIhUxgCLiIiISGUMsIiIiIhU9v8BUcsEhPSStvEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(10,10))\n",
+    "chart = fig.add_subplot()\n",
+    "ax1 = fig.add_subplot()\n",
+    "colors = ['r','g','blue', 'brown','purple','gray','pink','black', 'y', 'orange']\n",
+    "for genre in genre_dict:\n",
+    "    genre_data = data[data[\"genre\"]==genre_dict[genre]]\n",
+    "    ax1.scatter(genre_data['chroma_stft_mean'],genre_data['mfcc12_mean'], c=colors[genre_dict[genre]-1])\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1072,12 +1164,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Wykorzystanie algorytmu Bayesa"
+    "# 4. Wykorzystanie algorytmu Bayesa"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1133,9 +1225,16 @@
     "![image.png](attachment:image.png)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "W procesie trenowania i testowania modelu wykorzystany został skrypt losujący kolumny i zapisujący uzyskiwane wartości accuracy w celu znalezienia najbardziej efektywnej kombinacji cech. Uzyskane w ten sposób cechy to: mfcc4, m"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1147,7 +1246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1170,7 +1269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "scrolled": true
    },
@@ -1228,7 +1327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {