diff --git a/Nifty /Nifty Close Price Prediction using Neural Networks/Nifty.csv b/Nifty /Nifty Close Price Prediction using Neural Networks/Nifty.csv
new file mode 100644
index 00000000..453190f7
--- /dev/null
+++ b/Nifty /Nifty Close Price Prediction using Neural Networks/Nifty.csv	
@@ -0,0 +1,3710 @@
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\ No newline at end of file
diff --git a/Nifty /Nifty Close Price Prediction using Neural Networks/Nifty_closeprice_prediction.ipynb b/Nifty /Nifty Close Price Prediction using Neural Networks/Nifty_closeprice_prediction.ipynb
new file mode 100644
index 00000000..dbabb40a
--- /dev/null
+++ b/Nifty /Nifty Close Price Prediction using Neural Networks/Nifty_closeprice_prediction.ipynb	
@@ -0,0 +1,1121 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nifty Close Price Prediction(Using Neural Networks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 406,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 407,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('Nifty.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 408,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\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>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2009-05-12</td>\n",
+       "      <td>3554.649902</td>\n",
+       "      <td>3691.649902</td>\n",
+       "      <td>3534.199951</td>\n",
+       "      <td>3681.100098</td>\n",
+       "      <td>3681.100098</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2009-05-13</td>\n",
+       "      <td>3668.750000</td>\n",
+       "      <td>3709.600098</td>\n",
+       "      <td>3610.199951</td>\n",
+       "      <td>3635.250000</td>\n",
+       "      <td>3635.250000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2009-05-14</td>\n",
+       "      <td>3631.899902</td>\n",
+       "      <td>3631.899902</td>\n",
+       "      <td>3537.600098</td>\n",
+       "      <td>3593.449951</td>\n",
+       "      <td>3593.449951</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2009-05-15</td>\n",
+       "      <td>3597.850098</td>\n",
+       "      <td>3686.250000</td>\n",
+       "      <td>3597.850098</td>\n",
+       "      <td>3671.649902</td>\n",
+       "      <td>3671.649902</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2009-05-18</td>\n",
+       "      <td>3673.149902</td>\n",
+       "      <td>4384.299805</td>\n",
+       "      <td>3673.149902</td>\n",
+       "      <td>4323.149902</td>\n",
+       "      <td>4323.149902</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date         Open         High          Low        Close  \\\n",
+       "0  2009-05-12  3554.649902  3691.649902  3534.199951  3681.100098   \n",
+       "1  2009-05-13  3668.750000  3709.600098  3610.199951  3635.250000   \n",
+       "2  2009-05-14  3631.899902  3631.899902  3537.600098  3593.449951   \n",
+       "3  2009-05-15  3597.850098  3686.250000  3597.850098  3671.649902   \n",
+       "4  2009-05-18  3673.149902  4384.299805  3673.149902  4323.149902   \n",
+       "\n",
+       "     Adj Close  Volume  \n",
+       "0  3681.100098     0.0  \n",
+       "1  3635.250000     0.0  \n",
+       "2  3593.449951     0.0  \n",
+       "3  3671.649902     0.0  \n",
+       "4  4323.149902     0.0  "
+      ]
+     },
+     "execution_count": 408,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 409,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3709, 7)"
+      ]
+     },
+     "execution_count": 409,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 410,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop('Date', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 411,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "      <td>3681.100098</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
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+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3631.899902</td>\n",
+       "      <td>3631.899902</td>\n",
+       "      <td>3537.600098</td>\n",
+       "      <td>3593.449951</td>\n",
+       "      <td>3593.449951</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
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+       "      <td>3671.649902</td>\n",
+       "      <td>3671.649902</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3673.149902</td>\n",
+       "      <td>4384.299805</td>\n",
+       "      <td>3673.149902</td>\n",
+       "      <td>4323.149902</td>\n",
+       "      <td>4323.149902</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3704</th>\n",
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+       "      <td>22442.699219</td>\n",
+       "      <td>22442.699219</td>\n",
+       "      <td>320300.0</td>\n",
+       "    </tr>\n",
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+       "    </tr>\n",
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+       "      <td>22302.500000</td>\n",
+       "      <td>22302.500000</td>\n",
+       "      <td>277400.0</td>\n",
+       "    </tr>\n",
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+       "      <td>331300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3709 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Open          High           Low         Close     Adj Close  \\\n",
+       "0      3554.649902   3691.649902   3534.199951   3681.100098   3681.100098   \n",
+       "1      3668.750000   3709.600098   3610.199951   3635.250000   3635.250000   \n",
+       "2      3631.899902   3631.899902   3537.600098   3593.449951   3593.449951   \n",
+       "3      3597.850098   3686.250000   3597.850098   3671.649902   3671.649902   \n",
+       "4      3673.149902   4384.299805   3673.149902   4323.149902   4323.149902   \n",
+       "...            ...           ...           ...           ...           ...   \n",
+       "3704  22561.599609  22588.800781  22409.449219  22442.699219  22442.699219   \n",
+       "3705  22489.750000  22499.050781  22232.050781  22302.500000  22302.500000   \n",
+       "3706  22231.199219  22368.650391  22185.199219  22302.500000  22302.500000   \n",
+       "3707  22224.800781  22307.750000  21932.400391  21957.500000  21957.500000   \n",
+       "3708  21990.949219  22131.300781  21950.300781  22055.199219  22055.199219   \n",
+       "\n",
+       "        Volume  \n",
+       "0          0.0  \n",
+       "1          0.0  \n",
+       "2          0.0  \n",
+       "3          0.0  \n",
+       "4          0.0  \n",
+       "...        ...  \n",
+       "3704  320300.0  \n",
+       "3705  297800.0  \n",
+       "3706  277400.0  \n",
+       "3707  331300.0  \n",
+       "3708  265800.0  \n",
+       "\n",
+       "[3709 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 411,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 412,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Open         28\n",
+       "High         28\n",
+       "Low          28\n",
+       "Close        28\n",
+       "Adj Close    28\n",
+       "Volume       28\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 412,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 413,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dropna(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 414,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Open         0\n",
+       "High         0\n",
+       "Low          0\n",
+       "Close        0\n",
+       "Adj Close    0\n",
+       "Volume       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 414,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 415,
+   "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>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3554.649902</td>\n",
+       "      <td>3691.649902</td>\n",
+       "      <td>3534.199951</td>\n",
+       "      <td>3681.100098</td>\n",
+       "      <td>3681.100098</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3668.750000</td>\n",
+       "      <td>3709.600098</td>\n",
+       "      <td>3610.199951</td>\n",
+       "      <td>3635.250000</td>\n",
+       "      <td>3635.250000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3631.899902</td>\n",
+       "      <td>3631.899902</td>\n",
+       "      <td>3537.600098</td>\n",
+       "      <td>3593.449951</td>\n",
+       "      <td>3593.449951</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3597.850098</td>\n",
+       "      <td>3686.250000</td>\n",
+       "      <td>3597.850098</td>\n",
+       "      <td>3671.649902</td>\n",
+       "      <td>3671.649902</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3673.149902</td>\n",
+       "      <td>4384.299805</td>\n",
+       "      <td>3673.149902</td>\n",
+       "      <td>4323.149902</td>\n",
+       "      <td>4323.149902</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Open         High          Low        Close    Adj Close  Volume\n",
+       "0  3554.649902  3691.649902  3534.199951  3681.100098  3681.100098     0.0\n",
+       "1  3668.750000  3709.600098  3610.199951  3635.250000  3635.250000     0.0\n",
+       "2  3631.899902  3631.899902  3537.600098  3593.449951  3593.449951     0.0\n",
+       "3  3597.850098  3686.250000  3597.850098  3671.649902  3671.649902     0.0\n",
+       "4  3673.149902  4384.299805  3673.149902  4323.149902  4323.149902     0.0"
+      ]
+     },
+     "execution_count": 415,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 416,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='High', ylabel='Low'>"
+      ]
+     },
+     "execution_count": 416,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.scatterplot(df, x=df['High'], y=df['Low'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 417,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Volume', ylabel='Close'>"
+      ]
+     },
+     "execution_count": 417,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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eXJfCGgUCgeB8IyJNYUq8TsMTE3rz8Nvh6efT0oLg5l622gglXZKj6UK0/F28Drkl3+V2B23jz89M5tM9VXI9VFK0hmf/u49vK+t4sSAHJYpmI1Un6q2BfZoyk1k0LpvH1+7y2n/3ESPzburJovd3eYnBvNM1TZ6deSlxoX28pGjhpRBVvBTWKBAIBOcbIZrCmHD182lNQXBzL9torRqj2T+1I7XkG802Hh+fzZzVZX7+SAtu7snj63az4XR327LC/rKQmbmqlCUFOXSIC21RoVEpmXE6jScVoidERaBWKVDi5t4R3Zh4TToxWjX1Vgc7fqrjWJ2F0b06Mun0/pERKnQaFUnRESwpyAGaIlKf7qkKaqfgGS2MjFAGNc4Ml6ji5egbJhAIBK1FFIK3EW1dCB6utLYg2Gi2MWNVacCXba5BT05aIt8frmPxLb3onKTz2n60rpFDJ83848sDdO8ULxdex0dFEBcZgUoJTrebKpMVhUKBWqng98Vfy8frNCrevOsa/vrR3oCCJN+QzOheHXjk3TKP7/RMvy4TnUbF8VNWXt9UwZheHQE4YrQATUX6gYTQyjsHMfG1rV7Xf7Egx6+mSooWdkyIwmi28f/e+j5gl2CeQc8Tt/b2ey4XiyN1jUEjnx1F95xAILhEEYXggvNGawuCg6UZPY0mzTYnD7+zgz/f0guny40+uun4z384wdodR9hUXsOnp6NJ0CRGlhX25+XPyr1SZCvvHOS1HrPNSdHyb3itcAAK9rGx3Of6eens/NnIssL+WB0u0pOjUSngo13H+PsXB1lSkMOm8hoeHp3Fb//+lRxFClbc7TsSxmxzysXoRbkZ6DRqEnQRJEVraH86AiZZS2w+UOMV7ZI68mzO0OnFC0m4Rj4FAoHgQiFEk6BVnE1BsPSyPWq0cLC6wc9JG5q62X6qbWTLwRp2HK5j4bhsOsZFBozoFOVlBJxBt/lgjV+aq7rexsR/fMWcMd15aHQ3qkxWoCl9Nn3lmevnGfQ8NCqLX7/6FWab06uou87cdE/N1UcFKlr37LRbcecg1u08yo7DdXIqU3qewTryRmSlhLzmhUY4mQsEgl8yontO0CrOtiA4Xqeh0e5k6ortTHljG0s3lHuNQAEwNtrJSU3gy/3VzF1TRmJ04JdzsI644pIKJuU2zX/z2j8tgZS4SCa//g1uYPlpd3Hp+vmGZP40Mkv2eZKiYMUlFQAoFQpeLMihi15H+xDF3aWH6/yuLZFn0BMbqaa4pIIv91fz8Ns7MJptzT5PjVrJgRP1GM22kPsJBAKB4PwjIk2CVnEuBcHNCQStWilHczbur2b26MAeSsEiPlI6bMWdg2T38ZRYLT8cPyVHtWauKuWuoV15aHSWXAuVHKOhwepg4bhsvyhYrkHPloM1fFdZS1FeBh3jIoMWbe89auLPt/Rizrs7vZ5PviGZB0d28zLflFKZoZ5nrkHPB2XHWLqhXDhvCwQCQRggIk2CViHVKPl6SLXECiE5RhM0EiOlwzxTXPXWwB5KobybzDYnZpuTKW9sY+qK7fy++GsykqPp1yVR3v78J/t5bv0PtIttihodNVrQR2tZ9XWlVxTMM+JUUl5DfFQET/13H5NyM/zWlWvQc/vANP764R4W39qLTx+4ln/fPZhlhf3pk5ZAwT++orreO1p0ymIP+jx9o12e0SmBQCAQXBxEpEnQas62IDhep+Hxcdk8unqnV6RGEggrtx7y2j9ao2L68KaZg57puKpT1qDeTbkGPRa7Uy7u1qqVfP3jSa7pmsSjY7rzc20j0JRKu/10/RLAr7qnMHdMdw7XNnoVYnvWXdmdbjbsPcFXB0+ypCCHqcMMGBvtfvuaLA6WFOSgUMCX+0+Qk5rA07f1kWfuSSNipFSm5/OsNdswNtr9rg3CeVsgEAguNkI0Cc6Ksy0INllsPDQqiz9aHF6CY+XWQ0wc1MXLHNLlhq9/PMmsUVlAU5QoQRdBvcXO9OEGXG63tyGlIZnC3HRm+IgNiVHZHTC0i2HOmjI/j6mF47LlCFUwdBqVvA6AOzzsBTyRxI1WraS0starwDvXoOfFghz+9XWlVypTep6llbUh1yCctwUCgeDiIUST4IKi00Rw09ISivIyGN6tHQ6nm8Fdm1JdnnVE04dnsmHfcZ5bv5/n1u8HzgzAnZSb4WdIKaXsVmw9FFAw5Wcm83VFLYvW7qYoL4NJQ9IB6JwYJRtg2hyukBEsT5rrpDM22nnukx8CztxTQNBUpnDeFggEgvBFiCbBBSU5RkP/Loks3VBOcUkFL03sy1Fjo5zC0qqVVJksWOxO/v7FQa9jB3fVs3RDOQWDugRs0ZfMJO1Ot18kaepwg1yI7Xncr7qn8NhNPZn97k6+PVTLiwU5fhEsKX342b4zDt/NzsTTqIL6WZWU12CxBxZdwnlbIBAIwhchmgQXFF+zy2krt1OUl0G6PpoEXQQJURquSolh/vu7vCJGeQa9LFSCCRapO+7Dmfk4XG653kqtVDD6xY0BI1DdOsYx+50dXuNXivIymJLXlWiNCqfLzeaDNXLasHjSAJQKBaWH60KOSFEqFSGfQ7A0W7jPHBQIBIJfMkI0CS440RoVi8Zl02BzYLY5iY+KICVW6yUI/nJrb2obbJgsDuIi1SRGa6i3OABCCpZ+XRKJ1Khkx21oGnsSSDBBk+eTZ+TJMxKl06j4cGY+t1x9BSOyUuSC96UFOdQ02Lgl5wrmv7fLL6r15ITeNNoDX08iVJpNOG8LBAJBeCJEk+CCEmrYb/zpEWtGs416i4NGu5PkGA3RWjX1FgdOt5v8zGSKSyp48fRIE+9CcD2FQ9KZ8+5OFozLlj2NQtUJhapNMtuc1JptXJ2W6LdNEjBLg4gbo9l2Tmk24bwtEAgE4YcQTYILhtFs8xNMcMaDaElBDg02p7yP78Bb6bPb7faa6QaQEqfl0z1VcjG51bFDHh4cqk4oIercCq89hY3JYgfFme9Fmk0gEAguL4RoElwwmhv2W2e2e9kBFOVlyIIJvAfg/nGYgQiVklOWJk8jyfvI83ySp1EoAZMYrQlZm9RcRChU5Eyk2QQCgeDyQogmwQWjuWG/DTaHl/jwrTeCMzVHSzeU8970XH7z95Z5GgUSMGqlggmvbOaJCb0B/DrmFo7LDilwWhI5E2k2gUAguHwQoklwwWjOg6jBp1i7OS+kYMXdEr6pNV8BU1pZS3W9zSvV5+kGXme2caDKjcliJy4qguRo7+Obi5wJ926BQCC4vBCiSXDBaM6DyLe+qDkvJLVScU6pNUnEBfJ8AhjSVc/4lzd7ndNzaG5zkTPh3i0QCASXF2Jgr+CC0dyw35RYrdc2yVogELkGPSXl1UzOzSDPZ5+WFltLIi4QeQY9mw96izHfobnCvVsgEAh+WYhIk+CC0lxxtGfBtmQtoICAA35nriol90o9T07ojcXuanWxdbAC8fzMZAqHpMuGlp54pt2Ee7dAIBD8slC43W73xV7E5YDJZCI+Ph6j0UhcXNzFXs4ljdFsk0VVXFSE7NN0ymInWqtGo1JibLQRrW2bbjTP68VGRuB0uxn/0qagNVOrpw6RvZuO1DUGtRXoeDqNJxAIBILwpTXvbxFpEoQdgTrO2vv9Hkeft+sdqKoPWWTumXYTtgICgUDwy0GIJoHAh9am3YStgEAgEPwyEIXgAoEPzRWsC4EkEAgEv0xEpElwSSDVHQXzTGprRNpNIBAIBL4I0SQIe5obVXK+ONu024UWeAKBQCC4MAjRJAhrQo0qmfX2Dp6+rQ/t4yIv0ur8ORuBJ0SWQCAQXBoI0SQIGzzFQ8xpawHfeXSebNxfzcET9Thd7vMacWopLZ1F58nFiqIJBAKBoPUI0SQICwKJh1yDnnuvvyrkcXaXO6ggkbhQkZzWzqI7G5ElEAgEgovHRe2eW7x4MQMGDCA2NpaUlBTGjx/Pvn37vPaxWCxMmzYNvV5PTEwMEyZM4Pjx4177VFZWMmbMGHQ6HSkpKfzpT3/C4XB47fP555/Tt29ftFotBoOB5cuX+63npZdeIj09ncjISAYNGsTXX3/d5vcs8CeYeNhUXoPDGXpob6IuQhYkgThS18j0VaVc/+wX3PLyZq5/5gtmrCrlSF1jm61forWz6FoisgQCgUAQPlxU0fTFF18wbdo0vvrqK9avX4/dbueGG26goaFB3uf+++/n/fff56233uKLL77gyJEj3HrrrfJ2p9PJmDFjsNlsbN68mTfeeIPly5czb948eZ+KigrGjBnD8OHD+e6777jvvvu48847+fjjj+V9/vWvf/HAAw/w2GOPsX37dvr06cPIkSOpqqq6MA/jF0zVKWtQ8bD5YA35hsDz4XINehzOJkP7QMNxm4vkSDPkAmE02zhQVU9pZS0HTtSH3FeitbPoxMBfgUAguLQIqzEqJ06cICUlhS+++IKhQ4diNBpp164dK1eu5Ne//jUAe/fupXv37mzZsoVrrrmGDz/8kLFjx3LkyBHat28PwCuvvMKsWbM4ceIEGo2GWbNmsW7dOsrKyuRr3X777dTV1fHRRx8BMGjQIAYMGMDSpUsBcLlcpKamMmPGDB5++GG/tVqtVqxWq/zZZDKRmpoqxqi0kiN1jew5amLKG9sCbtdpVLw7dQgL1+5mU4D5c2qFgknLv+Gje/PpGB/p5+x9/bNfBL32pw9cy5UpMQHXdDZ1RkazjRmrSoOaYvqm2852fQKBQCBoO1ozRiWszC2NRiMASUlJAHz77bfY7XZGjBgh75OVlUVaWhpbtmwBYMuWLfTq1UsWTAAjR47EZDKxa9cueR/Pc0j7SOew2Wx8++23XvsolUpGjBgh7+PL4sWLiY+Pl/+kpqae6+1fEgSLwPh+f6SukX3HTXzz40n2HjXxwzETB0/vbzTbOFTdwMGqemb95/uQ1zPbnJxssJGTlsiywv68fEdfiicNoCg3g2iNCqVSwco/DOLnukYefOt7r7Tb2URyziU61VpTTMl5PBBi4K9AIBCEH2FTCO5yubjvvvvIzc0lOzsbgGPHjqHRaEhISPDat3379hw7dkzex1MwSdulbaH2MZlMNDY2Ultbi9PpDLjP3r17A6539uzZPPDAA/JnKdJ0ORMsAvP4+GwWrt3NJ3vOpDLzDHom5WYwc1UpZptTjgz95YM9PDw6i798sIc7BnVhY3kNfdISyTXovSJJEvmGZBKiNAy5Us/XFSfRqBWMyOrAorW72Bgg8jRvTRmP3tidk2YbOk3oX2/fdBm0vpjbl9aYYkoiK9jAX1EELhAIBOFF2IimadOmUVZWRklJycVeSovQarVotdqLvYwLRqgIzCPv7uTqtEQv0VRSXoMbKMrLYOmGclkQ5aQlsuC9XfRLT8LqaCryLi6p4MWCHAAv4ZRvSGbuTT2Y+I+vqK63kZ+ZzCOjs1iwdpefwNpUXoMSBZNy0zlUY2bS8m+Yfp2BPIOekkBiLDOZmEj/X39jY+japZbUGbXGFFM4jwsEAsGlQ1ik56ZPn87atWv57LPP6Ny5s/x9hw4dsNls1NXVee1//PhxOnToIO/j200nfW5un7i4OKKiokhOTkalUgXcRzrHL51QEZiS8hpyUhP8vt/k8730eWN5Ddd3TyEyQgU0peBmrir1SsEtK+zPn0Z144kP93D7wDSgyZfppNkeMCIFsLG8msgIFR0TItFpVBSXVDApN4M8g95rv1yDnsIh6cx5d6dXOu9IXSMWe+huvUDRqXMlXqfhypQYrk5L5MqUGCGYBAKBIEy5qKLJ7XYzffp03n33XTZs2EBGRobX9n79+hEREcGnn34qf7dv3z4qKysZPHgwAIMHD2bnzp1eXW7r168nLi6OHj16yPt4nkPaRzqHRqOhX79+Xvu4XC4+/fRTeZ9fOs3VB0lRo+a+lz6fOGVFgVvujDPbnCzdUM6UN7YxdcV2ijdV8N/dx9mw94SX8DI2hl6HsdHO42v3UJSXIYuxq9MSWTcjTxZjOWmJzFxVyvo9VXKdkhRJ23ywhlwfkSUh6owEAoHgl81FTc9NmzaNlStXsmbNGmJjY+UapPj4eKKiooiPj2fKlCk88MADJCUlERcXx4wZMxg8eDDXXHMNADfccAM9evTgf/7nf/jrX//KsWPHmDNnDtOmTZPTZ/fccw9Lly7loYceoqioiA0bNvDvf/+bdevWyWt54IEHKCwspH///gwcOJDnn3+ehoYGJk+efOEfTBjSXDu9Vh1Yf/t+L31uF6tFpYCpw68E3AHrk2auKgW8hVew63hu31hezaTcdOCMGMszJPPgW99jtjnlfXUaFb1TEzhqtHDK6mBybgY7fqrjzryugE+qUNQZCQQCwS+eiyqa/va3vwEwbNgwr+9ff/11Jk2aBMBzzz2HUqlkwoQJWK1WRo4cycsvvyzvq1KpWLt2LX/84x8ZPHgw0dHRFBYWsnDhQnmfjIwM1q1bx/33388LL7xA586dee211xg5cqS8z29/+1tOnDjBvHnzOHbsGFdffTUfffSRX3H4hSZc5pJJnV6B2unzDHpKD9f5fZ/r8730OdegZ+dPRgZkJDHlja94865rmHTKitXhQqtWUnq4Ti4gB2+hVHq4jvzM5ICpQs/r+Ua4XG43S07XTVkdLqI1auJ1ETz18V6Wbij3OkfvzvEMzEiiKDdDXlNako6OYqyJQCAQ/KIJK5+mS5nW+Dy0lHCbS3akrtGv0yvXoOfx8b1YtHYXG/aekL8P1j335teVPDqmO+VVDXROjGLdzqOolQq++fFkwFqlXIOenLREWdiMyErhgRuuYvEHe9lY7r2OyR7XW1bYX/Z+yjXomTUqi6c+2itHtKZfZ6C0srZF1wThmSQQCASXK615fwvR1Ea0tWgymm1MX1UaMKISyCjxQnHcZOFAVT11jXY5KqTTKEmI0tA+LhKrw0VkhAq3241aqUStUpCgi8DmcGE02zlqbKSq3spz6/cDTQLlzryuuHGzrKTCr3uuMDddFkL5mclMHWZgxqrtPDmhN5ERKowe6yguqZAFmiR68g3JPDiyGy98+oOXqPMUVYHw3H4xn7dAIBAIzi+teX+HjeWAwJtz9Qs6X9RbHEx8bavXdzqNihcLcijeVBHQtfsPr2zzqiVaVthf/v/S/gMzkshJS5RTYvFREcRGqlEpYUlBDh3jo3C53fzm71sw25zMWFXKiwU5rNh6yEdo6Zl/czblJ+pZVtifjvGRuHF7CSYIXrjuu72tPZPOd7o1XNK5AoFAcDkiRFOYEq5zyQKtS+pSK8rLYNaoLBqsTsw2h19tkoSvYNlUXkNRboZXOm1ybgbLSg7yq+4d6NEpjt8Xb2XhuGz5XJ7XlIRWalIUbjf89tUtVNfb5IhTIDuE5grK0/U6Pn3g2jb1TDrf6dZwS+cKBALB5YYQTWFKa4e/XiiCrUvqUpMESqjUVyDBEqNV8/IdfdFFqHC63eg0Kv44zIBGpeSzfVWYbU6/46RrSvznnsH8vvhrrxqqmatK5QJwT6SC9EA1TUMzk7kiIarNI0ChxrOca/rvfJ9fIBAIBEI0hS2hutUupl9QqHXlG5Ll7rV8g97LRkDCt6NOIiZSTVyUGo1axfw1ZV7H5huSeXfqEFQqRdDz5mcmo1Erefq2Pn4deKWH6/yOk1zIFeDlGH6+Rpic73RruKZzBQKB4HJCiKYwJVznkgVbV35mMnPH9uDJD/ew5eBJlk7MAYXC60Xu2VHnSa5Bz4dlTR5dgTraNpZXs3DtbmaP6k5hbgYu/D2UFo7ryZgXS/xSgdAkkFZPzWXh2l2yQDLbnPzr60qenNAbi9113keYnO90a7imcwUCgeByQoimMCZc55IFWpdaqaCyxkzBoC7YHC6mryxl2vArmT26O6csdnQaFTFaNX9et9tL2OQZ9PxpZBYF//iKJQU5Xuk2TzaV1+Bwub3qmACuSIyiY1wkAP27JAaMgOWkJfDd4VoeGpVFUb0Nt9tN58QoOsRFtupZnkuR9flOt4ZrOlcgEAguJ4RoCnNaM/z1QuK7LqPZRvkJFzNXlfLmXddw4rRZZZXJgtPtpsHm5IjRwswRV/HgyCx+rDEToVJQergOp8uF2eZstqPN7nTx9h+HcMriIFqjIl4XQedEnbw9WARs0bhslEBdo40uSbqzEp7nWmR9vtOt4ZrOFQgEgssJIZoEbUK8TkPnxCjMNif/3X2c7ytrg9Y0jenVkSNGixxVyjs9f665jrbYKDU2uxN9tMZP+BjNNhptTu4bkckjY7qjUihQKRXoPaJBaUSf1b21RZH1+U63hms6VyAQCC4nhGgStBkd4iIZmpks1xAtWLsroG+Tb0ebTqMCQne05Wcmy2m46nobB6sbiIuykRytocHmDBoFitdpztm7qK2KrM93ujVc07kCgUBwuSBEk6DN8Ix2VNQ0eJlV+na0Sam4XINePj5YR5tU6G1stLPw/V184mFUufjWXnyw46jXSBU4EwVafGsvHn5np5+g+sstvbA5XRgbmxdSbVlkfb7TreGazhUIBILLASGaBG2CFM2pt9pZND6bRpuTu//326D7a9VKcg16pg/P5LN9VUBTR9uqrYdYfEsvzHYnP9U2Ak0RqDEvltA3LYFJuRlsPnhSLiZPidX6CSaJL/dXc6jGHDit9s4OrvaYLxeqPkkUWQsEAoEAhGgStAGBiqQX39qL/MzkgGmtfIOedrFaxvTqSIxWRZ8rEnj5jr5yNOpEvZVn1v/gl6YrKa/BDRTlZchip7ni8brGwFGgkvIaJp/uwIPQ9UmiyFogEAgEIEST4BwJViS9aO1uiicNAPDalm9I5rGbe/LTSTNHjBYeX7fHz1spz5AcsK4JzoxckWiueDzUdl/BFaw+SRRZCwQCgQCEaBKcI8GKpM02J0XLv+E/9wxm9mgFDVYHNqeLLQdr+OmkmUnLvwl6TqkwPBieYqe54vFA7uMSgQRVsPokUWTdcsTQYIFAcLkiRJPgnAhVJG22Ofmxxsyqryt59MbujHphIwDTrzMEFzqn7QdC4Sl2iksqWFbYHyV4j17JTGbxLb1Y8P6ugOcINs4lVH1SWxVZX86iQgwNFggElzNCNAnOCunF35L02Mb91V61RVKXHHiPQ8kz6HlwZDc+21cVVFTl+YidnLQELHYX/dKTmHS6U69zYhTxkRF0TtKxYFw2Vod3Wi3YOJehmcmolQpKK2vPm5i5nEWFGBosEAgud4RoErQazxd/qKiRZzTHM+Vmtjm9xqFYHS66JOlwut0ULf8Gs83JiwU5ftGj3NMjV2obbCwr7E+CLoLP9p1g2srtXnVRK+8cRJekJqfwQGm1yAgl89/b5XVMfmYyU4cbGP1iUzSsKC+DIV31aNRKEqM1bSKg2lJUhGO0SgwNFggElztCNAlahe+L/82vK1lx5zU8vna3V+u/p5Gl53ebPAbmSh1w+ZnJTBt+JUXLt8lCZuXWQywan02DzckxowVoql8q+MdX9OuSyPThBv5n2dcBisj1XJEYRU2DDTiTUvN9WT99Wx9ZSEVr1Ww7VEvR6TqrFwtyeH1ThdccvLaIBrWVqAjXaJUYGiwQCC53hGgStArfF//tA9N48qM9TMpN555hV2JstPsZWeYa9Hy2r0pu8fd3CU9na8VJ2SW8Y3wU3x+u5devbOF/BndheLcUoKmrbkRWe4yNNsw2JzlpCV7nyjck8+DIbkz422aq621BhYRvlEatULBo7W4AlhTksHxThV/krC1STG0hKsI5BSb8rAQCweWOEE2CVuH74s9JTWDphnK+OniSFwtyWLH1UNDRKdCU9vpDfleitWpsjqZuuukrm8RVnkHPovHZHDnZSPdO8WR1iOW59ft5bv3+oOeaNsyARq0kWqtmw97jFPzjKzn69OX+ama9vYPHx2eTENX0wj5msvBTbSMKhYLtlbUUl1TQr0siL9/Rl+QYDQoUAWfmSec7lxRTa0RFsPRbOKfAhJ+VQCC43BGiSdAqfF/8Uvu/VKd019CuPDw6iyqTlcRoDfuOnZIjToCc8hqd3Z6pwwyM6dWR3CuTiYlUUWWyctsrW6iut6HTqCjKy2DOmB4cMTbSLlbLzp+MXuf6rrKWX3VvzzGThec/+YEHR2bx0mcHvNa3cX81B0408PqmCqYNu5KiN5pSgDqNijljuvOvu6/hp9pGtGol9VYHtebWRYNaU1vUUlERKv0Wzikw4WclEAgud4RoErQK3xe/Z/ec2ebE4XLzxId7qawx879TBtI1OZp+aYl+9U4T+qWyYV8Vg9L1FPzjK7/rSDVP+YZkOifoeOKjPfToFM+SghysDhfxURHERqopWv4NyycPZOKgLjTaHCwpyGGGh7CCpiL0bw/VsuSzcpYU5KBRKumUFMX8NWU88m6ZvF++IZmHRncLef+e0aDW1ha1RFQ0l36bO7ZHi9d3MRB+VgKB4HJGiCZBq/B98XuaS+o0Km7o0Z6c1ATS9dE8urqMgRlJjO7VgUm56U1dcnodH5YdY+aqUpYU5BAdGdrIUqdV8eGuo4zo3p72cZHy8N8tB2soLqmgb1oCx00Wpq7YDjQJnxcLcrwiUk6XWx69Mm24gRitmnlryvzqljaWV3PjTx2Djn/xjAadbW1Rc6KiufSbRqUM+xSYGBosEAguV4RoErQazxd/g9XOr/t25vF1u7l9YBpPfbSXjeU1vD8jVx55MuWNbUBTxGfN9Fy+P1yH2ebE6nBRZbKSZ9BTEsSTqcHq4O9fHOTFghyKfQq08zOTKRySjkqhkL/bWF6NizMiKdegZ8vBGnJSEwBwuNycsjiCjmlZtG43q6fmsmDtLr9reaaYzqW2KJSoaC79Zmy0iRSYQCAQXCSEaLrMuFD+Pb4v/gU39+Tht3fIRdT1lqYoj1TzpNOoeLEghyc/2MPk3HSgKbU36+0drLjzGhat3eUlnPIMeh4f34vKGjM5aQlevk4AKXFaFG4Fv3l1C0/f1sdrbZJY8ywcl/ZJ1EVw+GRj0Psy25xU1DSQk5YoX6tTQhSxkWo6eqTczldtUXPF4tHaCJECEwgEgouEEE1hTmtE0MX077E6XPRJS5RduRN0EUy/zkCsRs306wwMu6odxkY7E69JZ+fPdfxpZDdiNGq6d4zjjte+4skJvZk1Oot6i5OYSBUNVidK3Pzzqx9lASMJsNLDdew+YqRHp3jMNmdAV/JorZqctEQ5TadVK8k16HE43c26mKuVCi+Ppo/uzeeJD/bw51t6yc9eEjdSwXpOagJWh4vICBXbK2uJiTy7/7RaWiwuUmACgUBw4RGiKYxpjQi62P49bqC0stZLbFyf1Y5bcq7gu43e3+ca9FyXlcJvXt3CskkDePrjfXIKD5rqkube1IMfTjTw0MgsFq3b7Xe8FEHKDzJDLkKloLikQvaJqjpl5bGbelLXYCMlNpJ8gz6gtUCgmXQ/1zUyvm9nahrOpNySYzT8qnsKvx2Y5meEmWfQc2N2B47WNXpFp1qC6EATCASC8EXhdrvdF3sRlwMmk4n4+HiMRiNxcXHnfD6j2cb0VaVBC5J9RdDBE/W8U/qzX8RDEg6fPnAtV6bEnPO6gq515XY/ETL9OgOllbUB64f+cks2H+48yreVdSwpyCEyQuVljCmt+7qsdjx6Y3csdhfHTGecwYtLKuiXlsjkvHTZ50ki16BnbO9OdO8Yxz++PMCDI7vhdsOJU1aitWqqT1nRaVW8+sVBeqXGc3239iiVUG91EqNV4XLDp3uP8/cvDmK2OVlW2J/iTRU8NrYnKqUCk8VOQlQEaqWCh9/dGXSEzNjenbgxu8NZCR0pwtja9Fs4jlcRCASCcKY1728RaQpTWltoHCjSk2vQy51k59O/p7reFjBqIxlfBuLxdXtYPTWXRWt3M2NVaVBjzImDuvDbV79iWWF/OsZH4gaujWzH8G7t0KhUvPDJPj/BJEWh+qYlsPiWXvzlwz18WHZc3ifPoOexm3vy4KirMJodPPnRHr8Zd9OHG+h9RQL/+9WPlB6uo7SyDrVKQb3VgdXhosHmxOlyBy0o31Rew/0jrvKKTrWGs0m/het4FYFAILhcEKIpTGlNobHRbGN+gBZ66XNRXsZ59e8JtlapBskTzxqgWrONBeN6UlHdQGykmlmjsrA5XJyot6JReY9iqa63MeWNbacLxLNZ/d0Rru6cwKwbu1MwqItsReB5TEl5DZW1jVyZEgucEU0l5TVsqziJWqngve+P+Ak+6bmN7dWR+0ZcRdHyb3ixIIfH1pR57bussH/I52JstJMYHVj4eEaE4qMiiNaqqbc4MFnsxGjVaNRKbE4XTpcbs9VBvC700OCLnZ4VCASCXwJCNIUprRm5ESzSA00CYNoww3n17wm2VqngWhJK/dMS6ZgQyeNrm2qUdBoVxYUDmPLGtpCpPM86o5LyGhqsTrb9eJLnP9nPy3f0lT2aAmFstMt2A550jI9CH6vhT2/vDHic1IFnd7oonjRAtlJoLfPf28VSH8HiGRGSugpfD2Cn4OlgDqGjRuE8XkUgEAguF0K3EZ1nvvzyS2666SY6deqEQqFg9erVXtsVCkXAP0899ZS8T3p6ut/2J554wus8O3bsID8/n8jISFJTU/nrX//qt5a33nqLrKwsIiMj6dWrFx988MF5ueeWInVRBcLXxLC5qJQ2QnleX5jB1lp6uI7rs9rxYkEOpZW1bKusZeHa3bL4KMrLwHm6pK64pILJp20CPMk3JDM5N4Pikgr5u2Mmi2xP0FwnnFatDBjxitdFUGWyhjzW6nBRZ7Zz4pQ1oGCSjD0DIQm9jacFi8Rxk4VZ//leFjhFeRl+ggmaxr8s+aycorwM+TspamQ02/AlnMerCAQCweXCRRVNDQ0N9OnTh5deeing9qNHj3r9KS4uRqFQMGHCBK/9Fi5c6LXfjBkz5G0mk4kbbriBLl268O233/LUU08xf/58Xn31VXmfzZs3U1BQwJQpUygtLWX8+PGMHz+esrIyLhZSF5WvGAnURdVcVCoh6vxGGIKtdd9RE4/d1FMWBX3TEr3EQU5qAlsO1pBr0Muz63LSEllW2J+X7+jLijsHMSk33cvd25eWCJdAwkqtUgQ4whvpuECiC84IvXyD931LdVWS0JMEy5G6Rg5U1XsJsJzUhJB1Ub5Rsi99RJhEayKTAoFAIDg7Lmp6bvTo0YwePTro9g4dOnh9XrNmDcOHD6dr165e38fGxvrtK7FixQpsNhvFxcVoNBp69uzJd999x7PPPstdd90FwAsvvMCoUaP405/+BMCiRYtYv349S5cu5ZVXXjmXWzwnWmpiGA7T5YOt9ee6RnnEii7Ce2SK1eGiuKSCFwtygCaRIBWO5xn0TDpd0O0pmHwtBnyPl5CEy6qthwKu19TooPRwXUjrgeMmC0eMloDpPTgzpPi96bkcqjEHrKuCJsEi1RwVDEzzewahCLQ9UNQoHH4HBAKB4HLnokaaWsPx48dZt24dU6ZM8dv2xBNPoNfrycnJ4amnnsLhcMjbtmzZwtChQ9Fozrw0Ro4cyb59+6itrZX3GTFihNc5R44cyZYtW4Kux2q1YjKZvP6cD+J1Gq5MieHqtESuTIkJmGZrTVTqfGA02zhQVc/B6gZQQEZytLzWhtPCwTMVJ6FVKwNGmIonDeChUVm8ubXSrzNu7k09vVJ1vse/edc1LCvsT05aIm9uPcSjY3qQlhiFTnNGsOUZ9OhjNE2RorzAkaIZ12XS64p4BnVJIjVJR36A9KNOo2Lu2B44XU33FRmh8rJLgDOCRao58o16tSS96EugqNHF/h0QCASCXwKXTCH4G2+8QWxsLLfeeqvX9zNnzqRv374kJSWxefNmZs+ezdGjR3n22WcBOHbsGBkZGV7HtG/fXt6WmJjIsWPH5O889zl27FjQ9SxevJgFCxa0xa21CRdrtEawNvcnJ/RGp1ERq236FfNMxUkRIc9hv77WBNdntePhG7szOS/Dy7/p+8pa+nVJ9Lqe2eZk6YZy8jOTWXBTTyqsDjk6NHZJCf3SElg9NReTxY5Oq+ZoXSMKoG9aAtNXlnLX0K7cOyITh8uNTqNCrVQQp1Xz5w/3MKFfKk98uIfCIem43GcsBnQaFcWTBvDShnJmv3OmmNzT5qF/l0QWjsvmx5oGbE633z0H+uxJIKPN/BBRIzFeRSAQCM4vl4xoKi4u5o477iAyMtLr+wceeED+/71790aj0XD33XezePFitFrteVvP7Nmzva5tMplITU09b9drCRd6tIbRbGPWf3awsdw7JbTtUC2HTpp5acN++qQlkmvQB0zFSZ8V4DV3LtegZ9bo7jz7331cmRIrG3YO7qqnfZyWdnGRuN1ur2PyDck8Pj6bhe/v4tO9J7zWs7G8hoVrdzH9OgMf7DzK0g3l/O/kgcwd25NFa3fx/Cf7ef6T/UBTFGreTT05YmyyKpDqsb46eFKefWd1uEjX61j8wV6/e99UXoNSoWDdjDy+qjjJjS9ulA0ywT+dGOwZ5BuSmTrcwJQ3vvH6brHHKJdAiPEqAoFAcP64JETTxo0b2bdvH//617+a3XfQoEE4HA5+/PFHunXrRocOHTh+/LjXPtJnqQ4q2D7B6qQAtFrteRVlEuHs8HzMZPETDdCUiluyYT+bymv4trKOFwty0EWo5FSaJD50GjVWu5PJuRkU5XXF6nCSEqslPiqCo7UW/jjMIPs2xUdFEBup5vZXv8Jsc1KUl8Gs0VkcPtmIVq0kQx/NcZPFTzBJlJTX8PDo7meKs20O7g8w867KZGXiP75i4bhsL3NOKZolsaywf8B7h6bOt3qbg/ZxkRTlNRWEe0aUPJ+B1eEiRqPir7/uQ4PVQeVJMwA7fzby9Y81LCnIwepwER8VQRe9js6JunP6mQkEAoHg7LkkRNOyZcvo168fffr0aXbf7777DqVSSUpKCgCDBw/m0UcfxW63ExHRVAuyfv16unXrRmJiorzPp59+yn333SefZ/369QwePLjtb6YVhKPDsyTinG43P9U2BtzHV2zMXFXK0ok55Gcms3F/NcUlFRTlZTCyZ3sK/vGV3/GSr9OYXh35qbZJFG05WONVK7R0Qzk9OsYxdcV28jOTmTUyC7sz9ESgw7VmzDYnOo2KzolRPDmhN1aHixOnbPLIGWgSfalJUdRbnBRPGuA1jkZaW7tYLS/f0ddvXI18rZONTF2xXU7XPfz2Dp6Y0BvwLniXUpnSjLpordpv7lx+ZlOESQgmgUAguLhcVNFUX19PefmZf71XVFTw3XffkZSURFpaU5eRyWTirbfe4plnnvE7fsuWLWzdupXhw4cTGxvLli1buP/++/nd734nC6KJEyeyYMECpkyZwqxZsygrK+OFF17gueeek89z7733cu211/LMM88wZswY3nzzTbZt2+ZlS3ChCUeHZ08RF8oNO1DH186fjSwc15M/r93N7YO68PqmJoESqJ7HbHNSWlnLDT3ahzSujIxQsfjWXlydmtDkvq2LYPp1Bj8BI6FRKWUzSV+zylyDnqUTc1Cg4LWSgwHH0UjCx3dAr2cdk3RdqYBburfbB6bJEaZZo7L4qbaRhNPRI8+hvqIuSSAQCMKXiyqatm3bxvDhw+XPUo1QYWEhy5cvB+DNN9/E7XZTUFDgd7xWq+XNN99k/vz5WK1WMjIyuP/++71qjeLj4/nvf//LtGnT6NevH8nJycybN0+2GwAYMmQIK1euZM6cOTzyyCNkZmayevVqsrOzz9OdN0+4OTz7ijirw8Xuo6aAosez48vT8RrgzqFdee3Lg+SkJdI/LZGbenfk8bV7vFJdkl3AZ/uqghZJ52cmc2W7aF4vOehViJ0XQMBI5yw9XBfUTFKqRbq5d8eg42ienNCb4iDHQlOEaumGcr8CbsldXErx5aQmyGJQGr4MTT9zY6MNnVaNUqEgLioCfRilYwUCgeCXzkUVTcOGDcPtDp1Sueuuu7wEjid9+/blq6/80zu+9O7dm40bN4bc57bbbuO2225r9lwXinBzePYVcVq1MqhH0nGTRU7FeYqU0so6RnRPkSNN0iiVorwMpg6/kgiVkmitGqvDidHcZBsx5bQjtteIEYOe2aOzWPT+Lj+PpRIfAQNNAmvBzT2prDGjj9UEHSK8cX81D43qhk6j8otUbSqv4eHRWSGNKItOO5pLA4M9kaJvvoJq26Faas125q4p83q+0nkWf7CHBeOyxcBdgUAgCAMuiZqmXyLh5vDsK+JKD9eRk5bgV9SsVSupqbfy4MhuuNxuv/omu9PtFekx25wUl1RwdWoCr28q9zOo7H1FAgPSkyjKzSBGq6be6qBdrJYqk5VPQhR9zxqdRc9OcbSL0RKvi6D8RD36aC2NttBmklUmq5fg8sRsDexKLhGtVZOTlhjQwVyrVgYUVEV5GcxdvTPo0OCctEQxcFcgEAjCBCGawpRwc3j2FXFnokze9T2Sk/eU5d9w+8A0YrTev2IqpcIvWhMqZQZNwmHKG9t4f0YuL27YT05aIj06xoVc74lTViJUSp775Acvb6WVd17T7L0GcwBvTqi6XO6AYis/M5l2sdqAgspTVPoiRa+WbigXA3cFAoEgDLhkHMF/aYSbw7PvUF5PJ+6Vdw7inT8O4YOZeUzOzUCtUMgt+yql94y32gb/tGJL5q/lG/S43cgz3Zpz0m4Xq+UNDyEm1VbtPmokv5lZdYEK2XMNeiLUiqBz7vIzk+mi1wX8eS2+pRdLPt3P0g3lQWfoBUNaixi4KxAIBBcfEWkKY8Kpk0oScZ7t8Gabkx2H67hjYBodE6I4UFXPlDe2AWdsA4Zd1Y5lhf1RKBRsr6zFFaCGrbn5awBzb+pJvcUuR2pCOWnnG/RERai8Ul5SNKu0so7VU3NZsHZXwFl1M1eV8o/fe3cGStsO1ZiZnOtfY5Vr0DP/5p5ckagL+vNaMC4bq8PbSmBoZjKdE0PXKkniUAzcFQgEgouPEE1hTjg5PDcn4qRo1O6jJoonDeCUxUHVKavsZXSg6hTjru5EviHZq1uuuaiRPlrLd5W1VNY2ypGa4pIKlhX2Rwle4ig/M5mpwwwcONHgdQ7PNNiHu44ypldHrzosachuTloCFruTFXcOwul2Y3e45G1LCnKYEaCGq/RwHQ5nk/AL9vMK9uyAoGlYKfIlBu4KBAJBeCBEk6BVhBJx8ToNT07oTa3Zxp8/2OMXjZk3tifP/ncfhbnpgFsWO6WH68gz6L3GiEjkGfRoI5R00Ucz//3d8vd90xKw2F30S09i0unozxUJUez8qY4pb3wjt/FLeEaz/v7FQV4syPGzD5AiSiu2HmLioC5e9Uf5Bj1VJoufM7h03Lg+nc762f3lll7MfndnwO651dt/YvGtvaiut3GwuuGiucKHszO9QCAQXCgU7uZ6/gUtwmQyER8fj9FoJC4udJHy5YzRbGP6yu1+3WDQJICuTkukuKSCu4Z25fruKVSZrKgUChKiNTz18V5va4HMZBbe3BO7y4VSqaDB4qS63opCocDtdqNSKDDbnWjVSqpOWRmQkcjNSzZhtjmZfp2B0spa+XzLCvvLqUM4kz6U5tplJEejVIBCAd8eqmPR2t2yYMo16JmSl4ECBctKDvrNyZucm0HX5Gi6totp9fM6UtfIvDVlZHWMk9eSEBXBFYlRuF1u1Cqln6C60K7w4ehMLxAIBG1Fa97fQjS1EUI0NXGgqp7rn/0i6Pb/3DMYY6Mdq8NFtEaNw+VCoVAQF6lCp1Fjtjk5UW9Fo2pKexWXVJCTlsDYXh3p1TkejVrFgvcD1yO9ubWS2welyS39kqnmpvIaPxHlSa5BT05aIuP6dCKzfSyHqhsoP1HvlX7zHLEysmd7eeZd6eE69h018fRtfbwiLy2JzBjNNqavKg1oYjo0M5mnbuvDg299H3T7hbAhaG6NwgpBIBBc6rTm/S3Sc4I2pTlTTmOj3SviIwkegBtfLAl4jNR6b2x0EKN1U5SbwdRhBoyNdq96JLPNidXhYsWdg6g129n5c53s8eRwuZmQcwXz1pT5jU+RCsBzUhOI1qo5abZ5rdETz5l3ELibsaWRmeZc32sbLr4rfLg50wsEAsHFRIgmQZvSnCmnL1LkZ+HNoUfWWB2uptSVLoKUOC03LdkUcL+N5dVMyk1nyhvbZEE043SB9+zRWfRJS2RSgAJwKRX38Ns7mDu2R8i1dE2OZvXUIQG7GYPNDNx2qJYvfjhB/y6J1FsdxEVFYGy0hbyOyeIIuf1C2BCEmzO9QCAQXEyEaBK0KaFMOX1HiEhsKq9p1nYgMkKF2+2mzmzH2Bj6RW073ckmCbK5Y7rTLi6ST/dWUVpZG9BMUlrbl/ur0aiUIY1FO8ZHykLJaLZxoKpeTsO5XG4/weQ5f89zTt7KOweFvI+4yND/eV4IG4Jwc6YXCASCi4kwtxS0KcFMOfMNybIxZSBqGqxBjSPzDHp0GqVsPBkZoQq5hnR9tNyiv6m8hqvTElm59RB//+Igk0/Ph/NEikhJazM22lpkLHqkrpHpq0q5/tkvuOXlzVz/zBdUnjR7HaPTqFhSkONltCmx+WANeUHueWhmMonRGr81eG6/EDYEvqamF2MNAoFAEC6ISJOgzZE8iapOWTE22onSqNCqVdy8tCSoI7bd6Q5oHJlvSGbO2O64gbTEKPYeM5JnaBfUoiDfkEykWsm/7rqGQycbsdiduNxu7hrald9fk47V6WLWqCxcLrA4nJhtTrZX1nql6KK1Ec16UgVLw3kiRZgifYw2JaRRNAqFwq/+6ckJvWkfF+lnKOq5/ULUEgUyNb3QaxAIBIJwQYgmwXmhweZk4drdshiYfp2BvmkJAYVOrkHP9spaiksqKMrLYNaoLCx2F3aniy0Ha7jl5c2YbU7yM5OZf3NPnv14L5NyM3DjLbDyDHoeHNmNqlMWlm4o9za9NOiZO7YnR+qaut62V3rbCkh4Rk9CeVIFK5D2dCqXXMjvGNQl4DmkUTRrpuWiVCgCirNwcIUPhzUIBAJBOCBEk6DNCRSFefPrSpYVDkCh2BfQxFGK9CzdUC4PzPXtYNu4v5r5a3bRJy2BmUGcuZ0uFy9uKPdLhW0sr2HR2t1Myk3HaLadHqDrPfOuNdGTYAXSUvRIyRkX8qLTEbRAmG1OlAoFV6YE93gKB1f4cFiDQCAQXGyEaBK0Ob5RGJ1GxV9/3Zsln/5An9QE7r0+Uy7m9u1ekwqyJeHki9QdF8iZGyDPkBx0+K90bHWDnQff+t5LdCVERXBlSgzt4yJbdI/BCqSl6NGbd12D2eqU7zHonLxzqAsSLt0CgUBwYRGiSdDm+EZhivKaiqxLymv4ZO8JiksqWPmHa3j6470B3bXf/LqSfmkJ6DSqgDVQoTrt4iLVQY+TjtWqlQFF16cPXEv7FvqShuoSzElL4L+7j8vCT4o+gX+91qJx2WcldIRLt0AgEFx4RPecoM3xjcLkpHrXMpltTu584xseGpXFijsH8fIdfVlW2J+c011uU/K6otOoeGliX3Qa/065+KjAUZ5cg57vDxt5sSAn4HHSsYFsD6B1nkPBugQ9O/GqTlnJz0yWo085aYksK+zPy3f0ZcWdg5h2nYFEXetb9oMVoX+5v5qH396B0Rza/0kgEAgEZ4eINIU5l2IKxjcKEygyVF1v4/ZXv6IoL4MberTnp9pGj1qmb8hJS2BMr44U5WV4RYRyDXpiI9V+6S7P2qh+aYn8667BfLz7GMUlFXLUKe/0scFsD1rrOSQVSB8zWfipthE4k27s3yWR4Ve149qr2skCR7qPXIOeGddlkp6kO6ufZWtdui/F3yGBQCAIR4RoCmMu1RSMb5u6Vh04oOlZ+C2NJZGQRqd41hhJwqho+TfcPjCNqcMMON1u7A6XV23UxvJqJp2y8H1lHSv/cA13vvEN3TvGMW9sD55dvy9g6m5oZjIRSgU/HD+Fy+3GbHUQr9OQHN0kLqpOWTFZ7CRGabC5XF7bu3WIo0NcJNX1NvTRGm7NuYJorZp6iwOTxc68sT2IUCk42WAjMkJFtEZNgi7irIVLa1y6L9XfIYFAIAhHhGgKU5pLwYT7oFTPNnWny01+ZnLA6Egwl3BoilAlRWt4865rcLrcbDlYIwuj7yvruDo1wauI3PfYjeXVuHHz1t2D2fbjSdRKBRMHdsFkcXhFqUZ0T2He2B6YbU5+rmtEoVDIFgj9uiQybdiV/Ok/O3jpjr4cNVkwNtqJjFCxYd8J9h01sWBcNp0SorxML30H7balUGmpS/el/jskEAgE4YYQTWHKpTwo1TcdFKlWMm9sDxa+v5uN5WfuKd+QTGFuOjNXlQY8j1atpMHqYMZpe4Gc1ISmF31UBBq1kttf/SpowbcU3Sopr+GI0cKh2kbWfF/G/Juz5SG+VoeLDL0ON/DIuzv9itJfLMhh5qpSlnxWTvGkAcx/f1fAlOC8NWXMGdOdxNM/j/MtVEIVoXv6TJ3r75BI6wkEAoE3QjSFKZfqoNRA6aD8zGRmDDfQLz2Re0dk4nC50WlURGlUPPnB3oDCJ9eg57jJwhGjxa/T7T/3DGbD3io/nyXPYz2jV8ZGu+yZZLU7cbjcsgfUBzPz+PMHe/zOI32WaqpOnLIG3ScnLZEDJxpYvnkXc8Z0DylUfq5rpLrBdk4CpKUu3efyOyTSegKBQOCPEE1hyqU4KDVYOmjj/mruufZKnv9kP89/sl/+XqdRsXRijpxKk8g16Jk+PBOL3cnj6/b4XcdksdOncwK9O8ejROF3rFQQLqFVK+VidJPFIRec5xuaOtuC+TpJdVUAdUGGBEv7WB0uNu6vlgvCg/FjjZmpK7afswBpiUv32f4OibSeQCAQBEaIpjClpSmYcCJUOmjLwRryDcleAsdsczJ9ZSnzb+rBQ6O70WB1EhupJlKtYu3OI/z9i4MBo1DtYrU8t/4HRvfswLTrruTeES03y4yJVFFrtskz7Q5WN4S8J0lsBStml/YJtd0Tab+2ECDNuXSf7e/QpZwaFggEgvOJ8GkKU4L5AIXzoNRQ6aDikgrmju1BvsH7fnLSEkiK0fLbv3/F7a9+xU+1jTTYHHxTcTJo2m7nT0Z6dIqnV+cErHYXL39ejj5Gy+ubKli6odxLME3OzWD3ESOlh+vIM+ipMllJ10czKTedo3UWNKrQ/wlo1UryDclBi9XB2/up9HAd+QZ9wP1804Zf7q+m6pQ15PXPhbP9HbpUU8MCgUBwvhGRpjDmUhuUGiodZLY5USphwbieVFQ3eM2L84wMRaiU/P3zAywan8281buCpt6WFOTwY42ZB/79HUV5GWjVCv58Sy9+qm3E2GiXz71y6yEmDurCqq2HmHdTT15Y/wP333AVK7YeokeneDrFRwYdcZJ7WmTNvjGLp/+7L+B9+Xo/FZdUsHpqLgvWBi4a9y16rzxpJlqrPm91QmfzO3QppoYFAoHgQiBE0yWCG0BxsVcRmubSQe1itFgcLl7fVOHVqSaRZ9DTMU7Lbwaksr+qnj5pCUzKTQ8osKwOF5ERKrlIvG9aImqFjXaxGlRKBSqlgjG9OjImuyMuXEwc1IUXPvmBWTd2x+V08eiYHiz+YA/90hKZPtwA+I84mT+uJx+VHaWk/ASPjumB3bnLr8D9wRu6UbT8G1n09UtL4MNdR8lJS6QoN4PYyAhOWex+4tCT810n1Nphu6F+jvmZycREir82BALBLxOF2+12X+xFXA6YTCbi4+MxGo3ExbVwgFkzXIodTEfqGoN2dXU8veZDNQ08GqDFX5o7d9/1V+HGzU1LNwW9zoo7BxEbqeb2V78iJy2BnLRE9h41MW9sD5wuNxaHE6VCgdvdFOWKjzoTYZFa6Y2NNnRaNWqVAoUbGu0u6q0OIiNUJEdrsDqd1DbY0WlUxGnVONxuGmxOzFYnMZFqdv5Ux/z3d8tCKN+QzOS8dKavPCOO3puey5Mf7Q0aycpJS2TphnI+feBarkyJaZOfQVsQ6Oco/Yz+9XWl7E0lEAgElzqteX8L0dRGtLVoMpptTF9VGrAgd2hmclh3MEmiJFg66OCJet4p/Zmc1ASvKJI08uSje/OJjFAyZ3VZ0IjUYzf1ZPGHe/hVjw4MykgCQN+CNv5gQnThuGxMjTaitU3rNducPBRgv8W39mL+e7vI7hzP8G4pKBUKGqwOVEoFJeXVvPrlQa+aqoEZSQxK1/PS5+Ve5/JM15ltTlZPHcLVaYlez685f6SW7HcuXkvHTRYOVNVT55HulH5G4f47KBAIBC2lNe9vEWcPUy7lDqbm0kHGRruX75IvFrsTjVrJpNwM3PinzR4c2Y1jdRY27D3B7NHd6dquZRGaUK3089aUySLAaLb5CSZpv/nv7WLOmB7MWVPGc+v3o9OoeLEgh+U+KUdJFK3ceojeVyTwyOgsjgyxBK3lkuqEfjppZvY7O9joca5A0cXmopBVJgsNVgfz1pQ1e65g1FscTHxta8Bt4f47KBAIBOcDIZrClMu5g6klhcbGRjszTzuBSz5Iktgo+MdXPH1bHwAarI4WX7elQjTUfuv3VHHHoC70SU1g0pB0AFKTonjmN1dTb3FQ12gjUq3C5nRhNNvp0SmeaSu3U5SXwXeVtQEjZ1L7/8+1Zma9s8MvledrT3DcZOHH6gYKBqYxOTdDHvny5f5qHltTxpyxPdhUXs26nUebPZcnvlEpY6Mt5PM8ZbFzqLqBBpuDBpuThKgIUmK1bRrtau5Y4VouEAguJEI0hSmXcwdTS/2DfJ3APZH8jlrzHFoqRJvbz2z3XpeUqmqfEsOBqnquf/YLv2OKSyp4sSAHhY8Zp1TvZXW4qDPbuWNQF6bkdZWFkBSJ+nJ/NTUNNhpsTmb953uv6JHnyJeeV8Qz592dTMrNCGra+eX+ao6ZLF7iIlDkauWdg4I+A51GRYxWzSOrd3pHAk/fjxTJOpe6vOaOvRRr/gQCwaXNRfVp+vLLL7npppvo1KkTCoWC1atXe22fNGkSCoXC68+oUaO89jl58iR33HEHcXFxJCQkMGXKFOrr67322bFjB/n5+URGRpKamspf//pXv7W89dZbZGVlERkZSa9evfjggw/a/H5bgyQsAhGu5pYtpSX+QaHuX/I7au1zaKkQbW4/XyNLKUoFwQWX2eZk5qpSHrjhKpYV9mdZYX/W3z+UJQU5APy/f3/HmCUlTF2xnaLl31BaWcuLBTnoNCr5HE6Xu0kkBBjn8vqmCu6+tivXd09hY3mNbMoZjJ9qGzGam9YcLG25+WANeUE8p+aO6e43iw+a3N8ffnsHRrMNo9nGvDVl9ElNYFlhf16+oy/FkwbQOzWBx9aUydcPRHOu5MdNlpDbQ51bIBAIzpaLGmlqaGigT58+FBUVceuttwbcZ9SoUbz++uvyZ61W67X9jjvu4OjRo6xfvx673c7kyZO56667WLlyJdBU4HXDDTcwYsQIXnnlFXbu3ElRUREJCQncddddAGzevJmCggIWL17M2LFjWblyJePHj2f79u1kZ2efp7sPjSQsAs1xC1dzy9bQnH9QsPlqnh1crX0OLY1whdrP16BSQopSNedV5XS5iYpQoVUrUSoVWBwu5vrUHYH37LvikgqK8jJwuNxeKbk3v67k9oFpckF9t/ax/FjT5HDeEodyKR1Z02CT042SlcPOn+tQKxXMGp3FZJMVhUIhR7/6dUkku3M8s98tC3heSUQqFHD7wDTZdNTzGU7OzaCmIXhNVHOp1NqG8Kn5EylCgeCXw0UVTaNHj2b06NEh99FqtXTo0CHgtj179vDRRx/xzTff0L9/fwCWLFnCjTfeyNNPP02nTp1YsWIFNpuN4uJiNBoNPXv25LvvvuPZZ5+VRdMLL7zAqFGj+NOf/gTAokWLWL9+PUuXLuWVV15pwztuHU6ni9HZHeSXmVatpMpkweEMHUW4VGiuYNxTWBkbm1r/JQ+mp2/r0+oXU0sH3TYn2HwNKgGitU3/KTXncRStUfH5DydkwTOkq54787pyx6AufOuTkiutrOPRG7O4NecKHltT5iU8rstqx4o7r2Hh2l3y98sK+8vbSw/XhTTtLD1chz666X7dQGllrXwenUbVFBn6rJynPv7Ba/0fzMzH7nJRXlXvd15PTlns6LRqXt9UEXTQ8fybegY9vrkUqckSupbtbGv+WiuARIpQIPhlEfY1TZ9//jkpKSkkJiZy3XXX8fjjj6PXN6UMtmzZQkJCgiyYAEaMGIFSqWTr1q3ccsstbNmyhaFDh6LRnPmLb+TIkTz55JPU1taSmJjIli1beOCBB7yuO3LkSL90oSdWqxWr9cwIDJPJ1EZ33MRxk4XH3t9Fj07xtI+LBEChUPCz0cL893fxl1t7y99fzrTWmLE5WuqQ7btfpEbF9kO1AQ0qcw16eRxLvE7D4+OzeSSAD1XhkHQmvLKFa7om8a+7BzPfRwjlG/SsnpbLkdpGFAoFURFKdBo1j64u8xMePTrFs9DHddzqcLH7qIlcg16uoQLf7kM9haeF39jeHTlusrB43W6vfYryMlj6WTmbymvQaVQU5WXI0ayjxkY6J0QRpT6TNgxEbGQEdqcr5DBkpyu420lzKdK4Zgw2z6bmr7UCSAw2Fgh+eYS1aBo1ahS33norGRkZHDhwgEceeYTRo0ezZcsWVCoVx44dIyUlxesYtVpNUlISx44dA+DYsWNkZGR47dO+fXt5W2JiIseOHZO/89xHOkcgFi9ezIIFC9riNgNSZ7YxcVCXoKmNOrPtFyGazgctFWKe+31/uJaUuEhy0hICjkdp6jSLxmi2sXDtbq5OS+S+EVcFHCTco1M889b4C6GN5TUseH8XY3t1pF1cJN9V1NK/S1JA4ZGTmuBXJK9VKz3EUoVX9yHAFQlRfLTrGDNXlZKTlsDaHUf5/nAdhUPS2XzwzKw/6dySnYLv72B+ZjKzR2dxXVY7Nuw94bc2KdV54EToaFQgd3SJ5lKpidFtM9Baiiw53W4Wvb/LL00aSABJx5w025icm0Gf1ASvCKF0nLBkEAguP8JaNN1+++3y/+/Vqxe9e/fmyiuv5PPPP+f666+/iCuD2bNne0WnTCYTqampbXZ+t5uQqY25Y3q02bUEzROjjaDgH1sDWiDMXFXK+9PzgKZanE/2VPHJnipyUhOY8sY2+Rw6jYrp1xkY2bN90K7ATeU1FOVmULypglmjsviptjHgfoEKvUsP15GTluBn1QBNkcuyI0ae/2S/n7Gmy+1uii6dXpN0zN3XdqXKZKEoN4M7BnUhMkIl1zUt/nAvj9zYHavDFbB7Ll6nIT4qtGCIj2qKBgVLiYVKpbaPi2xRqjUUnpGlZYX9/QSThKcAChSN8uxe9BROl7ItiEAgCExYiyZfunbtSnJyMuXl5Vx//fV06NCBqqoqr30cDgcnT56U66A6dOjA8ePHvfaRPje3T7BaKmiqtfItSm9LfE0dPdlUXoOwcb+wJMdo6N8lMaDY8YxsSLU4Oo0KfYyGZYX9sTpcRGvUxOsieOrjvfToGNpx1lOIBCvoDvS9Z5TJ1xJhwbie/HC8nmWF/b2iXjqNioEZSYzp1ZEberSXR878aeRV3NCjg1+HnKdAUCsV/GV8LxpsDvk4T5+mlhTeN5cSC5VKPZeB1r6ptea6DU9Z7E3H/GeHl2UEeBftez73S9kWRCAQBOaSEk0//fQTNTU1dOzYEYDBgwdTV1fHt99+S79+/QDYsGEDLpeLQYMGyfs8+uij2O12IiKa/hJbv3493bp1IzExUd7n008/5b777pOvtX79egYPHnwB786b5kwbzbaWmzoKzp2WFpHHRUag06hYOjGHZz7ex7eVdRTlZXBDj/Y8dXoGnZQuC4YkiMw2Z9CC7tLDdeQZ9F51U5KtwdyxPZg7tgenLA5itGpUCrA6nOz82eiVRtJpVLw0sS+REUoee2+XX8SoX1oipZV1Xtf1FAgNVgeZp0e/BGPhuOym7sAAzwxoUU1QKBF0tjVvvt15zXUbxkZGcMxk8RNMEr4/10vdFkQgEATmooqm+vp6ysvP/MusoqKC7777jqSkJJKSkliwYAETJkygQ4cOHDhwgIceegiDwcDIkSMB6N69O6NGjeIPf/gDr7zyCna7nenTp3P77bfTqVMnACZOnMiCBQuYMmUKs2bNoqysjBdeeIHnnntOvu69997LtddeyzPPPMOYMWN488032bZtG6+++uqFfSAexGhD/2iiNZeU3r0saElkIzlGw9yxPSguqWB7ZZ1cE5STmiCnf1rS2QagVirYfcTI5NMvY8/99xwx8udbejFntbcg6dclka7J0Ryts/BaycGgUSKzzUlRXgZHjY0BncM37q/2S9tJSAIhVCRFiiB9e6iWorwM2T29c2IUHeIiiddpOFBVf9FsA3y780L9TIZmJhMTqabsZ2PIc0rRqtakCC8UwhZBIGgbLuqbd9u2bQwfPlz+LNUIFRYW8re//Y0dO3bwxhtvUFdXR6dOnbjhhhtYtGiRV1psxYoVTJ8+neuvvx6lUsmECRN48cUX5e3x8fH897//Zdq0afTr14/k5GTmzZsn2w0ADBkyhJUrVzJnzhweeeQRMjMzWb169UXzaAJQKhUhX6wqpeIirErQkshH37QEZr+zk+nXGeS6tDsGdZH3kdJoSvBz9pZqjfINyahVCh74VTd2HzEyID1JrlFKiIogNTGK59b/4OWv1Ck+ij1HjXxVUcPXFSeD1sPdNbQrDpebkT3bU2WyhkwDh4qKxUSqOVBV7/ci9k19BXJPh4s7Ksi3Oy9Yt6EkgFoyric1KYqP78uXRWG4IGwRBIK246KKpmHDhuF2B6/O+fjjj5s9R1JSkmxkGYzevXuzcePGkPvcdttt3Hbbbc1e70KhVioCRhikF6sQTeGLbxcaeKd/pDTaXUO78tDoLKpMTdYVUq1R37QECnPTmfiPrZhtTvINehaOz+ZkvQ1thApdhIqvDp7ko93HvQqPlxX2Z/a7ZSwr7M9z6/cHXNum8hoeHp3FEx82X1sFwWt9OidG8ei7O/lkz5maQulF3GhztiiCdDajgtoqYuJbbyX9TIryMpg6zIACaBerlWu0SitrQ0aj8g16Pt51nB2H62RRGA4IWwSBoG25qGNUBMHRR2v419eV5KQlyiMolhX2JyctkX99XSkbEwrCD0kMeAoO6YUrYbY5ef6T/fz2719x3GQhPiqCIV31vPb7/lydlujVibWxvIa5q8v47IcTjF1SwnXPfsHq73/2G7MiXa+5omYpuqRVK5ut5Qm0PT8zmR0/Gb0EE3iMMGnBoF9o+aggo9nGgap6tv14kt1HTbz73c/c8dpWrn/mC2asKuVIXeAOw1DE6zQsHJft9zMprayl0e7kzn9uQ6lQeNWqFZdUMDk3w+sYaPqHzNybespDk6WROuFAS4ZUCwSCliMKY8KUeJ2GBeOyefjtHX7pjXCrlxB4kxyjIT8z2UtwBEv/9EtLoEN8JMtKDjLz+qsY/ULgiGhJeY0cefQ8h2fNkXS9loxQgSYh1yk+Mnj0JDOZKpPF77uF43ry3vdH0GlUfl5LX+6v5pEbu4e8rhRBClVcv3BcNpUnG4g125m7usyrANuzNutcIianLDaKcjN4eHQW9RYnMZFqqk7PtDPbnF7pQal70tfOQatWctxk4cOyo/KzCCergYuZAhUILkeEaApjzqWlWnDxiNdpWHBzT7YePCkLEs/0j1QnFB8Vwf7jp0hN1PGb/qnNRkx8I0i+NUdSNCtkGikzWS40Ly6p4KWJfZk+3CCfz3O/e6/PpGNcJB/fly/7RZUermPMiyXkpCUE9CYCUCkVLTae9P0dj9Ko2F5Zx40vbqQoL4PSytqgtVmSYDzbovHYSA1PfLTPL/39xITezFxV6pUe9BR4gcxmH1+3x+O84WM1cDYpUIFAEBwhmsKcth4jIrhwnDRbWXhzNvPf28XG8mrMNidLN5STa9Dz2E09Gf/SJsw2J7kGPfd7uIcHI1AEyVNIFZdUsKywP8s2HgxYDydFcG58sSmaZbY5mbZyO3df25VZo7Kazmd3EalRsvfIKVJitcREqnn43Z1+KZ5g3kTQJJpaYzwp/Y4bzTamryqVrxXI9dzz+p6CsbURE6PZxtwA42lk89ixPfwsAySBV3XKSuVJM+Dt9C7dYzhZDbR0SLVAIGgZQjQJBOcBfbSGrQdP8tJnB5pa7nPTg6ZzNpXXMGO4u9lCYylC5ImnkMpJS0ClVDD3pp5YHE4eG9sTl9stG09KL8h+XRJlYWK2OXlu/X6eW9/kFJ6TlkhOagJ/ensH+ZnJPDqme9CamEDddUMzk9GfLs5ubZTUt/6mudosz+2tjZhU19tCei7NG9sj4FolgRetVZ+TG/mFoqX+YgKBoGUI0SQQnAdak84B0GlVQeuepELj8S9t8jpmaGYyV7aL5j/3DCZKo0IBrN9znL9/cRCzzSl3s2X5dMnNv7mn3+w7T7uDp2/rAzR5NdU1hI7geAoX3xdxa6OkvvU3LS1SP5uISXO1Po0h5uLBpZU6v5TWKhCEO0I0CX7xnC/jP8+XVa3ZhrHR7pfOkdBpVPQLMDdOq1ZSZbLyXWWt1zGSQOmYEEWizjutJRGsSFoB5KQlBpyhZ7Y5vcSKTqsiFF2To1k9dUibvIh9629aYgJ6thGTtqj1uZRS55fSWgWCcEaIJsEvmmDGf3+5pRc2pwtjY9sIqbioCF74dH/AVFeuQc+6nUcpzM3A5TM3LtegZ0peBjt/NrLyzkFoI5QkRGm8BEpL2so9166P1rDjcF3AeqHcAGnAUEXlidEa2sdFtugZNIdv/U2wyFt+ZjILbu4JwB/yMtrEp8kTUesjEAiCoXCHcpcUtBiTyUR8fDxGo5G4uOZNAwUXH9/CY0/yDHquTjszoLc1DspGs42aBhtuYP6aMjaW16DTqHixIIflmyq85sXlZyZTOCSdmatKgabC6pzUBGxOF+1itCToIqgz20nUaYJGckora7nl5c1B17N66hCu9pkRd6Su0a/OxTNFJxWoD8xIomeneNnZ3Hfff31dyYJx2W3mLO27Lp1GxdyxPeiblkCjzdmmqaVAz8AzgicQCH4ZtOb9fc6iyWKxEBnZNv/SvJQRounS40BVPdc/+0XQ7csK+zPljW3y56GZyTx1Wx/qLY6gqTwpctUnNcGvXV6nUVGUl8GQrnoiI1TER0UQE6n2c9aWkAqzb7n6Cq5MiTnr+/j0gWsDHi+lJesabVjtLjYfrJEH+noKKIAlBTlERaioa7TL6TxpX2k0Slulf6R1XYj6mwt5LYFAEJ605v19Vuk5l8vFn//8Z1555RWOHz/ODz/8QNeuXZk7dy7p6elMmTLlrBYuEFxImisG9u3e+nJ/NQeq6pn42lb5O88IlOfIiklD0v3SX5LlwNIN5V5CZv5NPbHYnV4RKM9ITnJe8PlvcPapJs86F6PZRrtYLTmpCQCUHTFS9rORJQU5WB0uOsRF8uGuY7JQ8n0ubTlc90LW34haH4FA0BrOaozK448/zvLly/nrX/+KRnPmL5zs7Gxee+21NlucQHA+aa4YOFD3Vp2Pl9KX+6uZ9fYODtU0UHXKKqf6mmuXrzXbKK2s5cCJemIj1Tw5oTcr7xzkNy5n4bimodEHqurl/Y1m79EXUqee70iS1hRJx+s0ZLaPpXvHON78upKeneL55seTTHljG1NXbGfMkhJKK2v9RrdICGdpgUDwS+CsIk3//Oc/efXVV7n++uu555575O/79OnD3r1722xxAsH5JFSEJlBBNAQWUhv3V1NeVd/sfp4YG+1y6k8SNz07xcmpoluuvoLkvAzMNqdf3VWg+qq2aivvlBDF47f04sF/f9esE7cnwllaIBD8EjirSNPPP/+MwWDw+97lcmG3i39xCi4NgkVo8k6nxopLKry+DyakwD+y5DugN9R5pGgVwJUpMVydliin7h4KMaE+UMTJ8/izTTvVWxxsDNAtB03CSUrhSbRVt5k0mDdYRE0gEAguNmcVaerRowcbN26kS5cuXt//5z//IScnp00WJhBcCAJFaCIjlMx/b5dX7Y5vl5svUnG01J4fyqjSs8BawnPivOQZFaVRtcpKoK38plpT69VWztLBrB9a2rEoEAgEF4KzEk3z5s2jsLCQn3/+GZfLxTvvvMO+ffv45z//ydq1a9t6jQLBeSVQMfDTt/XxElIxkWrmvLvTrwgazkSOJKGkVCjYuL9aNqqcOsyASqkgWqPi493HA5pbAtQ12pj//i5ZOLx8R9+Q6/asI2pL0dFcrVdbGloCXgX0ngQy5zxfRqQCgUDQEs5KNI0bN47333+fhQsXEh0dzbx58+jbty/vv/8+v/rVr9p6jQLBBSeQkFowLhurI7S30cxVpXw4Mx+Hy80pi53ICBVrdx6luKSCJQU5QQfQQtOwXE/h0FxdlFRHdNxkYdZ/vvdLqQVzBG+O5rrxOsZHhjxfa4VNS805RTRKIBBcbM7aETw/P5/169e35VoEgrCmJVPu+3dJJEEXIYuE0spaWSiFHMibmczmg97fh9p/aGYyMZFqfqxu4EhdY9AapLOxAziXIa9nI2yaSweesthbFY0SCASC88VZiabDhw+jUCjo3LkzAF9//TUrV66kR48e3HXXXW26QIEgnGjtlHvPVFewOqehmck8dnNPblpS4nWtUPs/Pj6bL384wervfuaOQd61hb5IabzWRIDOphvvbCNeLZkD19pRMQKBQHA+OCvRNHHiRO666y7+53/+h2PHjjFixAiys7NZsWIFx44dY968eW29ToEgrGipqPBMdUnpO2kgL0DnxCg6xEVS02Dzq3Py3P/h0Vk02lzoozWyi3jBwDQ2ldfI5wpGbGTEOaW23NA05TcER+oa+bG64awiXi0x5zxY3RDy+sInSiAQXAjOynKgrKyMgQMHAvDvf/+bXr16sXnzZlasWMHy5cvbcn0CQdjSkhZ/X1sDyRV8+eYf6dY+lm4d4ojXadBHa8j3sT6Q9i+trOXjXcfRRzddr97i4JM9VXIXWyh7AymNFyq1Fai1/0hdI9NXlXL9s19wy8ubuf6ZL5ixqpQjdY1++0qpM1/jT1+CCZuWmHO2JBolEAgE55uzijTZ7Xa0Wi0An3zyCTfffDMAWVlZHD16tO1WJxBcBrQkKhWv07D4ll48/M6OZsepSDVAUqF4sDRe/mnR0WB1tNq6oDX1Q1LqbNKQ9JDPIZSwae4Zne2oGIFAIGhLzko09ezZk1deeYUxY8awfv16Fi1aBMCRI0fQ6wP/i1cg+CXTkhlnnZN0PDmhN4dqzF6DcaVxKtLxUtTFs1DcM+1ndbhIiIrgypQY2sdFsv3QyZDX9Y0AtbZ+SBJxzRW6210udh8xEq1VkxAV4fc8Qj2jcylOFwgEgrbirETTk08+yS233MJTTz1FYWEhffr0AeC9996T03YCgaD1XJGoI0ar9hun4ikKpKiLb4RJ6tIbmpnMX27phdnq4MCJeiz20HPwfCNALelm80QScUEjXoYmY9BbX96M2eYk16BnxnWZdEnS0bEVVgFtNSpGIBAIzhaF2+12n82BTqcTk8lEYmKi/N2PP/6ITqcjJSWlzRZ4qWAymYiPj8doNBIXF3exlyO4zDlS18jDb+9g26FaivIy5NEmVyRGEatV85cP9pDRLobSylpy0hIprawNal3gm247UFXP9c9+EfTanz5wrTzmBZrSeTNWlfLl/mp0GpW8HqvDRXxUBBa7kxk+hp65Bj1je3fixuwOQvQIBIKLSmve32ft06RSqXA4HJSUNLVJd+vWjfT09LM9nUAgaAVS1OWYycJPtU3F2aWH65ixqpS+aQlMys1AqVCwdEM5pZV1IWuefEVLTKSa/MzkgCm6QPVDvqkzKeLlOXrGtzNQ6vr7ua6R6gabcPYOY4QLu0BwhrMSTQ0NDcyYMYN//vOfuFxNoX+VSsXvf/97lixZgk6na9NFCgSCwDy+bo+fuCkpr8EN3D30SgA/qwOrw4VWrSQtQHrsSF0j89aUUTgkHZfb7ecP9eSE3kBTNMpksRMfFUG0Vo3F7mTO2B643G7MVifxURE43W7Gv7Qp4MgYaJph92ONmakrtgtn7zBFuLALBN6clWh64IEH+OKLL3j//ffJzc0FoKSkhJkzZ/L//t//429/+1ubLlIgEPgTqmB7U3kND4/Okj9LVgeefPrAtV6fPbvmNh+oCVhY7nK5mb6qlI2nU3EvFuTw+qYKP3H1xITenLLYgwom8B4TI5y9ww/hwi4Q+HNWountt9/mP//5D8OGDZO/u/HGG4mKiuI3v/mNEE0CwQWguYJth9MdcgyLb5rNU4QFElnr7x/KwrW75X2K8jL8BBOcealOHW4g36APaHiZa9Bz3GThiNHidZxnZ96FTguJNJQ3woVdIPDnrEST2Wymffv2ft+npKRgNpvPeVECgaCJUC/y5gwfjWY7k0+7hQdKs/m+8IyN/iaX3tvtXi/RnNSEoAOIv9xfTVFuBpPzMgAFG8u9hxxPH56Jxe7k8XV7vI6TOvMudFpIpKH8aW0XpUDwS+CsRNPgwYN57LHH+Oc//0lkZCQAjY2NLFiwgMGDB7fpAgWCXyrNvchDGT7mGfRsq6yluKSCorwMpg0zoI1QkhClCdimf6SusVlrAp1G5fVZciQPhtnu5MG3vueuoV25d0QmDpebWK0ap9vNZ/uq+PsXB/3Sd7GREectLRRMgIo0VGCEC7tA4M9ZiaYXXniBkSNH0rlzZ9mj6fvvvycyMpKPP/64TRcoEPwSaemLPJjh419u6YXN6WJEVkqzfkbStfqkJoRM50VrvP+68KxJCoRWrcRsc/L8J/t5/pP9AEy/zhDS/iA5RnNe0kKhBGijzSnSUAEQLuwCgT9nJZqys7PZv38/K1asYO/evQAUFBRwxx13EBX1ywxlCwRtSUuFQ1sYPkrX+vZQbUhrAp1G5fUSDeUAPjQzmapTVr/vi0sqKJ40AJVCEdTZu62H8zYnQO8bkdmm17tcEC7sAoE/Z+3TpNPp+MMf/nBOF//yyy956qmn+Pbbbzl69Cjvvvsu48ePB5rm282ZM4cPPviAgwcPEh8fz4gRI3jiiSfo1KmTfI709HQOHTrkdd7Fixfz8MMPy5937NjBtGnT+Oabb2jXrh0zZszgoYce8jrmrbfeYu7cufz4449kZmby5JNPcuONN57T/QkEZ8vZ1JO4ARRnf62WWBN4vkQlB3AFeM3L87Qm8I1U9O+SSHqSLqTQa+u0UHMC9JEbu7fp9S4nhAu7QOBNi0XTe++91+KTSgN8m6OhoYE+ffpQVFTErbfe6rXNbDazfft25s6dS58+faitreXee+/l5ptvZtu2bV77Lly40EvAxcbGyv/fZDJxww03MGLECF555RV27txJUVERCQkJ3HXXXQBs3ryZgoICFi9ezNixY1m5ciXjx49n+/btZGdnt/i+BYK2oqXCoS0KmD2v1Zw1ge9LNC4qgmd+czX1FkfAl2qoF26wF29bp4WaE6AqpUKkoULQkrmJAsEvhRaPUVEqQ9cvyCdUKHA6g3uzhDrOM9IUiG+++YaBAwdy6NAh0tLSgKZI03333cd9990X8Ji//e1vPProoxw7dgyNpuk//IcffpjVq1fLqcXf/va3NDQ0sHbtWvm4a665hquvvppXXnmlResXY1Quby5kO7rRbKPObGfOmrKgrtxLTqfRJM+kYPu0ZI3HTRYefOv7czpPWz8faUxMoLRQa+bVQfNjYTb8v2uJjFC12fUEAsGlxXkZoyI5f19MjEYjCoWChIQEr++feOIJFi1aRFpaGhMnTuT+++9HrW66tS1btjB06FBZMAGMHDmSJ598ktraWhITE9myZQsPPPCA1zlHjhzJ6tWrg67FarVitZ6p2TCZTOd+g4Kw5EK2o/900szsd3bw7enRJ8FcueN1Gg5U1Z9zAXNLHMBbco62fj5tmRZqLnKlPy3wRBpKIBA0R6tqmjZs2MD06dP56quv/NSY0WhkyJAhvPLKK+Tn57fpIgEsFguzZs2ioKDA69ozZ86kb9++JCUlsXnzZmbPns3Ro0d59tlnATh27BgZGRle55I8po4dO0ZiYiLHjh3z851q3749x44dC7qexYsXs2DBgra6PUEYIkd8Vu/0M2j8cn81s97ewYKbeqJSKUiIijjnF+zPtWZmvbNDFi6e9UUAaUk6UmK18nWaSzsZG+3yuJO4qAhitGoarA6MjXZitGoi1UoWvL+LT/aeCOgAnp4c7Rdl8Y0oxWjV561dvzVpoVCRrpYWNIs0lEAgaI5Wiabnn3+eP/zhDwHDV/Hx8dx99908++yzbS6a7HY7v/nNb3C73X5u454Rot69e6PRaLj77rtZvHgxWq22TdfhyezZs72ubTKZSE1NPW/XEzRPW6aIpOjJpCHpfoJJp1FRlJdBTmoCVaesuAGny83xUxYarM5mr20026hpsOFwuXG53VhsDvTRWuqtDh68oRsPj1ZS22DD6XLzbWUtM04PvP1gZh7VDU0GlPE6TbN1Txa7k1v/tln+nGfQMyk3Qx6gm29IpjA3nc0HTwasZVp55yAAOVoUKKK08s5BF71dvyWRrmCRK8BLWP7SXcAFAkFoWiWavv/+e5588smg22+44Qaefvrpc16UJ5JgOnToEBs2bGg23zho0CAcDgc//vgj3bp1o0OHDhw/ftxrH+lzhw4d5P8NtI+0PRBarfa8ijIJMdqhZbRlisizRb1gYJrXNs95a54iw1OQAMwd24O+aQmYbd4iSkqH3T4wjdc3VVB6Og23dEM5tw/q4jWWRKdRMXdMd9754xDqzHYUCgVuN3y6t4rBXfXNmltuPugt9qRBvkV5GSzdUM7G8mpcuOXP0jUlQahQKDhU04BKqSBSrQwYUaprvLiu0a0xpvSNJAkXcIFA0FpaJZqOHz9ORETwf92q1WpOnDhxzouSkATT/v37+eyzz9Dr9c0e891336FUKklJSQGa3MsfffRR7Ha7vPb169fTrVs3EhMT5X0+/fRTr2Ly9evXX3R386N1jXz+wwlSYrVYHS5qzXa+rjjJsKvaieJUD0K9OGe9vYPHx2e3Kn3m2aLua+AYbN6aJEjuGtqV7CvieX1TBbPf2Slvlwwn57+/i16d4zlZb2XWqCyUCgUNVgd/GtWdRWt3UVpZx/TrDPRNS0QXocKFm7U7j1JcUoHZ5iTPoGf+zT35uc5MQrSGheOymbemzEs4SREkScB5sqm8Rk73eX7WaVTcfW1XRvfsyKK1u7wEYX5mMovGZfPtoVq/8zVncNnadv3W/iPhbI0whQu4QCA4G1olmq644grKysowGAwBt+/YsYOOHTu2+Hz19fWUl5/5y7miooLvvvuOpKQkOnbsyK9//Wu2b9/O2rVrcTqdco1RUlISGo2GLVu2sHXrVoYPH05sbCxbtmzh/vvv53e/+50siCZOnMiCBQuYMmUKs2bNoqysjBdeeIHnnntOvu69997LtddeyzPPPMOYMWN488032bZtG6+++mprHk+bYjTbOFxrxre50e12c7jWjE6jEn+p0/ScjhotQV+cG/dXU15Vzxubf2xxBMGzVsjXwDHUvLVN5TXcP+Iqnvvkh4BDbGe/u5P8TD3Du7Wnut7Kkx/tlfdbVtif7aejTr5RrFyDnhcLcpi5qpSS8hoWvLebBeN6YnW4OHDyFLNHZ/HoGAU/1TYCkBSt4Y7XtvqNKJHwHX/icLl5sSCHKpOFBWt3+a194/5q5q4p84pIBXs+nvi264cSREazjVqznbk+9WPNRX6am5cXLNIlhtG2PSIqLvgl0CrRdOONNzJ37lxGjRolz5yTaGxs5LHHHmPs2LEtPt+2bdsYPny4/FmqESosLGT+/PmyN9TVV1/tddxnn33GsGHD0Gq1vPnmm8yfPx+r1UpGRgb333+/V61RfHw8//3vf5k2bRr9+vUjOTmZefPmyR5NAEOGDGHlypXMmTOHRx55hMzMTFavXn1RPZqMZjtOl5t1O496vZCahp0aMJrtv/i/kKT0im8KzReb00W/9ETqzDbqzDYabE4SoiK8iqo98awVKi6pYOnEHJTAxvKaZuetOVzugAICmsTHgpt7svVgDWt9fq5WhytoFEv67JlWO2q00Gh3cv+/vsNsc7LqD4OY8sY2dBoV/ywaGFQwgX90KDUxij9/sIei3IyQa580JN3rO51GhVqp4OHRWVSZrCgUCrafnnfXv0uiV5F1sFTYkxN64wY2lVez5rufvUwy4Uy0cO7YHqiUCq8XcUvm5QWLdIlhtG2LSHUKfim0SjTNmTOHd955h6uuuorp06fTrVs3APbu3ctLL72E0+nk0UcfbfH5hg0b5hdJ8aQ5C6m+ffvy1VdfNXud3r17s3HjxpD73Hbbbdx2223NnutC4XC5WfpZedAX6KKbf9mmm8dNFn6sbqBgYBqpSbqQ+6Ym6kiIiuDPH+zxGw+ycFxPFG6I0qqptzgwWewkRXvXCilQMLpXRyblZtAuNnQdm+9QW51GxV1Du5JnSMbhdGOxOxmYoaeq3kppZZ0sbrRqJTmpCRSXVDD9OgM5qQlYHS4iI1Ts+KkOhQKuy0qhR8c4IiNUxGhV/Fxr5p9FAzlRbyUuKoL7RmTSp3MCPxw/FTT6k2vQU3q4Tl7b/Jt6NHkUjcoKKbQC3dP13VOoMlk5ccpG6eE6iksq6NclkQ9m5pOoi/CKIgVLhX3+wwk+3X2ce3+VyZ/+E1ywHT5pZsob2xiamczCcdnYXS4WvreLPmmJQe81PzMZtUqB0ewdNTKabURFqPz29+RCuYBfDtEZkeoU/JJolWhq3749mzdv5o9//COzZ8+WRY1CoWDkyJG89NJLfq37grOj0eEM+q/+TeU1NDpabyB6ufBzrZlDNWbqGu1ERqioMlnIM+j9ohTQ9OJUKeHlAAJ04/5q5q4uY9G4bGa/s4MNe5vq8XQaFcWTBuAG+qQm8FrJQfnY6dcZQgoST3QaFUsn5vB6SYU8sFbab/pwA72vSGDayu3y9x3itbw7dQiPr93tXVNkSGbq8Cv57d+/wmxzkhyj4Z9FA3n/