{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Deterministic Terms in Time Series Models" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:04.639694Z", "iopub.status.busy": "2024-04-18T11:33:04.639440Z", "iopub.status.idle": "2024-04-18T11:33:07.205560Z", "shell.execute_reply": "2024-04-18T11:33:07.204884Z" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "plt.rc(\"figure\", figsize=(16, 9))\n", "plt.rc(\"font\", size=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic Use\n", "\n", "Basic configurations can be directly constructed through `DeterministicProcess`. These can include a constant, a time trend of any order, and either a seasonal or a Fourier component.\n", "\n", "The process requires an index, which is the index of the full-sample (or in-sample).\n", "\n", "First, we initialize a deterministic process with a constant, a linear time trend, and a 5-period seasonal term. The `in_sample` method returns the full set of values that match the index." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:07.209408Z", "iopub.status.busy": "2024-04-18T11:33:07.208863Z", "iopub.status.idle": "2024-04-18T11:33:07.557494Z", "shell.execute_reply": "2024-04-18T11:33:07.556864Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " const trend s(2,5) s(3,5) s(4,5) s(5,5)\n", "0 1.0 1.0 0.0 0.0 0.0 0.0\n", "1 1.0 2.0 1.0 0.0 0.0 0.0\n", "2 1.0 3.0 0.0 1.0 0.0 0.0\n", "3 1.0 4.0 0.0 0.0 1.0 0.0\n", "4 1.0 5.0 0.0 0.0 0.0 1.0\n", ".. ... ... ... ... ... ...\n", "95 1.0 96.0 0.0 0.0 0.0 0.0\n", "96 1.0 97.0 1.0 0.0 0.0 0.0\n", "97 1.0 98.0 0.0 1.0 0.0 0.0\n", "98 1.0 99.0 0.0 0.0 1.0 0.0\n", "99 1.0 100.0 0.0 0.0 0.0 1.0\n", "\n", "[100 rows x 6 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from statsmodels.tsa.deterministic import DeterministicProcess\n", "\n", "index = pd.RangeIndex(0, 100)\n", "det_proc = DeterministicProcess(index, constant=True, order=1, seasonal=True, period=5)\n", "det_proc.in_sample()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `out_of_sample` returns the next `steps` values after the end of the in-sample." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:07.560967Z", "iopub.status.busy": "2024-04-18T11:33:07.560434Z", "iopub.status.idle": "2024-04-18T11:33:07.616224Z", "shell.execute_reply": "2024-04-18T11:33:07.615653Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " const trend s(2,5) s(3,5) s(4,5) s(5,5)\n", "100 1.0 101.0 0.0 0.0 0.0 0.0\n", "101 1.0 102.0 1.0 0.0 0.0 0.0\n", "102 1.0 103.0 0.0 1.0 0.0 0.0\n", "103 1.0 104.0 0.0 0.0 1.0 0.0\n", "104 1.0 105.0 0.0 0.0 0.0 1.0\n", "105 1.0 106.0 0.0 0.0 0.0 0.0\n", "106 1.0 107.0 1.0 0.0 0.0 0.0\n", "107 1.0 108.0 0.0 1.0 0.0 0.0\n", "108 1.0 109.0 0.0 0.0 1.0 0.0\n", "109 1.0 110.0 0.0 0.0 0.0 1.0\n", "110 1.0 111.0 0.0 0.0 0.0 0.0\n", "111 1.0 112.0 1.0 0.0 0.0 0.0\n", "112 1.0 113.0 0.0 1.0 0.0 0.0\n", "113 1.0 114.0 0.0 0.0 1.0 0.0\n", "114 1.0 115.0 0.0 0.0 0.0 1.0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_proc.out_of_sample(15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`range(start, stop)` can also be used to produce the deterministic terms over any range including in- and out-of-sample.\n", "\n", "### Notes\n", "\n", "* When the index is a pandas `DatetimeIndex` or a `PeriodIndex`, then `start` and `stop` can be date-like (strings, e.g., \"2020-06-01\", or Timestamp) or integers.\n", "* `stop` is always included in the range. While this is not very Pythonic, it is needed since both statsmodels and Pandas include `stop` when working with date-like slices." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:07.620467Z", "iopub.status.busy": "2024-04-18T11:33:07.619419Z", "iopub.status.idle": "2024-04-18T11:33:07.674740Z", "shell.execute_reply": "2024-04-18T11:33:07.674089Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " const trend s(2,5) s(3,5) s(4,5) s(5,5)\n", "190 1.0 191.0 0.0 0.0 0.0 0.0\n", "191 1.0 192.0 1.0 0.0 0.0 0.0\n", "192 1.0 193.0 0.0 1.0 0.0 0.0\n", "193 1.0 194.0 0.0 0.0 1.0 0.0\n", "194 1.0 195.0 0.0 0.0 0.0 1.0\n", "195 1.0 196.0 0.0 0.0 0.0 0.0\n", "196 1.0 197.0 1.0 0.0 0.0 0.0\n", "197 1.0 198.0 0.0 1.0 0.0 0.0\n", "198 1.0 199.0 0.0 0.0 1.0 0.0\n", "199 1.0 200.0 0.0 0.0 0.0 1.0\n", "200 1.0 201.0 0.0 0.0 0.0 0.0\n", "201 1.0 202.0 1.0 0.0 0.0 0.0\n", "202 1.0 203.0 0.0 1.0 0.0 0.0\n", "203 1.0 204.0 0.0 0.0 1.0 0.0\n", "204 1.0 205.0 0.0 0.0 0.0 1.0\n", "205 1.0 206.0 0.0 0.0 0.0 0.0\n", "206 1.0 207.0 1.0 0.0 0.0 0.0\n", "207 1.0 208.0 0.0 1.0 0.0 0.0\n", "208 1.0 209.0 0.0 0.0 1.0 0.0\n", "209 1.0 210.0 0.0 0.0 0.0 1.0\n", "210 1.0 211.0 0.0 0.0 0.0 0.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_proc.range(190, 210)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using a Date-like Index\n", "\n", "Next, we show the same steps using a `PeriodIndex`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:07.678857Z", "iopub.status.busy": "2024-04-18T11:33:07.677820Z", "iopub.status.idle": "2024-04-18T11:33:07.729037Z", "shell.execute_reply": "2024-04-18T11:33:07.728363Z" } }, "outputs": [ { "data": { "text/html": [ "
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constsin(1,12)cos(1,12)sin(2,12)cos(2,12)
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" ], "text/plain": [ " const sin(1,12) cos(1,12) sin(2,12) cos(2,12)\n", "2020-03 1.0 0.000000e+00 1.000000e+00 0.000000e+00 1.0\n", "2020-04 1.0 5.000000e-01 8.660254e-01 8.660254e-01 0.5\n", "2020-05 1.0 8.660254e-01 5.000000e-01 8.660254e-01 -0.5\n", "2020-06 1.0 1.000000e+00 6.123234e-17 1.224647e-16 -1.0\n", "2020-07 1.0 8.660254e-01 -5.000000e-01 -8.660254e-01 -0.5\n", "2020-08 1.0 5.000000e-01 -8.660254e-01 -8.660254e-01 0.5\n", "2020-09 1.0 1.224647e-16 -1.000000e+00 -2.449294e-16 1.0\n", "2020-10 1.0 -5.000000e-01 -8.660254e-01 8.660254e-01 0.5\n", "2020-11 1.0 -8.660254e-01 -5.000000e-01 8.660254e-01 -0.5\n", "2020-12 1.0 -1.000000e+00 -1.836970e-16 3.673940e-16 -1.0\n", "2021-01 1.0 -8.660254e-01 5.000000e-01 -8.660254e-01 -0.5\n", "2021-02 1.0 -5.000000e-01 8.660254e-01 -8.660254e-01 0.5" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "index = pd.period_range(\"2020-03-01\", freq=\"M\", periods=60)\n", "det_proc = DeterministicProcess(index, constant=True, fourier=2)\n", "det_proc.in_sample().head(12)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:07.733431Z", "iopub.status.busy": "2024-04-18T11:33:07.732320Z", "iopub.status.idle": "2024-04-18T11:33:07.773496Z", "shell.execute_reply": "2024-04-18T11:33:07.772869Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " const sin(1,12) cos(1,12) sin(2,12) cos(2,12)\n", "2025-03 1.0 -1.224647e-15 1.000000e+00 -2.449294e-15 1.0\n", "2025-04 1.0 5.000000e-01 8.660254e-01 8.660254e-01 0.5\n", "2025-05 1.0 8.660254e-01 5.000000e-01 8.660254e-01 -0.5\n", "2025-06 1.0 1.000000e+00 -4.904777e-16 -9.809554e-16 -1.0\n", "2025-07 1.0 8.660254e-01 -5.000000e-01 -8.660254e-01 -0.5\n", "2025-08 1.0 5.000000e-01 -8.660254e-01 -8.660254e-01 0.5\n", "2025-09 1.0 4.899825e-15 -1.000000e+00 -9.799650e-15 1.0\n", "2025-10 1.0 -5.000000e-01 -8.660254e-01 8.660254e-01 0.5\n", "2025-11 1.0 -8.660254e-01 -5.000000e-01 8.660254e-01 -0.5\n", "2025-12 1.0 -1.000000e+00 -3.184701e-15 6.369401e-15 -1.0\n", "2026-01 1.0 -8.660254e-01 5.000000e-01 -8.660254e-01 -0.5\n", "2026-02 1.0 -5.000000e-01 8.660254e-01 -8.660254e-01 0.5" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_proc.out_of_sample(12)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`range` accepts date-like arguments, which are usually given as strings." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:07.784976Z", "iopub.status.busy": "2024-04-18T11:33:07.776581Z", "iopub.status.idle": "2024-04-18T11:33:07.829492Z", "shell.execute_reply": "2024-04-18T11:33:07.828865Z" } }, "outputs": [ { "data": { "text/html": [ "
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constsin(1,12)cos(1,12)sin(2,12)cos(2,12)
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" ], "text/plain": [ " const sin(1,12) cos(1,12) sin(2,12) cos(2,12)\n", "2025-01 1.0 -8.660254e-01 5.000000e-01 -8.660254e-01 -0.5\n", "2025-02 1.0 -5.000000e-01 8.660254e-01 -8.660254e-01 0.5\n", "2025-03 1.0 -1.224647e-15 1.000000e+00 -2.449294e-15 1.0\n", "2025-04 1.0 5.000000e-01 8.660254e-01 8.660254e-01 0.5\n", "2025-05 1.0 8.660254e-01 5.000000e-01 8.660254e-01 -0.5\n", "2025-06 1.0 1.000000e+00 -4.904777e-16 -9.809554e-16 -1.0\n", "2025-07 1.0 8.660254e-01 -5.000000e-01 -8.660254e-01 -0.5\n", "2025-08 1.0 5.000000e-01 -8.660254e-01 -8.660254e-01 0.5\n", "2025-09 1.0 4.899825e-15 -1.000000e+00 -9.799650e-15 1.0\n", "2025-10 1.0 -5.000000e-01 -8.660254e-01 8.660254e-01 0.5\n", "2025-11 1.0 -8.660254e-01 -5.000000e-01 8.660254e-01 -0.5\n", "2025-12 1.0 -1.000000e+00 -3.184701e-15 6.369401e-15 -1.0\n", "2026-01 1.0 -8.660254e-01 5.000000e-01 -8.660254e-01 -0.5" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_proc.range(\"2025-01\", \"2026-01\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is equivalent to using the integer values 58 and 70." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:07.833758Z", "iopub.status.busy": "2024-04-18T11:33:07.832709Z", "iopub.status.idle": "2024-04-18T11:33:07.886728Z", "shell.execute_reply": "2024-04-18T11:33:07.885938Z" } }, "outputs": [ { "data": { "text/html": [ "
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constsin(1,12)cos(1,12)sin(2,12)cos(2,12)
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" ], "text/plain": [ " const sin(1,12) cos(1,12) sin(2,12) cos(2,12)\n", "2025-01 1.0 -8.660254e-01 5.000000e-01 -8.660254e-01 -0.5\n", "2025-02 1.0 -5.000000e-01 8.660254e-01 -8.660254e-01 0.5\n", "2025-03 1.0 -1.224647e-15 1.000000e+00 -2.449294e-15 1.0\n", "2025-04 1.0 5.000000e-01 8.660254e-01 8.660254e-01 0.5\n", "2025-05 1.0 8.660254e-01 5.000000e-01 8.660254e-01 -0.5\n", "2025-06 1.0 1.000000e+00 -4.904777e-16 -9.809554e-16 -1.0\n", "2025-07 1.0 8.660254e-01 -5.000000e-01 -8.660254e-01 -0.5\n", "2025-08 1.0 5.000000e-01 -8.660254e-01 -8.660254e-01 0.5\n", "2025-09 1.0 4.899825e-15 -1.000000e+00 -9.799650e-15 1.0\n", "2025-10 1.0 -5.000000e-01 -8.660254e-01 8.660254e-01 0.5\n", "2025-11 1.0 -8.660254e-01 -5.000000e-01 8.660254e-01 -0.5\n", "2025-12 1.0 -1.000000e+00 -3.184701e-15 6.369401e-15 -1.0\n", "2026-01 1.0 -8.660254e-01 5.000000e-01 -8.660254e-01 -0.5" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_proc.range(58, 70)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Advanced Construction\n", "\n", "Deterministic processes with features not supported directly through the constructor can be created using `additional_terms` which accepts a list of `DetermisticTerm`. Here we create a deterministic process with two seasonal components: day-of-week with a 5 day period and an annual captured through a Fourier component with a period of 365.25 days." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:07.905015Z", "iopub.status.busy": "2024-04-18T11:33:07.890617Z", "iopub.status.idle": "2024-04-18T11:33:08.006358Z", "shell.execute_reply": "2024-04-18T11:33:08.005729Z" } }, "outputs": [ { "data": { "text/html": [ "
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consts(2,7)s(3,7)s(4,7)s(5,7)s(6,7)s(7,7)sin(1,365.25)cos(1,365.25)sin(2,365.25)cos(2,365.25)
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2020-03-141.00.00.00.00.00.01.00.2217720.9750990.4324990.901634
2020-03-151.00.00.00.00.00.00.00.2385130.9711390.4632580.886224
2020-03-161.01.00.00.00.00.00.00.2551820.9668930.4934680.869764
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2020-03-281.00.00.00.00.00.01.00.4479450.8940610.8009800.598691
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" ], "text/plain": [ " const s(2,7) s(3,7) s(4,7) s(5,7) s(6,7) s(7,7) \\\n", "2020-03-01 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-02 1.0 1.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-03 1.0 0.0 1.0 0.0 0.0 0.0 0.0 \n", "2020-03-04 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n", "2020-03-05 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n", "2020-03-06 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n", "2020-03-07 1.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", "2020-03-08 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-09 1.0 1.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-10 1.0 0.0 1.0 0.0 0.0 0.0 0.0 \n", "2020-03-11 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n", "2020-03-12 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n", "2020-03-13 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n", "2020-03-14 1.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", "2020-03-15 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-16 1.0 1.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-17 1.0 0.0 1.0 0.0 0.0 0.0 0.0 \n", "2020-03-18 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n", "2020-03-19 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n", "2020-03-20 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n", "2020-03-21 1.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", "2020-03-22 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-23 1.0 1.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-24 1.0 0.0 1.0 0.0 0.0 0.0 0.0 \n", "2020-03-25 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n", "2020-03-26 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n", "2020-03-27 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n", "2020-03-28 1.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", "\n", " sin(1,365.25) cos(1,365.25) sin(2,365.25) cos(2,365.25) \n", "2020-03-01 0.000000 1.000000 0.000000 1.000000 \n", "2020-03-02 0.017202 0.999852 0.034398 0.999408 \n", "2020-03-03 0.034398 0.999408 0.068755 0.997634 \n", "2020-03-04 0.051584 0.998669 0.103031 0.994678 \n", "2020-03-05 0.068755 0.997634 0.137185 0.990545 \n", "2020-03-06 0.085906 0.996303 0.171177 0.985240 \n", "2020-03-07 0.103031 0.994678 0.204966 0.978769 \n", "2020-03-08 0.120126 0.992759 0.238513 0.971139 \n", "2020-03-09 0.137185 0.990545 0.271777 0.962360 \n", "2020-03-10 0.154204 0.988039 0.304719 0.952442 \n", "2020-03-11 0.171177 0.985240 0.337301 0.941397 \n", "2020-03-12 0.188099 0.982150 0.369484 0.929237 \n", "2020-03-13 0.204966 0.978769 0.401229 0.915978 \n", "2020-03-14 0.221772 0.975099 0.432499 0.901634 \n", "2020-03-15 0.238513 0.971139 0.463258 0.886224 \n", "2020-03-16 0.255182 0.966893 0.493468 0.869764 \n", "2020-03-17 0.271777 0.962360 0.523094 0.852275 \n", "2020-03-18 0.288291 0.957543 0.552101 0.833777 \n", "2020-03-19 0.304719 0.952442 0.580455 0.814292 \n", "2020-03-20 0.321058 0.947060 0.608121 0.793844 \n", "2020-03-21 0.337301 0.941397 0.635068 0.772456 \n", "2020-03-22 0.353445 0.935455 0.661263 0.750154 \n", "2020-03-23 0.369484 0.929237 0.686676 0.726964 \n", "2020-03-24 0.385413 0.922744 0.711276 0.702913 \n", "2020-03-25 0.401229 0.915978 0.735034 0.678031 \n", "2020-03-26 0.416926 0.908940 0.757922 0.652346 \n", "2020-03-27 0.432499 0.901634 0.779913 0.625889 \n", "2020-03-28 0.447945 0.894061 0.800980 0.598691 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from statsmodels.tsa.deterministic import Fourier, Seasonality, TimeTrend\n", "\n", "index = pd.period_range(\"2020-03-01\", freq=\"D\", periods=2 * 365)\n", "tt = TimeTrend(constant=True)\n", "four = Fourier(period=365.25, order=2)\n", "seas = Seasonality(period=7)\n", "det_proc = DeterministicProcess(index, additional_terms=[tt, seas, four])\n", "det_proc.in_sample().head(28)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Custom Deterministic Terms\n", "\n", "The `DetermisticTerm` Abstract Base Class is designed to be subclassed to help users write custom deterministic terms. We next show two examples. The first is a broken time trend that allows a break after a fixed number of periods. The second is a \"trick\" deterministic term that allows exogenous data, which is not really a deterministic process, to be treated as if was deterministic. This lets use simplify gathering the terms needed for forecasting.\n", "\n", "These are intended to demonstrate the construction of custom terms. They can definitely be improved in terms of input validation." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:08.010578Z", "iopub.status.busy": "2024-04-18T11:33:08.009545Z", "iopub.status.idle": "2024-04-18T11:33:08.029502Z", "shell.execute_reply": "2024-04-18T11:33:08.028874Z" } }, "outputs": [], "source": [ "from statsmodels.tsa.deterministic import DeterministicTerm\n", "\n", "\n", "class BrokenTimeTrend(DeterministicTerm):\n", " def __init__(self, break_period: int):\n", " self._break_period = break_period\n", "\n", " def __str__(self):\n", " return \"Broken Time Trend\"\n", "\n", " def _eq_attr(self):\n", " return (self._break_period,)\n", "\n", " def in_sample(self, index: pd.Index):\n", " nobs = index.shape[0]\n", " terms = np.zeros((nobs, 2))\n", " terms[self._break_period :, 0] = 1\n", " terms[self._break_period :, 1] = np.arange(self._break_period + 1, nobs + 1)\n", " return pd.DataFrame(terms, columns=[\"const_break\", \"trend_break\"], index=index)\n", "\n", " def out_of_sample(\n", " self, steps: int, index: pd.Index, forecast_index: pd.Index = None\n", " ):\n", " # Always call extend index first\n", " fcast_index = self._extend_index(index, steps, forecast_index)\n", " nobs = index.shape[0]\n", " terms = np.zeros((steps, 2))\n", " # Assume break period is in-sample\n", " terms[:, 0] = 1\n", " terms[:, 1] = np.arange(nobs + 1, nobs + steps + 1)\n", " return pd.DataFrame(\n", " terms, columns=[\"const_break\", \"trend_break\"], index=fcast_index\n", " )" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:08.033570Z", "iopub.status.busy": "2024-04-18T11:33:08.032523Z", "iopub.status.idle": "2024-04-18T11:33:08.089468Z", "shell.execute_reply": "2024-04-18T11:33:08.088861Z" } }, "outputs": [ { "data": { "text/html": [ "
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consttrendconst_breaktrend_break
551.056.00.00.0
561.057.00.00.0
571.058.00.00.0
581.059.00.00.0
591.060.00.00.0
601.061.01.061.0
611.062.01.062.0
621.063.01.063.0
631.064.01.064.0
641.065.01.065.0
651.066.01.066.0
\n", "
" ], "text/plain": [ " const trend const_break trend_break\n", "55 1.0 56.0 0.0 0.0\n", "56 1.0 57.0 0.0 0.0\n", "57 1.0 58.0 0.0 0.0\n", "58 1.0 59.0 0.0 0.0\n", "59 1.0 60.0 0.0 0.0\n", "60 1.0 61.0 1.0 61.0\n", "61 1.0 62.0 1.0 62.0\n", "62 1.0 63.0 1.0 63.0\n", "63 1.0 64.0 1.0 64.0\n", "64 1.0 65.0 1.0 65.0\n", "65 1.0 66.0 1.0 66.0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "btt = BrokenTimeTrend(60)\n", "tt = TimeTrend(constant=True, order=1)\n", "index = pd.RangeIndex(100)\n", "det_proc = DeterministicProcess(index, additional_terms=[tt, btt])\n", "det_proc.range(55, 65)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we write a simple \"wrapper\" for some actual exogenous data that simplifies constructing out-of-sample exogenous arrays for forecasting." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:08.093490Z", "iopub.status.busy": "2024-04-18T11:33:08.092393Z", "iopub.status.idle": "2024-04-18T11:33:08.105457Z", "shell.execute_reply": "2024-04-18T11:33:08.104859Z" } }, "outputs": [], "source": [ "class ExogenousProcess(DeterministicTerm):\n", " def __init__(self, data):\n", " self._data = data\n", "\n", " def __str__(self):\n", " return \"Custom Exog Process\"\n", "\n", " def _eq_attr(self):\n", " return (id(self._data),)\n", "\n", " def in_sample(self, index: pd.Index):\n", " return self._data.loc[index]\n", "\n", " def out_of_sample(\n", " self, steps: int, index: pd.Index, forecast_index: pd.Index = None\n", " ):\n", " forecast_index = self._extend_index(index, steps, forecast_index)\n", " return self._data.loc[forecast_index]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:08.109975Z", "iopub.status.busy": "2024-04-18T11:33:08.108887Z", "iopub.status.idle": "2024-04-18T11:33:08.133466Z", "shell.execute_reply": "2024-04-18T11:33:08.132861Z" } }, "outputs": [ { "data": { "text/html": [ "
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exog1exog2
0699
16428
21581
3548
4128
\n", "
" ], "text/plain": [ " exog1 exog2\n", "0 6 99\n", "1 64 28\n", "2 15 81\n", "3 54 8\n", "4 12 8" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "gen = np.random.default_rng(98765432101234567890)\n", "exog = pd.DataFrame(gen.integers(100, size=(300, 2)), columns=[\"exog1\", \"exog2\"])\n", "exog.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:08.137566Z", "iopub.status.busy": "2024-04-18T11:33:08.136527Z", "iopub.status.idle": "2024-04-18T11:33:08.141426Z", "shell.execute_reply": "2024-04-18T11:33:08.140817Z" } }, "outputs": [], "source": [ "ep = ExogenousProcess(exog)\n", "tt = TimeTrend(constant=True, order=1)\n", "# The in-sample index\n", "idx = exog.index[:200]\n", "det_proc = DeterministicProcess(idx, additional_terms=[tt, ep])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:08.145412Z", "iopub.status.busy": "2024-04-18T11:33:08.144343Z", "iopub.status.idle": "2024-04-18T11:33:08.172985Z", "shell.execute_reply": "2024-04-18T11:33:08.172370Z" } }, "outputs": [ { "data": { "text/html": [ "
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consttrendexog1exog2
01.01.0699
11.02.06428
21.03.01581
31.04.0548
41.05.0128
\n", "
" ], "text/plain": [ " const trend exog1 exog2\n", "0 1.0 1.0 6 99\n", "1 1.0 2.0 64 28\n", "2 1.0 3.0 15 81\n", "3 1.0 4.0 54 8\n", "4 1.0 5.0 12 8" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_proc.in_sample().head()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:08.177752Z", "iopub.status.busy": "2024-04-18T11:33:08.176501Z", "iopub.status.idle": "2024-04-18T11:33:08.208343Z", "shell.execute_reply": "2024-04-18T11:33:08.207741Z" } }, "outputs": [ { "data": { "text/html": [ "
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consttrendexog1exog2
2001.0201.05688
2011.0202.04884
2021.0203.0445
2031.0204.06563
2041.0205.06339
2051.0206.08939
2061.0207.04154
2071.0208.0715
2081.0209.0896
2091.0210.05863
\n", "
" ], "text/plain": [ " const trend exog1 exog2\n", "200 1.0 201.0 56 88\n", "201 1.0 202.0 48 84\n", "202 1.0 203.0 44 5\n", "203 1.0 204.0 65 63\n", "204 1.0 205.0 63 39\n", "205 1.0 206.0 89 39\n", "206 1.0 207.0 41 54\n", "207 1.0 208.0 71 5\n", "208 1.0 209.0 89 6\n", "209 1.0 210.0 58 63" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_proc.out_of_sample(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Support\n", "\n", "The only model that directly supports `DeterministicProcess` is `AutoReg`. A custom term can be set using the `deterministic` keyword argument. \n", "\n", "**Note**: Using a custom term requires that `trend=\"n\"` and `seasonal=False` so that all deterministic components must come from the custom deterministic term." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulate Some Data\n", "\n", "Here we simulate some data that has an weekly seasonality captured by a Fourier series." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:08.212533Z", "iopub.status.busy": "2024-04-18T11:33:08.211491Z", "iopub.status.idle": "2024-04-18T11:33:09.041607Z", "shell.execute_reply": "2024-04-18T11:33:09.040885Z" } }, "outputs": [ { "data": { "image/png": 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RERERGSohuX37djzyyCOQpItXtD366KPYtGkTtm7disWLF+N973sfBgcH8dBDD2HNmjXo6elJUsRkVE1qhWRFrn7zI4Ur5xYAAN7mHEkiIiIiIiIiIuMkJEdGRnDfffehpKQE73//+y/4ffv378cDDzwAu92OLVu2YNu2bXjqqadQW1uL66+/HidPnsSnP/3pJEZORiRatvVcaCOsq86F3SahvieAlv4RvcMhIiIiIiIiItKVYRKSDz74IE6fPo2f/OQn8Hq9F/y+hx9+GLIs42Mf+xhuuukm7esejwePP/44bDYbnnnmGZw4cSIZYZNBNfYZJyGZnebE8jLl7/R2VkkSERERERERUYozRELy9ddfx/e+9z189KMfxebNmy/4faFQCFu2bAEA3Hvvvef8fmVlJTZs2AAAeO655xITLBmeLMto6lVmSFbk6j9DEgDWV+cBAPY09ukcCRERERERERGRvnRPSA4PD+PjH/84ioqK8O1vf/ui33vq1CmMjCiVb2vWrDnv94iv79+/P65xknn0BkIIhKKQJKA8N13vcAAAqytzAAB7G/t1joSIiIiIiIiISF8OvQP4/Oc/j/r6ejz33HPIycm56PfW19cDAHw+H7Kyss77PeXl5Wd9L6UeMT+yJDsNbodd52gUKyuUv9u13QH0B0LIyXDpHBERERERERERkT50rZB8+eWX8eMf/xgf+tCHcNttt13y+/1+PwAgI+PCbbiZmZkAgKGhoYu+VjAYxNDQ0Fm/yBqa+tR2bQPMjxRyM1yoLlD+3u5vZpUkEREREREREaUu3RKSg4OD+MQnPoGCggJ873vfS/r7P/zww/B6vdovUVlJ5qdt2DbI/EhhdQXbtomIiIiIiIiIdEtIfvazn0VLSwu+//3vIz8/f1I/I9q0A4HABb9neHgYAJCdnX3R13rwwQcxODio/Wpubp5k5GR0TWpC0kgVkgDnSBIRERERERERATrOkHzuuefgcDjwwx/+ED/84Q/P+r0TJ04AAB5//HG8+uqrKC4uxpNPPomqqioAwMDAAPx+/3nnSIrEovjeC3G73XC73TP/g5DhNPapFZIGTUgebB5EOBqD0677TikiIiIiIiIioqTTdalNJBLBtm3bLvj7DQ0NaGhoQGVlJQBg/vz58Hg8GBkZwZ49e3Dttdee8zN79uwBAKxatSoxQZPhGbVle05BJrLTHBgai+BEux9Ly7x6h0RERERERERElHS6lWgNDAxAluXz/vrrv/5rAMDXvvY1yLKMhoYGAIDL5cLNN98MAHjiiSfOec3GxkZs374dAHD77bcn5w9ChhIIRtAzHARgvJZtm03CKq1tu0/naIiIiIiIiFJTOBrDL7Y34GjboN6hEKUs0/WMfuELX4AkSfjZz36GrVu3al8fGRnBJz7xCUSjUdx5551YsGCBjlGSXprUdm2fxwlvulPnaM6lLbZpGtA3ECIiIiIiohT1nVdP4//+/igeeOqg3qEQpSzTJSRXrVqFRx55BNFoFJs3b8a1116Lu+++GzU1NXjttdcwf/58/OhHP9I7TNLJeLu2saojBTFHch8X2xARERERESXdkdZBPLatFgBwosOPtoFRnSMiSk2mS0gCwP33349XXnkFN954Iw4dOoQXXngBmZmZePDBB7F79+5Jb+0m62nqUzawV+QZa36ksLzcB5sEtA6Mon2QH3xERERERETJEorE8M9PH0I0Jmtfe+NUt44REaUuXZfaXMjPf/5z/PznP7/o92zcuBEbN25MTkBkGkavkMxwO7CwJBtH24awr3EANy9L1zskIjIpWVZupCVJ0jkSIiIiInN47PVaHG8fQo7HiVuWzcKvdjbijdPd+NBlFXqHRpRyTFkhSXQhYoak0RbaTLRaW2zDtm0imp6xcBQ3PPoG3vvtN1HbPax3OERERESGd6JjCN//y2kAwL/euhh3rCoFALx1ugeRaEzP0IhSEhOSZClGr5AEJiQkm5iQJKLp2XqkA6e7hnGy04/bvv82/nKiS++QiIiIiAwrEo3hn393COGojE2LinDr8llYVuaDz+PE0FgEB1sG9A6RKOUwIUmWEY7G0KoOJK406AxJAFilbto+2jqIsXBU52iIyIx+u7sZAJCd5oA/GMHHf7Ebj71eq7VxExEREdG4H79Rh8Otg/CmO/H125ZAkiTYbRKurFH2T2w71aNzhESphwlJsoy2gVFEYzLcDhsKs9x6h3NBZTnpKMxyIxKTcbh1UO9wiMhkmnpHsKOuF5IE/P7vr8Q9l5VDloFvbD2Bf3ryAEZDPOggIn0EI1G8frILgWBE71CIiDSnO/34zqtKq/ZXb1mEwuw07feunlcAANjGxTZESceEJFmGaNeuyPXAZjPukgdJkjhHkoim7Xd7lerIK2vyUZWfgYduX4qv3bYEDpuE3x9sw10/3o4jrYOsliSipPvJtjrc97PduPHbb2D7GVYbEZExPPjsYYSiMVw7v0CbGylcPVdJSB5qGUB/IKRHeEQpiwlJsoxGdaFNpYEX2ghMSBLRdERjMp7e2wIAuHttOQDlkOMj6yvx60+uQ26GC0dah3DL997Cxm9tw3dfO42GnoCeIRNRCnnleCcAoKV/FPf+9y58+fnDGGa1JBHp6HSnH3sa++G0S3jojqWQpLMLV4q9aVhQnAVZBt7kQQpRUjEhSZbR1Ks8dFfkGnd+pLBKTUjua+xnFRMRTdpbZ3rQPjgGn8eJTYuKzvq99dV5eOHvNuDmpSVwOWyo7Q7gW6+cwnv+63W8/wdv48l3mni9IaKE6RkO4lCLMormzlVlAIBf72zCjY++gbfVh3xZltHYG8Bz+1vw1ReO4I4fvo0vPncYfaxKIqIE2XK4HQBw1dwClHjTz/s9om37DbZtEyWVQ+8AiOJF27BtggrJxbOy4XLY0BsIobF3BFX5xk+iEpH+nlKX2dy2ohRuh/2c3y/P9eAHf7UK/rEw/nS0Ey8caMXbZ3pwsHkAB5sHkJnmwC3LZiU7bCJKAW+dVpKOC0uy8cgHl+POVaX4l2cOoaV/FH/137twWVUu6nqG0TN8dvJxX9MAXjrcji/fvAh3rCo9p3qJiGgmthxSEpI3Ly254PdcM68AP3mjDm+c6oYsy7wOESUJKyTJMprUlu0KEyQk3Q47lpV6AbBtm4gmpy8QwsvHOgAAH1xTftHvzUpz4gOry/CrT6zDri9u1KqVntrTkvA4iSg1iYUQ16iVRlfU5ONPn70aH1lfCQB4p6EPPcMhuOw2rKzw4eMbZuMbdy7FguIs9I+E8cDvDuLDj+9CPcdMEFGcnOr043TXMJx2CRvf1Vky0ZqqHKQ77ejyB3Giw5/ECIlSGyskyRJkWdYSkpW5xk9IAsocyT2N/djb1I87V5fpHQ4RGdzz+1sRjspYUpqNRbOyJ/1zBVlu/MN1NXhmXwveOt2NrqGxs7ZLEhHNVCwm483TZyckASDD7cDXbluCO1aV4kjbEBaVZGPxrGykOccrvO9YVYb/frMe3371FN4+04sbv/0G/vG6Gnz6mjlw2Fk7QUTTJ6ojr55bAG+684Lf53bYcfmcPPz5RBfeONWNhSWTv88iounjpzxZQvdwECOhKGwSUJZjjoTkyorxOZJERBcjyzKe2qO0a999ierI86nKz8CqCh9iMvD7g23xDo+IUtyx9iH0DIeQ4bJri/smWlmRg4+sr8TqypyzkpEA4LTb8LfvmYOX778aV83NRygSw3+9fAo/396QpOiJyKrE/Mibl124XVu4em4+gPFqbyJKPCYkyRKa1PmRJd50uBzm+Gu9qtIHADjZ6cfgaFjfYIjI0A63DuJEhx8uhw23Li+d1mvcrrZtP7uvNZ6hERFpD/BX1ORP+z6sMi8Dv/z4ZfjMe+YAgLYIh4hoOk51+nGmaxguu+2i7dqCWGyzp6EfgWAk0eEREZiQJIsw00IboTArDVV5HsgysKehT+9wiMjARHXkTUuK4fVcuOXoYm5ZWgKnXcKx9iGc6BiKZ3hElOK2nTy3XXs6JEnC9QuVxMGxdl6niGj6XhTt2vPykZ126Xun2fkZKM9NRygaw8663kSHR0RgQpIsorHPfAlJAFhfnQcA2FXPhCQRnd9YOIoXDiht1pdaZnMxORkuXDu/EADwHKskiShOhsbC2NukjJ+ZaUISABYUZ0GSgM6hIHqGgzN+PSJKPbIs449TaNcGlAORq+cq17A32LZNlBRMSJIlNPUqGxkrcjN0jmRq1lXnAgB28RSOiC5g65EO+MciKMtJx+XqIcZ03bFKafd+/kArojE5HuERUYrbfqYX0ZiM6vwMlMdhsWCG24HZecr93LE2VkkS0dSd6hzW2rVF1fVkiEMVzpEkSg4mJMkSzFohuW62klw40jaEYc4qIaLzeGZfCwDgrtXlsNmkGb3WtQsK4U13onMoiB21PAghopkTD+5Xx6E6Ulg0S9lwy7ZtIpqOLYeUzpKr5xVMql1buHxOHhw2CQ29I2hUC16IKHGYkCRLEEttKuJwMp9Ms3zpKM9NRzQmc44kEZ1jcCSsJQ5vXTFrxq/ndthxi9q69Oz+lhm/HhGlNlmWtdbGa+YnICHJCkkimiJZlids1y6e0s9mpTmxqjIHAPDGaS7WIko0JiTJ9IaDEfQGQgDMVyEJjFdJco4kEb3bX052IRKTMa8oE7Pz4zOSQrRtbz3SgZEQK7OJaPpqu4fROjAKl8OG9bNnNlJiokUlrJAkouk52elHbXcALocNG6fQri1cMUe5lu1lsQhRwjEhSaYnyulzM1zImkJJvlGsm805kkR0fi8f6wAA3LBoaif8F7OqIgeVeR6MhKJ4+Whn3F6XiFLPtlNKBdG62blId9nj9rqiQrKuexijoWjcXpeIrG+Lul37mnkF03o2XK1WSIplXUSUOExIkumZtV1bEJu2D7UMslqJiDRj4Si2nVRaITctmvoJ/4VIkoTbVihVks/u57ZtIpo+MT8yHtu1JyrMSkN+phsxWal2IiKajLPatZdObrv2u60o90GSgOa+UXT5x+IZHhG9CxOSZHpmXWgjlOWkY5Y3DZGYjL2NPIkjIsWO2l4EQlEUZ6dhaak3rq99+0olIfnW6W50DfFmm4imbiwc1bo74p2QBMarJI+2Dcb9tYnImk50+FGntmtfv7BwWq+RlebE/KIsAMC+xoE4RkdE78aEJJleo1ohWWnSCklJkrQqyV11nFVCRArRrr1pUdGMt2u/W1V+BlZV+BCTgRcOtMX1tYkoNeys60UwEsMsbxpqCjPj/vraHEkutiGiSXpe7fyYbru2IBbb7GPbNlFCMSFJptfUp8yQrMiLz8IHPayrVudI1nOOJBEBsZiMV451AQBuWBy/du2Jbl9VBoBt20Q0PdsmbNeWpPgemgATNm1zsQ0RTUJ9TwA/e7sBAHDX6rIZvdbqCnWOJLvXiBKKCUkyPa1C0qQt28D4pu2DzYMYC3N4O1Gq2988gJ7hILLcDu36EG+3LC2BTQKOtw+hfXA0Ie9BRNb1RoLmRwqiQvJEux/RmJyQ9yAia5BlGV9+/jBC0Riunlcw49nbYrHN4ZZBBCN8NiNKFCYkydRCkRjaBpQHabO2bANKMrUo241QNMbWACLS2rWvXVAIlyMxH9U5GS5tNuWOWlZnE9HkNfWOoLY7ALtNwhU1+Ql5j9n5GUhz2jAajqKhN5CQ9yAia/j9wTa8faYXbocNX3v/4hlXbVfmeZCX4UIoGsORVlZpEyUKE5Jkaq0Do4jJQLrTjoIst97hTJskSVoVFOdIEtErRzsBJK5dWxCJhO1MSBLRFDx/QBn1sL46F9kzmNN2MXabhAXFnCNJRBc3OBLG1148BgD4h+tqUBmHMV6SJI3PkWTbNlHCMCFJptaonphX5HoSMr8omcQcyZ11TAwQpbIzXcOo6wnAZbclrBVSuGKOchCyo7YXssyWSCK6NFmW8fTeFgDAB2Y4p+1SFnOOJBFdwn/+6QR6hkOYU5CB/3N1ddxeV7Rtc44kUeIwIUmm1tSnzI+sMPH8SEFUSO5vHuAcSaIUJtq1L5+TN6MNkZOxpjIXTruE1oFRbR4vEdHF7G7oR1PfCDLdDty4uDih7yUW2xxlhSQRnce+pn488U4TAODrty+F22GP22trCcmmfh7aEiUIE5JkatpCGxPPjxTmFGQgP9ONUCSGg80DeodDRDp5OUnt2gCQ7rJjpbpJkm3bRDQZT+9tBgDcvLQEHpcjoe8lFtuwZZuI3i0SjeGLzx6GLCvV2uur47sEcGmpFw6bhG5/EC39XP5HlAhMSJKpWWHDtiBJkta2vauecySJUlHn0BgOqAcSmxYmPiEJjLdtb6/tScr7EZF5jYQi2HKoHQDwgTWJbdcGgAXF2bBJQM9wEF3+sYS/HxGZx8/ebsCJDj98Hie+uHlh3F8/zWnHYnX5H5eOEiUGE5Jkak196gzJOAwvNoL1s0VCkpVKRKno1eNKdeSKch8Ks9OS8p5XzFEW23COJBFdytYjHQiEoqjM82CN2s6YSOkuO2bnK/d4rJIkIqFzaAyPvnoKAPDFmxYiN8OVkPdZXcE5kkSJxIQkmZYsy9oMSSu0bAPAOrXVYG9jP0KRmM7REFGyJbNdW1hR7kO6047eQAinOoeT9r5EZD5imc2dq8qStkxw0SylQomLbYhIeOlwO0ZCUSwr8yZ0uRYX2xAlFhOSZFpd/iDGwjHYbRJKc9L1Dicu5hZmIjfDhbFwDIdbB/QOh4iSyD8W1tqmb1iU2EURE7kcNqxVq7PZtk1EF9LSP6LNmr1jVWnS3pdzJIno3d46o9yvvHdJMWy2xB2OrKr0AQCOtw8hEIwk7H2IUhUTkmRaYn7kLF8anHZr/FWWJAmXVSmJgZ11nCNJlEreONWDcFRGdUEGagozk/reYo7k22c4LoKIzu/Zfa0AlOtFWU7yOlPEpm1WSBIRAISjMe056aqagoS+V4k3HaW+dMRkcOkoUQJYI4tDKamxV5kfWZlrjfmRAhfbEKWmnXVKMvCaeYm9uT4fkZDcVdeLSJTjIojobLIsa+3aiWyPPB9RIVnfE2CFEhHhQPMAhoMR5HicWKweWCTSKrZtEyUME5JkWmJ+ZIUFNmxPtKLcB4CtSUSpZneDcgghqqSTafEsL7LSHPAHIzjKaw8Rvcvuhn409Y0gw2XHe5ckb6QEABRkuVGQ5YYsAyc6/El9byIynjdPK+3aG2ryE9quLayu8AEA9nLTNlHcMSFJpiVatq2y0EaYX5wFSQJ6hoPo9gf1DoeIkmBoLIyTncqD9uqqxG+ufTe7TcJ6damWmBFHRCQ8vbcZAHDzshJ4XI6kv782R5Jt20Qp783T3QCAq+bmJ+X9RIXk/qYBxGJyUt6TKFUwIUmm1Sg2bFusQtLjcmB2ntKGfpw33kQpYV9jP2RZuZ4VZqXpEoNo2+ZiGyKaaCQUwZZD7QCAD6wu1yUG0ZbJ7hGi1DY4GtZmOV45NzkjbhaWZCPNacPgaBh1PcNJeU+iVMGEJJlWkzpDssJiMyQB5YMPYEKSKFWIuUSrK5NfHSlsqFEqDXY39CEU4RxJIlL86WgHAqEoKnI9WKtDBTfAxTZEpNhR24uYDFTnZ6DUl56U93TabVhe5gPAOZJE8caEJJnS0FgY/SNhANabIQkAC0uyADAhSZQqxPzItTrMjxTmFmYiP9OFsXAM+zkniYhUYrv2navKIEmJn9d2PqJl+0T7EBdvEaWwt84kt11bWM3FNkQJwYQkmVKTOj8yP9OFTHfyZxkl2niFJIe3E1ldOBrDAbX9SK/qIwCQJAmXz1Fu8DlHkogAZbu2uD5tWlSkWxyVeRnwuOwIRmKo6wnoFgcR6estdaFNstq1BSYkiRKDCUkyJbHQpsJiC20EkZCs7R5GMBLVORoiSqSjbUMYC8fg8zhRnZ+payxijuQOJiSJCMDASBj+sQgAYHa+fiNy7DYJS2Z5AQAHmgZ0i4OI9NPcN4KG3hF1EV9yO0pWVigJydruAPoDoaS+N