diff --git a/.gitignore b/.gitignore
index 102937a..4b3e069 100644
--- a/.gitignore
+++ b/.gitignore
@@ -13,6 +13,10 @@ ipython_config.py
 aimenv/
 .venv/
 
+# CSV datasets
+static/*
+
 # Remove previous ipynb_checkpoints
 #   git rm -r .ipynb_checkpoints/
 
+
diff --git a/lab_1/image.png b/lab_1/image.png
deleted file mode 100644
index d7908a3..0000000
Binary files a/lab_1/image.png and /dev/null differ
diff --git a/lab_1/lab1.ipynb b/lab_1/lab1.ipynb
deleted file mode 100644
index 4c97118..0000000
--- a/lab_1/lab1.ipynb
+++ /dev/null
@@ -1,327 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Начало ЛР\n",
-    "\n",
-    "Выгрузка данных из CSV в датафрейм"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Education Level</th>\n",
-       "      <th>Institution Type</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Device</th>\n",
-       "      <th>IT Student</th>\n",
-       "      <th>Location</th>\n",
-       "      <th>Financial Condition</th>\n",
-       "      <th>Internet Type</th>\n",
-       "      <th>Network Type</th>\n",
-       "      <th>Flexibility Level</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>University</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>23</td>\n",
-       "      <td>Tab</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Town</td>\n",
-       "      <td>Mid</td>\n",
-       "      <td>Wifi</td>\n",
-       "      <td>4G</td>\n",
-       "      <td>Moderate</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>University</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>23</td>\n",
-       "      <td>Mobile</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Town</td>\n",
-       "      <td>Mid</td>\n",
-       "      <td>Mobile Data</td>\n",
-       "      <td>4G</td>\n",
-       "      <td>Moderate</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>College</td>\n",
-       "      <td>Public</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>18</td>\n",
-       "      <td>Mobile</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Town</td>\n",
-       "      <td>Mid</td>\n",
-       "      <td>Wifi</td>\n",
-       "      <td>4G</td>\n",
-       "      <td>Moderate</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>School</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>11</td>\n",
-       "      <td>Mobile</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Town</td>\n",
-       "      <td>Mid</td>\n",
-       "      <td>Mobile Data</td>\n",
-       "      <td>4G</td>\n",
-       "      <td>Moderate</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>School</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>18</td>\n",
-       "      <td>Mobile</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Town</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Mobile Data</td>\n",
-       "      <td>3G</td>\n",
-       "      <td>Low</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  Education Level Institution Type  Gender  Age  Device IT Student Location  \\\n",
-       "0      University          Private    Male   23     Tab         No     Town   \n",
-       "1      University          Private  Female   23  Mobile         No     Town   \n",
-       "2         College           Public  Female   18  Mobile         No     Town   \n",
-       "3          School          Private  Female   11  Mobile         No     Town   \n",
-       "4          School          Private  Female   18  Mobile         No     Town   \n",
-       "\n",
-       "  Financial Condition Internet Type Network Type Flexibility Level  \n",
-       "0                 Mid          Wifi           4G          Moderate  \n",
-       "1                 Mid   Mobile Data           4G          Moderate  \n",
-       "2                 Mid          Wifi           4G          Moderate  \n",
-       "3                 Mid   Mobile Data           4G          Moderate  \n",
-       "4                Poor   Mobile Data           3G               Low  "
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "dataframe = pd.read_csv(\".//static//csv//students_adaptability_level_online_education.csv\")\n",
-    "dataframe.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Диаграмма 1 (Круговая)\n",
-    "\n",
-    "Данная диаграмма (круговая) отображает распределение людей по типу соединения к интернету (4G, 3G, 2G). Это позволяет сделать вывод о том, что люди с низким уровнем заработка имеют чаще всего 3G и 4G (одинаково), чем 2G."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Распределение людей низкого уровня заработка по типу соединения')"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "rural_df = dataframe[dataframe['Financial Condition'] == \"Poor\"]\n",
-    "\n",
-    "network_type_rural_count = rural_df['Network Type'].value_counts()\n",
-    "\n",
-    "plt.figure(figsize=(6,6))\n",
-    "plt.pie(network_type_rural_count, labels=network_type_rural_count.index, autopct='%1.2f%%')\n",
-    "plt.title(\"Распределение людей низкого уровня заработка по типу соединения\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Диаграмма 2 (Линейная)\n",
-    "\n",
-    "Данная диаграмма выполнена на срезе данных и отображает количество студентов в разных возрастных группах. Из нее можно сделать вывод, что в подростковом возрасте (10-15) люди менее интересуются IT направлением, но в возрасте 20-25 лет количество учеников IT направлений начинает преобладать над не IT сферой (и в это же время количество не IT студентов начинает убывать)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x71e288175ac0>"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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nn39O27Zt+fvvvxP9HAQFBVG0aNFHnvfB63rvvfdo0aJFsvs8/AHF1KlTE30fw8PDEz4wSs5HH31E6dKl6dOnz2OvQ4yJieGTTz7h1Vdftdr7RCSjqZASyQYaNmyY8PeaNWsyePBgpk6dSu/evROmtjz4z/jMmTNJRh3OnDmT5D/rkydPAubRlpQYMWIEVapUoWvXro/cZ9SoUQwYMIDTp08nLBL88FS0kiVLEhER8cRPTUuWLMn69eu5c+dOuo1KVa9ePaH7VKtWrbh27Rrjx4/nk08+IV++fLi4uHDmzJkkzzt9+jQ2NjYULlw4w/JWqFCBqlWrMn/+fAoVKsTly5eZPHlykv1CQkK4fPky3bp1e+zxSpYsCZinYKbkE2tbW9sk+yXXdSwlihYtitFo5OzZs4nebzdv3iQ0NDTJe9PX1zfRucuUKUPdunUTisrHyZs3b5Lcy5cvT3R/1apVREdHs3LlSooUKZKwPbnpjRkpMjKS0aNH88Ybbzz2l+tHqVixYpLX/vAacyn9+ctoDRs2TGi6AiRpjPPvv/+yceNGRo8enahhxIOR1qfxoNj5b3Hwzz//ACQ0YFmyZAlOTk6sX78+0VS8mTNnJnvMEiVKJPoae3p60r17d/bs2UOdOnUA8zTUc+fO0bJly0dmezBSZm9vn+Lv2bPPPkuNGjUS7ic3xfOBw4cPs3DhQpYvX/7E9QinTZtGcHDwE7tIimQnukZKJBv64osv8PX1ZcCAAcTFxQFQo0YNvL29+eGHHxJNO1m7di2nTp1K0lVq0aJF+Pr6pqiQ2r17NytWrGDcuHFPHFXw9fXlueeeo1mzZjRr1izJVJGXXnqJ3bt3J/uJe2hoaMLr6dSpEyaTidGjRyfZz/RQW+K0un//PnFxccTFxWFra0vz5s1ZsWJFouk8N2/eZMGCBdSvXz/J9Lf/skTeXr168ddffzFx4kTy5MlDq1atkuzz4Hq59u3bP/ZYLVq0wMPDgy+//JLY2Ngkj4eEhKQpY0q0bt0aIElHuG+//RYgyXvzYffv3wdI0io9rR78wvjf70tYWNgjfynOKN9//z337t2z6EK+Kf35y2yS+55B0vdUWk2ZMiXh7yaTiSlTpmBvb59wXZOtrS0GgyHhAyIwjwQ/XKQ/SnLv4RUrVnD//v1HTrcF8wcfjRs35scff+TGjRtJHn/an9sPPviAevXq0a5du8fud/fuXb744gveeeedFM9iEMkONCIlkg25u7szefJkXnzxRb755huGDx+Ovb0948ePp1+/fjRq1Ihu3boltD8vVqwY77zzDmBuSvHJJ5+wbt06fvjhhxRNt/rrr794/vnn0+VT7GHDhrFy5Uratm1L3759qV69Ovfu3ePYsWMsXryYixcvkjdvXp577jl69erFpEmTOHv2bML0ne3bt/Pcc88lujg8pTZs2MDVq1cTpvbNnz+fdu3a4eDgAMCYMWPYsGED9evX54033sDOzo4ff/yR6OhoJkyY8NhjWyJv9+7def/991m2bBmDBg1KMqVy6tSpfPzxx+TLl4/z589z/vz5hMfi4uK4cOECGzZs4Pnnn8fDw4Pp06fTq1cvqlWrxssvv0y+fPm4fPkya9asoV69eol+mUxPlStXpk+fPsyYMYPQ0FAaNWrEvn37mD17Nh06dOC5555LtP+FCxeYN28eYG5wMmXKFDw8PFLccOJJmjdvjoODAy+88AIDBw4kIiKCn376CW9v72R/Wc0of/31F1988QV58uSx2DlS+vOX2Xh4eNCwYUMmTJhAbGwsBQsW5K+//iIwMPCpj+3k5MS6devo06cPtWrVYu3ataxZs4YPP/ww4VqyNm3a8O2339KyZUu6d+9OcHAwU6dOpVSpUhw9ejTJMY8ePcq8efMwmUycP3+eSZMmUahQIWrUqEFkZCSjRo1i2rRp1K1bl+bNmz8239SpU6lfvz4VK1bktddeo0SJEty8eZPdu3dz9epVjhw5kubX/tdff7Fz584n7nfo0CHy5s37yCmhItmVCimRbKpjx460b9+ezz77jJdeeonixYvTt29fXFxcGDduHMOHD8fV1ZWOHTsyfvz4hKkymzZt4vbt28yfP5/u3bun6FwGgyHVHcQexcXFha1bt/Lll1/yxx9/MGfOHDw8PHjmmWcYPXp0omuAZs6cSaVKlfjll18YNmwYnp6e1KhRg7p166bp3F9++SVgvh6oYMGC+Pv7JxpBKl++PNu3b2fEiBGMHTsWo9FIrVq1mDdvXoo6VKV33vz589O8eXP+/PPPJGvLQOJOY8k9vn37dr744guef/55wFyYFShQgHHjxvHVV18RHR1NwYIFadCgAf369UtTxpT6+eefKVGiBLNmzWLZsmX4+PgwYsSIZK9P2759e8K1Gnnz5qVatWqMHj36sVMrU6NMmTIsXryYjz/+mPfeew8fHx8GDRpEvnz5UtSwwVJ8fX15++23LXqO1Pz8ZTYLFizgzTffZOrUqZhMJpo3b87atWspUKDAUx3X1taWdevWMWjQIIYNG4a7uzujRo1KNIWwSZMm/PLLL4wbN463336b4sWLM378eC5evJhsIbVs2TKWLVuGwWAgf/78PPfcc3zxxRe4ublx7do1Fi1axIABAxg9ejQ2No+fPOTn58eBAwcYPXo0s2bN4vbt23h7e1O1atVk18VKjfbt26f436ePPvrosaPyItmRwZRec2BERCTDdezYkWPHjnHu3LkkjxkMBjZv3pxw3dfDZs2axaxZs9iyZYtlQ4pkUX379mXx4sVERERYO4qIZEK6RkpEJIu6ceMGa9asSXa0SURERCxLU/tERLKYwMBAdu7cyc8//4y9vT0DBw5Mdr8ePXqQP3/+Rx6nZMmSCdP6REREJHVUSImIZDFbt26lX79+FClShNmzZz+yS9aDhgyP0qBBgyTrComIiEjK6BopERERERGRVNI1UiIiIiIiIqmkQkpERERERCSVdI0UYDQauX79Ou7u7ilafFRERERERLInk8nE3bt3KVCgwGPXclMhBVy/fj3dFnIUEREREZGs78qVKxQqVOiRj6uQAtzd3QHzF0urcouIiIiI5Fzh4eEULlw4oUZ4FBVSkDCdz8PDQ4WUiIiIiIg88ZIfNZsQERERERFJJRVSIiIiIiIiqaRCSkREREREJJV0jZSIiIiIZAnx8fHExsZaO4Zkcfb29tja2j71cVRIiYiIiEimZjKZCAoKIjQ01NpRJJvw8vLCx8fnqdaQVSElIiIiIpnagyLK29sbFxeXp/rlV3I2k8lEZGQkwcHBAPj6+qb5WCqkRERERCTTio+PTyii8uTJY+04kg04OzsDEBwcjLe3d5qn+anZhIiIiIhkWg+uiXJxcbFyEslOHryfnuaaOxVSIiIiIpLpaTqfpKf0eD+pkBIREREREUklFVIiIiIiIiKppEJKRERERHKEeKOJ3edvsyLgGrvP3ybeaLLo+fr27UuHDh0A81Syx90+/fRTi2aR9KeufSIiIiKS7a07foPRq05yIywqYZuvpxOjXvCjZYW0t8BOqRs3biT8fdGiRYwcOZIzZ84kbHNzc7N4BklfGpHKTIzxELgdji02/2mMt3YiERERkSxv3fEbDJp3KFERBRAUFsWgeYdYd/zGI56Zfnx8fBJunp6eGAyGRNseVUhFR0czfPhwChcujKOjI6VKleKXX37h4sWLjx3hCgwMpFSpUnz99deJjhcQEIDBYODcuXMJ2z799NMkz38wkvbAjh07aNCgAc7OzhQuXJghQ4Zw7969hMeLFSvGxIkTEz3nvyNyffv2fWTWvn37pvnrak0akcosTq6EdcMh/Pr/tnkUgJbjwa+d9XKJiIiIZDImk4n7sSn7wDneaGLUyhMkN4nPBBiAT1eepF6pvNjaPLmTm7O9bYZ2EOzduze7d+9m0qRJVK5cmcDAQG7dukXhwoUTRrmuXLnCs88+y759+yhcuDAA+fLl45VXXmHmzJm89957CcebOXMmDRs2pFSpUonOU758ef7++28A3nrrLaKjoxMeO3/+PC1btmTMmDH8+uuvhISE4O/vj7+/PzNnzkzR6/j+++8ZN25cwvEfbIP/reuU1aiQygxOroTfe8PDP+LhN8zbX5qjYkpERETk/92Pjcdv5Pp0OZYJCAqPouKnf6Vo/5OftcDFIWN+hf7nn3/4/fff2bBhA82aNQOgRIkSCY/7+PgAEBVlHmnLly9fwjYwjwKNHDmSffv28eyzzxIbG8uCBQuSjFJFR0fj7Oyc8FxnZ+dEhdTYsWPp0aMHb7/9NgClS5dm0qRJNGrUiOnTp+Pk5PTE1+Lp6Ymnp2fC8f+bP6vS1D5rM8abR6Ie+TkJsO4DTfMTERERyWECAgKwtbWlUaNGaXp+gQIFaNOmDb/++isAq1atIjo6mi5duiTa7/bt23h4eDzyOEeOHGHWrFm4ubkl3Fq0aIHRaCQwMDBhv+HDhyfaZ/78+WnKnVVoRMraLu1KPJ0vCROEXzPvV7xBhsUSERERyayc7W05+VmLFO27L/AOfWfuf+J+s/rV5NniuVN07oySHlPe+vfvT69evfjuu++YOXMmXbt2xcXFJdE+Fy5coHjx4o88RkREBAMHDmTIkCFJHitSpEjC34cNG5boeqfhw4cTH599BwNUSFlbxM303U9EREQkmzMYDCmeXtegdD58PZ0ICotKdv6PAfDxdKJB6XwpukYqI1WsWBGj0cjWrVsTpvalVuvWrXF1dWX69OmsW7eObdu2JXo8KiqKffv20atXr0ceo1q1apw8eTLJdVUPy5s3b6J93N3dCQ0NTVPurEBT+6zNLX8K9/O2bA4RERGRbMjWxsCoF/wAc9H0Xw/uj3rBL9MVUWDuhNenTx9eeeUVli9fTmBgIFu2bOH3339P8TFsbW3p27cvI0aMoHTp0tSpUyfhsYiICEaOHAlA/fr1CQoKIigoiPv37xMdHU1YWBhgHlnatWsX/v7+BAQEcPbsWVasWIG/v3/6vuAsRoWUtRWta+7Ol+RH+yG7pz1hCqCIiIiIJKdlBV+m96yGj2fipgg+nk5M71ktQ9aRSqvp06fTuXNn3njjDcqWLctrr72WqO14Srz66qvExMTQr1+/RNu//vprvvrqK+7evUupUqXw9fXF19eX33//nXXr1iV016tUqRJbt27ln3/+oUGDBlStWpWRI0dSoECBdHudWZHBZDJZdknnLCA8PBxPT0/CwsIee6GdxSR07YPETScM5vsGWzDFg6MHPD8aqvUFG9XAIiIikv1FRUURGBhI8eLFU9Qd7nHijSb2Bd4h+G4U3u5OPFs8d6YciUpv27dvp2nTply5coX8+f83G+rTTz9N9Od/LV++nOXLlzNr1qyMCZnBHve+SmltoN/GMwO/duYW5x4PfRriUQBemguDdkKhmhAdDqvfgdlt4da55I8lIiIiIsmytTFQp2Qe2lcpSJ2SebJ9ERUdHc3Vq1f59NNP6dKlS6IiCkjorpccJyenhHblkjyNSJEJRqQeMMabu/NF3DRfO1W0LtjY/u+xfT/Bxs8g9h7YOkLj4VB3CNjaWy+ziIiIiAWl54hUTjNr1ixeffVVqlSpwsqVKylYsKC1I2Ua6TEipUKKTFRIpcS/l8yjUuc3mu/nrwjtJ0OBqtbNJSIiImIBKqTEEjS1LyfKVRR6LoGOM8A5N9w8Bj81gb8+gZhIa6cTEREREckRVEhlRQYDVO4Kg/dBhc5gMsKuSTC9LlzYau10IiIiIiLZngqprMwtH3T+Bbr/Dh4F4d9AmNMOVvjD/X+tnU5EREREJNtSIZUdPNMC3tgDNV8z3z88F6bWgpMrrJtLRERERCSbUiGVXTh5QJuv4ZX1kPcZc+e/33vDwh4QfsPa6UREREREshUVUtlNkdowcDs0HAY2dnB6tXl06uAsUINGEREREZF0oUIqO7J3giYfw4CtUKAaRIfBqrdg9gtw+7y104mIiIiIZHkqpLIznwrQ/29o8SXYu8DF7ebOfjsmQnyctdOJiIiIZCxjPARuh2OLzX8a4y16ur59+9KhQ4ck27ds2YLBYCA0NDTNx541axZeXl4ANG7cGIPB8Mhb48aN03weeTQ7awcQC7OxhTqDoWwb86jUhS3w9yg4vgTaTwHfytZOKCIiImJ5J1fCuuEQfv1/2zwKQMvx4NfOernSwdKlS4mJiQHgypUrPPvss/z999+UL18eAAcHB2vGy7Y0IpVT5CoGvZZD+2ng5AVBR2HGc7BhFMTet3I4EREREQs6udLchOu/RRSYG3L93tv8uJXt2LGDBg0a4OzsTOHChRkyZAj37t1L0XNz586Nj48PPj4+5MuXD4A8efIkbMudO/cjn/vrr79Svnx5HB0d8fX1xd/fH4BixYo9coRr1qxZvPLKK7Rt2zbRsWJjY/H29uaXX35J2PZg9O2/twcjaQ9cuXKFl156CS8vL3Lnzk379u25ePFiwuPJjez9d0Ru1qxZj8xarFixFH0N00KFVE5iMEDVHuC/H8p3BFM87JwI0+vBxR3WTiciIiKSMiYTxNxL2S0qHNa+DyTXdOv/t60bbt4vJcezQPOu8+fP07JlSzp16sTRo0dZtGgRO3bsSChqLGX69OkMHjyYAQMGcOzYMVauXEmpUqUA2L9/Pzdu3ODGjRsUKlSIiRMnJtzv2rUr/fv3Z926ddy48b/u0KtXryYyMpKuXbsmOdeZM2e4ceMGEydOTLQ9NjaWFi1a4O7uzvbt29m5cydubm60bNkyYZTtSbp27ZqQbeLEiRQqVCjh/v79+9P+BXoCTe3Lidy8ocssqPgSrBkKd87DrDZQvS80Gw3OXlYOKCIiIvIYsZHwZYF0OpjJPFI1rnDKdv/wOji4pvjoq1evxs3NLdG2+PjE12aNHTuWHj168PbbbwNQunRpJk2aRKNGjZg+fTpOTk4pPl9qjBkzhnfffZe33norYVvNmjUBEka2AGxtbfH09MTHxydhW926dSlTpgxz587l/fffB2DmzJl06dIl0euNjo4GoGDBgri6uuLp6Zkow6JFizAajfz8888YDIaE43h5ebFlyxaaN2/+xNfh7OyMs7MzAJ6entja2ibKaikqpHKysq2hWD34+1M48Ku5RfqZddDmGyjX9knPFhEREZEneO6555g+fXqibXv37qVnz54J948cOcLRo0eZP39+wjaTyYTRaCQwMJBy5cqle67g4GCuX79O06ZN03yM/v37M2PGDN5//31u3rzJ2rVr2bRpU6J9bt++jZ2dHS4uLske48iRI5w7dw53d/dE26Oiojh//n/dph8uSOPi4ixWYKaUCqmczskT2n4HFTrDqiFw+xws6gF+7aHVV+Ce39oJRURERBKzdzGPDKXEpV0wv/OT9+uxGIrWTdm5U8HV1TVhutwDV69eTXQ/IiKCgQMHMmTIkCTPL1KkSKrOl1IPRnCeRu/evfnggw/YvXs3u3btonjx4jRo0CDRPhcuXKBo0aIJo00Pi4iIoHr16omKyAf+Oyr2cEG6dOlSvvzyy6d+DU9DhZSYFasHr++EreNh5/dwcgVc2AotvoAqPczXV4mIiIhkBgZDyqfXlWxi7s4XfoPkr5MymB8v2cTc7dgKqlWrxsmTJ5MUXJbk7u5OsWLF2LhxI88991yajpEnTx46dOjAzJkz2b17N/369Uuyz9atW5MUV/9VrVo1Fi1ahLe3Nx4eHo/c7+GC1NvbO02Z05OaTcj/2DtBs1EwYIu5LXpUKKwYDHPaw51Aa6cTERERST0bW3OLcwAe/mD4/++3HGe1Igpg+PDh7Nq1C39/fwICAjh79iwrVqyweLOJTz/9lG+++YZJkyZx9uxZDh06xOTJk1N1jP79+zN79mxOnTpFnz59ErbHxMSwZMkSNm3aRPv27QkKCiIoKIiwsDBMJhMhISEA9OjRg7x589K+fXu2b99OYGAgW7ZsYciQIUlG7jIbFVKSlG8l6L8Jnv8c7JwhcCtMqwO7JmshXxEREcl6/NrBS3PAwzfxdo8C5u1WXkeqUqVKbN26lX/++YcGDRpQtWpVRo4cSYEC6dVQI3l9+vRh4sSJTJs2jfLly9O2bVvOnj2bqmM0a9YMX19fWrRokSjvrl276Ny5M0ajkY4dO+Lr64uvry9vv/024eHhCU0tXFxc2LZtG0WKFOHFF1+kXLlyvPrqq0RFRT12hCozMJhMFujhmMWEh4fj6elJWFhYpv+GZbg7F8wL+QZuM98vUBXaTQafitbNJSIiIjlCVFQUgYGBFC9e/OmbCxjjzddMRdwEt/zma6KsOBKVHURERFCwYEFmzpzJiy++mLB9y5YtfPrpp2zZsiXJc0JDQ6lSpUqitaIy2uPeVymtDTQiJY+XuwT0Xmkunhw94fphmNEYNn4GsVHWTiciIiKScja2ULwBVOxs/lNFVJoZjUaCg4P5/PPP8fLyol27xKN6Dg4Oj1wI2MbGJlEjiaxKhZQ8mcEA1XqD/z4o1w6McbD9G/ihvvlTHRERERHJUS5fvkz+/PlZsGABv/76K3Z2iXvY1a1bl6VLlyb7XA8PD4sulJtR1LVPUs7dB7rOhVOrYM17cPsszGwFNV4xL+TrpGmRIiIiIjlBsWLFyOlXCGlESlKv3AsweC9U+//OLAd+ham14Mxa6+YSEREREckgVi2kpk+fTqVKlfDw8MDDw4M6deqwdu3/fhmPiopi8ODB5MmTBzc3Nzp16sTNmzcTHePy5cu0adMGFxcXvL29GTZsGHFx6ixncc5e0G4S9Fllvo7q7nX47WX4ox9EhFg7nYiIiGQzOX30Q9JXeryfrFpIFSpUiHHjxnHw4EEOHDhAkyZNaN++PSdOnADgnXfeYdWqVfzxxx9s3bqV69evJ+oGEh8fT5s2bYiJiWHXrl3Mnj2bWbNmMXLkSGu9pJyneEMYtAvqvQUGWzixFKbWhIAFoH/wRERE5CnZ29sDEBkZaeUkkp08eD89eH+lRaZrf547d26++uorOnfuTL58+ViwYAGdO3cG4PTp05QrV47du3dTu3Zt1q5dS9u2bbl+/Tr58+cH4IcffmD48OGEhITg4OCQonOq/Xk6uR4AK/0h6Jj5fskm0HYi5CpqzVQiIiKSxd24cYPQ0FC8vb1xcXHBYHh4YV2RlDGZTERGRhIcHIyXlxe+vr5J9klpbZBpmk3Ex8fzxx9/cO/ePerUqcPBgweJjY2lWbNmCfuULVuWIkWKJBRSu3fvpmLFiglFFECLFi0YNGgQJ06coGrVqsmeKzo6mujo6IT74eHhlnthOUmBKvDaZtg9BbaMg/ObYFptaPIJ1BqoFqMiIiKSJj4+PgAEBwdbOYlkF15eXgnvq7SyeiF17Ngx6tSpQ1RUFG5ubixbtgw/Pz8CAgJwcHDAy8sr0f758+cnKCgIgKCgoERF1IPHHzz2KGPHjmX06NHp+0LEzNYe6r9jbpO+cghc2gHrR8Dxxea1qPKXt3ZCERERyWIMBgO+vr54e3sTGxtr7TiSxdnb22Nr+/Qf8Fu9kCpTpgwBAQGEhYWxePFi+vTpw9atWy16zhEjRjB06NCE++Hh4RQuXNii58xx8pQ0N6I4PAf++gSuHYQfG0L9odDwPbBztHZCERERyWJsbW3T5RdgkfRg9fbnDg4OlCpViurVqzN27FgqV67M999/j4+PDzExMYSGhiba/+bNmwnDcD4+Pkm6+D24/7ihOkdHx4ROgQ9uYgE2NlC9LwzeB2Xbmhfy3TYBfmgAl/daO52IiIiISJpZvZB6mNFoJDo6murVq2Nvb8/GjRsTHjtz5gyXL1+mTp06ANSpU4djx44lmi+7YcMGPDw88PPzy/Ds8ggevtB1HnSZDa7ecOsM/NoC/hwG0XetnU5EREREJNWsOrVvxIgRtGrViiJFinD37l0WLFjAli1bWL9+PZ6enrz66qsMHTqU3Llz4+HhwZtvvkmdOnWoXbs2AM2bN8fPz49evXoxYcIEgoKC+Pjjjxk8eDCOjpo6lqkYDFC+g7ld+oZP4PA82DcDTv8Jbb+DZ5pbO6GIiIiISIpZtZAKDg6md+/e3LhxA09PTypVqsT69et5/vnnAfjuu++wsbGhU6dOREdH06JFC6ZNm5bwfFtbW1avXs2gQYOoU6cOrq6u9OnTh88++8xaL0mexCU3tJ8KFbvAqrfg34uwoIv5fstx4JrX2glFRERERJ4o060jZQ1aR8pKYiJh8xewZxqYjOCc21xMVXrJPIIlIiIiIpLBUlobZLprpCQHcXCBFl9A/78hfwW4fweWDYD5XSD0srXTiYiIiIg8kgopsb6C1WHAFvPCvbaOcG4DTK0Ne34AY7y104mIiIiIJKFCSjIHW3vz+lKDdkKROhB7D9YNN3f3Cz5t7XQiIiIiIomokJLMJW9p6PsntPkGHNzh6n74oT5sGQdxMdZOJyIiIiICqJCSzMjGBmr2h8F74ZlWYIyFLWPhx4ZwZb+104mIiIiIqJCSTMyzIHT7DTr/Ci55IeQU/PI8rB0O0RHWTiciIiIiOZgKKcncDAao0An890PlboAJ9v4A0+rAub+tnU5EREREcigVUpI1uOSGjj9Az6XgVQTCLsO8TrB0INy7be10IiIiIpLDqJCSrKVUUxi0G2q/ARjg6EKY+iwcWwxaW1pEREREMogKKcl6HN2g5VjzQr7efhB5C5a8Cgu6QthVa6cTERERkRxAhZRkXYVqwICt8NxHYOsAZ9ebF/Ld9xMYjdZOJyIiIiLZmAopydrsHKDR+zBwOxSuBTF34c/3YGYrCPnH2ulEREREJJtSISXZg3dZ6LcOWn8NDm5wZQ/8UA+2fqWFfEVEREQk3amQkuzDxgaefQ3e2AOlm0N8DGweAzMaw9WD1k4nIiIiItmICinJfrwKQ/ff4cWfwSUPBJ+AX5rBug8h5p6104mIiIhINqBCSrIngwEqdYHB+6FSVzAZYc9U80K+5zdZO52IiIiIZHEqpCR7c80DL86AHovBszCEXoK5HWHZIIi8Y+10IiIiIpJFqZCSnKH08/DGbnh2IGCAIwvMC/keX6qFfEVEREQk1VRISc7h6A6tJ8Crf0HeMnAvBBb3g4XdIfy6tdOJiIiISBaiQkpynsLPwuvbodEHYGMPZ/6EqbVg/y9ayFdEREREUkSFlORMdo7w3AgYuA0K1oDocFgzFGa3hVvnrJ1ORERERDI5FVKSs+X3M0/1azke7F3h0k6YXhe2fwPxsdZOJyIiIiKZlAopERtbqP26uRlFyaYQHw0bP4MZz8G1Q9ZOJyIiIiKZkAopkQdyFYWeS6Djj+CcC24eg5+bwl8fQ0yktdOJiIiISCaiQkrkvwwGqPyyeSHfCp3NC/numgzT68CFrdZOJyIiIiKZhAopkeS45YPOv0C3ReBREP69CHPawQp/uP+vtdOJiIiIiJWpkBJ5nDIt4Y09ULO/+f7hueZW6SdXWDeXiIiIiFiVCimRJ3HygDbfQL91kKc0RNyE33vDwh4QfsPa6URERETEClRIiaRU0Trw+g5oOAxs7OD0avPo1MFZYDJZO52IiIiIZCAVUiKpYe8ETT6GAVuhQDWIDoNVb8HsF+D2eWunExEREZEMokJKJC18KkD/v6H5F2DnDBe3mxfy3fEdxMdZO52IiIiIWJgKKZG0srGFuv7mhXxLNIa4KPj7U/jpObhxxNrpRERERMSCVEiJPK3cxaHXcmg/DZy8IOgozHgONoyC2PvWTiciIiIiFqBCSiQ9GAxQtQf47we/DmCKh50TzdP9ArdbO52IiIiIpDMVUiLpyc0bXpoNLy8Ad1+4cwFmt4WVQ+B+qLXTiYiIiEg6USElYgll28DgvVC9n/n+odnmVumnVls3l4iIiIikCxVSIpbi5AkvTIS+ayB3SYgIgkU9zIv53r1p7XQiIiIi8hRUSIlYWrH6MGgn1B8KBls4uQKm1oRDc7WQr4iIiEgWpUJKJCPYO0OzUTBgC/hWhqgwWOkPc9qZr6MSERERkSxFhZRIRvKtBP03wfOfmxfyDdwG0+rCzklayFdEREQkC1EhJZLRbO2g3hB4YxcUawBx92HDJ/BLMwg6Zu10IiIiIpICKqRErCV3CeizCtpNBkdPuH4YZjSGjZ9BbJS104mIiIjIY6iQErEmgwGq9Qb/fVCuHRjjYPs38EM9uLTL2ulERERE5BFUSIlkBu4+0HUudJ0Hbvnh9jmY2QpWvwNR4dZOJyIiIiIPUSElkpmUewEG7zOPUgEc+NW8kO+ZtdbNJSIiIiKJqJASyWycvczXTfVZBbmKw93r8NvL8EdfiAi2djoRERERQYWUSOZVvCG8sRvqvWVeyPfEMphSEwIWaCFfEREREStTISWSmdk7w/OfwWubwKciRIXC8kEw70X496K104mIiIjkWCqkRLKCAlXgtc3Q7FOwc4Lzm2BaHdg9FYzx1k4nIiIikuOokBLJKmztof47MGgXFK0PsZGw/kP45Xm4ecLa6URERERyFBVSIllNnpLmRhRtJ4KjB1w7CD82hE1jIC7a2ulEREREcgQVUiJZkY0N1OgHg/dCmTbmhXy3fQU/1IfLe6ydTkRERCTbUyElkpV5FICX50OX2eDqDbf+gV9bwpr3IPqutdOJiIiIZFsqpESyOoMByncwj05V6QmYYP9P5oV8/1lv7XQiIiIi2ZJVC6mxY8dSs2ZN3N3d8fb2pkOHDpw5cybRPo0bN8ZgMCS6vf7664n2uXz5Mm3atMHFxQVvb2+GDRtGXFxcRr4UEetzyQ0dpkKv5eBVFMKvwYKXYEl/uHfL2ulEREREshWrFlJbt25l8ODB7Nmzhw0bNhAbG0vz5s25d+9eov1ee+01bty4kXCbMGFCwmPx8fG0adOGmJgYdu3axezZs5k1axYjR47M6JcjkjmUfM68kG8dfzDYwLE/zAv5HlmkhXxFRERE0onBZMo8v1mFhITg7e3N1q1badiwIWAekapSpQoTJ05M9jlr166lbdu2XL9+nfz58wPwww8/MHz4cEJCQnBwcHjiecPDw/H09CQsLAwPD490ez0iVnftIKwcAjePm++XagZtvwOvItbNJSIiIpJJpbQ2yFTXSIWFhQGQO3fuRNvnz59P3rx5qVChAiNGjCAyMjLhsd27d1OxYsWEIgqgRYsWhIeHc+JE8mvrREdHEx4enugmki0VrA4DtkCTT8DWAc79DVNrw54ftJCviIiIyFPINIWU0Wjk7bffpl69elSoUCFhe/fu3Zk3bx6bN29mxIgRzJ07l549eyY8HhQUlKiIAhLuBwUFJXuusWPH4unpmXArXLiwBV6RSCZhaw8N34PXd0KROhB7D9YNh19bQPApa6cTERERyZLsrB3ggcGDB3P8+HF27NiRaPuAAQMS/l6xYkV8fX1p2rQp58+fp2TJkmk614gRIxg6dGjC/fDwcBVTkv3lewb6/gkHf4UNn8LV/fBDA2jwLjQYCnaO1k4oIiIikmVkihEpf39/Vq9ezebNmylUqNBj961VqxYA586dA8DHx4ebN28m2ufBfR8fn2SP4ejoiIeHR6KbSI5gYwM1+5tbpT/TEoyxsHUc/NgQruy3djoRERGRLMOqhZTJZMLf359ly5axadMmihcv/sTnBAQEAODr6wtAnTp1OHbsGMHBwQn7bNiwAQ8PD/z8/CySWyTL8ywI3RZC51/BJS+EnIZfnoe1wyE6wtrpRERERDI9q3bte+ONN1iwYAErVqygTJkyCds9PT1xdnbm/PnzLFiwgNatW5MnTx6OHj3KO++8Q6FChdi6dStgbn9epUoVChQowIQJEwgKCqJXr17079+fL7/8MkU51LVPcrTIO7D+Qzjym/m+Z2FoOxFKN7NqLBERERFrSGltYNVCymAwJLt95syZ9O3blytXrtCzZ0+OHz/OvXv3KFy4MB07duTjjz9O9KIuXbrEoEGD2LJlC66urvTp04dx48ZhZ5eyS8BUSIlg7ui36h0Iu2y+X6krtBgLrnmsm0tEREQkA2WJQiqzUCEl8v+iI2DzF7BnOmAClzzQagJU6ASP+OBDREREJDvJkutIiYiVObpBy7HQ/2/w9oPI27DkVVjQFcKuWjudiIiISKahQkpEkipUAwZshec+Mi/ke3Y9TK0F+34Co9Ha6URERESsToWUiCTPzgEavQ8Dt0PhWhATAX++BzNbQsgZa6cTERERsSoVUiLyeN5lod86aPUVOLjBlb3wQ33YOgHiYv63nzEeArfDscXmP43x1sssIiIiYmFqNoGaTYikWOgVWP0OnNtgvu9dHtpNhvBrsG44hF//374eBaDlePBrZ52sIiIiImmgrn2poEJKJBVMJvOo07rh5mYUGIDk/hn5/y5/L81RMSUiIiJZhrr2iYhlGAxQqQsM3gcVOpN8EcX/tq/7QNP8REREJNtRISUiaeOaF6r3fcJOJvO0v0u7MiKRiIiISIZRISUiaRdxM333ExEREckiVEiJSNq55U/ZfnbOls0hIiIiksFUSIlI2hWta+7O96CxxKMsHQCbv4SosAyJJSIiImJpKqREJO1sbM0tzoGkxdT/3/cqCrERsHU8TKwEO76DmHsZmVJEREQk3amQEpGn49fO3OLcwzfxdo8C8NJcGBIAXWZD3jIQFQp/fwrfV4G9P0JcdMbnFREREUkHWkcKrSMlki6M8ebufBE3zddOFa1rHrH67+NHf4ctYyH0knmbRyFo9D5U6QG2dtbJLSIiIvIfWpA3FVRIiWSguBg4PBe2fQV3b5i35S4BjT+ECp3ARgPlIiIiYj1akFdEMic7B6j5Kgw5DM2/AJc8cOcCLO0PP9SDU6tBn++IiIhIJqdCSkSsw94Z6vrDW0fguY/B0ROCT8KiHvBTEzi3UQWViIiIZFoqpETEuhzdodEweCsA6g8Fexe4fgjmvQiz2sCl3dZOKCIiIpKECikRyRxcckOzUeYRqlqDwNYBLu2EmS1hXie4ftjaCUVEREQSqJASkczFzRtajTNfQ1WtDxhs4dzfMKMxLOoJwaesnVBEREREhZSIZFKehaDdJPDfDxVfAgxwahVMqwNLB5gbVIiIiIhYiQopEcnc8pSETj/BG7uh3AuACY4ugik1YdVbEHbN2glFREQkB1IhJSJZg3c56DoPBmyBUs3AGAcHZ8GkqrBuBESEWDuhiIiI5CAqpEQkaylQFXougX5roWg9iI+GPdPg+8qw8TO4/6+1E4qIiEgOkKZCav369ezZsweAZcuW0bt3b8aOHUtsbGy6hhMReaSidaHvGui51Fxcxd6D7d/AxMqw9SuIvmvthCIiIpKNpbqQ+uCDD2jVqhUNGjTg448/pn///ty/f5/vvvuOoUOHWiKjiEjyDAYo1RRe2wxd54O3H0SHweYx5hGqXVMg9r61U4qIiEg2ZDCZTKbUPKFgwYJMnDiRokWLUrduXZYuXUq7du3YvHkzPXv25Nq1rHfhd3h4OJ6enoSFheHh4WHtOCKSVsZ4OL4Utnz5v65+7gWg4XtQtRfYOVg3n4iIiGR6Ka0NUl1I2dnZceXKFXx9fXFxceHYsWOULFmSoKAgChUqRFxc3FOHz2gqpESymfhYCFgAWydA+FXzNq+i0HgEVHoJbGytm09EREQyrZTWBqme2mcymbCzswPMRZWNjfkQBoOBVNZkIiKWYWsP1fvAkEPQcjy4ekPoJVj+unkdqhPLwWi0dkoRERHJwlI9ImVjY4OnpycGg4HQ0FA8PDywsbHBZDIRHh5OfHy8pbJajEakRLK5mHuwbwbsmAhRoeZtPpWgySdQ+nnztVYiIiIipLw2sEvtgWfOnPlUwUREMpyDK9R/B2q8Arunmm9BR2FBFyhcy1xQFW9g7ZQiIiKShaR6RCo70oiUSA5z7zbs/A72/QRxUeZtJRpDk5FQqLpVo4mIiIh1WazZBEB8fDzLly/n1KlTAJQvX5527dpha5s1L+BWISWSQ4XfgO1fw8HZYPz/dfDKtIbnPgKfCtbNJiIiIlZhsULq3LlztG7dmmvXrlGmTBkAzpw5Q+HChVmzZg0lS5Z8uuRWoEJKJIf79xJsHQ9HfgOTETBAhReh8YeQt5S104mIiEgGslgh1bp1a0wmE/Pnzyd37twA3L59m549e2JjY8OaNWueLrkVqJASEQBC/jGvQXVimfm+wRaqdINGw8GriHWziYiISIawWCHl6urKnj17qFixYqLtR44coV69ekRERKQtsRWpkBKRRG4chc1fwD/rzPdt7KFGP2jwLrj7WDebiIiIWJTF1pFydHTk7t27SbZHRETg4OCQ2sOJiGQ+vpWg+yJ4dQMUb2i+fmrfDPi+Cvz1CUTesXZCERERsbJUF1Jt27ZlwIAB7N27F5PJhMlkYs+ePbz++uu0a9fOEhlFRKyj8LPQZxX0XgmFakLcfdg1CSZWgs1jISrc2glFRETESlI9tS80NJQ+ffqwatUq7O3tAYiLi6Ndu3bMmjULT09PiwS1JE3tE5EnMpngn/WwaQzcPGbe5pwL6r0Nzw4ABxerxhMREZH0YdH252Du3veg/Xm5cuUoVSrrdrZSISUiKWY0wsnlsPlLuH3WvM0tPzR4D6r3ATtHq8YTERGRp2OxQuqzzz7jvffew8Ul+3z6qkJKRFItPg6OLoKt4yD0snmbZ2Fzh7/K3cDWzrr5REREJE0sVkjZ2tpy48YNvL29nzpkZqFCSkTSLC4GDs+BrV9BRJB5W55S0HgElH8RbFJ9KaqIiIhYkcW69qVxJqCISPZk5wA1+8NbAdB8DDjnhtvnYMmr8GMDOP2n+foqERERyVZSPSJlY2PDe++9h5ubW7KPjxw5Ml2CZSSNSIlIuom+C3umw67JEP3/Xf0KVocmn0CJxmAwWDWeJC/eaGJf4B2C70bh7e7Es8VzY2uj75WISE5ksal9NjY21KlTJ9k1owwGA5s2bUp9WitTISUi6S7yjrlV+t4fITbSvK1ofWj6CRSpbd1sksi64zcYveokN8KiErb5ejox6gU/WlbwtWIyERGxBosWUkFBQbpGSkQkJe7ehB3fwoFfIT7GvK10c2jyMfhWtm42Yd3xGwyad4iH/yN8MBY1vWc1FVMiIjmMxa6REhGRVHDPD63Gw5uHoFpvMNjC2b/gx4bwe28IOWPthDlWvNHE6FUnkxRRQMK20atOEm/UNW4iIpJUqgupRo0aJTutT0REHsOrMLSbDP77oWIXwAAnV8C02rDsdbgTaO2EOc6+wDuJpvM9zATcCItiX+CdjAslIiJZRqoLqc2bN+Pl5WWBKCIiOUCektDpZxi0C8q2BZMRjvwGU2rAqrch/Lq1E+YYwXcfXUSlZT8REclZUr1i5NChQx/7+LfffpvmMCIiOUZ+P3h5Plw7BJvGwPmNcHAmBCwwt1NvMBRc81o7Zbbm7e6UrvuJiEjOkupC6vDhwwl/37FjB9WrV8fZ2Rkwd+0TEZFUKFgNei2Fizth0+dweTfsmQoHZ0HtQVD3TXD2snbKbOnZ4rnJ5+5IyN3oR+7j62luhS4iIvKwVHft+y93d3eOHDlCiRIl0jNThlPXPhHJFEwmOLfRXFDdCDBvc/KEukOg1uvgmPz6fZI296LjaP7dVq6FPnrq3vhOlehas3AGphIREWtT1z4RkazGYIDSzWDAFug6D/KVg6gwc2E1qQrsngaxul4nPRiNJt79/QjXQqNwd7LD290x0eN2/78Y7/LD14iLN1ojooiIZHJWLaTGjh1LzZo1cXd3x9vbmw4dOnDmTOJWwFFRUQwePJg8efLg5uZGp06duHnzZqJ9Ll++TJs2bXBxccHb25thw4YRFxeXkS9FRCT9GAxQ7gUYtBNe/AlyFYd7IbB+BEyuBgdmQnystVNmaZM2nWXdiSAcbG2Y1e9Zdo9oym+v1eb7l6vw22u1WTOkAa4Otuy+cJtvN/xj7bgiIpIJpXpq38qVKxP+3q1bNyZOnEj+/PkTtrVr1y7Fx2rZsiUvv/wyNWvWJC4ujg8//JDjx49z8uRJXF1dARg0aBBr1qxh1qxZeHp64u/vj42NDTt37gQgPj6eKlWq4OPjw1dffcWNGzfo3bs3r732Gl9++WWKcmhqn4hkavGxEDAftk6A8GvmbbmKQ+MRULEz2NhaN18Ws/bYDQbNPwTAhM6VeKlG8lP3Vh+9jv8C83XBP/WuwfN++ZPdT0REspeU1gapLqRsbB49iGUwGIiPj0/N4RIJCQnB29ubrVu30rBhQ8LCwsiXLx8LFiygc+fOAJw+fZpy5cqxe/duateuzdq1a2nbti3Xr19PKOh++OEHhg8fTkhISIrWvFIhJSJZQmyUubPf9m/MI1QA+crCcx+ZR7DU8OeJTl4Pp9P0XdyPjeeVesUZ+YLfY/cfveoEM3dexN3JjtVv1qdoHtcMSioiItZisWukjEbjI29PU0QBhIWFAZA7t7lD0sGDB4mNjaVZs2YJ+5QtW5YiRYqwe/duAHbv3k3FihUTjYq1aNGC8PBwTpw4kex5oqOjCQ8PT3QTEcn07J3MnfyGBEDTkeZGFCGn4fdeMKMRnN1gblghybodEc1rcw5wPzaeBqXz8mHrsk98zohW5aheNBd3o+J4fd4homKf7v85ERHJPp7qGqmoqPS76NloNPL2229Tr149KlSoAEBQUBAODg5JFgDOnz8/QUFBCfv8t4h68PiDx5IzduxYPD09E26FC6sjk4hkIY5u0OBdeOsoNBwGDm5w4wjM7wwzW8HFHdZOmOnExBkZNP8Q10LvUyyPC1O6VcPO9sn/BTrY2TC1ezXyuDpw6kY4I1ccz4C0IiKSFaS6kIqPj+fzzz+nYMGCuLm5ceHCBQA++eQTfvnllzQHGTx4MMePH2fhwoVpPkZKjRgxgrCwsITblStXLH5OEZF05+wFTT6Gt45AHX+wdTSvQzWrDczpANcOWjthpjF61Qn2Bd7BzdGOn/vUwNPFPsXP9fF0YnK3qtgY4PcDV1m0/7IFk4qISFaR6kLqiy++YNasWUyYMCHR9UcVKlTg559/TlMIf39/Vq9ezebNmylUqFDCdh8fH2JiYggNDU20/82bN/Hx8UnY5+Eufg/uP9jnYY6Ojnh4eCS6iYhkWa55ocUX8FYA1HgFbOzgwmb4qQn81h1uJj/NOaeYu+cS8/dexmCASd2qUMrbPdXHqFsqL+82LwPAJytOcPxaWHrHFBGRLCbVhdScOXOYMWMGPXr0wNb2f52iKleuzOnTp1N1LJPJhL+/P8uWLWPTpk0UL1480ePVq1fH3t6ejRs3Jmw7c+YMly9fpk6dOgDUqVOHY8eOERwcnLDPhg0b8PDwwM/v8RcRi4hkKx4FoO138OZBqNwdDDZwZg1MrweLX4Xb562dMMPtPn+b0SvNheT7LcrSpGzaO+8NalSSZuW8iYkz8vq8g4RFqgW9iEhOlupC6tq1a5QqVSrJdqPRSGxs6v5TGTx4MPPmzWPBggW4u7sTFBREUFAQ9+/fB8DT05NXX32VoUOHsnnzZg4ePEi/fv2oU6cOtWvXBqB58+b4+fnRq1cvjhw5wvr16/n4448ZPHgwjo6Ojzu9iEj2lKsYdJwOb+wBvw6ACY4vhik1YYU/hOaM6cxX7kTyxvyDxBlNtK9SgNcblXiq49nYGPimSxWK5Hbh6r/3Gfp7AEajmnuIiORUqS6k/Pz82L59e5LtixcvpmrVqqk61vTp0wkLC6Nx48b4+vom3BYtWpSwz3fffUfbtm3p1KkTDRs2xMfHh6VLlyY8bmtry+rVq7G1taVOnTr07NmT3r1789lnn6X2pYmIZC/5ysBLs2HgNijdAkzxcHiueVHfP9+HuzeffIws6l50HK/NOcC/kbFUKuTJ+E6VMKRDe3hPF3um9aiGg50NG08HM31rzhvlExERs1SvI7VixQr69OnDiBEj+Oyzzxg9ejRnzpxhzpw5rF69mueff95SWS1G60iJSI5weS9s+hwu/v+HYfYu8OwAqPcWuOS2brZ0ZDSaGDT/IOtP3CSfuyMr/evh6+mcruf4ff8V3l9yFBsDzHmlFvVL503X44uIiPVYbB2p9u3bs2rVKv7++29cXV0ZOXIkp06dYtWqVVmyiBIRyTGK1IK+q6H3CihYA2IjYedE+L4ybBkPUdljTb2JG8+y/sRNHGxt+LFX9XQvogBeqlmYrjUKYzTBkIWHuRF2P93PISIimVuqR6SyI41IiUiOYzLBP+tg0xi4+f9rIznnhvrvwLOvgX36Fx8Z4c9jN3hj/iEAvu5Smc7VCz3hGWkXFRvPi9N2cfJGONWKeLFwQB0c7J5qeUYREckEUlobpFshFRkZyddffw2Am5sbQ4cOTY/DZggVUiKSYxmNcHIZbP4Sbp8zb3PzgYbvQbU+YOfw+OdnIieuh9F5+m7ux8bzav3ifNLW8p1bL9+OpO3k7YRHxdG3bjE+bVfe4ucUERHLslgh9agCKTIykp9++olvv/0WV1dX+vfvn7rEVqRCSkRyvPg4OLrQPMUv7P8XnPUsAo2HQ6WXwdbOuvme4FZENO2n7ORa6H0alM7LzL41sbPNmNGhv0/epP+cAwBM6laVdpULZMh5RUTEMixWSNnY2FCnTp1Ei/ECxMTEsGfPHuLj49OW2IpUSImI/L+4aDg0B7Z9BRH/39UvT2l4bgT4dQSbzDd1LSbOSM+f97Lv4h2K53Vl+Rv18HSxz9AMX60/zdTN53FxsGXF4HqUzp/6RX9FRCRzsGghFRQUhLe3d6LtQUFBFCxYUIWUiEh2EBMJ+3+CHd/B/X/N2/JXhCYfwTMtIR1aiacHk8nEh8uO89u+y7g72rFscD1KebtleI54o4lev+xl1/nblMznygr/+rg5Zu5RPBERSZ7FuvYZDIZk1+JIj/U5REQkk3BwMbdFf+soNB4BDu5w8xj89jL88jxc2GLthADM23OJ3/ZdxmAwT6uzRhEFYGtjYFK3quT3cOR8yD0+WHIU9XISEcne0jQi1apVK9zc3PDw8KB48eI0bNiQUqVKaURKRCS7irxjbpW+dwbE/X+r72INoOlIKPysVSLtOn+LXr/sI95o4oNWZXm9UUmr5Pivg5fu0PXHPcQZTYx6wY9+9YpbO5KIiKSSxab29evXD4Do6Ghu377NhQsXuHDhAsWKFePixYsqpEREsrO7N2H7N3BwJsTHmLeVbgFNPgbfShkW4/LtSNpP3cG/kbF0qFKA77pWyTQzI2buDGT0qpPY2RhYNLAO1YvmsnYkERFJhQxtf3716lWGDx/OwoULmTlzJs7OznTp0uVpD5thVEiJiKRS6GXYOgECFoDp/z9A8+sAz30E+Z6x6KkjouPoNG0XZ27epVIhT34fWAcne1uLnjM1TCYT/r8dZs3RG/h4OLF6SH3yujlaO5aIiKRQhq8jFRoayjvvvAOAl5cX3333XXocNkOokBIRSaNb52DLWDi+BDCBwcbcLr3xcMhVLN1PZzSaeH3eQf46eZN87o6s8q+Pj6dTup/naUVEx9F+yg7Oh9yjbsk8zH21FrY2mWPETEREHs9ihdS9e/dwdXV96oCZiQopEZGndPMEbPoCzqwx37exh2q9oeEw8PBNt9N8+9cZJm06h4OdDYsG1KZqkcw7be7szbu0n7qTyJh4Bj9XkmEtylo7koiIpIDFuvblz5+fV155hR07djxVQBERyUbyl4duC6D/JijxHBhj4cAvMKkKrP8I7t1+6lOsOXqDSZvOATC2Y8VMXUQBlM7vzrhO5uvGpm4+z8ZTN62cSERE0lOqC6l58+Zx584dmjRpwjPPPMO4ceO4fv26JbKJiEhWU6g69F4OfddA4doQFwW7p8D3lcwjVlFhaTrs8WthvPtHAACvNShOp+qF0i+zBbWrXIC+dYsB8M6iAC7fjrRuIBERSTdpvkYqJCSEuXPnMmvWLE6dOkWLFi145ZVXaNeuHXZ2WWsRQk3tExGxAJMJzv0Nmz6HG0fM25y8zOtT1RoIDimbJn4rIpp2k3dwPSyKhs/kY2bfmlnqeqOYOCNdZ+zm8OVQyhfwYMmgupmqOYaIiCSWoc0mJk+ezLBhw4iJiSFv3ry8/vrrfPDBB7i4uDztoTOECikREQsymeDUStj8JYScNm9zzQcN3oXq/cD+0c0iYuKM9Ph5D/sv/kuJvK4sG1wPT2f7DAqefm6E3afNpB3cuRdD1xqFGd8541rFi4hI6ljsGqkHbt68yYQJE/Dz8+ODDz6gc+fObNy4kW+++YalS5fSoUOHtB5aRESyE4MB/NrDoF3QcYa5m9+9EFj3AUyuDgdnQXxskqeZTCZGrTzO/ov/4u5ox099amTJIgrA19OZyd2qYmOARQeu8Pv+K9aOJCIiTynVI1JLly5l5syZrF+/Hj8/P/r370/Pnj3x8vJK2Of8+fOUK1eOmJiY9M5rERqREhHJQPGxcHieeR2qu/9/jW3uEtB4BFToBDbmaW9zdl9k5IoTGAzwa9+aPFfG24qh08eUTWf5+q9/cLSzYcmgulQo6GntSCIi8hCLjUj169ePAgUKsHPnTgICAvD3909URAEUKFCAjz76KNWhRUQkB7C1hxr9YMhhaDEWXPLCnQuw9DWYXg9OrWLX2RBGrzoJwIhWZbNFEQXwRuNSNCnrTXSckTfmHyIsMulInIiIZA2pHpGKjIzMMtc+pZRGpERErCg6Avb+ALsmJXT1O0FJxsd0Jk+lVnzbtQoGQ9ZpLvEkYZGxtJm8nav/3qdZOW9m9KqBTRZqniEikt1ZbETK3d2d4ODgpwonIiKSwNENGr4Hbx0lpu5Q7uNEec4zx2E839wbgeHybmsnTFeeLvb80LM6DnY2/H0qmB+2nbd2JBERSYNUF1Lp0ORPREQkCaOjJ4OD2lA/6jsW2LTFZOuIzZXdMLMVzH0Rrh2ydsR0U6GgJ5+1Kw/A1+vPsOvcLSsnEhGR1EpT177sNMVCREQyh+/+/ocNJ29y1y4X5fpOwTDksLk9uo0dnN8IPz0HC3vAzZPWjpouutYsTJfqhTCa4M3fDhMUFmXtSCIikgppKqR8fHywtbVN9iYiIpJaq49eZ/KmcwCMe7EiVYvkAs+C8MJE8N8PlV4GDHB6NUyvC0v6w+2sPSXOYDDweYcKlPP14Pa9GAYvOERsvNHasUREJIVS3WzCxsaGJUuWkDt37mQfb9SoUboEy0hqNiEiYj3Hr4XR+YddRMUaGdCwBB+2Lpf8jsGnYfMX5sV9AQy2ULUnNHofPAtlXOB0dun2PdpO3sHdqDheqVeckS/4WTuSiEiOltLaINWFlK2tLTdu3MDbO3u0ogUVUiIi1hJyN5r2U3ZwPSyKRs/k49e+NbF9Uge76wGwaQyc22C+b+sANV6FBkPBLWv+3/TXiSAGzD0IwJTuVWlbqYCVE4mI5FwW69qnZhMiIpIeouPiGTTvINfDoiiRz5VJ3ao+uYgCKFAFei6GV9ZD0foQHwN7p8P3leHv0XD/X4tnT2/Ny/swqHFJAIYvPsq54LtWTiQiIk+S6kJq8+bNj5zWJyIikhImk4mRy09w4NK/uDvZ8VPvGng626fuIEVqQ9/V0GsZFKwOsZGw41uYWBm2ToDorFWMvPv8M9QpkYd7MfG8Pu8Q96LjrB1JREQeI9WF1L1799i4cWOS7evXr2ft2rXpEkpERLK32bsusujAFWwMMLlbVUrmc0vbgQwGKNkE+m+El38D7/IQHWa+lur7yrBrMsTeT9/wFmJna8OkblXxdnfkXHAEI5Ye0ywQEZFMLNWF1AcffEB8fHyS7SaTiQ8++CBdQomISPa189wtPl9zCoARrcrRuEw6XNdkMEDZ1vD6Duj0C+QuCZG34a+PYVJV2P8zxMU8/XksLJ+7I9N6VMPOxsDKI9eZs/uStSOJiMgjpLqQOnv2LH5+STsKlS1blnPnzqVLKBERyZ4u3b7HG/MPEW808WLVgvRvUDx9T2BjAxU7w+B90G4KeBaGuzdgzbswpQYELABj0g8DM5MaxXIz4v87F45Zc5JDl7PeNV8iIjlBqgspT09PLly4kGT7uXPncHV1TZdQIiKS/dyNiqX/7AOE3Y+lcmEvvnyxouUWeLe1g2q94M2D0OorcMsPoZdg+SCYVhtOLAPjf9ZsMsZD4HY4ttj8p5WLrVfqFaNNRV9i400Mnn+I2xHRVs0jIiJJpbr9+cCBA9m9ezfLli2jZElzh6Fz587RqVMnatasyc8//2yRoJak9uciIpZlNJoYMPcAf58KJr+HIyv965PfwynjAsREwr4ZsHPi/7r6+VSEJp9AXBSs+wDCr/9vf48C0HI8+LXLuIwPiYiOo92UHVwIuUf9UnmZ/cqzKetqKCIiT8Vi60iFhYXRsmVLDhw4QKFC5gUQr169SoMGDVi6dCleXl5PFdwaVEiJiFjWV+tPM3XzeRzsbPhjYB0qF/ayTpCoMNg9DXZPhZjHdfX7/4LlpTlWLab+uXmX9lN2cj82njeblOLd5mWslkVEJKewWCEF5sYSGzZs4MiRIzg7O1OpUiUaNmz4VIGtSYWUiIjlrDpynTd/OwzAd10r07FqISsnAiLvwPZvYPeUx+xkMI9MvX0MbGwzLNrDVgRc462FAQD82rcGTcrmt1oWEZGcwKKFVHajQkpExDKOXwuj8w+7iIo1MrBhiYQmCplC4HaY3fbJ+/VZDcUbWD7PY4xacZzZuy/h6WzP6jfrUzi3i1XziIhkZymtDVLdbEJERCQlQu5G89qcA0TFGmlcJh/vtyxr7UiJRdxM3/0s6KM2flQp7EXY/VgGzT9IVGzm7jwoIpITqJASEZF0Fx0Xz+vzDnIjLIoS+VyZ1K1q5muU4JbCKXIp3c+CHOxsmNajGrlc7Dl+LZzRq05YO5KISI6nQkpERNKVyWTik+XHOXjpX9yd7Pi5dw08nOytHSuponXN10DxmALPwQ2K1MmwSI9TwMuZSd2qYjDAb/uu8MeBK9aOJCKSo6mQEhGRdDVr10V+P3AVGwNM6V6NEvncrB0peTa25hbnwCOLqZgIWPOO1deVeqBB6Xy80+wZAD5efpyT18OtnEhEJOdKUyEVHx/PkiVLGDNmDGPGjGHZsmXEx2eO/2RERMR6dpy9xZg1pwD4sHU5Gj2Tz8qJnsCvnbnFuYdv4u0eBaHma2CwgUNzYEl/iIuxTsaH+D9XisZl8hEdZ2TQ/IOE3Y+1diQRkRwp1V37zp07R5s2bbh69SplypjXszhz5gyFCxdmzZo1CYv0ZiXq2ici8vQu3rpH+6k7CbsfS6dqhfi6SyUMhkx2XdSjGOPh0i5zYwm3/OZpfza2cGIZLHkNjLFQugW8NBvsna2dltDIGNpM2sG10Ps0K5efGb2qY5PZrkETEcmiLNb+vHXr1phMJubPn0/u3LkBuH37Nj179sTGxoY1a9Y8XXIrUCElIvJ07kbF0nHaLs4FR1ClsBcLB9TGyd56ay+lq7MbYFFPiIuCYg2g22/g6G7tVBy7Gkan6buIiTcyvGVZBjXOeh9kiohkRhZrf75161YmTJiQUEQB5MmTh3HjxrF169a0pRURkSwr3mji7YUBnAuOIL+HIzN6Vc8+RRRA6eeh51JwcIeL22FOe/OCvlZWsZAnn7YrD8BX60+z+/xtKycSEclZUl1IOTo6cvfu3STbIyIicHBwSJdQIiKSdXzz1xk2ng7Gwc6GGb1q4O3hZO1I6a9YPeizEpxzwbWDMKsN3LX++lLdni1Mp2qFMJrgzd8OcTM8ytqRRERyjFQXUm3btmXAgAHs3bsXk8mEyWRiz549vP7667Rr184SGUVEJJNaEXCNaVvOAzChUyUqF/aybiBLKlgN+q0FNx8IPgkzW0LoZatGMhgMjOlQgbI+7tyKiGHw/EPExhutmklEJKdIdSE1adIkSpYsSZ06dXBycsLJyYl69epRqlQpvv/+e0tkFBGRTOjY1TDeX3wUgNcblaRD1YJWTpQBvMvBK2vBqwjcuQC/toRbZ60aydnBlh96Vsfd0Y4Dl/5l3NrTVs0jIpJTpLrZxANnz57l9GnzP9blypWjVKlS6RosI6nZhIhI6gTfjaLd5J0EhUfRpKw3P/WugW1O6hoXdg3mdoBb/4BLXui9HHwqWjXS+hNBDJx7EIBpParRuqLvE54hIiLJsVjXvuTEx8dja5t1LyxWISUiknLRcfF0m7GHQ5dDKZnPlWWD6+HhZG/tWBnv3i2Y2xGCjoKTJ/RYDIWftWqksWtP8ePWC7g62LLyzfqUzKyLIYuIZGIW69oXGBhIt27dGDRoEP/++y/t2rXD0dGRMmXKcPTo0acKLSIimZvJZOLjZcc5dDkUDyc7fu5TM2cWUQCueaHPKihcG6LCYE4HuLDFqpGGNS9DreK5uRcTz+tzD3IvOs6qeUREsrNUF1IDBw7k1KlTHD9+nCZNmhATE8OKFSvw8/Pj7bfftkBEERHJLGbuvMgfB69iY4Ap3atRPK+rtSNZl7MX9FoKJZ6D2Hswvwuc/tNqcexsbZjcvSre7o6cDY7gw2XHSIeJJyIikoxUT+3z9PRk+/btFC1alNy5c7N//36qVavGmTNnqFWrFqGhoRaKajma2ici8mTb/gmh78x9GE3wcZty9G9QwtqRMo+4aFj8CpxeDQZb6PgjVOpitTj7Au/Q7ac9xBtNfN6+PL3qFLNaFhGRrMZiU/vu3r2Lr68vnp6euLi44OXlBYCXl1ey60uJiEjWF3jrHv4LDmE0QefqhXi1fnFrR8pc7Byhy2yo9DKY4mHpa3DgV6vFebZ4bka0KgvAZ6tPcvjyv1bLIiKSXdml5Unr1q3D09MTo9HIxo0bOX78eJYciRIRkScLj4rltTkHCI+Ko1oRL77oWAGDIQd16EspWzvoMB0c3WD/z7D6HYi+C/XeskqcV+sX5+Clf1l7PIjB8w+xekgDcrs6WCWLiEh2lOoRKYA+ffrQoUMH7t+/z8CBA+nQoQN9+/ZN9XG2bdvGCy+8QIECBTAYDCxfvjzR43379sVgMCS6tWzZMtE+d+7coUePHnh4eODl5cWrr75KREREWl6WiIg8JN5o4u2FAZwLjsDHw4kfelXH0S7rdmm1OBsbaP011H/HfH/DSNg0BqxwnZLBYGBC50qUyOvK9bAo3lp4mHijrpcSEUkvqS6kjEbjI2/x8fGpOta9e/eoXLkyU6dOfeQ+LVu25MaNGwm33377LdHjPXr04MSJE2zYsIHVq1ezbds2BgwYkNqXJSIiyfj6rzNsOh2Mo50NM3pXx9vdydqRMj+DAZp9Ck1Hme9v+wrWDgejMcOjuDvZM71ndZztbdl+9hbfb7Tu4sEiItlJqgupOXPmEB0dnS4nb9WqFWPGjKFjx46P3MfR0REfH5+EW65cuRIeO3XqFOvWrePnn3+mVq1a1K9fn8mTJ7Nw4UKuX7+eLhlFRHKqFQHXmL7lPAATOleiUiEv6wbKahoMNY9OAez7EVb6Q3zGtyMv4+PO2BfNiwVP2niWzaeDMzyDiEh2lOpCql+/foSFhVkiS7K2bNmCt7c3ZcqUYdCgQdy+fTvhsd27d+Pl5UWNGjUStjVr1gwbGxv27t37yGNGR0cTHh6e6CYiIv9z9Goo7y82rw04qHFJ2lcpaOVEWdSzr5k7+BlsIWA+LHkF4mIyPEaHqgXpVbsoAG8vCuDKncgMzyAikt2kupDKyPUoWrZsyZw5c9i4cSPjx49n69attGrVKmEKYVBQEN7e3omeY2dnR+7cuQkKCnrkcceOHYunp2fCrXDhwhZ9HSIiWUlweBQD5hwkOs5Ik7LevNe8jLUjZW2VX4aXZoOtA5xcAQu7QUzGFzIfty1H5cJehN2P5Y35h4iKTd10fBERSSxNXft+//33R/ZU792791MF+q+XX3454e8VK1akUqVKlCxZki1bttC0adM0H3fEiBEMHTo04X54eLiKKRERICo2noHzDhIUHkUpbze+f7kKtjbq0PfUyr0A3RbCwh5w7m+Y1wm6LwQnzwyL4Ghny7Qe1Wg7aTvHroXx2eqTfNmxYoadX0Qku0lTITVhwgRsbZN2bTIYDOlaSD2sRIkS5M2bl3PnztG0aVN8fHwIDk481zsuLo47d+7g4+PzyOM4Ojri6OhosZwiIlmRyWTi4+XHOXw5FE9ne37uXQN3J3trx8o+SjWF3sthfhe4vAtmt4OeS8E1T4ZFKOjlzMSXq9J35j4W7L1MtSK56Fy9UIadX0QkO0lT+/MDBw4QGBiY5HbhwoX0zpfI1atXuX37Nr6+vgDUqVOH0NBQDh48mLDPpk2bMBqN1KpVy6JZRESym192BLL44FVsDDCle1WK5XW1dqTsp0ht6LsaXPLAjQCY1RrCb2RohEbP5OPtps8A8NGyY5y6oeuERUTSIk2FVHqJiIggICCAgIAAAAIDAwkICODy5ctEREQwbNgw9uzZw8WLF9m4cSPt27enVKlStGjRAoBy5crRsmVLXnvtNfbt28fOnTvx9/fn5ZdfpkCBAlZ8ZSIiWcu2f0L48s9TAHzcxo8GpfNZOVE25lsZ+q0F9wIQchpmtoR/L2ZohDeblKLRM/mIjjMyaN5BwqNiM/T8IiLZQaoLqaJFiyY7rS8tDhw4QNWqValatSoAQ4cOpWrVqowcORJbW1uOHj1Ku3bteOaZZ3j11VepXr0627dvTzQtb/78+ZQtW5amTZvSunVr6tevz4wZM9Iln4hITnAhJAL/BYcwmqBL9UL0q1fM2pGyv3xl4JW1kKuYuYj6tSWEnMmw09vYGJjYtQoFvZy5eDuS934/kqHNpEREsgODSf9yEh4ejqenJ2FhYY9soiEikh2FR8XSYepOLoTco1oRL34bUBtHu/T5sExSIPwGzO1gHplyyWO+ZqpAlQw7/ZEroXT5YTcx8UZGtCrLwEYlM+zcIiKZVUprg1SPSA0ZMoRJkyYl2T5lyhTefvvt1B5ORESsJN5o4q3fDnMh5B6+nk780Ku6iqiM5uELff8E3yoQeRtmvwCXdmfY6SsX9mJUOz8Axq87zZ4Lt5/wDBEReSDVhdSSJUuoV69eku1169Zl8eLF6RJKREQsb8L602w+E4KjnQ0zetXA293J2pFyJtc80GcVFKkL0eEwtyOc25hhp+/+bBFerFoQown8FxwmODwqw84tIpKVpbqQun37Np6eSde98PDw4NatW+kSSkRELGv54Wv8uNXcafWrLpWpWCjj1jOSZDh5QM8lUKoZxN2H316GU6sy5NQGg4EvOlakrI87tyKiGbzgELHxxgw5t4hIVpbqQqpUqVKsW7cuyfa1a9dSokSJdAklIiKWc+RKKO8vOQrAG41L0q6yupxmCg4u8PJv4Nce4mPg9z4Q8FuGnNrZwZbpPavj7mjH/ov/MmHd6Qw5r4hIVpbqBXmHDh2Kv78/ISEhNGnSBICNGzfyzTffMHHixPTOJyIi6Sg4PIoBcw8QE2ekWTlv3mtextqR5L/sHKDTr+AwBALmw/LXISYCnn3N4qcunteVr7pU5vV5B/lpeyDViuSiVUVfi59XRCSrSnUh9corrxAdHc0XX3zB559/DkCxYsWYPn06vXv3TveAIiKSPqJi4xkw9yA3w6Mp7e3Gd12rYGNjsHYseZitHbSbAo7usPcH+PM987VTDd61+KlbVvBhQMMSzNh2gWGLj1LGx50S+dwsfl4Rkazoqdqfh4SE4OzsjJtb1v5HVu3PRSS7M5lMvPvHEZYeuoansz0rBtejWF5Xa8eSxzGZYPOXsG2C+X69t6HZp2CwbPEbF2+k+8972Rd4hzL53Vk2uC4uDqn+3FVEJMuyWPtzgLi4OP7++2+WLl2asIDf9evXiYiISFtaERGxqF92BLL00DVsbQxM61FNRVRWYDBAk4/g+c/M93dOhDXvgtGyjSDsbG2Y0q0q+dwdOXPzLh8tO67FekVEkpHqQurSpUtUrFiR9u3bM3jwYEJCQgAYP3487733XroHFBGRp7P1nxC+/PMUAB+3KUe9UnmtnEhSpd5b0PY7wAAHfoHlgyA+zqKn9PZwYkq3qtjaGFh2+Brz9l626PlERLKiVBdSb731FjVq1ODff//F2dk5YXvHjh3ZuDHj1r0QEZEnuxASgf+CQxhN0LVGYfrWLWbtSJIWNV6BTj+DwRaOLoQ/+kBctEVPWatEHoa3NDcj+XzVSQKuhFr0fCIiWU2qC6nt27fz8ccf4+DgkGh7sWLFuHbtWroFExGRpxN2P5b+cw5wNyqO6kVz8VmH8hgsfH2NWFDFztB1Htg6wunVsKArxNyz6Clfa1CCluV9iIk3Mnj+Ie7ci7Ho+UREspJUF1JGo5H4+Pgk269evYq7u3u6hBIRkacTbzQx5LfDXAi5h6+nEz/0rI6jna21Y8nTKtsaevwO9q5wYTPM7Qj3Qy12OoPBwIQulSie15Vrofd5e1EA8UZdLyUiAmkopJo3b55ovSiDwUBERASjRo2idevW6ZlNRETSaMK602z9JwQnext+6l2DfO6O1o4k6aVEY+i9HJw84cpemN0WIkIsdjoPJ3um96yGk70N2/4JYfKmsxY7l4hIVpLqQuqbb75h586d+Pn5ERUVRffu3ROm9Y0fP94SGUVEJBWWHrrKj9suAPBV58pUKOhp5USS7go/C33XgGs+CDoGM1tBmOWm15f18eDLjhUB+H7jWbacCbbYuUREsoo0rSMVFxfHwoULOXr0KBEREVSrVo0ePXokaj6RlWgdKRHJLgKuhPLSj7uJiTPi/1wp3mtRxtqRxJJunYU57SH8GngWgT4rIHcJi53uo2XHmL/3Ml4u9qx+sz6FcrlY7FwiItaS0trgqRbkzS5USIlIdnAzPIoXJu8g+G40zcrlZ0av6tjYqLlEthd62VxM3bkAbj7Qaxnk97PIqaLj4unyw26OXg2jUiFP/ni9jq69E5Fsx2KF1MqVKx/7eLt27VJzuExBhZSIZHVRsfF0nbGHI1dCeSa/G0sG1cXdyd7asSSj3L1pbjwRfAKcc0HPJVCwukVOdfXfSNpO3kFoZCw9axdhTIeKFjmPiIi1WKyQsrFJfFmVwWBIWPHcYDAk29Evs1MhJSJZmclk4t3fj7D08DW8XOxZMbgeRfO4WjuWZLTIOzC/M1w7CA7u0H0RFKtnkVNtORNMv1n7MZng25cq82K1QhY5j4iINaS0NkhT+/P/3lxcXDh37twj26KLiIhl/bT9AksPX8PWxsDU7tVUROVULrmh9woo1gBi7sK8F+HsBoucqnEZb4Y0KQ3Ah8uOcToo3CLnERHJzFJdSD1MizuKiFjP5jPBjFt7GoBP2pSjXqm8Vk4kVuXoDj3+gNItIC4KfusGJ5ZZ5FRDmpam4TP5iIo1MmjeIcKjYi1yHhGRzOqpCqmLFy9y7949LcQrImIF50MiGPLbYYwmeLlmYfrULWbtSJIZ2DvDy/Oh/ItgjIXFr8Chuel+GlsbAxO7VqGApxOBt+7x/h9HUf8qEclJUl1Ivfjii7z44ou0atWKqlWr0rRpU/Lly2eJbCIi8ghh92N5bfYB7kbFUbNYLj5rX0EzBOR/bO2h089QrTeYjLDSH/ZMT/fT5HZ1YFrP6tjbGlh3Ioiftwem+zlERDKrVBdSnp6eeHp6Urx4cb744osndvETEZH0FW80MeS3w1y4dY8Cnk5M71kdB7unnqkt2Y2NLbwwCer4m++v+wC2ToB0HjWqUtiLkS+UB2DcutPsvXA7XY8vIpJZaR0p1LVPRLKWL/88xYxtF3Cyt2Hx63WpUNDT2pEkMzOZzAXUli/N9+u+Cc9/Duk4gmkymXhnUQDLA66Tz92RNW/Wx9vDKd2OLyKSkSzWtS88PPyxNxERsZwlB68yY9sFAL7uUllFlDyZwQCNh0OLseb7uybD6rfBmH6ddg0GA1++WJFn8rsRcjca/98OExdvTLfji4hkRqkupLy8vMiVK1eS24PtIiJiGYcv/8uIZccAeLNJKdpWKmDlRJKl1HkD2k0GDHBwFiwdAPHp12nPxcGO6T2r4+Zox77AO3y1/ky6HVtEJDOyS+0TSpQoQXBwMB988AH16llmoT8REUksKCyKgXMPEhNn5Hm//LzT7BlrR5KsqFpvcHA1F1HHF0PMPegyC+zTZxpeyXxufNW5EoPmH+LHbReoWsSLlhV80+XYIiKZTapHpE6dOsWnn37KN998w5QpUyhSpAiNGjVKuImISPqKio1n4NwDBN+N5pn8bnzXtQo2NurQJ2lUoRO8vADsnOCftbCgC0RHpNvhW1X0pX/94gAM++MogbfupduxRUQyk1QXUvb29gwdOpSzZ89SsGBBKlWqxLvvvktoaKgF4omI5Gwmk4kRS49x5GoYXi72/Ny7Jm6OqZ5MIJLYMy2gx2JwcIPAbTCnPUTeSbfDD29VlprFcnE3Oo5B8w5yPyb9rscSEcks0twvN3fu3EycOJHDhw9z8eJFSpUqxcSJE9MxmoiIzNh2gWWHr2FrY2Ba92oUyeNi7UiSXRRvAL1XgpMXXDsAs1+AiOB0ObS9rQ1Tulcjr5sjp4Pu8tGyY1qsV0SynVS3P69atWqSRR9NJhPnzp0jMjKS+Pis96mT2p+LSGa0+XQwr8zej8kEn7UvT+86xawdSbKjmydgTge4Fwy5S0LvFeBVOF0OvefCbXr8vJd4o4kvOlagR62i6XJcERFLSmltkOr5IR06dHiaXCIikgLngiMY8tthTCbo9mxhetXWL6BiIfnLwyvrzMXUnfPwa0tzMZW31FMfunaJPAxrUYZxa08zeuVJKhb0pFIhr6c+rohIZqAFedGIlIhkLmGRsXSYtpPAW/eoWSwX8/vXxsEuzTOxRVIm7Kr5Wqnb58A1H/RaDj4VnvqwJpOJgXMP8tfJmxT0cmb1m/XJ5erw9HlFRCxEC/KKiGRBcfFG3lx4mMBb9yjo5cz0ntVVREnG8CwE/dZB/opwLwRmtYarB576sAaDga9fqkyxPC5cC73P24sCMBpz/Ge4IpINaEFeEZFMZNza02z7JwRne1tm9K5OXjdHa0eSnMQtH/RdBYWehagwmN3O3NXvKXk42TO9Z3Wc7G3Y+k8IkzedS4ewIiLWlaYeuosXLyZ37tzpnUVEJEdbfPAqP+8IBODrLpUpX8DTyokkR3LOBb2WwcLuELgV5nWGl2ZDmVZPddhyvh6M6VCR9/44wsSN/1C1iBcNn8mXTqFFRDJeqq+RsrGxISgoCG9vb0tlynC6RkpErO3Q5X95+cc9xMQbGdKkFEObl7F2JMnpYqNg8StwZg3Y2EHHH6Fi56c+7Iilx/ht32VyudizekgDCno5p0NYEZH0Y7FrpABOnjzJqVOnuHz5MjExMWkOKSIiEBQWxcC5B4mJN9LcLz9vN3vG2pFEwN7JPBJV8SUwxsGS/nBw1lMfdtQLflQs6Mm/kbG8Mf8Q0XFZb9kUERFIYyHVtGlTypcvT/HixXF1daVixYp899136Z1NRCTbi4qNZ8DcA4TcjaZMfne+61oFGxvDk58okhFs7c0jUTVeAUyw6i3YNfmpDulkb8u0HtXwdLbnyJVQxqw+lT5ZRUQyWKqvkQoMDMRkMhEbG0t4eDjXr19n3759fPLJJ8TFxTFs2DBL5BQRyXZMJhPDlxzl6NUwcrnY83OfGrg6punSVRHLsbGBNt+Cozvs/B7++hii70LjEWBIW9FfOLcLE7tWod+s/czdc4nqRXPRoWrBdA4uImJZ6baO1Ny5c/nss884e/ZsehwuQ+kaKRGxhh+2nmfc2tPY2hiY++qz1C2Z19qRRB7NZILt38Cmz833a78BLb5MczEF8O1fZ5i06RzO9rYsH1yPMj7u6RRWRCTtLHqNVHJefvllFi1alF6HExHJ1jadvsn4dacB+PQFPxVRkvkZDNDwPWg1wXx/zzRY6Q/GtF/j9FazZ2hQOi/3Y+MZNO8gd6Ni0ymsiIjlpbmQOnjwIPPmzWPevHkcOnQIe3t7qlWrlp7ZRESypXPBd3nrtwBMJuheqwg9axe1diSRlKs1EDpMB4MNHJ5n7uwXl7bGU7Y2Br5/uSq+nk5cuHWP9xcfJZ0myoiIWFyqC6ng4GCaNGlCzZo1GTJkCEOGDKFGjRo0bdqUkJAQS2QUEck2wiJj6T/7AHej43i2WG4+faE8hqeYGiViFVW6Q5dZYGMPJ5eb15yKvZ+mQ+V2dWBqj2rY2xpYezyIX/5/LTURkcwu1YXUm2++yd27dzlx4gR37tzhzp07HD9+nPDwcIYMGWKJjCIi2UJcvBH/3w5x8XYkBb2cmd6zGg526TbDWiRj+bWHbgvBzhnObTAv3BsVnqZDVSuSi0/a+gEwdu1p9gXeSc+kIiIWker/wdetW8e0adMoV65cwjY/Pz+mTp3K2rVr0zWciEh2MnbtabafvYWzvS0/9a5BHjdHa0cSeTqlm0GvpeDoAZd2wJx2EJm2IqhX7aK0r1KAeKMJ/wWHCL4blc5hRUTSV6oLKaPRiL29fZLt9vb2GI3GdAklIpLd/HHgSsKUpW9fqoxfAXUIlWyiaF3osxKcc8P1wzCzNdwNSvVhDAYDY1+sSGlvN4LvRvPmgsPExev3ChHJvFJdSDVp0oS33nqL69evJ2y7du0a77zzDk2bNk3XcCIi2cHBS//y0bLjAAxpWppWFX2tnEgknRWoCv3WgpsPhJyCX1vCv5dSfRgXBzum96yOq4MtewPv8PVf/1ggrIhI+kh1ITVlyhTCw8MpVqwYJUuWpGTJkhQvXpzw8HAmT3661c5FRLKbG2H3GTj3IDHxRlqUz8/bTUtbO5KIZXiXhVfWgVdR+DcQZraCkNQXQqW83ZjQuTJgXmtt/YnUj26JiGSENC3IazKZ+Pvvvzl92rwGSrly5WjWrFm6h8soWpBXRCwhKjaeLj/s5ti1MMr6uLNkUF1cHe2sHUvEssKvw5wOcOsMuOQ1X0PlWznVh/l89Ul+2RGIu6Mdq96sT7G8rumfVUQkGSmtDVJcSN29exd398evOL5//35q1qyZuqSZgAopEUlvJpOJtxYGsPLIdXK52LPSvz6Fc7tYO5ZIxrh3C+a9CDeOgKMn9PgDitRK1SFi4410m7GHA5f+payPO8veqIezg62FAouI/E9Ka4MUT+1r3rw5ERERyT4WFxfHxx9/TL169VKfVEQkG5q+9Twrj1zHzsbAtB7VVURJzuKaF/qsgiJ1IDoM5naA85tTdQh7WxumdK9GXjcHTgfd5ePlx7VYr4hkKikupO7evUuzZs0ID0+8RsTx48epWbMms2bNYvny5emdT0Qky9l46iZfrT8DwKh25alTMo+VE4lYgZMn9FwKJZtAbCQseAlOrU7VIXw8nZjUrSo2Blhy6CoL91+xUFgRkdRLcSG1efNm7t27x/PPP094eDgmk4nx48dTo0YNypUrx7Fjx2jdunWqTr5t2zZeeOEFChQogMFgSFKImUwmRo4cia+vL87OzjRr1oyzZ88m2ufOnTv06NEDDw8PvLy8ePXVVx85ciYiYmlnb97lrYUBmEzQo1YRetUuau1IItbj4GJetLfcCxAfA7/3hiOLUnWIuiXzMqxFWQBGrTjB0auhFggqIpJ6KS6k8uXLx6ZNm4iKiqJJkybUr1+fb775hnnz5rFgwQJy5cqV6pPfu3ePypUrM3Xq1GQfnzBhApMmTeKHH35g7969uLq60qJFC6Ki/rdIX48ePThx4gQbNmxg9erVbNu2jQEDBqQ6i4jI0wqNjOG1OQeIiI7j2eK5GfVCeWtHErE+O0foPAsqdwdTPCwbCPt/TtUhXm9Ugmbl8hMTb2TQvEOERsZYJquISCqkumvfrVu3aNasGadPnyYgIICyZcumTxCDgWXLltGhQwfAPBpVoEAB3n33Xd577z0AwsLCyJ8/P7NmzeLll1/m1KlT+Pn5sX//fmrUqAHAunXraN26NVevXqVAgQIpOreaTYjI04qLN9J35n52nLtFQS9nVvrXI4+bo7VjiWQeRiOsGw77ZpjvN/sU6r+T4qeH3Y/lhck7uHwnkufK5OOXPjWxsTFYJquI5Gjp3mzigbx587Jp0yb8/Pzo3r07//7771MFfZTAwECCgoIStVX39PSkVq1a7N69G4Ddu3fj5eWVUEQBNGvWDBsbG/bu3fvIY0dHRxMeHp7oJiLyNL748xQ7zt3CxcGWn/vUUBEl8jAbG2g1ARq8a77/96ew8TNI4ee5ns72TO9ZDUc7GzafCWHq5nOWyyoikgIpXtDkxRdfTHTfw8ODbdu28eyzz1KxYsWE7UuXLk2XYEFB5gX48ufPn2h7/vz5Ex4LCgrC29s70eN2dnbkzp07YZ/kjB07ltGjR6dLThGR3/dfYebOiwB8+1JlyvlqZFskWQYDNB0Jju7mQmr7NxB9F1qONxdaT1C+gCdjOlRg2OKjfPv3P1Qp4kWD0vksn1tEJBkpLqQ8PT2T3C9evHi6B8oII0aMYOjQoQn3w8PDKVy4sBUTiUhWdfDSHT5afgyAt5uVpmUFXysnEskC6r9jLqbWvGee6hd9F9pNAdsn/1rSpUZhDl76l4X7r/DWwgBWv1mfAl7OGRBaRCSxFBdSM2fOtGSOJHx8fAC4efMmvr7/+8Xk5s2bVKlSJWGf4ODgRM+Li4vjzp07Cc9PjqOjI46OmnYjIk/neuh9Bs49RGy8iVYVfBjSpLS1I4lkHTX7g4M7LB8ER36DmAjo9Iu5OcUTfNquPMevh3H8WjhvzD/E7wPr4GCX6qsVRESeSqb9V6d48eL4+PiwcePGhG3h4eHs3buXOnXqAFCnTh1CQ0M5ePBgwj6bNm3CaDRSq1bqVlAXEUmN+zHxDJh7gFsR0ZT1cefrLpV14btIalXuCi/NAVsHOLUKfnsZYu498WlO9rZM71EdDyc7Aq6E8sWakxkQVkQkMasWUhEREQQEBBAQEACYG0wEBARw+fJlDAYDb7/9NmPGjGHlypUcO3aM3r17U6BAgYTOfuXKlaNly5a89tpr7Nu3j507d+Lv78/LL7+c4o59IiKpZTKZeH/JUY5fCye3qwM/9a6Bq2OKB/hF5L/KtYXuv4O9C5zfBHNfhKiwJz6tcG4XJr5cBYDZuy+xIuCahYOKiCRm1ULqwIEDVK1alapVqwIwdOhQqlatysiRIwF4//33efPNNxkwYAA1a9YkIiKCdevW4eTklHCM+fPnU7ZsWZo2bUrr1q2pX78+M2bMsMrrEZGcYdqW86w6ch07GwPTelSjcG4Xa0cSydpKPge9loOjJ1zZA7NfgHu3nvi0JmXz4/9cKQA+WHKMf27etXBQEZH/SfU6UtmR1pESkZT6++RNXpt7AJMJvuhYgR61ilo7kkj2ceOIeUQq8hbkLQO9l4PH42eYxBtN9P51LzvP3aZEPldW+tfHTSPEIvIULLaOlIhITvXPzbu8tfAwJhP0rF1ERZRIevOtDP3WgkdBuHUGfm0JdwIf+xRbGwOTXq6Kr6cTF0LuMXzxUfQZsYhkBBVSIiIpEBoZw2tzDnAvJp7aJXIz6oXy1o4kkj3le8ZcTOUqDqGXzMVU8OnHPiWPmyNTulfDzsbAmmM3+PX/13UTEbEkFVIiIk8QF29k8IJDXLodSaFczkzrUR17W/3zKWIxuYrCK+sgXzmICIKZreD64cc+pXrRXHzcphwAY/88xYGLdzIiqYjkYPpNQETkCcasOcXOc7dxcbDlp941yO3qYO1IItmfuw/0+xMKVIP7d2B2O7i067FP6VO3GC9ULkCc0cTgBYcIuRudQWFFJCdSISUi8hiL9l9m1q6LAHz7UhXK+aohjUiGcckNvVdA0XoQHW5uRHH270fubjAYGPdiRUp5u3EzPJohvx0mLt6YgYFFJCdRISUi8ggHLt7h4+XHAXin2TO0rOBj5UQiOZCTB/RYDKWeh7j75kV7T6545O6ujnb80LMaLg627L5wm282/JOBYUUkJ1EhJSKSjGuh93l93kFi4020rujDm01KWTuSSM7l4AIvLwC/DmCMhT/6QsCCR+5eytud8Z0qATB9y3k2nLyZMTlFJEdRISUi8pD7MfEMmHOAWxExlPP14OsulbGxMVg7lkjOZucAnX/9v/buOzyqauvj+HdKek8oCRBqpCkdRBCUogIvoEgVQYrtioi94bV7Va6990K1gKiAKCoIXJCmVGkKCISSUNILySSZ8/4xzCQhARlKZib5fZ4nj2TOOZM1eZYns2bvvTa0GQmGHb4dB6vfP+np/VvVYuyl9QG4d+YG9qbkVFCgIlJVqJASESnBMAwe+GojWw5mEhPiz4ej2hHsr809RbyC2QL934SO4xzf//Ag/O8lOMm+URP7NKNdvSiy8gq5bfo68gqKKjBYEansVEiJiJTwzpJdfLcpCavZxLsj21EnKtjTIYlISWYz9H4eLn/I8f0vz8DCJ8otpvytZt6+vi0xIf5sS8rkseNrHkVEzgUVUiIix/20JZkXf/wTgKevuYiLG0R7OCIRKZfJBN0fgav+4/j+19dh/r1gL9uhLzYikDeGt8Fsgllr9/Plb4kVHKyIVFYqpEREgD+Ts7jnyw0A3HBJPa7vWNezAYnIP+s8Afq9Bpjg90/gm39BUUGZ0y5NqMZ9VzUB4LE5W9h8IKNi4xSRSkmFlIhUeWk5Nm6Z+js5tiIuaRjN4/2bezokETld7cfCoI/AbIU/ZsLM0VCQV+a0cZc3omfTGtgK7dw2fS3puTYPBCsilYkKKRGp0gqK7Iz/bB2JqbnERwfxzoh2+Fl0axTxKS0Gw7AZYAmAP+fDZ0MhP7vUKWaziVeGtiY+Ooj9ace4d+ZG7Pbym1SIiJwOvVsQkSrt2fnbWLErhWB/Cx+Oak90iL+nQxKRM9GkN4z8CvxCYPdSmHYtHEsvdUpEsB/vjmiHv9XML9sP8+7SXZ6JVUQqBRVSIlJlfbEmkckr9gDw6rDWNI0N92xAInJ2GlwGo+dCYCTsXwOT+0H2kVKnXFQ7gv9ccxEAL//0J8t3HPVAoCJSGaiQEpEq6bc9qTw2x9EK+d4rG9PrwlgPRyQi50Sd9jBmPoTUgEN/wKd9IGN/qVOGdohnaPs62A2484v1JGUc81CwIuLLVEiJSJVzIP0Yt01bS0GRQd8WcUzokeDpkETkXIq9CMb+AOF1IGUHfNIbUkpP43v6motoHhdOao6N8TPWYSss2zpdRORUVEiJSJWSayvklim/k5Jjo3lcOC8OaYnJZPJ0WCJyrlVLgBsXQHQjyNjnGJk6tNV1ONDPwnsj2xEeaGVdYjrPfb/Ng8GKiC9SISUiVYZhGDwwaxNbkzKJCfHng1HtCPa3ejosETlfIuMdxVTNiyD7EEz+P9i/1nW4bkwwrwxtDcDkFXuYu/GghwIVEV+kQkpEqoy3ftnJ/D+S8LOYeO+GdtSJCvZ0SCJyvoXWgDHfQe32cCwNpl4Nu5e5Dl/RvCa3d2sEwMOzN7HjUJanIhURH6NCSkSqhJ+2JPPyz38BjrURHepHezgiEakwQVEw6luo3xVs2TBjMPz1k+vwvVc2pnOjGHJtRdw2fS3Z+YWei1VEfIYKKRGp9LYnZ3LPlxsAGN2pHsMvruvZgESk4gWEwYivoHEfKMyDL4bD5tkAWC1m3hjehprhAew6ksPDszdhGNqsV0ROTYWUiFRqqTk2bpn6Ozm2Ijo1jOHRfs09HZKIeIpfIAybBhcNBnshfHUTrJsKQLXQAN4Z0Rar2cR3m5Jce8yJiJyMCikRqbQKiuyMn7GOfanHiI8O4p0RbfGz6LYnUqVZ/GDgB9B2NGDA3Amw8h0A2tWL5pH/awbAs/O3sXZvqgcDFRFvp3cUIlJpPfPdVlb+nUKIv4WPRnUgKsTf0yGJiDcwW6D/69B5guP7HyfCkklgGIy9tD59W8ZRaDcYP2M9R7PzPRuriHgtFVIiUil9tjqRqSv3AvDqsNY0iQ3zcEQi4lVMJrjyGej+qOP7Jc/DT49iAv47qCWNqoeQnJnHnZ+vp8iu9VIiUpYKKRGpdNbsTuXxOZsBuP+qxlx1YayHIxIRr2QyweUPQO9Jju9XvgXz7iTUz8R7I9sR7G9hxa4UXvn5T8/GKSJeSYWUiFQq+9NyGTd9LYV2g74t4xjfPcHTIYmIt7tkHFz9FpjMjuYTs2/mgmqBTBrUEoC3F+9i4dZDHg5SRLyNCikRqTRybYXcMnUtKTk2LqwVzouDW2IymTwdloj4grY3wOBPwOwHW76GL0ZwdfMoxnSuD8C9MzeQmJLr2RhFxKuokBKRSsEwDO6ftZFtSZlUC/Xng1HtCfa3ejosEfElF14Lwz8HayDs+BFmDOGRnnVoUzeSzLxCxs1YS15BkaejFBEvoUJKRCqFN3/Zyfd/JONnMfHuyHbUjgzydEgi4osuuBJGzgb/MNizDP/PruXdQQ2IDvFny8FMnpizxdMRioiXUCElIj5vweZkXvn5LwCeueYiOtSP9nBEIuLT6neB0XMhKAoOrCV29mDevaYOJhN8+fs+Zv62z9MRiogXUCElIj5te3Im987cAMCYzvW57uK6ng1IRCqH2m1hzPcQWhMOb6Hjkut5sqtjG4XH5mxm84EMDwcoIp6mQkpEfFZqjo2bp/xOrq2ISxNieLRvM0+HJCKVSc3mMPYHiKgLqX8zavs4hjeykV9o5/YZ68jILfB0hCLiQSqkRMQnFRTZuX3GWvanHaNudDBvDW+L1aJbmoicYzGN4MYFUK0xpsz9PJv2AN0ikklMzeW+WRuwa7NekSpL7zpExCc9PW8rq/5OJcTfwkej2xMV4u/pkESksoqo7ZjmF9sCc+4RPjae5GLrThZuO8y7S3d5OjoR8RAVUiLic2as3su0VXsxmeC169rQuGaYp0MSkcoutDqM/g7iO2KxZTIj4Hk6mzfz8k9/smLnUU9HJyIeoEJKRHzK6r9TXO2H77+qCVc2r+nhiESkygiKhBu+gYbd8Ss6xpSAl+hhWsuEz9eTnJHn6ehEpIKpkBIRn7EvNZdxM9ZRaDfo1zKO27s18nRIIlLV+IfA9V9C0374GTbe93+VS48tZvxn6ygosns6OhGpQCqkRMQn5OQXcsvU30nNsXFhrXBeHNwKk8nk6bBEpCqyBsCQKdByGBbsvOb/Do33f8Xz32/3dGQiUoFUSImI17PbDe6ftZHtyVlUC/Xnw1HtCfK3eDosEanKLFYY8B60vwkzBs/7fYx11Rt8t+mgpyMTkQqiQkpEvN4bv+zgh83J+FlMvDeyHbUigzwdkogImM3Q92Xocg8Aj/h9zv7Z/2bnoUwPByYiFUGFlIh4tQWbk3ht4Q4Anh3Qgvb1oz0ckYhICSYTXPEkRd0fA+A209ds+vh2cvJsHg5MRM43FVIi4rW2JWVyz5cbARjTuT5DO8R7OCIRkfJZLr+frB7PAzDQNo8/3hmFUVTo4ahE5HxSISUiXiklO5+bp/zOsYIiuiRU49G+zTwdkojIKYVddju7u7xEkWHikswf2PvBdVCokSmRykqFlIh4nYIiO7fPWMeB9GPUiwnmrevbYLXodiUi3q/BFbewuNVL2AwL9Q/9TMang8GW6+mwROQ80DsTEfE6T83bwurdqYQGWPlwVHsig/09HZKIyGnree1NfFD7OY4Z/kQcWErB1GshTw0oRCobFVIi4lWmrdrL9FWJmEzw2rDWNK4Z5umQRETcYjKZGD3qJh4KfopMIwi//aswpl4NOSmeDk1EziEVUiLiNVb9ncJTc7cAcP9VTbiieU0PRyQicmbCAv24Y8wNjLU/TooRhungepj8f5CZ5OnQROQcUSElIl5hX2ou46avpdBu0L9VLW7v1sjTIYmInJXGNcMYNegahtoeJ9mIgiPb4dPekLbH06GJyDmgQkpEPC4nv5Bbpv5OWm4BF9UO54VBLTGZTJ4OS0TkrF3TujaXXtKZwbYn2EdNRxH1SR848qenQxORs6RCSkQ8ym43uHfmBrYnZ1EtNIAPbmhPkL/F02GJiJwz/+7bjGp1GjMo73H2WupC1kH4tA8c3ODp0ETkLHh1IfXkk09iMplKfTVt2tR1PC8vj/HjxxMTE0NoaCiDBg3i0KFDHoxYRNz1+qId/LjlEP4WM+/f0JZakUGeDklE5JwKsFp4Z0RbCoJrMCDnEfYHNYHcFJjSHxJXeTo8ETlDXl1IAVx44YUkJSW5vpYvX+46ds899zBv3jxmzZrF0qVLOXjwIAMHDvRgtCLijh/+SOL1RTsA+M+Ai2hXL9rDEYmInB+1IoN4Y3gb0k3h9El7gCPR7SA/E6ZdCzsXeTo8ETkDXl9IWa1WYmNjXV/VqlUDICMjg48//phXXnmFHj160K5dOz799FNWrFjBqlX6dEfE2209mMm9MzcCcOOlDRjaId7DEYmInF9dL6jOPVc0Jotgrjh8J1nx3aAgFz6/DrbN83R4IuImry+kduzYQa1atWjYsCEjRowgMTERgLVr11JQUMAVV1zhOrdp06bUrVuXlStXeipcETkNKdn53DL1d44VFNH1gmo88n9N//kiEZFK4I7uCXRrUp2MQj8GpIynoHF/KLLBzNGw8QtPhycibvDqQqpjx45MnjyZBQsW8O6777J79266du1KVlYWycnJ+Pv7ExkZWeqamjVrkpycfMrnzc/PJzMzs9SXiFQMW6GdcTPWcSD9GPVjgnlzeBusFq++FYmInDNms4nXhrWmdmQQu1ILGG+bgNHqejCK4Jt/wZoPPR2iiJwmr3730qdPH4YMGULLli3p1asX33//Penp6cycOfOsnvf5558nIiLC9RUfrylFIhXlyXlbWLM7ldAAKx+Nbk9ksL+nQxIRqVCRwf68O7It/hYzP20/ynuR90LH2xwHv78flr3s2QBF5LR4dSF1osjISBo3bszOnTuJjY3FZrORnp5e6pxDhw4RGxt7yueZOHEiGRkZrq99+/adx6hFxGnaqr18tjoRkwlev641CTXCPB2SiIhHtKwTyZNXXwjAiz/9xYoL7ofLHnAcXPQ0LHwSDMNzAYrIP/KpQio7O5tdu3YRFxdHu3bt8PPzY9Gi4k43f/75J4mJiXTq1OmUzxMQEEB4eHipLxE5v1bsOspTc7cA8ECvJvRsVtPDEYmIeNbwi+MZ1LYOdgPu/GIDh9rfD1c+7Ti4/FXH6JTd7tkgReSkvLqQuv/++1m6dCl79uxhxYoVXHvttVgsFoYPH05ERAQ33XQT9957L4sXL2bt2rWMHTuWTp06cckll3g6dBEpYV9qLuNnrKPQbnB1q1qMu7yRp0MSEfE4k8nEfwZcRNPYMI5m2xg/Yx0Fl0yAfq8CJvjtI/h2HBQVejpUESmHVxdS+/fvZ/jw4TRp0oShQ4cSExPDqlWrqF69OgCvvvoq/fr1Y9CgQVx22WXExsby9ddfezhqESkpO7+Qm6f8TlpuAS1qR/DC4JaYTCZPhyUi4hWC/C28N7IdYQFWft+bxqQftkP7G2Hgh2CywKYvYNZoKMz3dKgicgKTYWgCbmZmJhEREWRkZGian8g5ZLcb3DZ9LT9tPUS10ADmTbiUuIggT4clIuJ1ftySzL+mrQXgnRFt+b8WcbB9Pswa42iP3rA7XDcD/EM8G6hIFXC6tYFXj0iJiG97bdEOftp6CH+LmfdvaKciSkTkJHpdGMu/Lm8IwAOzNrLrSDY07QsjZoFfCPy9GKYNhGPpng1URFxUSInIOVFkN1i5K4U5Gw6wclcK8zYc5I1FOwB49tqLaFcvysMRioh4tweuakLHBtHk2Iq4bdpacvILoWE3GPUtBEbAvlUwpR/kHPV0qCKCpvYBmtoncrYWbE7iqXlbScrIK3Pspi4NeKxfcw9EJSLiew5n5dH3jeUcycrnmta1eG1Ya8e60qRNMO1ayD0K1RrDDd9CRG1PhytSKWlqn4hUiAWbkxg3fV25RRRA27qRFRuQiIgPqxEWyNvXt8ViNjFnw0Gmr9rrOBDXEm5cAOG14ehf8GlvSP3bs8GKVHEqpETkjBXZDZ6at5WTDWubgP/M30aRvcoPfIuInLaLG0QzsU9TAJ7+bivrE9McB6pd4CimohtCeiJ80gcObwN7EexeBn985fivvciD0YtUHVZPByAivievoIgjWfn8sv3wSUeiAAwgKSOPNbtT6dQopuICFBHxcTd1acDavWn8sDmZ22es47sJXYgJDYDIujB2AUwbAIe3wkc9wS8Yco4UXxxeC3r/F5pf7bH4RaoCFVIiAjhalafl2jiSnc+RrHwOZ+YX/zsrnyNZea5/Z+W5tznk4ayTF1siIlKWyWTihcEt2Z6cxe6jOdz95QYmj70Yi9kEYTVhzHxHEZX6N9hySl+cmQQzR8HQqSqmRM4jFVIilZxz9Ojw8UKouDAq/e+j2fkUujEFz99qJjzQytFs2z+eWyMs8GxegohIlRQW6Md7I9sx4O1fWbbjKK8v2sG9VzZ2HAyMgIJjJ7nSAEyw4GFHC3WzpaJCFqlSVEiJ+CDn6FF5BZGrYMrO50hmPln57o0eRYf4UyMsgOphAVQPDaB6+PH/hgVQIyzQ8XhYAOGBVuwGdPnvLyRn5JW7TsoExEYEcnGD6HPyukVEqpomsWE8P7AFd3+5gTcW7aBNfCTdm9aAvSsgK+kUVxqQecBxXoOuFRavSFWiQkrEixyzFR0vgvJKT60rNc0uj6PZNrcaOARYzdQ4XhCVLIZcBdPxIikm1B8/y+n3oLGY4In+zRk3fR0mKFVMmY7/94n+zR1TUURE5IwMaFOb3/emMn1VInd/uYHvJnQhPvvQ6V288EloNwYa9VC7dJFzTPtIoX2k5Pyy2w1Sc21liqGSI0lHj//b3dGjmBB/VyFUsiA6sUgKC7A69iE5T8rbRyouIpAn+jen90Vx5+3niohUFfmFRQx9byUb92fQonYEs/sU4T/dzfVP1ZtBQk9HUVXvUvDTtGuR8pxubaBCChVScmZybYUnWXNUPLXucGY+KTnujR4F+pmLR41CA1wjSdXDnP92HHN39Oh8K7IbrNmdyuGsPGqEOabzaSRKROTcOZB+jH5vLCMtt4ARF9fm2T3DHY0lTja5OqSaYzRq12I4uA4Me/Fha6CjmEq4wlFcVWsM5/EDNxFfokLKDSqkxKnIbpCaYyu3IHKuOXKOKmW7MXpkMjlGj6qFBlAjPLDEmqOAMtPsQs/z6JGIiPiupX8dYcynazAM+KzLYTr/fs/xI+VMri7ZtS83Ff5eArsWwc5fIOtg6ScOrwMJPaBRT2jYDYIiz+vrEPFmKqTcoEKq8su1FZ6w5ijvhNbejq8zHT0qryAqOc0uJsQfqxeNHomIiO96beFfvLZwBwFWMwv7ZBK/+knILFEYhdeG3pNO3vrcMBwb+e5aBDsXORpSFOUXHzeZoU4HR1GV0BNqtVHnP6lSVEi5QYWUbyqyG6TklC2GjpQzzS7Hdvq7vDtGj8opjMpMswskxN+i0SMREalQdrvB2Mm/sfSvI9SPCeabcZdwcOMvHEs7QFBUbZp27IXF6kY/MVuuo5jatQh2LoSjf5U+HhTlGKVyFlbhtc7p6xHxNiqk3KBCyrvk5BeWv+aoZMGUnU9Kdj5uDB4R5GehRviJhVHxNDtn0RSt0SMREfFyaTk2+r25nAPpxwiwmskvLF7/dNbNftL3FY9W/b0U8jNKH6/R3NGwIqEn1O2sphVS6aiQcoMKqfOvyG6Qkn3CyFF2+VPscs9g9OifptY51x6JiIhUFu8t2cWkBdvLPO6cJ/HuyLZn3zm1qBAOrC0erTqwjlLrsaxBUP/S46NVV0C1C9S0QnyeCik3qJA6M4ZhkHN836OTrTly/js1x73RoxB/S7nFUPUTptlFB2v0SEREqp4iu0GX//5SatuJkpwboi9/qMe57aCamwp/L3Y0rNi1qOymwBHxxaNVDS5X0wrxSadbG+gjei/iLe2jC4vspBzvXHeqqXWHM/M5VnD6o0dmE8SEnmzNUWCp9UchGj0SERE5qTW7U09aRIFjzCgpI481u1Pp1Cjm3P3g4Gi4aJDjy9m0YudCR1G1dyVk7IN1UxxfJgvUae8YqWrUE2q1VtMKqVQ0IoV3jEid7w1NDcMgO7/wpCNGzml2R7MdnevcyYoQf0uZtUblTbGLDvHXvkIiIiLnwJwNB7jriw3/eF67epH0b1mLNnWjaBYXjr/1PM7isOXC3l8da6t2LTpJ04ruxzcF7gnh2rBdvJOm9rnB04XUgs1JjJu+rsx2eqczx7mwyM7R7OOjR9l5jhbfJ+59dLxYcnf0qFq5ex0FliqUqoVq9EhERKSirdyVwvAPV7l1TYDVTIvaEbSpG0mbulG0qRtJXETQeYoQSE8sLqr+/l/5TSucRVXdTmpaIV5DhZQbPFlI/dMcZ4DIYD/Gd08gJdtWaprdkax8UnPdGz0KDbA6CqByW3sXjypp9EhERMR7Od8/JGfklfkgFhwfxkaF+HPDJfXYuD+d9YnpZBwrKHNebHggbetF0ibeUVhdVDuCQL/zMP2uqBAO/F5cWJXbtKJLcWGlphXiQSqk3ODJQupMPlE6kcVsolqof3FBdMKokXMkqVqYP8H+Gj0SERGpDJwzWqBUSVLujBbDMNh9NId1iemsT0xjfWI625MzyzSCsppNNK8VTpt4x6hV27pRxEcHnfs9E3NSHE0rdv3iKK6yk0sfj6gLCT0cRVXDyyEw4tz+fJFTUCHlBk8WUqc7x7lNfCSt60aWWyRFBWv0SEREpCo6mzXWOfmF/HEgg/WJ6axLTGN9YhpHs21lzosJ8S+eDhgfScv4yHO7pYhhwOGtjoJq50JIXAlFJeIwWaBOB8doVUJPiGutphVyXqmQcoMvjEh9fssl57brjoiIiFQK56rrr2EY7E87xvp9jlGrdYnpbD2YQUFR6beKZhM0rhnmWmfVtm4kDauFYj5XH+racmDPr8WbAqfsKH08KBoadXeMVjXqoaYVcs6pkHKDN6yROtUc5/OyD4SIiIjIP8grKGLLwUzHdMB96WxITOdA+rEy54UHWml9fMSqTd1IWsdHEhnsf26CSNt7fArgQtj9P8jPLH28xoWOaYAJVziaVlgDzs3PlSpLhZQbvKVrH/zzHGcRERERTzqUmedaZ7U+MZ1NB9LJK7CXOa9h9RDaxEe5mlk0rhmK1XKW7deLCmD/78WjVQfXU+rdk1+wo2lFo+PTAGMS1LRC3KZCyg2eLqTg/O8jJSIiInI+FBTZ+TM56/g6K8e0wD0puWXOC/a30LJOhKuJRev4SKqHneXokbNphbMbYPah0scj6havrWpwmZpWyGlRIeUGbyik4NzNcRYRERHxpNQcGxv2pbkaWWzcl0F2fmGZ8+Kjg1yt19vUjaL52WwabBhwaEvxaFV5TSviLz4+WtUD4tqA+TxuUCw+S4WUG7ylkBIRERGpjIrsBjsPZxdPCdyXxo7D2WX2wvR3bhocX7xpcK3IM9w02JYDe5YXj1al7Cx9PDgGGnY/vndVDwiLPbOfI5WOCik3qJASERERqViZeQVs3Jfumg64fl866bnlbxrsGLFyFFctznTT4LQ9x4uqX+DvpWDLKn285kWOgiqhp5pWVHEqpNygQkpERETEs5ybBjtHrBybBmdRdMKuwVaziWZx4bStWzxqVTc62L1Ng4sKYP9vxaNVBzdQtmlF1+OjVT0hppGaVlQhKqTcoEJKRERExPvk2gr5Y38G6/els26vY2+ro9n5Zc6LDvF3tV5vWzfK/U2Dc47CrsWOomrXL2WbVkTWPb626orjTSv0frEyUyHlBhVSIiIiIt7PMAwOpB9ztV5fl5jGlnI2DTaZoEnNMMd0wOPNLBpVP81Ngw0DDm0uHq3auxLsJaYcmq1Q52JHw4pGPSGutZpWVDIqpNygQkpERETEN+UVFLE1KbN4rdVJNg0OC7TSukQTizanu2lwfrajaYWzG2DqrtLHg2Mca6saOZtW1DxHr0w8RYWUG1RIiYiIiFQejk2Di9dabdp/6k2Dnc0smtQM++dNg51NK3Yugt3/K6dpRYvi0aq6l6hphQ9SIeUGFVIiIiIilZdz0+Di9uvp7D6aU+a8ID/HpsFt60W5WrCfctPgogLYt6Z4tCppQ+njfiHQoOvx9VU9Ibqhmlb4ABVSblAhJSIiIlK1pObYjrdfdzSx2LAvvdxNg+tEBdGmbpSrS+ApNw3OPgJ/Ly5us55zuPTxyHrFnQDVtMJrqZBygwopERERkaqtyG6w60iJTYMT0/nrcFa5mwZfVCu8eK1V3ShqRQSWbb9utzuaVjhHqxJXlW1aEd+xeO+q2FZqWuElVEi5QYWUiIiIiJwoM6+ATfsyXBsGr09MI62cTYNrhgeUWGvl2DQ4yP+ETYPzs2HPsuJugKl/lz4eXA0adXe0WG/UA0JrnMdXJqeiQsoNKqRERERE5J8YhsHelFzWudZapbEt6eSbBjubWLSJj6JezAmbBqfuPj5a9QvsXgq27NI/LLZF8dqq+EvAehodBuWcUCHlBhVSIiIiInImjtmK+ONAxvHiyrHe6khW2U2Do4L9Sq21alkngrBAP8fBQhvsX1M8WpW0sfTFzqYVztGqmEYV8MqqLhVSblAhJSIiIiLngmEYHMzIc621WpeYxpYDmdiKSrdfN5mgcY0w16hV27pRxZsGu5pWLDzetOJI6R8SVb94tKrBZRAQVnEvsApQIeUGFVIiIiIicr7kFxax9WCmq/X6ur1p5W8aHGCl9fHNgtvUjaJ1fCRRQVY49EdxJ8Bym1ZcUrx3VWxLNa04Syqk3KBCSkREREQq0uHMvOMNLBxNLDbtz+BYQVGZ8xpWC3EUV3Ude1s1jQLrvhWO0aqdiyBtd+kLQqpDw+7H26yracWZUCHlBhVSIiIiIuJJhUV2/jyUxbrjhdWGxHT+PsWmwc726+3D04lJWuYYrdr9v3KaVrQs3rsqvqOaVpwGFVJuUCElIiIiIt4mLcfGhv3Fo1YbEtPJKmfT4NqRQY6iqk4olwbspEHGaqx//wLJm0qf6B8K9bs6CquEnhDdsIJeiW9RIeUGFVIiIiIi4u3srk2DHa3X1+09yabBFjMX1g6nS6xBD//NNMn+jaDEJZhyj5Y+MapB8WhVg65qWnGcCik3qJASEREREV+UlVfApv0Zri6B6/elk5pjK3NezVA/+sce5Ur/LTTL+Y2wI2sx2UuMbpn9HFP/nKNVNVtU2aYVKqTcoEJKRERERCoD56bB6/cdL6wS09mWlEnhCZsGh5vzGByzm14BW7gw9zdCc/eVfqKQ6o5mFY2cTSuqV+Cr8CwVUm5QISUiIiIildUxWxGbD2awbm/x3laHT9g0uJ4pmd6BW+gduJUL8zfgbz+hPXtcq+K9q+pcXKmbVqiQcoMKKRERERGpKgzDICkjz1VUrU9MY3OJTYP9KKSd+S8uN2/kSv/NJNhPaLHuH+rYCLhRj0rZtEKFlBtUSImIiIhIVZZfWMS2pKwSa63S2JfqGJWqTjpdzH9wmWUTl5n/IMaUWfri6IbFo1X1u0JAqAdewbmjQsoNKqREREREREo7nJXHhuMNLNYnprFxXwZ5BQVcaNrDZeZNXG7ZRFvTDvxMxRsJ2483rTAn9ISEK6DmRadsWlFUWMj21T9yLO0AQVG1adqxFxartSJe3klVuULq7bff5sUXXyQ5OZlWrVrx5ptvcvHFF5/WtSqkREREREROzblpsLOJxfp9aRw+coRO5q2Owsq8kbrmI6WuyQ+ohr1hd4KaXQUNu5dqWrH+xynUWvkUNUlxPXaIGA52eoI2vUZX2Os6UZUqpL788ktGjRrFe++9R8eOHXnttdeYNWsWf/75JzVq1PjH61VIiYiIiIi4Lz3XxoZ96axLTGf93lRS92+nXcE6LjNvopN5KyGm0k0tjoY1o7BBd9IKLDTZ+iYAZlPxcWdzwY2d3/BYMVWlCqmOHTvSoUMH3nrrLQDsdjvx8fFMmDCBhx9++B+vVyElIiIiInL27HaDv49msy4xnU17DmPbvZIGGau43LyJ5ua9pc41DDCZynkOAw6bYqj+6F8emeZ3urWBZycgngM2m421a9cyceJE12Nms5krrriClStXlntNfn4++fnF1XFmZma554mIiIiIyOkzm00k1AgjoUYYQ9vHA+3Izr+NTfvS+XTXTuw7FtH86Pd0YnO5RRQ4RqhiSWHL6h+58NK+FRq/O3x+u+KjR49SVFREzZo1Sz1es2ZNkpOTy73m+eefJyIiwvUVHx9fEaGKiIiIiFQ5oQFWOidUY2yvS7jpjn/j1+70puwdSztwniM7Oz5fSJ2JiRMnkpGR4frat2/fP18kIiIiIiJnLSi69umdF3V653mKz0/tq1atGhaLhUOHDpV6/NChQ8TGxpZ7TUBAAAEBARURnoiIiIiIlNC0Yy8O/RxDdSOlVKMJJ+caqaYde1V8cG7w+REpf39/2rVrx6JFi1yP2e12Fi1aRKdOnTwYmYiIiIiInMhitXKw0xNAcZc+J+f3SZ2e8Ph+Uv/Eu6M7Tffeey+jR4+mffv2XHzxxbz22mvk5OQwduxYT4cmIiIiIiInaNNrNOuhzD5Sh00xJHl4H6nTVSkKqWHDhnHkyBEef/xxkpOTad26NQsWLCjTgEJERERERLxDm16jKeo5gi2rf+RY2gGComrTtGMvYr18JMqpUuwjdba0j5SIiIiIiMDp1wY+v0ZKRERERESkoqmQEhERERERcZMKKRERERERETepkBIREREREXGTCikRERERERE3qZASERERERFxkwopERERERERN6mQEhERERERcZMKKRERERERETepkBIREREREXGTCikRERERERE3qZASERERERFxkwopERERERERN1k9HYA3MAwDgMzMTA9HIiIiIiIinuSsCZw1wsmokAKysrIAiI+P93AkIiIiIiLiDbKysoiIiDjpcZPxT6VWFWC32zl48CBhYWGYTCaPxpKZmUl8fDz79u0jPDzco7GIuEv5K75M+Su+TPkrvszb8tcwDLKysqhVqxZm88lXQmlECjCbzdSpU8fTYZQSHh7uFYkkciaUv+LLlL/iy5S/4su8KX9PNRLlpGYTIiIiIiIiblIhJSIiIiIi4iYVUl4mICCAJ554goCAAE+HIuI25a/4MuWv+DLlr/gyX81fNZsQERERERFxk0akRERERERE3KRCSkRERERExE0qpERERERERNykQqqS0FI3EREREZGKo0KqEpg1axYjR47EZrN5OhQRt82bN4+HH36YgoICT4ci4raff/6Zd955h6KiIk+HIuI25a/4Mm/IXxVSPu7tt99m2LBhLFu2jDFjxqiYEp/y9ttvc8011/D111/zxBNPqJgSn/LOO+/Qq1cv3n//fT7++GO9GRWfovwVX+Yt+atCysdlZmYybdo0XnzxRXbs2MGoUaNUTIlPyM3NZefOnUyZMoUxY8bwyy+/8Nhjj1FYWOjp0ET+0ZEjR1ixYgWffPIJHTp0YMqUKXz44Yd6Myo+Qfkrvsyb8lf7SPk4u92O2Wzm2LFjzJgxg/fff59GjRoxdepU/P39PR2eyCnl5uYSHBxMZmYm//3vf1m0aBHdunXjmWeewc/Pz9PhiZzS0aNHqVatGqmpqdxxxx3s3buXG264gVtuuQWLxeLp8EROKSUlhZiYGOWv+CRvuf+qkPIxRUVFWCwWVwEFjkYTJpOJvLw8ZsyYwXvvvadiSrySM1dL/ruwsBCr1Up2djaTJk1i4cKFdOvWjf/85z9YrVYPRyxSzHnfLXn/dd6T09LSGD9+vN6Mitcqef91KigowM/PT/krXs9b77+a2udDnAmTmZnJ+PHj2b17NwAmkwnDMAgMDGTkyJHcdttt7Nq1S9P8xKsUFRVhMpkoKiqioKCAjIwMAKxWK4ZhEBoaysMPP8wVV1zBkiVLePTRRzXNT7xGUVERZrOZrKwspk2bRnZ2NoDrg62oqCjefvtt6tevz7Rp0zRNSryK8/5rt9sByMvLA8DPz4+ioiLlr3g1b77/qpDyESWLqMaNG5ORkUGDBg0AR5XufIMaEBCgYkq8jt1ud+XvwIED6dq1K927d+fpp58mNTXV9SmpiinxRiXvv/Xq1WPp0qWEhoa6jpvNZgoLC4mKiuKtt97Sm1HxKs77b1ZWFqNHj6Z79+4MGTKE999/H8D1yb3yV7yRt99/VUj5AMMwXDfBiy66iMsuu4zPPvvMddzZ6cx5MyxZTO3cuVPFlHiccx1fp06dsFqtjBgxgsGDB/P8888zYsQIVqxY4TpXxZR4E+cfcef9t2fPnnzyySdlzrNarRQUFOjNqHgds9lMbm4u7du3Jysri86dOxMfH8+ECRMYNWoU+/btAxzvNZS/4k184f6rNVI+Ij8/n86dO5OXl8eWLVsAePHFF1m/fj379++nd+/e3HHHHYSHh7uuyc3N5bPPPmPKlCnExMQwa9YsLeAXj/n++++57777WLZsGdWqVQNg586d9O3bl3r16vHcc8/Rvn171/znjIwMXnjhBZYtW0a7du148cUXtWZKPCInJ4cLLriAVq1a8cMPPwAwdepUtm3bhtlspmPHjlx99dVA8ZqTlJQUJkyYwIEDBxg4cCATJkxwzesXqWiff/45zz77LCtWrHC9T1i2bBn9+vWjZ8+evPvuu9SsWVP5K17H2++/+r/CR6SmphIQEEC9evWYP38+I0aM4IsvviAoKIhWrVrx9NNPc+ONN5KVlQU4qvjg4GBuvvlmatSowapVq9i2bZuHX4VUZfn5+eTm5uL87CY/P5+EhAS+//57/vrrL5577jmgeJg+IiKCZ599FsMw+Omnn1xrAkUq2sGDB0lOTqZWrVpkZmZy/fXX89JLL7FixQqWL1/OkCFDmDRpEuBYc1JYWEhMTAyfffYZSUlJzJ49mwMHDnj4VUhVlpmZSWFhoauIKiwspGvXrvzvf/9j4cKFPP3004DyV7yP199/DfF6drvdMAzD2Llzp9G3b18jLi7OaN++vbFt2zbXOevWrTMCAgKM119/vdR1mzdvNkwmkzF79uwKj1ukpK1btxoBAQHGBx984HrMZrMZhmEYGzduNCwWizF16tRS16xbt86wWCzKX/GYwsJCwzAMY82aNUZwcLARHR1tdO3a1di0aZNht9uNjIwM49VXXzX8/f2Nn3/+udS1K1euNKxWq/H11197InQRl5UrVxpms9n49ttvXY8VFBQYhmEYc+fONQICAowffvihzDXKX/GkoqIiwzC8+/6rESkv5Oyq4+TstNOoUSNef/11rrjiCu69916aNGniOr9NmzZ06dKFNWvWAMVtTi+88EK2bdvGwIEDK/x1SNV0Yv46NWvWjIkTJ/Loo48yd+5cwDGvubCwkJYtW9KtWzc2bNgA4Bq1io2NZe3atcpfqTAn5q9z7WmHDh1YunQpNWrUYNy4cbRo0QKTyUR4eDhDhw4lLi6O7du3l7rW39+fX3/9lWuvvbbC4peq7WT335YtW3LzzTfz3HPPsWzZMqA4ty+77DKaNm3K1q1bgeL7r/JXKtqJ+eucjufN918tOPAyJTfY3bt3L02bNgVw9c5v1KgRr732GiaTydXpzGw2U1BQQGBgIC1atCjznM6CS+R8cy4MzcnJ4eWXXyYpKYmAgACeffZZQkJCGDJkCLt37+b+++/HbrczYMAA17qnkJCQMvuexcXFERcX54mXIlWQM39zc3P58MMPSU1NJSQkhAcffBCA9u3bs3jxYtcbUOcHVsHBwdSoUYOaNWuWerxt27Yeey1S9TjzNzs7m8cff5ykpCSsVitvvvkmkZGRXHfddezfv58nn3ySRx99lO7duwMQERFBTExMmaY+yl+pSCfL3zfeeIOoqCivvf+qkPIihmFgNpvJzs7moosuIicnh59++ok2bdoAjoLJMAyio6PLXDt9+nTWrVvHo48+ClBm0z2R880o0V2yY8eO1K5dm7CwMH7//Xd+++03fv31V5o3b86dd97p6tz34IMP0rRpUw4dOsSPP/7IXXfdBSh/peKVzN9LLrmEGjVqYLPZ2LlzJytXruSbb74BHKOkTs48/eqrrzhy5AjNmjUr9bhIRSnZ4rxDhw7Uq1ePxo0bM3/+fIYOHcpPP/1E9+7dycnJ4cMPP+TWW2/loYceonnz5mzcuJHVq1e71kkpf6WinSp/hw8fzoIFCzAMwzvvv+d14qC4zWazGSNHjjQuv/xy47LLLjPq1KljrF279qTn//rrr8b9999vhIeHG19++WUFRipSVl5entGzZ09j0KBBRmFhoWG32429e/casbGxpdY/HThwwHj//feNBg0aGM2bNzdat25tzJo1yzCM4jWBIhXt2LFjRteuXY3BgwcbNpvNyMrKMpYsWWI0bNjQ+OWXX8qcv2XLFuOZZ54xQkJCXPkr4im5ublG586djSFDhrjWlsyePdvo27evaz2UYRjG5s2bjYkTJxqhoaHGBRdcYCQkJBgzZ870VNgihmGcOn+d35fkLfdfjUh5mV27dhEWFsa4cePo27cvQ4cO5eqrr2bu3LllhikLCws5duwYmzZtYsaMGfTr1881pCniCYsXLyYtLY3XX38di8WCYRjUqFGDOnXqkJmZ6TqvVq1a3HrrrQwePBir1Up+fj7Vq1d3zc0X8YT58+djs9mYNGkSfn5++Pn50bx5c+x2O4cPHy517tGjR1m8eDHz5s1j+vTpDBgwQPdf8aivv/6aatWq8eqrr7rWliQmJvLXX3/Rt29fACZMmECfPn147rnnuPPOO7Hb7RiGQe3atV33X+WweMKp8rdPnz5YLBZuu+02evXqRU5Ojtfcf7WPlBdatWoVrVu3JjAwkJycHAYPHswff/zBnDlzaNeuneu8wsJCrFYraWlpREVF6SYoHpeZmcmkSZN49NFHCQ4Ods15vvrqq2nbti1PPvmkax2g87+A3oCKV/jzzz+ZMWMGjzzyCIGBga787dKlCyNGjGDcuHGlcjUpKYnCwkLi4+N1/xWPs9vtLFu2jEsvvRSr1crcuXMZMGAA48aNo127dsycOZOtW7eycOFCGjdu7OlwRUo5nfzdtm0bCxcu5IILLiApKYmioiLq1Knj0fuvCikvcuKbyZKNJwYOHMgff/zhGpmaPHkyGRkZjB8/XpuUilc4Wf4CDBw4kIYNG/LSSy8BsGjRImJiYmjdurUnQhUpl2EYrrn6JfO5R48e9O3bl/vuuw+ApUuX0rp1ayIiIjwZrohLyfstQHZ2Ni+99BL16tVj7NixrscjIyN56KGHmDhxoifCFCmXO/n74IMP8sgjj3gizHKp/bkXObGSdiZVUFAQX3/9NS1atGDQoEHcfffd3HjjjSQkJKiIEq9RXv46u0AZhkFwcDDgaIxy5ZVXkpqaWuExipyKyWRydYQymUyu/LXZbPj5+QGO/O3evTubNm3yWJwiJyr5JhQgNDSU++67z/UmtLCwkOTkZJo3b07z5s09EaLISbmTvxdeeKEnQjwpFVI+wllMAbzxxht8+eWX9O3bV2tKxCcYhkFERARz585lzJgxzJgxgx49eng6LJFTcn44YLfbiYyMZN68eYwdO5YZM2bQtWtXD0cncmqhoaGuf1utVhYsWEBKSgoNGzb0YFQip8dX8lfDGT7k3XffZe/evcyZM4f+/furiBKv5xwxjYqK4rXXXiMpKYnJkyczfPhwrSkRr+ccnapZsyavvPIKmzdvVv6Kz3Dm5p9//snSpUu58847mT59ern7TYp4G1/JXxVSPiIpKYnly5czbdq0UkWU/oiLN3OuM8nOzubAgQPMnTvX1V0SlL/i3Zx5mpKSwqZNm/j222+5+uqrlb/iM1JSUpgyZQpfffUVn3/+Oddee62a+4jP8IX8VbMJH5KSkkJMTIz+iEuFO9sb186dOzlw4ACXX3658ld8hjPvFy5ciNVqpVu3bspf8SmGYbB//36ysrJo3ry58ld8ii/krwqpCnBiN5J/evxE3lZ9S9XibAFdUFBAYWEhQUFBZ/V8ymepSCfLt9O9/5Z8Dm/8Iy6Vm/P+69zuRMSXnIv7r7erHK/CixUVFblamM+ZM4evv/6alStXAmW7lJyM/miLpzj/iGdkZJCQkMCSJUvO+jmVz1JRioqKMJlM5OXlsXz5chYvXsyePXsAx/23qKjotJ7HmbMmk0n5KxWm5P23UaNGrF692tMhiZy2c3X/9XYakTqPnJV4VlYWHTp0IDQ0lF27dhEdHc0ll1zClClT9AmTeC3nH/HMzExat25N06ZN+f7778ucV5k+WZLKo+T9t1OnTvj5+bF582batGlD165defnll4Gy+ev8XiOn4kkl779t2rShcePG/PDDD2XOO9n+fcpf8aSqdP/Vu5/zyGQyYbfbueGGG0hISGDp0qX89ttvvPDCCyxevJgrr7ySw4cPA47kcTpw4ICnQhZxsVgsZGVl0aZNG9q0aeMqovbv38+WLVtISkoCyn6ypPwVb+DcB8q5GfQPP/zA6tWrGTJkCFOnTmXIkCFAcf46P1PU/mbiDZz335YtW9KmTRtXEZWRkcHevXux2WxA8fsM5a94k6p0/1UhdZ7Z7XYyMjK4+uqrCQkJISEhgUGDBrFw4UISExMZMWIEgKsCP3jwIA0bNuT999/3cORS1RmGwc0338zu3bv57LPPABg3bhzDhg2jZcuWDBgwgIceeghw/NFX/oq3ycnJISsri5tuuonY2Fjatm3L+PHjmTJlCosXL2b48OGAI39NJhO7d+8mLi6Ob775xmc+DZXKyW63M2rUKBITE/nqq68AuPPOOxkwYAAJCQkMHDiQN954A3C8f1D+irepKvdfFVLnmdls5tChQ6xfv971mGEYNG/enNmzZ7Nx40buuecewFHBR0VFcdddd7Fw4UKys7M9FbYIJpOJf//738THx3PdddcxcuRIVq9ezb333svcuXO5+uqrmTFjBo899pjrfOWveBOr1cr+/ftL3X+Dg4Pp1asXH330EUuWLOHFF18sdf6wYcNYsWKFJ8IVcXEWUtHR0dx2223cdtttLF++nOuvv57JkydTs2ZNPvzwQ959913XNcpf8SZV5f6rQuo8cs71vOOOO1i6dClff/01UDwU36pVKx544AFWr17N0aNHAQgKCuLKK68kKCjItRmkiKe0bNmS77//njVr1rBkyRKmT5/OoEGD6Nu3L3fccQfDhw9nyZIlpKSkAMpf8ZzylvuGhIQwcuRIlixZwq+//up63GKx0LNnT6699lpWrVpFQUEBAPHx8Vx55ZVs376dwsLCCotd5MT8tVqt9O/fn6lTp/LFF1/w448/8vnnn3PLLbcwYsQInnrqKZo0acIvv/ziylXlr3hKefff4ODgKnH/VaeDc8S5MLS873v27MmCBQv4+OOPCQ4Opnfv3q7FdQ0aNGDPnj2uRAK48soradeu3Vm3mRY5XafK3wsvvJDFixezfPly6tWrBzhumhEREdSuXZu9e/eWulb5KxWtZIvo9PR0TCYTkZGRWCwW+vfvz4IFC/jggw8IDQ2lVatWAISFhdGiRQvmzZtHVlYW0dHRAIwePZo+ffqoEZBUmBPzFyAqKgqr1UrPnj2ZPXs2Bw8eLHX/rVOnDk2bNmXWrFnYbDZXvip/paI589dms7Fjxw6OHTtGixYtCAgIqBL3X41InQN2ux2LxUJ2djbjxo1jz549WCwW1wL8Jk2a8OCDD5Kdnc3rr7/O1KlTXdceOXKEuLg4V2HlrOqdSSVyvv1T/gI0btyYUaNGERISUura9PR0OnTogL+/P6D8lYrnzN+srCyGDRvGVVddRY8ePbjzzjs5duwYl156Kc888wz/+9//eOmll/j5559d12ZnZ9OoUSPXBwHOpj81atTwyGuRqqe8/O3Zsyd33XUXeXl5BAQE0KVLF4YOHUpgYGCpa3Nzc+nUqZPr/qv8lYrmzN/MzEx69+7Nddddx8CBA+ndu7fr/vvss89W7vuvIedETk6OcfHFFxsmk8no0aOHsWfPHsMwDKOgoMB1zsqVK42RI0caNWrUMNq2bWv079/fCAoKMmbOnOmpsEUMwzh5/hYWFpZ7flZWlvHxxx8b4eHhxnfffVeRoYq42O12wzAc+dikSRNj4MCBxuzZs43HH3/c6NSpk/Hmm2+6zv3hhx+Mrl27Gs2aNTN69uxpjB492ggICDC++uorT4UvVdw/5e/bb79d7nU2m8349NNPjaioKGPBggUVGbKIy4n5O3ToUGPNmjXGV199ZSQkJBjz5893nfvdd98ZXbt2NZo2bVrp7r/aR+ocKCoqYuLEiaxfv55+/frx3XffYbPZmDJlCvXr1y+1I3lSUhI7d+7k888/p379+nTo0IHu3bv7VM98qVz+KX9P3Odh7dq1fPHFF3z88ce8//77DBkyRPkrHlNYWMi//vUvUlJSmDVrFn5+fgAMGTIEm83GnDlzXOdu27aNTZs2MWvWLBISEujevTu9evVS/orHuJO/AMuXL2fOnDl8+OGHfPDBBwwdOlT5Kx5TUFDA4MGD8ff3Z8aMGa7R0auuuorbb7+d8PBwWrVqRUxMDFu3buWPP/7gq6++olGjRpXm/us7kxC9mMVi4YILLqBatWrccccdNGrUiFdffZXRo0eXKabi4uKIi4uja9eurutVy4onnU7+liymYmJiaNSoEXPnzqVLly7KX/Go1NRU/Pz86NevH35+fq577fDhw3n11VcpKCjAZDJhtVpp1qwZzZo1Y9iwYa7rlb/iSaeTv2az2TX9KSoqitDQUL755hvXh7AintS9e3eaNm3qKqJmz57N4sWL2b9/P3a7naNHj7Jo0SJatWpF8+bNK939VyNSZ2j27Nnk5+dz/fXXux7Ly8tzzWGeM2cOb775JgUFBUydOpV69epRVFRERkaG1o+Ix51p/qanpxMTE0NBQYHrk1Nf/zRJfE/J/M3Ly2Px4sV069atVIOTmTNn8swzz7Bhw4ZSc/BLjq6KeMKZ5q9zUX/Je7Xuv1LRTnz/YLPZsFgsWCwWVq9ezYABA3jwwQe59tpriYqK4rrrriM9PZ0VK1ZUylzVX5QzdOjQIaZOnUpubq6rog4MDHQt0L/mmmuYMGECfn5+jB49mr179/LOO+9w2WWXkZWV5cnQRc4qfzMzM11FFFApb4zi3Zz5m5OTQ2BgIH369CEoKKjUp5tms7lUw5Rp06YxcOBA14JmEU852/x1fvIPuv9KxSv5/gHA39/fVezHxMQwbdo07rnnHurXr09ERASdOnXCZrP5VEtzd6iQOkOtW7cmMzOTw4cPYzKZXDc8i8Xiuhk634wGBATQqVMn7r33Xu6//37CwsI8GbrIGefvAw88QHh4uCdDF3Hl75EjRwBcf6BLvqmMiYkhMDAQi8XC5MmTGTt2LIMHD9aIlHjcmebvkCFDMJvNymHxqJLvHwDX+wfDMEhISOCKK65wfQ+42qEbhlEppvKdSP83nqHOnTsTFBTE+PHjAccbUOcnnSaTqdSb0Zo1a5KcnMzXX3/NmDFjKmUiiW9R/oovOzF/rVZrmZGm3NxcQkJC+Oijj7j55puZNm0aI0eOVP6Kxyl/xZed7P3DiaOjJpOJKVOm8N577zFixAj8/f0r5QiqCqkz4LzhPfnkkyQnJ/PSSy8BjqH4E9+MTp48menTpzNz5kz69++vm6B4nPJXfNnp5C841vz9+uuv3HrrrUybNo3hw4crf8XjlL/iy043fzdu3MjEiRO5++67ef/997nyyisrbf6qkDoDzmH1li1b0qVLF+bNm8eUKVNcx5zDnCaTiWPHjvHtt98yePBgVxJVxopcfIfyV3zZ6eZvvXr1aNy4MXPmzCn1JlT5K56k/BVfdjr5W1hYyOHDh8nNzeXzzz93teivrNS17yzt27ePu+++m9TUVPr168d9990HlO0OpZugeCPlr/iyk+UvQHZ2NsnJySQkJCh/xSspf8WXnSp/CwsLOXbsGGFhYZU+f1VInQOJiYm8+OKLrFq1iho1avDJJ58QEhJCaGioWpOK11P+ii8rL38DAwOJiIjwdGgi/0j5K76svPwNCgqqUk2pVEidI+np6fzxxx/8+9//pqCggKCgIJ588kk6depUqlW0iDdS/oovU/6KL1P+ii+r6vmrQuo8WL58OX/++Scmk4nrr7/etXGeiC9Q/oovU/6KL1P+ii+rivmrQuocOnEalKZFiS9R/oovU/6KL1P+ii+ryvmrrn3nUFVJGqmclL/iy5S/4suUv+LLqnL+akRKRERERETETRqREhERERERcZMKKRERERERETepkBIREREREXGTCikRERERERE3qZASERERERFxkwopERERERERN6mQEhGposaMGYPJZHJ9xcTE0Lt3bzZt2uTp0ERERLyeCikRkSqsd+/eJCUlkZSUxKJFi7BarfTr18/TYYmIiHg9FVIiIlVYQEAAsbGxxMbG0rp1ax5++GH27dvHkSNHXOf88ccf9OjRg6CgIGJiYrj11lvJzs52HX/44YepVasW/v7+1K5dm4ceegi73Q7AkiVLMJlMzJ8/n5YtWxIYGMgll1zC5s2bXdenpKQwfPhwateuTXBwMC1atODzzz8vFafdbueFF14gISGBgIAA6taty7PPPgtQalTtxK8lS5awZ88eTCYTGzZsKPWc9evX57XXXnN9/8orr9CiRQtCQkKIj4/n9ttvL/U6AT799FOaNGmCv7+/62fcfffdJ/39njjqZzKZaN++/Wn9bnJycggPD+err74q9ZzffvstISEhZGVluV5beV8lX9vJzikZ+7Rp02jfvj1hYWHExsZy/fXXc/jw4ZO+NhGRqk6FlIiIAJCdnc306dNJSEggJiYGcLyZ79WrF1FRUfz222/MmjWLhQsXcscdd7iuu+qqq/juu+/YuXMnH330ER988AHTp08v9dwPPPAAL7/8Mr/99hvVq1enf//+FBQUAJCXl0e7du2YP38+mzdv5tZbb+WGG25gzZo1rusnTpzIpEmTeOyxx9i6dSufffYZNWvWBHCNqCUlJQEwe/Zs1/edO3c+7ddvNpt544032LJlC1OmTOGXX37hwQcfdB3fvn07N998MzfeeCM7d+4kKSmJTp06/ePzlhz1S0pK4scffzyt301ISAjXXXcdn376aanzP/30UwYPHkxYWJjrsYULF5b6GXXq1CkTx6efflrqnBNjLygo4JlnnmHjxo18++237NmzhzFjxpzOr05EpGoyRESkSho9erRhsViMkJAQIyQkxACMuLg4Y+3ata5zPvjgAyMqKsrIzs52PTZ//nzDbDYbycnJZZ7z77//NuLi4oxPPvnEMAzDWLx4sQEYX3zxheuclJQUIygoyPjyyy9PGlvfvn2N++67zzAMw8jMzDQCAgKMDz/88B9fE2AsXry41GO7d+82AGP9+vWlHq9Xr57x6quvnvS5Zs2aZcTExLi+//LLLw0/P79S51x++eXGXXfdddLnGD16tHHNNdeUe+x0fjerV682LBaLcfDgQcMwDOPQoUOG1Wo1lixZ4tZrA4xvvvnGrdh/++03AzCysrJOeo6ISFWmESkRkSqse/fubNiwgQ0bNrBmzRp69epFnz592Lt3LwDbtm2jVatWhISEuK659NJLsdvt/Pnnn67HnnvuOYKDg2nYsCGDBg1i1KhRpX5OydGP6OhomjRpwrZt2wAoKirimWeeoUWLFkRHRxMaGsqPP/5IYmKiK4b8/Hx69ux5Vq+1c+fOhIaGur6cz++0cOFCevbsSe3atQkLC+OGG24gJSWF3NxcABo0aEBBQQGzZs3CMIyziqWkU/1uLr74Yi688EKmTJkCwPTp06lXrx6XXXbZOfv5TmvXrqV///7UrVuXsLAwLr/8coAyvycREXFQISUiUoWFhISQkJBAQkICHTp04KOPPiInJ4cPP/zQree57bbbWLduHdOnT+eLL77gf//732lf++KLL/L666/z0EMPsXjxYjZs2ECvXr2w2WwABAUFuRXLyXz55ZeuonHDhg3UqlXLdWzPnj3069ePli1bMnv2bNauXcvbb78N4IqjQ4cOPP3004wdO5bAwEBCQ0NZtmzZOYntVG6++WYmT54MOKbnjR07FpPJdE5/hnMKZ3h4ODNmzOC3337jm2++AYpfv4iIlKZCSkREXEwmE2azmWPHjgHQrFkzNm7cSE5OjuucX3/9FbPZTJMmTVyPRUdH07RpU0aMGEGXLl2YPXt2qeddtWqV699paWn89ddfNGvWzPV811xzDSNHjqRVq1Y0bNiQv/76y3X+BRdcQFBQEIsWLTqr1xYfH+8qGhMSErBara5ja9euxW638/LLL3PJJZfQuHFjDh48WOY57rzzTuLi4njqqafYsGGDq3HE2TjV7wZg5MiR7N27lzfeeIOtW7cyevTos/6ZJ9q+fTspKSlMmjSJrl270rRpUzWaEBH5ByqkRESqsPz8fJKTk0lOTmbbtm1MmDCB7Oxs+vfvD8CIESMIDAxk9OjRbN68mcWLFzNhwgRuuOEGV7OHd955hy1btrBnzx6mT5/Ozz//TJs2bUr9nKeffppFixaxefNmxowZQ7Vq1RgwYADgKJR+/vlnVqxYwbZt2/jXv/7FoUOHXNcGBgby0EMP8eCDDzJ16lR27drFqlWr+Pjjj8/Z7yEhIYGCggLefPNN/v77b6ZNm8Z7771X6hzDMBg1ahRt27bl4YcfJiEh4ZyMlp3qdwMQFRXFwIEDeeCBB7jqqqvKbSRxturWrYu/v7/r9c+dO5dnnnnmnP8cEZHKRIWUiEgVtmDBAuLi4oiLi6Njx46uznzdunUDIDg4mB9//JHU1FQ6dOjA4MGD6dmzJ2+99ZbrOebPn0+3bt1o2rQpTz31FI888gg33nhjqZ8zadIk7rrrLtq1a0dycjLz5s3D398fgEcffZS2bdvSq1cvunXrRmxsbKlCAuCxxx7jvvvu4/HHH6dZs2YMGzbsnI6YtGrVildeeYX//ve/XHTRRcyYMYPnn3++zGvYsWPHOS3gnM97st+N00033YTNZivzez1XqlevzuTJk5k1axbNmzdn0qRJvPTSS+flZ4mIVBYm41yumBURESlhyZIldO/enbS0NCIjIz0djldx53czbdo07rnnHg4ePFimyBIREc+w/vMpIiIi4gm5ubkkJSUxadIk/vWvf6mIEhHxIpraJyIi4qVeeOEFmjZtSmxsLBMnTvR0OCIiUoKm9omIiIiIiLhJI1IiIiIiIiJuUiElIiIiIiLiJhVSIiIiIiIiblIhJSIiIiIi4iYVUiIiIiIiIm5SISUiIiIiIuImFVIiIiIiIiJuUiElIiIiIiLiJhVSIiIiIiIibvp/wXrRESIkHU4AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_cut = dataframe.iloc[0:1500]\n",
-    "\n",
-    "it_student_df = df_cut[df_cut['IT Student'] == \"Yes\"].copy()\n",
-    "nonit_student_df = df_cut[df_cut['IT Student'] == \"No\"].copy()\n",
-    "\n",
-    "age_bins = range(10, 36, 5)\n",
-    "\n",
-    "\n",
-    "it_student_df['age_group'] = pd.cut(it_student_df['Age'], bins=age_bins)\n",
-    "it_student_count = it_student_df.groupby('age_group', observed=False)['IT Student'].value_counts().reset_index()\n",
-    "mean_count_it = it_student_count.groupby('age_group', observed=False)['count'].mean()\n",
-    "\n",
-    "nonit_student_df['age_group'] = pd.cut(nonit_student_df['Age'], bins=age_bins)\n",
-    "nonit_student_count = nonit_student_df.groupby('age_group', observed=False)['IT Student'].value_counts().reset_index()\n",
-    "mean_count_nonit = nonit_student_count.groupby('age_group', observed=False)['count'].mean()\n",
-    "\n",
-    "plt.figure(figsize=(10,6))\n",
-    "plt.plot(mean_count_it.index.astype(str), mean_count_it , marker='o', label='IT студент')\n",
-    "plt.plot(mean_count_nonit.index.astype(str), mean_count_nonit , marker='o', label='Не IT студент')\n",
-    "plt.xlabel(\"Возрастная группа\")\n",
-    "plt.ylabel(\"Количество студентов\")\n",
-    "plt.title('Количество студентов на IT и не IT направлении')\n",
-    "plt.xticks(rotation=45)\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Диаграмма 3 (Гистограмма)\n",
-    "\n",
-    "Данная диаграмма отображает количество людей по типу соединения к сети Интернет. На основе этой диаграммы можно сделать вывод, что чаще всего используют мобильный интернет, чем Wi-Fi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Количество')"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10,6))\n",
-    "plt.hist(dataframe.head(20000)['Internet Type'], bins=4, edgecolor='black')\n",
-    "plt.title(\"Распределение людей по типу подключения к сети Интернет\")\n",
-    "plt.xlabel(\"Тип подключения к сети Интернет\")\n",
-    "plt.ylabel(\"Количество\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/lab_1/requirements.txt b/lab_1/requirements.txt
deleted file mode 100644
index f045b89..0000000
--- a/lab_1/requirements.txt
+++ /dev/null
@@ -1,39 +0,0 @@
-asttokens==2.4.1
-comm==0.2.2
-contourpy==1.3.0
-cycler==0.12.1
-debugpy==1.8.5
-decorator==5.1.1
-executing==2.1.0
-fonttools==4.54.1
-ipykernel==6.29.5
-ipython==8.27.0
-jedi==0.19.1
-jupyter_client==8.6.3
-jupyter_core==5.7.2
-kiwisolver==1.4.7
-matplotlib==3.9.2
-matplotlib-inline==0.1.7
-nest-asyncio==1.6.0
-numpy==2.1.1
-packaging==24.1
-pandas==2.2.2
-parso==0.8.4
-pexpect==4.9.0
-pillow==10.4.0
-platformdirs==4.3.3
-prompt_toolkit==3.0.47
-psutil==6.0.0
-ptyprocess==0.7.0
-pure_eval==0.2.3
-Pygments==2.18.0
-pyparsing==3.1.4
-python-dateutil==2.9.0.post0
-pytz==2024.2
-pyzmq==26.2.0
-six==1.16.0
-stack-data==0.6.3
-tornado==6.4.1
-traitlets==5.14.3
-tzdata==2024.1
-wcwidth==0.2.13
diff --git a/lab_1/static/csv/students_adaptability_level_online_education.csv b/lab_1/static/csv/students_adaptability_level_online_education.csv
deleted file mode 100644
index 7c0f34d..0000000
--- a/lab_1/static/csv/students_adaptability_level_online_education.csv
+++ /dev/null
@@ -1,1206 +0,0 @@
-Education Level,Institution Type,Gender,Age,Device,IT Student,Location,Financial Condition,Internet Type,Network Type,Flexibility Level
-University,Private,Male,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,18,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-University,Public,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Wifi,4G,Low
-University,Public,Female,27,Computer,Yes,Town,Poor,Mobile Data,4G,Low
-University,Public,Female,23,Mobile,No,Rural,Mid,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,10,Mobile,No,Town,Rich,Wifi,4G,High
-School,Public,Male,10,Mobile,No,Town,Mid,Mobile Data,4G,Low
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,27,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,10,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,3G,Moderate
-College,Public,Female,23,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,10,Mobile,No,Town,Poor,Mobile Data,3G,High
-University,Public,Female,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-School,Public,Female,11,Mobile,No,Town,Mid,Wifi,3G,Low
-University,Private,Male,23,Computer,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,18,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,3G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,3G,Low
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Public,Male,27,Mobile,Yes,Rural,Poor,Mobile Data,4G,Moderate
-School,Private,Male,9,Mobile,No,Town,Poor,Mobile Data,4G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Private,Female,18,Mobile,Yes,Town,Mid,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Public,Male,18,Mobile,No,Rural,Poor,Mobile Data,3G,Low
-University,Private,Male,23,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Public,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Wifi,4G,Low
-University,Public,Male,27,Mobile,No,Rural,Mid,Mobile Data,4G,High
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Public,Female,10,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Public,Female,11,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Male,23,Mobile,No,Rural,Poor,Mobile Data,3G,Low
