diff --git a/notebooks/MAST/exploring_jwst_transmission/comparing_spectral_types_jwst.ipynb b/notebooks/MAST/exploring_jwst_transmission/comparing_spectral_types_jwst.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "65f3eaf0",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5d84656f",
+ "metadata": {},
+ "source": [
+ "# Comparing Stellar Spectral Types with JWST Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "515b6fba",
+ "metadata": {},
+ "source": [
+ "***"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ea13b8cc",
+ "metadata": {},
+ "source": [
+ "# Learning Goals\n",
+ "\n",
+ "By the end of this tutorial, you will:\n",
+ "\n",
+ "- Be able to extract data for multiple targets that meet `SpectralDB` search criteria.\n",
+ "- Become familiar with cross-referencing outputs with `SpectralDB` against `astroquery` catalogs.\n",
+ "- Compare the strength of spectral features in G and K stars by eye."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d28853a0",
+ "metadata": {},
+ "source": [
+ "# Introduction\n",
+ "With the new (as of the creation of this notebook) JWST observations rapidly being acquired, accessing and making sense of the new data is crucial for many observational astronomers. Checking that our astronomical intuition holds with new datasets is a helpful way to make sure that we understand the properties of these datasets.\n",
+ "\n",
+ "\n",
+ "One concrete, applicable idea from stellar astronomy is that spectral features should vary as a function of stellar type. In particular, [stellar spectral classification](https://lweb.cfa.harvard.edu/~pberlind/atlas/htmls/note.html#:~:text=Each%20spectral%20type%20is%20divided,%22color%22%20and%20surface%20brightness.) holds that as stars get cooler, they will have stronger molecular spectral features. This is because at cooler temperatures, molecules are less readily broken apart by thermal motions. Because the abundance of molecules is higher in cooler stars, these molecules will absorb more radiation, creating deeper absorption lines. \n",
+ "\n",
+ "In this tutorial, we will leverage the unique capabilities of `SpectralDB`, a MAST tool, to search directly on wavelength, querying across observations to compare the spectra of different stellar types. Specifically, we will compare the spectrum of a G star to a cooler K star, identifying the stronger molecular absorption expected in the K star.\n",
+ "\n",
+ "For more information on using Specviz and SpectralDB, see the notebook [link previously written SpectralDB notebook].\n",
+ "\n",
+ "The workflow for this notebook consists of:\n",
+ "\n",
+ "- [Collating targets](#Collating-targets)\n",
+ "- [Downloading spectra](#Downloading-spectra)\n",
+ "- [Plotting spectra](#Plotting-spectra)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9253f3d4",
+ "metadata": {},
+ "source": [
+ "# Imports\n",
+ "\n",
+ "- *matplotlib* to visualize the downloaded JWST data.\n",
+ "- *sys* to report our Python version.\n",
+ "- *requests* to interact with the SpectralDB API.\n",
+ "- *numpy* to rework the downloaded JWST data.\n",
+ "- *warnings* to limit the number of warnings printed.\n",
+ "- *astroquery* to cross-reference our stars."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0f1a0b6a",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-09-23T04:53:37.986303Z",
+ "start_time": "2022-09-23T04:53:37.976121Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import requests\n",
+ "import sys\n",
+ "import numpy as np\n",
+ "import warnings\n",
+ "\n",
+ "from astroquery.simbad import Simbad"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b7782b1",
+ "metadata": {},
+ "source": [
+ "## Collating targets"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1ba13cf2",
+ "metadata": {},
+ "source": [
+ "Let's first submit a request via the `SpectralDB` API. As shown in the [SpectralDB documentation](https://mast.stsci.edu/spectra/docs/), these requests can be made over flux and wavelength. There are a number of molecular features blueward of 2 microns, so let's set our wavelength range from 0 to 2 microns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "ac013ae6",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-09-23T04:53:39.482130Z",
+ "start_time": "2022-09-23T04:53:39.470002Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "wav_range = '0,2'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d10f812a",
+ "metadata": {},
+ "source": [
+ "As demonstrated in [link other notebook], these conditions will be passed to the `SpectralDB` request as strings. Ranges are represented as comma-separated values, as above.\n",
+ "\n",
+ "To make some minimum quality cut on our observations (and reduce the time to query our observations), we'll also enforce that our observations have a minimum of 1 Jansky."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "314a423e",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-09-23T04:53:39.911252Z",
