5_Growth_curve_fitting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://pbs.twimg.com/profile_images/969600936321736704/WrwKmb8y_400x400.jpg\" width=\"200\" height=\"200\" />\n",
    "\n",
    "# Fitting Growth Response\n",
    "Rather than model the sensitivities, we can model the growth curve directly. For simplicity, lets assume the the growth factor curve has a gaussian shape, with 3 parameters, a $\\mu$ which determines the location of the curve, $\\sigma$ which determines the width and $A$ (Normalisation) which determines the height.\n",
    "$$ A\\exp{(-0.5((T-\\mu)/\\sigma)^2)}$$\n",
    "\n",
    "The growth curve should be the same for all countries and states. Lets fit this on the US states growing Maize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pylab as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pystan\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in climate temperatures\n",
    "clim_precip_maize=pd.read_table('./Crop_data_files/clim_file/temp_climatology_Maize.csv')\n",
    "clim_precip_maize.rename(columns = {'Unnamed: 0':'Crop_season_location'}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define states to fit over\n",
    "As we are modelling the growth function with respect to temperature, we need to use the absolute temperature measurements rather than anomalies. Lets add back the climatological mean to the anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "states=['Indiana','Illinois', 'Ohio','Nebraska', 'Iowa','Minnesota']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read in and add back mean temperature to get real temperature values\n",
    "temp_states=[]\n",
    "for i,s in enumerate(states):\n",
    "    maize_temp=pd.read_table('./Crop_data_files/maize_met_anoms/Maize_Spring_USA_'+s+'_temp_anom_real.csv')\n",
    "    maize_temp.rename(columns = {'Unnamed: 0':'Year'}, inplace = True)\n",
    "    tmp=maize_temp.iloc[:,1:].add(clim_precip_maize[\n",
    "        clim_precip_maize['Crop_season_location']== 'Maize_Spring_USA_'+states[0]].iloc[0,1:,])\n",
    "    temp_states.append(tmp)\n",
    "temp_states=pd.concat(temp_states,keys=states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">Indiana</th>\n",
       "      <th>0</th>\n",
       "      <td>-1.9534</td>\n",
       "      <td>-3.97301</td>\n",
       "      <td>2.59058</td>\n",
       "      <td>10.0881</td>\n",
       "      <td>17.3264</td>\n",
       "      <td>20.9624</td>\n",
       "      <td>25.5664</td>\n",
       "      <td>24.8831</td>\n",
       "      <td>20.2695</td>\n",
       "      <td>11.3892</td>\n",
       "      <td>5.57066</td>\n",
       "      <td>0.147049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.00883</td>\n",
       "      <td>0.874847</td>\n",
       "      <td>4.86918</td>\n",
       "      <td>13.9457</td>\n",
       "      <td>15.2778</td>\n",
       "      <td>22.6881</td>\n",
       "      <td>23.9615</td>\n",
       "      <td>22.5089</td>\n",
       "      <td>18.4416</td>\n",
       "      <td>11.8638</td>\n",
       "      <td>7.13905</td>\n",
       "      <td>-1.29025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-6.41284</td>\n",
       "      <td>-2.59879</td>\n",
       "      <td>4.7838</td>\n",
       "      <td>8.75127</td>\n",
       "      <td>19.9566</td>\n",
       "      <td>19.5645</td>\n",
       "      <td>24.1836</td>\n",
       "      <td>21.6212</td>\n",
       "      <td>18.3241</td>\n",
       "      <td>13.4818</td>\n",
       "      <td>7.37009</td>\n",
       "      <td>4.99193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.465296</td>\n",
       "      <td>1.96229</td>\n",
       "      <td>6.1551</td>\n",
       "      <td>9.13795</td>\n",
       "      <td>15.1196</td>\n",
       "      <td>22.1406</td>\n",
       "      <td>26.0552</td>\n",
       "      <td>25.6531</td>\n",
       "      <td>19.8001</td>\n",
       "      <td>13.6845</td>\n",
       "      <td>7.84777</td>\n",
       "      <td>-5.46126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-5.10546</td>\n",
       "      <td>3.22889</td>\n",
       "      <td>0.673888</td>\n",
       "      <td>10.3269</td>\n",
       "      <td>15.3663</td>\n",
       "      <td>23.748</td>\n",
       "      <td>22.7625</td>\n",
       "      <td>23.5065</td>\n",
       "      <td>18.6671</td>\n",
       "      <td>15.534</td>\n",
       "      <td>6.18488</td>\n",
       "      <td>3.9957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-5.70493</td>\n",
       "      <td>-2.89812</td>\n",
       "      <td>7.23863</td>\n",
       "      <td>14.3429</td>\n",
       "      <td>18.4319</td>\n",
       "      <td>20.7701</td>\n",
       "      <td>23.7437</td>\n",
       "      <td>22.1458</td>\n",
       "      <td>19.6811</td>\n",
       "      <td>14.5876</td>\n",
       "      <td>8.3624</td>\n",
       "      <td>-4.03283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.912048</td>\n",
       "      <td>-0.499908</td>\n",
       "      <td>6.81455</td>\n",
       "      <td>13.4268</td>\n",
       "      <td>17.8881</td>\n",
       "      <td>22.5552</td>\n",
       "      <td>25.2507</td>\n",
       "      <td>21.3784</td>\n",
       "      <td>21.0521</td>\n",
       "      <td>13.6069</td>\n",
       "      <td>4.95992</td>\n",
       "      <td>0.745674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.76537</td>\n",
       "      <td>1.84541</td>\n",
       "      <td>7.20209</td>\n",
       "      <td>11.7326</td>\n",
       "      <td>20.2266</td>\n",
       "      <td>23.5955</td>\n",
       "      <td>24.9677</td>\n",
       "      <td>23.7684</td>\n",
       "      <td>19.7501</td>\n",
       "      <td>10.013</td>\n",
       "      <td>8.71276</td>\n",
       "      <td>2.17935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-3.01212</td>\n",
       "      <td>-1.95634</td>\n",
       "      <td>5.6462</td>\n",
       "      <td>11.3608</td>\n",
       "      <td>18.0358</td>\n",
       "      <td>23.2077</td>\n",
       "      <td>25.5936</td>\n",
       "      <td>25.3732</td>\n",
       "      <td>19.5151</td>\n",
       "      <td>9.29096</td>\n",
       "      <td>6.93201</td>\n",
       "      <td>0.563471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.67857</td>\n",
       "      <td>-2.49459</td>\n",
       "      <td>6.04169</td>\n",
       "      <td>10.5955</td>\n",
       "      <td>15.5242</td>\n",
       "      <td>21.7484</td>\n",
       "      <td>24.2272</td>\n",
       "      <td>22.5472</td>\n",
       "      <td>18.1161</td>\n",
       "      <td>13.3909</td>\n",
       "      <td>5.81759</td>\n",
       "      <td>-6.4596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.44255</td>\n",
       "      <td>3.09323</td>\n",
       "      <td>7.68794</td>\n",
       "      <td>11.0972</td>\n",
       "      <td>15.7078</td>\n",
       "      <td>22.1507</td>\n",
       "      <td>23.4637</td>\n",
       "      <td>22.262</td>\n",
       "      <td>19.636</td>\n",
       "      <td>12.7797</td>\n",
       "      <td>8.92063</td>\n",
       "      <td>1.76187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-2.28052</td>\n",
       "      <td>2.21587</td>\n",
       "      <td>7.22717</td>\n",
       "      <td>13.4812</td>\n",
       "      <td>20.8521</td>\n",
       "      <td>23.6615</td>\n",
       "      <td>25.0505</td>\n",
       "      <td>23.7012</td>\n",
       "      <td>19.1759</td>\n",
       "      <td>14.5671</td>\n",
       "      <td>4.56245</td>\n",
       "      <td>2.32591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.16997</td>\n",
       "      <td>3.40981</td>\n",
       "      <td>5.74444</td>\n",
       "      <td>11.0505</td>\n",
       "      <td>16.1849</td>\n",
       "      <td>19.7391</td>\n",
       "      <td>23.0626</td>\n",
       "      <td>20.4427</td>\n",
       "      <td>18.2962</td>\n",
       "      <td>12.0058</td>\n",
       "      <td>6.20206</td>\n",
       "      <td>0.936968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.0173301</td>\n",
       "      <td>-2.03527</td>\n",
       "      <td>3.39191</td>\n",
       "      <td>10.4553</td>\n",
       "      <td>17.5315</td>\n",
       "      <td>21.5171</td>\n",
       "      <td>25.1457</td>\n",
       "      <td>23.8498</td>\n",
       "      <td>17.2116</td>\n",
       "      <td>11.6949</td>\n",
       "      <td>5.8006</td>\n",
       "      <td>0.151508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-5.85973</td>\n",
       "      <td>-1.65559</td>\n",
       "      <td>4.84746</td>\n",
       "      <td>12.7552</td>\n",
       "      <td>15.6909</td>\n",
       "      <td>23.4092</td>\n",
       "      <td>23.864</td>\n",
       "      <td>21.7388</td>\n",
       "      <td>18.9319</td>\n",
       "      <td>13.636</td>\n",
       "      <td>9.38717</td>\n",
       "      <td>3.63721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-1.37723</td>\n",
       "      <td>-0.592061</td>\n",
       "      <td>6.55102</td>\n",
       "      <td>11.1221</td>\n",
       "      <td>16.6471</td>\n",
       "      <td>22.5709</td>\n",
       "      <td>24.8979</td>\n",
       "      <td>26.0461</td>\n",
       "      <td>18.2937</td>\n",
       "      <td>14.0004</td>\n",
       "      <td>3.45992</td>\n",
       "      <td>-1.96513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-2.70339</td>\n",
       "      <td>-0.100481</td>\n",
       "      <td>2.02085</td>\n",
       "      <td>10.0078</td>\n",
       "      <td>16.7241</td>\n",
       "      <td>22.2153</td>\n",
       "      <td>22.6436</td>\n",
       "      <td>23.1465</td>\n",
       "      <td>18.5797</td>\n",
       "      <td>13.6224</td>\n",
       "      <td>2.99285</td>\n",
       "      <td>1.15791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-3.63169</td>\n",
       "      <td>1.65543</td>\n",
       "      <td>5.8699</td>\n",
       "      <td>8.89772</td>\n",
       "      <td>13.6574</td>\n",
       "      <td>20.9806</td>\n",
       "      <td>23.5137</td>\n",
       "      <td>21.363</td>\n",
       "      <td>18.5364</td>\n",
       "      <td>12.6404</td>\n",
       "      <td>3.90617</td>\n",
       "      <td>0.795719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.05681</td>\n",
       "      <td>4.4293</td>\n",
       "      <td>5.56694</td>\n",
       "      <td>11.4184</td>\n",
       "      <td>19.4995</td>\n",
       "      <td>21.8538</td>\n",
       "      <td>23.5531</td>\n",
       "      <td>23.4021</td>\n",
       "      <td>21.3572</td>\n",
       "      <td>13.7404</td>\n",
       "      <td>7.75199</td>\n",
       "      <td>2.84628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-2.0289</td>\n",
       "      <td>2.81877</td>\n",
       "      <td>2.99723</td>\n",
       "      <td>12.1992</td>\n",
       "      <td>17.8924</td>\n",
       "      <td>22.6487</td>\n",
       "      <td>26.0824</td>\n",
       "      <td>22.2634</td>\n",
       "      <td>19.215</td>\n",
       "      <td>12.9724</td>\n",
       "      <td>9.49661</td>\n",
       "      <td>1.37364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-2.26375</td>\n",
       "      <td>3.58884</td>\n",
       "      <td>8.04499</td>\n",
       "      <td>10.7229</td>\n",
       "      <td>18.4269</td>\n",
       "      <td>21.601</td>\n",
       "      <td>22.325</td>\n",
       "      <td>22.7456</td>\n",
       "      <td>18.5223</td>\n",
       "      <td>14.4603</td>\n",
       "      <td>4.87473</td>\n",
       "      <td>-6.14589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-2.02941</td>\n",
       "      <td>1.16934</td>\n",
       "      <td>3.36088</td>\n",
       "      <td>14.056</td>\n",
       "      <td>17.822</td>\n",
       "      <td>21.0009</td>\n",
       "      <td>23.6794</td>\n",
       "      <td>23.549</td>\n",
       "      <td>17.8994</td>\n",
       "      <td>12.9223</td>\n",
       "      <td>10.0884</td>\n",
       "      <td>3.22454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.27671</td>\n",
       "      <td>2.44643</td>\n",
       "      <td>4.32397</td>\n",
       "      <td>12.4996</td>\n",
       "      <td>15.0693</td>\n",
       "      <td>22.9426</td>\n",
       "      <td>25.562</td>\n",
       "      <td>24.2886</td>\n",
       "      <td>21.2787</td>\n",
       "      <td>11.7329</td>\n",
       "      <td>4.95447</td>\n",
       "      <td>0.417109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-5.02928</td>\n",
       "      <td>-2.90573</td>\n",
       "      <td>6.12223</td>\n",
       "      <td>11.9321</td>\n",
       "      <td>15.7047</td>\n",
       "      <td>19.9331</td>\n",
       "      <td>23.1963</td>\n",
       "      <td>23.4877</td>\n",
       "      <td>17.8939</td>\n",
       "      <td>12.3158</td>\n",
       "      <td>8.42407</td>\n",
       "      <td>1.40652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-3.58426</td>\n",
       "      <td>0.0155546</td>\n",
       "      <td>7.17004</td>\n",
       "      <td>12.4253</td>\n",
       "      <td>19.1386</td>\n",
       "      <td>21.2121</td>\n",
       "      <td>22.8543</td>\n",
       "      <td>20.7388</td>\n",
       "      <td>19.936</td>\n",
       "      <td>13.1411</td>\n",
       "      <td>8.02159</td>\n",
       "      <td>0.156068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-1.42508</td>\n",
       "      <td>1.99761</td>\n",
       "      <td>3.30135</td>\n",
       "      <td>12.6427</td>\n",
       "      <td>15.3971</td>\n",
       "      <td>23.8479</td>\n",
       "      <td>24.6321</td>\n",
       "      <td>24.3111</td>\n",
       "      <td>21.2757</td>\n",
       "      <td>13.5586</td>\n",
       "      <td>7.59132</td>\n",
       "      <td>-2.30366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.19659</td>\n",
       "      <td>0.528402</td>\n",
       "      <td>5.20404</td>\n",
       "      <td>13.5371</td>\n",
       "      <td>16.4012</td>\n",
       "      <td>21.2803</td>\n",
       "      <td>24.8531</td>\n",
       "      <td>23.6764</td>\n",
       "      <td>17.5355</td>\n",
       "      <td>11.0109</td>\n",
       "      <td>7.45237</td>\n",
       "      <td>3.79294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.254286</td>\n",
       "      <td>-5.87735</td>\n",
       "      <td>8.93536</td>\n",
       "      <td>10.4911</td>\n",
       "      <td>19.447</td>\n",
       "      <td>22.9623</td>\n",
       "      <td>23.2312</td>\n",
       "      <td>25.4311</td>\n",
       "      <td>21.4844</td>\n",
       "      <td>16.5682</td>\n",
       "      <td>6.42833</td>\n",
       "      <td>0.932309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-1.34529</td>\n",
       "      <td>-1.50774</td>\n",
       "      <td>4.07293</td>\n",
       "      <td>12.0195</td>\n",
       "      <td>15.6034</td>\n",
       "      <td>22.6876</td>\n",
       "      <td>23.768</td>\n",
       "      <td>22.7712</td>\n",
       "      <td>20.6028</td>\n",
       "      <td>13.1122</td>\n",
       "      <td>5.43167</td>\n",
       "      <td>-0.603224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-5.16117</td>\n",
       "      <td>0.970609</td>\n",
       "      <td>7.58417</td>\n",
       "      <td>11.6994</td>\n",
       "      <td>17.0706</td>\n",
       "      <td>22.4303</td>\n",
       "      <td>21.4693</td>\n",
       "      <td>22.0726</td>\n",
       "      <td>19.2709</td>\n",
       "      <td>10.904</td>\n",
       "      <td>8.79125</td>\n",
       "      <td>-0.142664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">Minnesota</th>\n",
       "      <th>5</th>\n",
       "      <td>-4.13192</td>\n",
       "      <td>-2.09789</td>\n",
       "      <td>7.74112</td>\n",
       "      <td>14.6075</td>\n",
       "      <td>19.3596</td>\n",
       "      <td>20.1646</td>\n",
       "      <td>23.4548</td>\n",
       "      <td>21.0549</td>\n",
       "      <td>18.0717</td>\n",
       "      <td>12.0754</td>\n",
       "      <td>1.33048</td>\n",
       "      <td>-6.24669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.568592</td>\n",
       "      <td>-2.66535</td>\n",
       "      <td>6.98464</td>\n",
       "      <td>13.3611</td>\n",
       "      <td>17.6148</td>\n",
       "      <td>23.0568</td>\n",
       "      <td>24.6877</td>\n",
       "      <td>21.12</td>\n",
       "      <td>18.3588</td>\n",
       "      <td>13.0724</td>\n",
       "      <td>3.81428</td>\n",
       "      <td>3.22258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.73212</td>\n",
       "      <td>6.88246</td>\n",
       "      <td>8.64409</td>\n",
       "      <td>15.2516</td>\n",
       "      <td>20.1553</td>\n",
       "      <td>23.9406</td>\n",
       "      <td>25.7057</td>\n",
       "      <td>22.3036</td>\n",
       "      <td>19.3415</td>\n",
       "      <td>10.8753</td>\n",
       "      <td>9.33057</td>\n",
       "      <td>3.09372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-5.01356</td>\n",
       "      <td>-3.92133</td>\n",
       "      <td>6.57787</td>\n",
       "      <td>11.9771</td>\n",
       "      <td>20.6204</td>\n",
       "      <td>25.6252</td>\n",
       "      <td>26.0471</td>\n",
       "      <td>25.4472</td>\n",
       "      <td>19.89</td>\n",
       "      <td>10.3594</td>\n",
       "      <td>6.6319</td>\n",
       "      <td>1.04807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.99412</td>\n",
       "      <td>-5.44154</td>\n",
       "      <td>2.61215</td>\n",
       "      <td>11.0271</td>\n",
       "      <td>16.805</td>\n",
       "      <td>21.4253</td>\n",
       "      <td>25.3328</td>\n",
       "      <td>23.4742</td>\n",
       "      <td>18.4592</td>\n",
       "      <td>13.8511</td>\n",
       "      <td>4.15497</td>\n",
       "      <td>-4.4039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.45338</td>\n",
       "      <td>1.93223</td>\n",
       "      <td>7.63872</td>\n",
       "      <td>11.9294</td>\n",
       "      <td>15.6109</td>\n",
       "      <td>22.5983</td>\n",
       "      <td>23.1922</td>\n",
       "      <td>23.1201</td>\n",
       "      <td>20.6556</td>\n",
       "      <td>12.6606</td>\n",
       "      <td>9.0294</td>\n",
       "      <td>-1.17773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-3.92862</td>\n",
       "      <td>3.11584</td>\n",
       "      <td>6.90338</td>\n",
       "      <td>13.1766</td>\n",
       "      <td>18.9713</td>\n",
       "      <td>24.2099</td>\n",
       "      <td>23.7534</td>\n",
       "      <td>23.4575</td>\n",
       "      <td>18.162</td>\n",
       "      <td>12.2784</td>\n",
       "      <td>1.67328</td>\n",
       "      <td>1.59081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.02583</td>\n",
       "      <td>4.47921</td>\n",
       "      <td>6.46294</td>\n",
       "      <td>9.77775</td>\n",
       "      <td>18.1172</td>\n",
       "      <td>20.4962</td>\n",
       "      <td>20.129</td>\n",
       "      <td>20.3516</td>\n",
       "      <td>18.3432</td>\n",
       "      <td>12.3589</td>\n",
       "      <td>4.81849</td>\n",
       "      <td>0.538691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-3.43702</td>\n",
       "      <td>-2.46786</td>\n",
       "      <td>2.72279</td>\n",
       "      <td>9.73175</td>\n",
       "      <td>16.2897</td>\n",
       "      <td>19.9857</td>\n",
       "      <td>22.7532</td>\n",
       "      <td>22.9996</td>\n",
       "      <td>15.7256</td>\n",
       "      <td>12.0615</td>\n",
       "      <td>4.78389</td>\n",
       "      <td>1.12847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-8.40981</td>\n",
       "      <td>-5.12762</td>\n",
       "      <td>6.79489</td>\n",
       "      <td>11.7895</td>\n",
       "      <td>18.4134</td>\n",
       "      <td>23.1323</td>\n",
       "      <td>22.575</td>\n",
       "      <td>21.8551</td>\n",
       "      <td>20.6646</td>\n",
       "      <td>14.7023</td>\n",
       "      <td>8.90711</td>\n",
       "      <td>2.68409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-1.03702</td>\n",
       "      <td>0.0433769</td>\n",
       "      <td>6.19355</td>\n",
       "      <td>8.84094</td>\n",
       "      <td>15.7857</td>\n",
       "      <td>23.2486</td>\n",
       "      <td>24.2362</td>\n",
       "      <td>25.6595</td>\n",
       "      <td>18.191</td>\n",
       "      <td>12.5708</td>\n",
       "      <td>2.95699</td>\n",
       "      <td>0.171366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-5.47606</td>\n",
       "      <td>-0.955331</td>\n",
       "      <td>1.75054</td>\n",
       "      <td>9.21849</td>\n",
       "      <td>15.3219</td>\n",
       "      <td>22.4267</td>\n",
       "      <td>22.399</td>\n",
       "      <td>22.8337</td>\n",
       "      <td>18.6896</td>\n",
       "      <td>13.6473</td>\n",
       "      <td>1.86508</td>\n",
       "      <td>-3.12346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-4.81259</td>\n",
       "      <td>-0.626674</td>\n",
       "      <td>3.82706</td>\n",
       "      <td>8.84177</td>\n",
       "      <td>14.111</td>\n",
       "      <td>22.5276</td>\n",
       "      <td>23.0689</td>\n",
       "      <td>21.6627</td>\n",
       "      <td>19.6917</td>\n",
       "      <td>13.8807</td>\n",
       "      <td>3.67768</td>\n",
       "      <td>3.90135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.0648863</td>\n",
       "      <td>6.23277</td>\n",
       "      <td>4.26645</td>\n",
       "      <td>12.6663</td>\n",
       "      <td>19.7089</td>\n",
       "      <td>20.2156</td>\n",
       "      <td>23.3634</td>\n",
       "      <td>23.4533</td>\n",
       "      <td>21.6834</td>\n",
       "      <td>14.0157</td>\n",
       "      <td>8.25804</td>\n",
       "      <td>3.28583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-3.52869</td>\n",
       "      <td>4.28585</td>\n",
       "      <td>6.01729</td>\n",
       "      <td>12.0433</td>\n",
       "      <td>17.6562</td>\n",
       "      <td>21.7146</td>\n",
       "      <td>25.3622</td>\n",
       "      <td>22.61</td>\n",
       "      <td>18.1921</td>\n",
       "      <td>13.1873</td>\n",
       "      <td>11.3955</td>\n",
       "      <td>3.40229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-1.5933</td>\n",
       "      <td>4.23661</td>\n",
       "      <td>9.78457</td>\n",
       "      <td>11.5349</td>\n",
       "      <td>18.2122</td>\n",
       "      <td>20.5942</td>\n",
       "      <td>23.2149</td>\n",
       "      <td>23.6865</td>\n",
       "      <td>19.0189</td>\n",
       "      <td>14.9355</td>\n",
       "      <td>4.32295</td>\n",
       "      <td>-6.65135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.115828</td>\n",
       "      <td>-4.55684</td>\n",
       "      <td>2.19694</td>\n",
       "      <td>12.6839</td>\n",
       "      <td>17.6015</td>\n",
       "      <td>22.0952</td>\n",
       "      <td>25.0501</td>\n",
       "      <td>24.1284</td>\n",
       "      <td>18.4943</td>\n",
       "      <td>12.6305</td>\n",
       "      <td>13.2101</td>\n",
       "      <td>4.03991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.96631</td>\n",
       "      <td>4.46447</td>\n",
       "      <td>1.3322</td>\n",
       "      <td>11.4276</td>\n",
       "      <td>15.0573</td>\n",
       "      <td>23.5834</td>\n",
       "      <td>25.7227</td>\n",
       "      <td>22.7024</td>\n",
       "      <td>20.6883</td>\n",
       "      <td>9.02409</td>\n",
       "      <td>6.13683</td>\n",
       "      <td>3.87269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-1.99306</td>\n",
       "      <td>-1.99945</td>\n",
       "      <td>5.05857</td>\n",
       "      <td>11.9879</td>\n",
       "      <td>16.0229</td>\n",
       "      <td>21.3427</td>\n",
       "      <td>23.9022</td>\n",
       "      <td>24.7499</td>\n",
       "      <td>18.8574</td>\n",
       "      <td>14.0685</td>\n",
       "      <td>5.58588</td>\n",
       "      <td>3.42526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-3.78915</td>\n",
       "      <td>0.0625075</td>\n",
       "      <td>7.669</td>\n",
       "      <td>12.9472</td>\n",
       "      <td>16.5216</td>\n",
       "      <td>20.3546</td>\n",
       "      <td>22.7068</td>\n",
       "      <td>20.3391</td>\n",
       "      <td>21.614</td>\n",
       "      <td>13.7204</td>\n",
       "      <td>9.09832</td>\n",
       "      <td>2.37138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-2.39663</td>\n",
       "      <td>3.38214</td>\n",
       "      <td>5.23942</td>\n",
       "      <td>14.1619</td>\n",
       "      <td>15.9437</td>\n",
       "      <td>24.1862</td>\n",
       "      <td>25.2585</td>\n",
       "      <td>23.1068</td>\n",
       "      <td>22.0523</td>\n",
       "      <td>14.496</td>\n",
       "      <td>8.43334</td>\n",
       "      <td>-0.212369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.67315</td>\n",
       "      <td>0.0749459</td>\n",
       "      <td>5.7632</td>\n",
       "      <td>14.9493</td>\n",
       "      <td>18.3958</td>\n",
       "      <td>23.0262</td>\n",
       "      <td>26.575</td>\n",
       "      <td>23.609</td>\n",
       "      <td>17.8316</td>\n",
       "      <td>11.4477</td>\n",
       "      <td>8.48189</td>\n",
       "      <td>5.58581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.461534</td>\n",
       "      <td>-4.37127</td>\n",
       "      <td>8.44598</td>\n",
       "      <td>11.1934</td>\n",
       "      <td>19.9066</td>\n",
       "      <td>23.7381</td>\n",
       "      <td>24.8627</td>\n",
       "      <td>23.9209</td>\n",
       "      <td>20.6349</td>\n",
       "      <td>15.8876</td>\n",
       "      <td>7.11577</td>\n",
       "      <td>-1.38013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-3.00906</td>\n",
       "      <td>-2.9919</td>\n",
       "      <td>3.2335</td>\n",
       "      <td>10.4267</td>\n",
       "      <td>15.914</td>\n",
       "      <td>21.8423</td>\n",
       "      <td>24.4014</td>\n",
       "      <td>23.2185</td>\n",
       "      <td>19.7553</td>\n",
       "      <td>13.6043</td>\n",
       "      <td>7.2154</td>\n",
       "      <td>-3.06519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-5.30291</td>\n",
       "      <td>0.752841</td>\n",
       "      <td>5.70794</td>\n",
       "      <td>11.5109</td>\n",
       "      <td>17.5029</td>\n",
       "      <td>21.3959</td>\n",
       "      <td>21.4957</td>\n",
       "      <td>21.8151</td>\n",
       "      <td>20.6255</td>\n",
       "      <td>9.79597</td>\n",
       "      <td>11.539</td>\n",
       "      <td>-1.48554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-3.89497</td>\n",
       "      <td>-1.74977</td>\n",
       "      <td>9.13581</td>\n",
       "      <td>15.0599</td>\n",
       "      <td>17.4754</td>\n",
       "      <td>22.4536</td>\n",
       "      <td>24.9831</td>\n",
       "      <td>25.4966</td>\n",
       "      <td>18.3786</td>\n",
       "      <td>15.0904</td>\n",
       "      <td>7.36189</td>\n",
       "      <td>-1.73295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-4.42845</td>\n",
       "      <td>-0.934544</td>\n",
       "      <td>3.87682</td>\n",
       "      <td>10.9312</td>\n",
       "      <td>16.6914</td>\n",
       "      <td>22.2601</td>\n",
       "      <td>26.4496</td>\n",
       "      <td>23.9896</td>\n",
       "      <td>18.7246</td>\n",
       "      <td>15.6735</td>\n",
       "      <td>9.0895</td>\n",
       "      <td>4.73814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2.49036</td>\n",
       "      <td>4.01522</td>\n",
       "      <td>13.5296</td>\n",
       "      <td>13.7233</td>\n",
       "      <td>19.8998</td>\n",
       "      <td>24.0971</td>\n",
       "      <td>27.6678</td>\n",
       "      <td>23.6544</td>\n",
       "      <td>19.7564</td>\n",
       "      <td>12.5577</td>\n",
       "      <td>8.40235</td>\n",
       "      <td>2.15723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>-0.870058</td>\n",
       "      <td>-0.306576</td>\n",
       "      <td>1.55386</td>\n",
       "      <td>7.75404</td>\n",
       "      <td>16.7774</td>\n",
       "      <td>21.9309</td>\n",
       "      <td>24.3047</td>\n",
       "      <td>23.9181</td>\n",
       "      <td>21.8939</td>\n",
       "      <td>12.6892</td>\n",
       "      <td>6.05913</td>\n",
       "      <td>-3.67604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>-5.5111</td>\n",
       "      <td>-5.5827</td>\n",
       "      <td>2.46772</td>\n",
       "      <td>10.2512</td>\n",
       "      <td>17.231</td>\n",
       "      <td>22.7577</td>\n",
       "      <td>22.8453</td>\n",
       "      <td>23.7806</td>\n",
       "      <td>19.9618</td>\n",
       "      <td>13.6042</td>\n",
       "      <td>2.57197</td>\n",
       "      <td>3.39578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Jan        Feb       Mar      Apr      May      Jun  \\\n",
       "Indiana   0     -1.9534   -3.97301   2.59058  10.0881  17.3264  20.9624   \n",
       "          1    -4.00883   0.874847   4.86918  13.9457  15.2778  22.6881   \n",
       "          2    -6.41284   -2.59879    4.7838  8.75127  19.9566  19.5645   \n",
       "          3   -0.465296    1.96229    6.1551  9.13795  15.1196  22.1406   \n",
       "          4    -5.10546    3.22889  0.673888  10.3269  15.3663   23.748   \n",
       "          5    -5.70493   -2.89812   7.23863  14.3429  18.4319  20.7701   \n",
       "          6   -0.912048  -0.499908   6.81455  13.4268  17.8881  22.5552   \n",
       "          7    -1.76537    1.84541   7.20209  11.7326  20.2266  23.5955   \n",
       "          8    -3.01212   -1.95634    5.6462  11.3608  18.0358  23.2077   \n",
       "          9     2.67857   -2.49459   6.04169  10.5955  15.5242  21.7484   \n",
       "          10    3.44255    3.09323   7.68794  11.0972  15.7078  22.1507   \n",
       "          11   -2.28052    2.21587   7.22717  13.4812  20.8521  23.6615   \n",
       "          12   -0.16997    3.40981   5.74444  11.0505  16.1849  19.7391   \n",
       "          13 -0.0173301   -2.03527   3.39191  10.4553  17.5315  21.5171   \n",
       "          14   -5.85973   -1.65559   4.84746  12.7552  15.6909  23.4092   \n",
       "          15   -1.37723  -0.592061   6.55102  11.1221  16.6471  22.5709   \n",
       "          16   -2.70339  -0.100481   2.02085  10.0078  16.7241  22.2153   \n",
       "          17   -3.63169    1.65543    5.8699  8.89772  13.6574  20.9806   \n",
       "          18    2.05681     4.4293   5.56694  11.4184  19.4995  21.8538   \n",
       "          19    -2.0289    2.81877   2.99723  12.1992  17.8924  22.6487   \n",
       "          20   -2.26375    3.58884   8.04499  10.7229  18.4269   21.601   \n",
       "          21   -2.02941    1.16934   3.36088   14.056   17.822  21.0009   \n",
       "          22    2.27671    2.44643   4.32397  12.4996  15.0693  22.9426   \n",
       "          23   -5.02928   -2.90573   6.12223  11.9321  15.7047  19.9331   \n",
       "          24   -3.58426  0.0155546   7.17004  12.4253  19.1386  21.2121   \n",
       "          25   -1.42508    1.99761   3.30135  12.6427  15.3971  23.8479   \n",
       "          26    4.19659   0.528402   5.20404  13.5371  16.4012  21.2803   \n",
       "          27   0.254286   -5.87735   8.93536  10.4911   19.447  22.9623   \n",
       "          28   -1.34529   -1.50774   4.07293  12.0195  15.6034  22.6876   \n",
       "          29   -5.16117   0.970609   7.58417  11.6994  17.0706  22.4303   \n",
       "...                 ...        ...       ...      ...      ...      ...   \n",
       "Minnesota 5    -4.13192   -2.09789   7.74112  14.6075  19.3596  20.1646   \n",
       "          6   -0.568592   -2.66535   6.98464  13.3611  17.6148  23.0568   \n",
       "          7     1.73212    6.88246   8.64409  15.2516  20.1553  23.9406   \n",
       "          8    -5.01356   -3.92133   6.57787  11.9771  20.6204  25.6252   \n",
       "          9     1.99412   -5.44154   2.61215  11.0271   16.805  21.4253   \n",
       "          10    4.45338    1.93223   7.63872  11.9294  15.6109  22.5983   \n",
       "          11   -3.92862    3.11584   6.90338  13.1766  18.9713  24.2099   \n",
       "          12    2.02583    4.47921   6.46294  9.77775  18.1172  20.4962   \n",
       "          13   -3.43702   -2.46786   2.72279  9.73175  16.2897  19.9857   \n",
       "          14   -8.40981   -5.12762   6.79489  11.7895  18.4134  23.1323   \n",
       "          15   -1.03702  0.0433769   6.19355  8.84094  15.7857  23.2486   \n",
       "          16   -5.47606  -0.955331   1.75054  9.21849  15.3219  22.4267   \n",
       "          17   -4.81259  -0.626674   3.82706  8.84177   14.111  22.5276   \n",
       "          18 -0.0648863    6.23277   4.26645  12.6663  19.7089  20.2156   \n",
       "          19   -3.52869    4.28585   6.01729  12.0433  17.6562  21.7146   \n",
       "          20    -1.5933    4.23661   9.78457  11.5349  18.2122  20.5942   \n",
       "          21   0.115828   -4.55684   2.19694  12.6839  17.6015  22.0952   \n",
       "          22    2.96631    4.46447    1.3322  11.4276  15.0573  23.5834   \n",
       "          23   -1.99306   -1.99945   5.05857  11.9879  16.0229  21.3427   \n",
       "          24   -3.78915  0.0625075     7.669  12.9472  16.5216  20.3546   \n",
       "          25   -2.39663    3.38214   5.23942  14.1619  15.9437  24.1862   \n",
       "          26    5.67315  0.0749459    5.7632  14.9493  18.3958  23.0262   \n",
       "          27  -0.461534   -4.37127   8.44598  11.1934  19.9066  23.7381   \n",
       "          28   -3.00906    -2.9919    3.2335  10.4267   15.914  21.8423   \n",
       "          29   -5.30291   0.752841   5.70794  11.5109  17.5029  21.3959   \n",
       "          30   -3.89497   -1.74977   9.13581  15.0599  17.4754  22.4536   \n",
       "          31   -4.42845  -0.934544   3.87682  10.9312  16.6914  22.2601   \n",
       "          32    2.49036    4.01522   13.5296  13.7233  19.8998  24.0971   \n",
       "          33  -0.870058  -0.306576   1.55386  7.75404  16.7774  21.9309   \n",
       "          34    -5.5111    -5.5827   2.46772  10.2512   17.231  22.7577   \n",
       "\n",
       "                  Jul      Aug      Sep      Oct      Nov       Dec  \n",
       "Indiana   0   25.5664  24.8831  20.2695  11.3892  5.57066  0.147049  \n",
       "          1   23.9615  22.5089  18.4416  11.8638  7.13905  -1.29025  \n",
       "          2   24.1836  21.6212  18.3241  13.4818  7.37009   4.99193  \n",
       "          3   26.0552  25.6531  19.8001  13.6845  7.84777  -5.46126  \n",
       "          4   22.7625  23.5065  18.6671   15.534  6.18488    3.9957  \n",
       "          5   23.7437  22.1458  19.6811  14.5876   8.3624  -4.03283  \n",
       "          6   25.2507  21.3784  21.0521  13.6069  4.95992  0.745674  \n",
       "          7   24.9677  23.7684  19.7501   10.013  8.71276   2.17935  \n",
       "          8   25.5936  25.3732  19.5151  9.29096  6.93201  0.563471  \n",
       "          9   24.2272  22.5472  18.1161  13.3909  5.81759   -6.4596  \n",
       "          10  23.4637   22.262   19.636  12.7797  8.92063   1.76187  \n",
       "          11  25.0505  23.7012  19.1759  14.5671  4.56245   2.32591  \n",
       "          12  23.0626  20.4427  18.2962  12.0058  6.20206  0.936968  \n",
       "          13  25.1457  23.8498  17.2116  11.6949   5.8006  0.151508  \n",
       "          14   23.864  21.7388  18.9319   13.636  9.38717   3.63721  \n",
       "          15  24.8979  26.0461  18.2937  14.0004  3.45992  -1.96513  \n",
       "          16  22.6436  23.1465  18.5797  13.6224  2.99285   1.15791  \n",
       "          17  23.5137   21.363  18.5364  12.6404  3.90617  0.795719  \n",
       "          18  23.5531  23.4021  21.3572  13.7404  7.75199   2.84628  \n",
       "          19  26.0824  22.2634   19.215  12.9724  9.49661   1.37364  \n",
       "          20   22.325  22.7456  18.5223  14.4603  4.87473  -6.14589  \n",
       "          21  23.6794   23.549  17.8994  12.9223  10.0884   3.22454  \n",
       "          22   25.562  24.2886  21.2787  11.7329  4.95447  0.417109  \n",
       "          23  23.1963  23.4877  17.8939  12.3158  8.42407   1.40652  \n",
       "          24  22.8543  20.7388   19.936  13.1411  8.02159  0.156068  \n",
       "          25  24.6321  24.3111  21.2757  13.5586  7.59132  -2.30366  \n",
       "          26  24.8531  23.6764  17.5355  11.0109  7.45237   3.79294  \n",
       "          27  23.2312  25.4311  21.4844  16.5682  6.42833  0.932309  \n",
       "          28   23.768  22.7712  20.6028  13.1122  5.43167 -0.603224  \n",
       "          29  21.4693  22.0726  19.2709   10.904  8.79125 -0.142664  \n",
       "...               ...      ...      ...      ...      ...       ...  \n",
       "Minnesota 5   23.4548  21.0549  18.0717  12.0754  1.33048  -6.24669  \n",
       "          6   24.6877    21.12  18.3588  13.0724  3.81428   3.22258  \n",
       "          7   25.7057  22.3036  19.3415  10.8753  9.33057   3.09372  \n",
       "          8   26.0471  25.4472    19.89  10.3594   6.6319   1.04807  \n",
       "          9   25.3328  23.4742  18.4592  13.8511  4.15497   -4.4039  \n",
       "          10  23.1922  23.1201  20.6556  12.6606   9.0294  -1.17773  \n",
       "          11  23.7534  23.4575   18.162  12.2784  1.67328   1.59081  \n",
       "          12   20.129  20.3516  18.3432  12.3589  4.81849  0.538691  \n",
       "          13  22.7532  22.9996  15.7256  12.0615  4.78389   1.12847  \n",
       "          14   22.575  21.8551  20.6646  14.7023  8.90711   2.68409  \n",
       "          15  24.2362  25.6595   18.191  12.5708  2.95699  0.171366  \n",
       "          16   22.399  22.8337  18.6896  13.6473  1.86508  -3.12346  \n",
       "          17  23.0689  21.6627  19.6917  13.8807  3.67768   3.90135  \n",
       "          18  23.3634  23.4533  21.6834  14.0157  8.25804   3.28583  \n",
       "          19  25.3622    22.61  18.1921  13.1873  11.3955   3.40229  \n",
       "          20  23.2149  23.6865  19.0189  14.9355  4.32295  -6.65135  \n",
       "          21  25.0501  24.1284  18.4943  12.6305  13.2101   4.03991  \n",
       "          22  25.7227  22.7024  20.6883  9.02409  6.13683   3.87269  \n",
       "          23  23.9022  24.7499  18.8574  14.0685  5.58588   3.42526  \n",
       "          24  22.7068  20.3391   21.614  13.7204  9.09832   2.37138  \n",
       "          25  25.2585  23.1068  22.0523   14.496  8.43334 -0.212369  \n",
       "          26   26.575   23.609  17.8316  11.4477  8.48189   5.58581  \n",
       "          27  24.8627  23.9209  20.6349  15.8876  7.11577  -1.38013  \n",
       "          28  24.4014  23.2185  19.7553  13.6043   7.2154  -3.06519  \n",
       "          29  21.4957  21.8151  20.6255  9.79597   11.539  -1.48554  \n",
       "          30  24.9831  25.4966  18.3786  15.0904  7.36189  -1.73295  \n",
       "          31  26.4496  23.9896  18.7246  15.6735   9.0895   4.73814  \n",
       "          32  27.6678  23.6544  19.7564  12.5577  8.40235   2.15723  \n",
       "          33  24.3047  23.9181  21.8939  12.6892  6.05913  -3.67604  \n",
       "          34  22.8453  23.7806  19.9618  13.6042  2.57197   3.39578  \n",
       "\n",
       "[210 rows x 12 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets read in our Stan model and compile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_d04a20d59a41a46fcc332e2654a5244e NOW.\n"
     ]
    }
   ],
   "source": [
    "# Compile model\n",
    "sm=pystan.StanModel(file='./stan/BMSM-GaussGR.stan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a first attempt, lets use the yield anomalies data and add back a constant 6. We will deal with fitting the Yield trend due to efficiency later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in Yields\n",
    "yields=pd.read_table('./Crop_data_files/Maize_median_yield_anoms.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Region</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
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       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Maize_Spring_USA_Nebraska</td>\n",
       "      <td>-0.06276</td>\n",
       "      <td>-0.15690</td>\n",
       "      <td>0.40794</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.40794</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.37656</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.50208</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.00416</td>\n",
       "      <td>0.18828</td>\n",
       "      <td>-0.37656</td>\n",
       "      <td>-1.506240</td>\n",
       "      <td>0.28242</td>\n",
       "      <td>0.62760</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Maize_Spring_USA_Iowa</td>\n",
       "      <td>-0.75312</td>\n",
       "      <td>-0.04707</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.15690</td>\n",
       "      <td>-0.15690</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.03138</td>\n",
       "      <td>-0.03138</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12552</td>\n",
       "      <td>-0.31380</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.62760</td>\n",
       "      <td>-0.37656</td>\n",
       "      <td>0.43932</td>\n",
       "      <td>-1.757280</td>\n",
       "      <td>-0.25104</td>\n",
       "      <td>0.87864</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Maize_Spring_USA_Illinois</td>\n",
       "      <td>-0.69036</td>\n",
       "      <td>-0.18828</td>\n",
       "      <td>0.31380</td>\n",
       "      <td>0.12552</td>\n",
       "      <td>-0.31380</td>\n",
       "      <td>0.43932</td>\n",
       "      <td>-0.50208</td>\n",
       "      <td>0.62760</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.31796</td>\n",
       "      <td>-0.75312</td>\n",
       "      <td>0.06276</td>\n",
       "      <td>0.31380</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-3.263520</td>\n",
       "      <td>0.65898</td>\n",
       "      <td>1.38072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Maize_Spring_USA_Indiana</td>\n",
       "      <td>-0.37656</td>\n",
       "      <td>-0.25104</td>\n",
       "      <td>0.50208</td>\n",
       "      <td>0.31380</td>\n",
       "      <td>-0.62760</td>\n",
       "      <td>0.50208</td>\n",
       "      <td>-0.37656</td>\n",
       "      <td>-0.12552</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.18828</td>\n",
       "      <td>0.18828</td>\n",
       "      <td>0.87864</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.69036</td>\n",
       "      <td>-3.640080</td>\n",
       "      <td>0.97278</td>\n",
       "      <td>0.69036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Maize_Spring_USA_Minnesota</td>\n",
       "      <td>-0.34518</td>\n",
       "      <td>0.15690</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.50208</td>\n",
       "      <td>-0.18828</td>\n",
       "      <td>-0.50208</td>\n",
       "      <td>0.25104</td>\n",
       "      <td>-0.25104</td>\n",
       "      <td>...</td>\n",
       "      <td>0.94140</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.00416</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.75312</td>\n",
       "      <td>0.75312</td>\n",
       "      <td>-0.62760</td>\n",
       "      <td>0.376560</td>\n",
       "      <td>0.09414</td>\n",
       "      <td>-0.18828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Maize_Spring_USA_Ohio</td>\n",
       "      <td>-0.37656</td>\n",
       "      <td>-0.06276</td>\n",
       "      <td>0.12552</td>\n",
       "      <td>0.25104</td>\n",
       "      <td>-0.69036</td>\n",
       "      <td>-0.25104</td>\n",
       "      <td>0.31380</td>\n",
       "      <td>-0.31380</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.81588</td>\n",
       "      <td>0.56484</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.75728</td>\n",
       "      <td>1.12968</td>\n",
       "      <td>0.43932</td>\n",
       "      <td>-0.43932</td>\n",
       "      <td>-2.510400</td>\n",
       "      <td>0.65898</td>\n",
       "      <td>0.12552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Maize_Spring_China_Jilin</td>\n",
       "      <td>-0.23500</td>\n",
       "      <td>0.11935</td>\n",
       "      <td>-0.03450</td>\n",
       "      <td>-0.04300</td>\n",
       "      <td>0.05020</td>\n",
       "      <td>-0.05020</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.13240</td>\n",
       "      <td>...</td>\n",
       "      <td>0.18101</td>\n",
       "      <td>0.58294</td>\n",
       "      <td>-0.18101</td>\n",
       "      <td>0.54988</td>\n",
       "      <td>-0.45689</td>\n",
       "      <td>-0.54988</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.194480</td>\n",
       "      <td>0.08097</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Maize_Spring_China_Liaoning</td>\n",
       "      <td>-0.49600</td>\n",
       "      <td>-0.05390</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.17770</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01228</td>\n",
       "      <td>-0.19889</td>\n",
       "      <td>-0.26599</td>\n",
       "      <td>0.46488</td>\n",
       "      <td>-0.93963</td>\n",
       "      <td>-0.81114</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.039145</td>\n",
       "      <td>0.51029</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Maize_Spring_China_Heilongjiang</td>\n",
       "      <td>1.41700</td>\n",
       "      <td>-0.28565</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.07270</td>\n",
       "      <td>-0.09070</td>\n",
       "      <td>-0.00440</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.35160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.38670</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.98415</td>\n",
       "      <td>0.28129</td>\n",
       "      <td>-0.28129</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.26910</td>\n",
       "      <td>-0.134550</td>\n",
       "      <td>0.07151</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Maize_Summer_China_Hebei</td>\n",
       "      <td>-0.10190</td>\n",
       "      <td>0.05095</td>\n",
       "      <td>0.01090</td>\n",
       "      <td>-0.17080</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.15120</td>\n",
       "      <td>0.07440</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00079</td>\n",
       "      <td>0.10930</td>\n",
       "      <td>-0.04865</td>\n",
       "      <td>-0.06144</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.004350</td>\n",
       "      <td>0.07125</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Maize_Summer_China_Shanxi</td>\n",
       "      <td>-0.07660</td>\n",
       "      <td>-0.16455</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.09990</td>\n",
       "      <td>0.30560</td>\n",
       "      <td>-0.38730</td>\n",
       "      <td>0.02570</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.00498</td>\n",
       "      <td>0.07901</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.44465</td>\n",
       "      <td>-0.00768</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.113000</td>\n",
       "      <td>0.30546</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Maize_Summer_China_Shandong</td>\n",
       "      <td>-0.09110</td>\n",
       "      <td>-0.09725</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.21410</td>\n",
       "      <td>0.13420</td>\n",
       "      <td>...</td>\n",
       "      <td>0.20345</td>\n",
       "      <td>-0.20345</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.02903</td>\n",
       "      <td>0.01979</td>\n",
       "      <td>-0.04882</td>\n",
       "      <td>0.01816</td>\n",
       "      <td>0.037410</td>\n",
       "      <td>-0.17756</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Maize_Summer_China_Henan</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.27150</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>-0.14270</td>\n",
       "      <td>0.26840</td>\n",
       "      <td>-0.28030</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.06260</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.05075</td>\n",
       "      <td>0.08355</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.08871</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.029670</td>\n",
       "      <td>-0.00006</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Maize_Summer_China_Shaanxi</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.21405</td>\n",
       "      <td>0.01430</td>\n",
       "      <td>-0.01430</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.02660</td>\n",
       "      <td>0.17180</td>\n",
       "      <td>-0.02660</td>\n",
       "      <td>...</td>\n",
       "      <td>0.15269</td>\n",
       "      <td>-0.14016</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>-0.10249</td>\n",
       "      <td>0.01875</td>\n",
       "      <td>-0.01875</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.090210</td>\n",
       "      <td>0.17491</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Maize_Argentina_All</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.10950</td>\n",
       "      <td>-0.11910</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.12250</td>\n",
       "      <td>0.20780</td>\n",
       "      <td>0.52420</td>\n",
       "      <td>...</td>\n",
       "      <td>0.88200</td>\n",
       "      <td>-0.54950</td>\n",
       "      <td>1.21300</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.87650</td>\n",
       "      <td>1.45370</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.869100</td>\n",
       "      <td>0.12670</td>\n",
       "      <td>0.23720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Maize_Bulgaria_All</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.14550</td>\n",
       "      <td>-0.11705</td>\n",
       "      <td>0.23410</td>\n",
       "      <td>0.50200</td>\n",
       "      <td>-0.89490</td>\n",
       "      <td>0.66950</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.77570</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-3.07330</td>\n",
       "      <td>-0.37800</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.54450</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-2.571600</td>\n",
       "      <td>0.43150</td>\n",
       "      <td>1.28800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Maize_Canada_All</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.09495</td>\n",
       "      <td>-0.66130</td>\n",
       "      <td>0.05750</td>\n",
       "      <td>-0.05750</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.14950</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12980</td>\n",
       "      <td>-0.03710</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.55150</td>\n",
       "      <td>-0.52230</td>\n",
       "      <td>0.67620</td>\n",
       "      <td>-0.31630</td>\n",
       "      <td>-0.153800</td>\n",
       "      <td>0.30000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Maize_France_All</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.29295</td>\n",
       "      <td>1.53020</td>\n",
       "      <td>-1.62540</td>\n",
       "      <td>-0.07930</td>\n",
       "      <td>0.41570</td>\n",
       "      <td>-0.41570</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.46630</td>\n",
       "      <td>-0.27040</td>\n",
       "      <td>0.57180</td>\n",
       "      <td>0.19850</td>\n",
       "      <td>-0.19850</td>\n",
       "      <td>-0.26960</td>\n",
       "      <td>0.88780</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.37010</td>\n",
       "      <td>0.96460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>Maize_Germany_All</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.33400</td>\n",
       "      <td>-0.06860</td>\n",
       "      <td>0.08350</td>\n",
       "      <td>-0.08350</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08890</td>\n",
       "      <td>-1.18320</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.36240</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.02450</td>\n",
       "      <td>0.87430</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.72465</td>\n",
       "      <td>0.20360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Maize_Netherlands_All</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.14400</td>\n",
       "      <td>0.26190</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.16670</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.20850</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.53190</td>\n",
       "      <td>-0.35100</td>\n",
       "      <td>1.04720</td>\n",
       "      <td>-0.56820</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.006500</td>\n",
       "      <td>-0.39415</td>\n",
       "      <td>1.39990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Maize_Poland_All</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.61400</td>\n",
       "      <td>-0.13665</td>\n",
       "      <td>-0.03790</td>\n",
       "      <td>0.27330</td>\n",
       "      <td>-0.29240</td>\n",
       "      <td>-0.09240</td>\n",
       "      <td>0.07340</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03940</td>\n",
       "      <td>-1.57200</td>\n",
       "      <td>0.75940</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.48060</td>\n",
       "      <td>0.60090</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>-0.30645</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Maize_Romania_All</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.14055</td>\n",
       "      <td>0.00440</td>\n",
       "      <td>0.23390</td>\n",
       "      <td>-0.23830</td>\n",
       "      <td>0.31510</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.40620</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.71390</td>\n",
       "      <td>-0.18980</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.90070</td>\n",
       "      <td>0.21160</td>\n",
       "      <td>-2.307100</td>\n",
       "      <td>-0.01755</td>\n",
       "      <td>0.29290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Maize_Ukraine_All</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>...</td>\n",
       "      <td>0.46270</td>\n",
       "      <td>-0.16490</td>\n",
       "      <td>-0.41840</td>\n",
       "      <td>0.17640</td>\n",
       "      <td>0.32840</td>\n",
       "      <td>-0.27990</td>\n",
       "      <td>1.42510</td>\n",
       "      <td>-1.364300</td>\n",
       "      <td>0.12650</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>Maize_UK_All</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>Maize_Brazil_All</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00310</td>\n",
       "      <td>-0.00220</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.14600</td>\n",
       "      <td>0.07170</td>\n",
       "      <td>-0.03030</td>\n",
       "      <td>0.04590</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.34200</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.07070</td>\n",
       "      <td>0.29480</td>\n",
       "      <td>-0.36550</td>\n",
       "      <td>0.15600</td>\n",
       "      <td>-0.15600</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.16270</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>Maize_USA_All</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.14370</td>\n",
       "      <td>0.05615</td>\n",
       "      <td>0.19750</td>\n",
       "      <td>-0.30980</td>\n",
       "      <td>0.06000</td>\n",
       "      <td>-0.06000</td>\n",
       "      <td>0.03740</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.07240</td>\n",
       "      <td>-0.10080</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.06730</td>\n",
       "      <td>0.74530</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.35600</td>\n",
       "      <td>-1.848400</td>\n",
       "      <td>0.34465</td>\n",
       "      <td>0.80700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                           Region     1960     1961     1962  \\\n",
       "0            0        Maize_Spring_USA_Nebraska -0.06276 -0.15690  0.40794   \n",
       "1            1            Maize_Spring_USA_Iowa -0.75312 -0.04707  0.00000   \n",
       "2            2        Maize_Spring_USA_Illinois -0.69036 -0.18828  0.31380   \n",
       "3            3         Maize_Spring_USA_Indiana -0.37656 -0.25104  0.50208   \n",
       "4            4       Maize_Spring_USA_Minnesota -0.34518  0.15690  0.00000   \n",
       "5            5            Maize_Spring_USA_Ohio -0.37656 -0.06276  0.12552   \n",
       "6            6         Maize_Spring_China_Jilin -0.23500  0.11935 -0.03450   \n",
       "7            7      Maize_Spring_China_Liaoning -0.49600 -0.05390  0.00000   \n",
       "8            8  Maize_Spring_China_Heilongjiang  1.41700 -0.28565  0.00000   \n",
       "9            9         Maize_Summer_China_Hebei -0.10190  0.05095  0.01090   \n",
       "10          10        Maize_Summer_China_Shanxi -0.07660 -0.16455  0.00000   \n",
       "11          11      Maize_Summer_China_Shandong -0.09110 -0.09725  0.00000   \n",
       "12          12         Maize_Summer_China_Henan  0.00000 -0.27150  0.12380   \n",
       "13          13       Maize_Summer_China_Shaanxi  0.00000 -0.21405  0.01430   \n",
       "14          14              Maize_Argentina_All      NaN  0.00000  0.10950   \n",
       "15          15               Maize_Bulgaria_All      NaN -0.14550 -0.11705   \n",
       "16          16                 Maize_Canada_All      NaN  0.00000  0.09495   \n",
       "17          17                 Maize_France_All      NaN  0.00000 -0.29295   \n",
       "18          18                Maize_Germany_All      NaN -0.33400 -0.06860   \n",
       "19          19            Maize_Netherlands_All      NaN  0.00000 -0.14400   \n",
       "20          20                 Maize_Poland_All      NaN  0.61400 -0.13665   \n",
       "21          21                Maize_Romania_All      NaN  0.00000 -0.14055   \n",
       "22          22                Maize_Ukraine_All      NaN      NaN      NaN   \n",
       "23          23                     Maize_UK_All      NaN      NaN      NaN   \n",
       "24          24                 Maize_Brazil_All      NaN  0.00310 -0.00220   \n",
       "25          25                    Maize_USA_All      NaN -0.14370  0.05615   \n",
       "\n",
       "       1963     1964     1965     1966     1967   ...        2005     2006  \\\n",
       "0   0.00000 -0.40794  0.00000  0.37656  0.00000   ...     0.00000 -0.50208   \n",
       "1   0.15690 -0.15690  0.00000  0.03138 -0.03138   ...     0.12552 -0.31380   \n",
       "2   0.12552 -0.31380  0.43932 -0.50208  0.62760   ...    -1.31796 -0.75312   \n",
       "3   0.31380 -0.62760  0.50208 -0.37656 -0.12552   ...     0.00000  0.00000   \n",
       "4   0.50208 -0.18828 -0.50208  0.25104 -0.25104   ...     0.94140  0.00000   \n",
       "5   0.25104 -0.69036 -0.25104  0.31380 -0.31380   ...    -0.81588  0.56484   \n",
       "6  -0.04300  0.05020 -0.05020  0.00000  0.13240   ...     0.18101  0.58294   \n",
       "7   0.00000  0.00000  0.00000  0.00000  0.17770   ...     0.01228 -0.19889   \n",
       "8   0.07270 -0.09070 -0.00440  0.00000  0.35160   ...     0.38670  0.00000   \n",
       "9  -0.17080  0.00000  0.00000  0.15120  0.07440   ...     0.00000  0.00000   \n",
       "10  0.09990  0.30560 -0.38730  0.02570  0.00000   ...    -0.00498  0.07901   \n",
       "11  0.00000  0.00000  0.00000  0.21410  0.13420   ...     0.20345 -0.20345   \n",
       "12 -0.14270  0.26840 -0.28030  0.00000  0.06260   ...     0.00000  0.00000   \n",
       "13 -0.01430  0.00000  0.02660  0.17180 -0.02660   ...     0.15269 -0.14016   \n",
       "14 -0.11910  0.00000 -0.12250  0.20780  0.52420   ...     0.88200 -0.54950   \n",
       "15  0.23410  0.50200 -0.89490  0.66950  0.00000   ...     0.77570  0.00000   \n",
       "16 -0.66130  0.05750 -0.05750  0.00000  0.14950   ...     0.12980 -0.03710   \n",
       "17  1.53020 -1.62540 -0.07930  0.41570 -0.41570   ...    -0.46630 -0.27040   \n",
       "18  0.08350 -0.08350  0.00000  0.00000  0.00000   ...     0.08890 -1.18320   \n",
       "19  0.26190  0.00000  0.16670  0.00000 -0.20850   ...     0.00000  0.00000   \n",
       "20 -0.03790  0.27330 -0.29240 -0.09240  0.07340   ...     0.03940 -1.57200   \n",
       "21  0.00440  0.23390 -0.23830  0.31510  0.00000   ...     0.40620  0.00000   \n",
       "22      NaN      NaN      NaN      NaN      NaN   ...     0.46270 -0.16490   \n",
       "23      NaN      NaN      NaN      NaN      NaN   ...         NaN      NaN   \n",
       "24  0.00000 -0.14600  0.07170 -0.03030  0.04590   ...    -0.34200  0.00000   \n",
       "25  0.19750 -0.30980  0.06000 -0.06000  0.03740   ...    -0.07240 -0.10080   \n",
       "\n",
       "       2007     2008     2009     2010     2011      2012     2013     2014  \n",
       "0   0.00000  0.00000  1.00416  0.18828 -0.37656 -1.506240  0.28242  0.62760  \n",
       "1   0.00000  0.00000  0.62760 -0.37656  0.43932 -1.757280 -0.25104  0.87864  \n",
       "2   0.06276  0.31380  0.00000  0.00000  0.00000 -3.263520  0.65898  1.38072  \n",
       "3  -0.18828  0.18828  0.87864  0.00000 -0.69036 -3.640080  0.97278  0.69036  \n",
       "4  -1.00416  0.00000  0.75312  0.75312 -0.62760  0.376560  0.09414 -0.18828  \n",
       "5   0.00000 -1.75728  1.12968  0.43932 -0.43932 -2.510400  0.65898  0.12552  \n",
       "6  -0.18101  0.54988 -0.45689 -0.54988  0.00000  0.194480  0.08097      NaN  \n",
       "7  -0.26599  0.46488 -0.93963 -0.81114  0.00000  0.039145  0.51029      NaN  \n",
       "8  -0.98415  0.28129 -0.28129  0.00000  0.26910 -0.134550  0.07151      NaN  \n",
       "9   0.00079  0.10930 -0.04865 -0.06144  0.00000  0.004350  0.07125      NaN  \n",
       "10  0.00000  0.00000 -0.44465 -0.00768  0.00000  0.113000  0.30546      NaN  \n",
       "11  0.00000  0.02903  0.01979 -0.04882  0.01816  0.037410 -0.17756      NaN  \n",
       "12  0.05075  0.08355  0.00000 -0.08871  0.00000  0.029670 -0.00006      NaN  \n",
       "13  0.08996 -0.10249  0.01875 -0.01875  0.00000  0.090210  0.17491      NaN  \n",
       "14  1.21300  0.00000 -0.87650  1.45370  0.00000 -0.869100  0.12670  0.23720  \n",
       "15 -3.07330 -0.37800  0.00000  1.54450  0.00000 -2.571600  0.43150  1.28800  \n",
       "16  0.00000  0.55150 -0.52230  0.67620 -0.31630 -0.153800  0.30000  0.00000  \n",
       "17  0.57180  0.19850 -0.19850 -0.26960  0.88780  0.000000 -1.37010  0.96460  \n",
       "18  0.00000  0.36240  0.00000 -1.02450  0.87430  0.000000 -1.72465  0.20360  \n",
       "19  0.53190 -0.35100  1.04720 -0.56820  0.00000  0.006500 -0.39415  1.39990  \n",
       "20  0.75940  0.00000  0.00000 -0.48060  0.60090  0.760000 -0.30645  0.00000  \n",
       "21 -1.71390 -0.18980  0.00000  0.90070  0.21160 -2.307100 -0.01755  0.29290  \n",
       "22 -0.41840  0.17640  0.32840 -0.27990  1.42510 -1.364300  0.12650  0.00000  \n",
       "23      NaN      NaN      NaN      NaN      NaN       NaN      NaN      NaN  \n",
       "24  0.07070  0.29480 -0.36550  0.15600 -0.15600  0.000000  0.16270  0.00000  \n",
       "25  0.00000  0.06730  0.74530  0.00000 -0.35600 -1.848400  0.34465  0.80700  \n",
       "\n",
       "[26 rows x 57 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_years=np.array(yields[yields['Region']=='Maize_Spring_USA_Indiana'].iloc[0,22:]).size\n",
    "data={\n",
    "    'n_regions':len(states),\n",
    "    'n_years':n_years,\n",
    "    'd_temp':np.array(temp_states.iloc[:,3:9]).reshape(\n",
    "        len(states),np.int(np.array(temp_states.iloc[:,3:9]).shape[0]/len(states)),6).astype(float),\n",
    "    'd_yields':np.array(yields[yields[\"Region\"].isin(\n",
    "        ['Maize_Spring_USA_'+s for s in states])].iloc[:,22:]).astype(float)+9.75,\n",
    "    'n_gf':40,\n",
    "    'temp':np.arange(0,40,1)\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets fit the model and look at summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit=sm.sampling(data=data,chains=4,iter=1000,verbose=True,seed=1308)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inference for Stan model: anon_model_d04a20d59a41a46fcc332e2654a5244e.\n",
       "4 chains, each with iter=1000; warmup=500; thin=1; \n",
       "post-warmup draws per chain=500, total post-warmup draws=2000.\n",
       "\n",
       "                    mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
       "mu                 18.98    0.02   0.63  17.69  18.57   19.0  19.39   20.2    871    1.0\n",
       "sigma               6.22    0.02   0.48   5.29   5.89    6.2   6.53   7.18    573   1.01\n",
       "norm                2.01  2.3e-3   0.05   1.91   1.98   2.01   2.04   2.12    519   1.01\n",
       "fdy[0]              0.02  8.4e-4   0.02 2.0e-3 9.7e-3   0.02   0.03   0.08    742   1.01\n",
       "fdy[1]              0.04  1.2e-3   0.03 4.0e-3   0.02   0.03   0.05   0.12    739   1.01\n",
       "fdy[2]              0.06  1.6e-3   0.04 7.7e-3   0.03   0.05   0.08   0.16    736   1.01\n",
       "fdy[3]              0.08  2.1e-3   0.06   0.01   0.04   0.07   0.11   0.22    733   1.01\n",
       "fdy[4]              0.12  2.7e-3   0.07   0.03   0.07   0.11   0.16   0.29    731   1.01\n",
       "fdy[5]              0.17  3.3e-3   0.09   0.04   0.11   0.16   0.22   0.38    728   1.01\n",
       "fdy[6]              0.24  4.1e-3   0.11   0.07   0.16   0.23    0.3   0.49    725   1.01\n",
       "fdy[7]              0.32  4.9e-3   0.13   0.11   0.23   0.31    0.4   0.62    723   1.01\n",
       "fdy[8]              0.43  5.6e-3   0.15   0.18   0.32   0.42   0.52   0.75    722   1.01\n",
       "fdy[9]              0.56  6.3e-3   0.17   0.26   0.44   0.55   0.66   0.91    723   1.01\n",
       "fdy[10]             0.71  6.8e-3   0.18   0.38   0.58    0.7   0.83   1.07    726   1.01\n",
       "fdy[11]             0.88  7.0e-3   0.19   0.52   0.74   0.87    1.0   1.25    731   1.01\n",
       "fdy[12]             1.06  6.9e-3   0.19    0.7   0.93   1.06   1.18   1.42    741    1.0\n",
       "fdy[13]             1.25  6.5e-3   0.18    0.9   1.13   1.26   1.37   1.58    756    1.0\n",
       "fdy[14]             1.44  5.7e-3   0.16   1.12   1.34   1.45   1.55   1.74    780    1.0\n",
       "fdy[15]             1.62  4.7e-3   0.13   1.34   1.53   1.63   1.71   1.88    816    1.0\n",
       "fdy[16]             1.78  3.6e-3    0.1   1.55   1.71   1.78   1.85   1.97    868    1.0\n",
       "fdy[17]              1.9  2.5e-3   0.08   1.73   1.85    1.9   1.95   2.05    922    1.0\n",
       "fdy[18]             1.97  2.0e-3   0.06   1.86   1.94   1.97   2.01   2.09    746    1.0\n",
       "fdy[19]              2.0  2.2e-3   0.05   1.91   1.97    2.0   2.03   2.11    553   1.01\n",
       "fdy[20]             1.98  2.5e-3   0.06   1.87   1.94   1.98   2.02    2.1    538   1.01\n",
       "fdy[21]              1.9  2.7e-3   0.07   1.78   1.86    1.9   1.95   2.05    657   1.01\n",
       "fdy[22]             1.78  2.8e-3   0.08   1.64   1.73   1.78   1.83   1.94    749    1.0\n",
       "fdy[23]             1.63  2.7e-3   0.08   1.48   1.58   1.63   1.68    1.8    858    1.0\n",
       "fdy[24]             1.45  2.6e-3   0.08   1.29   1.39   1.45    1.5   1.62    961    1.0\n",
       "fdy[25]             1.26  2.6e-3   0.08   1.09    1.2   1.25   1.31   1.42   1020    1.0\n",
       "fdy[26]             1.06  2.6e-3   0.08   0.89   1.01   1.06   1.11   1.23   1007    1.0\n",
       "fdy[27]             0.87  2.8e-3   0.08   0.71   0.82   0.87   0.92   1.04    881    1.0\n",
       "fdy[28]              0.7  2.9e-3   0.08   0.55   0.65    0.7   0.75   0.86    787    1.0\n",
       "fdy[29]             0.55  2.9e-3   0.08   0.41    0.5   0.54   0.59   0.71    717   1.01\n",
       "fdy[30]             0.42  2.7e-3   0.07   0.29   0.37   0.41   0.46   0.57    668   1.01\n",
       "fdy[31]             0.31  2.5e-3   0.06    0.2   0.27   0.31   0.35   0.45    635   1.01\n",
       "fdy[32]             0.23  2.2e-3   0.05   0.13   0.19   0.22   0.26   0.35    612   1.01\n",
       "fdy[33]             0.16  1.8e-3   0.05   0.08   0.13   0.16   0.19   0.26    597   1.01\n",
       "fdy[34]             0.11  1.5e-3   0.04   0.05   0.09   0.11   0.13   0.19    586   1.01\n",
       "fdy[35]             0.07  1.2e-3   0.03   0.03   0.05   0.07   0.09   0.14    579   1.01\n",
       "fdy[36]             0.05  9.0e-4   0.02   0.02   0.03   0.05   0.06    0.1    575   1.01\n",
       "fdy[37]             0.03  6.7e-4   0.02 9.9e-3   0.02   0.03   0.04   0.07    573   1.01\n",
       "fdy[38]             0.02  4.8e-4   0.01 5.3e-3   0.01   0.02   0.03   0.05    571   1.01\n",
       "fdy[39]             0.01  3.4e-4 8.0e-3 2.7e-3 7.1e-3   0.01   0.02   0.03    571   1.01\n",
       "pred_yields[0,0]    8.94    0.02   1.02   6.96   8.22   8.94   9.64  10.94   1818    1.0\n",
       "pred_yields[1,0]    8.75    0.02    1.0   6.75   8.08   8.75   9.39  10.71   2000    1.0\n",
       "pred_yields[2,0]    9.17    0.02    1.0   7.18   8.49   9.19   9.85  11.12   1756    1.0\n",
       "pred_yields[3,0]    8.69    0.02   1.03   6.69   7.99   8.67   9.41  10.66   1983    1.0\n",
       "pred_yields[4,0]    8.98    0.02    1.0   7.03   8.31   8.99   9.66  10.97   2000    1.0\n",
       "pred_yields[5,0]    9.77    0.02    1.0    7.8   9.11   9.75  10.45  11.77   1777    1.0\n",
       "pred_yields[0,1]    9.94    0.02   0.99   7.94   9.27   9.97  10.59  11.86   2000    1.0\n",
       "pred_yields[1,1]   10.08    0.02   0.98   8.14   9.42   10.1  10.73  11.97   2000    1.0\n",
       "pred_yields[2,1]    9.98    0.02   1.03   8.04   9.28   9.98  10.68  11.99   1869    1.0\n",
       "pred_yields[3,1]   10.36    0.02   0.97   8.47   9.68  10.38  11.03  12.26   2000    1.0\n",
       "pred_yields[4,1]   10.28    0.02    1.0   8.35   9.58  10.25  10.92  12.27   2000    1.0\n",
       "pred_yields[5,1]   10.16    0.02    1.0   8.19   9.52  10.13  10.83  12.13   1946    1.0\n",
       "pred_yields[0,2]    9.77    0.02   1.01   7.83   9.11   9.75  10.42  11.75   1953    1.0\n",
       "pred_yields[1,2]    9.78    0.02   1.02   7.79    9.1   9.77  10.46  11.81   1899    1.0\n",
       "pred_yields[2,2]    9.75    0.02   1.01   7.77   9.05   9.78  10.43  11.77   1967    1.0\n",
       "pred_yields[3,2]    9.72    0.02   1.02   7.81   9.01   9.72  10.42  11.75   2000    1.0\n",
       "pred_yields[4,2]    9.69    0.02   1.02   7.74   8.99   9.67  10.37  11.79   1896    1.0\n",
       "pred_yields[5,2]    9.57    0.02   1.01   7.71   8.86   9.54  10.25  11.61   1977    1.0\n",
       "pred_yields[0,3]    8.15    0.02   1.02   6.11   7.47   8.15   8.81  10.14   1988    1.0\n",
       "pred_yields[1,3]    7.88    0.02    1.0   5.88   7.18   7.89   8.56   9.81   1847    1.0\n",
       "pred_yields[2,3]    8.42    0.02    1.0   6.53   7.75   8.43   9.11  10.35   2000    1.0\n",
       "pred_yields[3,3]    7.84    0.02    1.0   5.94   7.15   7.84    8.5   9.84   2000    1.0\n",
       "pred_yields[4,3]    7.75    0.02   1.04   5.72   7.05   7.73   8.46   9.83   1837    1.0\n",
       "pred_yields[5,3]    7.98    0.02   1.01   5.97   7.28   7.99   8.68   9.91   1795    1.0\n",
       "pred_yields[0,4]    9.12    0.02    1.0   7.09   8.49   9.13   9.81  10.98   1786    1.0\n",
       "pred_yields[1,4]    9.13    0.02   1.04   7.02   8.43   9.14   9.82  11.13   2000    1.0\n",
       "pred_yields[2,4]    9.22    0.02   0.97   7.29   8.55   9.25   9.88  11.09   1921    1.0\n",
       "pred_yields[3,4]    8.86    0.02   1.01   6.89   8.17   8.85   9.56  10.77   1737    1.0\n",
       "pred_yields[4,4]    9.06    0.02   0.98   7.17   8.39   9.06   9.69  11.03   1836    1.0\n",
       "pred_yields[5,4]    9.22    0.02   1.04   7.15   8.52   9.23   9.92  11.23   1957    1.0\n",
       "pred_yields[0,5]   10.72    0.02    1.0   8.84  10.04  10.71  11.37   12.7   1791    1.0\n",
       "pred_yields[1,5]   10.73    0.02    1.0   8.78  10.06  10.73   11.4  12.66   1905    1.0\n",
       "pred_yields[2,5]   10.75    0.02    1.0   8.75  10.06  10.75  11.44  12.73   1978    1.0\n",
       "pred_yields[3,5]    10.8    0.03   1.02   8.74  10.11  10.83  11.49  12.78   1597    1.0\n",
       "pred_yields[4,5]   10.82    0.02    1.0   8.85  10.13  10.81  11.49  12.84   2000    1.0\n",
       "pred_yields[5,5]   10.97    0.02   1.01   8.94  10.26  10.98  11.66  12.98   2000    1.0\n",
       "pred_yields[0,6]    9.96    0.02   1.03   7.89    9.3   9.97  10.66  11.97   2000    1.0\n",
       "pred_yields[1,6]   10.04    0.02   0.99   8.03   9.42  10.08  10.67  11.96   1814    1.0\n",
       "pred_yields[2,6]   10.06    0.02   1.02   8.09   9.39  10.07  10.75  12.05   2000    1.0\n",
       "pred_yields[3,6]    9.75    0.02   0.99   7.79   9.07   9.78  10.42  11.67   2000    1.0\n",
       "pred_yields[4,6]    9.99    0.02   0.98   8.14   9.32  10.02  10.62  11.98   2000    1.0\n",
       "pred_yields[5,6]   10.12    0.02   0.97   8.25   9.44  10.12  10.77  12.03   1902    1.0\n",
       "pred_yields[0,7]    9.24    0.03   1.05    7.2   8.52   9.22   9.95  11.34   1749    1.0\n",
       "pred_yields[1,7]    9.32    0.02    1.0   7.42   8.61   9.32  10.02  11.18   1879    1.0\n",
       "pred_yields[2,7]    9.35    0.02   1.01   7.38   8.68   9.34  10.02  11.36   1939    1.0\n",
       "pred_yields[3,7]    9.82    0.02    1.0    7.9   9.13   9.82  10.51   11.8   2000    1.0\n",
       "pred_yields[4,7]    9.69    0.02    1.0   7.87   8.98   9.68  10.36  11.68   1838    1.0\n",
       "pred_yields[5,7]    9.93    0.02   1.01    7.9   9.24   9.92  10.63  11.93   1911    1.0\n",
       "pred_yields[0,8]    8.78    0.02   1.02   6.85   8.08   8.82    9.5  10.77   1962    1.0\n",
       "pred_yields[1,8]    8.86    0.02   1.01   6.89   8.19   8.87   9.55  10.79   2000    1.0\n",
       "pred_yields[2,8]    8.93    0.02    1.0   6.96   8.28   8.93    9.6  10.84   2000    1.0\n",
       "pred_yields[3,8]    8.85    0.02   0.99   6.88   8.19   8.87   9.55  10.73   2000    1.0\n",
       "pred_yields[4,8]    8.56    0.02   1.03   6.54   7.85   8.56    9.3  10.57   2000    1.0\n",
       "pred_yields[5,8]    8.31    0.02   1.04   6.29   7.62   8.31   8.99  10.33   2000    1.0\n",
       "pred_yields[0,9]     9.4    0.02   0.99   7.49   8.71   9.39  10.07  11.31   1960    1.0\n",
       "pred_yields[1,9]    9.51    0.02    1.0   7.58   8.83   9.52  10.17  11.45   1720    1.0\n",
       "pred_yields[2,9]    9.34    0.02   1.01   7.33   8.67   9.31  10.05  11.27   2000    1.0\n",
       "pred_yields[3,9]   10.12    0.02   0.99   8.18   9.43  10.12  10.79  12.05   1868    1.0\n",
       "pred_yields[4,9]    9.64    0.02   1.01   7.71   8.96   9.65  10.32   11.6   1838    1.0\n",
       "pred_yields[5,9]    9.31    0.02   0.99    7.4   8.62   9.33   9.99   11.3   2000    1.0\n",
       "pred_yields[0,10]    9.7    0.02   0.96   7.83   9.05   9.68  10.36  11.71   1762    1.0\n",
       "pred_yields[1,10]   9.49    0.02   0.99   7.51   8.81   9.48  10.17  11.46   2000    1.0\n",
       "pred_yields[2,10]   9.73    0.02    1.0   7.83   9.05   9.75  10.42  11.71   2000    1.0\n",
       "pred_yields[3,10]   9.25    0.02   0.98   7.33   8.59   9.25   9.93  11.09   2000    1.0\n",
       "pred_yields[4,10]    9.3    0.02   0.99   7.35   8.62   9.27   9.98  11.26   1708    1.0\n",
       "pred_yields[5,10]    9.6    0.02   1.02   7.68   8.91   9.61  10.29  11.62   2000    1.0\n",
       "pred_yields[0,11]   9.52    0.02   1.02   7.58   8.85   9.52   10.2  11.48   2000    1.0\n",
       "pred_yields[1,11]   9.64    0.02    1.0   7.74   8.97   9.64  10.33   11.6   1819    1.0\n",
       "pred_yields[2,11]   9.49    0.03   1.03   7.49   8.82    9.5  10.17  11.44   1399    1.0\n",
       "pred_yields[3,11]   9.66    0.02   1.01   7.65   8.98   9.67  10.34  11.65   2000    1.0\n",
       "pred_yields[4,11]   9.68    0.02   1.02   7.75   8.99   9.67  10.36  11.71   2000    1.0\n",
       "pred_yields[5,11]   9.72    0.02   0.99   7.73   9.04   9.73  10.37   11.6   2000    1.0\n",
       "pred_yields[0,12]  10.21    0.02   1.03   8.22   9.54  10.17   10.9  12.26   2000    1.0\n",
       "pred_yields[1,12]  10.33    0.02   1.04   8.34   9.64  10.34  11.01  12.37   1978    1.0\n",
       "pred_yields[2,12]  10.18    0.02   1.03   8.16   9.49  10.19  10.88  12.17   2000    1.0\n",
       "pred_yields[3,12]  10.71    0.02   0.99    8.8  10.02   10.7  11.38  12.68   1856    1.0\n",
       "pred_yields[4,12]  10.41    0.02   1.02   8.47   9.73  10.39   11.1  12.45   1937    1.0\n",
       "pred_yields[5,12]  10.51    0.02   1.03   8.54    9.8   10.5   11.2  12.52   1746    1.0\n",
       "pred_yields[0,13]   9.17    0.02   1.01   7.22   8.51   9.18   9.81  11.22   1828    1.0\n",
       "pred_yields[1,13]   9.16    0.02   1.01   7.14    8.5   9.15   9.85  11.09   2000    1.0\n",
       "pred_yields[2,13]   9.17    0.02   0.96   7.28   8.54   9.18   9.81  11.07   1860    1.0\n",
       "pred_yields[3,13]   9.54    0.03   1.02   7.54   8.88   9.53   10.2  11.58   1643    1.0\n",
       "pred_yields[4,13]   9.38    0.02    1.0    7.5   8.69   9.37  10.02  11.41   1862    1.0\n",
       "pred_yields[5,13]   9.47    0.02    1.0   7.56   8.77   9.44  10.13  11.52   1921    1.0\n",
       "pred_yields[0,14]   9.77    0.02    1.0   7.83   9.12   9.76  10.45  11.71   1699    1.0\n",
       "pred_yields[1,14]   9.89    0.02    1.0   7.91   9.21    9.9  10.55  11.88   1944    1.0\n",
       "pred_yields[2,14]   9.62    0.02   1.01   7.66   8.92   9.63  10.32  11.61   2000    1.0\n",
       "pred_yields[3,14]  10.12    0.02   1.01   8.16   9.42  10.12  10.83   12.1   1804    1.0\n",
       "pred_yields[4,14]  10.11    0.02   1.01   8.14   9.43  10.09  10.78  12.11   2000    1.0\n",
       "pred_yields[5,14]  10.05    0.02    1.0   8.08   9.38  10.05  10.74  12.03   1735    1.0\n",
       "pred_yields[0,15]   8.78    0.02    1.0   6.89    8.1   8.76   9.42  10.81   1932    1.0\n",
       "pred_yields[1,15]   8.52    0.03   0.99    6.5   7.88   8.55   9.21  10.41   1511    1.0\n",
       "pred_yields[2,15]   8.65    0.02    1.0   6.76   7.97   8.64   9.34  10.54   2000    1.0\n",
       "pred_yields[3,15]   8.46    0.02   1.01   6.49   7.77   8.46   9.15   10.5   2000    1.0\n",
       "pred_yields[4,15]   8.47    0.02   1.02   6.43   7.79   8.51   9.16  10.49   1983    1.0\n",
       "pred_yields[5,15]   8.37    0.02   1.03   6.43   7.69   8.37   9.04  10.51   1848    1.0\n",
       "pred_yields[0,16]   9.62    0.02   0.98   7.73   8.98   9.65  10.31  11.52   2000    1.0\n",
       "pred_yields[1,16]   9.57    0.02    1.0   7.71   8.88   9.56  10.26  11.54   1855    1.0\n",
       "pred_yields[2,16]   9.53    0.02    1.0   7.54   8.86   9.56  10.21  11.52   1990    1.0\n",
       "pred_yields[3,16]   9.83    0.02    1.0    7.8   9.19   9.83   10.5  11.82   2000    1.0\n",
       "pred_yields[4,16]   9.56    0.02    1.0   7.59    8.9   9.56  10.25  11.53   2000    1.0\n",
       "pred_yields[5,16]   9.38    0.02   0.97   7.51   8.74   9.37  10.01  11.27   2000    1.0\n",
       "pred_yields[0,17]   9.28    0.02   1.01   7.26   8.57   9.29   9.97  11.26   2000    1.0\n",
       "pred_yields[1,17]   9.26    0.02   1.01   7.26   8.61   9.29   9.95   11.2   1941    1.0\n",
       "pred_yields[2,17]   9.25    0.02    1.0   7.28   8.58   9.23   9.91  11.24   2000    1.0\n",
       "pred_yields[3,17]   8.99    0.02   0.98   7.11   8.35   8.99   9.63  10.94   1907    1.0\n",
       "pred_yields[4,17]   9.05    0.02    1.0   6.98   8.38   9.08   9.74  10.94   2000    1.0\n",
       "pred_yields[5,17]   9.13    0.02   1.01   7.11   8.45   9.13   9.82   11.1   2000    1.0\n",
       "pred_yields[0,18]   9.71    0.02   1.02   7.74   9.02   9.72   10.4  11.74   2000    1.0\n",
       "pred_yields[1,18]   9.68    0.02   1.01   7.54   9.04   9.69  10.36  11.64   1907    1.0\n",
       "pred_yields[2,18]   9.69    0.02   1.03   7.65   9.01   9.68  10.36  11.69   1765    1.0\n",
       "pred_yields[3,18]   9.49    0.02   0.98   7.61   8.81   9.47  10.15  11.45   1999    1.0\n",
       "pred_yields[4,18]   9.79    0.02    1.0   7.89   9.12   9.79  10.45  11.78   2000    1.0\n",
       "pred_yields[5,18]  10.08    0.02   1.02   8.04    9.4  10.12  10.77  12.07   1931    1.0\n",
       "pred_yields[0,19]   9.55    0.03   1.04   7.53   8.86   9.56  10.23  11.71   1666    1.0\n",
       "pred_yields[1,19]   9.77    0.02   0.98   7.79   9.13   9.79  10.42  11.71   1851    1.0\n",
       "pred_yields[2,19]   9.48    0.02   0.98   7.55    8.8   9.47  10.17  11.39   2000    1.0\n",
       "pred_yields[3,19]   9.57    0.02   1.02   7.57   8.88   9.57  10.25  11.68   1772    1.0\n",
       "pred_yields[4,19]   9.67    0.02   1.02   7.68   8.99   9.66  10.37  11.68   1798    1.0\n",
       "pred_yields[5,19]   9.71    0.02    1.0   7.72   9.04   9.74   10.4  11.74   1720    1.0\n",
       "pred_yields[0,20]  10.08    0.02    1.0   8.12   9.41  10.06  10.78  11.97   1901    1.0\n",
       "pred_yields[1,20]   10.0    0.02    1.0   8.09   9.35   9.99  10.66  12.07   2000    1.0\n",
       "pred_yields[2,20]  10.17    0.02   0.99   8.29   9.51  10.19  10.85  12.02   1948    1.0\n",
       "pred_yields[3,20]   9.64    0.02    1.0   7.61   8.98   9.65  10.31  11.53   2000    1.0\n",
       "pred_yields[4,20]  10.02    0.02   1.01   7.97   9.36  10.03  10.69  12.01   2000    1.0\n",
       "pred_yields[5,20]   9.98    0.02   0.99    8.0   9.36   10.0  10.65  11.91   2000    1.0\n",
       "pred_yields[0,21]  10.34    0.02   0.98   8.46   9.69  10.34  11.01  12.25   1979    1.0\n",
       "pred_yields[1,21]  10.35    0.02   1.01   8.41   9.69  10.35  11.04  12.29   1968    1.0\n",
       "pred_yields[2,21]   10.2    0.02   1.02   8.23   9.48  10.17  10.88  12.13   2000    1.0\n",
       "pred_yields[3,21]   9.69    0.02   0.98   7.82   9.03   9.67  10.36  11.64   2000    1.0\n",
       "pred_yields[4,21]   9.95    0.02   0.98   8.07   9.28   9.97  10.63  11.81   2000    1.0\n",
       "pred_yields[5,21]   9.58    0.02   1.01   7.62    8.9   9.58  10.24  11.55   2000    1.0\n",
       "pred_yields[0,22]   8.84    0.02   1.01   6.84   8.15   8.82   9.49  10.88   2000    1.0\n",
       "pred_yields[1,22]   8.98    0.02    1.0   7.03   8.35   8.96   9.68  10.88   2000    1.0\n",
       "pred_yields[2,22]   8.77    0.02    1.0   6.77    8.1   8.81   9.45  10.67   2000    1.0\n",
       "pred_yields[3,22]    8.9    0.02   0.98    7.0   8.24   8.92   9.58  10.71   2000    1.0\n",
       "pred_yields[4,22]   9.07    0.02   1.01   7.13   8.36   9.07   9.74  10.96   2000    1.0\n",
       "pred_yields[5,22]   8.84    0.02    1.0   6.88   8.16   8.83   9.53  10.89   2000    1.0\n",
       "pred_yields[0,23]   9.89    0.02   1.01   7.91   9.21   9.89  10.58  11.84   1963    1.0\n",
       "pred_yields[1,23]   9.75    0.02    1.0   7.75   9.09   9.75  10.47  11.66   1909    1.0\n",
       "pred_yields[2,23]   9.89    0.02   1.02   7.82   9.23   9.92  10.57  11.84   1898    1.0\n",
       "pred_yields[3,23]   9.52    0.02   1.03   7.53   8.82    9.5   10.2  11.65   1908    1.0\n",
       "pred_yields[4,23]   9.52    0.02   0.99   7.61   8.87   9.49   10.2  11.53   2000    1.0\n",
       "pred_yields[5,23]   9.42    0.02   0.98   7.46   8.76   9.42   10.1  11.27   2000    1.0\n",
       "pred_yields[0,24]  10.59    0.02   1.01   8.56   9.89  10.61  11.29  12.58   1810    1.0\n",
       "pred_yields[1,24]  10.67    0.02   0.99   8.77   9.99  10.69  11.33  12.57   1907    1.0\n",
       "pred_yields[2,24]  10.48    0.02   1.03   8.46   9.77  10.51  11.17  12.47   2000    1.0\n",
       "pred_yields[3,24]  10.72    0.03   1.01   8.71  10.05  10.72  11.42  12.67   1490    1.0\n",
       "pred_yields[4,24]  10.71    0.02   1.02   8.72  10.03  10.73   11.4  12.77   1949    1.0\n",
       "pred_yields[5,24]  10.53    0.02    1.0    8.6   9.84  10.54   11.2  12.52   2000    1.0\n",
       "pred_yields[0,25]   8.94    0.02   0.99   7.07   8.28   8.94   9.58  11.02   2000    1.0\n",
       "pred_yields[1,25]   9.13    0.02   1.02   7.17   8.44   9.11   9.81   11.2   1899    1.0\n",
       "pred_yields[2,25]    8.7    0.02   1.04   6.68   8.01    8.7   9.37  10.83   1965    1.0\n",
       "pred_yields[3,25]   9.24    0.02   0.99   7.22   8.61   9.26   9.89  11.16   1742    1.0\n",
       "pred_yields[4,25]   9.23    0.02   0.99   7.22   8.55   9.23   9.88  11.21   2000    1.0\n",
       "pred_yields[5,25]   9.25    0.02   1.03   7.24   8.54   9.24   9.94  11.27   1934    1.0\n",
       "pred_yields[0,26]    9.8    0.02    1.0   7.95   9.11   9.81  10.46   11.7   1927    1.0\n",
       "pred_yields[1,26]   9.76    0.02   1.02   7.77   9.09   9.74  10.44   11.8   2000    1.0\n",
       "pred_yields[2,26]   9.81    0.02   1.01   7.77   9.13   9.81   10.5  11.76   2000    1.0\n",
       "pred_yields[3,26]   9.64    0.02   0.99   7.67   8.98   9.65  10.28  11.66   2000    1.0\n",
       "pred_yields[4,26]   9.71    0.02   1.01    7.7   9.03    9.7   10.4  11.63   2000    1.0\n",
       "pred_yields[5,26]   9.69    0.02   0.98   7.77   9.02    9.7  10.34  11.64   1916    1.0\n",
       "pred_yields[0,27]   9.04    0.02   1.01   7.03   8.39   9.03   9.72  11.04   2000    1.0\n",
       "pred_yields[1,27]   9.03    0.02   1.01   7.11   8.37   9.02   9.71  11.03   2000    1.0\n",
       "pred_yields[2,27]    9.2    0.02   0.98   7.24   8.55   9.23   9.85  11.14   1824    1.0\n",
       "pred_yields[3,27]   9.16    0.02    1.0   7.27   8.48   9.14   9.82  11.15   2000    1.0\n",
       "pred_yields[4,27]   9.05    0.02   1.02   7.02   8.37   9.02   9.76  11.05   2000    1.0\n",
       "pred_yields[5,27]   9.07    0.02   1.02   7.12   8.33   9.12   9.76  11.04   1947    1.0\n",
       "pred_yields[0,28]   9.55    0.02   0.99   7.65   8.87   9.52  10.24  11.46   1944    1.0\n",
       "pred_yields[1,28]   9.57    0.02   0.99   7.61   8.92   9.59  10.23  11.55   2000    1.0\n",
       "pred_yields[2,28]   9.62    0.02   0.99    7.7   8.96   9.59  10.29  11.53   2000    1.0\n",
       "pred_yields[3,28]   9.38    0.02   1.02   7.44   8.71   9.37  10.07  11.42   1954    1.0\n",
       "pred_yields[4,28]   9.53    0.02   1.02   7.53   8.84    9.5  10.22  11.47   2000    1.0\n",
       "pred_yields[5,28]   9.35    0.02   1.01   7.35   8.65   9.36  10.05   11.3   1981    1.0\n",
       "pred_yields[0,29]  10.24    0.02   1.02   8.29   9.53  10.24  10.92   12.2   1895    1.0\n",
       "pred_yields[1,29]   10.2    0.02   0.98   8.33   9.54  10.18  10.86  12.12   2000    1.0\n",
       "pred_yields[2,29]   10.3    0.02   1.01   8.27   9.65  10.34  10.99  12.24   1899    1.0\n",
       "pred_yields[3,29]  10.29    0.02   1.02   8.32   9.58  10.32  10.97   12.3   1899    1.0\n",
       "pred_yields[4,29]  10.31    0.02   1.05   8.23   9.61   10.3  11.04  12.38   2000    1.0\n",
       "pred_yields[5,29]  10.38    0.02   1.01   8.42    9.7  10.39  11.08  12.37   1871    1.0\n",
       "pred_yields[0,30]   9.29    0.02   0.99   7.32   8.61   9.28   9.96  11.26   2000    1.0\n",
       "pred_yields[1,30]   9.46    0.02   1.01   7.52   8.77   9.44  10.14   11.5   1785    1.0\n",
       "pred_yields[2,30]   9.44    0.02   0.99   7.46   8.76   9.45  10.14  11.31   2000    1.0\n",
       "pred_yields[3,30]    9.6    0.02   0.99   7.67    8.9   9.61   10.3  11.49   1871    1.0\n",
       "pred_yields[4,30]   9.64    0.02    1.0   7.64   8.97   9.64  10.29  11.57   1923    1.0\n",
       "pred_yields[5,30]   9.68    0.02   1.02   7.73   8.96    9.7  10.42   11.7   1846    1.0\n",
       "pred_yields[0,31]   9.09    0.02   0.99   7.19   8.41    9.1   9.78  11.06   2000    1.0\n",
       "pred_yields[1,31]   9.02    0.02    1.0   7.03   8.35   9.04    9.7  11.03   1943    1.0\n",
       "pred_yields[2,31]   9.16    0.02   0.99   7.13   8.51   9.15   9.83  11.12   2000    1.0\n",
       "pred_yields[3,31]   8.92    0.02   1.02   6.93   8.23    8.9   9.61  10.96   1725    1.0\n",
       "pred_yields[4,31]   8.94    0.02   1.03   6.96    8.2   8.96   9.65  10.98   1750    1.0\n",
       "pred_yields[5,31]   8.85    0.02   1.02   6.85   8.18   8.87   9.55  10.74   2000    1.0\n",
       "pred_yields[0,32]   9.02    0.02   1.03   7.08    8.3   9.01    9.7  11.08   1929    1.0\n",
       "pred_yields[1,32]   9.04    0.02    1.0    7.0   8.41   9.04    9.7  11.09   2000    1.0\n",
       "pred_yields[2,32]   9.01    0.02   1.01    7.0   8.32   9.03    9.7  10.94   2000    1.0\n",
       "pred_yields[3,32]   9.37    0.02   1.03    7.4   8.68   9.35  10.07  11.38   2000    1.0\n",
       "pred_yields[4,32]   9.18    0.02   1.03   7.18   8.46   9.19   9.88  11.17   2000    1.0\n",
       "pred_yields[5,32]    9.0    0.02   0.96   7.15   8.35   8.97   9.66  10.92   2000    1.0\n",
       "pred_yields[0,33]    9.9    0.02   1.01   7.97   9.21   9.88  10.61  11.82   1963    1.0\n",
       "pred_yields[1,33]   9.74    0.02   1.01   7.77   9.04   9.78  10.41  11.73   1766    1.0\n",
       "pred_yields[2,33]   9.81    0.02   1.02   7.78    9.1   9.81  10.53  11.82   2000    1.0\n",
       "pred_yields[3,33]   9.13    0.02   1.03   7.16   8.43   9.14   9.82  11.17   2000    1.0\n",
       "pred_yields[4,33]   9.12    0.02   0.99   7.18   8.45   9.12   9.79  11.06   2000    1.0\n",
       "pred_yields[5,33]   8.71    0.02   0.98   6.73   8.07   8.68   9.36  10.61   2000    1.0\n",
       "pred_yields[0,34]   10.1    0.02   1.01   8.14   9.41  10.09   10.8  12.11   1827    1.0\n",
       "pred_yields[1,34]   9.97    0.02   0.99   8.07    9.3   9.94  10.65   11.9   1967    1.0\n",
       "pred_yields[2,34]  10.06    0.02   1.03   8.07   9.36  10.04  10.74  12.17   1859    1.0\n",
       "pred_yields[3,34]  10.19    0.02   1.01   8.19   9.49  10.23  10.89  12.03   1915    1.0\n",
       "pred_yields[4,34]    9.9    0.02   1.01   7.77   9.26   9.89  10.54  11.98   2000    1.0\n",
       "pred_yields[5,34]   9.45    0.02   0.99   7.55   8.79   9.43  10.13  11.32   2000    1.0\n",
       "lp__              -71.98    0.05   1.29  -75.4  -72.5 -71.63 -71.06 -70.59    661    1.0\n",
       "\n",
       "Samples were drawn using NUTS at Thu Feb 28 16:26:23 2019.\n",
       "For each parameter, n_eff is a crude measure of effective sample size,\n",
       "and Rhat is the potential scale reduction factor on split chains (at \n",
       "convergence, Rhat=1)."
