AirQualityEDA_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Air Quality Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  LSTM\n",
    "Long Short Term Memory networks\n",
    "\n",
    "The Long Short-Term Memory network, or LSTM network, is a recurrent neural network that is trained using Backpropagation Through Time and overcomes the vanishing gradient problem.\n",
    "As such, it can be used to create large recurrent networks that in turn can be used to address difficult sequence problems in machine learning and achieve state-of-the-art results.\n",
    "Instead of neurons, LSTM networks have memory blocks that are connected through layers.A block has components that make it smarter than a classical neuron and a memory for recent sequences. A block contains gates that manage the block’s state and output. A block operates upon an input sequence and each gate within a block uses the sigmoid activation units to control whether they are triggered or not, making the change of state and addition of information flowing through the block conditional.\n",
    "\n",
    "There are three types of gates within a unit:\n",
    "\n",
    "Forget Gate: conditionally decides what information to throw away from the block.\n",
    "\n",
    "Input Gate: conditionally decides which values from the input to update the memory state.\n",
    "\n",
    "Output Gate: conditionally decides what to output based on input and the memory of the block.\n",
    "\n",
    "Each unit is like a mini-state machine where the gates of the units have weights that are learned during the training procedure.\n",
    "\n",
    "We can see how we may achieve sophisticated learning and memory from a layer of LSTMs, and it is not hard to imagine how higher-order abstractions may be layered with multiple such layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import pyplot\n",
    "%matplotlib inline\n",
    "from matplotlib.pylab import rcParams\n",
    "import seaborn as sns\n",
    "rcParams['figure.figsize']=15,10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>CO_GT</th>\n",
       "      <th>PT08_S1_CO</th>\n",
       "      <th>NMHC_GT</th>\n",
       "      <th>C6H6_GT</th>\n",
       "      <th>PT08_S2_NMHC</th>\n",
       "      <th>NOX_GT</th>\n",
       "      <th>PT08_S3_NOX</th>\n",
       "      <th>NO2_GT</th>\n",
       "      <th>PT08_S4_NO2</th>\n",
       "      <th>PT08_S5_O3</th>\n",
       "      <th>T</th>\n",
       "      <th>RH</th>\n",
       "      <th>AH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>18.00.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1692.0</td>\n",
       "      <td>1268.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>48.9</td>\n",
       "      <td>0.7578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>19.00.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1292.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>955.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1559.0</td>\n",
       "      <td>972.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>47.7</td>\n",
       "      <td>0.7255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>20.00.00</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>939.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>1555.0</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.7502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>21.00.00</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1376.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>948.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.7867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>22.00.00</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>836.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1205.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>59.6</td>\n",
       "      <td>0.7888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TIME  CO_GT  PT08_S1_CO  NMHC_GT  C6H6_GT  PT08_S2_NMHC  \\\n",
       "DATE                                                                      \n",
       "10/03/2004  18.00.00    2.6      1360.0    150.0     11.9        1046.0   \n",
       "10/03/2004  19.00.00    2.0      1292.0    112.0      9.4         955.0   \n",
       "10/03/2004  20.00.00    2.2      1402.0     88.0      9.0         939.0   \n",
       "10/03/2004  21.00.00    2.2      1376.0     80.0      9.2         948.0   \n",
       "10/03/2004  22.00.00    1.6      1272.0     51.0      6.5         836.0   \n",
       "\n",
       "            NOX_GT  PT08_S3_NOX  NO2_GT  PT08_S4_NO2  PT08_S5_O3     T    RH  \\\n",
       "DATE                                                                           \n",
       "10/03/2004   166.0       1056.0   113.0       1692.0      1268.0  13.6  48.9   \n",
       "10/03/2004   103.0       1174.0    92.0       1559.0       972.0  13.3  47.7   \n",
       "10/03/2004   131.0       1140.0   114.0       1555.0      1074.0  11.9  54.0   \n",
       "10/03/2004   172.0       1092.0   122.0       1584.0      1203.0  11.0  60.0   \n",
       "10/03/2004   131.0       1205.0   116.0       1490.0      1110.0  11.2  59.6   \n",
       "\n",
       "                AH  \n",
       "DATE                \n",
       "10/03/2004  0.7578  \n",
       "10/03/2004  0.7255  \n",
       "10/03/2004  0.7502  \n",
       "10/03/2004  0.7867  \n",
       "10/03/2004  0.7888  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define header\n",
    "col=['DATE','TIME','CO_GT','PT08_S1_CO','NMHC_GT','C6H6_GT','PT08_S2_NMHC',\n",
    "     'NOX_GT','PT08_S3_NOX','NO2_GT','PT08_S4_NO2','PT08_S5_O3','T','RH','AH']\n",
    "\n",
    "#define number of columns from csv\n",
    "use=list(np.arange(len(col)))\n",
    "\n",
    "#read the data from csv\n",
    "df_air=pd.read_csv(\"AirQualityUCI.csv\",header=None,skiprows=1,names=col,na_filter=True,na_values=-200,usecols=use,sep=\";\", decimal=\",\",index_col=\"DATE\")\n",
    "df_air.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>NMHC_GT</th>\n",
       "      <th>C6H6_GT</th>\n",
       "      <th>PT08_S2_NMHC</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "     TIME  CO_GT  PT08_S1_CO  NMHC_GT  C6H6_GT  PT08_S2_NMHC  NOX_GT  \\\n",
       "DATE                                                                   \n",
       "NaN   NaN    NaN         NaN      NaN      NaN           NaN     NaN   \n",
       "NaN   NaN    NaN         NaN      NaN      NaN           NaN     NaN   \n",
       "NaN   NaN    NaN         NaN      NaN      NaN           NaN     NaN   \n",
       "NaN   NaN    NaN         NaN      NaN      NaN           NaN     NaN   \n",
       "NaN   NaN    NaN         NaN      NaN      NaN           NaN     NaN   \n",
       "\n",
       "      PT08_S3_NOX  NO2_GT  PT08_S4_NO2  PT08_S5_O3   T  RH  AH  \n",
       "DATE                                                            \n",
       "NaN           NaN     NaN          NaN         NaN NaN NaN NaN  \n",
       "NaN           NaN     NaN          NaN         NaN NaN NaN NaN  \n",
       "NaN           NaN     NaN          NaN         NaN NaN NaN NaN  \n",
       "NaN           NaN     NaN          NaN         NaN NaN NaN NaN  \n",
       "NaN           NaN     NaN          NaN         NaN NaN NaN NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See the end records of dataframe\n",
    "df_air.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIME             object\n",
       "CO_GT           float64\n",
       "PT08_S1_CO      float64\n",
       "NMHC_GT         float64\n",
       "C6H6_GT         float64\n",
       "PT08_S2_NMHC    float64\n",
       "NOX_GT          float64\n",
       "PT08_S3_NOX     float64\n",
       "NO2_GT          float64\n",
       "PT08_S4_NO2     float64\n",
       "PT08_S5_O3      float64\n",
       "T               float64\n",
       "RH              float64\n",
       "AH              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_air.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop end rows with NaN values\n",
    "df_air.dropna(how='all',inplace=True)\n",
    "#drop RH NAN rows\n",
    "df_air.dropna(thresh=10,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8991, 14)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "df_air.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO_GT</th>\n",
       "      <th>PT08_S1_CO</th>\n",
       "      <th>NMHC_GT</th>\n",
       "      <th>C6H6_GT</th>\n",
       "      <th>PT08_S2_NMHC</th>\n",
       "      <th>NOX_GT</th>\n",
       "      <th>PT08_S3_NOX</th>\n",
       "      <th>NO2_GT</th>\n",
       "      <th>PT08_S4_NO2</th>\n",
       "      <th>PT08_S5_O3</th>\n",
       "      <th>T</th>\n",
       "      <th>RH</th>\n",
       "      <th>AH</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7344.000000</td>\n",
       "      <td>8991.000000</td>\n",
       "      <td>887.000000</td>\n",
       "      <td>8991.000000</td>\n",
       "      <td>8991.000000</td>\n",
       "      <td>7396.000000</td>\n",
       "      <td>8991.000000</td>\n",
       "      <td>7393.000000</td>\n",
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       "      <td>8991.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.129711</td>\n",
       "      <td>1099.833166</td>\n",
       "      <td>218.607666</td>\n",
       "      <td>10.083105</td>\n",
       "      <td>939.153376</td>\n",
       "      <td>242.189292</td>\n",
       "      <td>835.493605</td>\n",
       "      <td>112.145137</td>\n",
       "      <td>1456.264598</td>\n",
       "      <td>1022.906128</td>\n",
       "      <td>18.317829</td>\n",
       "      <td>49.234201</td>\n",
       "      <td>1.025530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.436472</td>\n",
       "      <td>217.080037</td>\n",
       "      <td>206.615130</td>\n",
       "      <td>7.449820</td>\n",
       "      <td>266.831429</td>\n",
       "      <td>206.312007</td>\n",
       "      <td>256.817320</td>\n",
       "      <td>47.629141</td>\n",
       "      <td>346.206794</td>\n",
       "      <td>398.484288</td>\n",
       "      <td>8.832116</td>\n",
       "      <td>17.316892</td>\n",
       "      <td>0.403813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>551.000000</td>\n",
       "      <td>221.000000</td>\n",
       "      <td>-1.900000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.184700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>937.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>734.500000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>658.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1227.000000</td>\n",
       "      <td>731.500000</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>35.800000</td>\n",
       "      <td>0.736800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.800000</td>\n",
       "      <td>1063.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>909.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>806.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>1463.000000</td>\n",
       "      <td>963.000000</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>0.995400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.800000</td>\n",
       "      <td>1231.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1116.000000</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>969.500000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>1674.000000</td>\n",
       "      <td>1273.500000</td>\n",
       "      <td>24.400000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>1.313700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.900000</td>\n",
       "      <td>2040.000000</td>\n",
       "      <td>1189.000000</td>\n",
       "      <td>63.700000</td>\n",
       "      <td>2214.000000</td>\n",
       "      <td>1479.000000</td>\n",
       "      <td>2683.000000</td>\n",
       "      <td>333.000000</td>\n",
       "      <td>2775.000000</td>\n",
       "      <td>2523.000000</td>\n",
       "      <td>44.600000</td>\n",
       "      <td>88.700000</td>\n",
       "      <td>2.231000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CO_GT   PT08_S1_CO      NMHC_GT      C6H6_GT  PT08_S2_NMHC  \\\n",
       "count  7344.000000  8991.000000   887.000000  8991.000000   8991.000000   \n",
       "mean      2.129711  1099.833166   218.607666    10.083105    939.153376   \n",
       "std       1.436472   217.080037   206.615130     7.449820    266.831429   \n",
       "min       0.100000   647.000000     7.000000     0.100000    383.000000   \n",
       "25%       1.100000   937.000000    66.000000     4.400000    734.500000   \n",
       "50%       1.800000  1063.000000   145.000000     8.200000    909.000000   \n",
       "75%       2.800000  1231.000000   297.000000    14.000000   1116.000000   \n",
       "max      11.900000  2040.000000  1189.000000    63.700000   2214.000000   \n",
       "\n",
       "            NOX_GT  PT08_S3_NOX       NO2_GT  PT08_S4_NO2   PT08_S5_O3  \\\n",
       "count  7396.000000  8991.000000  7393.000000  8991.000000  8991.000000   \n",
       "mean    242.189292   835.493605   112.145137  1456.264598  1022.906128   \n",
       "std     206.312007   256.817320    47.629141   346.206794   398.484288   \n",
       "min       2.000000   322.000000     2.000000   551.000000   221.000000   \n",
       "25%      97.000000   658.000000    77.000000  1227.000000   731.500000   \n",
       "50%     178.000000   806.000000   109.000000  1463.000000   963.000000   \n",
       "75%     321.000000   969.500000   140.000000  1674.000000  1273.500000   \n",
       "max    1479.000000  2683.000000   333.000000  2775.000000  2523.000000   \n",
       "\n",
       "                 T           RH           AH  \n",
       "count  8991.000000  8991.000000  8991.000000  \n",
       "mean     18.317829    49.234201     1.025530  \n",
       "std       8.832116    17.316892     0.403813  \n",
       "min      -1.900000     9.200000     0.184700  \n",
       "25%      11.800000    35.800000     0.736800  \n",
       "50%      17.800000    49.600000     0.995400  \n",
       "75%      24.400000    62.500000     1.313700  \n",
       "max      44.600000    88.700000     2.231000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_air.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>CO_GT</th>\n",
       "      <th>PT08_S1_CO</th>\n",
       "      <th>NMHC_GT</th>\n",
       "      <th>C6H6_GT</th>\n",
       "      <th>PT08_S2_NMHC</th>\n",
       "      <th>NOX_GT</th>\n",
       "      <th>PT08_S3_NOX</th>\n",
       "      <th>NO2_GT</th>\n",
       "      <th>PT08_S4_NO2</th>\n",
       "      <th>PT08_S5_O3</th>\n",
       "      <th>T</th>\n",
       "      <th>RH</th>\n",
       "      <th>AH</th>\n",
       "      <th>HOUR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>18.00.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1692.0</td>\n",
       "      <td>1268.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>48.9</td>\n",
       "      <td>0.7578</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>19.00.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1292.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>955.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1559.0</td>\n",
       "      <td>972.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>47.7</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>20.00.00</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>939.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>1555.0</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.7502</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>21.00.00</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1376.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>948.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.7867</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>22.00.00</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>836.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1205.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>59.6</td>\n",
       "      <td>0.7888</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TIME  CO_GT  PT08_S1_CO  NMHC_GT  C6H6_GT  PT08_S2_NMHC  \\\n",
       "DATE                                                                      \n",
       "10/03/2004  18.00.00    2.6      1360.0    150.0     11.9        1046.0   \n",
       "10/03/2004  19.00.00    2.0      1292.0    112.0      9.4         955.0   \n",
       "10/03/2004  20.00.00    2.2      1402.0     88.0      9.0         939.0   \n",
       "10/03/2004  21.00.00    2.2      1376.0     80.0      9.2         948.0   \n",
       "10/03/2004  22.00.00    1.6      1272.0     51.0      6.5         836.0   \n",
       "\n",
       "            NOX_GT  PT08_S3_NOX  NO2_GT  PT08_S4_NO2  PT08_S5_O3     T    RH  \\\n",
       "DATE                                                                           \n",
       "10/03/2004   166.0       1056.0   113.0       1692.0      1268.0  13.6  48.9   \n",
       "10/03/2004   103.0       1174.0    92.0       1559.0       972.0  13.3  47.7   \n",
       "10/03/2004   131.0       1140.0   114.0       1555.0      1074.0  11.9  54.0   \n",
       "10/03/2004   172.0       1092.0   122.0       1584.0      1203.0  11.0  60.0   \n",
       "10/03/2004   131.0       1205.0   116.0       1490.0      1110.0  11.2  59.6   \n",
       "\n",
       "                AH  HOUR  \n",
       "DATE                      \n",
       "10/03/2004  0.7578    18  \n",
       "10/03/2004  0.7255    19  \n",
       "10/03/2004  0.7502    20  \n",
       "10/03/2004  0.7867    21  \n",
       "10/03/2004  0.7888    22  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Split hour from time into new column\n",
    "df_air['HOUR']=df_air['TIME'].apply(lambda x: int(x.split('.')[0]))\n",
    "df_air.HOUR.head()\n",
    "df_air.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO_GT</th>\n",
       "      <th>PT08_S1_CO</th>\n",
       "      <th>NMHC_GT</th>\n",
       "      <th>C6H6_GT</th>\n",
       "      <th>PT08_S2_NMHC</th>\n",
       "      <th>NOX_GT</th>\n",
       "      <th>PT08_S3_NOX</th>\n",
       "      <th>NO2_GT</th>\n",
       "      <th>PT08_S4_NO2</th>\n",
       "      <th>PT08_S5_O3</th>\n",
       "      <th>T</th>\n",
       "      <th>RH</th>\n",
       "      <th>AH</th>\n",
       "      <th>HOUR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>2.6</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1692.0</td>\n",
       "      <td>1268.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>48.9</td>\n",
       "      <td>0.7578</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1292.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>955.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1559.0</td>\n",
       "      <td>972.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>47.7</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>2.2</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>939.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>1555.0</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.7502</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>2.2</td>\n",
       "      <td>1376.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>948.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.7867</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>1.6</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>836.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1205.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>59.6</td>\n",
       "      <td>0.7888</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CO_GT  PT08_S1_CO  NMHC_GT  C6H6_GT  PT08_S2_NMHC  NOX_GT  \\\n",
       "DATE                                                                    \n",
       "10/03/2004    2.6      1360.0    150.0     11.9        1046.0   166.0   \n",
       "10/03/2004    2.0      1292.0    112.0      9.4         955.0   103.0   \n",
       "10/03/2004    2.2      1402.0     88.0      9.0         939.0   131.0   \n",
       "10/03/2004    2.2      1376.0     80.0      9.2         948.0   172.0   \n",
       "10/03/2004    1.6      1272.0     51.0      6.5         836.0   131.0   \n",
       "\n",
       "            PT08_S3_NOX  NO2_GT  PT08_S4_NO2  PT08_S5_O3     T    RH      AH  \\\n",
       "DATE                                                                           \n",
       "10/03/2004       1056.0   113.0       1692.0      1268.0  13.6  48.9  0.7578   \n",
       "10/03/2004       1174.0    92.0       1559.0       972.0  13.3  47.7  0.7255   \n",
       "10/03/2004       1140.0   114.0       1555.0      1074.0  11.9  54.0  0.7502   \n",
       "10/03/2004       1092.0   122.0       1584.0      1203.0  11.0  60.0  0.7867   \n",
       "10/03/2004       1205.0   116.0       1490.0      1110.0  11.2  59.6  0.7888   \n",
       "\n",
       "            HOUR  \n",
       "DATE              \n",
       "10/03/2004    18  \n",
       "10/03/2004    19  \n",
       "10/03/2004    20  \n",
       "10/03/2004    21  \n",
       "10/03/2004    22  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_air.drop('TIME', axis=1, inplace=True)\n",
    "df_air.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of missing values:\n",
      " CO_GT           1647\n",
      "PT08_S1_CO         0\n",
      "NMHC_GT         8104\n",
      "C6H6_GT            0\n",
      "PT08_S2_NMHC       0\n",
      "NOX_GT          1595\n",
      "PT08_S3_NOX        0\n",
      "NO2_GT          1598\n",
      "PT08_S4_NO2        0\n",
      "PT08_S5_O3         0\n",
      "T                  0\n",
      "RH                 0\n",
      "AH                 0\n",
      "HOUR               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Count of missing values:\\n',df_air.shape[0]-df_air.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_air['DATE']=pd.to_datetime(df_air.DATE, format='%d/%m/%Y')   #Format date column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the index as date\n",
    "#df_air.set_index('DATE',inplace=True)\n",
    "#df_air.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_air['MONTH']=df_air.index.month     #Create month column (Run once)\n",
    "#df_air.reset_index(inplace=True)\n",
    "#df_air.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>CO_GT</th>\n",
       "      <th>PT08_S1_CO</th>\n",
       "      <th>NMHC_GT</th>\n",
       "      <th>C6H6_GT</th>\n",
       "      <th>PT08_S2_NMHC</th>\n",
       "      <th>NOX_GT</th>\n",
       "      <th>PT08_S3_NOX</th>\n",
