{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Leveraging Experiment Lines to Data Analytics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time Series regression - tune - example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our objective here is to generate a model that is able to do time series forecasting."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Configuring the environment:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading required package: daltoolbox\n",
"\n",
"Registered S3 method overwritten by 'quantmod':\n",
" method from\n",
" as.zoo.data.frame zoo \n",
"\n",
"\n",
"Attaching package: ‘daltoolbox’\n",
"\n",
"\n",
"The following object is masked from ‘package:base’:\n",
"\n",
" transform\n",
"\n",
"\n"
]
}
],
"source": [
"# DAL ToolBox\n",
"# version 1.0.777\n",
"\n",
"source(\"https://raw.githubusercontent.com/cefet-rj-dal/daltoolbox/main/jupyter.R\")\n",
"\n",
"#loading DAL Toolbox\n",
"load_library(\"daltoolbox\")\n",
"\n",
"#load required library\n",
"library(ggplot2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cosine time series for studying\n",
"\n",
"Generate a cosine time series to use in the example, it starts at 0 (zero) and goes up to 25 (twenty-five)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"i <- seq(0, 25, 0.25)\n",
"x <- cos(i)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plots the time series:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAADAFBMVEUAAAABAQECAgIDAwME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\nQUiAjEQQEvg/y11GUNiIoksAAAAASUVORK5CYII=",
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 420,
"width": 420
}
},
"output_type": "display_data"
}
],
"source": [
"plot_ts(x=i, y=x) + theme(text = element_text(size=16))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sliding windows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Creates a matrix representing a sliding window to be used in the process of training the model. Each row of the matrix represents one moment of the sliding window, with 10 (ten) elements as attributes (t9, t8, t7, ..., t0)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"A matrix: 3 × 10 of type dbl\n",
"\n",
"\tt9 | t8 | t7 | t6 | t5 | t4 | t3 | t2 | t1 | t0 |
\n",
"\n",
"\n",
"\t1.0000000 | 0.9689124 | 0.8775826 | 0.7316889 | 0.5403023 | 0.3153224 | 0.0707372 | -0.1782461 | -0.4161468 | -0.6281736 |
\n",
"\t0.9689124 | 0.8775826 | 0.7316889 | 0.5403023 | 0.3153224 | 0.0707372 | -0.1782461 | -0.4161468 | -0.6281736 | -0.8011436 |
\n",
"\t0.8775826 | 0.7316889 | 0.5403023 | 0.3153224 | 0.0707372 | -0.1782461 | -0.4161468 | -0.6281736 | -0.8011436 | -0.9243024 |
\n",
"\n",
"
\n"
],
"text/latex": [
"A matrix: 3 × 10 of type dbl\n",
"\\begin{tabular}{llllllllll}\n",
" t9 & t8 & t7 & t6 & t5 & t4 & t3 & t2 & t1 & t0\\\\\n",
"\\hline\n",
"\t 1.0000000 & 0.9689124 & 0.8775826 & 0.7316889 & 0.5403023 & 0.3153224 & 0.0707372 & -0.1782461 & -0.4161468 & -0.6281736\\\\\n",
"\t 0.9689124 & 0.8775826 & 0.7316889 & 0.5403023 & 0.3153224 & 0.0707372 & -0.1782461 & -0.4161468 & -0.6281736 & -0.8011436\\\\\n",
"\t 0.8775826 & 0.7316889 & 0.5403023 & 0.3153224 & 0.0707372 & -0.1782461 & -0.4161468 & -0.6281736 & -0.8011436 & -0.9243024\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"A matrix: 3 × 10 of type dbl\n",
"\n",
"| t9 | t8 | t7 | t6 | t5 | t4 | t3 | t2 | t1 | t0 |\n",
"|---|---|---|---|---|---|---|---|---|---|\n",
"| 1.0000000 | 0.9689124 | 0.8775826 | 0.7316889 | 0.5403023 | 0.3153224 | 0.0707372 | -0.1782461 | -0.4161468 | -0.6281736 |\n",
"| 0.9689124 | 0.8775826 | 0.7316889 | 0.5403023 | 0.3153224 | 0.0707372 | -0.1782461 | -0.4161468 | -0.6281736 | -0.8011436 |\n",
"| 0.8775826 | 0.7316889 | 0.5403023 | 0.3153224 | 0.0707372 | -0.1782461 | -0.4161468 | -0.6281736 | -0.8011436 | -0.9243024 |\n",
"\n"
],
"text/plain": [
" t9 t8 t7 t6 t5 t4 t3 \n",
"[1,] 1.0000000 0.9689124 0.8775826 0.7316889 0.5403023 0.3153224 0.0707372\n",
"[2,] 0.9689124 0.8775826 0.7316889 0.5403023 0.3153224 0.0707372 -0.1782461\n",
"[3,] 0.8775826 0.7316889 0.5403023 0.3153224 0.0707372 -0.1782461 -0.4161468\n",
" t2 t1 t0 \n",
"[1,] -0.1782461 -0.4161468 -0.6281736\n",
"[2,] -0.4161468 -0.6281736 -0.8011436\n",
"[3,] -0.6281736 -0.8011436 -0.9243024"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sw_size <- 10\n",
"ts <- ts_data(x, sw_size)\n",
"ts_head(ts, 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data sampling"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Samples data into train and test."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"A matrix: 3 × 10 of type dbl\n",
"\n",
"\tt9 | t8 | t7 | t6 | t5 | t4 | t3 | t2 | t1 | t0 |
\n",
"\n",
"\n",
"\t1.0000000 | 0.9689124 | 0.8775826 | 0.7316889 | 0.5403023 | 0.3153224 | 0.0707372 | -0.1782461 | -0.4161468 | -0.6281736 |
\n",
"\t0.9689124 | 0.8775826 | 0.7316889 | 0.5403023 | 0.3153224 | 0.0707372 | -0.1782461 | -0.4161468 | -0.6281736 | -0.8011436 |
\n",
"\t0.8775826 | 0.7316889 | 0.5403023 | 0.3153224 | 0.0707372 | -0.1782461 | -0.4161468 | -0.6281736 | -0.8011436 | -0.9243024 |
\n",
"\n",
"
\n"
],
"text/latex": [
"A matrix: 3 × 10 of type dbl\n",
"\\begin{tabular}{llllllllll}\n",
" t9 & t8 & t7 & t6 & t5 & t4 & t3 & t2 & t1 & t0\\\\\n",
"\\hline\n",
"\t 1.0000000 & 0.9689124 & 0.8775826 & 0.7316889 & 0.5403023 & 0.3153224 & 0.0707372 & -0.1782461 & -0.4161468 & -0.6281736\\\\\n",
"\t 0.9689124 & 0.8775826 & 0.7316889 & 0.5403023 & 0.3153224 & 0.0707372 & -0.1782461 & -0.4161468 & -0.6281736 & -0.8011436\\\\\n",
"\t 0.8775826 & 0.7316889 & 0.5403023 & 0.3153224 & 0.0707372 & -0.1782461 & -0.4161468 & -0.6281736 & -0.8011436 & -0.9243024\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"A matrix: 3 × 10 of type dbl\n",
"\n",
"| t9 | t8 | t7 | t6 | t5 | t4 | t3 | t2 | t1 | t0 |\n",
"|---|---|---|---|---|---|---|---|---|---|\n",
"| 1.0000000 | 0.9689124 | 0.8775826 | 0.7316889 | 0.5403023 | 0.3153224 | 0.0707372 | -0.1782461 | -0.4161468 | -0.6281736 |\n",
"| 0.9689124 | 0.8775826 | 0.7316889 | 0.5403023 | 0.3153224 | 0.0707372 | -0.1782461 | -0.4161468 | -0.6281736 | -0.8011436 |\n",
"| 0.8775826 | 0.7316889 | 0.5403023 | 0.3153224 | 0.0707372 | -0.1782461 | -0.4161468 | -0.6281736 | -0.8011436 | -0.9243024 |\n",
"\n"
],
"text/plain": [
" t9 t8 t7 t6 t5 t4 t3 \n",
"[1,] 1.0000000 0.9689124 0.8775826 0.7316889 0.5403023 0.3153224 0.0707372\n",
"[2,] 0.9689124 0.8775826 0.7316889 0.5403023 0.3153224 0.0707372 -0.1782461\n",
"[3,] 0.8775826 0.7316889 0.5403023 0.3153224 0.0707372 -0.1782461 -0.4161468\n",
" t2 t1 t0 \n",
"[1,] -0.1782461 -0.4161468 -0.6281736\n",
"[2,] -0.4161468 -0.6281736 -0.8011436\n",
"[3,] -0.6281736 -0.8011436 -0.9243024"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"A matrix: 1 × 10 of type dbl\n",
"\n",
"\tt9 | t8 | t7 | t6 | t5 | t4 | t3 | t2 | t1 | t0 |
\n",
"\n",
"\n",
"\t-0.7256268 | -0.532833 | -0.3069103 | -0.06190529 | 0.1869486 | 0.424179 | 0.635036 | 0.8064095 | 0.9276444 | 0.9912028 |
\n",
"\n",
"
\n"
],
"text/latex": [
"A matrix: 1 × 10 of type dbl\n",
"\\begin{tabular}{llllllllll}\n",
" t9 & t8 & t7 & t6 & t5 & t4 & t3 & t2 & t1 & t0\\\\\n",
"\\hline\n",
"\t -0.7256268 & -0.532833 & -0.3069103 & -0.06190529 & 0.1869486 & 0.424179 & 0.635036 & 0.8064095 & 0.9276444 & 0.9912028\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"A matrix: 1 × 10 of type dbl\n",
"\n",
"| t9 | t8 | t7 | t6 | t5 | t4 | t3 | t2 | t1 | t0 |\n",
"|---|---|---|---|---|---|---|---|---|---|\n",
"| -0.7256268 | -0.532833 | -0.3069103 | -0.06190529 | 0.1869486 | 0.424179 | 0.635036 | 0.8064095 | 0.9276444 | 0.9912028 |\n",
"\n"
],
"text/plain": [
" t9 t8 t7 t6 t5 t4 t3 \n",
"[1,] -0.7256268 -0.532833 -0.3069103 -0.06190529 0.1869486 0.424179 0.635036\n",
" t2 t1 t0 \n",
"[1,] 0.8064095 0.9276444 0.9912028"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"test_size <- 1\n",
"samp <- ts_sample(ts, test_size)\n",
"ts_head(samp$train, 3)\n",
"ts_head(samp$test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model training\n",
"\n",
"Tune optimizes a learner hyperparameter, no matter which one. This way, in this example, an ELM is used in the hyperparameters tuning using an appropriate range. The result of tunning is an ELM model for the training set."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Setup for tunning using ELM\n",
"tune <- ts_tune(input_size=c(3:5), base_model = ts_elm(ts_norm_gminmax()))\n",
"ranges <- list(nhid = 1:5, actfun=c('sig', 'radbas', 'tribas', 'relu', 'purelin'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In [6] is using ts_elm() as base_model, but all time series models can be used. It is simple as changing the constructor.\n",
"\n",
"An LSTM could be used, as shown at In [7], as lines of comments. This example clarifies how to provide variability on workflow models by simply changing constructors.\n",
"\n",
"Options of ranges for all time series models are presented in the end of this notebook.\n",
"\n",
"Input size options should be between 1 and sw_size-2."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# tune <- ts_tune(input_size=c(3:5), base_model = ts_lstm(ts_norm_gminmax()))\n",
"# ranges <- list(input_size = 1:10, epochs=10000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The prediction output using the training set can be used to evaluate the model's adjustment level to the training data:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"io_train <- ts_projection(samp$train)\n",
"\n",
"# Generic model tunning\n",
"model <- fit(tune, x=io_train$input, y=io_train$output, ranges)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Evaluation of adjustment"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" mse smape R2\n",
"1 4.346951e-29 2.160724e-14 1\n"
]
}
],
"source": [
"adjust <- predict(model, io_train$input)\n",
"ev_adjust <- evaluate(model, io_train$output, adjust)\n",
"print(head(ev_adjust$metrics))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prediction of test"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] \"0.99, 0.99\"\n"
]
}
],
"source": [
"steps_ahead <- 1\n",
"io_test <- ts_projection(samp$test)\n",
"prediction <- predict(model, x=io_test$input, steps_ahead=steps_ahead)\n",
"prediction <- as.vector(prediction)\n",
"\n",
"output <- as.vector(io_test$output)\n",
"if (steps_ahead > 1)\n",
" output <- output[1:steps_ahead]\n",
"\n",
"print(sprintf(\"%.2f, %.2f\", output, prediction))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Evaluation of test data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" mse smape R2\n",
"1 1.066072e-28 1.041671e-14 -Inf\n",
"[1] \"smape: 0.00\"\n"
]
}
],
"source": [
"ev_test <- evaluate(model, output, prediction)\n",
"print(head(ev_test$metrics))\n",
"print(sprintf(\"smape: %.2f\", 100*ev_test$metrics$smape))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot results\n",
"\n",
"The plot shows results of the prediction. "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94\nAAAgAElEQVR4nOzdd2BUVfo+8GdmMjPpk94rpBFIIBQJHRQUFBQWvrACu/5YZbFTdhdYXcsq\na1fU1UVsYFmKsIsCipRIldAMkAQSSO9l0sskmfr74xLMhgApM3POnbyfv5LLzL0Pepl577nn\nnldiMplACCGEEELET8o6ACGEEEIIMQ8q7AghhBBCbAQVdoQQQgghNoIKO0IIIYQQG0GFHSGE\nEEKIjaDCjhBCCCHERlBhRwghhBBiI+xYB2Dm9OnTu3btYp2CEEIIIaRnBg0a9NBDD3X5R/23\nsCsuLo6KirrnnntYByGEEEII6a7q6urNmzff7E/7b2EHwM3NLTAwkHUKQgghhJDuksvlt/hT\nmmNHCCGEEGIjqLAjhBBCCLERVNgRQgghhNgIKuwIIYQQQmwEFXaEEEIIITaCCjtCCCGEEBtB\nhR0hhBBCiI2gwo4QQgghxEZQYUcIIYQQYiOosCOEEEIIsRFU2BFCCCGE2Agq7AghhBBCbAQV\ndoQQQgghNoIKO0IIIYQQG0GFHSGEEEKIjaDCjhBCCCHERlBhRwghhBBiI6iwI4QQQgixEVTY\nEUIIIYTYCCrsCCGEEEJsBBV2hBBCCCE2ggo7QgghhBAbQYUdIYQQQoiNoMKOEEIIIcRGcF3Y\nHTx4sKioiHUKQgghhBBxsGMd4KaKior++c9/PvXUU8HBwd15fXp6+u7duzMyMpycnGJjYxcv\nXuzh4WHpkIQQQggh/OB0xM5gMHz++efdf31SUtJzzz135swZf39/iURy6NChVatWFRQUWC4h\nIYQQQghvuBuxO378+OXLl0+dOlVdXd3Nt2g0mk8++USpVL722mthYWEA9u3bt2HDhvXr169f\nv14ikVgwLiGEEEIIN7gbsfvmm2++//777ld1APbv36/RaObNmydUdQBmzJgRFxeXm5ubmZlp\nkZSEDwaD4aOPPpo1a9bkyZP//Oc/V1RUsE5EeKfX669cuXLlyhW9Xs86CyGEmB93hd177733\n7bfffvvttwsXLuzmW44fPw5gzJgxHTcmJiYCSElJMXtCwgmTyTR79uzHHnts7969R48effvt\ntwcPHkz338kt7Nq1KywsLCYmJiYmJjQ09D//+Q/rRIR39fX1ycnJaWlpOp2OdRZCuoW7wk7a\nQXdebzKZCgsL7ezsAgMDO24PDQ0FUFhYaJGUhANbtmzZu3dvxy3V1dVPPvkkqzyEc2fPnl24\ncGFJSYnwa2lp6eLFi5OTk9mmIjx79dVXAwICxo4dGx8fHxMTk5SUxDoRIbfHXWHXU21tbVqt\n1sXFpdN2YUtDQ0PHjf/617/ubEf/RMWuy/+DSUlJJpPJ+mEI/15//fXW1taOW1pbW1977TVW\neQjnNm3a9Mwzz2g0GuHX3Nzc3/zmN7m5uddfcP2PCBEcOXJk2rRp/v7+CQkJr7/+ularZRJD\n9IWdMDzu6OjYabuTkxOAtra2jhtDQkLuaOfp6Wm1kMQSjEZjNzcSAiAnJ+fGjdnZ2dZPQkTh\n9ddf77SloaHhX//6V01NzbJly1QqlZOT08CBAz/77DMm8Qhv9uzZM2XKlEOHDpWXl1+4cGHt\n2rWLFi1ikoS7p2J7ytnZWSqVdroQR/u1lKura8eNM2fOnDlzpvAzTa8Ru4kTJ37xxRf/u81u\n7Ng76Tlo0iVfX98bN17/iNDpdGVlZf7+/nK53Lq5CKfy8/Nv3JicnDxnzpxjx44Jv+bm5j7y\nyCN6vX7ZsmVWDUc4YzQaH3vssU4bd+7cuX///nvuucfKYUQ/YieRSFQqVWNjY6ftwhZao9iG\nPfTQQyNGjOiwwR74T1racxoNzXEmXVi6dOmNG8+dG/6b3yQ98cSTTk5OoaGhLi4uK1asaG5u\ntn48wht/f/8bN548efLYMTWwG1gGXJvYvWbNGlY33QgniouLr8/f7ejUqVPWDyP6wg6At7e3\nVqutrKzsuLG4uBiAl5cXo1DE4mQyWWHh28BfYmPj4uPjFy/+natrcFXVmMGDz+r1dEOWdDZ3\n7ty77noF+AaIBaBQKO