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now use some more sophisticated diagnostics to check the inference is behaving properly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 of 2000 iterations ended with a divergence (0.0%)\n",
      "0 of 2000 iterations saturated the maximum tree depth of 10 (0.0%)\n"
     ]
    }
   ],
   "source": [
    "# carry out some diagnostic checks on fit\n",
    "from xidplus.stan_fit import stan_utility\n",
    "stan_utility.check_div(fit)\n",
    "stan_utility.check_energy(fit)\n",
    "stan_utility.check_treedepth(fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples=fit.extract()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine posterior samples\n",
    "Now lets take a detailed look at the posterior samples from the fit. We will examine:\n",
    "1. The posterior distribution on the growth curve parameters\n",
    "2. The posterior distribution on the growth curve\n",
    "3. Posterior predicitive checks (i.e. comparing predicited yield to real world yield)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1128d0d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(10,10))\n",
    "for i in range(0,2000,10):\n",
    "    plt.plot(fit.data['temp'],samples['fdy'][i,:],alpha=0.1, c='b')\n",
    "plt.xlabel('Temperature [$^\\circ$C]')\n",
    "plt.ylabel(r'$dy$ [tonnes ha$^{-1}$]')\n",
    "#plt.title('Growth Curve')1d_Gauss_temp_post_sample_growth_curve\n",
    "plt.savefig('./figs/1d_Gauss_temp_post_sample_growth_curve.pdf')\n",
    "plt.savefig('./figs/1d_Gauss_temp_post_sample_growth_curve.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure above shows growth curve samples from the posterior. Each curve is a possible growth curve. Where the lines overlap most represents the curve is known more precisely at these points, compared to where the lines are more spaced indicating there is more uncertianty. Our results indicate that the high temperature tail to the growth curve is known to a good degree of precison where as the the low temperature tail is more uncertian."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1bb2bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.DataFrame(np.random.multivariate_normal(np.array([20,5]),\n",
    "                                              np.array([[5.0,0.0],[0.0,1.0]]),2000),columns=['$\\mu$','$\\sigma$'])\n",
    "g=sns.PairGrid(data=df,size=2.5,diag_sharey=False)\n",
    "g.map_diag(plt.hist,color='Red',alpha=0.5)\n",
    "g.map_lower(sns.kdeplot, cmap=\"Reds\",alpha=0.8,n_levels=10,normed=True, shade=True,shade_lowest=False)\n",
    "df=pd.DataFrame(np.vstack((samples['mu'],samples['sigma'])).T,columns=['$\\mu$','$\\sigma$'])\n",
    "g.data=df\n",
    "g.map_diag(plt.hist,color='Blue',alpha=0.5)\n",
    "g.map_lower(sns.kdeplot, cmap=\"Blues\",alpha=0.8,n_levels=10,normed=True, shade=True,shade_lowest=False)\n",
    "\n",
    "g.axes[0,1].set_axis_off()\n",
    "\n",
    "\n",
    "plt.savefig(\"./figs/output.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure above shows the prior (red) and posterior (blue) distribution for our parameters of the growth function. The bottom left plot shows the 2D distribution of $\\mu$ and $\\sigma$, and plotted alongside are the two 1D marginalised probability distributions. It is clear that the infered posterior probability distribution has significantly shrunk compared to prior, indicating the fit is being driven by the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Posterior Predictive Checks\n",
    "Lets now do a couple of posterior predicitive checks. Lets first visualise the predicited yield distributions coming from the model with a violin plot and plot the real yield data on top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qRjSf7lhKZVytb2pqDtKFrg9dXQ6Z0tG6aDsqG2pWTUxYOqhNmWmmtE2aJmY+3W+T0ZejJkSsJWBEpoQQ3wfSwF/DznFd927XdQ9zXfew3r17m3TXokhrhiC7rtviO0evX/U2fqGsqlHTfUeQLRxchIlhIKBbOr9PU3CA/jvWgWkUoQ5cV3/X6RgsgGnHMSriq9WnppmvWL41uqaZrMYFoDatp3XRQaZIKUV0x0OWdNZpWDRMiv+aTHHtjYTjaPsUOwbWopZAiW5DIcTlwBnAJ9xWmuPdyJnQ0QtfLl4tKg3h7HM6VxXS6YyeuSKLFvbnBgwS6zn6Wk4TDYb335bVTJk4iZosRCbh7EaRaq5LAqHWTtMpGzySnNJYOLPkul5bG6xvvtIRaTX1mcB/y8Lkfk1ksG6/upuBupT3bmpTGu/I1a+wUBQzn0EEuEkqnpaAFpkSQpwCfBc4znXd6ua9peaD64KDS1JRUEI2u7eeX0NxalGpt/Hb6FWFdPZZHcclkVBcjIo0J0x2cfohvWbmYh0fPJMdqxGZ0l4AzZKi6j+r97ylSbV2bs7/qyKVcbQIUXYctLRPpm4UVS6BqtYhU5rjwdSXUxe6ztF1GX3NlGOg4S9GhvlUxtHeHJrIpZaATGqER4FZwBghxDohxFXAH4DOwEwhxFwhxJ37+T61YOYMq/fhimXX1Yq28d2nspnPINrMpW2pp02iUDytloaZJGWi/jdbjPTrzun1aVJ3y4Q4ph1Hq62JNizVoNFV1cRlx0HawO9EBzopUBzHpSZH01KTSkec3bxIZ/RdLcxKP2msF5mm3Ek6JNlxTQJUtJoBZhU3dDYSWR/m1uwzFauZcl33ooCf79sP99LsyGqm1Nt5BXy1NFMZR1tbYwItnynf5FUVBlnbd9pxKFN0vzNZ7E0EnklOFjOfE0gqDof6Bs2hnpA127G2dHFaU7Kq72yvuxnQNzc3apgyDh0S8iqxXDNQXTpDpzI1w4KJCVY10KQ6lcnrb1+dumZK35Skn1HcdUHDkAGgJfdziYUOyTCpVlCMbO91aY88qr6n7BwthguNLNp9BnSd8dJYBkGDBZvmBtKFjuDxT151n6msT48OYTUzuenCLM1Ay5KTJs2Ujr+JgWbKKDGkHkxSkZiY+XSzKnt+RDrywWn8LqrzrTYn2kvPB0kPKQ1NWHVdviYqlXaUNwUmPmkm9QBNSKeqOTTXbzWVVk9PY7oJ0YV+pHuDFk5zrWnNmqn2TabQU8U3kgQNFpwpEoM2zQMT9Hccss+oM7HMdlR67cAgASYt79BtEuWjW5wW9PMggX7komk5DZ1+XdfVJo7eZkC5WR6BUp1vuZqpWk0/Oh3omGb21RWa9arr1Ux9Jgu27pwziTLWifj1rzGqG1OjjOJFcJfI1coqtcs0rTUmFTD2J9o1mfJCpjV2j1nzlZZmyiycXRfNoZlSXbSbdgt6pFNXuJuY+UzMVy3pg5Q1F4O+z5S2w7xmHiQwLSSt11Z3QTHZ+HhkVWPO5SWylG/vOG4emapRJCZgWIhX0ccryOG8SlGbprtomqSQMNFM6YTu++9RdSyakKniaKYavo2iTEsZmkNbAu2cTOkJ6OyHNkmsp5vMUhdaZKqZNFMpzZ19SwsBLweYPkkwuV/VtrkEKpVWz4Wkq63J3qv+e9Lf1ZsFB6i3SxmYDnRMOpC/EKi849p0vg+SXnRcy2kTqoI0UwG/RUFXi6yr/QCzpJ06ofv+sdeSmimTXHLapXM003vkrk2WTBUBrqOZf0nTrgu5jnItrZlSb1NAphSeNzcxnpZmytVPDGkSQm9CiPTzRTnKQtKfc0ZVO6V7v6aaVX0zn1kBVK1ndfQXXd3SFrnvVaVfP3nSIVMtmVSyJiBnUtBvUTBesFs4pUg64yr73/nHkOozm5RYcQysA9q1JbMuNGm19nlkqgjl2mTQvsmUps+USX6fYlVKV00y6rpuwfOpaEBSeTtsvZ29rluZScZqXQJnsgPUCWX3L1yqTui6Pkj1BiYSMMvFZeQzpUOmDDTQaUd94cztU7Vfv8O5KjEB/W/TWDtOcgyGJfNVdZrXHYMm39UkCWZaI7u3PzeVsjuA4bzRl2kaedIMTHV5pYksmWp5OK6eajv7sbTIlIG/lQlU1dNhA1J2kKc0zRVZOAaaqaIQIg2/p6a26onqCsmUopDWJBimiSFNNH+6O2zdUj8pzV0y5PhbaTrSgto79hOZjIZfm87wrUtnGtvJ1o4LG6uqY1iXTJlopnRTijQGMxhqplTbm1Tc8KJgtZpqbaBN1oy8tCAaG4mWQLsmU7rVzrOCq17DV6UpNULL+0ypLJ6hAk9SYOZOJq2dvUbSwix025lEqaU1THXQVDRYnUzlCwzVUhNeokWlJkDTAqRbxFp32HtOznqNXVdPy9nkW6MunBt9IxXHoq65ojZgXioHjGi8pNx+Zcdg2H15aSHk31cxKgfougLoln7yyyMdMlYs1wX1Nk19qT5n7pgKmgutAe2aTOnuznM/nHo+DDOfk5YydYSp7GVV+SYh3pCtn9Vy9nowyxxdn9Yrg1Cvuej6BYbqwuklo9S4X0NHT12tYX1D/iWdd5zR9JlqMl/pLwzq5orcxJsqmqkAMqVKsDVkUm4fsotY2Dd0XbX3pTXf0k6TJk1TLumIiMZgHANNJain48k4jkGBcAN5aBCcpdM+Py2I1Uy1OHQTQ+arFFVV0/oRQmBS7kRNKxD2XLICKLe9DpkqRkhv2nG1K5bXZbykg6pm46ygU9ZM+R3QFcdhRldb0wwkWQcm5kVdE6FJHi/dEO9cQlKXzkiPp6D3orph03lH+SVh5BaxqG+oIhe1yJ8mWc1C1/+uafzqayqD/o5DxjHQ1Gf0XS20CnVramUdJ9+/V6cgdEugXZMpXSfnfGc3RadJgwghE6j6IIX7NUgKzIyZ0Eq3sIrZ69PLdq1MiDLZdhp+H43OsIpCtiAHmGIkVEbPl6Iub7FXb68bHZTtS0dQ6porsn1mNDSW9Rrf1XXdvO/oOPLvOGjM66YyUYEOmYoibSpzV2eem8hu0N/kNUfIf9DfcTBL2qkXRAFNxZlVoOuA7tdEqW4sWwrtmkzpODnnFp4ENftsXuintv+HVjPlsNwwQSP7vLnn6eQlMilaaWLm02lvEkmik0AwKNJSxz9Gb1FoGhe6Pic6yJIoLS2Rpi9cvhZDL9JM5bvW5ZigspCJcAvLj6ZbkkMFufM8lZbza4s6R4V8mmgb/f+WhXYUrEYEoV/jAuqbCfOgGj0/0Gx9PRXU+caSbHs/eTLJg7c/0a7JlE7qef8EVHFOzTUhaZMpg/pvKjtP04gb/+Kjs9jrOunrRprphv2bRJLoRIZm/YfyflOua2ZWwsb/b5V+ldvkLCq6miktLVzus2qa840TWUo8b9oJTvGiOo51ZJKf7MmUsYkiTCrfSUt+57zPTEY9iMI0ClZlHAX5/qiWCTLJ15fR3HBl+1Pt1/+8sr5PQXKoNZr62jWZ0mHe/g+nopkyiVbIQlczlVJ0RAxbnGUXbf97UY42MyimaxrloxoKnyvgVCNJdMK0g85Vja7TjfIxTY6nM37r0k3kUSd/UkojOCDjyxPVEhrHoESbMmVhTOdqFjrzxr/gyWjSogiTilzU8V8yTimi6dDduGlS2HwHyRJZ7V8WuoQIsjJYfY5n+1Pt179GyMrSIK1xa0zc2a7JlKcCNbNhq0zGXKc8/UKbyk0a+1YR7KECWuIaft8P0NNM6TgxgnqIdWO7BhJVpxgKn7uAqEaS6KQaMM0Blu1PdefoN73q+Jzo7Opr8iLG1PuszzjKjr/+Z1MVzvUaxDywXl1d/POG3dv+1kylfC4PIDcmosiIikz0fOGkTweag0zpOXRnv5FKGpSwjYOKdsokQll3jdKt8lFIpvQ1U62xpEy7JlM62o/CMh7ywt3/gbV29ppmvrTjKvkjhE34tARRqc/o+X7k9uEJnZYVAtlvaVLaRTWLs06qgebQROhEuJk6w4LeZqBWI/w+C6eBACqHpBcsuvLfNdc3Uq0kTICZT2I8hfWhW5hcFkGLncz3iSIjKgRfJxrbb4bXcdI39dWS7TNMlqjImEyDCVgnR5uuz1R27Kv0WZvKFK4ZBtGhrdEJvV2TKR0n54KdjcJHM6l1l4WuA299Rl4zlQ7wycnCdeN3HEECVUWjoJvgLgtHQ+sCuX4Nqn5P+poTHb+csPeiXjvRTFujG6WpijyfNIV0AZDvB6fUzkCDoVusOGiRrEnFm/nCxo1qeLlpSSPvt/jxH9WNyjfSMfMZR8E2g3lcdiyFyRIVGaObxw70nzXrG6zSZ9AzyZLGILlnHdBbGKp+RFD40VUiB5pDM6XrwJvJFEaGhCHuncRNsCAyorQY5Zi+dB0gTVJeqGo/auqbzlf16ckV5jqlenKhqplyXTWTRUGtRg1zqs4OOfd7qKQLgKZ3qpoQsjDQRMGcr0mmgkzE3vNGj6kwPxwV2ZQtuWNCOMN+8yOqD9nunYbxqyIfmiMKNqXpy6kTBWuqccyWsIm6VlRb3YjqplJKKmRKfwMe5A5izXwtjLRGNEewaltPHakzwHWKbKpGqcURtlgypbljzcI0MaROnbxUpslRU9Wkk/v9VU1KuefKaqbC/H90wspVFgb/d9XJq6Uj5Ew0Yrp5sQrIlGaAgGwi11SAaVy276jnkr1vHadhXTIVBdnes5pnlWmeyhRGPSqb+TLqZY1ys66DvHzJkweOGRlTfU6Tsmc6wRdBm1DZjWmQ3Gvp2rcyaOdkSr2emslHL9jZK4ez6/lMNQ1uubZx8jSObAUJC10ziZYpVCMZa74pyUybIC8E8oWsbKRPlD+bLByNxTOYJKsvLKrarEKnYXmym5crysQkr0nEZDViUcI/bmGIujdp0352PCh8m6Bry8gYIUT4Mcm+G7UfSulezOSS159rVCJIpc/s+x2w8u8c/69pdN/8Zt7vcTCJvG0qf6Oz3uhs1IIj8qQ2IgFjwJr5WhgpRQ2GV2y18OPW1qtNjix0NFM6PlOmPkiqCBIWKlmkUwZCoNHhUpFN6RRshWC7fq2srV/T5BAmpPQ0UwrEMdB8K/+usqYDVQFt4qeom/HaP1dUTJo6Wq3IdAEx8ybquKyMyToNK2mmAhOFxr/jCC4VSbRykW62zYBiwEjIGhDZRpOYpx2Hit3LGfP+T0imq5n85pep3LVYWo4bJRNO668Z2U2dyuYuaE7LaL5zgz1yoRu8tD/RbsmU67rK6QLCNA66min1RIt65Tga60JJPmucOIsTeKbRZiaaqeyipO5Mm2+qkxUEQd9e1ueq0AdJ7p7Dnk3lmXXeU3AxXfnvk9Yguo7jFmhwdc18Kr5wQSZN2bHoJ50y8sHEKTtqcZd91TqaiKBzZTKE50qPXhv+Q+WuRU3HJFVTjQXjW1Czml24lYMZCpze4/vMOC6k6pj41jfIlFTw9idmkC6p4JDXryaxd71Uv81R9kyHTOkkrA3rJ67/sFqqVjPVgsh+aJW8H2Eah6CQ5qg+w/6Og075G2h6PtkU/8lEtESLOWxOpnLOUzWFZgW5ipCFwoVWPuKm8DzZXFNB2g8ZhJER2bGRawZVWTxNzSTZ59M1mUXdRxhyv4WueTAL+YS1GrXCoshUTNMos7vsmGgqwK5AjjUXwOxmrOfGVznkf1/k0P9eTsd9awBISLIpnfsNCoxRyYKeG8ygMm90NtFpx2HUB7+l867FLDz8V+ztMZH3j7mXknQVY166Emp2xV4j93lVUwXolL/JItWomTLTcnrXir5G2HHdXIP7E+2XTGmYksJyvsiEcGbz3eRC3WcquGxEHPJynEhMjjiBFke2wga4dB4lA81Utm/VAp36CeMMAhL8ZEpyPIRpG6TNqDkXMC0xpBRYkFYnU6ZOzjoO6GG17mTb+zVROlnbVRBFpmSjNVX9KqPOjTNLJgSUV21gwlvfZl+X0QBM/t8XSab2xW7UmvpQv1/TElm6ckkr8GjZTIYse5A1oz/L9v7HA1DVbQzzjv5LITfiAAAgAElEQVQjHfd+BI9dCum6yEuYmPl0koxmkdbQaoURrzj5FCYLVAlvS6DdkimdgRaekTY+902wf4G61qUlcpyUxEi00kT0sNBV2WZh5DjZqP5XFB4aqngwW+z9k1220nrY4im7E8vtVzZDt7+8ShY6EXJqbcy0YbmaKelQ60xwAINMv67rFpr5ZHzoZD2vA/vUO5YLnQissIUuTtOTdFJMmnU9CTfNvKPvYP6Rt9Fp7yomzv4mCclwZR2fHtO8TbpyyT9uUnGO1fu2UPr0l9nbdQzLJ30779DOPkew6PBfwUevwz+vjbTjmpj5zIhY9tvIr1W6rgvNEXzRUjhAyJTcQAubdI4TT4yCI18UNVOOZsXyjNqzJhKCZDJYuicTgkQE2YoifDpkSicPjP8aMvCrwXVMkqpt/e9D1gwbRqa0HPylnVmDx42OlskkRYFKn+mMo1VfL+xZZZK51qUDsv8baqYMeJY0suNA5duEaRPiFtEBb/+crjs+YOG0X1LTeRg7+x7J0kO+T++Nr9B19q+V7ldFhhprpjTJVJCmLvQdOQ7881pE/V4WHHELTrK84JRNQ8+EE2+CBU/ASz8O7Td3vVKNojXZ0KoS82xgShDiTIVRfbS2+nztl0xp2JOjJl3chAz6sKoqVN2K5XW+SSWDsmTwpy8N+T2LqOeR2ak4jusjU3rlWZTJlHb4cuEzmZSFkWkbNgZkebZO/pnmKGGTfcdKBCxkEyKzMPi/oax/jMmiG5jJWYZMGfhMNQdU501UxvRIUj9/Br0+fJDVB13J1kGfbPx53ahLWDfiArrM+T3MnxHbv44mLezZdMiUmgmr8NxQ89Xbd8Hyl6g54Saquo4OPMV1wT3qeph2Nbx5O7x1V+z9quaEq9P0NQT1bxM1XOLT8DT04br03Pgq3bfMbjzW2hJ3tl8ypaH9iCQKMdcw3RVB89SFko1oCiNNpSEaqyyiBKmMf46uua3p/EzgdaIQ5M8mrV0KeKZ0Ri6TtG4ZhLDXKLvz1FkUwh1EVRYz9SifoO8gG1mnG71lMleDiJNM9YGo/HEmfrSy0XGqDsdRcij0GluXwFPXU933MJZP+lb+MSFYMuWH1A88Av51Hax/L7L/pmAGuZcTlP08C50oY7XAC0nN1KYFMPNHcNCp1E6+MvKaDgJO/TWMPQOe/y4sejr/uGOW7V2nzBVkNzrevz2/Jf3NoXcspr9Uij5rX+Dwmecy5fWrGbLk/sZjVjPVQtDRfkQJkDiSEyqgFUwAOj5TupqeMNJUWhI9JEySD0LhxFWJtoEcvxyVBI2a+XLCcpyAXMI6HRNh1PeXjdzS2UiEfTuVIqo6ppmw9yGj5dT1twqbkzJzNWyzEhfhafJdZQlTFFRNsJH3G3Ssvgoe/yyUdmTjSX/CTZQWnOImy9h95n1Q0Qf+dgns3RTah6r2I4p8729fzqBNZMGcqa+GJ66Cjj3g7DtitZGu60IiCeffC4OmwROfhzVvNV0uMIBCPVgk7Fph0IlQVh5LAJkUzH2E4Y9/gkmzrieZrmbhtF/ywVF/aDxFR/GwP9FuyZROFe+onX+cOjLU+VHRR0HVz0pX0xOmmQoz/2URJfhlTJSmDsfZ9ioFcXXzJ0U5uct8J50M0lqCxwcdFX4kSVaNGFPa1etrE4KIjczzho03mUVF18k5ct7E5m3y2JRwUhz85lfovf6lpmOSRKuuUdMjq5kKP69gPLguPP01TzN1/r04nfuHthWVveGiR6B2l0eoUrWB59UrOqBHzSt5v0E9ghE0Pwrm6r9/AFsXw7l3QkXP2Gs2ti7tCBf9DboMhEcvgG3LvHsNGPtKvoY596dCHP0BLTJtozVTvmOpGnj7Hrj9UPjntbiJUuYfcSuzTnmejcPPx02WNZ6q41+8P9GOyZR6+RATdXtzVABP61R29+dPktTYlIVooOJ8pkwX++AFUH4yZ9ur+Ajo+i5FEQwZzUnQQhAnfHKFS6e9qxiy5AGEkwJUyFT+RkKGdJpk6M6i0TTTDJopGSEdnLZCwtQc0WfcuwolYgYax7j3myVMA1c+Tt91LzLqg5vJFvGUySieG6kpW9JIaZ6/+wDMfxxOuAFGnhAZwJIQAvodDOfeBevnwDNfDxS82bEka0qK0p7Kkvs8Db+k3HbdkAzdue9o8bMw5z446noYeYLXTurqDajoCZc+AYkSePg82LtZe+yDmatFnW/8yPmARh1rOFi3F964DX43CZ77FnTuCxc9xodnP8/mIad7Wrqwtq0E7ZJM+e3nsrvzyDIIMTE34WY+Fc2Uei3BQsdquWcNS48Q5zNlor2DMM2UvODSqcUWNOHTEpF1UUI8zvQVVtg2TvhkHJdEupYRC27jiBfP4KB5v6TP2hcAb2GRiQbMfS+ypDNy8ZQck1nyqZR13cBXK+j7y4yl3G9QsWsJibSnHZHJXaPr5BxFzDMxvoYCSKb2MWLh76kv60bF3pX03PR6ZJtc5N6zbFCMtM/Uhvc9n56Rn4BjPD+pZIQgbTw2/iw4/gaY9wjMuiPvHH8eMDktcPNqdGU3BGHvqXHO7Nng+Yj1nwwf/6HUNQPRYzhc/BhUbYNHPkN99d6CU3SDTUzywplqpkT1Tnjll3DrRM+frO94uPxpuGomjDklknS2OTIlhLhfCLFFCLEg57ceQoiZQohlDf/tvn9vUw31OU5yIC9AonZ5ccnmwp1a97NmytdvWrJGXrgDevSQiN5lxHZrVLLEH5Yum4k8VBMRS4j0tQm6Ghex/N8c8eJpjPjwDjYPPpWaikEMWNUU/SQjQPzvReb9RpmhZH21stotWT+rKJ80XUdyKfNtQ7v+q57gyH+fyVHPn8jA5Y8gMvWxckL3u0bNydgNlIChS+6jrG4H8z52J3Ud+jBk6YNAvFwKujepDN0y5KRmp+cnVdEHzrsHGvLTRSX9zUthd+y3YfzZMPOHsKzJdJnK5CcvNl2wZTSr/iAVaY1uWPoIx/EWnn980Uu+ef59UNJkotJygxs4FT79EGyaT/fnrm7UWGehKw9NNMky8zRoA1hWs4VR837NqEeOgFd/BUOPgs+/DJ/9Fww/tnkcBVsYMpqpB4FTfL99D3jZdd3RwMsNf7caBE0+mY+e3TUlU/s48rmTOeS1q+i58TVwnUiiFVRfTKXfLFRqxkVdX0b46Jr5TLMxmyQ31c1iHuqXI6ElCkOcgFZObLprLfztEjr9/SKcZDnvHv8XFk6/mQ3Dzqfnlll0qFrn3VOMgE/5ci+BnJA1Nd/mlrABSQd9Q6dhXf+7lOPSY9PrjJvzA3b2nkZNxWDGvXcjR75wCmLe38AJf19h9xUXfBH1PuLeb1n1FoYuuZ9Ng09nd69DWTvqEnpufoOK3UtjNeYQYJqR0VTGkZMsUdizET79YJ4fUJSZrySXTSUScM6foM8EmPG5Rn+ggrJchppVOY15fh+yEaWRUbCzfg+rXvWi8nrlp0GIM8+GVqk46GQ44xYq1rzC2Hd/nGci1dXUxyYZjWir+m1K63Yw5t0bOfrZjzN06QPsGXIiXPsmXPQoDDpM6h5aK2LJlOu6rwE7fD+fDTzU8O+HgHOa+b6MEOiXI7HwZgdw7/UvUbHvI7rsmM+U1z/PkS+cRqd5D3oRKwGIclRWqV+UdhzlmnO6ZjNdM59JmCvoRbhl4f+uugWoZfuN9CPS1WD426Xr4X+3wh2Hw4r/UH3sD3nrpH+xs890ADYMOxcXwYBVTwLxfn1B70QmQ3fudUcsuJ2eG/+bc0ydGKk66HfZPo+D3v85iXQ1ED9vwkLhZcZ+x23zmfTm9VR1Hc3cj93Fuyc8wvvH3Eu6tAudnrsO/nQUfPhUwcuOyr1kQszjyNTgD25DuGlWHPx1ANaPvJBMsgNDlj4otYHX0UREmXYd14U3b4OlL8Anfw6Dp+Udj6qwUFBcoazCc0hPlsKjF0LNzgJZLZWZXvNYUx96xDxMDpRsmgsv/8TTvE25rOB4bLH5qINTr2DD5OsZuGoGwz9sMpHKWkGkUzkEQCf5ce7wPuj9nzNw5eNsGno2b57yIh8dfzv0nRDaNtpa1Lq0V7o+U31d190I0PDfPmEnCiGuEULMEULM2bp1q2Z3atCdGNmJ3m/NM9R0GsjrZ/6PBdNvJl3SiYqXvgO3jIN//9DTIuQg0lFZoeyJlxpBTTOl61wdlgKhJEYzFVnaQkJspTMuZTVb6LvmGTrvXOj9JkkgCzVT+j5TEL/gR32K2DIIMlFqq16HOz8GL90IIz8O171N/RHX50Ws1FUMYEffo+n/0ZONTsdR0E0qmSXJlbsWM+LDPzDmvZ8gnHTDsdjmBd9QZhPReI7rMva9mxiy7CEOfe1zJOv3xs6burQTOBZjzTM7VzP5tWtIl3Xh/WPuIVNaCUKwvf+xvH3Sk17ovuvA45fB3cfD8pcaB72JdkkpoikXWxbTZ/njrBt5CTWVQwBIlXdn47Bz6bf6KRI18TLVv1CaaqYqNszyiMKEc+HwawqOR5n5AktVdRsCFzwMO1fDjKuoq6/PO6yaVLIAmps8KZNxwDhPpqoY9J/robIfnHlboMkqjghEafcAVh/8VTYMO4+RC29nwMrHATk/UO+eC59LNwWFlJmvYSyV1u2g77oXWDfyIhZN+zk1nYfGag0jTcbthExJw3Xdu13XPcx13cN69+69v7sDghdZGZNQUghKa3fQY/MbbB5yGm6yjE1Dz+KdE5+g6tLnYMQJnrPkbZM8X4HVs8B1o4WskmbKM5XolgWI+s2PsBQIcakRtOqE1eyERc/Ac99mytMnc+zTH+Pg2d9g+sxzGTvnB7hV22PvFwpJgbSZLyyXUUxCwOg0ENF9Rmqm9m6GJ6+Bh86AdC1c/Dhc+FfoNjjQbLNh+Pl0rN5Ajy2zYgVIbb3e2M8+z+BlfwGgU9U6eq//d8MxGWKkrpnKzpvuW9+iy84FbBp8Kl22f8Chr16OW70zsm2YEI90uK/egfvXT5HI1PH+MfdR37Fv/nGRoG70GXDtLM/8VL0DHj4fHjwdVs8y0lRGvcLIsfTSjWRKK1g1/tq8n9eMvpykU0+neX+O7Bf0NFNhMqisZgujXrseeoyEs34fSBTCNFOJRARJGHoknP5bWPEynV79Sd4h2U1TKCTWXN00G0Hz/KC5P6d8z0dw3t3QMdidOKr8aVyhefBM1YsO+ynb+h3D+Dk/YNS83yCctJxpMuCeZS0oJg7o/Vf9g4STYv3IC3OORbeN0nLG1ZhtaZRottsshOjvuu5GIUR/YEtz3pQpdHfniYSgz7oXSLgZNg05o+mAEDB4Oow62tNKvXMvvPsgfPgv6D+ZkkOvQXQ4Lk+jkAvHcWN3GtC0u007bqy5LYtAHySJAa6bAV0qk3N9NaydDStf9XwGNs7zdvulnajpOZUNw85nZ+9p9F37PIOXPUTf9S/BKT+FyRdHSpmCArMKhW2DECd4tLUJYX06GfovexT3yd8h0rWeA+7HvgFlnRpPCXr8rQNPJFXWlQGrniB53LmR/Qaa+SQ1UyV1u+i35mnWD/8U3ba9y7DF97Jl0KlSZKrAl0Ihmd/QxfdQV96TDw//DZuGvsnBb36FsS9cCIOegcpgpXfUGK/POHQo9YVSp2rh0Ytg50fMO/YBqrqOCr+xZAkccjFMPB/e+zO89n/wwCmUjTyRziO+zN7u4wufxSCyKJQff/Q/WPo8mw/7Lqny/EW5ussItvU/jh5zH4ATvgmlHUKvr5NoMehphJPm4NlfJ5mqgguegfLOgW3DZEtJTAF1pl4OmxfS7e276F82go3DzwPkxm+U1JKRpEEpI2S0Lv5x3mftcwxcNYM1E77EkGFHh7aLdtKXIFMZBzdRyryj/8RBc3/BsCX30mXHfDIDHoIe4Xm+IFjDqlvDUybVRsZxwXUYuPIxdvaamldGJ06jG/WeZEhnS0KXTD0FXA78quG//2q2O2oGBJEpmd1NQgj6rXmGfV1Gsq/r2LxjjR+u22A46SY47jvwwWMw+04qnr2Oj3XoxbqRF7Nu5EWkOuQnZXNcl4TElM4OrIzj4l8LwqCTGBK850kmRN5gTiT0zHzCSdFlx3x6b3sL3pgDa9+CTL2XF2XQNDj2OzDiOJwBU5m7bFdjuz09J7Nh2LlMeP9GSv91nbdwnX4L9JsY2Lf/G2YakpzG1hMM0SjELSrZZ+1QtZ7R835D5e4l1HfoSX15T8q69oXeA6GiF1T09v5X2cf7u7xLwbW7bJ/H2PdupMvOhWSGH0fy9N8WOKVC8ELkJMvZNORMBqx8nGTdrtDdLgSPfRlzheu6DFw1g2SmlrWjP8vuHpMZ/+4P6b5lNm7/k2Pb+8mNVJ1G16Vy12J6bXqd5RO/jpMsZ9uAE5h3zF1M/t+X4IHTvOiergML+4v4dgVEy3Hgyath7Wzc8x9gV3JacEM/Ssrh8KvhkEvg7btJ/O9Wpq84h82DTmXFxOup7jKy8dS4RSE67UoAXNdzKegykJ0Tr4LqwlPWHHQFvV69EhbMgCmXhl7fTwp0y3CMXHAr3be+w7KjbmZ0n3Gh5+lGCgPwyV+wZ+0Cxr37Q6o7D2d3rylSPn+R71fCHBTsZ6um5elQtZ5xc37I7h6TWT7+OoZEtIsiAnEal3RuSZdkGUum3sienpMZ++6PEPefABf8pcCPzd++4DcJ95J0QLqXbKR81Ld1Xei+ZTYV+z5i1YTr8tvHbEKiNvdS46kFEUumhBCPAscDvYQQ64Af45Gox4UQVwFrgE/vz5tURdBORsbU0aF6I923zWHFhK8WzM6C+VhWAYd9DqZeyZ6FL1L/vzsYufB2hi/6ExuHnsPiQ3/cqKmS3bPmkinZ87ODe+DyR+m860MAOpUloSJYS5aLsbtr8/pKJgQsC9/hAvSsTVNS0xSSW16zme5b36Ek3eCc3+9gz49ixPEw5Egor2y634BJXNVtDPNOepRjql7yQqTvOhamfxFO+H8FO98wX7ioSRVZEiZuwc/UMWzRnQz/8I+AYHu/oymt30Pl7qV02DobFu4Kbpcs56AOPRlS1oP68h64iRJ6bXiF+g69mX/ErQw77lI6dwz+PmHPsmH4pxi8/GGvSOzhV4fectA4zzSUHAqL4ARwMxkGrXiEnb2nsa/bWKo7D2fkwtsYuuReqiepkylZ9f/QJfeRLunEulEXN/6+o+/RLPj4A0x+9Wp44FS4/CnoPiyvbVQixoLv+u/vw6Kn4OSfIyaeC4siFOlBsrusE3zsa+wefyk7X7qFIUsfpM/6F9kw7PzGeR6nmFL2/Vj4JGx4D875EyUdOkJ1TcEpO/ochdNnPIlZf/QIXwhp0Pk2/iv1Wv8ywxbfw7oRF7Jt5HkEl+j1UJoUCFG48SorkdAkJEtYePTvmPzC+Ux68zrePf7PVHcZRTrjRG70oszfMgqMurQDrsOAVTPosmM+gKfdrIyWo3331dOlYc513T4X4TosOOK3OKI0kmQE+o41IE7jEqRZ2jjsXPZ2HcO0t6/35sypv/bWp4D3ErTRkTHzRfmexpGpQSseo76sG1sG+RIDxJr5wq8ra71pKcSSKdd1Lwo59IlmvpdmgeO4gTuK2pRXfiRql9Jj1TMA+Sa+BoR+VCFIDTuBuYkpdNqzkiHLHmLQikep69iHlRO/Kn3fruvmmflkkNV+9F4/k3Hv/Zj6sm64iVJPeEiw9p6+fFxCAJui23VyXMpy7i9d2plNQ89iR58jqRl4JNMnHhTaNrQKvStgyiUw5lT4z09h9h+9xeSTv/CcXIWnQQua8HWpDJXl4cM4Mvlg1G5s5asMeeoblO1azpaBJ7PkkBuoqxjQeHhg946M69PRS6JXtbXhf9ugagtUbWX3pnWI6m2U1W6npH4Xa0dfzoqJ15MprWRQRLfJhCCZFAUEcG/38VR1H0/F+w9Hk6mIfGdRZKrrupfpWLWOZZO+A3jasLWjLmPUgltZv3Uh9IzW5hQ6pkqY1fesp++aZ1k36lLSZV3zju3qfRhc/i/4y3lwfwOhytHkRe2k87SCs+7wxtP0a+HI62K1FFGpBpyyzqyc+FXWjrqM4YvuZMiyB0mm97HgiFtx3Oh5E7XYFyye6Tp46SboOxEmXUDZ9kIi5d2s8J7r6a945vQRxweeVmCakQnGybnfjvvWMOHt77Kn+wSWTvk+nWLWMCEEpclEQT9lyXh1u+O4VCW6Mu/oO5n630uYPvNclk36DjXDv0LnjuURfco9Syj2bGDK/75Nz81vUF/eHVeUeNeMkaOdMw6VDVPVSZTy4eG/agoUiCAZiYQgkQgOctGNqN7XfTw7LplJ75lfgWe/AevmwBm3eCVpchA0d3R9rbK/d4rinFVb6L1+JmtHX4qTzP+GcStdVK3YOCtKS0PXzNdqEeeYWuBLkYNuK59id4+Dqek8NO93IeTss9VdRrB46k0k01UMW3w3mwefRlXX0VJlanIXfdlcU6mMQ7J+L2Peu4m9Xcfw9klP4iZK6dKxlMOH94ht/+HaXWzbW9f4d8/KMqYMic6/umFHNUs2FWbfBaiIIDUQLgQaf+/UA864FQ65FJ79Osy40jP9nXYz9V2GB7aNMydFOw0HtN272aujNf9x6DKU94+5m+39jy84zXXxwrm79Pf+58Oy5duoDjFPxEWqlZckqA7wRdg55jNUzL4RNs33NIA+xOU7C/Zw8dBn0Z+p7diPrQNPbPxt3aiLGbb4Lrq8fyeMjSZTOiHTnefeA7isPuiK4BMGToUrnoW/nOPtti/7Z6MJWKoo+YIn4cUbvPD0T/6iccUN0ppkEbXuZi+b6tCDpVNuoK5jH0Z/8BvqOvZn2SHfi9ysRTrS+hfPOffDrtUNJUSSlIfIrNKSBIlJn4H//ARm/TGQTPmTUYJkzr3s/ToZJrz1bcBl/pG34STLpWRhWUkhmSovjV/8qhu0PFVdRzH75KcZ/84NjH3/p9TteB3O/xN0GRDYLooEx97tgieY9PTXSWTqWTT1J6wfcQEIQXlpgmNGRwdNzVmxjeq6kHmediGCZJQmE9QFyII437KovXa6vCtc9Bi8+msvGebm+V6kZI5mN2huyvjR6aaY6bzobyTcNOtHXFhwLG5tjCKWccFSLY3WdTfNgKgdcaQQ2bacjtvms3lwoVZK1dFt6eQbSJdUMG7O98HJSOWCyRV4sma+VMZl1PzfUl67jUXTft5YqV02zNU/GKM0F1kUCH7JYxC+AHrJgnOODZoKV78Cp90M69+FPx2J+O/PG8t+5CI+V1TUopvTNpOGt+6CPxwGH/4Tjvsuqz4zM5BIQXwaiKh+49wTwgh/9djzIFkG7/818HjU+I4c+1uX0HXjG6wbeRFuookQp8u6sn7EZ6hc9s+CdCB+KIdM1+ykYsHDbB58ep7GrwD9JsIVz0Gi1IuqW/8eIJG36aM34B9f8EzN596d59mv7L/UAP83Xz3mKtaOupShS+9n8NKHIheGyHmTu3jW7IJXf+MRo5Ge8r9DyLzsUJLwHM8PuwqWvdiY9DIXQQlbM058QfWsE/TQpffTbfv7LDn0x40aFxl5WB5wzzKLX65/VH3HPsw95h4WHXoTpevfgj8e6RHkoPvV8Zmq2QVPfB5mfI7qymHMPvlfXqRZw/kyWdCjE/tGv+PQYvMxcjh27CcSnpvExY/DrjVw13GNGebrI1KKxCG8ykdEW8ehcuFf2dF7OtVdRsT24UeUOTRuvWlptDsyFRnlE/XRF8zARbBpyGkFh2KjUHzwdq7fp9v2uQxa8YhUm9zFSDq9/5rZDF7xCGtGf5Y9PSYpt/fvFMtL4tXwUe8i3nFSQpuQRSLpmbO+PAcmnEuHN3/LES+elpdMEiSSJcqUxVg3B+45AZ7/jqcN+dJsOOEGMomOoW3jdlRRjpVxkV8dQr5DWWUvGHu6F/iQris4rj32374bJ1Hm7ch9WDP6Cu9hZ/8p8p4LCm7Hmfnm3E8iVcXqsVcFHs4bSb0Pgs89Dx26wENnwepZkYS0ZPsS+NtF3m78wkcKIt0itRhRTMv/2YRgySHfZ8vAkzho7i9gUXgcTpRPSd7u+43fQc0OOOknjYt6x7Lg8dD4+7SrIFke+I3CnKjjfEhLEoKK3csYueB3bBl4EpuGnJl3LA5BsiTKKhB6X0KwftRFrPrUC9BzpKetfvIajwjlnRb1TQN+XPmql5x1wZOkjv0ecz7+KDWdh+WdIlOr0ST3mHZJL9mN2kGfhGv+C10Hw18/Ba/+hrpUKrCdTuRi3O8ArPwPpXvWsH5koWyB+Kox0ZsQS6b2K6IyiIfuxlwX5v+duoFHFuaeIZ4BB9nkNw05i239jmHU/FtI7FkffdPkm/akstGm6+j8769T02kgKyfk+2ZlJJO3+YVbBwk1fNTuMk4IRJkvQ3dxnfvCeXez/VMzcBNlTHn9Gia98SX6rH2Oit3LSdUXkor864a/B1GzE57+Ktx7oufz9OkH4bJ/eEKbaO1TXBRK1NG43W7Y4tmhNOlFbdXsgCXPFxz3lw3JRSiZqt0Ncx9l27AzSXUoNA3XVQxg3+iz4b2HvHxhAfAXoIaYepipWnjrLmqHnsC+bsFRYQVzqvswuPIFbzw8fB6d1gUX+i2r2czAZy+Dkg5wyQzPdOyHpmYq8GAiyYLpv2V3z0MQT17j5Z4LQDSZaji2e51HiCZd4BXGbUDH0mTgotOprEGLWNkHJn0a5j7i5cbKQVgpobjo5hI3zYS3v0u6tJJFU3+St+rJaKaCZEmQtqrwvoLvd2/FMPjci3D8//OCMP50NKx6rfF4pGYq949ULbxwA/z5LM+X6PMzqTnqW3ka2VzEbdaiC79HNg19H3HvSVrbDtBjBFz1b29MvfJzyv5+KSX1ewrayQRoaZn55jxApkMPtgwMDmKJI1OhaTaSQipKsyXR7shU5IIdRlI2zoPty6kdG5zDJ84hMNDBUQgWH3ojwnVIPP/NWFVGfQl/uKgAACAASURBVB6ZktAsvX4LpTuXs3jqTWRKKyKvFwb/pJXRTEX5PcSRsUiiG5NAs6r/0cw++SmWHfwtem56g0mzvsaRL57GmPvHeoL1iavh9Vtg6YuearvhfQcKO9eh/6onOPzZk+G9v8CR18GX32l0dm88LeKW9mfB8rD32KE04SWO7TIQ3n+44HgUCQ8dU3MfgVQVG8eGh9bvPORaqN/n+fIEwF9YPIvQ3e4Hj8G+zVQd9qXQPgMXx64D4crnoftwhs+8kp4bXsk7nEztY8rrV5Oo2wWX/B26Dw24SExOoshFOfigU9KBeR+700ub8uiFsHVpwTlSId6v/MLLx/bxH/juSdAxQKvTKZd0H/ElSNfAuw/knRNGmuIWz4p3fk+XnQtYfOhNBaleZELSg7RQMpqpsDFTm8p4PorHfw+umumlrXjoTHjx+5CqldM2bvzAy2o/+w6YdjV84XUYODWSDERFjYJZRQhdMqVcmqisE5x7J5x2M2UfvcLhL51H5a7FeafUpZzYDbgymdqzEZY8T/WEC0NzMMZZfUJNoa3MXwraI5mKGmhhI3/BDEiUkB5zZuDhuA+eDBGUtZWDWXXw1xBLX4SF/4i8Ri6ZiLVfb1kEr/+WPaPPZXv/Y4Ovp0GmTDVTcdE6cYkWo5ByHNxkGavHXcOr57zN7JP+yYLp/8eW8VdC5/6w+k14+SZ45DPwu4Phl4Ph3pOonPktBi/7M923vEVp3Q4qdy3msFcuZsI7/4/qyuHwhde8+mIhCQjDEMelohbsuOiiMFLboTTpmT8PuRhWvAy78zWeUSbNwPHgOPD23TBoGlU9JxUeb0B9r/Ge/87sO72dvQ+hvhRBC7bjwJu/h/6TyQw9JrTP0MSFlX3gimeo6T6WyW9cR5+1noZOOCkmvfkVKnYvY+2Jd+ZpdvyINAlFfLko7Ue6Q3fEpU94C/7D53uBDDmIIiBlyQRsWuAR2+lf8Mqr+BCkrcwjU30neH5Wb9/j1XtsQJi5NdKss/EDyt+8mU2DT2fLYH+Neznzip84CSGnmQq73zzZMWgqfPF1z1ds1h/gnhNIbF0Qes2Em/HqX97zcU+re8kTcPrNjclyI8uBKRaeV0GYb1Scz1RkUE3Y+icEHH41686eQSJTy7SXP0O/1U/lnRJXEF3ZzPf+w+BmqJ/02dBrxmk5kw1Rj360tkg+aIdkKlrtGnDMcTynxpGfoKSyZ+Fx4ndiUcJl47jLof8hnj9Otb9edBNyhUicQx9PfxXKO7N++o9CT5PJG1Jo5ovfOSYSIlQ7FUfGorSGcUIr97iTLGdf9/FsGno2Hx36Xbh0BnxjIXx3tWcKOP0WmHwhJEvpsPRpxrz/M6b+9zKO+9cRHPHvs+i0ZyULp/2SOR9/JDRBqClMMveGLTqNRPaQiz0NxrxH845HCdnAHeuKl2HHSjj8C/F5eo7+qpf24YPHCo6H+uUEjeOlz8P2ZXD0VymJIN+R2uBOPVh2ysPs6TmJg2d/nf4f/YNxc35Az81vsOiwn1E1+Ljwtnhlo8KgW+YjmRCeKfLix6F6OzzyaahrinqN3ISUJOClH0OHrnDMNwPPaTTp5aCAYB1xHezd6AVQNCDs24T6tKXr4Z/XQqeeLDk0WL7IaKb8mrTykqRUZu/6EA11gXwoq/DC/i+ZAdXbqXzoJIYuvgec/OfqsG8tY168yKt/OeZUr1TQ6BPzzomSPXGF53U0mVmEbZpiyZSMH2gIdvWcwtsn/oM9PQ5m4lvfYsiSJm1znOk3vCxX0EYt47kGjDieRK+RhccbIKNhClJmtDbnc2iHZCpqLAUSrbWzYc96OPhToWw3zswX6UdUWubVsKre4SWlDEEugYp03p1zn5dh/JRfUlsWnsZApsByaTLRqFVLJoR0Rtkw0tUhxNcniyjtUxz5CzMD5gnCjt1gyBGeQ+7pN8OVz7Hl2sW8dubrvHfs/Syd/F1WTPgqs059kY3DzwehP/zjprJJgc4gMlVakmhajHqMgGHHNOz8mt5LpNN70MR46y6o7Avjz47XpA0/1tsUvHl7QThiqPYjSDP1xm2e5mXc2ZECMRkXHl7WhfeOvZ8dvacz4e3vMuCjf7BiwvXed41B1Joe9W2idsONAn/gofCZhzxN0+OXQ8Zz+E0kROgiWb7mNa+g8rHfCs1u7ycnyYQoXIxHnQg9R3u5tRrGgnIE1qu/hs0L4IzfFZSwyUJGTpSXJPKIRscyubkWtiFw3ZDN2OiT4NpZZEZ/ktEf/B9T/3sZHarWgevSf9WT3uZpxyI45074zJ+honDDHOmDFLPJi9JyxnHHcDNftByVSgsSgvpMhvqOvXnvuAfZMuATjJx/C532rPSOxVhEwuR3XdDvy1+C3Wth6pWR5FAmgjxovFkzXwsgup5awI/zZ0BJRxhzWqiGKU6lmEiI0IWhrCQB/SfBUV/xFr+VrwaeJ6WZ2r3eS+Y34gSYdIG67TwA2Qkto4LPIizazMTWH0f+wmpAxQkAkRDUd+zLjn4fY82Yq1g14brGRSLOf9FEuyQdvRWAkmSi4PoFwmPKpbBzlWfebEC0/4YP21fA8pkw9UooKYsc48mE8F7W0dfD9uWw5Lm84+F+Ob7f18z2NgJHfgWSJZFhzzLZjZ2STsw75m42DD2Hj8Zczarx18W2Af3vKp0ravRJcObvPM3f019r/DBBC0dJwiXx0o+g6xDPjycEfq1v4FxLJOCIa2HjXFjjOcIr+bmsf9czh02+GDH2tFCZJvNtEj6yJ+OPCYoRv1lU9CR9/kMsPPxXdN61iCNePJMpr17BhHe+x97u41l23otwyEWhEz6jYzZrQBT5jnOQDtLwlyRFrGxR3jTlIOtX6SZKWTz1JzjJDox794fgOpEaOtcNTpoMIQFPcx6Aij4w9vRIwiSz7gSNN6uZagEoOQ1nUp5KfMypUF4ZynZ1fAQaf88KkeO/B92Heya6VGFG41x1fCbjFu6IXBee/Sa4GU9QCxGzo5IjU9n7DksMGNym8D0F7pT992RA/sLU/174crgQMMmMHPXd4wReVFuZnb3/nAKBNO4sKOsc6Iguhbfv8WonHnZlQ38S9zvubOg21NNO5SDMmblAY/XG7dCxh5ftPqbPuJ1ntqWTLOfD6b9h+eRvNyXljNEbRmVVloq6Czzm6/PQz8Jx34O5D8N/fwUEy4iB656FTR/AJ34YWazYP7dC5+vkizzt1qw7gPC5USAfUrXwj2s9TeUpvwTCv0HU+8tFrpwIi1D1Q3eDWJJMsHHYecz+5NPs7TaO7tvmsHTSd3n3uD/jdC30Qcu/bvixOKfsKJNx3LoR9H5lSGf0GiefF6u+Y2+WTf4O3be+w4CVf48kaXGkMu/47nVe3rMpl0Ky1LOAhLwLmUSuQfOutdXlgwOMTBUMlpWvev4NB38K8HZTQc7kMqrIUDKVHSylHeHM2zxtwqu/LjjP7/xX4G/y4T89f5MTbmjMZhs1+GWjzbITWkkzFfCsMu2V/dlyoJzJvAFRwi6WEBmkgYgaMzKCwN++QPCWdYKDz/fGRa0X6hxtcsg5VrcP5v4Vxp8DnfvF3m/jsWQJHPllT7u0ZnbT5UIjsHJ+37oUljzr5Q4r86JPgzRwBX3KPI//WMzrDdOIJRLxWquwawd+0+O/52Xzf/VX8N6fCzYhiUwdQ+fe4jnLT/xU5D0X5oQLuZGyTl5NtsXP4mxfFUpACjTBr/wMti2Bs3/vmcuJcJDWcAeQ8ceM21BF5WcrSXpmxdqKQbx7wsO8dtYs1oy9ChLJWE1aJIGLkUuR40VGA+07R4ZcxEUJRrb1Nd0w/NPs6D2d0R/8BrFvc3Aj4r9Nnvx+7y9eR1Mvb/wp7LlkyGPQOLRmvhaAEsGY/3fP6XNUk0Ni0EeS+XDh4ew5g2XEcZ5wfeN2ryRIA+rSmYIkhHm7/Zqd8Nx3PJ+V6deGP08O4shJFlmhLEMY/W3yfpMQliapBiIjbiKIlompzkQ9HTZmhJAzk/jPCbyXKZdBqtqrY0icn1bOH/Mehbo9XuRYA8JMt+Ab21Mu8bRLb9zW+FOgbxQ+zdSbt3u5nw6/Ju8cXb+RaId5ve8qUzsuNO9NEMsSwtMijzoRnv4a3df9N+/woOUPU7ZvHZz001gGqFStYNrVkCjBfevO0FMyGbdpc7NmNrz5B5h6Rb4sNCZTTeeFZXHPRWzuthg30MZvIxKky7oU/q7Rb5zradRclktuKkmScxCneY1s628qBIsO+ymJTB09X/tBYBuQILrZ45m0VwJs1CfyStiERyhr+kwprFcthdZ3R/sReYw+VQOLn4FxZ3o5SxoQqFKU+HBBeWACfz/5p14iwae+0hh5EuRzkkemZv7I06CddbunHZCA7N4lOyhVmH7QxJASAlGRLxHHcotAByFKFR/rCxSBqGeKm8xhx0uTCalkcwWaqaDrDZwKvcc2mvqktHCu65n4+h8Cg5rq7YWZYYTwEa2yCo8QLXmuMZ9SoAMqHgF2HBf2bvKiAA+5BCp65Z0TvmON81OMOKZLpjSdYb22IX0mS+HTD0G/ifR54Ro67/A2USV1uxj+4Z+oHvpxb5MVA7/WPJIgdOkPE88jMfdhkvXBdTShYQGsr/Ki97oNhpN/5numYFkoE5UH+XLCdLMF8RoZ3e9qkitK1yycRYH5VkJTE3XZuG8TNDVqOg9j1YQvU7HiWVj8bGC7uPWkkZAuexH2bvB8MXMQG6EcAd3SRC2N1ndHhpDWfix90UtE6FOv66oUw818vt879YBTfw0b3oeGnWPQzr6RYK163WP6R305MneOLrITXtYPAoIXQDkypadNiPOlj/SliPRd0idTUZocMFuwg/oObCeE55ew7h3YslgurH/lfz1zzvQv5EnWThFZ1wsE9OFXe1qmN2/HcdzI/Fb1Gccb507aS47qQ9jiEWfuMClrFBpVp+kMG3c/lFfCxX/HrejNIa9fQ8d9axi+6E5KUnupOTY8wtePXDkUO46O+BKifh8DVz0eeorj4gW07FgJZ99RkGtNd9MUdK6UG0DMkh1HtrTJVES/8YV4ozTQ6kRB5j1FycqoDRWEj9PVY64i1Wu855dbuzv2HvxolO1zHvDy/h2Un58sSANVkhRS+aKCLSGtj7q0vjsyhLQ9ecEML9pgeH7SSz9xkjXLBC2uoYnqJpznDbb//Ax2fhSumUrVeA7r3Yd7zqz+60fcj6wiOBsVIfOMWQSaQg2FQNT6F+dUGXU0MgIrxrQS5ecRN5nDCLgsmfKbnEIJ/aQLPEfyuQ9HPk8jmXr7HujU0xuDOQjVrAaRrIpeHon74DHSuzeGPwSQqdkD79zvOcz3LMw3E2oejyGr+yWPl0yh75B3HLsZ6dyX9EWPI9wMU177HIOX/4WNw86jZMDBsX029pEzBmLn64BDyAw+isHL/oJw0sHnrHoN3r4LDv9CgRwE/ZIwWWTfZyIhRyziNVPRCBszcf5aJu4HppumgihNCZIgNc8Vj7uJUvac9FtPi/zSTQXH41YHAbBztZcSYcplBRaUIGIuLQutZqo4kJoYtbth6b+98iEJ36Ll+3CyZpmgSVAWphIXAk7/rZfn6JlvUJsqFHZ16Qy89n+wY4Xnd9GQrTcXUSpdmfpZ0DQoo8LUg/r1O07KLURR97t/hmLWMTX4WHx6gyAnUplszmFmDdnFyD+eQt9vZR+PmM/7Gwk3uIgpNIyHnau9IIapVxREjpUkE4FjuCIgWSTgaZmcNIl37op8juTcP0Pdbi+tQgDChGycuUIq+jAE+8PMVyox38r6jWPBMXdSXr0JVyRYMfGrUj4jQfcns5jUT/siHas30Hv9zIJjydQ+Sp75ipez7MQfB7YP1kzJR/1m71c28ipOzMa94XBivv+WOV2fv7DzpMx8BmM/Sga7A6d6qTXm3JcXYALxpvOEEJ4FRQgvktWHQO2S5jtKSmq0Whqt745aAoufhUxdYxRfLpQcPXPgT1Ln/RYxWLoOgk/8CFa8TMdFTxQcTm750HPyPeQSr0xEAEx251lkB6Vq3o5y/3uSGNyRCREj7jeOzMbdeXgyVgnzbciCEptDxljIKqj/p1wKVVvp+NF/Qk8pSQh4515AeGU4AhBk6gsz/9FjBIw7i+S795NM7Qs8RTgpyt6500swOnBq4Dm6JuOobxc3lkO1hjLZmMPMfJLCvXbANN47/iHmHf1H0pX9lchJLoGUkUupUZ+kumIwQ5Y+WHBs9LxfI3avhXP+1Bhd6UfQd1Ahf40uBC208AVpUUtLEkYLbxzB0/X5C2sv5ZBtIPdj86id8H0v59lT10O6Tqod4G3k3v8LjD7Z87/zwcRk7B/r/rWntaB13tX+xvwZXhbmHAfcLKQcfwMgRGGG49jBMu3zMGga/Wb/hNK6nFIzToYRs77n5YvxOYXmQjqJYASy14gzefnhN2vI+FxFRr5EHIvjhXG7pjDhI1ewVW9BCcutoiNAYjVho06Cyr50WPBI6CnJdK23cxx3hlcwOADKxWmPvh5Rt4eBK4P9cvqueZbE3g1eKZoQlAdE0Mk4K0eNl1jNVMhxExInO986lCbZ3WsqO/odo+z3kXt/UgQlkWTt6Mvptv19umyf1/hzj02vM2jlY6SnX+dVDIi4V5nfwlCSENK+QxA/j+OITZCpOsx8rdJvFHR9/oLaCyFH6KOITZz5NzqliPD8+864xfOrfP2WxmNxfoily1+AfZsLHM+zCHofsmMp6UuK3Rr9paAdkqnYiLF9Wz0n3ImfCjw5Nr9PBAoT68W0TSThzNtJpvZy0NxfNv48ePlf6LJjPumTf+k5rIcgtniqBBrJlKJmqkCDJ7Wrj9AmRJA5IcLz+0B8BEsY0dNNxiorBEwcJ3PHUllJjKk5WQKTL6RkxUzKarYGntJl+T+hdpfnHxOC4GeNuN+BU8kMPYYhSx9EZOrzj7kuw5bcS6b3uLxwez90/SFMsqeHVSwwMVXLmslzF3eZhT6vj6S6D9KG4eeRLq1kyNIHACip38P4d25gX5eRpI+7IbK9fxEDNZ8pITxzjKw/ZqwpKWa+Bo1fmXccObU0/e90NNAyGm8wi1CWKnM1+iQ4+NPw+m9hy2LvWESOtUQCEu8+CF0GeW0DEGy50VtbVbS5LYl2R6ai0/vjJTl0M4EmPtDXTAWdK/XR+45nzbhr6L/6X/TY9DodqtYzcsHv2Nr/eOrGnBPZ1CR0P4tkw+5RhlhEXV9qAYzUJujvqHSzkctpphQJRg6CTVjquzGpNodcinAz9F/9r8JjrkuXD+6HvhNh6FGhlwhOxhrdt3Pkl+lQs4l+a/NDqntueo3K3UupP/zLkatVIJkKSzOQg+i6fhL+S5pZlUN9phQ0U1moLgqqPkgAmdJK1o24gD7rXqS8agMHzf05ZbXb+PDwX+PmpIQJg3Tm9RCUJoS01ttUA+1Fnub/JlMTMNJkLEGmgm5LWkbktJfdaJkE1UTXpcz545O/9LRUT1/fmGwr7NqVVWth5Suer1QieHwEWW5UtJx5/oKtMMcUtEMyFVs+ZP4M6D0O+k4IPEcnVDXsXJmPns44rBz7Rao6D2fcuz9m3JwfAILFh95IfUxJmChhLKuZyg5ymR1R3vVzTQ6SuWciC0LH3G+kf1icmc9gAQyOaJITAkFJIHWioaSER++DcAZPZ8CqGQVRGN22zaF024defqgoYuN7T4mERN+jTmJf14MYuuS+vH6HLrmP2o79cHxRg34EZRWXIQtRi4aMliiYxOltCISQ95nK/f4q/kde33o+SGtHXQbAwbO/zoCP/sHqsdewp8ckqbbSmddDoKKZitNA62iSZRbsKGIe912DSALIywghmgrMS1sTNN0lsv2FIY+sVvb2CNXatzyH9IhrD1j5OIgkHHpZZN+F30ZvbVUdgy2F1nlXBohadMurNsDa2V4ZjhCUJhN5E1oneqXxb4nJkcq4OMlyFh32MzpWraPn5jdYMfHr1FUMiKw5B+E7GdVoB50wU53BbZLgzqR8iEkofKAfhmSdscAITw2zsez7FVMupWLvSrpun5v3++Blf8Hp0M1T3UegcPzKmEgEq8dcReXupfTc9BoAXXZ8QI8ts1lz0OWIkrLYa/i/vczYDZvniUS8OQgKn1XWtyfIZKxCbvLmjaaZT3VnXlcxgC2DTqbb9vfZ23UMKyULQkP+eJU1L+bC07DKt4mK6pXROPoXbKlyJVHuEhLvWre8lr8P2e8aaeI20EwViNjJF8KIE7xUCbvXB357kamn74oZXn3bLgMi+/YH86hopnKJlzXztRCidi9dVzzl/WNiOJmC/EVEycynEQmYLYWyq/c0Vo6/ji0DT2LtqEsajsVppoKvH5ejxw8dtanfp0cGYb5LMrm89kv5EE1BKSsE/OOhJCmks0dD0/eVDoKYcC6Zkk6edip7jeqN9F4/k/TkSwPTawT1l4VMv0IINg0+ndqO/Ri6+F4Ahi6+j1RpZ9aPuECq9IVfyySTZiBsnss6E+ua83XNg0H9qG5iGseDxubno3HXsrv7RBZO/w1uMp7gZpG7iMmQaz9KEkLJhSDq28tcx/9uZExnUcRHhhQFh/3vPzIVVkMWoDTGRK6k4RcCzrjVS7j73LcI6rLP+pmU1m4PdTzPRe5YSiaE4iYk1zzeOmlL67wrA0TtbDov+6cXot1jROQ18lmwvplPpm1uzbmVE7/KB0ff0Wh3TsdopsIWddVoBy0ylVd3S07IhmaPlsjlFW3rj/NrCE6oKlvKwH95WcdhP3lUfc/KpX7KO7N96Gn0XfsciXQ1AINWPIrAxT3s8/H9+fqRNc+4yTLWHHQFPba+Rd81z9Jn/YusG3kRmdLK2AisoH5kNBmJBl8/P2T9c3Ty+4B5nTAT3w9VM1/u+9nXbSzvnPQk+7qNazouQXRzCZROFJVnxpUnU2HfPpkUemWYZOZ4VHJeKTJllgcpe48qJFk3vYfyprTHcDjhBljyHD1WP19weODKx6irHAQjPx59w5gRotzvaqP5Wghhu5dOe1ZQtm1hbHV28H04hY/uF3IykyMdUUkzFeszFeL8qKiZ0skDU66xKIS9D5lFO0ogx8nqML8nGeEshMgjraUlwSkPglBQrFjxPeuYdXaMuYCSdBV9171AIlPHwJWPsbX/CZT1Ghbb1r/jVel3/YjPkCrtzIS3v4Mrkqwd7SXuk6m37R9/siQu6DvIfhtd38hkUMJazUVQdTxk/cuaKzpOhuiaaNKgwcynopkKeTbZvgvHkplmSkYLbeJnC3q+cEF9yJA4LfP4EV+C/pMZOOvHlNQ3lZrptHcVPbbMZtfYi+N9LcgnQSZpQVpj9nNoh2QqbED2W/MMLgImRjvEQhODVrX35wqepKRJJx1BmOIqdYc5P+o6tqqgLCezuLRPjwgOSZerlB52zfiknkFaB5V31EEznN3vv6CaPFAneis94HCqKocxYNUT9F37LGV1O9k09rPSAQa5CfGk6qk1sKVMaSXrRl5EwkmxcejZ1HfsI33P/jEh+7yBRck1ouqC/o6CP2mgysKQOwdU05GA98yy7ycuMEOGeOpsmvx9qORxCg8Wkes7l7glEvLPGHSLyaScKarQSV91M6s+HnT9tEKTzkYW9iyBM2+npHYHo+f9pvHngSsewxEl7B53QfwNk0+CdCNZVdfklkTrvCsDBApT16XvmmdJD/kYdO4Xe43sh9PdYYA8e44iTHFkCkImlaJja5zQDUJuFIqOf0AuZARWGDGVEdRBwlLX+VGFTPkFl2r6iaz/iErdxA5lSTYMP5/uW99h+MI/sK/LSGoGfky6fb4mQm0crTnoSrYMPIlV465ValdoXpTUdGpG5EEhmVaKLPKX+tHQOKoks8xFSUKBTGmmDMmFaUh6QohYzXEuTIJFIP+dypaoEkIYZej2WwKUIx4b7lOlpFdwMmF9Z/vYMTXgEHZOvpqBq/5Oty1vk8jU0f+jJ9k68ESo7Ct1z3kR4Mr+gsmCa7Q2tN4700QQa+28cyEV+z4iExOmnYWqQ2AWuYue7GAxJVOBJR+Uw5fVyRToqaebu0iyDA9MJIJynOhppnTKaYT9Ldte1VFz47BzcEWCTlXrWDfqUjqE1deL6BPkHdCzSHXoyQdH30FtZWE5Cdk+kwmhXFcy7FpRMIks8i+8Km2hwSlbc3ddmgzW7oadGwYdHyQdx1+V7+nvL+93DQIpE8iQhUlOuMKSMIrjQUMzFahtl7jfsPkhk9tt1+Hforpi8P9n78zDJCnKxP1GVXfPfTIDwz0MIMMplyiHIocr64WuiogXXriHq6vrtcqKrnvoert44aroT8X7FpFDEBYQGe4BhvscYGaAOYBhpruq4vdHZHZnVWdmZXxRXVnV/b3PU09XV1ZURGZGRnzxXcHeK05nyX2/Zmh4A6t3f42H24NcMI/7kQpTXSTtoVty/29oVAZh7xO9fsN38kuq8ItqE+o5TiV5x2I6oZmSbqcgUU9LM15nac+KPsiSkOm0sj7lWlf+vkLrWF/y05oMz9iOR5cczcjgHB7e9eVeZqjm1WMBn7J2x339cnwCPgK2qGgVsP00lfJ8OeDuq6+WcqxspXDZLLM6+PkgxfdQ4qtSKWCGTxK6PUuyz/oIcSEReeODGWSLWZ/xIS09S5GULSEZ2wemzWLVIf/GrCfvZfm1H2Pz7F14fNvDC2vUmpUNE2viLoPebZmQcTfJNtjugXN5bMlzGZydvTVLEqkwJSlr84QpoWbK92H23JZvlAGJZkpo5ssaGItGb41TxftopoSJFuPs8jHeZr5oIvOZFOKJ/pZn/RdXH/cj6oOzvAISfH1ksqLqYgplI5cKUx30hfN5ZsabCH01U8UFovFlO6TpEQTWSMZDQ2fMfEXvT1LjV2S/0NHvBkRphu4dNygy88m2zhmoVlLTKhTal3LA8PiSI3lo6SuoNEZYvew1YCqFhcBmZYPAoy0wZwAAIABJREFUxB2wCOkGk06YGqg2+8fMX7eC6U+vYe2uLyk8CMU3vIjqs5X4AS7aWfLkpTxBK2ZcWK5n/g6Qa6biju01uKe0rcjgk51WoaBjdYCTaFLT56P1M6Z54vM17fiaSGBsUByZvpDNc/cA5Ns2+Gx9k4W3X47HNUqbPCSCY9uNpHPq9S0L0aQgXGFXTPHtWSDHB8mj/lFhSmBiMcZvfMkaC4qnrkia+SZ+kRcTsnecC1Yqlmw2JrXvFxTi0p6RImNEfD1uP/DD3LP8HTy47GRAlotRIhQNVtXM13WSA9WS+39LvTqDDbtkb7Y6vnykdhWobOJOUjhHT54wVaB8aFguyIWpeBL1cWBPE0amFXB0zpp8ij6U45L5CfOHhQQl+Kq2q54TJ7jzbC0iCRAokkg1WWcahf1yhL4UIdnpYWziLpomI6bZh86vLARqpqp+Anao2QzG+oHENNNOc9lKaHulfSnd/04mBPo7oPtFPEL6JtRFF02pqWIKXN/4etaG5nHXAf9MfWhO0+dFiMdxyWJioFI8LU0ZBAlTxpj3GGNuNsasNMacY4yZ3qmGhRA/BKYxwrYPnse6HY6lOm128fKV+IbL7LrJvxNNyMbMMdL+Gee98VlRdVozVfQ6h+wxltxiyNdvJDlp+qyUwV1X3z6YFpnkM3HGgq3Pfo1Zfa7oNRYLUy2CU6XiKyRH2/V4mmWkAQkxEo1jTMX4ClNhmh5IjGmCxaXBb7GWJiRAcY2jMWZUgyYxZSaR5g/zdrMQLJqguR/6aEhT/a0E+bRifK6Tr09xEt+cZd1GPOMbY3YE3gUcaq3dD6gCJ3eqYSHEN3fhmssZGt7AI7u8xEu4qURCgqSD++YGyhtninSbTghTRTIhp+HCnj3NUML96jIz/gonbO+NnavVwhs6J0maRkKEcx9ar4lkf0mfATJrRezjDD5qWvf0v0v6f/hqiSTnCs0TvmSfMJ+IvFZ8faay703xc5YsmmIkWu/WNhfacDtBPOn7LZjGt9Pn2Yuvp2RsGUjZ7LsIrf6NRetN1egWTKvQ2k7fBcyookJi9fHUynabUPXJADDDGDMAzAQeCm9SOPFDsOT+3zIyNI/HljxXoFGQqRRjybnoYJm7TUqB+ls3ZhbZlIX903dgh/EP3lBBASU0mV/SvCi5RkMDFdFqKtnvZHmF/OtMXuOq5wA0atIJ0BDFzPQwuY1uqeF5b5KTgE8OsGSdsh0AXF1SzZRkMgF/gTxL6+bjWzZYrRTO2dSKS6rrV2ZctKSnwCoSptJ8prrkpC/tD1I/rdbnpKgPX6rW29NEHqSZ8tTKdhuxMGWtXQ18BrgfeBjYaK09v/V7xpjTjDErjDEr1q1bJ2+pB/GAPDK0kNW7vQpbHfIepH036IzxVYnn7pJesJNKkoUmCTHz+QtT1Sbhr+iAlzWwSTRTEr+ywaoRlUtOftKB1pcmJ3LfRUQknPv0o1kZeaxmDhbPbyXJ9g4twpSH8AZyAQ7GhBTZ5r8h0Xxyk2SRz9OoVoxXzqYkvmY+SPED9d56JDbf+gvz7T7LLC/MTQj5KSzykPpptV4Xn0VISLJbcONoUV/KVioVI0ow3S1CzHwLgBOB3YAdgFnGmNe3fs9ae5a19lBr7aGLFy+Wt9SDuGPdftCHufOZH2z6rCh5O3Pn4ZszJG9QLfobodoPKdIVVXKlWXRQD9nXD2IN2Nh7XwarFdG5xvdD6icjKZNcPUrP1acfzZyWfg9nZXyeVaf763e+Tf5Lvtt4DMhXyaNaLUHEb4jPlG+5rGviMx5KNNCjCIqND6rx1EyNBhbIfJ4Arz0QIZHFXJoAWaSZajbzFaVVePJZhITk6wN3naR761WN36bZ3SZk5j0euMdau85aOwL8HDiiM80KIzVizDexnlAzFT9UxbMUZ7ercGK9wCzFklUCxIOsf7nkvSgqTA1W06OCikQCxsRaBJmfS/F8Ks11ytX/IBOmkgO6RJgaqPid66yhgdR7M2uaf+Z13+uUXBn7P+PyexNq1umWMDU0MN5dYdqgn+/fQICPl8sz5d/mvP/blo+0q75+sslx1OV48w+skT7norFFOO639omQhLW+2mCfjeJbqZhJqpnCmfeeY4yZaVyvOw64tTPNCiNtOxXfCbQiCFeFhM+JICvs+GP+JixZYj0ZlYoR+VJMa9JMFSuftamzJLu3dFuMkElXOhlJxp3Q/dQGq8ZrpVytmFQ/DL/8Vv4O6K5cckLxHNgDNhwe8HzOk4RF8/mXCd7upCL3maoY//FlnDAl6BMhCZdBoHEJ8AWSlgvRQDctaIW7QaT9344B4TgKTlsoTePTDUJ8pq4CfgpcC9wU/dZZHWpXEGmaKV/bblUQqQb+kS95D0Fhf6DkikqYWE/CgNCG3ZqnpyhpA1y3/BokuWAgmaNHOhl13+m9KjBxz2zRQvlopWAs4MNXjS9NqwBjSVVD8slJBLGKkU8KEi1yqEN3SEi6EZhmWrXN/nukyoSpoFxRUT+QCp2yRalc+JOm92hVVHjvAxvQl+K5tVfxG/FasNaeAZzRobZ0jLQb7DuA+OZzifGNfMl7CCQ7pUtWOOLUCMIV9nSp42TLd31TFYw6HAsFjCJJVFsZCKgzrteX0IAEiYP0rKEqjyb+94nkA3cvRZq/gbC+H+rLJi0r1UxJZLDxmzoLJkChxkWy2Ao180m3HQkRzAcDfaa63d5kn/Bx1G/9ru8+sANCdwmQpeLpJj0s58lpvWFFw++TuG0Q/Ov2jXzJ8gUCj0i35LlKJs+AgTLUQTrEXu+7ehyNGJMkNhVq4UJMSSDTYHQigtD3vrb6TvimKRgUTthDwYKjrA/H2ixJWTcpeBcDZCb50L0Eq1W5r4oRzDCtAolvH3bCn3/FyevkK5yE5CYE2XOazM/mOxaKIwHHBQdINFOya+S7NVG3mZTCFMhNSTFuk1rBqtNTrZ3lCwT+Yf8DwpDTEOfoUAd0nwmw9cH1va8h22K4++pdLDgjvkgzlWioRECpCgTHkAghcAsgyX0Z3R+yy+HW8SWWlHW5l7o3KYxbhEhcHgKchn2J03PESJKqijRiCfOiWKMrXTQJZ2JpShFpqphkXkNfJ3+Qzxng+lIPy1JTRZiSrc4lVCr+mzGmPbjGFH+gQ01JUlznDohw89QYhmwJA2HbYkj9XHz3aswq70MyrYfMQdo/23Wrut/XrO5MMxKtahS5JXaQlgnJA5WKOCu4QT4pSJ630FQDIVFUUg1c6J6WknsqjY6LGfAM3GgqKyw3VK3I8v0Njo3Bvn0qZIEYYqpTM19JNPvl+GumQm5aSERSjE9o7pgpqdvClNAnQphTaHz2XX9fComTM8j9w+KEfNJ7I9UIhOwvKfEXTMts70NILqNqgEagWpH1YWmACkSaKVFJGeP8XLxNWAGLS+E1iifqSsV/XJP6pIVGwYZopqTCqtTZPsR/tKy0IAZ5sFQ3mLTClCT8PklI2npvzVTK9yWbdEofZCnSlUJs6/d9kMdnRvYP8Q7xXZI+yIPVijiDtHQyGgvVFg54gtVqsoi3z4kwCzQ4fyup5s9Io3aFmjSIJ4XuPavJ58ZnQ9yYMswrIVGwxggd0AOi4yBs7zipmU+aAyy+rkOCpLNhmqmQSFb1mSqFkLBRkJlXpGXTBjefjho/wFIVs5TY6VLCkGBFFZp/RupLAXItHMgdYiFkxWpG6/ZFavqK76cx/vVK02xAvNqVm/kk1YZo0kIEKUnJwepYokTJRryhphkJowvELprNpBnFx+oMC/uXMCj0NQy5viFjS5yORIIx0rjz7jBphalQlW1I2npfISHt+z6CwqgTbkCbpUgHu6ogeit0U+eQCVCaxBXCVqySaCiI8jZV5SbNkChCSfCG1P/O1Ruy153QcT1w01VpSakcNpawVuLyEKZNkBC6PYtUW1Op+G8OPlpnpSJeNEnFBOlCIkTzF7JzAMjnKRe4ISraFaaEMCWN3pLiO4GlPYA+e34Z4xyOu23mgxD1dNhedyDTTJWR42SgIls9gnzSdaZFqbZGNnmOmg6EYd7SQTYkD5L0nob4foQgnXRjrYt0I95un2postuQFAW+m4PHSExmMSGJk8UaIrGJMPbR7a4WrtvmcV8mrzAVkNAMwrQ8vp07baL1HQyCNiMNIMw0I/BrCLiv1QDtUsXItURhSRrlZr4wwdG/3FhWcOkEKO9LYs1UwIQQ4gogJVQzJY34lWrqxabxQD9QabmhakU0X4BcgAP5fQ1b5An3nw1N99LF7P/dJCgDei+T7CSSFXrIQOnbudPaJ9K6lCFMBWgTRDmUooFOIqBIk4xCWKLFEF8t6R2V5m0CuckteB/CAIFIvo1H98z5ZRKylRLINXjiaNRgzZRQmBqoBAd9SAgz84UsCORzo/Qahyxoe5nJK0xF0UUVYcK5EJ8p36JpD6HvgxkiKIQgXS0YE5ZWQeoHF3KNpEXDnJXldYb4s0kIMfNBgJYzIA9SyAJEKqyGINZMVWOfKbnpt5uMOUh3W4iTC1OlWAYq8iSYg8KtXUITEYcELfUyk1aYgjCzTtik62vmG99JfDu5NOlhKCHampDEetIVttjMYQxWsjkffjnDWpGuWIPCtIX3NNSXQiooSDZmTpaV0k8+U+ETYHfPdSzZbXcFc6eZEhUN8rOVJ3GVL2il1oyBwOdcrvlTYao0WnPf+BDkgN4BYcp30peGs4ciFlaFtv6hwElBHG1j5AOeZC/AUKoVI75Gcj+tMM1UkDBVwqIpJM+UFOk1CnFAh+4mGYWxZ6bbEWNBJvkSfHpCtLJSa8bops4B6UgmI70t6gUitQlDWAimr6Nymt1btOdRCZ1UvqIKy1IsnbClZoOQKxtiSgoJLZfvgSUrNy1wAgzaZkIaHBAUtSsu2vVFXqhDd7cnwJAM/hBmHu+2Rhfk40tIig6pv1Ucad7te9PrTGphqgxfFZANPOMSUgqyFPeT2UFqgh3L3CsPfe42ZUVZSp2ygzVTXdZ+BCVj7aI5vxOEOnR3W2soZTSDf5e1H2VE3oZQqciz00tdAeIyKkw1M6mFqZAIt5CBUlIyOQFJd+Putl9DCFVhdFx4iLeomMu+20e+FJWKfKUs9pkKvTcBminx6ryL5vwyiTX0ZQj2EmIXDZ98e0nK0PSEXNkQbVhI31czX+eY1D5TAxW5z1QIks4Suv1Nv5n5pJunjvpMCQfZoFwwwuGyDCE3JPhC7uRczoq1UpFrBUIGdultLWNMGtUmiAWF7jdamtgXwpyyQ5zBpYS0V56dXr63qnR3Bei+/123mNTClDRiDAI1U4KiSQFKEr4ckrytDKSatND8M93O9wRh2gCxACdMCQIBjvaB20yITcYBOcDC/J66f1+lDERjYUgqk24TtIG1sM44nY6szhLMfCEa8wBBTJolfjIzqYWpwap8v69uE6qZqpiwiaHbVIRRKAPR/lndznESQpBjapDvnrysqL5owhY73Is1At3fiNeVlZUry7dG6kNXFtMG5SlFpJgQkaiEqSZE+KuYgGCTwZBs7/0xJ/syqYWp0O1Dusn0wWri/eTXTJW2110ZuWACRtkQ3w9pvSHdyCUC7K6TcyVEm1CCaSa0XimDfeIvFSNZVIYSkrepjMsbMo6GlA0J5OmvXlicSS1MhfhMdXugTApQMs1UvwlT5WT3DvJzESbtDLktUsEkZNUZIvyFZCmWm2bkpo6QZ6aMwIIQQpIeltFeaVQohCx8wvphtzEEBJoEBC3101ZK3WJyC1PVcgQMyWTU5DMl0EwZU47EHzKJhWTC7SdH2jImIhPQIYI0U0EToFy7VIbTcEhXKkPrUobvXghlTNhhgnln21KsTrkGOmwrJRWmWpncwlSlJAlDwFCTz5Rs37l+So1gAjQngyGRJAGaSiNUTZXjmFpOCHKIZirE1SrEUV9KHz1uQDnJY0MoZe9DyulLUkyAKi0s+39/9aVuMKnFy0qAOSgESWdJ7i8mUW+XkRYB5AOPWwH2l4q5275AITjhT1g2oF5pWgRXb0hfktYpp5/M6hB2b8qgHM0UfbMAhzj4QlY2ZM7ot77UDSa1MBWyb1EZxOGmkkGkrFVyiHlF2uQQX4qpRBkJGstYsZbl59JHQwsQmmOt+5TlolGG/10IYof5Ptvku9eZ1LNSHKrdbaQTykCc9VcgTPVbuKlBPhnpg1wMuQ9SyCAbYuYL8ZmS+tDJ6TfNVJDPVAnnWkp7AxZ5ZXWHbu90ADoGpzG5hamS/EakD/JA1QRFb5VBkP+uODVCfz3IZbW2jMvUbxFuU4kgrWEH21GUMvpvWYJ5CGXkWAt7zifngz6pHdCrlaAUbF1nsFIR54Ipq4OWs41H/9zTMilnI155WbmPVzlPeb91wxBtQhnBLf2WaqDfhISQ5qpmajyTWjMVYv8ug5Ady/utb5f1IPdTfwiljHPtN1NSCP0m1PfbBFiOZqq/rhGUo5nqx+s00UxuYcr01+TpzHz9pZmSEqJL6LdJrCzKuE79tNn2VKPvzOOlaab6y8xXRv8P6UrWCrMf9ziTXpjqp4k3bGuADjdmggm5LSGmpHIm+/66OWWZg8L87wIKS+vsfpVB9Fs/LGPC7rNLBJTjMN9P82q3CBKmjDHzjTE/NcasMsbcaow5vFMN6wT95C8FbuUonchK85kqoc6QBzlkSw0p/WZeCWEqhYdPJeGkDErZraDP5gwIcZhX60AnCXVA/yJwnrX2VcaYIWBmB9rUUfrplqvPVDH0QS5GKT5TU8jM12+9sN+E+nKS3fandqrb6DUaj1iYMsbMBZ4HnApgrR0GhjvTrM4QEuZaBpUgYap/zhNCV0UdbMgkppSVfYDiT7xFUN+JNeXQb9epPCFZah3ocEOK1ist11/doecJsXksA9YB3zbGXGeM+V9jzKwOtWtK0m8Z20MIOc1+mxSmEqXtT1YCffeo9ll7y3rOQ/bvnCr02+K9G4QIUwPAwcBXrbUHAU8BH2r9kjHmNGPMCmPMinXr1gVU54+hv8aPSkW+Oi/PV6WEOid12ER/o1rD3qXf5r+yzHxThSl0ql0hZFp6EHjQWntV9P9PccJVE9bas6y1h1prD128eHFAdZOfEM1UeavzcsKXlfaUE+Gm/aFX6TdtQhmt7a80z+XRZ12pK4iFKWvtI8ADxpi9oo+OA27pSKs6xEC1UkrmXinVijw1Qr8RcpZT5Rr1I+VoE7Q/FKHfrpI+5xNLyHMj2T92shMazfePwPejSL67gTeHN2nqErIxc78NO5rFfHIyle5Nvwlx/RbNF4J8k+/+u6/9pDCA/ru+RQkSpqy11wOHdqgtU56K6b88U1JC2quK+GKUZSbpfp1KEaYPVstuQl+g/UmRoLq6HqLSZxnby0IvUTFK8WebQvdmsm6L0StoVnuln1BhqoeoGPnGzDoIKK2UYdbpt/3fFKWVqbIgmCKn2TVUmOoh+m0vwbLQS9S79Ju5WVEUpROoMNVDhGx0PJXQa6Qok59yzNQ6tigyVJjqIfotyaiiKMpkQwUqRYIKUz1EiM/UVEIvkZJEn5nJiTr4K/2EClM9RD/mOCkDvUZKEp1zlU6hI4siRYWpHsKoZkpRFEXpAjrXdBYVpnoMaf+eSg/GFDpVpYdRDanSz2j/7SwqTPUY8m0Q9MFQFEVRlDJQYarHUJFIUfzot73JFEWZfKgwpSiKoihMLXcJpbOoMNVj6MOsKIpSDuouoUhRYUpRFEVRFCUAFaYURVGUnkO1REo/ocKUoiiKoihKACpM9RhG4/kURVEUpa9QYarHUM22oiiKovQXKkwpiqIoiqIEoMKUoiiKoihKACpM9Rhq5lMURVGU/kKFqR5DHdDbo9uHKIqiKL2EClM9hmqmFEVRFKW/UGGqx1BZSlEURVH6CxWmeoyBqt4SRVEURekndOZWFEVRFEUJQIUpRVEURVGUAFSYUhRFURRFCUCFKUVRFEVRlABUmFIURVEURQlAhSlFURRFUZQAVJhSFEVRFEUJQIUpRVEURVGUAFSYUhRFURRFCcBYa7tXmTHrgPu6VqFjEfBol8uWUWdZZbW9E1u239obUlbbO7Fl+629IWW1vb1btqz2StnVWru47bestZP6Bazodtky6tRz7d06p1J7p9K5ant7t6y2t3fLltXeiX6pmU9RFEVRFCUAFaYURVEURVECmArC1FkllC2jzrLKansntmy/tTekrLZ3Ysv2W3tDymp7e7dsWe2dULrqgK4oiqIoijLZmAqaKUVRFEVRlAlDhSlFURRFUZQAVJhSFEVRFEUJQIUpRVEURVGUAAbKboCiKMpkxxgzDzgB2BGwwEPAH6y1GwJ+8wXW2gtyjs8FFltr72r5/ABr7Y1tfnsJgLX2EWPMYuC5wG3W2psF7fxPa+2HPcvsBhwE3GKtXdXmu7sAa621W4wxBjgVOBi4BfiGtbaWU/ZlwPnW2i0+7YvKPg9YY629zRhzFPAc4FZr7e8KlJ2N6w87AzXgjqgdjQJllwMn0tyXfm2tvdX3HLqBMeYwwFprrzbG7IM771XW2nMFv/Vda+0bO97IDjDpovm0o/VmRwvBGDMEnAw8ZK290BhzCnAEcCtwlrV2pNQGKkoOxpg3AmcA5wOro493Al4AfNxa+13h795vrd0l49hJwBeAtcAgcKq19uro2LXW2oNzfvcdwIcAA3wKJ5zcDBwJ/Le19ps5Zb/U+hHwBuC7ANbad2WU+6W19uXR+xOjtl+Ce87/y1p7dk6dK4HDrLWbjTGfAnYHfgkcG9X5lpyyTwNPAb8HzsEJuPWs7yfKfQE4DKeQ+ANwXPQbRwPXWWvfn1P2JOD9wA3AMcAVOCvR/sDrrLU35ZT9IPBa4IfAg9HHO+HGxx9aaz/Zru0Zv/tma+23c44vx82pV1lrn0x8foK19ryccmcAf427ThcAz8bd1+Nx1/o/csr+uvUj3PX6I4C19mVtTqu7lJ2CvZMv4IPA9biB4PXR60PxZwG/++YC31mOe6Bmt3x+Qk6ZM4A/AyuA/8J1ko8ClwIfaVPfr1tevwGejP/3PL+jgPcCf9Xme88G5kbvZwAfj+r9FDCvTdl3ATsLr//3gR9Fdf0/4Be4Afps4Dttyu4OvA/4IvBZ4G/btTVR9oXAV6Nr+qvofeb9LPibHy1Y71uBpS2fvyWnjAFOAl4dvT8O+BLw90BF0M4/Fvzeopb/Xx/VexrRYi2j3CuAhdH7xbjJ9qboPu/Ups7PAUcKr//C6Bl7W3SdPgL8Fvg0sKBN2WOAM6O+8DPgk8AeBeq8DZif8vkC4PY2ZVuf8+Tz/lROueuB7aP3hwGrgL+J/r+uTZ03ATOBbaIxZUmivde3Kfsg8D3gjcCbote6+H1OuesS768Adov7F3BDmzpvSby/JtnfC5S9LjqvtwMXAWuArwFHtyl3c9R/ZgLrgZnR54PAyjZlb0x8fxFOqAA4ALiiTdnbgcGUz4eAOyTPRFT+/pxj74r68C+Be4ETE8euLdCXqtF12kTz/HFjm7LXRn3p+Tgh9fnAw9H73PtTxqv0BnT0ZEroaNFxUWcrq6MBf0m8fztu4D0DuJwcoTMaQAai92fhVo9HRWV/3qbOjTgt4WW4CX6xx/W/Mfo7EA121eh/k3edovtyAXA6boD+CvAfOPX/89vU+QXgXNyK76jodXL02RcnsC/9J06Y/gJwF/CPBfvSV4Cf4iba7wE/wU1oP2zXXtzgnnzdBGyN/2/XDxPvT8et0t8U1f/5nHLJCfBHwHtwK+xTgQva1LkOtwC5D/hv4CCP638uTvj/Km6F/D84E9a/Ab/KKfdJ4Ns4YfGnOOHr7bjJ+NVt6rydFAEemEebcQk3Ub+Y6LlOvJ6PMzFllbup5f/tcYLGu/L6Uco9vaHlWDtBbE7Ud38A7Bh9dneB+5Ks8y+edf4BODZ6/zPcxrTghMF2wtS1Lf8via7RlcADOeVWRn+nR/doRvR/Ndm3s+4NY1ahGTQLku0EsVXx+bV8vivODOvznDc9723aOzt6vzR69t5d8N5cl/Y++r+dYF7BjQsXAAcW7UtlvUpvQEdPpoSOFpUVdbayOlpLvVcTCTbALFoG4ZZytybetw5C7dp7XdTmvwK+iZsQz8NNvHPalF2JE4gXAE8wptGYnmxTxn2JBa+ZwCXR+10KDAKpGgOcANduAtyU8XoCqBXoS7HAOh83+X++QF+6Kfo7CDwGDEX/D+Td0+g7sQC2PHpWlgIPRO939ehL1wKzEu3I60u3Jd5f49uXor97Av+KE/JX4YT6Z7Qpe33iPq4uWm/yXKJrenn0fgHtJ8A34QTjrwIfjl5fiz47tU3Z3wPHZBy7NKfcFcDuLZ/NwWlf2o1nK4gWpSS0hNHzliucJL57CHAxTit8b4Hv1xPPyDBj2rAh2gv0O0d1XYrT2K3HafmvA44r2n9TjmX2fZxAfhlu/Px0VO9HcKbcr7Wp81M4AfDD0W98OPp8IXBzm7InAHdG/eKs6HVe9Fmu1hy3ED0wfq4Tr6U4F4qscre0/D87qvNzBZ7VqxjTwiU1hvNoI9QnvrsTbnF2Jm0Wo2W+Sm9AR0+mhI4W0tnK6mg4W/0C3MptRcuxvMHlJ0QmT9wq/dDo/TOAq9vU2Sp8DQIvw/kprGtT9j3A3ThNxLtwE8I3cILHGTnlbgKmRe8XkJi0KaaKPyzl88NoL5zcD2yXcSxztRsdv7Xl/ypO+PwJOQMtzULNeS3Hcge86DuvwE1GL4v+LyqYr8I5Ch/CeC1GXt//Ok4bNANnfn159PkxwJ98+lL02QE4U/mdBe7rApxAvZHIlBo9C5kaheiZiYX4XYA/J47lToCJ/ncy8M84AeNk2pgVQ17AM4E9Uz4fxPnl5JXdhXQN/47A8R5tMMA/AN8LOI/5wOEFv7s3zl/2lTiXhLbmbdpoqNuUPRx4TvQ+dic4qWC9L4q+/4LEZ5V4vGpTtoJzdn9ruC9eAAAgAElEQVQl8KrofbVAuW8CR2Uc+0FOuT8SLdgTnw3gTPP1NnWmng/OvLm/5/V+MfCf0vs10a/SG9DxE+pyR4uOizpbWR0NZ4q8G7gn+huvAmeTPwHOw/kp3YUTBEei8n8CntmmzjwhbUaBNu8A7BC9nx/d23HCTkuZd+Mmz7Nwk34sCC4mZ1Uffefg6Bxvwa02z8c5vF8FHNKm7L9ntQ34VJuyvyXFTBv9ZiOn3O9p8deLPl9Ci9kk5zdm4RYAvwYeLFjm4pZX7KczTlBvKTcIfAwneN4PNHBaiR8Au0j7UoH2vha3cFoTjREX4rS7q4HTcsq9BifMnx+198WJvpQ7PiR+Y7uoXx1EhrDd6bJl1KntnfiyGb837vnvxAu3YF+ScUzkuxja3ok615DXZIzm2wXYZK3dYIxZChyKW+17h/R61LkTznzzSMqxI621l3u2d5W1dmWBesVlM35vJu6hvqfN9+YAy3AC44PW2jUFfvsZ1trbJe2KyovO1RizL27FutK2CbHOKL8EtyI3uHMdd487iTFmBoC19umUYztaa1ePL5X7e7Nwpre1HmWeidMGfM2nrpbfqOIWC5sLfHcezrT5WMHfnm0TEUXCthlrbc0YM4DTSK+21j7cptxCXL+/03qkNDDGHIgz683DOWgb3AS1Afh7a+21OWUPwpkH59EcCZhbtqXO1nJ/Z629rmB7O1k2r7155zkhdQa2t+t1tiMvurNAWdEzFfIsBrZXXHaimFTClDHmQ8A7cM6zn8GpUS/Haae+aa39XJvyXRVsQtpb1rmWIfwFXqcggdMYcyiJXDA+AlkZZbW9vddeY8z1wDustVe1fP4c4OvW2md2umwZdWp7u9Le92YdwkWAL8wqm4dUOGlXLqS9E3WuE8VkE6Zuxk2WM3GmrGXW2nXR6vwqa+1+OWW7LtgEtrfr51qW8Cc918A6j8b58mzA+QNdjvN7GQHeYK19oJfKant7ur13WGv3zDh2p7V2j06XLaNObW9X2rsF5/Celoj0Pdba+TllRcJJoEAU0l5x2VKwPWBr7NSLsRD6Ki5ZXdKpu53D8c04Z9htcL4byQi3CSkb2N6un2sZ1yjkXAPrvC7x/d2AX0TvX4DLVNxTZbW9Pd3eLwG/w/ldHRG9XhN9duZElC2jTm1vV9p7BRk+m7QPbtkCfAIX9dr62tDpch1or7hsGa/Jppk6GxdKOwvYjJNoz8Nlwp1jrT0pp+yN1toDIn+Kh3EOd43o2Eqbr+kRlQ1sb9fPtYxrFHKunWhv9L6Ki1Y8OPr/Zmvtvr1UVtvbu+2NvvPXjO3MYHC+U7+2BXY6kJYto05t74TXuRfwuLV2Xcqx7WyO/6ox5gpc3rprUo49YK3duZPlOtBecdkymGzC1AAuA7TFJdZ7Ni56537gy9bap3LKnk2XBZvA9nb9XEsU/kTnGljnt6L6LsINequtte81zkn/Wmvt8l4qq+3t3fYqSi8gFU76TagpjU6ruvr1hYtMey0u/8sAbh+qM4EPECUinIiy/XSu/XaNAts7iMvUfiYu03Wc/HMG7RNZdr2stren2zsPl0H9VlxC1cei958kZZuZTpQto05tb1fbu8q3bBmvkPb23bmW3YAO37jZuESAN+MS8q3D7X33prLb1un29tu5TqX7qi99JV+4bNcfJJGrB5f/60O03zpHVLaMOrW9pbb3gwXKioQTabkOtFdctoxX6Q3o6Mm4DUhPxeXseC9uq4k9ge/QJqElJQg2ge3t+rmWcY1CzrVD7V3ZUvbUXiyr7e3p9mZuZZV3LKRsGXVqe3u3vdFxkXAiLVfmuZbxKr0BHT2Z8dtZXB39reByC+WV7bpgE9jerp9rGdco5FxLbG8ZfUnb27vtPR9nWt4u8dl2uMnowokoW0ad2t7ebW/0vTIEx1LOtYxX6Q3o6Mm4UMqjovcvBf7gcdO7LtgEtrfr51rGNQo51xLbW0Zf0vb2bnsX4Da3XYXbhPdxnJnkU0T7/XW6bBl1ant7t71R2TIEx1LOtYxX6Q3o6Mm4zU7/gkus939EO8jj9s96V5uyXRdsAtvb9XMt4xqFnGuJ7S2jL2l7e7S90XeWA8fTsqcYbTZgDylbRp3a3p5ub9cFxzKvU7dfpTegaycabXKbc7wUwUba3jLOtdeuUbtzLau9ZZTV9vZ0e98F3Ab8EpfB/8TEsWsnomwZdWp7e7e9ie90VXAs81y7/Sq9AV07Ubg/oOyECTYT1N6un2sZ1yjkXEtsbxl9SdtbYnuBm+JJCFgKrADeHf1/3USULaNObW/vtjc6XobgWMq5lvGabEk7b8w6hFtJThP+7oTsbh3S3l4714m6RtHxjp/rRLa318pqeye2bIH+e4u1dp/E/7NxyWdvAY611h7Y6bJl1Knt7d32Rt+9CTjcWvukcZu+/xT4f9baLxpjrrPWHtTJcmWeaxkMlN2ADrMd8EKcXTeJwfk8ZNJmwt5ugsqK2xtSVtrekq4RCM+1rPaWUXYStHcBsBC4G9jOGGOBPa21dxpjvobLOP6JwDpfB3wNuKcD7S1ULuIRY8yB1trrAaJJ6SXAt4D9J6hsGXVqe3u3veASzT4ZlbvXGPN84KfGmF1x/bjT5ULbG1K2+5StGuvkC/gmkZNoyrEftCm7BjgQ2LXltRR4aCLKBra36+daxjUKOdcS21tGX+rX9j4AnAL8M3B1XA63dcsePdheSZ07kcjR03LsyIkoW0ad2t7ebW90/I/AgS2fDQDfBeqdLlfmuZbxmlSaKWvtW3OOndKm+G9x9tnrWw8YYy6ZiLIh7S3pXLt+jSDoXEtpb0ll+7K9QB1Yi9tDcat1K99LcNsA9Vx7hf33wZxjl09E2TLqDCmr7Z34ssAbcfuTJsvUgDcaY74+AeXKPNeuM6l8phRF6S+MMfcCb8OtQt9mrT0q+jxp5jsbeNBae3pkYvge8Hlcnps68GFr7bejcvOA/wH+Gre59TdwSTUbxphT4zqMMQb4HPA6YBpwH3CKtXZlV05cUZRJxaTSTCmKMiVYgtsvbEfgBTj/jV9aa9fjBKl5wDJgG1zCwYdxpuIkfwU8D3gGbnuY5bi0B4qiKN5Uym6AoiiKJyPAv1lrR6y15wJPAnsZY6rAa4B/sdY+Ya29F/gs8IaM35iDE6KMtfZWa+3D3Wm+oiiTDRWmFEXpNx6LfDZiNuP8rxbhfK/uSxy7D6fBasJa+0fgTODLwBpjzFnGmLkT12RFUSYzKkwpijJZeBSncdo18dkuwOq0L1trv2StPQTYF2fue/+