Python/diabetes_data_reduction_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Factor Analysis and K-Means Clustering on Diabetes Patient Survey Data\n",
    "This is a sample of Python code to run Explorator Factor Analysis and K-Means Clustering on actual Diabetes patient survey responses.  The analysis provides the step by step for running the algorithms and visualizing the output. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os \n",
    "from pandas import DataFrame\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import from Excel\n",
    "Here I am importing the raw data file from Excel.  Data was downloaded from QuestionPro in .xlsx format.  I cleared the data with missing values, leaving 434 responses to the survey items (n = 434).  There were 25 questions in the survey, most were Likert scale response questions that the respondent had to answer.  Each question was measured using a 7-point agreement scale, where 1 = \"strongly disagree\" and 7 = \" strongly agree\".  A few of the other questions were binary (yes / no) or categorical in nature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    434\n",
       "B    434\n",
       "C    434\n",
       "D    434\n",
       "E    434\n",
       "F    434\n",
       "G    434\n",
       "H    434\n",
       "I    434\n",
       "J    434\n",
       "K    434\n",
       "L    434\n",
       "M    434\n",
       "N    434\n",
       "O    434\n",
       "P    434\n",
       "Q    434\n",
       "R    434\n",
       "S    434\n",
       "T    434\n",
       "U    434\n",
       "V    434\n",
       "W    434\n",
       "X    434\n",
       "Y    434\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excel_file = pd.read_excel(r'diabetes_data.xlsx')\n",
    "df = pd.DataFrame(excel_file)\n",
    "df = df.dropna(how = \"any\") \n",
    "df.count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>56514146</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
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       "      <td>1.0</td>\n",
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       "          A  B  C  D    E  F  G  H  I  J ...     P    Q    R    S    T    U  \\\n",
       "56502335  6  6  6  1  3.0  2  1  1  1  1 ...   5.0  2.0  6.0  7.0  6.0  6.0   \n",
       "56502044  7  7  7  3  0.0  1  1  1  1  1 ...   7.0  7.0  7.0  7.0  7.0  7.0   \n",
       "56521758  3  6  5  2  3.0  2  1  1  2  1 ...   6.0  5.0  6.0  6.0  6.0  6.0   \n",
       "56514146  7  7  6  3  1.0  1  1  1  2  1 ...   6.0  6.0  6.0  7.0  7.0  7.0   \n",
       "56518387  7  7  7  3  0.0  1  1  1  4  1 ...   7.0  7.0  7.0  7.0  7.0  7.0   \n",
       "\n",
       "            V    W    X    Y  \n",
       "56502335  2.0  6.0  7.0  6.0  \n",
       "56502044  1.0  7.0  7.0  7.0  \n",
       "56521758  1.0  3.0  6.0  6.0  \n",
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       "56518387  1.0  7.0  7.0  7.0  \n",
       "\n",
       "[5 rows x 25 columns]"
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Factor Analysis\n",
    "in order to reduce the dimensionality of the data, I used Exploratroy Factor Analysis using the factor analyzer module in Python.  This produced a result that most closely matched that from SPSS.  In determining the correct number of factors to extract, it is best to examine the Eigenvalues for each extracted factor.  Eigenvalues tell you the amount of variance explained by each factor.  Factors with an EV > 1 should be retained.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "these are the Eigenvalues produced by the Factor Analysis procedure: \n",
      "\n",
      "    Common_Factor_Eigenvalues\n",
      "0                    5.333385\n",
      "1                    1.973328\n",
      "2                    1.453436\n",
      "3                    1.314101\n",
      "4                    0.488146\n",
      "5                    0.370327\n",
      "6                    0.217004\n",
      "7                    0.204804\n",
      "8                    0.151623\n",
      "9                    0.113361\n",
      "10                   0.090478\n",
      "11                   0.050834\n",
      "12                   0.021145\n",
      "13                   0.007463\n",
      "14                  -0.014694\n",
      "15                  -0.045699\n",
      "16                  -0.092415\n",
      "17                  -0.122068\n",
      "18                  -0.133415\n",
      "19                  -0.151651\n",
      "20                  -0.153599\n",
      "21                  -0.191482\n",
      "22                  -0.250575\n",
      "23                  -0.279836\n",
      "24                  -0.313051\n",
      "\n",
      "\n",
      "this is a plot showing the Eigenvalues. \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      " The table below shows the factor loadings.  These can be read as essentially correlationsbetween each variable and its factor.  Here we have suppressed low factor loadings (below absolute value of .3) \n",
      "\n",
      "    Factor1   Factor2   Factor3   Factor4\n",
      "A  0.781048                              \n",
      "B   0.70955                              \n",
      "C  0.589779                              \n",
      "D  0.305919                              \n",
      "E                                        \n",
      "F                                0.539615\n",
      "G                                0.649173\n",
      "H                                0.668029\n",
      "I                                0.470189\n",
      "J                                0.426596\n",
      "K                                        \n",
      "L                      0.605093          \n",
      "M                      0.741453          \n",
      "N                      0.538103          \n",
      "O                      0.696321          \n",
      "P             0.82648                    \n",
      "Q             0.42858                    \n",
      "R            0.843011                    \n",
      "S              0.7021                    \n",
      "T  0.457194  0.584036                    \n",
      "U  0.522997  0.421961                    \n",
      "V                                        \n",
      "W  0.821093                              \n",
      "X  0.531271                              \n",
      "Y  0.358459  0.445334                    \n"
     ]
    }
   ],
   "source": [
    "#Factor Analysis using factor_analyzer\n",
    "\n",
    "import pandas as pd\n",
    "from factor_analyzer import FactorAnalyzer\n",
    "df_features = df\n",
    "fa = FactorAnalyzer()\n",
    "fa.analyze(df_features, 4, rotation='varimax', method='ml',bounds = (.3,1))\n",
    "\n",
    "#produce Eigenvalues\n",
    "ev, v = fa.get_eigenvalues()\n",
    "print(\"these are the Eigenvalues produced by the Factor Analysis procedure: \\n\")\n",
    "print(v)\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.plot(v,marker='o')\n",
    "plt.title('EFA Eigenvalues')\n",
    "plt.axhline(y=1, ls=\"--\", c='red')\n",
    "print(\"\\n\\nthis is a plot showing the Eigenvalues. \\n\")\n",
    "plt.show()\n",
    "type(v)\n",
    "\n",
    "\n",
    "#print the factor loadings for each factor\n",
    "print(\"\\n\\n\\n The table below shows the factor loadings.  These can be read as essentially correlations\\\n",
    "between each variable and its factor.  Here we have suppressed low factor loadings (below absolute value of .3) \\n\")\n",
    "FL = fa.loadings\n",
    "FL =FL[FL > abs(.3)].fillna('')\n",
    "print(FL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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>Factor1</th>\n",
       "      <th>Factor2</th>\n",
       "      <th>Factor3</th>\n",
       "      <th>Factor4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.054953</td>\n",
       "      <td>-0.284472</td>\n",
       "      <td>-1.328741</td>\n",
       "      <td>-0.131137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.092655</td>\n",
       "      <td>0.848037</td>\n",
       "      <td>-0.667054</td>\n",
       "      <td>-0.393525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.004259</td>\n",
       "      <td>0.517942</td>\n",
       "      <td>-0.218829</td>\n",
       "      <td>-0.433025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.889358</td>\n",
       "      <td>0.247111</td>\n",
       "      <td>-0.072290</td>\n",
       "      <td>-0.183916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.151305</td>\n",
       "      <td>0.885104</td>\n",
       "      <td>-0.502512</td>\n",
       "      <td>0.294696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Factor1   Factor2   Factor3   Factor4\n",
       "0 -0.054953 -0.284472 -1.328741 -0.131137\n",
       "1  1.092655  0.848037 -0.667054 -0.393525\n",
       "2 -2.004259  0.517942 -0.218829 -0.433025\n",
       "3  0.889358  0.247111 -0.072290 -0.183916\n",
       "4  1.151305  0.885104 -0.502512  0.294696"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FS = fa.get_scores(df_features)\n",
    "fa.get_scores(df_features).head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.24295709 -1.43064796 -0.12436277  0.01092374]\n",
      " [ 0.38138944  0.41556157 -0.62424693 -0.05311073]\n",
      " [-0.30932402  0.2929933   0.78838192  0.05545484]]\n"
     ]
    }
   ],
   "source": [
    "# K-Means Cluster Analysis \n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "kmeans = KMeans(n_clusters=3)\n",
    "kmeans.fit(FS)\n",
    "\n",
    "# Learn the labels\n",
    "labels = kmeans.predict(FS)\n",
    "centroids = kmeans.cluster_centers_\n",
    "print(centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FA3orRMbx24ZKaT5UUKUoyjFPBp6HyFycHKGannwyCLGlyKqnmmZQ5ipSBS9e3eKaHtv45NrrKfRn1N4ush5E5P4FXEOdBuOecYj8l9H8V6ZvXO4hKZfaqmMu5u1uV+fTbSkpCuxX4kG4qbtL4r7ZN+HqiSiYAr4rQGsDeifIvA1RMCl9QaOiHEEqp0pRlGNf4BUS6yMBRCD0JjLrZwihN/ao6pTlcaO545f7hBDgPQ3hPe3Indg1GPRuNUHfvhpbUUujKOTni50d63y6JSX//noJT82fS9e8PG4dPJRRmaMhOpPEfDADfOfG3SKM9jW11R6u8zxSWmCXgMhAaBl1PlZRGosKqhRFOaZJKUGW1PGAiLPrLE2J3vW1uryAtjGbzKSvwm7wnteo4wGoCIf5z7Kv+Wz96dzTJ8LIlhswpYEgxrzdbbnvq3HYsq4FDknMslhTWgw4hUyX7trJ3UMu46aOXztLgcRYWVbA+5t7U21mMLrHJZyZaeHS6xfUSimRgRch8K+aMg020nMKIvsRhN76sH8GinI4RGMnagIMHjxYLlq0qNHPqyjK8cnePSp1b0SRhWi5sDavpzEsK9rF1e++xffafstvB8/GZ+xbBpToCC0P0eIDJ3m7kRQHg1zw5quUhUK1BUhbeqN0yQ6xpdrLzqCnjmc77yMCkEmW+jy6zswJF1Mg3+DXc3YwcVN7opaODfhdLlr4/bx9+VVxS52p7Nj5J1bsnEG2q5pBBbvQNQnooBUgWnyUtKq9ohwuIcRiKeXggz1OzVQpinLsy7gJqp4isbCkF/wTGjWgAnho+jRCpsmkLT2pMt3c1+8rumaXY9oa31UNYGCPpxo1oAL487w5lASDcc2dd4fdFEc89GlRSFmkFMTeRtBO30K/HiMmNXLdEfrk7mHWro4ky58SQvDxxlIKfBczafMnhK19y4rBWIwdVVX87NOP+M9Fl9XevicY4JWlXzNtwzpcus6FvXqzdOcWPtto49ZGIRF4dIu/Dv+MEa12gF2JDL2LyLj+SP6YFKVOKqhSFOWYJ/zXIWPfQfgjwMKZWXGB5xRE5h2NOpbycIg1JcW133++ozPzdrfjks6rOLX1NiJWNQPNdUitVaM2Ep6yZlVcQLWXLSWrSoqZ88Ob+XT9OoqDAXr6PmRQ9gdsrs4lzxOmW3Y5v1o0Gpli75NpWQRjMd5btYiQmdgL0bRtFmzfxp5AgMKMDDaXl3PBG68SiEVrs7C+3bMbTUgsaRC1nbeugAm3zDmLKWe+Q+esSpZveZknVzltfU7t1DldPxpFqTcVVCmKcswTQkPk/hFp3gLh6YANnjGIlHWRjpyYbccFS238Vbw7fiIZRowMl4ktQZbfCe6RkPv3RkugDydpk7NXr+wisqNPc3VnC+E5FRnJgaBJoW9X7WNGtd7G5C3dCZiJtbQ0TWNg6zb8e2nqquguXWd3oNrpdThlIlWx+NINEmoqzseL2hovrh7AbwfPpjoKX27dwpKdO7jp5MH8dPioely5oqSPCqoURTluCKM7ZHZv0jG08PlpmZHBtspKAP48dDr5njCG5uQlaQKn3EPkSwhNBP9ldRwtfdy6nhBYCSR/GPoFZ7XfiCtiAzYy9DZo7TiwIs/4tpsp8ISIWAbmAcnspm1z59QpdMjJYU8wkPT8McuiXXY22yorWFdWWu9xW1JnYXFrAjGDdzf1Apyeic8tXsTFvfvQOTev3sdSlMOl6lQpitLsVEbCLNi+je/27G70quiHSwjBQ6PH4jUMCr0BBhTsqQ2o4oWQwZcbbVxGkqXGK7p+x1ntN+I3TMTehTgZBGszGL0AL+DMpLk0g/+N/5iBLbMTjmNLSVk4xNaKioTG0uBkYZ3cpi25Xh9T165p8Nhz3RE2VucwdWu32tssaTN59aoGH0tRDkdaZqqEEC8B5wG7pZR903FMRVGUA1m2zWOzZ/DGim9w6zqWlOR5fTx11jkMalN3UcpDIWUU7HLQchEiRYuYQzC+Szd+dPIQpq2byfrKHPrkpZiZsesoBZFm3fILWFa0K+62m3p9g99ItiwYBXMN5L8GoffA2gquEyksvIphOzawbM/ChPwsCUQsk7Gdu/DRurUJ9329cydztmzGbmCQ7NNjtPRWc9X0C4na+5ZKTdumMpKsNll6SClZvHMHy3cXkevxcka37mS60/c3ojRP6Vr+exl4GnglTcdTFEVJ8NjsGby1cjkRy6rd9h+Mxbhu0rt8eNUP6JSbm5bzSBlCVj7hLL8BIJC+SxDZ9yNEXaUFDu6rbVu5feoUopaFIIPLP7+Y4S138LcR08hwHRDAGL0P61wNcfvgYfzkkw8J7bcE2NKXfKkOAKEhtAJEziNxN68pXZA04R2c2aNt5RvRsLEPWCgJWyYPz5zOI2NPw63rRK1kzZUlGrL2uW7NJGQZTN3WI+GRfsNgWLsOqcffAHsCAf737XLWl5bSLS+fM7p1555PprKxvMy5Vin5xeeCX40ex4QBA9NyTqV5Ssvyn5RyFlD/RXBFUZQGqoyEeWPFN3Fv+ntFTZPnlixMy3mklMjSG50ZGMI1XyEIvYMsvdm5365GmludmawG2FZZwQ2TJ1IWDhGIRamOxYjYBvN2t+Un808/4NFeRObtabmm+jijW3duPGkwHl3HretoCHYG66j5JCXoLRJu7paXT2tfmF8OmMuc815j7vmv8sjJs2jjr0InwtqycEJAtde2ygp65BfQITsnSWEGSYYR4/oe39Avbzentt5Cu4yqlMNz6wandel68As/iC82bWDMf17g6QXzWbFzLuGKZ5i85CcQW0XINDFtG1NKTNvmNzM/5zczPj/scyrNl0pUVxSlWVhVXIxb12tnqPZnSsn8bVvTc6LYIjC/BQ5stBxxegWWXguxZVCzK0/6rkZk3YMQroMe+t9Ll2DaieOP2gZzi9qxLVhI+4wgSAuyf4VwD0nDBdXfPSNGcWXffny8bi1hM4aRlQ/8hcT6Xh7wXYxI0iNwSGsf3vByPJqFS7Mo8Ia5ousqzuu4nptnn82airrrb2lC47+XXMEtH0xidUmxU1BUQutMF8+PepsuWWUgv2JPKJ8xH15Cqr6CfrcLXTu8eYPKSJg7p04hYsb4w9AvOLv9BnQhEUJy2wlLmL2rPXfNOx1T7lt2/O/yZYzv0k2VdDhONVpQJYS4BbgFoGPHuntHKYqiHCjT7caqI98mK035LDIyy0nGTirsBF3IvUXEIfg60i5C5P7fQY+9eOeOlEtjbsPLqsitdGjbAtyjEVrjtc1ZXVLMi0sW8W3xHtplZXH9gJMZ0aEjUg5DVu2E4H+pKWrgNEh2DURkPxB3jGAsxk1TJrJs52ZMeTKGsPn9shHc228BN/RaTqYR5aVTP+Q3i0czZUv3pLNVHXNyaOH3AzDxymtYU1LMxvIy2mRm0a9lK+AGCH+MNDdRZbfEpRcTtRPrXgEEo8lvb4g3VnxD1LK5vsdyzmq/EZ+xLyB2aSajW2/lzj5LeGrlvuDXkpJ/LlqQlqDKsm02lJWha4IuuXmNWrdMOTSNFlRJKZ8DngOnTU1jnVdRlGPDCS0KyfV6CcYS3yx9hsHV/Qak6UwunMyI5MHPvmhqrzCEpyHNLQij7g+MdbVhsaVGfs5pCG/6E+7rMnXtan427WNiloUlJd/u2c2cLZu5rn9P7hs5Bi37l0j/NRCZBjIGnpEIV/+E4zz4+acs2bmDqOUES7GaXYF/WTGEXrmljGq1nUzN5O6+i5i+sxPVMVdcsVCvofPImPFxx+xZ0IKeBfsvMXrBdxEC6OAzgWdSXlf/VofXB/DT9Wv5w5ezAbip97KkCfs+w+IHPVbw15WD49rzbKooO6xzA0xa9S2/mzWDsGUipSTH6+V3485Iy5KmcuSokgqKojQLQgj+eta5+F0u9P0+sfsMg/6tWnNx7z7pOY/3TKChs14CovMO+qhr+w3AZyRfJsxwuzi5TdsGnrfhvti0gQvffI0+z/yVoc8/y08+mUrYNONmAUOmycvLvuHbNd/DLr/H2f2YcSMi89akAVV5OMTH69cmTS4PWS6e/e6k2u87ZlYx8fT3GN92C7qwEUhOKijmPxdeyogO9V/F8BgGN540OGmJBq9hcNewEfU+1oGKg0F+/NEHNd9JWnpTzVyCzzDJMOID/Y7Zh7dh4uO1a3hg+jRKwyGCsRgh02RXdTV3fjSFBdu3HdaxlSMrXSUV3gDGAi2EENuA30gpX0zHsRVFUfYa1KYdH1w1geeXLGLeti1kuz1c3W8AF/fug0tPT+Vx4eqN9J4N4Y+JzyXScVrcJKM5y2IHcWqnzpzfsxcfrF1dO+Pm1nUMTePZcy5AO8LLO699s5Tfz5lZm+xfVxX1qKXx9oZu/Dr3E6S5GgreR4jkbxnbKitxpch3A1hfGR9kdM6q5J+nfIKUIPGhZd+LltHwnXp3DRtBdTTK68uXOr9/CZomeOK0Mw8rQH3vu5WYtcu0gpKIjxbefX8LW6uzeG1dH1ZVFNA+o4pqc1+g7DNc3DzooH13a4ViMT5cu5o1JcW0y87m/B69eeLLWUl/N2HT5I9fzuKdK64GQFpFEFsKIgPcQ9Na9kM5NGkJqqSUV6XjOIpyuJzdWHqjtfZQGl/n3DweO+2MI3oOkfN7pGsABF8AazforcA9DkJvk5i0DWCBZ+zBjysEvx9/Juf17M3ry5dSHAwytF17JvQfSOvMOnbapUEgGuXxOTMJmyZt/FWc3nYzayvy+GpP27ilq71sNIojPiAG1g6ITAfvmUmP3SojM0UJBEdrXwCnefU1EHoHMAGBwEJk/gjhn3BI16QJwUOnjuXHQ4ezdNdOPIbBoDZtDzvA3lxRHrfI+/Kavtze52v8hsm07Z346fzxWFIQs42aoqgCgRMgX92vP8uLivj1F58TMmMMbdueu4eN4ITClgnnWbm7iGsnvk3MtgnGYnh1gyfmzCZmpQ52lxXtQsoYsuIhCH8IwkVtsn7OEzUzrUpTUbv/lGOCjMxDVj0B5mpAIN2jENkPIIzjO/9ASgtkFYiMeu1OUxxCaIiMqyHj6trbpJRIazNEv8Ips7D3wT7IuBOh1a8dihCCUzp24pSOndI86rrN27YFQxPc138+1/VYgZSwoqwFN8w6l5CV+Lfh02MMK9zhfCMDyMjnKd+wCzMyGNi6TYqlKckprUuYWHQ/07Zm4DF+zcXdvYxun4Xm7o/Q/Id9bTleL2M6dzns4+zVM7/A2XVY8/3zqwcyoGA3/fN389P54wnv9/PamxemCcGz51zI7+Z8wfbKytpZu2kb1jF7yyZeOP/iuOXNmGVx3fvvUhHZt8s0XEcwtZdb15GVj9c0B4/CfmU9zLJ72e35F+3yRzrHM2N8sGY1X27dQp7Xy2V9+tInSXCnpI8KqpRmT0ZmIcvuJO6NLjobWXIZFEw6aPLwsUhKE1n9LARfBhkGdKTvQkTWLxp1V9mxRAgBec8ig69D8D9gl4LeBZF5J8I7/uAHaGIx2+bs9muY0H0lXt15wx/cooju2WWsKi8gtl9ZAIGNR7e4sNO62lug7qKnJ7VKFVQJnl/VB/e6YoKxnQB8vtHF4DbteP78IRyNof7FJ/ThiS9n1QZGltS47cvv0T6jCstOnoqsCcFLSxfHBVTgBGYh0+QXn33CzOtvqt3BN2PTRiJ1LL8mYwjBud27QegB4l7vak8WYcXGR7n546t5aPQY7vnkI6pjUYKxGBqCN1cu5+q+/Xlw9Fi1k/AIUYnqSrMmpURWPkriC4wEGURWP90Uw2pysuJ+CDzvzFIRA8IQmogsvdaZvVLiVEYi/HPRAr732suc/spLPDl3NsXBxORkIQy0jOvQCqdT5J/Nf7b9imeWZ/DVtq1HRQ/CqGXx0bo1PLPwKyat+o6wuS+Bekjb9tzYc2HcLjYh4N+nTmVQi114NJMsVwS/HqNzViVvnfY+ma6a5wsfwnd+neeeui51zz5Tyrhdm8FYjIU7tvHqN0sP8UqPrGyPl1cuugyXtv8yomBbIIuYTP62GbNtlhbtTJlXVhIKsn6/RtGLdmxPWsg2FUMIcrxe7h3aGlLktuka9M0rYnXxHn4w6V2KQ8Han7uNJGyavLFiOTM2b6z3eZU/82DAAAAgAElEQVSGUTNVSvNm7wZrV6o7nTyQ44w0N0H4ExKLV0bB2gSRmeA9rfEHdpQqC4W44M3XKA4GidQsv7zw9WLeWPEN73//Wtpn5yQ859mFX/G3Bc5uv5hl4XW56JqXz2sXX0a2J7EgZmNYVbyHCRPfJmyahE0Tj2Hwmxmf8cIFFzOkbXta+P1kZia2ncn1RHht3AdsqipgY6AzLT1b6JO7h30TGV5wDwdX3cnXkXosXe3P2WG4hBtOGtSg5zWWIe3as+zWO/jv8m+YvWUjWfYMyiM6c3YnT6j3G64UZUj3EoRNpzzCr7/4nP99u7xBfQ5tKamMRLh/xmp+1C2PIS2qSTbZVBbxIoFcV5Az2m3Cb8RYuKcNK8sLAQiZMV76ejHjOh/fqRFHipqpUpo5QaqKysetyJzU98kgMvJJ442lGfjL/C/ZHaiOCwqilkVFJJK05cjszZt4euH82v6DNs7My5riYu6b9nEjjnyfqGUxYeLblIRCBGIxrJqZoapolBvef5eq4FpkbC1uvTDlMTpnlTCu9WJOzNuzb2lIawlZ9yBynz7octHojp3jSl3UR1koWdL/0cNruLjhpEG8dFZrzuqwlUXFbUj+eiNx6zo53tQBdcQy6ZFfwIdrV/PeqpUpi8CmYuPMhs3csosJM87izI+uZEcgfik/aBq8uq4vE7ovZ/b5r/PgwLnc1/8r3jhtMq+PnYy/pvTDjqrU7X2Uw6OCKqV50wpBb5PqTmgGuS5pJ3TqDjTVBPX+Jq36LukbnC0ls7dsjltCA/jn4gXJ+w/aFrM2b0q6bHikfb5xfdIt+EMLdzDljFfxlF+MLL0cZAn1+/1LQIDIRsu4PmUphf3dPmQYniQ1o+rSITuHv301j3s//YgXliw6eoMsGeHplf0I26kzwC7p3YddVdUHPdRzSxY1aNkvGUtqbK7O5poZ52HXTHYFYgYL9rRheyCT+/ovwKtbZLhMPLqN3zAZUFDE44NnIoBeBYk9G5X0UK+uSrMmhIDs3yDLbiM+r0pzdrxl/LiphtZ0POOAx5LfJ/wI77mNOpyjXfyOq73LMaL2v2HTxLtfwc6NZamrZbt1ne2VFbWtVhpKxtYggy9C9BvQWyH814Hn4EnFG8vKEoKqPrnFvDD6o305VLW7xDScl34L53pTVY+XYG1DmusQRveDjr1Lbh4/HzWax2bNqA1SBdCzoIDNFRUJ43NpOuvLSnlm0VdOLphh8NRXc3npgkvonp/Py0u/5qOaPK0Le/VmQv+T6pwJOqLcJ7OpelEdDxC8vmIZpkw9+yQQmLbNzjTNEtlobA9k8Z81J9I5q5L/bTyBz3d04sXRU/HqiUGbV7c5vd0mWvtj3DKocXtKHk9UUKU0e8IzCvJfRFb9EWLfABp4xjk73Yz2TT28Rif01k5bkeCbxNdU8oBrILgPvdL0sahPi5aUBtbw8/5fcXrbTRiazdKSlvzhm+FsD/cg54AcqfbZOewKJJ+RiFoWbbIOrd6UDH+GLL8HZ2OBBdZ6ZPRr8F0A2Y/WGVi1y87GYxhxyeB3nbgIr5ZsRsQGkQmeC0DECAZmUBIM0NIXxKMfEBQIF9j1a7mycMc2npgzK27WTwKbyss5qXVbvt61A01oaMJZxrJsO66K+96g68bJ7+HRDQKxaG3S9z8WfsXry79h8lXX1tnq50gRWh55Xo2diSlptSKWhSZEyjypzrm5ZLjddM7NpSSUntlMG43Hlp3ijBGBRNI9uxwtxZ9KzNZ5+JReDGydanZfOVwqqFKOCcI9BFHwNlLWFOI7zrcLi6z7kUZ3CPzTKdyo5YL/WkTGzUfdz0bKGE7B1qbJRnhgZE+68wSZRhRDc94QT26xm5dPncqS0IMJP69bBg1m5ce7CR2wLGhoGkPbtadlRsNLVkgZQVbcR+Iu1hCEJjuBlTt+dkHG1iKDb4C9nbPbnMhT3hCbY/te0ocW7kRL9SOVMSqMq/jljG+ZsSkHQ5hI4JruK7m330JcWk1gJCNgdKvXNfzhy9lJlyAjlkVxKMCn1/6Qudu24NEN1pYW8+LXi7GS7JQLmybBWCyu+GbEsigJBfnjl7P50xln1Ws86faDAafy53lzMOvILbelRBciofG31zB4cPRYwFkmvXPqlIQlQJfQiNUx01UXXQjGdO7CmpJiqsxc2pA8+st0CU7voT5UHUkqp0o5pgihHXVBQ1MQQqD5L0cr/Byt9XdoLeehZd5xVLWxkOEvsPecjSzqiyzqi112F9La2ejjGJzzPtmuWG1AtZfPMBmV+3JCqYTxXbpxTb8BeHSjNjE7w+WifXY2fz7znEMbRGQWqfPgwsjg/+JusQMvIUsuhdAbEPkCI/g8n571X05pvae2F17UTl1VXGJxw5SpTN+0gagtCFouQpaL19adyM8XjK15lAe830No+QcdvpSSpbtS/+42lpVR4Pdz5Yn9uKj3CewJBFJWYLekTGhZDWDaNh+sWX3QsRwpUlKbv1SXkR060iknF69h4DUM2mRm8dT3zmFsTXHScZ27cvuQYbh1HY9uoAFuTadtdvYhj61tVjbPn3cRs66/mV4df+4UpE2gI1wDEPrhNZpW6qZmqhRFaXR2aApUPMi+mRkTIp8iixdAiw8QeiMm0oY/RRMpZgisXWDvitsMIYTggdFjuOLEvkxe/R1V0Sgj23dkXJeuGCmnhg7CriJ5XhOAdAqN7v0uthaqniJ+ViuCLuDfYz7j9Z3P8F1JOdtj4yjwTkMQSzhiudmBteViv/52jrDl4tNtXdhSXUDHgpMROSly8w4ghMDQtDpa1Yi4nYEnFBbiXWvU2XswmajllCRo7A9Opm3zryULU/6G9spwubi230BO79qNHVVV2FLSPjs7Ybx3DBnOpSecyB++nM3UtavRBGyvrDzk8d0/6tR95/CcBd55EJoERHGWe/0gchG5fz7kcyj1o4IqRVEalZQWVP6OxKUuG2Q1MvgyIutnjTmiQ7q/e34B94w4JT1DcPeHlEs/XnCP3Dea4JuQJFACpxL6hN5VCO+ZSHsosmQZWHtw3lzB2cDh5Z9rLiAYS57Xo2ku5gefoHP3MQ26hDO7dmfq2jXYSX5eI9q3j9sZeEnvE/m/eV8mGb8ToKXKS+pV0KJJZqKdkht1F8116zrts3MY16UrQgjaHWTmaUtFBZ+sX0vMtlP8NuvnhBYtOLtHz9rvhRCInEeR/iuQoUlgVyI8o51Zx6NopvpYpZb/FEVpXOYa9r3JHygKoamNORrwnEbKl0K9BWhHPqlXGN3BfTJw4JueAOFG+C/bd5O9HWfnXhLSBHuP80wtF1EwCTJuAq0taC3AdxGi4H1KYqn7Dgph4HGlrmeVyi9OOZUsT/I37epoNG4WK8fr5d8XXkqW20OGy4VL08hwuSjMyOCXo8bgTVKawWsY/Gzk6DrHIGWIb4vW84vPPubqd9/isdkz2FpR0eBrOVCm24N1kLpS5/boxVuXfb/es5V/XzCvwTN1+3NpGrleL8+cc2HS+4WrL1r2Q2i5f0T4zlcBVSNRM1WKojSBpm/pspfIvAMZ+QxkNfHj8iKyft1oMyMi9x9OsnpkFggPYILWGpH3D4S2X1V3V3+IfElixXycvjPGvlkLZBjhGQm+ixHGvkDqot6STzesi9stuJdl27X5Pw3RLiubcZ27MnnNqoSZplUlxfxz0QLuGrYvSXpw23YsuOlWPtuwnp3VVXTJy2NMpy4Ymobf7eKJOTNrc5gMTfDrMadxWpfkVcCluR5Z+Sh2ZAHdpOS2zpn8ftkwXlnWndeXL+Mf55x/WBXEsz0ehrXvwLytWxKS0N2axg0nDebno+oO+A703Z49hzwer25w08mD+MGAk5OW76iMRKiMhGmZkYlbT51bp6SfaIp+VYMHD5aLFtVV80NRlGOVlBZy90iQybbquyHjh2hZ9zbumMwNyMrHIfolIMHojsi6H+FJ0/JeQ8Zi7QJzPWgFTpJ89f9BdC5OqZCx4L8Ryq8HeeDynQ5GV0TBByADyIqfOdX1hQdkzLmm3L8hjPbYUnLj5Iks2L41bheazzC4a9gIfjRoaIPHHbMsBvzr6ZSzL/k+H4tuvr3ex4taFit3F6EJwYktW6WcAZLWdmTxBUhZjdgvKA6ZOvcvHMuHW7vjd7lYeNNt+FyH3r55e2UlF771GoForLb6vs8w6Jybx9uXX4W/gcc+49V/x/UCrK8st4cPrppAh5zE9klF1dU8MP1T5mzZgqE55St+OHAQdw8bgX6o+X4KAEKIxVLKuns1oYIqRVGagB36ACoeIKFgq5aHKPgAoRc0ybikdBJ7hWiiIpP7jyW2Ell6DcgQ+2bQNCfpOPtRqHwEMEFaIDTQ2yHyXkLorbBLvg+xFcQvs2qgFSAKP0cIL6Zt88qyr3l52deUhYJ0ycvnrqHDOb3rwQt9JlMZCTPk+WdTtl/RhGDdj+85pGPXxa54CELvkmxJdE/Ix8gpE/C7PDw+/gzO79n7sM5VFgrx3+XL+GTDWty6weUnnMhFvfs0uJI8wOvfLOXhmdMTZr72t7cs695cs1M6dOLvZ5+HEAKvYcQFmoFolDNe+zd7AoG4Y/oMgwt7ncDj48+svU1KyY7qKnQhaJ15aHXVjjf1DarU8p+iKI1O852H1DKRVU+CuRYwwHumU7C1iQIq4KjKO5GVv00yG2U7t4U/RrSc68xE2budJT/XQIQQyNhyML8jMW/NBhmA0IfgvxRD07jhpEFpa2ic6fbgc7mIRZIsSwJtj9Sbd2Q6qXLM/K4YnbMq2B4soCQN7YPyfD7uGDqcO4YOP+xjXdm3P++uWsnSXckbwrf0Z/DQqWNZuGM7OR4PF/Y6gTlbtzD+1ZcoD4fRheC8nr15aPRYcrxeJq36lopwOCFIC5km7636lruHjaRVZiafrFvLb2d9QWk4hJSS9tk5/G7c6Qxrn7xRtNIwKqhSFKVJCM9YhGcsUpo4xT8bb1eXlBJii8DaDnqn2oDkaCFlCGLLUtxrQ2QGQrjAOy7x7ujXzuxV0gMHkdG5CP+laRvrXpoQ3DhwUNLeiD7D4I4hw9J+zpozp7xHALZ0yj30KWx5hM5/aAxN453Lr+bujz7go/Vra/PQ9JpZqOfOv4j+rVpzXs3s2h++nMUry76u/dmawOTV37FkxwY+vGIMn6xfl7KnoEvT+Gr7VjLcbn766dS4Jdr1ZaXcMPk9/nvJFQxQldYPmwqqFEVpUvVp1ptO0lyHLL0JZPm+G7VWkPcCwjhKPq0ftLJ28vtLQ0GeXxRl8torsGw4re1mbj3ha9pn7G2ro4HITetQ93f7kGFsrijnw7VOkU4hBFJKru43gCtO7HdkTuo9F4KvkazMRGnEy7ZADl3yshjStt2ROX8dVhXv4cm5c/hy62aEEIzv0pWfjRhNp1znd6AJwdk9ejJt43pMy6r9rVq2TUV439J4aSjIy0uXJJR1iNk2RdWVTFn2Kzx2NyB5YU+BwKXpPDZ7RtKct5Bp8se5s3n9kivSct3HMxVUKccdaZdB+COnoKLRBzxjEELtkDkeSBlGllxTE1Dtt0xibUaWXo3MfxOht23yWSuhZSCNHmCuSv4Ad+LyU3EwyPlvvEJZKETUdvrjvbOxFx9s6c7b4yfSI6cccCP8l9R5biktpyyDyEBoDVuy0zWNJ888mx8PHcGsLZvQhWBc566H3A+xPkTGzcjwZLDL2X8ZMGQa/PbrcfRu0ZIXL7i40X+nK3cXceW7bxHar+XOR+vWMmvzJt7//rV0zs1jTzDAzz79OK7chCUllmVx29TJzL/xVjLdbuZu3YKh6UlrZQUtFx9s7sD3u33DvF0FBK3EhHlT2gxo3YZtdRQYXbRj+2Ffs6KCKuU441TyfgBnYSAMIgNEDhS8jtAb/5OskigQjbKutIRMt5uuefnpfTMMT8UpRXBgcrANdhEUn4HUCpFZ96P5zj7k00gpIfIJMvCKE6C4+jl9F10n1PsYIutBZNnNHFgk1cZLxHs3B7YVfmr+l5SEQnFV0k2pEzAFv1kymv+O+wx8l4Fdgl16k5OL5eqPyPghwuiGlBIZ/A9UPwMyDFhI9xBE9m8bPIPXKTeXCbkDG/ScQyX0FlAwCVn9F+fDkoxRZfdmWfha7j51FH1btjqk40op+aZoF6tLimmVkcmojp0aVDH/4ZnTE0pW2FISiMX409w5/OOc85n43bckb8rj+GjdGi7v0xftIP8GNCEZ33YTJ+YVs7ysNWFr3+N9hsH9p5xKrrfuzReH3A1AiaOCKuW4Ic31Na1R9kuklQGQIWTpzdDiwyafoTie2VLyp7mz+c+yrzE0Dcu2aZ2Zxf997xwGtEpPvzIZXZ4k+Xt/Jtg7oeIX2Eg0X8N7+UkpkRU/h/CnQMi50dqKDH+GzHkSzXdmnc/fS3iGQd5zyKrHkOZ6bGmzsqyQ3y87lWWlX3B6ly387rTTyfU6fd6mrFmV0HYGQKKxpKQ1Vb4nyWIOsvzHNTsKAXMtMjQZ8v6GjK2A6uf3jRkgOh9ZchkUfpS0B6AtJbM3b2JZ0S5yvB7O6dGLQv+B4d6RJ/RWiJwnIOcJAHKAUw/jeLsD1Vz//ntsKXeWiIUQeAyd58+7iJPatD3o80OxGMuKkieg21Ly+cb1AGyrrExZqT0Ui1FU7SzbjurQCdNOkYyvR7mw0zoMTfLymA94ed1YXt9wMhXhMD3yC7hr2MjaumMntW7DwiQzUroQnNW9Z8LtSsOpoEo5bsjAqyRv72GDtQPMFeA6QnkfykH9Yc4sXlu+NC7nY2N5Gde89z8+uvq6pHV5GkxvhVO1PFVF973CUPUE0nt2wwPt6DyI7BdQAU4OVBgqf4H0jkEIT70OJTzDEZ4p/PSjt5i1ZQtlkb2zCRbTNqxjVckePrr6Oly6XkffPdCEixgZNf3g9h+XBVjI8p861dgTCoo6uw1l4HVE1o/j7tkTDHDVu2+xq7qaUCyGWzd4Ys4sHhw9lmv7N84s1ZEgpeQHE99hfVlp3E66QAx+MOldZl5/I/m+xIKb+7OkpK5qRXuT0k8oLMRvuAiaia9LfpeLbvlOIJvj9XLn0OE8s/CruGR0t2bSKauSs9pvAMCj2/yobym3jbkl6XkfOaULl0/cTtgEa29hVSHI8ni5d8SoOq9JqR8136ccP8w1pGzvIQSYmxt1OMo+VZEIr3yzNOnupahl8fyShWk5j/BdhLP0Ww92uTNr1UAy9Pa+maBkInMadLx1pSV8vGHXfgGVw0lSrmbahnUAnNQ69QxKgc9Pnj2RxH6LewdtkfrnEoHItIRb7/hwMlvKywnW5AxFLJOIZfH4nJks3dXwn9vRYsmuHWyrqkxaP8q0bd5asfygx8h0u+menzizt9fwmvIF5/fsnbQopwC8hovTu3Srve2OIcN57LQz6JSTi0CSaUSZ0H0lb457H7e+d4bSh/BflXA8KU3ssrvoyc28f/r/OK/DWrJdEXI9Flec2JcPr55A26y6exUq9aOCKuX4YXQBUiWkS9DbN+ZolP18V7wHt5785ci0beZsTU/AK/TWkP0Q4CX138JeNnAIFbjtunrNSZBVDTrcvG1bSdXWJxCLMW2Ds5R038hTUvTME9zXfxEiMjXlcZzb6yoEHf9z2FRexoo9uzGTBB4R0+SFJc23uPOq4uKUDZ0jlsnSovoFjL86dVzS34fPMPhFTQ/DTLebVy++jFyvlwyXG7em1/ZAfOPSK3Ad0GLmot59+OK6G1n7o1NZesmb/HLgQjJcez+I+MA1ALznJ5xTBl6AyAwgTOesMv48fDpLLn6ZRRe+xqND16oCoGmklv+U44bw/wAZmkLibJVwms26BjTFsBScN5e6KktnutJXlFPzX4l0D0IGXofI506CerKAwuiC0BveWBj3CIguIumskLTA1bClMbempUxUdmY0nJfxk9q05V/nXsgD06dRGgo6eUBahPv7z+OC9ivqcaZUQaYPfPE7BrdUVODStKTzXhJndq25KvT70UXyAF8Xot4zOiM7dOT58y/i0ZlfsKm8DAn0adGSh8eexon7Jc/3b9War268lS82bWB7VRWdc3M5tWPnOtvKaJ6hyBaTkIEXIfoVaNkI39XguyB5iZLAv0k+SxmGwMvIjNtVPmmaqKBKOW4IVy9k1i+h6nGcl/5oze4/LyLvefWi0oROaFFIjsebtMGvzzC4qm//tJ5PGN0ROb9BWrcjSy6s2Y6/9xO/ALyI7EcP7dj+y5GB50AeuMvQA54RCKNzg453WpduPDxzetL7fC4XF/Xat6NwdKfOzLr+JjaWlxGLrqWrdQu6VsdSJABucJ8Mviug4pfEv/m6AQOq/4Jd/RfwjEVk3kW7rKyU7WgE0Ck3ryGXeFQZ06kLWoqXApeuN+hvcVSHTnxy7fVURsIIu5RMOR+YiTRHIoyOccc9s1uPBo1TGF0QOb876OOkNONrsiU8oAon1/To6SbQnKnlv0YiY6uxy+7ELhqCvXsUdtWfkHUuEyhHgpZxFaLwU8j8Mfh/gMh+GFE4A2F0aeqhHdeEEPzle+fgMwz0/YJbr2FwQotCLu3T98icVy9EFLzvBBQix+mr5xmHKHgT4T7p0I6p5SIK3gC9O+ADkQW4wXs6IvevDT5eYUYGtw4eiu+ApSSfYTCifQeGtotfthZC0DUvnx6+Geha8pYxDo/z5R6JyP0Hmu9cRN5z4BoEePcrEhoEWel8hT9AllxE1+wyeuQXxP2uao9qGNyYptY3TcFjGPzz3AvxGy48Nctve6uc3z1sBD0LWiQ8Z9bmTVz05uuc8I+nGPz8Mzw5dzaB6L7NEJmxF8mo+B6y6lFk5ePI4nOxy++t6SZwZAlhgKhjdk1kcEjL3EpSqqFyI5DRxcjSG3A+Ae79ebtBL0QUTEJoadjVpChNQJrrIDIfhBs8pzk1gw7D+tISnlm0gAXbt5Lp9nBNvwFc3qfvITWsTbfv9uzmz/O+ZP62rRi6xrk9enL3sJG0zMhM+Rxprge7BPRuh93T8KN1a3h6wXw2V5ST7/Nzw8CTubb/wJT1heyKX0HorRRH08F3ESLzxwg9eYK7XfbjmgT1A2ekBLhHssv1V654+00qImECsRiummXKu4eN5NbBQw/5Oo8WRdXVvL58Gct376JdVjbX9BvACUla3fxv5XIenjk9bteqR9fpkpfPxCuuxm1OQ5bfT/yuSwAvZFyPlnVoTaallOysrsK0bTpk59Q5025X/R0Cz5O4BOiFjBvQsn5ySGM4ntS3obIKqo4wKSWy+HtgbUpyrxsybkTL+mljD0tRDouUUWcbfmRWzS0aYEHm7WiZtzfl0I6IJTt3MGHi24RNs/ZjkaFp5Hi8fHj1hDoDqwNJuxqsbaAVHFrOVn3PE/4YWXF/irpcPkT+Kwh38jxCKSWyqC/JS5AA6IhWyzBtnWkb1rNoxzbyfD4u6tUnPaUvmomwGWPw88+mWLZ28Zsx47is1S9rdh4nITIQLRc4fRwb4KttW3lg+jR2VFWiCUG2x8ODo8dxXs9eSR8vZQxZfgdEvmJfYOUF9xBE3rMNPv/xqL5BVdN//DvWWdvASrVbJAqhiaCCKqWZkVV/hMhsEuoaVf8LaXRHeOtX4LK5eHD6tIRyD6ZtUxEJ8/SC+Tw67vSDHkPKKLLyd86/eWGAjCFdAxG5f0LoR6CRrWe8swHD2sG+fDEAN7j6gOtguUGp617tvd+luzmnR0/O6XF8Fo5cuH17yk0EITPGu9+t5LKCOnauSqtmJrP+xW2XFe3ihsnvxf09hkyTn3/2MYaWvIinEC7I/RfEvkHWlMcQnjOcivoqlzStVE7VEReFOvvKpfokqChHJynDEHyb5LuJQsjqZxt7SEfUnkCAjeVlSe8zbbu2efDByPJ7a4pvRpxK/kQhthhZchnSDqRvwDWEcCHy36rpE+gGkQl4nNyuvBeSvplKGUGGP4PwRDB6pz640R0hfGkfc3NjHqTxdcyyQKsrad+GBqZ/PDl3dtJ6bmHT5PE5M0m1+iSEQLgHoGX9DC3rZwj3ABVQHQFqpupI0zuROglQA7eqYnu0kjIE5lbQchD6ofUPOyZZu6mzgGbSpe7mK2ZbdfZeS9Ya5kDS3FJTJ+jAxHEL7GpkaAoi4/uHNc5khF6AyH8JaRU7pSP0dggtN+lj7dA0qPw5zu/WrqmwLkgsN+FFZN2f9rE2R4PatHUCpyS8hsHZ3XuCfwJU/53EDyEuJ8BtYHCarM3MXkXVAcrCoYNWfFeOHDVTdYQJYUDmPUCSfzjCizgG80+aOykt7Mo/IXcPR5ZeidwzHrvkcqSquO7Q8olfTjrw/sNLyD7atMnMStmMVsNmdIfUlbNrRRfVMWMdgugXhz7AehB6C4TrxJQBlYytgop7a3phVtfkYUVxPndnULtTUO+AyPsbwqM+DAJke7zceNLghJ2ZmhBkuT1c2bcfIuN6cA9ydpbW8oPeHpH9cIPP6aqjfpVE4tIOVtRWOZJUUNUItIyrIPsBEHk4wZUbjN6I/FcRRtemHt5xT8oQ0i5F1kzly8pHIfSa02qkdplmObLkcqSdfBnoeCK0TPCMI/kMrA/81zfyiI4sIQQPDvXi1RMDSa9h8eNe79bjIF7qnN0Tjd+EeH8y8DzJ+yHGnK+CtxCF0xAtPkN4xjbu4BpJVSTCJ+vXMnXtGkqCdTXdjnfviFHcM+IUcr1evIaBS9MY26kLk668hmyP11mGzXsRkfs0eC8C7zmInN8hWkxJGeTW5ZwevTBSzJwObNWGLE/9+koqR4Za/mskmv9KpO8ysLaD8KjlpKOAtHYgK34D0bk4VdWzkf4fQugdEnPdbJAhZPANNbsIiJxHkSVrwCoCgoBwAgf3qKS9x5q7s1tPgqFBfr9sBCVhHzaC3jkl/HbQbHpkBpDmOoTRPfUBPKfW9NdLQvgRvkuPzMDrK7aCxNIJNYQHYVciXAhTjDkAACAASURBVH0adUiN6YX/Z++8w6Qqrz/+ee+907fv0gTpvUhRQEFRVAQbgmJDMSoaY4pJNImJGmNMTEzyi8YYNSqWWFEEQRFQEFAEQXrvS2/by/S5976/P+6w7O6UXWCX5nyeh4dnp7z3ndnZueee8z3fs2IZzyxeiCYUENa8yR/07stvBw+pU3ckhGB833O5s3dfigN+PDY7Hru91mMUcFyIcFx43Ht98PzBzN2RT0UoWGXAethH649DLzvu9VMcH6mg6gQihArVXHRTnDykWYosvgHMUqpOJmYReJ8j+WDZeZAKqhBKNuRNh+BsZGguCBfCNRJs550x4lepb7eCDZEBspwrz97LiFY7KAy6sSsGWY6oPkqk1zHvz8ruyYzHoOIpavoVuSxdpX1Qo72OeqE0AWNH/PukDsfpP9YQSBlAel+D4AxAB8fFCM89x32BOnPbFp5dvLCGzxTAO2tW0dTtYXy/OrvoAVAV5aisNY6VZmlpzBh7By8uW8L0LZsxpMklbdrxwMALaHsaO9mfKaR8qlJ8LzG9/wHvy8QKh+vAPggl583G2FKKUwRp+pBlP62mg1JABrGC73jZHAei6UKEUvdMOBn+Dul9EfTNoOQg3HeC63pKAiEW7N6JISWDWrWmRfqJHXArg18gy39tlbxrIEBtj9JkZoMcZ1NRIasPHiDd4WRo23a4bHX4I5WUwKefIjd+CwWTkOkSOtjgijTIUgG7VVpzDDzmPQ1/5022JphVmON08d299ydtVEjx/SDlU5UiRTKCn3PUAZVwI1w3Nsp2TldkZD2EFoGwgXMYQm15srd03MjyhyC8FAjHnbNcEye4rq9XQAUg7AMQOTXdxp9bvIiXln9nOaNLMKTJmG49+ePQy07cydwxDJxXQnBmNICUWKNqHIjs5497eW84zA+nT2XVwQMILCG3lPD3YcO5qlMcw8oNG2DCBJg+HWkYUdmEgZAAAp4rhcs9yLEZyK4/gqZLEOLYZtclsssA8EbClAeDZLtS9hEp6kcqqDqDkNJE+t8F32tgFoDSFDz3INxjrZp+imoku0J2YmUkIhw5qzpB6wnO4Y2+s9MBKUPI0vshvBzrfVKg8p9I9+2I9N+ctiVAaeyH0ELii7YVwGYZdyJARsB1DSLj0WM+3rTNG3llxVLChkG4Wmv+lE3rOSs9nfv7H3sG5mgQQkDGX8E5Ghn40CqL2wcj3GMaZIzWg1/MYMWB/TVeI8CvZs+iXXYO3fKqOct/9BE88ggYBmRkgBIEXQDVutoMCZ97EXN8yN8aMO4rcA47pr2l2eyUheJ5rlm468qmpUhRje9lUCVlBCKrQIbB1tvqZjoDkOUPQ/ALqjQb5n6o/AcysgaR9feTurdTDtcoqNxGXAPLw+7Dvv9amholA9y3I9y3WBYZKZAVT0ezObWyff73LLdu17UnZV/Hjb7FmmMo42UxTVDbIDKfsLpCtZ7HNM9vb0U5m4oKyXW5eW7xorhGjgFd59UVy7jvvAEnLFslhADHwOMqpcXjoLeSBbt2xgRUYJljvrJ8Kc8Ov8q64aOP4OGHweOBw2JvM0xMylAVkKlCWCKeOgTuGXBbzaAqpOu8u3Y1761bjT8coX/LVvyk/8CYgci39OzFG6tWEKq1P01RuKJ9xxM6d9IwTebv2sGiPbvx2OyM7NKVjjlnlkXJmc737gxhBmZCxe+p0kZIHekZj0h74LS9uoaoz0zwc2KDhAAEZyEj9yBsp8coCWnsA9MHWttjTunXhXCNsbJ6xj5qZiWckP44iuM8cExolGOf7kgZhMBk4pdPA0jffxGna1Cl5JF0PIvaHGGvn3C5NpX+Lfxi1mcsOmBiV1RMFHxxZsYdxq9HqAgFyXKevNKTKSVf5m9n4vo1VIbDXNy6Lbf2OueozCW3lZRgV9WYoAXAkJJ1BYesHzZssDJU1QMqsILcuCakgD2awXpiCvS9F7pbHYohXeemjyaytaS4SoD+2dbNzMnfxmsjr+f8VmdXLfHTARewYPcudpSVVs3wc2k2ct0unrjk0nq/zrqoCIWYtmkDW4qLaZOVxeiu3cl1H3kfSwMBbv5oIge8lfgiETQhmLByGbf17M0jF118Wp+fvk98r4IqGV4K5Q8TE3j4XkeKNETa+JOyr4ZABmcTv2QBEEEGZ5/yQZWMrLeybfquaIkFpOd+hOfeBv9CEYobcj9Cep+HwBRLoKt1RaT/skHans9ozGIQIrHeyNh7QrfToGg9LPNSI8EQYs/tR72klBJZ+TQ/mVnId4XNCJtaNMBIPltPAG5b41xU1AfDNLlv+jQW79tTFWysO3SICSuX8dGNt9KhnhmUph5PUtf5FmnRSsGECVbJr5YdAcJTNSsxLg47hDV47TX45z8BmLRhHduqBVRgBYgBXeehL2bwzV0/rPpOcdtsTL5pLLO2bWHKpg3opsnVHTszqmv3uoX09WTZ/n3cNW1y1R6cmsYzixfy/JXXcFm7DgA89MVMdpWXVdkk6FKi6zrvrVvDeS1bMrxDpwbZS4rG5fsVVFU+R6J5ZfheQnp+cBqXd3QSn+VMkjpgnwJIfTey5LaokzNHyi/eF5DojeINtc9rsnTfKFy2GxnSpu1pqZ2Q+k6IrLBOPI4hJ2Yem8iGZDPPlKaNv4dGQggBWf9BltxuyQMIYYU3TnCNBPvFR79oaBb5h2awtPAawmb9vl9sisLVnbpgVxvWHftAZSUHvJW0ysiIaf+XUrJk315mb9+GEGBXNb7du7tGeTJo6IQMnQdmfcZnY+848tzQgmhXYz6oeQj3eHCNQgiFZp40PHZ73DKnS7Pxgz79rC6/6dMtDVUtBCDVdtbatb/HhA209ogMAZ9+Co89BtnZvL9uTdzjAZSHQmwsKqR7kyOfU7uqMrJLN0Z26VaPd/HoCOoRxn8ypUZW8nCw97OZ01lw571IYNHe3VUBVXUCeoRXli9NBVWnCadrBHFs6OuT3BkB4yBorU7YdhoS4bgY6X8zTks0gBPhOIaTwQlE+iZET2K1CYDvFaTnboSIPyrkaAkbBr/6Yiaz87ehKQoCgSFN/njJZYzp3rNBjtHYSBlElv082nkXbfvHQGb8BcV1daMeWyhupHOE1SkWkx11gfvuRj1+fZEyCOGVgAB7X4Son9O0sHWDJrOR/g8gvCRqfXAL2PofU8ZUel9hXUk6mmISShCLKkJgRu1t3DYbLdLSefzioUd9rEQU+n38YtZnrDiwv6oUN6hVa54dfhWZTichXefuT6aw+tBBAtVO/vEu0yRWx9yusjLaZGVh+t6Ayn9RpeXUS5EVf4TwAvYrTzDqg/eoDMWWil2axvVdu3Np2/bw1ltWlipBECmEHWnrAmYlyArrRiUbhMdylVMB07QCqzvuqMqsxUMRIun9Dc2sbdswElkXSZiycT0XnN0au6rG1Z0B7K2oaMQdpmhIvl9BlfBEx47EQepwOgvWbX1B6w2RldTUujjA3gdsvU/WzupH6BsSZ9MUS0BsO6dBDvWnr+cxZ8d2QoZRQ+fx+PwvaZ2ZxYCWp35gLcsfswIqQjXPfOW/Q2qtEbZejXp8kfEHpL4FjN3RvykFcFgDYt03N+qx64Pp+x94n+FIx5iJTPslQm1tZUPt/RBqi4TPF0oOIu1+4P7j34yxj0x7Yt8pAfRr3oKmnjQMaXJlx86M6Ni5wbJUumly06SJ7KsoR5ey6jO/cM8ubpvyIZ/eOo5/LVnEyoMHYgwwE2FTFEqDAVqbEiqfIVZfF4DQXF5b25KyoBITVCgIruvSlT9derl1w5YtdR5TRKceQAL7Cimr1rmwdRv2ra+IW3bUTbNmt2Ejs6+yIuH7GjR0dpaXckP3HgkHMwO0zTr6cTYpTg7fr6DKdRP4XiX2C0ABW99jmsN0qiCEgJxXkZX/gMAkaySGUMF1EyL9V6e+yDFpFsEg7kDqY8AbDvPRhvWEjNgvuaCu88LSxQxoOaZBjtVYSLM0ic9WCOl9pUG8hZIhlDTI/RjCC5DB+ZafkevqRg/m6oPp/xQq/0lMqb/yKSRO6+9CRpDOKxCZTzdaM0QVaksGNduIJuKnqZyaxoMXXFhDPN2QzN2xnSK/D71WYBMxTXaWl/Ht3t28u2Z1vQMqsLK97bOzIfhZ9P2MfYyUQTq5v8GQF8XcZyKZv3PnkRvKyyHJoOB6oSjWOsAP+/Xn46g+qjouTeOuPv1ixsg0Ju2ysnBpWtymBJem0TknjxyXm6Ft2zN3Z35MtsqlafzovAExz01xavK9CqqE5x5kaDbouzkyKsJhjdjI/MvJ3FqDIIQDkfEYMv1hkJUg0hHiNNEJuW4E77+Ib3GQDcnmqh0Fe8rLsKkKoQQXhRuLChvkOI2KviNJ27+ESLIyd8NhzTO7+NQrLfueJb52Euv2wwFAcA5SPIXI/GOjbkek3Yet/GH+PWgOP/pmOBFTQZdWFsqlmYzu2p2B8bKjUTdxtmyxgoXMTOjcGUaOhOz6jyNZsndvwi7DQCTCd/v24dfrXw5zahqju3Ynw+FE+oMJZxoKJGm2xOtWhqt9fjMzrfJdLSTS+i6TYcAGSoaVsYqHaVrrAGdnZvL+DTfz4Ocz2FdZgaYoGKbknn7n8vOB9RsJVBoIMGPbFsqCAXo0acZFrdugHkPgd3n7jtjVL+P+DhQhGN3N6lh8+vLhjPt4EttLSwhGImiqigDuO7c/Q9u2P+rjpjg5fL+CKsUNuZOQ/qnRoblhcAxDeG5DKDkne3sNhhA2EKfX6xGeW5HBKVbnX1UGRgHsiMy/NVimLcflTppmzzkdnJOVvAT6syinwJy2k4WUITD21/PRQQhMQab/unG96hzDwbWSwc3eY/rwj3ljS3dWFbegqRvG9RvHkLZda36+q7mJc/izqigYhoE3EqHytw+z8JxeFNxyC9ffcCMt44i7q5PucKAJEZOpAkugneFwkOFwUBaMH4gKsDI7EiKmwfAOnfjDxVGrAdsAEs3KlMLDokNtE+6rW161hobOsZ3J1u9yR7QpQlrHMfYjtTYIEcfSQYga6/Rq2ozZ4+5iZ1kp3nCYjjk5OLX6XWRO3bSB3305G0VY9gwum41ct5v3r7/5qEcI2VWVd0aP4faPPyJs6AQiEZw2G4oQTLh2NBkOSyua4XAw9ebb+G7fXr7bvxe3zc6VHTtxVnr93PpTnBqkZv+lOGWQph/pfytavvSDbQAi7ccIW5wxFsfBTZMmsvLg/hidh0vT+P2QodzSs2G0W42JWTQK9E3EzqJzITKfQriuOaZ1pZSgb7PEwFqneo9faSzWHDrIy8u/Y31hIS3S0rirTz+Gte+YMMiW0kAeOgfL5b0eiDREzlsIW+M3KEh9FwRnImUQYR8I9vNjX0dtN/GorsqQku2lJYQNA2EYpAWDmIrC86Ou5d6//C2pRii/tISr33s7bsnboarM+8F4Jm9cz4tLl8R0zLk0jZ/1P5/uTZvhDYfp27xFTFBhlt4LocXULEdroDbj5Z1P8p+lK+Ku++q1oxl0dnTAfEkJnH++5VGlqlaGKrKZ+DpLBWxdEVhZIwmge8Fbgpz7AKL5NZap81FeiEmzBOl7nYjvUwr9lcza044Jm3tTEPQAoApBl9w8plfrejwawobBnPxt5JeW0jI9gxEdOzWYZUOKxqe+s/9SQVWK7x17K8oZ/cF7+CPhqi97t83G+S3P5uVrrjumFP+JRuo7kcU3R+e0RUvZwgWOyxCZ/3dMY4lkZC2y7EEwCqp0R7jGIDIePSlWI1M3beDRubMJ6npVxc6l2RjVtStPXXpFwueZZQ9FOxProxFyIJp8jlDPaogtx0WaXmRgKoTmg5KGcI2xRsDEC6hqu4lHOeTzUejz1pAu2XQdVzjC+7ffym//lVxD9/eFX/O/1asIVCvzuTSNBwZcwH3nDcAwTR6MdsQa0TKcqihc2bEz/xg2Iqmru5QhZMWfIDDtiJ+UfSAi829IJZenv/mKt9esquq0lUieuOQyrunUhX2VFWQ7XdZsvQcfhGnTIDsbKStA30v8AdaKZcKq5FjBl7EbSsthhAf5RFPACfYBiOwX6y1/kMYhZPFoMCs43NEaNhR8uo3Rc65nry+j6j17cuhl5Lk89GzarIZ556mAlJKl+/fxyeZNhA2dS9t14PL2Hay5kimOi1RQlSJFEipDISZvXM/8nTtIs9sZ070nQ9q0Pa2m0UuzEhmYbHVOKhnRk/UFx9b2b+xDFl19xCesCie4RqFkPtkwm64n3nCYARNeiiuedmkab4++kX4t4gdC0ihCFl8PZhmJtVUACmg9UPImN8ym4+1F34ssucmaEFAV/LqtoCrr3wgR7fDbsAFGjQKnM9b8EthUVBjXw8im6zh0g/QZM8g7L/n3/dwd+UxYsYw9FWW0y8rmvvMGMPjsNjUes7W4mHk78xECLm3bvt4Gn2AFjxgHQM2tklNEDINnFy/irdUrCZsGAri2c1eynE7eX7cWRViC+f5nteSZVm1pcttt4HQitXIwDiU+mJKDUM9CmgUQKICwiXy9BXSxGl7Cho0D5k20afV4vf6mzbLfQPBTahuy6qZg/oHW/GjhiKrbHKqGXVUIGwbXdenGn4Zejq2B/cSOBd00+fFnn7Bo724CkQgS62KxZXoGk268parMmOLYSAVVKVKkqDdmxVPW3L64ZTMHounXCKX+4ujj5dMtm3jkyy/iinsFcFOPXvz1ssTZKmlWIv0fQnB69IZgVGt1uEHFaTWo5H6I0NokWua4MYtvseaMxivTZjyOcN9g/VgtSxOPDYUFCb2OMgMBMm66mawXXmi4jTcQP/7sE+bv2lEjOFawSnbVX40qBLkuN/M8mbgeewzpMkAtIH6mSoDaDKHkIX3rIaAjH82Da2uWJSvCdn667HFev+6GOu0pzIO9iN9NCxFT0Gvy+Krmguo4o15bf7702IY5NyRvrFzO/337TUyp1RbNOP5rROP6153p1DeoSuUEU6RIAeFvSahDEnaIbDih2/GGwwmDCInVmQVRE1R9G9IsqfEYoaSjpI1HyfsYJe9jRN5niKxnwXGZJa5OewDR5ItGDaikcdAayB03MAhYZr2Q1E38MMksAHxOJ5mzZ0Npafx9RNZglv0as/gmzPI/IPXt9X8Rx8HW4uKYgAqsd6P2b9aQkspwiA+7d4W//c0aO1Oug5HINDMdWVoCYSNuQAXgsUVYV7CHCSuSX8BbiYXEjR8CsCnx7TCCus7kjespTyDyP5G8vipWuwZWJnDW9q0n1PD0+0wqqEqRIgWIZN1vZh33Nzx9myc25nRpNi5sfTZmxd+RBQORxTciC4ZgFo9DJuj8E0JBOC9FyX4JJfcdlLR7EEpmY23fwiyJDgNOcj9YtglJ3MTBmp8Xr6wrhCAvPQNx2E289iG8ryGLb7dKW5FVEPgQWTTaGixfi0NeL68sX8pfFsznsy2bE7p715evd+/ENOtfCQnoOnPyt8GYMYipn8CoG6zEYrmEMgMqTCgzwZuN8IcRo0Yj3+gaN6ACqAzbqQhL3lq9MulxhRCgdU94/x5fBkEjsTbLrqpsLi6q12tsTIoD8eZVWqhCUBaMN23j1Ebq+ZiVf7cuCnzvWyXmU5zvlaVCihQp4iPctyLLN3KkPFbjTjjBpp5d85rQt3kLVhzYX8P1XhECt03jurM+Av90amimIkuRxWMg73OEUvNEK6Vk2YF9HKispH12Dj0ytyH974NZCPb+CPftCLV5w74ItXXiIcAAWlfr/3q4ibs0G20ys9hbUV41zgagidtDntsNoVDMOlLfCd7a806jg5zLH0Y6Lqqyknhv7Wr+9PU8JFaXmsdm488L5vHhmFtplZHBnPztvL12FcV+P/3Pasndfc+ldWZys2RNEVSPAy9uvpufdF9Oh4wyioIu3tjSiw93dMOUR67tq7rhundH/Ot95O/zkVOegs0bEF4H5PZFdOkL115rlUq9/0V6X0TU0s4FdI03t/YCBGWhurNIIv1XyNL7qa3Bi5h2lpTfwtkZmeyuKI/7XN00yXSefL3S2RmZbC0pTnCvVV49nTC9r4D3eazPrA7B2UjvM5D7PqKBfAsbg1RQlSJFCnBeDYEpEF7FkcBKxfIJ++cxdRMeL69cM4pfz57FvJ352FWNiGnQPjuH/wy/AE94FLElGxNMHzIwGeG5s+rWzcVF3PPJx9ErdYFphmiXXsKrF31DM5cfIuuR/ncg+3WEvV+D7V8oaUjXKKsrLkYw74yOwaHebuLpdjtd85pY3ZBS4tS0IyLsam7ih5GBKSQd/RT8AtzXs7GwgD8vmF8jePVFIgR0nbunTaZ706Z8uSO/qny0raSYSRvW8cZ1NyQd6XRZuw48/c3XANzdeTW/6LkMt2btJ9Me5pE+i7mo+V5+sugKQOC22RjTrUeNNQ7Y8/j2wgfRhihc3KYtWc6aPnLCcw8ysp6A70tsirX/iKny9cFWvLSxL2AFG6aUzNuRz7trV1MaDDDo7NaMO6cPzdOs4Fs4BiMz/w8qn6jWrOHAlvUIY8+6DodnPY/P+7JG9+RhmnrS6JxE0G+YJvN25rNs/z6ynE6u7dytTm+xY+FnA87n4TlfxOzRqWmM7XkODi356V4axRBZBtjBcf6JGc6eaC/h1eD9DzV1bn6QAlnyQ2jy5Sk7JSQVVKVIkcKyTMieAIFpSP97IMvBfh7C80OE1uGk7Mljt/Pi1SMp9PnYUVZKE4+HdlnZyMAMZMSWeAB3cC5EgypfOMytH31QK1sh2Fyexe3zr+GLER8iRBhkGFn2U2jyTYMGkCLj90izzLJTECogLDPLjD8g7JbmVXpKIXIAIvssSwIl1+pui2OqKbC6H2Oo5iYOll+XNUw6UVAVBmlpsN5YtSKuIa4pJXsrK9jnrayhi4qYJhHT5KczPmXxPT9K2F3XKiOTW3v2ZtbW73iw11Kcas1juDWdC5vtZWCTA6wubU3vZs25rJ31WTNMk0fnzmbq5o1RKwYrI/TzgYNqjGwRQkPNfp4p+e9TXDYD3TSYf6ANG8osA1yXZuMn/QfywMzpzN+1oyow3FhYyFurV/Lu9TdxTjMrQ6m4rkA6L7d82jAtn7Zod+Z1XboxfcsmllZzn7cpKnZV4d9XXpPwBF/g83LzRx9Q5Pfhi0SwKQrPLfk25nU0BFd36sLm4iJeXbEMtWpAt2BI67b8ZnDsqKDDSGkiK/8M/g+rlasNZPrvUdwnZ2SX9L9NfJ2bBFkCkdXWTNtTkFRQlaLRkaFvkd5/WWJn4QL3GITnx43rYp3iqBHCZv1uTtIXaSKaeDw08XiO3CAcJHLxBkA5coU9bfPGGhmYwxhS5VDAw7cFLRnUbJ91owxAZDnY+zfQzkEIOyL7eczgQvC/BYTBeXWVOavpewNafo0gDChWoGgcAtNrOYfX/0BVbuJSGsiy+yGyIsnj7aB1A2B7aUnCpgBrdp7BkOZ7yXMG2FyWw/oyy2g0oOss278vabbq90MuYXjLtdESX+zvwaXp3N5pJ5ert3LHOX2rPOKe/24xn27ZRNgwami7nv/uW9pmZTOiY6eq26RZztiOO/FVrkfIEAObHuKFjeezurgJ9/Q7F7uqMX/njhqjeMKmQdg0+MmMT/n6znuqgiIhFLDFurtrisKEa0czc9sW3lu3hvJgkAvPbsOdffoldVi/b/pUdpWXVf182Bbj+e++pW/zFgxswHmPQggeuuBCbu/Vhy93bCdiGgw+uw0d67DFkL4XwD8ZK9CuFshUPIlUz0I46jfWp0ExdhO/wQNAAfPgidzNUZEKqlI0KmbgMyj/HVXlDxkC31vI4JeQ+7E1OihFiqPBMYh4J2gAhNvy64qy4sD+uCUbgIipsKEs90hQhTgiHm9ATO/z4H2FKm1IZCXS+xwyewJUPgPD7PAcVqebKgATpA+kt34NAoZhlf+uvdb6OfgJhJaQ2FVeBaU52C8AoFNOLmsOHYwbWPXJOcB/L5yFTTERSISA7RVZjF9wFRFpT9r1VuDz8r/VK5mXL/GIq7mlw3quab29RiedIuDKDq1Qso90qkcMg9dXLY/byRbQdf793aKqoEqalcji0QijgDTNCggGNd3DwKYF+D3PkpUxmLFTPkw427A0GGB9YQE9mzZL+Dqq3jVF4ZrOXbmmc9c6Hwuw5tABVh+K77UV0HVeXbGsQYOqwzRLS2Nsr971eqyUYfC9TlwtJUGk998nJ6jSekQ7Z+NkWqUOarsTvqX6kur+S9FoSBmBiseJ1ZOEwTiADHx0MraV4jRHCBekPw7UFgc7QTsHHJdW3dIsLS2hm7RNMcl1VDuZyEhV9qahkKHF4J2ApQ2JniCk3xLIl/7IKvdlq3C5B7zVr8xNMPYhIxusf8YeaxZePCoqjgi3Ael7h/gnyShae2s0TzQ7c2effnHNK5s5A7x+8QyyHSHSbBE8Nh23ptM1q5jXL5pB2DASBiObi4sY9vYbvLZiOZtKdJYXN+Px5Rcxbv41hI1qvw/hRjiH1nhuSSAQzZDFZ0c16wjp/x8YhVQvFQkBmgiREXwSKSUl/uRdcaWN1BX30rKlSe/fWRbfAuOEYhyKzlZMgL75xO2lGsIzjvg5H9Uqyzbw6LKGJBVUpWg8ImtInMINQuDjE7mbFGcQivsGRParYB8IIhPUNsi0B1ke/BPvrl3LF9u3EjYMxnTviZpAIxUyNLplHW6Ft1uz+LTWDbpP6XuD+AGOCeYhkFbGTY7NsLJU4WrZIhmxHocJZjno22MDq3DYsmIYP77a88pIjBuR8SRCPTLMuGteE5685DIcqopDtU5kHpuNu7tuw6nGFiBtiqR9Rhk/6G6PW/qSRiG/nDWZynCYsHkkoxgwbKwrzWNivhW4RkyBQRYF+kV8snkjM7dtoTIUIsPhqNHhWJsanXaBqSQy7URWgL6Nc89qiZpA8xQyDLrmJp6beDws3rsn6f3HI1Y3pWTG1i3cSiHohgAAIABJREFU/NFELn/rdX71xUy2HIutg5JBwqwvWH9bJwGhtYfM/8O6cHIDNqsLWW2HyP7vSdlTfUmV/1I0HlInqfalvkNvU5wWSBmG8ArAAFsfhOKp8zlHfQyzFIx9oDRFOAYiHAMBOOit5I6pH7G/chqmlGhCQVEEr1wziocuGMwzixfGmFBKCTfPHc17Q2fSo1knRNa/Gny/ljYkEQ6qMixdHMjf5iKeKgIUsMf7uzGtMTBaW+vHcBh8Pssss3s1nyVbH+s9intBo0OcdvQx3XtySdv2TN+yiZKAn15Nm3Np9mpEOL4ppiIUHuyfV+M2aZYjy37NnpI17CgbTbzTS9Cw8c62HtzcfiOLDrXi1W2jWFX4NrboXEDdNPnN4Iu4vF0HZudvixnN49Q0xp1TTaCczLICAUQY3/dcJm9cj1FLW6cJwYgOnWrq9RoQM8neFExu6n5sNiVSSn7x+Wd8mZ9fVdreVV7GzG1beP7Ka7m0Xft6ryWUTKR9QNT8t3Zw5QT3rce0x4ZAcV2BdCyC0BwwS8HWA2z9T9muv8OkgqoUjYetV9WVeCwOcAw/odtJkRwpdSsIEC6Emth8Mx6mfzJU/pmqIFrqyLQfITz3N8iXoDS9yPJHIDTXElnLMNJ+LiLzn6Dk8oOpk9lRWhqjC7r7kynMu2M8O8tKmbhuDTULbAo+XeG3K8bx2W33HPce46J1AmMHCQMcxxAILQSCcG06EhBPF0PAhDQlqrGqhvQhDR1RUWllqP72NxhTs7FAeO5DBmcTz8YB18iEpqd5bjd39jliKWGWn4VVzIjdu1OzIWxHsl1SSmTJHaBvoyKSgU2YifJH7PJmMujTcZSHnRzOMlUXo/9j0QL+cukVrC8soNDvq+rYc9ts9GrajHv7VWskcAyFwIfE73JUQOtE2IifuTOBgUlE9sfL+U0LmLM3GxmnIJTtCHNlpyOCeBnZivRPBHMvaD0R7lsQavwM2te7dtYIqMBypA/oOr/8fAZL772/zrE81RGZTyGLb6g5nxI32LoiPHfVe53GQChp4Bp1UvdwtKTKfykaDaG4Ie1+oLbfiWJpKTy3nYxtpYiD6XsXWXABsvh6ZOEVmEXXICNr6/VcGfoKKv54RFwtvUAQvC9bBpvHiZQSWTreCqgIR9cPQ3gpsuRmlu3fzb7KirhCa8M0eX/dGubv2pGwEJ1f5uVAZeVx7zMewjMeiOeqrlnZvKx/g3us1RUr3HBtjjUYeEQaBCSUm1E3ccP6v9ywslOjRsHUqTEBFYCwdUZkPw8iyxK6izRrD84RiIzH6793960J9h7dv33wkR/DS8DYBURol16OIRMH0oZUogFVfAK6zuurljPrth/wp0su5/J2HbiqU2eeG34174y+sUbAINJ+CMJJbEbcBWm/RAgbzy1ZlNAy4pnFizCS6LeOFSkNftF9boyNBIBTjfC73osQ0UDQ9L1hBTWB9yA0D3yvIIuGIcPfxV37/fVrEjZfSCnrLDvWRqgtEHmfQ/ovwXYe2C9CZD6FyHkbkWwiQIq4pDJVKRoV4bkPKdLA95/olZAJ9oGIjD9WTbJPcXIxfW9D5f9RQ/ujb0GWjLM6NLXknTay8l/EZkWw1vP9B+m+5fi8nyKrIbKJWN8aHcwSthYsJNFg+JBhsLbgIIEkc89URcEfiV/mkjIEwc+Rer6VvXNeFePWngxh743MeBQq/oR1DRuxLCHUVojs59BNha+KxnCg8hI6Z5mc17I7wvZj5BPL4Bc5MNsH28OIChOZoUDnNii3zk04eLnquI6LoekiCC+3glBbrxo6qnrt3dYdmfYj8P4X6703ATsIDZH9omXBcZjICsuSAst/alzHdby9rSeBJONdkrGtpBiHpjG6W3dGdUpH+l6C0F+gSMV0XoPw3ItQcxHqWZDzAbLi9xBZi9XZ6IG0X6K4bwLg2717YmYNHsYfCbOvsqJOd/ijR9Alq5z/XfwZjy4bwm5vBoqQeLQIvzlnMaPa5gMqUt8Glc9S8+8nZNkxlf4Ymi6KCWzKAomF9RJJZShRjjDJbpV0yzC3mmluimMjFVSlaFSEEAjP7Uj3WDCLrAxVyp/qlEHKCHj/RVwxtQwhvS8hsv6efBE9yZgVs8ISTovjCKAjy0iov5M+mtq2oiZYXxWCVhmZaIrCF9u3xT25qkKJe1KVkQ3IkjuxsmN+JC6o/Atk/QfhSGymWBvFfTPSOcxyMDfLLc2TfQDrCgu4c+rLhA0d3TTRFIU0+1omXXcfZ7EOsoJwoyVmtvbtRGS/AI7kAdVhhNAgqjk7VpS0HyMdF1uGsMYBK7vmviU2QBNRMXE08H2o11JCpsbE7d0QAoLG0Z1qsqJidKlvQxbfFA3Yolkf/9vI4CeQOw2hNkHYOiFyJ1omqzIASrMaQXyyUpgRdaZvaIRQkI5L6Jc3j5kjJnHQ7yZkapztqbDMUh2XIISC6f+AxNpSA0JfgXNYjVsHnd2G1YcOxvVf002Tc5o1xxsOM23TBpYf3E9zTzo39uhJu6z6fW5SHB+poCrFCUEIBY7ySjnFCUDfSeIOTQPC39S9hnAlEQzL6An3OBAurK+q+LqZi1o5E9smqCpje/ZGNw2+3rUzxvvIpWn8bMD5MZYCUkaQJXfX6qQLWBmEsp9Ck3kIJQdplliBktoyaalEKDngvqXqZ284zNjJH+KrliELGQa+SIQbpm7km9teRvX+6YjQXW2ByHj8hHoGBSIR5u/aQWXIpG/zB+mUm8RE0jkCKv9Z9aOqSH7fdxE/676cNaVnsaTyx0zcsIeIaSKlJNftpqnHw5qDB9FrZRmdmsbtUTG6LHs0Wu6tTgTMMqT3OUTmn6tuFUoWEBscj+7SnTdXryRixgYhHbNzaOppnIs8kf5bq4Qn/TR3H7Z1UEB4EOm/s3409pGw+04a1oVoLcb26s1rK5fHBFVOVWNIm7b4ImFGTnyHiGHg1y0X9zdWreChCwZzT7/zYtZL0bCkgqoUKb7PCEeSZgKwOtTqwHUD+N8h9opbAcdFCHGcw2YdVwB/TXCnHbtnJK+OzODOqZMxTEnQ0FGFwKaq/HrQRVXBwIRrR/ObOZ9TEvCjKgpSSn7a/3zG9z03dtnQPBK36kuk9zVkZDVEVlleU4B0/wCR9kDVaJNkvL5yeY2Aqjq+cJi5+/MY3mGGNY8NE5S8E9b1FNQjfLRhPX/95itUoWBIEwmc2+Is/nv1dXjsscGjUJsj034GvheqyoAAWU6FIe2HcUnGdTw02CS/tASbqtI2M4uigJ/RE9+lNBis0gi5bTZ6NGnK3X3OxYzsAH1lgl3qEPwMqgVVifjReQP4bOtmigL+KkG8IgROTeMvl11x1O9PfRFaG8idZjmWB+cAEpyXIzw/RWhRgbztHAh9Q9zyubCE9rXJc7v5YMzN/GTGpxz0VqIpKiFD54oOHfnLZcMY9vablFcby2R1UJo8s3gh57c6u15GpymOHZFIi9CYnHfeeXLZsmUn/LgpUqSoiZQSWTQcjJ1x7nWA54co6T9LvoZZiSy+EYz9HDk5OEBJR+ROQajNj3ufpncC+J6vccJGuMB5PUrmHwAo9vv5YP1a1hw6SMv0DG7tdU7MiA4pJdtKSgjoEbrk5iUcMit9ryEr/0ni2Xn26H3Vs3wucB3ZTzL6v/oSxYHEppQPXTCYn/Q/v851GpJAJMKfF8xj8oYNNfylDuNQVS5p246Xrr4u4RoytAjpexX0fFDPRnjGx5h7VscfiTBt0wZmbd+KQ9O4oVsPLmvXAU1RMEt/FG1OSIQdpfm6er22smCAV1csY8rG9YQNkwtbt+GBAefToY4RLo2NNEuQhZdWG+J8GBXUtoi8GQmDaSklW0qKKfH76ZibSxO3h+/27WX8J1PwxdEQKkJwQ7fu/O3yEY3wSs58hBDLpZR1pvpSmaoUKb7HCCEg869WqYsQR4IEO6jNLPFqXWso6ZD3MdI/JWrGqFudZu5bErbvHy1K2j1IWzek7+XoCbuF1VlXzZYj1+3mx/2Ta4iEEMnLWIdRW0WzePGCKpUqU84aBCDwETL9Z0mbMA56KylL4uItgGaNVJJKhJSSO6dNZs2hg3EDKrDKk/N27qDA501YMhOOQUdVonTbbNzaqze31hqrImUYQguSP9net97HyXK6+PWgi/j1oPpr4U4EQsmB7NeRpfcBupU1FgqIPHCNgdAcpGMIQsRmjIUQdMnNg2of54PexF2sppTsLi9vhFeRojpnbFAlZRgQNTtUUqRIEYOwnwu5k5De/1it8cIBruutLEM9O92EcFkWGY1okyEcgxGOwXU/sCFwDCXx12MyB2qb1XFXS1xcnfJQCIemVfkv1UYCV3aMHerbmCw7sI/1hQVxxc/Vcagq20tKaOpJoyIUZObWLRQFAnRv0oQhrdtWDUSujiklvnAYj91uibTrgwxDwp49C5H2UP3WOsUR9n5Wp2boG6Sx2xpubOwA7/PIw4L7rGcQjkvqXKtDdg5mgrfNpih0y0vpWhubMy6okqElyMq/gr4JEEj7+YiMRxFxXIRTpEhhYXkb/ftkbwOIXhAZe0FkINS8up/QCAhhh5zXre4/qZN0ll7Mk5Pr0FpnZCa0gABolZ5RQ7cUMQy+3JHPpqJC8txurunchSxnbe+35EgpiQSXcLD4AwwjiNMzjOa5I60OQWDBrl0EIhGau7zc0Wkt5zc5QGnYwaT8bny+rx0y6gOlmyZNPB5mbd3Cg7NngoSgYWXzVCG4s08/fjGgH24tTERm8OySxbyzZhVhw8ChWgL0X5w/KKYjT5qVSP8kCH1hvX/O0aDkgXkw/gtSWiHsfeLfdxoihA2cQ62MsZFPlX1I9GMiS38OeVPrtDfp0bQZ7bKy2FxcjFFrpp+qKPygd/2zeymOjTMqqJKhhcjS+6kh+gsvsvQeuVPq/ECmSHEmIU0vBGeCWWCNJnFcekpnbqU0rWyZ/w2sNjsdaeuByPwb4vBolhOIsPWCJguQpT+EyFLqypxYSGseYRJcNhvjzunLqyuWxl2xwO9jQ2EB3Zs0ZVdZGbdM/gBvOIQvEsGpaTy14Cv+b9gIru5cv6GyUuoU7rsLNys4S42gaOALfM3u/GfJazkVjzMXu6rSO6eA/108HU3RcajWzoY030vIUHl69UDe3d6zyp7CCqj8REwNq2ApyLT7OcfxNGrxLkxFIxhRyAh3586O0D69nK0V2Xy6ycvGokLeuO76I/szDkYdvSup+u4Or4racDiJ5wwvMh6t12s/nZD6bggvJdaPDSCC9L2JyPxjneu8NvJ6bv94Ege9lYQNE7uqYErJcyOupk1WQ/txpajNGSVUNwtHRKP82ijgHIHSGLO9UqQ4BZGhr5ClD0R/Clq2BsJluSRrHU7q3hJhVvwV/BOpmRVSQKQjmnx+0sxizUN9Lbf4OnFCxp9Q3ImF3IfZWFjAyInvxHWBV4Xgui7d+MewEQx96zX2VlTEDBh2ahozxt5B23p4DxUW/Ad3+EVcWk19WMhQWFnah0E9J7KtuAhn+RWc5a5tX3D4sSqTdp7D4G4v8u7aNewqmMbaklyKQpZdhkuNMHPEhzRz+rCpR/YqJRgSNAWCuoqJ4JeLr+SnF/2a3s2sBgazZDyEFxKrUXOA/TzLWJTDmS0J6b+rMvY8k5DBL5HlvwGZQBel9UDJ+xgpJdM2b+I/333Lnopyspwu7ujdh3v79a/KAB52Vl9fWECe282w9h3x2O1IaVhmutIHth4pA+aj4HsnVJdmCRiJ7PlNy0QtRYrvAdI4FA2oqgUn0mcZWJbcZXks1aPt/0QizXLwv0esjYEJMoj0vQtpP4HwAmRoAQgnwnkVwtY93nINvLk6HKpFOti6I9J+hrAPqNeSeysqcNlseOMMLDakZGtJMcsO7KPI748JqMAav/P2mlX8fkjizrrDKMG3cdljBfcO1aRP9mr2VxTQIeMQXr/O3H2tOb/Zfty1AjCHanBbh/UomSYHK/K5r+ty7l5wVdX9I9tsJdserBFQAQgBWlRG5dQsvdYLg6azvEQi8x6xSn3hxcT3SgtBeDU0/RoRWQ+oYO975o5OUZuQVK8X7aJ9ZvFCXl+5vMpzrdDv44WlS1i8dw//GzUGRQiEEFxwdmsuOLt11dNlaCGy/Fcgg4Bizc90jUJk/KGqDKybJmsPHSRimpzTrBlO7dTNbJ+qnDFBVYoUKSyk/0PifzlL6yo4vAiOwhH8hBBZbYm84wYwIQjORobmWPPlpB9QkL63kK6rEBl/rdPDyRo3MxMZ+haUTIRrNMLWrX5707qBnmAOotIC0WT+UXtItczISDhzThGCdlnZfLdvL3qCx0RMk63FxfU6lltN3BEmpWBXyW6aN/fz4JJLub7thpiA6jACExmYwug2m4hIpcbg2Ctb5eOxJbKfqImmSM7LmoksmguZ/8DKQiUyj/VCyd2Q+17cDrhTiZ1lpbyyfCmL9+4hzW7ntl69Gd2tR/2HG2u9rJJnjL0CVpbZPY4Cn5dXVyyrMYAaIKjrrDiwn4V7dnFR67YxT5eRrbHSGIDANKSwITIeZ8bWzTw2dw4R00QIq8Hg5wMu4N5z+8eslyIxZ8xAZaHkgNomwb0KOC47oftJkeKkoW8hvi4DS3St7zyRu6knDpJqlswC0LdXO+GYQNDSjAWmJF1ZGgeQhcOQFU9A8GPwv4Usvhmz4qmkgvHDiPQHsbQ9tXFC2oPHZMrZvUlTzs7IjNsNZ1MUthQX8fx3i2NOnofRhKB9HfP/DlMUSlziMRGku5qzrDCHRYeaY8rEpwSJjr/s3/TJXECfnMIaQ5Mj5tGdSlRhWL/L8kep89pe34r0vVevdQt8Xj7euIGpmzZS7E/sA9bQLD+wj6vfe5tJG9axs7yMdYUFPPn1PMZ9PCnh77A2QghE9otW5rPq86Zg+Z/djHAMYt6OfMwEgXZA15m6aUPc+0zvy5jS+k4wTMFubzoFATcQBP8kvtuzgV/NnkVZKIgvEsYbDuOPRPjXkkW8v27N0b0Z33POmKAKQGT8gdgvP2GNBUh7IN5TUqQ489DaY5lTxkFolgfTqYa9H4m/jpwgK4gbKMoA0jch6dKy7OdWUFY7IPN/CKH5dW5NOAZD5tMgskB4QKRZ/9Ifrpd+KhGvXDuKJm43HptVYrEpCg5VJcflZltpSY2T8Xl5B3hjyGcsGfk/Zo34gLEdN3DHOb1qrOcLh3lz1Qqum/gO177/Ni8vX0pFKES5dhd+PTZwCegan+7pS4+mLZm+7SBBQ2P67g74IvGDnIihMHzGjXywvT2KMLmv60pcqpVhmrqrc8LnJScStZ9IloUKQmAiYIm5pf9DZOBjS/IRRUrJk1/NY8ibE/j9/Dk8Nm82g994hWcXJx623VBIKfnFrBkE9EgNjVxA11lXcIiPEwQ68RC2rogmX0Laz8B+MbhGI3L+h5LxCAC+SCRmrE91diXwoSqp+AZFmEzc3pULPhnH1Z/fyCWf3cq1n9/AutJmfLzuU4J6bKYxoOs8u3hh3BJ0ivg0SPlPCDECeA4rjztBSvl0Q6x71PtwDIScN5GVf4fISqwM1SWI9N8gtNZ1Pj9FijMB4b4J6Xsjwb2OU6/0h9VSLjOegvLfULNE4QStjaWXjGvECZiHEq4r9T0Q2Uh8zU4A6X89qeP3YRTXVUjnFaBvAGlaGqrj1Pa0zsxi/g/u4fPtW1l58AC5Ljfd8prwwKzpNcp+o9ps5slzv8Gp6igCcp1BHumzBI3fIeWrCKFQHgwy6oN3OeTzEtR1PFqY7fu+ZUKZwZ3njmXpnqGcnzMXU1o6J4CFh9rQt+M/ANhTUYFEMGd/W3ZUZtExsxSneiSo8+sa727rzoFgOu/nd+euzmv5SfeV2FWDlzb2Y8HBVmyvzKJLZgkONdEsyXhIsPcHDAh+kuRhXsyyh6yh1ChYHYc6Mu2nKGk/4vWVy/lg/RrChlEjGJ2wYhltMrO4vluPo9jT0bGpuIjSBGauAV3nj1/NJd3u4KpO9fMeE0oWIu1e4N6Y+xxq8lN2sJr3WX5pCd/s3kWBz8d5rjT2+Zvyl1WDCBhHdFIby/MYO+9ymrmCQHxD18pQiOKAnyZuT732/33nuIMqYSleXwCGAXuBpUKIT6SU9Q/PGxBh72dNLJcmlvnniZmXleL0pLLUy6q51qiL3kN7kJFTP7PLUxmhtkRm/hXKfxu9JQS4QWiInNerRKmnGoprBFJtatkqRNaCkg6uW8B9IxQMSfzEZJk38yAIe2KxubG/3vsTQrNmtTUgDk1jZJdujOxi6bs+qFVqcaoRnjz3mxidkyZClsloaD44L+Ufi75hf2UFEdNkWMt8/jlwHlKCpphI30Iubt6WdaEX2XRgNkE9iM11IcO7jyDb6eLRubP5do81uNmQCrfOG8mDPb/jpvabcWsRDgY8vLihD+/nW4HJAX86v1oylP8bOI87Oq7nrs7r2OtrgscmcKSNsebcyUpQzwKjEEsvFV8zFTF0Zu8QDG73KJnBL4g7Ay/aAUpwNjGNDL6XMNX2vLRsW8ywbLCCmn9/922jBlXecAg1yXkmqOv8evZMNhQW8KtBFx7XsbJdLjRFSai1y3A40U2Th76YyRfbtwEyOsR6ODbFJGzG/u2HDJW9vgwSDVY3pcSdEqzXm4b4dh0AbJNS5gMIISYC1wEnJag6jBBnVGUzRQMjpeS9pybz3l+moNmjnS9hnZsfHsW4x2887YNxxXU10j4AGZgGxn5LlO28GqG4Ez5H6tvA2Adq60b3dJOhhcjKZ6zMj3CAcyQi/RfWRVHO6zGPN10jIfAJMSdV4UJ4fpj4QGqbJN17Iu7A2pNJjsuNUu2768Jm+zDMRJ9FPzLwEcJ5KVM3byBimrRLL+OZgXNxadV1PGGkkU9P14uc039SjRW+2rmDaZs31RhNEzBsPLV6ME+tHoRHCxPQbZi1SrOf7+vA0s/O4ro2W2jj8XJJp6to1nSs9fmqNuRYyggyMBUqnqB2YBUxBZvLMvjN/L3w1VtMueo6Ojs/IdZo1Q7GAeIGXDKA4X2eivClCd4j2FNejpSy0f6mu+Q2qVM3FdB1Xlu5jNt69aZF+rFfuA1o2SqhK71L07iqU2eeW7KI2fnbCBnVg0yFcILPkSEVbKqCECLmdShCcH6r1nGHaKeIT0NEHi2B6l4Ge6O31UAI8UMhxDIhxLLCwsIGOGyKFMfO7Le+YuLTUwkHI/grAvgrAoSDET78xyfMej3ZENfTB6E2QUm7ByXzcYT7xoQBlTQOYhZdjyy6Hln2ILLoOszim5FGQaPsywzMsDqR9LVAVLAc+Mg6vlkR/7VkPAa2PoALS2VgBxzgGgvOqxMeS6hNo+XOeCcFZ/KA7CQwpE1blGrnPoeqkzQWkD6klASiZZ8fdFqLpsRmHAQ6RDYjI1tr3P7WmpUE9JrBjoKJU9Vxazq3tN+EU41fdi0JuXhjS2+eWDmYB792x/18CWFDcd+IyHkTRCYIDwHdhl/X2FKew/gFVxI0dIK6zg0zcilXbwScVmZKuEFpBplPJXkDQDF2oya5iE53OBr1IinD4eC2Xr1xJRjOXZ0v8rfGvV2GV2CW/hyz6HrM8kdjfk+HyXO7Gdsz9liaEGQ5XVzbuSv/W70yrj4KEr8HWQ4nZ6Wn46y2rl1VyXQ4+PPQy+t8XSmO0BCZqni/qRhVm5TyFeAVsMw/G+C4KVIcM2898SFBf2wGI+QP8faTk7hy/PejW1TKCLL4lqguyYh62ACRNciS2yBvVoN6WklpRLMWtbMOOpjFSP+7iLT7Y54nhAty3oLIGssSQjjAMQyhnV3nMUXm35Gl4yGyGTBAqJYuKv131ty1UwiHpvHCVSO5b/pUdNNkRVFzbHGCJAsXOIYihKBjTi5bS4rpkVWETUnw9So0yxzZdiQ7V+iLNTX94NKpnOXxkW0PYldNSkJOpu3qFJOtAmji9FEY9LD60EGCeiShr5Gw94emi9h6YBpvr5nD2pJM1pQ0ofrpI2JKnt8wmN9f9AvQN1pBldbNakZI4t8klGyu69KVjzdtJBJnGHSO08Xqgwfo3bxFwjWOl99deDEAb65aiZmgi9WQkpAeuz/T+yJ4X8b6m5Cgb0QGPkVm/hXFFXvB8NiQS2jidvPyiqWEDQPTlFzcti1/HjoM3TTr3W14GLuiMqZ7D+47dwAT169h8sYNRAyDKzt24o7e/chzJ85up4ilIYKqvUD1b7ZWQP2FCilSnGAM3aBgd+JsafG+EsKhCHbH90BHEJoLspxYXysDzCIIL4B6DHKtN/oGEnsShSAwDeIEVWC1nGPvbf07CoSSDjkfWAFZZIXVuecchlBOzZEdF7Zuwxe338X/Vq9k9aEDrC3vQ7/sdSii+kWAAoob4boBgF9dcCE///wz9vgyOCenADVu4sYEpXmNW/q1OIvNxUVEqml0euUUoVULzH7ZcxlzD7TBG7FjRC0XBCZO1eDfF8zh/oXDqYzUPYtQCBvryrvx8a79cYdJ66bJlpIihJIWFa8ffqIH6RgKoS+B2hkYJ7jv4JGLLmHlwQPsrSiP0VbtKi/j1ikf8rdLB3N1y68gOCP61KsQnrsaxFVcVRQeGzKU3s1a8PCcz6vmIVbHpigMOrtmw5TUt4P3v9S8yDCsf+W/Qzoutt6PaihCcH//gdx7bn+K/X7S7Paq8lzYMJLko2LRFIUmHg/j+56Hx25nfN/zGN+3TtPwFEloiPLfUqCTEKKdsNphbgGStHGkSHFyUVQFuytxC7fmsGGzn5pi7oZGhlcmHsEifcjw6gY+oNVAkpij6RyrP0IIhL23dRJ133jKBlSHaZmRwSMXXcwHY26hf7e3UDy3YJXFPIAdbH0ROZOsgBEY1qEjj110CR/u6BNXjAwClNwYof1dfc5FU2qeBgJGzeef5fHy6bDJjGyzFY8WxqnqDG2xm0mXTaVbVjGDm+3zD09dAAAgAElEQVSlV9P6uW+3yshM+NvXhKBDdvwAR2Q+CWpLK3tVdaMb7P0RnnFkOBx8eus4xp3TJ0Y0LrHE4r+bO4dg+VtWJ6mxB3xvIIuuQRoJhjYfA1d16kzrrCxstd5Th6pybouW9GzarObeApOJDRQPo1gDphOgKQrN0tJq6J3sqsrILt3qZTiqKQr39D2X6beOI9MZz4ctxbFw3EGVlFIHfgp8DmwEPpRSrj/edVOkaCyEEAy/85IqgXp1bHaNy28fctoL1euNkgUkOhk6Gj74SOpibgfn8IY93hmAEDaUjEcRTRcjcj5ANJmLkvv/7J13eBTlFoffb2a2pickVOldkKKAdCuKioCAqOi1d6+9Yy/XjgW72DuCIooCigoi0kER6b0HSEjZPjvf/WMgEHY3CbCbBJj3eXg0M7Pzndlsds6c8jufI7TSXY8XtW3H+4MfY7u4hLC0I0vm5blApCIy3or4XDdIT+ftfgNIczhJttlJttkZv64FIaP0TblOUjHPdf6Nv857n38GvcvbPSfRMj0Pp6rTreZWHurVrULX0qlOXTJd7qiOlaaq/Oe49hHbZXgH0vMh4AQlG7Q24OyHSH8VkfFOyZBwu6qyrmBX1HmKAAqS6dty9tkSBCMfWfRMhWyvCKqiMHrwUPo0aYpDVXHbbDhVjfNaHcs7/QZEviC8k9hOVQiMXQdsw4O9TqZpRmaJ/tn+uFSNJJuNllnZzN+ymUmrVhDQdaSUlh5VHDiiBipbWFQUT6GXW7oNZ9u67fg9ZlrFmeQg+5gavDLjSZLTjw5NFhnehNx+JpEz9wAciOxfEGp2XNc0PJ9C0TOUTnkoINIQNSYg1BpxXe9oROorkd6xpuip7XiE69yINNK+6IbBnxvWsyvg57hsF8foV0I4l+gSB6UJ40RVayCyxlQolbY6P4+hY77Er4fwhEI4dkdVnjj5NAa1brPfdaxG7jx/d63fbvFX4QKtGSLz04jRNReO/ZJZmzZGXTdJC/L48b9zboOV++2xIWr+E/cHqT36TjlJybhjODiGZzQU/Q+INprGjch4u8LzJPclbBj8tnYNk1atwKYqtK6Rw7KdO8j1eJi1aQOBcLikmN2padhVFV8oRMgwaJKRyd3denJ6k6YHvO6RTEUHKltOlcVRS9AfZOroP5ny2e9IQ3LKRT04+YLu2J1HV/uw4XkXil7GdKwkZnrOYSqGJw1LzJreb6F4BBi7a9vsPRCpD0VEX6oLUl8LgWnmD46TK1QgfzgjDQ/S95VZ44ZuOliyiNhRFQ0cp6JkjKzQ+QO6zsRVK/gndxs1k5Lp36IV2UmRDzLGziFmLVxE8bcTkv+LkrxXIDNsGHR//21yoxTfAzgUnUl9v6ReUvF+ewSi5uIq0W+Thhe5/WSQuyh9jRpojRFZ38XV2bt+wrdMWb2qTFV2MB2th3udwtA2bcs87mjCcqosLCwqjAzON1XYw2tAbYJIugJxgAXhB7ym3D3gWTiq7bBcKcPIgnvBP5G9DifgOg+R+shRkyaWRiGy6CnwjS3jKDsiZ3aZWmgHtGY4F7n9FGLOsVSPQcmeUvLjz6tXcsvECVFFQEHSu9Z63u01MXKX1gylxoS42HwwSH21KTES3gZCMScH2Foj0l9DqFlxWyeg6xz35shSTQllkWy3M/fqGyo+EPoIp6JO1dFRjWthYVEmwt6x0uUFhBAgUit1zbKQRrGZLlOySwrApedt8E8iIj3qG4fUmiGSLq58QxPA0h3b+XrJYgoCfrrWa0Dfps1w7KNZJJRUSBluismWVVgtC4E4teDLQhA2kDGcKqOo1I8/rV4Vw6ECBUH3mtHGGTkRyXceoqGHhtAaQ42JoC+G8FbQGiG0JnFfxxsKHdhDgIS/tm2hU53qGT2urlhOlYWFxVGNNLzIokfBNwEzEhVG2rtB2gvgeY/odUU+8LwNh7lTJaXkf79P5dN//jI1j6TkhxXLeXbGNMYOuahE/VsGZiB33UCk9MY+CNXsMIwXalnzWgXYSkdSNSW2w2DXNBxJZ4NYy97Ilx1SH6jQ7MdEI4QAWxvzX4JIczpJttvJ80WfUxhpFFh16weONcvFwsLiqEVKicy/evcInCBmREqH4DTYccruOqIYGFupivKJePLr2jV89s/f+HW9pPPLEwqx3ePhph+/A0Aa+chd15vK9zGELU29qMtKOvHigRB2SLoCU0V/fxyI5JtKbTmrSQ3cWnT7DCk5rcVARM4fZoF75qeInBkorn5xs7e6owjBfzt3rZDyO5gOVbuatco/0KIUVqTKwsLi6CX0l/kvWgRGFlHmc6dIPyxqqmToH7MTUOYj7N3AeU5J3dO7C+ZGjKkBU/373+3b2VBQQF3t23JCFhq4zolwcuKBSLoRaXjB+4mZCkQCGiLtqVI1fzIwlRPtN3N81inM2VETf3ivc+fSNC7aZ+aerrbkp9Ur+XrJeAJ6mDOaNmNgy9YxO/SOJP5zXHvyfF7enjcHm6oiJXhDZuRu39+wS9O4p3vPUilgi4phvWMWFhZHL8GZxCyEBsxbjYNIyQknuKt36k9KiSx6EryjMa/RQPp/g+KXIPMLhFafTYXRZy2Cqfu0pbiIusmriC2tIMB9GUrq3fG/AEAIBZF6DzL5erMLUDhM4dN9OvWk4UXuugUhfLzd80dGLTuOj1a0IT/gpG6Shxs6nczgNuYYmYCuc8m4Mfy7PbdE1X3elk28PmcW44YOi+hANCORASCx8wMrCyEEt53YnSs7nMCczRvRhEKjjHRe+HMGE1cuJywl9VLTuLNrd85p3rKqzT0ssZwqCwuLA2JPyutIuMlQbtehAloTCK/dnf7CVPK2tUMkX5do6w6NwG/g/YrSDpEXDD9y182IGuNolpXFhsKCqEm9YFinQVo60BhwEtWxEm6ErXUCjN9vGSUVHD2i7wz8wp6uTJticH2rhVzfauHunRokJSPESQCMWjCXf3K3lRo47NN1gp5i7v/lpxKBTikNpOdd8IwioBfxV149DPvJdGh8Ey5bSoKusvJIdTg4tdHeYviXzzwbQ55FKBy2olOHiPXuWVhYVAgZ/MtUnw7NBxSk4yREyt0IrWFVm3bwOPtA0VNlHCAg4yOE/hfSPxlQEM4zwX5itXcqpfd9IFpRsgH6aqS+hutO6MyMDesjuubsikr3YxpQMzkZaQxAel6KUU6lgvP0BFh/ABg7QMaaJ6mbHXW7+fTvv0o5VHsIS8nv69dSFAiQ4nAgCx8E3/d8tbo+Ty7cq4RuTHmD2048hSs7Hnnz8RQhLIcqDliF6hYWFuUig3OReZdAaC7mfD4dAlOQOwch9fVVbd5BI9S6YOsSay84eqOoqQhHT5S0x1HSHkU4ulZ7hwqA8JbY+4QNwrkcX7su9/c8CYeq4tI0bIqCS7PROjubEWecZR6qZCDSX8UsGN8zI84NIhmR8W7Va4xpLSGWcKdwleoSLAjEVohXhaAwGDA/z77xTNmcw6MLulOs20v+eXWVETOnMXaJNYnNIjqWW2phYVEusvAxItM/0hy6XPwSIn1EVZgVF0Tme8id54G+nL3hGLupV5X6RFWadmhoLSG8nqghJhkArQEAw9q246ymzZm0agXFwSAda9ehQ63apRzHnzfW5t35N9E6eTZN0rzUy+hAt2bXYtMqJxUm9fWmZljgD9NRcp+PcA9FCBfYu4BSc/e17t9woCFcA0t+apqZxaLcaHpV5ty+HHcS+McDMGJRp1IF73vw6ZIRf4xjYBMbir15vC7R4gjBcqosLCzKRBq7QF8VY6+xu6bl8EUIG2R9C8Hfkb5xIP0I5+ngPAshnOWfoJoikq9GBqYS6QzbwdEDoe5tl89wubigzXFRz/PW3Nm8MvtPfLrObMziZZemccKySbzffxBKgqN2MrTIjJLKACVOU9EIpO9ryPrSdKwyPzSlMfT1IAQgQCSZs/OUvY7fLV268d8fv4tId7o0jSvaH292xO1+/fKC2HMMc312inMvIaX2eIRaM/4XXQ5HVF3jEYblVFlYWJRDeVpMh7dWE5hdZjh6Ixy9q9qUuCFsxyFTH4XChwAVUwldNUegpD1XoXPk+3y8NGsGgXDpCJBP15m3ZTPT16+jV4OG8Ta9FHLX3XubBErwg74W6fkUkXyV6SBmjQf9H/MBQKkF9s7m73UfTmnUmLu79+SZP35HEwoSScgwGNiyNTd36WoeZO8NPI1L1fFGiVQBhKXgl005nJv2MSKl8hTZl+3cwVO/T2X6hnVIKVGEQAhBh1q1uaNrDzrXtdTPqxrLqbKwsCgToWQg1foQjhatUsDeq9JtsqgYinsg0nkaBKaYY13sHRAHoNr929o1qIoC4UgdL28oxDdLF9OrQUM8wSC/rV1DcTBAh9p1aJ5VIy72S309hDfF2OsH35eQfBWwR5W8rfmvDC5t15FBrdowff06QkaYLnXrkZOUXLJfaPWQrkH0b7CaMWubEjL2n31nzoAcPrc7m/2ruaGS/PBlO3cwaPRn+EKhkseYsJQgJXM2b+Kyb8cy8sxzOLVx/EfcWFQcy6mysLAoF5H6IDL/OiJSScKFSLm1SmyyqBhCSQGX2cG23ethwfoVuGw2utQ9BpuisN3rASDbnRSRTgoa4TJV4wN6mPHLlnDflMkoQsGQBhI4vlYdhvc6iSy3m2x3UszXl4v0mONvYpkQEcGqGMl2O2c2bRZzv0h9mNu7fsj0bevY7nPgN/ZErPYO1faFbYxc1IhhXfykOROfJn56+tRSDtX++HWd4b/+xMmNGic8JWsRG8upsrCwKBfh6AYZbyOLngJ9mbnR3hmRMjwhw18t4otuGDz46898s/RfHKqKBMKGQZLdTmHAFDatk5LCYyedRo/6DUpe17XeMSXja/YnyWajZVY2906ZHCFT8MfG9fT7/GNURaFljWyeOe0MWhxM9EprTGyPSjGL1BOAEIKsrMv4/mIfQz7/H8sL0/fsKW2eqjJ9/TrObt4iIXbsQUrJ9PXryk20FweDLN+5g5Y1shNqj0VsLEkFCwuLCiEcJ6LU+BZR8y9Ezb9RMj9C2BJ7M7GID89Mn8b4ZUsIhsMUBYMUB4P4dJ0dXi/BcJhgOMzaXbu45vtxzN60seR19dPSObNpM5z76RdpikKW282SHbkEoug+gZmaCobD/L1tK0O++pxNRbHV22MhhAOSriHm/L+k6w/4nAdCisNF3fSyBjtr6NJIqA0HgkCgG/G3R0qJ9E/C2HkhRu4pGPk3IoN/xX2dIwHLqbKwsDgghHCYw27jjNRXYhQMJ7RtANsWXczCyR9TlF8c93UOBSlDyPAWpFG97CoLbyjEp//8FdHxFg2/rvO/36eW2vbc6X25tF0H3DYbLs2GXVU5tVFjxg65iCU7t1eoTSGg67wzb85B2S+Srofkq00pBZFs/lepi8gchbDFTuHFi77Nu+LW9q+rMgkZBt3qleV0xQchBCfWO6bc41RFHFxEsAyklMjC4ciCuyE0D4yNEPgZmXcJhndcXNc6ErDSfxYWFlWO4fsBCu7FMAKoiiQrE1KS5jL6yXdRU2/k4gcHV2n7uJRhZPFr4P0ApA6EkY5eiNTHEWp8b2LxZl3BLjRR8efnRblb0Q0DTTFfoykK93Tvxa1durHD6yXN6STZbjrVNZOSWV9QUO45Q4bBL2tX8winRuyT0geB6WZ9lO14hFa6g00IgUi+CZl0JYSWm2OCtKaV9nno17wlr8+ZxaaiQkL7RIFcmsaQ1m0i5gVGQ+7+zJQnlFoY8PPOvLmMXbqYoB6m2zH1+W/nrjTLyuK+Hr0Z8tXnMZ1jl6ZxV7ee2NToDuBBE5oPvgmUVueXgB8KH0I6T0MoyTFefPRhRaosLCyqFGkUQ8G9gB9FMeMeigpOt8HQG7cwY+wXTP7gt6q1sfBR8LwLshizWD8EganInYNNp6Aak+5wEjIiu/dioQgRtdDZoWnUTU0tcagALm9/PG4tuuxAxOvVyGd4wzsemXsisuBuZOHDyB19MfJvRsrIIddCuBD2dghbs0pxqAoDAaasXsX0Dev4aMBg+jZtjl1Vsasq6Q4n/+3clYd6n1LmOaS+ASP/euS245Db2mFsPxPpj67rVhjw0+/zT3hn/ly2FheT5/fxw8rlDPjyExZs2Uzr7By+HHwBnevUK/n9CEynt1ZSMk+ccjrD2raLeu5DQfq+JuZAbaGaMyYtSrAiVRYWFlVL4CdiPd+pmqT3uVv56LHRnHH5ycjQcmTxSAjONEetuAYgkq5GKBkJM0+Gt4HvGyCw3x4djHzwfQ/uIQlb/1CpnZJCyxrZ/L1ta7mpOgH0btCowt1jZzRpyq/NW/D98mX49Fjz98CpapzfurSUgwwugMIHiLhhB35DFj6OSHu8QjbEGyklr8+dxauzZ2FTzc+lbhjc2qUbz5x2Bt5QiDSns9z3SIa3IXcOAlmIOdoJCK9G7roVmfY/FNc5pY5/Z95ctnmKCe7jABtS4tN17pkyickXX06bnJp8MXgowbDZlRkyDAK6TqbLlThH09hFzGYBGd79oGGxBytSZWFhUbUYu5BEvyFrNsjI1tm+fge6dx5y5xDTCZMF5iBdz4fIHQNM1fdEEZxN7OdPH9I/MXFrx4kX+vQlzeGMGi3ag4Ig2W7n/p4VF14SQvD0qX34YMB5DG51LB1q1camKKV65ByqSp3UFC7aL4oii98g0lEFU39qHNIoqrAd8WTMksW8PmcWgbBO8e6ifr+u8/KsGUxevZIMl6tCTqf0jDIlIdi/cNwPRU8iZeno4diliwlG0QMD2FBQwKbCvYX+dlXFoWkk2+1kud2JjdzZexC9UWA3to6JW/swxIpUWVhYVC22NghsEMWx8nkUFs92Y3faEcUPULquA/M1xk6kZxQi5U42rdzCtnU7qN04h9qN4jQ+RNjMEE6sME9VDxSuAI0zMvn5P5fz+aK/+WXtapLsdk5r1IQFW7fwyxpT1PW0xk25pUtX6qell3O20ggh6FSnHp3qmLVQi3K38dLMP5i3ZTMuzcaQ1sdydcdOJNn3a27Q/yXmmypsEF4HSsWFSuOBlJKXZs6IWrfk03VG/PkH/Zq3rNjJ/JOJ9pk2F/KZyu+2vbMDYzlUYM4l9JcRCUwkwnUu0vMKGAEiHERhA6Vy5j8eLlhOlYWFRdwJBkKMf20i3705Gc8uDy06N+XiB4fQqkuUbi3bCaDVIxxYgaruvckaYQj6BdMmZNPnP50RxqhYq7FzzXgevz6fVQvXYrNrhAIhWnZpxoOjbyc9O+3QLsbew0xzREO4Sw3src5kutzc2PlEbux8Ysm2/7TrEPd12ubU5N1zzyv/QCULjNzo+2TI3F/JBMI62zyx01nrCnZh7B4PUz7lHVN6f7d69flh5fKoumB2RaVBeuJS3GUhlCTIHI3c2T8y1SeLzehx9k/mDEYLK/1nYXG4IKUPGZiJDM5Fyqp5aq0IoWCIu055hA8e+oLNK7dSsKOIOT8u4K5TH+GPcbMjjhdCIDI+BK0tQb+Cp0jF51HYss7OfRe1JqtOXa54og+xvq7COtzWL4vlc1YS9AXxFHgJ+kMsnrGMO05+BOMQdXuEkgwpdxCZAnGC1hocZRcrHwhSSuZt2cSo+XMZvXgRBf4YBcJHAMJ9KdHTSgrYWiLU2pVtEjZFLel6jIZL0yquVu7sC8Qo4hdJsJ9o7s1dukboge1Z8/au3cu0K+FI/+6u14gdYBSbdYUWgBWpsrCo9kgpkZ53wPMa5mBcAIFMeRjFfW5VmhaV376Yweq/1xHw7u3gkhIC3iDPX/k6Xc7uiGYr/dUj1CxsNccgPUtYOW0i079dx6a1WQy6oxe9z++KzaEic51m2mQ//pycTmGejbBe2nkKh8JsX7+D+T8v4oQ+B94VJaUPgn+BUBHuC0Gtjyx+ZffA3jRwD0MkXYEQ8Wlh3+X38Z9xY1mdn0cobGBTFB7+7RceP/lUBreu3DRYpeDqD4GfIThjn3EzLhBORNoLh3x6GVoEoSWgZJjDsqNoq/29bSszNqzHoWn0adyUuqmp9GvegvHLlpaSTwDT4Tqv1bEVXl8kXYn0fWPW/7FvpNMJKQ9HDHtumpnFJwOHcM/Pk1hfsAtVUbCrKnec2J1hx7U/kEuPP6F5Zez0IgNTEdW4WaMysZwqC4tqjvR+YTpU+zsUhQ8g1SyEo3vVGBaDH9+dgt8TrQAZjLDB4hnLaNc7+s3JntSKDn1b0aFv5D6ZcgcUPsH+3WL/zErF54lem+Pz+Fny5/IDcqpMJ3YUeF5lXyeWlIdQanxT4fMcKP/98XuW7dhecjPfI4Pw0G9TaFEjm7Y5caoRqyYIoUL6qxCcjvSOAVlkOj+uQea8woNEGnnIvKshvHL3qD4FEJA+suRvxa+HuGr8OBZs3UwwHEZTFJ79YxqXte/Ifd17M2fzJrZ7PCW1VS5No1ZyCnd161Hx61NrQI1xyKJnwT8J0EFrhUi5K+bfbPtatZl08WVsKiokoOvUT0uv2gjVHoS7jBmMApTUyrao2mI5VRYW1RgpDfC8EjVCA35k8UvVzqmK5VABCEGpCNaBoLjPx0BA0Qu73w8DtIak1DkDzTYdPRRZ92R32EhKdx/QOtI3GopfJaIovvBBpJqJcPQ8KPvLYlNhIXM3b4qIjoBZwPz2vDmM7HtOlFcmjiU7tvPLmlVICac0akzr7Jy4ryGEAo5e5IVPYOySxSzbuYPGGYsZ0roNOUkHJygp868HfQmwO10l92y/AWpMQGj1eGzqr8zbsplA2DwmvLtI/KO/FtImuyY/XHQp3yxZzHfLl4IQDGzRinNbtMJlq5gmV8n1qbUQ6SN2D6WWCKEgpcSvh7ApKmoMh6luSjVzUhwngxweY6cT4apADd1RguVUWVhUZ4x8KKu1PPRv5dlSQTqf3ZF1/24g6I+s+woFdFp2aXrQ51bcQ5Cu8yC8CYQDodbktEu28cUzf0IUpwqg9/ndKnx+KSUUv0JklyGAH1n0YkKcqtW78rCrKoEoHWCGlCzbuT3ua8YibBjcOukHpqxZRSgcRgJvzJ1Fz/oNefWsfnGPnExfv45rvx+HlOAP6zhUldfmzOLlM87m9CZlf1ak9INvPNL3LWCYTQ+hfylxqEqhI72f4HPdwTdLl5Q4VPvi00O8PncWZzdvwbDj2sct7SaEQEoYvXgRL8/6k22eYjRF4dzmLbm/Z2/SndW7yFsoKcjUB6DwSUpHil3g7AO246vKtGpHNYgrWlhYxES4iN3LjxmWr2b0v/FM7C57hHaOw+3gnGtPJzXz0FqwhVARWn2EaqbDajeuyUXDz8PhduxzjLnelU8PI6v2AXRNyXwwyhi7oi89WLPLpHZyStQo1R7qphxiB+MB8M78ufyyZhV+XScsZYkA5bT1a3lt9sy4rlUcDHLdhG/x6Tr+3U5OIBzGr+vcMmkCeT5vzNdKowi5YyCy8EkIzTHrfrzvAbEioSEILWK7x4OqxC4231BY/tidg+G1OTN5dOovbCkuwtg9bPrbZUsY+OVn+ELVt/FkD4p7KCLzXbD3AqUmaG0QaU8g0p6t0hFS1Q3LqbKwqMYIxQ32bkT/U7WBa1Blm1QuGTlpvDLjSVp0aoLNYcOV4sSZ7GTQ7edw7QuXJmTNYcMH8eT399Hl7I7Ua1GHbv078czkBznv5rMP7ERV5MQ2zcyiUXpG1M4yl6ZxZYfKiwSMWjA3qk6TX9d5/6/5u1NZ8eGHFcvK3D9u6ZKY+2TxSxDeQOmoYojYvz8F1Dpkud3oZTiwNQ8y7VgWhYEAr82ZHfG+hgyDXI+Hb5ZWv4hzNIS9E0rmKJSc31FqfI1w9bMcqv2w0n8WFtUckfYocsd5u9WZ99QrOUGtjUi+sSpNi8kxLeoycuZT7NicR3G+h9qNc3C4EiuS2e6kY2l3UsW7s6IhhAvp6Ll7ntn+N1477K4dkTJsjlPx/wBIhPMMcJyKEAf/lfrG2ecy5Ksv8ISCeEMhVCGwqSqXtetIj/oNDvq80dBDOkIIVK1056IhJXm+2LMMi4NBAmEdZznz/gK6zoQVy/hp9UqcqsaAlq3p2aBhhNO4pbgIb4wojV/Xy44a+b4mdlQqGg6E+yKS7Xb6NG7K5FUrS42EAdOBvSoBDuzsTRuwqQqBKBlqnx5i/PKlEYrzFocnllNlYVHNEWodyP4B6f0M/BMxI1QDEa7BZiQL2LJmG2Nf/J6/f/uXlKxkzr3+DHoOPhGlijuHatTJpEadzCq14UARqQ8jd/5l6u9EOLE3IaUfmfcf0JeXSAHIwC+gNoTMT02xxIOgflo6Uy+7kgkrljNjw3qyXC4GtW5Di6wacbkugMUzlvHmHR+ybM5KhBC0O+lYrn/xMhq1qQ+Yw5TTnU52xdDHSrLZyhx1A7DD62XQ6M/Y6fOWOEw/rVnFCbXr8k6/AdjUvY5ck4xMkmw2PFEcK7fNRssY1y6l3EeGIRYOzN+fAtjBfQnoqzG8n/JMlxSKvRoztiYRDIcRmAOjz2zanCHHti3nvAeOKEcI1EoZHTmIeIZyK8oJJ5wg586dW+nrWlgciSyesYx7z3gCPRgq6YBzJjnocGpbHh57J6oaHx2l6og08iAww/zB0Q2hxMeBk8aufZxYDVz9Ea4hCMWNUfQceD4icm6dHVzno6Q9FBcb4s2i35dwX98nIrovXclORs78Hw1aHwPAyNl/8sbc2fj3S1U5NY0r2nfkzm5lF+pf8/04flu7JiLF5tQ07uzagyv2iQQFdJ1u771Nvj8yOpZidzDjimsix9vsxth+JoRXRzdCqQGu/4C+wKz/cZwMhcNNR0x60Q1ByFD5YvWxPPN3N1LtDp7v05eTGjYq89oOlqJAgC7vvhnxngK4NBsP9z6Z8xPgzFnEDyHEPCnlCeUdZznIFhaHMYZh8MTQEfg9/lKSAn5PgAVTFjF97KwqtC5xSCkxikYgc3XJMvgAACAASURBVHshCx80/+X2wih8IS41P0JJR0m+AaXGeJQaX6MkXVoSFcT7BdEHAQfBN8aUwaiGvHbLe1HlLPyeAO/e/1nJz9ef0IUexzQw1cMxh6m4NBtd6tbj5i5ld1IWBQJMW7c2as2SX9f54K/5pbY5NI1PzxtClstNks2GTVFIstlIdzj5aODgmA4VgEi5FXBG2eOCpJtRUq5DyXgLJe0xKH4ZjLyS6JamSFyaztDGi+mWs5aCgJ8npv0a13qxfUlxOLi1Szdc+ymm21WVOikpnNuigvMELao9VvrPwuIwZvncVXgKoqdB/J4A41+fRK/BbZC+sRCcDUo2wj0EYTu8n4ql7yvwfAgEQe7jKHg/Qqp1EUkXJGZdaZgilTEJYTpc1atF3lvkY+0/GwBwuAzOuyaXsy7Ow50cZtlCN5+/sjfdpykKb/cbwF/btvLTqpXkbduFY8F2MpfuYr66kE5928eMfu7y+9GEQpDo8hb5+9VrSRmkueMLZp77MRgFeMKZrNOH0qLeDdijjGzZF+E8E5myDYpGmMKUYM4MTL4G4R66dw19NeiriayRA7emc3nzRUzbWp+tnmJmbdrIifWOKXPdg+Wa4ztRMzmZl2bOYF3BLlyajUGtWnNnt57l1qhZHD5YTpWFxWFM8S4vQo0dcC7cuRO5/RTzZoMPUJC+cUj3MJTUeyrNzrhT/BrRtaR84HkdEuRUCaEgldpgbIl+gJJG9OhJ1TFn80am/vMZz41dSrPjfKiaRBqw5z7eoWcxx3ZehuGbjOLqU/K643Jq8usj4/n7o6mEgjpG2OCnD34jp0E2L057jJSMyC65nKSksnonaZyxNz0rpYHMuwpCCxAEQECytpNjbe+DtwhSHyz32jbq/flgaQ18vhkk21SOrXMOZ2cfj23fgvjwNhA2c35dFOq4TSdZNwyW7NheyqkqDgb5dNFffL1kMSEjTJ/GTbmiw/EHLUzav0Ur+rdodQBDmS0ONyynysLiMKZJ+4aEAtG7pzSbynEnrgVZuM9WA/CD9zOkozfCcWJlmBlXpAyBsTX2AUYuUgajznqLC0nXQ9FTRDp1Lki6plq1mL85dzaLN3zC051+wqXtEz3aJ9CkKOB0SSgcjnSeUtLB+Ovn05nyyTQCvr2RQF+xn00rtvDClW/wyNd3lWyf+f08Pn1yLBuXb6ZG/wZsbZ9GaD/3yqVp3Ny5694NwT9B/5uIVKr0gXc0MulKs0kjBj+sWMadP01EDxvo0nTWXMv/ZPSStXw4YNDegnitUelo5j6EDVhWkAWYs/1quPdKZhQG/PT/4lO2FRfh3y3K+v7C+Xy5eBHfDB1Gw/QD0D/bD8uhOnKxaqosLA5jMnLSOOn8bjhckQ6EZlcYdE0s58OH9H6ElEGkvh5ZluBltUOj7GiQA0hcOkW4h4L7QsC+W9fKZf6/ayDCfVnC1j1Q1hfsYuSs6TzU4bfSDlVMdAj9VfLTl899G3XkkB7Umf3jAgrzzAjPF898wxMXvMjSWSsozvdg+2gJroU7UQ3TkXLbbDhUlVu6dOPUxk1KziP9P5bRwScg8GvUPSt27qTvJx9w04/f49d19H1q2Hy6zl/btjJu2V59K6HWAnsXon0mgobKO0v3SBlITmu0175XZ89iyz4OFZi6UkXBIMN/+SmG3RZHO1akysLiMOfWt64F4LfRM7A7bBhhg6Q0N/d/2JNa9ZfG1kIMLkDmdgIESB1pPxGR9j+EGv8Zb/FECIF0DQLfaCJ1imzgOi+h0SIhBCL1XmTS5RCYBkhw9ESotRO25sHwzdJ/aZ62A4daEYcKzM/BXidqx8adMY+02TXytuwirBt89MhXpaKlwpBkf7CcmvVSOGPkBTRucQwnNWwYZRRLWXZJiFLwv8PrZchXn1MYjD1f0qeH+PTvhQxp3WavTekjkPlXQmg5YRnCr4MqJA/P78Gywto4NcGrffuVmu03dsligjHGBs3dvInCQIBUh8McwI0VfbIwsZwqC4vDHLvDxt0f3MTVz1zMyoVrSclIokWnphDegNzxTOwXyl2UurEF/0DuHAw1Ju7tdKumiJTbkaE5oG8A9kQ73KDVQ6TcUTk2qDXBPaRS1joYTBFPvcwap1LIENiOK/mxdpNaFO5cGfVQPaSTXS+TaWP+ICXdIG+bhP20mOSmYpSJa2iUXpdPXv8Mw5B079+JDqe2RVEUhKMP0vcje39/++HoHbHp478XRJ3Ztz/7O11CSYXM0RD6Gy20kEKP5L1/M8gNh7isXQ6XtGsfMcTYp8ceHaMIhSXbc3lr3hx+X78WCRxfuw739ehN+1rVy7m2qFwsnSoLiyMYI+9SCM7F7EqrCG5IuRclQYXe8UTKIPgnI/3fgQTh6gfOPomrpTrM+G75Uh7+9Ud+PWsUKbZyfv/CBe5LUVJuL9n0x7jZPH3JKxEpQMWuonTO5pYXN3JyraUQNvAUKnz2ck2+fbcG+zpXmbUz8BZ6CXgDSAnOZCdN2jXgmckPYndqyLyhEFpG6boqJzj7oqRHPhCc9+WnLNxWRj0doAnBkGPb8OQpfco8rjwuGPMlszdvjLov3eEkZBh4Q8FSTqtT0/h44GCOr133kNa2qH5YOlUWFhaI9FfA1gaz7scJIgmwY9YdRcMLgZ8rzb5DQQg7wnWOqUWU+RbCdU6FHaota7bx5h0fcHvvh3jm0pEsmbUiwdaWJuAL8PFjXzG07jWckzyMGzvfy6wf5pf/wgPgjCbNcNrcjFjUGa9eOimx91najpmwcIO+DhlaVHJM9wGdGXTbOdidNuxOG4qqIB0qgWPcvPDKTE6qtQSbGsZml6TXCHP5vVu58oG9XZGaTaVwZxF+T6BkPX+xnxXzVvPe8M/NwdgZH4FrMGaNnAYiDZKvR6T9L+o1VSQEYNc0ru7YqYLvUmxu79odZxRZB5emUSclJcKhAlOL69Gp0WvBLI4OrEiVhcVRgAz9C6G/QclAygAUPrx7lmAUHGegZIysXAMrkTkTF/Do4BcIh8LmDDxFYHfauOCeAVz8YOLTeXpI57aeD7J60TqCvr0RJIfbwbXPX0K/686I21qbCgu5dsK3tE3+g1vazCLN7kcVEsPWGbu9Ifi+YW+UaPc4l9ThKPvoPOWu3870r2czeekyZiQVc0bv9TzUcQZJWmQaLugXDDuhNd4iB+GwgTSi31+EEFz93CX0u64PTrfDnKUofSDcCBH7Wf/szz5iyY7tMffXSk7m5TPPplOdehV4d8rnp1Uruf+Xyfh1c1ailJKbO3dlxMw/CESptwJT52ve1TeQ4kjsrEuLyqWikSrLqbKwOMKR+gbzhqU1RAg70ihG5nYluiq4G5E+AuE8pbLNrBQCvgBDal2FryhSs8jhsvPKn/+j8XHxHV68P79+8Qcjrn4jamedw+3gq22jcCXFV+tqxc6dbCzaReM0Qf20mmDsQO44h+ifAQciZ2rEyJ9O77zOTp+P93pOoFft6Gkxb5HCK/c1xBs8iVnfzyvTJrvLTt2mtXh5xpMVvt7j3niF4hgDmDWh8PMll1M/Pb1C56ooYcNg8fZcdMPg2OwcHJpGy9deilrEDpZTdaRipf8sLI5yZHABxvYzkTvORuYNReZ2xfC8b6YAU+4hUvXbCfZ2UQuEjxRmTYidYgsFdX4YlfjU5+QPf43qUAGomsLCX/6J+5rNsrI4uWETGmQ0RihJSN84YnffCfBPitjqDZmRqbCMfdtwJju59c3reWL8vdSoW/YcxqAvyKYVW/jmlR8qehm4yxhbowhR5libg0VVFI6rWYuOtevg2J0O7NWgYcwRyc0zsw4Lhyqg63y/fCnvzJ/DL2tWE44yWsjiwLGcKguLIxCpr0TmX7Z74KzfTPXJIih6Cen9ACXpYkTGSLC1B5EMaj1IvhWRMQohjtwBzAU7igjr0Z0JI2ywc3N+wm0IBcroXpOmDlTCMfKAWOsEwdgVsbV9rVoAjF/fFE8oeuO4IsK4Mk8G4Py7++Nwl+1cBP0hfnyn4o7skFZtsEcZkSOA1jk5ZLkrp2v1rq49S8kv7MGpaTzUu/pHeWdv2kjnUW9w35TJPD9jOrdM/J4e77/D6vy8qjbtsMdyqiwsjkBk8RulNIf24oPiV03FcUcvlKzRKDXno2T/gpJ8BUIc2TPImrRviKJE/9pzuO207to84Tb0OK9LTGdDD+m07dUq4TYIe6fdTQvRdjrB1i5i820nmoXbEzc2Zk1xGn59f+fGBcn/NeULgP43nsmpw3pgs5et3OOLEbWLxjXHd6JeamqpAnKbopBst/PMqfGrRSuPZllZjBlyId3q1UcVAkUI2tWsxYcDBtG5bnzquRJFns/LFeO/pigYxBMKETIMPKEQuZ5iLv7mKytidYhYTpWFxZFI8E+iDZA1MUBfW4nGVB9adWlG3aa1UW2R0Q7NpnHG5Scn3IYOp7ZFUSOTRw63g7OvOZ307LSE24CzjxmhjLgFaKDUAXvXiJecUKcuI888h1RHClf9PpjPV7ejMOjAkALURoi0/6EkX1NyvKIo3PbWdbwx/1lszujOulAExx2AE5nicPDt0Iu5/cTuNMvMon5aOhe3bc/EYZfRLCurwueJBy1rZPPJeUP494ZbWHz9zXwzdFjcCuQTyZh/F2NEqaWWQFEgwO/r11W+UUcQlvinhcURSRmFvzJsRiOOQoQQPDXpAR7q/wxrFq1DUVVA4k5x88R395KamZLQ9Wd+P48nLhgRkYJ0uO1cdP9ALrh3YELX34MQdsj6Apl/I+irdw8cDoKtLSJ9ZExF+lMbN2FWo+tYvD2XUPhCXNk5aFFkB/alQetjuPSR8/nksTH4vaWjUnannWEPDD4g25Psdq7qeAJXdSy3ZrhSsEVJR1ZnluzIxa9HT/0Gwwar8/M4qWGjSrbqyMFyqiwsjkTc50HxW0Tt7lLrILT6lW5SdSEjJ42Rf/6PNYvWsXbxRrLqZNCmR8uYacF44Snw8MQFLxLwRg73NcKSMy4/JeE27ItQ6yJqjEPqKyG8GdQGCK38zkdFCNrm1Dygtc6/qz96UOeLZ8ahqApG2CAlI5k737+RJu0aHuQVWBwM9dPSsSkKoShpPruqUis5sQ8WRzqWU2VhcQQi3Jchfd+ZN8tSOkQORNpTVWhZ9aFR2wY0aptY+YR9mfrVTMoaD/fzx1MZeveASrOnBLUJhJYiC69DhjeAkgHuSxFJl8atxk4IwbAHBjPo9n6sWbQeh8tOo7b1EzqjsSop8PsZvXgRv69fR5rTydBj29L9mOpxvecf25a3580lWnmAqghObdS48o06grCcKovDhrAeZu6kheRu2En9lnU5rnfravElVR0RSjJkjUV6PwTvWCAA9q6I5OsQWtOqNu+oJG9LftQoFUAoECJ3w45KtshEFr8M3vdNLTMAYxv+3OdYPOctpk2+jMufvIjMWhlxWcvpdtCqS7O4nKu6sipvJ4O/+oJAWC9Js/26djWnNWrCi2ecVeXfWXVTUnn6tD7c9/NkDCkJGmGcmoYqBKP6DSyRjbA4OKx3z+KwYNmclQw/+ylCgRBhPYyiKqTXTOOZyQ9Su9GBpSKOFoSSjEi+EZJvrGpTLICGbY7BmeyIKjzqTHLQrEPlRwhkOBc8o4DSzp7TLWl9fBFj3pzA9R0X8tZfz1dOAf0RwE0/fk9hwF9qhI03FOLnNav4ceVyzmrWosps20P/Fq3oVKcuX/37DxsKCmidncOgVseS5jw6ay3jidX9Z1HtKd7l4e7TH6NgRyHeIh8BXxBfsZ+ta3K565RHMSqpBXjNonV88cw4vnz2W9Yv3VQpa1ocOXTtdwLuFFfUSIXNYaP30G6Vb1TgNyB6obXDbdD73J0U5RUzZsR3lWrW4crq/DzWF+yKOqPQGwrxwcL4znc8FOqkpHJLl24836cvV3Q43nKo4oTlVFlUe376eCphPdJxkoakMK+IeT/9ndD1w3qYx4a8wH+73s8HD37BBw9+zg3H382zl71aaQ6dxeGPqqk8/+uj5NSvgSvFicNtx5XiIqNWOs//8kjcR9NUDJ1YY4oVBWw2SSio8+vnf1SuWYcpO7xeNCV2N2CuN8a8TYsjBiv9Z1HtWT5vNQFvdIHAkD/E2n820OmM9glb/9MnxzL7h/kEfHtTJHoozLQxM2nSviGDbj0nYWtbHFnUa1abj1a9yt9T/2Xj8i3UbJhNx9PaolZVW769G7GcKm+xwoyJZsrPCEc+PGzfuJPfx8zEW+Tj2O4taH9ymyqpF5JSJ2/dD8yZOA9JKsefNZSc+lWjF9UkI5NgOLpcgSIEx+XUqmSLLCoby6myqPbUrF8Dza5FHd9hc9rIqh3fAar7YhgGX788oZRDtYeAN8Do58ZbTpXFAaEoCu1PbkP7k9tUtSkIrSHSeTrS/zOCvbVewYAgd5ONPyenodlUug3oXOp1Xzwzjo8fHY2UEAqGcCU5qd2kJs9NeTjhWl/7YoRW8s5tN/Htu0moqgQE4dtm0PeyDtz42v2VKlEBkOV2c2aTZkxctZLAfs6VXVW57oTOMV5pcaRgpf8sqj1nXnEKihL7CXj/L/x4EvAG8BfHHqORtyUfGUWd2MLicEGkPYtwX4quOwj4BEG/YNp3adzevxlGWMGZ5GTo3f1Ljp8/ZRGfPD6GoD9EKBACCb5iP+uXbOTpS0ZWmt1ShvnhlRv4/n03oYCC36vi9yqEAgqTPlrA1y99VWm27MtTp/WhZ4MGOFQNt81Gss1Oks3GiNP70jo7p0pssqg8rEiVRbWnVsMcbhp5Ja/e/B6GHkYPhbE7bSiqwiNj78JZztDWQ8HhdmB32fAVRR/Cm1YjpcpbpC0sDgUhNETqHdhSbmbutz8z6r5v2bJ6F1JCx9OO5aZXryLnmBolx49+dlzUdLweDLPwl3/YuSWfrNrxkWAok+B0Pn/Jjd8XmToN+BS+ePpbBt12fqX/fTo1G2+fM4C1u/JZuHULyXY7Pes3tKQKjhKs37LFYUHfK0+lba/WfP/WZLas2kaT9g05+5rTE/7lrSgK51zbh29f+5GgL1Rqn8NlZ+AtZyd0fQuLykIIG90G9KVr/zPxFHixOTQcrsgHlg3LN8c8h91pY+ua3EpxqmRoNbmbYt/CivJD+L2BKmoAgIbpGTRMrwTn0qJaYTlVFocN9ZrV5rrnL630dS97bCjL565i2dxVBDym/ozT7eC43seWSotYHL4YhlHp9Tf7UrizCE+hl+x6WWi2qv1aFkKQnJ4Uc3/tRjXJXRddqDQUCJFTv0bUffFGaHVJSjXwFEYv8rfZFRwue6XYYmGxB8upsrAoB7vTznNTHmbR70uYMX4OQgh6nteFVic2t1J/hzFF+cW8N/wzfvpoGgFvgLrNanP54xfQ+/zK04vasnobL1z1Bv/OWIZqU1E1lQvvG8j5d/Wvtp+tIXeey7LZKyOGI6uaSutuLciul1U5hjhO4qyLn+fbd1MIBko7xDa7QZ9Le1Spo2xxdCKqosj2hBNOkHPnzq30dS0sLCwA/N4A13W4i23rtpfqKnW4HVzx5AWcd0viOzp3bS/gyta3UZRfjDT2fg873Q4G3nIWVzx5UcJtOFjee+Azxo74nrBuENbDuJKdZNRK56XfHyejZuK6cffHXzCXu059lLXLbPg9ZsTKlRSmTpNsXpz+Eq5kV6XZYnFkI4SYJ6U8odzjLKfKwsLiaOO7Nybx1l0fRy24drgdfLVtVMJrcT5+7Cu+ePobgv5QxD67087oLW+TlBY7DVfVbFyxhV8++x1PgZd2vY+ly9kdUbXK19vSQwXMHvcOv375LxIXvS84l24DTqsSWyyOXCrqVFnpPwsLiyonFAzx5/i5rFm0nszaGZw0tBspGckJW2/KZ9NjCsqqmsI/vy+h05kdErY+wB/jZkd1qAA0u8qSWSs5oU+7cs8jpeSXz6bz5bPjyN2wg1oNsrng3oH0Pr9bQlOI9ZrV5j8Pn5+w81cUzZZGtyF30m1IVVtyeLPT66U4GKRuaiqalTY9aCynysLCIiFsXL6Zd+//jNk/zEdKSYdT23LVU8No1LZBqeM2LNvEHSc9jN8bwFfkx+F28NYdH3Lfp7fQPUEaZOVF6CsjgG93ll1EbXfaKnSekTeN4qePpuL3mE7iql3reOGqN1g6ewXXvXDZoZppEYVFudt4c+4s/snNJdvt5rL2HTmrWQuUaloHVxar8/O45+dJLMrdhiYUNFXhhhO6cHXHE6ptXV91xnJHLSws4s7G5Zu5sfO9JdGYUEBnzo8LuLnbA6xcsKbkOMMwuKfPE+zKLcBXZCp6B7wBAr4gTw17mW3rtifEvpOGdsPhju7UhENh2vRomZB19+XMy0/GEUNjTVEUWndtXu451v27gckf/FbiUO3B7wnw3RuT2bxqa1xstdjLhOXLGDrmCyatWsmGwgLmb93CPT9P5vZJPxywEHDxLg+r/17Hru0FCbK2bHI9xZw3+jPmb9lMMBzGq4coDAR4edYMRs7+s0psOtyxnCoLC4u48849n+Ar8pcqwJYS/B4/r9/6fsm2hb8upji/OGpkyAgbTHj7p4TYd+blJ5OenYZqK11343A7GPbgYNwpiS9wPu2SXtRrXjsiIuVw2bn1zWsqJK3w2+gZ6KHos+aMsMG0MTPjYquFiV8Pcc+USfh1HWOfD61PD/Hz6lX8uXFDhc7j8/h55j8jOb/21dzW60Euqn899/d9kvzcynWu3l84H18oFDH90afrvDVvDp5g5Hgui7KxnCqLKkeGdyCDC5DhLVVtikWcmP3DgphP7YtnLCPoN7+sN6/cGnVYL0AoqLP2n4rdpA4UV7KLV2c/xckXdMfmMNX5c46pwX9fvZIL7x2YkDX3x+6089L0J7jg3oFk1cnEleKi3UnH8tTEByos6xDwBgnr0d+/sG6UvM8W8WHaurUoRE+JefUQoxcvqtB5Hjz3aaaN+ZNQIIS30EcoEGL+L4u4tfvwmE7ygeAp9PLNKxO4teeD3HnKI0x8/9eon4WfV68iZET//GiKwj+52w7ZlqMNq6bKosqQRhGy4G4I/A7CATKItLVFpL+IUGtWtXkWh4CU0b+o92DsjmDVapQTs0tLs2sc07Ju3G3bQ3p2Gvd8+F/ufO8GQgEdh8te+SNN3A4ueWgIlzx0cFXWx59+HN+/ORlfsT9inyPJUS2GNh9JFAeDpSJU+7PLH/l72J/l81axdNbKiCaFcChMfm4Bf4ybQ+8hXQ/axryt+dzY+V6K8ooJeE1HatmclXzz8gRemv54KZkJZxmjcwwprdE6B4EVqbKoEqSUyLwrIDANCIIsAgIQWoDcORQprSfsw5m2vVrH3Nf4uAYl8xo7nNoGV0p06QJVVTjn2tMTYl/pdVScbsdhWZTb4dS21G1eG5uj9M3P5rDRqG192vZsVUWWHZl0qF2HcAynyqVp9GzQsNxz/D31X8J69FmiviI/cycvPBQTGXnju+RvLShxqMCssduwbDOfPjm21LFDWrfBFcNxcqgabXOsh9sDxXKqLKqG0EIILwf2bykPgywA/+SqsMoiTlz9zMVRi7AdLjvXjdg7akhVVZ76cTipWcm4kk3nyu60YXfauH3UddRuXP2+1Dcu38xTF7/MeVmXMTjnSkbeNIqdW/KrxBZFUXj+l0foMbALNqcNV7ITu9NG76HdeGbSA4elo1idaZSeQff69XGopaOrAnBoGkNaH1vuORxuB6oW/dYrFHFI9XwBX4CZE+ZFddpCgRA/jppSatv5x7ahflp6xPU4NY2nT+uDakkrHDCW+KdFlSA9o5BFI4AY9QOuIShpT1aqTRbxZcmsFbx283sl3X71W9fjxpcup91JkTeegC/A1NF/suqvtdSol8Vpw3pWqjJ3RVn99zpu7fkAAU+gJIWp2lSS05N4c/6z1KhbSSNaouAp9JK3JZ+sOpmVUmhfFQT9QXzFflIyk6tsBM13H0zh4em/UdA6FaEbSFWhniuZ9y8cSuOMzHJfn7c1n4sb30goikaZw21nxNTHaH58k4OyrWBHIRcecy2hQPTvVc2m8mPgi1LbPMEgoxbM5ct/FuEJBWlfqza3dulGh9p1DsqGIxVL/NOieiOSMD9+0f74FRBplWyQRbxp1aUZr856Cm+RDyklSanumMc6XA76XHpS5Rl3kLxy46gS6Yc9hENhivOL+fChL7nj3RuqyDJISnWX+R4fzuTnFvDaze8x49vZgMCV7OSC+wYw+LZ+lRqN+33sTN666T2yvEHSkzSCOU7UIp0Ur0Fe02407lO+U5VZK4NLHzmfjx8bU0qA1pnk4KTzux20QwWQkpmMO8VFQaAo6v76retFbEuy27mlSzdu6VJ5My+PZKzYnkXV4OgDxCpmtiNc/SvTGosE4k5xHRE3e2+Rj6WzV0TdF9YNpo6xdH0Sgc/j579d7mP617MIBXRCgRCFO4v46KHRvHPPx5Vqy6j7Pi2pVVI9Oq41xdh3+Al4g7x7/6cVPs/Quwfw0Fd30KZHS9Jz0mjaoRG3vXUtt4+6vkKv94ZCrM7Po2C/wnhFUbho+KDoqXe3g8seu6DCNlocHFakyqJKEGoWMuUuKBoB+PbZ4wL3+Qhbi6oyzcIiKnpQLzMqogejFx8fLGE9zLQxM/nhnZ/xFvnocnZH+l1/Bhk5R1cU9+ePp7Fre2FEnZDfG2DcyIkMvXsAaTVSE26H3xtg65rcmPtXLVyLlLLCkbPOfTvQue+BjUIK6DqPTv2Fb5YuQVMEIcOgV4OGPH1qHzJd5oPLwJvPYlduAWNf/B7Nbt7iw7rBNc9dQtd+5WavLA4Ry6myqDKUpEuRtlbI4rdBXwVqbUTSleA4papNs7CIICUzmRp1M2PeWOPZaaeHdO4780mWzl5Ropa+9p/1fPPKD7z8x5PUT6DURHXj97EzY85ptNk1/p76Lz0HnZhwOzSbilAExPCdbXYt4anIzMxG4wAAIABJREFU6yeM58+N6wmEwwR22zF17RoGf/U5k4Zdhk1VEUJwxZMXMeTOc1n0+xJUTaXdSceWdNxaJJZDSv8JIYYIIRYLIQwhhOUCWxwwwt4ZJXMUSs6vKFmfIZynWh1LFtUSIQTXPv+fqONtHC47Vzx5YdzW+uGdKSyZtbzU+JmgP4Rnl4enL34lbuscDsTqlANAEFPnLN5oNo3OfTugKJHfT6qm0ntIYmuSlu7YzsxNGwiES3t1IcNgu8fDT6tXltqekpFMt3M70eWsjpZDVYkcak3VP8B5wLQ42GJhYWFRYULBED9/Mo27TnuUW3s+yJgR3+Ep8CR0zR4Du3D3BzdRo24mDpcdu9PGMS3r8uQP99OiU9O4rTP+9YmldIb2IKU57y9RMxGrI6dd3BtnUnSnIBwK0/6UyhM4vfHlK0jOSMZm35vksTlspNVI4cqnhyV07T83bogpPOoJhfh17eqErm9RMQ4p/SelXAJYkQULC4tKJeALcHvvh1m/ZGNJNGfl/NV89cJ3vDrrKbLrJU7aoNfgrvQcdCLb1m1Hs6kJkVEoyo/tHGp2jaK8Ymo2yI77utWRXkNOZOyL37Hu342lVMgdbgeXP3FBpcpH1GyQzTuLXmDMiO/5fexMhCI45YLuDLzl7ITXdTlUFSXGvVYAblv0AeEWlUuldf8JIa4RQswVQszdvv3oecqysLCIP18++y1r/1lfKj0W8AXZlVvAS9e9lfD1hRDUapiTMF2qlp2bxnxYDYfC1G1e+5DXMAwj5nzG6oTNbuOFqY9x3q1nk5qVgmpTadjmGO756L8MuvWcSrcns1YG1zx7CR+veo2PVrzKZY9fWCmF8qc3bhrz9+XUbAxoYannVwfKjVQJIX4GakXZNVxK+W1FF5JSvg28Dab4Z4UttLCwsNiPCW//FDE7DcAIG8z/eRGeQu9hLeNw0fBBzPvpr4gUoM1p45zr++BKij7apyJM/2YW7w3/nA1LN2F32f/f3n2HN1U1YAB/T3a62FD23mWXvQSRKUNkiZMhnwMEF4iIioDiQEURRUUZoigqiCBb9i6y995QRunMzvn+qAIlKV23uUnz/p7ne56vucnNa7Tt23PPPQdt+7fA4Pce9UkxyC5zqAmD3n0Ug97N3Uts/qxIaCiGNmqCaTu2weK8vb6fWadHuwoVUTcy50Wbci7DUiWlbOeLIEREmZUcb0n3mFanQUqCJaBLVdXoimj7SEssnZF2WxGXw4VyNUtn+7xLvlmJL1+ceaus2S12rJq9DrtW7cP0PR8F9GcWDJ5v2ARVCxXGFzu24fTNOBQJCcWg+tHoXSMq29NwLMlW3LgUh/xF8/HfvwK4pAIR+RWX04Utf8bg0NajiCgcgbaPtPCYI1W+Vhkc3uZ9IU6dQYeCkf63xU1WXDlzFavnet7/43a58fnz36Jp1+gsjyzZrXZMf2W2x+iX0+FCXGw8ln67Gr1e6pqj3JT72lWohHYVcn5ThM1iw7Th32PVDxug0WngcrjQrHtDjPhqCMLyhyqQNDjldEmFh4QQ5wE0BbBECLFcmVhEFIxiz13DE5WH4oOnpuKXDxdh1th5eKrKMPz+2ZI0z3vi7T7prhrdd2QPn91mn1tW/bDu1t6CdxMagXW/ZH319kPbjqU7mmG32LF67oYsn5MC19hu72PVD+tht9phTbLCYXNg88LteLHVWLhcyi5kG0xyVKqklAuklKWklEYpZTEpZQelghFR8Hn7oQ9w7fyNW/vrOexO2K0OfPf6Tzh0x8hUww518dynT8EUZkJIhBkhEWYYTHo8+MwD6Deqh1rxFRN3JR5Ou/dNce2W1G1asiyDmayBMGmdlHEk5gQObT3qMS/RYXfiypmr2LF0t0rJAh8v/xGRXzhz8BzOHr4At8tzT0i7xY7fPvkTb8x76dZjnQe3w/2PtsSetQfhtDsR1aIaIgqF+zJyrqnRpApWzFrrsXkzAJjCjKjcoEKWz1mtcaV0R78MZj3a9Gue5XNSYNq1eh8cNu+l3ZJoxfZlu9DkwQY+TpU3cENlIvILl0/FQqf3/neelBLnDl/0eNxoNqJRp3po1r1hnilUANDi4SYwh5pSt0W5g0arQf4i+RDdoU6Wz2k0GzFw4iMel021Oi0iCoajy5AHcpQ5My6fjsWfX63A4ukrEXuWS+uoxWDSp7tSvUYjYPKyawBlDksVEfmFyArF0r3kJTQCZaqru9+dy+mCJcnik8tkBqMen2wYj1JVSsAUakRIhBnGECMq1C6LyWvHQavN3pyxh4Z1xktf/w+R5YpCo9VAZ9ChVe8m+GLHpFydnOx2u/Hps9MxsMYITH95Fr56eRYGVBuOqcNm8LKjClo81CjdY3qTHm36tfBhmryFl/+IyC+UrV4KZWuWxondp+Bypr0EaDDpVbszLeFGIqa/PBtr5m2C2+VCROEIPDa2F7o+0z5Xd5MoUTESMw58guO7TuHy6asoWSkSFWqXzfF52/Zvibb9W8JmsUFn0GW7oGXFb58sxqo5G+C4aw7Psu/XoFTVEugxtFOuZ6DbipYpgp4jumDhZ0thvWOzamOIES0fbozK9bN+eZlSCTX+SoiOjpYxMTE+f18i8m/XLlzHi63eRPy1BFgSrdAb9YAA/vfh4+j+vO9/8dosNgyp8wpiz1yF03H7jihTqBEPDe+MgRP6+zxToJFSonfkYMRfTfB6vFCJAph3/msfpyIpJdb/uhU/vfc7Lp28gsIlC6L3y93Q/qn7oNHwItbdhBA7pZTRGT6PpYqI/InL5cK2Jf/gyPbjiCgUjtZ9m6FwiYKqZFk6YzWmjfg+zXY4/zGY9Pjp/HREFMw7c7lyg81iQ7eIJ7zegPCfZY55PhkxI8quzJYqXv4jIr+i1WrRrFtDNOvWUO0oWDNvk9dCBQA6vQ571hxAy4eb+DhVYDGYDDCY9Ol+jiERZhaqLIi/loCfJi3A3z9uhNPmQL37a+Pxt3rnaKV9Ug7H+ChbnA4nti3Zib++WYX9mw5zsinlSRrtPX5ECgC5OKcqrxBCoPPgdjCY9B7HDCY9uj7TXoVUgSn+WgKeqf8q/pi6DHGXbyIxLhkbft+KYU1Gp1nHjdTDkSrKsoNbjmBst0lw2l1wuVwQGg2Kli6EScvHemwnQhTI7n+0JQ5sOux1lMVpd6H+/VEqpAo8AyY+goNbjuD0wfOwJqWuvWUOM6FC7bJ44u0+KqcLHD+9twA3YxPS3CUr3RLWZBs++d90fL37IxXTEcA5VZRF8dcS8HjFobAkpt3QVqPVoESlSHx38NNcvSOKyJfsNgeGNR6Nc0cuwmG7feeaMcSIx8Y+jH6jHlIxXWBxuVyIWbYb6+ZvgRACrfs0Q3SHOpwUnQV9ig9G3JV4r8f0Rj1mn5iq2vzDvI5zqihXLPvub7icnvtCuV1uXLtwA/s2HELtVjVUSEakvP/Wi5r11s9YNuNvWJKsKFEpEk+O6xtwK5CfP3YJh7YeRWhECKI71IHB5NsFHrVaLRp3aYDGXbhSd3Y50lnHDUj9w/buJSvI91iqKEsObz8Ou8Xu9Zjb5cbp/edYqsgvXTp5BWcPX0DhkgVRoXbZTI+ohoSb8ezHT+HZj5+ClDLgRmKtKTZM6Psxdq3eB61OC6ERkFLi1e+HomXPxmrH8wvJCSlYMXMNtv21C6ERZnQY0NYvR9HqtY3CxgXbIb1sNxQSbkKxckVUSEV3YqmiLClWtgi0Oq3X0SqdTosCkflVSEWUvsS4JIzv8zEObDoMvVEPl9OFIqULY9yCV1G6atZWaVerUDnsDmz+IwZnDpxDweIFcF/fZpleAX3yoGnYtXrfv5vn3h7JeP+Jz1Ci4kRUrFMud0IHiMunYzGsyeuwJFlh+3chzO1Ld6Fu21p4+/dX/OrOxMff6oMdy3Z7zPEzhhgwaNKjflcCgxH/DVCWdBnSDlq99x8yQiPQuEt9HyciSp+UEqM7TsS+DYdgtzqQHJ8Ca7IN549cxIgWY5Fy19xAf3T28AU8WvY5TB48DXPemY/pL89Cv1JDsHnRjgxfG3flJjYt3PFvoUrLYXXg5w/+yI3IAeW9xz5DwrWEW4UKAKzJNuxavQ8rZq5VL5gX5aPK4P2Vb6J8rTLQm/QwhRqRv2g+DPtiMDo82UbteASWKsqi0lVLYtB7/WE0G25tyGn495t73IKRMBg9b5v2lQObU+9KfKzCc3ix5VhsXLCNSz0EuSM7juPMwXMeewpKKWG32rH6h/UqJcsct9uN1zqMx83Ym7Akpt41Z02xwZZix7v9P0XsuWv3fP3ZQxe8LmWQem6JozHHFc8cSK5duI5jO0/C7eVymi3FhgWf/aVCqnur0aQKvt4zGbOPfY7puz/Czxe/ZqHyI7z8R1nW84UuaNCuNhZPX4nLp2NRuUFFdHm6HQoVL6BapiXfrMSXL86E3WKHlMCV01dxfPcptHu8NYZPe1q1XKSuIztOwO3yXqytyTbsXnsAXZ/t4ONUmbf77/1IupkMb38buF1uLJm+EgMmPJLu6/MXywenI/3JzQUj1fue9Qc3ryZAb9SlubPz7uP+qnBJLl/jj1iqKFvK1iiN56cMVDsGgNQ5M9OGz4TdmnYCvTXZhpWz16LjgDao2rCSSulITeEFQqHVawCr5zGNRqBAsXy+D5UFF45fhtvpfXsXh82J0/vP3fP1ZauXQmT5Yjh78JxHMTOFGvHQC52Viprr3G43ju08iZRECyrVK4/wAmE5PmeJipFw2j3nh/4n2OebUdbx8h8FvC2LYtJd+dphdWDFrLW+DUR+o0nX6HT3nNOb9Og4oK2PE2VNZPmi0Oq8z2HUGbQoXb1Ehud4c/7LCCsQBmNI6hIKQqQWqhY9G6NFgNz9t3vNfvQv8wxebTcO4x7+CP1KDsHUF2bA5Uq/EGVGSLgZ7Z9sDaPZc3kJY4gBj47pmaPzU/DhSBUFvJREC9zp/HB1uyUSbyT5OBH5i5BwM1797nl8OPALOGzOWwXLFGpEt+c7olK98ionvLf67WrBFGb0OqFeq9Wiy5AHMjxHmWolMevY51g642/8s2ovIgqGodPg+1G3TVRALA9x5tB5vNF1UpqJ5EDqmnlarQbPfjIgR+d/bsoAJMYlY/MfO6DTayGEgMvlxgvTBiOqRfUcnZuCD1dUp4B37J+TeLHVWNhSPNfPMoWZMPSzgejwFCdyBrNT+8/i18l/4viuUyhatjB6Du+Cem1rqR0rU07tO4OX27wNp90JS5I1deK5EHjlu+fQpm9gLUCaHR88ORWrf9zgdcTRYDLgl8vfIDQiJMfvc/l0LPZvPAxTqBEN2teBOdSU43NS3sEV1SloVK5fAVWiK+LwtmNw2G5PytVoNQiNMOO+vs1UTEf+oHxUGbz6/fNqx8iW8rXK4tONE/DJkK9wZPtxSAk07lQXVRpUUDuaT+zbcCjdS7g6gxan959DzWZVc/w+keWKIrJc0Ryfh4Ib51RRnjDhz9Fo8mA0DCY9QvOFwGA2oGrDSvhs80QYzUa14xFl26VTVzC8+Rgc3HIUDrsTDpsDmxfF4LnoUThz8N4T1fOC0Pzpj0K5XW6EFcjcIqhEvsCRKsoTQsLNeHP+y4iLjcfF45dRqEQB/tVJecI3o35ASnxKmrWU3C43LIkWTBsxE++vGKtiutzX9Zn2+PKlWR5zqgCgUIkCKFMta6viE+UmlirKUwoUzYcCRf37NnmizJJSYsuimDSFKlza0RgXUUomImzVLrheS4a2enWgWzegQN5bd6rDgDb4+6eNOBpz4tb2LDq9DnqTDqPnjgiIyfYUPDhRnYjIT0kp0UHfF9ItUUYmoBNOoQkuQYPUn9tuCJQoXyR1zzeNBnjwQWDwYKBG5jY1tyRZ8PuUJVg2429YU2yo3aomHhv7MMrXKpub/1hZ5nQ48fePG7F4+gokJ1jQ4IHaeHjEgyhWlhsIk29kdqI6SxUhMS4JMcv3wOlwol7bKK7US+RHRrQci4IbV2Eg9kMDiRTo4Ubq6IzBbEC5GqVSn+hyAQkJgFYLvPsu0KvXPc9rSbJgaOPXcfnUlVt7AwqNgMFkwMQlo1Gndc1c/eciCiS8+48yZd77CzFn3C/Q6rSQAFxOF9o/2RovfPE0dzwn8gMvdSgM/ab9sEgtnHfcWyQ0AkVK3fEHkFabevnPbgdGjQIAXKrfEvs2HIIxxIiGHesiJNx86+mLpi3H5dOxaTZblm4JW4oNHw2chtnHp/LSGlEWsVQFsQ2/bcUP43/994fq7R+sq+ZsQJFShfHomIfVC0dEwMGDKDP7C1jKFYftUjzEv0uG6AxaFC1dGKERZs/XGAxwS4m4Ac/ibUcjXNQXhNAAbqcbz00ZgM6D2wEAln23BnaL59puAHDzSjzOHbnISeBEWcShiCA2e9wvXu+osaXYMH/yIricOdsCgohy6NtvAZcL5oIRKFezNMrXKoNytUqjXFQZhOZLf6mBq1cSYU+24AHbMViTrbAkWmGz2DFtxPfYs/YAAMBm8fze/49Gq/H6s4GI7o2lKoidP3op3WMOqxMJ1xN9mIaI0rhxA1i8GIiIuPWQTq+FXq/DvS7Kud1uJFxLRLLUozEuIUzeHo2ypdgxd+KvAIAG7euku2emlBJl/5urlYdJKXF81ynsXLkH1y7eUDsO5QG8/BfEwguEIu5KvNdjUroR4u3SAhH5xp9/pk4+13rfUDk9DpsTQgBuKaCBRBNcwircvpvv1L6zAIBHXnsI637eDEuSNc3rTSFG9B/TEwaT5ybDecnx3afwTq/JiLtyE1qdFg6bA40fbICRM4fCFJJ2wWCX04W4KzdhDjcrsiUO5V0cqQpi3Z7rAIOX3dl1ei2a92jElciJ1HT0aLZeptVp8d9N3QJAKaQdcc5XJHXkq0TFSHy05m2UiyoNg9kAc7gJoflC8PjbfdB3ZI8sv2/C9UT8MH4+nqn3Kp5v9Br+mLYMVj+9hBgXG4+X73sLl05egTXZhuT4FNitDmxbvBPvPvLpredJKTF/8iL0KjYIT1V5Ab2KDsLrnSYi9tw1FdOTP+NIVRDrM7IHYlbsxYndp2FNTv1r1RRqRMHI/Bg2dbDK6YiCXHx86tpTWaTTa2EKNcKSZIWEQOgdN6EYQ4x4aFjnW19XaVAR3+z9GFfOXIUlyYqSlSOhN+iz/J6xZ6/i+YavISXRcutuwjMHz2HRF8vw2ZZ3/W50Z8n0FXDanR6P260O7Fy5BxdPXEaJipH4/o2fsOCzv24tOgoAO1ftxdBGr2HGwU8RXiDMl7EVZbfacXDLUbjdEjWaVvEYnaPsYakKYgajHpPXvo1ti//Bqjnr4LA70apXU7Tu0zTPD/0T+b18+QC3942EMxJZvijOHr4AjdOBZJlakkyhRtRrWwsdB7X1eH5OF9H89NlvkHAjKc3Gx7YUOy6djMXcCb9hyAeP5+j8Svtn9b40S0ncSavX4siOE4goFI7fPlns8Ty3y42UBAuWfrsafV7t7ou4ilvyzUpMf2X2v0tmCLhdLjw5ri96vdRV7WgBj6UqyGm1WjTr3hDNujdUOwoR3alKlWy/VG/QoXxUaaScuwxjySjcV7052j91Hxo8UFvx9ecsSRbsWr0vTaH6j8PmwPLv1/hdqcpfJP2trIQQCCsQigObj0Bv1HktXzaLHet/2xqQpWrzHzvw5YszYUtJu5zGzDfnIaJwONo/cZ86wfIIzqkiIvJHXbumTlJ3ZW9pE42UCCsQhl5rvsaYn0agYYe6ubKgryXJCo0m/fsR754I7w+6DGkHU6j3y10ajQb12kZBp789N80bgzHrl0n9wXdv/OhRqIDUkcVZY3+GGrus5CUsVURE/qhgwdS9/BISsvf6hITUYpbLmyznL5oP5vD07xSuWLdcrr5/dtRvVxstejZOU6w0Wg2MIUa8/uNw6PQ61GpZPd2CYQo1ov1TbXwVVzFSSpw9eCHd49cv3bg1v5ayh6WKiMhfDR6cOlpl977yebrs9tTXDRqUO7nuoNFo8PibvbyO/BhDDBgwvl+uZ8gqIQRGzhyK1+a8gLpto1C2Rim0f+o+TIt5Hw071gMAGEwGPP/ZQBhD0s4vNZj0KFm5ONr2b6FG9BwRQnj886Q5rtFAH6AjcP6Cc6qIiPxVjRqpmyP/u5cfDJm4gcRuB5KTgfffT329D3R7riOSbqbgp3d/h1avBWTq3oRDpw5C/Xa1fZIhq4QQaN6jEZr3aJTuczo82QaFSxTErLd+xsm9ZxASbkbnIQ+g78juAXv57/5HW2H5zDUedz9qdRo079EIOj1rQU4INa6fRkdHy5iYGJ+/LxFRQPr1V+D111PnV0VEeF8Q1OVKveSn1aYWsV69fB7TkmTBoW3HodNrUb1J5Wwtz0C568blOAxv9gbiYm/emltlNBsQVjAM03ZMQsHI3L1cHKiEEDullNEZPY+VlIjI3/XqlTrqNGNG6krrbjcgZeo6Vm43IERqmerRI/WSn49GqO5mDjOj/v21svVam8WGk3vPwmg2oHytMv/e7k9KiT13DV+88B22//UP3G6JkAgTChTLj7ACoWjTtzk6P90OYflD1Y4Z8DhSRUQUSOLiUovV0aOpC4Tmy5e6/IIPJqXnBiklfnz3d8ybtBAarYDb7UZ4/jC88v3z2S5olFb8tQQMjnoJCdcT0yx9YQwxYsgHj6Hbcx1VTBcYMjtSxVJFRESq+fnDPzBn3HzY7trSxhhixKcbx6NS3fIqJcs7Zr71M3754A84bJ5rboWEmzE/dkbAzhHzlcyWKt79R0REPpOckIK4KzchpYTD7sBP7/7uUagAwG6x44fxv6qQMO/Z8OsWr4UKACCAYztP+jZQHsY5VURElOvOHr6AKc9+jYNbjkIIgXyFw9F9WCe409mKR0qJ/RsO+Thl3iTusTgrZOoaXaQMfpJERJSrYs9dwwtNX8fe9QfhtDvhsDlw7cINzHn7l3T34AMAU6jJhynzrvv7t4TB5P3ynkanQZUGFXycKO9iqSIiolz106QFSElIAe6awmu3OiDTGakymPTo/PT9Pkjn3+xWe+oE82xurg0A3Z7rgPxF80GnT7sUhzEkdYFTrc7LEh2ULbz8R0REuervuRvS3UdPo9HCYNTC4XDC5Ujd59BoNqB4xWJ4aHiXDM995uA5rJ67AUnxKah7X000694wTyxgGRcbjy9e+A6bF24HROqE8kde74mew7tkebmJ0HyhmBbzPr4b8xP+/nEDbBY7KtYpi4ET+99aQZ6Uwbv/iIgo17jdbnQ09IN0p/O7RgDvL38DmxbuwPalu2A0G9BpUFt0+V97mEK8b3r8n29GzcHCqcvgcjjhcrphDjchX+EITNk0IaAXsbQkWTA46iVcvxgHl/P2htrGECO6D+2Ipyc9pmK64MTFP4mISHXWZM87+9KQQN22tVC/XZ0snXfr4p1YNG057Jbb+yJaEq2wW+yY+MinmLxmXHbi+oWVs9cj4VpimkIFALYUGxZ89hf6juyOiILhKqWje+GcKiIiyjXGEEO6k6QBwBxuhkaT9V9F8ycv8lrYXE43Dm87hthz17J8TqXtXLkHoztNwIDqwzGu10c4tO1Ypl63/tctsHpZZgIA9AYd9q47qGRMUhBLFRER5RqtVotOg9tBb/S8MKI36NDr5a7ZOu/l07HpHtMb9bh67nq2zquU78b8iLd7foiY5Xtw/shFbFqwHa/e/zaWfLMyw9dqdff+1cwlEPwX/80QESnM7XZj8fSVeKLyUHQJ6Y+nqr6ApTNWQ405rP5g8Hv9Ubl+BZjDUpdIEAIwhRpR+76aeOS1Htk6Z5lqJdM9Zrc5EFm+aLbOq4QzB8/h90+XpBlJk1LClmLHtOHfI+F64j1f3+7x1jCFep9P5nK4UK9tlKJ5STmcU0VEpLDJg77E+vm3L+FcOHYJ04Z/jyMxJzDiyyEqp/M9o9mITzaMx67V+7BxwXZotAKtejVF7VY1sr1xct+RPbBvw2GP1dh1Bh0atKuNQsXVm6i+cvY6OB1Or8c0Wg02/LYVXYY8kO7rW/dpht8+WYyzhy6kWQndGGLEwImPwBxmVjwzKYMjVURECjqx5zTW/bLZY06MNcWGlbPW4uzhCyolU5dGo0GDB+pg+LSnMezzwajTuma2CxUA1G0ThafG94XepIfBbIBGq4Ep1Ijytcpg1OxhCibPuvjriXA5va8r5bA5kRyfcs/XG4x6fLL+HfQc3hnhBcOg1WlRLqo0XpszDD0zscwEqYcjVUREmbRj+W788sEfuHjiMkpUjESfV7t5rPOz4fetcNi9j1K4nG5sWrAdZUY/5Iu4eV6vF7ui7SMtsP7XrbAkWhHVohqiWlTLUVlTQp3WNbHu582wJFk9jumNOlRvUiXDc5jDzBg86TEM5vIJAYWliogoE2aP+wXzP1x0awQq9uw1HN5+DL1f6YYn3upz63lOuwtul/dRCrfLne5lIaUlJ6Tgj6lLsWLWWtitDjTqVB/9XuuByHLqzTXKDQUjC6DH0E5qx0ijVe+mmDF6LmwWe5r/FnQGLUpVKYGoFtVUTEe5iZf/iIgycOnUFfz8/kLPS3rJNvz8/kJcOnXl1mMNO9RNd886Y4gBDdpnbT2m7Ei6mYznGozC3Am/4cKxy7h67jqWffc3htR5BSf3nsn19w92BqMeUzZNQOX65WE0GxCaLwQGkx51WtfE+yvHqj6SRrmHI1VERBlY98vme44+rftlC/qNSr2LrXbrGqhQpyyO/XMSjjs2CzaY9KjasBKqN66c63l//mAhrp6/Boft9qiYy+mCJdGCj5/+ClO3vZfrGYJd0TJFMHXbJJw/dglXz11DyUqRKFqmiNqxKJdxpIqIKAPJCRY4HS6vx5wOF5ITbk88FkJg0vI38MDjrWAwG2AMSf1fhwFtMXHJaJ+MUqyYtTZNobrTyb2nEXflZq5nyKnEuCRcOXPVY1Wxf0cSAAAgAElEQVTxQFOqcnHUa1uLhSpIcKSKiCgDtVtWxx+fL/U68dgcZkKtFtXTPhZqwovTn8HzUwYi/loi8hUOh8Fk8FVc2FLs6R7T6rQZbx2jokunruDjp7/C/o2HodVpoTfo0O+1HujzandeNiO/x5EqIqIMNGhfB0XLFIZOr03zuE6vRdEyhRHdwfs8KYPJgCKlCvm0UAH49w4478cMJgOKli3s0zyZlXA9EcMaj8betQfgtDthS7Eh6WYyfnjnV3z/xk9qxyPKEEsVEVEGNBoNPl73Duq1rQWDSX9r4nHdNlGYvHZctvauy01PvN0HBrNnkTOGGPHkO32h1Wq9vEp9i75cDkuSFW532pXnrSk2/PbJEiTHJ6uUjChzePmPiCgTIgqF492lY3Dt4g3EnrmKomWLoHCJgmrH8qpKg4p454/XMHnQNCRcT4RGq4EQAk+M64Nuz3ZQO166Ni3cDvsdk/vvpDPocHDrMTTsUNfHqYgyj6WKiCgLCpco6Ldl6k7176+FH05Nw9lD52G3OlAuqjT0Br3ase7JYLpXPgmD0b/zE7FUERHlUUIIlK1RWu0YmdbhqbY4ueeM14n0QgjUaJbxSuSUPS6n69aIJmWff00EICKioNXusZYoWbm4x4iVMcSAF6Y97fcjbYFo3S+b8WSVYehk7IcHQx/F5MGpl4wpe4SUMuNnKSw6OlrGxMT4/H2JiAKNzWLDzdgE5CsSAVOIUe04uc6SbMX8jxbhr69XITnRgsr1yuPJd/qiTuuaWTqP3ebAgilLsOjLFUi+mYwKdcrhibd6o26bqFxKHngWfP4XZoz+EbY7dgrQ6bUoXLIQpu/5CCHhZhXT+RchxE4pZXSGz2OpIqJ7iYuNx6o563DxxGWUq1ka9z/aCmH5Q9WOlefZLDZ8+dJMrJy9Hhoh4Ha70apPMwz7fBB/2WXA6XDilTZv4/iuU7BZbq/Z9d+IV/sn7lMvnJ+wptjQu9ggr5dajSEGDHrvUTw0rLMKyfxTZksVL/8RUbq2/BmDxys8h5lj52HxVyvxzai56F/2GezfeEjtaHmalBJjHnwPK2etg91ihzXFBrvVgXU/b8Kr7cbB7fa+ZQ6l2vDbNpzYcyZNoQJSF0X9fOgM2Cz+u/iprxzcfAQarfcKYEux4++5G3ycKG9gqSIir+Ji4zHxkU9gS7Hfus3dlmKDJdGKN7pO4i+mXHR4+3Ec2X7cY3kBh82Jc4cuYPff+1VKFhiWz1wDa7Ln6vcAoNEI7F5zwMeJ/E9GV6lUuIiVJ7BUEZFXK2evhXR7/8nqdrmxacF2HycKHv+s2pvuek2WJCu2L9vl40S+l3A9ESf2nM7WpOk75wh5k95nG0xqNq+W7ibhxhAD2vZv4eNEeQNLFRF5dfH45XR/+dgsdlw+fdXHiYKH3qBL99KMRqvx+bY3vpQcn4xxD3+EfqX+h5dav4l+pf6Htx76AIlxSZk+R9NuDWEM8f4ZOe1ORLWoplTcgGUKMWLAhH4w3nXzg1avRf4i+dBxYFuVkgU2lioi8qpszdIeP3D/YwwxoFSV4j5OFDyaP9QIGo339YL0Rh1a927q40S+IaXEK23HYduSnXDYHEhJsMBhc2D70l14pc3bmZ5L1nnw/TCHmT2KqTHEiI6D2qJA0Xy5ET/g9Bz+IF76+n8oXqEYhEZAb9Sh7SMtMHX7e7wZIptYqojIq3aPtbrHL3Y9mnbL8EYYyoCUEge3HsW2JTtx9fz1W4+XrFQcnZ9uB1No2lJrCjWide9mqFinnI+T+sbuNftx4dglOOzONI877U5cOnkF/6zal6nzhOUPxdRt76HOfTWhN+hgCjXCHG5G75e74vkpA3MjesBq278lZh+fiiUpc7Ek5UeMnDkU+YuwdGYXV1SnHIu/loBNC3fAmmRFzRbVUDW6otqRVLV/02HMHf8rjv1zEmEFwtDtuQ7o+mz7gFu4MLxAGCYueR1vdH0P0i1hTbHBFGqE3qjHh6veCrh/Hn9zaNsxvNP7IyTfTIFGK+CwOdG4SwOMnDUUphAjnvt0AKpEV8S8SQsQe/YaCpUoiD6vdEPHQXn3sszuNQdgSfI+wdySZMXuNfsR3b5Ops5VrGwRfLDyTSRcT0TSzWQULlWI29zcA7+flcF1qihHFn7+F74Z9QM0Wg1cDhc0Oi0qNyiPd5e8DnNY8A0fr5m3CZMHTfNYG6daw0p4f+Wb0Oq0KqbLHmuKDRt/34YrZ66iVJUSaNY9mj+Ac+jq+esYVHMELIlpC4TBpEfDTvXw9m+vqpRMXfPeX4hZb86D0+HyOKbVa/H4m73x6JiHVUhGwY7rVFGu27P2AL4d/SPsVgesyTY47E7YUmw4sv04Pho4Te14Pme3OfDp/6Z7XRvnSMxJbAzQu+VMIUa0e6wVHh3zMFr3bspCpYCFny+F0+b0eNxuTZ0/FHs2OG8CaNWrSboT9LVaDVr3aebjRERZk6NSJYT4UAhxWAixVwixQAiRX6lg5P9+mrTA663LDpsTWxbvRPy1BBVSqWfP2gNAOnuRWpOtWDpjtW8Dkd/au/6gx7yh/+gNOhzdedLHifxDiYqR6Pnig17nknUf2gmlKvPmCPJvOR2pWgkgSkpZG8BRAKNzHokCxZmD59M9pjfocPlUrA/TqM9+1wjV3dJbjJCCT77C4ekflEB4wTDfhfEzgyb2x+gfhqN6kyrIXzQfqjWqhFGzh+Hp9x9TOxpRhnI0UV1KueKOL7cC6JWzOBRIipQqhGt33LF0J6fdiYLFC/g4kbpqNK0Ch5dLOgBgNBvQpEsDHycif9X12Q7Ys/aA133XDGZ90K+j1Kx7QzTr3lDtGERZpuScqoEAlip4PvJzvV7yHKYHUhcnrNa4MoqUKqRCKvUUKJYf7Z9s7bHooNAIGEON6DyknUrJyN806lQPzXo0SvP9o9NrYQo14o2fX4JWG3g3NOQF6+ZvwcAaI9BB3xc9Cj6Jr16ZBUuSRe1YFEAyvPtPCLEKQKSXQ2OklH/8+5wxAKIB9JTpnFAIMQTAEAAoU6ZMgzNnzuQkN/kBKSWmPPcNVs1ZB7vVAemWMIeZEF4wDFM2T0ThEgXVjuhzLpcL3435CYu+WAaNRgOHw4kq9Stg5KyhKFHR27dR7jh35AJ+/uAP7Fl7AGH5Q9H12Q5o/2Rr6PRcRcVfSCmxdfFOLJq2DHFX4lGrRXX0HNEFxSsUUztaUJo/eRFmvfVLmnmieqMepaoUx9TtkxRdjuG/X5NCpDMJk/xOZu/+y/GSCkKIJwE8A+B+KWVKZl7DJRXylqM7T2DVnPVIik9G9AN10OLhJkG/HozNYsOlk7EILxiGQj6+DLp3/UGM6fwu7DbHrb29TCFGVGlYEe+vGMtiRXSXlEQL+kQO9rhzF0idJP/CF0/jgSda5/h9Tu49g29GzsGuv1MXMa3frg6e/uAxlI8qk+NzU+7KbKnK0U9XIURHAKMAtM5soaK8p0qDiqjSILgX/Lyb0WxEuZqlff6+brcb7z06Bda77sq0pthwNOYEVs/dgA5PtfF5LiJ/tvvv/dDqtYCXK33WZBtW/bA+x6XqxJ7TGNFibJobVmKW78K+jYfw2aYJKF+rbI7OT/4hp3OqpgIIB7BSCLFbCPGVApmIKJuO7zqFpHjvf99Yk21YPH2ljxMR+T+X6957CrpcnouRZtVXL830uANYSsCaZMVXr8zO8fnJP+T07r9KSgUhopyzJFqhTWfxRABITqdwEQWzOvfVgDOddcNMoUbc16d5js7vcrmwd/2hdI/v/ns/3G43NJrcXY/7yI7jWDd/M+xWBxp1qo/oDnVy/T2DDSdXEOUhFeqUhcPm8HpMp9ei3v21fJyIyP9FFAzHwy8+iN+n/JVmorpWr0X+Ivlw/2Mtc/YG8vbkdK+Hc3m7OLfbjUmPf47Nf+yA3WqHdEusmLkWJSpFYvLacQiNCMnV9w8mrKhEeUh4gTB0Gnw/jCGeS13ojXr0frmrCqmI/N+ACY/g6fcfRYFi+aDTa6E36tG6dzNM3f4ezKGmHJ1bq9OiRpMq6R6v1aJ6ro4YLfl6FbYs2gFbig3SnVrgLElWnD10AVOHzci19w1G3FCZKI9xOV34+tXZWDx9FXQGHdwuFwpE5sfrc4ejWqPKascj8mtSSqQkpMAYYlT0TtnD24/hlbbjPLb2MoYYMXntOFSNzr2bfZ6sMgwXj1/2esxg0uPXq9/luDjmdT5bUiE7WKqIcl9yfDJOHziP0HwhKFujFNfEIVLZoW3H8OWL3+PIjhMAgGqNKuHZT57K9T92uoY/5nX1fiB1uZVvD3yCYmWL5GqGQOeTJRWIyH+F5gtFzWZV1Y5BRP+q3rgyPtv8LuzW1PWwDCZDBq9QRrGyRdLdq1VKiQLF8vkkRzDgnCoiIiIfMpgMPitUAPDI6J5etxQzmPRoP6CNT7PkdSxVREREeVjb/i3w4DPtYTDpoTfqodVpYQwxolarGnjmoyfUjpencE4VERFRELh8OhabF+6A3eZAgwdqo3L9CmpHChicU0VERES3RJYrip4juqgdI0/j5T8iIiIiBXCkioiCisvpws6Ve3H94g2UrVka1RtX5nITRKQIlioiChoHtx7F2G6T4LQ54XK5IQQQWb4o3lv2BgqXKKh2PCIKcCxVRBQU4q8lYHSHCUhJtKR5/NzhCxjVfjy+3fcxR6yIAsSJPaexbck/EEKgSdcGKB9VRu1IAFiqiChILJ3xN5xOl8fjLqcbsWeu4sCmw4hqUV2FZESUWS6nCxP6fYIdy3bBaXcCEJg74Vc0f6gRRs4aCq1Wq2o+TlQnonuyptiwYtZafPvaD/jzy+VIjEtSO1K2HN5+DHaL3esxt1vi1L6zPk5ERFk1d+Jv2LFsF2wpdricbricLtgsdmxauB2/fbxY7XgcqSKi9B375yRGtnsHTocT1mQbjCEGTH91Dsb+8hIad66vdrwsKVqmMLQ6DVxOt8cxnU6DApH5VUhFRJklpcSCKX/BluL5x5EtxY5fP/4TfV7trkKy2zhSRUReOewOvNZhPJJuJt/ajNWWYoctxYbxfSYj7spNlRNmTZen20Gr9/53pNBo0CjASiJRsLFZ7LAkWdI9HnclHm635x9NvsRSRURebf1zJxx2p9dj0i2xfOZa3wbKobI1SmPA+H4wmg3QaFN/9OmNephCjRi3YCQMRr3KCYnoXoxmA4whnnsY/ieiUDg0GnVrDS//EZFXl0/Fwm5xeD1mtzpw9pD3Xe/9Wa+XuqJB+zpY/NUKXD4diyrRFdFlyANcToEoAAgh0O25jvh9yhKP+ZFGswEPDe+sUrLbWKqIyKvICsVgMOthSfS8Y85g0qNM9VIqpMq58lFlMGzqYLVjEFE2PPF2bxz75yQObDoMa4oNAgLGEAPqto1Cv1E91I7HUkVE3jV5sD70Bj0ssHocExqBDgPaqJCKiIKZ3qDHe0vH4MDmI9iyaAeERoPmPRqhWqNKfrHOHEsVEXmlN+gxafkbGPXAO3A6XLAkWW/NZ3hz/ssoUDSfygmJKBgJIRDVvBqimldTO4oHlioiSlfl+hXw0/npWP/rVpw9dAFFyxRGm37NEZY/VO1oRER+h6WKiO7JaDbigcdbqx2DiHLRpZNXcPl0LIpXKIbIckXVjhOwWKqIiIiC1PVLcRjfZzKO/XMKeoMODpsD1ZtUwZh5L/ISfzZwnSoiIqIg5HK68GLLsTi8LXULp+T4FNitDuzfdBivtHlL9YU0AxFLFRERURDaungnbl6N99i6yeVw4er569i5cq9KyQIXSxUREVEQ2rfxMCyJnkumAIAl0YqDW474OFHgY6kiIiIKQuEFQqHTa70e0xv1CMvHu3yziqWKiIgoCLXt3+LWPph3EwJo3beZjxMFPpYqIiKiIFS8fDE8+sbDaTYpFgIwhhgxYOIj3BMzG7ikAhERUZDq//rDqNmsGn75aBEuHr+EUlVKoM+r3VGrZXW1owUklioiIqIgVue+mqhzX021Y+QJvPxHREREpACWKgpYUkq1IxAREd3CUkUB5cblOHw48At0CX0UHfR98VzDUdi5co/asYiIiFiqKHAkXE/Esw1GYfUPG2C32CHdEsd2nsRbPT7A+l+3qB2PiIiCHEsVBYzfPl2MxBtJcDldaR63Wez4fOgMuFyudF5JRESU+1iqKGCsmbcJDpvD6zFbig2n95/zcSIiIqLbWKooYEj3PSamCwG3izuqExGReliqKGC0fLgxdAbvS6vp9FpUqF3Wx4mIiIhuY6kKcHvWHsA7vSdjWNPX8cXw73Dp5BW1I+WaXi91RUi4CRqNSPO4McSA/330BLQ67xuDEhER+YJQY62f6OhoGRMT4/P3zWu+emUWFn+1EnaLDVKmjtZo9Tq8/furiG5fR+14ueLy6VhMG/49ti/bBUigWNnCGDzpMbR8uIna0YiIKI8SQuyUUkZn+DyWqsB0cOtRjGz3DmwpNo9joflCMP/Kt9Ab9Cok8w2H3QGn3QlzmFntKERElMdltlTx8l+A+uvrlbBb7F6PSbdEzPK8vSCm3qBnoSIiIr/CUhWgrl+KS3ebFrfbjYTriT5OREREFNxYqgJUrVY1YDAbvB6TUqJy/Qo+TkRERBTcWKoCVOfB90On97zbTW/QoXL9ClxegIiIyMdYqgJU/iL58NHfb6NwqUIwh5sQEmGGwaRHzRbVMH7Ra2rHIyIiCjreV1KkgFC5fgX8eOZLHNxyFDcu30T5WmVQqnJxtWMREREFJZaqACeEQM1mVdWOQUREFPR4+Y+IiIhIASxVRERERApgqSIiIiJSAEsVERERkQJYqoiIiIgUwFJFREREpACWKiIi8guJcUk4sPkIzh25oHYUomzhOlVERKQqh92BqcO+w6o566A36uF0OFGsbFG8MW8EytfillsUODhSRUREqvp48FdY/cN62K0OJMenwJZix9lD5/FiqzcRd+Wm2vGIMo2lioiIVHPt4g2sm78FNovd45jD5sCiL5erkIooe1iqiIhINUe2H4fe6H0mit3qQMzyPT5ORJR9LFVERKQac7gZkOkfD8sf6rswRDnEUkVERKqp3ao6NDrvv4pMoUZ0frqdjxMRZR9LFRERqUan1+G1OS/AGGKARiNuPW4KNaLOfTXRvEdDFdMRZQ2XVCAiIlU17lwfn295F/PeX4iDW44iolA4egzthLaPtoBGw7/9KXAIKe9xMTuXREdHy5iYGJ+/LxGREixJFqycvR4xy3cjNH8IOg5oi9qta0AIkfGLiSjgCCF2SimjM3oeR6qIiLLg0qkreKHpGFiTrbAm2yAEsPH3bWjarSFemzOMIytEQYzf/UREWfBu/ylIuJYAa7INACAlYE22YcuiHVj782aV0xGRmliqiIgy6cqZqzi55zTcbs9pE9ZkG37/dIkKqYjIX7BUERFl0o3LN6E36u9xPM6HaYjI37BUERFlUsnKkXDYHF6PCQFUrFPOt4GIyK+wVBERZVJEwXC06t0UBpPnaJXBbMAjox9SIRUR+Qve/UdElAUjvhqCpLhk/LN6HzQaAaHRwO1y4YVpT6NG06pqxyMiFbFUERFlgdFsxPhFr+HckQvYv/EwQsLNaNS5HsxhZrWjEZHKWKqIiLKhdNWSKF21pNoxiMiPcE4VERERkQJYqoiIiIgUkKNSJYQYL4TYK4TYLYRYIYQooVQwIiIiokCS05GqD6WUtaWUdQEsBvCmApmIiIiIAk6OSpWUMuGOL0MBeO7dQERERBQEcnz3nxBiIoAnAMQDaJPjREREREHCmmLD2p83Y9ff+xBRMAwPPNEaVRpUVDsWZZOQ8t6DS0KIVQAivRwaI6X8447njQZgklK+lc55hgAYAgBlypRpcObMmWyHJiIiCnSXT8dieLMxsCRZYUmyQqMR0Jv06DSwLZ6bMhBCCLUj0r+EEDullNEZPi+jUpWFNywLYImUMiqj50ZHR8uYmBhF3peIiCgQDW38Go7tPAm3O+3vYVOoEa//OAJNu2b4O5x8JLOlKqd3/1W+48tuAA7n5HxERETB4OKJyzi1/5xHoQIAa7INv32yWIVUlFM5nVM1SQhRFYAbwBkAz+Q8EhERUd527cIN6A062C12r8evnrvm40SkhByVKinlw0oFISIiChYlKxeH3erwekwIgfK1yvo4ESmBK6oTERH5WKHiBRDdvg70Rs+xDYNZjz4ju6uQinKKpYqIiEgFo+YMQ5XoijCFGqE36mEKM8FgMuD5KQNRo0kVteNRNuR4nSoiIiLKutCIEHy6YQKOxJzAwc1HEJovBM26N0RY/lC1o1E2sVQRERGpqGp0RVSN5oKfeQEv/xEREREpgKWKiIiISAEsVUREREQKYKkiIiIiUgBLFREREZECWKqIiIiIFMBSRURERKQAlioiIiIiBbBUERERESmApYqIiIhIASxVRERERApgqSIiIiJSAEsVERERkQJYqoiIiIgUwFJFREREpACWKiIiIiIFsFQRERERKYClioiIiEgBLFVERERECtCpHYDIX0kpceyfk7h8KhYlKkWiUt3yakciIiI/xlJF5MWlk1fwRtf3EHv2GjRaDdwuN0pUjMT4RaNQtEwRteMRUQBwOV3YungnNi7YBq1Oi9a9m6JB+zrQaHiRKK8SUkqfv2l0dLSMiYnx+fsSZYbD7sDjFZ7Hjcs3Id23vz80Wg2KlCqEWcc+h1anVTEhEfm7lEQLXr7vLZw/dgnWJCsAwBxmQpUGFfDusjdgMOpVTkhZIYTYKaWMzuh5rMtEd9m0YDtSEi1pChUAuF1uJNxIxLa//lEpGREFiq9HzsGZg+dvFSoAsCRZcWj7ccybtEDFZJSbWKqI7nJ4x3FYEq1ej1kSrTi286SPExFRIHE5XVg1ex0cNofHMbvFjj+mLlMhFfkCSxXRXQoUzQe90ft0Q4PZgHyFI3yciIgCiSXJCpfLne7xxLgkH6YhX2KpIrpL2/4tIYTwflBKtO7bzLeBiCighESYYQo1pnu8WJnCPkxDvsRSRXSXIqUK4X+Tn4QxxACNNvVbRKPVwGg24IVpT6NA0XwqJyQif6bRaND75W4whngWK1OIEf3HPKxCKvIFLqlA5EW3ZzugRtMqWDDlL5w7cgFlq5dCzxFdUL5WWbWjEVEA6DuqOy6euIw1P21MHfkWgNst8eAz7dFxYFu141Eu4ZIKREREueTSqSuIWbYbGq0GjbvUR+GShdSORNmQ2SUVOFJFRESUS4qXL4auz3ZQOwb5COdUERERESmApYqIiIhIASxVRERERApgqSIiIiJSAEsVERERkQJYqoiIiIgUwFJFREREpACWKiIiIiIFsFQRERERKYClioiIiEgBLFVERERECmCpIiIiIlIASxURERGRAliqiIiIiBTAUkVERESkAJYqIiIiIgWwVBEREREpgKWKiIiISAEsVUREREQKYKkiIiIiUgBLFREREZECWKqIiIiIFMBSRURERKQAlioiIiIiBbBUERERESmApYqIiIhIASxVRERERApgqSIiIiJSAEsVERERkQJYqoiIiIgUwFJFREREpACWKiIiIiIFsFQRERERKYClioiIiEgBLFVERERECmCpIiIiIlIASxURERGRAliqiIiIiBTAUkVERESkAJYqIiIiIgWwVBEREREpgKWKiIiISAEsVUREREQKYKkiIiIiUgBLFREREZECFClVQohXhBBSCFFYifMRERERBZoclyohRGkADwA4m/M4RERERIFJiZGqTwCMBCAVOBcRERFRQMpRqRJCdANwQUq5JxPPHSKEiBFCxFy9ejUnb0tERETkd3QZPUEIsQpApJdDYwC8DqB9Zt5ISvk1gK8BIDo6mqNaRERElKdkWKqklO28PS6EqAWgPIA9QggAKAXgHyFEIynlZUVTEhEREfm5DEtVeqSU+wAU/e9rIcRpANFSymsK5CIiIiIKKFynioiIiEgB2R6pupuUspxS5yIiIiIKNBypIiIiIlIASxURERGRAliqiIiIiBTAUkVERESkAJYqIiIiIgUodvcfERFRXnN89yls/2sXhBBo2i0a5WqWVjsS+TGWKiIiors4HU5M6PcJYpbvgcPmgBACcyf8iuY9G2PkzOeh1WrVjkh+iJf/iIiI7vLDhF8Rs2w3bCk2uF1uuJwu2Cx2bFqwHQum/KV2PPJTLFVERER3cLvdWPj5Utgsdo9jthQb5n+0SIVUFAhYqoiIiO5gS7HBmmRL9/iNyzchpfRhIgoULFVERER3MIYYYTQb0j2er0gEhBA+TESBgqWKiIjoDhqNBg8+2x4GL8XKaDag54guKqSiQMBSRUREdJcnx/VFVPNqMIUaAQEIIWAKNaL+A7XR99XuascjP8UlFYiIiO5iMOoxafkbOLD5CLYs2gGh0aBlz8ao2rCS2tHIj7FUEREReSGEQFTzaohqXk3tKBQgePmPiIiISAEsVUREREQKYKkiIiIiUgBLFREREZECWKqIiIiIFMBSRURERKQAlioiIiIiBbBUERERESmApYqIiIhIASxVRERERApgqSIiIiJSAEsVERERkQJYqoiIiIgUwFJFREREpACWKiIiIiIFsFQRERERKYClioiIiEgBLFVERERECmCpIiIiIlIASxURERGRAoSU0vdvKsRVAGd8/saBrTCAa2qHCFL87NXDz149/OzVw89ePel99mWllEUyerEqpYqyTggRI6WMVjtHMOJnrx5+9urhZ68efvbqyelnz8t/RERERApgqSIiIiJSAEtV4Pha7QBBjJ+9evjZq4efvXr42asnR58951QRERERKYAjVUREREQKYKkKIEKI8UKIvUKI3UKIFUKIEmpnChZCiA+FEIf//fwXCCHyq50pWAghegshDggh3EII3hHlA0KIjkKII0KI40KI19TOEyyEEN8JIWKFEPvVzhJshBClhRBrhBCH/mgzqPgAAAJrSURBVP15Mzw752GpCiwfSilrSynrAlgM4E21AwWRlQCipJS1ARwFMFrlPMFkP4CeANarHSQYCCG0AL4A0AlADQCPCCFqqJsqaMwE0FHtEEHKCeBlKWV1AE0APJ+d/+5ZqgKIlDLhji9DAXBCnI9IKVdIKZ3/frkVQCk18wQTKeUhKeURtXMEkUYAjkspT0op7QDmAeiucqagIKVcD+CG2jmCkZTykpTyn3//fyKAQwBKZvU8OqWDUe4SQkwE8ASAeABtVI4TrAYC+FntEES5pCSAc3d8fR5AY5WyEPmcEKIcgHoAtmX1tSxVfkYIsQpApJdDY6SUf0gpxwAYI4QYDWAogLd8GjAPy+iz//c5Y5A6TDzXl9nyusx89uQzwstjHBWnoCCECAPwG4ARd10dyhSWKj8jpWyXyaf+CGAJWKoUk9FnL4R4EsCDAO6XXItEUVn4755y33kApe/4uhSAiyplIfIZIYQeqYVqrpTy9+ycg3OqAogQovIdX3YDcFitLMFGCNERwCgA3aSUKWrnIcpFOwBUFkKUF0IYAPQDsEjlTES5SgghAMwAcEhK+XG2z8M/uAOHEOI3AFUBuAGcAfCMlPKCuqmCgxDiOAAjgOv/PrRVSvmMipGChhDiIQCfAygC4CaA3VLKDuqmytuEEJ0BfApAC+A7KeVElSMFBSHETwDuA1AYwBUAb0kpZ6gaKkgIIVoA2ABgH1J/xwLA61LKv7J0HpYqIiIiopzj5T8iIiIiBbBUERERESmApYqIiIhIASxVRERERApgqSIiIiJSAEsVERERkQJYqoiIiIgUwFJFREREpID/A7PiP6vbQTRUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.scatter(FS['Factor1'], FS['Factor2'], c=labels, s=50)\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1], c='red', s=300, alpha=.8);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 2 1 2 2 2 2 2 2 0 2 0 1 2 0 0 2 1 2 1 1 2 2 2 0 2 2 1 1 2 2 0 2 2 0 2 2\n",
      " 0 0 1 0 0 1 1 1 2 0 2 2 2 0 0 2 2 0 2 2 0 0 2 1 1 2 2 0 2 2 2 2 2 2 2 2 0\n",
      " 2 0 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 0 0 0 0 1 0 0 2 2 2 2 1 2 0 0 0 2 2 2\n",
      " 2 1 1 2 2 2 2 1 1 2 1 1 1 2 0 1 1 2 2 2 0 2 2 2 2 2 0 2 2 1 2 1 0 0 0 2 2\n",
      " 0 0 2 2 2 2 0 2 2 2 2 0 0 0 2 2 2 2 2 1 2 1 2 2 2 1 2 0 2 1 2 1 2 1 2 0 0\n",
      " 2 2 2 1 1 1 0 2 2 2 2 1 2 2 2 0 2 2 0 0 2 2 2 2 2 2 0 0 2 1 1 0 2 2 1 1 2\n",
      " 0 0 2 0 2 2 0 2 2 2 0 1 2 0 2 2 2 1 2 1 2 2 0 2 0 2 1 0 1 0 2 2 2 1 2 2 2\n",
      " 0 2 2 2 2 2 0 0 1 0 2 0 2 0 0 2 0 0 1 2 2 2 0 1 2 0 0 2 2 2 2 1 1 1 0 1 2\n",
      " 2 2 1 0 0 0 2 2 1 2 2 2 2 2 1 1 2 1 1 2 0 1 2 0 1 2 1 1 0 2 2 1 2 0 1 2 1\n",
      " 2 0 2 0 2 0 2 2 0 1 2 1 2 2 2 0 2 2 2 2 2 2 1 2 1 2 1 1 2 1 2 2 2 2 0 0 2\n",
      " 2 2 2 0 2 2 1 0 2 1 1 1 2 2 2 2 2 2 1 1 1 0 0 2 2 0 2 2 1 2 2 1 0 2 0 1 0\n",
      " 2 2 2 0 0 2 2 2 2 1 1 2 2 2 2 0 2 2 2 2 2 2 2 2 1 2 2]\n"
     ]
    }
   ],
   "source": [
    "print(labels)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}