       "      <th>NO2_GT</th>\n",
       "      <th>PT08_S4_NO2</th>\n",
       "      <th>PT08_S5_O3</th>\n",
       "      <th>T</th>\n",
       "      <th>RH</th>\n",
       "      <th>AH</th>\n",
       "      <th>HOUR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>2.6</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1692.0</td>\n",
       "      <td>1268.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>48.9</td>\n",
       "      <td>0.7578</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1292.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>955.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1559.0</td>\n",
       "      <td>972.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>47.7</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>2.2</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>939.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>1555.0</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.7502</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>2.2</td>\n",
       "      <td>1376.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>948.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.7867</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/03/2004</th>\n",
       "      <td>1.6</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>836.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1205.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>59.6</td>\n",
       "      <td>0.7888</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CO_GT  PT08_S1_CO  NMHC_GT  C6H6_GT  PT08_S2_NMHC  NOX_GT  \\\n",
       "DATE                                                                    \n",
       "10/03/2004    2.6      1360.0    150.0     11.9        1046.0   166.0   \n",
       "10/03/2004    2.0      1292.0    112.0      9.4         955.0   103.0   \n",
       "10/03/2004    2.2      1402.0     88.0      9.0         939.0   131.0   \n",
       "10/03/2004    2.2      1376.0     80.0      9.2         948.0   172.0   \n",
       "10/03/2004    1.6      1272.0     51.0      6.5         836.0   131.0   \n",
       "\n",
       "            PT08_S3_NOX  NO2_GT  PT08_S4_NO2  PT08_S5_O3     T    RH      AH  \\\n",
       "DATE                                                                           \n",
       "10/03/2004       1056.0   113.0       1692.0      1268.0  13.6  48.9  0.7578   \n",
       "10/03/2004       1174.0    92.0       1559.0       972.0  13.3  47.7  0.7255   \n",
       "10/03/2004       1140.0   114.0       1555.0      1074.0  11.9  54.0  0.7502   \n",
       "10/03/2004       1092.0   122.0       1584.0      1203.0  11.0  60.0  0.7867   \n",
       "10/03/2004       1205.0   116.0       1490.0      1110.0  11.2  59.6  0.7888   \n",
       "\n",
       "            HOUR  \n",
       "DATE              \n",
       "10/03/2004    18  \n",
       "10/03/2004    19  \n",
       "10/03/2004    20  \n",
       "10/03/2004    21  \n",
       "10/03/2004    22  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_air.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_air.drop('NMHC_GT',axis=1,inplace=True)    #drop col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_air['CO_GT']=df_air['CO_GT'].fillna(df_air.groupby(['DATE','HOUR'])['CO_GT'].transform('mean'))\n",
    "df_air['NOX_GT']=df_air['NOX_GT'].fillna(df_air.groupby(['DATE','HOUR'])['NOX_GT'].transform('mean'))\n",
    "df_air['NO2_GT']=df_air['NO2_GT'].fillna(df_air.groupby(['DATE','HOUR'])['NO2_GT'].transform('mean'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Left out missing value: CO_GT           1647\n",
      "PT08_S1_CO         0\n",
      "C6H6_GT            0\n",
      "PT08_S2_NMHC       0\n",
      "NOX_GT          1595\n",
      "PT08_S3_NOX        0\n",
      "NO2_GT          1598\n",
      "PT08_S4_NO2        0\n",
      "PT08_S5_O3         0\n",
      "T                  0\n",
      "RH                 0\n",
      "AH                 0\n",
      "HOUR               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Left out missing value:',df_air.shape[0]-df_air.count() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_air['CO_GT']=df_air['CO_GT'].fillna(df_air.groupby(['HOUR'])['CO_GT'].transform('mean'))\n",
    "df_air['NOX_GT']=df_air['NOX_GT'].fillna(df_air.groupby(['HOUR'])['NOX_GT'].transform('mean'))\n",
    "df_air['NO2_GT']=df_air['NO2_GT'].fillna(df_air.groupby(['HOUR'])['NO2_GT'].transform('mean'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zcvo6t7PQoDYXHzx17eTYpW5noUFd++E4t7PQYLwlbuegYfnD3M5Bw4nrucftLDSoBSc9/Ke4GGjMa2NP8tomuU00QqaUUkoppZRSLtEImVJKKaWUUsoVFo3X46qpRqKaar6UUkoppZRS6qCnETKllFJKKaWUK/ym8SJkTbXhoxEypZRSSimllHJJU20oKqWUUkoppQ5yFgfNAOS/m0bIlFJKKaWUUsol2iBTSimllFJKKZdol0WllFJKKaWUKxpz2PumSiNkSimllFJKKeUSjZAppZRSSimlXOE3OqiHRsiUUkoppZRSyiUaIVNKKaWUUkq5Qoe91wiZUkoppZRSSrlGI2RKKaWUUkopV/g1QqYNsoPN3f+CmT9Dyzj4/HW3c1O35Qvgf5M8WH445iTDyedUPigzd8Orj3soLADLgrMut+jZv/L8CeM8jLnQcOJYdw/oxfO9vPZCGJYFw08q44zzfJXm79klPPdYOAX5YFnChVeUcuQAP2Vl8OKTYWxa58VvwXEnlHHm+b4a1tJ4lizwVCrP6eeWVZqfsVt4/rEwCvIFy4Lz/17KkQMsZk3zMvWD0PJ0WzcJj75QTGon9/bPmoV+pkzyYSzoP8rLsLNDK82f+lIpG36zh931lUB+tuH+jyIB+PIVH6vn+zEGOvfxMOaqUESk0ctQ1VUPjKXf8O6UFJXy5E1vsWHZtmppQkK9XPPw2RwxsDPGGN741+fM+XIJiW3iuPXfFxMdE4nH6+G1h6awYPoKF0oBW38tYs7kLIwFh42Ips9ZLaqlWT+7gEXv5YBAfGooJ9yaCMC8N/ayZVERAEed3YJOQ5o1at7rsmdJDqve2A4WtD0+nkNPS66WJu3nvaz/KA0RaN4+kl43HOJCToNbudDwyYsWlgUDRwkjzqncqSZrt+HtJyyKCsD44dTLPXTvLxTkGl550GLrWhgwQhh7bdPojHPsIR24Z/hQvOLhg9+W89IvCyrND/N6eXz0ifRo1Yq9RUXcOPUrduTm0iYmhm//fgkbs7IAWJKWzoTvpjVavu8+cSjHdTqEYp+PO6d+x8r03dXSdE9O4pHTTiQiJIQf12/ioW9nAtAiIpynzxpNmxYx7MjJ5aaPvyS3uIRD4+N4eMxIuicn8fSMubw6b1H5sqZdfzkFpT4sy8JvGc565d0DXsbyfePx8MHSWvZNsrNvptj7Zp+U5s355oqL+c+cebwyf1HVxbtmYEJnbj3sFDziYcr2Bbyx8aeg6Y5P7sGjfc7n4jnPsyp3RyPnUjUF2iA7yJx+Epx/Jtz5sNs5qZvlh3ee93DLIxZxCfDg9R56H21o3aEizZfvCn2PNQw71bBzC/z7Xg8936z4vYr3J3no0c+FzFfh98PkZ8OY8GgxLRMNd14bQd9BZbTrUNEI+fidUAYdV8aJY8rYtkV4eHwEL75TxM8/evH5hKcmF1FSDDf9PZIhx5eRlOxeA8bywyvPhnHPoyXEJxjuui6CvgP9tK1SnoHH+Rl5ahnbtwiP3B3OkQOKOWa4n2OG+wG7MfbYhHBXG2OW3/Dp8z7GPRxGiwTh2RtLOHyAl1YdKi4Sx/wjrPz/OVPK2LHBrmObV/rZvNLi5hfCAXjhthI2LrPo2NPbuIWoot/x3Wl9aCJ/HzSRbkemct2/zuXm0Y9XS3fujaPIzshj3JD7ERGax0UBcN5No5g19Ve+fHMW7bskc//b13Bp/wmNXQwsv2H2S1mccl8SzeJD+OT2NDr0j6Rlu4r9kb3Tx+KPczj9X60Ij/ZSlG3XrS0LC9mzsZSxT6fg9xmm3r2L9kdGEhbVNC7+jWVY+eo2+t3dmYj4UH4ev4ako1oQ3TayPE1BWjEbp6Rz9H1dCI0OoSTH/Rsx+1h+w4fPW1z7sIfYBHjiBoseRxtSOlTcjPjuf4Y+xwrHnOIhbYvhpXstur/pJSQMRl9sT0vb7FoRKvGIMPGE47nkg09Iz8vjk4vPZ9r6DazPzCpPM/aI7uQUlzD8v68xulsX/m/oEG6c+hUAW7OzGfPGO42e72M7pZLaMpaRz79GrzbJTDz5eM5+9b1q6SaePJwJX/zAkh1p/Pe80zm2Yyo/bdjMlYP78/Ombfx37gLGDerHlYP78cS02WQXFfPQNzMZ3q1j0PVe8uaH7C0qPtDFA5x9M+J4Lnnf2TeXBNk3PZ198/JrjD6s8r4BuHv4cfy0cXOj5Le+PAj/130M181/lV3Fubwx6Bp+2r2aTfmVG9RR3jDO6TCQZdlbXcqp+/QZsoP0GTIRSRaR90Rkg4isFJGvRKSLiHQXkekislZE1onIvVLHrW4RGSUi80VktYgsEZH3RaS9iDzvvF8pIkXO/0tE5G+NVc5g+vWC2OZu5qD+Nq2BpNaQmAIhodB/qGHJz1V2h0Bxof1vUQHEtqyYtXguJKYYWndw/0Bev8ZDcmuLVq0NoaEweKifBXMq3+8QgcJCu3yFBUJcvCmfXlJsN+pKSyAkBCKj3C2TXR5DqxRDSCgMGlrGgrmVGyEihsIC+//A8gSaPT2EwcP8jZHlGm1ba5HQWohP8RASKvQ6zsuKeTXnacmPfnoPtcsqAmWlBn8ZlPnsfRQd63507OhRPZn24S8ArP51M9ExkcQlxVRLN/Lcgbz/n+8AMMaQm1Xg/A9RzSMAiGoeSWZ6TiPlvLLd60qJSQkhJjkUb6jQcUgzNv9SVCnNqu/y6XFyc8Kj7X0SGWv/3bvNR+vu4Xi8QmiEh/jUULb+WlRtHW7JXl9AVHI4Ua3C8YR4SB4Ux66Flbfz9ukZtB+ZSGi0fa4IbxEabFGu2LLGPjcnpAghocKRxwnLfq5+jO87PxcXQEy8/X94hNCxhxDadIpDr5RktmRnsy0nB59l8eWqNZzQqXJj5ITOHfl0+UoAvlmzjoHt27uR1UqGd+nIZ7+tAmDpjnRiIsJJjK4cCU6MbkZ0eBhLdqQB8Nlvqxje1S7b8K6H8tlvK53pKznBmZ5VWMSytF2U+d3/Qd6g+6ZzLftm9ToGdmhfad627BzWZWQ2ar7r0j22LdsKMtlRtJcy4+f7tN84Lumwaumu6jKCtzb+RKm/LMhS1F/FQRchcxpYnwJvGGPOdab1BloBrwNXG2O+E5Eo4GPgGuD5GpbVA3gWGGOMWeVMGwOkGmOudd6nAl8YY3ofwGIdlPZmQlxixRd8XAJsXF05zZgLDU+P9zB9qlBSDLf8y/7yKCmGrz+wo2vffuT+BXJWhpCQVFGW+ETDutWV73ecfbGPB+6I4OvPQigpFiY8Zt99PPpYP/PnhjDu7ChKSuDSq0ppXv3aulFlZQjxAfsmPqF6ecZe5OPBOyP4ZopdnnsfrX439ecfvdx+X8kBz29tcjKgRWJFHWmRIGxbE/wiZO8ui6x0i0697LJ2OMxLx54WD1xQDAYGnRpCq/bu38eKT25Bxs7s8vcZadkkpMSyd3dFF55mMXYk5uI7TqHnoM6kbc7ghfEfkJ2Rx9tPfMlD713HmMuPIzwqnPHn/KfRywBQkFVGdELF11B0vJdd60orpcnZaUeNPr0zHWMZ+p4bS/sjI4lPDWPh+zn0PM2irMSwY3kJcQGRNbeVZPmIjK/IT0TLUHLWF1ZKU5BmHxvzJqzBWIZOf0shsXf1LptuyM6E2IDjJjbBbqQFOulC4YW7LX6a6qe0GK59xP1joyatoqNJy8srf5+el0+v1snV0+TaafzGkF9SQlykfeOibYsWTL3kAvJLS3lq1lwWbm+cbmWtmkeTnhuQ79x8WjWPZk9+QZU0+dXSAMQ3iypPuye/gJZRUXWv1MArF5yJAd5ftIwPFi9rmMLUoFXziu0Ozr5JCbJv8qrvm+KyMv4xoC+XvP8JV/Q/6oDmc38lRrRgV3HFTZhdxTn0iG1XKU2XmBRaRbRg9p41XHjIMY2dxSZDf4fs4IyQDQN8xphJ+yYYY5YAXYA5xpjvnGmFwHXAnbUs6w7g4X2NMedzU40xwTsB10BErhSRhSKy8OW33LkT3SQFOf6qxivnzxQGjTA8/o7FjQ9YvPKYB8uCKW8KI84wRERWX4Ybgp1LqjYTZ8/wMvREHy+/V8T4h4t59l/hWBasX+3B44GX3y/khbcK+fyjUHbtdLeRGbQ8VbI0Z0YIQ0eWMel/xdz1UAnPPmqXZ591qzyEhUP7Q/48J9olP/o5YogXj9cubMZOi93bLO5+K4K7345g/VI/G5e5G/EDgj7DZqrsNG+Ih8Q2caxcsJHrRz7KqkWbuOKfZwIw9Iy+/PD+L1x01D1MuPAFbn/2Eneei6vHcWNZkJPmY8yDrTjh1gR+fD6TknyLdn0iaX9UJJ/dkc4PT2bQqmsY4m5P0rpVKZzxGwrSS+g/oQu9bjiE5S9vxVfQRO6S1+McsGimYcAI4YG3vVx1v4e3HrewrKZ5vAer3lWPmeDHFewpKODYSZMZ88Y7PDT9R54+5SSiwxqn8V+vfAf53B/ZC+e9/j5nTn6Xce9+ygX9etG3fZs/sLS6Bc9//fbNjUMG8trCxRT6mk53332ClssEzhdu6TaaZ1Z/FSSl+qs56CJkQA8g2BOd3atON8ZsEJFoEYkxxuTW8Jkn/miGjDEvAy8DWOldmua3lQviEmDvHmHfV8feDIiNr5xm9jfCTQ/ZV/kdDwdfKeTnwqbVwqLZ8NErQmG+/aUVGgbHn+bO5o1PNGTsrjj9Zu6p3oVv2teh3POIHUXqerhFaSnk5cCs6SH06ecnJARaxEHX7n42rPXQqrV7F/7xiYbMPQHlyahenunfhDD+YfsOf5fDLXxOeVrE2fPnzPQyeJj7F5ctEiBnT0XeczIMMfHBGx9Lf/Rz+rUV/ayWz/XTvpuH8Eg7fde+Xrautjj0iMa/8j/l0mMZdcFgANYu3UJC69jyeQkpsdW6HeZmFVBcWMLcr5YCMOvzXznxvEEAnHjeIO45/zkAVi/aRGh4KDEtm5GTmU9jahYfQn5GRR3Jz/QT1bLyto2O95LUJRxviBDTKpTY1qHkpPlI6hzOUWNbcNRYO6L0w5N7aJHSdPrIhbcMpSizItpXnOUjPK5y/iLiw2jRqRmeECEqKZxmKREUppfQoqP7X82xCZAdcNxkZ0BMy8pp5n1ruPoh+77uIYcLZaVQkAvNY2ly0vPySWle0Z8/uXk0uwOiTHaaPFJimpOen49XhOjwcLKL7XN2qd8+H6/YtZut2dmktoxjefquA5LX8/v24uw+PQBYtnMXyTEB+Y4Jlu98kmOiK6fJs4/lzIJCEqObsSe/gMToZmQVVo7SBrNv+VmFRXy/ej09WyezcOuBiwim5+WTElOPfdO8Oel5lfdNr5QURnXtzP8NHUJMeDiWgdKyMt76dekBy2997S7OoVVERcS7VUQLMkoqLjWjQsLo2LwVk/qPAyA+PJonj7qIWxe99Zcb2MP9jrPuOxgjZDWpuPKvrs6reBGJd54RWysitzVs1v6aUrvCrh2wJ91+Pmf+TKHX0ZV3RcskWLXEvhjeudVukDVvAXc8ZfHom/brhDMMo881rjXGADp1tUjb4WFXmuDz2Y2RfoMqN0YSkiyWLbYvNrdvsdPFxEJCkmH5Eg/GQHERrFvlpXV7d09PHbtapO0QdqcJZT6YOzOEvgMrNxATkgzLF9unkO1bBF+pXR6woxrzfvK6/vwYQNsuHjJ2GrLSLcp8hqU/+jn86OoNqt3bLYryocNhFafF2ERh4zILv9/gLzNsWmaR1M6d0+YXr//EdSMe4boRj/Dz10sZPnYAAN2OTKUgr6hSd8V9fvluGT0HdQag95BubF1rP2Oye0cWvYd0A6Bd51aEhYc0emMMIKlzGDlpZeTu8uH3GTbMLiC1f+Wwd+qAKHYuty+Ki3L9ZO/0EdMqBMtvKM6161fm5lIyt/ho1yei0ctQkxYdm1GYXkLh7hKsMov0uXtJOqpyd8Skvi3IWml3wyrNLaMwrZjIpHA3sltN+66wZydkphvKfIZffzQccXTlGxlxSbB2sX3eTd9q8JVCdNPocVnNb2npdIiLo22LGEI9HkYf1pVp6zdWSjNt/UbO6HE4AKO6dmbeVnvk0paRkXicCE27Fi3oEBfHtuxsDpR3Fy7l9P++w+n/fYcf1mzg9J72c0e92iSTV1wvYd8AAAAgAElEQVRaqbsi2F0RC0pL6dXG7uZ3es/DmLZ2AwDT12zk9J6HO9MPZ9qaymWuKjI0hGZhoeX/Dz60A+v2ZDRo+aqq175ZF7BvulXsm/Pe/YChk15l6KRXeX3hYl6cN79JNMYAVubsoH2zBFpHxhEiXkak9OSn3eUdrigoK2HEtIc47cfHOe3Hx1meve0v2RhTNvdvwzW8FUCwgTVWAMcGThCRQ4F8Y0xekPT7PnMksNQYkwn0dhpj0TWkd92t98H8JZCdA0P/BtddBn8b7XaugvN64fxrLZ4Zb3dDHDzS0CYVPntDSO1i6D0Qzr7S4o1nPHz/iSACl99mBe3C4TavF664vpQH74zAsuD4UWW0SzW893ooHbtY9Bvk55KrSpn0VDhffByCCFx7eykiMOo0H88/Hs7NV0SCgWEnlpF6qLuBVK8XLr+ulIfusrshDjvRLs/7Tnn6DvJz8T9KeempML78xP7yvsYpD8CqZR7iE+xBQdzm9QqnXR3K5HtKsfzQb6SX5A4evn3TR9suHro7jbMlM/30Os5bqWtMzyFeNiy1ePpqOxLYta8naGOusS2YtoJ+w7vz6s8TKS4q5emb3y6f99z3d3HdiEcAePWhKdz27CX84/6/kZOZz1M3vwXA5Ps+4YbHz+eMK4dhDDx101uulMPjFYaMa8mX9+3G+KHrCdG0bB/GgnezSewURmr/KNr1iWD7kiLev24n4oGBl8YREeOlrNQwZbwdoQiNEobflFDe1bQp8HiFwy9rx8KH12MsQ9th8TRvF8m6D3bS4tAokvrGktArhozf8ph160rEA10vbENY86bxtez1Cn+7xsMLd9vD3h89UkhJFb5806J9Z+GIgcLp4zy892+LGZ/6EYELbvWUHz8TL/ZTXAhlZfDbz36uechTaYTGxuY3hvt+mM5rY8/EK8KHy1awLjOTG4cMZHn6Lqat38gHvy3nydGjmDbuMrKLi7nJGcWvX7s23DRkEGWWhWUsJnw3jZzixnk29sf1mziuUyrfX3sZRWVljJ/6Xfm8z8ZdwOn/tUd+nPjVdB4ZM5KIkBB+2rCZn9ZvBuDluQt45qzR/K13d9Jy87jxoy8ASGgWxcdXnE90eBiWMVwyoA8nv/gmcVGRPH/2qQB4PR6+WL6aWRu2HNAy+o3hvu+n89rZAfsmI8i+OWUU0668jOyiin3TlPmNxWMrp/KffpfhFWHq9kVszN/NPzqfwKqc7fy0e3XdC/mL0N8hA6naF/nPzhnUYx4w2RjzX2daPyAKeA240hjzg4hEAh8C3xpjnq1hWUdgDxByasCgHhMAjzFmovM+FXtQjx71yd/B1GVxTvHBE2SO9TbO8L6NxTJN58L0j9pcFl93oj+RSYMHu52FBjNy+jq3s9CgNhcfPHXt5NimESVoKNd+OM7tLDQYr7vjHDU4f9MZS+cPi+u5x+0sNKgFJz38p7gY2Laj8e7etmuT1iS3yUHXZdHYLcwzgBHOsPcrgInATuA04B4RWQMsAxYAz9WyrGXAjcCbzrD3c4DDgAP/K4lKKaWUUkqpg17T6BvRwIwxO4Gza5g9dD+X9SXwZS3zN2MPJKKUUkoppZTaD/4m1ndMREYB/wa82D3u/lVlfgfgVSARyAIuNMZs/yPrPOgiZEoppZRSSim1v0TEi/37xCcBhwPnicjhVZI9AbxpjOkJ3A888kfXe1BGyPaXiFyG3TUx0Jx9P/6slFJKKaWUanhNbESC/sB6Y8xGABF5D/uRp5UBaQ4Hbnb+nwF89kdXqg0ywBjzGvaAH0oppZRSSqm/pjbAtoD324EBVdIsBc7C7tZ4BtBcROKdEdl/F+2yqJRSSimllHKFH2m0l4hcKSILA15XVslOsFEYqz7ldhtwnIgsBo4DdgBl1T61HzRCppRSSimllDroGWNeBl6uJcl2oF3A+7bYI7UHLmMncCaAiEQDZxljcv5IvrRBppRSSimllHKF1bRGWVwAdBaRQ7AjX+cC5wcmEJEEIMsYYwF3YY+4+Idol0WllFJKKaXUX54xpgy4DvgWWAV8YIxZISL3i8gYJ9lQYI2IrAVaAQ/90fVqhEwppZRSSinlCn/Qx7bcY4z5CviqyrQJAf9/BHzUkOvUCJlSSimllFJKuUQjZEoppZRSSilXNLUImRs0QqaUUkoppZRSLtEImVJKKaWUUsoVltEImUbIlFJKKaWUUsolGiFTSimllFJKuUKfIdMGWaObU2y5nYUGMzji4AmwrvY1rV8l/KPC5OCpZ+tKWrmdhQblz9zrdhYaTFppC7ez0KDahGe7nYUGc2jowVOWg01p7MFzfgYwB8+lAPnF4W5nQf1FHUSHkVJKKaWUUkr9uWiETCmllFJKKeUKv8aHdAsopZRSSimllFs0QqaUUkoppZRyhQ57rxEypZRSSimllHKNRsiUUkoppZRSrtBh7zVCppRSSimllFKu0QiZUkoppZRSyhX+g+nH7H4n3QJKKaWUUkop5RKNkCmllFJKKaVcYWl8SLeAUkoppZRSSrlFI2RKKaWUUkopV+goixohU0oppZRSSinXaIRMKaWUUkop5QodZVEjZEoppZRSSinlGo2QKaWUUkoppVxh6TNk2iD7s1m+AP43yYPlh2NOMpx8jqk0P3M3vPq4h8ICsCw463KLnv0rz58wzsOYCw0njjU0ZXf/C2b+DC3j4PPX3c5N3X6d7+WVF8KxLDjhJB9nneerNH/PLuE/j4VTkC9YFlx0RSlHDfBTVgbPPxnOxnUe/JYw7AQfZ53vq2EtjWfRfC+TX4jAb8HIk3z87bzSSvP37BKeeSyCfKc8l1xRQt8Bfnw+eOGZCNav8SAeGHdNCUf09rtUCtu2Xwv5eXIWxoKuI6LpfVZstTQbZhfw63vZIBCfGsbxtyYC8MsbWWxbVARAn7Nj6TikWaPmvSbXPHUx/Ub1pqSolCf+Pon1SzZXmh8ZHcFTMyaUv09oE8+0d2cz6ba3uOrxC+k19HAAwqPCiU2M4cykcY2Z/XK7luSx7LUdYEH74S3pcnpStTQ75maz+sNdiEBMh0j63tiewj2lzH9iC8YyGL/hkFEJHDIy3oUSVLb910LmvZKJZRm6nhBDryB1beOcfBa/txcEWqaGMeyWVgDMfyOTbYsKMRa06R3J0X+PR6RpXKgsmu/l5ecjsCwYebKPsVXOB7t3Cc88HkFuthAdY7jtrmISEt3/jjn2kA7cM3woXvHwwW/LeemXBZXmh3m9PD76RHq0asXeoiJunPoVO3Jzy+enNG/ON3+/mP/MmccrCxYBcMlRfTinZw9EhPeXLuP1RYsbtUwAx7ZP5Z/HDsMjwvsrlzNp0fxK8/u3bsO9xwyjW0IiN3zzBV9vWFc+7/UxZ9InOYUFO3dwxRefNXbWgzqufSoThgzD67HL8+KvVcqT0oYJxwyjW3wi139XuTwA0aFh/HD+pXy7cT3/nDW9MbNezZCkjozvOQqPePhoy69MXjun0vxzUo/i/EP74TeGQn8p/1z8ORvyMjgirjX39T4VABF4ftWP/JC22o0iKBdog+xPxPLDO897uOURi7gEePB6D72PNrTuUJHmy3eFvscahp1q2LkF/n2vh55vWuXz35/koUc/FzL/O5x+Epx/Jtz5sNs5qZvfDy8/G87ER4uITzT837WR9B9URrsOFRckH74TxuDjyhg1poxtW4QHxkfy8juFzP0xhDIf/HtyESXFcP3fozjm+DKSkt27mPH74aVnI7j/0ULiEw23XhtF/0FltO8QUJec8pw8xsfWLR7uHx/J5HcK+O6rUACenVxI9l7hvvGRPPl8IR6XOkhbfsOcl7I4+b5WNIsP4bPbd9KhfxRx7cLK0+Ts9LH04xzG/CuZ8GgvRdl2A3LrwkIyN5Zy5tOt8fsMX9ydTrsjIwmLcre3d79RvWnTKZnLDr+Fbv07ccNzl3PDkAmV0hTlF3N1v/Hl75+f9xBzPrMvRifd/nb59NOuGUnH3qmNku+qjGX47ZUdDLrnECLjQ/nxrvUk940hpm1EeZr8tBLWfbabYx7oSFh0CCU5ZQBExIVwzIMd8YZ6KCv2M/3WtST3jSGyZagrZQG7rs19OYNRE1NoFh/C1P/bQfugdS2bUx5pXamu7VpdzK7VxZzxdFsAvhi/k/QVxaT0iHSlLIH8fnjxPxE8+Jh9Prj5migGDCyjfWrF+eCVSeEMH+Fj+IllLF3s5Y3J4dx6V7GLuQaPCBNPOJ5LPviE9Lw8Prn4fKat38D6zKzyNGOP6E5OcQnD//sao7t14f+GDuHGqV+Vz7/7+OP4aePm8vedE+I5p2cPznzrf/j8fl4deyYzNm5iy97sRi3X/UOHc9FnH5Gen8eUcy7gh43rWb+3olw78vK4/YdvGHdk32qff/nXhUSGhHBej56NlufaeES4/9jhXDjVLs/UsRfw/abK5dmZn8dt075hXO/q5QG4dcBgftm5vbGyXCMPwr29Tubvc95iV1EuHwwbx4y0NWzIyyhP88X2Zby/2W7cD0vuwh1HnMiVc99hXe5uxs58Gb8xJIZH8+nwq5iRvga/cf/GhjrwDvhVhYj4RWSJiCwXkQ9FpI3zfomIpIvIjoD3YSIySkTWiMh6EbkzYDnDReRXJ91sEelUyzq7ishMJ+0qEXnZmR4vIjNEJF9EnqtH3qNF5CUR2SAiK0TkJxEZ4MxrKyJTRGSdM//fIhJW1zL/iE1rIKk1JKZASCj0H2pY8nOVu6cCxYX2v0UFENuyYtbiuZCYYmjd4c9xcPfrBbHN3c5F/axb4yGltUVya0NoKAwZWsb8OZXvd4hAYaG9vwoKhJbxpnx6cbHg90NJCYSEQGSUu/uoanmOGVrGL0HKU+SUp7CA8vJs2+KhZx/7ojk2ztAs2rB+rXsNmD3rSohJCSEmORRvqNBxSDO2/FJYKc3q7/I4/OTmhEd7AYiMtf/u3eYjuXsEHq8QGuGhZWoY234tavQyVDXo1KP4/p1ZAKyev55msVG0TK4eidmndadkYhNjWDa7+t3WoecMYub7cw9YXmuzd30hzZLDaNYqHE+IhzaDYklfkFspzZZpWRxyYjxh0Xb9C29h//WEePCG2vXK8hmwcJ1d10LL69qhQ5qxdX5BpTRrvs/l8JNiqtU1AH+pwSqzX8ZviGzhpSlYu9pDSpuK88Gxw8qYN7fy+WDbFg+9jrQblz17+6vNd0OvlGS2ZGezLScHn2Xx5ao1nNCpY6U0J3TuyKfLVwLwzZp1DGzfvmJep45sy8lhXWZm+bRO8S1ZkpZGcVkZfmOYv207IzvXeDlyQPRq5ZQr1y7X52vXMOLQynnYkZfL6swMrCAX83O3byXfV1ptult6JyWzJSegPOvWMPKQyuXZ7pTHBClPj8QkEqKimLVtS2NluUY9W7Zha0EW2wuz8RmLr7av4PiUbpXSFJRVbPvIkLDyMhX7y8obX2HekKBlPVj58TTaq6lqjJwVGWN6G2N6AKXAOc773sAk4OmA937geeAk4HDgPBE53FnOi8AFTrp3gXtqWed/ApZ7GPCsM70YuBe4rZ55nwxkAZ2NMd2BS4EEsfuQfAJ8ZozpDHQBooGH6rnc32VvJsQFdAGJS4C9GZXTjLnQMG+6cPsFHv59r4fzrrWvUkqK4esPPJx64V/nAG9MWRlCQlLFto1PNGRmVm4sn3NxKT/+EMIV50bx4PhIxl1XAsDAY8uIiDBcfnYzrrygGaePLaV5TKNmv5rMDA8JSRVXuAmJVrXynHdxKTN/COGyc5tx3/gorrzOvhueeqjFL3ND8PshPU3YsNZLxm73ToIFWX6iEyouDpvFh1CQVbkLZc5OHzk7fEy9M40p/7eTbb/aDbb41DC2/1pEWYlFca6ftOXFFGSUNWr+g4lvHceebRV3jzO2ZxHfOq7G9MPOGcjMD3+uNj2pfQLJqYksmbHigOSzLsVZPiLjKyJakfGhFGdV7q6bv7OE/LRSZt27np/uXs+uJXnl84oySplx21q+u3oVnU5LdDU6BlCYVUazgLoWFR9CQWaQurbTx+d37WDqHTvY7tS1Vt0iSDkikv9dvpV3L99Cm95RxLY7oPf46i0zw0NiYpXzQUbl88EhHS3m/GSX/efZIRQVCrk5jZrNalpFR5OWV1Ff0vPyadU8unqaXDuN3xjyS0qIi4wgMjSEfwzoy7Nz5lVKv3ZPJv3atiU2IoKIkBCGHppKSpVlHmjJzaJJyw8oV34eydGNm4eG1Co6mp0B5UnLz6NVs/qVR4B7Bg/l4bk/HaDc7Z+kiOakF1XcVNpVlEuriOp3ls8/pB/fjrie27qfwMO/fVM+vWdcGz4ffjVThl/NfUu+1OjYX0hjXyXNAmq7ldQfWG+M2WiMKQXeA05z5hlg32VqC2BnLctJAcpj18aYZc7fAmPMbOyGWa1EpCMwALjHGGM5n99ojPkSOB4oNsa85kz3AzcDl4tIVJBlXSkiC0Vk4dR3c6vOrr8gx2XVxwvmzxQGjTA8/o7FjQ9YvPKYB8uCKW8KI84wRLjf++WgFOycWfXJj1kzQjj+xDImv1fIPQ8X8cy/7Ocx1q324PHAK+8XMOmtQqZ8FEb6TnefG6lPeX6aEcLxJ/p47b0C/vlwIU875Rlxko+EBMMt10Qx+YVwunX343HzRn89vs8sC3LSyjjlwWSG3ZrIrOczKcn307ZPJO2OimTKHWlMf3IPSV3D8Xjdf6Yn6HNFtZRz6NkDmfl+9QbZ0LMHMuuT+ViWO1/6Qa81qhTNWIb8tBIG/7MjR93YniWTtuMrsBs5kQlhDHuiC8P/041tP+6lONvlZy/rcY42fshN8zH6gdYMuyWJWS/soaTAT26aj+ztpZw7uT3nTe7AzmVFpK1wPxpbk6rluvwfJSz/zcsN/4hi2VIv8QkWXpcDfEEPkyqVLtixZAzcOHggry1cTKGvcp3akJXFy78s4I1zzuTVsWewak9Go18015TnPysJMqBDfYtz0RG9mbFlU6UGqpvqW5Z3Ny3gxO+f5ckVP3BVt2PKp/+2dwenTnuRs2f+l3FdhhDm6pdn4/EbT6O9mqpG61MgIiHYka9vaknWBtgW8H47dqMI4ArgKxEpAnKBo2tZztPAdBGZC3wHvGaM2d8O3t2BJU5jK9i8RYETjDG5IrIVu8H5W5V5LwMvA8za3Ol3nzbjEmDvHmHf4b03A2KrPMM++xvhpofsO5kdDwdfKeTnwqbVwqLZ8NErQmG+/UUVGgbHn/YnPos3IfGJhozdFSfizD0VXRL3mfZ1CBMese8FdDvcwlcKuTnCT9ND6NPPT0iI3cWvW3c/G9Z6SW7tXiQmIdEiY3dFtCFjj6daeb7/OpSJj9gXjN0OtygtFXJzhNg4wxXXlJSn+78bomjd1r3+ZM3iveQHRLUKMsto1tJbLU2rLuF4QoSYVqG0aB1KbloZiZ299BkbS5+xdnfA6U/uISbFna5Yp141gpP/PgyANQs3ktiuoj9yQtuWZKbtDfq5Q3u2xxviZd3iTdXmDT17IM/d8NqByXA9RMaHUpRZccFblOkjIq5ylCuyZShxXaLwhAjNksKIbh1OfloJcZ2iKqVp3i6CrNUFtD665q6bB1pUfEilCGphZhlRQepaYtcIPCFC8311baePtOXFJHWJIDTSvmBod2QUe9aWkNLd/bto8QkWe/bUfj6ITzDcfZ99fisqgrmzQqhnkOOASc/LJ6V5RXQiuXk0u/MLqqTJIyWmOen5+XhFiA4PJ7u4mF4pKYzq2pn/GzqEmPBwLAOlZWW8tXgpHy5bwYfL7KjyrccMJj2vcRsDafl5pEQHlCu6ObsK8hs1Dw0pPT+P1gHlSYluzu56lufI5Nb0S2nDRT16ERUaRqjXQ6HPx6PzZh2o7NZqV3EuyZEVXVxaRcawu7jm+vHV9uX8s/doYEql6RvzMijyl9I5JokV2WkHKruqCWmMpmKkiCwBFgJbgVdqSRvs1vO+s/7NwMnGmLbAa8BTNS3EiVwdBnwIDAXmiUj4/me91nzWdG/3gLVwUrvCrh2wJx3KfHY0rNfRlVfXMglWLbE3486tdoOseQu44ymLR9+0XyecYRh9rtHGWAPq3NUibYeHXWmCzwezZ4bQb1DltnxCkuG3xfbF2bYtQqkPWsQaEpMMy5Z4MQaKi2DtKi9t2rv7QEznrhY7d3hId8oza2YIAwZVbiAmViqPB59TnpJiuxwAixd58XhNpcFAGlti53By08rI3eXD7zNsmF1A+/6VA9mpA6LYudy+mCzO9ZOz00fzViFYfkNxrr0fMzeXkrWllLZ93LlA/nzS91zdbzxX9xvP3KkLGXGBfVe1W/9OFOQUkZUe/J7T0HMGMSPIM2Jtu6QQHduMlfPWBflU44jtGEVBWikFu0uxyix2zM0muW/l/rrJ/VuQsdy+iC7JLSM/rYRmrcIoyizFX2rXq9L8MrLWFBDduiFP8/vPrms+8py6tnF2Ae37VR6Vs8OAZqQtsw+Q4lw/uTt9NG8VSnRiCOkrirH89jNkaSuKiG3rbhfMfbp0q3w++GlG9fNBTo492irAh++GMWKU+yPF/paWToe4ONq2iCHU42H0YV2Ztn5jpTTT1m/kjB72kxGjunZm3lb7nvB5//uAoS+9ytCXXuX1RYt5cd583lq8FICWUfY5IKV5c0Z26cTnq9Y0Yqngt13ppMbG0jbGLtepXbryw6YNjZqHhrR0dzqpLWJp29wpT+eufL+5fuW56fuvGPzmfxny1mQenvsjn6xe6VpjDGDZ3h10iI6nTVQsoeLh5LbdmZFWuX50aFZxM+245C5sybe7n7eJisXrRD9bR7bgkOgEdhQ23mAxbrLwNNqrqWqMW71FznNf9bEdaBfwvi2wU0QSgV7GmF+c6e9Te6QNY8xO4FXgVRFZDvSgSlSrDiuAXiLi2ddlscq8swIniEiMk/cDdlb0euH8ay2eGW93Qxw80tAmFT57Q0jtYug9EM6+0uKNZzx8/4kgApffZgXttvFncOt9MH8JZOfA0L/BdZfB30a7navgvF4Yd30J990ZiWXB8FE+2qdavPt6GJ26+Ok/yM9lV5XwwlMRfP5xKAjccHsJInDSaT6efTyCG6+IxBjh+BN9pB7qboPM64V/XF/MxDuj7GH8nfK845RnwCA/l19VwnNPRTDl4zBE4MbbixGB7Gxh4p1RiMcQH2+45U6XR1rzCoPGteTr+3Zh/ND1hGhatg9j4bt7SewUTof+UbTtE8n2JcV8eN0OxAMDLo0jIsZLWanF5+PTAQiLEobdlNAkuizO/3oJ/Uf15vVVT1NSVMITV7xUPu/FBQ9XGl3xuLOO5p7THqu2jGHnDAr6XFlj8niFnpe35ueHNmIsaD8sjph2Eax6P53YjpGk9G1BUq9o9izNY9rNaxCP0P3CFMKah7D7tzxWvLm5/DZYp1MTiWnvbjTJ4xUGjkvgm/vSMZahy/DmxLUPY9G7WSR0CqdD/2a06RPJ9iVFfHz9NsQD/S6JJyLGS+rAZuxcVsQnN25HBNr0iarWmHOL1wtXXV/MhDuiyrsld0i1ePu1MDp3tc8Hy5Z4eeOVcATo0bOMq28oqXO5B5rfGO77YTqvjT0TrwgfLlvBusxMbhwykOXpu5i2fiMf/LacJ0ePYtq4y8guLuamgBEWa/L8aacSFxmBz7KY+P10cksat6x+Y/jnj9N5c8xZeDwePly5nHVZmdw8YBDLdu/ih00b6JnUikmjT6NFeATDUzty04BBnPjuGwB8cNY5HBrXkmahocy97ErunPYtP211b0AMvzFMmGWXxysePljllKe/U57NdnleOskpzyEdubn/IEb+7w3X8lwTvzE8uPQrJg++EA/CJ1uWsD5vD9cfNpTle3cyI30t5x/an0FJh+CzLHJ9Rdy1yP7pgaPi2zOuy2B8loXBcP/SL8kubbrdllXDkgM9iouI5BtjgnZcEJGJQL4x5gnnfQiwFhgO7AAWAOcDa4B0YJAxZq2I/B07WnZWDcsdBUwzxvhEJBlYDPQxxqQ78y8F+hpjrqsj7x84655gjDEi0hl7sJGpTt7+Y4x5U0S82AOU5Bpjbq1tmX+ky2JTMzii6d5p2F+rfQV1J/oT8R64QG2j+zz/CLez0KC+6+X+b2U1lN4L3I+CNKSE0D9vt6+qzohZ6nYWGtRJ797udhYajD+yCQwJ2oCa8GM5+y2y9cFzDgBYdcY/3b+DWA+fbOjTaBctZ3Zc3CS3ifvj0gYwxpSJyHXAt4AXeNUYswJARMYBH4uIBewFLq9lUSOBf4vIvlvztwc0xjZjDw4SJiKnAyONMStrWM4VwJPAehEpBDKdZRkROQN4QUTuxe76+RUwvoblKKWUUkoppVQ1B7xBVlN0zJk3Mci0r7AbN1Wnfwp8Ws913gLcUsO81Posw0mbC4yrYd424NT6LksppZRSSilVWVP+fbDGoltAKaWUUkoppVzSpLos7i8RuRsYW2Xyh8aY/fqBZhH5Bag6PNdF+36/TCmllFJKKdXwrIPpQcTf6U/dIHMaXvvV+KphOQPqTqWUUkoppZRSDetP3SBTSimllFJK/XnpM2T6DJlSSimllFJKuUYjZEoppZRSSilX+E2T/GmwRqURMqWUUkoppZRyiTbIlFJKKaWUUsol2mVRKaWUUkop5QpL40O6BZRSSimllFLKLRohU0oppZRSSrnCrz8MrREypZRSSimllHKLRsiUUkoppZRSrrDQYe81QqaUUkoppZRSLtEIWSOL9Ra7nYUGs9pn3M5Cg+kW2sztLDQonylzOwsNxsvBU88APBHhbmehwZRYB9e+6RCW4XYWGkyedXB9vfsSfG5nocE02xDqdhYaVGGHg+f7xuM5uM5pfxb6DJlGyJRSSimllFLKNQfXLTSllFJKKaXUn4Zf40O6BZRSSimllFLKLRohU0oppZRSSrnCMjrKojNFyPsAACAASURBVEbIlFJKKaWUUsolGiFTSimllFJKuUKfIdMImVJKKaWUUkq5RiNkSimllFJKKVdY+jtkGiFTSimllFJKKbdog0wppZRSSqn/Z+++w6Oo1geOf8+WdJKQntACoYM06SC9WkCs2BvY9V71ekXwYrmKXa+ioNivPxUbKhYsKF0QaaF3SAjpvSe7O+f3xy5JliQQFTOR+36eJ08ye97dPWdn9sycec9MhDCJTFkUQgghhBBCmMKF3PZeMmRCCCGEEEIIYRLJkAkhhBBCCCFMITf1kAyZEEIIIYQQQphGMmRCCCGEEEIIU8g1ZJIhE0IIIYQQQgjTSIbsL2bzeitvzfPBMGD0RCdTLnN4lWdlKF56ypeSYjAMxZXTKukzwIXTCfOf9eHQPisuA4aPcXLB5Y563qXxbFpv5Y15vhgGjJno4MI62vPiU76UFCsMA66aVsmZnva8/KwvB/dZcBmKkWMcXNgE2nMis56A5WshrDl8+bbZtTm51b8onnzJiuGCC84xuOEKw6s8NR1mP2UlL18R0kwzZ5aLmCh32XOvWFi1zoJhwKC+BvfdYaBMPAGWvKmMNa/nog3oMjaI3heG1IrZv7qEjQsLQEF4vJ0x90QCsO6dPJI2lgFw5iUhtB8a2Kh1r88tT11Ov3E9qCit5Nlb3mB/YpJXuX+QH898e3/VckSL5vz04VpenfFB1WNDJ/flgXdv447hD7Nv8+HGqnq9srYUsOudFDCg5ahw2k2OqRWTtjaP/Z+koRQ0a+1PzzvbmlDT+u3b4OSbBeVoQ9NnnA/DLvH1Kl+yoJxDW50AOCqgpMBg5kfBHEx08u1r5VVx2SkGF9/nT5dB9katf01bfrXw33l2DANGTnQxearTqzw7UzH/KXtV/3zZDQ56DzBY/aOVrz6qPrxIPqSYM6+C+Pa6sZtQr+Et2vLgwNFYlWLh3q3M3/qLV/m0bn2Z2rEHTm2QW17GvauWcLSk0KTaVpt5zgiGdWpLmcPBzE+/Z1dqZq2YrnFRzLlwPH52Gyv3HGLO18uryq4Y2IvLB/bEZWhW7DnEs9+tYlBCa+4ePxS71YrD5eKZb1fxy8EjjdgqGN4yntmD3evjw91bmZ+43qu8f0xLZg8eReewSO748UuWHNoLwKDYVvxr0KiquITQMO748Uu+T9rfqPWvaWhUe2acMQErFj5N3sTr+1Z7lV8S35fL2vbD0JpSZyUPJX7JgaKsqvJY/xAWj7qNl3cv5+0DPzd29U0h15DJgOwvxeWC1+f6MPvJcsIiNTNu86PvYCet2lTv5D59z87g4U7GT3JyJEkxZ6Yf898rY+0KKw6H4rnXy6goh7/f4M/QUU6iYszbQbpcsGCuLw89WUZ4pOaft/nT/7j2fPyeD0OGO5ngac+/Z/qz4L1Sfl5hw+mAFzztueOGAM4yuT0nc/5EuPwCmDHH7JqcnMsFc16wsuAZJ9GRcNnNNkYMMUiIr455dr6V88YZTJ6g+WWT4sXXrMyZ5WLLdsWW7YpP3nAfvF1zh5UNWxT9epuzbgyXZvWruZz7cBSB4TYW3ZtGm/7+hLXyqYrJT3Ww+dMCzn8iGt8gK2X5LgCSNpSSdbCSi5+PxeXQLJ6VQes+/vgEmLvz6DeuB3EJ0Vzfawad+7Xj9uev4u+jHvWKKSsu57ahD1Ytz13xIGsWb6xa9g/yY/LNY9j164FGq/eJaEOz880j9JvVAb9wO2tn7iHqzBCCWvpXxZSklXPwi3QGPtwRe5CNioKmdRLGcGm+ml/GNY8GEhyhePWuEjoPtBHV2loVM/FGv6q/1y2uJO2ge1tr19PGrS8FAVBapHlhWhEJvc3bRRsueGuunZlPVhIeoZl1uy9nDnLRskb//Nl7NgYOdzH2PBcpSYonZ/kwd0AFQ0e7GDra3a7kQ4pnZ/s0qcGYRSn+PWgMV3z3EeklRSyedDVLk/ezLz+nKmZHTibnLv4v5S4nV3buxf39RnD78sUm1hqGdYynTUQoE557ix6tYnhw0iimvrKwVtzsyaN58POlJB5J49VrzuesjvGs2nuY/m1bMqpLAufP/T8cLhdhge7vVn5pGbe++wVZRSW0jwrntesuYOSTrzVauyxK8cjQsVz5tWd9TLmKH5IOsL/G+kgtLuQfy5cwvUc/r+euTTvC2YveASDE148Vl05jZcrhRqv78SwoZvU4m+k/v0tGWSEfDp/OsvQ9XgOur1O28dHhDQCMjOnEP7uN56Z1/1dVfl/38azK2NfodRfmavJDUqVUjFJqoVLqgFJqp1LqG6VUR6VUa6XU90qpXZ7H4z3xy5VSfWs8P14ptb3Gcg+l1Fql1A6l1DallF/td62KDVJKzfe892al1Eal1HSl1BlKqS2en1yl1CHP30v/zM9i/x4LMXEG0XEaux2GjHDx6xrvHbZSUFrqTkWUliiah+uqxyvK3QfalRVgs4F/gLk7yH17LMTGGcR42jN0hJP1J2hPSYkirEZ7yssVLhdUNJH2nEy/nhDazOxaNMz23YrWLTQt48BuhwmjDJat8e4uDiYpBvRxf+b9e2uWrXGvJ6WgolLhcEKlA5xOCA8zb91k7qskONZGcIwdq12RMDSQw7+UecXs+r6Y7mc3wzfIfeDsH+r+nXfEQVw3XyxWhd3PQni8neRNZbXeo7ENOrs3P37gPnO6+9eDBIUEEBZdO+t3TFxCNKGRwWz/eW/VY1c/MIWPX1iCo7xpDGry95cQEONLQLQvFpuFmMHNydhQ4BWT8lM2rcdFYg9y9xO+IeZlj+qSstdFWJyFsFgLNrvijGF2dq9z1hu/bYWDM4bXbsPO1Q469LXh42deWtm9v9FEx2psdhg0wsWGn61eMUpBWUnt/U1NP/9kZfBIV6PUuaF6RcRyuDCfI0UFOAyDLw/uYmzr9l4xa9OTKXe5193mzFRiA4PMqKqXUV0S+GLzLgC2HkmnmZ8vEc28M/YRzQIJ8vUh8UgaAF9s3sXoLgkATB3Qk9dX/orD5V4fuSXuvmxXWhZZRSUA7M/MwddmxW71Xtd/pl6RsSQV5FWvjwO7GRfvvT5SigvZnZuF1vXvS85u25HlRw5VrTcznNG8BUdKckkpzcOhXXxzdDsjYzp5xZQ4K6r+9rfa0VS3aVRMZ46U5LG/xgDuf4FLWxrtp6lqujUDlFIK+AxYrrVO0Fp3BWYC0cB/gae11l2A/kDtvH3t17MB/wfcrLXuBowATnQ08jqQB3TQWvcGJgBhWuttWuteWutewGLgXs/ymN/b1obIzVZERFV/ccMjNbk53jvsS652sGqpjRun+jNnph833F4JwMBhLnz9YPolAdx8RQCTLnbQLPjPrO3J1dWenOPac+nVlaxYamPa1AAenenP9NvdHdmgYU78/DTXXxLIjVcEcv7Flaa353SSkQXRkdXL0ZGazOP2Dx0TNEtXuruQH1cpSkoV+QXQs5umXy+D0RfYGH2hjcH9Ne3aNGLlj1OS6yQoonqgHxRupSTX+wCxINVB/lEnn81IZ9E/06oGXeHxPiRvKsdRYVBW6OLo9gpKss0/uAyPCyUrJbdqOetoHuFxzeuNH3HRAFYsqp4ClNCjNZEtwlj/beKfWs/foiLXgX94ddbSL8xORa5391ySVkFpWjnrZu9h7QO7ydpScPzLmKooRxMSUb1bDY5QFOYYdcbmZxrkZRi061H7wHfbyroHao0pL9vdJx8THqHJy/buny+8ysHqH63cdpkfT83y4drbau9O165oegOymMAg0kqKqpbTSoqICaj/bNmlHXuwPOVQY1TthKKCg0gvqK53RmEx0cHeA8Xo4CAyCoqrYwqKifLExEeEcmZ8CxbePJV3pl1M9xbRtd5jXLcO7ErNqhq0NYbowCBSj1sf0b9jAHxeQmcWH9h1Kqv2m0X7BZNWVj21NaOskGi/2gcnl7Xtx5Ixd3J3t7HM2bYEcA/ObugwhPl7VjRafUXT0aQHZMBIwKG1fuXYA1rrLUAOYNNa/+B5rFhrXdqA1xsHbNVaJ3qel6O1rrPXUUol4B7oPaC1NjzxWVrrJ39rI5RSNyqlNiilNnzyXslvfXqVuk4MHX/+dPUyKyPGO1iwsIyZc8qZ+4T7+qz9uy1YLLDgw1LmvVvKl5/YyUg19642DWnPqmU2Ro138vrCUh6YU8Z/nvDDMGCfpz1vfFjCK++W8sUnPqSb3J7T3fHXgN1zi4uNiYpLptnYkKiIitBYrZCcAoeSFT987GTpx07Wb1JsSDRx3TRgOzMMKEhzMOnRaMbcE8GKl3OoKDZo1duf1mf68/l96Sx9NpvoTj6oxjtxXC9VxwV5JzpzPPzC/iz/ZF3Vc296/DJem1V7qlOTc1wztUtTkl5B/9kd6XlnW7YvSMZRYt7Z8OM1pE87ZtsKB92G2LBYvSOKcg0yDhu072PuFQV1bk7HNebnZTaGjXPx8gfl/POxSuY96b6++Zj9uxS+vtCqbVObvVDH96eujgKYktCVMyJieHXb+jrLG1Nd1+Ee/72va3s7FmG1WAj282XqKwt55tuVPDf1HK+49lHh3D1+KA998adO9qmlzjr/xk0m0j+QTmGRrDxy+FRU6ferax3VsW19cOhXJi59ked3LOXmjsMAuK3zSP57YB2lrso/u5ZNjoFqtJ+mqqlfQ9Yd2FjH4x2BfKXUIqAtsBSYUWNw9Z5S6ti8Ih/AqPE8rZT6DogEFmqtn6rnvbsBiccGY3+E1noBsABg25GWv3vPFB6pyc6s3physmpPEflxiZ0HHndfGN6pq0FlJRQVwKqfbPTu58Jmg5Dm0KmbiwN7LUTHmXfmsq72hNVqj43ZnvZ07mrgqITCAsXKGu0Jba7p3M3Fgb1WYuKazsHZX1l0pDtLdkxGliIywjsmKgKe/7d7+ykthaUrLDQLgk++stCjqyYgwB03dIBm605F357mHJQFhtsozq7eLopzXASEeY+qgsKtRHX0xWpTBEfbCY2zU5DmIKqDL2deHMKZF7unAy59NouQWHMyF+dNH8WEa4YDsHfTISJbhlWVRbZoTm5afp3Pa9u9FVablf1b3Df98G/mR5uuLXjq6xkANI8O4aGFd/LQ1BdNvbGHb5idspzqA5HyXAe+zb0/a79wH0LaB2KxKQKifAmM9aM0vYKQhKaxKwuOUBRkV+8yCrM1zcLrPu+5baWDc2+pPWN++yoHXQbZsNrMPXAIi3T3ycfkZNfe3yz71sr9c9zrrKOnfy4qcO9jAH5ebmty2TGA9JIiYgOrM2Kxgc3IKC2uFTckrg239xzEJd98QKVhTjsuG9CTi/t1B2BbSgYxIdX1jg4OIrPI+yRvemEx0SHV2aXokCCyCt1tSy8o5oed+6tey9Ca5gH+5JWWER0cxItXnMf9n3zHkdzGzTynlxQTd9z6yKxjfZzIuQmd+O7wPpx//JDtD8koKyTWvzojFu0fTGZ5Ub3x3xzdzr96ngOboUfzFoyL68o93cbSzO6H1ppKw8n7h8w/GSD+fE09Q1YfG3AW8A+gH9AOuLZG+RU1phSefdzzhgJXeH5PUUqNbsgbKqVmea4TSz0F9f9d2ncySDtqISNN4XDAmuVW+g32HoBERBls2+w+2ExJcscFh0JElGb7FgtaQ3kZ7NtlJa61uR1Xh+Pas3q5jX6DvXd6EVGarZ72HElSVDogJFQTGaXZtsVa1Z69u6y0MLk9p5NunTRJKYqUNHA44NufLIwY7P355uVTdTb89fctTDnbvRAbpdmwReF0gsMJGxIV7dqYd4Y8qoMPBWlOCjMcuByaA6tLiO/v7xUTPyCA1O3ugX9ZoYv8VAfB0TYMl6a80L1N5hyuJCfJQave9V52+qf68rWfuG3og9w29EHWfr2J0ZcNBqBzv3aUFJaRm1H3QdSIiwaw/JPqO8iVFpZxads7ueaMe7nmjHvZ/esB0wdjACEJgZSmV1CaWYHhNEj/OY+oM72vi4vqG0LuTvfBTWWhk9K0cvyjfOt6OVO06Ggl96hBXrqB06HZttJB5wG1B4vZKS7KizWtutQxXXGF0/TpigAJnQzSjyoy0xROB6xdbuXMQbX75+2b3YcRR5MUlZWK4FB3mWHALyutDGqCA7LE7DTahjSnVVAIdouF89p14Ydk77vydQuL4vHB47hh6SJyyhsyAefP8cEviVzw0ntc8NJ7/LjrAJN7dwGgR6sYiioqyT5uQJZdVEJJRSU9WrnvUDq5dxd+2uW+cc9Puw4woF0rANqEh2K3WskrLaOZny/zrz6f579fzebkxj/EScxKIz6kOS2bedZHQmd++I13SZyU0IUv95s7XRFge34qrQPDaREQil1ZObtFd5al7/GKaR1YfTJteHQHkkrc08+vXv0W4374D+N++A/vHljHgr2r/mcGY3INWdPPkO0ALqrj8RRgs9b6IIBS6nNgIPDGSV4vBVihtc72PO8boA/wYx2xO4GeSimL1trQWj8GPKaU+m2nbU4hqxWm3VHJozPc0/ZGTXDSKl6z8G07CR0N+g12cc3NlbzynC9ffWpDKbjt3kqUggmTHbz8tC93TfMHDSPHO4lvZ+40EqsVpt9RwcMz/N238Z/goHW8wftv+9C+o4v+g11cd3MF857z48tP7aDgznsrUAomTnYw92k//jbNH60Vo8Y7iG/XtAdk9zwM67dAfgGMuAhuvw4uOufkzzODzQYz/+bilnttuAw4f6JB+7bw8psWunbSjByi+XWL+86KSkGfHppZf3cfeI0drlm/WXPh9e5tcEh/gxGDzdvWLFbF0OlhfP1wJtoFncYEEdbah1/fzyeyvQ/x/QNo1duPlC1lfHh7KsoCg65tjl+wFWel5ouZGQDYAxSj/x5Ra4qZGdZ/t5V+43rwZuKTVJRW8tyt1V3fy6sf9rq74rAp/fjXRc+bUc3fxGJVdL2uFRvm7EcbmpYjw2nWyp99H6US0i6AqL6hRPQMJntrEavu2YmyQKcrW+DTrOnsxqxWxTm3+PHff5ViGJo+Y32IamPlx3fLadHBSueB7oHW1hUOug+z15p6mpdhUJBtEH+G+fNirVa49nYHj9/vnoY4YryLVvGaj9+20bajQd/BBlfe5OC15+x8s8iGAm7x7G8Adm+zEBbhvilIU+PSmtlrl/Lf8RdjVYqP9m1jX34Od/ceytbsdJYe2c/M/iMIsPswb+QkAFJLipi2dJGp9V655xDDOsbz7d3XUe5wMmvR91Vli26/ggteeg+ARxb/xJwLx+Frs7Fq32FW7j3sjtm4nUcvGMcXd16Fw+Vi5qffAXD5wJ60Dg/llpEDuGXkAACmvbWo6qYffzaX1sxes5T/TrwIq8XCR3u2sS8vh7vOHMK27HSWJh2gR2QMr449nxBfX0a3SeCuM4cw7pO3AGgZFExsUDPWpTXurfrr4tIGj239hgWDrsKiFJ8lb+ZAURa3dx7JjvxUlqXv4fK2/RkU2Q6nNiisLGPmps/MrrZoAtSJrjswm+emHuuA17XWr3ke6wcEAC8CY7TWWUqpt4ANWuuXlVLLgX9orTd44uOBr7TW3ZVSzXEPvoYClcC3wPNa66/ref+PgP3Av7TWLs8dGXO01oE1Yt72vP4nDWnTH5my2NRY65lz/1fU2d40/rfUqeLQp8/UzXn57U8e9Bfy3YAWZlfhlOm0ovzkQX8hg5uZ97+LTrUOPhlmV+GUmrL0drOrcMoEHjA/A3oqlbY5ffY3gdG//zr/pmjH5IfMP4PYALO2XtBoB5SP9VjUJD+Tppu7A7R7tDgFGOu59fwO4CEgFfd0xR+VUttwX0Z50n+aobXOA54DfgW2AJvqG4x5TAPCgf1KqY24r1W77/e3SAghhBBCCCGqNZ25HvXQWqcCl9RRtA/oUUf8iOOWD+O+Ocix5f/Dfev7hrx3IXDTSWKubchrCSGEEEIIIby5mnZ+qFHIJyCEEEIIIYQQJmnyGbLGoJT6BTj+Vl1Xaa23mVEfIYQQQgghxP8GGZABWusBZtdBCCGEEEKI/zWGbpL32WhUMmVRCCGEEEIIIUwiGTIhhBBCCCGEKQzJD8knIIQQQgghhBBmkQGZEEIIIYQQwhQurRrtpyGUUhOUUnuUUvuVUjPqiblEKbVTKbVDKfX+H/0MZMqiEEIIIYQQ4n+eUsoKvAyMBVKAX5VSi7XWO2vEdADuB4ZorfOUUlF/9H1lQCaEEEIIIYQwRRO7y2J/YL/W+iCAUmohMBnYWSNmOvCy1joPQGud+UffVKYsCiGEEEIIIQS0AI7UWE7xPFZTR6CjUmqNUmqdUmrCH31TyZAJIYQQQgghTGHoxssPKaVuBG6s8dACrfWCmiF1PE0ft2wDOgAjgJbAKqVUd611/u+tlwzIhBBCCCGEEKc9z+BrwQlCUoBWNZZbAql1xKzTWjuAQ0qpPbgHaL/+3nrJlEUhhBBCCCGEKVyoRvtpgF+BDkqptkopH2AqsPi4mM+BkQBKqQjcUxgP/pHPQAZkQgghhBBCiP95WmsncDvwHbAL+EhrvUMp9YhSapIn7DsgRym1E1gG3Ku1zvkj7ytTFhtZE7uTzB/iowyzq3DKOLTT7CqcUnZ1+ny1HdpqdhVOKVdxsdlVOGX8rcdPq/9rs6vTpx+wn0b9M4Cynz7tOY02MwD0adQP+Pk4zK7C/6Smdmystf4G+Oa4x2bX+FsDd3t+TgnJkAkhhBBCCCGESU6f0+hCCCGEEEKIv5TGvMtiUyWfgBBCCCGEEEKYRAZkQgghhBBCCGESmbIohBBCCCGEMIXRsNvRn9YkQyaEEEIIIYQQJpEMmRBCCCGEEMIUriZ223szSIZMCCGEEEIIIUwiGTIhhBBCCCGEKeS295IhE0IIIYQQQgjTSIZMCCGEEEIIYQpDriGTDJkQQgghhBBCmEUyZEIIIYQQQghTyP8hkwyZEEIIIYQQQphGMmRCCCGEEEIIU8g1ZJIhE0IIIYQQQgjTSIbsL2bLrxbemueDYcDoiU7On+r0Ks/OVLz8lA8lxQrDgMtvqKTPAINVP1pZ/JG9Ki75kOLJeeXEt9eN3QQvG9dbeX2eHy4Dxk10cNFllV7lWRmK/zzlR7GnPddMq6DvABcOB8z7jx/791hQFph+awVn9HKZ1Aq31b8onnzJiuGCC84xuOEKw6s8NR1mP2UlL18R0kwzZ5aLmCh32XOvWFi1zoJhwKC+BvfdYaCa8AmjWU/A8rUQ1hy+fNvs2pxcyqZS1r2Rg2FoOo0JpueFobViDq4pZvPCPFAQFu/DyLujAVj/Tg5HNpaiDWjRy5+BN4SjmtjKufWF6+g/sQ8VpRU8fd3L7N98qFbMyKlDuOz+C9Bak5OaxxNXvUhhTpEJtfWWsbmIrW+log1oM7o5naZE1YpJ+Tmf3R9lgoKQNn70+3trSrMq+eXpJLQBhkuTMDGctuPCTWiBtz0bXHz1aiWGAf3G2xhxid2r/KsFlRzc6u6rKsuhpEDz4McBACx5o5Ldv7rQGtr3tnLeTXZTt7XN661e+5splzm8yrMyFC895UtJMRiG4spplfQZ4MLphPnP+nBonxWXAcPHOLngckc979J4hse1ZXbfMViVhQ/3JzJ/xzqv8hu69GNq+544tUFueSn/XPsNR0sKq8qD7D4snTSd75L38uCvPzR29Wu5f9IIhnVqS5nDwayPvmdXamatmK4tonjs4vH42W2s3HOIxxcvB6BTbASzp4wmwMeH1LxC/rlwCSUVlbWe31iGt2jLgwNHY7UoFu7Zyvytv3iV949pyYMDRtM5LJI7li3mm8N7q8pm9BvOqFbtAHhx81q+OrS7Uet+vEERHfhHl3OwKgufp2zg7YMrvcovbNWfS9oMwKU1Zc4KHt3xOYeKs7ApK7O6T6ZrSAsMrXlm19dszK3dl5+O5P+QNSBDppRyKaW2KKW2K6U+Vkq18CxvUUqlK6WO1lj2UUpNUErtUUrtV0rNqPE6o5VSmzxxq5VS7U/wnp2UUss9sbuUUgs8j49VSm1USm3z/B51krofVkp9WmP5IqXU256/r1VKaaXU6BrlUzyPXeRZXq6U6lujPF4ptb3Gcn+l1EpPe3crpV5XSgWc7DP9vQwXvDHXh5lzKnj+9XLWLLORkuS9s/70PTuDhrt46pVy/j6rgjfm+gBw1mgXT79aztOvlnPHjAoio7XpgzGXC16d68eDc0p5+Y0SVi6zkZzkvUl++J4PQ4Y7eeHVUu59oJxXXvQD4Ptv3Ac5c18v5ZEny3jzVV8Mo9ZbNBqXC+a8YGX+k04+f8fJkp8sHDjsHfPsfCvnjTP49E0nN11j8OJrVgC2bFds2a745A0ni95ysn23YsOWpnXAf7zzJ8KCp82uRcMYLs3PC7IZ968YLnyxFQdXF5N3xPvAoyDVQeKn+Zz7eBwXvtiKgddHAJCxu5yM3eVMeb4lF7zQkqx9FaTvKDejGfXqP7E3LdrHcm3HO/jPTa9y57zptWIsVgu3/Oc6/jHqIW7q9Q8Obkti8u0TTKitN+3SJL6RyuBZbRnzfAdS1hRQeMT78y1Oq2DvZ1kMezSBMc93pMd1cQD4hdoY9lgCo57pwIg5Cez9PIuyXHMP+g2XZvG8Sq57xJe7XvEjcYWTjGTvjuncG3248yV/7nzJn8GTbHQb7O4Hkna6SNpp8LeX/fj7PD9S9hoc2mZep+ZywetzfZg1p5zn3yhj9TIrR+rY3wwe7uSZV8u564FyXnvRvb9Zu8KKw6F47vUynppXxg9f28hMN7dPsyjFI/3Hce1PHzH2y9eYFN+V9iHeA/iduRmc983bTPzqTZYk7+H+PiO9yu/pOYxfMpIbs9r1OqtTPG0iQpn49Fs8tGgps6fUfTg0e8poHlq0lIlPv0WbiFCGdooH4JELx/L8ktVM+c+7LN2xn+uHn9mItfdmUYp/Dx7DNd9/zJhP32BSuy50CPVeN6nFhdyz8hu+OLDT6/FRrdrRPTyaj5mMEgAAIABJREFUiZ+9zeTF/8dNZ/QnyO7TmNX3YkExo9t53LnhHS5a9QLjY3vQNijSK+bbtEQuXT2Xy9e8xDuHVnF357MBmNLKfbh56eq53PrrW9zVeSJKbnbxP6MhQ9IyrXUvrXV3oBK41LPcC3gFeL7Gsgt4GZgIdAUuU0p19bzOfOAKT9z7wAMneM8Xa7xuF2Cu5/Fs4Dyt9RnANcC7Dah/X6VUt3rKtgGX1VieCiQ24DVRSkUDHwP3aa07AV2Ab4FmDXn+77F/j4WYOE10rMZmh8EjnPz6s/W4emlKS9x/l5YomofXHnSt/snGkJHmZpMA9u2xEBtnEBOnsdvhrBFOflnjnbRVCspK3R1SaQmEedpzJMlCj97u7GBoc01gkGb/XvPOsGzfrWjdQtMyDux2mDDKYNka7/ocTFIM6OOuf//emmVr3O1SCioqFQ4nVDrA6YTwMHMHyyfTryeE/mlb+qmVta+C4Fg7wTF2rHZFu6GBJK8v8YrZ80MhXScG4xvk/j75h1Z/r1yVGsPp/tEujX+I93fObIMm92PpuysA2PXLPoJCAwmL8c4AKqVQSuEX6AtAYLA/Oal5jV7X4+XuLyUwxofAaB8sdgsth4SQtqHQK+bw0lzaTQjHx7NufEPcfYTFbsFqd3/HXE6NNvGEzDFH9hqExynCYi3Y7Iqew2zsWlt/X5u4wkXP4Z4+T4HDoXE5wekAwwlBoeYdjLn3NwbRnv55yAgXv9bRP5dW9c/V+xuloKLcPairrACbDfwDzO3TeoXHklSUx5HiAhyGwZdJOxnXqoNXzNqMZMpd7v3K5qxUYgKqO7nuYdFE+AewKvVwY1a7XqO6JbB44y4Atian08zfl4hmgV4xEc0CCfT1ITE5DYDFG3cxulsCAPGRzdlw6CgAa/clMba792fRmHpFxnK4MJ8jRZ51c3AXY1t7n7NPKS5kd14WhvbejjqERvBL+hFPtsnBrtxMhrds25jV99IttCVHSnI5WpaHU7v4Pm0rI6K6eMWUOCuq/va3+nCsRe2ColifcwCAvMoSihzldA1p0VhVN5WhVaP9NFW/9Qh2FVBvZgvoD+zXWh/UWlcCC4HJnjINBHv+DgFST/A6sUDKsQWt9TbP781a62PP2wH4KaV8T1LnZ4CZ9ZStAvorpexKqSDcbdtyktc75jbgHa31Wk/dtNb6E611RgOf/5vlZivCI6s7o/AITW6298Z18VUOVv1o4+bL/Hh8li/X31Z7CsLaFVaGjHTWeryx5WRbiIiqPoqKiDTIyfFuz2VXV7J8qY3rpgby8MwAbrzdffY8vp3BLz/bcLkgPU1xYK+V7EzzBmQZWRBd4yRYdKQmM8s7pmOCZulKdx1/XKUoKVXkF0DPbpp+vQxGX2Bj9IU2BvfXtGvTiJU/zZXmOgmMqD6QDAi3UZLjfZBckOqgINXBl/cfZfF9R0nZVApAdGc/Ys/w54Prk3n/+iRa9AogtJV5Z1/rEhEXRuaRnKrl7JQcIlqEecW4nC5evPU1Fmx9loVHF9C6S0u+feOnxq5qLeW5TvzDq6f0+YfZKc/xznIVp1VSnFrBigcOsHzmfjI2V0+zLM2u5Md79vHdzbvpeH4k/mHe0wMbW2GOJiSiug8LjlAU5NQ9EMnLMMhLN0jo6e4T2nSxktDDypwry5hzZRkdzrQQ1dq8Pi03WxERVWN/E6nJPa5/vuRqB6uW2rhxqj9zZvpxw+3u/c3AYS58/WD6JQHcfEUAky520CwYU0UHNCO1pHrbSSspItq//rNKl7TvwfLUgwAo4IEzRzNn47I/u5oNFhUcRHpBdXsyCoqJDg7yiokODiKjoLhqOb2gmChPzL6MHEZ2dU/zG9+jIzEmnmGLCQgirea6KS0iJrBh9dmZm8mIlm3xs9po7uvPoNjWxAWat7FF+QWTUV5QtZxRXkikX0ituItbD+CL4XdzZ6fxPL3zKwD2FqUzIqoLVmUhzr85XULiiK7jueL01ODeXillw5352naCsBbAkRrLKZ7HAKYB3yilUoCrgCdO8DrPAz8ppZYope5SStW+4AMuBDZrrSvqKKvpI6BPPVMkNbAUGI974Li4jpj3jk3JBL6p8Xh3YONJ3hsApdSNSqkNSqkNn7xffPIn1EPXsV8//vKCNctsjBjn5JUPyrn/sQrmPuk9lW/fLgs+vtC6rfkZmDrbc9zyymU2Ro138NbCEh6cU8rzT/hhGDB2ooOICM3dtwbw+jxfOndzYWlaiYta6+aeW1xsTFRcMs3GhkRFVITGaoXkFDiUrPjhYydLP3ayfpNiQ2LTPYvzl9OA7412QWGag3P+HcfIu6NYNS+LihIXhWkO8lMqmfp6ay57vQ2p28pI21HWOPVuoLquMTr+u2W1WTnv5nHc0uefTG1xI4e2JTP1/vMbqYa/0XHtMVya4rRKznqoHf3+1ppNr6RQWeIeUAdE+DD62Q6MnduJ5OV5lOebfJ1SA7a1Y7audNF9qA2L1R2QnWqQecRgxn/9uf9dfw4kGhzaZt5Mhob0z6uXWRkx3sGChWXMnFPO3Cfc+5v9uy1YLLDgw1LmvVvKl5/YyUg1t0+r693r2wue37YbPcJjWLDDfR3TVZ36sOzoAdJKzb/m8pi62+Pdojq3PU/Ivz7+nssG9eKjOy4nwNcHh9PMWTN19WENO0ZZdfQwy44cZNF5VzB35HlsykzFaWK6vK4phnW15ePkX5i84jnm7vmOaQkjAFicspGM8kLeHXwr93Q5h8S8ZFxNIfUvGkVDburh7xmMgDuj9MYJYk/U590FnK21/kUpdS/wHO5BWu0naP2WUuo7YALugdJNSqmexwZfnimITwLjGlB/F/A0cD+wpI7yhcCduLN291A7m3aF1nqD533jga8a8J7Ht2cBsAAgMbnV7x4JhUdqcrKqP+Kc7NpTEn/61sbMOe4xaseuBo5KKCqAkObu8jXLm0Z2DNwZsezM6jPa2VmWqimJx/ywxM5Dj7sPgDt3NaisVBQWKEKba6bdWj0W/+edAcS1NK/jio50Z8mOychSREZ4x0RFwPP/du/0Skth6QoLzYLgk68s9OiqCfBcfTh0gGbrTkXfnuYPmk8HAeE2SrKrt/nSHCcBYd6j98BwK5Gd/LDYFM2i7YTE2SlMdZC2vZyojn7Y/d3nrlr1CSBrbwWx3fwbtQ3Hm3TreM6eNgaAPRv2E9UqnB2esoiW4eSk5nrFJ/SKByDtoDuBv+Kjn5l6n/kDMr8wG2U1MmJluQ78wrx3S/7hdsI6BGCxKQKjfWgW50tJWgU+7asv1/UPs9OslR85u0ppMci8M8rBEYqC7OrvbWG2Jjis7oFI4gonk2+tzrbu/NlFq05WfP3d8Z36WknebdD2DHPONIVHarIza+xvsmrvb35cYueBx92zFjp1Naj07G9W/WSjdz8XNpt739Opm4sDey1Ex5l30J9eWkRcjaxLbGAzMstqD7CGxLTh9jMGcen371NpuOvbJ7IF/aJaclWnPgTY7NgtVkqdlTy5eUWj1R/gskE9uah/dwC2p2QQE1LdnuiQIDILvadipxcUEx1SnTWLCQkis8h9UvhQVh43vrEIgDYRoQzvbN40v/TSImJrrpuAZmSUNvzk9UuJ63gp0X2DlhdHnMvhAvOmY2eUF3hltaL9gsmuKKw3/ru0bdzfbTJs+xSXNnhud/V5/zcH3khyafafWt+mQv4x9G+7hqyX1voOz1TE+qQArWostwRSlVKRQE+t9bHb5nwIDD7Rm2qtU7XWb2qtJwNO3BkplFItgc+Aq7XWBxpQf3BfazYMaF3H+6z3vHaE1nrv8eUnsANo1KtgEzoZpB1VZKYpnA74ebmNvoO8d3ARUZrtm92rNSVJ4aiEYE9+0TBg3Uprk7h+DKBDJ4PUoxbS0xQOB6xabmPAYO/BYmSUZutm9wHJkSQLDgeEhGoqyqHck6jYvNGKxapp3ca8AVm3TpqkFEVKGjgc8O1PFkYM9q5PXj5V2crX37cw5Wz3QmyUZsMWhdMJDidsSFS0ayODsVMlsoMvhWkOijIcuByag6tLaN3P+1qLNgMCSdvm3qDKC10UpjpoFm0nKNJG+o5yDJf7GrK0HWWEtjR3WhzA4nnfcXOfe7m5z72s+fxXxlw1HIAuAzpQUlBKbnq+V3zO0Vxad21JSIR7Kk+fsT1I3n200et9vObtAyhOq6AkoxLDYZCypoDYvt7TjeL6BZO1w31wVlHopDitkoBoH8pyHLgq3N+hymIXOXtKCIo72Qz2P1fLjhayUzW56QZOhyZxpZMuA2sPqLJSDMqKoXWX6l1waKTi0HYXLpfG5dQc2uYiqrV5ByntOxmkHbWQ4emf1yy30u+4/jkiymCbp39OSXLHBYd69kNbLGjt7qf37bIS19rcM/2JOWnENwujZVAIdouF89p05Ycj+71iujWPZs7ACUxb9ik55aVVj/999ZcMWTSfoZ/NZ87GZSw6uL3RB2MAH6xN5MIX3uPCF97jxx0HmHSm+9qkHq1jKC6vJLvIe0CWXVRCaUUlPVrHADDpzC78tMN92BQW6D6ppBTcNGoAH67b2ogt8ZaYlUbb4Oa0OrZu2nXhh+T9J38i7huChPq6b/bVuXkkncMiWXnUvDsT7iw4SqvAcOL8m2NTVsbF9mBFpvddH1sFVN+wZGhUJ5JL3VPO/Sx2/Kzu/cuA8ARc2uBQ8XHXPojT1qm+7f2vQAelVFvgKO6bZFwO5AEhSqmOnkHPWGBXfS+ilJoA/Ki1diilYoBw4Khn6uLXwP1a6zUNrZTndZ4HZgB1XThxP/Bbb532ErBeKfX1sYGmUupKYKnWOv03vlaDWK1w/e2VPHa/e1rIyPFOWsVrPnzbTkJHg76DXVx9UyWvPufD14vcX+pb762smrawa5uF8Aj3TUGaAqsVbrqjnIdmBGAYMGaCg9bxBu+97UP7ji4GDHZx/c0VvPScH1986oNS8Ld7y1EK8vMVD80IQFk04eGau2eYe+c7mw1m/s3FLffacBlw/kSD9m3h5TctdO2kGTlE8+sWxYuvWVEK+vTQzPq7e2A8drhm/WbNhdfbUAqG9DcYMbhprKP63PMwrN8C+QUw4iK4/Tq46Byza1U3i1UxaHoE3z6cjjY0HUc3o3lrHza+n0tEe1/a9A+kRW9/UraU8ekdR1AW6HdNOH7BVuIHBZK6rYxFf0tBKWjRO6DWYM5s67/ZxICze/POvrlUlFbyzPUvV5W9sulpbu5zLzlpefzfIx/z3IqHcTpcZCRl8fR1L5/gVRuHxaroeUMcax47BAa0Gdmc4FZ+7FyYQfMEf2L7BRPVK4iMxCKW/n0vygLdr4rBt5mNzMQitv033T0vQ0OH8yIJaeNnanusVsWkW3x484EKtAF9x9mIbmPhh3cradHBQteB7l1u4nInPYdbvaabdh9q5cBWgxduLUcBHc600mWAef+ZxmqFaXdU8ugM9zTxURPc+5uFnv1Nv8Eurrm5klee8+WrT919122e/c2EyQ5eftqXu6b5g3bvq+LbmXxXX62Zvf57/jv6UqxK8dH+rewryOaunmexLSeNpSn7uf/MkQTYfJg3zJ09PlpSyPTln57klc2xcvchhnWKZ8k/r6O80skDH39fVfbp367gwhfeA+CRz37isUvG4Wu3sXrPYVbtOQzA2b06c9mgngAs3b6fzzbsqPUejcWlNbPXLuW/Ey52r5u929iXn8PdfYayNTudpcn76RERw4IxUwjx8WVM6/bc1WcoYxe9id1i4ZNzLgegyFHJ35d/jauB0x3/nLYYPLXzS17qdy1WpfgiZRMHizO5ucNodhYcZWXmbi5tM5D+4Qk4tUGRo4wHt34CQHPfQF7qey0aTWZ5If9K/MS0djS2pnyzjcaiTjZPVylVrLUOqqfsIaBYa/1MjcfOBv4DWIE3tdaPeR6fAjwCGLgHaNdrrQ/W87rPAedQPUh6Wmv9f0qpB3APnvbVCB+nta79zzfcr3MY6Ku1zvbc/OMQ8L3W+lql1LWestuPe87bwFda60+UUsuBfxw/ZdFzx0mUUoOAp4AoT7tWAndprUupxx+ZstjU+KmmkWk7FeJt5h7InWp2dfr8i8FnchPMrsIp9UP3OrvTv6Qzt5w23RkA/QMbOumi6evgc3qdWT9v+e0nD/qLCNxubib3VCvuaP7/mDtVIuIKTh70F7Jx4mN/iZHOlDW3NdrO5LMhLzfJz+SkR231DcY8ZQ/V8dg3eN/84tjjn+GeanhSWuu7gbvrePxR4NGGvIYnPr7G3xVAXI3lt4G363jOtTX+HnFc2WE8Uyc9y2uBsxpaHyGEEEIIIUQ1yZD99tveCyGEEEIIIYQ4RUyd16SUmgVcfNzDHx+b5vgbXucX4Pg5AFcd+/9lQgghhBBCiKZHMmQmD8g8A6/fNPiq53UGnILqCCGEEEIIIUSjOn2u/BdCCCGEEEL8pUiGTK4hE0IIIYQQQgjTSIZMCCGEEEIIYQoDyZBJhkwIIYQQQgghTCIZMiGEEEIIIYQp5BoyyZAJIYQQQgghhGkkQyaEEEIIIYQwhWTIJEMmhBBCCCGEEKaRAZkQQgghhBBCmESmLAohhBBCCCFMIVMWJUMmhBBCCCGEEKaRDFkjO+wMN7sKp8y+imizq3DKWNFmV+GUcmir2VU4Zf4RdsDsKpxSy+InmV2FU6Z3wAqzq3BK3bPuErOrcMo80v8Ls6twSoWv9DW7CqdMTl+X2VU4pUJ22M2uwimT5x9odhX+J0mGTDJkQgghhBBCCGEayZAJIYQQQgghTKElQyYZMiGEEEIIIYQwi2TIhBBCCCGEEKYwkAyZZMiEEEIIIYQQwiSSIRNCCCGEEEKYQu6yKBkyIYQQQgghhDCNZMiEEEIIIYQQppC7LEqGTAghhBBCCCFMIxkyIYQQQgghhCnkGjLJkAkhhBBCCCGEaSRDJoQQQgghhDCFXEMmGTIhhBBCCCGEMI0MyIQQQgghhBDCJDJlUQghhBBCCGEKuamHZMiEEEIIIYQQwjSSIfuL2bPBxRevONAG9J9gZeQldq/yxa9WcmCrAYCjAorzNY984g/A12842L3ehdbQobeFSTfbUcrcsxJHNpWy9vVctAGdxgbR68LQWjEHVpewaWE+KAiP92HUPZEA/PJOLkc2lgHQ+5JQEoYGNmrdj5e8qYw1nrZ0GRtE7wtDasXsX13CxoUFnrbYGeNpy7p38kjytOXMS0Job3JbAFI2lbLujRwMQ9NpTDA961g3B9cUs3lhHigIi/dh5N3RAKx/J4cjG0vRBrTo5c/AG8JN39ZOZNYTsHwthDWHL982uzYNc/PsyfQb0YWK8kqevfdDDuw4WivGZrdy60NTOGNgAtrQvPPsEtZ8u42zLx/EuVcNxnAZlJdW8uLMT0jen2FCK2DfBiffLChHG5o+43wYdomvV/mSBeUc2uoE3H1aSYHBzI+COZjo5NvXyqvislMMLr7Pny6DvPvExjYsth0P9h2DRVn4cP8WXtm5zqv8hs79uLR9L1yGQU5FKfet+5qjJYUA7L/sPvbkZwGQWlrI9BWfNHr9azqw0cF3C9zf417jfBlysZ9X+fevlZJUtW40JQWaez909xOPTcojqo0VgOBIC5fODmrcytfj3qkjGHJGW8orHTz01vfsTs70KvfzsfHkTefQMjIUl9asSjzI3EWrAbhibB/OH9odl2GQV1TGw29/T3pukRnNYHireGYPHoVVKT7cvY35W9Z7lfePbcnsQSPpHB7JHUu/YsmhvVVlcUHNeGLYeOKCmqGB6775lJTiwkZuQW33TRnBWV3c6+ZfH3zPrqOZtWLumDiY8/p2JTjAl4H3v1yrfGyPDjx77blMfe59dqaY06cNi23H7DPHYlGKjw4k8srOtV7lN3TuzyUJ7j4gt6KUf677itRS9+cfFxDM4wPOJjYgGA1cv/xDjpYUmNCKxqW12TUwnwzI/kIMl+azlx1Mn+NDSIRi7t8q6DrASnSb6kTnpJt8qv5e84WTowfcg7PDO10c3mlw1zz3wc68f1RwcJtBQg9r4zaiBsOlWfNqLmc/HE1guI3P702lTf8AmreqbkNBqoPETwuY9EQMvkFWyvJdACRvKCXnYCUXPB+Hy6H5alY6rfr44xNgTtLXcGlWv5rLuQ9HERhuY9G9abTp709YjbbkpzrY/GkB5z8R7dWWpA2lZB2s5OLnY3E5NItnZdDaxLaAuz0/L8hmwkOxBIbbWPzPo7Suc93kc+7jcV7tydhdTsbucqY83xKAr2amkr6jnNju/qa0pSHOnwiXXwAz5phdk4bpN6IzcfGR3DDqCTr3as3t/76Quy54sVbc1NtGk59TzPTRT6KUolmoex0sX7yJb953HyQMGN2V6bPO41/Xvd6obQD3dvbV/DKueTSQ4AjFq3eV0HmgjajW1f3SxBurBwHrFleSdtC9nbXraePWl9wH+aVFmhemFZHQ29xdmkUpHuk3jqt+Wkh6aSFfTLiWpSn72F+YUxWzIy+DSUveotzl5IoOvZnReyR3rP4CgHKXk3OWvGlW9b0YLs2S+aVc8WgQweEW3ririI4D7ETWWDfjpgdU/f3rl+WkH3BVLdt8YPrc4Eat88kM6R5Pq6hQzp/1Ft3bxXD/FaO45vGFteLe/X4jG/akYLNaeOWeixjcPZ6ftx9mT3ImVz32PuWVTi4a3oO/XXQW9y/4ptHbYVGKR4aM4cqvPya9pIjFF1zJD4cPsD+/ejtLLSrkH8uXML1nv1rPf27k2by0aR2rjyYRYLNjYP7R8NAu8bSJCOXcOW/Ro00MD1w0iiteqL1uVuw8yAerE/lq5rW1ygJ87Vx+Vi+2JqU1Qo3rZlGKh/uO5+qfPiC9rJDPx1/n6QOyq2J25GYwed+b7j6gfR9m9B7FnWs+B+CZQecxb8caVqcfdq8bGan8z/hLTllUSmml1LM1lv+hlHqoxvKNSqndnp/1SqmhnsetSqmNSqlhNWK/V0pdfIL3ClJKzVdKHVBKbfY8f7pS6gyl1BbPT65S6pDn76V/UrM5stcgIk4RHmvBZlf0HG5lxzpXvfFbVrjoNcLqaQc4KzUuJzgd4HJBUKi5GYusfRUEx9oIjrFjtSsShgaS9EupV8zu74voenYzfIPc7fAPdf/OO+IgppsfFqvC7mchLN6HI5vKGr0Nx2Tuq6zVlsO/eNdn1/fFdK+nLXHdfKvaEh5vJ9nEtsCxdWOvak+7oYEkry/xitnzQyFdJwbXag+Aq1JjON0/2qXxDzFv4N8Q/XpCaDOza9FwA8d048fPNgCwe0syQcF+NI+s3YBxF/Xnw/k/AaC1pjDP/f0qLa6oivEL8DHt7GTKXhdhcRbCPH3aGcPs7F7nrDd+2woHZwyvnQHbudpBh742fPzM7dN6hseRVJTHkeJ8HIbBl0m7GNuqo1fMuoxkyl3uNm7OTiUmoGkNWo5J3esiLNZC8xgrVrui2zA7e9dV1hu/Y0Ul3Yb71FveFAzvlcDX63YBsP1gOkEBvkSEeM9GKK90smFPCgBOl8HupEyim7sH/hv2pFBe6V532w6mEdXcnE6jV1QMSYV5HCkqcG9n+3czLj7BKyaluJDdudno477c7UPDsSrF6qNJAJQ6HZQ76//ONZaR3RP4coN73WxNSqeZvy8RzWrPFNmalE52UUmtxwFunziYt5ZtoMJhXnt6hseRVJzHkRJ3H/BV0k7GtuzgFbMuM6m6D8g5SkyAeztqHxyBTVlYnX4Y8Kwbl/nrpjEYqEb7aar+qhmyCuACpdTjWuvsmgVKqXOBm4ChWutspVQf4HOlVH+tdbpS6lbgdc/jFwFaa/3xCd7rdeAg0EFrbSilIoHrtdbbgF6e93wb+Epr/afOLynIhpDI6o0pJEJxZI9RZ2xehkFuukH7nu4xd5suVhJ6GPz7inLQMPg8G9GtzR2Pl+S6CIqo3gQDw21k7qvwiilIdQCweEaae0rT1FBa9QkgPN6HTR/m02NyMM4KTdr2cpq3Mm+qUkmu06stQeFWMvZ5H7wca8tnM9LRhqbv1FBa9/EnPN6HDR8W0GOygbNCc3R7hVcmygyluU4Ca7QnINxG1t66182X9x9FG9Dn0ua07BNAdGc/Ys/w54Prk9Fouk4MIdTk9pxuwmNCyE7Lr1rOTi8gIiaEvKzq6VOBzdyZpavvHk+PAQmkJecw76HPyM8uBuDcqwZzwfXDsNltzLjylcZtgEdRjiYkorofCo5QpOyp+yRTfqZBXoZBuzqy+ttWOhg8xfxtLMY/iLTS6qlf6aVF9AqPqzf+0oSerEg9ULXsa7XxxYRrcWmD+TvW8kPKvj+1vidSlGMQHFm9bppFWEitd924yM8wiO9R3Wc4K+GNvxdiscLgi/zoNMj89RPVPIiMGlMMM/OKiQwNIrug7gP8IH9fzurZjg9+3FyrbPLQ7vy8/dCfVtcTiQ5oRmpxdTvSSorpFRXboOf+P3v3HR5F1TZw+Hd203svlBCqdEILEDooglIsoFiw66uCKKKvioDYFUV8/ayoWFAUsSKgKIj0joTepIYUSCd9kz3fH7ukbUKRsBPwua8rVzIzZ3eek7M7O2eeM2cbBQSSXVTI+wOGUt/Xn5XHjvDquuWGZ2LC/HxIziyrU0pmDmH+PtV2viprXjeUiABflu88yO19Ol6oMM8owtOXpNyyY0BS3kliQqo/BtzQuB3LEg8A0NAviGxLAe/1vJ563v6sSj7E1PilhreNcI6LMkMGFAMzgHFVbHsCePxUR01rvRn4DBhtX14HrAamAC+dWl8VpVRjIBaYqLW22h9/Qmv9ao3V5ALZsqyENj3MmMy2DlxqopXjR608PcuDp7/wYH98CQe2VZ9dc4qzOMZYrZCVVMzgFyLoOz6UFe+kUZhTQr32ntTv6MlPTyTxx7QThF3mXlpXQ1RRl8rR2OpiYegL4Vw+PoQu7N+GAAAgAElEQVRl76RRmGOlfntPojp68uMTySyelkr4ZW4ooxNKVdWnUoV0CWQnWbj6+Tr0fTSMFe+eoDC3hOwkC5kJRYz8KIqbPmpA4rZ8knYYm/G71FR1P17lK+FmFxOhdQLYuekQDw19k11/Heaep4aUbp8/azV39X2FmVMXcNPoyy94zFWp6jyjunfxtmUWWnV3cXifn0y3knLISpMOxl9frLJdqjnQXRPdijbBEczYua50Xfcf32HYr5/y8Kp5TO54OVE+jvdtOkuVUVfTODuXW2je3a1C24z9xJ+73/Tjmse9+e3DfNKTDP68oerwK79vTjGbFC/dO4ivl/zFsdSK9/AM6tKcltHhfL5o0wWI8syquh23utdZZWZlonNEPV5cs4yh339BlK8/w5u1quEIz9351EkpeHxYb17/aXkNR1UzqutPDYtuRZugSD7cZbvP1EWZ6Bxan5c2L+GaRZ8Q5RPA8IZtnRipcbRWTvuprS7WDhnAO8AtSqnKMye0AiofJTfa15/yFPAIMFtrvf80+2gFxJ/qjP1T9iGUG5VSGxd9lXbmB1TDPwSyTpS9s7NSNX7BVb+44ssNVwTYvrqEqOYm3D0V7p6KyzqZObL7vKp13ryDzeSklqXjc9OK8Q4yO5SJjvXE5KLwC3fFv44r2Um2x7QfEcD1b9blqmcjQINfpHEnZN7BLhXqkpNWgleluvgEm4mO9cJsr0tAHVeykmxZpo4j/BnxZh2GPBsOGvwjjZ2YwCvYhdxy9clLK3aoj3ewmahYb0wuCt9TbZNo4dDaXMKaeeDqacLV00T9Dl4O2TVx7gaPiuPt+eN4e/440lKyCIksO1kPifAnLaXiTfnZGXkU5BWxetF2AFYsjKdJq7oOz7vs5y10G2DMCZlfiCIrtew4lJ2q8Q2u+mNp2/KqhytuX2GhRTcXzC7Gf9Am5Z0kstwQxAgvX1LycxzKdY+IZnTrOO7981uKrGUdleP2skdzMlmbcoRWgeEXPuhq+AWbyD5R1jYnU634BlX9P96x3HG44ql2DIww06CNCyl/G9MhG9GnHbMn38LsybdwIjOX8KCyYYZhgdVnx54edTlHj2c6ZMdiW0Rx99WxjHv7JyzFxtQpOfckdXzK6hHp7cPxXMfXWXWP3Zl2nKMnsyjRmt8O7ad1qDGvsxu7t+Ob8bfwzfhbOJGdS0S5cePhAT6cqKZtKvN2d6NJRAgfjx7OLxPvom2DSN66eygt6zm/Xsn5J4n0LjsGRHr5cjzfceKX7uHRjG7VnfuWzS09BiTlZbMjI4WjuZm2tknYS6ugCKfFLox10XbItNbZwOfA2LMorqh4wa8XkAW0Ppd9KqWett8nlnguj9Naz9Bad9Jad7rypuBzeWgF9ZqZSE3UpCdbKbZo4peV0LKrYyrleIKV/Bxo0KKseQNCFQe2WSkp0ZQUaw5usxJW39jmD23qTnZSMdkpFkosmr9X5hIV61WhTHQXLxK322ZSK8guISvRgm+4C9YSTUG27SCWdqiI9MNF1Gtv3KQRYU3dyKpUl+jYivGUr0t+dgmZiRb8qqhL2mEL9dt7OOzDmWxtY+GkvT4HVuYS1bnieP4GXbxJ2mbLfBVkl5CdaME33BWfUBeSdxRgLbHdQ5a0I5+AesZ2MC8F82etZszg6YwZPJ01v++g/7WdAGgeE0XuyYIKwxVPWbdkB2272u4tiYlrWjqTYp3okNIysX1bcOxQqsNjnaFuMzPpx6xk2I9p25ZbaN7F8cJKakIJBTma+i2qGK64rLjKjpoRtqYlEu0bSD1vf1xNJoY0aMHiSsMOWwaG82LsQO5d9i1phWX3zPq5eeBmstUv0N2TjqH12JdlTLsA1GlmJj3RSkZyCSUWzY7lFpp1cRx2mGZvm3rNy9omP8fWngB5WVaO7iwmJMqYtP/cP+O5+bkvufm5L/lzy99c3bUFAK0bRZCTX1Rlh+yBa+Lw8XTn9Tl/Vlh/Wf1Qnr61P+PenkfGSeOy/vHHk4n2D6Ser/111qQ5vx/++8wPBOJPJOPv7k6Qh+3zKa5uFPsy/vmF4vMxZ1U8N0z7khumfckf2/5mSCdb27RtEMHJgqKzHq6YU1BE78nvM+iFmQx6YSZbDycx9uN5hsyyWPkYMLhBSxYfczwGvBA7iPuWz61wDNianoS/mwdB7rbzoLjwBuw38BjgTFatnPZTWxk/xuP8vAlsBj4pt24n0BH4o9y6Dvb1KKW8galAP2CmUuoqrXV10yTtBNoppUxaa6vW+kXgRaXU2V2KqmFms2LYA658NLEIawl0HmAmooGJRZ9bqNfMRCt752zLnyW0622uMHymbQ8zf8dbmf6ALVNxWSdTlZ05ZzKZFXH3BvHLsynoErjsch+CotzYODuD0CbuNIj1ol57TxK2FDB3zDGUCbrcEYiHn5niIis/T0gGwM1L0feREEOHLJrMih73BrHg2eMV6rJhdiahTdyIjvWifnsPErbkM2dMIsoE3Urrovlpgu2Dw9VL0d/gupyqT7d7Q/j1Wdv9bs36+xIY5cam2emENHGnQaw3ddt7krAln+8eOooyQefbg/HwMxPdzZvEbfl8/3ACSkHd9l4OnbnaZvyzsH4LZGZBn+Ew5k4YfrXRUVVvw9JddO7TnJlLn6SgwML0/84p3fb2/HGMGTwdgJmvLuSxN27iP5OGkpWeyxv2ckNGdad996YUF5eQk5XPtMccZzNzBrNZcfUDHnw+KQ+rVdPhCjfCGphZMquAuk3NNO9q62htXWahdS/Hr+nISLGSlWoluo3RY3xtSrTmmY2/83m/kZiUYu7fW9mXlcq4tj3ZlpbE4mP7eap9X7xd3Hinx7VA2fT2TfyCebHLQLTWKKV4f+eaCrMzOpvJrBh4vxdfTc7BaoWYK9wIbWDmzy/yqdPUXNo5276siFaV2ib1qJWFb+eilEJrTdwIjwqzMxpl5baDdG8TzU8v3klBUTFTPv2tdNvsybdw83NfEhbowz1Xd+FgUhpfTroFgG/+iOfHldt5eHgvPD1cefV+28EhOe0kj74zz+n1KNGaySuX8PlV12NWJr7Zs419GWmM69SdbSeSWXz4b9qGRvDBgGH4u3vQv0FjxnWKY8DcT7FqzYtrlvHl4BtQwPbUFL7etdXpdahsxa6D9GwRzYIJd1JgKWbSV2Vt8834W7hh2pcAjBvck6s6XIaHqyu/T76H79dt571Fa6t7Wqcr0ZopG3/js74jMSkTcw/Esy8rlUfa9GJbehJLju3jqfb98HZx4+0e1wGQmJvFfcu/xao1L/+1hC/63YxSsC09ma//drx/UVyaVHXjp2szpVSO1trH/vdUYCQwU2s9RSk1FJgEDNRapymlYoB5QBetdZJS6lXAorWeaN82B2intS6oZl/fAPuBSVrrEqWUB5CmtfYuV+ZTznJSj58OxFx8//Bq7Cs0bjhNTTPXgml/a5JFG3/yU1MeCzq7K78Xi6vjhhodQo2547dlRodQo55cd53RIdSY52J/MjqEGvXm1BuNDqHGpHUy/n66muS359L5vMnpdGnd73zg5gm1NyVUTrv5k5x2EhY/+Pla+T+52DNkANOAMacWtNbzlFJ1gdVKKQ2cBG61d8ZaAtcC7exltyilFmGbCOTZap7/HuA1YL9SKh3It5cXQgghhBBCiPNyUXbITmXH7H+nAF6Vtr8HvFfF43YCzSqtO+09aPZ71f5zhjJ3nDFoIYQQQgghRAW1efZDZ7loJ/UQQgghhBBCiIvdRZkhuxCUUusA90qrR9m/AFoIIYQQQghRwyRDJh2yUlrrLkbHIIQQQgghhPh3kSGLQgghhBBCCGEQyZAJIYQQQgghDFGbv7DZWSRDJoQQQgghhBAGkQyZEEIIIYQQwhDaaV8LXXtJhkwIIYQQQgghDCIZMiGEEEIIIYQhZNp7yZAJIYQQQgghhGEkQyaEEEIIIYQwhGTIJEMmhBBCCCGEEIaRDJkQQgghhBDCEDLJomTIhBBCCCGEEMIwkiFzsve7dzc6hBpTkpZhdAg1xuThbnQINaokJ8foEGrM0uihRodQoxasnmd0CDXm6rhLq22aBRYYHUKN+fyv+kaHUKOKxxodQc3xOmo2OoQa5ZZ96eQ3In68tM4FuNnoAM5ObbuHTCk1EPgfYAY+0lq/Umn7/cBooATIAe7TWu88n31KhkwIIYQQQgjxr6eUMgPvAIOAlsBNSqmWlYrN1lq30VrHAFOBN853v9IhE0IIIYQQQhhDO/HnzGKB/VrrA1rrIuBrYFiFcLXOLrfofdbPfBoyZFEIIYQQQghxyVNK3QfcV27VDK31jHLLdYGj5ZYTgC5VPM9o4FHADeh3vnFJh0wIIYQQQghhCGfeQ2bvfM04TZGqgnHIgGmt3wHeUUrdDEwEbj+fuGTIohBCCCGEEELYMmLlZ0WqBySepvzXwDXnu1PpkAkhhBBCCCEEbACaKqUaKqXcgJFAhemRlVJNyy1eDew7353KkEUhhBBCCCGEIXQt+uYErXWxUmoMsAjbtPcztdY7lFLPARu11vOAMUqpywELkMF5DlcE6ZAJIYQQQgghBABa64XAwkrrJpf7++Ga3qd0yIQQQgghhBCGqG1fDG0EuYdMCCGEEEIIIQwiGTIhhBBCCCGEMSRDJhkyIYQQQgghhDCKZMiEEEIIIYQQhqhNsywaRTJkQgghhBBCCGEQyZAJIYQQQgghjCEZMsmQCSGEEEIIIYRRJEN2Ebr/+RF07t+Kwvwipj0yi7+3HXUo4+Jq5sGXbqBNt6ZorfnslZ9ZtWALoXUDGf+/2/Dx88RkNvHJiz+x4Y8dBtSizINv3EbngTEU5hfx+t3vs3/LoQrbPX08eGNp6ffxEVI3mCWzV/L+Y7O4/7VbadenJQDuXu4EhPpxXdi9zgy/ggem3kznAW0pzCti2gMfsz/+cIXtnj4evP7rU6XLIXUD+WPOGj548qvSdT2GdWLirNE81PtZ9v11yFmhn9GD/7uT2EEdKMwr5LU732H/XwcdyvQd2Z2bnroOrTVpiRm8MuotstNOGhCto/snD6NznxYUFhQx7fE5/L3jmEMZF1czD065ljZdG6Otms+m/cKqX7dx1c3dGDwqDmuJlYK8It6a8C1H9qcYUIsze/oV+HMNBAXCz58aHc3ZudTa5oHHBxHbvSkFBRamTfmR/buTHMpM/eAOgkJ8KSq0APDU6FlkZeSWbu/RvyWTpt7ImFs/YN+uRKfFfjoX+zEA4Inr+tCjRUMKLBYmzf6N3QnHHcqMuSqOIZ1b4uflTrcn3ildPzS2JeOG9uR4Vg4AX6+I54e1250WO8CEq/vQq1lD8i0WJnz3G7uSHONvWSeMl667Eg9XF5bvPchLC/4EYHS/rgzv1IaM3DwA3vx9Fcv3HmJwu+bc1aNj6eObhYcy/N0v2Z18wil1OuWxm/rSvU1DCoosTJm5iD1HKtbN3c2FV+8fTL3QAEqsVlZsPcDb360E4PrebRnRN4YSq5X8Qgsvfv47B5PSnRp/eePu7Eu39g0pKCzmhXd/Ze9Bx3Z6Y8J1BAd4YzabiN99jGkfLcGqNaNv7UWPjo2xFJdwLCWTF99dRE5eoQG1cB75HjLpkF10OvdrRZ1GodwdN4XmHaIZ88pIxl39mkO5kQ8PJDP1JPf2eA6lFL6BXgDc9MhAVszbzILPVxDVLILnvniQO2InOzzeWToPjKFukwjubPkozWObMPbtuxjbo2I8+TkFPNB5QunyO2tfZNWPGwB4//EvStcPe3AAjWOinRJ3VToPaEudxuHcFfMkzTs3Ysz0UTzS74UKZfJzChjd45nS5f9b9gyr5m0qXfb08WDY/Zeza8PfTov7bMQOak/dJpHc0ewhWnRpyth372VstwkVypjMJh54807uaTWO7LST3PPqrQwbM5BZz841KOoynfs0p050KHf3e4XmMVGMef56xl33lkO5kaP7k5mWw739X7W9bwI8Afhz3mYWzl4DQJf+Lbn36SFMuvMjp9bhbF0zCG6+Dp58yehIzs6l1jaduzelbv1g7rzmLZq3rsdDTw3m4ds/rLLsqxO/q7Kz5enlxjUju7CriottRrnYjwEAPVpEExUawJAXP6FNgwgmjujHrdO/dii3bMcBvl4Zz89P3+Gw7be/9vLyd0udEK2jXs2iaRAcwMDpn9C2XgTPDO3HyA8c4588tD/P/LSY+KNJfHDbNfRsGs2KfYcA+HzVZj5ZtalC+fnxu5kfvxuApuHBvH3LMKd3xrq3aUj9sACunTCT1o0ieerW/tzx0lcO5WYt2sSmPUdxMZt4b/xw4lpHs3r7IX5dt5vvlm0FoFe7Roy7sQ9j3/zeqXU4pVv7htSLCOSGsTNp1TSSx++5nHufnu1QbuL0+eTlFwHw4vgh9OvWjMWr97Bh62Hen72CEqvmwVt6ctu1sbz75QpnV0M4WY0MWVRKlSiltiiltiul5iql6tqXtyilkpVSx8otuymlBiql9iil9iulniz3PP2VUpvt5VYqpZqcZp+XKaX+tJfdpZSaYV8fW25f8Uqpa88Qu1ZKTSu3/JhSakq55fuUUrvtP+uVUj3s681KqU1KqV7lyv6mlBrxj/6JZ6nrwLYsmbsOgN2bD+Hj50lgmJ9DuQEjuzHnrd8A0FqTnZ5r/xu8fD0A8PL1JC0560KGe0ZxQzryu/1As3v9frwDvAiKCKi2fJ0mEQSE+rFt5W6HbX1ujOPPOasvWKxn0u2q9iz5yrb/3RsO4OPvRVC4f7Xl6zQOJyDUj+2r95auu23itcz93y9YCiwXPN5z0W1YZxbPWgbArnX78AnwdmgnpRRKKTy83QHw9vMkLTHD6bFWpevlrVjyw0YAdm85go+fB4Ghvg7lBgyPZc57fwD2902G7UpyXk7Z1UkPL7daPSNU53YQ4Fi1WutSa5tuvZuzeMEWAHZvT8Dbx4OgEJ9zeo7bH+jH3M9XUVRYfCFC/Ecu9mMAQN82jfl5wy4Ath1OxtfTnRA/b4dy2w4nk5qd67DeaP1aNOanLbb4tyYk4+vhTohPxfhDfLzxcXcj/qgtK/vTll30b9n4rPdxddvmLNzq+Pl6ofWOaczCNTsB2H4gCV8vd4L9K9atsKiYTXtsFymKS6zsPnKcsEDbsSK3oKi0nKe7K9rAA0HPTo35dbmtLjv2JeHj7U5wgOPr7FRnzGw24epiLj12rd96mBKrbWH73iRCgy6iA/o/pZ34U0vVVIYsX2sdA6CU+hK4sdzyFCBHa/26fdkMvANcASQAG5RS87TWO4H3gGFa611KqQeBicAd1ezzLWC61von+/O2sa/fDnTSWhcrpSKBeKXUz1rr6j7ZCoHrlFIva61Ty29QSg0G/gP00FqnKqU6AD8qpWK11sn2GD+yrx8OaK31Bb0UGBzhT2piZulyalImIZEBZBzPLl3n7We7cnzbE4NpG9eUpEOpvDvhGzJTT/LF6wt48esxDL2rN+5e7ky40fFKtDMF1wnkxNGyYQWpCekE1wkkPTmzyvJ9b+zGn3PXOKwPiwohIjqULUuNG34ZXCeAEwlldTlxLMNWl5SqO719hndh2ffrS5cbt40itG4Q63+NZ/hDAy94vOcipE4Qx4+mlS6nJqQRUjeoQjuVFJfw1oMfMmPrNApyCzm2L4n/G/2xEeE6CI7wJzWp3PsmOYuQCH8yTpQNpfK2X6i47dEradulMUlH0nh3yg9kptqGJw0eFcd1d/XCxdWFJ29937kVuIRdam0TEubLiZSy43Hq8WyCQ/1It8da3vgp12AtsbLyj13M/sjW2Wl8WQSh4f6sW7GX62+Nc1rcZ3KxHwMAwvx9SMkoe12lZOYQ5u9zTp2v/m2b0qFxXQ4fz+S1H/8kJdOxXS+UMF8fkrPKxZ+dQ7ifD6k5ZfGH+/mQkl0WU0pWDmG+ZRcEbu7ajqHtW7DjWApTf1lOdkHFoXAD2zRjzBfzLmAtqhYa4ENyerm6ZeQQFuBDWlbVbePj6U7Pdo34evFfpetG9G3HLVd0xMXFzAOvG5eVDQ3yISW1rC4n0k4SGuRDWqZjXaZPuJ4WTSJYu+UgS9fuddg+uF9rlqzec0HjFbXDhZjUYwVQbWYLiAX2a60PaK2LgK+BYfZtGjiV7vEHTjdwPhJbh872QK232X/nlet8eXDm/nAxMAMYV8W2J4DHT3XUtNabgc+A0fbldcBqYArw0qn1F5JSjuNsK18JMruYCK0byM4NB3howKvs2nSQe565DoA+13Zi8Zx1jOo4kcm3vsvj/3d7lc/pLFXu+zQt1ueGbvw5x7FD1ueGbqz4fj1Wq3GXP86mbcrrfX0sf367tvSx/3n5Jj582nH4SW1Qdd0qLptdzAy5fwAPdPgvI+vex8FtRxj51DVOivD0zvp9UyeAnZsO8dDQN9n112HueWpI6fb5s1ZzV99XmDl1ATeNvvyCx/xvccm1zVkeB16d+B333/gu4++ZSev2UVx+dTvbceDRgcyYvsgZkZ6Ti/0YUJ1zyaQs236AQc99zIipX7Bu7xFeuPnKCxiZo6o/LvVZlLH5et1WrnzjE6575wtOnMzlv4N6VSjXtl4EBUXF7D+e5vgkF9jZ1O0Us0nx4n1XMWfJXxxLLbvgOXdpPNdMmMn/fbuCuwd3uVChntG5nAuMe+k7hv7nfVxdzXRsHVVh2+3XdqGkxMqiFbsuSJy1idbKaT+1VY3eQ6aUcgEGAb+eplhdoPzA+ATg1DvnHmChUiofyAa6nuZ5pgN/KKVWA78Bn2itM+1xdAFmAg2AUafJjp3yDrBVKTW10vpWwKZK6zYCt5dbfspenze11vurenKl1H3AfQCt/HpT36vVGcKpaPAdvRh4S3cA9sYfJqRO2TCRkMgAh2GH2em5FOQVsnphPAArft7MlTfZrrReeVMcE29+G4Ddmw7i6u6KX5A3WWnOu8o35P4ruOruvgDs2XiA0PpBpdtC6gWRllT1EJdGbaMwu5jZV8WN5H1u6MbbYz+5MAGfxpB7+zHw9t4A7N18kNB6ZXUJrRtIelLVmb6GretjdjGzf4tt0g9PXw8atKzL1AW2EbyB4f5M+XosU0a+ZdjEHkMfvJKr7rGd3O7ZuJ+w+sGcyj+G1AsmLbHiDdOn7t9LOmCbUGHZN6sZ+YRxJ2ODR8Ux8EbboWXv1qOERJZ730T4k1YuiwGQnZFHQV4RqxfZbtJfsTCeK0fEOjzvsp+3MOb56+DxCxj8Je5Sa5shI2IZdG0HAPbuTCQ0vGwYeUiYH+mpjpNapNkzgPl5RSz9dRuXtarL6mW7iW4SxtQZdwAQFOzDs9Nv4plxXxkyscfFfgwAuLFHO67r1hqAHUdSCA/0BftHSHiADyfOITuWlVdQ+vd3a7bx8JAeNRprVW7q0o4RnWzxbzuWQoR/2fC1cD8fjleKPznLljUrLePvw4mTts/3NPtkHgBzN27nvVHDKjx2UJvLWLjNecMVR/RtxzU9bYObdh5KISLIl3j7tvBAH05UkVECePq2Kzh6PJOvymXHyvttw26eurU/4LwLG9ddGcPQ/ra67P47mfAQX7AntkKDfUnNqP51VmQpYeXGv+nZuTEbttnOCQb1bkn3jo146Lnacf+luPBqqkPmqZTaYv97BXC6MQpVdU9PXToYB1yltV6nlHoceANbJ83xAVp/opRaBAzElmH7j1Kqnda60J65aqWUagF8ppT6RWtdUNXz2J8rWyn1OTAWyD9N7KfiL3+poxeQBbQ+zfPPwJaFY1Dk6HNO4cz/dDnzP10OQOf+rRhyV2+W/biJ5h2iyT2ZX2G44inrfttG27imxK/aS0yP5hzZaxtPfvxYOjE9mrP4m7XUbxqOm7uLUztjAD+//zs/v/87ALGDYhj2wAD+nLOG5rFNyM3Kr3a4Yp8b41haxT1i9ZpF4hPgzc61+y5o3FX5+cM/+PlD230tsVe2Zch9/fnz23U079yI3Oz80w5X/PPbdaXLedn53NhwbOny1AVP8OHEOYbOsjjv3UXMe9f2gRZ7VQeGjR7I0q9X0aJLU3Kz8hzaKe1YOlEt6+Ef4kdWajYdrmjLkd2Os+U5y/xZq5k/y/Z66dy3BUNGdWfZz1toHhNF7smCCkPiTlm3ZAdtuzYmfs1+YuKals7WVyc6hMRDthHNsX1bcOxQqsNjxdm71Nrm57nr+XmubfhxbI+mDL2hC38u2k7z1vXIyylwGK5oMpvw8fUgOzMPs4uJLj2a8df6A+TlFHJD/7LrglM/uIMP3/zNsFkWL/ZjAMCclfHMWWk7ze/ZsiEje7bj1817aNMggpz8onMarhji511avk/rRhxMufCz+H21Lp6v1tni79WsIbd0bcfCrXtoWy+Ck4VFFYYrAqTm5JJbWETbehFsTUhmWEwLvlxrOz0L8fEuLX95y8bsSynLhCkFV7Zuym0fOa8DMHdpPHOX2urWvU1DbugXw6L1e2jdKJKc/KIqhys+cE0cPp7uPP/ZbxXW1w8L4Ohx2+uxR9tGHDnu3HsXv1+0he8X2f7Pce0bcv3A9vy+ajetmkaSm1foMFzR090VL0830jJzMZsU3do3In6XbdBXl3bR3DosltHPzKGwqPbcRyourBq/h+wsJAD1yy3XAxKVUqFAO3tnCmAOp8+0obVOxJYJm6mU2o6tU7Sp3PZdSqlc+/qNZ4jrTWAzUD7NshPoCPxRbl0H+3qUUt7AVKCfPYartNYLz7Cf87JhyQ4692/FzDVTKMgvYvq4slkG3/79KcZc8TIAM1/8icf+73b+89xwstJyeGPcLAA+evZ7xr52M9fe1xet4Y1HZl3IcM9o/S9biB0Yw6e7plOYX8jr93xQuu29DS9VmF2x9/VdmTischIT+t4YV+V9Zc62ftFWOg9oy8z4VynMK+KNB8uuS7yz8tkKsyv2urYzk4ZPNyLMf2T9ws10uao9n+37Pwrzinj9ri6zX5IAACAASURBVLKpoN/f/Br3d3ictKQMvnhuLm8se5ZiSwkph0/w2p3vnOZZnWfD0l107tOcmUufpKDAwvT/zind9vb8cYwZbGuLma8u5LE3buI/k4aSlZ7LG/ZyQ0Z1p333phQXl5CTlc+0x2rn0FKA8c/C+i2QmQV9hsOYO2H41UZHVb1LrW3Wr9xH5+7N+OSnhym0T3t/yruz7+fBm23Dk156exRmFxNmk4nN6w/wyw+VB2PULhf7MQBgxc6D9GgRzfyJd1JQVMzkr8pO6uc8fgs3vvYlAI8M6clVHS/Dw9WV36bcw/drt/P+r2u5uVcMfVo1pthqJTuvgEmznTu0dPneg/RqFs2vj9rif/r7svi/H30L171ji/+5eX/w0vUDcHd1YcXeQyzfewiAxwb2pHlEKBrNsYxspvy0pPTxnaLrkZKdQ0KGMRN9rdp2kO5tGvLjS3dRUFTMs5+U/W+/nHwrtzz3BWGBPtw9uCsHk9L4YtKtAHyzdAs/rdjODf1iiG0RRXGJlZN5hUyZadyw39V/HaRbh0bMfetuCoosvPhuWSyfTh3FHf+dhYeHK1P/ew2urmZMJsWm7Uf58Xdb53T83f1wdXHhzUnDAdvEIK99uNiQujhNLZ5sw1lUTcxEo5TK0VpXOY1UFZN6uAB7gf7AMWADcDO25G4yEKe13quUuhtbtuz6ap53ILBEa21RSkUAfwHtAU/gqH1SjwbAGqBt5Qk7qordPmRxJDBTaz1FKTUUmAQM1FqnKaVigHlAF611klLqVcCitZ5o3zYHW6ey2mzcP8mQ1VYlabVn9qzzZfJwNzqEGlWS49ys54XkEt3A6BBq1ILVzr9h/kK5Om6o0SHUKGug40xoFyvrXzuNDqFGHR9beyY4OV+Wc5t0s9bzSr5kTmtwy7506gKw+pvxtfemqXKiP3/Faf/4Q7c9WSv/J07/HjJ7R2kMtsG9Zmydnx0ASql7ge+UUlYgA7jrNE81APifUupU5+dx+8yHo4AnlVIWwAo8WF1nrArTgDHlYp2nlKoLrFZKaeAkcKu9M9YSuBZoZy+7xT6E8gng2bPcnxBCCCGEEP9itbKP5FQ10iGrLjtm3zalinULAYehfVrrH4AfznKfjwKPVrF+FnDW4/DKx661TgG8Km1/D9t0/JUftxNoVmnd2MrlhBBCCCGEEKI6Ts+QCSGEEEIIIQQg95BxEXTIlFJPAyMqrZ6rtX7xHJ4jGFhSxab+Wmvnf+GGEEIIIYQQQnARdMjsHa+z7nxV8xxpwNnOAimEEEIIIYRwBsmQYTI6ACGEEEIIIYT4t6r1GTIhhBBCCCHEJUrLLIuSIRNCCCGEEEIIg0iGTAghhBBCCGEILfeQSYZMCCGEEEIIIYwiGTIhhBBCCCGEMSRDJhkyIYQQQgghhDCKZMiEEEIIIYQQxpBZFiVDJoQQQgghhBBGkQ6ZEEIIIYQQQhhEhiwKIYQQQgghDKFkUg/pkDnbgD/2GR1CjUkq8jc6hBpTaL20jgae5kunPu29lhkdQo26Om6o0SHUmAWr5xkdQo1qOP9eo0OoMaPjcowOoUZ98ZHREdQcc57REdQs17xL5/PGeneq0SGIfynpkAkhhBBCCCGMcen06f8xuYdMCCGEEEIIIQwiGTIhhBBCCCGEMWTae8mQCSGEEEIIIYRRJEMmhBBCCCGEMIbcQyYZMiGEEEIIIYQwimTIhBBCCCGEEMaQDJlkyIQQQgghhBDCKJIhE0IIIYQQQhhDMmSSIRNCCCGEEEIIo0iGTAghhBBCCGEM+R4yyZAJIYQQQgghhFEkQyaEEEIIIYQwhJJ7yCRDJoQQQgghhBBGkQ6ZEEIIIYQQQhhEhiwKIYQQQgghjCFDFqVDdrE5sjmfVR+lo63Q4gof2l/v71Bm/8pcNn2dBQqCo125fHwoAGs/y+DwpnwAOt7gT5Me3k6NvSopW06y7ZNjYIWo/kE0uybMocyx1ZnsnpuCUuDXwJNOD0eRd6KI9a8fRls1ukTTcGAIDQcEG1CDqp3YksWuzxLACvX6BdNoWIRDmaQ1Gez/NgmlwDfKk3ZjGxoQafVS/jrJ1k8S0VZo0D+Qy651bJuE1Zns/uY4KPBv4EHnR2xts+61w2grWEs0jQcFG942+zYWs3BGAdqq6TDAjV43uFfY/suMAg5uLQbAUgi5WVYmfOPHgfhifv2woLRcaoKVEU940qKbq1Pjr8r9k4fRuU8LCguKmPb4HP7eccyhjIurmQenXEubro3RVs1n035h1a/buOrmbgweFYe1xEpBXhFvTfiWI/tTDKjFmT39Cvy5BoIC4edPjY7mzHrXi+aZrv0xK8XXe7by3tb1FbbHRtTjma79aB4UykN//MzCQ3tLtz0V25t+9RthUooVxw4xZc0fzg6/goTNeaz9OA2rVXPZ5X60uz7AocyBVTn89XUGKAiKdqPvo+EArP8sjaOb8tBWqBvjSde7g1HK+JnUnri2Dz1bNKSgyMKkr35j17HjDmUeGhTHkE4t8fNyp+tT7zhsv6JtU6bdMZiRb8xmZ4Jx75sny9Vl4mnqMtRely7l6jKiW1tu6tGOEquVvEILz85dzIGUdGeGf1rjbutLXExDCoqKef79X9l7yLFu05+4juAAb8xmE/G7j/H6J0uwauPP7LuGNGNc88GYlIl5CRuYdXBZleX6hrfm5ZhbuGPN2+zOPoafqxcvx9xMC796LEjczLRd85wcuTCSdMguItYSzcoP0hn8bBjewS58/3gSDWI9CarvVlomM9HCX99lcc0r4bj7mMnPLAHg8MY8ThwoYsT0SEosmnlPpxDVwRM3L+NGrWqrZuvHx4ib2BDPYFeWPbWfiE5++NXzKC2Tk1TIvh+P0/P5xrj5uFCYZTtp9gh0oecLjTG7miguKOGP8XuJ6OSHZ5DxJ8raqtk58yidn26KR7AraybsIayjPz71PEvL5CYVcOCnZLo+2wxXHxcKsywGRuxIl2jiP06k+6SGeAa5sPSpv4ns5Idf/Ypts/eHE/R6oTFuPuaytglwodeL9rbJL2HJ+H2Gto21RDP/vXxuf8EbvxDFB+Nyad7VhbAoc2mZQfeV1WvtvCKSDtjeN43aufDg2z4A5J3U/O+ekzRub/xhs3Of5tSJDuXufq/QPCaKMc9fz7jr3nIoN3J0fzLTcri3/6sopfANsL0G/5y3mYWz1wDQpX9L7n16CJPu/MipdThb1wyCm6+DJ18yOpIzMynF83FXcMsv35Cce5J5w0ax+Mjf7MtMKy2TmJPN+OW/cF+bzhUe2zGsDp3C63Ll958C8N3gm+kaWZ+1SUedWYVS1hLN6hmpDJwSiXewC/P+e4yoWC8Cy33eZCVaiP8uk8Ev16nweZOyu4CU3QVcO70eAPMnJJK8o4DI1p5V7stZerSIpkFIAINf+oS2DSKYOLwft/zva4dyy3Ye4KuV8cyfcIfDNi93V27uGcPWw0lOiLh6Pe11ufos67KgUl0Wbt7N3DVbAejTqhGPD+vNAzN+cEboZ9QtpiH1IwIZ8ehMWjWJ5L93Xc49k2c7lHv6rfnk5RcB8NIjQ+jXtRmL1+xxdrgVmFA81mIoYzd+zPGCbD7pNpoVx3dxKLdih9LL7MYNUXFszzxSuq7IamHGvt9p5BNOI1/Hi7ji0nZR3kOmlNJKqWnllh9TSk0pt3yfUmq3/We9UqpHuW1fKqX2KKW2K6VmKqVOe5aolBpof47dSqktSqk5SqkopdQ79uWdSql8+99blFLDL0ilgeP7ivCLdMEvwhWzq6JxD28OrcuvUGbXbzm0vsoXdx/byaZngO13xlELdVq5YzIrXD1MBEe7cmRzvsM+nCljfx7eEW54h7tjcjFRNy6A5A3ZFcocXpJOwyuDcfOxnQS7+9t+m1xMmF1tL1+rRYPVubGfTub+XLwi3PGy1ysiLpCUjVkVyiT8kUrUgFBcS+tlfEeyvPTStnHD5GqiXnd/kjZWbJtDi9NpNDAYN/trrbRtXMvapqRYow1um4S9JQTVMREUacLFVdGmlyu71xZXW37bMgtteju2x86VFpp2csHNw/ir/F0vb8WSHzYCsHvLEXz8PAgM9XUoN2B4LHPes2VZtNZkZ+QBkJdTWFrGw8uNWnBRuVqd20GAY9VqpZjQSA5lZ3D0ZBYWq5WfD+zmigZNKpRJyMlmd/oJhyv5GnA3m3E1mXEzmXExmUjNz3Vi9BWd2FeIX6Rr6edNox7eHFlfMZ49v2fTcpCfw+cNQEmRxlps+9ElGk9/M0br27oxP2/cBcDWw8n4eroT4us4UmTr4WRST1b9vx8zKI5Plm6k0FL9McQZ+rZuzLzzqEtuYVHp355urtSmg0Cvjo35ZcVOAHbsT8LHy53gAMe6neqMmc0mXF3MtaIKLf3rk5CXRmJ+BsW6hN+T4ukV1sKh3H1NB/DFoeUUWcteRwUlFuIzD1dYJ/49jL/U+88UAtcppV7WWqeW36CUGgz8B+ihtU5VSnUAflRKxWqtk4EvgVvtxWcD9wDvVbUTpVRr4P+AoVrrXfZ1Q4ForfVo+3I0MF9rHVPDdXSQm16MT0hZk/kEm0nZV1ShTFaiLdPyw5PJaKum08gAojp4EhztxsY5WbQdZqW4UHNse2GFK51GKEi34BlcduLrGexKxr68CmVyEm0njism7Udb4bIR4YTH2M7O8lOLWPvKIXKTC2l5a2StyI4BFKZb8Awu+996BLmStb9ivXKTbPVaO3kP2qppMjyS0BjH4adGKUgvrtg2QVW0TZLttbds4t9oq6bFiHDC29vaJi+1iDUvHyY3uZDWo4xtm5NpGv+QsmtPfiGKhD0lVZbNPG4lI8VKo7aOJ4/blluIu9bY98wpwRH+pCZlli6nJmcREuFPxomTpeu8fW1Zv9sevZK2XRqTdCSNd6f8QGZqDgCDR8Vx3V29cHF14clb33duBS5REV4+JOWWtUFS7knah0ae1WM3H09kTeJRNtz8AEopPt+5mf2Zxg0hy0svxrvc541XsAsn9hZWKHPq8+bnp46hrdDhxkDqdfAivLkHkW08+equI2g0LQf5E2Dw5w1AmJ8PyZll7ZOSmUOYv0+1na/KmtcNJSLAl+U7D3J7n44XKsyzcr51ARjZvR239e6Aq9nM3e99eyHC/EdCA31ISS+r24n0k4QG+pCW6Vi36U9eT8vGEazdcpCl6/Y6bHe2UA8/jheUXYA9XpBNq4D6Fco0840k3MOfVSd2c0t0T2eHWCvJtPcXaYYMKAZmAOOq2PYE8PipjprWejPwGTDavrxQ2wHrgXqn2c8TwEunOmP2x8/TWi8/l2DtGbuNSqmNa75JOJeHVlTFC7bytXqrFbKSLAx9IZzLx4ew7J00CnOs1G/vSVRHT358IpnF01IJv8wNZfAFyyqvZlWqkLZqcpIK6f5MYzo+HMWW9xOw5NpOpj1D3Oj7ejP6v9Wco8syKMisXcP+KqhcrxJNbnIhsZOb0W5sQ7bPOIIlt5ZfFat0/4e1RJOTVETPKY3o/HAUm99PoMjeNl4hbvSf1pQr/u8yjvxpbNtU9TqrLse1bZmFVt1dMJkrljiZbiXlkJUmHWrHNayq7sXRlSpqdjERWieAnZsO8dDQN9n112HueWpI6fb5s1ZzV99XmDl1ATeNvvyCx/yvUMUL62zPMxr4BdAkIIiuX71Pl9nvERfZgNiI0308XWBVvW8cjmOQnWTh6ufr0PfRMFa8e4LC3BKykyxkJhQx8qMobvqoAYnb8knaYeyIDHCMH0CfZQspBY8P683rP53Tx/8FU/XteOd2Vvv1qniueukTpi9YwX1XdKmRuGpEVce3auo27pXvGPLg+7i6munYKupCR3ZGVTeLLrdd8Ujzwby1e4HTYhIXh9pxdvHPvANsVUpNrbS+FbCp0rqNwO3lV9iHKo4CHj7NPloBr59nnGitZ2DrQDJ914B/fB3AO9iFnNSyk/actBK8gir2qnyCzYQ1c8fsovALdyWgjitZSRbCmrrTcYQ/HUfYsjCLp53AP9LYjJJnsCv5aWUn6vlpFjwCK8bkGeRKYDMvTC4K7zA3fOq4k5NUSGATrwplfOt7kL47lzpdHW86dzb3IFfy08oylwXpFtwr1csj2A3/Jt6YXBReYe54R3qQl1yIf+Pa8Zb0CHKp2DbpFjyCKsbmGexKUFN724S74VvHndykQtyqaJu0XXnU7WZMBtAvRJGVWjZuMjtV4xtc9bWobcstDH7Aw2H99hUWWnRzwexi3HDFwaPiGHij7aRp79ajhESWvdZDIvxJS6k4pDQ7I4+CvCJWL9oOwIqF8Vw5ItbheZf9vIUxz18Hj1/A4P8lknNziPQuG18Z6e1LSl7OWT12YIOm/HU8ibxi2/tuacIB2ofVYX3yeVzEOw9ewS7klvu8yUsrdvi88Q42E3qZByYXhW+4K/51XMlOtJC0vYCwZh64etreZ/U7eHFibyGRrZx/D9mN3dtxfdfWAOw4mkJEufGv4QE+nMg6u4ySt7sbTSJC+Hi07a6EEF9v3rp7KGM/nue0iT1GlqvL9irqcvws61LZL3/tYeL1/Wskxn/q+itiGNq3DQC7DiQTHlRWt9AgX1Izqq9bkaWElZv+plenxmzYfviCx3o6xwuyCfMo+6wL8/DjRGHZsdnLxY1GPuG8G3sfAEFuPrzW/jYe/+tzdmc7Tsz0r6GNvxXAaBdrhgytdTbwOTD2LIorHC8dvQss11qvOJv9KaWC7feI7VVKPXZu0daMsKZuZCUVk51iocSi+XtlLtGxFT/gort4kbjdNitcfnYJmYkW/MJdsJZoCrJt2Yu0Q0WkHbZQv73jiaczBTT2IjepiNzjRViLrRxbnUlEJ78KZSJi/UndbjsQF2YXk5NUiHe4G/lpRZQU2U6yi3KKSd+Ti08dd4d9GMG/sTd5yYXkHS/EWmwleXUGYR0rdkbCOvmTvtM2JKMou5i8pAI8w2pH/ACBTbzISSokN6UIq8VKwqosIiu1TZ3OfpzYYTvZtLVNEV7hbuSnWSgpPNU2JaQZ3DZ1m5lJP2YlI9lKsUWzbbmF5l0cO76pCSUU5Gjqt6hiuOKy4irvK3Om+bNWM2bwdMYMns6a33fQ/9pOADSPiSL3ZEGF4YqnrFuyg7ZdGwMQE9e0dCbFOtEhpWVi+7bg2KFUh8eKcxd/IomGfoHU9/HH1WRiSKPm/H54/1k99lhuNl0i62NWChdlomtEffaXmwzE2UKbupOdZOGk/fPmwMpcojpXvI+nQRdvkrbZMl8F2SVkJ1rwDXfFJ9SF5B0FWEts95Al7cgnoJ4x7585q+K5YdqX3DDtS/7Y9jdDOtnu52nbIIKTBUVnPcQvp6CI3pPfZ9ALMxn0wky2Hk5yamcMbBmtEdO+ZIS9LkPL1SXnHOoCEBVSdkGnV4tGHEnNPE3pC++737dw+4RZ3D5hFss37mdQz5YAtGoSSW5+ocNwRU9319L7yswmRbeYRhxONH6WyF3ZCdT3CiHSMxAXZeaKyHasOF46yIrc4kIGLn2Ba5dP5drlU9mRdVQ6YwK4uDNkAG8Cm4FPyq3bCXQEys8X3MG+HgCl1DNAKLZ7zU5nh/2x8VrrNCDG3hnzOf/Qz53JrOhxbxALnj2OLoHLLvchKMqNDbMzCW3iRnSsF/Xbe5CwJZ85YxJRJuh2RyAefmaKizQ/TbB9cLh6Kfo/EuIwLMuI+rS9qw5rXjyAtkJU30D86nuwa04yAY09iezkT1g7H07En2TJuD0ok6LVrZG4+bpwfOtJdnx+qLSr3WRIKH5Rxs7gdYrJrGh5Z302vrQfbdXU6xuMb31P9n2TiH8jL8I6BRDSzo/UrSdZMX4nygSX3VoXN9/a83Y0mRXt7q7DqhcPghUa2Ntm59cpBDb2JLKzH2ExPqTEn2TxI3tRJmg9KgJ3XxeOx59k2+fJpW3TdEgo/g2M6/ybzYqrH/Dg80l5WK2aDle4EdbAzJJZBdRtaqZ5V9uJ4tZlFlr3cnUYDpiRYiUr1Up0G+MnJThlw9JddO7TnJlLn6SgwML0/84p3fb2/HGMGTwdgJmvLuSxN27iP5OGkpWeyxv2ckNGdad996YUF5eQk5XPtMccZ2erLcY/C+u3QGYW9BkOY+6E4VcbHVXVSrRm8urFfD5oOGZl4pu929iXmcajHbqzNTWZxUf+pm1IBDOuuAZ/N3cuj2rMuI7dueK7T1h4cC9xkQ347fo70VqzLOEQS478bVhdTGZFt3tD+PVZ2/3Izfr7EhjlxqbZ6YQ0cadBrDd123uSsCWf7x46ijJB59uD8fAzE93Nm8Rt+Xz/cAJKQd32Xg6dOSOs2HWQni2iWTDhTgosxUz66rfSbd+Mv4Ubpn0JwLjBPbmqw2V4uLry++R7+H7ddt5btNaosKu0YtdBerWIZqG9LhPL1WXu+FsYUa4uV9vrsnjyPXxnr8tNPWLo2iyK4pISsvMLeXr2IqOq4mD1loPExTRi7vS7KSy08MIHZbF99tIobp8wCw93V6aOvwY3VzMmk2LTjqP8sDjewKhtSrSV13fN438d78KkFPOPbeRg7nHubXI5u7OOseLErtM+/ode/8XLxR1XZaZ3WEvGbpzpMEPjJUnuIUNVvu/gYqCUytFa+9j/ngqMBGZqrafYJ92YBAzUWqcppWKAeUAXrXWSUuoe4C6gv9b6tIPalVJtgB+AIeUm9ZgMmLTWU+zL0dgm9Wh9NrGfz5DF2iapqPZMQnG+Cq21pzNUEzzNtfh+unPU3svYISg17dMBvY0OocYsWH1pfU9Ow/n3Gh1CjRkdZ+x3mNW0Lz4aYHQINeaSOQmw80muRdMcnyd9+6U1WmDtlS9fFGMBG735htPeFgceebRW/k8uhbPQacCYUwta63lKqbrAaqWUBk4Ct2qtT31pyPvAYWCN/Ur491rr56p6Yq31NqXUw8DnSilfIA04AjxzwWojhBBCCCHEv8WldpXiH7goO2SnsmP2v1MAr0rb36Oaqey11udUZ631AqDa6XC01oeAs8qOCSGEEEIIIUR5F2WHTAghhBBCCHHxk+8hkw4ZAEqpO3Gc/n7VqS9/FkIIIYQQQogLQTpkgNb6EyrO1CiEEEIIIYS40CRDdvF+D5kQQgghhBBCXOwkQyaEEEIIIYQwhmTIJEMmhBBCCCGEEEaRDpkQQgghhBBCGESGLAohhBBCCCEMIdPeS4ZMCCGEEEIIIQwjGTIhhBBCCCGEMbQyOgLDSYZMCCGEEEIIIQwiGTIhhBBCCCGEMeQeMsmQCSGEEEIIIYRRJEMmhBBCCCGEMITMsigdMqc7VBBsdAg1pq57ptEh1JgGbqlGh1CjXFWx0SHUmPFrbzA6hBrVLLDA6BBqTMP59xodQo06OPhDo0OoMWMSY40OoUZ1vGmr0SHUmDXz2xodQo3qfccao0OoMV9v6Wx0COJfSjpkQgghhBBCCGNIhkzuIRNCCCGEEEIIo0iGTAghhBBCCGEIuYdMMmRCCCGEEEIIYRjJkAkhhBBCCCGMIRkyyZAJIYQQQgghhFGkQyaEEEIIIYQwhnbiz1lQSg1USu1RSu1XSj1ZxXZ3pdQc+/Z1Sqnof1Lt8qRDJoQQQgghhPjXU0qZgXeAQUBL4CalVMtKxe4GMrTWTYDpwKvnu1/pkAkhhBBCCCEExAL7tdYHtNZFwNfAsEplhgGf2f/+FuivlFLns1PpkAkhhBBCCCEMobQTf5S6Tym1sdzPfZXCqQscLbecYF9XZRmtdTGQBQSfz/9AZlkUQgghhBBCXPK01jOAGacpUlWmq/LdZ2dT5pxIhkwIIYQQQgghbBmx+uWW6wGJ1ZVRSrkA/kD6+exUOmRCCCGEEEIIARuApkqphkopN2AkMK9SmXnA7fa/hwN/aK3PK0MmQxaFEEIIIYQQxqhFXwyttS5WSo0BFgFmYKbWeodS6jlgo9Z6HvAxMEsptR9bZmzk+e5XOmRCCCGEEEIIAWitFwILK62bXO7vAmBETe5TOmRCCCGEEEIIQ6halCEzinTILmIntmSx67MEsEK9fsE0GhbhUCZpTQb7v01CKfCN8qTd2IYGRFq9hM15rP04DatVc9nlfrS7PsChzIFVOfz1dQYoCIp2o++j4QCs/yyNo5vy0FaoG+NJ17uDOc+vgTgv+zYWs3BGAdqq6TDAjV43uFfY/suMAg5uLQbAUgi5WVYmfOPHgfhifv2woLRcaoKVEU940qKbq1Pjr2zPxhLmf1CE1Qqdr3Shzw0V45k/o4gDW0sAKCqA3CzNM3O9APjl4yJ2byhBa2jS3syQ/7ga2ja9IhvxTKfLMSkTc/Zv4f2daytsv7t5Z25sEkOJ1UpaYR5PrF3AsdxsAPbf9AR7Mk8AkJiXzb3LvnV6/FV54PFBxHZvSkGBhWlTfmT/7iSHMlM/uIOgEF+KCi0APDV6FlkZuaXbe/w/e/cdH0WZP3D88+wm2fReIZDQO6H33gRBwHYWUOxiOT3P8xQ5sfxOPb1T7/RU5FQsJxZOPRCxgQIiIIKUUAIJPaT3Tdlks/v8/tiQZFMoGnai932/XnnBzDw7+312Zmfmme8zz07syUNPX8Gdc18hdX/DZ5Y9Y2x8Ig8Pm4hZKd47sJuXd291Wz4kNp6Hh02ge3gUv/36E1YfPVi7bMGQsUxo1xGTUnx78iiPbP7a0+Gfk4V/gXWbITwMPnnD6GjOLH9nMQffOo52atqMjyJxVpzb8oz1eaS9cwJLuOvYED8lhrYTogBIW3aCvB3FAHS4JI6Y4T9rROgWUbirkCNvHwEnRI+LJn5mvNvynA05HH33KD5hPgDETY4jZrzrfFOZV0naq2lUFVQB0OO+HvhG+Xo0foCF08YxpksHbHY7C/77JfsycxqV6RUXzZMXX4DFy4sNqUd4/LN1ANw1YTgTu3XCqTUFZRUs+O8X5FjLmNGnOzePGgRAeZWdR1atpkPxEgAAIABJREFU5UB2nierRfYOK7uXZqCdkDAxjG4XRzcqk76piJQPckBBSIIvg3/XnvLcKr7/6zG0E5wOTadpEXSYYuy+NrZNBxYNnohZmXg/bRcv7/nebfmNPQZzZZe+VGsnBbZy/rjpM06WldAzLJo/D5tCoLcFh3byYvJmVh1NMagWwtOkQfYLpZ2afa+fYPDCLvhGeLP5wQNEDwwhMN6vtkxZpo3DK7IY9mhXvAO9qCy2GxhxY06HZtOSPKY+EkdAhBcr/3iS9kP8CWvnU1umOMPOrg+LmPFkGyyBZiqKXA2A7BQb2Sk2Ln7OdUJd9WAGWXttxPX2a/K9PFGXVS9XMO/PAQRHKl65p4zuw7yIbm+uLTPtlrqT95aVVWQedtWlY5IXt/8zEIByq+YfN1np1N/Yr6bToVn5UhU3Pm4hOFLx4u9s9BhmJqZ93ThAM26p206bVtrJOOQE4Ng+B8f2Obn7RVd9F99XyZFkJx37mjGCSSkeGzyFa75+j6zyElZMvY416amkleTXltlbmM3Mz5Zic1Qzp0t/Hug/nt9uXAGAzVHN9M9eNyT25gwe2YW27SK4fvbzdO8dz28XzODuef9qsuxTf/qwycaWn78Ps68cyv7kE028yjNMSvF/IyYz57MPyCqzsnLWNaw5fojUorptk1Fawr0bPuOWPoPdXjswug2DYtpywUdvAPDhjKsZFteOLZnG1edMZk+Dqy+BB54wOpIz007NgaXH6P9gVywRPvywcB+RA0PdzjEAMcPD6XZ9gtu8vB+LsB4pZ8hfeqHtTrY/lkJEUihe/sYcA8BVn8NvHqbXA73wCfdh96LdhA8Mx7+tv1u5yGGRdJzXsdHrUxenEj8rntA+oThsjqYHvT7PxnRJJCEilAueX0pSfCwPz5jAFf96r1G5h2dMZNHKNexMz2TJ3NmM7pzIt2lHee277Tz/9WYArhnaj9vHDuORVWs5WVTMNUuXU2KrZHTnRB6bOanJ9Z4v2qHZ9VoGIx/qgF+4F98sOETcoGCC29WdM0szKzn4cS5j/twJn0AzlcWum5u+oV6MebwTZm8T1RUO1t6bSuygYPzCjbmhaVKKx4ZOZu5X75NVbmXlhfP46kQaacV1x7R9Bdlc9Omb2BzVzO3ajwUDx3HnhpVUVNv5/cZPOWotJNovkFXT57Hh5BFK7JWG1MWjJEPWMqMsKqUcSqmdSqk9SqnlSqm2NdM7lVJZSqmT9aZ9lFJTlVIHlFJpSqkH6q1nolLqx5pyG5VSnU/znt2UUutqyu5XSi1psLy9UqpUKfWHM8SulVLP1Jv+g1LqkXrTtyilUmr+tiqlRtVb9k5NPfYopV5XSnnsCFCUVoZ/rAX/GAsmLxOxI8LI3lbsVib96zzaT4nCO9B1cW8JMTbj0lBuaiXBcd4Ex3pj9lZ0HBXA8a1lbmUOfFVCz2nBWAJdJ3K/0LoTuqNK46x2/WmHxi/EuJN9+kEH4W1MhMeZ8PJW9BnjTcqW6mbLJ6+302ds4+2xb6OdLoO88PE1LpsEcOKgk4g2qrY+SWO82L/Z0Wz5XesdJI2taUQqsNs1jmqotoOzGgJDjatPUkQbjlkLOVFahN3p5JNj+5ncrqtbmS3Zx7E5XNtrR14Gsf7BRoR61oaP7c6aT3cCkLInnYBAX8IjA89pHfNum8Dyt76jqrL5/fR86xcVx9GSQk5Yi13b5nAKkxPcD/vppSWkFOTibDCAlQYsZjPeJjM+JjNeJhN5Fe7Hj9ZmcBKEBhkdxdkpSSvDL9aCX4wvJi8TMcPDydtWeFavLTtZQWiPIExmhdnXTGCCP/m7is/8wvOo9FApfjF++Ea76hM5LJKC7Wc3SnX5yXK0UxPax9WDw+xrxmzx/PlmYvdOrNi5H4Bd6VkE+1qICgxwKxMVGECgxYed6a6M+Yqd+5nUoxMAZZVVteX8fLzRNVfBO05kUmKrrFlvJrHBnt1JC9LKCYj1ISDGB5O3ifiRIWRuK3Erc3RNAR2nRuBTcy1gCXGdb0zeJszerktZR7VGOz0aeiP9IuI4Zi3iRGnNMe3ofqa06+JWZnOj843r8z5iLeSo1fUdy6koJd9WTriv+w0D8evVUrfhK7TW/cDVSAGuqDf9CFCqtf5bzbQZeBGYjGsc/x+UUiu11vuAl4FZWuv9SqnbgT8B1zXzns8Dz2mtV9Sst0+D5c8Bn51F7JXAJUqpJ7XWbjl6pdQM4FZglNY6Tyk1APivUmqI1joLeAeYW1N8GXBTTR3Ou8oCO34RdRkK33BvitPK3cqUZboOsFsWHUA7NZ0viyOqX4gnwjsr5QXVBETW7YL+EV7kHnS/E1Sc4crqfbLgJNoJA64II36APzHdfYnr48e7NxxHo+k5LYTQepk1T7Pma0Ii6+5vBEcq0g803YApynFSmN10xih5g50RFxtXj1NK8jUhkXWNqOBIxYkDTZ/pCrOdFGY56ZTkqn9CDzOd+pp5Ym4FWsPwi7yIbm/cL2zE+gWSWV53cs8qt9Ivok2z5a/olMT6jEO10xazFyumXodDO3l572a+Sk89r/GejcjoIHKz6+qUl1NCRFQwBXmljcre+8hsnA4nG7/ez7JX1wPQqVssUTEhfP/tQS6dO8JjcTcU6x9IZpm1djqzzEr/qLjTvKLOjzkZbM44wQ9X34ZSirf2/Uha0c/6GRhRj62wCt965xhLhA8laY0bvDlbCynab8Uvzpeu17bDN8JCYII/Rz7MoP30GByVTgr3WQloa0zvhVMqCyvxCa+rj0+4D6WHGn9f8rfmU5JSgm+sLx3mdsASYaEiswIvfy9S/p5CZW4lIb1CSLgyAWXy7I2mmKBAMkvqvi9ZJaXEBAeSW1q3XWKCA8kqKXUvE1R3s+Z3E0cwK6knVlsl895o3P36sgG92ZB65DzVoGm2gmr8IupuUPqFe1OY6n49U5rpakyu/9MhtFPT4/IYYvq7GjLleVVsfvIYZVmV9L4mzrDsGECMfxAZZXXH5sxyK/0imz+m/aZzX9adPNxoflJEHN4mM8esZ3cT5BdPMmTn5XfIvgWazWwBQ4A0rfVhrXUV8B4wq2aZBk7dmg6h8Q+x1ReHq0HneqHWyaf+r5SaDRwG9p5FvNW4frH7niaW3Q/cd6qhprX+EXgTuKNmerWuAWzF9eNxjdRk2bYppbbt+TDtLEL6iRqcG7RDU5ZVyZBFXUm6qwN7lhzHXmbc3fBGmvgCNnzMSDugJNPO9P9rw/jfR/PtS7lUljkoybRTlF7Fla+256pXE8hIriBzb4Vn4m5CU78+0dypOnm9nV4jvTCZ3UtYC5xkH3XSeUAr6El8FtvmlN0bHPQeVVefvAwnOSecPPCWHwve9uPQLidHkpvPrp1vTT27pps5+s9O7EWfiFiW7Kvr8z/yvy8y6/M3uPu7lSwaOIn2gY2fc/S4purUxE741J8+ZP4VL3HvTa/Tu397Jk1PQinFrb+fypLnvvBEpKfXxD51tuflhOBQOoeGM+zdxQxd9jIj4hIYEtvkIVj8FGexIaIGhDLy+b4Mfbo34b2D2feS60I+om8IEf1C2Pbwfva+cJiQLgEo4zowuJxFfcL6hzHw7wPp92Q/QnuHkvqK6+aLdmpKDpSQeHUifR/riy3XRs6Gxs9unXdNfV/O4qeP6pf4+9pNjH/2VVYlpzB3aD+3ckMT47l0QC+e+Wrjzwy0BTQ4xjkdmtLMKkY/0pHBd7fnx8XpVJW5ziv+kT5MfKYLk1/oxvF1hdiKjHs8o6nzZHNbaHaHnvSNiGPJXvfnZqP8Anh21HTu27Ra2in/Q1q0QVbza9XTgOTTFGsL1O/kn14zD1wZptVKqXTgGuAvp1nPc8DXSqnPlFL3KKVCa2IIwNWQevQcQn8RmKOUapg+6gVsbzBvW838WjVdFa8BPm9q5VrrJVrrQVrrQb0vPV1b9exZwr2pyK/rfmArsGMJc78r5BvhQ/TAUExeCv9oCwFxvpRntZ6+yP4RXpTl1TUQy/Or8Q93P2sHRJhpPyQAk5ciKMabkDbelGTYObqljOiuvnj7mfD2M9FugH+j7JonBUcqivPqMkgleZqgiKa/Xskbmu6uuOdbOz2Ge2H2Mra7IpyqT92poCRPExzedFy71leTNLZuu+3b5KBdNzMWP4XFT9FtkJnjKcb1I8kstxJXrwtirH8Q2RWN74yPjE3kjt4juHndf6hy1jUgc2rKnigtYkv2cXqFxZz/oJtw0eVDeGnZfF5aNp+CXCtRMXV1iowOpiDP2ug1+bmueRXlVXzzeTLderXFL8CHxM7RPL3kOt785Hf06BPPo89dRZcezWcNz5esslLiAuq6R8UFBJFd3njbNGVqQhd25GRSXm2nvNrON+mH6R/t+Tr8WvmG+2Crd46pzK9qdI7xDvLCVNNdrO3EKEqO1GU1OlzchqF/6U3/hd1Ag3+s5wfAqM8SbqkdkAOgqqCqdvCOU7yDvGvrEzM+hrIjZbWvDUgIwDfaF2VWhA8Mp+yoZ7rHXj0kiY/nz+Hj+XPIsZYRV687YWxwIDlW9ziyS0qJDQ5sUKbxd2rV7hQm96i7HukaE8n/zZrMHe+upKjC1qj8+eQb7kVFfl0jqqLAjm+4+41Jvwhv4gYHY/JSBMT4ENTGUtsLqLZMuDdB7XzJ3++eXfOkrDIrbQLqjs1x/kHkNHFMGxmXwJ19RnDTNx+6nW8CvX1YOuEyntn5LTvyjBloyQhKe+6vtWqpBpmfUmonrsbKcVw/mNacpq7qTn1E9wAXaq3jgaXAs82tRGu9FOgBLAfGAVuUUhZcDbHntNZnd1Z3rasEeAu46yyKKxrf8HgJ2KC1/vZs3/PnCukUQHlWJeU5lTirnWRtKiR6oHt7MnpQCAX7XBdkVSXVlGfa8Iu2NLU6Q0R1sVCSaceabcdh1xzeWEb7we794ROGBpCZ7Mp82UoclGTYCYrxJjDKi6y9NpwO1zNkmXsrCI03rptC265mCk66uu5V2zXJG+x0H9o405WX7sBWqmnXo4nuiuurm2yoGSG+q4m8DE1BTX12baimx7DGMeemO6kohfY96g4loVGKI3scOBwaR7XmSLKD6PbGNTJ352eQGBRGfEAI3iYTFyX0YE2Dboc9w2J4fMhUbl7/H/Ir607mwT6++Jhc9Q6z+DEwKp7UYs+OPnbKJ8u3cvvVi7n96sVsWrefSdNdd7e7946nvNTWqLuiyWwiONT1/IHZy8TQUV05eiiH8tJKfjPxaeZd9HfmXfR39ien8/A97xoyyuKu3Ew6BIfRLrBm23TszlfHzq4XwcmyEobGtcOsFF7KxLDYdqTVGwxE/DxBNeeYippzTPbmAiIHhrmVqSysa+Dkbi8ioK2r0aWdGrvVdbPNeqyc0uMVhPc1trt8YMdAKrIqsOXYcFY7yduSR/iAcLcyVfXqU7C9AL82frWvrS6vxl7iajQU7y3Gz0NdMJdt3cXFi9/h4sXvsHb/IWb16wFAUnwsVluVW3dFgNzSMsqqqkiKd426PKtfD9amuLpgJ4TXZfcndO/EkTxXd7i4kCBeuOIi7v/oc47mF3miWm7COvtTmllJWXYVTruT9O+KiRvk/hxvm8HB5O51HeMqS6opzazCP8aHinw7jkrXDb+qUgf5B8oIbGPcdc6u/EzX+ebUMS2xB1+dcD+m9QqP5olhF3DTNx+Sb6s733ibTLwy7mI+OryX1ccOeDp0YbAWf4bsLKQD7epNxwMZSqkoIElrfaqv0Ps0k3E6RWudAbwOvK6U2gP0BoYClymlngZCAadSyqa1/ucZ4vo78COuhuAp+4CBQP2xlAfUzAdAKfUwEIXrWTOPMZkVPa9vx7Yn0tBOTfz4CILa+ZH6QQYhHf2JHhRKZFIwebutfHvvPpQJus1ti09QK+gOV8NkVgy/OZLPH81COzVdJwYR1t6H7csKiOxsIWFIAG37+5G+s4IPf3sCZYLB8yLwDTaTODyAjOQKPro7HaWgbX//Ro05TzKbFdNv8+Wth8pxOjUDJvsQnWBm7ds22nYx032Yq6G1e72d3mMaDwFfmO2kOM9JYh+j+/W4mM2Kmbf58PqfKtFOGDTFi5gEE1+9XUXbLiZ6DnPtR7vWubJj9evTe5SZQ7ud/ON2GwroMtBMjyYap57i0JqHt33FWxOuxKQUyw/tJrU4j3v6jiY5P5M1J9NY0H88AV4+vDjqYqBuePvOwRE8PnQqWmuUUizet9ltdEajbN2YyuCRXVm64m4qa4a9P+WlZfO5/erFeHubeeKf12D2MmE2mfhx62E++7hhwt9YDq1ZtGkNb027DLMy8cHBZFKL8vn9gJHszstizfFD9I2MZcnk2YT4WJjUvhP3DBzJ5A+XsvrIQUbEJfDlpdejtWZ9+lHWHj905jc10L2PwtadUFQM4y6DO6+Hy6YbHVXTTGZFt+vas+PJA+CEuHGRBLbz49DykwR38CdqUBgnPs8mb3sRyqzwCvSi53zXz6o4qzXbHnUNPuHlZ6bXHR0bddH2NGVWdJzXkX1P70M7NTFjY/CP9+f4f44T2CGQ8IHhZH6ZScGPBa76BHjR+VZXBkmZFIlXJbL3yb2gIaBDQO1w+J60PvUIY7om8uXd12OzV/Pgf7+sXfbx/DlcvPgdAB5d9TVPzJ6Cr7cX36YeZUPqUQDunTyKxIgwtNZkFFt5+JM1ANw+diih/r4smj4BAIdTc9mSZR6rl8msSLqxDd897vpJgoTxYQS382Xfe9mEdfIjbnAw0f0Cyd5lZc3vDqJM0PuaWCxBXuTsspL8VlbtrfIuF0URkmBcNtahNYu2fsVbk36DWSk+SEt2nW+SRpGcn8Wa9DQWDByPv5cPL411Pa1zsqyEm7/5iOkJ3RkS044wix+XdeoNwB++W82+QgO6x3paK85ceYo6m/7HZ1yJUqVa6yaH+GpiUA8v4CAwETgJ/ABcDRwAsoARWuuDSqkbcWXLLm1mvVOBtVpru1IqFtgB9K8ZbKPJ9z5T7DWNuCuB17XWjyilZgIPAVO11vlKqX7ASmCo1jpTKXUTcAMwUWt9Vg8w3b3jql/NbtfW4vk7aedLgo8xWY/zxVu1omcFf6Z7t/zG6BBaVNdnPdsd6HxKue3cRnZs7Y7MaPqnA36J7swYYnQILarC0Tp6D7SEzav6Gh1Ci5p96XdGh9Bi3ts5+MyFfkGOXnu/8c9AnIUeDz3nsWvj/f93T6v8TDx+21prXa2UuhP4AjDjavzsBVBK3Qx8qJRyAoW4GjvNmQL8Qyl16urmvvqNsZ/oGeDOerGuVEq1BTYppTRgBeZqrU/9Auti4BiwuSZD8JHW+rGfGYMQQgghhBDif0SLNMiay47VLHukiXmrgdVNzP8Y+Pgs3/P3wO/PUKbRezdRJrDe/7MB/wbLX6aZoey11q2n/58QQgghhBC/MK15sA1PMe7HgYQQQgghhBDif1yrz/AopRYClzeYvVxr/fg5rCMCWNvEoolaa+Of0BdCCCGEEOJ/kWTIWn+DrKbhddaNr2bWkQ+c7SiQQgghhBBCCOERrb5BJoQQQgghhPiVkgyZPEMmhBBCCCGEEEaRDJkQQgghhBDCEDLKomTIhBBCCCGEEMIwkiETQgghhBBCGEMyZJIhE0IIIYQQQgijSIZMCCGEEEIIYQzJkEmGTAghhBBCCCGMIhkyIYQQQgghhCFklEXJkAkhhBBCCCGEYSRDJoQQQgghhDCGZMikQeZpF4buMjqEFtPRu8joEFqM1fnr+ip4K6fRIbSYx4asMDqEFvXWjnZGh9Bi7hhRanQILerOjCFGh9Bi/tlmq9EhtKiei28zOoQWUxn16zk+Ayz/aoTRIbQYFVNpdAjif5R0WRRCCCGEEEIIg/y60gJCCCGEEEKIXwwZ1EMyZEIIIYQQQghhGMmQCSGEEEIIIYwhGTLJkAkhhBBCCCGEUSRDJoQQQgghhDCGZMgkQyaEEEIIIYQQRpEMmRBCCCGEEMIQyugAWgHJkAkhhBBCCCGEQSRDJoQQQgghhDCGPEMmGTIhhBBCCCGEMIpkyIQQQgghhBCGUJIhkwyZEEIIIYQQQhhFMmRCCCGEEEIIY0iGTDJkQgghhBBCCGEUyZAJIYQQQgghjCEZMmmQ/dLs26b56GUnTicMn6qYfIV7krMgR/PvvzmpKAPtgItuMNFriKKsRPPan50cPwhDJysuv6P1JUe3bzWz5EVfnE6YcqGdy6+qcluek634+199KSlSBAZr/rDARmRU6/kW7/zBxFsveeN0wvhpDmZdWe22PC9H8fLT3pSVKpxOuOpGO/2HOtm41syqD+q+isePKJ54qZLEzsbWbcdWM0tf8sHphInTqrn4Krvb8txsxT+ftlBWCk6nYu5NVQwY6qC6Gl5+xocjqWYcThg7qZpLrrY38y6ecWi7nS+WlKOd0G+KhZGX+7ot//Jf5Rzb7dpe9kpNWbHmvvdDAXh8ZiHRCWYAgqNMXLEo0LPBn4Xb/3E9Q6YNoLK8kr9e/yJpO440KjP+ypFcteAStNbkZxTyl2uepyTfakC07tJ/LGfLa/k4nZpuk4JJujS0UZnD35Wy471CUBCe6MP438cAsPXNfE5sd23Xtv38GHZjBEoZ+xOj+TuLOfjWcbRT02Z8FImz4tyWZ6zPI+2dE1jCvQGInxJD2wlRAKQtO0HejmIAOlwSR8zwCM8Gf44W/gXWbYbwMPjkDaOjad7CaeMY06UDNrudBf/9kn2ZOY3K9IqL5snZF2Dx9mJD6hEe/2wdAPdNHs34bh2xOxwcLyjmwRVfYrVVMqNPd24cObD29d1iorjklXdIycr1SJ3GtE/k4dHjMSnF+/v2sPjHrW7Lh7Rpy0OjxtM9Moq7vljFZ4dSAegRGcWfx00i0NsHp9b8c9v3fJp2wCMxNzQmMZGHxo/DrEy8vyeZV7b+4Lbcx2zmb9Om0js6hkJbBXet+pSTJSXM7N6dmwcPqi3XPSqKmW//m/25udw7ciQX9+pJsMVC3xf+6ekqATAmriMPD56ESZl4P20ni/ducVt+Y4/BXNGpHw7tJN9Wzv1bPuVkWQkAaVffz4Ei1z6UUV7Czev+4/H4hTGkQfYL4nRolr/o5I4nTIRGwt/uctJ7mCYuoe4C5Mt3Nf3HKEbPMJF5TPPKQ056vWXGywemX+ual3nUsCo0y+GAl5/35c9PlxMRpbnndn+GDq+mfaKztsxriy1MnGxn4gXV7Nph5s1XLdy7wGZg1HWcDlj6gjcPPlVFRKRm4Z0WBg53EJ9Q16j6+B0vho11MPkiB+nHFE8t9OGFoZWMmuhg1EQH4GqMPbPIx/DGmMMBr77gw6KnbIRHaR64w5dBI6ppV68+H77jzYix1Vwws5oTxxRPPOjLy+9UsHm9Gbtd8eyrFVTa4Hc3+jFqQjXRscbUyenQfPZyOXP+HEhwhInX7rHSdag3Ue3NtWWm3Oxf+/8fPrGRdchRO+3lAze/EOzRmM/FkGn9ads5juu6/pYeQ7tw10s3c9fwB93KmMwmbvv79dzU6x5K8q3c9NRcZt05lbcfXW5Q1C5Oh2bTkjymPhJHQIQXK/94kvZD/Alr51NbpjjDzq4Pi5jxZBssgWYqilzbJjvFRnaKjYufiwdg1YMZZO21Edfbz5C6AGin5sDSY/R/sCuWCB9+WLiPyIGhBMa7xxQzPJxu1ye4zcv7sQjrkXKG/KUX2u5k+2MpRCSF4uVvprWaPQ2uvgQeeMLoSJo3pksiCeGhXPD8UpLiY3l4+gSuePW9RuUenjGRRZ+sYWd6JkvmzGZ050S+TTvKpsPHeHbtRhxOzb2TRnHLqME8s2Yjq5JTWJWcAkDX6AhevGqWxxpjJqV4bOxErlnxH7JKraz4zRzWHEkjrbCgtsxJq5X71n7Ozf0Hub3WVl3NvV99xtHiIqIDAvjkN3PZcPwo1qpKj8Revw6PTJzAvP98SJbVysdz5rA27RBpBXV1uLx3b4ptNia8/jozunXj/jGjuWvVp6xMSWFlSs1nHxnJK7Nmsj/X9dmvPXyYt3buZO0N13u0PvXr9diQKVyz9j2yyktYMe061qSnklacX1tmb0E2Mw8uxeaoZk6X/jzQfzy/3bgCAJujmumrXzckdmEsj6RJlFIOpdROpdQepdRypVTbmumdSqkspdTJetM+SqmpSqkDSqk0pdQD9dYzUSn1Y025jUqpzqd5z25KqXU1ZfcrpZbUzE9USlXUe7/FZ4g9RCn1llLqUM3fW0qpkJplCUqp7TXr2auUmt9Sn1lTjh2AqDiIjFN4eSsGjFUkb258kWsrr/m3DIJrbrBafBWdeiu8vc9nhD/dwRQTcW2dxLbReHvDmPHVbNnkfr/gxDETSQNcF2N9+zkaLTdS2gETsW00MXEaL28YPs7Btk3uF1JKQUWZq/FcXqYIi2i87TZ9bWbEeEej+Z7mqo+TmJrtMXKcgx++c/+8lYLy8sb1UQoqba5GXVUleHmBn79xDcyMgw7C40yExZoxeyt6jfHm4JaqZsvvXV9Fr7E+zS5vbYbPGsyat9cDsP/7VAJDAwiPdc8yKaVQSuEbYAEgINiP/IxCj8faUG5qJcFx3gTHemP2VnQcFcDxrWVuZQ58VULPacFYAl3fJ7/Quu+Vo0rjrHb9aYfGL8TYxktJWhl+sRb8YnwxeZmIGR5O3raz+5zLTlYQ2iMIk1lh9jUTmOBP/q7i8xzxzzM4CUKDjI7i9CZ268SKXfsB2JWeRbCvhajAALcyUYEBBFp82JmeCcCKXfuZ1L0TAN8dOo7DqWten0lscOMM+fQ+3fm0pnHmCUkxsRwrLuJESTF2p5NPUg8wuaP75dBJawkp+Xk4tfux90hRIUeLiwDIKSsjv6KcCD/P38RIio3lWFERJ4pddVh1IIVJnTu5lZnUuRMf7d0HwGcHDzK8fftG67moezdWpdRl+HZmZpJbVtaonKckRbThmLXHkDd0AAAgAElEQVSQE6VFrm1zdD+T47u6ldmSfRybw9UjY0deBrH+rfeGn6co7bm/1spT/dYqtNb9tNa9gSrgiprpfsBi4Ll60w7gRWAa0BO4SinVs2Y9LwNzasotA/50mvd8vt56ewAv1Ft26NT7aa3P1Ih6DTiste6kte4EHAFerVmWCYyoiWco8IBSqs1ZfSI/QVE+hEbVZcNCI6HeTRcAps1VbPta89BcB4sXObns9tbXNbEp+XkmoqLqsmGRUU7y89y7HnXo5OS7Da5GweaNXlSUK0payfVKYR5E1Os+GRGpKWwQ/6XX2Nm41swdV/ny9EIfrrujcTe+zetbR4OsIE8RGV2vPlGagnz3+vzmWjvfrvHiliv9eOJBX26809XIGTbGgcUXbv6NP/Pn+DPzcjtBBp5vrPlOgqPqvgdBkSas+U0flYtyHBRlO0nsW9f4rK6C135XwtJ7SziwufmGnFEi24STc6LuQJCXnk9k23C3Mo5qB8/f/i+W7H6G904uoX2PeD5/7WtPh9pIeUE1AZF1n7V/hBdl+e77f3GGneIMO58sOMnK+0+S/qPrjlNMd1/i+vjx7g3HWXbDMdr28ye0nbENaVthFb4RdTFYInyoLGz8Pc/ZWsj3f9zD7ufSsOW7MhOnGmCOSgdVJXYK91mx5be+/e2XJiY4kMySuq65WSWlxDRoVMUEB5JVUnraMgCX9u/NhrSjjeZP69WVT/d4rttfbEAgmdZ6dSq1Ehtw7l2pk6Jj8TaZOVbTQPOkmMAGdbCWEhPo3rqPrVfGoTXWykrC/Ny7m0/v1o1PUjzXGD6TWP9AMstLaqezyq3E+jd/1+KKzkmszzhUO20xe7Fi2nV8dMG1TI7vcl5jFa2LESmGb4G+p1k+BEjTWh8GUEq9B8wC9uF67O/UpV0IkHGa9cQB6acmtNbJ5xpoTQZuIHBFvdmPAWlKqU5a60P15ltopoGrlLoFuAXgrsdjufCqsHMNxaWJa8iGj0tsX6cZOlkx4VITR/Zp3v6rkwWLTZhMxj5X8VM0rNsNt1ay+AULa7/0plcfBxGRTsytpDePbur6vkH8m77xYswUBzMur+bgPhMvPeXD0/+qxFSz16TtV1gs0K6D8bdwmqpPwz1o4zdmxl1gZ+bl1RzYZ+KFv1h49tUK0lJMmEyw5P1yyqzw0D1+9B3gIKaNMfVq8l2b+Trs22Cn+0gfTOa6AnctDSEowkRhloN/P1hKVKKZ8LhWsuNBk89MNdx+Zi8zF82fwm0D/kjm4WzufOFGrlwwm2WPf+ShKJtxFsc07YCSTDvT/68NZfnVrFqYwSX/iKeyxElRehVXvuq6a/7ZI5lk7q0grpdxXRbP5sH0qAGhxI4Ix+RtIv2rHPa9dIQBD3Unom8IJYfK2PbwfnyCvAnpEoBqPbvZr4pu8oDdsIz79K2jh1DtdPLJbveL/75tY7HZq0nNaXB39DxSTRzAzvXoGuUfwLOTp3Hvms8NGU+h6Uc9z227JMW6PvuD+Z777M+kyW3TzP42u0Mv+oTHcuVX79TOG/nxi+RUlNIuMJRlk67iQFEux0s932D2OOMvewzn0fSJUsoLV+brdI2jtsCJetPpNfMAbgJWK6XSgWuAv5xmPc8BXyulPlNK3aOUqt+Hp4NSaodSar1SavRp1tET2Km1rr1lW/P/nUCvmjq1U0rtron5Ka11o0ai1nqJ1nqQ1nrQT26M4cqIFeXW7bVFeRDsfiOcLV+4niED6NBTUV0FZSW0ehGRTnJz63bHvFwT4Q269EVEahY+auP5V8q59kbXXeWfcFPwvAiPgvzcugNxfl7jLonffG5m+FjXrtS1pxN7FVjrZfg2rfNqFdkxcGXE8nLq1Se3cX3WfubNiJr6dOvppKqmPt9+7UX/wQ68vCAkDLr1cnDooHGZ2uAIEyW5ddlXa56ToPCmW2R7NzTurhgU4Yo9LNZMQh8vsg8Zv41m3n4Bi3/8K4t//Cv5mQVEt6sb/CEyPoL8jAK38p36JQKQeTgbgPUfbKLX8G4ei7c5/hFelOXVDX5Tnl+Nf7h7KyQgwkz7IQGYvBRBMd6EtPGmJMPO0S1lRHf1xdvPhLefiXYD/Mk96NnnYBryDfdxy2pV5ldhCXPvJ+4d5IXJ27VPtZ0YRcmR8tplHS5uw9C/9Kb/wm6gwT/WPRsgzs7Vg5P4eP4cPp4/hxxrGXHBdRmK2OBAcqzuXdqyS0rduiK6ytRlzGYn9WR81w7c99Fnjd7rwt7d+HSPZzM0mWVW4oLq1SkwiOyy0tO8wl2gtw+vz7iYZ7Z8x87szPMR4hllWUvd6xAUSHapex2ySuvKmJUiyGKhyFb33PiM7q0rOwaQWW4lrl4XxFj/ILIrGm+bkbGJ3NF7BDev+w9VzrpzSk5N2ROlRWzJPk6v8JjzH7RoFTx1leSnlNoJbAOO4+oG2JymrpROXQneA1yotY4HlgLPNrcSrfVSoAewHBgHbFFKWXB1M2yvte4P/B5YppRqrkOVoul2e+18rfUJrXVfoDMwTyl13r497btBbgbkZ2mq7Zof12v6DHP/uMKi4eAOV8hZxzX2KggMOV8RtZyu3Z1knDSRlamw22HDN14MHeE+SmFxsWt0QoDly3yYPNXYkfvq69TNSdZJRU6motoOm9eZGTjc/cI9MlqzZ4frK3fymKKqShFcc5vA6YTvN5gZ3koaZJ27Ock8aSK7Znt8t87M4AbbIzLaSfIO18Vz+jFXueDQmnruNKE12Cogdb+ZNu2dTb2NR7TpaqYgw0lhlgOHXbN3g52uQxt3bctPd2Ar1cR3r2sQVJQ6qba7vk/lxU5O7Ksmsr3xaYuVL33B/AH3MX/AfXz33x+YdM1YAHoM7UJZcTkFWe53VPNPFtC+Zzwhka5D3YDJfTmectLjcTcU1cVCSaYda7Ydh11zeGMZ7Qe7P9+TMDSAzOQKAGwlDkoy7ATFeBMY5UXWXhtOh+sZssy9FYTGG/uQbFCnAMqzKqnIqcRZ7SR7cwGRA91vwlUW1jXYcrcXEdDW1ejSTo3d6vqOWY+VU3q8gvC+v4CDdyu07IddXLz4HS5e/A5rUw4xK6kHAEnxsVgrq8gtdW+Q5ZaWUVZZRVJ8LACzknqw9oCrA8yozgncNGoQt727Epvd/RioFEzt1YVP9xz0QK3q7M7OIjEklPigYLxNJi7q0o01Rw6d+YWAt8nE4gtn8tGBfaw+5Nm469udlUViaCjxwa46zOjWnbWHDruVWXvoEJf0cj2xMq1rVzYfP167TNXMW3XAmBEim7M7P4PEoDDiA0Jc2yaxB2vSU93K9AyL4fGhU7l53X/Ir6y7IRPs44uPyXV+CbP4MTAqntTiPI/GbxR5hsxzXRYrap6zOhvpQLt60/FAhlIqCkjSWn9fM/994PPTragmW/U68LpSag/QW2u9HaisWb5dKXUI6IqrsdjQXqC/UsqktXYCKKVMQBKwv+F7KaX2AqOB8zJOqdmsuOx2Ey8tdA17P2yKIi5R8elbTtp3UfQZrph9s4n3/uHkm48dKAVz7jXVdml65FoHtnKorobdmx3c/rjJbYRGI5nNMP+3Nhbd74/TCZOn2UlIdPLvpT506eZg6AgHyTvNvPmaBQX07lvNbXcZeze8PrMZrrvTzpMLXMPEj7vAQbtEzfI3vOjQ1cmgEU7m3mrnX896s/ojLxRw231Vtd02UpJNhEe6BgVpDcxmuOm3Vfz5AdfPEEyYWk27RM17b3jTqauTwSMczJtfxeJnLaz60Aul4I6a+kydZefFv1q45yY/0DD+gmoSOxpXL5NZMXW+P+8uKsXphH6TfYhKMLPu3xW06WKubZztWV9FrzHebl0A8044Wf3PMpRSaK0Zcbmv2+iMrcHW1T8y9ML+vJn6ApXlVfzthhdrly3+8a/MH3Af+ZmF/Pux5Ty7/lGq7Q6yj+Xy1+tfPM1aPcNkVgy/OZLPH81COzVdJwYR1t6H7csKiOxsIWFIAG37+5G+s4IPf3sCZYLB8yLwDTaTODyAjOQKPro7HaWgbX//Ro05I+rT7br27HjyADghblwkge38OLT8JMEd/IkaFMaJz7PJ216EMiu8Ar3oOb8DAM5qzbZHXacVLz8zve7o6NZ1tjW691HYuhOKimHcZXDn9XDZdKOjcrc+9QhjuiTy5V3XY7NX8+CKL2uXfTx/DhcvdnUXe/TTr3li9hR8vbz4Nu0oG1KPAvDQhRPwMZt5/dpLANfAII+sWgvA4IR4skpKSS/07MPMDq15eMPXvDXrUkzKxPJ9e0gtyOeeISNIzslmzdFD9I2OYfGFswix+DKxQyd+N2QEF7z7JtM7d2NIm3jCfP24rHsvAP6w9nP253lmhMj6dXj0629449JLMZkU/9mzh9T8fH43YgTJ2VmsPXSYD5L38My0aXx9ww0U2Wzc/emnta8fEh9PlrWUE8Xun/39Y0ZzUffu+Hl7s/GWm/kgeQ/Pb97s0Xo9/MNXvDXxSkxKsfzQblKL87in72iSCzJZk57GggHjCfDy4cXRFwN1w9t3Do7g8aFT0WgUisV7N7uNzih+3dTZ9KX+2W+iVKnWusnOZUqpR4BSrfXfaqa9gIPAROAk8ANwNXAAyMI1iMZBpdSNuLJllzaz3qnAWq21XSkVC+wA+uMaNKRAa+1QSnXE9UxbH611QTPr+QhXt8XHaqYX4WoYXqqUigfytdYVSqkw4Hvg0tM9r/bFkZ6t44q7BXT0/vX0a7Y6W8+IjS3BWxmXkWppO23xRofQot7q1u7MhX4hJu85+25SvwRHba37d7/OxT/bbD1zoV+QnotvMzqEFlMZ+es5PgOYKlv3jYNz4YxpPTd6W8KRuQt+ERun/+3PeezaeMdL97TKz6TVXYVqrauVUncCXwBm4HWt9V4ApdTNwIdKKSdQCNxwmlVNAf6hlDrV4fg+rXWWUupS4DGlVDWuxtn85hpjNW4EXlBKpeHKkm+umQeuLpHPKKV0zbK//ZTBQ4QQQgghhBD/mzzSIGsuO1az7JEm5q0GVjcx/2Pg47N8z9/jekas4fwPgQ/PZh015QuBuc0s+4rTjxgphBBCCCGEaEZrfrbLU34ZP1IlhBBCCCGEEL9Cra7L4rlSSi0ELm8we7nW+vFzXM/3uH5LrL5rpAuiEEIIIYQQ54lkyH75DbKahtc5Nb6aWc/QFghHCCGEEEIIIc7aL75BJoQQQgghhPiFkgyZPEMmhBBCCCGEEEaRDJkQQgghhBDCEDLKomTIhBBCCCGEEMIw0iATQgghhBBCCINIl0UhhBBCCCGEMaTLomTIhBBCCCGEEMIokiETQgghhBBCGEJpSZFJhkwIIYQQQgghDCIZMiGEEEIIIYQxJEEmDTJPu2P5zUaHIJpgj7QbHUKLUt5Oo0NoMREbLEaH0KKq7zI6gpbz71eNjqBlDbxqt9EhtJiei28zOoQWtW/+y0aH0GIunHy50SG0qBHvJhsdQot5/YdRRocg/kdJg0wIIYQQQghhCPlhaHmGTAghhBBCCCEMIxkyIYQQQgghhDEkQyYZMiGEEEIIIYQwimTIhBBCCCGEEIaQZ8gkQyaEEEIIIYQQhpEMmRBCCCGEEMIYkiGTDJkQQgghhBBCGEUyZEIIIYQQQghDyDNkkiETQgghhBBCCMNIg0wIIYQQQgghDCJdFoUQQgghhBDGkC6LkiETQgghhBBCCKNIhkwIIYQQQghhCBnUQzJkQgghhBBCCHFGSqlwpdRXSqnUmn/DmiiToJTarpTaqZTaq5Saf6b1SoNMCCGEEEIIYQytPff38z0ArNVadwHW1kw3lAmM0Fr3A4YCDyil2pxupdJl8RdmTIcE/jRxHGZl4oPde3jl+x/clvuYzfx1+gX0jomhsKKCu1eu5mRJCW2Dg/nixnkcLigAYGdmFou+XGtEFX5yHU6JCwri8xuv5fnvtvDaD9sBmDewP1f07Y1Sivd3JfPG9h0erVNDY9t24OFhEzErxXsHd/Py7u/dlt/UaxBXdu1LtXZSYKvgvm8/42RZSTNrM8bYNh1YNGgSZmXi/bRdvLx3i9vyG3sM5srOSTV1KOePm1e71SHQ24c1M2/mi+MHefiHrzwdfiP3XTmOkX06YKuy88jSL0k5nuO23NfHi6dunU58VCgOrfl212Fe+GgjAHMmD2D2qN44nE4KrRU8+saXZBVYjahGrfsvGceoHh2w2e08tOxLUtJzGpW588IRXDS4J8H+Fobf/2Lt/JlDenLPzNHkFJcC8N63u/h4yx6Pxd7Q/RePY3QP17Z56N0v2X+ycV1+O20EFw1y1WXYghcbLZ/ctwvPXDeDK59dxr70bE+E3aTCXYUcefsIOCF6XDTxM+PdludsyOHou0fxCfMBIG5yHDHjYwCozKsk7dU0qgqqAOhxXw98o3w9Gj/AwmnjGNPFtW8t+O+X7MtsvD16xUXz5OwLsHh7sSH1CI9/tg6A+yaPZny3jtgdDo4XFPPgii+x2iqZ0ac7N44cWPv6bjFRXPLKO6Rk5XqqWqe18C+wbjOEh8Enbxgdzdm57YHpDB7djUqbnWf+9CFp+zMalXn69RsJjwyisrIagAdvXUpxQRlRsSH84fHLCAjyxWw28frfv+CHbw96ugq1MnaUse31bLQTOk8ModclEY3KHPuuhN0f5KOA0EQLo+5xXd+W5drZ8nIW5XnVoGD8wngCo709XIM6Y9t2YNFQ1/n//YO7eTnZ/fx/46nzv9N1/v/jxrrz/5uTL6N/VBt+yDnJjWs+NCJ8cWazgHE1/38TWAfcX7+A1rqq3qSFs0iASYPsZ1BKReBqHQPEAg7g1NllSIMN8rOZlOKRSROY98FHZFmtfHTt1axNO0RafkFtmcv79KLYVsnEfy1leveu/HHcKO5euRqA40VFzHzznZYM6Zz93DoALJwwlg2Hj9ZOd4mM4Iq+vbnk7XexOxy8fvklfHP4CMcKizxZtVompfi/4ZOY88UHZJVZWTnzWtYcTyO1KL+2zN78HGasfAubo5q53fuxYPA47ly30pB4m2JSiseGTGHumvfIKreyctp1fJWeSlpxXR32FWRz0eo3XHXo2p8FA8Zz57crapffmzSG77OPGxF+IyN7J9IuOpTZC5fSu2MsC+ZMYN6T7zUq9/aX29l2IB0vs4nF917GiN6JbNpzlAPHc7jm8WXYqqq5bGxf7r5sNAuWrG7inTxjVI9E2keFctHjS+mTEMufLp/A3Oca12f93sO8t3EXnyy8rtGyL3cc5MkPv/FAtKc3qkciCZGhzHhiKX0TYvnTZROY848m6rLvMO9u3MWqB69rtMzf4s3Vo/ux+1imByJunnZqDr95mF4P9MIn3Ifdi3YTPjAc/7b+buUih0XScV7HRq9PXZxK/Kx4QvuE4rA5QHkq8jpjuiSSEB7KBc8vJSk+loenT+CKVxtvj4dnTGTRJ2vYmZ7JkjmzGd05kW/TjrLp8DGeXbsRh1Nz76RR3DJqMM+s2ciq5BRWJacA0DU6ghevmtVqGmMAs6fB1ZfAA08YHcnZGTy6K20SIrlh+rN079uOO/80k9/NWdxk2aceWE7qvpNu8666dTwbvkjm0w+20r5jFP/30jzmTf2bJ0JvxOnQ/PCvbCYsisc/wpvP7z9G/OBAQtpZasuUZFSx9+MCpjzeHkugGVtxde2yTS9k0vvSCOKSArBXOFEG9v0yKcVjwyYx94sPXOfOi67lq+Np7ufO/BwuOnX+7+Z+/n9lz1b8vLy5uls/o6pgCE8+Q6aUugW4pd6sJVrrJeewihitdSaA1jpTKRXdzPu0Az4FOgP3aa0b3zGpR7os/gxa63ytdb+alORi4LlT0y3dGANIiovlWFERJ4qLsTudfLr/AJM6d3IrM6lLJz7esw+Azw+kMrx9+5YO42f5uXWY1LkTJ4qLSc2vO7h1jghnZ2YmtupqHFqz9UQ6U7p09kyFmtAvMo6jJUWcsLrq+Mnh/Uxu7x7P5qzj2ByuE8qOnAziAgKNCLVZ/SLiOGYt5ERpTR2O7WNKuy5uZTZn16tDbgax/kG1y3qHxxDp58+3GUc9GXazxvbrxKdb9gOw53AWgf4WIkMC3MrYqqrZdiAdgGqHk5RjOcSEubbLtgPp2KpcdU0+nEl0WBBGGt+nE5/84KpP8rEsgvwsRAYHNCqXfCyLvJIyT4d3Tsb37sQn21x12X2qLkGN67L7WBZ51qbrcue0ESz9ZhuV9uoml3tK6aFS/GL88I32xeRlInJYJAXbC878QqD8ZDnaqQntEwqA2deM2WI+n+E2aWK3TqzY5doeu9KzCPa1EBXovj2iAgMItPiwM93VAF6xaz+TuruO498dOo7DqWten0lscONj2/Q+3fm0pnHWWgxOglBjv9bnZPj4Hqxd6eoJkrL7BIFBvoRHnkMFtMY/0NXgCQjyJT/XuB4a+Wk2gmK9CYr1weytSBgVxIkfSt3KpK0pouvUUCyBru+Eb4grn1B8ohLtgLgk1z7q7WfCy2LcpW2/yDiOWYvqzp2H9zPldOf/3Axi/eu+I5syj1Nmb/HLR1GP1nqJ1npQvb9GjTGl1Bql1J4m/madw/uc0Fr3xdUgm6eUijldeWmQ/YLEBAaSaa3rJpVlLSUmKLBxmRJXGYfWlFZWEubn6vISHxLCynlzWHbV5QyKb+u5wBvG9xPr4Oftxa1DB/HCd+5d5w7m5jM4Pp5QX198vbwY1zGRuCDjGjixAYFkltXVMbPM6tZYaeiKrn1Zl37EE6GdtRj/IDIa1CHGr/k6/KZzX9ZlHAZcN/X/NHAiT2w3PvtySnRYINn1uhjmFJYSFdr8PhLoZ2F0Uke27j/RaNmsUb3ZtMfY7RUdEkh2YV19sotKiQ45t31+Yt8uLP/jXP523QxiTvNZnG/RwYFkFf30unRvG0VsaBAb9hn/HaosrMQn3Kd22ifch6rCxhdX+Vvz2blgJyn/SKEyvxKAiswKvPy9SPl7CrsW7uLosqNop+eHHosJrjv+AmSVlBLToFEVExxIVknpacsAXNq/NxvSjjaaP61XVz7dc6Dlgv4fFBEdTG5Wce10bnYJEdHBTZb9/Z8v4cXld3L1reNr5/37pa+ZMKMfb6/5I4+9NI+Xnlx13mNuTkVBNf6RdV0M/cO9qMh3v7lizbBTklHFFw8e4/MHjpGxw3VzpiSjCu8AExuePsnqPxzlxzdzcDqMG7Ivxj/Q/dxZbiUm4DTnzq59WXfS+GOX4bQH/84mHK0naa17N/G3AshWSsUB1PzbuE+3+7oygL3A6NOVkwbZL4hqovuKbvCAomqikNaQW1bGmMWvMvPNd3j86/U8N2MagT4+jcqebz+nDnePHM7SbTsot9vdlh0qKGDJ9z/w5hWX8PrlF7M/Nw9Hyzy4+RM1EX8zR4GLO/WkT2QsryRvPd9BnZOmeko194nO7tCLvhGxLNnr6id/TbcBfHPyEJnlxj5jVV+T9WlmHzGbFE/cPI331u7gZF6x27JpQ7vTMzGGt77Yfh6i/Hmaq09T1u85zLTHXuPyp//N9weP8+erLziPkZ1ek8eEszxrKgX3zRrL31ZsaOGofqKzCDusfxgD/z6Qfk/2I7R3KKmvpLpe6tSUHCgh8epE+j7WF1uujZwNpz3Pe8zZ7FsNi9w6egjVTief7HbPhPVtG4vNXk1qTj7ip2vyPNnEDvjUA8u57ZIX+MO8JfQakMDEi1xd4cZd2Jev/vsj10x6mkW3v8l9T1ze5Do9ocndq0EoTqfGmmln8mPtGXVPHFteyqKqzIF2Qu7+CvpfG8XUpxIozbZz+JviJlboGU1fvzT9/Zndsafr3NnKzv/ijFYC82r+Pw9Y0bCAUipeKeVX8/8wYCRw2rtQ8gyZB9Tvrxp1yeUEDx3+k9aTZS0lLqjuTktsUCA5pWUNyliJCw4iq7QUs1IEWiwU2WwAVDkcAOzNzuF4URGJ4WHsyfLsw+8/pw5JcXFM7daFP44bRbDFglNDVXU1b+/YxfLkvSxP3gvAvaNHkmU1rjGQVWYlrt4dsbiAILLLSxuVG9kmgTuThvOb1e9S5XR4MsQzyiq30qZBHXIqGn+mI2MTuLPPcK74clltHQZEtWVwdDzXdBuAv5c33iYz5dVVPLVjvcfiB7h8XBIXj+kNwL4j2cSE19UnOiyQvOKmu78tvGYSJ3KKeHet+8AwQ3q058bpQ7j5r8uxV3t+e10xKolLhrvqs/d4NjFhQVBzYzUmNJDcc+iaWFxuq/3/h5uTufuiUS0a65lcMTKJS4fV1OVENrH1+orFhAaS28y2aSjA4kPn2Eheu+MyACKDAnj+xpnc9dpKQwb2sIRbagfkAKgqqKodvOMU7/9n777Do6rSB45/70x6Jp00CJBCCS0BadJJQu9dAbvi6lpR1wKKqCuuu5b9WVbUFewIKEpviXQpohAINYEkhJBGQpKZ9HJ/f8yQTjXMjez7eR4enblnJu+bc+fcOfc998aluhLgG+FL8nfJVa91bu2Mg495RYNnd09MCfXHjRthRs9wpnY398fh1Az8XWuM0a4GMussFc3IN9VaimhuUx3rhPCORLQL4p4v69+UYFTn9qyNa1rLFf8sxt7emxGTewJwMu4s3n5uVdu8fV3Jyaw/RmdnmpciFhWWsnVdLO27tCRm9UGGT+zO3Ie+AOBYbAp29ja4ejiRl2P9Jc5OXjYUnq8+0VqYU46jp029Ns3aOqKzUTD42uHawg5jWilOXjZ4BNnj4mf+nAX0MnA+vhitpBfUOXY6uZDZ0PHf33z8v2190zv+a0Gp1DqCa/IPYJmiKPcDZ4CpAIqi9AAeUlX1AaAD8LaiKCrm0wtvqap6+HJvKhMyK7CsT/0EoM0/373u0s2htHRae3gQ4OZKhtHE6A7teWr1+lptYhJOM7FzRw6cS2NE+7bsOWNecuXp6EhucTGVqkpLNzdae3iQkmv9m7vgKYQAACAASURBVF78kRymL1lW1ebxfrdSUFrGVwdiAfB0ciSnsAh/FxeGtWvD1K/rX4RuLbHn0why86ClwY30QiNjgzvw+NbVtdp08vThjb7DuGvT92QXF2oU6aXFZqcR6OJJgMGNjEIjY1t35PGdtW860snDlwW3juDumGW1cnhyZ3WuU4K70MXLz+qTMYDlW2NZvtW8f/TvEsS0iHA27jtB52A/TEWlDU7IHp7QF4OjPa99WfuukO1bejP3jige/b8fuWAsskr8dS3dGcvSneZ8BnQM4vYB4Wz4/QRdWlvyuYYJWTNX56r2gzsHk5hxddc5NZalu2JZusuSS4cgpvcPZ/2BE4S19sNYXHrJa8XqMhWXMmhe9Y0MPvvrFN5etUOzuywagg0UpRdRnFmMnacd5/ecp91f29VqU3qhepKW81sOjs0dq15bXlhOWX4Ztq625B3JwxBsnaWk3/4ay7e/mvtjUNsgZvYKZ23cCcID/DCWlJJV56RZlqmAgpJSwgP8iD2bzvjwDny97yAA/du05oH+Pbhz8XKK61zTpygwolNb7li83Cp53WxWf7eX1d+ZVyL0GtCesTNuZev6Q4SGtaTAVELO+doTMp1eh8HFgfzcQvQ2OnoNDOXAnlMAZKbn0e3WYDavPEDLIG/s7Gw0mYwBeLVxwJhWhimjFEdPW5J3Gun3pH+tNi17uZC0M5+QSDeK88sxnivF4GuHrZOOUlMlxXnlOLjZkBFXiGeI9e9MelHs+TQCXT2qj53BHXh8W/3j/4K+w7h7c9M8/ovLU1U1G4hq4Pn9wAOW/98MhF3L+8qE7E+kQlV5JfpnFk+dhF5RWH74CPHZ2TzRvw9x6RnEJJxm2aE43h49gphZ95JbXMyTlrsT9mzZgif796W8spJKtZJ5m2LIKy75U+VwOR+OH4uHowNllZXM3/wz+SXWz+2iClVl3u5ovhw+Fb2isCz+MPG52TzVrT+HzqcTnZLAnF6DcbK14z8R4wA4V2DkgegVmsVcV4WqMm/fJr6Mus2cQ8Ih4vPOMzt8AIez04g+m8AL3SNwsrHjPwMnAJBakM+srU3zNr07DyfSr0sgK1+/l+LScuZ/vqlq27fzZjLj1W/w8TDwwOjeJKZl881LMwFY9nMsP+2M44kpA3F0sOXNh0YDkJ5t5KkPtbsr5o6jifTvEMiaF835zFtSnc/Sv83ktn+Z76b65NgBjOreHgdbWzbNf4AVe+JYuGEPMwZ2ZXCnEMorK8kvLOalbzdqlQo7jiUyoEMga+fcS3FZOS/VyGXZ0zOZ9rY5l9ljBjDqFnMum+c9wIq9cXy0cc+l3lYTil4h+O5gjv7zKGqliu8gX5wCnDjz/RkMQQY8u3uStimNnN9zUPQKNs42tPmL+YJ/RacQOD2QI28cARWcg5yrbodvTdviExnYNpBNj5v7Y87K6v748aGZTFxo7o9X1v7MggnDcLCxYUdCEtvjkwB4aVQkdno9i+6aBJhvDDJ/jflmxD1bB5Ceb+LsBe2WlF3K06/AvoOQmweDp8Cj98KU0VpHdWn7dpyg58B2LFr3FCXFZbzzYvXx48Plj/LI1A+wtdPz+sf3YGOjR6dTOLDnFBt+MP+ZmU//tY4n5k9k4p39UFV4+0Xtxm6dXqHHAz78/NpZ1EoIiXTDvZU9sUvO49XGgYCeBvy7OpF2sIDVTySi6KDbXd7Yu5hv8HHL3d7EzE9BBbyCHWgzxF2zXCpUlXl7ovlyWO3j/+xu/TlsOf6/0NNy/B9sPv6nFhiZFWPuv2UjpxPi7oWzjS27pz3MczvXs72J3BzrhtLyKpMmQrmW6w7EpSmKMh8wqap62fvG/pEKmbhxypqVXbnRn4hi++eq/1+O13b7Kzf6EymvfwPBPy31JrsKufv0Q1qH0Gh2r72mk7NN3tGHPtI6hEYzauhUrUNoVH2XXHYl1p/Kol+tu3z7Rku691ltLgy8Rn2nvW2178a/LHu6Sf5OpELWSFRVna91DEIIIYQQQvyZWPPvkDVVN9n5TSGEEEIIIYT485AJmRBCCCGEEEJoRJYsCiGEEEIIIbQh97OQCpkQQgghhBBCaEUqZEIIIYQQQghNyE09pEImhBBCCCGEEJqRCpkQQgghhBBCG1IhkwqZEEIIIYQQQmhFKmRCCCGEEEIITcg1ZFIhE0IIIYQQQgjNSIVMCCGEEEIIoQ35O2RSIRNCCCGEEEIIrUiFTAghhBBCCKEJuYZMKmRCCCGEEEIIoRmpkAkhhBBCCCG0IRUymZBZm75E6wgaT6l7pdYhNBrnU7Zah9ColHKtI2g82T0qtA6hUTml6LUOodHoC7WOoHHtXhOmdQiNpsT75hmfAUYNnap1CI1m3eblWofQqIJXzdI6hEbjt+0mWzh2r9YBiKslEzIhhBBCCCGEJuQaMrmGTAghhBBCCCE0IxMyIYQQQgghhNCILFkUQgghhBBCaKNS1ixKhUwIIYQQQgghNCIVMiGEEEIIIYQ2pEAmFTIhhBBCCCGE0IpUyIQQQgghhBCakNveS4VMCCGEEEIIITQjFTIhhBBCCCGENlQpkUmFTAghhBBCCCE0IhUyIYQQQgghhCbkGjKpkAkhhBBCCCGEZqRCJoQQQgghhNCGVMikQiaEEEIIIYQQWpEKmRBCCCGEEEITitxlUSZkTdnc4YMZ1CaI4rIynl+1iaPpmfXadPLz4Y3xw3GwsWFbQiKvb9wKgJuDPe9OHk0LN1dS8/J58oe15BeXEOzlwYJxw+jk58O7W35h0Z7fqt4r5rH7KCgto7KykopKlcmffXvDcxzYKpCXB0agUxSWHo1j4W/7am3v1bwFLw2IILSZN49vWMP6U/FV2z4fN4lufv78ei6VB9b8dMNjvZQ5owczsH0QRWVlzPlhE8fO1e+njs19WDB5OA62Nmw/kciCtVurts28tSszbg2nolJl24lE3t64gz4hrXhqeH9s9XrKKip4a8MO9p5OsWJWZi+Mq85t7rJL5NbCh9enVuf2xqqtALT3b8a8iVE42dlx7kI+z363noKSUitnYDaoZSDz+kaiVxSWHj/MRwfr7Gf+AczrE0GolzePRa9hfeLJqm3NDS78Y+BwmhtcUIF71/3AWVO+lTMwmzN6MAPb1djX0i6xr02y9MfJ6n3tkchbmdKjCxcKCgH49+ZdbD+ZxJjwUO7r373q9e18vZnyn284np5llZwAnp84mAEdgiguLePFJZs4llo/r8dG9mVcj464OtnT+4UPq56f2ieM6f3DqaispLCkjFeWR3M6I8dqsV80d+RgBrY1j9cv/LSJow30TSd/H96YOBx7Gxu2xyfy+vqtADwe2Yeo9iFUqio5BUW88NNGMo0FjOkSyqz+PQAoLC1j/poYTmSct1pOA1sF8vKAGuPz7w2Mz/0t4/PG6vG5QzNv/j54CAZbOypVlQ/272VtwgmrxX05Dz8/mp4D2lNSXMbbL/5