+ "start_time": "2022-09-23T04:53:39.903327Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "min_flux = '1'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "34539530",
+ "metadata": {},
+ "source": [
+ "We can now submit our request, starting with the `base_url` upon which `SpectralDB` requests are constructed:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "780e7abc",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-09-23T04:53:40.495639Z",
+ "start_time": "2022-09-23T04:53:40.487193Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "base_url = 'https://mast.stsci.edu/spectra/api/v0.1/search'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8861fc22",
+ "metadata": {},
+ "source": [
+ "Now we create our dictionary of conditions. Note that the syntax for requesting a quantity above some minimum value in `SpectralDB` is `[quantity].gt: min_value` (where gt stands for \"greater than\")."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "a66ef317",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-09-23T04:53:40.655866Z",
+ "start_time": "2022-09-23T04:53:40.651025Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "conditions = {'flux.gt': min_flux, 'wavelength': wav_range}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7d79e81a",
+ "metadata": {},
+ "source": [
+ "With the `conditions` and `base_url` set, we can submit and read in our request."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "72d96756",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-09-23T04:53:42.141032Z",
+ "start_time": "2022-09-23T04:53:40.819042Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# submit the request\n",
+ "response = requests.post(base_url, json={'conditions': conditions,\n",
+ " 'columns': ['targetName']})\n",
+ "\n",
+ "# turn the response into a readable dictionary\n",
+ "response_data = response.json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0faf9d9b",
+ "metadata": {},
+ "source": [
+ "`response_data` is now a Python `dict` that we can parse to access the data we'd like. To get a sense for the data, let's take a look at the first entry of the value of the `results` key."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "455ad439",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-09-23T04:53:42.197510Z",
+ "start_time": "2022-09-23T04:53:42.142292Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'fileName': 'jw01120-o005_t002_nirspec_prism-clear_x1d.fits',\n",
+ " 'x': 0,\n",
+ " 'y': None,\n",
+ " 'wavelength': 0.5984808741858381,\n",
+ " 'flux': 2659528056.691551,\n",
+ " 'targetName': 'HWK-I 74230'}"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "response_data['results'][0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c0136bdc",
+ "metadata": {},
+ "source": [
+ "The response of the request returned the fileneame of the data, the average wavelength of the observation, the average flux of the observation, and the name of the target name observed. For 2D data, `x` and `y` are used for plotting, but they are not relevant to the 1D spectra that we consider here.\n",
+ "\n",
+ "Next, we can parse through these results to construct a list of unique target names. We'll ignore blank target names."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "5cc341e0",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-09-23T04:53:42.221734Z",
+ "start_time": "2022-09-23T04:53:42.198378Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['(2002 XV93)' '10 Hygiea' '175113 (2004 PF115)' '2 Pallas'\n",
+ " '230965 (2004 XA192)' '2MASS J12560183-1257276' '2MASS J15395077-3404566'\n",
+ " '2MASS J16194609+5534178' '2MASS J17430448+6655015'\n",
+ " '2MASS J17540383-2810466' '307982 (2004 PG115)' '52872 Okyrhoe'\n",
+ " '624 Hektor' 'BD+04 3653' 'ENCELADUS' 'F2M1106' 'GANYMEDE' 'GCRV 21765'\n",
+ " 'GSPC P330-E' 'HWK-I 74230' 'IRAS 05248-7007' 'JUPITER' 'Kopff'\n",
+ " 'NGC 2070 S7B' 'NGC 7469' 'NIRISS Focus Field' 'ORIBAR-NIRSPEC' 'Read'\n",
+ " 'SDSSJ1652+1728' 'SDSSJ1723+3411' 'SMACS J0723.3-7327' 'SMP-LMC-58'\n",
+ " 'TITAN' 'TYC 3986-834-1' 'TYC 4433-1800-1' 'VV114' 'WD1657+343' 'XID2028']\n"
+ ]
+ }
+ ],
+ "source": [
+ "names = []\n",
+ "for d in response_data['results']:\n",
+ " name = d['targetName']\n",
+ " if name != '':\n",
+ " names += [name]\n",
+ " \n",
+ "print(np.unique(names))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dd7f1116",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-09-23T04:40:38.974661Z",
+ "start_time": "2022-09-23T04:40:38.963267Z"
+ }
+ },
+ "source": [
+ "These targets span a number of different catalogs. We can use `astroquery`'s functionality to check the spectral type of each object, if it is a star.\n",
+ "\n",
+ "First, we instantiate a [`Simbad` object](https://astroquery.readthedocs.io/en/latest/simbad/simbad.html) and make sure that the spectral type is reported in its queries."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "303bff35",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-09-23T04:53:42.663971Z",