67b67R+NyuXXf9619zdDp/AG1tbe+99x49gkMAPP7444AE2AzMF7Yo\nlcqhQ4cCk4FZwEdALhANoL6+np7H7+cUCkWX25VKpZWTwDYKO2Ghk9OnT3fceObMGdywBgqx\nJevWnVOrJ7m7L7x0KfXixYtfffVxRkakk9Pl/PyxUVGH5s9fEBcXd/fdd3/11Vf0OAURXLgw\nDfi/JUtWbt68+dKlS3v37vz66yzgAHAXkAb8QXjZ5s2br169yjYqYe5Pf/rT+PFrgIeAeQDc\n3Nw+//zzJUuWABuAaOBDQAEsEF6sUqmYhiWM+fn5JSQkAMCDgN+v261/HxZiLOy0Wm12dnZ2\ndvb1afJTp06VyWQ7d+6sqqoStpw6dSolJSUmJiY8PJxdUmJBWq3hH/9QAab33vv1eiggwPn8\neT+5/Gpe3t07doxIT08/ePDg73//+xUrVjCMSjixc+fV6uoRLi5pn3/+yEMPPRQREQHAzq4c\nmA48BkiBz4A5wotpbXMilUrLy+8DsGBB0549e3JychYuXDhr1ixHR0fgKvA8oBdOmMmTJ/v4\n+LDOSxjbvHmz4xBH/BvYcm3Liy++eK3asy7xFXZqtXrVqlWrVq26/sCESqV64okn6uvrly9f\n/vbbb7/wwgtvvPGGm5vbE088wTYqsZzHHktubY0MDU3+3e8Gddw+YIDKzW0+kAOsBp4RNr7/\n/vvnzp1jEZNwZO1aNSB5/PH/WaLCw8MDMAEfATMAAHOF7fTUPCkpaczOTrCzK/3664dmzpwp\nzNgeMGDABx98oFQqgRrgKDDMz2/M5s2bWYcl7MXHx49+ZzQkCPw5cOnSpUlJSS+88AKTJKJ/\nKlYwdepUlUq1f//+CxcuODk5TZo0acGCBX5+frd/JxGh6mrNF18MBNq+/DK40x9lZ2er1ReB\nacBPwK+zXo4dOzZy5EjrxiQcOXu2LCdnlFyev27dqI7bx40bN3DgwJycHOAkcBLIAhATE3PH\nHXcwSkp4sXbtBWDCuHHn7OwCOm5fsmTJ+PHj//Of/3z+eXpW1qh77lklrIdPyKmQUzDgwKMH\nYr1iGcbgt7CbP3/+/Pnzb9weGBi4e/fuG7ePGjVq1KhRN24ntmfhwjMGw+QRI45OnDip0x+1\nr3WSBwwCfl0Ep5uNTIitevLJq8CkuXML7ezCOm63t7ffunXr7NmzS0tLgXEAfH19t23bRoue\nkF27PADjunUDb/yjyMjItWvXzp1bEh3tm5s7SpiER/q5gwUHW6JbPH7xiB3BsqqDGG/Fkn7L\naDQWFRUVFhYeP/69VFqwbVvcja+JiIhov3r+n6UNp0yZYpWMhEeNjY0pKXqptPrDD7u49hs1\natSVK1e++uqrefPmAVi6dOnQoUOtnpHw5cCBq83Ngzw9L4wfH3Sz10RGBo4aFf/zzz+Xl5db\nMxvh0+v5rwOYqZnJOggVdkQMTCbTG2+84e7uHhISEhYW1tLy1tq1n0VEdLFIoVQq3bRpU6cn\nz//617/SV3V/tnHjRr1+6ooVX3h4OHT5Amdn58WLF2/YsEEqlR46dMjK8QiHfvjhX0D42rXq\nW79szpw5RqNxz5491klFePZz4M/Q4dnYZ1kHocKOiME777yzZs0aofOvsHbJ3r3fdeoXd92U\nKVPOnz//8MMPC489/u53v3vllVesmZZwRafT/fOf/7S3t1+9+jbtfby8vEaOHHn27Nnq6mrr\nZCN80mq1W7Zs8fRsfuqpybd+5dy5cwHs2rXLGrEIxy5nXm59p3XArgFRnlGss1BhR7in1Wpf\neumlThtTU1N37Nhxs7fExsZ++umnR48eBVBRUWHZfIRv27dvLywsXLJkSZctxTqZMWOGwWA4\nePCgFYIRbu3atUutVi9evPi2q8tGRkbGxsYeOnSorq7OOtkIn77Z9g024G/Nf2MdBKDCjvCv\nrKxMGKvr5LYrjQUEBERHR584ceJmY3vEhl26dOnxxx+fMWPGypUrpVJpN9cynDFjBoB9+/ZZ\nOB3h2ueffw7gD3/4Q3dePGfOHJ1OR+dMP7djxw6FQjF79mzWQQAq7Aj/3NzcunymtTv94iIj\nn9RovvvsswwL5CL8+vbbb4cPH75hw4Yff/yxqqrKaDQmJyd3540jR450dV2wc+d4g4G60vVT\nBQUFhw4dGjVqVHx8fHdeP2fOHGDE++9X3v6lxEalpaVdvnx52rRp7u7urLMAVNgR/qlUqgce\neKDTRhcXlzlz5tz2vZGRI4Gp33xDd0n6kebm5ocffrhTU/YnnniiUzvpLslkMnf3pzWapdu3\nU0uxfmrTpk1Go/Hhhx/u5utHjBhhZ7fr9OlH6upab/9qYou2b98OYMGCBayDXEOFHRGBjRs3\nJiQkAMnAfkDi4uLy+eefd2dR0D/+MRIwXbjAxVUUsY6zZ8/W1NQAAAYDJ4BJAJqbm0+cONGd\nt0+fbgKwaRMtYNEf6fXGDz5odXAI+O1vf9v9dw0ZkmcyOb3zTqrlghGe7dixQ6lU3n///ayD\nXEOFHREBb2/v3bsPA6OUSq9PPvn46tWrwpJjtxUT42lvn1VfP6iqSnP7VxOboNPp2n+cDowD\nrjWM7jSGdzNPPx0NGM+coZZi/Uhra+s//vGPqKgoZ+fZ1dWvqVSfqlSq7r/9oYdcAWzf3q0T\njNiY8xfOX716dfr06T06ZyyKCjsiDtu25QKyIUMaH3nkkR41ixs0qAxQfPYZ9XTvL4YPH97+\nMKPQRO6UsD0xMbE7b4+N9XJ0vNLQEJuTU2uZgIQ7Dz/88N/+9resrKy2tsUAystf/fTTT7v/\n9scfj5NKq7OyYltb9RbLSDj1RO0TOIpJj3Rug8QQFXZEHA4cqAcwYYLitq/s5N57HQB8910X\nz9USm+Tp6fn6668DAEYCjcBVAM8//3xYWFg39zB8eAUg+/BDuhjoF37++ectW7YAADyBB4Cr\nwIlVq1a1tLR0cw8KhSwiIsNk8vjoo3TL5SR8OjfgHO7A/ElddEBlhQo7Ig5paQ4A5s4N7ukb\nly2LAQypqd4WCEU4tXz58o0btwMDpdKL48eP/frrr1988cXuv33xYk8Ae/caLJWP8OTcuXPt\nPz4IKIHPAVNjY+OVK1e6v5Pf/lYJYPPmegsEJPz6MuNLXagu8HxgoEsg6yy/osKOiENlZbhU\nWnmLvo03ExzsGh3959bW6fX19JnbjzQ2RgKShATD8ePHFy1aJJFIuv/eJUtiHRxera19WWhz\nQmybg8P1RnNjAQB7bth+eytXxsnlX5SVfUznTH+g0+nWr19/5513Pnb4MQB3193NOtH/oMKO\niMDJk0VGo4+vb37v3j5njr3BUHzs2DGzhiJcS0qqB5CYKO/FexUK2axZF6qqDqSm0nOOtu/u\nu+9ur+GGARrgCoCYmJioqB70hnJzs58z54fKyi0pKSmWiUl4YTKZ5syZs2rVqsNHDmvu1UCD\nb/7fN+npHN2Fp8KOiEBBwQnAb9GiXn5iTpkyBcDhw4fNGopwTSrdDixeuLAHz9l0NH36dFAL\niv4hLCxs/fr1AIBPgbcBg0ql+vrrr3s0ygtA6DqwevXqjz/+OD8/3/xBCR+++eab77//HgAS\ngTBgN5ormx999FHGsTqgwo6IwJkzZ4CKe++N7t3bx48fr1AofvrpJ/OmIjzLzDyoUu1NTAzr\n3dunT58ukUiosOsnli1b9uWXXwLvRER8+fe///3KlSsjRozo0R40Gs2GDRsA/PTTT8uWLRs0\naNDGjRstE5Yw9uvNn+GAAdgBAMnJyd1cUMkKqLAjInD69GmZTDZy5Mjevd3R0XH06NGpqalV\nVVXmDUb4VFNTk5ubO3z48C6b0XWHv7//sGHDkpOTaWpmP6HRaAD86U9/ev755319fXv69tWr\nVx8/fvz6r62trStWrDh//rw5IxI+/Pqp8iEQAHx/7beeDvFaDhV2hHdarTYlJWXIkCEuLi69\n3smdd95pMpmOHDlivlyEX+fOnTOZTL2+EhBMnz5dp9MlJSWZKxXhmVCEJSQk9O7tX331Vact\nra2t7auoEJty5513/vpLJdAGABMnTpTLezOj1xKosCO8u3DhQltb2x133NGXnQj/FA8ePH7b\nVxIbIExg7+ndtE5mzJgBmmbXb1y8eFEmk8XFxfXivVqttqGhi5Uyq6ur+5yLcGfOnDmdWh+5\nurp+9NFHrPLciAo7wrvk5GR0u23AzSQmJkqlyZs3LzdTKMI1YWWyPo7YRUdHy2SbPvtsxcSJ\nE1944YWmpiYzpSPcMRgMqamp0dHRjo6OvXi7QqEYMGBA+29vAvuFnwYNGmSmgIQv27dvf/zx\nxwGEhIQ89dRTly5dio7u5RRwS6DCjvDu5MkU9LmwUygU7u4KrXbAL79Qc3fbd+7cOXd39w7f\ntT1WU1MzatQog8HFZBp8/HjdSy+9NGbMGGEaFrE9V65c0Wg0w4YN6/Ue1q1b1/5jPHA34BUa\nGrp06VKzxCO8kUqlQmfLd9999/333w8K6vECqxZFhR3h3a5dL8tkv8TExPRxPyNHNgD49NMc\nc4Qi/Lp6taagYIePz8t9mcv8t7/9rbCwEBDuw04HkJ6e3t6pjNia7dtLgecDAyf3eg8PPvjg\nxx9/7OPjA6QDiIqas2/fPjc3N7NFJJzZu9cdeCg2dijrIF2gwo5wLTOzWqcLUanQ68cbr1uw\nwBtAUhKtC2/jdu7MA0a5uQ3py07an7P5ETAJhV2HjcTW7N8vB/7u7j68LztZunRpRUXF/PmD\nAQwbtpjuw9q2ixfvBT4cODCMdZAuUGFHuLZ1ay6AQYO6mJjcUw8+GC2RNOTlhfd9V4RnR440\nAhg7VmGOnZUAucAIgJeFDIglZGc7A3jggdC+72r69CAAPLUhIOZXV9fa1hbi5JRvZ8djEcVj\nJkKuS0pqBnDXXb2Z0dyJRGJwcvpFrw8cOnTOn//8Z7Va3fd9Eg6lpzsAmDMnuC87EbqVAAAu\nAyogAJ2WOSA2pLY2VCYrj4316vuu7rsvDDCWlNBNWFv2/fd5gF1wcC3rIF2jwo5wLSPDFcBv\nf9v7WfACvV4/bdq0pqbdgDY11fj222/HxcWVlZWZIyPhS2VlkERSM25cYF92sm7dutBQYfzm\nMgAgNi4ubvXq1X2PR3iTklJuNHp5ehabZW8+Pk5yeUlDQ4hZ9kb4dORINYDYWCPrIF2jwo7w\nS6831tZGyuWFgwb19Up648aNx48fBz4D3IHdACoqKlatWmWOmIQjly6pDYZAD488qbRPN0/d\n3d3Pnz+/du3a8PDvgYjp0xXJycntreKJTfnuu0IAUVHN5trh8OFfmEy/KS4uMdcOCW/On9cD\nGD9exTpI16iwI/xKSsoxmRSBgWa4kj58+DAAoBH4dcUK6h5re3buzAcQFdXY9125u7u/+uqr\n27e/DeSEhgY5OTn1fZ+EQydPagCMHq001w7vuqsFOHzpEs2zs1n5+S4A7ruvT/M9LIcKO8Kv\nkpLjgOvSpZf6viuTqYuHYbvcSERNr/8JmLpwoc5cO4yJiZFIJJcvXzbXDglvdLrDwPoHHvAz\n1w4HDx4MIJ0eoLBdOt13jo7bo6I8WAfpGhV2hF+nTp0CtNOm9X7V0OsmTZp048bJkyf3fc+E\nK6mpJ4Gk++832yrwLi4uQUFBVNjZsOLif7u6vjh+vBkeiRUMGTIEwKVLZrgiJRxSq9UNDS9P\nnLiZdZCbosKO8OvUqVNKpTI+Pr7vu3rsscc69a7w8vJ65513+r5nwpVz5855e3uHhJhz6nps\nbGx1dTU9Rm2TGhoacnNzhw4d2pflrDuJiYmRy+U0YmerUlNTAfSurbB1UGFHONXY2Hj58uXh\nw4crlWaY+yKXy3/66ad//OMfQtegYcOGpaam8tYHhvRReXl5aWlpH1vE3khYaZYG7WzSxYsX\nTSZTX5qJ3UihUERGRl66dMlo5PSpSdIXVNgR0ktnz541GAx9bBHbkYODwzPPPLN//35A4ukZ\n7e/vb649E06cPXsWgIUKu4yMDPPulvDg/PnzAMxb2AEYMmSIRqPJy8sz724JD9LS0kCFHSG9\ncOrUKQCjR4827259fHyk0uJjx940724JD3755RcAI0aMMO9unZ1HABc++STSvLslPLh48SIs\nUNg5Os4E9mzZUmHe3RIepKWl2dnZ8dwyjgo7wqmjR7MBqRlH7K5zcqrR6QLr6lrNvmfC1rlz\n52CBEbvExIHA0Px8d/PulvDg/Pnzcrk8NjbWvLv18YkEZh47pjXvbglzBoPh8uXL0dHRZpkj\nZCFU2BG+mEymrVu3Lliw4ODBVyWSTEssCevrWwdIf/qpyOx7JmwdOLDOweGrwMA+9Zy40YAB\nblKpur7ezLslzGk0utTUpYGBT9jb25t3z1On+gHIyuL3u5/0TnJygUbzRHDwTNZBboUKO8KX\nJUuWLFy48JtvzphMvibT5UGDBuXk5Jj3EAMH6gGcOFFl3t0Sts6eLdPphrm6mm2hk45UqhKD\nwTc/v84SOyesfP99nsHwmEw21+x7njQpGGipqDBD81nClW+/rQTeMBimsw5yK1TYEY788MMP\nX3zxBQBAuAN7uqamZtmyZeY9SkKCA4DU1Dbz7pawtWtXIYCYGLM1huooMLABwKFD5mknSjhx\n4EAlgLg4g9n3rFDIHB0LWlvDmprobqxNOXu2FUBiItd9aKiwIxw5cOBA+493AABOAzh8+LBW\na84PxwkTvAHk5MjNuE/C3IkTLQAmTLDIB64wBevkSRqxsykpKQYAEydapOOnv381ID90qNAS\nOyesZGfbA7j7bq4XVaDCjnBEr9e3/zgEAJACwGQymXc5qMmTgwFddTVNf7EpmZlOAH7zG3Mu\nTXxdYqIrgLQ08w/tEIby8lSAafbsMEvsPCZGD+DwYVrX2qao1X4SSePYsVzPuKXCjnBk3Lhx\n7T9GAlVAHYCRI0ead2qzo6N84MCxwJ3UK9aWVFeHy2QVCQm+ltj5rFn+wFgPjw8tsXPChNFo\nqqsLs7MrDQ93s8T+778fwG9lshOW2DlhorKyWacLcXHJl0rN1qfEEqiwIxxZsGDB3XffDdgB\nDkA2AHt7+w0bNpj9QIMHBzQ2NpaVlZl9z8TK9Hr9xo0b77pridHo5eiY0dLSYomjRET4enhc\nyco6Z4mdEyZOniw2mdy8vUsstP/p08OB7fn5yRbaP7G+77/PB6TBwbxPyaDCjnBEKpV+9913\nK1c+Bfi5uMyaP3/+2bNnzb7eLIDo6GgAmZmZZt8zsSa9Xj9t2rRHH330p5++AuIaG1eMHDmy\nsbHREscaNGhQQUFBc7NFHs4g1pednQasnTAh30L7Dw4OdnNzo46xtqS4OAP4aPx43j8EqLAj\nfLG3t7/zzjsB/OlPT27fvn3IkCGWOAoVdrbhgw8+OHLkCADAAKQDFy9fvvzcc89Z4liDBg0y\nGo1XrlyxxM6J9eXlnQZeX7TIzCvYXSeRSGJjY3NycuhiwGZUVR0HHlu82Jl1kNugwo5wR1i4\nLiIiwnKHiImJAUBf0mL3448/3rhx3759ljiW0Jzg8uXLltg5sT6hmdjQoUMtd4ghQ4YYjUbq\nMmwzhPFXCw03mBEVdoQ72dnZsEphRyN2YqfT6bq5se+osLMxFy5c8PDwCA0NtdwhhAqA7sba\njNTU1JCQEDc3izxtY0ZU2BHuWKGw8/T09PLyyszMs9whiBWMGTPmxo1jx461xLGEnt80+mIb\nampqCgoKhg0bZtGjDB48GHQxYCtKS0urqqri4+NZB7k9KuwId7Kystzc3Dw9PS16FJNpR2Fh\nRlWVxqJHIRa1Zs2aThcAnp6er732miWOFRwcbGf33YEDz1hi58SacnJy3n33XbTPtbWcmJh4\n4JMdOyxypUGsLC0tDUBcXBzrILdHhR3hi1arLyysioyMtPSBPD1lgCwpqcjSByKW4+Likpyc\nPHPmTADu7u4PPfTQL7/8EhQUZIljSSQSpXKgRpPQ0EDN6MTKZDKtWrUqNjb25ZdfBrBp06ZP\nPvnEcocLCPCSSmcXFY223CGI1aSmpoIKO0J64dixYp2urq7uFUsfKDLSCCA5ucbSByIW5eXl\n5eq6HDj12GNbNm/ebNEpU35+tYAdXQyI18aNG9evX6/VaoEngWdbW+3++Mc/JidbcKk5larI\nYPAvKKi33CGIdezZ4wgsjomx4NM25kKFHeFLcnIVAH9/O0sfaNgwBwAXL9Loi+hdviwHRgcH\nW7zJT2SkHsCxY9QkSqw6rHa+DHgOaAOwceNGyx0xJKQBwPffF1juEMQ6zp2bBnwaFWXByd/m\nQoUd4UtamgZATIzc0geaONEHQF6ewtIHIpZWVuYAIDHRx9IHGjnSEcD583QxIFbtzWbsgGjg\nEqADUFpaarkjxsdLAZw4wXuvAnJrra36lpZQe/sCJycRfGVQYUf4kpVlAjB8uKulDzRxYhCg\nVas9LH0gYml1dV6AJj7e4oXd5Mm+AHJzlZY+ELGQ8PBwAEAwIBeaFgIYMGCA5Y44bpwbgEuX\nLHcEYg0HDhQAyoAAcYzWU2FH+FJa6ghgwgR/Sx/I3t5OqSxuaQkwGk2WPhaxHKPR1Nbmb29f\naoW23BMmBAGtarW3pQ9ELGTNmjUAAKG8ywfg5OT09NNPW+6IM2eGAabCQpXlDkGsICmpAkB0\ntJ51kG6hwo7wpa7OSyJpjI31ssKxJk9+xWTyLikptsKxiIWcP18BOLi5WeMhGIVCNmDAcoPh\nfr1eHJ/vpJPf/OY377//vkIhrHKSGxwc/M033whLT1tIYKCLh8dak4lWyRErtVr93HPPff31\nRQCeniWs43QLFXaEIzqdQav1dXCw4JSXjhISvAEt9Z8QteTkCgCBga3WOdwddzTodBm5ubnW\nORwxu6eeemr48HkAnn56Vk5Ozr333mvpIyYmptfX/1BeXm7pAxGzy8rKiomJWSQBWB8AACAA\nSURBVLduXU1NIICvv16zatUq1qFujwo7wpHi4kLAberUt61zOGF5UuoYK2pSaRpw14wZVrqS\nFvpPUC8BUXNwOA28tWDBYLnc4g9pgRqLidkjjzxSUyPcDUgCtgDF69evP3bsGONYt0OFHeFI\ndnY2oBs82Br3YUEdY21CeflV4KexY63UvZGaRNmAlpbdMtnakSMDrHM4obC7cOGCdQ5HzKW5\nufn48ePtv70PLBJ+2rdvH6tI3USFHeGIFbrEdkQjdjYgJycHFn6wsSPqGGsD8vLyAgICFApr\nrFuhVqv/+9//Ali9enVsbOyOHTuscFBiFjqdzmTq4tE6rVZr/TA9QoUd4YhQ2Fmhn5jA3d3d\nx8eHCjtRy8nJkUqlYWFh1jlcZGSkXC6nwk68mpubKysrrXMloNPpZs2a9e233wIwmUwZGRnz\n58//5ptvrHBo0ndubm7CXZ1OxowZY/0wPUKFHeGIlUfsAMTExBQXFzc2NlntiMS8cnJygoOD\nlUorrS0nl8sjIiIyMjKMRqN1jkjMKy8vz2QytS9oZ1nbtm07ffp0p40rV67schyIcKhDq5Jr\npk+fPnfuXCZhuo8KO8KR7OxsJycnPz8/qx2xpWWVyVS7ezd1/xSlhoaGqqoqq92HFSgUf9Zo\njp88KY6FD0gneXl5+HWlYssS2sYDABYC3wFBAEpLS9VqcaxzSyZPnpycnCx0oI6MjHzppZf+\n+9//SiQWXzKzj6iwI7wwGo15eXkRERHW/Gfj5+cGqJKTrbEKGjE7YdmRgQMHWvOgLi4DgOFJ\nSbR6hSgJ54x1LgZcXFzafxwK3A9EApBKpU5OTlY4OjGLxMTEuLg4AMePH3/uueccHBxYJ7o9\nKuwIL86cKWtpqdRo1lnzoMOHOwJIS9NZ86DEXHbtagR2G413WvOgQ4fKAZw7p7HmQYm5JCU5\nAo95e1tjIu/s2bPt7e0BCF0ugDAA9957LxV24nL+/GCFYpaPj8WbFpoLFXaEFz//XAk4e3q6\n3P6l5jNhgg+A/Hzq/ilK584ZgFmurmHWPOiECV4Arl6VWfOgxFzOnh0K/MvHJ8wKx4qPj3/1\n1VcBAAUAgNCBAwd+/PHHVjg0MaPS0r9JJO/yfwf2OirsCC8uXGgCEBlp1XOyvfunpzUPSswl\nP18GYPhwKy1iB8BoNFZWHgMMWVnyKVOm7Nmzx2qHJmZRV+cOtA4daqXRlxUrVly8eHHhwnEA\nfHxGpaen+/tbvBE2MaPMzGqTydnVtZp1kB6gwo7wIivLCCAhwaojdgqFTKksbmkJ1uvpIUfx\nqahwAjB+vPW+KdesWfP0038E8k2mQUeOHL3//vs3btxotaOTvmtr81coSqVS642+xMfHr1+/\nHIBWG9B+Z5aIxunTFQB8fVtYB+kBKuwIL4qK7AGMG2fteQxeXlWAw6lTVmpQS8yoocFbIqkP\nD7fSiF1GRsZbb70l/Ai4Av4AVq1a1dDQYJ0ApI+E0ReVytoPS/n4OEmlNU1NdGdAfC5erAcQ\nEiKmFWqosCO8qKlxB1qGD7feWieC2bN/BgKamqiTo8i0tup1Oj8HB+tV5B3WJFsHTAKqAWg0\nmosXL1otA+mL5ORyAL6+DB58CQ7+yGR6Vq/XW//QpC+ysrQAoqKs0afEXKiwI1wwGk2trYFK\nZbGdnbXPydGjfYAy6j8hOqdPlwFyT886qx2xQxOq08AxoO2G7YRrFy40AAgLYzDvYtSo8wbD\nVyUltPyhyBQUSADEx7uyDtIDVNgRLpSVlQIDxo9/3/qHpo6xIlVbmw08OGHCJasdcdKkSTeu\nYuXn55eQkGC1DKQvWluzgS/vuIPBF5+wyG1BQYH1D036wmDIBA6PGePLOkgPUGFHuJCdnQ1U\nJiQwmFksdAOkwk50ysuvANumTLHeEQMDA9evX99xi1Kp3Lx5M43YiYXJdBx4aOZMqz6hJaDC\nTrTec3KaFR0tpvmRVNgRLghdYq3cQkDg4ODg7u5+7ty5rVu31tfXWz8A6R0mbSeWLVt26tSp\nP/zhDwACAwNTU1PvueceawYgfWHNfmKdCIVdfn6+9Q9N+qKgoCAsLExEi9iBCjvCiZycHAAR\nERFWPm5eXl58fHxtbW1DQ8PChQujo6OPHz9u5Qykd4RzxvoXA6NHj/7ss8+8vb3lcnlUVJSV\nj076Ijc3193d3c3NegsfXhcWFgYasRObysrK5uZm4f+diFBhR7ggjNhZv7BbtGhRZmbm9V8r\nKip++9vf0ridKOTk5CgUisDAQCZHDw0NLSkpMRgMTI5OesFgMBQVFVmnS+yNqLATI2GElQo7\nQnojOztbqVQGBwdb86BXrlxJTk4GADwAZAEPAigtLT1w4IA1Y5DeycvLCwsLk8nYtPYyGB7W\n6XadP1/B5OikF4qKinQ6HZP7sABcXV0dHVenpMxmcnTSO0JhJ9xGFxEq7AgXsrOzw8PDrfwl\nXV19vUuMFogAYoRfqqqqrBmD9IJarW5oaGA1+gJAIhkE3HfmDJ0qoiF8SbMq7ACYTEtqapZS\nkxsREUZYacSOkB5LT1c3Nl40GF608nEjIiKkUuGfQCEA4Np4obAACuFZUlIp8KlcPpNVgJAQ\nALh8uYlVANJTx4/XAnO9vGJZBXBzqwcUFy9WsgpAeursWQMwOiiI2cVA71BhR9g7dqwMCHdx\nsXbPCR8fnyeffBIAIEx8CQFw1113TZ482cpJSE+dONEMPCyVxrEKEBmpBJCdrWUVgPTUgQPu\nwE69ntk54+vbCuDcOTWrAKSnjhyZCpzy8KDCjpAeSklpABARweB58jfeeOPPf/6zUqkD6oCQ\nhQsXbt26tX0Yj/ArI0MLIDZWySpAfLwKQHExnSqiUVysADByJLMFyYSZWmlpjawCkJ5qaPAA\nmsW1iB2osCM8uHJFD2DoUCfrH1qpVL755puNjY1KZQUQ/NVXX3t7e1s/BumpwkI7AHfcwewD\nd+RIHwBqdedGFIRb1dWugHH0aH9WAWJi7NHee5SIQlubr719GesUPUaFHWGvsFABYMwYZhWV\nXC53c2sA7DMzq2//asIBtdoVMI0dy/BL2hPQNDS4swpAeqq52Ucmq1CpmI3yDh0qjPKyeY6b\n9FR6uhpwUqlqWQfpMSrsCHvV1e6ALjGR2Zc0gEmT9gMJjY2FDDOQ7mtq8pFK1T4+DEZ5r/Pz\ne0UieY5hANJ9NTUtRqO3szPL+W1jxvgC/1QojjLMQLrv7NlKAD4+LayD9BgVdoQ9jcZfLi9x\ncJAzzDBsmAy4UFpKy4eKQG1tq8Hg6+zMeA25+PizLS3bamvFd0HfD/38cykg8fJqYJghLMzd\nxeXZ1tZ/M8xAuu/ixQYAoaEm1kF6jAo7wlhVVZXJNGHEiH+yjRESEgKgqKiIbQzSHXl5+cCK\n+PhktjGEZUsLC2mUVwSKikqAH2NiWBZ2AEJDQwsKCkwm8dUK/VBVVQVwafBgliMOvUOFHWEs\nOzsbSB81Ssc2htD0ggo7USgpyQLenzqV8aRm4WKAmkSJgsFwEZgxfz7jBaXDwsJaWloqK2kp\nOxFwdT0ADJk3T8E6SI9RYUcYE7rEWr+VeyfClzSNvohCbm4u6JwhPZGXlwembScE1DFWRIRz\nRnRtJ0CFHWFOKOwiIiLYxggMDJTJZPQlLQo5OTkAGPYTE9CtWBERvqQ5OWeE5maEc/n5+S4u\nLl5eXqyD9Jgd6wCk/yotLX3rrbe2bt0KICsry2QySSQM1igWyOVyPz8/uhUrClyN2NHoiyjk\n5eUplUp/f5aP3oMKO/EwmUyFhYXMP2R6hwo7wkZGRkZiYmJDw7W5zCtXrrx8+fLHH3/MMJJE\n8vfi4ujmZq2Tk/gmVfQrOTk5zs7OPj4+bGMEBQVJpS+cPBnINgbpjry8vLCwMOZNZXx8BgL/\n79gxl9Wr2QYht1FRUaHRaMR4HxZ0K5awsnTp0utVneCTTz45ePAgqzwAJJJYYPz58zSvmWtG\nozE/P3/AgAEMx3cFcrlcIllWWno/2xjktqqrqxsaGphPsAPg7R0CbDp7diTrIOQ2hFFVKuwI\n6S6NRnPy5EkAgBtwCrh29cq2sPPz0wI4f56aT3AtLa28tfUFZ+c5rIMAgJNTldHo09DQxjoI\nuZWUlCJggr//ENZBrjUsqa+nhiW8S08vB8KCg9lfDPQCFXaEAYPB0L6SUxgwGrg2j0Gv17ML\nheBgAMjIaGaYgdzW0aNqYG1b22TWQQDAw6MRkJw9W846CLmVfftagGOVlTNZB4FUKlEqy7Va\nxlP9yG3997/uQF5l5RjWQXqDCjvCgIuLS3x8PAAgGABw7ZGFCRMmsIoEIDLSHkBODrXo5tqF\nC40AIiK4+OwSRnkvXKhhHYTcSmamFkBMDLMusR25uNSYTE5ZWXTOcK2gQIL29r6iw8WHI+mH\nNm7cqFQqgSAAQDGA+++/f/bs2QwjDRniCqCkhP5RcO3qVT2A+HiWXWKvCwuTArh8mUZ5uVZQ\nIAUwfLgb6yAA4OOjAXD6NOOGeOTWKiudACQm+rEO0hv0HUbYSExMPHPmjJfXUAChobK33npr\nx44dbKfDjxzpA0CtdmCYgdxWUZECwB13eLIOAgAxMQ4AcnNZTiEgt1VR4QRgzBguvqSDg40A\nUlPrWQcht9LQ4CGRNEVEiHI2JBV2hJn4+HiVajCADz985k9/+pNCwXiRkagoD4XicReXD9jG\nIDfT1ta2Y8eO8nJHwBAT48g6DgCMHesKvGVvf451EHIrDQ0eEklDeDgXI3YjRpiAHRoNrWvN\nL6PRpNX6KZWMmxb2GhV2hKXaWicAw4czXpBMIJVKwsKSqqt3sw5CupCdnR0XFzd//nytNhgo\nGj58yJEjR1iHQmJiAPAXne5H1kHITen1Rp3O396+lHWQa2bOtAfmy2Q/sw5Cbio1tRJwcHOr\nZR2kl6iwIyw5O7/p6PiYv78z6yDXBAcH19XVdVpgj/Bg0aJFWVlZgAz4CPhMrVY/+OCDdXV1\nbFO5uLi4ublRVzGeXb5cCqT7+PBS2FG7WP6lpZUDal/fVtZBeokKO8JSTc2eAQNOsE7xK6FJ\nFDUW401mZuaZM2cAAAbgb8A6AOXl5QcOHGAbDEBoaGhhYWH78j2EO7W1OcDI//u/fayDXOPn\n52dvb09dxXhmZ3cZ8Pnd78Q6xYIKO8JMXV1dU1NTQEAA6yC/Cg4OBhV2/Kmp6XptiOpq9qtJ\nh4aGtrW1VVTQQ46cysvLA08tBCQSSUhICBV2PBPGU8PDw9jG6DUq7AgzQv0kDJJxQijs6M4a\nbyIjI7vs8hkTE2P9MJ0IJzCdM9zKzc0FMGDAANZBfhUaGlpfX898IgG5GVH3EwMVdoSh4uJi\nAIGBHPVQp1uxfPL29l6+fHmnjdOmTZs0aRKTPB0J5wxNmeKWMGLHQ6PY60JDQ0HnDMc4PGd6\nhAo7woxQ2AUFBbEO8it393Dg3QMHolgHIZ299tpra9eulclkAGQy2e9///utW7d2OYxnZa6u\n0cDjR44YWQchXcvLy5NIJFyNvnh5xQL3nT6tZh2EdC0/P9/V1dXdXZSL2IEKO8JQSUkJOCvs\nwsMDgeVZWXGsg5DOFArFq6++On78eACVlZVffPGFpycXaxTLZAOAD3/+mYvFb8mNcnNz/f39\n7e3tWQf5VV3dHcDe/fvlrIOQLphMpsLCQvEO14EKO8LQwYORwHtOTqGsg/zKy8tRIqlpavJg\nHYR0LTNztLPzfA8Pjv4HjRzpDaCigqO6gQDQ6XTvvPNOTMyQsjJVQ4P266+/5ufJ5SFDnNHe\njZTwpqiovLWVryHenrJjHYD0X1evxgAjgoP5aq1jb69uaQkzGk1SKX3scqei4nl7e76Wg4+L\n8wHa6uu56GpArlu+fPmGDRuAGCCjqenr3/3ud2q1euXKlaxzAe0XA2VljHvtkC7t21cFaEpK\nDrEO0ns0YkeYaWxUAc1hYSrWQf6HSlUPKNPTafoLd/Ly6gAnZ2e+niWUySRyeUVrKxfdU4gg\nLS1tw4YNAADhYdhcAH/9619ra7noJZCQ4Avo6ur4+ugjgrS0RgCBgSK+UU6FHWFGq/VWKCpZ\np+jM27sVwPnzVayDkM4uXqwC4OHB3XLwzs7VJpN7WVkT6yDkmnPnri8tGwYAyAPQ1taWlpbG\nKNH/UChkcnl5S4sv6yCkC1lZWgAxMSKeXEGFHWGjrKzJZFI5O3NxAd1RUJAJQHo6dRXjTnp6\nHQB/fz3rIJ15ejYDOHeO1ijmhYODQ/uPwQCAghu2M+bsXG0yedDFAIcKC6UAhg4V8XgqFXaE\njZSUCgAeHi2sg3Q2dmwr8LxMdoV1ENJZTk4LgOBgGesgnQ0eXAV8VVlZwjoIuWbKlCnOzkIH\naqGxTRmAkJCQYcOGMUzVUVBQKXA0I6OYdRDSmVrtBGDMGH/WQXqPCjvCRnp6PQA/Px3rIJ1N\nmeIMvKzVXmQdhHRWUGAAEBHB3S2S+++vAX6v011mHYRc4+vr+9FHHykUCkBY/7zYycnpq6++\nkst5mTg1d+5ZYHJLSw7rIKSzxkZPiaSRt8nfPUKFHWElF3hm9Gi+JsKD2sVyzGTKBw4MHerK\nOkhn1FWMQ4sWLUpJSbGzswfqnntuZWZm5sSJE1mH+pXQfII6xvLGaDRptd68PXrfU7TcCWFD\nq80AXp08eQ/rIJ0FBgba2dnRlzSHHB13AD9MmMDdcy3ClzSdM7wZPHiwg4NrUFDISy+ls87S\nmbBMGnUV482lS+nA8EmTHgS+ZJ2l92jEjrDBYdsJgUwm8/f3pxE7DpWUlNjb23O1OrEgJCRE\nIpHQlzRvmpqaGhsbAwN5nCxF7WJ5c/Xq1cmTJ8fHxwP6pKRtr7zyCj8rWvcUFXaEDQ4bxV4X\nEhJSXl7e1tbGOgj5HyUlJYGBgRIJdwtHK5VKHx8fGrHjjXD1GBAQwDpIF4KDg+3s7OhWLCea\nmppmzpx59OhR4VedTvfss8++9957bFP1GhV2hI3i4mJ7e3tO2n12EhwcbDKZhG8FwonW1tbq\n6urAwEDWQboWGhpaUlKi13O3FEt/JvwT5vOcsbOzCwgIoBE7Tvz73//OysrqtPGll14yGAxM\n8vQRFXaEDW5HXwAolYnA2pMnuZvL1Z+VlpaaTCY+R18AeHqONhgeyMgQ95xrG1NaWgpeR+wA\nBAXFV1aG1NdrWAchuLGqA1BbW6tWi7IFERV2hIGWlpaqqirh+VMOaTQJwKvHj3O3FEt/Jty7\n5/acUavnAv85epS7Bbf7M+Gc4XPEDkBBwUqT6cy77+6pr+erX3Y/5O3tfeNGhULh5ibKHtBU\n2BEGLlwoA55VKO5iHaRrUVH2AHJz6bYaR1JTa4Fxnp4DWAfpWmgoAFy6RI0EOFJcXAEuC7uW\nlpY5c+aUlPwM4MUXN0dFRf3www+sQ/Vr8+fPb1/R+lcLFy60t+du1czuoMKOMHDqVD2wrqGB\no2WlOho82BVASQn96+DI/v0q4ERlJS+dAzoRLgZycmiUlyPffjsfaFQouBvlXb169bffftve\n6Cy0srJy4cKF9PANQ+Hh4Zs2bVKpVMBVYD+AyZMn08MThPTAlSsaALzeVcPIkT4Aqqp4aStJ\nAJSUSADExHS+quZEXJwKdDHAmfp6F8AxJqaLu2wM6XS6zz77DMD1wg5AfX39li1bGKYi8+bN\nu3jxMjDQ3t7j8OHDhw8fdnXlbi30bqKPIcJAfr4OwIABStZBuhYZ6QE0NzSIcnaFraqqUgAY\nPNiddZCu3XGHL4DKSroY4Ehrq4dUWmVvz9c6/PX19S0tQo/sfABAiLC9rIyevGGsvByA1MtL\nP3nyZNZZ+oQKO8JAaakU7Xc8+aRQVGi1fqxTkF/V1bkAxqFDfVgH6drAge4SSVNjI6d1Zz+k\n1xv1em8Hh2rWQTpzd3dvn5IvLKh07aHdgQMHsopEBGlpNQC8vUW/gikVdoQBtVoJID6ex0Xs\nBEFBJ02mTVVV9LQaL1pa3KXSaicnXpq438jZ+ZzBcIl1CnJNRkYVIHdxaWQdpDOZTLZ69WoA\nQAtQArQBCA4OXrx4MdtgJCurCYCf+K/oqbAjDDQ0uAK6wYO9WAe5qSlTjgBPl5XRdGYuCKMv\n9vZcryw4btzrev0D1dXcDRH1T8Loi6dnK+sgXVizZs2aNWsUCgUQBMxISEjYvXs3h73y+pvc\n3DYAoaF83bvvBSrsCAMSyQ8uLv+xs+P39BPWS6Pn1DiRl6cGrnh6VrAOcitC9086ZziRmdkA\nwN+fx3afUqn0tddeq6ysHDVqFIDDhw8PG8bp4979SmmpCcDAgY6sg/QVv9+sxFZptVqN5i9D\nh37IOsithISEACgqKmIdhABAQ0MxED9r1g7WQW7Fz88PwPHjx3U6WvSEPX//80DA/ffz2xhQ\npVINGjQIQEUF11cs/UdIyH+AxClTnFgH6Ssq7Ii1lZSUmEymoKAg1kFuRRixo8KOE5y3EADw\n4YcfvvnmmwCWL18eGRn5448/sk7U35WVlQBl0dH8zvdAe7szofUZYa66+gpwOjJS9JPsqLAj\n1iZ8SXNe2AkjdnRbjRNCN3duz5ldu3Y9+eSTGs21pp8FBQXz5s3LyMhgm6qfE84Zni8GAPj7\n+4MKO26UlpY6OzuLd/m666iwI9bG/+gLgODgYIlEQiN2nOD8S/q1117rtKW5uVm8y9bbBqFa\nEobEuEWFHVdKS0u5/ZDpESrsiLVx/iUtcHBwUKnuy8iIYx2EANx/Sefl5bX/GArECz/l5OSw\nykMAlJaWOjg4uLtzvbJgQEAAICssrGEdhKClpaW2ttbPBhY7ocKOWJ8wDBbMbUOxdlrtm5WV\nb+v1RtZByLVzhttbsR2+DE4C3ws/cVuG9hPFxcWcXz0CAEKAtu++m8k6BuF9vkePUGFHrC0l\nxR24x8+P938/KlU9oEhLU7MOQlBQYHB19XZxcWEdpGuPPfZY+49lgJ/wufrHP/6RYaR+rrW1\ntba2lv/CbsgQH0BaXy/6xzBtAOe3BXqECjtibRcu/B/wPf8j3j4+bQBSUqiwYy8nZ3db2ynW\nKW7q0UcfXb58OQCgDLBzcAjZsGHDuHHjGMfqx06frjSZMmtrn2Qd5DZUKqVEUqvRUFtq9vbt\nMwI/1dWNYR3EDKiwI9bW0uIpk1Xy1pn7RkFBJgCXLnHXkqi/KSlpNJlcnJ3rWAe5KYlE8u67\n72ZlZQlX++++u/3RRx9lHapfS0urA6KUSq7XOhEoldU6nTfrFASXL0uAKY6OvI84dAcVdsSq\n2tr0BgOPnblvFBGhAJCdLfqG0GJ3/rwagLu7hnWQ24iIiIiMdAZQViZhnaW/u3q1CUBgoAj+\nRzg51QOORUUNrIP0d2VlABAZaQu3xamwI1Z1/nwFIHNza2Id5Dbq6+szMw8COHToyuOPP14m\n/KMnLFy6VAfAz0/POsjtBQXJAOTl8diftF/Jz9cBCA9XsA5ye+7urQBSU7nug9wfqNVyAIMH\nc/0YdTdRYUesSujM7eWlZR3kVjQazZgxY/bv3wicamnJ3rBhQ0JCQnl5Oetc/VR2tgZAcLCM\ndZDbCw9XAuqaGt6vW2ye0PRTGEDlnI+PHkBGBr8zDfqJujoHAHFxIrh9f1tU2BGryshoBBAY\nyGNn7uveeOONjIwMoBQYA7wFoKKiYs2aNaxz9VMFBXoAAwfasw5yew88IAV8goP3sg7S36nV\nCgBDhohg9GX27KuAs5/fZdZB+juNxk0iqff0dGAdxAyosCNW1dRUCfw8aBDXc19OnDhx48bj\nx49bPwkBUFWlBxAdLYLRF2okwIn6emfAFB8vgtGXgQO9gGY6Z5jT6bwVChu5IU6FHbEqV9cT\nwPj77+e6sJNKu/h3IZOJ4FagTQoO/iegvPNOEYy++Pr6SqVSmpHJnKvrGlfXB1UqEYzyCgun\nUWHHVn19g8m0aNCgzayDmAcVdsSqRNF2Ytq0ad3cSKyguLhYLjf5+fmwDnJ7dnZ23t7eVNgx\nV1V1NDw8k3WKbhEKOzpn2CotLQF2DxtWzDqIeVBhR6yquLhYKpVyvrr3ihUrEhMTO24ZMGDA\nK6+8wipPP1dSUhIQENDlMCqHAgICysrKjEbqRMdMdXV1S0sL/20nBP7+/lKplEbs2LKlthOg\nwo5YWXFxsY+Pj0LB9TIEcrn86NGj77zzTnR0NIAHH3zwwoULbm60OjwDOp1OrVaLqIGjv7+/\nTqerqaG27syI60taLpd7enpSYceW8N9fmCNrA6iwI9ZjNBrLyspE8SWtUChWrly5YsUzwJC4\nuBncdim1eaWlpUajUSyjL7j23WBfVER31pgRurmL6JwJCAigwo4t4b+/iM6ZW6PCjlhPeXm5\nTqcTRWEnUKtjgbT9+7meEWjbiouLIaoP3NzcB4GWffu4XqnRtgmFnVhG7ADU1b3c2pqbl0dL\n2TEjzHEU0Tlza1TYEevJyCgH4n18IlgH6a6oKBcAlZX0PCwzoht98fWVA8jNbWEdpP8S3eiL\nUukG+FPzCYZEdzFwa1TYEes5eFALXCwomMk6SHcNHuwBoKZGyTpI/7V1qy9Qo1bfwTpId4WF\nKQEUFoqgAZqt2rcvBtgil4exDtJdPj4GAJmZ1C6WmdOnp0skb/r5+bEOYh52rAOQfiQ7uw1A\neLhozrqYGE9A39REE+yYKS4G4B4SomIdpLuENlYVFXTNzExubihwR3BwNesg3RUUJEV76zzC\nRFnZPRKJUi6Xsw5iHvTpQ6ynuNgEIDraiXWQ7rKzk8pk1a2tIlga11ZVVsohkt5QAqGJeHU1\n189927bGRldAFxkpmnMmPJxGeVkyGk16vZdSKZorgduiwo5YT0WFHEBcnGg+cAEolTUGg5de\nT8uSsVFX5wyYhg4VwerEgrg4b8DY0CCCBmi2qrXV086u0s5ONN9uUVE0ystSdnYtYO/i0sg6\niNnQmUSsp7bWCUBCgmi+pAGoVDVAcV6e7VzMiYtG4yaV1qpUopnm6OgokPxXOAAAIABJREFU\nl0prW1po1UM2Wlp0RqOng0Mt6yA9IMzlraqiUV420tKqAHh4tLEOYjZU2BHr0Wg8JJIaDw8H\n1kF6YObML4BwjYZWmWLAaDTp9T5KpZp1kJ6Jjp4vkw02mUysg/RHFy9WAlKVqpl1kB6Ij/eW\nSqf4+b3BOkg/JTy24utrYB3EbKiwI1ZiMpn0+kYHhxLWQXpGeE6KOjkykZVVA9i7uIjsacGQ\nEHlbW0NtrZgGjWzGpUu1ALy8xDT6olTaeXtnVFefZx2kn8rJaUH7Iyy2wXb+JoRzarXaZBpy\n553PsA7SM0KTGSrsmGhuLgKCp079jnWQnqG27kwVAEvHjhVZN3eh+QSN8jJhb38F+Pvo0bbz\nH58KO2INFRUVGzduBCCRSMT14SUUduXl5ayD9EelpcVAcUyMaCbYCYRzhppEMdHcnAN8Onas\nmD5kAAQEBGi12upqmsvLRArw4rhxtvPAExV2xOJ27twZFRX1/PPPA9izZ8/EiRPr6+tZh+ou\nGrFjSFgOXkQ96AR0zjAkurYTAmGUly4GmBD+s9tM2wnwuUBxenr67t27MzIynJycYmNjFy9e\n7OHhceu3PP/88xcuXLhx+8cff2wza0mLVF5e3pIlS5qamq5vOXHixFNPPfXll18yTNV99CXN\nkOj6iQnoVixDoj5nSktL4+PjWWfpd0pLS+3s7Ly9vVkHMRvuCrukpKQPPvjAZDJFRUU1NjYe\nOnQoJSXl73//e2ho6C3eVVpaKpPJfHw6r6Mhk1GXT8Z27NjRsaoTbNu27aOPPnJ0dGQSqUf8\n/PwkEklxscjm79sGkX5J08UAQyIdfaFzhqHS0lI/Pz9bqhb4Kuw0Gs0nn3yiVCpfe+21sLAw\nAPv27duwYcP69evXr18vkUi6fJder1er1bGxsa+++qpV45JuqKrqorO1Tqerq6sTRWGnUCgk\nkrJffhHTQ3Y2Q6SdueXyICB99+7yd99lHaX/KS0tValUTk6iaW8jaGuLAfbu2YMlS1hH6WeM\nRmNFRUVCQgLrIObE1xy7/fv3azSaefPmCVUdgBkzZsTFxeXm5mZmZt7sXeXl5SaTSXSf/v1E\nZGRk+4+u1ze6u7vfOLzKLYWiUafzYp2iPyouLnZycnJ3F1OrEgCRkT5AbHW17dzZEZGSkhLR\nDfECcHf3Ae7Lzqa21NZWUVGh1+vFeM7cAl+F3fHjxwGMGTOm48bExEQAKSkpN3uXMHxtY/9j\nbMbChQujo6MBAMeAaz1bnnvuOTs7vkaLb8HRsR5wLCqiu7HWlpn5hZ3dV6xT9JhKpZRIqPkE\nA+XljY2NL9nZPcg6SI8NGeIBoLpaZA+A24CUlCpgpVw+5vYvFQ+OvlxNJlNhYaGdnV2nEk2Y\nXVdYWHizNwqTKpqbm19++eWrV68CCAsLmz59+rhx4zq9srCw8Pq6FdXV1V5eNAxjcU5OTnv3\n7n300UeTkvyBKmdn52effXbFihWsc/WAm1trTQ3S0qqCg11v/2piJpWVzQbDCKmUr4vPblIq\na1pb6VLT2lJS1MCK5uYTrIP02ODBXoCBWgxb36lTrcA7tbVHWAcxJ44Ku7a2Nq1We+NtFxcX\nFwANDTcdLxEKux07dqhUqrCwsMbGxrS0tIsXL959991PPvlkx1fu3Llzy5Ytws8JCQkzZsww\n89+BdCUiImLv3h8dHKSOjum1tbUiGqsTeHvrc3Nx5UrDvfeyjtKfnD+vBpzc3cXUG+o6J6eG\n1taIwsKGkBC6GLCey5frAfj66lkH6TGFQiaTVdAor/UJbSdCQkT2rXRrHP1ldDodgBsn1AvT\nYNvabjp7vaKiQiaTPfDAAw899JDwgEVubu66desOHDgwYsSIjjd2Z86cef1h8itXrpj9r0Bu\nJiOjGvBVqVpEV9UB8PcHgJwcDesg/Ut6ei0QJsYvaQBubprqaqSlVVFhZ01ZWRoAQUFdP2bH\nOaWyRqOJ0OuNdnaiHKUWqZISI4CBA8XUwfy2OPqWdXZ2lkqlra2tnbZrNBoArq43/Xx88cUX\nO20ZMGDAH/7whzfeeOOnn37qWNhFRUVFRUUJP4tojVwbkJFRC/i6u4vy2dLQUAWAwkId6yD9\nSG5u7u7dvwAJjo6i7Ljq7a3PyUFmZv1997GO0p/k5+sADBhgzzpIb7i6Nmk08qtXq2JjaY6Q\n9VRUyADExNjUBRhHVwYSiUSlUjU2NnbaLmy57RrFnQwdOhRAXl6eueKRvsjObgTg7W1kHaQ3\n7r3XAHhGR+9lHaS/eOutt2JjY48dywaQlPTVwoULDQYD61A9c/fdBcBYN7errIP0L+XlEgDR\n0aJ8tnTkyJ+BBxsaqPmEVdXW2gOIi/NkHcScOCrsAHh7e2u12srKyo4bi4uLAdzsQQeTyaTT\n6W783BcWG3R2prmoXMjPbwMQFCTKFSDDw32BGlo71DqOHDnyl7/8pa2tDRA6iRVv3br1jTfe\nYByrh4YMcQSSa2pu+sgXsYSqKnsAQ4eK8kt65Mh6YFtNTTHrIP1LY6Mr0DpwoMjWVLo1vgo7\n4bbp6dOnO248c+YMblgD5brq6uq5c+cuX7680/ZLly4BuL4eHmErIuIY4DZrlihXDKFF4a3p\niy++aP/xfWA2cAXA559/zjBSL9A5w4S9/QGJ5COR3sqkdrFMSCSHXFz2SqWinJd5M3wVdlOn\nTpXJZDt37rzeruDUqVMpKSkxMTHh4eHCFq1Wm52dnZ2dbTQaAXh5eQ0ePLiwsHDLli0mk0l4\nTVFR0SeffCI8UcHkL0I6qaioAOpDQ0V5Je3s7Ozs7Hx9oRxiUR1alWQB3wENANRqNcNIvUDt\nYpnQ6T7x83tJqRTlnQE6Z6xPp9O1tj4dF7eedRAz4+jhCQAqleqJJ5744IMPli9fPnz48IaG\nhrS0NDc3tyeeeOL6a9Rq9apVqwBs27ZNeIR25cqV//jHP7Zt23b48OHQ0NC6urqcnByTyfTw\nww9fLwcJW8KnlTCMIUZ+fn50JW0d1x9v6qh9jWvRoBE76zMajWVlZeLtDUUjdtZXVlZmNBqD\ngoJYBzEzvgo7AFOnTlWpVPv3779w4YKTk9OkSZMWLFjg5+d3i7f4+Pi8+eabO3bsuHTpUnp6\nuqur6+jRo+fNmxcREWG12OTWhOEuX19f1kF6yd/fPzs7u6WlxcHBpp6K59CKFSs2bdpUW/s/\nD8Pe+OQ75xwcHNzc3OhL2prE3huKRuysT6TdqG+Lu8IOwKhRo0aNGnWzPw0MDNy9e3enjQqF\nYtGiRRbORXqvvLzc3d3d3l6UyxCgfQCmoqKCZm1aWnBw8Pfff7906VJhmqyvr+9bb70lxrXE\n/f39i4qKWKfoR4QyWrxf0t7e3nK5nC4GrEn4ry3eW0k3w9ccO2KrysvLbz3syrny8geBgkOH\nOq/FQyxhzJgxO3bsADB37tzS0tLFixezTtQbWu3Kpqb/lpc3sQ7SXwijL+IdsZNKpU5Oz2Zk\nPMI6SD8i9ouBm6HCjlhcc3NzY2OjqAs7Jyd3IOTqVWo+YSXCvfuIiAiR9ooFYDTGAtNSU6tu\n/1JiDjZwW02nm9fU9LBWK7JVG8VLuPEt6nOmS2L90CQicu5cNVBUWdl5SRoRCQ62A1BQIMrO\nGWL000//n707j4+rLvfA/5klM5klmTUz2dc2TWlpkbasFUSKUEB2QRB+ipf7AwQu0gsIVxR3\nEOEWUEDA+1NEEZT2Fi5FqAWhCC1gS+mSpEmapFlnz+yZzPr74zSxpGmSSWbmO3O+z/uvOJLX\n69O8vnPOc55zzvdJAr8PBk9kHWTuysriANrbacJNLvj9/ldfHQGuKy4u4NtqpaVBQNbWRhcD\nOfLppyrgTItFbC9PUGFHsq611QdUFxcX8HzrxkYVxqcKkhzYu1cOXCeRFPABV3hup7MzxDqI\n+D355JM1NTVvvFEB/P6mm374pz/9iXWiOTKZxgDs2+dhHYQXO3ZcALxjMlHHjpA0HTwYAlCw\nb8QCQHNzCQCnMx9fNhIlux0AGhvVrIPMXW1tEWjEcPZt3rz5lltu8fv9QBUAn6/1m9/85qRd\n7guF1ZoE0NFBz2XmSCikk0hCFRVim1BFhR3Jur6+KIDq6gKuipYsMWJ8qiDJAZdLBqC5uYAn\ncy9cqAFAm1dk2yOPPDL+YyUQBnyRSGT9+oLccra2Vg6guzvCOggvolFzUZFj5v+u0FBhR7LO\nZksBqKtTsg4ydwsWGIFoMFiQw8ULkc9XDKC5uYBv37e06EBd3uw7dOjQ+I8VwOG9Qnp7exnF\nmZfGxmIA/f308kQueDyjqZROrRbhU7BU2JGsczikKPDui1QqsViu1mi+yToIL4JBLRAt6Mnc\nn/tcGXCr0fhn1kFEbvyVRgVgBOzChwU6S2DlSg3wsFa7m3UQLuze7QCg14twrwMq7EjWCXcw\nhbuZhau+fsDj2RGPx1kH4cLYmFEmcxX0ZG6zWVNS8vtweAvrICJ32223AQCsgAQ4fOf7yCmU\nBWTFCjNwl0TyFusgXNi/3wvAYomyDpJ5VNiRrLNafyqTnbNwYWEXdhUVFclksuCm0ReiZDIJ\n/KCm5vesg8xXZWUlDRLItiuvvPLHP/5xUZECeAXYodVqn3zyybPOOot1rrkwm81KpZLWTG64\n3S5gZ0ODCF9vosKOZN3IyE6rtVUuL+zFRmPdc8bpdCaTTx1//Aesg8xXRUWF3+8PhWjHk+y6\n7777fvOb7wOXXHutvaen5+abb2adaI4kEkl5eTkVdrlRWvopsPKyy0Zm/k8LTWGfa0n+S6VS\nDoejoMdOCKiwyxlh7AStGTJ70WgUwBlnnGE2m1lnmZfKykqHw0GPfOSAWMdOgAo7km1utzsa\njYpgyjKdpHNGNIWdcM6gNZMDwpqxFvRumQDGH/mw2+2sg4ifWAfFggo7km3CWU0EJ2nhn0An\n6RwQ00katGZyQqiERHCcEeoMuhubVTt37rzwwgtffvllAE899VQgEGCdKMOosCPZJZrui0xW\nB/zvG28sYR1E/ERzkgYWAT//+9+LWMcQP2HNiOBioKhoCXDzRx/5WQcRrQ8//HD16tWbN28W\nbt8//PDDa9euFdm9byrsSHaJpvtitVqAS3p7q1gHET/RFHZSaTVw9yefFPb74AVBNMcZv38p\n8OQ//kEXA9ly6623RiKfme3x/vvvP/fcc6zyZAMVdiS73njDALwSCrWwDjJfS5aYgaTfr2Ed\nRPw+/LARuEOrLfjCrqWlFDR8IicOHaooKVlZXFzwQ/+amlQABgeTrIOIUywW27lzJwCgCGgB\nDh/Pd+zYwTBVxlFhR7Kro6MEuKikpOCvpIuL5VKpJxIp4FkIhaK19TTgv0XQsVu2rAyA16ti\nHUT8+vt/MzYmhiEfixfrADgcMtZBxEkmk8nlwoVWI9AGPCl8rlQW8MTLo1FhR7LL5ZKjwOeJ\nTVAqPfG4OZlMsQ4icuFwKRCyWgu+OVpVVSKRhEIhHesgIuf1RlKpErVaDM+lLVyoAWCzSfv6\n+lhnESGpVHruuecCAITrxsNvH59//vmsImUDFXYku/x+NYClSwt7cymBRuMHlD09XtZBRC4a\nNRYVuVmnyAy53BWNmlinELl9+1wASkoKfujnli1bzjvvJGDU59PU1dXdfPPNiUSCdSixefLJ\nJ8vLywHhJpIdwA033ECFHSFpCIVKgFBFhZZ1kAwwGCIA9u0TSc2Rn4LBaCqlV6l8rINkhlrt\nS6X0Hs8o6yBi1t7uBWA0FvbQz76+vquuumpgYACwAZUAfv3rX//0pz9lnUtsampq2trajjvu\nLACLFuk3btz47LPPsg6VYVTYkeyKRg1FRR7WKTLj5JPbgP8nkRhkHUTMWltdgEQE3RfBwoUf\nAT8eHnawDiJmPT0hABYL6xzz87vf/c7rFe4GvAr8FZAAeOyxx1IpevYjw/R6vdHYAuAb3zj/\n0ksvZR0n86iwI1kUDEZTKaNKJZJhfCedFAGeD4Xo2ZcsOnDAh/HmqAisXr0f+L7XO8A6iJj1\n9cUAVFYW9ulsYGBikXwb+AaQAuDxeMJhkVzk5BXh4e8FCwr+Qd4pFfY3geQ5m80O/D9Ll/6V\ndZDMoEECOTA6agN+c9xxIrnfTWsmBwIBD9Df2FjYLzbW1tYe/aHZbNZoxFl8sBUMRoF4S4s4\nn3+lwo5kkcczDPzhxBNF0q4QTtLCVqgkS6TSg8C/r1lDhR2ZraqqN4Ha888v7NPZN77xDaNx\n8l7W69atYxJG9Coq7pHJVC0t4ty+qrC/CSTPiWY7eAGdpHNAZGtGGP1JayarxDG3sLq6+uWX\nX66vrxf+p1Qqvf3227/zne8wDSVadrvdbDbJ5eLcL5AKO5JF4jjgTqDCLgdEM09MQGsmB+x2\nu0QiKSsrYx1kvs4666z29vZf/OIXAO6+++5HH31UKqVzdFY4HA7RHGSORouGZJHICjuVSqXT\n6egknVUiWzNCYTc0NMQ6iJjZbDaDwSCO4QFKpXL16tUA6J2J7PF6vZFIRDS3BY5GQwxJFgnd\nF+HcJg4q1R29vQXfGMhnNptNIpGI5phrMBgUiqtbWxtYBxEzu90+5ZsHBcpsLgdOaW+ndyay\nRTgxieYgczTq2JEsEppboum+AIhELo5Gv2W3h1gHES273a7X68XRfQGwbdu2ePxnAwPr1q1b\n19PTwzqOCIVCoWAwKKaTtNFoBbZ/9NGVrIOIFhV2hMzdrl1nSyT3Wwp959Aj6PVhjI8wItkw\nMHCqXr+GdYrMeOSRR84888xkchAwrl//5JIlS/7xj3+wDiU2AwMOQC+mq0ejUSWRBEdHxTBf\nOz9RYUfI3A0Oflki+Y+ioiLWQTLGZIoCaG8XycCrfON0hkZHnxsZ+S/WQTKgs7Pzu9/9LgBg\nGJAA5aOjo9ddd10ymWScTFzefTcIjLS338Q6SCbJ5e5oVJw7ceSDv/zFAgT7+1exDpItVNiR\nLIrHzQqFSDYkE1RUSAB0ddGt2KwQ5vDqdGJ4bPztt98eGxsDAAhvTlQA6O3tbWtrY5hKfLq7\nwwAsFlGVyyqVP5XSB4OFPf02bw0PpwBNRUUJ6yDZQoUdyZbBwQCgVqsDrINkUk2NHMChQ3TA\nzYquLj8AgyHGOkgGRKMTi0TY0frwvcJYTAz/uvzR3z8GoLJSVC8CarUhQNLWJqqr4vzhckkB\nNDZqWQfJFirsSLbs3esCoNePsg6SSU1NagC04UmWdHYGAVitYph6fuqpp47/aAcAWAEYDIbF\nixeziiRKg4NJAA0NxayDZJLBEAXQ0eFlHUScRkaKARx3nGhvdlNhR7Klo8MHwGSKsw6SSStW\naIHHNJqdrIOIk9B9qa4Ww0OZK1euvPHGGwEAB4DNQt/uiSeeEM0Lv3lC6L40NYnqtlpVVQRo\ntdtHWAcRp1BIAySbmycPcBMNKuxIthw8OAqgvFwM3ZcJJ5xQBnxbJnuTdRBxGhpKAaivF0np\n88QTTzz55JNLlniBC2tqdm3ZsuXqq69mHUpsPB4lgMWL9ayDZNKll3YDSwyGA6yDiFMkopdK\n3QqFOOeJgQo7kj0KRS+wfsUKUT2OptfrVSoVDZ/IknjcBuxqaRHJsy8ymezmm29+/fXXAZx8\n8snnnHMO60QiFAyqgNSiRaLqvgg7cQi7cpCMi8eNCoWYb3NTYUeyRS7fA6z7/OdF9VAzgPLy\ncirssqSs7EVgxerVouq+WK1WiURCJ+ksMZsvMZkWq9ViuH0/gQq77PH5fEDTypU/YR0ki6iw\nI9kisqGfEyoqKtxu9xHvPJKMsdlsUqnUbDazDpJJSqWytLSUTtJZ4nA4KipEVdWBCrtsstvt\nwHB9vaj2x5mECjuSLeIr7JLJ5DPPPLNv375UKrV48eL169fT1hWZZbPZysrK5HKxdXmtViud\npLMhEAiEQiHxjRCgwi57hL+qmE5MR6PCjmSLzWZTKBQGg3heKf/JT35y4403+v1+AN3d3evW\nrbvzzjtZhxIVu90uygOu1Wr1+XyRSIR1ELER39WjQKvVajQaKuyyQVgz4rsYOBIVdiRbbDZb\neXm5RCJhHSQz7Hb7j3/8YwBAM/BvQAuAxx9/vL29nW0w0fB6vZFIRHwnaQClpS3A6kOHnKyD\niI2Ih35aLDXDw3SCzjwRr5kJtG5IViQSCafTKaYvz549e+JxYU++1cBvgDOFz3ft2sUwlZgI\nB1yLxcI6SOb19V0LvLdjh6imsOQDEZ+kXa6/eDy7R0dFtQ9oPnA4HBDpcWYCFXYkK7q7XYnE\n9cXFZ7AOkjFqtXr8R2FClPWoz8m8HDzoBBaVldWyDpJ5ZWVJAN3dNGI4w8R6KxaHp4pJDxzw\nsA4iNiJeMxOosCNZsWuXF3jW6byYdZCMWblyZVVVFQDAAWBiQtSZZ57JMJWYvPmmFGjv6hLh\nZm8VFVKMz9UgGbRx4yKgZ2RkEesgmSdMFWtvp+ETGfa3v10CbFOpKlgHySIq7EhWjA/9TLAO\nkjFKpfL555/XaDTjoz8txcXF//M//yOmt0PY6uuLAaipEdvWFQBqa5UABgfptlqGDQ0pgfrK\nShF+B83mBICuLrp9n2EuVx1wem2tqHa0noQKO5IVPT2jGG9UiMZZZ53V3t5+773/BqSUytp9\n+/ZdeumlrEOJh82WAtDQoGIdJPMaGjQAHA6RvEiUP7xeJYBFi0S1o7VAOHgeOkRvUmdYJKKX\nSj3FxWLbU+lIojrvkvwxMBAHUFcnkqGfE6qrq3/2s/slEn8qZWlqamIdR1TcbjmABQtENc1d\n0NysA+DxiLAZyVYgINpp7jU1CgBDQ+K56ZEn4nGjQiHyG9xU2JGscDolABobNayDZEVJyTbg\nH6xTiI3QfVm8WIQn6cWLjcBwPC7m8ZRMjI3pxNp9aWhQA3C56PZ9Jg0OBgCVWi3yG9xU2JGs\ncLuLACxapGMdJCuWL/9FNHod7TebWaFQCRBbsECEz0tZLBqNZqHBcDfrIGITj5sVCnG+N/rF\nL6oA1cKFv2UdRFRaW90AdDqRH7qpsCNZIZW2A+8uWWJiHSQrhH2znE7abzaTYrG4XO6QSsX5\nIBpNFcu4vj4fUKzRBFkHyYrKSisQoTWTWR0dPgBGo8hHQVJhR7JCqXxIp7vYbBbnHm/C5pZ0\nzM2gZDKZSn3uhBMuYR0kW6xWq9vtpuHCGRQM2oFlp522gXWQrCgtLVWpVHSQySyt9hBwwZln\ndrMOkl1U2JGsEOaJsU6RLTSiO+NcLlc8HhfxmrFYLKlUyuVysQ4iHk7nMLC3pUW0tTJ1eTMu\nGOwHXl++XMY6SHZRYUcyb3R01OfzifskjfHRNCQjRL8dPF0MZJyI54kJrFarcMHDOoh4iH7N\nCKiwI5k3PDwMoKJCtFt700k646iwI+kS1oyIT9JWqzWZTLrdbtZBxIMKO0LmSPQnaaOxHPhc\nezvrHCIi+gOu1WoFNIcO0Uk6Y4Q1I+LjDF0MZJzo14yACjuSeaK/ko7HK4Fd7757Busg4iH6\ni4GenhVA8NVXq1gHEQ/RXwwcOnQJYP/rX+lWbMbYbDapVGo2m1kHyS4q7EjmtbYGgRUGQzXr\nINly3HEmAH6/CIdfsTI87ISoT9LCfrPUfMkg0V8M6HRawCKMZyQZYbfbTSaTXC7CHa2PRIUd\nybytW2uBfzqdi1kHyZaKCi0wGg5rWQcRj02bLgaC8XgN6yDZIoxK83hEfkbJpQ8/vEEi+YvJ\nJM7NMgFUVxcBGBykjl3GDAz8TCa7l3WKrKOjDMk8h0MGYOFCMdc9crk7GhXhjARWfD41oGlp\nSbEOki3CqDSfr5h1EPEYGTlZIpFKpaJtT1CXN7MGBwPx+NXR6C7WQbJOtF8JwpAwT2zxYjHX\nPUqlL5k0jI3RxXRmhEIlQLiiQrQXA7W1OiASDpewDiISyWQqkTAqlWKe5r5wYSkAt5v6L5kh\nzBMrLRX/rW0q7Egm/fGPf6ytrXU4pEDi7ruvP3jwIOtE2aLVhgBZV5eYzyu5FIsZi4pE/sao\nTOYeG9OzTiESPT1eQCnWeWIC6vJmVmenH4DJJP6rcSrsSMZs3Ljx2muv7e/vB8oB55tvvn7u\nuef6/X7WubLCbA4CnX194hxAnmPhcCyZ1BcX+1gHya7iYl8yaYzHk6yDiEFbmweATjfGOkgW\n1dbqgDF6ljdTenrCACwW1jmyjwo7kjH33HMPAEACWAE7gIMHDz7zzDNsU2XJxRe/BTQXFfWz\nDiIGra0uQFpSEmIdJLtOP/2HgMrrpYuBDBC6L2azmLsvEgms1su12utYBxGJ/v4ogKoqkc8T\nAxV2JFNisVhXVxcAQAP0Ap3C5/v372cXKoto79AM6ujwAdDrxdx9AVBdrQXiwiYdZJ56e0cB\niHd7nMNqax0jI58kk9TlzQCbLQWgrk78t7apsCOZUVRUpNUKtwyCQAvwFeFzo9HIMFX20LjY\nDCop6QYqL7jgY9ZBsku4GKA1kxE6XTvwjdWrRd7ltVqt8XicpoplhE73IfC9lSvFv/8oFXYk\nY6655pqjP/zqV7+a+yQ5QB27DHI4hoHhpiaRP0skXAzQmsmIWKwTeG7FCpF3X+g4k0ESyQfA\nT5YvF2ev4UhU2JGMefjhh0899dSJ/6lUKtevX79q1SqGkbKHui8ZJPoRAgI6SWcQrRmSLrvd\nLpFIRD9PDFTYkQzSarXvv//+jTfeCOCaa6759NNPv/3tb7MOlS3UfckgTiZz00k6g0Q/KFZA\nayaD7Ha72WwuKipiHSTrqLAjmSSRSISK5+tf//qiRYtYx8kik8lUVKQcHBT/Xpc5QN0Xki67\n3V5UVCTWR3gnCGuGXrjJCIfDIforAQEVdiTDhLuTFrFvFiSRSJLJQ/v2/YF1EDHgpPuiVpcD\n7W++eT3rIGJgs9msVqtEImEdJLvGxhqAN19/fSHrIAUvEAiEQiHRH2QEVNiRDBsYCAAy0Rd2\nABQKbyJhSiZFO940Z2w2m16vLy4W+YPwtbVGoMnnE/8jPtmWSqVuVUTGAAAgAElEQVScTicP\nJ2mTyQx8qbdXzOMZc4OTq0cBFXYkw7Zt+w4Q1evLWAfJOrU6ACj7+8U5WiM3XnjhheXLl3d0\nvOD3P3HfffeFw2HWibJIJpNIpe5IRMc6SMHr6fHEYo8kk19hHSTrWloMoKlimfDJJz7g7lRK\nnC/zTUKFHcmwSEQnkfjUavE/oCqMMxJGG5E5eOaZZ772ta/t2dMJrEgmK3/6059ed53IN9lX\nKr2JhJm6vPO0Z88IcFsodDrrIFnX0KAHoqGQyHcCyoGPP44BP/d6T2AdJBeosCMZFo+bFIoR\n1ilyQRgm3dUVYB2kIEUikbvuugsAILwzYQOwcePGt99+m2GqbFOrA4CCurzz1NkZgNjniQmk\nUolM5olG6VbsfPX1CfPE5KyD5AIVdiSTvN5IKqUtLuai1rFYUgB6e8V89zB7Dh486PcL9Y3w\n1MvhHQF37drFKlIO6HQRAK2t1OWdl0OHhHliIn9zQqBUehMJI3V558lmSwKoreXipjYVdiST\nWlvdAEpLudgEpKqqCOODpUm61Gr1+I/CezaHNwHRaDRM8uSGyZQA0NVFHbt5GRiIAaipUbAO\nkgsaTRAoOniQi9sg2eN0SgEsWFDCOkguUGFHMqmjwwtAr+ei1rnwwlGgpKXlLdZBClJDQ8Py\n5csBHNmxU6lU5513HsNU2bZmTQewymA4yDpIYRO2dWtsVM/0H4rBihV/By4PBGj7w3kZGVEA\nWLRIzzpILlBhRzKppycMoKwsyTpILlRXm4Ag7Tc7Z88//7zJZBov7OwKheLxxx9vaGhgHCub\nFi8uBv7p8w2zDlLYXC45gIULuei+nHCCB9jo89EexfMSDGqBVEuLyHe0FlBhRzKprm4voL3o\nom7WQXKBxsXO0/HHH9/R0bFyZStw4VVX1e3cufOGG25gHSq7aPhERmi124DHly0zsQ6SC7Rm\nMkIu36pU/oWH7RpAhR3JLLvdDoTq6ri4KrJYLFKplA6482E0GpuaioDNDz74n0uXLmUdJ+to\nxHCGvKRU3l1VVco6Ri5QYZcR0eh9Cxb8iHWKHKHCjmQSJ/PEBHK53GAw0AF3nrhaM3SSzgi7\n3c7DPDEBrZn5CwaD/MwTAxV2JLO4OkkDsFgsdCt2nux2u1arPeIlWTErKyujLu88JZNJl8vF\nz0la+JfabPSM3dwJ37jy8nLWQXKECjuSSUKVw9UxNxAIiHsQVrY5HA5+rgTkcrnRaKTCbs7e\nfffdyy+/PB6PDw0Nbd++nXWcXKCO3fwJZTE/JyYq7Egm2e12lUpVUsLF22oAbLY7gMFPPqH9\nZucokUh4PB5+CjsAwEN9fc+yzlCQnn766S984QubNm0CMDg4eNpppz3//POsQ2WdyWSSSn+6\ne/fFrIMUMKEspsKOkLngqvsCQKnUA5UHDvhYBylUTqczmUzyc8AFkEickEic7XBQlzc9Dofj\njjvumPTht771La/XyyRPzkgkEuDf7PYvsw5SwKiwI2SO4vGk3f7PYPBp1kFyR9ixr6cnxDpI\nodq50we84PdfwDpI7ghzWfbvd7EOUmC2b98+OiqMtDke+AbQACAYDH744YdMc+WCUjmSSJho\nqtic7d0L4JySkkrWQXKECjuSMQcPjgDVMhkXm0sJKipkAPr6xlgHKVStrWHg6rGxhayD5I7R\nGAPQ2UlTxdKTTE5se/4l4LfAKuF/pFLiL3c0miCgOHSI7gzM0bZtS4Ato6M1rIPkCBV2JGPa\n2kYA6PUR1kFyp7ZWCWBoKME6SKESRpWUl3Oxb4XAYkkB6O6mLm96TjnlFKVSCeDI4cIqleqk\nk05imCo3dLoxAG1t9CzvHHk8SgBLlnCxwSqosCMZdPBgAONjzjnR2KgB4HRyVJdkllATV1Vx\nsR28oLJSBmBggIt5yhlUUVHxwAMPADhyuPD69euNRvGfrU2mOICurgDrIIUqGNQAqUWLxL9U\nBFTYkYw5dGgUAE/vTmDBglKMj5cmc2C3JwHU16tYB8mdurpiUJd3Tu64447NmzcrFNUAvvjF\npX/7299uvPFG1qFyQXjoX2hvkzmIRHQSyQgn88RAhR3JoMHBGDjrvixfbgLOqKp6jHWQQuXx\nyAE0NvKyPw6Ak05SAv9eVvYB6yAF6fzzz5fLK4D4m2++uGbNGtZxcuTEEyPAT5TKQ6yDFKp4\n3KhQjLBOkTtU2JGMsdlSGG9IcEKnU2u1n/h8e1kHKVRerxJAS4uBdZDcWbrUAPwmmfyEdZBC\nNTaml8k8cjlHJ6/Vq2XA9+TyfayDFCSHI5RKadRqjm5kc/TdINlWX/8acMbnP89RYQfAarXS\npvBzptP9WSq9v7FRzzpI7lgsFolEQmtmHv6h0+1gnSGnaPjEfPT2OoHtZWUc/fWosCMZEwx2\nAu81NnK03QkAi8Xi8XhisRjrIAUpGv1LWdnTCoWMdZDcUSqVOp2OTtJz4/P5EomrVq58gnWQ\nnKJxsXM2NDS0ceOvgdOam38Vj8dZx8kRKuxIxtjtdqlUajLxVdhZrdZUKuV0OlkHKUi8jSoR\nUJd3zoS/G29rxmw2y+VyWjPp2rBhQ0tLy89//nMAr7322ooVKzj5G1JhRzLG4XCYTCa5XM46\nSE4JF9MOh4N1kMITCARGR0f5mfMzwWKx+Hy+SISjHR8zRfii8VbYCRfMnBQlmdLf33/99dcH\nAv96tG7Pnj033HADw0g5Q4UdyRiHw8HnSRr0+Muc8HmSBl0MzAPPa4YWTFo2bdp0ZFUn2Lx5\ns9vtZpInl6iwI5kRCoVCoVBZWRnrILnm860A/m/rVo42eckU4UTF4ZqJxc4A/nvnTpFPr88G\n4ZkHDteMUnlOJPLvNhtNopstj2eKQR2pVGpkRPz7nlBhRzJDaFlx2LGTyy3AhW1tHD3+nync\nrplA4Hjgjt27acRw2rhdMy7XpcDje/bQVLHZamlpOfpDrVZbUyP+ibFU2JHM2LEjALw2MnI+\n6yC5Vl+vBkA3SeZgxw4Ad6VSC1gHyTVhqlhfHxV2aTtwQAIsMxj4KuxSqZRC4QHw4ot/5+FO\nYkZceumlK1euBADUA4dbvPfff//4xGExo8KOZMb+/RHggmSylnWQXFu4sBTjExRIWv75Tz3w\n0OhoHesguVZTowBNFZuTbdvWAJ8CFayD5I7H4zn99NMPHNgG4Le/fb25uXnz5s2sQxUAhUKx\nadOmyy+/HHgD6NDr9Q899NC6detY58oFOhuRzOjvjwKoqODuUmHRIgMAn4+vbZkzwuWSAmho\n0LAOkmuNjRpQl3dOgkE1gJYWXqa5A7j55pu3b98OCI1tq8fjufbaa/ft21dVVcU4Wd6rqqp6\n+eWXpVKfXO7xeDwSiYR1ohzh7jRMsmR4OAGgpkb8Xe5J6up0wFgoxF11Mn8jI0UAFi7UsQ6S\na+NdXnrhJm2joyUSSdBoVLEOkiN+v3/Dhg0AAOG9eysAr9c7/iGZQSgUS6VKVaoAP1UdqLAj\nmeJySQDU13NX30ilEpnME41yNO00UwKBYoy3PLmyeLEJQCCgZh2k8MRiermcoxcIPB5PIiHc\nshcavIcfLqQd0WfpwAEPINFqR1kHySkq7EhmCO0HoRXBm9raJ4F1yWSSdZACEw5rJZKwxcLd\nxYDFoikqeqq4+P9YBykwkUg8mdQXF3O05UdFRYVGI3xBBoHXgf3C583NzQxTFZADB0YA6PVR\n1kFyigo7khlC+2HxYo6efZnQ0rIzkXiBh+2RMisWM8nlnL7iV1PzcCz2OOsUBaatzQ1ItdoQ\n6yC5o1Qq7733XgCAE7gAeBzA0qVLr7jiCrbBCsXBg0EAZWV8vahEhR3JjNLSXysU/8lh9wXj\nu2rR8Im0JBKJVOpPZWXvsA7ChtVqdbvdsViMdZBCMjDgAbpNpiDrIDl1zz33fP/731epDj9W\neM4557zyyisT/5NMz2YLA1GLha9Sh69/LcmeUGhDdfUm1inYoAlRc+B0OlOpW1et4vQZcKvV\nmkqlXC4X6yCFRKnsB5ouvfRd1kFySiaT/fCHP/R6vY2NjSUlJVu2bGlsbGQdqmDU138EKC+7\njK+DMxV2JAMSiYTH4+FwgKOAxsXOgfDn4nbNUJd3DridQQdAoVDU1NQEAoHRUb7eA5gn4S2T\nigq+1gwVdiQDnE5nMpnkcM6PgE7Sc8DtNHcBXQzMAa0Z0PuwaeJzzVBhRzKAzy/PBOEfTrdi\n08L5mhEuBoaHh1kHKSScrxk6zswBn3cGqLAjGcDnl2eCUlkJ/PD99+nBlzTwfJJ+4403Hnzw\nt8C1N9zw60svvbS7u5t1osLA85oBoNHUAas7OnysgxQSh8Mhl8sNBr42y6TCjmQA5wdco9EC\nfL+9fSnrIIWE2zWzbdu2tWvXDgyogecTia9s2rTp7LPP9nq9rHMVAGHNcPvIR1/fycB7b73F\n3XSf+XA4HGVlZVIpX6UOX/9akiXvvVcM/CiRaGIdhI3mZiOQDAZ53Oplzvbv1wLnGAzlrIPk\n2l133QXgyAlRvb29jz9Oe9rNbHh4VCaTG408bpYJoKZGAWB4OM46SCFxOp0cXj3KWQcgYvDp\npybge8nkJ6yDsKFQyKRSVyTC3czT+fjooy8B31UqudvvY+/evQDGJ0RZPvshmc6ePb9PpWS8\ndV8m1NerATidHM08nafBwUAkMmK3f8A6SK5x+g0hmeV2ywEsWFDCOggzCoU3HjexTlFIgkEN\nkFiwgK9nXwCUlgpj97xAZKKw0+noqmBm8bhRoeBontgkTU2lADweasfMVmurGyhWqbi7eU2F\nHckAn08JXueJCdTqAKCy2TgadjRPY2M6qdSjUMhYB8m1K6+8cvxH10Rhd8SHZGqDgwFApVbz\nNXbiSIsWGQD4/cWsgxSMgwcDAIxG7m5eU2FHMiAU0gKJxkY96yDMlJSEcXiWJZmVeNyoUPA4\nXfeBBx445ZRTAAAOwAJI7rnnni996UuMY+W99nYPgNLSCOsgzNTV6YBoKETP8s5Wb28YgNmc\nYh0k16iwIxkwNlYqlXrkcn6X03HHdQOPBIO03+ysDA8HAZVazWODU6PRvP/++y+99JJOtxt4\n/bXXtj7wwAOsQxWAgwf9AAwG7rovEyQSFBV1JJO0j91sDQyMAaio4O7mNb9nYpJB3HZfJnz+\n8wPAnYnEIOsghaGtTei+cDocSSqVXnnllZdcsg24uKmpknWcwtDTEwZQVpZkHYSl44//ejK5\nJpXirgU1N8PDSQC1tfSMHSFpGhnxA7+sqeFrMvckNFUsLXa7G9hRUcH1Vqs0SCAt3HZfjmSx\nWGKxGO16OEsulwRAfT13N6+psCPz5XLZge+cdBIVdlTYzZZKdQg49ctf3s06CEvCMHsq7Gap\nufldQHfBBQHWQViii4G0NDX9Dlh62mlU2BGSJs63gxe0t7cD2Lhx4//+7//SjZIZcTt24kjC\nP58uBmbJ6XQC/tpafl+9B62ZNI2M9AL7a2rKWAfJNSrsyHwJJ2mh/cChZDJ58cUX33nnnQA+\n/fTTyy677MILL0wkEqxz5TXOhwsLhH++0+lkHaQw0AUkqMubJofDUVJSolarWQfJNSrsyHwJ\nJ2luD7i/+tWvXn311SM/ef3119evX88qT0EQqhlu14yAbqulhbq8oDWTJofDweeCocKOzBfn\nB9yNGzeO/7gW+Krw04YNG1jlKQicrxmByWQBjuvq4u6Vvbmx2+1arZbD7suRrFYroO7r43f8\nxuwlEgmPx8PnQYYKOzJfQveF21uxweDEVviPAk8e9SGZAue37wVGowXY/9FH17MOUhicTifn\nCwaA210PhDZvPo11kALgcrmSySSfa4YKOzJfn35aDlxqMlWwDsLG8uXLx390AHpAAeCEE05g\nGCn/9fcrNJpajYa7t9WOVFqqlEj8o6P8TlievUQi4Xa7Ob93D2DhQh2AkREF6yAFwGazgdfn\nPaiwI/O1Z88FwEaDwcw6CBs/+MEPDAZhkr0DkABlOp3uRz/6EeNY+a27+4/R6D9Yp2CvqGgk\nFjOwTlEA2trcyeQhh+Ne1kEYa2kxAQgGub4fPUvvvBMFPrDbeRzWR4Udma/R0VKJJGA0qlgH\nYaOmpmbbtm1r166VSl0Ali1bs23btoaGBta58tfYWCKZNBQXc707saC42JdK6YLBKOsg+a69\n3QtUKxRc73UCQKejLu9sHTgQBU6VyehWLCHpi8UMcrmbdQqWli5d+vrrr69e3QzgyitvXbZs\nGetEea293Q1INRoeB8VOotWGAcmBAx7WQfJdd3cQgMnE76DYCXK5h7q8szE0FAdQXV3EOggD\nVNiReQmHY6mUXqXiejt4gdUqBdDXN8Y6SL7r6PAC0OupTXX4jyD8Qcg0+voiACwWCesg7KlU\nAeryzobDkQJQV8fjrSQq7Mi8tLe7AYlGE2YdhL3mZgBvJxK0KfwMursDAEwmrqe5C6zWGDBg\nt9Nd6RkMDkYBVFXx2H2ZROjydnZSl3cGbrcMQGMjj7etqbAj8yI0GwyGGOsg7K1ZkwTONpk+\nZB0k3wndF6uVui+47LI2oMZoPMA6SL4TZmjx2X2Z5Lzz/gQogGHWQfKdz6cE0NysZx2EATnr\nAKSweb0u4G/19XRfgDaFny2ncxQYo+4LaM3MmtB9aWrisfsySVVVKRCjNTOjUEgDJBcu5PF5\nROrYkXlRq7uBL51/fi/rIOwJGybRAXdGTU1vAcUXX0wPI9K42NmqrX0OOOvEE3Wsg7BHFwOz\npNX+oLT0JoVCxjoIA9SxI/NCs6EmGI3GoqIiOuDOSPgTlZfzuA3BJHSSniW/f69MtrO6mvft\nTkBrZtb8/tcbGxtZp2CDOnZkXoTjC5+7e08ikUjMZjMdcGdkt9tBFwMA6CQ9aw6Hw2QyyWQ8\ndl8moS7vbASDwXA4zO1Bhgo7Mi/UsTuSxWKhk/SMHA6HTCYzGqn7ApPJRF3e2XA4HHSQEdDF\nwGxwfmKiwo7MC3VfjqTXt0Qiy10uP+sgec3hcJjNZuq+YLzLa7fTXs3T4bz7MgkVdrPB+a0k\nKuzIvDgcjqKiovFhqbw7dOg2YMfu3SOsg+Q1h8PB7QH3aF7v1v7+XaxT5DXOT9KTGI0miWTf\ne+/dzzpIXhPWTFkZpw/yUmFH5mVgwGoyfU4ioT3JAMBojAPo6qKO3TEFAoHR0VHqvkxQqSJA\n8eAgzW45Js5vq00ilUokkrJwuJJ1kLzG+Zqht2LJ3CWTKYfjf1WqXtZB8kVZWQpAby/dWTum\njz/2AKN9fe+xDpIvSksjHg/a2z1VVbRJ29TefTcOvDIyMso6SL5QKLyRSA3rFHnt7383AL+K\nRumtWELSdOiQD1BqNEHWQfJFebkMwMAAzeE4ps5OP1Cs0ShYB8kXRmMM1OWd1v79KeAigHpU\nh6nVQUBFXd5ptLaagVsUCk5v31NhR+buwIERADpdhHWQfFFTowQwPJxgHSR/9faGMd7aJADM\nZqHLS9OWj0n4QglfLgKgtDQCoL2dxsUe08hIEYCFC0tZB2GDCjsyR/v27XvssRcBxGKDgQBd\nOwJAQ4MGgNtNX6tjGhiIAqiooFdiDxP+FP39NIfjmFwuCYD6ejXrIPmCnuWdUSCgAtDSwume\nSnQGInPx9NNPr1ix4o03dgLo69u5aNGirq4u1qHYa27WAe7RUbo3fUzDw3EAtbXFrIPkC6ER\nZbMlWQfJX0L3ZcECmid2mNmcBHV5pxUOa4DR8nIt6yBsUGFH0tbZ2XnHHXdEo1FAeOfIMTw8\n/PWvf51xrDxw4olGwFxV9TDrIPnL5ZJhvLVJAJxzjgSwHHfcRtZB8pffXwyOuy9HW7PGBpxU\nWdnKOkj+ikYNcjm/t6qpsCNp27x58+io8IaaHHACdgAffPDB8PAw22DMqdVqrVZLe4dOw+OR\nA1iwgNNnX45WU2MGnLRmpjE6WgKMlpfTxcBhLS0a4GOfb5B1kDwVjyeTSaNS6WMdhBkq7Eja\nQqGJ7Tx+CViAvwr/IxikW5A0VWwG9fU/B45ftox2tD5M2HeX1sw0iosfNJl+xDpFHqHhE9Nz\nODzAfU1Nb7EOwgwVdiRty5cvP/pDg8FQX1+f8yx5x2KxuN3ueDzOOkiecru7tNpenY66L4dR\nl3d6iUQiEHiuqelt1kHyCBV20xsZsQMPrly5m3UQZqiwI2m74IILzj333EkfPvLII0VFRUzy\n5BWLxZJMJt1uN+sgeYrmiR2NurzTcLvdiUSC1syRqMs7PZpgToUdSZtEIvnzn/+8bt06YY77\nkiVL/vjHP15//fWsc+UFupieRjwe93g8PB9wp0Rd3mlwPhtqSmq1WqPR0EHmWDgfFAsq7Mjc\nlJaWPvLIIyUlJc3Nzfv27bvmmmtYJ8oXFosFUAwO0jF3Ck6nM5lM0kl6EuryToO6L1OiLu80\n6GKACjsyR9Fo1Ofz0S2SSTo6zgbGXntNxTpIPqID7pSGhq4HDm7fTvvNToG6L1OKRu9zuf48\nNkZd3ik4nU6M37Dmk5x1gCns27fv1VdfbWtr02g0xx133LXXXms0zryD0dx+i8zZ8LAjlVLQ\nSXoSq1UJYHCQxsVOQThJ83zAnZJCYQAau7o+YR0kH9GamVIisTSVOqmz07l0KZW8k9EFZN51\n7N56663vfe97H330UUVFhUQi2bp167p16w4dOpSN3yLz8f77QSDS2fkt1kHyS12dCoDDQbNQ\np/D66zLgfbv9JNZB8otwAjp0aJR1kHz0wQcm4AdALesg+UWniwLo6PCyDpKPdu9eAPybwcDv\nxUB+FXbhcPjZZ59VKpWPPvroQw899NRTT918880ej2f9+vWp1DHPlHP7LTJPBw8GARgM+bWE\nmGtqKgHgdtMs1M/o7Ow877zzHnvsdeC0v/xl88aNNGjhX6qqikBd3mPYs6cOuF+h4Lf7MiWT\nKQ6gu5u2Dp1CW9t5wLNms4l1EGby66z85ptvhsPhK664YmJHtLVr1x5//PHd3d3t7e2Z/S0y\nT319EQBWq4R1kPzS3KwH4PMpWQfJI16v95xzznnzzTdTKTMAr7fj8ssvf+ONN1jnyhfC5Fzq\n8k7J61Vg/GtFJlgsElCX9xgikVKJZESt5nf7rfwq7N577z0Ap5566pEfnnLKKQB27dqV2d8i\n8zQ8nMD4CHMyobnZCCSDQTXrIHnkiSeeGH8uQrg54gDwne98h2GkvNLYqAV1eY8hGNQAqeZm\nemD6M6jLO4143KhQcH2TOo8Ku1Qq1dfXJ5fLq6qqjvy8rq4OQF9fXwZ/i8yf0wkAtbX0+udn\nKBQyqdQ7NqZjHSSPtLZOTCsXHvR2Ch/SkxICoR3l9dI10hTGxkolkpHi4nx8z48hoctrt9M3\naDKvN5JKlRQXB1gHYSmPvi1jY2PRaNRgmDxEsqSkBIDfP/VeAGn91nvvvbd79+ExI4FAoKWl\nJSPJ+SQ0GIRHysiRFi68YnBwn9CXIgB0uoky1wIkABcAvV4vkdB9fABoaTFJJGvLy0uAP7PO\nknficaNC4QCoY/cZK1cWA/+v1VoDfIF1lvzS1uYGqkpKuL5JnUcdu1gsBkCtnnwPS6PRABgb\nG5v/b3388cfPjevp6clQcE75fMUAWlpomvtklZUIBp3hcJh1kHzx1a9+dfzHcsAJJAFcffXV\nDCPlFYVCZjbv9Pt3sg6Sd9zucCqlValoh7/JjjvOCDybSPyTdZC809HhA2AwRFkHYSmPCjut\nViuVSiORyKTPhRNkaWnp/H/rlltueXvc2WefnbHoXKquvk0ub2xooIeaJ6OpYpOcccYZP/nJ\nTwAA3wRuBLB69eoHH3yQbaq8QoMEpmSzuYD/bmykHf4mKysrk0qltGaOFg47gRcXLBhhHYSl\nPCrsJBKJTqcLBCbfGhc+OdZuw2n9llKpLB1HE+vnyeUasFjGpFK6mzYZFXZH++53v7t9+3bg\nrYaGva+88sq2bduO7rLzzGKxBINB6vJOEgwOA/95+ulU2E0mk8lMJhMdZI5WXNwNXP2lLw2z\nDsJSHhV2AMrKyqLR6KTFOjAwAMBsNmf2t8g8OZ1Onrf2noYw/kiYcUkmCF/GU0899aKLLqKn\n6yahi4Ep0TyxaVCXd0o0dgL5VtgJW5Z8+OGHR3740Ucf4ajdTOb/W2Q+vF7v2NgYzfmZEp2k\np0QH3GnQmpkSzRObBnV5p0THGeRbYbdmzRqZTPbyyy+7XC7hkx07duzataulpaWhoUH4JBqN\ndnV1dXV1JZPJ2f8WySz68kyDTtJTojUzDaEpRWtmEloz06DjzJRozSCvtjsBoNPpbrnlll/9\n6le33377iSee6Pf79+7dq9frb7nllon/xul0rlu3DsCLL74oPKYzm98imUVfnmmEQvXA3ldf\nHbz3XtZR8gmtmWkEAsuADVu3Ki68kHWUvPHWW2+9+uqroO1IjyEaXQ2ctGuXb3ziEgHoOAMg\n3wo7AGvWrNHpdG+++ebu3bs1Gs2ZZ5551VVXlZeXZ+O3yJwJD5DRsy9TqqgwAnU2m491kPwi\nrBnOD7jHIpWWAye3tb3DOkheSKVS11577QsvvCD8z9tvv/2DDz544YUXpNL8usXEls+3FPjC\nJ598dNllrKPkE4fDoVQqj9g7k0d5V9gBWLVq1apVq471/1ZVVQmXcWn9Fsms117TA/vs9iHW\nQfJRS4sRQCBAMzk+4913m4BfSaU1rIPko/p6FQCXi94pAYCnn356vKo7H5ABm1966aXVq1ff\neuutjJPlk4oKKYCBgal3eOVWf/8ig8HC+etZdAFE5qK3VwYs0eloE7spVFWVAKPhsJZ1kPzS\n2dkA3KLT0YvqU1iwoBSAx5OPV9q599JLL43/+ADwkrCj9YsvvsgwUh6qrlYAGBpKsA6SR5LJ\nlNv9vN//KOsgjFFhR+bC7ZYCqK/XsA6Sp+TyERoXO4nQwmxuplElUxC6vH4/dXkBwOebeIyh\nTJgs/NkPCQA0NGgAuFx0Ev+XQ4d8QJFaHWIdhDFaE2QuRuqp67MAACAASURBVEYUABYtoo7d\n1JRKXzJpiseTrIPkkXC4RCIJWSx0MTCF6upSYHR0lLq8ALB06VIAgBQoAw7vB3n88cczjJSH\nxru8MtZB8khbmweATjd5EhVvqLAjc0Hdl+mp1UFA3tPjZR0kj8RiBrncwzpF/pLLR8bG6EoJ\nAO6///6SkhLABMgBBwCtVvuDH/yAda78Qs/yHq2rKwDAZOL99jQVdmQuRke1EknQaKRjytRO\nOWUzcProKG0xdVg0mkgm9cXFNM39mGpqnpVI7p7YnpNnTU1Nb7311uLFZwAAnKeddtrWrVub\nm5sZx8ozVVUlcvmvVaq/sg6SR/r6wgBotwYq7MhcxGJGudzNOkX+Wrx4FPjA47GxDpIv2trc\ngFSjCbIOkr8WL/4okXhuZITr4eUTVq1adcMN9wE4+eS6999//+STT2adKB9VVj6QSDzBOkUe\n6e+PAais5P32NBV2JG3xeBy4YNGih1gHyV80SGCSQMAB3LtkyW7WQfIXDRKYJBRyAu82NdF2\nHsdksVicTid1eSeMjnqBjvp6JesgjFFhR9LmcDhSqe0LFw6zDpK/6CQ9yejoMPDgKaf0sw6S\nv2jNTKLTtQFfOP98ujNwTBaLJR6PU5d3QnX1G8Ci886jjh0haaKZLTMS/jhOp5N1kHxBa2ZG\n1OWdhNbMjIQ/jjDThQCw2WygNUOFHZkDOuDOyGq1gk7SR6A1MyPq2E0i/CmErxKZEq2ZCVu2\nbDnxxBM3bdoE4Lbbbuvt7WWdiCXa6JykjQbFzogOuJNQYTcjWjOT0JqZEXV5Bdu2bTv33HMn\n/uemTZv27NnzySeflJaWMkzFEHXsSNrogDujsjIL8NyHH17JOki+oO7LjIqLK4Dvbt9ezTpI\nvrDb7RKJxGymGXTHpFTWA1/75BPet2276667Jn3S3d39+OOPMwmTD6iwI2lzuVygwm5aCkWR\nRHKR00l7NBwmFHbU5Z2GRmMBfrJ/P81XOMzpdBqNRrmcbisdk9/fAPzhgw8qWAdhbO/evUd/\nuGfPntwnyRP0nSFpe/vtE4Dfy+VVrIPktaKikVjMyDpFvti//ySp1GIymVgHyV+LF5uAVDCo\nZh0kXwwPV9TUUP9yOo2NNC4WAEpKSkZHR4EiwAQ4gQQAbu/Dgjp2ZA56exuB66xWukUyHZUq\nkErp/H7ahQsA+vu/CTwok/G+DcE0tFqFROKLRPg9Gx3J5QqHw+85HI+yDpLXmpv1ALxeBesg\njH3lK18BABwPDAO//OyHPKLCjqQtFNICicZGmms5Ha02DODAAZqOCgDxuEGhoMm5MygqGonF\naP4yALS2ugGUlIyyDpK//H7/Cy88CqRstuQVV1zR2trKOhEzDz744KpVqwDhEV4HgLvvvvvI\n1yl4Q4UdSdvYmE4qdcvltHimo9dHAXR0UDWD4eEgoFaraZ7YDIQur89HXV50dfkBGAxR1kHy\nVCwWW7NmzX//988BbzJp3rBhw6pVq/bt28c6FxtarXb79u1nnXUVgCVLyrZv3/7zn/+cdSiW\n6NxM0kbdl9kwm5MAenpCrIOw197uAVBaSt2XGVCXd4LwxTGbU6yD5Klnnnnm448/BgA4hE5V\nOBy+7bbb2KZiSCaTqVT1ANauPfGUU05hHYcxKuxIeoaHg4BKraZ6ZQYnn+wC/kOtPsQ6CHsH\nD/oB6PUx1kHy3YIFXcAvfT4X6yDsDQyMAaiooIcyp7Zjx47xH98B3hVO5du3b0+l+C2FhUE/\ntbUq1kHYo8KOpEd49qW0NMw6SL5buTIF/DKR6GYdhL2DB0MALBZ+Tzmz9IUvdAH/kUgMsg7C\n3uBgAkBNDe+vBRxLUVHR+I83ARcDSQAKhUIikTBMxZbbLQfQ3KxjHYQ9KuxIeuJxO3DriSfu\nZx0k39G42AnJpA14qaWFnpeaAQ0SmBAKhQB/fb2GdZA8dd555x394dq1a3OfJH8EAjIAixbR\n60dU2JE0jY4OAU+sXOlmHSTf0bjYCSUle4GvrllD7wTMgGa6T6ir+yOgW7OGdvWb2pVXXnnV\nVVcd+Ul1dfVjjz3GKk8+qKq6Wak01dXRhkFU2JE0CWcdGjsxIxr9OYFm0M0SdXkn0HFmRn/6\n059eeOGFk046CcBll122f//+8vJy1qFYstvtFouG55vRE2jyBEkPnaRnyWAwFBUVUWEHGhQ7\na3QxMMHhcKjVaq1WyzpI/pJIJFdffbVSqbz88stPOukkngctAEilUi6Xa9myZayD5AXq2JH0\nCO0EOknPSCKRlJWV0UkadDEwa1TYTXA4HLRgZoMe+RB4PJ5YLEYnJgF17Eh66CQ9e0VFXx8Y\nKEmlUpzfHbDb7VqtVq2m56VmYDQa5fILOjubWQdhLJFIuN3uhoYG1kEKgMlkBY7r6OB9Xxi6\nd38k6tiR9FBhN3vB4OWJxL0DAwHWQRij7sss+Xy+ZPJ/urv/85133uF5QzKXy5VIJGjNzEZJ\niRXY/8EHX2UdhDE6MR2JCjuSntbWc5XKb1P3ZTZKSyMYn7vArWg04XafXFKyinWQfLdly5bm\n5uZk0pZMms8666zPf/7zHg+nK2d42AnI6bbabFRVlQCRcJj3hxGpsDsSFXYkPU7nDYnEOtYp\nCoPRGMf41EtuHTjgTqVes9tpzUzHZrNdc801TqcTcABKQPf+++/fdNNNrHOxsXVrFIh2dFw1\n839KALncHY3qWadgbOPGMmBwaGgF6yB5gQo7koZoNJFMGoqLfayDFAZh0mVPD9dTOg4c8ALQ\n62kTu+ls2rTJ7Rb2hhQ2sbMA2LBhw8jICMNUrPT2RgCJyVQ0839KAKXSl0yaYrEk6yAsDQ5K\ngEqzuYR1kLxAhR1JQ0eHB5BqNFxXKrNXVSUHMDTE9YzU3t4QAKMxwTpIXjvirUZhEzsLgGQy\nyeeedsJXprKSCrtZ0WiCgKyri8drgAlutxRAQwPvt6QFVNiRNLS3jwAwGGg21KxUVysBDA1x\nXdN0d4cBlJfToWY6CxYsGP9R6NhZAahUqpqaGlaRGLLZUgDq62ma+6zo9VEABw5wXdiNjCgB\ntLTwfktaQEdbkoaeniCo+zJry5YVA7/VaA6wDsLS8HAcQFUVdV+mc+mlly5duhQAcAD4BxAC\ncOedd6pUPBY3Ho8MQFMT3VabFYtlDBgcGvKyDsJSKKQGUs3NRtZB8gIVdiQNhw5FANDLarO0\ncmUp8E21+m3WQViy21MA6uqKWQfJayqV6pVXXjn77LOBTcDn5fK37rnnnu9///usc7Hh9SoB\nLFpE3ZdZufzyvUC1wdDJOghLkYheKh1Rq+kCEqDCjqRFLu8D/r/jj4+zDlIYaJAAgGjUDXQ2\nNdGzLzNobGzcunXrli1bAFx//fUPPPCAXM7pBvKhkAZILlhgYB2kMNBxBkA8bigq4vpm9JGo\nsCNpUKv3AP92xhmscxQIlUpVUlLC+QG3ouJ3QPNpp+lYBykMixcvBsDtDnYCk+lKk2mVQsH7\nNIVZoqli4XA4lVq1bNkDrIPkCyrsSBpomnu6LBYLzwdcAA6HQyaTmUwm1kEKg8VikUgkwnwk\nbjmdvZWVXL9LnhahY8fzmrHb7UB7Y2OQdZB8QYUdSQPt7p0ui8XidrvjcX5vXjscDrPZLJNR\n92VWFAqFXq/n+SQdDAbD4TAdZGaPOnbUcZiECjuSBuq+zF4sFnv44Yf37NmTSqWam5sff/zx\nRILHt4lpUGy6LBYLz4UdTXNPl8lkkslktGZozUygwo6kwW63CwcR1kEKwN13333XXXeFQtXA\nV3p6grfffvt9993HOlSuBQIB6r6ky2hc6PfXBYOjrIOwQd2XdAkX2zYbvzci6VbSJFTYkTRQ\n92WWurq6Hn30UQDAdcCfgeUAfvGLX/T397MNlmN0wJ2D/v7/Avbs3cvpK35C96WsrIx1kEIS\nCLzT1/cx6xTMCGuGLgYmUGFHZsvjCYXDX1apTmUdpAB8+umn4z8KD75YACQSid27d7OKxMTA\ngBsoN5upsEuDwRAD0NHB6X6zwhQ1uhhIi0oVAdTDw5w27YQ1QxcDE6iwI7O1d68beNFuv551\nkAKgVqvHfxQKu8OXklotX9u5bdmSAobb2y9hHaSQlJUlAXR3h1gHYePVV2uAT73eFtZBCklp\n6SiA1lY36yBsbN16JvCaQlHFOki+oMKOzFZnpx+A0UjbEMxs9erV45eP/xr9WVFRccoppzBM\nlXt9fVEAlZX0UGYahD9Xf/8Y6yBs9PergWVGI+1OnAajMQ6gq8vPOggbQ0P1wAU1NfRW32FU\n2JHZ6u0NAzCbk6yDFICSkpLnnntOrVZPFHZarfYPf/gDb6M/h4cTAGpqlKyDFJLqagWAwUFO\nt8gZGSkC0NxMO1qnoawsBaCnh9MubzhcAoQtFg3rIPmCCjsyW0ILoaKCui+zsnbt2ra2tv/4\nj6sAaDSN7e3tX/ziF1mHyjWXSwqgoYEOuGlobNQAcDgkrIOw4fcXA2hpoWnuaRC6vAMDnN5O\nicUMcjmnt6GnRIUdma3h4TiA6mrqvsxWbW3t+vXfk0h2qdVDVVU8Pv8xMiIH0NRUyjpIIVm4\nUAeEw+EI6yBsjI6WSCRhs1k9839KxtXWKjHeIOdNNJpIJg3FxZzehp4SFXZktqj7MgdSqaSi\n4ssKxZ2sg7ARCKgBLF5M3Zc0rFplBDTV1Q+xDsIGdV/m4JxzpIB18eINrIMwcOCAG5BqtWHW\nQfIIFXZktpLJIWDXokUlrIMUGKvV6nA4UqkU6yAMRCIKIETPvqRFo9FoNBo+BwlEInHqvsxB\nTU0Z4HA4eFwzwsZAOh2nLxtNiQo7Mltm8/8AK048kbov6bFarbFYzOvlcVsyrfYLDQ0ns05R\neLidKuZyOYEzTzjhedZBCoyw7R+fa0YqHQS+duqprayD5BEq7MhsORwOlUrF205s8yfsh87h\nMTeRSLjdbquVWrxps1qtbrc7HufuxVi32wH8o6XFwzpIgSkuLi4tLeXwIAMgHB4CXvjc5zh9\nJnVKVNiR2bLb7TSzZQ64LeycTmcymaQRAnNgtVpTqZTL5WIdJNdoUOycCY98sE7BAM0TOxoV\ndmRWksmk2+2mk/QccFvY0aDYOeP2zprwT6Y1MwdWq9Xr9Y6NcfeoGR1njkaFHZkVl8uVSCTo\nyzMHJlM5sKCzM8A6SK5R92XOhD/a4CB3DRg6Sc+ZxWJJpVLC1FSu0HHmaFTYkVmhL8+c2Wwt\nQOdf/9rEOkiuCWuGJnPPwcGDZwHR//s/7vZyo8JuzoaGrgW63nuPuwtI6vIejQo7Mis9PR5g\nkdHI4y6789TUpAXgcnE3sYMOuHNmtaqAIg6nitEF5JwplQagqauLu6liDodDLpcbjbRdw7/I\nWQcghWHrVjnQ3tHxLusghaelxQDA6+VuYsfGjcuAQbd7iHWQwtPQoAbA3yN2eP/9NcAZJSVU\n2KWtokIKoK+Pu5dDOzquV6vdUil1qf6FCjsyKwMDcQBVVbRg0tbcbAQSwSB3m/Q6HAqgsrqa\ndoRP24IFJQDcbu66vIODnwOaKyo4nZM7H9XVCgBDQ9xNFfP7v6lS9bFOkV+oyCWzYrcnAdTW\nFrMOUngUCplUOjI2pmMdJNd8vmIAzc161kEKz6JFBoz/AbkyNqaTSt1yOZ2Y0lZfrwLA24Yn\n/f1+oFitDrIOkl/o+0NmRWgeNDXRZrNzoVCMxOMm1ilyLRhUA4kFCwysgxSehgY9EA2FuNsM\nPB43KpU8zmiZv4ULdQA8niLWQXKqvd0DoLR0lHWQ/EKFHZnZ8PCw3Z4CUF3N3YNiGaFW+wG/\nzcbXC2tjY6VSqVeh4O5+4vxJpRKZzBON8tXsHBwMAMUqFXVf5qKlxQjA5+PrEN3V5QdgNHJ3\nA3p6VNiR6aRSqXvvvbeurm5kpAhIXXLJ6a+88grrUIVn7dr1QHkwyNfD8PG4UaEYYZ2iUC1e\n/O9S6fJUKsU6SO60tbkBlJZy9/h/RtTWlioUF5eV/Zh1kJzq6QkBsFg4+prMBhV2ZDpPPfXU\ngw8+GIvFACngtNsHr7nmmra2Nta5CgyHwyfs9hCgUan4alJmUE1NIhZzjIxwVBl3dQUAmEzc\nbfKSKVbrJz7fR6xT5NTAQAxAZSW91fcZVNiR6Tz22GPjP54CWAGEw+Gnn36aYaRCxGFh5/PZ\ngMWnnfZH1kEKFYdTxeTyQeD2FSv6WQcpVFarVRjQzDpI7qjVHcDDn/scdew+gwo7Mp3+/ikO\nsn199G55ejgs7DweJ9De1BRjHaRQcbhmEolDwOMnn8zdtNNMsVgs8Xjc4/GwDpI7SuUnwF0n\nn8zd++PTo8KOTKempuboD+vq6nKfpKBx2H0R/rE0QmDOhD+dg6ftK2jNzBOtGSKgwo5M59vf\n/vakTzQazY033sgkTOHisPtCQz/nicOLAVoz88TtmqGB1JNQYUemc9NNN917771y+eFHU8vL\ny1944YWWlha2qQqOUNjZbC7WQXKHBsXOE7cXA9R9mTPh68Zbx06v1xcX063Yz6DCjkxHIpH8\n7Gc/+9nPfgbgnnvu6erquuiii1iHKjxGowXwvfXW91kHyR2n0wk6Sc9DIlEDfLx582rWQXJH\nqGKp+zJn4fBxwF+2bOFoG3mHw0FXj0ejwo7MLBQaBbB69WqNhruBpxmhUhVJJPFIhKOpYnRb\nbZ4qK43ASpvNyDpI7jgcjtLSUpVKxTpIoSoqKgOuOHCAl6P02NiYz+ejq8ej0e4vZGYbNnwB\nCIZCh1gHKWAKxUg0ytEB6L33vgpcq9dTYTdHLS0mIBkMqlkHyZ1Dh64rLeVo376ME0Y+uly8\n9Gu6ulyp1E1yOUcXzLNEhR2ZmcejADSNjTT0c+5UqsDYWJPDEbJYuLiedrmOA6oMBo7qksxS\nKGRSqZufLm84HBsdvbeoaB/rIAWspcUAnqaKffKJH3jS6dzGOkje4aW0J/MRCGiAVEsLd2Ps\nM0gYUy2MrBa3p556qrKycmxMD9ivu+46rh7/zyyFYiQeN7NOkSNtbW5AotWGWQcpYM3NRiAR\nDHJx6Qjg4MEgALOZow2ZZ4kKOzKzSEQnkXi1WgXrIAXMYIgC6Oz0sw6SXU899dS3vvWt4WEH\nYAQcf/jDHy6++OJYjLYpngu1OgCo7PYQ6yC50NHhBaDXR1kHKWAKhUwq9UYipayD5Eh//xiA\n8nIqYyajvwiZWTxuUijE32rKKmFMtTCyWqxisdh//dd/AQDMgAywA/jwww83bNjANliBKikZ\nBdDa6mYdJBeEr4bZnGAdpLApFCPxOC+3VoaGEgBqaqjjMBkVdmQGLlc4laJp7vN1wQUDQHVV\n1V7WQbJoaGjI6/UCAIR3JpzC5/v20YNTc3H66duBs1KpIdZBcqG/PwLAaqVT0rw0Nf0ZuDUQ\nCLIOkgtOJwDU1dGDvJPRt4jMYP9+NwCdbpR1kMLW1KQHBt3uYdZBskin00kkEgBAOQDAJnxu\nMNBrN3OxdGkUeMfvt7EOkgsDA3EA1dVFrIMUtmXL9gG/dTq52KPY7ZYDaG7m5QWj2aPCjsxA\nJhsAqs45503WQQobD9N+9Hr92rVrAQCfAl8D/gJArVZfcsklbIMVKK6GTxQXdwG/O/54Kuzm\nhYfjzAS5vB3YKrwLTI5EhR2ZgdNpA4aammhmy7xwcpJ+9tlnFy1aBDiAF4BPVSrVr3/966am\nJta5ChIna0ag0bwHXH/qqVrWQQobV1PF1OpfFhd/uaaGl5dFZo8KOzIDm80Gmg01b5ycpCsr\nK/fs2XPBBRcAuO2221pbW6+77jrWoQqVsGY4OUn//+zdd3xb1d0/8I+GJdmSJS95bzue2WSS\nEAKEEcIeTUkoUObzAGWEzqf0AX6UMsoqhfIUKAVKGCUUSoGUEELDSkKW43jvJduSZVu2NSxr\n/f64cRKMM617z71X3/cffclq4vPBudb93nPPPV9qVRIWEfI5w7FarXTATIoKO3IM3GdEamoq\n6yDSptPpjEZjJHzgajQablHd7bffnpubyzqOhEXUbTWr1apWq2k55hRFTmEXCoXsdjsVdpOi\nwo4cA/cZQTN2U5eSkhIJH7gYn+Wli4EpSklJUSgUEXLMcN3cxx++IScpcm7F9vf3+/1+OjFN\nilqKkWOgGbtwcbkedjjKRka8sbEy7/ljtVqjo6ONRlr7MiVarVajeW7//jjWQYTQ19dXVlbG\nOoXkGY2pwK+//TaddRDecScmmrGbFM3YkWPo7e1VKBRms5l1EMlTKDKA0tpa+e8329vbS1cC\nYREKrRwaOp91Ch6FQqFXXnll1qxZXq+3qanpT3/6UyBAexSfvORkM/DbhoYlrIPwjpuVpBm7\nSVFhR46houKxqKiPo6JoG4Kp4rqK1dcPsQ7Cr9FRv93+O4XiZtZB5ECnGw6FjENDo6yD8OXh\nhx/+8Y9/XFnZBVw2MpJx2223/fKXv2QdSsISEqIVCqfHE8s6CO8aGkaARbGxmayDiBEVduQY\nPJ45CkUh6xRyYDYHAbS0yHxT+Nra/lDoRo/nDNZB5CA21g0o6urk2dDPZrPdf//9AIBy4F3g\nBgCPP/54Y2Mj01zSplb3j43J/xmUTZtMwLaurtmsg4gRFXbkaKxWF6CPiaF+YmGQnq4C0NHh\nZR2EX3V1gxifniRTxP0YGxrkOctbUVHh8/kAANxKjwM96Hbt2sUqkgxERw+HQnFut491EH7Z\nbCEA2dnRrIOIERV25GiqquygfmJhwjWrtlj8rIPwq6FhBIDZTCulwiApSc6zvDrdwW3P0wAA\nB57/jY6ms/XJMxjcgKK+Xp6zvAfZ7SoAhYW0o/UkqLAjR9PYOAwgMVHmF3/CyM/XA7DZZL6h\nAzclmZ5Ony1hkJamhHxneefPnz+++J37314AJpNp2bJlDFNJXVzcGMYnzmXM4dACKCqKiGfG\nTxR9+JKjaWlxAaAnysNi4UIDcFFGxvusg/CLm5LMypL5li7CWLDAB9wTG1vPOggvoqOjX331\n1ejoaIB7htqq1WpffPHFhIQExsmkrKysF/jj6KjMt7JzuWKAYFERHSqToMKOHE1HxxiAjAwV\n6yBykJ+fBPzL7a5kHYRfVqsCQGGhnnUQOViwIBp4UqmsZh2EL+eee251dbVeXwDguuvOraio\nuPLKK1mHkrbly23AHSpVG+sg/PJ4TErloE5He/FOggo7cjTp6d8CFyxfLvNlYcIwGAx6vV72\njQRiY7cDT8yeTbsTh0EkdBXLy8vT6+0KReWf//xQSUkJ6ziSFyFdxYJBm07XwTqFSFFhR47G\n7W4CPiovp3UM4ZGcnCz7D1yV6t/AT8vLk1gHkYMIOUlrtT9NTT1Po6E7A2EQCV3FXC5XMDhv\n0aKfsg4iUlTYkaPhmn7S7t7hkpKS0t/fP77Fgzz19vYajcaYmBjWQeQgNjY2JiZG3ifpUChk\ntVqpVUm4cB/X8j5mqJ/Y0VFhR47GarUqlcqkJJp9CY+UlJRQKGS321kH4RH1Ewsv2c/yDgwM\njI2N0TETLvK+fe/3+59++umVK1cC+PLLLz/++GPWicSICjtyNL29vYmJidRPLFxkf2fN6/UO\nDQ3RFG8YJScn2+12v1+261y52wJU2IVLXFycVquV64zdLbfccvfddzc0NACwWCyrVq16/fXX\nWYcSHSrsyNHYbDb6wA2jkZHTgNe//FK2rT97e3tDoRAdM2GkUKwMBn/T2irb/WapsAsvhUJh\nNF7Y1raAdZDw27Fjx8svvzzhzZ/85Cderzw3ejxpVNiRIxoZGXG73bSOIYwCgWxgbVWVbGdf\naO1L2DkcZwH3V1Y6WAfhC3fM0CxvGLlc/29w8NlAIMQ6SJjt2LHj+286HI66ujrhw4gZFXbk\niPbutQPrPZ7LWQeRD66rWHe3bNtt7d7tBP4rKmo66yDywXUVa26WZ1cxAPX1w0Cu2ZzOOoh8\n6PUuQN3SIrfmExqNZvylCTj4GlotbYf+HVTYkSPav38YWOP1lrEOIh95eTEA5LvEDt98owae\nHxgoZx1EPriZrLY22fZr/ve/S4FWu72IdRD5MJm8AOrq5Hb7fsWKFeP9hV8CvEAmgMLCwqIi\nOni+gwo7ckStrR4AtPQljKZNMwLo75ftbukWSxBAbq7umH+SHCdulpdr1CZLdrsaQFER7Wgd\nBsFg8Iknnmhv/xbA5ZffetNNNw0MyKe8KywsfPjhhwEAaUAI6IuJiXnttdeUSqpkvoN+HOSI\nOju5fmKyrUKEV1qaAGBoSLZ1T1+fEkBhoYF1EPnIyYkGINNnHAFgaCgawPTptKdSGDz22GM/\n/elPfT4LAJ8v/qWXXlq9enUwGGSdK2zuuuuub775RqlMBwZ+/vM76+rqFi9ezDqU6FBhR46o\np4dmX8IsK8sIjLrdsayD8GVwUAuguNjEOoh8FBbGArDbZduVweWKBTyZmbL9pRCMy+V64IEH\nAADcao8UAJs3b/7kk08Ypgq7xYsXB4PJWu3go48+mpWVxTqOGFFhR46IO5fk59PsSzjFx/9e\no/kj6xR8GRmJBkIlJYmsg8jH9OnxwHqdbg/rIHwZG4tXq/tZp5CD5ubm0VFuK6VW4GvgwEbo\nVVVVDFOFXU+PE9DHxAyzDiJeVNiRI+JmX0pL41kHkZWCgg+czufldHPkcB5PnEIxaDTSQ2ph\nk5sbHxX1Y632NdZBeDE66g8GE6KjZbuZi5Di4g429f4aWAq8yX0RHy+rz/D9++0A4uJk+zjR\n1FFhR44oNvYdheK+oqIE1kFkJSUlJRAI9PfLdYriM5PpP6wzyIpCoTCbzXLtVtLQ0Af0x8a6\nWQeRg+zs7CVLlkx402QyrVq1ikkenjQ2OgAkJsr2caKpo8KOHNHY2DvJyX/W6ejhiXCScVcx\nl8vl810/d+5zrIPITXJyss1mC4Xktt8sgLExC5B8ySWvsg4iE6+99lpeXt7BL/V6/V//+te0\ntDSGkcIuKakeiL700krWQcSLCjtyRDabjbaDDzsZ0NcFsQAAIABJREFUF3bUG4onKSkpY2Nj\nDocM71dyxwx9zoRLfn5+TU3Nq6++GhsbazKZ6uvrL730Utahwqy3txcYzcmhW0lHRIUdmdzQ\n0JDH46EP3LCTfWFHx0zYcT9SWbZ1p4uBsNPpdNdcc01xcbHL5ZLlD5Y7ZmQ2DRleVNiRydEH\nLk+osCMnSvbHDH3OhF1qaqrf77fb7ayDhB8dM8dEhR2ZHHXm5olGkwWs27ZNhvcRuGOGPnB5\nkAFcuHlzcyAgty7DdMzwJDExH5jR3NzHOkj4UWF3TFTYkcnR7AtPoqJSgScqKgpYBwk/OkmH\nXSgUuv/++598sgL44MEH98yYMWP79u2sQ4UTnaR50tp6KVC5Zcso6yDh19PTo9VqZbaHS3hR\nYUcmt22bCvipUjmNdRC5KS6OB+BwyHCnt5oaNbAoMZFO0mHz3HPPPfDAA4FANwAguba29qKL\nLurp6WEcK3x6evoUCgVdQIZderoSQFubl3WQ8Ovt7U1NTVUoFKyDiBcVdmRyu3YlAr8fG6OG\nLWFWUBAP+FwuGfbz+Oqrc4BtUVG0qDlsHnnkEQAA99hECoC+vr6XXnqJYaTw+vbbVxSKNq1W\nhtc5bOXkaAB0dvpYBwkzr9dvtVY4nS+wDiJqVNiRyXH9xAoKZFh/sKVSKVSqAa837th/VGqc\nTj0QLC6W4fJBJsbGxiwWC4DDW38CaGlpYRUp7Pz+RLWadpoNv8JCAwCrVW6n+NrafiA1KkqG\nn59hJLd/dRIug4M6AOXldJIOP43GEQgkBoNy2292dDReqeynHa3DRaPRJCZyXXdtQAA4MBWa\nnp7OMFUY2WyuUChWr6emn+FXVhYPoL8/inWQMKupGQSQkCDDtYNhRIUdmZzTqQcChYW0QDX8\n9HonoGlvH2IdJMz8/kSNZpB1Cln5r//6LwBAALBzhZ1er7/22mvZpgqXqio7AKOR+omF3/Tp\nSUBoeFjPOkiYNTaOAEhOlmev7XChwo5Mzus1KZUDGo2KdRAZysurBf5ss8lqJ4KOjiFAp9c7\nWQeRlfvuu2/t2rUAgG+AusTExNdee62oqIhxrDBpaBgCNf3kh9GoVSqtPp+HdZAwa2sbBZCe\nTiemo6HCjkzO70/SagdYp5Cn5csrgf8aHe1mHSScqqr6AcTFye1EwlZUVNTrr79eVVU1a9YD\nwDnbt2+/7LLLWIcKm6YmF4CUFLmtSRCJkpKzlMqzWKcIs66uAIDcXA3rIKJGhR2ZhNU6CLyc\nkbGTdRB5kmUjge7uAaA2NZUKu/ArLy+fN28eAI9HVj/ezs4xAJmZtCiTF6mpqS6Xa2RkhHWQ\ncLLZFAAKC2NZBxE1KuzIJAYHrcCtp576Kesg8iTLwi42thkou+yyStZB5InrjCmnHewAFBT8\nB8heuVKGe62JAXfMcFtAy0Z29t+AJYsXU2F3NFTYkUnQdvC84n6877///o4dO1hnCRs6Zngl\ny8Kur68L6CwoSGIdRJ5kecwMDtYpFNvy8pJZBxE1KuzIJKhRLE+CweDq1at/9rOfAdiyZcui\nRYtuvfVW1qHCg44ZXsnyJE0XA7zifrAym7Hr7e2Nj4+nHa2Pjgo7Mgk6SfPkD3/4w9///vfD\n33n++edff/11VnnCiE7SvJJrYadWq5OSaMaOF9wvo/yOGe53gRwFFXZkElTY8eSNN94Yf7kc\nOJd7tX79ekZxwok7Zqiw40lqahqQ0dQkq+27ent7k5OTlUo6DfGCK4C6uvpZBwmbkZERl8tF\nHzLHRL9RZBJU2PHE4XCMv3wb+PP33pQwq9UaFRUVH087WvMiLi4F6PzqqxtZBwmbUChks9no\nQ4Y/o6PZgPe9985hHSRsuNsCdMwcExV2ZBLV1cnAiuRkujAKs7KysvGX3UAqoABQXl7OMFK4\ndHUFkpMzaPaFJ3FxOoXC4fHIp0Vmf3//2NgY3VbjT2lpEqAZGopmHSRsuNvKsmmpxx/6FCaT\nqKy8Gtg43qeShM0DDzwQHc19zvYAWiDRZDL9z//8D+NYUxYMhqzWbwcHN7IOImcazYDPJ5/l\naDt2DAJVfX0/Zh1EtvLy4oBRp1M+O4Ns2gRg4+DgYtZBxI4KOzIJrzdepepXq+nwCLPZs2d/\n+OGHs2bNAroBFBWdvmnTpvz8fNa5pqqhYQCIMhhktRWq2Oj1w4DeYpHJD7mmZgQoj4oysw4i\nZ2q1fWwsgXWKsKmpCQHnaTR0K/YY6MxNJgoGQ4FAAnVz58mZZ55ZUVGxeHEugGuv/dWCBQtY\nJwqDmpoBAHFxo6yDyBnXrm3/fjvrIOHR2uoBkJqqYB1EznQ6RzCY4Hb7WAcJj97eEID8/BjW\nQcSOCjsyUWurA9BSN3deZWdHAWhpkUmHqKamEQBmc4B1EDlLSvIBqKsbYh0kPDo7fQCysqJY\nB5Ezo9EFKGtrZfJgbF+fGkBxsZF1ELGjwo5MVFc3AMBkoj4/PCouVgPfut19rIOER1ubB0BK\nCs2+8Cg9XQEMd3fLpLDjOurR7AuvEhLGANTUyOT2y9CQDkBpqXxuLvOECjsyUUPDMICkJD/r\nIHJ2/vkqYGFS0uesg4TH+OyLhnUQOfvhD3sAU2rqbtZBwqO/PwpAcbGJdRA5u/DCHUB8fHwz\n6yDh4XTGAmP5+fJ5NpwnVNiRiYaGBoDtOTljrIPIGbfLQ3d3N+sg4WG3B0CzLzyTWfMJh0MH\noKyMHr3nUV6eCXDI5pjxeuPVartSSXcGjoEKOzJRXNx+YPEll8hkjbY4paWlKZVK2XzgTpv2\nKhCzYoWOdRA5k1lhl5DwO43myqws+WzGIUJyOmYCgQBwa27un1gHkQA16wBEdKjthACioqIS\nExNlM2PX29sLeDIyaEdrHsnpJA3A4fgiM5NWwfOL677FNWyQOpvNFgy+W15+MesgEkAzdmQi\nKuyEkZaW1tPTEwqFWAcJg97eXp1OZzLReikeGQwGg8Egj8LO5/MNDAxQ00++cRcD8ijsuCOf\nWpUcDyrsyETcpwB95vItPT3d6/UODsrhgTWr1UoHjAC4iwHWKcLAarUGg0E6ZviWkpIimyUf\n1Cj2+FFhRyaibu7CMJtzgdLmZslfTPv9frvdTidpAaSlpTkcTrdb8tsf0tWjMNRqdVJSEs3Y\nRRoq7MhEVqs1OTmZurnzraVlNVCzZYvkuzXYbDaafRFGV9d9wNiuXZJ/sIlmXwTj873c3v6Z\nDJZ80MXA8aOTN/mOYDBktSYmJRWyDiJ/GRlKAE1NbtZBpooWZQrGZFIBitpaB+sgU0UnacGo\nVKmhUH5bm+T3taZj5vhRYUe+o6lpwO/f1939OOsg8peTowHQ2Sn5jaA/+sgP9HZ0XMQ6iPxx\nt6Gamlysg0zVpk1JwGtjY9NYB5E/k8kDoLpa8l3Fvv56FvA7ozGddRAJoMKOHNLe3v7ww38F\noFBYnU7qFcuvoiIDgN5eyW+22drqBVJMJgPrIPKXlaUG0N4u+c3D6+sTgB8ZDMmsg8if2ewH\nUF8/zDrIVDU3LwB+mZ5Ox8yxUWFHDnj33XfLyspeeWUjAJttf0lJSV1dHetQclZWFg+gv1/L\nOshUWSx+ADk5kv8PEb/8/GgAFkuQdZCpGhjQACgpod5QvEtLUwBoaZH8kg+PJ06hcJhM9Dlz\nbFTYEQDo6em5/vrr3W43wK1gsFoslrVr1zKOJWszZ5qB0PCwnnWQqbLZFADy8qifGO+Ki40A\n+vokv7H88HAMECorS2IdRP6ys2Wy5MPnS9RoBlinkAYq7AgAfPLJJ8PD3Fw9N9FtBbBnz57G\nxkaGqeTNYNAolX1jYz7WQaZqYIDr5k6zL7ybPj0RwOCg5Fu3eTwmhcJhNNLsC+8KCmIA9PRI\n+6nY3l4noI+JkfwNZWFQYUcAYLyqw/iM3YENLYeGJP8slZiVl69QKJayTjFVw8MxoG7ugsjL\ni9NoytPSbmcdZKr8/kSNRg5bc4vfkiUGYEVW1nusg0xJVZUd4w+CkGOiwo4AwPTp08df6oAx\noBuAVqstLi5mmEr20tLSPB6P1JtPuN2xCoUrOZluxfJOqVSkpbms1lbWQabEZnOFQgaafRFG\nUVEK8NnQUBXrIFNSVzcEIClJ8jeUhUGFHQGAM84444ILLgAA3AVogSYADzzwQGxsLNtg8pae\nng7pt3XX6a7IzPwR6xSRIi0tzW63+3wSvoNvs/UCt5eXb2UdJCJwLYal3nwiELAAD82a1cc6\niDRQYUcAQKFQrF+//o477uAaTmRmpj/zzDM/+9nPWOeSOa49Tnd3N+sgJ8/r9Q4P78vNlXwv\nBKlIS0sLBoM2m411kJPncPQAzy1Y0ME6SKRITU2V+tUj0Ajce8YZku/TIwwq7MgBRqPxD3/4\ng16vLykp6ezs/MlPfkJdxfjGFXaS/sy1Wq2hUIjaTghGBscM9RMTWFpa2uDgoMcj4QVqXHsb\najtxnOjMTQ4ZHh4eGRnJyspiHSRScLdiJT1jR31+BCabwo6OGcFwP2quNpIo7oDnDn5yTFTY\nkUO4CoOrNogAuM+pri6pLhxxu92bN28GoNXSvhUCkUFhR7MvAuOOGUkvs6OLgRNChR05xGKx\nAMjIyGAdJFIoFNmA+5//vJx1kJOxadOmoqKiX//61wCeeOKJm2++2e+nZ9Z453SWAPv+8Y8c\n1kFOHp2kBTYyshj452efSfiBm97eXo1GEx8fzzqINFBhRw7p7OwFzdgJqKzMDOiGhqS3UUh7\ne/sPfvAD7kqA8+KLL/72t79lGClCJCcnAjM7O6V3zBxEhZ3AlMpM4KIqKW940tPTk5qaqlBI\nvrO2MKiwI4f8/e8ZgMdmm8s6SKQwmbQKhcPjkd5l6GuvvTa+efUvgI+BPADPPPNMKCTtDe7F\nr7w8AcDAgITvfVdWnq5U3pmURP3EBJKXpwNgsQRYBzlJY2OBvr6r9PoLWQeRDMn3HCRh1N2t\nAHQ5OUbWQSKIVts/Oiq9KdLD5urmASuBAIDBwUGXy2UwGBgGk72SkkTAPzIi4R+yxbIWUNJD\n94IpKpJ2i+Ha2v5g8KnBwW9ZB5EM+tUih9jtWgCzZlFvKOHo9cNATHu7xFq3ZWdnj7/MBIJc\nq5KkpCS9Xs8wVSTQaFRKZf/oaALrICcpGAwFAkk6HXVzFw43yzs4KNVZ3urqAQAJCbSJ3fES\nYwlfVVX1wQcf1NbW6vX6srKyq6++OiHhGJ9i//u//1tRUfH991944QVayXH8hoYMQLC8nG6R\nCCcuzt3fj6qq/pwcE+ssJ+C666578skn+/v7gQzACvgBrFu3jhbBCECnG3S7C/3+oFotvSvz\nxsZBIMFgcLIOEkGKihIAn3RneRsbRwCYzUHWQSRDdJ8Ln3322W9+85tvv/02LS1NoVBs3rx5\n3bp17e3tR/9b3d3dKpUq7XtUKpUwseVhdDRBqezT6cRY7suV2ewHUFsrsRm79PT0d999Nz+/\nEEgFLGq1+q677vrFL37BOldEMBhGAHVjoyRbDNfUDACIi6PZF+Go1UqVasDrld5aXk57uxdA\nZiadzY+XuE7hbrf7xRdf1Gq1jzzySG5uLoCNGzc+//zzTz311FNPPXWkyQC/39/X11dWVvbw\nww8LGlde/P6g358UE9MM0I7wwrnooobt23+Ymvo4MId1lhNz+umnv/PO1lNOiYqP99TUdNLU\nuGBOPXXT+++vc7n+BEhv1URT0zCApCSafRFUVtafOjtbgsFXpbi0savLByA7W6q3koUnrn/j\nTz75xO12X3HFFVxVB2DlypUzZsxoaWmpq6s70t/q7e0NhUK0SccU1dX1AxqjcYR1kMgybVoC\nYJVo84mamiEAGRkhquqENGOGF/iqv1+Sx0xLyygA6iAgsPLy3YHA63a7JHs622wqAIWFtH73\neImrsPvyyy8BLF68+PA3Fy1aBGDPnj1H+lvcJuy0re4UjY11AinnnPM+6yCRRdKNBBSKNuCK\nZcuaWQeJLNI9ZpxOZ1/fduAvRUUu1lkii6SbTyiVDcDG6dPjWAeRDBHdig2FQh0dHWq1ekKJ\nlpOTA6Cjo+NIf5Gb7XC5XA8++GBDQwOA3Nzc8847b8mSJTxHlpXu7m7ANm2ahDc+lSLpnqQB\nDA21Au8uXHgR6yCRRaLHzJtvvnnHHXdwk0ZPPx2dkvLwnXfeyTpUpODm1Ht6embOnMk6ywnT\n619VKL6eNcvDOohkiKiw83q9Y2Nj3+8ZEhsbC2B4ePhIf5Er7N555x2TyZSbmzsyMrJ///59\n+/adc845t99+++F/cv369f/+97+518nJyVT5HY7bmYzuaAuM+4FL9FYs9aBjQoqF3c6dO6+/\n/vrR0QPPTHg8nrvuuisnJ+eSSy5hGyxCSHrGrqenJz4+nhpSHz8RFXY+nw9ATMzEGSNuZyyv\n13ukv2i1WlUq1cUXX3zttddyD1i0tLT89re/3bRp0ymnnDLhxi45Eq62oJO0wHQ6XXx8vLRO\n0gdRYceEFAu7Z5555mBVd9Djjz9OhZ0wDs7YsQ5yMnp7e7kbd+Q4sSnsAoHA+vXrD39nzZo1\nBoNBqVR+/5ff7XYDMBqP2A7h/vvvn/BOfn7+9ddf/9hjj23ZsuXwwm7t2rVr167lXr/77rtT\n+C+QIe53Po1WNQsuLS3tmBv6iBN3MUCzvALjmmZK6yQ96VoaiR72UiTdGTun0+lyuejxrBPC\nprALBoMbNmw4/J0f/OAHarXaZDKNjEx8KpN755h7FE8wa9YsAK2trVNLGkG6uroAZGZmsg4S\ncZzOB12u2RbLSEZGLOssJ6arqys2NvYoF12EDxqNRqt9vrJSSnudTDqtm5WVJXySyGQ0pgP/\n+/XX0rsGoxmHk8CmsIuKivrggw++/77ZbB4cHLTZbMnJyQff5AqOI3WMDoVCfr9fqVRO2IuY\n+5LaVh6/7u5u7rYg6yARR61OAfL37WuRXGFnsVjoPiwjZ42MSOka7LbbbnvzzTcnvHnHHXcw\nCROBMjJSgAcaG/exDnLCqLA7CeLa7oS7bbpjx47D3/z222/xvT1QDurv77/88su//3RVdXU1\ngIP74ZFjqq5+Va1+l1pCCY9rPlFff8THg8TJanUODz+jVF7POkgk0uuHAF1Hh2SOmSVLlrz0\n0kt6fTZwFVCs1WoffPDBH/7wh6xzRYq4OJ1CMeR2S6lvIae6eghYajTSGrsTIK7CbsWKFSqV\nasOGDQf3Udy+ffuePXtKSkry8vK4d8bGxpqampqamoLBIICkpKTy8vKOjo433ngjFApxf6az\ns/PFF1/knqhg8h8iOSMjY37/TJVKSnMAssEtUWtslNjOXrt324Brvd5TWQeJRFxLrv37pbTf\n7A033PCzn70BvFFa+nBbW9u9997LOlFk0Wj6/X4z6xQnbPNmE/BlT4/EGvOwJaKnYgGYTKbb\nbrvt2WefvfPOO+fOnTs8PLx///64uLjbbrvt4J/p6+tbt24dgLfeeot7hPbuu+9+6KGH3nrr\nrc8//zwnJ8fhcDQ3N4dCoRtuuOFgOUiOrqLCBmSaTNSZm4GcHA2Azk4/6yAnhms7kZLiYx0k\nEpnN/uZm1NcPr1rFOsqJ6OoKAZg5M5HWwgsvJmbY6823Wl0pKVJq4dDTEwSQn08brJ4AcRV2\nAFasWGEymT755JOKigq9Xn/66aevXr366J8CycnJv//97995553q6uqqqiqj0bhw4cIrrrii\nsLBQsNhSV109CGSazWOsg0QirlWOpJ5xBIDGRjeArCxxzfpHCG7FUXOzm3WQE9PW5gdQWKhj\nHSQSmUzuwUFUVdklVNjt27ePO8jj4mh34hMgusIOwPz58+fPn3+k/zcjI+P7D15oNJqD+5iQ\nk1Bf78T4PUEisLKyOAB2exTrICemrc0HID+fdg1lgJvl7eiQ2HRpT48SQGmpxB4SkofERF9b\nG+rqhs46i3WU4xAMBm+66aaXX34Z2Ajg9tuvGBn52d133806lzTQ1TYBgPb2MQC5uXSSZmDu\nXDOwKiXlWdZBTkxPjwJASQntdcLAqaeqgTsTEnazDnJi+vt1AGbNmnyLA8KrWbP6gacDAWk0\nufnDH/7w8ssvAwBSgDGvt3fdunX/+c9/2KaSCirsCAB0dQFAURGtY2DAZIqOi/tmcHAX6yAn\nxm7XAZg588Q2mCRhMWdOPPCM3y+xwm5oyAiMlZZKaQc+2TjjDDdwN9DAOshxeemll8ZfpgO9\nQAjAX/7yF4aRJIQKOwIAGRn/AM5bsoRukbCRlpYmrUYCAPT695XKx8rKaPaFASl2FQMQClVG\nR3+tUtF5hwFpNZ+w2WwAAAVgAWq++yY5BvoFIwDgcFQCn5SU0CaQbKSnpzudzu+3XREzl+vV\njIxnNRrVsf8oCTe9Xh8bGyutws7r9Xq9P5w37z7WQSJUYmIigMrKSkl8zow/+xgCTgFWcm9O\nmzaNYSQJocKOAIDFYomPj4+OjmYdJEJxF9Nc61VJ8Pv9NpuN2k4wJLlZXovFEgqF6JhhYuPG\njRdccAGAjz76KD09/amnnmKd6Bi+v81hbGzsXXfdxSSM5FBhRwCgu7ubPnAZSk9Ph6TurPX2\n9gYCATpmGEpLSxsaGnK7JbPjCXWjZqWuru7KK6+0WCzcl06nc926dW+//TbbVEe3atWql19+\nOTb2wOqg4uLiDz74gLYwO05U2BEMDQ25XC46STMkuRk77iRBxwxDkltmxxV2dMwI79lnn3W5\nJja2eeSRR5iEOX4//vGPf/Ob3wD44x//WFtbu3z5ctaJJIMKO3KgnqAuywx5vWXA2xs3Smbr\nEK6wS6edD9kJBk8F/t+33zpYBzle3OcMFXbCa2trG3+5GLiZ27/2sDfFi7tumTt3LjUxPyFU\n2JEDJ2m6RcKQwZAK/KCuTjJPJXOzL1lZWayDRC6Xaybwm507JdMtho4ZVg67aL8H+DOQBolc\nlXV0dICOmRNHhR3Bv/8dAt51u09hHSRylZSYAPT1Sab5xFdfGYCfGAy5rINErqwsNca3FpeE\nmhoFUJSRQReQQrv55pt1Oq6NWycAIBvA4R3YRaulxatS6elu0omiwo5g//4o4DKtln55mJk1\nywxgaEgyG0Tv3FkMPBMTQ7fVmCkoiAHQ3R1iHeR4bdt2I1BjNh+t8Tfhw/z5859//nmj0cgV\ndipVzt133/3f//3frHMdW2XlS0CTWi3G3qdiRoUdOdAbqrRUMgu85CcpKUahGHG741gHOV4O\nhx7j9ShhorjYCKCvTzLnPI8nUaXq0+kkE1hOrrvuuubm5ksuOQXAsmVrn3zySfGvWhsd9QcC\nSdHRdtZBpIcKO3KggeP06dQbiqWoqD6fTzJdHFyuOIXCYTZLZopRfqZPTwTgcEhj78mxsUAg\nkBQd3c86SORKSkq64IJZAAYG9KyzHJeKChugiouTwHbKYkOFHcHwsAEIlJdLpqqQJb1+OBQy\nWK0TdyUQJ5/PrNX2sU4R0WpqvgY8g4O6e++9V/ytlvbtswFqk4lO0izNmZMEwGbTsg5yXCoq\n+gEkJ0tmFal4UGFH4PHEq1R26g3F1owZnwPXW60S2Jasrc0B6A2GYdZBItdPf/rTCy64APi/\nYPDNhx56qLi4uK6ujnWoo6ms7AeQlEQnaZZmzDArFF8C+1kHOS51dSMAsrLEfstYhKiwi3R+\nfzAQSNbp6BYJYwsWdAN/HRy0sA5ybBUVdgAJCZLpeSAzX3/99RNPPAEAWAc8BMDhcNxwww1s\nUx1dba0TQGamZB71kKWoKGV29o8CgYndusSpudkHID9fMnsFiAcVdpGuu9sK3Fhe/hHrIJFO\nQo0EHI4e4NWyMlrUzMYnn3zy/Te/+eYbh0O8mxX39Q0CPXl5dJJmLCsrq6+vb3R0lHWQY7PZ\nvECorIye6jthVNhFOru9B3j1lFNaWQeJdNx+oZLoKhYKNQHXnXee2Nd1ydXY2OQ3NH0+n8BJ\njl9KyudA+pVX+lkHiXRZWVmhUOhg31gxy8p6AdCuWEGF3Qmjwi7SUWdukZDQjB0dM2wtWrTo\n+28WFhaazeLdfaazsxN0zIgA18WB++cQuY6ODpUqmJlJG6yeMCrsIh03RSSJ9jLyxv0TSKKw\n4y73qeknKxdffPGqVasmvPl///d/TMIcp66uLoVCQZ8zzEmosOvq6kpPT6fdiU8CFXaRjjpz\niwQ3YyeJW7HcjB0dM6woFIoNGzY8+OCD06ZNA5CTk/PNN9+cddZZrHMdjcViMZvN442tCDNS\nKex8Pp/VaqUusSeHCrtIx82+0JU0cwaDQav9eVXVJayDHJvFYtFqtUlJtPEhMzqd7t577/30\n08+BS9LTb1m8eDHrREcTCoW6u7vpPqwYpKVlA3MqKoKsgxyDxWIJBoN0zJwcKuwiHd1WEwmv\n1xsI3GK3X/fFF18Eg6L+2LVYLOnp6eJvSSR7GRlpwNv79l3KOsgx2Gw2r9dLHzJiEBubDezZ\nuvUc1kGOgbstQDN2J4cKu0hXUXF+VNQv4+Ik06VUlvbt2zdjxgy/vyMUMp5++vmLFi3iPtdE\nyOUas9uXx8efxjoIgVqtVKuto6PifWaC09LSDRho9kUMiosTgNGREbF/4Hd00NM2J48Ku0hn\ns10N/BfrFBHN4/FceeWVjY2NAPfkRNrOnTuvvvpqxrGOYO9eWyj0d7udjhlRMBgGgsEEu13U\nm0V//LEPGGluvoJ1EAKlUhEVJYGLgTfeSAMcdvs81kEkiQq7iOZwjIZC8Xq9ePc1jQRbtmxp\nbGwEAHBPTqQD2Lp1a21tLcNUR7J//yAAs9nLOggBgIQEJ6DYtcvKOsjRNDaOAsjO1rAOQgBA\nrx8MheJF3pa6q0sBmPLz41kHkSQq7CJaZWUfoDAaRf0bLntW68Gz8qHCDmLd+qShwQmAHrYR\nibQ0H4B9+wZZBzmazs4ggGnTYlgHIQCQmOgCsHu3qC8G+vq0AGbMSGAdRJKosIto3OxLSop4\nN6yPBHl5eeMvOwAA2dwXBQUFTPIcXUvLGIDOzobXAAAgAElEQVSCAi3rIAQAcnKUAOrqRH0r\n1mqNAjBzJs2+iEJqqh9AVZWob9QMDRkB/6xZyayDSBIVdhGtsZGbfaHHG1latmzZ0qVLAQD7\ngeeAvQDWrl2bk5PDNtikLJYQgKIiA+sgBABmz9YAn46OivRRG87AQDSAOXPoJC0KBQUA6nt7\n+1kHORqPJ1Glsmk0KtZBJIkKu4jW2uoDQJ252VKpVG+99db5558P1AO3A59ec801f/rTn1jn\nmtz4LRKafRGFlSsNwDkxMZ+yDnI0LleCQjGUkqJnHYQAwOrVLqAkLm4b6yBH5HSOBYPmmBhR\nl55iRoVdRNPpaoFn5s2j22qMZWRkfPTRR52dnVqttrS09NVXXzUaRdr6OhhsA6pmzhT7U3UR\ngpvW7ejoYB3kaHy+eI2mj3UKcoD4m0/s3WsFlHFxTtZBpIoKOx41NDR8+OGHFRUVot1vVqX6\nArjz1FNp9kUUMjMzc3JyuC2jRSsq6tdm85lGI10MiEJsbGxcXJyYC7vBwcFQKGHp0v9hHYQc\nwG0OJ+bCLhhsBzJXrtzCOohUUWHHi8HBwYsuuqi4uPjCCy+cM2fOwoULGxoaWIeahMViUSgU\nXJdSIgY5OTnDw8MOh0jXNYdCoZ6eHmohICpZWVkdHR2hUIh1kMl1dXUBwbw8E+sg5ID4+HiD\nwSDmws5i6QAsZWW0kPckUWHHi5tvvvlf//rXwS937dp1+eWXj46OMow0KYvFkpCQQJ25xUPk\nd9bsdvvo6ChtBy8q2dnZo6OjdruddZDJcT1U6GJAVDIzM8Vc2HHZqJ/YSaPCLvw6Ozs3bNgw\n4c2qqqpPPxXdAufu7m76wBWV7OxsAO3t7ayDTI46C4sQd8yI9mKAK+zoYkBUsrKyRkZGhoaG\nWAeZHB0zU0SFXfgddiWUAfwauID7QmyfvA6Hw+Px0H1YUYmPLwWu2brVwzrI5Lq7uwGk0/bE\nYpKcXAicWlEh0qcTuGOGLgZEJSsrB0ivrxfpLjncBSQVdieNCrvwO+xwTAB+C1zKfcFdWIsH\nXRWJUDA4DXj1iy9EuuMXHTMiZLMtBL7++ONo1kEmx13o0jEjKi0tawHLJ5+I9AKyo6MjKioq\nJSWFdRCposIu/LKzsy+77DIAQBsAIBdAeXn52WefzSzTZHbvdgDX6nQzWAchh8ydmwigt1ek\nqx5ra51AXmoqnaRFpKwsFkBHh0gfnqDZFxHKyVEBqK8XaWHX1dWVnp6uUtHuxCeJCjtevPji\ni6tWrQJGgH4gd+7cuRs2bBDbMwpffgnglYGBOayDkEPmzk0BAoODsayDTG7jxvlAi9ebzzoI\nOWTOnEQAVqtIN6DZuvUxpXJvfDztqSQixcUxANrbA6yDTGJkxGu1fu7zPc46iIRRYceLhISE\nDz/8sLa2VqvtBbK+/np7SUkJ61ATcb/VBQXiKjcjXExMlEpldbtFuv1vf38MgDlzklgHIYeI\n/GJgdDRbraa9TsRl5sw4AD09atZBJrFnjw0oVatpIe/Jo8KORyUlJYmJI0DUvn2iW9e8a9eu\nmhoHgPh4UbcPj0B6fX8waHY4RLc5DgCn0wS4c3PjWAchh0RHq1Uqm8cjxosBm80VCpn0epHu\nyxix5s9PBTAwIMYmb/v29QNITfWxDiJhVNjxKy3NC2DXLhFtMRUMBq+55pr58+d3dwPAL395\nzSOPPMI6FDkkIWEEUOzc2cs6yCS83qSoKBvrFGSimBh7IGB2OsdYB5lo1y4rgIQEunoUl+Rk\nvULhcDrFeH+8rs4FgNZkTgUVdvw65ZQh4A8uVxvrIIc8+eSTf/vb3wAAaYDf5+v+1a9+JcI9\n9iJWSUkfsIHbJEJUBgY8oVCcXi/Sva8iWXJyH1BVW9vDOshE3G2BlBQ/6yBkIq3W5vfHiLBh\nSVubD0BhIa0ROnlU2PFr1SoAd42NVbIOcshf//rX8ZcZQC8QAPDKK6+wS0S+45JLeoErA4E6\n1kEm2rvXBiAuzsU6CJno8ss/AWa5XK2sg0zU0OAGkJmpYB2ETLR8+S9CoZT+/n7WQSbq6lIA\nKC0V6ZpRSRDj2kk5yc3NBdDW1sY4x2EOaz20FTiwkKuvT3SrACMW11VMhM0nmpr6gejkZFr7\nIjpc8yWxbYEOoL3dD6CgQKRP7EaynJwUAJ2dnUlJ4noWym7XAZg9W1yppIVm7PjFFXaiOkkX\nFRWNv1wL3MC9Ki4uZpWHTCDarmIGQy2QsnbtPtZByESi7SqWlfUv4JRzz6XbaqLDXQyIsGNs\nfPzvVarLpk+nwu7kUWHHL6PRGB8fL6oZu/vuu2/COyaTad26dUzCkO/jZuxEeJKmnWZFS7Qn\naZutEdhTWkp9C0VHtMdMX98XWVl71WoqTk4e/ex4l5ub29HREQwGWQc5YMWKFW+88YbJdGBn\nqenTp3/00Ud5eXlsU5GD9Hp9YmKiCGfsuMKOmn6KEDdjJ8KTdFdXl0ajMZvFuBVLhBNnYef1\neu12O5eNnDQq7HiXm5s7NjbW0yOiB9auuuqq2267DcCbb765f//+JUuWsE5EviMnJ6ezszMQ\nENe+8FTYiVZiYqLBYBDhLG9XV1dGRoZCQQ9PiI44C7uurq5QKESF3RRRYcc7o3E+cOO2bSIq\n7AC0trYCmDdvHusgZBJm8yk+34qmJivrIAdUVVVdddVVH3/8MYC///3vPh89PyE6mZkFra3i\nqp+42Re6dy9OmZmZCoWio8PCOsh3cIUmHTNTRIUd74aHTwVe3LLFyzrId7S2tiqVSrowEieb\nbS3w8X/+I4qdCHbs2DFv3ry33nrL4/EAuOeee1avXs06FJnIbv+L272/tVVEPR4sFksoFKKT\ntDjpdDqVqmr79r+xDvIdXV1doMJuyqiw411JiQ5AY6O4Jjnq6/NSUpZqtbQNgRhlZwNAVdUI\n6yAAcPPNN3u9XkABRHPvvPfee//85z/ZpiITJCV5MN7pQSToJC1yGo3C70/1+8Wy/hvjM3Y0\n4zBFVNjxbvbseACdnSrWQQ6x292Dg+vd7j+yDkImV1SkBdDYyL5D1PDwcGUlt712CuAG3uDe\n/+KLLximIt+Xnh4AUFU1zDrIIR9+GAK+GB4+lXUQMjmTaRiIqqoSUcfLjz+eBvxNo8lnHUTa\nqLDj3cKFKQD6+mJYBzlk27YeQJGUJIoJIfJ9M2eaAHR1sf/1VCqV4yvfcwEAB+4Oq9W0t7m4\n5OWpANTXe1gHOaS2FsBpen0q6yBkcmbzKIC9e0VU2DU1ZQFXZ2bSMTMl7M8cspeTY1Iohp3O\nRNZBDtm9ewBAVpa47g6Tg+bNSwZgs0WzDgKDwbBo0SIAQAEAoJl7/+yzz2YViUyqtFQPoL1d\nRLfVuroAoLTUwDoImVxmZghATY2IZnmHhkzAWHk57Y8zJVTYCUGn6x0bSxPPUoaaGg+AoqIo\n1kHI5EpKEgHX8HA86yAA8OKLLxoMhvHCrgXAjTfeuGLFCrapyASzZycA6O3VsA5ySF+fFsCs\nWSK6piWHKyjQAGhqEtGDfV6vWa22qlTier5bcqiwE0JmZhXwbmurWNY1t7SEAMyYQV2WxUuv\nr/T7RdHTvby8vLa2Ni5uDoBTT0194403XnjhBdahyETz56cCoeHhEOsghwwNGYDAjBk0+yJS\nJSUGAOLZyc7hGA0GE/T6QdZBJI8KOyGcf/4XwNq+PlGcpwH09OgALFhAH7jitXTpA4HAOYOD\noviMy8zMDIXygNB77z151VVX0X6zImQ0alNScg2GNayDAIDH4/nb3/7mcsUrlX1Op4h2YCGH\nO+usOKA4J0cs12l79lgBRXy8i3UQyaPCTghc90/xdIz1ei0KRdPcuSmsg5Aj4o4Z8TQWczoT\nlMq+5GQ96yDkiLKzUywWi9/vZxujvr6+rKzsmmt+HAqlBIMdRUVFmzdvZhuJTCo/P02lau7u\nbmEd5IB9+wYApKSw3w1A6qiwE0Jubi7EVNgFAjcUFp5PXZbFjOv+KZLCbnR0NBQqPOWUG1kH\nIUeTnZ0dCATYdi8MhUJr1qxpa2sDFMAlwL2Dg4Nr164VydwzOVxUVFRKSop4uoopFM3ALaed\n1sc6iOTRqV0IXGEnkpP04OCgw+HIy8tjHYQcjahm7FpbW4PBsZKSONZByNFwFwNsO8bW1tbu\n2bMHAOAHPgI+BWCz2TZt2sQwFTmSrKys3t5ekTQJdLsbgBeWLqWn+qaKCjshiGrGjusSy0Ui\noiWqwq6lpQUAXQyIHLdfP9vC7kgzczRjJ05ZWVnMZ3kPslgsoFYl4UCFnRDi4+NNJpNITtJc\nYZefT1t7ixpX2LE9SR/EFXYFBQWsg5CjEcOMXVFRkUo1SZed8vJy4cOQY+IuBkRyN5Y7dKmf\n2NRRYSeQ1NRlra3Tg0H2mxFwhR3NvohcRkaGWp1aX886B4Dxwo4uBkRufF2mhWEGs9l8zz33\nTHhz1apVS5cuZZKHHJ2oCruuri6tVms203YNU0WFnUCGhu4dG9tQWcl+WSgVdpKgUqmAXdXV\nz7MOAlBhJxFGYy7Q/49/XM02xm9/+9v777+fm7fTarU333zz66+/TlvkiJPLNRvY+/e/i2IT\n6c7OzszMTDpUpo4KO4GkpHgA7NxpYx0EVVVjgJkKO/HT6+3BYOLAAPvun83NzTqdLi0tjXUQ\ncjTTpiUBMcPDjJ9xiYqKuu+++1JTU1NSUpxO55///Oe4OHrsRqTS0pKB2S0tjBuWuFyu9evX\n9/f3R0dHM9+vRwaosBMI96zC/v0jjHMA27f/SqFoTUpKYh2EHENiohNQ7NzJuGFJMBiqqflP\nVNSHdCUtckqlIirKOjrK/k7WwIDHYnnGaPy5Wq1mnYUczZw5SQBsNi3DDNu3by8pKbn66p8B\nT1ZVZc6ePVskDxpKFxV2Aikq0gJoaGC89WIwGBobS9Vqu9nGIMcjLc0HoKJigG2MykpbKJQU\nE0MN6CTAYBgIheJ7epxsY3zxhQW4DFjANgY5ppkzzYBvaMjEKoDT6Vy9enVXVxdQCtwNnF5d\nXb1mzZpQiP16dOmiwk4gs2aZAHR2Mv6BV1bagJi4ONp6QALy8pQAamoYN9jZvt0GIC2N/R1h\nckyJiS4Au3YxnuXdsaMfQE4O3VMTO7VaqVbbGM7yfvbZZ+PPcXMPw3YC2LZtW21tLatIMkCF\nnUAWLUoFYLPFsI2xY4cN4wv+iMiVlsYAaGkJsI1RUTEMgNZkSkJ6uh/Avn2Mr9yqq0cBlJez\nvMFHjlN0tD0YTKysZPMEfl/fwQcKswFwhR0Am439enTposJOIAUF8Urlfp+P8bZk+/YNA8jP\np8VSEjB7dgIwOjjIuAqvr/cBmD6d8TUJOR65uSoA9fWMj5mWFgWAU06hZyZEbXh4eM2aNSMj\nNYBi1qxVZ555pvCbrU6bNm38ZSEAoBmAQqEoKioSOImcUGEnnPLytWNja9kuHWhs9AEoLdUx\nzECO0/LlaQpFTFLSo2xjdHZGAZg3L4FtDHI8Lr7YB2Tl5GxmG6O31wBg2bJ0tjHI0d16661v\nvvkm8BhwGmD5/PPPr7jiCoHbi5122mlnnnkmAGAaEOAKu5tuuik9nQ6ek0eFnXByc3M9Hg/b\nGWa73Qm458yJZ5iBHKeYmOikpCTmDUv6+mKB0JIltNeJBJSWpgFdXV2Mj5mhIbNSOZCTw2xJ\nPjmmrq6u9evXAwAqgK+AUQC7du367LPPhIyhVCrffPPNH/zgB0AR0KFWB26//fann35ayAzy\nQ4WdcLj2rNz+wKwkJDwF6M85J5VhBnL8cnJyurq6AgGWy+xiYq42m1clJtKtWAkQQ1cxn88X\nCt2bk/MiwwzkmI60pYjwZ6jk5ORnn30eeDIn5+OhoaE//vGP0dHRAmeQGSrshDPe8IflxXRL\nS0tSUpLRSFtXSEN2drbf7+/uZrY9jcfjsVqrSkoYb59BjpNer09MTGRb2LW2tgYCr5166n6G\nGcgxHeleZ0ZGhsBJALS0NAK/u/DCupgYuoAMAyrshMPN2DHcetHv93d1dVHPCQnJyckB04uB\nlpaWUChEzcQkJDs7u7OzMxgMsgrQ1NQEoLCwkFUAcjzy8/PPO++8CW8WFRWdffbZwodpaGjA\ndx6kIFNChZ1wuMKO4Um6s7PT7/dTYSchXGHHcAKG6xJLx4yEZGdnj42NWa3MtrJrbm4GUFBQ\nwCoAOU6vvPLKaaeddvDL0tLSDRs2MLkN2tjYCCrswocKO+Hk5uYCadXVzNZLcYsn6CQtIdnZ\n2YCyqYnZrViusKOTtFS43W6HwwHgV7/61a5du5hk4Ao7mrETv5SUlK1bt+7YsWPGjBkAtmzZ\nwr0QHhV24UWFnXCSkpIUisYdO37BKgAVdpLj85UAnrffXsgqAFfY0a1YSeju7i4vL9+6NRmo\nfvXVofnz5z/yyCPCx+BuxdLFgCQoFIoFCxZkZt4NbPrkE2Y7NjQ2NkZFRXE3KMjUUWEnKI2m\n2+tNDQbZbGVXV9cNqKmwk5A5c1IATV8fswXFVNhJyC233NLW1gYogTIgH4zm7ZqamoxGY3Jy\nssDjkpNmMBQAZ3/55TCrAE1NTXl5eVFRUawCyAwVdoIymRxATG1tP5PRN2w4E3CrVLSjt2RM\nm5agULiGh5nt4P/5579UqT5KSUlhFYAcp9HR0Y0bNwIYb8rEdd7E+++/L2QMny/Y1LQuPv4n\nQg5KpmjOHD2A2lo2y4QaGvqGhm5NTLyYyeiyRIWdoLgmrd9+y2Zdc3+/EVDNn087zUqJRtPr\n9bLZdzAYDLlcc6OiihQK6kEndh6PZ3y/wzYAwIE7oS6XS8gYO3f2BAI3BwIXCDkomaJly1IB\ntLez6e27eXMP8LvR0VVMRpclKuwElZ0dAlBZyWbG2+VKUalsRiN15pYSo9EB6BsbB4Qfeu9e\nKxAdF8dmgpmckPj4eO65e6AbGAaKuffnzJkjZIyvv7YCyMoaFXJQMkULF6YBnv5+Nm0Dd+0a\nBlBcTFePYUOFnaCKijQAGhrGhB96YMATDJoNBpYNzchJMJvdAHbtYvAPt327DUB6Op2kpeGw\nRkz1QD6gXbRo0Zo1a4TMsG+fC0BRkUrIQckUqdVKnc4yOprl9zPY/rC21g9gzhyD8EPLFRV2\ngpo1ywSM2O0MZuy2besBFImJI8IPTaYiLW0MwKZNdV6vV+Ch9+0bBpCfT1fS0nDxxRf/85//\nnDt3rkJRB6hXrvzJhx9+qFarhczQ2BgEMGeOUchBydQlJtqB6G+/7RF+6M5OHYClS2khb9hQ\nYSeo8883A0az+c/CD71rVz+ArCy/8EOTk/b0009//fUawPTKK5fm5+d/8MEHQo7e0OAHMH06\nNfmRjIsuumj37t233+4CTrnuuiWJiYkCB+jq0gE49VR6JFZizjhjP3Bpf3+98EP39ycAowsW\n0OLvsKHCTlBms1mv1zPpKlZf7wRQWEi3SCRjw4YNd9999+ioDRgG0N3dfdVVV1VVVQkWoLMz\nCsC8eWxW3pCTtnx5MrCnsbFa+KEHBuIBz9y5NPsiMWeeqQbe7+ioFXjcYDA0Opqp1Xap1VSN\nhA39KIWWk5PDpLBLT/8IiL3ySjZb6JGT8Pvf/37CO263+5lnnhEsQFzc0wrFxUuXTt4snIhW\nSUkJgPp6oWdfQqFQMPh/yckvq1R0ZpGYoqIijHeAEFJrazfwYn7+ToHHlTf69RNabm6uy+Xq\n6+sTeNy2tjbAWVpKW3tLxqQXAFz7EGF0d3+VkbHHZNIJNiIJi8LCQrVaXVsr9OxLb2/v2NhT\nixZtEnhcMnXFxcVgcTHQ0dEA3HXBBXsFHlfeqLATGrclgfCTdi0tLVFRURkZGQKPS05aevok\nU2WZmZnCjO5yuaxWKzWGkiKNRpOfn19fXx8KCTpDzzUTo46fUpSUlJSQkNDQ0CDwuNyI3Hwh\nCRcq7ITGtcNrb28XeNzW1tbs7GyVitbYScZtt9024R2dTnfLLbcIM3pLS0soFKJmYhJVUlIy\nMjLS3d0t5KDUJVbSioqK2traRkcF3d6Iu/lLFwPhRYWd0LKysoCorVv3jI0Jt5vd0NCQw+Gg\nLrHScuONN/785z/XaDTclzEx+ueee27RokXCjE5dYiWNW2ZXV1cn5KDNzc2gwk6yioqKgsEg\n94svGCrs+ECFnaA2btz4k5+8DHiefTYzIyPjtddeE2Zc7neVCjvJefTRRxsbGy+//Fmge/Hi\n966//nrBhuZO0lTYSZRKtQz44tVXBR2Um7ErLCwUdFQSJhrNmcD6994TtMlNY2OjXq9PS6O9\nTsKJCjvhVFdXX3HFFf39+wEVkG2326+99tpPP/1UgKG5FfdU2ElRdnb2mjXnAGltbdFCjssd\nM1TYSVRubjZw2r59UUIO2tzcrFars7OzhRyUhEtsbBGw5uuvA4KNyE0QTps2jbpRhxcVdsJ5\n4okn3G43YAPcwIEa66GHHhJg6FdeSQR6nc7FAoxFwu7ss3MAf2+voJvNfvTRWcBb6el0W02S\nzjknG0BXl6BtmqqrL01OvlHgXhckXBYvTgDQ3CzcOuyKii6v90cpKWcKNmKEoN9A4YyvXQgB\nbUAuoABC3A0vvrW3K4CUggK7AGORsIuN1Wg0LW53TjAYUioFurS1WGYqFCnZ2XphhiPhlZtr\nUir7hoaEu8PV2Djg8fyPXr9LsBFJeJ1xRhYQ6u0Vrh3cpk19wIsjI/8RbMQIQTN2wklJObgb\nex2gB3IApKamCjB0T080gEWLaDt4qUpK6guFYrZtE+ghR78/ODaWodNZhBmO8MFo7A4EUi0W\ngdpDf/llD4CMDLcww5GwS0qKUal6nE7hNiTfs8cJoKxM0AUDkYAKO+HcdNNN4y/3AwBmALj5\n5psFGHp4OBFwlZQI3TiShEt+vgfA5s0CFXa7d/cC2vj4QWGGI3xITx8GFFu2dAkz3K5dDgD5\n+dTbRsJMJmswmNTePiTMcFyfi3nzhJsjjBBU2AlnxYoVjz/+uE6nA/YDISDnrrvuOqza48XI\nyMgbb7zp9aZGRVn8fh+vYxH+zJoVBWDnToGmQ3bs6AOQni7ojlYkvIqKggC++Uaghxzr6nwA\nZsyIEWY4wof09BEAgl0MWCwxAJYto6aFYUaFnaDuueeehoaGRx9dBhgvuKDtqaee4nW4bdu2\nlZSUrF27Dojx+RpmzpzJ7UdAJGf1aiMwzWz+mzDD7ds3AqCggB5Vk7CLLgoB5+n1XwgzXHt7\nFICFC+m2gISdcYYVuMXtFqgZncNhViiGSkvpmAkzKuyElpWVdc89t0VHB/juKuZ0OlevXt3d\n3T3+BG5rfX39mjVrBO4yRMJi/vxpKlVrbW2VMMM1NPgBTJ9Osy8Stnx5DvBJe/seYYbr6zMB\nwaVLqWmhhJ13nh54wWbbL8BYbrfP58uIiaGFvOFHhR0DKpWqtLS0vr6e1+YTW7Zs6ezsBADs\nAgqA3wPYuXNnVZVAxQEJI51Ol5+fX11dLUxdbjJtBG4466x4AcYiPMnJyYmJiRGs+UQw+IHB\n8JbRqBVmOMIHrmerMB1ja2o6gA15efUCjBVpqLBjY8aMGT6fr76ex2Pabj+4uYkPaAG4Ig99\nfX38DUr4U1ZW5nQ6x4t1fvX3f6FUvjJ3bqYAYxGeKJXKoqKixsZGv9/P91gjIyMu168XLPgL\n3wMRXuXl5Wk0Gl5PTAdZrXXAmssv3yfAWJGGCjs2pk+fDoDXyTPu2msChUJRXFzM36CEP2Vl\nZQBqa4VY/tLc3JyZmanV0uyLtBUXF3u9Xr5XfYCaicmFSqXKz89vaGgQ4M4ANy846XmKTBEV\ndmzMmDEDPBd2S5YsOffccye8ecstt2Rk0CIYSeIKu+rqar4HcjqdfX191MpdBkpKSiDIxQBX\n2NExIwPFxcUul6u7m/edlRobGwFMmzaN74EiEBV2bHCF3d69PD6jqlAoXn/99dWrV3NfajSa\nu+6668knn+RvRMIrAWbs+vr6brjhBq4h9969e99++23+xiIC4Ao7AZbZcR10aMZOBgRbZscV\ndnTM8IEKOzbS09NVqvpPP32C11GSkpJuvfVWAD/60Y9GRkaeeuqp6GhBG8mTMCosLAF2v/PO\nrTx9f5/Pd+GFF7788stOpxOAw+H44Q9/+NZbb/E0HBGA2TwD2PDmm7yvvqDCTjYMhvnAk//4\nh4fvgRobG81mc3w8PaEVflTYMWMwePz+DL4b/uzbtw/A8uXLNRoNrwMRvhmNMVFR5uHhgmCQ\nl+Uvb7/99o4dOya8uW7dumAwyMdwRABz5uQDlzY35/E9EHcrNj8/n++BCN/i44uAu7dt47dJ\ntNfr7ezspPuwPKHCjpmcnCFA8dFH7byO8vzzM4HK5OT5vI5ChJGQ0BMKGffutfLxzSsrK8df\n/jfwDJAMoKenx2az8TEcEUBCQrRabRkZ4f3p5srK+QkJlxsMBr4HInw7/fQ0AJ2d/O5huXNn\nWzB4YUbGAl5HiVhU2DFTXg4AX33FbzvO9vZkoHTpUrowkoO8PB47xsbGxo6//BFwG+AGoFQq\n6WwtaXFx1lAovq6un78hHI7RgYFHAoH/5W8IIpiZM5MVimGHw8zrKB995ADeHxy8mNdRIhYV\ndswsWxYPoLKSx6fKR0f9bneeTtcaF6fjbxQimOnTVQB27ODl9v0ll1yi0+kAFTATqAecAFau\nXEmFnaRlZ7sAbN7M4/7+W7d2AUqzeZi/IYiQYmIsY2OZbjePvcUrKz0AZsygExMvqLBjZtWq\nHCDU0WHkb4h//7sN0KWl0a00mTjttAQAdXW8/NrOmDHj0UcfVaunA3pgN4CCgoIXXniBj7GI\nYEpKFAB27Bji45vb7fbbb7/9xz9+CMDw8F7BulwQXpnNg4D6q694vBhoaVEBWLiQnpzgBRV2\nzGRlGdXq7uFhHvsff/qpFUBZGe/7zhNhnHdeDhC0WOJ4+v533HHHJZf8PwD5+UMvvfRSVVVV\neno6T2MRYSxcaARQWxv+OwNut3vZskErvRIAACAASURBVGXPPffc4GA8AJvtm/nz5wvTjYrw\nKjd3DMCXX/I4I9DbGwuETj+dNlXlBRV2LC1f/otAIKe3t5en779rlw/A0qWxx/yTRBKSk/Vp\naZcqlTwuTGlqMgG4886lN9xwg05HN0okb+XKdOBag2F92L/z008/Pb6rIrfLSbPT6Vy3bl3Y\nByICO+MMH3Cvz8fjXugjI6kqlS01lZZ58IIKO5bmzcsCQoc9jRhmzc2xAC66KJun70+EN2eO\n3+Fo7enp4en7t7bGA8HLL6d9K2Ri2rTk+Ph/dXZuDvt3PmxzHO7ZrCYA27dvD/tARGAXXJAI\nPDQ4yNc/pdXqCgRSYmP5mtEgVNixxHfHWJXqyqSk88vKknj6/kR4XP+JmpoaPr55MBgcG/tN\ncvLvMjJollc+SkpK2traPJ4wbzl7WCvhncC/gcHvvkmkatq0aQqFgr+76hUVbcCWnBwe1/BF\nOCrsWOK1sOvp6bHZWufTBnbywmthV19f7/F8sGIF761FiZBKSkqCwSDXwSmMzj///PGXvwZW\ncq9WrVoV3lGI8GJjY1NTU/kr7NrbvwZWrFnDY6v0CEeFHUslJSVRUVH79+/n45tzPSdmz57N\nxzcnrPDaMXb37t0A5s6dy8c3J6wUFxeDh46x11577cUXf2e5Z1FR0WOPPRbeUQgTRUVF3d3d\nIyNh3lnpww8/zM/Pv+WWWwA899xzX375ZXi/P+FQYceSVqudNm1aTU0NH12bKioqAMyaNSvs\n35kwVF5erlAoqqt5Wde8Z88eAKeccgof35ywUlJSAh4KO4VC8d5775177rkATj/99CeeeKKi\noiIujq9HtomQuIuB8M7y7tix48orr2xtbeW+7OjoOP/88+kxaj5QYcfYjBkz3G5ffX1r2L8z\nN2NHhZ3MGAyGzMxMngq73bt3KxQKmuWVGa6wq6+vD/t3VigUQ0NDSqXygw8+WLduXXR0dNiH\nIEwUFhYC2Lp1q98ftt2yHnjggdHR0cPfcTqdjzzySLi+PzmICjvGPJ5rAOdf/9oX9u9cWVn5\n/9u784Coqv5/4O+ZYUl2VHBBlEURWdwxdzOXXPJRwf2be/mkWZr5/Wo9qeXK76m0lMweDTPD\nJTULF1JxQxNEQRBkURFFFBRFBUG2mfv7Y5J4ABWGC3O98379NXO49/QpLqf33Dn3HDMzM+6y\nLD/Gxv++f/9CQsI9cbvVaDQxMTGurq686SIzrq6uKtW6Q4f8RO+5uLg4JiamTZs2Vla1uNA6\n1bHDhw9/+WUQ8PG8eSccHR23b98uSreV3pzjota1gcFOzzw9GwAm588XvPjQ6njy5ElycrK3\nt7dKpRK3Z9I7W9umgOPhw+nidnvlypWcnJzOnTuL2y3pnZGRkZHR4Pv3B5WUiDzlIzY2tqCg\n4NVXXxW3W9KjhISEkSNH3r1bCKwExmRmZk6YMOH48eM177l+/foVGxs25KIN4mOw07M33mgK\n4MoVkdcI2Lnzmlp9W6n8SNxuSQq8vBQAIiJE3przhx8ygG12dkNefCi9bBo0yALMIiNFXv5w\n27Y04H88PXuJ2y3p0Zdffpmfnw+kACWAm7Zx+fLlNe958uTJAAAvoDtg+t+NJCYGOz3r1auZ\nQpF3924jcbs9fjwbsHd0FLlbkoIePWwBXLok8iZRx48rgPHNm3uJ2y1JgZNTAYATJ0ReEjY4\n2B74uUmTbuJ2S3p09epVAEAxkAK4a0PC08YamTVr1tSpU4E5wJ9AewALFizw8xN/hgAx2OmZ\nUqkwM7teVNQiO1vM5UMvXBAAvP46t1iWocGDWwDCrVvW4nZ79ao1IIwc6SRutyQFXl7GAM6f\nfyxutzdvNgWeDB/OfUrkw97e/unLc4Al4A2gUSMR7hEoFIrAwEBj477Ak6++eisuLo5PTtQS\nBjv9c3DIBlSHDt0Qsc8bN2wB4c03nUTskySiWTNLlerOo0di7p+t0QiPHjkbG990deWHAbl5\n/PjxrVtHAezbd3no0KHnzp0TpdubN3OKipysrK6YmRmL0iFJwfTp05++1C4y1xvA22+/LUrn\nV65kFxe7WFsnz5s3W7s+P9UGBjv98/TUAPjzz7tidajRCLm5TsbGN7kxlFzZ2NzSaOwuX84W\nq8Njx24IgnWjRtzkR27UavWQIUMOHPgKQElJq4MHD/bu3Ts8PLzmPW/bdgVQtmr1sOZdkXQM\nHjx4xYoVpqamQBgAoPfs2bNnzJghSudbt14FFB4evGZqF4Od/k2dCsDazGy/WB2ePJkmCJb2\n9rfF6pCkZtCgo0Dr27dFW81u//4MAF5eRWJ1SBLx888/nzp1CsgBZgPLARQUFMyaNavmPR89\nmgugZ0/erpObTz75JDExMTBwgYnJekvL4HXr1onV85Ej+QAGDDAXq0OqFIOd/r36ahsgR8SN\nxf74IwNA69YiL6FC0tGnjy1wOSlJtGAXEVEEoE8fC7E6JIk4e/bs05ffAke1r2JiYsotFauD\nuDgzAL6+zWrYD0mQs7Pz1KlThg07lpu7VcTNIRITbQFh0qSWYnVIlWKw0z97e/tGjRrFx4u2\nI/Irr/wBuE6eXCxWhyQ12h1jExISxOpQofgReHfMGGexOiSJMDWtZCklIyMjIyOjGvacnx/8\nyit7e/d2rGE/JFm9evUCEBYWJkpvRUVFeXkRlpYnOZG3tjHYSYKXl1d6enp2tjhTpi5ciAau\n9e3rLkpvJDX5+fl79+4FEBAQ4OnpuWXLFkGo0dIngiBcvhzs5HTIxaWSFUTppTZkSCULEw4c\nOLCGwe7GjRs5OSsGDNhck05I4rTB7tSpU6L0FhUVVVLy7rhx20TpjZ6DwU4StM8HibUBaGxs\nbP369R0d+UlaniZPnvzVV18BEAQhISFhypQpAQEBNekwNTU1Ozu7U6dOIhVIEjJgwICZM2eW\nbWncuPGGDRtq2G1kZCSALl261LAfkrJ27drZ2NiIFez+/PNPAD169BClN3oOBjtJ8Pb2BiDK\nNLuHDx/evHmzXbt2Ne+KJCgsLGz37t3lGhcuXJiXl6dzn1FRUQA6duxYo8pIqtavXx8cHDx9\n+vSGDRsqFIoTJ07U/FMfg50hUKlU3bp1S01NTUtLq3lvDHZ1hsFOErTBLjY2ueZdxcTECILA\nYCdXMTEx/92gAJCfn5+crPvFEx0dDQY7WRs2bNimTZs+/PBDQRDOnz9f8w4jIyMVCoWPj0/N\nuyIp6927N0T6NjY8PNze3t7V1bXmXdHzMdhJgpOTG3Drp58mHjhwoLCwsCZdxcbGAmCwkytz\n89KVAryAKOAz7RsLC90faGWwMxCdOr0B7Fq1yq6G/ajV6qioqFatWtnacha8zLVv3xf4et26\nmi5QcvXq1Tt37nTv3l2hUIhSGD0Hg53+paSkvP56T+BJQUHLN98c5uXldfHiRZ17i4mJBdCh\nQwfxCiQJGThw4NNslw60BYYC8PLyatWqlc59RkdHN2/evMxWQiRPffu2VygGJCXVdMX/+Pj4\nvLy8V199VZSqSMp69uwAzIiJaVvDfk6fPg2gZ8+eYhRFL8Bgp2cajWb8+PGXLl0C4gAboNnV\nq1fHjBmj8ypTv/zyoUIR6e7eRtw6SSIcHR3Xr19vamoKPATOAB2trd2DgoJ0/hx85kz6vXs7\n7ew+ErdOkiATE1XjxklqddNDh67XpJ8tW24C89zc+opUF0mXhYWJjU1yYaFzYuK9mvTz88+F\nwMguXXqJVRg9B4OdnsXGxj7dulG7jp0XgOTkZN2WDsrPL87Pd3vlFWtTUxPxaiRpmTRpUkxM\nzKJFi5o1uwgohg4NaNtW98/Te/emA6/b2tb0Ezm9FHr0KATw4483a9LJgQPWwFeOjrxjZxDa\ntn0IKLZsSalJJ6dPDwC2dejQXqyq6DkY7PTs7t3SLWIvAAC6a9/cuXOnul2VlJQEBp4BTJs0\nEW3bWZImd3f3pUuXrls3CMDJkzWa/hIeXgigZ09u8mMQpkxpBiAsrEb7gKWlNQIKR47kLHiD\nMHSoFYDDh3XfqiQl5UFhobOV1WULC95xqAsMdnpW5hGhI0ABMFr7prpTpnbs2OHo6Pj++4EA\nMjJCDh48KGaVJEkjRrQ0Mrp1+7bn48e67/GanGwBYOTIFuLVRdI1dKiLSpWRkdGmqEitWw93\n7uQVFLhaWFy1sqpkTwuSnylTWgPFycm6z8H96aergMLDQ5wV+OmFGOz0rGXLluPHjwcA5AKh\ngA1g/8Ybb1RrYnJYWNj48eMzMzOBtgCePDk7atQo7eOxJG+tW6cIgklQkO6/6+xsJ5Uqs21b\nPjlhKFq0SBEE6507dVwfZ8eOK4DK1fW+uFWRZNnbm1tYXM7Pd0tPz9GthyNH8gAMGMCvBeoI\ng53+bdiwYerUqUqlEpgGOHh62m3durVac+FXrlz59KV2lZPYJ0+efPHFF6KXSlKzYEEh0PDK\nlZ26nR4RcUujaWBnJ8Lqo/SyeOede4DHrVvBup1++PBDAN2713SrWXqJDBoUDgyOiYnQ7fSE\nBBtAeOstF3GromdhsNM/KyurwMDA7OzsEyd2GRsrlUqlnV31FppKSdFOa1UArYA04B6Aq1ev\n1kKxJC1+fj3q1VMfOHBAh3OvX7/+xRfHALRqlSt2XSRdEyf6AIlHjx7V7fTYWFMAI0Y0FbUo\nkrRJkxoBR86cOaHDuXl5xY8etTYxSXVzayBuVfQsDHZSYW1t3adPn379+sXFxSUlJVXr3Kcr\nkAmAR+ksvUaNGoldI0mOmZnZa6+9lpSUVK0cr1arZ86c6ezs/Ouvc4ERFy/+i5MyDYeDg4O7\nu/vp06efPHmiw+kFBVtfeeWH/v05KdOA9OzZU6lU6rb/RERELLC6Q4co0auiZ2GwkxY/Pz8A\nv/76a7XOeuedd56+zAciKzSSnA0ZMgRASEhI1U/x9/d/ug18NvD7o0dnx40bd+3atdopkCSn\nf//+BQUF4eHh1T0xIyPj/v3vXnttt1LJ/QMMiK2traen57lz53T4MBAbGwZ8+u67um9mTdXF\nYCctI0eONDIy2rNnT7XOmjJlyqxZs0rfmpqaLl++/M033xS7OpKioUOHAqjWLbe1a9eWa8nN\nzQ0MDBSzLJKwfv36AdDh29izZ88C6NKli/g1kbT17t27sLAwMjKyuif++eefAHr06FELRVHl\nGOykpUGDBn369ImOjq7u7RM3NzcAo0aN2rJlS2Ji4r/+9a/aKZAkx9nZ2cPD48SJE3l5VfpM\nXFxcXGb1xL+lp6eLXRpJ1GuvvaZSqUJDQ6t+yvnz58eMGTNjxgwAeXl5giDUWnUkRb179wag\nw7exZ86csbOza9myZS0URZVjsJMcX99RQG9//3PVOmvLli1KpXLNmjWTJk1ydnaupdpImoYM\nGVJQ0CIo6GxVDjY2Nm7SpEnFdicnJ5HLIqmysbHp1KlTVFRiZuaDqhx/8OBBHx+fXbt2ZWVl\nAfjqq6/mzZtXyzWStOgW7FJSUjIzM3v06KHznoekAwY7yenVyw84tm1bNTbqjo+Pv3DhwsCB\nA5s1a1Z7hZFkNW8+Hkj65puqbicwf/78ci22trbTp08Xuy6SroYNP1ars77++vILj1Sr1W+/\n/Xa5xq+//joqitPhDUjjxo1tbDaGhn775Elx1c/i97B6wWAnOd7edjY28Xl5HhERt6t4yqJF\nZwHPyZMn12phJFnTp3srFDnJyS01mip9Qfbhhx/6+Iwr/fN3dnbes2ePo6NjbdZI0tKvnxNg\nevBg4QuPvHLlSkZGRsV23fazppeXvb27RtNyx44XfxgAEB8f/8EHHyxZsgSAg4NDLZdG/4XB\nTor6938EKPz9r1Tl4IKCkuDgfygUfw4aNLy2CyNpMjMzdnC4pFY3+fXXKl0zT56UREevVipj\n9+zZe+7cuaSkpL59+9Z2kSQp06a5A0+Sk198j7/Ml2jrAX/ATPtGqeT/PgxL794A8NNPqbm5\nL1j5cufOnZ06dVq3bt316zOB6VOnTg0O1nFBbNIB/zKlaNEid0Bz/Hj9qhy8YkW0RmPXps1F\nG5t6tV0YSdYbb6gBbNpUpbu8CxeeU6ubuLs/8PUd0blzZxMT7sxtcGxsXmnQILGoyOXcuUru\nxpXVqlWr5s2bA27AO8BI4K+NiflhwKDk5uZmZPwC4MQJtbW19cSJE7OzK9/79cGDB//85z+L\niooAG2A+MKWwsHDatGmPHz+u25INF4OdFLVta29peSknxzMqKvOFB//4owbABx/Y1H5dJF1z\n57YGNOHhVfow8OOPVoCwcmUlj1CQ4ejSJRfAf/6T8vzDlEplYGCgQrECMAIWASUAPv7447Zt\n29ZFlSQNM2fOPHBgHXAL6CUIip9//nny5MmVPhx9+vTpR48eAQB6AkrgDID79+9r18qhOsBg\nJ1Gvv54NKP39XzCbISXlQXp6BxOT6zNmVONhC5IfLy87c/OknBzPlJQXPOe4efOl3FyvBg0u\nDB/OBQgMmq+vFYBffslevHhxXFzcc468d6+ZIPipVPFdu6b7+fnt3bu3zP7UJH9XrlwJCgoC\nAIQB9QEPAPv37z9//nzFg4uKtPd0jYClAIC/NjwsLHzxhE4SBYOdRC1Y0BJYn5r68/MP++ST\nOMC0T5/rfJicundPB/YeOHD6+YctXfoIwHvvqeukKJKoa9euffbZcOBeTo7psmXLfHx8vv32\n22cdPGdODqD4+OP88PA/d+/ePWLEiLoslfSuzI6FYYAAtNa+uXy5klsPnTt3BgD8L9AB2AaE\nATA2Nu7UqVNd1EoMdpLVrZtDly5bYmIC79y585zDDhxoCGiWLXOrs8JIslassAVGb9++cufO\nnbdvVz7Z7sKFO9evdzY2vr5oEQdZgzZt2rRbt24CTsAgAIWFhfPnz09MTKx45MaN8Xfu+FhY\nxC9bxg0nDJSdnd3Tl9uBfwB/7Y1U6Y7kLVq0mDDhM2AxcA+Yo21cunQpty+vMwx20uXn56dW\nq/fu3fusAxISEvLyFrq4/Pzqq03rsjCSILVa/c033wCIiIgYN26cm5vbpk2bKh72009bgE2j\nRl0zMuLfvuG6d+/eyZMnAQB/71ZSUFCwb9++igd/++12IH3JkmqsXkYy07Fjx/bt2wMAHgH7\ntY0uLi69evWqeLBGo7lwQQNoGjVaaW+v7Nq1a1BQ0IIFC+qwXkMn6cH9yJEjN2/e1HcVeuPn\n5wfA39//o48++u233ypOU92yZQuw7/PPJf1LpLqxatWqp5NgACAvL+/9998vNwOmoKAgKOhL\nK6uPv//ep84LJAl51u5zFZ9bPHbsWGzsyj59ps+f36H26yKJUiqVO3bsaN26ddlGa2trlUpV\n8eDvv/8+MXFpv34zMzNX37lzJzw8fMKECZwsVJekmwlu3ry5bt26pKSkKh4fHx+/cuXKiRMn\nvvvuu2vXrn3Wk9gvC41G89FHHwG4cePG6tWrR44c+eabb6rVf82LysvLi4+P37p1q6Wlpa+v\nr14rJUmoeH+uoKBg8+bNZVu2b9+elZU1bdo0S0vLOiyNJKdZs2YNGzas2N6hQ/n09umnnwJY\nuXJJXZRFEta6deuLFy8GBwevWbNmz549Q4cOvXDhgnb94bLS09MXLlxoaWm5efNyvdRJkGyw\nU6vVgYGBVT/+6NGjixYtioyMbNKkiUKhCA0NnTdv3o0bN2qvwtoWEBDw+++/l205ePDg6tWr\nHz58OG3aNCsrK29v74yMjKZNm5amPTJklc3FtM/M/Gu5nOLi4oyMjLVr1yqVytmzZ9dxbSQ1\nKpVq9erV5RoVCsW9e4/Ktuzfvz88PHzo0KHdu3evw+pIokxMTIYNGzZ37lxfX9+ffvqpefPm\n/v7+hw8fLnvMzJkzc3Jy/P39uZONHhnpu4DyTp06lZCQEBERcf/+/Sqekp+fv3HjRlNTU39/\nf+1G5iEhId99992aNWvWrFnzkt4B/vXXX8u8WwLcAS7s3LkvIiKi7I+Sk5NnzJixffv2uq+Q\nJMXV1fXSpUtlGnoDhyIifrl4MXXVqk/27NlTXFwMwNPT08XFRV9FknRMnDjRxMRk+fLlSUlJ\n9evX79u3b0hIxIwZ7UJDw1avdj158uSjR4++/vprhUKxbNkyfRdLklO/fv3t27e/9toAX9+4\n06fbNW2qAHD06NH9+/f37Nnz3Xff1XeBBk1ywe6XX36p7p22Q4cO5efnT5o0SZvqAAwePPj0\n6dNxcXFJSUlt2rQRv8raV2ayiynwL8AYQFSUOioqGfAFLgAbgRwAO3bsWLx48Uv6r0li+fTT\nT8ePH1+mwRLIvn17Urt2GcArwF+3dS9durR69Wrtt/xk4MaOHTt27Fi1Wq2dKbVrV+K4cY1+\n+cV2z56P1er/pz2mWbNmXl5cI5Mq0b179xEjdu3aNaRz5zC1+nVArVKpTExMNm7cyO3m9Ety\n//W/+eab33777bfffpswYUIVTzl16hSAbt26lW3s2rUrgOjoaNErrBvt2rV7+rIE6Am8C3yv\nUsUAzsD/AP8G/n6WIjk5WS9FknSMGzdu7dq11tbW2rceHqnBwclubnsAW2AzEAF01f5oyZIl\nBQUF+quUpKV0/vvo0W2++OIccEet9gc+A1oBSE9P5x07epZPP3VUKP5Qq3sDi4BZanVDQRC4\nELHeSS7YKcuoyvGCIKSlpRkZGTk4OJRtb9GiBYC0tLSyjY8ePbr11JMnT0QsW3RLliyxtbUF\nAKiBSOB7a+sFW7deBqwAb2AU8Pc2zPb29vqqk6Tj/fffv3v3bkxMTEpKSnx8/LBhfd96KwHw\nAPYAPsAhwAdAXl5eub8LIq20tBCgN3ATWAIkANMBbNy4Ud91kUStWrVSECYBt4BFwLfAtuLi\nYn4S0DvJBbvqKiwsLCoqqviUn7YlJyenbOMPP/ww/KmnyzhJVPPmzU+ePDlo0CAzM7N69eoN\nHDjw5MmTfn5+rVu7AvHA34vbeXp6dunChUMJAExMTNq1a+fi4qKdWmprawukAqOAvkAAcBGA\nQqF4+pmB6L/cvXsXuAL0Bq4BauA4gKysrEq3BCVKTk4GsoAJgACUAP8HoOprWVAtkdwcu+rS\nTgk3MzMr125ubo4Km9P5+PgYGxtrX+fm5kLavL29Q0JCNBqNIAilX5fs3LlzxIgR169f1751\ndXXduXOnkdFL/3uk2jB8+PBPPvkkNzcXOAGc0DYOHDiwzDryRH9r2VK7ffB1wAdoBFzTNr6k\nj6BRbatfvz4AIAwYAyiBKACVrqRDdUk/gUCtVpddTBXAhAkTdEsnFhYWSqWy4pyh/Px8AFZW\nVmUbe/XqVbpS9p49e3T4x9W9cl9Jt2vXLiEhISQk5Pr1687OzkOGDDE1NdVXbSRxjo6OP/zw\nw9SpU0sXpG3Tpk21FhIigzJz5swNGzZkZWUB2cBfS4EuXrxYv1WRZE2ePPno0aMAgF/LNuqr\nHtLST7DTaDS7d+8u2zJmzBjdgp1CobC2tq54+03b8vTzhKzUq1ePixJTFY0ePbp79+779u27\nffu2t7f3iBEjSm9aE5XTpEmT/fv3z5gxIzY2FoCNjc2yZcuq/hwbGZqJEydGRkYGBASUtsyc\nOXPq1Kl6LImgr2BnbGwcHBwsVm92dnYPHjy4e/du2WcI0tPTwXvCRICDgwOXlaIq6tKlS0xM\nTFpa2uPHj1u1asWPAfR869atmz59+smTJwVB6NOnT8XNS6juyWFuVrdu3S5fvnz27Nlhw4aV\nNkZGRqLCGihERPRCzZs313cJ9NJo3759+/bt9V0F/e3leyq2qKjo6tWrV69e1Wg02pb+/fur\nVKrdu3ffu3dP2xIREREdHe3u7u7s7Ky/SomIiIjq1Mt3xy4rK2vevHkAduzYoX0Y1tra+r33\n3gsICJgzZ07Hjh1zcnLi4uJsbGzee+89fRdLREREVHdevmBXqf79+1tbWx86dCgmJsbc3LxP\nnz5jx45t3LixvusiIiIiqjvSDXZjxowZM2ZMxXYHB4dKH7zw8fHx8fGp/bqIiIiIJOrlm2NH\nRERERJVisCMiIiKSCQY7IiIiIplgsCMiIiKSCQY7IiIiIplgsCMiIiKSCQY7IiIiIplgsCMi\nIiKSCQY7IiIiIplgsCMiIiKSCQY7IiIiIplgsCMiIiKSCQY7IiIiIplgsCMiIiKSCQY7IiIi\nIplgsCMiIiKSCQY7IiIiIplgsCMiIiKSCQY7IiIiIplgsCMiIiKSCQY7IiIiIplgsCMiIiKS\nCQY7IiIiIplgsCMiIiKSCSN9F6BPFy5c0HcJRERERNWQm5v7nJ8a7h27jh07dujQQazeoqOj\nQ0NDxeqNZC8sLCwiIkLfVdDLQRCEkJCQ+Ph4fRdCL4fc3NyQkJDU1FR9F0K1xdLScvTo0c/6\nqeHesXN2dnZ2dhart/Dw8KSkpPXr14vVIclbUFCQjY2Nr6+vvguhl4BGo1m1alWLFi14wVBV\nXLt2bd26dX369OEFY5gM944dERERkcww2BERERHJhOqzzz7Tdw1yUFJS0rx5806dOum7EHo5\nFBcXt2nTxsPDQ9+F0MuhqKioU6dOIs4eIRkTBEGpVHbu3LlJkyb6roX0QCEIgr5rICIiIiIR\n8KtYIiIiIplgsCMiIiKSCQY7IiIiIpkw3HXsxBIfHx8cHJyYmGhubu7h4fHWW2/Vr19f30WR\nVBw5ciQkJOT27dsqlcrBwWHgwIH9+vVTKBSlByxevDgmJqbiif/5z38aN25ch5WS/lXxYuCY\nQ8XFxX5+fs85ICgoyNLSEhxhDBKDXY0cPXo0ICBAEAQ3N7fc3NzQ0NDo6OjPP/+8RYsW+i6N\n9EwQhMDAwN9//12lUrVs2dLExCQ5OXnt2rXnz59fuHBh6WHazGdvb1/udJVKVbf1kv5V5WLg\nmEMAFArFs554vXPnjkqlMjL663/uHGEMEIOd7vLz8zdu3Ghqaurv7+/k5AQgJCTku+++W7Nm\nzZo1a8relSEDdOrUqd9//93eSDGuHgAABhZJREFU3n7lypXaUTUrK+vzzz8/c+ZMaGho//79\nAZSUlGRlZXl4eKxatUrf9ZKeVeVi4JhDWkZGRt9//33F9qioqM8//3zKlCn16tUDRxhDxTl2\nujt06FB+fv6oUaO0IyyAwYMHe3t7X7t2LSkpSa+lkf4dO3YMwJw5c0o/K9vZ2c2YMQNA6S6x\nmZmZgiA0bdpUX0WSdFTlYuCYQ8+Rn58fEBDg6ek5fPhwbQtHGMPEYKe7U6dOAejWrVvZxq5d\nuwKIjo7WT00kGZmZmQqFwt3dvWyjdoHZW7duad9mZGQAcHBwqPvySGqqcjFwzKHn2LRpU15e\n3ty5c0vv3XKEMUz8KlZHgiCkpaUZGRmV+5vRznRJS0vTU10kFfPnzxcEwdjYuGxjSkoKgNLJ\nMbdv3waQl5e3bNmyy5cvA3Bycho0aFCPHj3qvF7SsxdeDBxz6DkuXrwYGho6ceLERo0alTZy\nhDFMDHY6KiwsLCoqsrW1LdeufRApJydHH0WRhLRs2bJcy61bt9avXw9g8ODB2hbtsLtr1y5r\na2snJ6fc3Ny4uLjY2NiBAwfOnj27jgsm/XrhxcAxh55FEITNmzfb2tr+4x//KNvOEcYwMdjp\nqLi4GICZmVm5dnNzcwCFhYV6qIkk7PTp0999911ubq6vr6+Pj4+2Ufv82vDhwydPnqz99uTa\ntWvLly8/fPhwp06dyn3jRvL2wouBYw49S1hYWEpKyqxZs0xNTcu2c4QxTAx2OrKwsFAqlQUF\nBeXa8/PzAVhZWemjKJKi1NTUDRs2JCYmWlhYzJ079/XXXy/90WeffVbuYBcXl2nTpv373/8+\nduwYh12D8sKLgWMOVUoQhKCgIFtb2wEDBpT7EUcYw8RgpyOFQmFtbZ2bm1uuXdvC9UIJgFqt\n3rFjx+7du5VK5YgRI0aPHq391uz52rVrByA1NbX2CySpK3sxcMyhSkVFRWVmZvr5+VVxaTqO\nMLLHYKc7Ozu7Bw8e3L17t+zaj+np6QAaNmyov7pIEgRBWLt27fHjxz09PT/44IOKq4kKglBS\nUqJUKssNx9q3FhYWdVcr6VsVLwaOOVTRH3/8AUC7NGZZHGEMFpc70Z32PvbZs2fLNkZGRqLC\negRkgP7444/jx4/36NFj+fLlla4Rf//+fT8/vzlz5pRrv3TpEoDShcrIEFTxYuCYQ+U8ePDg\n/Pnzbm5uFdc04QhjsBjsdNe/f3+VSrV79+579+5pWyIiIqKjo93d3bXLlZEh27dvn5GR0ezZ\ns5/1/UjDhg09PT3T0tK2bdsmCIK28ebNmxs3btTOd67DYknPqngxcMyhcqKjozUaTdu2bSv+\niCOMwVKU/r5JB6GhoQEBAebm5h07dszJyYmLi7O0tFy6dCn3bTRwOTk5b731VsUlx7ScnJw+\n+ugjAHfv3l2xYkVqamqjRo1atGjx8OHDlJQUQRCmT58+bNiwOq+a9KmKFwPHHCrryy+/DAsL\nW7x4cefOnSv+lCOMYWKwq6lz584dOnQoOTnZ3Ny8TZs2Y8eObdy4sb6LIj27fPny/Pnzn/XT\n1q1bf/HFF9rXRUVFu3btunTp0rVr16ysrFxcXEaNGlVxDTwyBFW8GDjmkJYgCBMnTszNzd22\nbZt21ZuKOMIYIAY7IiIiIpngHDsiIiIimWCwIyIiIpIJBjsiIiIimWCwIyIiIpIJBjsiIiIi\nmWCwIyIiIpIJBjsiIiIimWCwIyIiIpIJBjsiIiIimWCwIyIiIpIJBjsiIiIimWCwIyIiIpIJ\nBjsiIiIimWCwIyIiIpIJBjsiIiIimWCwIyIiIpIJBjsiIiIimWCwIyIiIpIJBjsiIiIimWCw\nIyIiIpIJBjsiIiIimWCwIyIiIpIJBjsiIiIimWCwIyIiIpIJBjsiIiIimWCwIyIiIpIJBjsi\nIiIimWCwIyIiIpIJBjsiIiIimWCwIyIiIpIJBjsiIiIimWCwIyIiIpIJBjsiIiIimWCwIyIi\nIpIJBjsiIiIimWCwIyIiIpIJBjsiIiIimWCwIyIiIpIJBjsiIiIimWCwIyIiIpIJBjsiIiIi\nmWCwIyIiIpIJBjsiIiIimfj/bjWyRuqQbTwAAAAASUVORK5CYII=",
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 420,
"width": 420
}
},
"output_type": "display_data"
}
],
"source": [
"yvalues <- c(io_train$output, io_test$output)\n",
"plot_ts_pred(y=yvalues, yadj=adjust, ypre=prediction) + theme(text = element_text(size=16))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Otions for machine learning\n",
"\n",
"Options of ranges for all time series models:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"### Ranges for ELM\n",
"ranges_elm <- list(nhid = 1:20, actfun=c('sig', 'radbas', 'tribas', 'relu', 'purelin'))\n",
"\n",
"### Ranges for MLP\n",
"ranges_mlp <- list(size = 1:10, decay = seq(0, 1, 1/9), maxit=10000)\n",
"\n",
"### Ranges for RF\n",
"ranges_rf <- list(nodesize=1:10, ntree=1:10)\n",
"\n",
"### Ranges for SVM\n",
"ranges_svm <- list(kernel=c(\"radial\", \"poly\", \"linear\", \"sigmoid\"), epsilon=seq(0, 1, 0.1), cost=seq(20, 100, 20))\n",
"\n",
"### Ranges for LSTM\n",
"ranges_lstm <- list(input_size = 1:10, epochs=10000)\n",
"\n",
"### Ranges for CNN\n",
"ranges_cnn <- list(input_size = 1:10, epochs=10000)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "4.3.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}