AwrFz9dr8c9H9eDZzoaSkHIA5f1lMXk4B3n5uPPP6FJxdHNDrdSz690Z+3XGy3uubgrn/gK27wdMDVn+udTRXNiggkHl9oyxj9CE+iq2zr/kFMK9vJKGe3jwWs7pqjO7j35KX+kRWtQtx9+SxmNVsSk6wavyX89RdEfQJD6KktJzXPt7AiaT6Y8O7z06imbszer2OgydSeWtxDJUySfmfIhOyy1AUpQI4jPn3lAjcqapqrqIogcAaVVU712g7HzCpqvpWY/zsgW0CCfR0Z9iHiwlv4cf8UZFMW/RdvXbzR0Uxb000B1PT+HT6BAaGBLL9VBIP9uvF7sQUPv3lV2b17cmD/XryVsxOcouKeX3DVqJCQxr8uXd/uZwLRcWNkcIV6RSFVwdHcedP35NuMrLytplEn04g4UL1l6lUo5G/RW9g1i096r3+k9/342hjw/TOYVaJtyED2wXSupk7I95ZTFhLP14eF8ntC+v307zxUbz8UzSxKWl8fPcEBrQLZMfJJHoFBRDZIYQJ739NWUUFns6OAOQWFvHXr1aSZSygjY8Xn947iYg3P7VqbgPam3Mb+a/FhLXyY97ESKZ/2EBuE6OYvyKa2DNpLLxvAv3bB7LzRBKvTh7Kv9ZuZ39iKhN7dOK+Qd15f9Nuq+YAlv2s3xDuWLuc9AIjqybdweakUyTkZle1OWfM55mt65kV3rPe69+JGMUHv+9hZ2oyTja2VGq02H1gu0Bae7kz4t3FhAVY9rWPG+iPcVG8vNKyr901gQFtA9kRnwTAl7t+Z/Gu32q1XxN7nDWxxwFo6+vFBzPHW3UyNqCDeT8bvWAxYa39eHFKJDP/r35e246eZsnOWNbOuafW8+t+P87y3YcAGNwpmL+NH8TDn/xojdCrDGxr7pvh7y0mPMCPl8dEctun9XN4eUwU81ZFc/BsGp/cMYEBbQLZkZDEZ7t+472fzZ+NO3t35a+DbmX+mhhSc/O4c/Fy8otLGNAmkFfHDWnwfW8EnaLw6qAo7lxpGZ+nzSQ6sYHxOWYDs7rVHp+Ly8t5evN6kvJy8XF2ZvW0O9h+JgljaYlVYr+UngPa0bx1M+4b/Q6hYS159MVxPDlzYYNt33x+OfFHU2s9N/0vEWzfeJi1y/bRKtib1/5zN3ePaJRDfqObMBJmTILnF2gdyZXpFIVX+w/ljrXLzGP0xDvZnFxnjDZZxuiw2mP07rQURq34AgA3ewe23fYA288mWTP8y+oTHkRLPw+mPr2ITm38efbeIdz/cv2T3XPfX0NhkfmE5RtPjCWydzui9zSNkxhWUal1ANqTa8gur0hV1a6WiVcO8Ii1fnBUuxB+OnQMgNjUdFwd7PE2ONdq421wxmBvx8HUNAB+OnSMqPbmiVZU+2B+OnTU8vxRhliezyks4nBaBuUV2u/94b5+JOfmkpKfR1llJatPnmBocJtabVKN+RzPPt/gmaJfzp7BVKZNxeWiyA4hrDxg7qdDKem4ONjTzKV2PzVzMfdTbIq5n1YeOEZUB3N/3N47nP9u/5WyigoAcgqKADiWlkWWsQCAhMxs7G302Or1VsnposhOIaz6zZLbmXRcHBvOzdnejtgz5txW/XaMqE7m3AK9PdifaP5Cszs+maGd21ox+mpdffxIzr9AitGynyUcZ1hg7RMSZ035HM85j1pnP2vj7oVeUdiZmgxAYXkZxeXlVou9psgOIaw8aOmPs5Z9rc6Y0MxQZ187eIyojg2ffGnI6LBQ1h063nhBX4WIziGs2m/JK7nh/ezitvOWz0RNNauujna2mvyB0ajQ6r6JPXuF8fpsdd8MsYwDdXNQLZP+Aylp5BeXWN43DT9Xlxuey0Xhvn4k59UYn+OvfnxOzL1AUl4uAJkFBWQXFeLl6Gi12C+lT0QHYlYdAOD4oRQMLg54NruG36mq4mSwB8DZxYHsLG0q5VejZzi4W293+UO6evuTnFdjjD51nGGBtfc18xidVW+MrmlUUDu2piRSXKHNGN2Qgd1DWLfD/F3sSEIaBid7vNzrj28XJ2N6vQ5bG+se60XTIBWyq7cbsFopxtfFQHq+sepxer4JXxcDWaaCOm1M9doAeDk7VbXNMhXg6eR05R+qwmczJ6ECS387zLIDhxsnmUvwczaQZqqRo8lIVz//G/ozG5uPq4H0vOocMvJN+Loaan1x9HU1kJFX3U8ZeSZ8XM39FNjMne6BLXhiaF9Kyiv41/rtxKVm1PoZwzq15di5rKpJm7XUyy3vyrml18gtPiObiI7BbDl6muFh7fDT6NuBr5ML52rsZ2kFJrr6XN1+FuzuQX5pCQuHjaOlixs7U8/w5t7tmiwl8XG5xL5mqtMf+XX2NcuYADDj1nDGdevAkdQM/rl+e9WX/YtGdGnHo1+vuoFZ1OfjaiA9t0ZeuSZ83AwNTr4u5fZ+4dw16BZs9Xru/+j7GxHmZfm6GEirO1671hmvXS89XgM8GdWX8eEdMRaXcPfn9XOYcktntscn3qAM6vNzNpBmrDM++177+Bzu44etTk+yZYKmJS8fV7LS86oeZ2Xk4+XjSs55Y722T/19EpUVKruij/Dtx1sA+Po/P/P6J/cwbkYfHBzteGHWIqvFfjPzdTZwrqDmGG286jG6prEhofz38P7GDO0P8/Y0kJldnVtmjhFvDwPZufXHt38/N5mOIX7sjk3k571NcymsuHGkQnYVFEXRA1FAzW8qIYqiHLz4D3iocX9m/efqnhlqoMkfWkw1/fOlTPrvt8z69kdm9gynR6sWf+DdrkxpIMk/25LpP9pPep0OVwd7bl/4HW9t2M47t4+u1a6NjxdPDe/P/JXRjRPwNWg47jq5XSa5l5ZvYnqfrix7bAZO9naUlVt3QnlRg310lZ8UvaKjp18Ar+/exrgVX9PKxY0p7To1coRX52ryaLiN2Xd7DzH8ncVM+vBrsowFPDtyYK12YQF+FJeWk5CZXf9NbqAG96FrHMm+2xXLqAWLeXftDh4c2rtR4romVzEONKRmi3/H/ELEO/9lzeHj3NG7a612vQMDmHxLJ97evPMPBnr1lAaSutbh2dvJmXeGjuRvMRubxF2tGzzmNBDZm88v5+FJ7/PM3Z/Q6ZbWRI0198fgUWFs/ul37hzyT+b99Qv+tmBqg+8prk2Dh5Fr3GG8HZ1p7+nN9pSkxgip0TT4ObpEck+++QNjHlmInY2eHp1a3ejQmhRFVa32r6mSCtnlOVomW4HAb8DmGttOqapaddS0XEPWIEVRHgQeBPAZNxX3Hn0abDejRzjTupkvSzt8LqPW8hQ/VwOZptpnVNKNJvxcDbXbGM1nYLMLCvE2OJNlKsDb4ExOYeEVk734/jmFRWw+nkBYcz/2n0m9wquuX5rJiL+hRo4GFzIKTJd5RdMwvXc4U3ta+ulsBn5u1Tn4uhrIrHNmPz3fhK9bdT/5uhnIspwpT88zsfloQtV7VaoqHk6OXCgswtfVwHszx/LC9xtJycnDGqb3CWdKL3NucXVzczOQmV8nt7zaufm5Ve+DiVkXePCzFQC0bubOoNCgGx1+g9ILjDSvsZ/5OxvIvMr9LL3AyNHsTFKM5t//pqQEuvn6s+xE3A2Jta7pvcOZ2sOyr6U2sK811B+udfa1GmPCRcv3x/HRneNrvXZkl/asO2yd5Yq39wtn8q2W/Swlo1b11NfdQGbe1VfHalp/4AQvTo5qlBivZEavcKbeUj1e+9cdr+uMAxn5lx6va1pz6DgLZ07g/S3ma8ra+TbjtfFDefDrH8m10vW9YK5S+Ltc//hssLVj0ZiJvL1nFwcz0m5EiFdl7O29GTHZfN3RybizePu5VW3z9nUlJ7N+dSw707wUsaiwlK3rYmnfpSUxqw8yfGJ35j5kvl7pWGwKdvY2uHo4kZdzffurMEsvMNHcueYY7UJm4bV9FxgT0p6NSfGUq9pfjjF5aFfGR3QB4NjpdHy8qnPz8XThfAPVsYtKyyrY8fspBnQPYV9c8g2PVTQdUiG7vCLLpKs1YMd1XkOmquonqqr2UFW1x6UmYwDf7o9lwqffMOHTb4g+cYoJYR0ACG/hh7G4tNbyFzAvRSwoLSW8hR8AE8I6EHPyFAA/nzjNhLCOluc7EnPi9GVjdLS1wdnOtur/+wW3Jj7rxt7N61BGOoHu7gS4umKr0zG2XXuiE0/d0J/ZGJbsjWXSB98w6YNviDl2ivHdzP0U1tIPY0lpvaVW540FFJSUEtbS3E/ju3Xg52OWfjp2it7BLQFo7eWOrV7PhcIiXBzs+eiuCby7aScHztS/C9gNy213LJP/7xsm/983xBw5xbjultxa+WEqbji3wpJSwlqZcxvXvQM/HzHndvEGJYoCf4nszdI9h6yWR02xmekEunkQ4OJm3s/ahLI5+er2s9isdNzs7fF0MOfSt0Ur4i9Yr4K0ZG8skz78hkkffkPM0VOM72rpjwDLvlZnTDhvsuxrAZZ9rWv1vlbzerMhHUOIz6jOQ1FgeOe2rDtknWUy3+2KZerb3zD17W/4+fApxvWw5NW64f3sclo1c6/6/4Edgjlz3jpL477dF8vEhd8wcaFlHLD0TXjAFcbrGn0Tc9zcN609q3OIDA0h8fwFAPzdXHj/trE8t2IDSdnWXfJ3KCOdQDd3Alws43Pbqx+fbXU6Fo4ax4oTR1l3StulV6u/28sjUz/gkakfsPvnY0SN6wZAaFhLCkwl9ZYr6vQ6XN3NS/z1Njp6DQwlKd68jDwzPY9utwYD0DLIGzs7G5mMNYLYrLTaY3RIKJuv8S6J40I6sDrh2A2K8Nr8sPkgd835irvmfMW2/QmMGmD+LtapjT+mopJ6yxUd7W2rrivT6xT6dg0m+Zz17xSrKdWK/5oo5WqWVfyvUhTFpKqqwfL/3YCVQAjQguu8y2L719696l/4vBERDAgJpKi8nDmrNhGXZj4o/DRrJhM+/QaAzv6+vDFuGA42Nmw/lcRrG8xr3d0dHfj35NH4u7qQlm/kie/XkFdcQjNnJ354YAYGe/PtiAtLyxj10Zd4ODny4bSxgHkZ3Zq44yzcua/hwCxK3f/4majBrYOYN2AwOp2O5Ufj+HD/Xmb37svhzAyiE08R5uPLwtHjcbN3oKS8nKzCAoZ/az5DuWzybQR7eOJsa8uF4mKej9nI9jPXd0bJMf36z028ODaC/m0DKS4rZ+6KTRyxXAO24tGZTPrA3E+dWviyYPIw7G1s2BGfxOurzf1kq9fx90nDCPX3pqyign+tN9/e/i+DezFrUC/OZF+o+jkPLF5RddOPK1Ea6ZrmF8dH0K99IMWl5by4vDq3H56YyeT/q87t9WnDsLe1YeeJJF5fac7tjn7dmN4nHIDouATe3XB9y60Kgv/4UsfBLYOY1zcCvaJj2YnDfHhgL7N79ONwVjrRyacI8/bj42GW/azCvJ8NW/45AP1btGZun8EoQNz5DF7Yvomyyuvf951Srv+C7RfHRNC/nbk/5q7YxJFzln3tkZlM+tDSH80t+5qtDTtOJvH6GnN//GPKCEL9vFFRSb2Qz/yVMVUTup5BATw1rD/TG7hr4+Xor1x4vypzJ0XQL9T8GXpxySaOnjXntfzpmUx925zX7DEDGH1Le7xdzRXmH/bG8dHGPTw3YTC3tmtFeUUF+UUlLPhhC6cyrm/SXPYHLnN8aXQEA9qYc5jz0ybiLH3z40MzmbjQMl4392XBhGE42JrHgdfWmfvmvdvGEOjlgaqqnMsz8vLqaDKNBbw2bgjDOrblXK65YlNRqTLlk6v7cyQl3o04PiuW8fm3vczuZRmfkyzj86gan5uCAoYv+YIJ7Trwz6jhxOdU98MzMRs4dv76797Z/uPG+YL6yNyxdO/XlpLiMt55cUXVnRQ/XP4oj0z9AHtHW976fBY2Nnp0OoUDe07xyb/WUVmp0irYmyfmT8TRyQ5Vhc/e2cDvu6/99urrNi9vlFwu5+lXYN9ByM0DL0949F6YMvrKr7sewatm/eH3GNwyiHl9ItHrLo7Re5jdvR+Hz9cYo4dOwM3enpKKCvMY/f1iAAIMrnw/fgZ9vln4h79v+21r/DrFM/dEcWtYIMWlZfz9440cTzSPDV8uuJO75nyFp6sTbz0zETtb8z7325EU/v31Fioq//j38z3fPP2nWFMbFfGG1SYjMVteaJK/E5mQXUbNCZnl8WpgGbADK0zImrrGmJA1FX9kQtYUNdaErClojAlZU/JHJmRNTWNNyJqKPzIha2oaY0LWlDTWhKwpsMaEzJoaY0LWVNyICZmW/jQTssELrDch2zqnSf5O5Bqyy6g5GbM8HlvjYec62+ZbIyYhhBBCCCHEzUMmZEIIIYQQQghNKDfN2rHrd3PVZoUQQgghhBDiT0QqZEIIIYQQQghtyP0spEImhBBCCCGEEFqRCpkQQgghhBBCE8rNdVPY6yIVMiGEEEIIIYTQiFTIhBBCCCGEENqQa8ikQiaEEEIIIYQQWpEKmRBCCCGEEEIbUiCTCpkQQgghhBBCaEUqZEIIIYQQQghNKHINmVTIhBBCCCGEEEIrMiETQgghhBBCCI3IkkUhhBBCCCGENmTJolTIhBBCCCGEEEIrUiGzsgo7rSNoPOpNNJ0vbF2udQiNStXfPGeb3I7Yah1Co7LLv3n6xrbw5skFYNA9u7UOodEs39xX6xAaVd8lh7UOodEEr5qldQiN6vS4T7UOodF0THtY6xD+N1VqHYD2bqKv1EIIIYQQQgjx5yIVMiGEEEIIIYQm5Lb3UiETQgghhBBCCM1IhUwIIYQQQgihDamQSYVMCCGEEEIIIbQiFTIhhBBCCCGENqRCJhUyIYQQQgghhNCKVMiEEEIIIYQQ2pC/QyYVMiGEEEIIIYTQilTIhBBCCCGEEJqQv0MmFTIhhBBCCCGE0IxUyIQQQgghhBDakAqZVMiEEEIIIYQQQisyIRNCCCGEEEIIjciSRSGEEEIIIYQ2ZMmiVMiEEEIIIYQQQitSIfuTGRjUmhejBqPX6VgWG8fHe3+ttd1Or+dfo4fT2c+XC0VFPLFyHan5+VXb/V1c2PDAXby3aw+f7fvN2uHXM6hVIPP6R6DXKSw9GsdHv++rtb2XfwvmDYgg1MubxzatYf2p+FrbDbZ2RM+4h42nE3h5x8/WDL2eQQGBzOsbhV5RWHr8EB/F1snFL4B5fSMJ9fTmsZjVrE88CUAf/5a81Ceyql2IuyePxaxmU3KCVeOva1CLIF6+NQq9TuG7E4f46NDeWtt7+QXwcu8ocz5bVrEu6WTVtud7DiKyZTAA7x3YzZrE41aNvSHPTRzMgA5BFJeW8dKSTRxLzazX5rGRfRnboyOuTvbc+sKH9bYPDWvL2/eM4fZ3vuXo2QxrhH1Jz0yPoF8Xcz7zF23kxJna+djb2fDmQ2MI8HanorKSHYdO88EPOwGYPCiMqRFdqaispKikjNe/3ExiWo4WadQz+64I+nYNori0nNcWbuBkUv1+eve5SXi5O6PXi+TDVwAAIABJREFU64g9nspbi2OobCJnWDMOGDm0+BxqJbSO8qD9RJ96bc7+ksvxZZmggFtrB3o+2YrCrFL2/isZtRIqK1RCRnoRNMzL6vEPDAzkpYjB6BUdS+MO8/G++seYt0aOoLOPLxeKi3h8zVpS8/MZFxrKrJ49qtqFensz7quvOZaVxdP9+jGxU0dc7e0Je/8Da6dU5dyBAvYvykCthDZRbnSaVP/3m7wrn0PLslEA90B7+s9uDkBBVhl7Pkqn8Hw5KBAxNwCDj62VM6h2sx1vLmfuP2DrbvD0gNWfax3Npc0dMZiBbYMoLivjhZ82cTS9/tjVyd+HN8YPx97Whu3xiby+YSsAwzu25dFBfQjx9mTap0uISzMfX2x1Ol4ZM4TOzX2pVFUWbNjKvuSz1kzLOprI+K0lmZBdBUVRJgIrgA6qqh5XFCUQWKOqaucabeYDJlVV37pRcegUhflDI7l76QrSjUZW3D2DmIRTJGRXf5GaGtaJvOISoj5ZzOgO7Xh2cH+eWLWuavvcqEFsP510o0K8JjpF4dWBUdyx6nvSTUZWTZ3J5sQEEi5U53POZOSZmA3M6tqjwfd4unc/9p7TfnDSKQqv9h/KHWuXkV5gZNXEO9mcfIqE3OyqNudM+TyzdT2zwnrWeu3utBRGrfgCADd7B7bd9gDbzyZZM/x6dIrCa32HMHODJZ9xdxF9JoH4Ovk8vX0dD3apnU9ky2A6e/ky8sfPsdPbsGzUdLaePY2prNTaaVTp3yGQ1s3cGbNgMWGt/XhxSiQz/++7eu22HT3Nkp2xrJlzT71tTva2zBjQlUPJaVaI+PL6dQmipY87E+csonOwPy/cEcU9C5bUa/fVxt/47UQKNnodHz09hb6dA/klLokNe4/zw7ZDAAwMD2b2bYN5/N8rrJ1GPX26BtHSz4OpTy2iUxt/nr1vCA/M+7Zeu7nvraGwyLw/LXhyLJG3tiN69wlrh1uPWqES+9k5+r0UhKOnDVteOIV/D1dcWzpUtTGllXDyxywG/j0EO4OekrxyABzcbRj4egh6Wx3lRRXEPB2PXw9XHD2t96VfpyjMj4rk7u9/IN1o5MeZM83HmJwax5jOnckrLiZy0SLGtG/PcwMH8Piataw6fpxVx80nXto1a8bH48dxLCsLgJjTp/ny4EFi7rvXarnUVVmh8uunGUTOC8DJy5YNzyUT0NOAW0v7qjb550o58mMOw15vhb1BT7GlbwB+eT+NzpO98A93pqyoEkXD9UU32/HmSiaMhBmT4PkFWkdyaQPbBNLa053h7y8mvIUfL4+O5LbP6h9jXh4dxbw10Rw8m8YnMyYwoE0gOxKSiM/M5vFlq3llTFSt9lO7dwFg3MKv8HRy5NOZE5ny6bfI9OXmI0sWr850YCdwu5ZBhPv7kZybS0peHmWVlaw9doIhbUNqtRnSNoQf444CsOF4PH1at6q1LSU3j/jz2TQFXX38SM7LJSXfnM/q+BMMC2pTq81ZYz7Hs8+jNnD2pLO3D82cnNiRkmytkC+pq7c/yXkXSDFacjl1nGGBdXIx5XM8J6vBXC4aFdSOrSmJFFeUX7KNNXT19icpP7c6n9PHGNqqgXwuZNWrTLR1b8be9BQqVJWi8jKO5WQyKCDImuHXE9E5hNX7jwFwKDkdF0d7mrk412t3KDmd88aCBt/j0ZF9WbxlPyVl2vYNwKCuIazbbf6cx51Ow8XJHi+32vmUlJbz24kUAMorKjl+JhMfDxcACoqrJ8eO9raX3SetaWD3ENbvMOd1JCENg5M9Xu71++niZEyv12Fro28yJ1dzEgpx9rPD2dcOna2OgH5upO3Pr9UmKTqH4BFe2Bn0ANi7mc+L6mx16G3Nh+SKchW10rqxA4T71T7GrDlxnCFt6hxj2oSw4oi5j9afPEmfVq3qvc/Y0PasOV49QT6YlkZWQcOfK2vJTijGxc8WFz879LYKrfu7kPKrqVabhOhc2o1wx97SNw6WvslLKUGtAP9w875o66jDxl67r0832/HmSnqGg7uL1lFcXlRoCCsPmY8xsanpuDrY422oPXZ5G5wx2Ntx8Kz5pN7KQ8cYEmr+fJ0+n0Ni9oV67xvi7cnuxDMA5BQWkV9cQufmvjcyFW1UWvFfEyUTsitQFMUA9APuR+MJma+LgbR8Y9XjdKMJX4OhdhuDgTSjuU2FqmIqKcHD0QFHWxv+0rsH7+/aY9WYL8fXYOCcqTqfNJMRX2fDZV5RTQFe7DeYBb9sv0HRXRtfZwPnCmrkUnD1udQ0NiSUVaeONWZo18XPyUBazXwKjfg5X90R8WhOJoMDgnDQ2+Bh70gf/1Y0d3a9UaFeFR9XA+m51flk5Jrwcbv6/glt4Y2fuwvbjybeiPCumbe7gfScGvlcMOHjful8DI72DAgP5tdjZ6qemxoRzk8L7uOxKQN5a8mWGxrv1fL2MJBRI6+sHCPeHg3n9e7zk1m38GEKi0rZsvdkg22srTinHEev6oqWo6ctxdlltdqY0koxnSth24un2DongYwD1fkWni8l5ul4Nj50nHYTvK1aHYPaxw+4eIyp/bn3q3OMMVqOMTWNbt+e1ce1X6ZcU1FOOU7Nqn+fTp42FGXXnogYz5WRf66UjXOS2fB8MucOmCeR+edKsXXWsf2fqax7Jonfv8ikskK7swA32/HmZuDrYiAtr8ZnJ9+Er4uhXpv0fNNl29R1Iv08Ue1D0CsKLdxd6dTcB3+3Jj47FddFJmRXNgHYoKrqSSBHUZRbLM+HKIpy8OI/4KFLvYGiKA8qirJfUZT9+Xt3X3cgSgPPqXUK14pSv5WqwhP9+7B4/wEKy8rqbdeK0kBGV3uIu7NLV7YkJ5JWY0KnpQb75hqP196OzrT39GZ7SlJjhPQHNbQfXV1CO1KT2JJymhVjZ/J+xFh+zzxHuRan+2to4GNR77Nzudf+bfwg3lrZNCb/cG356HUKrz84iqUxB0g9n1f1/PItsUyYs4j3v9/B/WN636hQr01D49cl8pr9jx8Y+9eF2Nrq6d6pfpWmyaiTU2WFiimtlAHzg+n5RCt+X3iW0oIKAJya2RH1dluGvt+eM1svUJxr3fG6of3qakblmkNDuJ8fxWXlnMxuGisxLmpw+KqTb2WlijGtjKGvtqL/bH/2/Ced0oIK1ErIOlZEt7u8GfFma0wZZZzektfAG1rHzXe8uTnVG7saGrev0G8/HIgj3Wji+wdnMGf4YA6kpFFe2YTLPNdJUVWr/fvDsSqKp6IomxVFibf81+MS7VopirJJUZRjiqIctVzudElyDdmVTQf+bfn/7yyPPwROqara9WIjyzVkDVJV9RPgE4A2b7573XtDutGEv2v1mRE/FwOZpoI6bYz4u7iQbjShVxQM9vbkFhcT7u/PiPZteXZwf1zt7alUobS8nK9+j73ecP6wdJOR5jXOvvobXMgsMF3mFdVu8WtOT/8W3Nk5HCdbO2z1OgrLynhzz44bFe5lpReYaF6jguTv7EJm4dXlctGYkPZsTIrXfPICkF5oxL9mPk4uZFxDPh/E7uGDWHM19r3BY0jKq78U40a7rV84k281X+Z5JCUDvxprXnzdDWTlXd0SKmd7O9r4NeOzR6YA0MzFmffuH8fjn62y6o09pkaEM2GA+XqCo0kZ+Hm6cPHT6+thICu34Xzm3jWUlMxclkQfaHD7pl+P88IdUcDGGxD1lU0e2pVxEea8jp1Ox9ezup+8PV04f+HS/VRaVsHO304xsEcIv8Zpv3TZwdOGohoVsaKcMhw8ax9mHb1s8WzrhM5GwdnXDpfm9hSklWDXxqm6jactLi0dyD5WSIs+blaLP91owt+l9jEmw1T7c59uMrdJN5mPMS6WY8xFY0KbXnUMwMnLhsLz1X1TmFOOY52+cfKyoVlbR3Q2CgZfO1xb2GFMK8XJywaPIHtc/OwACOhl4Hx8MVq52Y43f1YzeoYz9RbzMebwuQxz5cq8Shw/VwOZdZa/Z+Sb8HOtroj5uRrINF2+3ypUlX9s3Fb1eMl9t5GcndtIGYjr9DwQo6rqPxRFed7y+LkG2n0JvK6q6mbLarvLfthkQnYZiqJ4AZFAZ0VRVECP+XThf7SI51BaOq09PAhwcyXDaGJ0h/Y8tXp9rTYx8aeZ2LkjB86lMSK0LXvOmEeH6d8uq2rzeL9bKSgr03QyBhCbmU6gmzsBLq5kFJgY27Y9j29ed+UXAk/WaDcltBNdvH01m4wBxGalEejmQYCLGxkFRsaGhPL4z2uu6T3GhXTgn/uaRhUmNiuNIFcPWhrcSC80Mja4A49vXX1Vr9UpCq529uSWFBPq4U2opzfbU62/1G/prliW7jLv4wM6BDG9fzjrD5wgrLUfxuLSS14rVpepuJRB8xZWPf7sr1N4e9UOq99lcfmWWJZvMefTr0sQ0yK7snHfCToH+2MqKiW7gQnmwxP6YnC057UvNtV6vqWPOymZ5oN6/7BgzmRaf8J80Q+bD/LD5oMA9O0axJRh3di8+zid2vhTUFRCdp2JpqO9LU6OdmTnFqDXKfTpGkzsCe1v7APg0cYJU1oJBRmlOHracHZXHj2faFmrTfOerqTsyqV1hAcl+eWY0kpx8rWjKLsMO4Mevb2OUlMF2ScKaDOmmVXjP5SeTqC7OwGurmSYTIxpH8rsdbXH5JhTp5jUqSMH0tIY2a4du89UL4NVgJHt2jF96TKaGq82DhjTyjBllOLoaUvyTiP9nvSv1aZlLxeSduYTEulGcX45xnOlGHztsHXSUWqqpDivHAc3GzLiCvEMcbjET7rxbrbjzZ/Vt7/G8u2v5jF5UNsgZvYMZ23cCcJb+GEsKSWrzgnzLFMBBSWlhLfwIzY1nfFhHfh638HL/gwHGxsUBYrKyukb3IryykpOnW8ad8RtVE3lQuCrMx4YbPn/L4Ct1JmQKYrSEbBRVXUzgKqqVzxjIhOyy5sCfKmq6l8uPqEoyjYgQItgKlSVVzb/zOJpk9ArCssPHyH+fDZP9O9DXHoGMQmnWXYojrfHjCDmwXvJLSrmyVVXN8HRQoWqMm/Hz3w5bjJ6RceyY3HE52Qzu1dfDmdmEJ10ijAfXz4eOR43eweigkKY3asvw5Z8oXXo9VSoKvN2RfPlyCnmP0lw4jDxF7KZ3b0fh8+nE518ijBvPz4eOgE3e3uiWocwu3s/hn2/GIAAgyv+Bhf2pKVonIlZhaoyb3c0X46Yil5RWHbyMPG52Tx1S38OnU8n+kwCYc38+GTIRNzs7BnSqg2zb+nP0BWLsNXp+H70DACMZaU8uXUtFRoPtjuOJTKgQyBr59xLcVk5Ly2pnqAse3om097+BoDZYwYw6pb2ONjasnneA6zYG8dHG5vOdZcX7TqcSL8uQfy04D6KS8t5ZXF1deubeXcw89Wv8fEwcP+YW0lMy+brl+4AYNmWg6zcEce0yK706tCK8opKjIUlzF+kTXWsrl8OJtK3azDL372fkpIy/v5xdVxfLLiTu+d8hYO9Lf98egJ2tnp0OoXfjqTwY7S2J5cu0ukVwu9vzq7XE6ESWkd44NrSgaPfZeAR4oh/T1d8uhrIiDUS/eRJFB10vtMPexcbMmONHP4y3TyrUaHtWG/cWlv3S3+FqvLKz1v4fPJkdDqF7+PiiM/O5sm+fTmckU7MqdMsOxzH2yNH8vN995FbXMwTa9dWvb5XQADpRhMpebWX8z03cABjQ0NxtLVl54OzWHY4jvd2X//y/euh0yv0eMCHn187i1oJIZFuuLeyJ3bJebzaOBDQ04B/VyfSDhaw+olEFB10u8sbexfzDT5uudubmPkpqIBXsANthrhbNf6abrbjzZU8/QrsOwi5eTB4Cjx6L0wZrXVUtW2LT2Rg20A2PWY+xsxZWX2M+fEvM5n4sfkY88ran1kwYRgONjbsSEhie0ISAENCQ3hxZASeTo4snDGe4+lZPPDNj3g5O/HfOyZSqapkGAt47scNWqQnavNVVTUNQFXVNEVR6v9tE2gH5CqKsgIIAqKB51VVrbjUmypN5e5aTZGiKFuBf6iquqHGc48DI4GW13Pb+z+yZLGpKTfcREsdbG+iXABVf9PsZrgd0e5v/dwIdvk3T9/YFt48uQAMet66k4QbafnmvlqH0KjuHr5V6xAazaJfBmgdQqM6Pe5TrUNoNB0/fljrEBrV8ZdnN3hlaFMzsv3zVjuYbDj55l+AB2s89Ynl0qIqiqJEA34NvHwu8IWqqu412l5QVbXWdWSKokwBPgO6AWeApcA6VVU/u1RcUiG7DFVVBzfw3HvAew08P98KIQkhhBBCCCGuQ837OlymzZBLbVMUJUNRFH9LdcwfqP8XwOEscEBV1dOW1/wE3Ip5ktYgucuiEEIIIYQQQhuqar1/f9wq4G7L/98NrGygza+Ah6Io3pbHkcDRy72pTMiEEEIIIYQQ4sr+AQxVFCUeGGp5jKIoPRRF+S+A5VqxZ4AYRVEOY746+LJre2XJohBCCCGEEEIbf6L7Waiqmg1ENfD8fuCBGo83A2FX+75SIRNCCCGEEEIIjciETAghhBBCCCE0IksWhRBCCCGEENr4Ey1ZvFGkQiaEEEIIIYQQGpEKmRBCCCGEEEIblVIhkwqZEEIIIYQQQmhEKmRCCCGEEEIIbaiVWkegOamQCSGEEEIIIYRGpEImhBBCCCGE0IbcZVEqZEIIIYQQQgihFamQCSGEEEIIIbQhd1mUCpkQQgghhBBCaEUqZFbmEZaldQiNxlRsr3UIjUanu7nOzjjYlWkdQqO54OisdQiNyu+nm+dzU3n/ea1DaFTfHeypdQiNRvEt0TqERrXo1/5ah9Bo/LbdXOfCO6Y9rHUIjeboXz7SOoRGNlvrAK6OXEMmFTIhhBBCCCGE0IpUyIQQQgghhBDakAqZVMiEEEIIIYQQQitSIRNCCCGEEEJoQypkUiETQgghhBBCCK3IhEwIIYQQQgghNCJLFoUQQgghhBDaqKzUOgLNSYVMCCGEEEIIITQiFTIhhBBCCCGENuSmHlIhE0IIIYQQQgitSIVMCCGEEEIIoQ2pkEmFTAghhBBCCCG0IhUyIYQQQgghhDYqpUImFTIhhBBCCCGE0IhUyIQQQgghhBCaUFX5O2RSIRNCCCGEEEIIjUiF7E+sT7O2PN1hDDpFx8qzv/LF6e0Ntov068yb3WZw164POZafauUoL6+/TwhzwkagU3R8n/w7/z25q9b22wK7MyO4JxWqSmFFKS8fWM0p43m6eDTnla5jAVAU+PDYNqLTjmuRQpX+Pm14vssI9Oj44czv/Dd+Z63t0wJ7MD2oJ5WqSmF5KfNjV3PKmFW13d/RjVWRj/Dh8a18fuoXa4dfT59mbXmmw2j0io6fzu7n8zr71+SWvZjWujcVqkpReQl/P/ITiaYsbBQ9czuPp6NbCypVlbeOreW3nESNsjAb6B/MvO5D0SkKy07FsvDo7lrb7w/txbSQrlRUVpJTUsize9ZwrjAfgOZOrrzRexT+Tq6owH1bl5JakKdBFrXNvjeCPt2CKC4p5+//2cDJxMx6bd6ZMwkvd2f0eh2xx1N5+78xVKoqj9wxkP7dQygrryA1I5fX/7MRU2GJBlnArc3aMTvUPI6tOvsrXyVua7BdhG9n3ug6k3t2f8Dx/FRcbZ14o+sMOrgGsPbc77x9bJWVI2/YoOZBzOsZhV7RsTQhlo/i9tbafn+HntzeNoxytZKc4kKe/WU9qQX5dPTw4e+3DsNga0+FWsmHh3ezJknbMW2gfzAv9xyCTtGxNOEgC4/sqbX9/g49uS2kKxVqJdnFhTy3Zy2pBebPTcKM5ziRax7fzhXmM2vr91aPv65BLYKY1zsKvaKw9OQhPjpcp2869eD2dmGUV1aSU1zEszvXV+XzxdApdPNuzq+Zqdwf/YMW4V/WU3dF0Cc8iJLScl77eAMnkuqPB+8+O4lmlvHg4IlU3lpsHg+0NHfEYAa2DaK4rIwXftrE0fT6cXfy9+GN8cOxt7Vhe3wir2/YCsDwjm15dFAfQrw9mfbpEuLSMgCw1el4ZcwQOjf3pVJVWbBhK/uSz1ozrcua+w/Yuhs8PWD151pH00TINWQ3/4RMURSTqqqGGo/vAXqoqvqo5fGDwFOWzfnAU6qq7rRsS7K0PW95PBh4RlXVMZb3+ReQCjgAH6uq+q41cgLQofBsp3E8um8RGcX5fNH3r2zPPE6iqfZg5qS347bWfTice8ZaoV01HQovhY/i/l1fkVGUz7KIWWxJO8Ep4/mqNmvOHmZp0m8ARPi147kuw3nwl2+Iz89k6tZPqFBVvO0N/Bj1EFvST1Ch0cFFh8LcsFHM+sWcy9JBs9iSfqLWhGvt2cMsS9pvyaU9z3Yazl/2fF21/bnOw9mREW/12BuiQ+H5TmP5677FZBTn81Xfh9mWeYxEU3U+G9Ji+SFlHwADfUJ5KnQUj+3/goktewBw28738bBz5v0ed3PnLx+holHfKAqv9BjOXT8vIb0on5+G30v02XgS8qv3syM5GYyPX0RxRTkz29zC890ieXzXTwC81Wcs/zmyi53pSTjZ2Gr+BQagT7cgAvw8mPb4Ijq19edvDwxh1txv67V78d01FBaVAvD602OJ7NOO6F9O8OuhZBZ+u4OKSpW/zhzAXRN78Z9vdlg7DXQoPNNhHI/v/4zM4nwW93mEHZnHSCqoP45Na9WXuBrjWGllGZ/EbybY4Euwi5+1Q2+QTlF4tfdQ7ti8lPRCI6tG3c3mlAQS8rKr2hzNyWDs2i8orijnjnZdeaH7YB7dvoqi8jKe2rmWJOMFfBwNrBl9N9tTE8kv02airFMUXu01jDtjviO9MJ+VI+8xf25q5HIkJ4NxJxebPzdtu/F8twge27kSgOKKckavW6RJ7A3RKQqv3jqEOzYuM/fN2LvYfKZO32RnMnbVl+a+ad+VF3oO5tGt5on+x3H7cLSxZUb7rlqlcEl9woNo6efB1KcX0amNP8/eO4T7X64/Hsx9v3o8eOOJsUT2bkf0nhPWDrfKwDaBtPZ0Z/j7iwlv4cfLoyO57bPv6rV7eXQU89ZEc/BsGp/MmMCANoHsSEgiPjObx5et5pUxUbXaT+3eBYBxC7/C08mRT2dOZMqn32p0BKpvwkiYMQmeX6B1JKIp+Z9esqgoyhjgL0B/VVVDgYeAbxVFudqj+1JVVbsC/YC5iqK0vEGh1tPJPYCUgmxSiy5QrlawOe0Qg3w61Gv3ULuhfHV6O6UV5dYK7aqFebbgTEEOZwtzKVMrWXf2CJH+obXaFJSXVv2/o40dquXLcHFFedXky05vU/W8Vrp4tCClIIezhRcoUytYlxpHhF/7Wm0Kyqu/WDnqbWtNUCL9QkkpuEBCjQmclsz7V07V/rUp7RCD6+xftfOxq8om2ODDvuxTAFwoLcBYVkxHtxbWCr2ecK/mJJsukFKQS1llJWuSjzI0oG2tNnsykym2fEYOZKfi5+QCQBvXZtgoOnamJwFQWF5W1U5LA3qEsGH7UQCOxKdhcLbHy925XruLX770eh22NvqqP/Wy71AyFZYzknEn0/D2dLFO4HV0dGvJ2cJszlWNY7EMbGAce7DtML5O2k5pZfXvvriijNjc5FrPaa2rlz/JxlxSTHmUVVayOukYw1rW3td2Z5yp3tfOn6va1xKNF0gyXgAgs8hEdnEhng5O1k2ghnCv5iQbL5Biyq3KZWhAu1pt9tTLxVWLUK9K12Z1+ub0MYa1alOrze70GvlkncPPqepcLr+knaGgrJSmaGD3ENbtsIwHCWkYnK5uPNBaVGgIKw8dAyA2NR1XB3u8DbXj9jY4Y7C34+DZNABWHjrGkNAQAE6fzyEx+0K99w3x9mR3ovnkTU5hEfnFJXRu7nsjU7kmPcPBXZsht+lSVev9a6L+pydkwHPA3y5WwFRV/R34AnjkWt5EVdVsIAHwb/QIL8HbwY2M4uplUxnFeXg71D4YtnP1x9fBjZ1Z2p0BuxwfBxfSi/KrHmcU5ePrUH+UmhHUk41DH+OZTkNYcGhD1fNhHi1YHfUwK6Me5pWDazWrjgH4OriSVi+X+l9Opgf1ZP2Qx3mq01AWHF4PmCdn97ftx0cnGl6qpQUfB9c6+1c+3g5u9dpNbdWblYOe4vH2w/nX0TUAnDSmM9inA3pFR3NHDzq4Nce3gddai5+jC2kF1X2TVmjE1+nSR8NpIeFsO3cagCBXT/LLivlowGRWj7iP57tGolOUGx7zlXh7Gsg4b6x6nJVtxNvT0GDbd+dMZu2nD1NYVMqWPSfrbR8T2Zk9B7VZUurt4Epmjf0ss4H9rJ2LeRzblaXt8r2r4evkwrl6+1rD/QIwrU0YW1NP13s+3MsfW52eZGP9L5vW4udkIK2wOpf0QmPV5LEht7UJZ9u5U1WP7fU2rBx5DyuG31XvBIgWfJ0MnCuo/sykFRrxdb7MONAujK2p2i61vlrengYys6tzy8wx4u3R8H737+cms/6jhykoLuXnvfXHA2vydTGQllcdd3q+CV8XQ7026fmmy7ap60T6eaLah6BXFFq4u9KpuQ/+bjIDEk3b/8KEzFFRlIMX/wGv1tjWCfitTvv9luevmqIorTAvWzx0ie0PKoqyX1GU/VnrD1zLW1/6ZzbwXM35iILCU6Gj+ffxdY3y824EpYEsGppSfZv4K8M3v8/bR6J5KHRA1fOHLqQyNuYjpm39lFnt+mOn0/CMXwMd0tASvSWJvzIy+j3ePRLNQ+0GAvBIaARfntpDYUXTOfvaYN80MOFdfmYv47e9w/snNvJAyGAAVp39zbLM8a883WE0sRfOUNHE7qB0qbn7+MBOdPH059Nj5mtlbBQdPb1bsuD3GCZsXEwrw/+3d+fxdk13H8c/v0wyIyGDISJIQxBJJFrz0CrFE2m1aqhSQg1tg1bLY3gonqc8tNXWVFWlOlBCW1OI1NR6ImSOFEFkjoTMc+7v+WPvk3vBHEiyAAAgAElEQVTucW/c8ay91/2+X6/7ep2z9r3xW/Y5e6+111q/tQ0n7bpvGSOtnlXTKaxplPjiGx/hP867k9atWzJ4715Vjn1z+AFs2lTBMy+92SRxfppqu7ZF9TCMkf2O57YZT5Qtpoaorq9e02OiE3fdi3279uTuaeOqlG/frgO3HnwcP/jnk0GnWNX2GgBw4q792adLD+6eXrkm66BRv2LYU/fxvVf+ytX7f55eHbdpslhroy7fmRP77MW+XXtw95Rx1R7Pmrqcq5E/eYTjL7yTNq1asn//XtX+TkifuG9W9536lC/GIxOmsmDFSv5y7qlc8cXDmTB7PhsrsnUPkhIVFeX7yajo15ABa9JphUDlGrIt/L5ReQ+t7mtfXHaymR0BfAYY4e5rq/sH3f1u4G6AIU9d0Sj32EVrl1UZdejedmsWr6t8mtm+VRt269SdO4eOAKDrVh25ZfA3uPT1BzKT2GPh2uX0aFc5itS9XWcWrV1R4+8/OWcq1+x3HPB4lfJ3Vyxmzab17NG5G9OWzm+qcLdo4Zrl9KxLXeZO5aoBx8GEZKTv6B324tL+X6BT67a4O+srNvKH98I1BhZ+4vPVucrnq9Qz86dwef9hMOURNnkFtxY9CLj3s+fywerFNf5tU1uwZgU9O1Sem57tO7FozSfPzUHde3Nh/4M45bnfs75iEwDzVy9n2scLmb1qKQCj57zFwO12hHcnlSf4Il/+4n78x1HJ2ogZMxfQfbtOkA5+b9+1E4s/XlXj367fsImXx8/kkCG78dqUWQAce9heHDS4D9+57uEmj70mi9Yup1vR56xb2858WHId69OxO7cPPReALm06cvPAM/jBhPuZkZHrWLEFq1awQ+lnbfXKT/zeQT134aJ9DuTk0X/Y/FkD6Ni6Db898iRumfgSExbPK0vMNZm/egU9i6Yg9mjfiYVrqqlLj95cuPeBfH30g1Xqsij93dkrl/Lqwg/o36U7H6xc2vSB1yA5N5WjJFs8NwM+x8lP/bFKfbLmK1/Yj2FHJNeDN99dQLeulXXr1qUTi5du+Xrw0hszOWTwboybOqvJYy126pABfHXQ3gBMmbcwGbmanRzr0bkji1ZUjXvh8pX06Fw5Itajc0cWrfzkeSu2yZ3/eaZyxskfv3Uys5aE++yJ1EZzGCHbkunA4JKyQWk5wBJg26JjXYDiluWf3b0/cAhwSx3WnjXY9GVz6dVhO3Zoty2trCVf6LkvLy6qfMq9auM6vjDmBoa9cDPDXriZqUtnZ6ozBjDl47ns0rErO7bfhtbWgi/t1J+x86tOr9ylQ5fNrw/r0ZdZKz8CYMf229AyfeK5Q7ut2bXjdsxdHe6CO3XpPHp1KNSlJV/acW/GLqhal17Fdem+B7NWJXU54+XfcvSzP+PoZ3/GAzNf5e63XgraGYPk87Vzh66bP19H99yXFxZVnTK2c/uum18f3O0zfLA6WRzftkVr2rZsDcABXXdjk1dUSQZSbpOXzKN3p23ZqcPWtG7RguN32Yvn5lZNnrLXtt25fuixnPviwyxZt7rybz+az9Zt2tJlq2Qtz4Hdd+GdZWE6l48+M5EzL3uAMy97gBfHvcMxh+4FQP89erJq9TqWlDTA2m3VevM6kpYtjM8N7MOsucln7oABvTl92FAu+8ljrFsfbg3Wm8vnsHP77ei5+To2gJdKrmPHjL2e4S/exPAXb2LastmZ7YwBTFoyP/msdUw+ayf03pNnZ79T5Xf6d+nGjZ/9IueMfYQlays/a61btOCuw4fz6LvTeHJW+Gnmpd+bE3rvyXNzPvm9ueGAYxjxj79U+d50btN284yFbbdqx+Dtd+LtQN+bgkmL59O7c9G56VPDuTnwaM4Z82iVc5NFjzw7kTOueIAzrniAF8a/w5cOSa8Hu/dk5ZpPvx4cuF8fZs37qOxx/+G1SQy/60GG3/UgY2bMZNi+yZrRATv2YMW69Xy4smrcH65cxap16xmwY9K8GrbvnoyZMfMT/26xtq1a0a51Mt5wYJ9ebKyoYObi8tdVpC6awwjZltwE/MTMjnH3JWa2H3AmcEB6/B/AN4CrzawlcDrwWOk/4u7/MrMHgO8Bl5cj8E1ewU3T/8ptQ86ipRl/nfM6765cxHl7fJ43l83hxUXZX2+xyZ3rJz3JPQedTguMR2dN5J0VH/KdPQ9n6sfzGLvgLU7tM5QDu+3KhooKlm9Yw+WvJ//7B3ftxYi+B7GhogLHuW7SEyxdvyZgXSq4YfKT3P25b9DCjFEfTGDmig+5qN8RTFs6j7EL/s2puw7lc9v3YaNXsHz9Gq54Y1SweD9N8vn6G78cciYtzXh8zhu8u3IR397jKKYvm8uLi2Zw8i6fZWjX3djoFazYsIZrJidprbfdqgO/3P9MHGfR2uVcNSlsuutN7vzX+NH87oiv08Ja8PC7k3h72WJG7nMoUz6az5i5b3P5wCPp0KoNvzz4ywDMW7WMc1/8CxXu/PeEMfz+yFMxgykfLeBPMxtn2nFD/HPCe3xuUB8evu1s1q7fwA23P7P52H03fYMzL3uAtm1bc9NlJ9K6dUtatDBenzqbx55NRvYuPftIWrdqxc+uOglIEoPc/Ovnyl6PTV7B/775V34++Fu0MOPvc8fz3qpFjNj988xYNpeXPtzyVMpRh15G+1Zb0dpacli3vfju+Hs/kaGxnDa5c/W4Z7n/81+jpRkPvTOFt5ct5uIBBzNlyQKem/MOlw8+gvat2nD7YcMAmLtqOSPGPspxu/RjaPed2Xardpy0WzKC8P1XnmT6x2Hqs8mda157lvuP+jotzHh45uSkLvsewpSP5id1GXQEHVq14VeHDAcq09vv3rkrNxxwDI5jGHdO+1eVbIah6nP1q89x/9FfTc7N21N4e+kSLh54MFMWL+C52e9w+ZDDad+6Dbcf/h8AzF21ghFjHgXgoWNPYbdtutKhVWv+9bXz+eHLT/HivPcD1qjSPye+x4H79eEvtybXg+vvqrwe3H/jNzjjigdot1Vrbr7kRNoUrgfTZjNqTPlH+ou98PZ7HLpHb0Z/5yzWbtjIFY+P3nxs1HmnMfyuBwG49onnufHEo2nbqhUvvfM+L77zPgCf77cbVx57BF3at+POU4cxY8GHnPPgKLp2aM89pw+nwp2FK1bxw1FPV/efD+bSa2HcRFi6DA4/CS46C046LnRUgWU42Ua5WOjsdE2tFmnvzwdGkkxFXAFc6u4vpse2Bu4gWVNmwNPAj9y9opp/ZwfgDWAPd69xrlpjTVnMgpVrtwodQqNp0SKa0wJA2zYbQofQaD7++JPZwvKsx2PxfG8qzg476tHYFiwMu86pMVmL7K6VqA/fED4rYGPp8Vw8dQFY2jeeyVbTz7sjdAiNqkWPt8JnoaqFYzqfVbZG2NPLf5vJ/yfRj5AVd8bS9/cB9xW9v4Ok01Xd3y4DTq3hWOm/Mw/IxmY4IiIiIiI54BlOtlEu8TzWEBERERERyZnoR8hERERERCSjIl8+VRsaIRMREREREQlEI2QiIiIiIhJGhUbINEImIiIiIiISiEbIREREREQkDFeWRY2QiYiIiIiIBKIRMhERERERCcK1hkwjZCIiIiIiIqFohExERERERMLQGjKNkImIiIiIiISiETIREREREQlCa8g0QiYiIiIiIhKMOmQiIiIiIiKBaMqiiIiIiIiEoaQemLvmbcbIzM5197tDx9EYYqoLxFWfmOoCcdUnprpAXPWJqS4QV31iqgvEVZ+Y6gLx1UcaRlMW43Vu6AAaUUx1gbjqE1NdIK76xFQXiKs+MdUF4qpPTHWBuOoTU10gvvpIA6hDJiIiIiIiEog6ZCIiIiIiIoGoQxavmOYlx1QXiKs+MdUF4qpPTHWBuOoTU10grvrEVBeIqz4x1QXiq480gJJ6iIiIiIiIBKIRMhERERERkUDUIRMREZFmxcy0D6uIZIY6ZCIiItLcjAsdgIhIgTpkETCzL4eOQZoHM2trZttXU97NzNqGiEnio2ualIGFDkAEwMw6hI5BwlNSjwiY2RvuPih0HI0tbeDvDjgw093XBg6pzsxstLsfHTqOxmJmdwNPu/ujJeWnAQe7+/lhIqu7CM/NV9z9kWrK2wA/dPcfBwirXmK7pplZS+AcYCeS788rRceudPfrgwVXD2bWA8DdF6QPaA4B/u3u08JGVntmNge4tabj7l7jsawys0u2dDwvdYqlHqXMbEegJzDZ3debWTdgJHCmu+8QNjoJTSNkkjlm1srMbgLmAL8Dfg/MNrObzKx12Ojq7BOjSTl3cGlnDMDdHwQODRBPQ8R2bs41s6fMbNdCgZkdC0wGuoYLS4C7gMOAJcBtZlbcoMzVaKCZnQf8C3jVzM4H/g4cDzxqZmcHDa5uWgIdgU41/ORRcfzfJ791iqUem5nZSGAi8AuS7843gTeBdsDgkLFJNmiELAJmthp4p7pDgLv7vmUOqUHM7KckF92L3X1FWtYZ+F9gjbt/L2R8dWFm75LcUKpVXecmy8zsTXffs67Hsii2cwNgZqcA1wN/APYm6XRe6O6TggZWRxFe0yYXYk6TSdwObAecArzq7gNDxlcXZjYFOICkITkL2D0dKdsWGOvu+wUNsJZiG4UtZWYT8vS5qklE9ZhO8kDzIzPrRXJ9O9TdXw0cmmSEsgzF4T3ghNBBNKLjgb5e9LTA3ZenT2NnALnpkAFbk9SnuvUKDuSt0b/IzIa6e5UF8WY2BPgwUEz1Fdu5AXgI6A9cDCwFjnT3t8KGVC+xXdPaFF64+0aS0cyrgedJRmnyZIO7rwZWm9lMd18A4O4fm1menvDGvoYsT+diS2Kpx1p3/wjA3T8ws7fUGZNi6pDFYb27zwodRCPy4s5YUeGmnN3wAWa5+7dCB9GIfgA8ZGb3Aa+nZfsDZwBfDxVUPUV1bszsYJKRl1eAnUmmyP3NzP4M3ODu60LGV0exXdPGm9kx7v50ocDdrzOzecAdAeOqjwoza+3uG4DjCoXpmt88LYM4KnQA0qzsZGa3Fb3vVvze3b8bICbJEHXI4vDKp/9Krkw3szPc/f7iQjM7nWSELE+iegrr7uPM7ADgAuDMtHgacIC7LwoWWP1EdW6AnwHnFI1ePmZmzwJXA5OAfsEiq7uormnufnoN5fcA95Q5nIbavObN3ecUlXcFLi1/OPVTGK2ISTqdtPDQcnczm1w4RI6m+n5KPSrcfUCYyBrkByXvX6/2t6TZ0hqyCJjZzsAu7v5y+v4SKqfB/MHdq1uLkVlpJqJHgTUkFy0HhpCsWRju7nMDhlcnZra3u08NHYd8UmznxsxauHtFDcf2dPc3yx1TfcV2TYNkawjgQpIppQ5MB25394VBA6unNHlMoS5vuvu7gUNq9sxsly0dz8uocw31MJIspVe4+5fKHJJIk1OHLAJm9kfgQXf/e/r+38DdQHugn7ufFjK++jKzI0lu+AZMc/cxgUOqszTrWBd3vzl9P5ckYYkBl7l7rqYrlTy5rHKIHD2BhfjODdTY6P9V3kYvY7ummdlBJIlW7iN5yGTAIOCbwGnFafCzLk2wdA/JVOWJJHUZQFKvs919ecDwpBrptgtfT7Ph5oqZ7QecCnyNZG3pI+7+y7BR1Z2Z/Y2q904HFpMkwvl9mKgkS9Qhi0BptqjirERm9pK7HxIuurpLE0Rs5+5PlZSfAMxz99wM9ZvZa8Ax7r4kfT/B3Qem6y1Gu3uuUsUXPbk04AmgypPKvDyBhSjPTUyN/tiuaa8C57v7hJLy/YC73P2AMJHVXbp+9H3gusKIrJkZcBVJxsUzwkXXvKWd5QuBHYG/As8CF5Fkk53o7sMChldrZtaXZE3yKSRbRfwZ+L67b3EEMMvM7LBqirsApwNvu/uPyhySZIzWkMWhbcn74sXKedx/6GYq1ycVe5PkKfmRZY2mYVoUGvyphwHcfa2ZtQsUU70Vd7jMbF2eOmDViOrcALcAJ5Y0+h83s1Ek+2DlptFPfNe0zqWdMQB3n2hmedtX6SB3P7O4IE3CdJ2ZvR0mJEk9AHxMsk/cOSTrltoAw9x9YsjA6mgG8BJwQmF6spldHDakhnH3F6orN7O/kjxAU4esmVOHLA4rzKxvIb11YbGymfUDVgaNrH66uvv7pYXu/o6Z5a0xtnXxG3e/EZL1PuSzYRmT2M5NTI3+2K5pZmbbuvvHJYVdyFdmQogvGU5M+rj7PgBmdg/JlLhenu7nmSNfIRkhG2tmTwN/ItLPXZo9OnQYkgF5uxFI9a4B/m5m3zSzfdKfM0mmLFwTNrR62dLoRIeyRdE4RpvZ9dWUXweMLncwDWVmgwo/QDszG1hSlidRnRvSRn81hXls9Md2TfspyeftMDPrlP4cDjyVHsuTV8zsaitpRZrZVYD2VQprQ+GFu28C3sthZwx3H+XuJ5Nkhv0Hyb6K3c3sDjM7Omhw9WRmXar52c3MriXJVCzNnNaQRcLM9gYuI1nMDzAVuDmPWeTM7E6SeeNXFu9Hll64err7ucGCqyMz60CyAH4ISepxSBbAjydJUZ6rp/1mNnYLh93dczOdNMJzcy4wgmS9yBtp8WDgJ8C97n5XqNjqI6ZrGoCZHU/V+kwjqc/fwkVVd+k6pd+QrE+cSJKcYCAwgSSpx7KA4TVrZrYJWFV4S/JwczWVSZc6h4qtodIHS18FTs7TfabAzN4j+a4UHmQ4STtnLHC9kuGIOmTNiJn9wt2/EzqOT1PUUB5KcsOHHDeUAcysD5UNsenuPrPkeH9311OyAGI6N7E0+msrL9e0GJnZbsBeVGbBze33RkQkNHXImpHSzGVZV9JQnla6z01MN/y8nBsz+/KWjrv7o+WKpVzycm6ao7ycGzO7eguH3d1/XLZgyiQv50akXGLZlkSahpJ6SGalHbAtbTb6AMm0mRjkZVXvX0hGLQsjl8VxO8mG3rHJxblpjo3+HFlVTVkH4GySBDIxnptcfG9EyqFkW5L7qdyWZJyZ5WpbEmka6pBJnsV0w8/LUPVXgJOBfYHHgT8W0hJHLC/npjk2+nPB3W8pvE4zXn4POIske9wtNf1dzuXleyNSDjFtSyJNQB2y5iWmDgzohl927j4KGJWu8xsG3JJuRfCfNe2zIuXRTBv9ubmmpUkJLgFOA34HDCpNgy8i0YppWxJpAnlLhSyfwsw6po3l6vy8rMFIXawPHUAdrQWWActJRmFKN/KNSW7OTZpK+XpgMskDt0Hu/sOI1yjk4ppmZjcDrwErgH3c/b+aQWcsN98bkTKIaVsSaQL6EETCzC4wsw+AWcBsM5tlZhcU/4673xckuKaT+Ru+mbUp3q/HzI4ws0vN7Nji33P3z5Y/urpL478beB04Avi5uw9092cCh1ZnZvaVGsrbpHsqAbk6N9E0+s2spZmdZ2Y/TtdeFB+7svA6R9e0S4EdgCuBeWa2PP1ZYWa5TnedPgQcZGbbFJfn5XsjUiYx7UUoTUBZFiOQNlAOBC4qZCJMMxT+HPg/d69u89tcMrN+7j4jdBy1ZWaTgMPd/WMz+wEwHHgSOAwY7+6XBw2wjsysgmT05WWSKaNVLiDu/t0QcdWHmT0DVAAXuPt7admxJDfHp919ZMj46io9N+uAjVQ9L7nbg8jM7gHaA+OAbwAvuPsl6bFos/eZ2bZZ70Sb2e3ufkH6+mCSRAUzgd2B89z9yZDxiWRVc9uWROpGHbIImNm/gQHuvrakvB0wyd37homs8ZnZB+7eK3QctWVmU9197/T1eOAQd19jZq2AN9x937AR1o2ZnckW1u65++/KF03DmdkpwPUkjcq9ge2BC9190hb/MMdy0uifXPhupN+V24HtgFOAV919YMj4mkoeOpvFMaYbxV/q7m+kDwEfcvf9w0YoIpI/SuoRidLOWFq2Jn1qnitmdltNh4BtajiWVcvNbG93nwosJllrtYbku5e7KcM5miJWWw+RPK28GFgKHOnub4UNqcmNIfvbRbQpvHD3jcC5aVr/54GOwaJqerlJUpLq7O5vQLJNiZm1DB2QSBaZ2S/Y8sPM3MwukaahDlkc5pjZUe4+prjQzI4C5geKqSHOIllzsa6aY6eUOZaG+jbwYDp1cREw3sxeIEkbf2PQyOrBzH5LzTcVd/ezyxlPQ6TTrW4HXgF2JplG+jcz+zNwg7tX9/mLQR4a/ePN7Bh3f7pQ4O7Xmdk84I6AcTW1PExZ6Wdmk0k+R70LI65m1gJoHTg2kawaX/T6WuCaUIFINmnKYgTMrD/JnlAvkyRbcGAIcBAwzN2nBQyvzszseeBKd/9nNcfec/ddA4RVb+lT46OBviQPQeYAz7j70qCB1UMNiTB6ASOBlu6+U5lDqrd0CukF7j6uqKwDcDXJ96ZfsOCaUB6mxTVXeTg3ZrZLSdF8d19vZtsBh7p7jJvDizQaM5sQ67RrqT91yCJgZrsDPUga/P1JnlxOA94G5rr7zIDh1VmaBnatu68OHYvULF0zcgVwKEkijN+4e+YzXxaYWQt3r3ZKr5nt6e5vljumcshDox/AzLoBF5Jc0xyYDtzu7guDBtaE8thQM7PWJOsv50a8vYJIo8nLNVjKS1MW4/Az4Ap3v7e40Mz2T4+dECSqenL3j4rf5/mGb2ZTqH4aUiHzXa6SekDSWQH+ExgI3Ax8O13nkyvuXlFDo/9XsXbGUpmfspimuv8DcB9wP0nMg4D/M7PT3P2VgOE1CjO7wN1vLyk+KkgwdWBmdwK/cPdpZrY18C9gE9DFzL7v7n8MG6GISP5ohCwCxZn8qjk2xd33KXdMDbGlGz6Qqxt+0fQeA54AvlR83N1nlT2oBjCzh4H9gf8lSYixqfh4aWc6y0oa/a9T2ej/JpDLRn+6jqfQ2WxD8iDj/eLzYmZdsn6ezOxV4Hx3n1BSvh9wl7sfECay+jGzS0qLgMtJ15G6+61lD6qezGyau/dPX48k2dbjRDPrATyVtxE+kXIwsxVUPpxtDxRmAOVuWxJpGhohi0PbLRxrV7YoGs8h7v7t9PVZwFvFN3wgNx2y4g6Xma3LWwesGkNIbirfJ0m8ApUjLg70CRFUPd0CnFjS6H/czEYBdwF5a/SfSBJ3hZl9m2Q66Sqgr5mdX9jrJuudsVTn0s4YgLtPNLNOIQJqoGtJ9h+cRuX3pSWQx7oUT0v+AvAwgLsvMMv84KtIEO6ex++6lJE6ZHF4zcxGuPuviwvN7GySJ/95oxt+Rrl779r8npn1z0Eymdga/dcAA0gewkwChrj7v9NR2keAPG0+atXtl5auL83ddhEkU2JvBToA17r7ajP7prtfGziu+liabnA7lyRx1Nmweb+4PD4AFBEJTh2yOIwERpnZaVR2wPYn2ctneLCo6i+aG76ZFS/cbWdmAylaw1PYwydCD5D9va5ia/Tj7gtg8wbq/07LZhWmMubIT4HRZvZ9oPAdGQz8JD2WK+7+AXCSmQ0DnjWz3NWhyHnAbSSJpEYWPnMk69+eCBaViEiOaQ1ZRMzsCJI1IwDT3P35kPHUl5n1pfKG/7PCZsRm9kXgaHe/dAt/nilmNnYLh93djyxbMGWUh2xxZnYuMIJk+mVpo/9ed78rVGz1YWYTgMHp+rGhhXT+6bYLk2paZ5pV6UOZy0hGlyCZ7ndzYeplXplZe5IpjAe4+6Gh42kqZna5u/936DhERPJAHTLJLd3wsysvaX1javSb2RBgiruvLSnvDRzs7r8PEZd8Up4zx9ZWXq4BIiJZoA6Z5FYebvhm1gtY5O5rLVkAdybJVL7pwK/zmC6+NvJwbmKX50a/mV29hcPu7j8uWzCNIKbMsbWVh1FyEZGs0BoyybM8ZPh4Ehiavv4fYDfgMeBIkoyF3woUV1PL/AbRzanRn8P9oVZVU9aBZD1pVyBX54aIMsfWgZ72iojUkjpkkmd5uOG3cPfCfiOfJ8l8VwH83swmBYyrQcystbtvKCnbzt0XA7j7Z8NEVidq9GeUu99SeJ1mvPweSZ3+RLJdQd40x8yx0VZMRKSx5S3zlkixPNzwZ5tZIXHH+8DOAGbWNVhEDWBmR5jZHGCemY1O1ycVjA4TVf24+y2FH+BukgyehUZ/nvZTKyht9D8GlZkX88bMupjZ9cBkkoeHg9z9h3mbfplaambHp1lWDwKehnxmjq2Dh0MHICKSF+qQSZ7l4YZ/DnCVmb1Isg3BRDN7HngOuCRoZPVzE/BFd9+epBPzrJkVRsPy0EGuQo3+bDKzm4HXgBXAPu7+X6XbE+TMecBFwG/Jeap4M+tjZvea2fVm1tHMfm1mU83s4eIHNO5+Y7goRUTyRUk9JHPMrA9wJTCPZN3VT4HPAW8CP3D398NFVzdmtjtJ+v4lQF+SRv8cYCuSZAszA4ZXZ2Y2yd0HFL3vDzwK/Ai4Kk+JPNJG/5dJOpa/cveVgUNqkMi2i6gA1gEbqTo12UjW93UOElgTy0Pm2PTh0h+BrYHTSTqZDwFHA6fFupWHiEhTUodMMiemG76Z/R24wt0nl5TvD1zj7ieEiax+zGw8cHzxNDgz2wn4O7Cbu3cKFlwdqdEvWZOH7KTF2RPTDch7VXdMRERqT1MWJYs6ufsd7v4/QOd0nc9sd/8NsG3o4Oqod2lnDMDdxwO9yx9Og/0I6F5c4O5zgMNJRjNzw91buHs7d+/k7p2LfjrF2hlLfTV0AFKjPEz7rTCzvum+d+3Th0uF2QAtw4YmIpJPyrIoWVSRTr/amvSG7+7jc3rDb7uFY7la1wPg7s8Vvy/Z6+qGMFFJHeWh0d9c5WHKymXA34AK4ETgcjMbAHQGRoQMTEQkr9QhkyyK6Yb/mpmNcPdfFxea2dnA64FiqrfI9rpqrvLQ6G+uMt9ZdvcxwGeKil42s+2Aj919U6CwRERyTWvIJBfyesM3s+7AKJKU5IUO2P4kGReH5y0luZlNc/f+6euRwOHFe11p/Uj2aZ1PdpnZFXnITmhm/YBhwI4kHfx5wOPuPqnF0D0AAARrSURBVCNoYCIiOaURMsmkmm74QK5u+O6+EDjQzI4gmdoH8IS7Px8wrIZojhvcxiYP20VEpbaZY3PSGfshcArJfn3j0uKdgD+Z2Z/Stb8iIlIHGiGTzCm54c9Ji3cCvg7ohh+QmY0FbgHmAmOBfmlnrBUw1d37BQ2wGYtpu4jYRJY59i2gv7tvKClvA0xz9z3CRCYikl/qkEnm6IafXTHtdRWbmBr9sYkpVbyZzSDZHH5WSfkuwGh3/0z1fykiIjXRlEXJogpgB2BWSXnP9JgE4u5vAcdUU/4M8Ezhvfa6CqKTu98BYGYXuPstaflvzOyigHFJXJljRwJjzOxtYHZa1gvYHdDnTESkHtQhkyzSDT//vgqoQ1ZeMTX6YxNT5tjngL7AUJI1vkYytfy1vCVdEhHJCk1ZlMxJ1yNVoBt+buVtGlYMzOwo4HaS784I4GJgc6Pf3R8PGJ6UyHHm2DfcfVDoOEREYqIOmWSObvj5p3OYDXlt9McollTxetgiItL4NGVRskj50/NP5zCAWLaLiE1kqeK3N7NLajro7reWMxgRkRioQyZZpBt+/mmvqzKLrNEfm7OpPnPsrcA0km0K8qIl0BE9dBERaTTqkEkW6YafUTFtcBuhmBr9sYkpc+x8d78udBAiIjFRh0yySDf87LqPyr2uXiXZ6+o6kr2u7gW011U4MTX6YxNT5lg9KBMRaWRK6iGZo0Xj2RXTBrexMbNjgF8C1Tb63f3pULE1dzFljjWzHYCvkXyupgC/cfeNYaMSEck3jZBJFh1nZiPRDT+LtNdVdml/qOwal2YdfTV0II3gp8AG4CXgWGAv4HtBIxIRyTl1yCSLdMPPrpg2uI1NTI3+2MQ0zW8vd98HwMx+Q2UCGRERqSd1yCSLdMPPKHcfA3ymqOhl7XWVGTE1+mMTU+bYzUlj3H2jmT52IiINpQ6ZZJFu+Bmmva4yK6ZGf2xiyhw7wMyWp68NaJe+N8DdvXO40ERE8kkdMski3fAzSntdZVpMjf7YRJM51t21VlREpJEpy6KI1JqZvUX1e121Aaa5+x5hIhMzeyNdQyYZowykIiKyJRohE5G60F5X2aWRsexS5lgREamRRshEpNa011V2aX+o7DKzP1M1c+wsd1fmWBERAdQhE5E6iGmD29io0Z9dZjalKHNsKyq3KBAREdGURRGpE+11lV3aLiK7lDlWRERqpA6ZiNSFWpLZpUZ/dilzrIiI1EhTFkWk1sxsDlDjflba6yocM9sErCq8BdoBq1GjX0REJNM0QiYidaG9rjJK+0OJiIjkk0bIRKTWtNeViIiISONqEToAEckVjYyJiIiINCKNkIlIrWmvKxEREZHGpQ6ZiNSa9roSERERaVzqkIlIrWmDWxEREZHGpTVkIlIXVfa6ChmIiIiISAw0QiYitaa9rkREREQalzpkIiIiIiIigWjKooiIiIiISCDqkImIiIiIiASiDpmIiIiIiEgg6pCJiIiIiIgEog6ZiIiIiIhIIP8PjIfWTjgRbvkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c0fc940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Use heatmap to see corelation between variables\n",
    "sns.heatmap(df_air.corr(),annot=True,cmap='viridis')\n",
    "plt.title('Heatmap of co-relation between variables',fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mosthusnejahan/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential, load_model, save_model\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "import glob\n",
    "from datetime import datetime\n",
    "from keras.callbacks import EarlyStopping\n",
    "import time\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6293 2698\n"
     ]
    }
   ],
   "source": [
    "''' Dividing data in test and train sets '''\n",
    "dataset = df_air.RH.values #numpy.ndarray\n",
    "dataset = dataset.astype('float32')\n",
    "dataset = np.reshape(dataset, (-1, 1))\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)\n",
    "train_size = int(len(dataset) * 0.70)\n",
    "test_size = len(dataset) - train_size\n",
    "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]\n",
    "print(len(train), len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#''' Dividing data in test and train sets '''\n",
    "#dataset = df_air.values\n",
    "#train_size = int(len(dataset) * 0.70)\n",
    "#test_size = len(dataset) - train_size\n",
    "#train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]\n",
    "#print(len(train), len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' Helper to create time frames with look backs '''\n",
    "def create_dataset(dataset, look_back=1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset) - look_back):\n",
    "        a = dataset[i:(i + look_back), 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back, 0])\n",
    "    print(len(dataY))\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6285\n",
      "2690\n"
     ]
    }
   ],
   "source": [
    "''' Creating time frames with look backs '''\n",
    "look_back = 8\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "testX, testY = create_dataset(test, look_back)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data size: (6285, 1, 8)\n",
      "Test data size: (2690, 1, 8)\n"
     ]
    }
   ],
   "source": [
    "''' Re-shaping data for model requirement '''\n",
    "trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
    "testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n",
    "print('Training data size:',trainX.shape)\n",
    "print('Test data size:',testX.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6285 samples, validate on 2690 samples\n",
      "Epoch 1/50\n",
      "6285/6285 [==============================] - 1s 187us/step - loss: 0.1783 - val_loss: 0.1027\n",
      "Epoch 2/50\n",
      "6285/6285 [==============================] - 0s 52us/step - loss: 0.0927 - val_loss: 0.0716\n",
      "Epoch 3/50\n",
      "6285/6285 [==============================] - 0s 52us/step - loss: 0.0628 - val_loss: 0.0617\n",
      "Epoch 4/50\n",
      "6285/6285 [==============================] - 0s 52us/step - loss: 0.0557 - val_loss: 0.0557\n",
      "Epoch 5/50\n",
      "6285/6285 [==============================] - 0s 54us/step - loss: 0.0525 - val_loss: 0.0528\n",
      "Epoch 6/50\n",
      "6285/6285 [==============================] - 0s 59us/step - loss: 0.0506 - val_loss: 0.0507\n",
      "Epoch 7/50\n",
      "6285/6285 [==============================] - 0s 64us/step - loss: 0.0490 - val_loss: 0.0488\n",
      "Epoch 8/50\n",
      "6285/6285 [==============================] - 0s 55us/step - loss: 0.0475 - val_loss: 0.0482\n",
      "Epoch 9/50\n",
      "6285/6285 [==============================] - 0s 63us/step - loss: 0.0468 - val_loss: 0.0462\n",
      "Epoch 10/50\n",
      "6285/6285 [==============================] - 0s 61us/step - loss: 0.0458 - val_loss: 0.0462\n",
      "Epoch 11/50\n",
      "6285/6285 [==============================] - 0s 59us/step - loss: 0.0452 - val_loss: 0.0450\n",
      "Epoch 12/50\n",
      "6285/6285 [==============================] - 1s 86us/step - loss: 0.0444 - val_loss: 0.0449\n",
      "Epoch 13/50\n",
      "6285/6285 [==============================] - 0s 63us/step - loss: 0.0439 - val_loss: 0.0442\n",
      "Epoch 14/50\n",
      "6285/6285 [==============================] - 0s 59us/step - loss: 0.0434 - val_loss: 0.0441\n",
      "Epoch 15/50\n",
      "6285/6285 [==============================] - 0s 55us/step - loss: 0.0431 - val_loss: 0.0437\n",
      "Epoch 16/50\n",
      "6285/6285 [==============================] - 0s 55us/step - loss: 0.0426 - val_loss: 0.0435\n",
      "Epoch 17/50\n",
      "6285/6285 [==============================] - 1s 83us/step - loss: 0.0424 - val_loss: 0.0427\n",
      "Epoch 18/50\n",
      "6285/6285 [==============================] - 0s 64us/step - loss: 0.0421 - val_loss: 0.0426\n",
      "Epoch 19/50\n",
      "6285/6285 [==============================] - 0s 55us/step - loss: 0.0418 - val_loss: 0.0424\n",
      "Epoch 20/50\n",
      "6285/6285 [==============================] - 0s 54us/step - loss: 0.0417 - val_loss: 0.0422\n",
      "Epoch 21/50\n",
      "6285/6285 [==============================] - 0s 66us/step - loss: 0.0415 - val_loss: 0.0419\n",
      "Epoch 22/50\n",
      "6285/6285 [==============================] - 1s 82us/step - loss: 0.0413 - val_loss: 0.0414\n",
      "Epoch 23/50\n",
      "6285/6285 [==============================] - 0s 55us/step - loss: 0.0411 - val_loss: 0.0414\n",
      "Epoch 24/50\n",
      "6285/6285 [==============================] - 0s 57us/step - loss: 0.0409 - val_loss: 0.0410\n",
      "Epoch 25/50\n",
      "6285/6285 [==============================] - 0s 76us/step - loss: 0.0408 - val_loss: 0.0404\n",
      "Epoch 26/50\n",
      "6285/6285 [==============================] - 1s 80us/step - loss: 0.0406 - val_loss: 0.0408\n",
      "Epoch 27/50\n",
      "6285/6285 [==============================] - 0s 63us/step - loss: 0.0404 - val_loss: 0.0405\n",
      "Epoch 28/50\n",
      "6285/6285 [==============================] - 0s 56us/step - loss: 0.0403 - val_loss: 0.0404\n",
      "Epoch 29/50\n",
      "6285/6285 [==============================] - 0s 58us/step - loss: 0.0402 - val_loss: 0.0401\n",
      "Epoch 30/50\n",
      "6285/6285 [==============================] - 0s 53us/step - loss: 0.0401 - val_loss: 0.0397\n",
      "Epoch 31/50\n",
      "6285/6285 [==============================] - 0s 54us/step - loss: 0.0400 - val_loss: 0.0396\n",
      "Epoch 32/50\n",
      "6285/6285 [==============================] - 0s 55us/step - loss: 0.0399 - val_loss: 0.0395\n",
      "Epoch 33/50\n",
      "6285/6285 [==============================] - 0s 53us/step - loss: 0.0397 - val_loss: 0.0393\n",
      "Epoch 34/50\n",
      "6285/6285 [==============================] - 1s 82us/step - loss: 0.0397 - val_loss: 0.0391\n",
      "Epoch 35/50\n",
      "6285/6285 [==============================] - 0s 77us/step - loss: 0.0396 - val_loss: 0.0389\n",
      "Epoch 36/50\n",
      "6285/6285 [==============================] - 0s 77us/step - loss: 0.0396 - val_loss: 0.0390\n",
      "Epoch 37/50\n",
      "6285/6285 [==============================] - 0s 71us/step - loss: 0.0395 - val_loss: 0.0393\n",
      "Epoch 38/50\n",
      "6285/6285 [==============================] - 1s 82us/step - loss: 0.0395 - val_loss: 0.0390\n",
      "Epoch 39/50\n",
      "6285/6285 [==============================] - 1s 80us/step - loss: 0.0393 - val_loss: 0.0391\n",
      "Epoch 40/50\n",
      "6285/6285 [==============================] - 1s 81us/step - loss: 0.0394 - val_loss: 0.0391\n",
      "Epoch 41/50\n",
      "6285/6285 [==============================] - 0s 70us/step - loss: 0.0394 - val_loss: 0.0395\n",
      "Epoch 42/50\n",
      "6285/6285 [==============================] - 0s 63us/step - loss: 0.0394 - val_loss: 0.0391\n",
      "Epoch 43/50\n",
      "6285/6285 [==============================] - 0s 63us/step - loss: 0.0393 - val_loss: 0.0392\n",
      "Epoch 44/50\n",
      "6285/6285 [==============================] - 0s 62us/step - loss: 0.0395 - val_loss: 0.0389\n",
      "Epoch 45/50\n",
      "6285/6285 [==============================] - 0s 59us/step - loss: 0.0394 - val_loss: 0.0388\n",
      "Epoch 46/50\n",
      "6285/6285 [==============================] - 0s 51us/step - loss: 0.0394 - val_loss: 0.0388\n",
      "Epoch 47/50\n",
      "6285/6285 [==============================] - 0s 58us/step - loss: 0.0392 - val_loss: 0.0387\n",
      "Epoch 48/50\n",
      "6285/6285 [==============================] - 0s 55us/step - loss: 0.0389 - val_loss: 0.0387\n",
      "Epoch 49/50\n",
      "6285/6285 [==============================] - 0s 49us/step - loss: 0.0391 - val_loss: 0.0384\n",
      "Epoch 50/50\n",
      "6285/6285 [==============================] - 0s 64us/step - loss: 0.0390 - val_loss: 0.0384\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_2 (LSTM)                (None, 100)               43600     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 43,701\n",
      "Trainable params: 43,701\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "''' Fitting the data in LSTM Deep Learning model '''\n",
    "model = Sequential()\n",
    "model.add(LSTM(100, input_shape=(trainX.shape[1], trainX.shape[2])))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mae', optimizer='adam')\n",
    "#history = model.fit(trainX, trainY, epochs=20, batch_size=100, verbose=2)\n",
    "history = model.fit(trainX, trainY, epochs=50, batch_size=100, validation_data=(testX, testY), verbose=1, shuffle=False)\n",
    "#end = time.time()\n",
    "# Training Phase\n",
    "model.summary()\n",
    "# store model to json file\n",
    "\n",
    "# To save model\n",
    "model.save('my_model_01.hdf5')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' Predicting 1 years data based on 5 years of previous data '''\n",
    "yhat = model.predict(testX)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c27deb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "''' Plotting the first 500 entries to see prediction '''\n",
    "pyplot.figure(figsize=(20,8))\n",
    "pyplot.plot(yhat[:500], label='predict')\n",
    "pyplot.plot(testY[:500], label='true')\n",
    "pyplot.legend()\n",
    "\n",
    "pyplot.ylabel('Relative Humidity', size=15)\n",
    "pyplot.xlabel('Time step', size=15)\n",
    "pyplot.legend(fontsize=15)\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lKU/JjTfemMWLF9/rc5smHDZg787hZuEQAABa7xGPeEQuv/zyXHbZZVm+fHlOPPHEXHjhhfn1X//1bNq0KWeccUZGRkaydu3aXHbZZfnYxz6WXbt25dnPfnZe/OIX52EPe1h27NiRCy+8MCeccEIe+chH3ufnnnjiiY39ncJhA+wcAgDA4ePcc8/NN77xjbzkJS/JnXfemTe84Q254IILsmjRorzpTW/K+973vgwPD2fVqlX5pV/6pYyMjGR4eDiPecxj8o53vCM33XRTFi5cmDVr1uSzn/1sFixYcNDPbYpw2ICF8yYa0giHAADQfkuWLMknP/nJe10/44wzcsYZZxzwnk6nk/e85z3363ObYpRFA4aGSka7nWzWkAYAAGgJ4bAhve5wNhtlAQAAtIRw2JBet2OUBQAA0BrCYUMmjpV65hAAAGgH4bAhvZGOhjQAABwRaq1NlzBnzea/rXDYkJ6dQwAAjgDz5s3Ltm3bmi5jztq2bVvmzZs3K58lHDZktDucLTuFQwAA5rbjjjsuN998c7Zu3WoHcRbVWrN169bcfPPNOe6442blM805bMjofA1pAACY+xYtWpQkueWWW7Jrl279s2nevHk5/vjj9/0bP1DCYUN63U42b7dzCADA3Ldo0aJZCzD0j2OlDRkd6WTnnvHs3D3edCkAAADCYVN63YlNWx1LAQCANhAOGzI6GQ51LAUAANpAOGzIvp1DHUsBAIAWEA4b0usOJ3GsFAAAaAfhsCFHzd97rNQ4CwAAoHnCYUM0pAEAANpEOGxIb2Ry59CsQwAAoAWEw4boVgoAALSJcNgQx0oBAIA2EQ4bMtIZysjwUDYbZQEAALSAcNigXnfYziEAANAKwmGDet1OthhlAQAAtIBw2KDRbkdDGgAAoBWEwwaNdjtGWQAAAK0gHDao1+1ki4Y0AABACwiHDXKsFAAAaAvhsEG6lQIAAG0hHDZIt1IAAKAthMMGjU4+c1hrbboUAADgCCccNmi020mtydaddg8BAIBmCYcN6nU7SeK5QwAAoHHCYYNGJ8Ph3cIhAADQMOGwQXYOAQCAthAOG9TrDieJWYcAAEDjhMMGje7bOdSQBgAAaJZw2CDHSgEAgLYQDht01GQ4dKwUAABo2sDDYSnlOaWUH5RSri2lvPoA7/9cKeXrpZTdpZQz93vv5FLKZ0sp15RSvldKWTGouvvBziEAANAWAw2HpZThJBclOSPJqUleVEo5db9l65Kck+RDB/iI9yf5L7XWRyZ5UpLb+ldt/y0cGU4pdg4BAIDmdQb8+56U5Npa69okKaV8OMkLknxv74Ja6w2T741PvXEyRHZqrZ+bXLd5QDX3TSklvZGOcAgAADRu0MdKlyW5acrr9ZPXpuNhSe4qpXyilPKNUsp/mdyJvIdSystKKVeVUq7asGHDLJTcX73usGOlAABA4wYdDssBrtVp3ttJ8rQk/zHJaUlWZeL46T0/rNaLa62ra62rly5den/rHJhet2OUBQAA0LhBh8P1SU6a8np5kltmcO83aq1ra627k1yW5AmzXN/AjXYdKwUAAJo36HB4ZZKHllJWllJGkpyd5FMzuPfYUsre7cBnZMqzioer3kjHsVIAAKBxAw2Hkzt+L0/ymSTXJLm01np1KeWNpZTnJ0kp5bRSyvokL0zy7lLK1ZP37snEkdK/L6V8JxNHVN8zyPr7YXS+nUMAAKB5g+5WmlrrFUmu2O/a66f8fGUmjpse6N7PJXlsXwscMMdKAQCANhj0sVL2o1spAADQBsJhw3QrBQAA2kA4bNjoSCc794xn5+7xpksBAACOYMJhw3rdicc+HS0FAACaJBw2bHQyHGpKAwAANEk4bNjo/Mmdw53CIQAA0BzhsGGOlQIAAG0gHDZstDucJLl7u3AIAAA0Rzhs2E93Do2zAAAAmiMcNqw34lgpAADQPOGwYbqVAgAAbSAcNkxDGgAAoA2Ew4aNdIYy0hnKZqMsAACABgmHLTDa7dg5BAAAGiUctkCvO5zNRlkAAAANEg5boDfSyWajLAAAgAYJhy3gWCkAANA04bAFet1OtmhIAwAANEg4bIHRbsecQwAAoFHCYQs4VgoAADRNOGyBXreTLRrSAAAADRIOW2C0O5zNO3ZnfLw2XQoAAHCEEg5boNftJEm27rJ7CAAANEM4bIG94dBzhwAAQFOEwxYYnQyHOpYCAABNEQ5bwM4hAADQNOGwBXrd4SR2DgEAgOYIhy1wVHdekhhnAQAANEY4bIG9O4eOlQIAAE0RDltgb0Oau4VDAACgIcJhC2hIAwAANE04bIGFI8MpRTgEAACaIxy2QCklvZGObqUAAEBjhMOW6HWH7RwCAACNEQ5bYrTbMcoCAABojHDYEqNdx0oBAIDmCIct0RMOAQCABgmHLdHrdjxzCAAANEY4bAnHSgEAgCYJhy2hWykAANAk4bAlerqVAgAADRIOW+Kobic794xn5+7xpksBAACOQMJhS/S6nSRxtBQAAGiEcNgSe8OhpjQAAEAThMOWGBUOAQCABgmHLeFYKQAA0CThsCVGu8NJ7BwCAADNEA5b4qc7h8ZZAAAAgycctkRvxLFSAACgOcJhSxw1X0MaAACgOcJhS2hIAwAANEk4bIl5w0MZ6QzZOQQAABohHLbIaLcjHAIAAI0QDluk1x12rBQAAGiEcNgivZFONhtlAQAANEA4bJHRbsfOIQAA0AjhsEVG53eyZadwCAAADJ5w2CI9DWkAAICGCIctMjrSyebtwiEAADB4wmGL9DxzCAAANEQ4bJHR7nC27NyT8fHadCkAAMARZuDhsJTynFLKD0op15ZSXn2A93+ulPL1UsruUsqZB3h/USnl5lLKOwdT8eD0up0kydZdxlkAAACDNdBwWEoZTnJRkjOSnJrkRaWUU/dbti7JOUk+dB8f82dJPt+vGpu0Nxw6WgoAAAzaoHcOn5Tk2lrr2lrrziQfTvKCqQtqrTfUWr+dZHz/m0spT0xyfJLPDqLYQTtq/kQ41LEUAAAYtEGHw2VJbpryev3ktUMqpQwluTDJKw6x7mWllKtKKVdt2LDhfhfahN6InUMAAKAZgw6H5QDXptt95dwkV9RabzrYolrrxbXW1bXW1UuXLp1xgU3ae6zUziEAADBonQH/vvVJTpryenmSW6Z575OTPK2Ucm6S0SQjpZTNtdZ7NbU5XI3uDYdmHQIAAAM26HB4ZZKHllJWJrk5ydlJ/t10bqy1/tren0sp5yRZPZeCYZL0usNJki07hUMAAGCwBnqstNa6O8nLk3wmyTVJLq21Xl1KeWMp5flJUko5rZSyPskLk7y7lHL1IGts0r6dwx1GWQAAAIM16J3D1FqvSHLFftdeP+XnKzNx3PRgn3FJkkv6UF6jjLIAAACaMuiGNBzEwpHhlCIcAgAAgycctkgpJaMjHd1KAQCAgRMOW6bX7dg5BAAABk44bJled9jOIQAAMHDCYcuMdju6lQIAAAMnHLaMY6UAAEAThMOWEQ4BAIAmCIctM3GsVDgEAAAGSzhsmVE7hwAAQAOEw5aZOFaqIQ0AADBYwmHLjHaHs3PPeHbsFhABAIDBEQ5bptftJIndQwAAYKCEw5b5aTj03CEAADA4wmHLjE6GQx1LAQCAQRIOW8bOIQAA0AThsGXsHAIAAE0QDltmVEMaAACgAcJhy/S6w0kcKwUAAAZLOGyZvTuHdwuHAADAAAmHLaMhDQAA0AThsGXmDQ9lpDMkHAIAAAMlHLbQaLejWykAADBQwmELjXY7dg4BAICBEg5bqNftZLNRFgAAwAAJhy002h22cwgAAAyUcNhCPc8cAgAAAyYctlDPM4cAAMCACYctNDpi5xAAABgs4bCF7BwCAACDJhy20Gh3OFt27sn4eG26FAAA4AghHLbQ6PxOkmTrLuMsAACAwRAOW6jXnQiHjpYCAACDIhy20OhkONSUBgAAGBThsIV6I5PhcLtwCAAADIZw2EKOlQIAAIMmHLaQY6UAAMCgCYct1OsOJ0m27BQOAQCAwRAOW2jvKIvNO4yyAAAABkM4bKFRzxwCAAADJhy20IJ5wxkqwiEAADA4wmELlVLSG+nkbqMsAACAAREOW6rX7dg5BAAABkY4bKled1i3UgAAYGCEw5Ya7XZ0KwUAAAZGOGyp0fmOlQIAAIMjHLZUb0Q4BAAABkc4bKmJY6XCIQAAMBjCYUv1hEMAAGCAhMOWMsoCAAAYJOGwpUa7w9m1p2bHbh1LAQCA/hMOW6rX7SRJthhnAQAADIBw2FKj+8Kho6UAAED/CYcttTccakoDAAAMgnDYUj07hwAAwAAJhy3Vs3MIAAAMkHDYUo6VAgAAgyQctlSvO5zEsVIAAGAwhMOW+unOoVEWAABA/wmHLaUhDQAAMEjCYUvNGx5KtzMkHAIAAAMhHLbYaLejIQ0AADAQwmGL9bodO4cAAMBADDwcllKeU0r5QSnl2lLKqw/w/s+VUr5eStldSjlzyvXHl1K+VEq5upTy7VLKvx1s5YPXs3MIAAAMyEDDYSllOMlFSc5IcmqSF5VSTt1v2bok5yT50H7XtyZ5ca31UUmek+TtpZRj+ltxs0a7w8IhAAAwEJ0B/74nJbm21ro2SUopH07ygiTf27ug1nrD5HvjU2+stf5wys+3lFJuS7I0yV39L7sZvW4nd2ze2XQZAADAEWDQx0qXJblpyuv1k9dmpJTypCQjSa47wHsvK6VcVUq5asOGDfe70DbwzCEAADAogw6H5QDX6ow+oJQHJfn/kvxmrXV8//drrRfXWlfXWlcvXbr0fpbZDkd55hAAABiQQYfD9UlOmvJ6eZJbpntzKWVRkk8neW2t9cuzXFvr2DkEAAAGZdDh8MokDy2lrCyljCQ5O8mnpnPj5PpPJnl/rfWjfayxNXrdTrbs3JPx8RltrgIAAMzYQMNhrXV3kpcn+UySa5JcWmu9upTyxlLK85OklHJaKWV9khcmeXcp5erJ289K8nNJzimlfHPy6/GDrH/QRrvDSZKtu/Y0XAkAADDXDbpbaWqtVyS5Yr9rr5/y85WZOG66/30fSPKBvhfYIr3uxH+ezdt3Z7Q78P9UAADAEWTQx0qZgb2BUFMaAACg34TDFuuNTIRDTWkAAIB+Ew5bbHS+cAgAAAyGcNhijpUCAACDIhy22N6GNFt2CocAAEB/CYct1pscZbF5h1EWAABAfwmHLTY6ZZQFAABAPwmHLbZg3nCGioY0AABA/wmHLVZKSW+koyENAADQd8Jhy/W6HTuHAABA3wmHLTc6v6NbKQAA0HfCYcv1uh3dSgEAgL4TDltutDvsWCkAANB3wmHL9UY6RlkAAAB9Jxy23GhXt1IAAKD/hMOW63U1pAEAAPpPOGw5oywAAIBBEA5b7qj5nezaU7Njt46lAABA/wiHLdcbGU6SbDHOAgAA6CPhsOV63U6SOFoKAAD0lXDYcqOT4VDHUgAAoJ+Ew5brCYcAAMAACIctJxwCAACDIBy23KhnDgEAgAEQDltudL5wCAAA9F9nJotLKZ0kw7XWHVOuPSvJqUn+udb69Vmu74g3OrL3WKlRFgAAQP/MKBwm+UiSnyR5SZKUUn4/yduT7EgyXEr5lVrr/5jdEo9sve7eOYd2DgEAgP6Z6bHSNUmumPL6FUkurLUuSPLXSf5ktgpjQmd4KN3OkHAIAAD01UzD4eIkP06SUspjkpyY5L9NvvfRTBwvZZaNdju5WzgEAAD6aKbh8NYkKyZ/fk6SG2ut102+XpBkfJbqYopet2PnEAAA6KuZPnP40SRvKaU8LslvJnnnlPd+Jsm/zFZh/JRwCAAA9NtMw+Grk2xKclqS/5rkTVPee2ImGtYwy47qdrJZOAQAAPpoRuGw1ro7yRvv471fmZWKuJdedzi3b97ZdBkAAMAcNqNnDkspx5VSVk55XUopLyulvL2U8rzZL4/EsVIAAKD/ZtqQ5pIkfzjl9Z8meVcmmtN8spRyzuyUxVSjjpUCAAB9NtNw+IQk/5AkpZShJL+b5DW11kck+U9J/mB2yyOZ2DkUDgEAgH6aaTg8Oskdkz8/MclYkg9Ovv6HJA+ZpbqYotftZOvOPRkfr02XAgAAzFEzDYfr89NB97+Y5PsyzUBzAAAgAElEQVS11psnXx+dZPtsFcZPjXaHkyRbdto9BAAA+mOmoyzel+StpZRfyEQ4PH/Ke2uSXDNbhfFTve7Ef6YtO/bkqPnzGq4GAACYi2Y6yuLNpZSbMzHn8PcyERb3Gkvy17NYG5NGJ8Oh5w4BAIB+menOYWqt70/y/gNc/51ZqYh7Gd23cygcAgAA/THjcFhK6ST51SRPzcRu4cYk/yfJJ2qt0ksf9IRDAACgz2YUDkspxyX5bJLHJrkhya1JnpzkvCTfKqU8q9a6YbaLPNI5VgoAAPTbTLuV/nmSxUl+tta6qtb65FrrqiQ/O3n9z2e7QH66cygcAgAA/TLTcPjcJK+qtV459eLk6/Mz0cGUWdbbO8pCOAQAAPpkpuGwm+Tu+3jv7iQjD6wcDuSnx0r3NFwJAAAwV800HH45yatKKb2pFydfv2ryfWbZgnnDGSp2DgEAgP6ZabfSP07yj0luKqV8NhMNaY5L8uwkJcnTZ7U6kiSllPS6Hc8cAgAAfTOjncNa6zeTPCzJxUmWJnlmJsLhf0vy0Frrt2a9QpJMHC21cwgAAPTLjOccTo6qeHUfauEget1OtuwUDgEAgP44ZDgspVyZpE73A2utT3pAFXFAvW4nd28XDgEAgP6Yzs7h1ZlBOKQ/RrvDjpUCAAB9c8hwWGs9ZwB1cAi9kU5uv3tn02UAAABz1ExHWTBb6sw2Y0fn61YKAAD0j3A4aDd8IXnLyuSWb8zotlENaQAAgD4SDgdtwViybWOyce2MbusZZQEAAPSRcDhox66Y+L7x+hndNtrtZNeemh2798x+TQAAwBFPOBy0kYXJomUz3zkcGU6SbNkhHAIAALNPOGzC2Kr7daw0STabdQgAAPSBcNiEsZXJxutmdMvo3nDouUMAAKAPhMMmjK1KtmxItm+a9i17dw51LAUAAPpBOGzC2KqJ73dOvynN6Hw7hwAAQP8MPByWUp5TSvlBKeXaUsqrD/D+z5VSvl5K2V1KOXO/936jlPIvk1+/MbiqZ9necDiD5w73His1zgIAAOiHgYbDUspwkouSnJHk1CQvKqWcut+ydUnOSfKh/e4dS/KGJD+b5ElJ3lBKObbfNffFsSsnvs8gHPaEQwAAoI8GvXP4pCTX1lrX1lp3JvlwkhdMXVBrvaHW+u0k4/vd++wkn6u1bqy13pnkc0meM4iiZ113NBk9fmY7hyN7j5UaZQEAAMy+QYfDZUlumvJ6/eS1Wbu3lPKyUspVpZSrNmzYcL8L7buxVcnG6T9z2OtOzDk0ygIAAOiHQYfDcoBrdTbvrbVeXGtdXWtdvXTp0hkVN1Bjp8xo57AzPJRuZ0i3UgAAoC8GHQ7XJzlpyuvlSW4ZwL3tM7YyuftHyc4t075ltNvRrRQAAOiLQYfDK5M8tJSyspQykuTsJJ+a5r2fSfKsUsqxk41onjV57fC0r2PpzMZZaEgDAAD0w0DDYa11d5KXZyLUXZPk0lrr1aWUN5ZSnp8kpZTTSinrk7wwybtLKVdP3rsxyZ9lImBemeSNk9cOT/djnEVvRDgEAAD6ozPoX1hrvSLJFftde/2Un6/MxJHRA937viTv62uBgzI283EWjpUCAAD9Muhjpew1/+hk4ZIZzjoczhajLAAAgD4QDps0tmqG4dDOIQAA0B/CYZNmOOvQsVIAAKBfhMMmLT4l2bQ+2bVtWst7XQ1pAACA/hAOm7S3Y+mdN0xr+Wi3k60792R8vPavJgAA4IgkHDZphh1LR7sTzWW37LR7CAAAzC7hsEkznHXY2xsOdSwFAABmmXDYpAXHTnxNOxwOJ4mmNAAAwKwTDps2g3EW+46VCocAAMAsEw6bNoNwuPdYqZ1DAABgtgmHTRtblfxkfbJ7xyGXjgqHAABAnwiHTRtbldTx5K51h1zqWCkAANAvwmHTxk6Z+H7HdYdc2hMOAQCAPhEOmzaDcRY/PVZqlAUAADC7hMOmLRxLukdPKxzOnzeUoWLnEAAAmH3CYdNKScZWTiscllLS63Y0pAEAAGadcNgGM5x1KBwCAACzTThsg7FVE91K9+w65NJet+NYKQAAMOuEwzYYW5XUPdMaZ+FYKQAA0A/CYRvs61h6/SGXLprfyaZth95hBAAAmAnhsA0WT8463HjoWYcnjy3MjRu39rkgAADgSCMctkFvaTIyOq2mNCuX9HLX1l25c8vOARQGAAAcKYTDNpjBOIuVS3pJkrW3b+l3VQAAwBFEOGyLaY6z2BsObxAOAQCAWSQctsXYquTOG5M9B+9EetLYwgwPlVwvHAIAALNIOGyLsVXJ+K5k0/qDLps3PJSTjl0gHAIAALNKOGyLfeMspne0VDgEAABmk3DYFjMKh6O5/vYtqbX2uSgAAOBIIRy2xVEPSjoLkjumEw4XZtuuPbl1044BFAYAABwJhMO2KGUGHUtHk8TRUgAAYNYIh20y3VmHSyfGWQiHAADAbBEO22RsVXLn9cn4noMue9Ci+el2hnL97ZsHVBgAADDXCYdtMrYq2bMz2XTLQZcNDZWsWNzL9bdvHVBhAADAXCcctsmMx1nYOQQAAGaHcNgmMwmHS3tZt3Frdu8Z73NRAADAkUA4bJNFy5Lh7vTC4eJedu2pueWu7QMoDAAAmOuEwzYZGkqOXTGjjqVrHS0FAABmgXDYNotPmfYzh4lxFgAAwOwQDttmbFWy8fpk/ODPEi7ujeSobic3CIcAAMAsEA7bZmxlsntbsvnHB11WSsnKpb2sFQ4BAIBZIBy2zYzHWQiHAADAAyccts0MwuGKxb3cfNe27Ni9p89FAQAAc51w2DaLlidD86YVDlct7aXWZN0dWwdQGAAAMJcJh20z3EmOffCMOpZ67hAAAHighMM2GluV3DGNY6XGWQAAALNEOGyjsclZh7UedNmi+fOyZHTEOAsAAOABEw7baGxVsmtLsvm2Qy5ducQ4CwAA4IETDttohh1LHSsFAAAeKOGwjcZWTnyfTlOapb1suHtHNu/Y3eeiAACAuUw4bKNjTk7K8PTGWUw2pfHcIQAA8EAIh200PG8iIE7nWKlxFgAAwCwQDttqbNW0nzlM7BwCAAAPjHDYVnvD4SHGWcyfN5xlxyzQlAYAAHhAhMO2GluV7NiUbL3jkEtXLFnoWCkAAPCACIdttfiUie/T6Vi6pJfrN2xOPcQuIwAAwH0RDttqBrMOVy4Zzabtu3Pn1l19LgoAAJirhMO2OubkpAxNMxwuTJJcf/vmflcFAADMUcJhW3W6ydHLp71zmCTX376131UBAABzlHDYZtMcZ7H82AXpDBU7hwAAwP0mHLbZNMPhvOGhnDS20DgLAADgfhMO22xsVbLtzmTrxkMuXbmk51gpAABwvw08HJZSnlNK+UEp5dpSyqsP8H63lPKRyfe/UkpZMXl9Xinlb0op3ymlXFNKOX/QtQ/cvo6l1x9y6colvdxw+5aMjxtnAQAAzNxAw2EpZTjJRUnOSHJqkheVUk7db9lvJbmz1vqQJH+R5C2T11+YpFtrfUySJyb57b3Bcc4am/6swxVLetm2a09uvXt7n4sCAADmokHvHD4pybW11rW11p1JPpzkBfuteUGSv5n8+WNJTi+llCQ1Sa+U0kmyIMnOJJsGU3ZDjl2RpEwrHK5a0ksSzx0CAAD3y6DD4bIkN015vX7y2gHX1Fp3J/lJksWZCIpbkvwoybokb6u13uthvFLKy0opV5VSrtqwYcPs/wWDNG9+smjZNMdZCIcAAMD9N+hwWA5wbf+H5O5rzZOS7ElyYpKVSf64lLLqXgtrvbjWurrWunrp0qUPtN7mja2cVjg8YdH8dDtDuX6DcAgAAMzcoMPh+iQnTXm9PMkt97Vm8gjp0Uk2Jvl3Sf5XrXVXrfW2JF9MsrrvFTdtmuMshobKRFOaO4RDAABg5gYdDq9M8tBSyspSykiSs5N8ar81n0ryG5M/n5nkH2qtNRNHSZ9RJvSSrEny/QHV3ZyxVcnW25PtPznk0pVLelnrWCkAAHA/DDQcTj5D+PIkn0lyTZJLa61Xl1LeWEp5/uSy9yZZXEq5NskfJdk77uKiJKNJvpuJkPnfa63fHmT9jdg3zmJ6HUvX3bE1u/eM97koAABgrukM+hfWWq9IcsV+114/5eftmRhbsf99mw90fc6bGg5P/JmDLl25pJfd4zU337UtD17cG0BxAADAXDHoY6XM1NjKie8zGGfhaCkAADBTwmHbjfSSox6UbLz+kEv3jbPQsRQAAJgh4fBwMM2OpWO9kRw1v6NjKQAAMGPC4eFgmrMOSylZtaSX6x0rBQAAZkg4PByMrUo235rs2HzIpSuX9LLWsVIAAGCGhMPDwd6OpXce+rnDFUt6ueUn27J9154+FwUAAMwlwuHhYG84vOO6Qy5duaSXWpN1G7f2uSgAAGAuEQ4PB8fOZJzFaJI4WgoAAMyIcHg4mL8o6S2dVjhcsWRhkmhKAwAAzIhweLgYO2Vasw6Pmj8vS0a7uUE4BAAAZkA4PFxMc9ZhEuMsAACAGRMODxdjq5K7b0l2HrrRzIolC7NWOAQAAGZAODxcjE02pbnzhkMuXblkNLdv3pG7t+/qb00AAMCcIRweLvaOs5jG0dKVS3pJkhtuN84CAACYHuHwcLF353Dj9GYdJsna2zf3syIAAGAOEQ4PFwuOTRaMTWvn8MGLF6YUO4cAAMD0CYeHk2l2LJ0/bzgnHr0g19s5BAAApkk4PJwsnt6sw2TiaKlxFgAAwHQJh4eTsVXJT9Ynu7YfcunecFhrHUBhAADA4U44PJyMrUpSk7tuPOTSlUt62bR9dzZu2dn/ugAAgMOecHg4uR/jLBwtBQAApkM4PJwIhwAAQJ8Ih4eTBccm849O7jj0rMPlxy5IZ6gIhwAAwLQIh4eTUiZ2D2//4SGXdoaHcvLYQuEQAACYFuHwcLPq6cmNX0zuvOGQS42zAAAApks4PNyc9tKkDCVfefchl65c0ssNd2zJ+LhxFgAAwMEJh4ebo5clj/7V5OvvT7b/5KBLVyzpZfuu8fx406HnIgIAAEc24fBwtObcZOfmiYB4EKsmO5be4GgpAABwCMLh4ejExycrnpZ8+b8le3bf57KVSyfC4VrhEAAAOATh8HD15POSTeuT7112n0uOP2p+5s8b0pQGAAA4JOHwcPXQZydjpyRfemdSD9xwZmioZMXinmOlAADAIQmHh6uhoeTJ5ya3fCNZ9+X7XLZqqXEWAADAoQmHh7PH/btkwbETu4f3YcXiXtZt3Jrde8YHWBgAAHC4EQ4PZyMLk9W/lXz/08nGtQdcsnJJL7vHa9bfuW3AxQEAAIcT4fBw96SXJkOdic6lB7BqsmOpo6UAAMDBCIeHu6NOSB7zwuQbH0i23Xmvt1cuGU1inAUAAHBwwuFc8ORzk11bkq9dcq+3jl04L4vmd3QsBQAADko4nAtOeEyy8ueTr7w72b3zHm+VUrJy6ahjpQAAwEEJh3PFk1+e3P2j5HuX3eutVUuMswAAAA5OOJwrHvILyZKHTYy1qPUeb61Y3MvNd23L9l17GioOAABoO+FwrhgaStacm/zoW8mNX7zHWysnO5beeMfWJioDAAAOA8LhXPK4s5OFi5MvXXSPy6uW7B1nsbmJqgAAgMOAcDiXzFuQnPYfkh/8z+T2a/ddXjEZDo2zAAAA7otwONec9h+S4XnJl9+179Jot5OlR3WNswAAAO6TcDjXjB6XPPas5JsfSrZu3Hd5pY6lAADAQQiHc9Ga85Ld25Kr3rfv0srFwiEAAHDfhMO56PhTk1OekXz14mT3jiQTHUtv37wzm7bvarg4AACgjYTDuerJ5yWbb02++4kkE8dKk3juEAAAOCDhcK465fRk6SMnxlrUui8cOloKAAAciHA4V5UysXt463eS6z+fk8cWphThEAAAODDhcC57zAuT3tLkSxdl/rzhLDtmgXAIAAAckHA4l82bn5z20uRfPpts+IFxFgAAwH0SDue6034rGe4mX37XvnBYa226KgAAoGWEw7mutyR53NnJtz6cRyzakbu3784dW3Y2XRUAANAywuGRYM25ye7tWbPx8iTJv9y6ueGCAACAthEOjwTHPSJ5yDOz4roPZXF3PBf947WOlgIAAPcgHB4pnvLyDG3dkL969LX5wrW35/Jv3tJ0RQAAQIsIh0eKlT+fHP/oPPm2j+Txy4/O//vp7+WurZ49BAAAJgiHR4pSkiefl7LhmrzzcTfmzq278pb/9f2mqwIAAFpCODySPOaFyYk/k+VffE3+4LSF+duv3pQrb9jYdFUAAEALCIdHkuF5ya++NxnfnXM3/uecdPRI/uST38nO3eNNVwYAADRMODzSLD4lee7bMnzTl/I3D/3n/PDWzXnP/1nbdFUAAEDDBh4OSynPKaX8oJRybSnl1Qd4v1tK+cjk+18ppayY8t5jSylfKqVcXUr5Till/iBrnzMed3bymLOy6up35rxTbs9f/v2/5MY7tjRdFQAA0KCBhsNSynCSi5KckeTUJC8qpZy637LfSnJnrfUhSf4iyVsm7+0k+UCS36m1PirJ05PsGlDpc0spyS9emBxzcv5o01uzeHhrXnf51WYfAgDAEWzQO4dPSnJtrXVtrXVnkg8necF+a16Q5G8mf/5YktNLKSXJs5J8u9b6rSSptd5Ra90zoLrnnvmLkl99b4a3/DgfOv5D+ecf3pa/+/aPmq4KAABoyKDD4bIkN015vX7y2gHX1Fp3J/lJksVJHpakllI+U0r5einllQf6BaWUl5VSriqlXLVhw4ZZ/wPmlOWrk2e8Nitu+9/54yVfyRv/7nv5yTabsQAAcCQadDgsB7i2/1nG+1rTSfLUJL82+f2XSymn32thrRfXWlfXWlcvXbr0gdY79z3l/0lW/nzO23Zxjt26Nm81+xAAAI5Igw6H65OcNOX18iS33NeayecMj06ycfL652utt9datya5IskT+l7xXDc0lPzyuzPU7eUDR787H/vKtfnajXc2XRUAADBggw6HVyZ5aCllZSllJMnZST6135pPJfmNyZ/PTPIPdaJTymeSPLaUsnAyNP58ku8NqO65bdGDkn/zX3P8tmvzZ72P5jWf+E527TH7EAAAjiQDDYeTzxC+PBNB75okl9Zary6lvLGU8vzJZe9NsriUcm2SP0ry6sl770zy55kImN9M8vVa66cHWf+c9rBnJz/7Ozlrz6ezbMPn894vXN90RQAAwACVuTy+YPXq1fWqq65quozDx67tyV//Qu7esC6/uOvN+eAf/nJOGlvYdFUAAMD9VEr5Wq119XTWDvpYKW02b35y5nszOrQzbx16V15/2bfMPgQAgCOEcMg9LX14yhlvyZry3TziuktyxXd+3HRFAADAAAiH3NsTXpzxU/9N/njeR/PRyz+ZTdvNPgQAgLlOOOTeSsnQ896R8dET8sbdf56//PTXmq4IAADoM+GQA1twTEbOel+Wlzvy6G/+ab5x48amKwIAAPpIOOS+nbwmu572qvyb4f+b//2RvzT7EAAA5jDhkIPq/utXZOOS03Lulnfl45/7fNPlAAAAfSIccnBDwxn79UuS4ZE8+kt/lPW339V0RQAAQB8Ihxza0cuz/bnvyKPL2lzz3t/Nxk1bmq4IAACYZcIh07J49a/m2oeck2duuyK3/cW/yreu/ELTJQEAALNIOGTaHvLv35F1z3x3jsvGnPo/np//+9d/nN07tzddFgAAMAuEQ2bk5H91drq/f1W+c8wz8pT1f531b12T23741abL4v9v776jLDnrM49/f1V1c+c0Cj0zyigjISEBkowQBgMOMlgYYeyD99jGu8ZrHFiD4Zw1xutd24fgXQNrE+TA2gYhL4sMxgQRJDBKIEARIQmNJvX0zHS8ucK7f1R19+2eljQz6uk76n4+59Spt0LXfe/Vq+l+7vvWWyIiIiIiz5DCoRyxyuAYz/udm7jj8g9QCacZ+sef4JFPvB2iVrerJiIiIiIiR0nhUI7a5a/8JZpv+ia3Fl7CGQ/9b/a953JaO+7udrVEREREROQoKBzKM7L15HGu/C83ceNZ78U1Zgj+5mVMfeYdEOpeRBERERGRZxOFQ3nG8oHHz//Cr/LIa2/hs3Y1Q/d8kNm/eCFu513drpqIiIiIiBwmhUNZM1eefzov/N1/4s9H/oRadQb3sZfT+td3QtjodtVERERERORpKBzKmhrrLfLW33gzn7/q09wYX03hzg/Q/MsXwRN3dLtqIiIiIiLyFBQOZc15nvErP34RZ/3aDfx2/g85ODuHu+EncJ//A2jOdbt6IiIiIiKyCoVDOWaet22Qd//uf+Z9Z/4d/yd6KXbHh4jfdx7c8m6o7u929UREREREpIPCoRxTfcUc73nDFeSvfT8/F/8PvtA4m+S29xG/7zySz70VZp7odhVFRERERAQw51y363DMXHrppe7uu/XcvePFdK3NJ+7aya3f/Aavbvwzrwm+gQeE5/4chat/D8bO7nYVRUREREQ2FDP7tnPu0sM6V+FQ1lsUJ3z5wX38y213ccnuf+B6/6uUrcXc9pfT97K3wfhhtV0REREREXkaCocZhcPj30MTc3zq1u8xdN8N/KL9G/1W58Do5Qy+/G34Z1wDZt2uooiIiIjIs5bCYUbh8Nljth7y6dsfovatj3Bd+zNssRkme86hdM1b6b3oNeDp9lgRERERkSOlcJhROHz2iRPHV+/fxY5bPsJLpz7BKd4+JvPbaL/gtxj/sTdCkO92FUVEREREnjUUDjMKh89uj0zM8O3P/y3PffwGzrYdHPSGeeL013PaT7yZ/pGTul09EREREZHjnsJhRuFwY5hrtLn9C59k+L6Pckn0XVoux7f7roHLf51LX/AS8oGGnIqIiIiIrEbhMKNwuLE453j0gbuZ+dqHOG//5yjR4h6ew8Pb38BZL3k9F20fxTSBjYiIiIjIIoXDjMLhxhXVpnn8y3/NwH1/y0i4l71uiM8VXoW7+I284vLz2TpU7nYVRURERES6TuEwo3C4CSQx9fv/lbmvf4ATDtxOy+X4TPwi7j7htVxy+Yt55QUn0lfMdbuWIiIiIiJdoXCYUTjcZCYfpHrbhyjcfyO5pMmdyXP4B/dK3Nk/xc9eso2rzhwl5+v+RBERERHZPBQOMwqHm1RjGvedj9O+/cMU5ncywTB/H76UB/IXcNbZ5/Pi513AC04fwfd0f6KIiIiIbGwKhxmFw00uieHhL5Dc/ld4j399cXfD5dljWwj7ttF/8pls2XY23vBpMHgKDGyDXKl7dRYRERERWUNHEg6DY10Zka7xfDj7VXhnvwqmd8CBh2nvf5SJxx6guvcRirM76Zu9G+/B1vKf6z0xDYqDp2brU+DEC2HsnC68CRERERGR9aFwKJvD4HYY3E7+zJdx6ovSXbVWxJcfmODr9zzArsce5MRkgvPL01xemOO09gEqj30Nm9+zdI1TXwxXvAVOvwb0yAwRERER2WA0rFQEmGuGfPH+fXz2+3v4xg8PECWOU4bLXHv+MNeeEnLqwW9gt38IqhOw5QK44rfgvFeDr5lQRUREROT4pXsOMwqHcjSma22+cP8En/3+Xv790QMkDsYHS5w3VuCnvX/nqv3/SH/1MZK+cbwX/SZc/EtQ6Ol2tUVEREREDqFwmFE4lGfqQLXF5++b4PbHDvLoZJXHDtQIo4hrvHv49eCzXOb9gKr1cNfoa9j7nDcyvnU7p4/1cFJ/EdPQUxERERHpMoXDjMKhrLU4ceyarvPo/iqPTFYJH7+DS3Z/nMta3yJ0ATfFP8ZH4lcxmRvntNEKZ4z2cPpoD8/dOsAl2wepFHSbr4iIiIisH4XDjMKhrBe3/2Fat/0v8vd9EktCHhx4MTfmX82X5raxe6YBgO8Z55/Ux2WnDnHZqcM8/5RBBsr5LtdcRERERDYyhcOMwqGsu/l9cOdfw10fheYsbL+C+vN/g3vDrdyzc5Z7ds5x35452rEjwThjrJeLtg/xvO2DPG/bEKN9JcDS2VDNS8t+Hnz1OIqIiIjIkVM4zCgcSte05uE7fw/f+iDM7X5m1wqKsP1FcPpL4YyXwujZepSGiIiIiBwWhcOMwqF0XRzCw1+A+kHAgXPgko6yI04SJmYbPHGwyhMHa+ycqtEMYzwS+ooB5/bWuLD5HUaajwMQVk7ATr+G4Kwfh9NeAuWhbr5DERERETmOKRxmFA7l2ShJHA9PznPnj6a440dT3Ld7lj0zDUbj/Vzl38tV3ve50ruPAauRYDxROItdQy9kfvzF5LdfxskjfZw8UKK3qGcwioiIiGx2CocZhUPZKJLEMTnfYvdMnV3TDXZPVWH3dzhh/zc5q3oX58Q/wDfHnCvxreQ8bk0u5J7c87ChUzhzrIfzT+7ngpP7OfekPoVGERERkU1E4TCjcCibRVKfZv7BWwgf/jLlnV+nXN8DwERwEk/EwzQjiPCJ8Snk8/SWC/SWi/RVigxUyuRzAXidiw/mQ74Cg6fA0OkwfBqUBrv7RkVERETkiBxJONQUiCIbgFcepP+S6+CS69J7GQ8+Ao9+hRMe+xonNKYJwzaNVptWq04rDAmrIW42oknMpDkKXkLBc+QtIbAEnxhLYohby1+oPJwFxdOXAuPCdqG3O29eRERERNaEeg5FNqkD1Rb375njvt2z3Ltrlvv2zLJrurF4fHywxAUnFDmvOM0ZwT62uj2MtnfRV3+CwtyPsLk9yy9YGYPhM5YHxvII5MuQK0OulK3L6QysnrfO71hERERk89Gw0ozCociRma61uX/PHPfuTsPiwxPz7J1tUm1Fy84zg/EKXNw7zbn5/ZwRTDCe7GW0vYve+hPkG/uf/sWCUhoY85UsOHaEx1wJ8j3QMwY9W9Kld8tSudivx3mIiIiIHAYNKxWRozJYyXPlmSNceebIsv3zzZC9s032zDSYmG2yZ7bJxGyDvbMj3EIWmXwAABo+SURBVDS7lb0zDWrtePH8Cg1O9faxrdTihHLCWDFhpJAwnI8YyEX0ByG9fkjF2hRp4UUNCBvQrkFzFuYn0mdFVvcdOrQV0p7HzuC4WoAs9IGfg6AAfr5jySlYioiIiKxC4VBEnlZvMUdvMcdZW1a/r9A5x3wrYu9Mk72zDfbONtk702DfXIsfzTe5fa7F5HSTA9X2IT/re8ZIT54tfUXGeotsGSkw1ltkrK/AYClgONdi2E0xEE/TE02Rr0+moXFhOfgo7Ph3aEwd/htaFhbzECyUC2l4zPfA6HPgxAvhhAtg7Ny0N1NERERkA9OwUhFZN2GccKDaYt9ci31zTSbnmkzOp+V9cy0m51tMzjU5WDs0RC4o5XwGyzn6y3kGyzkGyjkGynlGio4T/CpjNsMI0wzl2gwXjbIfY3EIcTvthYxDiLL1Ifvaabk5C5MPQGsufVHzYeSsNCguBMYTLoTy0Dp9ciIiIiJHR8NKReS4lPM9TuwvcWL/U/fCtaOEg7UW07WQmXqbmUbIdL3NTD3dnq6Hi+UfTMwz2wiZrofEycKXXQOL1+opBIwPlhgfLLN1qMTWwTLjYyW2DpXZOlSmp/Ak/wwmCczsgIl7YeL76XrHN+HeG5fO6RvPgmJHaBzYrmGrIiIi8qyknkMR2RAWhrbO1tMguXe2yc6pOrumG+yarrNzqsHO6Tr1jnsjAQbKObZmwXF8sMzWwRInDZQo5X2KOZ9i4FPMeRRyPsXAoxhOUzz4IP6+jtB44GFwSXrBQj/0ndQxVDW7z3FhyOohQ1lXLrmOSXlWzvTauc7Kfl5hVERERJ6UZivNKByKSCfnHNP1kJ1TdXZOp8ExLacBctd0g3aUHNa1cr5RDHwKOY9eP+IcfyfnsIOz3GMMMUfRiylaRN4i8haTJyJHiO8iAhfiuxAvDrEkHcpqq028czjMWx4Y8z3pUujNlp50cp6F7cVjfdmx3qXtfE8aWkVERGTD0LBSEZFVmBlDlTxDlTzP3TpwyPEkceyvttg726TejmhFCa0wphkmNMM4XaKEVpjQjLLtMDsnGuO74cXcHsVUWzHzzZC5RsRcM3zawOl7Rm/BZ7DoMVxyjBYco6WEkULMUD5mKJfO8trnp7O89nghFa9NwTWxMJvpNaynS7sG7So0Z2B2J7Sq6cyv7fnD+5CCYhoUi31LobHYl/aIFvs6tldZV0agOKCeTBERkWcphUMRkYznGVv6imzpK67pdZthzHwzDYrzzYi5Rvik5dlGyEQj5KHJkKlam7lmiHOr/1MdeLY4Ic9gOcdgOU9PMaDQ41MY8CjkPAp+OiS24EPFWvTQoEydsmtQTGoUkwbFpE4hrpGPa+SjKrloniCcx2/P4y08UqQ5l07Q064+9Zst9MHAtkOX/q3pujSo8CgiInKcUjgUETnGirn0/sXR3sIR/2ycuGzCnXY6GU9taXKe6Xo7XbJ9Ow7WqbbSHs92FKc9n0/Za1nIlkN7URf4nlHKpfddFnM+5V4YCloM+U0G/Qb9XpN+r0GfNRhy04zEkwyFE/Tv/iG9j3yNXFxfdr0k10OcBUV/cDve4PYsPI6nz6Q8WkERKqNpb6fCp4iIyFFROBQROY753tJQ2KPhnKMdJ9kQ2YRWFNPOQuPCsNnFcpQOk22EcTacNqYRLu1bHFobJky3Y/ZGMY1G+vONdky9HVFvx0SLs8Y6+qkxbvsZtwPpOtrPePMA45MPMm630muNtfuwIAuJY+kQ156xNDD2jKX7ekbT7cpYuk+9mCIiIssoHIqIbGBmRiHwKQQ+rO1o2SfVjhLq7YhaO6beytbtiHorptaOONCO2dGKqLci4sY0+fndFOt7CcPW4n2dzXYaOhe2n27utDJNRrw5TnTzbJmbY7Q6y/C+HzDg7qQ/mcHn0B7UxAKa+SHC4jCtykm0ercR9m4l7t9GMrAdBraTK1bI+R6FwCO/sPgege8do09PRESke9Y9HJrZK4D/CfjAR51zf7rieAH4e+AS4CDwOufc4x3HtwEPAO9yzr1nveotIiKHJw1ReQbKa3O9JHE0wphqK6Laiqi1IqrNaPl2K6bRjtjVjnm4HVFrpYG01opptNp4rVlKrQOUwykq0RRDbpYRm2U4nGO0PsP49ENstdsoWXvZa+93fex0Y9kyurje7UY54I/iBXkKgU8p71HK+fQHIaN+nRG/yjDzDNo8A8zT7+boS2bpSeYoR7OUoxmK4Qz59gyJXyQu9BMXBnDFASgNYuVBvPIQfmWIoJKWKQ0uX57JMFwREZFVrGs4NDMf+CDwMmAXcJeZ3eyce6DjtF8Bpp1zZ5jZ9cCfAa/rOP5+4PPrVWcREekuzzMqhYBKIWDLGl1zZe9mK0p4IIxx1Un8uScI5naSn3+CwvxOTq7t4sz6DirNO/Dc0nMyE3xm82M0vB6K7TkqjVkKrrnq6yXOmKHCtOtlD71Mu16m3EnM0EOBkP5mjQGqDNhe+nmEAasyQBXfnrzLtEmBhlcmsjyR5YgtILI8sZcjsRyxlyfxcsReDpeVEz+P8/K47LmaSVCCfAXyPVihguV78Io9+MUe/GIfuVIPQamPfLFCMZ9LJznKek9NQ3JFRDac9e45vAx4xDn3GICZfQK4lrQncMG1wLuy8k3AB8zMnHPOzH4WeAyorV+VRURko3ny3s0h4OzVfyiOYG4XTO+AmR140zsYnNnBYHMOysNQHsrWhy5eaYAhz6c3ThgNY5rt9H7ORsejUhphzGQY88TCo1PaIUlrHhrTWHMGvzmL35oh154hH85RiGYpRFW8JH1upu9CgiQkiEN81yZwNQIiii4kICTnInJE5AmzJcJ7ivC5Us0VqFNkyhWpU6RmJaYYYNr6mfYGmfUGmPUGmPOHmA8GmPeHSIISge/he0bONwLPW1znVwzVXRi6W+jYlw/85edkM/Au7Mv53uKw35y/sM/StefheQqwIiJHYr3D4cnAzo7tXcDlT3aOcy4ys1lg2MwawNtIex3f+mQvYGZvAt4EsG3btrWruYiIbG5+AIOnpMtRWggzfcXcmlXraCSJI4xj2q0G7fo8YSNdokaVuDlP1KziWlWSdrq2dg3CGl5Ywwvr5KMalXCeU8K9VML7KcfzrHJbJw0rM+sPMGMDTHsDTNsAUwxwkH5mXZlqnGM6yTOX5JmPcszEeRouT4MCDQrE+M/ofQaeLYbIpcDZETQXA6lPIQumhSyQFoKFc/xl5+UDD98Dzwwzw7O07BnZ9tI+WzyW7fMMP1uCbJ3LwvPhbKu3VkSOtfUOh6v9q7bya8snO+ePgPc756pP9Y+jc+7DwIcBLr300sP/SlRERGST8Dyj4AUUcr3Q0/vMLxi1obYfapNQ3Z8+G7M2Sam6n1JtkhOqk+nx6kPQmFr9GgGH/FXi/DxJUMYFReKgTOwXifwSsV8kNp+YIB1OS1qOzCdyPiFL63Bx7dF2Pm3n03B5aq5AtV1kvllgNikwnxQ4GOeZiQpMRzlqsU87Sjpm3+0+zyDwPXKepWs/DY+Bb+S8dL3YO+t7BN7S8cBLQ611/JnV+efUsjLLNhb5lgZWzwzfo6O8tF523CwNxGbkAm/ZY3GKOZ9SzqeU9ylm9+2m9++mx4rBkU38lCSO2DniJFucS/cl7pA/NFcyAJdg9YN49f14tUksW3uNKZJCH66yhaQyRtyzhaSyBVcaAvMOmSxrtVfzLP0MPDNs4XPJvjxY+LwWvlxYaWHG6WY7nTW63o4WZ49uZPsaYXrPdaMd0+iYXbodJQQdbSXI2kh+oU2saEsLxxfKXlYfs+wzsqW2sbDPbOV2Wm/f8+gpBPQW01sCyjlfPfnPEusdDncBWzu2x4E9T3LOLjMLgH5girSH8Toz+3PSh3IlZtZ0zn3g2FdbREREnlSQh/6T0+XpxCHUDkBrHsIatOsQNtJy2IB2DcJ0n7Vr+GEdwjrByvPiEJIYkjArh+nQ3ySEJFoqxyF03Ct6+O8pB5UeXL6Cy1VIchXioEIclElyFZKgRJyrkATlbF0iCsokQYUoKBMHJSK/TBSUifwyoV8i8ookeERJQhQ7oiy8LGynZUecJISrbEdJGlaj2BHFCe1sHSWOMF645tK5YeyotyOixNFeMeuvw2EuocdV6Uvm0kmTFpeFSZTS/f1ujgo16pSZoZdZepleWFwv0/Qw5XqZSiocdL1MJz00nU98RMHakSOmSJsiLYrWptcL6ctF9PoRAFECkTNiZ0SJEbl0O0ogwSPBSDBctqTbHkXajNgsI8wyajOM2hyjNpNtp5NTDTFHYId2f4fOJ2eHtp+28zlAP5NukEk3kC2DTDKwbHueUvaeQorWzsptitamsLA/2y4SUrK0XLKQAiG1JMeMqzBLDzOuwhwVZl22UKFKidX6VUo5n8C3tA3FjjB5+lmfjyUz6MkH9GRhcSE49hRW3+4tBvSVcvQVA/qKOfpKOXqLAaWcv6Y96M45mmFCrZ1OblZrxTgc+WyUR25hqLq/NIw952/sXvz1Dod3AWea2anAbuB64BdWnHMz8EbgW8B1wFeccw64auEEM3sXUFUwFBEReZbxc9B3InDi+r2mc1mAzAJj2IR2NVtq0Ooor9hv7RrWruK1qwTtGtSmslC7EGyPcBqEoAS5YvpMzqAIudLydVDMjneclytBoZCWXZK9jyQNvUmcbi+W46Wyi5ef265B/eDS0phOr7cav5A+L7Q8BOUTodCXBvrGFNQfhfrUoe99oaPPJz2/NIgrD+NKgyReniRs4NoNXNiAsI6FDSxu4kVNvLi5bMKnZVbutuw1jlJsORqFEZr5IRqFU5jNDzNRGKaRH6ZZGKZRGKGRH6KZH6bt9xC4ZjrbcWv/snWpdYCx1n62tw5Qaj1GMZw++kqtEFmOyMuTj5t4h3wAS5z5JIX+bKbjAaw0gFcewkoD6X+DoJjObBwUSfw8kZcn8QrpRFZ+nogckVcgtByRFWhbjtByhORJzEuDtkuH8DlnYJaWsayPdCGIp8cdgBlR7Ki2Y2qtmGorpNqMmM9mmq61I+azGacnZpuLM09XW9HTBtjAs8Xg2JsFx6V1jr5SGiDr7TgNe9m63k6vX2+nM1/XWwvHI45mcMBCr/3CkvfT3vHAM2745eezfbhy5Bc9TqxrOMzuIfxN4Auk/1vf4Jy738zeDdztnLsZ+BjwcTN7hLTH8Pr1rKOIiIhsMGZp7yb5dLvYD2s1922SZD2d9Y6AuWIJO8v1NJxGDYhaaS9o1Ez3NWeyY9myeKzBoXfhdPACMB88Pyt7HeVsv3npzLTlYRg7t2PCpKHVJ1TKlZePNV1N2MzC4sE0LNYPZttTi9vWmEqHa0btNOTmS1Dpz8JwKV3nymkQXnVfOXtsi6VB1iVp2F8sP8kCS2U/Dz1jUBmDnjH8Yj89ZvSsTQtYErXTIdXVfTA/AdWJ9IuHlV8ALFsXOr4IWPpCIPD89I9059J21ZhJw3xzJi1na2tM46/Yx/SP0nJzblmvucfi/wHrwwvSz97Lpfdse7n0yyE/t1SuBNCXBz+H85aGiUcJaa94HBPHMVGSEMcJcRwTJ9m6lhDPOZIkJkkS4iTBuYQEj1lXoer1UvP6aAR9tHL9hPl0SQaH0jBdHiJf7KVSzFEp+FTyAZVC2jMZxkm6ROmw3sXtOO2FX7YdJ4TR0nYx98zule42c93sYz7GLr30Unf33Xd3uxoiIiIiR2+h5zNqZkHP71gf/n15sgnFEcSt9IuIqJV98dDq2Ndcse4ou5ilrjyXlVdbZ8ehY1/SMdx7xdDvuL18GPiy49nakd3E6KUF87IvK56qnA0rjiO89izWmEm/pIgaT/75eLn0ubGdz5L18yvez8r3x1Mf+8n3Ht4Q+3VkZt92zl16OOeu97BSERERETkSCz2fwbr2+8hG4Afpkn/2DnM8Eqt+VRI2lnpeG9Np7/ZieToNkAvlmSfSgAodPed26PYhx1jaTqI1flfrS+FQREREREQ2plw2VLlvHe9zfhbTWAQRERERERFROBQRERERERGFQxEREREREUHhUERERERERFA4FBERERERERQORUREREREBIVDERERERERQeFQREREREREUDgUERERERERFA5FREREREQEhUMRERERERFB4VBERERERERQOBQREREREREUDkVERERERASFQxEREREREUHhUERERERERFA4FBERERERERQORUREREREBIVDERERERERQeFQREREREREUDgUERERERERFA5FREREREQEhUMREREREREBzDnX7TocM2a2H9jR7XqsYgQ40O1KyKah9ibrRW1N1ovamqwntTdZL8eqrW13zo0ezokbOhwer8zsbufcpd2uh2wOam+yXtTWZL2orcl6UnuT9XI8tDUNKxURERERERGFQxEREREREVE47JYPd7sCsqmovcl6UVuT9aK2JutJ7U3WS9fbmu45FBEREREREfUcioiIiIiIiMKhiIiIiIiIoHC47szsFWb2AzN7xMze3u36yMZiZjeY2aSZ3dexb8jMvmRmP8zWg92so2wMZrbVzL5qZg+a2f1m9pZsv9qbrCkzK5rZnWb2vayt/VG2/1QzuyNra580s3y36yobg5n5ZnaPmX0221ZbkzVnZo+b2b1m9l0zuzvb1/XfoQqH68jMfOCDwCuBc4HXm9m53a2VbDB/C7xixb63A7c4584Ebsm2RZ6pCPg959w5wAuAN2f/nqm9yVprAdc4554LXAS8wsxeAPwZ8P6srU0Dv9LFOsrG8hbgwY5ttTU5Vl7inLuo49mGXf8dqnC4vi4DHnHOPeacawOfAK7tcp1kA3HO3QpMrdh9LfB3WfnvgJ9d10rJhuSc2+uc+05Wnif9Q+pk1N5kjblUNdvMZYsDrgFuyvarrcmaMLNx4CeBj2bbhtqarJ+u/w5VOFxfJwM7O7Z3ZftEjqUtzrm9kP5BD4x1uT6ywZjZKcDFwB2ovckxkA3z+y4wCXwJeBSYcc5F2Sn6fSpr5S+A3weSbHsYtTU5NhzwRTP7tpm9KdvX9d+hwXq/4CZnq+zTs0RE5FnLzHqAfwZ+2zk3l37JLrK2nHMxcJGZDQCfBs5Z7bT1rZVsNGb2U8Ckc+7bZnb1wu5VTlVbk7VwhXNuj5mNAV8ys4e6XSFQz+F62wVs7dgeB/Z0qS6yeewzsxMBsvVkl+sjG4SZ5UiD4T845/5vtlvtTY4Z59wM8DXS+1wHzGzhS279PpW1cAXwM2b2OOmtP9eQ9iSqrcmac87tydaTpF96XcZx8DtU4XB93QWcmc16lQeuB27ucp1k47sZeGNWfiPwmS7WRTaI7D6cjwEPOufe13FI7U3WlJmNZj2GmFkJ+HHSe1y/ClyXnaa2Js+Yc+4PnHPjzrlTSP9G+4pz7g2orckaM7OKmfUulIGXA/dxHPwONefUM76ezOxVpN9C+cANzrk/6XKVZAMxs38CrgZGgH3AHwL/D7gR2AY8AbzWObdy0hqRI2JmVwK3AfeydG/OO0jvO1R7kzVjZheSTszgk36pfaNz7t1mdhpp784QcA/wi865VvdqKhtJNqz0rc65n1Jbk7WWtalPZ5sB8I/OuT8xs2G6/DtU4VBEREREREQ0rFREREREREQUDkVERERERASFQxEREREREUHhUERERERERFA4FBERERERERQORUREjgtmdrWZOTM7v9t1ERGRzUnhUERERERERBQORUREREREROFQREQ2OTO70sy+bmZ1MztoZh8xs97s2C9nQz2fb2a3mVnDzB42s1evcp3fNLMfmlnLzB4xs99Z5ZwLzexfzGzGzKpmdqeZvWzFaSNm9qns+GNm9hsrrnGemf2bmU2ZWc3MHjSzN6/phyIiIpuSwqGIiGxaZnYFcAswAVwH/DbwKuBvVpz6SeAzwGuAe4FPmdlzO67za8BfAjcDPw18Cnivmb2945yzgW8CJwL/EXg18Glg64rX+gjwvez414APmtllHcdvBmLgF4GfyV6392jev4iISCdzznW7DiIiIl1hZrcBkXPuJR37riENjBcAl5IGxXc65/57dtwDHgC+65y7PtveCXzROfcfOq7zIeANwBbnXNPM/gm4CjjTOddYpS5XA18F/tg591+zfTlgD/Ax59zbzWwE2A9c6Jy7d40/DhER2eTUcygiIpuSmZWBFwI3mlmwsADfAELgko7TP71QcM4lpL2IC71548BJpL2FnT4J9JGGTIBrgE+uFgxX+GLHa4XAD7PXAJgiDaJ/ZWavM7Oxw3mvIiIih0PhUERENqtBwAc+RBoGF5YWkGP5cM/JFT87STo8lI71vhXnLGwPZethYO9h1GtmxXYbKMJiMH056TDYG4CJ7F7Iiw/juiIiIk8p6HYFREREumQGcMC7gH9d5fge0iAGMAYc7Dg2xlLQ29uxr9OWbD2VrQ+yFCSPmnPuIeDnsiGnVwF/BnzOzMaz8CgiInJU1HMoIiKbknOuBtwOPMc5d/cqy56O0xdnJ83uMbwWuDPbtYs0SL52xUv8PDBHOoENpPcx/ryZFdeo/qFz7ivA+0hD58BaXFdERDYv9RyKiMhm9vvALWaWADcB88A24CeBd3ac96tm1gbuA34NOAN4PaRDPc3sXcBfm9lB4EvAi4H/BLzDOdfMrvFHwF3ArWb2XtKexIuBg865Gw6nsmZ2IfAe0vsZHyMdGvs24HvOuamn+lkREZGno3AoIiKblnPuG2b2Y6TB7eOk9yDuAP6N5fcQXg+8H/hvpD2Fr3PO3dNxnY+YWYH0URhvyc75Pefc+zvO+YGZXQn8KfDRbPcDwDuOoMoTWb3eSToJzgzpDKdvO4JriIiIrEqPshAREXkSZvbLpI+y6HXOVbtcHRERkWNK9xyKiIiIiIiIwqGIiIiIiIhoWKmIiIiIiIignkMRERERERFB4VBERERERERQOBQREREREREUDkVERERERASFQxEREREREQH+P5/tPM3PduqUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b63d748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot history\n",
    "pyplot.plot(history.history['loss'], label='train')\n",
    "pyplot.plot(history.history['val_loss'], label='test')\n",
    "#pyplot.legend()\n",
    "\n",
    "pyplot.title('model loss',size=15)\n",
    "pyplot.ylabel('loss',size=15)\n",
    "pyplot.xlabel('epochs',size=15)\n",
    "pyplot.legend(loc='upper right',fontsize=15)\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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