+ "start_time": "2022-09-23T04:53:42.653208Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "customSimbad = Simbad()\n",
+ "customSimbad.add_votable_fields('sptype')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "59f40948",
+ "metadata": {},
+ "source": [
+ "Next, we iterate through the objects and report out their stellar types. Because we've added the `sptype` field to our request, the `query_object` method that we will use will throw a warning for objects that are not stars. Therefore, we will disable warnings for while we iterate through the objects."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "74c7b98c",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-09-23T04:53:42.895088Z",
+ "start_time": "2022-09-23T04:53:42.812770Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2MASS J12560183-1257276 L8.0\n",
+ "2MASS J15395077-3404566 M0III\n",
+ "2MASS J16194609+5534178 G0-5\n",
+ "2MASS J17430448+6655015 A5V\n",
+ "2MASS J17540383-2810466 M4.5III\n",
+ "BD+04 3653 K5III\n",
+ "GCRV 21765 A0\n",
+ "GSPC P330-E G2V\n",
+ "TYC 3986-834-1 K0III\n",
+ "TYC 4433-1800-1 A3V\n",
+ "WD1657+343 DA.9\n"
+ ]
+ }
+ ],
+ "source": [
+ "with warnings.catch_warnings():\n",
+ " # disable warnings\n",
+ " warnings.simplefilter(\"ignore\")\n",
+ " for name in np.unique(names):\n",
+ "\n",
+ " # query the object\n",
+ " result = customSimbad.query_object(name)\n",
+ "\n",
+ " # ignore this object if no result is returned\n",
+ " if not result:\n",
+ " continue\n",
+ "\n",
+ " spectral_type = result['SP_TYPE'][0]\n",
+ "\n",
+ " # ignore this object if it has no spectral type\n",
+ " if spectral_type == '':\n",
+ " continue\n",
+ "\n",
+ " print(name, spectral_type)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "74ce5c36",
+ "metadata": {},
+ "source": [
+ "It seems that our sample of targets includes a K star (BD+04 3653) and a G star (GSPC P330-E). Their spectra should be different; K dwarfs, for instance, should have more pronounced molecular features than G dwarfs."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "27b38906",
+ "metadata": {},
+ "source": [
+ "## Downloading spectra"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e501582",
+ "metadata": {},
+ "source": [
+ "Let's start by examining the G star in our `response_data`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "cc6ad63a",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-09-23T04:53:44.090260Z",
+ "start_time": "2022-09-23T04:53:44.069665Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# access target files for G star\n",
+ "\n",
+ "targetname = 'GSPC P330-E'\n",
+ "\n",
+ "targetfiles = []\n",
+ "\n",
+ "for d in response_data['results']:\n",
+ " if d['targetName'] == targetname:\n",
+ " targetfiles += [d['fileName']]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "e08ad3d6",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-09-23T04:57:39.130258Z",
+ "start_time": "2022-09-23T04:57:39.115552Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['jw01538-o062_t002_nirspec_g140h-f100lp_x1d.fits',\n",
+ " 'jw01538-o062_t002_nirspec_g140m-f100lp_x1d.fits',\n",
+ " 'jw01538-o062_t002_nirspec_g235h-f170lp_x1d.fits',\n",
+ " 'jw01538-o062_t002_nirspec_g235m-f170lp_x1d.fits'], dtype='"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(8,6))\n",
+ "markers, caps, bars = plt.errorbar(k_star_wav_masked, np.log10(k_star_flux_masked), \n",
+ " yerr=k_star_log_err, \n",
+ " color='teal',\n",
+ " fmt='.',\n",
+ " ms=1,\n",
+ " elinewidth=1,\n",
+ " ecolor='blue')\n",
+ "\n",
+ "[bar.set_alpha(0.1) for bar in bars]\n",
+ "[cap.set_alpha(0.1) for cap in caps]\n",
+ "\n",
+ "\n",
+ "plt.title('K star', fontsize=25)\n",
+ "\n",
+ "plt.xlabel('Wavelength (microns)', fontsize=25)\n",
+ "\n",
+ "plt.ylabel('Log10 Flux (Jy)', fontsize=25)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "388f623f",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2022-09-23T04:56:53.450768Z",
+ "start_time": "2022-09-23T04:56:53.339384Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/zg/pp98nf5j52b0_pww3l0b71f40000gq/T/ipykernel_39107/480626688.py:2: RuntimeWarning: invalid value encountered in log10\n",
+ " markers, caps, bars = plt.errorbar(g_star_wav_masked, np.log10(g_star_flux_masked),\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Log10 Flux (Jy)')"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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UCyFEN7I0ycKaw6ksTbK091CEEKJD0bX3AIQQQrSdW8eZqar0XgohhDhGgmIhhOhGok0m7jPH09vU3iMRQoiORconhBBCCCFEtydBsRBCCCGE6PYkKBZCCCGEEN2eBMVCCCGEEKLbk6BYCCGEEEJ0exIUCyGEEEKIbk+CYiGEEEII0e1JUCyEEEIIIbo9CYqFEEIIIUS3J0GxEEIIIYTo9iQoFkIIIYQQ3Z4ExUIIIYQQotuToFgIIYQQQnR7EhQLIUQ3otGAyeS9FEIIcYyuvQcghBCi7Wi13qBYCCFEbZIrEEIIIYQQ3Z4ExUIIIYQQotuToFgIIYQQQnR7EhQLIYQQQohuT4JiIYQQQgjR7UlQLIQQQgghuj0JioUQQgghRLcnQbEQQgghhOj2JCgWQgghhBDdngTFQgghhBCi25OgWAghhBBCdHsSFAshhBBCiG5PgmIhhBBCCNHtSVAshBBCCCG6vS4VFG/YsIFLLrmE6OhoFEXhq6++qvV3VVV5/PHHiY6OJjAwkPj4eH777bf2GawQQgghhOgwulRQXFlZydixY3nttdfq/Pvzzz/Pyy+/zGuvvcbWrVuJjIzkwgsvxGq1tvFIhRBCCCFER6Jr7wG0pFmzZjFr1qw6/6aqKq+++ip//etfueKKKwB477336Nu3Lx999BG33357Ww5VCCGEEEJ0IF0qU3wyhw4dIjc3lxkzZvh/ZzQamT59Ops2bar3dna7nfLy8lpfQgghhBCia+k2QXFubi4Affv2rfX7vn37+v9Wl2eeeYawsDD/V//+/Vt1nEIIIYQQou11m6DYR1GUWj+rqnrC72p65JFHKCsr839lZma29hCFEEIIIUQb61I1xScTGRkJeDPGUVFR/t/n5+efkD2uyWg0YjQaW318QgghhBCi/XSbTPHgwYOJjIzk+++/9//O4XCwfv16pk6d2o4jE0IIIYQQ7a1LZYorKio4cOCA/+dDhw6RlJREREQEAwYM4J577uHpp59m2LBhDBs2jKeffpqgoCCuu+66dhy1EKKryiy18uYWC3dMMtM/3NTewxFCCHESXSoo3rZtG+eee67/5/vuuw+Am266iXfffZcHH3wQm83GwoULKSkpYfLkyaxZswaTST6sujO3G6qqICgItNr2Ho3oSpZst/Dt/lR0OvjHefHtPRwhhBAnoaiqqrb3IDqT8vJywsLCKCsrIzQ0tL2HI1qA0wkFBdC7N+j17T0a0ZVIplgIIdpHU+K1LpUpFkKIjqR/uImnZ8S39zCEEEI0QLeZaCeEEEIIIUR9JCgWQgghhBDdngTFQgghhBCi25OgWAghWklmqZW/rEkks9Ta3kMRQghxChIUCyFEK/G1ZFuy3dLeQxFCCHEK0n1CCCFayfwJZlwu76UQQoiOTYJiIUSX1p6Ls0hLNiGE6DykfEII0aV5PGC1ei+FEEKI+khQLIQQQgghuj0JioUQQgghRLcnQbEQQgghhOj2JCgWoh5ut7cW1e1u75GIjizbauWJxESyrdKLWAghOjMJioWoh0zQEg2xxGJhRWoqSyzSi1gIITozackmuj23GyoqICIC9Pr2Ho3obOabzbUuhRBCdE6SKRbdnsfjDYolI9z1bc/J4dx332V7Tk6LbTPaZOKx+HiiTaYW26YQQoi2J0GxEKLbWJSQwJYjR1iUkNDeQxFCCNHBSFAshOg2Xpo5k0n9+vHSzJntPRQhhBAdjATFQog2114dGyZERbFu3jwmREW16X6FEEJ0fBIUCyHa3EubNvGWxcJLmza191CEEEIIQIJiIUQ7UBSl1qUQQgjR3qQlmxCizd03ZQomg0HamAkhhOgwJCgWoh7Sv7j1+NqYCSGEEB2FlE8IUY8j5VZetiRypFyW7xVCCCG6OgmKhajHsp0WNuSlsmynLN8rhBBCdHWtXj7hcrm8O9JJpYboXG4ea6aw0HsphBBCiK6txSLVlJQUEhMT2bZtG3v27CEjI4OCgoJaQXHv3r0ZOHAgI0eOxGw2M336dIYPH95SQxCiRUWFmFgQF09USHuPRAghhBCtrVlBcVJSEh988AFffPEFmZmZtf6mqmqtn51OJ9nZ2eTk5PDLL7+wdOlSAGJiYrjyyiu5/vrrmTBhQnOGI4QQQgghRJM0uqbY7Xbz4YcfMnnyZMxmM6+88gqZmZmoqlrrqz7HXy8rK4tXX32VM844g4kTJ/LBBx/gdrubdaeEEEIIIYRojAZnilVV5b333uPJJ5/k0KFD/t/5BAYGMmbMGEaPHs2QIUPo168fPXr0IDAwEFVVsdlslJSUcOTIEQ4ePEhycjLJycnYbDb/Nnbs2MFNN93Eo48+yt///nduuukmNBqZCyiEEEIIIVpXg4LiH3/8kfvuu4/du3cDx4Lh008/nSuuuIKZM2cyadIk9I1s5up0OtmyZQsJCQl8+eWX/PbbbwCkp6dz22238corr/Dqq69y3nnnNWq7Qggh6uZ2Q1UVBAWBVtveoxFCiI5DUU9W63CURqNBURRUVSUsLIybbrqJW265hTFjxrToYJKTk1m6dCnvv/8+paWl/n37Jut1BOXl5YSFhVFWVkZoaGh7D0e0gKoq2LcPhg/3Bgqn+r3oXJxOKCiA3r1lERaQx0MI0T00JV5rcG1CZGQkL774IpmZmbz66qstHhADjB49mldffZXMzExeeukloqKiTlqfLIQQQgghREtoUFD83HPPkZaWxn333UdISOv3pwoODubee+8lLS2N5557rtX3J0RdciqsLE5JJKei7Va0c7vBavVeipaRbfWuTJhtlZUJhRBC1K9BQfEDDzxAQEBAa4/lBAEBAdx///1tvl8hoH1WtPN4vEGxx9Nmu+zyliZZWHM4laVJsjKhEEKI+skyc0LUQ1a06xpuHWemqtJ7KYQQQtRHgmIh6tEv1MR95nj6yXzKTi3a5H0ee5vaeyRCCCE6smY3Af76669lsQ3RJWm1EBIibas6O7cbKiqkTlsIIcTJNTsovvzyy4mJieGRRx7hwIEDLTEmIYRoMR6PNyiWOm0hhBAn0yLLxeXn5/P8888TFxdHfHw8H374IXa7vSU2LYQQQgghRKtrdlAcGhqKqqr+r59++okbb7yRqKgo7rrrLpKSklpgmEIIIYQQQrSeZgfFubm5vPfee0ybNg3AHxyXlpbyxhtvYDabmThxIm+99RZW6RMqhBBCCCE6oGYHxQEBAdxwww0kJiayf/9+HnroIaKiooBjAfKOHTtYuHAhUVFR3HzzzWzcuLHZAxdCCCGEEKKltEhNsc/QoUN55plnOHz4MF9//TWXXnopOp3OHxxXVVXx/vvvM336dIYPH86LL75IQUFBSw5BCCHaVLbVyhOJsmKeEEJ0di0aFPtotVouueQSvvrqKw4fPszTTz/NsGHDgGPZY19WOSYmhiuvvJLVq1ejqmprDEcIIVrNEouFFampLLHIinlCCNGZtUpQXFNkZCQPP/wwKSkpJCYm8oc//IHAwEB/cOx0Ovnyyy+5+OKLGThwII8//jiHDx9u7WEJIUSLmG82c0lsLPPNnWPFvGyrlZctktkWQojjtXpQXNO0adN4//33ycnJ4aWXXiIgIABFUfwBclZWFv/85z8ZOnQol156qdQei3al0YDJ5L0Uoj7RJhOPxccTbeocS+YtTbKw5nAqS5Mksy2EEDW1+cf9zp07+etf/8qTTz7p72WsKIr/76qq4na7WblyJdOnT+fSSy8lNze3rYcpupGcCiuLUxLJqaidOdNqvUGxrGgnupJbx5mZMSCWW8d1jsy2EEK0lTYJisvLy3nzzTeZOHEiEyZM4I033qC0tNSfIQ4NDWXhwoX85z//4ZxzzgGO1R6vXLmSKVOmUFhY2BZDFd3Qsp0WNuSlsmynZM5E1xdtMnGfufNktoUQoq20alC8YcMG/0Ied955Jzt27Ki10MeZZ57JO++8Q3Z2Nq+99hoLFixg/fr17N27l3nz5qHRaFBVlcOHD/Pkk0+25lBFN3bzWDPT+sZy81jJnAkhhBDdVYsHxbm5uTz77LPExsZy7rnn8uGHH2Kz2fyBcHh4OHfeeSe7du1i06ZNzJs3j8DAwFrbiIuL45133uHTTz/1/27FihUtPVQhAIgKMbEgLp6okPbPnMkkKCGEEKJ96FpiIx6Ph2+//ZalS5eyevVq3G43QK0Wa1OnTmXBggVcddVVBAQENGi7c+fOZdSoUezevZusrKyWGKoQHZpvElRQMPzjvPj2Ho4QQgjRbTQ7KH7kkUd47733yMvLA2oHwj169OCGG25gwYIFnH766U3a/uDBg9m9ezcul6u5QxWiw7t1nJmqSmQSlBBCCNHGml0+8dxzz5GXl+cvjwA4++yzef/998nOzubVV19tckAMoGnhflhWq5V77rmHgQMHEhgYyNSpU9m6dWuL7kOIppJJUEIIIUT7aJHyCVVViYiI4MYbb2TBggUMHz68JTYLwJNPPsk999zTYtu77bbb2L17N//973+Jjo7mgw8+4IILLmDPnj3069evxfYjhBBCCCE6j2YHxdOmTWPBggVceeWVGAyGlhhTLSNHjmyxbdlsNpYvX87XX3/NtGnTAHj88cf56quvePPNN+vscGG32/39lMHbXk4IIYQQQnQtzQ6KExMTW2AYbcPlcuF2u0+Y6BcYGFjv6nnPPPMMTzzxRFsMTwghhBBCtJNutYCtyWRiypQp/POf/yQ7Oxu3280HH3zAr7/+Sk5OTp23eeSRRygrK/N/ZWZmtvGohRBCCCFEa+tWQTHAf//7X1RVpV+/fhiNRv7v//6P6667Dm09a/kajUZCQ0NrfQkhhBBCiK6l2wXFQ4cOZf369VRUVJCZmcmWLVtwOp0MHjy4vYcmRIeVbbXyRKIsKtLW5HEXQoi20+Ca4ltuuaU1x4HBYMBkMtG7d29GjRrFmWeeSURERKvtLzg4mODgYEpKSkhISOD5559vtX0J0dktsVhYkZoKwGPx8e07mG5EHnchhGg7DQ6K3333XRRFac2x1KLT6ZgzZ45/yeiWkpCQgKqqxMXFceDAAR544AHi4uK4+eabW2wfQnQ1883mWpeibcjjLoQQbafR5RO+RTpa8wvA6XTy1VdfMW7cOH744YcWu8NlZWX86U9/Yvjw4dx4442cffbZrFmzBr1e32L7EKKriTaZeCy+cy4qotFASIj3srNpjcddowGTqXM+HkII0ZoUtea6zCcRHx/fqplij8dDVVUV2dnZ/k4QiqKgqiphYWGkpaW1ajlFQ5WXlxMWFkZZWZlMuusi0vKtvPCjhQfONzO0T/sGfU4nFBRA794gx2ktI6PYyr9/tnDXWWYGRnS+oF4IIUTjNSVea3D5RFv2I87Pz+eDDz7g0UcfxWazUV5ezhtvvMHf/va3NhuD6D6W7bSwIS+VXjvhyQvj23s4ooUtTbKw5nAqQcHwj/Pi23s44hSyrVaWWCzMN5s75ZkJIUTn1SFPoPXp04f77rvP3z4N4LvvvmvnUYmu6uaxZqb1jeXmsVK32RXdOs7MjAGx3DpOnt/OwDe5cInF0t5DEUJ0M81e0a41XX755fTr148jR46wf//+9h6O6KKiQkwsiIsnKqS9RyJaQ7TJxH3meHpL0rFTkMmFQoj20iEzxTUNGzYMgJKSknYeiRBCiNbWmSd1CiE6tw4