06-Logistic_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic regression (aka logit regression or logit model) was developed by statistician David Cox in 1958 and is a regression model where the response variable Y is categorical. Logistic regression allows us to estimate the probability of a categorical response based on one or more predictor variables (X). It allows one to say that the presence of a predictor increases (or decreases) the probability of a given outcome by a specific percentage. This tutorial covers the case when Y is binary — that is, where it can take only two values, “0” and “1”, which represent outcomes such as pass/fail, win/lose, alive/dead or healthy/sick. Cases where the dependent variable has more than two outcome categories may be analysed with multinomial logistic regression, or, if the multiple categories are ordered, in ordinal logistic regression. However, discriminant analysis has become a popular method for multi-class classification so our next tutorial will focus on that technique for those instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Updating HTML index of packages in '.Library'\n",
      "Making 'packages.html' ... done\n",
      "Updating HTML index of packages in '.Library'\n",
      "Making 'packages.html' ... done\n"
     ]
    }
   ],
   "source": [
    "install.packages(\"ROCR\")\n",
    "install.packages('caTools')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(tidyverse)\n",
    "library(modelr)     # provides easy pipeline modeling functions\n",
    "library(broom)      # helps to tidy up model outputs\n",
    "library(caTools)\n",
    "library(ROCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial primarily leverages the Default data provided by the ISLR package. This is a simulated data set containing information on ten thousand customers such as whether the customer defaulted, is a student, the average balance carried by the customer and the income of the customer. We’ll also use a few packages that provide data manipulation, visualization, pipeline modeling functions, and model output tidying functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(repr)\n",
    "options(repr.plot.width=5, repr.plot.height=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>default</th><th scope=col>student</th><th scope=col>balance</th><th scope=col>income</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>No       </td><td>No       </td><td> 729.5265</td><td>44361.625</td></tr>\n",
       "\t<tr><td>No       </td><td>Yes      </td><td> 817.1804</td><td>12106.135</td></tr>\n",
       "\t<tr><td>No       </td><td>No       </td><td>1073.5492</td><td>31767.139</td></tr>\n",
       "\t<tr><td>No       </td><td>No       </td><td> 529.2506</td><td>35704.494</td></tr>\n",
       "\t<tr><td>No       </td><td>No       </td><td> 785.6559</td><td>38463.496</td></tr>\n",
       "\t<tr><td>No       </td><td>Yes      </td><td> 919.5885</td><td> 7491.559</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " default & student & balance & income\\\\\n",
       "\\hline\n",
       "\t No        & No        &  729.5265 & 44361.625\\\\\n",
       "\t No        & Yes       &  817.1804 & 12106.135\\\\\n",
       "\t No        & No        & 1073.5492 & 31767.139\\\\\n",
       "\t No        & No        &  529.2506 & 35704.494\\\\\n",
       "\t No        & No        &  785.6559 & 38463.496\\\\\n",
       "\t No        & Yes       &  919.5885 &  7491.559\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| default | student | balance | income |\n",
       "|---|---|---|---|\n",
       "| No        | No        |  729.5265 | 44361.625 |\n",
       "| No        | Yes       |  817.1804 | 12106.135 |\n",
       "| No        | No        | 1073.5492 | 31767.139 |\n",
       "| No        | No        |  529.2506 | 35704.494 |\n",
       "| No        | No        |  785.6559 | 38463.496 |\n",
       "| No        | Yes       |  919.5885 |  7491.559 |\n",
       "\n"
      ],
      "text/plain": [
       "  default student balance   income   \n",
       "1 No      No       729.5265 44361.625\n",
       "2 No      Yes      817.1804 12106.135\n",
       "3 No      No      1073.5492 31767.139\n",
       "4 No      No       529.2506 35704.494\n",
       "5 No      No       785.6559 38463.496\n",
       "6 No      Yes      919.5885  7491.559"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "default <- as_tibble(ISLR::Default)\n",
    "head(default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>default</th><th scope=col>student</th><th scope=col>balance</th><th scope=col>income</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>No       </td><td>No       </td><td> 729.5265</td><td>44361.625</td></tr>\n",
       "\t<tr><td>No       </td><td>No       </td><td>1073.5492</td><td>31767.139</td></tr>\n",
       "\t<tr><td>No       </td><td>Yes      </td><td> 919.5885</td><td> 7491.559</td></tr>\n",
       "\t<tr><td>No       </td><td>No       </td><td> 825.5133</td><td>24905.227</td></tr>\n",
       "\t<tr><td>No       </td><td>No       </td><td>1161.0579</td><td>37468.529</td></tr>\n",
       "\t<tr><td>No       </td><td>No       </td><td>   0.0000</td><td>29275.268</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " default & student & balance & income\\\\\n",
       "\\hline\n",
       "\t No        & No        &  729.5265 & 44361.625\\\\\n",
       "\t No        & No        & 1073.5492 & 31767.139\\\\\n",
       "\t No        & Yes       &  919.5885 &  7491.559\\\\\n",
       "\t No        & No        &  825.5133 & 24905.227\\\\\n",
       "\t No        & No        & 1161.0579 & 37468.529\\\\\n",
       "\t No        & No        &    0.0000 & 29275.268\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| default | student | balance | income |\n",
       "|---|---|---|---|\n",
       "| No        | No        |  729.5265 | 44361.625 |\n",
       "| No        | No        | 1073.5492 | 31767.139 |\n",
       "| No        | Yes       |  919.5885 |  7491.559 |\n",
       "| No        | No        |  825.5133 | 24905.227 |\n",
       "| No        | No        | 1161.0579 | 37468.529 |\n",
       "| No        | No        |    0.0000 | 29275.268 |\n",
       "\n"
      ],
      "text/plain": [
       "  default student balance   income   \n",
       "1 No      No       729.5265 44361.625\n",
       "2 No      No      1073.5492 31767.139\n",
       "3 No      Yes      919.5885  7491.559\n",
       "4 No      No       825.5133 24905.227\n",
       "5 No      No      1161.0579 37468.529\n",
       "6 No      No         0.0000 29275.268"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(123)\n",
    "sample <- sample(c(TRUE, FALSE), nrow(default), replace = T, prob = c(0.6,0.4))\n",
    "train <- default[sample, ]\n",
    "test <- default[!sample, ]\n",
    "head(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will fit a logistic regression model in order to predict the probability of a customer defaulting based on the average balance carried by the customer. The glm function fits generalized linear models, a class of models that includes logistic regression. The syntax of the glm function is similar to that of lm, except that we must pass the argument family = binomial in order to tell R to run a logistic regression rather than some other type of generalized linear mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 <- glm(default ~ balance, family = \"binomial\", data = train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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YW2xWLR6XR0A7VazfM8vWy32WxGo5FhmEAgQH8gDMMEg8FgMKjT6cRORDo+RTye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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "default %>%\n",
    "  mutate(prob = ifelse(default == \"Yes\", 1, 0)) %>%\n",
    "  ggplot(aes(balance, prob)) +\n",
    "  geom_point(alpha = .15) +\n",
    "  geom_smooth(method = \"glm\", method.args = list(family = \"binomial\")) +\n",
    "  ggtitle(\"Logistic regression model fit\") +\n",
    "  xlab(\"Balance\") +\n",
    "  ylab(\"Probability of Default\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>term</th><th scope=col>estimate</th><th scope=col>std.error</th><th scope=col>statistic</th><th scope=col>p.value</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>(Intercept)  </td><td>-11.006277528</td><td>0.488739437  </td><td>-22.51972    </td><td>2.660162e-112</td></tr>\n",
       "\t<tr><td>balance      </td><td>  0.005668817</td><td>0.000294946  </td><td> 19.21985    </td><td> 2.525157e-82</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " term & estimate & std.error & statistic & p.value\\\\\n",
       "\\hline\n",
       "\t (Intercept)   & -11.006277528 & 0.488739437   & -22.51972     & 2.660162e-112\\\\\n",
       "\t balance       &   0.005668817 & 0.000294946   &  19.21985     &  2.525157e-82\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| term | estimate | std.error | statistic | p.value |\n",
       "|---|---|---|---|---|\n",
       "| (Intercept)   | -11.006277528 | 0.488739437   | -22.51972     | 2.660162e-112 |\n",
       "| balance       |   0.005668817 | 0.000294946   |  19.21985     |  2.525157e-82 |\n",
       "\n"
      ],
      "text/plain": [
       "  term        estimate      std.error   statistic p.value      \n",
       "1 (Intercept) -11.006277528 0.488739437 -22.51972 2.660162e-112\n",
       "2 balance       0.005668817 0.000294946  19.21985  2.525157e-82"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tidy(model1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>(Intercept)</dt>\n",
       "\t\t<dd>1.6597183798203e-05</dd>\n",
       "\t<dt>balance</dt>\n",
       "\t\t<dd>1.00568491489589</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[(Intercept)] 1.6597183798203e-05\n",
       "\\item[balance] 1.00568491489589\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "(Intercept)\n",
       ":   1.6597183798203e-05balance\n",
       ":   1.00568491489589\n",
       "\n"
      ],
      "text/plain": [
       " (Intercept)      balance \n",
       "1.659718e-05 1.005685e+00 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp(coef(model1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can further interpret the balance coefficient as - for every one dollar increase in monthly balance carried, the odds of the customer defaulting increases by a factor of 1.0057.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Waiting for profiling to be done...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>2.5 %</th><th scope=col>97.5 %</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>(Intercept)</th><td>-12.007610373</td><td>-10.089360652</td></tr>\n",
       "\t<tr><th scope=row>balance</th><td>  0.005111835</td><td>  0.006269411</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & 2.5 \\% & 97.5 \\%\\\\\n",
       "\\hline\n",
       "\t(Intercept) & -12.007610373 & -10.089360652\\\\\n",
       "\tbalance &   0.005111835 &   0.006269411\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | 2.5 % | 97.5 % |\n",
       "|---|---|---|\n",
       "| (Intercept) | -12.007610373 | -10.089360652 |\n",
       "| balance |   0.005111835 |   0.006269411 |\n",
       "\n"
      ],
      "text/plain": [
       "            2.5 %         97.5 %       \n",
       "(Intercept) -12.007610373 -10.089360652\n",
       "balance       0.005111835   0.006269411"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confint(model1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, we can measure the confidence intervals and accuracy of the coefficient estimates by computing their standard errors. For instance, \n",
    "^\n",
    "β\n",
    "1\n",
    " has a p-value < 2e-16 suggesting a statistically significant relationship between balance carried and the probability of defaulting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>1</dt>\n",
       "\t\t<dd>0.00478505710301834</dd>\n",
       "\t<dt>2</dt>\n",
       "\t\t<dd>0.582089268899191</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[1] 0.00478505710301834\n",
       "\\item[2] 0.582089268899191\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "1\n",
       ":   0.004785057103018342\n",
       ":   0.582089268899191\n",
       "\n"
      ],
      "text/plain": [
       "          1           2 \n",
       "0.004785057 0.582089269 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict(model1, data.frame(balance = c(1000, 2000)), type = \"response\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  Pregnancies = col_double(),\n",
      "  Glucose = col_double(),\n",
      "  BloodPressure = col_double(),\n",
      "  SkinThickness = col_double(),\n",
      "  Insulin = col_double(),\n",
      "  BMI = col_double(),\n",
      "  DiabetesPedigreeFunction = col_double(),\n",
      "  Age = col_double(),\n",
      "  Outcome = col_double()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "data = read_csv(\"data/diabetes_log_r.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Pregnancies</th><th scope=col>Glucose</th><th scope=col>BloodPressure</th><th scope=col>SkinThickness</th><th scope=col>Insulin</th><th scope=col>BMI</th><th scope=col>DiabetesPedigreeFunction</th><th scope=col>Age</th><th scope=col>Outcome</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>6    </td><td>148  </td><td>72   </td><td>35   </td><td>  0  </td><td>33.6 </td><td>0.627</td><td>50   </td><td>1    </td></tr>\n",
       "\t<tr><td>1    </td><td> 85  </td><td>66   </td><td>29   </td><td>  0  </td><td>26.6 </td><td>0.351</td><td>31   </td><td>0    </td></tr>\n",
       "\t<tr><td>8    </td><td>183  </td><td>64   </td><td> 0   </td><td>  0  </td><td>23.3 </td><td>0.672</td><td>32   </td><td>1    </td></tr>\n",
       "\t<tr><td>1    </td><td> 89  </td><td>66   </td><td>23   </td><td> 94  </td><td>28.1 </td><td>0.167</td><td>21   </td><td>0    </td></tr>\n",
       "\t<tr><td>0    </td><td>137  </td><td>40   </td><td>35   </td><td>168  </td><td>43.1 </td><td>2.288</td><td>33   </td><td>1    </td></tr>\n",
       "\t<tr><td>5    </td><td>116  </td><td>74   </td><td> 0   </td><td>  0  </td><td>25.6 </td><td>0.201</td><td>30   </td><td>0    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       " Pregnancies & Glucose & BloodPressure & SkinThickness & Insulin & BMI & DiabetesPedigreeFunction & Age & Outcome\\\\\n",
       "\\hline\n",
       "\t 6     & 148   & 72    & 35    &   0   & 33.6  & 0.627 & 50    & 1    \\\\\n",
       "\t 1     &  85   & 66    & 29    &   0   & 26.6  & 0.351 & 31    & 0    \\\\\n",
       "\t 8     & 183   & 64    &  0    &   0   & 23.3  & 0.672 & 32    & 1    \\\\\n",