5GVMSFJptTYp5yOV+ZZb34kAJR40+BNdyISk3G6k8OTiaxsj9quvaYyBzabPu2QgkhI7m/ux0goomssRKQ/sUCwKNuNdJdd11jEpts9jX26xkFE+nhTrY5cWe5DVpozqe+dm+FCTaFyaLyrntcgonhhQpJMqcniFZKSJGFBMedIEqWCPQ1ioY1+8yOFilwPSn3pCEdlLS4iSl2N6gLBSgMsEFzDlkmilCbmR16Z5PmRwuXVyqHtzjp2kRDFCxOSZEqiZbvSggttBM6RJLI+WZa1ah8950cKkiRpVZJv1/boHA0R6U0bkWOA+y1RIcmWSaLUE43JePuMkgi8KsnzI4X1TEgSxR0TkmRKTX3WT0gu0hKSrJAksqrG3hH0DIfgctiwtMyrdzgAgA01SuWBGBxPRKmrQa2QrDLA/VZuhgvVBUqlJqskiVLLkdZBDI6GkZXmwHKd7pfWqXMrT3T40cdDEaK4YEKSTCcYiaJtcBQAUGGAFqJE0SokO4Ygy7LO0RBRIuxW50cuK/XC7dB3PpsgWqGOtg2h2x/UORoi0pM2IscgM7u1tu0mJiSJUslbZ5RD0sur8+Cw65PCyM90Y16RMkfynXpWSRLFAxOSZDot/aOQZcDjsiM/06V3OAkztygTdpuEgZEwOoeYFCCyIjGncU2V/vMjhfxMNxbPUg5ExLwmIkpNYqmNESokAWC1SEhyxi1RSnnjlHI/cpVO8yOF8bZtLrYhigcmJMl0Ji60kSR9N9ImUprTjup8pSKBbdtE1iTmR4qqH6MQ85nePMW2baJUFQhGtCppIyy1AcaXfx1sGUAoEtM5GiJKhkAwgn1qVbRe8yMFzpEkii8mJMl0tI2PBjmtTyTRtn2MCUkiy+kLhFDbrVzPVhssIXn1PKUC4Y3TPYjFODKCKBWJed0+jxNej1PnaBRzCjLg8zgRjMRwtG1Q73CIKAneqe9DOCqjLCdd9+e/dbPH50j2DrODjWimmJAk0xlvHzLGaX0iLeRiGyLLEksZ5hZmIifDWOMn1lTmwuOyo2c4iOMdvP4QpSLtADjXOAfAkiRhdYXats3FNkQp4U11yd5Vc/N1747Ly3RjflEWACVRSkQzw4Qkmc74gHXj3CAnysIS5QOPCUki69mjLrRZU2Ws6kgAcDlsuFxtS3qDbdtEKanRYAtthNVVTEgSpRIxz/rKGn3btYX16rZttm0TzRwTkmQ6okLSKPOMEmmRWiFZ3xPAWDiqczREFE971IfpNZXGWWgz0dXzlBt/MUieiFKL0RbaCKJCck9jP2SZIyWIrCwcjeF01zAAYK1BDnDFHMkdTEgSzRgTkmQq0ZiszTTSe4ZIMhRkuZGX4UJMBk52+PUOh4jiZCwcxaGWAQDGrJAExhOSexr7EAhGdI6GiJJNtGxXGKhlGwCWl/vgtEvo9gfR0j+qdzhElECdQ2OQZaVzIz/TrXc4AIB1akLyVOcwejhHkmhGmJAkU2npH0EoEoPLYcMsX7re4SScJEmcI0lkQYdaBhGOyijIchvuYV+oyvOgLCcd4ajMtiSiFCRatqvyjdWRkua0Y/EsLwDlwISIrKttYAwAUOJNg82m7/xIITfDhQXFnCNJFA9MSJKpnFFL9qvzM2A3yIdSonGOJJH1iIfotVU5ug9ovxBJkrQqSTFQnohSQygSQ9uAUn1opKU2wupKzpEkSgXiOjTLa6xCFK1tu5YHtkQzwYQkmYpISNYUZuocSfIsKBYVkmzZJrKKPQ3KQ/Rqg86PFK6eyzmSRKmopX8EMRlId9pRkGWMNsmJ1qgJSXEtJSJrahUJSYN1xomEJDtIiGaGCUkyFZGQnFuYpXMkyaO1bHcMcXg7kQXEYrJW1WOUAe0XckVNHuw2CXU9ATSr83uJyPpEu3ZlnseQVdyiQvJkpx9DY2GdoyGiRGkfFAnJNJ0jOdu62cqB8ukuzpEkmgkmJMlUznSnXoVkTWEmnHYJ/rGIdkpIRObVOjCKwdEwXA6bduBgVNlpTqyq8AEA3jjNKkmiVCEW2hh1gWBhdhrKc9Mhy8CBpgG9wyGiBBEzJI1WIZkzYY7krjrOkSSaLiYkyTRkWU7Jlm2Xw4Y5Bcqfl23bROZXqx6szM7LgNNu/I9htm0TpZ7GPlEhaayFNhOtUUde7OEcSSLLajNoyzYAXD5HnSNZxznbRNNl/CchIlWXPwj/WAQ2CajKN+aJfaIs4qZtIsuo71Eqj2YbbHPthYjFNtvP9CIcjekcDRElg2jZrjDgQhthldq2vY8JSSLLEgnJUoO1bAMT50iyQpJoupiQJNMQ1ZGVeRlwO+w6R5NcC5mQJLIMLSFZYI6E5JJSL3weJ/zBCA42D+gdDhElgWjZrjJ0haSSkNzf1I8ID0uILMc/FsbQWAQAUGKwLduAMkdSkpRn1G4/50gSTQcTkmQaIiEp2pdTCROSRNZhtgpJu03ClTX5ANi2TZQKojEZzX1KVZJRZ0gCwLyiLGS5HQiEojjRwZE2RFbTPqjMj/SmO5Hhdugczbl8HhcWFivPaNy2TTQ9uiYkf/Ob3+CjH/0oli9fjsLCQjidTni9Xlx22WV4+OGHMTw8fMGfffXVV7F582bk5+cjPT0dCxYswJe+9KWL/gyZWyrOjxQWlihDkxv7RhAIRnSOhohmoq5bSUhWmyQhCYy3bW87zTlJRFbXMTSGUDQGp11Cidd4bZKC3SZhhbp0a18T27aJrMbI8yOF8bZtJiSJpkPXhORjjz2GX//614hEIli1ahXuuusurFmzBkeOHMEXv/hFrFy5Em1tbef83KOPPopNmzZh69atWLx4Md73vvdhcHAQDz30ENasWYOeHj4wWZFISM5NwYRkXqYbhVluyDJYBUBkYmPhKNoGlRtss1RIAuOLbQ61DKA/ENI5GiJKJNGuXZbjgcPgi7e0xTYNTEgSWY22YdvAByPrqpVr0F7OsiWaFl3vMh555BH09PTg6NGj2Lp1K5544gm89tpraG5uxpVXXokzZ87ggQceOOtn9u/fjwceeAB2ux1btmzBtm3b8NRTT6G2thbXX389Tp48iU9/+tM6/Ykokc50p26FJDDetn2ig23bRGbV0BuALCvtR7kZLr3DmbRibxrmF2VBloFXj3fqHQ4RJZBYaGPkdm1htTpHck9DH2RZ1jkaIoonM1RILi31AlAKZ8bCUZ2jITIfXROS69atQ25u7jlfz8vLw0MPPQQAePnll8/6vYcffhiyLONjH/sYbrrpJu3rHo8Hjz/+OGw2G5555hmcOHEiscFTUg2OhLVhwXNSNCFZrS7AaFIfFIjIfOq7x+dHSpKkczRTc+uKWQCA3+5u1jkSIkokLSFp4A3bwqpKHxw2CW2DY2jpH9U7HCKKI9FRYuSEZIk3DTkeJyIxGac7OTqOaKoM24fhcCiDa91ut/a1UCiELVu2AADuvffec36msrISGzZsAAA899xzSYiSkuVMt9KmXOJNQ6YBhxonQ4X6YNDUx4QkkVnV9ZhvfqRw15oyOGwS9jT241QnR0cQWZVo2a4w8IZtweNyYHm5DwCwgzPciCxlvELSuC3bkiRh0Syli+1Y+6DO0RCZjyETkn6/H//6r/8KALj11lu1r586dQojI0oyZs2aNef9WfH1/fv3JzZISqpUXmgjlOcwIUlkdmbbsD1RYVYarl9YCAD433eadI6GiBJFVEhWmaBlGwDWqzPcuFSCyFq0GZIGrpAEgEXqWK1jbRyrRTRVhkhIvvzyy7jvvvvw0Y9+FDfeeCNKS0vx8ssv473vfS++8Y1vaN9XX18PAPD5fMjKyjrva5WXl5/1vWQNIiE5pyB1E5IV6oNBMxOSRKZVp87CnV1gvoQkANxzWQUA4Nl9rZyVRGRBsixrFZJmmCEJjG+53VXHOZJEVhGLyWg3Qcs2ACyepcyRPNbOhCTRVBmi9/XYsWP4xS9+cdbX7r33XnzrW9+C1+vVvub3Ky1iGRkXfpDLzFQSVkNDF78gBINBBINB7f+/1PeTvlghOV4hOTQWweBIGF6PU+eIiGiq6rWWbXNey66aW4BSXzpaB0bx0pF23L6yTO+QiCiOegMhBEJRSJKyZdsMVlfmwGGT0Dowipb+UZSbYPYlEV1cTyCIcFSGTQKKstyX/gEdaS3bbUOIxWTYbOaaEU6kJ0NUSH72s5+FLMsIhUI4c+YMHnnkEbz00ktYtGgR3njjjYS858MPPwyv16v9EpWVZEypvmEbANJddhSoH8hs2yYyn/5ACP0jYQBAVb45H5jtNgl3r1U+L//3HS63IbIa0a5dkp2GNKdd52gm56w5krVs2yayAtGuXZSdBofdECmLC6rOz4DbYUMgFOUzGtEUGepft9PpxJw5c/C5z30OL730Evr7+/HhD38Yo6NKubZo0w4EAhd8jeFhJXGVnZ190fd68MEHMTg4qP1qbuaDlVGNhaPa5sS5KZyQBLjYhsjM6tU2yBJvGjwuQzQoTMsH15TDJgHv1Pdp1etEZA3j7drmGitxudq2zTmSRNYwvtDG2O3aAOCw27CgWMlTsG2baGoMlZCcaN26dVi0aBGam5uxZ88eAEBVVRUAYGBgQGvffjeRWBTfeyFutxvZ2dln/SJjqu0ehiwDOR4n8jKNXbKfaExIEplXfbd5F9pMVOxNw3ULigAAT3K5DZGliApJs8yPFNZPSEhyjiSR+YmEZInXuBu2JxJt20fbuGmbaCoMm5AExmdFdnV1AQDmz58Pj0e5QRJJyncTX1+1alUSIqRk4PzIceVMSBKZVl2PutDG5AlJALh3ndK2/cy+Fi63IbIQs1ZIrqr0wWmX0DY4hua+Ub3DIaIZEi3bpSaokAS4aZtougybkOzp6cHBgwcBAPPmzQMAuFwu3HzzzQCAJ5544pyfaWxsxPbt2wEAt99+e5IipURjQnKcqJDkpm0i89EW2hSY/1p2zbxClHjT0D8Sxp+OdugdDhHFSWOfOSskPS4Hlpf5ALBtm8gKzNSyDQCLuGmbaFp0S0geO3YMv/nNbzA2NnbO7506dQp33XUXgsEg1q9fj6VLl2q/94UvfAGSJOFnP/sZtm7dqn19ZGQEn/jEJxCNRnHnnXdiwYIFSflzUOKJhOQcCzzEzxRbtonMq65bbNg2V+XR+dhtEj64RqmSfJLLbYgso0lt2a4w4abq9ZwjSWQZ7YPmSkguKM6CJAGdQ0H0DAf1DofINHRLSHZ1deHDH/4w8vPzcdVVV+Gee+7BnXfeibVr12LhwoV4/fXXsXDhQvz2t7896+dWrVqFRx55BNFoFJs3b8a1116Lu+++GzU1NXjttdcwf/58/OhHP9LpT0WJwArJceIBoXVgFJFoTOdoiGiyYjEZDb3WmCEpfHCtstxmR10v6rq53IbI7PxjYfQGQgDMVyEJcI4kkZW0qi3bZpkhmeF2YLY66oJt20STp1tCcvHixfj617+Oq666Ci0tLfjDH/6AF198ES0tLbj++uvx2GOPYf/+/aioqDjnZ++//3688soruPHGG3Ho0CG88MILyMzMxIMPPojdu3cjPz9fhz8RJUIkGtMe4pmQBAqz3HA5bIjGZLQPnltdTETG1DE0hrFwDA6bhLIcc5z2X0qpLx3XzCsAAPx2N6skicxOLLTJy3AhK82pczRTt7oyR5sjyU4SIvMKRqJalaFZZkgC44tt2LZNNHkOvd64oKAAX/ziF6f98xs3bsTGjRvjGBEZUWPfCMJRGelOO2Z5zfOBlCg2m4TynHTUdgfQ1DeiLbkhImMT7doVeR447IYd3zxl91xWgb+c7Mbv9rbg/k3zkOa06x0SEU2TWTdsC+kuO1aU+7C7oR8763pNt5iHiBQdatFFutMOn8c8hyOLZmXjxUPtOMoKSaJJs85TEVnS6c7xdm2bTdI5GmPgHEki86lXN2xbYX7kRNctKESpLx19gRCe3tuidzhENAONfebcsD3ReNt2n86RENF0taoLbUp8aZAk8zz/LRaLbdoGdY6EyDyYkCRDq+3m/Mh3Y0KSyHzqLLRheyKH3YZPXjUbAPDTN+sQjXFuG5FZNfSIhKQ5KyQBzpEksoI2dX6kmdq1AWBRidKyXdcTwEgoonM0RObAhCQZGhfanKucCUki06nvsdZCm4nuXlsOn8eJxt4R/Oloh97hENE0Nagt22a+Tq2qUOZItnOOJJFptakVkmYb11WQ5UZBlhuyDJzo8OsdDpEpMCFJhiYSknMsVlU0EyIh2cwbbSLTsHJC0uNy4KPrKwEAP95Wy6okIpMSFZJVJm7ZFnMkAaVKkojMp31wvGXbbBaLxTacI0k0KUxIkmHFYjJbts+jgglJIlMJRqLav1erzZAUPnpFFdwOGw62DGIHkwBEphMIRtDlV7bamjkhCYy3be+o5bWIyIxa1ZbtWSZr2QbG27a5aZtocpiQJMNqHxrDSCgKh00y9TyjeBMVkv0jYQyNhXWOhogupblvBDEZyHDZUZDl1juchMjPdOODa8oBAD/eVqdzNEQ0VQ29SnVkjscJr4m22p7P5RMW27Bim8h8RMu22WZIAsqmbQDctE00SUxIkmGJdu2q/Aw47fyrKmS6HcjLcAFglSSRGdR1jy+0MdO2yKn65FWzYZOAbae6cZyVAUSm0tCj3E9UWaCKe2VFDlx2GzqGxtDYy/skIjORZRntYsu214wt28qm7RPtQ4hEYzpHQ2R8zPKQYZ3uVIYB13B+5Dk4R5LIPKw8P3KiyrwM3LS0BADwkzdYJUlkJqJCcrbJ27WBs+dIcoQEkbkMjUYQCEUBmLNluzLXA4/LjmAkpl1XiejCmJAkw6pVq4o4P/JcFdy0TWQaqZKQBIBPXz0HAPD7g21o6ef1icgsxEKbSgskJAHg8jlK2/Ybp7p1joSIpqJVrY7My3AhzWnXOZqps9kkLCxh2zbRZDEhSYYl5oeI5BuNY0KSyDzGW7at8aB/MUvLvNhQk4doTMbjb9XrHQ4RTZKo5KnKt8Y91/ULCwEoIyTGwlGdoyGiyTLzhm2Bm7aJJo8JSTKsziFlw1qRCeeHJNp4QnJU50iI6FLqUqhCEgA+pVZJPvlOM/oDIZ2jIaLJqFdnSFrlOrW01IuibDdGQlG2bROZiChImeU1X7u2wE3bRJPHhCQZlpaQzLbmVtqZ4AxJInMYGgujZzgIwDoP+pdy1dx8LCrJxmg4iifeadI7HCK6hOFgRLtOWWGpDQBIkoSNC4sAAK8e69Q5GiKarNYB5fnPjPMjhYmbtmVZ1jkaImNjQpIMaSwcRf9IGABQnM0KyXeryFMSki39I4jG+EFHZFRiLltBlhtZaU6do0kOSZJw77oKAMBOViYRGZ64TuVluJBtoevUpkVqQvJ4J2K8VyIyBVEhWWrihOS8oizYbRL6AiF0DgX1DofI0JiQJEPqUi/ebocN3nTr3BzHS3F2Gpx2CeGojA61kpSIjCeVFtpMtKA4C8D4/EwiMi4xP7IyzxrzI4XL5+Qhw2VH51AQh1sH9Q6HiCbBCjMk05x21BQoS1mPtfPaQ3QxTEiSIXX6Rbt2GiRJ0jka47HbJJTlqHMke9m2TWRUtWKhTYolJOeoN+KtA6MYDXGhBJGRiQpJq7RrC26HHdfMLwCgVEkSkfG1WaBlGwAWlypt2weaBvQNhMjgmJAkQ+oYVD6M2K59YZwjSWR8qVohmZPhQo5HqW6v6xnWORoiuhhtoU2e9a5TYo7kK5wjSWR40dh455eZW7YBYP3sPADA27UcXUN0MUxIkiFxw/alVeQqH9RNTEgSGZb491lpwQf9SxFVkmzbJjK2xl5rVkgCwHULCmG3STjR4ecBLpHBdfnHEI3JcNgk5Geae6np5XOUhOTB5gEMByM6R0NkXExIkiFpCcksc38YJVK5aNnmDTaRYXWos5BmmXgW0nRVFyjJjdpuVkgSGZmYIWnFSm6fx4U1lTkAWCVJZHRioU2xNw12m7lHdpXnelCR60EkJmN3fZ/e4RAZFhOSZEgd6lKbYlZIXlBFLhOSREYWjsbQ5U/da1k1KySJDM8/FkbPcAiA9ZbaCBO3bRORcbVaZH6ksKFGbds+06NzJETGxYQkGZKokCzkDMkLEjMkW/qZkCQyom5/ELIMOO0S8jNSr9pbtGyzQpLIuBrU+ZH5mS5kpTl1jiYxREJyV30fBkfCOkdDRBfSrlZIzrLIIe7lc/IBcI4k0cUwIUmGJBKSXGpzYRVqJUPPcAgBziYhMpx2dTlXUXYabCZvPZoO0bJd1x1ALCbrHA0RnU+9mB9p4Tm3lXkZmFeUiWhMxuunuvQOh4guQLRsW6VC8gp1juTx9iH0BUI6R0NkTExIkuHIsswt25OQneaET91i28wqSSLDaVfnR5ZY5KR/qipyPXDYJIyGo9rWTCIylsYe6y60mUhs236ZcySJDMtqLdv5mW4sKM4CAOxglSTReTEhSYYzNBpBMBIDABRmp16b41RocyR7mZAkMhrtYMVrjRvrqXLabVolN+dIEhnTeIWkNedHCqJte9vJboTUe0wiMpZ2Cy4CvEJr2+YcSaLzYUKSDEdU0vg8TqQ57TpHY2zlXGxDZFiiZTtVKyQBzpEkMrqGFKmQXF7mQ0GWG8PBCHbWsVKJyIis1rINjLdtb+diG6LzYkKSDIfzIydPVEg2MyFJZDgcPTFxjiQTkkRG1KB2WFh5hiQA2GwSNi4sBMBt20RGNBKKoF9dOmWlhOS66lzYbRIaekfQqiZciWgcE5JkOB3csD1pFayQJDKsVJ8hCUyskGTLNpHRDI6GtUULVq+QBMbnSL56rBOyzEVbREbSps6PzHQ7kJ3m1Dma+MlKc2JZmRcAqySJzocJSTKcTq2qiPMjL4UJSSLjEhWSJRY66Z+qOayQJDKsRnV+ZEGWG5luh87RJN6GmnykO+1oGxzDyU6/3uEQ0QRWnB8paG3bXGxDdA4mJMlwOv3KQ3wRKyQvSWvZ7h9FLMbTfiKjiMZkdPqDAFK7QrI6X6mQbBscw0goonM0RDRRfU9qLLQR0px2LJ6VDQA41clDEiIjseL8SGGDWGxzpofV2UTvwoQkGU7HoPIQz4TkpZV40+CwSQhFYlqrOxHpr9sfRDQmw26TkJ+ZutXeORku5Ga4AHDTNpHRNPSkxvzIibQxEl1MSBIZSavasm3FhOSqyhy4HTZ0+YMcYUP0LkxIkuF0+bkIYrIcdhsq1MoGPuwTGYdoPSrKcsNuk3SORl/V6my6uh5eo4iMpKE3NTZsT6Qt2uL1iMhQ2kWFpAW7StKcdqypygEAbK/lHEmiiZiQJMMRc9dYITk540sjeNpPZBTahm0L3lhPFSuSiIxJJCRnp1BCktcjImNqG7RuyzYAXDGhbZuIxjEhSYYSicbQM6y2bHtTt81xKpiQJDKedrHQxmvNG+upYEVSfMiyjIaeACLRmN6hkEU0qP8mK1NkhiQAzClU7pnqeoY5e5vIQNos3LINjC+22VHbiyivPUQaJiTJUHqGQ4jJUOauZTAhORliiy0TkkTGIWa6skKSFUnxEInG8OCzh/Ge/3od7/3Om3jjVLfeIZHJDY6E0T8SBpBaMyTLc9LhtEsYC8e0iiwi0pcsy+NLbSx6kLu01IsstwNDYxEcbRvUOxwiw2BCkgxFPMQXZrlhS/G5a5MlTvtru1h9RGQU4xWSTEiKCsn6ngArkqZhLBzFZ36zD0/ubgYAnOkaxkf/5x188he7tS3JRFNVr7ZrF2a5keF26BxN8jjsNlSqCVjO3iYyhr5ACMFIDJJk3Q45h92GddVKleT22l6doyEyDiYkyVA4P3Lq5uQrCcmOoTEMByM6R0NEwPhwdrZsA+W5HjjtEkbDUbSrh040OUNjYfz1/7yDl491wuWw4dG7l+MTV86Gwybh1eNduOHRbXj4j8fhHwvrHSqZjGjXTqWFNgI7S4iMRbRrF2S64XbYdY4mcUTbNudIEo1jQpIMRWzYLsq25ulYIng9TuRnKv971fO0n8gQ2rnURuO021CRq8yoq2MCYNK6/GO4+8c7sau+D1luB3758ctw+8oyfOWWRdj62atxzbwChKMyfvxGHTZ96w10MtlLU6AttEmhdm2Bs7eJjKVVHOJadH6ksKFGWWyzu6EPwUhU52iIjIEJSTIUbTMtKySnpJqn/USGEYvJWnKILdsKzpGcmsbeAD7w2A4cbx9CfqYbT35qPdarrV4AUFOYiV98/DL87L61KPWlo2NoDE++06xjxGQ22kKb/NRZaCNUq9cjtmwTGUO7Os+11Gfte6Z5RZnIzXBhLBzDsbYhvcMhMgQmJMlQOofEhm1rfyDFG0/7iYyjJxBEJCbDJgEFWaz2BiYkADjz8ByyLKOlfwQvHmrDv794DB94bDtuePQNNPWNoCLXg2f+9nIsnuU9789eu6AQn9s0DwDwwoFWyDJndNLk1PeOAEjVCkke4hIZidUX2giSJGFFuQ8AcKB5QNdYiIwidaZYkymIqqKiLCYkp4I310TGISq9C7LccNp57gfwGnUhv97ZiO+8dhrd/uA5v7eszIv//ugaFF6iY+DGJcX40vOHUdcTwOHWQSwr8yUoWrKSVJ4hKQ5IOoeC8I+FkZXm1DkiotQmZkhavWUbAFaU+/DnE11MSBKpmJAkQxFbtjl3bWq4aZvIOMbnR1r/xnqy2CJ5rrFwFN946QT8wQgcNgkLS7Kxotyn/KrwYXZeBmw26ZKvk+l2YOPCIrx4qB3P729jQpIuaWAkhMFRZRFSZV7qtWx7050oyHKj2x9EXXcAy9WKJSLSR1uKtGwDYIUk0bswIUmGolVIcobklNSoD/v1vQFEYzLsk3iIJaLEEBWSs3iwohEVku2DYwgEI8hw8/bjT0c74A9GUOpLx6ufuwbprulvFr1tRSlePNSOPxxqw5duXsjPALqoRrVduzDLDY8rNf8tVudnKAnJnmEmJGcgGpMxOBqGx2VHmtO625EpsbSW7RSokFyuHho29o6gLxBCboZL34CIdJaadyFkSCOhCPxjEQDcsj1Vs3zpcDtsCEZiaO0fRUUKVjwQGYU46Wel9zifx4W8DBd6AyHU9wSwpPT8MxFTydN7WwAAd64um1EyEgCunlcAn8eJbn8QO2p7ceXc/HiESBbV0q9co8pyrP/wfyFzCjOxq76PnSVTcKbLj2+9cgqdQ0H0B0LoHwlhYDQMWVaqTl+5/+pLjpggerdQJIYudWxJKiQkvR4nqvMzUNcTwMGWAVw7v1DvkIh0xeFWZBiiqijDZec8nymy2yTMzueMNiIjENcybtg+WzXnSGraB0fx1pkeAMCdq0pn/Houhw2bl5YAAJ4/0Drj1yNrax1QKiRLc1L38JLLAKfuP7eexB8Pd2BvYz/qegLoH1GSkQAwOBrGs/t57aGp6xwagywrn2N5KVItqLVtNw3oGgeRETAhSYahbdjm6eq08OaayBg4Q/L8xq9RrEh6dl8rZBm4bHYuKuO05fi2FUpic+uRDoyFo3F5TbKm1n4xry11r1HigIRzbSdnYCSEv5zsAgD8xx1L8du/WY9X7r8au7+0EV+7bQkA4Nl9LZBFhpJoksY3bKdBklJj3MiKCh8AzpEkApiQJAPh/MiZ4RZbImNgheT5jScAUvsaJcsynlHbte9aXRa3111TmYNSXzqGgxG8drwrbq9L1tOqJgBKU7hlW5u93aPM3qaL23K4HeGojIUl2fjQZRVYV52HuUVZKMhy49bls+By2HCqcxhH24b0DpVMRoy5SYV2bUFUSB5sGWASn1IeE5JkGJ3csD0j3LRNpD9ZlrWEZDEPV87CCknFvial3dHjsmtt1vFgs0m4dcUsAGzbpovTZkimUALg3cTs7VA0hpb+Eb3DMbwX9rcBAG5fOeuc3/OmO7FpYREA4Dm2bdMUtQ2IQ9zUuR4tKM6Gy2HDwEgYDb28/lBqY0KSDKNDTUgWcqHNtLBlm0h/fYEQQtEYJInV3u9WrVUkDSOWwhVJYpnNTUtK4r5tXLRtv36yCwMjobi+NlkHKyTPnr3Ntu2La+4bwTsNfZAk4Nbl5595e/tK5esvHGhDJBpLZnhkcqJlu9SXOvdMLocNi2dlAwAONPfrHA2RvpiQJMPQKiT5ED8t4sa6NxDigyiRTsT8yPxMN1wOfsROVJ6TDqddwlg4prVopZrRUBQvHmwHAHwgju3awvziLCwozkI4KuOlIx1xf30yv6GxMPxjEQCpPUMS4EHuZP3+oFIdeXl13gW7mK6ZX4DcDBd6hoN4U13YRTQZ2gzJFLseaW3bzYP6BkKkMz4tkWGIpTZMSE5PhtuBWeqNYqq3RBLppZ3zIy/IYbdpC1xS9Rr18rEO+IMRlOWkY93s3IS8x/vVKsnn2TpJ5yEW2vg8zrhX6JoNZ29fmizLWhv2bSvPXx0JAE67DbcuV9q5n93Haw9NntaynaIJyf1cbEMpjglJMgwxd62QCclp0+ZI8uaaSBcdauUfD1bOb16Rco061eHXORJ9iHbtO1eVwWZLzDZRMUdyV32fVnlCJHDD9jjO3r60o21DONM1DJfDhvcuKb7o94q27ZePdsA/Fk5GeGQBomMilVq2AWBleQ4A4HjbEIKRqM7REOmHCUkyBFmW0eXnUpuZYvsRkb5YIXlxC4qVmUnHO1JvE2vbwCjeUlsZ71wV/3ZtodSXjsvU6kvRakkkaPMjmZBEdb5yz1TXw3umCxGV1psWFiE7zXnR711W5sWcggwEIzGOjKBJmThCIpWW2gBAeW46cjNcCEVjOMbt9JTCmJAkQ+gLhBCOKksOCjK51Ga6qkX7EU/7iXShbdhOsRvryVpQnAUAONGeehWSz+1vhSwD62bnoiLPk9D3uo1t23QBXGgzTtwz9Qxz9vb5RGOydqhxsXZtQZIk3KEetjy7ryWhsZE1tKvt2t701BshIUkSlpd5AQAH2LZNKYwJSTIEsWE7P9PFRRAzICok61ghSaQLVkhe3MISpULyTNcwwim0iVWWZa1dOxHLbN5t89JiOGwSTnT4WTFPZ2HL9rgMt0O7VqfqXNuL2VHbiy5/ED6PE9fMK5jUz7xfHRmxs65PS34TXYho1061hTbCCrVtmwlJSmXM/JAhdKkLbYo4d21GREKyqW8kpR72iYxCHK4wIXl+pb50ZLodCEVjqO9JnQTAvqZ+1PcE4HHZsXlpScLfz+dx4YqafADAVrZO0gQtapKojBWSAHiQezFimc3NS0smXSxQluPB+mplZAQrtOlS2gZSc36ksKLCBwA4yIQkpTAmJMkQxEM8E5IzU5TtRobLjkhMRmPviN7hEKUUWZbRrp72p9ospMmy2STMV9u2j7enzsyk1453AQBuWFSUtLa0zeoCipeOtCfl/cgcxiskEzs2wCy0UTeskDzLaCiKreq14/ZJtGtPdMfK8bZtWZbjHhtZh0hIpuo904oyHwCgoXcE/QGOjaDUxIQkGYKYu8aE5MxIksRN20Q6GRgJYyysVCYXZnMW7oVocyRTaNP2oZZBAMBaddlMMtywuBh2m4QjrUNo4gEVARgLR9EzrHSkcIakgssAz++V450IhKIoy0nH6sqcKf3sTUuL4XbYUNsdwOHWwQRFSFYgZkimasu21+NEdb5yKHKgZUDfYIh0woQkGYK2YZsJyRnjzTWRPsT8yLwMF9Kcdp2jMa4F6hzJEylSISnLMg6pDxrLSn1Je9/cDJfWOskqSQLGq5HSnXbkeC6+MTlV8J7p/F5Q261vX1kKSZKm9LNZaU7csFip0H52H9u26cLEnNFZKdqyDQAryn0AgANNA7rGQaQXJiTJEMYrJFlVNFNzuGmbSBcdQ8qNdTHnR17UwhSrkGzsHcHQWAQuu01rV0+W9y5R5lX+kXMkCWdv2J5qksmqRMt2Uy9nbwu9w0FsO9UNAHj/iqm1awt3rFJ+7nd7mrVRJkTvJpbapPKSreUiIck5kklR2z2MH/zlDAZG2CJvFExIkiF0iqU2fJCfMZ72E+mDG7YnZ56alGsfHEuJG8JDasviwpKsSS+GiJcbFxdBkpSB+S39bNtOddywfa7i7DR41NnbTX38NwIAWw63IxKTsbTUixp1DNBUXTO3AKsqfAiEovjai8fiHCFZQSwmawUpJSl8TRIVkgdbBjhzNQkefOYwvvmnk/jQT3ai2x/UOxwCE5JkEJ1iqU0WH+RnqnpCQpIfbETJI26sWSF5cdlpTm3DbypUSR5Sqx6WqcPrk6kwKw1rq5S2bW7bpokVkqSw2aTxxTZdPMgFxrdr3zbFZTYT2WwSvn77UthtEv54uAOvn+yKV3hkET3DQYSjMmwSUJSVuh1yC0uy4XLYMDAS5kLSBKvrHsY7DX0AlPvPD/54hzbKhPTDhCTpLhiJolfdLMYH+ZmrzPPAJgH+sQh6hq1ffURkFOMVknzYv5QFxakzR1JUSC4t8+ry/mLbNhOSxArJ8xOdJXU9HHXT2BvA/qYB2CTgfctLZvRaC0uycd8VVQCA//v7oxgLR+MQIVmFOCApzk6Dw566KQmXw4bFs5R7IrZtJ9bTe1sAKFWppb501PcEcNePdqCxl9d+PaXuv34yDFEu7bLbOGQ9DtKcdpTnegCwbZsomcScLLZsX9rCEqVt+2SntSskozEZR9WE5HIdKiSB8TmSexr7tSpeSk0tagKgjBWSZ6nOVztLWCGJ5/e3AQA21OSjMA5dS/dvmoeibDcae0fw2Ou1M349so62AbZrCys4RzLhItEYntmnJCQ/dXU1fvfpyzE7PwOtA6O460c7cNri96NGxoQk6U60axdmuzlkPU44R5Io+drZsj1pokLyeLu1bwDruocRCEWR7rRrC8eSrdibhtWVOQCAPx1llWQqY4Xk+c0pVFu2U/yeSZZlPH9gfLt2PGS6HfjqLYsBAI+9Xot6VqGSShzizuL1SEtI7mdCMmHePN2DzqEgcjNcuH5hEWb50vHbT63H/KIsdPmDuPsnO3FEPUCm5NItIRkOh/Haa6/hn//5n7F27Vr4fD44nU4UFxfj1ltvxZYtWy7686+++io2b96M/Px8pKenY8GCBfjSl76E4eHUvpkwo45BpUKyOJsP8fHCTdtEySXLE4azs2X7khaICskOP2Ix6866PdSi3NwuKc3WtSXtJrVt+4+H23WLgfQVicbQoR4Ac4bk2cTillOdw5a+Hl3KoZZB1PcEkO6048bFxXF73c1Li3H1vAKEojF89YUjnG9OAMZbtmf5+PwnEpLH24YQjHC0QSI8tacZAHDbilJtwWBhVhqe/Jv1WFbmRV8ghI88vgvDwYieYaYk3e6Ot23bho0bN+K//uu/0NLSgiuvvBJ33HEHCgoK8Ic//AG33HILPvWpT533Q+vRRx/Fpk2bsHXrVixevBjve9/7MDg4iIceeghr1qxBT0+PDn8imi6tzZEnZHEjKiTPpPhpP1GyDI1FMBJSbiJ5uHJpVXkZcDtsGA1HLb3Z9lDLAABgaalP1zjeqyYk32no41bJFNXpDyIak+GwSXFpxbWSmoJMuB02DAcjaEjhWWJimc2mRUXIcDvi9rqSJOH/3boYLocNb57uwRYejBCgLROZxUNcVOR6kJvhQigaw7E268/WTrbe4SBePd4JAPjg2rKzfi8nw4XffHIdKnI96B8J42V2kiSdbglJm82GO++8E2+88Qba29vx4osv4re//S0OHz6MJ598Ena7HT/5yU/wq1/96qyf279/Px544AHY7XZs2bIF27Ztw1NPPYXa2lpcf/31OHnyJD796U/r9Kei6RhfBMEb5HiZWySqj/ihRpQMojrS53Ei3WXXORrjs9skzC9WrlMnLHydEgttlpfrs9BGKMvxYHmZF7LMtu1UJdq1S3xpsNs4Hmcih92GRepSicMp2rIXjsbwh4PK/Mh4tWtPVJWfgc+8Zw4A4P/94Rj8Y+G4vweZi3j+Y8u2krRfri6+O8i27bh7/kAbwlEZy8q82sigibLSnLhzVZn2vZRcuiUkr7vuOjz99NO46qqrzvm9u+++G/fddx8A4Je//OVZv/fwww9DlmV87GMfw0033aR93ePx4PHHH4fNZsMzzzyDEydOJDR+ih8ugoi/BeqDfudQEH0BbtomSjRxHWN15OSJ65RV50iGJ1Q6LC3VNyEJADctVZbbcNt2amodUCqROT/y/Jap/0YPt6RmQvKtMz3oDYSQl+HClXPzE/Ien75mDqryPOjyB/GL7Q0JeQ8yjza2bJ9lRbky65mLbeJLlmU8tVtp175rTfkFv+/9K2YBAN463c1OkiQz7FKblStXAgCam5u1r4VCIW225L333nvOz1RWVmLDhg0AgOeeey4JUVI8aFvWWLIfNxluByrzlE3bJ9qtW31EZBSs9J46cUpt1QrJU51+BCMxZKU5UJWnz0KbicQcyR11vTyoSkHjC208OkdiTEvUhOShFK2QfEFt137f8llwJmjebZrTjr+7tgYA8OIhtm2nsrFwFD3DyucQW7YVKyp8AJiQjLdDLYM42emH22HDrctnXfD7qvIzsLzch5gMbDnEKslkMmxC8vTp0wCAkpIS7WunTp3CyIhywrtmzZrz/pz4+v79+xMcIcVLBx/kE2Kh+rB/jAlJooRrH+C2yKkSi21OdFizQlIstFla6oXNAC2ylXkZWFSSjWhMxivHWCWZasQCCS60Ob9lZT4AwNHWwZRbbBMIRvCno8p8NVEllCibFhXBYZNwosOPBm7cTlni2S/daYfP49Q5GmMQLdsNvSPo56Fh3IhlNjctKYY3/eJ/125Tr39s204uQyYkOzo68POf/xwAcOedd2pfr6+vBwD4fD5kZWWd92fLy8vP+t4LCQaDGBoaOusXJV8kGkOXX01IsmQ/rhaWKAlJq7ZDEhlJ6wBnIU2VqJBs7B1BwIJbDUVCUiQ6jEAst/nLiW6dI6Fka1ErJMt4jTqvOQUZSHPaEAhFUZdiibKXj3VgNBxFVZ5H2/abKD6PC5fPyQMAvMTxESlLHJCU+NIgSfof2BmBz+PC7Hylm+KAuhCPZmY0FMXv1eTiBy/Sri3csmwW7DYJB5oHeGCSRIZLSEYiEXz4wx/G4OAgli5dik996lPa7/n9SmIlI+PCrU+Zmcp24UslGB9++GF4vV7tl0hkUnJ1+oOIyYDTLiE/w613OJYyXn3EZDtRoolZSJzPNnm5GS4UZSvX/ZOd1js4ERu2RdWDEayvVhIBe5v6IcupVQWW6lgheXEOuw2LZyn/Vo+kWNv2c/uVB/bbVpYmJTkkDka2HmHbdqo6pX7mV+dn6hyJsYgDgQNNA7rGYRV/OtoBfzCCspx07f7nYgqy3NhQo8zQfYFVkkljuITkpz/9abz22mvIy8vD008/DZfLlZD3efDBBzE4OKj9mjirkpKnQ10EUZSdZoiWNitZpFZInu4cRjga0zkaImtrG2TL9nRocyQtVsk9Fo7ipNqKvtRACcllZV44bBK6/UGtYo6sT5ZlHppMglg+dSiFFtt0+4N467RSMX3bivhv1z6fGxYVQ5KAgy2DWqKcUov4zF9Ucv6Ox1QlEpIHWSEZF6Jd+67V5ZPOM7xfnTP5woFWHtwmiaESkv/0T/+Exx9/HDk5OXjllVcwb968s35ftGkHAhcuoR0eHgYAZGefu9J9Irfbjezs7LN+UfKJhTYcaBx/ZTnpyHQ7EIrGUNfNsnOiRInFZLRrLdscPTEVVq3kPt4+hEhMRl6Gy1AJoDSnHYvVpMu+pn6do6Fk6Q2EMBZWDiY5HufCREIylSok/3CwDTFZSYRU5Sdn+VZBlhtrq3IBAFvZtp2Sjquf+QtK+Pw9kZaQbB5gMmyGGnsD2F7bC0kC7lw9+cOWG5cUI81pQ11PAEdarXVvalSGSUg+8MAD+O53vwufz4eXX35Z27I9UVVVFQBgYGBAa99+N1HpKL6XjK19cHyGCMWXJElYUGzNh30iI+kJBBGKxmCTlGpvmryFFq2QPKwmNJaWeQ03H2t1RQ4AYG8jE5KpQlRHFma54XbYdY7GuEQ185G2QURNuthmOBjBh36yA//x0olLJjTC0Rh+u1t5brp9ZXKqI4Wb2LadsqIxWesgEM8ppFhQkgWX3Yb+kTAae0f0Dse0ZFnG1148BgC4am4BynI8k/7ZTLcDGxcWAQCeP9CakPjobIZISP7Lv/wLvvWtb8Hr9eLll1++4Abt+fPnw+NR/kLt2bPnvN8jvr5q1arEBEtxJSoki7lhOyHEYhtu2iZKHHEdK8pOg9NuiI9V0xAVksc7hixVDXCw2XgLbYTVlUxIpprWfs6PnIw5BZlId9oxEoqivmdY73Cm5fWTXdhZ14cfbavF7/a0XPR7H3n5FE52+pGd5sD7lid2u/a7iTmSexr7teWWlBoaegMIRmJId9pRmZecqlyzcDvsWDRLeXY70DygbzAm9uKhdrx6vAtOu4Qv37xwyj8vxlf84WCbaQ+nzET3J6cvfOEL+OY3vwmv14tXXnkFa9euveD3ulwu3HzzzQCAJ5544pzfb2xsxPbt2wEAt99+e2ICprjqGGTLdiJx0zZR4onqI86PnLrq/Ew47RL8YxG0DVrnofRw6wAAYFmpceZHCqsqfQCUtnIrbjenc7VyfuSk2G0SFqvJALPOkdzTMH7Q8NXfH9GWh7zbm6e78aNttQCA//zAMuRmJGZm/4WUeNOxotwHWQb+dLQzqe9N+hIdEfOKs2Dn/oBzaIttmJCclv5ACP/6+6MAgM+8pwbziqZehXv1vAL4PE50+YPYUdsb7xDpXXRNSH75y1/GN77xDfh8vksmI4UvfOELkCQJP/vZz7B161bt6yMjI/jEJz6BaDSKO++8EwsWLEhk6BQnWss2KyQTQpvPxgpJooRhQnL6XA4b5hQoWzatcp0KBCM406VUVy0z0EIbocSbjlneNMRkZU4VWV8LKyQnTbRtHzbpHMk9jX0AgPxMF8bCMXzmN/swEjr74KHbH8T9vz0IAPirdRV475KSpMcJsG07VR1XP+sXsl37vFZW+AAwITldX9tyDL2BEOYVZeIz186Z1mu4HDbcvFS5LrJtO/F0S0j+/ve/x9e//nUAQE1NDX7wgx/gvvvuO+fX5z//+bN+btWqVXjkkUcQjUaxefNmXHvttbj77rtRU1OD1157DfPnz8ePfvQjPf5INA2iIqaEFZIJsaA4C5IEdPmD6B0O6h0OkSW1aglJHqxMh6jkPtFhjUruo21DiMlAcXYaCg06U3QV27ZTirhGlfHQ5JLEYpvDJqyQHA5GcKxNSfb8/GOXoTDLjTNdw/jqC0e174nFZDzwu4PoGQ5iflEWvnLLIr3CxU1qInRnXR/6AiHd4qDkEnPtF3KhzXmJCsljbUMIRqL6BmMyr5/swrP7WiFJwH/cuWxGM5Pfr7Ztbz3SgbEw/zskkkOvN+7r69P+7z179lxwJmRlZSX+67/+66yv3X///Vi6dCkeeeQRvPPOOwgEAqioqMCDDz6IBx98UNvGTcYWisTQoybJuNQmMTwuB6ryMlDfE8Dxdj+unOvWOyQiy2ljO+SMiKH2xy1SIXmoZQCAMasjhdWVOXjxUDv2ctN2SuAMyckT/26Ptg0hGpNN1VJ6oGkAMVn5LFpS6sV371mJe3+6E0/vbcHl1Xm4c3UZHn+rHm+c6obbYcP37l2JNKd+S44q8jxYVJKNY+1DeOVYB+5eW6FbLJQ8YowUF9qcX0WuBzkeJ/pHwjje7tcSlHRxgWAEX3ruCADgviuqsEpd4DddaypzUOpLR+vAKP58ogubl+pTSZ4KdKuQvO+++yDL8iV/NTQ0nPfnN27ciJdeegm9vb0YGxvDqVOn8NBDDzEZaSKdQ2OQZaUsOi/Js2tSidUe9omMRiy14Szc6VlgsQpJMXvO6AlJANjX2I8YB7Zb3vgMyclvGk1Vs/Mz4XHZMRqOorbbXIttRLv22irl3/f66jx8duM8AMCXnz+CZ/e14D//dAIA8NX3LZrWbLV4E23bLx3p0DkSSobB0bB2PVpQzArJ85EkCcvFHEkeGk7aN/90Eq0Doyj1pePzN8yf8evZbBJuXaEs+/rDwbYZvx5dmO5LbSh1tWvt2mmQJPOcQJuNttimgwlJokTgDMmZWVSSDUkCznQNW2Jm0niFpE/XOC5mYUk20pw2DI1FTJd0oakZDkYwOBoGwArJybDbJCyZZc62bbHQZnVVrva1v7u2Bhtq8jAajuJzTx1EOCrjpiXFuPcyY1Qj3rRUSUi+faZH+3tK1nVSPXic5U2D1+PUORrj4mKbqdnb2I9f7GgAADx8x1JkuOPTBLxpUREA4J36PsgyD28ThQlJ0o1YaFNs0BlbVsFN20BfIIT/88s9+NxvD+A3uxpxqtPPqiCKi7FwFL3q7Cu2bE9PQZYbd6wsAwD8+4vHTH3TNzgSRkPvCIDxWXRG5LTbsFxNmO5jBYaliXZtb7oTmXF6SLO6JaXmW2wTicawX/23vKZyvFXRbpPw7btXoiBLGdlT6kvHf9yxzDCFADWFWagpzEQ4KuPPJ7ht2+o4P3JymJCcPFmW8aXnDkOWgTtWleLqeQVxe+3Fs7LhctjQGwhp93YUf7wzId2ICklWFSWWaNk+0+VHKBKDy5F65xC/3d2MV44pN7rP7le2pXnTnVhdmYMls7IRk5XE0lgkirFwDGPhKFZW5OATV87WM2wyAVEdmeGyIzudH6nT9c83zseWw23Y09iPl450mHZWzzsNSsvk7PwM5Bh8FMnqyhzsqu/D3sZ+zm6zsNYB5SGKByaTt8yEm7ZPdPgRCEWRleY4pxW7IMuNH39kNb7/5zP43KZ5hqtMu2lJMb735zN46XAHblcPp8iaxPioBSX6jwswMpGQbOgdwcBICD6Pse8n9HSy048THX6kOW34ys3xXdLldtixrNSLPY392NvYj9n5GXF9fVKkXmaCDKNdfZAv8bJCMpHKctKR5XYgHJVR15OarXlvn+kBAFwzrwCXV+ch3WnH4GgYfz7Rhe/++Qy+/5cz+O+36vHrnU14em8LXjzUjq+9eAxnulLzfy+aPG1+pC/dMBUnZlTsTcOnrp4DAHj4peOm3Sy5o7YXgDK7zehWc9N2SuBCm6lbqi22GUQkGtM5msnZox6GrKrIOe8inlUVOfif+9Zq1Z9G8l51juS2U90IBCM6R0OJNL7QhhWSF+PzuLTkF6skL+71k90AgMur8xJyEMx7pcRjOQfpZuIMSUocSZKwoCQLuxv6cbx9KOVuAsbCUa1q6Su3LERNYRbC0RiOtQ1hd0MfaruH4bLbkOa0w+20I81pw0uHO3C4dRAvHmrTBsITnQ/nR8bPp66pxpO7m9DcN4qfv92AT10zR++QpmxHnZKQvHyO8ROSK9UNlLXdAfQHQoav6KTpadEW2vAaNVmz8zKQ6XZgOBhBbXcA802wDXiP+rAsFtqYyaKSbFTketDUN4LXT3bj5mXmrJCni4vFZG2GJFu2L215mRf1PQEcaB7Ae+YX6h2OYb1+sgsAEva/0aoJSwApMVghSboZT0jyJjnRUnmO5O6GPoQiMRRnp2FOQSYAdX5auQ+fvKoaD9+xDP/2/iV4cPNCfG7TPHzmPTW474oqAMCWQ+06Rk5m0MqEZNx4XA78840LAADf//MZ9A4HdY5oavoDIa0dbX117iW+W3+5GS5UFygVGPubeaNtVaJCsowVkpNms0lYPEu5bxJLqoxMluXxhTaVxr/2vJskSRO2bfO+y6qa+kYwGo7C7bChKs+jdziGxzmSlzYcjGjXvmviODtyolXq4e2pLj8XbyUIE5KkG7HUpsTHCslEG09Ipt6m7bfUdu0r5+ZPuqV20+IiuOw2nO4a1k5zic6nTas+4nUsHu5YWYolpdnwByP49qun9Q5nSnbVK9WRNYWZKMwyx9+H1RVsRbK6VlZITotYSnXEBHMkWwdG0TE0BodN0pIYZiPatv9yogtjYXOO7KCLE88g84qy4LAzBXEpK9TP54PNA6Ze9pdIb5/pQSQmoyrPg6oEzXcsyHKjMs8DWWZyOFF4NSBdBCNR9Awrm2lZIZl4YrFNKlZIvnVaTUjW5E/6Z7LTnNqWthcPtSUkLrKGtkFWSMaTzSbhy+pQ8ifeacLpTvNcs3bWKaMhLjfB/EiBs5Gsj2MlpkfMkTxkgoSkqBBaXOpFusuuczTTs7zMhxJvGgKhKN5U79vIWo5r7drGH4FgBAtLsuCy29A/EkYjNzyfl5gfmeiWdh7eJhYTkqSLzkGlFc/tsCHHYNv+rGh+cRYkCegZDqLbb642yJnoHQ7iaJtyIrthCglJAHjfcmWG0YuH2nkySRc0cakNxcf66jzcuLgI0ZiMr//xuN7hTJpYaGOG+ZGCSEgebB5E2CTLO2jyItGY9pnPed1TIyokj7UNGX6xzZ5G5TBkTaX55kcKNpuEGxezbdvKTogN2yk2y3663A47FqmjI1iZdy5ZlrFNnR95zfzEtGsLnCOZWExIki4mVhVxM23ieVwOzM5TStlPdKRO2/Z2NUGwoDgLBVnuKf3s9QuL4HbYUN8T0JKaRBPJssx2yAT5wk0L4bRLeP1kN7ad6tY7nEvqHQ7ipFrNaYYN28KcgkxkpzkwGo7iRApW0Ftdz3AIMRmw2yTkZU7tMzDVVeVlIMvtQDASw+muYb3DuShRIWnGhTYTiTmSrx7rRChi7CQwTd1x9fljASskJ21lhQ8AsK+JibB3O9M1jLbBMbgcNqyfndj7rjXqtXV/Uz+iMRapxBsTkqSLDnWhTXE2T+yTRdwApNIcyem0awuZbgeuW6C0AGw5zNN6OldvIIRQJAZJAop4LYur2fkZ+Mj6KgDAr3Y06hvMJIh27QXFWcg10bZqm03STv73qlVWZB0dQ8q9VmGWG3YbD3+nwmaTsLhUqU46bOC27cHRsHYYYsaFNhOtqcpFfqYLQ2MR7Kjr1TsciiP/WBjNfcoB7kJWSE7aGvXf9O4GJiTfTbRrr6/OS/ioirmFWchyOxAIRblbIAGYkCRdtHGhTdKJG4BUmSMpy/JZC22m4+Zlom27jW3bdA4xm60wyw2Xgx+n8fb+FbMAAO/U9xr+RHpHnXKtMVN1pKDNRmoa0DcQirsO9V6rmO3a07K8zAfA2G16+5v6IctAZZ5nyp0gRmO3Sdi0SKmS3Mq2bUs5pSbNi7PTkGOiQzu9icq8kx1D8I9xw/NEr59S2rXfk6Dt2hPZbRJWqNWqe1mtGnd8giJdtIu5a1xokzSptmm7oXcErQOjcNltuGz29KoGrltQiHSnHc19ozjUYtwKCdIHl0Uk1uJZ2ch0OzA0FjH8qAkxP9KUCUnORrIsdqPMjJgH++bpHsMeSop27TUmr44URNv2y0c7DX8QRZN3TC2GYLv21BRlp6E8Nx0xGdjPQ0NNIBjB7nrl2pfo+ZEC75UShwlJ0kW7uEnmqX3SiJuA2u7hlJjN89ZppZR/VaUPHpdjWq/hcTlw/UKlbZvbtundWrnQJqEcdptWHSBaoo2oa2gMtd0BSBKwvtp8SYHl5T7YJKB1YBTtakUdWUPHkLLQhiMlpmfd7Dy47Da0DoyiviegdzjnpS20Mfn8SOHyOXnwpjvRGwjhnXrjXvdparjQZvrEYcOeBv57EHbU9iIUjaE8Nx3V+RlJec/Vldy0nShMSJIu2rWlNrxJTpZSXzqy0xwIR2WcMfiA9nh4U50fedXcmZ2c3bJMaRvdwm3b9C5tXGiTcOvUQeW7DDxPTMw6W1icDZ/HfK1oGW4H5hUpB1ZHW41diUpTw5btmUl32bVEn7inMJJwNKZt3zXzhu2JnHYbNi4sAsC2bSs5oc7dW8gKySkT16A9TIRpxtu1C5O2HHdFuQ+SBDT1jaDLP5aU90wVTEiSLkSFZAlbtpNGkiQsUNu2j1m8bTsSjWlJgukstJnoPfMLkOGyo21wDPvYLkETaC3bfNhPGFFx+E5DH2IGbd/bqV5rRHunGc1WKwya+0d0joTiSSy1KeE1atrEoaYRE5JH24YwFo7B53FiTkGm3uHEjWjb3nq0w7DXfZq8WEzWFoGI8VE0eaJCcn/TAMJR63e4XYosy9pCm/ckqV0bALLSnJivHt7uaxxI2vumAiYkKenGwlH0BUIAeJOcbEtLvQCAQy0D+gaSYIdaB+Efi8Cb7sQS9c88XWlOOzYtUk7r2bZNE3GGZOItKfXC47JjYGR8k6zRiHbyy004P1Ioy1H+DostqGQNnWzZnrGr1KV4O2p7DJcMEC2cqytyYLPQFvUr5+Yjw2VH51AQ+9UKUDKvlv5RDAcjcNlt2uEXTd7cwkxkpzkwGo6mzB6Ai6nrCaClX9kRkOyDYG2OJBfbxBUTkpR0Ysh6utMOb7pT52hSy0p1Q5jVByO/pVYyXDEnD/Y43KSLtu0/Hm7naT1pOEMy8Zx2G9ZUKdUBOw3Ytt0xOIb6ngBsEnCZCedHCuW5HgBACyskLUOWZS61iYNFJdnIy3AhEIoa7t5JW2hTZd5rz/mkOe24jm3blnFcXUpXU5gJp52ph6my2SQtESb+zacyUR152ezcae8ImK7x/w6c5xlPvCpQ0rWpM41KfGlJm/tAipUVyoX0ePsQRkNRnaNJHJGQvHLuzNq1havm5SMrzYHOoSB280OIoFR69wwr1UecIZlY62YrD9u7DLjYZkedcq1ZUupFdpp5D9jKc5SEZHM/KyStYmg0gtGw8jnPGZLTZ7NJ2KCOfnlTXZZnBMPBCLbXKtcfqyy0mUi0bb90pIPzu03uRDvbtWdKHDqIJVap7PWT6vzIJLZrCyIheaR1CGNh6z5HJxsTkpR0HYOcaaSXWd40FGW7EYnJONw6qHc4CREIRrRS+pnOjxTcDjtuWKTcHD9/gG3bdHalt89j3kSUGaxXW6F31fcarkJ5R606P9LE7doAUJ6rJNVb+kb48G8RYn6kz+NEmtOuczTmJtq2jTRH8jc7GzE0FkF1fgZWVVgvIfme+QVIc9rQ0j+Ko21sUzUz0WbMhTbTt2ZChWQqf0aPhqLYVa8kZfVISFbkepCf6UIoGsPRNms+R+uBCUlKOi600Y8kSVhZrnyo7bfo/Itd9b2IxGSU56ajMi9+s2ruXF0KAPjdnmacMugsO0oeUek9i5XeCbeszIt0px39I2Gc7hrWO5yziOVZ60280AYASn1KhaQ/GMHgaFjnaCgeREKS7dozJxbbHGoZwMBISOdolAr9n75ZDwD42/fMictoGqPxuBy4Zp7yv/tLbNs2tRNqy/aCYlZITtfych+cdgld/mBKz3reWdeLUCSGUl+6Lou8JEnSDoD2cut53DAhSUnHzbT6svocSVHBcGVNfE/OrpiTjxsWFSESk/Hl54+k9AklAW2cH5k0TrtNa5Mx0hzJlv4RNPeNwm6TsNbkM9zSXXbkZ7oBKAsIyPw61cNfLrSZuWJvGuYWZiImA9tr9b8GPbWnGT3DQZT60nHbylK9w0mYm5aUAABeO96lcyQ0XZFoDE19ymziuUXW2QSfbGlOOxbPUpZ0pnLb9vMHWgEA18wv0K0YQNyPMiEZP0xIUtJpQ9ZZIakLMUdyX5M1y/7fPiMSkvFp157oq+9bhDSnDe/U92kfipSaxMEK50cmx3p1Ycyuev2TAYJo115a6kWmO7mD1RNBtG0393GxjRW0czxOXIkqSb3nSIajMfx4Wx0A4FPXVFt6SchadX7wma5hhCLG2nBOk9PpDyImA067hAL10IumZ606K3ZPiibCnnynCS8caIMkAbfreBAznpAcsORztB6s+ylGhtUmbpJ9vEnWw9JSLxw2pexf/Lewim5/EKc6hyFJyobteCvL8eAfrpsLAPj6lhNsbUxhWqU3E5JJsU7MkazrM8wNoGjXvtzk7drC+GIbJiStQLRss0IyPq6apxxyvnGqR9dr0PP7W9E6MIr8TDc+uKZctziSYZY3DVluByIxGXU9xhrXQZMj7pVKvOmwWXC0QDKtrlQX26Tgcs29jf346gtHAQAPbJqna1fKklIvnHYJPcOp3T4fT0xIUtK1i9lrrJDURbrLrm26s9ocSbEBe35RFnIyXAl5j09eNRvV+RnoGQ7i0VdOJeQ9yPhamZBMqmVlXqQ5begNhHDGAHMkZVnGTosstBHKctTFNmzZtoROMUOSFZJxsW52Llx2G1oHRtHQq0/SPhqT8djrtQCUexGrLyuSJAnzipVFKCc7OLvbjMYPb3kdmqk1aoXkqc5hDI6kTkFE59AY/vbXexGKxnDTkmL83bU1usaT5rRjSanSPr+3KfWSw4nAhCQl1WgoigH1IsqbZP1YdY7kLrViad3sxJ2cuR12/L/3LwEA/HJHA45YdFs5XRxvspPL7bBrg8SNMEeyqW8EbYNjcNol7SHB7Mpz1QpJtmxbQvsgE5Lx5HE5tFY9vdq2tx7pQF1PAN50Jz68vlKXGJJtXpGSkOQyQXPSDm9ZhDJj+ZluzM5XlnXus1hByYUEI1F8+td70eUPYn5RFv7rruWGWCS5TE1InmjndSkemJCkpBLVkRkuO7LTzD9zy6xEQtJqH2i76pWTqnUJrli6cm4+bl5WgpgMfOWFI4jFjNFCSskhy7K21IYzJJNnvfrveme9/ifSIim6vMwHj8san2XjLduskLSCTm7ZjruJbdvJJssyvv+XMwCA+66ossTc2smYry5CYYWkOXG8TXyJQ5HdKdC2Lcsyvvr8UexvGkB2mgM/+ehqZBjkuqdVbvOgJC6YkKSk0oas+9INccKRqlaWKx9oR1uHEIxEdY4mPgZGQtoHQzJmi3zl5kXIcNmxv2kAv9vbnPD3I+MYGAljNKz8u2H1UfKIyudddb26z5EUC22sMj8SGF9q09I/ovv/vjQzwUgUfYEQACYk4+lqdbHNzrpehKPJXbLy+sluHG8fgsdlx31XVCX1vfU0v1gZMcQHf3NqVw9vmZCMj1RabPPrXU347Z5m2CTge/euQmVeht4haeaLym0elMQFE5KUVNz6aAyVeR7kZrgQisZwrG1I73DiYk9DP2QZqC7IQEFW4jf5FXvT8NmN8wAA//HSCQyMhBL+nmQMogWpIMsNt8PaM7yMZHm5D26HDT3DIdR2B3SLQ5bl8YU2FpkfCShLByQJGAvH0DPM65mZdQ0FAQAuhw0+j1PnaKxjUUk2cjNcGA5GcKB5IGnvO7E68sPrKxM2I9uI5qkVks19owgEIzpHQ1PVyvE2cSUW2xxsHrDs5vlYTMb/vFWPf/u9ssTm/3vvAlwzr0DnqM42V01Itg2OYWgsdeZ5JgoTkpRU7dq2NX4w6UmSJKws9wGwzhzJd9T2hUTOj3y3+zZUYV5RJvpHwnhuf2vS3pf0xRYkfaQ57dq4CT3nSNb3BNA5FITLbsOqSmvMjwSU5FWJWk3HTdvmNvHwl90o8WOzSdhQo7Rtv3kqeXMkd9b1YW9jP1wOGz555eykva8R5GW6kZ+pHDJzjqT5iPsljreJjzkFGcjxOBGMxHCkzXoz7Bt7A/jQT3fi/714DJGYjDtWluJvrq7WO6xzeNOdWi7jNK9LM8aEJCVVm3aTzA8mvVltjqSYH3lZEhOSTrsNt60sBWCMRRuUHOM32DxYSTYxR3KXjnMkRXXkigqf5bbclnGxjSV0qPMji9iuHXdXzVXnSJ5O3hzJH21TNmt/cE0ZClPwv+mCYi62MSP/WBhDY0pVawkTknEhSZJWJbm3wRrPb4BSFfnLHQ1477ffxDv1ffC47Pj325bgkQ8aY4nN+YiFWyc7hnWOxPyYkKSk6hhkhaRRrFQ31lqhQnI4GNG2Xa+bndwWyokJEi63SQ3iYIVbI5NP/PvWc46kNj/SQu3aQlmOmCPJxTZm1jnIhTaJIhKSh1oGkjKq5WSHH9tOdcMmAf/nKuNVCiUDH/zNSVRqe9OdKbOEKRnWVFlrsU1z3wj+6r934asvHMVoOIr11bn402evxofXVxo2GQkA83lQEjdMSFJSTVxqQ/paXu6DJCnzXbrUagqz2tfYj2hMRllOetLbaJeWeuFx2TEwEubQ9RTRypZt3ays8MHlsKHLH0RdT/LnSMqyjJ11ykOAlRbaCGLTdgtbtk2N87oTp8SbjgXFWYjJwB8OtiX8/X7yRh0A4L1Lig211CGZ5hcrcyT54G8urRzTlRBisc3exn7TL6AbDkbwgR9tx466XqQ77fi3WxfjiU+uR7narWFk4wclvC7NFBOSlFTa7DV+OOku0+3QtoTtT+Jw9kR4R4d2bcFpt2GNutWbbdupoY1D2nWT5rRrN+P/u6sp6e9/pmsYPcNBuB02beyFlZRrLduskDSzTrZsJ9SH1pYDAH61szGhCYGOwTH8/qAynzpVqyOB8U3bJ/jgbyqcH5kYS0q9cNlt6A2ETP9Z/YvtDegcCqI8Nx0v/dNV+OsrqmCzGbcqciJt0zYPSmaMCUlKmp7hIIbGIpAkoCzH+CcfqcAqcyRFQjKZC20mWl/NhGQq4VIbfX1SfTD/9a5GdPmTW90t5keursyx5Ib1crVlm0ttzE3MkCzm4W9C3LG6DOlOO051Dmv3H4nws+31CEdlXFaVq43ZSUVzC5UKyZ7hIHqHgzpHQ5PVPqCOt+G9Uly5HXbMUf9NnOgY0jma6fOPhbUK8M9tmoeqfHNVgNcUZkKSgN5ACD28Ls0IE5KUNOIEoSLXg3SX9R7kzGhlufnnSI6FozigVngme36kwDmSqSMUiaHLr9x48CZbH++ZV4AV5T6MhWP48ba6pL63OHSw4vxIYHypTdvAKKK8lplWxyArJBMpO82pLbT71c7GhLyHfyyMJ3YqVeBG3DKbTBluB8pzlc/bU52cI2kWPLxNHLHoycztwj97uwGDo2HMKcjArctL9Q5nytJddlSpYzTM/N/BCJiQpKQ5pf5jnVuYpXMkJKyq9AFQhrNHojF9g5mmA80DCEVjKMxyozJPn8rbiXMkT3XxQ8nK2gdHIctAmtOGvAyX3uGkJEmScP+meQCAX+9sTNoM3FjM2vMjAWUJitMuIRyVtbZfMpdYbPy/HWe3Jc6H11cAALYe6UjINei3u5vhD0YwpyAD1y0ojPvrm838IqVt+6SJK8JSTSvH2ySMWKhywqTtwoOjYfz0TeVA+bMb58Fukjbtd5tXpFSqMiE5M0xIUtKc6lJONcU/XtJfdX4mstIcGAvHTDubZ+L8SL22sTntNqyuVKpNd9aybdvKGnuVVtaKXI+ht/9Z3dVz87GqwodgJIbHttUm5T1PdfnRFwgh3WnHsjJfUt4z2ew2Satmae5j27YZ9QZCiMRkSBJQkOXWOxzLWjzLi9WVOYjEZDy5uzmurx2OxvA/b9UDUGZHmmWmWiKJxTYnWSFpGm2DnCGZKPNNXiH5+Fv18I9FMK8oEzcvLdE7nGnjHMn4YEKSkua0+o9VXERJfzabhBXlPgDAfpPOkdR7fqQg2rZFBRVZU1PfeEKS9DOxSvI3u5qSUs23Qz1sWFOVA5fDurdPYtN2c7+5h+WnKvFvIT/TDafdun9PjeAj6ysBAE/saoprl8mWQ+1oGxxDfqZbaw1PdWKxDR/8zSEak7XRESVMSMadaNmu7wkgGInqHM3U9AdC2oHL/RvnmfrAZZ5IDPO6NCO8U6GkkGVZm/vClm1jEYPSzThHMhyNYW+jkkhdp/NMt/E5kr2cI2lhomqsnAlJ3V1Zk481lTkIRWJ47PXEV0mKhOR6i86PFMpyWCFpZu2DbNdOlpuWFiMvw4WOoTG8erwrLq8pyzJ+rC56uO+KSqQ5OXMdmFCJ1OFP6GZzio+e4SDCURk2CShipXbcFWenITvNgWhMRm1XQO9wpuSnb9ZhOBjBwpJs3Li4WO9wZoTXpfhgQpKSotsfxOBoGDYJqC4w1xYtq1ulbtrery6GMZMjrYMYDUeR43GipkDfUQDLyrxId9rRzzmSlsYKSeOYWCX5xDtNWjVGIsRiMnbVW3t+pCCS7S2skDQlsWGbC20Sz+2w4+615QCUebbx8PaZXhxvH0K6046/WlcZl9e0gtn5GXDYJPiDEbQl8FpP8SEW2hRnp8HBSu24kyQJC9Sq4ZOd5pmr2jscxM+3NwAA7t8419TVkQBQlZ8Bp11CIBTVZqbS1PEKQUkhqiOr8jJ42mswomW7vieALr+5bvJEu/baqlzdP9ScdhvWVHGOpNUxIWksV8zJw2VVuQhFYvjh62cS9j7H2ocwOBpGhsuOpaXehL2PEWgVkv2skDSjTjVZU8yEZFLcu64CkgS8daYHtd0zn2/44zeUau+715Yjh4vTNC6HDXPUg+dTJp2bl0raBpTrEDdsJ4622MZE/x5+8kYdRkJRLCnNxqZFRXqHM2NO+4TrEtu2p40JSUoK8Y90LhfaGI7P49IesF8/2a1zNFOza8JCGyPgHElrk2UZTb1MSBqJJEn47Ka5AIAn32nWqjLibWedcsiwdnau5efyaRWSbNk2JVEhWcyW7aQoy/HgenUL9m92Ns3otZ7e24I3T/fAJgGfuHJ2PMKzlHkmTMCkqjZtwzYTkokyz2SLbbr9QfxiRwMA4HOb5llmMeS8IvHfgQu3psvad9VkGCIhKf7RkrFcp95M/zlOM5CSIRqTsbtBLLQxRgsl50ha2+BoGP5gBABnSBrJFXPysW52LkLRxFVJivmRl1t8fiQwXiHZPjSGUCR+izooOTpYIZl0H1aX2/xubzNGQpFpvcYvdzTg8787CAC474rZ/Iw5j/lFrEQyi1YmJBNugckSkj/4yxmMhWNYUe7DtfML9Q4nbkSlKq9L08eEJCXFeIUkE5JGdP1C5YPhzdPdptnWdqJjCP6xCDLdDiyala13OADOniN5uosnZVbTqFZHFmW7OXrCYMQsyad2t6AvEIrra0eiMW08hNXnRwJAQaYbaU4bZBloH+RMJLNhhWTyXT23ABW5HvjHIvjDwbYp//wP/nIGX33hKADg4xtm4yu3LIx3iJYgNm2bJQGTykSFZKmP16FEEUU+7YNjGBwJ6xzNxdV2D2tzdh+4wTrVkcDECklel6aLCUlKOFmWcVqdITmfCUlDWjLLi4IsNwKhqPbgbXQizjVVObAbZCjyWXMk6zhH0mo4P9K41lfnYUlpNkLRGJ7d1xLX1z7WPgR/MIIstwOLZ1l7fiSgtMGX5Sh/x5v7mJA0GzFDkkttksdmk/Dh9RUAlBlpY+HJHezKsoxvbD2Bb/7pJADgH6+rwVduWWiph/V4Es8QZ7qHEYmyetvI2tTDrBIvKyQTxZvuxCz14OmkwavzHv7jcURiMq5bUIir5hboHU5c8bo0c0xIUsJ1DI3BH4zAYZMwO58bto3IZpNwnVo+/5pJ2