fFEdERBAWFkZIiKTxRNcnnQFETbJ4hxBCtJ0GlU94PB407fQp/dlnn7XbvoVoa1qtNygWAmTxDiGEaEsNijZHjBjBZ5991tpjOcEnn3zC6aef3ub7FUKIjmC+2cwlsbFSXyuEEG2gQUHx/v37ufbaaxkxYgRLly7Fbre32oCqq6tZsmQJI0aM4Prrr5cJdkKIduV2g9XqvWxrUl8rhBBtp0FBcWxsLKqqkpqayoIFC4iKiuKuu+5i8+bNLTIIVVX56aefWLhwIVFRUfzxj38kNTXVvxyzEEK0F4/HGxR7PO09ku5B6qiFEO2lQUHx7t27eeGFFwgPD0dVVUpLS3njjTc4++yziY6O5qabbmLp0qXs3LkTh8Nxyu3Z7XZ27NjB4sWLuf7664mMjCQ+Pp633nqLsrIyVFUlPDycF198kV27djX7Tgohurb2zOaKliV9ioUQ7aXByzyDd8m8V155hX//+98UFxd7N3Dc0s8ajYbo6Gj69etHeHg4gYGBqKpKdXU1JSUlHDlyhJycHDzHpV18w+jRowd3330399xzT4dcRlmWee56qqpg3z4YPhyCgtp7NKIpTrY8dnOXzpalt9uWrGgnhGgJTYnXGhUU+9hsNt5//32WLFnC9u3b697wccGyT327Gz9+PLfffjs33HADgYGBjR1Sm5GguOuRoLjzk6BYCCFETU2J15q0ol1gYCC33347t99+O6mpqSxfvpyEhAR++eUXf/nEqWJtg8HA5MmTmTlzJldccQXDhw9vylCEEEIIIYRotmYv8xwbG8sjjzzCI488gtPpJDk5mT179pCRkUFhYSGVlZUABAcH06tXLwYOHMjpp5/O6NGj0UvaRQghhBBCdADNDopr0uv1TJgwgQkTJrTkZoUQQgghhGhVslScEEIIIYTo9iQoFkIIIYQQ3Z4ExUIIIYQQotuToFiIDkYWohBCCCHangTFQnQwsqywEEII0fYkKBZCCCGEEN2eBMVCtKFsq5UnEhPJtlqbdHsprRBCCCFahwTFQrSht7ZZ+GpvKm9tszTp9lJaIYQQQrQOCYqFaEO3jTcza2gst403t/dQGqy52W0hhBCiM5CgWIg21D/cxNMz4ukfbmrvoTTYEouFFampLLE0LbvdFrKtVl62SOAuhBCi6Vp0mWchRNcz32yuddkRLU2ysOZwKkHB8I/z4tt7OEIIITohCYqFECcVbTLxWHx8ew/jpG4dZ6aq0nt5PI0GTCbvpRBCCFEfCYqFEJ1etMnEfeZ4etdRlaLVeoNiIYQQ4mQ6VO6koqKivYcghBBCCCG6oWYHxXfddRd2u73ZA/n5558ZO3Zss7cjhBBCCCFEYzU7KH799dcxm83s2rWrSbd3u938/e9/Jz4+nvT09OYORwghhBBCiEZrkfKJvXv3MmnSJF566aVG3W7//v1MmTKFp59+Grcs0SWEEEIIIdpJs4PioKAgABwOBw8++CAXXHABR44cOeXt3nrrLSZMmIDFYkFVVQDOP//85g5HiE5Peu52LbL4iRBCdA7NDop37NiBuUb/0rVr1zJmzBg+//zzOq9fUFDApZdeysKFC6msrERVVYxGIy+++CJr1qxp7nCE6PR8PXeXJnWcxTK6c2DndkNFhfeyKTrD4idCCCFaICgeNmwYmzdv5i9/+QsajQZFUSgpKeGaa65h3rx5tTpKrFy5ktGjR7Ny5Ur/70aNGsWWLVu47777mjsUIZpEo4GQkI7Tx/bWcWZmDIits+due+nOgZ3H4w2KPZ6m3X6+2cwlsbEdevETIYQQLVRTrNVqefLJJ1m3bh0DBgwAQFVV/vvf/zJ27Fh++OEH7rjjDi699FIKCgr85RL33HMP27ZtY/To0S0xDCGaRKv1BsVabXuPxMvXcze6jua67VVaIYFd0/kWP6nr+RRCCNFxtGhu7Oyzz2bXrl384Q9/ALyB8aFDh5g5cyaLFy9GVVVUVSU6OpqEhARefvllDAZDSw5BiC6tvUor2iqw685lGkIIIdpXi58wNplMvP/++3z44Yf+cgpfMKwoCnPnziU5OZkLLrigpXctRJfXEUsrWlJ3LtMQQgjRvlplmefDhw/z5ptv4vF4UBQFRVH8f0tPT6egoIAePXq0xq6F6NJOtpxxV+Arz5AyDSGEEG2txTPFH374IWPHjuXnn3/2Z4l79+7tryPevn07EyZM4D//+U9L71qIDkFKAJpO6m+FEEK0lxYLisvLy7nuuuu48cYbKS8vR1VVtFotTz31FEeOHOGVV17BaDQCUFVVxZ/+9Ccuvvhi8vPzW2oIQnQIpyoByCy18pc1iWSWStDcntrq4EWebyGE6BxaJCjesGEDY8aM4dNPP/XXDw8bNoxNmzbxyCOPoNVqufvuu9m6dWutThOrV69m9OjRfPPNNy0xDCE6hFN1anh7h4XVaam8vaPz1M12tex3ttXKdcuX8/lve3lrW+s+D53x+RZCiO6o2UHxI488wvnnn09mZqa/ROK2225jx44dTJw4sdZ1R44cydatW2v1JC4oKODyyy9nwYIFVFVVNXc4QrS7U5UA3D7RzO9GxHL7xM5TN9vRJ8BpNGAyNbzX9BKLhaIqGz0CArltfOs+D53x+RZCiO5IUX2RbBPV7DDRs2dP3n77bS677LJT3m7dunXcdNNNZGVleQeiKAwdOpTU1NTmDKfVlZeXExYWRllZGaGhoe09HNECnE4oKIDevUGvb/72sq1WllgszDebm1Qbe7Lx1Pc3txuqqiAoqHX6LTf3PnU0vsx3cn4+r82ezYSoqHqvW1UF+/bB8OHex1eIjqS1//eF6KyaEq+1SPmEqqpceOGFJCcnNyggBjj33HPZtWsXV111lX8baWlpLTEcIdpVe2RVnU7IyfFetoauNgEu2mQitaiInbm5LEpIaO/hCNFkHg9YrU1fcVEIcUyzg2Kj0cirr75KQkICkZGRjbpteHg4n3zyCe+99x6mLvJhK0R7rP7W3KWIu5tsq5XYnj0ZGxnJSzNntvdwhBBCdADNDoq3bt3Kn//852Zt44YbbmDnzp2cddZZzR2OEO2qq5UZdFUv/ryJb1JSObNfzElLJ4QQQnQfzQ6KR40a1RLjYNCgQaxfv75FtiVEe+noE9KEj3LcpRBCiO6uVVa0a6qaK98J0RnJimzN01aZ9vvPmkJYgEGeJyGEEH4dKigWoj00tp3XyfgmpHWU8XQ2b22z8M2+VDwqPHFufKvtpyWeJyGEEF1LN/zYFaI2rdYbhHaUdkYnG09XD5hvG29m1tDYVu8d3JqLkWzPyeHcd99le05Oi29bCCFE62l2pvi8885riXEA3vKJH3/8scW2J0RX4wuYu6r+4SaenhHf6vvx1X4DLZ4xXpSQwJYjR1iUkMC6efNadNtCCCFaT7OD4sTExBapBVZVtdVril0uF48//jgffvghubm5REVFMW/ePP72t7+h6aqpN9HupCNFx9Oatd8vzZzJooQEafUmhBCdTIvUFDdlUTxfANzMBfUa5bnnnuM///kP7733HiNHjmTbtm3cfPPNhIWFcffdd7fZOETXlVlq5c0tFu6YZKZ/uDcAbs2spGia1qwpnhAVJRliIYTohJodFC9btqzB1/V4PJSUlLB7925Wr15NXl4eiqJw7bXXcuGFFzZ3KKe0efNmLrvsMubMmQN428B9/PHHbNu2rdX3LbqHt3dYWJ2Wil5/bKKYdKQQQgghOr5mB8U33XRTk27ndDr5v//7P/7617/y6aefMnXqVBYuXNjc4ZzU2WefzX/+8x9SU1OJjY1l586dbNy4kVdffbXe29jtdux2u//n8vLyVh2j6Nxun2hGo9QOgKXTgRBCCNHxtVtLNr1ez6JFixg4cCBXXXUVd999N7GxsVxwwQWtts+HHnqIsrIyhg8fjlarxe1289RTT3HttdfWe5tnnnmGJ554otXGJLoWCYCFEEKIzqndZ5ddeeWVzJkzB7fb3ezlok/l008/5YMPPuCjjz5i+/btvPfee7z44ou899579d7mkUceoayszP+VmZnZqmMUQnQsORVWFqckklPR8u3bhBBCdBztHhSDNzAGSElJYcuWLa22nwceeICHH36Ya665htGjR3PDDTdw77338swzz9R7G6PRSGhoaK0v0fW43WC1ei+FqGnZTgsb8lJZtlOW7hZCiK6sQwTFgwcP9n//22+/tdp+qqqqTmi9ptVq8Xg8rbZP0Tl4PN6gWF4K4ng3jzUzrW8sN4+ViZJCCNGVdYhlnm02m//7/Pz8VtvPJZdcwlNPPcWAAQMYOXIkO3bs4OWXX+aWW25ptX0KITq3qBATC+LiiQpp75EIIYRoTR0iKK65il1YWFir7eff//43f//731m4cCH5+flER0dz++238+ijj7baPkXLc7uhqgqCgjrO0syi85FFVYQQQtTU7kHxL7/8wuuvv+7/ecKECa22L5PJxKuvvnrSFmyi4/OVOgQESFAsmu6tbRa+2ZeKRz3WU1oIIUT31eZBsdvtpqSkhOTkZL744guWLl2K0+lEURRiY2OZNGlSWw9JCNEN3TbejNPpvRRCCCGaHRRrm5mq8y3zbDAYeOONN5o7HCG6NCkdaTn9w008PSO+vYchhBCig2h29wlfUKuqapO+APr27cv//vc/zj333OYOR4guTbpkCCFqyrZaedmSSLZV+mgL0VwtUj7hC24bo0ePHowfP57LL7+cG264Qfr/CiGEEI20NMnCmsOpBAXDP86Lb+/hCNGpNTsoPnToUKOubzAYMJlMhIRIfyPRNNlWK/+2WLjrLDMDI6RrgBCi+7p1nJmqSu+lEKJ5mh0UDxw4sCXGIUSDSWZECCG8ok0m7jPH01vyA0I0W4dY0U6Ixrh1nJkZA2K7fWZka1YO5yx9l61ZOe09FCGEEKLTk6BYdDq+zEh3X3Dh/u8TsOQc4f7vE+q9TrbVyhOJMglHCCGEOBUJikW35HZ7uzi43e09kqZ78cKZmKP68eKFM+u9zlvbLHy1N5W3tlnacGRCCCFE59