       "\t 1     &  89   & 66    & 23    &  94   & 28.1  & 0.167 & 21    & 0    \\\\\n",
       "\t 0     & 137   & 40    & 35    & 168   & 43.1  & 2.288 & 33    & 1    \\\\\n",
       "\t 5     & 116   & 74    &  0    &   0   & 25.6  & 0.201 & 30    & 0    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Pregnancies | Glucose | BloodPressure | SkinThickness | Insulin | BMI | DiabetesPedigreeFunction | Age | Outcome |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 6     | 148   | 72    | 35    |   0   | 33.6  | 0.627 | 50    | 1     |\n",
       "| 1     |  85   | 66    | 29    |   0   | 26.6  | 0.351 | 31    | 0     |\n",
       "| 8     | 183   | 64    |  0    |   0   | 23.3  | 0.672 | 32    | 1     |\n",
       "| 1     |  89   | 66    | 23    |  94   | 28.1  | 0.167 | 21    | 0     |\n",
       "| 0     | 137   | 40    | 35    | 168   | 43.1  | 2.288 | 33    | 1     |\n",
       "| 5     | 116   | 74    |  0    |   0   | 25.6  | 0.201 | 30    | 0     |\n",
       "\n"
      ],
      "text/plain": [
       "  Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \n",
       "1 6           148     72            35              0     33.6\n",
       "2 1            85     66            29              0     26.6\n",
       "3 8           183     64             0              0     23.3\n",
       "4 1            89     66            23             94     28.1\n",
       "5 0           137     40            35            168     43.1\n",
       "6 5           116     74             0              0     25.6\n",
       "  DiabetesPedigreeFunction Age Outcome\n",
       "1 0.627                    50  1      \n",
       "2 0.351                    31  0      \n",
       "3 0.672                    32  1      \n",
       "4 0.167                    21  0      \n",
       "5 2.288                    33  1      \n",
       "6 0.201                    30  0      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Pregnancies        Glucose      BloodPressure    SkinThickness  \n",
       " Min.   : 0.000   Min.   :  0.0   Min.   :  0.00   Min.   : 0.00  \n",
       " 1st Qu.: 1.000   1st Qu.: 99.0   1st Qu.: 62.00   1st Qu.: 0.00  \n",
       " Median : 3.000   Median :117.0   Median : 72.00   Median :23.00  \n",
       " Mean   : 3.845   Mean   :120.9   Mean   : 69.11   Mean   :20.54  \n",
       " 3rd Qu.: 6.000   3rd Qu.:140.2   3rd Qu.: 80.00   3rd Qu.:32.00  \n",
       " Max.   :17.000   Max.   :199.0   Max.   :122.00   Max.   :99.00  \n",
       "    Insulin           BMI        DiabetesPedigreeFunction      Age       \n",
       " Min.   :  0.0   Min.   : 0.00   Min.   :0.0780           Min.   :21.00  \n",
       " 1st Qu.:  0.0   1st Qu.:27.30   1st Qu.:0.2437           1st Qu.:24.00  \n",
       " Median : 30.5   Median :32.00   Median :0.3725           Median :29.00  \n",
       " Mean   : 79.8   Mean   :31.99   Mean   :0.4719           Mean   :33.24  \n",
       " 3rd Qu.:127.2   3rd Qu.:36.60   3rd Qu.:0.6262           3rd Qu.:41.00  \n",
       " Max.   :846.0   Max.   :67.10   Max.   :2.4200           Max.   :81.00  \n",
       "    Outcome     \n",
       " Min.   :0.000  \n",
       " 1st Qu.:0.000  \n",
       " Median :0.000  \n",
       " Mean   :0.349  \n",
       " 3rd Qu.:1.000  \n",
       " Max.   :1.000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We are predicting whether a person has diabetes or not\n",
    "- Outcome 0 : Not Diabetic\n",
    "- Outcome 1 : Diabetic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "split <- sample.split(data$Outcome, SplitRatio = 0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GET TRAINING DATA AND TESTING DATA\n",
    "data_train <- subset(data, split == TRUE)\n",
    "data_test <- subset(data, split == FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>576</li>\n",
       "\t<li>9</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 576\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 576\n",
       "2. 9\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 576   9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(data_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>192</li>\n",
       "\t<li>9</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 192\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 192\n",
       "2. 9\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 192   9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Pregnancies'</li>\n",
       "\t<li>'Glucose'</li>\n",
       "\t<li>'BloodPressure'</li>\n",
       "\t<li>'SkinThickness'</li>\n",
       "\t<li>'Insulin'</li>\n",
       "\t<li>'BMI'</li>\n",
       "\t<li>'DiabetesPedigreeFunction'</li>\n",
       "\t<li>'Age'</li>\n",
       "\t<li>'Outcome'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Pregnancies'\n",
       "\\item 'Glucose'\n",
       "\\item 'BloodPressure'\n",
       "\\item 'SkinThickness'\n",
       "\\item 'Insulin'\n",
       "\\item 'BMI'\n",
       "\\item 'DiabetesPedigreeFunction'\n",
       "\\item 'Age'\n",
       "\\item 'Outcome'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Pregnancies'\n",
       "2. 'Glucose'\n",
       "3. 'BloodPressure'\n",
       "4. 'SkinThickness'\n",
       "5. 'Insulin'\n",
       "6. 'BMI'\n",
       "7. 'DiabetesPedigreeFunction'\n",
       "8. 'Age'\n",
       "9. 'Outcome'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"Pregnancies\"              \"Glucose\"                 \n",
       "[3] \"BloodPressure\"            \"SkinThickness\"           \n",
       "[5] \"Insulin\"                  \"BMI\"                     \n",
       "[7] \"DiabetesPedigreeFunction\" \"Age\"                     \n",
       "[9] \"Outcome\"                 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## COLUMN NAMES\n",
    "names(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "## LOGISTIC REGRESSION MODEL\n",
    "logit <- glm (Outcome ~Pregnancies +Glucose+BloodPressure+SkinThickness+Insulin+BMI+DiabetesPedigreeFunction+Age,\n",
    "                 data = data_train, family = binomial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = Outcome ~ Pregnancies + Glucose + BloodPressure + \n",
       "    SkinThickness + Insulin + BMI + DiabetesPedigreeFunction + \n",
       "    Age, family = binomial, data = data_train)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-2.5647  -0.7374  -0.4328   0.7582   2.9431  \n",
       "\n",
       "Coefficients:\n",
       "                          Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)              -8.220066   0.808624 -10.166  < 2e-16 ***\n",
       "Pregnancies               0.115334   0.035752   3.226  0.00126 ** \n",
       "Glucose                   0.035070   0.004232   8.287  < 2e-16 ***\n",
       "BloodPressure            -0.011440   0.005842  -1.958  0.05022 .  \n",
       "SkinThickness             0.003715   0.007822   0.475  0.63483    \n",
       "Insulin                  -0.001836   0.001073  -1.711  0.08702 .  \n",
       "BMI                       0.075934   0.016272   4.667 3.06e-06 ***\n",
       "DiabetesPedigreeFunction  1.068568   0.355411   3.007  0.00264 ** \n",
       "Age                       0.017272   0.010481   1.648  0.09936 .  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 745.11  on 575  degrees of freedom\n",
       "Residual deviance: 552.05  on 567  degrees of freedom\n",
       "AIC: 570.05\n",
       "\n",
       "Number of Fisher Scoring iterations: 5\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(logit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "## PREDICTING THE PROBABILITIES\n",
    "predicted_prob <- predict(logit,type = 'response', newdata=data_test[1:8])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>1</dt>\n",
       "\t\t<dd>0.903441439891495</dd>\n",
       "\t<dt>2</dt>\n",
       "\t\t<dd>0.201629708418517</dd>\n",
       "\t<dt>3</dt>\n",
       "\t\t<dd>0.883401406863321</dd>\n",
       "\t<dt>4</dt>\n",
       "\t\t<dd>0.813804018800802</dd>\n",
       "\t<dt>5</dt>\n",
       "\t\t<dd>0.524724205213701</dd>\n",
       "\t<dt>6</dt>\n",
       "\t\t<dd>0.638992565564301</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[1] 0.903441439891495\n",
       "\\item[2] 0.201629708418517\n",
       "\\item[3] 0.883401406863321\n",
       "\\item[4] 0.813804018800802\n",
       "\\item[5] 0.524724205213701\n",
       "\\item[6] 0.638992565564301\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "1\n",
       ":   0.9034414398914952\n",
       ":   0.2016297084185173\n",
       ":   0.8834014068633214\n",
       ":   0.8138040188008025\n",
       ":   0.5247242052137016\n",
       ":   0.638992565564301\n",
       "\n"
      ],
      "text/plain": [
       "        1         2         3         4         5         6 \n",
       "0.9034414 0.2016297 0.8834014 0.8138040 0.5247242 0.6389926 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(predicted_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>1</dt>\n",
       "\t\t<dd>1</dd>\n",
       "\t<dt>2</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>3</dt>\n",
       "\t\t<dd>1</dd>\n",
       "\t<dt>4</dt>\n",
       "\t\t<dd>1</dd>\n",
       "\t<dt>5</dt>\n",
       "\t\t<dd>1</dd>\n",
       "\t<dt>6</dt>\n",
       "\t\t<dd>1</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[1] 1\n",
       "\\item[2] 0\n",
       "\\item[3] 1\n",
       "\\item[4] 1\n",
       "\\item[5] 1\n",
       "\\item[6] 1\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "1\n",
       ":   12\n",
       ":   03\n",
       ":   14\n",
       ":   15\n",
       ":   16\n",
       ":   1\n",
       "\n"
      ],
      "text/plain": [
       "1 2 3 4 5 6 \n",
       "1 0 1 1 1 1 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## CONVERTING PROBABILITY TO PREDICTED OUTCOME\n",
    "predicted_outcome = ifelse(predicted_prob>0.5,1,0)\n",
    "head(predicted_outcome)"
   ]
  },
  {
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tNB0Aq/YI74aww7mLFi+V1m07WJQAVteRMhA5a47mea8VFBSEUMcKP2oQtG3XSRZGLZaNKjSHaqg4jAFb9dbrWuk9sCK8bfsOCxdHiHilKjUtMgHmQ0YGwDhNP8BqOATwDF2FrqCC3IwFNRiQXjBFV+URgYCVa5gfWDVHO06oUN21e68MHjbC0g3QV5gY7Tt21VXvhZbagF0HDh85YlEGVarXspX7ssqrr4pfy5VXI2ScGiJNm7c2MQ/zAmHue1Xsbo3bbhzBBsUYETnQX6/buGmziWHw6Ni5mxQpXsZMlu81AB4+fKSsdqqJMdrSOLDynpiYaPn8mANID0Be/RONEkC6BVbj06oBgFoFOO+mCvLrWjMBOyk80JSO+/cfmBGAGhD9+g82sb9oyVI1NS4J6jBgbDE/8HzsoIB6AC9evLSQf0QIPH78xJ6N8P7w0eNsHqDw5Nmz5zSK4LmlqaBY4ENtF2oEIG0F7eYPCZAACZCAbwnQAPAtfz6dBEiABEiABNwikJYBgG0AEdqOAznuzgGxi9B4bH2H3O+9Ko6Rz48VXqxgQ+BBuCIHHznyCClHPjdCz5HfjZVbrP5X0ZQAiHSE8CO/fdgIzQPXlfXhmoPeXPPkC6n4T35OXVsNh+jFSjpW9mEAVK5W08LNIeAhDrGCvUaLxHVQEY5UAmxVmLEBMMlyyXfoXvfIW0fNA4TaV6tZx6IDTp48bfdGDvs9zc9HtADy4tEPiN6586Ns9R2r8DBJ0If9Bw6oOH1iFfRhEiClAOYA7otV/HYdu1jo+9QZM21HBaQLIL0AUQ0tWrWzoniIdIAhgBoLMDwQ9YDCgRDYEL3xKoiRMgBTBmkI32MAYKUcY4QiiGgTti9EOgeq8GP8Ux84/4UWa/zWLgDggrQQrN6jnsLc+QtsDAcOGSZNmrWyiAUYNIgOQGRD1KKllu4ABtgyEc/HDg14jvN8RBckJCRa/5Dq0T2st+54sMBMGLQf/UfbnPPxyh8SIAESIAHfEqAB4Fv+fDoJkAAJkAAJuEUAIgrCynUXgN/VAPgpW3ZbxcdqPFbHkcdfo3Y9y8HH6vHJUxDfz2z7NqzmItcdBgBSB7BLAELVN0RvtJVkiEVU/z967IQMGjrcVvghnJEq8IcWsvtLc+f/VhGMiIMcGl3wU7YcahD8KWUrVE7JU0fxO4h/mBNY/UfEwGWtrg/RiHBwrCojtxyCGudkZAAgJQHFC9FGRCFAbKOeQMs27W2VHivmWBUHH9QJwKo3CuChL6jQP09z0SG+HQMA5sWBgweNCUyCTbprQKs2HawWws/an59/y2GGB8Q++opUA7Tzl99zaj2DAlJX6wrMQ567Cl6s+qPGQplyla34HUQ2jA60B0IYhRARPZDrr7+/2wC4lHRZV+HnSa/e/c282bFzl/UnrcmCPmN1PrUBcCEhQV4qBxTgw3aEyNVHWkhY777SQaMSwA7pEjAwYPKgLsCVq1dtlwOkcozSlX1ESiDloWevfhrBsUB3VjgkSD/AGIIzzCJEOyAiAkUTcT6iP5YsW66FB0/oTg03bTcIjAMNgLRGj++RAAmQgHcJ0ADwLm8+jQRIgARIgASyRCAtAwCh8BCoWJ1HPvbylatsez+EwCNfHuIPQs1Zjb2theuwuuwYAH9qbj/y8hFCDyHprCgfOXrcBD1qAyCa4N8//WYr3YgEQPE/O3QFH7UB8hUsqlvd1bfK9NO1Gj7yx2EAID2hbYfOWk8g2lbkk8PCkw2AwUNHuGcAaATDug3RlpKQX58HAwDpCWt1i0GEpDv9g9iE+IdATdMAqFjNRCq20IMp8kwNAIhYrOrDEMFWgTAKYKwgQgFREniFgLfCfholgJ0TojSNAqvmZgBoXYGyFarKkGEjLa0ApoIZABp2v1xX3JGmYPfS+2P7Q4zLvXv3U7bec50U6Ae2cYQg7z9wsEU4wDBBn9L6ScsAQBQGog9QkBH9RAoDIgnCx4zT9I/FmiYRbdsVTpk6Q9p36irYnQFmxtVr18w8gUmEwoiYEyjqh90NkMqA1I3IWXN0lX+/RU4gxQD1B/aoYbBECzMiYqRD5+5mLsDgWbY8uZCkM//Saj/fIwESIAES8B4BGgDeY80nkQAJkAAJkECWCaRlACDHv6AWtevWo7cJtiRdoUUI+sOHD61oHrZqgxh1flC5Pi0DAHvY41wTlPp64sRJE/SldWUbK+JYAS9fqaqtlPcI62Ph6QhRx+owCtWhUB/C37Hi3bFLd0sBQHoBtg2cv2DR5/SCZ5aLjr3iITxRo+B7UgAQAYAtDmFqYMtCFP6DEVGnfiOtCTDb8tYhuhEdgX5DyN69e8/ELIQ2IgBQ9R/CHsX9sGKN3Q0QOfD06TPZvCVOtzjsYvdFkUAU9IOhgQJ56KNz9OozQAarsMUzcT12I4DwBxukDSAMHhX3Hzx4oM9+LfFaNM9JAUBqwfemAKD9izUnH88aPWa85fcjqgFjo///+fWTmR4fPnydAgAD4NSp0xa2DxEOowT1GpYsXS6nT5/RNIybtquCs3rfRLdOREQDnvv27TuL1EB9gfvaDxQZRL2EoWpu4B7dw3ppCsFCqyuAiI4XOldQVwCpA7v27E3eelE5wPiBCRCtURsoCug6B525yFcSIAESIAHvEqAB4F3efBoJkAAJkAAJZIlAegYAcuL79BukIvmwif4Pn3Ov03rY9xgAEK/nzydYXYBadRtaiDtWwJs2byVjx0207eeQJ44DhQJRLA+h71h13rlrj/RTgY3zUQegdFkV3AOG2DkHDx1Wsb1VsPqPLQch/lNvA5jWLgDYku6IVrCHCTBMw/cRlQDRXVRFN8yGKG3Dfg1PRy0AhMsjVeCoRjCgOB4MABQ9RDg/0hUK6VZ9iFCYvzBK0xyOa7X9K1Zwr9+AwVYLAcYCaiJ0UbGLsHenn3jF1ogxG2OtLVevXlexHyfggxoIMBiw5SBy5q1Svq6gQ3zDsCiiOwign99jAGCMUccgdssW3V5vjJkrSNlAEb/kvPpPGjnwwUQ6TA9EMSACwrUIIAyAY5rCgQNbByLUH6v3KJIIgwRmAtITUDCxS7eeUr9RU91ecIEkXb5s6QvghvNgCMEIQG0I1AFAUUDUOcArigWivgJMAtRSeKrtAGuMAeYEdiToqNEAMBYQ0YBoDP6QAAmQAAn4lgANAN/y59NJgARIgARIwC0C3jIAsAp8Q4v1QUhj1RfiFiIXtQWQPw6BP0D3fccxUFd5p0ybYcI/KemyrRgjtLyCFtvLjvQEXeWvUq22dOjUTfoPGqJb7/XRmgP13d4GEFvuobAfVt2xOo8t93AgBB8h+TBAYCwg1H2Ivq6PjrEQeKxOr9C0iDJaowCmAWoeIAoAtQSwdV2s5v9DPKMIIgQ80hkQUVGvQRMr/uf0E6/Ii4fZgG3wEGVx6NARMwqwJSFSKbAbQj0Np0ehQrQFOfHIscfqP6IPvtcAwKo6iiZOnT7TDASspCNC4/bt2ya0H2jkAgQ8djC4pCvvt27d0W0Ad5noRrg+DACYG0eOHJWIKdOsZgHusXffAT33tsAEgjmAcautBgZ2jZilKQfnL1yQa3pfiHiYSZd0PFH1/6ZeA/Nggob4I5oABgDqUJzSaAIYNmfi482geKCmwQ2NLoDJM1B5de4aZtECaCNMC/6QAAmQAAn4lgANAN/y59NJgARIgARIwC0C3jIAEFaOQoAXLiTKYg0bh8DGCjpW7BHWj9x+50B1e+w0MHvOfE0bOGWCFGIVK+plylc28YtIABTK+zNvfsv7xwo7VuJRJ+B7IgCQW56o+8pjBRxRBqPCxwqq8qMQoUUR4P4auv+7FilEbYCGmnaAHH0rQqcr2AjX76Q7ExTWlXg8E2IcRgAMjYlqAiAnHzsMhGu4fa26DazQn7VZDQOnn3gtpJEWMECwfR6EdFLSZdtyEOkDRUqUsXNRKBDtyK0REIjMwM4CMAcQffC9BgDqAMBggDkxcPBQFd0d1WwZYvUdNsVusZoKK1ausRV8RD5cUWME2wZi1d0xAFD4EdsmYscH1DeAGTFVBf+q1WutICDqF/Tq21+w9WHVGrUt1x87P5w8dcbSOMaMnWARENEa8bBm7QZLe0DqRM/e/Syi4ooWddyhpgNqB0TOmmPpBTCMUJMBURAwekbqOCElArUImALg1n/qPJkESIAEfggBGgA/BCtvSgIkQAIkQAI/hgAMABS3S0hINOGF/PpCRUtZ5X9UX0fhPoRsfysF4K5WxUeufhvdug1CHAK1eau2JoBdt5p7r3UDUBQQK7oQdgj9Ru49VrMh+p0DkQEQ4wiXR345Vtyx7zzEa68+/a1tKK6XCyvkRUvY1n09evUxQVpURTN2AmjRup3tTIBV5ePHT1rYewltW5HiZaRRkxa2Op+k4ekQxs+fv1CRelqwu0FjTUmA8EbYPtqTQ0U9eGB1f+WqtSY8UTwPW+CtXL1Gi9N1MCMjR+58Zh5g5TtiynRbLcf2hKh7gC0Oq9aoYyaA00fnFaxgJERv3GTh7zBJUGRxwcJFJtKxCwN2CUD6A8L+GzdtYVsiIk2goBoeiBTopREK+/YfNIGPsHiMaVo/MDtgMoAjigvW18iChk2a2z1hcMB0mKrh/YcOH9V+3hVU7keYf381XlCHISExUbAij0gApG3ABMB8QfFHjHenrt2tyGO/AYNstwiE7SOsH5EWGEts8Yh243wUmEREBAoBoijgqdOnrf179+63e2Mu1ddzcG4j7TNSDhA5sk6LP165ckVQ/DG9fqbVd75HAiRAAiTwYwjQAPgxXHlXEiABEiABEvhhBLCSitVh5MRjH/oZkXM0n32RhoDvtBVvFGZzKuKn1QgIaIg8rNTO1lXgSN1ubtXqdbaaDXMBQg2SFLoUv+Net7QqPHLfZ0TOlkkRU784EGKOHHW0B6HlENwQrzAOsPKOkHkI1QhdfYZ4XKp58Tt27ZLNW+Ps+RDyqCGAYnPIIYcQR1G+5LbNtSr6CEfHdnbWNm0TctPPnj1nK+A4DyIe7ZqsbZk5a469Hx9/Ts2C59Z+5Lxj+zrksaMPkyOmWXh9cu2Ag9q/29ZuCHqI26XLV5gRkLqvaD/MEITK41ywgUlyXk0A8EFxvAhNf0BoPdoFARy3bbusUMMlctZcDbOfb4IeqQzow7fGCX199+69mQC79+y1QnxIU8C9sZKPwoqot4AohxfaFqzIx8Vtt0KQCM2HEYNUDpgAhw4fMVMA7cc98Iqq/Vv1/F179lhEA/hjXiGvH7UasFsAnuUcGCds54jUhCdPnlodAfQD8y5q8RKrNYBzUXMgcraOgaZgJCVdtvOccRP+kAAJkAAJ+JQADQCf4ufDSYAESIAESMB9AhBTENhY6UdO+LXr101sQ+hB/MMg+JbgwufYpx5CD3nkOCDcsfqfWpA694GoR2E6PAtC84tD945HjjhEIc7DPXCgLSgkh2gAVJe/qufheohtVN9H8TgUtsM2cng+qvG/efNWr3tjQhSfoW3Ykg5F7lLMCe0/+gAjA9EMOA/711ubPj8D70P8OyvsOB8mAAwGRAPgXIhXmA3IcXdMk2Q2z0x0I6z+i37qNWg/7g3B7Xpv/I17Q4yjLbg32oVzIcQRAn9dr0V/ILLRFjzL4ZvWLHA+A1OYH9ft3tom9FWPG3p/8HXGHPfE7gO4PwoDYo5gHLBV32Mt0geO6LszDhgXjMEjvTd2TAB/XIPn4XwYOM6zrE86hugH7o2+O0ydvH/j9bltxknv+dKln2n1ke+RAAmQAAl4lwANAO/y5tNIgARIgARIIMsEHGEIcQcR5hz42/nsWw/BOamv/R4xmtY1zrPx6vp8px3pXYP3U3/mev23PkPfMro/UiBc7/et81Ofl/rZrn203104u943zesy6Oe3xul7+plW2/FeWu9/1Y/PY5bc7i+vSX7vn7nleu333ht1JBw+GfWTn5MACZAACXiHAA0A73DmU0iABEiABJyGD2QAACAASURBVEiABEiABEiABEiABEjApwRoAPgUPx9OAiRAAiRAAiRAAiRAAiRAAiRAAt4hQAPAO5z5FBIgARIgARIgARIgARIgARIgARLwKQEaAD7Fz4eTAAmQAAmQAAmQAAmQAAmQAAmQgHcI0ADwDmc+hQRIgARIgARIgARIgARIgARIgAR8SoAGgE/x8+EkQAIkQAIkQAIkQAIkQAIkQAIk4B0CNAC8w5lPIQESIAESIAESIAESIAESIAESIAGfEqAB4FP8fDgJkAAJkAAJkAAJkAAJkAAJkAAJeIcADQDvcOZTSIAESIAESIAESIAESIAESIAESMCnBGgA+BQ/H04CJEACJEACJEACJEACJEACJEAC3iFAA8A7nPkUEiABEiABEiABEiABEiABEiABEvApARoAPsXPh5MACZAACZAACZAACZAACZAACZCAdwjQAPAOZz6FBEiABEiABEiABEiABEiABEiABHxKgAaAT/Hz4SRAAiRAAiRAAiRAAiRAAiRAAiTgHQI0ALzDmU8hARIgARIgARIgARIgARIgARIgAZ8SoAHgU/x8OAmQAAmQAAmQAAmQAAmQAAmQAAl4hwANAO9w5lNIgARIgARIgARIgARIgARIgARIwKcEaAD4FD8fTgIkQAIkQAIkQAIkQAIkQAIkQALeIUADwDuc+RQSIAESIAESIAESIAESIAESIAES8CkBGgA+xc+HkwAJkAAJkAAJkAAJkAAJkAAJkIB3CNAA8A5nPoUESIAESIAESIAESIAESIAESIAEfEqABoBP8fPhJEACJEACJEACJEACJEACJEACJOAdAjQAvMOZTyEBEiABEiABEiABEiABEiABEiABnxKgAeBT/Hw4CZAACZAACZAACZAACZAACZAACXiHAA0A73DmU0iABEiABEiABEiABEiABEiABEjApwRoAPgUPx9OAiRAAiRAAiRAAiRAAiRAAiRAAt4hQAPAO5z5FBIgARIgARIgARIgARIgARIgARLwKQEaAD7Fz4eTAAmQAAmQAAmQAAmQAAmQAAmQgHcI0ADwDmc+hQRIgARIgARIgARIgARIgARIgAR8SoAGgE/x8+EkQAIkQAIkQAIkQAIkQAIkQAIk4B0CNAC8w5lPIQESIAESIAESIAESIAESIAESIAGfEqAB4FP8fDgJkAAJkAAJkAAJkAAJkAAJkAAJeIcADQDvcOZTSIAESIAESIAESIAESIAESIAESMCnBGgA+BQ/H04CJEACJEACJEACJEACJEACJEAC3iFAA8A7nPkUEiABEiABEiABEiABEiABEiABEvApARoAPsXPh5MACZAACZAACZAACZAACZAACZCAdwjQAPAOZz6FBEiABEiABEiABEiABEiABEiABHxKgAaAT/Hz4SRAAiRAAiRAAiRAAiRAAiRAAiTgHQI0ALzDmU8hARIgARIgARIgARIgARIgARIgAZ8SoAHgU/x8OAmQAAmQAAmQAAmQAAmQAAmQAAl4hwANAO9w5lNIgARIgAQyIPDp0yf58OGDvHnzVl6+fCmvXr2Sd+/eCd7nDwmQAAmQAAmQAAmQQNYJ0ADIOkPegQRIgARIwAMEPn78KI8ePZL4+LOyY+cu2b17r1y/fkPev39PE8ADfHkLEiABEiABEiABEqABwDlAAiRAAiTgFwQg9JOSLsvylatl0JDhMnxEuBw4cEhev35NA8AvRoiNIAESIAESIAESCHQCNAACfQTZfhIgARIIEgIwALD6HzF1ujRs0lxatGwrsbFbLB2AaQBBMsjsBgmQAAmQAAmQgE8J0ADwKX4+nARIgARIwCHw7t17OXPmrEycPEXqNmgiTZq1ko2bYmkAOID4SgIkQAIkQAIkQAJZJEADIIsAeTkJkAAJkID7BLCij5z/t2+TC/49f/5c8/8fy6FDR2T02AlSq25Dadi4maxes1bu3r0rz549k+fPX1g6AGsCuM+bV5AACfgHAfzbh+P9+w/279mLFy/037fn/xz6b+FLLYD69u07+zeS0U/+MW5sBQkEEwEaAME0muwLCZAACQQIAYj/p0+fyunTZ2TlqjUSOXuuhf4PGDhU6jdsKoWLlZJiJctKpy7dZcLECJk6PVLmzl8oW+O2yZUr1+zLc4B0lc0kARIggRQCEPQwPm/cvGnFTucviJIp02bqMcOO6TNnyfoNMXJG06EeP35sRVBTLuYvJEACJOABAjQAPACRtyABEiABEnCPALb7u3Xrtqxdt0E6dOoqlavVkhJlKkjBIiUkZ54C8stvOeXXP3LJXwWKSFE1AkqVrSQ16zSQkeFj5OChw/YF2r0n8mwSIAES8D0BGADY5vTEiZMyfuJkqV2vkZQoXUGKlypvR5nyVaRX7/4SHbNRrt+4IW/ULOAPCZAACXiSAA0AT9LkvUiABEiABL6LAAyAmzdvyarV66RVmw5SpnxlKVi0pOTNX1j+yJlXfsqWQ35WE+DPvAWkQKHiGhFQWipXrSlDho2U/QcO0gD4Lso8iQRIwN8IOAbAsWMnZPSYCVK1Rh01PkvK34VL2FGsRFnp2r2XmaPXdBvUN2/e+FsX2B4SIIEAJ0ADIMAHkM0nARIggUAkgBSAhw8f2TZ/02ZEypChI6R33wHSpl1HqVSlpq385y9UTBo0aiZhPftKv/6DZMSo0bJ8xSo5fyGBYbGBOOhsMwmQgOX/Q9RfSkqSNWvXy6jwsdKn30Dpo//+4eg/cIjMnbfA6qHcv/9A3r17R2okQAIk4FECNAA8ipM3IwESIAES+B4CTh4sTICkpMty/vwFOaX1ANavj5Z+AwZrSkBNqVG7nsyMnCNHjx2T+LPn5IIKf+TNomAgDAT+kAAJkECgEcC/fYiAQvG/W7duSUJCov77djblOHvunFy9ek2Loj6ySCf+WxdoI8z2koD/E6AB4P9jxBaSAAmQQNARwJdg1wNfclEY6/Tp+M/bADbWbQBbSszGWKv+j89xuF4TdFDYIRIggaAn4PpvGH53/m1zfU19TtBDYQdJgAS8SoAGgFdx82EkQAIkQALpEXj37r2cOXP2swHQRA2AVrJxU6wVzMIXYv6QAAmQAAmQAAmQAAlkjQANgKzx49UkQAIkQAIeIvD+/XsL8589d760150BunbvKdt37JRXuic2DQAPQeZtSIAESIAESIAEQpoADYCQHn52ngRIgAT8hwBCYO/duy+HDh+VNevWy/roGLl46ZIVwaIB4D/jxJaQAAmQAAmQAAkELgEaAIE7dmw5CZAACQQVAYh81AF48uSp3L17T82Aexb+7+T+B1Vn2RkSIAESIAESIAES8AEBGgA+gM5HkgAJkAAJfE0ABgCOtIphfX023yEBEiABEiABEiABEnCXAA0Ad4nxfBIgARIgARIgARIgARIgARIgARIIQAI0AAJw0NhkEiABEiABEiABEiABEiABEiABEnCXAA0Ad4nxfBIgARIgARIgARIgARIgARIgARIIQAI0AAJw0NhkEiABEiABEiABEiABEiABEiABEnCXAA0Ad4nxfBIgARIgARIgARIgARIgARIgARIIQAI0AAJw0NhkEiABEiABEiABEiABEiABEiABEnCXAA0Ad4nxfBIgARIgARIgARIgARIgARIgARIIQAI0AAJw0NhkEiABEiABEiABEiABEiABEiABEnCXAA0Ad4nxfBIgARIgARIgARIgARIgARIgARIIQAI0AAJw0NhkEiABEiABEiABEiABEiABEiABEnCXAA0Ad4nxfBIgARIgARIgARIgARIgARIgARIIQAI0AAJw0NhkEiABEiABEiABEiABEiABEiABEnCXAA0Ad4nxfBIgARIgARIgARIgARIgARIgARIIQAI0AAJw0NhkEiABEiABEiABEiABEiABEiABEnCXAA0Ad4nxfBIIKQKfhP9HAiRAAiFD4NMnER5kwDnAORCKcyCkvt+GdmdpAIT2+LP3JPBNAvjS/1E+yHv+HwmQAAkEO4FP7+XDx3fy8d0b+fjmtXx8zYMMOAc4B4J7Dnx6+1Y+ffigvqean/wJGQI0AEJmqNlREnCfwIdPH+Tlp+fy5NMjefTpAQ8y4BzgHAjqOfD09V15deeavL2SJO+SLvEgA84BzoHgnQOXk+T9rVvy8cUL+fTxo/tfEnlFwBKgARCwQ8eGk8CPJ/Du0zv7sn/z4zW5/vEyDzLgHOAcCOo5cPtZgjy+cExeHjkgr/fv40EGnAOcA8E7Bw7slzfx8fLh4QP59P79j/9SySf4DQEaAH4zFGwICfgfgXef3srDT/fsC//VjxeFBxlwDnAOBO0c+HBRbj45K4/iD8nz/bvl1a6dPMiAc4BzIHjnwO5d8ubUSXl//z4NAP/7Cv5DW0QD4Ifi5c1JILAJ0ACg2AtasUdDi4Ze6jnwIVFumAFwUJ7vowFAA4QGEOdAkM8BGAAnT9AACOyv6plqPQ2ATGHjRSQQGgRoANAAoAHAORAyc4AGQPCudHIVm2PLOfD1HKABEBpf5tPoJQ2ANKDwLRIggWQCNAAo/kJG/KVeDebfoRchQAPga4FA0UQmnAPBOwdoAITs130aACE79Ow4CWRMgAYADQAaAJwDITMHaAAEr9ChiOXYcg58PQdoAGT8RThIz6ABEKQDy26RgCcI0ACg+AsZ8ccV/9Bb8U895jQAvhYIFE1kwjkQvHOABoAnvioH5D1oAATksLHRJOAdAjQAaADQAOAcCJk5QAMgeIUORSzHlnPg6zlAA8A7X6b98Ck0APxwUNgkEvAXAjQAKP5CRvylXg3m36EXEUAD4GuBQNFEJpwDwTsHaAD4y9dtr7eDBoDXkfOBJBA4BGgA0ACgAcA5EDJzgAZA8AodiliOLefA13OABkDgfCH3cEtpAHgYKG9HAsFEgAYAxV/IiD+u+Ifein/qMacB8LVAoGgiE86B4J0DNACC6Su7W32hAeAWLp5MAqFFgAYADQAaAJwDITMHaAAEr9ChiOXYcg58PQdoAITWl3qX3tIAcIHBX0mABL4kQAOA4i9kxF/q1WD+HXoRATQAvhYIFE1kwjkQvHOABsCXX3pD6C8aACE02OwqCbhLgAYADQAaAJwDITMHaAAEr9ChiOXYcg58PQdoALj7tThozqcBEDRDyY6QgOcJ0ACg+AsZ8ccV/9Bb8U895jQAvhYIFE1kwjkQvHOABoDnvzgHyB1pAATIQLGZJOALAjQAaADQAOAcCJk5QAMgeIUORSzHlnPg6zlAA8AXX6394pk0APxiGNgIEvBPAjQAKP5CRvylXg3m36EXEUAD4GuBQNFEJpwDwTsHaAD455dvL7SKBoAXIPMRJBCoBGgA0ACgAcA5EDJzgAZA8AodiliOLefA13OABkCgfj3PcrtpAGQZIW9AAsFLgAYAxV/IiD+u+Ifein/qMacB8LVAoGj6LiYvd+6QF3o8275NHm/dKg82b5b7m2PTPR5t2SJP9dznO7YnXxOn12zZrNclX/Noq36+LU5e6Oe496vP42DP0fee6GcP7fzNkt65zjV83ZnCjyxSsaABELxf4DPoGQ2ADADxYxIIZQI0AGgA0ADgHAiZOUADgEIpk4YHhDqE/7X16+Xk4kWya+ZM2TF9RtrHjBlyeP58ubJ2rdyL3SQ3YzZI/LKlsnfWLNmhn+2cMVOOLFggl1avMkMAJoEjXPE7jIKElSvl4Ny5sjsy0u6VuGqVmQ343NUwcK7jayrhm8lxDjqONABC9is+DYCQHXp2nAQyJkADgOIvZMRf6tVg/h16EQE0AFKEZtAJnR8s+J7pivy1deskbto0Ce/aTRpUrS61KlZO86hbqar0btlaNk6aJGdV+O+bPVum9OkrrWrXlTqVqki9ytWkT6s2snTkKDUGltkKvzMeTzRSIHHVSokaNly6NmkqTarXlF4tWsmyUeFyfsUKO5cGAMW+M18yfKUBkPEX4SA9gwZAkA4su0UCniBAA4AGAA0AzoGQmQM0AGgAZNIoeBIXJ4m6Kr9sZLi0rFVPfv0th/z7f9nk3z9lk//89Jv89+d/jp9//UOqlqkg84cOs9X7mImTpEfTFpI3d77P5/0uhQsUli6Nm0rMpMlyMzraVvUh7BHuf2rxYgnv0k1KFS0u2bPnkkqlysmEsJ5yPCrKIgZoANAAyFD4O/OcBoAnvioH5D1oAATksLHRJOAdAjQAKP5CRvxxxT/0VvxTjzkNABoAjjBy89UxAJaOCJcWNevKz79mNwPgDxXoBfMXkjLFSkrFUmVNrEP8d23STNaPn2BifqOK/O5Nm0vuXH/ZNf/331/l12w59NyyEtG7j5xYFGXpBa4GwKguXaVk4eLy+x9/SsWSZWV8j55ybCENgO8Wvm6Ob9DelwaAd75M++FTaAD44aCwSSTgLwRoANAAoAHAORAyc4AGAA2ATApDVwOg+WcD4Kdf/pDyJUrb6v6U3n1l8YiRslRD9ZeFh8vmiClyTsP7r2rawOYpU6TbZwMA0QKIGvjvz79Lvrz5pX39hnYdcvyfbtsmDz9HAIxUA6AEDIDfaQAErTjP5Fx0iwcNAH/5uu31dtAA8DpyPpAEAocADQCKv5ARf6lXg/l36EUE0ACgAZBJ0ZWWAfBLtuzSUGsBzOg3QPbPmSuX166RaxvWy3U9bm+MsXB+7BSwdeq0lAiA/6lp8JuKegj7nDny6Op+Genfpp1s1+KAt2KiLcQfKQA0ABjm75bQT29e0wAInC/kHm4pDQAPA+XtSCCYCNAAoAFAA4BzIGTmAA0AGgDpCaUM3k/LAEAYf7Oata1gH1b7sa0fwvhdD+T0o3AgUgDyaApANq0dgJSBogWLagRAAfk7399Sp2IVmda3vxzTHP87GzfaLgM0AGgA0AAIpm/b3u8LDQDvM+cTSSBgCNAAoPgLGfHHFf/QW/FPPeY0AGgAZCD00xNd6RkATWvU0mJ/Q+XUksW2fd8TNQFgBDzbvk1efC7ql2IA5P5L/tCcfqQN1K9SXaqXrWhGQLFCxaRDg8ayeuxYTRlYazUBaADQAEhvLrr1PiMAAub7uKcbSgPA00R5PxIIIgI0AGgA0ADgHAiZOUADgAaABw0A1ACoUqa8DGzXQaKGj5BNkyNky9SpslVX/I8uXCh3Nm2U+5s3/xMBoAZA9j9ySZXS5W1rvwEa+t9AjYCC+QpK6SIlZEj7DrJn1izZO3uWjOjUmTUAMjlWbgnkYH8GDYAg+sbuXldoALjHi2eTQEgRoAFA8Rcy4i/1ajD/Dr2IABoANAAyKfjSigDANoA5cuSWIn8XkQpaqR8r+jXKVZTaFarI8I6d5bRGBaAWgGsEAPL/K5QoI6N0m78Fuk1g/9btpFyx0pIvT36pVaGyTNBq/yvCR8vAtu2khEYGsAggIwGyZGjQAAip7/SunaUB4EqDv5MACXxBgAYADQAaAJwDITMHaADQAPCgAYDt/P6rVf1/0S0Bf/s9p23Zh237cubMI23rNZRD8+dZYb8vDYCcUq54KdvWL1Z3CogaNlw6avh/ofyFJX+eAtK4Wk0JV3OgY8MmUrhAYSsYyG0AaQJk2gSgAfDFd95Q+oMGQCiNNvtKAm4SoAFA8Rcy4o8r/qG34p96zGkA0ADwoAGACIBcf+aVkkWKSzVd/a9XqZrUq1xddwZQEd+1m5xZusSK+rkaANnUKChbrJRMDOsl++bMln2zZ8t0LQBYq3xlu1eRAkWkafVaUl0jCXLnyqdFA3PqTgFlzTA4tjDKdglAkcFMC8JM9p/PC1ATggaAm9+Kg+d0GgDBM5bsCQl4nAANABoANAA4B0JmDtAAoHDMpABOKwUANQAQzh/WvKVW8e8nS0eGa/j+GFk5eqzsmhkpN6M3aA2A2C9SAFIMgJ69tE7AArm4epVuEzhV+rRsI6W0DsCfGj1QWHcJ+EtTArDLwK+6awANgAAV35mcax41W2gAePx7c6DckAZAoIwU20kCPiBAA4DiL2TEX+rVYP4dehEBNABoAGRSlKVlAPyiAh0h+3MGDZZjWvTvtm7hB8GPwn/Y/u/5ju32mmYEgBoAJxYtknuxmyRh5UpZrnn/SAXImztZ+CO14F+aYgATgAYADYBMmwI0AHzwzdo/HkkDwD/Gga0gAb8kQAOABgANAM6BkJkDNABoAHjQAIA4b1aztizSPP5zy5fb9n+phRqMgPQMgJOLF8uTuK3ycMtmSxeY0W+AVCpVTrJnzy3/VgMANQZoAFD8p55Tbv1NA8Avv3t7o1E0ALxBmc8ggQAlQAOA4i9kxB9X/ENvxT/1mNMAoAHgZwbA021x8kJz+u9qJAC2ABzQtr2lAvySLTsNgEyOlVsCOdifQQMgQL+dZ73ZNACyzpB3IIGgJUADgAYADQDOgZCZAzQAaABkUvCllQLgiQgAGAAo6of7X16zRusHjJGWtepqDYAC8t+ff2cEQCbHiybA58gJGgBB+/09o47RAMiIED8ngRAmQAOA4i9kxF/q1WD+HXoRATQAaABkUlBCqF9eu0aiJ0yygn2li5a0av69W7aWdePHy8VVq+TZ9m1f8X2sIf6o9D+ycxepXraCbgFYWprXrCPzBg/VtIFldg0MgBdaLwDnntS6AFP79JUWagKUL1Fayur5rWrXszoDZ5YutXQB7gLAtIDvNjhoAITsN3waACE79Ow4CWRMgAYADYDMGABXVEhdfp8gl95ekMRX5+TCi3g9znzXkfAyXi6+PidJ7y7okWC/Z3S96zV4dmbazGs416/SAPhKoH63kMikcA6W+z9XcX9300ZB3v7aceMloncfmaJCHb+fiIrSAoAxJuJT9xemQKIW+YudHCGR/QequO8nUVozYN+cOXJDdwlAoUBcYyaAGgG3YqLNMFg2apRtDzhFz180fKTsVRPh+ob1VmeABgANgNTzLN2/aQBk/EU4SM+gARCkA8tukYAnCNAAoCjKjDCG+D/3/LQcurFfNp3YKMt3LJMlcYszPrYtkdV7V0ncmc1y5PZBOfXwuMTFb/nm9Uv1mjX7Vtt5x+8elkQ1D2AC0Ajg3HV77tIAoAGQSSMDohtiHlX+r61fLwm64p+oB36/HxurwnybifjUQgzXoRAgxP7F1avtmstr18odNROc8H/Xa/AMGA1X162zLQLxDOf8J5/rBdAAoAHgOme++TsNAE98VQ7Ie9AACMhhY6NJwDsEaABQRLktojR0Hqv3R24dlFV7VsrQKcOlWedWUr91kwyPhm2bSYe+nWXMnHGy7uBa2X5+m4ydN96ub9Am7etxTfs+nWXkzHAzD47qcy+9OU8DgCkM7keC0ACgAZAFAwDCO6MjtRjL6PzUYt7d81M/j3/THPhiDtAA8M6XaT98Cg0APxwUNokE/IUADQAaAJkxAC69PS97Lu2SGatmSuP2zSVnvvzy0+85ko/f9FUrWP/v84Hff8J7+vmvOf6UImVKS7venWXuhnlmArTX33Pk/Svlmp9+0/Ode+nrz3/klDwFC0qV+rWk39iBgoiAE/eOWApCZtrOa0J4ztMAoAGQSQPgC1HFe3AeBcocoAHgL1+3vd4OGgBeR84HkkDgEKABEMJiKAsryMj9P3BtryyMXShdBnWXstWrSJGypaVgyRKS+++C8ssff9o+1v/5+TfJljOX5C1USAqVKinFypeVmk3qS98xA2SJCvkNh9dJ67D2Zgxg32uI/xx/5ZP8xYqZUYBrYC78mv1PyZ4nr5StVlnChveWzSc3We0ApgJw/rpl6NAAoHALFOHGdnKuemIO0AAInC/kHm4pDQAPA+XtSCCYCNAAoIByS0B9Ng1QA+Ds01Oy78oeXcVfI/Oi50rk6kgL7ceKftFyZUy0/54rj5SrUUW6D+0pExZMksg1s2TRliiJObpB9l3eLVu1FoBjACBKoIAK/0btmsuQiGEWXTB+/kRp2qmVFCheTI2E3GYuNGjTVBZvWSSntX4AUhFYC4Bz+LvnMA0AiipPiCreg/MoUOYADYBg+sruVl9oALiFiyeTQGgRoAFA8fTd4sklYgCiGybAxTfn5LxW/z/75KQK8mNWqG/cvAlSpV5N+SN3XsmVv4A0aNtUZqo5sOviDi36d8yMgwu6E8D552dkZ8L2FAMAq/ylq1SUvqP7W9G/E/eOyu6LOy33v1LdmhYZAEOhct0aMm35NDl4bZ8+n7UAMjN+IXsNDQAKt0ARbmwn56on5gANgND6Uu/SWxoALjD4KwmQwJcEaADQAMisGHTC751X1AXAqv6UpVOkWsM6kj33X7Zi36RjS5m/cb4VDXQEO65BNf9diTtSDIBsOXJZiP+giUNk0/EYSXh1Vo5p1f+IxRFSS9MG/lQz4TeNAqhQs6pMWjRZ9uqzYEDgXpntA68LsflPA4CiyhOiivfgPAqUOUAD4MsvvSH0Fw2AEBpsdpUE3CVAAyDEBJDLKr6nxS/C8fdrSsCUpVOleioDYMGmBbrt3yFB7QBHsF9MZQD8mj2XlKpcUfqE95PVurvAsTuH1SDYLiNnhNuqf06tDZA9z19StX5tmbZiOiMAfuBYenpu+M39aABQuAWKcGM7OVc9MQdoALj7tThozqcBEDRDyY6QgOcJ0ACgAeApcZZVA+BnrfiPIoLNu7SW0bPGyEI1DaavnCFtwjpI4dKlJHvefPJ38eK6ZWBrWRy3WE7eP8YaADQB3Iv+oAFAUeUJUcV7cB4FyhygAeD5L84BckcaAAEyUGwmCfiCAA0AGgD+YgD8W3cMQB2AvwoXkZKVKkjF2tVtd4F8RYua+MfOAPVaNZZRM0fL9vNxkvgqOfzfiSjwVD94nyD+b4IGAIVboAg3tpNz1RNzgAaAL75a+8UzaQD4xTCwESTgnwRoAASx2PHy6nBWIwD+77+/ih3/+1X+9b9s8q+f9NDX//z8u+0O0Fh3Bxg7d7xsOLROTj86LlfeJ6akE1C0cx5/1xygAUBR5QlRxXtwt3EpLQAAIABJREFUHgXKHKAB4J9fvr3QKhoAXoDMR5BAoBKgAUDh9F3C6TvMhKwaAIgA+CV7TslTsKAU020Ey1WvIuV1C8FKWvW/U/+uMnPVTNlxYZuc0R0H8Cyu/HPuuj13aQBQuAWKcGM7OVc9MQdoAATq1/Mst5sGQJYR8gYkELwEaABQRLktotIxA7JqADg1AJp1biXhkeGCwoGLtkRZvn/0kQ1y4OoeOffsFMV/Ovw9NY5BfR8aABRVnhBVvAfnUaDMARoAwfsFPoOe0QDIABA/JoFQJkADgAaApwRfVg2A5F0AKkhv3QVg1Z4VcvzeEQv1R7j/2aenNOf/rFx+n8CVfxoA7hX+c+VFA4DCLVCEG9vJueqJOUADIGS/4tMACNmhZ8dJIGMCNABoAPiLAZAtRy4pW62yDJ44RGJPxMilN+czL/RcRR9/J0dnDtAAoKjyhKjiPTiPAmUO0ADI+ItwkJ5BAyBIB5bdIgFPEKABQAOABgDngKfmgN/fhwYAhVugCDe2k3PVE3OABoAnvioH5D1oAATksLHRJOAdAjQAKP48JdqcFICpy6ZK9UZ1JXuev6ygX9NOLS2f/+jtQ3Lp7T/F+y6+Pie7EndIm7AO8quu/mfLqREA1TUCYNJQjQDYyAgAZ9War56LYKAB4Dei6uXOHZLW8coToof38Ng4pzVGeI/jtDMwGNAA8M6XaT98Cg0APxwUNokE/IUADQAaAJ40AA5e2yeRayKlkW7ZV7BkCSlevqy079NJlmxbIsfuHv7SANAQ/72XdknXQT0kX5Eikq9oUanZpJ6MmhkuW89spgFA4e854e+wpAHgN6LlhYrIp9vi5MGWzXJ/c6w82rpFXuzY7jfto8BNFrgQ+08wTps3y93YTfJQx+s5xylw5ikNAH/5uu31dtAA8DpyPpAEAocADQAaAJ4yAFCg78zjk7Lt7FaZGz1Pxs+fKJOiJsvirYtkjwr9c89Pf1HE7/K7BDn98Lgs27FUxs4db0fkmlmCiv/H7hy2cz3VNt6H89zmAA2ADIWLs+L7YscOeb59m4l0CPW0jmf6OcSgc407ohmC/8zSpRIzcZKsHD1adkfOkrubNmbYPnee4e65Tj/Qp6fbkvuOPsKscD5zvafzHowLY4FzM8nD9b7+9DvG6fSSJbJ+/ARZPGKkbJk6TW7FxPh0nPyJj9+3hQZA4Hwh93BLaQB4GChvRwLBRIAGAIWRp8TxFRVXCPE///yMnLh/VI7cOiQI+z+lIv/Cy/ivtu9LPv+8Vfo/cuugHLl5UI7fPSLxT0/KxTfnWO3fWbXmq+ciAWgAZCjcIGqx4guRd0aF3/YZM2Srir7Ux/bpM+TgvHmSuGqVreC7uyqM1WTcY0y37tKvdVuZP2SoXFm3NsP2/UjBhb5jhTtx5UrZN3uO7NC+n1q8WG5tjJGnn40A1+cbq7itclXbjfMglG9GR5sZgM9czw3U3zFOW6dNkxGdukjXJs1kap9+cnH16qDoW6COiVvtpgEQTF/Z3eoLDQC3cPFkEggtAjQAaAB4zgC4aKIdwh7RAFjht0N/x3s4XJ/lvJf63Mvvk89Nfb7rtfyd8zZTc4AGQIbCDSvYd3Ql/tjCKJkzaLB0bNhYujRuKn1atpa+KtRxQLAPbt9RpvftJ9umTzfhDtPAHWGCZ8RGRMjAtu2lY4NGMk3vlbTGt8ISfYcJsXbcOO1vG2lbr4FM6tlb9s6eLTdjkoW9ax9x/i0V/DAKZvYfILMGDpIjCxaYiRAoBgDaiVV+jMdtNTqQjoFoBqf999QAiNMxHtW1m4Q1byEz+vWXSzQA3JrrrnPG67/TAAitL/UuvaUB4AKDv5IACXxJgAYAhVSmhBRXpb8wM8gwQP47ogGQoXDBSv71DetV1M6UASrOyxQtKW3q1rcV4DHde8hYO8JMGEcNG2Er5Tj/6Xb3DYBNkyNkQJt20r5+Q11Z7iuXfGwAoO8XVq4wY6NK6XKSP28BaVytlsweONhW+JEHD2HsiOPn29UwWLtWUxjGSG81SGCMxOlqOVIZYA54XexlovghxH7iqpVmcuycOVOOR0VZrr/TR5gDiG5YO368RA0fLlumTNXokOiA6Fsg8P/hbaQB8OWX3hD6iwZACA02u+o/BD59+iTpHf7TShEaAAEiXCi4Kbg5B7I+B2gAZCjcIGqvrV+vYd/TpVeL1lK2WCmZGNZL9s+ZK6c0xP205u3jiF+2zML/IQYfaxg8BK8jjvGKvHnncH3fEZZYcf6WAeB6jXMf11x85z4QUK7n4nfn/NTvv8xAIKPv55cvl4hefaRCidJS4K+/pVzx0tK9aXNZpznwEMpPtK/Os1EjIWnNGlk6YpR01SiJHs1ayOaIKXJ345cGgGs7nLal1xdHELpeg9+d61K/7/yd1nXONamfhWscbhi73bMiLYJhUs9esmrMWOuTM54wRe7HxmqawzozaG5Eb7AUEee5rq9OG5x7u36W3u9OG1N/7twLY+b6WUbnO9fx9fMuBTQA/Okrt1fbQgPAq7j5MBJIJgDx/+HDB3n9+o28fPlKXr16Je/evTNTwJ8Y0QCgAcDVa86BkJkDNADcNgAqlSpnxd9ua00AiK/0hBVEGsTiY10xRtg4VsGd477+jZVk14J6GRkAzr0Qku7cB/no+BuiFZ+7CkPcG8/As5zn4xU5/Shg6AhaXJNeHxwDANEItStUlrqVqkr1shWlTsUqMqpLVy2AN1VubNiQ8uzvMQBSuMRp29AXbZPTnwf6N1InUnN1roHZgHPubfryGjBGXx9u3WyGBAo2OizQpsdpPAvPThmDz+xwLgycdbq6P0RTOnq3aGUh/icWLdK2brKxBFfwe6T3xPX4PfkZW40t2oJzUnPF33adtjHlufqe9U3Px3spPGI32pil8Eg1thhrsEg5X+cWxhbn4/loj9P/9MY2ZN+nAeBPX7m92hYaAF7FzYeRQDIBGABPnjyR+PhzsnffATl85KjcuHHDTAF/YkQDgOIvZMQfV9CzvoIe6AxpAKQrfh2BlDoC4HsNAAhsiMnD8+fLal1FXjYqPOWAwNw3e3ZyrQAVchBrGRkA+BxFBlF9HvdarscK3S1gw4SJclIFKkS0I3whoi+vXSMH5s61z3EerkE7UMQwQYv6YbtBxzRw+pr61TEApvfrJ01r1JKezVrqyn4zaVStpqUpoE4BxPEjvZcjZDOKAMAzURjw6MKFEjNpkvXB+hM+2iIg4jWa4uGWLV+MC67BSjvC8bdMmaLXjElm8PkacNk/Z47sioy08HwIY/DHgXSMIwvmS8xk7K6QfB2eB464BmkWjiDHuQjpH96xszSvWUcaVKlu/Y3sP1CiJ06UQ/PnydX16+T8iuU2FgfmzdXfV9g4H49aqEUcp+v7c+2eMGVcecIUQDoF6ifguRe1WCSeC263NUICfYjWHSAwVsvDwzXyYIxsVD7xy5baWDmCHiYC5tWJRVHWJmdscT4iSI7ofMPnjsHj2gb+rlEANAD86Su3V9tCA8CruPkwEkgmgNX/ixeTZNbseRLWq68MHTZKtu/YKW/evPErRDQAaADQAOAcCJk5QAPgC5GWlkDKjAEAsfZIRSxE7lQVyU2q15aGVWta/nyDKjWkWY06MrRDJy36N0XTC9aZWEvPAMC9INYh5sd06yEta9VVYVpDmmgufpPqtaSdFuZbNGy4XFAhmrzCvEWr0q+SaBWPo3VHgY4NGtvzGunzm9aobbn52L4OWw4ih/9bJoBjAKDQXcvadU0Yz1QxPEwFcqs69TQlopWtlkP04z5Yef6WAQARjIgB1FOI6N1XOjVsom2rbVzqV64uHbSt84cOs7ZBQKPvuAYFB1FYMKJ3H0s/AD/0v6ly7d60hYzq3E36t24nPZu3tN0T0H+YIBDY+5UbKvUnP6tOyhi0rFXP+rFeDRScD8YnNbcfkQ3Vy1WQIn8Xlr/zFZTSRUro2NWwdIZ5g4eowF4ga8aNl9Fdu9u5a7RA4qkli2We7toAHuFaHHCzmhQwOZz5lGwGxVg6wZAOHa1uBHZ8QMFEFBrcM2uWjO0WZv1HnzCuOFBrYoHeF7tPYKyw6g8jBEbHFOWHooyN1YxBXYYWOi+QcgFT5rS255n2H/ycNvCVKQB+9WXbB42hAeAD6HwkCcAAOHfuvIwdN1GaNG8lnbv2kOiYTZoS8Nqv4NAAoPgLGfH3kWMd8mNNAyBDgZTaAKhYsqyJTKywQ5QhpB4HxOaTOA0HVyEM0YqV4p0qdCMHDJThumUcRCh2EZjQo5eGlreWHipckWN+YO48E6t3VAimVQMA9zquq73TVYSjsF7fVm3sHrMHDlIR2MdEOfLUne0HYQSgaj8EKsLYIZpnDRgk0/T5o7p0M5EcpkJ55edrHm39J4c/tUh0NQBa1alr4naDroQj+gDtwI4I48N6as78rJRV9PQMAJgDEK8oCgjxChNhjLYxUts2S4sKQsSjxgLaDIPigkYp4Jo7ujoOgQxWENjYbQEmxNxBQ4znxJ69dVeGZlKjXCVLUZjcq4+tymM8sBKOVfnpffvL6C7d9XWAXYNz+rdpZ9eN6x5mgvqmtu2yFjBERAUKMTZS0V+nYmWNdGhkz16h0QZoB2o9LB0ZbmPRq0VLWTR8hEUdOIUPwRbtv6SmCAQ4DkQ0IFICYwGRjp0UDuvuCNe1tgR2jRjfo6f1G2OGtmK8xuh3pG7ar8HtOli0A7ZUTI5mWCATlHnnRk2tD7gWEQq4ZmrfvrbzAooUwgyiAfBZ9O9yeWUEgF995/ZmY2gAeJM2nxWSBBDuj+Pjx492QPwj3//MmXgZNXqsNND/UWvXsYusWx+t9QBeWhoAzsH5zrW+AkcDgKIw5EUhjYHQSQ2gAeC2AVCiUDETfxB+q8eMk9Vjx8maseMtfPvQvPm28ovVfxSJwxZ427R4IIwArLhDpB/SEO0FQ4bZNoIoprdSQ7exCgyxumny5K92AcCq70YN7cZKN3LSsQp9UE0D3Asrz3FTp9lzbmj4Op4Zp8+DuMaK+PzBQ/XcuXJu+XJbRU4Wm2G2ugyxCeGOlWqYFqnFP/5ObQCM0YgCrD7jnjAzemp7umkf5qgYh/EAQyRJt8RLXQQQ5gbC8sFjZr8BJmpRWBBmANqGlIS9s2bbPXu3bGVbIeIzXIdwewjcrk2aSR81HZaMHGXXQKxj+z1EE8AUgAFQo3wlmaBGAe4J0Q2uRxcusEKE6LszBkiZQBpAmKY09NQFiaX6O8LzYeTgvstV7MMg6KF9m6xiHVEEKASJNAsYO7gW0Qb4PGrocDMckOqBc7FF5GQV+tg28vHWZHMFjJFCgNV/3BcGAUL7j2mECFbse6qRgFfUVDizZKndb0/kLDMDujdprtd1snQJtBv3wS4RiBDArhNIHQC/C8rpkP4OwwPbRzp1ANIa15B+jwaAr75e+/y5NAB8PgRsQLATgIiH4H/69KncunVbrl69JpeSkmRr3Hbp02+g1KhdX5o2by1z5i2QCxd0T/QrV+Xa9ety/8EDKw4II8BXPzQAaADQAOAcCJk5QAMgTeHrKpBcIwAgeBEWXq9yNd0DvqVtcwehjRXjEbrKj1XiRBVjVqhOi7JhS7xzy5dpSPYSE3zIb0eOeJSG7ENAImx7lq7kY9Ucq7sb0zAAEAq/VkPO29VraIJ1voaEQ0hDVEJUX123Vn/fYDUEIHBx79YaOo5oAUQGQBQiJPzk4kWCbe0m9eptue1hKnxREwDh74gycO2z8/vXBkAP2af56xCcCGHH6n8HjQIYqNsjInoB+fTo/5JUuwCgYCJYYOeAIe07SC/t+3QVvDATwAbthpCFqdJNhT5C29EPCPl9s+eYaG6nond8jzBbhYe4R1g92p2gOxEgbaCdrtQ30FX7iZ8NAEQ2IAoAKRZnVWyj/3gOVvCxGr9q9BiLwkAUw8wBA+09pA3g3kjNGNm5q45rW4s0gLnh5NSD+zIdZ8cAQPoFBDeeA+MAhggMGNQSwLloI3ZLwAo9oiZgomyKiLB0gzVqHmGrxK5Nmuo8GGjRAGgnmCDaAAYOxh1RCEgxwHuINGhWs7bUr1LNjAjURcDcQRHA22oiITLFqYHACACXlf9dTAHw1fdqf3kuDQB/GQm2I2gJQMA/evxYjh0/IcuWr5Rp0yNlwsQI6TdgsNSs00AK6zZCpdWtb9ehs4wdP0kmR0yTyNlz1SDQsEk1C96/f+8zNjQAKP5CRvxxpT90VvrTG2saAGkKX0cA4/VLA6C1FNbc8NZ16muIfTcTmwjnnqyiemb/ASocI0zoQnhi9RcC8PTixbaCP1dX7qdoNX2Eb0MIIq+8tlbTx0o4ttqDgEzLAEBaAXLKwzR0HEIQZgPC/3ereIawtsrvahJgtRur6OM0l7xqmQrSRHPrscqPsHM8F8c4FdDd9T41taI/7oVIBBgUCBd37bPze1oGALY/RCg/RC3y5we27WD3Cu/STXapwXBCBekiXZl23QbwtnKA+TFNw9sh7pugoKCaKRDraBd2GUA7B6k5gHZjdRvvwyBAhAVy4btoyPuSESNtddwxLCBwESWAqAekDsBQQUoAjAPwR/QEuJxV0Q+DYrZGLTgsEDWAMH+IaZgiiMzA+Ri7zRFTLV1iQNt2gnGD4eGIadQjSG0AXFETBkYN6hQM1j5gfGFgnF22XIV5rJkwqPmAFAZETiCC47BGi0zQ8P0m1WtaUUVEN2B8rH29+1hawIA27S2tATyQGrBrZqQWBpxsUQbYiQFGA8Zwh5oxiGCA8HfdBcAZR766GAGMAPDZ92tfP5gGgK9HgM8PegII57927bosVfHfQldHSuqXkfwaNpknXyH5LUce+SlbDvnl9z8lZ+78kr9gMSlYpKRU1Gq7I0aNloOHDvu0MCANABoANAA4B0JmDtAASFP4ugomVwMAOerlS5SxcG2EY1/UEHSsemMFGKHjEORYRYagxqoshN5y/d81FJZDCDvEb8s69VTUVZOyxUqpUC9v5gEEa3oGAMQuQsUhDLs1bWbV6XEfrIZDcJ/U+gAQoIgigFEwSFfjUbiunBrtEMQ41zkQGdCsphbCU0HZt3UbWakr1hC3jqB27Td+T88AQF4+hC3MDQhQ5KO3rF3PBDbEOEQzQuGR775ZV9NR7A59wKp63cpVpULJMlKnUhWLVHDahtcWteta+7pp2DvSCmJ1FX2O1geAAEZEA0Q8zAcnZQGiHKIXOyrAgEBhQscAQDoCcuyParQEIihghqDIYGs9B89qWK2GMaqv3z2QNoAIhMwaAIjCsHFS8wO1HrCqD1MIhRsxP9aPn6iRDc3tfbQF443PBuhYoaZEuWKlpZ7OCTB0eLSpm8yjoRb5QxrA4uEjbReEY5rSANMIpgiOThrBgL4jogE7EVzWeWAREp/rD6Qe05D/mwZA0GuQ9DpIAyA9MnyfBDxEAAbA5StXZN6CKKldr5Hk/qugCv6cJvz/8/Pv8u+ffrPjf79ml59/yynZsueWQkVLaoTAIN0icL9PCwPSAKD4Cxnx95FjHfJjTQPAbQMgo20AHVGKHPbhWusGK+0QhMgb36r5+tjqbYbmwUM0N6haPUMD4AVWuTX3HOHrWGGO1NX/ziquEUGASIT+GqYOUYm0AOSHI0KgSunyttqM95FXvlXb4hxoF7YCRGoAwvWdcPG0hGF6BgBy4SF4EXYOITtOV7IbqUiHyYGUhojevaVTo8ZfGAAQ4iM7dTVTopP2HdEACLV32uW8IrUAaQYoZHdkwUKNdhhshgVW7LGFHvL0ndV4vCICYj8MAI3IaKU8HAPgRnS08UakBsLysZqOe2F3BDxroa7Qo3gghDbE+kHd4i8rBgBMCYh91HQY0q6jDLNdHiLMWEABRmyfOCGsl/UN5hD4ox5AnUpVbacGFDZEgUWHg/Mapzn9qEFwQccK8+Dupk0WTbFbawRgO8Q+rVpblAC2ZhxoBQM1DUXrQ2B8HE5pjW3IvkcDwEPf9APvNjQAAm/M2OIAI4AUgPv3H8geFfPTZkTKoCHDpZdW323TrpOUq1hV8v1dVArD8W7YVMJ695M+/QfJcF0lWbV6rSQkXrT6Ab7qMg0AisKQF4U0BkInNYAGgMcNABRfQx42ivW10hVdrMIjZN8RZYgSQOE61AxoruHnEJ/figCAiMMBgflw6xY5vXSJLB45Ugap2GtRq46lEaC6P4wF5JQjFL52hSqWagCBj5B2iEG0K/UBc8G5f1qC8FsGAHLi0SZEH0BUI7wdtRGwUt9PowuwbSCKHDoRAMhVR8X9Dg0amThGUT6I+dRtwt9oL9qNIn+oC4CtAlFrwSkM6OTjo/0I8YexgtD75sraMQAQEYFIBBTMgwkD0wW5/3gm7o/fMQbYJhEpAFmNAEBbYIggTx/97Kch/Qu0NgFy9gdrmsQgPVAjAGMNEX9MzY1hHTtZCgLGzFI6tC9p8QBn17F6sUPng/YBUSNOHQEnsqO3PnffnNkaiZL+7g5pjXXIvEcDwFdfr33+XBoAPh8CNiDYCaAI4Nu3b+XRo8eW05+QmChnz52TmE2x0lMFf7WadaWxVjSeEaku/+nTcl4LASZevCS3b9+RFy9e2m4AvmJEA4AGAA0AzoGQmQM0ADxuACAXHPnuEPYQwcjtxk4AyFWHcIVgxu4BKCKIVfxvGQAXdSUXq9IIub+xYYMJTKy+o/Ad8uORN48QdlSyR3V8bGGHbQZRDK+fCmZEDDiF6BwTASv+2C0ARgTa6ojptARgRgYARClW4NHfeVqwDv1trikGjTS8HhX5scIOAwDPQiE9mCKoDYDdDCCMr+lKOISsY0Kgr9gNwSlqh9QBRDWgEB6MA+TPu25xh/MhqHFfnIOICqcIINiBT3u9DkUKYZBAMON5SA8AG5gWMGhQzNAxAFA7AIYCTJW+urqOlfnzy1ekrKanVQMAKQDoA4wF1BtA+gLqACD1A2kPqF8Q0buvFTRECsMDNSHADIUQEfKPVAmkCcDwcLbvc8brls4bhwfMC0QBoA1IwcDfMDqQdhGhKSJt9F6oaRA7JcIMFNwjrXEN6fdoAPjq67XPn0sDwOdDwAYEOwFnK78PHz5aQT/sCPD69WsV+2dkZPgYqa//Y4gCgGvXrZdnz5/bij/Oef/hn60AfcWIBgDFX8iIv48c65AfaxoAGQokiGBsAbdVRRZqAGSUAoAibKjiDvGJfHOITIhY5NpfVxGPugDYux0ivaaK5PQMAOT8o7AbKvwjXBz573hF1X4cCL2frqvajTX0u2+rtrJu3AQ5oAX6VowebcIb+eQoOIjzUJ8ARgDMB6QKwCjAVn7IkYeITk8oZmQAOKIXpsR+DdtHETvk8ZcpVlJKFC6u+elNzAC4owYADJCdM2boqntnE/NYfUd6QpJGS0DQon3n1STATgWIDkA4PSIeIMyxQg5h6+xscElFL4Q0mKKiPvqPegrVlaezDSAMABQXxO4BEOMIlz+rZgHENKIRIOybamQBzAqMR4oBoDywPSLew44EEPHoGyININwxF1y3AcQuAGYA7NppHGGuYLeDYfodBwYIeKD2Arb+Q3sR2QCRf03PQ9uRBoBUjvEaNQCzJEEZgAWYYLxg9MBAwk4OiOjAHMDfaBNqUMAAQGpE5ICBVg8AJsDWaVPlUdyWdMeVBsAJeX//vnzyYcFpX33HDeXn0gAI5dFn331GAHUBzp07L2P0S0pj3Xu3U5fuEh2z0af5/mnBoAFAURgsovCKijscl98nSNK7C3ok6O/J7+H9b/Uz5Vq9BtfiHs5737qOnwXYfz80ADI2AHTVHqIPorSPCc0KslRX2yEIsQKeWkyhCCAEOgQfVvmxzRyKtCHnHKveCEuH6KtfuZqF70/p/c8uABD5g9q1NyGHfeEhGG+oYI3REHvcA2ZBlIbEY7Ufuw6gAj/uj8JzqM5/SZ+L/ein9+tvoe2dGzWxz9BebAkI4Yrt6CZqLjrEJrbm+6YBoH0/r/vLR+qzUHAOAh+GAgS/EzkAEwCCFvdCTj/ENgoQFtcCv6hzgO3w7moEA86BKMdWieg/igQOVzMAnNC2lXos0t9naZ4+QuURMYAVdayKo/1h+r0B4naI5taj/8i1x64AEPnI8a9erqLU0t0NHAMAQhz36a2mDYwIhNtbSL5et0DHAlX5EYEBE8B1FwA8EwYOzkW0AiIPwHOjjg3ME+zYgPYgigFbAaI4H57lzAOs0GM3BhTqg7BHm1AbATs8ILoB4fw4LApAtyfEcxDB0UOjIsM1WgDbPGKegAnaj6iHFbpwgnFF9AMiGTAPsIMA5gLGFO1DSgjag2iGY2pwYB6mZ+w4bQ3JV0YApPXVNyTeowEQEsPMTvobARgAly4lyey587UeQH8ZPjJcduzc5dOK/2kxogGQdQHjCMXMvgaSiHS3j97qG9oF0Z7wKl5O3D8q+6/skSO3Dsq5Z6fk0tsLJua/1RZcn/jqrBy9fVAOXNsrx+8elvPPz5gZ8K3r+FnW//vxKkMaACnCLT0xBLGGFVkUphuroguh5lhBR753WgILwtgpjhepq8xYtcYqNFaDYQhgNRsV6SGCEfoNYwCGAVb6UdV/cs/eJk4hjLECfD821lb/cR+IUYhJCF6Ie4hGiEHUFMC5WDGHMQGxiCJ73fVzVJ/Hc3qogIbQRC49dhBwthF0QvDT6j/6DlNhmQregbolHsQoVs+tcOBn8wMMcMBIwKo9+tNRBTcq9w/XbeqQEw8eMEseaXg9DIXl4eEpK9/oD9rWQ9mgWOKknn0kWvnCUECuO1bMEVYPcwDMUAARWxmCJQrgIZIA4fqo8I/dDZwUAAjxY1oVH/UXemm/UewPhfiw/SCuQZ4+zAEUTUSbTy5eZCYF2omUC6y84zzsLNBF24hQfgjzE1GLbIV/TLceNo4bJkywaASHHyJALmgfIeAxZqhBgGgQjAlC9h1eYAuO2E0CdQ4QxYDruKirAAAgAElEQVR6BM7uCWCC6yerkQBjCAzO6RGj2wAOad/J5iH6gzoL2AkAv8PIwDxF359/HhenXXz9vBUgDYC0vvqGxHs0AEJimNlJfyPgFAY8cPCQbIiOkS1b4izv/72fhWDRAMi6gHHEJ1aOL745r8e57zjOf7HS7FURlMlQeEf8Q2hfevuNfupnzgq6t/qFtiWogD9884Cs2rNSxs+fIPNi5snOhO1yVk0AtOdbbUF/IPoXxi6QyYsny5JtS2Tv5V1y4WX8N6/71j35Wdb/2/I4QxoAGRoAlueuwg1F/LZobjhWn1FADsI0LQMA72EVGeH1CPdfOXqsrdAipB+r+hD2m3VVHKJuzbhxVuHdwss1Lx3iGOHdWOVFSDqEM+6FlV9ch+ttn3i9F1b9Fw4dbgIbIeiPNeQbbYWwvKvXHdKq9lhBjtQVf1yH82foSjHELlbqE7WOwLdW/yEYcT+07ZCK1/UqdPfMnm1GQ+rrHFGL/HmsPq8eO06iho2wVW+kMaDWgHMOro1XIYsweZgUrv1BdAS4YAUe4ti5BtfjPVTJxznWH+0TdhxYogUR0ad+KuQRbg8RDI4YH6ftiDpwnjVdn7lMiw5jLJF3D9aIagBDsMMz8Yq0CYTo4xlItcAzsEMBohhggmAMEUWBuYCaAo7AhqGCCAm8jwJ9iBZAiD6iBJ7qZ06f8AqzCP1E38AMYfwOD7wiTQFtgPhHX2BE4b5IZ5iaai6gBsMONZAg/jFu9hwdQ6ddfKUB4G+6wNvtoQHgbeJ8HgkoAdQFQBTAmzdv5OXLl/Lq1StB3j/e96cfGgBZEykQnhD8Zx6fkD2Xdsr6Q1qleP9qWbMv/WPtgTWy8XiMnX/64TG5rGHnHhc6mRT56bXDEf+Jr8/KMRXK285tTe5r6n5q3zef0orPd4+YGZLe/Tz9/hUN9T/75KRsORUrw6eOkEp1qkvHfl1l6fal1l4I/G89E+bBzgtx0n1ID6nZRAuZje6v47hKzjw6/s3rvnVPfpa1/7Z+CD8aABkKJBNqKqggxCEqEbrtWqgtLWGFayAiEYaN8yHkUfkdwhyCD/fBgRVhCGIIQYg2PAMiGiv5uNYRcngeRGbKfVzuBXHsCFcTfXofXPfPs2PtuXg+rneej2c556fVB7xnff/cD+wtn/pZqa/D+Wgr+oX2oo+pn4Nz8B7u90V/tH2IdsA1uIfTd6cdT7clX4NzwNEOFbswMiC0kXrQum49maHpCkmrk4vpges/HJLHAM902oY24HkYA4eh8zzUP8BnKLaXmhvah89c2+qwcJilcFAW6XGzc40Z+pZqfD+PF54BXugL2oj7fjGnnLmg7cRzcJ7TFr6mYYAwAsCfvnJ7tS00ALyKmw8jgcAiQAMgayIFK8vxT0/J9vNxMnXZNGnbq4M07dhSmujRqF1zqdeqsdRu3tBeG7VrJk06tJAWXdtI2PBeMm35NIk7s1kuvj7n9yITBgAiHE5qeP2qPStkwIRB0qxza+2j9ulzf/HauL2G3w7rZcL76O1DZgJ4IxrADAAdhy2nY2XEtJFSuW5N6dS/myzb8dkA0MiMpLcX5MKLeE0LOG3MYbykGBtmAGyT7kPDpFbT+tJ3DAyA1Wbs/BAh6mGDhm38zv+OaQBQLPn5KjFEL4QtDhPCKpghnHFgRR2FAxcMGabpEM0tZx/pBVaj4PM5FMFpiGA/H/MfOmY0AALrS7kHW0sDwIMweSsSCDYCNAC+UzikI9ggIM+/OGN548t3LpPwWaNl6JThMiRimIrJnrqaXE+KlS8j1RvVkY79u6hwHizDp4+wMHOcv+/ybgun93cBh35iFf3IrQMye90sFf+tTCi369NJBk8eYn0ePHmodB7QTdr0aC/9xg6Q2evnyO7EnZZLD4H+I/uI9iGH/whSAPaukgkLJsr8mPmaArDDUgCQmgFDYsWu5TJ3w1zZdlZDbHV1H31y+oYUgKjNC3VsIj6nAOyWBKYA/NBx+5FzIs170wCgAeDnYvCW7iCAEH0cSCeAuMeKPeoJoO4B0hlQVwF58CiUuGtmZIapDT9UYPo5z5DvOw2AYPva/t39oQHw3ah4IgmEHgEaAFk3ALAyfv75aTl255AK3h2y48I22a4h8ou2RJkgrli7unTo21ki10TKphMx+nmc7E3aZedjNTqj/PQ0hUw6hsSPOtcRycixn7l6pq76t5DWYe0t6mGrRjGgz9vObtE+L5Ihk4dJmzCt0Dyij/19WIvxgdGPahvui/aBIwT7iXtH1VhJLgJ4VvmiCCCiLDAmMGc69O2iQj9KTZt9kqjvp1yrBgKKAO6/slfH5rCcYxHAHzpmP3I+pHtvGgA0APxcsNouAKNGWX78Mn3dPGWKFjCcZdsFIvQfOxqgyOHAtu2tIv7ZZcstUiDkha6fj6vPxocGQOh9sf/cYxoAITv07DgJZEyABkDWDABHfDoiEmHmEJwQllhlHjB+kEUBIKd83cG1FlKO1WgIYghWHKghcFbD1888OqGfn7TXU1obAOH2eA8mAY74JyfsPEe0uoocPNPuobUIEJHgWvneEe+oiI97Qtwe09VwvOJv3Nv1fNf7Or8793AMgKadWkq3wWG2oo5q+bgeIhur7BDXnfp3lVbdtfq2FuMDh0vaZ9wDW/OhsN5p7ddxpx1oix4n7h2xPoAHzsXh8MX1MFnABSv11v7P18Rr7j9W/9EG5PKjHgPOsfe1TXge7r1i1zJN0ego1RrWlrFzx0v04Q1WNBDcwBTHqQfHUtqB6/BMtBXjkPDyrI2b07bk/vzzzNMPj5sB4Ywt2gO2MCSc9qJdeIbtTvCZydd9PP5VH62Nr87ZfHG4OGPDVzf+G6YBQAPAz4Ui9rlHIT1U5O+vuwRA6GMLvyEdOlqV/F5a1T+8q6Y3jQrXAnlRKeH/PhOYfs4z5LnQAMj4i3CQnkEDIEgHlt0iAU8QoAHghnhwY9UdQnfnhe0ycOIQC5VHSHz0kfW6qvzPir8jII/fOywxRzbIyt0rrHjg6n26DdX2JRZBgDz0uPgtdqw7uMYiCBCGn3pFHSI19uRGy1vHajxEMMwFe4YK0ZMPjtrnC2MXWiTCjFUz7RViHSv4ELhOTnxaghL3Qbi8YwA0UwMAKQ6rteI+hLOriEUePoyPNhohMGzqCO33BjsHTLAiv+fSLutr5JpZMmPVDD1m2iuq9scc3WAmAoT8ZU0bcBhBwKOdS7U6/+x1s1Oumb9xvsQci7Z2oc/Y+g/3iNJ+4nzbEvDqXkHhxRHTR6n4ryPFyv1/9s6CO64r6dq/5pt5J8mEOQ6DnZhiZmZmZmZmlGVmkmxZtmVmZmaIIQ46OMlMffWUfOV2W7IlqyW1uktr3dV07oE6R2vdvWtXVQlp2a2NDJ02XLP+z5X1Ot8Dqt44qBcVBBamLZA0VTNQDpBkh8u3L5MVGlawU8M1APQZdtX1QBDsvrpDww6Wab6B9JAOwDpKhKMK9DceXy/MMVgnc6f/9bpXkC/sY7BHR749YOOSt2DGqsfXuE/VF9iF9pntkX+Xjf9lJwCcAIhywEr2+50zZ0hi/wEm8e+n9e5Dr3FdukqKlsY7uXiRJhFc90gyv7gHu1G+twWyP04AROJRuVD24QRAodw2n7RbIH8s4ARANkBDDoB/AMKySwAA6EgEOEgT17Xr08lk8ySg6zK4mybT6yJDpw+XBI2ln7JsqvTR/AEjEkYawA1AdzDerkvbZVTiaE1e11vB4wyVsZNbIF1lgNc8RUHyyIRR0lJj9onfr99GL03aBxAeO3eceekBt1nF6meHAGAugOOtSkAA/Al7YF0QH3jTAf+U5UtYnSBdhnSXuq0a6tVI50LCxAbSuH1zuw8Qvu/GbvPqA5BP/XTU1sw826vqoHGH5jZ3Eg6ynlGz1ZuvoH/XlR3abo0MmTrU1jdhwUQLTUjav1qVCGPtno+KFpU3i3wgJSqUlRqN60rXIT2MUIBkST26TpMzdrPwhclLp8jKPSs0j8FMqwjQbVjPB+EMe4wIwR7sAYTDok2LrJ8O/bvIUiVx9l7fpYTEbqv0MEbn3EJVB/V0rayTBJDt+nSwfWRPUClArLBGPpN/oEP/ztKINWp77NOye1sZrTkNktWO5C2AGAn23V9z+P/rBIATAFEOEn/WrPeQAJRhPLZwgZXgo9QgF+X4KJF3PTnJKieQAZ/kgAUCLKPcjm4TLwOYP0/R0TuKEwDRuzc+M7dAgVvACYAcAohskgHZJQDOaFb65P1J0kZL1pWsWE7K16pq8nlALLH2JK1DGQAARL7edWh3+w7iIBT8bdEqBIQZNGzXVOXtowyEE2pA1nu80wDxTgO6Ss9RvY0omLhoonrEh2ipvHYGOCcummQkAPdk5mHODgFAGwA7kv9+4wZIq55tZfjMEaY8OKbAlUoJY+eNMwDcoltrnW9PncsoYS5Dpg3T7ztK8y6tNYFgH5mzbo6SALssJIDY/RG6JmL3qTAAkTFBgTLrHDJ1mIHmVXtWGbmwbNtSC00o9nUpy0WwRVUY5FtYoTaAlChfs6p8VqK4NOvSymySuCZRUjUvwx5VCaBUqN28gXxdpaIMU+KFexLXzLKKDU07tVDQrnY9t8lCNlgroQ+bT6eZPQ3Y9+1kSgHWiRqAPWzfr5MlfoScmbBwooxQe3RS5QR2H5U4RksmplpYA2skXIIkihAA1l7tQqgCJBD7g4oBRQMkS+je+/sc/A87AeBg0YGrn4F4OgOuACjw5+yCmoATAAVleR/XLVAILOAEQA7AQzbBP4AspwRAawW3xcqUkkp1qxswT9q3yuLAiRUHwE5aPEkAzTkhAPDo79ZkeADNNgos+2lyPsgEQghOqscZT/3YeeOlrQLvzpqwb866uXJCPdKZAczsEACEEAQ5ANorGCYPwDSV9++4uFX2qFcciT/fNe7QTAZOHiyskaR7eMGR0fM7AL+Jgm2qKODVJ7EiJAjhBo07Npcxc8fK9gtbLMQB73uakg0A93Sgv93WR26CdAJgkPa702TzjLFKwynwpldQkoV1E6qAOgIyBVulHknJIACGzxxpHnnmCPnAfV1VtYCXPpDhn9R8DUma16Hb0B5GKADaCTuA0OF9u94dTZGxcvdKswshCtiC8o9t1Q4QHoQE7FblAmtEjUDeBFQeVIdgzqxxkxIKKYc0eaSqJ/iOvXDQ/4z/t04AOPiLJ/Dna/Xz7gRAIXgSz5spOgGQN3b1Xt0CMWEBJwCeEUg8hQzIKQEAMC5Xs7KC/DZWig5gbMkCVcZPLDqgMScEAHkAAI8LNs5XgNrTwCagNGnfagPQuxVgAoCnLJumnvr20lCl6SP1dzLjM244wAwnAKgCwFyJ46fywR7NnL/jwlaZo/HuyOWpEIAKgHh8ZOvE1PfVfAh40smHQCz/YY3rJ7kfknZA9dbzm83TjdccIgBCADk9Ens+N1evvYFsjasHuJ9ScoR4e9QFEBqQD4QPhBIArAdVAkkC1yl4b6vy+8p1a8jkpVPVDlstNIG1oZQgBCBQAIxQAmC9fgZ0E8OPbJ81Y0+APIkKD6p9USo0UWICML948yLZprag/CBgHlXH8BkjZbXaHOBvNj+2zvIBQJDUbdnAVAjsFfboakRCa/P4E5JwTJMKEjZxUtdIjgfWmpVCI3y//HMW/9dOADggclDsZyCezoATADHxrP4si3AC4Fms5ve4BeLEAk4AZAEUngLwnwawckoAtFVvcfVGdQw841U+pZ5ePPGA12ciANRrjHJgjIYOEEtO38Si9x3bz6TpQ1WePkC97O0UiFZtUEuqN65jXnli9AHL4esLJwBqt6gvFetUk1bqGR+k0nr6G6xhBp0VqCNvx4M/X8HyARLXafw/eQCI16c8ILH8AFy+p18u1kpVApISAp4hF8ZpuMA+JQDw8Pcd018aaN4CchbgIQfok5QPjzjZ+alAAKGQNQFwWtYdfkgATHlAAARzyIwA2KBEw35VS6BEIKlhjSZ1Zdz88QbmSdiHtx9FAjkV+o0fIFvObDIlA3H85CdAzQFpQe4GwgGwUX9tB1lQQ+1drkZlC8nYpsQHxAzlExtoCAfhCSgQyCew8/I2IxzO/XZSLmiuAOyEvcL3xz9n8//YCQAHf/EE/nytft6dAIiTp/nHl+kEwOM28W/cAm6BBxZwAiCbwCGHhEBOCQBAYR2NP+83rr8C1bVWyg+glx0CgHZbNBadmPogBwBx6MSrA8Qr16shZatXtkR7SM87aow5F552Qg+QnSOxJ9Eg0vPzvz+dAKjVvL6UrlJePeb1DdAGfXbRUAJi1pfvWK7gPT2RH8nyqGwAKG7RtY3K3hMMKJP8LhS44tVfsnWxKRYatm1qoJlwBeLzZ2pWfPomASBEQq9RfSz2f7lm58e7jrf8yQqAnBMAaSc2mHoBm4zX+H1s20cJFCozYF/UABADLTU0A9uR9Z8qCagHyteqIl+V/9rsAyES2AfPP7kMUEJADkBycA8JDBOSZhkBxBotR4Img4RMIGyDNaICwGZOAOTif9YJAAdEDor9DMTTGXACIG6f950AiNut94W7BZ5uAScAcgEmnkAKPAsBULdlIwPslPM7+8sJA3oBATCJEAD14AOClypIxusNeAYMcm1SiT2x6KEEANJyZPfVNds92eR7qyeamPzZKq0PLpLgIVmnT7zyyPUZMxSYB+OElgGso/L1Ws3qaZ99LY49vb/ZWuJuoWW/P6jefIA/c0MuT0I9Mv0TS8+YSPupUhA6znElAJZuXWIgGK86SQoP39lv8fmUVKR8HiEAhBg0VtICRQD2oGQegJx4/2Vasi/zEIBnIADUHsjuURawLsIQuGZpYkAS8pHUD+KEkAYSLVLpgLCA4TNGGOlSpUFN+22KVhQItTfr55qvaof1GhJA/gHG2abqi0UaRjB6zhhL6NhIcyWg3qAiBLkCNinJY5Ua1KahdvP3OfgfVtvd+PGUfH9yr9zftd2BUDwBIV+rn/d4PANOADz9QThGWzgBEKMb68tyC0TCAk4A5AA8PAHwh4OwZyIAFKT3nzjQsuaHEgB7NY590uLJ5qnvoF77RZsWZBAEAGw89iSnA4xS3o/s+KYAUGn54ClDtfRccwOu8zfMN0ANUEX6zsV7xuI9QD0riTnjhBIAAFPCB4h7Jxle0B+x/MSpkxCQe9IJgDOW8I+8AHi/g8SAQTz7lQdExlHNcA/IB+DjHR+jQPiYetWZE3ND7k9YwxIlCQZMHGRl/ao2rKVr62ahA1QfWPpIFYBBltMAQuP8bxAAay2WH0UEZf7IAcC8mWNmIQAQIiRhBJyTL4EqAJAw5EqANBkwcbDZIFBOINEnaSHZ/OtrvoCWWgaR/AHkYsjM5nxH6ALrw16skbFQHCzbsVTzAww2AoBwAYiOuZqkEZUD7cPPm3/O5v+x7rUTAA/Kg8UjGPI1OwkQb2fACYBIPCoXyj6cACiU2+aTdgvkjwWcAMgmcMgB+AeMRY4AOGcAcvqqGVobvpmWAmxltenxlgMaAYMA53nr51rseJ0WDY0AoCygSddVQk75QHIMJCQlKIDcK5dCPO/M84wSACSaA5AChjMDk3wfSgA0UAIAwmGFxuLj6c/sHr4L7tug8fI91FNOyAEe8jWaKZ/EfZd0/ult0nMdkFSw86BuduElR5EQeMgDEA3hAHjHs46UnhAGYvPXHlxjCoJMFQA6x3Va7o+wBwgAvPfkOwjWnDUBcNzWRy4APP8oE0jWhxKhqyZXZK5k/SdvAuugTB/zgsAgX8FUTbJIKARqB35nv0h8COkC2OcVIiRjjUbGnLLfyP0AWdJKiQT2kNwJECDsWVb29u+f8v+se+AEgBMAXiPez0DcnAEnAPLnYToKR3ECIAo3xafkFogWCzgB8BTAkEPgHwCwSBEAgMbT949bIjrk5/VUJRBk18c7flpBJNnrkfpXqF1NqjasnU4AqPwf6fqKXSsMsDbQmPqeI/tYjP0h9SIHHvuj9w4qME5Rj/MyLaW3ycBpsIbQ13SQfsbi1aetmKby++wTAHjgkfxPWzFdywB2sDAAADTyd9YACMZLvlAz4UMSoBIYOm24KSFYAwCb5H6bT2+0NQGcaQ+h0UxVBSTNm7hokpXKI4QgMwIATzte/G7DekiV+jVloHrvk/cl2fh43s+ojUPLABLHjwKA79lLAHqyJjJE9dBIKyYAyimtOEgTH247t8XAPfZCpUDMfg+N3yccA4KAvAbMlz5OKHlBZQL2jDCCzSrrRzVAtYSVuldpJ3WNdw5Y2+Oa1wClQWutJsB45AOA1HECIBf/s04AuAc43jzAvt74PvNOAETL43a+z8MJgHw3uQ/oFig8FnACIBdg4gnkQEAA9NWkfmTZ76lJ68iED5gPJNyA6jNafg6AS3K4eq0bm7R9/YMcAADKwGtMoji81niCm2iSOOLOJyggxEOM5LytJhEkq3y1RkoAKLjeqmAe8Arwnr5yuoUANO3U0l7HaHu+48JbPVorBUzVftYfSzVJeijwD94/RgCoJ7zTgK4a27/8qQoA7kVhQFZ9gDUqAEA+pIWtQQmFdI96D+mgyQkHa7b8pduWWFWAQ5oDYJYqAQDaQ6cNM08/knvAMKECTVQBQKJDCAw8+oBv+viyTGmtcvAwBACv+/YLW2wckhEC4iFS5qTMMWIAO61RBQEKijJVK6rc/yEBEKx9x6WtMkzVC+RToGpCu74dzf7sDW2wFUoAvPTE7HcZokkLtURgV31lfcybUI6Rs0ZbOMGs5FmauyHN9ojyiUN0fUOm6hrnjVflwFQLU+gxordVRBiolRawyZG7BzPOT7A3/pqD/2EnAOIbDDkY9v2PtzPgBEDheSCP8EydAIiwQb07t0AsWcAJgByAhycA/nAQBsjHW0uZOAAvcfkbT6SatzsAi7wSMw8wJqYdD//4BeO1nFyaeejpkzZceL0hCsiw30qzygNgyYbfVr3QlJnjfkA1RACedsZGmg/hsEk95zM0hABAyn2EEbTRagBcHft3MXC9QPMD7Lq847HEfMG6mAMydpLykf2emHSS9JF7AAl70C6zV7tX25Alf7WWOAQMI8VHuo9MHg83knm83MNnjrA2Fjevpe8IAVi0eaGW2UsvWUgSQbLqcw/vAciUG9x9dYccfOBJJ3aehIPj5k8wzzuhEqgQ8LQv1r566j0kEIQQofTe3NS5pn7AK99B7dFYQy0gVggzCEIE2E/WPm/DPFs7Y0NUUBGAObJG1g7xw15h85mqUOiuSgBUCqyPebNGLhIypgP6AwrqD5hKAJCPXYI1sj/sKcqNBRsXyG6tFMB5CcbKzNb+3VP+n3WfPATA5d9xI/+ON7Dr632c4HECIJYe2XO0FicAcmQub+wWiC8LOAHwFMCQA9AfCr6u/HXe5OV4/fEyU+f9gMbfB4nvaJsOqs8YSEXiDrBeq4Aarzfx9qH94cEGgCIVR/pOpniA9FglGEg0R5m9eZpVngtP/rF7hzNyBABIAbMk2CNWnvtQDXChKkB6v+38FpOvB+qE0LGDuRKvj4w9PSP/QpWsU+pvl4Hr8Pbhn9PXetbA7noF2mTFH6PKg2AuvKJGWHMg2YgC8hQwFwA4c2OOk5egVkifN+umMgLyerL/E8OPmgJPPqqEacunmi1RHtAP15lfjhtRgEqAdY/RmHpsuUZtuu/GbrvmK8BPUO894QnE8wf7dVmBI7kSdlzcZpn6E3X+q1XCT3x/KChnnZAAhAJQuo/5TdSxgnWOnj3aSv/NWTfHlALp+QBOyg7dn8WbF6sNpmasEftMVsXAki2LZc+V9DXSP1e4ff1zNv+P1XZOAOQPAfDr1i3y8+ZN8v3GDfLt+lS5s27dU6+7qeusPfdxP1dhBOvB2r/bsD5jPb9s2Wzr+UXX9EPaRvue1/sP1loY1+lzzp//pVzZ2QmA+HqoD1mtEwAhxvC3bgG3wKMWcAIgm8Ahh0SAAV4F7SSsw8N74ocj5pEHiAYAjtfLShSQxZ94b5L5ARzx3PN9KKijLWCU3w9piT2A915NEscrMeN4obmfC8APsOQeLrzfAGl+269Al/uCi898f/b+CUsOSPvQcYP3QaZ++jqjbbnnhI4ZCn6Dtlm90jfzYn6QGcEagrng9Wd9QcI82qcD9/TxDqjUPv2eXfbKZ3IIMAfaAbwpj4gtyHNAXxAJ9MPF3GmLrdP72m3t2Bu+52Jdh9W+zBF7sw8P166EhJIMjInNbU8fjE3/wbozxlKb05axgjXyCtnA+umLtigUeI9CAhs8aY3BGP76jP+3am8nAPIHtAB4v1m7Rk4sWiQ7Z8yUtMlTZMOkyXpNkvUTuSbaK5/5Pm3yZNk2Y7q1576fNqUVWgIAkH8jOVkOzp0rxxcutPc/KtjnewiRs0uXytH58+31dspaI0pyBfLc810oiaJ82XMnAB596I2jT04AxNFm+1LdAjm1gBMAzwgkckgIOGBzO/sZiIIz4ARAvgElAPzRBfNl0ZChMrB1W+lQr6G0rlVXmteoLQ0rV5Pa5StJA33lM9+3r9tA+jRvKYuHDtP7FqhqYJ0C5s35Nt9IgjG8+ntmzZIRHTrKjN597P2ttelA/1pykiwdPlwmde8hy4YNl2O6VpQCkRzf+8ofkqtQ2NkJgJw+FsdMeycAYmYrfSFugchbwAmAKAAlTiZkeM8dJPt5zNMz4ARAvgFNZPynlyyWtePGyeQePWVI2/bSWwF+2zr1pUKJr+WD9z60Vz4D/Ie0aSfju3bT9uPlzNIlBorxmBcKkBXmgf9J175u/HhpWq2G9GraXN9PkKurV5uq4cKK5TKuSzfpVL+RjNfXXQkzhdCHwrhOn3MhIBqcAIj8g3Mh6dEJgEKyUT5Nt0BBWMAJAAdceQq4nNxwciOazoATAPkGNImDR/Z+R8EtcvjLq1aavD9FwXCvZi2kdLES0vMBOD6pYQJXVq2S6+odBwScdOcAACAASURBVAz/mJYmQcx8YQSZP23aZERGoyrVpEeTZpKiZEAGAbB8uYzp3EU6qOJhrL7unDlDcyOk5Nu+FEZ7+pxzQTQ4AVAQj9ZRMaYTAFGxDT4Jt0B0WsAJACcAnADwMxA3Z8AJgHwDmkESP7z4gHkUAcT270pIkMGqBihXvJQMVK8/n2/p9/x+/0E7kuPde5BADzBNH0FCQF7pjxCD7zZsMKUA7X/Uz8TXI6f/QV/5jvehyQdJRkibIMlgKLCkX77/YePGx5IWch9kBvML1hV6b/j7vCIAgjmyLoiSYG28x15B3oRgjvc3bzYb3FufnoyQNt+mptqF7bAZ66K/wN58z3pph62CNT+0+aNjQ/AEdg1NaMgcuDe0f/Ylfe6pwpwsCeIDm4bb0D/nAvSHKlKcAIjOh+98mJUTAPlgZB/CLVBYLeAEgIO/uAF/0eSJ9rkUjDLCCYB8IwDCARwAkoR3xMYTDlC+eGkZpAQAn/GA8zuAEVB7aN48k81vmz5dwwGWGjgFUNKn9aPtSS6YNmWKJRM8pMn2iKWn/boJE9SrPlP2Jc6WDZpscMGgwTJ3gJb71Gv5iJGyQxMSXlyxwsBtAJR5BTyjUmA+q0ePlvkh960aNUoOzZ0nN9cky8/aLphL+BqDz5EkAH7VNQfzhKC4uHKFJU9cOHiIzGFdAwfJIs2bkDphooVcALAhTGh7PSnJ2iaNGSNb1TapmngRG6wYOVK2TJ1mNjw4Z65s1CSM2G3zlKmySW26ctRobTNKds1MMNIGEH9L9w6bY1PyOgQ2xU4rRoyS7dNnyJXVqzLIFfYS5cdh3cuNmugR5Qf7QrJH5pCioR78dpu9f7C3gf38NULgHyLACYDC+nie63k7AZBrE3oHboHYtYATAE4AOAHgZyBuzoATAFFLAAAYAeHXklZL0ugxMkgTB47v2t0A5zUFsgB/gCHA9tyyZQKoHdGxkwxr38Harx07ztp3b9zMku9N793HYu17qgS/a8MmmoSwgXRt1ERl910NCF/UWPzAY44n/LKGIGzSSgUz+/TVpIVtpEfjptJN27fX+3o2aS6z+w+QvUoOAFgDQJ4VUI0UARCAf2wDsD+/bLkRIyQXZG5dGjbWV+bZVAbonFcpcD+3bKm1/V49+SRT7N+ytbSqVUeGtmuvVwf7PExf5ylw36KAf6kmIsTW3dVOfE8eBgia4ToGQP2ChiywJxAqAH9+I3SD8YOLPAdju3Q1MubSypVCDgRscEIrICxRcmJAqzbSTe05oVt3maB7OliJn8ndexoxELq3WdnTv88FIeAEQOw+wD9lZU4APMVA/rNbIJ4t4ASAg7+4AX/udS8Yr3s02d0JgKglAAD4gHvCBAD3AMbuTZrKPPVwUzYPEAwQBKwfmDNHpvXqI50VAPdt0craA1a7KxCuUKK01K1Q2RILTuzWwwA9bYcrUdCxfkNLQjhGweqWqVNtLGTreKrxgpOsEKA8qmNn7b+3ZfAf3l7Bts6FvAV43ElsGBAHWQHTiBEA6hnHLsjzzyqwZ40jlfQAsI/s0Emm9Oxl8ySkom3d+rbGVPXko3AgLAA1Q8Mq1eXjDz6RuhWrWBJGADheezz5O2bMMPs0rlpDKpcqK61r17X+E/r2kyXDhplXHwJgb2Ki8F2vZunAH5IgoU8/u7BxPyUZICOGqe1I+ohagPCB/bNnW7WD+pWqSsWSX5sdqX6QqGTKaiV52EdXAOQC3Ov/Q1ZnMON7JwDi9hHfCYC43XpfuFvg6RZwAsAJACcA/AzEzRlwAuDpgCE7oOIZ2ph0/0khAAp28ayTABDgiEcZUAnYJCQAcoDfAZZIygHmeKKn9uwtexJnqaR8nHRu0FiKf15Mqn5dTlUAnWTrtGmWWPCGSvcPq4R/poLWNnXqmVcfQHty8SKLXyeEgMz8vRXkA1C3TZtucvaba9bI4fnzJLHfAGmnALtvi5aqEphsAPeXLVlXKIgkARCEJiDx79eylXRVMgIAj42Q3JNccLOSGXjZ2+raJqi9AOyUHdydMMuA/5tvvis1y1YUVBEHFXRjD0ItqLaAsqGekgPlviplCgmk/2dVYRHE9kMmLBwyxMiTFjVqmcqCcIvbKSl2nVu+zED/QB2fpIeD27TVUIGFZiMUE9i1ZtkKUvbLkhbyAfFCqAUEhecAyAaAf4b/tQzwz71OADz9QThGWzgBEKMb68tyC0TCAk4AOPiLG/AXTZ5on0vBqBGcAIhqAgDggqefUnmAXGT7ePi3KZAH+EMikC9g8dChKstvplL9tiZ7B3BSag8CoHzxUkIYQJrK+W+qmoB7iDEHcG5VYI+EHRIAOTwx7pc0ph7FQXcF1pQjRBJ/avFiA8moEfgdrzbhAx3rN7CYe36HkHgEaIUAtUgSAEj/ISggJiBEID7w3pMbASB/TSsn7FdQDxHSRMsO9lQv/QYlSPgNAgDv++cff6bVCPR7vY/vsQnzP692RtVQv2JVqV2ukvVBRQZi/o1s0TACcgSgNmiu4L+f7gUx/ID/YO0QFNho6fAR2qa2qQjW6zjnlRjYNXOmEQAoMrgIUcCmAZkT9OGveUgEOAEQiUflQtmHEwCFctt80m6B/LGAEwBOADgB4Gcgbs6AEwAZwC2/QVd2FADMCeBJVnk8xQByQO9ijSNHis73gO9JGktO/Pl0lekDjvEoQwAQC1+rXEVTD5zUdkHYAP0COkliN0NVAIQCIOkHKJOIbkqPXuYF595ODRoZyB6pYQB2KfglBKCGerEbVK5qigS87wDfrGwYSQIA4oJY/U4658qlykjDytVM+UC8PvMjHwBx/i0UfJcuVlxa1qwjy9WLf0E99yTda6Dty6j3HU8888aG2DggAEjm10RDALgfEI/qIdgHVASA+d7NW0h7VUCgmoCcCV87JAVqi471G9n85vQfaIoLEgMSctCkanUjBqj4wNhZ2c2/zwMiwAmA/HmYjsJRnACIwk3xKbkFosUCTgA4+Isb8Ode94LxukeT3Z0AKDDwlRMCAOB+XBPIAR57qKef2Hzi2ZG8b9Ys9cSaA3rJzn9VvyM7PwQAbQHpeMuJ1Q8lAHhPHP18zZ7fRckDPP4p48dbv3jVq5Quq0C5hHmq29SuJ+3qNHhw1TdQ3ax6Tet/weDBcmzhAgPQWQHWSBIAKB7Wa1x/4yrVpdgnX1j1hEaVAdTBHOvb+2bVahr4HtS6nSXjgzAhxh/CgIoLkByH583V8nthBIDmWGhRs7aFDyD/x0MfEAAk6FupNsau2IscBMwnHMR/p+qM7TpWt8ZNVE1QRQmaHrJH1QeEUpAjAHIB9cReDdUIvzcrG/r3ESIDnACIlsftfJ+HEwD5bnIf0C1QeCzgBIATAE4A+BmImzPgBEDUEwAB+MSrjxx/SNt2lul/jWb5R+pP9nqk/xNVBQDAJZadpHMPCQAFoFkSAMtkgRIAePR7Nm2m94xXmXyCEgodTTkAUIV0oArB2rHjQ65x1j9ebjLbf6OecQiNrEBqpAmAVCU38NJX0kR6nTTMgVwGyOnD50hpPWT3gH9IEUIcIAAqFP9apvXsI0dU7YC3/hEFgBIArVQ10L5ugwyJfrAHDwmApkoCNJWVShCgSAgH8Y8QAJWwfzphAwHAXrRUgqFzg4aWmyD83qxs6N87AVB4nqSjc6ZOAETnvvis3AJRYQEnABz8xQ34iyZPtM+lYNQITgBkCVrzGnBlVwEQzANQv1Vr1QPIezdrabHqexSsj+3cRQYrAUBJOggBAG1AAFAFoHb5ijJG26AgCGLNAZ3I1vkOGTsEAEnrKPuHLH6MlgYkVp68AsT7ozRA/k6/XAB+pPHEvn+ncfFBv8Fcw18jSQAAuKlQQN4C5kg8/vbp0+WyltvLmKPOD889c8RD/4N65L9NTbUcANkiALRMIOUOgxh91oPN6JOqAoRidNbQCPIyUBKQ9Qdrph1hBZsJU9A2jVSpQGJBQjMIAXACIEJAPiTHRGD7bL26AiAqnrULYhJOABSE1X1Mt0AhsYATAE4AOAHgZyBuzoATABnALVvg4VlBRyb35ZQAIOkfCelIUke5v2Faxm/FyJEWu0/ce5qC4uvJq60sYEAAdGnQxLzkZOsHJN9W8I70n4s2mzR8YMSDsn5I0wH/5BRgjGbVa1m+AWTuVzSz/s9axx5wSwJBQD/A97om3GNefP8k+0WSAABc49Uny38TlflTAhCwzVx+VlIDuwLI761fb9/h+YcU4T6SAOaGAKD84L7ERCMd2ikBMUpLEBKKEagAsAMJA6kUsEzt1qZ2fQubQI1xTsMtUCCgBnAFQAGSAE4AFJKn8chP0wmAyNvUe3QLxIwFnABw8Bc34M+97gXjdY8muzsB8ETg+iRQm9vfckoAANoBucljx1oMOgnmyOBPvXtyAhycO8eA7n0Fvw8JgMbyddHi0lSBMqD+yPz55hHn94PqkZ7Rp68lAKR8IAqCs5pJH+8+ZfR6aEgAme7JB7BFlQc3kpMFAEzM/Dktiwd5sH3GdCMCIAWeZI9IEgB481E64FVvXbuuXbP69Zd9Sl6Q0R8wDuiHyEAxsWdWQnqZPQ2NyC0BALFA0r95Awea3SiFiIKC8oFmG7XP5VWrrOLCqE6dpVWtuhpO0cFyJKAeoByhEwAFCP4h4pwAiJnn9ZwuxAmAnFrM27sF4sgCTgA4AeAEgJ+BuDkDTgA8Ebg+CdTm9recEgB4l/FkA2JHqueZGHhKyVHrnqoAgHIAKv0GBEDXhk2kTLESVnee8oGA1WWa2X6Rlg0crQCV+HnUBFN79hIy0t9WuTxEA/kGFiohQMUBsugTHkCuABQHXIn9BliYAG1IJMiYT7JHJAkA1mhgelaieuAB2XVsDaM7dTFJ/rIRIzSx4WCZ1quPXYQwMEdyI+SWAGAPUBJAKozr0tXIB0IFsGtgm7maQwDSpJuGTwxt195CKAhDQCkBSeEEgBMAcfRIHVVLdQIgqrbDJ+MWiC4LOAHg4C9uwF80eaJ9LgWjRnAC4InA9UmgNre/AZrvqLcaT/wEld83rFJdS+p1t894sTPzqiMvp3QfHm8IgK+1nB3AnhKBQT37gAAgUV7Pxs2k2tflTHLeu1kLi0mvV7Gy1CxXQarq93iw5w4YZKX/ANWAa5Oxq5QeGTvEAhUCKAfIRT6B2uUrqfS+hknvV2viPfIDPI0AoF/yCxA3T8UCKhegKIAYuKSx+zN695V+uo4Zvfukl+bTeP3M7MvcuJD6E85AWT/UD2Ttr1GmgpUmZJ6UKCT2HrCNZJ+1ERJwYM4c89w3qlJDvfiDTUnwvZIq1ucWnYsSH8uVICH3AfMh3p88B6FzgSBh7F0JM2WKEieoELBpum0qal6CKkrK1LfSiyRJZJ2sn5wLlFhEuUDCRUiVQ1qFgLFD+/f3eUwQuAIguh6683E2TgDko7F9KLdAYbOAEwBOADgB4Gcgbs6AEwAFBr4Afj8ooL+q8fVk76fmPKXjrmpsPUA/M2AIiMTTDzBvWbOuVCxZxmT8p1WCHmSzf4QAUGl/XQWnlAhE4o/3f6bK/qf16i3TFWxT0o6wAEAufTNmcDE3yAY86IkKWmmPV51XvNzrJ0zUJIILzCOe2VxDgSxzwgtP5QLA/zkNNUAyD5iGBCGOfsPESfYKofCjziX0/tD3wfwA1YREAPCx3Uz1wrOudM9/bwt5MHuqfVkL66NvQiiww0ElAwgVgEygz1+2ps8Fe1ANYf3EiRZGgOc+fHzCLBibnAl4/iFkAsUB7+mfPWVvA1KFtVJFgHvICZA2aZLt9dNsFzq2v48AOeAEQGF7LI/YfJ0AiJgpvSO3QOxZwAkAB39xA/7c614wXvdosrsTAI+Au/wEWAY6FRQC9pGIAyh5/TEtPZFdODDkM21PL1lscvxO9Rtrkrl66qWemCHdD/okBMAUAEoANKxcVcZ17WrAE0CKx/3iyhX2GsT1ByA1dP1Bsr+b6j0nrv3hfSvkin7Gqw7pALANn2toP7znd4B0UDmA98F9jI3i4RudM68/btr4VEXBrw/6BNR/q/cArAlbYF3BBdgnpIE2EBBc2I85YGuk/MG6g/74TI4D1kYuBObJfZmt5+fNOra2va4e/tCxeX9d50PIAURGYBte0+eb3j97xOfg9/Ax/HMEwL6ek8fs6ARA7D24Z3NFTgBk01DezC0QjxZwAsAJACcA/AzEzRlwAuBxgJAZaMij7wB/XIBtA6kPPmcGCvkOcEocOdLzHk2aW3k/ysvh4Q7uoZ9QAqBRlWoqk+8hp5Q4AOACvIOLttwXXKFgKfgumFtwD6/hcw3GDr0/9H1GX1vS1xp8DoA3nxkn43t9H3p/Zu9D29p8QtaV1RxtnAfzD72f/sPnEjqf8PFD7w0fO/gcfv8j94StN7x//5wJcI/U/6ATAPH4aG9rdgIgbrfeF+4WeLoFnABw8Bc34C+aPNE+l4JRIzgB8FSgWdBgDOCI5Bzwf2H5ckkaPUb6a/w4sv41Kme/rB79wJPNXAGgjxMAPeXMkiUG/At6PT5+HoLbSIHkWO7HCYCnPwjHaAsnAGJ0Y31ZboFIWMAJACcAnADwMxA3Z8AJgKgnAAD0SNLx9KeMGy8TNFFgt0ZNZWznrpZUzhIGahuIAicAHFw7wfKUM+AEQCQelQtlH04AFMpt80m7BfLHAk4AOPiLG/DnXveC8bpHk92dAIh6AoA48e3TZ1i2/6bValrcPyXvNmoSOTz9gfc/nAAgSV/f5i2lWfWalhzvrCYKRBrvAPEpADGWvd++NvnNCYD8eZiOwlGcAIjCTfEpuQWixQJOADgB4ASAn4G4OQNOAEQ9IIYA2JWQoLXlO5jnf4yCfzLmk5QvHPybAkCVAPc2pJpiYOHgITJV8wWs13J217S6AGoCJwCcAIjrM+AEQLQ8buf7PJwAyHeT+4BugcJjAScAHPzFDfiLJk+0z6Vg1AhOAEQ9IP5FE+dRd/704sVybMECOaOefDLZhyb+CwV0ljNAgT6hAWSkP798mdWiz6q0YOi9/t7JgZg/A04AFJ4H8gjP1AmACBs0q+7+97//Cdfff/8t//nPf+TXX3+V+/fvy/1ffpHff/9d/vrrL/nvf/9rbbLqIyffMxZ9/vHHH9b/n3/+GbG+czIPb1u4LeAEgBMATgD4GYibM+AEQNQTAAGgRwkAiOfVstyrpz+Q/YeCtiCbPd5+FAJcJBGkbWbtQ+/1904AxPwZcAKgcD+k52L2+UoABCAYoAsQ5gKk8sp3AQCmXaz9BeD/559/litXrsrOnbtlzdp1smHjJjl85KjcunVbfvvt94iBdIA/fZ48eUoOHz4qly9fEUgA/3ML5MQCTgA4+Isb8Pdf3+u432snAKKeAIh5QOZx6X4G8/MMOAGQk0fimGqb7wTAHwpCf/jhB7lx46ZcvnJFLl26LFevXZM7d++aR/yvv/6OGAgOCIfMXvN7F5kDoPz69RuyMW2T9O47QGrWaSDNW7aVyVOmyb79B+Teve+MBInE3H786SfZf+CgzJk7XyZNniap6zeafSPRt/cRPxZwAsBBYdyDQicGCkaOXxB2dwLAwVd+gi8fy89bQZ8BJwDi54E+bKX5RgAEHnCA/p69+2XegkUycfJUGTt+kkyfmSgpqRvkzNlz8otK4mmb278A9AeSe2T3oTL7SIyRkzky3m+//Sbnzl+QBQuXSJVqteTfr7wpH37yhXTWMjbrN2yUb765ZWqInPSbVdt79+6ZumDg4GHSqXN3JQIWKPHyY1bN/Xu3QKYWcALACQAnAPwMxM0ZcALAAVlBAzIf389gfp4BJwAyffaNhy/zlQAg1v3I0WMK+idKuYpV5d0PPpE33vlAvviypHTo1E2SktfKXSUIIgHO6ePXX38zpcGp02fk+ImTggwe9QFkQCTGyMkBySAAzp2XefMXSYVK1eX/XnhF3n3/Y+nQsYusUwIk0gRAqpIK/QYMlnYdukji7Hm29pzM2du6BZwAcPAXN+CvIDzOPmZ0qQucAHDwlZ/gy8fy81bQZ8AJgLh90M83AgBPPF7p9RvSpEPnbvLWex8ZAP7Hcy/JK6+/I9Vq1JFZiXPk2rXrGbkAnnVXANt4+4m1X7pspfQfOER69Opr/R/ReHvi8JlPfv45AZCf1vaxImUBJwCcAHACwM9A3JwBJwAckBU0IPPx/Qzm5xlwAiBSj8uFrp98IwCI/T93/rwkKMivXb+RvPrme/LSq2/J8y+9ptfr8mWJr2XI0BGWsO6XX37N1EMPiOYCvAPwwy++D34jBn7n7j3Ss3c/KVG6nHxVsoz06NlX4++1HIyqDEiIF56MkP64PxiH16BNMBa/hyYrDP89aBe8hra3EIBzuVcAZDav8PG+/faeZKYACF1bYKvg3uA11AbBiQ7uy/yeR22SnfnRxv+i3wJOADj4ixvw91/f67jfaycAHHzlJ/jysfy8FfQZcAIg+h/E82iG+UYAUPZulwLyAYOGStkKVUz+/3mxEvL+x5/LK2+8Kx99Wkyl6p1l3br1cvv2nUw99IBPyubh2UfSf1TDCQ4f0Utfjx8/IecUXF9VBQEqgoOHDsvMWbMt0d5b735o4zVq0kKmz0iQnbt2y8VLl+XW7dsWIsB9x/T+o8eOy5WrV+UXnStj/fHHn/Ldd98LoQP8xnVJ7yNZHxn7f9WY/jt37uq4F+TYsRNyhLk8uI4ePS6nTp22/n5SxQHgOhIEAMCZeQHwL168ZHYgrILr+PGTGfO7qfkECCsIDwHgfuby88/3zU4nqBIQMm/scE0TFWKDUJDOe8oWYgtszz2MiU3Oau4G5vPnn/8xuxHqce+77yzfwdFj6TahbWCP7zUM42+t+uB/0W8BJwAcFMY9KHRiILpk+nm5H04AOCAraEDm4/sZzM8z4ARA9D+I59EM84UAwEv+3fffy4qVq6VJs1YC8C9eqqw0a9lG6tRvLJ8VLSEfKBFQo1Y9maYAHUAJSOUP4Mn9gEvAOACVUIG+/QdK1x69pUu3ntKley/9PEgmTJyskv8Vsmp1sowZN0EaNm4uH3xS1BQGL6ra4PNiJaVW3YbSs1c/S8SXtmmz5h1YIxMnTZVefQZomIB+v2iJVSX4XTP2Mx5Ewtx5C0xJgJogcc48zdh/UL65dcvIgy1bt1syw159+utcekln5qNX9559ZLAqGsjCf+jwEVs/oQdnz517phwA2AFSAnANwbF5yzaZPWe+JVEcPmqsjNBr3IRJsmjxMpsfyQbXpqQ+RgBgy58VyJ89d15WJSXbPcNGjJbhI8fIkOGjhPfLV6yW8+cvGmGRbv//aXLGX+WkEhoLFi6WkaPHyVBtO0LvGanjzpg5S/ZrFQOSDEL0ML/tO3Yp2TJL+0vvc+TosVrtYLqdAQgEFCH+F/0WcALACQAnAPwMxM0ZcALAwVd+gi8fy89bQZ8BJwCi/0E8j2aYLwQAcntK/k2eOl3KV6pqXv9qNevK6LHjVREwTHj/oQL1r0qW1fJ4A2Xfvv0m0Qd8cuHxpnrAXv0esFmnXiP5+LMvzav/7vufyHsffirFipeWukom9Nekd4DSxk1bWJsXXn5DyDPwz+dfln/r+9feKiJfajgAZfhmJiRq2zFSt0Fj+eizYvL+R58bcIdkoBrBjZvfyJq166Rjl+5KJHxhV3tN2AfBAMDGo01IQ72GTeTTL76S9z74VJP6fWJXkY8+k2IlShsJQRs84ZAGp8+czTEBkA7C/2uKA8A1Jf1YI2vo02+ggXzyHAzSjP/jxk+yObOG5LUpjxEAkAi3VGGxY+cumaL7AanBhVKgqxIpzVu1FSoHUJUBJQBEzH/+85cRAsuWr5Q+OmZ7TSoIwdG3/2ANq+B1oFUxuHXrtqkqtu/YKROUVIEM6aS2gxyh/8FDhuuYM5Qc2GkKijw6095tBC1gBMB/78r1vy/J1b9UYeOX28DPgJ+BGD4DN384Kd+f3Cf3d2+XX/Xh3C+3gZ8BPwMxewZ2bJPfjx2Vv779Vv73wPEawUdI7yqKLZAvBIDF4+9Kj8c32b+CY4Dm8pWr1GO91JICfvpFcXmnyMfSoFEzA7DI5vFWcyEnB/zjRa5YpYZVDiBsgCoCAPfPVFFQukwFqdegiQLiATJ6zDhp2bq9FP2qtLz02lsG/sm4T7JB7iEEAeJhhpYfBOxCStDuhZdflzbtOmoegiMmdyecAG940xatrWQfZfsgFhYvWSZnFMifVPl8wqw50ki/I88ACoPPiha3Ob2rZMAb77wvRT78zO5BRQAoR3Kf0yoAEACQKBASyPoB1KgnAPtpm7aoFP+oyfH3qRd+zZoU++6EetkhL8JDAAD0hFBs3bZDVq5KkmRtv3vPXiMoNm3eYooAwP0otSFtCFvAq786aY301nH7KuHAmrdt36FhAEclLW2zLFy0RPYdOKDzu6nrO6llHROkRat2MmTYSKvswN6hpNi6bbupEg4cOKhKhj+i+N/CpxZY4D//1TCYP27JzV/OybX7p/1yG/gZ8DMQ02fg5t3j8u3pffLDwZ3y097tfrkN/Az4GYjdM7Bvp/xy+rj85/t7TgAED75x8povBADl7QD6xOB/9GlRS/gHMAWw7lcwSFlAAPSrb74rZctXkanTZ8qFCxcN9FKy7/z58zJ/wSL11DeR198uYpJ+1AKdu/YwCfwM9eQTOkAIwHR9BSQvUpDeXZP+kVwQ4E7SwfKVqkk3DRuYqQAW4Jyq7VAhVKhcLccEAPNjXQcOHpKVq5Nkzrz5pgZgLvTZoXNXKfl1BRuXMod46JHFQwLklAAAtFO+EHsh1UcBQcgDYQDE3uPVhygJwgMuK8AH5KdoPoVwAoB23333neUKuHDxonnsAfn0cffut5Y4kL6xLUQHuQIIXWBPmrdoY8oD1kFIB/cwL3IRkE/hG1UAkOdhkHr6a+sc52u4AKQF82IfaXv12jVTQrAm/4t+C/z152/yro0opwAAIABJREFU061Lcvf8IblzZq9fbgM/A34GYvoM3D63T25eVEL78kG5ccUvt4GfAT8DsXsGbl49JN/ePie//fqD4oj8rY4W/U/AsT3DfCEAzqtcnpjxMuUrm3eckn8A9kuXLhsYXKrS8qrVa5vHHC96t569FdxuNRn+HxqLv3ffPvUmj1BAXV4l/O9ZvoAOnbrKho2bNLTgqiXlu3Hjphw6dNgS/CFdv3T5sixZulxqa8w/9xTRMIF2Kl1PSl5riQJJREdiOsISKlapnmMCgLn/8OOPJpOHBNi0eavOJ83IBzzigOCKlWsYqfG2JiFs36mLViDYZEnzckoAEAKB9H/VqmRp266ThTosW7FKx76u4RF/WJhEEC4BsAZsG5jXUIFwAoB2APdff/1NbmsCw/MXLpjd9uzdZ6QI4QqtVQXRpHkrId/BT1pNgZwBJFRsoaoNQg7Ik3Dw0BH5Vss6BuMx5vff/6C5AA4qSTHa7D5eCRnUBReUIMDeqBhoz8U8/C/6LfC3qj9+PXtGfty1Xb7fskmvNL/cBn4G/AzE7Bm4u3+bXLl2UM7/oImFfznll9vAz4CfgRg+A6flxu9X5P7fP8nf/3MCIPqfyiM3wzwjAAB4eJtJ3ofnulnLtiaHL6Jx9niwieUngzzeY2LLAepvF/nI4udr1WkgszV5Ht5t5Ock6+vYuZvG9Bez3yEQSDB38dJlk6gDaAHJPyogJ0M/9+CRRrZOfD6qgfc17ACvNiAc4IrXGzn+lGkzckQAIPdHzXD69BkDz0GiPRIOEoLQuFlLUzpUrlbL8hpQ4pByh800jACPPJULckoAMNezZ89r0r950lyl/63adJAd6oUntIJs+qFgmvdc93SNWZUBxD4QCltUkg/gB9R3UPu2btvR8iGUKF1eqtesZ1J/CADGZ+6EVzB+yzbtLcHh+g1plgiRZIn0STuUByQKpF0TtQUqDOL+GYsKC8yZBIuhc47ccfaeIm2BvzUXxq+nT8tPmijmx7SNfrkN/Az4GYjZM/CD7u3dvVvl8vVDcu6nk3L29zN+uQ38DPgZiOkzcO3Pa/Lz3z87ARDpB+go7y9PCQCAHrLwZctXSaWqtazc32sqxS+u8n1i9PEUk7keYP6VJuYjrv9ljdMvprJ9Muifv3DRpOYpqesVnHawJH3E1JMnABAOyMfzDJj8++//2nuIAL67feeOrNYM//UaNs2EANAyfipLJ4Z/qhIAlarWUAXA2/KCgnXGIV4d2Ts5AJh7k5AcAI2aNLeqAEjdCTUgaWGpshUV7H8hpfUV4NujV18D6ayJ8ANyC5A7YG3KOlMd5JQAwFt/SgkHvPCtFHyTrO/AwcPmxQ8Af/g5y4oAgJCBvIB0GaWhCkOHj7QwCpQQY8dNtLlX12oMdeo3yiAAsOcZrczA/CFMIAxIhkhYw6zEuZZskOSG5G1A5k/VA0iASZOnWZt2D9pC6mzbvlNJgmu6Rx4CEL5n0fg5gwDY5gSAEyBOAPkZiO0z4ASAEx5O+vgZiLcz4ARAND595/2c8oQAAJTi/ccjT4w/YL7oV6UUCL8hz/37VXlVgT4Z94mNJ3s/eQEA///S37jefu8jadG6nWzVEntXr16TtetSpW2HTir9/8ISBVYlhGB6gqoHLprnGUk5UnjK0KWrBlTerpnuQwkAKgUAWlO0r7taUeBX9VafPHVGiNmnP4gHvPV46rfv3GkSekIX5mnuAUgE5g6YhwBIUCCelJyi5f+mWVJCQgxQJwDO6Y/keuMmTJYqGtZA4kH6Jf8BSfkIO8gpAfDbb79b2b658xZqGENnIwAOhhEAAREQvKJyCFcAEPv/vcbur1JlRI/efaWTEi+EYuzZs0/zLFyQI0eOGjFA5v6mzVsbuEcBwF7e1zAAbHpKvcGoAUaPnWChGsynS7eeVt4PwoTqCYRGXL9xQ86dO29rJqdAa1UttO/YVXMIjLNwCdQC/hf9FnACILYBjwNa318/Aw/PgBMADv7iDfz5ev3MOwEQ/c/ieTHDPCMAAOV46AGbjZq2tFJ9LyqAflk97W+884FJ+SmbF5TOe0vj5F9+/W157sXXDIxXqV7L6twf0zCB9RrL3l296mT7B2yTaZ9kfmkaGmCJ7G7dMqLg6NHj5n2GBCC+PVkBd/1GTe0ewguQ5wO+z6o3++6331oIAF5pQhJIEsjYNTX8gOR3AHVK5Y0ZN8EUAs+/9JoRAEj9IR9oQ5z/F1+WkhdV4v91uYpafWC8hRhs2botgxxgTbklACA3SJ4HsUAoREslR8iRECTiC0A/QP0PjbOn/R1dP+UCQ3MA3NEkf5cvXzEvPvH8ZPRPS9ucEcsPMYKsH9tChODdh8RhLwHsyPxRRhA+QCm/efMXWltsNnb8JM0loHkB1Pa/PAgHIPkf816r+4BtyMHQtn1nu49+/C/6LeAEwENw4EDJbeFnILbPgBMADoYcEPsZiLcz4ARA9D+L58UM84QAAIgCGM+eO6eZ+aeYl58M/8j3ycRPCTtAJlL54GqrnuTKVWvKO+9/bAn5PitaQkF/HwO6xLuTUA6pPiEEAO7SZSpa/Xpq2QPiSVhHXoDlmhwPTzRx6RvTNlsyu9fffl8VBu9IKS0VSKz7rNlzrYwdCeoIJQAMv/nuB0KpwM9VldBBPdUAVsrYNVTP/ceffWm/oQDInAB406oY9O47wMA1Uvo2mqwPhQPKgdwSAABw1rNz126T31evVVcmTppiZfgCgB6A9OuaABHQjXIivAoA4RjHT5zUcorjNFShrZX6O3z4qHr3fzGQzz2zZs8x0oQQAEIOKMH4448/WVUGI06URMDLj8IAcmbCpKmmdBgwaKjZm7wKJBbMiPdXEoDygJARhAw000oChBHQp/9FvwWcAIhtwOOA1vfXz8DDM+AEgIO/eAN/vl4/804ARP+zeF7MME8IAMAowJFycV00Xh2vPYnwAP/DRow2L/O5c+flknqjg4ta8eQDKFepqoF8EvcRi06sOvXmqVeP1LyoetwB4vT37gefGDincsDX5Spp0rlWltSOygB4q0k+SH6B9z/+3EA497xd5GMpW6GKhSWsTl5rXnWA+ydffGXhB/T9jrb5VD8D4D/U8ATm/4/nXkpXAGgIwMyE2ZpgcK3J/EspEQHAR9nwvoYoFNWQBpLoFS9VVj7QvAD8llsCAA8/Xn2y6RNiULNOfSUimhlAP6zx9njkAf7nz1+ULVoacLvG2Z86ddrk96EKABQZ5BKgXCIqgj667qTkNXJG4/eR7EMw9OrT38iMytVqaqLGRJP903eS2p+yfjt27s4gA7ZoiMawEVqWUMkCQgJQD1ANgeSLVFs4cuSYze2Ekg5Lly230oWduvSw/ACUF/S/6LeAEwAPwYEDJbeFn4HYPgNOADgYckDsZyDezoATANH/LJ4XM8wTAgDpN9n956pEvK5mxie+/02V/RNXvnTZCsveT2K50L9vVZK/TpP9te/UVSX+JSxPACCa5HQQACSZ27R5i4xS73WJUuUyPOuAa0A7IJ9YfWL2Aax/av94oZlDYyUG3lP1QQDGySXQVRUIazWWHeIBOT+Z7T/6tJipC4jb//CTolKuYlULD6B8ITkCXn+riCX5owwecfPE9BMDj0IAAoA29FGxSg2dSxNVLNQUQhvIeYDn26oAHDshlAmsWr2OJR4k/0EXJSk2KHj+5tYtK9EXapfgPaoKQDMedjzzKAyoBoCSgqR8AH1yLUBObNu2w0A9yRMHDRkmgG7yB1AhwcoDpm6QwRq+AAlA0sNuPfpYH8Tqo8ior3ZsrGEbqCogckiomDBrjrTStig1evTqZ+0CSf+w4aMM/AP40zZtkXHjJ5pdyCXA3EhayFi8zp2/QHMvnLKSgMHa/DV6LeAEQGwDHge0vr9+Bh6eAScAHPzFG/jz9fqZdwIgep/B83JmeUQA/GHl4PACA0pbtGpngHDa9JlaJ/6AxYlTui/0jyRzJ06eNO8wmfVbaJUAgCmSfhLUUWOe+HKS340YOcYk7E2bt1FSoY2979yth4UJEJsOaKV/EtgBSuctWGx9Be1JfkfYAHH+15QsOHb8hJEAKAHSQXFH9YQPsP7mzF1g8e2UyOOaPGW6ecEpdwcpsXjJcunTf6CBY36nj4kqiyfMgESAHTp1s98m6X1k7r98+aoBdJQQ2AVVQ6K2heQgph+gn9kfKgDWhLKBcfHGE6KAx757zz4GygcPHS4rVq22329+841VM1i8dLnF8m9WZQDSfVMSXLho5AX5DXr27ifduveW7mpr5kS/EAwkB8Sbzz339YIsIedBV20L6cDecI0eM0E2Kegnt8CtByEGhFVASKD+oF/u6aHjTNfSj9ic/rJaZ2Zr9+8KzgJOADwEBw6U3BZ+BmL7DDgB4GDIAbGfgXg7A04AFNwzdkGOnCcEAEAVbzUx5Uc1ThxvOXJ8ZP+UpwOEAmhD//6jYQNkj790+bKB4d16z4GDh+TylSsG5P/8M728H2XmKN9H/P7u3Q8ufU9bytvhrQ76R2VAZQDAKQnqgvYHDhw0GTuAG0DNuMwVsmDP3n2mCgCQnzlzzuYD4EYpwIVcngR7AGPK3qEyANQGv9MHJfNQQHAfVRDok/u+VQ888fYBUMYuzJtyh5AWJPALt0uojfgN4AxZwpoYF7sGcz50SAkGtRfx9VQ5oE8+Q6CgLqD0Xvr9v1hcPmqCYH70QX8XL122+Zw7f97uIZyDC6KEfAF79+638YIxySlAdQDsiPLje92fi5cuGflAm+Dat/+gVTJgPyjZ+KR1hq7Z3xesBZwAiG3A44DW99fPwMMz4ASAg794A3++Xj/zTgAU7HN2QY2eJwRAAFQB4IBxgCEXIB5ygN/DASCf/1Zw++g9f9hnQGtwD+/pJ+gz41XH4XvAatDW+lSw+Wif6XPhO+ZCf4+P+7vNm/5oFzoe74P7uDe8b9b7tPv++utv7f/BGmgfzCUTu2R2MDIbFzswNvPhd1uXro/PXKF24Tc+h64r9H7aM6fQeyBoQvcysDvroB19pts7fczM2v75JzZ38J/Znkbrd04APAQHDpTcFn4GYvsMOAHgYMgBsZ+BeDsDTgBE6xN43s4rTwiAvJ2y9+4WcAvklwWcAIhtwOOA1vfXz8DDM+AEgIO/eAN/vl4/804A5NcTdXSN4wRAdO2Hz8YtEFUWcALgIThwoOS28DMQ22fACQAHQw6I/QzE2xlwAiCqHrvzbTJOAOSbqX0gt0Dhs4ATALENeBzQ+v76GXh4BpwAcPAXb+DP1+tn3gmAwvdsHokZOwEQCSt6H26BGLWAEwAPwYEDJbeFn4HYPgNOADgYckDsZyDezoATADH6AP+UZTkB8BQD+c9ugXi2gBMAsQ14HND6/voZeHgGnABw8Bdv4M/X62feCYD4fMp3AiA+991X7RbIlgWcAHgIDhwouS38DMT2GXACwMGQA2I/A/F2BpwAyNbjcMw1cgIg5rbUF+QWiJwFnACIbcDjgNb318/AwzPgBICDv3gDf75eP/NOAETumbkw9eQEQGHaLZ+rWyCfLeAEwENw4EDp2Wzxw8YN8t2G9XIrZa3cSE6W60lJWV4316yRO+tS5N76VLt4f3ON3pOc9T38dmvtWvk2NVW+13EYz/fq2fYq3u3mBICDIQfEfgbi7Qw4AZDPD9ZRMpwTAFGyET4Nt0A0WsAJAAdSuQWFgH9A+t7EREmdMFHWjhsva8aOe+zi+61Tp8nR+fPl0sqVcmX1Kjm2YIFsmTJVUrK4h374bXfCLDm/bLncSUlxEiDNz+yznlknABz8xRv48/X6mXcCIBqfvvN+Tk4A5L2NfQS3QKG1gBMADqaeFUwF9+HFPzhnrgxu005qlasoFUt+LeWKl5JyXz16VSpVRtrWqS8zeveVbdOnK6hPkMS+/aVVrbpSqWSZTO+hj8p6X+9mLSVp9Bg5u3SpkgBrTQXgSgA/u8EZzO6rEwAOhhwQ+xmItzPgBEChfUTP1cSdAMiV+fxmt0BsW8AJAAdR2QVPWbVDnr9t2gxpUrWmvP762/KvF16W//evF+36x3MvCdc/9Xrhxdek5BdfyYDWbUwdsGHiJBmqpMGXnxaV51545bF77F7t5/+ef1m++PgL6Vi/kSweOlyOqILg9gMSIKs5+fd+rjM7A04AOPiLN/Dn6/Uz7wRAbD/HZ7U6JwCysox/7xZwC4gTAA6UMgNKOfkunAB4/sVX5aWX35D33vlAin9eTMp+VVKvUublb1O7nkzv3Uc2T50qaZMmP0IAPPfvV+Xtt96Top98IaWKFpcSn38pH7//sbzy6pvy8itvGFHQo0lzUwIQQuAKAD+7OTmntHUCwMGQA2I/A/F2BpwAiM+HfScA4nPffdVugWxZwAkAB1E5BVHh7cMJgJcUrH/64afSuGoNmdCth3rth8mSYcNl2YgRliPgwOw5cnLRIov9D1UAvPH6O1KzbEUZ1q6jJPTrL9N69ZGeTZvLV58Vk3+reuCtN9+VuhUqy5SeveSE3k9CwPC5+Gc/z086A04AOPiLN/Dn6/Uz7wRAth6HY66REwAxt6W+ILdA5CzgBIADpicBpuz8Fk4AvPraW+bB79uitWyZNk0urlghl9Vjf3nVKrmWtFoz+q+Rq5oAkISAoQTAu++8L+3rNpRVGuuPzP/Q3LmyRMkDSAEUBa+qEqB88dIytG0H+80JAD+72TmfoW2cAHAw5IDYz0C8nQEnACL3zFyYenICoDDtls/VLZDPFnACwEFUKEB6lvfhBMBrSgCUUdn/UPXkA+K/W59eug/JfnDd0NJ/25QcCCUAirz7gXRt3FTSJk8xsuDq6tWySd83qlxdXnklPQygtIYG9FNiYX/ibCs9+Czz9Xvi98w7AeDgL97An6/Xz7wTAPn8YB0lwzkBECUb4dNwC0SjBZwAiF8wFCkgnBkB8HWxEjKwdVvZpeX7biQnyzfq9f9GkwVSMeDe+lQrG/gYAaA5Azo1aCwp4yfI6cVLNExgsawaNVpql69kOQDSFQClZEjb9lZ1wBUAfnZzeoadAHAw5IDYz0C8nQEnAKLx6Tvv5+QEQN7b2EdwCxRaCzgB4CAqpyAqvH04AUDCPnIANK1WSyb36CXLho+QFSNGGpin/N+pxYvl3LJlGgIw9REFADkAaperJCM6dJLZ/QdqssC+QtK/rz4rqhUEXpV33iqifdaUOQMGyhktB/i9KgrC5+Kf/Tw/6Qw4AeDgL97An6/Xz7wTAIX2ET1XE3cCIFfm85vdArFtAScAHDA9CTBl57dwAuB5zeb/4suvyztvF0nP6K+l/0p9UVwqlCgtvZo1l5WjRsn+ObNls8r7Q0MAKAX45hvvymcffWaJ/4ppecAPi3yssf9vWQgAJQRHdews+xIT5U5KilcB0Kz22dkfb/PQTk4AOBhyQOxnIN7OgBMAsf0cn9XqnADIyjL+vVvALeBlAB1E5RpEhhMA/3rhZfnn8y8JgP7fL71mCfxI4vf2m+9Jk2o1ZK568HclJFh8fygB8I/nXpJ/6T0vaMZ/CIQX9Z6XNfHfB+99KLVUGTC8fSfZookDr2siwSCXgIPbh+DWbfF0WzgB4OAv3sCfr9fPvBMA8fmw7wRAfO67r9otkC0LuALg6aDBgdWTbRROACDXx2v/yYefSMWSXxt4B8A3rFxNS/x1kJRx4y054OYpjyoAntf73lewX+bLklK9THmppbH/9StVlQ71Glo4wI4ZM6x6ADkEIAB8X568L26fx+3jBICDIQfEfgbi7Qw4AZCtx+GYa+QEQMxtqS/ILRA5CzgB8DhIcOCUM5uEEwBBDoAmGq8/qXtPWTpsuOUBWDlytGzVzP8nFy3KMgdAzXIVzdM/d+BgvY/cAaMkdcJEIwyuaVWA7zakVxTwPcrZHrm90u3lBICDv3gDf75eP/NOAETumbkw9eQEQGHaLZ+rWyCfLeAEgAOp3ILDcALgVS0DmF4FoJ3sVql/UAXgllYCyKgCkJT0WBnA90KqAJAk8KaWCuSe2ylr5dvUdULWf/f8+3nNzXl1AsDBkANiPwPxdgacAMjnB+soGc4JgCjZCJ+GWyAaLeAEgAOq3AAq7g0nAF5TAqDsVyVlWPuOcmjePPPah44BiL+h4P6xMoDvfiDdGjeTTVOmyjUlCDzLv5/N0HMTifdOADj4izfw5+v1M+8EQDQ+fef9nJwAyHsb+whugUJrgXQC4JT8tG2rx1R7QsBnOgNOADhQjwQ4z48+nABwMOSA2M9AvJ0BJwAK7SN6ribuBECuzOc3uwVi2wJOADh4yy3wcgLAz1Buz1B+3e8EgIO/eAN/vl4/804AxPZzfFarcwIgK8v4924Bt8CDMoCuAMgvABKL49xOSZF9iYnSq2lzk/6X+bKENK1eS6b26iPHFy202P3QdRMC8I3G9u+dNUum9ugl9SpWkS8/LSpVSpeVER06W4lAQgQ8BMCJhdBzE4n3TgA4GHJA7Gcg3s6AEwDx+bDvBEB87ruv2i2QLQu4AsBBVm6B1b3UVDm/fJkkjRkj03r1VuDfWxYOGaoZ/6fLldWrHkvcBwFwV5P6cQ9VAeYNHCQTu/eQhL79ZMPESXJ26VJNFrjusftyO0+/38+6EwAO/nIK/s78dlpO/3pKTvx8Qo79cFSOfn9EjnyXvevo90flxE/H5eT9k3Yd//FYxv30c0w/89uZX09L6LwY89QvJ+3eYExeT94/IfzGFdre3/u5ftIZcAIgW4/DMdfICYCY21JfkFsgchZwAsBBUW6BMdn57ypgv7xqpYH3MwrgL6xYLteTkzR7f+pjQB4CIP2eFLmuyf4uLF8uZ5YskbOa+f+qlvqjUgDef9rldm5+v5/v0DPgBIADpScBpcx+A2wfvndI1p/YIIu3LZVZKXNkZvIsmZH09GvexgWy9kiKbL+0Q7Ze2Car9iVJ4rr0+xO1n6U7lAS9sNWIhVBgD/jfc2OPrD2cIgs3L5LZ6+bKgs2LZcv5LQKJ4ASAn+PMzmpW3zkBELln5sLUkxMAhWm3fK5ugXy2gBMADpBCAdKzvAeoP+0K7/dp7e13T8roBEiEz4ATAA6csgJJWX0P2N5xeYdMXjZVOgzoIrWbN5CqDWpLlQa1nno17tRSRs0ZK0uUOIAM6D9xsNRp0dDur6X90N+stbNlx5Ud6UqAB57946oaWHN4rYycPUZa92ovdVs1klY928ncDfPkwO39pkjIar7+vZ/x8DPgBEA+P1hHyXBOAETJRvg03ALRaAEnAJwACAfn/tnPRKyeAScAHByFg6OnfUb+n3Y6TfqOGyDlalaRtz74UF56/S3596tvygsvvy7Pv/SaPPfiq/KcvvL536+9KS8+uD4t/pV0HNhNpq2YIePmT5D6bZrI6+8Wsbb0UfTrktJlaA9TFhy4cyDDs0/owEL1+Lfr20m+LFta3izyvrYtJaPnjpPd13bJ6V9OeQjA736Wn3Z2g9+dAIjGp++8n5MTAHlvYx/BLVBoLeAEgIO9WAV7vi4/2+FnwAkAB00BKMruK/H5yPcB33j0y1SvLF+W+1oA92+9/6G88Mob8o/nX5J//fsVef29IvJpieL6exm7qjWqIwMnD5HElNlGAOD9f+mNt+X//etF+cdzL8lr77wnlerVkIFThsoGDTEgXwCKA/IDLNi00Lz/n5UsLq+8+Y6NhyJglxMATn7kkPxwAqDQPqLnauJOAOTKfH6zWyC2LeAEgIOkcJDkn/1MxOoZcALACYDsAv+gHYD8yL3DsvHURlm6c5nMTp1r8f9Dpo8wOf/7X3wuz7/yuryqYL6yhgUMnTlSElTWzzVfQTxx/MmH1sjkpVOlbstGGQTA/3vuRXn+5deE++u3baK5AebK3pt7LfmfEwB+ToPzF4lXJwBi+zk+q9U5AZCVZfx7t4BbwMsARjjGOFaBk6/LSYFYOANOADiwyimgggDIqALwIIv/wbsHZbUm9OsypIcUK1NaXnz9TXn7w4+kRfe2knQgWY5QKYAs/1QN0IvkfdNWznhIACj4f+HVN+SVt95Vef8H8lX5r6XHyD6ycs8qu4cqA64A8LOa07OaVXsnAOLzYd8JgPjcd1+1WyBbFnAFgAO7WAB2vgY/x9k5A04AOKjKCiRl9T0EwCOXhgQg1U85uk66j+ht4QDE87/z0cfSqlcHWXds/SPtKd1Hpv9QAuCfz78s7378iXxRuqR8WKyofKAqgOqN68iYeeMtIeCB2wdkftoCDwHIodQ9qz2M9++dAMjW43DMNXICIOa21BfkFoicBZwAcOCUHeDkbfycxMIZcALACYDcgkHIgJM/n5B1SgD0CCMAWisBkHp8/SMx6qfun3yMACBfQImK5SwpYPna1eT9zz/TGP8vpXm3Ngr8F8qmM5us9F+rnu3FcwD4mc3tmXUCIHLPzIWpJycACtNu+VzdAvlsAScAHNjFArDzNfg5zs4ZcALAwVRuwVQkCIDnXnpVKtWtYSEEgP7iFcqqIuBjKVmpnPQa088qAExZMd1CCpwA8DOb2zPrBEA+P1hHyXBOAETJRvg03ALRaAEnABw4ZQc4eRs/J7FwBpwAcDCVWzAVKQKgcr2a0n/iIBkyfbg079paPtGqAq+/976Ur1XVqgIMnjZcGndsIZ9pVQGvAuDnNjfn1gmAaHz6zvs5OQGQ9zb2EdwChdYCTgA4sIsFYOdr8HOcnTPgBIADqdwAKe6NCAHw4qtSsW51GTpjhEr958jI2aOlUv0aVhbwvU8+lbqtGwvy/1rN6lv5PycA/Nzm5tw6AVBoH9FzNXEnAHJlPr/ZLRDbFnACwIFTdoCTt/FzEgtnwAkAB1K5AVKRJQBqyKg54yT54BpZumO5dB3WU4qVLW3lBD/+6kspVbmChQYU+fRTefmNt40IGDl7jOy6tktO/3LqkTwDuV2T3x/b/xdOAMT2c3xWq3MCICvL+PduAbeAlwG87/lWAAAgAElEQVT0MoASC8DO1+AERXbOgBMAsQ108gPIRk4BUENGzx0n609skM3ntkjCmkRp3KmllROkPOBr7xYxMuDF194Urk81RMAJAD+/z3LGnQCIz4d9JwDic9991W6BbFnAFQAOnLIDnLyNn5NYOANOADiAehYAFXpPpAmAjafTZN83+2TDyQ0yavZYSw74tpYUfP7l1+X/XnhFr5fl36++4QSAlwR8ZtWHEwDZehyOuUZOAMTclvqC3AKRs4ATAA7sYgHY+Rr8HGfnDDgB4ARAKJh/lveRJgDStOTfke+OyIE7ByTpQLL0GTdASlepIK+8/a784/mX5B/PveQEgIP/Zwb/nHEnACL3zFyYenICoDDtls/VLZDPFnACwIFTdoCTt/FzEgtnwAkAJwCeBfSH3hNxAuDsZjn2wzE5ef+EKQHmbVwglAb8+Mti8sIrr8s/lQRwBYCf29AzmNP3TgDk84N1lAznBECUbIRPwy0QjRZwAqBwALsfNm6Q77k2rLdXPnPFAijLizUE9jF75dBmubk3L9bifUbuf9QJAAdSOQVP4e0zCIBj66TnqD6WqI8s/WTvb9Ono6w/vuERb+2p+ydl28WtMn3VTKmn2f1feesdeV6BfeX6NWXMvPGySQmA4z8es+oCJPfbdmGbfj9OqjWqI2+9/6E8pxUDXnr9Lfm8ZAlNGjg2PQngr54EMHxf/HPW/9tOAETj03fez8kJgLy3sY/gFii0FnACIHLgIi+B2r316+V6cpJcXrXSXu+mrjMiIC/HLMx9A+Kx0Y0HNruatFrurEsxAuVJxAm/QbTQ9urqVWZv+qCvJ91XmG0VT3N3AiBrkOAAKnu2CQgAYvYHTRkqVRrUErL2F69QRrqP6C1pGtMfastTv5yUHZd3yOzUuVba75OvvpL3P/9cGrRtKlOWTZWtF7bK8Z+OZdxz+N5hWb0/WXqM7CNfV60oRT79TD4qVlTK16oqE5dMlj039shpJwAy7BVqa3+f+Rl2AqDQPqLnauJOAOTKfH6zWyC2LeAEQO4IgAAwfqde5m9TU/Val+X1nYJ42j8LkASMJo0ZKwl9+8mqUaPl+MKFBlILCrwF68DDfm991uvmN9o8y5qfdW2MxbinFi+W5LFjZXrvPrJoyFA5NHee3Fq79olz4d7bKSnadq4sGDxEZvTpK2vGjrO+6PNZ5+T35e7/LFL2cwIgc4DgwCn7doEAAIDvv7VPVu1PkumrZ8po9eSPXzxZlu5cpt/vfwSc0vbwvUOyWT3989MWmtd/pCb7m71ujlYAWC8H7x4USIJgD3i///Z+WbVvtUxZPk0z/4+1agFTVkyX1OOpcvT7I6YWCNr7a/b3Ll5t5QRAbD/HZ7U6JwCysox/7xZwC3gZwFyWAQTcXk9KkiPz58mmKVNkrYLFZAXqodcaBaGpEyYYqLyd8mQAmhXQObNkiYzp1EXa1K4nI9p3lO3TZ8jNNWsKDJDiJf9m7Ro5u3SJ7EpIkJTx4x9b8zr9bvv06cLcIUiyWlukvwfE3123TnYnzJKxXbpJ8xq1pHezlrJh4iS5pnvF3LMak99uJCfL+gmTpEfT5tKyZh0Zp33s1jWiAsjqPv8+OgD+0/bBCQAHS7kFgRAApgJQaT/J+wDre2/utfh9gD6S/9AxaAuoR+Z/6NuD1naPtj9494Ac+/Go/Uab4J5H+lYygbb0zzjHfjgqhAmEtg/u81c/21mdAScA4vNh3wmA+Nx3X7VbIFsWcAVA7oALHn/Af2L/AdKlYWNpUaO2tKlTTzrUbygdGzSyq5O+9m7eUpYMGy5XVq0yj/jTgEr473izh7RpJw0rV5N+LVvL5ilTlQBILjBA+p16w88uXSop48bLsPYdDGS3VnIiWHPH+o2ks657YOs2smjoMPOgQ34AsPNaDUD/7Mu+xESZ3KOXtKvXQAa1biebJk81sgYyAok/4RSXVq6QG2pHvPvcx4Vd0yZPkf6t2khH3ccpPXvJXu3LCYDc/a+En+mC+OwEgIOkrEBSTr8HhHPh4Q+u4LvwvoLvg3bpr+n3B78F9wSfeX20fTrw5/ugrb/6ec7OGXACIFuPwzHXyAmAmNtSX5BbIHIWcAIgd6AGUIh3eESHTlK3YhWpX6mqdGvcVIa16yCjOna2a7R67id17ynrJ060+H1UAzkFPxAAg5UAaFC5qvRt0arACQAA87EFC2T+oCHSrm59qVK6rBIgTTLWPKxdR+nVrIW0V/CNJ33ewMFyctGijFh6gFhObZDd9oB45ndG1QnYfM6AQbJi5Cg5On+B3FISAvCPKmGtkherRo+RvbMSbV8gBriX34/Ony/LR4y0ea9X5QDtPQQg7/Ysu3ub23ZOADhgyg5g8jZ+TmLpDDgBELln5sLUkxMAhWm3fK5ugXy2gBMAuQM1dxUs7pwx0wB/k2o1pY96+pcNH2Ee4yPz5snRefMNTBKzj7f5nhIGgaeZVzziEALpV/rn0N8DoJwVARDaNr2vkP6079DfA/AUfPfI2A/mEvwWtM3q9VsF2Cgf5iq4bq8EQOOq1c3Tf/jBmvclzraQgOEartCoSnVpXauuAu5xcnnlyke87Yxn83jEDtjj4dxD5xDML+M+bZeV7e