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-University,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,10,Mobile,No,Town,Mid,Mobile Data,2G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-College,Public,Female,23,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-School,Public,Female,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,High
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Male,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-University,Private,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,3G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Poor,Mobile Data,3G,Low
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-University,Private,Female,18,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Public,Female,11,Mobile,No,Rural,Poor,Mobile Data,2G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Wifi,4G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,11,Computer,No,Town,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Poor,Wifi,4G,Moderate
-School,Public,Male,11,Computer,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Computer,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Mobile,No,Town,Mid,Wifi,3G,High
-School,Public,Male,10,Mobile,No,Town,Mid,Mobile Data,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Public,Male,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Private,Male,23,Mobile,Yes,Town,Poor,Mobile Data,3G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Male,27,Computer,Yes,Town,Mid,Wifi,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Low
-College,Public,Male,18,Mobile,No,Rural,Poor,Mobile Data,3G,Moderate
-University,Private,Female,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-College,Private,Female,18,Computer,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Female,18,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Public,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Public,Female,11,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Rich,Wifi,4G,High
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,3G,Moderate
-University,Public,Male,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Low
-University,Private,Male,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,High
-College,Public,Male,18,Mobile,No,Town,Poor,Mobile Data,4G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Public,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Low
-School,Private,Female,18,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Private,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Moderate
-University,Public,Male,23,Mobile,No,Rural,Poor,Mobile Data,3G,Low
-University,Private,Male,23,Computer,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Poor,Wifi,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Female,23,Mobile,No,Town,Poor,Wifi,4G,Low
-University,Public,Female,23,Mobile,No,Rural,Mid,Mobile Data,2G,Low
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Rich,Mobile Data,4G,High
-School,Private,Male,10,Mobile,No,Town,Rich,Wifi,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Low
-University,Public,Male,23,Computer,Yes,Town,Mid,Mobile Data,3G,Low
-School,Private,Male,9,Mobile,No,Town,Poor,Mobile Data,4G,Low
-University,Public,Male,27,Computer,Yes,Town,Rich,Wifi,4G,High
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Wifi,3G,Low
-University,Private,Male,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,High
-University,Public,Male,18,Mobile,No,Rural,Poor,Mobile Data,4G,Moderate
-School,Public,Male,10,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-College,Public,Female,18,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Male,23,Mobile,No,Rural,Poor,Mobile Data,3G,Low
-University,Public,Male,27,Mobile,No,Rural,Mid,Mobile Data,4G,High
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,Yes,Town,Mid,Mobile Data,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,3G,Moderate
-University,Public,Female,27,Mobile,No,Town,Mid,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Rich,Wifi,4G,High
-School,Public,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Town,Poor,Wifi,3G,Moderate
-College,Public,Male,18,Mobile,Yes,Town,Mid,Mobile Data,4G,Low
-University,Private,Female,18,Mobile,Yes,Town,Mid,Mobile Data,3G,Low
-School,Private,Female,18,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Public,Male,11,Computer,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,9,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Computer,No,Town,Rich,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Public,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Wifi,4G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Public,Male,18,Mobile,No,Rural,Poor,Mobile Data,3G,Low
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Wifi,3G,Low
-College,Public,Male,18,Mobile,No,Town,Poor,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,Yes,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Public,Female,11,Mobile,No,Town,Mid,Wifi,3G,Low
-School,Public,Male,10,Mobile,No,Town,Mid,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Public,Male,11,Mobile,No,Town,Mid,Wifi,4G,High
-University,Private,Male,27,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Public,Female,27,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,3G,Low
-School,Public,Female,11,Mobile,No,Rural,Poor,Mobile Data,2G,Low
-School,Public,Female,18,Mobile,No,Rural,Rich,Mobile Data,4G,High
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-College,Private,Female,18,Computer,No,Town,Mid,Wifi,4G,Moderate
-College,Public,Female,23,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-School,Public,Male,18,Mobile,No,Town,Rich,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,18,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Private,Male,23,Mobile,Yes,Town,Poor,Wifi,3G,Low
-School,Public,Male,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Female,23,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Female,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,9,Mobile,No,Town,Poor,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Wifi,3G,Moderate
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Public,Male,11,Mobile,No,Town,Mid,Wifi,4G,High
-University,Private,Male,23,Computer,Yes,Rural,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Rural,Poor,Mobile Data,3G,Moderate
-University,Private,Male,23,Computer,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Female,27,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Private,Female,23,Mobile,No,Town,Mid,Wifi,2G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Wifi,4G,Low
-University,Public,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Public,Female,11,Mobile,No,Town,Rich,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Female,9,Mobile,No,Town,Poor,Wifi,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Public,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Computer,No,Town,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-College,Public,Male,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-University,Public,Male,23,Mobile,No,Rural,Poor,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Town,Poor,Wifi,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Public,Male,27,Mobile,Yes,Town,Rich,Wifi,4G,High
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,9,Mobile,No,Town,Poor,Mobile Data,4G,Low
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,9,Mobile,No,Town,Poor,Wifi,4G,Moderate
-College,Public,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,No,Town,Poor,Mobile Data,4G,High
-School,Private,Male,11,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Public,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,10,Mobile,No,Town,Mid,Mobile Data,2G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Female,18,Mobile,Yes,Town,Mid,Mobile Data,3G,Low
-University,Public,Female,23,Mobile,No,Rural,Mid,Mobile Data,2G,Low
-University,Private,Male,23,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Low
-University,Public,Male,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Poor,Wifi,4G,High
-University,Private,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Moderate
-College,Public,Male,23,Mobile,Yes,Rural,Poor,Mobile Data,4G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Public,Female,9,Mobile,No,Town,Rich,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-University,Private,Male,23,Tab,No,Town,Rich,Wifi,4G,High
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,No,Town,Rich,Wifi,4G,High
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,9,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-University,Public,Male,23,Mobile,No,Rural,Poor,Mobile Data,4G,Moderate
-School,Private,Female,18,Computer,No,Town,Mid,Wifi,4G,Low
-College,Public,Male,18,Tab,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Rich,Mobile Data,3G,Moderate
-University,Private,Male,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Private,Male,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Female,23,Mobile,No,Town,Mid,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Rich,Mobile Data,3G,Moderate
-University,Private,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Public,Male,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Public,Male,11,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Moderate
-College,Public,Female,23,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-University,Public,Male,23,Mobile,Yes,Town,Mid,Wifi,3G,Low
-University,Private,Female,23,Mobile,No,Town,Poor,Wifi,4G,Low
-University,Private,Female,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Female,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Female,23,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Computer,Yes,Town,Mid,Wifi,3G,Low
-University,Private,Male,23,Mobile,Yes,Rural,Poor,Mobile Data,3G,Moderate
-University,Public,Female,27,Mobile,No,Town,Mid,Mobile Data,4G,Low
-School,Private,Male,10,Mobile,No,Town,Rich,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-University,Public,Female,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,Yes,Rural,Rich,Wifi,4G,Low
-College,Public,Male,18,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,10,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,10,Mobile,No,Town,Poor,Mobile Data,3G,High
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Poor,Mobile Data,3G,Low
-University,Private,Male,23,Computer,No,Town,Rich,Wifi,4G,High
-College,Public,Female,18,Mobile,No,Town,Mid,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-College,Public,Female,18,Mobile,No,Town,Mid,Wifi,3G,Moderate
-School,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Public,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Male,27,Mobile,Yes,Town,Mid,Wifi,3G,Moderate
-College,Public,Male,23,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-School,Public,Female,10,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Public,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-University,Public,Female,27,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Public,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Low
-University,Private,Male,23,Computer,Yes,Rural,Mid,Wifi,4G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,3G,Moderate
-University,Public,Male,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,27,Computer,Yes,Town,Poor,Mobile Data,3G,Moderate
-School,Public,Female,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,High
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-University,Public,Female,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-School,Private,Female,18,Computer,No,Town,Mid,Wifi,4G,Low
-University,Public,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Low
-University,Private,Female,23,Mobile,No,Rural,Mid,Wifi,4G,Low
-University,Private,Male,23,Mobile,Yes,Town,Rich,Wifi,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Poor,Wifi,4G,High
-University,Public,Female,27,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,3G,Moderate
-University,Public,Male,27,Computer,Yes,Town,Mid,Wifi,4G,Low
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Public,Female,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,High
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Male,27,Mobile,Yes,Town,Rich,Wifi,4G,High
-University,Private,Male,23,Computer,Yes,Town,Rich,Wifi,4G,High
-University,Private,Male,23,Computer,Yes,Town,Poor,Wifi,4G,High
-University,Private,Male,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Male,27,Computer,Yes,Town,Mid,Wifi,4G,Low
-College,Public,Male,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Private,Male,27,Mobile,Yes,Town,Mid,Wifi,3G,Moderate
-University,Public,Male,18,Computer,No,Town,Mid,Wifi,3G,Moderate
-University,Public,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,10,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Computer,No,Town,Mid,Wifi,4G,Moderate
-College,Public,Female,18,Mobile,No,Town,Mid,Mobile Data,3G,Low
-School,Private,Male,9,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Public,Female,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,High
-School,Private,Male,11,Computer,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Public,Male,23,Mobile,No,Rural,Poor,Mobile Data,4G,Moderate
-University,Public,Female,27,Computer,Yes,Town,Poor,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,High
-University,Public,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Private,Female,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,4G,Low
-University,Public,Female,27,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Private,Female,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-College,Private,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Female,27,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Public,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Private,Female,23,Mobile,No,Town,Mid,Wifi,3G,High
-University,Private,Male,23,Mobile,Yes,Town,Rich,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-University,Private,Female,23,Computer,Yes,Town,Rich,Wifi,4G,High
-School,Public,Male,9,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-School,Private,Female,18,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Public,Male,23,Mobile,No,Rural,Poor,Mobile Data,4G,Moderate
-University,Public,Female,23,Mobile,No,Town,Poor,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Wifi,4G,Low
-University,Public,Male,23,Mobile,No,Rural,Poor,Mobile Data,3G,Low
-University,Private,Male,23,Computer,Yes,Town,Poor,Wifi,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Mobile,No,Town,Poor,Mobile Data,4G,High
-University,Private,Male,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,High
-University,Private,Male,23,Mobile,No,Town,Poor,Mobile Data,4G,High
-School,Private,Female,11,Mobile,No,Town,Rich,Wifi,3G,High
-University,Private,Male,23,Computer,Yes,Rural,Mid,Mobile Data,3G,Moderate
-College,Public,Male,23,Mobile,No,Rural,Poor,Mobile Data,3G,Low
-School,Public,Male,11,Mobile,No,Rural,Poor,Mobile Data,3G,Low
-College,Public,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Wifi,3G,Low
-University,Private,Male,23,Mobile,Yes,Rural,Poor,Mobile Data,3G,Moderate
-University,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,3G,Low
-School,Public,Male,11,Mobile,No,Rural,Poor,Mobile Data,3G,Low
-University,Private,Male,23,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Male,23,Mobile,No,Town,Poor,Mobile Data,4G,High
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Low
-University,Private,Male,27,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Public,Female,18,Mobile,No,Town,Mid,Wifi,3G,Moderate
-University,Public,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-College,Public,Male,18,Mobile,No,Town,Mid,Mobile Data,4G,Low
-College,Public,Male,23,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-School,Public,Male,18,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Computer,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Town,Poor,Wifi,3G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Poor,Wifi,4G,High
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-College,Public,Male,18,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Private,Female,23,Computer,Yes,Town,Rich,Wifi,4G,High
-University,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Public,Male,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Wifi,3G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,Yes,Rural,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,3G,Low
-College,Public,Female,18,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-College,Public,Male,18,Mobile,No,Rural,Poor,Mobile Data,2G,Low
-School,Private,Female,10,Mobile,No,Town,Rich,Wifi,4G,High
-University,Public,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Female,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Public,Female,11,Mobile,No,Rural,Poor,Mobile Data,2G,Low
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Moderate
-University,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Rich,Wifi,4G,High
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,4G,Low
-University,Public,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Moderate
-College,Public,Female,18,Mobile,No,Town,Mid,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Public,Male,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,3G,Moderate
-University,Private,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Private,Female,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Moderate
-University,Private,Male,23,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-University,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Moderate
-School,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Male,18,Computer,No,Town,Mid,Wifi,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,10,Mobile,No,Town,Poor,Mobile Data,3G,High
-School,Public,Male,11,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Male,27,Computer,Yes,Town,Poor,Mobile Data,3G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-College,Private,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Rural,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Public,Female,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-University,Private,Female,23,Mobile,No,Town,Mid,Wifi,2G,Moderate
-College,Public,Female,23,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,10,Mobile,No,Town,Rich,Wifi,4G,High
-School,Private,Female,9,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Male,23,Mobile,No,Rural,Poor,Mobile Data,3G,Low
-University,Public,Male,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,3G,Moderate
-University,Public,Female,27,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,No,Rural,Poor,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,27,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Rich,Mobile Data,3G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Poor,Wifi,4G,High
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Low
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Wifi,4G,Low
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,3G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Male,23,Computer,Yes,Town,Mid,Mobile Data,3G,Low
-University,Public,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Low
-College,Public,Male,18,Mobile,No,Rural,Poor,Mobile Data,2G,Low
-College,Public,Male,18,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,High
-University,Private,Female,23,Tab,Yes,Rural,Poor,Wifi,2G,Low
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Private,Female,23,Computer,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Poor,Wifi,3G,Low
-College,Private,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Public,Male,18,Mobile,No,Town,Rich,Mobile Data,4G,Moderate
-University,Public,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Public,Male,27,Computer,Yes,Town,Rich,Wifi,4G,High
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,3G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,Yes,Rural,Rich,Wifi,4G,Low
-University,Private,Male,27,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Male,23,Mobile,No,Rural,Poor,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-University,Public,Female,27,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Public,Female,9,Mobile,No,Town,Rich,Mobile Data,4G,Low
-University,Public,Female,23,Mobile,No,Town,Poor,Mobile Data,4G,Low
-University,Private,Female,23,Tab,No,Town,Rich,Wifi,4G,Moderate
-College,Public,Female,23,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Public,Male,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-University,Public,Male,23,Mobile,No,Rural,Poor,Mobile Data,4G,Moderate
-School,Public,Male,11,Mobile,No,Town,Mid,Wifi,4G,High
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,9,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Male,23,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Female,10,Mobile,No,Town,Poor,Mobile Data,3G,High
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,10,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Public,Female,23,Mobile,No,Town,Poor,Mobile Data,4G,Low
-University,Private,Male,23,Computer,No,Town,Rich,Wifi,4G,High
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,High
-University,Private,Female,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,9,Mobile,No,Town,Poor,Mobile Data,4G,Low
-School,Private,Male,11,Computer,No,Town,Mid,Wifi,4G,Low
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Public,Male,11,Mobile,No,Town,Mid,Wifi,4G,High
-School,Private,Male,10,Mobile,No,Town,Rich,Wifi,4G,High
-University,Public,Male,27,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Female,18,Mobile,Yes,Town,Mid,Mobile Data,3G,Low
-University,Public,Female,23,Mobile,No,Town,Mid,Wifi,3G,Low
-School,Public,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Female,27,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Rich,Mobile Data,4G,High
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Female,10,Mobile,No,Town,Rich,Wifi,4G,High
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Moderate
-University,Public,Male,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-University,Public,Male,27,Mobile,Yes,Rural,Poor,Mobile Data,4G,Moderate
-University,Private,Male,23,Mobile,No,Town,Poor,Mobile Data,4G,High
-School,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Public,Female,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Public,Male,27,Mobile,Yes,Rural,Poor,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,Yes,Rural,Mid,Mobile Data,4G,Low
-University,Public,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Rich,Mobile Data,3G,Moderate
-School,Public,Male,11,Mobile,No,Town,Mid,Wifi,4G,High
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Female,18,Mobile,Yes,Town,Mid,Mobile Data,3G,Low
-University,Public,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Moderate
-University,Private,Female,18,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Public,Female,11,Mobile,No,Town,Rich,Wifi,4G,Moderate
-University,Public,Male,27,Computer,Yes,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Rich,Mobile Data,3G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-University,Public,Female,23,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-University,Public,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Public,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Rural,Mid,Mobile Data,3G,Moderate
-School,Private,Male,9,Mobile,No,Town,Poor,Mobile Data,4G,Low
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Low
-School,Private,Female,10,Mobile,No,Town,Poor,Mobile Data,3G,High
-University,Private,Female,23,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-University,Private,Male,27,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Public,Male,11,Mobile,No,Rural,Poor,Mobile Data,3G,Low
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Public,Male,11,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Poor,Wifi,3G,Low
-School,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Poor,Wifi,4G,High
-University,Private,Male,27,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Public,Female,10,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,3G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-School,Public,Female,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Public,Female,9,Mobile,No,Town,Rich,Mobile Data,4G,Low
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Private,Female,23,Computer,Yes,Rural,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-University,Private,Male,23,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-College,Public,Male,18,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Low
-College,Public,Male,23,Mobile,Yes,Rural,Poor,Mobile Data,4G,Low
-College,Public,Male,18,Mobile,Yes,Town,Mid,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-University,Public,Female,27,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Town,Rich,Wifi,3G,High
-University,Public,Female,27,Mobile,No,Town,Mid,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-University,Public,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Rural,Poor,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Female,18,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,3G,Moderate
-University,Private,Female,18,Mobile,Yes,Rural,Poor,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Poor,Wifi,4G,High
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Rich,Wifi,4G,High
-School,Private,Female,18,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Male,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-College,Public,Male,18,Tab,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,Yes,Town,Poor,Mobile Data,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Wifi,4G,Low
-University,Private,Female,18,Mobile,Yes,Town,Mid,Mobile Data,3G,Low
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Moderate
-University,Public,Male,23,Mobile,No,Town,Mid,Wifi,4G,Low
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,3G,Moderate
-School,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,No,Town,Poor,Mobile Data,4G,High
-University,Private,Male,23,Mobile,Yes,Rural,Poor,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Male,23,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Private,Female,23,Mobile,No,Town,Mid,Wifi,2G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Public,Male,18,Computer,No,Town,Mid,Wifi,3G,Moderate
-University,Public,Male,23,Mobile,Yes,Town,Mid,Wifi,3G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-College,Public,Male,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-University,Public,Female,23,Mobile,No,Rural,Mid,Wifi,4G,Low
-University,Private,Female,23,Computer,Yes,Town,Rich,Wifi,4G,Moderate
-University,Private,Female,18,Mobile,Yes,Town,Mid,Mobile Data,3G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,Yes,Town,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,High
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Female,23,Computer,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Tab,Yes,Rural,Poor,Wifi,2G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Male,23,Mobile,Yes,Town,Poor,Wifi,3G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-University,Public,Female,23,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,10,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,Yes,Rural,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Moderate
-School,Private,Male,10,Mobile,No,Town,Rich,Wifi,4G,High
-University,Private,Female,23,Computer,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Female,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Public,Female,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Public,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Female,23,Computer,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Female,18,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Computer,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,3G,Low
-School,Public,Male,10,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Public,Female,11,Mobile,No,Town,Rich,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-University,Public,Male,27,Computer,Yes,Town,Rich,Wifi,4G,High
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Male,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Rich,Mobile Data,3G,High
-School,Public,Female,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Public,Male,18,Mobile,No,Town,Rich,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-School,Public,Male,11,Mobile,No,Rural,Poor,Mobile Data,3G,Low
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Female,10,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,High
-University,Private,Male,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Female,18,Mobile,Yes,Town,Mid,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Computer,Yes,Rural,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,Yes,Rural,Mid,Wifi,4G,Moderate
-School,Public,Male,18,Mobile,No,Town,Rich,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Female,23,Computer,Yes,Town,Mid,Wifi,3G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Moderate
-School,Private,Female,10,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,High
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Public,Male,18,Computer,No,Town,Mid,Wifi,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,High
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Female,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Female,10,Mobile,No,Town,Poor,Mobile Data,3G,High
-School,Private,Female,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Public,Female,18,Mobile,No,Town,Mid,Mobile Data,4G,Low
-College,Public,Female,23,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-School,Public,Female,11,Mobile,No,Town,Mid,Wifi,3G,Low
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Moderate
-University,Public,Male,27,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Private,Male,23,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-University,Public,Female,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-School,Public,Female,10,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Private,Female,23,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Low
-University,Private,Female,18,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Moderate
-School,Private,Female,10,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-School,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,18,Mobile,No,Town,Poor,Mobile Data,3G,Low
-College,Public,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Female,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-School,Public,Female,11,Mobile,No,Town,Rich,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-School,Public,Male,18,Mobile,No,Town,Rich,Mobile Data,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Female,18,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,27,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-University,Public,Male,18,Computer,No,Town,Mid,Wifi,3G,Moderate
-College,Private,Female,18,Computer,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Wifi,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,10,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Private,Female,23,Computer,Yes,Rural,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,4G,Low
-School,Public,Male,18,Mobile,No,Town,Rich,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Computer,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,High
-School,Private,Male,10,Mobile,No,Town,Rich,Wifi,4G,High
-School,Public,Female,11,Mobile,No,Town,Mid,Wifi,3G,Low
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Male,10,Mobile,No,Town,Rich,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Male,18,Computer,No,Town,Mid,Wifi,4G,Low
-College,Public,Male,23,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Female,10,Mobile,No,Town,Poor,Mobile Data,3G,Low
-College,Public,Male,23,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Male,27,Mobile,Yes,Rural,Poor,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Poor,Wifi,4G,High
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,3G,Moderate
-College,Public,Male,18,Mobile,Yes,Rural,Mid,Mobile Data,4G,Low
-University,Private,Female,23,Mobile,No,Town,Mid,Wifi,3G,High
-College,Public,Female,23,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Low
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Male,10,Mobile,No,Town,Mid,Mobile Data,3G,Low
-School,Public,Female,11,Mobile,No,Town,Rich,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Private,Male,23,Computer,Yes,Town,Poor,Wifi,3G,Moderate
-School,Public,Male,10,Mobile,No,Rural,Mid,Mobile Data,2G,Low
-College,Public,Male,18,Tab,Yes,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Tab,No,Town,Rich,Wifi,4G,High
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,10,Mobile,No,Town,Rich,Wifi,4G,High
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Low
-University,Public,Male,23,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,High
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-College,Private,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Private,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-University,Private,Male,23,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Poor,Wifi,3G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Low
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,3G,Moderate
-School,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,10,Mobile,No,Town,Mid,Wifi,3G,High
-School,Public,Male,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,High
-University,Public,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Moderate
-School,Private,Male,10,Mobile,No,Town,Mid,Mobile Data,3G,Low
-University,Private,Female,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Male,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Rich,Wifi,4G,High
-University,Private,Female,23,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Wifi,4G,Low
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-University,Public,Male,27,Mobile,Yes,Rural,Poor,Mobile Data,4G,Moderate
-School,Public,Female,11,Mobile,No,Town,Rich,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Public,Female,27,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Public,Male,18,Mobile,No,Town,Rich,Mobile Data,4G,Moderate
-University,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Private,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Public,Male,10,Mobile,No,Town,Mid,Mobile Data,4G,Low
-School,Public,Male,10,Mobile,No,Rural,Mid,Mobile Data,2G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Male,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Low
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,3G,Moderate
-College,Public,Female,23,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,High
-University,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Public,Male,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-University,Private,Female,23,Mobile,No,Town,Poor,Wifi,4G,Low
-School,Private,Male,11,Computer,No,Town,Mid,Wifi,4G,Low
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-University,Public,Male,18,Computer,No,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Public,Female,18,Mobile,No,Rural,Rich,Mobile Data,4G,High
-University,Private,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Public,Female,11,Mobile,No,Town,Rich,Wifi,4G,Low
-University,Public,Male,18,Mobile,No,Rural,Poor,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Poor,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Female,23,Computer,No,Town,Rich,Wifi,4G,Moderate
-University,Private,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Wifi,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Public,Female,23,Mobile,No,Rural,Mid,Mobile Data,2G,Low
-University,Public,Female,27,Mobile,No,Town,Mid,Wifi,4G,Low
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-College,Private,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Public,Male,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Rich,Mobile Data,3G,Moderate
-University,Private,Male,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,Yes,Rural,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-University,Public,Male,27,Mobile,No,Rural,Mid,Mobile Data,4G,High
-School,Private,Male,11,Computer,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,18,Mobile,No,Town,Mid,Mobile Data,4G,Low
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Public,Female,11,Mobile,No,Town,Mid,Wifi,3G,Low
-School,Private,Female,10,Mobile,No,Town,Mid,Wifi,3G,High
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,High
-University,Private,Female,23,Computer,Yes,Town,Rich,Wifi,4G,Moderate
-University,Private,Female,23,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Male,9,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Wifi,4G,Moderate
-College,Public,Female,18,Mobile,No,Town,Mid,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Town,Rich,Wifi,3G,High
-School,Private,Male,11,Mobile,No,Town,Poor,Wifi,4G,Low
-University,Private,Female,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-University,Public,Male,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Male,23,Computer,Yes,Town,Mid,Mobile Data,3G,Low