PuK9oJ0R66wqp4Z8RE8dOt8wBv7+K6yAIVHZ8sNy2EEB1Dg4LiW265pbXHAYCiKCxdurRN9iVEdyALVHR8SywWVqSmAvBYfHz7DkZ0Om43VFRARATo9e09GiE6twYFxe+++y6KorT2WAAkKBadXmaplTe3WLhjkpn+4S2b+ZN2dF3PfLO51qUQjeHxeINiWdBHiOZrcE1xU1esa+zqdkKcikYDJpP3sqkyS608uzmRzNKWn4D29g4Lq9NSeXtHy9fx+trRLU2SGuGuItpk4rH4tpk4mllq5S9rWud1L4QQnV2DMsWPPfZYa49DtKLWzFy2B63WGxQ3xztJFn7ITCU0CZ68ML4lhuV3+0QzGqV1Mn81G/XXrEUN19b/gNT1/Lvd3jrkoKDOW1MtGs93wKbXwxPnxrfzaIQQomORoLgbkA/CE9081kxhofeypfkyf62hZqP+Jzcm8s2+VDwq3DjSzOIUCw/EmBkaVDtAruv57woTDdtKToW13se2s7lseBwbMtK5bHhcew9FCCE6HOk+0Q20Zuays4oKMbEgLp6oTrzaeM3OEi/+vInlGbsxbrXzrzm1W7TJ8988y3Za2JCXSq+dLX9Woa19vS+FUpuDr/elMCEqqr2HI4QQHYr0Ke4G3G6wOzp3T96W5s3+JZJT0XlrK32dJbwlEb6JsCdOiK2rZjXbauVlS9MX9ehOi4LcPNbMtL6xrXJWoa3dNt7MrKGx3bZFX1foTy6EaD0SFHcDrTnxq7PyZf+W7ewaj8mfz5jC3IFm/nzGlAZdv7kT9rrToiDHzip07tIJOP5AqvvxlQ1JpwYhRF0aXD6xa9cuAEJDQxk0aFBrjUe0Ajl9fqJ5Y7w1xfPGtMxj0t4LMDS2HKTmhD1o/MQ7WRREdEbS0lAIcTINzhSPGzeO8ePHs3DhwlNet7y8nPLycmw2W7MGJ1pGW7Z86iyiTd4gsqUek86WOfVN2PPd/8Zm0Lp7xlE0X3u0h5OWhkKIk2mViXbh4eEoisJFF13EypUrW2MXopvraC3FJHPacjracytaR3t0xTn+DIkQQtTUqjXFsiCHaC1ZZd6JXlllHWOil2ROW05He25F67h4WBwhBgMXD2u79nDHnyERQoiaZKJdJ/FrRg5TF7/Lrxk57T2UDqGu06CN6YiQW2nl3UOJ5Fa2fuDVkqeJW2I1P4Aj5d7uE0fKW+/+N7VDhZzi7h6+3Z9ChcPBt/tT2myfLfX/I4TomuStoZN48McEduQf4cEfE5q8ja60xOv8CWYuHhbL/AnHToO+8NMm3thq4YWfNp3y9u8mW0jMTeXdZG/g5XZDRUXrtGpqye4fvtX8mltW8M5Ob+D5Tit232hqnfWt48zMGBArp7i7uNsnmvndiFhun9h2z3NL/f8IIbomWbyjk3hxxkzuS0jgxRkzT33lo7KtVt7aZuH64WYMBrjhy+WUVNuo9tgJCzBySVwcK1JSTuiY0NBOCu1Z++krV6hFUWpfnsTxtYUejzcoPlJu5YNfWraLRHt3/6jreZo/wYzLRa2DipbW1DrrmDDvxNCgoFYaWAcgddOtu/Jjd9KVVlwUor1JUNxJRIaEcE7/QUSGnNhzq74g9q1tFr7em4rVCk6NnV35uahAXkUl6w5lsPZQOuXVDsqqvUGy7/ZvbbP4lw++faK53gC5NZYKziy18tyGTYDCQ9Om1Fujm1lq5c0tFu6YZPZf54GzpxAeaGhQ8FlzueSaliZ5s7oeteUm/7T3h39dz1OdBxUtrKn78GXzujJZZlu0lK604qIQ7U2C4k5iyXYL3+5PpcppJ0hv5I5J3sDv+Y2b2JabhdXuOCGQmz0kjjWp6ZzXL47l6RYqnA5UVH5IO0ifoFD6GEMZ3TOEHGsFi7dvJ6WwmLheEVw8LM6f4Xts7Xo+2p3MocIKll5+MdlWK89v3ES124kBA/OGT6H38ZElJ8821xXQ+ry9w8IXKbtRVdDrVcIDjwXrNW/nezy0Gnhwsjer2BLB57zR3se1PbtINDaL2BKrdLVk/9b27tncUB1lnB1lHKJzumWcmfJy76UQonkkKO4Efs3I4cvdKSgaBavDydr0DGxuO9tzc9iTXwioDO/Zm9lD4vjLmkR/sLl4u4UduUf4w3fvo2gUtAAaLXaPhwOlBewrzmNQWA9yKq1Uuhx8/FsyWkXDpowsqlwuLj89DktONjaXg9VpKViOmPnjqhXsystFURR0ioZ1h9KJHzSIR+K9K6n5srx6PSSmp+NRvQFmzSD4pZ838dHu3eRXWunfw8R8sxm3G/72QyI/H87A4XbRMzCYNQcOkl9VyeGSCgK1erbkZFHldKDXHzv9f/NYc70Zt/qC72yrlf9stXBJPzMBASbCwo7dJiasYdnNkwX2jXX8thqbRfRdvzHNXo7fp29yW1Aw/PGM+s8ONETNMw1PnBvf6Nu3lY4yzo4yDtE59Q838fCUeHqHt/dIRFtqyc8gcYwExZ3A3WtW8VtJLgrergmzh8ZR4XCSnJeHqnqDocOl5Vz00fvYXC6W79uNQWMg01qCHRd2lwsADQomvQ4t4FI9uPBwoKwIAAVQAbfq4fv0g6ioTHv3HfqbwtCgUGyv4o+rVrC3sAA33p26VA97y3I4mFyAwQBhgQaWp+7G5fYwKLwHsT16sSYtjdTCYn48eIgqp51X58wERcGtevgqdR82l5OssgqiQ0P4IiWZKpcTgBJ7NQA6RcP23Gwyy8updDoYF9mX2yd6g7WnZ8STUWzl8Q0J6HUKD0+vXW7x8ubNfLQ7GZvbwX1TpvjfQB5ft55P9iSTOrCC5865uNap+qwyK+9uPPUbzfE9Vhv7BpVZauVvPySyuyCf8VF9seRkN7lfa7bVyr93WZjRyww07M3x+PHXrLFubpDWWXo21zfOtv6w6SyPlxCi43hp02Y+Sk6myuXg1dkz2ns4XYYExR1cZqmVrDIrviRgQXUF7/1mwaQzUuVy4EFFBcqc1f7bpJYcC3QBtCi4UdErGvAolDqr8aD6fw8QoNFj83gDUvXo76rdLjKt5Ri0WmxuFwdLSjBodFS7XbXGaPe4+CB5JyE6IxV2Bza3k6pCF0l5ObhUDzvz8gjQ6NiQmcHNy79FVWFIeAS78nNwqG42Z2by/IUXHh0reMB/f12qh3J7NR6PB5fHzaCwHrWyl6/+upkP9llQAKMRXr7o2JtDflUFJdU28qsqeHnzJv67axffHUzlcFk5NpeD30qyya208tzKTXg8CrHaIbz847cE6PXodPCP8+LrfV4uHhbH6tQDWDLzGP3aYvQ6cLjdDQ5s395h4fOUZKpdLjyKi9+NiGvyRLylSRZ+PJKKzQbnTjz1vuHEyX++Gutqt5Uiq52z+g1qdJBWswygteuVW0J9Nc++0pxTvQZaexxCCFEv36lBWQ+iRTU6KE5ISEDbgHO6qqo2+Lo+iqLgcrlOfcVuZMl2CzmV5cCxQBHA6rKf8rZ6NJiMgUzo1Z/E7P04VDd257HCU62iwa26UQCHp/bjrkFhkCmCcwYMYtORdPaXFlHmqK51HaNWi/1oIWtRdRVFVPn/VuVy+L93udxUayE5P4/fCvJRVZVZQ2LZXaCACg6Pm/krVlDtcqLVaHF7ahfHppeV4Ft9+LM9u7lwy2ncMmkM2VYriRmHsLucaBQNOeXelnOXxcXxdUoKewsK8Xg8JKanMzGyH8XVVRRVV6EACgojevViye5NfLx/O3qNgs21BdWj0ifYxC1jTwwIa2YQP9yVTHJ+PtWeI97HQtHQKySYzJIKMkut/gxjffWiFw+L461tFjweDwpKk0sV3G64+jQzJSVwUe+GB7H11V//a8tmPk/ZzXWjRjc6S/rCRm9mvtTm4JVZMzptreycoXH8cCCdOUPbblEJIYRojEVnTa01v0i0jCb1KVZV9aRfiqKgHG2LdarrHv8laps/wcycobEE6w0MDutBiN5Q5/U0nNiGzImHKpeDnKpShoT2pIcxCIOi9V/ToXqDz8FhPegREIQeBeXotk4L680HV8wlOjSECZHRaGtsXwEMaAnWGQnQ6mv9vi6q4s06u1QPDo8bp+phTfoBPKqKgkJFtYPsynIURWFwWDia41qqeWp9r3JHwjdkllr5+w/r2Z2fD3gzygkH03jD8ivnffAub1h+xVrtQKfRkltRwZpDB2odVBi1OmwuJx+lWnC53bhUFY9HJdCg55bYKZz59hKu/ex/pOZY/RPY3t5hYfm+3Zz59hK+2LOH6qOZde9j6aGgsopP9+7iiXWJ/n7Qx/fqzbZaue+7BOZ9+RWlNhuKoiG9tOyE3sq5lVYeWJPAn79dc0Jf6Zr9pj0eCFFNLDw9nt4BzQ88FVQ0CihKE/4Xj8tcNLVPcXtbmZZChdPByrTGLyrR1AVLhBCiMWQV1dbRqExxQ4NWCW5bTv9wE9/84RoeXZvIR7t2E24MZPaQOL7Zvw+Hx4kK6BUtQXq991S8qhIWYKTa5cblURkUGk6OzYpWUbhh9BhKbA5+PZKFBzel9mo8qkqfkGCCDHoC9TrKq+1YnXZyKss4/4N3cHg8nDdgMH0CQyh32EFReHzquTyxaS2ldhuh+gDsbidaRUOwXo9Bo8PmclHhsqMAWkWLR3Vz/Cui2u1Ci4JRqyXPVgGARtHwwRVzeX7DJn48fJAwg5EjleU4j8scuz0ezli8GKfqxqUeC5lL7LZa16uZ2a52uwjQ6OgdHEyV007PwGDWHT6Ize0NbHvrQqhS3Yzv05dnd36P0+Pm073J4NTz/lWXoNXC5OgYnvnpJ5yqh+OpeDPeHlVle14OqpqDXn+sXvSMyBjOWfouA8JC+To1BbvLe5Cg86jY3Uqt3srZVit/WrucA2WFaFAIDzLUOo2/ZLuFL/ft5ecj6bxw/kwW/7qL6mqFq/pPYVQDa4p9We+p/WN4btNGnj9/JkZnCIoGrh8zmvumTDnlNo7PBF83ZgzbcnK4bswYoOVrZZva27exGevm9HBuaj229C0WQoj21+CgeNmyZa05DnEK8yeYWXswnaIqG5VOBwoKZ0UOZXBEON9n7KfYVkXPoEDsLje9AkIoc1QTpDMwOSaGHUfyGBsZybWjxrDo+wQUBWYPHYZbhWq3E7cbwoMMXDtqNH9auYoDJUXY3W5sR2uHNx05TH9TD4b1juDjK+fy1jYLeq0GrVvD9OihGAPg+/SD9AkM4b3f/Y6Pf0tm8fZtOD1ujIoOj0aDQaOl3FmNirfGWaMohBoDiAoJIaWwABWI7z+IL/ekEB1mQq/RUOVyMq3fIDZkHeLM6AGU2e2kFOVjV93+QBqOTRI8lWqPiyPWcjyolFTbCTya5VaAXFs5oLAuK82/LRVYk5lCtjWe/KoKrln+ea2AWIcGF7UDZL1GS2xET45YrVw87Njp94d//J7fivIJzNEToNMxIDwcg07LoB5h7C8sJjE9na1ZOZwRE8UT6xNJys+mb3AIDo+bSVExtfYxKSqGF37+mQOlHm5Z8RUFlVV4PBCgMXAB8Sfc77rarfnqZj/8LYm8igquWv4p06JP48esFK4bPbpBwePxAeCK1BTKqh2sSE3hjJioFq+VzSqz8u+fG982ri27O5zsQKBm+U1kcO3xN/W+NTaY/jUjh3sTEnhl5kwmD4xq8H6EEKI7aHBQfNNNN7XmOMQp9A838fGVc3lzi4X3kndg8zhIKsrizVmXcftEM/d/n8BfzjmbJzcmsjMvD1SI7h2KyWDE4fbQPzyElWkpFFXZCDHo+TkziwqHg94hgZTaHPxuRCxnxESx/OqreHOLhd/y8/gmbR8AZ0YPYFK/GO6Y5M203T7RjNVux25XmDd8Cot+Xk653UaF084ne3axaOpUXC6VMpuTpJw8cqpLUBSV0WGR6DQ6+oeGsOrgfjwqaDUaNBotvY0BRAQG8fYOC5fGxXH9mFG4XAq/ZmeiVTSgUfn2+mu5+MOP2V2Yiwe1VjCswVtmUXPyIIBJb6TK6cR9NHj1HP2bTtFgMgYQ5g7GgQOnx0OZs3amGaDSaeeJ9Yl8tmc3lUfrpDUoeFAx6nSEanVEBARiddoZ2bMvpXY7wXojZdVFfJS8i+25OWSXV5BVXgqA0+0i3BDAkPAIsirKOFxWxqHyEpxuN3euXsnpEX35fN9uqj1OMitK0SkaXvhlIxePOM0/pic3rqf6aA14SlEhcwaOpG9ICJf18QZiJ2u35ss4zxoSR0JKOjeOH8s/N69Di5Y9RbnebiYetc7tHP+74wPA1q7Fret+NERdgeqpemXX7MzRGCc7EKg5ge/v59S+TlPvW2Pb9z24NoGk/CM8uDaB9TfPa/B+hBCiO5DuE51I/3ATd0wys7+giNXp+wnVB3Lb6uV88vu5bFowD4AfDh1kZ24+igJn9Y/hz5OmgN3ALePM6PXgcnknwX13II0exkCeO28mK1JT/KeKfR/q965eQ2C6N5M6qk/vWh/00SYTL180E6cTCgrg/jPP5rqcz3CpKhUOJ/3DTbx26Uyqq+HXPVY+PbyJgAAFVYUfD6aTU1mBSW9kSHgEp0X0YE9hAU6Ph4NlxQCE6PW8fJF3OWtfZuuF82fSP9zEGdFR7CnKQ6N6Sw40gOZoDXu128nwnr3JKC+jwmnHoNESoNOhUcDlUelhDORIZRl6jZbhEX0YEtaL/QUljOgbw7dHDwCOF2IwsCI1xR8Q61BQjgbFE/pEcfbAQf6JfXmVFaxITWFYz3DCAgzkWivZkZ2Lze3Ec7SkyKNAldvFmoz9uD0ejFodA0PDyamwsjM3D0tODu6j2WiPqmLQaXnxwpm1griRvfqwNfsIHrxt8X7ISmXF3JsIqzKRVWbl+q8/41Bpib9Vj6/d2sWnxfFEYiK3jDPzwc5k0q1FfLF/N32DQqh0uLhrVDyptiz+NMX7WvAFhwVVVvYVFfHyzJl8uz+lVsD49Ix4sq1W5n+1gpX7DxCkN7AyLaVZWUiHAwoLwaazsjTpWOB6fFnDybKkNf9WV6B6ssC3tZblPllZxvHLjreWVy6ayaKEBF6a2fDl4js66dcqhGgpEhR3Mku2W0gpLeK2cePZlpNDUZWNpUkWf3Zp0dQpuFwqoLDorClEBh9r7K7Xw9MzvD11AzUGbhxlJjbKxJRBJwYw902ZgtN5bDt1ySy18upmCxgdBOi8Nc0h+mMT77RaGBZp4v7+U3l7h4XJ/WL4NTOb28ZN4O2k7bwycyYf797lD5D/etZ0nvt5I9eOGuPfxuSBUf6AH8AUYCTMGIDd7cbhctErOIi5caMoqKpgxYEUpsTE8NSwC/nT6m/5x7Tz2J6bQ4XDSYjBwA1jR/NB8i70Om+A/mFyMi6Xh+IjZQRodfQxhFLkqiBEa0SrVQnUGzhUVuzPLoOvXZw3aHXg5ukZ8Ty6NpFv96cSaNCiaCClqIjsCiu51spaXUI0KHhUFbvLyaDQcLSKliGhEewoyKbS5ahVH33sNhqe37CZ1JJCsivKsbntXD58BN+mpmB3uyl3VlPpsnP11x/x7tQFrNyyid2FeaCq/Hw4k8xS70TBogo7t32zgqyKUv67YzdHKspRFZXBYT1wu1QKq0pZm7uX0EADC1esYnt+Ns+efz7nDRnIR8m7Kaiq5K7Vq/j3rNmsPZheqzTkhZ828e6uJFyqh75BIVwS6w2+m9p1YtuRHP68MoExUb3YfCSTDYfT+fjKuScEt1syc7hndQKvzpp5wmv4VBnUkwW+zVkZ8WT1yzXHn1Fcu6QlJsy7z6CgJu22wSZERbFu3rzW3Ukba+sWekKIrkuC4k7Gl23ytWF5c4ulVubJl6X1cTpP2AT9w008c1G8/+e6Mm7Hb6cur27ZxMf7d3PxsFiuGTEGULhv6okB9DtJ3g+tnzLTKa92sCx5u392f822Mi9t2syBkiI+/i253kzjNaePZsOhw0QGmci1VfDG7NlMHhhFttXKcEuEPxi5bOQ9ANzCGB5bl8g3+1LpaTLw74uPZaA3HDyMw+3B6qhG0Wv4x9i5TBkdwrvJ3qzTlZ99RlrZsYBYwVt2EWo0MiA8jDcunl3rObn89DhWHUghq6yC5Xt/42BVUa2x+6bSOVUP5Q47t080s+5QOoVVFfW2gSl3VvPVgd9QVe9cvD15Bby+dQsu1YNRo8Oo9faNLrPb+G/aJnZbM1A9KiFGA4dKS3jp500EG4wsT9uB1W1Hq2gos1f7A/0fDx0iSK+nxFHJRynb0Wu0uDwePKjcuXoVt4ybgN3lQkGh3Obgtq9XkF1RzkfJuzgjJorMUisbD2fhORrQqyp8lJzM92lpfHfgAGcNGMB9U6acMjiuWet67/erSCrKxqVxEGI0kFJUyMubN/PKrNoN6h9am8CuwiM8tDaBDbfM8//et2Lh7/qb61yCHOoOfFuihVxD65ePL5fQaqEtutZlW70dUc6LjuO7QyksPLPzZ1ebMzFSCCFqkqC4kzk+W9YSE5kaW5fo4y1bgLBA40lX1Jk3xkx1NVw6PI4vdqVw9fg4Vh7wlmzUuj8NaEa+Mi2FDGsJ+0oKuPr0UXydkkJ0WAj9w+vP7tWVFVyZloITD+cPHci2nBwKK2xsKUvh6h7x/vH85azpzP3iI9yoRAQEEh0UTk5lOdePHlPr/ta8D2fEeAP0NQcP+FflAwjWGZnQN4rNRzIYFNaDmYNjuW28mTMiY7g293NsLpe/QFqDQmRAOFWeamxOBwF6HeUOO1pVww+HD/pLMeJ69sblcfNbUT4aRcMv+QfJtRejaKDa5aLS5eD/tv1CVGAYlW5v+Yde0XBGn4FsLcgkyKCjqLqSgmrv9nwdNAaaelBgq8Ttgfd37qSHIRhF1XKwrBi36kGv1eIL8d/eYaHMbiPcGIjd7cag0bE56zDBBgMHS0pILy0jWG84IUA8/kDsgR8T2JF3hAd+TOD0Xn3YlZeH6oGx0X05VFrsz3rXDOAWTT6bO1Z+y6LJZ/tPoV9+ehwP/JBAQYWNigp4blDdr4m61Axoay5N3tPg3WdDVjtsSMeNzFIrheV2zBGDmDe6ZQO5U5USvLXNwtd7U0lISafS5UBv6PzZVVn8RAjRUprUp1h0HhqNNwOlaYVnetHUKdw23syiOrLD4M1KvWxJRFHgPnM8/cJCMBggJjykzv6Ki86a6t3eWVPr3ef8CWYGh0UQqNXzW0E+3+5PZcn2k/fB9WUFa2b/bh1nZsaAWO6dMpWPr5zL7+JGMG+MudZBgSUvC5PRiF7RculpI/jksmu58fSJ3HvmyduVRZtMfHbF1YzsEcnQ4D7cMMLM3j/9iQlRkYQaAwk3BvLQNO+S1FtzswjU6dEpGgzo0KJhbuwott7wZ769+gaG9eiN3e2dOqgqKh7VuxLh3GGjWXHdtSy95HfEhIRh0Go5bCvA6XETqNP5a6BVINtW5s8MR4WYSLMWEhUUyvjeUbVKQwAMipbfDR/OgglmHB4XJfYq0q3FVDrtuFUPWhTCDYFcc/powLsIid3twoN3bB48FNuqsbtcxISGMi1mMEVWB79m5PCXNYlszcrhicREssqsWK3eAzKAF86fyfi+/Xjh/Jn8/Zx4hvfoy6HyYj7YtZMqp5P8iqpaz3NmqZXnf95IsM7Iluws/yn0+9ckkFZUyoGSImICI076PNW0NSuH1akHGBbeiyKrg5c3b2b5vt2c8+5SkvJyAHg32VuH/PaO+l9vDekdumS7hQ2ZGeg9hhO6UDSX73Go73/itvFmLhocy9/OmMk5MQM5XFzBnd+c2AtbiI6oZo92IVqDZIq7uIaclnW7oaICIiK8dccNdaoMje8UsVNjR+8x4tQ4WHc4vd4Z9g3J+PQPN/G/a7wdMnwT3Jpy2rRmDadWC/+8IJ6CgtrXmT/BTHGFHVB4aJq3BOCJHg2r+4wOC+HiYXHMHWJm3BATer036PfVgS/Z7q0Dv228mSKrHbdbIb/UwfrcFPqagjGZ4Ls9KaSVFfmX1fZliPVaLa/MvpD+4Sb6h5v44sqrufjjD4/uWUGnqZ3u16IQojMyMLwHbtVNenk+wTojFYXHum0MDetJoNaAOSraf5BTWmVnxYF