rZFC+XaKmPMjxTG50gyIWk1IiHJVjpjuucyJSHwxDtNkOX4jEzo9gfxFbVyaV11rmEOPxKtnIttTGk4GNHGSrBCMrk+uKYc+Zku1HYH8B8vnbjk98diMv7190fx2OvKEpsHb1qAz90wn8nIiyjLSYfHZUcoEkNDL69NRtbOpTZJMV9r2zbupu03T3fj1eNdcNgkfHGz9aq/eV2aOSYkKeG0Ddv5GXA5+FfOqK5T27ZfO9EZt4f5RBkLR/HWmR4AwDXzjHXStr5aSZDuqu/jHEmLaWaFpKHdunwWPC476roDcan0Pt3px20/eBsHmwfgTXfisxvnxSFKcxivkOTNtZmI+ZFZbgcy3Q6do0ktPo8L37xrOQDg59sb8OcTnRf83mhMxr88cwi/2NEISQK+fvsSfOqaOckK1bRsNkkb/cR5bcY1Fo6iVx2dUsqEZEKJMQZGXfQUicbw7y8eBwB85PJK1BRab7ktr0szx+wQJdxpbaGN9S5CVnJlTT5cdhua+0ZR223s+Yc76noxEoqixJuGxQaZHyksLfUh3WlHXyDEOZIWw5ZtY8tKc+LW5bMAAP/7zsy23b51ugd3/HA7WgdGUZXnwXOfuQJLSq3fri2U5yoPkS39bNk2k051fmQRqyN1ce38Qnx8g7Id+/O/O4Qu9b/HRJFoDJ976gCe3tsCu03Ctz64HH+1rjLZoZqWWGxj1AQMjc+PzHDZkZ3Og5FEMvpimyd3N+Nkpx/edCf+6fq5eoeTMOK6ZNT/DkbHhCQlnPjHObeQ8yONLMPtwHp1YYPR27ZfO65UHly3oNBw7U0uB+dIWpVISFbmMSFpVPeuU9q2/3ikA/3TXG7z5DtNuO9n78AfjGBtVQ6e/cwGVBek1oFauaiQZMu2qXDDtv7+v5vmY2FJNvoCITzwu4NndUqEozH8028P4IUDbXDYJHz/npW4fWWZjtGaj1ggcYoP/obVNqFd22j36FajtWx3+g3X3TY0Fsa3XjkFALh/41z4PC6dI0qceayQnBEmJCnhTqlVYvO40Mbwrl8g2raNm5CUZVlLmG5cVKRzNOfHOZLWE47GtFN/zpA0rqWlXiyelY1QJIZn97dO6WfD0Rgefuk4vvDsYURiMm5bMQu//uQ65GZY9yb6QrjUxpy4YVt/bocd37tnBdKcNrx5ugf//VYdACAUieHvn9iHLYfa4bRL+OFfrcJNS0t0jtZ8FqgtqnzwNy5xr8T5kYk3pyATDpsE/1gE7YPnVmTr6ft/PoO+QAhzCjLwV+utXQU+MTFMU8eEJCWULMs4o/7jnF+cWhUmZnSdmpDc29iPgZHpVRcl2tG2IbQPjsHjsuNyNfFnNJwjaT2t/aOIyUCa04aCTLfe4dAFSJKkLbf53ykstznV6ccdP9yOH29Tkgf/dP1cPHr3Crgd9oTFamSiZbtnOIjR0OQ2BpP+WCFpDDWFWfjqLYsBAN/800nsbezDZ36zF3862gmXw4affGQNblhcrHOU5jRPfZZo6A1Meps5JVerlpDkdSjRXA4bqguUhbFGahdu6AngZ2/XAwC+fMsiOO3WTjmJTdsNPbwuTYe1/3aQ7loHRhEIReG0S6jM44ZtoyvP9WBeUSaiMRnbTnXrHc55vaq2a19Zk480pzGTBUtLfXDZbegLhLQbMzK3ifMj2YJkbO9fMQvpTjvOdA1jT2P/Rb83GpPxo221uOW7b+Fw6yC86U58/96VuH/TvJT+7+xNdyJLXYrSOsC2bbPo4AxJw7jnsnK8d3ExwlEZH/zxTrx6vAtuhw3//dE1uFY9/KWpK8h0I8fjREwGznBOtyG1D6oJSS8rJJPBiIttHvrjcYSjMq6ZV4Br51v/eleQ5YZPvS4ZfQ+DETEhSQl1Wt2wXZ2fafnTEau4boHSBv1ng7ZtG71dG1BOLOcUcvC6lXChjXmctdxm14WX29R1D+OuH23Hf7x0AqFoDNcvKMQr91+NW5bNSlaohiVJEspy2bZtNmKpTQkrJHUnSRL+486lKPGmIRqTke6042f3rcXV8wr0Ds3UJEkab4/k/ZUhTZwhSYk3vlBlSOdIFLvqevHysU7YbRK+fPNCvcNJCkmSOEdyBpghooQS/yjncsO2aVy/UDnJev1kNyLRmM7RnK1jcAyHWwchSePt5UY1vvnOGDcINDPNakKS8yPN4R51uc2Lh9vPGT8Rjsbw0zfqsPm7b2Jf0wCy3A785weW4b//eg0KmcjRlOUoD5NcbGMeYoYYZ0gag8/jwk8/uga3LCvBrz5xGa6oydc7JEuYzwd/Q+MMyeQyWoXkt189DQC4e2055qbQ/ghxXTrZwQrJqXLoHQBZ26lOLrQxm5XlPvg8TgyMhLGvaQCXzc7VOyTNayeUdu2V5T7kG3yOn/g7b5QbBJoZVkiay/IyLxaWZON4+xCe29+Kj22YDUBZNPXVF45on01Xzc3HN+5cxgen8xCbtlv6WSFpBuFoDD3DQQBAERPrhrGk1Ivv37tK7zAspUbtQGFrpPHIsqyNKirl52pSiAKI2u5hhKMxXTsS36nvw466XjjtEv7u2hrd4tDDvGIelEwXKyQpocQ/ynmskDQNh92mzfsQCUCjEO3a1y80bru2sIAfTJYiEpKVeUxImoEkSbj3snIAwBO7mtA5NIZ/enI/PvSTnTjVOYzcDBf+885l+OXHL2My8gLEYhtRHUzG1u0PQpYBp11CXgpuhqfUMadAJCQDOkdC79YXCCEYiUGSgCKvsQsHrKLUl44Mlx3hqIz6Hn3/TXzntVMAgLvWlKdcQnoBR0lMGxOSlDCxmKwNnE6lkm0rEO3Qfz5unDmSI6EI3jrTAwDYaIKEpJhxVNcdQChirNZ3mhpZltHUywpJs3n/ylKkO+043TWMa775F7xwoA2SBHxkfSX+/MA1+ODa8pReXHMpokKSLdvmINq1C7PSYLPx7zVZl5jR3dgbQDDCjbZGIuZHFmS64XYYc/Gk1dhskladp2dX1p6GPrx9phcOm4TPvGeObnHoZV6h8t+gdWAU/rGwztGYCxOSlDAt/aMYDUfhcthQyYd4U7l6XgHsNgmnu4a1RIze3jrdg1AkhvLcdFNU3JZ405CV5kAkJrOtyOQGRsLwByMAgLIcXsvMIjvNifctLwEAjIVjWFHuw+//7kp87bYl8HlYQXYpZWqFZFPvCGRZ1jkauhSx0IbzI8nqCrPcyHI7EJOBRoPco5JCtGuXpFh1nN6MMLf+O68psyM/sLosJe+VvR4nctXuBF6XpoYJSUoY0ao6pyATDm7YNhVvuhNrq3IAAH82SNu21q69oMgUVU2SJLF83yJEu3ZRthtpTp74m8n9m+bh/Stm4Rt3LsWzf3sFlpZ59Q7JNCpzMyBJwNBYBH2B0KV/gHTVIRbacH4kWZwkSagWcyS7eOBrJO2DYn4kr0PJpPdClb2N/XjzdA8cttSbHTmRGOvUxFE3U8IsESXMqS7OjzSz6xcobdFbj3boHInS/v/aCSUhuWmR8du1hfkGaKGgmeNCG/Mq8abjOx9aibvXVrCNdYrSXXZtBhRntRmfqJDkQhtKBTXqHMkzTEgairZh28sKyWQSm7ZPdupTIfldtTryjlWlKE/he2XREcoKyalhQpIS5jQ3bJvaTUuLIUnAzro+3YckH2wZQM9wEFluB9ZWGWfr96VoNwg6tlDQzImEZCrfZFFqGl8ewYd+oxMzJGexMolSwJzCDAC8NhmNmCHJZXHJJTqymvtGMayOGEqWA80D2HaqG3abhL+/dm5S39toKvKU61JTHw9xp4IJSUoY0bI9t5AVkmZUluPBNfMKAABP7m7SNZZXjytt41fPL4DLYZ7LFlu2rUFsGa7MzdA5EqLkqi5Q/s7X8aHf8LSWbc6QpBSgVUjy2mQoYoYkE5LJlZPhQmGWstVcPH8ny3deVTZr376yFBV5qX1wzwrJ6dH1yf7kyZP43ve+h/vuuw9Lly6Fw+GAJEn493//90v+7KuvvorNmzcjPz8f6enpWLBgAb70pS9heJgfTEYQnbBhmxWS5nXPZRUAgKf3tOi6KVrMj9xkgu3aE4m/+22DYxgc5cY1s9JatvN4g02pZbxCkqf9Rtemzm4rYUKSUsAcbYZkALEYl24ZhWjZLmVCMunm61AEcbB5AH85KaojU3d2pCBmSDIhOTW6JiQfe+wx/OM//iN+8Ytf4MiRI4hGo5P6uUcffRSbNm3C1q1bsXjxYrzvfe/D4OAgHnroIaxZswY9PT0JjpwupalvBMFIDGlOG9scTez6BYUozHKjNxDCK8f0WW5zpmsYJzr8sNskvGd+gS4xTJc33ak9HCb7xJLiR9xYcIYkpRq2bJtDLCZP2LLNRABZX0WuBw6bhNFwFO3q333SVygSQ/dwEABHR+hBj64sMTvy/StmoSqfXUSiQrR9cFTXQh6z0TUhuWTJEnz+85/Hb37zGxw/fhwf+chHLvkz+/fvxwMPPAC73Y4tW7Zg27ZteOqpp1BbW4vrr78eJ0+exKc//ekkRE8XI5IvNYWZsHORgGk57DbcvbYcAPC/7yS/bfuFA62444dvAwDWV+fC53ElPYaZ0uPEkuInFIlpWyN5uEKpRsxpa+4bQTAyuUNjSr7eQAjhqAxJgta2R2RlTrtNS4Bw07YxdA6NQZYBt8OG3Azz3a+bnZhbv7+pPynvd7hlEK+d6IJNAv7hutSeHSkUZLrhcdkRk4GWflZJTpauCclPfvKT+OY3v4l7770XCxYsgM126XAefvhhyLKMj33sY7jpppu0r3s8Hjz++OOw2Wx45plncOLEiUSGTpdwWk1Izitku7bZfXBNOSQJeOtMDxqStNymPxDC3z+xD//05AEMjUWwrMyLh29flpT3jjcmJM2tbWAUMRlIc9pQkMkHfUotBZluZLkdiMlsQTIyMT+yINMNp908c5aJZmJOARfbGMnE+ZGSxGKUZLt6bj4cNgkHWwZxpHUw4e/3Ha06shSzWR0JAJAkSeumauzjPdNkmequJRQKYcuWLQCAe++995zfr6ysxIYNGwAAzz33XFJjo7Mda1e2Cs8rZkLS7MpzPbh6rlhu05zw9/vLyS7c+O038OKhdthtEj67cS6e+dsrTDsomYttzE2bH5nr4Q02pRxJklCtzWrjQ79RiSruEs5toxRSo16bzvDaZAhtWkKS7dp6KMxOw83LSgAA//N2fULf60jrIF493glJAv6OsyPPIhKSTTzEnTRTJSRPnTqFkRHlP+6aNWvO+z3i6/v3709aXHQ2WZbxTn0fAGB1ZY7O0VA8aMtt9jYndCbGQ388jo/9bDe6/EHMKcjAc5+5Ap/dOM/UFR/zi5QWihMdQ5BlDl43m4kJSaJUxCok4+tQZ+iVZDMRQKmDM26NRUtIco6tbj62YTYA4MWD7ej2BxP2Pt/7s1Id+b5ls7SDAVJwsc3Umeopv75eyfb7fD5kZZ2/8q68vPys772QYDCIoaGhs35RfNR2D6NnOIQ0pw3Lyrx6h0NxcP3CQhRkudEzHMKrxxOz3GZfUz9+8kYdAOBjG6qw5R+vwrIyX0LeK5nmFGbAbpMwNBbRHhrJPJq1hCTbUSg1cdO28bUPioU2TEhS6hDXpjNdvDYZQeuAejDCSm3drCj3YWWFD6FoDL/Z1ZiQ9zjePoQ/HVWqI//xelZHvltFnvK80NTH69JkmSoh6fcrLY8ZGRd+MMzMVD6cLpVgfPjhh+H1erVfIpFJM7ejTqmOXFWRA7fDrnM0FA9Ouw0fXFMGIHHLbX70ei0A4AOry/B/37cYaU5r/N1xO+yoVmernGDbtumMV0jyBptSE6uQjE/MkCxhQpJSyBy1MqtnOIjBkbDO0VDnEK9DRnDfFVUAgF/vbErIMjqxWfvmpSWo4a6Ic1TmskJyqkyVkIynBx98EIODg9qv5ubEz8ZLFTvregEA66vzdI6E4ulDaysgScCbp3viPhfjdKcfLx9TTts+fU11XF/bCLjYxrzEDYVZZ5gSzZRo2a7rDnDshEGJGZKskKRUkul2oFgdU3CGBya6EwnJYo6O0NXmpSUoynajZziIPx5uj+trn+gYwktHOtTqSG7WPh/Rst3UN4JYjPdMk2GqhKRo0w4ELlwCOzysfCBlZ2df9LXcbjeys7PP+kUzJ8sydqkVkkxIWkt5rgdXactt4lsl+aNtSqv2DYuKLHnaxsU25iTL8oSWbSYkKTVV5Hlgt0kYDkbQlcCZVDR94xWSrOSm1CLm17GCW38iIVmY7dY5ktTmtNvwkfWVAICfvd0Q14PE7712BgCweUkJ5hVZ73ktHmb50mG3SQhGYrxnmiRTJSSrqqoAAAMDA1r79ruJSkfxvZRctd0B9AwH4XbYsLyc8yOt5t7LlNEGT+1pQTgan+U2rQOjeOFAKwDgb99jzVkk84vFYhsmJM1kYCQMfzACACjLYUKSUpPbYdcS8ty0bTyyLGszJNkqSamGS7eMIRyNoWc4BAAoYoWk7u65rAIuhw2HWgaxr6k/Lq95qtOPPx5RKi7/gbMjL8hpt6FUnaPa2Ms5kpNhqoTk/Pnz4fEoN8V79uw57/eIr69atSppcdG4XfVKuzbnR1rT9QuLkJ+ptAE8u68lLq/532/WIRKTccWcPKwo98XlNY1mvnqKWNs1jEicErmUeGJ+ZFG22zIzTYmmgw/9xjUwEkYwonyusDKJUo2YI/n/t3fncW4XdP7H398kk8x9X53OTO9LWkrLDVW5XBERD8SDVdHVdX8i64GuXOrqoiiu6IKuqKviBQsKoiCsIKfSchRaEAR6zbTTaTv3fSWT5Pv7I/lmOsw9k+T7TfJ6Ph59POwkmXwG0+98v5/v5+Bmib2sjc5ZbkOluV6bo0FZvk/vOK5GkvSzrfsnfU57vz9WXT8bNz28R6YpvWV9tdZW01k6ndim7S7mSM5GSiUkvV6v3vrWt0qSbrvttgmPHzhwQNu2bZMkvfOd70xqbIh4KtquffLyUpsjQSJkuV366JZlkqT/uPdlNXYs7M5P12BAtz8TqWr+xBkrFhyfU9WW5CjX61YgFNZ+7palDCshuYQN28hwy9m07ViHo/Mjy/O93AhGxlnJsckRWqx27YJsuVyGzdFAkj58WuR67U8vtehwz3Ds650Dfn313r/r9G8+ojf856N69NW2Gb/XntZ+3RedR/mvZzE7ciZWV0m8dy6kq5RKSErSlVdeKcMwdMstt+hPf/pT7OtDQ0P66Ec/qlAopAsvvFBr1661McrMFJkfyUKbdPfxNyzXyctKNRgI6V//d8eCNrj9fNt+DY+GtH5xobasLI9jlM7ichmxWSu0bacOKyFZx/xIZDgqJJ3LqnBhoQ0ykVUheaBzMCEbhTE7bdGEZBVV2o7xuppCnbysVKGwqV89dUCD/qBufGiP3vifj+mWrfsVCIUVCIb18V89qz+9NPXym/0dg7rstp0yzcis/9fVUB05Eyok58bWhOSOHTt0yimnxP7cd999kqQf/ehH475+5MjYP5LNmzfrhhtuUCgU0nnnnaczzzxT733ve7Vy5Uo9/PDDWrNmjX74wx/a9SNltMaOQbX1++X1uNK29RaS22Xov953nEpys/TSoT5960+75vV9Bv1B/WLbfknSpWeslGGk9x1VFtukHhbaABErolVIDVQhOY41P7K6kIU2yDyVBT4V+DwKm9IBqpFsY90YYX6ks3zk9EiV5K+fOqA3/uej+u5DuzXgD2r94kL9/CMn6m0bazQaMvXJ23bG5vkf7dFX23TB95/QrtZ+lef7dMVbKPiajfpoZ1UTXXGz4rHzzfv6+vT0009P+Hpzc7Oam8fm0/n94zcUffazn9WGDRt0ww036JlnntHg4KDq6+t11VVX6aqrropt40ZyWe3am+qKmbeW5hYV5ejbF23UR3/xrH76RKNOW1Gms9dVzel7/O8zTeodHtWy8jy9+ZjqBEXqHGuqqZBMNWMVklzoI7NZCclDPcMaCgSV67X19BFHaWGhDTKYYRhaXpmvFw72aF/bAJt/bdIanSFJQtJZ3vS6KtWW5Ki5e1j9kpaW5epz/7BGb92wSC6XodevqpDP49KdzzXrM3c8L/9oWO85sU7hsKnvP7pX331ot0xT2lxfrJs/cDz//84SFZJzY+sZ5RlnnDHvVfTnnHOOzjnnnDhHhIWwFtqcTLt2Rjh7XZX+6fRl+tnWRn3+ty/o/z79hlm3jPmDIf3PXxskSf/yhuVyZ8C8mTVUSKacsQt9EpLIbCV5XpXmedU1GFBjx6COqSmyOyREHaFlGxluRUWeXjjYo70strFNKxWSjuR2Gbr27ev134/u1ds3Ldb7TqxTlts17vFvXXissrNc+vVTTfrCXX9Tz3BAzzR266FXWiVJHzilXl8+/xh5PSk36c82VmdVz9CoeodHVZSTZXNEzsYnC3Fhmqaeis2PZKFNprjiLWt0TE2huodG9enbdyoUnt0Nhj/sPKzWPr+qCn165+bFCY7SGayNdE1dQxr0B22OBjMxTTM2pJ3KI0BaXm7NkaQFyUla+iLLCjhOIVOttDZtM+PWNq39zJB0qjPXVurOT5ymD56yZFwy0uKKJi2tpaXX3f+qHnqlVV6PS99697H62js2kIycozyfR+X5kX8LLLaZGZ8uxMWBziG19vnldbu0ub7E7nCQJD6PW9+/eLPyvG493dil7z+yd8bXhMKmfvj4PknSx7Ysz5itoKV5XlUURH457W6lStLp+kaCGgpEBuRTeQSMtW3vowrJUY5QyY0MZx2b9pKQtE1rX6Rlu5oKyZRkGIa++NZ1uuzMlZKkmqJs3fn/TtV7TqizObLUNda2zU3cmZCQRFxY1ZHHMT8y4ywrz9PX3rleknTjw7tnbJl58O8taugYVFFOlt5/cn0yQnQMFtukjtZodWRxbhbHNEDSiko2bTuNaZrMkETGi1VItg0qPMtOHcSX1bJdSUIyZRmGoc+/eY3+9JnX68HL36hja4vtDimlLYm2bbNsa2YkJBEXTzdGFtqcTLt2Rnrnplqds65SYVP67p93T/k80zR1c7Q68pJTlyjfl1mLEaxh67uokHS8sc21nFwD0lEVkrRsOwaV3EBkXpvHZWh4NKQj0ZuJSJ5Bf1D90VFEHIdS39rqwoy7PkuE+miFJC3bMyMhiQUbPz+ShTaZ6vNvXiPDkO578YheOtQ76XO27u3U35p7lZ3l0iWnLU1ugA5gVUj+/XCfzZFgJq0sigDGWR5NSDZ2DFCF5BBWdWQJldzIYFlul5ZaM24ZKZF0VkdJntdNIguIomV79khIYsEOdg3rSO+IstwG8yMz2NrqQr19Y40k6T8f2DXpc25+PDJj8n0n1qssP/MGXx9XVyxJerG5V8FQ2N5gMC1roQ0VkkBEXUmOstyGRkbDOtw7bHc4kHQk+v9DNfMjkeFWVDBSwi7W/Eg2bANjlpRFjklUSM6MhCQWzKqO3FhbrBwvd+gz2WfftFoel6HHd7fr6ejnwvLCwR5t3dspj8vQx16/zKYI7bWiIl8FPo+GR0O0bTuc1bLNCTYQ4XG7tLSMTdtOwvxIICK22IYKyaRr6+d8CXgta4bkkb4R+YMhm6NxNhKSWDArIcn8SCwpy9N7T4xsZPv2g7tkmmNtfTc/FpkdecFxNaotybUlPru5XIaOqy+WJD1/sMfWWDA9qwWJC31gDJu2neUIoyUASUcttqFCMulaYjdwM6/zCZhKaZ5X+T6PTDPSTYqpkZDEgpimGVtow/xISNK/nrVKPo9L2/d367Fd7ZIid6wfeLlFkvT/3rjCzvBstynatr2zqcfWODC9WIUkF/pAzPJoW2RDBxf9TmC1bC+iMgkZbqxCkurtZIu1bHO+BMQYhqH6aJVkE3Mkp0VCEgvS3D2sQz3D8rgMHb+E+ZGIVGpYC2v+84FdCodN/fgv+2Sa0jnrqmKbpjPVpuic1Z1N3TZHgulQIQlMNFYhycm1E1AhCUSsiFZIdgz41Ts0anM0mcU6X6oq4DgEHM1abLO/gzmS0yEhiQV5cl+kXfvY2iLletmshohPvHGF8n0evXykTz/b2qi7dx6SJF16ZmZXR0rSxmiF5L72QU6aHWpkNKSuwYAkltoAR1tBW6SjjM2QZKkNMlu+zxO7gbi7jRndyWQlJLkxAoxXX2ZVSJKQnA4JSSzI1n0dkqTTV5bbHAmcpCTPq39+/XJJ0tfue0WjIVMnLytlC7siM0WWRn9BPd/cY28wmFRbtP0oO8ulopwsm6MBnMNq2W7r96tvhBsqdmuhQhKIWVsd6cB5tYWEZDK19DFDEpjMktLIOdOBTrpKpkNCEvNmmqa27o1USJ62goQkxvvo65epNM8b+/snzqA60kLbtrNZc9mqC7NlGIbN0QDOUZidpcqCyEVnA5u2bdU/Mqp+f1ASCUlAktYuKpQkvXqkz+ZIModpmrGbuJW0bAPjWC3bB6iQnBYJSczb7tYBdQz4lZ3l0uYlxXaHA4fJ93n0yTNXSpKOqSnUG1dX2ByRc2yKbtpmsY0zjd3t5+QaeK3YYhvatm1ltUkWZHuU72NkDkCFZPL1DI0qEApLkiqpkATGsZbaNHcNKxQ2bY7GuTiDwbxt3Rtp1z5xaal8HrfN0cCJPnLaUlUU+HTCkhIqzY6yqS5SIfn8wR6Zpsl/G4dhoQ0wtRUV+XqqoUt720hI2slaaFPD/EhAkrTuqArJcNiUy8W5VaJZN3BL87xcCwKvUVOcoyy3oUAorJa+ES0u5vf1ZKiQxLxZCUnmR2IqLpehCzbWqIYD8DhrFxXI53Gpd3hUjR20PTqNdaFfRUISmGBldLHN7lYSknZiwzYw3vLyPHndLg0GQmruHrY7nIzQSkcJMCW3y1BtSbRtmzmSUyIhiXkJhsJ6urFLkrSFhCQwJ1lul46tLZJE27YTxSokOcEGJlhbHa1CamFOm53GNmxznAIkyeN2aVVV5IbJKxyfkqKVhTbAtKy27aZO5khOhYQk5uWF5l4N+IMqzs3S66ItEgBmL7bY5iCLbZyGyiNgausWRea0NXcPs2nbRhyngIliN0yOMEcyGVqjC22quYELTIrFNjMjIYl5sdq1T11exowWYB421RVLokLSiVpjF/qMGgBeqzjXG6vK28XyCNu09EZaUqmQBMZYN0xeYdN2UlgzJCtJSAKTokJyZiQkMS/MjwQWxqqQfLWlX0OBoM3RwBIKm2rt544/MJ2jl0fAHke4cQJMEDs20bKdFG20bAPTWlaeJ0lqYGfAlEhIYs6GAsFYVRcJSWB+qouyVV2YrVDY1IvNvXaHg6jOAb9CYVMuQyrP99odDuBIa6sjVUgv0xZpmyPMkAQmsI5NB7qGNOjnZm+i0bINTG9VZeSYtK99QKGwaXM0zkRCEnO2fX+3AqGwaoqytTQ6FwHA3G2qL5Yk7TzYY2scGGNd5FcWZMvj5lckMBmqkOw1FAiqdzgyv5MZksCYsnyfKgp8Mk1pdys3TBKthS3bwLQWl+TI53EpEAyruZu27clwtYU523ZUu7ZhMD8SmK9YQrKJxTZOETu55iIfmJI1p21XS7/C3PFPOmvDdp7XrQKfx+ZoAGexqiRfZcZtQgVDYXUMRCokSUgCk3O7DK2oyJck7WkdsDkaZyIhiTnbuo/5kUA8xDZtN/XINLmod4LWaEJyESfXwJSWluXJ53FpKBBSE5sjk67lqA3b3BgGxntdtIKbxTaJ1T7gl2lGEi5leYy4AaaysjKakGwjITkZEpKYk+7BgP5+OPIL/rQVZTZHA6S29TVF8rgMtfX7Y63CsNeRoy70AUzO43ZpdZVVhcRFf7KNzY9koQ3wWmujFdyvMuM2oaz5kZUFPrlc3BgBprIqmpDcS0JyUiQkMSdPNnTKNKXVVfmqpIIIWJAcrzs2i81aFAV7tZKQBGbFattmsU3yWaMlOE4BE62tjlZItvTRfZJAVqU27drA9FbGEpKcL02GhCTmZGt0fuRpK2jXBuKBOZLOEquQ5AQbmJZ10f8qbZFJd6R3WBIbtoHJrKjIV5bbUP9IUIfpPkmYtn4rIemzORLA2VZVjVVIcpNkIhKSmBMrIbmF+ZFAXLBp21la2RgJzIrVFvkKLdtJ10LLNjAlr8cVWyLxymGOT4linS9xAxeY3pKyPHlchgYDIUZ0TYKEJGbtUM+w9ncOye0ydPLyUrvDAdLCprrIYpsXD/UqEAzbHE1mM03zqNlsnGAD01kXrZA82DWs/pFRm6PJLByngOlZ43CYcZs4Lb3RGZIkJIFpZbldWlqeJ4nFNpMhIYlZs6ojj60tUkF2ls3RAOlhSVmuSnKzFAiG2Qhps76RoIZHQ5KYzQbMpCTPG6uM2dXCXKRkamHWLTCttdVWBTfHpkSxWrapkARmxmKbqZGQxKzRrg3En2EYOra2WJL00uFee4PJcNZFfnFulrKz3DZHAzjfukVc9CfbyGhInYMBSVRIAlNZu4gZt4nGUhtg9lhsMzUSkpgV0zS1bV+nJBbaAPG2vCJSxt/UOWRzJJmthXlIwJxYF/1UdydPW1+kTTI7y6WiHLpVgMlYN0saOwY1Eu18QHyNzdxmqQ0wk5VUSE6JhCRm5UjviNr7/XK7jNgSDgDxsaQ0V5J0gISkrVppgwTmZB1VSEk3tmE7R4Zh2BwN4EwV+T6V5XkVNqXdrVQkxdtwIKS+kaAkqYpzJmBGVkJydyubtl+LhCRm5cVDkVbSVZX5tDICcbakLFIheaCLhKSdrEURVEgCs7MuOqdtV0u/wmFOsJOB4xQwM8MwtDZaJfnqERKS8WZVR+ZkuVXg89gcDeB8KyryZRhS7/CoOgYCdofjKCQkMSt/jyYkNywusjkSIP3Ul1kVkoPcNbNRSx/zkIC5WFaeJ6/HpcFASAe7uaGSDGzYBmZnbXV0pASbtuPOSkhWF2VTqQ3MQnaWW/XRjjjatscjIYlZsSokN9SSkATirbYkR4YhDQVC3DWzUUusFZILfWA2PG6XVldF2pBeoQopKazjFKMlgOmNjZTg2BRv1g3cygLmRwKztbKCxTaTISGJGZmmqRcPRe4uHlNDQhKIN5/HrZqiHElSU9egzdFkrpbosgjmIQGzF6tCYo5kUlAhCczO2uhIiVdb+ug+iTNruRY3RoDZW1nFYpvJkJDEjNr6/eoY8MtlSK+L3m0EEF/1LLaxHRWSwNzFqpBoi0yKllirZI7NkQDOtrIyX26Xoe6hUbVGE2iID0bcAHNnVUjuISE5DglJzOjFZmuhTYFyvCy0ARJhSRkJSTuNjIbUPTQqiWURwFxYi21o2U4OKiSB2cnOcmt5eWRpIHMk46uVlm1gzlZVRc6XSEiOR0ISM7LmRx6zmOpIIFGsTdtNbNq2hXVynZ3lUlFOls3RAKljbbRCsqlrSAP+oM3RpLdAMKyOAVolgdlijmRiHL3UBsDsrKiIXOu19/vVGy2CAAlJzMLfD7NhG0i0JUdt2kbytUSrjqoL2RgJzEVpnldVhZEqmV0tXPQnUlv/iExT8rpdKsvz2h0O4HhrF43NkUT8WC3wtGwDs1eQnRXrbtjbzvmShYQkZhTbsE1CEkgYZkjai3lIwPxZVUgstkms2I2TIm6cALOxrpoKyXgzTXOsQpJzJmBOVlay2Oa1SEhiWm39I2rt88swxi44AMSfVSHZORig7dEGLcxlA+bN2rRNFVJiHemlTRKYi1XRrbYNHQMKhsI2R5MeeodH5Q9G/ltWMEMSmBMrIbmnlYSkhYQkpvX3Q5GLixUV+crzeWyOBkhfBdlZKo224NG2nXyxCkku9IE5W7eIxTbJwI0TYG5qinKUk+XWaMhkRnecWO3aJblZys5i2SkwF6sqWWzzWiQkMS2rXXt9DdWRQKJZbdtNtG0nXexCn/YjYM6sDopdLf0Kh02bo0lfVEgCc+NyGVpRGVkkQYtkfIxt2OY4BMwVLdsTkZDEtGIJSeZHAgkXW2zDXfyka2FjJDBvy8vz5HW7NOAPqrl72O5w0lZLX+S/LTdOgNlbWRFNALSTAIiHtv5IhWRlIe3awFytiiYkD/UMa5ARXZJISGIGf2ehDZA0S1hsYxurQpKlNsDcedyu2F3/3a20bSfK4R7rxkmOzZEAqSNWkcTMtrhojyYkmR8JzF1Jnldl0RFdDe2M6JJISGIanQN+HY5epL+Olm0g4ZaURdqKmrr4BZVMobAZu+O/iAt9YF6WVUSOX/uZgZswzJAE5i6WkKRCMi5ISAILE1ts08YNXImEJKZhtWsvL89TQXaWzdEA6S/Wsk2FZFJ1DvgVCptyGVJ5vtfucICUtCx6Q6Wxg4RkIgRDYbX1k5AE5mpldInEvrYBmSYzbheqfSCakMwnIQnMx1hCkpskEglJTOPvhyMbtpkfCSRHfTQhebhnWIFg2OZoMoe1KKKyIFseN78WgflYWk6FZCK1D/gVNiWPy1AZiQBg1paU5crjMjQYCMV+32P+2qM3RqiQBOZnFYttxuHKC1N6sdlaaEO7NpAMFfk+5XrdCptSczdVksliLbSpouoImLdl5ZEbKo3MREqII0fNuXW7DJujAVJHltsVu2FCAmDhaNkGFmZVVaRqm+NRBAlJTIkN20ByGYah+lI2bSfbkR421wILtTTasn24d0QjoyGbo0k/1vzIam6cAHMW27RNAmDBrIRkJQlJYF6slu0DnYPyBzlfIiGJSXUPBnQoepF+TA0JSSBZrIRkE3Mkk8Y61tWWsNAGmK/SPK8Ksj2SmIObCEdISALzxsy2+BgZDalvJChJqsjnWATMR2WBTwXZHoVN5m5LJCQxhZcOR6ojl5TlqiiHhTZAsrDYJvmauyMJycUkJIF5MwxDy8pZbJMoLb2R41QNCUlgzqyE5D4SkgvSEV1o43W7VJjjsTkaIDUZhjF2k6SVYxIJSUyKdm3AHkuibY9NXVzQJ4uVkKwtybU5EiC1WW3bLLaJv7EKSW6cAHNlXfzvbefifyGOnh9pGMyyBeZrVWW+PC5DndEkfybj1gYm9fdDkQ3bG0hIAklFhWTyWQuEaNkGFia2aZsKybizZkguokISmLMVFfkyDKlrMKCuwYBK87x2h5SSrIRkOfMjgQW55q2v09ffuUFZbuoD+S+AScUqJJkfCSTVktLIBf2BriGFw6bN0aS/AX9Q3UOjkmjZBhYqtmmbhGTcMUMSmL8cr1uLiyO/41lsM3/t0WquinwSksBCFOVkkYyM4r8CJugdGlVTdMPv+sWFNkcDZJaa4mx5XIYCwbBa+0fsDiftHYq2axflZKkwm3m5wELQsp0Y4bCp1j4qJIGFiLVtk5Cct6NbtgEgHkhIYoK/Rxfa1JXmqDiXlgYgmTxuV6xSj7btxKNdG4gfa6lNa59fQ4GgzdGkj44Bv4JhUy6DyiRgvlZWWJu2+22OJHWRkAQQbyQkMQHt2oC96ksjbY9NJCQTbmyhDQlJYKGKc70qzo1UGu/v4PgVL1a7dmVBtjy0eAHzQoXkwpGQBBBvnNVggucOdEuSNtSSkATsEFtsw6bthBurkGTDNhAPtG3HH/MjgYVbVRVJSO4jITlvzJAEEG8kJDHOyGhIT+ztkCS9YVWFzdEAmSm22IYKyYSjQhKIL6ttm8U28dPSGzlOMT8SmL+VFQWSpMO9Ixr0M1JiPqiQBBBvJCQxzlMNnRoKhFRV6NMxNSy0AexgVUhay6WQOGMJSSokgXiIVUiSkIybI31USAILVZSbpfJoZd++dqok58o0zVhCspKEJIA4SemE5G9/+1udccYZKikpUV5enjZu3KhvfetbGh0dtTu0lPXwK22SpLPWVskwDJujATLTkjIqJJOFpTZAfC0tjyT3admOn5ZeNmwD8bCyMnJ+xRzJuev3B+UPhiUpltgFgIVK2YTkZz7zGb3nPe/R1q1bddJJJ+ncc89VU1OTrrjiCp111lkaHh62O8SUY5qmHnk1kpA8Z12lzdEAmctaatM7PKqeoYDN0aSvAX9Q3UORG1iLSUgCcTHWss0NlXgZmyHJcQpYCGuxzR4SknNmVUcW+DzK8bptjgZAukjJhOTvf/973XjjjcrPz9fTTz+tBx54QHfddZf27NmjDRs26IknntCXvvQlu8NMOa+29OtQz7B8HpdOW1FudzhAxsrxumPtMFRJJs6haLt2UU6WCrOzbI4GSA9LownJjgG/+kfoWIkHKiSB+FhZwabt+WJ+JIBESMmE5HXXXSdJuvLKK7V58+bY18vLy/WDH/xAkvT9739fvb29tsSXqh5+pVWStGVlOXe+AJuNbdomIZkoh3po1wbirTA7S2V5XkncUIkH0zRjCcnqQhKSwEKsqoostmHT9txZCclyEpIA4ijlEpKHDh3S9u3bJUkXX3zxhMe3bNmiuro6+f1+3X///ckOL6U9HG3XPntdlc2RAKiPbtpuYg5bwrBhG0iMpWzajpuuwYACobAMQ6oiIQksiNWyfaBrSIHoPETMDhWSABIh5RKSO3fulCSVlpZq2bJlkz7nhBNOGPdczKxjwK/nD/ZIks5ay/xIwG6xCkkqjBKGDdtAYrBpO36s+ZHl+T55PSl32g44SmWBTwU+j0Jhk8Vbc9Q+EE1IstAGQByl3JlNY2OjJKm+vn7K59TV1Y177mT8fr/6+vrG/clkj77aJtOU1i8uVDUzigDb0bKdeGzYBhJjWXTTdiMX/AvG/EggfgzD0AprsU0rbdtzQYUkgERIuYRkf3+/JCkvL2/K5+TnR37RTJdk/MY3vqGioqLYHyuJmakefiXarr2Wdm3ACZaUWS3bJCQThQpJIDGslm0qJBfuSB/zI4F4stq2WWwzNyQkASRCyiUk4+Wqq65Sb29v7M/BgwftDimh9rb16/4Xj0z6mD8Y0l/3tEuSzl5HuzbgBEtKI0mylr4RDfiDNkeTnpghCSRGrGWbGyoLdqQncpyiQhKIj1hCsp2E5FyQkASQCB67A5irgoLIdrTBwanvug8MRH7BFBYWTvkcn88nny8zDqiNHYN65w+2aWQ0pNI8r05ZXjbu8acbujQYCKmywKf1NUU2RQngaCV5XtWV5uhg17C27+/SmWu4WRBPg/6gugYDkqTFJCSBuLIqJLsGA+odHlVRTpbNEaWu2IbtIo5TQDysokJyXpghCSARUq5CcunSpZI0bUWj9Zj13Ey3pDRXb1hVodGQqX/51XNqeM0dwYdfaZUUWWbjchl2hAhgEqdGbx48ta/T5kjSz6Fo1VFhtkeF2SRLgHjK93liVTS0bS/MEWZIAnFlVUg2tA8oFDZtjiY1hMKmOqMJycpCEpIA4iflEpKbNm2SJHV2dk65tObZZ5+VJG3evDlpcTmZy2Xohvds1HF1xeodHtU//Xx7rDLINE09ZM2PXMf8SMBJTl0RSUg+2UBCMt7GFtowPxJIhGWxtm0SkgvRYs2QJCEJxEVtSa68Hpf8wbAORUe3YHpdgwGFTcllSGV5JCQBxE/KJSRra2t14oknSpJuu+22CY8/8cQTOnjwoHw+n84777xkh+dY2Vlu/c+HTlBtSY72dw7pX371rPzBkHa3DuhQz7B8Hpe2rCy3O0wARzl1eeTf5EuHetU7PGpzNOmF+ZFAYi21Nm1TITlvpmnqSC8zJIF4crsMLY+OldjT1m9zNKnBmh9ZmueTm246AHGUcglJSbr66qslSd/85je1Y8eO2Nc7Ozt16aWXSpIuu+wyFRUxD/FoFQU+3fLhE1WQ7dH2/d268q4X9VC0Xfu0FWXK8bptjhDA0aqLsrWsPE9hU3qmscvucNIKG7aBxGLT9sL1Do9qZDQsSapiyzYQN2zanpvY/EgW2gCIs5RMSL7jHe/Qpz71KQ0MDOiUU07RW97yFr373e/WypUr9eKLL+r000/Xtddea3eYjrSqqkA3/+Px8rgM3b3zkL73yB5JtGsDTmUtoXqSOZJxNdayTYUkkAhWy3Yjm7bnzZofWZrnVXYWN42BeLEqJPdzfJoVNmwDSJSUTEhK0o033qg77rhDp556qrZt26b7779ftbW1+uY3v6lHHnlEOTlcZE5ly6pyfe0d6yUpduf97HVs8AWc6DTmSCYELdtAYlEhuXCxDdtURwJxVR+9YdLUxfFpNmIJSTZsA4gzj90BLMR73vMevec977E7jJT0vpPq1dg5qB893qCNtUVaVMRFOeBEVoXkK0f61D0YUEme1+aI0gMt20BiLY1e8PcOj3Lsmic2bAOJsaQs8ru/qYsKydmgQhJAoqR0QhILc8Wb12pzfYlet6jQ7lAATKGiwKdVlfna0zagpxs7de76RXaHlPIG/UF1DQYkSYupkAQSIsfrVnVhtlr6RtTYOUhCch5arIU2xSQkgXhaUhpJSB7uGdFoKKwsd8o2DSYFMyQBJApH3wzmchl68zHVqiulQghwslNXMEcyng71RC7yC7M9KsrJsjkaIH1Zm7Zp256fsQpJbpwA8VRR4FN2lkuhsKlD0Y4JTK29P3IsIiEJIN5ISAKAw526nDmS8TS20IabMUAiLWOO5IK09DFDEkgEwzBUHy3IOEDb9oyYIQkgUUhIAoDDnRxNSO5uHYidFGL+DrHQBkiKpWzaXhCrmpsZkkD81Zdai204Ps2EGZIAEoWEJAA4XGmeV+uis16fokpywVhoAyQHm7bnbzQUVlM0kWv9dwQQP7HFNp0cn6YzMhpS30hQEglJAPFHQhIAUgBt2/HTTIUkkBRHt2ybpmlzNKmlqWtIwbCpnCw3LdtAAsRatqngnlZHdKGN1+NSYTb7cAHEFwlJAEgB1mKbp1hss2BjMyRJSAKJVBetQu73B9U3HLQ5mtTS0B6p2lpekSeXy7A5GiD91FsVkrRsT+vo+ZGGwbEIQHyRkASAFHDSslK5DKmhY1Ct0UUHmB9atoHkyPG6VZ7vlSQd7Oaify72tQ9IklZU5NscCZCelpSOJSSp4J5aG/MjASQQCUkASAFFOVk6pqZIkvQkVZLzNhQIqnMwIElaTIUkkHCLo4n/ZhKSc7KvjYQkkEi1JbkyDGkoEFLHQMDucByLhTYAEomEJACkCKttm4Tk/FkbtguzPSrKybI5GiD91UUT/1ZlMmYnViFZyUIbIBG8HpdqiiLHp6YuFttMhYQkgEQiIQkAKYLFNgtHuzaQXNa/tYPMaZs10zS1z5ohWU6FJJAoLLaZWfvA2AxJAIg3EpIAkCJOXFYqt8tQU9cQ7Y/zxEIbILnqSqmQnKuuwYB6h0dlGGObygHE35IyEpIzoUISQCKRkASAFJHv8+jYWuZILoSVFGF+JJAcsQpJbqLMmlUdubg4Rzlet83RAOmLTdszIyEJIJFISAJACrHatp9q6LI5ktREyzaQXEfPkGST7eywYRtIjiWlkQpkEpJTIyEJIJFISAJACtmwOFIh2dAxYHMkqYmWbSC5aooj/9aGAiF1D43aHE1qaIgmJJdX0K4NJBIzJKdnmiYzJAEkFAlJAEghi6IX9y29IzZHkprGKiRJSALJkJ3lVmW0sobFNrNjtWxTIQkkltWy3THg16A/aHM0ztM3ElQgGJZEhSSAxCAhCQApZFFRtiSprd+vYChsczSpZTgQUudgQJJUW0zLNpAsddEqJBbbzA4t20ByFOVkqTg3SxJt25Ox2rULsj3KzmKeLYD4IyEJACmkPN8nj8tQKGyqYyBgdzgppaUvUlWa63WrKHoBAiDxrIpkFtvMzB8MxSpJV1TSsg0k2pJSFttMhfmRABKNhCQApBC3y1BVYaRK8nAv1UZz0RZNSFZyYg0kVV2JVSHJBf9MDnQOKWxGKpKY2QYknlXB3cQcyQmYHwkg0UhIAkCKqY62bTNHcm7aonf6KwuybY4EyCy1R23axvT2tVkLbfJlGIbN0QDpb0l0juSBrkGbI3EeKiQBJBoJSQBIMdYcySMkJOfESkhWFHJiDSSTVYHEUpuZjc2PpF0bSIYlpZF/a2zanoiEJIBEIyEJACkmlpDsodpoLtr6adkG7HB0haRpmjZH42xs2AaSy9q0zQzJiUhIAkg0EpIAkGKqiyIX90f6qJCci/Y+WrYBOywqypFhSP5gODaTDJNrYMM2kFT10QruQ93DCobCNkfjLMyQBJBoJCQBIMUsYobkvIzNkOTEGkgmr8elRdFlXMyRnJppmkdVSNKyDSRDdWG2vB6XgmGTUTivQYUkgEQjIQkAKYaW7fmxWratLeUAkqc2tmmb49ZU2vr9GvAH5XYZsTZSAInlchmqi46VYI7keCQkASQaCUkASDGLoi3brf1+hcLMY5utWIUkS22ApKstjRy3WGwzNWvDdn1prnwet83RAJljSVl0sQ2btmP6R0bVEW3Zri3mBgmAxCAhCQAppqLAJ7fLUChsxk4WMT1/MKSeoVFJtGwDdqBCcmb7OmjXBuxgzZFsokIyZm/0BkllgU9FuVk2RwMgXZGQBIAU43YZqoom1Zh3NDtW25HX41JRDifWQLLVxTZtc8E/FatCkoU2QHLFEpJUcMfsiR6PVlcV2BwJgHRGQhIAUlA1cyTnxGrXrsj3yTAMm6MBMg8VkjPbF92wvZwKSSCplkRntjJDcoxVIbmykhskABKHhCQApCBrjiQVkrPT1hf578T8SMAetdEKyUPdwwoz+3ZSDbEN2yQAgGSyEpJNXUMyTY5PkrS7tV+StKqK4xGAxCEhCQApyKqQbOkjITkbsYU2zI8EbLGoKFtul6FAKBz794gxw4GQDkUr3klIAsllVXAP+IPqGgzYHI0z7GmNVEiuqqRlG0DikJAEgBS0KJqQPEzL9qy09VkJyWybIwEyk8ftih23mCM5UUNH5OK/NM+rkjyvzdEAmSU7y63qwsjxiTmS0qA/GLtBsoqWbQAJREISAFKQ1bLdQsv2rLT1R1u2qZAEbFMXrUI6SEJygn3tbNgG7FRfxmIbizXPtjzfxw0SAAlFQhIAUlBsqQ0JyVmJtWwzQxKwjTVHsrmLyu7XsjZsLy+nGgmww5JSFttYxtq1OR4BSCwSkgCQgmqKIwnJ1r4RhVgQMSNatgH71ZWyaXsqDR3RCslKKiQBO9STkIzZ3cZCGwDJQUISAFJQRb5PLkMKhk11DrAgYiZWhWQFLduAbawKSVq2J7IqJFloA9hjrGV70OZI7LeXCkkASUJCEgBSkMftilX70bY9vWAorM5BWrYBu1mbbKmQHC8cNmNLbUhIAvZYUhapTmaGpLQneoNkVRUbtgEkFglJAEhRi4pJSM5G52BApim5XYbK8khIAnapK41USB7uGWbUxFEO9w5rZDSsLLcRqyIFkFzWDMnWPr9GRkM2R2Of4UAoVsVOhSSARCMhCQApalFssQ3VRtOx5keW53vldhk2RwNkrsqCbGW5DQXDplr6uJFiaYhu2F5aliePm1NzwA7FuVkq8HkkSQczuEpyX/uATFMqzfOqLJ+buAASi7MeAEhR1YWRSpoWKiSn1dYf+e/DQhvAXm6XocXF1qbtzL3gf6197bRrA3YzDIPOE0l7ogttVlIdCSAJSEgCQIoaq5DM3BPn2bAW2lSy0AawnTVH8iBzJGMaoxu2l1WwYRuwU1Vh5Lwqkyu490QX2qxmwzaAJCAhCQApauxOPhf207FatlloA9jPmpHYzKbtmAOdkf8W1gw7APaojiYkWzP4Rm9soU0lC20AJB4JSQBIUVRIzo7Vsl1ByzZgu7po0u1gFzdSLNZW3/oyEpKAnaqLqJDc0xpp2WahDYBkICEJACmquihSadTaN6IwG2un1NpHyzbgFFRIjhcKm7H/FkvKaNkG7GQlJFszNCE5MhqK3SBZScs2gCQgIQkAKaqywCeXIY2GTHUM+u0Ox7HaY0ttSEgCdrNmSDYzQ1KSdLhnWKMhU163K9YuCsAe1Rk+Q7KhfVBhM7JxvIIN2wCSgIQkAKSoLLdLFdEkG5u2pxZbasPFPmC7umiF5JHeYY2GwjZHYz+rGqmuNEdul2FzNEBmiy216c3Mm7zWhu1VlfkyDI5HABKPhCQApDCrbZs5kpMLh021s2UbcIyKAp98HpfCJjdSpKMW2tCuDdjOatnuGPArEMy8GybWhu2VLLQBkCQkJAEghdVYA9i5sJ9U91BAweh8zXLajwDbGYahxdEqyYNdzJE80DUoSapnwzZgu9Jcr7LckcpAayFeJjm6QhIAkoGEJACkMOtu/uFe5rFNxmrXLs3zyuvhVx7gBMyRHNMUq5AkIQnYzeUyVFmQuYtt9rRFKiRXV1EhCSA5uDoDgBS2iArJabXRrg04Th2btmMOkJAEHKW6KDPnSPqDodjxaBUbtgEkCQlJAEhhzJCcXlu0woGFNoBzWO3JDR2DNkdiL9M0daDTatlmhiTgBJm6abuxY1ChsKmCbA83cQEkDQlJAEhh1gzJI7RsT4oKScB5rHZAa4FCpuocDGgwEJJhRLZsA7CfVSGZaS3b1vGYDdsAkomEJACksNiJc69f4ejyFoxhwzbgPFY7YEPHgEZDmbfJ1mK1Ry4qzJbP47Y5GgDSURWSGdZ5Ys2PXMWGbQBJREISAFJYVWG2DEMKhMLqGgrYHY7jWFsySUgCzrG4OEd5XrdGQ6b2Z3DbdpO1YZv5kYBjVBVlZsv2XmvDNvMjASQRCUkASGFZbpcq8iPJtiM9mXXyPBttfdEKSWZIAo5hGIZWV0eqcHa19tscjX1iC22YHwk4hlUhmWkt27utlm02bANIIhKSAJDiFjFHckrMkAScaXW0LXB3Bs+RbIomJKmQBJzDSkge6R2RaWbGKJxAMByrVl9VSYUkgOQhIQkAKa46Q9uLZmKa5lEt21RIAk5iVUjubsngCsmuSEJyaRkVkoBTVBZGbmAGgmH1DI3aHE1yHOgcVDBsKt/nid3kBoBkICEJACluUVFkO+uRDBvAPpO+kaBGRiMLM6wLDADOsKbKqpDM4ISk1bJNhSTgGNlZbpXkZknKnBu91kKblWzYBpBkJCQBIMXFWrZ7aNk+Wnu0OrIg26PsLDbYAk6yOro4YX/noEZGQzZHk3yD/qA6BiIjJWjZBpylOnqjN1MSktaNIdq1ASQbCUkASHHVRWPzjjAmttCG+ZGA41QU+FScm6WwKe1rz7w5klZ1ZElulgqzs2yOBsDRqqNdFa0Zcl5lHY+WV5CQBJBcJCQBIMUtyrA7+bM1ttCGeUiA0xiGodUZ3Lbd1BVZIFHP/EjAcTJtNre1UZz5kQCSjYQkAKS4RUWZtxFyNqyFNlXMjwQcyZojuaslcyskl5TSrg04TVV003ZrhiUkmbcNINlISAJAirNOnAPBsLoGAzZH4xyxlu1C7vgDTmTNkdyTgRWS1oZtFtoAzlMdPW9oyZCW7bERN5wvAUgu2xKS999/v77yla/obW97m2pqamQYhgzDUHNz84yvDQQCuv7667Vx40bl5eWppKREZ5xxhu68884kRA4AzuL1uFSeH7mrzRzJMWMt29zxB5zIatnelYEJyaZohWQ9FZKA41Rl0GzuoUBQ/f6gJDpKACSfx643vvjii9Xb2zvn1w0NDelNb3qTtm3bpuLiYp177rkaGBjQI488oscff1yf+9zn9O1vfzsBEQOAc9UUZ6tjwK8jvSNav7jI7nAcwWrZriAhCTiSlZBs7h7WoD+oPJ9tp6VJdyA6Q3IJMyQBx6nOoJZtqzoy1+tWfgYdgwE4g20Vku9617t03XXX6U9/+pPa2tpm/bqrr75a27Zt04YNG7Rnzx7dddddeuCBB/TUU08pPz9fN9xwg/74xz8mMHIAcJ6a6GKbg9E2QLDUBnC6kjxv7IbBnrbMmSM5GgrrcE8k0UHLNuA81mzu7qFRjYyGbI4msayka1VhtgzDsDkaAJnGtoTkz372M1111VV685vfrIqKilm9pru7WzfffLMk6eabb1Z5eXnsseOPP15XXHGFJOnrX/96/AMGAAdbVhGpstnfOWhzJM7RHpshSYUk4FTWYpvdLZnTtn2oe1ihsKnsLBcjJQAHKsrJks8TuUy2KgjTVSvjbQDYKKWW2tx///0KBAKqr6/X6aefPuHxiy++WJL01FNP6fDhw8kODwBss6w8kpBs7CAhKUnDgVBsJhIn2YBzZeIcydhCm9I8KpIABzIMQ9XRKsmWNG/bbjuqQhIAki2lEpI7d+6UJJ1wwgmTPr58+XKVlpZKkp5//vlkhQUAtlseTUg2tJOQlMbmR+ZkMRMJcDJr0/buTEpIRivZ62nXBhzLStCle0JyrGWbm7cAki+lrtIaGxslSfX19VM+p7a2Vl1dXbHnTsXv98vvHyvB7+vri0+QAGADq0LycO+wRkZDys5y2xyRvWLzIwt9VCABDra6OtqynVEJSatCkoQk4FSxxTZpvmm7NdqSToUkADukVIVkf3/kZDUvb+qNhPn5kTvtMyUYv/GNb6ioqCj2p66uLn6BAkCSleZ5VZjtkWmOXexmMmvmE+3agLOtqoyct7X2+dU7NGpzNMkRS0hSIQk4Vqa0bFsVkpUkJAHYYM4Vkl/4whd0zz33zPmNfvKTn2jLli1zfl2iXHXVVbr88stjf+/r6yMpCSBlGYahZRX5euFgjxo7BrQmWnWUqWIn2GzYBhytIDtLi4tzdKhnWLvb+nXi0lK7Q0q4pi6rZXvqG+wA7BVr2U7zCkmro6SKG7gAbDDnhOThw4e1a9euOb/RwMDAnF/zWgUFkQvswcGpZ6RZ71NYWDjt9/L5fPL5OPACSB/Ly/P0wsEeNbDYJnaCXcEJNuB4q6vydahnWLta0j8haZqmmrpo2QacrjoDZkiapkmFJABbzbll+9e//rVM05zzn3PPPXfBwS5dulSS1NTUNOVzmpubxz0XADJFbNM2i21iS20qGdIOOF4mzZFs6/drZDQst8vQ4pIcu8MBMIVYy3YaV0gO+IMaCoQkMeIGgD1Saobk5s2bJUnPPvvspI83NDSoq6tLkrRp06akxQUAThBLSFIhqXZrqQ0t24Djra6MJCR3taR/QtKaH1lTnK0sd0qdhgMZxUpItvWPKBw2bY4mMaxukgKfR3m+lNp1CyBNpNSZ0HnnnSev16umpiZt3bp1wuO33XabJOmUU05RTU1NssMDAFuRkBxjVTRU04IEON6aoyokTTM9L/wtBzojx+clpcyPBJysssAnw5BGQ6a6hgJ2h5MQY+3aVEcCsEdKJSRLSkr0iU98QpJ06aWXqrOzM/bYjh07dP3110uSrrnmGlviAwA7WQnJzsFAxmyrnYp1kl1dxEk24HQrK/NlGFL30Kg6BtLzwt9izY+sZ8M24GhZbpfK8iLnEOnatt3WF11ow81bADaxrTb72muv1X333Tfh6xdccIG8Xq+kSIv2D37wg3GPX3fddXrmmWf05JNPatWqVTrrrLM0ODiohx9+WKOjo7r88st1/vnnJ+VnAAAnyfN5VFXoU2ufX42dgzout9jukGwxHAipbyQoiZNsIBVkZ7m1pDRX+zuHtLu1P62XUe2PtmwvJSEJOF51kU8dA3619o1o/eIiu8OJO+vmLedKAOxiW0Jy3759evrppyd8fefOnbH/nZ098eCYm5urxx57TN/5znd066236v7775fX69Wpp56qyy67TBdddFFC4wYAJ1tWnhdJSHYM6Li6YrvDsYW1ETPP61ZBdpbN0QCYjdVVBbGE5Okry+0OJ2Gaoi3b9bRsA45XXZitlw71pe2m7dZohSQt2wDsYlvL9s9//vMZN3M/9thjk77W6/Xqyiuv1IsvvqihoSH19PTo8ccfJxkJIOMtK8+XlNmbtq3WKu74A6ljTYZs2j4QbdleQoUk4HjWeUS6tmy39kfPl1gACMAmKTVDEgAwveXROZINGbzYhhYkIPWsqkr/Tdu9w6Pqic73rS8lIQk43aKi9E5ItnG+BMBmJCQBII2waXusZbu6iBNsIFWsiSYk97QOpO2m7f3R43JFgU95PtumJgGYpViFZJq3bFfRsg3AJiQkASCNLKsYS0im60X9TGjZBlLPsvI8eVyG+v1BHUnTaiSrHX1VZb7NkQCYDevGZmsaJiRN04z9XJW0bAOwCQlJAEgjdSW5crsMDQVCauv32x2OLawT7Gru+AMpw+txxSq803WOpPVzrY5WgwJwtuo0niHZNxyUPxiWxFIbAPYhIQkAacTrcamuJEeS1JChi21aadkGUpKVkNyfpiMndrcOSJJWVVEhCaSCquh5RN9IUMOBkM3RxJe10KYoJ0vZWW6bowGQqUhIAkCayfQ5kmMzkUhIAqkklpDsHLI5ksTYE62QXEOFJJASCnwe5Xojybp0myPZxvxIAA5AQhIA0syy8kj1TWPHgM2RJF84bFIhCaSodL6Z0j8yqsPRts9VJCSBlGAYRtq2bbeyYRuAA5CQBIA0c/Rim0zTORhQMGzKMKTyfO76A6lkaRonJK127apCn4pysmyOBsBspetiG6tlm4U2AOxEQhIA0szy6EV9Qxpe1M/EumAoz/cpy82vOCCVWBWSzd1DCkSXLaSLPSy0AVKSVSF5JM0qJGnZBuAEXK0BQJqxLuqbOocUDKXXRf1MrJaqalqQgJRTWeBTrtetsCkd7E6vOZKxhTaVJCSBVFKVrhWStGwDcAASkgCQZqoLs5Wd5VIwbKq5e9jucJKqhRNsIGUZhqGlZem5aXtPW3ShTTUbtoFUkv4zJKmQBGAfEpIAkGZcrrGL+nScxTadsYU2nGADqShdF9vsjrZss9AGSC3WDc5027LdGm3ZruQGLgAbkZAEgDS0vCIz50jGEpKcYAMpaWl5rqT0Skj2Do3GLv5XVVIhCaSS+tLIMamhfUCmadocTXyYpqm22FIbbuACsA8JSQBIQ2NVRgM2R5JcLbEh7SQkgVQUa9nuTJ+E5O5ou3ZNUbYKstmwDaSSFZV58rgM9Y0E06ZKsntoVKOhSHK1goQkABuRkASANLSsPFKFk05VRrPR2ssMSSCVWTdT9nekz1Ib2rWB1OXzuLWiInJO9eqRfpujiQ+rm6Q0zyufx21zNAAyGQlJAEhDsQrJ9sxKSLbEZkiSkARSkXXsOtw7rJHRkM3RxMee6IbtNdUkJIFUZP3bfaWlz+ZI4qOtPzo/kupIADYjIQkAaWh57KJ+RMOB9Lion8nIaEi9w6OSqJAEUlVpnlcF2R6ZpnSgMz2qJGMVksyPBFLS2kWRhGS6VUhyrgTAbiQkASANleR5VZwbmVWWTrPYptMSbdfOyXKrMNtjczQA5sMwjLTbtG0lJFfTsg2kpHXVhZKkV9OlQjKWkKRCEoC9SEgCQJpKt4v6mRzdrm0Yhs3RAJivdFps0zUYUMdAQJK0kgpJICVZFZL72gflD6Z+10krCwABOAQJSQBIU5mWkGzljj+QFpbGFtuk/rHLqo6sLclRno/KbSAVVRdmqygnS6Gwqb1tA3aHs2DW+VIlCUkANiMhCQBpypoj2ZAhi22sE+xqTrCBlLY8jW6m7IkmJNfQrg2kLMMwtDa62GZXS+rPkWyNLrWpYqkNAJuRkASANLWsPNIe2NiR+nfzZ6OlN3qCzYZtIKUtTaOE5C5roQ0JSSClWQnJV9MgIdnGUhsADkFCEgDSVMa2bBdwgg2ksmXRGZJt/X4N+oM2R7Mwu1sjN4RWVzE/EkhlaxdFFtu8ciS1F9uEw6baohWSlYy4AWAzEpIAkKaWlefJ4zLUPTSqg11DdoeTcEcvtQGQuopys1SSmyUptRfbmKYZa9lmwzaQ2tKlQrJzMKBQ2JRhSOX5JCQB2IuEJACkqRyvW5uXlEiSHtvVZnM0idfSSwsSkC6WxRbbpO7NlI6BgLqHRmUYbNgGUt3qqgIZhtTe71fHgN/ucObN6iYpy/Mpy00qAIC9OAoBQBo7Y02FJOmxXe02R5JYkRYkKiSBdBHbtJ3CFZJWdeSS0lxlZ7ltjgbAQuT5PFpSmisptRfbtFsLbWjXBuAAJCQBII29cXUkIbltX6f8wZDN0SRO11BAo6FIC1IlWyOBlGfNkWxoT92EJAttgPSytjoyRzKV27ZbWWgDwEFISAJAGnvdokJVFvg0PBrS9sZuu8NJGKtdmxYkID2kQ4UkC22A9LJ2UXSOZAovtmnto0ISgHNw1QYAacwwjFiVZDrPkRxr1+YEG0gHYzMkUzchyUIbIL2kw2Kb1uj5UmUBFZIA7EdCEgDS3BlrKiVJj+1O3zmSLb3RO/6cYANpwaqQ7BwMqG9k1OZo5s40Te0mIQmkFatle3drv4KhsM3RzE8bLdsAHISEJACkuS2ryuV2GdrbNqDm7tTdWDudFusEm4U2QFrI93lUEZ0Hm4pVkq19fvWNBOV2GVpekWd3OADioL40VzlZbvmDYe3vTM3zKVq2ATgJCUkASHNFOVnaXF8sKX23bbdGZ0hWc8cfSBvWYpvGFExIWtWRS8py5fOwYRtIBy6XoTWxtu3UnCNpLbWhZRuAE5CQBIAMEGvbTtOEpFUhSUISSB9Ly3MlpXZCcnUl7dpAOlkXW2yTenMkg6GwOgaokATgHCQkASADWItttu3rkD8Ysjma+GulZRtIO0tTeLHNHjZsA2nJmiOZiottOgcDCpuSy5DK8klIArAfCUkAyACvW1So8nyfhgIhPbu/2+5w4o4KSSD9LI8mJBtTcFbbLqtCspoKSSCdpHLLtnXztqLAJ7fLsDkaACAhCQAZweUyYlWSj+1qszma+BoZDalnKLKFl4QkkD6sCsnG9gGZpmlzNLM3GgrrlSORZMW6RYU2RwMgntZGE5LN3cPqGxm1OZq5Odg1LElaVJRjcyQAEEFCEgAyxBlrrIRkes2RbItujPR5XCrM8dgcDYB4WVIaSUj2jQTVPZQ6F/67WvrlD4ZVmO2JLeYBkB6Kc71aFB0PszvF2rZjlduMkgDgECQkASBDvH5VuVyGtKdtQId6hu0OJ25i7dpF2TIMWpCAdJHjdccu/FNpsc3Ogz2SpI11xXLRFgmkHatK8pUUS0haCdTVVYySAOAMJCQBIEMU53q1qb5EkvR4GlVJWgnJKtq1gbSzLAUX27wQTUgeV1dsaxwAEmNtdBTDq0dSa47k7miFpLWYBwDsRkISADLIGWk4R7K1l4U2QLqKbdruTJ2E5PMkJIG0tja22CZ1KiRHRkOx4+jqalq2ATgDCUkAyCBnrKmUJG3d26FAMGxzNPFxdMs2gPRizWBMlZbtvpFR7WsfkBRp2QaQfqwKw10t/SmzcGtv24DCplSSm6WKfJ/d4QCAJBKSAJBRjqkpVHm+V4OBkJ490GV3OHFByzaQvmKbtlMkIflic69MU6otyVE5F/1AWlpekacst6EBf1DN3akxk3tXtJpzTXUB87YBOAYJSQDIIC6XoTesirRtp8scSVq2gfS1rDxXUmSGZCpUItGuDaS/LLdLKytTq23b2rC9hoU2AByEhCQAZJhTV5RJkl5o7rE3kDgZa9mmGglIN3WluXIZ0mAgpPYBv93hzGhnU48kEpJAultnbdpOkcU2VoXk6moSkgCcg4QkAGQYqwXyUE9qtBlNxzRNtfVFkhSVBVRIAunG53FrcUmOJGl/x5DN0UzPNE0qJIEM8bqayBzJFw/12hzJ7OymQhKAA5GQBIAMs7g4cnF/pGdEobDzWyCn0z00qkAospyHGZJAeloaXWyz3+FzJA/3jqhjwC+Py9D6xUV2hwMggTZE/42/lAIJyd7hUR2JjrehQhKAk5CQBIAMU1WYLY/LUDBsqq1/xO5wFqQleoJdlueV18OvNCAdLYtWdTc4PCH5fLRde+2iAmVnue0NBkBCHbO4SIYhHYneiHAyqzqypihbhdlZNkcDAGO4egOADON2GaouilQTpsp2yKm0smEbSHupUiFpzeXdWFtsaxwAEi/f59Hy6M0Sp7dtMz8SgFORkASADFQbncl2KMUTkmMLbUhIAulqWUU0Idnp7ITk8yy0ATKK1bb9YrOzE5Kx+ZEkJAE4DAlJAMhAi4tzJaX+YpvD0fhriklIAulqWdlYQjLs0Lm3wVA4ViW1qb7Y3mAAJIU1K9bpFZKvtrDQBoAzkZAEgAxkba1N9ZbtQ7GEZI7NkQBIlNqSHHlchkZGw2p16Nzb3a0DGh4NqcDn0fLyfLvDAZAEqbDYxjTNWIXkahKSAByGhCQAZKDaWEJyyOZIFsZqOV9MQhJIWx63S3WlkaruxnZntm0/f7BHknRsXZFcLsPeYAAkRSostmnv96tnaFQuQ1pZyc0SAM5CQhIAMlBtNIGX8i3bvSQkgUxgbdpudOgcyecPdktifiSQSVJhsc2uaHXk0vI8ZWe5bY4GAMYjIQkAGchq2T7cMyzTdOZMtpmEwqaO9ETaN2nZBtKb0zdtv3AwkoxgwzaQWZy+2GYX8yMBOBgJSQDIQIuKcmQY0shoWJ2DAbvDmZf2fr+CYVNul6GqQpbaAOlsWXm0ZbvDeWMmBvxB7W6LXPQfx0IbIKM4fbGNlZBkfiQAJyIhCQAZyOtxqaogksRL1cU2Vrt5dWG23MxsA9La0vKxTdtO87fmHplmZHREZQE3R4BM4vTFNtZCm7XVJCQBOA8JSQDIUFbb9qEUT0haPweA9GW1bDd1DikUdtaYiVi7dl2RzZEASDYnL7YJh03tbh2QJK0mIQnAgUhIAkCGWhxbbOO8FsjZONzDQhsgU9QU58jrcSkQCsf+7TsFC22AzOXkxTYHu4c0PBqS1+PSktJcu8MBgAlISAJAhqpN9QrJaNw1xbRIAunO7TJiF9SNDlts8/zBHknScXUl9gYCwBZOXWxjzY9cWZEvj5vLfgDOw5EJADKU1eqcqjMkxyokuesPZAInzpFs6R1Ra59fbpeh9YsL7Q4HgA2cutiG+ZEAnI6EJABkqLGW7dRMSFpxUyEJZIZl0YSkkyokrXbt1VUFyvV6bI4GgB2OrS2W5LzFNq9aG7ZJSAJwKBKSAJChaksilYWHuodlms5aEjEbVkKylqU2QEawFts4KSG5s6lHEvMjgUx2TE2hIxfbWBWSa6pISAJwJhKSAJChrArJfn9QfcNBm6OZm76RUfWPRGJeVERCEsgEVoXkfgclJJ/Z3yVJOmEJ8yOBTJXnwMU2gWBYDe2RYyUVkgCcypaEZFtbm375y1/q4osv1qpVq5Sdna3c3FytXbtWn/rUp7R///5pXx8IBHT99ddr48aNysvLU0lJic444wzdeeedyfkBACAN5HjdKsvzSpKaU2zTtjU/sjg3S3k+2iSBTGAlJA92D2s0FLY5Gmk4EIotsThpWanN0QCwk9MW2zR2DCoYNlXg86imiNE2AJzJloTk5ZdfrksuuUR33HGHcnNzdcEFF+jMM89UV1eXvve972n9+vX685//POlrh4aGdOaZZ+rKK69UU1OTzj33XJ100knaunWrLrroIn3+859P8k8DAKlrcYpu2h5baEN1JJApqgp9yslyKxQ2HbGMa+fBbgXDpqoLsxkdAWQ4py22ebWlT1KkOtIwDJujAYDJ2ZKQLC0t1Ve/+lU1NTXphRde0G9+8xvdd999amho0Pve9z4NDg7qfe97n7q7uye89uqrr9a2bdu0YcMG7dmzR3fddZceeOABPfXUU8rPz9cNN9ygP/7xjzb8VACQeqyL6FRbbHOoZ0SSVENCEsgYhmFoSVlk9m1jx4DN0UjbGyPnqScuK+WCH8hwTltsY82PXM38SAAOZktC8qabbtKXv/xlLV68eNzX8/Pz9dOf/lQFBQXq6urSfffdN+7x7u5u3XzzzZKkm2++WeXl5bHHjj/+eF1xxRWSpK9//esJ/gkAID1YFYZOqDaaC6uikwpJILMsr7AW29g/ZuLZA5H5kSctZX4kkOmctthmV0vkps1a5kcCcDDHLbXJzc3VmjVrJEkHDx4c99j999+vQCCg+vp6nX766RNee/HFF0uSnnrqKR0+fDjxwQJAirMSerRsA0gF1qZtuxfbBENh7TgQqZA8YSnzI4FM57TFNrtaoy3bVEgCcDDHJSRHR0djS20WLVo07rGdO3dKkk444YRJX7t8+XKVlkZOCp9//vmExQgA6aK2JNL+mHot25F4adkGMstSa9N2p70JyZeP9GkwEFJhtkdruOAHIOcsthkKBGOdL6ur8m2NBQCm47iE5E9/+lN1dHQoJydHb3nLW8Y91tjYKEmqr6+f8vW1tbXjnjsVv9+vvr6+cX8AINMsTtEZkrEKSRZJABnF2rTdaHOF5DONkXbtE5aWyuVifiQA5yy22dc2KNOUyvK8Ksv32RoLAEzHUQnJF198Uf/2b/8mSfrSl76kqqqqcY/390eG8+bl5U35PfLzI3eBZkowfuMb31BRUVHsT11d3UJCB4CUZCX0ugYDGgoEbY5mdkZDYbX2WUttsm2OBkAyWS3bh3qGNTIasi2O7fsjCckTadcGEOWUxTZ72iLXzCsrqY4E4Gyeub7gC1/4gu655545v9FPfvITbdmyZcrHm5ub9ba3vU0DAwO64IILdOWVV875Pebiqquu0uWXXx77e19fH0lJABmnMDtLBdke9Y8Edah7WKtSoPWwpXdEYVPyelwqz+POP5BJyvO9KvB51O8P6mDXkC3HLNM09ez+yPzIk5ax0AZAxGsX25TbVJ24py2y0GYV7doAHG7OCcnDhw9r165dc36jgYGBKR9raWnR2WefrQMHDujNb36zfvOb38gwJra/FBRETjoHB6du07Hep7CwcNp4fD6ffD4uZAGgtiRXrxzpU3NPaiQkrXbtmqJsWiWBDGMYhpaW5+nFQ71q7Bi05ZjV0DGozsGAvB5XrEUTAPJ8Hi0pzdX+ziHtbu23LyHZGk1IVjr/nA5AZptzy/avf/1rmaY55z/nnnvupN+vra1NZ511lnbv3q1zzjlHv//976dMFC5dulSS1NTUNGV8zc3N454LAJheqm3aPtzLQhsgk9m92GZ7dH7kcXXF8nnctsQAwJmWV0SqEhva7Ztza7VsUyEJwOlsnSHZ3t6us846S6+88orOPvts3XPPPcrOnnoe2ObNmyVJzz777KSPNzQ0qKsrcpK4adOm+AcMAGmoNjpHsjlFEpJW4nQxCUkgIy0ry5UkNXYM2fL+z0TnR57E/EgAr2H34q2R0ZCauiLHRiokATidbQnJjo4OnXXWWfr73/+us88+W/fee69ycqa/uDzvvPPk9XrV1NSkrVu3Tnj8tttukySdcsopqqmpSUjcAJBualNs0/ahHmuhDQlJIBMtjV3wTz0OKJFiC22WkZAEMN7yisjxqaHdnuPTvvYBmaZUnJul8nyvLTEAwGzZkpDs6urS2WefrZdeeknnnHPOrJKRklRSUqJPfOITkqRLL71UnZ2dscd27Nih66+/XpJ0zTXXJCZwAEhDYy3b9lQbzZWVOLU2hAPILFYF0n4bKiRbekd0sGtYLkPaXF+c9PcH4GzLy6Mt2zZVSO61FtpU5k+6kwEAnGTOS23i4WMf+5j+9re/yTAMlZaWxpKMr/WOd7xD73jHO8Z97brrrtMzzzyjJ598UqtWrdJZZ52lwcFBPfzwwxodHdXll1+u888/Pwk/BQCkh8Up1rJtLbWhZRvITFZCsqVvRMOBkHK8yZvjaFVHrltUqILsrKS9L4DUsCJaIXmwa0j+YCjpc2ZjC21SYEkhANiSkLTmPJqmqd/85jdTPm/p0qUTEpK5ubl67LHH9J3vfEe33nqr7r//fnm9Xp166qm67LLLdNFFFyUydABIO1Zir63fb8vJ81yYpjm2ZZuEJJCRinO9Ks7NUs/QqPZ3DmrdosKkvXesXZv5kQAmUVHgU77PowF/UE2dQ0lPDO5ujS60qWShDQDnsyUh+dhjjy3o9V6vV1deeaWuvPLK+AQEABmsNM+rnCy3hkdDOtIzEpvP5kQ9Q6MaCoQkSYuKpl6CBiC9LS3L0/NDPdrfkdyE5DPRDdsnMT8SwCQMw9Dyijz9rblX+9oHk56QHGvZpkISgPPZumUbAGA/wzBibdtOX2xjxVee71N2lnMrOQEk1vLojZNkzmnrHR7Vrmj1ERWSAKYydnxK7mIbfzCk/Z2RY+KqKiokATgfCUkAQKxtu9nhi21YaANAGttkuyeaIEyG5w50yTQjMywrCnxJe18AqWVZdLFNY3tyF9s0dgwqbEoF2R5VcowCkAJISAIAVGtVSDp8sc3YQhvatYFMtrGuWJK0o6knae+5fX+3JOmEJSVJe08Aqce6YZLsTdvWQpvVVQVs2AaQEkhIAgDGNm07vGU7ttCmiApJIJMdV1csw5CauobU1j+SlPfcHp0feSLzIwFMI5aQbE9uy/YeFtoASDEkJAEAsZZtp1dI0rINQJIKsrO0JrosYseBnoS/38hoSH9r7pUkncT8SADTWBadIdk9NKruwUDS3ndPdKHNShKSAFIECUkAQKxlu9nxCclIJVRNMQlJINMdH22d3tHUnfD3evFQrwKhsMrzfVpSlpvw9wOQunK9HtUURUbLJHOxjZWQTPZmbwCYLxKSAADVlkQusFv6RhQMhW2OZmpWBediEpJAxrMSks8dSHxCckf0PY5fUsxsNgAzWl4RqVLcl6TFNoFgWPujMytXs2EbQIogIQkAUEW+T163S6GwqdZ+v93hTGpkNKSOgUhsJCQBWAnJF5t75Q+GEvpeVhXm5noW2gCY2dgcyeQkJPd3DioYNpXv86i6kMV/AFIDCUkAgFwuQ4uim6udOkeypTfSrp2T5VZxbpbN0QCwW31prsrzvQqEwnrpUF/C3sc0zdg2781s2AYwC9YcycYktWxbG7ZXVuZTxQ0gZZCQBABIGpsj2dQ1ZHMkkzt6oQ0n2wAMw4hVLO5IYNt2c/ew2vv98rgMbVhclLD3AZA+rJbtZFVI7mljwzaA1ENCEgAgSVoRPXm2TmqdxkpIstAGgCUZcyStdu1jagqVneVO2PsASB/LoxWSBzqHFAqbCX+/sYU2JCQBpA4SkgAASWNbGa22H6dhoQ2A14olJJu6ZZqJuejfGW3X3sT8SACztLg4Rz6PS4FQWM3die882dvKhm0AqYeEJABAkrQ62uazu9WZFZKHrZbtYoa1A4hYv7hIWW5D7f1+NSdo/m1soQ3zIwHMkstlxOZIJrptezQUVkN0ViUt2wBSCQlJAIAkaXX0rnpz97AG/UGbo5nocC8t2wDGy85ya310rmMi2rZHRkN6+XBkYc7m+uK4f38A6cvatL2vPbGdJwc6hzQaMpXrdaumiHMkAKmDhCQAQJJUkudVeb5PkrS3zXlt27RsA5jM8fWJmyP5t+ZeBcOmKgt8HHsAzMny8uhim47EVkjujc7+XlmZL5eLpX8AUgcJSQBAzOoqZ7Zth8OmDveOSKJCEsB4iVxsE2vXri+RYXChD2D2rJbtxgS3bFuzv1dVMj8SQGohIQkAiLHatvc4rELyQNeQAsGwstyGqouYIQlgjDXb8dWWPg3EedzEjgPW/MjiuH5fAOnPatm25jsmChu2AaQqEpIAgBgrIem0Cskn93VKimy5zXLzqwvAmKrCbNWW5ChsSi8c7Inb9zVNUzuiG7Y3s2EbwBwtr4gkCFv7/HG/WXI065yNhTYAUg1XdQCAmFjLdouzEpLb9nVIkk5bUWZzJACcKBFt283dw+oY8MvjMmKLcwBgtopyslSe75WUuLbtYCgcm1FJyzaAVENCEgAQsypaIXm4d0T9I6M2RxNhmqaeaohUSJ62otzmaAA4kZWQfDaOCUlrfuQxNYXKznLH7fsCyBxji20S07Z9sHtYgWBY2Vku1ZYwYxtAaiEhCQCIKcrJUlVhZNO2U+ZI7mkbUMdAQNlZLm2so0oJwERWS/XOA90Kh824fE9rfuQm2rUBzJM1R3Jfgiok97SyYRtA6iIhCQAYJ7bYxiFzJLftjbRrn7i0VD4PVUoAJlpbXaBcr1v9/mDcbqbE5kcuISEJYH5ii23aE3OTN7bQhnZtACmIhCQAYBzrpHZ3qzMqJLdFF9qcyvxIAFPwuF06rq5YUnzmSA4HQnrlSJ8kaXN98YK/H4DMtCzast3YkZgKyRebeyWN3UwGgFRCQhIAME5ssY0DKiRDYVNPN3ZJYn4kgOnFc7HNi4d6FQybqizwaXExc9kAzI9VIdnYMSjTjM84CUsobMaW/p2yvDSu3xsAkoGEJABgnFWxlm37KyRfOdKn3uFRFfg8Wl9TaHc4ABzMaq22ltEshPU9NteXyDCYywZgfupLc+VxGRoKhNTSNxLX7/3SoV71jQRVkO3RhsXM2AaQekhIAgDGWRWtkGzpG1HvsL2btq07/yctK5XHza8sAFPbXBdJSDZ2DKptgRf+1kKbzUuKFxoWgAyW5XapvjRXktQQ58U2T0RnbJ+6vIxzJAApiSMXAGCcwuws1RRlS7J/sQ3zIwHMVlFulo6JVlK/78dPzfv4ZZrm2EIbNmwDWKBELbbZGk1IblnFSBsAqYmEJABgAqtt287FNqOhsJ5hfiSAOfjWu49VTVG2GjoG9fb/3qr7Xzwy5+/R3D2sjgG/styG1tMGCWCBlldEOk/2xbFCcjgQ0rP7I5Xcp6/kHAlAaiIhCQCYwAmLbf7W3KuhQEgluVlaW832SAAzO6amSPf+6xadtqJMQ4GQLr11h77xf68oGArP+ntY8yNfV1Ok7Cx3okIFkCGWl0crJOO4afvZA10KhMJaVJQd+/4AkGpISAIAJogttmmzLyH5ZHR+5KkryuRysVQCwOyU5fv0y386SR9/w3JJ0o8eb9AltzyjrsHArF4fmx9ZX5yoEAFkkLWLIqMknt3fpb6R+MzmtuZHnr6ynMVbAFIWCUkAwASrHdCyHZsfuZz5kQDmxuN26erz1un7F29SrtetrXs79dab/qrt+7umfd22vR2654XDkpgfCSA+NtYWaVVlvoYCId31XHNcvucTe6LzI2nXBpDCSEgCACZYVRlp2W7v96tnaHZVRfE0MhrSs9EqpVOZHwlgns4/tka//+TpWl6epyO9I3rvj57U9x7eo1DYHPe8UNjUjQ/t0T/+9Gl1D43qdYsKdc66KpuiBpBODMPQh05bKkn61ZMHFH7N8WeuugYD+vvhPknSaSu5aQsgdZGQBABMkOfzaHFxjiR7qiR3NHUrEAyrssCnFRXMRgIwf6urCnTPv27RuzYtVtiUbvjzbn3wp0+rrW9EktQx4NeHb3lG331ot0xTeu8Jdfrdpacpx8v8SADx8a5Ni1Xg86ihYzDWbj1f26IjbdZWF6iyIDse4QGALUhIAgAmZedim6ei7dqnrShjNhKABcv3efSd9x6nGy7aqFyvW9v2deotN/5VP32iUW+96a/6654O5WS5dcNFG3X9u49lmQ2AuMrzeXTh8bWSpF9s27+g77X1qPmRAJDKSEgCACY1Nkcy+QnJbbGEJCfbAOLnwuNrde+/btG6RYXqHAzo2j++rNY+v1ZW5usPl50eSxgAQLx96NQlkqRHdrWpqXNo3t/HqrBkfiSAVEdCEgAwKbsSkoP+oJ4/2CMpsmEbAOJpRUW+7r70NF1y6hK5XYbetXmx/vDJ02PHPABIhOUV+XrD6gqZpvSrp/bP63s0dQ7pYNewPC5DJy0rjW+AAJBkJCQBAJOyLs73JHmG5Pb9XQqGTdWW5KiuNDep7w0gM2RnufXVt6/Xy//xZn3nPccpz+exOyQAGeCSaJXkHdsPajgQmvPrrerIzfUlHLcApDwSkgCASa2szJdhSJ2DAXUO+JP2vk82jM2PBIBE8nmYFQkgec5YU6n60lz1jQT1h+cPzfn1zI8EkE5ISAIAJpXjdauuJFKhmMxN29ZCG9q1AQBAOnG7DH3wlEiV5M+37ZdpmrN+bThsamt0w/aWVZwjAUh9JCQBAFOyNm3vaUvOHMkBf1AvHe6TJJ28jJNtAACQXi46oVbZWS692tKv7fu7Z/26l4/0qWdoVPk+j46tLU5cgACQJCQkAQBTWpXkxTbP7u9SKGyqrjRHNcU5SXlPAACAZCnO9eqdmxZLkn6xbf+sX2fNjzxleamy3FzGA0h9HMkAAFOyKiST1bL9TGOXJKojAQBA+vrgKUslSX/6e4taekdm9RrmRwJINyQkAQBTWlU5ViE5lzlH8/V0LCFZmvD3AgAAsMPragp10tJShcKmbtnWOOPzR0ZDsZu2W0hIAkgTJCQBAFNaWZmv7CyXeoZG9UJzb0LfazgQ0t+aeyRRIQkAANLbx9+wXJL0qycPqHPAP+1zH3qlVf5gWJUFPq2szE9GeACQcCQkAQBTys5y6y3rF0mS7th+MKHvtbOpW6MhU4uKslVXyvxIAACQvs5eV6n1iws1FAjpx39tmPJ5gWBY//nALknS+06ql2EYyQoRABKKhCQAYFrvOaFOknTvC4c1HAgl7H2eOqpdm5NtAACQzgzD0GfOXi1J+uW2qaskb3v6gA50Dqk836d/iVZVAkA6ICEJAJjWyctKVV+aqwF/UPe/eCRh7/N0Q6ck6STatQEAQAY4e12lNiwu0vBoSD/+y8Qqyb6RUd348B5J0mfftEp5Pk+yQwSAhCEhCQCYlstl6D0n1EqS7ng2MW3b/mBIOw/2SJJOXs5CGwAAkP4Mw9BnzlklSfrlkwfU8ZoqyZsf26fuoVGtqMjTe6MdKwCQLkhIAgBmdOHxtXIZ0jONXWrsGIz793/hYK8CwbDK831aXp4X9+8PAADgRGetrdSxtZEqyf85qkrycM+wfvZEZAP3lW9ZJ4+bS3cA6YWjGgBgRouKcvSG1RWSpN8moErSatdmfiQAAMgkU1VJfvvBXfIHwzppWanOWVdpZ4gAkBAkJAEAs2K1Ct21o1nBUDiu3/uZ/dGFNrRrAwCADHPmmkptrB2bJfn3w726e+chSdI1563jZi2AtERCEgAwK2evq1JpnletfX79ZU973L7vaCis5w50S5JOZqENAADIMJEqyejG7Sf360u/f0mmKb1tY4021hXbGxwAJAgJSQDArHg9Lr1z02JJ0h3b49e2/eKhXg0FQirOzdKqyvy4fV8AAIBUccaaCm2sLdLIaFg7mnrkdbv0hTevsTssAEgYEpIAgFl7T7Rt++FX2iZsgpyvZxoj7donLS2Vy0VLEgAAyDxHV0lK0odOXaK60lwbIwKAxCIhCQCYtTXVBdpYV6xg2NTdOw7F5XvGFtosp10bAABkrjPWVOi8DdVaW12gy85aaXc4AJBQJCQBAHNiLbe549mDMk1zQd8rFDb17H5rfiQLbQAAQOYyDEM/+Mfj9afPvEHFuV67wwGAhCIhCQCYk/M3LlJ2lkt72wa082DPgr7XK0f61O8PqsDn0bpFhfEJEAAAAADgaCQkAQBzUpidpfM2LJIk3fHMwpbbPBVt1z5haYnczI8EAAAAgIxAQhIAMGdW2/a9fzus/pHReX+fp6MLbZgfCQAAAACZg4QkAGDOTlpWqhUVeRoKhPSH5w/P63uEw6a2748mJJkfCQAAAAAZg4QkAGDODMPQ+0+qlyTd9nTTvJbb7G7rV8/QqHK9bq1fXBTvEAEAAAAADkVCEgAwL+8+vlZej0svH+nTC829c379tr2R+ZHHLylRlptfRwAAAACQKbgCBADMS3GuV2+NLrf536eb5vz6R3e1SZLeuLoirnEBAAAAAJzNloRkX1+fvvSlL+mtb32rVqxYoaKiInm9XtXU1Ojtb3+77rvvvmlfHwgEdP3112vjxo3Ky8tTSUmJzjjjDN15551J+gkAAJJibdv3vHBYfXNYbjPgD8Y2bJ+5tjIhsQEAAAAAnMmWhGRbW5u+9rWv6S9/+YsqKyt19tln64ILLlBlZaXuuecenX/++frkJz856WuHhoZ05pln6sorr1RTU5POPfdcnXTSSdq6dasuuugiff7zn0/yTwMAmevEpSVaWZmv4dGQ/rDz0Kxf98SeDo2GTC0py9Xy8rwERggAAAAAcBpbEpLV1dV68skn1d3drSeffFK/+93vdOedd+r555/XI488otzcXP3gBz/QAw88MOG1V199tbZt26YNGzZoz549uuuuu/TAAw/oqaeeUn5+vm644Qb98Y9/tOGnAoDMYxiGLo5WSd46h+U2j74aadc+a22lDMNIWHwAAAAAAOexJSGZn5+vU045RR6PZ8JjZ555pt73vvdJkh588MFxj3V3d+vmm2+WJN18880qLy+PPXb88cfriiuukCR9/etfT1ToAIDXeNfmxfJ6XHq1pV/PH+yZ8fnhsKlHdo0lJAEAAAAAmcWRS22sRKXP5xv39fvvv1+BQED19fU6/fTTJ7zu4osvliQ99dRTOnz4cOIDBQCoONer86PLbW6bxXKbvx/uU3u/X7let05aVpro8AAAAAAADuO4hOT27dt1xx13yDAMve1tbxv32M6dOyVJJ5xwwqSvXb58uUpLIxe3zz//fELjBACMufjkSNv2vX87rN7h6ZfbPBJt1379qnL5PO6ExwYAAAAAcJaJPdNJ9uUvf1lNTU0aHh5WY2Ojtm/fLq/Xq5tuukmnnnrquOc2NjZKkurr66f8frW1terq6oo9dyp+v19+vz/2976+vgX8FACQ2Y5fUqLVVfna3Tqg3+88pEtOWzrlc2nXBgAAAIDMZnuF5D333KNf/OIX+s1vfqPt27crPz9f3//+9/WJT3xiwnP7+/slSXl5U29kzc/PlzRzgvEb3/iGioqKYn/q6uoW8FMAQGYzDEPvjy63+d9npl5u097v1wvROZNnriEhCQAAAACZaM4Vkl/4whd0zz33zPmNfvKTn2jLli0Tvm61Vvf19WnXrl36r//6L3384x/X7bffrt///vcqKCiY83vNxlVXXaXLL7889ve+vj6SkgCwAO/aVKtv/t+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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gen = np.random.default_rng(98765432101234567890)\n", "idx = pd.RangeIndex(200)\n", "det_proc = DeterministicProcess(idx, constant=True, period=52, fourier=2)\n", "det_terms = det_proc.in_sample().to_numpy()\n", "params = np.array([1.0, 3, -1, 4, -2])\n", "exog = det_terms @ params\n", "y = np.empty(200)\n", "y[0] = det_terms[0] @ params + gen.standard_normal()\n", "for i in range(1, 200):\n", " y[i] = 0.9 * y[i - 1] + det_terms[i] @ params + gen.standard_normal()\n", "y = pd.Series(y, index=idx)\n", "ax = y.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model is then fit using the `deterministic` keyword argument. `seasonal` defaults to False but `trend` defaults to `\"c\"` so this needs to be changed." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:09.046211Z", "iopub.status.busy": "2024-04-18T11:33:09.045116Z", "iopub.status.idle": "2024-04-18T11:33:10.554628Z", "shell.execute_reply": "2024-04-18T11:33:10.553981Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " AutoReg Model Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 200\n", "Model: AutoReg(1) Log Likelihood -270.964\n", "Method: Conditional MLE S.D. of innovations 0.944\n", "Date: Thu, 18 Apr 2024 AIC 555.927\n", "Time: 11:33:10 BIC 578.980\n", "Sample: 1 HQIC 565.258\n", " 200 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.8436 0.172 4.916 0.000 0.507 1.180\n", "sin(1,52) 2.9738 0.160 18.587 0.000 2.660 3.287\n", "cos(1,52) -0.6771 0.284 -2.380 0.017 -1.235 -0.120\n", "sin(2,52) 3.9951 0.099 40.336 0.000 3.801 4.189\n", "cos(2,52) -1.7206 0.264 -6.519 0.000 -2.238 -1.203\n", "y.L1 0.9116 0.014 63.264 0.000 0.883 0.940\n", " Roots \n", "=============================================================================\n", " Real Imaginary Modulus Frequency\n", "-----------------------------------------------------------------------------\n", "AR.1 1.0970 +0.0000j 1.0970 0.0000\n", "-----------------------------------------------------------------------------\n" ] } ], "source": [ "from statsmodels.tsa.api import AutoReg\n", "\n", "mod = AutoReg(y, 1, trend=\"n\", deterministic=det_proc)\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the `plot_predict` to show the predicted values and their prediction interval. The out-of-sample deterministic values are automatically produced by the deterministic process passed to `AutoReg`." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:10.560020Z", "iopub.status.busy": "2024-04-18T11:33:10.558399Z", "iopub.status.idle": "2024-04-18T11:33:11.418666Z", "shell.execute_reply": "2024-04-18T11:33:11.417993Z" } }, "outputs": [ { "data": { "image/png": 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OzCrJSCTS7KMQERFRA0SjUSwsLHRktwdQqZLM5XLrRS9ERNQcbZeQvOeee/CpT30Kuq7jne98J970pjfhfe97H44ePYonn3wSJ06cwKc//elmH5OI9hnDMHDjxg3oug6n09ns4zSM0+mEpmmskiQiIupAG7s9OjWe2VglaS5EJSKivdd2CUkA+KVf+iV84xvfwNvf/nZcuHABX/nKV+D1evHoo4/i+eefr3prNxFRvayuriIUCnXUIput+P1+LC0tsUqSiIiow6yurq53e3RidaTJ6/Vy4zYRUZNJgiUuAIBUKoWuri4kk8mObrckosZ4+eWXce3atX2z7CUcDmNiYgL33XdfR39gISIi2k/Onj2L2dnZfRHPmDMk3/zmN8Pj8TT5NEREnaPa/FpbVkgSEbWSUqmExcXFjm1t2oxZJRmNRpt9FCIiIqqDXC6HlZWVfZOc83q9yGaznCVJRNQkarMPQETU7sLhMLLZLHp6evbk9YQQSJeASN5AJC8QKVT+VzOAt0/YMOpr/F2T0+lEMpnE9PQ0ent7WSVJRETU5kKhEAqFwr4Zf2XOkpyZmcHBgwf3TSKWiKhVMCFJRLRLS0tLEEJAUZS6Pq9uCFyI6FjOrCUe82I9CVkyNn/MM0safuqEHW89oDY8Sej3+7G4uIgjR46gt7e3oa9FREREjSOEwMLCAmRZhizvTRNdtiywmjMQzlf+d3Xtf/tdMn7yuB1+e+MvO71eL8LhMObm5nDy5MmGvx4REd3ChCQR0S7kcjmEQqG636prhsAfnS/ipVV907+XAAQcEvpc5h8ZsykDFyM6Pn+lhJdXdfyLO+wIOBv3ocLpdCKdTmN6eho9PT2skiQiImpT6XQa0Wi0IVWC8YKBl8I6VvMC4Q2Jx5y21SMMvBjW8ZE77bi7v7EfVyVJgsvlwvT0NA4ePAi3293Q1yMioluYkCQi2oVwOIx8Pl/X9ibNEPjjlyrJSFUGTg8q6HfJ64nHPpeEHpcEm/zKBKAQAk/Oa/ibayVMRnT8+lN5fPgOB+4dbMxbvSRJ8Pl861WSe9WyTkRERPUVCoVQLBbR1dVV1+ddSBv47bN5pMub/73fLmHALaHfJaHfLaPbIeEb82UsZQR+/4Ui3jSu46dP2OFQG3fp6fP5EA6HMTs7yypJIqI9xIQkEVGNGtHepBkCn365iBfDlWTkL7zegTurrA6QJAlvnbDh9l4Ff/pyEfNpA394vogfGtPx/tvscDYgmHc6nUilUpienkZ3dzerJImIiNqMYRgIBoOw2Wx1/T2+lDHwO89XkpHDHgmnehX0u+W1BKSMfpe0aaLxoVEVX7xewhNzGr4d1HAlquPn7nLgcKC+o3FMZpWkOUuSVZJERHuDW7aJiGqUTqcRiUTq1t6kGwJ/eqGIcyEdqgT8WwvJyI1GvTL+w4NOvPOQDRKA7y1o+I9P5zGd2Lz9ezfMKsmFhQUkEom6Pz8RERE1VjweRyKRgNfrrdtzrmQN/PbzBaRKwAGfjF9/wIUPnHTg7QdteP2AijGfvGXVo12R8P7bHfh3p53odkhYyQn85nMFfGWqBN0QdTvjRj6fD5lMBnNzcw15fiIiei0mJImIamS2Nzmdzl0/l24I/NmFIp5f0aFIwL9+vQN37WJukk2W8FMn7Pj4GSd6nBJCDQzmXS4XisUipqenIURjPigQERFRY6ysrKBcLsNms9Xl+cI5A799toBkUWDMK+HfnXHCY7NeeXmqT8FvPOTCfUMKDAF8eaqM33qugJXsFpv9dmFjlWQ+n6/78xMR0WsxIUlEVIN6tjcZQuB/XCziubVk5P/ndQ68bqA+EzVu731tMP/Y2QLCufoF82aVZDAYRDKZrNvzEhERUWNpmoaFhQU4nc66tGuvriUj40WBEa+Ej59xwbeLbdleu4T/524Hfv4uB1wqMJ008P8+ncd3guW6X4J6vV6k02lWSRIR7REmJImIalCv9iZDCHzmYgnPLFeSkf/qdQ7cU+clNB5bJZj/ubVgfiph4D8+nUcwXb+k5MYqSSIiImoPkUgEqVSqLuNnonkDv/N8AdGCwJBbwsfPOOF37D7JKUkSHhxR8ZsPuXBbj4ySDvz5pRL+64tFJIv1S0rKsgyn04np6WlWSRIR7QEmJImIahAKhXbd3mQIgc9OlvDUkgZZAj56d2M3Yr9hRMV/foMLh7tk5DXgzy4UUa5T+7YkSfB6vQgGg0in03V5TiIiImqs5eVlGIYBVd1d/BEvVJKRq3mBAbeEf3+fEwFHfT9q9rpkfPyME+87YYcqAS+t6vjkc3kUtPolJX0+H6skiYj2CBOSREQWaZqGYDC4q/YmQwj8+aUSvr+oQQLw83c5cGaoMcnIjfrdMv7tPQ74bEAwbeDLN8p1e263241CoYDFxcW6PScRERE1RrFYxNLS0q63SieKlQU2oZxAn0vCvz/jRLezMR8zZUnCOw7Z8B/f4Fqfkf2Fq6X6Pb8sw+FwYH5+Hpqm1e15iYjotZiQJCKyaLftTUII/NXlEr63UElG/txdDtw/3PhkpCngkPGhOxwAgK/PlHE1Vp/t25IkMYgnIiJqE+FwGNlsdlcJyVRJ4HeeL2AlK9DjrCQje12N/4g57pPxL+90QALwvQUNL4bqF3d4vV4kk0msrKzU7TmJiOi1mJAkIrJoN+1NQgh87koJ3w5WkpH/8i4HHhzZu2Sk6d5BFW8cVSEA/PcLReTK9Wl3MoP4UChUl+cjIiKixlheXoYkSVAUpabHZ0oCv/t8AUsZgYBDwifuc6LfvXcfL2/vVfD2g5XROZ+dLCJRrM9sbDO+m5+fr/viHCIiuoUJSSIiC3bb3vT0koYn5yvJyI/caccbmpCMNL3/djv6XRKiBYHPX6lPuxODeCIiotaXy+WwsrJSczyTLQv87rkCgmkDXY5KZeTAHiYjTY8ct2HcJyNdBv7nZKlusYfX60U4HEYymazL8xER0WsxIUlEZMFu2ps0Q+D/TFVmNv7EURseHq19IU49uFQJP3dXpd3pqSUNz6/Up93J6/UiFAohlUrV5fmIiIiovkKhEPL5fM0Jyf85WcRcyoDPDnz8jBPD3uZ8rLTJlVhGlYELqzq+HaxPLON0OtcvoYmIqDGYkCQismBpaanm9qbvLWhYzQt0OST86KHmJiNNx7oVvOtw5Sx/fqmIRGH37U5mEM/lNkRERK1HCIGFhQUoilLTcr7phI4XQjokAB+714nRJiUjTeM+Ge89bgcA/K9rJaxkdx/LSJIEp9OJubk5lEr1W5pDRES3MCFJRFSlbDaLUChUUzVBURf4u5uV6sgfO2yDQ6ltO3cj/MRRGyb8MrJl4DN1aHcyg/j5+XmUy/Xb4k1ERES7l0qlEI1G4fV6a3r8l25UEnRvGFFxsKu2+ZP19rYJFSd7ZZR04E8vFKEZu2/d9nq9SKfTXG5DRNQgTEgSEVUpHA7X3N70rXkNiaJAn0vCj4w3b27kZtQN7U4XIzq+VYd2J6/Xi1QqxSCeiIioxYRCIRSLRTgcDsuPvRrTcSlqQJEqF5qtQpYk/Is7HXCrwEzSWL8E3g1FUSDLMudiExE1CBOSRERV2E17U14T+PvpSjXBjx+xQZVbpzrSNOqV8VNr7U5/c7WE5czu2p3Mf09zc3MM4omIiFqEYRgIBoOw2WyW4xkhBL50vRLP/PCYuqcbtavR45Txz05VkqxfvVnGVFzf9XN6vV6srq4iHo/v+rmIiOiVWuu3CBFRi9pNe9P/nS0jWwaGPVJTt2rv5K0TKk71yigZwJ9d3H27k8/nw+rqKhKJRH0OSERERLsSi8WQTCZrimcuRnTcSBiwycCPHWmd6siN7h9W8eCwAoFKLFPQdhfLOBwOlEolLCws1OeARES0jglJIqIq1NrelC4JPD5TaRt6zzE7lBasjjTJkoSPbGh3+uou253MIJ7LbYiIiFpDKBRCuVyG3W639DghBL50oxIXvOWAim5n636M/MBJB3qcEsI5gS9c3d1CGkmS4HK5sLCwgGKxWKcTEhERwIQkEdGOzPYmu91uub3pa9NlFHRgwi/j9GBrDH7fTo9TxgfNdqfpMqYStbc7mUH8/Pw8g3giIqIm0zQNCwsLcDqdlh97LqRjLmXAqQDvOmwtmbnXPDYJ//JOByQA31vQ8GJod7OxvV4vMpkMlpeX63NAIiICwIQkEdGOYrEYEokEPB6PpcfFCwaenK9UEzxyzAbZYjKzWR4YVvHAsAJDAP/9QhHFXbQ7mUE8l9sQERE1VyQSQTqdthzPGELgy1OVSsN/dNAGn73145nbexX86KFKW/lnJ4tIFGufjS3LMmRZ5lxsIqI6Y0KSiGgHoVAImqZZbm/66s0yygZwLCDjzr7Wr47c6GfX2p1COYG/vlZ7uxODeCIiotawvLwMwzCgqtbmWT+7rGMpI+CxAT96sDVnR27mJ4/ZMO6TkS4D/3OytKs4xOfzIRqNIhqN1vGERET7GxOSRETbqLW9KZwz8N2FSovQPzluvdW72Tw2Cf/izkrr9neCGmaTtbdu+3w+RCIRxGKxeh2PiIiILCgWi1haWoLL5bL0OM0Q+PKNysXkOw7Z4La1TzxjkyX8/F0OqDJwYVXHd4K1t247HA6Uy2UutyEiqiMmJImItrG6ulpTe9NXpsrQBXBHr4ITPe1VHWk62avgweHK2b+yiwU3DOKJiIiaKxwOI5vNWo5nfrCoYTUv4LdLeNuB9qmONI35ZLz3eKXD5X/fKO1qDI3H48HCwgLy+Xy9jkdEtK8xIUlEtI1a2psWMwaeXqrcwj9yvP2C941+7IgdEoDzYR1zqdqrJN1uNxYWFlAoFOp3OCIiIqrK0tISJEmCLFf/8a+kC/zd2oXkjx22waG2T3XkRm89oKLfJSFdBr61iypJt9uNbDaLpaWlOp6OiGj/YkKSiGgLxWIRy8vLltubvnyjBAHg3kEFh7raszrSNOKVcd9Q5Xv46i6qJD0eD7LZLDdUEhER7bFsNotQKGS5OvLbQQ2xgkCPU8KPHLA2d7KVKLKEHztSuSD++kwJRb22KklZlmGz2TA3NwfDqH1JDhERVTAhSUS0hVgshlwuB7fbXfVjZpI6zoV0SAB+8qi1JTit6t1HKt/HuZCOhXRtAbgsy1AUhUE8ERHRHguHw8jn85YuWAuawN9PV2ZHvvuIDTa5PasjTW8YUdHnkpAqYVezJL1eL+LxOFZXV+t4OiKi/YkJSSKiLcRiMRiGAUWpvsrxSzcqVYQPjqgY9XXGW+yoT8bpwcq/g7+7WfvGbW6oJCIi2nurq6uQZdnSgr1vzJWRLgEDbgkPj7ZvdaRJlSX848OVKsl/mCmjVGOVpN1uh67rnItNRFQHnfFpmYiozoQQWF5ehsPhqPox12I6JiM6FAn4iaPtPTvy1X58rdrz+RUdi5naKhztdvv61nIiIiJqvFKphHA4bKk6MlsW+PpM5YL1J47aobZ5daTp4VEVvU4JyaLAd3dRJenxeLC4uIhsNlvH0xER7T9MSBIRbSKVSiGTycDpdFb19UIIfOlGpXrwh8ZUDLg76+113Cfj3kEFAsBXd1ElaW6ozOVy9TscERERbSoejyOfz1cdzwDA4zNl5DRg1CvhgeH2noW90cYqya/tokrS7XYjn89zuQ0R0S511idmIqI6icViKJVKVVdIXozouB43YJMrs5Y6kfl9PbesY7nGKkmPx4NcLsflNkRERHvAHD+jqtW1XaeKAk/MVaojf/KYHbKFNu928PCYih6nhERR4HsLtVVJSpIEu92Oubk56Lpe5xMSEe0fTEgSEW0iEolAkqSq5i1VqiMrwftbDqjodnbmW+uEX8Hr+itVkn8/XdvGbUmSYLPZMDs7yyCeiIiogYQQWFlZgd1e/ZK9r02XUNSBQ34Z9wx0TnWkySZLeNeGWZJlo7YqSa/Xi0QigXA4XM/jERHtK535qZmIaBfK5TLC4XDV7U1XYwbmUgacCvCuw52xWXsr716bjfnMsoZQtrYqSZ/Pxw2VREREDZbJZJBKpaqOZ2IFA0+uzVb8yWM2S0tw2skbR1UEHBJiBYHv11glabPZYBgGgsFgnU9HRLR/MCFJRPQq5rylagfAP7NcCWbvH1bhs3dm8G463KXgrj4Fhqi9StIM4rnchoiIqHHi8TiKxWLVCcknZsvQDOB4t4w7+jqvOtJkV25VSX5tugxtF1WSy8vLSKfT9TweEdG+wYQkEdGrxGIx6Lpe1bylki7w/EolIfmGkermM7U7s0ryqSUNq7naZ0kuLy9zuQ0REVGDRCIRAKiq0tEQAs8uV0ap/OjBzq2ONP3wmIouh4RoQeAHi7VVSbpcLhQKBS63ISKqEROSREQbmPOWbLbqFtO8vKojrwG9TgnHuvfHW+rRgII7endXJel2u5HL5RAKhep8OiIiItI0DaFQqOrqyCtRA4migMcG3NXfudWRJrsi4Z2HKrHe39dYJWkutwkGgzCM2i5oiYj2s/3x6ZmIqErZbBbJZLLqdu2nlyq36g+OqE3bRCmEgKZpKBaLexYQ//haleQPFjVE8tZfU5IkKIqCYDAIIWprlSIiIqLNJRIJ5HI5y/HMfUMqVLmzqyNNPzKuwm+XEMmL9e/fKq/Xi2QyuV6NSkRE1WNCkohog1gsVvW8pUxJ4MJqpb3pweHGtWsLIVAul5HP55HJZJBIJBCJRBAOh9f/JBIJ5PN5hMNh5PP5hp3FdKxbwe09MnQB/EONVZJerxfRaBSJRKK+hyMiItrnYrEYNE2rquOjqAu8ELp1wdpMQgjour4nr+VQJLxjrUryqzdrq5K02WzQNA3Ly8v1Ph4RUcfbHwPPiIiqFI1GAVQ3b+n5FQ26AA74ZIz66n+/I4RANBqFYRhQFAWqqkJVVfj9fni9Xng8HjidTjgcDjidTthsNkxPT2N6ehr5fB7d3d0NnQH140ftuHK2gO8taPjHR2zocVr7d+BwOJBMJrGysoLu7u4GnZKIiGh/EUIgFApVNQsbAF4K6yjoQJ9LwtFAY+tVhBAwDAOapkHTNJTLZWia9ooOD0mSIEkSenp6IMuNPc+bx1X8w0wJq3mBZ5c1PDxa3ciejVwuFxYXF3H77bfDbrc34JRERJ2JCUkiojW6rluat2Ru125ENYEQApFIBF6vF3fccccrko+KsvVsp9e97nXo7+/H5OQkwuEwuru7GxYc39aj4ES3jGtxA1+bLuNnTzosPV6SJDgcDgSDQRw/fnzb74uIiIiqk8/nEY/Hq27XfmatXfmB4caMnzEMA7FYbD3pKMvy+iWr1+uFz+eD1+uFy+WC0+mEruu4cuUKwuEwAoFA1XFZLRyqhHcctOFvr5fx1ZtlPDisQrHYsu7xeBCNRhEKhTA+Pt6gkxIRdR4mJImI1iQSCWSzWfj9/h2/djVn4HrcgATggeH6JtLMZKTH48GZM2fQ19dX9WMlScLY2BgCgQAuXryIYDAIp9MJn8/XkGrJHz9qx+88X8B3FzT848M2dFuskvR4POuzlwYHB+t+PiIiov0mHo+jUChUFT+kSwIXI2vjZxpwwWoYBiKRCLq7uzExMbGedDT/d6sqzr6+PkxOTmJubg75fB6BQKBhXR9vPmDDP8yUEcpVqiQfslglqSgKJEnC4uIiE5JERBZwhiQR0Rpz3lI1LU5mdeTJXtlyEm47u0lGbuT1enH//ffjnnvuAQCsrq5C02ob2L6d23tkHAvI0Azg6zPWZ0nabDbouo6lpaW6n42IiGg/MhesVNPufHZt/MyEX8aot74fDc1kZCAQwH333Yfjx49jfHwc/f398Hq928ZbLpcLp0+fxunTp+FwOBAOh1Eu1zazeidOVcKPHrw1S9KoYdmex+NBOBxGJpOp9/GIiDoWE5JERGvMeUs73cALIdbbm+pZTWDOjDSTkf39/bt6PlmWcfToUTz88MMYGBhANBpFLper02krJEla37j97aCGRNH6xm23242lpSUUi8W6no2IiGi/MQwDoVAIDkd1Y1TW45k6L+czL1jNZGRXV5fl55AkCQcPHsTDDz+MsbExxONxpFIpiBoShjt5y4QNHhuwkhN4btn6Uh2Xy4V8Po9QKFT3sxERdSomJImIUJm3FIvFqpq3NJcysJwVsMvAvYP1CeDNZKTb7a5LMnKjnp4evOENb8Dtt9+OQqGASCTyiuHxu3WqV8HhLhllA3h8xnoVpsfjQTabZRBPRES0S8lkEplMpqp4JpwzMJWojJ+5v47jZ8xkZFdXF86cOYNAILCr5/P7/XjggQdw9913A2hM14dLlfD29SrJkuUqSUmSoKoqgsFgQxKmRESdiAlJIiJU2rWLxWJVg9OfXqsmeP2AApe6+3lGZjLSbE+qZzLSZLfbceedd+KBBx6A3+/H6upq3VqfXlklWUZesxaIy7IMSZKwsLDAIJ6IiGgXYrEYyuUybLad5yA+24DxM2Yy0ufz4cyZM+ju7q7L8yqKghMnTuChhx5a7/rIZrN1eW7TWw/Y4FaBpazA8yvWqyS9Xi9isRji8Xhdz0VE1KmYkCQiAhCNRmEYxo7zlnRD4Nnl+g1/35iMPHPmDAYGBnb9nFuRJAkjIyPrrU+xWKxuCcC7+hQMeyQU9VvtX1Z4PB6srq5y9hIREdEuhMPh9SUr2xFCrF+w1mv8jJmM9Hq9OHPmDHp6euryvBv19vbioYcewsmTJ1EqlRCJROoWy7htEt42UUnkfmPO+qWt3W5HuVzG8vJyXc5DRNTpmJAkon3PMAysrKxUNW/pclRHqiTgswF39O2uvclMRjqdTpw+fbqhyciN3G437rrrLvj9fiQSibo8pyRJeNN4JYj/1nzZ8ocDl8uFQqGAlZWVupyHiIhovykWi+txxU7mUgZWsgK2Oo2fefUc7N7e3l0/51ZsNhvuuOMOPPDAA3A6nUilUnV77jcdUKFIwFTCwHzKWpWkJElwOp1YWFhoyCJBIqJOw4QkEe17qVQK2Wy2qnlLT6+1N903rEKVa2/X3piMPHPmDAYHB2t+rlr4fD6cOnUKuq6jUCjU5TkfGlVhk4GFjMDNhLUZlZIkwWazIRgM1nW+JRER0X4Rj8eRz+eri2fqOH7m1aNn+vr6dvV81ZAkCcPDw+uVkvVajBdwyLhnsHLh/J1gbR0fqVQKq6urdTkPEVEnY0KSiPa9WCyGUqkEu92+7dcVNIEXQpXb8jfsor3p1ZWRe52MNI2Pj+PgwYNIJBJ1SQJ6bBLuX9vS+e0agniv14tEIoFYLLbrsxAREe030WgUQggoyvYdHBvHz+wmngGa1+1hOnjwIA4cOFC3WAbAesfH00ua5bnYqqpCCIGlpaW6nIWIqJMxIUlE+144HF5frLKdF8M6Sjow4JZwuKv2t89UKgWHw4HTp09jaGio5ufZLUmScOrUKfT09NQtCfim8coHm+dWNGRK1oJ4c/YS27aJiIisEUJgZWWlqmU2V2KV8TPeXY6fEUIgFos19YJVlmXccccdCAQCdVsmc3uPjCG3hIIOPLds/YLV7XZjaWkJ+Xy+LuchIupUTEgS0b5mDkSvpr3JXNby4LC6Y/JyK2aL9O23397UZKTJ5XLhzjvvhCzLyOVyu36+w10yDvhkaAbwVA3LbczZS/XaAE5ERLQfpNNppNPpKtu1K9WRux0/E4/HYbfbce+99zY1pvF4PDh16hQA1CUJKEkSfmR9LrZmeS622+1GPp9HKBTa9VmIiDoZE5JEtK/FYrGq5i0liwKTkd1v106lUuju7saBAwdqfo56GxoawtGjR5FOp6Hr1ga4v1pluc1a23YNy228Xi/S6TRnLxEREVkQi8VQLBZ3XNBX1AReDN26YK1VqVSCYRi4++67MTw8XPPz1Mvo6CgOHz6MVCq161gGAB4eVaHKwHzawHTSWiu4LMuQZRkLCwt12wBORNSJmJAkon0tFovBMIwd5y09t6xBoFIBOOSp7a1T0zSUy2UcO3Zsx3mVe0mSJNx2220YGBhALBbbdfD8wIgKpwKs5ASuxqwF8YqicPYSERGRRZFIpKrxM+fDOgo60O+ScDRQ+0fBZDKJwcFBjI2N1fwc9SRJEk6ePIm+vr66xDJeu4T7h3Y3FzsSidR1AzgRUadhQpKI9i1z3lI1yUGzXXs3w9+TySR6e3sxPj5e83M0it1ux5133gm73Y5MJrOr53Kp0noV6beD1luvPR4PlpeX69JCTkRE1OnK5TLC4TCcTueOX/vM2kzEB0ZqHz9TLBYhyzKOHTsGWW6dj5MOhwN33XUXbDYbstnsrp9vfS72soZs2VqC0+FwoFgsci42EdE2Wuc3CBHRHstkMkilUju2ay9lDMykDMhSZd5SLcrlMgzDwLFjx6Cqu9to2Sh9fX04ceIE8vn8rmc4mkH8CyEdyaK1IN7lciGXy3H2EhERURUSiQRyudyO8UyqJHDRHD+zi3btZDKJ4eHhpiyx2Ul/fz9OnDiBXC6361jmSEDGuE9G2QCeWrRWJSlJEux2O4LBYF1ayImIOhETkkS0b8ViMZRKpR3nLZnVBHf2KfDba6smSCaT6O/vx+joaE2P3yvHjh3DyMgI4vH4rtqdDvgVHOmSoQvg+wvWPhDIsgxFUTh7iYiIqAqxWAy6ru944Xl2WYMhgEN+GSPe2j4GFgoFqKqKo0eP1lxh2WjHjh3D8PDwrlu3XzEXO1jbXOxkMoloNFrzGYiIOhkTkkS0b62urkKSpG0DaiHEre3aNbZrl0olAJUAeadZlc2mKAruvPNOuN1uJJPJXT3Xmw5U/n19Z0GDUUMQH4lEdn0GIiKiTmaOn7HZbDt+rRnPPFBjPCOEQDKZxOjoKPr7+2t6jr2gqiruvPNOeDyeXc9wfHBtLvZy1vpcbJvNBk3TsLy8vKszEBF1KiYkiWhf0jStqnlLUwkDkbyAUwFeP1BbMtEc/N4KWyir0dXVhVOnTqFcLqNYLNb8PPcNqXCrQCR/a0N5tRwOB0qlEmcvERERbSOXyyGRSOzYrh3KGriZNCABuH+4tnimUCjAbre3dHWkKRAI4OTJkyiVSruKZVyqtJ7ArWUuttvtxuLi4vrlNBER3cKEJBHtS/F4vKp5S0+vVRPcO6jCoVgPvlt18PtOJiYmcODAAcTjcRiGtYoAk12R8PBobRsqJUmCw+HA/Pw8Zy8RERFtIRaLoVgs7jh+5tm18TOnehUEHNbjESEEUqkUxsfH0dPTU9NZ99rBgwdx4MABJBKJmmMZYHdzsd1uNzKZDOdiExFton0+HRMR1VE185Y0Q+Dsyu62a7fy4PftyLKMO+64A93d3YjH4zU/z4+MV1rIXgrriBWsfRgwW60ikUjNr09ERNTJzN+R2116CiHWL1gfHKmtOjKfz8PpdOLIkSMtXx1pMmOZQCCwq1hmwq/gsDkXe9FalaSiKJAkCYuLizW/PhFRp2JCkoj2HSEEQqHQjvOWLqzqyJaBgEPC7b3W3y7Nwe/Hjh1rm+B9I4/Hg1OnTgGofC+1GPHKuK1HhgDwXYtVkjabDbquc/YSERHRJnRdRzgc3rE6ciZlIJQTsMvAPYPWL1iFEEin0xgfH0d3d3etx22KjbFMPp+v+XnebM7FDlqfi+3xeBAOh5HJZGp+fSKiTsSEJBHtO/l8HolEYsf5kWY1wf3DCmSLCUVz8PvY2Bj6+vpqPmuzjY6OYnR0dFdD4d+0ViX53QUNumG91WlxcXFX85+IiIg6UTKZRDab3XH8jLnM5p5BBS7V+gWp+RpHjx6t6ZzNNjo6isOHDyOZTNa8dXs3c7FdLhfy+TznYhMRvQoTkkS078RiMRQKhW0TkkVN4KXVSsD54LD1aoJ8Pg+Hw9EWg9+3I0kSDh8+DFVVa04K3juowGcHEsVb/06r5fF4kM1mOXuJiIjoVWKxGMrl8rYdH7oh8Nyy2a5dW3VkJpPBxMQE/H5/zWdtJkmScOLECfh8PqTT6ZqeY7dzsW02G4LBYM0JUSKiTsSEJBHtO9XMW7oS06EZQK9TwoTf2ltlO7c2baa/vx/Dw8NIJpM1PV6VJfzQaOXDktUgXpZlzl4iIiLaRCgUWp9RuJXLUR2pEuCzVRbaWJXJZODxeHDkyJHdHLXp3G43Dh06hFwuV3NScONc7Gje+lzseDyOWCxW02sTEXWipiYkP//5z+ODH/wg7r77bgwMDMBms6Grqwv33XcfHnvssW3nbHzzm9/EO9/5TvT19cHlcuG2227Dr/3ar3E2BxFtyzAMhEKhHectXVxrx7mzf/tAfzO5XA5Op7PtqyNNZpWkoig1V0n+8LgKCcBkREc4Zz2I5+wlIiKiWwqFAmKx2I7t2i+vdSbcO6hCla2Pn8nlcjh8+DC8Xm/NZ20VExMT8Hq9NccTG+dif2/B2gWr3W5HuVxmxwcR0QZNTUj+yZ/8CT73uc9B0zTcc889eO9734vTp09jcnISv/qrv4rXv/71WFpaes3jfv/3fx9ve9vb8Pjjj+PUqVP4sR/7MSSTSXzyk5/E6dOnuZGViLaUTqfXE4bbMecD3dlnrZrAbG06ePAgurq6aj5nqxkYGMDQ0FDNsyQH3DLuWPt3+R2LVZIulwuFQoFBPBER0Zp4PI5CobBjQnLjBatV6XQaXq8Xhw4dqumMrcbj8ey6SvLNG+ZiaxbmYkuSBKfTiWAwCE2zFgcREXWqpiYkP/WpTyESieDSpUt4/PHH8YUvfAFPPvkkgsEgHn74YUxNTeFjH/vYKx5z/vx5fOxjH4OiKPja176G7373u/jbv/1b3Lx5E295y1tw7do1fPSjH23Sd0RErS6ZTKJcLsNut2/5NeFcZRulIgEnLbY3ZTIZuN1uHD58eLdHbSmSJOHIkSOQZbnmKsk3jVdmL31/sYyyxSBeVVUsLCxw9hIRERGwvqBlu/EzG+OZ23usxTOGYSCfz+Pw4cNwu927PW7LOHjwIDweT81VkvcMKvCbc7HD1udip9NpFs8QEa1pakLy/vvvR09Pz2v+eW9vLz75yU8CAJ544olX/N1jjz0GIQQ+/OEP4x3veMf6P3e73fjMZz4DWZbxpS99CVevXm3s4YmoLcXjcQDYtpXarCY4GpAtbaPc2Nrk8/l2d9AWNDg4iMHBwZqrJO/uV9DtkJAuAS+ErAXxXq8XsVgMiUSiptcmIiLqJKurq1CU7ZOMZrfHkYAMt81au3Y6nYbf7++Y6kiTWSWZzWZruuRUZQk/NGbOxS5be6yqQtd1LC8vW35dIqJO1LJLbVS1Ukmzcc5bqVTC1772NQDA+9///tc8ZmJiAg899BAA4Mtf/vIenJKI2okQAqurq9tWRwK1t2t3WmvTq5lVkpIkoVQqWX68Ikv44bUqyW/PWwvi7XY7SqUS27aJiGjfKxaLSCQSO46fuVhjPGMYBgqFAo4cObLja7SjiYmJXVVJ/vBYZS72paiBUNbaXGy3242lpaWau02IiDpJSyYk0+k0/tN/+k8AgHe/+93r//z69evI5XIAgNOnT2/6WPOfnz9/vrGHJKK2k8vlkM1mt11ooxkCl6OVAP4OCwG82dp05MiRjmptejWzSrLWjds/PKZCloBrcQNLmeqDeEmS4HA4EAwGoevWqiuJiIg6STKZRLFY3DGeuRKtLSGZTCYRCAQwMTGxq3O2Kq/Xi4MHD9Y8S7LfLa/P5PyOxeU2brcb2WwW4XDY8usSEXWalkhIPvHEE/jQhz6ED37wg3j729+O0dFRPPHEE/jRH/1R/PZv//b6183MzAAAAoHAlu2Q4+Pjr/haIiJTNQH8jbiBog747cABf/VvkZlMBj6fDwcPHqzDSVuXLMu7qpLsdsp43VoQb7XVyePxIJVKIRqNWn5dIiKiTpFMJqHr+npH2WamEgYKOuCzGM/ouo5SqYSjR49uGy+1u0OHDq0nB2uxPhd7oYySXn1SU1EUSJK06eJWIqL9piUSkpcvX8Zf/MVf4K/+6q/wxBNPIJ1O4/3vfz/+/M///BVbatPpNIDKh9KteL1eANhxxlmxWEQqlXrFHyLqbOb8we0GwJvtTXf0qZC3mTP5aoVCAaOjox3Z2vRqQ0NDGBgYqPl980fWgvinl6xtqLTZbNA0DSsrKzW9LhERUSeIxWI7zo+8uLoWz/QqluKZZDKJ7u5uHDhwYFdnbHVerxcTExM1z5K8u19Bj1NCpmx9Lrbb7UYoFKo5GUpE1ClaIiH5i7/4ixBCoFQqYWpqCp/61Kfw9a9/HSdPnsT3vve9hrzmY489hq6urvU/ZmUlEXWu1dXVbasJgI0Jyerbm8rlMmRZxvDw8K7O1y7MKkmg8r1bdUefgoBDQrYMvLxqLYh3uVxYXFys6XWJiIjanaZpiEajO1YvTtYwfkYIgXK5jMOHD8Nms+3qnO3g0KFDcLlcNSUGZUnCG0crMeVTi9bbtvP5POdiE9G+1xIJSZPNZsORI0fwy7/8y/j617+OeDyOD3zgA8jn8wCw3qa93S8Nczix3+/f9rUeffRRJJPJ9T/BYLBO3wURtaJCoYBUKrVtBWOiYCCYNiDBWgCfzWbh9/vR29tbh5O2h+HhYfT399c0S1KWJDw4cqtK0gpzCP3q6qrl1yUiImp3qVQKhUJh23gmWRSYS1XmNN/Rt/1F7Eb5fB5OpxODg4O7Pmc78Pl8u6qSfGgtIXkpqiNesDYXW1EULC4u1vS6RESdoqUSkhvdf//9OHnyJILBIM6dOwcA67PZEonEevv2q5mJxZ3muDkcDvj9/lf8IaLOlUwmUSgUtq0oMKsJDvpl+O3VtTcJIVAsFjE+Pr5j+1QnMaskzWoKqx5aS0i+FNaRKVmbvSSE4OwlIiLal5LJJMrl8rYdH5ORymXfhF9Gl6P6du1sNov+/v71EVj7wW6qJAfcMo53yxAAnrF4wer1ehGNRmteEkhE1AlaNiEJ3JoVaW4hO3HixPr2WjNJ+WrmP7/nnnv24IRE1C6SySQMw9g2abg+b6m/+sRiqVSC3W7H0NDQrs/YbnZTJTnmkzHhl6EL4Nll661Oy8vL69XzRERE+0U8HockSZC2mQs5GbG+XdswDAghMDo6uu1zdxq/37+7Ksm1C9YfLGmWHu9wOFAsFrltm4j2tZZNSEYiEbz88ssAgOPHjwMA7HY73vWudwEAvvCFL7zmMXNzc3j66acBAO95z3v26KRE1A6i0ei2yUhDCFyKWg/gM5kMuru7EQgEdnvEtqMoSl2qJK22bZuzlxjEExHRfmIYBiKRyLbdHoYQ6wlJK+Nncrkc3G73vmnX3ujQoUNwOp3I5XKWH3tmSIUqA0uZW23y1ZAkCXa7HcFgEIZR/eOIiDpJ0xKSly9fxuc//3kUCoXX/N3169fx3ve+F8ViEQ888ADuvPPO9b/7xCc+AUmS8NnPfhaPP/74+j/P5XL4yEc+Al3X8cgjj+C2227bk++DiFpfuVxGLBbbdt7SbNJApgy4VOBIV3VvjUIIaJqG8fHxfVVNsNHIyAj6+vpqqpJ8YFiFLAHTSQNLmeqDcVmWIcsyZy8REdG+kslkkMvlto1n5lMG0mXAqQBHA9V/1MvlchgeHt72uTuV3+/HgQMHkMlkLMcVbpuEewYqid+napiLnUgkEIvFLD2OiKhTNC0hGQ6H8YEPfAB9fX144xvfiJ/5mZ/BI488gjNnzuD222/Hd77zHdx+++34m7/5m1c87p577sGnPvUp6LqOd77znXjTm96E973vfTh69CiefPJJnDhxAp/+9Keb9F0RUSsyB8BvV1Fgbtc+1atAkatLLu634e+b2VglqWnWAnG/Q1qvRq1luc3q6ur6IjMiIqJOl0wm10fFbOXCWjxze68Ctcp4Rtd1SJKE4eHhupyzHR0+fLjmKsmH15bbPLukQTOqT2ja7XZomoaVlRXLr0lE1AmalpA8deoUfuu3fgtvfOMbsbCwgK9+9av4+7//eywsLOAtb3kL/uRP/gTnz5/HgQMHXvPYX/qlX8I3vvENvP3tb8eFCxfwla98BV6vF48++iief/559PX1NeE7IqJWlUwmoWnatgPgL9bQ3pTNZjEwMLA+73a/Gh0dRW9vb01VkuaGyqeXNBgWqhKcTicKhQJCoZDl1yQiImpHiUQCAOo+PzKbzcLr9aK/v39X52tnXV1dNVdJnupV4LdLSJdvxZPVcjqdWFhYsHypS0TUCbb+dN5g/f39+NVf/dWaH//Wt74Vb33rW+t4IiLqVLFYDLIsbxnAZ8sCNxOVluFqA/j9Ovx9M2aV5HPPPbdj4vfVXtevwK0CsYLA1ZiBk73V/fuXJAk2mw3BYBBHjhzZ9/8/ICKiziaEQDgchs1m2/JrcmWBKYvxDFDp+Dh48OC2z70fHDp0CPPz88jn8+uLVKuhyBIeHFHwf2c1PLWo4fUD1cdBHo8H8Xgcq6ur+7pClYj2p5ZdakNEVA/VDIC/FNUhAIx4JfS6qntbNIe/DwwM1Omk7a3WKkm7IuG+4Urg/tSi9bbteDyOeDxu6XFERETtJp/PI5PJbDvj8UpMhyGAQbeEfnd18Uy5XIaqqkyGAQgEAhgfH0c6nbZcJfnwaCWZ+1JYR6ZU/WNVVYUQAktLS5Zej4ioEzAhSUQdLZ1OI5/Pb5uQXG9vqrI6D6gkJEdGRvbl8PfNqKqKI0eOQNd16Lq1diVz2/a5kIaiZm32UrlcZts2ERF1vGQyiWKxWNU8bCvVkZlMBn6/H729vbs+Yyc4fPgwHA4H8vm8pceN+2SM+2RoAji7Yu2C1e12Y3l5edNlr0REnYwJSSLqaDsNgBdC4OLqWgDfX10Ar2navh/+vpnR0VH4/X7Li2aOBmQMuCUUdeCFcPXJTEmS4HA4EAwGLSdBiYiI2kkikYAQArK8+ce3jfFMtfOwhRAolUoYHx/f8nn3m66uLoyPj9e0NM+8YLXa8eF2u5HL5RAOhy2/JhFRO+NvHiLqaPF4HJIkbTljcDEjEC8K2GXgeHd1AXw2m4XP59vXw983Y7PZMDExgUKhYKnVSZKkDUF82dJrejwepFIpRCIRS48jIiJqJ5FIZNsZjytZgWhBQJWA23uqi2eKxSLsdjsGBwfrdcy2J0kSDhw4AEVRUCqVLD32gREFsgTcTBpYzhhVP86cc764uGi5VZyIqJ0xIUlEHUsIgdXV1S2rI4Fb7U0nehTYleoWoxQKBYyPj1ta3rJfjIyMwOFwWG47enAtIXk5aiBWqD6It9lsMAwDKysrll6PiIioXRSLRSQSiaratY/3yHCo1cUz2WwWPT09CAQC9Thmx+jt7UVvby/S6bSlxwUc8np16tNL1udih8NhZLNZS48jImpnTEgSUcfKZrPIZrM7zI+sBIzVzlsqlUqw2WysJtiC3+/H0NCQ5VanAbeM490yBIBnLAbxLpcLi4uLlisZiIiI2oE5P3K7udXr87D7qrssFUJA0zSMjY1t2UWyX8myjImJCWiaBsOo/pIUuNW2/fSSBsNCtaPL5UKhUOBcbCLaV5iQJKKOtdMA+KImcC1WCTSrTUhmMhkEAgH09PTU7ZydRJIkjI+PA6jM2rTiodG1tu0lzVLLktvtRjab5ewlIiLqSMlkErquQ1E2j1VKusDVmLX5kfl8Hk6nkxesWxgeHobX67Vcsfj6AQUuFYgWbsWY1ZAkCaqqIhgMsm2biPYNJiSJqGMlk8ltB8BfjevQBNDrlDDk2bk6QAiBcrmMsbExDn/fxuDgILq6uixXSZ4ZVGGTgaWMwFyq+iBeURQIIbC0tGT1qERERC0vFottmYwEgOtxAyUDCDgkjHmrb9ceGBiA1+ut1zE7itPpxPj4OHK5nKUEoV2RcN/QrQtWK7xeL+LxOBKJhKXHERG1K36iJqKOtbq6uu0A+I3btatpVyoUCnA4HKwm2IGqqjUtt3HbJNwzUPnA9QOLGyo9Hg9CoRByuZylxxEREbUyTdMQjUZ3mB95a/xMNfGMYRgQQmB0dLRu5+xEo6OjsNlslkfCmB0f51Y0FDULyUy7HaVSiXOxiWjfYEKSiDpSoVBAKpXadt7SxfV5S9Vv1+7t7YXf76/LGTvZyMgIXC4X8vm8pceZQfxzyxo0w9rspXw+z9lLRETUUVKpFAqFQlXzI6tt187lcnC73RgYGKjLGTtVT08P+vv7LS+3ORaQ0e+SUNCBF8J61Y+TJAkOhwMLCwvQ9eofR0TUrpiQJKKOlEwm1ysaNxPOGQjlBBQJONm7cwBvGAYMw+Dw9yr5fD4MDw9bbts+1avAb5eQLt9KGFdDlmXIsozFxUXOXiIioo6RTCahaRpUdfNlNbGCgcWMgITK79Bq5HI5DA8Pb5vkpEqC8MCBAzAMw1KCUJKkW3OxF8uWXtPj8SCZTCIajVp6HBFRO2JCkog6UjKZhGEYW85cMqsJjgZkuNSdE4z5fB4ul4vt2haMj49DlmWUy9UH44os4cGRyv/PnqqhbTsSiViuZCAiImpV8XgckiRteRlqXt4d6pLhte8cz2iaBkmSMDIyUtdzdqrh4WH4fD7Ly23esLZt+3LUQKxQ/Vxsm80GXdexvLxs6fWIiNoRE5JE1JGi0ei2A+BradceGhqC2+2uy/n2g4GBgZqW2zw8Wpn7+VJYR6ZUfbWj0+lEsVjk7CUiIuoIhmFgdXUVdrt9y6+ZrCGe8fl86O/vr8sZO53dbseBAweQz+ctdWAMuGUc75YhADxjcbmNy+XC4uKipQtdIqJ2xIQkEXWccrmMaDS6ZSuSZghciVY/b0nXdVYT1EBRFBw8eBClUslSED/ukzHuk6EJ4OxK9UG8JEmw2+0IBoMwjOqrEYiIiFpRJpNBLpfbMp7RDYFLFhOShUIBY2NjW7aA02uNjo7C4XCgWCxaetxDI2bbtmYpDvJ4PMhms5yLTUQdjwlJIuo4qVQKxWJxy/mRN+IGCjrgtwMH/Du/DWazWXg8HlYT1MBcbmN1+/XGIN4Kj8eDRCLB2UtERNT2kskkSqXSlhWSM0kDOQ3w2Cot2zsplUpQVZXjZyzq6urCwMCA5ZEwZ4ZU2GRgKSswm6r+olRRFAghsLS0ZPWoRERthQlJIuo4Ow2Av7