Et1BRlEmJClOKokwKrLV14MfAfxhj5kR5cN6Lc1hvwhjzLGPMs40xg8BTwBacM7uiKIo3KkwpijKZ+EeccHQ3bg+9H+CS/LUyFxfptx5nCnwM+EyX2qgoyiRDUyMoiqIoiqIEoJopRVEURVGUAFSYUhRFURRFCUCFKUVRFEVRlABUmFIURVEURQmgq9vJLFq0yC5durSbVSqKoiiKooi45pprHrXWLm73va4KU0uXLmXFihXdrFJRFEVRFEWEMea+9t9SM5+iKIqiKEoQKkwpiqIoiqIEoMKUoiiKoihKACpMKYqiKIqiBKDClKIoiqIoSgAqTCmKoiiKogSgwpSiKIqiKEoAKkwpiqIoiqIEoMKUovQw1tqym6AoiqK0QYUpRelhVJZSFEXpfVSYUpQexpiyW6AoykSjGuj+R4UpRVEURSkRlaX6HxWmFEVRFEVRAlBhSlEURVEUJYC2wpQx5lvGmLXGmJUpx95njLHGmEUT0zxFURRFUZTepohm6mzghNYPjTE7Ay8A7u9wm5Quo86PiqIoiiKnrTBlrb0UeDzl0OeBDwA6E/c5KkspijIR1Bs6uChTA5HPlDHmZcBqa+0NBb57mjFmhTFmxbp16yTVKYqiKKgWWVF6FW9hyhgzE/gI8NEi37fWnmWtPdRae+jixYt9q1MURVEiVNGjKL2JRDO1O7AbcIMx5l5gJ+BaY8ySTjZM6R46PvcuqohQlMmPPub9z4BvAWvtTcC28f+RQHWotfbRDrZL6SLOdKCpthVF6Sw6tihThSKpEc4BrgT2MsY8aIx568Q3S1EURVEUpT9oq5my1r62zfGlHWuNUgrqh9G76K1RFEXpfTQDuoLVKbtn0egtJUm/9Yf+am15qCG0/1FhSlEnZ0VRJgQdW5SpggpTCg0d8XoWvTNKkn7rD6r1VqYKKkwpmqW4h1E5V+lntP8qnaLX5ykVppRS1o795vtRFrqyV5L022OjWu9iGHWaakuvzxkqTCk0elzin8r0+PihdJl+E651aFGmCipMTQC9LkG3ogNe79JnXUmZYPqtP6hmSpkqqDCVQYh9tt/GDx3wepd+00QoxZgq2uCpcp6KosKUosJUD6Nz0eSkJryx/fasNmz/aeoVRYIKUxlMpQFgCp1q39Fvk6cysfRbd2hY23dtLgO9Rv2PClMZTKW+3eshp52k38wOtlF2C5Reot+Ea2v7r82KIkGFqQym0vM/lYSpfkN9ppQkjT4Trq3VHlwEvUb9jwpTGUylIUDqvwFTyxxaBmXIuVNJuC6r/0rHl37T8lj6r81loONoe3r9CqkwNQH0+k1vpVaXL3dDBDEpU2ngKWMimlrCVNkt8KPPmqs+UwXRS9T/qDCVQcgAUMZkHzIBjtSnThqIMgjx0ypDmJpKWtl+o9+0PC6ar+xW9D56jdrT69dIhakJoMfv+ThGAjRTOvG2J+QKleEj0+uD1lSm34Qpa23ftbkM9Bq1p9fnGhWmMgjTTHWuHd1gOESY6rNz7TfqeoEnJeLb2mfdwdq+a3IphDzm/RahLKXXh0IVpiaAfltlbB2RC1P9dq5lEGL2LWOgnEr3tN/OtKx5U9oPG6qZKsRUukYhlpBeRoWpDHpdpdhJhut18YQvHdzLGjvKqDakznIc/Lte5ZTCBjhll6WplE72Dau50ooQcl/77XGV9qVeH5cmtTAVpBEIUbv2+l1PUG9YGg25A3s/nWs/Ui/Baaqsezpcmxrn6kxfwuetz0w6DWvVVF2AfruvIUi7Q68rOCa5MCUvGzLI9tNzUYsma/FeYf10siUR0g/LyAFWVmLIfhPMQ4Q/6amWdY2ktTYacjNfyGK439J79Ft7y6DXh4dJLUyFEKKa7qdJIX6I5ZqpTrZm4ikjbUXIiqpWQtqKfuq/oYSca4g2V1prvWHL6cPCKmsNK75OIae5ebgmL1wCIcJUv+Xdm6zjkgpTGYSopsvQ1hhhuTjHlHTS7rcVVcgKW6qJCBkDQiItpZQ1aJVlcpMS4kcUVlZUNAhpe+sNK9auBqUUKeEahYyFQRpocclyCFlc9rLgqMJUBiEPRj8JGHFba0LbTi937k5ikQ8CIZdopBQ/oq5XCZSjxi9HmLJizbe1ck1PGYzUG0E7LEjpt8TJIRrofkN6mXo9AawKUxmEPBh9NNaNDnTS8y3DubQsXzgpciHMBmWnl1LefnVl1Nn959wGaKbqJaUakNZZa1hxKHy/+UyF9KWplOtP3H9tb2vhVJjKQKqpgf7S1sTq5RGpMFWCCj8E8XMcEM4uHddrjXIioSwl+ZaVIiTIy0rbWw+IcKvVy9FMSascrjUYrvVPe0MIMueHBDP0tIgxHrFWlnL8BYuiwlQGQWa+Hr7hrdRGfab8e7i1trTILynl+OXIBdWQ1AjSM7U9rk5vJUyL3H3th0tHIjcRljG+SM91uNaYUn5/UqaSZkrafxuqmepPQswrpayKhOVGoslacr4hjrQhK4wyHFOD6hSOk3EOsG4HNNiS1rri4ICSgkXEk0KAxrHWsNSFY1PIuUqe11q9Qb1h2TpSF9crpZ9ModbKg1tceXHRUgiJgu1lf0EVpjIIMfP1U+6l2J9B4tcQEuJdFv00yMYmWHE0VAnRZiHInbLldQYl55UKyQGTQiMgOi4ESXtjbYtU6xJylmVMutI6aw0bNt/0mTQVFLjRw6c6qYUpI80XgDN/SYWiUpwfhb1sJPJnkApT/eYz1U973YUGB8jNfOUMWmJH/YDeVIY5v9GQm8dD8jaFIDnXeM/P4Vqj674uZbgfSG9LvWGDovn6TZiSLw57+1wntTAVQj1AFV+W07CE4bpTwUvUzI2GXIgLuURlbBNUhvYjNG2FlLIGrXLuTYAwJc3NFuiALk5jIiol943cGo0p1sq0U2E7B5TgpyXswCOROVSCLck6IM+5F6ak6GV/ZBWmMghZAfaTmS8e8CQPR8ikUBZhmYZl5aQTdhxhKV21hrS3jNsaIthIBewQk1mII63cJCSfeKVIhevkmCIZX8LSVvSH9g7GIjQl80ZZwSJlJKwNCdzoBm2FKWPMt4wxa40xKxOffdoYs8oYc6Mx5hfGmPkT28zuE5K5t5/CcuNBbqtg5VgPEThLckAXa9IC3LLFUV+REDUi1kQEDFp95FsG8v4v1S6BfBPqRsCWMPWGPPeY3IdO1pNirTcIhSmxptJSQgCh+DkfDQIS9CdLgItHSVGW8qSd/e+AfjZwQstnFwD7WWsPAG4H/qXD7SqdWkDm3nLywPjXmbTV1+vW+3xDhKmyFFrS1WNIe8Vh5dH96L5mqhxTtdQyE9JUqaAKiCds6YRSqzewNmC3AlGpSJsgcUBP5JeSRQt3X/MXgnTxXQvY0itEi1yGlj5koVaWv2BR2gpT1tpLgcdbPjvfWhvvJPlnYKcJaFup1AJWgL3sJJdkS0vI8hbP1WOt0QjQ3pVzjcrIbC8eZBthwpSURoBfQwhyISFs42Ap0pW91NQxGt1ZgtlX0t7k9ZFcq263F+LggDBNsi+hwpQ8JYOomCsbsLWW+kxl8xbg91kHjTGnGWNWGGNWrFu3rgPVdYcQ34R+0UxtbRGefPPBhEShuBVV/2g/Qtor1XDG11YaWi6PIiwpYqyEKJ+Q/D7dzpcTksYE5BOgWJPWCBOmxAFADbm2ZrjeENcr1XIGmflsyF533ddMhZj5QqMeJ5ogYcoY8xGgBnw/6zvW2rOstYdaaw9dvHhxSHXeBKkiG/JBq4w8MJIqgzVTgZFFcp8IWTkIM/OJBwGhpmfUzCfehFpUrBTfhJD8SWWYOoLaK5zsY0253GdKVExszk+2U3KtysoS3+2+FG7m6x/rQIgmLcStpBsMSAsaY94EvAQ4zvbyhjkCRs0rZThXW4sRJMiS1DlOmPLUTI3UG/KBvVFOrimx03DIoNVwAlUFv/s6Egm3I8K9zYIGrW7nBQowLVrkWpfYD6Na8bs3IRsOS6NghwMib0MQmyUTY4NIMxViDgrxBxL2JeniO0TjGPKshsglYv+7RlgS4zIUFUURaaaMMScAHwReZq3d3NkmdRbZNgiujGTibYQ+yFJtgqDOLSONlv99hSl/p/UYS4CtP0AMk2sTyjDrBJr5AhJDdludHrKps22ERn75Fw6ZsJ0Z1f/mjApTddn2LN3WCIw0mfkE1zhk8RJgMpaPwbJ6w3ahCDnXsEW/BLdoktVZqzdKyR9WlCKpEc4BrgT2MsY8aIx5K3AmMAe4wBhzvTHmaxPcThHSrhJPuNIIlDKcdyUP1JZaq2bKr6OORNFF0kGgDMdJqZBQa8h9KaQRLKE+Mv2kTm/YMP+7kNWupGzIcy71/dgaPa9SF4RumvkajeY9BCWLLvliICAJZmBuq6DAAqEpVO4HKisHYftoBmmmethnqq2Zz1r72pSPvzkBbZkQrPXfViZ+EEUr1gAHuxBEwlSLJsrXAX0kEbo/WPWru4zEkI2AsNx6wIpKshdbcgKT5egJGLRKWAGGhEwH5a4RCo4hJgdpiHcyYGRrrcFg1c+wIH3cJDn3WrWp0qTAElx7u6/BkJp+RwLMtyEa3SALSshYKHXwr8ujx7vBpM6ALs+KLPeZKutBlrR1a6uZr+Zv5gOZGSpkhSIlRNANua+SlX1yYHUaQL/yIQ7zIwFpQaSEJsnttv9SPSDisVZviJ6Z5OLH1yQPYckdfc2SrecnSgos7IMjQVHGYSZjyRAxutOBRDMVECwSti1X97XetbotJdFoUSa3MIVsNRbiM1XaRqSe+VHS9oNqNPxWR0G2/kb3Q3rdgyxPUyAdoEOFKWdK9TSzBKZy6LY6PWSrCGuteKVcazFHeZVryK6xtGzSDC8RpqTPmyTnXutCTZIUWLp4Gak1ArZnCdnpwF+bZu3YdRE7oEvNzQHCVNDekhL3meg8R7ocfOHD5BamhCvWuHNKVue1ujNfSR2zpfhqelpzTI19XnyQTpr5fAlKrBeiXQoQiKSaE8mAt7XFydhXk9EQaqbiLNviLWyCTDPd10xJUxyMbULtX3Z4NJN58bKNhm16NiXClNTO56J2/fpDWvt8U6+It2epx476Ul9OUbWiMa3WGHN3kAgKQcJUwKI/KPGm8JlJ/u1FJrUwJfXLCRESxrQ13V3ZN6zfaiFrMC7qhG4TDsMyB/QwTYSEkGSUIRrHkXpjVJVflFYNoa8/mzxrdXRPhStAscAZ0B+k5xondwyJwPIdI6wd04T5lN1aazSNZU8PC/zohNLUcK1DwpQgWljCcICmh0DB3LcPN2WJF5r5QgJ5ur1BuBsLBZGsieetVzMxTWphCmQDyFh0RUj4cvcdeH36WKhmKjnQdd3MFxDlIxmwoAM+U75mkpb746+ZClsBdnsx4BLAhghT/uVGBSKhOV9Stum58Si7ebjW9P/TIp8p7yKA6xO+EYRp7Xt62H+HBQkh+bgaASZjSaBJU2JTgW9kyCIvxK9SquEfqbux0FfoHG4JvuhFJrUw1QnNlG/nHlVHdj2xnt9DlaXpKNpRk5OIyExi5cKJdCUWkhdLGm4dp4/wFYbGa6b8zXwhfV+8ZUmACVaejTzMnC+ZGGpCzVTTc+NRttVEJhGmpH4uEuEkTXDy1kwJx4eQBW1o6L6/MNXsG+k7ljYCovlCzlV8b4TmumTf61VT36QWpqQJ2Jq1LjLHy27fcN8tFLLaV3TADNVMxVv2SAgJrZXmxZL69EiFk1DNlDQzcnz/u51rKsShW5zfJza3ie6rzMSdzGbvpelp0UyNCExvofsX+mgEUjVTvsKUcEE6GmUsTMcguU6x+da3L7XeQ997GmIet8gXP1LNlFTQTfa9bisqijKphalO7HbuLUFH3/f1cYmRTl81T9VpVocs2lGTGh7JgxViNpNrpuS+cFJ1ejx5+g4AoVv9SPMnNfV9SX6rgM10QZ7bLSRDt6QfjgrJvuYK4fVN85Hy7ROSa9RoWG/NVFY0qK+ZryaMyIszxEsTm0oWITWhlrN1qyjfraOCNLohmimhskBqglUzX8lIo3ySJhXpJOibTTwUXzNfpjBV8CEJ1UylpWYoSogTOci0htIcJ7EPWshKLO3/dogTCAYsJEC+0h3zX5JN+BK5PGSD2dGIMd9JQXh9O6HpkZxnso1F/SmzovYk0Xy+gk0t8u8CGBbsaSnVmI9Fd8r7Q9r/7QjbOkeeOFksTEWCru94lux7UkXFRDOphSmXM8S/THLvK59UAe77sUq8uzfcd4WS9dAWVa2HTrp1Qe6amFAVs8RvSuoztVWwEmuk5FORaCFkW5aUo04PiQyVJt4MCaGXRowNC69vanScZ0SfbDHgrxHImuxGag0vTVOoQ3c3Nw6WLgZa+4C3OT8go3hIShFRSpDamKDrKxBJ+mG3meTClH8+jOHEygb8NEzJpGLdvuG+Pj1ZE21Rs0VTSK9wZS8184WE5bq/UrOkXDjxEa7TMtEP1/wifcRmycDJKFgzJcztJknGOtofBM+q1FzRtMIuWDZpaksi3bHAh2aNQEFhKue8fIQFyfZPzZsr+99XacoWqZ9Waxt9y9cb8i1Wum3mk/T90e+PqDBVKpJovtbVno+dPzipXgC+k2eWIFNUa5McFCUhvSN16+0fkKxPVK4h137UopBe/6zVY5vTFhWo0iYta/0GEecMKxcSWt8XJdTJWarlDHMi94+gioW+kEmh6BiRJTT5RnhKtOVNLg/1YuXzni8fAVIS3ZYUjKWLJtFejULN1LhAE29hTDYuQVh6mlCtt89zY61VM1/ZSNSYrX4IPn4JScGrFhCGL8EnT0+ej0lRbV7rdie+k+BIXbY/GcgjJeOB1jsKKjHAhgyWhc0k0fembX6Yva79OJXaFve5x+Q5IjRLhoYgS4WpeO822Wavsu1vxgQ4v8FZ4kcUkxxPigpT2f6NclOJpEzR/pf3fBV99uLnzFdLlOzzksWEb1R0jNTs29p/fPtTTbgggMAtpwTXtrkvFT/PeMeAtN/pJSa1MGXxT+Y3Ljmeh2aqE7lgpLgJpVgnazdYFBlMQhykrbWiLSpipL480oSUIaaDrYLJMx5Q97zx0+x85/eZ/+gK97nH5Cn1SUtO0KLQcuGqPk5qKtHojgiDA+JzbTT8tJ3JNoZEaRZdcGUJtb6TiuTaNkVRFdVa52iciwq90sXLSJMw1cWoXcHOAdaON9/63tOQxLNSLVy8GPcVdJP9z+c8W6+RZOP3bjC5hSnrny5/c4vwtGWkXrjTjA9nl/ic+HeSeGuXopNnO+1BkQcsJHQ/3o9KKkyNCDLoJk0zvvXWmvyIAjRTHj4nszesYrv7fwfA7A23eZUHNwD5Z+du9heUrAAlE1hyESJ5ZpxgHha163Ouye/6aKBHUlIGFFlwZQkgPto4J1yH+bkU3aw4b/wo2idH01Z43tfkTgMS85d0I9742hYVOGH8FkHgOY4mIqJ9Tb4gTykymsbE89q2RskXHcNbhSmJJaQbTGphSrLNRFpnLqphai3brYg+360t2j0D7Y43Gnbcw+sTWZR03pUIj7VGw/tBbjbN+D2IyYfZZ0IarjWb2or2h+Fag91XfpHa4GyGpy1gzsZV7nOPumuCyXO8tlGQZVswOCe1vxJtrlTLKfXhGP+cFyubvl+dXDjxeQZiQdnX9UCigc4bh4r2j9H9Cz0XBNc+Mj0AACAASURBVK3f9+2PdYHTO4wJCnWP/T/TBCAfISM0wk3qaxj3O9/rNN6kWVDTmdJnezFx56QWpiQPRqtmCooP8K2dQ6SZ8i7hbzdvd03aJZQIzXkTPwhOO9UdJ1FpskSQJ7JsHTyK9oeBh1ew+KGLuG+vt7FpwX7M3nibd91xvp2QLYYkA7REqEkKU61m9nbE5ynx2ZMIujD+PhZ2JE+5/0XKZt1DH+2sNHRfIkzljS9F+6NUM9V6fr7nK1mEQHP/KfqcpgUW+ASaNJvNZIsQiRYulld9LYut/d9ncVnks7KZ1MKU72SSFYJc1G+qE5op0fY3owNPZzpYuzakCZw+k6A0cWGMZEXVnEG3OzlOpH5l26/4NMPTFvLAnm/kyXnLmbXpLkxjxGuQl2x30tq+omadtHp9eLrFj8hL+1f31whA2MKn9TmXaq6zPmsl66x83BhGQ/c9UxO0+gAVeXbybkPRWzQi1EyFbs9Sq4flkwMf4Tprf1T/xbvMPG5Fe+zF98T33kg1U2n3sNsbsRdhUgtTvlmgM0OQBZ077f8iSLbj8N1jrK2Zr035NOEyTcDKoslXRbJha8M2+UYUqrPFz8VnwJSuAMf1hyLnevclzHvkSu7Z+++oD87iyfl7UWmMMPOJezyFqUbTX0l7sz4rUq8PrYK4T1/qVBJMn/vaKjwVncjS6ihSNk9gKjq8xdfGx0E6tT8UaG+exqyo8BcL8b5ZzFsnWd9JVxK4Ya1t6k9SS0ZM0f4kiQyNcX62MvN4PHb6jKFpSYSLR7OOr0fqbzuRTG5hylODkTVQFPVraJ3gJU6BEs1UzXPibFdFuwHvqRQtlI+jflPEmG/G39H8M56rooDJc0tTbiCPycjXbGYtXPRvbJm5Pat3PxmAJ+btBTgn9KITSxwtCX6TSfpk75k2QLCAaBXOfSJopcJUiGZqvDAlN/OFanqK3l1Jtve0tnkvEAOTQnpvJB2omRoWJBPeMtLsSF5YmMroc4XNhAHC1EjdjaOyVCR+i3cI60vpmikVprqKr29N1s0tMgCld5bumPni9tWEydt8SdMcWOtj7khqpmQOpr6q+HH2eh+zTuI++iVxbTGbNdqYzW77Pay+hrv3+Qca1WkAbJ6zG43KIHM2rio8yMfRkuA36KRP9hOrmbLWjtMI+/jfNZlgPVJHSKNRRxJpHGKKatLkE0p2Xy+qeR9NC+Kh6Ql1/DWNGode/Fr2+cuHwHoKNTWZmW/c9iyeqQok2zBJ0+lkzQ8iM59vUE1IXjfBnpZSf0FICG2Jvi7N+j6RTGphyjeZX9ZEUKTDpX2nJgnhF+0LZVPfTxRZg0XxSUXmgwTy/DNSez00n6/PCtDLbNZowB8/QWPh7jy89BWjH9vqEE/NWcbsDbcV7svS/fViTdqylV9im4cuzm9vBpLUEa3zpfQaewnIQmGqkwEqUOz+5Psg+YWX+yT6THX8LSAsVyoGgB3u+SnzH72WHe79OXve8EkAjClWdyxE+Zr5QpJgxosQt9lx8Xpb+0TRcTA0f1iyz/rueyjdDglkWkNpJCuMWV72+csHWXrLV73r7haTW5gKdEqNKXLjsh4M713AAzbZbH0/ETQaNnOAKpyUMiAKZVTF7DlpSyOw6i1BCa3/5+EVhbLyZ7D2Fkae+yFsZbDp0JPz92L2xtsiE2f785Y622+pNajUNrPbrV9hzxv/G1p8QYrgvVddwIq1tT6fZ621PxQdK9LaNlIrljA37dq01VaSr4UtqriJ6/YRjtME4yLjS8VAdeQplt38JdYvOoT793gDu95+Nrvc9m1MQWlKMmEPpwrmPucr60utwnTRYJyshUfRhWKIj+5ofxDMF50TpoprphY9dDE73PdLjB2zwvQak1uYavjtT5bVOQoJUwGCWBKJ9tLXb6TdcJY34LWm9k/SjSiUmjDKR6qZCsk7lnY9Ugfp+ghc/B+w3X5sXX7iuMNPzlvO9KfXMLh1faEJv3mPyGLnGUduzdmwCmMbzN50F/MevcZbM1Vr+K2QQ/1yJBvxuu/KBvcsrWy7PhEn1k2jnTavE0kwJdn/04S8IuOZMYZdbv8W07Y8yh3P/CC3H/hh1u74V+x5wyeZc9dvC9Ud9wGfbNdpAUQ+i4mkCdRn3G7tE0UiUmMH8NR2FEyMOs5H1+Nc43miLrCejJoIAwTOuA1FxjO79QmWX3MGT87dk3uXvx1QM1/X8fWZyhrsiiS7kw6USay1okRTvqkG2i0OKznH8ya6IhN36wPk7zjpPymk+UEUHXjS2le0bNqgmCrsXvc9WH8PHPuvWDv+kXxifuSEvvH2Qokam3zSPAXcuY+vBKBencZOd//IO9qy3vDb2yw0grApOCAg0tL9VvvyWXW06/t5/bVdeHre5Fp0fBvVRHjcz7Q2F5nEpm9Zx663fZM1O/01m7Y5ECpVVj77M2xcdBCLL3gX3HdF4bp9NjsOMSVB89jpM76kp4rJv85xIE0a0sW7j1Y4JD1NvGjxM6unf7fIM7fr9Z9l2tNruOVZ/4GtDgGdSwPUSSa1MFVr+G0zkdep2j3QWStEn5vesLLUCFs9HborbaSpvON5D2yRQaB1MvLJ+Jus3y/kXz7ISsPDW7dmyfy9kS3wp/+GnQ6DZ7ww9f4/mYjoKyKnNKVy8Azbn7v+JrZO35aHlr6SbR88j/rm9YXKg9NuxT4nRUm7viM1mTbCy4k8pY1FhLgszVS7uvM0SO1MFnnXs+j4NuYz5TMejf/tIs/qkuu+SKU+zJ37v3es3MB0bjjyq9Tn7gznnAxrV2WWt7Y5v1VRISGtr3tppoTuEk+PpEc355E3nxR5ftKuiXS/O19hKv6+1O+06fM216l+31XsdMf3eGCP1zvBPP5cNVPdxddnKk/wafdwZT0cPupIK9j+BlpMHQUGj2qe6ol8YSp3UhD6nHgNAoIHWRqSnlVPkboLBzOs+CY88RAc969gTKqwNDx9McPTFjB7Y/YElKQpZLpW9/KzmrP+ZjYt2JeHlp1Etb6Vbe76RWHBJtZI+TxzWRNl0WucNHX47H3o05YiZdv1p7xno92CK09gKnKtk4kofcxmab9tbRuBat3tLFh1Dg/u/lqenrNr06GRaQvY8MofQHUafP9VsOnh1J9ondwLC1Mp32s0ipeX7HSwtVZPXTQV0UzFzNx0J8tWfpFKbQtQbBxNi1z1EYqkuzrA2PhSdAy1NtvPNjfysTaM+e272DpzCXft/56mQ7o3X5epe/pM5Q94bTRTGce9MrBbfwf0WsuWAEUmoYE2wlTe8WRdg1vXM/exG0bDnos4BYauHkdVzF4mnXR7fZFJpbCprrVcRhRS0+9tfQIu+ywsez7s9rzsHzOGJ+ftNbqtTDuSwmOjUaxPbK01qI48xaxNd7Fp4X48sWAfNi7Yjx3u/gnDBa+1JNIya1AspOVsEcyLBgdkfadYCpQsc4XMDQDaX6/c7VmKCMr15v5QVJslWiBe+DHswAzu2ecfUg8PLNwNXvcT2Pw4fP/VsGXT+Pa2pjcoOHGGphpoDtwoVmfWnqRFNVML1v6ZZ110Mstu+fJoxGMR7V/qOCo0afqmj/DdWzVtQ+fksUwu/wKVdatYdfDHqA/ObjrUrTRAPkxaYarRGMsZUjjrbq6jp8eA16inf96GhmfGdkhLPth+4DDGMFBNF5iqVTMa2pxG3Lx561bwnD+8hMMuejWHn/fX7HTH92D4Se/2ujb7m+y8nJRTfr/oHlijA41tjN7XkOjOprJ//ipsfgyO/ejoR1lKwSfnLWf2xjsw1t+vp8ggO1xvMHvDrRgsmxbsB8BDy05izsbbqN1/ddvyMHZundBMFRngU3PXFJg4pXWmba8yWratT6XczJc3JhSZeKXCSVa9me257wq47XdsOvSdjExfmPqVgWoFdjgQTvourL0FfvxGqA3ntzcgetanvCSlSLYPXfuAhCX3/YqDLn0rW2cs5qGlr2Tnu37AotUXtdf+kX4PfSMf0963IykYFR1D88afzOu07na49NNsXf4KHt3hmNSv9NqWMpNWmEo6dRZdKed14Fwh58l1zL3rNyxfcTpH/O44jvnFwUzb/AjgN7G4vbYKfx1Iy5dT7MEYqqbf+qzPYxqNBjvf/l0OueSN1AdmcuvBH6M2OJfl1/0bz/r5kfCHj8D6ezPLZ2mJihLb2Osee7hlbhNU0Pdpzvqbec4fXsLhf3gxszbeWaje7MjQ6AZvfhyu+B9Y/hLY6ZDR41mRlE/M34tqfQuVDfe2rbc1yqeIgDFSbzB3vXM+fyISph7Z5SXUBmYycP1325aHhGZKaG5IIk2UW2QxIa0z73i7Ppy/UMsvmzcmFBkvpMJJlpCXei7Wwvn/CnN2YMsh78j8zVGt957Hw8u+BHdfDL95V9OJtF7n4pmyw4KAJE7ZWWNIbputZejyz7DfVe9nw6JDWHHsj7j1kI+zacG+7HP1vzD09Jq2i+pQn6mmjZl9hLBWE2yR5zQnt1nqs9houD4xOJNNR/9bZlnfiO6JZtIKU8mBoGhOitwVYPLYlk1w23lw3r/AV46Az+zB0ovfyZIHzuXp2btSrT/NoocvafubrfjuJQjjM99uLegjMziQIUxlfA7A8FMsuuCd7HX9v/PY9kdz1fE/Z/Uep3D18T/hL8f9mPU7HA1XfQ2+dBD88HVw7/+NG+3TBjafRIKiPbAyB7w25es1Fl9/Js+68NUMjGxiYGQTz7roVcwtEN6dPRFFbbn8C87Md8xHmo5nKQVjJ/TK2lty6xU7zNcscx9fyZYZ2zE8YzEA9cHZrNn5xQzd+otUc0wro+H3nTDzFUjWKE2CmV1ngEAkdANo97vtKJK2qfU6FRWmshaCqfti3vJLWL0Cjv0IA9NnZf5m02LtoNfD8z8MN5wDf/z3zPYVF/7kJmNoDeQJzWKeUWd9BH71TmZe/ike3vVErnvu/1IbmoutDrHy2Z+lWt/Kvld9ANsmY7y070N0PYa3sHzF6Sx85P/8zIOt802Bsnl9P1XQvebbcP+V8ML/ZOv0RdllPRO6TjQDZTdgomhNwDaDqvi3KvWtDD3wf7Diz3DPpbD6GrB1GJgOOz8bjvsoN087iEdm7oU1VY783bFs88ilrN79ZK+BsmH9HOZhvANfrHqdPph/vtOyhKkszdRjd8GPXs+stbdy537v4d693wFm7LubtjmQ25YcwuLthuHq/4UV34ZVv4Xt9ofn/B3s90oYnJ7hg1Qwcmuk2dnz6eE6c6cPZheIEPm5PH43/OJv2emBq3hk5xex6uCPUWls5YAr3s0zLvtHqN0Gx38cqumPUFa4e61u4YlH4KqzYP9Xw3b7NB3PCg54au4eWFOhsnYl8PLMZqdNAkV8RkYaTjMVm/hiVu/+Gna85ydw00/gWW/N/Y140Cy6mW4jZ8PqQite4fY30jQmuaa6NqvkvOPtJvu8gJAiwtRIvcHglsfZ7sHf89DSv2GkPqd9IbL78LjPa8Nw4cdh233gma9lcGt6uWolxYXg6A/Apgfhss/AvB3h0LeIzZJZQnzxJJj+2prMRM9RlHLT+W7Z6Myad1/C5sP/mZt3Oq3pBm6eu4zbDjqdfVZ8hPqVZ8Jz/ymz3vTs9AXTvWwdZr+r3se2q89n9qY7uWNZuhmtSL1FrlPenDbu2KaH4MKPwW5Hw4GnMPLY5uy29JgTelthyhjzLeAlwFpr7X7RZwuBHwFLgXuBk6y1xWOou0BzzpBiF71izKhD56yNt7PooYtZuPZK5j96DdX6VjBV2PFgOOo9zmF452fD4HQANt31GHarC5F9bMlzWXL/bzCNEeqN6YXb3GjYwhmNY7Iyy7YTprI0UKmfrzoXfvEOqAyw7sQfcO+0Q8Z/h0irMncHOO6j8Lz3w40/dpqqX/09XPBROPQtsOgVMLBNU7nC5rqWcy26B1aW6SdVyLAWrv2u0zpWBrj1iM+zeqcXjx6+5vnfZa8bP8VOV54JD10Pr/42zN523M/kBSTYSz+DaYzA8z807njWxNkYmM7m2UuZtUagmSoi3GzZxMwn7uGRXV7a9PGmBfszvGgfhq45u60w5ZsZOS+/UhEVvnQD1EwTbJs6vSaFDpbNi75tF5kLUH3oWp594WlM3/wwi1dfyMaXfxeYmVsmT9Ad17ev+bbLk/a6n0KlylDG0DOYtlAzBl78ebfA+N0/w5zt2Tr7iKavFNUS1TPuX9bnzd9pzkUXO1e3y9ie199GGg2mVaKLsfFB+P5J8OhtcOJXeHrPV8H9G8aVeWi3V7HNI5ex7cWfgGXPc/NNCmljVxy5mGtdsJbqBR9h29Xn88T8vZn/6LXUN6wG0n3cWmkVpoosnApbfADOfT/Uh+GlXwBjghYw3aaIme9s4ISWzz4EXGSt3RO4KPq/p2jer66gMBUNTAvW/pnnnP8y9rzpswxteYzVy05m48v/H3zwHnjbhS6MfdnRo4IUNN/Yx7Z/LgO1p5j32PVeae/rDYGZT5ihe9pA+ojXpLFq1OGiT8APXwvb7A7v+BPDS5+f+ZtNq7DBGXDIm+DvroA3/hp2ehZc+mkO+eXz2Peq9zHn8ZtGv1r0oWg9ryLnmYw+aWWckPHkWjjntc5ev9Mh8PdX8PAuL276iq0Oseqgf6Xx8q87DeXXnwf3XzXut7P63PQnH4BrzoaD3uCuaQt5Y/eTC5bDmpXZXyBrxdr++k579GbnfL6wWTOFMWza9/XwyI3w0HWF6i7qu5Gb2bvAcyOOtMy4HvU2wSp5Mo/zd5RF3bVTnGQFi0AbYcpaWPFtdv7VK7FUuHuff2CbNZez7Xl/68xNOeRpc5qu35aN8KdPucXlHscDMJjR3qzPqQ7Aq74N2z8TfvJmqg9f23S4SP/NW4gWSjVQS9fwtyNPmznahx++Ab5xHGx8wAmcB70uW0gzhlsP/QTM3g5+9jbYOj6ox9psf9G21+rKM5l53f9y3zNO5abnfA6A+ff9oXCuv9Z6i8ythX3+bvm1s2Y8/19g4TIgzLTebdoKU9baS4HHWz4+EfhO9P475NkdSiIpMRe1rQ5WDENPr2G/K9/D5tlLufSl/8dVL/wNtx/0EewzToDp8zLLJm/s49seTsMMsM3Dl3pJz3Xrlz0asnbjbl9nW83U5sddLpjLPgMHvxHefB7M38V/lWyMEzxP+SH2H6/hwd1PYfHqi3j2ha9kt5vPBIqb+Vo1UUWEqbwBsenYqnPhK4fDXX+EF/4XvOFX1GbvkDlAD+/7aidYD0yHs1/kzHaJe5clKCy75UxnHj36A6nHqznS1Ob5y2HDfbn+S1IBY9ZjTrhtNfMBPLHnK2BghhMCc0huAVKEiUhcWKTufEEh+5itb2X+uqtZtvJLHPLHUzjyd8eyy6pvjuYHynt084aBdguovFQlmWb5kafh1++E3/4TT23/HP7ygp9z937vZtXBZzD7vgvg529vijoeV7xozr3Lv+giUl/widGVwEC1QiWlWVl+mgBMmw2n/BjmbMceF7yFGU/cN3qoSAh+6KQrTTXQNhnrHRfAt18ElQF4yx9gd2dSy9N31YbmYV9xlnM1OO+D446P1LMFx9xn/aafwvmn88TuL+GOZ36IzXN358m5e7Ldg38ovHuANDigLU9vgHPfB0sOgMPfOfpxiEa320gd0Lez1j4MEP0db+eIMMacZoxZYYxZsW7dOmF1/jRHZhTrKIOmzv5X/hMDtc3ceMSZDM8YO62BnCi3OA1DTH1wNhsXHcw2j1zmuZ1M/up2/PfTk6EVMX9lRvMNVJwG4utHw72Xw0u/BC/7n1EtXN5k3y6zem3+btx+0Ee47KWX8fjiZ7P9fb90nxcUOMdtVlzgPPMmheFawzmB/+qdTvs2d3t4x5/g8L+HSiV/C5B6A5bsB6dd4lbkv3+/M4UOb848p5mb7mT7+37F8MFvdebQFPKE1acXLndv1t6afU5C09esx25iy8ztGZm+zbhj9aG5sN/fuME4ZaXcWndhzVSgU3bauRYRzPOuR9N9azScKffyL8L3XsmiL+/FoRe/jt1u+TLV+ha2zNyBZ9z4KY4893h2vPMHzjwhoN0jn2oei0gdl9bfB996odum6Hnv5/bjv8XItAUAPLjH63josA/Dzb9w/T7Pty+D0edi42q48suw/0ku3UGbNreLFGb2tvC6n2Gt5aDL3srgFreGL6IlCp10083j7ceXvN8euO5s+MFrnJblbRc2+UfmpZ8BqOx2FDzvfe4ervx507G2Y1oa91wGv/w72OUI7n3uZ0f9Xdfu9ELmr7uaLevTE6i2+33fvWczueCj8NSjbq5J+KHm70s5OYSpwlhrz7LWHmqtPXTx4sUTXd0okgRsu1z/WRY8eg23HPrvPDVvj6ZjmSpq0n0tHl3yXOZuuAXzZHEB0u0lWPjrbBlJT4ZWZJWRpZmaffM58M0XAhbecp4z1SXIGwTa+W+M5iEanM26HY9n5pP3M23zw4UfyFZNVLGQ/+wHbsaaFfC1o+D678NR74W3/RG23Xv0eJ7KfPR3Z8yHk8+BY053PmLffAE8dlfqZLT7yi9Rr85g82HvyvxdY0zmddyyMGpbjqkv7XyLXN/Zj92UqpUa5ZBTXR6xlT/L/Eq8ki8cLebjS9F6PGW/RQh0iLWWxqN3uACKH70BPr0MzjraDfQb7mfLvidzwxFn8qcTr+IvL/g51xzzPVYc8302z96Zva/9GObLz4Lrz8nV+KTRzok8zwdm3Lh054WuzY/f4/rlsadTbwm+eWS/06Iouh84bUDKtc7v+9Gxi//T5V879vTxbU4TpvI0U/FvL1jGDUd9jWlPr+HA/zuNgWGnhW03hufvzNC+T0h3ZkjtS7bBHjd+mlkXvA/2OA7e/Hu3UEuQN1SOPv9Hf9C5R/zmn2DD/aPHC2sNY9bc4qKrF+wGJ3+fp+3Q2KGdT8BgMbf9LrtBCSSRlnmL7IrBRX1f+x23iG0RyvPGiMmStHONMWZ7gOjv2s41qTN4Z3i99bdsd9PXeWD3U1iz60vHHc5bVaUN6o8veS4Ac1ZfWqC1Dt+NmbMcq4tobFqj+Sr1rSxfcTozz/sn2PUIOO1Pqc6P+Zqp/DqT92H9tocBsGDdXwptJA3jHVGLbBWRNpCa+jC73/Q5nnnByW4iOfVcOP4MGBhq+l7ebzcdq1Tg6Pc7f4iND8JZxzD3/ouavj/n8ZVs9+B53P+MU6llJDSMyRKmGnN2hGlzYc3NmWXTrmOj0WZ1vmUjM564N1OYMgY3qC/eO9fUF6/ki+63mJvZW5C0EIrt6xevaE19mOlPPsD29/6Cfa76AEf99mhmn/Uc5wi9+lrY60Xwiq/De2+Fd17NU8d9knU7/RW1afNHf2vD4mdxzTE/4LrnfsO5Afzyb+GrR8Atv2oSUnLN422kqSzNlDGJcanRgD99Gr73Kpizg9OYLn9RdL7N16rRsM7MfOS73XZG558+TqBqqxFYc7NbhBx2GizYddx30gSntpopnACzcdFBrHzO55m7fiWHn3cCS+77NVtT9r9ralMR36U29Y7/rIhPZvP/lfpW9vvze1m66htsPuBNTqCdNntcubz+MNodqoPwN99wAuvPTxsV0nPznbUe2/SQc9cYnAGv/xnMXNg0bzw1d0+emrMb027/Tf6JZvx+kcVLXveuNIbhN++GBUudgN+C1IexDKSpEX4NvAn4ZPT3Vx1rUYdI2rvbClOP3w2//Du2LH4mtx84/oYOVE1uVEfaw/rE/OVsnb6IBQ9firVvbxsVAv7CVPY+Ye07eHKAnv7Uava/4h+Zt34ljSPfS+W406GS7qCev8rIP8fkg/fkvL0YGZrHgrVX8ciuJzJcb+SaUiF7wMtb8bY+i7M23sm+V72PuRtu4aFlr2KH13wBpqWHinsnadzzeGcm/NEb2PuStzNtn3/g7n3eCZUqu6/8PMND87nvGW9hnwI+MmkGo2q1AtvtmytM5WVer2bcUx6+AWC883lExRg3Ih5yqvPhePhG2P6Apu+Mi4aqN5ieVV9EiB9R6nlay8DIJkbWrmfo6cfgqXXR61F4au3o+8PWP8LQlkcZHHli7PeG5rN+28OpLT+G2Xsf70wzLf05c/4zhsd3OBpzzKvg1l/Dxf/hQuC3PxCO/VfY4zjyunY7jW5W/x6oVty48vQG+MXfwu2/d+k2XvpFGBrL9dQ6ptQa1p3b8R93vlVXnum+f8yHE9/Jvjn1RgMuOMMJj897X+E2TxssIExFE/26HY/nL8f9lOXXnsF+V72PLQ/+HF7+BVi8V2q5UP876WboyY3JB7eu54DL/54Fj17DHQd8gDnHvpeZGalT8sbKpv6wcDd4yeecj9tln4WjP1DcpLll49iWPW8+F+bvTKN1yyVjWLvTCSxddZZ7TmZl53UCmQN6njVj55vOhMfuhDf8AobGR5jmOq+nbApfJkVSI5wDPB9YZIx5EDgDJ0T92BjzVuB+4NUT2UgJhbPZjjztBj5TYe0JX8duHRr3lYE0b8oEqQO/qfDYdkex6OFLaNTrVAfay601z70EszRT9YaL9sjztahWDNWqYcH9F7LP1f9Cxda46aivsv/xp+TWaXIuRVthqukhrrB+8bNYsO4vQBQkMP7SjzJSb6QOIltGGszJyT6RvDXz113NQZe+hfrALG448ius2/F4dsgQpMa1t/VYVp9asBTeej4Pn/NOlt3yZeY+fiOrdz+ZRY9cxh0HfID60Jy2gsLgQAVStIuDsTB1w4/ciaVcb6+s1TFRlN4TC/ZNPTw6Fh5wkjN5XfsdePFnm74zPhN/+/Qcyeuw7QPnsuzmL49ul2Oc2iWz7MyG5fDROi3V2lMMbV1PpZEWpWZg5kKYtRg7axFPzN+b4WnbMDJ9G7ZO24ZNC/fnyfnLwVQ4YOd5zM7oUG0XEpUK7Pty2PulcOOP4JL/gu+/EnY5gpmHvh8G0q9vW2Eqb7eCNTfDCy4g9gAAIABJREFUj17vzEB//d9OU9TSztZ7P/ocGQMnfApGNruIvMEZLu0L+QLIrNWXw50XwF/9O8xYkPqdtGjhIpqp5HP1xML9uPrYH7PjPT/hGSs/6zR+h7/TadWGmhODBu9fOFJn4ZrL2emO7zHriXuA6L606cPP3jqmMRvc+jjV2mZuPPwLrN35Reybc1vzggrG9YcDTnLm20s+CbsdTW32/pllR5//2rAzVa9b5fZCjBY/aePWmp1eyG63ftVF0h1yauZvj9Qb4xZAbq/H/Lkm61Rnb1jF9iu/Ds88hf/P3pnHyVFch/9bPbOHVve5QghJIAECyVzG3GAwYPCJHd9xfMbHL4mDYzuHc9k/57Kd+Bc7dhLbxCa24yvG8RWfGDBgwGAOCRAgMAJJSEIXktC92t2p3x/VM9sz00d11e70zO77fj772d3uqanq7uqqV++9eo+lL0gs3ylkzvBa6zcknLp0lNsyajTm0EqNGfKTP4WtD5qdJDMWw7Z9TR/JGuySXuRnjrqIBRu+x9DTq+GYMzPbbRIz20vbaULiwFB6B2dgHyff/Zf0P/4t9s04iQfP/RTMXpb8+ZDUCSVjrGz059k992zmbb6BnoNPMzA8HUgOwOmTnLbKgif/h0qplzuv+CFHUiLrVkkzPaauyLom8ehZ/8Cemadw4qq/Zc7WXzLQO4+nlr0RsNgKn9DfasLUkX1m8owxryQJ46lm1C2rGZiykMGeePNj7Zn3zTLCwgPfMru3IivJphhgg8PMIJ3qbZiy+2FW3PWnHJqymH0zjOahpBR9U3sSyw4ODrPv0IjgNFyezJGeWQz2zGbBwsVMmTUfJs81P32za06tuqJZszbFKyHl2VgHzwxKcNpvw8pXG8Hz1k+w4DuvpHv+haxb+T72NWgA0/wxIVmjM3/j/8LtHzQaorf+CBad03w5ujleVF3fDQKzyWTwsAmW2NUHZ787uY/qCgvv+ShMXwTPe2dim119ppre86DE5qWvJzj5ZZz44CdM5oA1/wNXfgyWv6R2450d0I8chAe/xcm3/it9z/6GgZ7Z7Jn7PLRSdAUBvVNSVnjA/r2Ha11GqzKblv0Oz845PfM60+aU2MX7iz8BG++E77yDymuuJ8lDp1LNSfb9P4AnbzFm6oigErcA3z/jJA5NXcykh7+fKUwlHU9duMe9N5VhTrrnLxnumU5wxd8nlk1bn6vUPZGtZ1xGQG+cYLU2x5pWS6u+ZgI0XvgBOOEKSnsOxX5fWpyXNHb1n4dGoR6/0UqYGhyuWEePBgufnqS5aOOd8N13M2/PRp5c/m6eWPGH6FI3MywGuzT/jizfj8aXMeo3NbgsXn1fxTeJKVoza9ttPNN/Xk2QyrK8+pgOtFJsXvp69s04iZPu/RDrl7+TSnlS2JT0sqlhK/rDiXjbQ03C1HBKrJ1UoXPLKg7OSV7t1qnpz3iL0bo89F04/Y21w00xwCz89rTWlI/s5ZRfXc1gz0zuvfi/aglye7oC5h6fvGFlxzMHeSxm4QMwfeF0pkyzD5ZrS9piIXZyLHfDWe+E097I3l9+lml3fZqzb/gtti28gs1LX8/hSf0M9symrNIDaDYKJmr4CMff/3EWPf5fsOg8eM2XYGp/bNm4DQnV5O+1xWVQgld+zmjpf/Kn0NVH5ZhXxn7f/I0/ZMquh4wvT1fyPY71mXIRpkIOdc+CV/y7ic/2o/fDf78Rjr8CXvRxmHVsqsEn9tyzm+Hu/zA+gId2MzzjJB4662NsPeal6JIRoHq6Ai5M6YMAax7ZljufKoQbTUoqdkdr7HzTOw1e9UW49gpm3fxBOO3jsQPYcEXDjR+BB79lTMynvr7ufOz9DU19i9d+wYTE6YtfVCWFGDoyVKEvReaMM/Md8/hXmb7rQTZc/GkWJ9QHWebQ5DqLYFwKU0lpJuqEqa0Pmpfy2Itq+dGShIEs81WSP9Rgzyz2zlrJtHU3wCXN8UIaOTJcGZW8ZpCgiRg6Ard8DG77JEw/hidf+i2emDTi+5JllgG/aMyNwlTUb2po+I0JpQxJ15olTFWbNHnv4/Qe2s6u/gtq57KEqdGIcbJ39qnc9cJ8LoVJqX66Smpkt+G2h2oOxlVSt/wnOeEe2g27n2Rg2WsTy9ZpyhafB7OPNxqXqDDlEAMMrTn57g/Se2AL917y1ZogZUNatPK0+5DpupjmLOvqL9jdx+DZ7+He2Vex+LH/ZNFj/0n/pp/VTmtVMr4qk+eFv0ON2uQ5MGUe3ZPmMG1XiSM9s0FXWPnrP2HGzvvYfco7mXnVR42jcgJpwR3rxsNSl4nm/403wA/+kCmXK5h5ef01Dg+w9MFPsn/WCqasfHXy9dIsONU5y6eQmJ6leh2Lz4V33wp3fd6YUf/9HLjwj2FFspZsRHWkYdPdcOdnzQYBNCx/Cfrs/8NdB5Y1dY5R2/afQHcp4FBM2J7E+3TM8+CSP2fqTX/H/JnnsXVJc3jHWQ9/BX71SZNt4sIPNJ1Pur9bj76CxY98Hh79scmZGEPiGJxxnxo17b0HNrN0zSfZOf8i9i27KqNsmqtKe0lT41OYinEmHBisQHUhVc2R1DvDSPuho2zSs0mzb0O6RuaZ+Rcx7ZHPmkkrwb+gypGhSnxOpwRyxeDYvtY4MW59wKzurvwolWcV7DxQ+0ivhYNomkkiy7esaUKP+E09nbV7K2uQTSJs7uyttwHwTP/5I6dsEpslMJauj92leKG2uxxAT5/xy4oJj5B7yzTUnM8H5p2aWLau/ytlwmVc/1cm3lUo3B1sFKYsNFNT7vscMzbfwKOn/QXPzqnfOZq12nfdvaWUQqnk708zHfjkyCuXAoa7p/LEyqvZePybmbrnEboHnqH78C4Wdu9j8tDu0FF+h9kQc2AnDJp3MwDOinzXULmPB8/5JNOf93pmpghSkK7RbdLUl3vgdV+Fr72GeTdczdxz/4UdC19YO73wN19l0sHNrDnvY6zMeNcbhamas3wGiaak6HWUuuC898CKV8LP/gJ+8Xf0r/o62075a3bNP7+pbFA5Ag9cB3d91mQt6Jlu8oWGOxGHhivwaHMIm0qFzLHYtS+BuUdx70mqe8YF7+fgIz9n+X0f4dk5Z3BoyqLaqbmbb2DhnR+GE14EL/qn2E6ZtENx38wVVKYvInj4+4nCVLKZL/1lrbt/WrP83g8DsPa5H2FWRj/qKiffwyzzeKsZp8JUyjbXqj159wZ46w/r8qolCUVZGpdSykPdveAijnv43+CJW4y/SQrVgc9mJxRYak4qFfj1583um56p8PqvG18DoLerPolkUoqZKEopyiUVO2FlqfHjHEGrflPB3s2JO3Ug2eSWJUxVB7RZ227jwNTjGJgcHyyznUi6j7Xn078ydkdf7uzsUHM+H+5/DiTE42zaZXnqb8ONfwP3fhle9DGgWZhq/L+JDXcw/fa/Z9vCK3jq+Lc0nc4SVtMDb2YM7pEcnI1YbVlP+M40ogP/UM8MdvefW/t/7uKZTJ4cYyc5ciAUsHaydt06Kvu203XkWXYsuISD05Yyz2Lxk1uj290Hv/1NBq69iufc+T7uP/+zPHPURZQH9nDsI59l5/yL2L+gWWBp+pqGPmOjlYLkZxd7HdOPhtd+2Thn//CPOePWt7H1mBfxm1P/nIG++XQd3sXRT/w3x6z7GhzabnxCX/wJOPUNdeEK0sbRuvx6MZjxJb58ZvywtMDJSQQltl72GY755mWsvPP93POCb6CDLqbvXMXKO9/HgTmnMOXV1yYmYE/c6a0Ugye8jJ57rzG7Qyc1ezxaCboxRBdj/U/9mDlbb+XR0/6Kw5OPznShSRMsbczGraS9WjNKxCf/DR/4nf8Oj/wvXP4RY7KIkKRZyRKm0jRXB+eeahxEH78h9TuiW1Ztg4ymxfKpaG1iHv3XK+CnHzROiL//q5ogBTCpwaw3KWX3VJRcSZIjxPkKVf2mejf/KrVskgO1jcN+MDzAzB1388z8C+qO+6xrPKxFmSv0JDNf7Xj/Cti1rhZtvYpT4uAtq2DmEsopW6KbBv3Js2H5S+H+b8DgYbTWHGqIA3RkqJLs9L5/O1z3NoamL+bh533UwvbWjE+MKteYT+nOsOmkTQqJK+zuycYvbuFzOXjs5Ww57jVsWP4ODk4zOR2tdse5bNzomcqOq77G/mnHc8odf8DM7Xdx7COfozy4j9+c8idWyZW7SqruftlOfEkTdtXPK5Zll7HnrbewbsXVzN18I+f+9EU8544/5IIfXsSyNZ/k4IwTTQy4P7jb+LA1xH3yCqvg0SeSNhZkWQgGJy/gkTP/jum7HuC4hz5D374nOfW2dzMwaT6PX/qF2BADtbIpC66Dx78UKoPw2E8TyrpZB2oLjcowxz30afbNWF7bjJO1CEkaCwF6EjT4RTFOhanmh3t4cNg4Xv/8Q2YiiOT/qZIkJWepE9MGte6ubjjuEnj8xlTbRXRws82Snvaa9639Dvz7ebDpHrNb5w3fqNPCQbOPlI3PVNrnsgaBuMuv+k1N2pIhTCUMalkB+TSa6TvvpTR8uM7EZ86lkzp5jqGGOe7+KhUVplaaQH471tZ9xilx8JbVcNRpqZNd7LnnvhUO74FHfsDAUPOWaYCDcf14eAi+/XY4/CzPvPgLDHc1BzS0wcefLW3xk7ZSTt/Jmr3CTrJopJp1QuImFau4TY4bN4K+Gax6/rUcmnwMp972bo55/L94eslvcWDGiVbClFKqrt+kTYpR0t7ntOfa1d3Hkyvew51X/pg9c89k9rbbefrYV3HHlT/m8Su+AsdfnujH4ZPWyGfR1JtgCUg6XkWj2X7MlWw+9jUseeTznHHzm0AFrLroiwzFpIOKkrar9/C802DawtCfrJnEvmTpMzVv88+YvO9Jnjzp92quNVkuNGnWEpv+30raqzWjRJxdeHifWQ0z/Ri46t9iZ8OkgTTLFygIVLIgVg5MSoF9W1JzqkW1aT7JI8sDe1j5q/cx9+fvMWaz37vN+LjEXG/jpG074DVqtMB8fdYgEEvoN9WXIUwlDaQ2juCzt95OJehiz9yz6k9kFPUJUJo2kGauWOMmznJp5Dv7w3hFDaa+3BPRwV0mcfKC01MCQyakt1lyoQlsee+X8kXiv/kfYP0v4aX/zOCck5rPW5K+eytDmErNdecoTFkI10lCk8175xy3yVGY6goUgz2zuO/5X+JI71y0KrFu5XvDc3bjRLR9tmOLay62qm/NoSmLWH3hf3DzK+9j7XM/wsFpyzKF1dQApRmLtTTBMqtPJC9K7cbRR0//Sw5OXULXkX2svuDzHJqyKHOBmJoQugKc/HKz8I9Jpp6k1crymSoFxrHs2Ec+x4Gpx7L96BfWn0shTWCyNR23ivZqzSjRpJmqDLP45qvh0C547Vdi7cGQ/HBSs52HJEnQPeUAloYhudbdGPsZqBegbNIYxDFr6+2cc/3LmLfpZ+w654MmJ9Ss4xI/X4oIgaWSslohA/TFmAN7u0pWTvNx7J57Nt17NxizZAJJg2xmkFMNs7f9kj2zz2C4a3LDqWzfmiSyNFNptyJLEAsC1STc1Gn9Zh5r4gE1CFOp2po4tWDoL8WC0xMnu0SNVRDAGW+GDbdzZNva2I80vYeP/cxEcT7jzSYOU0GkCUxpgoLPM4UEgahs55jd+HxKJZWZMQDSd/OlUR0Ljkyax68vvY67Lv8eA33zAftQMT0RocDGzKd1egaItHOp1oFMYcpdM2UdyTyGxEVphsal6gdaKfdxzyXf4M4Xfp+9s08Nz6WT6Rpy8lUwPGDe1QYS+1KGYK6UYt62W5i6Zy3rT3p3XXaNrL6UtEDv6Qqc55uxYtwJU1rrJmHkuIf/lRlb7zDOhw1pMKJUnasbsdk1kCRB95RLxlFy3smpflPRcA42aQwaWfrAJzjj1rcx1DWFuy+7jmfPvDrRCTFKdZDrySHlx/lW2fhbJU0aVb8p1t+WWDZpUMuKJh4c2M7UPWtjd/lk4TN5pu8Ky6471QQbBKY/NezoS7sXsYPo06vN76NOTfHTSnmup70RgjI993819nRdIurdG0x+sfmnmJ1GGYzlMJk0uZZKKmPnVnIS6iztNSRpHO3eu8bxxbac6w6s6AJyqGcGB6eNLMps/Z/qNVPZ40OWkjktknm5FCQ+m6z2pgtTlv5AcecyXvTe7gwNdALR04O9szg0dUnkXHrZtFtc0cDCs2DqUfDw95rOO++o1polD3+WQ31Hs3VRfd5bm1RKca9Wu2mlYBwKUwNDlTrfnNlP38JxD/8bW5a8iiOnpMcyAvcEnZlOw8suhQ13mB06MUQnnryaqam7H+LYtdewZckr+fVl301MC5LWvjw7IyZ3NwtpcccaSXpx9k8/keGe6cb8k0CSoFCppAfB7NpwKwDPhImno2RtXU4bmLIGgbS51UYT0bg6bVqtVnP0Ra49bTKKPbdlldFcTpqROBmlTtpT5sGJL2bK2m+hhpuzCdYG36EBuO4tpq2v/XIt2KOPT5qPCTbxXbV4zxNdARwXXD2Ofoq2pqAkDVRWYvG05247kUWv10b4y1oY6Yw5O3kXbMYuY48kyWnP3cYfqPGdmxQjYDXiY27ODDAaBHDSy83Cf6B+e69rnCnW/5JpO1exYfk70EF9KA8bk3FcqBjb96aVjD9hKqLV6TmwhRV3/Qn7pp/I2jM+VL9STiDuxbMRNBLNfNUBZdllMHwkUfsSbXdc0NE0Fq/9AkPlyTx62l9RKeeL/FztqHmEqTj1dNyxRhJ3SqmAwwvOTdVMpan404SIno03c6RnJvtmxPjnZAw8Pv4QaZqKrEEW4vzZGu5v/0pjtt63NXIw+UbECpxb7ocFI6kv4vp+Zuyx576V0uHdzNv886ZTtUH2p39uBLdXfrbO7OwjEKVG4nd0arVxaE0yhduYyONMFjax3aD52dj6JyYJAzZmvqQubOv4G22zzfiSJUxlnc9tqg5J2wXrtZnBRlvZtGjyC5ycVafVxpmTr4Khw/Cb62uH4tISVRke1qnmQ375/xjsncOWY5sDvTovQtosLAKMR2GqFk+qwnPufB+BHuKB8z5DpTzJSkiJG2htVNSZZpJF5xo/lwRT35FIJNyBHJF3e/c/Rf+mn7Bp6RsY7k5O2ptE1XHT5sWvEgSq6aW3MfOlVXFk4Xmwez3seSr2fNrLmjjgaU33+pvZ1X9+bIbmrPQ3aeezJvvUFavFANI4WTYNKDFO6OkZ1hs4sBOe3QhHnVY7FK+VzXiux13CwJSFHP3EfzedGhyqmDx+93wRzn9vXVgOyBJW3TV/WWUz43ilkKmBTiFuorQVirobhBtbgSbJTGWzcSNxt5mlVqA7pzCVpTXJarGzZsrDZ8op5EUEF41jriTJDaSdrRVddI6Jxh/Z1Zd1HxLPb7oXnriZbc95J5VSc36zsTaPt5L2a5EnNUduFfDEivew5qx/qtmUbZLiNkfuTfaTiJI5yJZ7zA6ox+Od0KOC3vCwzlTDV1n82H+iVYmnjn9z3XFbn5NqZ+5OiTQbR6YZKoa0gefIotCnacPtsefTTVgJJ7c9RHBwB8/0XxB7OktDlDsXW4Q0U4jVVvisFWv/yeZ3TCR0K7aE/lJ1mqk4dXpGW4OA7ce/jlnb72TSvg11p7p2PQb/+15YfD684ENNRVNX2BkTUXqaiQxflUT/Rs+NJhnE1WsrmCil6uq2KVdJydWYZb6CZFOKtZ9XaSSzhO3mljSyd8EmtTf9XvmE2UhM/WS5saBRYLV5rj4CXPou4/BcUIKTXmY0U2Esu6z7kHj+l5+A3hk8e9KbYk9baaYSNm60G+3XIk+iznC75l/IzqMvHTlnEXKgSSNguXJM6uB1k+qyy0ywxV1PNH2uURtlI/h1Hd7Fgie/zdZFL6vttKliGwep+vLl0UyBhRkqhjThRc072aT3SfCbSnWuTjq37ibAJJyObU/GAO8zYae97HaTdobD8aSZJiZMRDOVK6jk0+FOvqNG0si4+gtuPfZVVFSJo5/8Vu1YaXA/J976+9A9BRIiMvussNOa5Rq7xqYPJwpiFpNgrGYqR6ycaB+w6UNZ5qushNtxbQuCHGNiuECzFaSyxqzMiOKOZj4/YcpP4Gy8xzblUnejZtzr1Cj+0aInXwWDB2uWlKz7EDsGb3vI5Po75/dQvfFWExuXh7jnZ9sHW8m4E6Z8dmaAm8YF4h94KWjYHbQsFOwatFNDw5Ume7SNSXL+b75GafgwG078Xas2xlEVKGzCP0SJXm8Q2K0U0l70crkMSy5I9JtK28mT+MjX3URl7vImQbNWZ8aqyMc3Ic1/w24r/MhgkZgktuqEPvLJxO9rqnPLapNeo3daXdvi2pvFod557DzqYo568jvGEV1rTrrnr+nbt94IUlOT7r+7k3NaklNXQdfGdOYasBbirynPpBD9rJVDd8YQkmW68TFLwsj7bitM+eyQhfh7YmNZSJMpM/20PLScpvwoa6Yy6rU2rS8+H/pm13b1ZekxY+/TL//ZLKbOelesxqwUKKewIJBvEdIq2q9FnqQne82v2rZVw8eHVGi4vbOXmhhBDcJUfC7BjJHwyEGOWvsVdhx1CQemH9902ibujWljuHrMGbMjem22g2X6ikoZYSrBbyptFR27aho8BBvuQC19QeJqLGvCTlOZZ6nTk/qN7SDb6G8SO+j0r4Cdj8KQ2UmXa3fcllV1Jr7GOtOONTJc0Wxe+jp6Bp5h7tO/YOHjX2P+Uz/i8ZXvY3hxvIkVMpJmZwm6Hg7o0fhqUVz9nmy1NXHxw/L4fuT1QUpbgEC2tiFWmLJMOQVG66DUKApTGV/j2n+zBKY0EuMgWQqdmRpoizJRssYl63AvpbLJFPLYz8xYmpdn1sFD34Ez3w59s2IXTrbxyuL6oWimWkDaaszK6dJhdwXET8yxHXvZpfDkrbUJEJJyCWbsPFz9NboGdrFh+TtiT1ub+cIVvk0AwLpykWuzFqZStAldpcAIUxDrN5X2XGMFrQ13wPAAaumlzpGnUzVpGdecNMja5j8sBaqWQDuxHf0roDIEOx8DcmyZ3r8d9m5uEqYa+6tS2Wr4aqDFZ/ov5HDfURy35l844f6PsuOoS9iw/J2p2mCfQItpycVtfBxdJ964Xau2Y0RjHeWMuFZpZW3Mr5mhBjKGw15Px18Tty+wcsSG7B2yLiEvfCfdrHvkml+vVj7S5lJgFzg57Z3M7hdp2uuGAydfBUf2w7qbMv3Vmp7N7Z+CoKuWti2uzbauJY3P1dYS0mrar0We+KwywLx80Wdss+UfwoB+DYNG7IS77DIYPABP3Vk7dDhGC5UaxqEyDL/6V/bPOY09c86M/YjNhGLaqOp+2xJ96W3s3qZMxgQ4b0Ws31TqtlsSzHzrboJSNyw+z9mXIm1gy5pUkgfZHBNvWH9iO/tXmt+hqc9692HV+Tyyky9aX5VyKdskWVugBCU2H/tqpux9nIFJ/Tx09j+CClKF4Mb8bXVtybi/6VvSHZ1aHePJuQpTeSeErsi7amMeyd4dl2XC8hMcwWi8bSdNpVT6pg+HXZpjvesrceOR5X3Ka7oF81665nnMpb0+9iLjm/nw97N310bPP7sJVn8DzngTTO0P2xxnuXHTTDmlLWsB406YSsNWzuqt2zWTQw3f0JFjB+clFxqJPRIi4VBMDrODcXnNqjzyA9i9nu2n/J/Et8NWmKr5NeR0QI9OWLZardScaIEyS44Yv6msxxarmVp3kwlH0d3nFt0b87InjSFZg1ZPQuReW+E8WkfiJD97mREYwx191rsPt6wCVFM2gMZrshnsooLs5uNez44Fl/LAef/KUPd0INvU5Pps0vq3y+5bY/rL7sdBoJp3WuYY3KPPMm8U58z+MMqMxpb0PJopyIoonl62uxQ0va82AqtP8NikBUGcVi+OaITvPMJ10viTtSjOtWQudZlwJo/+hFKlOShvlLpnc8dnAG3CoVTbFfPwbOeoRrN8OwbshAkmTNkSfVh5VmJNk1FcuIGeKbD43Dq/qThhKjZJLBiJ8LZPwaylHDz2isS22K4Gq0JRXs1U2UEzlfS5upV2jN9U1q6jprN7n4btD9cc/l2dhpMGShunVqVU7CSbJ9N5LW9iUl2lMsxdXtNMpQcKjZx7ejXMOQF66nfYNPaBvKakI5Pmcv8Fn2XfzJNrx7J3jLk9mzTh38ZU0vhc80xkzbGB3Pye8oYLGHlXbX2Qss6nfyAucGdebVrZModg7fO2fTiGuPfVRvjzCR6bVEee+1SN5ZbHJJl0L7IWxVahEaKc/AoY2Et5/S2p31urd/8OuPfLcMrrYMaikfM2PsUpRN+5dowxBeNQmErdrWB5tXWRe3M88KYBOqnsssuMNmHv0wAcPDLU9JFDg8Px5q31vzST4Xl/SFdXV/P5ENvVYDlczdkKRHHfb/tSWEWPjvGbytZMNRx44hfm99IXAH7Z2X1inMQ56+bRTFWfSeqz7F85IkzZOsxvWQULTmv+TMMLktePLo4sN8UkH7Ks+xS7UAmx6cuNfTGXMOUQGyiu3tzCVPj5PCt6n/PQHLS1JyuIawNdQT7NVFKblLLVODqEbPEMsOvqf9f42XyaqfjdcVk+ePlGeeDY50PPdIK1P0j0U6zLaXnnv5vo6Re8r6G9zdfmKkzlNTW3inEnTPlk8a5SXWl2lfNlpm4y8yW9HMsuM7/XGe3UwRhnc60T/KZu/zRMngunviFjt1m+l9nGByNKqc7MZz+4x1VTN/nF+E1lfXvTd667yUTwnWeihMdpDpRy3zlj+zLHpw/Jv/pMXZH3r4D9W+HATjuH+X1bYd/TTc7nYExYpToh2d8vJ0sSjhOauspBpiCXdK225jrrhU8MLilAqkT7et5AuTXNlLUm2N3vrIqPBg9CzVQOF4KkscR27HbZHeeb+qnxniSGMskon+fe+uyOSyL2UsvdsPzFsPaH9NK86AdGclroniLwAAAgAElEQVQe2gN3f8E4rs+p32EeL0zZtzdvjLUiaM9WeZC63dryhazlq8u5crQeeOadbDJzP34DgzExpqo0mf+2roHHfw5nvxu6elMnzzxtd5H0o1qMPP5WcW2uGxhi/KYyV9hRaapSgXW/gKWX1FSRseY2i+zs4Bdrp1HrUi7Z7daJfh4yrj+SViY1OW31XEzk87rPeWhOXIi7vzbau6TccbYTUuP7kWuV3KSZao2Zr9oP7P0hk33+bJ3YG8fTPBMgmDE3zySfdG3WO78ag2BaPBur9yZH+S6LjRt1ddTicdmXcTWbpSkbEtt88lVw+Fnm7Lwr9nTtHv36P2BgL1z4gdj2uvizVRHNVAGkrUqtzVHhijHvyrF5VZTw0JWCpZfCul9waCDZsa9pl98dn4GuyXDm74bti7+e3FuuHSbNIBjxG8ozWMY9g6b6G/ymMnf5ROvf9iAc3Fkz8YFf5Om4z9n6PbmG2ahidX8jO/rSnmOtb25ZZQL2zH9O7Oeiz8emX2TOGRnn+2LMfHHH4ojPo2kpTDW+qzkEouao1S0y8+V839J2S7pkdgiC/KbfcinI5UKQtDCzvWarTUANpPUZm/vUaArNq73rrs03OYRyR7OZ027J4y6B7qnM3vjT2NM95RIcOWBMfMdf0bSxBZL82fIErI1optowYCeMQ2EqrUPadta8kXurNK2K0upbdikc3kNl072JHxmImv/2PAVrvg3PfQv0zQKSBwrXXUJ5cQmrECegNj2XGL+p1Ki/0REiTCHDcRfXDvnEn4kXxNzMfHmFqbKNJmLKXGPS3PZQbFDIKnXC1JwToXty7OfqfOFGQZ2eNY963V+PXHeNfSKfmW+kDtvcnVVcfA2r1ITrHJrgxHQnDpkdXMaJksp3f5LGEtu6o8+mZOn8njYWWEW29zSFuuyotlqUxpCaIitpHO/qhROvZPr6n6Iqg02ne7sCuPdLcGhXrFZq5HPuGl0x8xVAqsrWVjMVOApTDSlAUh/6cReDCiiti098DA0R2+/8rHFQOef3RtqZ8P35U8O42dqrL2aeQaBxFWfqbygf4zeVGuAxWn7dTUZbE0lhEidkWGum4vyebB3QPQYPyDF59q+ohUdI6nO9XSXTf55enWjiM+Xz7ZrxdXIueTwbH81UkxbZcWDPHXfJIzRCNZZdHk1P0r20NVX7mn2DwN4smVaHrQks2t4ey/b2did/zub5+vjfgVtKr7j+atOHU53t057TyVdRGtjDzB2/bjrVq4aM1WTJhbDo7MSvaDTfu8Rny+uP1kras1UepKpsLR/eSIJOdzNfplN33yw4+ky61/8i8SO11DiHdsN9X4aVr8rcbgoOL3POGFON9efRTMUOAo3PLMZvKunZ1WkGjhyAjXcaf6kGXBIzJ7XXNoq5tdk3gepzyZyM+lfAjrUwPBQ7QNUiK+97GvZvi93JVyVvUMmsid1mIm1ss+2ORx+NY6PZIa/Jofpq510l1wknDouYriDIJZwkTVi2fbgrZ39opBQo69RWkKJtdzDf2pZpDNRcxdbH0SdFEIyk8sojJLsuJJyzDiy7DN3Vx7ynftZ0avpvrjNjS4pWCuoF+7z+o9V7nNcfrZWMO2Eq7QWyjjBrs4sq4/utBudll9K9bRVdA7tjT9eiS99zrQnrf/7VdeeTOn/+LdeOmikHc6h1IMAGvymr4I4b7oDhI3X+Ukl12Goi4oJv2k68QYPzrctOKLAQ6vtXmu3Iu55IF1a3rDK/UzVT+dTpcZH/o9hoLZt8kByiRyd9V3p5N9OBUiMTQd5nGu0TLits25QjVZIEU1uB1SfIKBiH5zxzX9J7aVu3qybNx9xsvYs7AZdx1NXEndTPM3eud01iaNkLmbf5epOBI0RVhphy97/BgjPqXCvi2+zuRO5qLWol7dsyR+Ki4IJRdtjnkMuvcYH61a7VC7XsMhSaWdvuiD1d0RoGD8OdnzMO6w1Ow0qpWNVwXrNdnpVulLxbtSFH3KYGvymr4I7rboJyLyw6r7nePP5sEZRSdW3uKufTDOTNpxalK49mCmDbmvgVa/XebVkNqjTitB5D9T7lUacnfc4m7g3ExQayFHRjJhSXHUK2YTLi6nHx3+gOdyK6xPHqyumj5RoUtUq3o8BZRWGfdD2tDlsB29XHy3VXabXO6Johtwa6pHKbQ13DriTdR6uF08mvoHtgFzN33lM71v/Ujwj2rIeL/jhzN0r0fuaJtwcjYVvyWotaybgTppJ2sOTp4NWdai7mr5owZfMiLzid4Z4ZzN76y9jTFQ088E04sL1JK1WrL9YRMeeuMUe1aVc14KenZip2EGjwm0pcYUfNFY/fCIvPNw6TDTTepzz9ITrx2PpLjdTjbiapJTrO6odzTzRCUkJ4hDrN1Nzl0N2X+FXVZ2EbOgIsg7GmEL0veTQvvoltowufvKaDkfc8/zbt7nLgvMI245J9W5PMeS6O+k7JZVW+QJGJwp9l3UaAN3/nWVT6xoSL9oPcDuhBQCnnXBOb1shCQE4aR2120JZPfCHDpV7mbQp39ekKxz7yeRPq54QXZdcdqcPWzBylK8iOP1ck7dsyD2IFjLwTWc74KFVG1P8WZYMSB455vhGm4qIf6mHj2HfUqSYSbQy+0XchPcVAGnl3MkGzNiFRY9jgN5XpSPvsJtj5aKyJz9Trpv0wn/VQT0cdYvOq/8OJIVO7U+4x6WHShCmtw8jnySY+GHl38jhkJ/ruOTiD5+m7Pg7o0c+6mK+qAq7rGOHqRFsOglSzaiNxQkgQ5AghUdd/80+ACrt4ViN1xFsW8ky+LruxY0Og5AlV4LNoClTNbyoPUcGoVFJWz6e7HN9/rDRF3ZPZc/TFzNtkTH1zN9/A5L2PwwXvt0ovUjeOOvSlvAuJVuMlTCml3qeUekgptUYp9Q2lVLNKoABiBQyHmC4uD64n54r14DEX03N4B1OeXdt0bvqGG+CZx03CyIQBKV7Tk3OV49g/TaqIvJqahpgsafcp4jeVucV7XX0KmUaizz9vHC4vW79nXCFr7Wj/CiNMxbSvp1yCvZtN/K0U53Pz2fwCRtLkYb0Dy3Eiavxs3udavVcuiVNdwoJU6SoFzmEngiCfWb1cCpra2JtD6xgEI64ELpoplVMzpZTyCpQLbsJU7HvjGIzVZa5xcbXodTSb9cWZNC2F1b3HvZiewzuY8cx9HPvI5zg8dTGseKVV2aiCwqUvuSzeW4mzMKWUOhq4GjhTa70SKAGvH62G+eCbBwjMIOTy4GpOw5Yq5v0LLwJg9tMNpj6tmb/mczBjMZx0VWL5uIEn7woyj09DlJJD5y41DBypA1bEbyrTAX3dTTBlPsw7Kf5zkXry3p86M19OQbXa7/L6RED8RJhI/wp4diM9Q/uaTvV0BVbO5zDih+cbQBAcd2DleE+bssnnfK5dHo7gtbyJTq4AyqmcqTfI/b427WTN2Ye7Sx7CFBaBXRuIa28eIdlFmIoT/HMtKMLPKpV/F3hUYM1DndksjzDVXbY6FsfAkssZDro5YdXfM233Gp45/fdNwnVLfHwNx7VmCigDk5RSZaAP2OLfJH98s3gDlAK3kAHVF9i27PDkfvbNWM7sbbfVHZ++816m7FgF574ntbP6qqch/2BXpaT8tHeQMWBF/KYad8bVfVdl2CQ3XvqCxIup25WU2++pFPu3Dd0OA3t93baaKeNU3rVzbZPQ1lMOjPN5UB5xVk+hy3HbciMupqS898nHvFJN2usSZ62WdNhBM1UOAqewCGDGpbxCuWtYkCrdjqFioLqbL1+5RsHANiJ+lZrwl0czZRP/Lq1OD/87cBPoJzlqzOO0ULb3uNw3jWfmX8i0PQ9zeFI/h056jXW9MDImuAiPrhq8VuEsTGmtNwOfADYCTwPPaq2vH62G+eCbVBGgFMTn/srC5UV+Zv6FzNh5L6XB/bVjSx79IoM9M+H0N6aWbQ4MWcq1igPj1+CClT9PDNEXKXXybPCbSvQPe/p+E4srwcQHfrGBenw0U7WJyO1Vs3ZwrtvRV19XT7kUOp+fBF2TLOoMcr0vyWa+/MKUn6DbumfjspO1is8KWymVe8NI431x1a66aNNcFmqNk31e03otJlEOgTXus7kWFJ6LJpdy9cFj7cs33t88PnQ95YDtx1wJwIYT30FPT/Z4EqUW6NlBMHfdFNYqfMx8M4GrgGOBBcBkpdTvxHzuXUqpe5RS9+zYscO9pTmIDxeQUzPlMGiBmy/FM/MvIqgMMnO7SSTZt/dx5m65ke3L30xS2o8qzVFlHR6po7BfDgKne5Rr8qzzm6r/bM15PSaFTCPR2EC5hSkP7YfP4AE5JoRpC4wWb9tDzWEgSip0Pk/3l6pizGd5TCR+Zr7qtmfIv0J3nVAgEk/OKbK3aa+rK0De3Vu1spbhJqKMRvDYUk5/tCouZXwiZcPIu+YiDGUdS6zTc9Hk6zOV55k23d8cPnQ95YBtx7yENWd/gk1L3+AeO8+h/5eC9Jh2ReMj5l0GPKm13qG1HgS+AzQF+NFaX6O1PlNrfebcuXM9qrPHNaN2FBcfF4gO0PZl98w+naHy5FqIhMWPXstwqZftJ70ps2ycZiovrma+QLndo1wpNSJ+U41amtr3rPsFzD/F5KlLIVcMsGi5UlSr5WZycF1RWZdTypj6tj1U18buckCwb5PJm5XhLxWtM4+QnHQ/XXxO8mumon0pr5BQHdhd3vOqZsphUnBcqIHb+9a0kzW3Zspdk+bij+mbILwasiXPmF8u1Vsi8gqPtV3czrs0HRaljjHAXIPkgrlOHZTZuvjl6FK3Ux5Y2/hzjZQD9/emFfgIUxuBc5RSfcqItZcCj4xOs/yIzaidc9ArB2627y4HyVuXutk97xxmb72V7kPbOGrD99my5LcY7p2dWbYxt5mLMOVK3lQRVaI+Kpmal4jfVGwOrIF98NRdJnF0Zr1uqvhq7DIXh/uywyo5Si6NVv8K2P4wvREXO+MvVXU+t9NMqZxCsq8DevSz+VM4RVbneXexVuPJOUYiz3ufqqicUcGjuAhvTZqpvHHoPMwrLjKYaxDXKl0ltzhePnkTa76yjpoT10XpSEytHMJUUwL2/O9prQ0O4V7c75HbprBW4eMzdRfwbeA+4MHwu64ZpXZ5EZtR2yH2hwvVoHG2knd1UN05/0L6Dmxi+X1/g9LDbDzx7dYDro+pA5ytfAQ5M8JXyTVoRfymYrOzr78dKoOp/lKNdbnsSnKNDZQr7lgMue5v/wo4sp++gyP7QHq7Qn+poCs18nljnXmqHY20Rq7pWXy2pENoOnAx5zuursEIGK5lnTRTjcKUgznUdTx0WWw1myXzm5JcND1141JuAc5v0eTaH7prmyjs623cPZhH2954fa6aKRdKjvNNq/Dy5tJaf1hrvVxrvVJr/Sat9cBoNcyH0XBAd1Vrd5WCXKu4ai275htz1rzNP2fbwis4NGURtmJOj6Pt3BcT5iB/ubrYSzaDQOg3Nelg/WZR4y91I3T1wTHJ2cob63VdtboJYe7mIMgrTBlhqW/3iILYhEVYDf0nm+CeFuQVklN3WlriOhn57OaD0HTg7AzrLmC4zgkuk0msRjcHri4P4OZC4J3T0nHXl2sqGlOnm2a1inOsv5LRcubti655Kav1gVsYiLy5JevKjtc4U+1M9IEDTnmwfDRTeTp2dUVyaMoiDkw9FjC7JPK0wWc3lA8u257BQZsQ+k31PX1n3eGecmCcz5dcYCUoWCcOjqG7FDhNntVEwM6q7Tz3d95yQNG7ayQAbE8pNPMdZWfiAzOw593hGRdAM2/ka3BZ6UZMxo6O5C6aKVeNFlSFqdZNutF76uJI3mrNFDSMaQ5jt8v7Vh+iI1/52qLJVTPlmtKrbFKs5B2HXTMzRDfyuNTrE3iz5LEIaQXjUphSqt4PwnWQdaGk8g1W0QF54/Fv5amlv82+Wc9pOpeGr6nDNZ1MoNzy+lnHmaoS+k31bvpV3eFJBzebCPEWJj6IpABx3Eni7PfkECm+Sq5n0z0ZZh1H186Ha4f6Dm6Cw3usnc+rdaqczW0ckPObSdx2jFXvq8sqGULBxuHRNAafzYVyN627vKtmPDTlXMaHIHDP3+l6nSPhDfJP2O6+nG4CBoSLpsAtLYwp71QsdxiTKj0eWjgfZ/ty4LYoBfcNT63CPnRph9FVUgwOVf92mDxdBYwgX2braOfYvOwNiefS8FlR+eA7aHWVLSMbh35T5aduh0iA8ymbw6jxlsKUj2aqq6QYquQuVqvXZwDJRf8Kgm0PoZRJx9e380FzPIcwFaj8K+XGd8zFadhlcO72WCWD27WCn/9GoHCWMtzNQQFDw8NucbEcBU4fRkzybgKyvy+nW4qhVmvwyjkD7FbJ7WoRobsccGDAzQriswhRHhrdVjAuNVPgn+3cRwB2cbyNP2fXiKqQoJSbmtlnlezib1INNZBrwl1yAcGe9fQcGPGbmrTxFph2tEnya0F1sHOadD18ZHLl2GusN29b+1eidj1Bjz4MQM+O+6HUbTK756gz76U27fJxWO06m+m8NAJuA7Tr9m6fOn2ohXJwddL3uFYXfNKOuJpR63ymHDaM+O16dB1b3DQ9UTOoq7+Vy7PxGwvdNXitYNwKUz7bXMF9EIB8ZqQ0277twFf2tNf7dFBXP6LunPnfqn5Ts3f+2vxfGaZ7w62w9BLrCygHOXLdNZDXfBslGpQyd9ncwtQKQDN9/zoAylvvN8fK3bnq9NVMueyGcn02XomDHU1uyjGVEoT56pxKuo9LtTHC1e+vxWY+n80irtrG6OLV1YTl/p47FaPs6H4QDd3juunDXTOVuxjg52vYCsatMNUYuDAvfpop+8Jp8ahsB77qdzivzp2HPHcbdk+5lG9lE/pNzdp5NwDTdq9BDTxrbeIDT+dHR5Mm+AWby+u7VE0rM+3ZR1Fo1NOrc5n4wMimeZvbOPnkHaCNdsltOPIxo7Y65hO4b9wAd+GkGnHdpf/7aOFc8UnPEjg+024P0xd47vB01Xo7jmlV4dplp6afoOuz+UKEqULwcSYEP81Uns6Zpk627azV1VArty/X6vZQ4+d6LqHf1IxtJuXO7K23oVFw3CXWX+GjYlbKw4/Ow+ckd40zFkP3FKbsWcuUQ0+hBvbm2skHZmDPO2j5br/3iT+TNzFzFGM6cK/XBaX8FmsujJj5HP3DWjyJ+aRncQ2m6mvN8EnE63p3XXdajmgqXfytVN3vPLhqDQGvjRutYEIIUy6aKZ+HlqeDpr0ItgNfbZdaC1MZVHFdUTlNgEsuoHf/RnoObGH2tttQC06HvlnWxX18P1xXu9WyPquxfAUCmHcyk3atZcbuNeZYTs2Uy4DX2IdcNFOuZj4f863PStdnZ5GrNti1udW2urzrQeB3n1zw2THm6pMW7bOuCax9FlxO5Rw0SxBJe+YSj6swzZT7fNMKxq8w5e0z5V53Hrt5mmnD1uzhs0vNlHfvBj67i3LXG/pNzd1yE9OfWZ3LxAe+23LdTR0tH2T7V9Czay3Tdq2BUg/MOym7TEOdeS+10SySty/6DLLGzNf6ocxLE+y8OHc3NcOIuS8PPppVV0YWiO7vXO46w/iELnEJq3W6R8V3vE5HraGPn23ZR5gK/Bal7cy4FaaiKRPyJHKs4mXmy1E2CJIHKluhbGSgbH1ncx4EXHZgzVvBcM90lqz9PEoP5xamfOKUuDoqg+/OL4dC/SsoDexh5lM3wPyVUOrKVdzFp6dxweISJNf12fgKya64m3TcJxRn/66SuyaiCF+VkcneXcjISzU+YUvyaDbV7VbO9dn4zBm+z6bVAmerGLfCVJGaqbx9JWm1aDvw1QLGeWiYXPFxys79UgUBhxacS++hbQyXJ8PC5+UqXt1F6ILPdnafQdbF36+aVqZ33/rcJj5wm3B9c3a51gue5luPV8ZHM9XqaaF6b1u5O86H6r113ZTguqvOx//ORetXxccNwKWoT/qbmv+dT5w1B9pclhr/wpRSbh3G57nljuKc8Pk8ndVnp5oPPisql9Xj4DHnAXBgwbm5tvtX8RkAXMdKHzOqk4a0PxJTykGYclk5Ngo0Lu+cz07LVgdLBL8NCc67+TzM6ua3m8amqKCdrkKR88YYD82Uj++pcxowRz8in5yh1XfNRzM7Hhm3wlQ1WWa3QzoC8Btkc8foiXmR8trtSx5xb4pAOa52hxdfCMDBRRc71eu+6vQLjdBSeqczPO0Y83fOnXzgPkjWJsBAOfucuOAdjdwRn/AGrtW6TkS+ueNavZuvFLgl8K3iHLfJMT0LFOMc7RMnzVgzXDRT4Xvu4UjuQrtrpsZtOhkwq4yW+qqE5K0yziSSd9J32c4+GvhopnLHUALoX8Hdl3ydmSec51SvXyoDp6KFaAyH5pyEOrCdYO7y3GV9wl0cHhx2XmX77Ghy90Fqvc+Uqde5qBPVBZuL+RWK8VfxGdOc4zZ5aTmdinnhFSfN0ZpRLeKTm9KtWHtLU+NamOoqB84d3GfwyO28GzP5RIOO2uAzCBRBoNxeja5SwLNzz6S/u8epXp+BUrsOWgU8l0NnX83uxVcwv5T/FXft+j6RkcFnxdrC0BMRnAOxqtaHRvDdHVeEMGXiwrXWzGfinTkVLWYxi3tfcvWzrSbOdtfMds48lYfxLUyVgkIeW973vzdmt2Fex2Of3HE++EwKLi/jSGLb1k4KgVJUXAXzAp6LXng2+2fm95cC93tUfTat1n74OEj7mK+cA7F6aBNcW1sOt/27mrmdtMie+JlvHTVTJXfBvAgzlHGXcCvrEyjX9R0HMfN1JGWPmBZ+Zr58hSfFCFOTuvNppkoeTq0++EwKLkVHYmq1WvvhXrbV/iYQOvi32MRd1Ui5Pxv3la7rHfZxrPbRRDgLNR5+e14bIZxLutPl6O8KHptFfEyLBb3nrg/HK1CulzDV5lKRI+NamPJxGvYz8+X7fG9Xc8fszWnm89nRVAiO2oSRLd7uqm0XAqWczXxFjB0marVbWdfm1jRTHjuTXPDRTPkE+/R533x2LrqglHILsxFSxAToE+rFJ/5dJwlTPvV2BYFz//fp+yJMdSBGM1XQaiEHcWa+3u6cZj7HUANF4RofRSlFySMMhJ9Z0qloYStWd3OF4+Bc9hN0ncdnVzUnfpqpTsNHmHLu+x6Trleaq0KEKadiXrj6noJvoFwPZcM4fefG6WUZAo/O0sr3oqccNA3qcaa/NEyU7VFs1Bjjc3+7AvdYMK4vsvIyJrUen1yCrowEAmytCcsnoriXz1QHLV7AT9NTRN931nDikcOwgEWID15a2ZK7n62reRA6772xpYOm3/y45Bir0soXw6jg64WnOG1VGiXHhJdF4bMDy2dF5az88NBMFeeY2tp71OWZZsLZ78nHAd1L++FctBC8hKkCrtVL++Ex7ndSaATfJOo+KZxc6bT3xpZxLUx5rTI86nWpMqqCd9myWkT+LPCbAH3io7gOtEWsHougCOGvOjC7DrQ+E6C7A7rPhN1ZfamT4mKB74TtLhAVEU7HFeVh4vYJp+OTuUw0Ux2IUh5JFVssPkc1Uy6+DT6rjCLwMZp1ldx3+fi45bhShImkCJ+pWmTkFvuz+WimpvXmSwLdyXhpegrowz6mJB8/IucdhIUIU+7WFx8/2/EqEPkwroUpE0G39fW6DDw9kR19Lr4CpaCzHNBdd/NBMdnZfcu2Go8FqzMjmil3J3+3cq33D+tEOk8z1XqtocLHNcStnC/OJu6SQ7L5atkCNge0+ys+voUpjwm71UTNei7CVOARU8uHVscyAr+Ace67+TprIvJJputKdfJrtYa0qPe8M0aWEXy07YXESvPSpDmWC/wC+xZBETlDWxk6qFMY58JU56xYowKUk2aqgMkTPJO9OvtMFeNI20m7+Xx8plypCvStFrBFM2WHj4xbRBT/IgQ4n3GpqD7o6+PoQiGCbpu/5ONamPLZ0dRqotoWF81LJ/lLQXUC7JwVVWfdXT+nbB8CD3NzET5TE4lOu0dFCHAmblNn3adiwoI4Fx23jOugnUVpa1zw1kx1WO/2aa6PY6pP7JpOo4g+0RUEzpOgs0agoOlPF1CnDx0yFNbw6b4+2iX3flgM7ouQYsx8nSbU2zKuhalOMvP1jIKZr5PweaEKUf+r1qdn8aWIhYTX5gDXcgWYNDuRTpvEivGDa73Tuy+u1foEefa5VOfFlnuVLWH8C1MFKCJcOlrUtNdTyhewE8ZviP44ilD/K+WeFLQoilBWdnlFre6s+9tZre3A9hbQ4E4UzIvwQSosplYbM66nYJ+dGa0mCEzOORjJcZarfIdcZxVRE489hWimCsppVEgqj44zrUt7bXDe8TvK7RhrvDYkdNBmp1YxroUpn3QyPrhW2VNyz23WYeO6n5q44661qC3Tra+ziECLgh1tPhe1DZ12n3xiarnSaWNwK/ASppRSM5RS31ZKrVVKPaKUOne0GjYadNoun3IpQClXYapzrhM6L6K4D0VtDigkanUhJtiWV9mRtPvKvpGizHydho+voXOdnXijxhhfn6l/AX6qtX61Uqob6BuFNo0aRU26zlv+S8orhtJEYSL5h/lQSNRq6b9tS6fNf0WM350XGKEYRDPVjLMwpZSaBlwEvBVAa30EODI6zRodOs2ZsCsI6JogvdQrovgotkMYXYoJtCg9woZO014XodE1c0Zn3acicLGejHd87shxwA7gP5VSq5RSX1BKTW78kFLqXUqpe5RS9+zYscOjOjc6Kc1EySOLd6e9/355wjrsYguikPRCBeVNFLLptNtbnHlcyKK3K/+O8/GOjzBVBs4APqu1Ph04AHyw8UNa62u01mdqrc+cO3euR3X56bTB2Zj5Wu9M2GlMnCv1o4g+UVQMMCGbiXSffNJcTRQmUn9oBT7C1CZgk9b6rvD/b2OEq7ah0wQMo5lyeyQT6cWYSNfaaYMYEzEAACAASURBVBQUGUGwoNPMfEUxUW5Tp82P7Y7z0Ke13go8pZQ6MTx0KfDwqLRqFOmk7lJS7nnNJhIyCNhRiJmvg8zqgtCIuBAIrvju5vtD4GvhTr4ngLf5N2ni4uUzNcptaWdkvGtfRPvRvsijsWOiCFQT5DJbhpcwpbVeDZw5Sm2Z8Hj5TE2gN2MCXWrHIZGR2xfR6ArC2DGuPRw6bYwtKSUre6GjkRhg7YsMLdl04i3SjuU68VrbGRn62ggx89khK2w7Jor/kvQGO8QfU4giAXZHl3F/NzspAWopEAd0G+QW2VFEIl4fzaqY68aWTkvM3GkU1X3lqbYH416Y6iSCQHkFPRSEohF5SOhkOjFfnQjJ7cG4FqY6rYtJaAQ75A61L8U4oLe8SmGcItpRwZVxLUxBZ028gVLOCSRlDBAEQRCEYhj3wlQnIUk2hU5Huq8gCBMR36CdbU0pUB0lnPhopiYSnfRMJxoS2kMQhInIuNZMddqkGyiZjITOpku2WwuCMAGRka+NUEo5m0m0a+Q2QRAEQRC8EGGqjVBIQEobxBQqCIIgtBMiTLURQaCc03FMpFgjnWa+FQRBEMY3Iky1GSImCIIgCEJnIcJUmyEO6IIgCILQWYgw1WaILCUI+RCzryAIRSPCVJshDuiCIAiC0FmIMNVmyCJbEARBEDoLEaYEQRAEQRA8EGGqzRDNlCAIgiB0FiJMtRniMyUIgiAInYUIU4IgCIIgCB6IMNVmiJlPEARBEDoLEaYEQRAEQRA8EGFKEARBEATBAxGm2gyx8gmCIAhCZyHCVJshqTEEQRAEobMQYUoQBEEQBMEDEaYEQRAEQRA8EGGqzRAjnyAIgiB0FiJMCYIgCIIgeCDClCAIgiAIggciTAmCIAiCIHjgLUwppUpKqVVKqR+ORoMEQRAEQRA6idHQTL0XeGQUvkcQBEEQBKHj8BKmlFILgZcAXxid5ggSs1MQBEEQOgtfzdSngD8FKkkfUEq9Syl1j1Lqnh07dnhWN/6RCOiCIAiC0Fk4C1NKqZcC27XW96Z9Tmt9jdb6TK31mXPnznWtThAEQRAEoS3x0UydD7xcKbUe+CbwAqXUV0elVYIgCIIgCB2CszCltf5zrfVCrfUS4PXATVrr3xm1lgmCIAiCIHQAEmdKEARBEATBg/JofInW+mbg5tH4LkEQBEEQhE5CNFOCIAiCIAgeiDAlCIIgCILggQhTgiAIgiAIHogwJQiCIAiC4IHSWreuMqV2ABtaVqFhDrCzxWWLqLOostLesS3bae31KSvtHduyndZen7LS3vYtW1R7XVmstc6OOK61Htc/wD2tLltEnXKt7VvnRGrvRLpWaW/7lpX2tm/Zoto71j9i5hMEQRAEQfBAhClBEARBEAQPJoIwdU0BZYuos6iy0t6xLdtp7fUpK+0d27Kd1l6fstLe9i1bVHvHlJY6oAuCIAiCIIw3JoJmShAEQRAEYcwQYUoQBEEQBMEDEaYEQRAEQRA8EGFKEARBEATBg3LRDRAEQRjvKKWmA1cCRwMa2AL8TGu9x+M7L9da/zzl/DRgrtZ6XcPxU7TWD2R893wArfVWpdRc4ELgUa31Qw7t/Aet9V/kLHMscDrwsNZ6bcZnFwHbtdaHlVIKeCtwBvAw8B9a66GUsi8HrtdaH87TvrDsRcA2rfWjSqkLgHOAR7TWP7IoOwXTH44BhoDfhO2oWJRdDlxFfV/6gdb6kbzXEPnOt2mt/zOjzqOBu7TW+yPHr9Ra/zTju88CtNb6bqXUyZjrXqu1/rFDO7+itX5z3nKtYNzt5iuio0Xqzd3Z2qGjhQPBWcAarfX1KZ87GzNY7FVKTQI+yMig9Q9a62dTyl4NfFdr/ZRD+7qB1wNbtNY3KKV+GzgPeAS4Rms9mFJ2KfBK6getb6S1NVL2CuAV1Pel72cNHhnf+SGt9d9Y1LsQuFFrvT5y/O1a62sTyijgNWE7vw28APMerAU+ZzNIN3zfTVrrF1h8bo7Wemfk/98h7EuYiSx2gFFKvRK4RWu9K5ys/x/h5Al8QGu9KaXOfwb+R2t9e55rCsvOAt6DeZZfBP4COBfTl/5Ba707pewlwKuo70tf0Fo/nlHnm4EPA9cDm8PDC4HLgY9orb+S9zrC792otV6UcO61wKeA7UAX8Fat9d3hufu01mekfO+7Me+2Aj6OEU4eAs4H/lFr/cWUsp9uPAS8CfgKgNb66oRy39NavyL8+6qw7Tdj3vOPaq2/lFLnGuAsrfVBpdTHgaXA9zDvAFrrt6eUPQQcAH4CfAMj4A4nfT5S7lOYfl4GfgZcGn7H84FVWus/SSn7WuBPgPuBS4A7MFai5wBv1Fo/mFL2z4A3AN8Equ/IQsz4+E2t9cey2p7wvWl96WrgDzDvyGnAe7XW3w/PZfWlDwMvwtynnwNnY57rZZh7/fcpZX/QeAhzv24C0Fq/3OriWsS4EqaK6GjheafOVlRHU0r9Wmt9Vvj3O8O2fxd4IfC/SfdJKfUQcKrWekgpdQ1wEDNxXxoe/62UOp/FDFrrMIPWdVrrHUmfbyj7Ncw96gP2AFOA74T1Kq31WxLKXQ28DLgFeDGwGtiNEa5+X2t9c0qdnwJOwEwC0b70ZuA3Wuv32rQ95nuz+tI/ABcA94Vt/5TW+jPhubS+9O/APKAb2Av0AP+Lue5tae1VSjVqKRTm2h8F0FqfklK21ial1F9hNBhfB14KbNJavy+h3MNa65PDv/8buBO4DtP336i1vjylzmqOz7nAf2OE41VJn28o+2PgQWAacFL497cwgs2pWuurEsp9DOgHbsQI2E8CjwG/jxHCrkup81Hg7EYtlFJqJmbxdUJK2cb3vHYKeIHWenJCudXAi7TWT4cLtq8Af6G1/o5SapXW+vSUOh/EjEWTMPd5Waihmgn8Qmt9WkrZTZgx7PqwjQCfAP4YQGv95YRytTYppe7A9IEnlVJzMIuKU1PqjPale4HnVRcPSqn7M8quwghdr8bMEysxY+E3tNa3pJR7KPzsJIyAfHQozHVhhKmVKWUfAM4JPz8H+JrW+gql1CmYhc95KWUfA1Y0LiDDBedDWuvjM+qNPQWcoLXuSSj3IHCu1nq/UmoJZsz/L631v1j2pdMw49FWYGFkMX5X1tiCWVx9AbNIVJi54/UAac+nEHQb5LQZrR/M4NYVc7wbMwGmlX0g4edBYCCj7IPAlPDvJcA9GIEKzIuVVq6EERL2AtPC45OABzLqvA/4KnAxZjV0MfB0+PfzM8quivx9N8YUADAZeDCl3CPR+hvOrc6qE7P6eiFGI7AD+CnwFmBq1rMJf5eBbUAp/F+l3afq/Q3/7gNuDv9elPZcqn0p4biy6Et7E372AUMWfakc/j0D+DHwSZu+FP7uAp4BuiP3LPGZhp/5QdiXlgOLwz78VPj34hx96T5gcqQdaX3p0cjf9+btS+Hv44G/xmhN1mK0PydklF0deY6bbeuNXkt4T28P/56J0eim9iVgeszx6RZ9aTfwEsL3OvJzMUZIzmxv+P9RwL3A1TS8uzFl74v8fX/S804oOxWjVfo6RsAAeCKtTEydv85Z588wgiXA/1T7LDC7sf1p9Yb/zw/v0a+Ap1LKrQl/94bPaFL4fwljmsx6x6uKjEkN71BWX1ob906G7+qjGWW3YQSbxQ0/SzBa/6RyDzf8PwUzdv+z7bsa9xwtygbA+zCKhtNs+1JRP+PNZ6oCLMCspqIcFZ5Lox+4AvNiRFEYNWwaJR2a9rTW65VSFwPfVkotZmR1FseQNirlg0qpdVrrveF3HFJKZbX3TOC9wF8Cf6K1Xq2UOqTtpPUgXGUGmJd6R1jvAaVUon8BsCZi8rxfKXWm1voepdQJQKKpLURrs1q8Hrg+XMG9CKNJ/ARGy5DW3m6MsNeHmYR2YVY7XRn1loHh8LNTw4ZsDOtP47BS6iyt9a8bjj8PyPKv2INZHW9rPKGUyjJzlnXo46G13qOUehlwjVLqOsyiIIlqmUGl1N1a6yPh/0NKqVSzhdb65aHZ7RrgE1rrHyilBrXWje9RHJOUUqdj+lJJa30g0o60em9WSv0N8NHw71dorb8XmtKyTLA6rOM3wN8Cfxuu6t+AET6XpZSt9v2pwBSl1JLwnZ1N+v2tKKVmaa13YcaYUtiG3aGJNY2/B+5TSl2PEVLBCPSXh+1P407gYNx7HWq8ktinlFqqQ38pbTRUF2PMXysy6qwopbq00X68JFJfLxmblrTW+4A/Uko9F/iqUupHWWVCTlVK7cWMlz1KqfnaaMO6Ce91Cu8AvqKU+r+YvrM61DjNBN6fUbbu2WmttwKfBj4djt9J/Egp9UuMMPUF4FtKqTsxgu6tGXX+GPipUuoWzBh4HdRM0Fl96Y+AG5VSv6G+Ly3DmK/T+CFm0b+68YRS6uaUcluVUqdVy2mjoXopcC3GNJnGEaVUn9b6IPDcSH3TyZiTw/nik+HY90ml1Dba2c+7aGluNH8w/kaPY2zX14Q/Pw2PXZlR9ovABQnnvp5R9iZCyTlyrIxRrQ+nlLsL6Av/DiLHp5Oxeox8diHmZfxXYKNlmfXAExhTxRPA/PD4FNJX59OBL2FMdXdhBKgnMGa0UzPqTNOqTMoo+76wng2YVeONwH9gVngfTin3Xox28RrMiu5t4fG5wK0ZdZ4RXuPDhAIgxox7F/DcjLJ/h/HhiDv38YyyPyRGsxh+ZyWl3E8ItaMNx+fTsNJP+Y7JmNXmDzAmOpsyv2j4OSo8PpuUDO8YIfj/AhvDnwpGc/d1YJFrX7Jo7xswK/RtGP+nGzAr383Au1LKvS7sf9eH7X1JpC+ljg/h52ZizBMfwJi8Xg/MdL0Oi/pOBY5PuO9vzCi7iHgN/9HAZTnaoDAuBF/1uI4ZGBOTzWdPwvgJvgpjpgwsylzs0bZzMeY6MH5afwy81rLeF4efvzxyLAB6LMoGGGf3V2HMk+cQauDHqC8tJJwjYs6dn1E29nqAOcBzcrbjJRiT+phcp+/PuPKZAlBKBRjHwKMxL/Mm4G5t4VToUedCjJZpa8y583WCo6xSqkdrPRBzfA5mUkp0RIwp8xJMx861a6bhO/qAfq31kxmfmwochxEYN+kYDUxMmRO01o95tG0BgNZ6i1JqBsa3ZqNu1hw1lluBGWTX6IxdQQnl5xPpS3HPeDQJfQnQWh+KOXe01npzc6nU75uMMb1tz1HmVMwE9rk8dTV8RwkzkB60+Ox0jEbuGcvvnqIjmzwc26a00dqVMaaPzVrrpzPKzcL0+8e1wy48pVQ/kc0MNu+Nb9ki6pT2jn17E77P+b1wLVtEnb5lx4rxKEwtAvZqYyJZgjGHPaIttvQmlF2rtV4zVmWLqFPaa1dnWP5MIru38ghkRZSV9rZfe5VSpwGfw2h2N2EE84UYc/Dva63vSyl7OvDZsGx0J2Bq2YY6G8v9nk5x2B/DsmntTbvOManTs70trzMLlbG5ZSzKFlGnb9kxo2jV2Gj+YLbzPokx6bwj/P1FjIPq+9utrLS3rdv7fMxGghswfnQ/BG7H7FQ6pt3KSnvbur2rMbv5Go+fQ7aDtFPZIuqU9rakve9P+PkAsGssyhZRp2/ZIn4Kb8CoXoyZJCdh/DX2Ub9LLWuXRMvLSnvbur2rIp8/FhMjC4zT8PXtVlba29btTdyxhzEZjnrZIuqU9rakvYcxmxY+HPOzZyzKFlGnb9kiftrXM96NYW12wh0BDmG2iKPNLrV2LCvtbd/2lvRIHKyNmC3EaK1/rkwMqnYrK+1t3/b+RJldbV9hZAfWMZiYZVkBYF3LFlGntHfs23sf8D2t9b2NJ5RS7xijskXU6Vu25Ywrnyml1Jcw25snYwJKDmE65wswsYxe205lpb1t3d5rMY6hN2J2CG3WWr9fGSf9+7TWy9uprLS3fdsbln8RI5kZqhtjfqAtMh24li2iTmnvmNd5IsbE1RTwWCnVr1Oc2F3LFlGnb9kiGG/CVJn6lBpnY7ZCbwT+TYcxcNqlrLS3rdvbBbwTOBmT9uFarfWwMrvt5umUGExFlJX2tm97BUEY/4wrYUoQBKHdUCb0w59jNBHzwsPbge8DH9MpYRZcyxZRp7S3pe19BSOBjse0bBF1+pYtApvItB2DUmqKUupvlFIPKaWeVUrtUErdqZR6SzuWlfZ2RHvXNJR9azuWlfa2b3sxuf92A5dorWdrrWdj8mjuIYx+PQZli6hT2tu69l7cUHb3GJYtok7fsq1Ht4EX/Gj9YCTWt2Jidrwfk7freODLZEROLaKstFfaO1GvdYK1NzFnWto5n7JF1Cntbd/2TrRrLeKn8AaM6sU0J+W8O/wdYAI1tlVZaa+0d6Je6wRr7/XAn2KyC1SP9QN/BtwwFmWLqFPa277tnWjXWsTPuDLzAQeUUhcAKJMgdhfUEiZm7YUvoqy0V9o7WmWlve3b3tdhYp3dopTarZTahQn2OQuTy20syhZRp7S3fdvrU7bT2lsMRUtzo/kDnAL8GmN7vg04ITw+F7i63cpKe6W9E/VaJ1J7w88tx+STnNJwPDUBu0/ZIuqU9rZveyfatbb6p/AGtOxC4W2dVFbaK+2dqNc63toLXA08CnwPWA9cFTl331iULaJOaW/7tneiXWsRP4U3oGUXChs7qay0V9o7Ua91vLUXeJBwZQ0sweT4e2/4/6qxKFtEndLe9m3vRLvWIn7GVToZpdQDSacwjmttVVbaO7ZlO629PmUnYHv7MdHtn4qeqpZVSj0E/IHW+uai24tJRbMfQGu9Xil1MfBtpdRisv2tXMsWUae0t33b61O209pbCONKmMIMaldg4lBEUcAdbVhW2ivtHa2y4669ysRw+gCwFNgLfBcTxK8//Lsf+KO4slrrFa1ubwpblVKnaa1Xh23br5R6KXAt8JwxKltEndLe9m3vRLvW1lO0amw0f4AvAhcknPt6u5WV9kp7J+q1ZpXDCFHbgCuBLoya/8fA3cB/YgbUr7ZLezPqXAjMTzh3/liULaJOaW/7tneiXWsRP5JORhCEtkIpNQ3YArxda/2tyPEpwBPAB4FFmDx5h4FXYvItvkVrfU/42fXAO7TWNyileoCPM7Kd+lvAn2mtB1pzRYIgjHfGW5wpQRA6n/OAXuA70YPa+E/8BLg8PPRy4JvADOAHwL8mfN9fAucApwGnAmcBfzXqrRYEYcIiwpQgCO3GHGCn1noo5tzT4XmA27TWP9ZaDwP/hRGU4ngj8Dda6+1a6x3AR4A3jXajBUGYuIgwJQhCu7ETmKOUitsgc1R4HmBr5PhBoDehzAJgQ+T/DeExQRCEUUGEKUEQ2o1fAQPAb0UPKqUmAy8Cbsz5fVuAxZH/F4XHBEEQRgURpgRBaCu01s9iTHGfUUpdqZTqUkotAa4DNmFMenn4BvBXSqm5Sqk5wIeAr45ikwVBmOCMtzhTgiCMA7TW/6iUegb4BCNxpr4HvFFrPaBUrph9fwdMA6qBN68LjwmCIIwKEhpBEARBEATBAzHzCYIgCIIgeCDClCAIgiAIggciTAmCIAiCIHggwpQgCIIgCIIHIkwJgiAIgiB40NLQCHPmzNFLlixpZZWCIAiCIAhO3HvvvTu11nOzPtdSYWrJkiXcc889raxSEARBEATBCaXUhuxPiZlPEARBEATBCxGmBEEQBEEQPBBhShAEQRAEwQMRpgRBEARBEDwQYUoQBEEQBMEDEaYEQRAEQRA8EGFKEARBEATBg0xhSil1rVJqu1JqTcy5P1ZKaaXUnLFpniAIgiAIQntjo5n6EnBl40Gl1DHA5cDGUW6T0GIqFV10EwRBEAShY8kUprTWtwK7Yk59EvhTQGZiQRAEYUKjtUyFExknnyml1MuBzVrr+y0++y6l1D1KqXt27NjhUp0gCMK4QrTBgjC+yC1MKaX6gL8EPmTzea31NVrrM7XWZ86dm5krUCgAGdYFQRAEwR0XzdRS4FjgfqXUemAhcJ9Sav5oNkwQBGG8olTRLRAEYTQp5y2gtX4QmFf9PxSoztRa7xzFdgktxNj6ZXQXBEEogkpFEwQyBncyNqERvgH8CjhRKbVJKfW7Y98soZWImU8QhLFAfMOEiUKmZkpr/YaM80tGrTVCIcgmFEEQxoLBSoWeoFR0MwRhzJEI6AIVkabaFlnZC53MRBpalDjCTWhEmBKENkYEXUEY/8hb3vmIMCXIhN3GyJMROhkZW4SJgghTAsNiShIEYQwQWcoOiZ7e+YgwNQZ02oshspQgCGOBaKbskLvU+YgwNQZ02vjRacLfREIejRBlaLhSdBNyId1XmCiIMCV4mflkt5kg5GeiCMkT5Tp9EQ1e5yPClMCwx4ssg8DYomVtL0TotN4wkbTePtc6gW7TuEWEKYGKh+VAxgBBaB1FTbqugoIICcJEQYQpgSEfaUoYU2QyGp+4PtaiNJWu/VAzsbRTrsgtyubw4HDRTUhFhCnBSzMlCEJ+JoqmR2vdcW0W2pN270ciTAlemql27+CdjtzfsaWoGGsT5bFOlOv0RXwjOx8RphLw2aXWaa+FBO20Y6KYKzpt+30n4mw2K8pnqqCynYTPs5kgQ4sX7S5wijCVwJEJNKEMDvsIju3dwUeTIoTOIu7vxHmixeG6C7Y4nyl3s+REWYQIExsRpsaAThs8RDPVvnRYV/KiiJhlnfaudppmSk+o5ZY7EmKm8xFhKoGJ1LcHxWdqTPGKPzOK7bCucwI902Lur/bQTBWDc5+QviSMEu0+LokwlYDPeqrNn3kTopkaW/x8KeTZjDeM6cu1bDH9wUf4E61LNjIEZ9Put0iEqQQmkjPh4ATyDyuCDusOwhhT0e5LtU7TTPkIjhMJWTRl0+73SISpBPx2r7T3Q2/ERzPVaSvsIvC51iJWrIU5ORdRZyH318Ohu6B1j6t2aSJppXzec7EOZNPuAWBFmErAZxBo4+cdy5DHbj5n9X+H3SMfvC61iMm+w56NXxiTIpze3R9rUcKJmPmy8blKn/yoE4V213KKMJVAUSvAVqO1ZriinSekCTNQdtgk1om43uOi7pGzdgn3qOBFTbqu8mqlUkwE9CNDnTWAT6QsFM5uJR6LkFYgwlQCE0UzVVUvO6vxJ9Ag4Ozn4tEfZMU6tvjcXlcBQ2ucO1MR4SPAL85UEX24CAHbp0qfLBRF9QlXXE2aFY9dsK1AhKkEvISptpaf66kOdEOOHXyiTPZeGxI8+sNE8qXoNKdsH1z7RFHvm48mrQgtfzG+cO6V+rhauFKUtt21WhGmOhSfQauNn3cT1QWRayedUJO9jybCEVchtxPppPcGipmMiph0wc8BvRAtUUG+cK74aKZcKS4ArGtfau8xQoSpBHz6djtLz41UX2JnzZRjuc65Q4ai2ltEnrxOG2S96vTSOLrX6VrvUEUXIsT5+EwVY+ZzKzdc0P2dSIsm5zlDNFPF4fNS+KwU2vdxN1Nd6bqueIuIUVWY34jjk/V5/ydSDLAiNH/F5D50r9Voeka1OVb4mCULyWnZYX5aPhrHTppvwEPQLagv2TKuhSmjFnS7+V6du4Pmv+pk7TJpVyoeaTG8Yi8VMzg7T/Yew92RoYlhIoFinqvP2FxEZHufd84LV03acDETYCECp5jzrXDt+2bX+Sg3ZhQZ18KUDz6du4jJyFVbcyQUoly2Eg8XtEr2wfU++VymzyA7MDTsUXNn4bw7zuPp+Agm7n5E7v2pqNW5j9lMNFPZDHvlR+2sQdjdzNfeLjQiTCXgtVW1cxaONSHqiINmymegLOqVGHCMP+OVT82tGODeXvAbtIqgEI1jITvN3LVLwwVopvzcJXQhWpdOGoO11hPKzOfqQzdckP+dLeNamPIxzQwNuzsitrP03Eh1sh4YdDDzeUwKhQVadN5J4u7p4mPS8fGZ8olaXQRF+ML5DM4+sdmcQw0UIJz4aASGK7qQTRSdJJgbAdmj3s6ZbgD3zV3D2j24dCsY18KUD1p7BBfrILV2TZhyMCd5aaaK2jHmUa/zjiaPnVsTKW9iEX3J5/4W4Ug7VNEMtzg8gk9i5qHhCoMFhHMoZAz22PJfhLm5iLdca+1s9al4Cp1jjQhTCfj4A3WSivnw4HD428UBvfPiTPlo0tyjQPus6lvvu1fUE+20SNmuZYcqFa+FWss1U/j5ZBYRQ6mIvuQTjLLT/MpctY0V7a6Zquj2DhI9roUpjYejp4dvQhGDhytVYcpJM+UxCBRn5nMsp/2ccF2oxQBrsZmkqN2oRXQJH8HEOZ9lxb3eQY/dcT5uCy5Fq3k/i9BMFRHc1H2+8PMFKkIz5bUhwWs3nwhTHYf2EBSKWWXkLxPVfAwN5/dtGKpUnF+Mot4JV0HBRzPleo9qz6bF5q+iBF0/k6bjs/GYdF2bOzhccd69NVSpOPvR+cTxctFyDg4bIcw16bBPfyjC6d154T5sTLc+GuxW4+N76rMAb2dLSKYwpZS6Vim1XSm1JnLsn5RSa5VSDyilvquUmjG2zXTHOc6UhwRdzO6V/HU2aqPy7hzz85nyMJs5laqWddcmeCW2daDaj1o9gBSlSi8iufig165d9/HBVVszNNx6M5/rJOYTww78zFCFBBN2bO+gRxYK7eHPVsT75jNnjIfdfF8Crmw49nNgpdb6FOAx4M9HuV2jgpezsYejZ1H5s/LS6CdVNfnZYrRZ7gJRIbEHC/Br8C3Xcs1UUSlLHOc/r2CJXpop1/Gh4lRvpVI1m7VWUHDdbVbVSA0OV1ran3x8Db3a6bpoCtvq8ly1dte2FzH++riGdPxuPq31rcCuhmPXa62Hwn/vBBaOQdtGBddb7xMfpVNWRY3q97yxprw0U5WCdpM4lhv2MfN5DB4+5Z23s3uEFPHB3ffDfXXu86663qMjwxWnuG5HPDU9rrjuNqu2V2ucNHHuY/f/Z+/N4yypyvv/d9W9fbt7epm1e/aN2VcYQEBAwGEVCKAxURONC8pP4040evefJgAAIABJREFUGpeoiSb5Gr8xrnGPX4mCSwwoEVkGkF0EZmBghtn36ememZ7et7vU74+6dbtu1Tmnzjk19Izz4nm9eA19q86tulVnec7n+Tyfp/RHpaY/iuBZONgp5GlOhBVTcP5OVJ1HXTsenKl3AHfJDjqOc5PjOE85jvPU4cOHj8Pl9C2NMnKajJsT4UzZ9LFYmM8wo893OC0hfNJkx1k1K7cd+4GcBsEA+4QGe/j/jyuzzvPs32sa1MT+vdqhS2kW3TRmW8ImvFmzcR5tLa0W11hzDdNUoUgjq5BuHrVrV/TS0WdO5qhPKmfKcZxPAgXgx7JzPM/7jud5Z3ued3ZLS0uayxlbmjBHqWTvFJ0QXRWLHxv9fWOKTHknqnyIXbtU8HQKTSGwD0WlchzHXBgyHfcjTduxfq95yzBfPkU4KI3Z9v0wB9OWhG5jvj7b2G5AwL4/hMOhppa2aLatWRe+TjG3nLC6lJqWtW3oOM5bgWuBS72TuDiQzZ2NVBAB88Zpa1GVSh6u61i0M79WlHBuOuEVSqWKuGnG8J5PFDx9IiYBa2eqmDbMZ9WMwgmYtHx0ybItaRS6/d+bzVhcN0WYLxUyNYaOCYxuLk1tJKUzZZ24UbQn+HseeObTL5DOuQ7/a2JeCmTqRMy/qebRP/ZsPpE5jnMV8DHgOs/zBo7vLR1fs1k8AyfKpnOnrZ1l29I2dbn6b0NnKsVOOc2YOBHwdBqBUl+ozh45tA2RpBILPQEZY7a930tRniUNv8Z+8fSsFpU0dTQhLX/UnuMFY41M2VM00tAPbMPx+VRhPs/6xZ6IOTiNM5W2IsRLbTrSCLcCjwNLHMfZ7zjOjcDXgSbgXsdxNjiO862X+D6tzHa3G+z8bDv3Hwv5MUZAN0am7JETjxP1nOwnylTq6Rbt0qaWpxLWG+NQtY8u2ba1J8ynndxNzfO8yvxivHkJhX1tFvw0zrXNM6rmTJmLAtuiyLacNEhHPxgp2DUcTuEkp6JLnIBNf6p6ridgk2diiWE+z/PeJPj4+y/BvRx38/BwMMds8ynCfCdKWCyNDszo32bfkSZ1P41uUxpLkxmXRqKg5HlkDPtihUthMUl7KSatQqlk/Vutw9RpJtkUoY5UHA4rnuJom5Fiiboa/fhieLzmix65rNlzTodMpZtfTDXs0lhaLqfnpOtLppSHUS6c3Xoz1shqGrNFl4olf8P0Ry2N8Mds1sjUCQzz2ZrNLiMujWCqM2Vf7iTNJJBOP8yuXZpMEtvfWsnysdnVp+AgjTVaAyk5UykXlLGUQMmnCH2Fz7fl19iYrS5W9f3abAiMmwB+H/Q8+3kpTcjYNNTneaOVJ+wI6OlKptnaWKOchRTCpmNlp7YzhR1UPIoI2BX/LZXsO5t9mrbZ+YVinFdgmsGYJsxX8jx7sbkUOTdpSrukQTBsnLjgXdjs6m37PqRTGj4xZSbsFpRgt5s2QcDE0jhE4YXESvTTWj7CM1aKjy6aY8qZShlZSKNsb/pegpI7YEnST7HWpKpLae2Ye+VxZxsFGXvZIV07pZ0pf2CYt0tD/D1RHrTpBCD7bSYDOviteYvf6nn2jk0aZMq2aHAaBMMGrRwplCoOmC13LxUylSIbysZKaZAp7BzzSv+1QTCOQ+jLmKMYDvON4aKSL5YoFs3Up9PyMSGd8we2ztTYSl5UkfRtpRFSjHNr3lSKa4L5uwneqQ9WnJzo1CntTHmldMrgBcMJBNKrVtt2E9NCvLLJTXfSC6dLj3WYL00Y1bomWqlk7YjZ8K3Cgqo2qFiasFkaMVZbSxMe9xOa7HgY4X+N2lr23+E/SmTKfHMZFQS2DVXbWGVDa+kkW1MBiubjJhz9sFEHT8MXTENet90I5y03MCdqI2Fip7YzZbljTbNbSKsNZGumi6csdKQbUgo/FxsHpViyr7NkO5bSiFHmU4T5bJyh6HuILk5J5mcQ2j7fE6EzZY/82SPQXtW/JmZb/y08bkzDt+HrjaUciY0kQ3RTViqZo1PWC/YJInQXSiXja0afqel7te37YL8hCK5rY6OZrHbIFIxtyNjETmlnyna3m6YMQgXGHPMwiRnPJS0yVbVLtvBu0kwCaWrO2U7QxZJnFc4M2po+o5gzZVjqB9KJdo51mDqNUKjt7jxNuZ7jMrcYLgrhHbktH8jGhi10kKJF1MFiQ5AyMcAqgcjSwQgQLeNxnk83ztMi/GOv/h84ummSL14O84252U6yqTJu0gotWhN4jw8ypQvHh2FXG8exWLLfeaZxiGzLTOSLJfu2FihGtN+ZohimYd+w2aIucGIyCG2RtDTlemy1jMLv1eSdliI8NtsQlk2b4LpGzpTAcRI5WCo7EYriJct5KV8JLZoiU5H6qIbh0DTJTmkQ6LQZsKbvJuyIv4xMnQDzLJAIz/PS7R5TCi2mQaZMBobsd9mE+WwcxzQk5zS7KVsnoVjeeVql9VoQU2OcE8N+aMuZCkKh1hIHtkKLKR04KwmUFGPVVmXbtl5dlCdiM+ZsXqmtivlQXuRMmTkKaZ0pa9Flm2SGCundrHHUwTRFpoopwuNpyrPYZLqHN0ym72a4ahNizr8bCzulnamiRUbTSLFU1cbYmSrYwZiBWTtThrsM2f3pQqhpY9hp0u9tJw9beYNCqE/YECc9z5w0GUemLDhTFo9pFK0Z2/5btCDvVtpaLgppsr5stYyiO2xdtChe+smO52Vqw5aUh8GR9M6UbbZk0I1sJEVs56WCJU8rOq5Nx3mxaL8J8ROsrJqmRmXTOVMvI1NjbmnqX1X+No3tlmFaWyjSWhvIcIciGwy6i0PaIqb+Amj3jGwX+kKpZMV7quaHmbW3DSWlDfN5WPKIKtk2Y0tMHSmjdzYhC2shwBSFg22qJERRb9CfX6Lt7EQ7x47jNSQ4d9DUmbLog7bOX2A+MmU/bowTTSJIlGkoNJ+mWoFnv4GxebZpohnDob5j6pSPlZ3SzpRNWCatPkowOGy95zRiaCaTgGyx1O3k4R1UvliyEGGznwTs5Q3sHLjwwmXqyNmGktI69bayIGmRVXvSu7lzMnpNy9pxwbtJgWqZPKfhQimG3OkuDNHr2G5gTM1mASwUS0IH1dRRsFqwwwiGZdJGGv6d6biJcstMkalC0bPeXKaJDtisby8jU3/EZpOZEV2oTV9ckPli70yl4AMZLAqyHYnuohSeqDzP/PfmU8DTtgt9EAIwXXjDfcI2pdeccJlu8bTNlgwIsNbOVIoQrO11fX0f+9BMwWIzYMPLEfOI7JApW56iqeUtFkARKgU2Yb6UC7Ytl9MyvAhmm4GwMG9gpg7gSKFkneyUJunDxlG15d/ly9U63MIgeKWXkakTYcclzGeiCF5WCgY7kpyXQrXalMArG/S63xHXQTIkTqYgOds7U3YLdnWBWXPOFJhNsqJSP8bP1zJlOnhGnmefWm5jFZTIoqizLRk8+H2eZ46IVYR9DdqJHCfd0Fd0wTRVJAc7Z2rEou8PS36TCUcM7PpCFWJueD2wlxqw2QyI1gjTcT5SLFpTStIkfdisb7aO7lC+iFsYYs3D72Lp059lpGC++RkLO6WdKZuBEU1VNVP9TQdFeh7WEuiFolmRTVkmnW6JgbTESb/0gj3CZGO2BUXTaQOZT7KiPpc3nEBsBUpTc+FSZgfZlXCy436EUUbzmmrmyJTIcbIN84G9Bp6JRfuD3twgvy8j4c+UYT6b7/CTVIwvG1JdN0OmolYsmUluDOdL1ok1ttIIpbITZuqo2talHBkeYvVj72PC4T9wrPUcPM9eeuiltFPamfK1YExfePX5JiGA8CRStIxlp0kt1/2tSQMv6Xip5KUSm/MrpadBplJyZIwzbuxDBxVkKuUkG72PJLPNcBsuFMkOd+EUR6w2BGl5cNZhPosddpWenKWDnTbDTReZEqE0Y+FMhTdJuouYysE06VM2fSEWHjf8DltS9ogF11Cq9WfwjILvsEGKbMV5K0k1pmT70D1qK+IXC4y7891MOfQQm8/6B9rnXFv+rpedqTE1G/g/OvhMvOB4PSqzF54GuDRBepJ2I0mhGtHvMolj25I1AyulRCLMyY+jv82UKzCafn8cJlmD52W7W833HeOVv30Ni579P1bcBFvtsFSaT0W7ZIbwOzEP/Zr3JaGQpcDBEpkMrTQxm8SNaH/X+b2qZ2nynG1KTkWfk3Ex6ZScKZPweNoqFAGXCOw4TLacqdFixabzaJQaktD3SyX49Qeo33YnW0//OAcXvKFyyCYD96W2U9qZyluoFIs6su4klDbN1bMkDQdIj+5imzSAkoA40SJrs+O0DtelDGGlIYOLFkSVBROOCfE96Efjenay5ndvIzfYXvW5jtly0iat/wa1w0dpPXA3QyMF4/Y2RUjDk7qdgKY5ClwoVpN/jecJCwkUkeOk259E92cu05Ee6dGZ01TzpQmCaJNtFl1kjesBWvLvbMjVcq0/3c27/bwEKSRFjpMMhHLN8Dz47cdhw485evaH2bvkHVWHXw7zjbHlLXasooGgOzjS7oo8LLVgDMNXSZdIQq5Eg8AExQhCFqWSnWZUwVJWocJzMU1fTqFxEg7PaO9Yywv1tD2/YnL7Yyx67kvlzw2QKZuJsns/0178IcN1LdQNdsChjWbtsdMGsi2xUrmmRX+IFZg1IDuHhSFtOZWB6YY7hJyplxiZGinEHQud/q9y2oyQKSvOa+S9WnGmxoZrmLbYfPhdGCunl39jGmTKpK3neTEkShT2rtj9n4cnvw2vfB9tZ3wodvhkLClzyjtT5rX14i9Ye3CUO3Qm3+//bbhb8Dy7UF8llKQ5cSTxspLmL9HANYrzh56x6QRfKSiaYtG21Q4L2pqEHqyyocr3N/nQQ3g4TN9zB81Hn5VmSYnMhuBfuv+fcLwSz17wTTwcanfdZ9Q+uK6ppSG9B/3BOEwXcZ6MiNEW9yvK0DS5tqi/mzqQps/IljAf/p1uYQinNIpwmjhHnkVpl7R6XEUD7mnYqpTtNZ/z8USmbLKpIZ2mlkn/E2msSdfHR74MD38JznwrXPF5oQ6cbVTjpbRT1pkK1IZN9SxEg1d/p1Fk5vZbufiOc6gdOGRZnNaoCRDKhNK8XtI1km4hbUpvmlI0owVFzScPm7pQ0d25aSaJTdHskUKJmqFOmo+9wJ4lNzJc18LiDV8w4gmYhH0BaH8B59mfsG/hm+mZfDo9k1bRtO9+/fbBvVuRhkM7bMv+YBxyiGbtWpB+/f/XDdPJ7y/pvcoyM03HgOlYEzlOOoT5kudR37eXJU9/lovveAWLN3yhckz3PQWOlEmYTzR/m/bHvAUfM3pdXZRI1if0aSX2iHngRI0VMqWtsfbkd+G+z8LK18O1XwbHET6PaKLYyWCnrDMV1NgzSTWVEgI1K3l73ftZ9NwXcUt5mo89bwy9+gVbX/rwVdIVku5BtODZPmfTKukVPZcxSssVLZYmE5eNtspIocSk9kdx8OiYfRXbV93MhKMbqN/yP9rXzZe5H9r96b7P4tU2s2vZewA4Mv0Smo5soNR7WPuawb2bWnhSNRZ2DOlimTgXaVLobbKSVFyyJJ6Z7LgJglK0cBJEYZiBJML8gaeZ/8B7Of+uK5i56+fkcxNp3X+3L8mPvkK+zYJ9PEKhNkXJ433Jngen+jxqqbKMU4T5bJApkeMUG+sbboXffASWXA2v/Ra4GUD8PF5GpsbQbCDQVCnpnsf8Jz6FU540Grq3WRWntSGgB46FbW0zU5MNXBtnynyyMwtppr2m6N3r9qdSqboWm65zXSh5TD70MCO5CfRMWEHbvNfSM3EF0578Zxjp1/qOkTKsrrXg7noItt1D91nvo1A7AYAjM16Ng0dh6z1a16u+rlkfDJNnTctjVJX6MVSfDptJCDVOpNVAaxT3luTjyBZ3kwUlQE9MQtQix0mITHkebLsXfngtfHctzQceZveSd/LINQ+wY9WHqR06QlPX5uBkrWsH79IopJ4iecg/d3QDbnJdG5I+yOdRm3qNxpypFEW+ixZzsDhkHGq/6Vdwx1/D/Ivh9f8JmZrKIWEm68vO1NiZDWlY2rk1Fs/Cc79gStvv2L7qwwyOm0ljzzYr1WobBelwmMBW58f2etWf6127GiUy5H1YFuINI2DDKRAM2WfCtsXooqsJ/xcKTG5/hM5pF/q7M8dl6xmfIDfQBo99Te87dNHKUgnu/XtonkXH8rdWPu6dsJzhula8rXdrXS8wGzmSKAJiUhC3KmRs8l6j3BqDttHMKZ3FU/VMksa8zJkyEukNsiUN2ogyxIrhDOnCiI8m/Mf58OPXQ+dOuOILrH/9o+xY/RFG6ls5Ou1VAExp+x2QTDEILAhDGTnIKZEpW90xG5SzVPKkMiI2BPaRYtFoEzOatGTulNjo9Yl+03ChfM/b74NfvANmng1v/AnU1FXOkT2nl7P5xtDCnnpaZCqxcw904t79cbonrWLfwrfQN36RHTJloasC5no5jvEVqi1tkWTbGk0QQqYMQxbh6xQNMnbSKE9HnXhdp77u6GZqh45wdNqFlc+6Wl7BkblXwyP/Dt37E7+jIj+R9Hw3/Q8cXA9rP8mgVzv6ueNwZPpF1Oy6H4p5rfuuJAcY9uEoAqLM8olYFcHfBHEU6Cdpi/PaIFOKr04a83Ik2DwEZtJGpoE11NflO/VfOR1uf7f/4Wu/DR/YAOe/j1JNY+XckbopdE9cyeS2hwD9BBsbgrTotxkpbVuGzWxKkKmcWv2SXtXhZrv+YO6U2LQVjRHPg8KuR+G2N0PrUvjLn0NtY9U5sudkWwbnpbRT15kKa3CkRKYSO+ndn8QZ6mLz2V8AN0N/8yIaenfiFfJGzoKt7sdIVfp9cnsnwZtyE05IG+tPU+tu1EkwRKZMBeMi10v6TGRRxEIrrbxYYvKhhwE4OvXCqmM7zviYzz2577PK7wgXUFVuJAojsO4foHUFrH5DLNR1ZMarcUd6Ye8TifcNdrtdz/MYzFfrWSXyckKWt10AI+eaJBbEneTkdipHLclfSDveYPS3mYRmon0nN9jOwuf+lYZvrIZ7PgWTF8Bf/je85zE4/Y2QzQm/5+j0ixnfucFX1te8dtB/TeZDWXkW3e+wpQLYyDGonAGdjWKQYBU2E62p4B6tMmED1NswkSdqTZ0byd72BpgwG95yO9RPEFwrfYh7rOyUdaZsNDisuAk77odnf0LvWe+lb8JSAPrGL8It5anv22OEThU9W3n/UJhPC5lST2kqZ0pV8013t5CmjEfFmTJV37UsfyPKGtH9ndFFVyd8VfQ8Jh96hN4JSxmpb606NjBuBpz/ftj4c9j3pPyedZG/p34Ax3bD5f8Abia2eHa2nk/JzcE2vVBfhZhqsGscLsQzsEycqfBvNeGNpJH3GIpxZFIiU5ZhPjPOlFkma1hdOzfYwbI/fIIL/3ctc7d8n6G5r4Z3PQBvuxMWXRbbnTmRv49OuwjHKzG5/ZHEjVxgQegzLQEd7MJmJhQNG2RKNc/r/Gah1IBJ/0/BWw3aptm8NHRvZc1DN1Kqn+Q7Ug1ThO1k65lpwslY2CnrTFVNsilQCFAQEkf64dcfgskLOXLmByof949fBEBj93azemolu8KT1UhPemRKdVzlxNjoq5jCtTYLNogKM2s62ILfq/s7o86TDrm6NNjLhCNPV7gmVcdKHlz4YWic5qsDS95FGGGS9v2hbnjoizD/Ilh4aYwsD1CsaaB/2rmgyZuyKSTdPxxXWR8wUF6v0gAzmtzt9OSKJS8WTtTpSypelE35JjATvTWtJRh+h0uf+RzT9vyKA6f9OY+95h4OXfEfMPNMaVs3Mn90T1rNSG5COdSn503ZhPnk2djmzpQt1wr0SsqoQruelxz6FTlOJhv3NELEwbVNnLfwfF3fu4czf/d2PDfHsdf/HMbPlLZTvbux4Aeb2CnrTEWFFnVMNQCEL/WBf4KuPfAnX2WYUYi7v2kBHg6N3VuNnCn7gq3hMF/y9TLR2c7geFp4OuDVjH6f2e7ieJSECX9PkgnDfJpthRksCW2dPQ/jegWhM+V5UKppgMs+Cweeho0/E36H1g770a/AwFEflXIc6aTVOevVcGQrdO5S3jeYL9ggfkYmBHSbuokyPTmdsSrUWNNBppTZfAnOlOK+dHkypqGZIIxeM9TJlIMPsH/hm9ly5mcYbJyTuIhFkSncDJ3TLmTKoYfIOHr3a6ODlLZwcLUSv3nILOmzsCU5iclVKDR1myQW5iUmzUlhCyuZVwjkGha8x9qBQ5z5u7fheAWeufiHjDTN1WonspMt1HfKOlM2oSQj6PXAM/DEN+Gst8O8C6oGYilbx2DjHBp6to8JZ8r0t2aTnCkFNKXqwDqOYBTZstVHSVtzUXeyFIb5NN+RUPQwIYTl7lhHITuOrsninb8HsPoNMONMnzs13Ke8rnDX2XMQHv+mL4w3Yw0gfw9Hpl/i/8+2ZIkEmywf0fMYzutnBNosgGkKzIoRAT1EK7Dpu37JvM3fgpJ/v0nIlPEmT3SeKTJVHqfT9v4a1ytwcP7rtO4HxJuxI9MuIjfcSf1hvRJFJwaZsivPIpRkSOB0Jv2qpOMix8nEAQxvWEwTPoJXosszDFND5m75Prmhw6y/6Af0j1+YOM5Va4rNWvlS2inpTHmeFwl9aTpTStQldKyYh199ABpa4fLPCa/RN34Rjd1bjRb9Qskz5gJFiYg6C4LjOFL0yXXBVSFTKXcK0WdsOiCCa5hoaonImtoyBSnSrUUTshJS9zyyO+/nWOt5eBkxmbfkef5LuupfoLfNR5ii101Cph74JygV4NJPVz6SoW099bNh8iLY+lv5fQffYcjLAfluWndhsAnNpFl0ZYK1SQhr4DDN3vojVvzh4yzc+G+c8ei7yYz0Jm5CVIivdpjPEJkK7mnG7l/SPXEl/eMXx47JTLRZOzrtVXg41O+9X+v6o6KdWqcD6aRtIAVnSnBdUzFiU0uDTEWFSU3CfDaaWuHNQlPXZnonrqB34gr/XpKcKcXxl8N8Y2D5YnVZFl1ugWoXVLXoP/Y1aN8I1/xfqBsPxAdUX/Ni6vv2kB8e1L5vU7G44Lrh29adOLIZscOUddVdIu1OIdrelEgYXvR1ERCh/ozGNaNOeWA6CKKoSCwkOAmdO3G79whDfIFV+uicc31k6bGvQtfeqnOGIpypKqez40XY8GM4510wcd7o/ar4gouuhN2PCFGwsNnUPpQ9Dx1kIEyS9r/LzLGIfa7R3lbYt1TymLXtFpZs+DwdMy/nxTM/w6RDj/KK+/+cmq7dyraqfq67oJiGx4ueR+OxTTR1baZt3utix1Qm2qjl6ybTM2kl9XvMnCldCRTPi3PZArPSbdJs43liakbS3JTEHEs6LhYT1nOKomF0o7C6hSxIZYx6Hg3dW+kLOebFhPer1Gd72Zl66U0cw05+8Fov7ugOePBfYNl1sOxa6ff3j1+I6xXJHNuhedf+fRuLUVpkFoHcaZI5WYEpibQanVuEvNkK8+lLMcS/X2vhLMYzZnTby9KUlTu57esAlM5UlV3+OcCBez8jvUapFHEe7vss5BrhVR+paqMKSxQWXA7FEdj1O+Xt2HCm0lQdiJ6jW9IoDTIlKxWS1B+aNv6Qpev/kY4Zl7LxvC+zf+Ffsv7i/yQ3dJQFv7oOdsqfrWqDZepMmaj3z9h9OyW3hvY512jfD0CNZA45Ou1iatqegYHOxOubljtRvTudsV6KJBbol4QR31/SBjFJfibpuOg9amejWki2jF7XvKZl8C5zQ4fJjXRVErTCx2Smev8vI1NjYLahGZWj4PkpFn54L1sHV/9rdduIkxB437mjW3Ruufwd5pypmOaN5oCSTXi5jLpLqCZSHfX2NLpNpVL1LlA/ZCFw4DScVtX3J+3IZMiKst32+yhOmM9g4xzpKVWPePwsuOAD8MIvq7SgpArdux+FrXf5GYENk6vOUS0eg9PPgdrmxFCfjc6UTI3e1inSdZJ1vy9+jrjfKO/3qR8w7ZFPcXjGWja+8iuVEO6x1nP5w2W/IF/fAre81i/yKjDVuNJBssMIqzbiUhhh2t47ODzjUvK1E6uvmXBJ2UbtyPSLcfB8OZkECxApU2dReEzLSa4+R1d/yVYDTEWlcBz1cRD/3kJRL7IRXTOG8yXtiIiNDESwpjV2bwWgb/yS2LGktiKzEbh+Ke2UdKaEk6zOBK94NyUPWH8L7HkErvhHaJpWOVaMZKgBDDTOo+RkyXXqO1M2nKmhkerz85LwUtRqJE5TNsmZSiE+CGIExLY8i36at4hEbo9gJB0D+b1Jw1eFYdj9MPn5r068ryq74IPQNAPu+hiUSn7oK/J7h/NF/+Xc+/f+uee9J/Y1ylASGViw1q+/pnjJpgu2sqTGS+hMyRY6rQQKmSMmu9+nfwh3fpjeOZfy3Cu/GuPCDTbOYcu1v4RFl/tFXu+8OaY4rxpXWmMuRHvQ5RrW7b6P3PAxDs57reCaCchUVjyH9ExciVc/SSuZoVLQ3JDjJT5m1yfSOFMypzswVRJQUra18rqWvD/budQk4jPqTC2KHZOZNvXmJLBEZ8pxnB84jtPhOM7zoc8mOY5zr+M428r/TlR9x1ibbWfxFN6U03cI7vk0zHsVnPlXVcdEC7OXyTHQNI/6rq0ad1z+nvJCaFJjSRRO0omByzlT6oGcdmIXF620g/J1F23RoNMZiKo+kxQukaXLS+sC7n0c8gMU5l+aeF9Vlmvww31tG+DZW8UZhPkibLoDDjwFaz8JNfWxcxKTLxZf6RPeDz0nPc+0zISSXGqJHOqM8zQimHIkQvCdz/wIfv1BWHg5+y//tjSpoFjT5Ncku+CD8NT3fZRKIxQGyZpxonvWeUYNm3/GcF0LnSKJjoS2NbI5xM3gLbjUD2cnbGYqiSaaC2ZaEUx7x1wS5kv4fSqHKYm3qvp+299qm5BjikwN17WQr50UO5bUVni92KDzAAAgAElEQVTMQpPxpTQdZOqHwFWRzz4OrPM8bxGwrvz3SWO2BS9V76b5gU9AYQj+5CuxGUw2bvqbFzHOxJmqkC71O4lILVpH9FAWzstJdpXHy1IVDrbMyBMNuqA6vMpUPKKkhVc2yRZlUPz2deDWUJhzvvJ7hbby9X6R0HWfY6i/J3Z4aGgY1n0OWpfD6W8S369i8s8XS7DwcsBRCngGz0RHtBDUC45WmM9SeFNJtreskxdDP9b/l08JWHApvOG/8DK1wnZQnk7cjK/59dpvw77fw3fX+skCqPkzOs6UcWim7zDj9qyjbe71eG42djhpDZOh3gDOosth4Ai0rVd+h2kygwrB0JlPhbxKzT7c2PUiizb8C6+860pW/P4jNHa9mLhBVNEpknirUa2+qvuxRFd1N0AxvT6DxK7GCPk8fExmaUPcY2mJK6fneQ8B0W3S9cD/K////wNuOM73lcrSLNgia9l/D/Xb/xcu+bhfjypiskWhb/wi6vr2wciA1nVsOCcixyka+hOZLJyXhEypJm+bXTKY6MBEnSk94qQslJSYkVe+r9zQEWZv/RFTDqyrHEuaoI21gbavg7mv9MnhChM+Y9eF1/wf6Gun9vF/jx1ueP4W6Nzpi326GeH3qkUlgcYWmHmW1JkqRIQw9ZypdGiCOKNJf6cssqQwu6xt1eK54Sdwx/vgtEt8xKmmDhXYUOUsnf5GeNv/wkg/fO8y2HqPsq1OSMgY0d34c5xSgTZBiA/UCD4oUO+Mg7PwMsDxQ8YKC+5Zl0eq5NZoIBjGa0ZvOzz2dSbfcinn3XMds7ffwlD9NFoO3Md591zHgrv/CnY+KPU8XVcuT6NyRiGdmj6kc6asOVOlIg0926tCfP4xdVvVzznJfCni2w49m+p5XhuA53ltjuO0yk50HOcm4CaAOXPmWF7OzNKkjUYtO9LD0mf+gXzLCmrOf7/wHCkyNX4RDh7e4S04M9corxPmj+QLHogjAlXmeZ4wrDOQT0amwgT0hu7tZEe6/P8vjYOBOmm7ut4hxh8ddQ4LuWb6mxeB4+jtklPpNh2/kjCQsGDnBxm39Q7OeOGnTDr0CK5XpJBt4OFrH6KYa9JApuTHY05ETxt0vACXfS6uHh0xKUox62xY/QYa13+butbrGWqcDUAm38fU9V/xw9OLrpB+rxacvvhKX6Oq77DvXIXMhkuhresmMdssTdW7sRURrLR79qdw+1/DaRfDm26FGn8sqYRwY8dmnwM3PQC3vgl+8ufMPvvjbJn/NqEnrRMSij6TxHez4ScMtZ5RlXVlYtJMYdf1Ex9mnuU7U5fIAxpVQsSFEvU58SYgMNUv0llzteal/CC8+L/w7G2wYx14JUpT1/Dimr+nfc7V5GsnkR3pZtb2nzBn+y3wo+th+ulw/gdg+Q2QqV5uc1lXKJhZmxAdSCsXIKRaaBaOj5fl0pNGqO/fR6Y4VEU+D46pTFkgXOvNjp3ZOlPa5nned4DvAJx99tlj8uuPJzK18LkvkRs+wtHX/ogpmRrhObKdT1+zPxl5HZsSnamq4rQGSI3IT9BRtA1g5tZ9d7H68Q9qXQ9gavm/qvuom0Ln1Avon30xTP0TaJT61kJCqS3ErKtSLC3MXCoBoUna83zu0rO3wgu3M2O4h6H6aexZ8k56Jy5j9eMfYubOn7F36Y1JlA/1hBddzHaUEa+Fl8XqmkVNefjSz8CmX7HouX9l4/lfBXzF4Zqhoz6vSrGgKxejoH8vugIe+AJsvxfO+Iuqc6KITtowny33Q+e6tunWnqco8l0qwXM/h9vfDfNfBW+8tYqbpsrOEvoe42fBO34Lt/81s5/6ZzJHXmTzWf8Y413JsnLDZlRKqe05aN9I70VfkJ+T8GpkiEvl80WX+/Iy/UdjWaUQL/czXCgmOlNK0+Fyyvh3pdLonLDpDhjugeZZfkbs6jey35vB/iP9lTaF3Hh2L38Ph1a+kwv77/M1Cf/7Rj/M/sr3wZo3+zxHfARqkPhcnYRMqXmrdpsYncSnYimuqVUo+mNChZB63ij5POqgm/CDT3azdabaHceZXkalpgMdx/Om0pqwfpaWMnh1R51w+A/M2nkbexa/g9ppcmdI5kwNNs6h6OagY3PitW3K38icCR1V2lzWxSmOsPC5L9E7fgnbzvB3iYtam2iqk3eLYwMj7ApNHrUD7Uxuf4TJhx5i+p474JGbYdoqnyuyYC3MOQ+yo3wREQHbFmLWL2AtWQCDz4/ugOd+6u84u/ZATQMsv55dM/+EHQ1rwPEnt84dtzFn2w/Zt+gtlDw5BwaSlHsjv3f7Or948dQVZBKSB5RhnfEzaVv5bmZu+DL7Dv+Bgca5zN3yAw7NvpqW6WeiWo6UE3TwP9NPh6bpfqgv6kxF3o0Od0M1f9tmW6Z1ppThTsVPGr/jV/DQh2DuBfCm2yA3ruq4KnwuRZdyDfBnP6T9zn9gxtP/xrje3Tx3/tcZqR9FBZMWXhCEZlQaSht+DJkcfYuuh17JOQn+m54z9c/+JmL1n8fOs62jmcaic259724mbrsTdt3ui+LmGmH59X4Ydu6FFQ+4eEj8kPJODs56G6z5K9jyG79SwV1/6//uV7wLzrmJXFa8OU/irSozqpUty/dmOQfL9KiG8kUaauVrRsnzaOzeiodTARgCO3VcKXtn6lfAW4F/Kf97x3G7o+NgaUqABOYWh1n21KcYaJjFjpUfYLnFfXhuloHm02g4nOxMmZaEAbkwpI6Tkcu6zNpxG+P697H+Vd+jc+oFAGQWToacvFsU+4bpzHRVfdY2/3XglZg9sp0lvb+HHQ/A41+HR/8dasb5IaYFaymdtpZioTmGkNgoFMOorkqSJoto8cyOdJNdfwds+YVP+MXxOS6v/qQvxpproP9AN3QPVdrsWfIO1jx8E1P330VhQnwRCJu2Hlep6OvuLL0GHEdJNnZdQRHZiO1beiOTt97G4vVfoHfiChyvwI5VNzM+IVSigswrt+s4/kL4wu1++n4IqY2H+TScGiW5NLG59TgPFiO3MMjMnT+jmKmjb8JS+sYvpliaIL9fyTtt3fcb5j/xNzDnlfAXP60gD2FTOcFKB9lx6D7nZtpz81jx5N9yzn1/WqlrBprOVOQ5STdbhRF47mew5Gq8uonQ2y88zUnwpuTOVPl/pq+BcVP8UJ/ImbIM50tNg34QXHPKwQeYt/nbTDj6DJ7j+nPC2k/741PwXqUUgmA8uK4/nyy71teCe/Sr8NAX4bGvMmfR6+mf91YGI8V+k5wplQOSxA8rSISIdTY/ts4U+MjUYOMcStnqTOIkYEo13yUJm461JTpTjuPcClwCTHEcZz/wGXwn6meO49wI7AX+7KW8SROTVYQP6mcl6SgFNn/TN2no3cUzF/0npey45AYS62teTOPhZxLPsyllIEOmdKDXukIf8zd/g6Otr6xS3K7NquH0Gtku2nHJt66GNa+Ciz4Cw71+CZLt63xnYdvduMAF42ZwdNqFdE69kCPTL6KUHWdViDSwkWKJOgmpOrAKhO15TGl7gOm7b6fl4DrcUh5alsJln4NVfwbjZyq/5+i0i+hrXsjcLd9nxwp1l1ehGFUTyMH1MNTlo3hYIhghG3Jq2bb6o6x64maauzaxd+FbGGycw0ixRL0Sm9K0RVf6Kf97H4f5F1U+tkm/r3I4S0VqRrrJ1/lp04lZPhJist51oaF7K6se/xCNPdsrn3uOS3HiApi+CqathKnlf5umg+MI76l1329Z+cTf0Nuyhua/+JlwwQW105MUqqvJuOydfRWDjXM446EbWfHkR3ny0l+Am9EL80UWQekz2nY3DHbCGX+pXMR01rCM68TeTybov64LCy+F7ff5m4nI+LVJNEm7rI4US0zseILVj76XwcbZbFv9t3QtvJ5XrF6pbJfEX6qag+ec5/93eCs8/jUmbriN8zf/mI5ZV7B/wV+Qr/Ud+fraJhhSkGaHCzR2dQsPZWoaYVCOmhcLJRq7jpX/cuhvXoDn1mhtfmSSOzpSPH4mnzkHT7WG/dE5U57niXOpwVAQZ2wsSQlX5Uz5Oy6P3NAR5r74XQ7OvYHOaRekup/+8Qtx9v4Khnqgrll6XnWRTb3wVVIJBRUS4T7+VXLDx9i++qOV2TGbkWeYBKZK261aMGqbYMlr/P8Aju1m8MV76H3+Hqbt+w2zdv6MtrnX8cK5X9JGmEQDfjhfoq5G7SQEu67pu/+HFX/4OCO1E9m/4E3kzvoLpi05T291AHBc9i5+B8uf+gTNbY/CnGulp6qcgapj2+8DnFFnKuPGws2BJaVMB3XC2mdfw+zt/0Vj91Z2LX8vYFb/UGmnXQKZnB/qUzhTOjvdsC195nPM2PUL9ix9J7uWvxcvG9fCCptKeFPVl7xSiZk7bmPxhi9QqGnimYt+wGDjHBq7NtPU9SIzh3eQPfCUryofWP0kmLaSTMsKpjOX3vFL6W9eyJS2B1j5xM30TD6dF1/9fc6rlWdiqpyppA1e4GD3TlzO1jWfYtUTH2bWjltpW/qWRKQSDEpObfiJH25esJZM14j0+3QWMZEzVbVRWHSFH1o/uN5PnlDerx5FQ3osoa3neWSO7WT1Y+9noGkef1j7U4q5Jq1MyaRwfka00WtZDNd9jQNn3Ez+sW8ya8etTN0vlxyJWiNwnvbZ1VYbabtr2bvZsepmrYQPFTKlMqcwRH3fHtpnvyZ+LOERqzeXf2TO1B+bqTxs3Sy5lv334noF9iy5sfKZWhJAfrCiq3H4RT9LR2IjVc6UHYeo6pgKieg5CI9/k6Pzr6N30ujOqz7BKQH1gq5c7CfOY2DVW3lu/PU4pTyrH30f44+Mas0kIUwjErK9X51dzD0ILHAkJnU8znBdC49c+yCeW8NpkxuUL1a0aLTNvY4Fz3+Zlo3fgXPlzpSa0B36Y/s6mHkmjBsVsqvJuMJ3m1TqpzIhOg7rX/U9aka6KkhP0mSZFLqpWG0jzLvQV7G+cpSkbBPmC2xixxPM2nkbfc0Lmb/5W7Tuv5tt534BFl8jbaPaSORLJWpFfWmwC379QZZtup2jUy/khXO/yEjdFP9Q4xwOz7qSxlnjaW2ug6FuaH8BDj3vFzU/9DzZZ/6TFQU/7FtysoBHz6TVrH/V98hmxYhUYCoEKQldCo+r9tlXM2Pnz1jw/Jfpmn81IE/2AB/909Jn6+vwHeTz3weZLI4jd6Z0wP2s6xD9hirnZMFan4u47d6YMxUrd6KFTKmQtIQ6d72dnP7w/4eHw7MXfotirgkYrfWocoSV4fyEMZcdP50XV3+E3cvezcTDT+KU/CzspdOblRl9fUMFdhwWFx2fMaGeliY5MtU/XGB7h992wQtfZcLhPwC6nCnxOYMJUjy5rh24XlGITCXNOkpxUw1UdiztlHOmlGhNUocpv5up+++iv2k+/SGBMdVrU6U99zf73AY6NiudqfAEopulpiLpKpGIB/8ZSgU6zvlo1aqfhPCAIsyXcAxGn7/n1tA15Sxa2h4gO9xFoXaC70wpri97dzop+PnypNZ07Hl6Jq3Cc33nK8nBEA1kL5Nj36K3sHDjv/kL7TRxGECd0lu2wWO+KvlFH606LnOmEvVnQr+nWNNIsWYUKUlTFDTWvRddCb/9mK9dNek0wC7MB+AWhlj21KcZaJjNk5f9NxOOPMOypz/NGff9BXS9w9fGqhsfa6cqM5IvesQoHPufgl+8HboPsG3VR9iz9J2VxAKh1Y2Huef7/5Wtp3+IFzY+Q1PXizR2vYhbGmbnig9QrGnETXi8Kh5MbUY97qr6oeOw5cy/57x7ruO0DV+E1f+pbCtynIpFgZOw8efgFeF0P7FAtVBlNMLNNVkXIlnFVU7juEm+RML2e+HVf1d1XqzeaEpkSglgFPNkf/l2cv37eebiH8bqYg4X1M6UGplSd4qgTxRrGjkyY23l85VLW5U3PdQ3zOHaLuGx5tZGWqbIHfuB3mEO5/y2Ew//gZk7f4pTKlAoalAIZMhUgrObO+pzhqOCnZDs6KrD4ydXNbyT626OgylrjCWwWl3HoWaok4mHn6Rj1lXVI1Q1WBVPcbhplk/CTsjoi4b5dFJGVb9VeqzjRV+d+Zx3kZ00v+qQTvqx6zpkJBNtTVY9MMIOQk8ZEWs+9oJ/vwlonOy4nmhciUy+j4aenfRMHHV+knZjsl3R/tPe6PPoHv+6tK2W1MDOB8Er+ZmPIZMtvEnEVKV+UoLTaRQmWVzWq9o6WmPNptSP6zrM3/R1xvXtYfPZ/0gpW0/ntAt4/Mo7ObD0Rr+u3TfOgxd/E2ur7Pvha5dK8Mi/ww+u9F/K2+9iz7Kb1I6UxDzHZaB5Ae1zrmHH6r9h2xmfqDisSeNVhSrW1qjvJbpZG2hewJ7Fb6d15y9hz+PKtlqhGc+D9T/2nZvWpYCan6cTXhGdE1v8Fl0BB56B/iNVH0c5OEP55PlQ3X8VB3/7cbK7f8fms/6RrpZXxA4n9WMjCZSIicZzRoNqkQYNC6+BPZNWkSkO0dCz3VokF5KfUW3ni5TcGgYb58WOJf1W1bh52Zl6iU2FyCQJk7kOtB64B8cr0T77qsgxu2wc181AyxJIyOgLT26el17JWZopdd9nIdcEF300hgTphPlA3sGTOnd4se+duALw0SL/mPrd2FZnD4pQN3VtxsGjp3xdSOb1yN5roXYCPcve6O/muw8ov0Np29f5CMjMs6o+lkH8Sc6UWj9J/ZxUU1ps9zjpNJiy2Ccsly0uDJncf2uPbGLulu9zcN6fcmzqKAJUyo5jzys+ATfeB/UT4bY3wc/e6oeiNL6/knDQ1wE//lO47zOw5Gp498NKdBhQPgjVepNEmHccR+4kJ4wbUT/ctfyvGW6YAf/7N1CUi/RqkYYPPeeLxobkLtRhyeRlQ/RbY58tvAzw/HEQsigyrzMfKrNgZYee/C784Xv0nvXXfkaywJIcBaWKv4WDXavxbPNpnKnQnNc9aTUAzZ3P6ZVSsnSmao5uKRPd44GwpJ+rmvN0ki/G0k45ZyoNMpVxHFr3381A41z6xi+tOmZb5Tvrun5NNAUyJeQ1aEDbxrui3Y/C1rvgwg/BuEkxZypphxyYbDJNmmTDldQLufEMNM6huexMJQ1IWdgoqV2w6DZ3+tfpDSFTiWE+xQTdu+YmH1V68tvK75CaV15ETrtEqIwsMm3OlOExUMPtwiOLrvCzNYd9/kXUuUl0pooFxt97M/ncBLae/rHY4YzjwKyz4KYHYe2nfK2er7/CR1U9T+1MFT1fnuM/LoA9j8G1X4Y//xHUT8BJUOpXoRjK0K1GFFX0XrMZJzHxQnS8lB3H/nP/3neCFH1QhkxVzS/rfW0pVv5p5aM02Ycg3hDE+u/0M6ChxefflW2kUBLOa0kkZ9VYFT7f7evgro/Bkqs5cu7fxY+H7kdl6jJMychU9LZ1aqOqEK+kNS48bgYb55Kvaaa5c6N/TNG2pCjrk+SIZY9sFob4IDmZQdXXXuo6sqZ2ct3NcbA0Uvu1I8eY2PEE7dEQH2qHScUVyriOn37f1y6tBG9b/kY1ucd2yp4H9/49NM2A894DxJ0nHc4UyDt48mJf/Tt7Jq4cDfMlTgLi36qDTAE0HXuBofppVYKHSRlusnAmgDd+ri/i99R/+pmapnb4Reg9WN6dV5tsIUuFTCWgcKr1XNj3F18FxRHY+SCeF98MlEoJ7+b336KmfQNbzvw0hdq4tlPlmtmczyl796PQugzueC/ccgNO127h1zqlPI0PfwFuea2Par3rfjj7HVXj2ZZfkyR3kRSKEjkYSVIkIHcU+udf5RegfuCf/ZJEApPxjSrOSWEYNv7M11Gqn1g5ruprOouY6HfF2rmu3/93rPMlEpDzb5J4UyqHNNZ/D2+Bn7/d70+v+y55T942ifunztpVNgXiz0TLmVLV5ksM84WOOw49k1YxvvO5xLaJ87Ps+GAXmb6D9DWLnakkuRdlP3w5zPfSmpKUndDRphxch+sV6YiE+EC9U3NdR8qbymUdH5kCKTplq9iu5OVEP9h0h092XvvJSpmL6OSu2zltOT3R5987cSX1/fupGe5M3MXJw3x6k0fzsY1VIT7QyLZRTdAZx6+5NdwD62+JHU8Mm22/z/9jQVxh5KVAppJ+q2oxEq7lc86D2vGw7W7yRU+IzEh39Z274P7Pk19wJR2z4unSwvtpWQxv+w1c839h/9PMve1S5mz5fiUDCqCu/wBnPfBmxj/9NTjzLX59u6kriJpt5ldSLbAkdErkYOigwbKNnOu6cPUXfaf2nk8Kz0l0Trbe7SdCnPHmquM1GVc+px2vMB/4ztTgMZ87hRyRT9pcaouiDnTCT97gO+lvug1qGxNqRNpLiujUjouuKzoh1GKKNS76W3smraaheytuYVA5lyY9Bunx8prXN0HsTCVxpqRRkKyrJQsylnbKOVOqCS1pQZm0+zcMNMymd0Jc7zxpApG9dD/Mt8z/Q8KbEi06qVV/w1bM+7WhWpfD6aOyYbnMKMzsOMkFNgMTnadDnIzu3AMSetOxFxL5BbKFWQeZyuT7aOjdRe+kVVXHkiYeFQSddR1f0mDuhfDEf/jP2MS2r/MRS4FQqAz5q0lCplKGuKXHRO81UwMLXg1b72FEstAJ35nnwZ0fBjfL8JX/KoWJhPfjuvCKd8J7n6BvxgUsfvb/cPa6P6exazMt++/m3Huup7F7K22XfQOu+5pUQNM688s+IRKQOBgai6cyrD7pNL9O3PP/7Sc0REzKcwlKymz4iS9KuuDVgnsTo2Y6aFr0dzmO5LdWJBL8UJ80LJkwH2rpERVG4Kdv8aVh3vgTmOAXA0+D6KY1G2RKjZDqE9DBJ6G7XpGmrs1KpD6JEyg93uFHHqIFjgNLkjeQjY+TDZWCU9CZSnrpUhvopPHgo/EsPnxHIYnXIK2UnnGgeQbUNiuQqXgn1smGUt1R1e0+/UM/jf2yz1WpDTuOU5mofbFIPU9fNMnqECejr6Zngo8aNHc+n7irlzk+Sc5UoVSi6dgm/3pRZCrholo8ufPfD937fOQvZMoyCPkBn8sjCPGBPFU+iauiLM+S8Futyp0svgr6DlE8uEF4WBgiefY22PkAXPYZHIXivLoG4Sx2Xv59Np73ZeoHDnLOva/j9Mfez0DTXH5/xR10LbhO3ha1k6ytt2Vh4jCfHjIlml4qC9GFH4IJc+E3H/UdhpDJnJDhfMkn6G+7B1a/IaZCDnLUTAdNEzkJwjExbhLMeoUvkYA8rJbEIU0cq54H/3sz7HkErv96VSKC9ZpBEvE9uS9FnQIdJyFNaDE6j0ZJ6DJLekJyZ2ozXm0zw/XThIeTnpHrOsJN5MnGl4JT0JlSV9RWNNzyG5xSIZbFB3oTXk4iC5DLuL5z1rpM6kyJJhAdnR5lJfqgkw71+BXa573Kr6sWu2//t5lkRogmU53OHR03xVwT/Y3zaD72fKISrmzXVCh6icTg5mM+wTIsi+AfS3CmVAKllaKtV/iZbY9+paqDqXyB2gNPQHG4onoeNdGzlO7sQ5ZmklU6U7KXs+hywMHddq/wcGxD0HcY7v47mH0unH2j2qlJ6A8loH3ONTx+1V0cOO0N7Fr2Hp569a0MNs5JdMyT6h/Kb0r9vUkmJGVrLgoidKrSH2rq4ep/hSNb4YlvVI77ivgyZKrk1+HzirGi1bHv1/y86pzI71KGrxZe7iuh93VInSZlcWb8/it7rTUZFx7/hh+Ov+ijwnqAMkvsSynLndggUypLumLUYRqpb2WofhrNnRtTadFJrX2Tv/ZJnoWOzIYw6/FlZ+oktk13UGieXZXtFZgOrJ2Y4day1HemBKPTtoq3Fprw2Ndg4Ahc/jlhhw6cKBPYVNy5k5+RaGfaO2klzceeT5x41FmaCc5U5/MMjptBvm5y1bEkHoBWCRDXhVe+z08v3/VQ5bgK4ajbcz9k62GuuFSRaEKt0UAOlQkJCROlypmWljtpmAIzzyK38x7h4Rgq8tuPw0g//MlXwY1nMoVNt+5WvnYiW876LDtWfRgv45c3SOKqqObvNKhV0i1r84gEJuRbhdsuvhKWXAO/+yJ07QP8cSHrEoVCCTb8GGae7Uu3CEyUkFKTdRNReohzrpS/M9jkbV8nnfeSwnyOI6cZNO9dB/d8yk8YueQTgraq71VelhpVYpLGBjU6l+pwptJsQkToU0BC19Gakl9XcGHPg45No5xhgemomKcZN2NpJ98dpTSrgTHYBTseIL/kOuFJOl6wiigH+B1qsLNKKycwUVw+ScQSEngCGQd6D/nCkiteF9MyGv2O0TCfrtkiU6L77Zm4grqBNnJDR5Vt02RpNh17oaJrZWLadaFWvwEaWn3HtWyq9Sa3+0GYdwHU1AmPZwTCqDp9UInKJrRVqVord4+LryLXvoEawfurWhi33g3P/wJe9ZGKMOTxcKZszBZNUDphrp2as+4mRisT8DX/4neCu/1Uf9W4aOra5C90ElRKfk39eSL8e5W/c9pqf/xsv9c60UR2jYauLbTe89cw/XS44VtC6NFWQxDU86aOYxQNYenMo2lCiyL0unvSasb17cEZPCZoEXyv+p6Eh3vbYKgLZ+oKqdOkU7xdS2bjJLCT745SmpXXvuUuKOXxlom5FjodXI5MlS9aXkBEJHRRSE8lzJZ0TSjLNTz4zz4x+tJPS88LOrlJ0UhRR9Z5RqKJqWeiTwofd2Sjsq1tlqY73E1D3+5YiA8SQjqMFh0WWdUzqKmDc2/yeR/tm8rfLW5Y17+f7LHtUr5UYNEJpFZTtsLWEh1zmS2+AgePKYd+FztUWRiHe+HOm6FlmU+WLluaHbbqcBKCZKsZpzqmwzdMhUwJNjCxzybMgYs+Apt/DdvuUzpTM3b9N16mtkpbSuuaBs5UeIwof2cgkbB9Hfm8WIBUp1B3LLQ4dJQzHnk3Xq4J3nQr5MYJ2ykR6CSF7pQSElH0SgfNSlTLOzUAACAASURBVCOoKtpw9ZQTc7LtYu4jWKKy5bmQ1uVqTnGCvYxMnSCz2u1uugOaZ5GdHS8nAJocgSR+gUIeQYRM6UweqoGTO7YNnrnF19gp109TfYeJNH9WkDZt63D2TlyOh0PdkeeUbVV8INWikTtc5ktFMvlAD/0Q3bNPCI60PftGv2zQ4z5nRTYJTz70iP8/AkmEsEVRB50+aFQSJmKJjrnMpq0mP24qUw4+GDtUQRPW/SP0HIDrvuqnpGvcU5Jvb+v0JB23F+dN7ktZAa9Hd9zVRfqD60ocm/PfD5MXwm8+Qik/KPwupzjC1L13UlxyDdTHNb4Csw3nBxZGXRL776LLYaiL+sPrhYc9L3lODD9LtzjM6Y++l9zwUfpfd4ufBCQx1YKe9H5kzqXrairFWxDQ06BhQmeqvKGtVTlTCd1bOJd2BM7UMnmGsgYyZbt5H2s7+e4opalgQ+FkONTti8Ytv76sXRE/RctRkBDQK527oQXqJ0mcqfgkoTN5qCaBuoc+7y/uF/+t8juCZ5K0AEUtro9i55gUaxoZaJpHbYfcmfI8T8lvUvGBcuXvFYX5dCBm7V3RuEmw5s3w3E+h95D0eU4+9BCl8bNhSryCetjiyJTOULUPjcm1jNRhMRyH7llrmdz+CE6pWh6iWPJg35Pw5HfgnJtipVzUyJT6t6hDM8qm0gUnKWvXeG6JWDh7NjDdcEW0bmZtNiN+Rtlan4x+bBc1T4hrR7a0PUBupIvC6jcJj1euIUBDdaskgAEyBb40g+My4cCD0lOSCNKVe/M8lj31KSYcfYYXzvki2dlnat9n1JLkSGRCx1HnV3rt0PdnM44ewqnaRFs4GcVcE/1Np5HrkDtTVtaxyZfdGDdJ+hyT6rlCeoR0rOzku6OUZrxj3Xq3L3q34gbhZAd6O4xEGNNxpGVlZJl7STwBmVc//sjTZLb+Bi78oE8S1vgO0zpHNouC7Bo9E1dR0/6stF1SxFOFWtV0PMtgwyzytRNjx447xHzee/zsqN9/W9hnnFKeSe2PUzptbeJWL+ZMWWRLhi2JNCx7NyouVWBdsy4hm+9jwpGnqz4vjgzBr94PzTOFoWZXkYGVhBqq3l3SPduKoqZBMETfIUQ4JRZdnJXVChasheU3UPvEv1PXty92ePquXzJU30pp3sXKawoRARNuZRiZSuq/9RNh9rlMOviQ9JQkCYPabAanVGDxhi8wfc8d7Fj5ITpmX5V4z2nqv9VJ5SP0nKnwuqHNn1M4tEnPWTaseiatoq59vZR4mRh2Fx3v2FTRWLSt5ypr+zIyNQamgtuFzscLt/slVmae7Z9j+eK00ohbl/plREId1k9fFnfgJHkEYbkTz2PRs1/0dwTnvTfxvoPvSItM6RDYZc+od+IK3L426G0XHk+SMFAdzR56NqYvVbkfy/cqdWwmnQbL/gSe+j654kDs8PijG8gW+nEXqflS/jUiYb7UxFR1W9n7V2UrBdY59XxKbg1TDj5Q9fmMF77l9/dr/w1qm8T3Jfl+lYgoqFGipJCbbbke7YQEhYX7k0lovS5Xfa5sEa/Ylf8EToYl6z9fff3Bw0w+9BBtc28QakuFrSYTd3ZNFrGciTMFlBZcRvOx58kNHhYeT0o0qSv0cvoj/x9ztv2IvYvexq5l7yGTcRLnJiVdIqGtrDi8btH4sLOmmwSkuqekTZesl3ZPWk1m4LAfjhdY0uYmxqkqFf2yPWV6i+j964ZCo46pjkzMibCT745SmnLHGj023OuX9Vh+fYWNbKtSLLpuJgrbti7zy4+EOqzKYUqSRxAtOC0H7mHC0fVwyd9JCZdV9+0GBHSzrmBHnBRfI1BCp00MM1sn7A4ewzm2S0g+B733Klq0lAvD+R+AoW4mbf1p7NDkQw9TcjK4p6kRAdE1ZEKeYVNH4+ycEx3kJJ8ZR2freUxpe7DyWUP3dmY//01Y+Xo/bV96XUmWT0J/SkPClaIJibt6R3pf+shUdVhH13IRnmJiHc3xMxm+8KO0tD3AlAPrKh9P2/srXK9I27zXapCK446IiQMY3hDojLXiAn+TMbn9YeFxpZTJke20/uwaJnU8waazP8/WNZ8Ax9HMxE7TlyRhPs1waLhP6faHWgkdBXSQKUl0oCzeGZT1ibVLuKfY13buhMJQpZyTOJNVMxQajYKchKVk4FR0phROQWyXvfVuXzxx+fWVj2yF9UQDIYaECUjoqlBekjMV/TlOKc/Cjf9Gf/MCOOMv1TdcNlvOVPQ56xAJZZNF7wSfhM5BMfnUetiUlbkHWlYLD9sWbFWScGedDXPOZ8Kz362qGwe+M9XbcibUjU+8ro2YXxqCtOz16SAuxZLH0ekX09C7i/rePeCVWPbUpyhkG+Cqf1G2lZZhSlhYVLv4pLay96dT6DtRTy7Bws/TJLTuOE7Vfevca+EV76aveRFL1n8etzAInseM3f9D1+QzGGhekBi6gficaeIAhn+fjjPlTV3FcF0rk9vEoT6pftiO++F7a3EHj/HMxT/k4Gmjopxp3mnSMZA7NlGOm8qCuVRnDgUx9y6wpOcsmyP6JizFc2vgwNPC48YcxhD5HNIJ1uayEc2ykxCVglPRmTIRH9x0OzRO8xWZyxZ9wTo150A8EGL30lKWRwg7Uwo9qXxBjclEO/iMnb+goXcXu874W8hkE+7YtwDdMnamQr8tkaRcNtkE4NQ14kxZXHF+YscTIWaJlZGukSnxTD7QG5RWWjvnv59s735a999d+ahm6CjNx16gb+ZFidcEO2cqDUE6DTJVLHkcmX4JAFPaHmTWjluZcPQZtp3xdxTHJXD2LBcFpVhiwqIkQw10SMO2zl9gVYRjQzQ4fN91OmHfbA0vnvkZ6gcOMH/zt2g69oJfu3De6wC9MR9DpgzuOfitWY1yXACeA0envYrJhx6JbURAQOfxPPj9t+G/Xg/Nsyi+8366WqozsnXeaZrwreM4QodNl4AOo33HZA625f3JDpcytRRaVsidqYT7iR1v3+R/Wl7zRPdrJrMx+jxfapkYWzvlnCnVYK/qrMN9sO1eWH5d1bbchvgL/qITDSPGJt5xk3znrQqZkjtTSZypsI+Ryfdz2qavcWzK2RybtVbrnsGeMxWeZHQXBSmvLOvCjDVSZCrp1qTx/IPrYeI83IZJ8usmmNUksPgqipMWMHfL9ysrwOR2XxJhYO6rE68J1bt6XYdedU7SDlvWNmkx8TyPYsljsHEOfc0LmL77f1j43Jc4OvUC2ubekMhzUfYJhaVBpuqyGSGaEOUliUyGJunulquRKbPpN4xM6SworuPQ1XoObXOvY+6W77Hgha9QzNTSPvvqyvHE+438XjNkSk6dEJnnwZHpF1GT76G5M56QUtWTCiPw6w/CXX/rh5FvvJvs5HlxsVuNcJus7+smCIicczNkyr+GCVIp6js6TqsqOaMw7Qx/QyuIpyZ9b+x4xyafQ1pTD6RDpqLnvoxMjZEJSdllq+qs2+7xY7rLb6g6JxoC0IGJK98f6ajCl966tEq4UyXOmRTmC//SOVt/QO3QEbad/jFcg93jKGfKnoCu29Z1xZyTXKbsTPUdgp622HHHERd6DX+v0A6uhxlrpM6PFvlRsMNMnARcl9K576X52PNMOPwHwA/xjdROojRVHHKMXUNXPTpkypIwOs6YiPeX0C7cfY9Mv4Tmrk04XonNZ/0DOE6yM2X5btLwXFzXEb5XHdJw2jBflSq4Rlp42EwXlMBZ2rb6Y5QytUxp+x2HZ15OIddcPq5xv5GBZyPua+I0dk69gJKTYYog1Fe5cv9RuOUGeOb/wYU3wxt+XElwsMuCFc9LuhvMaF9yHDPUJZi/dDM7wT7TUvX+vBlnwkgvHN0mPC6bg4Wfd2yCqaNlZBLLISWYaTLDibCT865SmBqyDf3cTbf7JQzmnFd1TnSXYVY+QWMX17rcz3Ioe/8qLamkhSgY7G5hgLlbfkD7rCvpmXy6EcfImjNlkYUC4gFfk3Fhxhn+HxISunHJh4FO6NoL08+wDiWBvfpuds2bGKmdVEanSkw+9AhHp15IbY1e+NVxRqul6y5G6hRvDdRFMCsmtQv30cMzfQLxjpUfZKhxtn88IRPTNssnjWo1iJEDHWdK7vy99GG+YC5yHL3rBeNipL6F7St95fmD833Fc53yN+HvAEFCTYK5ro+o6jpgjgOFXDPdk9cwue134pPaN8F3L4H9T8HrvgeXfSYSWYjrcemY6F3o3nd0w21KkLbZ0Ir0mZI0sSBhXAUlxyQkdNkcHPs8P+gT0FtHs6ijvCcwC9eF5+qTUWMKTkFnSk0mLL/0kf5QiE+NRBkhU9FyBqJ7aVkK+QHo2gOoheiSROqCAdt64D6yhX72LXwLYLbDqdTmS0FAN4L+BQOhJuPCtFXguNJQn1I/TDTIg++ZsUauvquBDGTc6hCbblqukxtH25K30NL2ANP2/prccCdHp11o1p+CItSak4cyxdsyqynJqQlr/3RPOYvHr7yTvYvfLjwuMq2acwJTcZd0+n/Uccpl3VTyHjbvyHSHHQ6b6SzW4cewf+GbefzKO+mc6hfX1l3sw33ChC8VWDYjJ0tHLViUj0y/mOauTeQGq+uY1mz/LXz/cj/E9/a7YPWfxb7DTuxWPL/obhKjG3CTMR6+tkk9Sls9RJUTnmldDLlGKW9KFQ6tssNbwCtVyOeBxaI+RsiUfXh8rOzkvKsU5se5BZ+Hd1Xb7vUdmlAWX2DRydJkYGiVBohk9Kk0lJIWosABmrbnDgbHzaCr5eyqz3UseF4m6BKYZ+qozs1lXcg1wJQlUhK6sRhrgHBNPz11aKZapVh/13l02V9RzNSx9OnPAtA57UKr2ma677MmE9/9BaZzXRsULtpF+8cvriLzJXRha+Qv6uRW2mq+04ba6nE9TpPjIrs3XQSkyjkxHHM5Q6TScUI6UY7jv5uy6S7cVeF8Q2FfkL8nkQVnHZ3uJ2lMPlSWSPA85m7+Ng3/81d+5YCbHoBZ4sLt0X6eJkSuL1VgXvopbMHzMZXKiFoaeRqAXE2NT7dI60wFmXxTq/X90tQbDd+3qcD0WNkp50yBGLKt2lVtugPGTYG5F8TOc12najdj5j27yr8BaFni/1vmTanApySxyozr+CJ87Y9yaO51PrKDuWOkq/cRNlu9HLHeSPmzgIQu+N3GIo0H1/sEyPoJqQTjovdsMpAzjVM4OO91ZAv99ExYzkjdFCPnPBtCInRNtqjbykCkJnvaOFMW76Xymeaziob5dAnDIqfU70vmYT7TZztarUC/nVxhXq99NoXzB/641eZUlm+2b/xShupbmdL2EG5xmBW//yiLNv5fCstu8BEpRZ292Bxsof81eu+azlSUGmJQcgdCGdUGyJRtdpyUPxqgnTPWQPvzUBiOnSOt8hH9vGMTZGph4vyqj6NznxmFJjQHvxzmGzuTZToAfjx3692+UrVEAdg0a0Z2XeECVtcM42eHkCn59yWVUcllXKbtvRPHK9E2dxRlMw3ZmQ5+iCj3WqRLCz+bsQb6O6DnYOwcmcMmzbg5uMH/PuwhcdG1TRbAupoMexe/Hc9xK9IBJhOIDZdC9v06yEnarCShJdy6MMyn2R/TOGLRMN+4nB6XTeQMS+vkCcyoXl3EgrC0qT6VyHSRKZtEiLBlXH0+ZmUcOw5Hp13EpPZHOOuBNzN976/YvvLD5G/4biU7TGZRJE2X8iAaY9rIdeQ8k2LQYMdbFRPQ7Tl/le+beZZfXq39+dg5qjm4yto3+aBBRJ4nPNYzBuHf6LVtws1jYSfnXaU0Jfdj+32Q74cVN8TOCcx29xiDmGWTZesyYY0+U3Ndh+l776B74koGmhdI7yPJbAh94Q6dNsxXeV8KErpRbaf+I9C9D6b735dm0Y2ea4L61dW4DDbN5feX387uZTeVSZjmu09b1enAsprSCmK9HPW1k+a1pMvmMnHRQ93FyDZECHFnSrf8h/AZGWxGwokFps5U1gKZkjlNur2wGhGwCfO5Rk5CcO7R6RdTk++loWcbz57/dXYvf49WlnKNpfMnehe6z1l73pdY8F7T6kzpbEJkY6vSdma5KLSAhC7b1MXW245NsRBf9Nqma051GaaXw3xjZspQ0qY7oH4SzL1Q2j44VzdrptJOF2JuWQpHtkIxLkxnZB2baTq2iUNz1fIOSWaTahqWObDVC4l9NnUlOBkhCV26oxJ9HvCuysiUGP0wF9UDU5TIv0bfhKWUsuOMJxAbLoW4wrreb43yhmo0SNlpauhBXNkb9CfaNAVQsxm3Klyg6xBlXCd2jXrNDM3AasrOrXk9THNkSvZ+dJ368G+1CfNlHLPfGdzXkemXsHvJu3hq7a0cnnWF/10a31N1v5ZhpMp3GXDTwtcy5kxZaP1ZiQkrzql8Pn42NLRInCmNMN9AJ/S2xcjnUD3GTNeobGhNNqWxjJWdnHeV0qRhvvwQbPktLLtWqRAeRgRMUlzDHdV1VcjUch9K7dyp/L7EKz97G56T4dCca6o+Ntkpg312xKiOjAlnSkAaDp5Tbpw/CEXOlEkWVdB++unla8Z3x2bhNrsFJW2WT43FjlWkvKzbH6L3p0PKzgqQpbDpIWJ2zykt4lhX5Uzpv5voczENhdZmXasNjOMETpgJMiX+XLdP2SI94euY9N9gs1LK1LL99I/SN2F0UdbhFNkWkhYiUwZIXJrwrU191GzGjQuUajgorusIncxKW8fxQ30CErpMF63q8yDi0np8kangGZluQMbSTlFnShLm23G/L0q2XB7iAzuxOYiGBxUdu7VcVubwZuVCpJw7SiXY+HP6Zl9Cvm5y1SFTr982Bl3RQUqLTIWf8/SyCm+ETCaDsIWDsm0DTF7o89Mk59lIFIAhShRFXEyJqQESYfB+RI6T7m+tjWjB6Ia+VDtFHUc7LkdiF14Bs2ccRidNJvfo/eo+p8BqMvqk7KhlM/q6TSBffPSz+UYzAm0cwIzrGBGrpVpGrm7JKkf4/ybtAjMLE9rxKsEOgQb78KIodF815mac6UdOhnqqztFCpiI1+WT3Z7rhH1WJP3ldlpP3zlKYFLLddDvUT4T56vpoWQs4PbhGMBcoJ/UpSwAHOjYn1W2XH9r9MPQcYGDpn8YOmS7aNinPMDppmPHKEgTuZpwBA0ege7+y3ejnEmSqHOILrC5nF0qCamfBxLGJqzGbOrnl3VgKBw70nSnHcaqQLd0JT67jpYfsxpApzeckDGkaZKZWNk2GIouxTEALZ8o2I8k0bCYb2/pCmk6l/1uF+TSdoMBkfUkXjcuG5EFMxrio39iGCU3XjQrqYtAHoXqs6/IiQcb7C3028yzAi3FXZc+j6vOOTX4hd0HGZa0u2CCwAAG33YSMhZ2SzpQQ/SAPW+6CpddApkbZftSZMns8jjPKp1AO5Nw4mDjPd6YUA0jZb577KeSaKC5+TXUb1wJCtXSmaiycqfBOFwTvKnCCIgNZGuuPTgx9HdBzoEI+Dyy6QNsiUybQf7R8junOPpgcjRw4IWfKDq0xQbREptsvwteprdEn6YsWQJONRPA+TJGEqPOkU9MvbDUZx1pywkRRHORogsncZsPVCsx1jg+SZlbGJlP1r46JxrXJOwqesw2nJ9D6M3E6oXrcmWzUEueIYA6O8Kakm6bw5+2b/BCfYF0L16+1S2YQl/05WeyUdKZEE8W4/Q/BcE9iiA9CYokWLy7o1IkLZ+ty6NisdJikUPzIgE+kX349tXUNseub7LLBfEcUWC7raBcDDSxKOI4txFNXgJuN8aYSiZOBRcjnlfNivByDxSSs9m4YEk2jK2ST5RMN1YGZ42hDEpUtWtphBwsHDuLvVFedPrBRhXmz/h++R3/zYrjLdl3rRcF0vMkFa835QHbIlFkJGqmyvQW6Zct5Un2WdE0bcrSvxWXermqsGsxnYl5l6LOGyf5mP8KbkqG+lefkeT5nShDii55r05f88PjJ67KkujPHcT7sOM4LjuM87zjOrY7j1B2vG0tjoo5Vv/1OH36cf3Fi+zSwtvZut3UpdO4g441IT5E6U1t+AyN9cPobrPkmYbMl9dkuCjnVjqqmXkhCD8P3su8Cyu0cmF5dUDj2nAwWwDSZOmmIqcEuzmQhEWXHmfSJao01vXZyVXC99mGkxyRkVpt1YyinUSmlTOCsmr2X8PM06Uej13WsFwXXkIMkcxTNkCk7BA/8Oex4hCVteJlGyFIkScVEoypoD6OheRMzRRsDsyV0R+dCoebTzLPiyJSUgF5u23MAhrurChxHLQ33ySYDdizN2plyHGcm8AHgbM/zVgIZ4I3H68bSWHQQOcURstvugiXXQDaX2D7NC6/wiHSQqVKB2q5d0lOkHefZ26B5FsyNlycx3SUrr5NgtotCVexc9JwkJHQRAhKbMNs2+CUnylXkRdesMVx0bcN8fts0yFR6+B/MFvxaC8fRSLYi4Zqmmk1KxzzBbERRo9exEbw1UQWPWsYxW+TTooYwyscx7Ydg7vxJkTSDeSb4DlNeWpq6iWmyzWyfrW3fj68Zgt8640zo2Q+97ZWPEstyVTL5FM6URQZ4pe0pHubLAvWO42SBcUBcuvoEWHTHOvnw4zhD3UqhzrCZ1kSrapsNQgcJj7bFz+irO7ZFeoqw4/R1+FmJq/8MXDeWImsjwGlSYLPq/lzXalBUTwKigbwGBjuha2/19XQK8QrI59Fr2pbxEF4vqW2V4Kdh+NV1rN5NeOdp6jgmvpuENmHTfVbZzCjCaeoQpUm3tlGeDs63vd+gve0GxrStURasxHIZ+7Ck6yRkJQuuJTKjkJ0lF86m7wdmm5EXtLVDpuzuN055EPThmeXahwdH0SlR7U9fi7H8YfsL/r+KMF8apzN7qob5PM87AHwJ2Au0Ad2e590TPc9xnJscx3nKcZynDh8+bH+nBhbdsU4/cDfUNsNpl2i1d8uIgB0yFZAfE9pOWQROhtyxrdJThNff+AvwirB6FASsTbGjAntnynZRqHJsZM4UxEJ90efhupFB2XvIF4wTOVMpnlFAJK+aODQtcK5tMlGybnrkz1Zp2CTDTeZQ24i5plHvt66JlqIPW8sFWDpTruNo19UD+fM0rRFpu4hlHLMNQdpC0hBCpl4qaRuBBU6Uacg4MFsOUWBp6ncK16rpq30B5RgJvfrcqut2bILmmX7GvMQqTqfFc8paOp1jZWnCfBOB64H5wAygwXGcN0fP8zzvO57nne153tktLS32d2poQYdxSnkm77sXllwN2Vrt9jWWu7GsLhExWwuTF1Jz5EX5KaKO89xPfTHKQKuK6kXEZqds6UvZO1NJoa+pK8CtiWX0Rc+NDciAfB7J5NO6ZoLlMq51oVcIUnvNnlV0U6BrtVXyBoZp+xXB2nTkXdPvqLV0TtIgjgFiZ7OZSEXKNuQRVbU1RCtl79+0RqR1CQ9Hv6gyKDJDDe83ttHSsHTCm/bRDEjHzzVtH82KE4bWcw0+whQhoUevU/V3xyYlKgVhp9MOwTslOVPAZcAuz/MOe56XB34JnH98biu9BYNyYscTZEe6Yfn1CS2qLeM6VmKWo7sijZfeupTMUXmYLzaxHN7iOxirq6lp4V2UzaSXxpmyWoiSkKlsrU9ijJHQq68lDPE5LkxbFfvKtGm5NVk7Z2p0J2abMWnhTNXYOxiVDDcjwULJAmhBck6FJoxRmA8Mx3nEXNceDXYd8+zO6KWyGSd5sxcyW04PBEhaemTKtC/ZIERV42YMw3xgzyGyba+Fws080w/zhbirsZJpwd/FAhzequRLQfqNxKnKmdoLnOc4zjjH33JfCqSv3nucLNiRTd33W4o1jbBgrVH7mkxcrl+vXXnB1hnMrctxju3CLQwKD8cG9LO3+dDrqtdLz7MqU6Fd9rTaTCf2wKoF7iT3KyChxwdy5NptG2DKYqhtFF+3snu0c5JNU+iDdmBP8rfipKXYYdsgLrJzzdCEAMEbOzQheCU20iBZC6dz9LpmobqqtoaEbsdxUiu2pwmvuI5jtFmTIesmiQn+Rnhsx41NSZiq9hb9yBdULa83hu21wuMzzoTBY3BsNEkq2t8rf3fuhOJwojPlpnDMT1lkyvO83wO/AJ4BNpa/6zvH6b5SWy7r4pQKtBy4l6H5l0ONmWqDLSnQSICtZSkOHs394oy+qomlXD6GBWuhsbXqvPCCa1fzy7gJYN+5tUo+zFgDQ11wbHflo/hADj0fz5OSzwOzVbaHINRhj0zZhkmsuBQpOFOO4yN4Jk6NiJgafK7/HQabkJClCvM5o3w2U0vjJJuiNWEzzeYDQfkbw1qCbiqOF0bhbRn6YDKv2SJpNpmsgVXoHdYyM+nmB9P3E15bpL81IKGHeFPxOprlvzvK5HOFLAKUkSnrhCf7tmNhaZApPM/7jOd5Sz3PW+l53ls8zxs+XjeW1mqzLhMP/57cSBfDS64zbm8LR2ZdA4i57MWP790mPFw1gex5FLr3welx9Yk0CwpoFFSWmO0OO5gAfFK3wpmCqlBfLKU3PLB7/3/2zjzMkqJK9L+4tfROL3Q33XQDzU6zbzIqoIji8lREnacguM04zo6Oy6gz83SeznN0Ztx1dHAZZZRFmBF3BUVwBdkaaJqmaXYaeqH36q3q3hvvj8i8ddfMjBNVN29Wnd/31VdVNzNuRGZGRpw4WzwNQxsShamQxHqD/TIn3NHoFeGKNXCFLU3vERK1CHgHcLjkjv5pIILyeIVEFpXkfckY+QJGYuYI3XA7xDQjE2oa2+ebjFXa3sEs2poODAg1qzEhSVzBf5zIZM1YuBz6pzX4TbXsNxr/v2GVc7GYf3Rqe0OCLyakZqrXGewvsfCJn1Lun4E9zM/EB/4bdNaXy9yx5x0GfYPM2rm25VDLXkv3XAWDM50jfRODWYSTBCRlIFbhh/ibJHS/hcdC32CDE3qLma++fILzeUwsDElMAFInXEnizcZ6JSZJbxFX1gAAIABJREFUuS8FRGYSb7NBh0E2I24RIvNlq/3t2eaQaL4Qh2Nj5KZ1WaZsecJaiEwzAWOEL+38cnzGGalPWmgwQ6kkN/NJNS4DfU6T7DsOZ1qE9A24qL46zVSzkFn7f+MqmHd4qgUoNC2IdK7qBhNWmBroKzEwvJVNB57L4LQZ6QWakKqK+306S18/zD+KGdtaNVMNk9HIHlj1PVh+PgxObzk3JEMxhGimZINWbApNnPz6B11UX51mqjXbe93/Cc7nta8MiCSRTvYheVXAb5PjmHpfConmZKDP38Qt3cE+xgj97+J2SlJPhETz9QX0JYPBCEdeSX0tSVw9tS7S9zwu60toImIXGONfb2jEb8g2QXI/IlkC2Pj9TB2Hl5wGT9/tHMxpHYNrz2bjqlQTn2uvaqYKx2BfiXuf+znuO+NfhCHt8pfCq+yCY9om7mxYATzwI7ev4Emvb/sVtXB2gb8UyE0OpQD7d6bUEweeAk/dXXNCbx7gGgbdp+6CBcvbCpv1dYLsPvUJs72HRvOF3F/o3qTQLDxJkh6GpCiQpJ6I6xWFaQdotZxmSkbotiMgcdT3cyKvR6KBCxXMpT5p8ZZVvlvJ1MoHOOoH+REFCFOp+bQOPBXKe2CTiy1rK5gP74Ytj6Q6n0OoMCUTzrvFhBWmapNBqU/sSC6v2+O2LlzOwNA6+kaGGj5u6LR3Xw2zDoRlZ7f9itFd3bv7OEsl+SDbXyqlOxsvPtnt9bTl4Vp9bf0arHXmwAM7m/ggTLBxjr/exep8KWTPRtoNR316uiNgjI02wb+tccoLqYN/iDkf5EKyFNG2I0mJFjNgAjRTkmKJ5vyMSNs7IMwnB+FaF2mdIYl9U/vCklPd78hvakp/X20cLJWiZ7NpNWAzCVMhAqcJCNzoBhNXmKpX/wckWwypOxNRkrMZOxr9pmqT09AmWPuzaPuY9hPUQID/RighDpepA0+KE3ptwt6xDnZtSnQ+h9H7JIvAEpar+VJ0V/0f93lJvZI9F6cMhGk/TMCqc6BUEpu4+0pGZEqNTdUiH0VC/BT9yzTnVfN9tiUj9fAaI2Gqi5q0EGEqZANr6Q2WaqZqmuu0ezvvMJg6p60T+pT+PtePN65yBw44LrXeMP87uRWlG0xYYWo0xFs4yAYkB/MSMCJhaub2Rr+p2gu98r9bto9pJg5dlqRFCCXEmTBVm7BwOfRNaXBCj232DcJYBudzGH2mkmSsIauiIF+KEPW/MCBBEr4cqv0IiRgbCHnPhStlp4Xo/vsmMl81tbObiX0lYljHXEYZCfHxCtlMN8SnR7qQkPbf0XxyKWWNcdqpda0L2trCduP9Lupv7rLUekPur2qmciQPG3Zcb2bmLMP2T+ssTN1zlXOqTnHu6xc4DY8F42rr7xuARcePCkvU7+HW7Hze585NqTNzDrAmQlZF0j324npFdQb0h1ib5kOoaSYky7bbO07eD6VBFNK+HxKRJGlr/YQpjvgV66b8yVMzFdKXQsx80j4hDYyJzeOZ3tMlpznt0/BuYHTsrT2XDffBgqM7Wk4a6g0QiEoBvobdYEILU6HOblK8OkuphJ1/NDN2tBGmNq1xgkKCViqmv5TPjtpBWwNkae+BpzhhqloFOmww+9Rdzl4/MC21zpAQb/FqN2CA7vYgCzItUahmyjezdz1B2gThgktqXonpptks1tKDzH0hyAFdYpbM0ccrKKdWQGZ7aX+Q7EEYM1AqZdPoHniqs46svwdos6DdeH8mfykIFDjR1Ai50S8MGwW5rwr4d25zwPI2minjNjU2pZbtY9ox4Jm1eqwQaxNK7bNmt7D4ZBjeWXNCjyftmoq55nx+UoY6QzRE8rV5f8CzCclOH3KtvpNRsGYqYBIL8VXpF2rEQkw6eUwHIQEJ3Z6/wtNshOUykpubZRGlEPqehyxoM5RtckKPn8dAn4HdW2Bofaa0CBCWAR3VTOVHHtEVkrJm4XKm7N1I/75ttc/6S8A934bDXgCzFqXXGfBShSB9MfqyrqianNBHX+So625/AnZvTnU+h7BtMUwp0A8jQLCR4Pq+qKgoQ3fskB3j65cTojUM0cKJkywG3F8I8EESm5rlAQmGsMWld33GBG2JBGEac/mYFqKplNcpN49nHA9nLYL9ltaEqYbo8Q3RNjIZNVMSF4IYybjUTSa0MBWiig8y8/kWjjrizDpT38C6W2H74223j2lHqNlBijxkOuOgteAY6J9aE6ZaVthxpF8GYSrkHhlCBZvuajGcpkcowAkFm1jAlUTQloxce9cXEC0p2bza1ZmPA7qUkIS1JkArKyXWbPpuSxQjFYicb6RUMxUgTEk1UwGaHi/t6pJTapnQGyIBN7r8U1mFKZAvYAxh/objTXFGAwFBmqkAwcS76IJjAJhRZ+obXPltGJgBx7w8Y535CFMhCT8zvRh9/c4BP4roiyfpWkTeUyug1A8LM4TlGrk2oRSQaTFEFS8eZPsCHKSRXWpN+yFMkhvkz9ZlzZQrK3+mUvFEvg1NnTbBu055P4zy7XrTooH2RBqNHaK9DtJMBYyjXUk1sOQ02PoI7N4yurdqybgNjqfNzWQ9iem2k363mPDCVIgULMW7c89eSmVgZk0zNcAwZtV34djzYTDbVjgh6t4QQlZUmZt74CluS4Nqtbb666vXTC08NnVPKAgdeOTOpVK/HAhQ/5tQdbpEMxVN2NLoohwcf0P8DKWpSEyAQ7eU+s3FfQkR/qRMCRSmxL6yAZqeEMFcen9DzGZe4+GBkd/UU3c2blUVO5973LM8rEXdYEILU2Fmvu75TGEM++YeVXNCP+DpX7jM3ye23z6mHSE+JyGIV9jG4z4tPhmGh2Dz2trzHCiZzJnPY0LuUUiEUB5RliGbrhrhU61lBRdOgN3OYg75aKbyoD8gms8ECKtSMieV7EAeiSFDXnO5ZirkWn2EqZMBA+vuHNVylvCK5IsJMUv2MhNamMrPAd2/zPC8o5mxfQ1Yy6JHr4NZi+HQ53nVGZIbS4p84PF5kUed0ONrLJUMbHsM9mzN5C8F8izmEOb8mIf630vzN0aEaD8gQPgL8HMJmQCLJIiNmvm67/YgIW5nSGZ7CSEm45DxV1oyKKrUeMxVU2fD/KNg3R21ft+3c53bMzZjJF+tXuFr0+uLlwkvTHXbVwVkglhlwXIGh7cxY8daZj95s0uHkCEJWkwepgMIGQQ8Xqr5R8HAdCdMxdqPkvFyPoewDLpGrK8JE6akhJh9pYJjf83Ml8MEGCB0SgkxEUq1dxaZE1JtAsxBUJBQ25RcKkyJhet8ovmkhOYA8xrTlpwK6+4k7vb9z6x2f3hqpkLy9fUyvd26QEJWGUHClKTOyAn98JWfxthypkSdDXXmJExJMXgINnVO6HEOmVLJRM7nA36RJAH5nvLQTEkJExylPlNhmqkwYar7E2Cvr5Tr6a8PZxfQbRNLaF+Saj9CfA1DtHch+anE/la+17rkNNi1kb6d6zAG+jZFe/JFW6Jlr9fr9MIwoYWp/oCIpm6b+foiVenCdTcwvP+xqVujNGMIS1MgJcS84nWfak7oldHV41N3uc01+6dk/po8Ei3mIeTmsd1JrJGST9iiYkF+IyF9PxchWRzNJ88zBXLhRHp7Q7WcYj+iUj4+PSFmPvl745lR/MDR5J015/P9ljoToAfdT7TRHSa0MOUteY9hvb4MzlnE8OAcAPYd979FdRapi3pppsAJUyO74Zk1UW4VIufzbCa+mBABIyR8WUqIY2pIWQkD/WF+OfJgBiOe7EPkoTyEKSkhDuiQg2aqFKaZCjH7FinaLCQwpuTjMwVugV8agKfudAFAG1d5+0tBbyfeDGHCC1P5aKb8y04d6GfX7COxpoQ9Ln37mGZCnHDzwHvgWRxF7D21wq0etz8Ge7dnjuSLCdFMyXMDdZ8gR9rAnE1SzZT0RpVM2MpeSpHet5DtZCBAwA7MEyVvr7zvi01uObzpIe4d3sl5+6c4d4t1dzJgKrDpAW9/KVBhqpCEqP9DkFRZKhnWH/kGHln+FwzOWyKrV1QqH7x9euYf6ZKYRhF9fetdEk9fzVRXk7HWyuXTB6XtlU4Kg4FOw1KC/MNCfCNz0kRIiJ13QzaZ7Sax8Ndtx/egLafy6A8E+kz5NnrJqfDUCmbsfBiqIzJhqlAzVXYmtDAVlFcooF7pS7XjiFfxyAmXivaiCnGQzgNvM1SpDxafCE+voL/PUHp6BfQNwgI/58eQKB9pNEnIc+nqQBmXFY4KwWa+PHymAt70Xs97U09IBnTo/tgyGrXb3faGWDPywDhpSoS3mQ+cE/rwTuY99mP3v5r5akxoYcoE+EzlMQFOGegT7zxufJ0Jx4gQ7Yd30QNPgafvYcBUI+fz46F/0Osr8vChC3ku4rxAQSHT+Zj5pHfJEBYeLqVIwpQzXxXHz6ummQpIPyEhxNcwqN6ALVZCBEfvPhw5oc978FowfS5tjSfF6IH+TGhhqpSTH5G0ysG+UkDGX1mdoYjvr+TZHHgKlPcwY/tDLrLP08QH+UyAIc9GHC0Z6JgqYSDQz0WKKeVkYsnJrCMhdCP0bl9rvBAWRx/moNHNS7YW+wtKctHNPxIGZzE4tA72P8IrknqiM6GFqTy2QQB55x7sN2JhqkiqaYjzo3gSOaHPfeT7LvOup/M55JUlPp86ux18YYyhr8+Iw9nljr/F8Y0Mr1P+bKRjC+Tj59JXKnVdsCmauwTI+4RofCj1jY67AhMfFCtww4eJLUyRzyAQYibp9kSUFwbBoLX/ETA4k/1WX+X+L4hmKq8nk8fG11P6SwGbOsvIKy1I0RxpJb6YMXkML31GvoOFFPk+B/n1B3FgjLQ7LInyTQmcz0HNfIWkaKuM/j7T9dDlvBD5eJVKsPhk+ndvgv6ptazxvl8xWcgjKmlKf/YtkMaq3pC0IEG+kQXSekP3oyxDCdoSSVhnXn0pBPE2V9IGLznN/RYKUxOVYr1dnoSsFIKyggvL9ZdK4pVYURxLY8StramYj4e+Ae/ik8UcBN1PjQCB2g9xHq/iaROkhPSlEGEqj7skDb4IRVprXq4WIQ7oIo56GVuf///gyBfLyk9QJrYwVTDNVMhKLA+TTgji5xKb9gQmPsjLzJeX+r/79Qb55RTMzyUfwVxeZ9E0U0HCXx6BJgUbg8X9t3+Qvaf+sXck9USnWG+XgCKtPEsmn32h8kDc3qXPwpo+OOQ5ouIFu01B5JEuIMTHJczkVizTTB6EaHryEE7y2fuweIjf84DZP0QLN1Hfuf68GzCeFFIz1eVw9sIx9xB2/MltzF58mKj4ZAmhh3wEbGmSRQgLZ5cSJPyJS+ZDiHCSh2BTNLNkXuSxzVXRtHDdYEJrpkK0UtbaMWxJNkKSjBZNMxXU3DkHi7+gSJrKvAi5QyFJFvPQpBUtCjaEovlVFq29edF1nymKl4qnG0xoYQqKtUIJSTJatL4dtI1HQK8t2n0KIY9LDVL/j2E7lFaKNgHm5YA+WQjK/q+CbgsTWpgKCXPNg5Ako5NJM1W0ay0aIe9MHtne8+oNReuGRRoLQd/zrOQRyZrHe97rBAlTxpg5xphrjTGrjTH3G2NkXsHjROgWCt2mZPKxf+dBSHuDzDoB9YrrzCv/TMGizYr0rkLxJoWC3d6ub0sE0H3njnDysGYU7V3tBqEO6J8BfmKt/UNjzCAwfQzaNGbkNdiJfT+CNmydPJ27aCvWyeSnpWNs71K090Yn7N5lMo1pWRELU8aY/YDnAW8BsNYOA8Nj06xiE7JSkI53RRt4JpPwlxe57Eupz7VnKdqjCfHxmkw+PWIzX5CpTl52ohJi5jsM2AT8pzHmLmPMV4wxM5pPMsa83RhzuzHm9k2bNgVU111yCOYDVOLPQtGityaTmW8SzWGFo2iCbtE0aUUj5P7qo2klRJjqB04FvmitPQXYBby/+SRr7WXW2tOttacvWLAgoLqJT9HyYoUQ5DOlAue4EubPps9GGRtUmBpfwvJ46bNpJkSYehJ40lp7a/T/tTjhakKQzyam2kUnIkV7piEmEtVM9S5Fk00m06bkSvERd1dr7XrgCWPM0dFHLwRWjUmreoC8zEG6sh9f9O6OL0WLIJxMFO0u6XPtXfTRtBIazffXwLeiSL6HgbeGN2nyYijegCclRPtRtBd5Mk0KqpnqXYrWD/NKOluw26T0CEHClLV2BXD6GLVl0mP0RVYKTh6G6qJFsuZF0W5THmPhZIoCDEHvUitqle4htIOOPzpYji+6GOhd1CNTUcYPFaZ6DB3w0inaHSpae5WJSdEEXR0LlSKhwlQPYSaT05QyIdFwdkWZ+BTN/64bqDDVQ6jP1MRkMj3TyXStRaNogm7BmqtMclSY6jF0/EinaKuiorU3hJBEgMr4Mom6oaJ0HR35eoyirR4VRSkGOrJMTPLY+Uz7UisqTPUYKkulo7dIUfwpmoa0XyNvM6F3qTdQYarH0AiWdDS9gaL4U7S3pl9NxpnQ8bA30N7aYxRs8agoiqIokx4VphRFURRFyYwu+ltRYarH0E6qKIqi9DJF87/rBipM9RjqM6UoiqIoxUKFqR5DBX5FURRFKRYqTCmKoiiKogSgwlSPoYopRVHGAw2hV5TxQ4UpRVEURVGUAFSY6jE0SkJRFEVRioUKU4qiKIqiKAGoMNVjqF5KURRFUYqFClOKoiiKoigBqDDVY6jLlKIoiqIUCxWmFEVRFEVRAlBhSlEURVEUJQAVphRFURRFUQJQYUpRFEVRFCUAFaYURVEURVECUGFKURRFURQlABWmFEVRFEVRAlBhSlEURVEUJQAVphRFURRFUQJQYarHsDbvFiiKoiiK4oMKU4qiKIqiKAGoMNVjlEq6OZ+iKIqiFAkVphRFURRFUQIIFqaMMX3GmLuMMT8YiwYpiqIoiqIUibHQTL0DuH8MvkdRFEVRFKVwBAlTxpilwMuBr4xNcxRFURRFUYpFqGbq08DfAtVOJxhj3m6Mud0Yc/umTZsCq1MURVEURektxMKUMeYVwEZr7R1J51lrL7PWnm6tPX3BggXS6hRFURRFUXqSEM3UmcD5xphHgauAc40x3xyTVimKoiiKohQEsTBlrf2AtXaptXYZcCFwo7X2kjFrmaIoiqIoSgHQPFOKoiiKoigB9I/Fl1hrbwJuGovvUhRFURRFKRLGdnFnXWPMJuCxrlXomA880+WyedSZV1lt7/iWLVp7Q8pqe8e3bNHaG1JW29u7ZfNqr5RDrLXp0XPW2gn9A9ze7bJ51KnX2rt1Tqb2TqZr1fb2blltb++Wzau94/2jPlOKoiiKoigBqDClKIqiKIoSwGQQpi7LoWwedeZVVts7vmWL1t6Qstre8S1btPaGlNX29m7ZvNo7rnTVAV1RFEVRFGWiMRk0U4qiKIqiKOOGClOKoiiKoigBqDClKIqiKIoSwJhkQFcURVE6Y4yZDbwUWAJY4Cngp9babbk2rAPGmEUA1tr1xpgFwNnAA9ba+wTf9VFr7d+NdRvzxhjzPGCDtfYBY8xZwLOB+621P8y5aUoOqAO6oijKOGKMeRPwIeB6YF308VLgPOD/WmsvF37vedbaGxKO7wcssNY+1PT5idbaexLK/SnwfsAAHwfeAtwHnAn8i7X2qwllP9v8EfBG4HIAa+2lSddU9z2HAqcAq6y1q1POPRjYaK3da4wxUXtPBVYBX7bWlhPKng9cb63dm6VddeU+DZyBU0j8FHgh8GPg+cBd1tr3ppSfiROuDwLKwINRO6oZ6j4GeBWNgvn3rLX3+1xD03e+1Vr7nyl1LgFutdYO1X3+UmvtT1K++wzAWmtvM8Yci7vu1dbaHwnaebm19k2+5brBhBOm8uhodfV6d7Ze6GjRquoMYKW19vqE8/4At/LaYYyZhhtw40Hro9ba7QllLwW+Y619QtC+QeBC4Clr7c+MMW8AngvcD1xmrR1JKHs48GoaB60rk9paV/YlwAU09qXvpg0eKd/5QWvthzPUuxT4ubX20brP/8ha+7UOZQzwv6N2Xguci3sPVgNfyjJIN33fjdbaczOcN99a+0zd/5cQ9SXcRNZ2gDHGvBq42Vq7JdJ8fIJo8gTeba19MqHOTwL/ba39jc81RWXnAX+Fe5ZfBf4OeA6uL33UWrs1oewLgNfS2Je+Yq1dm1LnA8AfNGuhjDFzcePFUb7XEZV/3Fp7cIdjrwM+DWwEBoC3WGtvi47daa09NeF77wX+AJiG2/7riEhDNRf4hbX25ISyT+L2ab0eJ0gB/BvwHgBr7Tc6lLvOWntB9PerorbfhHvP/9la+/WEOlcCZ1hrdxtjPg4cDlyHewew1v5RQtk9wC6cIHQlTltY6XR+Xbn7gONx92gdsCSqfwAnTB2fUPZ1wHuBu4EXAL/FudycAFxsrb03oez7gIuAq4D4HVmKGx+vstZ+LK3tHb43qS9dCvwl7h05GXiHtfa70bG0vvQh4GU4ofMGXL+6CXgR7l7/v4Sy32v+CHe/bgSw1p6f6eK6xIQSpvLoaNFxUWfLq6MZY35vrT0j+vtPorZ/B3gx8P1O9ykaQE6y1paNMZcBu3ET9wujz1+TUOd23KD1EG7QusZau6nT+U1lv4W7R9OBbcBM4H+ieo219s0dyl0KvBK4GfhfwApgK064+gvrNujuVOengaNwK+r6vvQm4EFr7TuytL3N96b1pY8CZwF3Rm3/tLX2c9GxpL7078BCYBDYAUwBvo+77g1J7TXGNGspDO7aHwCw1p6YULbWJmPMP+DMQVcArwCetNb+TYdyq6y1x0Z/Xw3cAlyD6/sXW2vPS6gz3uNzAXA1Tji+q9P5TWV/BNwL7Acsj/7+Nk5LdJK19lUdyn0MOAD4OU7AfgRYA/wFTgi7JqHONcCzmgX4yPR3u7X2yISyze957RBwrrV2RodyK4CXWWufjhZslwN/Z639H2PMXdbaUxLqrH+md1trT6o7llZ2FvARXF98r7V2nTHmYWvtYZ3KNH+vMea3uD7wiDFmPm5RcVJC2fq+dAfuXlfbtb9dvTih6w9x88TxuLHwSmvtzQnlVlprjzfGTAWeBg601u4xxvQB98bt6VD2HuDZkfA1H/iWtfYlxpgTcQuf5yaUXQMc17yAjBac96X0pU7aSAMcZa2d0qHcvcBzrLVDxphluDH/v6y1n8nQH+7FzYlTgPXA0rrF+K1pYwtucfUV3CLR4OaOCwGSnk8u2B7Y02asfnCD20CbzwdxE2BS2Xs6/NwL7Espey8wM/p7GXA7TqACt0pJKteHExJ2APtFn08D7kmp807gm8A5ONXyObiX+vnA81PK3lX39204UwDADNxA0Knc/fX1Nx1bkVYnbvX1YpxGYBPwE+DNwKy0ZxP97gc2AH3R/ybpPsX3N/p7OnBT9PfBSc8l7ksdPjcZ+tKODj87gXKGvtQf/T0H+BHwqSx9Kfo9AGwGBuvuWcdnGp3zvagvHQMcEvXhJ6K/D/HoS3cCM+rakdSXHqj7+w7fvhT9PhL4PzgT1GqcKe2olLIr6p7juqz11l9LdE9/E/09F6fRTarzzbhFxBdxmrC/A74UffaWlLJbgZcTvdd1P+fghOTU9kb/LwbuAC6l6d1tU/Z2onEUN/nFn08F7k4qW3fuacAvcBqpRzOcf2fd37/v1Mc6lP0pTrAE+O+4zwL7p7W3+V4Ai6J79DvgiYRyHwd+hRs//xW3cPl7nEbuSyl13suoImNa0zuU1pdWt3sno3f1gZSyG3CCzSFNP8twWv9O5VY1/T8TN3Z/Muu72u45ZihbAv4Gp2g4Ofrs4Sz9L4+fieaAXgUOxK1a61kcHUviAOAluMGrHoNTwybRZyPTnrX2UWPMOcC1xphDGFV1t6NsnUp5tzHmIWvtjug79hhj0tp7OvAO3Av8XmvtCmPMHptNWi9FKvsS7qXeFNW7yxjT0b8AWFln8rzbGHO6tfZ2Y8xRQEdTW4S1brV4PXB9pA5/GU6T+G84LUNSewdxwt50YDawBbfaGUiptx+oROfOihryeFR/EnuNMWdYa3/f9PmzgDT/im241fGG5gPGmDQzZ7+NfDystduMMa8ELjPGXINbFHQiLjNijLnNWjsc/V82xiSaLay150dmt8uAf7PWfs8YM2KtbX6P2jHNGHMKri/1WWt31bUjqd6bjDEfBv45+vsCa+11kSktzQRrozoexGlBPhKt6i/CCZ9HJJSN+/4sYKYxZln0zu5P8v2tGmPmWWu34MaYvqgNWyMTa+fGWvuNSMP0EpzJ2OA00B+wCWbFiFuA3e3e68h82ImdxpjDbeQvZZ2G6hyc+eu4lDprGmbbaG7dH3h3Stm43B3GmHNxmrtfZyhykjFmB+7eTDHGLLLOtDhIdK8TeBtwuTHmH3F9Z0WkcZoLvCulbMOzs9auBz4LfDYav9tirX2fMeY57k97ixl1J/gKTnOTxI+AnxhjbsaNgddAzQSd2JeAdwI/N8Y8iFvwgFscHoEzXyfxA9yif0XzAWPMTQnl1htjTo7LWaehegXwNZxpMolhY8x0a+1unIAd1zeblDk5mi8+FY19nzLGbKCXg+bylubG8gfnb7QWZ/++LPr5SfTZS1PKfhU4q8OxK1LK3kgkOdd91o9TrVcSyt0KTI/+LtV9PpuU1WPduUtxL+PngcczlnkUeBhnqngYWBR9PpPk1fls4Ou4FfWtOAHqYZwZ7aSUOpO0KtNSyv5NVM9juFXjz4Ev41Z4H0oo9w6cdvEy3IrurdHnC4BfptR5anSNq4gEQJwZ91bgtJSy/4Tz4Wh37OMpZX9AG81i9J3VhHI/JtKONn2+iKaVfsJ3zMCtNr+HM9FlKfOLpp/F0ef7k7DDO04I/kfg8einitPcXQEcLO1LGdp7EW6FvgHn//Qz3Mp3HfD2hHKvj/rf9VF7X17XlxLHh7rvOCDqV6cAB0ivIWNdJwFHdrjvF493e8fqWnHa2eeodnaLAAAgAElEQVRkPHc5zk/wtTiXiVKGMucE3mfRdeLM7+8Bzqv7rARMyVC2hIscfC3OPPlsIg38OPWlpURzRJtjZ6aUbXs9wHzgBM92vBxnUh+X6wz9mVA+UwDGmBLOATZeAT4J3GYzOBUG1LkUp2Va3+bYmbaDo6wxZoq1dl+bz+fjJqWOjohtyrwc17HFIcjGmOm4AeGRlPNmAYfhBMYnbRsNTJsyR1lr1wS07UAAa+1Txpg5ON+ax22r5qi53HG4QXalTYkK6lB+EXV9qd0zHksiXwKstXvaHFtirV3XWirx+2bgTG8bPcqchJvAvuRTV9N39OEG0t0Zzp2N08htzvjdM21dkIewbcY6rV0/zvSxzlr7dEq5ebh+v9Z6pDQwxpyMM+vNxo1HBjdBbcP57t2Z4TsOoC4QIss7Jy3X1N766MNtwJ/bBP+0lLKp1yq9zm6XDb3O0PZ2+D7xeyEtm0edoWXHi4koTB0M7LDORLIMZw6732bIj9Kh7Gpr7crxKptHndrebHVG5U+nLnrLRyDLo6y2t/faa5wz+J9aa29t+vzZwH/YZAfpU3C+Vl6TdqBAFNJeUdmU6xw3AU5aNvAeBQtiHb43MbhlPMrmUWdo2XEjb9XYWP7gQvUfwZl03hb9/irOQfVdvVZW29vT7X0+zhH3Zzg/uh8Av8H5uhzUa2W1vT3d3o4BCzgtV1LZFbi0Cs2fP5sE52ppuTFor6hsYHu7XjaPZxqd864OP+8GtoxH2TzqDC2bx0/uDRjTi3GT5DScv8ZOGqPU0qIkul5W29vT7b2r7vxDcTmywIXQX99rZbW9Pd3ezwI/xPldPTf6eX302edTykqFk5DJPqS9orKB7c1D+Ov6M42O78UFXXyozc+28SibR52hZfP46V3PeBkV6yLhhoE9uBBxrItS68Wy2t7ebW+fHc2D9TguhBhr7Q3G5aDqtbLa3h5tr7X2UmPMyxhNJhz7cn7Bpifn/bEx5oe4YJY4eusgXL6zpOSx0nJB7Q0oK25vHmVzeqbg0o9cZ629o/mAMeZt41Q2jzpDy3adCeUzZYz5Oi68eQYuoWQZ1znPxeUyel0vldX29nR7v4ZzDP05bsBcZ619l3FO+ndaa4/ppbLa3t5tbygdJu3vpU3a0nJ5EdLevMpKCXimR+NMXC0Jj40xB9gEJ3Zp2TzqDC2bBxNNmOqncUuNP8CFQj+OWzHs6qWy2t6ebu8A8CfAsbhtH75mra0YF2230CbkYMqjrLa3p9s7G/gAbvJcGH28Efgu8DHbY5sdh7S3aNcqZbJcp5KdCSVMKYqi9BrGmJ/ictF9w0apNYxLufEW4IU2eesc0aQdKBCFtFdUNi8BLuD+dv2ZNpW9gNFEx+NaNo86Q8vmQSnvBowlxpiZxpgPG2PuM8ZsN8ZsMsbcYox5cy+W1fYWor0rm8q+pRfLant7t73AMmvtx21djjJr7Xrr9sBMC+/+Ni568AXW2v2ttfvj9uDcRpQ5e4zLhbZXWjakvXmUzeOZ1pc9p6ns1nEsm0edoWW7j+0BL/ix+sFJrG/B5ex4F27friOBb5CSOTWPstpebe9kvdZJ1t7rgb+lLkM2LnP2+4CfpZTtuN/aeBwbg/aKyga2t+tl83imOV5rodqb10/uDRjTi2nK0YHLfA5OA7e618pqe7W9k/VaJ1l75+I2xl2NW1VvwW1N9HFgXkpZqXASMtmHtFdUNrC9eQh/XX+mOV5rodqb18+EMvMBu4wxZwEYt0HsFqhtmJgWC59HWW2vtnesymp7e7S91m1m/J+4jWgPstbOs9Yut9a+D7f1VRKvx+VJu9kYs9UYswWXKHQe0DEaNaBcUHsDyorbm0fZnJ5pXmWL1t58yFuaG8sf4ETg9zjb86+Bo6LPFwCX9lpZba+2d7Je6yRr76XAA8B1uE3GX1V3LHVDc+AY3F6UM5s+T9u8XVpO3N7AsqL25lE2r2eaV9mitTePn9wb0LULhbcWqay2V9s7Wa91orUXuDeeDIBluG1p3hH9f1dKWdGkHTLZB7ZXVDawvV0vm8czzfFaC9XevH5yb0DXLhQeL1JZba+2d7Je60RrL7Cq6f+ZuOSxnwRWpJSVCichk31Ie0VlA9ubh/DX9Wea47UWqr15/Uyo7WSMMfd0OoRzXOupstre8S1btPaGlJ1g7V1ijPkna+0/jGWdbcoO4MwI9wa2N7FOYL0x5mRr7QoAa+2QMeYVwNeAE1LK9llrh6JyjxpjzgGuNcYcQrKvlrRcaHulZUPam0fZPJ5pXmWL1t5cmFDCFG5QewkuuqIeA/y2B8tqe7W9Y1W219r7G6DfGDPDRpnmjdtP6xJr7Tkpda7qUnuX4vyfLgCqKWVD6nwTbiujGtbaMvAmY8x/pJSVTtohk31Ie6Vl8xDgQsrm8UzzKlu09uZD3qqxsfwBvgqc1eHYFb1WVtur7Z2o14rzcdgH/F3dZ28DbspQ58PAP2WpE+iXthdnOrDxd4zX/Q35wQl8izocO3Osy+X1E9LevMpOlmstWnvz+tHtZBRFGXOMMY8CX8LliTnMWrutXjNljDkG+BxwGrAJ+D/W2m9HZb8O7AUOB56N2z3+TTba/84YY3Eh6e/ECUKHGmM+A7wGmA08CLzTWvur6PwzgH8HjgL2AN+ybpPiZcAjwIC1tmyMeS3wCeAV1tqVxphrgLOBabj9+P7cWnvfeN0zRVGKy0TLM6UoSu9wOy4vzHvqPzTGzABuAK7A7U12EfDvxpjj6k67GPgIMB9YAXyr6bsvwG1cfWz0/23AybgcNFcA1xhjpkbHPgN8xlq7H05A+3ZzQ40xb8UlXHyRtXZl9PGPcVnOF+IEuuY2KIqiAKhmSlGUsSfSTL0NWI/znzoCt7HrJcAXgb+y1p5dd/5/AE9Za/9vpJmaaq29MDo2E9iO2w/tiUgz9UJr7Y0J9cd7et1tjPkl8Avgc9baZ+rOWYbTTL0X+CPgxdbaJzt83xycv9Qca+12/zuiKMpERjVTiqKMG5GW5wfA++s+PgT4A2PMtvgHp4laVHfOE3XfMYTLOH5gu+MAxph3G2PuN24T4m04c9/86PAf40x8q40xt0VOrPW8F/hCvSBljOkzxnzMGPOQMWYHzgeMuu9UFEWpMdGi+RRF6T0+hDOTfSL6/wngZmvteQllDor/iDRT84Cn6o7buuNn4/breiFwn7W2GmmmDIC19kHgImNMCedXda0xZv+673ox8BNjzHpr7X9Hn70Bp0l7EU6Qmo3TTPVcSLaiKPmjmilFUcYVa+1a4GpcRmNwmqqjjDFvNMYMRD/PMsYsryv2v4wxZxljBnG+U7daa5+gPbNwYeqbcOkYPgjsFx80xlxijFlg3T5626KPK3Xl7wNeCnzBGHN+3XfuAzYD04GPyq5eUZTJgApTiqJ0gw8DMwCstTtx2qALcdqm9Tjn7yl151+B02htwUX8XZzw3T/FOYuvAR7DRQLWC14vBe4zxgzhnNEvtNburf8Ca+3dwCuALxtjXgZcHn3XOlzeq1u8r1hRlEmDOqAriqIoiqIEoJopRVEURVGUAFSYUhRFURRFCUCFKUVRFEVRlABUmFIURVEURQmgq3mm5s+fb5ctW9bNKhVFURRFUUTccccdz1hrF6Sd11VhatmyZdx+++3drFJRFEVRFEWEMeaxLOepmU9RFEVRFCUAFaYURVEURVECUGFKURRFURQlABWmFEVRFEVRAlBhSlEURVEUJQAVphRFURRFUQJQYUpRFEVRFCUAFaYURVEURVECUGFKUXoYa23eTVAURVFSUGFKUXoYlaUURVF6HxWmFKWHUVlKURSl91FhSqFS1SlbURRFUaSoMKUoiqIoihKAClOKoiiKoigBpApTxpivGWM2GmNWtjn2HmOMNcbMH5/mKd1AI8YURVEURU4WzdTXgZc2f2iMOQg4D3h8jNukKIqiKIpSGFKFKWvtL4EtbQ59CvhbNOCo8OgDVBRFURQ5Ip8pY8z5wDpr7d0Zzn27MeZ2Y8ztmzZtklSnTEDUtJgNvU9KkalqpLAySfAWpowx04G/Bz6Y5Xxr7WXW2tOttacvWLDAtzqlC+QxX6uMoCgTH33NlcmCRDN1OHAocLcx5lFgKXCnMWbRWDZM6R5VlWwURVFyQzV4xafft4C19l5gYfx/JFCdbq19ZgzbpSgKurJXio0zU5u8m6Eo406W1AhXAr8DjjbGPGmM+ePxb5bSTVQzpSjKeKAjSzb0PhWfVM2UtfailOPLxqw1EwRrLcYUZzWmslTvos9GURSl99EM6ONA0SZA1Uz1LlbXrEqB0bFFmSyoMKWQh+9jgRR3uaJzkVJktP8qkwUVphTNZdTD5JO2QvuDUmyK1oeL1l6lFRWmFCqTKCx3pFLNuwle5GHmm0TdQRln1MynTBZUmFImlVdO0cb2PAQbXSUrY4UK5tnQ21R8VJhSNGFcD5PHyn4y9QYVHMcX1UxlQ29TOr3+rqowpejqsYexxbJKFo4eH58Ljy7UlLGi191RVJhSdPXYw1Ty0Expd1DGiKrtfY1CL6BjcDq9fodUmFJ6XuIfS4qWtykfM1+x7pHSu1StVeFcGRN6vR+pMKXoqqiHycNMot1BGSus1fElC3qL0un1RZ4KU0pOEWPdrzPPeqWUJ5PWsGgPR0nFWptTUuBiZQVWgTOdXr9FKkyNAz3+zFuoVNXLuVeZTCbYSXSpk4aqaqYyofeo+Kgw1YHJNIlNJu1H0ZhMz0Y1UxMP9ZnKxiR6zcX0ej9SYaoDe0cq4rJFmxTKFc1l1KuUC5axXVHqqVStal0yULQ5Iw/UZ6qghISk9/Yjb2VyaT/yboEfI3kIunn5s+VTrTKOqAN6NibREDxhUWGqA5MpWWIe+9XltRLr9dVNM0XbSzCEos25+8py7fVkoWItebhkFk3TM5ncSqT0+i1SYaoDIZNuwd5jRso5CFNdrzGqt2B73eUi6Ob0dIom6IYICZMlM3jV2lwSzxYN1d6l0+sCsgpTHQhZKRRtUhieRNqPPAatkPubx7PJa57v8bFyTJksl1qt2kJpXUIm7BABuUj3KC8svS1QqTDVgZC+3cPPuwVrLeWK7fpKeTL55UivNa+JSFfJ2SjaoikPygVzQA9pash1BpWdJIKYtb09t6ow1YHJslKIr1OqipeaofKaiIo08JRzEqby8hfs5YFyrOnlFfZYUqnaXAJccjHnB5QNMhlPkr6E7W2NrgpTHShPkkSWsRAlnbSl5fJ6/4uU7b1qw1b10rQKefm4FG1SCGlusa5UTqVqqeQQkVo0JlP0uBSL7elFiApTHQjRCBRpUoivU9rmAl0qkI/GUaqFq1RdJJRUmybVCOS1kChYVwqiaO+NlHLV5tKfimTOh8ljCQnBqmaqmITk9ynSQBm/xNKJVyoo5KeZysMHSVaupjXscptDBLgQennV2Y48WlskMzU47WguC5gc+lKI60LIuFSw10ZMr2fTV2GqAyGrqR5+3i3ElykdpKXjZF4+U0FRmmLtnfDexv5s4mcjL5eH0Fmk9wYCI7+EZfNKsCu91krVihemIfd3MmmmihYIIR5H6W2rjwpTHQiJcCvSCjsWGsWaKfGELSoWTJgwJSsnrbKckwk2r9xAeTi+F+dNdeQ1cQ7tK4vKjQSY+SZLRDXkMy6FEJL/LmRx2MvPVYWpDoRMKD38vFuIr1EsOArrtTYfZ8J8IosKppmq5hUNVaQ3J0ArGzApFC1wo1yp5vLOiRcgY9yOrBRtS68wTZq8YC+PESpMdSAkLL1IW9EE+0wJr9WSz8RQycEZNtRnStpkabmKzSu/VderDBPoAwQi6aSQl5lDstgqV6pYK99hIcjM17tzbltCxqVeNn2NJVXb2476Kkx1oBqQbC4XJ2dp5Fbkz1CW+jVIHdCr+awCi7RKDs0BFlJvHmY+uSYtH9+aXPzKChS4UQ5cqIUQlGqgy2b1OHGylHy2yOp+2Yq1Pb0/nwpTHagEaKaK5LwbD3QjXfZrsHTfyTl40BKWCxWm5D4nAQ7oBRI684qECjElhfiz5YGkO8RbIUm3RAq5Unn+uwATbEgKlBz6YQh5KQx62R9ZhakOhOx23svSczOxI6FUyAhxju72e2Ft6MQr90GSUFvZC56NtXLvgpCFRAh5pLcKC2cX1hnwbPLKJSx5b2LzXkUYzBMk6BZoEK4ERs/mcaUhdYYInb3sW6bCVAeqVblGoJel52aGowFPGp0hT6nQfc1UiJo45JlKTQ4hJtgQYTUvM5/4fQuoMz/NlHBCycW0KHtX61MiSDTfIYJuSPCQXAMtLFfNR5MWQh7a4Ly218qKClMdKFer4hcyr+04JMQq+H1CJ1G5P0/3NVOhGhe5NkFWciTATOImhRAzn6hoECEmWCl5bE7rNKSyOvNJgCl7V4frxpRhwfiSV94mKVIBuVyVJza1IdJfAHkEWZWr+USGZkWFqQ6ETLx5TApGWG7fSCxMVUTlQ/zKuu8zlc+2OdI6YwFXNhFZ8SCbh2aqGlBnXpNukE9lgXymqkKzZP0iQNKHQ5Dnzet+7rx4rpHUG7JoCiGPfliuWHFkaDdIFaaMMV8zxmw0xqys++xfjTGrjTH3GGO+Y4yZM77N7C5xSK9UKOplVWQzeyMhSqKZqlblGow8JuygFSBh6mkJsYAr10zJyMMBPT+n94CyAeO6dALMJU8aMu1q/QJN1IcDLlW6ybcNEE3kminb8NuHvBJZ5mF9Ga5Ug5KFjjdZNFNfB17a9NkNwPHW2hOBNcAHxrhdY4JU2o87tTQKJa+NYn0ZqVRrO7pXKta7o4Zol9wmvl32mQrJHWblw6z0OmOt4d4Rf61hiM9UuSJ39AyZUOR+LgGmuhDNVEi0pFSLUSBTaP0CLe7LPoQ8V/kWNuIqAxKbhvlGFi2aT1p0uFwVu6N0g1Rhylr7S2BL02fXW2vj/QVuAZaOQ9uCkTrnDQdGuA2Xi6GZ2tM0STf/n0Y14EWu2u5HZpSr/gJjPfJwdv9Je6Rug1iRMBWwxg5J2hli+hLnOgsx84UEFgTsZynfm6/a9QAX6YRd32/3CtwIQoJF8rAOSOuMxySJk77UBBtKHlrZkQmgmUrjj4AfdzpojHm7MeZ2Y8ztmzZtGoPqxp+RAF8VyEczJemee4ebhKlhX2FKrpnKIzKjXAnzTZAiuU/1gm1ZoDUMjebreqRlaHBAYC4vCXlsJF3NQdCVOszXaxH2CjRTUkIzZXc7B1hYCpTwPHYSuq2ZstYWXzOVhDHm74Ey8K1O51hrL7PWnm6tPX3BggUh1XWN0GRzvSw917O7SXhq/j+NIHNFQM4QqZA7EvvCCXPehAyyvlU2C7q+2qkAH2f3bLpsTsorHUMeDug2YO/DSjUg7L+Lwl+9CwFITdXdF1ZDhBPpeFbTTAnmjZDI25CggG5HRY9UnJWpl+dWsTBljHkz8ArgYlukxEoZiDuZJMKtWrVie30IkkfQKkz57QwfGvEo9f+Qqon3BeTUCjGbSQbo5pW8rwnWIvdJc4JudwetPHzo4nql5KGZCvL7E5WSOTk3C08SYSpkoSadkYJyWwnHs2BhKgdfw25HN9cUHOXum7mzIhKmjDEvBd4HnG+t3T22TRo7LLIBZDjAzFfJKSpJUuOekUbhydvMF5BszuUM6e6EHQ9WImfYQM2U92TUJMj7tlmax6scoL0LITS7cbcjLSGfjaTDgihExaK+FLYYKFesOMJOQh47M0g1hiHzTbUaItSLigFhvoaS+TG+N3mMTVnJkhrhSuB3wNHGmCeNMX8MfB6YBdxgjFlhjPnSOLezq8QajLJgG4QibRI7Nma+7vtMhUSDQPf3CpP0ieaVvK+WVPpsQvw3QghZhOQRQg+hex/K6gwRxOSaVYGZuo0mytffJQ8zXzUgarcifKixJUNi0QjpSyF+T902j9dr7bqdsywr/WknWGsvavPxV8ehLeOCeyH9UlrWT1z7ylWmDfZlLpvX/kG+70Wlalu0HcNlF0HWV8p2v0JX2N02h4YlwQxL5hdq5vN14LXCFWuIySGEvBYhYsHaytsbcq2V0GzZAiT9t53gtHekwowpqVNOjbAACllZi1xjIx3PQi0hhUt2KzHz1d2bXvWbmtAZ0KWdpV7I8NUIVKzcFyiEUJ+GGB/fnJCorziyrpvsqyUolaYakGEFWpfmgVXSD0W+CYHaOynOXCErG/JsnGOrf+kQc5Dr+8LtmwJM61Ikmp52/dVXMxVivpIufFxwgFxIFpmwKvJxKTR3npQQF41gzZQKU/kg6S4NyeY8B4DRBGxdnowCzUhpn7cjJOorV58piW+Cle9HJVkpxwNsjG+bpavzkbokrpKJIcTJOcTMJ58AZZoe5wcnn3Sl2uuQPUO76fPXrr/6al5C8kyFCObSshKhc7hcrZnpJJqpEHeJIJ+pAAFOMmfUvy95bNeWhQktTEnWq+VK4+Dq6/gbC1Fdd+AdAzV80udt6wyJ5guYUKQal5BBKyQDuq85tL6t9Z/5IE2C2Wzi7hbxpCDfn0xYrzDbuzN9CesMWISELGBColF93/N2/dVXo5BHxKO0D9bqDdDgSd63SkUeeRviMyU1aUpMxqBmvtyRqOKbO7Sv6nWkGjsTdveB+2pNOgpTHpop6RYgNjKFSu+RZJysH8ilminxitXTf6ndpOMbDCFdsTZqZWWbX0uI74/kHkt3OgD5xBuSPDMkk3OIaT3MB8mvcLvr810QSBdbUhM3BL7nAmfw+vfN+ZH63qOqWLgOiToP679h9eWReigLE1qYAv/VWKsw5ddp4gGj2w/cV6jpNLD5rB4rwhd5pGYK7a5marR+oWZKumIVaKbafu7R7nKlKtqeotFfUOaoL6FmHhe02Wmm5M9GMmmHCDVOmJL3/ZAte6TlxkSY8hUUpIutAL8yGyCISRKqhubjKgfkhAsJ+JAvhGX9t/4d7dVovokvTHlrpsJ8VeJO1u0H7ttBO70MIx77Co4It2eJX37pICDxXWpc2XRXM+Xrl9Op7/j0xZGKzBxUn99KuieghPj+SLfUCMkzJR3cQzJeSwX6IEFBVMo/OKBcaTVTw+g2XVmRCpwhUcZVK9fYSATs5nfaN2o3RDAPCWQIEaZEUcZq5ssXyQAbGkUVEuYagrfjY4cO6af9iE2anoJcJLBVq7LVZ2hobbXq/0JKB4Fq1U2APhNvp7b59Cmn/ve/t/UCVDc1U7EWTSZMBZj5hFGlzjwoe8dHhCbuuA912wHd15TUaQzxHSfEe6NGfq8STXIlwHm9KojkDs0nJ+1LEO3MIDFxV+VCvdSsvi9wMdwNJrYwJViLNU8gvi90LZdRpXv+JuDfsTsNjj4T8IhQwzTc8GLIXkhfmgd4b2FKaNYZFRKy19dJiPFp80jZ+jv8NuUe882IH0JISgapU2vcj2SaKad9kUxGI5WqOCEwyM3jIUkwfe5RJ+2275goDYGvJZ4VCgpSYVXiQxrqVhL3JQmhiX1Fvoae/qNxPfVCqpr5ckCi/m9+UL6D3ui+ft3XTPkMlp0mdy8NSuwf5mEahPCcIaF5SsC/zVLTgUR719FnyqNPDUeDrE+faDU5SPZTkw3sIcKUNAlmvAiQlI2fp6S9tcSonouQECEBwpJR+iyYOt0TX22GTzBMPeUAn8yQFB1VgdalOVrcN3p8JEq0LAuikPWJmlAv0MxKnPRbrEWqmeo+EsfUtvlRvLQKbgDotvTsKziOdHiLfLQf8aDuO6GEOoOLJr8m4Wmf5yq5LPRBqvnQ+WiVOppJ/BzQXZnsbW4Wnnw3VwZ5NN7ovokCPy3hBBgP6pIku7WFhETDGdfrrU3213I21N2lVANJ98RLuyo0wUqFVZCboWy02PIWplp8dGXaO9E4Gqptl5j5rP/Cp3kurXR5n8esTGhhqiqwf7cTgrJqmap1OWDyEKZ8XoxO/h4SR2nfF7lesBBtoSCY/JpNDL7mRWmiRck96ugz5TkRgd/qsXlzZUmWesngvK9crS0ERLl2hH4u8aAu8sETBprU9yFvX8OAPdxA7lg9Em9+nbH/JfV1L59MYWLfkP0lpVpOicm4fr6I8elP9QkwJRpSaeRikOZvDARO6M0s6BNamJJkRm73kLKuFhqEhIA8HBJ8BEeboGrN6pRYvwrzFqbKgcKUcMLu1IYsSAd2yX6AnSZLn8ErbqufZqq1jb6mPkmkZb1pQ2JaLAsTF8ZCucSRXGqWrG+nNAJXnONHnMrBT4hLOs8nok+qJSqHaGuE5vz43vosuNr1HZ/+1OB7Kt3XL0TzJ7y/Y7HPo685tBtMcGHKb8Vardq2nTL7IBJmvoLu5IJJe+GzDLohAlH9yyHzkZFPfqNt8I3SlGYUj33ofJKhhpn56lesfo7v4fupyQTdsAhCiR8G1PlMSQRAYdRuw7YYno2un8QkzuRSR2VfU3XSdWWdvMuRNkyihQsxfUnN+RLNVKctd7I+2/p7Iw3kkbw3QRszj5Fmqts+yVmY4MKUn2aqY7qAjA+uvkNLw/6lVD18ptKEriydvf6eSBObNv+dFd+Bo9wm4sXb0bNSFb3A8UBQrWa/1o6RlhkHofrn5+fwK9fKSuqL2RuqmRImLpRqVqF+02xPYbMiezb1dUmFDOkCz3fyTBJGOvlqtnxHzVQnHx+kAkZIUslQ3zJrs7/nI4HjqCSyDkbnSdHCp+pvbh4LjXk3mNDClK/PVGh+n+byEh8baS4YH81U2ryT5XvqHbh9tUuNmgiZWceHvW2en+/LOCxMtOi7PUuST0HW627UfgSaHQR7AvpS76tVFjiXSifAeGKXvKdjopkKyLskeW8kWuD6vUqzlk96Flkn0vhd83021lpxcEBcn8hPqybAeZjpAqN2Q6OiqwJncBhddEldNEL2Lxz9TDVTXcXXPhvauVtNSbIOLsHHcTItwjHL1zQM7B5anuaNWiW2b98onXaCk2+k2obN8bgAACAASURBVHC5Kho86uvOkt046RlmHeSlmqm2m9N2YT+11sSFnnUKNyuWBlDU570J0dz5tjk0oarkXWvY0zLjO5M0hmU38wn9MUODW8SaqdhkHKaZSvq8mX2BmqlyVRYFO6qV9RfoK9Wqd79v1297MdfUhBamfCPcOqmFs0r9LbmMumjma05slkTaLcnyLdINcUO36wH/3F/tkk/6akCGy1WRr0rjXnfp9ylxVS/wieumQyzIIi1D81uJNVPCiLwGzarAXBzj6/vX+M6FCfaZ66y7vqx9IamfZu3Do/5hIdo7mSZOIpiPCIS/0KjdxuTHsoV7iFZWFHkrMPO1yyvVzU3YszKhhamq5zYT4WpXy4ztaznu1vdSKu8V7r/lXQTwC91POyuLwFA/MPuE0DdPPpWq9Z7MfMOXO7147cx/7RjdnsJ3f7zGPfkyaaYSTSTZnk2jZirjBNhBGPf2TxOYSEI1UyNVoQl2JJ4U/AbmBh8v7+0/6rUJcnOHd5SltaK8YfsEGuikLpp1Hq2ZYLscKSzeq1GQT67T85dYQsTJbgOEKZl/owssyFpvJyWBaqa6jG9n6bynVEa16/Awx972fhY/9l3223KPSK0u3Si27LmZbijNgkHWF6vdgO47yPtub9FJiMm6XYp0BSjZEX4sggPqBZqswlBo34/x7YPWtgrT3k7d4klhNDjA5zrrhRqfwAJofB6+yYDr5VR/jZj1zogf1zv6d1bNVNJRP+dqXyFBen9jykLBfNRkPBZmvozvrNDVIkby3lTqIt4leehqUcYZF11jkXOvW0xoYcp3+4JODy7rg5+96gpmb7kHgFnbVwsTqXkXAfw2vDRpx03aGa3+EyHClO8Kx3f12On7M+cPE5oOmq81mzCVfDzLVdcPxpl9VDqc55u/xleTVZ+wM8a3P4xUqqLILWkUYbNwLjVz+whhLaZQoUbMP1jE35yfJLBlHd/iMTfESX/EI80AjKYKkAmd1VqdWc1YwYv3Bs2UxH8p3BncV6vrG/XYabyV7G053kxoYcrXAb1TJ86U5mBoI0vu/Be2LHw2w4NzmLntAdFqQeqA7uOEmyYrpYlS1tqWwTyrdqmdNsh3Q11fJ8ZOk1ZWwUhqOtg73HhulnuUNohn6R/1fTWzv1+H68oayl6r29cPaAzCnl1Qg1+91toms1mIxlEmFPlMRC2mUKGvVogPkk8OpFBiE5i/A3ql6X+ZD5NkUeBbZ6d3LusYE6IhBWHOp8C9BBnawIJ1P2Nkz+5MpyddU69ppya2MOXZWZJeoNTJ+/p/oFTew+pTP8TQ7KOZuX2NyElOOlT5+I2UUqSpvlLy8X3lakt6hawCUTuBYreHMBXvbeaVP2kMV4BeZr7mVdxI9lUrwNRd6zj+d+9kcM+m2mdZ5rKGSSGrv18HIajs6XTvPwmFhz1LzA3NGjE/zZRMK9t87rCHFqNlEvNOOltt+J2V+nqsDZ/Asj4lSQZ/aPVD8tpgPMChu15Tn1XACNFMtTOP+2gr470EfTV/ze+m77u66J4vcNJv/oJZXzwRfvr38MzaxPPHamuibqDCVB1Jq9vEB/fIL+Geq3n06D9h936HMzQnEqZGyj7NxXo6zMfEzsNjJUylaa7aCT9ZBaJ25+3xuE+1ZH6BjtVAy+bHnWh0GvYw87W51rQBr/75L3zyehY98SOO+/17vfZpqZ9QQrcA8Uki6L7Hb4ALTcgX5+Xy1aCFCEStE0rGlAFV2zDR+gQ0NJ/nK0BK82K1CnF+5ffbfDdTdz1Z+z/dgcAh2VsSWsdpn+sdq10dsvSH+l0K+oZ3smDdDbV3PMs7G2oelyQZhdb74nufpm1by+6ZB7PvoLPh1i/B50+Db7wS7rsOKiMt5ydumt1jTugTWpgqe5qDks7tGPJdHoYfvpvqnGU8uvzPABiafTR9lT2Utj3q01wxI56ruP4UzVN/Kblb7B5uFX6ymLDKlWrbF2DPcPaXwjefS9IzzZqvqiHXToDPFPiZNGdtvY+q6WP/Db9l2f3/kblc4x6R2epLWkj4rF69V/RtJh4fQaG2g32gENcNM187Qbrdu9SO0Bxg0o2ZWwTHDNca+1wO7tnIaTddwuk3XsTg3meiY9nqrSXtDDBLtvs/CWnqCUnUbjxW9w9v59Sb38xJv/lLDl31xdr3pdF2oeYzjsapHLyF1WYzavbxbKRSZfrOR9g+72TWv+RL8Der4IUfhC2PwjVvhk8dBz//CGx7vKFM5+9Tn6mu4a2ZSji347HffhaeWcPQuf9MtX8qAEOzjwJg+tYHvCYXaxHZ+WqruKyaqZKhk7xkTLqZT+r31CkVwb5yxXs/qqxapbHYJ0wagtxupZiWjqF+stlv60o2Lz6H9Qf9Lw6777PMfuaOTPXWtzfrtkaJm9N6DLgVz0SAnSaerNqe+Bla6ydQtQhEHvW1bE2UsWx7rax/RKlvvVBn5vOY/MqV1gjhLP0/Hj6Wrf4yplpmYHgbJ/zunZhqmay6qfge+/ankFx/DaY6r6ACf01luVqNBKm3Mmv7A2xd8CwOu++z7P/0TZkUAG0DeTzaPCJ28G/u+x4C/Z4hpu5+mt2zDnX9cdYBcPa74R0r4OJrYclp8OtPwqdPhG+9Dtb8lOGExUY38zhmYUILUz4RbpCcTKyt4++WR+CX/wrLz2f3IefWPh6afSQWw8ztnsIUstQIkhwnnbRP/X3pXaLdi1yp2tRBpDZYVSscueKfWfTYdwE/c0dtR3hBNnCfY/VI1P8jldb9ACFd6DTRZNM3MsT0nY+wY+7x3H/6P7F3+oGccMu7KO3d5tVeyNYvYqHTVIY5+s4Ps2Ddz0aPeQy4PtsaQUIOsIzannqh2kcL3TzxZDWRtPXxytjW3fvk5vG2mimv1AruXKmzfEyW+2QMDO7ZwJKHrmT9IRdw/2kfYe6m33PEPf9KyjqtRv377SPMh6TZqD/X6z4J8qSVd23h1JvfwsztD3D3cz/PXWd/haE5x3D8Le9hYPujqeVDtd7SLXdCzKjlTc4/avesZY3vUakPjjwPLroS3nEPPO+98PTdcMXrOOKqM1m26osNfqO179Novu7hGzKdJMi0TBDWwo//Fkr98NKPNWoD+qeze+YhzNr2gFdnk/pMje6Snr3wQAehaaAvfbTr9NKmqZlHKhas5Zi7Pswha/6TpWu/VXcsq5DiF+WTlzDVadJJm4xiGXfWtvsxWHbMO47KwEzufc6nGdz7DP0/+OtEL3RrbYv2IctkX65YqFY47vd/y0Frv8kxd3yIUnmvO5ZxMrM2Cg7wEqbCNFMNk66XZqp1UsiiHW27GXTGene38Q3MKky107r5vO/xhB+qWc3Sl0rGOK2UrfDIsX/O+mUX8MQRl3DImv9k1oPXpZZv9nPMep1tNWliM5+H1q/iKcDt3sL0K1/DzO1ruPvML7D5wBdQ7Z/G3c/9PBjDCb/+S6p7hxK/InSLrPie+jpxN99Pnz5Y2fQgALtmHdpZWJ1zEJz79/A3K+F1/8Xe/Q7jiJWf4qwfPJ8TfnspczfeOvp9ggTB48kkEKY8zWxZuf/78OD18IK/g9lLWjrlrtlHMWP7Gq/OKrTyjSaM8whdHuxv/+indPi8nk4vbZqaeaRS5bD7PsvSh65k39T5zNr2AFQr0TE/wSbrIDkWwlT9dWWNwJKar+LggP223AvAzrnHu9/zTmDtCe+m9MAP4feXdSw/XGmNtMyyUq5Wqxx910dY9MSPePqQ85mydxNLH7oyOpZaHBi9nz5jXOiuA/VaMx8NWvNklFU72u59rmTcmqh94EZGn6lK1ZnM9m2pDVRe2yFF5/qEsrfVTGUQMqbs2cCSh67i6WWvZs/MgwFYc9L72Tb/VPa/8d2wfmWmttb+z5rCpM39kAqPXuYrHz+t3Vvg8lfRt3kNd5/572xefM5o/TMP4t5nf5KZ29fA9y9NnJBCAzfiNlY8Axma51MfYdVs7qCZakffABx7Pg++5HJ+87LrefyoNzN34y0c+PA1tVN6zMpHf94NGC/i0NG0yDUR+3bCj98HB5wAZ/wp0Lp63znnaBasu4Ede4dg1tRMX+trIolp9umZ0t+XWqaT0DTYl1w2KZw27WWedudlHLTqC6w79A/ZNv90jrvt/UwfeoTd+x2RWfsRa12yvsRJ40SWez3cJg3EcKXK1FLyfeo0iKdNZrG/2qyt97F32iKGp86vHXvymLdy1O674Pp/gIOfDYtPav3+Nvclyyp7wR2fZPFDV/Do0X/C2pPey5Q9GznkgS/z5OEXUrH7pZaHUY2Ue5bpfbCdJiHGx5za7u802t6nkSpTB5Lb3en5DVeqqSbyvW2EqX379mF3PI3ZtQmGNsKujdHv6P+hDdhdm3ju9vUM7NuKwbL2hHfx6PI/y7wAqQ+jD/EFgmzC2AF3fxFjqzyy/M9H29A3yD3P+Sxn3vhauPoSePsvYNrctuWlfk/t+kzWzZmby0ujACFBgNu9BS4/HzatYccF32Bz36ktp2xZdDYPHf9Ojlj5KVh6OjznL9p+VafAjWrVUspgS63XXg+Xq0wbTH9f43Pr8XnnzJaH2DttEdX+6bU0MWlttRb2zFrG2pPex8PHv5P+kZ2jx8SJhMaHCStMDVeiPYCs85vqZNaqp2RMRxNFg1B208dg59Pwusuhz93CZofoodlHY7CwcTUsOCtTm62VZUBvjkLJIkx10kx1+nz0+zsPTomrubuvZv6vP8TGJS9m9WkfZsYOt0qZtfV+du93RHYtUW0/teymp05kEabarcT3jlQyTLodsq6nDD5xpOV+W1eyY97xDcf6+vrggi/Cl86Ca94Kf3ozTJnVVG+7FWvKvbrlSyxe8VnWHfqHrD3xPQA8dNylPOsXb2DpQ1dSXfqO5PIRvpqpJK2BZLPXrGXqt8SoZ2+5wmwGUurr8FxHqkwf7FyuPtHtQWu+zpKHr2Fw3zMM7Nvmxolm+qfBzIUwcyF2zjI2zjyB4anzXbqMx3/Ao8v/LPMCpD6MPo6UTAsygQ5mvjRhbMdTzF19BU8vezV7Zx7UcGh42kK2v/KrzPv2q+F/3g4XXU27SJgWv6eM2rT2C4ns44Q0BUpzHbGFoGEniV2b4fJXwTNr4KIrKC86Gx5v7//46PI/47CRByld/w+w+ERY1jp/JCUiziIY1Y8J+8qVTGXaBXTFn2XpTwNb17Jr1qHebY2p9k1huG9K5vO7zYQVppoFjCzCVJISq3Zs/Uq45Ytw2pvhoGfVjjc7SQ7NPhqA0qb7AR9hyl+aanScrLDf1OQJAWCww/1IE6aSoug6DkBrfgrX/Tm7lpzJymd/AlvqZ9d+h1MtDTBr2/1sOOSV3tsLxBGAaVvfJH1tllvdTpuwZ6TCnJRynbQGlZTVY1/J0DcyxIydj7D+kFc1HOsvGZixP7z2yy43yw/fDa/+j4aO2zaCMGl1fs+34SfvY+vBL2H1aR+ufdf2Baez+YDncsjqL7PhOW8DpqdcsX8OsMQIwsz5seo1U1n7UGeBKI1OgmmaIBcnul345E85esVH2bb/yWybfxrDU+ezeMnBTJu72AlPMxa434Mza89ieKTC6gddaoGRwdkcveKjTNv5GOVojEmjXbTZ9MH0ob/d+5yq/fjVJzFUeeTYP297uLr0DHjpP8OP3gM3fxxe8IHU9maNQGz3/GKzfJoGpFxt9Ff1ERLaaWcaLAS7NjuN1Oa1zsn6iBdS2jXc+QuNgQv+Hb7yQrjmLfD2m2H2ktrhaoJ1YLhSZVoGrbAkDUTnvQSr9KVo6rGWKTseYctBr6h9tHckmxDXCZM5a1l3mLA+U/UTSFZbctKL02eMW3L/4G+cevqFH2o43rzS3TPzICp90xh4ZlXmNjszX+bTazTY+jOu4qSaKW9twmO/g2+/CRafyBPnfYVqtLKwpQGGZh/FrG3u/mSVIWONT7XanQy40qgZaeZeYwxztrt7smPucQ3Hav1z2Vnw/PfBPVfD3Vc2nOMlTEVCLsvOZu3zPoMtNU6wDx/310zZt5mZ917esb31jG5imu1hJvuz+Zt2MvvWBDi9S823w5Uq03es5djfv5/t807ijnO+yerTP8zDx1/KzhPfDMee70y3+x/utI11AnJ9X9q05EUALFx3g9j8FeKDlPQ525+EO7/BzmNez94ZS9ue0l8y8Ky3wUlvgJs/Bg/8uOWcln0IA/J4Jba3jnb3MsS8WPts1zNu4VMnSAH0JQT6GAOlabPhwm/ByB749huhvG+0XQnvRlY/uoYNrAM0f0mfN7DrGfqHd7C7TjOVxf8uabE8Hh48IUxYYaq+g2QVppKSVfaXSnDX5fDk7+HFH4Hp8xqOt0wgpsTQ7KMY3Hx/5jZLfaYaHSezXWtHYSpFg+eVt2n9Srji9TD7ILj4WsoDMxoO75yznFlbV0HG627eE9AnfLn996WfI93+JjEBbIqwMXubc9DdMbfRzNcQafm898Kys512atOa2sft7knbgIFYyD3geLjwCiqlVhvV9vmnsfmAM5l95xdgeFdim6EubUXGAT2pL2V1Jm/Y2FawPVCWzxvOEQYWjOzazkm/+UsqfVO557mfw/aN3u804ab+XuydsZSdc5azYN3PMgutUofujnvHdbrPv/4U2CpDZ3Q2C/f3ldws+IpPOp+//3k7bH6o4ZyWfQgztrfTs8ky/re7l9L7C5GWdNcz8I3zYctDcNFVcPho+pyBpLkmHoMXHO3M+uvucJHjcbvGQKMriVxM0kylUY7GqN2zlo3Wm2H8TlJwjIs/dACpwpQx5mvGmI3GmJV1n80zxtxgjHkw+t3ekzBHGjVT2V7G/oTVQv++zXDDh+CQs+Cki1qOt+tQQ7OPYuqW1ZnVLr57zkGrQ3jWa5VqppLa1/CSb3kYvvkamDIT3vgdmDG/5fydc45lcHgbU/asz6SRa94T0Me5VEo7wSnL4JykWUkbpPfbspK90xczMnX/hs/76gfgUh+85sswMA2ufatbwdJ+tVdujjarCblL4ZL/hqmdHcwfPu5S+vZshtu+kthmGN0UOeuAnqR8yppeYVhgrgjZ+LqjZiqpbLXKjB/9JdOGHufe53yGfdMXNRxOu1/NmoiNS85j9ua7YGhDanuhVSiS+KMlfR8QaaUuh1MuoTT3kI7fWVsQDEyD13/TpZa56mLYN5oKoDXrerb3vJOmI1PepzbPIKuWp93YX96xwWmktjwMb7gaDn9Bw/GkFDQNx449H856F9zxdbjjGx3bWqs3g0a3XKk2pJ4IfW+yCOdxWoTdTT5TafQlCExZTLDdJItm6uvAS5s+ez/wc2vtkcDPo/97ir0izVTnhzP1F/8Iw0Pw8k+06Bfr91mqZ2jO0fTv2wo712eqvyLQTEn3GOu0MkrLM5X0rtb8vXauh/96tdtr6Y3fcblDaHX43jn3WABmbV2V6bqbVzI+eVXakWVh085nKpNmKmHASxOYZ25Z2aKVgjb9c7/Fzmdqw0q3aSidn39t4GoQcq+rCbmdbv/2+aew++Bz4DefaZjw2uGbCDDxmWd8DRq3+smaeNOVKZV3s/iR/6ltdZLW7moHx/XmdrTw608y9aGf8OBJ72PbwjNaDqeZNJuFrY1LzsNgmf3YDYnlRss3OUhnEHY7jWnQYTHwq0+4TnT2e5IFhfpxZ87B8IdfhWcegO/9Va0TSpJgQkLfz5Jnrc0zyKKZKrdJRTKwdzNzr3mtS+r8hqvhsHNayvX3lTruQtESaX3uP8BhL3B+Zk/eEbyrg3derLhcgDBV3fQg1dIAe6aP+n5leV+TdjZLErTyIFWYstb+EtjS9PGrgG9Ef38DuGCM2xVMg004Y2fp5KQ+Z9Nt9N1zJTz3r2HhMS3HO710O2dH5264L1P9vtvfQDvn0jAtXJqjftIEWLXAnq3wX6+BoU1wybVOVV1/vI6h2UdjMczadr/I5JZFmGp436ylb2So/bE2NJsVY7LkmkrSrCQ+473bmbrj0bbCVNtnc+R5rl/e/lXsfdd1HNj2jlQ6CrmQHGa8+fR3we507VQ8aWc1IyXdwSyvQbMjbubcVOVhljx0FWf+6DyOu+39nP7z1zFt56OpwlSiv2Cnutf+DG78J3Yd9WqeOPLNbU9Jkz2bBbhds49i98yD2f+J6zMFrDQLT1k0Lkl9tOU+bXsC7vwvOPWNMOcgBjpot/v7TKsj+OHnuv3Z7vsO/O7zQOuWS5Vq+k4W9ekfmsnim9NeM+V/bwf3PsNpN72Rvh2PwcXfhsOe37FspzQ0LZ+X+uAPvwYzF8G330h1aGPm9rSjaz50dZgta9k98xB3LR71JrneJPmd5YHUZ+oAa+3TANHvhWPXpLGhIfQzowajnYnLVIZZfseH3CrqeX/bplTngSfeo4+N2YQpic9U6/YJ2bVw7YSJtE2QkzDl3XDFhbD5Qec8ueS0huPN11YZmMHuWcuYtW1VpkmhRZjKoCGqv5rFj13HC75zKqfd+AaWPHQV/cPbE8u2S4AZkya0Jipdkg4+fTdAS1oESNAanvtBd6+/99dMGXqy7SnDQ1tGhdyLG4XcNPYtOg2OeJHbhzJBOxUPqt3a5qH5GcSRZh2xFu7/Pode/SKW3/FB9sw8iPue9TH6y7t41o0XMm3TPYn1Je7d2W4S2/ooXPvHcMBxbDznXzpK72kLqBZBwhg2LjmPuRtvobw7fXuh5u/PpHHx8fn71Sfc77PfDSRECndaqJ35Tlh+PtzwQaoP3dQ+bUXKGB6nwml7TGjmy6JhrTfBDu59hlNvehPTdq3jyZddDoc+L7Gsl6vF9Hlw4Tdh92b2+8Hbo30OW8mUO28M83glfV5P35a1Df5SkNHMlzAfhcxV48G4O6AbY95ujLndGHP7pk2t++uMF/XhtJm1NW0ezsFrvu5yIr3sX2GwfXh4p4GnPGUOe6cd4KmZynRqjeaOnHVjW2NMS0ft6zOpqQY6fl91hOW/+mvnoP/ar7T4CEB7AcM5od+fSRPRLDxl8g+ru5x563/NyMAsBvdtYfkdH+Ss654LV78R7v8BlFtDlZMGiTShNVWD14mn7gJgZ1MkHyT4s/UPwmu/CliOv+VdmOpIw+FSeQ9zv/tGl+Pmwm/B0tNaviIpzNgY4JwPOO1UQvb12hYVHjl6QvDar+7xW+BrL4GrL8ECd5/579z+git5+tDXcPu5V1Hpm8bJN16CXXtjx/qS3qsW08vwbrjqEsDC6/+LSn/n1BJpE2C7a9q05DxK1RGqa36aWBZataRZtN+ZN33f9jjc9U049U3OB4+EhMCd+m+cCmD/IzHX/hFTdj3VckraGJ78rmbxhWt9nzMJU9F3z93wW077xcVM27WOu87+MjsWPTu1bKfFUcdF0+KT4JWfYcqTv+WIe/6t7SkSbVpmYUqqmaqU6d/+WIO/FGRb+CeZjCeKMLXBGLMYIPrdUe9orb3MWnu6tfb0BQsWCKvzozlrtXPSTu8wzWaUqbvWcdiqL7DloBfD0c1uY43f34mh2UfDhmzpESpVmzlHT0y7jpxVM9B8vUkRJjFtIyhslWN//wHmPXUTvOJTcOyrWs9xJ7Z8snPOsUzbvQ6zt9mS3Ip0c9qY2ZvvYuvC5/C7l/6YW1/036w74g3w+O/g6ovhE0fBD94Fj99ak/qSVOZpA4g48/pTKyjPWsrIlHkthxKDA+YdytCLP8WczSs4/N5P1z421RFO/N2lTHn69o5CLiSbPEvGuGzMR5wHv/2c2wGgDbVtjbq0z8O+NhNgi3/Mpgfgyjc4QWrb4/DKz3LfBT9xKQaii94961Bue+FV7JlxEFzxOrj32rb1Zd6ayFr4/jucL9trvwrzDkvURqYpE9pNkNv3P5l9UxdQWv3D5MK0+mRlEaYqWX3+fhX5kEZaKXALtXamvsRkwlNmOUG/so8Tf/tXlCr7Gg6nCUTJOcsyCFNt8udlctRffy+n3PxHnHbzWyhVhrnreV9h28IzMtXZeX/UhPf8pAvZccJbOWTN1zjg8dZnn+XZNmv+rM22AO/kL9jp8xrbHsNUR9g9s1GYqlbTn03SrgJpOw78f/bOO86Oq7z73zP3blEvu+qyJEu2LFmWbFlyERhXDBiMaQEMvIBJgBBCIJAQElIg8FKSlySEJJCYkkACJpgQcICAsXEDgyXbkqtkq9nqbaVVL7t7z/vHmbk7994pp6xuWZ3f57Pa1cycOWdmTnnOU35PvWHbmjuAyAHg7cAPhqY5QwPbHE3Vnfi81f8XgG2XfyyzXNZEeWTceci965SfSg4GStIorxmkE8bpINJMtR/fQ+HUYTTS8tUuulIyf/WnmLblDp6/6A9h2S2pZRM1U6ETemdPvsBZ7Qw+UEr3kyi3N9S4tJ/Yx8ij2+jtWgpCcHjiYjYu+zP40Fp48+0w7zpY8y342kvgC0vhns8gw1xSScj7TpmLZ1bBHavpn1KbJgbIZV0/es4r2Tb3ZuY882Um7nqgLOR277yPnS/6DCxKd23MDkEO/7j6T+D4/lTtlGneRFckJh2OBO5DO+GO98MXL4fnHoBr/xx+71FY9nYGEriKT42YwiPXfBM581L4r9+CX32x5po8C2L5m6+8FZ74Dlzzp8qnLadsnmYqcYMlAvZOv47iprvKkZyp5asEDZ1Iyaxryvc78HyolXp7BakkJGunOtpyJpjuc+l96T8w7sCTnPfoX1ZMGHkbJ1eajSQtSabj+oHn4XvvZuJ/vJgxB57k2Qv/hAdv+Cm9kxSRs45Qk+a3mhVVDrD3hX9Bb/fFnL/qo4zqfabinM63taWBSLsm108rpL44OvbsmlN5QnJbxryUFyxVb+TS4AohbgOuBrqFENuAjwGfBb4jhPgtYAvw+tPZSFOkkahlpXuAygVl0va7mLTjbp5d8pGy+joNWf3wyPjzlNll33qYcn7mffotNFOJtn7NxWzMwWc5Z+Vn6N55HwClQjuMnjLIwjxqkvp/BNS0cgAAIABJREFUjJm5vTSW4qlO+tvGghCc/fQXmbXh33l+/jvYufh3mG3UeqWZAujY9xRwQ+a1iald+gcyNTbRJx3XswaAg90Xlc8JIVRCzfkvUT8nDsG6H8Jj34b7/oqu+z7L8q6L2DXrJnbPenmFtihv0so6mypoHe+FA5th8VsST4/IEab6SyWeveijjO95lAse+jB7p1/LtC13sOGCD3LsvDcxPaNsFmdL+dzMZXDuS5V26pJ31VAqRIJ9pAl22Tnq5N1KWgBPHe2FVbcqYajUr3JnXvlhxRwfIk146W8fy6k33U7nHe+Bn/4JHNkFL/7L8g4ir01SgtjyIPz0o3Deyyu0NVll84SptMVqz8zrmbnp27DpPiPNuZ6ZL30OKbf3gc+BCOCKD9Zc01EMqPau00mifmjOS+ld+DucvfZLHJq4hO3zbgbyI/IytYY6kcK6qWiO7VfauJW3ggg4tOx3WX3WLfS3V48FDWEqxRKQF/IvgzYeX/EFLvvZa7nwwd9l5Yu/V65fx2cq6dv2DeTnpUzTIuWuWT0hLcLoBGGqb4DRHeliSKZmSsOSUk/kClNSylpSJYXrhrgtQ4ZMRtoMRJJu24n9nLf6kxwZN5+t89/G1NxkjOkd+HCU8mHP07nCVEmaR/MlseHm7jIOboN7Ps35a75Ff9sYNi76PQaKoxhf6mVycFAlWD20HXasUQlX5eCiNSX8KQVtnOqYSOfx3eyY8xrWX/gRxlksnn2dEzkxYgqd+7L9ypISDkN++pzIB2xcz2pKosjh8YO+SDVftXMsXPRm9XNoBwdXfovCE99hwepPMH/Np9m86H1sPl8lHs2L5rNC6HxemLEUquSEYkHkRloOlCSlYidPXP55Lr3rtczY/F2en38Lzy18DxNz2pvlf1CxU776I/Dla2HlvyghJUR10mKdxL/xcTN90+1M33w7/e3jONXRzcDIbtg3pzLFyugp0Dm+HC8d95kTA6eYufE2pq37Ipw4AItfrzRDE2sn8Ex/tkIHvP7f4McfVnQQR/bATf+ghO68T35oB3zn7TB+NrzmnyviujMpRXJum9bXDky6jFL7WIJ1/5MpTPVVldfZbGVygJWkcq5f8y1Y/ps1WilINunp5Aw92V9iy6L3M+bAk5y3+pPM3PBNIOyfGSlwugdKXBbTXg20jeLApMvomXoFRyZdlFouQm4ewr7jKo3YLz4Ppw6rOeLqj7L35Dj699US2upsitOEpjwhoSRVnsPHX/AFlt37Vi749Qd57IVfQhbatQJ5kjbgOutO2rjJFeB6NtDXPo6+jlo6yjzNVCb3Y5P5TA3L3HyJKlvNyIFC3xGWPvBO2k4e4PEVX0AGbVp5/dJwbMzZyKCI2P0kLP6NzGv7B6RxJFTSxJgqOB4/AA/8LTz0LwDsX/Junjj7t+jvUJnm+sePYPL0KgLHUkmVO6oy2B/cu53dO7bSfnIf7Sd6ODliMpsWvR9EkMtIm3b68PiFjM0RptKcFfOE5EHN1GMcnrCQUrEzdi6jvWOnc3jZe1k3/a2M7l3H/DWfZvYzX+G5Be9CBm1WCalzETqft511McWt/RWTXp5WCgY1pEfHncPjK/6esb1r2bzwPSBE7iSbmUopfm7GMpj/MnjwH5XWJ9RO1eRT09AES6n+mfvUF5j79D9xZNx8glIfo3vX0X6iB55OiFgKikq4GjWJswoTmNg2kb72CUzaficjj27j8PQrGHPjp2B6+gKa9SoGSlLV8Yq/gTFT4Z5PKSbrN3wdMnKeiYFTiNvfoZji334HdI7LfngDpGlWZKGd42dfx6hn/hcG+stJ1yuukbX+otXCVRKyAygk3P85EIVErRRAZ4JJT0czdbKvBEGBJy/7G859/P/RduoAoASMEaPSO1R/3wDHTwy6UrSf6GH2uls5e+2X6C+OhDVXKq6meddC97kVk1E18XG8LaX+foLHb4N7Pg2Hd6i+f93HyhvjgV3J/oM683gaT1Ief1L0aQ52X8y6iz/O+Q//KUsefB+Pv+AfkTI/L2uS4JTXXinTXVBKJTLzpJb2refY6DmJC0De/J3mx1tIotloMIalMJX0gXSEqTZ5igt/+V5G967jsSu+yKEu5bviwrQqC+3I7vkIjYg+G56pJHVyjTq274RSSz/wN3DiIFx4M1zzUXr7JtK/d3BXlWguCwJlIhnVBZMX0j/5JFvGJIdj572ntMF2ePz5dK97QO3+2kYkXmPLWi2EQJT6Gbv/CbbPrbRG52mJo0nryPgFbDvnLSx58PcY2/MYByctz92NZb2J1IjJnWsUBcfIiYxo6+HwwKAwoZOYNt53eqZfQ8/0QWfzvG6VZSqtCWm/+o/h1quVUH6V0k7VUnRomJpL/Sx45C+Yuek/2THndaxd/glkoBaDtoLgqrOKSjN0dE/4e2/F/4OeHUzc/wztJ3s4Mm4+jy77BANzr+GS6bXO+xXV6kRaCgFX/ZES3H70Ifj6TYibvkHalDl/zacR21fB678OkxfmP3sMeYJ51vljZ9/AqGf+G7b+WuVsrELfgKwp36eR/DfrHRUPbVFaqUveCWOTjccdCcJ/nhkJBqPq+jvGs/aST8XuF/Cic9MDmPb3HmftjkMVxwqnDjNx76/p2vMgM/f9Cp79iToxdkYoWF0Dc6/mZFtC2nIp6d55L9zzd7B3HcxYrgI45ryw4rI009fAgMxNxJ42/+TOSzFd5o65r0fIARY+8hdc+ODv8tx1/5JdmBSC0hyzZF4fLUlIVSL1bODoxMsST+X596Zr75pLkIJhKkwlTeS5Zr6Bftq//y469/yaJy/7HD3Tri6f0olyy8TkRSpqLAd9pZK5ZiotLxRAaQAe/47aXR/cqriCXvxxmLoYgPb9xyrK6ewcXXg/0jRBhycsRMgBFfWYELYP6T4IedEghUAw+uCzFAaOc7CrUluRp0mLTyD7J6+gJAp07foFByct1yXoTkRqrTtWw/SlAIxoL3D4xKAwNaJdpw9mCAlOZr6quqcvhfk3wK/+AS57N3SOS0gBkjPe+o7T9eN3MXLTT9i84LfZuPhDlTtXIRS3zsiJQC1RrpSSlev2lLVbUdkOrVQ/WZF1VeeWv0MJVN/9TSZ8+yY6X/BlToyqNGtN2/xfnLXxW8gXfACR4eR/OnBi9rVQ6FD0HgnCVKpGd6BEZ5Au3GQtntPWhGbPFK0UQGdiNF9+H06KqlPH9TTQcQy0j2HvjOs5MOslzDxvsjJNbrwHNt0D6/4H1vwHAO1TlnDOhMvomXIFvd3LGNO7lnMf/2sm7F3FwIS58IZvKB6shPkij0Iiy0k6bS413bxHfmULH/kL2u55D8z+T2jrTL1eawNehbz5To2bhHafPEJweCfHZtea2yH/u6bNS82WSgaGaaJj42g+KeGHHyB45kc8e/GfsWv2TRWn24p2Gpfy7acsgkPblLksA7oUDhGq/VQinOobgPU/g3+5Er7/HhjZBW+7Q+VhCwUpqNVG6JgzXRJPpqmvIyd0dqWTJqYm2UyZfCMUA6HymEGNMJUn/MV35/3tYzk08UK6dv+i5lwSst5F4qnjB9RkP021sdqsp7Ord8nOkvXtExeEq/9YaTlDk3FtctoMoeZ4L/z7axmx6ac8c9GfsXHJHxingD/ZHyNpjJU92ZfPTp/1nhKLLrwR3vrfBMf2sPznb6yIoBqz/wkWPPIxeiavQF775wZPMIi8R8883zFKaVjW/TDxwdK42PKE3bR3NOLIFro2fg+WvUOlM0prVlV/bS8GWmaZtHGeF76fNd7Kc9aEOUo4fsM34I82wzt/Dtf8GaXiKGY9868su+/tXP39ZVx69+sZeXgzay/+OD1vv19RvaQSrtpHEabNpbnuEglCy/Z5N/P0sk8ybvu9iuql70Rq+eQNeF5/sAy42a8i+ao5piLkKQ+CQCRq6prN+RyGqzBlqpm662MqxPeqj7B74S01p1OZe0PkzRFicuj0nMM31Rey+Oqa+pIExDH7n2D2j26Gb/6GyiX4G1+Dd92TmNag+rnykhxDdifOC+lNK3pi1ExK7WOthCkdNfG4ntWc7OzmxMhKjUKeg3S1wNQz9QrG7n+CtpMHcicXrei4OHaoaMPI16daeNJx3nWBkZkPVDvPe4VKAXK8Vz+t0aEd8K83wLZV9LzsS2yd/zar9maZEfP6RFZkXep3nfNCDrxRMcAsv+fNjN+7irYT+7nwwfdxqrObJ1f8nfK1SkHW+pgnYuQSqi64UWmewwCGONIoBfJSMaVtFs5++ovIoAhX/H5m+WrNlI5WCnIoDjLmxUxhKulcUFBa8Ks+zO7XfY/7Xr2SNVf8C9vn3syGxR/iwRt+xvZz3kxfjvEmK2ovzwk9bS7N2+SlPeqOeW/kuRd+RqUw+vabUwWqxMTMeRuQzLMZ2BclOJ6TeDovLyVUJXgPkbfWNALDUpgyygD+i8+rqJ1L3glX/0niwpEnZORppoJpYWqQPdnCVNRuXdLDuFZmxJEtXPCrD3LZXa9jxIFn4Ia/ht9dBRe8LlWKqeZ9cTXz5aleUwUxIeibtAh2pgtTqSzzOe+qrRAwrucxpZWq+k65SZ2rquyZegUCycTdD+Yy1We9isTusjMUpkLNVPW3SHLo1bpvdC6nbJpmqpjFin/1R0Lt1D/rJdze+yx89SWKPPMtt3PivDRy1/wNSpbmK5dQNStSLcufatIiHr72PznV2c3S+97BxfffQtuJHh5/wT/S1zEx8/27aHQzk70GAs67QVEUrPthzXlbYSrpLYw4/DxTn/8Bu+e/WTnnZ6BYCCpyp+lpVrN59rIW/Mz3m9OZ+gZKDLSNZt/0a3h26Z/y3ML3MNA2StWpkfw6DXmb4iSBQIj8TV7W4+w/701w0z/Cxp/Dt99Uw0GWlJgZ8s1t1n59IcfUsdFzEk/rcIAlcU01W5JjGKbCVBJdQGIEy6PfUFqpC16n0sUIkeg4macVyOrcQQCMmQYjJihG5Kx2D5gxSEcM0KLUx/Kfv4lJO37OpoXvZeVNd8Nlv63SjGTARjPl4jOVVbZ/8mKVdqeUPMmnOnrmafGO7mPkkec52LW05lSexrF6d35owmL62sfRtesX+Q7oxpqp1SqkfqRynq7uc3qaKfsFJU2wzHxH0y5UWpFffbEmR1yN5mjbw4qFvP8E3PIjmHdNtmCeM1lmTcJZof+5oeM5p0+MmsHD197GkfELGdO7jnXLPsHhMI+i8TcPYb0JicqO6oZZL4AENvQ0M18eCWbSezp77ReRQRvbF70ns2yE+IYgl7CT/LGcdd5lk5ed1Dm7TUY5DKuQ5IurFzme088ufiu86h+Vf9htlQJV2vO4aHMhw+2hZz39Y2ZURFFXlsu8LZD8/bxmqg6QUiamQqhRJ679H5XyYd518OpBTphqjUAQ5AsZubtOIZQTekZEX38sUaeOtA6DA2Nczxo6TuzlqUv/ik2Lf58TwSgtvpFiISi3PQhOv89UliaoNHUx9B8vq4WrkfZOcsO8t60Cav2lIF9AqZkgggL7J7+AibsfYCBn8skUsNPMfNMHBb744iOEHtuvFot5CtK+fa6AfdVH4ORBup/8SsXhk/0Dg33w2Tvh669UNAq/dWfZlOmyAGYKUxnnXCgtos/W1zGRR67+d1Zedzs7z36tVlltHq8EZG9gwu+z8Eal+Q41ARGSiG5BQ5iq+v+Iw88x7fkfsG3emzg1ojuzbIT4+NLZDOQN5aw5zakvZVSct2nK0mTmkYUmjWkdIUFrnC/9P/Cqf4JN98JtN6tckWTk13PUTKWiZwN94+el31fDgJj0Tlzoik4Xmq9FjkgbFxEXBgCb74fv/qbizHnjv1docGx8VbQmuymLYM/aVBtDfAHQ1UxFKuiunfdTEkV6pqiwXSn18/NFi2V7Qd8np2CZBiFTfT01TKGy64nE02n+B7k2922rkKLAoQkX1JzKExSSbt0z9Qo6j++h/cCzmWWzNEE1p47th97nK7iR4hqh9mKglYA6S5uTJ+imaaByJ61pS5ALbmTGun+leOpg+bDKuyVVCP1tNytun9/6GUycm1snkJjbLY4k7XME0ywCuoi/w1Kxs0ydAvnh7C7kg1nny3PPgleo31WmvjShSTf5e4S5T/8TpaCd5xe8S9uBJr4x1dF65wkuWaczhVWDQJNq5GmXsgS8vG5YLAQ1/SZPWw4G43zpW1QC6U33lQWqNKEp3wE9u02J56WEfRvoGz834aTefSFZM+uj+eqA3IGx/VGl+pw4D978HWgfVXFNtWZKx48oVw0PSpg6dUQtmgmI7xh0c+tFAljXrgc42L2UgfYx5XO6TuxlYUrTQRTsyeay8iwFk+arEO9dtU60kK6JKJVynnXrSk50nU+pWMtflfdtk/pSz1QVfj5q632ZZbPeRI1DceQvFdNMBYEoL8A6EyykC7mQH/0Sry8OnR3gqSs+QrHvCLOe/bfBg1Iif/F38P3fUSH7t/xIsZjHYOz0HkOWr0rWIpYrk2ZqFLPum6eVzYqWzH7WLEGsrN0YP0uZXddWClOpgRsGwtTIw5uZuuV/2DbvzZzq1NNKQeX31enDLm4w2cJUHhO/3Tmd83mo/vY683CuD10cF70ZXv0lpUC47Y30nahO8qOQq5nKS6WUdP7IHjh1OFMzpYOk8aE7J9YTzdciR2QJU3LfehXlNnIivPV7Zf+UOGodf/U0U2kTQXkinBJF9CWb+uKdWXeiGyhJ2o/vZWzv0+ybemXFOV3tVtQpjYSptJDeXL+cjAWlvUORHaY4oWcJTKmaiNIAbH+UU9OSuat0/DiqcXLkNI6MncfY7fdnXpepmaquNmQ+Z1plgmPTb6OlwchAUj06dZ/sXsjumS9l1vqvUzzZC7LE/DWfoePeT8Ci18JbboeOMTXlsibEPEE301clYw7IE3qyIudc/AVdhKms8xWCwoJXwraVcHgXkO5sDPnzQ/lppOScx/6aUtDBcwveVXUyGxXaVR1hKufGWZ+uWAjy5+AUZFNl2EtLOias6m+rs3nJjKhO6ocXvUmlN9r8ABO+/1aC/mM1l0iZ3Sd0SDtrEObk65uQYebTeL3tCdRE3sxXB6R9nI5juyh+87Uq6uWt39dm7tVdyHIJ2CYtAERqRF+fhWaqv1Sia9cDAPRMfVHFOWPNlEHnTDMZ5WqmMt5lW0HAtCWKHiHhI1o5eu55GvqO0j99eeJpW7qB/VNexKhdD9VEysSR9S5q+sqONTDhbBWkEEP0bXQnjqwFXcfnKkmA0SNaLLH5/N+l2HeEOeu+zAUP/SGz1v8bRy96J7zuq1DsSG5vIUhd6PI2MS6mGVvfMheH+UwtXM471uYAK5v6lCN61pjJ422KhM7J237C5B13s2nR++jrVMmi80zG5bYZmvlyKWZy6k1zI8gbPzpCjw3yhEOw4/oz0kxFuPBmeM2/0LnjV1z0wG8nClRZm3irN9SzAYB+R81UkguKyea/Xmi+FjkiaY5tO3mApfe/A04eVMSVXekftybCTXshSwktjzp3x2iVdDUlos9WM9W1635Odk7myPiFNed0YGPmS1sAs8xMkL6gt0U+QVOXKPLKg9tqrsnWTKWc27pS/Z55Sc2pYkFY2917pl5BMHASnn8w9RojR/0daxJzyUUTqy5HT2aGdY1+bDtp9Q9IjoxfwO6ZL2POM19m6pYfsn7xH9BzxSdynYnShKZcYSpjiOQSqlqahLI51rKfM2seyRem0n0UKwSMyQuVT1roN5UbHZfxngKh5s3zHv0EhyZcwJb574id0xSmYu3WEeZzSX/ztH+WqUdcTHXG5LxVqO4Xrm4lmSbNC9/Itqs/z4R9q1j6wLtrBCpda0YSEn3H9q2HQgf9Y2oTYZvAVmNebzRfixxRPZEW+o5y0QPvYsTRbZx6/W01ppRqtBUqTXa6piCt1ACTz0838w1YCFP9fXTt/qXSSlWNXG1hKjIlGWimbPMlpTs5h+Wib5NA3pn1PKn+M9sehpHdtHXXOkC6kGAemHQJpUKH4nJJQdYkW/Geju2Hg1sq/KUiRMKUrmYq06FbYzFLYvrXqTuahDde8H6OjZ7NU5d8hucX/janNPpgWgLnPF6t3ES8Gch04M2oNusd5pmSFAN4yjlLQazmuBCKqmLz/XC818mhuxAI5q/5NG2nDvL0JZ9SRJ2xczqI2qfDnQTpbNflei21f7qbkSTkCVqZ3G4ar8nKzJeVoianH+47+yaevPT/MX7fwyx94F0U+gZzs+paRLTRsxG65iEyPqqWwJkkTHkz3+lHfAIRA6dY8uD7GHPgKZ64/PP0n7Uit7wQwthxEjK0LvHyUy6A/ZvKYapxxNM76ApTI/Y+Rtupg+yb9qKac3lhuRGsNFOW+ZKESHZyLk92UxYBoiaiLy89SOrpbSvhrEuTucMs/KXK9RVHcHTKJbDh7tRr0vqDEFXvqewvVauZinwF8iLb8uoEvX5sQ1gLg8LUsbHn8ODLf8bOs18H6An0I9qThak0IStC1p3zun52ZF3WxC9SFyu991v7TELkL/ZGtBULXwmlflj/s1yqgSxha+TzP2fa8z/guQXvrtF66yaZLRpuBiCZ7XrwnJ1f2unUYlhriUJUt00vC4W9715/SbJ79it58rLPMX7fI5z99BcHz2lS8iQh0QTbsx66zslm8Ne4tw1dUSPQfC1yRHkClyUueOjDdO3+JWsv+TT7ZlynTRcQH5S6C5mWtmbKIpAllYW8CnFGZ92w5TFb70WKgP1TXlhzTjdfctQ+Hc1FhLRJSychdNIgKN+vfZQKoa9yQs/bYSeeP7Zf2exnLqc9wTlVZ7eatWs6PPNK2LsWDm5PPJ/OKF5Fc5DifB6/R1YUZPW9U7UfGs9ruwNME5p0zAZJQlN7MTBO9aN7Dtw4n9Lehw4/UJK2TYf2Iu3bJfaxGcth9FSVyNcWJw4x7u4/4sjYeWw+/701p3UJE6P3bEKw6CIopL2n0ypMOWgrIclnKr+MSyBEJDDtnnUjvd3LmLDnV+VzWZufvCmo5vxAn8o12nVO5jyqk6+xRuA0oPGpJ4adMFUWmETAoYkX8OyFf8zOOa8BzP2IwEQzleYzFddMpUf0xQWogZLUWojGbb+XgxMvor99XM25PG1OhEhY1FHDl8skXFssCL2BkVC24n5TF9eY+az2S9seVr9nXooQwopRPMtUd/Ssq9UfKaa+1Im9+vl3rlF+LiPG11wbfROTb5M20ehoB6rbrLsDTCPJtNVMpWmrhgqZvmWW2g+b9wt6/TDVfJWkXQ0CWPByWH9XZoBEJu76OMHhHaxd/mlkoTaLgq7ZzNRMDVmRwvkLr5HQOUTIurfO2lEtPOmMt6xNaz4NxOCY7O1expjetWVTX9YmJDflUfX5A88rDWn3uU5Ez6DecfweLlaF04nmbJUD4qrK5xe8iy3n/WbsnBldQPXfWUidZON+KBPmQNvIxIi+6lxjudqpI3sZ3fMEPdOuTDytG9IbDUwTzVTSZOryniqFqSUqaeux/drtSXzUbStV5Gboi2SThzBroJ+aeJ7SAJgKU9XHq5jP42iz0Bq67M6rF3bdHWBaX9PRBCdppvJMfK7Qdug2KKvT/5MEJ63Fs2oxya1zwY3Qd5S25+/LvG9i/37ul/DwVxm49Lc52J3cL3X7RSH0gTIJ9Ej9Njpa74T3oZvZwRbZfooafSL2LoXQ60dBkB48k+T3GEd8SPZ2LyeQA4zd/1jNuZo6TYWpkBaBrnOc2OkjVKQmakITHwxDYSpLo5ObeiRENAjUZGAeuZJ0L0BlKp+8sCai71R/LR/MyZx0D9Eivm9qrb8UGJj5wnabTHiJIfSaC2DSBFNxv2lL1O+YdiqvZYnjfNsqpQnsGA1AZ9VCpscflnGuEMC8a2HTPYn5BLWcYY/uU4Jjgr+Uqj80k2gsJFn16i4oNYS1mjvANKFJRzOV9B10vo0LUn1rtDQJQ6uZ0t2EGI25OS+CjnG0rf+x1r3L6DsOd/wejJ9N4bo/T73MjJMu0PaxgoyoaEtqD53v4uJEnjZGdKOF4++yrdoFIANalpAExG/f27UUiWD8vkdy6zNm6Q9pEeg6x8l0GyH+nl2Ch04nhp0wlbUb1tVMRYKRyaSh7fwYRfTFdvPVWil1LKetG+7iVGcXhycs0m5jEqIIGZMFO6kz6+4WMn2mIDGtTD7RYhVKA7DtEZh5aflQtb9KXrQYZDvDFgMB51ynqBx2rKk5n/Y+Ko5H5RJoESBu5jPQTCWyBetNPh3FSt8y3W+aNq500roUAlGzIOmY+bLeSB6/T2pUqaVfGWhGSyZcozvHJC3aqd+n2A7zX0px/U8Qpf7Ue9Ys9vd+BvZvhJu+gOgYnSoomGgGihlalCQMtaCrJUxlOUjnCVOp41xvzFW4lBitN7UN08nhGT870D6GI+PPY/xe5RKRmU804zsmKh32rYcRE2HkRL0MITmozPPYnGJLc7bKAVkCU14G8AjRADTZUaV94JpJYMoFcKxHUe2HSMrsnpmItFSCjXfTO+1FypTlgMBCFd/ZXlunrjYh2WcqVveoLhg7o8IJPV8zVXXF3nVw6nAFv1R1+3Tam5uiYu41qnUba6P62lOcwSsm2Z3pzucw+F6M+mHSoqupYar2LdP9pmkbGN3ooJpv42iCzVsAtf3ZEuASMZYs6Opqpgw3MAtvRBzfn6l1qKAa2P4oPPgPcPHbYO7VqXWCmeawEAijjZqWhj+t7GnQTOWZt9J50vSeuRAMRogaCVMpz5q78aw639u9jHH7H0OU+jQS1RtoZXs2qmAi8tIhmW/CmzGSD4ajMGXDlF2FMvO0wUdLm3hqhanICX3Q1JckOCUJWGXsWA3HeuidflXqJSZ8lFkamCQkCQq6TsNaBGxVTuh5i2PN6W2r1O+zBjVTcYGiUBB6k3OWP0QxUILf9IsSKRKEEIkaoQrBZscalSOyszaAAMKv8VBlAAAgAElEQVSJNsj344nDZcGGykWg2jSahjShSXe8VftI6fSlrIk/b2Pg4lfm4uScqDnJ8XGJkCQ4ZQo1865DFjqYtP1niacrHLr7T8EP3gejp8D1n8ysM+t4EgqGmim392vgVxaDi09P2hgxETg7LLj+Ev1WNb5L9fP0di+j2H+M0b3rcl1a0jZlifWGtAjgHvEIVX7MXpiqD6xyuFWhTBdgYvpKCXuu6aCRMBVzQk8y6Z1IMP2VseEuQHBoRrK/FOizFIN+6H0EIUStD5KDma9GEJ26BPY9W+bjyt9tVR3YukqpmCfOLR+KL9i6QkKWyrx8bt61Sng7cbDmmlzTTIbzOSjNgSlLu+0kGyG+COj7TCWPq4GS1IoqrTHB6kRaZjQtr++nCQM6C6CLA3rSwqGrtakeI4UgZ0PQMRox71ombb8rMUKjYgP1y8/DnqfgFX9bEVWa9P11o3bj7TTRrLoIuknztY6wmtW+XM1UgpYezIQpG64/29RPtcKUSrU1ft8j1qTLNfWeOARHdpeFqax1VHeN9ZqpBiAz0bGmU3ZbWTPlphFI7NwjJ8KYaRX0CMmaqSxh6mcw42LkyK7US0wWYZPJMUJnu7k2AWrfSaKdf9oSxccVEzizxlyNsLVtpTLxxY7HJzddFXzWoC1H4cy7DuSAYp2uviZLcDyyFw5tS/WXAvOFKK3NJpqE+LWuminQi+iLCwptSRuQBDhpE1IWOp331JGgbdQVMJJ9evS+b3Wf1fqmC29kxLEdjOmtjR4ub6D2rIX7/lolpF7w8qo6kkyLZs6/pj5TaffXir4NakmB9YTcjMVeg3csaW4yiUhts9JM2X2b6vnk5MipHB81k/H7HrEeNzXHY87nkM1sr6uZio+dZmQ/h2EoTGUJTNrCVJkuQP/1BAmOtKm7kymLNMx8KcLUsf2w/RE453on8jbbayPUaqbszHyJpIVTEyL6slKAxE8dP6C0WmdV5uNrKwRl3wTdXWPW5FR+jrMuhfYxiaa+pHrKi8LOyPk8XTMlhDAyNav7uyUFNdVM5QV16Jj6KiN19NqazTyd7/uRxGSuswAmaxv1+lNSu2wccNPaUYP5NyBFwKRttaa+YiFQgRo/eB90jIEb/rrmmkTB3JDjJzDUrqZtdHTrHXJG8Zw1IElLD/obNojxcZlseix9I5O+RW/3xYzf+zB5uoPUb1Pd7p6N6nfoMwVuUZpQKUCdTqoLFzRnqxqM6AObmPmgdvFM1dZMPh/2PgMDKtLmeILgVColR/mx8edKa3Pu9ZntO+2aqdjASjRnpqCtaieXuBCNnwWd4yuc0LV9ZLaFDrcJyY2jQa/tLF9MZxQvTyCFNjj7SuWEXiWtV08yFVqXiPk8EhxTYDpxuCYFjdqsk+YE8tMW6aQ1imt79Akh3ZxaE/mtNLSriRpoA2fj6m6s+31redI0+vCoLg5PuZTJCX5TxYKAh/4Ztj8MN/wVjJ5Uc42r/11Uj1kgj5sPkg05b9b40Hneai09mJn52srrjdt70gqqSRg3vd3L6TjZQ/HgZuM6E+vtWQ8ImHB2Zr2gv8ZGViJTH7x6wgtTCSiGk55JSDrUTtCp2popF8DAKejZQKkkK/LyxZHohL7hLuUPNH1ptmbKYNKz0UzFOW5MQ1XjgzJxIhOixgk9Mzlt/Ny2VSrCccaymuuiQW/S3iQn8hpT1LxroHeLyrsYQ/XkU+FXtmMNdJ0LnWMz6zch7ITK6KC0dmShHHyhyXmTJyvpkMfGzek20T0199N4Z9VjVVd4TNRAa75fkaClsU1irav5ODj7pYw+tJ4Rh5+rOD7yyFa4+5Nw7kth8euT63Q0GYO5ma+QYKozqbf6Op33lCUw6ZHd1roumLwnG6b4pOfSqTNpro/8ptq3rzSuM/F4zwa1IW7rLB9yScMEsQh7w/mwnhh2wpRLyPTgdSJ1UGehJsQ7xTkxHtGXxSdVY+orlZQ5ad61EBT0HKQ1YOKsHiE+cE0dArXYbKcuUX5lofYuM1N6hWZqpdL8dYypuS4a0CYTndakdc516neVqa+W2yrWP3aszvSXimCj0u6oKmMkPBo6w+bJSlIj5qPCH0I3kCHjvegQyFZroUa0FbSjJmtpNkyE88FrTShJgqA6AbueAHds7ssAKrVTUnLWL/8YgiLc+LepE6NrMAOYm/lUvZXPFgT6GjEbCpRM30gtYaoqc0CS60IGornNlE+uWqmjR0Rc+zxHx87lVPt4itsfyiyb7mtYdXzfYCRfhLTNve78NphDtnlFluZtmSVcOEPiaCsERvwokDxBJ6J7vprI9jyd6Wheo7Ha9Tgc3QPnvBjIyzFmoJmykPZdoiu0CNimLYH+E+W0BGnaMyFi50qlkKxzeUq9ZoJC2rU1bZ44V6m0N1YLUykT+5E9cHhHpr9UBBuVdrXmxMQ0Y7NLdkX82+rWm7Vw6CyANVpkg3yANmz6EWwEoggVwQGaApwcN5NDEy6ooEiYvvl2Rm3/JbzkEzBuZnpbk8x8xpqp5FQ4Wah1ttcXdONzsC4FSlZ/0UsTZGGCjSGaq03m7KR8ozoR1YnzqAg42H0xYsuvas/FoBXNJ2UFx1QEF/4wUM9bLAhjTX09MeyEqaHIAxRda6yZqlExpwyqYrsSqHY/xakMB94an6kNd6nfoSZkKGj6Ta+NYJO/MEKFw3HaOyo7oSsm9NT0CXFz1L5n4eTBCubzOGwEBe2omXOug80PKM6eENXvpTzpRMznKWlk4rAywVpEx8Xr02FSjpDLAabxqqOJMqpfB2ljq6i5gNYShRoIU7qBJglw2oTE6tEt21YI2DPjesb3rKH9+G46ju1i/mOf5cSMFXDxLZllbaPjKu9hviGo9Xsy0CRbRKMWU3If6o4dm7yflfWba6agsh8WCkLLtSNtbByatAyxf2MFmXQ1qrWjkOAve3gn9B2t0Uwl1WvMQWah4KgnmrdllsiajE0TbpouZPFdUe6kPmWREqYyzHw1gtaGu9QCPHpy2Mbk+5vkFFTXm3cDG21ChPiEnDrxdM+HYifsVEk4tVirt4U2/7NShKmijTCluTufd52aRLb+unyo2r+mvOjuWA2IwTyEGbDSTOmYUVNQNnFr9ok8YUpXGxzVp/u8hSA50lFXsLFJL5RWh0nZXH/BDFSOG80gikLA3hnXAzBp+90sePTjiFIfh17yt9l8IyltNG1zIRDGbgRWzvYh4nOw0Xex9EGCWgHTdD6M5lLT9aYiW4Ejd97RqeGcueXXiecH68wRHKtoESIkZ74wFToDb+arJ4aKLsDG1h/v0LmT+uTz4eBWBo7Xkj1GqODvOd4LW1eWTXyQvpMx3eHYaD+URsiuvo6qSMBEFIrqHYVO6KnpeuLHt65UUYAT5yW32SJ1jrYT7pwrlOk2TEA9eG2CSXPnGqUGT/DrqoaNMOVKcFcMAu1vmid0ZQUOxBHdxqQvJpk1TCI149BN1K2urRLELBz8wVzQrUyKqxlSXgw4OnYeR0fPYe5T/8CkHT9n46Lfp9CVPEaqUd1GG58pY2Gq5tvYCatGLOSW0XGQJHBaBI0YZjqASmFR9x2l0S+cnLRYbV7zhKmqd1IzbvYpt4wazdQQ+N/ZWIvqiWEnTGVJriYfohgE2gtBhLgaNHcgTrlA1bOvllAvQkVY+aZ7FTnkudfH2pjcPtMF2DbUtGhhNgNNzRSoiL6dj4OUqYtdRfltDytKhDROk0AYa+G0mYY7x8JZl9U4oVdoiaIJb8dqLX8p0BdGKuu0j7QEM/V7Urh/HKZCmRknkT2/T3uhMqmzWcqdSu2SiRbYRWsYtTEI9KN12woBCMHeGdfTcbKHgxMXs3X+2w0c/QefVQhzM58QZqmtwM3MF19wXQNNdPtS9YbCXDNl7lcGduM8Ncl3xwgVAZ3jN5WvmdoIxREqv2pOvab+T0ULEuN64owSpkwmAhtbPwx2rtzOHUb0te1bm3pJRSqODT9TOdxmDDpXu2RYj8O2g0a8KMY+EeFilDs5T1sCJ3rh4Nb8FCAnDqoExwn8UhGGilE8dSGad63SpMX8DiodjgM4vEv5FWj4S0F+Kp0kuGg/wJwbaCi0wYM+U66mGX2agvYkQVenXof3WylcGzoqR3xEFmbqnXNew5Fx83n6ks8gg6K2MFWtRTbtj1aaqeoACksNnsn7TRTMLclYTWhpwJ47qbIf6pt9U4/Puly5VZw8klq++j3VClProWtezYbWlf8OmptjChyFKSHEB4UQTwkhnhRC3CaE6MwvdXrhSuYXv9ZmIYsGcO4kO3Y6dI6jo2dd6iVlUUpKpfGYe40yf8XamATTnZENaScMLqKm5KbtMY1W5jueeqH6veuJ1PdZPr79EUDWMJ9Xt9fc5KDpgA6DFAkb76lpX9lBNHI+16BFAPNdPVSOAdOIMTCftFJ99wr6potoQUpiJk9D8rfR74u2QRRxrZaLE7m1o7LBeIvad3TcOfz6pT/k6Lj5Rhoml0ATUMKU6TRaG7hhF/VoovlIirzWNfNVp0sx3bCZcnFFsNk0BSm+hu3FAGa9QFk/tj+cWr7WBFtN2LmhxsSX1j7jqMfCMBWmhBAzgPcDy6WUFwAF4OahapgtMtlsDSYvW21NmcsobyAKAVMuoKMnXTNVxu6nlDYj5i8FarJImqiMhSkLoREGJ3eTBRAG2537PaYsUuFgOx/Pzwu1dRUgEsk6I9jsbJLecTo31oUwsquCIqG8S46+yc41qp05zOcRbL5NhW+Nof8GmCdYThsrZhGlgXEZ191ufFExERSEGAwusV3oq//WwWCaKzNBt1r2MtkoakXeZiAQ5trV6m9hKrDa5LpLZMQ3eN64gGs6xwSBXci/rTk/6drOtiDciIpMv6kaUtT4//tPwYHnE4Up7dy1GbBJ/F5PuJr5isAIIUQRGAnscG+SG4ZOmLJ7Ne0mu6Ipi2jfvy6f+XBDyBFTJUxVmyoimOfPMrq8DJsFEAYXo9zv0T5SsYTvelzlU0uop/ys21bCpAXKFJoCFVRg1NQaPpfMpLZBoLSHG+9RnFckkGDuWK0iFTtGa9VvI0zFNYU2/dhU6HQl5FP3MDdhJY0xk/LRBF/QTFRcUbb6u2qiLabVMt30RJsW47ZWaSfN0gu5+d/ZaPerKRnM3RbCecnSD658LI10ObFOkfi3Lmyi1GwDTZJzhhbU3Dnlgky/qUwH9APPKc1WFccUJGvETMeOEMLKh7ResBampJTbgc8BW4CdwEEp5Z3V1wkh3i2EeFgI8fDevXvtW6qJasfSCEFg1mFNtS3x+uO/MzH5fAp9R+g8tj3xdLkFG+6GKYth7LSaa1yiUMr1WHbQaMG14qkqBnrvKHJCJ8MZvFRSzucZJj6w89+I2jpYX867Pec6RawaJrKO8s4NClNrtJ3PgVgn0EcQE4ZsdryFwGzSSqvDlOtMCMNIyyRSSRNhqmgwVoewrI3mBOxD6F3oDVwyHYD9Rq0ysa25NhnM3lNHsXLdMGFdh8p+a6M9sRGm4qmjzISpFM0UKL+pravKmSeqUaM1jP+/J4rkqxWmQMN5PQeFQFjNh/WCi5lvAvAq4GxgOjBKCPF/qq+TUt4qpVwupVw+aVJtMs2hRqq2xlAdbysBR2YVrcERRvSN7n0m8bQQwIlDaqcQ+eNUIXFgGIcvG11exmC+JJsFRY9ckWlL4NA2OLa/5ruWfT96NihH9RSyzgg2PlOQEpGXhnnXqt+hqa9Cg3FoJxzZpe0vBQ5awzIRYB00UynaL1MfRdO2Jo1zUxNY2n1yy0YmNwszalshsNKGDW5e3N6TjcAJ9dNMwaAJtmjgdxfBZpNXTUjZacC6DpVRqzbaYFtmb6PNe4gkx/ryBnz2CsWXF8uLWlFfcdBsXEPuW+aYSqbdqKEjMVyTBfbrcj3gYuZ7MbBZSrlXStkHfA94wdA0yw1DYZ+1JVodVDFrfPTJCwEYczBNmBKw+X4o9VdQIsTh4jhZUY8FXHg/1OKpUbbMhP54eg6siKwzI5IPlGDimp4ld9IaMxUmLypTJFRoMHZGzuf6minrbyPstYaBMFvoUzVTBn3DJtKyWlhT/dHcTGijFYiEKNuF0zRoI16X6ZhLZeLXQNy8brr4gb0iwSUXm42ZDyrnTeNIy7hp3WJOtHUriXziXEyahSC2sT3rcvU7w28qvgmpmJ/2rYdRk2DE+MRyrpopFcwwPIWpLcDlQoiRQj3hdYCGN/XpR9JAMA7DtHXKNolw6xhN39jZjEoTpkD5S7WPURxGCUgSnEwcJ8v1WECxxNt1oWIQ6L2jaWFE387H05mRt62CjnHKFykDEbu3KSqJADXafM61ajI6dTRkow8noB2rlUP91MXaddtqpqLntHleczOfu2bKJlKn2rnallHcirTWwhm8XDbF/y8PEaeXadnq92LMC2fpHwb6yeWrYcM7FqFQEFZuGi7BAZEAVZEr1KK8KWzm4ExS1HEzYPysTL+p1LRcKZF8g/Vq+p6mwIazrJ5w8Zl6CPgu8CjwRHivW4eoXU4YijBMeyJLNeHpdpRTXQsZk2LmCwDW3wVzr4JCW+I11cmVO9rMSATBfsJz4f0o6k54IyfC2Jmw6/H0HfbWVTBzmZY60da/a7BOjX407zoo9cFzvwDUpNMW0SJ0nwfto7TrFpaibjnS0uLjmkbNpLEqG/koCjtCvrhztXESXgczX9l/ycaMKuw1usWC5iYkBtfoOG3uvATY9l8Xnz9bckfjTVMMZcHcklLHRTNlntMvJ1H3rBVqM5gSGDUoTFXVmytMGc6jVQiC4auZQkr5MSnlAinlBVLKt0opTw5Vw1ygzVqdARfuJZOF6FT3QkYeeY6g/0TNuc6D65W/UIqJDzQGhgasqRGCwNrWH5hEZkxbkqiZai8Gyqdsz9O5/lIRXMnxtBaiWSsUC3Bo6isGAcWAkPlc318K7AVdZaqz68dCmNWb1gdMFm1bwbyGFNUApsmV43AJvnDZhLQF5lqXar8uU82UrcM82PffqE/Z5A011axGqBjnhvxsqQKGJlw2pabfM9fcNmuFCqLZvymx/KCDf6zc8V44uldbmLINZhiWmqlmRpKZz5wuwMGPyGAC6OtaiJAlRh3eWHNu7Nb71B9VlAhxVDubm+QJc4XLomBUdupi6FlPR5Ws3lEMYMej5JF1xmGzAzRW/7d1wpwXlp3QiwVB27HdaoIyieRzQDEIrPtwYOion5qiwmBhMa0zglFwQBUiDY9Nnyhr/ixNOtZuBIWgnHlAF9WLralQZOsw7wInYdXQ5y+CS07LSDtrI3CCgzBlMQdX0xTUaIlmrVC/U0x9ib57PeH6lUCLEME5mIFhrJlqViR9KNNObu2rIoSR83p/9/lAckTfmO33wqSFMG5mavliONFFMFVPu6BoKDjGURBC/x1PXQKyROf+Spe89mIQknWSSdZZUa/FztFqkp13nVJ7H3ieQAjadofO55ppZCJYa6YC+wla7QANhKmUd9JhsLu3JeQbCs2UFbFp5JNmw6PkQD5o6mQPtcKU6fO2F4W1kGCL6P3YCNiB5SYv/p5s/e9sNC5g76NbCAJL83iGYNM9H0ZMSBemkgI3epITHFfU6TVTrYdEM5+xU3Z9NFMD4+cwUOhk9MHKtDKFvqOM3LkylRIhDldbtLXPVMFMcKyuU3vCm6Yi+jr2PVVxuL0YKOfz7vPU4NeA7cRTTh+iu6iUU8v8nEBAcffjxs7n4OAzZZk8NarTpGSqZspg0bZJOwJuE3Q5hY2lA7qtGdXJzFcw97eq/j42Zj7bjBAu/phg55htuhmI4MJtFU+RZQP76HHLoJq2jHETBCqqLyWiLzG4pWeDmt8mnJ1aZ1vF+7UxGdtpr+uFYSlMJfNMmT2q7TdT7N4GhYMCR8aew+iDz1YcnrD3IYLSqUx/qQiutmhrwVHYa6aCwGDJHncWdI6nsPuJikmnoxAKU5omPrCbZCPGdiOtQPd85Ti/8W6lidj1mGJobx9pVLeTBsP22xj6TBUTUpaAaYoW9wXQhlG5aElTUHCY2F3SYthRSAxebxNtVrTgACvX57AZALv+H5hovWOoSMNUx4hHcNAiW47z3JyLs1coASmWtD1CmWMtXu++9TB+NhTbU+uMiHnBzrfMdF6qN4anMFWoZbO13THYwKSuIBAcGb+gxszXtfN+SsWRg/ZrzfpsOqnL7tFW7RoItZHRghBlJ/T4s3Yc3AzH9+fyS8VhrxHQSH8ThxCKImHT/QRygGCXIfN5iLr4pFVD2ORTq9SImnLf2LLTdziY+SAMDrBhiXdIuloo2AtiNhrHuLBrE21mGx0HQ6CZshambMx8g2XM/cqEVbkI9kKneYos0NDolv2marVTiTlZezZm+ktBtHmx1+DZftd6YVgKU/FEpGCrUrSv3zQk/Mi48+g42UP7iX3qoJR07bqfYzNeCMUOo/rSwtSzYPuowmLRjWA8MKYugT1P0yZK5UPtux5Rf2hG8oG9b0J70TxqhnnXwsmDjHvux4ije439pVzgsgDavKNalm0zc7OtP4SLnwvYa/CctEuWNBBgH6nmwovl4uNlCxcTrK2WsxhuwhU3nJ3Aartpd4rateoPg2US2zztQih2ZgpT5UCIUimXFqFcl4OQLERTZ5MZnsIUVE2yDXKe1EEg4Mg4RTY5OiTvHHnkOUYe3cax2ddo3SM+QdqYLGzhMskKDBfPqUug/wRjjmxWdQcQbF+lCE0nnad9G9vXY8PnwtyrQQRMXvNP6v+GtAgucNFMue7qwVywsWU3bnMwzagydu8pCOwFc9fgANsIQrCbC136ku3iF2k9bARdW/87CKMlLdcLF6oYFw2eHQt/jjWj2KGCehKc0MtRsNE9Du+A/uNawtSgL5z3mWoZuEYOuMBE6i4ESjMFgxF9XTvvB+DknGs16xt8PhuThZt2yaooQhi6OYdO6GMOPA2Ez1wm69TXgjhxaplOWiMmwIxldO5fixSFci7GesDWMRXsJnaXZLoQUSOY1xstBEJYmg4sNXhOEXmWofug+qHNeI2TSprCxT/MFq6aKWt+q8CFUNXFT9G+L9kUjcZKISv34awVsPMxOHW04vBg3s+w3L78SL7BsvYaUpfvWg8MW2GqUU7ZYDYBFAJBX+dETnZOLjuhd+26n6NjzlYOfRqoTLJZv94WCPv3ZDwwus6FYiej9ithqr10DPY8ZWTiA/tJyyaKClAUCaC0Z4bO5y6wcVSOYLNY1+RNdMipZoKaXbJFeauIPEtNGtib6sA+7Uj0fqzMKwFWfjkuaITPFChNp612yUp77QhbLeegqS7jw85aAXJABfnEy1Y7oEcJjnN8plRZByEZ+41/PTBshSn3bOf2dZtMztEkd2TcfEYffIag/wQT9q6kZ+qV2gMzHvlSz84mDDm1Kspi+I4LRZiyiBE9ih5h/IEnQZaMnM8xrTNeva1wMk9pF8X0i+0qtoSN30cEm2LVY8yKR8aiLwWBYgS3XgAtOKaieq2DLwL7ceNCqQC2G8vBf+uF8pxWR2oEcI2Crb851NrZXidp9lmXqJZV+U3VaKZ6NkDbKBgzLbdeLSEuBc1s4oNhLEy5OqC7wGTXGS3Qh8efx6iD65m451cUBk6yb9qV2oPaJamtK1wmHmOt1tQldPQ8BVIydt9qdWzmcqv6TWEtnMxYxtE518Pi1w19ozLgov1wjaoDO/JY+xQrbn4utnDp+9bs9NaaVXvNlKs53wYupKi2Cc1B9SVr7Z9LBK0lbL9NtInI3IR0jlOuCVV+UxHFQbkv9WyArnlaO1UXfjYvTDUIrj5TtgsRmO3s28uaqfMolE5x1vp/Z6DQSe+kS7VNJeWkqw0QplxIO41f8dTFFE4epPPYdkbvXa1MfyMnGtdrA2vNVKHIvld+vayhqhdcdsk2mtxqM58NeaztmGuzyE8WwWXM1IthfijqjJ7TNiS9EeYVF/oJ2+/q6vdk24dt369tRPWgGTXnWWevUL6pA/2xOgXtxZjv3r71Wv5SEKZSsny/TS5LDV9haigyVFvDoGhkpogi+rp2/4IDky6lVOjQNkFEqvBGaKasJw8bb6tpFwLKCX3knkeNTXxRvTZw0SY05Ls4mPls/I+q8+LZaKZc2utCNWALWw2Gi6bHttxgIl47l4d6txfc6CdctEvW3zWo/4JvrZnSJUWddTn0HYVdj1ccLq+p/Sehd4uWv1RUr2luyQj1jso3RXO3zgGNjOYzFTA6CgFHx55DSagO2jPtSkC/85Q1Uw3obC7EkMbCyeTzkSJg8rY7KZ7Yb8R8Xq7WQTNlu9g3QpgSwk27aoqOYiVRbqtoply+jUtZa6He2mdKw0cmBYrfp74aF3DjSrPVLrkK5vXW4NlqpiK/vdxxc9bl6neV31R5Td2/CZDamikXbWM9E23bYPgKUw3kmTJmjy4GyEI7x8bMBWDf1CuNUpeUs3jX2fkR7Du41ZzTPpLSxHOZsu1/1f8tNFO2cBFOGiFMuWg/bCCEKAtQQWCbyNR+4bTVJjhpplz6vrWmx01TY+P4K7DnbXKBDVt7BBfzYD352Vzh0JUo6LDpj5sB42fV+E2V19Qokk9TmGozWNdaDcPzqRj82Ip/pt5OgWbXR4tQb/dSjoybz/Exc4z8VgY1U/WX+G0nEIGdQFWaupig1EepbRRMPt+qXhsUnEwzjRGm6k0XHJn6Oi20UuBCqGqXXw/czDIui64947VduXI+NYvoxUA0RivgQjPgol2qN1eUC4QLQWmgGQU7a4XSTElZPtQe9SMDjilwo2xpdgxbYSoIbd9tDrsbW5iqxKNF6Nmlf86qa79dcUwHQbibqnfUIthP7sLWqXXqYgD6pi41Iut0hYsTbmOEqfrXG20AbM3qLj5pNiH04GYKdTPz2ZazNPMVNR2Ok+oULqx79nCxKFj3BwfNVEPM+Tgkqtd91lkr4Oie0KSnUO5HPRth9BToHDu0da2kLYMAACAASURBVLYghq0wBWpSb4SAoZ3AN0S0CJUKHQy0jQ6PmQkKKgqlQRoQq3J29YnQCb1/en0oESI0wmnYBY1IvRD1WRt/KXA08zXANOPCKeQSvWUDF1eARgjm4KaZcjHJu0Rp1hsuJuOirhaunPR40NRXfkc9+pF8UTkvTLUg2gvBoDqyjjCdeJIWH9PwdLWgtM7ntNZCnHUJ+6a+iIHzXzPELcqGCFxMmo2ZPOo9Z7U7aqasgwNcnFpdhCmHMPh694jIF8jGXOdiSnKBy3zm0h/qyc/mCpdvo00e2z1fpcaKCVMVhJ0GwpTLxqfZ0TqrrwVckla6wNxnqraNprv7toKwZnNuJYiO0TxxzdcQUxbZlXfw8bKeLBv0Wert5xL5X9Q92asLv4/D9GC72Nv6C+JQLgiEU1RzQzRTLmZUh75kK8M1KtrMttaiLrFvEKiovlhEX0EIOLYfjvVo0yJEt2r2qDxbDG9hysGZ0AW2PlN5x7JQLGhEZpwGuFAN2KIR39XFobtRc0e9F8B2By4jcKELcDE3OwRfNGD2dNFpuQhTDcmu4GDms9YiBw6s7Q1ilXTp+9rfdfYKpYU6shcI+4NhJF9UZ7MzmdtiWAtTjUg8CeYCRlL0k7mZzz6iyQXWDrEuzqUOZH62aERKDVfUe/2Lvom1MGUtrDpoExzekfWCLdwShNvCyaG7EQzoDXDwd/KNdJh+Xd6u27jRLFzlNxWIuDClr5ly0SI3O4a1MNUoZzfTvhJFHsZhauZziWhyQSPGhUt4rXUUlYMTbqOmjnp75hQdiCHBJcLNxXxbf+1Ho+CygXExh9rCSZhyCIyx9ud00nK6CI4Ozva6bZ52IRQ7y6a+IBCKFiEowoTZ2nW6JPludgzTx1JoFKeFTeeuzW1mrpkaro591SgWgrrb3RXTsH3ZRqDe9Ub9r947TzetoX299v2hMQ7dLnx7DdFMOdRpL5g7vKNG+UxZu1oYCI7FDpixrKyZKpv5JsyBQpthvcNznRrewlSD7LM2VcZ9pNqK5sJCEAxfW3Q1GpGjycXW36hovnp3h2iStNWQOiV7bYDZrFG8QrZwYZ5ulAa63nDZNLWaY7WxeXzWCtj5GJw6qjYvhpF8EbyZrwXhEjJdb1QmZjb/LI3wI4LGmLBcohYbEZDXMM1Unb/OYCb6OmsNHdKduLyjRkQKu8DluzSEeLYB0Xzglgy9EXBxQDcqO2sFyAHY9jBCSkXYaSNMtciabIrWmg0MEYjW+XBxM5+NMDWcoySq4eT74cIN5M18mYh2nPUmcnWRGVvt2zQsMXMD3lOj5jMXWoVGwMkB3aTwWZcAArb8iuDQNhg4aSVMDdd1algLU4UG+SbYoFIzZc4grcLDh7JFemhEpFpjohYdTEkNc0GvL4JAjTdrZnAXM9+Z8YqdxpsTb1MD+rDLfFZvhnnXsi6wrdZ4zegcB1MugC2/QuwPI/kMOKYG6x2eg3VYC1OiQWGYNjXGfaZMOaZALWCNCsGvNxpFd9FymqkG1BmYRAgNEdwE3cagIcJJi2mmGuZraFmuUQFAtvN+MbDIWzt7BWxdRWHfM+r/Vpop4yItgWEtTDUqp5QNXM18w9WpLwmtFrXYsAW7QazVjdh5tlr3b4hDdwOi41zQagKci4N/I2Cl4J91OfQdRaz9PrSPUUmOTetttcGqidb6+oZoJT8iVzNfw8JyG1BnY6J87KfZM0VjCI1xyj6T3q8LXObCRrzjxvmztVZ/stekWYzVsy4HINi2CrrmWX2kFnu92hjWwpRQ+v+WQEcxKHcyWzNfI9AILUSrBBU0Gg2JtGxQLkzfI/LRCOLNVsNwXeiTYKWpHDcDxs9Sf1v4S8Hw9SEd1sNLOJD51RtCiPJCZMOj1Gq7KRe0mgNja7XWDS7EkC5otf7fCLN8q2jpIzQsmq8htdYf1nE8s16gflv4S8HwFVidhCkhxHghxHeFEOuEEGuFECuGqmFDgUA0Sj1tV2eUiNSOGsGqypZEqy0KZxIalVi81XpEQ/zKGlTWozlhPY/OUqY+W2FquKLoWP7vgZ9IKX9DCNEOjByCNg0ZXPxcGoH2okrMbCOMnUkCRiMERyll/SttQTQi0rI9ZiL3SMeZNEdIKa3m0TPnDTn0hwWvQK6/E3H2VVbFh2s3tBamhBBjgSuBWwCklKeAU0PTrKGBoLU+XHshKGunTNFKz+mKRi0KXpzKRyP82bwPnR5ajUOpcVGwDaq4zrB+ztGTkW/8FsKPuwq46OTnAnuBfxVCrBZCfEUIMar6IiHEu4UQDwshHt67d69DdeYQDaJGsJ3c2wqBdd651tLBeQxXtBpthYceWs0nzRat+Jytx3/Xeu9YBy7CVBG4GPiSlHIpcBT44+qLpJS3SimXSymXT5o0yaG64Y+iQy7BFpwDrHEmPasLzqQUILZoxcXTFmfQo55RsGZ7P0MSsNcLLsLUNmCblPKh8P/fRQlXTYNWk4DbgqDlkqc2Aq32XYfr5JGEVjO5tVp7zyQ0StA9UwTsM+Qx6wbrlVtKuQvYKoQ4Lzx0HfD0kLRqiNBqObsKDpqpMwmt9E3hzJmcwQsnzYxW0xo2hJy37jWeeRiu79g1mu/3gG+GkXybgHe4N+nMRTEQ1j4nLTZPegxTtNqC7eFxpuJMSnNVDzgJU1LKNcDyIWrLaUErmYQC0Zi8Zq2GRr2hMyn/oS28Zqp54b+MRxzDVahpFFw1Ux5DiEIg/ILdpBBCeO2fBnz/bV6cSYvnmfSstmjUvme4fhnv7dxECIT9zr6VNHCu8BNl86IBBOgeHh4W8PPo0GJYT32t5oAeBPbaD29e8WgGeJ+p5oX/Mh4epw/DWphqNQjsFyMvTHk0A2zySnp4NAu8z6oe/J6pFsN65mu1BKiBEH5nrwH/hpoXRc+T1rTwgoLHUMGbCGsxrGe+VvverWaW9Ghu+AnPw8PDoz4Y1sIUtNaCEojW0qQ1Ci30ST08PDw8zgAMf2Gq0Q0wRcs1uP5oJQHZw8PDw2P4Y1gLU6225ApxZlEceHh4eHh4DAcMa2Gq1SDwxJAeHh4eHh6thmEtTLUaa7XSTHl4eHh4eHi0Eoa1MBWI1vKvEbRWez08PDw8PDyGuTDVaoKJEKJh+ZI8PDw8PDxON1psWdbGsBamWg3DtI95eHh4eHgArafk0IUXppoIPprPw8PDw8Oj9eCFqSaC8B7oHh4eHh4eLQcvTDUZvM+Uh4eHh4dHa8ELU00Gn+jYw8PDw8OjteCFqSaDl6U8PDw8PDxaC16YajJ4zZSHh4eHh0drwQtTTQYvS3l4eHh4eLQWvDDVZPDUCB4eHh4eHq0FL0w1GbxmysPDw8PDo7Xghakmg5elPDw8PDw8WgtemPLw8PDw8PDwcIAXppoMwzVvkYeHh4eHx3CFF6aaDF6U8vDw8PDwaC14YcrDw8PDw8PDwwFemGoyeCufh4eHh4dHa8ELU00G7zPl4eHh4eHRWvDClIeHh4eHh4eHA7ww5eHh4eHh4eHhAC9MeXh4eHh4eHg4wFmYEkIUhBCrhRA/HIoGeXh4eHh4eHi0EoZCM/UBYO0Q3MfDw8PDw8PDo+XgJEwJIWYCrwC+MjTN8fDw8PDw8PBoLbhqpj4P/BFQGoK2eHh4eHh4eHi0HKyFKSHEjcAeKeUjOde9WwjxsBDi4b1799pW5+Hh4eHh4eHRlHDRTL0QuEkI8RzwbeBaIcR/VF8kpbxVSrlcSrl80qRJDtV5eHh4eHh4eDQfhJTS/SZCXA38oZTyxpzr9gLPO1dohm5gX53LNqLORpX17T29ZVutvS5lfXtPb9lWa69LWd/e5i3bqPbaYraUMl8TJKV0/gGuBn44FPca6h/g4XqXbUSd/lmbt84zqb1n0rP69jZvWd/e5i3bqPae7p9irrSlASnlvcC9Q3EvDw8PDw8PD49WgmdA9/Dw8PDw8PBwwJkgTN3agLKNqLNRZX17T2/ZVmuvS1nf3tNbttXa61LWt7d5yzaqvacVQ+KA7uHh4eHh4eFxpuJM0Ex5eHh4eHh4eJw2eGHKw8PDw8PDw8MBXpjy8PDw8PDw8HCAF6Y8PDw8PDw8PBwwJDxTHh4eHh7pEEKMA14GzAAksAP4qZSy1+Ge10spf5ZxfiwwSUq5ser4Einl4zn3ngogpdwlhJgEvAh4Rkr5lEU7Py2l/KhhmbOBpcDTUsp1OdfOQuWJPSGEEMAtwMXA08CXpZT9GWVvAu6UUp4waV9Y9kpgt5TyGSHEFcDlwFop5Y80yo5G9YezgH5gfdiOkkbZBcCrqOxLd0gp15o+Q+ye75BS/mtOnTOAh6SUR2LHXyal/EnOvS8FpJRylRDifNRzr5NS/tiind+QUr7NtFw9MOyi+RrR0WL1Gne2Zuho4URwKfCklPLOjOsuQ00Wh4QQI4A/ZnDS+rSU8mBG2fcD/y2l3GrRvnbgZmCHlPIuIcSbgRcAa4FbpZR9GWXnAa+hctK6LautsbIvBV5NZV/6Qd7kkXPPv5BSfkKj3pnA3VLK52LHf1NK+bWUMgJ4fdjO7wLXosbBOuCfdSbpqvv9XEp5rcZ13VLKfbH//x/CvoRayBInGCHEa4D7pJT7w8X6bwgXT+APpJTbMur8W+C/pJS/NHmmsOxE4H2ob/lV4KPAClRf+rSU8kBG2WuA11HZl74ipdyQU+fbgI8BdwLbw8MzgeuBv5RSfsP0OcL7bpFSzko59wbg88AeoA24RUq5Kjz3qJTy4oz7/jZqbAvgr1DCyVOofKx/LaX8akbZL1QfAt4KfANASvn+lHLfl1K+Ovz7VWHb70WN889IKf8to84ngUullMeEEH8FzAO+jxoDSCl/M6PsceAo8L/AbSgBdyDt+li5z6P6eRH4KXBdeI+rgNVSyg9nlH0D8GHgMeAa4EGUlWgx8BYp5RMZZT8CvAmVDzcaIzNR8+O3pZSfzWt7yn2z+tL7gd9FjZGLgA9IKX8QnsvrSx8DbkC9p58Bl6G+64tR7/pTGWXvqD6Eel8/B5BS3qT1cHXCsBKmGtHRwvNWna1RHU0IsVJKeWn497vCtv838BLgf9LekxDiKeBCKWW/EOJW4Bhq4b4uPP7ajDoPoiatjahJ63Yp5d6066vKfhP1jkYCvcBo4HthvUJK+faUcu8HXgncB7wcWAMcQAlX7w2Z+9Pq/DwwH7UIxPvS24D1UsoP6LQ94b55fenTwBXAo2HbPy+l/IfwXFZf+iIwGWgHDgEdwP+gnnt3VnuFENVaCoF69mcApJRLMsqW2ySE+DOUBuNbwI3ANinlB1PKPS2lPD/8+z+BXwO3o/r+W6SU12fUGeX4nAT8J0o4Xp12fVXZHwNPAGOBheHf30EJNhdKKV+VUu6zwBTgbpSAvRl4FngvSgi7PaPOZ4DLqrVQQogJqM3X/Iyy1eO8fAq4Vko5KqXcGuAGKeXOcMP2DeCjUsrvCSFWSymXZtT5BGouGoF6z+eEGqoJwD1Syosyym5DzWF3hm0E+BzwhwBSyq+nlCu3SQjxIKoPbBZCdKM2FRdm1BnvS48Al0SbByHEYzllV6OErt9ArRMXoObC26SU92WUeyq8dgRKQJ4RCnNtKGHqgoyyjwOXh9d3A9+UUr5UCLEEtfF5QUbZZ4FF1RvIcMP5lJTy3Jx6E08B86WUHSnlngBWSCmPCCHmoOb8f5dS/r1mX7oINR/tAmbGNuMP5c0tqM3VV1CbRIFaO24GyPo+DYFsgpw2Q/WDmtzaEo63oxbArLKPp/w8AZzMKfsEMDr8ew7wMEqgAjWwssoVUELCIWBseHwE8HhOnY8C/4HKi3hV+Htn+PdVOWVXx/5ehTIFAIwCnsgotzZef9W5NXl1onZfL0FpBPYCPwHeDozJ+zbh7yKwGyiE/xdZ7yl6v+HfI4F7w79nZX2XqC+lHBcafelQys9hoF+jLxXDv8cDPwb+Tqcvhb/bgB6gPfbOUr9peM0dYV9aAMwO+/DW8O/ZBn3pUWBUrB1ZfemZ2N+PmPal8Pe5wJ+jtCbrUNqf+Tll18S+43bdeuPPEr7TX4Z/T0BpdDP7EjAu4fg4jb50AHgF4biO/VyNEpJz2xv+fxrwCPB+qsZuQtlHY38/lva9U8qOQWmVvoUSMAA2ZZVJqHOlYZ0/RQmWAP8V9Vmgq7r9WfWG/58avqNfAVszyj0Z/u4Mv9GI8P8FlGkyb4xHiowRVWMory+tSxqT4Vh9JqfsbpRgM7vqZw5K659W7umq/49Gzd1/qztWk76jRtkA+CBK0XCRbl9q1M9w85kqAdNRu6k4poXnsjAFeClqYMQhUGrYLBRkaNqTUj4nhLga+K4QYjaDu7Mk9EulUj4mhNgopTwU3uO4ECKvvcuBDwB/CnxYSrlGCHFc6knrQbjLDFCDem9Y71EhRKp/AfBkzOT5mBBiuZTyYSHEfCDV1BZCSrVbvBO4M9zB3YDSJH4OpWXIam87StgbiVqE9qN2O2059RaBgfDaMWFDtoT1Z+GEEOJSKeXKquOXAHn+Fb2o3fHu6hNCiDwzZ1GGPh5Syl4hxCuBW4UQt6M2BWmIyvQJIVZJKU+F/+8XQmSaLaSUN4Vmt1uBz0kp7xBC9Ekpq8dREkYIIZai+lJBSnk01o6seu8VQnwC+Ez496ullN8PTWl5JlgZ1rEe+CTwyXBX/yaU8HlORtmo748BRgsh5oRjtovs91sSQkyUUu5HzTGFsA0HQhNrFj4FPCqEuBMlpIIS6K8P25+FXwPHksZ1qPFKw2EhxDwZ+ktJpaG6GmX+WpRTZ0kI0SaV9uMVsfo6yQlaklIeBn5fCLEM+A8hxI/yyoS4UAhxCDVfdgghpkqlDWsnfNcZeCfwDSHEx1F9Z02ocZoAfCinbMW3k1LuAr4AfCGcv9PwIyHEAyhh6ivAd4QQv0YJuvfn1Plj4CdCiPtQc+DtUDZB5/Wl3wfuFkKsp7IvnYMyX2fhh6hN/5rqE0KIezPK7RJCXBSVk0pDdSPwNZRpMgunhBAjpZTHgGWx+saRsyaH68XfhXPf3wkhdtPMft6NluaG8gflb7QBZbu+Nfz5SXjsZTllvwpckXLuWzllf04oOceOFVGq9YGMcg8BI8O/g9jxceTsHmPXzkQNxn8EtmiWeQ7YhDJVbAKmhsdHk707Hwf8G8pU9xBKgNqEMqNdmFNnllZlRE7ZD4b1PI/aNd4NfBm1w/tYRrkPoLSLt6J2dO8Ij08C7s+p8+LwGZ8mFABRZtyHgGU5Zf8vyocj6dxf5ZT9IQmaxfCepYxy/0uoHa06PpWqnX7GPUahdpt3oEx0OmXuqfqZFh7vIiPDO0oI/jiwJfwpoTR33wJm2fYljfa+CbVD343yf7oLtfPdDrw7o9wbw/53Z9jeV8T6Uub8EF43AWWe+AOUyetmYILtc2jUdyFwbsp7f0tO2Vkka/hnAC82aINAuRD8h8NzjEeZmHSuXYjyE3wdykwZaJS52qFtK1DmOlB+Wn8IvEGz3peH118fOxYAHRplA5Sz++tQ5snLCTXwp6kvzSRcIxLOvTCnbOLzAN3AYsN2vAJlUj8tz+n6M6x8pgCEEAHKMXAGajBvA1ZJDadChzpnorRMuxLOvVCmOMoKITqklCcTjnejFqVUR8SEMq9AdWyjqJmqe4wEpkgpN+dcNwaYixIYt8kEDUxCmflSymcd2jYdQEq5QwgxHuVbs0XWao6qyy1CTbJPypyooJTyU4n1paRvPJQIfQmQUh5PODdDSrm9tlTm/UahTG97DMpciFrA/tmkrqp7FFAT6TGNa8ehNHI9mvceLWNBHpZtE1Jp7Yoo08d2KeXOnHITUf1+g7SIwhNCTCEWzKAzblzLNqJO397T396U+1mPC9uyjajTtezpwnAUpmYBh6QykcxBmcPWSo2Q3pSy66SUT56uso2o07dXr86w/HJi0VsmAlkjyvr2Nl97hRAXAf+M0uxuQwnmM1Hm4PdKKR/NKLsU+FJYNh4JmFm2qs7qcr8jMxz2T2PZrPZmPedpqdOxvXWvMw8iJ7jldJRtRJ2uZU8bGq0aG8ofVDjvZpRJ553h76+iHFQ/1GxlfXubur1XoQIJ7kL50f0Q+CUqUumsZivr29vU7V2DiuarPn45+Q7SVmUbUadvb13a+6GUnz8A9p+Oso2o07VsI34a3oAhfRi1SI5A+WscpjJKLS9Kou5lfXubur2rY9efjeLIAuU0fGezlfXtber2pkbsoUyGQ162EXX69talvSdQQQsfS/jpPR1lG1Gna9lG/DSvZ7wdBqSKhDsFHEeFiCNVlFozlvXtbd72FuQgD9YWVAgxUsqfCcVB1WxlfXubt73/K1RU2zcYjMA6C8VZlkcAa1u2EXX69p7+9j4KfF9K+Uj1CSHEO09T2UbU6Vq27hhWPlNCiH9DhTePQhFK9qM657UoLqM3NFNZ396mbu/XUI6hd6MihLZLKT8klJP+o1LKBc1U1re3edsblr+BwcwMUWDMHVIj04Ft2UbU6dt72us8D2XiqiE8FkJMkRlO7LZlG1Gna9lGYLgJU0UqU2pchgqF3gL8kww5cJqlrG9vU7e3DXgXcD4q7cPXpJQDQkXbTZYZHEyNKOvb27zt9fDwGP4YVsKUh4eHR7NBKOqHP0FpIiaHh/cAPwA+KzNoFmzLNqJO3966tvfVDBIdn9ayjajTtWwjoMNM2zIQQowWQnxCCPGUEOKgEGKvEOLXQoi3N2NZ396WaO+TVWVvacayvr3N215U7r8DwDVSyi4pZRcqj2YvIfv1aSjbiDp9e+vX3quryh44jWUbUadr2fpDNoEX/FD9oCTWW1CcHR9C5e06F/g6OcypjSjr2+vbe6Y+6xnW3tScaVnnXMo2ok7f3uZt75n2rI34aXgDhvRhapNyrgp/ByiixqYq69vr23umPusZ1t47gT9CZReIjk0BPgLcdTrKNqJO397mbe+Z9qyN+BlWZj7gqBDiCgChEsTuh3LCxLxY+EaU9e317R2qsr69zdveN6K4zu4TQhwQQuxHkX1OROVyOx1lG1Gnb2/zttelbKu1tzFotDQ3lD/AEmAlyvb8C2B+eHwS8P5mK+vb69t7pj7rmdTe8LoFqHySo6uOZyZgdynbiDp9e5u3vWfas9b7p+ENqNuDwjtaqaxvr2/vmfqsw629wPuBZ4DvA88Br4qde/R0lG1Enb69zdveM+1ZG/HT8AbU7UFhSyuV9e317T1Tn3W4tRd4gnBnDcxB5fj7QPj/1aejbCPq9O39/+ydd5xcVd3/33dmtqT33klIIwkloQjSBKQqPhQRFRBRH0Ts2PtjebA8WH+KKEhRFBQpioL0nkAaIQFCem+btkk2uzsz9/z+OHNn78zccu45yc5u9nxeL15h586Ze+69557zOZ9v67j97WrXWo3/DqlyMo7jLAo7hHRc61BtbX8PbtvO1l+Ttra/B7etyTmRpWj2AgghVjuOcxrwN8dxxhDvb6XbthrntP3tuP01advZ+lsVHFJkCjmpnY3MQ+GHA7zYAdva/tr+Hqi2h1J/lzuOc6YQ4vFO0t+4c252HOcoIcRCACHEXsdxLgBuA6YfpLbVOKftb8ftb1e71vZHtaWxA/kfcCvw9pBjd3e0tra/tr9d9Vpj2u0FzuxE/Y0750hgaMixkw5G22qc0/a34/a3q11rNf6z5WQsLCw6FBzHWQ18BHgS+CqyJl43ZJHqTwohdjuOcwewSAjxf47jjEAWiv2EEOLXjuNMQEbeDQD6Anch6zJmgBeAa4UQ69v5siwsLA5hHGp5piwsLA4dfKjw3+nAYUBP4FeFY88ApxX+/1RgZeFfgFOA54TcKaaAPwBjgNHAft9vWFhYWBwQWDJlYWHRUfEB4CYhxEohHVG/ArzPcZwMkkyd7DhOCkmefgScVGh3auE4QojtQoj7hBBNQog9wPdpI10WFhYWBwSWTFlYWHRUDAfW+P5egzTVDRFCrED6Vh0FnAz8E9joOM4kfGTKcZzujuP81nGcNY7jNALPAn0dx0m343VYWFgc4rBkysLCoqNiI9I852E0kAO2FP5+BrgEqBVCbCj8fSXQD1hY+M7ngUnA8UKI3kgVCzpgaLWFhUXnhSVTFhYWHRV/Bj7rOM44x3F6Aj8A7hFC5ArHnwGuR6pNIOt2fRJ4XgiRL3zWC+kntctxnP7At9qr8xYWFl0HlkxZWFh0VNyGjMR7FlgFNCPJkodnkGTJI1PPA919fwP8DBkJ2ADMRkYEWlhYWBxQ2NQIFhYWFhYWFhYGsMqUhYWFhYWFhYUBLJmysLCwsLCwsDCAJVMWFhYWFhYWFgawZMrCwsLCwsLCwgCWTFlYWFhYWFhYGCDTnicbOHCgGDt2bHue0sLCwsLCwsJCC/PmzWsQQgyK+167kqmxY8cyd+7c9jylhYWFhYWFhYUWHMdZE/8ta+azsLCwsLCwsDCCJVMWFhYWFhYWFgawZMrCwsLCwsLCwgCWTFlYWFhYWFhYGMCSKQsLCwsLCwsLA1gyZWFhYWFhYWFhAEumLCwsLCwsLCwMYMmUhYWFhYWFhYUBLJmywHVFtbtgYWFhYWHRaWHJlIWFhYWFhYWFASyZsrCwsLCwsLAwgCVTFhYWFhYWFhYGsGTKwsLCwsLCwsIAlkxZWFhYWFhYWBjAkikLCwsLCwsLCwNYMmWB41S7BxYWFhYWFp0XlkxZWFhYWFhYWBjAkikLCwsLCwsLCwNYMmVhYWFhYWFhYQBLpiwsLCwsLCwsDGDJlIWFhYWFhYWFAWLJlOM4tzmOs9VxnMUBx25wHEc4jjPw4HTPwsLCwsLCwqJjQ0WZuh04p/xDx3FGAWcBaw9wnywsLCwsLCwsOg1iyZQQ4llgR8ChnwJfBMSB7pRF+0LYJ2hhYWFRNQg7CXd6aPlMOY7zbmCDEOJVhe9+zHGcuY7jzN22bZvO6SwsLCwsOiFc15IEFVgu1fmRmEw5JAF+lQAAIABJREFUjtMd+BrwTZXvCyFuEULMEkLMGjRoUNLTWVhYWFh0UrTm3Wp3wcKiXaCjTI0HxgGvOo6zGhgJzHccZ+iB7JhF+8FuiiwsLA4GrOJi0VWQSdpACPEaMNj7u0CoZgkhGg5gvyzaEdJebwv0WXQ9uK4glbJj/2BB2K2aRReBSmqEPwMvAZMcx1nvOM41B79bFu0JO91ZWHQOdDYfJKtMWXQVxCpTQojLY46PPWC9sagK7IRn0VXhWFHqoMJOLWqw96nzw2ZAD0Fn2wGawErxFn50pbFvcXBhQ/4tugosmbKwypSFRTujqxDWrnGV5rCks/PDkikLXPsiW1hYHATYqUUN9jZ1flgyZUG+i+ySOyOyNk+PRWeGnVqUYEln54clUyHoSmPbkqmOCzvJWnRmWH9MNdj71PlhyVQIupINuxpkqqv4jJiiGiZY+2QsDhS60DRqBHufOj8smQpBVxrbVpmy6Kqwi9jBhStEl9qY6sLeos4PS6ZC0JUGd7YKZKoL3V4jWKJr4UdnHA1daS7VhTXzdX5YMhWCrhThlrNOzh0WXWkcdiV0lafqiq5zrSawe6bOD0umLMjmq6BMWZKgBLcKPNc+G4sDBSGE3RAowN6jzg9LpkLQlQa3Db/vuMhbB3QLHzob0XVF15pLdSHsFByLjr5OWTIVgq7kq5KrhjLV7mfsnKjGOLRr38FHZyNFuv2VDugHuDOHIDqbz1Q1xm9HJ+WWTIWgC3EpWjs44+/KqEraig4+aR1IdJ0rNYPuOHS7kJnPJN1LV9q866KjDyNLpkJgwrw7m0N3Z+tvZ4PRJFsNM18Hn7QOBXS2W6zbXyG6DlEwIY1d5BYd0rBkKgQmg7uzvRc5g4ttyeW12nWlBdvkUvNVMMF2FSWhM6KzPZm8K6oSRNHZ0NnMvhaVsGQqBF1pQTFx7OtCt0kbJhNlNZSprjT2q7WIdbZ7rNvdfBcy8xltmrrIPTJBR79FlkyFwGQC6OgPvRy5vH6W4s42UVajjI3RJFuFbX1XUhKqNXo72WujDSGEJQoKMAkC6iqqVkd30rdkKgQm47OjP3Q/PHKhyzE6m62/k3XXyASri660+FXjUoVBhFtnezR5V3/D1VVIAphtSruKT1pHHw6WTIXAZIB29Ifuh7dw6l6v7oTXmQinKUzGQzXSVnSVyRmqMw6F0D9vZ3tvupLPlNF73oXeOV109DtkyVQIusqCki8qU7pmvgPZm4OPaux2TRbAakyyXak4rcll6pqMhcF5q/VYdMewWyUzXzXM+SYwMvMdwH50ZIgOPi9ZMhUCIzNfx33eFXANlSntBaVqi0IVzmmkTOlv6/XVxuo8n9Zc+0sYJtfZotnfLpV7SYiqEJtq5M4z2TR19OzeHQEd/Y2xZCoEJrupziTFewuu7vV2tkWhGt01uUcmdRP11cbqLPad7Zxm97d9z1kt5F1hROp10dnuk42ojke1NnmqsGQqBCZRVJ1JYS6SKc1FW/dSq/VSVGfB1mznCnIG41D3Uk0W+84Go6hd3XbCpDyL5kkNYTKWqpPeo91PaWC6FVXZNJmYzKoyf4uOrU5ZMhUCk8Hdke265fCuM6u5aOv7jXSee2QK3Z15Xujv6sFkkq0O6ayOaqjf1iSdSDWUqWr4geby1THzdaY52ES9M0EnukWAXDM68nO1ZCoEMgpF14/oAHfmIMKTl3UdIHXngGq9E0bkpJ39yvKumblNO9Gi4Xl1UZ3IOhOyqtfOZPHsbI7VeVdoB1GYXGlnuk3V2jSZwOSM2hHgVpnqnMi7+vJ0Z8rT45EoXZt9Z/O1qsZ5de9RrhBWrk3iqqCcmKAaQ8JIEdBWl/QXFJPoTjPi2P7qqpkZqvMoPXlDc772hlb7jGYwcz/ouGurJVMhMJkEOlNaBS/qRYdMCYNIHUF1JjwT1VC3t9rKVIHo6i6gJukuOttuVxdmXEqfJJj40enCTE3Qa5fLdx1lSnc85AyV4OqY5Nt/HLrWAb1zwkiK78hPvAxeOLpOmLdJdEW1IjNMdoC60Fem3JJ/k8LEL6er+LmYLWL67XTPa6ZMaTfV6q8obEh103t0tmg+bbNvXl+Bdl39bPomqIr/uVWmOidMdlTVSLSoa6bzlCkdMmUku1bJ/m2U8kLX1GE4jrR39gb9rYpDbLufsZqpEdpf9TbxSdM5bTG4Rdsfs/2fTS7vGvj0tP97bqLwV8Psa3JeuQnRPu1BhyVTIci5rvaOSjfNQDXQnM0DegkTXaFPTgRVymWkKUwZTTya5/QmV93xZOQgXZVn0/7nNMk8beLLpm02c/Ujmkweqc45TZVVExiZ87VNsHrtiulpdJQpg7FUDbOvyXlNHfUPNg5pMuUaTDw5gyiUakweuoPbU6Q8UpUEJmVHqmX/NkvG2r7n9Mi8TtoKk9IL0syn1dQInc0BXXssGYx9k8XTBFrKVK6guOjmsOt0Zj5NtVF4ypSeq0WXSWNiEGHfHoglU47j3OY4zlbHcRb7Pvux4zhvOo6zyHGc+x3H6Xtwu9m+cF1BPi+0TWedpWil6wqyubbUCEkXFpOor2rZv6tR/kY3AayJmUQYSOI5w+giXWhHzxq8bybvqkk6EZNEi9pqsNEYTt7YcyHQNRt3tpxa2uM3b6ZMVSM9jZnJ2MD9oJP7TN0OnFP22WPANCHEDOAt4CsHuF9VhacE6Nr6O0udpeZcqRqVVJ0y8a3JV2mHrR2mjYmaoGcmacsBprFjRX/Cq5bPVHungAAzFdkkylJ/QTEgYgYLoM749dfHa8klV76rYYYyOafu+M0WzaF6mybtqNJ29qErnlfX/aBKgTGqiCVTQohngR1ln/1HCJEr/DkbGHkQ+mYM3dueNcy9ZOKHoQudF6M565b9nWzCc4VZMd3OJMULIbQnH93dY9YgbYUJTIIvTFANxcXkXdX1qTRRE/Ju+5tgdVM5tPjmEx2fTBM/xc6orOqMRZkVXOu0nc+MeggoU3H4MPDvA/A7HQae6Uu3in2nUabKyFNzwut1DfKjVK9mVxWUKU01wRtHrTmdHavQ3k3I4IsqkKkqKFNZg+gt7Sg1A3XJFfomWJNFV6e//s1a+cZN6byJW7TBLOpRD7rvjLfO6KwbZiZjrWayrcESV40EsO0BIzLlOM7XgBzwp4jvfMxxnLmO48zdtm2byenaDZ483aozuA0c102gMz4ryFRCZSpnlIvL7IXUhS7PFaL9M/d6JEpnHArMFoVqbAhMCJyJs73uGNZ5Lt45dReUbN41MlXrwBV6mqzfjUAnwEUXJgqG0DTJg3nuMJ11w8hnqgp+T2BY5qoD6xTaZMpxnKuAC4APiIjRJ4S4RQgxSwgxa9CgQbqn04bOgyvuFDSUqY5u1/WjPLdUS8Ldo8lCVC3JVj8Bpr6ZT5eIFUm9pkKqA7dAkNt7Q2C6CdGfoPXMM9KvTD9ARZc45l2hrYgZ5ffReDbNrW0EqqlVw2dKc9ibRKPqv+X6PnQ5kyoURuWmtJoBZlHR2qqs5rvaXsjoNHIc5xzgS8CpQoimA9ulAwchBI7jJG5nokxVK+LAhDQW/054vSbpI4yc111BOpX8uboG5zSJjtORp/2Rlnr+JmYErr2VKZM0G0Y+U66e0mNiCjVRplrzrr6ZT6uVXn9dV7Dfp0bt11CmtAMoDO6vEEDyqUWeV1fhNHjPjfJMVYEQQfuOw/aESmqEPwMvAZMcx1nvOM41wK+AXsBjjuMsdBzn5oPcTy3omjrMbNjVyh6tYUYqu76kL3NeI51Csa3Bi2FiczcpsWLi05O0z/5n05rXW4h0xoRHjnVUWTArmG2SAFZ7gnb1FkGTzYCJg382L4r5m5LCzEydrE1zLl9yvqbWXPiXQ2BiDqqGyqntM2WyeTd4b0yWKRNzm3at0g5u9YlVpoQQlwd8fOtB6MsBR1vYaLKthkcqXFcuDjVpdWto1cLKD4QylXABzbpuVZQpXZiYZowSlGrY+v0Tq8441FWmimqYgT+QVjtNUuNBaEoKuk6tOVeQNUjqqxW5JaRaqUOuvfY60InaLTfrtWTdxIqyfk1L/fdcCHBNonZdQSrJNfpMvlrKlJHarv++mZn5NNt1gWi+DgvdHat/d510p513peza3gzaJKmeh6TXmsvrFzHVSRJqCv/ElRQmi71OEkxToqsbgdWW20qfPOogb+LnIgyy0+ua+fL6C3ZW873JFhfd9lUi5JyWrPH+AB+ppKY+bQUjr+9XZpooNOli78+/pVMf1dikqQmT9c0oaWcHVqYObTKlvaC0tUkqqRfrqWkP8PZpl3dFRc23pINVhpXr5dsxkeJNZHgTs6S2nK4xyZaTp6REV9cJt7VkI6GjniQ/J8gFsL19gUThHuk815zryhQHGuMpm3e1lD8TcxC0b9b1IOIURLCioG0KNfBnE7Rv6L4/ZUQ2l5zYyw1tdYijLszMfNqnPeg4pMmUrnNeiTKV8Ol5A8XE0VkHSSeesMU5iQLSllRSY0ExKCSti6yrt4iBWViuTgqJ8ueT2J9Nc8daspFox+djtsPWe89zBu9qMdGiJpnSUf5MfDnBYBHTGPtBqRASp17RrkAhtOpZgplDt07evfLM8O2ZnsZE4zExt+lvSjt2NN8hTaZ0lSn/DjnpC22SMwRAI/gQSD5RhknKychUweygMbnnDKR4XXdjT5nSUf9MkozqmBcryFRSZUrTv8DExG0CGVWn11agNyZMMk97bXTUtKymwtSWxFWXKGg10yK6QfNL0pIyuoQom3PJ5/WclYVBQlWd6OZytS55SS/XiDjqwqhAuIGvoa5pvT1wSJMpnSiUcmfhpJNl3lCZ0kVSZ9iwCbklgXOrSbmTbN7VXqx154CiWVLT4bjcLKoCUZD+k46Hcr+YpMRTd8fqHxe6Kp4OXFc/B5ius73Jxke3bS7vFueXpKSo1TA4wEgRSLpZC8hZl9QnSL/QfGFe0hhPJjmqcvnkc0RFSa/E98jMD1QX+uc0UP6E/rW2Bw5tMqWxyyifHJM+vGqRKUkC1c8ZNiEnmeBN8qO0GpApXZjkUMrndZUePQWjvI86gRA6E09JSoZ2TBaqm+8JDMx8BoWkvTZJF08Tn7Ss75w6qouJr0pin7+ATVniaGFNR3tvI6KjfJvkMsppqESmxeZNgoCqoUzp+qR5Iod1QK8SdPJSlD+sxDtPV2/xNEVSM1SoMqU44eV8ZS2S7jjdgsrTnos1tBFjnUlaOpdqLLquRzjb12fqgChTmslCdWAyUbrCLM+azqJbzMdlEKWZWJkqyT2mMxb11YQk82iYj1XS+6ybAsJ757QCYzTJlCioJkmvsVzBS1qFIuu62ik6dF0evPPqQLvou/dMLZmqDnQeXDkJSjrBV0uZSmpKCpvIVRUQP4FK6gtRraikollShxTl9XJqecTNNDVC8rQVer4UpmY+bX82A2VKN6FqkVxr+vz5/1WFCZnybwJ0qzPoIGnkbdj9THqfdVNAZIvPVYcU6ZlDiwp0wmssnzuTzqUmypQQBv5LBuWQtPyYDXwU2wuHNpnS2J2XD5KkD69tV6RrstBqljgKJWwiV53g/XJ00t1Ua77tHmmZKwx8przzJoVuPTXPB81EhdBpr6NMuW6pWpj0uZrAJD+bdhoIg+i4rOZ73lJCVpNuQtq+r6MamuRJOyAuBBqmaj2S7KkYevdIZyzp+I+6AXNK4vfcJD2NpkoE+v5s0r9Rh6zK83VkU98hTaZ0JNvy3XziaL4ig25vZSrZjsrUZ8rvKJk0GV/WUP0wSfoG+kqEXhi8bJNkkgycZNvBZ6rcXJvUfwPMU3vo3GPdkGmTQtK6meL9309qHveTW50Ej9opFRK6EISVIkrq66WrVmZNzPmaUbDeGEryXILGTuIIT4P3RlclApOIUqEVtVutlC1JcGiTKa38PqLsbz3H4fZ+4DoJN4M/V/sN/0KbdNE1fTF0HWmzBkRXNy9WS+HemE6yWtnpE26xPRPDpHnfZsiafySOLAJzoqujJuguusUF0CAbeeJ7bECIWgxMhGAQHZdwIxE1hyQhC7rmW29DrGXO14za1SHmQc9Dx5wP+mRK5/56Fh+t/Gyaz9RkE9JeOKTJlI4kaFrGwyTCDQwrarejX4OfQCW125s6Oeuqft7Cp+NInnf18mJ5oc9SKUruj+bBq8+nCh2C0Zx1SeVbGLnyL0xY/FOy2Vzi39AdvyYJYHUndu8+6xTsLvY3ofpRUj4kgRm1tSw7dtJs4qB3b6GtWoKqeSZqnKqOYd3s9K6PDGlF7bp6mfh15v2g55FkzfJMfN7/J4VuFYoiWdXxqXT1Nlyma0Z74JAmUzoLSqVDoJ6Mr+tcrRtdkVSeDnuJVO9XeX6UJPfJf491E37qoM3hWMeR3NWaZP3Xqqr0mPqzQZuPV5Lx1JzN033PKhzh0m3fegZsfj6xQ6zQnOd0HXhBr/YhlNdFU79OP3lL6vekG7hREUKf8LkkPZ8fSSOpot5p1XevmJ3eJPWEhplPV+X0nmuS+SxsrKuSFP/z0HWX0FH5TVxZdKMldd/V9sQhTaZ0slaXk4S8qx7C7/dT0XXe1d3ZJ0kDIUS4lJ1XdDY1IZ0lC4rGfdJZOP3BCDpkLOsKrZ2YfzypmkMPRA4w7xqTLAzNuTw9GlcA4KZqGLHynsQKiG5iSN2s4GCgTJU8Gz1l1cRUl6RWZHN5puykde4M8rolddQ/EMqUrtm3NH1E8kVXtzKDNx+25lx1BS/k2aveI9M6mjoZ2+W5CuS6Su+qNfNVAXkNM59JtfMDwZ5N6kKpLmRxL5DK75iYQ0sjmvT9VRK18U3KuqkRdArb+hUEZTIVpkxp7HqTTJbNWZcejcsRTor14y9n4MYnye7aqNwe9CNtTMzjOsEB5WazJH5/uiaHvCsqnLNV54nyOag5l0+kOuYKUWo6C2DSMjZRm5WkhEznuXrQ2aiZmoyFUF/swxz1VTd7/vZhvxUFXQd0E+uL7v018dFtLxzSZErPb0S/2rn/5U2y6/TDqC6U4ksY16+446Yhvbp+Ix5M8gJB8onH75uQZALJ5t0SBVBV/TggRag1Ihebs3l6NK5kf4+RrJtwBSmRp+61u5Xbgx6ZyhokgAX5nidVG8vNZEkiUv0ll5Kk9wgiTqpjoqlsDnJdPTU46UbEny7DdPxCEn8gPVNSi4FqKM+nl59NRzkJU910lCldM59RtKSWGqaXr29/CZmyylS7IykLbs7mA7+vrkzp7ToPBJLk1Irja3GELmiySfIyl5pJkt8jrfI1/l1c4jIeetGHuuPhgCpTCa61JefSY88K9vWewP5eY9g++G30WHI3uOrPSM93ydTsm9wEW75BSmLOrMharZybrfJ7qrvsoDkoSZ91iyS35No2EqrzYNT8obqQFp2cE/bXfz/zGuNCt9adjlUiLCmpakZzU6dsXZXIeyY6alhO4/7m8qWbJatMVQFJbcJhD6mpNafVXodBt0fF8rhzxP1MEBlJ4gvhfzF06/qZtEmeLFGPiOmOB1OfKb+SpvpcXFeQa22l+55V7Os1HoAN499H7d71sOJJpd+Q5zaU8DXIdTaXXIEuJyJJJmhdkhz0PWUiFkCcmjRMk0kKmUMpgVIlb1FDTtU0qZvxuvKdO/gZxcvnNNUNQWhEteKYMKlCAfpmPhP/Rh3lr3ycN2fz2ulxDiYOaTIl1Rr1Bxe281J9IU2LVoK+z1SSFyPuW3ETXtDLrixNly9EOj4cOTfxy+Sf4FxXPx9MNZUpdX+T5FE+rXmXbvvWkXKz7OstydS24WeQrR8Ac/+g9Bugp0z5F2mdkH8dBaLcbFb+dxR0/QV1lSkhRCDJTKSmafqk+TeS+7Nqm8qo+UN1ftONvK185xIqUxq17irGg3I0XojPlOI7VGJa1PQP0zG56Y4l2UYkTt5aPs6FSJ4ouj1wSJOppJJtmHKgqijoyv9+6FbU1llQdBF0XaovVvlEo5PjJy+STwImoeX++5rIN6zshVd2TDVUpnTk/5acW4zk88iUSNey+bCL4a1HoFHNEV1nzPsJRbljuApa827iRbd8Mm7NqSdlLR/Dqtesq0y15NzAEidJNmu6Zj4/yUxCOMOg+pR0y/WU35Mk49HLUZVUmdIl12FmPtWahCWmxXZ0BjdxQNdpGzTu9ilai9oThzSZ0vGZCoKqomAqMQvNWknFUhyKE48Td9yJ/kbQZKE6aQW1TTrB60wCFWYdTSUiySRQfk9UJ2rT7PR+U45qm9acjOSDNjIFsH7cpSDysOCPSr+jY+YrnyyT7DpFwYk2qSJmFLVbTsRUk7EGbMrKf0u1HSQz7RSjJQ0UvJasGuGMmj/i5h4P7t4GJs/9Js6OZYotJCqU7ySEszCGhEioQOcribkKTJUp/3mSksDie6OT7d1AmdKpCLGvpZI46SjYBxuHNJlKKtmGvUCqIcWmErMQaCWaKhZXVnwJY7gSqZjjQYtOi2KodmA9qqTRdRqLZ/mCvU+TTCWZZAOJY0z7qEKrqtec1fBJa81LZaq52xDyNT2Ln+/rMQZ33Gkw/04lR3Sd3Wr5eFL1UQQ5FpIufq4rAomIKpkqJ6hJ1D+Vzyq/E7LJS2Da0TbzlS1kKvcoav6I26gB4OYZ9vj1jFz5F8Y/8TFo2RPfhuAo4yRzsP+5JiEZ2ma+kHMob5oM1pu2WpgahMjzv9MhU8W26vNv0Jg7ECrpgcYhS6b8kq16GYSo/CjRvyFEpZkt6cQluZROdIUonE+tbSpmQos7HjS4VUO1A1WthE6xSU2DQogKf4+g3U4YWjXNfDrEMWpyU53gS5U0tXubK5Cpfb0nVBzLHnUV7F4Hy5+I/R2dCB+TyDqdavIyR1PA562qxFNPiQj6XlZhfgp7r5KYdnRqhgb5aqk8myjCFLdRA+Dp/6XnhudYP/5y6htXwYPXKzlbmW7UdAuw65BrTxkKgso49qcTSXJef3v5b/JUGf5oviS+T/40G0k2AkHEyZKpdoRfslX1r4kaxHEMvtUXQVX8LLEyJbQc0D0yojpRZmJmtHTM8bAJVWWiDfQbSfBiec8oCZnan81XqD26DsfGylTMmIi6LtVr1snjlc3l6b5nZYmJr/gbE86GHoNgXrwjemuCzQsEpyNJ8mz8JUPU60oGf0/Fjy6br/RfUnfyr/x9lSSPYb+fxJG3zWcqWaqM8mtVUaai5o+4uYW3HoVnf8zWCZfy5szvsHLG5+H1B2D2b2LPG3SfkigvuoEmOspU1Jqk0mddldODrqnORA0rLVasGhlamegWkplv2wuZanfgYKE8N1BNOp43Ri1WceM7iOEnDcHXtPL5kqjJhSxOSs+kUzhO+GYvimwFqTwemrJ5+sX0NdBvJJE87Zk01e/U3gAVKokDo//Z6kxYfsS1j7ss1xWkYhakkjxeiouC07iRTK6pmBbBjzw1cPQH4YWfS0f03sPD+1cIDqhJq3nHBCbJTRLyX5a4sL4mHdsmbCJX8XEMWmRV1LggEuYhrt+RGcVdl7pU/DXrpBrQfVfTEfNPJJnasQr+/lEYOoNlM78FAlZN/Ajjml8n9dg3YPjRMOZtoc2D7lNS8tjWTv8995TzqGv1rzVD1v6Tw1/9Ial8C1BQ9mLe8e7AKYWxuGvQsSw68VeJTGc65rag7zdn83SrjR9/3nfb/l914xPyrnbA+nyHLJkqj2jqXhvfJiqSLi7KLujl83ytMgpEzoNOoWOPpXu7XJUFpSadCuxzTSYVScbCIotATZkKejmSOOq31exSv09NLZW/n88LmrN5pXulk/AzFyDDQ/yuNe75u0KQinHj9S8KKhM7QGbHUgD29ak08+VcAcdcBc//FObfBad9KbhvBX+vvCtQuK1AsAqVKDjAryao+i6FOnRrmqk123mI63ec6bcuZhZ3XUGqaTvj3/gNa6Z/ChgU3aCAIPO7CsmIGmuZVMhcmN0P914JgHjvnTRvKUzYjsP+835FjzvOgL9+CP77Weg1JPAngvxak5BHfXN+8H2KIhneejJ4/aMcMecL7Ok7hcYBRwLSzWJE326R52xqzbFzXyt1+7cweMNjDNz0NM0Dzlfuszfnenm1VNeochKUhNTo5MUKew6uqy6StBe6BJlSVRP8hKn/5udo7j6MpoIPSWxW8IgIrIziwuIKHY+p0gFuTKZiFIUos5EKKWo2lKc9MpOkmnyY2Wh/qxqZypYt2Crqn27izdgcYDHHg87RksvTvTb6Va/dWYjkC1CmhBDQfxwcdrp0RD/lBghQQ7yajqYRtF7dORVnZZ33POzZqJCxoHPIqKjoBSnquasEJYQeU9h8ZV2X0W/dxuhld+Cma3Gn/ThW3YQwtT3+HtVmIshU2Pzyrxtg8yK4/B5aeo1GbG4oHmpO96DHe++C358Jf/swXPkgpCvHc1Cwka4juW7hdv9vRZGpvCsYuPFJpr30WRr7H8mCU24lX9MDgFQKRkwOJowetm7by8pt+3DcLG975DzGv3YTKye+U7nPJQXYcy49FUlJRY3IJEEQGiV34opmdyQy1XF6coCh4zfivYuD1v+HY569hhMfOY/jH30XY9+4GXaujmwbWgIkwUsphF7STp1SBrWZ4EdfF/J58fcjTJexJqwDYP/WqSYfZZZUOV/5YqaiTumOB92krX6UjwGV8V+7cxmttX3J1vevOFZcsGddDY3rYfnjgb+hoxoGjRm569TwDzOcoFWIQti9jGsbuSjEmKKi3Q/i71OuuYkRK+9FOClGLbuT7O4NsW0gROlReC6h6hNQE3Rs/p0y9cbJN8CkcypTzORcGDoN3vVzWPM8PPGdwN8OmltkdGzy6Dgdk1nJZzEuHqkVTzHjxU+yp+9kFpz8uyKRAjn+4xRqj8SIVA0rpn2aXruX0uOt+5X7rJPZPui7SdrqKH9R0fg6tQEPJg5hMpX8xXCFoKZ5O1PmfZPGfkew9Kivk890Z8JrNzHg1uPgd2c1yMxEAAAgAElEQVTAS78OTGAYHnGT1G8q+QAp2WUoRiSFkabadLRSEzWA49JHHIiopGLNuUTqR/Dvq0wEQYugyqIbqn4YJlaNI1utAWZYFZJRt3NZYCQf+Py4Jp0HPQaHZkT3ri1JvpuwSVU9t5tv7CuS8jBCoKKo6aYpiFwUYjYGUaq4Sp+d1+6ltnUXr8/6Po6bJ/Xsj2PbQPA7prKJqYnYkFVs4jYugIdvgMNOg9O/ClSqH8X39MjLYNY18OIv4PWHKn7bhCRD6aZOdbHPF6LGK38rov2q5+h+/xXs6z2eBafcRr62V8VX4t5z/z3aMuo89vSdwoiFP4Vcq1K//XNfEjcLkwLh/nuqWiA8ygKhk3D0YOKQJVP+AaK6YxWuy5R53yCd3cuS437EuolXMveMe3j+/CfZ8/ZvQL4VHv0K3DQV/nA+vHIr7JNydHgJkCQh/HrKhH+Aq9qww+TRmgiJHswGd9g9SpJwzluUVEmJEME5hUBtwQ4aO2oOx8H3InaSjnn+cWQ76PnHTpZCUL97eWAkH/jUj3TBEX3Zo7B7fcX3PJKSZJILe46q5pmSUjSKE7untPXd9gozn7yc6S9+kpHL/0S33StwdTcEcX5PEb8bd61RtzPWzCcEtfN+x56+k9k09iI2HPZeMq/+EbaviG5H8HNUeba1EaaXEjeCph3ST6rHILj41qLpuNwsXzJ+z/lfGDETHrgOGpaXfM90A+N/hsp1NMMU6LBzrp0Nd19Gvs8Y5p9yO7m6voFfi3MrKVHznRTLp3+ObnvX4c67Q6nf/nuahBCV+zPqZOH3oBb1GPHeaOTIOpg4ZMlU6Yuh9sCHrHqAwRseZ8W0z7Kvz+Ft7XuMZO+xn4Brn4Pr58JpX4F9W+Hhz8FPJsJdF9HzjXvItDZW9iPBYHM1UiNk824JwVG91jAzX9RECKUv+YCNTzHh1R8xfOVf6b39VZzWvZFto1Q6VdLpERnVxTabD0+Cqes4rDIJhBGuuCSyel5zbQjzQYrEvgYyLbtCyVTJmJx5lfwgICO6p7AkSZQbpjCqFEMtjyxVNTmI1mYOX3gjM5/6IPVNm+izYxGT53+HEx85F+enU+C+j0pH+11rK9rqRgJG57DT95mKvU2rnyfT8AbrJlwJjsOqqddBqgae+kFMQwMyFTK3pFNOm1+Z68L9/w2Nm+C9d0CPgcXvlT/HEnKVqYNL75DE/t4roHVf8ZCJq4U/BxKoJyFOpFSunwd/vAR6D6PxvX8LNKl7iDtz+dy1fegp7Bw4C+fZH5fck7A++5+j8nsjRIDPlHrR4fJxrmJdiAwK61hc6tB1QE+sTO1ax6QF32XnwFmsnfih8O8NPFxGM536RdiyBBbfB4vvY/SKGxiZqqFh6ClsHHcJDSPOUD93Aa5IXsW7soSN2vlCyVSMzxRAt71rmbjg+wza9BTCSeEI3zn7jobBU2HwlLZ/BxwONfWRBKg179INdWdwVek+2ldFz1ynMjmHloqIjeaL/t1Y+T+oTErcZLntTYBQM18Jwes3Fsa/Q/q5nHxDiSNwMfw+gZnPJHFheWSpN7FHOldvXMiUf36U7rveYv34y1k244vkM93ptm8d/ba8xKT9C0ivfApeu1d+v99YGHcKjDsVxp5MSzZ4jMaNR5NI4ajFKna+mHMz+fp+bB59AQCt3Qaz58hr6D3vV/D2z8DQ6aFNdclUmAtByefP/QSW/QfO+wmMnFXyvfI5rUI56TsKLv49/PFi+Mdn4KJbwHGSq0T+c5aRItXIaGU1bNMi+ON/Qff+cOVDuOmBsH1XbL/CfrviOTgOy2d8nmOfvBzm/BZO/lxo+4qIPMUNeHO2kEtRuPTbOofG/tPJ1/SkWSHABQLyceVcqItuE/XoVIIv2hOxd8BxnNuAC4CtQohphc/6A/cAY4HVwHuFEDsPXjeTQYjKXUYkXBce/AQIl9ePuzEwUskpD0d3HOkUOXQanPFNFsx+gv6r/sGQdf9i8MYnePmMe2kccFQiB3RXJC90XJlETdXMF7zgRCpT2Wb6vnITo+b+EuGkeWvGl1h3+BXU799Mj93L6LdvOWNya2DrGzJbtpuV7Zw0DBhPn74TGVc3jn19JrK39+E09RoLjjxf0iKzqmHwUWRKZWcUHAqv4msV/NtCJE+XkQRBkn0smWoopEUIM/OVX8rMD0lVYPljMOnc4sc6WbZNyFS5OcirJt8jKFdAPidTOzxzI5m6/iw4+fdsH3ZK8fD+nqPZ33M04yZ8gm41KUkwVz0r/3v9QUkegSN7T2DH4BPYMfgEdg4+nlxtHyDeZ8okGWvU4cj5YucaWPovdh75cdxMffHjhiM/Tu8ld8ET34UP3BvaPOj9ECI+11nYHFJXU/h8+RNSGZtxGRz7kYrvlT/XbM6tfGcmnAGnfw2e+h6MOg6O+6h2BC2E5NTKKpApFQK35XW480Ko7QVX/QP6jMDZ2xLbpzCEvV+7B86k5bCzqHvhZzJYpFtw1r+KABXFudQr8zTujZsZv/hn5NPd2DLqXLLpq2HiybF1ysqfj8o8YRp80Z5QUaZuB34F3On77MvAE0KIGx3H+XLh7+DkM1VAS640G7nrykEfqrq88ntY9Qwrj/se+3uODvxKRIAKAtjRdzrbj5rOyiM+xSkPncjw1fdLMpWwfENS6bL8RWhVDN2vC3E0D71Hb/0H/v0F+u5czeZR57HsyC/T0n0o0LYQNdW9kzHjC3J9Piv9Mra+LsnV1tep2bSEw3Y/jFNQOtaNfz9LZ34bUHcoL5YjULyv0Vnt9WR8FfUv7rxh6TJUSpdFISgNxP5sTKqBbW+Rz/SgpdtQtZNMOhd6DoF5tweSqSSJEsN2lyrvQVjNrgoy1bBcmpQ2zIVpF7NwyldpdCqdfqFAThynoKxOgeP/W9Yk3LwId+Wz7F/8GMNX3ceo5X8kn65nyXE/ZOuocxXyh0Uci7nOKMIUufd65XeAw5bJV4Cve03pXnDSZ2RU3JqXQhNh5kMeQlyi0FTKoTZTmXqlNp2WptP7PiLv7QU/rRjw+TJzm4f92Ty9yknayZ+H9a/AI19BDDuK1lzw3K1CpgJ9DXN5+lAT2S5s01Tc7DUsk0QqXQtXPQT9xgDxNQqjSnpFRX/uPOHLDL37TJlg98xvB34nSCFSWTP2Z/P0aZjHuCW/ZNvwM2it68+Qdf8i8+e/S+vDMVfAkZdDz8EVbYWorJuoZH41DL5oT8SSKSHEs47jjC37+ELgtML/3wE8TQciU0ETW0suH0wUGpbDY9+ECWfRcPj7IGShDAzpLcArtgqQr+nJthFnMmTdv1h61NdozSkmmaIQxptUmcpW7s5b8y51McmtlM18O9fAI1+BpQ/DwIk0XPRXFmeODGxbEhKdroHBk+V/BazavIcNW7fTY89KJi74Pv23vFg8lpRMqSajjJKCY3c2rouzZQmjVj1L/61zEE5K7sQmnAP0iWwadT1Rk0CFAlqGuLqJQQkvhZAEMDTvzbY32d9nfCiTq/AdSdfA0VfA8zdJR/Q+IwG9rMphz0BFwg9S3EpMFq4Lc2+F/3xD+tpcfCtMv4TWZQ0QYtoIfP9SaRh+NNnBM1jY/304+VZ671jE4a/9hBkvfZple9ex46iPx/Y3DCbWitC2rfukmjb13eypHQzNbb5lLbk8HH8tzLlZEqqr/x347E2iHoPIVH0qC/deBW4O3nsX1PaoaBdmctqfzdOrvozYpFJw0W/ht6fAvVeSOf3vgX5IKspL0AZJJaVImKKedQXsWAl3vAsQUpEa0Kb8RmWJh+gE6FH+hPv7T4Hpl8Dsm+Uz7lW5QQquESki84MBtOzZwbTZn6e5+3AWH/9j8jU9eeuorzJ115MMWf5XuY4+8T8w8Rw5P0w4s+gGEOguYapMdTAzn66tYYgQYhNA4d9KKlqA4zgfcxxnruM4c7dt26Z5umQIegkCWXA+Bw9cKyfad/+SmghJNyrct1yu3DTmQmpadzNw09PJTB5CJLYD6/r0hPs1FO5Bthme+TH8v+Ng5VNyl3PtC7hjTwlsB/F1t/KuwM10Y0+/I2gYdio99q4m0yL9BlSTcCZNeRF1OyteRiEkuX7lVjnp/2QCk+4/h0kLf0CPxuX03vEq02d/jhl/ngl/vRre+Ke8TwEI29VDNNGKUkAh+h4HOYh6iIzYaXiLpoDM58XfDfrwmCvl/Zp/V/Ej79momrZdNzzgQmXBDlp4i2Ni9wb440UyGeTYk+C62XKRIY5gh5/P65NI17J70Czmn3oHm0edz+Gv/YSxL31FqrEhiFo7TdXIQCy6B5p3w/HXBvtV1naHU74Aa1+CZY8F/kRocIDCuxq0WRvy4ndg43x4z69hYPB4CyNTocSmWz9JzJq2M23O56SKWNFfBTNfiDIVh7B7kWlcB3e8G3LNMsnooIklx6Pe83TKiVSJonxPc3lXpphws/DMj4K/E/BcY++REAx46ovU7d/K4hNuIl/TE4B8TQ+2jL8ErnkUPvEKnPBxWDcH/nwZ/GwaPP4d2L5Cq04pmPkatjcOugO6EOIW4BaAWbNmtcvVK7PgF38uZeKLb4Xew6jdHe4QGOVLVD44dww5iZb6gQxb8yANo9Sz0srUCEmVqSAVziXYiNGGVMohk3ZKXsxM2pGL9bLH4d9fkDurqRfC2T8oqg/pVLitP66Asn+BbOw/A4DeO19jx9CTlUhkRcRNNr5MUKR5RQC71rX5xqx6FvYUcoj1HgGHv5Ol3Y9h64DjaOk+DIRL34Z5DFv3MCNWPQpL/g51vWHyBTD9YumknJa756gJL4ooRCU8dJxoMlVu3vYj1Mm0eTfs2cT+CRFkKug3+42Rfivz75SLcjrTZoJVJFO66p2HQAU6m4dF98rcRW4Wzr8JZn24hLFEmg4SmBXcdB2LT/g/6Wv1xm/gT1vhvXdCfaVqGaUoxqkUiYmYENIJediR5IYfS+6thpLDXqSac8xV8NKvpJow4cyKFT48bUX88y2fL4etvp/ei++EEz8FU94V2i6oQgLEOEkPP4r9Z/2QAY98hvFLfsGK6Z8tOaxC/pQ34GUIGqd1TZs5+umrENndOFf9A4YcUfGdqMzdcZvS6HQBAvofJss/zb8DTrxe/u1DsDIVc63z76TvqodZNuMLxbI3Hoqq3qCJ8M7vwRnfgrcekRutF34Gz99E/cgTGTr8PWwdeTZupltoP8oR7S8Y27xdoUumtjiOM0wIsclxnGHA1gPZKVMEOg2XvyybX4On/hemvgemXQyEm75Sqegot3JThUhl2DLqAkau+BPp5l247mCl8g05VyRKRglhJk21xay+Js3efJv836tlM9zzOXjjHzBgAnzw73LB9CHKcTq0VEQB/kWssd80BA59ti+SZEpj8UwSKemhtrmBfltn02/rbPpvnQ17C+Hv3QcUorYKkVv9D0MA69/c2kYmnBS7Bh3LrkHHMvR9vyC9+lkZzfnGP+DVu+VvTL0Qpl1MPnd4WBciJ8Oocj5xpRO0Sv1sewuApj7BzueRmHk13PMBGZU1+bziOWTi0PiCzNF+ZfHPtjwIoaZlByMe+x9Y/S8YdTy85zclphUPutFxgUecFCumf5bW3mOY9MrX4dazpVN331L/nagFMm7xjCJbgfd45dPSgf49N9McQCRc14tUq5VO3H//qNwYFJQ7DybBAX7lu+euN5k871tkR55IzRnfimwXqkzFvOtN097PzqXPM+6N37B7wFE0DD+9eEyF/IW5hsShfJzWNjdwzDNXUdu8newV91M7/KjAdlEbz7h5VCmY4dQvwsK75Rp38e/K+hykTEU8021L4d9fYufQk1gz6ZqKwxX3KV0jCfOUd8kE1wvvxpl3F9Ne/iLZBd9ly+gL2DDuUrLdgu+NH1HiQkdTpnTNfA8BVxX+/yrgwQPTnQODWOada4H7r5US8fk3Fbd3ulnBg8b2prEXknKzDFn3b+WH7gaULomDSeFg73qdfCtj37iZox88S6pSZ3wTPv5iBZGCmEkgxkbl3/Xna3uxr/dh9N6xCFB7MSpKGSTI4QWyqOgpD53I9NmfY+i6f7Gv9+Fwzo3yWm9YDpfeLlWMAdJ/KJsPN0O1uil5f97za/jCcnjf3TKL86t/gdvPZ+b9JzNxwQ/ovf3VCmkniidk0qnQxTUubUVUHq9Q/4RCJN9+HTI18WzoORTm3U4u75aocSomkijCpPIe+BeAARuf4oRH30XvtY9Jk/TV/w4kUpDQ9KvYbvP4S+Tmo3GjrJSwYV7J8ShlO+65RpHSQKI157cyEea0i0Lz3BXJybRLYPAR8OT3SsyU+QgTrEoeMe+aMq2NzHjxenI1vdl/4e8Ca+r5oZsRP+8Klh7zLRr7HcERc75AN2+ThEnUbjIzVE3LDo555kPUN21m4cm3kB82K7RdJp0KNfXF5fpTIlO9hsrgidf+CpsXl3wnSIUKfRezzbIeYm0PFh/3w2L0tR+eA3sgeg+HU25g4xUvMO+0u2gYfjrDVv+d4x+/iCkPnS/HatOO0OvpTIglU47j/Bl4CZjkOM56x3GuAW4EznIcZxlwVuHvDoPYF+PpG2HLYnj3L6HHgOLHurmXggbSnr5T2dt7AsPWPKichjGXkEy5rghUI5TJVNph8PpHOeE/72LCazexd9RpcP3LMkomE5wAxGSHXX6fGvvPkGRKMVlp+QKtcp3+tWbgxidpre3Ly2f+jWcufJnXTvmNtPEPOSLQiSFK+i4hJ5k6mHw+XHKbJFaX3MaeATMYueJPHPfEpZz4rzMYvfQ2UjnpXxVHHENJfcw4jPalCDm2bSmk62gJiWKFCDNTukZG8Cx/jJbtpQkuD0TEYxyyrktNyw6mvPJVjn7+v2mt68+ic++Ht382ML0JKAQdmGx2DzsVPvIY1NTLCglv/KN4KOrZxS2eURuYindux0ppYpl5NWTqIjK2F96dVEpunnauggVt/m9RY1/Fv7EmncLJt3LEnBuo37eRRSf+nNq+w2LbhZaEUahf6KbrWPS2X4DjMOPFT5LKS5cElY1a0GZDxTzoNUvlmjj6mQ/Tbe8aFr79ZnYNOjbWdSFMaY57z5XN1G//DNT3hie/W/KdoLQyoRGCj30Ttiwm/+7/R0t9sGu0EPHva6sLOwcfz5Ljf8Jz73qBN475Nq6Thn9/Ef5vsiRsK57qeJk4EyCWTAkhLhdCDBNC1AghRgohbhVCbBdCnCGEOLzwb4eilsEvRuGztXOkHffoD8Kkc0q+EzapRZleQuE4bBpzIX23z0dsX6XUJJ/QzBe2+1cpH8JbjzL+/vOZ8eInAVhw8u/Yet6tFeaJcpiRqdK/G/vPoK5lO/VNG5Uyf+soU34i0KdhAbsHzqSx/wxEKhMbORdXsTwQtT1g2sW8dvKvefbdL7HkuBtp7j6Cia/eyEn/OpORy/+Em4vOMVPMx1OG+pgITS1ysm0pDJiAE6EYRN6loiN6aRkLFaIbtVjFbSpEPsfQt/7Mif8+m2GrH2DV5I/x8pn30dhncmQ7I0T63xUODpoEH3lSEvR7roAXfyVLuiSpV1eGKGWqgmi9/HtJJGd9GFDMCj7xbGkWfeZH0NoEmJtga0UzR77wcQZtepqlR3+d3QNnxpqpIXxMxJ3Ta9XccxSLj/sRvXa9wYRFsgZhHJdyQ+rrqZgHvfs0ceEP6LXrDRa97RfsHPK2kmNhCF9v4shU+LGSDWu3fnDSpyW5Xju7+LGyT/HSf8PLv4UTPkF2/FmRfYq7Vv98mavtzYYJ72fOmX9H/PdzMm/d8ifgrvfAL46Ep39YLFcV5YgfF9nc3jgky8kELXTZvJDhwg9cC71Hwtn/W/Gd0Hp1mgkWN495NwKH9OLwxHh+5NyAzLYRCMopFPU5IP0pbj0L7n4v6dZGlhz3Q2af/TDbh50aqoj4EW3mSza4d3tO6DsWKSlTFaUMFMogeISppnk7PfauZtfAY9qOxXQ3uqhzfIdztb3ZNPYi5p9+F3NP+yNNPUcxef53GHrHSdI50+ev5kdYosDQ1AYFaCWGbFgKgyZGTkyRk1bf0TDhTOpeuxvHbbseFTIVtcOOvL8b5iF+fyZT5n2TPX0mM+edD7Jixg2IdO1BrdelXOqn5yD40D9h6rvhP1+Dhz9PXSq8X3HvnbLPVMseqS5NfQ/0liqQUiJLx5FOw3s2wcu3ANFKQ+yGr7mRvn+/nAGbn+f1md9lw4T3k045sZstCCcwquWjALYPP521h3+I0cvuZODGJ+PPGXI9XoLdKLhCMHjdvxm58l5WT/4o232+WnEqaBiJjhsPsUE1fhx/rSxO/vh3QMjiwkH3soJ0N26U9Q+HzoAzvxVPSGO+EKo4DjoCzvsRfH6pDATrfxg8/QP46TS46yL6r/kXTj64eLMlU+2AYDLlwmPfkjL4e34t5c8yhDn+qZRYCUJL92HsHHw8qdfuUUomk8uLRKkUosKIK17ktbPh9gtkArnGjXDBz9jx4RfZNPa/ECmpSqiQKcdxQm39Kk72fuztM4l8qpbeOxYphYdXlEFQ8MvxutR3+wIAdg04ungsLkmd6e7cj12Dj2Pe6Xcz/5RbcbsNgIeul2knXvtbhbQdRqbqQxQrD9H+PgHHsvtlHrFBk40UR2ZdTWbfZgZuerr4kYqZL2pRDryWph3wj09Ln6Q9G3nthJuYf9qdJXU0XTd6AYwdoxGHEy1iNd3gkttlcsy5t9Ltvg+QzgbXrqyLybIddf9LNjCv/gVaGqXpugDlEitjT4IJZ8ks8ft36RdmbtoBd15IesMrLD7h/9g4/jJAfUMa5fSeJNJ52Ywb2NN3ClNf/jK1+7dEfjc2Oi4CmT3rmTL36+zuP4OV0z5dcizenB/83OPGQxSprzhS20M6o699EZY/Hk6u/Z+7efj7x6Rv8SV/gExd7LXEPZqwcVjcsNbUywCIKx+ET78qI4S3vcm4J6/j5H9K/9Meu98qaatCztsTXYZM9dzwnMwIfMJ1MO7kwHZhL3yc4hLFkDeNuRBn5yqZgiEG2TIn3jhELVhFX4mNC2RxzdvOliadc34In5wPs66mtrbUL0p1wkuHsKm4+1ROXkS6lj39ptJnx6JYkxtUJiiVUUnRhMp7Nn0aFuCmatjTb5rvWPT5oibZOC4V+NOOw46hJ7Pt8kekw3qmHu67Bm4+SfrXFGakbmHKVOwkm/BYwzJ5ZOBEoyhCDj+bbPchjFjxl+JHKpFQUTv3kkXVdWW29V8eIxW9E66j8ZqX2DL6gkB5Mc7XJTLHT8S7nDjnTSoFZ30H3vVzUiue4tin3k9d0+aKr8UqUxEDtTj3uK505h0xs6TWXZjPVODidsY3oHkXvPhLPZVzz2a4/XzYsgT30jvl8ylAdUMamQMs4rGWPzeRruW1E35KOt/C1Nk3BOaf8qCdoiOfY/ILn8URLotPuAmRKk0qGuczFWbOjxsPUXNl4JFjroK+Y+CJ79CaC1bDS8bD8zfB6ufgvB+H5gNLikRFqPuNhXd8DT7zGmvPvZOdg45n5Io/8bZHL+CIOV8ofi2pJeRg45AjU3m3siRLprWRSbO/jBg4UTpbhkDXzBclcGwdeTYiUy93jTHIJlSmohas7KbF8JcPwC2nyTIaZ34HPr0QTrhW7gKonOBUJ7yakEEct1MIuk/SCX0xKRH8kvsRtDDERdx45+y7fR6N/Y4oqVEWJxNHJnCMmSgjzWYpRzqsX/u8dFrPt8I9H5TPatljdAt5DnF1whIpJwANhZ3eoEmRYzzWZzCdoWHiZQzY/Bz1+zYAemkr/Ch2d+MCuPVMqUgNmgLXPgfn/IDWdGX2bA9x+Wsicz5FJUWN6q+ICOOe+SH4wF+p37eeY5+4hF47X2/rSyp+fokkU17TlU/C9mXSrONDokVs2JFwxEUw+9eIveFqTuAmY9da+MO5Uun8wL2kp5xfQlrjwv09RHKXiINB96ip92G8ecw36bdltvSTDTtnFJmKeqme+SF9GubzxqzvBpYhi1NrlApCByBqmg1U2zOFFBibX4Ml9we2K64la+fIdArTL4Wj3h/ZDz/iJICw8l+R72oqTeu4M3jtxF/w3AXP8daRX6Fh6CnFw7oWo4OFjtWbA4AgMjJxwfeobd5G9t2/lvJ7CNKpYBNWPJkKH935mp44k8+XeVxywbZfD9m8TLqoSqiCBmK3PauZNvtz9PrDqTIJ5WlfgU8vkpEdZeUbylMZqCtTwdcbVXIHgnf9jf1nkM7vp27nssi2uaBK6cQvnI7j4ORb6bVjMbt9Jj6IcawmWQLHyvOGHysu5qmUzHF23Ry48Newfwf86RJ6/+Vd9Ns6p7RNSsUMm9DMt22pDHUeMMHIQRpg42GXAjB85V8B9SzoYci07IJ/fg5uOV0mVv2vW+DqfxUTIEaRtagUERBHTvSUKYhZPCecwZvn/Q3hZJj51PsZuPEpID7tCkSTv+I7PPtmWS9x6ntKjicu/vuOr0Ouhe6zI8hH+YU2LIfbzoWm7XDlAzJFCKXzSVzEYvG3NXOAhRH+TWMvomHsBfDk92FdsHUgUoULUzlXPQfP/piNYy8qUeD8iCdTIWa+mECTqPUmdGxPvwQGT6X78zfiuJWZ+ltzLmL/Tlk3sc/IkpRBKoj6Zi7vht7HOAXbGzfZ+v6snXQ1W8a0JXzV9WU+WOhYvTkAKCcigzY8xvA1D7B6yrW0Djk6pFUbgh5Q3M48aqeQSgEz3gf7d8rkhhHwJj5VMuWfBOr3bWDKK1/lbY+cy6ANT7B31vXS9nzalwP9w0Bel/99UY1aDCutUxNT2ynoRfec0LttWxjZNrSgaMy9SjnQe+cS0m5rifM5xPtMmZQyiDIXVRxKZ+DoD8D18+D8m0jtXsvMp6/g6Kevovd2eV/qM+nY/iYuprvtTeg3DjJ1kapX3OQOsLd+GNuHncKIVX+jtrmBbDabOJu/7KjL8JV/49h/ngXz/iCVlk/OhSMvKw3h8cUAACAASURBVLlxUXW94ohcVD60yA2BxuX44Q6awitn3EtTr8OY8cJ1DF3zkBJRDTNnFDPiNyyH5Y/BrGukCuF1V4hQ36fQ+zdgPBxzBT0X30X93nWBXylxRdi8GP5wjiybctU/YdRxxUMlZErVzKdJpkKTCTsO69/+A+gzAu77sMz4X37OpBnxm3ZIn6L+h/HWMd8IbRsXsBBk5otLEA0xqTLC5ohUGs74Jpldqxi+6r7KvroC96HPyAoQl9wWumboIOpdjVOww0yhoObj25446OVk2hv+ibSmeTtT5n6Dxr5TWTXl4/TLuRCcPqmtTTpVkbspqi4fRO8cHceB8e+QSfQW/QWmBO9i/OG52ZyAmDIp0Gbrr2newXGP/RfpXBPrJ1zB6ikfY9zYcfTq3j2yveM4ZNIpsjmXTDq6HpQfuiG9Qfdpf88xZGt6021rDJkKq2Afk3/GcRz6bJ8PUKFMxZncjbLvqihT5cjUwrHX4Bz1flY+8itGLv41s568nNln/4O6YVOjz6eDhrdkKD9QHzLG02lHKc9aNueyfvzlHPX8tZzy0IkIJyUzwvcYBD0GFv4t/f96tzfd9tbTWteffKYHvXa9waT536bv9oXsHjST2ot/DkOnB54zyrcwzmcqatMQaY4ydNGozaRo7TaYeafdyZEvXMcRc77AunQLjLs+sl1Yn4qbk5dvgVQNzLq65Li/AHs5cnnp0B34zp/6JcTCvzB+yS9YcvyPKw4X+c76ubL+YW1P6Tg8sDTrv38+UPFviYt+iyJakc+0W1+4+DbpN/qPz0iy4C8vFLGeV/RJCHjweti3DT7yOPndPUJJdpwfaBAZUNm4RI3RyPE78Rz2D5nJuNd/xaYx7ylxeRi+6m+k33hAJrwdGZ5sNAxRTy5qcxO38YkiTJZMHWQUWbAQTJn3TTLZPSw57U5EulapSnXQwhEnUUdGQjmOVB6mXSIr2O/fKfN/hPUbz0xRU/GdcngL+vjFPyOT3cPLZ93P3r4yz46qKFCTdsjm1GV4CL5HjhNPpgInPMeRflNboslU2OIZF1WXcqBvwwKaeoyktVtp0rm4yK7IulAxQykynD2OtNZ0Y8f0a9gw7CxO/uepDNz4DNnR06LbJEU+C9tXwKRzgfC0C3FO79C2u2wYdjoL334z9fs2UNuyg5G1e6lt3g77GmDjQvlvS5syMKjwH0A+XUcq30prXX+WHHcjuw+/mBOHhtZPj04qGfNwwsap40Qv+nHPLW48eefN1/Rk4dtvYfpLn2L0i1+DHi6c9KnE/a1Np6C5ERb+SZqLe5berzhTdN4VwYtv7+E0Tv8QQxf+ltWTP8q+PqVFegVCuhDc/T55zisflLUag/oXcw0l/YmZtKIuJzbD/JBjpVPzE/8jN7fHXFE8HpmJv7xPr/welj4s65UOP4pU49bQfse95rXpFI5TOlerEARt/0bHYdOsL3HYw+9l1PI/smbyRwDo3ricSQu+R3bMqdSc+OnApiaibBRhilOmwhTzTNqJLG1WDRxyZMqvVOwcdDw7Bx1fnAxU/DjKB7PjmEXbFCfnIy+DOb+RDoCFhHp++MmUSuZdAAT03Pk6I1bew7rDrywSKVCvW1SbTtFEPpEzX7ApVMFcEfKd3f1n0P/N38o8YLXBjsVhE17cdaaAPtvns2PIiZXHYiY7XZMDRC+sKkEo9TVpdnUfxt7eExiw5Xl21YQvth4iC+KWf7BjlSwEPNBTptIVEzuokani2HUcGoa/o/h5n9F9GdCzTArOtUhStW8buxo2smH9Wmqad1Dbsh03Xc/aiR8iV9ubXjG+RCbZ00OjdtOpaH8UTcd1D/4F383Us+jEX3Hcwq/Q67FvyBxRp3818CGGBsZkUpJIte6VpUPKEHcfcq4gTAjZOfMT9FzyR8a/9lMWvf03Jcf6rXsKnr9emoivfECWLgnsX9u1qMwvcVNWlFLsOA41mVRgdu/i/TvpMzLP3r+/KJOUDpLrQhT3LtnEbVkCj35NppA4/uOF84a3jSPfjuNQl0mXpLhRuU/RZCq6/a4hx9Ew9GTGvHkL6w+7DJGuZfrsz5HP1NN49i8ZGOP3GoaoZxNVfiguj1ddppJwQnwwTjVwyJEp/8S+buKVwcciUC6z1sRMsKBY7mHYUXLhevWeQDLlnwRUnXeFEHJHUdeXlUd8svSY0i+0vbxJnPmCdj9KZCrkPjUOmIEj8rBpEYx5W+B3wia8uAUj3biWmuYGdg04puJY3GSXODrOf16TvE205ZTaMeQkRqz8C81OpdNoJWLMzX4UavJ5C0oq5VBfk67IMt89JlEohBdYDtx1Zuqk/0qfEYg+U9mU2RnYNm7XGVlSQzNZYmxZF13zSgHlmzKRrmXHOb+m14t94dkfSVJ09g8qVujQ8kIpZDqEUcfDiMrxbeIwL+r7s2bSRxi/+Gf03r6QxgGyKO3gdf9i0uwbYOg0WYvQV46rHEkd0E1zGXkqezmKzzuVloEMN58ky5d85HGoqY9Jx1A41tok29T3kQW0C6Qjk0qRCwl4UCLYmVQJmVIx85mkMcm7ghXTP8/xj72HMUtvJZPbS69db7Lg5FsY0j1cCTZBVPmh+ECe4HlJZZPX3uhYOtkBQJT8r5LDqXziUk1kmQ4Z4EV/K8eR6tS62VIVKEOpMqVGpgaueZh+DXNZMe2z5Gr1HAa9iSaJMhU0MdbGOJ9HnaOx4IReXhzWj7AJL+6ZZja8DMDugZWLTVxh5sgkmDF01aQgNLRNqtuHnEg630LPLfF5yiIDIcqPbSuQqYFtJpwgU19c1nUI36TESfhRBCRu8TUxO+jWPjRRBMJ+v7a2RtYIPeE6mP1reOiTFTmRwvrbf9MzsqZegCoFCkpPxF1MpxzWHn4VLXUDmLDo/0AIhq/8G9Nnf469g46Gq/4RSaSgzMynNI/GfiUSYfephHz0HibJ0JbX4PFvA9Fm4eJm7dGvyoCNi34rM9wXYLpp0llvosZaHKnP5QV7+k1l86hzGbv0d4xedidrD/8Q24edlqj6RjmiBIfoQJ743w5KVtwRlalDjkyZZq0ujx6Iy0brIdQp279wTn+v/HdRZXkZ/+4+bKdfgtYmxs6/kT19p7Bh3KVKfQzsXzq5MhWkGigtJiHfaa0fiOgzKppMhZCmuN2ss24OuUwP9vY+vOKYau6bIMQtVFGESUnFKIzDXYOOw03V0G3ds7FtEoX8b1sqyyrV9Sp+FKRCda+NF6/Lk6kWP48Le45YOOIiQ6OOxjn+6pbxiPXLiUGoP6bjSEXq1C/JcjD3XVOSRiWTTgVu1gYu/gP0GgZT3h177qTIpFPka3qweurH6b9tDlNf/hJT536V7UNOYvnZdypFe/nvp4oyFffc4n2QgufqinNPPFua6eb8BpY+QtTe1XUFvP6gjCw96dPS38qHaJUo+QbT1MwXuwkpTFwrp30GhMuevlNYNuMGIHouNeS5RggiTlaZagdEl9SIb18us6pGDISaDvyf9x0FY0+WUX1lnfHv7ltj8uQA8MLPqGvaxNKjvy7la014L1+SyAhdM1/YPXIcZObmDXND24YR4dg6YeteZs/AowPvkUmekrihFHWtKuf1JpB8TQ92DziamtXPxLZRLjsCxZp8fgRNUEkc0MsRZ672HHDDjkUhOilqZNPQsR5XrieVCo9sVHl/gq6puFlzHOkzddZ3pV/lPR+U5X68vpXNS90bl9Nt3TNw7DWQDg5WiSMfUeTFe8fXH/Y+9ncfwfA1D7Bl5Nm8etJvqK0PT5jqh3evZGLS+OU4TsmJc/BXmoM9nPUdGSn64HU4ezaF/qbTuE6qhcOPgdO/nqjPqma+2L4G/G7Yb8e196bLpl7jmPuOvzD/1D8g0rUlx4IQN5aiVXEz/9GgOai+tuNRl47XI0OY+LlA5aSoKieGTf4Vg3vGZbI+4PpS4pBImdq1Fl74OQ1jzmfXoGMDv6K6k9DzmdJUpiImO2fETHlde7cFfieqZlcomhthyxL2DQkO9T2YobXh2Y3VxpO//c6hJ+FsXhR6bzxEyv9+huG6spTMoMkl3ymftFSCLyCcNMVnp48gJwb16qIcxSH8Gagk0AwnYioh7amKflfMGyd9Ci74qcxJ96dLpWM6lURv1LI/ItJ1MLM0HYIf8Q7Q4ce8eyTStSw+/sesmPYZFp/wU0S6VtklwD+3qKRdSaecyD7FpVdI5AuXqZPpErL7Gf7UpwPLzThujuGPf0q+L5fcWpLDy0Ok6VfBnF/eN+UqFAegWkfjgCPJ1vVvOxbRLjaSVXNzo1LPNdD9wCpTHR9e9ID/bxUo74qmXihrsi0qLS/jN4nEluL4zzcAh9Uzvxz6FdWK2m1mPnUhN9BnSoFM1YQoETXplFSmADbOD2yrlbRz/SuAoGVYCJmKUSKiEHe3wn47Tv3wUJtOFSehvSMLtSRXRatTkfK/32y2ex1km0r8paCSwNRl0kqTna4y5Z0jCGF5rzyYKAKhRFfh2YQXoVbcdPnOLVXKgL7O+jBc9DtY86IsTN60o+T3M62NDF99P/kjLpZ5u0IQRyqj7pP/Hu0eNItVU68rFkQvV8nC4M0JSdKuRKurMWbYgOeajgqhHzQRzv0RPTe+yJilv684PO71/0f3La/ABTdB/8OC+xQyb6ZSakShfCyq3quga1UpTRRpHjeKTAw/ZlSqiuDxZn2m2gHRCTTj23vhqh5UyZRynaX63jDpPFh8X4lfhL9ocd4V4QvRqufg9Qfg7Z8l13NEaH9UnTm9ySBJzo5UqtLhPs7Hpfi9QOf1lKwL5qRC/aZCzXxRDujrXgYnhfCIWhniXshoeTr6ek0XXcdxikpJdvCRUN8XVjwV2UY5ysdXk8+PSlVWbUyEEVoVMhV2jrj7ZBLRlEo5gYugyrse5pAfR/6CzlGbiVBsZlwKl90l66nd8S66Z3cUDw1f9TfS+f2kQhzPPcSRyiiyVZdJhSoKquPCU+JMg1s8xC28gWbUuHnt6A+yc9y7ilGLHvpufZlxb/yGnYdfAjPeG9o8jOCpKv0VZj4DMqVyziiF0MQcFzWWohRFlT6Xb3LSKafDlZKBQ5BMmbBrD/6Hp7r4hZklAifoIwvlZZY/BkinwHJn3eYg5918Dh75MvQZDSd9SrvGmB+eFJ1EmYLKl151cAeSqXQK6nrKQrYhZCpMmYoMKlg3GwYfQW33voGH46RipQKzIQj77SQ7Km8c1tXWwLhTYOVTkbbqqEWrRAHyIvnKzHyVqqxaX8Oi+WTR8Wjbuu59Skc8ALUoqsrfV1n0w1QZlajH8nPE3t/J58P774UdKxnx9/+ibt9GcPOMXP5Hdg86ltSIoyKbx6VsiZojyjeVfqgG5YB83xPlsAv5rkqFhiBlMVZtdBzWn/R9WroPY9rsz5Fu3UNNy06mzbmBph6j2fi2/4lsrlsNotjed72ZdPQz8UM3e7rumhFLzCOORz1/lcjm8neuo2U+99Axe2UAU4dAKH1YymY+VZ8pkBEh3QfCq9LU15JzK9bI5qAIqfm3w5bF8M7vQk23mAKoatdaVKYSJmvTcZyU36vsV1ElGHGMJFMBhCFM/XDdkMRvbl76pY06LnChS6fjdzdRUXfx2d5Tge1VF13wmUkyKTlmGje0qUoh3w916PY/n21vyvHXvX/Jd8p9mFTMXkKIyDwycbndgkhTTabSt6gckaknEkRLlnymsBiFOb6qmr4Sq97jT4crHiC1v4Fjn7qc0ctup/u+9WydelVs01RI4XZQzXWmnxXfQ03aSWTmi8z2HgNdguHW9WHx8f9HfdMmpsz7JlPmfo3alu0sPuEmRF3P6HOGvCM6vk9JSGfQs1FRDBMFqfjgOOFO79LXTW+uVEmpU64km7hnHEx0zF4ZIJpMqf2GN1Aj7e1lCC1aGdQ+XSOreL/1COzfGUicmlvLFqGmHfDk92DM26XfFWalLzx4v6FKvjxUOE4q3qcg0lZ82UbMlIrdzso8XFHmvMCIvq2vyySIo08IJDDdFRaEyFD4BBF5JZ9pZJqvTafkogqRpr5INcF/Xl9NvsrvtbVXuca4aMq41BW6Yc8mDuhQeW2ZdPhiEde3upqUsqJQm9FYPEcfT/P7HySVb2biqz+kufswmg47V6lp2CZJJ8koqBXi9aMmk0rkQpBoUxrQtvzRq7RzHNg98GhWHvEphq57mMEbHmf59M+zp398CafQgARFcl3j841MQjp1cy9F1jDUVJ9UyyglPeZH6Sak4/lLQZcjU8lUpiRyYtB3a9PpCJ+IyyDfCkseKPGX8lCRo+fpG2XF83N/WLRl6u4y/PASjqouBh4q8nEZRKEUJxGvwOaGSif0KHNe4IK+drb8d9RxwaG1ChNP1DUpmYQ0z9t2DvlMajIp6DdWlu9YGe03FbZrK16LEFKZCiFTSRd7lfpvUQhWiOLPa5oUtXwxUiUIpjlvdFRvgJqRRzH39D+xt/fhrJx6PXV1CpXQCSdNSdJzlHyWcCHLpJxEG7UwpULVH6h8oVXpr7fxXD35Y2wd8U42jzqftRM/VPjN6LbhplCNTVOC8aCbxiTq3YhLrBq23qiQsNDa7hrm0CT3qT3RMXtlgMj6WQl9ppIw4KBdUeQLNfxoWV5m0T3BypSfYG15XRbYnHm1LONQgGl5i2I/NZz5SnYKNWqhzxDjODloCmS6VaSNgOhFOdDMtO5l6DkE+o6RDotl51Uxt0U9fxVSFJwpPvkkW+NNVuNPh9XPyyLFIQhaoEsU1r1bJSkfGEKmEpod4tKNxKUBCzTNGJorVPZM5WkQVN/1IPNtIj+4EjNqMt+jlr6HM/uch9l42KXKKkZoHcIEJY38SGpiSYc4+4dBN+TfQ3n/VMZwUcVPpVl00q9Y/LafymAY1CJDg6a+JAS7VoNM6W7Uos4RN6aizHxxCCNNNaqKrj+bfgd0PodDkUxFKVOKL7WOMhW0K4ps75WXWfsS7s7VFYeLypQQ0um8rhe8ozRpXGSW7QQ+UDpMv3SHnXzi8KMoPaczMPyoCid01xWRi3ZgqZl1c2TNssJMp1O2ISoxnIp/gmkUijcBFSei8e+Qpsv14aVlgp5FCVku1uQ7MMpULGLIlG6aDZMQetBXVqFyoUymNvoXhYSmdQ0iFkaalKKogsZSYmUqpbyJjeqXim8N6L3nJibjVCrYtK4zJpKY+YKiLVU2iCauC2FjSYVMhZEmVT9mq0xVAZFqTcIHl/ShVU7QMYO7UF6mx5v3VRwqhpW/+U+ZX+j0r1U4DJs64XowJ1NmxK1kYRgxEza9WqK+xOVarfDL2bMZdq2RZCrkvCrXXJdJB77sjqNmPtCNeCx+v3Du4qI79mS5Y17xZPg545SebTFkyq9MHYgdYMwwdJxK5ULlPhlnntZMlgiVC2WSoAL/tdUpJAn1Q+edC1WmtJ30kypT6pHFcACUqfINralTtpI5tPI7qukjQG+9Sbx5LztXOVRcPUyUqXAToeo4dgL/vyPhkCNTByKaTycrOGjsigrlZfosu6/CXtKSdyHbDI9+TZq/Zn24onmUjTuJn4KObOpfUHTNHB5KFrYRx0C+BbYsKX4kVFLX+7FujvzXT6Y0iU3QQllfo5bMUjfi0YM3XosTTre+kmxGOKEH138rS4tQ20vWdItpr1RPLZYsxf6E1rOJVmWTP5tEylTZmEgSVFBa/FdfmVIdS2GkSa3YdhCZSkYAU05CnyljMpVwQ4uZUzaY+5bpJDeV5237vkp0MoSPGxVXj9CcWgpjKdTMp5Gf8IBs8g4COmavDGA6yYJ+7iWdXREzLqO+cTW9dywq+TifF7gv/lIqLOfeKE1gFf0MmSgVcrKU/I4G09dVpsrvSUV0kJdg02fqS1zLfO0cSNfJRKAFlE80ymTKwIm8kiQku89tZj5fu8NOl1ni9+9UOieU3d+GpVKVChkfXh/j8hB5iK/ZldyfQuU+hZEE1X6bEF3dklNQmq4gaToSnWTC4Ukl1dS78seXRHGB+LD5in4ZOKBD6fyiGqEZHbqfXJmqzahHd/rPkXRT698kqfq9hj33OOdziH7n4hBGgFTfgVLzeMekLR2zVwaIdspWu1wvw3dSBqyzK2LqheTTdQxb80Bp26bNOM/fBJMvgMNOC2xqkpPFj6STOsh76d3rZMpUTLRN3zHQfUBJRF9sKYPyD9bNkQqXr5ZWpSlJbbIzid4qn6CSKlNeH0s2AeNPB+HKTPgBCFSmSnJMLQ018UHbO6I6YcWWLFEiU6XfURmPYaWJVM3b/pB0SBqWbpZE0Lu+xOlICudxnCSpSMI2XCqqY2X2ch1lStUiAOZzmk4IfWTUroZvWdLxoBPNB6XEUTUwwCQgIWzOVPFnCyLJoeWUAs9tyVS7I0pyTGT6SsUnDiyHllNrfW8aRpzBkLUP4+TbystMWPQTmXjy7O+HNk1UJT0CSfyr/PAmkSSTR3lkXcUk4DhSnfIpU3G9K9n5ZvdLnyufiQ8qx4XqszWJaCqfiJP7m0hSX3J9I4+F2p6hKRIiHfz374K9Wypq8gV9V3VMxN1HFVGi/NkkIUTlSEKK/Dt7XWUqSS46D5mCOpU0HUnSwsEQZebT20wkjeZLpZzYUiR+lJNcD0mLK4N6X6Oc+VV+o2ITnbBuXCbhO+eh1PSldk4T36VwIqZgXgycl9THsTcvJSFg7Y1Dj0xF3OikBTeTTpI6O5S8K9g4+kJqW3cxcPOzAPRpmM+wtQ/Rctx1Mr9QCA4YmdJQpsCXQiLhBBsbCThipsyF1LIHkBNy1DtXon5sXAButoJMVdQSVLzm4IgmdUnd3zXVCa/YPpWqXPTSNdIRPcQJPWj8F8dDsSbf5IrvtLVPZuJ2nPAs26C2aFcoUwZkSsVc4UE3QqhkwdbYJadTjnJS3ZLzJlQNISJpp2bZkqSqd8pRTyDcdg69Uj9Q2t//z957x0lylAf/35oNl3U6XZTuJJ10SMIoSychogLGgJABG34EY5KNcXzhdcLG2MYJR2xw9iuDyMEmGpMsEEgySCidckancEG6oEvSpZ2dqd8f3T3bO9vd0/U8u9Mzs8/389nP7vZ0TVV3V1c99aQq29Yin7cyvk9azVTruSp2oSjre5SnKdaYQ8u8c1kCZlCetVRkc4jZuJsMnDCVbK459XhYYsrhoTDHSZjqEFhGGGs0PbtWPZ+xOUex6tH/At/klFv/jEPzVrL/vHcWls1bxQWr4oW9QOo4mX6JMn0wVq8HPGyd2Hi06NlNan/L+fz8Seek/cvK+tVACbNZAe1mklAht5aXSmHdxbD7Edg1NVN8Vv9vPZ8d90W/l+drpiYy4pdva9FkKRmkS5t0lNFmybkhZjMIz8XVzlDNiRYwE/mIwsax7OPhi4lQXyCI+kbo3Nd+T8v6PsFk829pv7KM3GFA6cz2mjQbEGugA8akhPSCp2z/zVuYlumO077VjyAXV69qpWAAhSnQTYAJSQcPqjcd9lyyvkbT42sjPHHcZSzf+l2Oe+CjHLH7Lh484900hud3LJ+1cgp+mYWS/pzhmlqDl62ZOif6vWUieWfpjNebboSlz4AFyyadkx4IwpIIZmh6ujjxZpY58eLod46pL9fZfsf9kWP+kcfn1pdkxA+5R3mCQa1WvEt9wpxJK+yABLAZz0GSpmM0oE6IJt/kkiX+G0V75hWRaB9CNJz5qQbCFxOS/usEmqn2BVbo4nBCu1q+vfNHpwb4lPWN1KTZgDgXV+Bc015vyLVmPY8yz0hjCZHuJZgg9WPuJr3bMgXZIb2h6ulwVfzwUK1lTio7ACQ5kh4//pXUmnVOvv2v2LP0HLYdd1l2Mso2slbnIQ7hUN5/qJ2RoZpoMkm3OfNFnH9UtHVKym+qMEozmRi8n0jW2d5WYTRIpikpRJhSTkaZg8eyk+CINbkpEtonyklmvmUnQ624f4zUwp5r/ma65b4jNB1DQqY/W8DEm4wJkr2+JBN2wnDNiRYwE6aOgEixaUw1EDqGAjjCx1Gtn1br2YSku8gKNCmZP6x9I95uLNzb6wnLUTX1WJlnlPf8y/SLrHNCtyYaFc433ULVMufcrzvn7nbO3eWc+6xzbu50NUzDdGTuHXJhjpOteoaSAbqkZioWmJ5achr7F52Ix3H/2b8PznXciiOqRyfxQ7gjbEK0ialuRZV7n1afOymir1QumCcfggNPTjHxtdcp2dKl07E8RpWTUWZdzsG6i6Jkrs2pWxGlJxHnUvdnx32FJr6E0ME9T9AtvX2TcFLQ+LPBhJlBIuRORGCF9/9aRqLSMgzHJhpp/2vVX5MJU5L7VHOdA0jaaZ9kQ7ZmgYmxouxWJZAtOAVtCTPJjy58MSvxoZNGuGW922VuVe4m1CXqHkkpGhJCheThwK2Juo1YmHLOrQbeCaz33p8GDAGvn66GaZDuqJ1Gop6GlFN2yYGnmUhMznH/OX/Avev/tLVbebOENJWXVDIEyXVCNFiJclR10kxBJEzt2wL7HgfyfTwm5dRq+UtdMLWtKWfw0ECE9OATGk2S9suRrKpyhZoTL4722Nt665SP2tX/zjkYOwB7NuXuyZdmZMgFOcNqMiODXODM9pmSaKYkwlS4b1lC5AMkMZlFJvWQ/pdEhKYJMRNOfjbhGjwoF9GZpn0bp9DxbMo2TCXIFKYCMttP3nMx1Aoii6hOhGsIG5OkZr52DRyE+SKLUge11TWwmilgGJjnnBsG5gNb9U3Sk/Xyicx8Ao1NqPo/vYHvrpXPY+uJr838LI/2a3VOcq1Bp7eQOtKmV265L1SSvHNrpJ3K85maJBht+iHMXZwZ+p92Bg/V3E3y6QmMJkkmLul+Urm+YideFP3OMPVlahOefBDwhTmmErImYEkbyw7w6T6gyVkGYROZRphKhCHJJDjkZGY+SBYw8v4LYfdIq1mNHNB1Zr5QzVQriCLg2WSa+YIcpCfODX0+LjAXV7pcaF44yBacytY/NXJRzivieAAAIABJREFU/r5K8rNJXVK6gViY8t5vAT4APAY8Duz13l/Zfp5z7h3OuZudczfv2LFD3tIAsgaLkFUGyHMvhSZgK/KLGi+jmcrYdDV08JJqpqS2/uT5DBVF6Rx9Brihlt9U3v2ctPrcdGPkL5Uj4E1EHwbuiTYin1ASp2Hpiir3/ixYFmV4z3BCz/Sl6LAnX5rhrJQMRecrI8aGUiteSTReQuhCQroHJ8iiHhOk7w3kR54V0S6chG510tLoCh3QQ2kf04KFqaxktx3QWjOSe1M4phUQmhZholz4+JK1yCnbp6YEBwgWL3n/d2JI6GvYLTRmviXAK4ETgGOABc65n20/z3t/ufd+vfd+/fLly+UtDSA7cqB7TtlQvnMWaZ+mbOCbQXvnDtW6aNDmyynM0TMyD1ae2hKmOkYlHdgV+QRl+EslSH1kpJoTSIezT7MwBZGpb9ONrXxcrTqzHLp33B8Jp0etK1XndGStDpvM4gSwgYPzpDxegVF5mmeTvN+ScUIjTEm0we3CU8gYkdboSh3QQ2n3r5H41kDYs2m/R6GCeTIOSaPNxPPNcBRZGlI+61ypZipEMJ+U7FYQBR6aTb/baGbeHwce9t7v8N7XgS8Bz52eZunIesAhm5GCPF3AhPNjufrGGwWaqYLPEjRq1wSpsC/t3IntveMguWY9bLkVms38PaWSF3JznEYhI5IvoSXEBfaFtAAVquFsaT+Eg2yhsLru4ihB6SM/mFxnqq7WxLnzfjjqxElb7OQRugLMdUwVaIlC+m97Hq/Qvu9iR3DJs5nQTMkc0KWuH8MCQWxqHqTuLQic8DqTMVzittAywYbkSmvbmSFYMFdoOUGeP2lYIFxrdg7QLN4le0umGeqQvLlqNMLUY8AFzrn5Lup1LwLunZ5m6RgdnryyGRZs+aBRwyd1lqFIM1XGZyptIgGZZkqyeoQkfYSoKHOGhzoP6qvPhcN7YddDnSfsTTdEmpfE16rg3JCQaZgsQIVHFukG2cJx8tgLYHjuFFNfOhley6TZYU++NEO1sAjPvPsZIqRI/Ze0A/TIUC14bICJCTvEt2yirEyjm5QNHZummPlCUw0k743gPknnvkQADBVqYMIxW5cnUCZwSs350ojq4Vq42TfrPSk7Pk3tS7Jghm7lLOsmGp+pG4AvABuAO+Pvunya2qVmrsI0A3KfqVBfimKfqWap75iUpTjQHwgUmqlauaSMWYwOl8gZkghGm28uMPOlhKlVp8Pogvw6hRnb0wKU1MwnHmSL7u/IXDj+uVOc0KdEFjXqsGtj4Z587XWGTETToZmaI9TgaaKoIFnZyyKpQObrIslhly4bqjVv18qH7h03GicpFQlT0vGhpUUOH8+6nbcJwrdhakfSByHZqSOsrZlJrUs+W40/m1aYkmxN1E1UDjbe+/d575/pvT/Ne/8m7/3h6WqYlvTqK1SbAHIzX2gkSVH6gzJ5pkDfSaVobNijQ7XObV12MowuhC235Go/RoZcJCxsuQWOm5oSYdK5LWfwsDbPU5j5ksy90kG24+Cx7pLIhLd3S+tQepuhOcO1SJBqjhfuyZcm1MyXJ8SEaIqGa5E2Wbr5L8hMqdKJV+MzVavpgj5C5bd24Sl477gyC58cpFPfxKbOMkFXFBij6Esa7R0oLCGC3EvtAmrIZt1VaqZgcM18Pc1chTYBNJ07sdeXFKYKBCZfwgEd9FuWSPunxsw3MlxCwKgNwTFnR8JUzrmjQzXYdhfUDxQ6n8OESjx0tTxpL0GJKalWU/hMdTihtbXM1ZMOJwPm6HCt1J58k+sMG7RyMyMHvHcjwzWRlmeyZir8Pa8JI4SGWhpomc+UdD9Myeq8ve8FR6QO1cSboUsnv5bmr4sC8uTJPty0CN0XpiQRblO36wnTICfVSZ30o79lmikTpipgsjAlU8VLSEJjp2Nn65KKqUk7hndzI0iN2nW47JYlq8+BJ+5k1NczPx4drsFjSbLOfOdziJNRCvdSS/zwJP41Ep+9hI79aOWpsGAFPPTdSYcnOXTveCA6WNbMFzgZpcPn04RppvQToKTvD0nzycU5byRlhxRmPicoOzo8oamU+iDNmGY1B8nWOQnS/HfSjOLp86XmOrklJDz3kka7lIyFkPQrmTuAZHHpnBP793aDARamUtoE4YpVgnRSyKLst0g3vGzVoxnYxSkkSvqqrD4XmnXctrvzN7DedEO0V93iNYVfpQlJHx2uFadyKGBEmHsGSmimnIsSeG68GlI+dqNDtYnr3Xk/LD6u0J+svc6QSdA5N9V0EJhcUrrFyiSnYYG/oDzQRL61hcY8XnMyTbImEEKzGJAykStKqpkKr1OjOUnMr912QA9NiwBTFz+he+Ql82n4riIT74xUM9XDmREGV5jSRvlIH1rohF00Z5Wdz0aUEr8G8Uqs7H1KnNC33JLvOLnpxo4mPghPRjmpnuFacBRgwlAtbAuQNKUE3XWXwIGdkbkzZiTtk1ZyT75WnYL139Tw+7B7NSzNWZbWTAn2ydOY86VlXU1urpBqwzSBEENCJ30NGjNqt/e6m6hX3ie6Gd1Zq01e/IT6gWp2Dpjoh+HX65gei89MMbDClD5yQK6uDencRfWU7ThJpw7ZJ2m6UNn6y5Q9YjUsXAlbbpny8g4NOYaf3gr7Nnc08UHs+CvVpCkEMamgACW1ECdeFP1OpUgYHY4nwGYTdv6o1J58CZL7pNmyBOT+d9qFhEozJfQj0kbzSdAk3hyu1RRmSVGxlv+cRPtXc9Nh5guvVxoZCorFu7AvaQK0JvljBtLa/kacGiG4WNcYXGFKbfqS1VsL9C8oGszLvpgtFX6XI25AbusvPQg4B6vXw5ZbsrfFSDY3Pq6zMDVcq8nbK/TDAK1Zp4zAeTQs/7FJflMjQ7Embe9jMH6wdI4pkD3T9mcTGtIujXBL+n6tFraxbateRf+Vm/l0mggJoXuGTqpTca1ShpRmPsnt1TpIh+5pmUblaiEoqvEp1mR715StTZMv8kwxsMJU8tCckyabkz+0kFxPRQJT2YFTm31Xg1TTE+RIu/ocePJB5jefnnR47kjsfD4yH1ae1vFrak6niRALREJBAQKE+nUXw6PXQ/0gEAkWI7Va0J58CSJHcGXEmFTgHBly4nc8qldULFo0qTRTsno1Pn8g07io+q9wHJVsVpwg70sTz1Pu+N5dDZ50TNNEu2sEc42jvvlMVYRzThx9FZWX1x3iu1H00pZdlVUqTCkGgdL3OPabWvjkHZMOz0k0U6vPhaGRjl/jVNoEXdkZHwROvBgah+Gx64FUIEQiTJWM5APZKrndrBfaF11If5hULnrHux2SDjIfLUiutbtmPpVmShG4IU+NIJ90xbnDUmUkgrIms720F0rrVOXUUgpTGi3ywCbt7HVGarXWrtpdrTegoxSa+UpO3iNxdIZUmNL0T435qvSLcczZAMzfftukw/PdYXjizlL+UglSU512CxC5Gr/kiWufB7WRVjb01jZDO++PUifMP6p0nZJHOlUzFWjmUwyUIxWE7oMuq323NVOaPEhdWQy0IdmsOKt8UJl4HJU6+WvM+Rozn6RosvgZCUxvABPaTcnickTha+gU5vFuMNDCVBUrKgiT9IvODVkdjShW5xo05qvSRecdCUtPYnTbZGFq0ZO3gW8ECVPSSTc0sKC9rJTSZpLRBVEG+NgJveWTFrAnX4I231PW/53QCFPDiqSSmoANqZnPodFMiYqptjupYhKr1WT76yVI+8PwUPhedwlDTpYAFnQaflEUbGu7HoUTudDJX6Ph72FZarCFKU0uGMmeUOl6y59bIEwFmBFGhuRZtqvAETh5rj6X2tZbIJUVfv4Tt0R/HHte6a8RC0RD8oFSY0oKqvLEiyJN3dPb48zeRAk7A0x8IPNzaReeQidtaW4g0OZ8ktUJsk2OIUk+KEOT1w2666QPuuCWmkYbrNgjTxOh3O1oaukiZMKHThLdKddMDQ85VaCUaaYqIhqgu3+JIauiokknpKOPDnc/4gYUGjwXONCuPhe3fzuLxra1Do0+fnO039y8JaW/Rmzmc4otQLo1wK67OPq98ZooaebBHXB4b+k9+RIkOwa0D5Dhmim5tmZYEWmp0RpKy0aaKVmdmh0HQJrfRxdoIqUKbXBostk0mvFXKl5LtybSRIBr9qXU5GczzVSFaHIDaQips9DMF9DRo4SU3X+c0kEg2OE4dkJfsufO6H/fpLalXLLONHKBSKHV6tbK/uizIsFy4/eoORjdHW8jE5CwE2QTYLtzf6gJwDl5JJTGnK8RdDUuBPLJU1RMtzGzRpOm0fzVFGYzqZZT0Zc0c410iJCaYIdjM6rI76mm02qZz1Qf0nLC7Xa9AXU653ITmIXYs4cUTrgaxIMAgZP2qtNgaJTFuyJh6oj9G3GH9sKxFwTVK/dp6P7AHl7REJxwITz0PWrA6O4Ho+OBmikpycJAmjxWGnmrEaY0gq507pQ6DUd16kzNkolMM4mpNFMKs5lGM6XZJ6/bSANjXKz1k6XKcOJ3rlaTa/g15vFuMNDC1HDZ/d+mmdCXMU87FaKCrSL7Och9IoIHgOE5sOp0Fu68HYClu2Jn9ADnc1G9MUNOHpGnmbCDWXcxPLWV2q4HGNn1AMxZHGWQ7wIjCj8MqEjQVU72/UKiiZC0WSP8adAsDjXmUI1fpRTNolQTlCAVAEN3OEjQBoX1sGJqsIWpqnymQjtLVscMDVkdUphJNMx4yH+a1ecyb+cd0GyweOcGmL8Ulq4L+grxNh4aU5LGzBda9sTIb6q28WqGdz0Ymfi6NAJpwu9Bl2ZDrOlRDA8arYu0qCT6ChItvfxiqzCvaDQ9Gg2eJpqv20Qm2O5fqzRASxfxaBnQK0OzV1I3ydq1OziDtCLypQpEbV19LrX6fhY89RCLdmyItFKh3yM2r2jCl7v4XJYcD0eto/bw92JhKiwtgoaRlJlPQhU+UxrtkmZokfsaysvpND3iomJULhoK3zJ5mg1ZnaD0PVX4h0mvVRyR57qfFqRbDLQwFUU69L4fUZZmKjTFf1XZYbtaY+yEvmzr1czZuzHY+Rx0ApF4EFC8ZaL2rrsY9/C1DB3YEbTBsZYRRf4ZUGgNFT49XdUaTiorLipGqtWCakyamrx50vtblf+d3AFdl0xYOjZJs//P+D6lFTLQwlRVPlOhq4ysXbtDw9M1mpO+mRSOWkdzzhEc+9Cno/8Dnc81aLJA6+6voOyJF+PGD0V/d1EzNdzKZdRdobOKDN1RvfKyVSTYDdkztJ0qFqWq6DhhOU0EuEbgrCIQQhMBLt/0vfc1TFIGWpgaqshxMrTOLC1UloBVRK/vW9SOqKm1Go1VZzP3wOP42ggcc5agXrk6XaM56SonvADv4v7TTTOfIpcRaLPpV6CZ6unYoqlItQnQZQ10jEbg1CT7lC4GNIsmjbAqTrNR00U9iupU+Hj1+vw20MKUUzi7dZMswSlUmILejnRoR/pC+dXnAFBfcTqMzBPUK0MTSdL1SXfuYtzqc/HD82DxcV2rNolm0uSRkaDTGsrKactWQRXCiQZVEkyxtkZjhpLVqUVj0pRqg+VpYjSpEWTlusVw1Q2YSTS+FN0ky5dhTqhmqtbbkQ7TxjGR31R99fmMdrFa+XqqokHgwndzaMtdzOtiNGuyqXi3fSI0WtlZ8Ma0kPqyQTXjqC44QOFH1GVtjRaxMKXwX9J0B3Gd8iq7woALU72dfj6hVnOMDtcYG2+2jgVH81W2Kuruyzh0wvPYv3At9ZMu7Wq9Ks1UFc/mpBdTP/YiwnV3clqJIbucPLaqRVM/LNTSVKHp0VBJEkyFYF5Vf9BEhmpSxUiRC3C9/b4NuJmv9x9AQlp4ck4iTPXHdWoZWnAUP3z5lXDcc0TlxQOPqJSuTi3d7hNDFWmmnHPi8PB+GR+mgyp2g9BQRXRcreZUyTerQGXmq0Cj2w+uNxIGXJiqJspHUmXarDc6XAse5Ae0f2aiSaCpCUHuK80U3ddWtoQp4QXLHdflg7vGNNNv75xG01PFtepSimjMfLI6q+oP4r5fUZqCfjOjlmWghalaH2mm0knQJI6i/aaZ0rRWk2yuCqoIgwdZEIMGzf5voEtI2W/9vwp0G273lxlVWlJjMq5KA61ZWMrz7snKRWUH813tnxlJQFWaKQmjqbDlUUFyvUHtoFlU4Tjp6L9Q+G4vJJJn0m05tyqfqf7qDbrnUsUm6lUkVNX42VammVJoejQLGClSk3yvM6CXFVHVYCfpaGnthSRVf78IjdOBarPMvpsC+4dE89FtraGjPwJNqkZn0qxAWFVU2Y8+jt1GkxRV1ZfEJXubwRamKlqxSkhroyRmoX4xZyZo2qvdeVxKn93irlOVZsrVqnk2/fbO9ctYWCUa01e/3V1VRF4FPlO9zmALUxV1b0mtkzRTIjOfoNI+ZVjhg1SV/9JswMXm1+5rpmaPNkFDv81hOjOfsNws6kcqgUizqfOA3uKBnlk0uYG09YYyWTMV/gWDKu1nUcl+i7Pn9qrQRENJqWpvvn6j3wSFqsZujQ9SP6GZM3Rmvv66T2VRCVPOuSOdc19wzt3nnLvXOSdL/jODVPHgJC/jqFIz1W+TvSqar8/ywMwmRobC03po6ad8clVit6gc0tvUb32witQTMLj9UJsB/e+Bb3nvX+OcGwXmT0ObZiVpYWqOYHd300wZvcCcke4ruzVb/ejq7S9mU3v7TbCpAhOIphexMOWcOwJ4IfBWAO/9GDA2Pc2aHqoy80lIMu82Gl61u/tsYKiPckzNNiSRqFr66T03ylNJBGHXa6wOze01QWwqmpHvRGAH8FHn3K3OuQ875xZMU7umhapWrFLmxBORZEKqqoN677te56BuRzAIhG6DNB2Yma8c/XaP+qu1/YdmdrTUE1PRjHzDwDnAv3rvzwb2A7/bfpJz7h3OuZudczfv2LFDUV04fTZ2MDJco1aTRav1m5lP09wq9s5yzlkkYAkk/n5a5gx3N9N7v9JfI0R19NlQKkaXJsY0U+1oRr7NwGbv/Q3x/18gEq4m4b2/3Hu/3nu/fvny5YrqZPTTamxkqDZrJmxVnqk+eqazjdnSfw1jNqPTTA0m4pHPe/8EsMk5d0p86EXAPdPSqmnC0V8PTrPnXL9ppjSYy1TvYsKUMV1ohjSp+0E/Lb61zKY5oxtoo/n+D/DpOJJvI/A2fZNmL5FmSrpNyuyhnzY5nm1UsYebYRj9w6AKrCphynt/G7B+mtoy69FskzKbMDNf7zKb+q9mOw7DqJqqttYa1DFioJf4TrEDeBVozHz9dJ1aBnXX8UFgUAdKo/sMqgajV7C7O73YtNRD9KNmqooBz2z9vYs9G8MYfCrIiNPzmDDVQ/SjMFUFdot6FxOmDKM/MM3f9DLQwpSjvzpMJEzJyvbTdWqxCbt3sUdjTBdVJASG2TOWzo6r7B4DLUz1GzXnTFAwDMOoCBt9DSkmTPUQQzUTpspgt6h3sUfTu1j0YWdsbDGkmDDVQ9ScRUOVYVD3dhoEZouJxOhtpP3Q+q8hxYSpHqJWc7Z6LIGNd72LdV/DMGYjJkz1EEPOiSejqpw1q8Dm697FVvaGYcxGTJjqIWrOWXZvwzAMY8YxK8j0YsJUD+GcrezLYE76hmEYRi9hwlQPUVOY+WYTtqIyjMHHFpZGPzHQwlS/vYummTIMwzCM/mPAhan+EkxMM2UYhmEY/cdAC1P9hsNyKBmGYRi9TZ/pKbqCCVM9hIukKWFZ692GYRjGzGPzzVRMmOohnJn5DMMwDKPvMGGqxzCJ3zAMwzD6CxOmegzTTBmGYRhGf2HCVI9hDuiGYRiG0V+YMNVjmJXPMAzDMPoLE6YMwzAMwzAUmDDVY5hmyjAMwzD6CxOmegzzmTIMwzCM/sKEqR7DNFOGYRiG0V+YMGUYhmEYhqHAhCnDMAzDMAwFJkz1GGblMwzDMIz+woQpwzAMwzAMBSZM9RhDtp+MYRiGYfQVJkz1GLbRsWEYhmH0FyZMGYZhGIZhKFALU865Iefcrc65r01HgwzDMAzDMPqJ6dBMvQu4dxq+xzAMwzAMo+9QCVPOuTXAy4EPT09zDMMwDMMw+gutZupDwLuBZt4Jzrl3OOduds7dvGPHDmV1hmEYhmEYvYVYmHLOXQZs997fUnSe9/5y7/167/365cuXS6szDMMwDMPoSTSaqecBr3DOPQJ8DrjEOfepaWmVYRiGYRhGnyAWprz37/Her/HerwVeD3zXe/+z09YywzAMwzCMPsDyTBmGYRiGYShw3vvuVebcDuDRrlUYsQzY2eWyVdRZVVlr78yW7bf2aspae2e2bL+1V1PW2tu7Zatqr5TjvfedHb699wP9A9zc7bJV1GnX2rt1zqb2zqZrtfb2bllrb++Wraq9M/1jZj7DMAzDMAwFJkwZhmEYhmEomA3C1OUVlK2izqrKWntntmy/tVdT1to7s2X7rb2astbe3i1bVXtnlK46oBuGYRiGYQwas0EzZRiGYRiGMWOYMGUYhmEYhqHAhCnDMAzDMAwFJkwZhmEYhmEoGK66AYZhGIOOc24x8FJgNeCBrcD/eO/3KL7zxd77bxd8fgSw3Hv/UNvxM7z3d3T47lUA3vsnnHPLgRcA93vv7xa088+9978XWOYE4GzgHu/9fR3OPQ7Y7r0/5JxzwFuBc4B7gH/33o8XlH0FcKX3/lBI++KyLwS2ee/vd849H7gAuNd7//USZRcS9YdjgXHgwbgdzRJlnwm8ksl96ave+3tDryH1nW/z3n+0Q52rgRu890+njr/Ue/+tDt99PuC99zc5555FdN33ee+/IWjnJ7z3bw4t1w0GLpqvio6Wqje4s/VCR4sHgvOBu7z3Vxac92yiwWKfc24e8LtMDFp/7r3fW1D2ncCXvfebBO0bJdpMe6v3/jvOuZ8BngvcC1zuva8XlF0H/BSTB63PFrU1VfYlwKuY3Jf+q9Pg0eE7/9B7/ycl6l0DXOW9fyR1/Oe891fklHHA/xe38wvAJUTvwX3Av5UZpNu+77ve+0tKnLfMe78z9f/PEvclooksc4Bxzv0UcI33flc8Wf8t8eQJ/Kb3fnNBnX8HfNF7/4OQa4rLHgX8GtGz/Ajwe8BziPrSn3vvdxeUvRh4NZP70oe99z/qUOebgfcBVwJb4sNrgBcDf+y9/0TodcTf+5j3/ricz14LfAjYDowAb/Xe3xR/tsF7f07B9/4i0bvtgL8iEk7uBp4H/LX3/iMFZf+h/RDwJuATAN77d+aU+4r3/lXx36+M23410Xv+F977jxXUeRdwvvf+gHPur4B1wFeI3gG89z9XUPYgsB/4JvBZIgG3kXd+qtyHiPr5MPA/wIvi77gQuNV7/9sFZV8L/DZwO3AxcB2Rleh04I3e+zsLyv4O8Abgc0DyjqwhGh8/573/y05tz/neor70TuBXid6Rs4B3ee//K/6sU196H/Ayovv0beDZRM/1x4nu9fsLyn61/RDR/fougPf+FaUurksMlDBVRUeLPxd1tqo6mnPuRu/9+fHfvxC3/cvATwD/nXefnHN3A2d678edc5cDB4gm7hfFx3+6oM69RIPWQ0SD1ue99zvyzm8r+2miezQf2AMsBL4U1+u892/JKfdO4CeBa4BLgduA3UTC1a94768uqPNDwMlEk0C6L70ZeNB7/64ybc/43k596c+B5wMb4rZ/yHv/j/FnRX3pX4AVwCiwD5gD/DfRdW8raq9zrl1L4Yiu/X4A7/0ZBWVbbXLO/T6RBuMzwGXAZu/9r+eUu8d7/6z47/8Afgh8nqjvv9F7/+KCOpM9PpcD/0EkHN+ad35b2W8AdwJHAD8W//2fRILNmd77V+aU+0tgJXAVkYD9MPAA8CtEQtjnC+q8H3h2uxbKObeEaPF1ckHZ9ve89RFwifd+QU6524CXee8fjxdsnwB+z3v/Jefcrd77swvqvJNoLJpHdJ+fEWuolgDf896fVVB2M9EYdmXcRoAPAL8F4L3/eE65Vpucc9cR9YGHnXPLiBYVZxbUme5LtwDnJYsH59ztHcreSiR0vYZonjiNaCz8rPf+moJyd8fnziMSkFfHwtwIkTB1WkHZO4AL4vOXAZ/23r/EOXcG0cLnuQVlHwBObV9AxgvOu733J3WoN/Mj4GTv/ZyccncCz/HeP+2cW0s05n/Se//3JfvSWUTj0RPAmtRi/IZOYwvR4urDRItERzR3vB6g6PlUgu+BPW2m64docBvJOD5KNAEWlb0j5+dO4HCHsncCC+O/1wI3EwlUEL1YReWGiISEfcAR8fF5wB0d6twAfAq4iGg1dBHwePz3hR3K3pr6+yYiUwDAAuDOgnL3putv++y2TnUSrb5+gkgjsAP4FvAWYFGnZxP/Hga2AUPx/67oPiX3N/57PnB1/PdxRc8l6Us5x12JvrQv5+cpYLxEXxqO/z4S+AbwwTJ9Kf49AjwJjKbuWe4zjc/5atyXngkcH/fhTfHfxwf0pQ3AglQ7ivrS/am/bwntS/Hvk4A/INKa3Eek/Tm5Q9nbUs9xS9l609cS39MfxH8vIdLoFvYlYHHG8cUl+tJu4OXE73Xq5yIiIblje+P/jwZuAd5J27ubUXZD6u/b8553TtlFRFqlzxAJGAAbi8pk1HljYJ3/QyRYAnwx6bPA0vb2F9Ub/78qvkfXA5sKyt0V/54bP6N58f9DRKbJTu94osiY1/YOdepL92W9k/G7en+HstuIBJvj237WEmn988rd0/b/QqKx++/KvqtZz7FE2Rrw60SKhrPK9qWqfgbNZ6oJHEO0mkpzdPxZESuBlxC9GGkckRq2iCEfm/a894845y4CvuCcO56J1VkW4z5SKR9wzj3kvd8Xf8dB51yn9q4H3gW8F/ht7/1tzrmDvpy0XotXmTWil3pHXO9+51yufwFwV8rkebtzbr33/mbn3MlArqktxvtotXglcGW8gnsVEDIhAAAgAElEQVQZkSbxA0RahqL2jhIJe/OJJqFdRKudkQ71DgON+NxFcUMei+sv4pBz7nzv/Y1tx88DOvlX7CFaHW9r/8A518nMOexjHw/v/R7n3E8ClzvnPk+0KMgjKVN3zt3kvR+L/x93zhWaLbz3r4jNbpcDH/Def9U5V/fet79HWcxzzp1N1JeGvPf7U+0oqvdq59yfAH8R//0q7/1XYlNaJxOsj+t4EPhT4E/jVf0biITPZxSUTfr+ImChc25t/M4upfj+Np1zR3nvdxGNMUNxG3bHJtYi3g9scM5dSSSkQiTQvzhufxE/BA5kvdexxiuPp5xz63zsL+UjDdVFROavUzvU2XTOjfhI+/HyVH1z6RC05L1/Cvi/zrlzgU85577eqUzMmc65fUTj5Rzn3CofacNGie91AW8HPuGc+yOivnNbrHFaAvxGh7KTnp33/gngH4B/iMfvPL7unPtfImHqw8B/Oud+SCToXtuhzm8A33LOXUM0Bn4eWiboTn3p/wJXOeceZHJfegaR+bqIrxEt+m9r/8A5d3VBuSecc2cl5XykoboMuILINFnEmHNuvvf+AHBuqr7FdJiT4/nig/HY90Hn3DZ62c+7amluOn+I/I1+RGS7vjz++VZ87KUdyn4EeH7OZ5/pUPa7xJJz6tgwkWq9UVDuBmB+/HctdXwxHVaPqXPXEL2M/wQ8VrLMI8BGIlPFRmBVfHwhxavzxcDHiEx1NxAJUBuJzGhndqizSKsyr0PZX4/reZRo1XgV8O9EK7z3FZR7F5F28XKiFd3b4uPLgWs71HlOfI33EAuARGbcG4BzO5T9MyIfjqzP/qpD2a+RoVmMv7NZUO6bxNrRtuOraFvpF3zHAqLV5leJTHRlynyv7efo+PhSCnZ4JxKC/wh4LP5pEmnuPgMcJ+1LJdr7BqIV+jYi/6fvEK18twDvKCj3urj/XRm39+WpvlQ4PsTnLSEyT/wmkcnr9cAS6XWUqO9M4KSc+/7GDmWPI1vDvxr48YA2OCIXgk8pruNIIhNTmXN/jMhP8NVEZspaiTIXKdr2HCJzHUR+Wr8FvLZkvZfG5784dawGzClRtkbk7P5qIvPkBcQa+BnqS2uI54iMz57XoWzm9QDLgNMD2/FyIpP6jFyn9megfKYAnHM1IsfA1UQv82bgJl/CqVBR5xoiLdMTGZ89z+c4yjrn5njvD2ccX0Y0KeU6ImaUeTlRxw6Kmmn7jvnASu/9wx3OWwScSCQwbvYZGpiMMid77x9QtO0YAO/9VufckUS+NY/5qZqj9nKnEg2yd/kOUUE55VeR6ktZz3g6iX0J8N4fzPhstfd+y9RShd+3gMj0tj2gzJlEE9i/hdTV9h1DRAPpgRLnLibSyD1Z8rsX+lSQh7Btzkdau2Ei08cW7/3jHcodRdTvf+QFUXjOuZWkghnKvDfaslXUae2d+fbmfJ/4vZCWraJObdmZYhCFqeOAfT4ykawlMofd60uE9OaUvc97f9dMla2iTmtvuTrj8utJRW+FCGRVlLX29l57nXNnAf9GpNndTCSYryEyB/+K935DQdmzgX+Ny6YjAQvLttXZXu6XfYHD/gyWLWpv0XXOSJ3K9na9zk64DsEtM1G2ijq1ZWeMqlVj0/lDFM77MJFJ5+3x748QOaj+Rq+Vtfb2dHsvJAok+A6RH93XgB8QRSod22tlrb093d7biKL52o9fQGcHaVHZKuq09nalvb+R8/ObwK6ZKFtFndqyVfxU3oBpvZhokpxH5K/xFJOj1DpFSXS9rLW3p9t7a+r8E4hyZEHkNHxlr5W19vZ0e3Mj9ohMhtNetoo6rb1dae8hoqCF92X87JmJslXUqS1bxU/vesbLaPgoEm4MOEgUIo6PotR6say1t3fbO+Qn8mA9RhRCjPf+2y7KQdVrZa29vdveb7ooqu0TTERgHUuUs6xTAlhp2SrqtPbOfHs3AF/x3t/S/oFz7u0zVLaKOrVlu85A+Uw55z5GFN68gCih5DhR57yEKJfRa3uprLW3p9t7BZFj6FVEEUJbvPe/4SIn/Q3e+2f2Ullrb++2Ny7/MiZ2ZkgCY77qS+x0IC1bRZ3W3hmv8xQiE9eUhMfOuZW+wIldWraKOrVlq2DQhKlhJm+p8WyiUOjHgH/2cQ6cXilr7e3p9o4AvwA8i2jbhyu89w0XRdut8AU5mKooa+3t3fYahjH4DJQwZRiG0Wu4KPXDe4g0ESviw9uB/wL+0hekWZCWraJOa29X2/sqJhIdz2jZKurUlq2CMplp+wbn3ELn3J845+52zu11zu1wzv3QOfeWXixr7e2L9t7VVvatvVjW2tu77SXa+283cLH3fqn3finRPpp7iLNfz0DZKuq09navvRe1ld09g2WrqFNbtvv4HvCCn64fIon1rUQ5O36DaN+uk4CP0yFzahVlrb3W3tl6rbOsvbl7phV9pilbRZ3W3t5t72y71ip+Km/AtF7M1E05b4p/14gSNfZUWWuvtXe2Xussa++VwLuJdhdIjq0Efgf4zkyUraJOa2/vtne2XWsVPwNl5gP2O+eeD+CiDWJ3QWvDxE6x8FWUtfZae6errLW3d9v7OqJcZ9c453Y753YRJfs8imgvt5koW0Wd1t7eba+mbL+1txqqluam8wc4A7iRyPb8feDk+Phy4J29Vtbaa+2drdc6m9obn/dMov0kF7YdL9yAXVO2ijqtvb3b3tl2rd3+qbwBXbtQeFs/lbX2Wntn67UOWnuBdwL3A18BHgFemfpsw0yUraJOa2/vtne2XWsVP5U3oGsXCo/1U1lrr7V3tl7roLUXuJN4ZQ2sJdrj713x/7fORNkq6rT29m57Z9u1VvEzUNvJOOfuyPuIyHGtp8pae2e2bL+1V1O2n9rrnPs3otwx27tVp7aspk6irWieBvDeP+Kcuwj4gnPueDr7W0nLVlGntbd326sp22/trYSBEqaIBrWXEOWhSOOA63qwrLXX2jtdZXulvd+Pjz0b+Hrri5y7DTgTOMF7/0vOuZ8i2ous6vZOKeuc88BJ3vsfTVOdTzjnzvLe3wbgvX/aOXcZcAVw+gyVraJOa2/vtne2XWv3qVo1Np0/wEeA5+d89pleK2vttfYO2rUS+TbcD/yfpCzRwHc/0bY+a3upvVll43Y+YxrrXAOsyvnseTNRtoo6rb29297Zdq1V/Nh2MoZhTBvOuUeADxM5i54XH/sAkUbnz4ATgD8CNnvvfz9W3X8K+CBR/pgG8Hve+4/GZT8G7CfymXghcA/wM977h+LPnwn8I3AusAP4A+/9f8afXQp8ADgW2Ad80Hv/gfizX4jrO4pIm/ZL3vutzrlrgRcQbYztgZ8nynfzSSJt2zDwg/j8zdN57wzD6F8GLc+UYRjV80PgCOfcjznnhojyxXyq4PxVwGJgNZHw8s/OuSWpz98A/DGwBPgR8H4A59wC4NvAZ4j2OHsD8C/OuVPjch8BftF7vwg4DfhuXO4S4C+IctUcDTwKfA7Ae//CuOyZ3vuF3vv/IBonPwocDxwHHAT+Kfy2GIYxqJgwZRjGTPBJIp+oFwP3AVsKzq0Df+K9r3vvvwE8DZyS+vxL3vsbvffjwKeBs+LjlwGPeO8/6r0f995vAL4IvCb1vc9yzh3hvd8dfw7wRuAK7/0G7/1hos1Un+OcW5vVOO/9k977L3rvD3jvnyIS5i4MuBeGYQw4JkwZhjETfBL4GaL97D7R4dwnY0Ep4QCwMPX/EzmfHQ882zm3J/khEpRWxZ+/GrgUeNQ5d41z7jnx8WOItFFA5NgKPEmkGZuCc26+c+7/Oecedc7tA64Fjoy1boZhGCZMGYYx/XjvHwUeJhJmvjRD1WwCrvHeH5n6Wei9/+W4DTd5719JZAL8CtEu9ABbiQQxoGUuXEq+9uw3iTRlz/beH0HkuwU9GJ5tGEY1mDBlGMZM8fPAJd77/TP0/V8DTnbOvck5NxL/nBf7ao06597onFvsva8TOaA34nKfAd7mnDvLOTcH+HPgBu/9I/Hn24ATU/UsIvKT2uOcOwp43wxdj2EYfYoJU4ZhzAje+4e89zfP4Pc/BfwE8HoibdMTwF8Bc+JT3gQ8Epvmfgn42bjcVcAfEPlXPQ6si78j4Y+Aj8emw9cCHwLmATuJnOu/NVPXZBhGf2KpEQzDMAzDMBSYZsowDMMwDEOBCVOGYRiGYRgKTJgyDMMwDMNQYMKUYRiGYRiGguFuVrZs2TK/du3ablZpGIZhGIYh4pZbbtnpvV/e6byuClNr167l5ptnLFLaMAzDMAxj2nDOPdr5LDPzGYZhGIZhqDBhyjAMwzAMQ4EJU4ZhGIZhGApMmDIMwzAMw1BgwpRhGIZhGIYCE6YMwzAMwzAUmDBlGIZhGIahwIQpwzAMwzAMBSZMGYZhGIZhKDBhyjCMWYv3vuomGIYxAJgwZRiGYRiGocCEKcMwDMMwDAUmTBmGYRiGYSgwYcowDMMwDEOBCVOGYRiGYRgKTJgyLKLJMAzDMBSYMGXQaJowZcxOnHNVN8EwjAHAhCkDE6UMwzAMQ44JU4ZhGIZhGAo6ClPOuSucc9udc3dlfPZbzjnvnFs2M80zuoG5TBmGYRiGnDKaqY8BL20/6Jw7Fngx8Ng0t8noMt4MfYZhGIYhpqMw5b2/FtiV8dEHgXdjLjd9j2mmDMMwDEOOyGfKOfcKYIv3/vYS577DOXezc+7mHTt2SKozDMMwDMPoWYKFKefcfOC9wB+WOd97f7n3fr33fv3y5ctDqzO6gGmmDMMwDEOORDO1DjgBuN059wiwBtjgnFs1nQ0zuof5TBmzFUtYaxjGdDAcWsB7fyewIvk/FqjWe+93TmO7jC5iOTsNwzAMQ06Z1AifBa4HTnHObXbO/fzMN8voJk1bnRuGMQM0baVmzBI6aqa892/o8PnaaWuNUQm+WXULDMMYRBreU8O27DEGH8uAbphmyjAMwzAUmDBl0DBhqmcZb5jacCaxrj+z2ELNmC2YMGWYX0MPc7DeqLoJhmEYRgdMmDJMM9XDmJxr9DM2tJSjYS9632PClGEvci9jj8aYpcymHGBmDu1/TJgyTJjqYSyhqtHPmIxQDrtP/Y8JU4YJUzOMxifNHo3Rz9hioBx2n/ofE6YME6Z6mCpMHbPJvGLMLDa0lMNeuf7HhKkcZlNIer1hb7IxwfgsmgE1V2pRsJ2pSjC3Z2N0GxOmcphNE4pppnqXKp6MrZLLYbepM1UNLf32bMwBvf8xYSqH2dS36025Fk66AjRTUjmqGGRnk/+Gph9aH+6M3aNy2Hq2M73elwZamNLc/Nm0UqiPy4UpqQZvFt1eFXafZha7vTNL08vHYefke/r1+sTbjma+MZNmbzDQwlTTyzvabBKmxhT+YVItRj/e3SoGrSq64Szq+irsNnXGe2/9qQQmEPU/Ay1M6TRT09iQHqeuEaZm0X2SXqrmFll2+pnFbu/M0vQmdJbB/Fb7n4EWpkD+Is+WlUKz6Wk2u/8y95saXoPmWhsVRFrOJq3sbLrWKvB4u8clsEVT/zPQwpQq7LnPOrdUGEp8nsaFTujS+9RfdzeiCgGwikG2z7q+iqqutd8WE9L2NptVBVH0F33WHYwMBlqYAvkg0G8rBel1JkKUVBiT3qY+u70qNJdaRb6zfltIaKgqcrHfzDrSLtE0n6lS9Ft/MKYy0MKU5iVWZAvoKxLNlDRxp9yPyAaPMlSRUHU2PRnNJKYZX2bLPW56b4JCCVT9cBrbYcgZbGFKMWX3nWZKWG48nqylGhCxma+/bq8yH5G8Xk1wgBQ/SxYSoAs00SwI+q3/S2k2+28srQKNNrjfTMaDykALUxpmiwO62swnnHj70ZRUhRauCmGqH5+NFM17rtJ8CwuPKXLCadAsSmfLWKpBs+PGbLm7vT4sDbwwVYVPTyXaBGF7EzNSXSpMCV/lRtPbiqoEVUyes2mV3G9ak34zmTWaZuYrg+YezabFTy8z0MJUFStHbVkpUqEmMe9Js6CLNVr0/kojjco/Rlh2vNGsZI/IfhMwNFThM6VJZNlvvoaNphf3p34TzDVoFk2z6Db1NAMtTGnQ9M9+ylrd0kwJtWnigbKikGkN3W7ueEWreu9nz0SmM6/I/QU1ZfuJRpzHrtv0233S7ELRb+OolF6/yoEXpuSD1uxwLk1WRNKXWTpQaoIDqsDTfa1AYiLpts9JJEx1tcrK0CRFlT6Wpvfisv32WJrei3PY9VsflAbxeO9V+6OaFbU3GHhhSopKM1XBkCfXTMVmPuGkIk/22V8rqio2zW5FWkr3l1TsS1nFs6nCUbmuUJtoEtZK+5Nukdf9+ztelWaqgjFY+p5qNNC+one1CnpdWz7QwpRmha3zt5KXlSIdPMZawlT4iOe9fKDst2R+Gh8vsc+UMtJSOrh7qtGAVDEpjGs0UyphVVhnBb57GhpNhWZKUW8/uVro/Mr6T4Mnpdcvc7CFKVUemD5zQBdW2TLzCdTMGu1SVdqPKlY3Ys1UklBVPBnJNVqzJQJLE3mr2TVA3A8reizinSSa8ve831wtxEFATS8W6jVaTmN6GWhhChSrBc2LXIFaW5u35vB4Q1SnJmlnFWOA1DdM016p9i4RpqR+Pf221U8V1Y4rUnRofKakgli/mXTGm/KIVM2VSu+TJmWLJmpXWq8mMrTf6PXrHGhhqqmImtHY+ftlwEteYoiuN3SVHq06ZXVXp5kSllO4zEsF80YrB1h3HXgbVflMVVCnpg9rtDXSPqG5R86Ji8qTdiq0Lppr7ad0DBObzQuEKRTJhPtknkro9ZClgRamNFJ7U5G5t4o8PZIa27U0oaY+jbmi6ftr/0OVZkpYsK70mdJM2FWlZJCgyxWl0RLJ6mw2NX1JVq4qdM7V8nqrCGYQuzwkGmhBmyPf0+7fXw3SZ9Prst9AC1Manx7V6rFPJqLD9cnSzOFAYarhdfeon5JDeo1/mFQYSlasXV7Ze4WDtAZtRKqEcYVPj1jjqBDgqvIjkpT13tNoyB3QVZqpirYJkjCuEKaicWm6W2RIGGhhSjMpaAa8SoQEQZVazZRGg6FxTNWgM/PJkPaH5HlIhQV5tFl/Oeqr6lT0wypST1Q1cUp6fyuAQrwYEBUDqhmDxXNNIkyJfKYUuRRFpaqj191nOgpTzrkrnHPbnXN3pY79jXPuPufcHc65LzvnjpzZZspQmaEU6mlNuHU3maqZCnNCbzTkqRGq2gBV7EOnMvPJyrU0U6IVq07QrcLMpwr6UGiJxA7SYjOfLqVIFUiqrSvSroByS68qXAiUbgCS8dBjDui9QhnN1MeAl7Yd+zZwmvf+DOAB4D3T3K5pQZPPpd6QD7L9ElbeLjxJzHxiFX5FZj6Ns7FGMJegmYyaCvW/5r3RoPGl0Phbdds3UmUe7ydhajzRTHVXswryZMIaNJu+p3+HoHFl6TcH9L7XTHnvrwV2tR270ns/Hv/7Q2DNDLRNjSYqSbM6r2JzWgntwlOomW+8oTOFVqHBUznMKzVMoYwpstOrTEkV7ZtYxSKkqdBMaRzXq3DK1iARFA43osVasynbakXTHTR5m6SozXxCB/TZsjWRp7cFwOnwmfo54JvT8D3TjsZpuN5sivdaqiQSSjLYtWmmDtXDzHzjzaZ8G5pGVVuWyMpptm2QaiE0m1BrhClNbiAN4nB2oU4gMbdpzKESmhXkMoLuCwrpxZkkv5tmfNBtYC0sJ37f5Ob8pkKzqkET9CHFN3vb2V4lTDnn3guMA58uOOcdzrmbnXM379ixQ1NdMFF6g/BySRRKXWme6XXafaZCNVN1ZaROFRO2RlPZzUSA6c1PRdnpFUKCJi1ItyPGknKSiUwziYH8PddEClcVASvp+2nN96F694Qpjb9gFcJqaw9OkQZatwuFlKoUBgOpmXLOvQW4DHijL7hC7/3l3vv13vv1y5cvl1YnQjoBajNP942Zr20yCPWZGm80xRPKeEUrKrmvilyrJVHFp5+FRJhqeLlj6rjCX1Cn/eiuNqGhfM91wqqoaLwfZvd9ZCR1phdroh0WpMEtmtxWiglbvAtFbA5NfoegutYKFj4aNK4W3UAkTDnnXgr8DvAK7/2B6W3S9CE18yUrBOnWI1LzoIbQyxxvNKdMIlG24vJtH2s0xRqQaAPUKsx88oFSrE0QDHjpvtdo+mChVZO2YjywH0wX3fb9SLSqEu1qU9F/takRxGYoYbmk3lAOpQSodi14GarJ46W4T8KCh1tbenXXnK/TwlWgmfKaBDUzT5nUCJ8FrgdOcc5tds79PPBPwCLg286525xz/zbD7RTREIYgazJPawZZDaEvVJ6gGPJCazZJ1jivazRaUtOtKupLoCFt918LHWg10ZL1htwXTpdUUl6npKjG8VcX3CLXXmt89zSTp0TgTPfhQyLNlGLxouqHsnLSYSl5tyWm0CjDvKxejWBShYaoodDodoPhTid479+QcfgjM9CWaafhZd1lXOH4W1X+pNAq8wSgsfEmC+aEfcfYeJN5o0NB9Ws0Uw3vqSHbaExj0tFoeuqNJnNHyt+jKTnA6g0Wzun4uraQCn8+jrKsxMFUoZmSvOkTDv4yzaq8PzQVJsLu+8h44SSW7sMSQUGXJ02xobnrnj9bPWUhOBwYAAS6fIiaaUq1aBLX2dvpEQY6A7q0o40r8vtoV0VSgjVTecJUoJkPJkKgQ4gmlO5P2NJNg7tt5psSadmlrX40EYSgMyXpHNDDy2k0U5qBXSOISbXtoNO4hF5rvTFZYAyNFAaFma8pT7uiSYIpWagdbNPehQopOp+pajRTVWwQ3g0GWpiSquK1+X0qyR4dPFnna6bKkky4IgfppleYkkTFAHn+Gc2GrRKzWftKPnQykvqyJX1f6i+oeTbigVJYrJUUVSCdaEwOGp80jVCv0WhpxxeJP5BGE6zymRL2J4kp9ODYxHvdbAqCgJryiGrdwqf7mimNq0U3GGxhSrhC0azOq9pzLjT8Pu/ayr7M9dj5HMKFqait1YTXasLZpYJYXWA2axeeQoUp6WSv8YMDxT5hGl8goTahJUwJJ3qxT9p4U+W71+09QyVjWnv/qY83gydCjfZOExwg7cOSOtPCVNb/neuUm4y9wjCgGbq7KdR3k4EXpkSaKWV+n2az+5laI7ND+fPzNA9lJ31NQj7tnl3i/fUUgq7UYT7yQQpPITFVMxXugK7p++MNqc9VcJFWuW6aoUDnM6VJljjWaFIfb4pzY3U7cEMinGSNnaHjRCWaKeSCQr0R/kwPti2S2v/vROTfKF+ESFGlMRFr/kyYqozE8TeUxF8lNFVAVKc8ElBDqNSe7Js15bhEmApO9jkxYUsmFLFPgyDfU0KUFVwWbeN92ITtvZ+6b2KomU9oDkrXKzHNSInyYnVXq6UR6pOBXdLmusKUKhmT0mUlSCaxrHsaKpCJtcgN+bPxioWwRGuo1UDXG7JxCXQmedUm1BpfOBOmqkGqTUgLB5LNf6H7iTtD/cPUmqnUeaGDXlqwkPrmSFDnXhKUlfiVHR5vThnoQkPLpf4Fhyf1/XCnYbl2SZcVXPJYNYsmqfP62PiEeVwirI43FCbCLjp0ZwpTXdJM1Vv5w2Q+r/IFV3gfbu8DoRpoqcIAlAJRBSbCeqNZyX6uZRlwYaopmqwP1eXCVGtrgG5rpgLNOnkDW1kNymTNlNwEIPXNkaAxv0rTBSTXF1I2q881m2H3Sio4asPZ5SZY3ea/kud6WLFoGhdO2GkBVRLhNtbo/p6hEr+crPNDzVFS85V24+BuaqambDYfujAdlydO1kxRmqg6jauFNBq7Gwy0MDUmiKJqN7EEb/7b2mepuw+94cM0J3nnlp300+eFDgBaYUrjwChedQpXRRORoSHCVHafC9EUNZqyAe+QcrKXonGklTplH56GRZPGFy40K7g2B5g4klWwB2fWuBv6jKR7RGryBDa9QugM3IYpvf9mQqg5f0yg+U7XL0UTVSd1fDfNVEU0Y9X0eKBTYLuJJXSQTSRn6apKSqjDcN5AU3YwSF9f6D0+pPTL0fhMyU0HshDk5D6FCJx5k2zIvZLmI0pHE4myVkufjcIfQhJZp82DlPb7C2FSVvDgOuO+FKgJTpDeX4m7RNb5oe+PNGJS47eqWXA1mj4o11TWmBDqW6nZ+qyKiDxNWUlUdDcZWGEq6Vze6yay0FBVbaSalNDJKG+gaZR0Ck9fn/dhA/VkU5LALye4RIQumq8ZR2rKTJqhPlMhx7NoCIS/ZtNPaqfIzCe9v5oILB/uw6F3/JVpPzQO/tqxRaotH2+Ga/izzDGh3yFd/Ggd/MVjRLMZZIbKuh8hz1bjtwo6U53GjUUuTMncdrrFwApTh4WmpPbQ1NDVucRHZjoImYySPE9Fn3eivVOHXO9kU1L3JmypOSjSvEV/h9rsJZGLeea8kH4sMZu19/VuCroa85UkOe+U9zywH7ay/wcKRIcUCwnt2CLVlksSQ2Zp7EKFOXl6j0TQDS+rzU4fFlE99X6EBI6kxwORSVOR3kMTra6KqDYzX/eRrgCnrFiFmqluOlZDmGaq04tQZsXSfn0h16veAFVj5hPm9mn9HfgyS/JxFe2bWJYkJUPIBKbNug46f4hu+kwdGmvTQHdJsJkOzZR0dS4NoZf4qmjzTDWb8sS+mmSs0sCNRut9CxCmcp5H2QXbdPieahY/UiQCcuK2YGa+CpA6l05ZnQdO9kld0hw90pVCJLWXq7NTZy7zLrd36rIDZbPp9WY+jWZKkNuqrlCnTy4btuJcuPseLvjWpcw58MSk46XqbU32If6Ck5+FxFdGbiJRaKYkwpRSCzcmfM8nj0vdczYGhWYq7gchY9NYxn6dQf1X6PfkvVeb+TQCXFAQUM7zKCuopK9PMt9onO01Qk23c8J1i8EVpoRRSe0+UtF+SeXLt3xkxKtH+YBXdrDs1Jk7rVe891MGxrKRSVP27BKZ+YKLADd+NlsAACAASURBVPK0FenBTRPeXXalnJRZuekbLNz3I1Y99jVAtgm11vE9dMLX+EM0mzK/Homjcvt7fjhgk1nvfUtYCJ3E0s8jdKWd9Atpslupz1SocJLeaipNqM9f9F2BGrGUSV66Z6hkDE7KhNzjPKGpbJ/QRIZC4mzffWFKJawK+343GFhhSuqbkOU7UdafIokgBJmKGeR5ekJ8ZDr1xU5fkx6w0sfK0P4sGgKNhHYACE5AmJoZwn1HUpqpkmWTdh61/YdAJFRB+ckh3Q9DJpSsZxi876Lw2YwJNGmtOiWaqXrWoqmkeSUlLISEsqefS0KYMBVuMk4j1UKEmjSnI4CiJTgGvm+ahMtRfXK/Sgjrv/mJk8N9K0XuEgpne6l2tCmMMk6/N72aBX2AhSmZb0LWSrzsgDmmHOxApnVJOmjZwa5TFZ0k/6z7KdVM5R0rQu7AGAsYioztedvw5DE54qZz2UQwGB7bxxG77+Lw3OUcsfsu5j31aOmJRRrlMx37qUlz6mlMWBLH1EOKPjxpoSYwXU06FjL5jof1pXakk1Dos8kbL0M2Ox4XLny0fkSSKFiYuEchZfPezbLv7KQ0JgJ3CU2qGLEbi5dtJD1pQdujTugDLEylBrySTuR5iRnLdhyNb02CRJiaeJG708k05qCsAS40UZ1UzTuxiW+gMKXY1Dk9WJWpN+k3S3bciPNNHjzz3UCknZL4UgT5qSjz3oDcybnlg5Tha9OJ0Amw2ZyaLBHK9+H0xFUfL++rqI1wmyQkB05mXhh8kd5Kpuw4WHRe+bFUv/CRLGilmqm6QBOsNfOlgyYO18MDOKTbrSULPmnWdc39he5uQRbCQApTU7KYl53ocx5SafV/6jyp/4dEag+117tOn7viMzK1dwEmknZCJ2ypzDjhxNgdB/R0SgUoJ+wmA82SbdfTGJrLtjUvY8/Sc1i56RulnYDr48L2xt+99p5/Zenj17SuIQTpqlFj5qsHZp7O66tlzfnt/lZlIwEzhakQp26FUN/0skWIxGxWdF5ZYUOSn639/Pp4WDJhiBfUksle4CubnDvnwBOcfOv7GTm0K/quEu+A936KNio0IlXrbC/LMC9Liiod07rJQApT7VnMy+5rpg1Jb38JupWVNpnwytbXQVbqKGxpTHXZ2o/QiUHqM5U4tQYO0FJhqu1hlhE0Eu3BUdt/yJ5l6/FDo2w79lIW7b2f+fseKqVdSPfDMD+VJvOeepRn3PVBTr3htxk5vEsleJYlHYEVqqWEcDOfdrue9kmr7CSWbeaTmWFDhYxoa5ZwgSp9T0IFoczvK6l5lJp9p+a/C9Wshm/qDLIo7uTajnn4ixz34Mc59+o3MXpoZ6lrPjw+1ck/NMG0dPP21uJdpJmS+WlNh9VnphlYYWrqsc4dLe/Fk+xXB+H+NSBcPQZqXGodpKmhWgfNVMYKvqw/RNZAFfpyyPfOCl89gnwSm9IfStTbbHpGD+5g4b4H2bXiAgC2HfsSPI6Vm75RaiBKtzE0K/4xD38B72oM15/mpNv/OvheSzVLraSoEl+geNuosmgdpKck/BwrVy7bzFc+gjDdf0J9VpLnGPrqpOsp65dT1M9D04NoUpFIyof2pYRkfmkElE/aduSOGzk8dwXz9m/mnKvfhH/q8Y5ls55F+D6yTZFgMuHPJjOjaoRV6H4Ox7IMqDAlM0PlTRxlJ6QpYf8C/w+JmNDSuJRUa3cSljoJW3nCSBkhJWt1HrrCEUegdNmptX3iKDNweQ9L4ii+XSufE9U5byV7lp/Hyk3fLOVTJ9WkNep1jnnky+xcdSGPnvJzHPPIlxjd9IPS5UPrS5D6eMGEqaKbex+2+2CW1kwptLJT9wyVpawIFY4l+fqKrqnsBCzVTGmFqXqjSV2wWJMEJdQbTVxjjCOfvJVta17CrS/8MHMPPM66r78O9m0tLJvV50LNfPWAdDppJGlXEsRZ7RULiW4xkMKUdM+jPCfWsg9fkxUcotWnyAE90JdiuIMw1enzPFNMmWio6dBMSdTL6U1BNWa+w8JQ9uj/cu0+avv11EeO4Kkjn9U69sSxl7Jw349w2+8JqjckncKRW69lzqHtbD3xNTz8rF/lwII1HP3998D44VLf0V536TIKXyBJ/pmshJJQTkDx3osTfmrM41MWamLNlNzMV1aAKxLYyr67EwEJci0yhGddb8TBCaEWgrQgU8bcVo/9KRftuYehxiH2rDifPcvP49YXfoSRA9vho5fC3s255bNT+ITv1iF6X1vjqMxEKBm/03OLaaa6SFakSxmTW977U3Y11/7ihgtTiFRToZoT5xzDQ9kC09CQo9ZBmMp31O/8MmetTEM1RRI1sTQapD1BaYgav124LHN/nIv8pXaveDbUhlrHt695Cd7VGLrnyx2/Q5KPaKzR5JiHP8/hucvYefRFNIfncf8572Pu3o3wg78v9R0gG+jS2kqNoFs+11mOZlXqq1I2dYoi9UR73wk36cgWEu3mlTJCRtFwWVrgjettBEacacbg0DQmCeON5qQ5p8yzSZ7Hku03ArB72XoA9i47l9sv+RgceDISqPY8llk+q47QpKiN5tTky2Vo5dQSpp5oCCJ+pUm4u8lAClNZGqYyg5Y2sWq7xiZ09RjJUhJ7criT6OhQ9qMfqRV3iazs5xPt6Fx31nMIDafXmpJCBoF280pyrFzZyf2hzGRU2/MI8/ZvbvlLJdTnLmXX8mdTu/crHTvq4Tbhr8xkNL7vCZZtvZrHj38VvjYCwJNHX8jO4y+Faz8ATz7U8TsgSegq97Pqhgk2bywoEyqu0S5pkqJOEcwDM15Lt2c5PN4A36Q2fhDvy46j+XWU7Rpp7WGQQKSwDkjzBB5oG/cPlNFMxc9jyc6b2L/oROpzl7Y+23PUmfDmr8ChPfDRl8Ouh6e2VZEnDSbGUImpThPNJ/GZak9ZFLopebcYSGEqa1Wh2eW6/ABQvZmv7MA+Mpz96Edzjqe/P6+NnepOm9rSjCkSYZYuI7hHkD1AlV0Ztb/03neue/jR/wUm/KXSbDv2Umq7HoIn7ij8DolGrHbHf1Dz42w94dWTjt9/9ntheA587ddLvQjehzs5S7LEJxwWPNei97LTBJF1L8sGX2SZx0v7aSl3DmhlxA/VTNWbrLvr73n+1y5k0e67u2Jiad+OpmydjYyJOmTilfpGtvvQHRgb71im2fTQbHDkzlvYvfz8ts+gefQ58OavwthT8LGXT1nMZArmAT66Sb+TPM9kvJYFi4Tnw2r3QQvZ+qmbDKQwlfWwymg/8rRCZbRFmfvViTRT4UgmlJE8zVSO+S+haDDu9GJqoyVb55ecvNKkJ6OxgPKanFqZTqIdVq0jj/0vh+eu4MCidVM+27HmJ/C1YbjrS7nl0/vGtdrbaULxntE7Ps2eZedw4IjJ9R4cXU7zkj+Eh6+BOz9f/D3Ictek+0XoRtRpAWU6os06CRu56VNK9OHMHGsl+6LWR0aaY+1wo8kRu25ndGwP51z9FhqbbulYpshNoFNaFph6j8sG8mS/q+ECRlYbimjXRJXxmWo0PYv23sdw/Wl2Lz9vyudN7+GYs+At/w31g5FAtfNHhe0LyW0ojZZMl5H6W4UK9O39vMyitAoGUpjKcnArM8BrhN1sc1BoZm9ZagSJk2iema+TZqrIRNZxIsr5vB5oGpJsRtr+QpZN5Jo1iWmEqcJ6vWfo0e9HJr6MWac+Zwn+hAvh7i/ldtYsn56O17rpBoZ3/4ita1+T+fHYWW+B1efC//weHNiV+zWJUBDq5Ny+0Al5tgcnCVPlnkvR93cyQeQJI536fpF5vMzkoo3eak2AAZPQeKNJo+FZsG8jT654DvXRIzji86+BTTcVlityueyUEBjkpjrNuwqTBfOQsbtdmDpUQnPigSN3RPdxT4Yw1Sq96nR469egUYePXQo77gfyBZnSDv6paMlws7zGzBdppkLqzBJOe9FvaiCFqSwHtzKdLO+MMs89c4uVelhHlexaNNY2eZa1m48OZw9qeUJWQtHKttOqKG9QLOuL0apHsH+WZPUI2YNqOQfTZubEVehPsf0eagd2ZJr4EvypPx05pW7ZkN3ejOff8Vo3fJLmyAK2HfuyzI/r3sFlH4oEqe/8Ue7XJEJU6HpAs/nvIYFmqlHQhzstuvL6cCchpdA8XuKdzcwrVDK/VVI/hL1nh8abDNWfZu7BJ9i98jnccvGnacxdCp/8KXjsh7nligSmoRLClDRyUStwTkpvEGAebK+j7KbZS3bcxIEFx3J4/qriE1eeCm/9evRifezlsO0e9VY0ST/2XrIjhMxkDDD3oSs5/X9/mfEnHyldJnsMNs1UV8jqaGU0U3kr6jIr7fysyuUfeuRvIg9dDqkv38xX3CWKhJhOL2XRCjNoT6tm+JYP7QNeGSdRkDsc5w3ihYLNw9cCsLvN+TxN85SXw9BopJ0qWW/hwHP4Kbj7y+xb95M0RhZknlJveDj6DLjgl2HDx3Mn0uSRhPbhKVmrA3zoDqYEijITZyeTWqfFT95kJdXKQmczVl7UVVntKky8XyHv2eF6g/lPRc7P+xedyOH5R7PxJ/8TFq2ET/40PJKdg6wotUqnHHeQMaaV3oB66v1oNMr7lkkjxrJ84TqVd77JkTtuytRKZbLimfC2b0BtGP/xy1i4597M00rv3zlN6UhCOfK+z7Ji61UMf/hCuO8bpcoczFg0hObU6gaDKUxlDJhlXqg8gauMIKbNqgyy1AhTV3HlOtlwjtCUlzIhoUgh1GkSnQ5hqtn0sW9A2I2aopkqnRto6nll2ponwBQ+n43X0DjyBA4tWJ17ip+zGNa9CO7+cubDyBrEC6/17i9DfT9Pnvy63FNaAvRF74Ej1kTO6I36lPOS5x8sTAl9ZJrNyfuTlRWQNeSaqjsIgGmB4IS7/5nTr3sXI4d3T/ksi7yJOWTCb+VtChiPDtYbLHhqIwD7Y1+6p0dXRFqSxWvg069pLQDSFCX9LSdMyca05H4s2/o9zv3ez7Jg74OTjnfioEDLCdlCbSfNyfCuBxkd2zPF+Twh8x4uOym690NzOOfqt7Bo99Scc2U3s27fwzCEZDwINvN5z4LtG9i56gU0Fq+Fz70BrvyDzLEkTfb9NWGqK2Q95DKTb945ZRIC5gtT5R+6ZBPI9nrL2sBHcga1TM2U93BwN+x4gJHN17Fi0zdY8+AnOfHOD3Hc/R/FNcZapxVRtAIqq2oO3dQ5+u6pJjedZqpz2fwJMKfdjXF49AeMH/+Czo067adh3xbYfOOUj7Kuq3Dg2fBJWHYKTy87O/eU1mJizkK49G9g+z1w3T9OOc+3NFPFzW8nqw+XIZn8lm/5Nuvu+ACr7rmCxu3/CRuvge33wv6dUwTOMg7QRUj9npIxaf5TD3PCPf/Eys3f5LzvvJqFe+7rOCnlPb+QvdgkGcUP1Zss2PcQTTfMwYXHAbEWZtGqyI9nyVr49Gvhoe9NKle0IOuUEDiqtz2nVnmfKdesc/Ktf8qSHTdy3lWvZfnmK8svnFL1hOQOy1pgdhojRjZfB8Ce5eszP8+9TUvXcehn/5vG8HzOufrNLNp156SPywbWpINUQgTs8VSkpfeBW8rs2sjI4V3sWP1idr3uq3De2+G6f4CPXQZ7t+QWm46tc7rBcNUNmG7ywu/L+NgUboPQ9IWRbtpNkiESpsquLPK+P/E/mjM8lFMioqWZ8k0W79zA3AOPM+fQTo545GkY2wVPb4f9OyZ+YoHp6PgHwLsazjdZ9dhXuevZf0tz6UmFdRbv2VXWeThWMQudlBPKhC9D9kCTCKxFviHBwvXjt8HhfR2FKY+HU14Gw3OjqL7jJpsE81Zxme3dcX8kkP3En1E0NE3SzD7zUnjmZXDNX0dC3ZK1rY8mfKbKP5tmnHF6cnvLm3VqjcM868bfZaT+VHTwtraT3BAsWAYLVsDC5bgFK3jG4QXU5yxlbO5SDs0/OtIOuOh96OQgnWvm6xjJGn2+7s4P0Ryawx0X/B3PvOUPOe+q17Gt+Xew/Gdyy2q13mnn9yCfqXqDFfs2cnDhsa3cY4lztVu4Ioo0+8Qr4TOvg9d/Bk76cUBv5mt//mXNmYfrDY5+5MvM37+Ze879U1Y//HnOvO7X2Dv+ELz0D6Egh97Y+OSQ/WSPvjwNfqucsD+MbL6eQ/NWcnDBsVM+q9WK++H44uO5/eJPcc7Vb+aca97KrS+8gn1Lz4w+E2x9ptsiy9NhqmnhN92IA/YuPYuFjMLL/xaOew7897vg/70Afvrf4RkvmlwmJ3CjF6P5Bk6YytNwJGGjRS9Hp8m+yJ8or2yIc1+zWX5lkZBnhuosTDkW7P0Rz7zlD1my8+bWcV8bgQXLYeHyaAJaeWpqMlrBtsYiNh5cwNjcZdRHj2T51qv4sZt/n2d/+6d4eP3vwzN+NXf5Px3bTCT3M8TM154HBgoEjBSNps90Vk6cNvOc+CF/gGo2c/rSxqsBGD/u+bA992sj5iyCk14M9/wXvPQvJmVKz7rWJJR47khbn9jwCagNwxmvxz+Zfz+nyEYv+2v45/Ph678Fb/x863lL9n/LmihLm3XGmyx7/GpG6k+x4YVX8NSSUzl9yRhH+b2wfzs8vSP+nVoY7PwRxz69jaHGxBY5Dz/zF3nojN8EOk/2+e958eA+3vQs2nUnKzd/k43P+lV2HnMJNy45nTOu+z8c851fhkMPwCV/MOlZJuRNHGWEekgSqcbtjFMxdNrlAKJ3ZP5TD7VMfBD139ZibcGyCYHqc2+A130KTn4JQwVCy3CHpMBJvWkSv6dO/pxjhw9ywj3/wt6jzmTria/libWv4pQNf8zqGz8Ie+6Fn74c5i7OLJvnvL6oU50SQdd7hjZdz87l52eOlZ32Rm00PYcWrOGWiz/NuVe/ibP/9+3c+ONf5ODC48rv1jHJZypcw5n+fx7lpKnGpptgeAFPH3HSxPec/ho4+kz4zzfDp14NL/xtuOh3W+9BXuBGXwpTzrkrgMuA7d770+JjRwH/AawFHgFe673fPXPNLE+nHcuLZIwiwac+7mE0v2xR2H9ZGj48R0/mFhWdOlr9IPP+92949vX/QGN4Pvee+yfsXr6esTnLOP9ZJzJ/zkh+fbsOsP+Jp1r/71jzE+xdeian3vBunnHDe2Hv9fCKf4T5R00pW3RtZa87OS8kmi9roJw0KeRQdB8PjzcK00gUmzQzJoaHr4GVp8H85UB++gFHPNCe+lNw73/DY9fD2ue3Ps9bxR+qNyYLU+NjcPvnIi3XwuU0dxakPGgfzRavhovfC//zHrjnK1FbmHg2IdpVTf6kQ/UGqx75CofnrmDXiudAbYgDixdx1JL5heW+f/92moeeYvTQTtbd9SGOf+AKtp7wag4uWlsY1p82cbTT6T1vND3PuPNvGZuzhEdP+XkAxuYt55aLPsFZd/8FS7//QXjiTnj1h2HekkllC30NS2ihs/arm5shtE0pN3aY+U8/xo5jJmsLDtVTdc4/Ct7y1SjC73NvhNd+nOEVF+d+51BHn8wcZ/t6o+NiduVDX2Dega3cu/7PwDmaQ3O4d/37qR1zJkdf/8fw7y+KNGjLT55SPtuJvMmiuYXNlaUo2LWR2tPb2HNKtvN5J4E+ec8Ozz+aW194Bed/59Wc8YNf5aYX/QeN5sLiBhPd4/RiNEQwab/eEK2W23wje446A2pDk8stOwnefhV847fg2r+GTT+EV38EFq4o8FEsvyjoFmV8pj4GvLTt2O8CV3nvTwKuiv/vCTQJ+Qoj1TpM3lL1f3v9oWa+rHoLr/Oh78G/PpeRH/wt2469lOte+i22rHs9B454BuNzjmSkw8Cc1XnH5q3k1gs/yqPnvgce+B/41+e2NC1piq6trGkoeUYhQmfeYNHJnFSkIen0XIt8CaY4K9cPwmM3wAkXJtamXFq3/+SXwsj8SQk82zNHp5lyDx74FhzYCWe/CSj2d8v87Px3RCvKb/4OHNoLpIQppQk2K3oni8bTT7LsiWt54rjLWivZMu/bUK1GY2QhBxet5YGz3kuzNsopt70//qzAvFJwXZ000HMeu5al267j4R/7ZRojExOeHxrlsee+P0o9sfEauPziyN9r0nfrfA2nOHSXNKO6PY9Sa9anJHKd8l7MWwJv+kpLw7BwY36UVsdN1IUmzfrhA5xw77+ye9m57Fr5vNRFOJ445U1RNvGDu+HfL4H7vzmlfHYUbGehXqSpfDSKgtyd4y/VKX1E+v06uPA47rzg71i470GeddN7SvkwaTbNFm8kPbaf2vZ72Lv07MzvYXQ+vOpf4JX/HOUx+7fnwyPfL3SOD90tYabpKEx5769l6lL5lcDH478/DrxqmtslppNGoIiiwbKT+S1vUAvx7Wk2JXtnTb2mzMiip3fAF38BPhk/qjf/F/dc8DeT9oRyrvNgl/uiuxo7Tn8HvP07MLoQPvGqKFJjfKx1StGEXfayJzRTAcKUJLKOyS/8gr0PMnf/hJNkx8G9SMvZPghsuhEah+HECzsOpC0TwOgCOPklkamvMT6lve1M+ezWT8Kio6PIQDrsp5Z1cGg4EgD274Cr/hRIBweEpDbINvOVMXfPf/Cr1Jp1Hl/7ytaxMsJFuouPzVvOxlN/jWWPX8Oyrd8rNJmJff68Z8WNf8nB+cewZd0bpn4MsP5tUaRW/UCkQbnnq6W+WzZ5lhMS5u99CIjSIqTJ7GfzjoQ3fRlWr+fIr7+DlY99PfN7O5nqpClm3C0fZ+7BbWw87V1TTGf1hoe1z4NfvAaWroPPvh6u/qtJwQnSHFWifE+PXoefvyxzlwMoYWpuezd2rXoBPzrtN1i16Zss3vAvxQ0mI3o2IGeTNFjk/2fvvMPkusr7/zl3ZraqraRdaaVV78WSLRvJBTfcK9h0YloIEBKwQ4AQSPIjCYEAIYEkBIhpIaF3Y2PcMLZxl2zZkqzetSpb1FcrbZvz++PMnb1z55Zzz13tzO7e7/Pss7v33jPnzLmnvOct35f9LyJkH8cnnqvK+fXPeXfAe3+nXBm+ewsVz34ZpI/2z4Ca4WzCNJpvkpTyIEDud4Pfg0KI9wkh1ggh1rS1tRlWp4/ATUyDpdgPYRojv0zYUaPOog4Qr+9bYAPPZuGF78JXLlBh8Jd/HD7wDMy+oshMlUlZof4XQS4PliVUCoT3Pw7nv0tFanzramjflnsiaMPW1Ez1Rdd++C3Qodqlo/uYseluLnzwZi568CZWPPYOyKrP0jHraN/b9bhylJ5+UeBCalkuzeCS25V2afcfQttUsHidOADbH4Fz36aEIoIZOXznxdQV8Kr3wupvQvML/cJUFJ8pjw1LSj2n43Hbf0nH2Pl0jF2Yv6ZlVnd18b65b+fU6NnMf+kzWH1nfIsFzc3A8bjxV1S3rWPn0jvJpiqLbue7d/oqeN9j0LAIfvJ2ePSfIBucy0ynr00SsPf0Zak5sRMoFqZ819iqMXDHz+mbupKlz32ESXvuLbhtWeGCgq9mKkiw6e6k8tkvc6R+lSdHW34NHtsEf/wALHsLPPZZ1cddymXB1Nzs1/+B42H3U4gZF2P5mDxDzXwe/b9n4Xs5NO0GJj7/Odj+u8Dy7rkVJeK8OGWaZtlc5PGJ8cpRPpCOYdISNQ+W3MboJz/LuU++P08j4kRUrsGzjbNOjSClvFtKeYGU8oL6+vqzXV1wupOQRcQ0nYzNfeSFKC+8LyKzt5TFkVDgSBzculmlILj3TjVAP/AUXPlJyChHAPcpMSyVDAQ7kKacWpNbvgxv/r5i6/7vy+CF/0EOwOA30UxFirQ8fRRe+B/4zk1M+c4FzFv/RXrTo2ie81ZqTu2jYf/DgIYwFSSYu9u+83GVrqVqTODJvejevGuUFjBH4BnUJwV1vvR9ddo77478JWOt4Wv+VoXK33cXvT3dubr0x3BkCgkbR3YyqvUFDs54bYEmQkeYEi5pSqYq2HLe31HTsZf0c/4n+0jvNH+jB373ac7Uzefg9Nd6PlJwfhkzRZEznncHPPEvyrE7Z0b1gklePz3zlaT25A7OVDfQVzG64F7gAbFyFN1v+TFH61ey9PmP0bj7l/lbYVopMKSeWPMtrFOt7Fx6p+ftgveWqYbbvg7Xf06Z+755NRze4RN+Hz6W/DjVfMfDsb1wfC/MuMTX0TxUmPKqUwg2vuqfOTNuPvzsj+HITt/ybk1Ub5++r64poSrNa+geN5ueSuUP2BU2TytHw+u/xZErPsf4lqdZ9fDrGHO4MFQ3arDW2YapMNUihGgEyP0Oiz8aNMTxLwiKogi6p72JhSAqs7evabGrU5levv5qaNsMt34F3nkf1C8oeK4yHV2YCjJDFS0Ci25WWrCmV8G9dzHn9x/wPGGAviBr90+UDdtv4uYX6J4zylz2oz+CL85XobodLbSv/BhP3fgIa676EZvP+3901k5j+tbvqLIhJI3aZrMzx+HAizD7ckD1od9iWiTIZqphwY3KEb2vJ3BxyY/DbBbWfg9mXgrj+zUO0RMZ5VA1Bm74PBxaT+3abwLhfeOEnykldMNf9xMkgkPTbym4bHpaPTL5ElqnXkPm6S/B8WbPZ8LISD37f+334MgO2ld93DNSD4oUZZCuVHP2xi/C9kdYfP/t1JzY4VlWx8fSrYnQERL6sionX6dLKwXhfZypHs1Lr/5vjjRcxOLn/5rJe+5R13WEqaikqF0d8OSXOTP9cl828aI1WAjF5v+OX6lIz7uvpGZPsTZHL22Ud7t8x8qeZ9TvmZf4HkzDIh799pRsuoYdV92t/vnRHdB9yvO5OCSY7rGj5W8lJex7nlMNK/KX+vpkuDAkBCeWvoPVr/kRUqS44NG3MW3rd/O3o/oXn22YClO/Bt6Z+/udwD0D05z4iCPYBEUGBAlTzolj9XUx/lB/moUowlRPn7emyf/54mfHH3qK+b+4Fv7wRRV2+sE1sOLtnvY5dxSQW7jy7lnRngAAIABJREFUQqCZz6uPxjQq59RrPs3Y5kdZ9dAt1LU8XfSYLpliXjOlaQ7N+tAbILNk9j4F93xQCVA/eQc0r1Zmq/c9Bh9cTeu5d+bJCrFS7Jv/TsYdXsvY9rWhG6u22WzP00pLNOuy/CU/odbz+tLblTZt52OBGqR8e/c8CUd35x3Pbbi1NQX3wt7Nolth3nWMf/6LVJ46oB3BGpT8Nyy0nHU/5tikC4vympkkX7Wx9dxPqHfx0N/51BvxA7s74fHPw7RVnJ55re9jnsKzELDyvfCOX5PpOsbKR97AxP3FG77O8mKS5LuvL0vNyZ0FtAj5eyGVpi2BzFTz8qu/ztGGVSxa87eMOrZZS5jydej2Ozyt/gZ0tnPior/y/Uzf1s66DN73GLJuOuc8/j5mbvxawamuOxcxFgTfFGRZH+F6z5OKnqFhsS+5aZhmKmjt6Ro9Dd7wbWjbBPf8uecpNU7i4OL0ZRrlju6CznY66lcUXNZxXs9Kycnx5/DcNb+kvfFyKrqOFNwrJ4SObiHED4FngAVCiGYhxHuAzwHXCCG2Adfk/i8LOCe61XuaitMt+f/DTGh+rOBAIGGn86XOffkLrHji3XmVZBTpuacvqmaq//tkzhxmybMfZcUT70YiVPTKbV9XXDA+cG/OWsJUkPbOr7hlwSV3svmmX9CXrmXF4+9m7stfyDOnR0E+L1TE7Og2Rh3bzNyX/4VX33cFc+5/i/IjW3iTcp79y01w/WdhynkqtNr17g7MfD09mTFM3/rtyIECvtj5uCLgbOpPK+EnTHm+nzmvgcqxsOEXgdql/Fd58f/U84tvLbgfJDCFyrlCwI3/gkSycO2n9ZOt9vkn/w00yTevgSM7OTD91qJbUZO2OnGmtoneC+9UZtNdfzD+nDye/284eRCu/ntSAYJEoCZi5iWsveEeOkfP4tynPsCsV75S4JCrEwXrNsXobJyio4VMz8kifykI38SEEGRSFtlUJesv/BK9mbGc88xdVGVPh9br69DtNR7OnICn/h3mXUtP4/n+HxrU3LoZnH77/bRMv4m5G77EOc/cRaqnX6MTtuEHrQOe/bTnaUVUaaV8g33CUnqFRt7OvQqu+pRa2576ctEzkVNO5T+7+PDT1RMucLJvNQDHxp9bcFlPmFK/eyvGsu6Sr7JjicOUW16ylFY031ullI1SyoyUsklK+S0p5WEp5VVSynm53/4kNYOM/ACWkvMfu4NznvlwfvEJW3eCzFxB9+wJNebIOqZt/x4ADc0P5uqMJhxFYSi2nxXZXlb+7o15QsDVN/4mbzYKgntzDuOrgZC8WyEqjNMTz+G5a37F/tlvZuaWb7Jg7T/m7wVpRpyw+1qfMb2//yftvY8LH7qV6Vu/w8m6xWy79D/go9vgtq8poSSEe6cvU0vznLfQsP9hMif2Bj4b9G0KnPx3Pa5YzDP9hDZ+Qm1VxuN6ulKZUzf/BtnTVXzfidPHYNOvlcYyU11wK+g0rMNaTd0MDpx7F/UHfkfNnkfDnydY8AkUptb9GJmuorXpuqJbOgEfQcO07+K7YOx0RfnQ52LJj0Jpc/ooPPklmHctzLg4xBcuxCm7tpE1V/6AAzNex5xX/oNp276Xvxe2vPT0FTuw66Scso6ooJHOMV7CVHCd0G/S66mawIYL/5Wajj00Pf03oQ2O5ND93H+rfr7iE8GNCXlvXVSxYdW/snX5x2nY/xBLn/tI/70Qk2jQYbnou5xsgcPbYYaibvA354cIU4GBPDlccpfif3vkH1SwiQOmNBB+BJqhe1bz81AxiuOj5haW07LCOCoUomCNLjOXqeGXmy//soWgec4fUde+Ji/ghC08QcJERcBimJVKoFm45v/RXVXP0YkX0ND8EEh/x3QvdPdKPVtyDvZmNK7teapPNfPKys+zc+ld9OBPuulEZcZAMxXwSGgkoIBsuprNF/wjB2bexuR99+e1U7rca7YQ1ZcNz5cIhZvrlF0/o3PUDP5w65O8/Oqv0zrjJsVvEgHNc+9AYlH/yrcDnwvqi/ydjlaV525WoeBbxFSeg+/4XHI7dB2ncs9jvnVKCWz4GfSeUWZfF0z9BZ3YN/9dnK6ZytR1/6nlBBfIxeUb0dENG35O37wbCviabOjkCwv6PqKiGq77DLS+Amu+VXgvZFcu+Ngnv6w0J1d9CjA/qIH6Ttl0FRtXfp5To2ZS1/Zs4PNO+EVLhvm6WIe3AnDKI3xfZ945v9PRhlXsXPxBxm77hfIhC4BvVLR7TTx9DJ75T1hwE0xdEXiQCxu/3X1ZEIK9C97DrkUfoP7Ao1R17APCk24HrdVFmqkcv5QtTPkJ2DrmUD/kv6kQirOpYXGBQ7pfxLiOH51/pGWYMLUapq6g21Wtjr+VlutCmWDYCVNOHJx5G+2TL2Puun+luiNYkwA+J3+U2jUwR5OEpu3fY8yxjWw57285OPN11Jzax6jjm9VtzZduCwq62il705jU/CB9qeo8U7FKdxL+GcU+UzE1UyESkbNsS9P1pHs6GN+qNoYwQcyG3TdS6pn67BNtuusYda3P0dJ0PT2Vip3dxFm5q2YyLdNvYvzWH6tTsQ+CuiLfD7ueUL9dwpSfUOsWfvOYfTlU11G11d91USKViW/SUmg8t+h+kHZEd3E/k7XYvfC9jD38En3bfx/6vJF/447fwekjdC1+o9HnQogWTghYdAvMvgJ+/xmVLDmHoHcqhGMMnzgAz30dlr0JJi8Fgg9jOoEfdiUn65Yw+ujG/KWwpcV0A0wf3U5vupau6klF93Rmjfv77lr0Ac40vRru/1gRKakTfma+ovHw7FdV8MYVii866JAXpjF3akj2z34zUlhM3fkTINxsHIkCZc/TkKmFxmVARN9IB7T9Gytq4S3fB4QKrunq8E+araWZ8uEACxI4u0/BoQ30TX1VkWJBRzMVJDCVlyg1DIWpgs1eCDZd8GmksFi05m8RId3vpxGo9rmex4lm5mz4d9obL6e16TraplyFFJbSTqEfqWYLQLomrJ4+Cdk+6psfor3xCrLpftONjoP2gEfzhSxazk3s6KSL6U3XUr//oaJ7QXAubjqT0VbD1x98FEv20drU7wgcpgH0Exz3LPhjUr2dir8rYllwLPy7Hlf+S1MKhRs/odZX2E1lYNEtVO98EKvXmyepsn2jSqZ83ts97VxxzFBgO+vCwVmv50x1gwqACIFW9KEbL/8IaibSOc3fjB12GPEzowiRSwAuhMo/2H0KfvcPjvuam9jjn1d8ZFd+Mn/Jb20BvUOMjZN1i6nuPOAbFeuGr4N/iMYlfXgbp0bP8hwrUTVTAFgpOm7+mgp5/8k7fSPN/N57gSa68wg881UV+JATTCJFGbvgHC9dNZNpb7yCKbt/jsj2hAYERRampq9S8xV/ATtI8IYwLb7r5vhZOYf0zXDPn3vm7gQ9M5/vWAoSzA+sBdlHT2Mx27uWMBXwyHChRihbuCdOV00j25Z/nPGtzzJhyw8Cy/oteEELIUD1I59EyD42r/gUCEFP1QSOTbwgz0mk88p7nMlINZ1oe/qy1LWvobLrMC3TCjP+6FDtuyetjjBlWcLX5yQsh6nz3WRTlbQ3XkHD/kcg2xfqJwBq8jgXN53JaPdpQ/PDnKlp5GTd0v7PC9kU/BbhjnGL6Jh6ifLZ6PV2otcym+16QuXVc/lq+WlIA82wS27H6jnFhEOPe96u2/pjSFUobYkH4pihoD9CLJuqZM+C95La+5TaPAIQqML3unnmuOIGWvp6+oR/WlHP6E0HtMwr9Qtg1Z8qbd7+FwFNbWP7NlXmgj+Gupn5+0HvLvSw5sDJusUAjD62MeRJBb85ErbGpI9uL0ojY0PncOj1fTNjGuH134D2rUpD5QEtLq+n/xO6Owp8pYKc+MMcut1Cz/7Zb6byTDsTDzwaGrSkzT3WeUSZjmdcnL/kp2n21UDnEBgs4nXPdkjf+Csyz/6HZzkdR3IjDrB9iqzz9KQVRbd0IgGD1ujEzHeW4TVx9s9+M4cbLmLSs5+BY/t8y1ZX+GimfK4DsPl+Krbfz84lH+JMbVP+cuvUaxl1fCs1J3dpneSc2iht5+o+SUPzA/Slqjg8+bKie2GwLJHvr1TKn9/IDW0epKL7heVam66lousI4w6/GLrgQfGk1bG5SwmpnlOMP/QHWqdeW7DahL2WoO44uux9cPKAipiJWNYSAo7uURQFsy4ruu+lqRAiRJiaeSl91ROYtK84L5rV18X47b+EhTd7JqCG4NOwjubEebLdP/tN9FVPhMe/EFgmaCPyXNc33qPS7ix7czCfXMgG6LdZFQmNl/8V1NbDb/8Kslk96pRH/0lFZ1720cL7lvCsN/S9unByXE6YOupvKnPCKGdo10nSHQc8I/lAz/HXSwCvzFjKfHr5Xyni2JeKD7fB0XEos+tz/60oQSYtzt8LSqActq6518rDky/lTPVkmnb8ONRkHKiZco7vvTl+qRn9eQNNNVNBGlLfQ9wld8GS2xn79GcZf8g7UjVMo+vLARZUrnk1jJ9DV2Zc8edFOAx7ocwUU8NQmPLhbNl0wT8BWcUG7vOGqvyiqPw2k64OuP9j9E5cxN757yq41dp0DQD1zQ/raaYcpHS6+Y56+nppaH6Iw5Mvoy9TW3BPN42NvVFGWdD9hKYwzZTb7+zw5MvosypoaH4oVBCDYlW0jp0fYMKhJ0hluwtMfDoI2jxPz7gSJi5QTrAe4yk0Om5XToPkEXXp9S4q0iGpflJpOubcRP2Bx7B6Owtu1e9/mHT3cU/H83ydPgKGEHpmvjOO5MTZdDVHz30/7Py9ojHwQTCxqce9dT+BCXNh6opYfHJ+m1VRv1eNhav/Xm0I634Ubkra/yJs/BVc/EEYVZxhy0sDVZlOBY4zN3oq6zhdM4XRR1/Re96P2DdofTi8HcCTYwr0NAKeY9ju98s/rkhjf/MRlaVB87OzUioqhN7TcPlfF9wL0myHab3ddUorzf7Zb2RCy5NYx/YEltVOEL7naUhVqkwHOXhZPEL9cwmJFPa9IeC1X6Fr/ELOeebDnj7EoflGfYhTffcrKdXcmbbSUwul54CeaKZKBj8V/plR0zhxyd/AjkfVqcgD6ZTlqSGpqvDppsf+GU40c+qaf0FahRF0XTWNHB9/Dg05n6AwdBtopmoOrabyTFuRiQ/0navtE2QUYcpPUMiECETuTbkvU8uRyZfSsP8hKjQ2FLfwpEs019D8EN2V4zk2oVDVHObzHhghZKXgoj+HQ+vzufEKyoYJUzsfh1GToH5h0X2nxtCGjnaoY+6tpPpOU3+g0Pl7yq6f0VU7FWZd4Vs2yE9LJzjATQTZOv+PoLpOpUTxQaRT57F9qp+XvRmECM5XF6KV9TWvePXB8rcqBv+HP0Wq+6TvZ6YsofyraibARR/0fMZr86z2W1sccHf/yXGL8ma+sFfjZ6YKNPO15SL5PGgRQNPM5/qumbTVLzRaKbj9G5CpgZ++S5Gb5hDoI3PiEDz/DTjnTVA/v+BeHGoPr035wKw3IoVF3eYfRi7reW/3k2ocpftzM3qNhzCXEjDUTAFU1LLrqrtBCJY/9WcFfFoQvu/4a6Z8+uDobpUIvelVnoJTFJ9XL0ShHRoMDDthKkgo6FnxHqVmfeCTKuLGA16D2XORPbgOnv0anP9ueqeuLL6PMvWNPbIOeXx/aLudA1U3mm/srt/SZ1XQ3nhF0T1dUkm7v6KE4/ppKsJMdV4agdama6jqPEhl+8uh9bqZe3U0U1bfGSYefIzWqVcX+SaFClNhC/SyN0PNRHj6K0X3A304BMpfatZlvo1wjzkdv6XTk19FV1VDgamv6lQzE1qepm3uGwJVh35+Wn7X3XALtp2iGi78M9j6ABwMf7ehWP9T9fscFcUXyfHXBV/qCa/valnKGf1UGxVP+QuGdYeegp2PwaUfVSl2PODlLqCzebo3yJN1i6k5uZtUz6nQMey30QX2UftWpEhxuna6522d1EPFHHauvh3TCLffrRyjf6vYy8N8dtLP/Dv0dSszofteoGYqnHrCDdsRfdyWn6j8inFw5gQcWlfgLwVqDSk+NOkQJ/vfCxsPp2qbWH/hl6k9sZ3Fqz9R8OXDDiF+2kxfLWezIutk2kpPwakvK0MtKImZr4QIdKTNpOHW/1QT8r4Pe74pT1u/+1q2D+77C+V/cvWnfOtrm6pMfamtvwltt9Ox2jcPVUEbsozf+wCHJ1/qybejqwK1hSjt8GzM+VG86mhrfA1ZkSKj0UdFmimfyBQnKvc9Sbr3VP5dOBHGPxNkfkkJoYg2V74Xtj0IbVsK7weVPbwFTrV6+kvZcPdVmB8FgBQpWqZdz4SDj5Pq6QBgyq6fIxG0znlDYNmKlOUpa+ls9uCTs2vl+6ByDDwRHtkXiFz6GKZdqKKTiOCr4gF/UlSf7zp1Bax4O9bz/03tye0e7csy7cUvwNhpyvHcB15mPj1NROH/J+uWIJCMOr45PCmuTz8F+pW1b6Fn7ExkqiK0bX7IuMaT5/oy9yq49C9h7f/Byz8OfG8Vp1vIrP0fpSmcUGx+FMLf51PXF9SN/bPfTOZ0mwp6MECewmDf84o4euYlRc+437+JcF1QZ6imUnJk8iVsP+ejTGp+gKk7+jVvocnboxCqgvremVpoWOyrIAhXHARppkKKDjJGlDBVmbbURLzq79SJed1PvJ9xwNNBdM23Yf8LcN0/K1OGDzrHzKZjzFzE5vtC212omdIwXzWvprLzEK3TbvC8rTvQbC1TFM2UnwbKRJjqrRzHsYZVpDbfG9poL81UmKq3avtv6M2M4kjDRUX3whbZQB8Zuw9e9SfKF+LZrxbcD9LSpfd480s5UXSy19QQtUy7kVS2m/oDv4NsH1N2/4Ijky6me9TUwHJCCE/fQF3NlGfOrupxsOr9inU9gFvICwWv5tA6pcFY/ub8pUDziobPlJfgGKgVuOpTUFHLwrWfKRqnDc0PUtO+XlEhOJjsdT5fZ/N0j8N+J/SNoXQkfma+wD5q30ZP3Vz/+5pwald9+/aKT8L0i+G+DyNz5kUvzNr0dZB9cLl3FCCYM4r7dWH75MvoqW2EF77jW1ZLsNnzJFhpZeZzwUSYCnrlYQdEe97sWfAejo8/h6adP87fC9sz/IRdX5eS5ufVQcRK+UcChpj6An3SyoxpatgJU1os5qv+VOVC++1fKYr/gPKZlMvx98RB+N0/wuwrVVoOgm3YrVOvRex5Ck4dDmy3c1BpZeLeeA9Zq4K2xis9b+sOs7xmKoIw5bUw6kQD+tVxZPr1yunVpd1xw62ZkjKEubevl8z2B2hrvNLzlB2FF8u3bO1EWP4WxX/kIHgMOiWLXX9QYfN1M3w/30QzJQQcn3AuZ2oambT3fsa3Pk1V50EOzHqDViYUt58L6PlqeeXsymZzY3rVB9Tp1EM7pZ1Y/OUcrcPi1/V/ftRIQAeEEFSkvL5rQB/XToQr/4a6Q0/ludEARLaHOeu/RFfdAmX2DYB3/4a/V7dg3lU9ie7K8Yw5ujHUed2XSN6vk/p64fAOeurm+X6mbuon5xj250hLwxu+BZkq0r94tydPWuWpA0zd+RO6l76tgG7CDT+hKQqZcOGNFEcXvkX52R7d7fmIFk3BnqdhygpFoumC+7ASNz9q2Hd1ZghpmXYzo49toubkLiDcmuGngfIs190JhzbANOUC45vAOkYuzXLDsBOmUpbwDJMtcoB87X9Bz2m4/yMF4m+orf/BT0BvF9z0r/nZEjR+25quRcgsbCkOWXeiKwp/kpSw8R6ONV5KX8Xo4GdDYAtTOtQENrwWxkqNzT6dsjwne8esXJTdpnt9y/ZlpSc5XGd3r8fTOex9Guv0Edp8ovjCNqKgE23B97jogypNy+pv5i/5OeOnRZ9yRg0w8YGpZkqAsGhpuoEJLU8yY8t36K4YR+vUa7ScyL3MUIG0IDn4JSzu6u2D2gnwqveo5MHthSayoP7Nbxh9vSoNzrxrC2gd4qaZ8OrPUMHxgvfQWbeA+S99DqtXJe2dsuvn1Hbs5vjFfx2a29FLINYiynWPJSE4OW4xo49tDNW6RNZMHd0N2R76xvsLU7pmM+f3DRQSxkyB2+7Gat3I/Jc+U3R71qavAXD6og8H1ucXBRemMQ+aGscXvhWE5UvQG3rg6u5UUZ4ufykb7jGnpanUPYR4wDk1WnJWjUn7lBkzbNb4zSvP6zmyTjuBu78wFSHfWplj2AlT4L2xFy1k9fPhyk+oDdzBFRTICr7tYfXsZR8rsNsHbVQnxy2CcdMDBQUoNJP09slgB9H9L8CJZg7PKI7iy7cpsLZ+2EKUDjWBDa+NqEpj0wXvRVWMblSTbtOvfcv5Re6dCRI8N92LTFfRPvlSz9thIf9B3DUFm1j9fJh3nYo06jkTWHbssY3QdTzQxAemqX7U75ZpN2Ble5jQ8iSHZtyKTFVo5T70FjDCx0WoCv/iDylT6JP/VnA/aFPIC/e7HoOOliKtT5B51yR037I0BJtUmkMXf5rqzv3M3PwNrN7TzH7lKxybsILeuf5z0YbXeAuLgAVvofNE3WJqj28jLYOdo/36wlcz1a5MbX0T/M182sKUoz9D3QjmXU3vRXfRtPPHTNrb7xZR1bGPKbt+zv7ZbyI7pingA/wPhGEHxcC1b8wUNbfXfs/TET2ULqN5NWR7CvilnAg9vPt9rsE9N7pqJnN04vme3HRe8NNyel5vVmSdNL2Kvqx/jtowp/dAzV+kzONnH8NSmPJaFD0Xyos+BFPOg/s/mjfRFJlX7P+7O+E3f6m4hS65s+CZQILGlFBpD3b+XkV1+MBt2gtkh934K7AyHJ1W7FTd3ya9gWYv1DpcQjY8HWk1U2L4kvktukX5xvio032FKb+IvmwWNt2HnHMV2bR3MuMwATJooytaoC/+IHS2K0dp/PuzriWXpDZEM2Wi/re/z4nxy+jMEcgemKVM0TqLrGmotp9ZOu8HOKoBzn+XMoU63q+W5m/dTxTf0/zrCu7HNQ4URUt6mP280D3tIg5Nu5EZW77BvJc/T+WZVrYv+wgVmiHtbiFbRyPsJYicrFuMJXvJHPY3jcuAROu+Amd77vMmzve+T7gPkg3nXNfRwGWv+BuOTVjBojV/R/XJ3YDSSklhsXvh+0MPBH6m8DBBLliot9TYPdXqaV0INFVbQpn4hKXSyHjARJgKXJciOtu3TLuRUce3Unt8e6BoEuaHWHR/nyLrpHZCLILdYBqIwKKDjhEjTHkO0lQaXvtVJeTkwnPdZfPlHv88HNsLN3+pgCsEgjfllJUTFPq6YZs351RvX7YoBYZvvqOciY85VyIrvUOwIZxAs799ouC3DmoqilN51FbqaqZ8/HIW3az+2ewd1Rc5YeuBtXDyANbiW31PN6En1ijJf2deCpPPgWf+C7JZ3wVv3KGnVSZ3D1JHJ5z9pMuSna9SCHYvfD8HZt5OxzjFY6Xzfr20NTqBCVo5uy65U5nBnvxS/lLQZ1ekLEWKu+leWHJb0ZyLC1MH/4qUxbblfw1YTNvxA9oar+BY/au0Azjcm53O5uc1Dk+OWwJApnW9b7lgNnE/YWobjJpMqnqsb1nd7+oUbnQOa1Y6w/qLvkTWyrDsmTupPb6Vxt2/ZP+ct9FVMznUVO3XrjB/w6D7aUvAvGtgzFR44X+K7oea+fY8pdaFKu/+dPrRKaqE6D50TkQ1abY2XY8UFpP2BUdTh/pTOe9LqTRTOYf7OJxwwVx/5SVNjRhhyvdkNGmx4i3Z8HPYdF9xvrpUClo2wjNfgXPv8AxvDTzZWEKZsEZN8jX1eQkKvk7oB9YqoW7xa4MjxjSlKfu5KMJUyhJFGgsd3xrw3rQq0haMnw2TzonUR+q6j2Zq069VBM3864x9KVTwgfe9ogVYCKXpbN8C2x8h4zHerL4ualtWh5r4oHCz1yXOdH6fA3PezMaVn8v/rzMeig8SmtoanZxdY6bAeXfA2u9DjnctcPymhBKsezph2VuK7gf1ho763/fQFIJMyqKrZjI7l3yIPquCHef8pfo8TQHDuTlYVvDJO99Wj88+PWoavelarJZ1vuWCcx/6mErbt0L9/Ni5GqFwPOr5hgm6ahp5ZeXnGX1sMxc8+jaklWH3wvfm7wfBr44wQS5oHFakLXUIWPEOT0f0QGfwbLcy8/mY+CCCX5kDcZOSO9FdXc/R+pXK1BeUZzBEmCoQto7tUWSd05QwFSQwhflMaQUBlQmGpzCl4zPlxKs/rE4P932YdNexQn6UFIpTqmosXPtpz+Kh5grLgoU3KZ+rntNFz3iZqnzNVxvvUULCghuLHVMd0NVM2c/pmgVt1Lg0UaMq/RPPOuG1YOSvLboF9j5bFGEJGn45TkiphKlZl0F1HRnDkGnwXqBTKeF9KlpyG4yeAs98xXO8jT28FquvyzOFjBuWJfJ169ITBH2finT0TVt30/TlkHG/m0v+AsilBCHYXJFJWbDuR8rfcFqxiSQO1w6E+EZqlNuz8E944tZn8po/3fLOzSFo/jrhuXkKi1PjlwQSokaOzpJSsZ9PDBam9Df9/u+q4xsmhMCy4PCUK9m94D1kek6wb+4f0V1dD4TPVy9BwrL8HdP7y2mYzc57u6cjeuBmf+glFZgSIExlUv2J4/WFVP9k82Gf4VWspekGak/uouKwf/LsMDfEgvv7cmSdOefzoGTRYQS7A+UfNhgYnsJUFM0UQCqjovtOH4EHPlEwuUa98n3Y9xxc+0++SWIty58WID/BF90CPadgx++LntHWTNkmvlmXQ834wMVFZ/GCfuk+6sCsqSg0Q+n6THmb+RzCFBK2FKucI4XWtm6CIztzn2eemBm8F1rfyMV0Bax6H+x6nEzb+qIFr67lWaRIBS6uTtjaP10NUdAY1zHNuIUpXXOOdjb5uhlKy/Tid+Fki5o3PifpytNtilF82Zs9TwZxwsPBXAvn7BNeLvG/AAAgAElEQVQ7kjYVMP+D2qZ7svZ7r6cnLIGWVxSJsAfChKmifayjVQVHTJxPRYBWVluYyj2X9jt8eMCekzvO+Us2rPoiO5d8KH/PhHpFxxcuSJuT1zCPnerpiB7ogL4vl9x4ejHHnQ0h+g9NuvNNCOFv0oxAvmyjtek6siJF7Tb/AKBIgnlzP1ln0T13uZDPDXw3Eeh8BgPl1ZoBQmRhCqBxudJQrfsRDQcfU2XOtFP7xKeVP8zytwYWD2UFn3mp0m55mLG8hSmPBfLQOji6Cxa/NrBO0Kc6yPtMRdVMZfo1UdUZ/WSt3v5suQWvYZFyWvToI9+8UF6nnk33AgIW3AT4L8I68qZf0mFfnP8uyNQinvlq0QY9vvUZeiYt90034le3rj9PsPpfx+eqULjRNRloa6ZAMV73dasE0fhrjMdsv0exRvtwN8U9sZqk6wHziEcbhZopvf7100z21C9VZtDDHqzshPNtFW2QDudzIURgvkYd2O82CoedvW5JK8OhGbcWBI+Eaaa83qHOGA6cN85FwsMRPWgNEXuegvpFih5Eo/4owoFnVLSI1tc2eqrGc7ThQmq2/dpXBRU2lgpu78uRdabUHhEkMIVppgbSpHm2MSyFqUqP04jWYnnZx6B+EbOe/VtS3SeZ99LnVIbym/4t1Hbg9/n5hSeVgQU3qonoCrHVzqi98R4QKVionLXjOCLasFXrUZ35nMmfdWkRoHhjKAhJF0Jpk3Y9AaePFjznm8qgTxb7fmy6F6ZfCKMnAfE0U5EF8+o6WPF22PAzRne35i+nejoYc2Qd2ZnhJj53PbqbddBCqq1NcPRJXM2UpzZxwhxY+gZY/W04ddi3L2s2/0wRHU705jsK9hfU2UALTSVx+tg0FZPuZuAnvPROWqb+8DH16aaUyiNHi2BH8vkJcVGFey//wbAybqRTwsgBXadubcHcwxHdbw1Jiz7Evuc8fWyL2miQ0suLALYibYX74Pncbpl2I5kTe5RPrgHya3B3J7RsKGB7j5NHcyB89wYL5dWaAYLx6TFdCa/7LzKnWznvD39C495fIy/5i6IM5Z51+gpTjuuLboEzxxRpowNeEWlF16SEV34Fsy7Nn3SCNs8oJxQTdanTAV3XxAfF7SraKBbdCtle2PpgwWXtCXlkJ7Ssz5v4wN8kpMW9ZKLlXPWnILNM2fp/+Uvj2lZjyT6sOdGFKd33Y1nCd/OI4o/R/7deGT8HU1/H00s/ojQqz/6X5zitPb6NdOv6QEbxYK2snn9O1NB9+7Pdgpxu/kJVXjj+1vez8nqvqfoFir/LR5iK5OcCKpKvYpQKFsD7e+lGltrtTlkiUri+aUSeX1mdctrClIcjul/Zscc3Q3eHL1mnE/0pvfT7yet76WoMvdA69RqklVbkunFw8CW1fueYzyEsWXF46iffe4kwdfbhfgGR1J9Tz+fI8vcz7vBaOkfPxLr0I1rF/KLZChakOa+BTE2RGcvLTNLd58o71/IKHNmRN/GB/2DydZD2gYkwVWmwEUGOBd2xaBSVnXKeOv25+iiI56RAjbwpR/iX095BkJlPxynbIMXK+Fmw8GbGb/4+qZ5T6lLrM/RZFWRmePPNeNcdXf3vN86jCAv5vzXHkB9XjCLr83hvDQth8a3w3N1U9RVzr03ec4/yLVv6et86gzYe3bnufC7K4aM4n9rZ1UxBoY9ivt7qSpgU7IQehKLcZm1blCYwd/jwDhbRiyy1kU75+/d4Pu+nRdYSpoods3XGfZD/XVFzXI7ofu2ta8uRVmr4R5qY+bzGXJxUNL2V4zgz/XJ1YPcQcMLeeP5z9/WTddoIEpjCBH4vDRyYmzTPJsqrNQMEZyQUaKo/HTi+6qPsm/M2tlzyb4GJS53wIrIsup6pVqrizfcV0MZ6aaayWZdz9cZ71CRe2K9x8RtMOqldnDCxPZuE9Ho9X1TWspQgtP0R6D6Vvxxkdy+4tele5f/myHvnr5kyi+bTEkwu/hCpruM07lYnvfEtz3Cy4XxEhTeBqBdsLUakvIkei2yUhadAwNB8r26ONCd8mbYv+xh0n6R+Y2F0FDJL4957Ye5VMKpeq51R7hU851wjYghTUTQCpgKc1/pSU5GGxmXKlzKqSc8L7dsKyDo9g0UiCI6gBIRIhwFDegNQ2ka30KXlK+jz0UJ4UFe4HNH9zM1jW3OklaMnh9ZvlNLLK8+jxrsJquH0/Fvh+D5oXlN0LzQpvH2/ebWiuamdmL8XmKw4TJgKcJ+JsqcPBoalMAWFLyGKGh4gXVHDlvP/nq6Gc7XL+NVRpLFadKtKj9G8On+pu887GievsZJSsZ7PuKRgg/FbeKKqP000U2rhstXTUYWplOffeSy6RYUUb38kf0lLVXzioIokcQicEJx0OAymTq1MW0nPlAuYvvV/qDjdxujjWzg15dXh5Ryw/TGiLLJeG3RRsu6gOp1mKI3+CfN58D2VTj4H5t/AuJe/SaqnI3+5rm01VZ0HESFJgwfCl8J+Lkq0GRRrBaKsL4WaqQjClIdmqjqTUgeHM8cVt48L4e4zjge6OuBEc6EwFdPZHtQYijJ+/cacCVEo6I0Fv7nh238XvDvviO4pEMsso1tWa5n4oH89iZtsXivlVMA475pzg0oo7mHq00oWLaXSTDWtDHw2CjIuS4aNKNrgwUL5tWiA4JTcoy4AmXR0G7aXGj6VEsWTed61asBuVmas3r6sb8qHvGNv22blHOow8an2lU6YcpaLSqsQaiKcfhHUTCgw9QWpivP7+eaciW/RwAlTXmPAK8DBC72r/pyaU/vyyVu7pulRIrjrjtK/Rj5eBXU6NnuNcmHCVOD9yz+G1XWMpu0/yF+avOce+jKjVLBGAAZCmLL7Kup8cWuJdAlr3XVFqdddZ2UmlzR88nJ14WAxeWfYqCkQFg5vU78dwlRcZ3vI+ZgNgMuBtjDlap/uGu4lOPkeQOZenXdE92rXqONbSXcf16ZASec1U3GFKXMtHICoGgtzr1GmPtemFPYO05bIkXW2QtMFoe2IAi+f3KgKksHA8BWmXAzSUWBrBCKdHL3U8F4vvGoMzL5CCQpS+oaVgyMaauM9gFBaLQf8yNsGS5iyN/moZsJAMx