+yNeqax8g55BGUk8u/4Hnjg9xmSTz/bC08XqyTEa/nfKQIxmO3PXlLGpvIZlhf1DPvcrEqIYkdWO2wd1Yfkm/3uSUojz1pTJ5psQOhWWEqslq1McVSb/OXWeSBG+L/dX8+jqncwalcXG8hrZz8r/XpMpHJLO6Bc20r9LohzxkCIizQ0nvhAu4JdLdOZyTnVeDqJW0La0evZcly5d+OCDD6itraW8vBy3201mZqZcQyRoG5r7V39LowKXGz+d9PYyArguqx3zburJorW7vf7yzjc0FTAfNTb62QZIlJTXcMRooUeneFk0mW1OipZ/w5wx3enjU8MkzVsD75e01D332b4q8g3JbCyvpigvg+KSCj8xJx03pldHuXC8eFMFOWmJlFbW+os7jy43aeCtb9Ssab8aXMDtA9MCzpDLSI6mssbM87dfTbeUWDYdqPZK/4aqT8oz6Plo1zEKTgumQPekRMGSghxmrCr1elGGSoVZHS5yUhOCbpfwTCl6ri/YvDzPui4p4vHUbX1kkRJsOHFbDKNtyYv2corOXK6pzstF1AralrMe2JuYmMiAAQPaci0CDxKiQqcHmtt+qdJcsfBsH8EEsGHvCZQKBdOGX8mkIenyi7PKZKHK1NhsW7yx0e734jbbnDzybhn/vnuw3/eeL2mdRk1spJp6qx2LzUVWhzhu7t2JOWvKmi0aL8rNoFuHWJ5d/4P82Xd/TwuAaI2a4d1SUCsVlFbWBT3vw6ObBtBKHXcS/757MP8oOUhOWiLRGjXt4yK9nmUwQZif2RS1mbmqlCUFOUEF6Mbyau4ZdiUvFuTQaLPLP0uHy03xpAFyvZPnmrRqJVaHi91HTS1KKUp4niOQx9Sywv5e+3y5v5rahjMRkUBiq2tyNB3jI895/EtLXrSXU3SmrQcshwOXk6gVtC1nLZoE55eUWC35mckB/2LNz0wmpZnamnCipSFu6YXz7aFaWSj8WN1AWpKOuKiIppdekBf2J3uqKBiYxpQ3vIc55xr03D/iqpDrk17cgYjS+LfUe76klxX2Z8ob31CUl8Gwq9rhbLTjxM3YXh2J1oT+z8vqcOF0nREovmsI5gmV75EKM9ucXsLK4XITFaFkRFZ78gzJNNqcfHtarERFKJmcm0FUhIp6q8OvrieQIDTbHOhjNEz8R1PUprlCeGOjnRVbD7H4ll5+c/FyfdYNTbVp6frooIIt16Bnsof/lYRaqWiVyAIwWRx+9+v5XFdPHXLOEaaWvmgvp+hMWw9YDgcuJ1EraFuEaApT4nUanmzB6Idwp6X/8pZeOHuOmnjzrms4ZWka9xGtUYECDp6oR4Ei5LUCvdA3ldcwa5RSTpn5Ir1gg6WIoiJUwQuNDcmUHq6TX77FJRW8O3UIz3y8j/tGdKO6PnSdjlatJDZS7fXZk2DddFIaTkrXvTSxL0eNjagUCjrro1jw3i4vcZlr0FM8aQDRWhUzXy3l1f/pR4Y+mhMB1ucvCLd5RW0imymg1qqVbCqv4afaRr+Xjm8XYK5BT8f4KGIj1fRNS/ASbBFqJSqFgi0Ha7xElnQ/JeXVTM7NQKtW0qNTvFw4n6BrGu1StPwbv7V5PutAnGtEpDUv2sspOtPSMTWXEpeTqBW0LUI0hTFtObT0YtDcv7wXjcumpsFKdKQajVLJlNwMrkiMYuH7Z176kit2dud4VIrQRoqeosMz+mK2OnlkTHe+q6xl0bo98gs416DnzryunDhlIT4qgr//Tz86xUdhdTg5UW8lPiqCkw1WHhqVxV89PJWkY+eM7c4tL58ZU2K2OTlaZ6FbhzgabXY6J0YFjRbmGvQcN1m4wkM4ttYTqig3g7uv7UpkhJJ1O4821USV+NdESfVGs0ZlkZOWQHyUhoVrd3F1iM4zSUzmZyZ7RW3cbnfQonvPCE+wlOim8hoeGd2dUT07cNxkOe2OnsqcsT34ubZRti148+tKlk7sy/eH6/wEkxR50mlUvFY4gGc+3uvna/XEhN5eYis/Mxljo63ZTrvjJos8n6+1hb+tedFebtGZS/3vKl8uJ1EraFuEaLpEcAPNBFrCjub+5V1+op4Zq0p5sSCHNzZV0CctkX94dKp5pqcWrdvDe9NzW9xKHzCtlZnMuhl5NNgc1FucaCOUaNVKiksOyl1YT3y0x08czb+pJwPSk7wKjUsP13G0zuJXkH+i3so1XfU89d8fKK2sY/W0XBa9v9srytXUPZeJxe7EDbII8U1RNZcKAxjeLUU2yQxUEyWxsbya2UoFj4zujlKpoKS8RjbTlK7nub7JuRms3HqIB2/oxpJPf5C3qRQKJuVmoEDhd0+eabRQTuGHTpqZumI7uQY9rxUO4MmP9vDUx2euMaJ7Cu/8cQhWh4v7R2Ty6JjuqBQK3Lj59lCdLIaK8jJ46uO9AUUinIlo5Wcm89hNPbj91a8CdhXmGvRyp13ftASv+XytKfxtzYv2cozOXE4Djy83UStoOxTu5syQBC3CZDIRHx+P0Whsdj5eS7nUuzdKK2u9IjG+vHxHX3YfNckdY8WTBnilVaZfZ5C3Tb/OQP+0RBxut1/Kqqkl38CUN77BbHN6HedLnkHP1WmJLN1Q7nf+YMfkG/T0OX2M/F1mMjf26ug3KuWJW3s1/cxOn+f+X2XSKS6SjHYxOFxu2cfps31VfH+4jn5dEhmV3ZH57+1i4/5qkmM0FE8agEqhwOl2c/PSTUGf33/uGUxUhIoxS0rk5zl1xfag+0vptld+1497/u9b4ExErm9aImqVgkRdBBFKJSiahI/CDQ12J/UWB2qVErVSwZQ3vuHJCb2JjFBhbLTLIlIq8s7PTPbrOgy0Dt+fh7QeSfD6drX95ZZeNNqdnGywodOoiNKouHnppqCdpO/PyEWrUvFtZS1J0Rru/t9v5fsddlU7jKejYZ5rhyYRleOxpqGZyS0q/DWabcxYVRr0RRvoHFK93+UQnbncOFLXGFTUijFWlxeteX+LSFOYcjl0b0j/8vZMlUmGjdsra9FFqLxSUBEq71Ca57ac1AScbnfA9vK9x0zoYzT0TUugpLwmZFqrpLyGyadnr/meP3iUpoZJHvPa8g3JPD4+G4UbPpiZh9nmJP60w3h1fVOxenJMU01a+7hIGu1O7E4Xmw7UyC9nz8hMz47x/GlkNyYNSScpWsNTH+2lT1oineIjQ9ZTadRKGnw60VqC57+SA3WefXRvPkoFHDdaWOLjb5Wfmcxff92bh/6zg7/+ujertlb6RZxmj87i2f/+QCB8i7Q9fx4QvI7ry/3VPPzODi+B5VsQ78vhk410TY5m9js7ZQ8q6X5zUhP8mgYkfOfztbTw92yiR5dTdOZSoaWNKZdbylHQNgjRFKZcDt0byTEaftU9hd8OTPNLleUZ9Izo3p7jJov8XVK09/14pqektvSctMDipuxnIw+NymKWAszW0B5W0nl9zx+KaK2al+/oS3xUBB3jIln0/m4+2Vslb5cigCaLneQYDSvuvIaFPnPc8jOTeW96HjaHk6NGC7Pe3kFOWgLbKmvp0TGOqSu2s6ywv2za+NLEvkwf3jTn0TeyNjkvndtf/Yr/nTJQ/j6Uz1K+Qc/On40AaNTKkGlOhQK2/VjrN8IHmgxBcbt5ckJvUmK13NirA5Ny073Slr8v/polBTlYHU6/gvRAnXCez76lghe8C+IDHaNVK+WOOd9n09zP23d7Swt/xYv2/HMuhpOtjd4LUSvwRYimMKW5QaTGZryHwoF4nYbHburpZ0YJTS9AN3uZNSpL/k6lUJBv0MsvWs/ISVPtUXBjyQn9Urn91SbH7BV3Dgq5Lum8vucPRYPVwaqtlTx2cw/+8sFuNh886Tfu5IsfTjAgvWnmmq9ggibB8dh7ZbKJ5ZKCHCx2F3/6z/cUTxrAssL+6E7bFJhtTqat3M7d13Zl3tgeOJxuTlkdqJUKSsqrmb6yKbqycX+13BkYqm1/6ukaKp1GxVGjRRYfgWqZjI12Pw8nr/sor+GeYQaqTtmY/W5ZwH2mvLGN96bn0mh30WhzcMrioPRwHQ+/vcMv6pgco0GnUbXI0sB3u29UyPNeSg/XMbZX0wBx32fT3M/bd3trCn/P9UUrXKiD01rRIz1LY6ONRJ2GeWvK/GxLLqXoveDiI0RTmKLThv7R6LShW7/DhXqbI+Q4GJvD1fSCq6xDq1YydbgB1+ltntGB0sN15Pi0pVsdLtrHabE7XVhsLp6+rQ9atRKL3dmignHf84eK0qTERjIpNx27w8XmgycDFprnGvQM6aqnQwjBsam8hgd+dVVTV5/NyRUJUbx9zxAWrd3Fdz8ZWXXXNbw/I5d6i5PYSDXHTRbufGMbL/+uL7e/6j9n8dUvD7J0Yg4omkSZ9HymDTOgVimJ0ij5eNdxprzxDTlpCRTlZaBVKfn+5zpZsJptTlmMzVxVypt3XUN1ffOiPSbE76jZ5uRkg51Jr3/NnDHd+XDnMb6trG3Wd6q1YiYQkvj719eV/M81XeSCXs/fnaRoTbM2FBIXsvD3Uq9jPJ+0tmTB91lKUdxAXCrRe8HFR4imMEWrVgZt7c4z6IlsYf3KxcbU6Ai5/US9lcm5GYzt1dR+vufYKYonDeCUxUG91cHNfTqx6P3dHpEC7xfudVnteOTGHqe9iZr+ctRpVCwr7I9SofD6C1YadSKlh6RzKgjuhp1r0DM5L4PqeivFmyq4Y1CXoHU3m8prmLOmjJnXZYa85zqz3aueJs+g5678K3koIZL57+3yi6K9VjiAz/dVeUXhJMw2J//31SEmDUn3ckPffLCpfmpJQY78vDaV1/CHvK4kRGv4uuIkz633nh03OTeDa7omAc2LE12Eik4JkSwr7O9Vp+ZZUB19upbt8XV7WFbYnx9rzM36TpUermuRpYEn8VERrLhzEFq1ksgIFcdNFt7d/hMLx2XTPi7Sq85Ieha/6p7Cn2/JZs7qMq/6I9/fkVD1SG0dEboc6hjPJ60pWQj0LJuLYgrvJUFLEKIpTLE5XEzKzcCN/0t80ukoy6VAXDOGgmlJOpRA54QoZr9bxvTrDHILPZwpIp+Um45KoeDxcdmYbc4mR+vTnWj/3X2UfumJ3DPsSpxuN3aHi69/PMmDN3TjoVHdqDJZUSkVdEnS8fi63fJL3WxzsnLrIR4alUV1vQ2708WjN3bH7Qaz3ckpi4PtlbXs/NnI1xUn5VRQyKLx/dU8cmP3Vj2jktPdewve90/plZTXsGjtLmZeb+D67u157L0yvyG8d+VfSdHpzkFPAomMeF1E0DZ9BfDQqCw+21dFuxht0MjbiKx2JERrAppoShGjnLQEbM4zc+W2VpxkZM8OxEdFMCWvq5/AajIhzeJkvY1bc67gsdPdhBK+Ysbzmp//cIKlG8pZcecgPv/hBN8frmPxLb3kDqdQdUa+38dEqmmwOlh556CQ9UjnIyJ0OdQxnk9a44MV6Fk29w8B4b0kaAlCNIUpDVZHwE6x0sNNPjX/NyV03U64kBitCRk52HvURN8uiRw80QCELgQ225002p1EqJQs3VDu17WVfUW8VyfVgC5J3PnPbXINzU8nG3nspp48ONJJRXWD/DylWihJoI3s2R5To52E0y+oPlckyFGZUO7hEkoFLU79SERr1UFTeiXlNTxyYw+O1jUytlcnr9+Hmnor7eO0XNM1qUXO2NoIVcjrFNXb+PsXB4MXoWcmc++Iq3jio+D+SHPGdCclLhKj2Y7D5ZZTcs9/4h3Z8u18+6m2kQff+p7V04ZwY3YHOXIWGaGiY5yWZ/77Q1CjSzgzQ3DphnIeeXenV2QmWJ3R2dQfna+IkHChDk1rfLACPctQKXjhvSRoKUI0hSnxUZqA7eBntof3v4qk1IXZZufPt/Ti0Xd3egmnfEMyC8f35JuKk/x+2de8/Lu+gHcIPZhJ5XVZ7fjTqG48rMzimLGp+04Sk9JLNc+gJzUxirfuHsyx0x16Ww+d5O4V3/L6pAF+fkbBrpVr0HNT745yobI0KsW3CFxysb59YBpu4LGbewaMChUGiJYA1FtCd/zVW+28sflHsjvHk9m+yfMpWquia3I0TreLB2/oxuIP9oR0xs416KltCF2r5Ha7vYrQpcibVIQeHxXBiXpryJqt+0dcxSufl5PVKZ4berT3ihx67gfenW9atZKivIzTZqDe++s0Kv5zz2AKBqX5/QNC+pl7zhA8n5GZ8xUREi7UoWmN4WSgZxksBX8pG4r+kgiXBgkhmsKU5BgNI7qnkNUxzu/lvPeoKaz/VeSbuvjw3lweu7knFruTeouTeF0EpkYbv1/2NeNyrmD+zT1xu5uiGJ4h9GC1Qxv2nsDucDHv5h4Y2sXw6GpvQZZn0LNwXDbf/HiSt0t/9jteSut5EqpO6fG1e7xe7hEqJaWVtX5CbsWd17Bo7S6Wbihn+aQB5KQlekWF2sVq5aiWLzGRoQv7ozRqru/enpwuCVRUN6BRKWm0ObBoHcRGRrD4gz1+QsNTmHxfWcfcm3o0G63oGB8l1ylp1Uo+KDvmlUZbPmlAs/PnjI12Hhrdnb9+uIdhV7ULKbCkzjcpAje4qz7gPxTMNicflB3j+8q6Fs8QPF+RmfMVERIu1KFpjQ9WoGcpDaSeM6Y794+4CmOjnbQkHSmxWiGYwpxwapAQoimMeXh0dx57r8zP32j+zdkXcVWhMZptfPHDCSYNSadgYFrTC9at5IOdR+W6ICkNNv/mnlgdLqIiVJystzE5N53qU1b+cktTAW+7WG3QSNu3lXWoFUoa7Q7m3dQTh8vNqUY70Vo1Lrcbq8NBvE4T9IXtG6YPbW5ZzT3DrmTphnKK8jJ4bE2Z33l7dIr3shnYVlnrJ6ymX2cgJy0h4JoarI6Qhf9VJguz393p5U7+3vRcyn420kUfHbQraFN5DY+O6UG/tAScLhdKhSJk6tBksbF8049Bt2+rrKVvWmLAa0mkxGk5XmfhodHdOXCiPuS+1tPdk9LYlnxDctB9pSjfwrW7g4598eR8RWbOV0Tochyt0ta01Acr2LPMSUsgJS6Sv39xgIXjsoWz9yVAuDVICNEUptSZ7X7pHWiqO3nsvTL+Mr5XWP0lajTbqGmwoQDW7jjiVwczOTed3lckAE0vcklM5Br0LBqXzdglJU1db5MG8PRHe/m2so5Xftcv4LWkVNqf1+0my6OOJyk6omnA75oy7hjUJehaP9tXxfThBpQgi43mCus1aiX5mclBxZXv94FSAcUlFRQXDkDpM7ctPzOZDrGRPD6+F3MCRM3+fEsvahus/O13fdGqm3yNiksqAGgfF9msZ9ehmgb++H/buW9EJmU/GSnMTceF288wc/7NPfl491Em56UD7qDGlJ3GRAbs5JPOs/MnI4O66nG6XWSmxITssMtIjmbOmB44XS7uG3FVyGJdaSDy2N6d+NPILI7UNcppupVbDzFxUBevrrfzFZk5nxEhYY7ZPC2tQ/N8lsZGOzqNCpVSgUqp4Onb+ohneokQbg0SQjSFKQ3N+Bs12EK38l9IjtQ1Mm9NGXcMSqO4JEBL+f5q3G43/dOT/FJWVSYLlTVmeQDrXz/aS+npQbIqpf+EYp1GxZKCHKI0Ku7MvxIXbjafHk9SlJchz48LZHgo8fcvDtL7igRu7nMFD43OospkJSVOG/IeoyJU/GlkNyz2wOLKV3RJqQCpkF+nUWO2OYjSKOmXnsjkvAx0GhVOl5stB2u48bRoXDoxh0fG9MBothMTqSYyQslfP9zDurLj8rnzDcm8WJCDxebE5nQ12xUUfzryIXk6rfzqkNfPISEqgkSdht++uoXq+qa5bncN7So/GzhTM9YvLYEr28UwOS9D9tOSyDPoeXBkNwr+8RVDrtTz0Kgs5r+/y0sE+nbYrdt51GvG21O39QkqSHINeo6aLAy7qh06jYpYrZq6RhtDuuoBvIbsns/IzPmOCAkX6rZDPMtLn3BrkBCiKUxpCFD34kmwIaUXGqPZxrw1Zdw+MI1EnTZomqikvIY/DjNwx2tb5e+kVOPtr24BzkRrpl9n4PVNFQxIT/JKJUkRpuWbKryuk29I5t2pQ7A73fILuPRwXdDZbWabkxVbD1EwMI2aUzaSojXoItRB01Z5Br087iVYPU8g4eJZyC8NqV1x5yCe/2Q/943I5NsfT3rdh9nm5PZXt8oDY4GAQ4Q3llfjxs3csT1QKhV8/sOJ4MacmcnERp65t+krm4Tc4K561EoFHeMj2HvUiNXholuHWKrLazDbnDz/yX52/lTHvSOuorreRo+OcSwpyDk9isXNyq2VXsIrPiqC2Mimbj2zzUlWxzgW+QgmOCOy5o7pTru4SD8/JF9fJc/7WDQum0RdhFdHHDT9DraPi2REVsoFi8yIiJBAcGEItwYJIZrClPjIiKCDbotLKpr9RTrfSJ0MVkfTC/L1TRXMGB7a1FGtVLBuZh6mRgdxUSpcLrDYnTw5oTcKhQL16ciSJJ4GZiQx76YezD/tXxSsWHtjeTUL1+7m7qFXyt8Vl1SEbJufM6YHP1Y3YLY7cZlhy8FqFozrybw1ZX7psXk39eS59fu4MiVWPt43XNwSU8Zcg57YSDW5Bj15hmSvFnxPPCNloeawuVxNQnL3EWPAsSh5Bj0Lbu7JR7uOMtpnRtyW0+aX/dIS6ZOWQPGaXV72FtJ4kxc/2e81Yy/foGdM747MuC4ThaLJJsDzfJKYD1Ujtqm8hnlje6BSKgL6IXVKiGLxrb04VGOmrtEup+D+vG43C8ZlE6/zPt/FiiaIKIZAcP4JtwYJIZrCFK1awfLJA1ny6Q8+IyeSWT55IJFqxUVrwfTsZPi/KQMZdlU7lm4o95ojF4h4XQRPfbSXLR5jSDxf8itPz4yTUl3ZneL5oOyo7E0UqjB80/9v78zjoqrXP/6ZfRhgGGBkS0B0UNlU1EQF1EpzwfV2K9FKRVvV6lY3szQ1K1vsVmpe6+ZSv1xu3cpKy3LJUjRXSnBLFEVDQBCGZfaZ8/tjPMc5zMIiy6DP+/XyVZxz5sz5njMz53Oe5/l+nvxyvDDy+vs7Tptnj8tgtkEkBIJ9pXh1y3HsOHWF2z5VE4weESp7cbrZhiqDBX5yEUqrjHh3+2nc0yeSi4p8OyvNqd7sRJEW80cnOPWccyxSnpYag6x1h+y2BIznc1zXeiErLQZ9owIRoJBALBKiotYEC8NAIhLggf7R+KxOyo2N/Jwrq0W3UCXPgdyRPfllmJrayaW9hUIqwvoZKbxp/h38ZVjy/Uk8MUQDrd7sZN3g6vhdUWu0one064Jyrc6EF77KdVnHYLSQMzZB3Ep42wQJEk1eislqw/Kdfzqlu/bklwEC4LXxiZi1MafVp2A6zmRQSEWIUPmgoKyWW++pf5vVyuDZu7vhj4talxGjfefKkaYJ5lJdRosNH/5yDhse7o+lP57CJA/F3QBgsTK899eZrHh3+xm8u/0Ml/KKCJDjh9zLbqfnj+sZgd7RgdBVWVChM0EmFqJLiD/PD8hssTrVZuVcrMTkj3/DxH5ReHFkHGpN9t5xBrMVEpEA8RHXjTdX7MrHiIQwj2NhW4KwKckNBy6gV6QKS3867VTAnZUWgx4dVUi6LcApkrQ8M7nRTXBZ7P3jTDzBtXJyb+w6dQXP3d0NZ0pq3F5vVT0+YgazFUWVepefVW8r/CQIom3xpnQ4iSYvxcrAbX3QnjNlqDZYWnUKpmM6jn1fdvr91GupIXZWGlAnHaYJxoKxCSitNkAplyA5SoW5X+c6vQc746y0yojUa+JJZ7JixieHsGbq7RAJnAvD+cdodpmmcoz2jEwIw4tf57l8PWvMuOXYZRw6f9WlGEjTBEMsdvZpYvmjsBIAMKBzMAQAJqzch9VT+jptyzCeBKYaHfxluFJtxLyMOKzNLkByVKDb1CQDBr2iAp2iSa68i1zhTuC4ci+XiYVQSEUQCIC+MYGIUdtzZXWFHJuGdNc0ed+5cqz65azLz6q3FX4SBNH2eEs6nESTl1JdT6Nbd4XiLfEk7piO2/Rwf245W7fSMyoQqZpgblZaRlK40wy5i1f1mLnhKHQmKz7N6ufyfdgZZxsfTkGf6AQcuVDB3XgnfvQblmcme/QYOnyt3isrLQbzMuJRa7TAVyaGAHan6+fu7obyehyxS6uN+OjXc1iWmezU8Dddo8Zzw7sha+0hLBibAAHgNDNs3ug4TFi5D/HhShRe1SE9Vu2yfcOu0yUuBWbqNYH5xg8nse9sOf77aH+8+HUeslJjPNY3PT5E4+QGzloEZKXFeBQwgQqpU51W3RYl7LLcv7RYlpmM17eexJHCSi79KRAABpMNAgGwN78M09Ydwhv39HA5Pna/OpPV5WfV2wo/iabhLQ7ONxt0XtsWEk1eisKFa7Ujvh7WN+eTeEmVAXP+9wcX9QpQXL9hsWmd655EBZi54Siy0mIQqpRDJhYiQuWD8hojJ5gAwGJzX9DD3khnbzyAlZN6Y+GYBLzynb0x7OyNOViWmQwGjJNYcbwR5xRWYGRiGP69Ox+TUqKxJrsAM9I648RlLYZ0C/E4Xja69eTGHPz3kQGYlmrkWQNk/uc3XqPfrBoTDGYrl6K7XGmAzmT/e/GWE9g6Ow2vf3/SKQL24S/nMKBzMEb34PeSK602wl8qwjv39kRZjYkTeQ3xkdr2VDp0Jit8ZSIcLbzeYsSdPxR73h5ccwDrZ6TAZLXhSrURHfxlyLuk5aUk2W2PF9lTqzmFlchKi0FiRAAuVeivpRKF+GDXWU58OVouAPbWP7v/vMLbr6vPqrcVfhKNx5scnG8m6Ly2PSSavBQfqchjdEAmce/N01xP4kWVepwvq+WlCWUiITdLjK09qutJJBELIRIIcOj8VZy9Uo27uoehT1Qgd8M+WlhR70wzncmKJzYcxQeTemNUUjimpXWGWCSA2leK18YnQWe2Qqs3c2LGsb/atNQYVOnNmDc6HgazBRlJ4Zxo05tt9b43APSJDoRcak9FSUUCVJos6B0ViH4PBkEsEGDfuXKnliipmmCnMVToTLgzLhRigQDP3d0NL4wUoFpvQbCfFGFKObqF+vPy9H2jA/lT6kvtjtr1eTGpfCQQCgQQCKzwkYgABlw9k0ws5PyhHGfQOfZuq9CZcaCgHBEBPugY6IO+nYLw1RMD8VeFHsB1n6aPH+qLD38557Yn4OJxiZj/TR72nCnj6rfYa/LQmoNOVhmuPqstXfhJT+oti7c5ON8s0Hn1Dkg0eSliocBt+mbWHbG46ibN1FxP4uwXNLNfFG+50WbD1NQYMLDfSFlnaMfZVwqpCGum9OV8iN7bYW8/8tiQLhAJBTCabRgWFwoBTrl1nQauz4Db9Eh/XKk2osZghUggQGmVEdHBCiz99SzirjmCL723JycENh0sxLN3d8W9q/ZzppmsgAhSSDC+VwQWfOt6ltuTG3M4B/NxK7KdbvIKqQjfzUrDsWuiqO7r2Vly7BhkYhEWbznhtJ9BsWosv+YYDgAMALgo2WKjLp46tKfHqnH4QgXmfmWvE1s9pa9TzdjqKX3dWhwA9jYuh89XYMmEjugYdH1Of7hSjrIaE4J9pZjQ6zZo9SaPPQGFOI6pAzvh8cFdIJMIoZRLcPhCBS+65HgO3H1WW6rws6FP6q0hrG5W8UaF/C0DnVfvgESTl2KwWhGilGG0i/qgEKUMYgGcUhjNOQWT/YJOHdiJt1xba+aiSiMSQjGmRwRe3XqC92XuEx0IoUCAPp2CMC2tMy+9xXr5DI0LwUsZ8ag1WWCzAWarDfscIkYsyVEq/HSihBexeHJjDj6c3AdP3tUVS3867VTLMz0tBsVaI7qH+WOvQ8uWdI0aL4+Jx08nijkbAr3JCoVMBDACFGn1+O+j/QFGgPs/2u/SQFRnskIgsEdxSquNuFprgp9MDJPVBq3OzJsll64Jxh+XKnnNflkOX6hAhc7MRWVY6t7A2ajLgm/yXBa5D4pV44k7NMhad4hb5iqV50l0sZGxPWfK8OLXubwn1rrFl2dLazx6MO04dQWZKdGY9PEBThgO7toBP0QHNvqz2tyFnw19Um+NFMjNnGahQv6Wgc6rd0CiyUsRQoCiCgNiOvjCVyZGjcEKP7kIvjIRiir0iAiU4+17e6Ki1oQqgwVKHzECFVKEKuXN8v7sF9QxmgQABouViyolR6owe2MOvnhsAKYONHDC7lRxFXxlIvTrFIRKvRlCAFaGQWqXYAyK7QC92QKbjQEDBnKJCDqjBWEBcvxRJ3qTrgnG/DEJKCirxeopfa+38ogOxJGLFejZUYVRiWGYnhYDX6kYfnIxxCIBKmpNEAsFePKuWMwbHY+iSj0AAdR+UvxVocdb2/7EW/jT5bhXTu6NE5er3DbWHRSrRvC1iECAQorLlXpcuKrD8l1nnGaQTUvrhFkbcngRJZastBjM35zrNEPSVag9QuWDpff2RHmtCQvHJMBqY6AzWRHgI4FYKMDIZXt4581VKs9VLzzAObpX3xOr2k+K8+W1LtexsKKN3VeXED+vmC7ckCd1AI1OgTQ2YlS3TrAh79GeoEL+loHOq3dAoslL6eAnw9VaE/JLaxCqlMNosUFnEqKkygBNiB98xCI898UfLfakyn5B1+wtwOaZqVj83QnsyS/DYYcZbTkXK5EcpUJJldFpuvsHP5/FI4M64+74UFQZLPCVieAvk8BgscLGMHbxV2nAnC+PIS7MH7PujEX/zkF4emgsrDYGvjIx/GViLPruOHY6mFCmXWvwe+j8VXy89xwnABzd0wF70XF+aQ1SOgdh1gZ75EchFdkjSR6QiYVuBUa6i+hIuMoHVQYzF7nSmawQCwXYe61lic5kdRn58RStcSVc3EVdcgornCJirqJKbN3ZvIw4zMuIR0FZLa+uCQBm3alB76hAXNWZYC6ugvCaS3uwgwgIUEjRMdDz58tRtLFPvy0RNWpsaquhT+qNSYE0NmLkqk6wvvdob1Ahf8tA59U7INHkxSzfme9yen16rBpje0a0aEGg4xe0qEKPuaO6Y6rWAH+ZGH9Lvg0LvsnjxEWxVu/yJv3ejjMY0jUEEz/6jVvONty12hgYLTa8eU8P5P6lhc5kxb6z5Xj7x+sRoJGJoXhhVByeHtYNtUYLAnwksFhteHD1Aax6oA/++b9jvPdzFCGrp/TFi1/nIV0TzE+PMQKXbVAAe2SrpMrgVNjONrXtEuLnMpLHMMDYFdluz2V9RdyuaGio3dXTpzvRlxylQohSDhtj4zl5swaadQu72SjUku9PYtG4RE4EhCnlHpvqOno7Nebp150QqrtcLhZiwbfHsePk9fYuDXlgaMiTemNSII0tzHVXJ+jpPdoj3ubgfLNA59U7aPyveTPy66+/YsyYMYiIiIBAIMDmzZt56xmGwcsvv4zw8HD4+Phg6NChOHOGX8x69epVTJ48GUqlEiqVCtOnT0dNTQ1vm2PHjiE9PR1yuRyRkZF46623nI7liy++QPfu3SGXy5GUlITvv/++2cfbGMpqTC4FE2B/Eg7ydf0FcUwz3AjsF3RQrBo2AN/nFWNd9nlUGy2478P9mJoag/8+2h/+MjFSOgdh8bhEpGvUvH2ka4IR4CNGeuz15TqTFbM35kBvtk/bn/7JYby34wxmbjiKjKRwbJmdhv8+0h+bn0jF40M0OHelFqVVBpy7UoNirQH3f/QbLlboUVih83j8bHRnT345F33KSovBuztOY15GHDfTjSVVE4xpaTEID/BBqiaYE2HTPzmMTQcLEaP2dZv6DPSVIq3O/q6fAzVKq428ZYNi1fVGaxoqNlhx6wgr+jKSwvG/xwZg5eTeWD2lL5KjArHhwAUUa4288bsr7M7OL8fa7AJ0C1fihS+PQauzf64cPxuOsCJrzd4CbpwNffotqtRj1sYc3PWvXzBh5T7c9c4vmL0xB+fLavHs57/zlr/8TR6evCsWa6bejpWTe2PN1NvRI1KFBd/kccfY0HPFwh5rY1IgDU331d2+PhF9M6RZ2EL+nc8MxuYnBmLnM4OxPDMZ4V5Yr6XVmXC2tAY5hRU4e6XG42eorWlP5/VmpU0jTbW1tejZsyeysrLwt7/9zWn9W2+9hWXLluGTTz5BTEwM5s+fj+HDh+PEiROQy+03sMmTJ+Py5cvYvn07zGYzpk2bhkceeQQbNmwAAFRVVeHuu+/G0KFDsWrVKuTm5iIrKwsqlQqPPPIIAGDfvn3IzMzEkiVLMHr0aGzYsAHjx4/H0aNHkZiY2HonxIH6nng9+fY015NqhMoHb9/bExfKdViztwBfPzEQRy9UoluYv1M6TiEVYX5GPF4Y1R0XynVc6ufMlRosHJuABQ6NcFmfo8XjElFQroPRYkXHQB/kXtLivg/3A7DfyAd2DkaAjwQ6kxVFWgNe3XqSS0VJRZ5vPI43JvZcsSmxh/p34tqg+MrEXJH6rA053Huz3kKRgQqEKmUen+JClXK8PiEJL36d69Ts97UJiQjwkaBfpyBePQ/gXMjP0hix4e7ps090IDp38ONN80+PVWPmHRrM2nCUZzxZX3Nd1ljTMW3kOLutUm+C0cwv5K/79OspneYpYjNvcy56RQVyfQIVUhEyU6Lx5rZTLmuzymvdp7Ya+qTu7rqkx6rhJ7/+k9nYwlzHOkF3Rfk3U5rFWxycPdEeC/Lbw3m9mWlT0TRy5EiMHDnS5TqGYfDee+9h3rx5GDduHADg008/RWhoKDZv3oyJEyfi5MmT2LZtGw4dOoS+ffsCAJYvX45Ro0Zh6dKliIiIwPr162EymbBmzRpIpVIkJCTg999/x7/+9S9ONL3//vsYMWIE/vnPfwIAFi9ejO3bt2PFihVYtWpVK5wJZ+p74vX0tNqcT6oGkxVmqw06kxWXKw2Ij/BHn+gELN5ynFeX0ScqED0jA5D3lxbf/FHE3RBWT+mLoqt6PD+iOx43WKB16FpfXGXgZn2xNUmO3kJhSjlGr9jrchabvUDdvTu4Y4rIsZcdABwurODaoKybejssDMOrDXKcqScWCRr0AxUV7It37ut1vTBfLkag7/XCfFf7aK5Qu7vp+QDw3aw0p2X/fWQAao1mvD4+CSarDRW6hgn0uiLA8ce7pMqAIF8pBsV2cJqUUN+NyVPEZm9+OTdrEPAcFQOAhWMSmnSuHGu2XF2XVE0wpgzshHlf53KpysYW5jrWCTa0Zo5oOcj3iGgKXlvTVFBQgOLiYgwdOpRbFhAQgJSUFOzfvx8TJ07E/v37oVKpOMEEAEOHDoVQKMSBAwcwYcIE7N+/H4MGDYJUev3DP3z4cLz55puoqKhAYGAg9u/fj2eeeYb3/sOHD3dKFzpiNBphNF5Pu1RVVTXDqK9TX9Ff3ZSP47rmfFKtNVmw/1w50jVqHC6swIDOwcj8zwFkpcVg7qg4XNYaAAC5f2mx+89SDIsLQ1TQ9X5kORcrEREgx9r9JYi/5qlktNjQNzoQHVU+nNFk3ZqkVE0wFo5NcDuL7USRFs8O7wYbGI+zwdI1apRW2c8VK54cb1o6sxXPffEHr37JsUB6w4yUBp+rUKW8UbMXm9OLyN3TZ0OWnS2tcdrGEfa8uRPjnkSRr1SEl7/JQ89IFaYOtBtryiUiHC2swIJv8rD03p6NiqrWFxWzenCbZ6nvSZ2NsJ4trUGlg8hno2hGi/2GKpcI3RqluvoeOn6nG1MzR7QM5HtENAWvFU3FxcUAgNDQUN7y0NBQbl1xcTFCQvhtMcRiMYKCgnjbxMTEOO2DXRcYGIji4mKP7+OKJUuWYNGiRU0YWcOoL5XA/r/juvRYNV4Z17zpxNprbTi+mZmKN384hbvjQ9E7yn7j6hsVCAbgPfmv2HUWC8fEY9HYBBgtNhhMVgT7StG5gx+W7zrDu+G9f38vLBybiEXfHXfqezbrjlhsP1Hs0psoXROMSSnRmPHJIUzsF4XpdbygOJ+kWDX+Obwb9py5gtVT+iLIV8pFp9ibVmSQj5Ngc8RXJsbZ0poWMyD0hlC7J4HORu3cifH6ntYXj0/ExH5RbovMy2tNjYqq1tdOxlVUsinUGCyY9PEBl+t+PVOG0moj3tx2ijN6za6Tln19QpLTda37nWbPB/udJsHUupDvEdEUvFY0eTtz587lRaeqqqoQGRnZrO9RXyRieWYyiqsMuOTQ6mLUsj3oGx3YLDl5rc4Ef7kYOpMVJqsVmSlR0JssWDQuEQu/PY7DhRU4UaTl6oMcozTbjhcjPiIAa/YWYNXkPggNkOKVsYnQmS2oNdqn5R8oKEeESo5RSeFcFILtvxamlCE+LAAmmw2LxiagxmDF5So9pCIhOvjLuBYmji7krOXAumm3w2JloPaTYvzKfdyNlJ0lxkan2Ne6ixbUddoGvL/eoSl4SklNS43Bfw8Wuk0b1fe0rjdZ602nhfjL3Iq2NDepVrdj8Wme1HR9N1St3owdJ0ux72y5yyilyepa3LWU0znReMj3iGgKXiuawsLCAAAlJSUIDw/nlpeUlKBXr17cNqWlpbzXWSwWXL16lXt9WFgYSkpKeNuwf9e3DbveFTKZDDKZrAkjaxz1RSJe3XqyRXLyRZV6vLb1BB4frEF6rBomC4P1By5gUko0Pvz1BHpGqtA3KhBjeoTj1S0neVEEtuA4a90h6ExWPLb+CB4d3Bl3dQ+Bj0QEoUCAWqMVd3YPxZUqA0qqDAjxv34uEyKU+PZYEd7dbp8pyQqiUYlhMFlsCJBL0Cc6kDduVkClx6rRv3MQ3v7xT66BreM2jmmRAB8JgnylmNg3Ei9+nesU0avrtF333AK4adpgON7MtXozFFIRREIBREIBlt7b0+246n9at7hMrwLX02meRNvLYxLwxg8nuWU5Fys9itzmSk3Xd0NlG2q7i1IO7e6+MbQ3RBcJ8j0imobXiqaYmBiEhYVh586dnEiqqqrCgQMH8PjjjwMABgwYgMrKShw5cgR9+vQBAOzatQs2mw0pKSncNi+99BLMZjMkEvsP4fbt29GtWzcEBgZy2+zcuRNPP/009/7bt2/HgAEDWmm0TaOlcvJanQmvbT2BJ+/qije3ncRzw7tBb7RgUko0Nhy4wNUm6cxWXK014eWx8TCabSi8qkOAjwQmixXBflJO2OhMVry7/QwOFlzFrDtiYTDbe8qx/eLq9kT7bnYqPvzlHPc3e2Oa0Os2xEcEQKszYeYdGtgY53qmJ4ZocPC8fZlIKHD6UWT3xbb4cIzaOT79u3Ladjy3xVUGJ8Ha3qNQnm7m7ma/1ScufGUij+vZ81s3AuMjFeFoYSUm/ec3TOwXhckp0QCAsAAZhsWFgoHz7LlFYxOaTYzUd0P1lXr+6WSjFDdrfzlvpyHnnXyPiKbQpqKppqYG+fnXn9IKCgrw+++/IygoCFFRUXj66afx6quvIjY2lrMciIiIwPjx4wEAcXFxGDFiBB5++GGsWrUKZrMZs2bNwsSJExEREQEAmDRpEhYtWoTp06djzpw5yMvLw/vvv493332Xe9+nnnoKgwcPxjvvvIOMjAxs2rQJhw8fxkcffdSq56OxtFROvqzGhL/17ohXttib2j7UvxNuC/LBx9+f4hVzy8RCZJ8tx4kiLeIjApBTWIFpqTHYdLAQw+PDMGd4dzx1lxUWG8M9mf98uhQf/nIOOpMVMrHQqUYlXROMnSdLncRKeqwaYpGA+zHMWnfIZVpk+ieHuCiQSCho8I9iXcHgymnbkUsV+ltm1o2nQu8bFReO6bS61yBMKedZNbBCFoDLa9+c1HdDVUhF9UYp2uN09puBxpx3SpcSjaVNRdPhw4dxxx13cH+zNUJTpkzBunXr8Pzzz6O2thaPPPIIKisrkZaWhm3btnEeTQCwfv16zJo1C3fddReEQiHuueceLFu2jFsfEBCAn376CTNnzkSfPn2gVqvx8ssvc3YDADBw4EBs2LAB8+bNw4svvojY2Fhs3ry5zTyaGkpL5eSrDGaEKGXck7zObMX3uZcxPT0GK37O56fiNGrMGx2Hy5X2WXSbDhbioQGdwDAMcv+qxPe5xR5tAVjjSXbZ/DrpGHb5lIGdMPJ9e73Wk3fFeizeNlpsvB5xTflRrO/cuuNmm3XTkGnZNyou3FFXRGl1JvS91vi37rUfFKvGw2kxdXdxQ5Ge+m6o9U3UoOnsrU9TbAQoXUo0BgHDMPXP0SXqpaqqCgEBAdBqtVAqla3ynlqdCbM35ri9ITX1h/lsaQ1Kq43I/I+9/cnqKX0xe2MOPpjUG5e1eoQq5TBbGXQM9LlWKG5BjdEKiUiI3adL8dGv9tTaojEJuD0mCPM2800f2QLjTQcu4Jm7u6HGaPeC2n+uHJsOFmJivygM7BwMkVCAWpMVRwsrsGZvARf52TAjxe3MJgBYPyMF0UEKdLxmfdAUPJ3b9Fg1enqY+r75iYHoFRXY5PdubTwJi7OlNbjrX7+4fe3OZwajS4gftw9X4qKoUu9WXDTWybgx+3IVcUiPVWPxuEQEKiTNcqN0N+6GnjeieaHzTjSFxty/vbamiaiflsrJq/2kMJivp6ZyLlaiT5QKMzccRVZaDEKVctgYBiVVBhy7ZIBYKECfToEwmGy4Oz4MSbcFcK9btusMPnqwLyp0Jp7nzYYDF/BwehdIxEI8seYgJvaLwt3xoYgPV0ImFsJPLuZmyNVl37lyD/3j1DCYrW5nLzUUT+f2lXGJGHUtTeSK9jTrpr5URkNTwJ6e1pszBdLQfbmLOOw5U4aXNudidI8IDO7a4YbTZO7GTdPZ2wY670RLQ6KpndMSOfkAhRTVRgs3S2nN3gKsmJQM7D3v5LUz645YiAR2X5vtJ0u5+qbkSBXiw5VIjlThp5PFiFT5IDbMH7VGK+6OD8Xd8aH4+XQpuocpUVZjwopd+UiOVHGNZFdO7u22pmjN3gJ8OysNC77NcyoGnpLaCbMbaUrpDk9O22yaqC7NOeumpYuIG5LKqC9NKRULcfZKDfxlYujNVtQaLag1WaHykSDE/3r7GVZcsGM6V1YLpY/J7Zg8jb0h6RRPkyTY1jAtmSaj6extA513oqUh0XQT0BI5+Y6BCrw2IQkvXeunNmtDDh4Z1BnP3t0NJqsNcondL+f05Src3ikI56/1p1uWmezSyHDkmASM/yDbSQitntKX26ahfjx2V2aLS38o1tiyuX4c3Z3blp510xpFxJU6M6YO7ITMflGcSzebBmVrs+ozvvw+rxhr9hZg9ZS+WPlzPtdaRyEVYf7oePSOUkFnskLpI4FcLMSCb49jx8nrNiHpsWosmZDES6U2x9gb4jLekvVnNJ29baDzTrQ0VNPUTLRFTVNLo9WZcLK4GkofCap0ZqgUEvhKRag1W1Glt0deFBIRzDYrDp2vxJZjRcgprORMJjmzyioDSmuMnO8SS6omGMlRgfijsBJP3KHB9E8OcaJqyYREj0XkWakxWOPCNBHg13O1ZLTGUx3Pje531sYcl5GSG6lVc6SoUo85//uD1z/QsQWNzmTlarNc1RGlXyva/yHvMhgGOHT+KnctWBPRuqaW6Ro1nh3eDTM+OYSyGhNv+SvjEhDkax9TY8bu7vrWV9uyekpfTP/ksMf6sxv97DRnLRfRcOi8E42FappuItrS56WsxoSJH/2GWXdqkFNY4SRQFFIRVk/pi8/2n8djd2gQo/Z1apWSHqvG/NHxeKvOjLj0WDUWjEmAwWzBiPgwyKRCrJjUGwaz3YogVClDaIDcqbdcukaNKamdMOfLY3jj2iwld01PW7oQuKVm3TSX/5a7z45WZ8Ivf17B1NQYZKZE86JMMvEFzrLBbGNw9koN1L5Sl+7z4z/IRnKUCi+M7M7z2nLXVHdPfhkYMFgz9XZevdqe/DIUlOvw8rfHMS8jrsFjb6oVgmNUsyn99Boa7aLp7G0DnXeiJSHR5MW0tc8Lm+Jw15U9OUoFg9mG2DAlyqtNUPvbW6XozVboTBbIxHb373e3n8aTQ7siMyWal0obu2Ivd+Nkn/wBe+uMXlGBWLO3gOfHE+AjgcFsxexrkRDW3fuFkd1x8aqea8GikIparRC4JWiOYlZPnx2zxYYtx4p41/LO7h3w+aP9IRWLsPi747wI1KBYNV6fkIS3fzzNS60ppCIkX4vSrJzcmxNffaMC3c4s3JtfjscNFmSlxfC2EYsE2HOmjBNl9Y29qVYIjtG0pvbTa0ykj6aztw103omWgkSTl9KcP9xNhS2qdBQoD6d3hq9MDJPFbhEwc8NR6ExWpGmCMTU1Bk9utEcQ7uzeAXNGxOHNbScRH2GfTbcuu4B3M2ZxfPJ3bJ3h6MV0ff85nNDSmazIKawAAN4NuF+nIABo00LgG+FGi1nr++yMTAzjCSaFVIRJKdE4dkmLrbmXnSJEv54pw8LvjiOzXxQy+0XBaLHBRyJCsJ8U7+3406l+bUyPcCikIreF/Fq9mefPBQCBiobVoLFjb0g0rkuIn8sI2ZMbc9A3OrDJ/fRuJh8ugiAaB4kmL8UbfrjVflJuar+jgFH7SbFiUjJGJoYhtYsaAT5iCARAaZWJa42Sc7ESkz/+Dc8M64quof6oqDXhlXGJePmbPJ5wYmtjCspqsXpKX+RctLfOeGZYVzwxRIPSaiPkEhFighV4betJ3o3YMWrgSLXBjPoK9eorBG7LtOiNFrPW99mZMrATbxmbTstKjXFZI6aQijCxX5ST6GWF7G/nrnLXJTu/HK9uOekUSXKkrhN8qiYYFqv9iuVcrES6JtiluHYce2OtEMKUcpTVmBDsK8WEXre5tSkoqzGhvNaENVNvd/IHq7vvuq+jVikEcfNDoslL8Qa/kQCFFK+MS8C8zfyp/d3D/FFjtCJr3WFeVOmjPWed6ov6xQTDbLFi959XMG9zHhaOTcDzI7ujxmCFUi6GUi7Gwu+OY+epK9zrUjXBCFHK8dCag9wN68MH+2DmnRpkpkTBVyZGrdHCmy3niK9MDKFA4HFs7Ow8V+exrdOiN+q/1ZCZY44kXzPqZPu71cVdjdLe/HIw19Y7CqQ9+WV4fEgXl6KprhM8K3y1OvsxbzpYiM8fHYAF3x53Ov+OY29sNK6+dI2ra56qCcayzGSnz5jjvtv6s0IQROtCoslLaWu/Ea3O/sQtEgCje0RwdUVRQQqUVBkgEgjw7v29EB2kgEQkRKXOiMXjEmGy2FClN8NPLsHPp0tw6aoOIQFy5BRW4GKFnqtbYm+WRosEcREBeHpYV64uyZUYEgsFuFprwvRPDrstTGf3e/hCBdI16iYVAntDWhS4sWLW+j47de0cWBHlzuYh2YP7OZvqrItULOR8vliuO8EX4q7uIVxkkW0CrZCKsDwzGa9tPYGekSpMHdgJRosNKh8Jbgv0gcFshVZnjww259Ryd9ec/Xw5ikLHfXvLZ4UgiNaDRJOX0pZ+I0WVerz8TR4m9ovChgMX8NCATli9pwB78sucBItCKuIsBir1FgT4SPBrfhnW7C1AcpQKQQopbGCQHBWIJ4ZoIBULUWu04PCFCjy5MQf/e2wgd0PyJIRKq4z4S2uvS3FXmO6YrhvQOQiLxydi/ua8BhUCsykWo8Xa5mlRlqYWs9b32SmtNvKWsWIp52IlUjXBTtegbmSqLq7WByqkePOeHrhQruM5wW88cAGZKdGY/PEB6ExW3vWYlxHHeT05Rh6B6/YUxy5WclEcV9G49GuO7Y2hIUaYgHO0yxtS6ARBtC4kmryUlmqR4gmtzoTSaiMKr+rw+BANThdX4dglLYQQYOadXfD8yO4wmiwY2yMCi7fYZ1ixtU5sfQubUmPdwiMC5LhSY8CopHAUa/V4alMO59GTHqvmTDLdCaF0jRpP3KGBSiHG4n+fAMAvTH9iiAZWhoHZYuNFqHaeuoKH0/V4+96eqNSZPBYCO6ZYVk7u7fEctYc2DPV9dtj/Z9flXKxEmibY7TVQ+TQucsWK0QCFFH4yMS5rDThXVoveUYFIjlRBJBDgnft6ooOfDFKxEAaz1R6V8ZHgxa/zXL4HK15W7MrnojhsNK5uofeoZfbGzg1NkdWXzvSXi7Ht6XQEKqQIVV5vFu4NKXQWqqsiiNaBRJMX05p+I+5qOlZPvR0GkwXLduUjp7AS/320P8avzEZWWgymXkvZySUiCAVAB38pPpnWDwqpCD5SEWxgUHhVB5VCgn/9eAo7HKIH6ZpgTEvthGqDmYtusEKITQWqfCQIU8qhN1twpcqEPlGBnNmlzmTFH4UV6BWpclnXBABXdWYE6kx4+8fT6H6tpUt8uBIfP9QX0cEKhKt8nFIsnpzIgfbThqG+z47jOqWPBBP7RuLFr3N51wAAQpQygIFTqo0lrY6Te11Rz/739e9Puo18LR6XCKnIBLPNc/k+G9FioziA3dX81S0nnArHG5IiY4VGfde82mDBfR/+5lSr1NYpdBaqqyKI1oNEk5fTUn4jjk+mQb5SzPs6z8l9Ozu/HEt/PM1NUZ91pwa5l7To48aHh02hrNiVD4VUhHkZcega6o/yGhP+OaI7HhuiwZUaI6Qie6pm1oYcrJzUG9Ou3aCz88u5/bKz6r7LLcKHv5xD/85BeHlsPGoNVpTX2tNLHfxlbpv6AnYBdKlCjx0nS3n+QsB1d+m6KZbcv7QNmr3VHvD02XG1rq7IkkuEWPjtceRcrMT6Gf2xeMtxnnBK0wTj9QlJsNgYDO0e4lHUvzIuEfO/yXNZ3B2u8kE0fHG2tMbjeBzFTaXehIXfHcfUgZ1cXivAc4rMUWjMulPjMi0J8Ovf6goxb2jZQXVVBNG6kGi6Ban7ZLp6Sl+X7UoAu9fR1GtT1JMjVZi9MQdbZqdh/jf8GXWOPkqA3fgyRCnHql/O4pFBnTFh5T4ncZOqCcbha9O62VQbOx19/7lyrlddeqwa8zLi8frWE8hMieaKyWfdqeFFn+ru23GWVl3YG6pjikUhFaFnRxVSYoJgg3un8damtVIvroTU0nt7oqzGBL3JjCUTklBrsqLKYIFSLkagLz9d5Qr2s3bkQoU9Onnts9Qx0AdhSjnv/Rrq4g0ARrMNe86UIbNflMf3d5Uiqys0GlIjx+IoxNoihV4XqqsiiNaFRNMthqsn04YW+hotNuhMVpTXGJGVGoOXRsWh1mSF1cqgg78MJqsVn2T1g59MDAEAhmFwe6cg1BqtSI5SuRVZrEllr0gV9GYrfr9YiVFJ4UiIUKJjoA9yL2lRWK7DjlNX8Lc+kdw+1uwtwIaH+4MB42aW1gWP46o2mHkplqy0GHy89xzXP8+xGTDrNN7atHXq5UYinXU/a47RSTbSV/e96nPxBuwCdt85+/VuSjq1rtBwrJHLSo2Bv1yMaoN7SwtHIdbWLTu8qa6KaBpUj9a+INF0i+HqydTVjcdxVpyvVIw1U2+H2s+ekgj2k+G9nWeQU1iJp4dqMDw+HHqLFQKBAH4yMSpqTTBbGVhsNsSG+sNktWHOiO6w2QCz1YZqowUMw0AkEPDMMJ/cmIPlmcn4vbACyZEqHC+qwme/XUB2fjlXoO14rDqTFTM+OYQ1U2/H4wYLtA6ztDYcuIAXRsZh/AfZbs8Fe4NjoxuOU+tdpR/7dQpq