-University,Private,Male,23,Tab,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Mid,Wifi,3G,Moderate
-School,Public,Female,11,Mobile,No,Town,Rich,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Wifi,4G,Low
-School,Public,Male,9,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-School,Public,Female,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Private,Female,23,Tab,No,Town,Rich,Wifi,4G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Female,23,Computer,Yes,Town,Rich,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Wifi,4G,Low
-University,Public,Male,27,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-University,Private,Male,23,Computer,Yes,Rural,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,3G,Moderate
-College,Private,Female,18,Computer,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-University,Public,Male,27,Computer,Yes,Town,Rich,Wifi,4G,High
-School,Public,Male,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Public,Male,23,Mobile,No,Rural,Mid,Wifi,4G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,3G,Moderate
-University,Private,Female,18,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,High
-School,Public,Male,11,Mobile,No,Town,Mid,Wifi,4G,High
-University,Public,Female,23,Mobile,No,Town,Mid,Wifi,3G,Low
-School,Private,Female,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,High
-College,Public,Male,18,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-College,Public,Male,18,Mobile,No,Rural,Poor,Mobile Data,2G,Low
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Female,18,Mobile,Yes,Rural,Poor,Mobile Data,3G,Moderate
-School,Private,Male,10,Mobile,No,Town,Rich,Wifi,4G,Moderate
-College,Public,Male,18,Mobile,Yes,Rural,Mid,Mobile Data,4G,Low
-University,Private,Male,23,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-University,Private,Female,23,Computer,Yes,Town,Rich,Wifi,4G,High
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,3G,Moderate
-University,Private,Male,23,Mobile,Yes,Town,Rich,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Wifi,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Female,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Rich,Mobile Data,3G,Moderate
-School,Public,Male,18,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-School,Private,Female,10,Mobile,No,Town,Mid,Wifi,3G,High
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Moderate
-School,Private,Male,10,Mobile,No,Town,Mid,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Wifi,3G,Moderate
-College,Public,Male,18,Mobile,No,Rural,Poor,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Female,11,Mobile,No,Town,Rich,Wifi,3G,High
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-College,Public,Male,18,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Public,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,Yes,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Public,Male,18,Mobile,No,Town,Mid,Wifi,4G,Moderate
-College,Public,Male,18,Tab,Yes,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,Yes,Rural,Mid,Mobile Data,3G,Low
-School,Public,Female,11,Mobile,No,Town,Rich,Wifi,4G,Moderate
-University,Private,Male,23,Computer,Yes,Rural,Mid,Mobile Data,3G,Low
-School,Public,Male,10,Mobile,No,Town,Mid,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Poor,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-College,Public,Male,18,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Female,18,Computer,Yes,Town,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Computer,No,Town,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-University,Private,Female,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-University,Public,Male,23,Mobile,No,Town,Mid,Wifi,4G,Low
-University,Private,Female,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Rich,Mobile Data,3G,High
-School,Public,Female,18,Mobile,No,Town,Mid,Mobile Data,4G,Low
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-College,Public,Male,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-College,Public,Male,18,Tab,Yes,Town,Mid,Wifi,4G,Moderate
-University,Private,Male,23,Computer,No,Town,Mid,Wifi,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-School,Public,Female,11,Mobile,No,Town,Mid,Wifi,4G,Low
-School,Private,Female,9,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,Yes,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Computer,Yes,Rural,Mid,Mobile Data,3G,Low
-School,Private,Female,9,Mobile,No,Town,Poor,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,Yes,Town,Mid,Wifi,4G,Moderate
-University,Public,Male,27,Computer,Yes,Town,Rich,Wifi,4G,High
-University,Public,Female,23,Mobile,No,Rural,Mid,Mobile Data,2G,Low
-School,Private,Male,11,Mobile,No,Town,Poor,Mobile Data,3G,Moderate
-University,Private,Male,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-School,Public,Female,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Private,Female,18,Mobile,Yes,Rural,Poor,Mobile Data,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-University,Private,Male,23,Mobile,Yes,Rural,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Computer,No,Town,Mid,Wifi,4G,Moderate
-University,Public,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Low
-University,Private,Male,23,Computer,Yes,Rural,Mid,Wifi,4G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Private,Male,23,Mobile,Yes,Rural,Poor,Mobile Data,3G,Moderate
-University,Public,Male,27,Mobile,Yes,Town,Rich,Wifi,4G,High
-University,Public,Male,23,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-College,Public,Female,18,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-University,Public,Male,23,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-University,Public,Male,27,Computer,Yes,Town,Rich,Wifi,4G,High
-School,Public,Male,10,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,4G,Moderate
-School,Private,Female,11,Mobile,No,Town,Rich,Wifi,3G,High
-School,Public,Male,9,Mobile,No,Rural,Mid,Mobile Data,4G,Low
-College,Public,Male,18,Mobile,Yes,Town,Mid,Mobile Data,4G,Low
-University,Private,Male,23,Computer,Yes,Rural,Mid,Mobile Data,3G,Moderate
-College,Public,Female,18,Mobile,No,Rural,Poor,Mobile Data,4G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Low
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Male,10,Mobile,No,Town,Rich,Wifi,4G,Moderate
-University,Private,Female,23,Mobile,Yes,Town,Mid,Wifi,4G,Low
-University,Private,Male,27,Mobile,Yes,Town,Mid,Wifi,3G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-College,Public,Male,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Poor,Wifi,4G,High
-School,Private,Male,11,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Private,Male,23,Computer,Yes,Town,Poor,Wifi,4G,High
-College,Public,Male,18,Mobile,No,Rural,Mid,Mobile Data,3G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Mobile Data,4G,Moderate
-College,Public,Male,18,Mobile,Yes,Rural,Mid,Mobile Data,4G,Low
-University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Town,Mid,Wifi,4G,Low
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Moderate
-School,Private,Male,11,Mobile,No,Town,Mid,Mobile Data,3G,Moderate
-College,Private,Female,18,Mobile,No,Rural,Mid,Wifi,4G,Low
-School,Private,Female,11,Mobile,No,Town,Poor,Mobile Data,3G,Moderate
diff --git a/lab_2/lab2.ipynb b/lab_2/lab2.ipynb
new file mode 100644
index 0000000..2c9ee97
--- /dev/null
+++ b/lab_2/lab2.ipynb
@@ -0,0 +1,3287 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Лабораторная 2\n",
+    "\n",
+    "Первый датасет: Набор данных для анализа и прогнозирования сердечного приступа (https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['HeartDisease', 'BMI', 'Smoking', 'AlcoholDrinking', 'Stroke',\n",
+      "       'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory',\n",
+      "       'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'SleepTime',\n",
+      "       'Asthma', 'KidneyDisease', 'SkinCancer'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "df_heart = pd.read_csv(\"..\\\\static\\\\csv\\\\heart_2020_cleaned.csv\")\n",
+    "print(df_heart.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Столбцы на русском:\n",
+    "\n",
+    "HeartDisease - сердечный приступ \\\n",
+    "BMI - ИМТ \\\n",
+    "Smoking - курящий ли человек \\\n",
+    "AlcoholDrinking - выпивающий ли человек\\\n",
+    "Stroke - был ли инсульт\\\n",
+    "PhysicalHealth - физическое здоровье\\\n",
+    "MentalHealth - ментальное здоровье\\\n",
+    "DiffWalking - проблемы с ходьбой\\\n",
+    "Sex - пол\\\n",
+    "AgeCategory - возрастная категория\\\n",
+    "Race - раса\\\n",
+    "Diabetic - диабетик ли человек\\\n",
+    "PhysicalActivity - физическая активность\\\n",
+    "GenHealth - общее здоровье\\\n",
+    "SleepTime - время сна\\\n",
+    "Asthma - астматик ли человек\\\n",
+    "KidneyDisease - нефропатия\\\n",
+    "SkinCancer - рак кожи"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 319795 entries, 0 to 319794\n",
+      "Data columns (total 18 columns):\n",
+      " #   Column            Non-Null Count   Dtype  \n",
+      "---  ------            --------------   -----  \n",
+      " 0   HeartDisease      319795 non-null  object \n",
+      " 1   BMI               319795 non-null  float64\n",
+      " 2   Smoking           319795 non-null  object \n",
+      " 3   AlcoholDrinking   319795 non-null  object \n",
+      " 4   Stroke            319795 non-null  object \n",
+      " 5   PhysicalHealth    319795 non-null  float64\n",
+      " 6   MentalHealth      319795 non-null  float64\n",
+      " 7   DiffWalking       319795 non-null  object \n",
+      " 8   Sex               319795 non-null  object \n",
+      " 9   AgeCategory       319795 non-null  object \n",
+      " 10  Race              319795 non-null  object \n",
+      " 11  Diabetic          319795 non-null  object \n",
+      " 12  PhysicalActivity  319795 non-null  object \n",
+      " 13  GenHealth         319795 non-null  object \n",
+      " 14  SleepTime         319795 non-null  float64\n",
+      " 15  Asthma            319795 non-null  object \n",
+      " 16  KidneyDisease     319795 non-null  object \n",
+      " 17  SkinCancer        319795 non-null  object \n",
+      "dtypes: float64(4), object(14)\n",
+      "memory usage: 43.9+ MB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HeartDisease</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>Smoking</th>\n",
+       "      <th>AlcoholDrinking</th>\n",
+       "      <th>Stroke</th>\n",
+       "      <th>PhysicalHealth</th>\n",
+       "      <th>MentalHealth</th>\n",
+       "      <th>DiffWalking</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>AgeCategory</th>\n",
+       "      <th>Race</th>\n",
+       "      <th>Diabetic</th>\n",
+       "      <th>PhysicalActivity</th>\n",
+       "      <th>GenHealth</th>\n",
+       "      <th>SleepTime</th>\n",
+       "      <th>Asthma</th>\n",
+       "      <th>KidneyDisease</th>\n",
+       "      <th>SkinCancer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>No</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>55-59</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Very good</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>No</td>\n",
+       "      <td>20.34</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>80 or older</td>\n",
+       "      <td>White</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Very good</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>No</td>\n",
+       "      <td>26.58</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>65-69</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Fair</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>No</td>\n",
+       "      <td>24.21</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>75-79</td>\n",
+       "      <td>White</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>No</td>\n",
+       "      <td>23.71</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>40-44</td>\n",
+       "      <td>White</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Very good</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
+       "0           No  16.60     Yes              No     No             3.0   \n",
+       "1           No  20.34      No              No    Yes             0.0   \n",
+       "2           No  26.58     Yes              No     No            20.0   \n",
+       "3           No  24.21      No              No     No             0.0   \n",
+       "4           No  23.71      No              No     No            28.0   \n",
+       "\n",
+       "   MentalHealth DiffWalking     Sex  AgeCategory   Race Diabetic  \\\n",
+       "0          30.0          No  Female        55-59  White      Yes   \n",
+       "1           0.0          No  Female  80 or older  White       No   \n",
+       "2          30.0          No    Male        65-69  White      Yes   \n",
+       "3           0.0          No  Female        75-79  White       No   \n",
+       "4           0.0         Yes  Female        40-44  White       No   \n",
+       "\n",
+       "  PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease SkinCancer  \n",
+       "0              Yes  Very good        5.0    Yes            No        Yes  \n",
+       "1              Yes  Very good        7.0     No            No         No  \n",
+       "2              Yes       Fair        8.0    Yes            No         No  \n",
+       "3               No       Good        6.0     No            No        Yes  \n",
+       "4              Yes  Very good        8.0     No            No         No  "
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_heart.info()\n",
+    "df_heart.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Объект наблюдения: состояние человека\\\n",
+    "Атрибуты объектов: сердечная недостаточность, ИМТ, курящий человек или нет, выпивающий человек или нет, был ли инсульт у человека и т.д."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mean_menthalhealth = df_heart.groupby('AgeCategory')['SleepTime'].mean().reset_index()\n",
+    "\n",
+    "plt.figure(figsize=(14, 6))\n",
+    "\n",
+    "plt.plot(mean_menthalhealth['AgeCategory'], mean_menthalhealth['SleepTime'], marker='.')\n",
+    "\n",
+    "plt.title(\"Диаграмма 1\")\n",
+    "plt.xlabel(\"Возрастная группа\")\n",
+    "plt.ylabel(\"Время сна\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Между атрибутами присутствует связь. Пример, на диаграмме 1 - связь между возрастной группой и временем сна\\\n",
+    "Примеры бизнес-целей:\\\n",
+    "    1. Прогнозирование инсульта на основе ИМТ.\\\n",
+    "    2. Наблюдение за изменением времени сна в зависимости от возраста.\\\n",
+    "\\\n",
+    "Эффект для бизнеса: влияние количества сна на здоровье, влияние ИМТ на здоровье, влияние возраста на инсульты\\\n",
+    "\\\n",
+    "\\\n",
+    "Цели технического проекта:\\\n",
+    "    1. Первая бизнес-цель: вход - ИМТ, целевой признак - инсульт.\\\n",
+    "    2. Вторая бизнес-цель: вход - возрастная группа, целевой признак - время сна."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверка на выбросы"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Пустые значения по столбцам:\n",
+      "HeartDisease        0\n",
+      "BMI                 0\n",
+      "Smoking             0\n",
+      "AlcoholDrinking     0\n",
+      "Stroke              0\n",
+      "PhysicalHealth      0\n",
+      "MentalHealth        0\n",
+      "DiffWalking         0\n",
+      "Sex                 0\n",
+      "AgeCategory         0\n",
+      "Race                0\n",
+      "Diabetic            0\n",
+      "PhysicalActivity    0\n",
+      "GenHealth           0\n",
+      "SleepTime           0\n",
+      "Asthma              0\n",
+      "KidneyDisease       0\n",
+      "SkinCancer          0\n",
+      "dtype: int64\n",
+      "\n",
+      "Количество дубликатов: 18078\n",
+      "\n",
+      "Статистический обзор данных:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>PhysicalHealth</th>\n",
+       "      <th>MentalHealth</th>\n",
+       "      <th>SleepTime</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>319795.000000</td>\n",
+       "      <td>319795.00000</td>\n",
+       "      <td>319795.000000</td>\n",
+       "      <td>319795.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>28.325399</td>\n",
+       "      <td>3.37171</td>\n",
+       "      <td>3.898366</td>\n",
+       "      <td>7.097075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>6.356100</td>\n",
+       "      <td>7.95085</td>\n",
+       "      <td>7.955235</td>\n",
+       "      <td>1.436007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>12.020000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>24.030000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>27.340000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>31.420000</td>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>94.850000</td>\n",
+       "      <td>30.00000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>24.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 BMI  PhysicalHealth   MentalHealth      SleepTime\n",
+       "count  319795.000000    319795.00000  319795.000000  319795.000000\n",
+       "mean       28.325399         3.37171       3.898366       7.097075\n",
+       "std         6.356100         7.95085       7.955235       1.436007\n",
+       "min        12.020000         0.00000       0.000000       1.000000\n",
+       "25%        24.030000         0.00000       0.000000       6.000000\n",
+       "50%        27.340000         0.00000       0.000000       7.000000\n",
+       "75%        31.420000         2.00000       3.000000       8.000000\n",
+       "max        94.850000        30.00000      30.000000      24.000000"
+      ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "null_values = df_heart.isnull().sum()\n",
+    "print(\"Пустые значения по столбцам:\")\n",
+    "print(null_values)\n",
+    "\n",
+    "duplicates = df_heart.duplicated().sum()\n",
+    "print(f\"\\nКоличество дубликатов: {duplicates}\")\n",
+    "\n",
+    "print(\"\\nСтатистический обзор данных:\")\n",
+    "df_heart.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "На основе полученной информации видно, что пустых данных нет, но есть дубликаты. Удалим их и проверим данные на выбросы:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Коэффициент асимметрии для столбца 'BMI': 1.3324306428979513\n",
+      "\n",
+      "Коэффициент асимметрии для столбца 'PhysicalHealth': 2.6039732622480822\n",
+      "\n",
+      "Коэффициент асимметрии для столбца 'MentalHealth': 2.331111549136165\n",
+      "\n",
+      "Коэффициент асимметрии для столбца 'SleepTime': 0.6790346208011537\n"
+     ]
+    }
+   ],
+   "source": [
+    "cleaned_df = df_heart.drop_duplicates()\n",
+    "\n",
+    "for column in df_heart.select_dtypes(include=[np.number]).columns:\n",
+    "    skewness = df_heart[column].skew()\n",
+    "    print(f\"\\nКоэффициент асимметрии для столбца '{column}': {skewness}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выбросы незначительные. Очистка данных от шумов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6dYq+vr6YD6P//Oc/gXMf5H7zm98wZ84cdu3aFTOZSHR7F4S+HVi3bl3k9qTwLU7JfPSjH+Xzn/88n/70p7n++uvJz88fkubvf/877e3t7Nq1i9WrV0deb2hoSKu9YW+++WbMt2BvvfUWqqpG2rx79258Ph+PP/54zK1Ig2+fmjt3LgB1dXVxJzGD01x99dVx08yZMwcIfZuRKM1gqcZLmMViiSlz3bp1uFwufvSjH/GTn/wk4z612+1DYjx06FDCOL/xjW+Qn58f2aAiniVLlrB9+3ZWrVrF17/+dZYvX859993H3r17E+bJ1Ny5c9E0jaqqqri3cQ6Wzrkxd+5cXn75Za666qq0blF78cUXKSsrY+bMmVm1YdOmTdxxxx0cPnw4svPg+vXrE8afbHyEx1wuH0dQXl7OE088QWlpKb///e/53Oc+x9q1ayPftFVUVAChPyKFb0kMe+ONNyK/j/a73/2O2trarG4nFkLknqxJEkJk7dFHH8Xn83HVVVdllG/Lli2UlpbyyU9+Mu7vw3/91gY9xu373/9+VnFCaMexn/zkJ5Gf/X4/P/nJT5g2bRpLly5NWO++ffuora1NWvZtt93GK6+8wgc/+MG0/krvcrl4z3vewyuvvBLZPWyweLH4/X5+/OMfpyw/2n//93/H/PzDH/4QgH/5l39JWE9XVxcPPfRQTL53vvOd5Ofns3Xr1iG7boXz1tTUUFVVxfe//306OzvjpikpKeHKK6/kJz/5Cc3NzUPiPXPmzJDXUo2XRPx+P4FAIHI7W676NJ7GxkYeeOABNm/eHLPuabDu7m5uuukm1q1bx1e+8hWuvvpqysvLh11/tPe///3o9Xq2bNky5BzSNG3IJCKdc+OGG27g5MmTQ75ZA+jv76evry/yc3t7O0899RTr1q0bVjvKy8t5+9vfztVXX83VV1+dcH1TqvExbdo0Vq9ezf/8z/8MuYVtcP+k6/zzz4/c1vfDH/4QVVVjJseXXHIJJSUlbNu2LeZ2yj/96U+8/vrrXHvttTHlBYNB7rnnHm688cYhu2gKIcaGfJMkhMhYX18fP/zhD/n617+OXq9H07TIbU5hp0+fpre3l507d3LNNdfErBP4y1/+wv/+7/9GNkkYrKCggNWrV/P//X//HwMDA8yYMYO//OUvGX+LEm369Once++9NDY2cv755/PLX/6SQ4cO8eCDD0bWaLz73e9m165dvO997+Paa6+loaGBbdu2UV1dTW9vb8Ky16xZw5kzZzK6jenhhx/mv//7vykuLo77+5UrV+J0Orn55pv51Kc+haIo7NixI+MPdQ0NDaxbt441a9ZQW1vLzp07ufHGG1m0aBEQmvyYTCauu+46NmzYQG9vL9u3b6ekpCRmElNQUMD999/PJz7xCZYtW8aNN96I0+nk5ZdfxuPx8Mgjj6DT6XjggQe47rrrWLx4Mbfeeivl5eUcPnyY1157jT//+c9AaOL2tre9jYsuuojbb7+dOXPmcPr0aWprazlx4gQvv/xyTBtSjZewvr6+mNvtduzYgdfrjWwpnqs+jecf//gHF154IbfeemvSdHfddRf9/f389Kc/HXadicydO5dvfOMbbNy4kcbGRt773veSn59PQ0MDjz32GHfccQf/8R//EUmfzrlx00038atf/YpPfvKTPPXUU1x++eUEg0EOHz7Mr371K/785z9zySWXUFtby5e+9CX6+/uZNm1azHUh/O3Uzp07ed/73pezWwzTGR//9V//xdve9jZqamq44447qKqqorGxkT/+8Y9Jvx1MR1lZGffddx+f+MQnWL9+PWvXrsVoNHLvvfdy6623csUVV/CRj3wksgV4ZWUl//7v/x5TxokTJyK3uQohxonR31BPCDHRhbfATvffU089pWnauS17Fy9eHLP1bri86C17T5w4ob3vfe/TCgsLNYfDoX3wgx/UTp06pQHapk2bIunS3QJ8wYIF2osvvqitWLFCs1gsWkVFhfajH/0oJp2qqtq3vvUtraKiQjObzdqSJUu0P/zhD0O26A3HG73Fd7z+ibcFeKI44/1+79692vLlyzWr1apNnz5d+8IXvqD9+c9/junTRMLl1dfXa9dff72Wn5+vOZ1O7e677x6yhffjjz+uXXzxxZrFYtEqKyu1e++9V/uf//mfuNsPP/7449rKlSs1q9WqFRQUaJdeeqn285//PCbNs88+q11zzTVafn6+ZrfbtYsvvlj74Q9/GJPmyJEj2sc+9jGtrKxMMxqN2owZM7R3v/vdMVtGZzJebr755pgxl5eXp9XU1Gg7duyIqTfdPs10C3BAe+yxx2LSDh43P//5zzVFUbQ9e/YMSZfLLcDDfvvb32pve9vbNLvdrtntdm3+/PnaXXfdpb3xxhtD2pnq3NC00LbW9957r7ZgwQLNbDZrTqdTW7p0qbZlyxatq6sr0pZ0rgnZbGudaAvwdMaHpoW21g5fUywWi3bBBRdoX/3qV9OqK1r0FuDR3vGOd2izZ8/Wenp6Iq/98pe/1JYsWaKZzWbN5XJpH/3oR7UTJ07E5Av32ac//emY1wdvcS6EGF2KpuXgT2hCiCmlsbGRqqoqnnrqqSFPns8m3Ui78soraWtry+mahPFu8+bNbNmyhTNnziT8tkqIXJ8b4e3vk206oCgKDQ0NWW3qIIQQo0XWJAkhhBBCCCFEFFmTJITIWF5eHh/96EcTPq8m03RCiMlh5cqVKdN89KMfzWobciGEGE0ySRJCZKy4uHjIRg3DSSeEmBzuuOOOlGnkmiCEmAhkTZIQQgghhBBCRJE1SUIIIYQQQggRRSZJQgghhBBCCBFl0q9JUlWVU6dOkZ+fj6IoYx2OEEIIIYQQYoxomkZPTw/Tp09Hp0v8fdGknySdOnWKWbNmjXUYQgghhBBCiHHi+PHjzJw5M+HvJ/0kKT8/Hwh1REFBwRhHI4QQQgghhBgr3d3dzJo1KzJHSGTST5LCt9gVFBTIJEkIIYQQQgiRchmObNwghBBCCCGEEFFkkiSEEEIIIYQQUWSSJIQQQgghhBBRZJIkhBBCCCGEEFFkkiSEEEIIIYQQUWSSJIQQQgghhBBRZJIkhBBCCCGEEFFkkiSEEEIIIYQQUWSSJIQQQgghhBBRZJIkhBBCCCGEEFFkkiSEEEIIIYQQUWSSJIQQQgghhBBRZJIkhBBCCCGEEFEMYx3AVBFUNfY3uGnt8VKSb+GiGQ6+/ad6Xj7RRYHZwNvOK6bXF0RRYMWcYpZVuXih0c1zR9o42dHP9EILy2a7eOKVU+yua2YgqGE1KBRYTZzq9o1184QQYtgUQMsgvdOq0NWvoQ563aSDAhO0eYfmub5mBu4+L0da++joH0ANBukdOFe/SYEgEBgUyIq5NhTNQmf/AB19Pk53+yP16s8GbtTDQDCUP8ygQKHNSFCDkjwDer0eUBgIqBj0Cu19PtRAkP6ARlDTUFVQtVD9CuCyGbl6YTF/P+zG4wswEFTRKxpWk5F502y48i00nemjzePHqNOxaLaDi2c4KbKbePlEJ/5gkDdbevEHVUDhbfOKuHzuNHR6hZYuLy8ebefpt87Q3e+nz6+hne0Hsx4CKBgUqCyysWCmgy5PgDyzgfcumYFBp6O110dbj4/2Pi/NnT40TaO9z4/ZoKPT48di1GExGuj3B+j1B1k0s5AvX1uN1aQHhr4vLq1wsu9oO789eII+X4DSAjNLZruYXmjl0ioXep0SydPS1U9rj4/6U92c7PRQ7rCQbzGiahoHmzrQgMoiO/d/aAkmg44dtY00tnsAjcUzCykrtIIGbX2+SN0Hmjpo6fbi7vVRaDXS2T+AK89MSb4ZNaixr7EdVYVu7wAoUFVk56YVleh1Siims3lddhNljnMxh9v6/NF29r7VxskODwoKM1xWVs4tpma2k0f3NdHk9lDhsnHTikpMBt2QPirOM8fEPKT8I+08d6SNU539lDmsuOxGivMtlOQnzpdL4TbWHmkHNFbMKWb53KKY4xY+1rmMIaaP7GZUTWNfg3tIDPHSRx/7TGIbrfaM5PHKlYkWbyYUTdMyeU/KqQceeIAHHniAxsZGABYsWMDXvvY1/uVf/gUAr9fL5z73OX7xi1/g8/l417vexY9//GNKS0vTrqO7uxuHw0FXVxcFBQUj0YyU9tQ1s2V3Pc1dcd6xE8j0w4IQQggxEVxTXcIHamZm9L5Y7rCwblE5j7/cnNF7abp0SmhymilFAatRj8cfHPK7coeFTddVA/ClXa/S6RlIO5bbV1WxZLYzaR9lW34435qF5WmlT8eeuua4MRTajHzokplDjluuYkjn81Whzci3338RaxaWx00/+Ninii1eGSPZnpE4Xrky0eINS3duMKaTpN27d6PX6znvvPPQNI1HHnmE++67j5deeokFCxZw55138sc//pGHH34Yh8PB3XffjU6nY+/evWnXMdaTpD11zdy586BMeIQQQgiRM9n+MTX8N/4H1tfk5IPsnrpmPrnz4KjHkOnnqw2rq3jw6YaU6ZPFlqjOkWxPro9Xrky0eKNNiElSPC6Xi/vuu4/rr7+eadOm8eijj3L99dcDcPjwYS688EJqa2tZvnx5WuWN5SQpqGq87d6/jchfvYQQQgghsqEAZQ4Lz37xHcO6NSqoalz+7b/R0p3555zhxJDN56tMJpXxYktV50i2J1fHK1cmWryDpTs3GDcbNwSDQX7xi1/Q19fHihUrOHDgAAMDA1x99dWRNPPnz2f27NnU1tYmLMfn89Hd3R3zb6zsb3DLBEkIIYQQ44oGNHd52d/gHlY54bVYox1DNp+vMvlGIF5sqeocyfbk6njlykSLN1tjPkl69dVXycvLw2w288lPfpLHHnuM6upqWlpaMJlMFBYWxqQvLS2lpaUlYXlbt27F4XBE/s2aNWuEW5BYa49MkIQQQggxPg33c0ouPudkU8Zofb6KrifdOkeyPePlc+VEizdbYz5JuuCCCzh06BD79u3jzjvv5Oabb6a+vj7r8jZu3EhXV1fk3/Hjx3MYbWZK8i1jVrcQQgghRDLD/ZySi8852ZQxWp+voutJt86RbM94+Vw50eLN1phPkkwmE/PmzWPp0qVs3bqVRYsW8YMf/ICysjL8fj+dnZ0x6U+fPk1ZWVnC8sxmMwUFBTH/xsqlVS7KHRbG392YQgghhJiqFEK7kF1a5RpWOZdWuSgryO6D8HBiyObzlQJpp48XW6o6R7I9uTpeuTLR4s3WmE+SBlNVFZ/Px9KlSzEajTz55JOR373xxhscO3aMFStWjGGE6dPrlMj2nDJREkIIIc6R98XUkvVRtv0XzrfpuuphL6rX6xQ2r6se9Riy+Xx1x+qqtNInii1ZnSPZnlwer1yZaPFma0wnSRs3buTpp5+msbGRV199lY0bN/L3v/+dj370ozgcDj7+8Y/z2c9+lqeeeooDBw5w6623smLFirR3thsP1iws54H1NZQ5MvtLy8QeVkIIIUR811SXsC3D98Vyh4UNq6soz/C9NF3ZfpZTFLCdfTjuYOUOC9vW17BtfQ2FNmNGsWxYXZWyj8qyLL/MYcnp9sxrFpYnjKHQZox73HIRQ7qfrwptRratr2Hj2uq46Qcf+2SxJapzJNuT6+OVKxMt3myM6RbgH//4x3nyySdpbm7G4XBw8cUX88UvfpFrrrkGOPcw2Z///OcxD5NNdrvdYGP9nKSwwU8kvmiGg2//qZ6XT3RRYDbwtvOK6fUFURRYMaeYZVUuXmh089yRNk529DO90MKy2S6eeOUUu+uaGQhqWA0KBVYTp7p9Y9YuIYTIlUyf++K0KnT1a6iDXjfpoMAEbXHWDF9fMwN3n5cjrX109A+gBoP0Dpyr36RAEAgMCmTFXBuKZqGzf4COPh+nu/2RevVnAzfqYSAYyh9mUEIf0oIalOQZ0Ov1gMJAQMWgV2jv86EGgvQHNIKahqqGHmwZ0ELxuGxGrl5YzN8Pu/H4AgwEVfSKhtVkZN40G658C01n+mjz+DHqdCya7eDiGU6K7CZePtGJPxjkzZZe/EEVUHjbvCIunzsNnV6hpcvLi0fbefqtM3T3++nza2hn+8GshwAKBgUqi2wsmOmgyxMgz2zgvUtmYNDpaO310dbjo73PS3OnD03TaO/zYzbo6PT4sRh1WIwG+v0Bev1BFs0s5MvXVmM9O6kY/L64tMLJvqPt/PbgCfp8AUoLzCyZ7WJ6oZVLq1zodUokT0tXP609PupPdXOy00O5w0K+xYiqaRxs6kADKovs3P+hJZgMOnbUNtLY7gE0Fs8spKzQChq09fkidR9o6qCl24u710eh1Uhn/wCuPDMl+WbUoMa+xnZUFbq9A6BAVZGdm1ZUotcpkV3e3L0+XHYTZY5zMYfb+vzRdva+1cbJDg8KCjNcVlbOLaZmtpNH9zXR5PZQ4bJx04pKTAbdkD4qzjPHxDyk/CPtPHekjVOd/ZQ5rLjsRorzLZTkJ86XS+E21h5pBzRWzClm+dyimOMWPta5jCGmj+xmVE1jX4N7SAzx0kcf+0xiG632jOTxypWJFi9M4Ock5dp4mSQJIYQQQgghxtaEe06SEEIIIYQQQowHMkkSQgghhBBCiCiGsQ5AjB+Re72j7qsuKbAMuZcZiLn/FwXaeofe6+wPqOyobaTJ7WFGoQUFhSa3h9PdXjRNwzugsnBGAS67iW5vAE0Dp81Ekd1EW4+P15q78PiDlOSbybMYON3lI6ipuPsGsJn0XFpVxPrlFRw63jnknvZdB0/Q4wugqiqdntCCA1XT6OwfwKjTcUmli2WVLkodofaF7q330tbrY+9b7QDML8/jrdO9NLZ78AZUXFYD1TMcfPeDS6g71cUz/zzDU683c9QdWvhQ4bTyuXecz5//2UpjWy9N7R4gdD//VReWcfrsQ9Xmltj50ZNv4fEHcdlNPPqJFTzwj7d46o1WNODCUjv5VhPNXV5mOKwsmO7AlWfi4LEO6k92cqLTi6pBkd3EuiXTMev0nOjqZ6bDiqZAQ1svB5o66PMO4O4PYNErVBXbueqCUl4+1UmfP4iqarj7/OSZDRTaTOgUsJsNXFhWQI8/AECh1Uhxnjn0nIOzxzh8vFu7vbj7/ORbjPy1voUe3wDuXj9Om5GACueX5aNXwG4ycLontGZueqGFy+dOY9GsQr79p9c5dLwDUKgostLR58cX0JjlsrFu0Qxeb+ni94dOMhDUWDmniK+8ewH9/iAfevA5TnV5sZv0fOyyCmYU2SkriB13/f4g33qinoa2PiwGHfNK8jHodayYW8TyOUVx75UOj9WG9j40TSPfbOR0t5fSAgs9vgEURaHCZWd+WT5ujz/ueoBE97gnW0cwOK/LZuL15m5ebOrAZtLznoum82Zbb+TnDyyZycrzioe0YaTuCU9U7nDqy3WsycobTl3R16/B60Si637+SDu1R9sAJekYE7Em4joGIcTUImuSBAB76prZsrue5q7kT0cO714TnngMVu6wsOm6al461sH2ZxpQR3l0ZbrwW0wO4XH324Mn+Gt9a8J0hTYj337/RTG77mx9oj6rsRquExhy7ugUEpYXzrdmYXna5100m0nP925YFGlDvDKi68hWonLXLSrn8Zebs6ov17EmKw+GHpd064o3JnQK3L6qio1rqyN1f2nXq0OuhfHGmIg1UmNWCCHSIRs3nCWTpNT21DVz586DOZlcyCRFTATbzm5PuvWJen7ydENWZWQ71sN/K79jdRUPPt2Q9fmybX0NQNxzN1xHttuwZnpNSKe+RGVmG2uy8hLFnU5dqcbEhtVVLJnt5JM7DyaNb9sk2QI313I9DoQQIlOycYNIS1DV2LK7PmcTG5kgiYlgy+56+v1Btj+T3QQJsh/r2tl/25/JfoIE8LXfvcrmx+Ofu+HXtuyuJ5jhV2TZXBNS1ZeszGxiTae8bOL0B9SUY2L7Mw187Xd1KWPMpu8nu1yPAyGEGEkySZri9je4M7rVR4jJoLnLy7eeqB/120GjDbfu1t4BWroTn7saoXbub3BnVG6214Rk9aUqM9NYh3PdSlbXjtrGlMdF1aC115+ynmz6frLL9TgQQoiRJJOkKa61RyZIYmpqPLuxxmSX6Tk+3GtCvPzplpnrdJmW0eTO7ZiQ62us0Ty+QggxXDJJmuJK8i1jHYIQY6KyyDbWIYyKTM/x4V4T4uVPt8xcp8u0jApXbseEXF9jjebxFUKI4ZJJ0hR3aZWLcocF2XhVTCXlDgv3rK1mLHcc1ikM67wryTNSVpD43FUItTO8bX+6sr0mJKsvVZmZxjqc61ayum5aUZlyTOgUKMkzpawnm76f7HI9DoQQYiTJJGmK0+uUyHa5uSCTLTERbLquGqtJz+2rqrIuQ0nw/+nkUyBSd7bnzNffexGb11XHLSP886brqjN+9kz0NSHdnKnqS1ZmNrGmU142dZkMupRj4vZVVXz9vQtTxphN3092uR4HQggxkmSSJFizsJwH1tdQ7kh9i0OhzRh5VlI8ZQ4L29bXsGF11Zj8lV7eWqem8rPj7prqkqTpnDZjzNbMG9dWZz1Ww2N92/oaygadO8nKK3NYeGB9DRvXVvNAnLyp2Ez6SBvC5+7gMsJ1ZLuVcqJyyx0WNqyuGnKtSKe+XMearLxExyWduhKNCZ0S2v5749rQs3y2ra+Jey0cPMZErJEas0IIkWvynCQREX4Ceku3F3evD5fdREmBBTRo6/NFnooORJ6UXmw3gwJtvb4hT02PfmL9jEILCgpNbg+nu71omoZ3QGXhjAJcdhPd3gCaBk6biSK7ibYeH681d+HxBynJN5NnMXC6y0dQU3H3DWAz6bm0qoj1yys4dLwz8tT2pRVO9h1tZ9fBE/T4AqiqGnnYo6ppdPYPYNTpuKTSxbJKF6WOUPtae3209Xhp6/Wx9612AOaX5/HW6V4a2z14Ayouq4HqGQ6++8El1J3q4pl/nuGp15s56g4tMq5wWvncO87nz/9spbGtl6azGwNUFtm46sIyTp9djDy3xM6PnnwLjz+Iy27i0U+s4IF/vMVTb7SiAReW2sm3mmju8jLDYWXBdAeuPBMHj3VQf7KTE51eVA2K7CbWLZmOWafnRFc/Mx1WNAUa2no50NRBn3cAd38Ai16hqtjOVReU8vKpTvr8QVRVw93nJ89soNBmQqeA3WzgwrICevwBAAqtRorzzKH1AWePcfh4t3Z7cff5ybcY+Wt9Cz2+Ady9fpw2IwEVzi/LR6+A3WTgdI8PgOmFFi6fO41Fswr59p9e59DxDkChoshKR58fX0BjlsvGukUzeL2li98fOslAUGPlnCK+8u4F9PuDfOjB5zjV5cVu0vOxyyqYUWSnrCB23PX7g3zriXoa2vqwGHTMK8nHoNexYm4Ry+cUxf0rdXisNrT3oWka+WYjp7u9lBZY6PENoCgKFS4788vycXv8Q8Z6+NyJHocHmjpC50ieecg5FB1DdF6XzcTrzd282NSBzaTnPRdN58223sjPH1gyk5XnFQ9pw+D6B9eRrUTlDqe+XMearLzh1BV9/apw2bhpRSUmQ+zfFYOqxvNH2qk92gYoSceYiDVSY1YIIVKRh8meJZMkIYQQQgghBMjDZIUQQgghhBAiKzJJEkIIIYQQQogohrEOQOTu3ux07s2PXm9U5rAOWWMUzhdUtZj78W+87Nzan+I8M6qqsfdIG6+e6IpZH/Rio5tfv3iMg8c7Mep0rJwbWk9iMujY3+Cmsa2H/9nbyJkeHwqh9TozCm2c7PLQ6wtSYDHywUtm0dnn56nDLfzzjAezQUe5w8L88nxmOW280ODmlZNdDARVjHqFfLOBCpeF1p4Ap7q92Ix6rEYFXxB8/gAaoCgKTpuJi2cWUHukndPdfoJn+81mVLhiXjF6g54jZ/o40dGHf0DFZTNwXpmD1091cMajAqG/KjjNYDSbMRCkxxuk2x+6Y9WogzyzHh3Q0R+MlG8A3rmwhIPHugCNGQ4DB46fe2jlpbMLaOzw0tHnR9NA1UA9+zun1cCC6Xk0tPfT5w3gD6r4AhqKFno6vUrovxZ9qI39gVAsClBg1tHjU1HPxu2yKZgMJgKaxkBAJd+soyjPwoAKBkXjZGc/vb4gQVVDrwNfILRYvTjPyHkloRj0iobNbMQ7oGIxKPT0+znTF8AX1FAAs0HBZtRjN+vRUJhRaKXHO4DHHyDPYqSyyH5262sdig5KCkz09Qc53eOlpduHbyCIosD80nzmleXxl9da6eof4LwSOxfPdPDEqy10ewPMKbaz+vxplBZYIuM4qGo88lwj+xvaae7yUpJnpKM/tFZqQAWDHnSKgt1koKrYhk6nQ1U1Gtr6aOv14bAaeffF5TR3+ag71YXdpKPfr3Kqy4vZqOP6pTM40+3nWEc/s51WpjutvHy8C6tRh81koLXXy+kuH2UFZpZVFVFdXjBk/VK8NSyLZhZy757XaWz3UFlk45611ZHzJfp829fgBjRWzClm+dyiuOuaWrr6cff5ceWZKck3owY19jW2AwrLKpy83tzN/x0+DcA11WXcenkVep0y5PyPd01I51qSzlqg8Fqfo219tHZ7KSkwM6c4L+6an3Subcn4AyqPPNfAC40d2E163l8zk5Xzhq7pildPsvWW49F4WuOTKpbxFKsQQiQia5LG2J66Zrbsrqe569wTxssdFjZdV53RLj/JygGG/C4svDtTeHMDCO2e1T8QJJcjw2TQ4Q+oqRMKkQWbSU+/P8h4vZiVOyysW1TOL188EXOuJWI26PAlOV8KbUa+/f6LWLOwPO65nwm7SU+fPxj5Od41IZ1rSbI04d+9dKyD7c80oMY5UDoltL32xrWxjyTI9hq59Yl6Hny6YciYsJv0fPeGRUPypurHbK7LoyVX7yOjEct4ilUIMTXJxg1njedJ0p66Zu7ceXDIm3j472npboearJxJfXCFmOI2rK6KOxHIteFeSzLJH95mG7K/Rm59op6fPN2QtJ7obboT1ZNJnWMlV+8joxHLHQnG63jtWyHE5CQbN4xzQVVjy+76uG/K4de27K4nGO9PrhmWI4SYnLY/M/ITJBj+tSST/NufacAfULO+RvoDKg+mmCBF501WT7p1jpVcvY+MRiwaicfreOxbIYSQSdIY2d/gTnp7jAY0d3nZ3+AeVjlCiMlrMn6eVDXYUduY9TVyR21jWpOycN5MrqHpXpdHS67eR0YjFkg+Xsdb3wohhGzcMEZae9J7U06VLt1yhBBiomhyeyjON6eVdvA1sMntSZAydd6RzpdruXofyYVc1TFe+lYIIWSSNEZK8i05SZduOUIIMVFUuGxZXyMrXLa068n2+jlerru5eh/JhVzVMV76Vggh5Ha7MXJplYtyh4VEm54qhHb8CW/Hm205QojJazLumqxT4KYVlVlfI29aUZnW9TCcN5NraLrX5dGSq/eR0YgFOLv9f3zjrW+FEEImSWNEr1Mi2+UOftMI/7zpuuqUz45IpxwhxOR0+6qqUTnPM61jONei21dVYTLosr5Gmgw67lhdlbKecN5k9aRb51jJ1fvIaMSiEDq2iX4P46tvhRBCJkljaM3Cch5YX0OZI/b2gjKHJaOtUJOVs219DdvW11DuiH8LQ6HNGHkuSpjdpEfJ8ftUoodECpELNpN+XP9RoNxhYcPqqiHnWiLmFOdLoc3ItvU1bFxbzQNJzu902E36IWUPjjOda0l5VJpE16INq6sSfvulU2K3/4bsr5Eb11azYXX8CaTdrI/Z/jtZPZnUOVZy9T4yGrGEx+t4iFUIIVKR5ySNA7l6+niycsK/a+n24u714bKbKHNYI7c2DM4XVDV21DbS5PZQ4bJx42UVHDreGXoSfZ4ZVdXYe6SNV090YTPpubSqiPXLK3ix0c2vXzzGweOdGHU6Vs4t4ivvXoDJoGN/g5vGth7+Z28jZ3p8KEBlkY0ZhTZOdnno9QUpsBj54CWz6Ozz89ThFv55xoPZoKPcYWF+eT6znDZeaHDzyskuBoIqRr1CvtlAhctCa0+AU91ebEY9VqOCLwg+fwANUBQFp83ExTMLqD3SzuluP+HHZ9qMClfMK0Zv0HPkTB8nOvrwD6i4bAbOK3Pw+qkOznhCD/bUAU4zGM1mDATp8Qbp9odOIaMO8sx6dEBHfzBSvgF458ISDh7rAjRmOAwcOH5ucfmlswto7PDS0edH00I7QIUfI+q0GlgwPY+G9n76vAH8QRVfQEPRQrtBqYT+a9GH2tgfCMWiAAVmHT0+FfVs3C6bgslgIqBpDARU8s06ivIsDKhgUDROdvbT6wsSVDX0OvAFQh9ci/OMnFcSikGvaNjMRrwDKhaDQk+/nzN9AXxBDQUwGxRsRj12sx4NhRmFVnq8A3j8AfIsRiqL7GdvudGh6KCkwERff5DTPV5aun34BoIoCswvzWdeWR5/ea2Vrv4Bziuxc/FMB0+82kK3N8CcYjurz59GaYElMo6DqsYjzzWyv6Gd5i4vJXlGOvoHcPf6Q23Ug05RsJsMVBXb0Ol0qKpGQ1sfbb0+HFYj7764nOYuH3WnurCbdPT7VU51eTEbdVy/dAZnuv0c6+hnttPKdKeVl493YTXqsJkMtPZ6Od3lo6zAzLKqIqrLC3B7/DHnYlDVeP5IO7VH2wCFFXOLWDSzkHv3vE5ju4fKIhv3rK2OnC/R59u+BjegsWJOMcvnFsVcIyLnd1c/7j4/rjwzJflm1KDGvsZ2QGFZhZPXm7v5v8OnAbimuoxbL69Cr1OGnP/xrgnpXEsGp4mX3x9Q2VHbyNG2Plq7vZQUmJlTnMdNKyoT/jEl22ukP6DyyHMNvNDYgd2k5/01M1k5rzhh3uh6iu1mUKCt1zes6/JoydX7yGjEMp5iFUJMPfIw2bMmwiRJCCGEEEIIMfLkYbJCCCGEEEIIkQWZJAkhhBBCCCFEFHlO0hQR7x5wiF13sLTCyYGmjpT3iWdz3/7g+uPVFY4nZl1FnpmAqvK7l07S5w+yrNLJzSurCKoa33qiPrKO44trLuTVk120dHs53dnPM2+eodsXYNFMB1++dgFWkz427jwzgYDKY4dOcqKjn/J8M55AkP6BIDaTnnnT8gF4s7Wb5k4vngGVudPyqJlZyMHjbl5v7qE/oFJWYMZqMjC/LJ+500LrKgav8Rjc1vDPLd1eWrv7ee1UDyfdHnzBIKX5ZsocVvLNRlp6vEwvtHL5vGKWVbp4ocEds5Yl+jVVA6fNhMtuwt3np63Hy94jbfR6Q2uBVs4tYlq+heJ8M2UFsTHEW1fiD6g8vLeBPa+1cLrLi6aplBXaeOeFpSyY7sDd7497vENrQBp5obEdm8nAB2pmckmli0f3NUXWt0WvPQmvT4n+nV6nxKzbWVbh5I2WHl485sZmMvC+xTNQVY2f7m2g2zvAopmFfPnaaqxnNyDIdHwOHhdo0NbnG5I3+jgWmo389fBpmtzn1hFZozZAiNcPK+cVA/DcW23sOngiMp7XL6+MrPfL9fqMXK79GOm1k+msdUpVZqZ5M2lTvHVfZQWynkYIISYrWZM0Beypa2bL7nqau849yTy8e1WnZyDymk4JbRoQVu6wsOm66pgdh+KVFS3dPIPrihdPLl08s4AzPf6EceeKooDVqMfjD0ZeG9zWwT9nXRehTRuykSiGcoeFhTMK+L/61rTKjj7eW5+o58FnGkh1RdEp57YC3v5MQ0wcigJGvQ5/QE2QO7Frqkv4QM3MjMZnqvE8OO5kx+2a6hK2f2xZwn4wGXQogC9F2+KdQ9mI17Zsy85VWYnKWbeonMdfbo57HFLVk+wYJsubSZuyrUMIIcT4Ixs3nDXVJ0l76pq5c+fBrD5Mh/82Gt6aNZ2ysskjJq7w8b66uoS/1reOaSzpiB6fQM7HZkWRlab2/mGVMfgcykai8y6bsnNV1nCvRfHqSfeaNDhvJm3Ktg4hhBDjk2zcIAiqGlt212f9ITCcb8vuevwBNa2ysskjJi7t7L+JMEGCc+Nz8+Ovsfnx3I/N4U6QIPYcCmbxlWOy8z7TsnNVVi6uRYPrSbfMwXkzaVMmcWd7vIQQQoxPMkmaxPY3uId9e5kGNHd52VHbmHZZ2eQRYrRoQEu3j5bu8Ts2w+fQ/gZ3xnlTnfeZlJ2rsnJxLRpcTyZlRufNpE3p1jGc4yWEEGJ8ko0bJrHWntx9CGxye1InykEeIcQ52ZzD6eZJJ12uysrVtSi6nEzLDKfPZf/kIo8QQojxSSZJk1hJviVnZVW4bKOSRwhxTjbncLp50kmXq7JydS2KLifTMsPpc9k/ucgjhBBifJLb7SaxS6tclDssDGdzWoXQ7k03rahMu6xs8ggxWhSgrCC0ffN4HZvhcyi8NX4mUp33mZSdq7JycS0aXE8mZUbnzaRN4bSpDOd4CSGEGJ9kkjSJ6XUKm66rBsjqw0k4z6brqjEZdGmVlU0eMXEpZ/9dU10y1qFEpDM+N69bwOZ1uR+bFUXWYZcRfQ5l8/ydZOd9pmXnqqxcXIsG1xNdZiZ5M2lTOG06MWd7vIQQQoxPMkma5NYsLOeB9TWUDfpraKHNGHk2Udjg9/cyhyVmW9tEZWWTZ3Bdzjjx5NLFMwvS+ovwcCkK2KIeKgpD25qrz1HDKSZRDOUOC9dUl6Rddvh4b//YMjasrkJJI6NOgQ2rq9iwumpIHIpC5EGzmbqmuoRtGYzPdMbz4LhT1f+Pz78jYT+YDTrMabRt8DmUjURty6bsXJWVqJxyh4UNq6sSnp/lSeoJl5lp3kzalG0dQgghJjZ5TtIUEe/J8kDMa0srnBxo6kj59PnosortZlCgrdeXdp5EdYXjiX6ifUmemYCq8ruXTtLnD7Ks0snNK6sIqhrfeqKexnYPlUU2vrjmQl492UVLt5fTnf088+YZun0BFs108OVrF2A16WPjzjMTCKg8dugkJzr6Kc834wkE6R8IYjPpmTctH4A3W7tp7vTiGVCZOy2PmpmFHDzu5vXmHvoDKmUFZqwmA/PL8pk7LY+bVlSi1ylJ2xr+uaXbS2t3P6+d6uGk24MvGKQ030yZw0q+2UhLj5fphVYun1fMskoXLzS4qT3aBiismFsU85qqgdNmwmU34e7z09bjZe+RNnq9AfIsRlbOLWJavoXi/NBtZtExuHt9uOwmyhzWyPHzB1Qe3tvAntdaON3lRdNUygptvPPCUhZMd+Du98c93v6AyiPPNfJCYzs2k4EP1MzkkkoXj+5rosntocJl46YVlZGJkD+gsqO2MeZ3ep3C80faI21dVuHkjZYeXjzmxmYy8L7FM1BVjZ/ubaDbO8CimYV8+dpqrGcnp5mOz8HjAg3a+nxD8kYfx0Kzkb8ePk2TOzT+7ll7rv5E/bByXjEAz73Vxq6DJyLjef3ySg4d70x53mUj3nmfbdm5KitROeHXE43JdMrMNG8mbYrUEXV9KivI7fESQggx8uRhsmfJJEkIIYQQQggB8jBZIYQQQgghhMiKTJKEEEIIIYQQIoo8J0kMEVQ1nj/aTu2RdkBjWYWLf7b2crwjdk1JOvfzp7MWKpN1A8V2M6qmsa/BjaZpFNqMFOdbIutsXmh0R+JeMaeY5XOLIvVFr1coKbCABi1d/Rw60QkoVBbFrpcJ6/UG+PdfvsSxjn5mO61854OLqW/uTrrmJdkaiej2R7cnHPPCGQ7+49eHIvXd/6El5FkMMX3R0tVPW6+fzn4/CkTaGt2PydZQDI5hcBuCqsaO2kYa2z1omkaexYBOgUKria7+ARQlts5EYyHemqNEGzOks24tnfVuqeqOt2aptceXdC1LuKzGdg+gsXhmIdOdtnGxHiWbdTW5Wv+UzfrEsZTr9k/0OIQQQiQma5JEjD11zXxp16t0egYSptEpcNWFJdSd7Ka569wT5ssdFjZdVx3Z5WlPXTNbdtfHpAnvYBdd/uB8g+MZXEYiigKDR7PNpMdk0CVtz+C23b6qio1rQ1sEr/vRM7xyojutvBBqy7pF5Tz+cnPcmOO1Px0XzyzgX6+cl7QvCm1Gvv3+i1izsDxpv6WKwWbS0z8QHNKXier80CUzh7S33GFh4YwCnny9FTWqnMH9GxYvXp1CTN544yRevmR1xxu38UTXtfWJerY/0xBTVrKYRlOi9seLKZO02dYdbaz7ZrBct3+ixyGEEFOVbNxwlkyS0renrplP7jyYdf7w30EfWF8DwJ07D5LO4IrON/gDcLpl5NqG1VXUHm3PaII0XmxYXcWDTzeMSb+la8PqcxOldI/z4HEy0uNDAa6uLuGv9a0p043FFtCJ2h/vfMok7XDqzkXZIyHX7Z/ocQghxFQmGzeIjARVjc2P1w+rjPAb/+bHX2Pz4/Vpf3gNp9uyu57g2T/XB1WNLbvTLyPXtj/TMCEnSMC4nyBBqH/9ATWj4xw9TvwBdcTHhwYpJ0jhdNFjdzQk67fB51MmaYdb93DLHgm5bv9Ej0MIIUR6ZJIkgHNrdoZLA1q6fRmXpQHNXV72N7gj8aRzi91ImcifUyZC6KoGO2obMz7O4XGyo7ZxTMfHYNFjdzSk6rfo8ymTtLmoezhlj4Rct3+ixyGEECI9snGDAKC1Z3x84AzHMV7iESOnye2hON+cdd7xZjTHbLp1ZRLTSJQ5nDy5MpLtmohxCCGESI9MkgQAJfmWsQ4BOBfHeIlHjJwKly3r41zhsuU4muEbzTGbbl2ZxDQSZQ4nT66MZLsmYhxCCCHSI7fbCQAurXJRVjD8N2cFKCsIbTWdyYa2CqEdnsLbU19a5aLckVkZuTSRd+OdCKHrFLhpRWXGxzk8Tm5aUTmm42Ow6LE7GlL1W/T5lEnaXNQ9nLJHQq7bP9HjEEIIkR6ZJAkA9DqFzeuqUydMIvzmv3ndgkhZ6X6QAth0XXXkWSF6ncKm69IvI9duX1XFxTMn5m6Id6yuGjeTh0RuX1WFyaDL6DhHjxOTQTfi40MBrqkuSStd9NgdDcn6bfD5lEna4dY93LJHQq7bP9HjEEIIkR6ZJImINQvL2ba+JvIcnUR0SujDY7kj9punMoclsoXtmoXlPLC+hrJBaZw245Dyo/MNjideGYkocT5b2Ez6lO2JplPObU/9+N2rMp4olTssbFhdNaRvwuK1Px0Xzyxg2/qahOVC6JlF29bXsHFtNQ8kSZsqBptJH7cvE9UZr73lDgvXVJcM+UYuun/DEh3nwXkHj5NE+ZLVHW/cxlN+tq7tH1vGhtVVCb9ZLE8wdkdDovbHO58ySTucunNR9kjIdfsnehxCCCFSk+ckiSGCqsbzR9upPdIOaCyrcPHP1l6Od3iocNm4aUUlJoMurafGx0sDZPS0+egyiu1mVE1jX4MbTdMotBkpzrdQVmBhaYWTFxrdkbhXzClm+dyiSH0t3V7cvT5cdhMlBRbQoKWrn0MnOgGFyqJzbYvW6w3w7798iWMd/cx2WvnOBxdT39wdiQcF2np9MW0JxxxdZ5nDOqT90e0Jx7xwhoP/+PWhSH33f2gJeRZDTF+0dPXT1uuns9+PApG2RvdjdFp3nx9XXug2yHgxDG5DUNXYUdtIY7sHTdPIsxjQKVBoNdHVP4CixNaZaCz4Ayo7ahtpcseOnVTHuSQ/dDwPNHWkHCeZ1j14PKFAa49vyHGKritcVmO7B9BYPLOQ6U5byrE7GtI5D7NJm2ndic6F8STX7Z/ocQghxFQkD5M9SyZJQgghhBBCCJCHyQohhBBCCCFEVmSSJIQQQgghhBBR5DlJE1hQ1Xj+SDu1R9sAhRVzi6iZ7WTn84280NiB3aTnvUtmoAY1frq3gVNd/ZQXWFh93jSK8828fKITDZjltKJp8GJTB80d/XT2+wkENTRNwx9UUYACq5GqkjwMOh1FeSbaeny09/g40dmP1axnwB/gTG+AIKGdmgwK5Fn0mHTgDWgENFAUBUXT0CsaeoOBAVWl2GpgQAN3rx9fUENTwWAANQgBLfQU+ngsBjAb9PgHgviCoXQT9b5RM+Ab6yByxKyDoAqBsz8bdRBQQ8fGooMg5342