WNUoVcxYKaQqGA0dHRbVumaHMejwcjIyOWZy89MKJAAnAzaWAlW30Qb7fboWkaZy8REVHbSyQSVc2PPNmrQJF3jmey2Sy6urrQ29tb13N2OnO5DQBLy23cNgn3DNQ+F3tlZcVy/ERE1E6YkCSijhOLxSDL8o4BfDXt2uVyGbIsY3h4uK5n3E9qWW4TcMjr7WdP1TB7aWFhAaVSydLjiIiIWoUQAuFwGDabbcuvsRLPCCFQLBbXfyeTNYODg/D7/ZarJM1t288ua9CM6tu23W438vk852ITUUfjbyMi6ii6riMSiWzZrp0oGphPG5BQXQDPaoLd6+/vR09Pj+Ug/g1rQfzTixoMi7OXMpkMZy8REVHbyuVyyGQyW86PzJQEZpKVDoJq5keao2zYrl0bm82GAwcOoFAoWJoHeapXQZdDQqYMXFitvrpSkiSoqopgMGjp9YiI2gkTkkTUUdLpNPL5/JYJSXP4+0G/DL99+/Yms5pgbGxs243dtD1ZljExMYFyuWxp2cw9AwpcKhAtCFyLWZu9JEkSFhcXazkuERFR05nzI7eMZ6I6BIBRr4QeZ3XzsHt7e9HV1VXnk+4fo6OjcLlcyOfzVT9GkSU8OFxbx4fX60UsFkMsFrP0OCKidsGEJBF1lGQyiXK5vOW8RyvtTaVSCTabjdUEdTA8PAy3221puY1dkXBmaK1K0mIQ7/F4EAqFkMlkLD2OiIioFSSTSRiGsWV79aSF7dpCCGiahrGxsS3H2dDOfD4fBgcHLccWD41W2u5fCuvIlKqvdrTb7SiXy2zbJqKOxYQkEXWURCIBAJsG3EIIXI5Wn5DM5/Pw+XwIBAL1POK+5Ha7MTY2VvO27XMhDSW9+iDe5XKhUCgwiCciorYUiUS2nB8phNhwwbr5Ar+N8vk8XC4XL1h3yVxuI0kSNK36i9Jxn4xxnwxdAGdXqn+cJElwOp0IBoOW5nATEbULJiSJqGMIIbC6urpldWQoJ5AqAaoMHA7s/PZXLBYxODjI4e91Yra+W1k2c6xbRq9TQl4DXrI4e8lmsyEYDFpqEyciImq2YrGIRCKxZbv2YkYgURSwy8Dx7p1jlFwuh/7+fng8nnofdd8ZGBhAV1eX9bnYI7V3fKTTaayurlp6HBFRO+CnbCLqGNlsFtlsdssB8NfjlYTW4S4ZNnn7liWzTaqvr6/u59yvent70dvbaymIlyUJDwxXgvhnapi9FI/HOXuJiIjaSjKZRLFY3DKeubYWzxztlmFXdp6Hres6BgYG6n7O/UhVVRw8eBClUsnSspkHhhVIAKYSBsK56i9KVVWFEIJzsYmoIzEhSUQdwwzgt6qQvB6vBIDHu6tr13Y6nejp6anrGfczc7mNruuWqhbNqoILq5y9REREnS+ZTELX9S0X6t1YS0hWE8+Uy2XYbDbGM3U0MjICl8tlaQxNt1PGyd7KR+9nl61XSa6srFgee0NE1OqYkCSijpFIJCCE2LLF+lYAv/NbXz6fR09Pz5bVCVSb4eFheDweZLPZqh8z6pNxoIbZS0BlliRnLxERUTuJRqNbJiMB6xesXq+X27XryOv1YmRkxFIsAwAPbmjbtlJdaS4F5AUrEXUaJiSJqCMIIbYdAJ8oGgjlBCQARwPbB/BmexOHv9ef0+msabmNGcRbbdv2eDzIZDIIh8OWHkdERNQMmqYhFottOT8ykjcQKwjIEnCki/Owm2VsbAyyLFu68Lx3UIVNBlayArOp6jtFJEmCqqoIBoOWEplERK2Ov5mIqCMUi0WkUqktKxpvrFUTjPlkuG3bz1tie1NjjY6OwmazoVgsVv0Yc/bSjYSBVQuzlxRF4ewlIiJqG6lUCoVCYZt52JXfgRN+GQ5153nYADgPuwH6+/vR3d1taS62S5Xw+oHKpbjV5TZerxexWAzxeNzS44iIWhkTkkTUEZLJJAqFwpYVBdcttmt7PB62NzVIb28v+vr6LAXx3U4Zt6/NXnqmxtlLVluriIiI9loymUS5XIaqqpv+vZXxM2Zis7u7u65npMqF58TEBMrlsqWqRXMu9nPLGnTD2lzsUqnEtm0i6ihMSBJRR0gkEjAMY8uZS1bmLZntTdvNb6LaSZKEAwcOWF5u8+CGbdtWZy/l83kG8bQnNE1DKBTC9evXsbS0hHw+3+wjEVEbicfjkGUZkrR59aOVhTb5fB7d3d1wuVx1PSNVjIyMwO12W7rwvKNPgdcGpErApahe9eMkSYLT6UQwGISmWbuYJaqFGc8sLCwgk8lwXAA1xOZXb0REbSYWi22ZQMxrAvMpMyG5/T0M25v2xtDQEDweD3K5HLxeb1WPOT2k4i8vl7CcFZhLGTjYVV3CeOPspcOHD2/5IY+oVuVyGdFoFKFQCMvLy8hkMtB1HbIsw+l0oqenBwMDA+ju7kZ3dzcvO4hoU4ZhYHV1FXa7fdO/z5QEFjKVpMCxHeZhA5WEwuDgIH/vNYjb7cbo6CimpqaqjmVUWcL9wyqenNfwzLKGu/qr/zju8XgQj8exurqK4eHhWo9NtCVN0xCJRBAOh7G0tIRMJgPDMOBwOOD3+zE0NISenh50d3dv+T5FZAUTkkTU9jRNQzwe37Jd+2ZChwDQ75LQ7dw+Icn2pr3hdDoxMjJiKYg3Zy+dXdHxzJJWdUISeOXsJc4GpXool8uIRCIIhUJYWlpCLpeDrutwuVwIBAJQVRWGYaxX5y4sLEBVVbjdbgwODqKvrw/d3d3wer1MFhARACCTySCfz29Z0TiVqFTUDbkl+B07z8NWVZW/8xpsZGQEN2/ehKZpW7bZv9qDI5WE5AshHQVNwLnDLFCTqqrrc7GZkKR6MeOZjUlIIQScTicCgQAURUGhUEA8Hkc4HIYsy3C5XOjv71+fpdrV1cXFWVQTJiSJqO2l02kUCoUtE1tmu/axKtub+vv74Xa763pGeq2RkRFMT09bDuLPruh4dkXHT50QUOTqgni73Y5EIoHl5WV+OKOalUql9STk8vIystnsK4L2V/8cy7IMj8cDj8cDIQQ0TUM+n8fU1BSmpqZgt9vh9/sxMjKCo0ePVv3fARF1plQqhVKptOUM6/XxMz3VxTNutxuBQKCeR6RX6evrQ1dXFzKZTNX/ro90yRhwSwjnBF4M6+tzJavhdruxvLy8beKaaCdmEnJlZWU9njEMAy6XC93d3a+JR1wu1/rPm67ryOfzmJ+fx+zsLOx2OzweDwYHBzE2Nobe3t5mfEvUphj5ElHbS6fT0DQNNptt07+3stCG7U17p6+vD36/31IQf+fa7KVkUeBKTMcdfdX9GjNnLy0sLODEiRNM/JBl4XAYL730EpLJJIQQcLlc6Onpqbr9WpIk2Gw22Gw2+P1+CCFQLBaRTCaxurqKdDqN173udVu+jxFR50un0xBC7Dg/8liguoU2Y2Nj/H3XYKqqYmxsDBcvXtz2/3cbSZKEB4dVfOVmGc8saZYTkmYi6dChQ7s5Ou1DQgjMzc3hypUrr6iEtBLPKIoCr9cLr9cLIQTK5TLy+TyuXr2KYDCI06dPY2hoqMHfCXUK1tUSUdtLpVIAsGkQqBkC04nqFtqUy2UoisIKuj1iBvHFYrHqQdmqLOG+IXO5TfXD4IHK7KVUKoVwOGz5rLR/CSEwMzODZ599FqlUan0epM/n29UsSDNJ3t3djUAggOnpabz44osolUp1PD0RtZNoNLplArGkC8wkq4tnzN+pnIe9N4aHh9e3YFfrwbUk5GRER7JY/bIQWZYhyzIWFha4ZIQs0XUdk5OTOHfuHAqFwno84/f7a45nJEmC3W5HV1cXBgYGUCgUcPbsWSwuLtb59NSpmJAkora3XQA/mzJQMgCvDRj2bH9rXSgU4Ha7OT9yDw0NDdUcxL8Q0lDUqg/GzdlLS0tLls9J+5MZvL/44osQQqC3t7chC2kcDge6u7sxMzODc+fOoVgs1v01iKi1lctlJBKJLedhzyQNaALw2yUMuHeOZ8z3FWq8QCCAnp4eZDKZqh8z5JFxuEuGAPDcsrWt2V6vF5FIBMlk0uJJab8qFAo4d+4crly5sv5Zp97xjCRJ6O3thaZpOHfuHObm5ur6/NSZmJAkorZWLBaRSqW2DOBvbJgfuVMbTT6fx+DgINub9pC5ddhKEH80IKPfJaGgA+fD1qskl5eXkcvlrB6V9plCoYDnn38ely9fhsvlQldXV0NHOdjtdvT29mJ+fh5nz55FPp9v2GsRUetJp9MolUrbxDO3xs/s9F5UKBTg9/urXhpHuyNJEsbHx6FpmqWqRfOC9ZklawlJh8OBUqmE5eVlS4+j/SmZTOLZZ5/FzMwMAoFAQ+fkS5KE7u5uCCHw4osvYnp6mpW8tC0mJImore0UwN+aH7lze5MQgu1Ne6yWIF6SpPUg/mmLVQUulwu5XA6hUMjyWWn/SCaTeOaZZzA7O4vu7u49W3Jls9nQ19eHpaUlnD17Ftlsdk9el4iaL51Or2/G3sz1KsfPAJUFXENDQ5yHvYcGBwfXY4xq3T+kQpaAmZSB5YxR9eMkSYLD4UAwGISuW7uYpf1lZWUFzzzzDEKhEPr7+2G32xv+mpIkIRAIQJZlnD9/Hjdu3GBSkrbEhCQRtTVzoc1mbQeGEK+oKNhOsViEw+Hg/MgmGBoagsvlslQR9uDwrdlLqZK12UuKoiAYDDI4ok0tLy/j6aefRjgc3rPgfSNVVdHX14eVlRU899xzSKfTe/r6RNQcqVQKkiRtmkS0Es/oug5ZlhnP7DGPx4OhoSFLF0l+h4Q7+irx6zM1tG2nUimsrq5aehztD0II3Lx5E8899xwymQwGBgYaMnJmO11dXbDb7bhw4QKuXr3KuJs2xYQkEbW1ZDK5ZQC/nBXIlAG7DEz4t3+7y+fzbG9qEo/Hg8HBQUtB/LBXxiG/DEMAZ2sI4qPRKBKJhMWTUicTQmBqagrPPfccstlsU4J3k6Io6Ovrw+rqKp599lnOCSPqcEIIRCIR2Gy2Tf9+MSOQ1wCnAoz7do5nOA+7OUZHRyFJkqWqxTcM32rbtpKwUVUVhmFwLja9hqZpuHDhAs6fP78+17FZ1dI+nw9OpxOTk5OYnJyEYVRfCUz7AxOSRNS2zAB+qwoms5rgSECGKm//i7hUKmF4eJjtTU0yOjoKIYSlIH69bbvG2UsrKyuWHkedS9M0vPzyyzh//jxkWW5q8G5SFAX9/f2Ix+N47rnnEIvFmnoeImqcYrGITCaz4/iZIwEZyg7xTD6fb0p1NwEDAwPwer2WLlhfP6jAqQCreYGphLVkjdvtxtLSEgqFgtWjUofK5/N4/vnncfXqVXg8Hvj9/qbHM16vFx6PB1euXMGFCxc4ZoBegQlJImpb+XweuVxumwC+unlLmqaxvanJagni7x+uzF6aThoIZWubvaRp1pKZ1HlyuRzOnj2La9euwev1tkTwbpJlGX19fUgkEnjuuecQiUSafSQiaoB0Oo1isbjjBWs187ANw0B/f3/dz0g7s9lsGB0dtTSCxqFIuHewtuU2brcbuVyOF6wEAEgkEnjmmWcwNzeH7u5uuFyuZh9pndvths/nw7Vr1/DSSy8x/qZ1TEgSUdvaKYA3KwqO7RDAFwoFtjc1md1utxzEdzkknOzl7CWqXaFQwNmzZxEMBtHT09NSwbtJlmX09/cjk8ng7NmzXMhE1IFSqRR0Xd90oY0QYv2Cdad4plQqwW63M55pouHhYaiqilKpVPVjzI6P51Y0aIa1udiyLGNhYYHz+fa5WCyGZ555BpFIpGUrpF0uF7q6ujA1NYUXX3wR5XK52UeiFsCEJBG1rVQqBcMwNp3zFisYiOQFJFRanLbD9qbWYAbxVgKUN4zsbvZSMBi0fE7qDJqm4aWXXsLKygr6+vq2nN3WCiRJQl9fH3K5HM6fP29piysRtT5zHvZmogWBWEFAkYAjXTvHM2alNzVHb28vAoGApY6Pk70yuhwSsmXgYsRaO6vX60UkEuFc7H0sm83ihRdeQCaTQX9/f9PmX1fD6XQiEAhgZmYGV65cafZxqAUwIUlEbSuRSECWN38bM6sJJvwyXOrW7Zdsb2odfX196OrqQiaTqfox9wwosCtAKCcwk7Q2e8nr9WJ5eZlbjPchIQQuX76M+fl59PT0tHTwbpIkCT09PUgkErh+/TqrYYg6hBAC0Wh0x/EzE34Zjm3iGaAyi3J4eHjL2IgaT5ZljI+Po1QqVf0+LUsSHhha6/ioYS52oVDA4uKi5bNS+yuXyzh//jyi0WhLzL+uhsPhgNfrxfT0NKLRaLOPQ03G31ZE1JYMw0AsFtsygL+x3q69/duc2d7E+ZHNJ8syDhw4YCmId6oS7h2oBPFWl9u4XC4G8fvU9PQ0rl+/Dq/X21aV0bIsw+/3Y2ZmhvMkiTpELpdDLpfbcX7kTvGMYRich90ihoaG4HQ6LS2bMdu2z4d15MrVXzhJkgS32435+XkUi0XLZ6X2ZRgGLly4gMXFRfT19bXVRYTb7UapVMKVK1e45Gafa5+fWiKiDbLZLPL5/DbzI6tbaJPP5+Hz+dje1CIGBwfXb/urVevsJXO5zdzcHOfY7CPLy8uYnJyE3W6H2+1u9nEsc7vd0DQNly9fZhBP1AHS6TRKpdKOG7ariWecTicTki3A5/Otz/6t1oRfxohHQtkAXghZn4udTqexvLxs9ajUpoQQuH79Om7evImurq5N58+2MkmS0N3djaWlJczPzzf7ONRETEgSUVtKpVLr1Y2vli0LLKSrS0iWSiUMDQ21RYvDfuD3+9HX12dp9tKpXgU+O5AuAZei1hI0Pp8PqVSKi0L2iUQigZdeegnlchk+n6/Zx6lZIBBAKBTC7Oxss49CRLuUSqUghNi0uilTEljMVC7adlpok8/n0dPTA6fT2ZBzUvUkScLo6Oj6WKBqH2NesFrt+JBlGYqiYG5ururXo/a2sLCAy5cvw+12t+1/8zabDTabDVevXuVs7H2MCUkiakvm3L/NAviphA4BYNAtocuxdaJR13VIkoTe3t5GHZMskiQJY2NjMAyj6qBakSU8MHxruY0V5uzAubk5zuTrcPl8Hi+++CLS6XTbzFnais1mg91ux7Vr1ywl74mo9cTj8S1bLacSlUu2IY8Ev337edi6rmNwcLAhZyTrBgcH4Xa7LSVazFjmasxArGAtsej3+xGJRDiTbx+IRCJ4+eWXIUkSvF5vs4+zK11dXUgmk7h27Rrj8H2KCUkiakuxWGzLRRQ3qmzXLhQKbG9qQYODg3C5XJaC+AfXgvgXQzrymrWAxufzIRwOc0NlB9M0DefPn8fq6mrbJyNNXV1dSKfTuHr1KoN4ojZlzsPe7fiZcrkMm82G7u7uup+RauN0OjEyMmIplul3yzjeLUMAeHbZ2gWr3W6HrutYWFiweFJqJ5lMBi+++CLy+TwCgUCzj7Nr5mzs2dlZrK6uNvs41ARMSBJR29E0DfF4fJsA3py3tP1bXD6fR19f35Zzm6g5XC6X5SD+UJeMQbeEkgG8aHH2ksPhQKlUYhDfoYQQmJycRDAYbJuN2tWQJAl+vx9zc3McOUDUpjKZDAqFQhXzI7ePZwqFAjweT0ckKDrJyMgIZFmGplUfl7xhxOz4sD4j2O12Y2Fhge2vHapUKuHFF19EPB7vmMtVoPJzWy6XceXKFUv/rVBnYEKSiNpOOp1GsVjcNIAv6QLTiUpFwXbzlsz2poGBgYadk2o3MjICSZKqDkwkSVoP4p+y2LYtSRJcLheCwSA3VHagGzduYGpqCn6/HzabrdnHqSuXywVd1xnEE7WpVCqFcrm86QVrSReYSVbf8TEwMNAxFy6dor+/H36/39JymzNDKlQJCKYNBNPW2rY9Hg9yuRyWlpasHpVanLlRe2lpCb29vW21UbsaPT09CIVCmJuba/ZRaI911k8yEe0L6XQamqZtmlyYTRnQBOC3V2ZIbsVsb2K7dmsyg3gr8/HMhOSVqIFo3loQ7/V6kclkuKGywywuLuLy5ctwOBxwuVzNPk5DdHd3IxwOY2ZmptlHISKL0uk0hBCbVjrNJA3oAuhySOh3bR3PmPOW+/r6GnZOqo2iKBgfH0exWKx6tIbHJuF1A5XE8g8Wy5ZeT5Ik2O12zM7OQtetV1hSaxJC4OrVq5ienkZ3d3fbbdSuhqqq67OxrSTwqf0xIUlEbSeVSgHApgH8rfYmZdtWhnw+D6/Xi66ursYcknZFVVWMjY2hUChUHcT3u2Xc1lOZvVTrhsrZ2VluqOwQ8XgcL7/8MnRdb+uN2jtRVRVOpxPXrl1bX/ZFRO0hGo1umVy4sRbPHAvI28YzZscIL1hb09DQEOx2O0qlUtWPeWj01qI+zbA2I9jr9SKRSCAcDlt6HLWu+fl5XLlyBR6Pp6PHTHV1dSGVSnE29j7DhCQRtZ3tA/id27WBSgA/ODjYcS0PnWRoaAg2m81aEL9WJfmDRc1yMOPz+RCNRrmhsgPkcjm88MILyGQy++JDullNzCCeqH2Uy2UkEolt5kdW4pkTO8Qz5nILt9td9zPS7gUCAfT09Fiq+rqzT4HfDqRKwGTEWqWjzWaDEALz8/P8fdABVldXceHCBciyDI/H0+zjNJQkSejq6sL8/DxnY+8j/CRORG2lWCwilUptGsAbQqxXFJzYZgA825vaQ09PD3p6eiy1bZ8eUmFXgFBO4GbCWqWj3W6HpmkIBoNWj0otRNd1vPzyy4hGo+jr6+uYoe/b2RjEc+wAUXtIp9MolUpbxzOJtQrJHRbalMtlDA0N7Yv3unYkSRLGx8eh63rVCUJVlvDg8K0LVqs8Hg9WVlZYNd/mstkszp8/j0KhsG8WVm2cjV0uWxtZQO2JCUkiaivbBfCLGYGcBjgVYNy39dtboVCA0+ncF5VT7UySJIyNjaFcLlcdxLtUCWcG14J4i23bQCWIX1xctJQEpdZy8+ZNBINBdHd376sKaKfTCSEErly5YqmqmIiaI51Oo1wub9rxsZgRyFcRz2iaBkVR0N3d3cij0i4NDg7C6XRa2n5ttm2/FNaRKVmrdHS5XMjn81hcXLT0OGodhmFgcnISsVisozZqV6O7uxurq6uYnp5u9lFoD+yfSJ2IOoK50GazTZLm/MgjARmKvP38yO7u7o5dctFJhoaG4HQ6kc/nq36MGcQ/t6yhpFsL4rmhsr1Fo1FcvXoVTqdz0621na67uxuRSIRBPFEbSKVSkCRp23nY1cQzbrebCckW5/F4MDQ0ZOmy84BfwQGfDE0Azy5bu2CVJAkulwtzc3OsMmtTMzMzmJ+f33eXq8Ct2dg3btxY3xtAnWt//XQTUdtLJpNbBvA3Niy02Y6maRgcHGzI+ai+vF4vBgcHLQXxt/XI6HVKyGvA+bC12UvcUNm+SqUSLly4gGKx2NFLbLajKArcbjeuX7+OZDLZ7OMQ0RaEEIhEIrDZbJv+/fVYdfFMoVDAwMDAls9DrWN0dBSSJFmKLcwL1qdq6Pjwer1IpVJYWVmx/Fhqrng8jsuXL8Nut3f0Epvt+Hw+zsbeJ5iQJKK2YQbwm1U+CSHWB8BvF8Cb1ZVs124fo6OjEEJUvf1aliS8YW25zVM1zF7y+XxIJpPcUNlGzFblcDiMnp6efdXa9Gperxf5fB5XrlxhEE/UoorFIjKZzKbJhmrjGfP3Iudht4eBgQF4vV5LF6wPDqtQJGAmaWAxY20utqIokCQJc3Nz/F3QRsrlMi5cuIB8Pg+/39/s4zSNORs7GAyya6nDMSFJRG0jn88jl8ttGsBHCwKxgoAiAYcDO8+P7OrqauRRqY4GBgbg8XgsBfFmVcHFiI5EwVoQr6oqhBAM4tvI4uIipqam4PP5Nh3nsJ9IkoRAIICFhQXODyNqUel0GsVicdML1mhBIF7cOZ4pFotwOBy8YG0TNpsNo6OjKBQKVT/G75BwV3/ld1qtF6yrq6uIx+OWH0t7TwiBq1evYmVlZd9frgKcjb1fMCFJRG1juwDerCaY8MtwKFv/Ai8Wi+jp6WF7UxtxOBwYGRmxNEdyyCPjaECGAPC0xdlLQGXeUygU4obKNpDNZjE5OQkAcLvdTT5NazAvbaampqquLCaivZNOp6Hr+qYLbaqNZwqFAjwez74dUdGOhoeHoSiKpbmOD611fDy9pMGweEnqcDhQKpWwsLBg6XHUHCsrK5iamoLX6930vWE/6u7uRiwW489wB2NCkojaRiqVghBi24U2x7u3f1vTNA29vb0NOR81zvDwMGRZhqZVn1x8ePRW27bVSkeXy4VCocAKsxZnGAYuXryIZDLJpQ6v4vf7EYlEsLq62uyjENGrJBKJLaufqo1nSqUSBgYG9n0VVTvp7e2F3+9HJpOp+jGvG1DgtQGJosBkxPpcbLfbjWAwaKkyk/ZeLpfDxYsXoes6PB5Ps4/TMhRFgaqqnO3ewZiQJKK2UU0Af2ybeUu6rkOWZbZrt6G+vr71AdfVOjOkQpWBxYzAXMpalZgkSXA6ndxQ2eKmp6cRDAb35RbKndjtdhiGgbm5uWYfhYg2EEIgGo1uuaziRhXxjHnJFggE6n4+ahxZlnHgwAGUSqWqL0pVWcL9w7XPxTZH3nAOX+syDAOXLl1CLBbjCIZN+Hw+xGIxznbvUIzeiagtGIaBWCy2aQCfKQksZSqB3XYBvDlviQF8+1FVFWNjY5Zu+D02CfcOVH4evl9DEG9uqFxeXrb8WGq8WCyGy5cvw+FwbDrGgSo/w8vLy0ilUs0+ChGtyeVyyOVym75vZUoCi1XEM+VyGTabjResbWhgYAB2ux3FYrHqx7xxrePjhbCObNlax4csy1AUBXNzcxzh0aLm5uYwOzvLy9Ut2Gw2znbvYPyJJ6K2kM1mkc/nN01I3khUqgmGPRL89u3nLfn9/i2rEqi1DQ4OwmazWRpsbS63eW5Zg2ZYC2IURYEsy5ifn2cA1GJKpRIuXLiAQqHA+WnbMEcPcPYSUetIp9MolUqbxiJTFuIZl8u1r7fwtqtAIICenh5LHR8TfhmjXgmaATy/Uttym1gsxhEeLSiRSODSpUuw2Wz8fLINr9eLlZUVJJPJZh+F6owJSSJqC6lUCqVSaduFNse3qSYAKvMj+/v7OW+pTfX09CAQCFgK4u/oUxBwSMiUgZdXrc+e8Xq93FDZYswtlKFQqOW2UOqGwOWojs9dLuIvLhXx/IpmuZqlniRJgsvlwuzsrKVqHCJqHHMe9maVUGY8s111JFDp+Ojr62M1VRuSJAljY2PQtOrnW0uStH7B+oMaOj7sdjt0XeflVIvRNA0XLlxANpttuWpnQwhcier43zdK+P5CGfFCc6trnU4nisUigsFgU89B9cf1TUTUFsxtx5slH24mzHlLWwfmZpsK27XblyzLGBsbw+rqKoQQVSWiZEnCgyMqvj5Txg8WNdw7aO3XnsPhQDKZxMLCAuf6tIjl5WVMTU3B5/O1xBZKzagE7c+HdJwPaUhvGDn67aAGCcCRgIxTvQru7FNwqEuGIu9dEtXr9SISiWBpaQmHDh3as9clos3F4/EtE4nVLLQRQsAwDP5OamODg4NwuVzI5/Nwu91VPeYNwyr+f9fKmEoYWMkaGPJYS0Z7PB4sLi7ixIkT8Hq9tRyb6kgIgWvXrmF5eRm9vb0tcbmqGwLX4gaeX9HwQkhD6lUNSWNeCXf0KbijT8HxbgV2Ze/ObF6wzs/P49ixY3A6nXv22tRYTYvky+Uyvve97+Hxxx/Hd77zHdy4cQPZbBa9vb2477778PM///N417veteXjv/nNb+L3fu/3cPbsWWSzWUxMTOCRRx7Bo48+yjdZog4Ui8U23a6tGwKzyUqy8UjX1hUFZntUq91AkjWDg4NwOBwoFotVByMPryUkL6zqSJXEtm1wr7ZxQ+Xx48cZADVZNpvFxYsXIYSo+kNcI5QNgUsRHedCOs6HNWQ3JCG9NuCeQRUOBbgU0bGUFZhKGJhKGPjKzTLcKnByLTl5R5+CXldjK5zM+WGzs7OYmJhgRRVRE5nzsDfr9ijpt+KZ7To+NE2DqqqMZ9qY1+vFwMAAgsFg1b/LAk4Zd/YpuBDR8dSihkeOW5ud7Ha7EQqFsLS0hOPHj9dybKqjcDiM69evw+PxNPVydeOl6oshDZkN8YzHBtzRqyCcr7w3LWQEFjIaHp/VYJOBE93KeoJy1Cs1PKlqdi0tLS3h8OHDDX0t2jtN++n/7ne/i7e97W0AgKGhITz88MPweDy4fPkyvvrVr+KrX/0qfu7nfg6f/vSnX/PD/fu///v45V/+ZUiShDe+8Y0YHBzE97//fXzyk5/El770JfzgBz9AX19fM74tImoATdMQj8c3DeAXMwZKBuBSgUHP9vOWvF4vPB5PI49KDeb3+9Hb24tQKFR1cnDUJ+OQX8ZMysCzSxr+0UGbpdf0eDxYXV1FMBjEsWPHajk21YFhGJicnEQ8HsfAwMCev37ZELiwquNcSMNLYR35DV1zfjtw76CKM0MqTnS/sgIymjcwGdFxMaLjclRHTgPOhSrJTKAyK+4tB2x4ywG1YcG83+9HNBpFOBzG0NBQQ16DiHaWyWRQKBQ2TULNpw1oovJ+0u/a+r3AXNDH+ZHtbWRkBPPz8zAMo+qLoodH1UpCcknDe47ZIFv4nSFJEhwOB2ZnZ3Ho0CHYbNZiIaqffD6PCxcuQNd1dHd37/nrb3ep6lu7VD0zpOC2HgXqWjyTKQlciuqYjFT+xIsCk1Edk1EduAYEHBLu7FPw40dt6GvQRassy7DZbJiZmcHExMSmhSrUfpqWkJRlGY888gh+4Rd+AW984xtf8Xd/8zd/g3/6T/8p/uzP/gwPPfQQPvjBD67/3fnz5/Gxj30MiqLgq1/9Kt7xjncAqGyse/e7340nn3wSH/3oR/HFL35xT78fImqcdDqNYrG4afXz9Fo1weEuedvArFQqcX5kBzBnLy0tLVXdtg1UltvMpEp4qoaEpCzLcDgcuHnzJg4cOMCh400yMzOD+fn5pmyhXMka+K8vFrCcvTXvK+CQcHpQwZkhFce6t37/6XXJ+OFxGT88boMhBKaTxnpAfzNhYDkr8LkrJcQKAu89bmvIe5TNZoNhGJifn8fg4CDfB4maJJVKoVwub3rBOrMWzxzqUrb9b7RQKGB0dJQJpTY3ODgIj8eDXC5XdXff6wYUuFUgVhC4GjNwstdaQsa8nAoGg6wwaxIhBC5duoRoNNqUy9XLUR2ffrnwinbs7S5VTV67hPuHVdw/rEIIgaWsWI9lrsV0JIoC31/UMBnR8e/OODHibUyc5vP5EI/HEQ6HMTw83JDXoL3VtITkm9/8Zrz5zW/e9O/e97734Rvf+AY+85nP4C//8i9fkZB87LHHIITAhz/84fVkJFApQ//MZz6Dw4cP40tf+hKuXr2K2267reHfBxE1XjqdRrlc3jT4nt4QwG9FCAEhRFNuIan+zNlLuVyu6orXB4ZV/PXVEuZSBoJpA+M+a4GS3+/H6urq+uwa2luJRAJXrlyB3W7f84TwZETHH79UQE6rBO0PDqs4PaTiSGD7S5DNyJKEowEFRwMKfuIokC0LPDlfxv++UcY/zJRR0AU+cLvd8vNWw+v1Ynl5GalUiq2eRE2STqe3vEybTlaqpg93bf/7yTAM9Pb2NuR8tHccDgeGh4cxNTVVdULSrlSSQt8Oavj+YtlyQlJRFKiqiqmpKYyPjzOp3QTBYBCzs7MIBAJ7frn6rfkyPnelBENULlXvXbtUPb7NpepmJEnCqFfCqFfG2w/aUNIFbsQNfP5qEUsZgcfO5vErp52Y8Ne/gtG8YJ2bm8PQ0BAvWDtAyw4Sev3rXw8Ar9ikVCqV8LWvfQ0A8P73v/81j5mYmMBDDz0EAPjyl7+8B6ckor2QSqUgSZvPJplO7BzAm9u5+SG8M7jdbgwNDVnatu21S3jdQCUw+sFieYevfq2NVZLcVry3NE3D5OQkstnsnrYoCiHwzbkyfu+FSjLyaEDGbzzkxs/c7sCxbqUuSUOPTcK7j9jxoVN2SAC+Na/hMxdL0I36b+Y2FyhwyypR80Sj0S3nxc0kbnV8bEXXdUiSxHimQwwPD0OWZWha9ZuzzW3bL4R05DXrvyu6urqQSCT4u6AJMpkMLl26BFmW93QmuWYI/OXlIv7yciUZ+eCwgt/5IRd+9qQDt/XsPp6xKxJO9Sl49D4XDvplpEvAb58tYGptSVe9+Xw+rKysIJFINOT5aW+1bELyxo0bAPCKUtzr168jl8sBAE6fPr3p48x/fv78+QafkIj2ylYBfEETWMxUgrHtAvhisQi32w2fz9ewM9LeGhkZAVD5cFath9eC+GeW9JoSPn6/H8lk8hUXZdR4U1NTWFpaQk9Pz57dhGuGwF9cKq1XEjw0ouLf3+dEl6Mxr/8j4zb83F0OyBLw1JKGP365iHKdk5Lmgqa5uTkm1YmaoFwuI5FIbFrlnS0LrOQq/81v1/FhLnRjQrIz9Pf3w+v1WrpgPdIlY8gtoaQD51aqT2SaNlZJlsvWL2ipNuYc7GQyuacdW5mSwKfOFfCteQ0SgH9yvBJvNGJDts8u4eNnnDjeLSOnAb97roDL0fonJZ1OJ4rFIuPxDtGSCcmVlRX8+Z//OQDgkUceWf/nMzMzAIBAILBlYmF8fPwVX0tE7a1YLCKVSm0awM+lDAgAPU4JAefWb2eFQgF9fX3cLttBBgYG1mcvVevOPgU+O5AqCVyMWA+QNlZJlkqlnR9AuxaNRnHt2jW4XK49ay1LlwR+9/kCvrNQCd7fd8KOf3GnHbZNZirV04MjKv716xxQpUrlyx+8WERRr29S0uv1Ip1OY2lpqa7PS0Q7S6fTKJVKm8Yz5vzIQbcEr337+ZF+v39Pq6uocVRVxfj4OAqFQtWPkSRp/YL1B4vWE5JApUoyHo+zSnIPzc7OIhgM7unl6lLGwH9+No8rMQMOBfg3r3fgHx+2N/T13TYJH7vXiTt6FRR14PdeKOB8uLaf062YF6zBYBD5fL6uz017r+U+nWuahg984ANIJpO488478fM///Prf5dOpwFg25lh5gyOVCq17euYSY6Nf4io9WwXwN+aH7n1WxnnR3Ymu92O0dFRSwlJVZbw4PDugnifz4dEIsFb2T1QLpdx8eLFLRdaNcJi2sB/fiaPa3EDTgX4xXsdeMehxiya2cw9gyp+8V4n7ApwMaLj984VamrJ24osy1BVFbOzszAMo27PS0Q7M+dhb9bxYc6P3C6eASrvi/39/Q05HzXH4OAgVFW1dNH5hlEVEoBrcQPhnPX3crNK8ubNm5baxak2yWQSly9fhs1m23ShVSNcWNXwG8/mEc4J9Dol/PoDLtwzuDfrQxyqhF+414F7BxVoBvBH54t4drm+P2cejweZTIYXrB2g5RKSH/3oR/Hkk0+it7cXX/ziFxv2H+1jjz2Grq6u9T9mZSURtZZ0Og1N06Aor21hqmYAvKZpUFWV7U0daHh4GIqi1DR76aWwjkzJeqJHURTY7XZWSTaYEAJXr15FKBTas2qCl8KV4H01LzDglvAfHnTh7v693/13R5+Cf3faCZda+bD5O88XavpZ3Yq5ZTUcDtftOYloZ9vNw56pYkGfYRicH9mBenp6EAgELLVt9zhlnOytxL5P1XjB6vf7EYvFWCXZYLqu4+LFi8jlcnvy364QAv93tozff6GIvAYc75bxH9/gsrzMcbdssoR/dbcDD44o0AXwpy8X8d1g/UYEyLIMm82GmZkZS+ObqPW0VELyF37hF/CZz3wG3d3d+MY3voHjx4+/4u/NNu3t3rAzmQwA7Dj4/tFHH0UymVz/w2oXotaUTCa3WWhjDoDfOoAvFAqct9Sh+vr64Pf719/3qzHhVzDuk6EJ4GwNs5eAyu+XeDzO3xsNFA6H1zePbrUAol6EEPiH6RL+64tFFHTg9h4Z/+8DLox6mxciHetW8O/POOG1VRIV/9+zeSSK9alo3LihUoj6L88hotcSQiASiWw6ekIIgZtVLLQxu0UYz3QWWZYxPj6OUqlk6T35odHKz9LTSxqMGt7LVVWFoiiYmppilWQDTU1NYXFxEd3d3Q2/XC0bAv9zsoS/vlqCAPDGURUfP+OEf5sxEI2kyBL+5Z0OvGlchQDw2Usl/N/Z+iUlzXg8FArV7Tlp77VMQvJjH/sY/uAP/gCBQABPPPHE+pbtjQ4ePAgASCQS6+3br2Z+QDS/disOhwN+v/8Vf4iotZgB/GaV0smiQLQgIAE4uMNCm97e3oYnNWjvKYqCsbExFItFa0H8yO7atjdWSXIgfP0Vi0VcvHgRuq5vO6KlHkq6wH+/WMLfXi9DAHjzuIqPnXZuO8NtrxzsUvDo/S4EHBIWMgKPPVdAJF+fpKTP58Py8jLH1RDtkWKxiEwms+n4mVhBIFUSkCVgwr/9PGyPx9Pw90Xae4ODg3A4HJYWjt07qMCpAKt5gevx2n43mLMkFxcXa3o8bW8v52CnipX5199frMy//pnb7Pjnd9ihNnj+9U5kScIHT9rxjkOV7/+vr5bwlSlryfetqKoKIQRmZ2d5wdrGWiIh+fGPfxy/93u/h66uLjzxxBNbbtA+ceIE3G43AODcuXObfo35z++5557GHJaI9kw+n0cul9tiAHylPH/YK8Glbv3L1jAM9Pb2NuyM1FyDg4Ow2WyW2qcfHFEhS5UZpEuZ2oJ4v9/PWZINIITA5cuXEY1GGz73VTcE/vB8EU8vaZAl4GdP2vHBU46mB+8bjXpl/Or9TvS5JIRyAp98roCV7O6TkuaGSrbqEe2NdDqNYrG46QWr2a497pO33XxbKpXQ39+/ZzNtae/4/X709vZa6vhwKBLuW5uLXWvbtqqqkGWZVZINUC6XMTk5iUKhsOUy3npJFgV+49k8rscNuFTgl+514O0H927+9U4kScJPHbfhJ49VkpJfnirjb66V65JE9Pl8CIVCSCQSu34uao6mJyQ/8YlP4Hd/93fR1dWFb3zjGzhz5syWX2u32/Gud70LAPCFL3zhNX8/NzeHp59+GgDwnve8pzEHJqI9s10Aby602a5dW9d1yLLM9qYOVsvspS6HhLv6Kj8336lxno05EH5qaopVknW0tLSE6elp+P3+TefG1tNfXy3hYkSHXQF+5bQTbzmwN1u8rRpwV5KSQx4JsYLAH50vQDN2F8RLkgSXy4W5uTlL212JqDbpdBq6rm+x0Ka6BX0AEAgEGnI+ai5JkjA2NgZd12vq+Di7otW8AK2rqwvRaJRVknV2/fp1rKysNHwOdkkX+MPzBazmBfpdEv7DAy7c1YT51zuRJAnvPmLH+2+rfKZ7fLaMbwd3nwR3Op0olUosEGhjTU1I/vqv/zp++7d/G4FAYMdkpOkTn/gEJEnCZz/7WTz++OPr/zyXy+EjH/kIdF3HI488gttuu62RRyeiPZBKpSCE2GKhzc7zlgqFAuctdThJkjA+Po5y2dpN65sPVIK17y9qKO4iiE8kEqwyq5NcLodLly4BAFwuV0Nf61vzZXxzvhII/9ydDpzsbWzyc7d6nDI+cV9lpuRCRuDJ+d0H8V6vF+l0mhsqifZAIpHYMikxU8WCvlKpBJvNxoRkBxscHITL5UIul6v6Mce7ZQx7JBT13VVJKoqCmzdvcjlInYTDYdy4cQMej6ehI6OEEPjsZBFTCQNuFfjYaSdGmjj/uhr/6KANP3WicgH8xeslpIq7v2B1u92Yn59HPp+vxxFpjzXtJ/bv/u7v8Fu/9VsAgKNHj+K//bf/hg996EOv+fMrv/Irr3jcPffcg0996lPQdR3vfOc78aY3vQnve9/7cPToUTz55JM4ceIEPv3pTzfjWyKiOtsqgBdCVBXAF4tFBAKBTVu+qXOYs5esVHrd0aeg3yUhrwHP1rjchlWS9SOEwKVLlxCLxRreqn0pouNzVyot/v/kmA2nh1qvkmAzAYeM9x6vVBZ8+UYJ8cLuWrfNDZWzs7P8EErUQEIIRKPRTWMRQ4j1lu3tOj6KxSJcLlfDWz+pedxuN4aGhix1fEiShDevVfd/O1h7CyyrJOvHnINdLpcbPu/176fLeGZZhywB//r1Tgx5WjsZaXr7hA0Tfhk5Dfjb69WPXNqKx+NBNpvlz2+batpPbSwWW/+/z507h7/4i7/Y9M8Xv/jF1zz2l37pl/CNb3wDb3/723HhwgV85StfgdfrxaOPPornn38efX19e/mtEFEDCCEQj8c3bdcO5wSyZUCVgTHf1m9j5XKZ7wf7gM/nQ19fn6UgXpYkvGm8koj61ry2qyCeA+F3b35+HnNzcwgEApDlxoUmyxkD/+2lAgwBvGFExbsOt2ab9lbeOKbiSJeMgl5pOd8tn8+HWCyGcDhch9MR0WbMedibxTPLWYGCDjgUYMS7dVtnsVhEX19fQ98fqflGRkYAwNIl0UMjKuwKsJipfbnNxlmSvKCqnRACV69exerqasNbtZ9f0fClG5XL8A/cbm/5To+NFLmy6EZCZcHk9fjufuZkWYbdbucFa5tq2m+1D33oQxBC7PhndnZ208e/9a1vxde//nVEo1EUCgVcv34dn/zkJ3lzSNQhcrkc8vn8phUFZrv2hE/ecgGFYRiQJIntTfuAOXvJMAwYRvXB+BvHbFBlYC5lrFeoWLWxSpID4WuTTqdx6dIlKIoCp9PZsNfJlAT+y4sF5DTgaEDGh++wt8zA92rJkoQPnqoE8WdXdFyK7C7wttlsEEJgbm6OGyqJGiSdTqNUKm0+DztR+W/4oF+GvMX7kRAChmGgp6enoeek5hsYGIDX67XUtu22SXhwbbnNk/O1d2uYVZIc41G75eVl3Lx5s+FzsGeTOv77hcpG9rceUNerZNvJkYCCHxqr/Nz+5aXirmdj+3w+xONxrKys1ON4tId4zUZELWnbAN5s1w5s367N+ZH7hzl7ycr8GJ9dwn1DZhBfezLR7/cjFotxlmQNDMPA5OQk0ul0Qy8PNEPgv71UQCgn0OuU8G9f74SthbZpWzHhV/DWicrP7V9dLqK8yyDe6/ViZWUFyWSyHscjoldJp9NbzsOeWV9os3XyQtM0qKrKeGYfsNvtGBkZsZSQBG7NxX4hpCNRrL1KUpIkVknWKJ/PY3JyEoZhwO12N+x14gUD/+XFIkpGZfzQz9z22s9J7eK9x+3rs7G/Obe7S31zVicvWNsPE5JE1JIymQyEEJu2J1Uzb6lQKMDn8zV8OQa1BpfLZXn2EgC8efzWhspMqbYAZuNAeFZJWnPz5k0Eg0F0d3c3rFpRCIHPXS7hSsyAUwF+8V4n/I72TEaa3nPUji6HhJWcwNdndje/1Ol0olgssiqGqEFSqdSWf7e+oG+HC1an0wm/31/3s1HrGR4ehqIolmZTT/gVHA3I0AXwvYXa45Curi5EIhH+PrDIMAxcunQJ8Xi8oZXMRV3gD14sIlEUGPFI+Fd3O6C06eUqAHjtEt57opJQ/T9Tu5+N7fP5EA6HecHaZpiQJKKWtNVCG80QmE3tvGG7XC5jYGCg7VoyqXYjIyOQJMnSzf6RgIwDPhllo7Jxu1YcCG/d6uoqLl++DKfTuWkldL08MafhOwsaJAAfvduB8W3mzrYLt03CT68F8V+9WcZqrvYgXpIkOBwOLCwssCqGqM62W2hT0gWC6Z3jmUKhgJ6eHths7deWSdb19fXB7/dbv2Bda9v9TlCDXmPlvM1mgyRJ3Lht0ezsLGZmZtDV1dWwOa+GEPgfF4uYSRnw2iqXq25b+3/GeeOoiqOB+szGdjgcKBaLnIvdZto/KieijiOEQCwW2zRJsZA2oBmAxwYMuLeetwSA7U37TH9///qmvWpVNlRWqiS/HSzDqLHNw6yS5CzJ6hQKBbz88ssolUoNnf388qqG/7UW4L7vhB2vG2iPjdrVeGBYwe09lWT656/sLoj3eDxIp9OvWDhIRLuXz+eRz+c3jWfm0wZ0AfjtQK9z68SCruvo7e1t5DGphSiKgrGxMRSLRUutp6cHFfhsQKwg8PJq7clEs0pyeXm55ufYT6LRKC5dugS73d7QOdhfmSrj+RUdigT8m9c7MeDujDSOLEn42ZO3ZmNPRmqPoSVJgs1mw+LiItu220hn/CQTUUfZLoCf3jBvaavqR3P2JBOS+4vdbsfo6KilOZIA8OCwCpda2d5+Obq7ID4Wi7HVaQeGYeDixYuIRCLo7e1tWBXzQtrAn7xUhADwQ2Mq3n6wc5KRQCXw/sBJBxQJeGlVx/lw7UG8zWaDpmmsKiCqs0wmg2KxuGk8M1NFPKPrOmRZZjyzzwwNDcFms6FUqv6yya5IeONYpUryW7uYi21W4k5NTVlaFLgfFYtFvPzyy8jn8w0dqfDskoav3Ky08H/wlB0netpno3Y1Xjkbu7Sr2dgejweJRAKJRKJOp6NGY0KSiFrOdgttbs2P3L69ye12w+v1NuyM1JqGh4ehqqql2UsOVcJDI7tfbqOqKmRZZpXkDqanpzE7O4vu7u6GtTal1jZqF3TgRLeMD55sv43a1Rj1ynj7wcqHx89dLqGo1x7EOxwOLC4usk2PqI7S6TQMw9h0oc36gr5t4hlzfiQTkvtLd3c3enp6kMlkLD3uR8ZVSAAmozpWsrUnE7u6urC6usoL1m0IITA5OYlwONzQy9WbCR3/Y7KyUftHD6r44bHOHN1gzsYO7XI2tt1uR6lU4gVrG2FCkohaznYbKW9WGcD39/c3LNlBrauvrw9dXV2Wg3hz9tJLYR3R/O6C+Egkgps3b9b8HJ0sEong8uXLsNvtm85Uq4eyIfCHLxYQyQsMuiX8m9c7obbx0Ped/PgRG3qcEqIFgb+/WXsQ7/F4kEqlEI1G63g6ov0tmUxuGYvMJKq7YPX7/Q1tBaXWI0kSxsfHoWmapdbTAbeMO/srsfO3g7X/PjBnSV6/ft1SleZ+Mjs7i+npaQQCgU0/r9RDNG/gv75YhGYAd/cr+KkT7btReydum4SfqcNsbEmSYLfbsbCwwArfNsFP60TUcrbaSJnXBJYzlcDs0BYbts3Arbu7uzGHo5YmyzIOHDiAUqlkKYgf8cq4rUeGAPCdXWyoVFUVTqcT165dQzwer/l5OpE5N9L8gN0of321hBsJAy4V+IV7nPDaOzcZCVQqfP/p7ZUg/h9myljO1BaA22w26LrOqgKiOjHnYW+2jCZbFljJbR/PAJUFff39/Q07I7WuoaEhuFwu5HI5S49783il4+MHixpKu6ia7+7uxurqKm7cuFHzc3SqWCyGS5cuwWazNeyyoGwI/MH5IlIlgTGvhI/e7YDcgZ0eG92/YTb2565Yi+M38ng8SCaTjMPbBBOSRNRSzI2Um7VrzyYNCFSGv3c5Nv+lXC6Xoaoq25v2saGhITgcDhQKBUuPM6skvxvUoO1ifo3P50M+n8elS5fY/rrGMAxMTk5idXW1oa1NL4W19dlZ/8/dDox490eYc8+Agrv6FOgC+NwVa4sQNnI6nVhcXOTIAaI6KBQKyOVy246fGXRLW16aGIYBSZIYz+xTHo8HQ0NDlrdt39WvoM8lIVsGnluu/b1cURR4PB7cuHEDq6urNT9PpzHnRuZyuYb+t/m/b5Qxt7ZR+xfuccKldnYyEqhUN/7s2mzsl1d1nA/XFkPbbDaUy2VesLaJ/RGpE1HbMBfabNbOuT5vKbB9u7bL5WpoBRa1Np/Ph4GBActt2/cMKOhySEiVBF4I1Z5IlCQJPT09WF5exszMTM3P00lmZmYwMzPT0NamRNHAZy5W5iy9/aCKu/o7a4nNdioLbuxQZeBS1MDZldp+fs1t22zbJtq9TCaDUqm0bTxzaIfxMw6HgwnJfWxkZASSJFm63JQlCW9aq5L8VnB3l0sejwflchmXLl2yNJu7UwkhcOnSJYRCoYZerl6O6utzFD98hwP9HbJRuxojXhk/ujYb+/NXSihq1i9YzbbtxcVFtm23gf3z001EbWG7jZTT6wtttk5oFAoF9Pb2NizpQa1PkiSMjo5CCGEpEFFlCT88thbEz+8u8LbZbLDb7bh69SqSyeSunqvdRaNRXLp0CXa7vWGtTYYQ+B8XS0iXgXGfjH9yvHPnLG1lwC3jHx+uBPF/fbWEfA1BvKqq0HUdoVCo3scj2nfS6TR0Xd80Htm4YXsrxWIRHo8HHo+nYWek1jY4OAiv12u5SvKNYzaoUuXnzEx+10KSJHR3dyMUCmFqaqrm5+kUc3NzmJ6eRldXV8M+Z2RKAv/9QuVy9UfGVNw7uH8uV03vPmJD79ps7K9O1xaPm3OxY7FYnU9H9caEJBG1lO0W2lSzYdswDPT09DTsfNQehoaG4PF4LAfxPzKuQpaAa3EDi+nd3ar6/X5ks1lcunRp397Qmq1NjZ4b+eSchsmIDpsMfPRuB2wdvMRmO+88ZMOAW0KiKPB/btS2iMDlcrFtm6gOUqnUphVUQgjcrGKhTalUQn9/f8OqsKj12Ww2jI2NIZ/PW3qc3y7hzNDacpv53b2Xq6oKj8eD69ev7+vq+Xg8jsnJSaiqCpfL1ZDXEELgzy8VES8KDLkl/Mxt++9yFajMxn7/2mzsr8+UsVTDbGy73c627TbBhCQRtZStAvh4wUCsICABmPBv/talaRoURUEgEGjsIanlORwOjIyMWB4G3+OU8fqBShD/rV1sqARuVRYsLCxgdnZ2V8/VjoQQuHjxIsLhcENbm4JpA39zvZJ8++nb7BjdJ3MjN2NXJHxgLYj/xryGYA1JdbfbjWw2i0gkUu/jEe0b283DjhUEUiUBWdo6nhFCQAjBBX2E4eFh2Gw2y9uuzbnYzy5ryJRqn4sNVKrNisUiJicn9+VlValU2pO5kd9f1HAupEORKperjn0wN3Ir9wwouLu/Mhv7ry7XNhvb4XBgYWGB89xb3P6N2omo5ZgB/GYbKc3qyFGvBOcWv6ALhQKcTifnRxKAyuwlRVEszz1683jl5++pRQ2FGtpeN7Lb7bDb7bhy5QrS6fSunqvdzMzMYHZ2tqFzI0u6+P+z9+dhkuRnfej7jSUzIve1svbqfaveZnp6ZjTSCAskHUCyOIBtgQUcm8XHNubw2OBjLPz4ufaxQdjHXF+uFxmzXc49YBuQQRh0kSVhBNKI0Wyame7p6Znu6W26u7bc14jMiLh/ZEV2dVdGZGZVLpWV38/zzAOaru6Mmanlzff3e78vfvHVGhomcH5Kam0XnWTnpmQ8MS3BtIDfu9b7LUl7bHtlZWUAT0c0GTRNQ7lcdl1osxgS4ZXa1zO6rsPr9TI/khCPxxGLxXrOxT4aFbEYam4s/uq93TUR7WzsSRzd3pobGY/HB3a4ulI28RtXmj+zv/uYBwdd4hwmgSAI+L5TXnhE4ErGxOV0701FOxebY9t7GxuSRLRnuG2kbOVHRt3zlmKxWNvfT5MnmUwiEon0PLZ9KiFi2i+gZgBf22URDwCRSASlUgmXLl2amNHtTCaDy5cvQ5blgeVGAsBvv6Xj3ZKFsBf44TMKRxs3fffR5vfAl1cNbFR7/5zz+Xy4d+8elxgQ7VCxWGw1FR/1Tis/svOCvlAoNLBnpPEgiiIWFxdRr9d7uiUmCAI+uPQgF9vcwQ2zrexR5bfeegvZbHZXf9Y4uX37Nt555x2Ew2HI8mAOPRumhV98TYNmACfjIr790PaLGZMo5Rdb2e5fuNV7Pe7xeNBoNDi2vcexIUlEe4bbRsob9oZtlwK+0WggmUwO7PlovEiShMXFRWhab6MeoiC0Rp3++E5jR2MiW20d3b59+/au/qxxUKvV8Oqrr6JarQ70ds9r641WgfojZxWEFTYjbfMhEcsJERaAP95Bfpidv7q+vt7/hyOaAKVSCYZhtG1gdFPPaJqGZDIJUeRbNWrmYquq2nOW5HtmZagSsFqx8EZ69weiwWAQtVoNly5dmogx2Fwuh0uXLkEUxYHlRgLAZ6/VcSNvwi8Df+OsApGHqy0fOtCsx19bN7Ba7v1zWFVV3L17dyI+X8cVf8oR0Z7htJHStKyONwoMw4AoihxvoofMzMxAURRomtbT73t2XoZXbOYTXsvtvoj3er2QZRlvvPFGz2NX40TXdbz00ktYXV0daG5kQWtu1QaADx+QcW6Ko9qP+vBmEf/ld+vQjN6a6pIkwbIsbtsm2iGnPGzTsrYs6Gs/8WFZFhf00UOCwSCmp6d7nvhQZQHvm39wS3K37APWe/fu4Z133tn1n7eXlctlvPTSSyiXywPNpr+aMfAHm5uk//oZBQkf2zNbzQREnEtKsAB8aQefw36/H4VCYaIXMu11/Iwnoj3DqYBfLVuoNgCvCCw4LKzQNA2KorAhSQ+JRCJIJBI9NwEDHgFPz/aviAeAaDSKYrGIy5cv7/rW5V7UaDTwyiuv4M6dO0gkEgPLjbQsC79ySUNBt7AQFPBXjjOioZ3zUxKmfALK9Z1FD9hj270uUiCadG552PfLFmoGoEjAXLD9gU2j0YAsy6xn6CHz8/OwLKvnm172xMcrawbSO4jweJTH44HP58Obb76JXC636z9vL6pWq3jxxRexsbGBZDI5sMPVct3Cf3xNg4XmQfhTMzxcbedDB5r/Xv7sbgPVHrPdPR4PTNPk2PYexoYkEe0Jbhsp39kcbzoYESGJzgttwuHwQPPqaPwIgoDFxUUYhtFzfuO3bGYvvbBioKDtvoEoCAKi0Sju3LmDO3fu7PrP20sMw8Crr76KmzdvIh6Pt30j3i9/fKeBV9cNyCLwN8+rjkshJp0oCPjg5hvRL97qLXsMaN4qqFQqHNsm6pGu6yiXy23jZ97JbdYzYdFxLFPTNC7oo22mp6cRDAZ7viU5HxRxMt6M8PiTd/uzITsUCqFSqezL0W1N0/DSSy/h/v37A41NsCwL/9dlDemahSlfc4ELtXcmKWHGL6DaaC6c7JWiKLh79+5EbogfB2xIEtGe4LaRspsA+EajgampKS61oG2mp6dbzZVeHIpIOBQR0bCAP73bn1uSiqJAFEW88cYbPb+p2KtM08SlS5dw7do1RCKRgS6Vulsy8Z/fbN7Y+/hxLxZDe7eM2Qu3YN+/IMMrAe+WLLyZ6a0hb49tc9s2UW+KxSI0TXPdsH3IZYNurVYb+MEOjR+v14uFhYWecySBB7ck//TdBhpmfw5Y4/E47t27hxs3buz6z9sr6vU6Xn75Zbz77rtIJpMDm/QAgK/dN/D8igFRAP7meQU+me9fnIiC0MqS/OKt3hc02du2Oba9N+3dSp6IJorbRspu8pYAcLyJ2vL5fJibm+u5IQkA37LYvCX5P243dr2h0haNRpHP5/HGG2/siabVbliWhTfffBNvvfXWwG8o100L/+FVDXWzeVpuj/DsJY1GA4VCAaurq1hfX8fq6iqy2SxqtdpI/lsHPAKenbM3VPbeVPf5fFhZWek5g5VokhWLRZim2baZYR+wHo46vwUzDAOJRGJgz0fja3Z2FrIs9xylcSElIaIIyGsWXl7tz41Gj8cDRVHw5ptvolAo9OXPHCV70uP27duIx+MD26gNAOsVE//X5ebP1f/5iAdHo4NrfO6UXc+sra1hdXUV6XQalUql52mjfnnfvAyfDKxULFza6O1zWJZlmKbJA9Y9ig1JItoTnDZS1k0Ltwt2Q7L9tyxd1+HxeDjeRI7m5uYgimLP4xpPzcoIeIB0zcILK/0p4u3lS7dv38a7777blz9zFCzLwltvvYU33ngDfr9/oBsoAeB33tJxp2gi5AF+5Kx3z2yhbDQayOfzWF1dRS6Xg8fjwcmTJ/HMM8/g7NmziEQi0DQN6+vrWFtbQ6FQQL3e+wj1Tn3wwIP8sPVKb28k/H4/t20T9chuzjw6saEbFu4U3esZLugjN4lEAtFotOcJC1kU8BcWmvX1f99BhIeTcDiMcrmMS5cujaxR1Q+maeK1117D9evXEY1GBzrpYZgWfvE1DTUDOBYV8RcP752b0FsPVbPZLGRZxvHjx/HYY49hamoKjUYDGxsbWFtbQy6Xg6ZpQ6tlfLKA928uaPrird5Hr1VVxb1791Cv92fiifpn710vIKKJ5LTQ5k7RRMMCQh4g6XPOW/L5fAgGg4N+TBpTU1NTCIfDKJVKPW1LVCQB/9MBD373Wh2/f13HkzNSXxphqqq2ini/3z+Wt2Fu3ryJy5cvQ1EUBAKBgb7W5Q0Dn7/ZLEB/6KyCqDLa89R6vY5KpYJarQZJkhAKhXDo0CFMT08jkUg8dLBy6tQplEolZLPZViFfKBRaiytUVYXP5xtYTtV8UMTphIjLaRNfut3A957s/o2WfcNrZWUFCwsLA3k+ov0mm822Hbe+XTRhWEDYCyRU53qGC/rIiSiKWFpawssvvwzLsnqKKfrmRRmfu1HHtZyJKxkTy4nd38qzt27fvXsX165dw7Fjx8YuOsmyLFy5cgVvv/02wuFw2+zXfvqDd5r/DXwy8L+eUxyz8Yel0WigXC5D0zQIgoBgMIgDBw4glUohmUy2vpcdP34c1WoV2WwW6XQaKysrKJfLyOVyEEURPp8PqqoO9GbpB5c8+MKtBl7bMLBSNjET6L5u8vv9yOVy2NjYwOzs7MCekXrHhiQRjZzbRsp3cpt5S1HJscjRdR3T09MDe0NP40+WZSwsLOD111/vuYj/0AEP/uhmHXdLFl5aNfBkn7YgxuNxbGxs4Pnnn8eTTz6Jqampvvy5w3Dnzh28+uqrrWbcIJXrza3aQPMN1eOp0ZQulmWhUCigVqtBlmWEQiEcOXIEqVTKdbxLEASEQiGEQiEsLS3BMAzkcjlkMpnWLYT19XWEw+GB3TL98AEPLqc1/Om7dXzXUQ+UHrKq/H4/VlZWUKvVuDSMqANN01AsFjvmRzr9DNI0DZFIZOBNERpf09PTUFUVtVqtp58ZMVXEX1iQ8aXbDXz2mo7lRH9+3ni9Xqiqitdffx2maeLEiRNj05S0LAtXr17FG2+8gWAwOPBJjxt5A79/vXlD7weWFUz5R/O+xTRNFIvF1qFqMBjEoUOHWk3IdvWMIAjw+/3w+/2Yn5/HmTNnUCgUkMlksL6+jo2NDeRyOViWNbDN5NMBEeemJLy6buCLt+r4/uXuv0/aY9urq6tsSO4xbEgS0ci5bqTMu483Ac0frLFYbGDPR/vD7Owsrl69Cl3Xe3qzF/A0b0l+9nodn72m44np/tySFAQByWQS6XQaX//61/HEE09gZmZm13/uoN2/fx+vvPIKLMvq6bbpTv3GFR2ZmoVpv4DvPTGaLZSGYSCdTiMQCODMmTOtJuROAu8lSUIikUAikcCxY8dQrVZx9epVvPXWW7AsC36/v+/Pf25KQsovYK1i4bl7DXzzUvcjYn6/HxsbG1hfX8fi4mLfn41oPymVStB1ve0Nx3fyzdgPt3qmXq8jkUiMTUOHhi8UCmFqagp3797tuYH20cMefPlOA1ezJq6kDZzqwy1J+5lKpVJrdPvUqVNj8Tl848YNXL58GT6fbyA/e7fSDQu/9JoGwwKempHwzOxociPr9TrS6TSi0SiOHDmCqampbZMd3RBFEdFoFNFoFIcPH4au69jY2MDrr7+O9fX1gW0o//ABD15dN/CVuw38pePenpYB2WPbp0+f5tKwPYTXiYho5NwW2nQq4E3ThCAIzI+kjqLRKOLxOEqlUs+/98MHPFA3txW/stafLEmg2ZRMJBKoVqt48cUXce/evb792YOwvr6Ol19+GfV6fSjNyBdWGnjuXgMCgL9xVunpZl+/1Ot1bGxsIJFI4L3vfS9Onz6Nqampvm3f9Pl8OHfuHE6dOoVyuTyQ7euiIOCDm03IL9zuLT9MFEUIgoD79+/3/bmI9ptisQjDMNp+f7iRcz9gtSwLlmVxXJtcCYKAhYUFWJbVc25jXBXxTZtZkp+93ttinE7sG4aXL18ei0zJ27dv49VXX4XH4xlK5NNn3tJxr2whogj4X5aVkTRsq9UqMpkMFhcX8U3f9E04c+YMpqen+zJm7fV6MTc3h6effhqxWAzr6+sD+Rw4nRAxFxBQM4CvvNtblmQgEGAu9h7EhiQRjZy90ObRAr5ct7BSbr5xPuSwYVvTNHi93oGPjdL4EwQBi4uLaDQaPYdwB70CPry5HOSz1/q7kMRuSmqahhdffHHPLrrJZrN46aWXUKlUEI/HB15M5zQTv765hfKjhz04Ghv+bQK7eF9aWsJ73/vegd3EFkURZ86cwZkzZ1CtVlEsFvv+Gu+fl6FIwL2ShSuZ3pfbrK6uolqt9v25iPYT+2v30e+P5bqFlYp7PWPnyvKAlTqZnp5uNVd69dHDHkgC8GbGxNVM/w5YgWbDJxAI4MqVK3j99ddhGP398/vl3r17eOWVVwBgKF9vV9IGPr+5iOWHzngR9A6/GVkoFFAul3H8+HE89dRTA7sRGo1G8fTTTyOZTGJ9fb3vnwOCIOBDm/X4F2/XYfZQj0uSBMuyuG17j2FDkohGLp/PA9hewN/cHNee8gkIOfzw1nW9lWlC1MnMzAz8fj8qlUrPv/dbDzZvSd4umvjGev8LrHg8jkajgZdeegm3bt3q65+/G5Zl4d69e/j617+OQqEwsGygR1/z1y7pKNWBxZCI7zw63NEaOy+yXC7j5MmTePLJJweeLSUIAk6ePImzZ89C1/XWpt5+8XsEPLu5ofILt3rbMunz+VCtVnmrgKgDpzxsOz9y2i84NiPshTY8YKVOFEXB/Pz8jg6JEj4R7x/QLUmgeYAVCoVw9epVvPrqq3uqKWlZFt599128/PLLaDQaQ5n0qDYs/PLrzcPVv7Ag4/zUcBPz7Jx+0zRx/vx5PPbYYwMfVw6Hw3j66acxPT2NjY0NNBq9b8V28945GT4ZWK1YeH2jt88vn8+H+/fvQ9f7/7lPO8OGJBGNnNNGym7ylnRdH0qDhPYHv9+PmZmZHd0qCHofjL3+Xp9vSQIPtlValoWXX34ZN27c6Ptr9KpWq+Eb3/gGvva1r6FUKg3ta+1P7zbw6roBWWhuoZSHuIXSLt4ty8Ljjz+Oc+fODXRr5FaCIOD48eN47LHHWstv+vk5YH/+fmPNwFql+1uS9tj2Xo8UIBolXddRKpVc42cOdahnIpEIs82oK3NzcxBFEfV6bwdMAPAXN29JvpE28Xa2/w1Dn8+HcDiMa9eu4ZVXXul7Q2onarUaXnnlFTz//PPQdX0okx4A8JtXdKRrFqZ8Ar735HBzsA3DwPr6Onw+H5566qmhbkEPBoN46qmnMDs7i3Q6vaPPUyeqLLSiB75wq7fPLftSAg9Y9w42JIlopNw2UrYW2kSdRzVN02TeEvVkfn4egiDs6NT+Ww954JWAWwUTr/b5liTQbEhFo1GIoohXXnkF169fH0lT0h5p+cpXvoKrV6/C7/cjkUgMZZP9esXEf7rSPLn+7uMeLIaGV6oYhoG1tTUEAgE8/fTTOHLkyNAPOwRBwOHDh3HhwgUAzQObfn0OzAVFnElKsAB86XZvbw78fj/W1tZ2dLuYaBKUSqVWjMyjtm7YdtJoNJBIJAb2fLS/JBIJRKPRHeViJ30i3rd5Y/6z1/rXKNpKVVVEIhFcv34dL730Ul8bUr2wLAv379/Hn/3Zn+Htt9+G3+8fWjPylbUG/uxuMwf7R84qPS1g2S1d11vLZd773vdibm5uaK9t8/v9eOqpp7CwsIBMJtPXW4kfXPJAAHBpw8C9UvcHrBzb3nvYkCSikbI3Uj669diyrI4bthuNBiRJYt4S9WRqaqq1EbJX4S23JD97vf+3JG2RSASyLOPVV19tbV8eFl3X8frrr+NrX/sacrkcUqnUwMeVbaZl4Zde11AzgOMxEd92cHg3heziPZVK4b3vfe9IN54LgoADBw7g4sWLkGUZmUymb58DHz7QfBP6p+82UGt0/2f6fD7UajWsra315TmI9ptisdjKgdzKsixc72KhDTCcPDvaHyRJwuLiInRd39HPh49t3pK8lDZwLTeYsWpFURCLxXDz5k28+OKL0DRtIK/jRNM0vPbaa/ja176GfD6PqampodUzBd3Cr11q/vN+2yEPTsSHl4NdqVSQzWZx4MABPPPMM0MZTXeiqiqefPJJLC0tIZvN9u1zIOUX8Viq+e90JwesKysrQ/98pPbYkCSikXLaSJnVLOQ1C6IALIXbf6ti3hLthMfjweLiImq12o6K+G876IFXbN546TW7phfhcBherxevv/463nzzzaE0JdfW1vCVr3wFV65cgaIoSCaTQ7kVafv8zQbeyppQpOZtAnFItxMrlQpyuRwOHTqEZ555Zs/cul5YWMCTTz4JRVFaY+S7dTYpYdovoNoAnrvX/agTt20TuXNaaJOpWSjozXrmgEM9o+s6PB4P6xnqyczMDBRFQa1W6/n3TvlFvHdusLckgeb25Xg8jlu3buHFF1/c0bPuxNZ6RlXVodYzlmXh1y9rKOjAQlDAdw0pB9uyLOTzeVQqlaHlX3fD6/Xi4sWLOHToEHK5XN8+B+xlk1+520Cl3n195Pf7uW17D2FDkohGyqmAf2fzNsFCUIQiOS+0CYVC225XEnUyMzMDj8ezo/GRiCLgm5ceFPGDbBSGQiGoqorLly/jG9/4BjY2NmCavW1I7ka9Xsfly5fx3HPPIZ1OI5lMDn1R1LtFE595q/nf46+e9CLlH06JYhfvp06dwsWLF6Gq6lBet1uzs7OtjZgbGxu7/nwTBQEf2rzl+4VbvX3+BgIBrK+v7yiDlWi/y2QyrgttFkMivC71jKqqCAaDA31G2l/C4TCmpqZ2/D35Y0c8EAXg9Q0D7wzoliTQPAhOJBK4c+cOXnjhhb5GkTxqaz2TyWQwNTU19HrmuXsNvLRqQBKAv3FOcfy67yc7/xoALly4MNT86254PB5cuHABR48eRT6f39FCpkediouYCwrQDODP7vZ2wAqAB6x7BBuSRDRSTgX8O628JedvU/V6HclkcmDPRvtXPB5HPB7f0dg2AHz7IQ88InA9b+Jyuv8Nwq2CwSD8fj/eeust/Omf/im+/OUv49q1a31rCm1sbOCrX/0qLl26BFmWkUwmt91YHrSG2RzVbljAuSkJf2FhOEV0qVSCYRi4cOECzpw5M/R/7m6lUik8/fTTCIVCfWlKPrsgQ5WA+2Wrp89fe2x7Y2NjV69PtN/U63UUCgXXPGy3ekbTNMTj8T37PYj2JkEQsLCwANM0d3RYmfKLeGbW3rg92IxHj8eDZDKJe/fu4U/+5E/wla98BTdv3uxLY8q2tZ7xeDyYmpoa+tdUumri/97Mwf6fj3pwIDz417ebkX6/H08//TQOHz68J5d9yrKMxx57DCdPnkSxWNz1f3tBEPDhzQPWL96qw+yhNvL7/VhdXeXY9h7AhiQRjYyu644LbW4V3DdSMm+JdsPO6Gs0Gjsq4qOKiA8s2rckd5bf1Au/34/p6WkEg0Fks1m89NJL+NKXvoSvf/3ruHv3bk9h8bquI5vN4s6dO3j99dfx1a9+Fevr60gkEggGgyMpYj97vY5bBRMBD/BDp71DeYZarYZKpYLl5WUcOnRoTxbvWyUSCVy8eBFer3fXzWifLODZeXtDZfefO4IgQBAEjjkRPaJYLLbNwwaAG5sbtp3yI4HmQq1YLDaw56P9a2ZmBsFgcMcHrB870lwO8uq60fpcHRRZllu51Kurq3j++efxxS9+ES+//DJWVlZ2tGzQrmmuXLnyUD0TCAQG8E/gzrQs/MolDdVG8+v9o4eGM6pdLBYhyzIef/zxkeZfd0OSJJw9exbHjh1DoVDY9dTPe+dk+GVgvWrhtR6WTfp8PlSrVWQymV29Pu3e3rnHS0QTx95I+WhT0bIs3Cw0f0AddMhbqtfrkGWZeUu0Y3NzcwiFQiiXyzv6PPrIIQ/+x50G3s6ZuJIxsZwY/Cm4ncVkWRYqlQpu3ryJW7duIRQKYXFxETMzM63tkfbHlEollEolFItFZLNZlMtl6LqOer0OQRDg8/mQTCZH1pC7ljPwB5s3M/7aaQVRdfBnpfV6Hfl8HseOHcPx48f3fDPSlkwmcfjwYVy+fBl+v39XeVgfOuDBF2838Nq6gdWyielAd3+WqqpYXV1FvV5ve7udaBKVSqW2C21My+q4Yds0TYiiyHqGdkRRFCwuLuKNN95AKBTq+efZTEDEM3MynrvXwGev1fF3nxhsLSMIAlRVhaqqME0TlUoF165dw/Xr1xGJRLC4uIjZ2VlEIpGH/lksy0K1Wt1W09jLMXVdRyAQGGk988e3G3gjbcIrAv/rOQWSOPjnqFQq0HUdjz/+OGZnZwf+ev0giiJOnjyJtbU15PP5XR3GKLKAb1rw4I9u1vGFW3U8luquvWVv206n02Pz722/YkOSiEbGaSPlRtVCuQ5IAjAfcg6A50Ib2g1FUbCwsIArV67s6GZgTBXxFxZkfOl2A5+9pmM5MbzgcEEQEAgEEAgEYBgGSqUSLl26hKtXryIej8Pn87WCw7du4PR4PPB6vQiFQpBleeSNOM2w8EuvabAAvGdWwlMzgy9LTNNEJpPB/Pw8zp49O9SlPf1w/Phx3L9/H9lsFolEYsd/zkxAxLmkhNc2DHzxdh3fd6q7LF77cyuTyWB6enrHr0+0nxSLRViWte176lrFQs0AvCIwF3DOj/R6vZz4oB1bWFjAtWvXoGnajnKQP3bYg6/da+Ab6wZuFYyhjBkDzcZUMBhEMBhEo9FAqVTCa6+9hjfffBPJZBJzc3NoNBrI5/PI5XKoVqvQdR2maUIQhD1V09wvmfitq81R7Y+f8GKmy0O+3bAnzU6ePInDhw8P/PX6yefz4cSJE3jhhRda3wN36oNLMj5/s47LaRN3Sybmg939u/d6vbh//z6Wl5fHrhbcT/hvnohGplgstkYAt7JvRy6ERHgcThc1TUMsFttTgc00fhYWFuD1enecIfPRwx7IAnA1a+LNzGBHnZxIkoRIJIJUKoVAIIB0Oo07d+60ttDH43GkUimkUinEYjEEAgF4PJ6RNyMB4Leu6litWIgqAn5gefDLqSzLwsbGBhKJBC5cuDCWN/wURcGpU6dgWdaus48+dKD5/fPP3m2g2ugudkCWZZim2QrPJyLnPGy7nlkMiY63pTRNg8/nG/riDdo/otEopqenW4siezUbFPH0bLMJOciN225kWUY0Gn1opPvFF1/Eq6++itu3b6NarbamRKanp/dUTWNs5mDrJnA6IeJblgb/3sQwDGSzWRw4cACnT5/eEzVdr5aWljA3N4dcLrer6KMpv4jHU83P3y/2EEPj8/lQKpWQz+d3/Nq0e2xIEtHIZDKZtg3FWx3GtYHmD+J4PD6wZ6PJEIvFkEqldpy9FFdFfNPCgyzJURIEAYqiIJFIIJlMIhwOQ1XVPbsk4dX1Br50u7kV8UfOehHwDL6YzmazCAQCuHDhwli/+Z+fn8f8/Pyui/gzSQkzfgE1A/jze91vqPR4PFhZWRl4dirROLBvcLW74XNzc1z7QIeFNolEYiwbCrQ3CIKApaUlANhRDiMAfMcRLwQAL68ZuF0YzQEr8GCkO5lMthqPyWQSkUgEPp9vT9Y0/+2dOt7Jm/DJwA+fVSAO+GvZPlxNpVJ47LHHxvZyhiiKOHXqFBRF2XU29ocPNA+Evnqv+wNWr9cLXdeZIzlibEgS0Ui4baS0bxQccGhI2gHIHG+i3bKLeNM0d1zEf/SwB5IAXMmYeCs7uiJ+nGRqJn7ptebtvg8tyTiTHHwxbd/Ifuyxx8b+MMMu4u3T/R3/OYLQaqg/v9J9Q9Ln86FQKOz4Ng7RfmIvtHFb0Od2wGpZFqLR6KAejybE9PQ0wuHwjr8vzwVFPDWzeUtywBu395MraaN1q/QHlhXEB5yDbeceRiIRXLhwYUcj+ntJPB7H4cOHUS6Xd7Xg5mRcxLRfgG4A31jrrhYXBAGSJGF1dXXHr0u7x4YkEY2EHUL96EZKy7Jwa3PL30GHGwV24c/8SOqHmZkZRCKRHRfxCZ+I98/vjVuS48AwLfyHVzWU6s1Dh+85ufPcoG5Vq1XUajUsLy9jfn5+4K83DNFoFMeOHUOlUtlxMx0AntzM7byaMZHTunszoCgKdF3n2DYRHiy0eXRk27Ks1sSH0wGrYRiQJIkHrLRrHo8HBw4cgKZpO769bt+SfGnVwJ3i7rYfT4KCZuEXN3Ow3z8v471zgz9cLRQK8Hg8ePzxxxGJRAb+esNw/PhxxGIxZLPZHf8ZgiC0csi/3uMBazqdRq1W2/Fr0+6wIUlEI+G00CZTs1DcXGiz4BBKrOs6fD4fAoHAMB6V9rl+FPH2LcnLaRPXeEvS1e9eq+OtrAlVAn70vOKYE9sv9m3so0eP4vjx4wN9rWE7cuQIEonEror4Kb+IwxERFoAXV7q/VSAIAtbX13f8ukT7RaFQAIBtI9frVQuVBiCLcFyyoGkaD1ipb+bn56GqKqrV6s5+f0jExc1bkr9/nQesbkzLwn98TUNOszAXFPD9pwZ/uFqpVNBoNHD27Nl9tVSuX9nYT80231O+vm6gXO+unvf5fKjVajxgHSE2JIloJJw2Utrj2vNBEV7JOQA+Ho9zIxr1zdzc3K6K+Cm/iPdt3pL8vREFwo+D19cb+IN3mv9+fuiMgukBb6E0DAOZTAaLi4s4e/bsvsto83q9OHXqFADsqoh/enZntwrW1tag63zTSpMtm822zbXbutBGdlloEwwGt02LEO1EKBTC3NzcrqI8vuNIs7H24oqBu7wl6ehz79RxKW3AKwJ/57wKRR5sfaHrOkqlEo4fP45Dhw4N9LVGoR/Z2AtBAXMBAQ0LeGWtu3pGFMVWJieNBt/NE9FIdNpI6TTeBDQzJGOx2MCejSZPOBzG7Ozsror4v7h5S/JS2sALPTR2JkW2ZuI/vt5smn3zotw6yR4UO2cpmUzi8ccfH9vQ907m5uawuLi4qyL+yc0bMW9lTWRq3b0B9fl8qFarDIOniWYvtGnXUGwttHGpZ+r1OhfaUF8tLi5CFEXU6zs7HF0MiXhiWoIF4D9f1bm8rI23sgb+6+bh8/cvezEfGvzhqr1Re3l5eV9+v+hHNrYgCK3a8uv3u59WUhQFq6uru8qwpJ1jQ5KIhs5tI+WtzQLeKT/SMAyIosjxJuq73RbxKb+Ijx5uNtn/v29oKOos4m12bmRRb77Z+asDzo20LAuZTAbBYBBPPPEEfD7fQF9vlARBwKlTpxAIBHacgxpXRRyLNr/nvtDl2LYkSTBNk2NONNFKpVJr7PpRnRba2I2e/ZIDR3vD1NQUYrHYrpaO/eVjXsgi8PqGgefu8YB1q6Ju4dPf0GBawDNzUitDfFDsw9Xp6Wk89thje3LLeL/0IxvbzpG8nDZQ6rIOt5uguVxuR69Ju8OGJBENndNGSsuycLNDAW8vwmEAPPVbKpVCNBrdVRH/sSMezAcFFHTgN67sfIR2v/ns9TqubuZG/p3HFMc4hn4pFouQJAmPP/74RGyvDYfDOHbsGKrVKhqNnb15tG8VPH+/+9+vKAru37/PWwU0sew87HYLbeyJD6d6ptFocKEN9Z0kSTh48CDq9fqOvzfPBkV819Hm5/RvXNGR6/Lm/H5nWhZ+6XUNWc3CTEDAX1tWBnpb8dGN2pMQ7bDbbOy5oIjFkAjDAl7qcmzb4/GgXq/zgHVE2JAkoqErlUqo1+vbCvicZqGgA6LQvEXVjqZpCAQCUFV1GI9KE2RrEb/TESWPKOCHzygQAPz5faPrDJv97PKGgf92vXnr9K+dVjAz4NxIXddRq9Vw+vRpzM7ODvS19pLDhw9jampqxyf8T05LEAC8kzexXul+bLtYLLaWehBNmlKp1DYPO12zUN5c0Oc0zqlpGhRF4cQH9d3c3BwCgQAqlcqO/4xvO+jBwbCISgP49Tc4ug0An7/ZwGvrBmQR+DuPqVAHnBtZKpVaG7Un5eBiazb2TjdfP7UZQ/P1Lg9YBUGALMtYXV3d0evR7rAhSURDVywWW1tat7JvE8wFBMcbVLquM2+JBmZubg5+vx/lcnnHf8bhqIRvO9Rstv/6Zb3rTX/7UU4z8YuvabAAfNOCjGfmBjvaZJpmK2fpyJEjA32tvcbj8eDUqVMQRXFHy5miqogTcXtsu7si3uv1Qtd15kjSxMpkMm3zae38yIWQCI/DQhtd1xGJRNrmaRPths/nw8LCwq5qGUkU8MNnFUgC8Mqagee7jPPYr67lDPzOW80lbt930ut4caJf6vU6qtUqTpw4gVQqNdDX2mvm5uawtLSEfD6/o0a4vajvSsZEoYex7Uwms6smPu0MG5JENHSZTKb9RspWfmT7fBTmLdGg+f1+LCws7Log+a6jHkz7BeQ0C//5zcncQmxaFn7xVQ0F3cJCUMD3nRpsbiTQ3HYbjUZx5swZiOLklTgzMzNYWlpCoVDYURFvZy91+8ZTEARIksRbBTSRDMNwzMPuZkFfo9FAIpEY2PPRZFtYWIDH44Gm7Tw+ZjEk4juObI5uv6F13dzZb0qbuZGG1bx994HFwedGZjIZzM/P49ixYwN9rb1IEAScPHlyx9nYKb+Ig2ERpgW82OUBq6qqqNVqPGAdgcmr1olopFw3UjJvifaAhYUFyLIMXd95I9ErNW8WCAD+7G4DlzYmb3T7v12v40rGhFcCfvQxFcqAcyMrlQoEQcCZM2cQCAQG+lp7lV3EB4PBHY1RX5yRIQrArYKJ1XL3Y9sbGxs7Hq0iGlf2Qpt29cytDvWMfWDAeoYGJZFIIJlM7ioXGwA+etiDxZCIYh34v9+YvGxsy7LwK5c0pGsWpv0CfvDMYHMjASCXyyEUCuHcuXP7eomNGzsbu1ar7Sgb+6nZzbHtLhuS9iH2+vp6z69Fu8OGJBENlftGSvcbBfZCG+Yt0SAlk0kkEold5+Idj0n44FLzFP3XLumoNibnZsGVtIHfu7aZG7nsxVxwsOWGYRgoFos4cuQI5ubmBvpae10wGMSJEyd2VMSHvQJObY5tP89bBUSuisVi2zzsbhf0eTwe1jM0MIIg4MCBAzBNc8cbiwFAFgX88BkvRAH4+oqBl1Yn64D1v99q4JU1A7IA/OhjCnwDzo2s1WowDANnzpyZ+O8Phw8fRjKZ3FE2tj3xcTVjdr2USVVVrK6u7urrhXrHhiQRDZXTRspczUROsyAAWHIo4DVNQyQSadvMJOoXu4g3DGPX24P/ynEvpnwC0jULv/3WZIxuFzSrlRv5/nkZ75sfbD6avYUylUrh1KlTzJcFcPDgQczMzOxoS6W9bbvbMHhJkmCaJjY2Nnp+LaJx5rTQJqtZKG4u6FtwyJnTdR2qqiIYDA7jUWlCzc7OIhQKoVQq7erPORiR8JEt2dilCRndfidv4LeuNmu37z3pxYHwYG8rGoaBXC6Hw4cPY3FxcaCvNQ48Hg+Wl5chimLPUxhJn4gjEREWgBdWu2sw+nw+lMvlHW/4pp1hQ5KIhsqpgG8ttAkKjqOdjUYD8Xh84M9I1K8iXpGb4z0A8Me3G3gzs79PXQ3Twi++VkNOszAXFPD9Q8iNLBaLUFUV586d42HFJlmWcfToUQiC0PMtySdSMiQBeLdk4V6p+1sFKysru27gE42TTgtt5oOi44I+TdMQj8cndhyThsPr9eLAgQOo1Wq73pL9HUc8mAsIKOgWfnMCsrHL9Qe5kRenH0y8DIqdG5lMJrG8vMzD1U3T09OYnZ3d0dRSrwesHo8HjUaDEx9DxoYkEQ1VNpttX8C38pbcF9owb4mGQVEULC0toVqt7rqIX05I+MBC83P+Vy9p0Iz9ebPAtCz86iUdl9MmvCLwd86rUAY82qTrOmq1Gk6dOsXlEI+Ynp5GJBLpOT8s6BVwOtn8Pvx8l0W8z+dDqVRCPp/v+TmJxpHbQptO+ZH274/FYgN7PiLb/Pw8FEXZdc7v1mzs5+418I21/Tu6rRsWfuHlGtarFqZ8w8mNLJfL8Hg8OHfuHFRVHehrjRN7akkURdTr9Z5+75MzzVrm7ZyJdLW7A1NZlrGysrLr2p+6x4YkEQ2NPYqwk/xIO6eJDUkaFruI382GStvHT3gRVwWsVSz817f3380Cy7Lwn97U8dV7DYgC8LcfUzDvMKrYz9fMZDJYXFzEkSNHBvpa40iWZRw6dAi6rvd8c/HpzSL+hZVGV0W5x+NBvV5HOp3e0bMSjZtyuYxarbajDdumaUIUxYnPh6PhCIfDmJ6e3vXEBwAciUr41oPNA9Zfv6yjUt9/TZuGaeHffUPDW1kTPhn4sccVBDyDbUbW63WUy2WcOHECqVRqoK81jqanpxGLxXo+YI2rIo7Hmt+HX1jpbkLJ7/cjm82iUqn0/Jy0M2xIEtHQlMtlx4U29ojTwYhzfiTzlmiYotEoUqnUrjdUAoDfI+CvnW5+3v/3mw1cy+6v0e3PXq/jC7eatyV+5KyCx1ODHW0Cmreto9Eozp4929qOSA+bn59HIBBAuVzu6fc9npIhC8C9soV3S53fcAqCAEmSsLq6utNHJRorpVIJ9XrdtSHpttDG6/XygJWGQhAELC0tAcCOthU/6ruOeTHtF5DVLPznq/vrgNW0LPzy6xpeXTfgEYG/e0EdeG6kfbg6Pz+PY8eODfS1xpUkSTh48CDq9XrPB6z2cptut22rqgpN03jAOkSs4IloaOyNlI8W8HnNQtZeaONwq0rTNMRiMeYt0dD0u4g/PyXjfXMyLAC/ckmDvk9Gt79ws97aqP19p7x479zgm5H2KP2ZM2d4SOHC5/NhaWkJlUqlp/Ejv0fA2anm99pus5f8fj/S6TSq1eqOnpVonNgHVY+OceZqJvKb9cyiy4I+n88Hv98/6MckArDzCI92FEnAD21mY//puw1c2tgfB6yWZeE3ruj48/sGJAH4O48pOBEf/HuOXC6HUCiEs2fPto20oqb5+XkEg8Geb/penJEgAHgnb2K90rmZaX9P56K+4WFDkoiGxmmhza1Cs5iZCQhQHTLnTNNk3hIN3czMDCKRSF9GnQDgr570IuwVcL9s4fev95aFsxd99W4dv7EZbv9dRz348IHBbtQGHmS3HTlyBPPz8wN/vXG3uLgIr9fbc/TA01tuFXTTzFRVFbVajWHwNBFyuVzbm9ndLOjTNA2JRIJLK2hoZFnGwYMHoet6X7LxTsQfLHn5tUsaqo3xP2D93Wt1fOl2AwKakx6PDWHSo1arwTAMLC8vIxKJDPz1xpmqqjhw4EDP2e5RRcTJePN7dbe3JH0+H1ZXV/tyGYE6Y0OSiIYml8u1veHYabzJvp7P8SYaNlmWsbS0BE3T+lLEB70PRrc/d6OOm/nxvVnw8moDv3Kp2Yz8nw7I+I4jg29G2qNNqVSKWyi7FI1GMT093fPNmMdSErwisFqxWhm/buzmDG8V0H5nmiay2WyHhTbON6ssy0I0Gh3U4xG1NT8/D5/P17dsvL9y3IukT0C6ZuF33hrv0e3P36y3Dom/75QXzwxh0sM0TeRyORw8eBAHDhwY+OvtBwsLCzta0PRgbLu7mtvn86FcLiObzfb8jNQ7NiSJaCjcCvgH+ZHtC3hd16EoChuSNBL9LuKfmJbx1IwE0wL+3Te0rkZI9poraQP//lUNpgW8b07G9570DqU5WCwW4fV6ce7cubbfS2g7QRBw8OBBAL1FD6iygHP22HaXRbyiKFhZWYFhjG+jnaiTSqWy44U2hmFAkiTWMzR0gUAA8/PzPWcKO1HlB6PbX7rdwCtjunX7K3fr+E+bkx7ffcyDDw1h0gMAMpkMEokETp8+zcPVLkUiEczMzOxgbFuGKDQPjFbKnWtuWZZhGAZzJIeEDUkiGopuCninG5LMW6JRCoVCWFxcbEUO9MP3LyuY8glYr1r41NdrXRVIe8WNvIFfeLmGhgk8npLwQ2e8EIdQTOu6jlqthpMnTyKZTA789fYTe0NloVDo6fc9Pdvb2Lbf70epVEIul9vJYxKNBbeFNq0bki4L+rxeLzds00gsLS1BluWeb5g5WU5I+MBi8+fEv31Fw4tdjsTuFS+tNvCrm5Me33pAxscOD6cZWSqVIEkSzp07B5/PN5TX3A8EQcCBAwcgCALq9e5jj0JeActx+4C1u89Rj8eD+/fv963uJ2dsSBLRUDgttCnoFjK15jf7JZeGZDKZ5Akijczhw4ehqmrfbkmGvQJ++mkVswEBmVqzKXm3uPebkndLJn7+xRpqBnAqLuJvn1cgiYP/urQsC9lsFgsLCzh69OjAX2+/kSQJhw4d6nlD5bkpCYoEbFQtvJPv7lZBo9FgjiTta8ViEZZlbcuQLGjNeqbTgr5gMAhFUYbwpEQPSyaTmJ2d7flwys33n/LiqRkJhgX8+1c1fO3eeDQl30gb+PQ3mpMez87L+J4hTXo0Gg2Uy2UcP34cqVRq4K+336RSKcTj8Z5jaJ6a7X1RX6FQ6FuGPDljQ5KIhqJUKsE0zW0F/K3NDL0ZvwCfw0IbAAx7ppGKRCJ9vyUZU0V88ikfFkMi8pqFn/t6tbXgaS9ar5j4Vy/UUKoDhyIifvyCCq/D0oZ+27qFsl0OLXU2NzeHYDDY07ieIgl4rIdt24IgQJZl3iqgfS2fz7dtXNzsYkFfvV7nQhsaGUEQcOTIEciy3POiMyeyKOBvnVfw7LwM0wL+42savnxnby/teydv4P/9cg0NC7iQkvCDp4cz6WHnYM/MzOD48eP8PrADkiTh4MGDaDQaPR2wPjEtQxKAd0sW7pY6/z5FUaBpGg9Yh4ANSSIainw+77qR0mm8yTAMiKLIvCUaucOHD0NRFFSr1b79mWFFwE89qeJQWESxDvyLr9dwPbf3mpJ5zcK/erGGrGZhLijgJ59QXQ8Q+knTtNYWSo457pzP58Pi4iIqlUpPzcKnZh+EwZtd/D6fz4dcLtfXrxOivcJuKOwkP9L+uuMBK43S1NQUZmZmkM/n+/ZnioKAHzrjxbcsybAA/NplHf/95t5sSt4tmfh/bpn0+FtDmvQAmrerVVXF2bNn4fEMZzx8P7IPWHu5vRjwCDiT7O2AVRAErK+v7/g5qTtsSBLRwLkV8LdaBXz7W0+apkFRFDYiaOSi0SgWFxdb43r9EvQK+N+fVHEsKqLSAP7PF2q4mtk7TclrOQM/+3wVqxULCVXA/35RRdA7nOLd3kK5tLSEpaWlobzmfra4uNjzhsqzSQk+GchqFq7lOt8qUFUVtVqNYfC0L1WrVVSr1R1t2G40GlxoQyNn35KUJKlvtySBZlPyB0558e2Hmo2233xTxx9c31vbt69ljZFNetTr9VYOdjweH8pr7leqquLAgQOoVqu9HbDOPMiR7Ob3qaqK1dXVnvIqqXdsSBLRwLkV8N0stGHeEu0Vg7glCQB+j4C/f1HFqbiImgH8/Is1XNoYbQ5T3bTwO2/p+Jk/r2G1YiGuNhunMXV4pUM2m0U0GsWZM2fa3rCm3kSjUUxPT/eUveSVBDyeat6SfL6LWwX2f6eNjY2dPSTRHlYqlaDrevt6Jt95oQ0PWGkvSKVSmJmZ6WuWJNBsdn78uAffebTZlPydt+v4zFv6yCM86qaF376q42eeH82kh30xY35+HkeOHBnKa+53CwsLPR+wXpiWIYvA/bKFd0vdTXxUq1WObQ8Yq3siGjinAr6kW9ioNn8gOI041et1LrShPSMajWJhYaHvtyQBQJEF/L0nVJybkqCbwP/rJQ2vrI2mKXmrYOCfPlfFH7xThwXgmTkJ/+x9PswEhlc22E3f06dPw+/3D+119zN7Q6Uoij2d+Nu3Cl5c7W5sW1VVrKysoNEYj+UGRN2yv/c/mmVb0i2k7QV9DgttdF1HJBLhqCaNnH1LUhTFvt6StP/s7zzqxfecaNb8/+2dOv7Tm6NrSt4uGPg/vlbDH954UM/8o6d9Q5v0AJqxVcFgkDnYfRQOhzE7O9vTAatPFnBuc2y7mwNWWZZhmiYnPgZspA3Jq1ev4t/8m3+Dv/7X/zrOnj0LWZYhCAL++T//5x1/7xe/+EV85CMfQTKZhM/nw8mTJ/GP/tE/4iYkoj2oWCzCNM1tP4Tt25HTfgF+z/bCwLIsWJbF8SbaMwRBaN2S7OVUtlteScCPP67g4rSEhgX821e0rjcC9kPDtPDZazr+j6/V8G7JQsgL/G+PK/ib51QE2nyNDoppmsjn8zh06BDm5+eH9rqTYHp6GrFYrKci/kxSQsDTzBK9muk8tu3z+VCpVJDL5XbxpER7j9ONsk71DNAc2U4kEgN7NqJeTE9PY3p6uq9Zklt9+yEPfmC52ZT877ca+PXLelcHWv1imBZ+/7qOf/q1Gu4UTYS8wI89Nvx6RtM01Ot1LC8v8/1MH+34gLWVi93d2LbH48HKysrIb/nuZyNtSH7605/Gj//4j+PXf/3XcenSJRhGd5lZ//pf/2t8+MMfxh/90R/h9OnT+NjHPoZ8Po+f/dmfxcWLFzkmRLTHFAqFtjcc7Y3CTrcjG40GPB4Px5toT4nFYpifnx/ILUmgubHyb59X8MysBMMCPv2qhq/eHXx+zd2SiX/+5zX87rU6DAt4YlrCzzzrxxPT8sBf+1GZTAbJZBLLy8u8Hd1n9obKer3e9YZKWRRwIfWgiO/E4/Gg0WjwVgHtK+4LbdzrGftnBRsStFcIgoCjR49CkiTo+mCyHj+45MEPn/FCAPAn7zbwS69rMMzBN3bulUz88+dr+K9vN+uZCykJP/M+Py7ODLeesSyrlYN94MCBob72JEilUojH4z1FDzw2JcErAmsVq5X768bn86FQKPR0iEu9GWlD8syZM/j7f//v4zd+4zdw5coV/MAP/EDH3/PKK6/gJ3/yJyFJEv7wD/8QX/7yl/Fbv/VbuH79Oj74wQ/i6tWr+Ft/628N4emJqBvdbKR0y4/0er0s4GlPsW9JejyegdySBABJFPA3zin4poXmxspfel3H/+eyhnfyRt+boKZl4f93o47/x3NV3CyY8MvA3zyn4MceUxAe4kiTrVwuQ5IknDlzBqqqDv31J8H8/DyCwSDK5XLXv+fpWXtsu9HVG0reKqD9RtM0lMvlHeVH6rrOA1bacwZ9SxIA3r/gaW6yFoCv3TPw776hYa3S3WFYr0zLwudvNuuZG3kTPhn4G2e9+N8eVxBWhl/PZLNZRCIR5mAPiCiKOHToEBqNRtcHrKos4Hxqc2x7pfNlOEVRoGkaD1gHaPjXHrb4kR/5kYf+dzdfqJ/61KdgWRZ+8Ad/EN/+7d/e+vt+vx+/8iu/gsOHD+Mzn/kM3nzzTZw8ebLvz0xEvemugHfesD0zMwNZHum3KqJt4vE45ufncePGDaiqOpBbfKIg4AdPe6FIwBduNfAnd5p/zQcFPDvvwTNzEqLK7grc1bKJX35dw9ub25PPTUn4wdPeoS6u2cowDJRKJSwvL2N6enokzzAJVFXF0tIS3njjDQSDwa4+f0/FJYQ8QFEHrmZNLCfcc7B8Ph9yuRzK5TKCwWC/Hp1oZOw87HaZtp02bOu6DlVV+bVAe4qdJbmysuK4rKkfnp6V4ZWAf/eKhpfXDLy8VsWJmIhn52U8OSND7cNymfVKs565mm1+LZ5JSPihs17ER1TP1Go1mKaJ5eVlBAKBkTzDJJibm0MoFEKpVOr6AstTMzJeWDHwwkoDHz/uca2BBEGAKIpYW1vDoUOH+vXYtMVYtep1Xccf/uEfAgA+8YlPbPv1AwcO4H3vex8A4Hd/93eH+mxE1J7TQpty3cJ61T0AvtFoIB6PD/wZiXplF/Fer3dgtyTt1/nESS/+wZMq3jMrwSMCd0sW/stVHT/xJ1X865dqeHGlgUaXI1DluoU3Mwa+cLOOX3ldwz9+roq3cyZUCfjBM178vQvKyJqR9m3qVCqFEydOcFR7wBYXF3vKQpVEAec3x7ZfW+88tq2qKjRNQzab3dVzEu0VTnnYW+sZp5FtTdMQj8e50IL2nOnpaczMzAz0liQAPJ6S8Q+eUnEmIUFA82DrVy7p+PH/UcEvvabhSrq7pWlb1RoWrmUN/OE7Ov7xV6u4mjWhSMD/suzFT15URtaMNE0TuVwOBw8exOLi4kieYVIoioIDBw6gUql0PZFxLilBFoGNqoX75e62bW9sbPSUVUndG6trR2+99RYqlQoA4OLFi20/5uLFi/izP/szvPLKK8N8NCJy4FTA27cJpnxC2013lmVBEASON9GeFY/HMTc3h1u3bsHn8w3sdQRBwHJCwnJCQqVu4esrDXzlbgPXciZeXTfw6rqBoAd4Zk7Gs/MyDoQlmJaFtYqF20UTdwombhdNvFs0W1tgtzoVF/HDZxUkfaM9oyyVSvB6vTh79uzAbmnQA5FIBDMzM7h9+3bXn7/npiR85W4Dr60b+N4OQyh2QzmTyfANGe0Ldk7Zo4clW+sZp2UZhmEgFosN9gGJdkAUxaHckgSA4zEJf/9JCemqia/da9YyKxULX73XwFfvNZBQBTw7L+N98zJS/gc1iWVZyNQs3Ck265nbBRN3iibWKhash/58ET9yVnno945CJpNBPB7H6dOnebg6BAsLC7h27Rqq1WrbG+yPUmQBJ+MSLm00a+i5oPvni6qqyOVyyOVymJqa6tdj06axakjeuHEDABCNRh2bFHbRa3+sE03ToGla63/3EoZKRN1zKuA7BcDX63V4PB6ON9GeZd+SvHfvHmq12lDyDv0eAR9Y9OADix7cK5n4yt0GnrvXQE6z8IVbDXzhVgNTPgF53YLuEI2TUAUshkQshkUciYg4NyVBHHHBXK/XUalUcP78eSSTyZE+y6SwN1S+++67re+3nZxOSBAF4F7ZwnrFxFSHN32KomB1dRWmaTI/i8ZeNptt+3XSqZ6xP/95wEp7lZ0lubKyMpSGS8In4i8e8eKjhz24nmvWMs+vNJCuWfjs9To+e72OEzERS2ER7242IcsOl9MiioClkIjHUhK+eVEeeT1TqVQgiiLOnDkz0MNqeiAcDmN2dhY3b97sqiEJAOeTzYbka+sNfPsh9/pHlmUYhoFsNsuG5ACMVUPS3m7klsNgNy86NRg/9alP4Z/+03/av4cjorYcC/guAuC9Xi8bkrSnJRIJzM7O4vbt20NfwDIXFPHxE178pWMeXE4b+MrdBl5eNVqjg14RmA+JzeZjSMRSSMRCSHS8wTMq9qj23Nwcjh49OurHmSipVAqxWAz5fL6reIyAR8CxqIirWROvbRj44FLnWwXlchnFYhGRSKRfj000dLquo1gstr09dosLbWjM2bckV1dXB35LcitBEHA0JuFoTMInTnnx8mqzlrmcNnA1a7byIAFAFIC5QPNAdSksteqaUSyrcWIYBorFIk6ePInZ2dlRP87EEAQBS0tLuH37dtefv+emJPzGm8BbWRPVhgVfhxxTWZaxvr6O48eP9+uxadNYNST76ZOf/CR+4id+ovW/C4UCR4qI+sy1gO9iwzYX2tBeN4pbko+SRAHnpmScm5JR0i28kzeQ9ImYCQgjvynQjUKhAL/fj7Nnz/LrfcgkScKhQ4fwwgsvdH2L8dyU1GxIrhv44JL7rQKv14tcLtfaNEo0ruw87HZLE252qGd0XYfP5+v65g7RKMzMzCCVSmFtbW0kkwpeScB75mS8Z05GptYc6S5oFhY2D1XngiK80t6taezD1WQyiZMnT3JUe8hSqRQSiQQymQwSiUTHj58OiJj2C1itWHgjbeCJaff6U1VVZDIZaJoGRVH69diEMVtqY58slstlx48plUoA0HHLkqIoCIfDD/1FRP3ltNCmUrewWmne4nLaSMm8JRoXyWQSs7OzeyL6I+htNifnguJYNCN1XYemaTh16hSi0eioH2cizc3NIRgMtuqnTs5NNYv2K2kDuuEeBi8IAgRBQCaT2fVzEo1SsViEYRjbDk221jMHXDZsx2IxxhbQnmbfkgQw8uUdcVXERw978VdPKXj/ggcHI9KebkYCzf6Ex+PB2bNn2bAaAVEUcfDgQRiGAdM0O/8GNA9YAeDVdYeMoy1UVUWtVuOivgEYq5+MBw8eBADkcrnW+Paj7ty589DHEtHoOBXw9u3IhOq80AYAx5toLNi3JGVZHujG7f3GNE1ks1ksLi7i8OHDo36ciaWqKhYWFlCtVrv6+IWggLgqQDeBNzPdFfFra2swjM4fS7RX2e87Hr31dLv4oJ4JtalngOYBKw9caBzMzs5iampq4Bu395t6vY5yuYzjx48jlUqN+nEm1tzcHAKBgOvlta3ObzYkX1s3Om7oliSpVbdSf41VQ/LEiROtcYcXX3yx7cfYf//ChQtDey4ias+pgG+NNznkLdXrdciyzIYkjY2pqSnMzMzsiVuS4yKTySAWi+HcuXO8OTRiMzMzkCQJjUaj48cKgoBzyQdFfCeqqqJSqfANLo21bDbbNlKiUx62aZoQBIH1DI0FURRx9OhRWJY18luS48KyLKTTaczPzzNfcMQURcHMzEzXB6zHYxK8EpDTmhvcO/F4PFhbW+vYvKTejNU7AK/Xi49+9KMAgN/8zd/c9uu3bt3Cc889BwD4ru/6rqE+GxFt51TA3+qwkVLXdSiKwoU2NDYEQcDRo0chSRI0TRv14+x5pVIJsizj3LlzrovqaDgSiQRCoVDXtwq2jjl1Ksw9Hg/q9TpvFdDYajQayOfzDnnYnesZLuijcTI7O4tUKsVDpC5lMhlEIhGcP3+eOdh7wMzMDARB6GoqwysJWI73dsCaz+e7bnhSd8aqIQkA//Af/kMIgoBf+7Vfwx/90R+1/n6lUsEP//APwzAM/KW/9Jdw8uTJET4lEbkV8J0C4DVNQyQS4Q92GitTU1N7JktyL9N1HeVyGSdOnMDMzMyoH4fQ3B45Pz/fdeTAckKCJADr1Qf5eU4EQYAoikin0/14VKKhc8rDBrpbaKOqKg9eaGzspSzJva5cLjenBs6d4y3oPWJqagp+vx+VSqWrj7cPWF/bYI7kqIy0Ifnyyy/jPe95T+uvP/zDPwQA/OIv/uJDf//+/fut33PhwgX8/M//PAzDwEc+8hF88zd/M77ne74HR48exZe+9CWcOHEC/+E//IdR/SMR0SanAr7asLBa7rzQJh6PD/wZifrJzpKUJIlZkg7s/J2lpSWONu0xqVQKkiR19QZUlQWcjDdLyG7D4NfX1/nmlsZSsVhEo9GAx/PwVvlqw8JKmQttaP+ZnZ1FMpnkLUkX9XodpVIJx44dw9zc3KgfhzZ5vV7Mzc11fYvRbki+nTVRrrsfsNrfx9mQ7K+R/nQsFAp4/vnnW39tbGwAAN59992H/v6j429/7+/9PXzhC1/At37rt+K1117DZz/7WQSDQXzyk5/ECy+8gGQyOYp/HCLawqmAv10wYQGIqwLCSvuFNpZl8aSRxlIqlcKBAweQy+W63vI3SbbmRkpS+zfwNBqJRALhcLiHse3mDfbX1jvnTvp8PtRqNeRyud08ItFIlEolWJa1LQ/7TrFZz8QUAZE29QzAhTY0niRJwvHjxyEIAsdT27AsC5lMBnNzczh16tS27w00WtPT012PbSd9IuaDAiwAl7q4JenxeLC6usocyT4a6TzkBz7wgR3/x/zQhz6ED33oQ31+IiLqF6cC3h5vcspbqtfr8Hg8zFuisSQIAk6dOoWNjQ3kcjne9N2iVCpBkiScPXuW44t7kCRJmJ+fx6VLl7r6+HNJCf8JwNWMiVrDgio7vyGTZRmNRgO5XA5TU1N9emKi4djpQhu7BuIBK42j2dlZHDp0CG+99RYUReEt3y2y2SzC4TBzI/eoqamp1rbtcDjc8ePPTcm4W6rjtXUDT8+6//f0+XwoFosolUr83t4n/M5CRAPhWMBvBsC75S0pisJv8jS2/H4/lpeXYZomR7c31et15kaOgVQqBVmWuxqtngkImPIJaFjAlUznWwWSJGFtba0fj0k0NIZhIJfLOSy0cT9g5UIbGmf2AWs8HueI6haVSgWWZeHMmTNdNbto+DweT29j20k7R7IBs8NlOUVRoOs6vyb6iA1JIuo71wK+w40CXdcRDod54khjbWFhoTW6PeljHaZpIpPJYHFxESdOnOBo0x4Wj8e73rYtCMJD27Y78fl8yGQy3EJPY6VcLkPTNIeFNt0dsLIhSePK5/NheXkZlmXxgBXNhZ3FYhHHjh3DwsLCqB+HXMzMzECSJDQanWNljsVEqBJQ1B9M8jmxa9hMJtOX5yQ2JIloAJwKeK1h4X4rAN55ZJtjrjTuBEHA8vIyIpHIxOfmZbNZRKNR5kaOAUmSsLCw0HXT8Ly9nXLd6Nh4t7dTTvrXA42XYrGIer2+vZ4xLNwrudczmqZxoQ2Nvfn5eRw8eBD5fH6is7Ety0I6ncbs7CxzI8dAMplEMBjsatu2LAo4k3xQz3SiKArW1tYm+uuhn/gTkoj6zqmAv70ZAB9VBESV7d9+mLdE+0kgEMDy8jIMw5jYW2HlchmiKOLs2bO8JTQmUqkUPB4PdF3v+LEn4xI8IpCpWbhbcm9ISpLU2rJONC4cF9psLuiLKAJiavu3U6ZpcqENjT0esDblcjmEQiGcP39+28JO2ntkWd7Rtu1uGpKqqqJcLqNQKOzqGamJDUki6rvdLLSRZZkNSdo3FhcXsbi4OJGj21tzI2dnZ0f9ONSleDze9bZtryTgVMIu4juPRXE7JY2bXC7X9ma3Xc84jWvbn+OsZ2g/2JqNPYkHrJVKBaZp4syZM4hEIqN+HOrS9PR012PbZzdvSN7Imyho7jWK1+tFvV6f6AZ9P7EhSUR951TA3+pQwDMAnvYbURRx+vRphEIh5PP5UT/O0FiWhUwmg/n5eRw/fpyjTWNEFEXMz89D1/WuGod2GHw3OZKqqiKfz3d9Y4FolOwbvTtdaOPxeNiQpH1jcXERS0tLE3fAaudGHj16FIuLi6N+HOpBIpHoOhc7poo4EBZhAXh9w72BKQgCBEHAxsZGn550srEhSUR9tdsCPhKJcBSC9pVgMIhTp06hXq93NQa7H2QyGUQiEZw/f54LqsbQ9PQ0PB5PV9u27TGnt3MmKvXOOZKapvFWAY2FSqWCWq3msNCmcz2jKAoCgcBAn5FoWARBwOnTpxEOhyfme7idGzkzM4Pl5WUero4ZWZYxPz/f9UKmcz3mSK6vr8MwOn8suWNDkoj6yqmA1w0L90qdR7a50Ib2owMHDmBhYQHZbHbf3ywoFovMjRxz0WgUkUikq1sFKb+I2YAA0wIup90Lc1EUYVkWcyRpLDjlYdfNB/WM28RHNBrlIi/aVwKBAE6dOjUx2djZbBbBYJC5kWMslUpBkqSeDlgvpQ0Ypnut7vP5UK1WJ6Y5P0hsSBJRXzkV8HdLJgwLCHmAuLr9hJELbWg/s0e3A4HAvg7BLpfL0DQNp0+fxtzc3Kgfh3ZIFEUsLCx0P7bdQxg8cyRpXJRKJZimuW1L9t1is54JOtQzAGAYBmKx2DAek2iolpaWsLCwsO9Ht/P5PERRxPnz57mcaowlEomuc7GPREUEPEC5DryTd9+gLcsycyT7hA1JIuorpwL+wbi21HbkodFocKEN7WvhcBinTp2CpmldndSOm2q1ikqlguXlZRw7doyjTWMulUrB6/V2FTNwfqo5lv/ahgGzwxtUn8+HYrHY1ZsDolGyGxKP2jqu3e77HBfa0H42CdnYxWIRhmHg/PnzWFhYGPXj0C5IkoT5+fmubvSKgtBabtMpF1sQBEiSxBzJPmBDkoj6qlMBv+Qw3qRpGhfa0L538OBBzM/PI5PJ7KubBbVaDcViEcePH8fJkyfZjNwHotEootFoV43DYzERqgTkNQu3C+63ChRFgaZpHNumPc1ezNUuP/L2lgPWdur1Ohfa0L4WCoX2bTa2Pelx5swZHDx4cNSPQ32QSqVaNxo7OWcfsHa5qG9jY2NfXjIYJjYkiahvuing3fKWuNCG9jtJknD69Gn4/X4Ui8VRP05f6LqOfD6Po0eP4syZM20PJGj8CIKA+fl51Ov1js1zjyhgObE5tr3R+VYB0Fx8RLRXVatVVKvVHS200TQNiqLwgJX2tf2Yjc1Jj/2pl7HtM0kJAoDbRRPZmvsBq6qqzJHsA75rIKK+cSrgG6aF20X3Ar7RaDBviSZCNBrFiRMnUK1Wx/5UtV6vI5vN4uDBgzh37hwXOOwz9th2N6NOveRIKoqC1dVVmKZ7sU80KsViEbqub6tnDNPCnQ71DBfa0CTYb9nYtVoNhUKBkx77kCiKXY9th70CDkWa39s7HbDKsgzDMDjxsUtsSBJR3zgV8PfLFhom4JOBKT/zloiOHDmCubm5sb5Z0Gg0kE6nsbi4iMcffxyyLI/6kajPIpEIYrFYV7cK7Ibk9ZyJou7+Oa2qKsrl8r65JUz7j52H/WhTcaVsoW4CqgSk2tQzAA9YaXLsl2xsTnrsf9PT0/B4PF3mYnd/wCrLMtbW1nb9fJOMX21E1DelUgmWZW0r4G8Vmt/Ql0IiRC60IYIkSTh79izC4TDW19fH7qaYYRhIp9OYm5vDhQsX2o410vizx7YbjUbHxnlcFbEYEmEBuNThVoG9LIe3CmivKhQKbW9I3bTrmXD7esayLAiCwHFtmhh2NnY6nUaj0Rj14/SsXq8jk8ng4MGDOH/+PG8271OxWKzrsW37gPXyhoGG2fmANZvNdnX7ktpjQ5KI+sZpZONWF3lLXq+XDUmaKNFoFE899RSi0Sg2NjbGpilpmiY2NjaQSqVw8eJFqKo66keiAUqlUq1FNJ2cS9q3CtzflAqCAEEQkE6n+/KMRP3U3UKb9vVMvV7nAStNFEmScOHCBSwsLCCdTo/VTclGo4FMJsNJjwkgiiIWFhag63rHA9YDYRFhL1AzgLeznXMka7UaD1h3gQ1JIuoLtwK+U0NS13WEw2EutKGJE4/H8dRTTyEWi43FTUnLsrC+vo54PI6LFy/C7/eP+pFowMLhMOLxeE+3Cl7fMGB2KPhVVcX6+joMo/NIFNEwaZqGcrm8o4U2uq5zoQ1NHJ/PhyeffBKLi4tIp9NjsXnbnvSYmZnBE088wUmPCZBKpboa2xYFAWeTzeb0qx3GtiVJgmVZbEjuAhuSRNQXTgW8aVlbNmy3H4NoNBqIx+MDf0aivSgajeLpp59GIpHY0w0auxkZjUbx5JNP8gbQhOhlbPtoVIRPBkp14J1851sFlUoF+Xy+n49LtGtOedimtXVBX/t6RtM0RCIR3rSiiaMoCi5evIiDBw/u+RFWe9JjamoKTz75JCc9JkQ0GkU0GkWlUun4sQ9yJDvHEMiyjNXV1bHNhB81NiSJqC9KpVLbAn6tYqFmAB4RmAlwoQ1RO+FwGO95z3swNTWFjY2NPdeUtCwL6XQawWAQFy9eRDQaHfUj0RBNTU1BURTUajXXj5NEAWeT3YXBezweNBoN3iqgPadUKsEwjG1ZchtVC9UGIIvAbJt6BmjeuuIBK00qr9eLJ554AocOHUIul+v4M2MULMvCxsYG4vE4nnzySU56TBB723Y3Y9unkxJEAbhXtrBecT9g9fl8KBQKqFar/XzcicGGJBH1RbFYbLuR0h7XXgyJkMT2C20kSWJDkiZeMBjE008/jenpaayvr++ZcHhd17G2toZAIIAnnngCiURi1I9EQxYKhZBIJHoa2+7UkLRzJDc2NvryjET9YudhP7rUxh7XXgyKkNvUM5ZlwbIs1jM00TweDy5cuIAjR44gn8/vqaakXc9EIhFcvHiRX6sTKJVKtRbruQl4BByNNltlr3VY1Gcf2PKAdWfYkCSivnAq4G+1xrWZt0TUSSAQwFNPPYW5ubmRb6y0LAu5XA75fB4HDhzAs88+i+np6ZE9D42OPbZtGEbHWwV27tLNgomc1nlse2NjY6yWIND+l81md7zQxuPxsJ6hiSfLMh5//HEcP358T9wcsywL+XweuVwOS0tLeO9734tYLDbSZ6LRsMe2+3nAKooicyR3gQ1JIuqLbDbbdinNrULzm7jbhu1QKMQwaaJNfr8fTz31FObn50fWrKnX61hbW4Msy3jiiSfw1FNP8SbBhEulUq1tkm4iioBDm9/vX+9QxPt8PlSrVeRyuX49JtGu6LqOYrHoutBmyeWA1ev18nslEZrLPs6fP4+TJ0+iWCx21QAaBLuekSQJTzzxBJ5++ml+jU4wQRCwsLCAer3e8YD1/FTzgPVK2oBuuH+soijMkdwhNiSJaNecCnjLsjpupKzX68xbInqEqqp48sknsbS0NNSNlfYtgmw2i8XFRXzTN30TDh8+DFFkuTDpAoEAkslkb7cKOow5ybIMwzDYkKQ9w2mhjWVZrQNWt4kPLrQhekAURZw5cwanT59GtVpFqVQa2mtbloVCoYBMJoP5+Xk8++yzOHLkCOsZwtTUFLxeb8fFSwtBAXFVgG4Cb2bc6xlVVVEsFof6Ob5f8CuSiHbNaaFNpmahXAckAZgPbf92Y58ihcPhoTwn0TgZ9sZK+xaBKIq4cOECnn76aX5tUosgCJibm+tqbNtuSF7aMNAw3T9WkiSsra317TmJdqNUKqHRaGxrKuY0C0UdEAVgoU09A/CAlagdURSxvLyM06dPo1aroVgsDvw1G40G1tfXAQCPPfYYnnnmGS7jo5ZIJIJ4PN7xgFUQBJzrclGfoijQNI1j2zvAhiQR7VqxWIRhGNsKePt25HxQhMdhoY0sy8xbInKwdWNlsVjE6uoqSqUSTNM9m68XlmWhWCwim81ifn4e73//+3H06NFtC6qIUqlUa8zazaGIiKAHqDaAG/nO2ykzmczAG+5E3SgWi62FS1vZedhzAQFeqf1CG0EQOApK1IYgCDh58iTOnj2Ler2O9fV1VKvVvo+3WpaFUqmEdDqN6elpPPvsszhx4gTrGXqInYvdaDQ652LbB6zp7hb1ZTKZvj3npGBDkoh2zT7tdCrgnca17YU2LOCJnHk8Hly8eBHvf//7cezYMQDAxsYGNjY2UKvVdlXQ27cILMvC+fPneYuAXAUCAcTjcVQqFdePEwUBJ+PNIv5KF2NOtVqNY9u0J2QymbYj1w/qmfaNDR6wErkTBAHHjx9vZWRrmoa1tTVks9m+ZGUbhoH19XUYhoFz587hfe97H28skyN7bLtTJNLJuAQBwErZQrbmfsCqKArW1tb6emlgEjDkhIh2rXMB79yQjMViXGhD1IEoipiensb09DSWl5exsrKCO3fuYGNjA/l8vrWp3im7zLIsNBoN6LoOXddbxb8gCJiensbZs2dZuFNXZmZmcPfu3daNMCfLCQkvrhp4I23gO444/3mSJME0TWSzWW5xp5Gq1+soFAo7WmijaRoX2hB1YC8UmZ+fR7FYxMrKCm7fvo1cLgfTNOHz+RAIBDrmPFqWBcMwHqppTNNEKpXC2bNnkUwmh/RPROMqHA4jFAqhVCpBURTHjwt4BBwMi7hRMPFG2sD75p0/N1VVRblcRqFQ4OF+D9iQJKJdsQv4dt/MOzUk6/U6EonEQJ+PaL9RVRUHDx7EgQMHkM/ncf/+fdy5c6dV0Pv9fsiy3CrU7ZNaWZbh9XoRi8UQjUYRDocRCASQSCS4hIG6Fo/H4fF4UK/XXQ+TlhPNm2TXsiY0w4LSZszV5vF4sLq6ihMnTrg2OYkGyc7DbtdUvL1Zz7gttEkmk/B4PAN9RqL9QBAEhMNhhMNhHDlyBOl0Gvfu3cPdu3exsbEBQRAQDAahqiqA5vsFu6axx2wlSYLX623d3I9Go1hcXOQlB+qKIAiYnZ3F5cuXO37sqYS02ZA08b5554/zer2txZBsSHaP70CIaFfsjZSPFvA5zUROsyAAWHJZaMPbBEQ7IwgCotEootEojh07hvX1ddy7dw/37t2DpmlQFAWpVArRaBSBQADBYBCBQIDFOu1KNBpFMBhEtVp1/Vya9je3U2ZqFt7OmjiTdM7wUlUV+XwetVoNPp9vEI9N1FGxWES9Xt92QFPSLaRrzZrF6YZko9HgLXOiHZAkCalUCqlUCqdOncLq6ireffddrK2toVAoQBAEeDweeL1eJJNJxGIxhEKhVk2jqioPsmhHEokEBEGAYRiuOaPLCQmfu1HHlYzhOh1i50im02kcOnRoUI+977AhSUS74rSR0r5NMBsQoMjtF9pIksSGJFEfyLKM2dlZzM7OYnl5GYZhwO/3dxx7IuqVKIqYmZnBlStXEIlEHD9OEAScikv46r0G3kgbHRuS6XQauVyODUkamU552NN+Ab429YxlWbAsi/UM0S4pioKlpSUsLi6iWCxiY2MDXq8XwWDQNZaGaCfi8XhrUZ9b/u+xmAhZADI1C6sVCzMB5wa4oijY2Njo2OSkB/hOhYh2pVAoANhewHfKW9J1nXlLRAPg8/kQDAbZjKSBSSaTEEWxY3D7cqL5OXilw3ZK+8/iYhsapUwm03bk+lah+fnrFD9jH8qyniHqD3uk+/Dhw1hYWEA0GmUzkvrO6/UilUqhWq26fpwiCTgaa37/f6NDPaOqKiqVSuv9MXXGdytEtCvZbLZtkXC7w0ZKe8yb46NEROMlHo9DVdWORbydI3mzYKJcd98G7/F4sLa2tqut8UQ71Wg0kM/nd7TQRtf11mIxIiIaH6lUCqZpdqw97HqmU0PSztjmAWv32JAkoh2r1+uOBfytDgHwXGhDRDSeVFVFMpns2JCMqSJmAgIsAG9mOt8qsHMkiYbNzsNuV890s9AmGAzygJWIaMzE43EoigJN01w/7lS82ZB8M2PAdGleCoIAURSRyWT6+pz7GRuSRLRj9kbKRzdsl+sW1qvOAfBcaENENN5SqRQMw+jbrQJVVaFpGm8V0EjYediPjmxXGxZWKnY9037igwesRETjKRQKIRwOdzxgPRQRoUpAqQ7cKbrH1dg5kp1ibaiJDUki2rFisei60GbKJyDg2R78awf9siFJRDSeEokEvF4vdF13/bjlzVsF3eRIWpaFfD7ft2ck6pZTHrb9xjOuCgh72y+0AXjASkQ0jgRBwOzsbMdaRhYFnIjbB6zujUbmSPaGDUki2jGnjZQ3W/mR7b/FaJrW2ppHRETjJxwOIxgMdrxVcDIuQQBwr2whW3Mv4mVZxtraWh+fkqg72Wy27UbUW3n3eoYHrERE4y2RSECSJDQaDdePY47kYLAhSUQ75rTQptNGSnuhzaOj3kRENB5EUcTs7GzH3KWgV2j9LLiS6XyrgDmSNGz2Qpt2NcmtzRuSSyEesBIR7UexWAw+n6/rRX1vZQ00TPccSQDMkewSG5JEtCNuGylvdbghWa/XEYvFBvp8REQ0WIlEAqIowjDcbwucSnQ3tq0oCmq1Gm8V0FCVSqVWY/FRrQV9ER6wEhHtRx6PB9PT0x0PQ+eDAkJeQDOAd/KdcyTX19eZI9kFNiSJaEecCnitYWGl3Dw1OtAmAN6yLFiWhXA4PJTnJCKiwYjH493dKog3y8030u5LcCRJgmmabEjSUNl52I8utNENC3dL7jckecBKRDT+pqamYJqma40iCkJr23Y3i/qYI9kdNiSJaEeKxSLq9fq2Av520YQFIKoIiCjtF9rIssy8JSKiMacoCqampjo2JI/HJEgCkK5ZWKu4b+WWZRnr6+v9fEwiV8ViEZZlbcvDvlsyYVpAyNNcavMo+40rD1iJiMZbIpGAqqodb0l2myNpL/3jor7O2JAkoh0pFosQBGFbAd9pXNu+VcmGJBHR+EulUh1vFSiygKPRB7ck3aiqilwuxxxJGhrnPOzN25FhcVutA3ChDRHRfhEIBBCJRLrOkbyeM6E13HMkBUFgjmQX2JAkoh1x3EjZoSGp6zqCwWDbrCYiIhov8XgciqJ0XG7TulWQ6dyQrNVqvFVAQ9FdHvb2Wgdo1jM8YCUiGn+CIGB2dhb1et3146Z8AhKqAMNqLrdxwxzJ7rAhSUQ9azQayOVy7TdSdrHQJh6Pt71tQERE4yUcDiMUCnW8VWAvtnkzbcBkjiTtEXYetls9c9Bl4iMQCPCAlYhoH4jH45AkybUpKQjClgNW90ajqqool8soFot9fc79hg1JIupZqVRq3QzYqm4+CIB3KuC50IaIaP+wbxXouu76cYcjIhQJKNaBd4vuRTxzJGlYSqVS2zxsw7Rwp/hgZLsdHrASEe0fsVgMgUCg67HtbnIk6/U6D1g7YEOSiHrmVMDfLZowLCDoEADfaDQgSRKCweCwHpWIiAYskUhAFEU0Gg3Hj5FFASdidhHf+VZBNpvtOAZOtFv2zZVHm4r3yxbqJqBKQMrfvuHIA1Yiov1DlmVMT093zLA+FW+20G4XTJR09xxJAMyR7IANSSLqmVMBf3PLuHa7GwPMWyIi2n/i8Tj8fn/XY9vd5kjyVgENmnMedvNzdCksQmxTz9gHrKxniIj2j6mpKQBwzX2MqiLmggIsAFc61DPMkeyMDUki6plTAX+7iwD4QCDQNquJiIjGk8fjQSqV6nirYDnRLDvfyhhomMyRpNEyDAO5XK7DQhvnBX1er5cTH0RE+4i9qK9jPRNvvtft1JBkjmRnbEgSUU/cCvibXRTwzFsiItp/pqamYFkWLJeFNYshEUEPUDOAG3nmSNJo2Qtt3BqSTnnYuq7D7/dDVdWBPiMREQ2P3+9HLBbra46kruvI5/N9e8b9hg1JIuqJ00bKrQHwTg1JAMxbIiLahxKJBLxer2vuoygIOBnvrohnjiQNWrFYRL1e39aQNC0Lt1sLbZwnPnjASkS0vwiCgJmZGTQaDdcD1hNxCQKAlbKFbM35gFUQBAiCwBxJF2xIElFP7AL+0YU2nQLgDcOAIAjMWyIi2oeCwSAikQgqlYrrx3V7q8DOkeStAhqUUqkEy7K2NRXXKxaqDcAjAnMB54U2kUhkGI9JRERDlEgkIMuy66K+gEfAoUizldapnlEUBWtra64NzknGhiQR9cSpgO8UAK/rOhRFYd4SEdE+JAgCZmdnUa/XXT/Obkhez5nQDOZI0ug4L7Rp3nZZCImQxPYHrKIosp4hItqHotEoAoFAxwPWU62JD/cIGlVVUalUmCPpgA1JIupJJpOBLMvb/n43AfCqqsLn8w30+YiIaDTi8TgkSXK9VTDtFxBXBTQs4O2s+60C5kjSoBiGgWw223bJHhfaEBFNLkmSMDMz0zEyxj5gvZIxXG8/2jmSPGBtjw1JIupaNxsp3QLgE4kE85aIiPapeDwOv9/vGgYvCMKWse3Otwqy2Sx0Xe/rcxKVy+WOC20OhJzrGZ/PB7/fP9BnJCKi0UgmkwAA03SuU47FRMgikKlZWK04NyTt977ZbLa/D7lPsCFJRF1zKuBNy2oV8E4B8MxbIiLa32RZxvT0dMftlKfizfLzShe5S7VajbcKqO+cFtpYloVbxebn5YGIc0OSC22IiPavRCLRyrJ24pUEHIt2lyPp9XqZI+mADUki6ppTAb9esVAznAPg7dMljjcREe1vU1NTsCzLtei2b0jeLJgo150/Tpbl1s18on6ys7webSpmNQtFHRAFYCHY/m0SD1iJiPY3n8+HeDzeOUeyh0V95XIZpVKpb8+4X7AhSURdcyrg7duRiw4B8PZCG27YJiLa3+LxeMdbBTFVxGxAgAXgzYx7ES9JEjY2Nvr8lDTpcrkcRHH72yC7npkLCPBKPGAlIppUMzMzMAz3fMjl+IMcSdPl4xRFYY6kAzYkiahrTgX8zS4X2jBviYhofwsEAohGox3Htpd7uFWQyWSYI0l9Y5omstmse36kQ/wMF9oQEU2GeDwOj8eDer3u+DGHIiJUCSjXgdsF57xJ+zJPJpPp+3OOOzYkiagrdgHffiPlZt6SS0MyFosxb4mIaJ8TBAEzMzOuBTzQW0OyVqshn8/37RlpspVKJWiatuMN26qqIhAIDPQZiYhotKLRKILBoOsBqyQKONG6Jem+qI85ku2xIUlEXbEL+HYB8Lc7FPCGYSAajQ76EYmIaA9IJBKQZdm1KXkyLkEAcL9sIVtzLuKZI0n9ViqVWjcdH9VNQzIWi7WdFiEiov1DFEXMzMy4RtAAvR2wMkdyO/40JaKuOBXwmZqFYh2QBGC+TQC8aZoQBIHjTUREEyIWiyEQCLjeKgh4hFbTp9OtAuZIUj8Vi0VYlrVtaqOoW8jUmjdXlhwakqZp8oCViGhCJJNJiKLYyg9ux25IXs0aaJjMkewVG5JE1BWnAt7Oj5wLim0D4O2t3FxoQ0Q0GSRJwvT0dF9vFaTT6Y5j4ETdyOVykKTtGZH27chpvwCfvL2escfseMBKRDQZ7EV9bgesC0EBIS+gG8D1XOccyWw22/fnHGdsSBJRV5wKeLshedDhNoGd08S8JSKiyZFMJgGgw62C5s+NN9LuWyztHEneKqDdcl9o0zkP2+PxsCFJRDQhVFVFMpl0bUgKgoBT8e4OWJkjuR0bkkTUkWsBn2feEhERPSyRSLQaiU6OxSTIQjP6Y7XiXJwzR5L6pVwuo1ar7XihjaIobEgSEU2QVCoFw3A/OLUnPq5kOk98lEollMvlvj7jOGOHgIg6cirgLcvCzc0bBQcjzFsiIqImn8+HSCTi2pBUJAFHog9uSbqRJAnpdLqvz0iTp1gstm46PurBxMf2aRCg2ZCMRqNtp0WIiGh/isVi8Hg8rrExdkPyes6E1mCOZC/YkCSijorFIur1+rYCPqdZKOiAKABLoe3fTuyTJOZHEhFNFkEQkEqlOuY+9nKrgDmStFt2HvajUxvluoW1zVu6TjckDcNALBYb+DMSEdHeEYlE4Pf7XQ9YU34RSZ8Aw2out3HCHMnt2JAkoo5KpRJM09xWwLcW2gSEtgttmLdERDS57LgOw3AuzlsNybQBs4scyXw+3/fnpMnhlId9e7OeSfoEBL3tF9pYlsV6hohowkiShKmpqR4W9TlnZwPMkXwUG5JE1FE2m22/0GYzP/JgxHm8iXlLRESTKRaLdcyRPBQR4ZWAUh24V3LPkWw0Ghxzoh1zy8PutKDPnhJhPUNENHkSiUTrYMrJyc3FNm43JIHm2DZzJB8Y64bkb//2b+MDH/gAYrEYAoEAzp8/j3/5L/8lx3mI+mg3BTzzloiIJpeiKIjH464NSVkUcGwzR/LNDmPbzJGk3bDzsNvWM/nNPGyXesbr9bIhSUQ0gWKxGLxeL3Rdd/yYk/Hmz49bBRPVDjmSmqbxgHXT2DYk/+7f/bv4+Mc/jq9+9at46qmn8G3f9m24ffs2fuqnfgrf8i3f4rqanYi6V6lUnAv4Dg3JRqPBvCUiogk2NTWFRqPh+jEnNm8VdGpIKoqCjY2Njn8eUTulUqnVWHxUq55xWNCn6zoikUjbZThERLS/hUIhBAIB1x5TXBWR8gswLeBtl1uSoijCsiw2JDeNZUPy937v9/ALv/ALCAaDeP755/H5z38en/nMZ/D222/j7Nmz+MpXvoJ//I//8agfk2hfsDdSPlrAZ2sm8poFAcBim4akZVkQBIG3CYiIJlgsFoMkSa5NxK1jTm7jUPb4N4t42olisQgA2/Kwqw0Lq62FNu0nOnjASkQ0uURRxPT0tOsNSQA4EbMPWDvnSK6urjJHEmPakPzZn/1ZAMA//If/EBcuXGj9/WQyiX//7/89AODf/tt/y+Bzoj5wKuBbC22CApQ2C23q9TpkWeaGbSKiCRaNRuHz+TrnSIpAUQfulTvnSLK+o53I5XKtDadb3dqsZxKqgJDLQhvWM0REkysejwNAhxzJ7iJoVFVFqVRCpVLp3wOOqbFrSN69excvvPACAOATn/jEtl9/9tlnsbi4CE3T8LnPfW7Yj0e07+Tz+bYFfGuhjcNtAi60ISIij8eDZDLpOubkEQUc2cyRvOpSxAuCAFEUsbGx0ffnpP3NzsNWFGXbrz1Y0OccPyPLMusZIqIJFovFoCiK6wGrPfFxs2CixhzJroxdQ/KVV14B0OxQHzp0qO3HXLx48aGPJaKdsSwLmUymfQHfxUKbcDgMWZYH+oxERLS3JZNJmKbZ1XbKbm4VpNNp5khST9zzsJufcwc6LLThDUkioskVCAQQCoVcG5IJn4gpX3c5kgC49wRj2JC8ceMGAGBpacnxYxYXFx/62HY0TUOhUHjoLyJ6mNtGylsdAuDr9XrrajsREU2uWCwGj8eDer3u+DEPGpLujUtVVVGtVjm2TT1xysMGttQzLg3JUCjU9vcSEdFkEAShuxzJVi62e44kNY1dQ9LOswsEAo4fY49UuDUZP/WpTyESibT+spuYRPSA00bKXM1EbnOhzVLIeaENbxMQEVEkEukqR9IjAgXdwv0uciQ55kS9KJVKME2z7UKblc3PN6cImnq9zoU2RESEWCwGURRhms7NxhOx7nIkqWnsGpL98slPfhL5fL711507d0b9SER7TrFYbFvA2+Pas0EBirw9X7LRaECSJOYtERERJElCKpWCpmmOH+OVesuRTKfTfX9O2r9yudy2WgYAbhdMWADiqoCwsr2eAZqHrOFweMBPSEREe10vOZI38iY0lxxJahq7hqR946pcLjt+TKlUAgDX4kFRFITD4Yf+IqKH5fP5tgX8g/EmLrQhIqLOEolEX3MkNzY2mCNJXbEsC9ls1iE/slnPOOVH8oCViIhsqqoiEom4NiSn/CISqgDDAt7OcWy7k7FrSB48eBAAXG802r9mfywR9c5eaONWwDNviYiIuhGLxeD1el2zl07EHuQudcqRrNVqzP+mrlQqFVSr1bYL+rrJj+RCGyIiAh7kSLplYgMPDljdJj6oaewako8//jgAIJ1OOy6tefHFFwEAFy5cGNpzEe03bgX8zTwX2hARUfdCoRACgYDrrYIjURGyAOQ0C6sV9xzJer3OHEnqittCm242bAcCgba1EBERTR47R9IwnJuNJ+LMkezW2DUkFxYW8OSTTwIAfvM3f3Pbr3/lK1/BnTt3oCgKPvKRjwz78Yj2jUKh0LaAz2sWsh0W2liWxdsERETUIopiTzmSbkU8cySpF8ViEZZlbYug0RoW7pfshTbOB6yxWAyC0D5fkoiIJks0Gu24qM++IflO3oRmMEfSzdg1JAHgp3/6pwEAP/dzP4eXX3659ffT6TR+9Ed/FADwYz/2Y4hEIiN5PqL9wHmhTfNN4kxAgNpmoY1hGJBlmQ1JIiJ6SCKRAADXcewTXY45KYqCjY0N1xsKREBzoU27huLtYnOhTVQREFXbvyUyTZPvJ4iIqEVRFMTjcVSrVcePmfIJiG/mSF5njqSrsWxIfud3fid+/Md/HKVSCe95z3vw7d/+7fjLf/kv4+jRo3j99dfxvve9D//sn/2zUT8m0VjLZrOQpO1La1rj2h3ylhgAT0REW0WjUXi9Xtdbkg8W23TOkaxWq8jn831/Tto/TNNEOp1uHz/TYaGNYRgQRZH1DBERPWRqasr1QFQQBI5td2ksG5IA8Au/8Av4L//lv+CZZ57Bc889h8997nNYWFjAz/3cz+GP//iP4fP5Rv2IRGPLMAxkMhnXAv5gpP2GbU3TEAwGudCGiIgeEgwGEQqFXG8VHImKkAQgq1lYrzo3JD0eD+r1OhuS5KpUKqFWq7nnYXOhDRER9SAWi0GWZTQaDcePORmzD1jZkHQjj/oBduPjH/84Pv7xj4/6MYj2HbuA9/v9236t00ZK5i0REVE79nZKt+xHZTNH8q2siSsZAyl/+581giBAEASk02kcOnRoUI9MY85eaNNu7PrWZgSN04I+Xdfh8/l4yYGIiB4SjUZbkxpOh1Z2BM07ORO6YcEr8b1xO2N7Q5KIBqdYLKJer2+75VjQLGRqmwttHBqSABAOhwf8hERENI7sAyvTdM5UOhGzcyTdc5dUVWWOJLkqFAqwLGvbIalmWLjbYaGNruuIx+M8YCUioofIsoypqSnXxTbTfgFRRUCDOZKu2JAkom0KhQIAbCvC7YU20wEBvjYLbRqNBkRR5HgTERG1FYvFoCiKaxF/Iv5gzKlTjmSlUmn9zCJ6VCaTgSxvHwi7s7nQJuxtvmFshwttiIjISTKZhGk6510LgoCTzJHsiA3JCWEYBl588UWUy+VRPwqNgXQ63baAv9lhXJsLbYiIyI3P50MkEnFtSB7bzJHM1CxsdJEjmcvlBvCkNO4ajQZyuVzH/Mh2NyBN0+RCGyIichSNRlt1iBN7Ud/VLBuSTtiQnBCNRgPpdBpra2ujfhTa4+wlAe2W0jzIj2y/0EbXdfj9fqiqOtBnJCKi8WTnSLoV8Ios4FCk860CO0cyk8n0/Tlp/BWLRWia1rYhadczB1zyIz0eDyc+iIiorUgkAr/f7z7xsRlBc20zR5K2Y0Nygui6jvX19VE/Bu1xhULBsYBv3ShwKeCZt0RERG6i0ShEUXTNfjzZGtvuLkfSLZOSJlOhUECj0djxxIeqqm2X+xEREUmShFQq5dqQnAkIiCgCGiZwI886pR02JCeIZVlYX193vZVAVCwW2xbwRd1CutY82TngUMBblsW8JSIichWLxaCqaodbBc2fM53GnJgjSU6c8rB1w8K9UueGZCwWgyjyrRIREbWXSCQAwDVH0q5nmCPZHn/KTphqtYpsNjvqx6A9LJ/Pt8bgtrqZ31xo42+/0MYwDOYtERFRR4qiIB6Pu+dIxiSIArBRtbBRdb5V4PF4oOs6cyTpIZZlYWNjAx6PZ9uvvVs0YVhAyAPE1fYTHYZhIBqNDvgpiYhonNk5krquO37MyS2L+mg7NiQnTLVaZdYSObIL+Hb5kd0utGHeEhERdTI1NYVGo+H466ostH7eXGWOJPVI0zSUSqX28TOt/EipbcSMZVkQBIEHrERE5CoUCiEQCKBarTp+jN2QvJYzUTeZI/koNiQnjCiKWFlZcbxWTJOtVquhXC67NyQjzgttfD4ffD7fQJ+RiIjGXywWgyRJrk3JbnMkFUXB+vo6cySpxV5os9MDVi60ISKiTkRRxPT0tOsNydmAgLAXqDNHsi02JCeMz+dDPp9HuVwe9aPQHuS2kbK10MalgOdCGyIi6kY0GoXP5+twq6C73CU7R7JYLPb1GWl8FQoFGIbRdqHNrS4akoqiIBAIDPQZiYho/MXjcQAdciQ5tu2IDckJo6oqNE1jjiS1VSgUYFkWJOnhW5ClLhbamKbJhTZERNQVj8eDZDLZMUdSALBetZB2yZH0er2o1+vMkaQWOw/7UXXTwrvFzZFtl4ZkNBrdVgsRERE9KhaLQVGUDov6mj9P3CJoJhUbkhPGLs42NjZG/CS0F+VyubYFvD3eNO0X4Pds/3XTNCEIAsebiIioa8lkEqZpOt4q8MkCDkbsbdvODUn75xZzJAl4kIfdbtrj7uZCm4AHSPraT3Q0Gg3EYrFBPyYREe0DgUAAoVDItSF5avOG5NtZEw3mSD6EDckJpKoqVldXYRjs0NMDpmkik8k45C01P1fcbhN4vV4GwBMRUddisRg8Hg/q9brjx9i3CjqNOTFHkmzlchnVatV1oc3BsMiFNkREtGuCIHTMkZwLCgh5AJ05ktuwITmB7KwljjbRVuVyGZVKxT0/MuLckPT5fPD7/QN9RiIi2j8ikQj8fr/rrQI7R7LTmJOqqiiXy8yRJBSLxdZB6aPseuZAuP04dr1ehyzLnPggIqKuxWIxiKLoeCjKHElnbEhOIPs2AnMkaSu3Av5BAHz7Al7TtNY3YiIiom5IkoSpqSlomub4Mcc3cyRXKxayNfccSV3Xkc/nB/CkNE4KhQJM02xbk7RuSLocsHLig4iIetFVjmTczpHkDcmt7Rov9wAApvZJREFU2D2YQIIgQJIkrK6ujvpRaA+xF9o8WsCXdAvrVfeFNpZlIRqNDvoRiYhon0kkEq45kn6P0PrZ86ZLES8IAgRBYI4kIZvNtl1I09iy0MZtw3YkEmm7nZuIiKgdVVURjUY7THxs5kjmDOZIbsGG5IS4m6vi1fSD/+3z+ZDJZFy/aGiyZDKZtgW4fTsy5RcQcFhoA4DjTURE1LNYLNa63ejkRJdj24qiYG1tzbG5SfufYRjIZrPtF9qUTDQswC8DU1xoQ0REfSIIAlKplGsm9nxQQMADaMaD2/rEhuREyFfq+JZ//VX8ylUJBb1ZpPt8PtRqNd4kIADNAjyXy+1qoQ0bkkRE1KtQKIRAINDVrYJuFttUKhXmSE6wUqmEWq3mmod9wGWhDcADViIi6p0dX+a0OFgUhK4X9U0SNiQnQMTvwfHpZhbOtc1oJVEUYVkWG5IEoEMBX+g83sSFNkREtBOiKGJ6erqrHMmVioWcS46koijQNI1L+yZYoVBAvV6Hx+PZ9msP8iPb52E3Gg0utCEioh2JRqOtS19OTjJHchs2JCfEkweiAIBr+QdjTF6vFysrKxxtIhQKBTQajfYFfJ4LbYiIaHDi8TgAONYjAY+AxdBmjmS2c44kl/ZNrkKhAABtb0De6uKAlRMfRES0E4qiIB6Po1qtOn6MHUHzdtaAwRYMADYkJ8bFA808nLdzD/6ez+dDsVhsFW80uZwK+HKdC22IiGiwotEovF6v6y3Jk13mSHq9XuZITrB0Ot02D7thWrhdfDCy3Y6maQiFQm0PZ4mIiDqZmppyHNkGgMWQiIAHqBnA3QpbcQAbkhPDviF5t9xsMgHNor1er3NsmxwLePs2wZRPQNDLhTZERNR/wWAQwWDQdczpRJc5kqqqolwuo1Qq9fUZae/TdR2FQqFt/My9komGCfjk5pK+dur1OhKJxKAfk4iI9qloNApJktBoNNr+uigIOL6ZI/lOqf304aRhQ3JCTIUUpFQLFppXhIEHo03r6+ujfTgaKU3THAt4LrQhIqJBEwQBU1NT7pu2N3Mk75ct5DXn24+KokDXdeZITqBisQhN0xwW9D24HSlyoQ0REQ1AJBKBqqpd5Ui+U2QrDmBDcqIcCTeLratb8pdUVcX6+rrrinra3+wC3m0j5cEIF9oQEdHgdMqRDHoFLGzmSF7NOt+SFAQBlmUxR3ICFQoFGIbhOvHhlB9pL7QJBoMDfUYiItq/vF4v4vG4+8RHrPlz6GZJgmEyXoYNyQlyNNL8v1vzl3w+H6rVKgv3CWYX8JK0/dr4gwK+/ZVyXde50IaIiHatlxzJTmPbiqIwR3IC5fN5AO0X2tgHrAdc6hlFUXhDkoiIdiWZTLrmSC6FRfhkoGYKuJZ2rnkmBbsIE8S+IXmzYKLWaP7/sizDMAzmSE6wfD7fGt/fqlK3sFppfp443SgwTZMLbYiIaNeCwSACgUCHWwXd50iWSiWUy+W+PiPtXZZlIZ1Otx3XNkwLd4qdN2wHg8G2v5+IiKhbveRIvna/MsxH25PYkJwgcaX5l2kB13IPxrY9Hg9WVlZ4k2ACuRXw9u3IJBfaEBHRgAmCgFQq5Z4juZm7dK9koaAzR5IeqFarKJfL7RfalC3oJqBKwHSAC22IiGhwotEoVFV1nfj4pgUZH5nT8dRiYIhPtjexITlhjkWb//fRse18Po9KhR36SeNWwG8NgG+HC22IiKifYrEYAOccyZBXwHyw2VC66nJL0r7xzziayeG20ObW5oK+JS60ISKiAfN6vYjFYq4TH09My/jATB0HYtvfg08aNiQnzLHIZiG/JRDe7uBzbHvyFAqFVm7So27mm58jbuNNXGhDRET9YudIut2StLdTujUkgeb0B3MkJ0ehUIBpmm3zsFsL+hzqGTtHmw1JIiLqh6mpKceRbXoYG5IT5tjmYpt3ciZ0o1mk2zcJNjY2RvVYNCLFYhGmabZdSnOzw0ZKLrQhIqJ+CoVC8Pv97jmSdkMyazp+DMAcyUmTy+Uc65FWPRNpv9DGvlnJhiQREfWDnSPpttyGmthJmDBTPiCqCGhYwDv5B8W8qqpYW1vjF82EyWazbW8TbF1oc8ChgDcMgwttiIiob+wcSbfcJXuxzZ2iiVKHHElN05gjOQFM00Q6nW477WFaFm53sdAmFApxoQ0REfVFJBKBoiiuB6zUxIbkhBEEAcdjzf/sW8edVFVFuVxGPp8f1aPRkJmmiUwm07YAt28TTPkEhB0W2giCwNsERETUV/F4HIBzjmREETAb2B4/8yhRFGFZFhuSE6BcLqNWq7VtSN4vW9ANQJGAGZeFNvbnHRER0W4pitIxR5Ka2JCcQPa401tbCnmPx4N6vc4cyQlSKpUcC/jruebnxqEIF9oQEdHwRKNReDyevuRIer1erK6uMkdyn7PzsNsesG7mYS+FnBfaWJaFcDg88OckIqLJwRzJ7rAhOWEsy2qNO72dM9EwH+RISpKEtbW1UT4eDZFbAW+P8x+Jth/X5kIbIiIahGAwiEAg0JccSZ/PxxzJCVAoFGBZVisTfasb9kIbhwNWwzAgyzKCweBAn5GIiCZLNBqFKIqMxOuADckJ4fF4oCgKGo0G5oICgh5ANx6M5gLNwj2dTvNq8YQoFosAsK2Atyyr1ZA87HJDkgttiIio30RRxNTUlGuO5MnN6JnbBRPleuccyWw22/fnpL0jk8lAluW2v9Y6YOVCGyIiGqJoNApVVdlb6YDdhAkhiiISiQQ0TYMoCDi+eUvyrUdyJGu1Gse2J0Q6nW670CZTs5DXLIgCcMAhAJ4LbYiIaFA65UhGVRHTfgEWHo6feZR94Ma6Zv9qNBrI5XJt42fqpoXbmwfvh6POB6zBYJALbYiIqK+YI9kdNiQnSDQahWk2CzN73OnNLeNOkiTBsiwW7hOgXq87FvD2bYLFkAivxIU2REQ0XIPIkbTrH9pfisUiNE1rW8/cKZpoWEDQ01zS14690KbduDcREdFuMEeyMzYkJ0g4HIYkSTAMAyc2x53ezhowt9xA8Hq9WFlZYQD8PlcsFqHrumtD0m1cm+NNREQ0KKFQqPscyUznHMlyudyKKaH9pVAooNFotB3Zfidn1zNS24YjF9oQEdEgMUeyMzYkJ0g4HIbX64WmaVgKi1AloNponiDbfD4fisUiCoXCCJ+UBq1YLKJerzsU8M1vmG4NSS60ISKiQekqRzLe/Bl1s2Ci4pIj6fV6W1MBtP/Y9Wq7hqN9wHrIZaGNJEk8YCUiooGIRCLMkeyADckJoigKQqHQthzJN7fcLrALd45t72+FQgGCIGwr4A3Two1W3pLzhm0utCEiokGKx+OtG2xtf10VkdrMkXw71zlHMp1OD+IxaYQsy8LGxgY8Hk/bX38nv3nA6pIfqSgKN2wTEdFAqKqKaDTKhqQLdhQmiCAISCaTqNfrAIATm7cLtgbC202qjY2NkTwjDZ5bAX+vbEE3AFUCZgPt85S40IaIiAYtEonA6/W65kieiHU3tq2qKtbX1zkytc9omoZSqdQ2fqZct7BSbjazD7ts2A4EAm1/PxERUT8wR9IdG5ITxs7JsSxrSyFvPHQDQVVVrK2ttRqXtL+4FfDXN2+ZHIqIENuMP3GhDRERDUM4HIbf73e9VWCPbb/ZYbGNqqool8uMo9ln7IU27TZk39gc157yCQh5udCGiIhGgzmS7tiQnDDhcBiyLKNer+NgRIRXBEp14F7pQUPS5/OhWq0im82O8ElpUAqFguNGSjtv6YjLuLbX6+V4ExERDVQ3OZL2YpubBRPVhnOOpMfjQb1eZ12zzxQKBRiG0T4PO++eh23jQhsiIhqkaDQKVVVd65lJxobkhAmFQq0vCFkUcGQzV+fqlrFtWZZhGAZzJPepYrHYCnJ/1Dtbbki2o+s6VFVFIBAY6DMSERHF43EAcMyRTPpEJH0CTAu4lnXPkRRFkTmS+0w+n3e83WjfkHTKw240GhBFkRMfREQ0UKqqIhKJMEfSARuSE0aWZcRisVaH/mT8wdj2Vh6PBysrK45vAmh85XK5tgtpag0Ldzdvyh5xaUjG43EutCEiooGLRqOt241OWvEz2e5yJJnjtD/Yedjtpj0sy8L13GZD0qWe8Xq9bEgSEdHApVIpxuE5YFdhAsXjcZhms1A7vqWQ39p89Pl8yOfzKJfLI3lGGgzLspBOp9vmLd0smLAAxFUBUbX9twYutCEiomHpd45ktVpFPp/v6zPSaFQqFVSr1bYNyUzNQkG3IArAgbBzQ5ILbYiIaBgikQhzJB2wITmB7Lwc0zRxJCpCFoCcZmGt8vBiG03TOLa9z5RKJVQqFYf8SPe8JS60ISKiYRJFEclksqscyRt5E5pLjqQsy2g0GsyR3CcKhULrluOj7DzsxZAIr9R+pFvXdSQSCS60ISKigYtGo1AUhTmSbbAhOYHC4XDrC8IrCTjcJkfSLtCYt7S/5PN56LreviHZ5XgTF9oQEdGw2FMdThEyUz4BcVWAYQHXcs5j24IgQJIkbGxsDOpRaYjy+TxM02wbIWM3JN0W2liWxYU2REQ0FMyRdMaG5ATy+/3w+XzQdR3AlrHtzMOFvKqqWF1d5dXifSSXy8GyrLY3At7pEADPhTZERDRsnXIkBUHACXts22WxDdCsazY2Nlr1D42v9fX1ttu1gQcL+pwakvZiPx6wEhHRMAiCwBxJB2xITiBBEB4agToR235DEmjmSJbLZeRyuWE/Ig2AZVlYW1trO96Uq5nI1CwIAA665C3FYjEutCEioqHpKkcy1n5B36NUVUWtVmNdM+Y0TUMul4Oqqtt+zTAt3CjYNyTbH7BqmsaFNkRENFTRaBSiKLZ2eVATOwsTKhqNwrIsWJaFozEJogBsVC2kqw++QOy8JeZI7g/VahWlUqltAW/fjpwPClDl9nlKhmEgFosN9BmJiIi2kiSp6xzJd3ImdMM9R9IwDDYkx1w+n4emaW3rmXtlC7oBqBIwG3TOj/T7/W1/PxER0SDYOZIc234YG5ITKhwOtwpznyy0thBezT5oSAqCAFmWsbq6OqrHpD6yC/j2C23cx7XthTYcbyIiomFLJBKuOZLTfgFRRUDDAq675EgCzabk2traIB6ThiSXy7XGrh9lL+g7FBEhOiys0XUd8XicC22IiGhomCPZHhuSE2rrYhtgy9h2ZvvYdiaTQbVaHfozUn/lcjmXAHj3vCV7oQ3Hm4iIaNi6yZE8aedIdjG2nc1m+YZgjKXT6bbNSODBgr5DDuPatkgk0vfnIiIiciIIAqamptBoNEb9KHsKG5ITyuv1IhwOP2hIbo47PZojaectcWx7/G1sbMDj8Wz7+6Zl4UaHjZRcaENERKPSTY7kiVj7OuZRzJEcb/V6HZlMxnHcutOGbcMwIAgCD1iJiGjootEoADBHcgs2JCdYIpFodeiPxyQIAFbKFvLag5Eo+zZdOp0exSNSn9gB8O3GtVfKFqoNwCsB80EutCEior2llxzJax1yJCVJgmmayGazfX9OGrxCoYBarda2ntEaFu6Wmm/yjkSd6xlFUdiQJCKioYtGo62DUWpid2GC2eMqlmUh4BGwEGq/bdvr9WJlZYWd/DHmFgB/Pdf8730wLEISudCGiIj2nng87pojORsQEPYKaJho3fp34vF4sLa25vhn0d6Vy+XQaDQgy/K2X7tZMGFaQFQREFPbv8XRNA0+n48LbYiIaOh8Ph/C4TAbkluwITnBQqEQPB4PdF0HABx3yZEsl8soFApDf0bqD/cAeHu8iQttiIhob+p3jmQ+n2c+9hjKZDIQRbHtQppO49pA84ZkIpHgQhsiIho6O0fSqZaZRGxITrBQKASfz9cagTq5Oe70VvbhmwVerxe6rjNHcoy5BsC3NmxzoQ0REe1NkUjkoZqlHac87EepqtqKMqHxYRgGNjY22o5rA8CNDgv6gOZUEBfaEBHRqNgxaJw+bWJDcoKJooh4PN4q7o9vBsK/WzRR0h+MMQmCAEmSsLa2NpLnpN1xC4DXDQvvFjfzlrjQhoiI9ig7R9JtzOnkZh1zLWuiYTqPY4uiCMuymCM5ZgqFAqrVaueFNtH2B7CGYUAURU58EBHRyESjUSiKwm3bm9iQnHCxWKyVoRRRBMwEBFgA3s5t37a9sbHRGu+m8eEWAH+rYMKwgLBXQFxtP77EhTZERLQXJBIJ1xzJuaCAkAfQu8yRXFlZYY7kGMnn86jX6/B4PNt+raBZ2KhaENDMxG6HEx9ERDRqPp+PP4e2YIdhwoXDYQiCAMNoNiDt2wXtciSr1SrHtseQWwC8fZvgSLR9HhPAhTZERLQ32DmSTrcKBEFojW13kyNZKpVQLpf7/pw0GNlsFoIgOORHNv97zwYE+D3OB6x+vx8+n2+gz0lEROREEASkUile9tnEfwsTLhwOQ1GUB2Pbdv5S5uGbBZIkwTRNNiTHUDabdQ6Az7nnLXGhDRER7RXhcBiqqrqObZ9wqGMeZedIcmx7PJimifX1dcf8yE7j2kCzIRmPx7nQhoiIRioajTrGj0waNiQnnJ0NaDckT2xu2r5ZMFGpPzzG5PV6cf/+fY43jZFOAfCdNmxzvImIiPYKWZY750huNiTfzhmuOZJ2U4oHreOhVCqhUql0Uc84v7UxTZMLbYiIaOTYkHyADckJJwgCkslka/V8wici5W/mSL6V3T62XSwWUSwWR/CktBPFYtExAL6gW1ivNt+sHeJCGyIiGgOdciTngwICHkAzmoerbrxeL1ZXV7npcgzk83nout62IWlZVscN2/ZCGx6wEhHRqPn9foRCId7YBxuSBLROi+3i/tTm7YIrj+QvKYoCXdc53jRGcrkcdF1vGwBvj2t3ylviQhsiItorotEoZFl2zJEUBQEnHPKwH+Xz+VAul1Eqlfr+nNRfuVwOANq+eVutWCjXAVkEFkLuC20YQUNERKMmCAIee+wxLCwsjPpRRo5dBkI4HH6ouG81JNMP3xiwg8TX19eH/oy0M24B8Dc6jGsDzRsF0Wh0UI9HRETUk0gkAp/P15ccSa/Xi3q9zoPWPc6yLKytrbU9XAUejGsfDIuQRecDVp/PB7/fP7DnJCIi6lYoFHKMIZkkbEjStsU2J+PNT4s7RRMl/eGRKFVVsba25ngzgfYOy7JcA+CvtwLg3RfacLyJiIj2ClmWkUgkOuRINn+uvZU1YHSRI5lOp/v7kNRXlUoFpVLJMW/Lnvhwip8BuNCGiIhoLxpZQ/Jzn/sc/sk/+Sf42Mc+hrm5udYtrnfffbfj79V1Hf/iX/wLnD9/HoFAALFYDB/4wAfwO7/zO0N48v1HlmVEo9FWQzKqipgLNHMkr7bJkaxUKrxNMAaKxaJjAPzWvKUjLvmRXGhDRER7TaccycWQCL8M1AzgVrHztu319XXmSO5hbvmRQOcFfQAX2hAREe1F8qhe+BOf+ATy+XzPv69SqeDDH/4wnnvuOUSjUXzbt30bSqUS/viP/xhf/vKX8ZM/+ZP4V//qXw3gife3RCKBu3fvtv73yYSEe+UG3kgbeGL6waeJLMswDAOZTAZTU1OjeFTqkl3AtyvAu81b8vl8XGhDRER7ytYcyXZjvKIg4HhMwjfWDVzNmK6NKlVVUSqVkM/nEYvFBvnYtEO5XA6WZbXNs26YFm5vLi86wokPIiKisTKyG5Lf/d3fjZ/92Z/FH/3RH2Ftba3r3/fTP/3TeO6553D27Fm8/fbb+MxnPoPPf/7z+PM//3MEg0H8/M//PP7gD/5ggE++P4XDYQBo3RCwcyTfbBMI7/F4sLKy4ngzgfYGtwD4bvKWNE1DIpHgQhsiItpTIpEIVFVtTXa0c9JhQd+jPB4P6vV662cm7T3r6+uQ5fZ3KO4UTTQsIOABpnzO+ZGKorAhSUREtMeMrNPwq7/6q/jkJz+Jb/3Wb+36pl02m8WnP/1pAMCnP/1pJJPJ1q898cQT+Kmf+ikAwM/8zM/0/4H3uXA4DK/XC13XATwo5O+WLOS1hxuPPp8PuVwOlUpl6M9J3bHzIx0D4LvIW7IsiwttiIhoz/F4PB1zJE8lNnMkMwYaHXIkRVHExsZG35+Tdq9Wq6FQKDjmR17PPRjXdsqH1DSNC22IiIj2oLG6+vS5z30Ouq5jaWkJ73vf+7b9+ic+8QkAwJ//+Z/j3r17w368sRYIBOD3+1u3DUJeAYubo7yP3pK0F+BkMpmhPyd1p1KpoFgsOgfAb96QPOIwxmYYBgRBaN2cJSIi2kuSySQMw/n242JIRMDTzJG8me8uR5IL+/aeXC6HWq3mmB95o5Uf6b7QJhaLcaENERHRHjNWDclXXnkFAHDx4sW2v3748GHE43EAwDe+8Y1hPda+IAgCEonEQ+NPpza3VD467mSP8PI2wd6Vz+ehaVrbAr6+JW/JacO2/XvZkCQior0oEom0ciTbEQWhFT/zRoexbVVVUa1Wd5RtToOVz+dhmiYkqf0B6jubC/qc6hmgGUfEfFAiIqK9Z6wakjdu3AAALC0tOX7MwsLCQx/rRNM0FAqFh/6adNFo9KFcyFOJzfyl9PZCXlEUrK2tud5OoNHJ5/OOAfB3Cs28paBL3pKmaQgGg443EoiIiEYpGo1CURTXse1llzpmK7uxmc1m+/qMtHsbGxuO+ZHluoX75Wbdeshl4kMUReZHEhER7UFj1ZAsFosA4Lr1NxgMAkDHBuOnPvUpRCKR1l+Li4v9e9AxFQ6HIUlS67bB8ZgEAc2NzNnaw+NOPp8P5XKZtwn2KNf8yHznvKV6vY5EIsHxJiIi2pO6ypHcvCH5ds6EbrjnSEqShNXV1b4/J+2cruvIZrOOh6P2KP6UT0DY63zAyokPIiKivan9kaOLf/AP/gF+//d/v+cX+uVf/mU8++yzPf++QfnkJz+Jn/iJn2j970KhMPFNyXA43MqHlGUZAY+Ag2ERNwomrmRMvHfuQf/a3kqZyWRaY/K0N9RqNeTzeccC/nqH8SbLsrjQhoiI9rxkMonbt287/vpMQEBUEZDTLFzLma0bk+34fD5kMhlUq1X4fL5BPC71KJ/Po1arOdYjrXHtDvmRoVDIMVObiIiIRqfnhuS9e/dw9erVnl+oVCr1/HseZY9blMvljq/T6SRUURSOoz5CURSEQqH/P3v3Hd9mee6P//No72HJ23FiZ08IEAgkQAhwCBDCTKFACxRKaUt7WiizZbWH8qNl9EtP2bRAgVNmKDTsQBhJGCGb7Njx3rJly9p6nt8fshQ71rTlIfvzfr14vcgzpFuGO751Pfd1XXA4HNFdqDNs8nBAsi2EE4oO/e/SezfBlClTRmrIFENkAR8vUFzZkbgAfDAYhFKpZHoTERGNahaLJZrZESutVxAEzLLJsb4+iJ1toaQBydbWVjgcDhQXFw/lsClFTqcToVAobsp2NOPDEv+/q8/nQ1lZGTM+iIiIRqG0U7ZfeOGF6A6qdP5ZtmzZoAc7adIkAEj4NLy2trbPtZQeu93ep0B8vMY2wKHdBInSpWj4JSoA3x2Q0OgOp62Vx6m3xPQmIiLKBmazGRqNJknadngdszNJHUk27Bt92traYtbCBsLZHBVJOmxH6qKbzeahGSARERENSlbVkDzqqKMAABs3box5vqKiAg6HAwAwf/78YRvXWGIymaJBZCBcR1IuAK0eCS3u/nUkvV5v9GdOo0NbW1vcbpSVPelN+ToBhgT1liwWS9wdCURERKOBSqVCTk5OSo1tKp0i3IH4dSSBcKZIY2MjG/aNAsFgEG1tbXFTrR1eCU6fBJkATDTFz/hQKBTM+CAiIhqlsiogedZZZ0GlUqG6uhrr1q3rd/6ll14CACxcuBBFRUXDPbwxwWQyQaVSIRAIAAA0CgFl5ti7JGUyGURRZEByFAkEAgkX8Ad60rXLEtRbCgaDsNlsQzI+IiKiTLLb7QkDiDatDPk6ARKAve2JA41s2Dd6dHZ2wuv1xi2vFNkdOcEog0rOhjZERETZKKsCklarFT/96U8BAD/72c/Q1tYWPbdp0ybcf//9AIDf/va3IzK+sSBS+Nvn80WPRbpUxkrbVqvVaGhogCiK/c7R8HM6ndEFeCyRBfzkOOnaoihCEATuJiAioqzQu45kPJF1TLK07UjDvt7rSxoZTqcTgUAASqUy5vlKZ/IHrH6/H2azOe5rEBER0cgasZzMP/zhD1i9enW/4ytWrIBKpQIQTtF+9NFH+5z/4x//iK+//hobNmzA1KlTsXTpUnR3d2PNmjUIBAK44YYbsHz58mH5DGORXC5HTk4OampqokGpmTY53q4IYFebCEmS+hQG1+l06OrqgtPphNVqHalhUw+n0xm3uL8kSajoCH8ZK4vTYdvv90OlUnE3ARERZQWLxRJ9kBqv1Mgsmxxra4PY5Uj88FQQBCgUCjQ1NWHq1KlDMVxKUXt7O2QyWdxmNAc6knfYZsYHERHR6DZiAckDBw7gq6++6nd88+bN0X+PlXaq0+mwdu1aPPTQQ3jxxRfxzjvvQKVS4fjjj8f111+PlStXDum4xwOr1Yqqqqron6dYZFAIQIdPQpNbQoH+0OIwspugtbWVAclRwOFwxF3AN3RL6AoAKhkwKU69Jb/fD61WG+2yTkRENJqpVCpYrVY0NjbG/d01o2eHZE2XiE6/BFOcGsrAoYZ9brcbOp1uSMZMiYmiiJaWlugGhX7nJQkHOxNnfERqofMBKxER0eg1Yinbzz77bNLO3GvXro15r0qlwq233ort27fD7Xajo6MDn376KYORGWIymSAIQjQNWyUXMLlnR92uw9KdIrsJGhoaoos/GhmhUAitra1x07UjtbMmW2RQyOLXW7LZbHG7WhIREY02yepImtQCSgzh33u7k6RtR7p2sz72yOnq6oLb7Y5bD7vOJcEXAjRyoNAQez3j9/uhVCpZgoaIiGgUY9SB+jGZTFCr1X3qSEa6VMaqI6nT6dDe3o7u7u5hGyP119nZCY/HE3cBv6fnv900a+zdBEB4R4HFYhmK4REREQ0Ji8UCQRASBiUj65idMdYxvUUeyLW2tmZugJSWSP3IeDsk9/U8YC0zyyCLk9Lt8/mg0WhgMBiGbJxEREQ0OAxIUj9arRYGgwFerzd6bEavxjaH74SM1G5iEfiRFakfGa94+5728I7X6TmxA5KhUAiCIDC9iYiIskqkjmTvdcvhZkYerCbZIQmE1zWNjY0JA5w0dNrb2wEgbv3ISMZHogesfr8fOTk5kMvjX0NEREQjiwFJ6kcQBOTl5cHv90ePlVtkUMmALn84Vebw6wVBQFNT03APlXpJtIBv9YhweCXIBWBynALwke7cDEgSEVE2UavVsFqtCQOS061yCACa3BLaPImb22i1WnR3d6OjoyOzA6WkJElKWD9SkiTsTfKAFQg/ZGVtcyIiotGNAUmKyWw2QxCE6G5IpUzAVGtPHckY6U5arRbNzc190rxp+EQKwMerHxlJ155okkGtiJ/eZDAY4r4GERHRaJWbm4tgMBj3vE4poMwcfx3Tm0KhQDAYZObHCOju7kZ3d3fc8jOtHinpA1ZRFCGTyfiAlYiIaJRjQJJislgsUKlUfQKMM3ueRO+OE5D0eDxcvI8Ql8uVsAB8ZDdBovSmQCAAm80WN0WKiIhotLJYLJDJZKnVkWxLvEMy0rCvsbGRDfuGWUdHB3w+X9wdkpF07WQPWFUqFRvaEBERjXIMSFJMBoMBOp2uT0Byhu1QQFI8bIEul8ujaTY0/JxOJ/x+f9wF/J6eBfz0nNhTPtLZng1tiIgoG5nN5uR1JHMO1ZFMFmjUarXo6OiA2+3O6DgpMafTCeBQc6HDpfKA1e/3Q6fTQafTZX6ARERElDEMSFJMgiAgNze3T0CyzCSDRg50B4Carv67CzQaDRoaGlgEfgRE6lzF2t3Y6ZPQ2B3+4jXVEnsBH2mGw90ERESUjTQaDSwWS8KA5FSrDAoBaPdJaHInDkhGgpsOhyPTQ6U4JElCc3Nz3OZ8wKEdkvEesALhHZLM+CAiIhr9GJCkuCLFwCO7COQyAdNy4qc7RYrAc/E+vCIL+GS7I0sMAgyq+OlNbGhDRETZLFkdSZVcwJSeetg7k3TbjuzQY+bH8PF6vejq6opby7rTJ6EhyQNWAMz4ICIiyhIMSFJcZrMZCoUCgUAgeixRHUmlUolgMIjW1tZhGyMBnZ2d6OrqSlA/MvzfalqCbpQ+nw8WiwUKhWJIxkhERDTUUqkjOTP6YDV5NodGo0FTU1PCICdljsPhgM/nS7qeSfSANRgMQi6XM+ODiIgoCzAgSXGZTCZotdrDGtuE/5fZ4wghJPZPd1KpVGhoaGAR+GEUWcDH21EQqbc0PUG9pWAwCJvNNiTjIyIiGg4WiwUajabPuuVwsxLUwz6cVquF2+2OlkWhodXa2gpJkhLUj+x5wJpgPcOMDyIiouzBgCTFJZfLYbPZ+izsS00y6BSANwQc7Oyftq3T6eB0OtHZ2TmcQx3XmpubIZPJYtZKcgckVHdGCsDHnu6iKEIQBO4mICKirKbRaGAymRLWkSwzy6CWA6449bB7i2R+tLW1ZXqodBhRFNHU1BT34SqQekMbo9GY8HWIiIhodGBAkhLKycnpk/okEwTMiHSpjJG2rVKp4Pf7mbY9TPx+P1paWqDVamOe398RggQgTyfAqok93SPdubmbgIiIsl1eXl7CFGuFTIhmDOyKUQ/7cEqlEo2Njcz8GGIdHR1wuVxx1zOeoISqyAPWBA1tAoEA7Hb7kIyRiIiIMosBSUrIbDZDLpf3WdxH60jGWMgLggC5XI7GxsZhG+N45nA44PF44i7gU91NoNVqodfrh2SMREREw8VisUAQBIhi/GDjTFv8B6uH02q16OjoQHd3d8bGSP05HA4EAoG4HbYP9DxgtWsF5MR5wBoJGvMBKxERUXZgQJISMpvN/eoxRRbyeztCCMaoI6nT6dDW1ga32z1s4xyv2traIEkS5PLYAcc9PV+2psdJ1wbC9ZZsNlvcmk1ERETZwmKxQK1WJ0zbnmU7VA871jqmN41GA6/XC4fDkdFxUl9NTU2Qy+Uxy88AwB5H8nrYgUAACoWCJWiIiIiyBCMQlJBKpYLFYumzsC82CDCqAH8IqHD234EQWbyz5tLQkiQJDQ0NcXcT+ENS9L9Poh2SkiTBYrEMxRCJiIiGlUajgdlsThiQnGCUQa/sqYcdYx3TmyAIEAQBLS0tmR4q9fB4PGhra4NOp4t7zaGGNokfsKrVagYkiYiIsgQDkpSU3W7vk7It9K4j2dY/3Smy0665uXl4BjhOdXZ2oqurK+4CvsIpIiQBFrWAPF3sHQehUAiCIDC9iYiIxgRBEJCbm4tAIBD3GpkgRMvP7EwxbbupqSlhbUoauLa2Nni9Xmg0mpjnA6KEAyk8YPX7/bBarVAoFEMyTiIiIsosBiQpKbPZDJlM1qce08wEjW2A8OK9sbEx4RcCGhyHwxFtSBNLJF17mjV2B27g0G4CBiSJiGisSKuOZIwHq4fTarVwu91M2x4ikUaI8UrHHHSKCIqASQUU6GOvZ4DwQ9acnJwhGSMRERFlHgOSlJTZbIZare5bR7InILm/Q4Q/FLuOpNvtZtr2EGpuboZMFj/YeCi9Kf5uAp/PB4PBALVaPSRjJCIiGm4WiyVaPiaeWT3rmH1x1jG9KRQKhEIhBiSHQCgUQlNTU8J1yJ6e9cxUa/wak5HgMx+wEhERZQ8GJCkprVYLg8HQZ2FfoBdgUQsIiuGg5OHkcjlEUYw+9abM8vv9aGlpidtdOyRK0f8u03MSF4C32WxxF/hERETZRqvVwmg09nmQerhk65jDKZVKNDQ0RDs5U2Z0dHTA5XIlrh+ZQkObSMYI60cSERFlDwYkKSlBEJCXl9cn/VoQBMzMCf/vEy9tW6PRoL6+PmHKFA2Mw+GAx+OJG5Cs6hLhCwF6ZbgJUSySJLGhDRERjTmRdYvf7094zcyebts7U0jb1ul0cDqdcLlcGRsnhdczwWAwbt1HUZKwryN5Qxu/3w+tVgu9Xj8k4yQiIqLMY0CSUmI2mwGgz86AGUnqL2m1WrhcLrS3tw/9AMeZtrY2iKIIuTz2boE9PbsJplrkkMXZ/RgMBqFUKrmbgIiIxhyr1dqv/vXhZiVo0Hc4tVoNv9/PtO0MkiQJjY2NUCgUcTM1arpEeIKARg6UmhJ32M7JyYlbh5KIiIhGH/7WppSYzWaoVKo+6U9zegKSBzpEdAf6pzAplUoEAgHWkcwwSZLQ0NCQsN5StH5kTuLFOxvaEBHRWBRr3XK4SGObyk4RnmDiVGxBECAIAlpaWjI6zvHM4/Ggvb09brYHAOxtDweUp1jjP2AFwjUkrVZrxsdIREREQ4cBSUqJ0WiETqfrs7C3aWUoMgiQAHzX2n93gSAIUCqVqK+vZ82lDOrs7ERXV1fcBbwoSdGAZKJ6Sz6fDxaLJW6aFBERUbbS6XQwGo0JG9vYtTLk6QSIErAnTvmZ3jQaDZqamvqUsKGBa2trg9frhUajiXvNoQZ98b+yhEIhyGQyZnwQERFlGQYkKSWCICA3N7ffToO59nDAa1uMgCQQ/kIQKVhOmdHW1hYt3h5Lg0tCdwBQyYGJCdKbgsEgbDbbUA2TiIhoxAiCgPz8/KTBw3TStrVaLTweD9O2MyTS+DBemrUkSdESNMkesDLjg4iIKPswIEkps1qt0UYoEfPs4d1121tDMXdBRmousdt25jQ3N0Mmk8Wtt7SnZzfBFIsMCln8hjaCIHA3ARERjVkWiwWCICSsIxlJ297pSN6AT6FQIBQKsRRNBoRCITQ1NSXcHdnkltDpl6AQgDJz4oY2er0+4WsRERHR6MOAJKXMbDZH60JGTMuRQS0HnD4J1V39F/ORmktNTU3DOdQxy+fzobW1NUm9pUh6U+LdBCqVirsJiIhozLJarVCr1QnTtmf27JCs6RLR6U9eXkalUqGxsZGlaAapvb0d3d3dKa1nyi0yqOTx60f6/X7Y7fa4D2qJiIhodGJAklJmMpmg1Wr7pG0rZUJ0Mb+9JX7adktLS8IvBJQah8MBj8cTdwGfanqT3++HVquFXq8fknESERGNNK1WC7PZnHD9YVILKDGEA1m7U0zbjtRypoFzOBwIBoNQKpVxr4k0tEn0gDWSuWM2mzM+RiIiIhpaDEhSyuRyOWw2W/86krk9Ack4dSQjNZeY4jR4bW1tEEURcnnsxXmrR0K7T4JcCO8oiMfn88Fms8Wt20RERJTtBEFAXl4egsFgwutm2RKvY3pTq9XRbAUaGEmS0NjYmLSpXqTRUKKGNsFgEAqFgiVoiIiIshCjEZSWnJycfrWY5vU0ttnXIcId6J/CFAl6NTc3D/0AxzBRFNHY2Ai1Wh33mkj9yDKzDOoE6U2SJMFisWR6iERERKOK1WqFIAgIheIHG+flhgNj2+LUw+5NEATI5XLU1dUxbXuA3G432tvbodPp4l7T7hXR4pEgAJhiYUMbIiKisYgBSUqL2WyGTCbrs9sgVydDgV6AKAHfxUl30mg0aGxsTLpLgeKLpIglqrcUSddOlN4UCoUgCAIX70RENOZZLBZoNJqEadvTc2RQJaiHfTi9Xo+2tjambQ9QW1tbNJAYTyRdu9Qkg06ZuH5kpMY5ERERZRcGJCktZrMZGo2mf9q2PXG6k06nQ3d3NxwOx5CPcaxyOBzw+/1QqVRxrznU0CZxujZ3ExAR0Xig0WhgtVoTBiSVMgGze9K2t8aph334a/p8PmZ+DFAk3T1R2ZhU1jNAOGXbZrNlbnBEREQ0bBiQpLSoVCpYLJZ+C/tI2vb2ltjpTgqFAqFQiDWXBqG5uRlyuTxuF8kOn4gmdzi9aWqSDtsGgyHhzgQiIqKxIpU6knPtiRv09SYIAhQKBWpra5m2naZgMIimpiZoNJqE16Xa0AYAH7ASERFlKQYkKW12u73fwn56jhwqGdDuk1Drir04V6vVqK2tTVjHiWKLFNBPtICPLN5LjDLoE6Q3BQIB2Gy2uIFNIiKiscRisUAulycMSs7radC3v0OEy588yKjX69He3g6n05mxcY4H7e3tcLvdCcvPdAck1HYlD0j6/X4olUo2tCEiIspSDEhS2iJ1JHs3t1HJBcyIdKlsib3g1+v16Ozs5C7JAXA4HPB4PEnqRyZPb5IkiQ1tiIhoXEmljqRdK0OxQYAEYEeceti9qdVq+P1+pm2nyeFwIBgMJqz5uK89BAlAgU6AWZ24fqRGo4HBYBiCkRIREdFQY0CS0mY2m6FWq9OuI6lUKqOdoik9bW1tkCQJcnn8nQKRHZLTc+JfEwgEoFQqmd5ERETjhkqlgs1mSxiQBHp1204xbVupVKK2trbPA1qKT5IkNDQ0JG1AE03XTrCeAcLZIzk5OQnXRkRERDR6MSBJadNqtTAYDHHrSO5tF+EJxk530mq1qKurg9/vH/JxjhWiKKKhoSFhM5u+6U3xp7XX64VWq2VAkoiIxpXc3FyEQrHrXEdE62G3BiGmUBvSYDCgo6MD7e3tGRvnWNbd3Q2n05kw2wNIvaFNKBSC1WrN2PiIiIhoeDEgSWkTBAF5eXkIBAJ9jufrZcjXCQhJwM446U46nQ4ul4spTmno7OyEy+VKuICPpDfl6wRY1Ik7bNvtdu4mICKiccVqtUKhUCSsIznVKoNGDnT5gYOdyXc9KpVKBAIBrmlS5HA44PP5EtbD9oUkVDqT148URREymYz1I4mIiLIYA5I0IGazGQD67TSYkyRtO9Iluq6ubmgHOIY4HA74/f6EOyRTSdeWJAmiKMJms2V8jERERKOZ2WyGVqtNmLatkAmY3bOOSTVtO9Kwj2nbybW0tABAwqZ6FR0iQhJgUQvI1ca/zufzQaVSMeODiIgoizEgSQNiNpuhUqn61ZGMdKnc3hI/LUqn06GpqQnd3d1DPs6xoKmpKRrIjSeV9KZgMAiFQhENJhMREY0XCoUCubm5KdSRTD0gCRxq2NfW1jboMY5lwWAQTU1NKadrT7fKEq57fD4fdDoddDpdRsdJREREw4cBSRoQg8EAnU7XLyA5I0cOhQxo80qo744fkPR4PGhqahqOoWY1n8+H1tbWhAv43ulN0xOkN3m9Xmg0GgYkiYhoXLLZbBBFMaU6kpVOEZ3+5HUklUolgsEg07aTcDgccLvdqdePTNLQxu/3Izc3N2HQkoiIiEY3BiRpQGQyGXJzc/sFJNVyATOsh3ZJxiIIAhQKBWpraxN+KaDwAj4SSIwnkt5kVQuwJ0lvstvtUCgUQzFUIiKiUc1qtUbrPsa9RiPDBKMMEoAdccrPHE6j0aC2tjZhfcrxzuFwIBQKJVyDhEQJ+zuS14+MrB3Z0IaIiCi7MSBJA2a1WiFJUr+g4tzcQ10q49Hr9Whra0NHR8dQDjHrtbW1QZKkhE1o9kTSm3ISpzeFQiHWjyQionHLZDJBr9cnTds+Ipq2nVqAUa/Xo6uri2nbcUiShIaGBiiVyoTXVXWJ8IUAvRIoNsRfz/j9fiiVSlgslgyPlIiIiIYTA5I0YGazOeZOg0i60x6HCG8w9g5ItVoNv9/PtO0ERFFEQ0NDwmY2ALCrLVI/Mn7QMhgMQi6XM12biIjGLblcnlYdye2tIYgpZHIoFAqIoojGxsaMjHOscblccDqdSes97nGEd0dOscghS6F+JDtsExERZTcGJGnATCYTtFptv7TtAn24M2JQAnY54qdtq9Vq1NTUIBRKLSVqvOns7ITL5UpYb8kdOJTeNNvG+pFERESJRDIFEpWMmWyWQacAugPhsiip0Gg0qK+vT5gOPl45HA74/X6o1eqE1+3tlfGRSKQEjUzGrzFERETZjL/JacDkcjlsNlu/gKQgCJhrP7S7IB69Xg+n08kUpzja2trg9/sT7pD8ri2EkAQU6ATk6+NPZ5/Ph5ycnKS7LYmIiMYyi8UCpVIJv98f9xq5TMCcnnXM1hTrSOr1erhcLrS2tmZknGNJS0sLBEFIWFZGlCTsa0+e8SFJEkRRZP1IIiKiMYABSRqUnJycmDsco3UkW0JxdyEolUqEQiE0NDQM6RizUaTeklwuT7iA39bTOCiSXhZPMBiE3W7P6BiJiIiyjdFohF6vh8fjSXjdvGgdydQCknK5HJIkMW37MJHyPIma8wFATZcIVwBQyYFJpvhfT4LBIOtHEhERjREMSNKgWCwWKBSKfilKM3PkUAhAi0dCY3f8tCitVou6urqEOxXGI6fTidbWVhgMhrjXSJKEbT07N47ITdC1MhSCTCZjujYREY17MpkM+fn5Sdcdc+3h36tVnSI6vKmlbWu1WtTX13NN00tTUxO6u7uT1o/c3hP4nZUjh0IW/0FspASNyWTK6DiJiIho+DEgSYNisVig1Wr7FYjXKARM66kBlCxtu7u7G83NzUM6zmzT0NAAn8+XsN5SVacIp0+CWo7ozzoW1o8kIiI6JCcnB0DiOpJmtYAyU/J1TG86nQ5ut5trmh6SJKG2thaCIEAuT5zJEXnAmizjw+fzwWazQaGI/yCWiIiIsgMDkjQoCoUibsfKyO6CbQkW8pEUp/r6+iEbY7YJhUKoqamBWq1OnK7d83OdZZNDmWA3gc/ng8ViSZouRURENB5YrVao1ep+NbAPF03bTjEgybTtvrq6utDc3Ay9Xp/wuu5eDfqSBSRDoVC0MRERERFlNwYkadDsdjtEUey30yCyqNztCMEXir8LQa/Xo7GxEW63e0jHmS1aWlrQ2dmZMF0bOFTX6ogU6kfm5uZmbHxERETZTK/Xw2AwJK0jGamHvaM1hKAYfx3Tm06nQ0NDQ8wHteNNY2MjvF4vtFptwuu+aw1BlIAigwC7NnH9SLlczowPIiKiMYIBSRo0q9UKlUrVr45kkV5AjkZAUAT2OOLvLtDpdPB4PNxR0KOurg6iKCZMR+rySzjQs5sg0tE8FlEUIQgCF+9EREQ9BEFAfn5+v3XL4crNMhiUgCeI6O/cZCJrmvGeth0KhVBdXQ2VSpUw2wMAtkYa9NkTp2GzBA0REdHYwoAkDZrJZIrZsVIQBMyzJ+9SGaktVFtbm7Ce03jg8XhQX1+ftPj7jtYQJAAlBgG2BLsJvF4v1Go1u1ESERH1YrVaIQgCRDF+oFEmCNGHfltT7LYtk8kgCMK4L0XT2tqKjo6OpNkeoiRFa3SmUj8y8hCciIiIsh8DkjRoMpkMeXl5MWsxRdKdkhWENxgMaGtrg9PpHJIxZovGxkZ4PJ6kAcltLUEAibtrA+HFu8lkYv1IIiKiXiJ1JJOlVs/r+T2bamMbILxLsrm5eVyXoqmvr0coFIJSqUx4XXWniE6/BI0cmGZN/LUkGAzCbrdncphEREQ0ghiQpIyIFBg/fIfjLJsccgFockto6o6/C0GtVsPv96OpqWlIxzmaSZKEmpoayGQyyGTxp2Y6uwkCgQByc3OTpksRERGNJ1qtFmazOWlAco5dDgFATZcIhzf1tG2v1ztu07a9Xi/q6uqSPlwFDu08nW2XQ5GgQV8oFIJMJmO6NhER0RjCgCRlRLyOlVqFgKk9T7wT7S4QBAEqlQrV1dUJ06fGso6ODrS1tSVNb6roEOEKAFoFMMWSIHDZ83NkujYREVFfgiAgLy8PwWAw4XVGlYByc/h3baLyM4e/tkwmQ11d3bgsRRNpVJisuzZwaG2YqB42EM74UKvVDEgSERGNIQxIUkbo9XoYjcaYHSujdSRTSNvu7OxEa2vrkIxxtGtoaIDf74darU543dZei3d5gt0EkcU7A5JERET9RepIhkKJ1yeRbIRUA5JAeF3U2tqK7u7uQY0x26Sa7QH0bdCXLOPD6/XCbDazBA0REdEYwoAkZUSkY6Xf7+93bm5P/aXdbSH4Q/F3CiiVSgSDwXHZbTsYDKKmpgYajSZpenXkC1Eqxd8NBkNKKVNERETjjcVigUajSZq2fUTP79udbSEExdR2PEZed7yVonE6nWhtbU2a7QEcatA3wShDjibxVxKWoCEiIhp7GJCkjMnJyYFMJuuXcl1iEGBVC/CLwN72xLsLtFotamtrYwY2x7Lm5mZ0dnYmTW/q8Iqo6uzZTWBP3NDG7/cjLy+Pi3ciIqIYNBoNrFZr0oBkqUkGk0qANwTsbU+trIwgCFAoFOMubbuhoSGaoZHMttZwunyydG1RFCEIAtO1iYiIxhgGJClj4u00EAQh2m17a5J0J71ej+7ubrS0tAzZOEej+vp6SJIEhSJxkDGS9l5mlsGkjh9ojHz5Ybo2ERFRfKnUkZQJQjRotq0l8bW96fV6OBwOOJ3OQY0xWwSDQVRXV6eU7SFKEnb0rAmPSCHjgyVoiIiIxh4GJCljtFotLBZLzDqSR/YsNr9tCkFMsFNALpdDkiTU1dUN2ThHG7fbjYaGhpSKv0fTtVMo/q5Sqbh4JyIiSsBisUAulycNSh4xgDqSkWZ/1dXVgxpjtohke6SSrl3pFNHV06BvcoIGfQBL0BAREY1VDEhSRsXbaTDHLodWATi8hwqYx6PX69HU1AS32z1UwxxVIt0oky20g6KEHa2p7ybQ6XQpfSkgIiIar1KtIznbLodMAOq7JbS4U0/b1ul0qKqqGhfNbVLN9gAOBXZn2+RQJGjQB4TXNKwfSURENPYwIEkZZbVaY+40UMkFzM8LL1C/aki8C0Gn08Htdo+L5jaSJKG6uhoKhSLpQntfuwhvCDCqgEnm5LsJWD+SiIgoMZVKBZvNljQgqVcKmNKzk297a+q7JA0GA9xuN2pqagY1ztHO7Xajvr4+pWwP4FAJmmQPWCMlaKxW6+AGSERERKMOA5KUUVarFVqtNubC/tiC8KLzmyRp25FC8JWVlUlTqLJde3s7HA5HSjsZI4v3eXYFZAkCjVy8ExERpS43NxehUChp85lIuZRk9bB7EwQBGo0GlZWV8Pl8gxrnaNbQ0ACPx5NSWnWnT0KlM7zLNFlDG7/fD7VazYY2REREYxADkpRRSqUSdrs9Zh3JOXY59ErA6ZOwx5E43clsNsPhcKC+vn6ohjoqNDQ0IBAIQKVSJb02Ukh/XpLdBH6/H0qlkvUjiYiIUmCxWKBQKJI+BI38/t3VFoI/lHrnbKPRiM7OTtTW1g5qnKOVJEmoqalJKdsDALb3dNeeaJLBokn8VcTr9bIEDRER0RjFgCRlnN1uhyiK/XYaKGQCjupJ2/66MfGiP7KoPXDgAEKh1HciZJNAIICampqUulG2ekTUuSQICAd2E4nUjzQajRkcLRER0dhksVjiZnf0NsEog0UtwC8Cuxypr01kMhlUKhUqKirGZOaHw+FIOdsDSL1BHxBe09jtdshk/MpCREQ01vC3O2Wc1WqFUqlEIBDod+64wvDic2NTECEx8e4Cs9mM1tZWNDQ0DMk4R1pzczO6urpSS9fuWbxPtcqgV6ZW/J2LdyIiouQUCgVyc3OTBiQFQcDR+eF1zNcN6T0sNZlM6OjoGJOZH/X19QgEAlCr1UmvDYkSdrT1BCRTrB+Zk5Mz+EESERHRqMOIBWWc2WyGTqeLubCfmSOHUQl0+YFdSdK2lUpldJekKKbW0TKb1NXVQZIkyOXJdwhsTXE3gSRJEEWR9SOJiIjSkJubGzO743ALC8OZHt82BdNK25bL5RAEARUVFWNqTeP3+1FbWwutVpvS9RVOEd0BQK8EJlsSfw0JBAJQKBSsH0lERDRGjUhAsrm5Gc8//zwuvfRSTJ06FRqNBjqdDjNmzMAvf/lLHDx4MOH9fr8f999/P4444gjo9XpYrVYsWbIEr7322vB8AEpILpcjLy8vZkBSLhNwdEFqadtAOLjZ0tIy5jpud3d3o7GxMaVulP6QhF09uwmO6El5jycQCLB+JBERUZpycnKgVquTNp6ZbJEhRyPAGzqUvZCqSObHWFrTRLI9Uu6u3fMzm2OTJ2zQB4TrR2q1WphMpkGPk4iIiEafEQlI3nDDDbjiiivw8ssvQ6fTYcWKFTjllFPgcDjw17/+FXPmzMGHH34Y8163241TTjkFt956K6qrq7Fs2TIce+yxWLduHVauXInf/OY3w/xpKBabzQZJkmLuNDiu4NDugmCStG2lUglJksbcLsnGxsaUu1HucYTgFwGrWkCJIXm6NhfvRERE6TEajTCZTDGb8vUmEwQc27OO+SqFB6u9RdY0lZWVSXdiZgNJklBbWwtBEFLK9gCAba2ppWsDh+pHpvraRERElF1GJCCZk5ODe+65B9XV1di6dSteeeUVrF69GhUVFbjkkkvQ3d2NSy65BO3t7f3uvf3227F+/XrMnTsX+/btw+uvv473338fX375JQwGAx588EH85z//GYFPRb1Zrda4Ow2m58hgVgvoDgDftSXfXWA2m9HU1ISmpqahGOqwE0UR1dXVKXejjKZr58qTXu/1erl4JyIiSpMgCCgsLITf70967cKeethbmkPwBNMLLJpMJjQ1NaG1tXVA4xxNXC4XmpqaUt4d2eEVUdUZfrg815444yNSgob1I4mIiMauEQlIPvLII7jzzjtRXFzc57jBYMAzzzwDo9EIh8OB1atX9znf3t6Oxx57DADw2GOPwW63R88dffTRuOWWWwAA99577xB/AkrGaDTCYDDETNuWCQKOSaMovEqliu6SHAs7ChwOB9rb21Pqgi1JUp+AZLJruXgnIiIaGJvNBrlcnrQT9kSTDPk6AQER2NycXtq2Wq1GMBgcE7skGxoaomnVqdjeszuyzCyDSZ34AWswGGT9SCIiojFu1DW10el0mD59OgCgpqamz7l33nkHfr8fpaWlWLRoUb97L730UgDAl19+OSa7GGYTQRBQUFAQtxbTcT1F4Tc1p1YUPrKjoLm5OaPjHAkNDQ0IBAJQqVRJr21yS2jxSJALwCxb4oBkZPHO+pFERETps1qt0Ol0SdO2BUGINrf5qiG9tG0g/NC2vr4eTqdzQOMcDSLZHiqVKqVsDyD1Bn1AOF1brVYzIElERDSGjbqAZCAQiDa1KSws7HNu8+bNAIBjjjkm5r3l5eXR3WFbtmwZsjFSaqxWKwRBiFn7cYpFBqtagCcI7GhNvrtArVYjFApl/S7JdLtRRhbvM3Jk0CqS14/UaDSsH0lERDQASqUyblO+wx3bE5Dc0RqCy5/eukSj0cDn8yVt4jiatba2oqOjAwaDIaXrg6IULdOTSv1Ir9cLm80GpVI5qHESERHR6DXqApLPPPMMWltbodVqceaZZ/Y5V1lZCQAoLS2Ne39JSUmfa+Px+Xzo7Ozs8w9lltVqhUajiZu2fWxBT9p2ikXhTSYTGhsbs7ruUlNTE1wuVxrdKMM/m3m5iWstAVy8ExERDVZubm7cpny9FRtkmGCUISSFm/SlQxAE6PV6VFdXw+VyDWa4I6aurg6hUCjlNceBDhGeIGBUhlO2kwmFQrDZbIMdJhEREY1ioyoguX37dtx0000AgDvuuAP5+fl9znd1dQFAwmBO5EltsgDjfffdB7PZHP1nwoQJgxk6xaDVamE2m+PuNIjsLtjcHIIvhbRtjUaDYDCYtbskA4EA9u3bl3I3Sm9Qwh5HeHdpKulNXLwTERENjs1mg1qtTmmX5HE9zW2+HEDatl6vh8fjQXV1ddr3jrSOjg7U1NSk/HAVOJTxMSdXDlmSFO9QKASZTMZ0bSIiojEu+barw9x8881466230n6jp59+GosXL457vra2Fueccw5cLhdWrFiBW2+9Ne33SMdtt92GG264Ifrnzs5OBiUzTBAE5Ofno7GxMeb5crMMdq2AVo+EbS0hLChI/r+jyWRCQ0MD2tra+jQ1ygYHDx5ES0tLykHDnW0hBCUgVyugQJ+8+LtcLmf9SCIiokHQ6/Uwm81wOBxJy6scV6DAa3sD2O0Q0eEVYdGk/pxfEARotVocPHgQ5eXl0Gg0gx36sBBFEbt27YLX60Vubm7K90Ua2sxL0l0bCGd8aDQarmmIiIjGuLQDkvX19dizZ0/ab5QoJaWxsRGnnnoqqqqqcMYZZ+CVV16JWSA70pW4u7s76fskq6OnVquhVqtTGToNgtVqhUwmQygU6rcrUBAEHFugwDuVAXzVEEwpIKnRaOB0OlFRUQGbzZZyIfWR5nK5sHfvXmg0GigUqU27bb26ayf7nJHFO3cTEBERDVykKV9TU1PSa3N1Mkw2y3DAKeKbphBOn5he4pHBYEBLSwtqamowderUgQ55WNXX16Ourg5msznlNZjDK6KmS4QAYG6KDW3sdjvX6URERGNc2inbL7zwQrS2Tjr/LFu2LObrNTc3Y+nSpdi7dy9OO+00vPnmm3EXIJMmTQKAhOkttbW1fa6lkWW1WqHVauN2rIzUkdzWEoI3mFoatslkQn19Pdrb2zM2zqEkSRL27t0Ll8uVcsMZSZKwrWc3wREpFH/3+XywWq0pde4mIiKi+Gw2G+RyOYLB5KnYxw2i27ZMJoNarUZlZSUCgUDa9w83v9+PXbt2QRCEtIKFkQesky0yGFTJg5iBQCCt3ZdERESUnUa0hmRLSwuWLl2KXbt24dRTT8Vbb72VMGXlqKOOAgBs3Lgx5vmKigo4HA4AwPz58zM/YEqbSqWCzWaLW4tpokmGfJ0AvwhsaU7ebRs41J2yoqIiK2pJtra2oqqqCkajMeXdBLUuCQ6vBJUMmJGTPCAZDAazLoWdiIhoNLJardDr9XC73UmvXVAghwBgf4eIFreY9nsZjUZ0dHSgrq5uACMdXgcOHIDD4Ug7lTqSrp3K7khRFCEIAtO1iYiIxoERC0i2trZi6dKl+O6773Dqqafi7bffTlqr56yzzoJKpUJ1dTXWrVvX7/xLL70EAFi4cCGKioqGZNyUvtzcXIRCoZjBw0jaNgB8lWK3bUEQYDQaUVtbC6fTmdGxZlooFMKuXbsQDAah0+lSvm9rc/hnMdMmh0qeWv1Iq9U6qLESERERoFAokJ+fn1JjG6tGhhk54eX01ymuY3qTy+WQy+WoqKhAKJTag9mR4HQ6sW/fPuh0upQa80UERQnfpZnxwRI0RERE48OIBCQdDgdOPfVU7NixA6eddlpKwUgg/MT6pz/9KQDgZz/7Gdra2qLnNm3ahPvvvx8A8Nvf/nZoBk4DYrVaoVAo4qY+Rbptb28JwR1IbcejVquN7pIczWpqatDU1JTWk35JkrC+Pvyzmp+XfPHudruh1+uRk5Mz0GESERFRL5Gsg1QyMQ6lbQ8soBhpohOvCeBIkyQJu3fvhsfjgcFgSOveve0ivCHApBJQakr+tcPr9cJgMKT0vYCIiIiyW9pNbTLhmmuuwbZt2yAIAnJycqJBxsOdd955OO+88/oc++Mf/4ivv/4aGzZswNSpU7F06VJ0d3djzZo1CAQCuOGGG7B8+fJh+BSUKovFAp1OB4/HA6VS2e98iUFAkV5AfbeEzc1BLCruf83hBEGAwWBATU0NJk+ePCqfpHu9XuzZswcKhSLm547nQIeI+u5wunbkS06y9ykpKUm5WQ4RERElZrPZoFar4fV6kwbHjslX4J87/ajuElHvElFkSO95f+T3d0VFBQoLCyGTjWhFpX7q6+tRW1sLi8WSdjPB3g36ZCnc6/f7kZeXlzVNC4mIiGjgRiSCEanzKEkSXnnllbjXTZo0qV9AUqfTYe3atXjooYfw4osv4p133oFKpcLxxx+P66+/HitXrhzKodMAyOVy5OXlxd3NKAgCji1U4M39AXzVGEopIAmE/19obm7GwYMHccQRR2RyyBmxf/9+dHR0pF2Y/bO68O7IBQUKaBWJF+SiGK5XxeLvREREmaPT6WCxWNDW1pY0IGlQCZhtl2NbSwhfNQRx/tT0G8yZTCY0NDRg//79mDZt2kCHnXGRRjaSJKXd9VqSJGxtCa9p5qVQP1KSJNaPJCIiGkdGJCC5du3aQd2vUqlw66234tZbb83MgGjI2Ww27N+/P7rYPNyxBeGA5HetIbj8UkpdGAVBgF6vR1VVFcrKylLuYD0c2tvbceDAAej1+rR2OviCEr7u6dR5UklquyM1Gg3TtYmIiDJIEAQUFhamnEZ9XEFPQLIxiPOmKNPe4adSqaDVarFr1y6YzWbk5+cPZNgZV1FRgba2tgE1zqvqFNHQLUEhA2anEJD0+XxQqVSjMuuFiIiIMm905YTQmGW1WqFSqeD3+2OeLzLIMMEoQ0gCvm1OvSi8Xq+Hx+PB9u3b49aoHG6SJGHPnj3w+XzQ6/Vp3ft1YxDeEJCvEzDNmnx6ejweWK3WtBrmEBERUXI5OTlQKBQIBAJJrz0qXwGlDGjsllDdlX63bQAwGAwIBALYunUruru7B/QamdTZ2Yl9+/ZBq9Wm1cgm4vOejI9j8uXQK5MHaD0eD0wmU9p1KomIiCg7MSBJw8JoNMJgMMDj8cS95tiC8GI3skMwFZE6pLW1tdixY0dKxeeHWqTWktlsTnuHRGTxfmKxIum9kiQhGAyioKCAtZaIiIgyzGq1Rh98JqNVCNEu0gNtbhNZ07S3t2PLli0j+qA10sjG7XbDaDSmfb8/JGFDT4O+xSmW4vH7/SgsLOSahoiIaJxgQJKGhUwmQ35+ftwdksChBi67HCI6/akHFpVKJUwmE/bv348DBw4MeqyDEQgEsHv3bgBIu9ZSY7eIve0iBACLipOnawcCASiVSthstoEMlYiIiBKQy+XIz8+H1+tN6fpD3baDA35AKpPJog9aI7UbR0JDQwNqamoG9HAVADY3h+AOAjkaAbNsyb9uBINByOVylqAhIiIaRxiQpGFjt9shCAJCodg7B/J0MkwyySBKwMbG9HYFaLVaqFQq7NixAw0NDZkY7oBUVFSgtbUVVqs17Xs/r+0p/J4rh1WTWrq2wWBg8XciIqIhEqmdGGkil8gRuXJo5ECbV8L+joGlbQPhB61GoxF79+5FTU3NgF9noAKBQDQYqtFoBvQakTXN4mJFSt21PR4PtFotA5JERETjCAOSNGzsdjt0Ol3itO3CnrTtNAOSQDgtPBgMYsuWLXA6nQMe50C5XK4B11oKiRLW1R9K106F1+tFQUFBWk1ziIiIKHU2mw0ajSalXZIquYD5+ZG07cGlW+t0OshkMmzfvh0dHR2Deq10DebhKgC0eUR81xZ++Lw4jTVNfn4+lMrU0ruJiIgo+zGSQcNGpVIhPz8fbrc77jXHFoQXrnscIjq86e0uEAQBNpsNXV1d2LRpU8opVpkQaWTT3d09oFpL21tD6PBJMKqAI/OSBzNDoRBkMtmAul4SERFRarRaLaxWa0p1JAFgYU/a9teNIYTEwaVbWywWdHd3Y8uWLfD5fIN6rVR1dXUNqpENAKyrD0ICMCNHhjxd8q8akiRBkiTk5uYO6P2IiIgoOzEgScMqPz8fQPzUJ7tWhslmGSQAXw6gKHwkKNnc3IwtW7bETQ/PtJaWFlRVVcFkMg2o1tJnPalNJxQpoJClltqk0WhYP5KIiGgICYKAgoICBIOp1YWcbZNDrwQ6/RL2tA88bTvy3jabDY2Njdi+fXtKaeODEWlk43K5BvRwFQBESYqma6ea8eHz+aBSqZiuTURENM4wIEnDKjc3F1qtNuFOg0h6z0fVgQHtLpDL5bBaraiqqsLOnTuHvCB8KBTCrl27EAwGodVq077f6ZOwtSUcOD0pxU6UHo8Hubm5aTfOISIiovTk5ORAoVCk1PVaIRNwTH54HfPlINO2gfCaxmKxoLKyEhUVFYN+vUQaGxtRXV0Ni8Uy4E7Xe9tFtHgkaOSI/hyScbvdMJvNMBgMA3pPIiIiyk4MSNKw0mg0yMvLS5i2vahYAaMSaPVI2Ng0sB2OKpUqWhD+4MGDAxxtch6PB5s3b0ZjY+OAay2trw8iJAHlZhmKjamlNomiiLy8vAG9HxEREaXOYrHAYDAkXLv0Fknb/rYpiOAg07aB8NpJrVbju+++Q3Nz86Bf73CiKOLAgQP49ttvB9XIBjjUzObYQgXUitSCmoFAAIWFhQMOghIREVF2YkCShl1BQQFEUYy7c1ElF3DqxPBOwXcrAwPe4ajT6aBQKLB9+/YhWcA3NTXhiy++wP79+2E2mwdUiF2SJHxeGwAAnFSSemqTWq1mujYREdEwkMvlyM/PT7mO4/QcGSxqAd0BYEdrZkrHGI1GBAIBbNmyBd3d3Rl5TSBcM/Krr77Ct99+i1AoNKi1hSco4ZumcEDypBTTtYPBIORyOdO1iYiIxiEGJGnY2e32pGnbp5YqoZQBBztF7HYMvGaSyWSC3+/H5s2b0dXVNeDX6S0YDGLXrl3YsGEDnE4n8vLyBryb4ECHiPpuCSoZcFxhaot3j8cDk8kEk8k0oPckIiKi9ESayKVSx1EmCFhQkJlu2xGCICAnJwft7e3YunVrSunjiYiiiMrKSnz++efRNG2z2TyoXYpfNwbhDwEFegGTLal9xfB4PNDpdAxIEhERjUMMSNKw0+l0sNlsCVOfjCohWgz9vYOBAb9XpCB8R0cHNm3aNOgulZ2dnfjyyy+xbds2KBQK2O12yGQDn0af1YW/UCwoUECbYmqT3+9HQUEBU5uIiIiGic1mg0ajgdfrTen6yEPGTc0heIOZqWUtk8mQk5ODmpoafPfdd+js7BxQFonL5cI333yDjRs3wu/3Iy8vDyqVatDj693MJtU1isfjQX5+PhSK1B7KEhER0djBgCQNO0EQUFhYiFAolHAh/V+TlBAAbG0Joc418F2SvbtUbtu2LeUvE71JkoSqqip88cUXqKurQ05OzqCLr3uDEr7u2TmRarp2JLWJ6dpERETDR6vVJn2Y2ttkswz5OgG+EPBZbWZ2SQKAUqmE0WjE7t278fHHH+Pzzz/H/v374XQ6kwYnI2uZzz//HAcPHoTJZBpUA5veGlwi9neIkAnAoqLU1jSR8ebm5g76/YmIiCj78HEkjQi73Q61Wg2fzxc33blAL8NR+XJ82xTCe5UBXD134B2lFQoFLBYLDhw4EG1Ak5eXh5ycHFgsloRP5n0+H7777jtUVFRALpcjLy8vI4v3bxqD8IaAfJ2AaVamNhEREY1m+fn5qK2thSRJSdcBgiDgjElKPL/Tj/cPBrC0VAGFLDOZDTqdDlqtFj6fDy0tLWhoaIBarYbFYkFxcTFyc3P7pV+73W589913qKqqyuhaJuLznoyPuXY5LJrU1jRerxdqtZprGiIionGKAUkaEUajEVarFa2trQnrL545SYlvm0LYUB/EhVOVKS9yY1Gr1cjLy4PH40FTUxPq6uqgUCig1WqRl5cHm82GnJwcGI3GaBp2S0sLtm3bhpaWFlgslkF1njxcZPGebmpTeXn5gBroEBER0cDl5ORAqVQiEAiklOK8uFiBN/f70eaV8HVjCCekuHMwFYIgQKPRQKPRQJIk+Hw+OBwONDU1QaVS9QlOulwu7Ny5Ex0dHbBYLFCrB/6AN5aQKGF9/aE1Tao8Hg9ycnKg1+szOh4iIiLKDgxI0ogQBAFFRUVobGxMuNNgilWOKRYZ9neI+Kg6iIumDa7GkUwmg16vjy5+A4EAvF4vKisrceDAAahUKhgMBhQUFEAul2Pfvn3w+/3Izc2FXC4f1Hv31tgtYm+7CAHAohQX75IkQZIk5OXlZWwcRERElBqLxQKDwQCPx5NSQFIlF3D6RCVe3xfAOxV+HF8oH5L6z4cHJ/1+P9rb29Hc3AyVSgVRFCEIAnJzcwdV9zqe7a0hdPgkGJXAkXmpr5UCgQAKCwtZE5uIiGicYg1JGjG5ublQqVTw+/0JrzuzLLwb8JOaQMYKw0dEajHl5uYiLy8POp0O3d3d2LlzJ7Zt2waZTAa73Z7RYCRwqJ7UvFw5rGmkNmk0GqY2ERERjQCZTIaCgoK0GuQtLVVCIwdqXRK2t4aGcHRhgiBE06Aj6xqj0YicnJwhCUYCwBc9GR/HF6Welh4IBKBQKLimISIiGscYkKQRYzabYTabkxaIn58nR75OQHfgUAfHoRBZxFssFuTn56OgoABGozHjT+5DooR1A0htcrvdsFqtTG0iIiIaITabDYIgQBRTa7anVwo4eUL4d/3qisBQDq2fyLomEx204+nyS9jcHA60nliSejmZSE1sq9U6VEMjIiKiUY4BSRoxgiCguLgYfr8/YWdIWU9heAB4vyqAkJjZXZLDbVtrCE6fBKMq9dQmSZIQDAZRUFDA1CYiIqIRYrPZoNFo4PF4Ur7njElKyAVgT7uIAx1Dv0tyOG2oDyIkAZNMMkwwpv61wuv1Ij8/P2FTQSIiIhrbGJCkEWW326MF4hNZXKyAUQm0eiRsbMruxXxkl+cJaaQ2BYNBKBQK2Gy2oRwaERERJaDRaJCbm5s0u6O3HI0MCwvDgbd3Kod3l+RQkiQp2qBvcRoZH5HdpXa7fUjGRURERNmBAUkaUVarFUajMenCXiUXcOrE8C7JdysDCXdUjmZOn4StLeGA6knFqac2ud1uGAwGWCyWIRoZERERpaKoqAiSJKWctg0AZ5WHf+dvagqhsTv1+0azqk4RNV0iFDJEA66p8Pl8UKvVfMhKREQ0zjEgSSNKJpOhqKgopQLxS0uVUMqAg50i9rRn52J+fU9qU7lZhuI0Upt8Pl+08zcRERGNnNzcXGi12rR2SRYbZDgyVw4J4QerY0Fkd+RReXIYVKmXk4nUxNbpdEM1NCIiIsoCDEjSiMvNzYVcLk+atm1SCdGUoGxczEuShM9rw+M+qSS91CZBEJjaRERENApotVoUFBSkFZAEDu2SXFcXRIc3Ox+sRvhDEr5sSL9BHwDWxCYiIiIADEjSKGCz2WAwGFJa2J8xSQkBwNaWEOpc2bWY398hor5bgkoOHJdGapPH44FGo2FqExER0ShRWFgIAAiFUq9rPc0qxxSLDEEJ+LAqOFRDGxabm0PoDgA5GgGz7alnbwQCASgUCuTk5Azh6IiIiCgbMCBJI04ul6ectl2gl+Go/PDC9/2D2bNLUpQk/Gu3HwBwXIECWkXquwI8Hg/sdjs0Gs1QDY+IiIjSkJubC71en/YuybN7dkl+XBOAJ5id9bCBQ+nai4oUkKWx09Hj8UCv18NqtQ7V0IiIiChLMCBJo0JeXh4EQUhpp8GySeHF/PosSnn6vDaIA04RGjlwwdTUm9lIkoRQKIS8vLwhHB0RERGlQ61Wo7CwEB6PJ637jsiVo0gvwBMEPqnJngervbV5RHzXGl6vpdNdGwC8Xi/y8/NZE5uIiIgYkKTRwWazpbzTYGqvlKePqkd/ypPLL+HVveHdkedNUcGqSX3a+f1+qFQqpmsTERGNMpE6iOmkbcsEAWeWhR9MfnAwiICYfbsk19UHIQGYbpUhX5/6mibSlZw1sYmIiAhgQJJGCaVSmdZOg8hi/pOaALyjPOXp1b1+uAJAiUHAaRPT20ngdrthNBphNpuHaHREREQ0EJG07e7u7rTuW1ikgEUtoMMnYUP96H+w2lt3QMJHVeGdnSem0aAPCO+OZE1sIiIiimBAkkaNdNK25+fJka8T0B0Ip0OPVvs7Qvi0Z3w/nK2GQpZeR0m/34/CwkJ2oiQiIhpllEoliouL007bVsoEnNFTfubdygBEaXQ/WO3tzf1+dPqBQr2AhWk06APC9SOtViu0Wu0QjY6IiIiyCQOSNGrY7XZotdqUFvYy4dBi/v2qAEKjMOVJlCT8c2c4VXtRkQLTrOnVSwoGg5DJZNxJQERENEoVFBRALpcjGEzv4eiSCQpoFUBDt4QtzamnfI+kmi4RH/V0B79sZnoPWSVJQjAYjKa5ExERETEgSaOGWq1Gfn5+yh0rFxUrYFQCrR4JH1aNvl2SH1cHUdUpQqcALp6uSvt+j8cDnU7HgCQREdEoZbPZYDQa007b1ioELJ0QfrD6TmUA0ijfJSlJEv650wcJwDH5csyxp/eQNRAIQKFQICcnZ2gGSERERFmHAUkaVfLz8wEcKnyeiFou4Pyp4UDfq3v9ONAxenYYdPhEvL4vvDvyomkqmNTp7wbweDzIz8+HUpl6V24iIiIaPgqFAsXFxfB6vWnfe/okBRQyYH+HiH0dydc9I+nLhhD2totQyYDvzxjYQ1aDwQCLxZL5wREREVFWYkCSRpXc3FxoNJqU6zGdMkGBBQVyhCTg0S0+uPyjY4fBy3v88ASBMpMMSyakV2MJAEKhEARBQEFBwRCMjoiIiDIlPz8fCoUCgUAgrfssahkWF4XXCKsr0rt3OHmCEl7eE37IunyyEjZt+l8ffD4f8vPzIZent7OSiIiIxi4GJGlU0Wq1yMvLSzltWxAEXDVbjTydgDavhKe3+0Y87Wm3I4QN9SEIAH4wWwXZAGoluVwuGI3G6I5RIiIiGp1ycnIGlLYNAMvKlBAAbG0Joa5rdO6S/Pf+ADp8EvJ1As4sSz9rI5L1YrfbMz00IiIiymIMSNKoU1BQAFEUUw4s6pQCfn6kGgoZsKUlhPcOjlw9yaAo4fmdPgDAyRMUKDenvxNAkiR4vV6UlpYyXZuIiGiUk8vlKCkpgc+X/kPRAr0MR+eH1wrvVI6+XZJ1LhEfVoXHdelMFZRpNLKJ8Hq90Gg0rIlNREREfTAgSaOO3W6HRqNJqx7TRJMcl844VE9yX/vI1JP8oCqAepcEoxK4aGr6NZaAcFqTSqVCYWFhhkdHREREQyFS8zndtG0AOKs8/PBxfX0Q21pGT5M+SZLw4i4fQhIwP0+OI3LTL0EDAG63GzabDVqtNsMjJCIiomzGgCSNOnq9HjabLe3Up1MmKHBcgRyiBDy21YeuYa4n6fCK+Pf+8BeR701XwaBKfxcBEE7Xzs3NZeF3IiKiLGG1WmE2mweUtl1ulmPpBAUkAE9s86HFPTpSt79pCmFnmwjFABvZAOGgZigUYgkaIiIi6ocBSRp1BEHAhAkTIIoiQqHUdzoKgoAr56hRoBPg8Ep4arsP4jDWk3xplx++EDDFIsOi4oHtIgiFQhBFEaWlpRAGUHuSiIiIhp9MJkNJSQn8fv+Aall/f6YK5WYZugPA37b44A+NbD1sX1DCv3aHG9mcXaZEnm5gXxn8fj9UKhXTtYmIiKgfBiRpVCosLITRaITL5UrrPq1CwM/na6CUAdtaQnh3mOoxbW8JYmNTuJHND2cNrJENAHR3d8NgMLC7NhERUZbJy8uDSqWC3+9P+16lLFwP26AEDnaKeGFX+q+RSW9XBODwSrBrBZxdPvB61i6XCzk5Ocz6ICIion4YkKRRSaVSYdKkSfB4PGnvNJhglOHymeHUotf3BbB3iOtJ+kNS9IvD6RMVKDWl38gGCKc1eTwelJSUQKUaWGoUERERjQyLxQKr1TqgtG0AsGlluO4IDQQAn9UG8VntyDS5aewWow90L52hgko+sIesoigiGAwy64OIiIhiYkCSRq2SkhJotVp4PJ607z2pRIHji8L1JB/d4kPnENaTfLcygCa3BItawPkDbGQDhNOalEoliouLMzg6IiIiGg6CIKCkpASBQGBAadsAMMcux/lTwzsSn9/pR1Xn8Dbpk6TwQ9aQBMyzyzE/b2APWYFwMxudTsesDyIiIoppYIXuiIaB0WhEUVERKisrodPp0rpXEARcMUuNg04PGrolPLnNhxuOVg84lToWSZKwvj6I/1SEdxFcMl0FrWLgr+9yuWC325GTk5OpIRIREdEwysvLg1qths/ng0ajGdBrLC9X4kCHiK0tIfzvZh/uPkELvXJ4dhhuag5hR2sICgG4dKZqUDsb3W43Jk+ezO7aNOSCwSCCwdHToZ6IaKyQyWRQKpVDlunAgCSNaqWlpaiqqooWRU+HRiHg50dq8PsNHuxoDeE/FQGsmJyZVOgWt4jndvqxozW8c2FerhzHFQ58F0GkgQ/TmoiIiLKX0WiEzWZDU1PTgAOSMkHAtfPUuHu9By0eCU9s8+FXR2X2oWos/pCEl3pK0CwrU6JAP/BEqmAwCEEQmPVBQ8rtdqO1tXXAZRKIiCg5pVIJo9EIu90OuXzgMY9YGJCkUS03Nxd2ux1tbW0D6tBYYpThB7NUeGaHH6v2BTDVIsdM2yACh5KEj6qCeH1fuKO2QgacN1mJZWWDe2rQ3d0NnU6HwsLCAb8GERERjaxIEK6+vh6SJA14baBXCrh+vhr/86UX21pCePtAAOdOGdr60qsrAmjzSsjRCDhnEI1sgHDWh9lsRm5uboZGR9SX3+9HTU0NlEolCgsLoVar+VCfiCiDJElCKBSCy+VCR0cHPB4PJkyYkNGgJAOSNKrJZDJMmjQJzc3NEEURMln6T+tPLFFit0PEuvog/neLF0tKlDihWIFiQ3qvVdcl4u87fDjgFAEA06wyXDVbjcI0XycWj8eDqVOnDng3BREREY0OeXl50Gg08Hg8aZec6W2iSY4f9jxUfXN/AJMtMsyxD83SvdktYnVPI5vvz1BBPYgSNJIkwefzYcaMGRnfSUEU0dzcDLlcjokTJ/L/MyKiIWQwGGA2m1FdXY3W1lbk5+dn7LUZkKRRr7CwEAaDAS6XCyaTaUCv8cNZKtS6RFR1hhfcqysDmGiS4YQiBRYWKmBWx194B0UJ/6kI4O0DAYQkQCMHvjddhSUTFBlJn/L7/ZDL5UxrIiIiGgP0ej1yc3NRX18/qIAkEH6our9DxKe1QTy+1Ye7T5DBrs1sT8pvm4J47jsfgiIwyybDMfmDC+54vV6o1WpmfdCQkSQJbrcbVquVwUgiomGg1WphMpnQ1dWFvLy8jO1IZ0CSRj21Wo1JkyZh27ZtMBqNA/qfX60Q8LuFGmxpDmF9fRDbWkKo6hRR1enHy3v8mGOT44RiBY7Kk0MlP/T6+ztC+McOH+pc4W6ZR+bK8cPZKuRoMvdlwOVywWq1wm63Z+w1iYiIaGQIgoCioiLU1tYOOLujt8tmqlDVKeJgp4i/bfHh9uM0UMoG/0WgOyDhxV1+rK8PNwMpNgi4avbg015dLheKiooG/BCZKJlAIIBQKMSGSUREw8hoNKKjowOBQCDt/h7xMCBJWaG4uBj79u0bVPqTUiZgQYECCwoU6PJL+KohiPX1QVQ4RWxrDWFbawgaObCgILxrcktLEB9VBSEBMKqAy2eqcWyBPKP1aSRJQiAQwMSJEwf9hYWIiIhGh95p23q9flCvpZIL+PmRaty9wYNKp4iXdvlxxWz1oF5zW0sQf9/hR4dPggDgzDIlzp+qHHSgUxRFSJKECRMmsJ4fDRlRDJdP4u5IIqLhE/k7N/J3cCYwIElZwWQyobCwEAcPHhx0+hMAGFUCTpuoxGkTlWjsDteX3FAfRKtHwud1QXxeF4xeu6hIge/PUMGgyvzC2u12s5kNERHRGKPT6ZCfn4/q6upBByQBIFcnw0/mqfHwtz58UhOEQSVgSYkCtjTTtz1BCf+324/PasPrnAKdgGvmqTHFkpnATnd3N/R6fUbrSxHFw6A3EdHwGYq/cxmQpKwgCAJKS0tRXV2NQCAApXJw3R97K9DLcOFUFc6fosS+dhHr64PY2BSEUSXgshkqzM0dumnS3d2N8vLyjARZiYiIaPQoLCxEVVVVRtK2AWBergLnThHx5v5wXeu3DwRQbpbhmAI5FuQrkKtL/B4720J4ZrsPbd7wrsjTJypw4TQV1PLMfcFwu92YMWMG1OrB7eAkIiKisY8BScoaeXl5sNlscDgcsNlsGX99mSBgeo4c03PkuHK2asifugYCAchkMpSUlAzp+xAREdHwy8vLg16vH1RTvsOtmKyEVS1gfX0Qe9tFVDjD/7yyJ4BJpnBw8ph8BQr0h4KTvqCEV/b6saY6vCsyVyvgmrlqTM/JbLqr3++HQqFAUVFRRl+XiIiIxiYGJClryGQyTJo0CS0tLRnbbRDPcKSAuFwumM1m5ObmDvl7ERER0fDSaDSYNGkSduzYMeCmfIeTCQJOnqDEyROU6PCJ2NQUwjeNQex2hJveHOwU8dreACYYZVhQIEexQYaX9/jR7A435ztlggIXT1dBo8j8OifSpG8oHhoTERHR2MOAJGWVoqIiGAyGjO42GAmSJMHv92PixIksyE1ERDRGlZaW4sCBA3C73RmpJdmbRS3D0lIZlpYq0emXsKkpiG8ag9jlEFHTFf4nIkcj4Edz1JhjH5o1R6RJX2lpKZv0EY0SkyZNQlVVVcJrHn74YfzqV78angERER2GAUnKKmq1GhMnTszoboOR4PF4oNFo2MyGiIhoDDMajZgwYQL27t0LnU43ZOsWk0rAkglKLJmghMsvYVNzEBsbQ6h0hjA/X4FLpqugUw7dmsnj8UCr1aKgoGDI3oOIBmbRokWYMmVKzHOzZs0a5tFkr2effRZXXXUVrrjiCjz77LMjPRyiMYEBSco6xcXF2LdvH7xeL7Ra7UgPZ0BcLhcmTpwIo9E40kMhIiKiITRx4kQcPHhw2NYtBpWAk0qUOKkkcw0Ak4msawwGw7C9JxGl5pprrsGVV1450sMgIuqHORWUdcxmMwoKCuByuUZ6KAMSDAYhCAImTJgw0kMhIiKiIWa1WlFUVISurq6RHsqQCIVCEASBTfqIiIgoLQxIUtYRBAETJ06EIAgIBAIjPZy0RZrZ5OXljfRQiIiIaIgJgoCysjLI5XL4fL6RHk7GuVwuGI1GrmuIslxtbS1+8YtfYOrUqdBoNDCbzVi0aBGeeOIJhEKhftc/++yzEAQBV155JRwOB371q19h8uTJUKvVWLJkSZ9r16xZgwsuuACFhYVQqVTIy8vD+eefjw0bNsQdj9vtxl/+8hcsXrwYVqs1WrrrnHPOwUsvvdTn2qqqKtx///1YunQpSktLoVarYbFYsHjxYjzxxBMQRTHme3z77be4+OKLUVJSApVKBZPJhPLyclx44YX497//Hb1u0qRJuOqqqwAAzz33HARBiP5z+GclotQxZZuyUl5eHqxWK5xOJ3JyckZ6OCmTJAk+nw/Tp0+HQsHpR0RENB7k5uYiLy8PjY2NyM3NHenhZIwkSfB6vZgyZQqUyuFLESdKRJIkeAL9A2jZQKuUj0iN/G+++QbLli2Dw+FAaWkpzjvvPDidTqxduxbr16/HqlWr8NZbb0GlUvW7t7W1Fccccww6Ojpw4okn4uijj+5z3W9+8xs8+OCDkMlkOOaYY3DiiSeiuroa//73v/H222/jqaeeigb7ImpqarBs2TLs3LkTOp0OixYtgs1mQ11dHT7//HNs374dl156afT6f/7zn7jjjjtQVlaGadOmYdGiRWhoaMCGDRuwbt06fPDBB3jttdf6/GzXrFmDM888E4FAAEcccQSOP/54hEIh1NXVYfXq1QiFQjj33HMBABdddBG+/PJLrFu3DpMnT8bixYujrzNjxoyM/XcgGm8YEaGsJJfLMWnSJGzcuBGiKGZNR0ev1wu1Wo2ioqKRHgoRERENE0EQUF5ejsbGRgQCgTETvPP7/VAqlWzSR6OKJxDCrDvfH+lhDMjO358BnWp4v6L7fD6sXLkSDocD1113HR555JHo31EVFRU49dRT8f777+Oee+7Bvffe2+/+1atX49RTT8Ubb7wBk8nU59xTTz2FBx98EFOmTMHrr7+OefPmRc999tlnWL58Oa677josXrwYU6dOBQCIoogLLrgAO3fuxH/913/hhRde6PMgx+v14uOPP+7zPmeccQbOO+88zJkzp8/x+vp6nHXWWXjjjTfw2muvYeXKldFz9957LwKBAF544QVcdtllfe5zOp3YtWtX9M8PPPAAnn32Waxbtw6LFy9mUxuiDMmOKA5RDMXFxTAYDOju7h7poaREkiR0dnYiPz+/3y9rIiIiGtsKCgpgs9nQ2dk50kPJGJfLBZvNBqvVOtJDIaI4rrrqqj4pxoenGr/66quoqqpCUVER/vKXv/R5YFJeXo4HHngAAPDXv/4VXq+33+srlUo8+eST/b7fiKKIu+++GwDwr3/9q08wEgBOOukk3HHHHfD7/XjiiSeix99++21s3LgRhYWFeP311/vtKtdoNDjrrLP6HFuwYEG/YCQAFBUV4U9/+lP0c/bW1NQEAP1eCwj3LFi4cGG/40SUWdwhSVlLo9GgtLQUO3fuhMFgGJH0hnS4XC5otVrMmDFj1I+ViIiIMksul6O8vBxff/01QqEQ5HL5SA9pUERRRDAYRGlpKdc1NKpolXLs/P0ZIz2MAdEqM//3wqJFizBlypR+xyOpxmvXrgUAXHLJJVCr1f2uu+CCC2C1WtHe3o5vv/0WixYt6nN+/vz5KC8v73ff5s2bUV9fj8mTJ+Poo4+OObZIUHT9+vXRY++99x4A4NJLL4XBYEj+AXv4fD588MEH+Oabb9Dc3AyfzwdJkqINxfbs2dPn+mOPPRY7d+7EZZddhttvvx0LFy5kSS2iYcYZR1mtpKQEBw4cgNfrhVarHenhxBUKheB2uzFv3jzuIiAiIhqniouLYbFY0NnZmfXrAbfbDZ1Oh4KCgpEeClEfgiAMe9rzaHbNNdfgyiuvjHu+rq4OAFBWVhbzfKQxV3t7e/Ta3iZNmhTzvoqKCgDAgQMHkj60aGlpif57VVUVgPRqM3755Ze4+OKLUV1dHfeaw3en33fffdi2bRveffddvPvuu9BqtTjqqKOwZMkSXHbZZZg5c2bK709EA8O/qSmrWSwWFBUVobKyEmq1etTWkmxvb0dubm7Mp5NEREQ0PiiVSpSXl+Pbb7/NqhrYsbjdbkyePHlUPxAmoqEX7++ASGfrgoICnHFG4h2rdrt9wO/vdrtx3nnnoampCVdddRV++tOfYsqUKTCZTJDL5di7dy+mT58OSZL63FdQUICNGzfi008/xUcffYR169bhq6++wrp16/DHP/4R9913H2655ZYBj4uIkmNAkrKaIAiYPXs2HA4HOjo6RmXHbY/HA5lMhlmzZo2ZIvZEREQ0MCUlJdi7dy9cLlfW1pQOBoOQyWQoLi4e6aEQ0SBF5nFkR2MslZWVfa5NxYQJEwAANpstrSYwpaWlAIDdu3endP1nn32GpqYmHHXUUfj73//e7/y+ffvi3huppRlJHfd6vXj22Wfx85//HLfffjsuuugiTJ48OeWxE1F6svexLFEPvV6P2bNnQxRFeDyekR5OH6Iowul0oqysDPn5+SM9HCIiIhphGo0GkyZNgsfj6bdjJ1tEgqmHN5sgouwTCca9/PLLMZvWrFq1Cu3t7TAajXFrQcayYMEC2O127Ny5E999913K9y1btgwA8H//938pNS91OBwADgUyD/fCCy+k/N4ajQbXXXcd5s2bB1EUsW3btug5lUoFIPxAhogygwFJGhNKSkpQXl6Ozs7OaHrAaNDR0QGr1cpGNkRERBRVWloKnU6X0pft0UaSJPh8PpSWlmZ9Yx4iAlauXInS0lLU19fjhhtu6BNwq6ysxI033ggA+MUvfgGNRpPy6yqVStx1112QJAnnn38+vvjii37XhEIhfPzxx/jyyy+jx1asWIH58+ejvr4eK1euRFtbW597vF4v3n333eifI7Ue16xZg507d/a59sknn8TLL78cc3wPPPBAzJqTu3fvju6qnDhxYvR4SUkJAPR7DyIaOKZs05ggCAJmzZqFtrY2OByOQdUhyRS/3w9RFDFjxgzWVyIiIqIog8GA0tJS7N69G3q9PqseWjqdThiNxmg6JhFlN7Vajddeew3Lli3DY489hnfeeQcLFy5EV1cXPv74Y3i9Xpxxxhm466670n7t66+/HtXV1fjzn/+ME088EbNnz8aUKVOg1WrR2NiILVu2oKOjA4899hgWLlwIAJDJZFi1ahXOOOMMvPvuuygtLcXixYths9lQV1eHrVu3wmKx4ODBgwDCXb7PPfdc/Pvf/8b8+fOxZMkS5OTkYMuWLdizZw9uv/123Hvvvf3G9j//8z+46aabMGPGDMycORNarRb19fX44osvEAwG8cMf/hBHHXVU9PqFCxeiqKgImzdvxlFHHYW5c+dCqVRi+vTpuOmmmwb2wyca50Zkh2RnZyfuuOMOnH322Zg8eTLMZjNUKhWKiopw7rnnYvXq1Qnv9/v9uP/++3HEEUdAr9fDarViyZIleO2114bpE9BopNFoMHfuXMjl8hHfcSBJEtrb21FcXMwFOxEREfUzceJEqNXqUVduJpFgMAi/34/p06dDr9eP9HCIKEMWLFiALVu24Oc//znkcjlWrVqFzz//HPPnz8djjz2G//znP9GU5XT96U9/wrp163DZZZfB5XLhvffew+rVq1FfX48lS5bg6aefxsUXX9znnokTJ2Ljxo24//77MXv2bGzYsAFvvPEGqqqqcPLJJ+P+++/vc/2rr76KP//5z5g+fTq++OILfPDBBygtLcX777+Pa665Jua4/va3v+Gqq66CQqHAp59+itdffx2VlZU4/fTTsWrVqn51L1UqFd5//32sWLECtbW1eOGFF/DMM88kjV0QUXyCNALFa/bv34+pU6fCYDBgzpw5KCwshEwmw/79+7F161YAwM9+9jP87W9/63ev2+3G6aefjvXr18NisWDp0qVwuVz4+OOPEQwGceONN+KBBx5Ie0ydnZ0wm81wOp1ZW2CcwoHAHTt2YOfOnbDZbFAoRmYTcGdnJwRBwEknnQSz2TwiYyAiIqLRS5IkbNy4EZWVlcjLyxvp4aSkpaUFeXl5WLRo0YitsYi8Xi8qKytRVlaWVgoxERENXDp/96YaXxuRlURBQQE2bNiAY445pt9i5pNPPsHy5cvx6KOPYsWKFTjjjDP6nL/99tuxfv16zJ07Fx9//HE0Nffbb7/FkiVL8OCDD2LJkiVYvnz5sH0eGj0EQcCMGTPQ1taG5uZm5ObmDnsaVDAYhMfjwZFHHslgJBEREcUkCAImTZqEmpoa+Hw+qNXqkR5SQh6PB3K5HDNmzGAwkoiIiAZtRFK2DQYDFi5cGHMxc8opp+CSSy4BAHzwwQd9zrW3t+Oxxx4DADz22GN96gQeffTRuOWWWwAgZo0IGj+USiXmzp0LtVoNl8s17O/vcDhQUFCAyZMnD/t7ExERUfaw2+3Iz89HZ2fnSA8lIUmS4HQ6MXHixKzZzUlERESj26jssh0JVB7+pPidd96B3+9HaWkpFi1a1O++Sy+9FADw5Zdfor6+fugHSqOWzWbDjBkz4Ha7EQgEhu19u7u7oVQqMWvWLO4eICIiooQEQUBZWRkEQRjW9Uq6Ojs7YTQaMX369KxqwENERESj16gLSH7zzTd4+eWXIQgCzjnnnD7nNm/eDAA45phjYt5bXl6OnJwcAMCWLVuGdJw0+k2ZMgXFxcVwOBwYjlKpoijC5XJh8uTJo6LLNxEREY1+BQUFsNvtcDqdIz2UmILBIHw+H6ZPnw6DwTDSwyEiIqIxYsS3cN15552orq6Gx+NBZWUlvvnmG6hUKjzyyCM4/vjj+1xbWVkJACgtLY37eiUlJXA4HNFr4/H5fPD5fNE/j/ZUGUqfXC7H3Llz0dHRAafTCYvFMqTv197eDqvVimnTpnH3ABEREaVEJpOhvLwcLS0tCAQCUCqVIz2kPtrb25Gbm4tJkyaN9FCIiIhoDBnxgORbb70V7awNhOtLPvTQQ/jRj37U79quri4AgF6vj/t6kSe3yQKM9913H+65556BDJmyiNlsxuzZs7Fx48YhLRjv8/kgSRJmzZrFbn9ERESUluLiYhQXF6Ompga5ubmQyUZHEpPX64VcLsfMmTNZioaIiIgyKu2Vxc0334y33nor7Td6+umnsXjx4n7HI6nVnZ2d2LNnD/7yl7/g2muvxb/+9S+8+eabMBqNab9XKm677TbccMMN0T93dnZiwoQJQ/JeNLImTpyI5uZmVFZWIi8vL+OLfEmS0NHRgbKyMhQXF2f0tYmIiGjsk8vlOPLII+FyudDW1ga73T7i2RaRRjaTJ09Gfn7+iI6FiIiIxp60A5L19fXYs2dP2m+UrNuxyWTCggUL8OKLL8JiseDRRx/FPffcgwceeCB6TSQ42d3dnfR9TCZTwvdTq9VDtluORheZTIY5c+agvb0dHR0d0TqjmeDz+dDR0QGTyYSZM2eO+JcHIiIiyk46nQ7z58/H+vXr0dXVlXQtO9S6urqg1+vZyIaIiIiGRNpbxV544QVIkpT2P8uWLUv5Pa666ioAwKpVq/ocj9Suqa6ujntvbW1tn2uJgHCa/5w5cwAAbW1tCAaDg3q9YDCI1tZWuFwulJaWYuHChUO2m5eIiIjGh9zcXMyePRs+nw9er3fExhEKheD1ejF16lSub4iIiGhIjMpiMJEakc3NzX2OH3XUUQCAjRs3xryvoqICDocDADB//vwhHCFlo+LiYhx11FE4cOAAHA4HBEGA0WhMa6esKIro7OyE3++H3W7H9OnTUVRUxJ0DRERElBHl5eXo6OjAgQMHoFQqIZfLh30M7e3tsNvtKC8vH/b3JiIiovFhVAYk16xZAwCYNm1an+NnnXUWVCoVqqursW7dOixatKjP+ZdeegkAsHDhQhQVFQ3PYClrCIKASZMmYcKECWhoaEBlZSWam5vhdDphNBqh0WjiBhYlSYLb7YbL5YLJZMLcuXMxceJEFngnIiKijJLJZJg7dy5cLheam5uRm5s7rA8+vV4vBEHAzJkzR13HbyIiIho7RqSF30svvYRvv/2233FJkvDGG2/gd7/7HQDg2muv7XPearXipz/9KQDgZz/7Gdra2qLnNm3ahPvvvx8A8Nvf/naohk5jgFwuR0lJCRYvXowTTzwRkyZNgs/nQ3NzM1wuFyRJ6nN95FwwGMTMmTNx8sknY/LkyQxGEhER0ZBQq9U48sgjYTAY0N7ePmzvG2lkM2HCBBQUFAzb+xIREdH4MyIRlQ8++ACXXXYZSkpKMG/ePFgsFrS1tWH37t2oqqoCAPz85z/vF5AEgD/+8Y/4+uuvsWHDBkydOhVLly5Fd3c31qxZg0AggBtuuAHLly8f7o9EWUgQBOTl5SE3NxcdHR2orq5GdXU1mpqaoNVqodVq0dnZCUEQMHHiREyfPh1Wq3Wkh01ERETjgMViwdy5c/H111+ju7s7WtJoKEUa2cyYMYPlaIiIiGhIjUhA8sc//jHMZjPWr1+PTZs2oa2tDUqlEiUlJbjiiitwzTXXYPHixTHv1el0WLt2LR566CG8+OKLeOedd6BSqXD88cfj+uuvx8qVK4f501C2EwQBVqsVVqsVU6ZMQXV1NQ4ePIjOzk7YbDbMmDEDBQUFXJgTERHRsCopKUFHRwd27twJlUo1pCnUoVAIHo8HRxxxxIh3+CbKBK/Xi0AgMNLDSJlSqYRGoxnpYRARDRtBOjw/dZzq7OyE2WyG0+nkIozg8/nQ0dEBm83G1GwiIiIaMcFgEF9//TWqq6uRl5cHmSzzFZdEUURraytsNhtOOukk1o6kUc3r9aKyshJlZWVxA3herxefffYZ3G73MI9u4HQ6HU466aSMBiVrampw//33491330VtbS2MRiOOPvpo/PKXv8TZZ58d8567774b99xzT8LX3bVrF2bMmNHnWDAYxD333INnn30Wzc3NmDZtGu688864G4a2bNmCBQsW4KqrrsKTTz45sA84ykmShAceeADPPfccDhw4AK/XGz2+du1anHLKKTj55JOxdu3atF43slFmvIdyDh48iLKyMkycOBEHDx4c6eFklWeffRZXXXUVrrjiCjz77LMp3ZPK370RqcbXGGkhikGtViM/P3+kh0FERETjnEKhwBFHHAGXywWHwwGbzZbRrA2PxwOn04mcnBzMmzePwUgaEwKBANxuNxQKRVb8Px0ZbyAQyFhA8ptvvsGyZcvgcDhQWFiIM888E21tbfjkk0/wwQcf4M4770wYeDziiCNw5JFHxjxnNpv7Hbv11lvx4IMPory8HGeffTY++eQTfO9738Orr76Kiy66qM+1oVAIP/7xj2G32/GnP/1pUJ9zNHvsscdw8803w2w248wzz+TGpyywZMkSfPrpp/jkk0+wZMmSkR7OmMeAJBERERHRKKbX63HEEUfgyy+/RFdXV0a+1IqiGG2YM23aNMycORNarXbQr0s0miiVSqjV6pEeRkqCwWDGXsvr9eLCCy+Ew+HAxRdfjH/84x/R+f3NN9/gzDPPxO9//3ssXrwYp59+eszXOO+883D33Xen9H7Nzc3461//ilmzZuGbb76BTqfD7t27MW/ePNx99939ApKPPPIINm7ciFdeeQUWi2UwH3VUe+WVVwAAr776ar+f87HHHotdu3ZBp9ONxNDGhOLiYuzatSsrHjpQbCPSZZuIiIiIiFKXn5+PWbNmwefzwefzDeq1vF4vmpubYTAYcNxxx2H+/PkMRhKNIatWrUJNTQ0sFgsef/zxPvN7wYIFuPPOOwEAv//97zPyftu3b4ff78dll10WDbDNmDEDJ598Mr777jt0dnZGr62ursYdd9yB5cuXj/n+D9XV1QCAqVOn9jun0+kwY8YMlJaWDvewxgylUokZM2Zg8uTJIz0UGiAGJImIiIiIssDkyZNRVlYGp9OJ1tZWeDyetGqISZIEh8MBl8uFKVOm4MQTT0RJSQkb9xGNMd988w0A4Oijj465A/G0004DAKxbtw6NjY2Dfr+2tjYAQE5OTp/jNpsNAOByuaLHfv7zn0MQBDz66KODfl8AqKurw0033YS5c+fCaDRCr9dj2rRpuPLKK7F+/fp+19fW1uIXv/gFpk6dCo1GA7PZjEWLFuGJJ55AKBTqd/2zzz4LQRBw5ZVXoru7G7fddhumTJkCtVqNgoICXHHFFairq+tzz5IlSyAIAiorKwEAZWVlEAQBgiBEd52uXbsWgiDETQvesGEDzjzzTFgsFhgMBhxzzDH4+9//nvTn4fF48OCDD2LhwoWwWCzQaDSYPn06br755uh/p8F+vt7S/fmnO75EDh48CEEQMGnSpH7nIj9vAHj99dexePFimEwm6PV6LFq0CO+8806f6yP/PT799FMAwCmnnBJ9DUEQ+tVZbG9vx1133YUjjzwSRqMROp0Oc+fOxf/8z//ErF179913R//7V1dX4+qrr8aECROgVCpx5ZVX4oknnoAgCFi2bFncz9vW1ga1Wg2VSoWWlpbo8Y8++gi/+MUvcOSRR8Jut0OtVqOkpAQXX3xx9O+C0Yop20REREREWUAmk2HevHnIyclBfX092tra0NXVBZlMBp1OB61WGze4GGnYZzabMWvWLEyYMIGBSKIxKhIAjAQED2e32wGEH1Js2rQJZ511Vr9rNm3ahFtvvRUOhwNmsxnz58/HOeecA6PR2O/aSEBo165dfY7v2rULKpUq+n6vvPIK/vOf/+D//b//hwkTJgz480WsWbMGF110ETo6OpCXl4dTTz0VKpUKBw8exEsvvQQAOOGEE6LX966rWVpaivPOOw9OpxNr167F+vXrsWrVKrz11ltQqVT93svpdOKEE05AdXU1TjzxRMyZMwcbNmzA888/j08//RRbt26N1tZctmwZJk2ahNdeew3d3d248MILYTAYACBuXc7eXn31VXz/+99HKBTCnDlzMHfuXNTU1OCaa67Bd999F/e++vp6LFu2DNu3b0dOTg4WLFgAo9GITZs24c9//jNeffVVrF27FhMnThzU5xvoz38w4xuou+66C3/4wx9wwgkn4KyzzsLu3buxfv16LF++HK+//jrOP/98AIgGX9977z00NTXhjDPOQEFBQfR1pkyZEv33nTt3YtmyZaipqUFhYSEWL14MpVKJr7/+GnfccQdef/11rF27Nmat1X379mH+/PlQqVRYtGgRJEmC3W7HJZdcgl//+tf48MMPUVdXh+Li4n73vvTSS/D7/bjggguQm5sbPX7dddehpqYGs2fPxqJFi6BQKLB792688soreOONN/Cvf/0LF154YcZ+ppnEgCQRERERUZZQqVQoLy9HWVkZuru70dLSgoaGBrS2tqKlpQWCIECv10eDk5IkoaOjA6FQCGVlZZg9ezb0ev1IfwwiGkJ5eXkAgIqKipjnex+P7OI73Ntvv4233367zzGz2YxHHnkEP/zhD/scP/LIIzFx4kT84x//wNlnn42FCxfi6aefxrZt27BixQqoVCp0dHTgv//7v3Hsscfi+uuvH8zHAxDuIH7hhRfC6XTi1ltvxT333NMnkNjc3Iy9e/dG/+zz+bBy5Uo4HA5cd911eOSRR6K1BysqKnDqqafi/fffxz333IN777233/u9+eabOOOMM/D5559H6/i2t7dj6dKl2LJlCx599FHcdtttAMINfoDwrrvu7m488MADMXfxxdLY2Iirr74aoVAIDz30EH79619Hz61ZswbLly+PeZ8kSfje976H7du34+qrr8bDDz8cDR4Hg8Fo06GrrroKH3/88aA+H5D+z3+w4xuoRx55BBs2bMBxxx0XPRbpJH/rrbdGA5IzZszAs88+iyVLlqCpqQm33nprzN2rHo8HK1asQE1NDX73u9/hjjvuiH5ut9uNa665Bv/3f/+HX//61zF3tL700ku4/PLL8fTTT/erb3vBBRfgxRdfxD//+c/o/0O9/eMf/wAAXHXVVX2OP/DAAzj55JNhtVr7HH/zzTexcuVK/OQnP8FZZ501KkuzMGWbiIiIiCjLCIIAg8GAsrIyHH/88Tj11FNx7LHHoqSkBMFgEC0tLWhpaUFzczPUajUWLFiAY445hsFIonFg6dKlAIBvv/0Wmzdv7nf+8ccfj/577/qOQLg0xB//+Eds3rwZDocDDocDX3zxBZYvXw6n04krrrgCL774Yp97VCoV/vrXv8Lj8eCMM86A2WzGjTfeiMLCQjz88MMAgFtuuQWtra14+umnIZMdCkPESm9NxUMPPQSn04lzzjkH9913X79djXl5eVi8eHH0z6+++iqqqqpQVFSEv/zlL30aoZSXl+OBBx4AAPz1r3+F1+vt9356vR7/+Mc/+jQVs1qt0cDRRx99NKDPcbhnnnkGXV1dWLhwYZ9gJACceuqp+MlPfhLzvvfffx/r1q3DkUceiccff7zPTlaFQoE//elPmDNnDj755BPs2LGj3/3pfr50f/6DHd9A/f73v+8TjASA2267DWazGXv37kVNTU1ar/fcc8/hwIEDWL58Of7whz/0+dw6nQ5PPvkk8vLy8M9//jPaOK63nJwc/O///m/MZls/+tGPAKBfejgAbN26FZs3b0ZBQUG/tO7zzjuvXzAycnzlypVoa2vDJ598ktbnHC4MSBIRERERZbHIrshJkyZFg5PHHXccJkyYgPLycpx00kmYOHFinyAAEY1dS5cuxUknnQRJkrBixQq8/fbbcDqdqKiowG9+8xs8//zz0YDc4X8v/OAHP8Btt92GI488ElarFVarFYsWLcLbb7+NX/ziFwCAX//61/D7/X3uO+ecc7Blyxbcfvvt+PGPf4w///nP2LFjB8rLy/HFF1/gqaeeitYaDIVCuPPOO5Gbmwu9Xg+z2Yyf//znaQUn33vvPQDAtddem9L1a9euBQBccsklMYNBF1xwAaxWK7q6uvDtt9/2O3/MMcegsLCw3/GZM2cCQMI6i+mIjPOyyy6Lef6KK66IeXz16tUAgAsvvBAKRf9EWJlMhpNOOgkAYtZ2TPfzpfvzH+z4Buqcc87pd0ytVqO8vBxA+v/dIp/j4osvjnk+Uu8zGAzGrN942mmnxUzlBsJ1KydNmoQ9e/Zgw4YNfc5Fdkf+8Ic/jPnzq6+vx1NPPYUbb7wR11xzDa688kpceeWV0RT/PXv2pP4hhxFTtomIiIiIxhC9Xg+9Xp/ROlxElF1effVVXHDBBVi3bh1WrFjR59yvfvUrfPHFF9i4cWO/RjSJ3H333Xj00UfR0tKCr776CieeeGKf87Nnz+6X7uz3+3Httddi8uTJ0e7eN910Ex5++GFcffXVOPfcc/H555/jgQceQGNjI15//fWUxlJVVQUgnGqbikjgqaysLOZ5QRBQVlaG9vb2mEGqeN2wIzsKY+2qHIja2tqE44x3PJKGf8cdd+COO+5I+B69G6JEpPv50v35D3Z8A5Xp/26Rz/GDH/wAP/jBDxJeG+tzJErdjzQXuvvuu/GPf/wDxx9/PAAgEAhEdyUfnq4NIFpmIBAIxH3tw3dCjxYMSBIRERERERGNIXl5efj888/x0Ucf4eOPP0ZbWxvy8/Nx7rnn4phjjkFRUREAYO7cuSm/Zk5ODvLy8tDQ0BANnCVz3333YdeuXVizZg00Gg26urrwt7/9DSeccAKefvppAOFdbNXV1Xj55Zexd+9eTJs2Lf0PPMRG+w5zURQBAIsXL8bkyZMTXjt79ux+x4b68w12fAOV6c8V+RzLli1Dfn5+wmtjPRRMVsfxyiuvxD333INXXnkF/+///T9otVq8/fbbaG1txcKFC/sFgN944w3cfffdMBgM+N///V8sXboURUVF0TrSt99+O+677z5IkpTmJx0eDEgSERERERERjTGCIOD000/H6aef3uf4gQMH0NDQAJvNhqOOOirl1wuFQnA6nQAQs9v24fbs2YP77rsPV155ZbSu5c6dO+H3+/t0XwbCgaqXX34ZW7ZsSSkgWVpaij179mD37t19OiDHE+laHK/RD3CowU+sDsfDpbi4GLt378bBgwdjno93PNK1/Nxzz8VvfvObIRrdIen+/Id7fENlwoQJ2L17N66++mpcdNFFGX/9iRMnYunSpVizZg3eeOMNXHbZZdGakpEak7298sorAIB77703Zvr8vn37Mj7GTBrdYX4iIiIiIiIiyphIA5drr722XzOSRN566y243W4IgoBjjjkm4bWSJOEnP/kJzGYzHnzwwehxQRAAAN3d3X2uj/w5cj6ZSGOPp556KqXrIx2TX3755ZhpuqtWrUJ7ezuMRiOOPvrolF5zKJx88skA0K9xUMTzzz8f8/iZZ54JIJyqPxy74dL9+Q/3+AYqMh+CwWDM85HPEQkEDoXezW2amprw7rvvQqvVxqxb6XA4AMTejdnc3IwPP/xwyMaZCQxIEhEREREREY0hO3fu7Fc3LhgM4o9//COeeOIJTJkyBb/97W/7nK+ursYLL7wQM2D35ptv4pprrgEQbrhSUFCQ8P2feeYZfPrpp3j44Yf71KmcNWsW1Go1Vq1aFQ2muN1uvPDCCwCA+fPnp/T5brjhBhiNRrz11lv43e9+169+XnNzM7744ovon1euXInS0lLU19fjhhtu6BNwqqysxI033ggA+MUvfgGNRpPSGIbC1VdfDYPBgA0bNuCRRx7pc27t2rV9OqT3du6552LBggX4+uuvcdVVV8WsX9je3o7HH388brAtHen+/Id7fANVUlICANFmMIe79tprMXHiRLz66qu45ZZb0NXV1e+axsbGlAO1sVxwwQWwWCz4+OOPce+99yIYDOLCCy/s0wE9ItJ06Mknn+zTaMrpdOKKK66I7mgerZiyTURERERERGNOoiYPo8lQjPPJJ5/EE088gaOPPhrFxcXw+Xz48ssv0dTUhClTpuDDDz+EXq/vc4/D4cAPfvAD/PSnP8X8+fNRXFwMj8eDnTt3RlM/TznlFDz22GMJ37upqQk333wzli1bhksvvbTPOYPBgBtuuAH33XcfZs+ejUWLFmHTpk2orKzE97///ZTSf4FwyvBrr72Giy66CPfeey+efvppHH/88VAqlaiqqsLmzZtx6aWXYvHixQDCnZVfe+01LFu2DI899hjeeecdLFy4EF1dXfj444/h9Xpxxhln4K677kr1RzwkioqK8NRTT+Hyyy/Hf//3f+Ppp5/GnDlzUFdXh88//xy/+tWv8PDDD/e7TyaT4c0338TZZ5+N5557Dq+99hqOOOIIlJaWwu/3o6KiAtu3b0coFMKVV14Zs1NzOtL9+Q/3+AbqwgsvxD/+8Q/cfPPN+Oijj5CXlwdBEPCjH/0IJ5xwAvR6PVavXo3ly5fjT3/6E5588knMmzcPJSUlcLvd2Lt3L3bt2oW8vDz8+Mc/HtAYNBoNLrnkEjz++OP461//CiB2ujYQblD1/PPP45133kF5eTkWLlyIQCCATz/9FDqdDj/60Y/w97//fcA/j6HGgCQRERERERGNGUqlEjqdDm63e0R3W6VDp9NBqVRm7PXOOussHDx4EJs2bcLGjRuhVqsxffp03Hjjjbj++utjNteYMGECbrnlFnzzzTfYv38/Nm3aBL/fD7vdjuXLl+PSSy/FxRdfnLRRyK9+9Sv4/f64gct7770XVqsVTzzxBP79738jPz8ft9xyC37/+9+n9Rn/67/+Czt27MBDDz2E9957D++99x4UCgWKiorwgx/8oF9AaMGCBdiyZQvuv/9+vPvuu1i1ahXUajXmz5+PH/7wh7jmmmtGLBDW2yWXXIKSkhL8z//8DzZs2IADBw5g+vTpePzxx3HttdfGDEgC4WDml19+iWeffRYvv/wytm3bhq+//ho5OTkoKirCddddhxUrVmRsB2i6P//hHt9AnH322Xjqqafw2GOP4eOPP4bb7QYQrnEaqXs6e/ZsbNu2DY8//jhWrVqFbdu2YcOGDbDb7SgpKcFvfvMbnH/++YMax49+9KPobthJkyZFSw4crqysDJs3b8bvfvc7fP755/jPf/6DgoICfP/738fdd9+d9OHBSBOk0ZzAP4w6OzthNpvhdDpjboUlIiIiIiKikeX1elFZWYmysrKEgQuv15s1OySBcBB1JAMxRESJpPp3L5B6fG3kw/9EREREREREGaTRaBjgIyIaxdjUhoiIiIiIiIiIiIYNA5JEREREREREREQ0bBiQJCIiIiIiIiIiomHDgCQRERERERERERENGwYkiYiIiIiIiIiIaNgwIElERERERERERETDhgFJIiIiIiIiyiqSJI30EIiIxo2h+DuXAUkiIiIiIiLKCnK5HAAQCARGeCREROOHz+cDACgUioy9JgOSRERERERElBWUSiXUajWcTid3SRIRDYNQKASHwwG9Xp/RgGTmXomIiIiIiIhoiNntdtTV1aG2thZmsxlKpRKCIIz0sIiIxgxJkhAKheDxeOB0OiGKIgoLCzP6HgxIEhERERERUdYwmUwAgNbWVtTV1Y3waIiIxi65XA6dToe8vDyoVKqMvjYDkkRERERERJRVTCYTTCYTAoEAQqHQSA+HiGjMkclkQ7oDnQFJIiIiIiIiykpKpRJKpXKkh0FERGliUxsiIiIiIiIiIiIaNgxIEhERERERERER0bBhQJKIiIiIiIiIiIiGDQOSRERERERERERENGwYkCQiIiIiIiIiIqJhw4AkERERERERERERDRsGJImIiIiIiIiIiGjYKEZ6AKOFJEkAgM7OzhEeCRERERERERERUfaJxNUicbZ4GJDs0dXVBQCYMGHCCI+EiIiIiIiIiIgoe3V1dcFsNsc9L0jJQpbjhCiKqK+vh9FohCAIIz2cjOvs7MSECRNQU1MDk8k00sMhGnM4x4iGFucY0dDh/CIaWpxjREOLc2x0kSQJXV1dKCoqgkwWv1Ikd0j2kMlkKCkpGelhDDmTycQJSjSEOMeIhhbnGNHQ4fwiGlqcY0RDi3Ns9Ei0MzKCTW2IiIiIiIiIiIho2DAgSURERERERERERMOGAclxQq1W46677oJarR7poRCNSZxjREOLc4xo6HB+EQ0tzjGiocU5lp3Y1IaIiIiIiIiIiIiGDXdIEhERERERERER0bBhQJKIiIiIiIiIiIiGDQOSRERERERERERENGwYkCQiIiIiIiIiIqJhw4DkKBYIBLBmzRrcdNNNWLBgASwWC5RKJQoKCrBixQqsXr064f0fffQRzjrrLNjtdmi1WsyYMQO//e1v4XK5Et63f/9+XHnllSgpKYFarUZJSQmuvPJKVFRUZPLjEY24gcwxURSxfv163HnnnVi8eDFsNhuUSiXsdjtOP/10vPjii0jWK+zbb7/FypUrkZ+fD41Gg7KyMvziF79Ac3PzUH1UohEx2N9jvT366KMQBAGCIOCaa65JeC3nGI0Xg51joijiueeew2mnnYbc3Fyo1WoUFhZi6dKlePTRR+PexzlG48Vg5lhbWxtuu+02zJ07F3q9HiqVCiUlJVi5ciU+++yzhO/LOUbjxYsvvogf/vCHOOKII5CXlwelUgmz2Yxjjz0W9913X8LYBeMdY4BEo9aHH34oAZAASAUFBdLZZ58tfe9735PmzJkTPX7ttddKoij2u/ehhx6SAEiCIEgnnXSStHLlSqmgoEACIE2fPl1qaWmJ+Z5ffPGFpNPpJADS7NmzpYsvvliaPXu2BEDS6/XShg0bhvpjEw2bgcyxffv2Rc/l5ORI//Vf/yVdfPHF0oIFC6LHly9fLvl8vpjv+eqrr0oKhUICIC1YsED63ve+J5WXl0sApPz8fGnfvn3D9fGJhtxgfo/1duDAAUmv10uCIEgApKuvvjrutZxjNJ4MZo51dHRIJ510kgRAMplM0rJly6RLLrlEOvHEEyWLxSIdffTRMd+Tc4zGk4HOsf3790tFRUUSAMlms0lnnXWWdNFFF0kzZ86M3vfggw/GfE/OMRpPFi1aJAmCIM2aNUs644wzpO9///vS0qVLJa1WKwGQpkyZItXV1fW7j/GOsYEByVFszZo10oUXXih99tln/c7961//kuRyuQRAeu655/qc27RpkyQIgiSXy6V33nknery7u1s69dRTJQDShRde2O81u7u7o784b7vttj7nbrvtNgmANGHCBMntdmfoExKNrIHMsf3790tLly6V3n33XSkYDPa5Z+3atZJer5cASPfcc0+/16yrq4v+AnziiSeix4PBoHT55ZdHF57JgjNE2WKgv8d6C4VC0oknnigZDAbpiiuuSBiQ5Byj8Wagc0wURWnJkiUSAOknP/mJ1NXV1ee8z+eTvvnmm36vyTlG481A59iKFSskANLZZ58tuVyuPueeeOIJCYCkUCikmpqaPuc4x2i8+fLLL6W2trZ+x1tbW6XFixdLAKRLLrmkzznGO8YOBiSz2NVXXy0BkE499dQ+x1euXCkBkK655pp+9xw8eFCSyWQSAGnXrl19zv3tb3+TAEjTpk2TQqFQn3OhUEiaNm2aBEB6/PHHM/9hiEaheHMskT/84Q8SAGny5Mn9zt10000SAOm0007rd66rq0sym80SAOm9994b1LiJskUqcyzyBPxvf/ubdNdddyUMSHKOEfUVb44988wzEgDpjDPOSOv1OMeI+oo3xwwGgwRA+vrrr2PeN3XqVAmA9MYbb/Q5zjlGdMhnn30WzUrrjfGOsYM1JLPY/PnzAQA1NTXRY36/P1rL5NJLL+13z8SJE7Fo0SIAwKpVq/qci/z5kksugUzW938NmUyGiy++GADwxhtvZOgTEI1usebYYO6JzLFYc9NgMGDFihUAOMdo/Eg2x/bs2YPf/va3OPnkk/HTn/406etxjhH1FW+OPfLIIwCAm266Ka3X4xwj6iveHNNoNCndb7fb+/yZc4zoEIVCAQBQq9XRY4x3jC0MSGaxffv2AQAKCwujx/bu3Qu32w0AOOaYY2LeFzm+efPmPscjf073PqKxKtYcG+g9XV1d2L9/PwDOMaKIRHMsFArhiiuugCAIeOaZZyAIQsLX4hwj6i/WHGtqasLWrVshl8txwgknoKKiAv/f//f/4brrrsNvfvMbvPrqq/D7/f1ei3OMqL94v8fOPPNMAMA999wT/W4W8dRTT2Hfvn2YO3cujj/++OhxzjGiQ7q6unD33XcDQDQQDzDeMdYoRnoANDCNjY149tlnAQAXXnhh9HhlZSUAwGKxwGg0xrx3woQJfa4FwhO+ra0NAFBaWprwvpaWFnR3d0Ov1w/uQxCNYvHmWCJutzu66+Twew4ePBj992RzrPfcJBqrks2xP//5z/jqq6/w8MMPY/LkyUlfj3OMqK94c2zbtm0AAJvNhqeffho33ngjAoFAn3vLy8uxatUqzJs3L3qMc4yor0S/x/785z9j586dWL16NUpLS7Fw4ULodDp899132L17N84++2w89dRT0R1gAOcYjW8ffPABXnrpJYiiiKamJmzYsAFdXV1YtmwZ7r///uh1jHeMLdwhmYWCwSAuv/xyOJ1OzJ07Fz/5yU+i57q6ugAg4eQxGAwAgM7Ozn73Jbo3ct/h9xKNNYnmWCI/+9nPUFlZiaKiItx+++19zqUzxzi/aKxLNsd27NiBu+66CyeccAJ++ctfpvSanGNEhySaY5EvZA6HA7/85S9x7rnnYvv27ejq6sKGDRtw3HHHoaKiAsuWLYteC3COEfWW7PdYfn4+1q5di8svvxxtbW1YvXo1Xn31VezcuRPFxcVYunQpcnNz+9zDOUbj2c6dO/Hcc8/hn//8Jz744AN0dXXh0ksvxbPPPguz2Ry9jvGOsYUBySx03XXXYc2aNbDZbHjttdegUqlGekhEY8pA5tgf/vAHPPfcc9BoNHjllVdgs9mGYaRE2SnRHAsGg7jiiisgk8nw97//vV+NHyJKLtEckyQJQHiuHX/88Xj11VcxZ84cGAwGLFy4EB9++CHy8/PR0NCARx99dKQ+AtGolmytuHv3bsyfPx9vv/02Hn30UdTU1MDpdGLt2rXIz8/HjTfeiLPOOguhUGiEPgHR6PKrX/0KkiTB7/dj//79ePDBB/Huu+9i1qxZ+Oyzz0Z6eDREuMrPMv/93/+NZ555BlarFR9++CGmTZvW53xk23J3d3fc13C5XAAAk8nU775E90buO/xeorEk2RyL5aGHHsKdd94JtVqNVatWRQsp95bOHOP8orEs2Ry79957sWnTJtxzzz2YPn16yq/LOUYUlupaEUDMDACj0YjLL78cAPDRRx/FvI9zjMazZHMsGAziwgsvxP79+/HUU0/hpz/9KUpKSmAymXDyySfjgw8+QEFBAT788EM8//zz0fs4x4gApVKJyZMn44YbbsC7776L9vZ2XH755fB4PAAY7xhrGJDMIjfeeCMeeeQRWCwWfPDBB9Gubr1NmjQJANDR0dFnW3JvkS5wkWuB8ATNyckBAFRXVye8z263s54CjUmpzLHD/fWvf8WNN94IlUqF119/HcuWLYt53cSJE6P/nmyO9Z6bRGNJKnMs0gHx7bffxpIlS/r8E6nVtXr16uixCM4xotTmWHl5ecx/j3VNQ0ND9BjnGFFqc+yrr77Czp07oVarccEFF/Q7b7Vao01vegf9OceI+jruuOMwa9Ys1NTUYOPGjQAY7xhrGJDMEjfffDMeeughmM1mfPDBB3E7Q02fPh06nQ4AopP2cJHjRx11VJ/jkT+nex/RWJDqHOvtb3/7G375y19Gg5Fnn3123GtNJhOmTJkCgHOMxqd059gXX3yBTz/9tM8/VVVVAMKNBCLHIjjHaLxLdY5NmzYtulOktbU15jWR473raXGO0XiX6hyLBDt0Oh3kcnnMayI18RwOR/QY5xhRf5HAYHNzMwDGO8YaBiSzwK233oo///nPMJvN+PDDD7FgwYK416pUqmhQ5KWXXup3vqqqCuvXrwcAnH/++X3ORf78r3/9C6Io9jkniiJefvllAIj5pI8om6UzxyIef/xxXH/99dFg5PLly5PeE5ljseamy+XC22+/DYBzjMaedObYli1bIElSzH/uuusuAMDVV18dPdYb5xiNV+nMMYVCgfPOOw9A391ZvX344YcAgGOPPbbPcc4xGq/SmWPFxcUAgPb2duzbty/mNV999RUAoKysrM9xzjGiQ1pbW7F161YAiJZGYLxjjJFoVPvtb38rAZAsFov09ddfp3TPt99+KwmCIMnlcundd9+NHu/u7pZOPfVUCYB04YUX9ruvu7tbKioqkgBIt99+e59zt99+uwRAKikpkdxu9+A+FNEoMpA59uSTT0qCIEgqlUp6++23U36vuro6SafTSQCkJ598Mno8GAxKP/jBDyQA0oIFCyRRFNP+HESj1UDmWDx33XWXBEC6+uqrY57nHKPxaCBzbNeuXZJSqZSUSmW/32N/+tOfJACSXC6Xtm3b1ucc5xiNR+nOMb/fLxUXF0sApJNOOklqbm6OnguFQtJ9990nAZAASJ999lmfeznHaDz57rvvpP+/vfsHSa2P4zj+idAIqSHMRCJdIiOXQtCtGgqXiIqGFoeiya2tphrcgkaXIIK2tqaoqCAb+kMUOBRlQ0vR2GAR5PdON55uXZ9L3uf03Ov7BS6e8/v9OMPHgx9/6srKij0+Pr47dnFxYT09PSbJ4vH4m2P0HX+PKrMfthfgf2NtbU2Dg4OSpGg0qo6Ojg/P83q9mp+ff/PcwsKCpqamVFVVpe7ubvl8Pu3t7en29lZtbW3KZrPyer3v5trf31d/f78KhYIikYgikYhyuZxyuZw8Ho+2trYUj8d//8UCX+AzGTs9PVVXV5fMTOFwWLFY7Kfzf/+9u39aXV3V2NiYXl5eFIvFFAqFdHR0pOvrazU1NSmbzb5+XQf405VzH/vI7Oys5ubmNDExocXFxQ/PIWOoJOVkbHl5WePj4yoWi4pGowqFQsrlcjo/P1d1dbUymYwmJyffzUXGUEk+m7Ht7W0NDAyoUCiovr5esVhMdXV1Ojs7Uz6flyTNzMwonU6/m4uMoVLs7u6qt7dXHo9HnZ2dam5u1vPzs25ubnRycqJisaj29natr6+rpaXlzVj6jr/EFxeiKGFpaen107NSj2Aw+OH4zc1NSyQS1tDQYDU1Ndba2mrT09P28PBQct3Ly0tLJpMWCATM5XJZIBCwZDJpV1dX/8FVAl/nMxnb2dn5pTGlXl6Pj49teHjYGhsbze12WzAYtFQqZXd3dw5cNeCccu9jP/q3HZLfkTFUinIzdnh4aCMjI+bz+czlcpnf77fR0VE7ODgouS4ZQ6UoJ2P5fN5SqZSFw2Grra19fV81NDRkGxsbJdclY6gE9/f3lk6nLZFIWCgUMo/HY2632/x+v/X19Vkmk7Gnp6efjqfv+POxQxIAAAAAAACAY/hTGwAAAAAAAACOoZAEAAAAAAAA4BgKSQAAAAAAAACOoZAEAAAAAAAA4BgKSQAAAAAAAACOoZAEAAAAAAAA4BgKSQAAAAAAAACOoZAEAAAAAAAA4BgKSQAAAAAAAACOoZAEAAAAAAAA4BgKSQAAAAAAAACOoZAEAAAAAAAA4Jhv+sawbvrKFwMAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = res.plot_predict(200, 200 + 2 * 52, True)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:11.421932Z", "iopub.status.busy": "2024-04-18T11:33:11.421458Z", "iopub.status.idle": "2024-04-18T11:33:11.443387Z", "shell.execute_reply": "2024-04-18T11:33:11.442736Z" } }, "outputs": [ { "data": { "text/plain": [ "200 -3.253482\n", "201 -8.555660\n", "202 -13.607557\n", "203 -18.152622\n", "204 -21.950370\n", "205 -24.790116\n", "206 -26.503171\n", "207 -26.972781\n", "208 -26.141244\n", "209 -24.013773\n", "210 -20.658891\n", "211 -16.205310\n", "dtype: float64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "auto_reg_forecast = res.predict(200, 211)\n", "auto_reg_forecast" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using with other models\n", "\n", "Other models do not support `DeterministicProcess` directly. We can instead manually pass any deterministic terms as `exog` to model that support exogenous values.\n", "\n", "Note that `SARIMAX` with exogenous variables is OLS with SARIMA errors so that the model is \n", "\n", "$$\n", "\\begin{align*}\n", "\\nu_t & = y_t - x_t \\beta \\\\\n", "(1-\\phi(L))\\nu_t & = (1+\\theta(L))\\epsilon_t.\n", "\\end{align*}\n", "$$\n", "\n", "The parameters on deterministic terms are not directly comparable to `AutoReg` which evolves according to the equation\n", "\n", "$$\n", "(1-\\phi(L)) y_t = x_t \\beta + \\epsilon_t.\n", "$$\n", "\n", "When $x_t$ contains only deterministic terms, these two representation are equivalent (assuming $\\theta(L)=0$ so that there is no MA).\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:11.446592Z", "iopub.status.busy": "2024-04-18T11:33:11.446003Z", "iopub.status.idle": "2024-04-18T11:33:12.279508Z", "shell.execute_reply": "2024-04-18T11:33:12.278777Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " SARIMAX Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 200\n", "Model: SARIMAX(1, 0, 0) Log Likelihood -293.381\n", "Date: Thu, 18 Apr 2024 AIC 600.763\n", "Time: 11:33:12 BIC 623.851\n", "Sample: 0 HQIC 610.106\n", " - 200 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "intercept 0.0796 0.140 0.567 0.571 -0.196 0.355\n", "sin(1,52) 9.1917 0.876 10.492 0.000 7.475 10.909\n", "cos(1,52) -17.4351 0.891 -19.576 0.000 -19.181 -15.689\n", "sin(2,52) 1.2509 0.466 2.683 0.007 0.337 2.165\n", "cos(2,52) -17.1865 0.434 -39.582 0.000 -18.038 -16.335\n", "ar.L1 0.9957 0.007 150.751 0.000 0.983 1.009\n", "sigma2 1.0748 0.119 9.067 0.000 0.842 1.307\n", "===================================================================================\n", "Ljung-Box (L1) (Q): 2.16 Jarque-Bera (JB): 1.03\n", "Prob(Q): 0.14 Prob(JB): 0.60\n", "Heteroskedasticity (H): 0.71 Skew: -0.14\n", "Prob(H) (two-sided): 0.16 Kurtosis: 2.78\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n" ] } ], "source": [ "from statsmodels.tsa.api import SARIMAX\n", "\n", "det_proc = DeterministicProcess(idx, period=52, fourier=2)\n", "det_terms = det_proc.in_sample()\n", "\n", "mod = SARIMAX(y, order=(1, 0, 0), trend=\"c\", exog=det_terms)\n", "res = mod.fit(disp=False)\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The forecasts are similar but differ since the parameters of the `SARIMAX` are estimated using MLE while `AutoReg` uses OLS." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2024-04-18T11:33:12.282852Z", "iopub.status.busy": "2024-04-18T11:33:12.282370Z", "iopub.status.idle": "2024-04-18T11:33:12.322132Z", "shell.execute_reply": "2024-04-18T11:33:12.321448Z" } }, "outputs": [ { "data": { "text/html": [ "
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AutoRegSARIMAX
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201-8.555660-7.985653
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" ], "text/plain": [ " AutoReg SARIMAX\n", "200 -3.253482 -2.956589\n", "201 -8.555660 -7.985653\n", "202 -13.607557 -12.794185\n", "203 -18.152622 -17.131131\n", "204 -21.950370 -20.760701\n", "205 -24.790116 -23.475800\n", "206 -26.503171 -25.109977\n", "207 -26.972781 -25.547191\n", "208 -26.141244 -24.728829\n", "209 -24.013773 -22.657570\n", "210 -20.658891 -19.397843\n", "211 -16.205310 -15.072875" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sarimax_forecast = res.forecast(12, exog=det_proc.out_of_sample(12))\n", "df = pd.concat([auto_reg_forecast, sarimax_forecast], axis=1)\n", "df.columns = columns = [\"AutoReg\", \"SARIMAX\"]\n", "df" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 4 }