1R+z9p56aUoUxFGEVOpNMJpt2HeunBMvg2LVWDA2AWOW7wXgfsYf4D5F5kpoOJq3fv9kGu778De342qsB1RL4i11VUTTaOuHIqLxkGi6xdDqTU7L6t541H5SrJl6O6qvCRGBwIqjhRU4VaTFp1n98PrWE8gprMQHk3pDLhHixc25SI4KRE5hBU4XV9vNCb85zkubsbPp/OUivLfznAdrAQMWj0/CQ6sPYOHYBO5G5m5aPNtUOCstBgM6B8PKMFxK7oe8y04RLhbHGV6vjk/Ei1/n1htxc+x71hpPhu099dLUKIijCNHqTAhVynk1U+W1Ji764y615ilFVp/Q8JWK27xWqaF4Q10V0TTa+0PRrQqJplsMhdT5kjveeHIKK/HxlNvx5rZTLus7/rX9T3SPCED3iABc1uq5XmXsdHAAmLLmINZNux0mK4MagwUKmQg+YhEEAsBks+GVsYlY+B3f8Tk9Vo1FYxNQWK7DW9tOYlzybZyIYdNi6Rq1y5ukzmTFHxcrnWbSKaSiem+oWp0Jr2w5gV5RgYgM8vxk5y+XtOqTYXtPvdQrTmRinC2t8Sg+3UVxWKFQN7UGAFFBCoT4y9zecOoTGiqFpM1rlRqKN9RVEU2jvT8U3aoIGIapr00X0QCqqqoQEBAArVYLpVLZ1ofjEq3OhMtVBizecsIp+sLOdkuJCcbCb/PcNtZNjgrEyMQwCAUCFFVed/heObk3nlh/lLc/Nr1ntNjQMdAHP50owZq9BejfOQiPDOoCX5kIlyr03Gw6xz5fq6f0BQDM3piDZZnJ2HDgAialRGNtdgFXc8RGlEKUMlypNuKz3y5gVx1TRMdZfKXVRnRW+yI84HoB8tnSGtz1r18AwKPT+KBYNd6+tyee++IPlz90bI1Oc/3IaXUmzt9ILhG57IO285nB6BLi16h9tmbthFZnwuyNOS7FSXqsGqOSwjH3q1xuWWPEZ1Gl3q1QCG+m17Pny1WtkrfVoXg6VsI7ySmswISV+9yu3/zEQPSKCmzFI7p1acz9myJNtwiXruqw8Ns8/OPurtwU/7riI1Qph85scTuFm40oXSjXOa2rWxflWCMC2EUQ+/euU1cwOSUay3ed4SwKWFix1cFfhkqdGRsf7o/jRVr8du4qfjt3lVegrZCKoPaT4Y0fTmL/uatYlpkMo8XGEz3sLD7WdHPzEwN5NxPHaI5bg81YNV6fkASd0dIqT4YN6YPW2NRLW9ROeIqCPHGHBlnrDvG2b0xa4kYLsBvyendRLm+sQ2nLuiqiaVA9WvuERNMtwF8VOsz96hh6RgXij4ta7DhZgn4xQVg8LhELvslzEjeeYGeT1cVTQW7d6eLsfur2LWPTaWuzC3jH5CgY6hZor5t6O+IiApCV1hk+EiFeHZeI8loTzw/KMWVX94fI8YdLZ7LihS+P8eq52MLyRd8dxwuj4qCQitz6QjU0XeYpStGQPmjHLlY2KvXSlrUTrsSJWCjAyGV7XJ7HxojPGxUKTXk91aEQzQXVo7VPSDTd5Gh1Jlwo12FPfjmmpsZw6a7SKgNe/ibPpcuzJwJ8JNh/bbp3RICcE0qN8boBrkemTNbrxddZaTFYm13gJLzYv9kp6qwL+dHCChitNqzYlY8Vu/Lxw1PpMNsYvL/zjNsUWt0fIrWfFEv+loQQfxmMFhtClXLk/aXFq1tPOt3UDRYbr3lrXRryZFhflKK+PmjzM+LxcFpMo27MrV074UoUOqYRcwor3ApPwLtrtagOhWguqB6tfUKi6SbHPnXbfhNifZae3JiDT7P6uezz5SlilKYJhr9cjDV7CwAAH0zqjVl3aACA1wZl5hANxCIhzFa7SWVdrxvHyFO48no6w3HKf12y88vxxBANJn98gLefsT0iuOhPQVktnvviDyzLTAbD8L2b3P0Q1Zqs+P7YZaeZfo6pMJY9Z8rw+OAuLo+xIU+GDYlS1Ff8bTBbG/1j2poF5Q1JXbXntER7L84nvIu29vkiGg+JppucKoOZi+qw/9WZrE6d7lnYiJEQ4NU2pWvUeG54N2StO8QJiZkbjuLRwZ3xythEGMxW1BgtEIsEkIpEWLn7DO7pE4k/6kQVHCNPqZpgBPiIsfXJNNQaLbB5nvEPoUCANVNv54qis/PLsXjLcS76IxMLnZr5yiUiBPi4/iHiRIyL9jEAXEaVZBKhU0i9oU+GDYlStISgaC2R0tDUVXtOS7RnwUd4J1SP1r4g0XSTo5RLsPNUKdKvRXfYKJI7J2VWdGx6pD+eFwAVOjOUMglOXNYi8z/8Pm86kxWHC67CbGW4VigDOgfDYLbi6aFdYWOAlzLiAQHwl0MX+ic35iA5SoVp14rKo4MV8JOKUKQ1eByLzmTB9E8O8yJBbNrRMXrFFqGv2JWP7f8Y5HaGWX2pMMd6KxaVj7TJT4YNiVLEqH2bXVC0lkhpaOqqPacl2rPgIwjixiHRdJOj9pPi9OUqTEuLwYbfCrmZc54a0/aJUiHvLy2KtAacKNLiwf6dEKP2dTKKTNeoMS2tE2Zt4LdCqZvWUkhFWD8jBVdrTYgPV2J5ZjLPAXzeN3nISApHmkaNNE2wyzorR1HkKhLkqm4KAAqv6uArE7uc1VSfiKlrduloiNmUG3tDohQtIShaS6Q0JnXVXtMS7VnwEQRx45BouskJUEix6NosucSOAQhSSDB/dDzkIiH6xwQBAgEvOpCmCca0tBgIIMCOkyWYlBKN//vtPHpGqjBnRHcAgN5kRYBCgmKtAXKxEOum9YNCKsK248Uue3XpTFZcrTVxnk4srBBiozqvbDmBhWMTsOjbE041RnVFkWMkKMBHwlkKuMLdrKb6RIxjNK45booNjVK0hKBoDZHS2NSVk/WDwHm5N9JeBR9BEDcOiaZbgAiVD9etvtpgho9YhHmbc3GksBJbZqWhoLwWRosNUUEK2BgGy3acwb5z5chKi4FYIMDTQ7tCIhJCqzfDYLaiY6APrAwDlY8UKoUYi7ecQFxEgNtZUekatVMNVV0hZLTYsONkKf4xLBYjk8IwNbUTFFIxdCb3jVONFhvSNWr8WVLt5n3touzXM2UorjI43dQ8iZj0WDU6q33x+aMDoPQRI1AhRahS3uhz70hjohQtUefQ0rUTjU1deaPfUUOhOhSCuDUh0XSL4Pgjf7a0hkvLlVYbuQjQrDs1OFGkxeSUaExLi4FWb4aVYTgnb53JilRNMBaNTcCMdYfx8ZTb8da2U3h+RBze2naSS/3VNYZ8ZVwCwADfP5mOwqs6SEQCJyHERnUKr+pRpDUgVCmHr0yEzP+4b5yq8pFgenoMGAZOM/5SNcGYPyaBa9h7qUKPMKXcSZi4EjHpsWrMvEODjOV7ueNrrpv5zRylaIwoJL8jgiDaIySabkEca0985SLu/9mZc+uvtSxZf+CCkxCZfWcsTGYb5o6KQ6nWgMeGaMAwNszLiIfRasP8jHgwAGqMFvhIRCipMmDptlP454g4/Gv7aew4Wep0PI71SlKRkKtTmnWnxq39QXqsGhEqOUYts3e7d3QKZw0pC8pqeREoVx46dUWMr0yMwxcqeLMEgea9mbfHKEVD24Y0VBSS3xFBEO0REk23II61J6VVRq742nG6vlggwJwR3SESClBea4LFyqBEa4DRbMXf6wgKAPh2Vire3HaK15rlSrURMrEQabEh+D6vCM8P7w6j2corPndM06Vr1Dzn8Po62OvNVu44XHknrZl6O/ceORcrEezr+iZcNwrn2A/NkVv1Zt7YNFpDRCH5HREE0R4h0XQL4lh7MufLY1g/oz8WbznOCacVu/KRrgnG4vFJqDGaIRUJ0cFPgmOXKrF46wmX9UOXtYZr6TnnFigvj47HW9tOwWxl0KdTEJ4f2R2lVfYaJzZN1ztKhbmjuuPvq/Zzr2VF3PyMOLw0Kh7ny2uh8pEgOliBcJUPtDqT2xqaNE0w1H5SDO3eARNTovHkxhxM6HVbveeGbuZ8WiqNRn5HBEG0R1yb9XgRnTp1gkAgcPo3c+ZMAMCQIUOc1j322GO8fRQWFiIjIwMKhQIhISH45z//CYvFwttm9+7d6N27N2QyGTQaDdatW9daQ2x12NqTQbFqlNWYMPnj3zAtNQZbZ6fh80f744en0rFwbCJsjA1vbjuNvfllMFlseG/HGbcz1MRCwTX/pUCsntIXKyf3xuopfZEcFYirtSa8lBGPPy5W4r0dZ3D/h78h52Il5BIR0jRqrJ7SF6+OT4RQCPSp09W7T1QgOijlEAkBP6kYB89fxYGCqzhfVoMirR4vj0lAeqya95pUTTCmpsZg2c4zmNTfLpj6Rgc2yEOHbuZ8GpJGawqscHcF+R0RBOGteH2k6dChQ7Bar9+o8/LyMGzYMNx7773csocffhivvPIK97dCoeD+32q1IiMjA2FhYdi3bx8uX76Mhx56CBKJBK+//joAoKCgABkZGXjsscewfv167Ny5EzNmzEB4eDiGDx/eCqNsfdjak0uVelwo1wEAtp8sQb/oQCikQujNNhRXGjA/Ix5lNUb8fLoU6Rq1k3s2YJ+lVlJl4KJU/HVqjOsVgSlrDuLxIV0w8w4Nak1WBCokkAiFgAAwWWw4X6bDH39Vok+nQExN7QSjxYYAHwn85WJM/Og3ztvpj4uVGN0jHK9sOYFdp65AIRUhKy0Gjw/uAqFQAJ3JiqOFFVyReWa/KPSNDmywXQCZF/Jpqcgb+R0RBNEe8XrR1KFDB97fb7zxBrp06YLBgwdzyxQKBcLCwly+/qeffsKJEyewY8cOhIaGolevXli8eDHmzJmDhQsXQiqVYtWqVYiJicE777wDAIiLi8PevXvx7rvvuhVNRqMRRuP1afRVVVU3OtRWJ0AhRWm1EU+sP8otU0hFWJaZjC1//IWnh3aF3mSFRCTER7+ew4pJyQAY55qka75OdY0p2dYrmR/9hm5h/ghRyvHg6oPcLLzkqEDcHR+KsSuy3R7jmqm3c9Ettjfdwm+Po1dUIHadusJz/07XBKNnVCBPuAX4SHgppPoKmulmzqclI28380xCgiBuTrxeNDliMpnw2Wef4ZlnnoFAIOCWr1+/Hp999hnCwsIwZswYzJ8/n4s27d+/H0lJSQgNDeW2Hz58OB5//HEcP34cycnJ2L9/P4YOHcp7r+HDh+Ppp592eyxLlizBokWLmneAbUCIvwzpsWouBeNYDF5SZUCIUo4KnRk6kxWzNtiXT02NgdnKICpIAYlIgAqdCQqpCAvGJkBntMJgtkIhE0EhEaNSb8KyicnY59C417H4O62L6xQNi8FsRXqsvUA8PlwJANibX87ZGzjCtlRxJNChKLmhBc10M79OS0fe2uNMQoIgbl28vqbJkc2bN6OyshJTp07llk2aNAmfffYZfv75Z8ydOxf/93//hwceeIBbX1xczBNMALi/i4uLPW5TVVUFvV7v8ljmzp0LrVbL/bt48WJzDLHVCVBI8eY9PXh1QWxLlGqjFQ+tPohQpRzpsWouqjP9k8N47LMj+PuqfTh8vgJSkQiFV/W4eFWPshojGIaB0WyDzmRBoEKKvyr1SI5UYem9Pbk6J7b/nFgk8HB09kjRorEJWLO3gOfQXbfFiavl6bFqCABcKK9FSZXBY0GzVsevzQlQSNElxA+9ogLRJcTvlr2xO9a/OXKrRt4Igri1aVeRptWrV2PkyJGIiIjglj3yyCPc/yclJSE8PBx33XUXzp49iy5durTYschkMshkshbbf2sSofLBisxkFFcZUKW3wGy18SJD//rpNBaPS8T8zbm81FyfKBU6d/DFL2dKkRgRAIPZCplYiL1ny5GRFIZXt55ETmEllmUmY012Ac82IO1asfbe/DL3XkwaNcKVcmw7fhnJUSqeHYG7hsPs8lRNMKYM7ITRK/YiOUqFF0fGeSxo/qtSj7Jak1v/oYb6FN2MUOSNIAjCTrsRTRcuXMCOHTvw1VdfedwuJSUFAJCfn48uXbogLCwMBw8e5G1TUlICAFwdVFhYGLfMcRulUgkfH+9u59BcsGkSrc4Erd6MgZ2DER+u5Iwi39txGi+PTYDZwuCy1h59y/1LC53JioMFV/Hu9jPcvlI1wRjfKwKz7tBgxc/5XLqP7RUXppRDKRfjL60eceFKjEoKx+ItJ3iiJl2jxtxR3XGpQo/D5yt4LVfSHMwwHUmPVaODvwyrp/TlOY5n55ejuMrgcfzny3V4Yv1Rl+m69tzuo7mgNBpBEEQ7Ek1r165FSEgIMjIyPG73+++/AwDCw8MBAAMGDMBrr72G0tJShISEAAC2b98OpVKJ+Ph4bpvvv/+et5/t27djwIABzTwK74e9OYqEAvjWmGCy2jCwczD2ARi3IhvLM5N5jXfZ2WusG3dkkA9+PF6CkkoDOgTIMTopnFsH2M00O6t9cemqDrUWGz7dfx45hZV4ZFBnPHVXLKw2Bn5yMeRiEcw2GyRiAeIjAjgBlK5RY8HYeLz+/UnecafHqrF4XCJGLdvj1hbBE2yEqq7/ELX7IAiCIFgEDMMwbX0Q9WGz2RATE4PMzEy88cYb3PKzZ89iw4YNGDVqFIKDg3Hs2DH84x//QMeOHfHLL78AsFsO9OrVCxEREXjrrbdQXFyMBx98EDNmzOBZDiQmJmLmzJnIysrCrl278OSTT2Lr1q0NthyoqqpCQEAAtFotlEpl85+ENkCrM6G81gSrjYHVxkBnssLKMLjXwYCyLisn98YT64/i21mpeG/Hn+jRUYU0jRpWGwN/uRhSsQhXqg1QKST46UQJGAZIui0ARosNMWpfbM29zPW5YwVZcqSKa96b+5cWUrEAaRr7rEqdyQqxUIAAHwl0JgvGfbDP5XHNulODPworeOlFFnYmn+Osu53PDEaXED+cLa3BXf/6xe142e0ay62c7iMIgvAmGnP/bheRph07dqCwsBBZWVm85VKpFDt27MB7772H2tpaREZG4p577sG8efO4bUQiEbZs2YLHH38cAwYMgK+vL6ZMmcLzdYqJicHWrVvxj3/8A++//z46duyIjz/++Kb1aGoorlIyZ0trPL4mKkiBddNuh0QkxG/nrmJSSjRW7s7HpJRoLNt1xqmX3bTUGMzemIM+0YF48s5YnnBx9H1aPaUvL8L1Fv7kve+2p9Ihl4ow604NJ7oc2XSwEJ8/OgALvj3Oixo5zuRzhPUfagmfIkr3EQRBtE/ahWi6++674SogFhkZyUWUPBEdHe2UfqvLkCFDkJOT43EbwvMU9PRYNURCAYJ8pagxWpCVFoO12QVIjgrE2jqF4MD1fnLzMuIQFiCHr8z9xzHnYqVbc81UTTCOFlYgRCnHySItlmUmc+k8wJ5CXJ6ZjNe3nkDPSBWmDuwEX5kYtUYLr/bJEdZ/qLl9iijdRxAE0X5pV5YDRMtTUmXAqctVOFhwFaeKq1BSp4A6QCHFK+MSkaoJ5i1Pj1Vj/uh4VBvMuFJthFBgN6PMzi/n/uuK7Pxy9IoMhMXGQC4ROLVEYTlRpMWzw7shvc77spGiV7eexNrsAsRFBGBddgGy0q77Nc3LiMPKn/Ox49QVzjJhb34Z1mTb++TVFUyO/kPN3e6jpdqSEARBEC1Pu4g0Ea1DYXktFn53HPERAUiOVKGsxgiVwgSj2YpqgwlSsRhSkRBlNUYkRwVyBd5sEfUbP5xEfEQAcgorkBwViORIFQD3nkosl7V6PPLpEaj9pFg99XYAcEqhTUqJxoxPDmHN1NsxtdrIvW/dWXJZqTFYsSsf8zLiMaRrByikIgiFArz4dR7vPdfsLcCyzGQA4Am6uv5Dze0QTg2BCYIg2i8kmm5R6hYiy8VCFGsNeGywBr5SEUxWG6r0Fuw7W46TRVrMGRGHcR9ko3/nIPxzeHenHnNs+5UNBy5gWmoMNhy4gOEJdsNQd55KLLcF+nCu5JnX+sw9MaQLBAIBrDYG+x08o65UG3m1TXVhBZrBbEXfTkEAgJzCCqftHJ3PXxoVB5PF5tZ/qDl9iqghMEEQRPuFRNMtiGMhMjtDbUDnYIiEAkiFAlgZBlqdGWEqOQbFqtEvOghv/XiKS3nlFFY4GVKyImReRhyCFBLMvjMWvlIx1wLFnYFlqiYY2/KK0TNShccHd4FMIkSgQgqpSIhF3x1Ht3Al5yau8pEguJ50GCvQ5BIRcgoroPSRwM9NrRRbaD6h122IjwjwuN/m8imihsAEQRDtFxJNtxiOhchsdGjttdoeFrZO6G8r96FPlAoLxiagd7QK3ULtUzFnb8zBiknJEELAK8xOjlIhRCnHA9ea8n74YB9MGdiJiz4B/FRYuiYYU67NXGMFzKBYNVcMvfTenk7RHQBuRUfqNdPLNE0wtuRe5sa05G9JvP56jrS2UKGGwARBEO0XEk23GI6FyOzsNnez2rLS7PVBC789jgVjE3CmxG43oDNZkfuXFnNHdcdUrcFlfREAiIUCzL6WAhMLBJiXEQ+LzQa9yYYaowVqPykmfvQbrxD71zNluKw14FxZrd2/yE/q5IPkSnSwQm/jgQuYWsdCYPGWE1gz9XYIru2fpa2ECrUlIQiCaJ+QaLrFcCxETo5UOdUmsbBF1QCwN78cZgvDq01KjAjA93nFyCmscNs3LudiJRdBcjSQZD2XVk7u7dK9u0Jnht5sxc5TpTh9uQqLxiXy/IsiVD5Y8rckFF7VwWxjEKiQwGJlIBcLEefgHs6iM1mRte4QfngyHRYb4xVChdqSEARBtD9INN1iOBYi1zerzXF9jcHM9XtL0wTDaLG5nYGWqgnGvNFxmLByH/c3ayCZ6tA3zl2BuM5kwfRPDnOvW/BNHpbe25MTGVqdCS98leuUbls5ubdbEagzWVGhM6FXVKDHMRMEQRCEO0g03WI4FiLXN6vNcb2/jwRr9hZgYJdgzB+dgLIaI28GmqP9QM7FSjAAll8TVGzaLjlKxYknd013HUUVK8SSowJRVmPiRJM7r6P6xkMz0wiCIIgbgUSTl9PcPcocC5Hrm9XmGFkSiwT4+KG+CFXKUVBei87Bvlxxdd3oTrpGjT5RKlTqTEi4NitteWYyJ576RAXi1QmJeHXLCaf3rNvShE0TOvoXufM68jQemplGEARB3CjtomFve6AlGvZertRj9+krCFHKYLTYIJeIUFJlwJCuHRB+gz3KtDoTSqoNEAqEWPTtcd4sOEfx0jtKhZfHJOD97adxT99IzNpg7xM36w4Nak0WrN7LLyRPj1Vj4ZgEGCwWWG3A3vwrMFkY3N4pCGKhAGKRAEKBAKv3nMP8MQmoMVhQXmsviM65WOmyb9zKyb3RLdSfKwh310SXnQ34yb7zTn3d3rynxw2fM4IgCOLm46Zr2HsrotWZcPFqLRjwNS3DMLhYoYNCKrrhQuI/LmrxY95l9OkUiOdHdsOVahMiAuSQiIS4WmvE/x4fCMk136ZHh2hwWWvA8sxknCqugq9MBBvD4J/Du+GlDBEqak0wWmw4dqkSf1Xo8VelDiFKOToF+0EmFuJCeS3CA3wwc8NRLM9MxpbcYvxjWDd0CfGDraQa93243+1xBvhI4CMVcX+78zrSmaz478FCLL23J2oMFq8o+CYIgiBuHkg0eSkVtSaIhHbvIwYCVOvt6bnwABl0RjMqak03JATKa03o0TEAQb5SSERCVBssyP2rEtFB4ThfXguT1QahUAiLSACGAZb+eJoXjfrg57PISovBwM7BMFtsCFRIoDNZ0S3MH35yEWI6+EJnul5IXqQ14NWtJ5EcpeLSfmzKTSoSum3Gm6YJRoRKjktXawHYZ8558jp6ZVwiQpVyhDZPsK9JNHdKlSAIgvAOKD3XTDR3eu78lRoIBAJkny1DqFJ+PT2n1WNgFzUYhkGnDn7178gNZ6/UYOE3edhzLbWmkIqwekpfrPw5n1sG2FN1c0Z0R4Bcgpc252JvnXXT02IgEQohk4iwfNcZnmnmuuwC3vbpsWosGpuAi1f1qDVZ0FntC6FQgFqjGUofKRZ8k8ffXqPGc8O7YdUv+XhkUBe8u/1PzvgSsDcXrqg1ocpggdJHjECFFKFKeZPPSXPg6LbOMihWjTfu6cGzTSAIgiC8g8bcv0k0NRPNLZouldeiQqeHSuGDGpMVVdciTX5SESp1egQqfNAx2LdJ+9bqTJi14ShPHM26U+PWcylNE4x5o+Ox5dhlJEeqYLTYEBnkgx+Pl2DN3gIAwMw7umBkYhhMVgbVegv85CIoJGJUGy24VKGHv1yMiAA5Xt16AjtPXeH2naoJxitjE2GyWrA1t4TbPzsLj61x+m52KsYsz8bOZwajS4ifV4oTrc6EWRtz3DqPOwo+giAIwjugmqabARsDpY8PXvg6lydk0jTBeG18EmBrutYtqzHxBBPg2ehyb345tDozb/2sOzX4o7CCK9p++8c/8cHPZ11GmNjCcoPZiv3nrvL2nZ1fjoXf5mHuqDi37w/YDS8Be0rPsRWMI7+eKcMLXx7jxElrp8ncWSGwx+Zom0AQBEG0P0g0eStCAV6sI5gAu4B5aXMu3piQ1ORdu5qyX5/RpdKH73G0Zm8BVkxKBhz6z+lMVmw4cAEvjYqH9VrBulRkjxhtOHABc0fGca1ZHNmTX44XBQKP72+x2kWiVCzE5SqDR3FSXmtCrcna6pEod1YILNX1rCcIgiC8GxJNXkq1yeoyVQbYhVO1i/YjDUXpwuSxPmNIH4kQaZpgLoKkM1kxa0MOHhnUGc/e3Q1mqw0KqQhyiQhHLlzFwu9OcFGoVE0wZt0RC6PFiuRIlcv9l9UYka4JdoqAsa8/WliBVE0wLmsN8JV6/thabUyDIlHNjavz6giZaxIEQbRvPN8piTajSt9yUQt2yr4jrDGkK1I1wfjpZDEWj09EmsM2OpMVh89fhcpXgmc+/x33frgfNoaBxcZgeWYyVk7ujdVT+iIjKRwGsxV/VRrcRrTMVgZTUmOcjoFN7Z0o0mLBmASsP3ABtSaLx/FZbUy9abKWwNV5ZSFzTYIgiPYPRZq8lLrpsLrcSNSCnbLvGI1Zs7cAq6f0hdAh3QbYa6imXjO6/L99hfhgcm+8KBJyFghmqw1/letwsUIPADhXVosirYE3i421G1iemQyx0DkNx0aS1uwtQFZaDKandYZYJIDaTwYBgBKtAZNTovHGDyex69QVxEcEeHT+1tUjqloqTebJCuHNe3pQPRNBEEQ7h0STl+IrFfHSYY6kaYLh62D22BQiVD54ZWwCakwW1BqtEAsFKLhSiz6dAvGPYV1RqTchQuUDuViIV7eehM5khc6kx9gV2Vz0Z9q6Q0iOUiHZoQmuv0zssqA7VROM0mojUGeypqMo05ms3GvTNMFYMDYB5TUmTF13CKun9MWua7Pu3DUKZsVJXUfxukjFQpy9UtMiheERKh8sz0xGWY2JzDUJgiBuMkg0eSsMg4VjE7HwW753UZomGAvHJjqJj6ZQXmuyCxkAz33xBzbPTMW3W4rw0a/nsHJyb+Re0iIqyAcjE8PwzLBuuKy1R5NcNeAF7MLIXy52Mqpka5rClTKcL9dh08P9AYHd6VsuFuK1a6KMJV2jxvwx8Vjy/UlMTokGwC9Ud9UoODpYgY7XjC+1OpNLx3D2WL7PK8aKXfktVhgeoCCRRBAEcTNCPk3NRHP7NF0ur8XlGiNMVga+MhFqDFb4yUWoNVohFQkQ7idDeBN9mgC7p9DxoirozXaxMv2Tw/jwwT6QigQI8pXBbLUhyFcCoUCA/WevIjxAjgCFBBKREHqzFWaLDfvOlXM+SumxaszLiAfD2FBlsEAkFMBsZaCQiiAWCmBjGExdewjdw/zxz+Hd8f7OPxEfEcCl5Bz9mTqrfbF46wnsOnWF84/KSo3B9E8Oux3PtqfS0T38+nkvqtQ7pckce+qxIo38kwiCIG5tyKfpJkHtK0P2uXKE+Nsb9upMQpRWG5Ha2XXBdmMoqzHh4PmrCFXKwTAM0jVqiIUCzNyQg2WZydhw4ALiIwKQEh2Evp0CYbExqDaYIZeIEOInhVAgQFoXNfrFBCFQIYHFyuBqrRHhAXJYbYBIKECRVm+vYRILcVlrwPsTk+EvF+PD3fl4OL0Lpn9yiJeSA+xRpkXjEpxScaVVBrd1TGmaYAT68kWPY5qsQmeCVm/mImSOUS3yTyIIgiAaCs2e81KENgZCAL0iAxCu8kGwrxThKh/0igyAUGBffyPUGM3o2VGFLmoFunTwxdxR3VFaZUBylApPbsxBfEQAkiNVqDZZUHhVh5zCCuw/V45KnQn5V2ox4d/7UGuyIFAhQY3BCrFIAJ3Jir+v2g+t3ozcv7Q4WliJGqMFBrMNHVU+UEhFuFpjQmyYEkIBgz4OtVCAXfwsHp+I8hojt4xNxV3VmbB4XCLSNWqn1ywcmwi5C8uEAIUUXUL8IBIKMPtaCpGd1bdm6u2YdacGCqmI/JMIgiCIBkHpuWaiudNzp4urIIAJPhJ7G5VqvRn+19qo6M0GMJCgW1jT3+dCWS1e3JyLnMJKPDq4M4bFhcBPJoHJYsOi74479Z+blhqDjQcu4OUxCdh3thxbjhW5jPqka4Lx/IjuEAsFsDAM9CYb/GUi+EhF0JttKNYaoPaX4mRRFYq0BiTdFgCjxQaVjwSBCikeXHMAb97Tw2UqTu0nxZv39EC4ygdanRl+chFKq4yY8+UxfDYjBeFKucuIUWFZLQwWGxZvcT2uzmpfdL6BPn4EQRBE+4XSczcBDICiShsua6837K02WlBSZUB4gA/CVTdmlGiy2jjR8+72M3h3+xkopCLMvKMLFo1NhN5ihd5kha9MDIEAYGwMJqVE44GPD+DV8YmYfWcsAP7stfRYNV4dn4i3fjiFjJ4ReGL9Ud57rp7SF9M/OQyFVMSrYwrxl+HPkmrM+PQwdCYr5xlVV5SV1ZiwJrsAyVGBTjP0zl2pxetbTzoVdhdV6pF9thxbcp1FXnZ+OQQA3rmv1w2dS4IgCOLWgESTl+IrEeGz384jLiLAyfNo58kSLByTcEP7rzE6exnpTFa8/eOfiFH78QTP/03rh1CVHOuyC3CxQo/H1x/F54/2xwsj4iAQ2F/nJxPBZgO25hYhNswfMWrnInV2BlzdOqbVU/rixa/zuL/dWQo4FnLXRSYW4vCFCvzy5xX0jQ5EjdGCIF8p5n2dh6mpnTy6q9cYLAi98eAgQRAEcZNDoslLkQCYPzoB8zbn8gRGmiYYr01Iwo025PDU8qNuS5UDF67i7KFqTE2NwfMju6PWYIVCIsaC7467dN5O0wTjzu4hTm1R3LVqqesSXtdSQCEVQyIS4ExJtVMhN2AXU7l/abEsMxlrswsw96tcAHYxtie/DJkpUR7PBdU0EQRBEA2BRJOXorNZIRWI8Mq4RHtq7lpNk0wshBiA3tb03nPA9ZYfrryMSquNvHVs5GdNdgEXsVFIRVg9pS/AgOfJxKboDBYLFo9PwvzNedz6nIuVTh5OgGsx5RiNWj2lL2ZvtM/q6xMdyBNqbPTpeJEWax2OD7guxurrq+cro68BQRAEUT90t/BSTFagTKeD2t/ZePEvrQ5KheyG9u+p5ccdXTtgcNcO3Do28jM/Ix7zM+Kh1ZvhIxVBIhLgzb/3gN5kdet+vfS+nqjQmVCtt8DfR4x7et+GeZvzeOKmpMqA9Fi1y6hVqiYYORcruWP44rEBmDtKgPNltZCJhZyNwPLMZLy7/QzvtaxYclcjxe5fKqJJpARBEET9kGjyUnwlIrz9awHirk39ZwvBcy5W4mSR9oZrmoD6W36w6yr1JhjNdjPLxf8+AZ3JyrUsCa/HTTtUKefVZGl1JozuEcE5ecvEQuQVaTE9LcYpalW3hklnsqKkyohAhQQbDlzgOaW7agTMiiV3NVLpmmBMSY2BVm8C0HSjUIIgCOLWgCwHmonmthwovFIDmwCYt9m5jcqr45MgZBhEteI0ea3O1Gz91Fy5dQ+LC8GisQmoMVpxsUIHwC56WMdxwC6ikqMCkdYlGBEqH8zbnMftg52Z54hCKsKyzGR8kn0eRworuBl7ABCilGHnyVJ89Os5fDcrDV1CyHKAIAjiVqQx92+vFk0LFy7EokWLeMu6deuGU6dOAQAMBgOeffZZbNq0CUajEcOHD8fKlSsRGhrKbV9YWIjHH38cP//8M/z8/DBlyhQsWbIEYvH1INvu3bvxzDPP4Pjx44iMjMS8efMwderURh1rs/s0lVTZW5FYbGAg4GqaBGAgFQthsTHo1o6nfHkSYZeu6vDCV8d4YpGNOm06WIjXJiQhVCnn7SNQIcX8b/KcarQUUhHWTL0dNhuDSr2ZS+mxYozaqBAEQdza3FQ+TQkJCdixYwf3t6PY+cc//oGtW7fiiy++QEBAAGbNmoW//e1vyM7OBgBYrVZkZGQgLCwM+/btw+XLl/HQQw9BIpHg9ddfBwAUFBQgIyMDjz32GNavX4+dO3dixowZCA8Px/Dhw1t3sA74ikV4/YeTuKdPJEKUMtgYwGqzobTKiC+PXMSLI+Pa7NiaA09NbTsGKfDmPT1woVzHEzr/PViIxeMSuXRf3X24qtHqGx2ITkEK2ACX9Vtv3tODBBNBEATRILw+0rR582b8/vvvTuu0Wi06dOiADRs24O9//zsA4NSpU4iLi8P+/fvRv39//PDDDxg9ejSKioq46NOqVaswZ84cXLlyBVKpFHPmzMHWrVuRl3fdJ2jixImorKzEtm3b3B6b0WiE0Xi93UdVVRUiIyObLdJ0/koVBAIRXtqc65See21CEhibFZ06tN9IU0NoSkrQ02uaM8VIEARB3BzcVJGmM2fOICIiAnK5HAMGDMCSJUsQFRWFI0eOwGw2Y+jQody23bt3R1RUFCea9u/fj6SkJF66bvjw4Xj88cdx/PhxJCcnY//+/bx9sNs8/fTTHo9ryZIlTqnD5kRvYaA36LBkQpJTG5UrVTr4yG/+m72naFRTXtOU/REEQRAEi1fPtU5JScG6deuwbds2/Pvf/0ZBQQHS09NRXV2N4uJiSKVSqFQq3mtCQ0NRXFwMACguLuYJJnY9u87TNlVVVdDr9W6Pbe7cudBqtdy/ixcv3uhwecjFIqz4pQD/PXIJRZV6lNWaUFSpx3+PXMKKXwogF4ua9f0IgiAIgvCMV0eaRo4cyf1/jx49kJKSgujoaHz++efw8fE81b2lkclkkMluzCvJExKhAI8M6oLlu87wHMFTNcGYfWcsJEJBi703QRAEQRDOeLVoqotKpULXrl2Rn5+PYcOGwWQyobKykhdtKikpQVhYGAAgLCwMBw8e5O2jpKSEW8f+l13muI1SqWxTYVZSbUAHP6mTp1FptREd/KUorTGgYzB5CxEEQRBEa+HV6bm61NTU4OzZswgPD0efPn0gkUiwc+dObv3p06dRWFiIAQMGAAAGDBiA3NxclJaWctts374dSqUS8fHx3DaO+2C3YffRVihkEtz/0W8I8ZchRClDoEKKEKUMIf4y3P/hb/CR3mj3OYIgCIIgGoNXR5qee+45jBkzBtHR0SgqKsKCBQsgEomQmZmJgIAATJ8+Hc888wyCgoKgVCoxe/ZsDBgwAP379wcA3H333YiPj8eDDz6It956C8XFxZg3bx5mzpzJpdYee+wxrFixAs8//zyysrKwa9cufP7559i6dWtbDh3+UhG6h/k7GTYC9hl0/lKqaSIIgiCI1sSrI02XLl1CZmYmunXrhvvuuw/BwcH47bff0KFDBwDAu+++i9GjR+Oee+7BoEGDEBYWhq+++op7vUgkwpYtWyASiTBgwAA88MADeOihh/DKK69w28TExGDr1q3Yvn07evbsiXfeeQcff/xxm3o0AYDNZsVr45OQpgnmLWctB2w32LCXIAiCIIjG4dU+Te2J5nYEv1hei215f2FE4m1OlgPs8kiqaSIIgiCIG+Km8mm6VTHarBiWcBvmfu1sbvnqhCQYKdJEEARBEK0KiSYvRSQQYtUv+VgwNgEWK8NFmsQiAVbtzsejg7u09SESBEEQxC0FiSYvRSwQ4G+9I/HKtyewJ/96v7R0jRqz74qFWEA+TQRBEATRmpBo8lIsNgZqPylGJYVhamqn6z5NVQao/aSw2KgUjSAIgiBaExJNXooAQFmNATEdfOErE6PGYIWfXARfmQjlNUZ08Gs5N3KCIAiCIJzxasuBWxmJUACGEeDclVqUVhlRoTOhtMqIc1dqwTCgNioEQRAE0cqQaPJSynUGhCnlqCuNBADCAuQo1xna4rAIgiAI4paFRJOXIhFLMOPTw+gbE4QIlQ/UvlJEqHzQNyYIMz45DImY2qgQBEEQRGtCNU1eilwsRIhShmH/+tVpXZomGHIx6V2CIAiCaE3ozuulmGw2zB+d4LKNystjEmCy2droyAiCIAji1oQiTV4KYwPe33ka01JjMGdkd272XGmVEe9uP42n7ura1odIEARBELcUJJq8FIEAuKdPJNZkFyDboY1KqiYY01JjQN6WBEEQBNG6kGjyUlQKKd7adgrJUYHISo3hzC1zLlZi44ELeP1vPdr6EAmCIAjiloJEk5ciFwsxf3QC5m3OxYpd+dzyNE0wXpuQRIXgBEEQBNHKkGjyUqr0Zry97aTLmqa3fjiFF0Z2R4BC2taHSRAEQRC3DCSavJRakxVb80qwNa/E5frZd8W28hERBEEQxK0N5Xi8lCqDxeP66nrWEwRBEATRvJBo8lJ8ZSKP6xX1rCcIgiAIonkh0eSl+EpFSK1jbMmSqgmGr5REE0EQBEG0JiSavJRAhRSz74x1Ek6pmmDMvjMWgVQEThAEQRCtChWCeykBCimigxQY3SOC59NUWm1EpyAFzZwjCIIgiFaGRJMXE67ywajEMJTVmFBtMMNfLkHf6EASTARBEATRBpBo8nICFFISSQRBEAThBVBNE0EQBEEQRAMg0UQQBEEQBNEASDQRBEEQBEE0ABJNBEEQBEEQDYBEE0EQBEEQRAMg0UQQBEEQBNEASDQRBEEQBEE0ABJNBEEQBEEQDYBEE0EQBEEQRAMg0UQQBEEQBNEAqI1KM8EwDACgqqqqjY+EIAiCIIiGwt632fu4J0g0NRPV1dUAgMjIyDY+EoIgCIIgGkt1dTUCAgI8biNgGiKtiHqx2WwoKiqCv78/BAJBs+67qqoKkZGRuHjxIpRKZbPu25uhcdO4bxVu1bHTuGnc3gDDMKiurkZERASEQs9VSxRpaiaEQiE6duzYou+hVCq96oPWWtC4by1u1XEDt+7Yady3Ft447voiTCxUCE4QBEEQBNEASDQRBEEQBEE0ABJN7QCZTIYFCxZAJpO19aG0KjRuGvetwq06dho3jbu9QYXgBEEQBEEQDYAiTQRBEARBEA2ARBNBEARBEEQDINFEEARBEATRAEg0EQRBEARBNAASTW3ABx98gE6dOkEulyMlJQUHDx70uP0XX3yB7t27Qy6XIykpCd9//z1vPcMwePnllxEeHg4fHx8MHToUZ86cackhNJnGjP0///kP0tPTERgYiMDAQAwdOtRp+6lTp0IgEPD+jRgxoqWH0WgaM+5169Y5jUkul/O2aS/XvDHjHjJkiNO4BQIBMjIyuG3aw/X+9ddfMWbMGEREREAgEGDz5s31vmb37t3o3bs3ZDIZNBoN1q1b57RNY383WpvGjvurr77CsGHD0KFDByiVSgwYMAA//vgjb5uFCxc6Xe/u3bu34CgaT2PHvXv3bpef8+LiYt523n69gcaP3dX3VyAQICEhgdvG2685iaZW5r///S+eeeYZLFiwAEePHkXPnj0xfPhwlJaWutx+3759yMzMxPTp05GTk4Px48dj/PjxyMvL47Z56623sGzZMqxatQoHDhyAr68vhg8fDoPB0FrDahCNHfvu3buRmZmJn3/+Gfv370dkZCTuvvtu/PXXX7ztRowYgcuXL3P/Nm7c2BrDaTCNHTdgd8x1HNOFCxd469vDNW/suL/66ivemPPy8iASiXDvvffytvP2611bW4uePXvigw8+aND2BQUFyMjIwB133IHff/8dTz/9NGbMmMETEE35DLU2jR33r7/+imHDhuH777/HkSNHcMcdd2DMmDHIycnhbZeQkMC73nv37m2Jw28yjR03y+nTp3njCgkJ4da1h+sNNH7s77//Pm/MFy9eRFBQkNN33KuvOUO0Kv369WNmzpzJ/W21WpmIiAhmyZIlLre/7777mIyMDN6ylJQU5tFHH2UYhmFsNhsTFhbGvP3229z6yspKRiaTMRs3bmyBETSdxo69LhaLhfH392c++eQTbtmUKVOYcePGNfehNiuNHffatWuZgIAAt/trL9f8Rq/3u+++y/j7+zM1NTXcsvZwvR0BwHz99dcet3n++eeZhIQE3rL777+fGT58OPf3jZ7L1qYh43ZFfHw8s2jRIu7vBQsWMD179my+A2thGjLun3/+mQHAVFRUuN2mvV1vhmnaNf/6668ZgUDAnD9/nlvm7decIk2tiMlkwpEjRzB06FBumVAoxNChQ7F//36Xr9m/fz9vewAYPnw4t31BQQGKi4t52wQEBCAlJcXtPtuCpoy9LjqdDmazGUFBQbzlu3fvRkhICLp164bHH38c5eXlzXrsN0JTx11TU4Po6GhERkZi3LhxOH78OLeuPVzz5rjeq1evxsSJE+Hr68tb7s3XuynU9x1vjnPZHrDZbKiurnb6fp85cwYRERHo3LkzJk+ejMLCwjY6wualV69eCA8Px7Bhw5Cdnc0tv1WuN2D/jg8dOhTR0dG85d58zUk0tSJlZWWwWq0IDQ3lLQ8NDXXKZ7MUFxd73J79b2P22RY0Zex1mTNnDiIiIng/JiNGjMCnn36KnTt34s0338Qvv/yCkSNHwmq1NuvxN5WmjLtbt25Ys2YNvvnmG3z22Wew2WwYOHAgLl26BKB9XPMbvd4HDx5EXl4eZsyYwVvu7de7Kbj7jldVVUGv1zfLd6c9sHTpUtTU1OC+++7jlqWkpGDdunXYtm0b/v3vf6OgoADp6emorq5uwyO9McLDw7Fq1Sp8+eWX+PLLLxEZGYkhQ4bg6NGjAJrnt7I9UFRUhB9++MHpO+7t11zc1gdAEA3hjTfewKZNm7B7925eUfTEiRO5/09KSkKPHj3QpUsX7N69G3fddVdbHOoNM2DAAAwYMID7e+DAgYiLi8OHH36IxYsXt+GRtR6rV69GUlIS+vXrx1t+M15vAtiwYQMWLVqEb775hlfbM3LkSO7/e/TogZSUFERHR+Pzzz/H9OnT2+JQb5hu3bqhW7du3N8DBw7E2bNn8e677+L//u//2vDIWpdPPvkEKpUK48eP5y339mtOkaZWRK1WQyQSoaSkhLe8pKQEYWFhLl8TFhbmcXv2v43ZZ1vQlLGzLF26FG+88QZ++ukn9OjRw+O2nTt3hlqtRn5+/g0fc3NwI+NmkUgkSE5O5sbUHq75jYy7trYWmzZtatAPpLdd76bg7juuVCrh4+PTLJ8hb2bTpk2YMWMGPv/8c6c0ZV1UKhW6du3arq+3K/r168eN6Wa/3oB99u+aNWvw4IMPQiqVetzW2645iaZWRCqVok+fPti5cye3zGazYefOnbzIgiMDBgzgbQ8A27dv57aPiYlBWFgYb5uqqiocOHDA7T7bgqaMHbDPElu8eDG2bduGvn371vs+ly5dQnl5OcLDw5vluG+Upo7bEavVitzcXG5M7eGa38i4v/jiCxiNRjzwwAP1vo+3Xe+mUN93vDk+Q97Kxo0bMW3aNGzcuJFnLeGOmpoanD17tl1fb1f8/vvv3Jhu5uvN8ssvvyA/P79BD0Zed83buhL9VmPTpk2MTCZj1q1bx5w4cYJ55JFHGJVKxRQXFzMMwzAPPvgg88ILL3DbZ2dnM2KxmFm6dClz8uRJZsGCBYxEImFyc3O5bd544w1GpVIx33zzDXPs2DFm3LhxTExMDKPX61t9fJ5o7NjfeOMNRiqVMv/73/+Yy5cvc/+qq6sZhmGY6upq5rnnnmP279/PFBQUMDt27GB69+7NxMbGMgaDoU3G6IrGjnvRokXMjz/+yJw9e5Y5cuQIM3HiREYulzPHjx/ntmkP17yx42ZJS0tj7r//fqfl7eV6V1dXMzk5OUxOTg4DgPnXv/7F5OTkMBcuXGAYhmFeeOEF5sEHH+S2P3fuHKNQKJh//vOfzMmTJ5kPPviAEYlEzLZt27ht6juX3kBjx71+/XpGLBYzH3zwAe/7XVlZyW3z7LPPMrt372YKCgqY7OxsZujQoYxarWZKS0tbfXzuaOy43333XWbz5s3MmTNnmNzcXOapp55ihEIhs2PHDm6b9nC9GabxY2d54IEHmJSUFJf79PZrTqKpDVi+fDkTFRXFSKVSpl+/fsxvv/3GrRs8eDAzZcoU3vaff/4507VrV0YqlTIJCQnM1q1beettNhszf/58JjQ0lJHJZMxdd93FnD59ujWG0mgaM/bo6GgGgNO/BQsWMAzDMDqdjrn77ruZDh06MBKJhImOjmYefvhhr/thYZjGjfvpp5/mtg0NDWVGjRrFHD16lLe/9nLNG/tZP3XqFAOA+emnn5z21V6uNzulvO4/dqxTpkxhBg8e7PSaXr16MVKplOncuTOzdu1ap/16OpfeQGPHPXjwYI/bM4zdeiE8PJyRSqXMbbfdxtx///1Mfn5+6w6sHho77jfffJPp0qULI5fLmaCgIGbIkCHMrl27nPbr7debYZr2Wa+srGR8fHyYjz76yOU+vf2aCxiGYVo4mEUQBEEQBNHuoZomgiAIgiCIBkCiiSAIgiAIogGQaCIIgiAIgmgAJJoIgiAIgiAaAIkmgiAIgiCIBkCiiSAIgiAIogGQaCIIgiAIgmgAJJoIgiAIgvBafv31V4wZMwYREREQCATYvHlzo/fBMAyWLl2Krl27QiaT4bbbbsNrr73W6P2QaCIIggDQqVMnvPfee219GARB1KG2thY9e/bEBx980OR9PPXUU/j444+xdOlSnDp1Ct9++y369evX6P2Im3wEBEEQXsKYMWNgNpuxbds2p3V79uzBoEGD8Mcff6BHjx5tcHQEQdwII0eOxMiRI92uNxqNeOmll7Bx40ZUVlYiMTERb775JoYMGQIAOHnyJP79738jLy8P3bp1A2BvfN4UKNJEEES7Z/r06di+fTsuXbrktG7t2rXo27cvCSaCuEmZNWsW9u/fj02bNuHYsWO49957MWLECJw5cwYA8N1336Fz587YsmULYmJi0KlTJ8yYMQNXr15t9HuRaCIIot0zevRodOjQAevWreMtr6mpwRdffIHp06fjyy+/REJCAmQyGTp16oR33nnH7f7Onz8PgUCA33//nVtWWVkJgUCA3bt3AwB2794NgUCAH3/8EcnJyfDx8cGdd96J0tJS/PDDD4iLi4NSqcSkSZOg0+m4/dhsNixZsgQxMTHw8fFBz5498b///a85TwdB3DIUFhZi7dq1+OKLL5Ceno4uXbrgueeeQ1paGtauXQsAOHfuHC5cuIAvvvgCn376KdatW4cjR47g73//e6Pfj9JzBEG0e8RiMR566CGsW7cOL730EgQCAQDgiy++gNVqRVxcHO68804sXLgQ999/P/bt24cnnngCwcHBmDp16g2998KFC7FixQooFArcd999uO+++yCTybBhwwbU1NRgwoQJWL58OebMmQMAWLJkCT777DOsWrUKsbGx+PXXX/HAAw+gQ4cOGDx48I2eCoK4pcjNzYXVakXXrl15y41GI4KDgwHYH1SMRiM+/fRTbrvVq1ejT58+OH36NJeyawgkmgiCuCnIysrC22+/jV9++YWrZVi7di3uuecefPTRR7jrrrswf/58AEDXrl1x4sQJvP322zcsml599VWkpqYCsKcJ586di7Nnz6Jz584AgL///e/4+eefMWfOHBiNRrz++uvYsWMHBgwYAADo3Lkz9u7diw8//JBEE0E0kpqaGohEIhw5cgQikYi3zs/PDwAQHh4OsVjME1ZxcXEA7JGqxogmSs8RBHFT0L17dwwcOBBr1qwBAOTn52PPnj2YPn06Tp48yQkbltTUVJw5cwZWq/WG3texVio0NBQKhYITTOyy0tJS7ph0Oh2GDRsGPz8/7t+nn36Ks2fP72oNXAAAAuRJREFU3tBxEMStSHJyMqxWK0pLS6HRaHj/wsLCANi/6xaLhfcd+/PPPwEA0dHRjXo/ijQRBHHTMH36dMyePRsffPAB1q5diy5dujQpeiMU2p8nGYbhlpnNZpfbSiQS7v8FAgHvb3aZzWYDYH8qBoCtW7fitttu420nk8kafZwEcStQU1OD/Px87u+CggL8/vvvCAoKQteuXTF58mQ89NBDeOedd5CcnIwrV65g586d6NGjBzIyMjB06FD07t0bWVlZeO+992Cz2TBz5kwMGzbMKa1XHxRpIgjipuG+++6DUCjEhg0b8OmnnyIrKwsCgQBxcXHIzs7mbZudnY2uXbs6hfQBoEOHDgCAy5cvc8sci8KbSnx8PGQyGQoLC52eiiMjI294/wRxM3L48GEkJycjOTkZAPDMM88gOTkZL7/8MgB7Gv6hhx7Cs88+i27dumH8+PE4dOgQoqKiANgfgr777juo1WoMGjQIGRkZiIuLw6ZNmxp9LBRpIgjipsHPzw/3338/5s6di6qqKq5e6dlnn8Xtt9+OxYsX4/7778f+/fuxYsUKrFy50uV+fHx80L9/f7zxxhuIiYlBaWkp5s2bd8PH5+/vj+eeew7/+Mc/YLPZkJaWBq1Wi+zsbCiVSkyZMuWG34MgbjaGDBnCi/rWRSKRYNGiRVi0aJHbbSIiIvDll1/e8LFQpIkgiJuK6dOno6KiAsOHD0dERAQAoHfv3vj888+xadMmJCYm4uWXX8Yrr7zisQh8zZo1sFgs6NOnD55++mm8+uqrzXJ8ixcvxvz587FkyRLExcVhxIgR2Lp1a5PN9giCaD0EjCf5RhAEQRAEQQCgSBNBEARBEESDINFEEARBEATRAEg0EQRBEARBNAASTQRBEARBEA2ARBNBEARBEEQDINFEEARBEATRAEg0EQRBEARBNAASTQRBEARBEA2ARBNBEARBEEQDINFEEARBEATRAEg0EQRBEARBNID/B67HQsSWdAOxAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(df, x=df['Volume'], y=df['Close'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Splitting of Train and Test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 418,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df.drop('Close', axis=1)\n",