KYACRj30DSQuU68D5exYCGqhi5bdpKAqOvp8QdRQMegUKM3TcbpHJXg2r0E5Oza00O8VJfT/GmA1KmiaRv8AkTLMelA10CkK0wvN1Mx28UZrL/lmPbOK7Jzp8XKktY8uj58eXxCDAkX5Zi6rKqTuVC/9/iAGvcIF0+wcOtmNxx/EaTMwpyiPNk+okSvmunBZTTx7pJ1u7wDzS/No6fbyQmMngaBKnllHRZEdu8XENLuJ6S4rBRYjh5t76B8IcFG5gyffOM2RM32Y9DrecWEJhVYjtUdD6/TMRj3nlebR3utHVTVauvvp8gxgNOhw2ozML3cwpziP+WX5uD3+yPqv8Hq+YFCl2zeATtHFXacXvZ6m0GLkr6+30NDuwWJQsJmMeAeClOSbKbAaQYNOr5+2ngHsZj3V5QUUWA38+sUTdPX78Q6ozHRZOa8kn3vWhnYtDF/TVA2cNhPF+UPX0g1e6xe9fq04zwwatPX5KLQY+ctrLbxyqosCi5E7Vs3hbedPS2tt2+JZhTy6rynpmrbhrCcayXVJI1F2JusLx9J4jnMyrEWbDG0QIl1juiZp69at7Nq1i8OHD2O1Wlm5ciX33nsvF1xwQSTNlVdeyT/+8Y+YfBs2bGDbtm1p1TFZ1yTtqWvmS7tepdOT5NOlEEKkITx5jEenhLZs37i2mj11zWzZXU9zl3dE4jDoFAJq/EDCOzLGu+bplNDkNh0mg47/+vDimJ3k0mmXToGrLiyh7mR3TLpyh4VN11VntDNdvPqyKWe0yt76RD3bn2mI6ePocTFejOc4R/KYj5bJ0AYhYIJs3LBmzRo+/OEPs2zZMgKBAPfccw91dXXU19djt9uB0CTp/PPP5+tf/3okn81mS3vCMxknSXvqmvnkzoNjHYYQYgq5prqE/6tvnbDf2A627eyW23vqmrlz58Gs2xX+G3q6W3gnqi/Tckar7K1P1POTpxsS/n7wlv5jZTzHOZLHfLRMhjYIETYhNm7Ys2cPt9xyCwsWLGDRokU8/PDDHDt2jAMHDsSks9lslJWVRf5NlslONoKqxubHXxvrMIQQU8xfJ9EECWDT7+vwB1S27K4fVrvCebfsrieY4uusoKolrC+TckarbH9AZfsziSceANufacAfUNMPdASM5zhH8piPlsnQBiGyMT5u1D2rq6sLAJfLFfP6//7v/1JcXMzChQvZuHEjHo8nYRk+n4/u7u6Yf5NJ6H78ybKCRQghxsbpHj87ahtzcuugBjR3ednf4E6abn+DO2l96ZYzWmXvqG1MeRujqoXSjaXxHOdIHvPRMhnaIEQ2xs3GDaqq8pnPfIbLL7+chQsXRl6/8cYbqaioYPr06bzyyit88Ytf5I033mDXrl1xy9m6dStbtmwZrbBHXWvPyKwFEEKIqabJnfgPbtlIdX1O9/qdzXV+JMpOt39y3Y+ZGs9xjuQxHy2ToQ1CZGPcTJLuuusu6urqePbZZ2Nev+OOOyL/f9FFF1FeXs5VV13FkSNHmDt37pByNm7cyGc/+9nIz93d3cyaNWvkAh9lJfmWsQ5BCCEmhQqXLaflpbo+p3v9zuY6PxJlp9s/ue7HTI3nOEfymI+WydAGIbIxLm63u/vuu/nDH/7AU089xcyZM5OmveyyywB466234v7ebDZTUFAQ828yubTKRVmBeazDEEKICa0038RNKyopd1gY7gbGCqFdvsLblCdyaZUraX3pljNaZd+0opJUuzvrlFC6sTSe4xzJYz5aJkMbhMjGmE6SNE3j7rvv5rHHHuNvf/sbVVVVKfMcOnQIgPLyqbmLil6nsHndgrEOQwgxxVxTXcLZR1tNClvesxCTQcem60I7nmXbrnC+TddVp3xejF6nJKwvk3JGq2yTQcftq5K/L9++qmrMn0M0nuMcyWM+WiZDG4TIxphe2e666y527tzJo48+Sn5+Pi0tLbS0tNDf3w/AkSNH+M///E8OHDhAY2Mjjz/+OB/72MdYvXo1F1988ViGPqbWLCxn2/qayHNDhBBiOJQkn210Smj75O0fW8YD62soc4zcLTWGJB+ynDZjwmteJp/NzAZdZPtvCF1P02mXTglNFMsHpStzWDLa/jhRfZmWM1plb1xbzYbVVUP6ODwuxsP23zC+4xzJYz5aJkMbhMjUmD4nSUnwzvzQQw9xyy23cPz4cdavX09dXR19fX3MmjWL973vfXzlK1+Z0s9JCguqWuTp9KCwYm4RNbOd7Hy+kRcaO7Cb9Lx3yQzUoMZP9zZwqquf8gILq8+bRnG+mZdPdKIBs5xWNA1ebOqguaOfzn4/gaCGpmn4gyoKUGA1UlWSh0GnoyjPRFuPj/YeHyc6+7Ga9Qz4A5zpDRAk9JclgwJ5Fj0mHXgDGgEtdLwVTUOvaOgNBgZUlWKrgQEN3L1+fEENTQWDAdQgBDQSbs1rMYDZoMc/EMQXDKWbqJuPmoHJsl+hWQdBFQJnfzbqIKCGjo1FB0HO/Ww6+7WEUQ99SZ6JrNeBcnYsBLXQQkq7SUFVdPT5gqihYtApUJqn43SPSvBsXoNydmxood+HH5qqAVajgqZp9A8QKcOsD+2CpVMUpheaqZnt4o3WXvLNemYV2TnT4+VIax9dHj89viAGBYryzVxWVUjdqV76/UEMeoULptk5dLIbjz+I02ZgTlEebWcfgrpirguX1cSzR9rp9g4wvzSPlm4vLzR2Egiq5Jl1VBTZsVtMTLObmO6yUmAxcri5h/6BABeVO3jyjdMcOdOHSa/jHReWUGg1UnvUjaZpmI16zivNo73Xj6pqtHT30+UZwGjQ4bQZmV/uYE5xHvPL8nF7/JTkW1ha4eSFRje1R9oJBlW6fQPoFB2VRTZuWlEZ8xf4oKqxv8FNa4+XQouRv77eQkO7B4tBwWYy4h0IUpJvpsBqBA06vX7aegawm/VUlxdQYDXw6xdP0NXvxzugMtNl5bySfO5ZW43JoItc01QNnDYTxflmygrO3coT2uHTi7vXh8tuosxhZWmFkwNNHbT2eCnOM4MGbX0+Ci1G/vJaC6+c6qLAYuSOVXN42/nT4v7FO7pdJfkWFs8q5NF9TTS5PVS4zvXD4HSXVrmy+gt6rsoZrbL9AZUdtY1D+mO8Gc9xjuQxHy2ToQ1CTIiHyY6GyTxJEkIIIYQQQqRvQjxMVgghhBBCCCHGG5kkCSGEEEIIIUSUcfOcJDF6cn1PcbLysq0rqGo8f7Sd2iPtqJqK02amON9Msc3E6y3dvNjkxmYy8IGamaycV0xQ1Xho71H+Wt8KaFxQkk9NhZPpTlvM2oJZThvziu38/pVTePxBamYXoihwzO3hdLcPTdPwDqgsmlnI5ecVs3xOUdJ4o9vnspk43NLNMXc/oLF4ZiHTnbYhbe71Bvj3X77EsY5+Zjut3P+hJeRZDARVjefeauO3B05wotPDjEIb718yA51OYV+Dm0BQ5Y3T3TSc6SOoaiyZ7eSDl8zisjlFHGjqiFmnUZJvAQXaen2RNSfhNRupfo5e05HseCZKF5PGbo6JI3pdSaJYhjMeU423sVhPMpb38I903anWf8j6hfFJjosQQqQma5KmmD11zWzZXU9z17knY5c7LGy6rjqr3WmSlQdkVdeeuma+tOtVOj1JVvNHMegUAurIDONCm5Fvv/+iuPHGa3s80W1e96NneOVE95A0FUVWzvT48fiDcUoYPp0S2pAg3Z+jJTueg9OtW1TO4y83J0wT3p0s+tgOrjvb8ZhqbOdq7GdSTq7Pt0yMdN1bn6hn+zMNQ8bR7atCO4mNZdtFYnJchBBTnWzccJZMks7ZU9fMnTsPDtkFLvz3w0y38UxWXqJBlaquPXXNfHLnwbRjGC3bBsWbqO2JKMDsIitN7f0jEt9ISnY8R6o+yGw8phrbd6yu4sGnG4Y99jM5h3J9vmVipOve+kQ9P3m6IeHvr6ku4f/qW8ek7SKxsRyTQggxXsjGDSJGUNXYsrs+7ofd8GtbdtcTTPMbmXTKiydZXUFVY/Pj9WnVP9qi403W9kQ0mJATJBj9rdUzHY/pjMXtzwydIGVaVybnUK7Pt0yMdN3+gMr2ZxJPkAD+GmeClKv6RXbGckwKIcREJJOkKWJ/gzvpbWEa0NzlZX+DOyflJZOorvDzT8aj6HiH03aRnkzGYzpjO9nnvnTryuQcyvX5lomRrntHbWPS/kxlJNsuEhvLMSmEEBORbNwwRbT2pPehPtfpMikjF2WOpHB84z3OySSdvs7V8UhVzkicGyMxlkb6HG5ye7LKl6v6RXZG89ouhBCTgUySpoiSfMuYpMukjFyUOZLC8Y33OCeTdPo6V8cjVTkjcW6MxFga6XO4wmXLKl+u6hfZGc1ruxBCTAZyu90UcWmVi3KHhUSbvCqEdjgKb9E83PKSSVTXpVUuygrG5xt0dLzDabtITybjMZ2xnWx343TryuQcyvX5lomRrvumFZVJ+zO6npGoX2RnLMekEEJMRDJJmiL0OiWyjfPgN8nwz5uuq077WRnplJdpXXqdwuZ11WnVP9qi403W9kQUQtt8T0TJjudI1pfueExnLN6+qgolye/TqSuTcyjX51smRrpuk0HH7auqkqa5prpkxOoX2RnLMSmEEBORTJKmkDULy3lgfQ1ljthva8oclqy2fk1W3rb1NWzLoq41C8vZtr4m8jyddBhG8E3daTMO2f4bErc9nvKzbf7H59/BxTPjbzVZUWTFZtLnJOZ4BndRqp+jJTue0codFjasrqI8SRqnzTjk2A6uO5vxmGpsb1xbnZOxn8k5lOvzLRMjXffGtdVsWF0VdxxtWF3F9o8tG7O2i8TGckwKIcREI89JmoJy/bT1ZOVlW1dQ1Xj+aDu1R9pRNRWnzUxxvplim4nXW7p5scmNzWTgAzUzWTmvmKCq8dDeo/y1vhXQuKAkn5oKJ9OdNhbPKuTRfU00uT3MctqYV2zn96+cwuMPUjO7EEWBY24Pp7t9aJqGd0Bl0cxCLj+vmOVzipLGG90+l83E4ZZujrn7AY3FMwuZ7rQNaXOvN8C///IljnX0M9tp5f4PLSHPYiCoajz3Vhu/PXCCE50eZhTaeP+SGeh0Cvsa3ASCKm+c7qbhTB9BVWPJbCcfvGQWl80p4kBTBy3dXty9Plx2U2hdgQJtvT5K8i0srXByoKkjchyS/VycZwYN2vp8SY9nonQxaezmmDjCt/JEj4nBsQxnPKYab7ka+5mUk+vzbaTizIY/oLKjtpEmt4cKl42bVlRiMpz729tYtl0kJsdFCDGVycNkz5JJkhBCCCGEEALkYbJCCCGEEEIIkRWZJAkhhBBCCCFEFHlOkgDSv0d9JNYzPfdWG7sOnqDPH2RZpZObV1bFrGvINu6gqvH8kXZqj7YBCivmFmW0xijeeprBeRPVP7juy6pcoMG+xnY0DQptJpw2Iy+f6EQDqors3HhZBYeOd9LS7aW120v9qW48/gFKC6zUzHZSXmjl0ioXQVXjkecaeaGxHZvJwPsWz8Bg0GW1/ijVmqBcHu9kfZVOHdHrX2Y5rcwvK8Dt8ae9Jqilqx93nx+HNbbfb1pRiV6njNgajZFc/5HNeZvOuBZCCCGmOlmTJNhT18yW3fU0d5170nq5w8Km66pjdjtKN10m9X7uVy/T5w/GvK4Ad6yuYuPa5NuBJ4sH4Eu7XqXTMxCTp9Bm5NvvvyhuvPHKiza4rYnqX7eonF++eGJI3blgN+mH9NdgOgVULfufo/swV8c7WV89/nJzyjq2PlHP9mcaYuKMliiuVMcUQFHAatTjierX4YzrVPWPdtmZjmshhBBiMpONG86SSVJye+qauXPnQQYPgvDflcPbwqabLpN6P7nzYNI0G5JMlJLFk86AHrytd6LyBpcNobYCKdNPVMn6MJvjnU7fJqtj6xP1/OTphozyZFNvqvIyletzJpuyMx3XMlESQggx2cnGDSKloKqxZXd93A9Q4de27K7HH1DTShdM9Gf+OPVufrw+ZboHn27AH1CzijuV6HiTlRev7C2769n8+GuTcoIEyfsw0+Odbt8mqqPfH2T7M8knSPHiyqbeZOVlKt1zayTLTnbe5jIeIYQQYjKSSdIUtr/BnfQ2JA1o7vKyo7YxrXT7G9xp19vSnbi86HJ31DbGzZ8snnREx5tJeeG2tnT7hlX/RJbJ8c72WIXr+NYT9QlvsUsWVy7GSKbjOlq659ZIlp3qvM1VPEIIIcRkJBs3TGGtPel9gGpye3JaXrrpEtWdSf504shVeVNNOv023L5tbE9v7OWyzlyUNxLnQqZ50j1vsylbCCGEmOzkm6QprCTfkla6Cpctp+Wlmy5R3ZnkTyeOXJU31aTTb8Pt28qi9Mbe4DpzeUyzKWskzoVM86R73mZTthBCCDHZySRpCru0ykW5w0KizX8VQjtf3bSiMq10l1a50q63rCD1hzEFuGlFZcZxpyM63kzKC7e1rMA8rPonskyOd7bHKlzHPWurSXd36ui4wvUOR6bjOlq659ZIlp3qvM1VPEIIIcRkJJOkKUyvUyJbPQ/+IBX+edN11ZgMurTSpfusFb1OYfO65Nt7Q2gb8HjPS0on7lSi401WXryyN11XzeZ1CzKqbyJREvx/9M/pHu90+zZRHVaTnttXVWWUR69TIvVme3yyGdfR0j23RrLsZOdtLuMRQgghJiOZJE1xaxaW88D6GsoG/dW9zGGJ2RI43XSZ1LttfQ12k37I7xSSb/+dKp5t62vYtr6GQptxSD6nzThk++9k5Q0uO9zWROnLHRY2rK6KW3cu2M1D+2uwwZ9zM/05ug9zcbxT9dXgb3wG17FxbTUbVlcl/UYpXlzhelN9o6QoYBs0DrMd19Fyfc5kU3am41oIIYQQIfKcJAGEthXe3+CmtcdLSX7otpt4f1VON10m9T73Vhu7Dp6gzx9kWaWTm1fG/wYp07iDqsbzR9qpPdoGKKyYW8TyOUVJ440ur9huBgXaen0J25qo/sF1X1blAg32NbajaVBoM+G0GXn5RCcaUFVk58bLKjh0vJOWbi+t3V7qT3Xj8Q9QWmClZraT8kIrl1a5CKoajzzXyAuN7dhMBt63eAYGgy4S59IKJweaOiIxZfrz4D7M1fFO1lfp1OEPqOyobaTJ7WGW08r8sgLcHn/KuMLlt3T14+7z47DG9vtNKyrR65Scjut02j2aZWc6roUQQojJSh4me5ZMkoQQQgghhBAgD5MVQgghhBBCiKzIJEkIIYQQQgghosjDZMWIiF6To2rgtJkospvo8Phx2U2UOayR7YZTrakIr6c41dnPwWNuTnd78Q6oLJzuoCjPRHGemTKHlaUVTl5odLP3rTZOdnhQFIUZTivLK4vQ6ZWYNTv7jrTz25dO4PEHqZldCBocONaBxx+kOM/ETJeNlXOKqalwsqO2kb/UtwBwTXUZt15eFVnDcqrDw6ETnahaaAOEJbOclDosoEFbn49iu5lAUOWxQyfx+AMsqyziAzUz+cJvD/FGSy8Oq4G1F5VTWmCl0+On0Gqkrc9P3cku3mjpIc9iYH5ZPksrzm6bHmctyeD1Jqqm8XxDOyc7+tE0jRlOG5fPK46sxwqqGs+92RZp/7JKFzevrEy5Diyytqfbi7vXR4HVyCsnOgmoKm09fkoLLFQVn1tb1drjpTjPjBrU2NfYTkDV6PUGUBSFyiIbH1o2m1++cIwmt4cKl42bVpyLId5am2RjJd76s2WVroRrrQa3KdP1Qunky8VapFT9kOm6ueI8c2RsjvZ6pOH0x0iu6xqvotfgDT4/RtNU7HshhABZkyRGwJ66Zr6061U6PQNJ04V3gItOV+6wsOm66shOW3vqmtmyu57mLm/KehUFRms020x6PP7g6FSWQLnDwrpF5Tz+cnNa/VNoM/KhS2ay4/ljQ2JXFLhjVeIdBTM5DtnSKXD7qiqWzHYOqSvZWAHijrfB4yGdsTU4TTzp5Mu27FT1xOuHaJmeP5nGlK3h9Ecu+nKi2fpEPdufaUCNGr/h8yPZrp+5NhX7Xggx+cnGDWfJJGl07alr5pM7D2adP/z3yQfW1wBw586DTOoBOs7E23p9T13zuDwOCmQUUzpjKzpNvA+BifoiF2WnU08qmZ4/mcSUrXT6LFHdw8k7UW19op6fPN2Q8PepHo+QK1Ox74UQU4NMks6SSdLoCaoal3/7SVq6fcMqRwFKC8yAQkv3yH1zIYbSKXD4P/8l5ra3t937txH9Bmk0KYSeC6RpWsJxGk7z7BffEXNbUaq+SGfcJio72nD7PJ02ZhpTttLps0R1DyfvROUPqMz/6p9ivkEabPA5OhKmYt8LIaYO2d1OjLrQepXhTZAg9O1AS7dPJkhjQNVgR21j5Of9De5JM0GC0Nhq7vImHafhNPsb3DGvp+qLdMZtorIzqSeVdNqYaUzZSqfPEtU9nLwT1Y7axqQTJBh6jo6Eqdj3QggxmEySRM609kyeD9NTWZPbE/n/qXxMB7c9l32RrKyx6vORqDfdMuOlG07eiSr63MtFumxNxb4XQojBZJIkcqYk3zLWIYgcqHDZIv8/lY/p4Lbnsi+SlTVWfT4S9aZbZrx0w8k7UUWfe7lIl62p2PdCCDGYTJJEzlxa5aKswDzschSgrMAc2vJajCqdAjetqIz8fGmVi3KHhcmy6kAhtDtXWYE5YZvCacLbbYel6ovocZtp2ZnUk0o6bcw0pmyl02eJ6h5O3onqphWVpFriM/gcHQlTse+FEGIwmSSJnNHrFDavWzCsMsJvypvXLWDzuupJ8+F8orh9VVXMgnC9Tolssz2a0v1wn02Zm66rjozTwWVEpxm8ID26LxLlC4/bTMtOt55U0mljNjFlK50+S1T3cPJOVCaDjttXVSVNM/gcHQlTse+FEGIwmSSJnFqzsJxt62siz3NJxmkzDklX5rBEtpZds7CcB9bXUO5I7xslZRTfr20m/ehVlkC5w8KG1VVp94/TZmTD6qq4sStK4q2FMz0O2dKdjWHb+hrKBtWVaKxsW1+TcLwNHg/xxtbgeqLTxJNOvmzLTqeewjj9kGkc2caUreH0Ry76cqLZuLaaDaurhnyjpEtyjo6Eqdj3QggRTbYAFyMiqGo8f6Sd2qNtqBo4bSaK7CY6PH5cdhNlDmvkVo1UT3MPP/H9VGc/B4+5Od3txTugsnC6g6I8E8V5ZsocVpZWOHmh0c3et9o42eFBURRmOK0sryxCp1do6/VRkm9haYWTfUfa+e1LJ/D4g9TMLgQNDhzrwOMPUpxnYqbLxso5xdRUONlR28hf6lsAuKa6jFsvr0KvU0IxdXg4dKITVQt9iFkyy0mpwwIatPX5KLabCQRVHjt0Eo8/wLLKIj5QM5Mv/PYQb7T04rAaWHtROaUFVjo9fgqtRtr6/NSd7OKNlh7yLAbml+WztMIVuv1QIdKOcF+F+6e1x0ux3YyqaTzf0M7Jjn40TWOG08bl84pZPqcokv65N9si7V9W6eLmlZUp/zodrqel24u710eB1cgrJzoJqCptPX5KCyxUFdu58bIKDh3vDMWTZ0YNauxrbCegavR6AyiKQmWRjQ8tm80vXzhGk9tDhcvGTSsqY7YeHzwuko2V6PEGCivmFrGs0sWBpo60xlayNMn6YiTKTlVGdD8U281xx0SiMorzzJGxmW1M2RpOf+SiLycaf0BlR21j3PNjNE3FvhdCTG7ynKSzZJIkhBBCCCGEAHlOkhBCCCGEEEJkRSZJQgghhBBCCBHFMNYBiOyM1H3iicoNv97c2c9LxzvQgKqi0PqTg00dPPvWGV490YXZqEfVVLo8fhRF4fzSfJw2M6qq8c/WHo629TIQUCmwGLCYDJgNCl3eAO29fnq9AcwGKLKbme608erJbvwDAVRNw6hTUFEosBjR6xR6fQFUTcWk19PrG8AXADWqHQqhp8InohD6C0Hw7H8vKDGj0+k53OIhmKKPyvN0+DU9Hn+A/oHYWuaXWCi0Wzl2ppdTvQNp9blRCcUaSBCww6SwaFYhDe0eTnb5UBOk059t9OD47cbQuPAHNYIq2IyQbzHQ5Q3iC2joAL0OTHrQG/SomkKfN4Cqgd2sw27WEwxqeAMaTpueEx3+SF8bFVAVMOnAYTFitxgZUDW6PX5QFPKMOlp7/QyoYDMqrJo3DZNRT3ufj35/EKNOo6XbjyegUpJv4aoLi/lr3Wma3P2omorNZMAfCKJqChaDgkGvw6BTMBkUWrv9DKgaFqOO2YUmjncNgAZWk57SfCOd/QG6vUGCqka+xcBsp4VpBVYUFNr7/FiNOkoKLDhsRtCgxxtA0zRUDRrO9PDG6V4CqkaBxciS2Q4uLHXwXEM7Xf0DlBaYmTctj5bu0MM0yx1Wur0DtHb78A4EmV+eT2N7H94BlcoiO1edX8KBEx2E10stmlnIvXtep7HdwyynldlOG6e6vcxy2jh/Wh4vHHNH0obXkoUNXoOGAq09Pty9PgptJjoHrfuLd10IqhrPvdXGroMn6PUFmZZvosBqxKBTWDGnmOVzQ3XGWxcTXo83+BqR7hqawWukwmvWQOGys2uu9jW4AY3LqorQKQqtPV7aen109g+gJOiXXIjXt8nWe+WinpFe5zPWa4rGuv6xMBXbHDaV2y4mH1mTNAHtqWtmy+56mrvOPe283GFh03XVw9pxKFG56xaV8/jLzTGvCyFGR6HNyLfffxFrFpbHPUeTiXdd2FPXzOd+9TJ9/sR/Dii0GVlW6eTJ11tjJuWKAlajHk9U3nKHhYUzCoak1Smh7aqjd2PLNP5kovslF1LFlotrbKJ6clX2WNY1HusfC1OxzWFTue1iYpGNG86abJOkPXXN3Lnz4JBvScJ/p8l2a9ZE5QohxocNq6t48OmGjM9RhXPXhT11zXxy58GRCC+h8LbVI3WN2ZaD7ajTiW2419hk9eSi7LGsazzWPxamYpvDpnLbxcQjGzdMQkFVY8vu+rhv5OHXtuyuJ5jofqwsyhVCjA/bn8l8ggSha8OW3fX4AyqbH6/PdVgpbX+mgX5/cMSuMdlc86Kle/0bzjU2VT3DLXss6xqP9Y+FqdjmsKncdjG5ySRpAtnf4E56m4oGNHd52d/gzmm5QoixN5zPF81dXnbUNkbWUI0mVYNvPZGbW+ziyeaaFy2T61+219h06hlO2WNZ13isfyxMxTaHTeW2i8lNJkkTSGtPem/k6abLNr0QYuJpcnvGrO7G9pGtezjXsGzyjmSeXFyPR7Ou8Vj/WJiKbQ6bym0Xk5tMkiaQknxLTtNlm14IMfFUuGxjVndl0cjWPZxrWDZ5RzJPLq7Ho1nXeKx/LEzFNodN5baLyU0mSRPIpVUuyh0WEm2mqRDaSebSs9vo5qpcIcTYG84uuuUOCzetqKSsYPQ/pOgUuGdt9YhdY7K55kXL5PqX7TU2nXqGU/ZY1jUe6x8LU7HNYVO57WJyk0nSBKLXKWy6LrSd7uCLUfjnTddVZ/xMgmTlCiHGh9tXVaGQ+TmqELoumAw6Nq+rTpk+125fVYXVpB+xa0w217xo6V7/hnONTVXPcMsey7rGY/1jYSq2OWwqt11MbjJJmmDWLCzngfU1lDli/yJc5rAMa4vNROWWOyxsWF1FuUO+JhdiLDhtRratr2Hj2uq452gy5YOuC2sWlrNtfQ12kz5pvkKbkWuqS4Z8e6UoYBuUt9xhiZtWp5zb/jtcd6bxJxPul1xsK5xObMO9xiarJxdlj2Vd47H+sTAV2xw2ldsuJi95TtIENVJPtU5Ubvj15s5+XjregQZUFdm58bIKDjZ18OxbZ3j1RBdmox5VU+ny+FEUhfNL83HazKiqxj9bezja1stAQKXAYsBiMmA2KHR5A7T3+un1BjAboMhuZrrTxqsnu/EPBFA1DaNOQUWhwGJEr1Po9QVQNRWTXk+vbwBfANSodiiQ8pknOiB49r8XlJjR6fQcbvGQ+BGbIeV5OvyaHo8/QP9AbC3zSywU2q0cO9PLqd6BtPrcqIRiDSQI2GFSWDSrkIZ2Dye7fAl3OdOfbfTg+O3G0LjwBzWCKtiMkG8x0OUN4gto6AC9Dkx60Bv0qJpCnzeAqoHdrMNu1hMMangDGk6bnhMd/khfGxVQFTDpwGExYrcYGVA1uj1+UBTyjDpae/0MqGAzKqyaNw2TUU97n49+fxCjTqOl248noFKSb+GqC4v5a91pmtz9qJqKzWTAHwiiagoWg4JBr8OgUzAZFFq7/QyoGhajjtmFJo53DYAGVpOe0nwjnf0Bur1BgqpGvsXAbKeFaQVWFBTa+/xYjTpKCiw4bEbQoMcbQNM0VA0azvTwxuleAqpGgcXIktkOLix18FxDO139A5QWmJk3LS+yW1y5w0q3d4DWbh/egSDzy/NpbO/DO6BSWWTnqvNLOHCiA1BYMbeIRTMLuXfP6zS2e5jltDLbaeNUt5dZThvnT8vjhWPuSNrlc4pizu3oc7TYbgYFWnt8uHt9FNpMdHr8uOwmyhzWhNeFoKrx3Ftt7Dp4gl5fkGn5JgqsRgw6hRVzilk+N1SnP6Cyo7aRJreHCpeNm1ZUotcpca8R8dKaDEP/DhcTf54ZNaixr7EdULjs7O04+xrcgMZlVUXoFIXWHi9tvT46+wdQEvRLLsTr27ZeX06vsYPryXXZY1nXeKx/LEzFNodN5baLiWNEHya7Y8cOtm3bRkNDA7W1tVRUVPD973+fqqoq3vOe9wwr8FybrJMkIYQQQgghRGZG7GGyDzzwAJ/97GdZu3YtnZ2dBIOhv1sXFhby/e9/P+uAhRBCCCGEEGI8yHiS9MMf/pDt27fz5S9/Gb3+3L3pl1xyCa+++mpOgxNCCCGEEEKI0WbINENDQwNLliwZ8rrZbKavry8nQU126d6zGy8dEHPPfEBV+d1LJ+nzB1lW6eTmlVUx6wCCqsbzR9upPdIOaFxWWYSKxm8PnuT15k40FUwGPQOqSoHFyNvnl9Dk7mXfETet3V4GVNC00JqZ6PsyFcBiUPAHNFTArAeLUQ9odHqjVwcJMXHZdOBVY9e7xWNSwH/2BNGROn20PCNEL19zmHX4BlTSPY0UwKCE1rTpFcizGFi3sJznGttp6fKCBk67EUUJXWM8/gD+oIqCwgynjcWzCnnHBaXsfuUUr53spKHt3Lq88No+ow5KC8wY9QptvQMEVY3yAjPzy/I50eXlRIeHgSDMKbZRPd3B0gpXaAG3Bi1d/exvbOfZN9vo9QcpyTPQ1O7Fr4b6ymkz4Ato5Jl0FNqM9PpVDHodc6fZWLNwOtMdVvwDQX669ygnO73YTXrmTstjptOG027EZTfT3uvjtZOdnOzyMb3QysLpBUzLN1NsN/N6SzcvNrmxGvUsmO5gWoGFsoLQ9dQfUPnWE/U0tnuoLLJxz9pqrCZ96Lp5pJ3ao22E14Ytq3RxoKkj4XolYEie5XOKAJKu84x3fR+89jPR+i449z7R0u2lrceLu2+Alq5+phdaWTn33PqyZO87mfxuaYUz0g/x3pdytQ5lJNaHDadMWWuTG5OxHydjmzI1Wfsg4zVJ1dXVbN26lfe85z3k5+fz8ssvM2fOHH74wx/y0EMPcfDgwbTL2rp1K7t27eLw4cNYrVZWrlzJvffeywUXXBBJ4/V6+dznPscvfvELfD4f73rXu/jxj39MaWlpWnWMtzVJe+qa2bK7nuauc0+eLndY2HRddczuL/HSFdqMAHR6Em8IoAB3nN1Rak9dM1/a9WrS9EIIMRWZDTp8gaEz0YtnFnDM3T/kuqkooT8YxVNoM+IPqHj8sdum2Ex6TAZdTFnlDgvrFpXz+MvNaV/fdUpoK/XwToFh8d4n4sX2oUtmDqkv/L4DJHxPivc7nULM5jHx4o73npaJVO3KpvzhlJnu+7ZIbjL242RsU6YmYh+M2MYNP/3pT9m8eTPf/e53+fjHP85Pf/pTjhw5wtatW/npT3/Khz/84bTLWrNmDR/+8IdZtmwZgUCAe+65h7q6Ourr67Hb7QDceeed/PGPf+Thhx/G4XBw9913o9Pp2Lt3b1p1jKdJ0p66Zu7ceXDIrmvhuXZ4m8xE6TJxTXUJf61vHUYJQgghxpPoLdWH+z6RbAfQVLuDplM2kNXWz+m0K9Pyh1Nmuu/bIrnJ2I+TsU2Zmqh9MKK72/3v//4vmzdv5siRIwBMnz6dLVu28PGPfzz7iIEzZ85QUlLCP/7xD1avXk1XVxfTpk3j0Ucf5frrrwfg8OHDXHjhhdTW1rJ8+fKUZY6XSVJQ1XjbvX9L+FcshdDzBP7x+bdzxX1PJf3LoBBCiKlHp8Dh//wX9Dol6fvJeBB+T3v2i+9I+7abVO+T2ZQ/nDLTfd/OpI1T0WTsx8nYpkxN5D4Ysd3tAD760Y/y5ptv0tvbS0tLCydOnBj2BAmgq6sLAJcrdI/zgQMHGBgY4Oqrr46kmT9/PrNnz6a2tjZuGT6fj+7u7ph/48H+BnfSi7QGNHd52VHbOK7f+IQQQowNVYMdtY0p30/Gg/B72v4Gd9p5MmlXuuUPp8x037czaeNUNBn7cTK2KVNToQ+ymiSF2Ww2gsEgf/vb3zh58uSwAlFVlc985jNcfvnlLFy4EICWlhZMJhOFhYUxaUtLS2lpaYlbztatW3E4HJF/s2bNGlZcudLak95FusntGeFIhBBCTFRNbk/a7yfjQSaxZtOuVHmGU2a6eSfS8RgLk7EfJ2ObMjUV+mBYk6Q//OEPVFVVcfXVVzN37lx27dqVdVl33XUXdXV1/OIXvxhOSGzcuJGurq7Iv+PHjw+rvFwpybekla7CZRvhSIQQQkxUFS5b2u8n40EmsWbTrlR5hlNmunkn0vEYC5OxHydjmzI1FfpgWJOkb3zjG/zbv/0bvb29fOtb32Lz5s1ZlXP33Xfzhz/8gaeeeoqZM2dGXi8rK8Pv99PZ2RmT/vTp05SVlcUty2w2U1BQEPNvPLi0ykW5w0KiuzIVQruB3LSiMmk6IYQQU5NOgZtWVKZ8PxkPwu9p4S3C05FJu9Itfzhlpvu+nUkbp6LJ2I+TsU2Zmgp9MKxJ0ltvvcVtt92GzWbj4x//OG+++WZG+TVN4+677+axxx7jb3/7G1VVVTG/X7p0KUajkSeffDLy2htvvMGxY8dYsWLFcEIfdXqdEtlSdfCACv+86bpqTAZdwnSZuKa6ZBi5hRBCjDe3rwo9By/6/SRbSoL/T/W7TMredF11Rgu2k71PZlv+cMpM9317vC1KH28mYz9OxjZlair0wbAmST6fD7PZDIDFYsHv92eU/6677mLnzp08+uij5Ofn09LSQktLC/39/QA4HA4+/vGP89nPfpannnqKAwcOcOutt7JixYq0drYbb9YsLOeB9TWhhyxGKXNYYrZJTJSu0GaMPJMiEYXQNrHbP7aMbetrUqYXQoipyJzg4awXzyyIe91UkrzPO21GbCb9kNftJv2QssodFjasrqI8g+u7Tond/hvOvU8MLmewQpsxbn1lDgvb1tewLcF7UqLfDf68Ey/uwe9pmUj0/jec8odTZrrv2yK5ydiPk7FNmZrsfZDxFuCf/exnI///3//936xfvx6Hw0EwGORHP/oRwWAwSe5BlSd413nooYe45ZZbgHMPk/35z38e8zDZRLfbDTZetgCPlu6TiZM9kT38xPCAqvK7l07S5w+yrNLJzSurYp7MHlQ1nj/aTu2RdkDjssoiVDR+e/Akrzd3oqlgMugZUFUKLEbePr+EJncv+464ae32MqCGHqCoEfvcDAWwGBT8AQ0VMOvBYtQDGp3eoQ9oFGIisunAq0KqEW1SwH/2BNGROn20PCP0Rj0/1GHW4RtQSfc0UgCDAgEN9ArkWQysW1jOc43ttHR5QQOn3Ri53nr8AfxBFQWFGU4bi2cV8o4LStn9yileO9lJQ5uHYFTZGmDUQWmBGaNeoa13gKCqUV5gZn5ZPie6vJzo8DAQhDnFNqqnO1ha4Qq9aWrQ0tXP/sZ2nn2zjV5/kJI8A03tXvxqqK+cNgO+gEaeSUehzUivX8Wg1zF3mo01C6cz3WHFPxDkp3uPcrLTi92kZ+60PGY6bTjtRlx2M+29Pl472cnJLh/TC60snF7AtHwzxXYzr7d082KTG6tRz4LpDqYVWCgrCF1P/QGVbz1RT2O7h8oiG/esrcZq0oeum0faqT3aBiismFvEskoXB5o6ItdeFGjr9cVcmwfnWT6nCCDu9T7Z9b25s5+XjnegAVVFdm5aURlzXY8WLqel20tbjxd33wAtXf1ML7Sycm4xy+cWJawv/L6Tye+WVjgj/RDvfSnZe1omouuN19/ZlD+cMtN93xbJTcZ+nIxtytRE64MRe07S29/+9qS/f+qppzIpbsSNx0mSEEIIIYQQYvSlOzcwZFrweJsECSGEEEIIIUQuDWtNEoQ2Xzh69Cg+ny8X8QghhBBCCCHEmMr4m6QDBw5w9913U1hYyI9+9CM++MEPcujQIQoKCvj973/PFVdcMRJxijGSzX2m2dzf3tLtxd3rw2U3UeawpnW/fDr3ykeXEV6bFVCD9HqDKIpCZZGNGy+r4MUGN7996QQef5BllS5uXnluDUCqPshkjdnzR9p57mgbJzv60TSY4bRw+dxpkXUDyfpzcB8la+/g+k519FNWYMGVZ6I4/9yajOHcg59Ou1OtqxvP9y4P5x7r0bw/e6LdCy6EEEJMBBmvSbr88sux2+04nU5eeOEFVq1axRe+8AV++MMfcujQIZ5//vmRijUrsiYpe3vqmtmyu57mrnNPSy53WNh0XXXCHUuS5QGG/E6ngBpnBCbLU+6wsG5ROY+/3Jy0rOgyvrTrVTo9UavjU1AUuGNVFUtmO5P2Qbp9tKeuOWkMhTYj337/RUP6NV75qdobjitVm5Mdy1TtSqfd8dKEd8GKjivVmBoL2Yz9XOQdzTiFEEKIqWjENm7Iy8vjwIEDVFRUkJeXx0svvcRFF13E0aNHufjii+nt7R128Lkkk6Ts7Klr5s6dBxk8OMJ/n463tWOyPBkNshwZqXrDfXDH6ioefLohZR/tqWvmkzsPplX2tqh+TdSf6cT1k6cb0s4z+FimOvbptBtIO/ZkY2osZDP2c5F3NOMUQgghpqp05wYZr0nyeDy4XC4sFgtWqxW73Q6A3W6PPN9ITGxBVWPL7vq4H3DDr23ZXU8w6muMdPKMtpGqN7wd+vZnhk4Uouvdsrsef0Bl8+OvpV325sdfI6hqSfszVVwPpjlBCos+lukcx3Tavfnx19KOPdGYGgvZjP1c5B3NOIUQQgiRWsZrkgC2b99OXl4egUCAhx9+mOLiYnp6enIdmxgj+xvccW/vCtOA5i4v+xvcrJhblFaeySjZ589wH+2obaSlO/1NTVq6fexvcMPZ/NnIdGIVfSzTOfbJvnsOl5epeGNqLGQz9nORdzTjFEIIIURqGU+SZs+ezfbt2wEoKytjx44dMb8TE19rT3ofcqPTpZtnqmlyezLOMxZ9Ga5zrI/jRKk/Xrrh5M3UaNYlhBBCTEUZT5IaGxtHIAwxnpTkWzJOl26eqabCZcs4z1j0ZbjOsT6OE6X+eOmGkzdTo1mXEEIIMRUN6zlJmqaR4b4PYgK4tMpFucNCok2EFUI7aIW3c04nz2SkU0jZRzetqKSswJx2mWUFZi6tcg2rPzPJM/hYpnPsk+0uHS6vrMA8rDjGSjZjPxd5RzNOIYQQQqSW1STpZz/7GRdddBFWqxWr1crFF18cc9udmNj0OiWydfbgD2HhnzddVx3zLJZ08sT7XTqynXjlasIWrz0KcPuqqoS/h1AfmQw6Nq9bkHZdm9ctQK9TYvozkzgVQrvPZSL6WKZzHG9fVRWpK97vN11XHWlzOscg0ZgaC9mM/VzkHc04hRBCCJFaxpOk733ve9x5552sXbuWX/3qV/zqV79izZo1fPKTn+T+++8fiRjFGFizsJwH1tdQ5oi9XafMYUm4tXCyPNvW17Atzu8SfYYrT5Kn3GFhw+oqylOUFV1v+Pk86VIU2LC6Km794T7YuLY6rT5as7A8ZQyFNmPM9t/hfA+srxnSzrB47Q3HlU6byxMcy1THPp12Jyqj0GYcEleyMTUWshn7ucg7mnEKIYQQIrmMn5NUVVXFli1b+NjHPhbz+iOPPMLmzZtpaMhs++GRJs9JGp6gqrG/wU1rj5eS/NDtO6n+Op0sz+DfLa1wcqCpg5ZuL+5eHy67iTKHNWme8O8SlZWo3uePtlN7pJ2AGqTXG0RRFCqLbNx4WQUvNrj57Usn8PiDLKt0cfPKSkwGXVp9kG4fBVWN54+089zRNk529KNpMMNp4fK501g+tyhhv4bLH9xHydo7uL5THf2UFVhw5ZkozrdQVpD6WOai3fHSABmPqbGQzdjPRd7RjFMIIYSYakbsYbIWi4W6ujrmzZsX8/qbb77JRRddhNc7vnZTkkmSEEIIIYQQAkbwYbLz5s3jV7/61ZDXf/nLX3LeeedlWpwQQgghhBBCjCsZbwG+ZcsWPvShD/H0009z+eWXA7B3716efPLJuJMnIYQQQgghhJhIMp4kfeADH2Dfvn3cf//9/O53vwPgwgsvZP/+/SxZsiTX8Ykcy9X6haCq8dxbbew6eII+f5BllU7WL6/k4LEOao+0AxqXVRWhUxTa+nwU55lBg7Y+Hy6ricOnezje4aHCZeOmFaH1P9GxFdvNoEBbrw+XzcThlm4a2/to7fZRnG9Gr0CBxYSigwKLka7+AUDDaTNTnG+mJD9UX0u3lxca2znQ6KbHF6Qoz8SKOS48fpXWHh/9/iAXzXDgspvo7B9Ap8BlVUWgwfMN7Zzq7MeVb+Slpi76fEFmOi0sne2kuTt0W+mC6QU8+Xorff4AdpOBqy4s5dWTnZx09/H66T6MOoVp+SamF1ho6fFj0im4+/00tXvQNI1ZLju3rKzijdPdBDUNHQo2k566U12c6fGhaODKM6FTFKwmPaChadDa48MXUFGAC0rzmF/q4NmjbRxt68Oog8UzC8k3G3mzrZegqgJgMxmwmQy8c0EZZQUWDrd0c7Stj3+29GA0KAwENM4vzUOv02Ez62nt9lGWb6HbN0Brj498s4H318xk5bxiIP66ouhj6LKZeO1kF/93+DSaBueX5uGwGTHo9KyYW8TyOUUxeVq6+nH3+XHaTHR4/BTaTLT2eHnmzTM0d3qxmXQ4rEbsZiNLK5zoUGjq8NDa7WVavhmdopBn0aMoCj39ARQFKovs3HhZBQebOqg92gYoLKtw8s/WXo65+wBYNLOQrv4BCq1GOvsHcOWZKck7N/7SXTsX7/wK5zvV2c+h4x1AKKbwmE92fj1/pD0Sc7z+yvYcziR/Juv+hlPPVCb9JIQQ41PGa5ImGlmTdM6euma27K6nuevcurFyh4VN11VntBPWnrpmPverl+nzB3MSl06Bqy4soe5kd0xsYnwyG3RYTXo6PQOR18odFtYtKufxl5vTPoaFNiMfumRmRnnGik4BNc6VMvr8iXd+JcqnU0JbqW9cO3Sb9z11zXxp16sx/QuJ+yuTcziTa0A67ckkbzbXmslO+kkIIUbfiG3cABAMBnnsscd4/fXXAaiuruY973kPBkPGX0yNOJkkheypa+bOnQcZfLDDf69Md8vgPXXNfHLnwZzHJ8REFX421YNPNww5v1LZsDp2opTN+ZXuOZzJNSBR2nTqztW1ZrKTfhJCiLExYhs3vPbaa5x//vncfPPNPPbYYzz22GPcfPPNnHfeedTV1Q0raDEygqrGlt31cT/whF/bsrueYLw/eQ8qZ/Pj9TmPT4iJTAO2P5P5BImz+fyB0O2QofPrtazqh+TncCbXgGRph5M3k2vNZCf9JIQQ41/Gk6RPfOITLFiwgBMnTnDw4EEOHjzI8ePHufjii7njjjtGIkYxTPsb3ElvZ9KA5i4v+xvcKctp6R7ft0UJMRay/SyrarCjthEIn1++rMpJdQ5ncg1IlXY4edO91kx20k9CCDH+ZXx/3KFDh3jxxRdxOp2R15xOJ9/85jdZtmxZToMTudHak94HnlTp0i1HCJG+JrcHyM35laiMXF0DcpV3ql9LRuN4CCGEGJ6Mv0k6//zzOX369JDXW1tbhzxgVowPJfmWnKRLtxwhRPoqXDYgN+dXojIyuQZkG0cmeaf6tUT6SQghxr+MJ0lbt27lU5/6FL/5zW84ceIEJ06c4De/+Q2f+cxnuPfee+nu7o78E+PDpVUuyh0WEm0qqxDaUenSKlfKcsoK5E1biMGy3bFZp8BNKyqB8PllzqqcVOdwJteAVGmHkzfda81kJ/0khBDjX8aTpHe/+93U19dzww03UFFRQUVFBTfccAN1dXVcd911OJ1OCgsLY27HE2NLr1PYdF1oB63Bb8rhnzddV53y2Rx6ncLmdUO3LBZiKlMIbeetMPT8SuX2VVWR5yWFzq8FWdUPyc/hTK4BydKmqjtX15rJTvpJCCHGv4y3AP/HP/6Rdtorrrgi44ByTbYAP0eekyRyIVfPSXLajNwgz0ka8rtEz0lK1F/ynKSJS/pJCCFG34g+J2kikUlSrFw93T2oajz3Vhu7Dp6gzx9kWaWT9csrOXisg9oj7YDGZVVF6BSFtj4fxXlm0KCtz4fLauLw6R6Od3iocNm4aUUlJoMuJrZiuxkUaOv14bKZONzSTWN7H63dPorzzegVKLCYUHRQYDHS1T8AaDhtZorzzZTkh+pr6fbyQmM7Bxrd9PiCFOWZWDHHhcev0trjo98f5KIZDlx2E539A+gUuKyqCDR4vqGdU539uPKNvNTURZ8vyEynhaWznTSf3eVvwfQCnny9lT5/ALvJwFUXlvLqyU5Ouvt4/XQfRp3CtHwT0wsstPT4MekU3P1+mto9aJrGLJedW1ZW8cbpboKahg4Fm0lP3akuzvT4UDRw5ZnQKQpWkx7Q0DRo7fHhC6gowAWlecwvdfDs0TaOtvVh1MHimYXkm4282dZLUA1tMW0zGbCZDLxzQRllBRYOt3RztK2Pf7b0YDQoDAQ0zi/NQ6/TYTPrae32UZZvods3QGuPj3yzgffXzGTlvGKAuOMo+hi6bCZeO9nF/x0+jabB+aV5OGxGDDo9K+YWsXxOUUyelq5+3H1+nDYTHR4/hTYTrT1ennnzDM2dXmwmHQ6rEbvZyNIKJzoUmjo8tHZ7mZZvRqco5Fn0KIpCT38ARYHKIjs3XlbBwaYOao+2AQrLKpz8s7WXY+4+ABbNLKSrf4BCq5HO/gFceWZK8s6Nv5J8C0srnBxo6qCl24u714fLbqLMYR1y/gw+v8L5TnX2c+h4BxCKKTzmk51fzx9pj8Qcr7+yPYczyZ+oPdnkzfZaM9lJPwkhxOga0UlSZ2cn/+///b/Iw2QXLFjAbbfdhsPhyD7iESKTJCGEEEIIIQSM4MNkX3zxRebOncv999+P2+3G7Xbzve99j7lz53LwYGZPihdCCCGEEEKI8Sbjb5JWrVrFvHnz2L59OwZD6DFLgUCAT3ziExw9epSnn356RALNlnyTJIQQQgghhIARvN3OarXy0ksvMX/+/JjX6+vrueSSS/B4PNlFPEJkkpRaNvfEx6wfilpvFM4PxF1fFF7X8EKDm9qjbWgaFNpMuGzn1oOUFcRf4zJ4TURxnhlV1djX4EbVVArMRg6f7qHPH6A038ySWU7KCq2RtUmHjncQUNXQWh8U7GYD71s8A4NeR2uPl7ZePx19fk52elA1jfZeP2d6fKiaSnuvn6AGRXYTH1gyky6/n+ePhNaYlDksXFNdSq83gMtuoiTfQkBV+d1LJyPrtW5eWYVep/D80XaeeeMMzx5pAzQWz3Ly5Wurz643Otevpzo8HDzeQUuXl9PdPkoLLJQWmCmwGgCFXm8AjdBC+sWznJQ5LKhBjdqGNk51eEGBGYVWls8pQlU1Hjt0Eo8/wNIKF/PL8nmhsQMNjUKrkeI8MyUFlkhfDgSDHDnTi3dAo6rYxhfXXMirJ7sia4bCxyjecSzOi12r4w+o7KhtpKG9DwVYMstJaYElMh7ijZ1E625ixlSKMRdvHEevfWrr9eHuG6Clq5/SAgs9vgF0ihJZL6TXKSO6TiTTdUGJ1iflovzh5Mm14ayBGulYxutaoYkSpxBCjBcjNkkqLS1lx44dvPOd74x5/c9//jMf+9jH4j5odizJJCm5bHZXipcnWqHNCDBkd64wBUg16BLtlpZot7CJwGzQ4QuocX93TXUJH6iZmbRfxxtFgURXj3KHhYUzQptaZHK8Eh33VGMq3u9T7TyXiKKA1ajHE7V7Yy53HMt0h7l4O90V2ox8+/0XxY0nV+f0aO+yNpzd9EYjlvG469xEiVMIIcaTEZskfepTn+Kxxx7jO9/5DitXrgRg7969fP7zn+cDH/gA3//+94cVeK7JJCmxPXXN3Lnz4JAJS/hvkA+sr4n7oS1eHiHGm/A4vmN1FQ8+3TCsMZvsnMhEJufcnrpmPrkz+TrPbYPiyeU5nas2pyPd68poxDQe+iMdEyVOIYQYb0Zs44bvfOc7vP/97+djH/sYlZWVVFZWcsstt3D99ddz7733DitoMXqCqsaW3fVxP5SEX9uyu55g1J9xk+URYrwJj9PtzwxvghRd1uBzIhOZnHNBVWPz46+lLDM6nlyf07loczoyua6MdEzjoT/SMVHiFEKIiSzjSZLJZOIHP/gBHR0dHDp0iEOHDuF2u7n//vsxm80jEaMYAfsb3ElvPdKA5i4v+xvcaecRYrzRyN3tmfHOiUxkcs7tb3DT0u1LWWZ0PCNxTg+3zenI9LoykjGNh/5Ix0SJUwghJjJDthltNhsXXXRR5OdAIMBzzz0HhCZSy5cvH350YsS09qT3oSQ6Xbp5hJjMsj0PsjnnMkk/kuf0SJ77I92fI1HmWF8LJ0qcQggxkWU8SXr88cfjvt7Z2cmtt97Ke97zHpxOp0ySxrmSfEvG6dLNI8Rklu15kM05l0n6kTynR/LcH+n+HIkyx/paOFHiFEKIiSzjSdJ73/vehL9TFIVdu3YNJx4xSi6tclHusNDS5Y17X7tCaGvr8NbK6eQRYrxRCO1Wl4tb7uKdE5nI9JwrKzCnvOWuPCr9SJzTw21zOjK9roxkTOOhP9IxUeIUQoiJLOM1SQDNzc2oqhrz79SpU7mOTYwgvU5h03XVwLndkMLCP2+6rjrmeRvJ8ggx3oTH6O2rqoY9XhOdE5nI5JzT6xQ2r1uQsszoeHJ9TueizenI5Loy0jGNh/5Ix0SJUwghJrKsJkk63dBsiiIX44lmzcJyHlhfQ5kj9paMMocl4faxifJEc9qMkefWxJPOSCl3WNiwuoryQfVM5Pd8syHx6XZNdQnb1tcMae94luyUL3dYuKa6JOPjlei4pxpThXF+Hx7HG9dW80AGfasoYDv7cN/BZQ13S+VMzrk1C8vZtr4mbrudNuOQ7b8zLX84eXItUQyDx89oxDQe+iMdEyVOIYSYqDJ+TpJOp+Mb3/gGTqeTgoICqqqquOSSS+jo6GD69OkEg8HUhYwieU5Satk8sT06T3GeGTRo6/NF8gPnfm83gwJtvaHfL61w8kKDm9qjbWgaFNpMuGxGOvsHcOWZKSs4F8Pg2JZWODnQ1BGpV1U19jW4UTWVArORw6d76PMHKM03s2SWk7JCK2jQ0u3l0PEOAqrKmR4fCgp2s4H3LZ6BQa+jtcdLW6+fjj4/Jzs9qJpGe6+fMz0+VE2lvddPUIMiu4kPLJlJl9/P80c6gNCHkmuqS+n1BnDZTZTkWwioKr976SR9/iDLKp3cvLIKvU7h+aPtPPPGGZ490gZoLJ7l5MvXVmM9+6E83N5THR4OHu+gpcvL6W4fpQUWSgvMFFgNgEKvN4BG6EPk4llOyhwW1KBGbUMbpzq8oMCMQivL5xShqhqPHTqJxx9gaYWL+WX5vNDYgYZGodVIcZ6ZkgJLpC8HgkGOnOnFO6BRVWzji2su5NWTXbR09ePu80eOUbzjWJxnosxhjRw/f0BlR20jDe19KMCSWU5KCyyR8RBv7MQ77kPGVIoxF28ch8ts6eqnrdeHu2+Alq5+Sgss9PgG0CkKlUV2blpRiV6nZHxOjNQ5F1Q1nj/STu3RNkBhxdwils8pShrPcM/pkWhzOpKd76Md03joj3RMlDiFEGK8GLGHyVZWVqIoCj6fD7fbjd/vx2az8Z73vIdf/OIXMkkSQgghhBBCjEvpzg0y3rihsbEx5ueTJ0/y97//PfIg2ePHj6PT6ZgxY0amRQshhBBCCCHEmMv4m6REjhw5wlVXXYWiKJSUlLBv375cFDts8k2SEEIIIYQQAkbwm6RE5s6dO+RbJjH+JV3zM2gtUbL1IuF74Iez1uKyKhdosK+xnei1FxBaa9LS7cXd68NlD617iVkTAxRaz62JWTyrkJ3PN7K/oQOPP4Arz8jMQhuFNhOdHj8nOvo52tZLny+A3WRg+VwXR1p7OdzSi3cgSHmhhSvOK2HV+dNiYjjR4eEvr7Xg8QewmQxccX4Jj710gh5fgNkuGx9ZOosDJzo46e6nrc+PdyDIDKeVC8ryebOllx6fn46+AfxBDYfVyB2r5rBiXjEvNLp57kgbJ9z9qJrKmR4vxzu8BIMqRXlmVp03jcvnFaPTKbR2n1s/daqrn+mFVi6rdPHqqU5+vv84nZ4BLEaFlVXFXH/JLAwGXcz6n9D6Kx+d/QMoKCyrdPLP0z00tPdxpttHSb4ZRQG72cDpbh8znFZWzi1OeSwONHWcXe/jp9PjR1FgWYWLf7b2csztiawb0+t1XFblQqcocdcihceEBjgsRjr6/TR3eik7u3ZIURQqXHbml+bT1ueLacuKuUUsq3TFrFuLt+YpeqwOXmsVL01zZz8vNrl5o6UHRYF3XljGLW+rwnR2M45IWd1eWru91J/qxuMfoLTASs1sJ+WF1rTXiqRzDiVKk+nrE0W68eeinSPZV9HjJPr8mWjHQwghpoKsvkl69tlnCQaDXHHFFbzxxhv88Y9/pKamhiuvvHIEQhwe+SYpsT11zWzZXU9z17mnsuuSPFOm3GFh3aJyHn+5OSZPucMS2Y52cHnh3w3eaWlPXTNf2vUqnZ6B/5+9P4+P66oP///XvXd2LbNIliVZiiQ7q+Ikjp3YVhZTKJCQNiEN8GkLTgK0IfDg8+inBfqDQKntthAohbK0H5ukH9qQAN8CgRI/SM0SSjbbsWPHJo6y2ZbkTbIsjTQjafZ77++P8cgjaWY0I81II+n9fDz8gMyc5X3u3HvuHM095+SM0WXTsFnUjOkUmJP9mipsGtYsMSwluT6LXOdNPlLn1n++eGrWx1lRIFuvlutcLSQNJM+/j2xq49qLvDnTTS4316pjma7Jyfmypcl2bea6ZhfCCmj5HJNC0hWjrmK1o9h1CCGEmF7JFm746le/ygMPPIDVauXzn/88X/3qV2lububIkSPs2LGDD3/4w7MOvphkkJTZriO9fOyxg0UZZOQarKT+Npq+JO2uI7189LGDRahZiMLM1cA6V/3ZlmfOdk2mX0NAUa7bTNdlOcrnmNy6uiHvdMWoayby6W9znRtCCCGKJ9+xQcH7JG3fvp1HH32U559/nr/9279l27ZtHDx4kH/6p3/i61//+mxiFnNEN0y27ews2pfFXOWk3tu2sxPdMNENk61PvFKkmoUozHwOkFJS10K6XNdk+jW09YlXitKGyddlOcr3mMQSRl7pcrUz37pmcqzy7W/NWdQhhBCi+AoeJJ06dYobb7yRNWvWYLFYeNvb3gbA7bffzvHjx4seoCi+fV3+aR8NKiYT6A1E2NflP/88fnTO6hainKRfC+mmuyZT+Yp57WSLpVzke0we3dOdV7pc7cy3rpkcq0L623L+PIQQYqkpeJDkdrsZHh4G4N5776W2thaAWCyG3W4vanCiNPpH5m6ANLne+apbiHIy+TqYz+uiXK/JfOPq8YdmXV6+dc3kWBWap1w/DyGEWGoKHiTdcsstnDx5Ekg+epcaJO3du5err766uNGJkqircsxbvfNVtxDlZPJ1MJ/XRblek/nG1eJzzbq8fOuaybEqNE+5fh5CCLHUFDxI+u53v8u73vWuKa/feeed/PSnPy1KUKK01rf5aHA7mKsFZxWSqzetb/Oxvs1HfbX84iiWpvRrId1012QqX321vWjXbbZYykW+x+Tujta80uVqZ751zeRYFdLflvPnIYQQS03Bg6Rs3G43Ho+nWMWJEtJUZXyJ42J84VKy/P/0/95yezuaqqCpClvvuLIItQpRuHLYiSZ1LaTLdU2mX0Opa2e27Zh8XZajfI+JzaLmlS5XO/OtaybHKr3sXJRZ1CGEEKL4ijZIEgvLrasb2L55LfXuiY925Lo/N7gd3L+pjYZJeerdDnZsXsuODOXVux1TlrW9dXUDOzavxeOyThtnhU3Lmm6uvkpU2LPHsJTk+ixm+70udW4V4zgrOWJJP1cnn8fpsUyXBpLn3/2b2qZNl15uriWes12T6ddQtjTZrs1c1+xCWG46n2NSSLpi1DWbduQ65xbC5yGEEEvJjDaTXUhkn6TcJu8uv67Fy4GeIfpHItRW2EGBgdHohJ3nc+1IX8hu9bphsvfYIHuODwAKG9p8YMIL3YOAQseqGjaurAHIuEv9uhYv+7v87Dk+gAl4nDZqK5PvrWn28NjebvZ1DRGKJfBVWmnyuPC4bAyHYpwaCnN8YJSxaIIKm4WNq3wc6x/ltb5RInGdBo+Dt1xSx82XLpsQw6mhEL98pY9QLIHLZuEtl9bx05dOMRJNcJHPxZ+ua+bAqSFO+8MMjMWIxHVWeJ1cVl/Fm32jjERjDI3FiekmbqeVj9y8ko6La9nf7Wf3sQFO+cMYpsG5kQgnhyLoukFNpZ2bL1nGjRfXoqoK/cEIA6MxhsZinAmEafQ42dDq4+Uzw/xg30mGQ3EcVoUb2mp573XNWCwqA6NRaivtYCYnhg+MRhkOx1FQuL7VyxtnR+gaHONcMEpdlR1FgQq7hbPBKCu8Tm5YVTvtZ3GgZ4i+QJiB0RjDoRiKAte3+Hijf5QT/hCGaVBtt6JpKhvafKiKwsDY1HMrdU6YgNthZSgco3c4Qn21g5FoHEVRaPFVcPnyKgbGohPa0rGqhutbfRfO4fNtnlxP+rnaFwjjH4vhq7RTX505Te9wmBd7/LzeN4KiwDuvqOeDN7Vhs6gTywpG6A9G6DwTJBSLs7zaydqLvDR4nDmvhVzXZKZ82dIU+vpCkW/8xWhnKY9V+nmSfv0stM9DCCEWspJtJrvQyCBJCCGEEEIIASXcTFYIIYQQQgghFrMZDZIeffRRbrzxRhobG+np6QHg61//Oj/72c+KGpwQQgghhBBCzDVLoRm2b9/O3/7t3/KXf/mXfOELX0DXdQA8Hg9f//rXefe73130IJea9GfiJ8+pWNPs4fsv9NDjD9HsdXH58ir84dj4s/MAe48NJue3DIXoC0R4s3+EkUgCiwJul5VQLEEskVyQoK7agV1TGQrHwATDNBmNxgmEDQySk9MX9fOYQixClVZImGAYYJoQz3IRWxRo8jgYDkUZjZmoKjitGoZpohvJfzH9fFoVXFYFTVVRgGBUxzBBz1C226GxwuvCriXn2Wiaxu9fUUd7QzXPvnmOJw6fJhhOoBsmmgJWi8oldZX8/mX1nAyEOHI6iGmaVDstuGwWDNNkeCzGudEYTptGe0M1713XzHWtPh7b283eYwMcGxgDM7nASMuyCoZDcSIxnVA0zplgFNOEVcsquPmyWna93MdIRKettoJNq2oZiSdQFXV8Ttv+bj/PvN7PLzrPktANLquv5AMb2ghE4vhHo1Q7LPzudADDMDk7EgXToMJu5cpGN74KG4dODtEXiBKO65imSYXdwvo2H/fe0IamKux+c4DHXzpFKKZzfauPzRtbOHhiiD3HBgGTaxrcbH/uOL2BCI1uBw/fcz2vnx3JOZdpJvPRYgmDR3Z3sb97CIdVo8quoagKbTUV3N3RmjHWe29Ivp5tLuuUOYbHB8fb1bGylo2rarLOJ9v95gA/PniS08Nhmrwu3rO2iRsurkU3TB7d002PP0SLz8XdHa3j8wHL0UKfAyiESCp4TlJ7eztf/OIXufPOO6mqquLw4cOsXLmSI0eO8Hu/93sMDAzkXdYzzzzDV77yFQ4cOEBvby8//elPufPOO8ff/+AHP8gjjzwyIc8tt9zCrl278q5joc1J2nWkl207O+kNFL7rusdlJZYwCKW+1QghhChIqf8wZFEVEkZxamhwO8aXF59832hwO7jjmgaeONw75fUtt7fz0okhHnqmK2tbFQU0ZWqsCuC0aRPuM6oC6clSdf/ni6cYDsUn5Pe4rHzprqsmrOS360gvn/jh4Yz3Lsv5wVZ6FKoC993cxgO3Tb+0+lzLdA9PHXNZvVCI8lCyhRucTievvfYaLS0tEwZJb775JldffTXhcDjvsv77v/+b559/nnXr1nHXXXdlHCSdPXuWf//3fx9/zW634/V6865jIQ2Sdh3p5WOPHZRfboQQQpREuTwdsOP8kue7jvTy0ccOzqiM+zeV10Ap2z089RuSLPMuRHnId2xQ8ON2bW1tHDp0iJaWlgmv79q1iyuuuKKgst71rnfxrne9K2cau91OfX19oWEuOLphsm1nZ1ncvIQQQixO5XKP2frEK7zt8uVs+dmRGZfx8LNdfPKdl5fFo3e57uEmyYHStp2dvKO9Xh69E2KBKHiQ9IlPfIKPf/zjRCIRTNNk3759/OAHP+DBBx/k3/7t34oe4G9/+1vq6urwer287W1v4x/+4R+oqanJmj4ajRKNRsf/OxgMFj2mUtjX5Z/RI3ZCCCHEQtMXjPLonm7OjsRmXIZhwqN7uvmzm1cWMbKZme4ebgK9gQj7uvx0rMr+HUYIUT4KHiT9+Z//OU6nk7/5m78hFArx/ve/n8bGRr7xjW/wJ3/yJ0UN7tZbb+Wuu+6ira2NY8eO8dnPfpZ3vetd7NmzB03TMuZ58MEH2bZtW1HjmAv9IzJAEkIIsXT0+ENlUUYx5HsPl3u9EAtHwYMkgA984AN84AMfIBQKMTo6Sl1dXbHjApgw6Lrqqqu4+uqrWbVqFb/97W/5/d///Yx5HnjgAT7xiU+M/3cwGKS5ubkk8RVTXZVjvkMQQggh5kyLz1UWZRRDvvdwudcLsXDM+EHec+fOcfDgQd54442CVrSbjZUrV1JbW8vRo0ezprHb7VRXV0/4txCsb/PR4HYgTyoLIYRY7Oqr7dzd0cryKtuMy1AVuLujtXhBzcJ093CF5Cp3qa06hBDlr+BB0tjYGB/+8IdpaGhg06ZNbNq0iYaGBv7sz/6MUKi0P3ufOnWKwcFBGhoW3+owmqqML+UqAyUhhBCFyPe+US73l613XInNorLt3atnXMZ9N7eVxaINkPsenvrvLbe3y6INQiwgBfcun/jEJ3j66afZuXMnw8PDDA8P87Of/Yynn36aT37ykwWVNTo6yqFDhzh06BAAXV1dHDp0iBMnTjA6Ospf//Vfs3fvXrq7u3nqqad497vfzcUXX8wtt9xSaNgLwq2rG9i+eS317pn9HO91WXHZMs/VEkIIMb1Sf4W1FPFLcoPbwY7Na9mR4b7R4HZw/6Y2Gia9Xn8+z/2b2nK2VVEyx6rAlPvM5GSpuj0u65T8Hpd1fPlvSN73dmxem/XeZVGVKXGqSvkt/w3Z7+H1bocs/y3EAlTwPkm1tbX8+Mc/5vd+7/cmvP4///M//K//9b84d+5c3mX99re/5a1vfeuU1++99162b9/OnXfeyUsvvcTw8DCNjY28853v5O///u9Zvnx53nUspH2SUtJ3666ttIMJA2NR6qocrGn28P0Xeujxh2j2urh8eRX+cGx8V2+AvccG2X1sgFNDIfoCEd7sH2EkksCigNtlJRRLEEtAhV2jrtqBXVMZCsfABMM0GY3GCYQNDMpnTw0hRP4qrZAwwTDANCGe5SK2KNDkcTAcijIaM1FVcFo1DNNEN5L/Uvt7WlRwWRU0VUUBglEdwwQ9Q9luh8YKrwu7lvx6q2kav39FHe0N1Tz75jmeOHyaYDiBbphoClgtKpfUVfL7l9VzMhDiyOkgpmlS7bTgslkwTJPhsRjnRmM4bRrtDdW8d10z17X6eGxvN3uPDXBsYAxMqLBptCyrYDgUJxLTCUXjnAlGMU1YtayCmy+rZdfLfYxEdNpqK9i0qpaReAJVUelYVcP1rT72d/t55vV+ftF5loRucFl9JR/Y0EYgEsc/GqXaYeF3pwMYhsnZkSiYBhV2K1c2uvFV2Dh0coi+QJRwXMc0TSrsFta3+bj3hjY0VWH3mwM8/tIpQjGd61t9bN7YwsETQ+w5NgiYXNPgZvtzx+kNRGh0O3j4nut5/ewIfcEI/tEovgob9W4n69t8479MpN83UvcD7fxGrJleB4glDB7Z3cX+7iEcVo0qu4aiKrTVVHB3R2vGWO+9Ifl6epnrWrwc6BnKWPfe44Pj7epYWcvGVTUZf03RDZPdbw7w44MnOT0cpsnr4j1rm7jh4lp0w+TRPd30+EO0+Fzc3dFaNr8gZZLrmAsh5l/JNpN1uVwcOHBgyp5Ir7zyCuvXr2dsbGxmEZfIQhwkCSGEEEIIIYov37FBwX+K6ejoYMuWLUQiF5axDIfDbNu2jY6OjplFK4QQQgghhBBlouAlwL/xjW9wyy230NTUxDXXXAPA4cOHcTgc/OIXvyh6gEIIIYQQQggxlwp+3A4gFArxve99j9deew2AK664gg984AM4nc6iBzhbC/Fxu1jCyOv568nPPa9r8fLCsUEeP3iKU8MhVnhcXN5QyWgkOanA7bQyMBblyKkA0YRBsy/5zHfyuf4eXugapHc4RCxh4rKrnBuJEYomiBsmFlXBqimMRhLE9OQ8JbsGcSPznIQUFTBKc5iEKInUPDwVaPbYQVWJxXUGxmLEs5zMGqCqYNUUNNNkLHHhvNeAaodGMKqPXyvjdSjQ4nWgqHBuJEYsYZDQQU/L63GqoKhEdZO4brC80sqGVcvQFIUjZwKAQoVNxT8WxzB0EoBiKqAoXOS1cWY4RiRh4nFZuLKxGlOBo30j+McSxA0D1TSI6gaGqdDkdaIAEd0gFEmgaQrRhIkC1FXZuGV1Ix+6sW3CnBSP3cquzl72HBvEH47jdVi5eHkFt65upNHtJGEY/OTgKV7tDQJwWUM1V9ZXE4jG6R0KYwCqotDoduKtsOKrsDMciuFxWhkOx/G6bJwbjdJ5Jkg4nuD61ho2b2zh0MnhCXM+gCnzQHTD5D+e7+KXnX0AvP3y5Vy5wo0/FBuf79k/EsE/FsNXaae+Ovdcnkx1aKoy3md3DY6hANc2e2nwOHPmSacbJruPDvCTg6cYi+lc3+rl3huyr9xWrDkvxZ47k6u8fOqaybwqIYQoVMnmJC00C22Q9OCTnTz8bBdG2qeiKsmlTtNX8tl1pJdtOzvpDcju3UKIueWyaYRSKzqUgdQqasOh+PhrM42xwe3gjmsaeOJw74T+NVMdDW4Hq1dU89Sr/RP67OnybLm9fXyls11HevnkDw8zNilWBfhIhhXcMvX9k8vMR7HKyac8YNq6suXP9FnMJk4hhCjZIOkXv/hFxiW4jx07xoc//GGefvrpwqMtoYU0SHrwyU6+/UxX1vdTS57uOtLLxx47KKvOCSHEApP6/WP75rUAfPSxgznTpy91na3vTy8zn4FDscrJp7xs96nJx6GQe9pM4xRCCCjhwg3vfe97+fGPfzzhtW984xtcc801XHrppYVHKoDkI3YPP5t9gATw8LNdhGM623Z2ygBJCCEWoFTfvW1nJ1t+9sq06R96potYwkA3zKx9f3qZeqaftNIUq5xCysskva6tT7xS0D1tJnEKIUShCl644Yc//CF//Md/TCAQ4C1veQsf+tCHOHHiBD/+8Y+59dZbSxHjkvDonu6Mj2ukM0z44pPyiJ0QQixkJuTdj5sk7w/tje6ceVJl7uvy07GqJmu6fV3+opSTb3m5FHIcsuXNN04hhChUwYOkd73rXfz85z/njjvuIBqN8oEPfICf//znZf8oW7nr8YfyStc9mF86IYQQi0OPP0RtlT2vtP0juQcd071fqnSlMt/1CyEWrxltWX3zzTfzm9/8hsrKSurq6mSAVAQtPlde6Vpr8ksnhBBicWjxuaircuSVdrp0xSqn0HSlMt/1CyEWr4J/SbrrrrvG/39jYyNf+tKX2L17N16vF4Cf/OQnxYtuCbm7o5UvPPlqzkfuVAU+e1s7v361n75AROYlCSHEAqQA9W4HhmFydiQ6bdq7O1rRVIUGtyNr358qM7XseDbr23xFKSff8nJJ1WWaJmeD0YLyFxqnEEIUquBfktxu9/i/a6+9lnvuuYfW1tbx18TM2Cwq993cljPNfTe34bRp40uqyi4RQgixsKT67S23t7Pt3VdOm/4jm5L7JWmqkrXvTy9zuv2DilVOIeVNV9fWO67MmCabmcQphBCFkn2SyozskySEKHcLYZ+kCps2Ze+hfGTbm8frsmIi+yTNpDyQfZKEEOWjZPskdXV1kUgkuOSSSya8/uabb2K1WmltbZ1RwKWy0AZJwPju7T3+EC0+F3d3tGbceX3yLuTrWry8cGyQxw+e4tRwiBUeF5c3VDIaSd583U4rA2NRjpwKEE0YNPtcvGdtE9e1+nhsbw8vdA3SOxwiljBx2VXOjcQIRRPEDROLqmDVFEYjCWJ6cmUhuwZxA/RcjwgCRmkOkxAlkdrbRQWaPXZQVWJxnYGxGPEsJ7MGqCpYNQXNNBlLXDjvNaDaoRGM6uPXyngdCrR4HSgqnBuJEUsYJHTQ0/J6nCooKlHdJK4bLK+0smHVMjRF4ciZAKBQYVPxj8UxDJ0EoJgKKAoXeW2cGY4RSZh4XBaubKzGVOBo3wj+sQRxw0A1DaK6gWEqNHmdKEBENwhFEmiaQjRhogB1VTZuWd3Ih25sQ1OV8b7HY7eyq7OXPccG8YfjeB1WLl5ewa2rG2l0O0kYBj85eIpXe4MAXNZQzZX11QSicXqHwhiAqig0up14K6z4KuwMh2J4nFaGw3G8LhvnRqN0ngkSjie4vrWGzRtbOHRyeLzvSz1yld4frm/zoRsm//F8F7/s7APg7Zcv58oVbvyhGLWVdjCTE//9YzF8lXbqq5P5NFWZ0r9mq0NTlfE+u2twDAW4ttlLg8eZM0863TDZfXSAnxw8xVhM5/pWL/fe0Jax30+ln67MfBSrnHzKy6eubGmKHacQYmkr2SDpLW95Cx/+8Ie59957J7z+2GOP8W//9m/89re/nVHApbIQB0lCCCGEEEKI4ivZZrIvvfQSN95445TXN27cyKFDhwotTgghhBBCCCHKSsGDJEVRGBkZmfJ6IBBA18vnGXUhhBBCCCGEmImClwDftGkTDz74ID/4wQ/QNA0AXdd58MEHuemmm4oeoJjds9xzGV9fIMzAaJThcBwFhQ1tPlRFYWAsmjOm8fzBCP7RKL4KG/XuC8/z7z02yJ7jA3C+TEzY2z3I6aEwpgkrvA5uXLWMjed3Xd97fJDdRwc4PRym3u2gpsJObdWF+QbA1Llcxwf54f4T7D4+QFw3afK4uLrJjcWi0ux1oQAnh8KYpkmlQ8OianSsqmHtRV6+/0LPhPljmqqw9/gge44NAiYdK2u5vs3HgZ4h+oIRBkYiDIfiKMrUY7Sm2cNje7vZ3z1EhU3jrrVN3HBxLbph8sjubvZ3+3HZNN5zbRPXtfkm1P3H11/Ef+4/QfdgCMM0qLJbOBuM0uh1srG1BlVTOBuI8NLJIQzTRFUU1jR7qa92YJgme48Pcno4TKPHyYZWH2/0j3JyKESz18Xl9VX4Q7Hx43WgZ4j+kcj4vI6+YIRDJ4cAaK2pGD8O+7r8nBkOT3gvFWeP/3zZy6vwh2OzmsOQfu7UVTuSc01Goxdeq3KAAgOjuc/FbOdm/0gEn8vGa30jnPCPAbCm2UujxznheBTz2itkfkexY0gvP/UZT3cdCyHEYiJz4UTBc5I6OzvZtGkTHo+Hm2++GYBnn32WYDDIb37zG1avXl2SQGdqoc9JymcFomKvUjTb+LLJFFOu/B6XlVjCyHsVLZdNQ4GcK1plWm0qNYm+GBSSy7lHExNn+CsKzHQdScv5icvlsgylqpBzPy9IttdpLXwFtNmuhlVoPbmuj3zLn3w8inHtFbpSWDFjmK7dsrKYEGKxm8/vVaL0SrZwA8CZM2f4l3/5Fw4fPozT6eTqq6/mf//v/43PV36bui3kQdKuI7187LGDU74cp/6OsX3zWoBp05Tqgs4WXzaTYyo0v1j8cg1Y8znvC6knVVam62M25+Zsr71c1/1Mr7XZ1l2MsoUQYiHI57uX9H0LW0kHSQvJQh0k6YbJTV/+Tda/5irA8mo7oNAXzJ6m3u3guU+/reg/EU8XXzapmJ7+67fylq/8j+zzJAqSz3lfSFmZro+Zntv5lD2dYtQ90xgKqbuUfYsQQsyXfL57Sd+38JVsdTtIPl63efNmbrjhBk6fPg3Ao48+ynPPPTezaMUU+7r8Ob+smEBfMJrzi6IJ9AYi7Ovyz3l808X06J5uGSCJguVz3hdSVqbrY6bndj5lT6cYdc80hkLqLmXfIoQQ8yWf717S9y0dBQ+SHn/8cW655RacTicHDx4kGo0CydXtvvjFLxY9wKWqf6R4A4hillWsMnv8oSJFIsTsTD6X5/Pam89rdSZ1lyJeIYSYL6XsL8XCU/Ag6R/+4R/YsWMHDz/8MFardfz1G2+8kYMHDxY1uKWsrspRlmUVq8wWn6tIkQgxO5PP5fm89ubzWp1J3aWIVwgh5ksp+0ux8BQ8SHr99dfZtGnTlNfdbjfDw8PFiEkA69t8NLgdZHviVQHqq5PLWudK0+C+sOx1KeIrVCqmuztac7ZPiEzyOe8LKSvT9THdtTebsqdTjLpnGkMhdZeybxFCiPmSz3cv6fuWjoIHSfX19Rw9enTK68899xwrV64sSlACNFUZX+538sWa+u+td1zJ1jtyp9lye3tJJhem4iuk5PSYbBZ1vH1CpChZ/n/6f6ef97OtJ9P1kevam23Z08nnus8nrpnEkG+7S923CCHEfMmnD5a+b+koeJB033338X/+z//hhRdeQFEUzpw5w/e+9z0+9alP8bGPfawUMS5Zt65uYPvmtdRP+sWm3u0YX4IynzSlji/fX5QmxzRdfo/Lisum5R2Py6ZRMU16r8s6vldSSjG7OgWwW6ZeVsosKrGoSln94pbPvUFRKOizS6l3O9ixeS078jzvZ/Jr5uSyMsl2XWUy+XjM9trLdU1nOzbFiiGfds9F3yKEEPNlPr9XifJS8BLgpmnyxS9+kQcffJBQKDn53m6386lPfYq///u/L0mQs7FQlwBPl8+uz/O5M3Sq7r5AmIHRKMPhOAoKG9p8qIrCwFg0Z0zj+YMR/KNRfBU26t3O8Z+z9x4bZM/xAThfJibs7R7k9FAY04QVXgc3rlrGxlU1yfTHB9l9dIDTw2Hq3Q5qKuzUViUf0UqVmX6s1rV4eeH4ID/cf4LdxweI6yZNHhdXN7mxWFSavS4U4ORQGNM0qXRoWFSNjlU1rL3Iy/df6KHHH6LF5+LujlY0VWHv8UH2HBsETDpW1nJ9m48DPUP0BSMMjEQYDsVRlKnHaE2zh8f2drO/e4gKm8Zda5u44eJadMPkkd3d7O/247JpvOfaJq5r802o+4+vv4j/3H+C7sEQhmlQZbdwNhil0etkY2sNqqZwNhDhpZNDGKaJqiisafZSX+3AME32Hh/k9HCYRo+TDa0+3ugf5eRQiGavi8vrq/CHYuPH60DPEP0jEWor7WBCXzDCoZNDALTWVIwfh31dfs4Mhye8l4qzx3++7OVV+MOxKedIIed9+rlTV+0AE/pHoxdeq3KAAgOjuc/FbOdm/0gEn8vGa30jnPCPAbCm2UujxznheBTz2svV/snvFTuG9PJTn/F017EQQiwm8/m9SpRWyfdJisViHD16lNHRUdrb26msrJxxsKW0GAZJQgghhBBCiNnLd2xgmWkFNpuN9naZUyKEEEIIIYRYXAoeJN1111053//JT34y42CEEEIIIYQQYr4VPEhyu90T/vv73/8+t99+O1VVVUULaikol2dds80HyjTHAZgSc+q11LwTwwQFkwqbhf6RC/NhwjGdL+7qZHA0hsum0lLjJKFDOG4wPBZnJBbHaVGJJkzAxDTBYVUJxw1smoK3wk5MNxkcDROKJxdIsKlgt6mMRgyMDG2zKlBlVxiNmcSMC685LQrBeO6nTFXApim47BoaJudC+vh7zR4bdZV2Dp0eQTen5lMAfeLLvHWlm6GYQfdgiLGITqp6FVhV6yQUN4kbBuFIjJF45ng0FXSDjG2dzOtUqatyMBYzUDAZiyYYDusZ86qARYXE+bIVQEu9kV6fAjVOFYfNSkw3SegGppncgdxp1bBqCroJA6MxErqBXYNKp5VAOE4kkSzCpik4LRCKmxhGcoGHCptKbZWDKqcFp81Chc1Ck8/Bi13DjEbjOK0ahmlyeihMJGHgtCi4bBZGozomsLzajoZJIKqjANGEQSim47RqrKyr4M5rV/D9vScIRhJUOyx88MY2wjEdX4UNn9PGrs4+9nYNYtUU7lyzgisb3ezr8o/Pz7rx4lpWN7r5yx8cZE/XIIYJTW47b71iOXarBbfTSjCSnIfXsaqGjStr0A2TR/d00+MP0eR2EjcN/ue1cxiGiYlJNK6jKAorl1Vykc/FDRfXsnFlck5d1mtsKMShU8MYZnKhhmubvTRMmhM1PkcsEOalk8OcHYlQYbNwRUMVdVWOKdf2dPONCumnCu3TdMOcMN8wdewy1d87HOalk0OYQIuvIuM8tlL2qcUuu1z6/2JKn5vqH4vhq7wwD3Sht22+LcbzRYiFYMZzklKqqqo4fPhw2S7/XY5zknYd6WXbzk56Axd2bG5wO9hye/ucrpqSKY4UVQEj7cxIrQg3HIrnfE2Ipc5uUYklDArtWCtsGlaLWvA1NvlanU6u9On9UCH9VKF92q4jvXzmJy9PaZfHZeVLd12Vtf5sMd9xTQNPHO4tSZ9a7P66XPr/Ysr1WS30ts23xXi+CDHfSr5wQ0pFRQVHjhyhra1tNsWUTLkNknYd6eVjjx2c8gUq9TehuVpeMlscQoilK9UPfWRTGw8905VXP1Von7brSC8ffexgzjjuz1J/IYrRpxa7vy6X/r+Y8rmXKCzMts23xXi+CFEO8h0bFLxPUjAYJBgMcvbsWf75n/8ZXddpbGycVbBLhW6YbNvZmfFmknpt285O9EL+LFzkOIQQS1eqT3j42cwDlMn9VKF9mm6YbH3ilWnjyFZ/IWbbpxa7vy6X/r+YCrmXLLS2zbfFeL4IsdAUPEjyeDx4vV4aGxv57Gc/y9e+9jXsdnspYlt09nX5cz46YgK9gQj7uvzzGocQYukyyf34Xno/VWiflpz/GJ02hmJ975tNn1rs/rpc+v9iyvdeshDbNt8W4/kixEJT8MIN//M//wOA0+nkkksuwev1Fj2oxap/JL+BSb7pSh2HEEJkU0g/kko7X33PTOotdn9dLv1/MRUa60Jq23xbjOeLEAtNwYOkt7zlLaWIY0moq3IUNV2p4xBCiGwK6UdSaeer75lJvcXur8ul/y+mQmNdSG2bb4vxfBFioSl4kPTNb34z5/t/8Rd/MeNgFrv1bT4a3A76ApGMzxkrQL37wrK/8xWHEGLpUkguzZ7tkbfJ/VQhfdr6Nh/11fZpH7lTFcaXl5+N2fSpxe6vy6X/L6ZUm6Z75G4htm2+LcbzRYiFpuA5SX/5l3/JZz/7Wb7+9a/zz//8zxP+ff3rXy9BiIuHpipsub0duLA6TUrqv7fc3l7y/Q/S4xBCiJRUz3PfzW3JwVKW91P9VKF9mqYqbL3jymnjuO/m2a+WOts+tdj9dbn0/8WUalM+ES+0ts23xXi+CLHQFDxI+tznPoeqqrz97W9n7969dHV1jf87fvx4KWJcVG5d3cD2zWupd0/8ibze7ZjT5TxTcTS4M/9UP7nf9bis43u2pHgzvCbEUme3qHl9aZyswq7N6Bor9DtSrvSpfuiB29rz7qcK7dNuXd3Ajs1rM7bL67KyI63+bP1Tuga3g/s3tU1JW4w+tdj9dbn0/8U03b2kYQG3bb4txvNFiIVkRvsknT59ms997nP813/9F3/913/Npz71qbJd4a7c9klKKZcdtMd3SQ9G8I9G8VXYqHc7Wdfi5UDP0IT4gCkxp147Mxzm0MkhDBMUTCpsFvpHojR6nWxsrSEc0/nirk4GR2O4bCotNU4SOoTjBsNjcUZicZwWlWjCBExMExxWlXDcwKYpeCvsxHSTwdEwoXjyL2k2Few2ldGIgZGhbVYFquwKozGTmHHhNadFIRjPfdqrgE1TcNk1NEzOhfTx95o9Nuoq7Rw6PYJuTs2nAPrEl3nrSjdDMYPuwRBjEZ1U9SqwqtZJKG4SNwzCkRgjGfYNVQFNBd0gY1sn8zpV6qocjMUMFEzGogmGw3rGvCpgUSFxvmwF0FJvpNenQI1TxWGzEtNNErox/kiU06ph1RR0EwZGYyR0A7sGlU4rgXCcSCJZhE1TcFogFDcxjORjXRU2ldoqB1VOC06bhQqbhSafgxe7hhmNxnFaNQzT5PRQmEjCwGlRcNksjEZ1TGB5tR0Nk0BURwGiCYNQTMdp1VhZV8Gd167g+3tPEIwkqHZY+OCNbYRjOr4KGz6njV2dfeztGsSqKdy5ZgVXNrrZ1+Xn9HCYRo+TGy+uZXWjm7/8wUH2dA1imNDktvPWK5Zjt1pwO60EI3EUFDpW1bBxZQ26YfLonm56/CGa3E7ipsH/vHYOwzAxMYnGdRRFYeWySi7yubjh4lo2rqwBclxjQyEOnRrGMJMDnWubvTR4Jl6rtZV2MKEvEOalk8OcHYlQYbNwRUMVdVWOKdd2Kv3AWDRjP1RIP1Von6YbJnuPDbLn+ACkHbtM9fcOh3np5BAm0OKr4PLlVfjDsQn1lLJPLXbZ5dL/F9P4vSQQxj8Ww1dpp756cbRtvi3G80WI+TQnm8kePHiQT33qU7z55pt84Qtf4J577plpUSVTroMkIYQQQgghxNwq2SDpd7/73ZTXfvazn/GVr3yFSy65hAMHDhQebQnJIEkIIYQQQggB+Y8NCl7dbs2aNSiKQmpslf7/Dx06NLNohRBCCCGEEKJMFDxI6urqKkUcYoHK9ax0pvdg6pyLyc9WT86XmkMxPu/JMFFVhTXNXuqq7LzWF6R7YIzO3gD+sTh2q8pd1zbx4ZtWAvDI7i72dw9RYdP4w9UN/OaNfg6fGgYUblhVw7IqB74KG/0jEZ578xzBSIJrmjx87g/asVlUnnv9HA89d5xgJM41TR4+864rOHxqmKff6Of5NwcIxXQq7BZuXFXDpkvrWNvi5bG9PezvHsRls/CetU3ccHFtxmfIdcNk7/FB9hwbxDRNPC4rXpeNgyeGeL1vBEWBd7bX88Eb29BUZXwOh2GC12WjpsLG4FiMoVAUVVGnzOtIzfvYfXyA00NhdF3HH0rgsmusb63h3htasVlUYgljfA5Ns9eVcc7HdJ93SqqsrsExFKbOnck0ZyE1h6d7MASYXN3kIRiOj8+Ry/YMfrZzJds5V1thBwUGRqMZ57NMji1TedPNBSjF/IFymhOTLa/MmxBCCLGYzGpO0kIgj9uVzq4jvWzb2Tlhj4wGt2N82dLJ76VWsxoOxaekT63Sk6lMNceeLaWmMPu9WgBcNo2v/a9rJqxGtOtIL5/5ycsTjkcuqcHMdDwuK1+66yqAactXFLhqRTVHTgezHuNcn+nkz+/BJzt5+NmujGVl+xxdNo1wXCdXTzS5HsjvXMl0zk0u945rGnjicG/GvV4ml5cpjnS5romZrkRV7DJnU162vJmO4WzbLYQQQpRCyeYkRSIR/umf/gld1/n0pz/NT3/6U37wgx+wdu1a/uZv/gaLpeAfp0pKBkmlsetILx977OCUAUShg4rU35m3b14LkLHMxWTH+WVbdx3p5aOPHZzvcPKS6zNN//xeOjHEt58p3S/Nyvl6UsdvPs6V9PZO/vKf65rIlmc6xS5zNuUVesxn024hhBCiVEo2SLrvvvt46qmnqK6u5tJLL+WFF17gPe95Dz/4wQ+4++67+cd//MdZB19MMkgqPt0wuenLv5l2l/V8KSSXcQaFvmBxyixX9dV2nvn/vY1N//g/i6atqc+vfyRa8l/8GtwOnv7rt/KWr/xP0c6/QqV2un/u02+b8BhirmsiU57pFLvM2ZQ302t+Ju0WQgghSinfsUHBm8nu3LmTRx99lF/+8pf8+Mc/5l//9V/52te+xre//W1+9KMfzSposTDs6/IX9QuqCfQFo4tm0JBLXzDKo3u6F1VbU5/fXDwS2RuI8Oie7nkbIEGyvb2BCPu6/OOvTXdNZMoznWKXOZvyZnrNz6TdQgghRDkoeJA0PDxMa2srdXV1uFwuLr/8ciC56l1fX1/RAxTlp39k8XzBnw89/tB8h7CglcvxS78O8r0mCrl2il3mbNLN9pqXPkMIIcRCU/Agafny5Zw5cwaAhx56iIaG5LPmw8PD+Hy+4kYnylJdlWO+Q1jQWnyu+Q5hQSuX45d+HeR7TRRy7RS7zNmkm+01L32GEEKIhabgQdInP/lJDCO5wtb73/9+KioqADh48CB/+Id/WNzoRFla3+ajwe2gWDMMFJJzdeqri1dmuaqvtnN3Ryv11YvnS2Pq85uLKScNbgd3d7QW9fwrlHI+jtTy4jD9NZEpz3SKXeZsypvpNT+TdgshhBDloOBB0l/8xV+wYcOGKa9/8IMf5Nvf/nZRghLlTVOV8SWhJ39pUrL8/2xSabbecSVb78hc5mKy9Y4rsVnU8bYuBLk+0/TP776b20oex5bbk3tXZTv/Si1V35bb2ycsRJDPNTE5z3SKXeZsysuVN5uZtlsIIYQoBwUPkoQAuHV1A9s3r6XePfEXkXq3gx2b17Ijw3tel3V835r09KklgrOVOZ/fr4pVdYVNG1/+G5LHb8fmtVOORy42S36Xq9dlHf8MpitfUeDqpuqcxzjXZ5r++T1wWzv3b2rLWla21ytsGso0B7ohrR7Ifv5NriPTOTe53Ps3tdHgzvzL3uTy6ifFkS7XNTHTZbCLXeZsysuWN9sxnE27hRBCiPlW8BLg08078vvLaxUjWQK8tHTDZF+Xn/6RCHVVycdq0pdFnvwekDV9tjLXtXg50DPEmeEwh04OYRgmqqqwptlLXZWd1/qCdA+M0dkbwD8Wx25VuevaJj5800oAHtndxf7uISpsGn+4uoHfvNHP4VPDgMINq2pYVuXAV2GjfyTCc2+eIxhJcE2Th8/9QfJXi+deP8dDzx0nGIlzTZOHz7zrCg6fGubpN/p5/s0BQjGdCruFG1fVsOnSOta2eHlsbw/7uwdx2Sy8Z20TN1xcm/Gv6bphsvf4IHuODWKaJh6XFa/LxsETQ7zeN4KiwDvb6/ngjW1oqsLeY4PsOT6AYYLXZaOmwsbgWIyhUBRVUelYVcPGlTUTPoO9xwbZfXyA00NhdF3HH0rgsmusb63h3htaxzepfXRPNz3+EM1eF5cvr8IfjuX1mU5uV6qsrsExFODaZi8NHuf459gXCOMfi+GrTD5iub7Nh26YPLqnm+7BEGBydZOHYDiOr8JGvduZsZ5c50q2c662wg4KDIxGJ8SfKmdybJnKm+5XkXyOUaGKXeZsysuWtxTtFkIIIYqtZPskeb1eDMPgr/7qr2hrm/p4zb333lt4tCUkgyQhhBBCCCEE5D82sBRa8LFjx9i6dStf/epX+ehHP8rf/M3f4Ha7ZxWsEEIIIYQQQpSLguck+Xw+vvnNb3LgwAGOHj3KxRdfzLe+9S10XS9FfEIIIYQQQggxpwp+3G6yZ555hk9+8pMEg0G+/OUvc+eddxYptOIo18ftppvLk5qnAibXt/h4o38kuYmmaVLtsKGqsKGtBkx4oXsQw4BgJA4KtNVU8P4NLbzY7efxg6c46Q9htyigKDitGqZpMBSKc3YkymgkgVVVWb2iirpqJy92DxEMx9H1BKMxE9OE1AlizNvREmJpUUhedxrgsCrYNIW4bhKKmxmvQw1I/zOVSubrVSW5GEUirde3quC0aXicVuoqbbxxboyRiI4JWBRwOy0sq7Rhtagc7R8jfD6zCmgKxM2pdbhdFuqrHTRUO1BUBd0weLVvFKuqcHlDNf/w7qv4zE8Os/f4IIYJ7fWVPPJnHdgsKv+w8xWePz5ANG5gGAbhhEGV3ULHqhqiCZPeQJgVHhfvXdfEhpU17O/y89yb5/jd6QCRuE6T18lda5pQNYXdRwc4dGqYk/4QFi05B/Hzf7gap00bn6+35/gAumESCMfpD0aIJAxWN7qptGs8+XIvZwIRKu0W/nRDMx++cRUvdvv58YGTnB5OxnFlQzXLqu3UVTvAhIGx6LTzLfuCEQZGogyFYqhKsi83DJPHD57itb4goHB5QxXvW9fMhpU1Bc2LS28XKGxo86EqSs64cpWVa55ZPu+n38s6VtaycVVNQXPVijHXbbbtEEIsLiWbk3TXXXdNec0wDJ566ilCoVBBvyg988wzfOUrX+HAgQP09vby05/+dMIgyzRNtmzZwsMPP8zw8DA33ngj27dv55JLLsm7jnIcJO060su2nZ30Bi7sQt/gdowvsfuZn7zMcCg+X+EJIcSidnVTNSf84ZL2s6k+PbW6X6Z+Px+pAXO2ctPtOtI77f0jV/7JZWW7T926uiGv9zPF4nFZ+dJdV+W16uF0deRjtu0QQiw+JRskfehDH8r5/r//+7/nXdZ///d/8/zzz7Nu3TruuuuuKYOkL3/5yzz44IM88sgjtLW18fnPf56XX36Zzs5OHI78NuMst0HSriO9fOyxg0w+6JNvhEIIIRau1O8Q2zevBcjY78+23PQv8buO9PLRxw7OOH+6XPcpgI9sauOhZ7pyvv/tZ7pyxrFjmuXhp4shn+XlZ9sOWcJeiMWpZIOkUlEUZcIgyTRNGhsb+eQnP8mnPvUpAAKBAMuXL+c//uM/+JM/+ZO8yi2nQZJumNz05d8U/JdEIYQQC48CLK+2Awp9weL1+wrJfaie+/Tbxpdfv/FLT9EXjM4of7rp7lMKyf3VjBzfHNRp3geor7bz/Gd+P+vS/tPFkC3+YrUjnzqEEAtTvmODGW0mGw6H2b9/Py+//DKlGmN1dXXR19fH29/+9vHX3G43GzZsYM+ePVnzRaNRgsHghH/lYl+XXwZIQgixRJhAXzBa1AFSqtzeQIR9Xcl9CZPznPIbIGXKn266+5TJ9AOg6d6H5HHJVH++MWSLv5AycsWZTx1CiMWt4EHS9u3bqaurY+PGjaxZs4bW1laef/75ogfW19cHwPLlyye8vnz58vH3MnnwwQdxu93j/5qbm4se20z1j8gASQghRHGk7ikzvbdkyjeX96lsdeUbQ650xWqH3LeFWLoKGiT9+te/5uMf/zjve9/7ePzxx/nud79LS0sL7373uxkcHCxVjAV54IEHCAQC4/9Onjw53yGNq6vKbx6VEEIIMZ3UPWWm95ZM+ebyPpWtrnxjyJWuWO2Q+7YQS1dBg6Svfe1rfPCDH+Q73/kOd955Jx/4wAf4zW9+w7JlywpasCEf9fX1AJw9e3bC62fPnh1/LxO73U51dfWEf+VifZuPBrcDebpZCCEWP4Xk3Jv66uL2+wrJFdjWt/mA5L2lvto+4/zpprtPKSTnHOWSzxSe+mp7xvrzjSFb/IWUkSvOfOoQQixuBQ2SnnvuOf78z/98wmsWi4V77rmHZ599tqiBtbW1UV9fz1NPPTX+WjAY5IUXXqCjo6Oodc0VTVXGl/me3DfLwEkIIRaPVJ++9Y4r2XpHe9HL3XJ7+/iCApqqsPWOK2ecP10+96n7bm5LLnyQ4X3l/PvT2XrHlVkXRMgnhmzxF1JGrnbkU4cQYnHLe3W7O+64g5///Ofceuut2O0T/2J1+vRpjh49yu/93u/x+OOP51356OgoR48eBeDaa6/la1/7Gm9961vx+XxcdNFFfPnLX+ZLX/rShCXAf/e73y3oJcBB9kkSQoj5JPskyT5Jsk+SEEtX0ZcAv+eee/je977H+973PpxO54T3urq6ePXVV/mDP/gDvvOd7+Qd5G9/+1ve+ta3Tnn93nvv5T/+4z/GN5N96KGHGB4e5qabbuL//t//y6WXXpp3HeU4SILcO3xP3qX8+hYfb/SP0OMPgWlS7bChqsld2jHhhe5BDAOCkTgo0FZTwfs3tPBit5/HD57ipD+E3aKAouC0apimwVAoztmRKKORBFZVZfWKKuqqnbzYPUQwHEfXE4zGTEzzwg3amLejJcTSkvpirAEOq4JNU4jrJqG4mfE61ID0bbxVMl+vKslHjBJpvb5VBadNw+O0Uldp441zY4xEdEzAooDbaWFZpQ2rReVo/xjh85lVQFMgbk6tw+2yUF/toKHagaIq6IbBq32jWFWFyxuq+Yd3X8VnfnKYvccHMUxor6/kkT/rwGZR+Yedr/D88QGicQPDMAgnDKrsFjpW1RBNmPQGwqzwuHjvuiY2rKxhf5ef5948x+9OB4jEdZq8Tu5a04SqKew+OsChU8Oc9IewaAo3rKrh83+4GqdNS/azxwbZc3wA3TAJhOP0ByNEEgarG91U2jWefLmXM4EIlXYLf7qhmQ/fuIoXu/38+MBJTg8n47iyoZpl1Xbqqh1gwsBYdEqfnpLq9/uCEQZGogyFYqhKsi83DJPHD57itb4goHB5QxXvW9fMhpU1HOgZynivyCS9XaCwoc2Hqig548pVVrb7VL7vp9/LOlbWsnFVTUG/zkxXx1y0QwixuJRkn6Tm5ma+973vsWnTpgmv/93f/R1Hjx7lu9/97swjLpFyHSQJIYQQQggh5lZJ9km68cYb+cY3vjHhtVAoxHe+8x1uuummmUUqhBBCCCGEEGWkoF+SXnnlFdasWcPb3vY2/uAP/oBQKMR3v/tdIpEIr7/+OlartZSxzoj8kiSEEEIIIYSA/McGlkIKvfLKK/nhD3/IX/3VX/GrX/0KgA0bNvCjH/2oLAdIIrf057BrK+05n6dPPef+/LFznBmOsMLrZGNbDaqqMDAapbbCDgoMjGbPv6/LT18gjH8shq/STm2Fjdf6gpzwhzBNqLRbODMcZmA0SjRh0Oh2UOm0oKDQPxLFBCptGpctr+KN/lGCkRjDY3GcNgstNS4a3Q5++8YAYPLO9no+eGMbmqok4z46wKFTQwyMRKm0a1TbrbzZP8ZwJI7bYWFlnYuuc2GiCR2Py8p1rTVYteS8gB/uO8lrZ0cwDBOPw8L6VT6avBUEwnH2d/k5HYhg0xRW1lZwXZsPh0Xj2kYPPzh4kpNDYS7yOvnnP74Wp01jX5ef00Mhdr3SR+9wGEVRuOniWjpW1vD62RFe7BnCYVVxWBWOnh1jJKpjtygsr04e08uXV7Ovx8+ZoTCNHifXXeTlqdf7OT4wSjRucGl9FRZV4eoVbgbGYjzz+jm6BsewqAor3HYsmorTbmFZpYPRaILeQIRGt50Ku5WzIxGOnxtF13VURcVq0VAVuLy+mvdd18wNF9eiqcr4Z9k7HObgiSH6AmHCcYOrmtzcfMkyNq6smTC/bl+XnzPDYQ6dHMIwk3Ni1jR5qHc7QYH+kSjnghGOnB7m9HAEh1XjysZqRqMJ+oMRwnGDa5o83HhJLRtX1gBMmD9w1Qo3X971Kt2DIVprXHz2tnacNm3Cebv7+ACn/SFASZ67K2tQFYX+kQj+sRhel42hUAy308rhU8PopokKVDqs9AUiNHoc3LhqGde3+abMD5kcz7oWL/u7/ONzQjpW1Uw4JtNdi+nXT6FzJRb73IpSzC2Z0A9O048JMR8W+3UtRLkq6JekdP39/dhsNjweT5FDKi75JSmz6VZaymeVolymWz1ortgsKrFEeSw5YT0/+X6hctk07t54EU8c7s35WaZWrwKK/rlX2DSsFnXac/Ed7XW8Z21T0VeKnLzSmMeV/ONQeh2T06TSZVvRK9vqWndc0zDlWE+3stliXqWrFKuUFdIPCjEfFvt1LcR8KMnCDQuRDJKm2nWkl489dnDKF7l0qb9RfWRTG99+pqvgOtLzP/RMV866hFgqdmxeO+GLTT7XYrrUdbU9z3KypV9opmtftn4mV/sL6QcX+vETC9Niv66FmC8lWbhBLHy6YbJtZ+e0X8rM8/8emsEAKT3/w8/KAEmIlG07O9GN5BWR77WYLpU233IypV9o8mlftn4mW/sL6Qcz5Rei1Bb7dS3EQiCDpCVmX5e/oMefZtv9Sv8txAW9gQj7uvxA4ddiillgOZPTLzT5tC9XP5Op/YUc+4V+/MTCtNivayEWAhkkLTH9I3M/L0gIcUHqGpzttVhoOQv12i9W3OnlzKTMhXr8xMK02K9rIRYCGSQtMXVVjvkOQYglLXUNzvZaLLSchXrtFyvu9HJmUuZCPX5iYVrs17UQC4EMkpaY9W0+GtwO8l08dLaLjMoqpUJc0OC+sGx4oddiilJgOZPTLzT5tC9XP5Op/YUc+4V+/MTCtNivayEWAhkkLTGaqrDl9nYg9wBIOf/vI5vaZlRPKv99N7fNeqAlxGKx5fb28f1N8r0W06XS5VtOpvQLTT7tS/Uz+ba/kH4wU34hSm2xX9dCLAQySFqCbl3dwPbNa6l3Z/+Zvt7tYPvmtTxwWzs7Nq8d3w8mX+n5t29eS0OOukrJZimfU9yqLeybWYVN4/5NbdN+ll6XlR2b17KjBJ97hV3L61x8R3vdjM7b6Uz+BD0u65Q6Mn3KqWMyebnebNdig9uR8Vinrqt8y8mWfqGZrn2pfqaQ9hfSDy704ycWpsV+XQtR7mSfpCVswk7zlXYwYWAs807zumGy99ggzx87x5nhCCu8Tja21aCqCgOj0Wl3qk/V1RcI4x+L4au0U1th47W+ICf8IUwTKu0WzgyHGRiNEk0YNLodVDotKCj0j0QxgUqbxmXLq3ijf5RgJMbwWBynzUJLjYtGt4PfvjEAmLyzvZ4P3tiGpirJuI8OcOjUEAMjUSrtGtV2K2/2jzEcieN2WFhZ56LrXJhoQsfjsnJdaw1WTeHyhip+uO8kr50dwTBMPA4L61f5aPJWEAjH2d/l53Qggk1TWFlbwXVtPhwWjWsbPfzg4ElODoW5yOvkn//4Wpw2jX1dfk4Phdj1Sh+9w2EUReGmi2vpWFnD62dHeLFnCIdVxWFVOHp2jJGojt2isLw6eUwvX17Nvh4/Z4bCNHqcXHeRl6de7+f4wCjRuMGl9VVYVIWrV7gZGIvxzOvn6Bocw6IqrHDbsWgqTruFZZUORqMJegMRGt12KuxWzo5EOH5uFF3XURUVq0VDVeDy+mred10zN1xci6Yq459l73CYgyeG6AuECccNrmpyc/Mly9i4smb8s0+lPTMc5tDJIQwz+WjUmiYP9W4nKNA/EuVcMMKR08OcHo7gsGpc2VjNaDRBfzBCOG5wTZOHGy+pZePKGoAJu89ftcLNl3e9SvdgiNYaF5+9rR2nTZtw3u4+PsBpfwhQkufuyhpURaF/JIJ/LIbXZWMoFMPttHL41DC6aaIClQ4rfYEIjR4HN65axvVtPg70DI3XnXrUJT2edS1e9nf52XN8AFDoWFUz4ZhMdy2mXz/ZXi+0nMViuvbNpP0T+sFp+jEh5sNiv66FmGuymex5MkgSQgghhBBCgGwmK4QQQgghhBAzIoMkIYQQQgghhEhjme8AxNzKNg+ptsKOYZrs7RrkzFCY+moHvkobbqeNX3b20heIUOWwEoknGAnHCcdN3C4rqqJw4yU13NBay+v9I+zrGqQvGCWa0MEEiwo2q8aZ4RDBsI5FVVjb7CEYiXNsYAzDhGqHRsIAl00jltAZGosTMy5MgDfS4k+tYGWe/zdfUjEUq6yUTGVqgNupMRTWC65TZeLxm6nGahveSjutXgfDEZ2xaILugTECER1ItiFVj0UBj1PDZbfisqn4x+IEQnGiGQJJfZ5WTaGuykal3UIobmCaJvGETjimkzBNErpJTJ/YFgvgsqs4bRrDoTgJAyqtCi67hdGYjs2i4XFqKIoKJpimweBYHIum4nZacVg1llfZUFUVw4DjA6OEYgmsmkq1w0KTr4J1F3lRVIXuwVFe6w3SG4gC0N5QxW1XNbLC65owL+mUf4xfvNLH0f4xRmNxmj1O3nFlPRZF5VQgTLPXxaV1lezrTs7Tqvc4qLBa2PVKL4FwgkvqKriu1cfp4TBnAxFQFCrtFtobqllWZaeu2kEibvDTw6cZjSYAqKmwMjgau5C2vppl1Xbq3c7k/KhuP3uODQIm17f4eKN/lJNDIVp8Lu7uaEVTlSlzcvpHovhHo/gqbOPlpOZi+Vw2OnuDHOgZosKmcdfaJq5r9fH9F3ro8V8od/KiKZPnVaSXOd08i1LMRZrObMuUeSRCCLGwyZykJWTXkV627eykNyA7dAtRLKnV7YZD8XmOZCpFgVw9vKKA06oRiuk5y1EVMAq4U6hKclnuB25LLmGcqe+ZXGaD28GW29unrNiVKW962unen4nZllmKmIQQQhSHLNxwngySknYd6eVjjx2c119fhBBLy/2b2rj2Im9efU/qN5b0pY2z9VuptB/Z1MZDz3RlfX8myyRPV+d0Zc42vxBCiNKShRvEON0w2bazUwZIQog59fCzXWz52St59T2pNNt2dqIbZs5+K/W47cPPTh0gZSorX9PVOV2Zs80vhBCifMggaQnY1+WXR+yEEHPOMOHsSDTv9CbQG4iwr8ufV7+Va6yRXla+pqtzujJnm18IIUT5kIUbloD+ERkgCSEWjmL2WYWUlW/abOlmm18IIUT5kEHSElBX5ZjvEIQQIm/F7LMKKSvftNnSzTa/EEKI8iGP2y0B69t8NLgdyOKzQoi5pCqwvMqed9+jkFwFbn2bL69+S1XI+n56Wfmars7pypxtfiGEEOVDBklLgKYqbLk9uRSvDJSEEHPlvpvb2PbuK4Hp+57U+1tub0dTlZz9Vmp/rftubsv6fnpZ+ZquzunKnG1+IYQQ5UMGSUvErasb2L55LfVuecxDiGLyuqzjeyWVG2Wa7+KKktzEeTqFfqdXleTy3w/c1p6175lcZr3bMWV57Gx5U2kfuK095/szWWp7ujqnK3O2+YUQQpQH2SdpiUnfBb620g4mDIxFqa2wY5gme7sGOTMUpr7aga/Shttp45edvfQFIlQ5rETiCUbCccJxE7fLiqoo3HhJDTe01vJ6/wj7ugbpC0aJJnQwwaKCzapxZjhEMKxjURXWNnsIRuIcGxjDMKHaoZEwkl/WYgmdobE4MePCX16NtPhTf0FOLQE8X1IxFKuslExlaoDbqTEU1guuU2Xi8Zupxmob3ko7rV4HwxGdsWiC7oExApHkJqRKWj0WBTxODZfdisum4h+LEwjFiWYIJPV5WjWFuioblXYLobiBaZrEEzrhmE7CNEnoJjF9YlssgMuu4rRpDIfiJAyotCq47BZGYzo2i4bHqaEoKphgmgaDY3EsmorbacVh1VheZUNVVQwDjg+MEoolsGoq1Q4LTb4K1l3kRVEVugdHea03SG8guVJbe0MVt13VyAqva/zRqX1dfk75x/jFK30c7R9jNBan2ePkHVfWY1FUTgXCNHtdXFpXyb5uP2eGw9R7HFRYLex6pZdAOMEldRVc1+rj9HCYs4EIKAqVdgvtDdUsq7JTV+0gETf46eHTjEYTANRUWBkcjV1IW1/Nsmo79W4n61q87O/2s+fYIGByfYuPN/pHOTkUosXn4u6OVjRVudAnVNhBgf6RKP7RKL4K23g5B3qG6B+J4HPZ6OwNcqBniAqbxl1rm7iu1cf3X+ihx3+hXJtl4t/g0vueuirHhDLrqpKPoGX7hWVy3slpp3t/JmZbZiliEkIIMXuymex5MkgSQgghhBBCgGwmK4QQQgghhBAzIoMkIYQQQgghhEgj+yQtEKnn2/uCkQlzBVLPuWd7f02zh8f29rCva5BQTOeqFW5qKu3UVtqoq3JgmCZ7jg9yeiiEacLZYJjO3iCRuEHcAE0Bh0Wl2qGhm2C3qMTiOmfHEvN9SIQoW5PnrPns4I9mT8v59FY1OQctaiQXNlhWZaPSonDcH8Ug+VetFo+FgbCBYSgYRnKeWtwA00xer4YCLotyfg6Tk2A4Tm8wTDCso5vgsECD24mJgtOqEgjFMU0Tg2S+/tEYugnVdgsX+Zx09o2iGwYrPA6ua6nhZCDMycEQTqvKRTUurmvxcSYQJRZPsPf4ICMxnWavg8uWV9HZOwrAxlU+6iodDIfinAmEaXA78FXYqKmwMxSKTejPgPG5PD6njc6+5Pwnp1XDaVV5s38EULisvorrWnw0eJL93Pdf6OH4wBh9gRBDoQSqovC2y5cRj+n8v709xBIGTR4HP7jvBo6eGx0v/9W+IC/2DOGyabzn2iZuuKR2Qp+aa05RpjTp8afmffaPRjP225nohsne44Pj88g6VtaycVVNXvOZZpM3n7Kub/PlnEcWSxg8uqc759y0cjTfcRdj/tpSnQO3VNtdanJck2RO0gKw60gv23Z20huYukt7g9vBHdc08MTh3ozvCyHEQpFaJXA4FJ+3GFw2jbs3XjSlT21wO9hye/v46nSZ+uV8459cVsquI7185icvT8nvcVn50l1X5VwZbzZ58y1LUZKD8UztePDJTh5+tgsj7X1VSS7T/sBt7XnXPdfmO+5M51G286OUZSxES7XdpbYUjqss3HDeQh8k7TrSy8ceOzivK7kJIcRSl/ob6vbNawFm3S8r58tKH3R99LGDOfPsyLKE+GzyzqSslNQxeXt7Hb/q7M+aLrUcfLl58MlOvv1MV9b3Sx13tvt7+rk23WdWjDIWoqXa7lJbKsdVFm5YBHTDZNvOThkgCSHEPEv1w1ufeIWtT8y+XzaBbTs70Q0T3TDZ+kTntHm2PvEKujGx5tnknSzfslJSpeUaIAE8/GwXsUQxNiMonljC4OFnsw+QoLRx57q/p15LnR+lLGMhWqrtLjU5rlPJIKmM7evyyyN0QghRJkygLxilL1icfrk3EGFfl398Pul0+oJR9nX5J7w2m7yT5VtWuny+LhkmPLqnu6ByS+3RPd1M912vlHFPd383uXB+lLKMhWiptrvU5LhOJQs3lLH+ERkgCSHEYlZoPz85fSH5p0tbyntOjz9UsrJnIt94ShV3vsc6V7pilLEQLdV2l5oc16lkkFTG6qoc8x2CEEKIEiq0n5+cvpD806Ut5T2nxecqWdkzkW88pYo732OdK10xyliIlmq7S02O61TyuF0ZW9/mo8HtYOktuiiEEOVHAeqr7dRXF6dfbnAnl9Zd3+ajvnr6Lx711fbxZcZTZpN3snzLSpfPcVAVuLujtaByS+3ujlamW9G4lHFPd39XuHB+lLKMhWiptrvU5LhOJYOkMqapCltuL78VgYQQYqlJfXHYeseVbL2jfcJrMy1vy+3taKqCpirjZeay9Y4rp+xVMpu8k+VbVkqqtHe01+VMd9/NbWW3X5LNonLfzW0505Qy7vT7++RPJfXfqfOjlGUsREu13aUmx3Wq8uq1xBS3rm5g++a1NLgz/3Wvwe3g/k1tWd8XQoiFwuOyju81NF9cNi1jn1rvdowvf5vql+snpck3/oa0slJuXd3Ajs1rM+b3uKw5l/CeTd5CylImfTdKHZOH77me+ze1TfllRlXKd/lvgAdua5/XuLOdR/UZzo9SlrEQLdV2l5oc14lkn6QFIrX7cV8wknHn9mzvr2n28NjeHvZ1DRKK6Vy1wk1NpZ3aSht1VQ4M02TP8UFOD4UwTTgbDNPZGyQSN4gboCngsKhUOzR0E+wWlVhc5+xYYr4PiRBlS2Hiql8+O/ij2dNyPr1VBQ2IGskvasuqbFRaFI77oxgk/6rV4rEwEDYwDAXD0DGBuJHc5FNTwFDAZVFYVmWnrtpJMBynNxgmGNbRTXBYoMHtxETBaVUJhOKYpolBMl//aAzdhGq7hYt8Tjr7RtENgxUeB9e11HAyEObkYAinVeWiGhfXtfg4E4gSiyfYe3yQkZhOs9fBZcur6OwdBWDjKh91lQ6GQ3HOBMI0uB34KmzUVNgZCsUm9GfA+E7vPqeNzr4gB3qGcFo1nFaVN/tHAIXL6qu4rsVHgyfZz33/hR6OD4zRFwgxFEqgKgpvu3wZ8ZjO/9vbQyxh0ORx8IP7buDoudHx8l/tC/JizxAum8Z7rm3ihktqJ/SpuXacz5QmPf7aSjuY0D8azdhvZ6IbJnuPD7Ln2CBg0rGylo2ravL66+1s8uZT1vVtPg70DGU9JrGEwaN7uunxh2jxubi7o7XsfkHKZL7jzudcm4syFqKl2u5SW+zHVTaTPW+xDJKEEEIIIYQQsyObyQohhBBCCCHEDMggSQghhBBCCCHSyD5Ji0Qhz8/3BcL4x2L4KpNL2a5r8bLnzQEeeu44gXCcereDd7QvZzSSoMJu4Yf7T3Di/IZ6F9dVck2zB/9YjOePDnAuGEVRweOy0VDtwDR0uociGLqBYZqE0qYuOTSwWjSq7Bo2TWE4YhCKxYnpc3mkxFyqsEAoMXF+zmRWBeJ5PvTr0BQUFYyESXRSHosC61s99AUi9I3E0A0TTTUxDJLzbawa9dV2quwar58Lk0gkQFFQlOTqYtU2FRSFsbiJpigsq7JRW2nlzf4Q4VicaMwgYiTrsquwekUVwYiOaUJNhY2zo1ESukldlZ33XtfEq73JuTPNXidRPcHOQ30EQlFQwGG10OR1cW9HK7/s7GXXK2eJJwzqq22879qLCJsGmFDlsPBq7winh8Os8Dh479pmrmvz8f0XesbnT7x/QwsHTwyx59gghmngcdoIhOMoClnnpMQSBv/+fBe/fKWP0UiCyxqqeF9a2V2DYyjAtc1eGjwT59Ck9zW1FXZQoD8YYWA0xnAolrPelEz9lW6YWeeFpObH7D46wOnhMI0eJzdeXMvGldPPt8nVN2abi5I+H8c0TTwuK7VVDuqq7Bi6yQvdg4BCx6qaGcWwrsWbc25PMS32uQVCCFEqMidpEdh1pJdtOzvpDVzYBbnB7WDL7e3jK5FkSiOEWPw8Litfuuuq8b7gwSc7+fYzXQWVkepPgLz7kcn1pmTqi1w2jXA8OeBMUZXkEszXXuTlMz95meFQPO86ctWVastLJ4Z4+NkujEl1/v4VdezvHspYXyHtzBWDqjCh3sn9dbHkc28QQoilRhZuOG+xD5J2HenlY48dnPKX+tTfCbdvXguQMY0QYunYsXktL50YKniAVIx60/9YU4q+KNMS17n6xlL0hYXEMFl6f12swUs+9wYZKAkhliJZuGEJ0A2TbTs7M96AU69t29nJ1idekQGSEEvc3/7Xyzw0xwMkgK1PvIJumDn7q9natrMTPe2nmXz6xvmMIVtMk8uYqXzvDcWoSwghFisZJC1g+7r8OR97MYHeQIS+YJYNWoQQS0b/aHxe/ljSF4yyr8s/bX81G72BCPu6/OP/Xcq6ShVDqr9OL2Om8r03FKMuIYRYrGThhgWsf0TmFwkhyt9c9FXpdcxX31iMGIoRe75lyD1ECCGyk0HSAlZX5ZjvEIQQYlpz0Vel1zFffWMxYihG7PmWIfcQIYTITh63W8DWt/locDvItpirQnIlo/pq+1yGJYQoQ3WV1qx9RSnVV9tZ3+abtr+ajQZ3cmnrlFLWVaoYUv11ehkzle+9oRh1CSHEYiWDpAVMU5XxZXkn3wxT/73l9na23nHlvHw5EkKUj7+78yo+sqlt1uUU2pdsveNKNFXJ2V/N1pbb2yfs/ZNP31hshcQwWXp/XYw9jPK9N8h+SUIIkZ0Mkha4W1c3sH3zWurdEx+bqHc7xpd4TaVpcMujFUIsNR6XdXx56gdua+f+GQyUGtwOdmxey44MfU0+9aZk668qbBrKpO/rqgL3b2pjx+a1eFzWjHV4M9QxXV3159ty/6Y2Jo8RVAXe0V6Xtb5ixTC53vT+uljyuTcIIYTITvZJWiTy2VU9laYvEMY/FsNXaae+Orn7+543B3joueMEwnHq3Q7e0b6c0UiCCruFH+4/wQl/CICL6yq5ptmDfyzG80cHOBeMoqjgcdloqHZgGjrdQxEM3cAwTUKJC/U7NLBaNKrsGjZNYThiEIrFielzeaTEXKqwQCiRe9llqwLxPHshh6agqGAkTKKT8lgUWN/qoS8QoW8khm6YaKqJYYABuKwa9dV2quwar58Lk0gkQFFQlOSvHNU2FRSFsbiJpigsq7JRW2nlzf4Q4VicaMwgYiTrsquwekUVwUhyA9SaChtnR6MkdJO6Kjvvva6JV3tHAIVmr5OonmDnoT4CoSgo4LBaaPK6uLejlV929rLrlbPEEwb11Tbed+1FhE0DTKhyWHi1d4TTw2FWeBy8d20z17X5+P4LPfT4Q7T4XLx/QwsHTwyx59gghmngcdoIhOMoCnSsrGXjqpopfUEsYfDvz3fxy1f6GI0kuKyhivelld01OIYCXNvspcHjnNCfpPc1tRV2UKA/GGFgNMZwKJaz3pRM/ZVumDy6p3u8XXd3tGKzqOPp9x4fZPfRAU4Ph2n0OLnx4lo2rsxeR666UnliCSNjnan69hwbxDRNPC4rtVUO6qrsGLrJC92DgELHqpoZxbCuxcuBnqGc/XWx5HNvEEKIpUQ2kz1vqQyShBBCCCGEELnJZrJCCCGEEEIIMQMySBJCCCGEEEKINLJPUhma/Az5mmYPj+3tZn/3EBU2jbvWNnHDxbUA48/Ng8mG1hpUTWFgNDrhOf/vPHecn7x0imA4gc9loa22kuFwAodVxTBMTvjHOOEPkzBAU5Jzh0YTuWMUQpSeQnL+U2ouVL40INtUvyqbQlw3iegX0tZVqEQMhWhCJxRPvq4CK2scnB2JETdMKu0qCR1CMZ1YWjxWBQzzQn1WFS7yubhv00pWeFx09gZ4sXuIUEzHxEQ1DQ6cCBBOJJ/0rrIqXLeyhm/96TqcNo3dRwd4/MApTvjHGByNYpgmMd3ANCEc0zFJLvSwutHNLVc1MhyKceikn+eODmKacEldJf9273peORPgRy+e5MCJIYZDMSrtGm+7vJ7P/UE7h08Ns+fYIAld52j/KJGESVuti0/fegUvnw7QNTDKt379Bv1jMVRF4YaVPv75j9fy05dOZZwzBcn5TY/s7mZ/9yAOq0al3YKiKKgKrGny0Oh1sa7Fy/5u/3ifnT53KzU/qnswhGkaVNit9AcjNHqc3LBq4hyvTPOMYgmDL/y8k8Onhql2WPnIzSu56dJls5qzpRsme48Nsuf4ALOZg5XPPKjZzp2aLn+m94G86ywkf75tKfZ8sVLMP5swB7HSDiYMjEUX9Py29DmHk69DIdKV9ZykrVu3sm3btgmvXXbZZbz22mt5l7HQ5iTtOtLLtp2d9AZy74Rut6hoqkIox6oHLpuW830hhCgn6vkB10KgKnDfzW08cFs7Dz7ZyUPPdjHd3VRRmJLG47JyfauXp17tz9l2j8vKl+66CmDKPcJmUYklpo6kbRaVb/7Jmqwr2WW63zS4HePLh3/mJy8znBo1T4pjJmUWM08h+TO9n1rBML192eosJP8d1zTwxOHeadsy2zYXegxmYrrvI7Mtfz7sOtI7o/NaLC6LYuGGrVu38uMf/5hf//rX469ZLBZqa2vzLmMhDZJ2HenlY48dzLkSmBBCiPJxdVM1vzsVnO8wcsq0RHm2+41C7tUoZ1omkHHp8ZnkKST/Rza18dAzXXm1KVOdxbgvTy53tm2erNjl5SqzWOXPh11HevnoYwdzpsm2nL9YXBbNwg0Wi4X6+vrxf4UMkBYS3TDZtrNTBkhCCLGAlPsACWDLz46gp/1Mlet+k+89aNvOzoLLLEaedPnkf/jZ/AZImeos1n05vdxYwphVmyeb7TEstMxilD8fdMNk6xOd06bb+sQrZd8WMXfKfpD05ptv0tjYyMqVK/nABz7AiRMncqaPRqMEg8EJ/xaCfV3+aR+xE0IIIQp1diTGvi7/+H8X437TG4gUVKZZpDzp8slf6Pfd9DqLeV9Olfvonu5ZtXmy2R7DmZQ52/Lnw74uP33B6dvUF4yWfVvE3CnrQdKGDRv4j//4D3bt2sX27dvp6uri5ptvZmRkJGueBx98ELfbPf6vubl5DiOeuf4RGSAJIYQojfR7TLHuNzMpc7Z58nm9GPpHIiUpv+f8xuz51D8f6QpNO5s8c6nU7ReLU1kPkt71rnfxvve9j6uvvppbbrmFJ598kuHhYX74wx9mzfPAAw8QCATG/508eXIOI565uirHfIcghBBikUq/xxTrfjOTMmebJ5/Xi6GuylGS8lt8rrzrn490haadTZ65VOr2i8WprAdJk3k8Hi699FKOHj2aNY3dbqe6unrCv4VgfZuPBrcDWYBSCCFEMS2vso0vWQ3Fud80uB0FlakUKU+6fPIXuqpzep3FvC+nyr27o3VWbZ5stsdwJmXOtvz5sL7NR3319IOf+mp72bdFzJ0FNUgaHR3l2LFjNDQsvpVHNFUZX3ZVBkpCCLEwXN0093+IK/Qese3dqyfsAZPrfpNv2Vtuby+4zGLkSZdP/vtubkPJ8H4mk+ss1n05vVybRZ1Vmyeb7TEstMxilD8fNFVh6x3t06bbeseVZd8WMXfKepD0qU99iqeffpru7m52797NH/3RH6FpGn/6p38636GVxK2rG9i+eS317un/2mG3qLhsWs40FdO8L4QQ5WQhfTdRFbh/UxtP/O+buX9TG0oesWdK43FZeUd73bRt97is7Ni8lh0Z7hF2S+Zbud2iZl3SONv9pt7tGK8ntRdQOu/5OAotM9sy0TPJU0j+B25rz/i+12Wd0r5MdWYr35Mhf4Pbwf2b2miYpi2zbfNkxS4vV5nFKn8+3Lq6Iet57clxXoulq6z3SfqTP/kTnnnmGQYHB1m2bBk33XQTX/jCF1i1alXeZSykfZJSJu+avabZw2N7u9nfPUSFTeOutU3ccHFyKfT0XaM3tNagagoDoxd2w9YNk+88d5yfvHSKYDiBz2WhrbaS4XACh1XFMExO+Mc44Q+TMEBTwKHBaGJ+j4EQIvmXWrsKkan7hOakAdm2ka6yKcR1k4h+IW1dhUrEUIgmdFJ7LKrAyhoHZ0dixA2TSrtKQodQTCeWFo/1/AawqfqsKlzkc3HfppWs8Ljo7A3wYvcQoZiOiYlqGhw4ESCcSN56qqwK162s4Vt/ug6nTWP30QEeP3CKE/4xBkejGKZJTDcwTQjHdEySfwBa3ejmlqsaGQ7FOHTSz3NHBzFNuKSukn+7dz2vnAnwoxdPcuDEEMOhGJV2jbddXs/n/qCdw6eG2XNskISuc7R/lEjCpK3WxadvvYKXTwfoGhjlW79+g/6xGKqicMNKH//8x2v56Uun6PGHaPG5uLujFVva4CSWMHhkdzf7uwdxWDUq7RYURUFVYE2Th0avi3UtXvZ3+8f77I6VtWxcVYOmKsQSBo/u6aZ7MIRpGlTYrfQHIzR6nNyw6kI6mHqPWN/mI5Yw+MLPOzl8aphqh5WP3LySmy5dNu1fxTOVlV7P3mOD7Dk+ACh0rKph48qaWZVZzDyF5M/0PpB3nYXkz7cts21zqcubXGZtpR1MGBiLFq38+aAb5oTvTunXoVgaFsVmssWwEAdJQgghhBBCiOJbNJvJCiGEEEIIIcRckkGSEEIIIYQQQqSxzHcAS1Wpn4+urbBjmCYvdA1iAlU2C6/2BQnFdGor7YzFEoRiOr4KCyeHIvQOhQjHdewWDVVVqHJYON4/Rlw3UFWF1hoXiqnzar9ssiZELgrJXehz0QCLpuB2aDRU2zjcO3GDSeV8mpZaO+eCMUZjJub5123n5ygpgN2i8GcdrXQNjbGva4hEIsFY3EQ3QFGhzedkcCzOUDg5ydDrUPnkLVfQ7HPxy84+jg+MMTgSxeuy4h+LYVEVxmI6Cd1ENw10w2RgLJnXrkFLjYtowkBBYWVdJWua3Pz29X7ODEeIxOJEEiY6Chd5nXzmHZcTTOgcOjlETDd4vTdAjz9CQtexqio2i8poTMeuKdS7nXzibZdiagpf//UbBMJxllXaeN91TYxGDfyhKL3DERrcDnwVdmqr7NRW2HitL8jJoTDNXheXL6+ifyTCoVPDGGby+FTaLJwJhhkcieJyWFjfWsO9N7SiqQr7uvycGQpx6NQwoHCRz8WldZXs7xkiYegEQgne7B/BNOGy+krWXuQjEI7jq7BR73bmnPNSW2EHhQnzQwuZk+Nz2njt7Agnh6bOf0rNE3r+2DlOD4UxAP9oFKfNwoY2H/fe0DZhrlShZjIPafL9aF2LlwM9Q3M2JymfvAth/k+xFBJbqdpRzsdHzJ2Ffh7InKR5sOtIL9t2dtIbuDDgSK22MpyaNU1ypZwtt7dPWW0l3/xCCCEmUgCnTSMUy7a0RX7S++dMfXK2tJlMl19VkktZX3uRl8/85OWc/bwCfGRTGw/cNv1yx5niyFS+x2XlS3ddlTH+TLGr5xfzSJlJ+6fLU0je2ZRf7HhLrZDYStWOcj4+Yu6U83kgCzecV26DpF1HevnYYwen/UszXNiDIH2JzULyCyGEKJ3UgOShZ7py9smZ+vKUUvXp9xc4UNp1pJePPnYwZ5rJSyTnG/tM2p8rTyF5gRmXP9M65+sLYCGxlaod5Xx8xNwp9/NAFm4oQ7phsm1nZ943w1S6bTs70Q2z4PxCCCFKxwQefjb3ACmVDi705Sml7NMfeqaLWCK/teN1w2TrE69Mmy49/kJin0n7s+UpNO/WJ16ZUfmZzCbeUisktlK1o5yPj5g7i+k8kEHSHNrX5c/6OEU2JtAbiLCvyz+j/EIIIUon3/t8el+eUso+3QQe3dOdV9p9XX76gtFp06XHX2jsM2l/pjyF5s3VrlzlZzKbeEutkNhK1Y5yPj5i7iym80AWbphD/SMzvxnOJq8QQojykN6Xl7pf7/GHpk9UYByptDONfSbtz5SumMduNnHMJl0xlSK2QttRzsdHzJ3FdB7IIGkO1VU55iWvEEKI8pDel5e6X2/xufJKV0gcqbQzjX0m7c+UrpjHbjZxzCZdMZUitkLbUc7HR8ydxXQeyON2c2h9m48Gt4NCFj9USK4Gsr7NN6P8QgghSiff1WzT+/KUUvbpCnB3R2teade3+aivtk+bLj3+QmOfSfsz5Sk0b321fUblZzKbeEutkNhK1Y5yPj5i7iym80AGSXNIUxW23J5cbSifG0sqzZbb29FUpeD8QgghSkchuTS3Qu4+eXJfnlLKPv0jm/LfL0lTFbbeceW06dLjLyT2mbQ/W55C86baVWj5mcwm3lIrJLZStaOcj4+YO4vpPJBB0hy7dXUD2zevpd498WdGr8s6vtdRSr3bMWWZxELyCyGEmEgBXDZt1uU0nO+fH7itPWOfnC5TX56SrU9PpyrJJb13bF47bT+vUPjy36k4spXvdVmnLP+dK/bJ331m0v5ceQrJO5vyZ1rnfCkktlK1o5yPj5g7i+U8kH2S5kmmXYiBWe2SnZ6/tsKOYZq80DWICVTZLLzaFyQU06mttDMWSxCK6fgqLJwcitA7FCIc17FbNFRVocph4Xj/GHHdQFUVWmtcKKbOq/3lP9FOiPmkwLTLImuARVNwOzQaqm0c7p04wV45n6al1s65YIzRmIl5/nWbChEj+f/tFoU/62ila2iMfV1DJBIJxuImugGKCm0+J4NjcYbCCQC8DpVP3nIFzT4Xv+zs4/jAGIMjUbwuK/6xGBZVYSymk9BNdNNAN0wGxpJ57Rq01LiIJgwUFFbWVbKmyc1vX+/nzHCESCxOJGGio3CR18ln3nE5wYTOoZNDxHSD13sD9PgjJHQdq6pis6iMxnTsmkK928kn3nYppqbw9V+/QSAcZ1mljfdd18Ro1MAfitI7HKHB7cBXYae2yk5thY3X+oKcHArT7HVx+fIq+kciHDo1jGEmj0+lzcKZYJjBkSguh4X1rTXce0Mrmqqwr8vPmaEQh04NAwoX+VxcWlfJ/p4hEoZOIJTgzf4RTBMuq69k7UU+AuE4vgob9W7nlP45vU+urbCDAgOj0bx3mU/P73PaeO3sCCeHQrT4XNzd0Tr+q5BumOw9Nsjzx85xeiiMAfhHozhtFja0+bj3hvx/QcoWx95jg+w5PgAodKyqYePKmpzxT74frWvxcqBnKK97WbYy8slTSN7ZlF/seEutkNhK1Y5yPj5i7pTreSCbyZ5XroMkIYQQQgghxNySzWSFEEIIIYQQYgZkkCSEEEIIIYQQaWSfpDKQ7Vl2nyv9mXsnl9dX4w/FpjzXqRsme48PsufYIGDSsbKWtS1evv9CDz3+EM1eF5cuq2T/CT+mCRV2C8+92c+xc2OoiklCNxmJ6BimiQrEjGRcxrwdESHEbClArUvBRGMoksAwoMqucnl9FeG4zmt9YxjGhblOelpeqwIJ88LcKocG9R4HKtAfjBJOJN/RzGQ/kZhUt9Oi8EfXNRIYjbO/e4hQzMA8P8fJMJNzqeK6SeR8pZqSnO90xzUNHD4Z4ODJYXTDpNljZ0NbLb99vZ/uoeR8SKsKa5rceCrtXN/i47K6KvZ2DXL41DADo1Eq7RYuWVZJz1CIN86OEo0nUFWFmgo7HatqubW9Hn8kzrlghFfOBAjFdZZV2qh2WDkTiHBuNMK5YJSxqE5/MJqc0+lQ+fBNq7iopoL66qnP1U/uw5PzQf2k+uONq5Jzeiakq7SDCQNj0QlzSEFhQ5sPVVGS71XaMXSTF7qT702eIxRLGDy6p5se/8T5S6m6+gJh/GMx3E4rh08NYwLNXheKCacC4SlzntIVOj9p2rmyOdoyk7lQhZh8nN6/oYVDJ4fLbq6EEKJ8yJykebbrSC/bdnbSGyhsQYQGt2N8icXP/ORlhkPxUoQnhBBiklT/e+vqhrz6cI/Lyh9f18QTh3sL7uuzlfelu67ipRNDPPxsF0baXVxV4PevqOPI6WDedalKcinz9BXxdh3pzXhvSdU9eXWqTMchtVJervtT6tj854un8q6rUA8+2TnlOE2W/pkKIRY3WbjhvHIeJO060svHHjs47UpYmeSzgpYQQojSUEjuRfTQM12Lpi9OLR2+60gvH33sYM606UuCz+Zelo9My4/n68EnO/n2M13Tpkv9hrSQlicWQsyMLNxQ5nTDZNvOzhnfVBbLTVkIIRaqh59dPAMkSLYnHNPZ+sQr06bdtrMT3TBnfS/LR6quQsUSBg8/O/0ACS7cU2dalxBi8ZFB0jzZ1+UvymMXQggh5p4JOR/fWogME774ZCd9wei0aXsDEfZ1+efkXpaqq1CP7uku6DMyZ1GXEGLxkYUb5kn/iAyQhBBClJfuwdD0ic6by/vYTOrq8effltnWJYRYfOSXpHlSV+WY7xCEEEKICVprXHmnratyzNm9bCb1tPjyb8ts6xJCLD4ySJon69t8NLgdyIKjQgix8CgkV4VbTFQFPntbO/XV9mnTNriTy2bPxb0sVVeh7u5oLegzUmZRlxBi8ZFB0jzRVGV8Ce+Z3FwW2b1ZCCEWnPtubkNh8fTH993chtOmsfWOK6dNu+X2djRVmfW9LB+pugpls6jcd3NbXmlTpc+0LiHE4iODpHl06+oGtm9eS7278J/2690Odmxey47Na8f3ohBCCFF6DW4H2zev5YHb2vPqwz0uK/dvaqNhBn19Jl6XlR2b13L/prYpv5SoCryjva6gulTlwvLfkLw3Zbu3pOpOXyY7273M47JOe3/ynj82+dZVqAdua894nCarP/+ZyvLfQogU2SepDEzerR0FBkaj+Fw2XusLcnIoTLPXyeX11fhDsSm7g+uGyd7jg+w5Nkhqh/e1LV6+/0IPPf4QzV4Xly6rZP8JP6YJFXYLz73Zz7FzY6iKSUI3GYnoGKaJCsSMZFzGvB0RIcRsKUCtS8FEYyiSwDCgyq5yeX0V4bjOa31jGIaJeT6tnpbXqkDCvLAsskODeo8DFegPRgknku9oZrKfSEyq22lR+KPrGgmMxtnfPUQoZmCaBrphYphgtyjEdZPI+Uo1BVpqXNxxTQOHTwY4eHIY3TBp9tjZ0FbLb1/vp3soOZneqsKaJjeeSjvXt/i4rK6KvV2DHD41zMBolEq7hUuWVdIzFOKNs6NE4wlUVaGmwk7Hqlpuba/HH4lzLhjhlTMBQnGdZZU2qh1WzgQinBuNcC4YZSyq0x+MYgJVDpUP37SKi2oqqK+e2P/C1D7cME1e6PKT6o83rqpBU5WJ6SrtYMLAWDQtzyCgsKHNh6ooyfcq7Ri6yQvdyfc6VtWwcWXNeP2xhMGje7rp8Ydo8bm4u6MVm0Udr6svEMY/FsPttHL41DAm0Ox1oZhwKhCekGcy3TDZe2yQPccHMtadKX2qfan7FDChzdnaUmhdhZp8nN6/oYVDJ4cnxCq/IAmxNMhmsucthEGSEEIIIYQQovRkM1khhBBCCCGEmAEZJAkhhBBCCCFEGtlMtozlM1ep0e2gxz/GqaEIrTUuPvXOy/nxgZMT5iLt6/FzYnCM350O4B+LYtNUYrrOSHRRP2kphCiQFYhPl0YB/fxcJI2Jc5nSNbutNHorODUUQTd1hkbjRLNMdLRq8JGb22jyVfCf+07wZv8YCd3A7dC4rNHNqYEReoZiGCT/sudzWVjf5sOiafSPRugPROgbiaJiUu2wctnyamKGgdtpoccfJhQ1aPTY+cjNq7jp0mVoqkIsYfBvzx7ney/0MDgaxW5RuXV1A5//wyvZe3yQv3viCKcDERTgysYqvvtnHbhdVmIJg0d2d7O/24/LqnFZQyWjUR1MqLBpPHf0HGcCEUBhZY2LRq+LNc0eAuE4vgobddUODN1k9/EBXj4VwGnXuK7Fx6V1lfzXoTO82hukymHhnVfWc09H64R5M2uaPeNzTSfPP9p9dIDHD55iLJpgWZUdt9OGpl6Y39Q/EsE/FsNXac84ryoc0/nik510D4ZorXHx2dvacdq0nOfCdPN81rV42d/lzzjPKNtcKsg8t6lc5wtlaoemKgsmfiFEdjInqUztOtLLtp2d9AZk528hhCgWu0Vl06W1/Kqzv+C81Q4LI9EE5XLXVBX4/SvqeP7oIKFYtuFqZg1uB1tub+fW1Q3c9939GY/HO9rrePie6zPmf/DJTh5+tgsjx7FQFKYcK4/LyvWtXp56tX9CXlVJLkF+7UXeKfe+9FjLSaZjoCjgtGoTPo9yjV+IpUoWbjhvIQ6Sdh3p5WOPHWRRfzBCCCHmlQJc1VTN704Fs6bJNFB68MlOvv1MV4mjuyD1G0w5LdFdyDEox/iFWMpk4YYFSjdMtu3slAGSEEKIkss1QAL4VWc/4bRfRWIJg4efnbsBElxYin7bzk70XD9dzZFCj0G5xS+EyI8MksrMvi6/PGInhBCi5PL9uv7FJzvH//+je7pzPmJXKibQG4iwr8s/95VPMpNjUE7xCyHyI4OkMtM/IgMkIYQQ5aN7MDT+/3v8oRwpS68c7pGzOQblEL8QIj8ySCozdVWO+Q5BCCGEGNda4xr//y0+V46UpVcO98jZHINyiF8IkR8ZJJWZ9W0+GtwOZLFQIYQQpZTvfeazt7WP//+7O1qZj9WsFZKrxK1v88195ZPM5BiUU/xCiPzIIKnMaKrCltuTNyQZKAkhhCilq5tyr/r6jva6Cfsl2Swq993cVtKYJt/7Uv+95fb2sthvqNBjUG7xCyHyI4OkMnTr6ga2b15LvVt+lhdCiGKyW1Te0V43o7zVDgtKGX3HVZXkIMY1zaavmTS4HWzfvJYn/vfNWY9Htn2SHritnfs3tU37a0qmY+V1WXlHe92UvKoC929qY0eGe1/9+VjLafnsbMdAUZjyeZRj/EKI6ck+SWUsfdfx2go7KDAwGsXnsvFaX5CTQ2Ea3Q56/GOcGorQWuPiU++8nB8fOEmPP0Sz18WlyyrZ1+PnxOAYvzsdwD8WxaapxHSdkeii/uiFEAWyAvHp0iigm2AAGpBtC9Nmt5VGbwWnhiLops7QaJyokaVMDT5ycxtNvgr+c98J3uwfI6EbuB0alzW6OTUwQs9QDIPkX/Z8Lgvr23xYNI3+0Qj9gQh9I1FUTKodVi5bXk3MMHA7LfT4w4SiBo0eOx+5eRU3XboMTVWIJQz+7dnjfO+FHgZHo9gtKreubuDzf3gle48P8ndPHOF0IIICXNlYxXf/rAO3y0osYfDI7m72d/txWTUua6hkNKqDCRU2jeeOnuNMIAIorKxx0eh1sabZQyAcx1dho67agaGb7D4+wMunAjjtGte1+Li0rpL/OnSGV3uDVDksvPPKeu7paOXQyWH6RyLUVTlY0+zh+y/00OMP0eJzcXdHKzaLim6Y7D46wOMHTzEWTbCsyo7baUNTFTa0+VAVhf6RCP6xGL5KO/XVyce+0n/VCMd0vvhkJ92DIVprXHz2tvYJvyBlEksYPLqnezye929omRDvuhYv+7v87Dk+ACh0rKph48qa8eOfnjfVFph476urmhprOcnUDk1VFkz8QixFspnseQt5kCSEqCqRwgAAGg1JREFUEEIIIYQoHtlMVgghhBBCCCFmQAZJQgghhBBCCJHGMt8BLGXZnrue/Pq6Fi8HeoZypvM4rPzq1bMcHxilPxAmYSRX4FnX4uOaZje/eKWXV84EietQX21HPT+jtqbSxvGzQXpH4uhmco6BRQPdgMSifhBTiIUp1zwgBbBrCnWVFoYiyXkyYDASy12mCqgquKwqFgUiukkofqEDqLZreFxW6t12zgUjnAlEienJ+kySk+4tClQ5LVg0jcvrq6h2WBiLJhgKJ3BaNVYuq+DTt17B3uODfOHnnQyHYkTiOlUOC82+Ch6+53pe7Q3y/LFznBwKcy4Y4ZQ/TMIwqKmwcd1KH8+/MYA/HKfaYeF965qoq3byu1PDGIaJqiqsafbS6HEm58J0+3nuzXMcOjXEwEgMRYEr6qu585oVvNY/wq87zzIaTXDp8iraG6uS84oAj9NGtdPCriO9HD4ZQDdM1jW7ubujjWAskXGOSXKeUhf7uvyEYzqrV7ipqbRTW2mj3u3MOCdlwpzTSjuGYfJClx8w6VhZy8ZVNTnzTI4j3/vGdGYzHyhTXmBO5ufkE3eueVBL2UKaA5bLYmmHKB8yJ2me7DrSy7adnfQGLuy+3eB2cMc1DTxxuHfC66oCRtqnlC2dEEKI5ApjpbyzNbgdbLm9nVtXN/Dgk5089EwXuapLTw+Z+//JPC4rX7rrqpx5UuUCU97LdN9IjyGTXHVMtzJbprwelxWA4dCF5UDyLa8Q+cT94JOdPPxs14Rjoipw381tPJC2D9RSM5vPvJwslnaIuSELN5xXjoOkXUd6+dhjB3PeVIUQQpSn1N+m395ex686+/POs33zWoCC+v8dOfKkfsnLt37Ox5DpS2O2+9J0+XLlnUkchcon7pdODPHtZ7qylnH/pqU5UJrNZ15OFks7xNyRQdJ55TZI0g2Tm778G/kFSAghFrBCBigp9dV2QKEvmH//v7zKhqKoBeXJRiG5Z89zn37bhMeQprsvZcuXT95CyytEPnHXVdk4Nxqb8AvSZKoCr/39u5bUo3ez+czLyWJph5hbsrpdmdrX5ZcBkhBCLHAz+etiXzBa8GDn7EisKAMkSMbcG4iwr8s/4fXp7kvZ8uWTt9DyCpFP3GdHcg+QIPlY4qN7umcVy0Izm8+8nCyWdojyJIOkOdY/IgMkIYQQ82fyfSjf+1KmdLO5p832fljM+2mPP1S0shaC2Xzm5WSxtEOUJxkkzbG6Ksd8hyCEEGIJm3wfyve+lCndbO5ps70fFvN+2uJzFa2shWA2n3k5WSztEOVJBklzbH2bjwa3A3kyVgghFq6Z9OH11Xbqqwvr/5dX2QrOk41CcsWv1NLcKdPdl7LlyydvoeUVIp+4l1fZmG4qiqrA3R2ts4ploZnNZ15OFks7RHmSQdIc01RlfMlWGSgJIcTCk+q739FeV1CerXdcydY7Cuv/t717ddY8Spb/n61+gC23t0+ZwJ7rvpQr33R5C42jUPnEve3dq7nv5rac5dx3c9uSWrQBZveZl5PF0g5RnpZWr1Ambl3dwPbNa6l3T/z5t8Ht4P5NbTRMen3ytZ0tnRBCiOQ+SaVU73awffNaHr7neu7f1Dbt4KDhfPpbVzdk7f8n87is7JgmT73bwY7Na9mR4b3J9436tBgyyVXHdEsoZ8vrdVnH90oqpLxC5BP3A7e1c/+mtinHRFWW7vLfMLvPvJwslnaI8iNLgM+jbLtD57tzeno6j8PKr149y/GBUfoDYRIG2Cwq61p8XNPs5hev9PLKmSBxPfnIh3r+W0RNpY3jZ4P0jsTRTdAAiwa6AYlFfWYIsTBpgJ7lPQWwawp1lRaGIvr5JdgMRmK5y1QBVQWXVcWiQEQ3CcUvdADVdg2Py0q92865YIQzgSgx/cIy2KoCFgWqnBYsmsbl9VVUOyyMRRMMhRM4rRorl1Xw6VuvYO/xQb7w806GQzEicZ0qh4VmXwUP33M9r/YGef7YOU4OhTkXjHDKHyZhGNRU2LhupY/n3xjAH45T7bDwvnVN1FU7+d2pYQzDRFUV1jR7afQ4WdfiZX+3n+fePMehU0MMjMRQFLiivpo7r1nBa/0j/LrzLKPRBJcur6K9sYrRaPKoepw2qp0Wdh3p5fDJALphsq7Zzd0dbQRjiQl9cEosYfDI7i72dfkJx3RWr3BTU2mnttJGvds5JT1M7P9rK+0YhskLXX7ApGNlLRtX1eTMMzmOfO8b08lVx0zyAjMurxD5xB1LGDy6p5sef4gWn4u7O1qX3C9ImczmMy8ni6UdovRkn6TzynmQJIQQQgghhJg7sk+SEEIIIYQQQsyADJKEEEIIIYQQIo1lvgNYKnTD5Lk3zvF/n36TV04HicYNUJLzhiwqBCPGlB3cVS7s6r6on4kUQogyVwlgVRiLmxn7YxUwZlq2FUbj2d9vrNIYipioCigmJEyDaCI5D6zB4+CW1Q1UWTUe2duFP6SPx6eSnDemn/9fRQGHRaHSpuF1WTk7GieeMBiLX4jc69Kor3Jg0xT6glGCkQTRhIlxvrwVHjt3Xd3EL17rpWswTExPHg8L0FLj4I/WNqMbBo/uPcFYTKe2woZFhUBEx6oprG12E4jqROMGzV4n71nXzA0X16IbJv/+/HF+8cpZxqIJLq+v5r1rm7i2xcuDT3ay+9ggccPg6sYqojpEEyZttS4+fesVvHRiiMcPniIUS7C22QvA/hNDnA1EsGkKDqtGbaUNFBVVgRVeJzesrOX6Nt/4vK3aCjsoMDAapbbCTiyh8/+e62I4HMOmqVxaX4VVU8fnvaXmW+09Nsie4wOAQseqGjauTM4lC8d0Pv+z37HrSB9x3aSh2sHn3nUFDruF3ccG+N2pAC6bxvq2GjZvbOHFLj+Pv3SKU0Mhmjwu/mjNClDgp4dOE4rpXN/qY/PGFg72DE2o7/rWZBvODId5sdvPiz1+EoZJx8oaPn3rFfzwxZPs7/bjsmm859ombrikdnxO895jg+w+PsCZoTD11Q6qXRZe7xtlLJZgeZWDa5s9NHpdE+b2TJ7X9f4NLRw6OUxfIIx/LIav0k5dlR1MGBiLZpw7l+2YZZKaZ5Refn119jnck+fB1VYmY+kfiWTMX2y6YbL3+CB7jg1iYuJxWqmttGedm5jexrmcS5X6HLsGx1CAa5u9NHiyxzhbC3W+2IKYk/Sv//qvfOUrX6Gvr49rrrmGb33rW6xfvz6vvOUwJ2nXkV7+4v87RCwx01uoEEIIsThZNYW4PvdfRVILj8yEx2UlljAIxfQpr1/kc/K7U8FZx5cPRYFCvsW5bBp3b7yI/3zxFMOhHCPzNA1uB1tub+elE0M8/GwXRoEHLZUf4DM/eXlKvR6XlS/dddWUVeh2Hell285OegORjGXecU0DTxzunfB+ajXF6dqWiqmYK9/tOtKbsX256szUxlLElu7BJzuzfo6lOi5z3cbpLJqFG/7zP/+Te+65hx07drBhwwa+/vWv86Mf/YjXX3+durrp96iY70HSriO9fPSxg3NerxBCCCHEfMt3MLojbbnuXUd6+dhjB0v6FI0CRVsiPN/veul1Zmtj6veVUixf/uCTnXz7ma68Y5yt+WhjPhbNwg1f+9rXuO+++/jQhz5Ee3s7O3bswOVy8Z3vfGe+Q5uWbpj87X8dme8whBBCCCHmRb4DnW07O9ENE90w2bazc06mGaTqnA3dMNn6RGdeac3zdcYSRtY2pl4rRmzpYgmDh5/NPUBKKdZxmes2FltZD5JisRgHDhzg7W9/+/hrqqry9re/nT179mTME41GCQaDE/7Nl31dfvpHp9mgRAghhBBiiesNRNjX5Wdflz/jI3bFZqbVORv7uvz0BfOPtzcQ4dE93TnbWKzY0j26pzuvRyWLeVzmuo3FVtaDpIGBAXRdZ/ny5RNeX758OX19fRnzPPjgg7jd7vF/zc3NcxFqRv0jpb/IhRBCCCEWg/6RyJx/d5ptfTPJ3+MPlazs2dZZrLrzzV/O35XLepA0Ew888ACBQGD838mTJ+ctlroqx7zVLYQQQgixkNRVOeb8u9Ns65tJ/hafq2Rlz7bOYtWdb/5y/q5c1oOk2tpaNE3j7NmzE14/e/Ys9fX1GfPY7Xaqq6sn/Jsv69t81FXa5q1+IYQQQoiFoMGdXBp6fZuPBnfpvzgraXXOxvo2H/XV+cfb4HZwd0crDW4H2RbBLlZs6e7uaCWfVbeLeVzmuo3FVtaDJJvNxrp163jqqafGXzMMg6eeeoqOjo55jCw/mqrwd3eunu8whBBCCCHmRb674Wy5vR1NVdBUhS23t+edbzZSdc6GpipsvaM9r7TK+TptFnV8WfTJtaf+uxixpbNZVO67uS2vtMU6LnPdxmIr60ESwCc+8QkefvhhHnnkEV599VU+9rGPMTY2xoc+9KH5Di0vt65uYMfmtdgsZX+ohRBCiDln1ebnS9JsavW4rLhs2pTXvS4rVzfN3RMsSoGNcNk07t/UNr6fUD4a3A52bF7L/Zva8volYrL68/l3bF6bsV6vyzph+W9Ifnfavnlt1l+UGtwO7t/UNuV9j8uaV9sa3I6iLj+d+q6Xq+7JdabaWD+pDfVFji3dA7e15/wcS3Fc5rqNxVT2+yQB/Mu//Mv4ZrJr1qzhm9/8Jhs2bMgr73zvk5SiGybPvXGO//v0m7xyOkg0boCSHNlbVAhGjCnLJKpcWCax7D8kIYRYxCoBrApjcTNjf6wCM90uvNIKozn2vmys0hiKmKgKKCYkTINoAiwKNHgc3LK6gSqrxiN7u/CH9PH4VJIDAf38/yoKOCwKlTYNr8vK2dE48YTBWPxC5F6XRn2VA5um0BeMEowkiCZMjPPlrfDYuevqJn7xWi9dg2FievJ4WICWGgd/tLYZ3TB4dO8JxmI6tRU2LCoEIjpWTWFts5tAVCcaN2j2OnnPumZuuLgW3TD59+eP84tXzjIWTXB5fTXvXdvEtS1eHnyyk93HBokbBlc3VhHVIZowaat18elbr+ClE0M8fvAUoViCtc1eAPafGOJsIIJNU3BYNWorbaCoqAqs8Dq5YWUt17f5ONAzRP9IhNoKOygwMBqltsJOLKHz/57rYjgcw6apXFpfhVVTWdPspdHjHH9EaO+xQfYcHwAUOlbVsHFlDZqqEI7pfP5nv2PXkT7iuklDtYPPvesKHHYLu48N8LtTAVw2jfVtNWze2MKLXX4ef+kUp4ZCNHlc/NGaFaDATw+dJhTTub7Vx+aNLRzsGZpQ3/WtyTacGQ7zYrefF3v8JAyTjpU1fPrWK/jhiyfZ3+3HZdN4z7VN3HBJLZqqoBsme48Nsvv4AGeGwtRXO6h2WXi9b5SxWILlVQ6ubfbQ6HWxvs03/tf+WMLg0T3d9PhDtPhcvH9DC4dODtMXCOMfi+GrtFNXZQcTBsai1FU5JuRP1ZvpmGWiG2Zy9bi08uurL5SZer9/JDJeFzD+Wm1lMpb+kUjG/MWmGyZ7jw+y59ggJiYep5XaSjv1bmfWOjO1odS/rqQ+x67BMRTg2mYvDZ7sMc7WfLQxl0WzmexslcsgSQghhBBCCDG/Fs1mskIIIYQQQggxl2SQJIQQQgghhBBpZJAkhBBCCCGEEGlkkCSEEEIIIYQQaWSQJIQQQgghhBBpZJAkhBBCCCGEEGlkkCSEEEIIIYQQaWSQJIQQQgghhBBpZJAkhBBCCCGEEGlkkCSEEEIIIYQQaWSQJIQQQgghhBBpZJAkhBBCCCGEEGlkkCSEEEIIIYQQaSzzHUCpmaYJQDAYnOdIhBBCCCGEEPMpNSZIjRGyWfSDpJGREQCam5vnORIhhBBCCCFEORgZGcHtdmd9XzGnG0YtcIZhcObMGaqqqlAUZb7DEWmCwSDNzc2cPHmS6urq+Q5HiIzkPBULgZynYiGQ81SUA9M0GRkZobGxEVXNPvNo0f+SpKoqTU1N8x2GyKG6ulo6S1H25DwVC4Gcp2IhkPNUzLdcvyClyMINQgghhBBCCJFGBklCCCGEEEIIkUYGSWLe2O12tmzZgt1un+9QhMhKzlOxEMh5KhYCOU/FQrLoF24QQgghhBBCiELIL0lCCCGEEEIIkUYGSUIIIYQQQgiRRgZJQgghhBBCCJFGBklCCCGEEEIIkUYGSaKkHnzwQa6//nqqqqqoq6vjzjvv5PXXX5+QJhKJ8PGPf5yamhoqKyt5z3vew9mzZ+cpYiHgS1/6Eoqi8Jd/+Zfjr8l5KsrB6dOn2bx5MzU1NTidTq666ipefPHF8fdN0+Rv//ZvaWhowOl08va3v50333xzHiMWS5Gu63z+85+nra0Np9PJqlWr+Pu//3vS1wqTc1WUOxkkiZJ6+umn+fjHP87evXv51a9+RTwe553vfCdjY2Pjaf7qr/6KnTt38qMf/Yinn36aM2fOcNddd81j1GIp279/P9/+9re5+uqrJ7wu56mYb0NDQ9x4441YrVb++7//m87OTr761a/i9XrH0/zjP/4j3/zmN9mxYwcvvPACFRUV3HLLLUQikXmMXCw1X/7yl9m+fTv/8i//wquvvsqXv/xl/vEf/5Fvfetb42nkXBVlzxRiDvX395uA+fTTT5umaZrDw8Om1Wo1f/SjH42nefXVV03A3LNnz3yFKZaokZER85JLLjF/9f9v725CotrDOI7/vE5qhY0v5UwikwaBpi00odSghUGICBYUwWAj4y41XyCcEhdR6iIIalMYqRtNgpBeNlGWlSA1mIZudGFkC6025iKtcP53dc+duXUvbZpxrt8PHJjz/z8Dz8CzmB9z5pxHj8zBgwdNQ0ODMYY5xdrQ0tJiDhw48K/7gUDAOJ1Oc+nSJWttcXHRxMfHm1u3boWjRcAYY0x5ebnxer0ha0ePHjVut9sYw6wiOvBLEsLq8+fPkqSUlBRJ0tjYmL5//65Dhw5ZNdnZ2XK5XBodHY1Ij1i/amtrVV5eHjKPEnOKteHevXsqLCzUsWPHlJaWpvz8fN24ccPaf/v2rRYWFkLm1G63a9++fcwpwqq4uFhDQ0OamZmRJL1580YjIyMqKyuTxKwiOtgi3QDWj0AgoMbGRpWUlCgvL0+StLCwoLi4OCUlJYXUOhwOLSwsRKBLrFcDAwN6/fq1/H7/D3vMKdaC2dlZXbt2Tc3NzTp37pz8fr9Onz6tuLg4eTweaxYdDkfI+5hThJvP59PS0pKys7MVGxur1dVVtbe3y+12SxKziqhASELY1NbWampqSiMjI5FuBQjx/v17NTQ06NGjR0pISIh0O8BPBQIBFRYWqqOjQ5KUn5+vqakpXb9+XR6PJ8LdAX+7ffu2+vr61N/fr9zcXE1MTKixsVHp6enMKqIGl9shLOrq6vTgwQM9ffpUGRkZ1rrT6dS3b9+0uLgYUv/hwwc5nc4wd4n1amxsTB8/flRBQYFsNptsNpuePXumq1evymazyeFwMKeIuO3bt2v37t0hazk5OZqbm5Mkaxb/eddF5hThdubMGfl8Pp04cUJ79uxRVVWVmpqa1NnZKYlZRXQgJOG3Msaorq5Og4ODevLkibKyskL29+7dqw0bNmhoaMham56e1tzcnIqKisLdLtap0tJSTU5OamJiwjoKCwvldrut18wpIq2kpOSHRyjMzMxox44dkqSsrCw5nc6QOV1aWtLLly+ZU4TVly9f9McfoV8xY2NjFQgEJDGriA5cboffqra2Vv39/bp7964SExOta43tdrs2btwou92umpoaNTc3KyUlRVu2bFF9fb2Kioq0f//+CHeP9SIxMdH6n9xfNm/erNTUVGudOUWkNTU1qbi4WB0dHTp+/LhevXqlrq4udXV1SZL1bK+LFy9q165dysrKUltbm9LT01VZWRnZ5rGuVFRUqL29XS6XS7m5uRofH9fly5fl9XolMauIEpG+vR7+3yT99Ojp6bFqlpeXzalTp0xycrLZtGmTOXLkiJmfn49c04AxIbcAN4Y5xdpw//59k5eXZ+Lj4012drbp6uoK2Q8EAqatrc04HA4THx9vSktLzfT0dIS6xXq1tLRkGhoajMvlMgkJCWbnzp2mtbXVfP361aphVrHWxRgT9PhjAAAAAFjn+E8SAAAAAAQhJAEAAABAEEISAAAAAAQhJAEAAABAEEISAAAAAAQhJAEAAABAEEISAAAAAAQhJAEAAABAEEISAAAAAAQhJAEAokp1dbUqKytD1t69e6eEhATFxMRIkoaHhxUTE6Pk5GStrKyE1Pr9fsXExFi11dXV1vnPjszMzHB8LADAGkJIAgBEvba2Niv0BEtMTNTg4GDI2s2bN+VyuazzK1euaH5+3jokqaenxzr3+/2/t3kAwJpDSAIARLXJyUn19fWpvr7+hz2Px6Pu7m7rfHl5WQMDA/J4PNaa3W6X0+m0DklKSkqyzrdt2/b7PwQAYE0hJAEAoprP51NFRYWKi4t/2KuqqtKLFy80NzcnSbpz544yMzNVUFAQ7jYBAFGEkAQAiFrPnz/Xw4cP1dnZ+dP9tLQ0lZWVqbe3V5LU3d0tr9cbxg4BANGIkAQAiFo+n08ej0c5OTn/WuP1etXb26vZ2VmNjo7K7XaHsUMAQDQiJAEAotLg4KDGx8d1/vz5/6wrKyvT8vKyampqVFFRodTU1DB1CACIVoQkAEDUWV1dVWtrq+rr65WRkfGftTabTSdPntTw8DCX2gEAfgkhCQAQdR4/fqz5+XmdPXv2l+ovXLigT58+6fDhw7+5MwDA/wEhCQAQdVZWVtTS0qLk5ORfqo+Li9PWrVt/+iwlAAD+KcYYYyLdBAAAAACsFfySBAAAAABBCEkAAAAAEISQBAAAAABBCEkAAAAAEISQBAAAAABBCEkAAAAAEISQBAAAAABBCEkAAAAAEISQBAAAAABBCEkAAAAAEISQBAAAAABB/gSFZpoFbjoJIAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Выбросы в датасете:\n",
+      "       HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
+      "32               No  45.35      No              No     No            30.0   \n",
+      "57               No  46.52     Yes              No     No            30.0   \n",
+      "90               No  44.29      No              No     No            30.0   \n",
+      "105              No  58.54      No              No     No            30.0   \n",
+      "107              No  45.42      No              No     No             0.0   \n",
+      "...             ...    ...     ...             ...    ...             ...   \n",
+      "319636           No  47.55      No              No     No             0.0   \n",
+      "319693           No  44.29      No              No     No             0.0   \n",
+      "319709           No  51.46     Yes              No     No            30.0   \n",