4o2MfLf0aVCheWL7eQBeaNxH+H7tmQtu1tvxYNGWahDIQN0C9EAOEN5/UeVA70QV4AG0d/D51DMLfQ78Lf0yb47hGbs++P9Jd+Fn/YGN4+WGN4+6xDGYJ5+euj/99OADiwiyVg52vx85ydM+AEQD4/WEfJcE4ARMlG+DTcAtFoAScAHgUIOQVMAQEA0G2lIHdMp66yc2aCJptbkwH6AvAX+krCQEDmNc1OD8AEGF9euUozz682CTrKggAcMqcnEQDI8UluRy4CQgzS+1ppfTEGcnjAazA+AJf5WdsHY1/V+7jfZPr6+9PsEBAA8wYOEkIc2ioJsHXatIxx8JZfXLFcktTD3q5OfalY4muZrCoIkusxNmtjHtiPLPuh82b+zOcbzXHAvEPnwhro+/H1ptuOe7At97Fu1g/gpz2fAfJnly6zhIqEakBcjO/aTfbMmqXzXWFzY0604z4ubAI5EIxJiIDZ9AF4Z37Mi/Vw383kNTb3gFCw35iz9sN+B2vl9ZruN/2ypmB/0tuzR9p+9aO24XzYGrV9QJKE2sffP/n/2QkAB0zZAUzexs9JLJ0BJwCi8ek77+fkBEDe29hHcAsUWgs4AfBkwPA0QJUZAbArhADI7H7A3rlly2SLAualw0bIrL79ZWaffprhv78sHDxU488ny4lFCw14AgbpIysCABBIhQDCEFaqzH3OgIHpfWl/9LV16jQbC284fQG8AcUk55uvsvyEPv3twpO/QiXvyOH5PbN5h36XGQGwLYQAYCxIjP2zZ8vAVm2lUskyMlQ97tunTVeiYbUBXsD1cQ0jIGFiYr8BNu/ADoQWbNW2gObQcbEdwBkbrxgxKuM+bLd4yHDZouvF2w9oP6fee5Ilrp8w0RIC4s0/tmChkRKD2rSXSqXKSpkvS0gLTfQ3pnNXWaBj0i+2PjhnriUCJHEg7wlfQOmxauRozSUwRU5rSMAdBfvB/vAKMXBQCY6NkyZZiAb3MF+IAeYMybBs+EiZZWvtL7P7DZTl+plkhZdQRihxQT9UiwD4736wxjn9B6bvk64RtQL5H1AnhBIMoTby91n/TzsB4MAuloCdr8XPc3bOgBMAhfYRPVcTdwIgV+bzm90CsW0BJwCyBgvZAVLPQgDgAQf8D9e8Ac2q15LqZSpIla/LSeXS5aRuhSqWsX6JJs47rUA08AyHEwCAULzJgOhtCpTHdO5iCoRa5StpPL72peC2YeXqBmz5Ha96UNsewIlioVa5SjZmVR27ZrmKmsivtnrDuyuInWPeZ4BrAHDDbfE0AoD2zJ2QAMaqoWsc3r6TEQ9XVHUAIIZsgLBoU7u+zZc5M3fIghplK2oCwbYGmvGS43FnTO4D1JNfAdvVKFtBmH9lzUHQpGpNGY0CQ4E66wUoj+zYSbo2aqLJAHuanTYqoB/atoNU1XGKvPuhvP3We/LZR58ZGdCyVh3LF0CyQAiIro2aSvfGzfT9YAX1k2V6rz42Zt8WrS1/AIoB1shasRN7BJnTW3MfkBhx3fgJllsAcM9+oTRgjGDONXWNzarXtrXw+8M9Wm3gnzU21TWyT6wR2zSuWkNGduhsa0SxESg7wvfHP2f+f+0EgAOm7AAmb+PnJJbOgBMAsf0cn9XqnADIyjL+vVvALeBlABW85QYshRIAlIwD7AI8ifdHqg3YxxsN+A7APOX7SE5HckC84tSpn6n15gHfvZq2sCoCgxT8bpg42WT8ePnDCQBUAueXL5N9s9Mz3ZNsj1J3E7r10P76ysRuPRX8dzNVAQQAigO81qk67kgFlj2aNDfP/ERtP02B7ahOnS15YRcFy9x/YE46CfCsBAD3sWZAPgqAamXKPyinN0uJjSUG4qf27K3hA42lQ72GlqWfRInT1BbMD/BNycMBmol/zdhxNn+ANOEVM1Td0L5uA+msVRcIK8B+lPsbrV782f0HmRef8INU9fyTMLGZlgGkT1QSJC5MHj1WEyoGCoCSWuqvrt2PYmLnzJlaPWC29jPQCJFWCth5T6lDcju0qFnbEhvOVfXEcVUTBF54gDiVApgvlQPGKdinLOQJzf2AAoGkkD2bNrc9n6HzTejbT6ZohQL2DOJlgu79zhkzjPTZowQNigZCSqiqwLlgj8bpKwQHCoId02da+IATADn7/3UCwIFdLAE7X4uf5+ycAScA4vNh3wmA+Nx3X3UBWOB///ufBNd///tf+fvvvx+7+D5ow2tB/7kCIGcAIpwsCAgAEsrVqVDZyuFBAszuP0Al5YNVsj3EsslvUlLgpHqI76gnG0Jgn0rjkxSI4lkG3CNPJz5+weAhVj4PUEz2emTkeOLDCQDuO7l4kSSPHWvZ9iEfAIl7FHDTF0kH9yQkGtAG9AL+N0+dKpPUEw6oxIsMQD6i2e5PaT/bNCQADzfx+gDPlSp1x9sOwAxfM59DFQAAXuLpt2j/eOrTY+HXGvhdOGSoVgloYIkAWRsSfMiFuer576zgn3lPUrKCUAXWytz36+9I46kiQGWFUUqUbFdwjJpg1ajRpgxoqXMc26WrSe7J9s9vkAO7FDzTD3PHA9+7eSshOSNKgCD/wJklS2X1qDFC2cJGVasbMQHAh1BBUYGtqVrQXIkDwgMA+9hwl5IDlDWEbAHgozSA5IHYQY2RpKEMbdUOAH2k/gd1HYQkjFZyBcJiqJIAhBQwP8ITmDPkQlOdHyqFxSrvR91A3oSBSiQwN4gZ5nZ6yWLbK0IUWOeJhYvk1hotq6jkUFYkTWb7Fu/fOQHggCk7gMnb+DmJpTPgBEBBP2kXzPhOABSM3X3UOLNAAOoB/X/88YfcvfutXL5yVS5cuKTXRbsuXrokN27clJ9++ln+/PM/GURAQZrKCYDIEACD2rRTGXsZqVCitALOGpYYD1DXo0kzA6x4bXclKGBURQCx3si9Ad/7Z88xEEvsOEBv7sBB5kEGGE9U7zbx5wDMzAiAEwokV40ebWOYF1m9+QBIYuCvqTycC6/5VQWn5BRYrGEFgOo2tRU8q7ccAoC4dOL0AabTeqZL3MsXLy0zVAVwbtnSDNVCOHAMCAByB1AGELAK2AdkA2zxtuMxxyOO7L2/vgbx+SQLpGwi3nQ89AD1SyqnJ2cACQ2Jpaefqb16a9nDatJdbUh+A7zz9NlP7+HekdoHgPj8smUG+C8r6Af4A+Jv6vUoAdBZqMqQDtaTJG3SFOlUv5H1M0PVF4ByFAuQF6glyI+ATSEAIAOwP6QKnnuUB/1btjZlAvvInrKXKCeaVa9pQJ/8BewPpQQhVAhXgHRZO3a8KgwSjQTZoaQGIQMQDU31PhQQ7Am5GPpqNQmqJxBKgN3OskbN9cD6gjUGihKqBoTvj3/O3CZOADiwiyVg52vx85ydM+AEQEE+ZRfc2E4AFJztfeQ4sgAEAN59wP/t23dkw8Y0mTx1uowcNVZGjBwjI9TjOHrsBFm4aIkcO35Cvvvue/nrr7+MBChIMzkBkDlQyC6AChQAg1UBQGw3HutA6o10e1L3HpbcbrV6dQHGJLVDNg5IpdY8nuhxKl/H05ser95UapevLPUrVbXYcCT0WREAeL4JNwAkNqpaQz3TDU0uDsg8PHeeScSD8APGJjygnvbLPGlLLDzjcg1WAqNLwyZSTWPNv/q8mPUD4AUUZ+ZhNgJA+yRBHQC23FclTc5PX6M1HwEqCEoidlGwjOd7jZYAhFCAkMBTDqgHxM9UQH1cvdmAf8bhAoRTGQHQj7IAAE1yQIiDtePGG/CHZAmS95FE8JCul76DjPq8ZkYAQDAQO0+cP+EHEC0QAAB8q7yQCQEAGXBGFRTE/APQ+6iqABUF3ntsdEG/X6ffQ3IwX4gA5oNqgJCG8koKVdAqCG1VXUFeA8IRUAUMUxsR6lBb8zaQu4H9Z85ctGlYpZrZFgUENoMMCtbIOqxKhKoxsntWvZ3malB73d27VS5fPyTnfjop2Xl49jYOsvwM+BkozGfACYCCfMouuLGdACg42/vIcWQBCAC8//fv35czZ8/KMJVQl1VJ+EefFpUPPv5cPvjkC/msaAlp3rKtJK9JkavXrhtZUNBhAE4A5A5ABQQAIBwv7wCNdweQHl0wX+vPLzFJO9JyvLaAf8rEkcGdOHO8w4QO4P3F2wwYraPgv+xXpSwpH15yPPSZEQAkjSPPAJ5rpPUdVIbfoFI1aaH99G/ZxuLEIQfwQlM6jtAASIpy2neJz79UEoCkf7UMXLdSRQBgmnnUq1TFgGdi/wEGbiEQnkQAEOoAGC+m55tEgsTM0x9S+G6Nm5pUPnXCBAP0kAnYgkz2EBCA5RUK8vGiA/oBZ4BUgC2S+rUa+w/QRl0wvlt3UzdgD7L1U3oQ0IyXnPUmKhgnLAJAjkeeK6IEgEr2yd0AkQI4B8xD3OzQMADCGiADWHPv5i10X0fYd4QyAN6Lqm0+10SD5EHAVigisBGvJPljDa31M7kBCCtAFYKNbI1q08a6fttTVQug1CD3AOsLtZmD++z9HzsB4ECuMAM5n7uf32c5A04AxBEYCVmqEwAhxvC3boG8skBAAPz88305dfqMDFCQUlTB1utvFZFX33hXXnvzPXmnyP9v77z/o6y6tf9nnV/e855zPo8eHxUVFKT33nvvHUIPLUBC7y2BEHrvSO/YC4oN9dGn+6r7vb5r2DjgJE5CEufOrB/uz4SZu+x97T0617XWutb/hM5de4ayMvX2VnkA2QIuAGT3wz1XCU4UAIh4E6leIJM20tIx/8tEnCG7EFvOp3Z+XO++FjHGD4Do8ipFuqn/RxDAJLAqAYBINgSd6PQRCQK00iOlvrOyEFrKUX+AxkPpAWIDaeSIDUT/+4h0km5eUVgYDqllHW3r0g+8AigZIBX/IcRchPxp/NNLACDy+B9g6ndQBJV7HS0qEjkuttR42hTiJUB7u3d37LT2hPgCQKLJjECgSCezjwUAZQ1AgruJAGOAh4iBkEJ3BMSNtfIRwESPbgcIGgN1PzIKIOR0DqhNAQBvAvwNmAueAONk1sgaUq9/WmUXZDkQyaedoKXs63xwRyx4+bk/a01aqexitKX3YwqYjjeGjrRQJGOA+5Mdck/XY95IlwTWFJGArgED2qcyDDgXMYm1ybQ+T6+X/zv13xkXAJxA1YRA+TW+b5K8B1wAqKtf/rl9XxcAcnt9fHQNBAGIPCUA//znv8KDB5+Hir37wrwFC8PkqTPCJDmTc0ybMSusXqsa6avXw8OH33gJwJFkk39IVXUFAFLNV6i2HdJM1BcifnXDRktdJwWdCPD0AYPNAX9BFQIABBiCHgkgQsAdRfsRFyD6lBC0eK2JReFJpY9p5bSRo0QBEvrpnnIj3hBuO2QoB1GPzvLx3jEyn04iowCwAc8CkXSi3xj5ka1g93h0z8f34N+6P2SfsgeyA8AA3wBq7qPQwPmMBSKMOIC/AJFyPAmIvn8t/wQ+J5sAkkxJAEIJ8yIDgdIC3oOw17YAgEjBcxFqaNGHv8HycePN8A8hgjKMrQWzraQBcQYhgi4Ljf/cyIwDmQ9+BQgJv8Fcc0rHir/B0uahDIr0tod0atij/7ZQDmDCic5NXxv/u/L/rrgA4EQuyUTOx+77tyZ7wAWABkI0qjkNFwCqCZif7gjUBAEEgFQWAD4A/zaC/8n9+2YEiBkgx0cffxw+//yL8IPKBH780U0AGwJRqa4AQN0/hBVii5ndLpFhzPoge0StaXmHyRwRX0zuqsoAIBoOCaQ2najxAwkC/PucnPAx0MP9ntT+1ZOnhH2qK18ul3+i5Dx70wzVtatEgfFDRiO5haBDyE0cUMQeIpppndIFAKL0g3RPothRPMh0Dff6UlFrIttEzxEAiJCfKi5+hIHEB52DmEEqP9F80v/HS7Ag0k5LPebKQQo8ZByhAGGAunvmNkiZBZQlECHPKACIxHMtZonU39MisGjseCvZ4H6M/2kTQDwAIOJghABBLT6ReYQU1opSDcwVMQbkvraeOu/9naVhozpBtFPWBU7/+BjgzcD8okjyxaM5YGCYWsMKy3KgVAIfA0QhTBzpKMA6kjVhnggqB6CrBPumsjXKtAb5/p4LAE6gakKg/BrfN0neAy4A1ORXffKvcQEg+WvoM0gQAulCACZ/Tx/4BMRWgLkwLfcAqDxamA1Zqq4AADGlhRzklyg49fu06MMXAPK4SJ9h1Ed9OyS5MgEgtgE8sGSJiQg45FPvD1HF1R/yTH05ZH+DSCj3JvKOCR2kelK//jKWW2BGhDwb0ntNmQiko5ep1R4mdhDVyshlTQQA8HwoUYHIOM76RLLpSLBMBBxR4O7WFA5XRHZxwh+vNHvKGRAzrojk43eA9wGtAEmvj2IAYgGlDvgKDJOzP6Sb9zIJABBvIvBkXUxWBJ/MgRmDBlspBNhR5884yGyIbQDTBQCuR3jh3mQbUFKB9wMZAJRg8FwTEiRkIKJQYoGAgbfCmJ59zPOAZ9OpAdwvr1+nsokl4eiyZTYm6vvJ7tg+e46tYxR46GBAhwjKQ1hTWkTSGtAFgOp9f10AcCKXZCLnY/f9W5M94AJALvzarv8xuABQ/5j7Ex2BxCDgAkD1CMTTogACADX/OLgTnS2UMRzt/DD8y0SeIX6QOyLG1PlTS75K0VwI3TLV0EN6qadPdQEYGdK7ABQotZ/MAFLPEQDIJiCFnig6hnREwTHDo8RgorILSJ2fo3FB6iHPmMthoDdKwsOADp1VWz4gFI0Zb8QVAk0NP9FwiOYlkXAi0JnmkCLyB+z5kG2c/od27iqRobjKDACuowwAAo2jP+nxQ3RdjKKT5r9W4yhSWcQ0mSmOUleCGSqHYPxkJiCUgB3zpXSCFoQICeCH+eIIufrPGTrMzifVnlT9yYrKRz8F8Iqp9wgetFmkEwFiDFH8LQWzzGcAnIjwY8oITog070gciOn2X6qLA8IMrR9bqWtCG4kAMwcOMXGCSH4k5QgBCD5btEa0hOReUyXAFKlsANNADuaBbwDtDS8ps4AMAcwhZw0eajjgN8AcyR7geWRbsA9Yf3CMY3p6X/q/M3+vXQBwAlUTApVv19z7x91w9+93w52/3Q53/no73P3bncB7HPmGRUOYrwsAiflJXqsDdQGgVuH0mzkCDQsBFwAyE4VsCRTRZFLCV4qEQtAgdfSwh/xlIs8Yt0FEcdkfqmg1zvtEdWnBx/UzRCRJLcf4jXsRlYdQQmhXKIUfgQDSiFfA7a1bLBpOBwJINPchAh4P7hfb72FKSIkBJBNCO6F3XxMIEC24joN7UHawTzX0EOTYmi8TFowJPwNS8yHdkFL62xMhzzTveA8+A7P3SndapHuZyHCKHHcKQ5S+z9gZC+R/sYz/yAzAOJHncQ119LTPI3tguM61Q6SYbApEGD5n7PgjIEggMiACEJ1nvHFs1OjTrQGMKF8AB0QHsiTOSNDZMXuupfSTMUELPrININvMgzlyr3USSmgliNki90cksFaCmiPP4TwyCsgKAHPuz/wwQGTtba4ygkTQYSxkINzbtj1UqL4fU0H2xNNzRKggAyJ9feKcIsb+Wvl32gWA7AWAe/+495j0RfJX3dekkKeq5lXZHDJdU9m5SXqfeUH6r3x9JZz+8HQ4eu9oOH//fLj5/Q0XAP6Z/fcnl9bcBYCG9bs929m4AJAtUn6eI5CHCLgAUDlZyIZIQQohpieVYo/5HMT8ff07RoGfvgdkjewAyPLmGQWKuI8zQk/0fdus2WGPPADoCIAXAESUFHCIJOnkmOxB/kiDx/kfQo/YQCr/KqX8E73nftwLQsp4iJpHIk/veOrMESC4T7EEBaLtCApcV6w2dLvnz7dsAcZYFZnnM6Lyl/V82vwhGiBSEGGvipDyGQdjglTjV7CloEDZD+Mfj4MMBqLezBciH+/J3zwPQp6ar8atsXMtKfuk0lMGwb2jQAFW4HOqpMTGG8f2laL4EPN9ixZZtJ9nklFAqcTNzVss8wJxg3WgHAIizzhYT+5xX+tBdgYGi5tF7s+qBAN/gzi/+Mo11PmD+V51NcCfgJKHiDnP3a51x8cAkYY5kgXAHkifI2vK+MhqwAsBESU+4+k95v+u/DvtAkB2BCaS/zuK/N78/ma48d31cP3ba79zXNd5N8ItnX/376mIcS6RoKrGAum9/ddbRnJv/CU1B6LfvJ/pOt4nKg42t364mYqSJ2zOmebFe/cU+b+mtT50+3Ao3rUyzF41L2w6qv8mfnbB1rWy6/z97L5bfwROLgDk4Y97TdkFgPxcd5+1I5AVAi4AVE4WsiFSkDBEAAgl0X1eY1o2n2W6B+9D4DCjg5BD7nmFAFJ3Hw+IbLwXqfOUG/AZ2QWQeUg4996Jlz0AACAASURBVOE9iGP6vfg346H1XiSKvEJIIce/XsOzU0fqmr32eSTdmcbPe3avR8+H+Nqz9G8IVmXXxPfjeJgb42fev469PHwmLB6IcDNfzn38PMMt09hT54NLFF64jr95j7mCZbqgET9n3DyL58dncl3ElWut84Dwf2Isadizjtw/fh7naeN+hNWTmMc1T82V+eOpAOYcPO+36/PbOaY/x//+/X0X1+OrCyfCh/evhHe+v52R3P0RP9Bz7ZmREF948FbYd31/2HZqe1h/eGNYd5BjQ+bj0Eadp9Kbm4fCpS8uGinOtXlVNp6bIv2nFO0uv7Qn7DxbGvZeVWnXJ2eN4D99jZF/kf23Pr8Y9l7bH/bfOBjOfHTGxBGEj6fPT9q/mcNFzW3Tkc1h4PihoVmH1mHKkhnhyN2jVhKQtPn4eO8FFwCy+jnc4E5yAaDBLalPyBGoPQRcAMiOODjBcpx8DyR/D3gGQHZRSkjutW+uhQoR4ZnFs0Of0QNDu95dQqtuHUKLzu1CU5HCJm1a2GtLvde6e8fQtmfn0G/s4DB/3cJw6NYhEeLriSHDb0noWLN/XRg+Y0zoMaxvGD17vIkcFx5c+M0cwObWD7fCnit7w4RC+Y8snRE2iiyf/uh0IkSPlICRynggs4N1Ts/aeCwAKOo/aOLw8GantmFq0UyVAhxzAcBLAGrvx6ffqc4RcAGgziH2BzgCyUXABYDkkxonpr6Gvgey2wMuAGQvANz8y81w/N0TYe2B9WHu2gUWBR47d2LoPWpAaNq+VfivF14Ib7RrFUYWjA2TFk21zyH/m46myDCkMinR17Mfnw2FGxebiPGnRi+FV5q/EUZIDNh/48DjcgaIM/PhlYwBsh0QCxA9inYsD8feOWZlBLk+Z8aPOMPabj62NazasybsuVwRLn156fFc+fzo28dMFCnctCRsO71TGQ9veQmACwDJ/bGbhyN3ASAPF92n7Ahki4ALANkRBydYjpPvgeTvARcAshcAiART93/+/rlw4r2T4YjM4EiRX7K1yEjvy02bhL7KDNhxdmc4dOewPj8WTrx/UrXi580zIEnp8Gc/PhMKNywK7Xt3DX9q9HL47z+/YNkOy0tLAuIAtf7pAgA+AVtPbgtdB/cKfSSILN2+LBwTYb6tzIBcFwBYF8h+6fldYXpRQRg2bVRYUVZiJRDR94BXfADOf3re3qckgKyHiEGuz9HH9+T33EsAsv1F3LDOcwGgYa2nz8YRqFUEXABIPqlxYupr6Hsguz3gAsCTxKAqogTZ44AwQggxyXtLRnCbj22xqH/j1s0tSn7qw1OWQs45HJjj8UqUHC8A0usvi3Dy97lPzoUzItQQSj7HaR4yevXhFbsHBnRxTNwHU8ErX13W9Zctah0/j+Miy4B7nJNL/ekPz9jB/TGsu/rwqhHybEjrGQkACzctDj2H97OyhlfefCO83rZFGDhhWFizb73GfMbM/iIm6QIAGRFLtz0pAHAeeDEGMENEOKPx4RUABowZ80CwjffkFfd9UvIZP/NgTlxzQUT8snC49MUlw/OqrkeUiNfbs/Re6rrUNVyHjwH4c0/w5HxEHaL7GPwNUI1/+z5drYyh9HyZzZO1Ym24hms5mMd1lQuwTmQCsJacw3owbtaM13h/rmGsXGN7Qs9ljMybuTA3xseByMAa83nEgvtx3bVvrtrn4MDB+eDHnrHnP3p23DP+mvn77QJArf5sTszNXABIzFL5QB2B+kfABYDsiIMTLMfJ90Dy94ALAJkJQjbECXIGqdt6YlsYNWucPACamxAAOYascY9IAqktp4XcluNbw1rV1mMiyN/LFWlesnWZ7rHdPif1fOvJ7ZaCfk6ZBukR9BtKQz+hNPVdF3aHHWd2yoTuyGNCD9G0Z4gQ7n6rPKwQmS3cuMSOxcpQWLNvXahQjT7ZC7dFqhlXVXNkDos2LwkDJgwN3Yf2saPLoJ6heee25gewQ8aAiBYQdO5VlQDA55wHCWZ+lFAskUBg45PIULRjRShT9B1yzhyMxItIE2GHWO+X6eKafWvDoi1FVpawWK+rKtaYOSFu/OtlwrhHmRiMGREA7CHlZGes1rwXC1/S9hduXqrMhOXmZUApg4kGKus4qSyN1XvXhtGzxocWXdqFRm++HroN6W0iADiaECA/AzI+tmitN2vdDsrTgSwQPCHWH9pkXQG4DyIG8+Uw8q/5kA2ydv96W/Nj7xwX6b9qZB1xYvfFcpsLa5TCY4kJEWUXdgmvC0+s75WvL5sBZfHuVWHBhsV2/sJNSw2/7ad32P6J+66qtfXP3ASw/n9Z58YTXQDIjXXwUTgCOYmACwDJJzVOTH0NfQ9ktwdcAKh7ASAS0tJzZWGU/AGokx9VMC6Mmzcx9B8/xDwEZq+eb+R4gdLu8RWYv35h2CdyeV1R50jYINyIDbNWzg2TF00zIYHotRFwEc1TH5w0vwFMCgfovpBYjh6K4g+aNDzM0TNw9CeiTTvDeN9Mr1EAGKzruHayXO+nLSsIrbp3CB36dg3z5IFAWzwi2vZ8Rb9jCUB6BgAZCYgYkP9db+0Oc9fMD8Onjw69RvQPXQb3Dp0G9AjdJDBMWTLdrqf0gGsg/3QVQDDgWQMmDAldBvXS0dOyEoZMGRkmL54eBikjgbILzim/VG6Rc0QACPqcNQvC0KkjQ88R/YRDH3sO2BPlB9+jIvQXJTAgFEyTqV+7Xp3Dc6+8HP7Pn/4UGjV73ebJWBdvWWriCd0eRs+ZIJFnXCgpX6VSj9JQtHNFGKqSAcaDOEBmQBQAiOAjaiwvK7Y1pnMA14AtEf/Sc7tCQclcMxYk06Kr4dE99B45IExbOtNEEbIcYhbBgZsHRPwXGXYd+nYLXbW2XMO1M1bMtnmkC0aZ1tXfS33fPQMgJ39+1/mgXACoc4j9AY5AchFwASA74uAEy3HyPZD8PeACQN0LABBBovkbD28KXQb2CM+/2khO8m2MnM5bV6hofYkR8wM3D5pxYC8R1gkLpogglilKffUxUSc1nMg34gEklswBCCfR9RjFHiOCCmGHWEJOl+ngGRDUvqMHhRnLZ4V9atUHca+KDD4WACYPDyNmjrXIOdF2jACbywUf0o3TP6LEXUXrM2YAyAOBtHTOwVwPAsw4JhZONSK7SMQasQKSDrHnfQgyrRZjBH+6RIfOEgkwFoTwY0wIIYfw8t7LIuqvNG8axqhLAeLIJT2LLIKdwo6I/uTF06yUgSwDrpuqMbTp0Sl0E3HG7A+jQrwK1qud47j5k617w6stmopk9xOGcyzzgIwKxA7u0X1oXyPdEHGyMIp3r7SOEGRGkF2QwiNVWkDEHnFh7NwJ4XV1iJgqUk8LyePvHg8bheVkiR6s1fgFk5WhsFgZF0slssyUIDBMIlE/GUlOC4eV5XHz+xuWfcAzWXc6UCCY4LOwYOMiM5xcsH5RqLi894mMkarWN98/cwEgub/Rn2XkLgA8C3p+rSPQwBFwASD5pMaJqa+h74Hs9oALAPUvADz3yksimh3C7FVzLQoNSSZye1reAbSXq64AwPU7zpRaVgDkEEIJ0eR9jlMfnAoLFPEm6k7EnHR3It9VkcBfBYARVt6wUsIDJBgBgpKA1j06hlmr5ik9/4B5EWQSAI7eO2IR+cNKgYeIcx1tAjfJMyHW/Z9474Tds5vMAxkfJJrncCzR30TsU1jNk3Cxz0oCKAuouFJhWDVu9aZEgCaWWUFJhdXiizAjcixU2j8knVR7RAHKNSif6Dm8v6X6z1g+2zIMyIg4qzr69Yc2hqFTRoXO/XuE2avnPWrdeM0wxLMgXQCAsFNGQFbDBOHdTG0gESXwEsCngQyG04r0g3X/cUOEV6ewrLTYhBrmQRZH71EDTeih5AOjSKL9lHVgKtlLUf0uA3uGDRKNEI/osIA5IUIJogi4R0+CCmVJlKuUgBITLwHI7vvsAkAD/yFfyfRcAKgEGH/bEXAEQnABIDvi4ATLcfI9kPw94AJAdoQhE1km1TsbD4D0DICuInB4BYyYOcZqybme+3D/M6ozJxW9OgIAJnIQR4hzf0XEiYrPEXmF6B5XvTkHtf+FqhnvN2Zw6KEINqUAENpMc4rvPSkAjFf7u/VGpCGZXE86PuULRPFJpWcMpMDTBSCWABy6fSiclicBJom836pbe8tGIMqPKEDknawHUuv7jhkUOovwIoBAiLkX2QztVW5Aij3RfbICILgcRNeZF+S6qVovEu3feiIlAKQ+v2I1+kTbERnwTkBUIPI+UBH2tkr3p9Riu9r5sQZkHZClMGL6GJvb/PWFhh3ZFdwPwSJdAEBcoJ6fzIslW5dauQDjRESAsFOacVDzh+j3lLAxdOooW2/GgAcCZL6rMJyv7Ax8ACD+iAcIN3Qg6D9ucGjdrYNlITBP7ku2AOIEewTPBPwcyORgD4AHIkzcS3Ed/TXz99sFgPz8te8CQH6uu8/aEcgKARcAkk9qnJj6GvoeyG4PuACQmSBkQ5xqIgAQ6W7RpW2YrlR8DORoLRdJW/UFgCKLeEOyuR8Eu2mHVimPAZkSTlw41Y4xc8arrrx/aNm1vaW4T1k6w6LFVc3xaQFg7YENRvKJpBNVJ5JPdB7CjKkhqfSbj6eIfhQAqFmH6BftWB6adWwTnn+tUeilcYxWuj7XT1w4JYyfP8nKApp3bqfxdTDSD0Em04D7tO3Z2SLrRNuj3wF4pUj5Wbu+TfeOYZzIfCwBgLRDhhE5NqlMYa4EC8oPeNbgKSPCm0rXf71dyzBM9f0IDaTtV1sAkEEi5B9vA0ohBqtUoo8i+pRbYBaIKz8knZIJyg0QTSDyJsaojAGx47WWzWxdxmlc6XiQ5s9aNW3fUmUJk1QeUibDyG0mWOC/AO6IF5gBMgYEADI9mHfcS1WtrX/mJoBZ/RhugCe5ANAAF9Wn5AjUFgIuAGRHHJxgOU6+B5K/B1wAqH8BgJT2uZjoiRynR22rKwDghk96O4QTQti49Zuqh39D0ehuFkEeMnlE4MDIr9/YQUZQh6oGf6kI+YVPL1QrAwABAKKJIR3GdmQEDJ40InSXud7ER2n96w9vfCIDgGj2Xokcc2T892qLZuF/Xvyz1fPTWeDx2DQ+/k2UHH8A6tzpkAC57aRod7veXdRScLnq5k+Yw38kuLxC3KdKzOio+ZKGjwBwTmPDK4ESANLvp8g3YIjGSekDLQwxRGyienywwnsAR/+LyiyoiQBw6v1T1gZwr1L6Z6+cZyUOZC3skmcAWQerK9aa4IGQseno5nBSJo1E+ykVYA+89EZjwyNiYWsFHsqsQLBh3RZKLGAuZErQkYB1xsuBOZP9gK8BGRaYHkb/gYiRE/3Kv9ueAVBbv5iTdR8XAJK1Xj5aR6BeEXABIPmkxompr6Hvgez2gAsAlZOE3yNQEK3qlgCQAUD9PKnfh0XcMwkAuLqPlyHdTtX106oujuPc/fNhpYzrRsqUr/+4oSJ/jwQApY9DPKlDhzBDhom6rxIBfnwook4rQIz8IJNE0+N9M71mygBAAGDOjPnArYNhlowGe6menvHOleM+bve4+scMgH3X94U9inrPktcBxnqNZNaHFwDGhET408cGWWdsZEWQNcC/O/brZkS3eJci3fIxoK6e53OQAXBedfMQ/CgA4AFAWn45nQOELynzpNpTM0+bPVLrGWd3RdBbKr1+uKLzmxVZfxYBACxOqSwCQQQcEDMwFMTAcd7aQntvtLIxmBNZAZgCYsRIxwGMAxEwnsCCNWOtHuEB+UfQYIxH7h4N207uMPPHwRIKOg/oaWaMdCpYqIwEhAgyNMAn05r6e79+110AqNef1TnzMBcAcmYpfCCOQO4h4AJAdsTBCZbj5Hsg+XvABYBfSUF1CRJE61kEACL3TwoAZ6y+G7O8VE37Nku7j6T3jFL9l+8stuh5X9XzIwBgegcBxSGfunbq2yHPnIsZXeq4bkZ917+9bgTRetXLub+q+VYlANzTtZBZjOkwHISADxTxRbRo2aV9ysxv2zIZ1e23AwPC19u2sLR2iDLjvfbN1SfGRykE46OFH2R345FNlk1AyjumePgFkOYOFmQhQHSPvn1ULfnGWjSdloqbjm22GnpEEqLjbXp2MsEBMk7WAhFyCPUYRdERF2jnlyoBUNeBdA8ApecjIFDWUKkHgAg35or4O1x5eEX33WdiA90REHcWySNggvAYptp/fBJ4NuMHk9mr5pnBHx4AiBYpLFib1HpR149AAx6sFS0bETxoj4jxH2uDOSBtGfF8YJ74MdCRAF8AxlzV2vpnXgKQe7+862dELgDUD87+FEcgkQi4AJB8UuPE1NfQ90B2e8AFgNwRAKhZn6XWc9SMQ+SJBGN8B+mFaGIUN2fV/NBTLeI4EADIELj85WWVFBRaXTkElPZwkNO7Io6RMEM+OfeKzPogl3xWFRGsTADgGu4JIWW8JeWrzcCvrUgodesvvt7YygKWSgA4qEwDSDoRfLIe3pQPwCIR58N3jhiB5z5xbpe/umSiAmND1KAMoJ+MAVurvp/2gJga4tYP+X9sqqi0+q7CipR+ouyIBuWXyu0ZRON7qpVfsVosMk6EBYwKScGnXr9Fl3bmARBLABAdtpzYbjX7iAOQdMwNf08AgJwzHvCi9SIt/GjTOHzaaHUUGBkKiudYNgCknvMQMpYIG8bXvk9XW+NLX17UZ6n6ffCA9IMHa8+6cZ3hovmnzP6u21wY33JlNTBPPAVoW8geQSioam39MxcAEvnjvBYG7QJALYDot3AEGioCLgBkRxycYDlOvgeSvwdcAMgdAeCiotCkqfdRzXhnpdLPWDHLCCPkj9r2dUotxyAO13vq/Okbf/UhkfTr5qQP4YS84jhPZPvCgwvhuiLGkEmyDXYqEo6BHySRa6oiglUJAPE67oHTPdFyxvufzz8X/uP//peNbbHGdkREnzGUX94jJ/+RMgJsbd4EJeWrLAuACDeRf6LztAakTv7w3cPmor9H1+AtgKjQXmUNPANPgVS6/gUZ45VaJ4UXmrxqosMolUVEAYAWfRDidr07W/YAYggReIwEESC450tNm0gIGPHIA+CiiQ9kCoyRQeGbGifmexj2WXReJPyMRIQnugA8ygBASEGUQFxYIzPEsXMnhW5DeocWSu/vJn8E1pMOAggm8bzSc7usZAPTRsaAwz9tCInu8zyM/ajrpwyArgu0bGTsGBrStYCxsCf4bKUEGPwC6LKA1wJdDyiViGvkr5m/314C0FB/wVc9LxcAqsbHP3UE8hoBFwCST2qcmPoa+h7Ibg+4AJCZIGRDnIjWxhIAnO1JcyetHPIM4eMeMcJNa7hNilB3Fzls01MeAGozlyoBuGnncC6EmJp56sIhdD2G9zURgPpuCPA4pZTTBx4i3Xlgr1QGgASA2yJ8RNWJLGN0R2o5qfH0sqdd3Xyl4FMHj6CAuZ4JACK1Vc2RqDkGc0Pkmj9Kc6NVHx4A6dcwR0oBEBbGzp0Qnn+1UfiP//wvI+wIAESoiXxTq79631oTARAoiMATrWZeHBBXHPoXirjvvbbXIt+nPzz1uF0ehnm9RvS3coM5quFfsEHzUR09Nf6vykkfN32IO6SZWnui+tTFN23fygSHgpVzzFxwpqLxvE8mQuPWzVNCiTwAyDgAe8ZLuQLZAZ36dwuTl0y3tHrmh8keAgAO/BB8xJdTH5x+nGVBpB5vBTwG2vXqYp4HdDwgxZ/P2AccMXMC/wPaBraVSEHLvwK1C2StOGbLMwHxg3tRhkAmAOUWZEJgXIiJIOeB4fDpowwbzAEREmKrxPR18r9/+x13ASA/f+a7AJCf6+6zdgSyQsAFgOyIgxMsx8n3QPL3gAsAvyUH2RImCB2kmLRy6/eutG7SwHHmJ9rLfSLpg5gRYR6lVPVBk4aFEmvh9quxHedCDnGjp63cBJE90sQhkX2U1k9q+QhFuSH2w5RePm7eJDP1IxLMsxgHUWLM7jAJ7C3C3FMCAtkEHHQBmLxoWtioKDvR8ChQVDZX5oCXwFT1nMfEj8gz0en085kb6eZErFfuWW316LTzQzRYvXdNqkZe4gTp90Srae/H+JkPRoG8cvRXHTu4lJSvNMJLZgHzwvkf40LEFQQAvBF4pd6d+UeTPcz0+PfWE1sVST9jz8UxH/wQQ+waeQLQ9m+0zBLpNoB4MFMEGsd+sAMPSinIkIBkd+rf3bDHXBHij8EeIgjXM1YIPJF9sI9rjJAACce9H6FgmrDbf/2ApfdH3DgX3wfmtlpz49wuylZgjLZWwgMRB0Jfoj1CdgRZEoxzwoIpJgB1H9bHzuUZvZT+j/8Ba3UUn4TvU2JDfJ6/Zv5+uwCQ1c/hBneSCwANbkl9Qo5A7SHgAkD9k5rvDh8K3xw6GL7cvy98VrEnfFK+O3yy+3cOncO5X+karuUeSSSkjPvhwQPhi337woO9ezWf/YmeTxLXIJ/H7AJAZoKQDXGCzBHdPf3R6VCmGnVq9ksv7DLCxmfcg1dIIpFwIsabj201Ikm0mLRxasjTz6XeG7JMivuy0pJAOjut8aipX713Xdgsp3yiykSEifoT/bdn6D4QWVoLkkrP+USziZYXblxkqfCQfyLK0U2/qjmSjn7o9uGw4+xO1d/vMpL/dG15nBvnHpf7PinqRduXh/UyqDuk0gDmFwmy1cnLmJDo/LLSYov2My8OMg0QB8Dk0qNruO7GX8DslM11ua4pVEs85oSpHun2O9QlAZd/UvrHSwDANwASzppQa79KZoBE6nkGr2Q/YJ6HWSAHvgKnNSYECuZCvf9ZMjWEE0IK5QLFEhJ2ni2zNT5y74iNn24FYEP5xZNrd9tS8OkCsEotANkTlEAwl4g153Ng+IcZIoIBY2ONGGehjqVgqPfBENGFcTFO1o8sCTI6WFdel24rsnKP42o7yLkR7/g8f838/XYBoPZ+MyfpTi4AJGm1fKyOQD0j4AJA/QsA34rAf7pnT7i1ZUs4VVwS9iwoDLvnzbdjl17L5s6zY9fc1Ht8trdwoc4tDnd0zWe69ttDyRQAGPdHu3aHK+s3hHOrVodbm7eEBxUVms/BRAoa+Uymkzh3FwAyE4RsiBNEDsIF4cRcj6h5JlKffh713DjOQ5rTjd/i8zgXos19KBvAzR9BgJR87k/0nrIDUu9j9J9ruY4oNqnsPIPzuS4e/Jtria5zbnxeZa8IE9wf4z3mRtQ6ncjG62xuOhdCy3MZM3X6XBvnF8fHvCDoRM7jvBgfJRPM7WlMqK/nucz36WsQBojKR0M/shvKzpdJBEmJDsyTe0YceF4cG2PgQDC5+Zebj0mzYS9BJeIXrwFr1pg58Rk48jd4p2PJ3+DA+vJs7l+Z2AKW4EFmiGEhESl9rbhH+jNS3REuPoEb5zO/KHpE08e4Nv5a+XfbBYB6/mGdI49zASBHFsKH4QjkIgIuANS/AEAE/O3t28P+RYvDwpFjwrDO3cPADp1Dv3YdQ4+WbUOHpi10NLe/+7XrZJ+N7NYzLBo5OhxasjS8s32HRc2TSMCY++V168OayVPD/OEjQ6nEjre3bVdWQHKzGpK4Dvk6ZhcAKicJTqD+GGwg0mQMENGGRCNq8G/et89E0iHNa9VSkHr8tj07WzSciHm2Aoev7R+ztrmCuwsAufjru+7H5AJA3WPsT3AEEouACwD1LwB8IxL8QVlZOFVSEjZNnxnmDRsRCgYPDRP79DPS//JzL4aXn/+z/m4TJvZWna0+WzBilJ17duVKuzapEfOvD+wPJ1cUh5mDhoRBHbuE4vETw/WNmwLvJ7WsIV/JdBLn7QJAfhOhXCFk6eOA7JNFsE/186TRU+pAFD5G3GNN/KhZ4wP1/71G9VdZxXrLEkjPOki/p//t+zx9D7gAkNif6M80cBcAngk+v9gRaNgIuABQ/wJAJg+AD3eVWWS8ZMKk0F3En4O/L69bF/gMn4DPlCpvHgAJjpZD9E8sXxGmDxwcBnToFJaPGx+ubdzoAsCR+t+HSSTwzzpmFwCcGKUTo1z4m7T8EzLJwwRvWlGBGSziEVB2flcov7THav3pcNC6R0e1HOwaMCk8eOugZQrELIFcmIePIXe/Wy4ANOzf8ZXNzgWAypDx9x0BRyC4APDHEC+ICEJAPMgKILV/4/QZoU/b9jo6hA3TZoR7So/ns3je1wcOhM/37pWBXoWZCKYbAsZzUiZ7e8008It9MtoT6cZwkOs+13UY7/E+PgT395SH+xIXeMVkkPOe9hfgvmQccB+eG69BlPhU13Ffnsl5v0fQaioAfHc4hVccx2PzRMZeXh4+1diZUxxHasyHTFiI8+ZzGz/na9yZsInn4LOANwF4RIx5Nv9+oGeBwSe6Dxjw/IhdPPfJtQD7ikeHxqD7cj7vxcyHbLD7PWz989//LrsAkLskJV8JJHXzMcWftortendR28Me1hYRt3yc73vJ4b/vmEFhvtoj7rlSYV4FZAjkK2Y+7+p9j10AyM8f+y4A5Oe6+6wdgawQcAHg90lDfRAriCMCACUBUQDYOG2m1cfzGWOA3N6WCeCBxUusdv60Sgg+3r0rfPtISOAVgvrOju3hyNKisGnGzHBYr2QRnFu1SkaCi2Q0uMD+PlK0zD5fOXFyKJ4wMayaNDlsLZgVTsuUEEIb7wkx5bkQ3Ytr14byBQvC6slTwgql7q8YPyGsnTIt7F+42Mz8INqR+FaGWU0EgHhPxI+Pdu2ycWwrmG1jWD5ugsY/6fFcwYdnIGJAtK9u2Bj2FC7UuAvDIWGB5wDnr9O4jy1brnutCydlrrinsDDsmr/AsC2fXxg2CztMGM+vXiOMd4evhcFHwvqMSjB2zJ7zBAZgCHYndB9KOyKpRzDB64HnYO7IGDBz3DlnbtgysyBU6JlXN2ywNXMBoH6+hy4AVI84ONGqe7wws6P2n64AtPObUDgljFWrO1r9cUxUi8QCtfAr2b3SIv+YFOKUT/TfvcW1xQAAFxpJREFU16fu16chYOwCQFY/hxvcSS4ANLgl9Qk5ArWHgAsA9UM8KiPE8f1sBAAIJb4Bi0eNCePlDQBxv670+Rj15h6QVboFLBgx0swDIbv7Fi4ygjyl34AwrEv3sEjXc49JffuH0T16hRHdeuj9bmFE1x4yJRwdzkosIML9UPeDdH8oUouAsH7qdKvdH8M1OndIp65huO43a9DQsHP23HBn61bLEojiQZxb+mtNBQDm/l5pqch6SVg3dZr8EvqHoZ0Zc3cb/5ievcO84SPCbpH4d3bsMFINGd+zYGGYqHmO6N7Txk7pwSj9DRaICBUi5CUi8OA5QkaLswYPC3OHDg/TBgwKS0aPM6PGu1u3hXd37jQiv2TM2DChd1+7B7hxjNQxpkdv82lAcIH0pzIFKgy3JaPH2nlgjfdBPEomTgon5IdAVoILAPXzPXQBwAlTrhE6M/qTDwDE/uT7J8O+a/tDxZW9j4+9V/eFQ7cOmWu+tcl75MbvAoDv5Wz3sgsAtfebOUl3cgEgSavlY3UE6hkBFwDqh3ikk+BMf2cjAECez5SsNJJO14AJMg2EQH6hqD8EErJ+b9s2izBDTDu92TKsVGT/wCL1d5YQ0KNV2/DK/76k13ZWgw8xhYSWSEgoEDHt375j6K3yg6Vjx4WLyhqAmH4iQYFMgzWK+kOKZ4hAU7e/UvdDSJjaf6AR58l9B1j0/ENF6BlHZYS2JgIAqfeQebwDlivzYEq/gRrHEBH0sWGlxk8mAuOA2GOoSPbDR/JNQAjYOL0gdG7eKjR58ZXQq3U7Efyhdg3ZEUTmK5QVMUeEv2OzFuHNVxqbsLFInRnWTpkayiUm0KrwhkwKIfazdd6gTl3C2F59bA14NpkTiAIIEP3laTBn2HDD4X2JFWRNHC1aZuvUTPdu07hpGCuhYPGosVbeQRcIBBx8HSrDK9Ne8fdq/p11AcBJU7akqb7Oi3X89/6eam2IsR/GgOkHEf/Y9s6Jv+/h6u5NFwDq+Yd1jjzOBYAcWQgfhiOQiwi4AFBzMlGbRCwbAYBI/63NWyxyDeHERI/UcqL+fPblvn3hkog76fmDRVQRAfYpPR8Su1pt97qICD//388Z2SXq/ZZS+t8v3Rk+EFmFDEOO277ezISC0jnzFHHfGW5s2mQRd543maj5rNnhltLsIbh8Rrr8JJH/7q3ahGVKx7+yfr3q2yushCATPjURAJgb82IOkG+6JZTNnScsNpswcE8R9+2z5iiK31ctFbuFFRoHn1FSsUFlFO3VUvGl514Ifdt1sPT9D8pKzf8AD4RTxSVhrkSD1k3eCG+89IoJHDGKTxYEJQcXVAZANgAiQV+JJGuVgcB4KAkA+5t61oZp08PAjp2FQ1sTJvgcjLjXWGH36gsvmQCwcMRoiTYrwnvKKIgeAFVlTGTC0N+r+XfWBQAnT9UlT36+75mk7wEXAHLx13fdj8kFgLrH2J/gCCQWARcAak4mapOIZSMAEAmHkELWSWXvrAg/ZQA3N20WmcTUr9w+m6FoPunxS8eMM/J6ad16q9XvqQh481ebWFvB08okgODyXAg26fukxPdr1zE0frGRovwTwrUNG+1+MxVt792mvSL+o5UNsNLOJc0dMcIi40OGhW4tWpsQcHDxUnUtkC+B7psJn5oIAJgWHlyyNDCvkYryLxg+0qL81Pu/LZKPILFb9fVkKPSVeeLkfv3DJdX2RwGgi8aGsDFn6DATQ5gvY2MsZ1euCvNVLtHxzRbKFGhpAgclD2QxcN67O3aGCpURkOLf8rU3VGYwUr4Aqw27SNwh8ggfZAKQdUF5wiGN947GxSsCQLNGr4UB7TtZVgGCSxxDJoz8vbr7TroA4GQu6WTOx+97uLp7wAWAxP5Ef6aBuwDwTPD5xY5Aw0bABYC6IxvVIXLZCACkiVNbfl0p6aTCQ+gRAiDh7+0stY4B2xQJH676fKLkx5UyT908bfYw66OzQJfmrS39nPT4mKqPYd7Hu3aHkyonmCTy+tJzf7ZsAO67ZUZBGNyxi1LoG4VuLVsbuSXdn4PI/0jVzUN6m778qkXAd8jgjug288k0/5oIADjzk3nA+Js1amyZDKO69bIxMA4I92D5EUDyX3/pVct+wLMgCgA9hFPvNu0srR+xII4tCgCFI0eFnm3ahoGdOoejy5aZ4R9Ekc/Bev2U6WFIx64qIWhvJoK0ZYTAsx4c3I9yCfwHurRoZc/fOnOWGQwelGEjmQnt3nhT3gH9LOPgC2VqZMLG36v776ILAE6eqkue/HzfM0nfAy4ANOzf8ZXNzgWAypDx9x0BR8DbAIro5QLxylYA4DxS83GSx1RutCLTdA64Jjd5Uv2LJQwQ/S8cMcqM6/AHuKEMAQQAouPdW7YxMk1NfSSxRLJJh39rzVozwWv0/ItmklcmF3wyDHq2bhteVAp9i1dfD33adFCWQKfU0baTiHUHKxkgvX7m4CEW8SZLoTYzABAAMCAklf9//+f50Ozl15Rx0Cb0a9vRxtFXr5Bz3uOVmn7wiAIAtf9kNtBi8a4yHZ4WAMhsYPzDVTJxXOn5hov2BcaDdFAoGT8pZTiozw8vXWZkP0b/IZQmAkgQINqPSIJQsXrSFPNroGMDXg1dlF1AhgIZB4g4ubDn8nEMLgA4mUs6mfPx+x6u7h5wASA/f+y7AJCf6+6zdgSyQsAzAJIjAEDYIJuk+tO6Did/nPgh+5QFEIGerxT1yXK9p40dveYhs5QIRAGgmwSArUr1zyQAXEAAUDo/9epT+w8KZWoZiJ8AbQlbNX5D2QZDzHNgr7oK0FmAw/5etNjIr0XdlVkAwWWcmQhmTTIAPpcAsE4CQFel8rdXJJ3uBZuUmUBbw/RxMBYINzX7mBc+IQCodh/jv7sySfytADAm9NPnlBdkFABkeDi0c1czCMTUD0Eizi8KAOBMmQICAGICpoln1TIwCgBdlRlAtgYdFlwA+OO+cy4AOHmqLnny833PJH0PuACQ1c/hBneSCwANbkl9Qo5A7SHgAsAfR0bSCXI2GQBRAIBAUv9OFgDdAEgxxwSPfvS0mFuolPbDIqOYAqYLAETv8Q2gjR7GeZBxi14rq+DDsl1WMjBFbvpNFWGfP3yUSH2RRd4HqQQg5TcwyYz/iPDjcP9Jebm9YoTHgeBA1DxGx9PnF/+uiQAA4UbQgKRDrovGjg8XJVZgwhfHwHg+fmTKF8fxrAIAY6UDwAaJD2RV8GxEhs+ULUGGA9hxPDx40DwBSmVMiEiBWLCtYJb5EFACQAaACwC58T1zAcDJXNLJnI/f93B194ALALX3mzlJd3IBIEmr5WN1BOoZARcAcoOYVEcAgHySBUBLwOFdu1vt/QJlAdAKj+g/qe4Y+EH+0wUAPANI45+tKP8FGdlhHPiNyCsEm/M3K6o+tEs3q+kn4o55YEXhQqtdh9iSWk9LQAg242UciBHUxN/bvs1EABMAHpHjSPrTX2siAPAMug1gpoeRHq3+iK5DxBk/Y2EOCADvy38AEz/+/awCAMQePwMwGK/6/U7qAkBJBJ4KD9TpgOdy0PXgojoq0JaQEgvW4dDSpSbSUBbgAkBufMfYhy4AOHmqLnny833PJH0PuABQzz+sc+RxLgDkyEL4MByBXETABYDcICcQSQgr9fyk3FNHvlEt7N7ett1IZiTRMeoM0aYNX4Ei/mQB0PKvvyLkmP+RFv+x1eETnf61BACnfgz+qIcvVfbAPd0bsnxny1Yjubjs91AbO1Lh6VH/rsgvkXa6CTAe+tyXTExlAeD0/5GMA0mppxxhj1z4EQzwEuCZjDOOOf21JgIA1yA8UOpA60NED9L5r6zfYONnLLTiOyNRgBR9xA0yEp5VACCTAXM/2iXi8E+XAJ69RdH9K/IYSGGQejbjwXyRVoDFEyfa2GIbQBcAcuM75gKAE7mkEzkfv+/hmuwBFwBy8dd33Y/JBYC6x9if4AgkFgEXAHKDnGQrAEQyDcmGoBPtx4mfHvWk6dOyj1p8SHMqPf1XAYAo/ot/eiF0VSeAWYOHWsnAatWqF4ngT+03MAzq1MXI/3r1tL8ucYEoOun+RLPJGuiq+nbS8CHDayZPNV8BPALmDRtp5Jz+9vdVFlDbAkAKm+0SKVKt/hAwRmjOPHeVyh4w3Fs8aqyNgXGdkiBBNsCzCgDgR5YDGBwpKjITPzAcpiwJOgekMJgalqkkgewESiUWjR4Tji1fbuUJYHFEnRRcAMiN75gLAE6eakKe/BrfN0nfAy4AJPYn+jMN3AWAZ4LPL3YEGjYCLgDkBjmB5NLKj8j86O5y99dBjT8kHxIaiX985Xzq348o4k07vuavNrG2gMUyrCMazuecm54BgBs+Zn60zRvdvbe1puO65q80sdZ6tAAsUx373W1bLbWf53I9Kf7Hly83Ezta/rV87XV7Hte2bdJMmQedwmIR36uKikfyX1kGAJ9jhLdw5OgwrlcfeQxMC7Tmiy0J4/zSX7kXgsbb8i3Ys2ChiRedmtF68LXw5iuN7WiuV8axTt0O3leXBM6nBSIYIpCM7dnHsKX9YTo2pO6vnDQ5jO3VO0wbKJd+jY3Uf1LFGUMUUfAYQODAyI9MAHBk/i10tHu9mZH/5eMmhPOPsg+YJ2UCiBGUTgyRL0Ch5szzvlJJQ/r8/O/6+w56CYCTuaSTOR+/7+Hq7gEXABr27/jKZucCQGXI+PuOgCPgbQAfEb0/moTFdHNq8aOz/VVqzZWCnolMQ875DMf7gsHDjMwThS5XKj4O/1E0gIjGLgCk8ZMlQNR+2yPTwBXjJ1hdO5kEB5csCXfUJo+ae54ZD+7xQVmpItsrzH2fMgDuwbWrFH3HgDAVdd/9+JrK8GRc7+7cYWUDlBnQehDHft7/7nDlRJCxYGp4TyUHJxiHSiUYw3KNgYPa/G2zZut+a1Lihc5/II8D8KRbAM+idh//ArBmfDyTeZ1X+0Q+P6xoPTX/8fM4B56NaGBCiLot0IEAw0Xmz0EWBcIJz2Yu4MU1iBDvSLRAPGFdKJXged/o83hvf618zesCGxcAnDxVlzz5+b5nkr4HXADIzx/7LgDk57r7rB2BrBDwDID6JSCVkZpIMr8QySV1nOg+KfgQTz57+jrexwjwxIpiCQBDLfpdNHacEWqui9ekCwB9JQBgUrdl5izLEiAaDuHlQDTguV/tT7n4pz/PyKxIK+SZ895ThD1eR7SdFHk+M+L7O4KK3UvEmPPva/yfS8SIhDn9mU//zXUQc7wPuBbvgjiG+Mo4uB/n2fnCCDzBkrnxdzqeqbEoUq9r+Bw8Y+lEpuczzsfPVnZBfC61/pQcpEwVU+Sfe6fuv998ERAGuLay+z/9PP933XwvXQBwMpd0Mufj9z1c3T3gAkBWP4cb3EkuADS4JfUJOQK1h4ALAHVDNGpC4CJphMBGEmvvZSDVkFFS3LfKkA7jv0ly/6cNHWnyD9NEA86LGQBRANg+e44RecgohJhIuB2PSCvPTB9/HBev3x5K1cU/vsau/TVbIP26TH8/ca+05z39zKevhbg9cW0c8xOvT44jnl8ZnvFzXp/A+6n5x7HE85+c+2+x4zyuiedne//4HH+tu++kCwBOnqpLnvx83zNJ3wMuANTeb+Yk3ckFgCStlo/VEahnBFwAqDuyUdtEDiJJ+jgp+kSUL61dZ/Xw9KjHqR8XfFz4jXg+Eg0qEwBwsOczzq3tcfr9krOn8m2tXABwMpd0Mufj9z1c3T3gAkA9/7DOkce5AJAjC+HDcARyEQEXAJJD1og8Y0aHQ/9x1cFvLZgtR/ruZnK3Q1H922rnhziQTuqeEADknk+bv+2z51oLOxcAkrP26Wvqf9d83VwAcPJUXfLk5/ueSfoecAEgF3991/2YXACoe4z9CY5AYhFwAaDmZKK+iRhO+Zjmlcjpf7iIP673Y3r0lgndVPWdX2+u84gE6eOC5OOyv1GmeUOUKcA1ZfPmW92+CwDJWfv0NfW/a75uLgA4mUs6mfPx+x6u7h5wASCxP9GfaeAuADwTfH6xI9CwEXABoOZkor6JGIQdoo9j/9T+A8O0/oPkSD/TWsthYAf5fzqlnxr/90tp47cirJZjP20CaXX3mYzv0g3x6nsu/rzk7LuGtFYuADh5qi558vN9zyR9D7gA0LB/x1c2OxcAKkPG33cEHAFvA5jBYC9XCQ/kHpf79+Xaf2vzZjtwoMfFvjIyzzWY/eG4/65c/3H+x42+svNzde4+LhcMamMPuADgZC7pZM7H73u4unvABYD8/LHvAkB+rrvP2hHICgHPAEgOsYLME+WHvH+tbAAO/n7sYJ9BzOCaeN3DgwfN+C9mCvB+bZAqv0dy9lC+r5ULAE6eqkue/HzfM0nfAy4AZPVzuMGd5AJAg1tSn5AjUHsIuADg5C3fSaHPP3++Ay4AOJlLOpnz8fseru4ecAGg9n4zJ+lOLgAkabV8rI5APSPgAkD+kB8nur7W+b4HXABw8lRd8uTn+55J+h5wAaCef1jnyONcAMiRhfBhOAK5iIALAE4K850U+vzz5zvgAoCTuaSTOR+/7+Hq7gEXAHLx13fdj8kFgLrH2J/gCCQWARcA8of8ONH1tc73PeACgJOn6pInP9/3TNL3gAsAif2J/kwDdwHgmeDzix2Bho2ACwBOCvOdFPr88+c74AKAk7mkkzkfv+/h6u4BFwAa9u/4ymbnAkBlyPj7joAj4G0AMzjnOyHMH0Loa51fa+0CgJOn6pInP9/3TNL3gAsA+flj3wWA/Fx3n7UjkBUCngGQXwTICa+vdz7vARcAnMwlncz5+H0PV3cPuACQ1c/hBneSCwANbkl9Qo5A7SHgAoATwnwmhD73/Nr/LgA4eaouefLzfc8kfQ+4AFB7v5mTdCcXAJK0Wj5WR6CeEfjp738Pf793N3x/+lT4y9EjfjgGvgd8DzTYPfCd1vbrt06Fj+5fDe/+cCck/Ye9j9/Jqe8B3wNV7YF3/vl2uP/v++GHn34IP/3yUz3/wvTH/ZEIuADwR6Lvz3YEchyBn/7xj/DPD94Pf718KXx//ryOc344Br4HfA802D3wzY1L4bMv7oSP/vZe+PBfH/jhGPge8D3QgPfAh+HzHz8Pf/v5b+HnX37O8V+kPrzaRMAFgNpE0+/lCDQwBH7+8cfw47ffhn8/eBD+9emnfjgGvgd8DzToPfCPLx+EH/72dfjLj9+G7/7fd344Br4HfA806D3w15/+Gv79y7/DL7/80sB+wfp0qkLABYCq0PHPHIE8R4D/Ifzy88/hl59+8sMx8D3geyAv9sDP+m8e0TA/HAPfA74H8mEP2G+94AJAPv3kdwEgn1bb5+oIOAKOgCPgCDgCjoAj4Ag4Ao6AI5C3CLgAkLdL7xN3BBwBR8ARcAQcAUfAEXAEHAFHwBHIJwRcAMin1fa5OgKOgCPgCDgCjoAj4Ag4Ao6AI+AI5C0CLgDk7dL7xB0BR8ARcAQcAUfAEXAEHAFHwBFwBPIJARcA8mm1fa6OgCPgCDgCjoAj4Ag4Ao6AI+AIOAJ5i4ALAHm79D5xR8ARcAQcAUfAEXAEHAFHwBFwBByBfELABYB8Wm2fqyPgCDgCjoAj4Ag4Ao6AI+AIOAKOQN4i4AJA3i69T9wRcAQcAUfAEXAEHAFHwBFwBBwBRyCfEHABIJ9W2+fqCDgCjoAj4Ag4Ao6AI+AIOAKOgCOQtwi4AJC3S+8TdwQcAUfAEXAEHAFHwBFwBBwBR8ARyCcEXADIp9X2uToCjoAj4Ag4Ao6AI+AIOAKOgCPgCOQtAi4A5O3S+8QdAUfAEXAEHAFHwBFwBBwBR8ARcATyCQEXAPJptX2ujoAj4Ag4Ao6AI+AIOAKOgCPgCDgCeYuACwB5u/Q+cUfAEXAEHAFHwBFwBBwBR8ARcAQcgXxC4P8DXjh1QyFqQdUAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONFUSION MATRIX\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **true positives** (Top-right quadrant): these are cases in which we predicted the customer would default and they did.\n",
    "- **true negatives** (Bottom-left quadrant): We predicted no default, and the customer did not default.\n",
    "- **false positives** (Bottom-right quadrant): We predicted yes, but they didn’t actually default. \n",
    "- **false negatives** (Top-left): We predicted no, but they did default. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   predicted_outcome\n",
       "      0   1\n",
       "  0 112  13\n",
       "  1  33  34"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## CREATION OF CONFUSION MATRIX\n",
    "confusion_matrix = table(data_test$Outcome,predicted_outcome)\n",
    "confusion_matrix"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Accuracy = ![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.791666666666667"
      ],
      "text/latex": [
       "0.791666666666667"
      ],
      "text/markdown": [
       "0.791666666666667"
      ],
      "text/plain": [
       "[1] 0.7916667"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## CALCULATION OF ACCURACY\n",
    "accuracy = (112+40) / (112+13+27+40)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC determines the accuracy of a classification model at a user defined threshold value. \n",
    "It determines the model's accuracy using Area Under Curve (AUC). Higher the area, better the model. ROC is plotted between True Positive Rate (Y axis) and False Positive Rate (X Axis). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zo7d67lxZkzZ+7evbuwsHD58uUyR0hERG5JRJlVjxAKnXNDssl+j3DTpk2jR4+e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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ROCR Curve\n",
    "\n",
    "ROCRpred <- prediction(predicted_prob, data_test$Outcome)\n",
    "ROCRperf <- performance(ROCRpred, 'tpr','fpr')\n",
    "plot(ROCRperf, colorize = TRUE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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