+okNoFNygn9N4uIDgXW963bNO9MGEe1C8ouO/XPlPSzowGASZAevEtdNY2MXnf/fRmRpFtPE+rnI3+/o1nJokyHqKaocK5jALuTz2fvtmvYcbWb2P1dmL1nmFS8wOcmHUDhJhD/fokCudTPooq4vgv8pmK0r8DJEzl/5+0WEX4evhNhZmxC26354Qpx9zxHEsR+yod4X341Qn6a0xRgIvmwcdTmPJ72OGIXnFyb9HtcW05q4NGJB+QJxWOInRqp0xzIdA/zAKW3g4nD8C+ZwvueeWVtJH30bXNg9MKNVOByYo1vrLXYSURpgYRoRt2AOwJqZuSBZT6VDvFyqJbVDRIywZ6ev03m7xm6pVfKZVxLtTfhh95W1ThyJSvI2No5tPaUBbdCl0nFE0CPqkvcshKoPOIipJcdEvRDI0TIeRnNtNBJpOhecG7ADhav5LKigqtcjbSliJOjLsZRdkAneUHghQvjJ+Gyz5GRdcRmnb8mIkHHyXd00HnQn/Hc2fbvKZn1GAI529dONeTVEovn5oNE580KF5L8mtNploJQIeKNVNh46ZgfWvbooSyuln5SwOxtlhCRFpH42qm3KZJ3T720kIFmsZzjuhV679XdKuubTW9o6cq9n4N2OMnaqCJibN9aHqW+ddBugo2FJv6fINb7Ov7bLLOJQX3g9I76QhTXia9KNrgwcLwFaYcgk1kTU1O86CTgsMJ9wv2Mv0ByoQhLNh0b7hmqm0LtG0qMvHZGJDT46Cb+TRMsLMuh4rRKi0MIY6KEtjyW5B9RSY+8O4jXT8Zp28Y6Cf/tXFk3hvpGDOHQ9Nvjuy8G0XLYsNLCxst2ixnWkzp1R3q9xxyPzXzYo41rGLGlm8xdedPOVM9iex0Pd8yr0jLSCZNA40AFJ6KI7sQOCNZY0RpFtTbuNxTMxX0/oqE9PatKgo13S/wex2yopv5hHZqK786o9Qb2E8B8Ko1cFjkHNFTL32fFA7eQCkZ17aanqn+rOdupHMWhihz3U3tAfEjF1OWgMrRyhVl4z1FzPqhPrrNhWSd+ToDmqUTBORl9YmiDR4slF+LBghxos1MQlUhgmNq7USYfjFsujc4o3ZWwsYcxb+HkODXxsjCo2GOI/vEGVV4K9hQ/NqaqYL518Lm3yD7esMd0DfdC2OaFLWCC17vP4qaOI62Jl09hmev/y0t02/WNjnkyxoJU17q/+jfdTDDjg8s/xCVZ1qZ0PIUh6bfQmVlRquc18Yb5f3YfRvFQRrca0u0d2oauOHmECuYN5OXqaAWV07LtOWfw61oXLVvg4mF5nH3QcL+zCiwhIiUXcF3wzbwmYoSWOA1z0LbnXNEbzz0WP5SzcldVHYdJqvpfA62k370+ebsE92I3eDk4rnyS29Xvk8uPsRAzVTPaTi0voASwUYw9YSZMJVopgYRzhcfhYEX+heMqAPc/dIDN+xFt0Drxn7HTw/09uWi+KZdmCfSc8Nr84y64ZsmjMzktBdR2dO1Q9IX3QKdh8nueSbw87JdJxWRnoeJD7zfQyRhyjCcHQq/a1TNlGVF4+hx15ev18ABfcAI8TSGRlfTJRyboITggzNeGysXYZT3kzLUTDnT7kQ9qIFqo2VFn3e+hKqNOSZ0l6kvMIeb83pfr0pJ48E2b5r82kZU7apfTsCoQQXqb/057iU4ha5rc6+GMU00bv9R/pLNL5WaqR+5G5XY1Ib7kKcTsRtkcs23Yd51ylzniurzWzMrM5ZiTs/2egpTwaSooU0uyrARJRfsYGL4ClOGHDLQfyKL7FgdSZhSpJKV2+/3fSR9dIei5vcx8UF8h2OI5htWUC5VzAatgzyzdxj/19xrIFWJsKP0fJDZ9Sj0dQVq79zt9OMFC2qv++/IZU1OnwZaVbejqAkPkn46onj3ASoyKTad/49sXfZxTo1boJ/aJebYtxd5ndNxUT0G3EDOelMGc86XVmHyOer3wZeKyvimuXIK9sf2QLanKHADig+i0X2moqWq8hMA9XMnOkywEQ4vnpqpsHbnHNHHHvgD1R17ARjXtoauqolUNHinQfJC2rLMxqDBuhS0p+WFqYoaWHC9soo4Up/5zcuqdKqf6ieiZkpnHhRFsqb1c8EOJoatMJX2O8VpIpOyIgsZbjtu4KYwtgmmrKB6+299Hxm9KydoLb7V9xnvtAJRNSCRHs8jbUVzLrWRyUdLhkyIylEw9yrE5nsDnXMqt/0GaiaqfHw+8I2G0oBTGIksTNl+ZRG5jNzlo8D9/qNsKrbWJU5IecHnaWwSlWmLU+MWsHfhewD97+xJKmkgTJloZu2Nx2RtSafMNBHOMgXvp2qschz3oEfwN5s5+q6tP8Fx8XNW4P9hsISILCi432Eqgoa20vDw4iUUa42L8+5ACospO38KUlLX9jwnGlZiRdSQmgjlJlko/PrRslyWmCW3w+kjsOvx/CU/01p1RUo5n9fN8iTa1RLgQtrs7J/qivIUW8qzVQOATE5dbKJOB7MB7hzQWo7Ki26hqnUtlZ0HPW+P3fUbJemPbdKqU7teF8zNfJZRJKCV61utxW7RLYgT+xl9dIP3Z/V1kdnxECy8SZ0UfVAUDRVhQhaY6iIKU/ZJOSorvVfdunBqRKPkUsvXmbK0N6IwvxIdfxnneI0idHr6h0URHIW5ZiofCWi0tliGApyjn9zlfZzQ/cwhBf6d+QTHxdoU03djw7KEVg5MJ9wa/qhmarubogReeB0KMzoHxbFTOTPraqbs/jk1HbupOn2Izkb9lFH5uox8phyHPEMeLq/PApQJs3IMvPLL/CU/S0tV2lKaqWneZJ1B7jK6e2xh0Ef5mfggpjAlhPiwEOIVIcQGIcQPhRB6uVcGCSlDzQnYWpc4vkAaLzzHG9Ww/+GiW9Ude6k5/EqgiQ+KFxqtel0w2UxATYQ4gpiWT9r865EiRUPzQ56361qexuo5VcTB5UYczVTk9+pAnt3Y0AfJZLOucjHMR027EMUhNpQZWeNjnBtJ3NQ5kfxkBkAzZVrWTAjrL1O0rjUuU+a600cLLvsJlwX91L4NRk1WGi4XCg4SBhu+JaKvL24hyC8/mx/sORplrnq9R92Nvmf5O6k80868lz8PQHeTfiRfvq6YPlO6Aqdvehb3OMlUqajzTfdCbzfgH51ec/qACoDwMPFB8JzWne9OYaocOaYghjAlhJgK3AlcIKVcCqSAtwxUwwYCylRnttmrE5V5lI/W5jlxLl11C6hvLhamGpofVH+ECVMezMhRYZriKG3gIG1D2+myZjzMupSG/Q95mvoa9j+MrBgNsy4L/Jji5LRRUoBoRB/6wCRZcWHdJpopy/NvXURhrRYiWKDWOcyYpliJbeYT5gJRv/O6iYBhZvJ1jsOijb4xx4R+aH3BZV9tglMz277FUysF5gnNbQj8Iwr94NamRTn4gNkBxjhtFJCafw1nahqpP/Ao3RXjEA0LtevNf0ZsYUqvj3ydyL36auntKlXRzt8D3r6n6ZQgc8CbrNPZTr9poisoF6aHK0+DWtxWpYFqIUQaqAEOxG/SwMGEp8eGSVi6M5RY91Rzcvb11LWvJnPmSMH1hn2/pWPislDit2LNlIm62FwgMtVqZVL6ZlSx6BZqT+6i9sSOwuvZXur3P0LvvOsK+HG8UEC0GFEIdFIamDqgm2qmTMZv3FNc1LHv9x51TeymRJbuRTWqSdMWaEzGcN553eC1plPR6ALcdYKHJsMnrYwfH09+85VSmfk8/KXUc/GEKRPNVDFBabR6baf5KC4Ink7vmpqtyooM+2e9EYBjE8+nMqNH7eGEyTw39ZnyqstznZh9pdJWOgg8PTX8zashU1NE1hn6+R6fp1N+2Jn5pJT7gS8Ce4GDwHEpZZEtRgjxPiHEGiHEmra2NvOWGsDUsQ+i86PYsDcGXQGlY9aNCJml/sDv8teqTjUz9ugGjs64IbS8mwnaTDNl1kdx+jdlRdAaLrwZiVDaKQfGta+hovsYcqF3FJ8TsZzIC0KQo31fU84yG/GFqej1Rg3V9tPO6EasmfavO4zexKRpeuBKx9RMmdTpJMkt+p6j6mH0lCK/KV/NlD0uOlqV9sEjkg/MtB8FEFrsGK62mec+BGekZUzNlG7aqJRFy9w30Jeq4nDj5UZrsIlAb5rlw5N3z+vdpitU0vjNv4GeM0Dxu8g7n08pJut0oqbC+54vsbULcShmBgtxzHx1wGuBWcAUoFYIcYf7OSnl3VLKC6SUF9TXF3v6n03E0ZwYq+LT0TbP7omL6axtKhAUbBPf0ZnByV5tOE9Qgym1m0Tq2EhFKTt6Mh31K6h3+U01ND9EX6qK9LyrQz/CuTBGpxswL9sf9WXWTybdGycvJUQf+37fTTufmuGm4GaBNtKcWGaHJrt/jH2mDMZDqNa7cVkR15RXf1qW47AX4HwO8WhBwGyNKCI/jppbNRV9znkmdY5yABozlSdvfpz9s98UmZwXDMeRQ8sURcDwMq35ll96G3SfhO2PAB6CruhWY26at7+UDS+hKWUJI1+vwSQUjoI4rboa2CWlbJNS9gC/APRpXwcBqYh5oZwwz1cXTcUshKBt6rWMb3maVPdJACbte4ATdUvoGTND6zPiLnimSBlE6tiIGmV5ctZ1jDm2kaqOfeqCzFK//2GONl6KVTUqtLypXw6oRcseRloRPg7YCUJNU/bEPrGanJKtaPXGyX0IhdqhqA7+WqmJApC2oqU7cZYDc3+rOGY+3zonL1PCUXdnfzs9eMcqUg4NXl6Y8tFMGZpgbZgsD4pHqP//qJope82P8m685kkUE3llOkVPZR0Iy1AzFblIrt5olhDw1lb7CqyzLofq8XkCT/dzY468kiPr9PaXsuElTFVX6GuS4/L1DQbitGovcKEQokaoHrkK2DQwzRoYpAwXSrusCaLyz6QsQWvTtVjZHiYeepzKUwcYe+RlWpuu025DHDNUHJiaK+yyUTbsM3OUls6OfBxzZD1Vp1s4NuN6rfIZh0nIpI9s50sTbaVp9BaYbUYFJ1bD6M4ozfXbZKM5k5v5lg2E5sTkwGXKnm6XNTLzhaVvalwOMgstrxRcDuQda9+q2K59Miw4x5LJJmaq4Xdu2pFzjdprcIT36p2Gyay8aT+ZoCJt5dwtorQ1gmYqlVE8h1segO7OIkGspvVF9YdPJJ+NWg8zn9c1PzhJcsuRsBPi+Uw9B/wMeBFYn/usuweoXQOCOJu9abmoDseZlMXxCefSVVVPQ/NDeXNfS9P12gu1afLUuDBxLrURNQ1NauJsTo5bmO+fhuaHyIo0Z2Zdo/0ZJtnZbWQicC+5EcfcbOrPZiqcgHqvUer11UwZRFOZRksGtSMIURPx5ssZaD/6y5oJcP1mPj9hyk4rU+g3FRik0r5VmfgC3nc+xdAgCgn25q4iwaL7wSmOwQiaKZc2LKo22R63ppt9PK6/aO/FS0gMfLdLboeeU7DtwSJBrLLlBaib6UnW6YSXdjGKxjHOGBwsxGqZlPJTUsqFUsqlUsq3Sym7BqphAwFTdTrEOClE3LAzKQuERevUa5hw6Aka9/yak+MWcnr0TKNM6YNp5hOGp06wBTH95yvTKVqnXsvY9rVUnG6lYf9DHG24kPSoOu3PyMTQJmjzYnkgHcXZ3gXTM1icKEIRUTPld4IfFGHK4QtnYuYrhWbKMtSY22uKb51jp0F1XZETetEGWKCZ2ubrfN5fb07To+mQ7YQwXI7yXFEmpltDs7pTGxaVjiHO4QXM95tMyoqlzQUNAXDmq6G2ATb8orCslGQOvBBq4gPVP+4xH0WYsrW5g7m/RUX5tmwAYJKE11nWBFHDcu3n2pquId3byZijG2htuj53L4JAloOpj5gpTIVVEdHMV5WxaG26FoFk1savUtOxh9amayM5p8bTTEXP1WgjZUU7JTthygFmh0CbjOOoJlh/VmX9fjY9ecbxhYOcz5RBH9v0ESaaw5ShxjzUhUAI5Tflokdwv598H3d1wPF9vs7nNqIG1ThhLCTkBDeTzdM0cXBlDEoRu52mAUCmy3ZF2iBTR9QUQVZK8R1ue4jKbL8/XlXnAUTHIV9+KSeEEEVrtR9thx/iHGgHA+XbsgFAKTRT+VNcRHr/o/Ur6alQDMQtOWFK96RdmIdwcO3JcXymonRxZTrFqTHzODVqJtN2/ACJoG3KVZGcPTNh0VABSFvR1ek24pibhaFuykT9b8MS0d6r32IcRVNUkVY+babUE+pvM42jkUAUg/3fNAo2nfP7Cxy/jcuhdWNBgtqizdN+L4e3q98+zuc2bN8jIy1cjMOAs+5IZQ3T9cQhu7X72EQ7CuaH0rRlRe4jd/JqLYF16e3QewZr24P58Vd3NJdYO8RfyoZ7rY7KbB/nQDsYGN7CVJxNzPCdReWZsgeytDIcnPE6jk48n84xswvuhSHPP5MyOynHgelimYrI41WZtkAI2pqUj9SxiSvorq430n6Yaqbi+N+ZLpamdr6KtPnCE1nQ9dl4Ipn5cpq0qOO3QJgy2MgqDExXEC+owLJiZB1IhWyejcuhrxvaNucvFWmm7P/zkXzehJ39dSrth8naYnwotZOhG7yflGEi6TjEkKUg57XLxdZM6bR52oWKx8xh6qs7/BKkq2HSUs16U67/o/VVOsYBcTBQvi0bAFglMK/Y0S+69ToH1Nbz/oYXXvPD/nu6mimD6JWBQhxhNcpCa1mKYqC16TqA/O8oi1ccYcrUdAB2RJNRUWNhNW2Zq8SjplLyWxSjvBvtxNcBdZuUN8/dGaN/Y/CzZcI2TzutjMNvyv0e8hGe7VtBpGD87MA6TR3m4yDvbG/iqB8xus1GHLLbuOS8podgk5RpmVThYUlrnloWLHkdbH+Y6mwHAGMOvwRTg8k6C+p1CMZmWmjzNXgwMKyFqTgaAVPzSlRCPiGE74lad3OwFxxTNvI4iMOeHlXQrUhZnBi/jDVXfI/mOW8Doi1e+ZxdhpvuYJt17LImSMfx8YqoNXSGzzsR5XRvKpwU0oKYOSubwCTdlI2oB4mCelPF+dEKMH6Oojpw+E35UiO0b4Xxs0JTMfm9Xx2YH0rNHfxNDz7OdSGyZioiv6Abg8XXBx5kt7rzZslt0NfNxObfYfV1UXNko7aJD1y+vQbm9VQJhPooKN+WDQDiaATiJP+NOrj9GaSj+UyVQmo3XgQMTLB2fxxrWIlMqQ3AzMxnajow1fTEcdQ3KhZT+Iuu0XWfbqP6FJkKJ/a7NOZuiqG9i2NGNdY4ho1Dy4LJ5wRqpvJzps0/J58TKWGeNsr8UGp+QDQd+05zdVRznZUbf4OZNgrMv6tRBHjTq2DsNOp23cfooxuwsj1azudedZrMnbRVTEBbThj+wpSxZsoM6ZQVedP1moBRThyh/DNnEaaaKcWyHa2Me2GNqt2ys5eb9FMqhmCeiuiD5EQcHi9T4S9u0lWAyoh9bOrnYjtlD/YmBuZ1QjzNd2ibG5fBofWQzQKFOQzzYfB9vXBkh54wFcNfML5mavBM8oWaKTMtpzkFirkW2WSNMCK7FQKWvI7a5ieYePBxdU2DFsGGc76YWAfiuFoMBoa5MGWuETDdxCD6qcZrodJNEguOU9wQ0kyZLAJun7Cop9Y4vh9RI9wKyhqG0MeB0tSYl40Kt2AReQ7ECBZRkYvmwokpzP1jMD6tpXQ0YpOXKZLFIzty9fWbdfLmq2N7lKO6hjCVjhNAYYh8IumYab2iwN7gTYXzOAcY48OWgZkP3GmYIpg0l9yOyPYyfet36R07I5Ss0wnnuDXt3zj78tnGsBam4jFPm9cb2bHOYwJGWeTttDkl8Zky3BWMzHyuyB6THHvmEVjxkjqbwtzXKp5WK4j0KgAAACAASURBVCp8I8Y0EUeLnE75+x2G1htLmDLXQpgOCa01zcsJ3Y4yjhjJBzEzScQI+Ve/DYUpw6TX6ZQ5MWQcM1QczbVR/k6HSTOSFm7KefSNnUGq7zS9jedHqjMT078xzoFrMDCshSkRy2dq8E6sXgMk6iKSsqKbFwcCg7kIuL9f1M3MNMoHotMFOBFnATA/sQ6u03tcYUql/4hcraorhpAcR9CNY/oyrVVrQ6lfCFYGDvU7ofdrptzCVDBhJ5j7o4G5sNqvmTIVbMzqrYgRfp+K4RsZZ66afFXjKFgh6F74OgCyU/Wdz4vqNBJ2zS0hg4FhLUzFOZ3HeWlRJ6OXUBB1EcmUuT3ZDWFF31Dc/RRVeIxj+oplMi6J+XWQhamorMouxNEiD7Z/mA1jkw5xfA01TG7pCpi02NMJPb+JtW2FUZOgelxonaXQCNgpd0zXtDgarTjkvKYHAmNfK2E2lmyhOmOQ+zB77h2cHLsAOe/aSOXi0piYJs0eLAxzYWrwHX8h+sTwmrwmm1FpqBHMyplMDHc/mWgAjQWMWJoeo2KAuQbDEubvxqSc+6QZ9d3EcCOK5TMVx5xv+l6FMDWO24EQGqXttDJSAh45Q9v1IvnA1ggM/tqSihGRWgr/u1gBTzHcUeLMVyNH8IlzeO66e0lNmBWpnM0V6Kw/UvkY/TsYGNbCVBw+l1jCVFSfKY+BFZX5NzRk+iwhThRKVLj7NeqiF+eEXQquqDiIc4ozWpxjOqCrg4/5xhnnvZrCeAMkfpRbKBqXw+kjcLw5V87hgyQltG/RFqbi9G8cxHF8j2NejJU2apDnujBcgfs1ldFLhybcDkBlXqiPXlZZmiIXGzQMa2EqjmYqzpyIuvB4nQ6ia6bMT3FxMKjaj5iaqTjCtUCZJo3KxtFMxdnsjcsZLLBxNVMxFso4UZpxZkwpFnbt72k7oef8pgqSJJ9qgzPH9TVTMXix4iAdkfrECVOhpiIdT8s52GYoUzNff2Lx6ImZ+wOeos+5jNvcHAEi0UyVFqaak1jCVMTCXrnNog62OLnC4iBWeHhEuCdvVA2giKmZimOaGWwMtkrcPfaibkhxFkrbv8YEsYTVOKYZ0yhY3e85aYmS/nN+U/n8nZbodz6v1xOm4hxC4sA0QhNKw89WmmhqswNB2hLhSbMDYLrux0m7E8d1YTCgl1RniKJUPlMDopkyCS0vZx2oCyabSfGGHX1CDrajpypbGo3hUNIaQhwW6NKYuGP5TBmW1RbMK2phwrx8Whm7ezIpAW1b1D8RNFOl2MRM/HlsGJtRS0SBYgrTsWQfLE2FItN3Eyell/I1LN89blhrpkp1ooq6KXjxfETNDZWOwVJcCgy1DRviOYMPdp1x/HJMijmdS8EwWieOP1sJVrJSLOyRxm/j8rxmyhY2lWZqm8rfN2aqXp0lMq+UglIkjn9YqdZf03FYEcPZ3lRrWJHL82gy1xOfqSGKOItH1LJeyY6jpjMohfNjHBhv2I7ZZJq3ywTxND0xxlIss+TgjgdbgEqlzBbLOM7GpdjsTX3oIIbvXpRyjcvg5AHoaMvPm7Rl5SL55mkP6KFyKHUijplvKAlTcWqM8129iKZ1EJqoOwCK7LZ897hhLUyZ2pPtsqYoBU+PJcxJD4cS0jG1H8YMxcZxM6U5TcVx6DZdsPqdWk2joYyKlUxzEqdGc/eDCA9PXqZ+H3o5b97O+0xpmvjA9meLUO8AoSQC3BCL2hUxNrl0LIJSc5N8KawDg4Fhvf3G8U2IV2/0Ms4QVZOTfbnnLXIj7oYNZv5P8XymjIqWaJEd/FOcbTIwDi031sKVzl/FFHHC77XRmBOmDq7r10z1dsLxfdrO5/l6SyBNlWTTjXEIGUpuFhDPP8yYiysG3UVZS1IMc2EqDgZ7bXaGqFYaRToMLWHKFPYmZBqaW7LTYwkw2AKGHTQRh/TQBEKFxw06SqVx1EZ1HYybDofW5WgGwDqyXd2LoJmCUnGlmZc1ZpiPsY6WhpomXpqrOLlKjeoU5uTScQ60g4FhLUyVqt9NXriTpNNESIhj1hlKKGJyjghjzZRRKYVSCbnGvFiG9cV9N+ZmSfOyMscQblRvCVaYyHXmnNDTKYuUZSnnc4CJCyJ9zEgx85UqAjwOTGuNEwFu7t8Yj+uvnDGshSkoTVi6CZwbkAmhWcoqb+c8N0xbagudg21KgvI+FXkhDm+TCex3EjV4Im69gtLRT5R9nZOXw5GdcOaEei/tW0GkYPysiPUOrf6N4ys7lHwjVb3mmrTBdraPm+exnPe4YS9MlQImk9HptGtiJhlKkXxxEIf0DQbfKVuVNS4aC4O9uGfScd9NHNOMUdHhjzwT+vqcMLUF6mZCujLSx4wUzZQQ5hrdUmmmzImIzd+rsUYrhp9WubuxJMJUmcCpjTIlNBsJqIgdMRZD+1H2iuZClIoawZSDJo6Zz1ybYN5HQ8L/znZCP7ROrTHt26A+mokPSmdyM4W5j7P5rCnVElyViZ4SBuJlHSgFjUm5b3HDWpgq9853omIAzHxDCabvJq+ZMkjQGafeOCiVEDbY37U/mm/wBRtjP5chZvaNPJZGT4ZRk+Dgy1RaWTi8XXFMRa23FJqpGLuTcbYCK0bZIXbYipOeJQ6NieleVe79O6yFqaEE52neRJgaYrKUMWKH38c4FRkvAiPk3eQz0cdIA2KCUi2ypaFjMCg0eRkcXEdVx37o647sfA5DTzNlCkEMf6shtpvGSc9iKnAqPy2jomW/jg6x1z98UaCZSsx8vrAjHU0ZeE0RJ0nyUIoqjQObXd4kGhVimGZKtIoNmVyYjcugbTPVR15R/0ekRYDSbGRDKdfdUEQsn6lYkbfD81CaCFNlgkyqfxKbaKbKfaANFGzNVCkytJvCVLiIi8HW2Ni0E8bcNXH82YbOcIgFo6/ZuBxkHzXb7lP/G5j5SiHYlEozNVxD992Ik54lTjSfef+Wdw8nwtRZgMlYEULEilQr90iHgUK/9mN4nm4GEoP9Xe3ciaYm2Dgo94W2pMillana9ZDyn6oeF/kjSnEgKEny6hjDaKhZB+Jw3cYJ+Eg0UwnOOuLkNivzcTZg6Nd+GJqSRkxPlQbp1OCbQ0eSacYIdTOhciyi94yRia9UGGqknUNxCJoHbpiWEzHyo5Y3EmHqLMD0pWdyG5GJL8ZI2UysXFqMoWTmKxVK0UNxnM/No6jKf6EdKBj1kRD9FAkGJr5SoXSM4sNTc+KGFYPoOc67Ga6ciIkw5YM4KltTlXgmZZmbr0bMdqK0UnESFo8UlMLsYOLvFxcqZ9fIeLHG39Im7zSI5CsVSvVKh1o6GVPEiZ+Ixc9mHM1X3v0ba+UTQowTQvxMCLFZCLFJCHHRQDVsJEIlnhzcSKihiHSMNAgjCaXoozj+UsYRQiPoKGE8z/PCVKKZCsJIWkdLwesG5i4a5Y50zPL/DjwgpXyDEKICqBmANo1YxNFMjSRYMYTOBGcXcTRTxma+GOSDQw3Gp/NFt9B11WeonHX5wDboLKJUpKgjZCiVjP1/uJr5jIUpIcQY4DLgXQBSym6ge2CaNTIRJ/HkSEImZZ6LLends4tk/JYpMtVkV30AUmapR0qB0vGzDb4fUSkQZ6rG+arD9Rwc52vNBtqA7wgh1gohvimEqHU/JIR4nxBijRBiTVtbW4zqhj+UmW9kTOQ4yKSssrefj1SYjt+4SMZDOJIuCkc8aoSBa8dgoFSaqeG6V8URptLACuBrUsrzgFPAX7sfklLeLaW8QEp5QX19fYzqomOoLbBxfIGG2FeNhVI4OSfQQ0n4iEbS4I+BodZNQ3H9HkqI5TNVonrLGXFWvmagWUr5XO7/n6GEqwSGsBIznxYSf6nyxZDJVzcCMdg5E0cahprwV6o0TEOsm7Rh3J1SykPAPiGEHWt7FbBxQFo1QpESwngzGqbj0xNxNoVSpXYZKUjOAuWLobbZlwojpZfifM9YPlPDdBzGjeb7EPD9XCTfTuDd8Zs0cmFZ5slTR9JCacqgm+DsoxTJf4drdNBIR/JWzy5KRoo6TF9sLGFKSvkScMEAtWXEI14SyKGFWKSoifqjbFGSFCDJeEiQIDJKlYdwuM7WxOZRRrBimPlGEoarmng4IJFrEgxljKThWzrN1PDs5USYKiOoXEmlbkX5I3HSL18M14UyQYIECYKQCFNlBEskJgsdJNq78kUyfBMMdYyU5WWofc9yb24iTJURLGHO7D2SUKqQ3gThSEywCRIkCMJwXSGSbamMIESyGekg6aPyRfJqEgxljCQz9cjJQjg4iEuNkGAAYQlzn6kRtAYk2rsyRrJAJxjKGEn+mCNpzxgMJJqpMkIczVRyokpQDhhBwzBBgiGNRMM/sEiEqTKCJRIxQQfJGlC+SN5NggRDAyNJCzcYSISpMoIgOS3oIOmiBAmGP5LI5vKFlLLUTSg7JMJUGSGOz9RIQqK/K18k7yZBggRBGK5CciJMlRGEGFm+T6YYpnMxQYIECRIMUSTCVBlBJJopLaRTybAtVyTjN0GCBGcD5b62JLtSmSFh906QIEGCBAkKUe5Wm0SYKjOU+XhJkCBBggQJEriQCFNlhiSaL0GCBAkSJBhaSISpBAkSJEiQIIE2yt3kVgokwlSZIRmjCYYykuGbIEGCkYhEmCozJDw9CRIkSJAgwdBCIkyVGRLNVIKhjET9nyBBgpGIRJhKkCDBgCEhVE2QIMFIRCJMlRmSvSjBUEaimUqQIMFIRCJMJUiQIEGCBAkSxEAiTJUZkpN9ggQJEiRIMLSQCFMJEiRIkCBBggQxkAhTCRIkSJAgQYIEMZAIU2WGxMiXIEGCBAkSDC0kwlSZwUpiyxMkSJAgQYIhhUSYSpAgQYIECRIkiIFEmEqQIEGCBAkSJIiBRJhKkCBBggQJEiSIgUSYSpAgQYIECRIkiIFEmEqQIEGCBAkSJIiBRJhKkCBBggQJEiSIgdjClBAiJYRYK4S4byAalCBBggQJEiRIMJQwEJqpu4BNA/A5CRIkSJAgQYIEQw6xhCkhRBNwE/DNgWlOggQJEiRIkCDB0EI6ZvkvA38FjPZ7QAjxPuB9uX87hBBbYtYZFROB9kEuW4o6S1U2ae/ZLTvU2hunbNLes1t2qLU3TtmkveVbtlTtNcUMraeklEY/wM3AV3N/XwHcZ/pZZ/MHWDPYZUtRZ/Jdy7fOkdTekfRdk/aWb9mkveVbtlTtPds/ccx8lwC3CiF2Az8CXiOE+F6Mz0uQIEGCBAkSJBhyMBampJSfkFI2SSlnAm8BHpVS3jFgLUuQIEGCBAkSJBgCGAk8U3eXoGwp6ixV2aS9Z7fsUGtvnLJJe89u2aHW3jhlk/aWb9lStfesQuTskAkSJEiQIEGCBAkMMBI0UwkSJEiQIEGCBGcNiTCVIEGCBAkSJEgQA4kwlSBBggQJEiRIEAOJMJUgQYIECRIkSBADcRnQEyRIkCBBCIQQY4HrgamABA4AD0opj8X4zGuklA8H3B8D1Espd7iuL5NSrgv57MkAUspDQoh64FJgi5TyFYN2flZK+cmIZWYB5wEbpZSbQ56dDrRKKc8IIQTwLmAFsBH4hpSyN6DsrcBDUsozUdqXK3sZ0CKl3CKEeDVwIbBJSvkbjbKjUONhGtALbMu1I6tRdiHwWgrH0q+llMY5coUQ75ZSfiekzqnAc1LKDsf166WUD4R89kpASilXCyEWo773Zinl/Qbt/F8p5TuilhsMDLtovlIMNEe9kQdbOQy03EKwEtggpXwo4LlVqMXihBCiGvhr+hetz0opjweUvRP4pZRyn0H7KlBcZgeklI8IId4GXIxKsH23lLInoOwc4DYKF60fBrXVUfY64HUUjqV7whaPUiC3ibwR1c6fAa9BzYPNwNd1FmnX5z0qpXzNgDe0xBBCjAc+iHqX3wI+CVyEGkuflVIePQt1vgP4FPAQsD93uQm4BvgHKeX/Gn7uXinldJ97b0Kl+2oFMsC7pJSrc/delFKuCPjc96PmtgA+jxJOXkERNX9BSvmtgLL/4b4EvB34XwAp5Z0+5X4lpXxd7u/X5tr+GGqe/7OU8n8C6twArJRSdgohPg/MAX6FmgNIKf84oOxp4BTwW+CHKAG3z+95R7kvo9bMNPAgcFXuMy4H1kopPxZQ9k3Ax4CXgSuBp1FWonOAP5JSrg8o+3HgrSii7Obc5SbU+vgjKeXnwtru87lBY+lO4M9Rc+Rc4C4p5T25e2Fj6VPADah+ehhYhXqvV6P6+jMBZX/tvoTqr0cBpJS3an25QcKwEqZKMdBy940GW6kGmhDieSnlytzf7821/ZfAtcC9fv0khHgFWC6l7BVC3A10ojbuq3LXbw+o8zhq0dqBWrR+KqVs83veVfb7qD6qAY4Bo4Bf5OoVUsp3+pS7E7gFeBy4EXgJOIoSrv5MSvlYQJ1fBuajNgHnWHoHsE1KeZdO2z0+9/9JKf8x5JnrcnX9Tkq523H9j6WU3/Yp81WgAagATgCVwL2o790S1F4hhFtLIVDffQuAlHJZQNmJUsp2x/93kBPMUVoBzwVGCHEb8LiU8khO8/Gv5DQRwEeklM1e5XJl/w34uZTyKb9nAsreD6wHxgCLcn//BCXYLJdSvjag7JXA6ykUzL8ppdweUucWYJVbCyWEqEMdvuYHlHXP8/wt4DVSylqfci8BN0gpD+YObP8LfFJK+QshxFop5XkBda5HrUXVwB5gbk5DVQf8Xkp5bkDZZtQa9lCujQBfBD4KIKX8rk+5fJuEEE+jhIpdQoiJqHmwPKDOjVLKxbm/XwBeZR8ehBAvh5RdixK63oDaJ5ai1sIfSikfDyj3Su7ZapSAPDUnzGVQwtTSgLLrgAtzz08Evi+lvE4IsQx18Lk4oOxWYIn7AJk7cL4ipZwXUq/nLWC+lLLSp9x64CIpZYcQYiZqzf8/KeW/a46lc1Hr0SGgyXEYfy5kbXkRtR58E3VIFKi94y0AQe+nJJCDnL/mbP4AW4GMx/UK1AYYVHadz896oCuk7HpgVO7vmcAalEAFamIFlUuhhIQTwJjc9WpgXUidLwLfQ+VFvDz3+2Du78tDyq51/L0aZQoAqAXWB5Tb5Kzfde+lsDpRp69rURqBNuAB4J3A6LB3k/udBlqAVO5/EdRPdv/m/q4BHsv9PT3ovdhjyee6CBtLIZ+7N+T+Z4EnUCfzHcCH/Prc/V1zvzPAYaDC0We+7zT3zK9zY2khKqnnTGBf7u8ZYePQ8fffok7p7wR+CnwpoNxGx98/Bj6MEiDfBTwcUmdbbo7tAb4AnBeh/19yvMf9umMY+BzwHeAO1GbyL8B7c+P6jWFjCRjrcX1s2FhCCf832fPa8XMFSkgOHA+O/xuBF4A7g8aRxzt92XUvbN6Mzo3dH6AEDICdGu/FWefzEet8ECVYAvzcHrPABHf7g+rN/T8510fPAPsCym3I/a7KvaPq3P8p59j2ezf0KzKqKVyPN4SU3ew1J3NzdUtI2RaUYDPD9TMTpfUPnau5/0eh1u5/C5oz7nfnfo8aZS3UuvAwcK7uWCrVz3DzmcoCU1CLrBONuXtBmARch5oYTgiUGjYIKZkz7UkpdwshrgB+JoSYQf/pzAu9UqmUO4UQO6SUJ3KfcVoIEdbeC4C7gL8BPialfEkIcVrqSetW7pRpoSZ1W67eU0L8//bOPVauqgrjv9UHlbaklIdUuWCtgCjBVlsVUwxoBMUCGklASYySoAT+4OWjRGNQ1KgRUYlGQCmVkGqEPxBBsILyNC3VUrAKCgG8abHVgLyfrZ9/rF07nc45Z+7se2afmbu/ZOfOPft8s7515pyZtfdee28rzC8A1rcMed5rZosk/dHMDgIKh9oCJG8trgRWhhbcMXhP4oXA3hV6d8GDven4j9ATeGtnaoXdKcDWcO5uQchosF+GF83sHZLubjv+dqA0v8LMni6qwr88y3AcHhxsMbMvAyvMbJ6kc6i4lwAkvWJmayS9HP7fYmalwxaSjg89RZcBF0q6zsxekdT+HBX5tA0fAd4d7qMVeMBfhMktrw+QdFJ4vdzMzq6wuUHSIjM7EG+lXmVmk/FW688k/b2Eu+3e3w2YaWZzwzO7J97oKsISSYcCmNnP8V61z5nZNcAdePBYhK8Da81sJR6kggf0RwFfrfB1FfB8p+c69HgV4Rkze4NCvpS8h+pIfPjrkAqb/zWzqfLejyUt9l5FxaQlSc8AZ5vZQvxzuaGKEzA/PDcGTDOzOfLesF3Y8V7phFOBK8Pz8hSwLvQ4zQbOreDu8ExJ2gRcDFwcvr+LcIOZ3YEHUz8BfmFmq/BA9/YKm78GbjKz2/DvwKvh/0PQZc84wNnALWb2IDveSwfgw9dluB5v9K9rrzCzW0t4m8xswTaevIfqWGAZPjRZhpfNbLqk54GFLfZmUfGbHH4vvmtmV4e/m2lynnfqaG48C55v9BA+dn1ZKDeFYx+o4F4OHF5Qt6KC+ztC5NxybAretb61hLcamB5eT2o5PouK1mPLuSP4w/gDKno9WjiPAg8Dj4S/c8LxmZS3zmcBy/Eek9V4APUwPow2v8JmWQ/drhXcc4Kdf+CtxluAH+MtvPNLeGfhvYuX4S26U8LxvYHbK2y+Lfj4V0IAiA/jrgYWVnBHgX0K6gpbu6H+/rb/J4d782q8G7+IdyOhd7Tt+BzaWvol7zEDb21ehwcs3XAewIfnFrJzL0bZvXQpcAEeXH4H+HA4/h48UCmzudOzAbwF+AbwUAX3Y3gLfTM+ZHcz3vLdCHy6hHcvsEd4vT+wqqWu8HNpOWc2Hvh9Bh/y+igwu5tr3EsB5gMHdjg+FR9CK+PuT+ce/n2B941Bg+EpBFdF+LE7PsTUzblvwvMET8CHKSd1wTkyQtu78OE68DytzwIndmn3g+H8o1qOTQKmdcGdhCe7n4APTx5G6IGv6V4aIfxGdKhbXMHt6A+wF3DoGHUswfMaa/EztgxVzhSAmU3Cczb2xR/mDcAadZFUGGFzBO9l2tShbrEKcjvMbJqklzoc3wt4jUoSETtwluA39phmzbS9x3Q8CHik4rzdgHl4wLhB0uYu3vsglfcYVPFfCyDpMTPbHc8rG9XOPUftvEPwL9n1qpgVVMCfQ8u91Okz7sD5Gj7pYSdtZvYtSUtLuNcD31ZbT0R4zy9IGtNyJmY2A5gh6V9j4MzHf8Au6eLc37cdOlneC7Innve3qIA3Fe9V3ZYcPILn1P0KOE/SaInN0jyNLjRPxntkt5jZFHzoY6Okf5ZwTsKHFP+GD4eeLumGkO/1fUknd2F3H1omM3Tz3MRyU9jMeuvXW/B+M9Uy+akf3BQ2Y7l1YRiDqf2BpyU9GZLlFuGt/copvQXcByStr4ubwmbW253NwF9ES8JxLwHZWBASM5H0Qoe6fSVt3Jm1wzk96x1PX0PAMk3evV917ixgiqTHu3zv6C/SXnwNwzDz8N6vrpc0MLMFwCV4z+4GPDAfwSdTnCGpcDjUzN4K/ChwW2cClnLbbLbzTpd0T5d6x5NbprfMz1psRurtu80qWMVEqTq4KWzGcuvCUAVTZnYecBrwEttnkNyFd4NeLumiJnGz3kbrPQIffnoSH8K6Cx+qeQX4uLpY4qGfgU2M3pS+9jv4i/W1R5vrgNMkrW47fhhwqcpnm/XETWEz6+2L3qIcMAO+KGmP8eamsBnLTQI1YKxxvAq+Fsqu+CyOZ9hxllrVLIm+c7PeRuu9p+X81+NrZIEnDa+s4B6Bzza7GZ/QcD3+o30rsF8d3Ei9ffc1xTWK8TXSZuGMPapzvHriprCZ9fZF74v4pIXzO5Qn6+CmsBnLTVGamxnfG7bKZ8K9DLyATxFHPruoidyst7l6J2v7Olij+BRiJP3WfA2qMnwPOFrSv81Xcr5I0mIzOwpPJj+6Bm6M3hS+prhGMb7G2LzRfFbblWyfgbUfvmZZ1QKwvXJT2Mx669e7FrhW0p/aK8zs1Jq4KWzGcvuOYRvmW45Pb56BLyi5Bb8534uvZXRik7hZb6P1LsMTQ2/BZwhtlHSueZL+WkkHl3DvU1iMzjx3aI3Cwq1m9hdJhVPTe+VG6u27rymuUYyvMTbDOcewfWeGbRNjrlMXOx30yk1hM+ut3eYbgSfUYcFjM9tHJUnsvXJT2IzlpsCwBVNT2HFLjXfiU6FHgR9Keq5J3Ky30Xqn4osyvhmfFr9M0lbzBPFXq2QNphSBTaTevvuaMPjrydcYmxkZGRMAasBYYy65DFPB1/I5A1/761NsX4V9V6pXFO+ZO0i+Dto1itQ7C19B/X58qPnx8PqbwO51cFPYzHr7qveBfnFT2IzlpijJBYyrM77o5AV44vFT+LYTq4BPNJGb9Q6E3vVt3E+mvs/HW++g+ZrqOkXY/A2wlJaFD/HFVM+jeuucnrgpbGa9SfUurYubwmYsN0VJLmBcnYFf4nt7jeDbCHwJOBD4KRUrp6bgZr1Dq7fvgc2g+ZriGkXeSzE2C/dMK6uL4aawmfU2V+9E8zVFSS5gXJ3ZeTuLNeHvJHyhxkZxs96h1ZsicBwoX1Nco8h7KcbmSuDztGwxhO8FuhS4uQ5uCptZb3P1TjRfU5QxbU0xAHjOzA4HMLPj8M1wkW+YWDUXPgU36x1OvXMlLZe0Qb4w6PGSHgROwTcDroM7aL6muEYxvsbYPAlf6+w2M/uPmT2Br0+1B76XWx3cFDaz3ubqjeEOmt40SB3NjWfBNzu9G1/d+E7goHB8b+DMpnGz3qHV+wfCptnAcfgeddvqqrq2e+IOmq8prlHkvdSzzXDOwfh+kjPbjpduwB7DTWEz622u3onma79LcgF9cxROGSRu1ju4ekkU2AySr027RlW+Ruo9E98g+VrgUeBDLXVr6+CmsJn1NlfvRPM1RUkuoG+OwuggcbPeodWbInAcKF9TXKMYX6tsAn8mtKyBufi2NGeF/++pg5vCZtbbXL0TzdcUzsXwdwAAAS5JREFUZai2kzGz+4qq8MS1RnGz3nq5qfRW4CvAFePNHTJfa7lGUJuvVXonS3oWQNKjZnYkcI2ZvY7qnLReuSlsZr3N1RvDHTS9STBUwRT+Zfh+fCPSVhie89A0btY7hHoTBTYD5WvC4K8nXyNtbjKzBZLWAUh61syOBZYBh9bETWEz622u3onma/+RumtsPAu+4ejhBXUrmsbNeodW72ZgAb6JbmuZCzxWB3fQfE1xjSLvpRibI7QsPNhWt7gObgqbWW9z9U40X1OUodqbLyOjCTCzy4ErJN3ZoW6FpJPr4KZAr3oH7RoN2ueSkZHRX+RgKiMjIyMjIyMjAsO2aGdGRkZGRkZGRl+Rg6mMjIyMjIyMjAjkYCojIyMjIyMjIwI5mMrIyMjIyMjIiMD/AKrIAOFZRiozAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cdc9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5*len(states)))\n",
    "for s in range(0,len(states)):\n",
    "    plt.subplot(len(states),1,s+1)\n",
    "    plt.violinplot(samples['pred_yields'][:,s,:],showextrema=False);\n",
    "    plt.xticks(range(1,36),np.arange(1980,2015),rotation=90);\n",
    "    plt.plot(range(1,36),fit.data['d_yields'][s,:])\n",
    "    plt.title(states[s])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most points lie in the distribution, indiciating a reasonable fit. Lets now quantify\n",
    "\n",
    "### Posterior Predictive checking and Bayesian P-value maps\n",
    "\n",
    "When examining goodness of fits, the typical method is to look at the residuals. i.e. $\\frac{data - model}{\\sigma}$. Because we have distribution of $y^{rep}$, we can do this in a more probabilisitic way using posterior predictive checks. For more information on posterior predictive checks, [Gelman et al. 1996](http://www.stat.columbia.edu/~gelman/research/published/A6n41.pdf) is a good starting point.\n",
    "\n",
    "For our case, the best way to carry out posterior predictive checks is to think about the yield in one year. We can look at where the real yield value is in relation to the distribution from $y^{rep}$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WrSIiIoIZM2YU+mVjypQptgpXCFEK4eHhvPDCC+Tl5REZGcmbb77J888/z+HD\nh3nxxRdN7vf111+zZMkSYxuK+/bu3WuLsAu14cufANsmYM5ehG+VBEyr1TJ9+vQC2+rXr2/88+nT\npwvdb+XKldYIR9i5Ja89qToEi2vQoAEHDhzg8uXL6PV66tat+1BhfaVKlQCUN2AUQpROWFgYbdu2\nRa/XU7t2bZo2bcqhQ4fo27cvoaGhJvebPn06oaGhNG7c2Lnv9FjoFmROTg6TJk3il19+ITs7m2HD\nhtGhQwcLBWk98qiVUK56pXLFLypjUlNT2bVrF2lpaQW2//Fx9dq1awMwYsQIm8YmhLCcGjVqkJSU\nRHJyMq6urvTu3ZuGDRsWuY+rq6vTNV0tjEarQ/P/td/m7POgjz76CG9vb959913S0tLo0aOHJGBC\nlISKGWTWNmzYMHx9fXn88cdNrhk7dix9+/Zl1KhRzv0tWIgy6ty5c/ztb3/Dw8ODixcv0qZNG65c\nuYKrqyvvvfcejz32WKH7NWjQgPPnzxebqDk6S3XC79y5s3EOr8FgMD7QZ+8kARPKKZlBZmU5OTkP\n1Xw9aM+ePUyaNIkBAwawYMECfHx8bBSdEMISZs2axbJly6hXrx7ff/89H330EStWrOCTTz4hNjaW\nNWvWFLrfzz//TGhoKLVq1cLd3d24XWUNmBIW6oRfsWJFIL8F1qhRo/jb3/5mieisThIwodzy/i1U\nh2BxAQEBXLhwocATwA+qUaMGq1evZuPGjYSFhREVFVXgillAQIAtQhVClFJaWhr16tUDoGnTpsTG\nxgLQpUsXli1bZnK/MWPG2CQ+c6g4D2tK0QfM1Pr//ve/DB8+nNdee43u3btbIjyrkwRMKFelolvx\ni8qYwMBAevTogY+PT4Hi+4SEhIfWhoaGcuLECWJiYoyF+RqNptC1Qgj7odPp+Oqrr2jdujVffPGF\n8e9vcnJykdMsWrVqRVpaGnfv3sVgMJCXl6e8J5iS87BWm/8yd58H/PbbbwwaNIjY2FiefvppCwVn\nfZKACeUOJP8XgM5NaiqOxHKWLl3KvHnzjIX2ppw4cYKJEycSEBBAQkICnp6eNopQCPGooqOjGTly\nJJUqVeLOnTssW7aMc+fOERkZyYIFC0zut2jRIuNT/zqdjpycHJ544gmltyC3nfgZgF4tTdetWpyF\nbkGuWLGC33//nWXLlhmvPK5atarAlAF7JAmYUG7tscuAYyVgXl5edO3atcg1c+bMYevWrUycOJFe\nvXrZKDIhhKW0adOGQ4cOcfnyZerUqYOHhwcGg4GjR48Wud+ePXv4/PPPmT17NtHR0Xz11VccPnzY\nNkGbsP3kfwDbJmAatOYX4Rfh/UqUAAAgAElEQVQyC3LKlCllsm+iJGBCuVWvt1QdgsUFBwczZ84c\nXnjhhQKDtv9Y1/XVV1+xfft26tatqyJEIYQFeHh40KRJE+PPJalpqlKlCtWrV6devXqcO3eOl19+\nmfXr11szTPsknfCFUMuznKvqECzu/q2ETz/91LjtwbquLVu24OrqeL+7EM7iwT5/D/L29i50u4uL\nC1euXKFevXqcOHGCtm3b8vvvv1sjRLum0WrRmFkDZu56e1ZkApaZmcnOnTv55ptvuHXrFlWrVuXp\np5+mW7duxsc+hXhUSmaQWdmhQ4eKXSPJlxBl20svvURqaipAgZFCkP+FKyUlpdD9/vrXvxITE8Py\n5ctZtGgRu3fvJjg42Nrh2h8Zxl247du38+mnn9K+fXvCw8Px8fHh999/59SpU4wePZpOnTpJ3Yqw\nCBUzyIQQ4lFt2bKFfv36sWrVqgLj9orz3HPP8dRTT1GhQgV2797NhQsXaNq0qRUjtU+WbENRFplM\nwKpXr86qVase2t60aVPCw8M5cuSIVQMTzmPdG61UhyCEEGarWbMmo0aNYtGiRSxevLjE++3fv5+/\n//3vfPbZZ1y9epWhQ4fyzjvv8Pzzz1sx2qIpOQ9rNfkvc/dxECYTsHbt2pGTk8P58+e5ffs2np6e\nNGjQwFhQ3L59e5sFKRxbebeyMTbC0uLi4op8vyw+1SOEs+nRowfPPPOMWfusWLGCDz74AIC6deuy\nc+dOIiMjlSZgas7D5o8icqQqfJMJ2OHDh5k/fz516tShQoUKZGZmcunSJcaOHUvHjh1tGaNwcLu+\ny3/8ueeThc9NK6t++eUX0tPTC9SG/PEpSFMFukKIsqV69epmrdfr9fj6+hp/rlmzJnq93tJhmeXD\n45cBCH+6ju0OaqE+YGWVyQRsxYoVbNq0CQ8PD+O227dvM3DgQEnAhEVt/jq/AaAjJWDvvvsuGzZs\noGrVqsZtDz4FOWLECJP737lzx6rxCSHUqVKlCps3b+bVV19Fo9Gwa9cuqlWrpjSmj7/Pb4gtCZjt\nmEzAcnJyHuoi6+7u7lAFcMI+bBjcWnUIFvfJJ5/w2WefUaNG8QPG4+PjWbx4MXfu3MFgMKDX60lL\nS+O7776zQaRCCFubPn06Y8eOZcaMGUD+lfH58+crjsr2NJpSNGJ1hqcg+/TpQ8+ePWnRogWVKlUi\nIyODkydPEh4ebsv4hBNw1TnOX6j7atasWaLkC2Du3Ln87W9/Y9OmTURERBAfHy9tXoRwYHXq1GHn\nzp2kp6ej0+kK3GlyKlKEX7jevXvz/PPP8/3335ORkUGlSpUYPny48sukwvEomUFmZU8//TRz586l\nQ4cOBa4k/7EG7L7y5cvTtWtXUlJScHd3Z+rUqYSGhtoyXCHEI7h79y4HDhx4qObzjTfeKHI/Ly8v\na4dm1+QKWBGSk5Nxc3Ojc+fOzJ49m3379jF27Fhq1ZJ+TcJyVMwgs7adO3cCcODAAeO2B2vA7nNz\ncyM7O5vatWuTkpJC69atyc7OtlmsQohHM2bMGK5fv46/v7+U6ZhDowFzO9s70D9fkwnY5MmTuXfv\nHpmZmSxZsoSXXnqJGjVqEBMTw+rVq20Zo3BwW/76tOoQLK4knfDv69ChA0OGDGH27NmEhYVx8uRJ\neUJSiDLk0qVL7N+/HxeXsjvdT8V5WBqxmnD58mU2btyIwWDgxRdfpF+/fgDOOTBUiBJatWoVERER\nJnt8Fdbba+jQobz00kv4+vqybNkyvvnmG7p162btUIUQFvLHlhJFOXPmTJHvF1ai4NBkFFHhcnNz\nSUxM5NatW9y4cYMffvgBDw8PcnNzbRmfcAKbvr4CQN9WtRVH8ugqVaoEmN/j6/vvv2fLli0MHTqU\nS5cuFWhfIYSwb/7+/gwYMICgoKACNZ8P1oCNHDnS5GeYKlGwlZVHfwBgSLuSj1R6VHIFzIRp06ax\ndOlSGjduTGxsLOHh4Xh7exfbvVsIc338ff4wbkdIwMLCwoCie3w9aOXKlRw7doyrV68ycOBAli5d\nyk8//cTw4cOtFaYQwoIyMzPx8/PjypUrRa4zpzTB1hJSrgO2TcCkD5gJjRo1YunSpdy4cYM7d+7w\n6aefGr/dC2FJGwe3UR2CUvv27WPbtm307t2bypUrs3XrVvr06SMJmBBlxKxZs8xan5mZyfz58/nh\nhx9YtGgRCxYsYMKECU7Xfkaj1aAxswhf4wxtKL7//numTZuGTqfj4sWLBAQEoNVqiY2NNWvquxCi\naC4uLsYZqwCenp5luphXCGcxevRoFi1aRPfu3Qt9f+/evYVuj4uLo3r16ty4cQN3d3cyMjKIjY11\nvmascgWscPPmzeP999+ncuXK/Pzzz6xatYrIyEiio6ONQ0SFsAQlM8jsSM2aNTl8+DAajYbs7GxW\nr17Nn/70J9VhCQFAxo8XLPp5HnX9Lfp5KkVERAAQExNj1n4pKSnMmjWLI0eOUL58eebNm+ekD96U\noggfJyjCz8zMpHLlykD+/0FcvHgRX19f7t27Z7PghHOI///aA0dIwJYuXVrk+4XVhsXExBAdHc35\n8+dp3rw5zZo1c75vwkKUQU2aNAGgVatWZu2nfeC2W15e3kPbbK2cq87mx5QifBMCAwOJiIigbdu2\nJCYm0q5dO3bv3l3i8SpClNT6QeadvOzZrVu3gPy+QD/++CMdO3bExcWFhIQEGjZsWOg+p0+fZv36\n9dy9e5e8vDznHUsihJN46qmnePfdd8nKyiIxMZGNGzfSurXambhKzsNyC7JwkydP5vDhw1y8eJE3\n3niDZ599lsuXL9O1a9diP1Sv1zN16lTOnz+Pm5sbcXFx+Pn5Gd9ft24d+/btA6B9+/aMGDGCrKws\noqKiuHHjBhUrVmTOnDlUqVLFAr+iELZz/1bEgAED2Llzp/G/4WHDhhEZGVnoPgsXLqRjx46UL1/e\nZnEKIdQZP348K1eupFKlSixcuJCgoCCT5wdHptFqS1GE7wS3IAGCg4MJDg42/lynTp0SfWh8fDzZ\n2dls2bKFpKQkZs+ezfLlywH4+eef+eijj9i2bRtarZa+ffvSsWNHjh8/jr+/PyNHjmTfvn0sW7as\n0KaVwvGs+dePAAxqW1dxJJaTmppa4AuEp6cnN27cKHStv78/y5cvp2XLllSoUMG43emaMgrhJFxd\nXRk+fLhdPem8OOHfAIzq0MB2B9VoStGI1QmugP3rX/8yuVPbtm2L/NCTJ08SFBQEQPPmzUlOTja+\n5+vry/vvv49Ol3+/OTc3F3d3d06ePMngwYMBaNeuHcuWLSv5byHKtC9++A1wrASsYcOGvPXWW7z8\n8ssYDAa2b99Os2bNCl176tQpTp06xbZt24zbVDdlFEIULzw8vMiapAcfWDN3vS0du5h/HrZpAqbV\nmN9WwhnaUGzdupXk5ORC70sXl4BlZGQUqGPR6XTk5ubi4uKCq6srVapUwWAwMHfuXBo3bkzdunXJ\nyMgw9hmrWLEit2/fLu3vJMqY919/SnUIFhcXF8fixYt555130Gg0BAUFmeyCbc/NGYUQpvXv3x+A\ngwcPkpGRQWhoKDqdjj179uDp6fnI6x2fPAVZqIULF9K/f38iIiKoV6+eWR/q4eFBZmam8We9Xl+g\nr9G9e/eYNGkSFStW5O23335on8zMTCf9j1E4Cg8PDyZNmqQ6DCGEFXXq1AmA1atXs3nzZuOTjMHB\nwfTp0+eR1zs6eQrSBJ1Ox9y5c7lz547ZHxoYGMjnn39O165dSUpKwt//f31fDAYDkZGRtG7dmiFD\nhhTY58iRIzRt2pSjR4/SokULs48ryiYVM8is5cknnyzyBPHtt9/aMBohhC3cunWLe/fuGR+kyczM\nJD093WLrHZY8BWna448/XqoPDQkJ4dixY4SFhWEwGJg5cyZr166ldu3a6PV6vv76a7Kzs0lMTARg\n7Nix9O3blwkTJtC3b19cXV2lD5IT+fanNNUhWMzHH3+sOgQhhI1169aN3r17ExISgsFg4MCBA/Tu\n3dti622hcgW34hdZmhThW55Wq2X69OkFtv1xfNHp06cL3W/x4sXWCEfYuRXhjnO1848d7A8cOEBK\nSgpDhw4lISGhyE7Xp06dIjExkZycHJ599lmzGzsKIdQZPXo0TZo04fjx4wBMnDiR9u3bl2i9RqMp\ndr0tqDgPa0pRhO9IsyAdp5pNCDuycuVKNm3axIEDB8jKymLp0qW89957ha7dvXs3o0aNIj09nczM\nTMaNG8fWrVttHLEQwlxnzpwxvnx9fenZsyc9e/akWrVqnDlzpsh9q1WrhpeXFxUqVHDeHoAabele\nDkIm/grllh2+CEBk8BOKI7Gcffv2sW3bNnr37k3lypXZunUrffr0KbTvz7p169i2bRvVq1cH8ufL\nvfnmm8pvSRTnu59+tfhnViznbvHPFMJaTD3ZDEW3ktm9ezcLFy6kU6dO6PV6xo0bx8iRI5X+nZ9z\n4BwAEzo3stkxpQi/GCtWrOD999+nXLlyxm1F9QgTwlxnf/1ddQgW5+Ligpvb/2oqPD09CzwJ/Ed6\nvd6YfAHUqFFD+Vw4IUTxSttCxh6/dH370y0FRy1FET5OlIDt37+fxMRE571EKqxu6WuBqkOwuJo1\na3L48GE0Gg3Z2dmsXr26QH3YH3l7exMfH0/Hjh2B/EkSXl5etgxXCPEIMjMzmT9/Pj/88AOLFi1i\nwYIFTJgwgYoVKxa6Xr505ct2ceOei3lXvbNdFDwsYCXF/ht/7LHHClz9EkIULyYmhrVr13L+/Hma\nN2/O0aNHjXMiC1s7Z84c2rdvT/v27Zk9e7aM4RKiDImLi6NSpUrcuHEDd3d3MjIyiI2NNbn+/peu\n++RLl3Mq9gpYTk4O3bt3N/by0mg00iJCWJSSGWRWVqNGDdavX8/du3fJy8srMBniQZ6enhw4cIDL\nly+j1+upW7culy9ftl2wQohHkpKSwqxZszhy5Ajly5dn3rx5RT71HBMTQ2RkJDNmzMBgMODm5sbS\npUttGLFj0ev1TJ06lfPnz+Pm5kZcXBx+fn4l3n/JkiVF1vNZS7EJWEREhC3iEE7sUmqG6hAsZtWq\nVURERDBjxoxCi0X/eGUrLS2//1lERAQffvghVatWNW6PjIzks88+s03QQohH8uDtw7y8vCJvKTZo\n0OChL12makRtpaZX2b3TFR8fT3Z2Nlu2bCEpKYnZs2ezfPnyEu//3nvvkZWVRXp6Oo0bN+bFF1+0\nyRVJk//GP//8c5577jl+/PHHh96THkXCkv4e9qTqECzm/jzTypUrF7t23LhxHDt2DKDAzFUXFxdj\nPZgQwv499dRTvPvuu2RlZZGYmMjGjRsLnaN8X2pqKrt27TJ+CbsvOjra2qGaVJbPwydPniQoKAiA\n5s2bk5ycbNb+Go0Gd3d32rZty9mzZwkLC2PhwoU0amTdJ0JNJmD3/8NITU21agBCOJLatWsDMGLE\niGLXrl69GoC33nqLWbNmWTUuIYT1jB8/npUrV1KpUiUWLlxIUFAQkZGRJtcPGzYMX1/fUk+bEQVl\nZGQUKPPQ6XTk5uaW+KpivXr1GDVqFACdO3emZ8+evP3223zwwQdWifc+k9H17NkTKNn/kQjxKBZ8\ndh6AsS80VBzJo7s/VmvUqFEl7lcjyZcQZZurqyvDhw8vtM9fYXJycuyu5mva3vzGsW93D1Acifk8\nPDzIzMw0/qzX6826pVu5cmWSk5Np0qQJAHXr1uXWLeu35ZBGrEK5X9OzVIdgMXv27GHSpEkMGDCA\nBQsW4OPjozokIYSVjB49mkWLFtG9e/dC39+7d2+h2wMCArhw4YLx4TZ7UJb7MQYGBvL555/TtWtX\nkpKSzP7nOmXKFMaMGUNAQAD+/v6cP3/eZNsgS5IETCg3r1cz1SFYTI0aNVi9ejUbN24kLCyMqKio\nArcZAgLK3rdLIUTh7j+kZqrFjCmBgYH06NEDHx+fAldqTHXOF0ULCQnh2LFjhIWFYTAYmDlzpln7\nN2rUiD179vDFF19w4cIF2rRpw4svvmilaP/HZAK2e/dukzv16NHDKsEI4ShCQ0M5ceIEMTExxsL8\nokaTCCHKnoSEBAICAsx+MG3p0qXMmzfPWDMqHo1Wq2X69OmP9Blubm4EBwcTHBxsmaBKwGQC9sMP\nPwCQlJRE+fLlefLJJzl9+jS5ubmSgAmLUjGDzJpOnDjBxIkTCQgIICEhAU9Pz0LXhYeHF1knZu0C\nUCHEozl+/DgnTpwwu9zAy8uLrl27WjGysiFX50KOztXsfRyFyd9k3LhxALz55pusXLnSuH3QoEHW\nj0o4lbQ72apDsJg5c+awdetWJk6cSK9evYpc279/fwAOHjxIRkYGoaGh6HQ69uzZYzJpE0LYj3/+\n85+sWLGC0NBQZs6cSdu2bUu0X3BwMHPmzOGFF14oMDNWZYlCPZ/CxyZZk8GQ/zJ3H0dRbCp58+ZN\nfv/9dzw9Pbl169ZDfUuEeFSzXmmqOgSL+eqrr9i+fTt169Ytdm2nTp2A/HYUmzdvNjZuDA4Opk+f\nPlaNUwjx6LRaLZGRkQQHBzNlyhQSEhIK3FZ84403Ct3vfnH+p59+atymukRBxXlYbzCgNzOjMne9\nPSs2ARs6dCg9evTAy8uL27dvm11sKIQz2bJlC66u5l1Sv3XrFvfu3TMOvM/MzCQ9Pd0a4QkhrECr\n1aLRaLh48SJZWcU/1X3o0CEbRGX/8q+AmZdQOVD+VXwC1qlTJzp06MDNmzepWrUqOp3OFnEJJ/LO\nvrMATH6xseJIHp25yRdAt27d6N27NyEhIRgMBg4cOEDv3r2tEJ0QwpIMBgMrVqxg9erVjBkzhn79\n+qkOqdTe2vk9YNsrYQaDoRQJmONkYCYTsD59+pgsEN68ebPVAhLOJytHrzoEpUaPHk2TJk04fvw4\nABMnTqR9+/aKoxJCFCcsLIw7d+7wz3/+0656epXGpdTM4hdZmNyCNGHBggW2jEM4sRk9mqgOQTkf\nHx+eeOIJXnnlFc6cOaM6HCFECTRp0oQJEyYUKKQXZihFET6Ok3+ZTsDud4G9du0a7777Ljdv3qRz\n5840bNjQJh1ihSjLfvvtN7Zt28aNGzcKbJ8yZcpDa3fs2MGaNWu4d+8eISEhREZGMmbMGLkNKYSd\nk5roR+PstyC1xS2IiYkhNDSUnJwcWrZsyTvvvGOLuIQTmbb3jHEOmaMYM2YMycnJeHl54e3tbXwV\nZsOGDWzZsgUPDw+qVq3Kzp07Wb9+vY0jFkII29JjKNXLURRbhJ+VlcXTTz/N8uXLqVevHu7u7raI\nS4gy7bfffuPDDz8s0VqtVouHh4fx55o1a8rDLkIIm2pcy/a9Bw2U4gqYMyVg7u7uJCYmotfrSUpK\nknvdwuLe7u548xF9fHxIS0szedXrj7y9vUlJSTE+9PLRRx/h5eVl7RCFEMJIxXlYivCLMWPGDObM\nmcOtW7dYs2YNU6dOtUFYQpRNcXFxAOh0Onr16sUzzzxToDVFYTVgkyZNYvTo0Vy5coW2bdvi7u7O\nsmXLbBazEEKoYNAb0OvNvAJm5np7ZjIBy83NxcXFhSpVqjBnzhxbxiScTMzuZMAxnoa8f8WrRYsW\ntGjRokT7ZGVlsWfPHi5fvkxeXh5169YtVT8xIYQorb9t/g6Av4c9abNjyigiEyZMmMD8+fPp3Lmz\n8daIwWBQPi5BOJ5yrsU+C1JmjBgxAoCcnBw++eQTXnrpJVJTU9m3bx8DBgwodJ/x48fzySefUL9+\nfVuGKoQQRv9NL76Dv6VJJ3wTJkyYAMjIBGF9jtAB/0HTp08nMzOTl156Ca1Wy8mTJ/nPf/5T6C3I\nhg0bsnfvXlq0aEGFChWM20tSPyaEEGVVnkFPnsG8RtzmrrdnJhOw1157jYkTJ9KxY0dbxiOEQ/ju\nu+/4+OOPAahatSqLFi3i5ZdfLnRtQkICBw4cKLBNo9GQkpJi9TiFEEIVZ+8DZjIB27BhAzExMRw+\nfJjJkycbBwWXhF6vZ+rUqZw/fx43Nzfi4uLw8/MrsObmzZv07duXjz76CHd3dwwGA+3ataNOnToA\nNG/enHHjxpXutxJliooZZNaWk5NDdna28anh3Nxck2tPnz5tq7CEEMJuGABza+odJ/0qIgHz9fVl\n1apV7Nmzh379+tG2bVvje2PHji3yQ+Pj48nOzmbLli0kJSUxe/Zsli9fbnw/MTGR+fPnk5qaatx2\n5coVAgICWLFixaP8PqIM8q7geK1NgoODefPNN3n55ZfRaDR8/PHHJuc7Zmdnc+TIETIz82ex5eXl\nceXKFcaMGWPLkIUQTizQr7LNjylXwIpw8+ZNjhw5gpeXF3Xr1i3xh548eZKgoCAg/0pWcnJygfe1\nWi1r164lNDTUuO3MmTNcu3aN8PBwypUrx1tvvUW9evXM+V1EGTWhcyPVIVhcdHQ0GzduJCEhARcX\nF0JCQggLCyt07ZgxY/j5559JTU2lcePGnDp1ilatWtk4YiGEM1NxHpYEzIR9+/Yxb948Bg8eTL9+\n/cz60IyMjAKdvXU6nbGtBcCzzz770D4+Pj4MGTKELl26cOLECaKiotixY4dZxxXCXuh0OgYMGGDy\nycc/SklJ4bPPPmPq1Km88cYbGAwGpk2bZoMohRBCHWnEasIHH3zAmjVrzLrydZ+Hh4fxdgrk14Td\nT75MadKkiXH8SsuWLbl+/bqx7YVwbOO3nQJgXq9miiOxnO7duxe6fe/evQ9tq169Oi4uLtSpU4cL\nFy7QpUsX7t69a+0QhRDCaOiHJwFYEV6y/oWWIAmYCZMnTy4y+Tpz5gwBAYWPLggMDOTzzz+na9eu\nJCUl4e/vX2wgS5cuxdvbm4iICM6dO0fNmjUl+XIStbzKqQ7B4mJiYox/zsnJIT4+nurVqxe6tkKF\nCuzdu5dGjRqxdetW6tWrR1pamq1CFUIIbt3JtvkxpQ+YCV9++SXr16+nc+fONGzYkKpVq3L79m1O\nnTrF/v37adSokckELCQkhGPHjhEWFobBYGDmzJmsXbuW2rVr06FDh0L3GTJkCFFRURw5cgSdTses\nWbMs8xsKuzf2hYaqQ7C4B2u4nnnmGcLCwhg2bNhDa2NjY9m6dStRUVFs376d/v37F/ugixBClHWG\nUlwBc4oasCFDhnD16lU2bdrE6tWruXXrFlWrVqV169ZMmDCBWrVqmfxQrVbL9OnTC2wrrMv3H5u8\nenl5sXLlytL8DkLYvVu3bnH9+vVC36tTpw7R0dGkp6cTFxdXoH5SCCEclYwiKoKvry9jxowhLy+P\nmzdvUq1aNbktKCxOxQwya3uwBuzXX3+lT58+ha69dOkSUVFRxsarLVq0YM6cOUV+yRFCiLLOQCme\ngnSgTmBFV8YDBw8eZNasWXh5eZGRkcHUqVMLfYpRiNKq5+N4V3z+WAOm0WioUqWKyVmPb731Fr16\n9SI0NBSDwcCWLVuYPHkya9eutVW4Qggn9+wT1Wx+TCnCL8Z7773Htm3bqFq1Kr/99htDhw6VBExY\n1KgODVSHYHGtWrUiNTWV9PR0IL9u4eLFizzxxBMPrb17926BHmHh4eFs3brVZrEKIYSK87D0ASuG\nt7c3VatWBaBatWpSnyJECcyaNYuNGzcW+Pui0Wg4fvz4Q2sff/xxvv32WwIDAwG4cOECjz32mM1i\nFUIIFaQGrBgVK1bkzTff5KmnnuLMmTNkZWWxYMECoPiRREKUxIh/fgvA0tcCFUdiOQcPHiQxMZHK\nlYsf73F/AkTDhg1xcXHh7Nmz+Pj4GOvICusdJoQQlvT6mq8BWD/IdlM45CnIYnTs2NH45xo1alg1\nGOGcGtfyVB2CxdWpUwdPz5L9XtHR0VaORgj7kX7mW4t/pleA43x5UyUrJ8/mx5RbkMXo2bOnLeIQ\nTiwy+OG6qLIuPDyc/v3707p16wJTIEaMGPHQ2gfrxe4rrF5MCCEchRThCyEsbsmSJcbmxcW5Xy9W\nqVIl47c7U/ViQgjhKPQG8xMqvePkX5KACfVUzCCztrt377Jq1aoSrTWnXkwIIRyF9AETQrFAP2/V\nIVhcgwYNOHfuHI0aNSp2rTn1YkIIYQ0d/lz4rFqrKkUNmCM9BikJmFBuSLvCG5SWZdevX+fVV1/l\nT3/6E25ubsbthT3RaE69mBBCWIOK83D+LUjz9ymJ27dvExUVRUZGBjk5OUycOJEnn7SvaSuSgAlh\nBea0aDGnXkwIIRyFNZ+CXLt2LW3atGHgwIFcunSJcePGsWvXrtKEaTWSgAnlBq//BoD3X39KcSSW\n06pVyXvpmFMvJoQQ1tDnH/kP/Wz569M2O6Y1E7CBAwca7z7k5eXh7u5udnzWJgmYUO6Z+rafQWZP\nzKkXE0IIR6HHgN7MovrC1m/bto3169cX2DZz5kyaNm1KamoqUVFRTJo06ZFitQZJwIRyg9rWVR2C\nUubUiz2KYxd+sujnVXB3tejnCSGci6WugPXq1YtevXo9tP38+fOMHTuW6Ohos+5K2IokYEJY0S+/\n/EJubi5+fn4m18hILyGEMzKUogi/pPnaxYsXGT16NH//+9/t9u6CJGBCORUzyKzt8uXLDB8+nGvX\nrmEwGKhcuTL/+Mc/qF//4SeNWrVqxalTp0hMTCQnJ4dnn33WLr+tCSGEJekNBvRmZmAlbdw6f/58\nsrOzeeeddwDw8PBg+fLlZsdoTZKACeU6qug/Y2UzZsxg8ODBxlFeO3bsYNq0aXzwwQcPrd29ezcL\nFy7khRdewGAwMG7cOEaOHEnv3r1tHbYQwkl1a1rT5sc0GMyf7VjS5faWbBVGEjChXPjTdVSHYHE3\nbtwoMEc1NDSUdevWFbp23bp1bNu2jerV8xPRiIgI3nzzTUnAhBA2o+I87OzDuLWqAxDCEeXl5ZGW\nlmb8+ebNmybX6vV6Y/IFUKNGDbRa+asphLCdu9l53M3Os+kx7z8Fae7LUcgVMKFcv/e/BGDj4DaK\nI7Gc/v3706dPH7p06e7cDjoAABclSURBVALAJ598wuuvv17oWm9vb+Lj4+nYsSMA8fHxeHl52SxW\nIYQYuDa/FteWfcBkFJEQinVrWkt1CBbXp08f/Pz8SExMxGAw8Pbbb/PMM88UujYmJobIyEhmzJgB\ngKurK++9954twxVCCJvLrwEzfx9HIQmYUK5vq9qqQ7CK8uXLU65cOXJzcwvMeHxQgwYN2LVrF9eu\nXSMvLw8vLy9q1Khhw0iFEML29AZDiZ9q/OM+jkIKTYSwgt27dzNq1Chu375NZmYm48aNY+vWrYWu\n3b9/P6+88gr169fH1dWVHj16cOjQIRtHLIQQtnW/CN/cl6OQK2BCORUzyKzNnCcbV6xYYWxPUbdu\nXXbu3ElkZCTPP/+8TWMWQghbcvanICUBE8q92uIx1SFYnDlPNur1enx9fY0/16xZE71eb/UYhRDi\nPhXnYT2luAXpQE9Byi1IoVyvlo/Tq+XjqsOwqPtPNt5X1JONVapUYfPmzeTm5pKXl8f27dupVs25\nB5QLIWxLxXk4fxSRwayXA10AkytgQr2cvPyrPa46x/k+YM6TjdOnT2fs2LHGtQEBAcyfP99msQoh\nxM3MbACqVHSz2THlFqQQivV//yvAsWrAPD09OXDgAJcvX0av11O3bl0uX75c6No6deqwc+dO0tPT\n0el0eHh42DZYIYTTG7bhJGDb87C+FMO4zV1vz6xyyUGv1xMbG0ufPn0IDw/np59+emjNzZs36dSp\nE/fu3QMgKyuLkSNH8tprrxEREVFk53DhWMJaPU5YK8e4BZmWlkZaWhoRERFkZGRQtWpVfHx8SEtL\nIzIyssh9vby8JPkSQjgNeQrSCuLj48nOzmbLli0kJSUxe/bsAoMxExMTmT9/PqmpqcZtmzZtwt/f\nn5EjR7Jv3z6WLVvGlClTrBGesDM9n3ScIvxx48Zx7NgxAFq3bm3c7uLiYux0L4QQQm5BWiUBO3ny\nJEFBQQA0b96c5OTkAu9rtVrWrl1LaGhogX0GDx4MQLt27Vi2bJk1QhN26P78sfJuOsWRPLrVq1cD\n8NZbbzFr1izF0QghhP0ylKIRqyRgxcjIyChwK0Wn0xXoBv7ss88Wuk+lSpUAqFixIrdv37ZGaMIO\nKZlBZmWSfAkhRNFkFJEVeHh4kJmZafxZr9cXOYrlwX0yMzPx9PS0RmjCDvVv46c6BCGEcGoqzsMG\nSnELUvqAFS0wMJCjR48CkJSUhL+/f4n2OXLkCABHjx6lRYsW1ghN2KHuzWrRvZnjDeQWQoiyQsV5\n2NweYKWZHWnPrHIFLCQkhGPHjhEWFobBYGDmzJmsXbuW2rVr06FDh0L36du3LxMmTKBv3764urpK\nHyQn8ntWDgCe5VwVRyKEEM7p17S7ANTyLm+zY0oRvhVotVqmT59eYFv9+vUfWvfHgcPly5dn8eLF\n1ghH2LmI9ScAx6oBE0KIsmTMliRARR8wM0cROU7+JY1YhXpvPFtHdQhCCCFsTJ6CFEKxzk1qqg5B\nCCGEjcktSCEUUzGDTAghhFrShkIIxVTMIBNCCKFWaZ5qlKcghbCgiKB6qkMQQginpuI87Ox9wCQB\nE8p1bFxDdQhCCOHUVJyHpQZMCMWu384CoHqlcoojEUII5/RDagYA9X08illpOfIUpBCKjfznd4DU\ngAkhhCqTdp4GbHseNvz/y9x9HIUkYEK5YcEPN+kVQgjh2KQIXwjFghtWVx2CEEIIG8tvQ2HuLUgr\nBaOAJGBCORUzyIQQQqil1xvQmzlbyNz19kwSMKGcihlkQgghFCvFU5COdAlMEjCh3MjnG6gOQQgh\nnJqK87CeUtSAOVAZviRgQrm2DaqpDkEIUYbdPPEvi35elZZtLfp5ZYGK87A8BSmEYldu3AGgdtUK\niiMRQgjndObXdAACannZ7JjO3ohVqzoAIaK2nyJq+ynVYQghhNOavvcs0/eetekx77ehMPdljh9+\n+IEWLVpw7949K/0WpSdXwIRyY0L8VYcghBDCxvR6PXl6vdn7lFRGRgZz5szBzc3N3NBsQq6ACeXa\n1KtKm3pVVYchhBDChvL7gJn/KtlnG4iJiWHs2LGUL2+fLY7kCphQTsUMsrLgs+8vWPTzKpZzt+jn\nCSHEo7BUI9Zt27axfv36Attq1apF165dadSo0aOEaFWSgAnlVMwgE0IIoZalRhH16tWLXr16FdgW\nEhLCjh072LFjB6mpqQwaNIiNGzc+UryWJgmYUC66c0PVIQghhFNTcR62Zh+wgwcPGv/8/PPPs2bN\nGrOOYwuSgAnlWvhVUR2CEEI4NSXnYemEL4Ra56/eBqChbyXFkQghhHM6+dNNwLaJmK36gB06dMjs\nfWxBEjChXOyeZEBqwIQQQpW5B84Dtj0P6w35L3P3cRSSgAnlJnX9s+oQhBBC2Jizd8KXBEwo1+xx\nb9UhCCGEsDFJwIRQTMUMMiGEEGpZqg1FWSUJmFDu/vwxqQETQggnUopGrCXsQlEmSAImlIvt3lh1\nCEII4dRUnIf1lKII3yqRqGGVBEyv1zN16lTOnz+Pm5sbcXFx+Pn5Gd/funUrmzdvxsXFhWHDhvHc\nc8+RlpZGp06d8PfPH8zcsWNHXn/9dWuEJ+yM3HoUQgi1VJyH9QY9eoOZw7jNXP9/7d17UBTnmgbw\nR8HhNsjlLJhYcTxCQcqE9QIWm41iNB4LTTRkEYEMTo5RK2qMtxhFDXhBA5qNHkurFDSlZEfkpqZ0\nU6tmiZeJ1xVKNGjAk7EgIRgDopGew316/0AnmeMlgjPzjfTzS6Uq093z9YPGzpvur9/PmdmlACsq\nKkJLSwvy8/NRWlqKdevWYdu2bQCA2tpa6PV67Nu3D83NzdBqtRg+fDiuXLmCCRMmIDU11R6RyIld\n/PE2AE7GJyIS5eTf6wAAI0L+xWHn7Mzi2r//TndhlwKspKQEUVFRAIAhQ4agrKzMsu/SpUsYOnQo\nVCoVVCoVNBoNysvLUVZWhsuXL2PKlCnw9/dHSkoKAgMD7RGPnEz6/3wHgHPAiIhE2XL07wAcXYDx\nLUibkyQJarXa8tnFxQVtbW1wdXWFJEnw9v6t47mXlxckSUJQUBDCwsLw8ssv4+DBg1i7di02b95s\nj3jkZNJiwkRHICIiB5O78BYkC7A/oFarYTKZLJ/NZjNcXV0fuM9kMsHb2xuDBg2Ch4cHgI5VzFl8\nKQeXICIiUh6l3wHraY9Bw8PDYTAYAAClpaWWifUAMGjQIJSUlKC5uRkNDQ0wGo0IDQ1FSkoKjhw5\nAgA4c+YMXnzxRXtEIydUUlVvWYeMiIiU4V4B1tm/uwu73AEbO3YsTp06hcTERMiyjPT0dOzatQsa\njQZjxoyBTqeDVquFLMtYuHAh3NzcsGjRIixfvhy5ubnw8PDA2rVr7RGNnJCINciIiEgsrgVpBz17\n9kRaWprVtuDgYMs/x8fHIz4+3mp/v379oNfr7RGHnFx67L+KjkBEpGgirsMyuvAIsht1YmUjVhIu\nOED9xwcREZHdiLgOmwGYO1lQdZ8uYCzAyAmcvXYTAPBS0J8EJyEiUqaiKzcAAH95oY/jTtqVOV2c\nA0ZkO3/736sAOAeMiEiUHd9cA+DYAsxslmHu5KSuzh7vzFiAkXD/GTdYdAQiIotfDIdtOl7gyHE2\nHa+7UHobChZgJJzmT56iIxARkYPxLUgiwUSsQUZERGLxDhiRYCLWICMiIrHku3919jvdBQswEu5v\nCUNER3hi+89/a/Mx1W5uNh+TiOhBRFyHuRYkkWB9fT1ERyAiUjQR12E2YiUS7HjFLwCAUc8HCk5C\nRKRM/32xBgAwcXBfh52Tk/CJBNt23AiABRgRkSi7z1YBcGwBJsudf6TYjZ5AsgAj8bZoh4qOQERE\nDsa3IIkEC/R2Fx2BiIgcjJPwiQQTsgYZEREJZe5CAdbZ450ZCzASTsQaZEREJBYfQRIJtm1KhOgI\nRESKJuI63DEJv/Pf6S5YgJFw/l4q0RGIiBRNxHXYjC48gmQfMCLbOVx2HQAwLuxZwUmIiJSpsPhH\nAMDkYf0cdk4+giQSbNepSgAswIioe7p+ZL/Nx3w2Otam4+0tqQbg+AKMb0ESCbTjr8NERyAiIgdj\nI1YiwXq79xIdgYiIHMyek/Db29uRkZGBsrIytLS0YO7cuRg9enTnQ9oRCzASTsQaZLtPlth0PE83\nvkhARNQZraaGTj+CbP+H9FjHHThwAG1tbcjLy8ONGzdw6NChrkS0KxZgJJyINciIiEgMtVoNHx8f\n3Cza26Xv+/j4QK1WP/KYkydPIiQkBO+++y5kWUZqamqXzmVPLMBIuOx3IkVHICJSNEdeh319ffHV\nV19Bkh7vbtY/U6vV8PX1tXwuLCzE559/bnWMn58f3NzckJWVhfPnz2PZsmXIycl5oty2xgKMhPNQ\nuYiOQESkaI6+Dvv6+loVUU9i8uTJmDx5stW2hQsXYtSoUejRowciIyNRWVlpk3PZUk/RAYi+uFCN\nLy5Ui45BRKRY+jOV0J+pFJzCdiIiInDixAkAQHl5OZ591vnaHPEOGAmX938dDQD/Y+hzgpMQET0d\nfvziv2w63pe/hAAAdP/+Z5uOK0p8fDxWrlyJ+Ph4yLKM1atXi450HxZgJNzuGf/2h8dkFZ2x6Tm9\n3PnWIhFRd6VSqZCRkSE6xiPZpQAzm81YtWoVKioqoFKpsHbtWvTv39+yv6CgAHl5eXB1dcXs2bMx\nevRo1NfX48MPP0RTUxMCAwORkZEBDw8Pe8QjJ9PLhU/CiYhIWexSgBUVFaGlpQX5+fkoLS3FunXr\nsG3bNgBAbW0t9Ho99u3bh+bmZmi1WgwfPhxbt27FhAkTEBsbi+3btyM/Px9Tp061RzxyMiLWICMi\not80/dLRj7EyZ5tNxvtz0mybjNOd2aUAKykpQVRUFABgyJAhKCsrs+y7dOkShg4dCpVKBZVKBY1G\ng/LycpSUlGDmzJkAgJEjR2Ljxo1WBVh7ezsA4Oeff7ZHZLKxz46efexjD5c3AQAuX3F/6DG2bnTa\nauPxmlW27+bfrLJtxkYb/8zudviZbf377NnLthmlp2DShmtj117tf5j2xn/Ydrwm244HAOamRpuO\n19Zosul47U1NNh0PANptnLFFagEA/Hz7V5uM51rNF6vu1Sv36pd/ZpfLiSRJVk3SXFxc0NbWBldX\nV0iSBG9vb8s+Ly8vSJJktd3LywsNDQ1WY9bW1gIAkpKS7BGZnECu6ABERAr3V1sNtHmXrUZ66tXW\n1lpNw7rHLgWYWq2GyfRbdW42m+Hq6vrAfSaTCd7e3pbt7u7uMJlM6N27t9WYYWFhyMnJQUBAAFxc\n2DeKiIiInFd7eztqa2sRFhb2wP12KcDCw8Nx7NgxvPbaaygtLUVoaKhl36BBg7Bp0yY0NzejpaUF\nRqMRoaGhCA8Px4kTJxAbGwuDwYCIiAirMd3d3TFs2DB7xCUiIiKyuQfd+bqnhyx3di3yP3bvLcir\nV69ClmWkp6fDYDBAo9FgzJgxKCgoQH5+PmRZxsyZMxEdHY26ujokJyfDZDLBz88PGzZsgKenp62j\nEREREQlnlwKMiIiIiB5OMQ2YzGYzVqxYgYSEBOh0OlRVVYmOpHitra1YvHgxtFot4uLi8PXXX4uO\nRHfdvHkTr7zyCoxGo+godFdWVhYSEhIQGxuLwsJC0XEUr7W1FYsWLUJiYiK0Wi3/rAh28eJF6HQ6\nAEBVVRXeeustaLVarFy5EmazWXC6B1NMAfb73mSLFi3CunXrREdSvIMHD8LX1xd79uzBZ599hjVr\n1oiOROj4D8uKFSvg7v7wtiDkWOfOncOFCxeQm5sLvV7PdjxO4MSJE2hra0NeXh7mzJmDTZs2iY6k\nWDt27EBKSgqam5sBABkZGViwYAH27NkDWZad9n/uFVOAPao3GYkxbtw4zJ8/HwAgyzLfbnUS69ev\nR2JiIgIDA0VHobtOnjyJ0NBQzJkzB7NmzcKoUaNER1K8AQMGoL29HWazGZIkWd70J8fTaDTYsmWL\n5fPly5cRGRkJoKOv6OnTp0VFeyTF/BvzqN5kJIaXlxeAjt+befPmYcGCBYIT0f79++Hv74+oqChs\n375ddBy669atW6ipqUFmZiaqq6sxe/ZsHD58GD169BAdTbE8PT3x008/Yfz48bh16xYyMzNFR1Ks\n6OhoVP+u8assy5Y/Gw/qK+osFHMH7FG9yUic69ev4+2330ZMTAwmTpwoOo7i7du3D6dPn4ZOp8N3\n332H5ORkSxNkEsfX1xcjRoyASqVCUFAQ3NzcUF9fLzqWomVnZ2PEiBE4cuQIDhw4gKVLl1oegZFY\nPXv+Vto8qK+os1BMARYeHg6DwQAA9/UmIzHq6uowbdo0LF68GHFxcaLjEICcnBzs3r0ber0eAwcO\nxPr16xEQECA6luJFRETgm2++gSzLuHHjBhobG+Hr6ys6lqL17t3bsnqLj48P2traHrrkDDnWCy+8\ngHPnzgEADAaD0/YQVcwtoLFjx+LUqVNITEy09CYjsTIzM3Hnzh1s3boVW7duBdAxmZKTv4msjR49\nGufPn0dcXBxkWcaKFSs4Z1KwqVOnYvny5dBqtWhtbcXChQvZu9JJJCcnIzU1FRs3bkRQUBCio6NF\nR3og9gEjIiIicjDFPIIkIiIichYswIiIiIgcjAUYERERkYOxACMiIiJyMBZgROR05s2bh6ysLMtn\nSZIQHR0NnU6Hmpqah35v+PDh923Lzc216pINdLyanp+f36lM6enpyM3NtXzevn07YmJikJSUhGPH\njgEAampqMGXKFCQlJeG9995DY2MjAODo0aOYNGkSEhISUFBQ0KnzElH3xAKMiJzOqlWrkJeXh++/\n/x4A8MknnyAhIQF6vR59+/Z94vFHjhyJhISExzq2vr4eM2bMwNGjRy3bKioq8OWXX6KgoAA7d+7E\n5s2b0djYiOzsbIwfPx45OTkICQnB3r170draioyMDOzcuRN6vR75+fmoq6t74p+BiJ5uLMCIyOn4\n+/sjNTUVKSkpOHfuHKqrq/HOO+9Ap9PBaDSioaEB8+bNg06ng06nQ0VFhdX3i4uLERsbi6lTp6Ko\nqOi+8ffv349PP/0U1dXVSEhIwPz58xEbG4uVK1fed6zJZMLcuXMRExNj2WY0GhEZGQk3Nze4ubmh\nf//+qKiowMCBA3Hnzh0AsKwPaDQaodFo4OPjA5VKhYiICJw/f97Gv2JE9LRhAUZETunVV1/FgAED\nsGzZMmRkZFite5iZmYmXXnoJer0ea9aswapVq6y+u3r1amzYsAHZ2dl47rnnHnmeyspKfPzxxygs\nLITBYLhv6aV+/fph8ODBVtuef/55FBcXQ5Ik3Lp1CxcuXEBjYyOeeeYZ5OTk4PXXX4fBYMC4ceMg\nSZKlYzrQsTadJEld/FUhou5CMZ3wiejp8+abb6KpqQl9+vSx2n716lWcPXsWhw4dAgD8+uuvVvvr\n6uowYMAAAB3LkP3www8PPYdGo4FarQYABAQEPNZ6fsHBwUhKSsKMGTPQt29fDB48GH5+fpZiMSoq\nCsePH0dycjI++OADq3VoTSaTVUFGRMrEAoyInjpBQUF44403MHHiRNy8eROFhYVW+/v06QOj0Yjg\n4GB8++238PHxeehYv7+z9rjq6+thMpmQl5eHhoYGTJs2DSEhIVbrAwYGBuLOnTsIDg5GVVUVbt++\nDU9PTxQXF2P69OmdPicRdS8swIjoqTNr1ix89NFHKCgogCRJeP/99632p6WlYcmSJVCr1fDy8npk\nAdYVfn5+uHbtGiZNmoRevXphyZIlcHFxQWpqKtLS0mA2my1rNvbq1QtLly7F9OnTIcsyJk2adN8d\nPSJSHq4FSURERORgnIRPRERE5GAswIiIiIgcjAUYERERkYOxACMiIiJyMBZgRERERA7GAoyIiIjI\nwViAERERETkYCzAiIiIiB/t/GzHskWS7sKAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "fig=plt.figure(figsize=(10,5))\n",
    "\n",
    "\n",
    "# This is  the colormap I'd like to use.\n",
    "cm = sns.diverging_palette(220, 20, as_cmap=True)\n",
    "\n",
    "# Get the histogramp\n",
    "Y,X = np.histogram(samples['pred_yields'][:,0,:], 25, normed=1)\n",
    "#C = [cm(((x-X.min())/x_span)) for x in X]\n",
    "C = [cm(((((x-np.mean(samples['pred_yields'][:,0,:]))/np.std(samples['pred_yields'][:,0,:]))+6)/12.0)) for x in X]\n",
    "\n",
    "\n",
    "plt.bar(X[:-1],Y,color=C,width=X[1]-X[0])\n",
    "plt.xlabel('Yield in 1980')\n",
    "plt.ylabel('p(Yield in 1980)')\n",
    "plt.axvline(9, linestyle='--')\n",
    "plt.axvline(2,linestyle=':')\n",
    "plt.annotate('Yield in 1980 that \\n model cannot explain',xy=(8, 0.01),  xycoords='data',\n",
    "            xytext=(8, 0.3), textcoords='data',rotation='vertical',size='large')\n",
    "plt.annotate('too much Yield in model \\n compared to real Yield in 1980',xy=(3, 0.01),  xycoords='data',\n",
    "            xytext=(3, 0.3), textcoords='data',rotation='vertical',size='large')\n",
    "\n",
    "#ax1 = fig.add_axes([0.05, 0.80, 0.9, 0.15])\n",
    "ax1 = fig.add_axes([0.82, 0.15, 0.02, 0.7])\n",
    "\n",
    "norm = mpl.colors.Normalize(vmin=-6, vmax=6)\n",
    "cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cm,\n",
    "                                norm=norm,\n",
    "                                orientation='vertical')\n",
    "cb1.set_label('$\\sigma$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can calculate fraction of $y^{rep}$ samples above and below real yield value. This is often referred to as the Bayesian p-value and is telling us the probability of drawing the real yield value, from our model which has been inferred on the data. This is tells us if the model is inconsistent with the data, given the uncertianties in parameters and data.\n",
    "\n",
    "* $\\sim 0.5$ means our model is consistent with the data \n",
    "* 0.99 or 0.01 means model is missing something.\n",
    "\n",
    "We can convert this to a typical '$\\sigma$' level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Bayesian_Pval(yields,pred_yields):\n",
    "    import scipy.stats as st\n",
    "    Pvals=np.empty_like(yields)\n",
    "    n_reg,n_years=yields.shape\n",
    "    for r in range(0,n_reg):\n",
    "        for y in range(0,n_years):\n",
    "            ind=pred_yields[:,r,y]<yields[r,y]\n",
    "            Pvals[r,y]=st.norm.ppf(ind.sum()/pred_yields[:,r,y].size)\n",
    "    return Pvals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pvals=Bayesian_Pval(fit.data['d_yields'],samples['pred_yields'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'P value')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bsowrv/L1CbUTcVIz5ML5uUwTugZkYdVy0oxXIDMawbJoQWMAXSqkl2FVdeHo9yNJ0/f1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IjLqCBfQbdB8IJdCGwePBYLMNua232UmwdYjQZ2fjIAVWOzgbkOMZbELBqmrMNhtmq43EQVg1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\nFStWcOONNyJJEh6Ph7vuuguA6dO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BcPvjffnyBe3t7VAqlaJWA/2K0WhEW1sbRo8e3ac5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\n+R/vF/Pnk9wrEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "for s in range(0,len(states)):\n",
    "    plt.plot(Pvals[s,:],label=states[s])\n",
    "plt.xticks(range(0,36),np.arange(1980,2015),rotation=90);\n",
    "plt.legend()\n",
    "plt.ylabel('P value')\n",
    "\n",
    "plt.savefig(\"./figs/pvalues_all.png\".format(states[s]))\n",
    "plt.savefig(\"./figs/pvalues_all.pdf\".format(states[s]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows the P values for every state. Most years are between -1 and 1, indicating that the yields are a good fit given the uncertianties in model. The exceptions are 1988,1991, 1993 2002 and 2012 where the yield was dramatically lower than predicited. This would suggest a failure in the model, most likely being the assumption of "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit Yield trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array(yields[yields[\"Region\"].isin(['Maize_Spring_USA_'+s for s in states])].iloc[:,21:]).astype(float)\n",
    "plt.plot(np.arange(1960,2015,1),np.array(yields[yields[\"Region\"].isin(['Maize_Spring_USA_'+s for s in states])].iloc[:,1:]).astype(float).T\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (herschelhelp_internal)",
   "language": "python",
   "name": "helpint"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}