+      "319725           No  53.16      No              No     No            29.0   \n",
+      "319794           No  46.56      No              No     No             0.0   \n",
+      "\n",
+      "        MentalHealth DiffWalking     Sex  AgeCategory      Race  \\\n",
+      "32               0.0         Yes    Male        70-74     White   \n",
+      "57               0.0          No    Male        65-69     White   \n",
+      "90              10.0         Yes  Female        70-74     White   \n",
+      "105              0.0         Yes    Male        65-69     Other   \n",
+      "107              0.0          No  Female        45-49     White   \n",
+      "...              ...         ...     ...          ...       ...   \n",
+      "319636           0.0          No  Female        55-59  Hispanic   \n",
+      "319693           0.0          No  Female        25-29  Hispanic   \n",
+      "319709           0.0          No    Male        55-59  Hispanic   \n",
+      "319725           0.0         Yes    Male        25-29  Hispanic   \n",
+      "319794           0.0          No  Female  80 or older  Hispanic   \n",
+      "\n",
+      "                       Diabetic PhysicalActivity  GenHealth  SleepTime Asthma  \\\n",
+      "32                          Yes               No       Good        8.0     No   \n",
+      "57                          Yes               No       Poor        8.0    Yes   \n",
+      "90                           No               No       Fair        7.0     No   \n",
+      "105     No, borderline diabetes              Yes       Poor        3.0    Yes   \n",
+      "107                          No              Yes  Very good        7.0    Yes   \n",
+      "...                         ...              ...        ...        ...    ...   \n",
+      "319636                       No               No       Fair        7.0    Yes   \n",
+      "319693                       No              Yes  Very good        7.0     No   \n",
+      "319709                       No              Yes       Good        7.0    Yes   \n",
+      "319725  No, borderline diabetes               No       Fair        5.0    Yes   \n",
+      "319794                       No              Yes       Good        8.0     No   \n",
+      "\n",
+      "       KidneyDisease SkinCancer  \n",
+      "32                No         No  \n",
+      "57                No         No  \n",
+      "90                No        Yes  \n",
+      "105               No         No  \n",
+      "107               No         No  \n",
+      "...              ...        ...  \n",
+      "319636            No         No  \n",
+      "319693            No         No  \n",
+      "319709            No         No  \n",
+      "319725            No         No  \n",
+      "319794            No         No  \n",
+      "\n",
+      "[8905 rows x 18 columns]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(cleaned_df['BMI'], cleaned_df['PhysicalHealth'])\n",
+    "plt.xlabel('ИМТ')\n",
+    "plt.ylabel('Физическое здоровье')\n",
+    "plt.title('Диаграмма рассеивания перед чисткой')\n",
+    "plt.show()\n",
+    "\n",
+    "Q1 = cleaned_df[\"BMI\"].quantile(0.25)\n",
+    "Q3 = cleaned_df[\"BMI\"].quantile(0.75)\n",
+    "\n",
+    "IQR = Q3 - Q1\n",
+    "\n",
+    "threshold = 1.5 * IQR\n",
+    "lower_bound = Q1 - threshold\n",
+    "upper_bound = Q3 + threshold\n",
+    "\n",
+    "outliers = (cleaned_df[\"BMI\"] < lower_bound) | (cleaned_df[\"BMI\"] > upper_bound)\n",
+    "\n",
+    "print(\"Выбросы в датасете:\")\n",
+    "print(cleaned_df[outliers])\n",
+    "\n",
+    "median_score = cleaned_df[\"BMI\"].median()\n",
+    "cleaned_df.loc[outliers, \"BMI\"] = median_score\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(cleaned_df['BMI'], cleaned_df['PhysicalHealth'])\n",
+    "plt.xlabel('ИМТ')\n",
+    "plt.ylabel('Физическое здоровье')\n",
+    "plt.title('Диаграмма рассеивания после чистки')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Разбиение набора данных на обучающую, контрольную и тестовую выборки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: 181029\n",
+      "Размер контрольной выборки: 60344\n",
+      "Размер тестовой выборки: 60344\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "train_df, test_df = train_test_split(cleaned_df, test_size=0.2, random_state=42)\n",
+    "\n",
+    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
+    "\n",
+    "print(\"Размер обучающей выборки:\", len(train_df))\n",
+    "print(\"Размер контрольной выборки:\", len(val_df))\n",
+    "print(\"Размер тестовой выборки:\", len(test_df))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение ИМТ в обучающей выборке:\n",
+      "BMI\n",
+      "27.41    5906\n",
+      "26.63    1941\n",
+      "27.46    1456\n",
+      "27.44    1416\n",
+      "27.12    1258\n",
+      "         ... \n",
+      "17.93       1\n",
+      "34.10       1\n",
+      "23.27       1\n",
+      "13.81       1\n",
+      "28.58       1\n",
+      "Name: count, Length: 2305, dtype: int64\n",
+      "\n",
+      "Распределение ИМТ в контрольной выборке:\n",
+      "BMI\n",
+      "27.41    1972\n",
+      "26.63     657\n",
+      "27.46     494\n",
+      "24.41     474\n",
+      "27.44     463\n",
+      "         ... \n",
+      "16.76       1\n",
+      "19.93       1\n",
+      "38.99       1\n",
+      "35.34       1\n",
+      "32.80       1\n",
+      "Name: count, Length: 1969, dtype: int64\n",
+      "\n",
+      "Распределение ИМТ в тестовой выборке:\n",
+      "BMI\n",
+      "27.41    1931\n",
+      "26.63     646\n",
+      "27.44     506\n",
+      "27.46     475\n",
+      "24.41     452\n",
+      "         ... \n",
+      "34.89       1\n",
+      "30.75       1\n",
+      "41.06       1\n",
+      "39.91       1\n",
+      "20.27       1\n",
+      "Name: count, Length: 1988, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def check_balance(df, name):\n",
+    "    counts = df['BMI'].value_counts()\n",
+    "    print(f\"Распределение ИМТ в {name}:\")\n",
+    "    print(counts)\n",
+    "    print()\n",
+    "\n",
+    "check_balance(train_df, \"обучающей выборке\")\n",
+    "check_balance(val_df, \"контрольной выборке\")\n",
+    "check_balance(test_df, \"тестовой выборке\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Оверсемплинг и андерсемплинг"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Оверсэмплинг:\n",
+      "Распределение ИМТ в обучающей выборке:\n",
+      "BMI\n",
+      "27.41    5906\n",
+      "26.63    1941\n",
+      "27.46    1636\n",
+      "27.44    1595\n",
+      "27.12    1258\n",
+      "         ... \n",
+      "40.92       1\n",
+      "26.98       1\n",
+      "31.22       1\n",
+      "29.59       1\n",
+      "16.00       1\n",
+      "Name: count, Length: 2305, dtype: int64\n",
+      "\n",
+      "Распределение ИМТ в контрольной выборке:\n",
+      "BMI\n",
+      "27.41    1981\n",
+      "26.63     657\n",
+      "27.46     494\n",
+      "24.41     474\n",
+      "27.44     465\n",
+      "         ... \n",
+      "43.03       1\n",
+      "30.64       1\n",
+      "42.48       1\n",
+      "31.61       1\n",
+      "35.34       1\n",
+      "Name: count, Length: 1969, dtype: int64\n",
+      "\n",
+      "Распределение ИМТ в тестовой выборке:\n",
+      "BMI\n",
+      "27.41    1931\n",
+      "26.63     646\n",
+      "27.44     580\n",
+      "27.46     533\n",
+      "24.41     452\n",
+      "         ... \n",
+      "14.37       1\n",
+      "17.28       1\n",
+      "18.04       1\n",
+      "35.98       1\n",
+      "17.41       1\n",
+      "Name: count, Length: 1988, dtype: int64\n",
+      "\n",
+      "Андерсэмплинг:\n",
+      "Распределение ИМТ в обучающей выборке:\n",
+      "BMI\n",
+      "27.41    5173\n",
+      "26.63    1708\n",
+      "27.46    1456\n",
+      "27.44    1416\n",
+      "27.12    1104\n",
+      "         ... \n",
+      "35.27       1\n",
+      "42.80       1\n",
+      "28.58       1\n",
+      "34.10       1\n",
+      "38.83       1\n",
+      "Name: count, Length: 2282, dtype: int64\n",
+      "\n",
+      "Распределение ИМТ в контрольной выборке:\n",
+      "BMI\n",
+      "27.41    1972\n",
+      "26.63     654\n",
+      "27.46     494\n",
+      "24.41     470\n",
+      "27.44     463\n",
+      "         ... \n",
+      "32.31       1\n",
+      "33.76       1\n",
+      "38.38       1\n",
+      "42.11       1\n",
+      "31.23       1\n",
+      "Name: count, Length: 1969, dtype: int64\n",
+      "\n",
+      "Распределение ИМТ в тестовой выборке:\n",
+      "BMI\n",
+      "27.41    1706\n",
+      "26.63     559\n",
+      "27.44     506\n",
+      "27.46     475\n",
+      "24.41     398\n",
+      "         ... \n",
+      "28.22       1\n",
+      "39.93       1\n",
+      "39.17       1\n",
+      "28.24       1\n",
+      "41.90       1\n",
+      "Name: count, Length: 1966, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from imblearn.over_sampling import RandomOverSampler\n",
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "\n",
+    "def binning(target, bins):\n",
+    "    return pd.qcut(target, q=bins, labels=False)\n",
+    "\n",
+    "train_df['BMI_binned'] = binning(train_df['BMI'], bins=2)\n",
+    "val_df['BMI_binned'] = binning(val_df['BMI'], bins=2)\n",
+    "test_df['BMI_binned'] = binning(test_df['BMI'], bins=2)\n",
+    "\n",
+    "def oversample(df, target_column):\n",
+    "    X = df.drop(target_column, axis=1)\n",
+    "    y = df[target_column]\n",
+    "    \n",
+    "    oversampler = RandomOverSampler(random_state=42)\n",
+    "    x_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
+    "    \n",
+    "    resampled_df = pd.concat([x_resampled, y_resampled], axis=1) \n",
+    "    return resampled_df\n",
+    "\n",
+    "def undersample(df, target_column):\n",
+    "    X = df.drop(target_column, axis=1)\n",
+    "    y = df[target_column]\n",
+    "    \n",
+    "    undersampler = RandomUnderSampler(random_state=42)\n",
+    "    x_resampled, y_resampled = undersampler.fit_resample(X, y)\n",
+    "    \n",
+    "    resampled_df = pd.concat([x_resampled, y_resampled], axis=1)\n",
+    "    return resampled_df\n",
+    "\n",
+    "train_df_oversampled = oversample(train_df, 'BMI_binned')\n",
+    "val_df_oversampled = oversample(val_df, 'BMI_binned')\n",
+    "test_df_oversampled = oversample(test_df, 'BMI_binned')\n",
+    "\n",
+    "train_df_undersampled = undersample(train_df, 'BMI_binned')\n",
+    "val_df_undersampled = undersample(val_df, 'BMI_binned')\n",
+    "test_df_undersampled = undersample(test_df, 'BMI_binned')\n",
+    "\n",
+    "print(\"Оверсэмплинг:\")\n",
+    "check_balance(train_df_oversampled, \"обучающей выборке\")\n",
+    "check_balance(val_df_oversampled, \"контрольной выборке\")\n",
+    "check_balance(test_df_oversampled, \"тестовой выборке\")\n",
+    "\n",
+    "print(\"Андерсэмплинг:\")\n",
+    "check_balance(train_df_undersampled, \"обучающей выборке\")\n",
+    "check_balance(val_df_undersampled, \"контрольной выборке\")\n",
+    "check_balance(test_df_undersampled, \"тестовой выборке\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Датасет: Цены на мобильные устройства (https://www.kaggle.com/datasets/dewangmoghe/mobile-phone-price-prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Unnamed: 0', 'Name', 'Rating', 'Spec_score', 'No_of_sim', 'Ram',\n",
+      "       'Battery', 'Display', 'Camera', 'External_Memory', 'Android_version',\n",
+      "       'Price', 'company', 'Inbuilt_memory', 'fast_charging',\n",
+      "       'Screen_resolution', 'Processor', 'Processor_name'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_phones = pd.read_csv(\"..\\\\static\\\\csv\\\\mobile-phone-price-prediction.csv\")\n",
+    "\n",
+    "print(df_phones.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Столбцы на русском:\n",
+    "\n",
+    "Unnamed: 0 - идентификатор \\\n",
+    "Name - Модель \\\n",
+    "Rating - Рейтинг \\\n",
+    "Spec_score - Оценка характеристик\\\n",
+    "No_of_sim - Доступние сим-карты\\\n",
+    "Ram - Объем оперативной памяти\\\n",
+    "Battery - Батарея\\\n",
+    "Display - Размер дисплея\\\n",
+    "Camera - Камера\\\n",
+    "External_Memory - Внешняя память\\\n",
+    "Android_version - Версия Android\\\n",
+    "Price - Цена\\\n",
+    "company - Фирма\\\n",
+    "Inbuilt_memory - Встроенная память\\\n",
+    "fast_charging - Быстрая зарядка\\\n",
+    "Screen_resolution - Разрешение экрана\\\n",
+    "Processor - Процессор\\\n",
+    "Processor_name - Название процессора"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1370 entries, 0 to 1369\n",
+      "Data columns (total 18 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Unnamed: 0         1370 non-null   int64  \n",
+      " 1   Name               1370 non-null   object \n",
+      " 2   Rating             1370 non-null   float64\n",
+      " 3   Spec_score         1370 non-null   int64  \n",
+      " 4   No_of_sim          1370 non-null   object \n",
+      " 5   Ram                1370 non-null   object \n",
+      " 6   Battery            1370 non-null   object \n",
+      " 7   Display            1370 non-null   object \n",
+      " 8   Camera             1370 non-null   object \n",
+      " 9   External_Memory    1370 non-null   object \n",
+      " 10  Android_version    927 non-null    object \n",
+      " 11  Price              1370 non-null   object \n",
+      " 12  company            1370 non-null   object \n",
+      " 13  Inbuilt_memory     1351 non-null   object \n",
+      " 14  fast_charging      1281 non-null   object \n",
+      " 15  Screen_resolution  1368 non-null   object \n",
+      " 16  Processor          1342 non-null   object \n",
+      " 17  Processor_name     1370 non-null   object \n",
+      "dtypes: float64(1), int64(2), object(15)\n",
+      "memory usage: 192.8+ KB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Spec_score</th>\n",
+       "      <th>No_of_sim</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Battery</th>\n",
+       "      <th>Display</th>\n",
+       "      <th>Camera</th>\n",
+       "      <th>External_Memory</th>\n",
+       "      <th>Android_version</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>company</th>\n",
+       "      <th>Inbuilt_memory</th>\n",
+       "      <th>fast_charging</th>\n",
+       "      <th>Screen_resolution</th>\n",
+       "      <th>Processor</th>\n",
+       "      <th>Processor_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Samsung Galaxy F14 5G</td>\n",
+       "      <td>4.65</td>\n",
+       "      <td>68</td>\n",
+       "      <td>Dual Sim, 3G, 4G, 5G, VoLTE,</td>\n",
+       "      <td>4 GB RAM</td>\n",
+       "      <td>6000 mAh Battery</td>\n",
+       "      <td>6.6 inches</td>\n",
+       "      <td>50 MP + 2 MP Dual Rear &amp;amp; 13 MP Front Camera</td>\n",
+       "      <td>Memory Card Supported, upto 1 TB</td>\n",
+       "      <td>13</td>\n",
+       "      <td>9,999</td>\n",
+       "      <td>Samsung</td>\n",
+       "      <td>128 GB inbuilt</td>\n",
+       "      <td>25W Fast Charging</td>\n",
+       "      <td>2408 x 1080 px Display with Water Drop Notch</td>\n",
+       "      <td>Octa Core Processor</td>\n",
+       "      <td>Exynos 1330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Samsung Galaxy A11</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>63</td>\n",
+       "      <td>Dual Sim, 3G, 4G, VoLTE,</td>\n",
+       "      <td>2 GB RAM</td>\n",
+       "      <td>4000 mAh Battery</td>\n",
+       "      <td>6.4 inches</td>\n",
+       "      <td>13 MP + 5 MP + 2 MP Triple Rear &amp;amp; 8 MP Fro...</td>\n",
+       "      <td>Memory Card Supported, upto 512 GB</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9,990</td>\n",
+       "      <td>Samsung</td>\n",
+       "      <td>32 GB inbuilt</td>\n",
+       "      <td>15W Fast Charging</td>\n",
+       "      <td>720 x 1560 px Display with Punch Hole</td>\n",
+       "      <td>1.8 GHz Processor</td>\n",
+       "      <td>Octa Core</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Samsung Galaxy A13</td>\n",
+       "      <td>4.30</td>\n",
+       "      <td>75</td>\n",
+       "      <td>Dual Sim, 3G, 4G, VoLTE,</td>\n",
+       "      <td>4 GB RAM</td>\n",
+       "      <td>5000 mAh Battery</td>\n",
+       "      <td>6.6 inches</td>\n",
+       "      <td>50 MP Quad Rear &amp;amp; 8 MP Front Camera</td>\n",
+       "      <td>Memory Card Supported, upto 1 TB</td>\n",
+       "      <td>12</td>\n",
+       "      <td>11,999</td>\n",
+       "      <td>Samsung</td>\n",
+       "      <td>64 GB inbuilt</td>\n",
+       "      <td>25W Fast Charging</td>\n",
+       "      <td>1080 x 2408 px Display with Water Drop Notch</td>\n",
+       "      <td>2 GHz Processor</td>\n",
+       "      <td>Octa Core</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Samsung Galaxy F23</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>73</td>\n",
+       "      <td>Dual Sim, 3G, 4G, VoLTE,</td>\n",
+       "      <td>4 GB RAM</td>\n",
+       "      <td>6000 mAh Battery</td>\n",
+       "      <td>6.4 inches</td>\n",
+       "      <td>48 MP Quad Rear &amp;amp; 13 MP Front Camera</td>\n",
+       "      <td>Memory Card Supported, upto 1 TB</td>\n",
+       "      <td>12</td>\n",
+       "      <td>11,999</td>\n",
+       "      <td>Samsung</td>\n",
+       "      <td>64 GB inbuilt</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>720 x 1600 px</td>\n",
+       "      <td>Octa Core</td>\n",
+       "      <td>Helio G88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Samsung Galaxy A03s (4GB RAM + 64GB)</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>69</td>\n",
+       "      <td>Dual Sim, 3G, 4G, VoLTE,</td>\n",
+       "      <td>4 GB RAM</td>\n",
+       "      <td>5000 mAh Battery</td>\n",
+       "      <td>6.5 inches</td>\n",
+       "      <td>13 MP + 2 MP + 2 MP Triple Rear &amp;amp; 5 MP Fro...</td>\n",
+       "      <td>Memory Card Supported, upto 1 TB</td>\n",
+       "      <td>11</td>\n",
+       "      <td>11,999</td>\n",
+       "      <td>Samsung</td>\n",
+       "      <td>64 GB inbuilt</td>\n",
+       "      <td>15W Fast Charging</td>\n",
+       "      <td>720 x 1600 px Display with Water Drop Notch</td>\n",
+       "      <td>Octa Core</td>\n",
+       "      <td>Helio P35</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0                                  Name  Rating  Spec_score  \\\n",
+       "0           0                 Samsung Galaxy F14 5G    4.65          68   \n",
+       "1           1                    Samsung Galaxy A11    4.20          63   \n",
+       "2           2                    Samsung Galaxy A13    4.30          75   \n",
+       "3           3                    Samsung Galaxy F23    4.10          73   \n",
+       "4           4  Samsung Galaxy A03s (4GB RAM + 64GB)    4.10          69   \n",
+       "\n",
+       "                       No_of_sim       Ram            Battery     Display  \\\n",
+       "0  Dual Sim, 3G, 4G, 5G, VoLTE,   4 GB RAM  6000 mAh Battery   6.6 inches   \n",
+       "1      Dual Sim, 3G, 4G, VoLTE,   2 GB RAM  4000 mAh Battery   6.4 inches   \n",
+       "2      Dual Sim, 3G, 4G, VoLTE,   4 GB RAM  5000 mAh Battery   6.6 inches   \n",
+       "3      Dual Sim, 3G, 4G, VoLTE,   4 GB RAM   6000 mAh Battery  6.4 inches   \n",
+       "4      Dual Sim, 3G, 4G, VoLTE,   4 GB RAM  5000 mAh Battery   6.5 inches   \n",
+       "\n",
+       "                                              Camera  \\\n",
+       "0    50 MP + 2 MP Dual Rear &amp; 13 MP Front Camera   \n",
+       "1  13 MP + 5 MP + 2 MP Triple Rear &amp; 8 MP Fro...   \n",
+       "2            50 MP Quad Rear &amp; 8 MP Front Camera   \n",
+       "3           48 MP Quad Rear &amp; 13 MP Front Camera   \n",
+       "4  13 MP + 2 MP + 2 MP Triple Rear &amp; 5 MP Fro...   \n",
+       "\n",
+       "                      External_Memory Android_version   Price  company  \\\n",
+       "0    Memory Card Supported, upto 1 TB              13   9,999  Samsung   \n",
+       "1  Memory Card Supported, upto 512 GB              10   9,990  Samsung   \n",
+       "2    Memory Card Supported, upto 1 TB              12  11,999  Samsung   \n",
+       "3    Memory Card Supported, upto 1 TB              12  11,999  Samsung   \n",
+       "4    Memory Card Supported, upto 1 TB              11  11,999  Samsung   \n",
+       "\n",
+       "    Inbuilt_memory       fast_charging  \\\n",
+       "0   128 GB inbuilt   25W Fast Charging   \n",
+       "1    32 GB inbuilt   15W Fast Charging   \n",
+       "2    64 GB inbuilt   25W Fast Charging   \n",
+       "3    64 GB inbuilt                 NaN   \n",
+       "4    64 GB inbuilt   15W Fast Charging   \n",
+       "\n",
+       "                               Screen_resolution             Processor  \\\n",
+       "0   2408 x 1080 px Display with Water Drop Notch   Octa Core Processor   \n",
+       "1          720 x 1560 px Display with Punch Hole     1.8 GHz Processor   \n",
+       "2   1080 x 2408 px Display with Water Drop Notch       2 GHz Processor   \n",
+       "3                                  720 x 1600 px             Octa Core   \n",
+       "4    720 x 1600 px Display with Water Drop Notch             Octa Core   \n",
+       "\n",
+       "  Processor_name  \n",
+       "0    Exynos 1330  \n",
+       "1      Octa Core  \n",
+       "2      Octa Core  \n",
+       "3      Helio G88  \n",
+       "4      Helio P35  "
+      ]
+     },
+     "execution_count": 242,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_phones.info()\n",
+    "df_phones.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Объект наблюдения: телефон\\\n",
+    "Атрибуты объектов: название модели, рейтинг, оценка характеристик, сим-карты, оперативная память и т.д."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(14, 6))\n",
+    "\n",
+    "\n",
+    "plt.scatter(df_phones['company'].str.lower(), df_phones['Spec_score'])\n",
+    "plt.xlabel('Фирма')\n",
+    "plt.ylabel('Оценка характеристик')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Диаграмма 1')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Между атрибутами присутствует связь. Пример, на диаграмме 1 - связь между фирмой и оценкой характеристик\\\n",
+    "Примеры бизнес-целей:\\\n",
+    "    1. Прогнозирование цен на основе оценки характеристик\\\n",
+    "    2. Прогнозирование оценки характеристик на основе фирмы и цены\\\n",
+    "\\\n",
+    "Эффект для бизнеса: влияние фирмы на цену, влияние характеристик на рейтинг\\\n",
+    "\\\n",
+    "\\\n",
+    "Цели технического проекта:\\\n",
+    "    1. Первая бизнес-цель: вход - оценка характеристик, целевой признак - цена.\\\n",
+    "    2. Вторая бизнес-цель: вход - фирма и цена, целевой признак - оценка характеристик."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверка на выбросы"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Пустые значения по столбцам:\n",
+      "Unnamed: 0             0\n",
+      "Name                   0\n",
+      "Rating                 0\n",
+      "Spec_score             0\n",
+      "No_of_sim              0\n",
+      "Ram                    0\n",
+      "Battery                0\n",
+      "Display                0\n",
+      "Camera                 0\n",
+      "External_Memory        0\n",
+      "Android_version      443\n",
+      "Price                  0\n",
+      "company                0\n",
+      "Inbuilt_memory        19\n",
+      "fast_charging         89\n",
+      "Screen_resolution      2\n",
+      "Processor             28\n",
+      "Processor_name         0\n",
+      "dtype: int64\n",
+      "\n",
+      "Количество дубликатов: 0\n",
+      "\n",
+      "Статистический обзор данных:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Spec_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1370.000000</td>\n",
+       "      <td>1370.000000</td>\n",
+       "      <td>1370.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>684.500000</td>\n",
+       "      <td>4.374416</td>\n",
+       "      <td>80.234307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>395.629246</td>\n",
+       "      <td>0.230176</td>\n",
+       "      <td>8.373922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.750000</td>\n",
+       "      <td>42.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>342.250000</td>\n",
+       "      <td>4.150000</td>\n",
+       "      <td>75.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>684.500000</td>\n",
+       "      <td>4.400000</td>\n",
+       "      <td>82.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1026.750000</td>\n",
+       "      <td>4.550000</td>\n",
+       "      <td>86.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1369.000000</td>\n",
+       "      <td>4.750000</td>\n",
+       "      <td>98.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Unnamed: 0       Rating   Spec_score\n",
+       "count  1370.000000  1370.000000  1370.000000\n",
+       "mean    684.500000     4.374416    80.234307\n",
+       "std     395.629246     0.230176     8.373922\n",
+       "min       0.000000     3.750000    42.000000\n",
+       "25%     342.250000     4.150000    75.000000\n",
+       "50%     684.500000     4.400000    82.000000\n",
+       "75%    1026.750000     4.550000    86.000000\n",
+       "max    1369.000000     4.750000    98.000000"
+      ]
+     },
+     "execution_count": 244,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "null_values = df_phones.isnull().sum()\n",
+    "print(\"Пустые значения по столбцам:\")\n",
+    "print(null_values)\n",
+    "\n",
+    "duplicates = df_phones.duplicated().sum()\n",
+    "print(f\"\\nКоличество дубликатов: {duplicates}\")\n",
+    "\n",
+    "print(\"\\nСтатистический обзор данных:\")\n",
+    "df_phones.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Видим, что есть пустые данные, но нет дубликатов. Удаляем их"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "В наборе данных 'Phones' было удалено 553 строк с пустыми значениями.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def drop_missing_values(dataframe, name):\n",
+    "    before_shape = dataframe.shape \n",
+    "    cleaned_dataframe = dataframe.dropna()  \n",
+    "    after_shape = cleaned_dataframe.shape \n",
+    "    print(f\"В наборе данных '{name}' было удалено {before_shape[0] - after_shape[0]} строк с пустыми значениями.\")\n",
+    "    return cleaned_dataframe\n",
+    "\n",
+    "cleaned_df = drop_missing_values(df_phones, \"Phones\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Рассчитаем коэффицент ассиметрии"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Коэффициент асимметрии для столбца 'Unnamed: 0': 0.0\n",
+      "\n",
+      "Коэффициент асимметрии для столбца 'Rating': -0.06697860128699223\n",
+      "\n",
+      "Коэффициент асимметрии для столбца 'Spec_score': -0.7393772365886471\n"
+     ]
+    }
+   ],
+   "source": [
+    "for column in df_phones.select_dtypes(include=[np.number]).columns:\n",
+    "    skewness = df_phones[column].skew()\n",
+    "    print(f\"\\nКоэффициент асимметрии для столбца '{column}': {skewness}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Видим, что выбросы незначительные\n",
+    "\n",
+    "Очистка данных от шумов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Выбросы в датасете:\n",
+      "      Unnamed: 0                       Name  Rating  Spec_score  \\\n",
+      "99            99                   Vivo Y02    4.35          54   \n",
+      "214          214                Realme C30s    4.55          58   \n",
+      "802          802  Vivo Y02 (2GB RAM + 32GB)    4.50          53   \n",
+      "803          803                   Vivo Y02    4.35          54   \n",
+      "1344        1344                    TCL 501    4.25          55   \n",
+      "\n",
+      "                      No_of_sim       Ram            Battery      Display  \\\n",
+      "99    Dual Sim, 3G, 4G, VoLTE,   3 GB RAM  5000 mAh Battery   6.51 inches   \n",
+      "214   Dual Sim, 3G, 4G, VoLTE,   2 GB RAM  5000 mAh Battery    6.5 inches   \n",
+      "802   Dual Sim, 3G, 4G, VoLTE,   2 GB RAM  5000 mAh Battery   6.51 inches   \n",
+      "803   Dual Sim, 3G, 4G, VoLTE,   3 GB RAM  5000 mAh Battery   6.51 inches   \n",
+      "1344  Dual Sim, 3G, 4G, VoLTE,   2 GB RAM  3000 mAh Battery      6 inches   \n",
+      "\n",
+      "                                 Camera                   External_Memory  \\\n",
+      "99    8 MP Rear &amp; 5 MP Front Camera  Memory Card Supported, upto 1 TB   \n",
+      "214   8 MP Rear &amp; 5 MP Front Camera  Memory Card Supported, upto 1 TB   \n",
+      "802   8 MP Rear &amp; 5 MP Front Camera  Memory Card Supported, upto 1 TB   \n",
+      "803   8 MP Rear &amp; 5 MP Front Camera  Memory Card Supported, upto 1 TB   \n",
+      "1344  5 MP Rear &amp; 2 MP Front Camera             Memory Card Supported   \n",
+      "\n",
+      "     Android_version  Price company  Inbuilt_memory       fast_charging  \\\n",
+      "99                12  9,999    Vivo   32 GB inbuilt   10W Fast Charging   \n",
+      "214               12  6,950  Realme   32 GB inbuilt   10W Fast Charging   \n",
+      "802               12  8,999    Vivo   32 GB inbuilt   10W Fast Charging   \n",
+      "803               12  8,489    Vivo   32 GB inbuilt   10W Fast Charging   \n",
+      "1344              14  7,990     TCL   32 GB inbuilt   10W Fast Charging   \n",
+      "\n",
+      "                                 Screen_resolution             Processor  \\\n",
+      "99     720 x 1600 px Display with Water Drop Notch   Octa Core Processor   \n",
+      "214    720 x 1600 px Display with Water Drop Notch             Octa Core   \n",
+      "802    720 x 1600 px Display with Water Drop Notch   Octa Core Processor   \n",
+      "803    720 x 1600 px Display with Water Drop Notch   Octa Core Processor   \n",
+      "1344                         540 x 1092 px Display             Octa Core   \n",
+      "\n",
+      "      Processor_name  \n",
+      "99             Helio  \n",
+      "214   Unisoc SC9863A  \n",
+      "802            Helio  \n",
+      "803            Helio  \n",
+      "1344       Helio G36  \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(cleaned_df['company'].str.lower(), cleaned_df['Spec_score'])\n",
+    "plt.xlabel('Фирма')\n",
+    "plt.ylabel('Оценка характеристик')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Диаграмма рассеивания перед чисткой')\n",
+    "plt.show()\n",
+    "\n",
+    "Q1 = cleaned_df[\"Spec_score\"].quantile(0.25)\n",
+    "Q3 = cleaned_df[\"Spec_score\"].quantile(0.75)\n",
+    "\n",
+    "IQR = Q3 - Q1\n",
+    "\n",
+    "threshold = 1.5 * IQR\n",
+    "lower_bound = Q1 - threshold\n",
+    "upper_bound = Q3 + threshold\n",
+    "\n",
+    "outliers = (cleaned_df[\"Spec_score\"] < lower_bound) | (cleaned_df[\"Spec_score\"] > upper_bound)\n",
+    "\n",
+    "print(\"Выбросы в датасете:\")\n",
+    "print(cleaned_df[outliers])\n",
+    "\n",
+    "median_score = cleaned_df[\"Spec_score\"].median()\n",
+    "cleaned_df.loc[outliers, \"Spec_score\"] = median_score\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(cleaned_df['company'].str.lower(), cleaned_df['Spec_score'])\n",
+    "plt.xlabel('Фирма')\n",
+    "plt.ylabel('Оценка характеристик')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Диаграмма рассеивания после чистки')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Разбиение на выборки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: 489\n",
+      "Размер контрольной выборки: 164\n",
+      "Размер тестовой выборки: 164\n",
+      "\n",
+      "Распределение оценки характеристик в обучающей выборке:\n",
+      "Spec_score\n",
+      "75    48\n",
+      "86    35\n",
+      "80    34\n",
+      "84    32\n",
+      "85    23\n",
+      "78    23\n",
+      "83    23\n",
+      "77    19\n",
+      "79    19\n",
+      "82    18\n",
+      "89    17\n",
+      "88    17\n",
+      "71    16\n",
+      "73    15\n",
+      "72    13\n",
+      "74    13\n",
+      "87    12\n",
+      "69    11\n",
+      "76    10\n",
+      "81    10\n",
+      "67     9\n",
+      "90     9\n",
+      "70     8\n",
+      "68     8\n",
+      "91     8\n",
+      "64     7\n",
+      "93     7\n",
+      "92     6\n",
+      "66     5\n",
+      "94     4\n",
+      "63     4\n",
+      "96     2\n",
+      "95     1\n",
+      "65     1\n",
+      "60     1\n",
+      "61     1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение оценки характеристик в контрольной выборке:\n",
+      "Spec_score\n",
+      "75    18\n",
+      "81    12\n",
+      "74    11\n",
+      "79     9\n",
+      "82     9\n",
+      "85     9\n",
+      "84     8\n",
+      "86     8\n",
+      "76     7\n",
+      "78     7\n",
+      "77     7\n",
+      "83     6\n",
+      "89     5\n",
+      "71     5\n",
+      "72     5\n",
+      "80     4\n",
+      "70     4\n",
+      "88     3\n",
+      "68     3\n",
+      "65     3\n",
+      "73     3\n",
+      "67     2\n",
+      "87     2\n",
+      "63     2\n",
+      "95     2\n",
+      "93     2\n",
+      "90     2\n",
+      "94     1\n",
+      "66     1\n",
+      "92     1\n",
+      "69     1\n",
+      "98     1\n",
+      "61     1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение оценки характеристик в тестовой выборке:\n",
+      "Spec_score\n",
+      "75    15\n",
+      "84    13\n",
+      "76    11\n",
+      "82    10\n",
+      "81     9\n",
+      "80     9\n",
+      "77     8\n",
+      "83     8\n",
+      "86     7\n",
+      "89     6\n",
+      "78     6\n",
+      "79     6\n",
+      "87     5\n",
+      "71     5\n",
+      "74     5\n",
+      "85     5\n",
+      "70     4\n",
+      "94     3\n",
+      "72     3\n",
+      "73     3\n",
+      "66     3\n",
+      "91     3\n",
+      "88     3\n",
+      "92     3\n",
+      "93     2\n",
+      "96     1\n",
+      "64     1\n",
+      "90     1\n",
+      "67     1\n",
+      "62     1\n",
+      "65     1\n",
+      "68     1\n",
+      "95     1\n",
+      "69     1\n",
+      "Name: count, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_df, test_df = train_test_split(cleaned_df, test_size=0.2, random_state=42)\n",
+    "\n",
+    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
+    "\n",
+    "print(\"Размер обучающей выборки:\", len(train_df))\n",
+    "print(\"Размер контрольной выборки:\", len(val_df))\n",
+    "print(\"Размер тестовой выборки:\", len(test_df))\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "def check_balance(df, name):\n",
+    "    counts = df['Spec_score'].value_counts()\n",
+    "    print(f\"Распределение оценки характеристик в {name}:\")\n",
+    "    print(counts)\n",
+    "    print()\n",
+    "\n",
+    "check_balance(train_df, \"обучающей выборке\")\n",
+    "check_balance(val_df, \"контрольной выборке\")\n",
+    "check_balance(test_df, \"тестовой выборке\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Оверсемплинг и андерсемплинг"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Оверсэмплинг:\n",
+      "Распределение оценки характеристик в обучающей выборке:\n",
+      "Spec_score\n",
+      "85    48\n",
+      "78    48\n",
+      "75    48\n",
+      "82    48\n",
+      "64    48\n",
+      "73    48\n",
+      "79    48\n",
+      "87    48\n",
+      "86    48\n",
+      "80    48\n",
+      "70    48\n",
+      "83    48\n",
+      "68    48\n",
+      "74    48\n",
+      "71    48\n",
+      "72    48\n",
+      "66    48\n",
+      "93    48\n",
+      "77    48\n",
+      "88    48\n",
+      "69    48\n",
+      "89    48\n",
+      "84    48\n",
+      "94    48\n",
+      "76    48\n",
+      "95    48\n",
+      "90    48\n",
+      "63    48\n",
+      "81    48\n",
+      "67    48\n",
+      "91    48\n",
+      "92    48\n",
+      "96    48\n",
+      "65    48\n",
+      "60    48\n",
+      "61    48\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение оценки характеристик в контрольной выборке:\n",
+      "Spec_score\n",
+      "75    18\n",
+      "94    18\n",
+      "72    18\n",
+      "82    18\n",
+      "70    18\n",
+      "74    18\n",
+      "68    18\n",
+      "88    18\n",
+      "71    18\n",
+      "80    18\n",
+      "92    18\n",
+      "86    18\n",
+      "66    18\n",
+      "81    18\n",
+      "84    18\n",
+      "79    18\n",
+      "73    18\n",
+      "76    18\n",
+      "67    18\n",
+      "95    18\n",
+      "78    18\n",
+      "85    18\n",
+      "83    18\n",
+      "77    18\n",
+      "89    18\n",
+      "98    18\n",
+      "69    18\n",
+      "90    18\n",
+      "87    18\n",
+      "65    18\n",
+      "63    18\n",
+      "93    18\n",
+      "61    18\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение оценки характеристик в тестовой выборке:\n",
+      "Spec_score\n",
+      "80    15\n",
+      "94    15\n",
+      "82    15\n",