+    "y = df['Close']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 419,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 420,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Scaling the train and test datasets so that the neural networks can train them\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 421,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.31598844, -0.30929386, -0.3100082 , -0.29989211, -0.41733415],\n",
+       "       [-0.89740031, -0.89596151, -0.88957808, -0.89455692, -0.01771546],\n",
+       "       [-0.59514771, -0.59986299, -0.58869703, -0.59316927, -0.2335959 ],\n",
+       "       ...,\n",
+       "       [-0.83887091, -0.8426177 , -0.83480871, -0.83986614, -0.38279328],\n",
+       "       [-1.01202371, -1.0177746 , -1.00727417, -1.01405834, -1.07600937],\n",
+       "       [-0.95944717, -0.96582086, -1.00259935, -1.00716601,  0.12476563]])"
+      ]
+     },
+     "execution_count": 421,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train_scaled"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 422,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2944, 5)"
+      ]
+     },
+     "execution_count": 422,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train_scaled.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Training(Using Neural Networks 4 hidden layers are there)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 423,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.optimizers import Adam\n",
+    "from keras.losses import Huber"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 424,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\srija\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Dense(256, activation='relu', input_dim=5))\n",
+    "model.add(Dense(128, activation='relu'))\n",
+    "model.add(Dense(64, activation='relu'))\n",
+    "model.add(Dense(32, activation='relu'))\n",
+    "model.add(Dense(1))\n",
+    "optimizer = Adam(learning_rate=0.001)\n",
+    "model.compile(optimizer=optimizer , loss='mean_squared_logarithmic_error')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 425,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 59.2406 - val_loss: 15.0415\n",
+      "Epoch 2/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 11.3710 - val_loss: 4.6508\n",
+      "Epoch 3/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 3.8473 - val_loss: 1.8259\n",
+      "Epoch 4/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1.5693 - val_loss: 0.8513\n",
+      "Epoch 5/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.7576 - val_loss: 0.4327\n",
+      "Epoch 6/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.4248 - val_loss: 0.2876\n",
+      "Epoch 7/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.3174 - val_loss: 0.2373\n",
+      "Epoch 8/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2522 - val_loss: 0.2131\n",
+      "Epoch 9/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2297 - val_loss: 0.1950\n",
+      "Epoch 10/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2057 - val_loss: 0.1795\n",
+      "Epoch 11/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.1887 - val_loss: 0.1652\n",
+      "Epoch 12/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.1727 - val_loss: 0.1506\n",
+      "Epoch 13/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.1566 - val_loss: 0.1356\n",
+      "Epoch 14/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.1402 - val_loss: 0.1204\n",
+      "Epoch 15/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.1189 - val_loss: 0.1050\n",
+      "Epoch 16/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.1065 - val_loss: 0.0910\n",
+      "Epoch 17/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0927 - val_loss: 0.0781\n",
+      "Epoch 18/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0787 - val_loss: 0.0663\n",
+      "Epoch 19/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0683 - val_loss: 0.0565\n",
+      "Epoch 20/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0556 - val_loss: 0.0485\n",
+      "Epoch 21/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0480 - val_loss: 0.0415\n",
+      "Epoch 22/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0412 - val_loss: 0.0357\n",
+      "Epoch 23/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0354 - val_loss: 0.0308\n",
+      "Epoch 24/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0310 - val_loss: 0.0266\n",
+      "Epoch 25/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0270 - val_loss: 0.0228\n",
+      "Epoch 26/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0225 - val_loss: 0.0195\n",
+      "Epoch 27/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0199 - val_loss: 0.0168\n",
+      "Epoch 28/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0167 - val_loss: 0.0145\n",
+      "Epoch 29/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0148 - val_loss: 0.0127\n",
+      "Epoch 30/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0125 - val_loss: 0.0110\n",
+      "Epoch 31/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0114 - val_loss: 0.0096\n",
+      "Epoch 32/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0100 - val_loss: 0.0085\n",
+      "Epoch 33/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0079 - val_loss: 0.0075\n",
+      "Epoch 34/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0072 - val_loss: 0.0066\n",
+      "Epoch 35/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0061 - val_loss: 0.0059\n",
+      "Epoch 36/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0060 - val_loss: 0.0053\n",
+      "Epoch 37/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0051 - val_loss: 0.0048\n",
+      "Epoch 38/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0052 - val_loss: 0.0043\n",
+      "Epoch 39/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0044 - val_loss: 0.0039\n",
+      "Epoch 40/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0038 - val_loss: 0.0036\n",
+      "Epoch 41/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0035 - val_loss: 0.0032\n",
+      "Epoch 42/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0031 - val_loss: 0.0030\n",
+      "Epoch 43/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0028 - val_loss: 0.0027\n",
+      "Epoch 44/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0027 - val_loss: 0.0024\n",
+      "Epoch 45/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0029 - val_loss: 0.0022\n",
+      "Epoch 46/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0021 - val_loss: 0.0020\n",
+      "Epoch 47/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0022 - val_loss: 0.0018\n",
+      "Epoch 48/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0018 - val_loss: 0.0017\n",
+      "Epoch 49/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0016 - val_loss: 0.0016\n",
+      "Epoch 50/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0014 - val_loss: 0.0014\n",
+      "Epoch 51/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0013 - val_loss: 0.0013\n",
+      "Epoch 52/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0014 - val_loss: 0.0012\n",
+      "Epoch 53/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0012 - val_loss: 0.0011\n",
+      "Epoch 54/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0011 - val_loss: 0.0010\n",
+      "Epoch 55/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 9.8512e-04 - val_loss: 9.4533e-04\n",
+      "Epoch 56/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 9.7006e-04 - val_loss: 8.6534e-04\n",
+      "Epoch 57/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 8.8427e-04 - val_loss: 7.9957e-04\n",
+      "Epoch 58/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 8.2493e-04 - val_loss: 7.4493e-04\n",
+      "Epoch 59/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.8714e-04 - val_loss: 6.9442e-04\n",
+      "Epoch 60/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 9.0120e-04 - val_loss: 6.4736e-04\n",
+      "Epoch 61/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 6.6088e-04 - val_loss: 6.0027e-04\n",
+      "Epoch 62/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 6.7174e-04 - val_loss: 5.6277e-04\n",
+      "Epoch 63/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 6.5245e-04 - val_loss: 5.1573e-04\n",
+      "Epoch 64/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 4.8866e-04 - val_loss: 4.8083e-04\n",
+      "Epoch 65/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.1655e-04 - val_loss: 4.5872e-04\n",
+      "Epoch 66/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 4.5414e-04 - val_loss: 4.2382e-04\n",
+      "Epoch 67/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.7204e-04 - val_loss: 4.0264e-04\n",
+      "Epoch 68/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 4.4724e-04 - val_loss: 3.6530e-04\n",
+      "Epoch 69/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.3715e-04 - val_loss: 3.3879e-04\n",
+      "Epoch 70/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 3.7364e-04 - val_loss: 3.1466e-04\n",
+      "Epoch 71/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 3.3107e-04 - val_loss: 2.9607e-04\n",
+      "Epoch 72/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 3.4258e-04 - val_loss: 2.7702e-04\n",
+      "Epoch 73/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 2.7917e-04 - val_loss: 2.6252e-04\n",
+      "Epoch 74/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 2.6866e-04 - val_loss: 2.5226e-04\n",
+      "Epoch 75/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 2.5664e-04 - val_loss: 2.2911e-04\n",
+      "Epoch 76/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 2.2652e-04 - val_loss: 2.1589e-04\n",
+      "Epoch 77/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 2.2165e-04 - val_loss: 2.0281e-04\n",
+      "Epoch 78/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 2.1508e-04 - val_loss: 1.9009e-04\n",
+      "Epoch 79/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 2.1575e-04 - val_loss: 1.8505e-04\n",
+      "Epoch 80/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1.7238e-04 - val_loss: 1.7020e-04\n",
+      "Epoch 81/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 2.5712e-04 - val_loss: 1.5892e-04\n",
+      "Epoch 82/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 2.2433e-04 - val_loss: 1.4611e-04\n",
+      "Epoch 83/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1.3760e-04 - val_loss: 1.3105e-04\n",
+      "Epoch 84/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1.3312e-04 - val_loss: 7.6752e-05\n",
+      "Epoch 85/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.7292e-05 - val_loss: 4.9295e-05\n",
+      "Epoch 86/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 8.3661e-05 - val_loss: 4.1589e-05\n",
+      "Epoch 87/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 5.2693e-05 - val_loss: 3.7660e-05\n",
+      "Epoch 88/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.8083e-05 - val_loss: 3.5493e-05\n",
+      "Epoch 89/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.3970e-05 - val_loss: 3.3614e-05\n",
+      "Epoch 90/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 3.9255e-05 - val_loss: 3.2295e-05\n",
+      "Epoch 91/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 3.7438e-05 - val_loss: 3.1674e-05\n",
+      "Epoch 92/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 4.1618e-05 - val_loss: 3.0788e-05\n",
+      "Epoch 93/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 4.8194e-05 - val_loss: 3.0421e-05\n",
+      "Epoch 94/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 4.6475e-05 - val_loss: 2.9424e-05\n",
+      "Epoch 95/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 3.8921e-05 - val_loss: 2.9264e-05\n",
+      "Epoch 96/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 3.7035e-05 - val_loss: 2.8174e-05\n",
+      "Epoch 97/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 4.4513e-05 - val_loss: 2.7533e-05\n",
+      "Epoch 98/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 3.6359e-05 - val_loss: 2.8184e-05\n",
+      "Epoch 99/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 3.7233e-05 - val_loss: 2.6300e-05\n",
+      "Epoch 100/100\n",
+      "\u001b[1m74/74\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 3.0896e-05 - val_loss: 2.8732e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit(X_train_scaled, y_train, epochs=100, validation_split=0.2, batch_size=32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 434,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'loss': 3.4638414945220575e-05, 'val_loss': 2.6299672754248604e-05}\n"
+     ]
+    }
+   ],
+   "source": [
+    "min_values = {}\n",
+    "for key, value in history.history.items():\n",
+    "    min_values[key] = min(value)\n",
+    "\n",
+    "print(min_values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 427,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1c7599592d0>]"
+      ]
+     },
+     "execution_count": 427,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(history.history['loss'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 428,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m92/92\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 585us/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_train_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 429,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 430,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 431,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test = np.array(y_test).flatten()  # Ensure y_test is a 1D array\n",
+    "y_pred = np.array(y_pred).flatten()  # Ensure y_pred is a 1D array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 432,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    test_actual     test_pred\n",
+      "0   9615.000000   9602.689453\n",
+      "1   8711.349609   8706.838867\n",
+      "2   7252.899902   7231.701172\n",
+      "3   8792.200195   8664.859375\n",
+      "4  10977.549805  10986.929688\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_pred = pd.DataFrame({'test_actual': y_test, 'test_pred': y_pred})\n",
+    "\n",
+    "# Display the first few rows of the DataFrame\n",
+    "print(df_pred.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 433,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 433,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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31GMf6nfqq7eQAIB6tTAhSYcUb7Tbrd8Bn/0XGHw2sEvnvDa9sb4BvfPaYmGhNQbuv/9+AGniKI1HHnkEZ599Nj755JMsH2bvvfdmyixbtgx9+vRBeXk5Jk2ahEsuuQSEEOy9995Zd/AM+vbti9deew2XXHIJJk6ciN133x0PPfRQ1n0cAH77299i/fr1GD9+PGpqanDIIYdg6tSpHjJyjHDRqVMnpdQOMWLkCy1DFMnjXZSIvcJx9XNzfSN0xIEtO5q0yucVj/8c2PgNsPx94Mwphe5Ni4aWkOPHUR46dKhvmZEjR2LkyJG+bQ0dOhSffvqptMy4ceMwbtw437pixIjRglEii3YMPbiDoDam9DR2Dilic9XGb9L///bdwvbjB4A4C3mMGDHygxWzgW9nhF6thWjMVS0VW3YkC90FJXg3zHrCbCmIvkSYpCZGWIiFnBgxYkQPJwU8Mgp4/BfhE18jWs3yukjmsbEv15RKEFRKACAElvZDKn4BwimBPpY6YiEnRowY0cOhtAc7NheuH0UK/QX8BwCLFXJ0UQpaklQxm9R4SDaWnHk4FnJixIiRB0Q4mZfYpFtwlKhrtdUCXzPxW4IJAXYWieZtWw1wUw/gBW/C7WJGLOTEKGnMmDEDlmVhy5Ythe5KDClyK5RhkHWlumO0HLCimIkmp/jh28dnzgBu7gXULMxHd/ggJN2P2/dLa2Q/f8H/miJCLOS0MAwdOhQXX3xxaPWdffbZGD16dGj1xfhhojGZIwd/vyXcYGRRmXpKU9+hglK5s1w/iVEspOK/T8dvCV78avr/c8NL+aFtxtv4DbDoldDazzdiISdGwUEIQTJZGh4fMUwRoSYnNlfpoQTNVcQh0PeuKv77LAnisRNN2pR8IRZyVEEI0FhfmH+Kk/jZZ5+NmTNnYuLEibAsC5ZlYfny5fj8888xatQotGnTBt26dcPvf/97bNiwIXvd888/j/79+6NVq1bYddddMWzYMNTX1+O6667DY489hv/973/Z+mbMmCHtw/Lly2FZFp5++mkceeSRqKqqwkEHHYSZM2dmy2RMTG+88QYGDx6MyspKvP/++3AcBxMmTEDfvn3RqlUrDBgwAM8/z+afefPtd7Hvj0ej1V5DcOyxx2L58uXKrzBGARGpINIChJwWcAtRgkBfY0dKQphT62OYn4/uUyn1wN9FG/W66NC0HbipZ2Ha/r/vgYpdfItNnDgRX331FQ466CDccMMNAIDy8nIcfvjhOPfcc3HnnXdix44duOqqq/Cb3/wG77zzDtasWYPTTjsNt9xyC375y19i27ZteO+990AIweWXX45FixahtrYWjzzyCAAoRzu+4oorcNddd+GAAw7AHXfcgZNOOgnLli3Drrvumi3zl7/8Bbfddhv23HNPdOzYERMmTMATTzyBBx54APvssw9mzZqFM844A126dMExxxyDVatW4fQxf8LYs36D80//Feat2o7LLrvM4IHGyDcIFcsm1uQUGqWw+LsVTkX2jt+/C2jbAxjw20DVqGpylm/aAW+myPxg5ead6FOgtsNALOS0ILRv3x4VFRVo3bp1Nhv73//+dwwcOBA33XRTttzkyZPRq1cvfPXVV6irq0MymcSvfvUr9O6dzmjSv3//bNlWrVqhoaFBmt2dh3HjxuHkk08GkE4HMnXqVDz88MO48sors2VuuOEGHH/88QCAhoYG3HTTTXj77bezSVb33HNPvP/++/jXv/6FY445Bvfffz/69u6N26+9FACw39EDsXDhQvzzn//UfVQx8gxSgtvBUjB3GKEkNBxg+kkcR1+TE9X7W/sF8Pa16b8DCjmqfVy/bWfBhJySGS8CxEKOKspbpzUqhWrbEJ999hneffddtGnTxnNu6dKlGD58OI477jj0798fI0aMwPDhw/HrX/8aHTt2DNJjJht8WVkZDj30UCxatIgpc+ihh2b//uabb7B9+/as0JNBY2MjBg4cCABYtGgRDh00QNhOjGJG7F1VLCjFJYsYeVdFdKfbN4ZWlWMVP2PEnV6j1BALOaqwLCWTUbGhrq4OJ510Elfb0aNHDyQSCUybNg2zZ8/GW2+9hXvuuQd//etfMXfuXPTtG+3eYZddcs+zrq4OAPDaa69ht912Y8pVVlZG2o8Y0SNKTU5LCKRnsoj/kFBUFskQs96r31YBBY0SF3KKX4yMoYWKigqkUjk2/KBBg/DFF1+gT58+2HvvvZl/GSHDsiwcddRRuP766/Hpp5+ioqICL730Erc+VXz44YfZv5PJJObPn49+/foJyx9wwAGorKzEypUrPf3s1asXAKBfv36Y/+kCYTsxihcxJ6eIUIKLVlG5kIc43nyDARYBSl2TU/xPOIYW+vTpg7lz52L58uXYsGEDxo4di02bNuG0007Dxx9/jKVLl+LNN9/EOeecg1Qqhblz5+Kmm27CvHnzsHLlSrz44otYv359ViDp06cPFixYgCVLlmDDhg1oampS6sekSZPw0ksvYfHixRg7diw2b96MP/zhD8Lybdu2xeWXX45LLrkEjz32GJYuXYpPPvkE99xzDx577DEAwAUXXICly5bjihvvxJJvluOpp57Co48+GviZxYge9LpQKpqX0p7axSgdrhHdzyIKBhiiJqcUXMitEjCpyVDavY/hweWXX45EIoEDDjgAXbp0QWNjIz744AOkUikMHz4c/fv3x8UXX4wOHTrAtm20a9cOs2bNwgknnIB9990X11xzDW6//XaMGjUKAHDeeedhv/32w6GHHoouXbrggw8+UOrHzTffjJtvvhkDBgzA+++/j5dffhmdO3eWXnPjjTfib3/7GyZMmIB+/fph5MiReO2117Jmsz322ANPPDQJU6bOwIDhp+KBBx5gCNUxihj0ihPiIuGtPIYvSnBnTkjxCMdmWiVBXYpLcCEF09IbLSxiTk4Lw7777os5c+Z4jr/44ovc8v369cPUqVOF9XXp0gVvvfWWdj/69euHuXPncs8NHTqUa7KwLAsXXXQRLrroImG9o4Yfh1OOG5z+0TNNSD7nnHO0+xcjv6A5J2HTc6zQhaYYRYciykL+zbpt2CekuopDbJPDKvGkYbEmJ0aMGJGD3f2GO2mW9hRcCJTG3pxNQl483lXJpvAiAKtqcgqpfCtxGScWcmLo4aabbkKbNm24/zImLi52bgU2fAUkG/LX2RhFA3qRCpsnHNX8X7CIuavnAdVPRdhAaQg5TD+JvsgS1dpMk+iD16UY8biQ76zENaWxuSqGFi644AL85je/4Z5r1aoVdtttN/6ua9O36f9vWQl0DkvZG6NkQI2J0LkVJT4Je/DQcen/d9gD6PPj0KsvQUqOmYt9RDdKQszl5JugMwLoCkxOiX9fsZATQwudOnVSTu3AhRMn4vxBgiLilIr2O5+yAE/wq/1uEdpFIOSUIorJXBWqJrIAAqf+JqNUvlg+YnOVD8KPzvrDRrFH94/fdzRgVPwhP+Ni8boJG4vXbIuo5tJT5aSHTHG8Z0JKW5Oji1JMyUKj+J9wgZBIJACk0wrECA9NqeJWfW7fvh1AOrFpjPBAyzVxMMACoxTtVSge76pwIx7n/13othmmy3whEJurBCgrK0Pr1q2xfv16lJeXw7ZjeTAQkukJqhEOyM6dxtU0NKVQltlZBKjHDUIItm/fjnXr1qFDhw5ZITdGOGAFm9LIXdVSRaeSEXHoBJ1G5qpoEK5mQ/VtxMRjU8RCjgCWZaFHjx5YtmwZVqxYUejulD62rAcAJFGGsnrzYddQuwGVTlrbgvplYfSMQYcOHbQzrsfwB7MbdMKdNFuGuSp/91A6EY9zIEa9Ln5NjlMCWrVSN+HHQo4EFRUV2GeffWKTVRi49xQAwGqrJ3Yf+7JxNQueeAD7b5me/jFuXhg9y6K8vDzW4EQE2jsmTBfcdIXRTMLFv/wYogQWVsD1/A20J9ERj/Mf8bigipwS30TEQo4PbNtGVVVVobtR+qhbBQBIBH2eOzajqrkuxO+ldEDPkyVOZCx9lIaQw8JAyIlKmCsIJ6dw74yErHnNN2KiSYySQimq2mOA0baEr/6OhaaWCEZIMfKuikqTU+I+5JoodXNVLOTEKDGU9gf3QwWt4g8zmFpLRmSaiBIxV9EwMRFFRzymxm9AAcAphQzfsZATI0aMGD6gNTkhLz9WRJNwXrWGnFuI6r5KQXsAABZY76qiIZiH2I1S0Ew7Jb4piYWcGDFiRA7GgTx076rS5gykEfECXuK78WLKQg4SniZHmXhcQBS/GCZH8T/hGDFilDwIGw1QXvjbGcC/fwrULFSr27xbPxzQucNKcNUyeceRaUlCjflU/C8jjngcI0aMGD5gODl+YfEf/wXw3Xw0/ecUpboZs86mb4Hl75t0sWWD4bQU/8LqBiFO0cTJCdWFXFHijIyfpdJ2HAwwRowYMXzAaHLULnHqN+q3c/fA9P8v+ADofpD+9S0UtJBASkTGsRCMxxXVfbICuwML5rG1SsFcVepCjtYTnjBhAg477DC0bdsWXbt2xejRo7FkyRKmzM6dOzF27FjsuuuuaNOmDU4++WSsXbuWKbNy5UqceOKJaN26Nbp27YorrrgCySSbnXrGjBkYNGgQKisrsffee+PRRx/19GfSpEno06cPqqqqcMQRR+Cjjz7SuZ0YMWLkCbS5StW7St11lVNuzWeK1xYLojUJ0DyoEpFxQrAKlUCcHGUNTSGjAf6AzFUzZ87E2LFj8eGHH2LatGloamrC8OHDUV9fny1zySWX4JVXXsFzzz2HmTNn4vvvv8evfvWr7PlUKoUTTzwRjY2NmD17Nh577DE8+uijGD9+fLbMsmXLcOKJJ+LYY49FdXU1Lr74Ypx77rl48803s2WeeeYZXHrppbj22mvxySefYMCAARgxYgTWrVsX5HnEiBEDABrrgX8fB8y8JZTqaIHFUZw0VTkVPELq8o31nJIlhhBNFKwmpPi1B4DrvRZRFnI6LUlwvkrxi5yq32uxQmu0T506FWeffTYOPPBADBgwAI8++ihWrlyJ+fPnAwC2bt2Khx9+GHfccQd++tOfYvDgwXjkkUcwe/ZsfPjhhwCAt956C19++SWeeOIJHHLIIRg1ahRuvPFGTJo0KZs+4YEHHkDfvn1x++23o1+/fhg3bhx+/etf484778z25Y477sB5552Hc845BwcccAAeeOABtG7dGpMnTw7r2cSI8cPFJ48D380D3v1HSBXSC5baTliZOMqZhNdvCy95a17x2dO5v0NcXBxak1P86yoAbyoQXe+q6NI6hPdeSsFchRL3Xgz0hLdu3QoA6NSpEwBg/vz5aGpqwrBhw7Jl9t9/f+yxxx6YM2cOAGDOnDno378/unXrli0zYsQI1NbW4osvvsiWoevIlMnU0djYiPnz5zNlbNvGsGHDsmV4aGhoQG1tLfMvRowYXtTX14VaH7PjVdbkqCG6+Cl5lgY2fQu89MdIqqbNVaUQmyUN6r0aaUyiJx4Hjfmkaq4K8060Scw/1LQOjuPg4osvxlFHHYWDDkoT/GpqalBRUYEOHTowZbt164aamppsGVrAyZzPnJOVqa2txY4dO7BhwwakUilumUwdPEyYMAHt27fP/uvVq5f+jccoKKILkBaDxsLVWyOrW5XIWCpLcWioXcP8DNOjxqGfeTE+2K/eBDYuZQ6xnzqBpfnpR0awDjVFSTG+DBY/2LQOY8eOxeeff46nn37av3CR4Oqrr8bWrVuz/1atWlXoLhUfPnkc5L4hwJb42cQIEVHnrvruE6Chjj4SHHlef9Zv2x5d88wzL7KFddl7wFO/Ae4ZxB5nomSbIA/E46DBAEvAdljqQo6RC/m4cePw6quvYtasWdh9992zx7t3747GxkZs2bKF0easXbsW3bt3z5Zxe0FlvK/oMm6PrLVr16Jdu3Zo1aoVEokEEokEt0ymDh4qKytRWVmpf8NRwHEAuwjtsS//GRaAupevQJszS0eAjREuwk69QGi7fsicnIMaqoF/Hwt02d+gZ8UBixBs3FKHLtSxMN8AS5AtroWVrJ7n2yOThTa6YIAhmquU9Qzh3Yt2TT8kF3JCCMaNG4eXXnoJ77zzDvr27cucHzx4MMrLyzF9+vTssSVLlmDlypUYMmQIAGDIkCFYuHAh4wU1bdo0tGvXDgcccEC2DF1HpkymjoqKCgwePJgp4zgOpk+fni1T1Ni0DLilL/DO3wvdEyG+/X5Dobtgji+mAI/+zKP+j1E40ItsoLgbC58HXr0U4Lmhr1+c/bMUzZpOqim6uhlzVXEJOV+t3SY4w46ZYknrECrxuAQSdP6g4uSMHTsWTzzxBJ566im0bdsWNTU1qKmpwY4dOwAA7du3x5gxY3DppZfi3Xffxfz583HOOedgyJAh+NGPfgQAGD58OA444AD8/ve/x2effYY333wT11xzDcaOHZvVslxwwQX49ttvceWVV2Lx4sW477778Oyzz+KSSy7J9uXSSy/Fv//9bzz22GNYtGgR/vSnP6G+vh7nnHNOWM8mMpAZNwE7twCzbi10VyQozkVCuDvbthb4ZnpaffzcWcDy95B8/ar8di6GBCbEY867fmEMMO9h4PMXQupX8cAt5IRKNi1ioW9zfSP/BNVlC8WUoPOH5UJeLI/dFFrmqvvvvx8AMHToUOb4I488grPPPhsAcOedd8K2bZx88sloaGjAiBEjcN9992XLJhIJvPrqq/jTn/6EIUOGYJdddsFZZ52FG264IVumb9++eO2113DJJZdg4sSJ2H333fHQQw9hxIgR2TK//e1vsX79eowfPx41NTU45JBDMHXqVA8ZuRjx/dYm7FboTpQs+F+cc+dBsJ1GNP3qEZQ3H1v9/Xfok7d+hYi69cBTpwADfw8cNqbQvQkFTDDAEHaG2zatQVtZe4FbyD+cZHSaHCaVRgmsq26YyWgtKBhgIdM6lOTXlIOWkKOyG6iqqsKkSZMwadIkYZnevXvj9ddfl9YzdOhQfPrpp9Iy48aNw7hx43z7VGxYtaUhFnJChu2kd4NLZ7+EDDOjriEpvqCYMWMC8P2n6X8tUchRdEmVzTZffL8NP5I3qNRGMYG4NDlRcXKsIpNyRAs9EyfHiJMTERhOTjCBpyTi5PxQXchjBIBtnuskhhy1O3OCTSnyMgBgx3aKo/DmXwvUi5CJxzpZyJWQj4U6v8JAlJwcdqEqLiGHC0LQeu186qdJ7qros5Ard2vVR8C8yZ4LlBN0FvCd/aA4OTHCQSmQzYyw4Rvg/h8Dn79Y6J6UNL5cQwk5c+4tXEdChIVwzVXw+YaKhr+hAbcmJ8xljSYel8Sz+fJ/2O3D66kDJpycPBCPVaWch48HXr0EWDrddaIEBE5TfDsTeOh4YO0XBe1GC11tixvEapnJ35MvXgCsXQg8Xzjyd0uYMpzAZMbgCP856mdblO1eSyG+iB4InFR05lV6YS46IYcj9JIFz7K/i4qTo5Zglnvphm9Yc20JbHi5m5KdW/2T4D7+c2D1R8B/T42mY4oo/ifcElECA9sE6zYW3u28yKZvIxRD2P3Qw/XRgpuqjV/yGCyfb6iYvYlEIJEKOaVlclizyZVyp5g4OQYCewZL1m6DQ4U/KEScHG3wvtd7DwP+dTSwbJbv5alt6yPolDpa5mpb7CgBTo7Jbq+hqbQm0pJBCS7YbpiRSGVSTrD+FCUi5OQwcYqKbSvAEVi31LmyyBN9c1Vkyj6GRK/Xpy3bmxghp9hiFvHAHS91zYF4v3rT9/rGpLnmKwzEQk4hYBW/kNMyUGSTuSLc816p7cK5YHgMYdxPy5u63JycUOumvIB0c0BFDZ6DQMJxx87R12/mI+KxuIz4IadStCan+IUc2b1savRfywptHm15M0UpoAQ0OSXx8f1Q0BI0OSRkTYLvDrj0nhlx3Oaq8L5BJgt5CYynhON2py+eLOS+LuQzbwG5fX9h/j+SMtDkFHA6lo2X6pqGPPbEDLGQUwCQEtDkFFr6NkWp9puGW8AsgTXJF8xEGUKcnBYpg3s0OSGmDyDmPJKowRNgEsQl5Dj65qqCuZC/+w9YdTWof/MGzkm4zFUlsATLNFcVu/heXuhPtQSecAuEXfzeVcU1DeZgLMSkmrD6gZOx+o3bwu1QHlAIDkXoJo2QIx5bfhuFEpQMvZqcUCunf0TXTkgoI15zlT7ykYVcXOzL77dyj6dorVpBlmC95yLT5FjlrYJ2JnLEQk4hUALmqpYAWiD6+t3/YPeat7H73BsL2CNDlOCC7Ub4WcgLvT+MALyko2FVzXCiImsmNCRIcIEvuojHtOnV4HJayFHWNoVoutS+QPy92hWtA/UlH4iFnEKgBMxVLQH0x7xp86aC9SMoSoFD4YfwAx7LJ/2SfGKO21wVEScntFqjQ7nbXEUI9HselSAcTCvGxEMqCXOV+B7LKvw1OYWmEJTAE255sGhNTgtYwGJEi6Jz+TUBI+WoaSykmpy8LA751RZZEXJy6N24VQLeemUcIadodHc08VjyLEU9Zjg5yncV3t3re6mJx2FFWfGLEMXfwxYIQnNySmDCKSYU0VSXP7QIQThcVU4JhBfRggUCK0AkXT+w8VyKfzyVgzVXEYM4OZENEsXxK+ovMeFHFdS7SrxGldnF/yHGQk4BQGtynGSESflaIPwmupYgD7izRBfmnkJulKkuD3FySmwgEAKOd1WYPIzSIh67zVUmyEecHBNTspMqsY2t9B5V7iU2V/3wQAk5qSgzDwdAqWbwpoWg4t9j8EHcwQBDEQoKC2K0MHiiIlKnSvXtShChdxUT8bgEPm23JsdS/QaYm8uDJkfyLMXmKureFF9GQTXYsj6WwFiKhZwCgM67k2yK0G30B4jDtk4tdBdCgEuTU5CEnSFPqkycHMP70QqHH0bAweBVaDUXpbmKEjIP/eoOkJf+WNTSju2KYaAcJycPQo6lqBUTte6k9N9zQUV6Ke/IH4XejsRCTiGQyHFykil3PIjiQMvL8lzKiGAxatwObFwqaTFc7RFLXjQLBkhIiQVR04AFAitKTQ61+CdIEtZnTyO1bnFk7YUNVfI9ozGMoh+EsJpIg0ZKjR8l17wWf/9b1kxRIqADmRUrJ6dUzVU/OKz9EvjqLf3rJh0B3DMIWPVx+H3igCEvqmYhd8GhrvPLQh4G8m0isEPgoQjB0RItW7cluvYKBNpVPgqT5mP3XIsjNrxINag/T5IINXbRQGauKv51IhZyCoAEpYpNJQtjrnpnwXJMmvIuUgUxheQLpXlvnrQOsnd0/xDgqVOA1fP0Gtm6EgCQ/GIK93S0Qq5Z3Skm549fE9H1v7EphadvHYvnHrs71HotVzDAMOOL8HbjboJ7vkEcBwtuHYUhy+5VKKwmckYdbuHsTRMDt0dzckpiMynblCh4B8dxcn6AoCecVLIw5qp+LxyLsdWjMeO9WQVpP4v6jcDcB4HtEQTrK4H5Qw3+N7J8nhkX6cvvtxldpwsmEr5hxGM6vkg+NDkifDLrZZxa/wROWfa3UOu1XFF+wxy+3Ede4AV2zYolOLh+tlJZQoiHp8ODWQyaIDDQ5BhqMgsFmSBXCjJaLOQUBLmRkSyQuaqHlRYqWq+YVpD2MyDPngG8cQXIs2cWtB/FDM8kM2cS8MZfmBlmS81yo7rrm/iq8/A5WYyUY1QDvTj49y6i2bduPfZZeEckVdseM0aYLuTFZyLRIeCqeuQxXmR5EHJMnAKMhJxCciQlz77v0v8AU/+vqKWd4s8U2SKRGxAF5+REmC9HBdaK9E7OWv5eQftRTHDPZ56J9M3/S/9/wKnZQ4n6GtPW+IfDnrQM8jp4NTn04hD9pO9pgRCk7jgAuzrRaF/tKBN0chfj4l2YTOFoeeAFh5yS6x/xuBCmHG3hT6J57bT5M+DDz4D+JwO7DeaWic1VP0TQ3rSpAruQt+CIy4X+uEzh4eQI7mPD5s3Zv1vtXGvYWr52iLSQY0o8prgMPpFWw+A6eL27HCQiEnDSbz26b9EpQrKr3zukUVa3hnv8i5Xr8I97H8CCZWkhn9b45Ofrl7iQC26PFnKkfcxHzB8VKHxLq9euF54rtJ9uLOQUBJQmRyTk1G/Mjwqw5ISc0hRcosC62p3Zvzs0iScZKfKkBichaHJ01PyRuA9HTNKP1LxS4p/NHm+ewz2+aPIF+OuGq/DN5DEA3J5LedDkmMzRjNlVxncpjrlZ5R63bC/OUChALOQUBPSgSXKIx+TbmcCteyL1HP/DDhUFIsG1hMza+YLK4tqG1OWhJwHAvG+zMUdoU0SA8bN65XLMfOnfaGhs0LrOKZJFxwT8MVTobzD48vNrTAcA/CrxPgB2OsuP/G7iQq54TURzpL6G27+8THOqQhiPErGQUxBQKlWOJmfD1JsBAIkvX8pDV/hq7ChNPa9Ur8av//4YPl62Uf/iQs/LeYB7bo7SLZbkyUuJMSMYTt6O4g64uRXhmaqHf4JjPrscHz75d632S1swj8Bc1bQz0EJsRSCF0Jocj2ZsZy0w/9G0ljys9oJeJXl+ItPbutqdWLB6i1HLRlAQ7ov5y4iFnEKAcSH3Cjk1W3d6jkXXF4F3TYRN1j0/Di+kLsKMx66LsJXShdezif82otyoRroJNnQhZ8xVARbXzlYtAKDLmne1rovU9TdiAYqnyQnU5La1wD+6AU/9NkAl4YO9T3b8bH/+T8ArF2HHYyeH2KDJNarRm3Pl6Du5+OaJeOX+q7Ho+60GjeubRUtbuI+9qwoCWrVHCp2gswAq+NPK0ovLWPKM9mqq84GWKvHYDdEcE8bd5SsgXNBQ+IDLc8Zv3EYwMZdyotSwF6rt8/+L1gDw9Zuh1hsYlCDq3iy0/uZVAECrdZ+G157BY2VfhT4n56mKmwAA0+cfhX49z9DvgDb8b7KY5aBYk1MAMOwEjgt56ItzskGcp0iwOy00I14EnWdTxN+dD9inbylwcozfl8BkEPazYyZsA0+fVz5djhsm58LpF4KUydWGhKjdiVIoD/t5LVhtpkVgEIG5iuZNhV07713LBF/Rhkw9Dxe1GeY8qzY7vleqJzCKWYJRQCzkFAQK3lUhYtv9xwP3DMLOxZwcRwUwVwVqQ2PmKieN2LR2le5lRYgo30Z+nkxQl+6OL/4Od6dyHBq/RTuop9L3W3Zg4Xe1bJ2ce3BEQk6xLQzc/gToYwjDJgpicJTCL1cbZvCeLQNOTrft3wBL39FuKxTopm4osrEfCzmFADUIHIdnrgp3kLTd+BkAYNlb//KcswroMWI0x2k8mr2T36DT/QdhzYoledfqkM0rsP3dO4Gdkh3vlpXA1tX+damoi3U6RyNfLuTMD/0x9+PEF5IKfVvUxnmPe3OB8RbQFCdq7ycPX4gNN+yJ2vXqO+30IlE6mpwwEEVqjig94PhCjkE9ito/+p3tufVD4D+/RNO6r/UbDAoFoSWj0Vrwv7uw5YbeWPnFh1H3ShmxkFMQUGrIPCbo3NHIEaiKcPILGyvn/i/vbdbfNxStZ16HVU+M5Rdo2gHc1R+480DAh5clciEXERP1EI2QQ1JJrJh6N7auWNB8IHicHPZktOO2Q80HOL/sNVeT3jYdTsTwQaseQ2eyCYunTNBqM1qyd9gCVIDe7tyaNqFHASc6jQJfUDRwIaf+ZjQgn78IPPDjLLWAN94WfUHziXzeQWj3r17PwZ9eiw5kK8iL54fUdnBoCzmzZs3CSSedhJ49e8KyLEyZMoU5b1kW99+tt96aLdOnTx/P+ZtvvpmpZ8GCBfjJT36Cqqoq9OrVC7fccounL8899xz2339/VFVVoX///nj99dd1b6cwYDQ5+RNyeEPVErqQB8Pbj1wXsIZwkW9zVZumdG6wVqv56Sp2bl2X/btppzzGjYomp9JqAj5+CKjlR4YVIqIHs/j1Sej94d/Q/pGfNB+hF58QBJSIicdPVngFFN57IJK0KKki2j+ErcnhCqDbFKJu79gC3LwHcMcBkWgRiVaYAT04PE6WUU2Cq54/B6hZCPxvHEAIKh4bKb1S+vgcB6mHhiP1n5MDfwtKpHX3symizbO2kFNfX48BAwZg0qRJ3PNr1qxh/k2ePBmWZeHkk1m3vRtuuIEp9+c//zl7rra2FsOHD0fv3r0xf/583Hrrrbjuuuvw4IMPZsvMnj0bp512GsaMGYNPP/0Uo0ePxujRo/H555/r3lLeQQ8HXpycMELS88D96CMajMNW3OlbJp/eT4FaWvh86PZwOkLojqSrd6Zq/NcuQ9ODx2lexG8r6Bjc9u1HzG92kQ2uySmEWytvkUsZ5H778NuNeGDmUu89uH+HKARwhZxAMW7Y31vf+idw+77YOuMe6XXO6k/Sf2zfgCgk7Cg94Ph1B3av8mBn/VaAENjrvuCczT0z2ffRsGklEt99hMTSt0Ea2U2U9rAq8Tg52i7ko0aNwqhRo4Tnu3fvzvz+3//+h2OPPRZ77rknc7xt27aeshk8+eSTaGxsxOTJk1FRUYEDDzwQ1dXVuOOOO3D++Wk12MSJEzFy5EhcccUVAIAbb7wR06ZNw7333osHHnhA97byC8aFPI+aHM5IFHFyinnQaoPAeD4lm5bBeiEdMh7X6XuUiCcigampaSf2qWXt2SIbPk9ILK/7Tq+DBUhdFY4mx0+4iMCFnEc8lmXSFqwmsx7+C45LfIq32j2DEXRxV59NBc1N9Y24/LnP8JtDd8fIg3qkD0Zsrmo/O+3W3H7GNcDQP/MuAAAsXrsNB2R/hf+OhETwEMD1ropgovxuyw7sSZxAn+amRhvNbx7b67Zil8q22XO6fY5q050vRMrJWbt2LV577TWMGTPGc+7mm2/GrrvuioEDB+LWW29FkuKmzJkzB0cffTQqKiqyx0aMGIElS5Zgc3NSwjlz5mDYsGFMnSNGjMCcOXMiupsw4WeuikqTw2uqtFzIzWDua7N8xfJIWhZqIl65CJ0aWcIqYyYp5ISTbABm3Qqs+Uz7UvoeQtHC5Ps5EMJtU0oiFQg5V5Y/i8H212jz+ZPyJrU6mMOtby7BsiWf4cIn5ubq4vXdsH4ZdpJy6fnITXgG5qr12xpw7mMf453FPuY23jPUFNjLUzvQ7pP76RoETYn6rnZPlYnc0r59W62kpD/UXN7dZYpHMIpUyHnsscfQtm1b/OpXv2KOX3jhhXj66afx7rvv4o9//CNuuukmXHnlldnzNTU16NatG3NN5ndNTY20TOY8Dw0NDaitrWX+FQKMZGya4G35B+kw5UEh/EiLZ5AGh3lihIZk9NmbmfD2C57mlKAFBCrYWYR+vNznNede4J2/A/86Wr8dhiQdfGxpc0w4OeI0G+Rqj556+E68NWe+UZU2cRPOwzFX9Vg3E+9WXoanK26kqg5buuD3rc5qrX5ZBIKqI8pCvupj4TV3vPAu/rj0/+GFx++V1q0rnPOe0OBv7kbPNW/nygjqDLoRoK/eWR8wppGKdqyIl4tIIx5PnjwZp59+Oqqqqpjjl156afbvgw8+GBUVFfjjH/+ICRMmoLKyMrL+TJgwAddff31k9RvBYDA7Hz8M+43Lkeo+AIkLZilfx1tcRMTjfKCoIxJ/8RLQfo/Iqmdeu89ixsjETri5qi2dfU7NQuN2iEBQk0PmXeXXYK5A3dK5aPOf4VhzyIXoMfpGyUWy5giXk/On+klwpt4HDNnCuUr+ptxnw3qvP6lNO2AMsr/JHgvdhVwwZrdbbXwuozglEfABGeIx/eE8PIxTOo1frr4Fh9lf4bCKrwCIx4curynI7OY4KSQ4xwfMPI/6pcZZa6jfEqAnqijeuTwyTc57772HJUuW4Nxzz/Ute8QRRyCZTGL58uUA0ryetWtZ1WHmd4bHIyoj4vkAwNVXX42tW7dm/61atUrnlkLB999+iSO+8SPlygfMpg8eAQAkarxmg7e/XIvRkz7Asg31SrWWWpwcU8FIq63vPwWeOxt46KdGbSmB1mz4CbrCvA4haEQED4b3nLdsN09BYuncbzOkpTTG7YYX0puqHtV3K1/jbY8wi5xDPThhlmVdTYzruYTJheDz8TTr37QMWCGnA+xIyIWcyMHck9r99UgqxDPauBSJz5/3ac8EvOtJCCbd3PVNLk2O/rBU+dZ+gELOww8/jMGDB2PAgAG+Zaurq2HbNrp27QoAGDJkCGbNmoWmptykOm3aNOy3337o2LFjtsz06dOZeqZNm4YhQ4YI26msrES7du2Yf/mG9Z9fsAc4g9lvDG6uF6vez318HqpXbcHFz1Rz6uUNxOJx9YsSOp/guuU8r4bo4GtMY1Tw0RJIZfiGIzirgs1dpTjmZIoc3zpy7e1sCj7G05ScXJ0phakzaF6wUN90GJuZuw8BHhkJrFskLLKzzDWnrv0CuOtg4LOMGZbW5ATvkhu8uEV+aEu2+Re6ZxAqp17qORzV0q4iWEiFFerhNm5nhRx9XbD/XbpLFJOWXlvIqaurQ3V1NaqrqwEAy5YtQ3V1NVauXJktU1tbi+eee46rxZkzZw7uuusufPbZZ/j222/x5JNP4pJLLsEZZ5yRFWB+97vfoaKiAmPGjMEXX3yBZ555BhMnTmTMXBdddBGmTp2K22+/HYsXL8Z1112HefPmYdy4cbq3lFf0IOtcRwwGAzVGR9z+Dj5evslTZOt2NQ6CXWLmqnCNNXys3LQ98jZYno3GVWF7jwg5Od7jgZ68wQ5b9q5Nzaz/eOINo+vSu+v0s3eIFcE4JJ7v4fCF1wKbvg2nds6iabwM1SwEPRpoD7NkeVu27AvnAVtWAC/9Mf07Yk6Oy41P6YoOljxOlXp74V0f+DunBfKA3E0ljV/xyDQeaAs58+bNw8CBAzFw4EAAaX7NwIEDMX78+GyZp59+GoQQnHbaaZ7rKysr8fTTT+OYY47BgQceiH/84x+45JJLmBg47du3x1tvvYVly5Zh8ODBuOyyyzB+/Pis+zgAHHnkkXjqqafw4IMPYsCAAXj++ecxZcoUHHTQQbq3VFAIhrjy9VNqf4sH/+0lzFncxYtTb4m7B6pDY1HKwzNh13x5e0yiPvZMCD3xd3E3gVsIYDU5IZjZDEv/9ZtTzdqjvKsI1DQ5vKSKPld4jjQ+f36mA2oEUGHV4Y3pbY2EEY631W7J/p2qZDU5pInaMCQb0f3rZ0LrBw+OiJMTEaRNBIhzpGauknBy6L93BPSu0s1dVWTQJh4PHTrU9wWcf/75jEBCY9CgQfjwQ/+8FgcffDDee48fLTaDU045BaeccopvXUUNnrlK4+NsZTXi3+W3A0gLmftaq3ByYhZeI14BMzsOqTQChSQe/xAg2vHTk4KfCYoItCBhmK6i0otZIp4KACsME6mPWSLsBY6QHE8irXNReXLBn+6m9WvQHQD5zy+BbWtgXfABkDDwFwkpJQEALFq7nVm/t21ej/bNfzs26ziybWcjsmLP7LvRefkrudY57+i7LTvQvV0Vl3SrgsBclmQjMHsisNdxwG6DVFqUdcb3arF3VXiaHNKgYI4LiPDN6OEhUu+qGCoId3C8VXkVAGCfnVsBnOQtkGoCuaNfdvotJPHY5N6NdgxFqK1iXMF9u0dNWII8VsbQ2G1qmWg83TTR5Mi8R9S7Es4nxgo5KpocX68513ne2G5KpseJ9e276d9rFqB8d5XF19VWiN+AZSeYntbXbqRbYspup4WcFR+4avLOPb+4+SUcesC+MA7nyhN+FTVgO0gFKuf+C/Y7f0+HSlAI/hnV1MLz5PO0LRlfzPtuDCjkmGRaL6I5N07QWWBENRb2dZZyjzsbvoFVvz77O5RddaGQ5w8pTD4M23UfTQ6jvaGDnUUXJ0dQ2LgZI+KxvMYAZw1ao7yrCCwQpdQbQYRCPpbrkr8/fQLYvimkZ56GlSgDfW/bKSHHE7WZGq9bXDxB3vc0r+pPOP7r64z7xtMopBrVntlWqy1WLPJmn5c3GM0cFFSTw8wTjQE5hip9KR6ZxoNYyCk4eN5VkXH2sXqz64MvIolbBXnprXDhj4Yobfnt2kRZyCPl5ASt1l2vybOTncyvcE4IshoBAgvBgu7zwfvuAxOc/zcWtZN/xY94rKUNyxW2EmXMsGnctpFXDABgUwdWuQj9ouSmJyfkNAVpNxnBKd329jq1YHhNVgU21IWYHd2Qk2OBKH3bis5VIaS6UCEeF+86Egs5BQZ/YpODiIKbMHXwy7jVoIX1rgqGKIKJNVfMPxyqqYivnfEtmy9zVdjxP0LW5OTfzErgZDU5gBOCuYqJHST66l1VmJgB2m34VGCu0qiLEkhslyanqX5zrn8STY7bvKJiktEFfZ+ZZ1Vfp2quMfCaiyRODpTyX8h6yo6TYH1UGXPFK+LEQk7BwZt8onSSdrdXWE5OaSFUEYcmBvpUTCS/giNPnJzQs5CrC4ZhgBAn+57SmpwIps4IFxPud66xQDtUWgw7Uca8m9T2zbxL0mUlPbYQ/gaLcDZtO+uiTN9TrOYqWtgLOsermKuKV8yJhZyihM+AUVhr+DtD4qm7lDk5oWs1OGA3n9FocrRcyEO4Z0alL9I2cI7rCN8eAYWEOelCa1INJ1dWrk1l7yo/cwWd4gCK/VQygfBMU7xj6u8hmcx5ZNqJcvbud1DmIPcmills2XOpCLJ1st9H+m9Z7qagPLugJuPIcleFGi27eAUYFcRCTsHBGUARjSkLxaXJyVdshSAfKWtlMXlWKqZFHyFHJNgY3pfS8wg4sbldyFmetVrd0ifnN25Dn5hZ76pINDncVsXColY9Ac1VtJBj2QnQb8duoIUI9U1UJOZmjglw5w6xuYpwypu2FxYILCUejVzTyZYM1qFYyIkRBM0D6NY3F+PmNxZH3BaHGFjKmpw8W4JNWhNew2hn1N+BP39HoQ5qceEHjRQgiB3VgJMj15bkf+LNEGXTmhyVtA4hTa+Ki0wy5WBd7U5wXxQv4rHGUEo25cxViUSC0SglGsWakoRsvIYdvRvs2M48tdQOcUTj4BqTQJdDtMlV+c6TDsH/qr/jk6WDCm/uDoVSpjCI4+QUAbbtbELn98bDhoONP3kyUu+qWne6B9FMV7xjtmAIVQUs+NuvdBjmKjbDcWThAJlfoQukIWhytHpEch4vBIBjWb4VKPgHMBB994So+XKd8fBcfPjtRjzWypvSxWrg8VLUhYxUKpmry9WZqiRVt8Rc5SUeR8HJ8UY8Jo44sazIw0ujRck5M9MiAKVX8/aXazHzk9cwseOeeOdKd5Z1dXO4LxSk4WI2acVCTqFBCFI7tuKcsjcBAOvq1kUn5Ozcij6vn8Z8eyJNTj5MSUHbCD2PUzOE/QpTyOFwB8SFqYUihLZpVXg+coFlWspA1UQq7ZvGuxBHnVZHOq1Ds3eVpWquUufkSNt21N7SkJX/wl2VM1CT6uTR0Tt1670XaDzDFKXJcfellUO7hrN10vOLm38ShbmKX6fgPhvrQTYu9ytVEKg8m5+lpmF85TeYuu0wAC4hJ8/eVZ5riuhpxkJOwUFgUbsJy0mFMjx40+IhZBHHJbWQnByDa/Lw7bDfNE0ODfNZ0RGP1YWcMKZiWjjUMlfpteL6qX8P0lL5Jh7DndYh3CzkFscpINe2Gi4qewkA0N3a7Dnn7PCalHS0azQnRybIuGERIvzQo9ikcDWdgrFSf9sA7NJIB0bVHyeyb1elPp4AYYEoCTmD7G8AACMTH3v7parJIcQ4no+7xWJFzMkpMNzjL99aP6GKPIQdflSalmz9hh+WXr8oLYpJc8K4gjqaiHDNVczEHGLgQ3mjVJOKgnUQ4nHoLAJCss+ewIKjEvHYb/HwOAHwemQhjHfBNQ1pjCWHFnKIWDh2zycyF3Keu3dg8EIVCL41WsApNgQ2/4TIyVERuIrYWhULOYUHce34SF5VfaJggOG43UbgeUA9qry7kBvcj0hYZCYOn/tgs5CHveApCLOUBkMd7izk4cbJCSWgoEZZxlxlEjQuQH/C2CzwBEud75PW5BDX7t+SaA2k3lURE4+N5jBNrYbsGWbHiMm8EdS1XVFzWuoZxlUQCzkFhgXCEPJUFrEwoxRHGSfHbxK182F7SveE/eU36dDvgykanipHdRLylmVOGPTHNbGJwuRw2g+yrMsWQslFEviZ+LxeNkGQNiZlalIzV4VjBvCMXsNKgmUhdyTmKlmNck1OYc1VIbXoX0LlPl2ZwgMHA+REfg5QmW8RryBUPIJRLOQUGJ7xozCgEiTpW0YVUbqQR5Z2IVO/at89JkH5M2Y/WJHAEwxsxGMN01UInWDD6QuEMMkvE+gIdblS4RCPxfXrFc5xchCOucrTG2+PCPVfIMhtB9XkUB5bhIAeN7L0FFIhJ4JggOAK09EtuGG5kDu37O2qN2jFipocFW24Ul+KR6hxIxZyCg52ciPE31yVIGKXSF3YEQoiUZir8k08poPahSm00ZOL7y3RKngn+IKnGycny0UJpJmgd5ZhRDyW18FE2hU8YVNzlQNbTZPj0wKr3ZLw4xSEUt+ecM1VGvGZUvTGihU/WW2wS8iRfLCRmLPpbzcjIioHn9Tvj9Qc1/yUVAQJO7WTvTbUiMcyzZuKliZO6xAjCChCoyoSIeZ8idJcFUUcDBqmnBxzYSXMD5l6Nj4TBDtd+S/evmDs/eY8Gx2EGoFVoY4Ilk9GA6Yk8PmUUV0XiOKuXLcxHTMGnbvKy8mhxpOkSs94dcLTSEs7oPHIwmRaZYUsSQeE33CYfKU8mKtiISeGBATK7n7NKAtTkxOpKjeKpSZs04lfWepvA6FKdAWryTH19jITIh2V3FVMiyG8xzA5AoCvJkepCq2yOU2Oqgt5eD0K/rwszljRqZUOBgjC6nJMM15Ho8nhaKwU+2Qi4EjnBEOJKe1PF5CTozhXqRCcVZ6fu0QxkZVjIacIQA+09IcvHyBlIWpywiQxexC5dK9af4B+EO6fGpeLgoRQtfl5V9ECkROwQ4AryqvAFViXM7TqY+CJk4H1S0StUn+qdly8SvgJSipmTT1zVe45yIQcYqwlk3lXRSQU6mhyKCGHuGLfqJLK3WfCjTvV3BdB3BkVhD5bNVcoFSQEz8sJ/M5z18soCWoCYPEILCaIgwEWGBZxaXJAfCfwMpIMTa8qngDyqzHJJ/z7RZ/XCNqn0wdaI+NbmvYUCt4HR3fnrXLfDzdHXN34DXDRZ5wqaDObasRjGfIbxNJybT5ExOM0py5zkQYnJ301t1QoiwznHeoIGbS5SsbJ8btjpv1UBGkdOL+inIdk32OWk2NUccDxXeCIx7uRtYHaDBOxJqfAIGA/QpXxVIZS8a4qFk6Oa+rTmUAYe1WIk6UjJmvK+hBKzBy6bYV7yuVsUpCst6ziHmYX9DAEaPWy4oCXeg1mnr2MeMwuqD6cHMXehDHsuIKlVjBAlnhMjwWbEcJlfXCdjToLeYhxrWRXBCkjzlcWniZHOrYcAtRvxD2PPIa7H30ye3zNpq1IZcaHyhxRnPtZALEmpwjAhvBWWbjLS0TICa5yjQY6E4jDCBXUcYfAts3VaQ4x1BCxJCGjtk2FI9V0g9xyRjtLmbkqz8EAQSizgyUkHtPJT33pTtRjsCDRqkbEYdIZB3SSy3R31FzIZXAiWBm59xTlCiypOvP+ZXO6UI+epzg51vpFwMPH4s/Nv7ds/Rm+/vwjHDbt13i9/Wk44ZIHJL0sDcSanEKDEIY/4De4nZSDCitETk4BgwEGrj8fJgsPXwqY9a+LseqGfti4vsb3cvE6J+DZ8EqKtDeGz5f1elPYpens+BUCF4YSBsB3EaCfWQi2XcpcRQAQK8EtxgivGu0SiBejMMY5VyjU4uTQQo7DCHBuLV1T3UZuHZ7WovC+5AjTqoJX2Lmr1NoUVhyoXvnVuVY3vP8oe6ppB1rPvB4AcMLW/yr3pZiIxm7EQk6hQQjj7fLQe0shVS+GPJiiHJzFysnx/2jZNBvu645e8wh6Yw2WTPmnf1OiYHvUmrNi2iQpOVFkzjTd7XmJ7txS1F+GmiZBfeGELfARDA08QqRlSU6TQyxxWgc94rGKZ4sVjvt9UE0OZa5yC2P0+zxsy1SU37Yn7n7o3zjnoffkdUZtrtLk5ITi9cfrkvQ9i8xVQTk5aubwlRvrfKtS+V7zsuE0RGyuKgLQA+SyZeelP848Ze6NNhhgxBGPNTQM9M7TNMKwbHLXR24XO+T7x/HJW4MxaOTZKj3K/Wk4FpQS7nF+qWgm1NJ9qj3/ospdhZxQQGCBiIjH1JjUyfAuz2ARfPHlaXK04uSk3CZyQe6qZly4+nL/SqNI68AdW6r3SZTGOHOFCl/FJPREiAJXYNOuUpycYE1EiVjIKTAI2I+grbXDp3y4oykRpQQeQQI+gY7FF3pzBq05CebZJE7QydbV8N1Cpf7QN2LKaWAnXVEdVDqL5vIqa3Ymwq3nvkMmcPsKmIY8EWl9GU2OzIWcJtv7BQN0/RYTpIOb3njPS2shpTk5YG/NdKMURYJOvheZGmyQsNKNNbersCkQmShDjHgsH//ec2aPoHilnNhcVWC4XVP9kI/M22EhenOV4SKvMSlrx4tRhcZ7JCIBwXRxoa8T3FNQwcB9vdmzk2lyDKoLUEU6rUPmCgsQaHIcU+2atO3c38bvhff8TTU5hNV4mPYpCu9LXnRodY2VwX0o1W1Qb+As5KZXcr45JQ/M4kUs5BQcjuaOJuzh5M+hMK45Eu8J6m9VQYEQc3OVq56wYMynYX6EsIMWkl3Fv8wQgWZFgjDD8zc3yJirZHFycp3wiRrjuQWeFsIK5Tvi567SqJcmHsOBn7lKBdHktuM8Q8V2oor+Lm+ef9LJEyeHf62+WT4fOQVNEQs5BQaBLkckZE6O4AMIY5EgEeeu0oHxfCpwIQ9Wqbc22W6TjXIc3Oyju4PONBPIS8nAhVxWSsdcFQYIiOs7VdHk6HBy+Il5A4STc9XDe14a845Lk8OYq0xN3hGYq5jcbtn3pXaflgknJ6rklSHGyQlOqFbQ5ETMvwyCWMgpMHQlYNXxqvqxhhIoTYAo4mDQMOUn6WlyaE+k8D5kPbMG3xvK9P4dJU4OXUS/He/4C3PShYIWK2Qhh+SevWPZQuIx2y+NQDmS0kWhyXHY3FV0b42FnCiIxzzTXoTTkIo2WSYIRRUbSTULOQ/eoI3+18cu5DEk0M1CHu5gsgCs3Lg9F90yVBTPwNcxVzHn6bxRoXo8mHFymAnTVFOmwMlhihssYvKUBYr3LpERfJ9fyAK2RUBpHsScHNoUGBqJNYRbCarJYc1VBGwWcsPNRhSOCdz3rmquikYb4TsUOQUCm6vYBjRKcrSJSt5VxTPXuxELOQVGelCpD2hlgUh5giU447anceFTHyv3QRkRk6RNs4mbtxGGRiNz3FQLxf9brw6JCa4Z9MIVyvxl4O0k00b6P7+wzVVOVtgjgFCTo2eukgmCuSPhaL44deh8Pw5rrgrDey0KEwd/TlDtn8l9BBcA3M+BKFzj36Ti+3G1QxxHMaeau5rYXBVDAIu4bf1yhB10qdJKYlblJThxyV9DrRcwX8ijht/zZrQ+jKko1E6wP6WeRAJTj6F3ikNdJ86dw/mhoZpQvR9T+Mb+CHtnSeg6ZS7kZhGPgYgjHvPq0PEylETJViXsup9GGKk5vOBsSrSIx2FS1jOMKnn7btO1BRJYaLBEc4YPCHEEorbfdcU51wMGQs6sWbNw0kknoWfPnrAsC1OmTGHOn3322bAsi/k3cuRIpsymTZtw+umno127dujQoQPGjBmDujo28uKCBQvwk5/8BFVVVejVqxduueUWT1+ee+457L///qiqqkL//v3x+uuv695OUcA4d1GIOCHxkefYR8s2YexTn2Bt7U6jOp2IiceWRoJOU+8qWSJNpR2salBH2dwq4uGYjgVNE1zQEUccx3DnL8ldlWdNDqjNCJG5kPt4tQjDAcibDgxuLButinPfMqH+C5gLK/nT5KhBFoRV3KBxc9kKeA4aQUOFMARsSSd5oR48jyAqN/k8QVvIqa+vx4ABAzBp0iRhmZEjR2LNmjXZf//973+Z86effjq++OILTJs2Da+++ipmzZqF888/P3u+trYWw4cPR+/evTF//nzceuutuO666/Dggw9my8yePRunnXYaxowZg08//RSjR4/G6NGj8fnnn+veUkGR3iCqCwP5ClFuAfjdv97DVws/xl+e/8yojqil+4ff/1a5rF5X6EB4NAfGrX1RaDd0cxUtoJgtEo4mJyfoBOY4TmCznwf+RIfgbTC15Uw0sojHlsgTLnOIFjDp63iLC6ce42+Km9ZB5/rcn+7YXuoBRb2mkfDh1WAUPK2DTGAhokTG+YmTo6K1UYrsXsSaHO2Ix6NGjcKoUaOkZSorK9G9e3fuuUWLFmHq1Kn4+OOPceihhwIA7rnnHpxwwgm47bbb0LNnTzz55JNobGzE5MmTUVFRgQMPPBDV1dW44447ssLQxIkTMXLkSFxxxRUAgBtvvBHTpk3DvffeiwceeED3tgoHokc8zudQmlR+N0Yk5uH2tWMBHKFfQcQDf/j889FwwieoLNccxjrEY8h25kFcql0qapkLudADIz+uu4F3lcQBu/gEqi5Tq1+jYTTiqjOXu0qsyfEZW8wO23vWe8QKxezL3c1rPSOXoBUCJydq76oMVO/dLEGni0/jOLn3mslC7qNJ4Wq8Q3X71tFcG46TIhZyIuHkzJgxA127dsV+++2HP/3pT9i4MZeVds6cOejQoUNWwAGAYcOGwbZtzJ07N1vm6KOPRkVFRbbMiBEjsGTJEmzevDlbZtiwYUy7I0aMwJw5c4T9amhoQG1tLfOv0Eh/WBpCjuICRQK6dhAAIxLzAAC/bvqfWR0Rk9EOtFfAaZSnwciAeRw+/WJIt3QyS63e+cDUuyoEby9drYpsWVZuzyhOjnnuqrCnXMJw58SaHL8M76Lvl0CwyFrs+zL9qrlpHXS0Ba73x3XV1kUE84PF2ZTwvhPe3GRR/1WFVBOi/FjUx4kyTDk53PUoFnIYjBw5Eo8//jimT5+Of/7zn5g5cyZGjRqFVCr98dfU1KBr167MNWVlZejUqRNqamqyZbp168aUyfz2K5M5z8OECRPQvn377L9evXoFu9kQkDZXFZ8mJ5xggEUy8N1rrF9xhjehkrHboEvGdcnNIUo10G7OEZmrvIJiCDt/pn51QdUPD85aivtmfCMt4zZXiTQ5/u75fHOVtO0QTJRc3oyhJqfZ/Sf7U4V4nCQ2PLNKnrKQ88AzEZkk3HV/P7w5T/atizQ5wXNXmUU8JoR4ExErXV683lWhJ+g89dRTs3/3798fBx98MPbaay/MmDEDxx13XNjNaeHqq6/GpZdemv1dW1tbeEHHs8v1K14IwSHADr4I4d8vgbbE6H5EzGMtqQsAsKMx5dpQm04s/tqpMF3IHeK4KglByPF9hWpt1Dck0e6tS1FupbDpsBfRaZcKfkEX8ZhYCW4xR0JUT1fD75co4rG7HtMnxzfZ6NTg/ib0hNa0EcclEEQs5MiOOU4K7jdoMsu5TVHMbyt7UF4HV2sTUMih/tYjHvM0XP7vqVjneiAPLuR77rknOnfujG++Se+UunfvjnXr1jFlkskkNm3alOXxdO/eHWvXrmXKZH77lRFxgYA0V6hdu3bMv0Ij7S6ov+CVBqKX7lUnSi3vKpEmx0CNKyyhMcET4uCDbzbgyPHP4uH3lhrVQUMl2rJ76tYH88CZOlTV53IX7HDGVmP9FpxaNgMnJ95DqlasBU7fQ/OOW8LJ0eF7qT0Hi+/Orwmed5Vpolq3uUoliB7X5T4KTo7iUZ72JIxggLy5xe89u7VKQp6OXkeY+lTKpX/KtY+q9RQTIhdyVq9ejY0bN6JHjx4AgCFDhmDLli2YP39+tsw777wDx3FwxBFHZMvMmjULTU25KJvTpk3Dfvvth44dO2bLTJ8+nWlr2rRpGDJkSNS3FC4ICWAbzw+MDSsRmKu8Ow+FfniuURdynKjMVVpPleDNFx7Gp1UXYOg3NwfvD2NSUUi+51BmGtUm6L89/VTtt8SFXCNOjnSSp4LcyeokVJXp5yDwmvMxV5lpL9ymIn3wduPmZkNWaE0o2DMc3vPKm7mK8x44gr6RJsdNPOa9c+n8TrjRjQN7ehHhD/llvA3QD82FvK6uDtXV1aiurgYALFu2DNXV1Vi5ciXq6upwxRVX4MMPP8Ty5csxffp0/OIXv8Dee++NESNGAAD69euHkSNH4rzzzsNHH32EDz74AOPGjcOpp56Knj17AgB+97vfoaKiAmPGjMEXX3yBZ555BhMnTmRMTRdddBGmTp2K22+/HYsXL8Z1112HefPmYdy4cSE8ljyjgGkd1GBqrsqDJsfoeWhochQydptAV0AZ0/AfAMDRiYVUJabxSfTaDurd4zgpdaGDhnTY+dWh+GwYBq1PAEOaGyMwVzFEda6QIzbbqeUxMuTkaJjO+H1gg2LqLsJcR4hINDk8jRWnHNe7KhpNjt+j4sX7Cj7T6PLumtt1aV0Bte+1RZmr5s2bh4EDB2LgwIEAgEsvvRQDBw7E+PHjkUgksGDBAvz85z/HvvvuizFjxmDw4MF47733UFlZma3jySefxP7774/jjjsOJ5xwAn784x8zMXDat2+Pt956C8uWLcPgwYNx2WWXYfz48UwsnSOPPBJPPfUUHnzwQQwYMADPP/88pkyZgoMOOijI8ygAiNbaqaodCZQxOiREI+S4tDIKphfL9Yz9c1eJNB0hfsg6E4/Db1t0H/OWb/KpzyxOjo7DHuPMZsDh8O+Sn6CqWg21eEtuMO1dlUnrYAG2WTBAWb+VhBzDxYSvpQqiydFDirfURBEnh6PB4D1XnjnIaMb0KCnpAxbnmKtN4jVNEVjB507V8c+5a5O0DsWsydEmHg8dOlS6SLz55pu+dXTq1AlPPfWUtMzBBx+M9957T1rmlFNOwSmnnOLbXnFDz1xVrKkSeMiHcK+U6dy1yJoSj0PdrRD3xCarWzA+BBPhi//+Ow79xx3ipjUnUGJgrmLbI8qDgY4zImsvrIjH9LOwRDybbNnMc+B4CmUL+bi2awosbjqy6QgMGieHlR00eYTIPDN381FocihhOttHnpATjvnO/Ri4miSfernPIaAAqBrx2Ltp5Gm4VOfY4kScu6rAILSxX6V8JFFCI0I+vCdUNFtE5rni0wYljJh9x6LF0KQutUpuKn/Y5zLdhVYfjIHFndZB0qZ6dFp1To68WK6c7aPJydZpWR6tT+a79BNixAu72jMxFbR5pFqduizXfekKBFyNQQQLI5cexPWu8j4P1RxcrsrZX9o8RD4nJ/hmVk009r5HNXOft1DxrkuxkFNwEE3BJf8Ss7HpKw/mKhWOgleO1NACyEw7KvKVkKBqKHQxxw05OQ6dhygPqmjidbQVF1UUcnzfu6L+g1qU/DQ5dJuW671mtTy+LuSy+v3NVaaCgY656t+zvuXEDWKfp/5SzonvbJhgVtqO6n3yvKssYhBEVWGT4DO/884H1hpT49rW5uSwKHVNTuhxcmLoQTvisa6a2OH6NeQFSqakoG2oCIiutAJ+2h9RMMAwoWuiDEwcFVwnmsDYODmk+Zg6mLLEgSq3KTQThqomh7lPSdvEHSfHLeQ4ABL+3lV0e65zwozwWqZWPvhZyL11bW9MYsObt6AcSWw4dBI6t6n0ltUwP2bA966KQJPDMdOoEo8BEyHSJb5z37kYFvjPMmiGdtNcccRAS6fbRr4RCzkFBiF6Kk5eSYdYQpUcN6usaseyKB5NjjfCqEobeoKkKMpxqJwczzuXmUrA7b6xup9ZiGWNsn+bpgpxXBGPZWYB5WeswauSVyMWOtztEeY5sF9c1lylsWtXXUzCSG7KfeacZ5jcWYery9MJldfUjgfa7OFpl7h+q8DxhN5DJPODqiZCGIdG95tyz0cGBG/3Rk07dppPi1qcHG4wQAVtVRFrcmJzVcFBtJS/6jtdq7l84QZfPpomgp0QW8hhvmXf5y3U5IQ5KbN9kIkPlmh3ZexCrkk8zrYdUigBRU6OnHgsvwfLNc2L26M5V5J+UeMsndbB8p731OGzqydqY8BiBG1hF6VQTlJJLf5WsoF/vYEmh8/JMf+e1Dc34PZVrAEOOmnpa1x5Zrug8zb9vnYh9cCiV4Dm9ykPDmjohRcLOTFEsACtxYpHrBMpuYFw1P+mwzcfJGnHSflPCIG8qyTmB6U138wLhynq4bRkjocRJ0cweryKHL023O0pmMjSZdXuyXd3ydyApJgjeNfektQ78wo5TpZ47BcMUH1XzTuqFTiUgionR6itc2ssNNt3OPVG4l2lai4SzU26wptHs6z/sXDHRIjmqi5kI/DMGUhOuy59jinovg6eZxBVfrt8IRZyCg2ip5rU5uSEMZGYWqvyQDxOC30hf2CCFd7z6AM0q/ceBToN090TQ6bmv6Ogjsu0poW4OVESsM9FFvFY7/mJ26ODpklqcGiNqzficVagDxAnR6WPpu9c1btKXD2tYXMM+hFuMEDh98MVpjmaHOG41+yTW0jQfOcWCJxU+MRj3tzbMP+pbJviC800OVF4yoWFWMgpMJTMLa4rtOoPIbWCeWDB6Ad+OkibbyGXsKLhmSPNXaXQP+EJtg9GLqORmquCCajMpOeoa3JUvwXfxUjx2fhFKM6eSxdI/83NXcUzV/EqEi/OKtwH08WEz4MyFMJEJDEJHG7uKvP5QfycqXGWlXE0zFWBiccmdeqbuEzQkFTg23DGoRonJ3YhjyFA5N5VeUiSKWw7Yu8JAHCIgrnKReTztZGLNB1hfshhaO8Mn68S2Za1Nxk0Qv+prgILj3isWI2jxsmhvWC4nJzmzYTjF01aoOmxwF9MCCzWpCbsoRyq3lXuIJW5dpkXqg0+Jyd8F3J+58I3B2Wr8cgIvHcorYEZgxkEfjbcfvhHYOZroszaKxbEQk6hQfSEAZ7ELNO0RJEkU9iWux/5yF3lKAiJAXZnURG3dcnm/N2UYd+oCVRJM8BdDH2nbqqouiaHSbMQKOKxGtiYQeLxmr4HipPj6lvW/GHIySGeGkX9UCjEgaomh82ITWtFvDt+HXDfZSBzlYomJv03zwwZnSZH01xFBHHSIph3+O/A3WyI80yRIBZyigFaQo525boXeKBqrPJmCI/g4+AQY/zasdycEN/ydFm1nb64MsHxMJ5NhMRjRtDj1uHn3ZQ7n3bXVWnT1bcgCToVn42v5oWuMnPe4mhyMnmtaPOXrA5XCdmtstcYEo8VOTmOgmZLh2OVvYZ7hxGYqzjCNG9DIXaKCPpd6puevN8S0cvZzK9UdlJyysyFPNbkxJBANyaCRA3JK234tTDB4ExdhyPxrnKZq1Ip3w/MzXvyW6Dp2oK67wqfnZZHnUBbFQLxWGyu8gqFtOeN77hyX0/9lkVgVSceazw/6aSu6EJOOQikzVXuODkk84e0L6KxJzNbh5G9ikc85i/INEeJOSHojyryk4WcC65GLSTisZu/ws0oLn9eDvdbCuhdpbhOuMtxlUpKLcacnBgiEOK76DLFlQWH5jg5eRx87o8oX8lEfVvRFQYYkwNlG/fUE+DZ6mjvhCcMNTkyMwSn1cxfdCoDHfK2d+evKuSIEVaCTlVNTlrOzD0Jb2vNAhAj3PloT1zcKO67sCz2OzLctHAFS94xRyDkSMyPKuCVDhQnR6hl4o0zucbKVYFeP1zvg55vc5sCvzp5kkXAeduYk+M1jatpcrR6l1fEQk4xIKAmR1o6j95Vno8hCj6Q61kRR0UTxu6S/YuLyoZ3P3o8LFEwN8OJUDHFAtMBzyEfcxX9DB3CNKNqmpH1zG/iVfVCUvWuoscQ4ZmrMpnafZ6t6P6kPKVQiMdqQg6tVaD5St7rQ5hXgpg4FIRzubVTJXSCQjc8JnqqrozmT1KlBT4nJ7CpX3mzwLvOJeQoVVW8Uk4s5BQcmpocXaVEQQdfHojHxN9c5TaXKJXP/i3T5KhASMrRqMF/QtcBUdFeMIuxySTs2vkzi08ImhyN9qWlBCk8PO3BoTQPHBfy5nHC5AXjyRVCbpjU6Cy4Rh2q5iqHGu+MtsPVV91upL3TXMcCeBAJ5zWuxopzSEg81uuHCg/Rb6zqkpVVIDNX6YdwUNkIxeaqGCLoqn65akhJ8Tx6V3najoCMxptU/AU58W6LD74QEKrA6O6EjwqZf8LHG8jgHN1qrrx+Hex7cgePU5tkpakutLKQS0qJzBbucoS+Z14wQKJUHy34sKkSxIuPS7xQ6q8bqrmr2LxtAq2UiCMmgQVeHCDz70klTk7ub56QHhLx2PMZU/U2C8LydybY5AY2V/EO+ZO/W2IW8ljIKQqoD5BCaHKMicd5GPhKnh4E4E9+svJ0/dkfroL+zyUM4jEcUS0SQUYm3CqYq5hszgbmKvpZuRN0qkZcDeJCrhw0z21Wk5TL8m44wQCz35mOBkyBzEtgKb0vP6iGIHBStPZGIOwbcHL4q26A+UFBAykbI6KIx0EEL3f7/HxmnEs4gnHwBJ1iTY7fld5L9TZFxYZYyCk4dL2reB8njwjZ/P8CanKE+WFCBHEc37nS8pi0dCIeE95R4RFVhCJ8Sm5cSKxMn2V6wq9bXkLHXAXjtA4BoFgN+5wkmjHQC7vl+eIy9WjlrnIttMJFmTGBmT2fhEFaB7mTg34/3Hmx8hYMkHefKX9NkIqTh1c3pfeuhBnHA38HnHtWIEITxyzicazJiSGExUye/tBeBAzVnszANs3qEIl0z1F563pE+ClymHxG+Yp4LGNk8M0Dskk0JRFy2KSU/DJMCAEmCJ63Du71bq6L4g5bOQu5TmoOWSll4jGy90BgAbZLk5O51m/XLhCkpM+E+dvsm7K5BCHOQkiNG0cohOj3wULwgILspf7Cea493r3z781W5GhRFfn2S64hhGAzGGyu4c8NKppnzlen9JpiISeGCERTk6PJmtepmx+vwRxilTCvbBCTmN/iZB4MkI3ca9BBYRuuZyN3ORIcl2geZJocIvOakVxGx8nRmISJy1xlS7lEavWG5UKuSjxOk3Kay1peTk5WANIw47GmOYnJSn3oaoKnyaH6lBI0XAzmKq12eE3z35NO+hdR3TqwwBdcozFXNbcpHedqBHVvkVjIiSGFxkKj+VXpCTlUlFpmEjdOQ252nQTe3aC/ucrdFy2ickReA1ou5BBNTOI6ZOYqtip/AYrPT9V5hkR5iDvKQnw4Qg6cpNI1rDbK60KePefjuSaMeKy68IT4TfG5Vinu395Ngl4/eO/LQgDvKqW0DuK2Rd+fxZRRMVe5ynDTYvho9zianKBZvbnfLHe599dEqW2EYiEnhgR6mhy9RVfHPT1FT/hhTKZ52Kk5xO2543+N7zNkOBC66mt3y8FdyEVlZSabVEq8gFi6bvG8xdCn/8xzc5nbVHNXycr5TrxK74pNfun/bTVrazjE48wGwTdlhpCTI8tCHo0qhys2M3Fy1N6FCnjWsiiCAfLGGW+squSuUtPkuIQErkAlrcAlTGYOa87zrvvhCrDwcnJ4m0b3yFDj5MQu5DFEUFqkqeLcYxI+h44mJ0mbMdS8XKQIeF9KcIi/6URbvS5YiEL8kD3vRdI/4a5O0h+Z6dGPVCzsB9O0umDpNSnK7pVTE3enGwYnh3XfVU3rwHUhz45BuQApE+KEX5mK5s0E3IjM1PNwWCGMKmQgbHFVC+FDkcQrNufqCjmStrJ/y8cq14Ncm2for5FR0gUTmbAt7YHBNflBLOQUAzS8DHS9pXQ0ObSJQ8abiKJtjVpdbagEJmP3V6oEU8/f7kUpwK5a51oi3OVL6mDMMO7L6IVWVMY72dPCrr8mx6URYIjHkq5xCMomKnT1iMe0eUbWL2SfG+GZq7JxcnzaI/wfcuKx5sKrCu7iTwt9omz14fQhmLmKJ0CqawzFEY9FcYKEFQn7lW1ftoEB4c7/uu9ZqJlytdZcu7iI6fiKOTkxZAiaoFNaSqNu2lxlB5iAck1HP/CJgibMvev3lXEEmpxQb0dHABRq5sUdkk16fm7O7kZ1OA389tQXH17ag+jGkQW/NAxUydyz4hCPczFRBDyW7CGRAC14zrDyuoAwQp9Ak6NE9neBy8kJ2VyVTvHCHHH9n4KOuUqm4fP85mnHhJcjPXI0NzC80p45jtOPTMRj6f3w3lPxCjAqiIWcQkODlJktr1u/atEkJeSEoYWJQJPDc0P1V+QQrb6I3cbD+9i1ajIwV8k4OaxXj39PuBOfhgu52yQr11p4tUy8CZufpoCtyR/suJCaq0BzFSxYLhdyJ+Xl5PCJvTKNCL99LS2kDjh1OcwiL3nGmv0IKiirQBRqwe8+xRVmBFfxc3BrJfmaOnlbJOWOlwTtuVMeF6u5HQXWAV+AVX9WxYhYyCk4VNIS0MV5uz1ZcfW6k6mckGPCySlEFnJCHIV7dH24WnwOA1MBXc5t1lDuA12d/nOUu5DLF2LAtSg1l6FvhUuWZBuhyuq6m7N1mHt8aLbn85yz/eC5kGdNE34CpKg9yVemSMbWB48MIhDw3cKZ0aLmr7lSroknuAjNojwByz9eUdZMK+mmnFsnHr90H/lpHXQ1OSqCiX9ahzQnx32VSl9i4nEMGXQWAW01pnp5J0VrcvTNVZ6PQeu+DOPkOAYTrhYnR//jZcibwrQOwXfC8jg5AWPRMDv65v/Ty4aSYJkrS/dfqoXhsKKNhBwlzRFrrpJqcqhIsAS22IXcT1Diuhj7mBCYS0JcTLhDSkQ8FnVIDXkxV3m0EIT5H1NWsAngcXLkGj72HN8U7Cc8G8amoUu73hWvyypzrLF2rXgVObGQU2gIw3oLoDt4dYK20Xlr/M0B/ohGg+maVFTEPuJwvXaExWlFjIS/IqpSjRyu83AE2hapd5Us4rGKCY4SUgxMDcyiTdyCqGxB92oteIuAH09ANbYHs6hDtqi5Eim6c1dlF0S5Zkg0gvgJLNNn2HcRJjj9Ewo57Psz64cnGYZRLSKI+XnqGwR22PoLKUp8FWkZ/iZNJaUE24Tb5MW73t+FnPutKZmrYk1ODBE0OTk6QgsAV3Aqn6KMuUptUaLhEbbykLsKTgoq/SOM+c2vPH+hUhVGlSI9h2KuEvdHJuQw5zQmaX6qBz5kZXWDO/M1OWFwcgBwPIiEQmrmPMdclSV6+whzwpQhEC8mfJ5HCPBzIZeRqDUXNb4mJ8C9CAUDngaPV1awceCZqzTmMV69/pswRcFMAo+jgSatgb3O/a7D3rTlF7GQU2Ckh4bGhKEstFjN9esIOXwXclUSsnci09BQKZqrvDsPNRdy9zXqbYgXLWGYPyXBwfeAqy09db+bzMhcl2qStpWuQL5Y+xM3qcXCw5WQXMtrN6C5Sjy22AzfmUu4Qiqh+gMLlsdcpabJkY0nIaLy8OM1xcTJ4e/43eZHFWRmI/aY+SZIROqmj2e0t/yyGpwcWUckXJicJlJ8uQXCN50F5eRwr/f3ruLdbZygM0Yg6CbolEFsp1ZDilr8aBdy08koGtdfzqTiK7Swi2zUaR3U7lunXrlmgX9KZq6ihRz/HS2Xb6LBa9KLeMxbJHgTrxxG5qos0ZTnfkuNM8sbJyfbms94YSMss/cqMzrT5fj1GnyjXLWZl5PibdfEXMVV0WnXIusBIQ63X9xjCmkhcuNBMl7Fl/sc9OmLtneVi5PDa0eRkxMHA4wRMjjqQVlprhqyeZ9Ez5+Z/2uYq+gdRYJ25VX84DzeVfmQ7h3HX+3tOq9TnklPoLiQ6BJ7m1vSKJs9IbxE6lIqCxTIqZrbig4nxm1GkF4qN6Hk6lc3VykHhWv+mxtjiFHj27DcnBxFc5W3zlxJXqZwAsv1XftzSdTBac/hCznsgCBaHDdAoIkMoMkReldxNWWczgq+ZZZ4LNYketvIVkx3Utx+pj3CYxXqrQkAYCkQj3OCuex7MNPkhOv1Fy60hZxZs2bhpJNOQs+ePWFZFqZMmZI919TUhKuuugr9+/fHLrvsgp49e+LMM8/E999/z9TRp08fWJbF/Lv55puZMgsWLMBPfvITVFVVoVevXrjllls8fXnuueew//77o6qqCv3798frr7+uezuFB9EUBmQmCsVdsAgs8Vg/U7U3TH0UnBy2DYc7Sciv0TJvacaUSV8StneVQNti6F0FhRxl3OzrGnm8PJogVeIxj9PAacv2FSDkp9Owmjld7CXcZ0cIdf8Wk5G9+SLvtdztNF87omxCECn1TBYZP82v9F5MFjX2mQXxruLBTTzOPF/+sxV4V3G0lVIepKtqrvec37fOGW/aEY8V+F3KmhyJCU5yoX+ZAkFbyKmvr8eAAQMwadIkz7nt27fjk08+wd/+9jd88sknePHFF7FkyRL8/Oc/95S94YYbsGbNmuy/P//5z9lztbW1GD58OHr37o358+fj1ltvxXXXXYcHH3wwW2b27Nk47bTTMGbMGHz66acYPXo0Ro8ejc8//1z3lgoMoqVqlk6F3ElDx7uKjnjscP/WQj6Ix5yEct4y7CLrTyfhCznpnyr3pPLB6wi2/oKI55xEk8NycswmMJ3UGFrmKu7k7IVvWgfF3SfhLGpcDQd93sr+h7qUt6CJNEJMrcI2s20pmFqNzFUCk0/ub3EWcl2hisvJCWKuEo1JxTrF8pX325dVqRafRv1bkR6TwK25lTuoyL5Fw3dSxEJOme4Fo0aNwqhRo7jn2rdvj2nTpjHH7r33Xhx++OFYuXIl9thjj+zxtm3bonv37tx6nnzySTQ2NmLy5MmoqKjAgQceiOrqatxxxx04//zzAQATJ07EyJEjccUVVwAAbrzxRkybNg333nsvHnjgAd3bKhjSat/gC176FOfD1zJX5YScBHGy87ixuUq5ZXWYEY/1vMVoDZRbI5G2Wcuh9My10jrw7eSyRcKRcHIsR+RFB8Fx3qKvbi5yxy+RamF4LuS8BJ2a2jtRCXohz3w/fFNfbgElluU1V0n4PGwtPKHKT/BjGlIppQbufOF9HgBnPGgvapzxG8hcxdF+OKzolYtrxLteFCfHPW6hda+8iNb+eyr3vVjaWi4v8ZhThkM89og4huaqaGb7cBA5J2fr1q2wLAsdOnRgjt98883YddddMXDgQNx6661IUikF5syZg6OPPhoVFRXZYyNGjMCSJUuwefPmbJlhw4YxdY4YMQJz5swR9qWhoQG1tbXMv0KDaE4Ysh00b3LW2XGlBHFyVDU57o8h+/Eq3J/pJ6IS8dhyPwWdLIo6ZNtmqLmQa7xzkzMpmZYn962Ju8ERchhThromhXh4U5qCOrf+MFzILdd9ZIQqkYYjc5wXDDDTH0ow8NWAqQk5KmPQjP/GWwmp5ypKXqog6LsRugs5OMIv4fPzuO9Bw4VcPpbEmhBLQUhKaxPdY1lfU+bJXcW7XhR9na3Ie1mJm6u0NTk62LlzJ6666iqcdtppaNeuXfb4hRdeiEGDBqFTp06YPXs2rr76aqxZswZ33HEHAKCmpgZ9+/Zl6urWrVv2XMeOHVFTU5M9RpepqakR9mfChAm4/vrrw7q9EKEzQFQXiOYBrbEjCN1clY+B79IS8Mu4vat0wBIJVRYT7nvwllLuASEGLuQScrGKJgeuRc190DcLuUBjkT4nhnsxTf+PN/HKoSJUuTNAZxY+oZCaOW55XcidzKLpK0DThH4VIcdSHHO+RXhXcerhLfKuxZ97pRxREI/dWtV0f3l18oQ5gaaTuX9/c5W7bq4mR8O0Kz0mg0to4xHDlURTgdZY4UKDa/KDyIScpqYm/OY3vwEhBPfffz9z7tJLL83+ffDBB6OiogJ//OMfMWHCBFRWVkbVJVx99dVM27W1tejVq1dk7alA24Wcu9O1xOd0NAZC7yq1OjwfEaXC9/+8VPeGbrWsWu4q2aLrLS6+dxXPKbVnLt4BuiGedGQCryROjop3lWtR89avPnG7TVuq3k7ZcpxddxjeHARw9VOyKFEre3qc86P3shwfHqHU0wPeCV5PpeW0g4QK6mKJ03xOjm6UdkkHzK8kBI7jMKYIx2FdyKXBADW+Y1lZj5WIe0s+3zZHA6+r5XIL5qr54rzfkVlfitm7KhIhJyPgrFixAu+88w6jxeHhiCOOQDKZxPLly7Hffvuhe/fuWLt2LVMm8zvD4xGVEfF8AKCysjJSIUoFDrE8rqLSCYMQRs0ojdnAFXLU+0YCanJE5qq0JiIaZHZ00n4RT4YZnRaov9i2hCRQlYeuZa4SuLtK0zpIhBzi713FW1hZE5SGJsc4GCCnL5k6fAeVyvO1GG5GjgMk2iyQbEnYfE6Ob5wcgelJfZEQjDkN7p2sLlEwQOY7UNGeusB7VUG9q3h5o7iu7RqaEq4Luc4GRPue+LOITlLbdHmFLOTZtyAWmnWFNJ8LiwKhc3IyAs7XX3+Nt99+G7vuuqvvNdXV1bBtG127dgUADBkyBLNmzUJTU84LZNq0adhvv/3QsWPHbJnp06cz9UybNg1DhgwJ8W7yACLX5PDstZ4y2Tg5PG8O9USbrJBDT8ABzVUhfgBeQUplwmXdIuUyJXFpcthJT810oOJCzj7TBau34s5pXwnK8g/LFkZZnByb0eQIJnueec9HSyFC+jJ6PEnK+vFEmuEneCtrH6mFnDgOlq/djMfuuEpQuFnY4wQDzPbRz8tJEmBPfImK677BN8Zd/PmaHOZuQ+L/BMuPRzyCHSGE2RDIg07y22aju4uFXraE4IjE3Mr2RUB014Cnj9w2vV+eV0jjbCjUeoDkirlKJfMNbU1OXV0dvvnmm+zvZcuWobq6Gp06dUKPHj3w61//Gp988gleffVVpFKpLEemU6dOqKiowJw5czB37lwce+yxaNu2LebMmYNLLrkEZ5xxRlaA+d3vfofrr78eY8aMwVVXXYXPP/8cEydOxJ133plt96KLLsIxxxyD22+/HSeeeCKefvppzJs3j3EzLw34CTnsINPV5OhMSPTCWGaFwMnJTBKGV9N4Z/FavPjJdxjjTldAHMVb9F9QCCE4c/JHGLl2G47IfBkS05UIlsGu+rd4C/+eUQ6Uc3vGv0jWH5kLOVFI68C04yXV+ueuos87rr7qaiN5C6S/YKsEmpMDgvlPXYsLE89xCubugVg2LI9pNleHrA8ic5YwkrHrnJhDFdJGQhDxWNEuIwQ3AWmQLjusQJPukltjmGvbA2FaB5p4rTB/KTwX2aOyqHaUL+J2Q30zzJru3fOpd9OoSjwue2S49LwS8TkCaGty5s2bh4EDB2LgwIEA0vyagQMHYvz48fjuu+/w8ssvY/Xq1TjkkEPQo0eP7L/Zs2cDSJuMnn76aRxzzDE48MAD8Y9//AOXXHIJI5y0b98eb731FpYtW4bBgwfjsssuw/jx47Pu4wBw5JFH4qmnnsKDDz6IAQMG4Pnnn8eUKVNw0EEHBX0meYacSe+e4PXNVTpCDp+r4b+giDrkXRxNcdWjb2P756+hfmcj24SKJsdtrhI8k6RD8NnXy7GrlfO6Y9TXYCcTz0LXDMY+LvqwXX3oYNXjivJnBWX1iZuyuCm2QjBAv2eqxclxHKb/2i7kiiYsvfPe9kAIDtz+saQ6+r0mmNOZdy7LWu+GajBARmwKU8Zpvqh2ZxPmLN0Ix3FpRxyREKaiPWVhEe8VtiAgnyq83lVsCw4huOn1RUjxzI+iiMcuc7TjEKzaVC/vB6XhpVvKPjNfTY5fPCV/eLVNnOstXrQi9zNzOHONf1/8NoC65rcwoa3JGTp0qL42gcKgQYPw4Ycf+rZz8MEH47333pOWOeWUU3DKKaf41lVM4D4dVdKhqGzWkYrelWZMWBofi2D3b6rJMcniK8L0ysvRztrOa8T3Wje5W/hMCMGCqvM9x+jr9L2rRNAw9xjs3qXEY1l2aWkzCs8w04ZHba+gjYDb2pMRcnjmqvA1FwBQ6ewQFcw9CMvyyq7Kpgm+pkccrwiu9yzS+Jh8Y+lrfvuvD7FoTS3+PvogHMjEDfKaboDmBS0EzVEQsirhmasc1ixdiUZMfW8OdkvU4kj3SqeY1uFfjz+Ow769V6gOIMTBb//1ISrLbTz+h8MF79dnfud+q5rPxkM8FtfIJjH1166FESeHOARWgZJIRepCHsMf/NwlOahE1Mwc4VtJNDQ5KYGQoyiFuzNTa3l2+Vh+uQIOACDln5mduzvhFEt5NVmenbkmJ0dSyL8MVZYfpl1yieBdAoBN/L2reHwRNuS9ppCmSLLlLdZGAcqUOTls8Lsqhz/OaI0hASdBZ0Y4EAgGOxpTaFWRYI651IuSTvoLl0bE4+ZLUjVf4KLER3h1fhUO3EfwjnkcLQ1YzYY3tv0AQg7xjpX071yd7awdmFV5Cb50enuvFwqV9LN28KflF0rtHZvqG9F15WtIIoHtjYO5z0zF+9PTj+Z6tmIXtIdckwRwHA0knBw5F8z7/alycuRnQ9qUGCAWcooBivmomg9wConP6WlyROYq/wXt7tc+xoVkJXMs1fzhRTnA04/OX6sgU9Hmjvovpn4uwt4y3usty9Ka4EULukz4lHlb2Apxcri3IFqfeW0w8YVSLjW+BBxhiDeGEz4ZIpWt/64brSI7fcta8EY8zlomaG1oc/kX5q/GZc99hgm/6o9Dy+n7U+HksEZR22lKayFs98pr8o2lr3mrMk20/t+2JAihAqwypkP2Ov20Dl5BPWzisduFPIMD7BWcy/09FlXmzrKGLbi34h4AwPbUlYLPWlyPBX5an2z0bUVGiQ4nh2mfw8kxY874aXIKZ66Ks5AXHH7aAX8hJzsoBaHOVSHyyFExDRw994+eYy99sgo7m1JK67mpGKQ23bLPWPTB+XE/CBwlLY2ozEPvfYsfTZiO5RvqtdxnxbwjQ04OQ7blQ0pOhMK4YrQP7CJpWxKzHyN0+PVS2gHun264F7VWEJiraE2OZcOte8+GS0h5BcjnX3gKH1T+GVOnPCEUgKXaUqrcoQuvBx4ZySlioslhr9mvaRH7rplEs+x1JqYm9xVB0zp4vnwX8VhegchcJd6g8FDRuJWqMgX6LtUIu6K+UFpDBagIOWrEX95co3IfxavJiYWcPIM3aOWJFv11EHKCpsZiGkCTc4i91HPslvJ/Y+HKDYHU0r5wUv71E7UJh5sjSYNIyitHtzv7jSdx/Y6bcduUOTAX6+i+ScaNZNG0dePkcLkFGgsUx8wnkpHYKLs+heWNUvUoPifioAqNgnIktxjxFotMvznf0H8r/oHdrI14rOKf4D9XOTzzwyqvq24YaR0SJOlyVxdomohavjga/HcQ7Btwf6868XuELuQQcZIEoN634zjaWci9nCuWQ6isyfE8C04ZXvseLg9nE630sv00OYUTcmJzVYHhF/HYG/BKPFkE9q5Kir2rNm/bjjatKlFeluCWEaHdxs9Advux1jU6SNvmfUu5lhaBaYAzqbmFHP1Af7m/J1fcBgCo2vgvoG17/3o4dTB9k925JKpxgqikdSCcP9V3uW5ug/seiOMANm8s8dTs+hOkcsRYLa+PTJ28BJ2ZODlN9EFOc3zBQS6EK+kq9eHqXwIp9nkI+EUBWmSgyvXjgRDi5QC6iMfy6xW8q5TW9pxQ5KS841ypIq53VcbUr2g8UtHk8IIBKlkK/B+Er3dVINNkMMSanAIjvfnQmOBEniZrv4C9+VtBA2pwkvz4KZVWExK37YkPbv65cl0ZJMrKEcaEKIa/CSktSFIHRORNJVKxfxl68uVNELsm1+ppt4R2cpm5Slw/SzzW2aXRC4CfkMMKhx5uk0jQ5CXMDKqlkEYfVOVgUJsRTu6qLF/HkXuuibOQC0yosLhD5eVH/4lPPvlIse8icDQ5zCJPCWRu4rFme7zvIJCHHCFeDR/PhCXqj0KCTiXQyW6dlMscyfxPANHGqXnsKwo5boGPL3Tw6vJuoqPIXRVrcn5AcA9aX02OwuBoZTUC9x8Jnm5Ay8MpxVfVA2lPhaHJ95XrysBOlEc6wJuSSVSv3IwRskKKH66fuS9tdVGpRy50JUiT1kQialJurpIQj+n4JKK6/SYtPyGHWcu9Y1zIi+JoN0xibCg/X0VzJHEoQdmyAdvtXdXcV8cn0KJAAE6lHO6WU5Qn6ufLbwKW3wQMauaEGHxj7mfUm3yH1YuncftKDxRvmhSVtrxICu5ZHV5Ti9F7p+AWzn2Ros1VLCeHtzlQ70v6GlVzVTLp+t55WsRmwVzqQu7eEEJRk+NbJhZyfsDQ81RQF1r0d8EkKRZyTJEo54bwDQ2PfrAc1U4ZRlTJSrmeseCZ8IjXrEpdJRko+8x5H38ZadTcCevvrmSCZRknX5O0rkxfBYue4CqqDcczkQsFF25Sy4ATpFRBQ5Nr/TSC9HmXuapZaPNNfip4hnvY6xWvERZSKONf71F1b1LnRd5fArOMBLxxVmlpRt6mQIiTFgzpXilHPxePP9bMqlCZm5PDpQyIL08LscHNVY8+8wyGlfXGsQf0aL6OU6WCJoenNVaNeCw9HWtyftiQ78i9g1CpTtVdBA2JJscYdoVSH0wdF3tba3FsWbW0jAWXrV5oruL3jC0T/IMvI02aC7dAtS71yJFwckATj/3bzD273DHHZ9KiyeoyF1n5cZ5wpQZmtyoryGhyZAUpzx2euQocIccnkq0yfyTEUjoQC6ImzXkv6GFt0q0ki1uffgvbGh38m2lCQ/gS3FuC0YCqEI9zghpxkq5xpzoHc85nNIOWrXRL/yifjHueb8Cx4++TtOnl5HjnENNx5CPkFJCTEws5hYafR4Bbza9qc85ocnTiE0Qh5OiFhNHGX8qf9i/keYZq5QC5d5WoHmnOH6SFHJ3JhBB97xR1F3JBHcT7p3EwQOLlnAh3dlxNjskEqSgaSMm17lPZJ+FxIc+e8tHk+OW28pRXjKlkFgzQ57ka5G3LF+7YcY3nGM8sKoJIyGY0OQrP1KKEnHScHs5mStInC0Sa1NXR2Pz9jrzubZtC1lzFMQmz17HHSl2TExOPI8a2ujq8f+NwTJ18AwDvtCayuWfgjQWhabYosCZHZ+KJCsq5WHjeVW71teaCwzdXJbXi5IBjJxfVnb3CXf+6xcCjPwNWzEaZUs4g+WLsL3i4zBseAdE/VlF232kwfA5sXIAPHxFkE6egF/xNrMnJXGv5ZHgfP+VzbtvyVhViM0XBeRAKgARaIQSgEZwxAMo3fYOgmhzme1fhoqQoIcdjLmveaKqOK7rerLlKfYneVN5DWqcK8dhvDhQhKIcvSsSanIjx2WsP4MepucDKuQDG8wvpmKtUNTkKuwgPDIWcZMoRDiTiqIbri3IaJMzHKw6NrxAnRxgFli4mv99y6GlyRLtoubnKde6/vwU2LwceGYUytMvVITKFMcIdr355/90u5KqaHPYdGIxhCj9a8QAadl4rfdLs+5V4q4EWcG1PrJwM0dvySZlxffljubaVFg81GEWU9XuuovPE0dbsBMlTpYqeU/+A1hU9/AtC/O2wkbr9n6nt5ObMdCBIzobA795DSNAJAHVV1L0rele532Nk3lUFTNAZa3IiRqJxm08JIv+YDDk5JgsEvSvRwc4msWaAwO1xkH8M2PERKlNU/hcR8VghUZ5u8k3ehKEr5PDIgKK6s5e4c1fVrsn+mVBK0MnT5HBU8QLQk+fmOi/RWmwmC5d47Lf4M5MvJ3cZi1wwQG9aB44mh9NvNsWAqtZBpVz4CxN83OF1kA8hBwA6NK7xLwTx+GO4ZArP3WI4OfwUDbJ7F6V1yDxvRyOrZWPrbq6reTW64WqbZ7JXEcb9zFUFXANiISdi+KqkSfY/gtPuRVax3ewF0XNyduwU5/tJf/iFFXIAYOCGl33L+EX7JD6mRQD4bssO3PTaImmZctKktRMmECmf1c1VtG0/oeBCzso4xNOe36RFlz1+0f9hv7qPmPMqqTVydQQLGqdKPJZqYQhNXvcm6MzVoee55geiSGozaUknySkTEsCorfyYrJSh4N2n8okmKE2O46TAD4Hg2xlhP3TMVYxAxGvU4hCPOX2JQpODVCzktFjw5GL2lw8nx3NKbcLPfmAaA5ZWveqgYYc4S24h1ZQiqHn2pOGNmyF/nhc++THa1HxI1+opU46kloZty/YG7sSjY65qpNxty6GnyTGJpO3ubxuLFYRF44Jw3PgXrt4ibUsGUT42qsHsnzL3b6a/llfIyWgBbZ+IxzT0eFlyhJG7ynuez8mxXGbbkoSQeEyXUTFX0cTjlCv9hev/3PaIoC+Z+VtDNOSZepkj3uVeKa2D0hrip8mJzVUtF+5B5Bq0BH47cjNNTnbQaWhRLL9AZgI07BQLOSD6GYsLBV4APfbd+Gtyhq17BP8szzm28jQ2FZaeCe/9xd8JzkjqcC3uDsmNuzLoRTwmIPjq6f/DgNp3qYN+1/nZ6P2vyzy7m9+Qa8bk7WgIGsoxbizYLjOClYmTIwnC6GlbcQyoRS2IwANNqJXS/55tn6zxeYfgeXW06qgy/n1OEGrOdFKuahU1kbIEnUpJNTOX+L0jTjBAD/E4InNVHCenJUNhxysdIO5zaoNFnrSTD9tQyGmUaXIc9QBdeYOQUOk9JBdyvBecideZ3+IpSv2h3ECRVVXhXvTo1hIKCw5939u/molBX02S1u8Qi1nI/EL2izU53uPnJ17z7a8Ijh8hl+qHI+Pk0Fo8y+tCnhHkbR/vKjP4CzBmMoS6uYopa/hBF9c0oCbc+4F+3+4cbdlvSKrJgfd5ktwxHXMVI2DzLGDcychsfdFFzMlpwXBPPjwlojQLuUfQTh/4NtEXjfv+TKEH0Zurdp1/l7z9IpNyhO7LnN2G5bLRs7lpVHY9IoEq+DMR5TsCwIm9IeKQ+PejcetaztFgmhxxGnLvPf2u7B2ftiS90PEgknDSCKixYHm9q7K8JWahIfhmncTxQNeJQFpEfzz5vnuBJyGh/qvVnvYVEULBG01lg1hGcmMmlUox74H3l6cNgNGYAWCCl+p5nfrFfOLEyeE4trjnsFDMVSbefyEhFnIiRHL7Vuy/8il5IUpq5593Dbjm3w5s7Bh5l/CyfGpyui59QXjOcYrQWCVU5Ph4Rng4Od6KbGXOVPCPXrpI+ZhJfcHUzaEnup0yPH3zqV7wEvySm2rDLwquoiYn/QRy5ipRnBw6+el3m7dj2B2zhHWqjhUlGcfkWfnKqfyglv7aZz7C5CAFh8pD9e9vgrgSdHI0ObKWPMmDXf0jGt5V/nnY2FEM7i8zc5XfeIg1OS0Uix8dhw7OZp9SuYXz4/YjsAVtmLOeOAb0YJHYazM5l3Rs9aZCjhREjX8SbZwcb2tffl+L3z88FwsoUit3t+H+eGkNBFeT4/7Nv/cwKAqNTRKirIuT47OX9MDf88ZPiDLc2bmOBw0i5reDpIVNGUmZkFxeIsvmZSFvJh5Ti15PUoP3Ki7S7TKnbYXvJwpzlSgUQpFpZo0QknKsnObkEIer6fXXJnLGnYkmxyd6N8lqcnJQ4+T4w2/TVshggLGQEyG6rxPv4jJgbLKW5RnUoojHhLOb5NYftiZn51bPIccSU7u2bm/C9oYIhKcgIATn/HsmKpe+ibP/NYM67P1QbY93lRzej51/TRg7GzbmiruBoLtmStnOGWee0AauceunpRDfPztRJ1PB7kOHk0OS4nG6ZXuTy1zl4uRkNTm5BesIezF6BUy8SZTL6T8n31qZ5KXuK/XH77YGvzhEeYTC96GyMNPEY+KkuDnTZHOwOBinvgs5E+6CUy0vl5Z7vhq48O/oAtfGXGXek5S5sel0OBVtfeuICrGQEyF4A5i/482pwd1yM/2hpVbMRffFzSRUy4Jti1+fiqrUjYRPtFYAwM17AI0s0bi+orOw+Oa3/on7756g0YvoQQhwZfJfeKjidlyP+6njvF0Mzclx5aZBmoh85fOf4cFZSwF4CbfZt+kSDqMOjuYlHgvGipLgxhFyfF3I5RBHPGaPJ5uCCch+mkxV76onPvg6d89WQsjJsVW+oUzbCmNAPeKx/ng6fMvr+O7ZKySV8k2Hg79/EhWpHfrt2Uu0r4kO4ZiraE/FlCsLuVekUG8nU4+xuYrbKmd+c33Hac9PV5mAnJxOR/wObdrEQk6LhPJCRrmmeq6gBmHikeHotu79XP0STU6O2a8++dEkOhlqvv2cPSCZDEYk5jGh7E0Qharz5MR7AICTEh9i89ZavPqfO1Czxuuq7RFJmQBpBHOXbcKz81bjptcXAw11KLO8mpz10+9JC4fM4Wj5CW5XZr5nBfD9lu24e/rX3hP0ZM0bZ748Mh9zlcjVmrAmkqZkg7QeP/ibCighVhLx+8GKO5vD9gOWbXtcyDP1JLRcyBXGgHIIBrNvZLcvH/SYyHNV8s1V5U4DBm18xai9okFImpx09PJMlSnmLajwIi1OX9LE44wmx1/MvT95Uvo6xUCUUhdyUR/9ykie528P7YXWFYVz5I6FnAihStiyGCHHx1yVPa5mrtLh5ChpcgBsf+VKLHltItVItAt2+CEW2P5+9q8x+NnS69H6jQs9JT05l1y7pbqdSfwp8TJOtD/E1n+N4rbW5T1vxmSVicMRSSYKUH3vP0/MQYd3r8ZXa7YA31cDqSQakw4218sFXk/8JlfbOtF0aTjMwgqkGgMmjfXj5NDCnE+cnAqnWXthJcQu5ErJT9WhTPINsBEQ88aKiSgcMpSel4KQQ8+ZjuOqt5nDJamnDEn8aNVDzDH6nTtWwrcPA/fs2dwcba7iaaXVtDui6xYfdLmwjNQ8bRfWry6OkxMhVLO3Zhckyyu5S3cBElWmiXcVE9hKgj3rq4GPq4ETL2puI/hkKNuxOE4K/p+6TmPsMxm6/S0AwP7WSk9RyzVxuJ9n200LcFX50+kfm7xNCceAwjNzYPnGmxHBTbh1JPuZM8umoeaF04ANs7H5gDPwTPkvcEHiM6oE51pP/3Pvz3H8Q8MLh6Wr3mRTQE2ODoHaR8jJ7pTtBId4nG5HdaMAqM8PvuWWvovO//MK6Mr9IIQvdQs0OS0CIWlyKmhNDnG43ChZNW2xnddydqwRBSHHKitP/9836CCHk6OxPrQbdDIWfj0d/Rs+5dQtazsWclos1Dg56aMA3/4qTEHgo8kxM1cZEgMj3vGlkkmUh1ifznTNxJSAa6dGHFTskBBLkZ5EUsTyBOBTWTSCeJy5UxT4tdZ9w2wAQMcvn8AxmO2qjMfJEXtXEXd4ew6EgrFrJ5xMBtPk+KZ1oBcGnwSdGWK+ZdkS7yoNc5XCd1NJdmL/WefLC/1ndKCJPCGKG+UTE6q0EY52rBysCzlDLyApwOELPjKkmZnN5ioVTo5dnulA7piiJkeLbyNdb4pX6xebqyKEmpswob43r7lK/DGKkwSyV/t34mNnXwBBhJxoJ0DHL9y+LgT9/cbaw3OMJR7zqvL/uOvQ2nMsoRB4MdBT9V3cxeiODa4j3nEm2wE6xF88E+cPc2mgGvU1Od8k9kIjKZO2k4FOWoesptP2BgPMtJOA+ljdO7XUt0zf5LfK9ZlCZGqwRC7kLQFheVdRkzxJsd5VBzZUAzd0RKe5t+h2Lts/FU0O7OYyHFMZjR2NSSRTjmvjpsPJEadZtSXPU4VWESViISdCqEwL6Y8iY67i7ZhFdVuwbfEHoKrJOaHhJjxEfgGAJdFpIQQhR1ZDKhUuz0HIB7ErPMe8aR1cHgx+3jsgqLd28RxXE3LMP0/iCgeglc3YPZFxXcjFvwlJ+cfNEJrxWFW6qSYnI2bxEn6K2lMXchJeU3GWeFy8O1oRhCbRFqe9oRE+mTttrvKiZ+0CrXqAnGnUj5OTJLlwBoxQynl3P0rNx3/u+zvbjp/G1ckJRZZtCXNpyb73WMhpweCbqzi/M/IILM41gkFoKZqrfBab4ccdj7OP3g8AUGYs5ERMPA5ZyBF91rwdre1S2bObJRVSKMFOm6fJ8ddQBFlivOYq9YlGqazkvolD/LlEAkKwW5OTMvCuIrCyglqabiLri46Q0+xdxSEeZ+op09DkFAuEpNES4eR8b3XDh04/vYtUYg9ppiJIB4zU6wYPFiFsTCYJHNhZgZv2rhJ145yNt7uO+JmVc5ww6XpTxMJ9LORECN5E742IC+QGGiciiZR4rCDk+LgmXTxsX7TdJb0Ilxuaq0Kxx0rWVRK2Jod6LxmzBiDiU9DEYzDvw1LQ5ADAzjKvi26ZgiZHRhb2v9h8sfW8Cq4mR3zf6YnRMAKqy7vKMYyTk9PkhBMnB8hpcizbhu32GGm+nbC9q/IBXyFn07dahOp8w4ENa9e99S5SMVfpanKcFFSSqfrBQm5c+pmrUrBBsuYqvzg5mfpz52RmJiDzLTM9E9Qp0+QUVsyIhZwIoUzqoiIe804L61cYPEp25bK0mYa3C00puDHreACZ7AhTAfglXFDPpNHKUZq5mhyPC7lbs+PD+QBBU4Ij5CjGJDJFME2OAlzCM12/46T8s5CLhA8Xr8AxMlfR3DYH0juiJ3kf4nGWs2ZzXMibn3dZCQo5oqz0FnFAvnoTuHsg+jhez8NigcOJQB0KNNUybuJxgIazQoOfuSqFBKXJiYIoTrLrggXxPBJzcn6wUB1oYsKYaZwcXghvEeyySgD8aJcppSFSYsRjqr9NoDU53g+VFcoceDL9+nJygGS5N9pnmYK7fpianEC5wSQRWXn1Eyb2k6BK0bh2u76bCDlUKAbH8TCMPD3JXuajrcgKOVaZ59srb6wFoBcMsPhBsO6d+wrdCV8QcLLC+0DJvKIt5DhKm0o/WJQLOXyEHMeyYdleTo687+rEY/LkKTkh2ObFPm8+FZurfpjgO4tzvKeymhzbO+gCupCraXLEDtoqC61eLBfx/QivCJi/yI0mKrFlkhZylLKQg/ntp9LuSdZi37oPPcdVhJwwOTk68LxznpAj6Z3jmAcDdJsDU036Qg6BBSfzbfhkIWeEMR+NYYazZtteF/LynRuby+RXyIky8aFFHKzZujOy+sOCSsA8D6IIsKiYjNgPFkguwbKfkANKi0UIkGzwDYBJd9HPrFy27F2qrHimtyTjvuQ0ObNmzcJJJ52Enj17wrIsTJkyhTlPCMH48ePRo0cPtGrVCsOGDcPXX7Nh4zdt2oTTTz8d7dq1Q4cOHTBmzBjU1dUxZRYsWICf/OQnqKqqQq9evXDLLV43vOeeew77778/qqqq0L9/f7z++uu6txMplNzzXCHwZWkE2AutrATPbztbg28f7DLxh+QoqIF1TFAmwz0VsiYn2ZDLvdVEmat4Hzwt+BDAmyNIgZzY1tnmOVahYK4KFCcnRI2CxVv8m8dlQ1MT3p/xhisomj/xWNWFnKRMgwFmNDl+wfQoDpBPgtqs96GdgHvqJPXrAUJQztGGRgldcqxm5dHVHSIcJPRyPDVf5Qd9Tg4JT6mtwcnJaHtaOfVITuiD+vuOkXZD14U8W9YShy2RRTwW63/yA20hp76+HgMGDMCkSZO452+55RbcfffdeOCBBzB37lzssssuGDFiBHbuzO0ITj/9dHzxxReYNm0aXn31VcyaNQvnn58LeFVbW4vhw4ejd+/emD9/Pm699VZcd911ePDBB7NlZs+ejdNOOw1jxozBp59+itGjR2P06NH4/HNXXqUCQjWiaXYysbzeVWJzVRqi0P9ZdazCbsSWfEgq5qrIk02G7V3VkBOoHZK7Px7x2ONCrqhp84Oxu74i3At22OaqzCLx/hN/x49nnMrk7LIcR4F4LDrPevQ4kszgMmSNVMQRms4ILMZs4ScYZoj5lp2A5SIeJ3ZsCBSbyBT+wQ7NkX42hV2gVJDeiGn2U8W7StdcRVLaghEPFsXJIZwwIfSc7yBnrtq3YSHKUtuxy4YFypGMtYUcAWTmKpHbeb6gHShz1KhRGDWKn6OHEIK77roL11xzDX7xi18AAB5//HF069YNU6ZMwamnnopFixZh6tSp+Pjjj3HooYcCAO655x6ccMIJuO2229CzZ088+eSTaGxsxOTJk1FRUYEDDzwQ1dXVuOOOO7LC0MSJEzFy5EhccUU6i+6NN96IadOm4d5778UDDzxg9DDChsqrJUD2g+PtOUUfWqZusfowU8B/xyLTCDmShArEcdKeJiGYq+intfjDN7DzvXvQ/bd3ofse+4Y/kTflQqnT3jBK5iqPW63ZbreSNPoOkCDCoydBZyAXcm8/MsNy/5X/9ZxziGOsyXEvPiacHEIRj2XBGi2X0OrLyWkeKxbHXFXRsAmpVFO46UcU4ESpySniKLY0CEyIx+FzckDcuavMQJureJwcOt0Lba5ivzm1d6eT1iGtxdHX5OjypcJGqJycZcuWoaamBsOGDcsea9++PY444gjMmTMHADBnzhx06NAhK+AAwLBhw2DbNubOnZstc/TRR6OiIhecbcSIEViyZAk2b96cLUO3kymTaYeHhoYG1NbWMv+iBWdxcA2S9EIm5uTIdqG8+th6ofTRyYUc8bnMQhV2SO/9p56KQ+o/wJb/nJXug4/Xizao+miPMl54e9sl5HhC3RsuMhVWtC65fqYXGTxjiicoyOLkKJirRMK311wV0IXcN+Jx7rztl6ATGRdybzDAXZo2B05BYYJoNTnRamjDQpqTo0s8jkCTE5ILOUDNqTxNDu3JaNmwOGVUe661kbJ4EfnTkJqrCqwMDFXIqampAQB069aNOd6tW7fsuZqaGnTt2pU5X1ZWhk6dOjFleHXQbYjKZM7zMGHCBLRv3z77r1evXrq3qAWP6Um4IGbK8cxV4trT5+VCjsoQlpmrZEJOZoLV4+Sol+3c9D2A8HkHhNJy0N4wXE2OVBNGQtm5iRBkbnCb3oLxe8RCDq9ex0nB9slpIk7QyfIFSFBNjpQETVhzlY/AkPEysewyz8agnbMl/MjcCohSyAFxSsFalebjaK6k5akdChXra3JCCQZIjUs+J4c2V3GSxQLSlEKBODkCyHO2tSBNTrHj6quvxtatW7P/Vq1aFWl77lcrVC1L4uSI1O0ZO6fvEFXS5JgJObl8PdEs9Ilm80DYEzltyinzNVdRcEc8prVwESCIhswTJ0drEfDX5GTGJTeqt8L7EpqRnOCaHDqeByHid+T+gvzMVdlylg331FlBGuH4BN6MAlGaq9LmvOKXchxLn3g8aMubvmV0E2sSR8z/0oGfdxUTkwo2V9sjn5cMhRwYEo9bkrmqe/fuAIC1a9cyx9euXZs91717d6xbt445n0wmsWnTJqYMrw66DVGZzHkeKisr0a5dO+ZflPBoZTi5TVjvKn0euignUU6TE5CTI5k8HIPgVzqTQCYuSejmKocWcnJ18+PkiOPiWIQwWqGwEaYmR6c2D5edK5CI36PS+xKOA5bzRFJBNTmSjOgk+590e4rvMm2uchGPLRKtVkWAsFOe0AjTQy9KBMnxJq9Y31wVhpibZr5kzFXee2PNVXxNjqpnnJaQY9tG5qpCI9TR0bdvX3Tv3h3Tp0/PHqutrcXcuXMxZMgQAMCQIUOwZcsWzJ8/P1vmnXfegeM4OOKII7JlZs2ahSYqpPu0adOw3377oWPHjtkydDuZMpl2igHuAcTf6VEmD6v5N31WOD/LNTk6cXLsRABNTkhuprxeZjgyKpm+tdqidr+0JsePk+NO6xC1uSrIxGEHCMOvw8nhTXopBY8o4Tt1P08DIQcW3S9pBCbGXCVXuVPVJ7zeVQBAwhbGFRCtC3lpcHKIxUmYGkrFhSEeg/Ku4hGPvZocPf8hY3OV5EuS1VNympy6ujpUV1ejuroaQJpsXF1djZUrV8KyLFx88cX4+9//jpdffhkLFy7EmWeeiZ49e2L06NEAgH79+mHkyJE477zz8NFHH+GDDz7AuHHjcOqpp6Jnz54AgN/97neoqKjAmDFj8MUXX+CZZ57BxIkTcemll2b7cdFFF2Hq1Km4/fbbsXjxYlx33XWYN28exo0bF/yphARvzBu/SVQ9GKDfFJ7zroqQeOyE9VHzkdGyhO1CTi9s5T7mKpZAy3Mhj9JcYA4vJ8e8ZT4nR+IyqvC+eML3ts1rMWDRbexBI8GB5eSIzVVuIUetrXQwQO93kUoWQsiJUpNYvLtzGsSyI3FT1p7aiBPKhozxruIIMEx0cYs/FqXzEnVfWp6xkmfM2yDmLisxF/J58+bh2GOPzf7OCB5nnXUWHn30UVx55ZWor6/H+eefjy1btuDHP/4xpk6diqqqquw1Tz75JMaNG4fjjjsOtm3j5JNPxt1335093759e7z11lsYO3YsBg8ejM6dO2P8+PFMLJ0jjzwSTz31FK655hr83//9H/bZZx9MmTIFBx10kNGDCBMvv/A4muq34mTLrZXha3IsipPD2R9y2wjTu0pKPLYSwhUyba7S+6h1dg5lmfwtYU/kFF+FJsjyPlR3qHSPd1Wkm13zyoNocrzd8E/rQCOlwKPhXf/t5PMwADnNjbm5KmfCoLMoi0unoarJSeeu4hCuDT3BgiDqODmlwsmJhtyqaa4KacNDx8kRuZDn/rb5m1TJuGCflA7x2Ht1BnYRk9S1hZyhQ4dKJzjLsnDDDTfghhtuEJbp1KkTnnrqKWk7Bx98MN577z1pmVNOOQWnnHKKvMMFwFELr8Gu2Oo5/uXTf8Mgi2X1e4xOBOxgIXxhxX/noi7kgKN6zyBsc5Vod8ibTDMCSOicHMFipqLJoWGBwImQt6AXf8h1bQDvKk+2J84Ykg0rlffFm0O6bfvSW9DYhbz5/05K4mnCCkDK5io7AZuryTEXchxi+Xqk8RB68loaxOEussUGYvGFzsD16qpyUqmQcldR/ECeezg19oSmOtWxrGWuEq8FiSJO66At5MTwR6NVyRWQB614mFOa5uSIIx4T4l56/DQ5VP0+sCXeVTJSnyMxBYhgNNzD5h0IBDM6am8GXk4OS0SOkhMRJBhgIpAmxz3SxBGPeUgFIh7T7RJYJpwcWOlNAMl8P/y2+jV9iRrSMXu7qs/MthPc3XMQc5Xxmw5p/DWRhCclxc7GJmxtTCLvEQ41kfZAioJ8rM/JCSf6O8ltuAzj5Mg3n2acHGLLvKtkio/COnHHQk4EEAk5XBB6EeEYq5rJyo7jjj2cLivKsaxFPJa5kEsGqBnxWH8SCD0LuUaf2d0160JOAE+27zDBE7pUYYeYKJLrZZMNBOmFUpRinnaIV87w+WaE/zZLXkJ3bBCW625tzv6t+swsO8ElYQbR5JBmx3ddhGWuSiLB8NMAYN/kV+iYqBNcUTwwiZPjBk+Tpp/WQX/TJ4KceGyzf3O9qyLQ5BgGAyw0flBxcvKFRrvKv1AzLOR4HoSXhRwZIYdPJBXFh8hxcvwHny11IZds40w4OQbq3LDJleYZullOjkVIQfIVqcCrlQiQ1oErkGS8q7xQM1f5PzcLZu+eIBd+fvev/6N8nbomp4xr4iVhRplWvS4sTY7l3e92tIpfwAGQFgQCaguSPHWVQRbyUMxVJKfJ8Ytm7FhlsHjeVZJxwZpodb2r+OO0mInHsZATAZo0hZzMsJXRjt1CTqakKIGmjncVP5hUpn25JkeXbCfaOch2FKGbhEwJgi5zVTq5apEKOWFqcngTWHZcGRJwlSZX0+drSblkIqhrcry5qwBFM50ApkJOWMEAUyWs1CchEI93osJ7UDtOTli5qyhOTsLMu0o55pOOJkfC3ZTyyWIhp+UhqSHkMPthDicns6i6F/pcxGOB+jAz6BS0FrZkEPoFA9QVQExs1qFrcgwFE8ulyQFIpOaqIEiEmNZBN3eVo8BNcVQXA5N3b3ir7mcmgs3JXQWo3bcI5pqccL6NFGwmu3UpIQzicaPFEXI0TTAWcTBo7sWB+gH4e1d5hByeJj4CTk464rFy8aJBLOREgFRCR5NDm3B4Qk6Ok+O90j+OjYpZIAjxWHcnKdbkiFFITg5zmXthJihecxX45k0VuD33uCZGmZCj5BHFMYG524UJ56v5tRiYL2QqdxpWoix07yrT/X9YnBwHtlArHLjuiIUnInDp10ETyr31GpirwoAFkhuLXOKxy7sqkAu5Rr9sMSdHel2syWl5SJWpCznM9MaJk5MzV7k0OT7eVUDzR6owCUqFHKkLKYk0d04WRaLJgcdTp4g5OWAFQ51pxj2m+M+LcMsCUIr8SzjRv7nClPHz1Z9YVc1Vtm0LIh4HcCE3nIrD0uQ4sI374IeohKcsOLnEdNHE0+ToSp4hbsbknBz/ODmqc5wOYdiCbSQg6ScrChexkBMBnEQr9cIEUn5DNkyOW5jwMVelr1VbhE1zV5EQNTmyGSVsTo48s7gYKYdg2braXD3FzMkJFJhMJGrTh8QJOtU0OQo51UAM35XZjlOVx2TbZfwgbQXQ5IT1bRDLikwYCcffSAIrEdiM0mR7hRxPdHM/hCRwWlT8Jj8hJ63J8ZbJCDn/SY3AhamLPTVkoBOLy1STU2hOTumyzYoYpExdyKGJx3xOTrO5ivA1OfJgfY7STs9Uk0OIfpwYFeJxilhIUES20KO6Ggom7RY+iiH2CqoeEsBTK1qUuTQ5mbgxKnAX4wkaya014usN4+S4+2ilK/Ovy12PoZBTRlJKi6WVEEU8Di82kSrCNFdFpcmJmsiRNlcF1eRUcirWFCBDmgtYc5WceAwrAYs3Rzd/X/t0b4cBBx4MzBC1piPIWUbpM2JzVQsEKdfQ5ID4MPKbB7vAuyoMTU5CkqBTzslJaWfhlmeETiPljggUeloHs93vgbSAA8Dc+yd6lIUYt4LnXWWvXyQsr+ZCrti2gSYnHXHGQMjRMFfxyPpBBA5T4nGqST1Y4owBt0nOWkY8JlN8XnlIeJWFQDxO8TQ5upycACEEaNDeVVxNDhPx2OZvUjPzkmV5hAzLUJPDTzukcJnBNWEiFnKiQHlrreLZQWfZ8EjWGScpwcIsipMDZDQtKpocSR1Sc1VK21wl+qjoD8Eds8KtxQqKMBMPhi6AhQS3JkcP/nFyOtV/nT5lyMnh75I5ZjIj4rHZjlPZXJUoi4CTYyrkNCiX3b3v/sJzu5GaCM1VvHvTSDPiQ1wmdnAX8qTN0+To1XH4d48H6kMGtHcVLwYOPVZEnmU5To7cI0rLXGWJ4+T4XKh/TYiIhZwooKHJYcxVHIpWxi7sDQYoj3gMpM1VKpoGGSdHbq4CHA6BVIaOqOUep3cXSVdgMhKySciceOyqp4g1OQl39NYAwQB5OZ12T30nvl5hR6squBq9K0uugRTB/cxEsO0yvgt5AcxVKZXo0pkWJN85YE5+9gPvqeqNRx+EEAzQ4WhydF3Ig+SaY5EzV1mcODn0WCF2gqvJoYUcd84pmuCvky/NMjQDFzqtQyzkRAC7Qk+Tk9kpEwknR0w89km7oKhpSAl2S3JNjmO0086ggXjdNgFO9NHQvatC0uQQUrSanGBwq7e9z6tCoiky5eR4ekFMicfRwrItrgt5EE0O/ciXW7spX0Y0NDncHEcUohNyOHNLmLt7ix+cUQfcsB8hBPYzgQV17yoi9K7KxNmB51mb3pUsGKD0OsP2wkIs5EQAu1JdyLEoTo7FSeuQ+eUJBphxIZd93Bq5pUQTnNyF3Ank3dFI8d4Z4rFLyOG5GwfBoHp5dnst5FGTswntAlwdJBig9/nbFoGT4r/7zdu2K1SZrnPV92vw/P3X4utl3woKGsY0inD3mBCaq8w1ObRGdk3345Sv04nN4ycI5FPICaJZ9MAuC/y+nQTHXBW9XxgXNPGYp6Vh5nxb5F2Vy4fofnp7kO8N+2WZCaexuarlIaGryZF9TM2D1aveD1eTI5pIZEJOOhig+SK/w+Kb9VJuc1WgjNpRgsDKY9+22h3z0o5bcyPiMKUEZsTh304Q1p3hV2RU5ismn4Nfr70LDY+ezOmHmdbN1LtKGYkyrnlk0LwrjKs0TuugY67yEQSiIB4vO/QaUW+U6/B9NhY/YaoOSCIE4nFIsECyplOeuYpJ0CkIBjiQ2siZ8NO4/fIxdwqvi4WclgcdTQ5ILiaCiL3+/ZYdeH3+UvZgc0HZ7stxUsqaBhG3R05sdgKRgnfY/OdEa3JSqRSQKk6TEC+OSyORmwSCoL68U2R10/AIOYLJ3oSD4mSF83SdP07OAQAcZH3rdV0HMeRPRSvkJBKJiCdu9cXVSYXHyQk7f9WSsv3R92d8wU9n4fV9GiFEPCa8AK4FEnIYbpiPuQoi76pMWcsKjRNjKkjGQk4LRKKMp/rkg42TY3uIYIQAr078M8759BTPlYB8l5NKqWcJNzFXEYegfqc5D6GREnJExOOmxoZAvJ9IwYmT0xRh6KmGSnMhR2e6tl3PWxQVVWSukiE7zhQXEMs40WiEQo6dkHokmoD9jjWEHA1NDi+AIQ034T8o5FHZNbyr/JapEOLk8IScQmlyaFg2J92EOxig5N4teF3IzfsSe1fFaEaiwl/I+aZsn9yPDCeHU44QgvPJc8J6pJqWVDKwuUo2eTQ2NWHKY7LYG3I0lu3CPe5QmpzGxp3amc7zhXSwOrZvyQiFnGTVrgGuVp9oerlt9oLnn3KS+pHvM16BCu+U5qvVEY2kt5YVKSfHsoObR9wwTuugIeT48UbdZuLAaG4vqJCzsmJPeTMhxMkBNxVPEQg5CQ7BnX52Au8qVy3h9MWyQzN95ROxkBMBVDQ5mw48M/t3RovBHUCC3YRK7qpUsolrruLFnRCbq8Qf0Ibvl+PCsinC835oKmub/ZvW5NDcAB1BTYToEgQSj3t1lHl6yC5dIqtbBpEmJ2VgRsy+W8Vdcub5Lujxa+22okKirMzXUylfWDDvfeWysj5P6XERUhbf29EcmTnKC53FcntZB+n5MOLkWLw5uwg0OQlexGOL5eRAGgIkPE0OTOPkFBixkBMBEuW8mAtupB+9BQKn+WPiRVEVxmrIuJBLdqxOKsXlNPAEGuGkI6m/vGGT8JwKnIo2uWbovlDDMplsMhZy1pIO+GPjxcaB1vzhjZMTXWh8wN7FXJMThKNSRXby60wmtWvNCoEcTQ6vjzlXWB2hwor0PaTNVeGOKfreRRwoHsaUvaFcVsbJGXry/wtfk9MM/tyi+fzOfQcY+HvuKSsEc1XxanJ80jrYCdiSiPWA5a/C04Dc27Y4EQs5EaCs3F+Tw0jXjWmX21atvERc8Xznr8lpaGrChtodnuP8BUCgyZHtWAPudAgl5LA9obQ6yaR26ogMTtnlUZxyxv+LbMGzCEeTE6GZxK7kP6+osUdqJfe4yLtKhowpUoXvQEd+le1W3SCmrq6KsGw5D8IEkXqDNUPK3bDL4ETEyeGe03g/Fgiw+2DgF/fyC9hlgbUVVgXH07MINDl+cXJg+ZmrQta+FDiwnwlKr8clgISCkJObhAnaNa0HAFTt2otT0M9cJX6Fs567ByfufNVznG9SEXzQUsk94CRQmYv7Qgs2tMdSKmWuyZl15bEYdkC3SDU5bk1ZpBqESj6HSQUik1MQEANzVXaCViUeZ0Io8IK1ia5BdEKDQ6y0RiRkIYoN8BYNZJocO2EjxSG5BkFOkAlBk9OM5KAx3ppC8K5KlBcn8TjBi3jsipPDC0yZK2vuFcVDrMmJAQAoUyAeZyRiixB0Sm0AALTp2sdbTvChZca5zFx1Wt2j3OO8RV+UOFPuXRVs4bSr/IPbpZqSUu+q6R38uRqRaXI4nBzHHa05RJS3autfSABhYtQAMNHkZBbzpqRCuhFKiGzVox8+aDMCH1YepdBG+uooEJXAzJirImkB0sXQtm2Q0M1VYk6O6V2W/fwOb022jaBLmc0TcgLOb2HA4piiPFnI/TQ5YQrkRcJF00Es5ESAsgr/XWdmV5UgTeiMrQCAjt37eMqJliYV4rGwbd4xUUNS9WSwhbNtezq4HZ0Zl9bkNAqTjK5BZ3Q95ATfdqLUrrjj5ETZVlkrc3NVFOkRnGRSe/xlzJ+1O7yhB/j7/UwOnwSOuvxZ9Dz1TrV2IjJXlVnRL3xd26qHoNCChJth22VwQtbkZMCNeBzqwlsWeCG3EhxX7QJ7dTrE4mrSGe29bfukWwhXyIk1OTEAABWcXYEHzQNvV2cjbItgJ8pR1b6rt5yPytTEVZZL8BQJLLJAUwHVuX379KHaZ2rO/uWkxJocS5FU50S04B3UUI22DTXMMXdKijBRUVUYTo4IJnFyMryQrTu8rs/ucWmBitnTPLkqxacJ2QtkodMntLpEoO+974GHR9KGLVmg7IQdupATZCOmA8uWexgp1cGZR0WBKL8eNhkfdfDfXAWFUoBWKwGb44GVOx+yJicWcmIAQFmlv3dVxk7axkp7rmy0OwsGo1DF0nw2pBgIBuaqoEH6rH1Goqn7QGlfHIl3FQF/clpf1QfLD7s2V0eEw3x3sobtU4TEvMrW5uaqSDg5BuYqq3lCruUIOdzymRw+zWp7S3GSDTNFwVMp9VxSpqA1G9bBv8XmfX8behtSTo6diMxcFTXCiFvE0yyJNDnJqo55CXDnwD/xqKUQJydUTk7IQTDzgdLrcQmgXMFc5TYDbS3naHEgVuQQBRfyMCDzyDAJ68/UnSjDlp8/mv6bIR5TQo4jj9rM+4C7/OUz9Dnx0uxvGTk7bEQpUFUGMVcZmBZrISc6O05Sex1rqmifvrYuTbaXxTCi02ZkeAdq+XPC1eRsIu2wxNk9tPr4oMmkNjYddHYETciFnNA1OVLicXhIe7sFNFfxDgo2V2luU/RCTgo2V5ai5xhil3EDBmZhWcY5p/j1xZqcGADKyhQ0Oa7Ru72qu6CknyYnnFco2ulLhaiUeUoHIP0MMkIKI+TQGcmTYnMVsSwll8YoA/S5EUWSwwwSZeaLkCjJZhCYeFftaNcHANB17Xt45+3X/VrIjsuMkCP1JMleFW7uquMP6AqrwtyzTQVu00QUnCJ+HK7mc7ad5rZEgKh9lKwQspBzISAe5ysQpDhpcu54ZXm5jwk3bBfyWMiJAcXdpmvCadpFIOREwMnhdkd0XMbJCSjkiOzFtJBDUvyozbn+qS16+UKkwQCDCDkG/Cm/K1IG8YucjnsBAH6W+BA/ff80T642NzLea7aWJgfG5oQGeJ/xXl12QVOCE0clVLj5SBEIOZJvOQq3+NBM6n5jNxE8Tg63BcH4tqwonpUXKdjcTRz9PLt1bOcbJ0fl2Sy391DqU7FE+tZBLOQUCq7Ba7Xnq8OFDH9LPIE0GWTCNiEew4CTwWmY/l/z37l7dnzSOqh8wFEKHt62opsEEtLIpnJEwclxTOLkdNpLfI5DPM6OS1uDeAxzDWcjR8gBgGTEQo7n3iNYRP0ExKjMupETjy3ztA4vdDgb80+Zyz0nGt+hmn8kcCw+JyfRtD37d5cBw+X9saAkkKm+e2lw2CJFLOQUCG6uS3nH3bjlxHuYjHTgfYVmmbANggE6ATU56QYAgNnR05+kk2qScHIsj1fFzU2nekrllZMToTq3LJC5ykSTI58cSSqlvYA57XkBL8XIcnIycaUUJlkLjvFOuxF8U3OyzBuN3ATzd/kJvrK9CSe9Qk4ozbHwW5wjCnAYtc7DTpinddhnwI8x+MD9uX0Uha6wbDsv2mEHNlejt6XTwWgkCSysOARl3faXe1cpanKU0/vF5qoYqnAPvNYd9cxVWfdMzsfdaJBoTzjGpeaqMDQ5HHMVJdSQVFLoygmwav0vnd54IPVzT5moXMh5SEaU/wcAbF70U9VrIxByHINI1HaFWFhwv2cLBDaazVXN966kySEEpktrSjCJOyEJOanKDtg2+P95jrs5ONFwcvyEnJCXg6y2OVoEIR5LrxNs4tKakzwJORztrdVuN9Rf9DX6Xf4mAJ9vQjGcgvJGMNbkAH369EkTSl3/xo4dCwAYOnSo59wFF1zA1LFy5UqceOKJaN26Nbp27YorrrgCySS7oM6YMQODBg1CZWUl9t57bzz66KNh30ooWJ7oje+sbt4TrglFmO/KZ4bgmqsEKncZhDt9ieRuhWCu4geAU4uTQwCmf5Zl4bPxwz3l8mmuSlll+GDPi7DU7htqvf9NHhtQkxMF8Vj//ctSnuyOtcxviyYeJzLmKjVNjqmQkORoQQkhcMpCMlcpeuZEwcnxNbNEpMmJ3FyVKDfvu+yZCDiHtp3Ih4wDBzZXS0MsCx077ZoNOutnxlbS5KjeUAkKOaFvOz/