+      "77    15\n",
+      "75    15\n",
+      "79    15\n",
+      "96    15\n",
+      "83    15\n",
+      "76    15\n",
+      "71    15\n",
+      "64    15\n",
+      "78    15\n",
+      "84    15\n",
+      "91    15\n",
+      "74    15\n",
+      "93    15\n",
+      "87    15\n",
+      "89    15\n",
+      "81    15\n",
+      "66    15\n",
+      "86    15\n",
+      "92    15\n",
+      "88    15\n",
+      "73    15\n",
+      "90    15\n",
+      "67    15\n",
+      "85    15\n",
+      "72    15\n",
+      "62    15\n",
+      "70    15\n",
+      "65    15\n",
+      "68    15\n",
+      "95    15\n",
+      "69    15\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Андерсэмплинг:\n",
+      "Распределение оценки характеристик в обучающей выборке:\n",
+      "Spec_score\n",
+      "60    1\n",
+      "61    1\n",
+      "63    1\n",
+      "64    1\n",
+      "65    1\n",
+      "66    1\n",
+      "67    1\n",
+      "68    1\n",
+      "69    1\n",
+      "70    1\n",
+      "71    1\n",
+      "72    1\n",
+      "73    1\n",
+      "74    1\n",
+      "75    1\n",
+      "76    1\n",
+      "77    1\n",
+      "78    1\n",
+      "79    1\n",
+      "80    1\n",
+      "81    1\n",
+      "82    1\n",
+      "83    1\n",
+      "84    1\n",
+      "85    1\n",
+      "86    1\n",
+      "87    1\n",
+      "88    1\n",
+      "89    1\n",
+      "90    1\n",
+      "91    1\n",
+      "92    1\n",
+      "93    1\n",
+      "94    1\n",
+      "95    1\n",
+      "96    1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение оценки характеристик в контрольной выборке:\n",
+      "Spec_score\n",
+      "61    1\n",
+      "63    1\n",
+      "65    1\n",
+      "66    1\n",
+      "67    1\n",
+      "68    1\n",
+      "69    1\n",
+      "70    1\n",
+      "71    1\n",
+      "72    1\n",
+      "73    1\n",
+      "74    1\n",
+      "75    1\n",
+      "76    1\n",
+      "77    1\n",
+      "78    1\n",
+      "79    1\n",
+      "80    1\n",
+      "81    1\n",
+      "82    1\n",
+      "83    1\n",
+      "84    1\n",
+      "85    1\n",
+      "86    1\n",
+      "87    1\n",
+      "88    1\n",
+      "89    1\n",
+      "90    1\n",
+      "92    1\n",
+      "93    1\n",
+      "94    1\n",
+      "95    1\n",
+      "98    1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение оценки характеристик в тестовой выборке:\n",
+      "Spec_score\n",
+      "62    1\n",
+      "64    1\n",
+      "65    1\n",
+      "66    1\n",
+      "67    1\n",
+      "68    1\n",
+      "69    1\n",
+      "70    1\n",
+      "71    1\n",
+      "72    1\n",
+      "73    1\n",
+      "74    1\n",
+      "75    1\n",
+      "76    1\n",
+      "77    1\n",
+      "78    1\n",
+      "79    1\n",
+      "80    1\n",
+      "81    1\n",
+      "82    1\n",
+      "83    1\n",
+      "84    1\n",
+      "85    1\n",
+      "86    1\n",
+      "87    1\n",
+      "88    1\n",
+      "89    1\n",
+      "90    1\n",
+      "91    1\n",
+      "92    1\n",
+      "93    1\n",
+      "94    1\n",
+      "95    1\n",
+      "96    1\n",
+      "Name: count, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_df_oversampled = oversample(train_df, 'Spec_score')\n",
+    "val_df_oversampled = oversample(val_df, 'Spec_score')\n",
+    "test_df_oversampled = oversample(test_df, 'Spec_score')\n",
+    "\n",
+    "train_df_undersampled = undersample(train_df, 'Spec_score')\n",
+    "val_df_undersampled = undersample(val_df, 'Spec_score')\n",
+    "test_df_undersampled = undersample(test_df, 'Spec_score')\n",
+    "\n",
+    "print(\"Оверсэмплинг:\")\n",
+    "check_balance(train_df_oversampled, \"обучающей выборке\")\n",
+    "check_balance(val_df_oversampled, \"контрольной выборке\")\n",
+    "check_balance(test_df_oversampled, \"тестовой выборке\")\n",
+    "\n",
+    "print(\"Андерсэмплинг:\")\n",
+    "check_balance(train_df_undersampled, \"обучающей выборке\")\n",
+    "check_balance(val_df_undersampled, \"контрольной выборке\")\n",
+    "check_balance(test_df_undersampled, \"тестовой выборке\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Датасет: \"Удаленная работа и ментальное здоровье\" (https://www.kaggle.com/datasets/waqi786/remote-work-and-mental-health)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Employee_ID', 'Age', 'Gender', 'Job_Role', 'Industry',\n",
+      "       'Years_of_Experience', 'Work_Location', 'Hours_Worked_Per_Week',\n",
+      "       'Number_of_Virtual_Meetings', 'Work_Life_Balance_Rating',\n",
+      "       'Stress_Level', 'Mental_Health_Condition',\n",
+      "       'Access_to_Mental_Health_Resources', 'Productivity_Change',\n",
+      "       'Social_Isolation_Rating', 'Satisfaction_with_Remote_Work',\n",
+      "       'Company_Support_for_Remote_Work', 'Physical_Activity', 'Sleep_Quality',\n",
+      "       'Region'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_remotework = pd.read_csv(\"..\\\\static\\\\csv\\\\Impact_of_Remote_Work_on_Mental_Health.csv\")\n",
+    "\n",
+    "print(df_remotework.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Столбцы на русском:\n",
+    "\n",
+    "Employee_ID - идентификатор \\\n",
+    "Age - возраст\\\n",
+    "Gender - гендер\\\n",
+    "Job_Role - специальность\\\n",
+    "Industry - образование\\\n",
+    "Years_of_Experience - опыт\\\n",
+    "Work_Location - место работы\\\n",
+    "Hours_Worked_Per_Week - количество часов работы в неделю\\\n",
+    "Number_of_Virtual_Meetings - количество виртуальных встреч\\\n",
+    "Work_Life_Balance_Rating - рейтинг баланса между работой и жизнью\\\n",
+    "Stress_Level - уровень стресса\n",
+    "Mental_Health_Condition - состояние ментального здоровья\\\n",
+    "Access_to_Mental_Health_Resources - доступ к ресурсам по психическому здоровью\\\n",
+    "Productivity_Change - изменение продуктивности\\\n",
+    "Social_Isolation_Rating - рейтинг социальной изоляции\\\n",
+    "Satisfaction_with_Remote_Work - удовольствие от удаленной работы\\\n",
+    "Company_Support_for_Remote_Work - поддержка компании удаленной работы\\\n",
+    "Physical_Activity - физическая активность\\\n",
+    "Sleep_Quality - качество сна\\\n",
+    "Region - регион"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 5000 entries, 0 to 4999\n",
+      "Data columns (total 20 columns):\n",
+      " #   Column                             Non-Null Count  Dtype \n",
+      "---  ------                             --------------  ----- \n",
+      " 0   Employee_ID                        5000 non-null   object\n",
+      " 1   Age                                5000 non-null   int64 \n",
+      " 2   Gender                             5000 non-null   object\n",
+      " 3   Job_Role                           5000 non-null   object\n",
+      " 4   Industry                           5000 non-null   object\n",
+      " 5   Years_of_Experience                5000 non-null   int64 \n",
+      " 6   Work_Location                      5000 non-null   object\n",
+      " 7   Hours_Worked_Per_Week              5000 non-null   int64 \n",
+      " 8   Number_of_Virtual_Meetings         5000 non-null   int64 \n",
+      " 9   Work_Life_Balance_Rating           5000 non-null   int64 \n",
+      " 10  Stress_Level                       5000 non-null   object\n",
+      " 11  Mental_Health_Condition            3804 non-null   object\n",
+      " 12  Access_to_Mental_Health_Resources  5000 non-null   object\n",
+      " 13  Productivity_Change                5000 non-null   object\n",
+      " 14  Social_Isolation_Rating            5000 non-null   int64 \n",
+      " 15  Satisfaction_with_Remote_Work      5000 non-null   object\n",
+      " 16  Company_Support_for_Remote_Work    5000 non-null   int64 \n",
+      " 17  Physical_Activity                  3371 non-null   object\n",
+      " 18  Sleep_Quality                      5000 non-null   object\n",
+      " 19  Region                             5000 non-null   object\n",
+      "dtypes: int64(7), object(13)\n",
+      "memory usage: 781.4+ KB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Employee_ID</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Job_Role</th>\n",
+       "      <th>Industry</th>\n",
+       "      <th>Years_of_Experience</th>\n",
+       "      <th>Work_Location</th>\n",
+       "      <th>Hours_Worked_Per_Week</th>\n",
+       "      <th>Number_of_Virtual_Meetings</th>\n",
+       "      <th>Work_Life_Balance_Rating</th>\n",
+       "      <th>Stress_Level</th>\n",
+       "      <th>Mental_Health_Condition</th>\n",
+       "      <th>Access_to_Mental_Health_Resources</th>\n",
+       "      <th>Productivity_Change</th>\n",
+       "      <th>Social_Isolation_Rating</th>\n",
+       "      <th>Satisfaction_with_Remote_Work</th>\n",
+       "      <th>Company_Support_for_Remote_Work</th>\n",
+       "      <th>Physical_Activity</th>\n",
+       "      <th>Sleep_Quality</th>\n",
+       "      <th>Region</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>EMP0001</td>\n",
+       "      <td>32</td>\n",
+       "      <td>Non-binary</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>Healthcare</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Hybrid</td>\n",
+       "      <td>47</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Depression</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Decrease</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Unsatisfied</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Weekly</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>Europe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EMP0002</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>IT</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Remote</td>\n",
+       "      <td>52</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Anxiety</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Increase</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Satisfied</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Weekly</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>Asia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>EMP0003</td>\n",
+       "      <td>59</td>\n",
+       "      <td>Non-binary</td>\n",
+       "      <td>Software Engineer</td>\n",
+       "      <td>Education</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Hybrid</td>\n",
+       "      <td>46</td>\n",
+       "      <td>11</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Anxiety</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No Change</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Unsatisfied</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>North America</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>EMP0004</td>\n",
+       "      <td>27</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Software Engineer</td>\n",
+       "      <td>Finance</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Onsite</td>\n",
+       "      <td>32</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>High</td>\n",
+       "      <td>Depression</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Increase</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Unsatisfied</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>Europe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>EMP0005</td>\n",
+       "      <td>49</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Sales</td>\n",
+       "      <td>Consulting</td>\n",
+       "      <td>32</td>\n",
+       "      <td>Onsite</td>\n",
+       "      <td>35</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>High</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Decrease</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Unsatisfied</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Weekly</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>North America</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Employee_ID  Age      Gender           Job_Role    Industry  \\\n",
+       "0     EMP0001   32  Non-binary                 HR  Healthcare   \n",
+       "1     EMP0002   40      Female     Data Scientist          IT   \n",
+       "2     EMP0003   59  Non-binary  Software Engineer   Education   \n",
+       "3     EMP0004   27        Male  Software Engineer     Finance   \n",
+       "4     EMP0005   49        Male              Sales  Consulting   \n",
+       "\n",
+       "   Years_of_Experience Work_Location  Hours_Worked_Per_Week  \\\n",
+       "0                   13        Hybrid                     47   \n",
+       "1                    3        Remote                     52   \n",
+       "2                   22        Hybrid                     46   \n",
+       "3                   20        Onsite                     32   \n",
+       "4                   32        Onsite                     35   \n",
+       "\n",
+       "   Number_of_Virtual_Meetings  Work_Life_Balance_Rating Stress_Level  \\\n",
+       "0                           7                         2       Medium   \n",
+       "1                           4                         1       Medium   \n",
+       "2                          11                         5       Medium   \n",
+       "3                           8                         4         High   \n",
+       "4                          12                         2         High   \n",
+       "\n",
+       "  Mental_Health_Condition Access_to_Mental_Health_Resources  \\\n",
+       "0              Depression                                No   \n",
+       "1                 Anxiety                                No   \n",
+       "2                 Anxiety                                No   \n",
+       "3              Depression                               Yes   \n",
+       "4                     NaN                               Yes   \n",
+       "\n",
+       "  Productivity_Change  Social_Isolation_Rating Satisfaction_with_Remote_Work  \\\n",
+       "0            Decrease                        1                   Unsatisfied   \n",
+       "1            Increase                        3                     Satisfied   \n",
+       "2           No Change                        4                   Unsatisfied   \n",
+       "3            Increase                        3                   Unsatisfied   \n",
+       "4            Decrease                        3                   Unsatisfied   \n",
+       "\n",
+       "   Company_Support_for_Remote_Work Physical_Activity Sleep_Quality  \\\n",
+       "0                                1            Weekly          Good   \n",
+       "1                                2            Weekly          Good   \n",
+       "2                                5               NaN          Poor   \n",
+       "3                                3               NaN          Poor   \n",
+       "4                                3            Weekly       Average   \n",
+       "\n",
+       "          Region  \n",
+       "0         Europe  \n",
+       "1           Asia  \n",
+       "2  North America  \n",
+       "3         Europe  \n",
+       "4  North America  "
+      ]
+     },
+     "execution_count": 251,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_remotework.info()\n",
+    "df_remotework.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Объект наблюдения: работник\\\n",
+    "Атрибуты объектов: возраст, гендер, специальность, образование и т.д."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mean_isolation_rating = df_remotework.groupby('Work_Location')['Social_Isolation_Rating'].mean().reset_index()\n",
+    "\n",
+    "plt.figure(figsize=(14, 6))\n",
+    "\n",
+    "plt.plot(mean_isolation_rating['Work_Location'], mean_isolation_rating['Social_Isolation_Rating'])\n",
+    "\n",
+    "plt.title(\"Диаграмма 1\")\n",
+    "plt.xlabel(\"Место работы\")\n",
+    "plt.ylabel(\"Рейтинг социальной изоляции\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Между атрибутами присутствует связь. Пример, на диаграмме 1 - связь между местом работы и рейтингом социальной изоляции\\\n",
+    "\\\n",
+    "Примеры бизнес-целей:\\\n",
+    "    1. Прогнозирование изменения продуктивности на основе опыта работы и уровня стресса\\\n",
+    "    2. Прогнозирование ментального состояния на основе рейтинга баланса между работой и жизнью\\\n",
+    "\\\n",
+    "Эффект для бизнеса: влияние ментального состояния на продуктивность, влияние качества сна и количества часов работы в неделю на ментальное состояние\\\n",
+    "\\\n",
+    "\\\n",
+    "Цели технического проекта:\\\n",
+    "    1. Первая бизнес-цель: вход - опыт работы, уровень стресса, целевой признак - изменение продуктивности.\\\n",
+    "    2. Вторая бизнес-цель: вход - рейтинг баланса между работой и жизнью, целевой признак - ментальное состояние."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверим на выбросы"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Пустые значения по столбцам:\n",
+      "Employee_ID                             0\n",
+      "Age                                     0\n",
+      "Gender                                  0\n",
+      "Job_Role                                0\n",
+      "Industry                                0\n",
+      "Years_of_Experience                     0\n",
+      "Work_Location                           0\n",
+      "Hours_Worked_Per_Week                   0\n",
+      "Number_of_Virtual_Meetings              0\n",
+      "Work_Life_Balance_Rating                0\n",
+      "Stress_Level                            0\n",
+      "Mental_Health_Condition              1196\n",
+      "Access_to_Mental_Health_Resources       0\n",
+      "Productivity_Change                     0\n",
+      "Social_Isolation_Rating                 0\n",
+      "Satisfaction_with_Remote_Work           0\n",
+      "Company_Support_for_Remote_Work         0\n",
+      "Physical_Activity                    1629\n",
+      "Sleep_Quality                           0\n",
+      "Region                                  0\n",
+      "dtype: int64\n",
+      "\n",
+      "Количество дубликатов: 0\n",
+      "\n",
+      "Статистический обзор данных:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Years_of_Experience</th>\n",
+       "      <th>Hours_Worked_Per_Week</th>\n",
+       "      <th>Number_of_Virtual_Meetings</th>\n",
+       "      <th>Work_Life_Balance_Rating</th>\n",
+       "      <th>Social_Isolation_Rating</th>\n",
+       "      <th>Company_Support_for_Remote_Work</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>5000.000000</td>\n",
+       "      <td>5000.000000</td>\n",
+       "      <td>5000.000000</td>\n",
+       "      <td>5000.000000</td>\n",
+       "      <td>5000.000000</td>\n",
+       "      <td>5000.000000</td>\n",
+       "      <td>5000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>40.995000</td>\n",
+       "      <td>17.810200</td>\n",
+       "      <td>39.614600</td>\n",
+       "      <td>7.559000</td>\n",
+       "      <td>2.984200</td>\n",
+       "      <td>2.993800</td>\n",
+       "      <td>3.007800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>11.296021</td>\n",
+       "      <td>10.020412</td>\n",
+       "      <td>11.860194</td>\n",
+       "      <td>4.636121</td>\n",
+       "      <td>1.410513</td>\n",
+       "      <td>1.394615</td>\n",
+       "      <td>1.399046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>51.000000</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Age  Years_of_Experience  Hours_Worked_Per_Week  \\\n",
+       "count  5000.000000          5000.000000            5000.000000   \n",
+       "mean     40.995000            17.810200              39.614600   \n",
+       "std      11.296021            10.020412              11.860194   \n",
+       "min      22.000000             1.000000              20.000000   \n",
+       "25%      31.000000             9.000000              29.000000   \n",
+       "50%      41.000000            18.000000              40.000000   \n",
+       "75%      51.000000            26.000000              50.000000   \n",
+       "max      60.000000            35.000000              60.000000   \n",
+       "\n",
+       "       Number_of_Virtual_Meetings  Work_Life_Balance_Rating  \\\n",
+       "count                 5000.000000               5000.000000   \n",
+       "mean                     7.559000                  2.984200   \n",
+       "std                      4.636121                  1.410513   \n",
+       "min                      0.000000                  1.000000   \n",
+       "25%                      4.000000                  2.000000   \n",
+       "50%                      8.000000                  3.000000   \n",
+       "75%                     12.000000                  4.000000   \n",
+       "max                     15.000000                  5.000000   \n",
+       "\n",
+       "       Social_Isolation_Rating  Company_Support_for_Remote_Work  \n",
+       "count              5000.000000                      5000.000000  \n",
+       "mean                  2.993800                         3.007800  \n",
+       "std                   1.394615                         1.399046  \n",
+       "min                   1.000000                         1.000000  \n",
+       "25%                   2.000000                         2.000000  \n",
+       "50%                   3.000000                         3.000000  \n",
+       "75%                   4.000000                         4.000000  \n",
+       "max                   5.000000                         5.000000  "
+      ]
+     },
+     "execution_count": 305,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "null_values = df_remotework.isnull().sum()\n",
+    "print(\"Пустые значения по столбцам:\")\n",
+    "print(null_values)\n",
+    "\n",
+    "duplicates = df_remotework.duplicated().sum()\n",
+    "print(f\"\\nКоличество дубликатов: {duplicates}\")\n",
+    "\n",
+    "print(\"\\nСтатистический обзор данных:\")\n",
+    "df_remotework.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Видим, что есть пустые данные, но нет дубликатов. Удаляем их"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "В наборе данных 'RemoteWork' было удалено 2423 строк с пустыми значениями.\n"
+     ]
+    }
+   ],
+   "source": [
+    "cleaned_df_remotework = drop_missing_values(df_remotework, \"RemoteWork\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Рассчитаем коэффицент ассиметрии"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 307,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Коэффициент асимметрии для столбца 'Age': -0.0039235316463557286\n",
+      "\n",
+      "Коэффициент асимметрии для столбца 'Years_of_Experience': 0.017598366445551735\n",
+      "\n",
+      "Коэффициент асимметрии для столбца 'Hours_Worked_Per_Week': 0.044648255942281\n",
+      "\n",
+      "Коэффициент асимметрии для столбца 'Number_of_Virtual_Meetings': 0.013099681093504066\n",
+      "\n",
+      "Коэффициент асимметрии для столбца 'Work_Life_Balance_Rating': -0.005133932183379798\n",
+      "\n",
+      "Коэффициент асимметрии для столбца 'Social_Isolation_Rating': 0.00804653798135057\n",
+      "\n",
+      "Коэффициент асимметрии для столбца 'Company_Support_for_Remote_Work': 0.0032439806320526494\n"
+     ]
+    }
+   ],
+   "source": [
+    "for column in cleaned_df_remotework.select_dtypes(include=[np.number]).columns:\n",
+    "    skewness = cleaned_df_remotework[column].skew()\n",
+    "    print(f\"\\nКоэффициент асимметрии для столбца '{column}': {skewness}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Видим, что выбросы незначительные\n",
+    "\n",
+    "Очистка данных от шумов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Выбросы в датасете:\n",
+      "Empty DataFrame\n",
+      "Columns: [Employee_ID, Age, Gender, Job_Role, Industry, Years_of_Experience, Work_Location, Hours_Worked_Per_Week, Number_of_Virtual_Meetings, Work_Life_Balance_Rating, Stress_Level, Mental_Health_Condition, Access_to_Mental_Health_Resources, Productivity_Change, Social_Isolation_Rating, Satisfaction_with_Remote_Work, Company_Support_for_Remote_Work, Physical_Activity, Sleep_Quality, Region]\n",
+      "Index: []\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(cleaned_df_remotework['Age'], cleaned_df_remotework['Number_of_Virtual_Meetings'])\n",
+    "plt.xlabel('Возраст')\n",
+    "plt.ylabel('Количество виртуальных встреч')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Диаграмма рассеивания перед чисткой')\n",
+    "plt.show()\n",
+    "\n",
+    "Q1 = cleaned_df_remotework[\"Hours_Worked_Per_Week\"].quantile(0.25)\n",
+    "Q3 = cleaned_df_remotework[\"Hours_Worked_Per_Week\"].quantile(0.75)\n",
+    "\n",
+    "IQR = Q3 - Q1\n",
+    "\n",
+    "threshold = 1.5 * IQR\n",
+    "lower_bound = Q1 - threshold\n",
+    "upper_bound = Q3 + threshold\n",
+    "\n",
+    "outliers = (cleaned_df_remotework[\"Hours_Worked_Per_Week\"] < lower_bound) | (cleaned_df_remotework[\"Hours_Worked_Per_Week\"] > upper_bound)\n",
+    "\n",
+    "print(\"Выбросы в датасете:\")\n",
+    "print(cleaned_df[outliers])\n",
+    "\n",
+    "median_score = cleaned_df_remotework[\"Hours_Worked_Per_Week\"].median()\n",
+    "cleaned_df_remotework.loc[outliers, \"Hours_Worked_Per_Week\"] = median_score\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(cleaned_df_remotework['Age'], cleaned_df_remotework['Number_of_Virtual_Meetings'])\n",
+    "plt.xlabel('Возраст')\n",
+    "plt.ylabel('Количество виртуальных встреч')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Диаграмма рассеивания после чистки')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Шумов нет\n",
+    "\n",
+    "Разбиение на выборки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 311,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: 1545\n",
+      "Размер контрольной выборки: 516\n",
+      "Размер тестовой выборки: 516\n",
+      "\n",
+      "Распределение оценки характеристик в обучающей выборке:\n",
+      "Hours_Worked_Per_Week\n",
+      "23    49\n",
+      "22    47\n",
+      "28    44\n",
+      "45    42\n",
+      "40    42\n",
+      "32    42\n",
+      "59    41\n",
+      "41    41\n",
+      "34    40\n",
+      "33    40\n",
+      "54    40\n",
+      "24    40\n",
+      "30    39\n",
+      "25    39\n",
+      "56    39\n",
+      "35    39\n",
+      "26    39\n",
+      "49    39\n",
+      "20    39\n",
+      "57    39\n",
+      "27    38\n",
+      "53    38\n",
+      "42    37\n",
+      "31    37\n",
+      "39    37\n",
+      "37    37\n",
+      "46    36\n",
+      "52    36\n",
+      "36    36\n",
+      "55    36\n",
+      "29    35\n",
+      "48    34\n",
+      "50    34\n",
+      "38    34\n",
+      "44    34\n",
+      "47    33\n",
+      "58    33\n",
+      "51    32\n",
+      "21    31\n",
+      "43    29\n",
+      "60    28\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение оценки характеристик в контрольной выборке:\n",
+      "Hours_Worked_Per_Week\n",
+      "37    19\n",
+      "25    18\n",
+      "33    17\n",
+      "44    17\n",
+      "22    17\n",
+      "57    16\n",
+      "41    16\n",
+      "45    15\n",
+      "36    15\n",
+      "27    15\n",
+      "34    14\n",
+      "56    14\n",
+      "23    14\n",
+      "53    14\n",
+      "48    14\n",
+      "39    14\n",
+      "32    14\n",
+      "52    14\n",
+      "24    13\n",
+      "21    13\n",
+      "43    13\n",
+      "28    12\n",
+      "42    12\n",
+      "50    12\n",
+      "38    12\n",
+      "55    12\n",
+      "29    12\n",
+      "60    11\n",
+      "46    10\n",
+      "59    10\n",
+      "20    10\n",
+      "58    10\n",
+      "51    10\n",
+      "26    10\n",
+      "49    10\n",
+      "54     9\n",
+      "31     9\n",
+      "35     9\n",
+      "47     8\n",
+      "30     7\n",
+      "40     5\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение оценки характеристик в тестовой выборке:\n",
+      "Hours_Worked_Per_Week\n",
+      "53    20\n",
+      "31    19\n",
+      "50    18\n",
+      "35    17\n",
+      "51    17\n",
+      "28    17\n",
+      "60    16\n",
+      "27    16\n",
+      "47    15\n",
+      "56    15\n",
+      "36    15\n",
+      "26    15\n",
+      "38    15\n",
+      "21    14\n",
+      "43    13\n",
+      "59    13\n",
+      "30    13\n",
+      "52    13\n",
+      "46    13\n",
+      "24    13\n",
+      "33    12\n",
+      "34    12\n",
+      "39    12\n",
+      "48    12\n",
+      "57    12\n",
+      "22    11\n",
+      "42    11\n",
+      "23    11\n",
+      "20    11\n",
+      "55    11\n",
+      "32    11\n",
+      "58    11\n",
+      "49    10\n",
+      "25    10\n",
+      "54     9\n",
+      "40     9\n",
+      "41     9\n",
+      "44     8\n",
+      "29     8\n",
+      "45     5\n",
+      "37     4\n",
+      "Name: count, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_df, test_df = train_test_split(cleaned_df_remotework, test_size=0.2, random_state=42)\n",
+    "\n",
+    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
+    "\n",
+    "print(\"Размер обучающей выборки:\", len(train_df))\n",
+    "print(\"Размер контрольной выборки:\", len(val_df))\n",
+    "print(\"Размер тестовой выборки:\", len(test_df))\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "def check_balance(df, name):\n",
+    "    counts = df['Hours_Worked_Per_Week'].value_counts()\n",
+    "    print(f\"Распределение оценки характеристик в {name}:\")\n",
+    "    print(counts)\n",
+    "    print()\n",
+    "\n",
+    "check_balance(train_df, \"обучающей выборке\")\n",
+    "check_balance(val_df, \"контрольной выборке\")\n",
+    "check_balance(test_df, \"тестовой выборке\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Оверсемплинг и андерсемплинг"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 312,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Оверсэмплинг:\n",
+      "Распределение оценки характеристик в обучающей выборке:\n",
+      "Hours_Worked_Per_Week\n",
+      "43    49\n",
+      "48    49\n",
+      "31    49\n",
+      "45    49\n",
+      "42    49\n",
+      "35    49\n",
+      "50    49\n",
+      "23    49\n",
+      "36    49\n",
+      "38    49\n",
+      "27    49\n",
+      "37    49\n",
+      "60    49\n",
+      "34    49\n",
+      "53    49\n",
+      "58    49\n",
+      "24    49\n",
+      "56    49\n",
+      "32    49\n",
+      "39    49\n",
+      "46    49\n",
+      "40    49\n",
+      "57    49\n",
+      "59    49\n",
+      "21    49\n",
+      "41    49\n",
+      "52    49\n",
+      "55    49\n",
+      "25    49\n",
+      "26    49\n",
+      "29    49\n",
+      "54    49\n",
+      "33    49\n",
+      "47    49\n",
+      "49    49\n",
+      "44    49\n",
+      "28    49\n",
+      "51    49\n",
+      "22    49\n",
+      "30    49\n",
+      "20    49\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение оценки характеристик в контрольной выборке:\n",
+      "Hours_Worked_Per_Week\n",
+      "24    19\n",
+      "46    19\n",
+      "39    19\n",
+      "43    19\n",
+      "48    19\n",
+      "22    19\n",
+      "30    19\n",
+      "32    19\n",
+      "20    19\n",
+      "60    19\n",
+      "52    19\n",
+      "33    19\n",
+      "57    19\n",
+      "36    19\n",
+      "51    19\n",
+      "40    19\n",
+      "31    19\n",
+      "45    19\n",
+      "27    19\n",
+      "50    19\n",
+      "38    19\n",
+      "58    19\n",
+      "53    19\n",
+      "55    19\n",
+      "34    19\n",
+      "29    19\n",
+      "49    19\n",
+      "41    19\n",
+      "35    19\n",
+      "28    19\n",
+      "21    19\n",
+      "23    19\n",
+      "37    19\n",
+      "56    19\n",
+      "25    19\n",
+      "47    19\n",
+      "44    19\n",
+      "42    19\n",
+      "54    19\n",
+      "59    19\n",
+      "26    19\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение оценки характеристик в тестовой выборке:\n",
+      "Hours_Worked_Per_Week\n",
+      "21    20\n",
+      "50    20\n",
+      "43    20\n",
+      "53    20\n",
+      "30    20\n",
+      "54    20\n",
+      "47    20\n",
+      "44    20\n",
+      "59    20\n",
+      "57    20\n",
+      "55    20\n",
+      "48    20\n",
+      "39    20\n",
+      "46    20\n",
+      "35    20\n",
+      "49    20\n",
+      "24    20\n",
+      "28    20\n",
+      "42    20\n",
+      "45    20\n",
+      "32    20\n",
+      "52    20\n",
+      "26    20\n",
+      "34    20\n",
+      "25    20\n",
+      "41    20\n",
+      "51    20\n",
+      "22    20\n",
+      "36    20\n",
+      "38    20\n",
+      "60    20\n",
+      "29    20\n",
+      "37    20\n",
+      "58    20\n",
+      "23    20\n",
+      "20    20\n",
+      "40    20\n",
+      "27    20\n",
+      "56    20\n",
+      "31    20\n",
+      "33    20\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Андерсэмплинг:\n",
+      "Распределение оценки характеристик в обучающей выборке:\n",
+      "Hours_Worked_Per_Week\n",
+      "20    28\n",
+      "21    28\n",
+      "22    28\n",
+      "23    28\n",
+      "24    28\n",
+      "25    28\n",
+      "26    28\n",
+      "27    28\n",
+      "28    28\n",
+      "29    28\n",
+      "30    28\n",
+      "31    28\n",
+      "32    28\n",
+      "33    28\n",
+      "34    28\n",
+      "35    28\n",
+      "36    28\n",
+      "37    28\n",
+      "38    28\n",
+      "39    28\n",
+      "40    28\n",
+      "41    28\n",
+      "42    28\n",
+      "43    28\n",
+      "44    28\n",
+      "45    28\n",
+      "46    28\n",
+      "47    28\n",
+      "48    28\n",
+      "49    28\n",
+      "50    28\n",
+      "51    28\n",
+      "52    28\n",
+      "53    28\n",
+      "54    28\n",
+      "55    28\n",
+      "56    28\n",
+      "57    28\n",
+      "58    28\n",
+      "59    28\n",
+      "60    28\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение оценки характеристик в контрольной выборке:\n",
+      "Hours_Worked_Per_Week\n",
+      "20    5\n",
+      "21    5\n",
+      "22    5\n",
+      "23    5\n",
+      "24    5\n",
+      "25    5\n",
+      "26    5\n",
+      "27    5\n",
+      "28    5\n",
+      "29    5\n",
+      "30    5\n",
+      "31    5\n",
+      "32    5\n",
+      "33    5\n",
+      "34    5\n",
+      "35    5\n",
+      "36    5\n",
+      "37    5\n",
+      "38    5\n",
+      "39    5\n",
+      "40    5\n",
+      "41    5\n",
+      "42    5\n",
+      "43    5\n",
+      "44    5\n",
+      "45    5\n",
+      "46    5\n",
+      "47    5\n",
+      "48    5\n",
+      "49    5\n",
+      "50    5\n",
+      "51    5\n",
+      "52    5\n",
+      "53    5\n",
+      "54    5\n",
+      "55    5\n",
+      "56    5\n",
+      "57    5\n",
+      "58    5\n",
+      "59    5\n",
+      "60    5\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение оценки характеристик в тестовой выборке:\n",
+      "Hours_Worked_Per_Week\n",
+      "20    4\n",
+      "21    4\n",
+      "22    4\n",
+      "23    4\n",
+      "24    4\n",
+      "25    4\n",
+      "26    4\n",
+      "27    4\n",
+      "28    4\n",
+      "29    4\n",
+      "30    4\n",
+      "31    4\n",
+      "32    4\n",
+      "33    4\n",
+      "34    4\n",
+      "35    4\n",
+      "36    4\n",
+      "37    4\n",
+      "38    4\n",
+      "39    4\n",
+      "40    4\n",
+      "41    4\n",
+      "42    4\n",
+      "43    4\n",
+      "44    4\n",
+      "45    4\n",
+      "46    4\n",
+      "47    4\n",
+      "48    4\n",
+      "49    4\n",
+      "50    4\n",
+      "51    4\n",
+      "52    4\n",
+      "53    4\n",
+      "54    4\n",
+      "55    4\n",
+      "56    4\n",
+      "57    4\n",
+      "58    4\n",
+      "59    4\n",
+      "60    4\n",
+      "Name: count, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_df_oversampled = oversample(train_df, 'Hours_Worked_Per_Week')\n",
+    "val_df_oversampled = oversample(val_df, 'Hours_Worked_Per_Week')\n",
+    "test_df_oversampled = oversample(test_df, 'Hours_Worked_Per_Week')\n",
+    "\n",
+    "train_df_undersampled = undersample(train_df, 'Hours_Worked_Per_Week')\n",
+    "val_df_undersampled = undersample(val_df, 'Hours_Worked_Per_Week')\n",
+    "test_df_undersampled = undersample(test_df, 'Hours_Worked_Per_Week')\n",
+    "\n",
+    "print(\"Оверсэмплинг:\")\n",
+    "check_balance(train_df_oversampled, \"обучающей выборке\")\n",
+    "check_balance(val_df_oversampled, \"контрольной выборке\")\n",
+    "check_balance(test_df_oversampled, \"тестовой выборке\")\n",
+    "\n",
+    "print(\"Андерсэмплинг:\")\n",
+    "check_balance(train_df_undersampled, \"обучающей выборке\")\n",
+    "check_balance(val_df_undersampled, \"контрольной выборке\")\n",
+    "check_balance(test_df_undersampled, \"тестовой выборке\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lab_2/requirements.txt b/lab_2/requirements.txt
new file mode 100644
index 0000000..7902035
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