++GOkKFvm559/juOPPx6nnHJK9th5552HG264Ifu7devcLimVSuHEE09E9+7dMXv2bKxZswZnnnkmysvLcdNNNwEAli1bhhNPPBEXXHABnnzySUyfPh3nnnsuevTogREjRoR9S8GQXom9x10DT7xLFwkfYnNV0irX3j4Jd/oGk4AOeB8gSzz20eRQ3asos9G+NSdyaR6FnCTKcdSZN2Drxv8H3LNfaPUO/vN/fLINy9EJtQZX+ZirDDQ5SuEV6B40azIzk70KHyKtCTTV5JRxvx1eMtltgy4A1n6J+u++RHdsUKqfWPwFMh+cHL/M6eF/JxIhJ0T1TpBggBkzKLc7Ik1OnlzICSzuuuBuWWbCtQhR0tCpmtlLkXgcupDTpUsX5vfNN9+MvfbaC8ccc0z2WOvWrdG9O98889Zbb+HLL7/E22+/jW7duuGQQw7BjTfeiKuuugrXXXcdKioq8MADD6Bv3764/fbbAQD9+vXD+++/jzvvvLPohBwCvibBzckRCjmi3FXZ+jmcHBMhR+DlIh3UYRCPKRDHgWXbXk2OwJWTwGKCw4l2I45lR68zb0Yqu/MKdxLct5t5IMCoYGKuUkl5koGFHGE6o5JXMVdZhBh7HYqI4+279gZWs8fa/vyfAIC66/ZWb8CyYXGFicJ6V6UbzZ9ZN0zYAYIBSq8TbOLyRjxGgr+xcb8n2XsjTrianAjN8VEh0rfV2NiIJ554An/4wx+YB/3kk0+ic+fOOOigg3D11Vdj+/YcW3zOnDno378/unXLmXhGjBiB2tpafPHFF9kyw4YNY9oaMWIE5syZE+XtmEEwwNwDT7xLF0UizmhyvPUnDTg5QkgGdSjmKmoxyqSJYDQ5jox4DEXPgTy6kGeefcRBGvMBP7nQxFxVVqln9klk4+RkODmKxGPDdy7a0e5/wp+wovsIrPzxzZz21EEE7sfuMRoFJ8d/cQ7ZXCW7h1BDt5QZf2+ZPnLN5kJOTn60GY5l8ze/nOeaEjCHCXGUZNeWzMmJVCybMmUKtmzZgrPPPjt77He/+x169+6Nnj17YsGCBbjqqquwZMkSvPjiiwCAmpoaRsABkP1dU1MjLVNbW4sdO3agVSv+RNrQ0ICGhobs79paExW+PriaBEVNDkE6KqxX0yImHicteTBCnUVf+kGTMIScIOYqSzE4XP4+TJKJGluiu2IafndgkqW5vNw/UGYGtpXj5Ng6nBziGAu2IuKxVVaJ3hc8a1QnW5FovHrd58OGb0oMTt/q0Qq7YIdpiwCiN1fZCXPisWw8Jbet51/DeU4Nf5qHyvsPNeqDCGlzFa9/HCEHNhLgzJMkpSYAqj6/WMhh8fDDD2PUqFHo2bNn9tj555+f/bt///7o0aMHjjvuOCxduhR77SWOoREGJkyYgOuvvz7SNvjgLORuTY4gpDohBA4sD2cmO3FwBnDKR6Wo404stfeGba7ianLkaR3oj1NsrsqfwJERcqLgVOQT01MDMSCxTFrGJHdVeaUeJyfnXcUPBpgiFhKuDUAQF3KzEAAaK7Zlc0epJxljBJpAWcRjEb7YdTgSDVsxuG6GcbtRa1LtQFnIxdf9ZPvb/CtsTpC+9j25ZYMgTTxWzNUGG+AKOWrmKtXnV4qcnMh06itWrMDbb7+Nc889V1ruiCOOAAB88803AIDu3btj7VrWyyLzO8PjEZVp166dUIsDAFdffTW2bt2a/bdq1Sq9mzIEbwr0cnL4r8IihC+UZN0zORExw4xlELGQw36AXiHHkWhyCNTysuSTeEwSzUJOgDo2lPcEfvVvbECHUPqki/fanohDr57mG0DQhHhcVq5JPEaGeJzh5LDjkZeywyLE+J1zhRyDaNEiiMxV7mNRyMh+CyZv3iCwAswnluv/wWoRnk8kYBq/RZr3SXSN7XXTUBVGdOBYCSRUzVUiDSRxlPhKjiLFIRZyKDzyyCPo2rUrTjzxRGm56upqAECPHj0AAEOGDMHChQuxbt26bJlp06ahXbt2OOCAA7Jlpk+fztQzbdo0DBkyRNpWZWUl2rVrx/yLGoQIFlnXQBXlJSLE8exUmytIn+eG/faLbKpjrhLXJfN6Um+A0sRwNDmWk2Ti5nhB9U+wMkSdxJRpKwRz1bJdBgIH/yakHumjoqIc7VuX+2r8TIQcK6HHF0uQjLkqw8lhnyuX1B9Ak2OSiVsr0KLlH6o/Xa4QLuT8w6ZCTjaJsEZbJrADpHUwISyns567+hBwjvmk6y9B9mKz3RNYab6RtweeIymRSZ6ofQspypLw4T6XYSPac8vFQk4zHMfBI488grPOOgtlZbmXtHTpUtx4442YP38+li9fjpdffhlnnnkmjj76aBx88MEAgOHDh+OAAw7A73//e3z22Wd48803cc0112Ds2LGorEzH2Ljgggvw7bff4sorr8TixYtx33334dlnn8Ull1wSxe0EQiIhCMbk3rkJiMe+mb55E6bPB6c1KUdurpILOUQSJyfdPYq4LJiw8kk8RiK4ucrJpDCIILaNCjLjR03I0btP3eeS4RlkiceWxZAs+UJOkGCAnLgkRjXxYVn8aLm8lBZhw1/I4Z833yRYzP/YSg2r5CBIuhMTDZDNSSOhmm5EhIaytrB+/yJWWz2yxxzYyvcmEnIsRU5Oys5x5aQRnSNK4holIhFy3n77baxcuRJ/+MMfmOMVFRV4++23MXz4cOy///647LLLcPLJJ+OVV17JlkkkEnj11VeRSCQwZMgQnHHGGTjzzDOZuDp9+/bFa6+9hmnTpmHAgAG4/fbb8dBDDxWV+3hNr1EAgDY/vYLLCXGTABOCwSMWcsSkvjAXdUsyqK0QiMe8XQatDCa+mhwFVWw+icchCDmZibdgrB5LLmQ5zUKGnyaHl0NN1/3WTTy2LFaw4Qo5xDwYoInWQk+TIyLJRs/J8YegzYB9iTwYoF1mrGHI3Frvbp00ruHM5wE1HJkxQZudHCvB1/DzzFWid+coeldRmhyZJaAUNTmRiGXDhw/P7spp9OrVCzNnzvS9vnfv3nj99delZYYOHYpPP/3UuI9Ro/s5TwJbVmLXTn2x5e3bvAVUzVWigHuSYIB+26SwvKvsiIjHICQ35ztJWDxCXaYPKpycfJKA7bS2MZCQk33meQru40bzmLLp90AhCRsVSPkKOUIypAYS2Tg5GSHHYki6Dmf8p4UOU5diHifHqCo+hNoStyYnmjFL/jgLDe/dg6ovn/Ocayzjx2IKaq7ia7KNquTCTpgTjzPcyI4HHo+6GQejzcYF/tfYCW97QbOgZ6Y+sJppLidHx1wFopbE2KXJEaEUhZzSD+ZRrLATQKe+APhChWqcHD9zFU+VbJKMUQR5NM3gnBz6OWQyrtOanLVb6mGJggFaltLkkk8X8qy5KsAsTjKmmQKbq0TtZyZU4qSkI024u9RAho9Gfx/09+TxSgJgk5TxouMIvBxl0OXkhOntogurxwBU/eYh7rlWR52PmamDcU3TOfQVAdyGM/fACyFtWCWvFduck5MVOm0bdaMfUbrE5hCPA6NZyqGFdmLZysRjoVegormKGfcCk2r6VCzkxOCAK+S4pOVEmSBOjjB1gkSTE3bIdAHsPMTJ+cW6+9C4fYukAv+Ix/nU5FhlIcTJKbS5yocTRAs5MqRCNLnQ3iu+5ioQYxNJqJ6JvPoFyR3zkoXcB4fvuzuqzpmCP1/FBjw0/37S11kheqfxkAiQhZwmDKuSh207ARiQ7uVoFnJAj3NBxGMNTY6qdxUSlCbHsiScnNITckqPRVSK4HJyXEKOKBigSJMjMVeFmoyxAOYq9874R1jILw8LtHlcLOTkT5a3myeLIOYqkjHNFEyTkxFy+ItTZrfpK+SEqEGjvw9Wk8M315o+/+g5OQIXcq+/jnY/wsARe+7KHrAsYwEi856iltcSiTJzzSn1LlT5YpadCMezlEazFpu4NDnc9jm36lgJ7ubWchwuh8jTfEKNkyPOsVi8iDU5eQB38XVzcgSutUQQWjxXp1wTEhRuYYxG6JocgZAjBSWEiT7lfMbJscqad0QBhJyM9syOeAcsbN/HuyorvPhM9GFmf6ddaR0fIceGYyzYEi7RPlRSjsDZyK3JKZgejwPD95i9Bd7qq1OR/PlbZQnzfFK0kKMcEM8KPW9fxrmC1uSIx7AOJ8eByvsjzVxCwI+TEws5MThQE3L0ODmZy7mcHKk3kiakQk64nJxsvcqLisVEcS0GTU5GyAmkyWl2Yw5TWNWCj3dVlmuTT00ONfH6mavsAFnI+UJOiBDlrgqZyBomSGBODq9Swyo5SNjlAYjH1L1pmKvC1mJnHgg9Vwk3CVqcHMUEnYy5KubkxNAET5NAcwxSRBK5V8DJkaV1CJeUI/5AwhByaIjMVVIwk5Sg3nyaq8rUczOJK0nf0xYUKPN481jkB6HU4OSEKeSUCcxVAuK9sSYn4mCAVvY/3DPFiYACV9TCulVWBtOljDblqKa9sG07nPAZNLLEYzNNjkzIUQI1bxGJkCNOJF28iIWcfMDHBi9bDHZ884Go0ub/8cPahwWZ6jKBwpqriPt6YTDA/As5vBAKqsjsllaNeBgLnT6YNvj+UPqm0QHpaUdRk2OWB4oPm9KwOIz2TsRJM1yYuTvVMNM6WOBNu8VAPBbC1IU8y8mJnniswjvhgTbHq2opbDsRet6+LCeHdiEXfYcamhyLqHFyrDLKXCUZfKVoriq9HrcQ0ANJ5mp7zM7p/BMB4uTowJKR0EIOBkg4uat8L7fp6/nIpyYn0SzklAviHimheSI54sihaDz8U/Qvy/NexGeydywbIP6anDA5Oawmx9+7ylhKMJjEtcarZXHlL0+cHJUotZzkpOEjQO6qPElqZYHi5FCaHEVBybLt8InHHHOVzuZMFPDUIiklc5VFmatkLuSxJicGF9yJWMAxUEd6EPImIN/EijqtSD6QRBicHE5aBz1OjsKzy6e5qjy9I0qUV2DLr542rCT3TisUBZzG427wL6QKX01OjngsW+BFSQNNYDPfC22u4ox/4ph7BEW+U7UE1GN9Tk4yT3tUd3R2jSub/xutIGYnyqWbMRkYTbXifdp2IpwNHo0M8Zgyl4qFSw1NTjoDlm/zVhnLyRGWK0FNTizk5AN+5qogi4FPnBnuJRrVy2JkhG6uQlrQsXV2p4w5rfDmqgQ1WXQ4eBS2k0pJaQEMJpKKn1yk344Q8hGSnVCdlNQ0GmY6DdaUIPeuChInx0yTo1PY4l5h0t+kz7yx9WcPYkWbQ7TrZWBZrm9M81rkwVxVVmbuXUWNH1WTV1rICVeTY2m4kPOJ6xJOjqa5CrYtnPf5CUOLG7GQkwf4eVcZRYaVmatC5OTIuCVhaHLcbRFH3N6Xh/4D6390da68S5NTDN5ViXKWeGyipSv4bslHi5ARXtIRqiVCTkjPPUnYevzSOthBspDznr3v96SlG+X3zWOuCq7JaX/ob/F9x0M1+iZAQE5O2Ngx5FLmt81Ls6AI2kSlYiLMXBO6dxXJmKvo4KY6xGP+WLBJSknLZXkcJgTmqti7KgYfPG1LDkHMVbwP0zd7tEYrsrQSiYB5iQCwnBxCsrwcHpKtOqPLyL+4Li8uc1VZOau54aUd8EXBJxJ5n1OUJkdmWgxLg+b+Ptj8PiJOjqn5IgCXSqkB1Tg5CpwclcUrjA1PwIjHYaPV8PH4ov0xuVZsWzlasQdMxGNFTo5lSc1VtdgF2350mWZHOJwcjWCAIk2OpehCblOaHCclTtdixZycGDxwVX/UAA7iauuXu6px7KfYce57rvMa9UvIpWEIOe4P0JG051WVWi5VbOE1OW4hh5sxmMjfQKE1OX6TImOukvC/wvKucgvttEDAe7d26N5VcuiZY9xjljpO/1JYmFIKnBzZpkEZQTNsh22usizUlXXwHDMBo8nRMHnJkgYTADv6n6XVj5y5SiVuD8dcJXxHikIOPW+lki2KeFx6BrYSBH/AyHkFvnXK0jpQsREquuypXTcNJyX+mMvC0ORQ8DNX8Vw8mR1vEcTJKavw1+SkkJDymQq+W/KZFLPaEx/icVjminKLHWcZ7y5A5l1l+M4j9q4ScXJMkBSE8mcQUL4gsMxJ3KFxcngJPl3HQggGaGsIOQnZZgzqnlpZcIQcIc+G60LOH7cWUfM0pON7EYkpThSZv5gRa3LyAo65ijpkxl2wvRVlWwvPXCULX14WSloHSj1LiFyTw5mEmCzmwivzN8zbtm3H/OaZU/zMDIXW5CgTj4kjNYdEJVwqaXIM5Qjesw9XDyGIk+O6DxWPGDVid7Dep8P6BPWuCh/uqO6qfBpvRVTvNOqw/TZ4mkRoK2uuMgsGKDZXpZSEHIvOXeWkuG0A/I1msSMWcvIArkmJFnJMzFUZRQ53cIdIPJaaq4Knj/CqUtU1OQQsEU7klJVPTU7Hbr2Z3yJNjhSF9mDwcyFnzFXRa3K89fpzcoipCdhgp6pzl5ZlCdYc10EFTUBKKTpzsLmAwDIXIELqA79Kl5Bj6F1FX6fD60lIN3j6CWJJlnhMa6ZFnBx1c5Wl2he6TKpJ+O2KEkkXM2IhJy+Qc3JMNDnZGoWchHAgIx6XheBCzsClyfmw22nMac8uwnJ/8ILJVPB83V47YcCubO1u3FMm5fO+LYPUAuFCVZOTko61sITLFcexEZ/90jrYhJhzZbmTeLhpUvjEY09B36pUhJxQ8tgFDAYYiSbH9cRMc8XR1+kISn4u5LpeSFlum4q5igNROhKLpLhawVdTP3IVpLSjTlJMPI41OTF44MccCMjJyalyDHul2pDEPhtCtFVPnByKk0MnjQO8pgQCNoaHSKsgmizCjMgrAq9HfpqcREVVNJ1Rhc+QyjxPyyfisci7qrrraL3+dNmP+cmkdRAFwzRd9IxMhXrEY74jgn5/VYScUMSzgOYqPkE6oIbJw8kJrsnREZT8YoSpmBuZ8hlODv2sdeLkCMatBcIV3noNPdtTMguJucqONTkx+PDh5BiZq5or4ExAdohZyIlEkxM6iMNycspYEi8vWBerShdpcvgfbDLEYHUi8NS+fkJO2669peejh+K0QBwjTc6A8/+NNae8hkaiNmFWtGrD1suYq7zPNy3kGC56JuYqHTdtix8nxy0Qqiy4St5rAV3I99i7f2Ahp8EOX2h3a1KMNTnUc9fRvvjFCNPWePDi5AjGMD/uGr89W6DJSbjnVrot2cZWQciJykxtiljIyQt45GBa5e7/Qcxz9uXXaUQ8Vh+E7SujFQTcxGOm5y5Nju3araxpvR/rAipoQ6TJ+cTqp9PV0LC6+zCsLOsjzDK+625757lHLqguGIbeVVZZBXoc+GPssNQWv8qqXdw15NrgvNsgRhKTiK6ae3bBLl/fhVzkUcOC/36+Ofoe6VXJM19F3eEXoedxfwrAyUnfQ6PFi/oddCEMx1xlG2pyZMRjCwRWQveZNd+PAienjFO3OB0J4W4ObXeoCxqpJCqdeu4pFc9P67LFvmXyiVjIyQN4vAHCqNz9X0Njt0F4ad+bcweyLuTeQVee4LgYluW4IiounalEKwDAvkeM9C0bBPTEsml7IyZN+zJ3zqPJSd/rgpNex7ROv8P+Z93r2oGoc3JeTf0IqSHhpULYZHfC18MmK5UdeMFD2OOaz7AT/ImmfcfOofXLBP6TfbMY4Uc89hnXTeBrTdyLd9UubbjlRG2INDkf9/uL55gbXCFHUxvyzQF/FtdvWXziqEGCTjUhh4+9f3qm9HzZnj9BmxNuABLl5jyM5ltq5GhyAruVe8xVwZOI6nBypOEziL4Il+VO2f6cnO499/A2KQsGyFnmE+VuTQ7VY5JCN2sLtz4l4nHb7v5l8ohYyMkD/PZtSuaqyjbo0zm388/uBl2T4Y7uh6LyuP8DAGzY6+Tscfuc14A9hgBj3lbqc+LKr4GLFsDq1FepfBh45cUncPXnJ2Z/u4WcjDr54MFH4fgL70fnzl0YjwjhxMlZMPb++ZXo2a2LsC/1rXdX7vcGdECn8cuwz49P9pxzazMewS9865NNtstOeAqrK4K/kwaZqcg3Tk7mD7mQ01jWTngOABotdyj5NDadN5/5XVXlFnIo3pYwQa33Hg777dX4rPWPPMeZa4O671+9Gs7ex4vPC+Pk6Gsi8mGuAmBs+ssIbsmEV8jRYjFx7sEdhNLYXGXolSULhEpgGaQ/8JqrRM+9+/5Heg9S4/bjA/4v+/d6uysIR5NT5ko/w/InxOYqnVhCxYLS63EJgruj1fSusir5u1n6I93QZn+0OvM5dBjye+Diheh8+kO5grsNAv4wFeh1GJf4uLG8J3ugsi3QMXpuCD05XbTln+w5t5DDUZXS9y+cOHm7/UQCZRKC7y5jZwrPmWLqiXPws8v+LS3TSOSTY9/DT8Tu/1cduC9yU5F8wUhWpIWXyh1rhZyc9/a8FFVH/lFej0DIcavE7TJW8KDddx3bW0fCIsJ0Gk1lYq0QAFhlJi7k1MirbAu7dQdhWWLZAk6OvrmKKKSgCMO7yjz5ZbOQY7cK3AcPXIKPu49bxnyIKd0vZI5Vd/+1pxpdgnAGMk6OJTARyZAV5BjisYBn02ZXzkFKA5SoxGP7TcL/Ukei8ue3cceSm3tmWRbWVqQ1RHsPPV3cz9i7KgYPKZ9ti4qroF3ZlhlgvIjH3/Q4EWjdKf2jwx7igFSc/rT94xu+fYgC9OS0k7g+PCUhh+bkqGtyLDuB8gr+5LvM7gPs0hk46xVRt43w4/17oktbeVbyJafMCLVNEXZCIuT4LLANnQ8CAOy+fbGQdPuTM69F207dpPUkBYt0wkebQu+ieUIOAKSSjdI6RDAhHrux134HY1ZiCL9+WODSNQwSdKYS/hnuQ/GuMjQFZe4hWcb7zoJxcrzBAHP1ffuzZ9GhVz+MvuBGfLz/ldnj7X9yvqceXc3E185uAPxjhOlqcuyMpypNPOb0LXn8TYIKct+MZVk467QzMOyaV3DYQftzBTn3XGrBQtcrPkbDnxegy97ipK6lmNYhFnLyADvZ4DnGRjz2Hzjt9hzomgg55qoA7uQVnftgcfkBxtfPHPII8/s7u4d2HfUWSzB1Z8bluffSwRB1XMht20ZZJV/IyarC+x4t7a8K6B2634S6uKwf+h90cOA23fiy4iDPMfmYk4+jqt0PRopY6IzNaGdtF5br3KmTtB6RJodeIHYQbxk60rZog2A3bJO2LUKFW40P+Jp8eDFbdht9naAs0H1X3nNxPXMVTg7HDBQFjDU5zY4DDlfICRiJWWauqmqfO15GPyOORsPn3j5tPwzbSU6YfPFHzwKQxwgzIb7vNryZHyhxIa/FLig7aiy/AmpuzFgOdqksa66GJ+SwwjyxLFjlVajcVa69TyT432wxIxZy8gAeU51NRyB/DXMH/AP9Dj3Olaep+VNiPgTF1yn4/kxd/+b2uxqHHXls9vfa372NdVX6vJHtVo4c7RALljtODs/spCLY8dJB2GWoEAo5ehOwank3AdB9XZVTp9WuKhzdFHU+E39lZSvUWGI+0ze7/RIAsEvXPth6xOX4pOIwAMCsVH+mXNIWaCKo3WIDRxBSSQybaDITcso9wRzN0KPv/tzjxLLRvvcAz/HvBl2KRpLAg6mfKbehIuSEkhzT1ETRLNzQTg+hQUI8poVkmzZJ88w2ftG9W3dh6ARXnZDeMJSFqMlZfPhN6HJwM4/LEpurpKZHRpPjnyIk4dbkKG6Qq1q1wn86jsV/2vxBqXwxoPQi+5QgKp0d3oO067SPJueIX45LX8IM3gCaHAKuoGMcodZKoHWbDvi6w49hJ3dir30OxXca8rNDLNgWwc7ELshskGyLwHbxI3iBqGgTnthcxdHkJBKoqBJxBSIIQw/+jopGK4HbZlA4CtwNGn48BUJS2JFoA6TWeU9etQJ7Uzvp9qP+hn1/ejVufuZV9NuHdY0XmZpaVeUWpgaOBxqzixaM+bKkmcBY3krO2eGBN+5at+mAuZ1/hSM2vOgqbAHlXuHkhGOHYuqun+LEPl1z5VyoQ2us7nAY9t+S5ouRMn8hZ5eK4OYFUx4GKU8LNyQKIcejPaP+phZ8m3rW3IXcR6C3O+8NZw1vcyWfI3TMYE1tKK23JE7O/2/vzOOjqLK+/6veO0unsydNViAkBBKWAElYZIuEkAcB0YeJgCgoEwwKCIqMKDC+iqPvoM4Mg8+oA74POAzMCKKgyK4oiyAMBhRBgYAkBMWQAIEk3ff9o0l3VXf1Snc6qZzv55MPTdWtW/d0Vd86dc6558iczUt8Jcfm3KL5xbx0y3Ich0lPvGj+zxL3VpMGGrLktAAak5hJ37M8OeZDOPvPXuZ58CWcTA5wHNJmb0aneTsAjoOxYAmqmR4VJsdv/LbYFiCX2birxPzB/Dw5Dr0KorXD5FBrrJPvwfgHrH16GKzpNCEe7zq7Mo3Xq0QCCn2A0UN3gav7iJkYbsodKANavd0DOkStwDMPjsWYfKHbzDZW43z0YJhK1kPGi8USy7Gi4AV9OrI+qrxUclRB4rmLvCF35kq7bc3fbWXhm4LtchmH4p7J6KB3HKR7C0r8qrYuEHBHyekyfiHqFJE429U+HsVdvA42Vd6WRST27U5nKmcFOvnzhEzFT53hPF+ZGEGGDNEg9uvj3rV8/iVxhH2/Hig5gvqD/Jc2m+eCs3mGc+LmEltCrlE7WV3lAkdpEMSolrk///sLUnJaAC2zt+TwbxF3LSiCFOTN/3pjyXHorvLydlDaT2J9e/dB0+xvYRrtPOmY+bxmVEwYu2Sr5ETo7B9AfPmVzD72ydxIbHWVDAqepchksk4gnpr4NY7OazsMm+/3V6U1MPfHsDzoHljl0XndpTnnkbuIlyGxotJoXa5ScgeTTf6Ui9GDIEsfIZhAG0WsPUo3aqZpjI6sYs5lU2tD8VliKa4w9+Xz7H4xnz8+vwTnZI7TFIhlGjcXW+TdQ6LKqxC13oDQZ39AyoRXPRijzXm9tPBaFAylbTLHO7eV2sfkWMfIz+Ui51vNOKA8WJhCwJXFJTI5U7T8S3CPscDCy8CCC4icus5mbMw9N3pze5m4kmNrZXIa7My3crthyQmPNqARfCu4f16QLwYHJuEqH1JyWoAg3LTbxr/xnLmrhIGX9sulhW8Md3Y5vXVXqULCRbcbwoNg6JQluo9Pcw2sVFOFYDtfyalDEFR6kWBm3mQS5MjdI6bkyORCpdFoXY1jO4G6Qss5W8njOPBYP2kVjgXl4buR/0DHOVsRkWIfIOwLTCJKqPOHvfi+8rDBOBpZjC59CtCktFc4Gzz0fptsLBFib4dNIonk3CkMG6mw/82Zcf541QSH4q5pf4DuuXMuz+EVPBmd1U5ramq0PxQ2CqjodXV+Tm/wrp4XwN1WckLiOt7R+UX7tltCzrOY8iw5CrUw8Lj7vE9wQpUlepwtN6BBZFyK45c/hcqcakMs1scjSw5vHhIEENuugHLTXWX3+xUZn1KL8wprkLH3aQLEaY6/u9H3CRct/Q8pOS2A+EPQPSXnvNya3VLwgxQLPL7DyczbwGNNqONVNKrwDt4OR6DkVKlSXMoXwhykIhdRcmwfMJzJ+lDxl9PPdiKJS05H9tNbkZE3yk9nNMN85K6KvOf/oOfj74GTyWBS2Ss51XLPVtTZBc5a7mnr+W+KJBQUFIZ1MNagUS94NJZm1LdjchQK9x/szu6XOplw/MLv1vGRmpAwka0MJrXeerSDFAi+xuuEebeDuDOHlqA8aRK+H/CaD0dlG5Mj7q5S8NxVuJ2jqImzWnAdBQgf7vUS5PNPm+91D2cET+dRPc9CzV9RausmdO6ust6v2qgkm33i47kc2s3y2df1ppLKNmJP0Xb0v8tJYswWgpScgOGekrM3dab1CMFy6duXThCNf4dKjpuWnEpNJ8H/tTonsSQc5/EbvoVw6wqt+mDXGYiDOfG3d66h1m5bQ71QIboTS44z+BWz/WUSdonS08BPB4qDzqrMMrW98lGj8UyhtYspEbn/GlRiD3vBQaJbtT3HA/NOud3eslchsuLLwyXkfILnfI2a8esErS3dOhlLSIgOB+/+t912pc7q4uTciMnxCV7G5MhuKzmcTI7uU5ejy93iK3IOm9KwzZjjUd92K4347ireA59vyWn+KSoarlq2qZXiAbgyrQ5qrVn5cGZxEx2bB864C7HDYMgcYD2Wf//Z/B7kToKdjTeuWD4nd+1rMx5hPxWpEwAAjfE5vDa+nZtS4qIwOLev64YtACk5AULwRicyuf/KQvBi+vuY8N+T+QdZPrLmj/y3LK8L6Vk6cNmigclxTieckELCnNda2pow26vRdE5JxJuxS3BK1hHqgY951QcAKG5cttum1wljLmR8S46bqfCNzPX3ZQR/1YOflZwhC4DY7qiQ27zJibg1OADl0Q6WK9++zxpsMtUG85QcTmuvfKiT+3k2XpuHtD61l7lv3j1tdKnkOCEkxvNjvHpRcHy/yEKjoc8q9Kr/fv2HC/7PgUGjtyo5Mh8td3eFwM1a9CpQutet4xRq+1gcMYJVCkToPbvO9rmJeJ95lhylIPDYTCb7wbpNZKUbAJiCrPeOOxnpAaCJmdudVbrnnmuc/BESZmwQLh4RKDnuK5fqq1b3arBOGD7An3cup01A0qTlAGBdtg6Aa/RP+orWgM+VnMWLF1uir5v/MjKsOSNu3ryJsrIyREZGIiQkBOPHj8elS5cEfVRUVKC4uBhBQUGIiYnBU089haYmoR9+9+7d6N27N9RqNTp37oxVq1b5WhT/wldYeG8eJ0b8AyeVGfh1/Ho8WzLcktAJsDUbm49X8Aqt3enqKkeWnAsTPrXZIjxPqN65kjN08gIsj37e4/Eo5TKUzpiNtOePID1nqOsDHKC6+bPl88Why3A+Zz4ik4QBcXx3lTOzMJ9rnOuHDN807vfVb0OeAWZ8geP5ywSbOd5EfyHePLGxgXPQ7bcrcWXSp/Z1rG7fB8oZnwk2K3gPVblG+FC6GD0QaeOe9Wi4jKd8fZ37Orr0HmLfRiMe72Udq6ffqX/SA7gN554lx7YtYH6w66Ksq6vkvO+vgclxLEg8y/KdotDwrHa504E48Ti7K+PW4mzmDOtxbio5HJjH7pLoAVPM5wxJs9vHt+Qo+Wkibn+ffzWOBQCsCLFPrPdB+itYH/4oeva3rphyd0HGD/d+jL26Yugn/69g+5ddF2JfxDjBtl/HvQdlp0F2fQiUHA8saDHDy9AEOY53+G/7PnnfbX3fx4Dby8c7d7G6q26FJrp9rraGX/LkdOvWDdu3WwtB8v3bc+bMwebNm7F+/XqEhYVh5syZuPfee/HFF18AAIxGI4qLixEXF4cvv/wSlZWVePDBB6FUKvHSS+aU1mfOnEFxcTFKS0uxZs0a7NixA4888gji4+NRWFiItgZfucjsPwroLx6jwdfQZSazeyU0lp90z92JwlE78e0JXXNtmlnH28AUUGmcP+xD1AqUPfYksOT3bo7PMxqZHErOcYI4VZPVXWUYPE20Dd+S4y43uCCEOYgDasZRVl9/MnJ4AT44NhFjatcAEC6jNUxdA1ytgCGyM8BxiOicC3DCF4hmZYyL7oID+lHIrdlidw5FsF7w/+AJb4vmf3EG38LUfdgD4o2CnCs5nj4cvYk9UGicP6w96VEusLx6NhYOQHiM1SXIeMkyjw/4E3qNmAQsvgPLlwPSc4bii8+L0BSWjMFO2kX0KEJDeGfgxAoAgMqusKo4rtVO+xaxgx4CS+2GiBhzlna+8ZUfZ8NP+NmcoiLnwZex8PN78fh99s+KMSX29dbcvWfSe+QhvYd9AdigyATcrKsErB4lNEZ1s2sHADKlY3eVMxK69Qc6n0M3lch3zrvPlDwrl1zG4bPRn6Oi4hwmdvXPoofWgF+UHIVCgbg4+3LrV69exTvvvIP33nsPw4YNAwCsXLkSXbt2xf79+5GXl4dPP/0UJ06cwPbt2xEbG4uePXvihRdewPz587F48WKoVCq8+eabSE1NxR//+EcAQNeuXbF371689tprrVLJuXDPOjTufBk1Mf3Q60dzfgxBZXs3k7Uld+2H78KHIuPXXQjvaHYZRSamW/abGh2tKLHFUfkDd35UnODHV8eFINKdydqPVow6BCECjjPc/pS7CAl7p+LDqGn4jYM2MubcknMkqD96zVyDy6/0RjR+BQDUy4IBo70rjE+TTAk3EvT6FI7jEKsPBm7rdvw3ak6uAKLs33756JKspSUUTeJKXES0MMjYNqeHW+PkKTmOauLIgyJEt/N6cetcJmaOTLBt/RMXhxpFFLo1ltsdczhtFpRVXyN7mKO7xswv8ijEmS45bXNVGYOwxmp06291EXqjcOkird+7kbcCy5/2KZlchgFz14ruu5LzBCIaKoHeD5o38B+oWvfcaZyj7KROD+LAJVrdo8xk/ZHx3VUqnoLa2GBO9ZCbFo/ctDFun8rTmBwxbFMhBKvF+5TzLfOeBnyrxXM8KXj9xEcIY+nuyskGcnxfSqY14ZeYnFOnTsFgMKBjx46YOHEiKirMS4MPHz6MxsZGFBQUWNpmZGQgKSkJ+/btAwDs27cPWVlZiI21+p4LCwtRW1uL48ePW9rw+2hu09yHI27duoXa2lrBX0uQ0LsQqfN2QRZrrQ3FXw3AtK4mcstByHhiA9jck+gy2DzxhoXzXEVXKxwc6B4qpXsPqiCN9Yd4Q+aeSdqf3OCcj2FoQTHO//Y73DvjRYdt5ILVVfaPDA4MCIqAkZdbojbMda0vI+ddZlFPOGkSCcrmB2LyJnqxlWbfMvNS0k9DxmD/gLfRpYfV7aFoEq9NldRjGH6W8e49uecPbI6Xcl/mQMFWhrj4bbh52uYrapu/JHLeVw6V+5yJv0f23I/sMm/b9V3yT5wK6o2z99gHCjcTNr8cePoMVHrry9+eDuYEff9sGuJaAABgTKAYZiRZ50ixvDquaLprwe1/n/H82JGv4FbKUESM/B0w/m1enTfr96t2YskJiUnx+JzOYLw8V3IFX8mxKlpNDSKZ593p+45jHYHEkbNxzGS2utfEDUBwpLh7SBBM7quK33IF0P8JIOdhINx5bSop4nNLTm5uLlatWoX09HRUVlZiyZIlGDRoEMrLy1FVVQWVSgW9Xi84JjY2FlVVVQCAqqoqgYLTvL95n7M2tbW1qK+vh1YrvrRy6dKlWLJkiS/E9IrMnLsAMT0s2HlMiwCOAxdqbyUDAMU152+TvE5Et6b2HArs+dzl0Qk9hwNnzSm9G9UuYiYc8eAm744T4UdFRyQ0VTncz3EcMgzOH5ZyXtHHakTCdr1Y82oO/tt3lyl/RvlqGYL7TUYqxDE6KF3gS77Iewvptht5E7MgNkLEoiab/D62HNmNu8c9DKVCOLGqjA4KcCpUuGAYiagLq819eGXJ4SlfDoKy1TpXGVPd03Kar1vqiFJgvTXGjJPJoDS5awEVJz6tF/D0LueNFGrzH4/7SqZh2dZsjOjjYWHcsW8Cl7+FupO1gCwziqWpcDGkofOB3g9AEeZ5PIYi77dQ5Nm7doyN1pcFZZC9klMzfh1ufP1PdP7vF4E/mGNXzDE5d2aLMvFWW8l4MTlynoJqbPDuOmtUSoikOvOIFEMcri74Cia1AnonCxDkKu8Cj10ywruUClLA55acoqIi3H///cjOzkZhYSG2bNmCmpoarFu3zvXBfmbBggW4evWq5e/8+fMten5lVCpQ+gXw5HeCh40i9M5SX6/T3Id6psKt3JmuGztBO+hxYOQfgMcOOG0X0X0E6hVm33/sMC9XPXV05t33jGsFr+J940C8ErfMdWMb6phZIb4Sk4spbAl2GXtgdeJiu3bNSs6hjHkAgA+0YxEUFoXuZWuQ2nekw/49rRvlKfs0d2HqqP72O3j3V3SEc0U0vXNnjLr/ETsFBwCu5Jvf9j/W3We3T8crNOlNULWMl+fF9vitmpH4jMtBag/74ExvaFZywrsNx4Wid3nnlUOmCow1MkyrxJNj+6N7gt6t9hYLY88S4O7fQ8GrmG4SSR7oukMO0Cf51JXceMvq3tSK1AHTZxXCMOXvkNmszjM2eW6J4iO05PDe3XmyGWK8K5uim/A/aJKp8evARV4OznzdwrRKQRkaMYTuKvPv8f2MZfiZ6fBBd9fZ431NhTwRLOO/gPvsS5S0FfxeoFOv16NLly44ffo07r77bjQ0NKCmpkZgzbl06ZIlhicuLg4HDx4U9NG8+orfxnZF1qVLl6DT6RxacQBArVZDrXZQ+biliDMHeHG11mWMKp0XS115FM/5H5yuegn9k+6sHyhUQF6pW+20UzcBdZUITi+6s3P6gKLcbvih41qMjvT8YVU39TOUH/gQvUeXYsl14N9fF2HeAEd2GaDo/unYe3wohqU5bsPHURFKX6FWOJo0re8vtivJPGHAsNH4Mf17FMTZK+LJXfsC5vUCCFJ7PpXItI6Vr4Kn18LEGJRyx+9hjUzusgSF6HG8gogymQwxD6zAT+89jKb+c9Cajfl2kvJlN3qh5PgBTbA15kOpcn+u1TbVONzXoHH9EhgWbHXzyG3cPHWFb8B45SzCO/Zxezx8NKm5wLMXES5SINgdGtVuhiPARsm5bckZN2EqLlz5De6JaJmUAXyuy0LB/WaN6L5jUaOQ/fMWHO3wADIurIeGax33oC1+V3KuXbuGH374AZMnT0ZOTg6USiV27NiB8ePHAwBOnjyJiooK5Oeb4wDy8/Px4osvorq6GjEx5of2tm3boNPpkJmZaWmzZYtwxce2bdssfbQJ+OUI9LFOGromWK1AVrL7fewJHonR1/+NfaZMePqNWdw1hp4Aenp0bK3GAN3Nix6e0TUcx6FzjHeFFQ3JXWBIngsASNECc0fYOX5uY34bU8hlGJjtqI09/lZyHFde5z0AQ2KAadsBlXeTZMcO4veW3JANFtsd4GQC15O7BKX2xdqmIahkkZhj27eMg9yFK6oJcsh5xTorEYV4/IyfuFjYpiXkuxltV+KEJXRF2NP7PR5/S+MsyRxrJUpOXFIaDmUtgiIkAj3dDJzlwBBkFI+P/CZkADpPet1lH/oO6biUMgbQhCHWJoYqNP8ht8bhFC8UnMcbZiKFq8LkXkPcPkah4sfkWDOAJ3rxAucLnMUUZpWuQuW3+9AjcwCOfzUOuk+ewM+DXkDvFhyfO/hcyZk3bx5Gjx6N5ORkXLx4EYsWLYJcLkdJSQnCwsIwbdo0PPnkk4iIiIBOp8Pjjz+O/Px85OWZl96NGDECmZmZmDx5Ml555RVUVVVh4cKFKCsrs1hhSktL8Ze//AVPP/00pk6dip07d2LdunXYvHmzr8XxG/yEc8GR8U5a+p7cR17Hux/mIX+Ig2RwfiJkyjrU/3MqFAUL4f9wXN+ww9gLw+VHcLbTZHizBsHfSo7DbLy2D5hEP2QflcnB/fZzAMz+fG7QJU6H3SOWISXUO+tqI6dAeO13lv9zj+7Al1teR8rdM+za8pUcfvyGr2v2+JO2oOQAQJ/xT3p2AGMIMYmvjsyaZ5++QBSOQ+xD/8+z8/qZl55fjOu3jIgJdT+1goKXhsHbmmG+xOREyeEUasRnDQEAdM8bAeR9hySHrQOHz7/FCxcuoKSkBL/88guio6MxcOBA7N+/H9HRZpPja6+9BplMhvHjx+PWrVsoLCzEX//6V8vxcrkcH330EWbMmIH8/HwEBwdjypQp+P3vrTlWUlNTsXnzZsyZMwdvvPEGEhIS8Pbbb7fK5eOOqK+3BnTqI8QDif1FTLgOUx6c3qLnBABZfBa0s79q8fPeCR0f34R/H/8W/zXIO1M3P5eJf3BkyWmhh/cdKgnT7+rkupEDjFAgpeG0xY8T1yEFcY++LtqW/y0p+cUc25CS4zQ0N0BxRb4ijNV5vIq8tROqUSJU49nrnKAMha9WV90BplagaN0pPpdg7VrxfArNaDQaLF++HMuXL3fYJjk52c4dZcuQIUNw5MgRr8bYGohN7op/GwfiCheOR7UtVIOG8JjUGB1SY3JdN3SA/5UcR0jsiSFCfVR3bFfk4f6qZVgd8TgmuXlckqFlXyp8hdgV/TxjIVTnPkPP4pZ/afEVWqUMQcZbgR5Gq4BvyWkNP+GWSIHhb9q+mtZG0agUuPvZjVA5Cawk2j5M7udAdwfuqrZkofAU0/TPcf3Lt2AoXIgR8gh8cGQMxuWIZ5Bthu+u4sJTgKJXAI3evwP1MWLuqkG/eQrAUy0/GB/wc88yRB1djvB7XwXecz85n5RR8mJyOFMLZxEVoVLjXh2u1gwpOQFE56EpszUQ4Mo/bQ9FYAKP3a230xaRGbIRep95OW0YgDEDero8xi67cK59jpfWjifVrdsCUWNfAoqfQ7CSX3qBg8xJtW2po+K5q0xeJHj0FfPDlyHx8h70HPF0wMbgK6Q7ExL+wd9FJqWGn9xVm4zmdXGfhE0Q3S9lS443+L04KuEdtxWcfXJzmZodems+poYA1H0LNIJl9wFUcl4oexij56zAwAyRbOptDJoJCY9gkV0CPYS2hcI/7qr/NTyH7Jt/Q+5g8WKurcKh3wq4mPkoAKBuSOAynfsKKV/RpOlrsTnrdfR79A1c/c0mXNZ1w80HfJcVva2g4CXkZCxwSo5KIUNygJat+xpyVxHu8dvPgC//Au2whYEeSZuC85OS8970fFysqXc8EbXU6qpWjuH+V4G6uYjRtWyaBn/ASdiN0yE2Bh3GP2z+T8ZgIOPLwA6oNRBAS46UICWHcI/4HsD4twI9ijYH5yd3lVLu/E2L3DO34ThAAgoOAFzPLEH7c+C0Y1pR7qO2DL3uEa2G/5g6YneQ4zpQbZGrkd6kEPQBZMmRBA0Dn0ZTcBwwdgXCx/3fQA+HaEFYK1hdJQVoJiRaDR0eXYugUH2gh+FT8vsPxjQsxsLEd1039iEcKTmSQFXwLBRPnQR6PgAoKZ9Wu8JE7ipfQO4qIuDceOw/uFlbjajEdFwIdAFVH6PTKPHmc7OgcFF92NcwWl1FEG2aSl1WoIcgCUjJIQJOUEwKgmJSAADd0joBFYEdj69xVknbX5AlhyDaJgNuvoFUWRWe6HF3oIciCUjJIVoVyrzpwMWvgC7Sis1paSjwuJ2jDQ/0CAgvWf/MBJz95Tr6pUYEeiiSgJQconWh1AITVgd6FG0fsuS0T6ZtA7YvAUYuDfRICC8x6LUw6LWuGxJuQUoOQUgRUnLaJ4n9gIc3B3oUBNFqoJmQIKQIBR4TBEGQkkMQUoSTdBEAgiAI9yAlhyCkCCd33YYgCELikJJDEBJEn5Ae6CEQBEEEHAo8JggJ0im7Pw5deA3BMSnoGujBEARBBAhScghCovQZNTXQQyAIgggo5K4iCIIgCEKSkJJDEARBEIQkISWHIAiCIAhJQkoOQRAEQRCShJQcgiAIgiAkCSk5BEEQBEFIElJyCIIgCIKQJKTkEARBEAQhSUjJIQiCIAhCkpCSQxAEQRCEJCElhyAIgiAISUJKDkEQBEEQkoSUHIIgCIIgJEm7rkLOGAMA1NbWBngkBEEQBEG4S/Nzu/k57oh2reTU1dUBABITEwM8EoIgCIIgPKWurg5hYWEO93PMlRokYUwmEy5evIjQ0FBwHOezfmtra5GYmIjz589Dp9P5rN/WTHuUGWifcpPMJLOUaY9yt0WZGWOoq6uDwWCATOY48qZdW3JkMhkSEhL81r9Op2szN4yvaI8yA+1TbpK5fdAeZQbap9xtTWZnFpxmKPCYIAiCIAhJQkoOQRAEQRCShJQcP6BWq7Fo0SKo1epAD6XFaI8yA+1TbpK5fdAeZQbap9xSlrldBx4TBEEQBCFdyJJDEARBEIQkISWHIAiCIAhJQkoOQRAEQRCShJQcgiAIgiAkCSk5fmD58uVISUmBRqNBbm4uDh48GOghec1nn32G0aNHw2AwgOM4bNy4UbCfMYbnn38e8fHx0Gq1KCgowKlTpwRtrly5gokTJ0Kn00Gv12PatGm4du1aC0rhGUuXLkXfvn0RGhqKmJgYjB07FidPnhS0uXnzJsrKyhAZGYmQkBCMHz8ely5dErSpqKhAcXExgoKCEBMTg6eeegpNTU0tKYrbrFixAtnZ2ZZkYPn5+fj4448t+6Umrxgvv/wyOI7D7NmzLdukJvfixYvBcZzgLyMjw7JfavLy+emnnzBp0iRERkZCq9UiKysLhw4dsuyX2lyWkpJid605jkNZWRkAaV9rAYzwKWvXrmUqlYr9/e9/Z8ePH2ePPvoo0+v17NKlS4Eemlds2bKFPfvss+z9999nANiGDRsE+19++WUWFhbGNm7cyP7zn/+we+65h6WmprL6+npLm5EjR7IePXqw/fv3s88//5x17tyZlZSUtLAk7lNYWMhWrlzJysvL2dGjR9moUaNYUlISu3btmqVNaWkpS0xMZDt27GCHDh1ieXl5rH///pb9TU1NrHv37qygoIAdOXKEbdmyhUVFRbEFCxYEQiSXbNq0iW3evJl9//337OTJk+x3v/sdUyqVrLy8nDEmPXltOXjwIEtJSWHZ2dls1qxZlu1Sk3vRokWsW7durLKy0vJ3+fJly36pydvMlStXWHJyMnvooYfYgQMH2I8//si2bt3KTp8+bWkjtbmsurpacJ23bdvGALBdu3YxxqR7rW0hJcfH9OvXj5WVlVn+bzQamcFgYEuXLg3gqHyDrZJjMplYXFwce/XVVy3bampqmFqtZv/4xz8YY4ydOHGCAWBfffWVpc3HH3/MOI5jP/30U4uN/U6orq5mANiePXsYY2YZlUolW79+vaXNt99+ywCwffv2McbMyqFMJmNVVVWWNitWrGA6nY7dunWrZQXwkvDwcPb2229LXt66ujqWlpbGtm3bxgYPHmxRcqQo96JFi1iPHj1E90lR3mbmz5/PBg4c6HB/e5jLZs2axTp16sRMJpOkr7Ut5K7yIQ0NDTh8+DAKCgos22QyGQoKCrBv374Ajsw/nDlzBlVVVQJ5w8LCkJuba5F337590Ov16NOnj6VNQUEBZDIZDhw40OJj9oarV68CACIiIgAAhw8fRmNjo0DujIwMJCUlCeTOyspCbGyspU1hYSFqa2tx/PjxFhy95xiNRqxduxbXr19Hfn6+5OUtKytDcXGxQD5Autf51KlTMBgM6NixIyZOnIiKigoA0pUXADZt2oQ+ffrg/vvvR0xMDHr16oW33nrLsl/qc1lDQwNWr16NqVOnguM4SV9rW0jJ8SE///wzjEaj4KYAgNjYWFRVVQVoVP6jWSZn8lZVVSEmJkawX6FQICIiok18JyaTCbNnz8aAAQPQvXt3AGaZVCoV9Hq9oK2t3GLfS/O+1sg333yDkJAQqNVqlJaWYsOGDcjMzJSsvACwdu1afP3111i6dKndPinKnZubi1WrVuGTTz7BihUrcObMGQwaNAh1dXWSlLeZH3/8EStWrEBaWhq2bt2KGTNm4IknnsC7774LQPpz2caNG1FTU4OHHnoIgDTvbUe06yrkBOGKsrIylJeXY+/evYEeit9JT0/H0aNHcfXqVfzrX//ClClTsGfPnkAPy2+cP38es2bNwrZt26DRaAI9nBahqKjI8jk7Oxu5ublITk7GunXroNVqAzgy/2IymdCnTx+89NJLAIBevXqhvLwcb775JqZMmRLg0fmfd955B0VFRTAYDIEeSotDlhwfEhUVBblcbhehfunSJcTFxQVoVP6jWSZn8sbFxaG6ulqwv6mpCVeuXGn138nMmTPx0UcfYdeuXUhISLBsj4uLQ0NDA2pqagTtbeUW+16a97VGVCoVOnfujJycHCxduhQ9evTAG2+8IVl5Dx8+jOrqavTu3RsKhQIKhQJ79uzBn/70JygUCsTGxkpSbj56vR5dunTB6dOnJXudASA+Ph6ZmZmCbV27drW46qQ8l507dw7bt2/HI488Ytkm5WttCyk5PkSlUiEnJwc7duywbDOZTNixYwfy8/MDODL/kJqairi4OIG8tbW1OHDggEXe/Px81NTU4PDhw5Y2O3fuhMlkQm5ubouP2R0YY5g5cyY2bNiAnTt3IjU1VbA/JycHSqVSIPfJkydRUVEhkPubb74RTIrbtm2DTqezm2xbKyaTCbdu3ZKsvMOHD8c333yDo0ePWv769OmDiRMnWj5LUW4+165dww8//ID4+HjJXmcAGDBggF0aiO+//x7JyckApDuXAcDKlSsRExOD4uJiyzYpX2s7Ah35LDXWrl3L1Go1W7VqFTtx4gSbPn060+v1ggj1tkRdXR07cuQIO3LkCAPAli1bxo4cOcLOnTvHGDMvu9Tr9eyDDz5gx44dY2PGjBFddtmrVy924MABtnfvXpaWltZql10yxtiMGTNYWFgY2717t2AJ5o0bNyxtSktLWVJSEtu5cyc7dOgQy8/PZ/n5+Zb9zcsvR4wYwY4ePco++eQTFh0d3WqXXz7zzDNsz5497MyZM+zYsWPsmWeeYRzHsU8//ZQxJj15HcFfXcWY9OSeO3cu2717Nztz5gz74osvWEFBAYuKimLV1dWMMenJ28zBgweZQqFgL774Ijt16hRbs2YNCwoKYqtXr7a0keJcZjQaWVJSEps/f77dPqlea1tIyfEDf/7zn1lSUhJTqVSsX79+bP/+/YEektfs2rWLAbD7mzJlCmPMvPTyueeeY7GxsUytVrPhw4ezkydPCvr45ZdfWElJCQsJCWE6nY49/PDDrK6uLgDSuIeYvADYypUrLW3q6+vZY489xsLDw1lQUBAbN24cq6ysFPRz9uxZVlRUxLRaLYuKimJz585ljY2NLSyNe0ydOpUlJyczlUrFoqOj2fDhwy0KDmPSk9cRtkqO1OSeMGECi4+PZyqVinXo0IFNmDBBkCtGavLy+fDDD1n37t2ZWq1mGRkZ7G9/+5tgvxTnsq1btzIAdnIwJu1rzYdjjLGAmJAIgiAIgiD8CMXkEARBEAQhSUjJIQiCIAhCkpCSQxAEQRCEJCElhyAIgiAISUJKDkEQBEEQkoSUHIIgCIIgJAkpOQRBEARBSBJScgiCIAiCkCSk5BAEQRAEIUlIySEIgiAIQpKQkkMQBEEQhCQhJYcgCIIgCEny/wH78bvbUi0LAAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_pred.plot(legend=True)"
+   ]
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Nifty /Nifty Close Price Prediction using Neural Networks/Readme.md b/Nifty /Nifty Close Price Prediction using Neural Networks/Readme.md
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/Nifty /Nifty Close Price Prediction using Neural Networks/Readme.md	
@@ -0,0 +1 @@
+