9WO12AnQ6QvRG8mwVqB64c/VKBoVGsCHzEFUuJyoqAVo9Q8N7sCn7MAaNDpfqRlUhtaSQ5MJsSmwOfsw4QHppGelFVkyYuGeamSG9TUSHhRA/aBDRYSFEBps4IyqGXcVHvPfZDYXVFezKLOZPX6/h4elTeOnnTewrLmBgaATzJ3gPZkqq7FS7nWzLycbhcfPs9u+5K34MDXHX6lVYcrPZlZ+HW/UwNbo/6WUluDwervziY54bfy05FVYMWm2teuqmmDM0jjUp6ZzV++TbaUrv4kti41h3KJ1LYuvedv9wE/+8IJ6EXTl8mfobpY5qgvV6IkIMnT5jfCpdtRd0ayY+OhqpHxetTYJiwZFyKy9bLDwQbmbocauiNWdmt29GvRMHaw6ncu6ggcwYENvsGfY1g+emdjloyMFCXXXVDa37fHzdej75LZmM4greH3Kxf3u+tno1u308NG0qr2+2MHPEaCJDQrh3shmTCRZMNLMiNZXdBbm4UY/1T1bxB9XgLQXx1OiuHKQzEBWs5YzIaDZkpmN3e3CrYHe5KbBVoEGDy+Mm3BhCsc3GsB69mHnaUBIOpHGgtJDtR/L4248/4lQ9mPRGSqpt2FxOf5/p/GorxfZKtuUd8d9fA1pMQQFsz8/Brar+RVEqHQ6mDRjIT4ezCNBp6RtoYlduPqvSUjGg5+vDSSy+5GIeX7eeAyUl2NwOXpwxg3snT6WkuoK1mQdRUHC4XXydnoxW0ZBpLWZjVgaldjvnhIUwqKcJtxvKqxx8um8XTvXYQUS21Vqr729mqZXnNmzCd6AD3rr83gEhgHe5cxXYnJOF2+PBA+TbKnl8x1dkVxfj9Li57esVLL74Er5OSWnS/8XKtBQqXA5+Lkjh99T/+m1KWdO3+1OocDj4dr+3V3TN++37P/Z44I/rPiXfVoECDO7Vt03qcds7KG1qL+iOrq3q0TsCqR8XrU2CYsGynRY25KXSayc8eWF8rb+9tGkzHyUn+9t7NUa0ycR95nhsOit6j4E7zjAT6DJRz9ynDqspBwaWnCNUuR38mJVCtnW6/0O4rmz4ku0WVqalUt0P/nl+PGFh+K/75Lnncc3/vDXHHjwEaQ38fvjoWh8K8yeYScu3sjotlQq3jUxrKWHGQHKrKnB4PKiqh2rVTVpZ4dESCA0BOgNnDxjIlSNG+SdtbsvOpqjKxp/XfEtWeXmt0gqdVovqUnH6Om+ox1YZ1KDgwoPd7eLMyP6szTy2AEpBVRXf7k8lr6oCraJBUUCjUah2Oil223Db3Pz+8y/QKgpWu52PdiUzzBTJx/u289BZZxMZFkJRdSVf7tsLgFv1sPrgfn+njk1HDvOXNYlcEhvHmowD2I8GxAaNlh4BgbU6s4C3BvqzfclUOp3odICq8t/kXd4VGbVG7G4nTjxM6B3DobJC8qsrcase0qsK/I9HanEBv1/+KYE6HZuy0/nv7+YSrjXVG+wdP4Fv/gQzNhucG3rsOWypgPH4muatWTn8adVKyqqrySwvJ6+ygn5hId5yHY2OfsGhjOzVt+k7bISGBKWt2V6tqb2gRcch9eOitXWDEy7dm9vtzTa53fVf5+axZqb1jeXmsScefauq6l3QoRlLd/cL9ZYq9A83ddjTfL7652zribVqp6rTrMuEyH7oFG8nh6VJJ7/d/AlmpvUfSJXHgRVrraDo+c0bcbrdRBgDmdA7hrU3zOPdKy8+IWA4VFZEsNaI4eiNA3Ra/m/mbK6MG8VpYb2ZEX06lwwZSd+gUKKCwji9V29/n2FfGcbHV87l8uEjeP2ii4nr0Qvd0beHEJ2BpTN/z21jJnHtMDNG7bFjaf3R1QmDdEb6h4RRYKvyLraCdyqezeWixG7zhpOqSkpREfExQxgYFoFy9CXldLnQKRo8qORXV3DP2m/YmnOEP333LR//lswXe/fgrPEC9nW6UPD2n168fRt3rFpBldOOTtEQoNVxRdzpXDZ05AlnJW6faGZIj3DvaoaqSoXTidVhp9rlIlCvxY0HFZXkwhwm9+uP9uhj4AuIexgDCDUGoEGD3e0ip7yCaz5fzr5sq78++nhvbbPw1d5UXvhpE39ZkwjAH88w802WhZwK7+stq8zqr7X2Odlr8ni+WkugVk3znatXsS3nCKklRdjcTlakprA7u5hyhx27x8lBazHf7N/bqNd2U/mC0pP9PzTlf62hfPMIWrsXtDhGaoBFZyOZ4i6uIadgo0JMLIiLJyrkxL/dPWkqarWRuyc1/YNEq/XuH8BgaPJmWtXJskhNOWX36PTpaD16UJVTfgj3DzfR02Tkq72pLE2qXdv5wvkzT7ks75LtFopsNnoGhPDC2RfxytaNvHKR9/of7zaQZ7MyOXIgN08cw5+/K2Z4j0ienjn9hMC6Zhbmh4Np5OyyotfomD00jrNO68s1E72r6l37mYPP9iajV7QoGgW724XJaOC8oYP4YNdOtIqGCGMQ5w0ZzOHicjbnZhCkNeBS3djcDtakpxBqDMSg1aFH610yXNGgOboaoUt10zcwlP+bcTFPbkwkraQYjaJQWm07GrJ6a6B7BASyv7iQClc1Tk8wdrcbj+ohzBjEnROn4CoN4fVfLNw19VjWMdpk4vPfX+2vSV/w7Tdo8Lbac4N/NcQqt4PvDqZydvRgtudlUel2oFUUDFodQTo91U4XZ8UM4rfCPHbkZ/OKJZH3hlxS5/Pjy95WuRx8uz+VKqedrdk55Ftt/rMzdb3+Xtm8iY/27KbY5e01vbsgnzdmz0ZvgEUJCTww5WxW/JbGwfxKtpcdwuXxUOW0s+isqf5s66hefdmenY1GUdAoGortVaxO34fT7cLXc8TpcbfaKoQ1NeT/qCn/aw3NLrdVL2hxTFeuAW7vciDROiQo7kTSi6y8tsnCnVPNDOrZNjUIAyNMPDWj63+QnGxFsaacshvU08Qbl82koIAGlYvUt//jFy+pS83T8UOi4JyYQfQLO3qEo3hbpxkMCg+vS+C3ojwC9Dpiwk4xKEVBr9ExuEc42/Oya9UwPz/jQn7OOkx2hRXcHnQaLbOGnsYDZ09Bo1FRFIX7pkzB7YZhr/3L2+rN7WSAKYLMilKcqptiexV6jZaehiCKHVUEagxEBARhd7mpdDkY2bM3/1ifSFSICafbQ0xIGBllZeRWlVNUXUleVQV/mTqNB9cm4EHltPAISqpt5FZasTq8y41vOpRDpcdGQMCxD2RfTXGFw8mdq1eRWV6GB6j2ONGjIUhrYGzvKLblZ+HxqFjyM6n2eLtroILT46bKDYW2Spbv99Y4u1UPv+ZmcO577/LKRTP9bfl8JRO+109mqZUgnYEql4PCKhuh+kBmH11l8OLT4vzP/9asHO5LSKCHMZBKp4Nfs7LYX1KER/Xw5zUrySovJ6+yktSib7A7PRTZK71PGWB1OHnx5828l7SDZTt38J/ZlxCk16PXKaw7lM6u/FyCdHpUjQGrs8q7KqSiOWlv8JbS0Im0jf1fa2jg1RVrbzt6YNaVa4Cb2spUdGwSFHcir/yyiY9+241TY+dfc9pmmdbW+iDpaEuz+uqf26Leua773pz99w838cS58ezbB+/tTqyVcVw0dQpBOgN3TDKTXVbBvQkJvDxj5infxK8dOZotWdk8dNbZ/Hok64QFYj6fezULV65kaI8IYnqEcP9Z3u4cNZcPfnRtIj0NIRTaK+gXEkp6eTEhegMu59F8rEel2FGFqqpUeexEB4dR7rBR6XawJTeTKpeL5KJcdBoNewrzCTUaGd83ih8z0rA5Hdz9w0qqnE48qGSUl/Hl769l4cqVjO4TSYXdyYGyPIIMhlpZ0Pu/W8PnKb+hAAatltgevRkW3ou9hYVotd7JiOcPHcQLF87k9198SqXLjhYNwboAegYGYgow4PK4yausAMCletCgUGqvJiO7hDtXr+LX+bfW2/P4srg4bvv6G/SKlj/HzeSj5F18nrKb4ko7T8bPJCgIbvxqOdtyjtDDGEiw3gAKKCroNTqs1U6qHE5QVQK1BmzOYx1EVGBTZiZOt5syVzVlrmqu+OIjhoX35sEpZ/F96iFG9ozEHBXNT+mZBBt1VDoc2D3uk/YG70jqCgK7cuB1Kh05MHO7IVxr4p/nx3e4sQlRHwmKOxXluMuOpTFZi+ZM4OvsWvOU4s1jzCgKtbpb+DJv/cNNp8w6+6xMS6HC6cCSl1Vn5m7ywCgsC2876TZqrr74+Lr1/Dc56Wj7Nu8iIgNDeuHwOHF4XJwdM4hgg4H8ikp25Gez6IypfLw3maE9IthTlEdacTFRQWHsKSpAxVvjq6gaf0+OCruD/+5M5rXZc1ienMLe4jys7mps1d6M8P+uuQqAVQf3ox69larCiF69WJ+Rjs3l5vK4EUSZQlgw0UxksImvL7uVZXu9nSocDliRnkxJeSU6RYsWDW58RcQqdrcLLRqqHW4yS63MHhLH9/vTmdAnhmu/WE5RlQ2dDhLT09lbmo9e0fLKngSiwwMptlVxqKSUZzYncsckM/PGTGBHbi63xZ2FQ28jw1rM/pIiVFTSiovQarX0N4WTXlbsL/cAUFA4VFaM3ePy/86DSkppPvf8sIpqp5MAvZ5/TD+PnoYQrhgVx1uWbewuyK+1tHpbamym0xcEFlRbeXvHsQNLmXzV8XTVbh+ia5OguBO598wp6D0G7jyz4RmRhvQg1mggJKT5E+Aa9SbYgNXruqrWzGzFhLVMgNASY+wfbuKOSd4s6YIJZgK1evIrK/n5SAaBOgNTomLYX1rE6X16sTU7m/BAAymF3qWxy1WbP+i+Z2UC+ZVVTO47kEtOH8qNK77ApAskyhSMw+0htbiA9PJi3tm1jY1Z6WRbrXiOTo1zqm6S83O44pPPiAoJodJpR0EBVCICg0gpLiTXZkWDgqJR/Y+d0wn9TCb/GZmkg1aMRpXlqbspqKok1GDE7vZQ7fZmqivddgwaLWXV1Vzx8ae4PQo2t4PH1v/IvsJCwowBzBkax5yh3kxxZkUpe8uzybLrUQFL3hHSy8rQ6WB9ejpOt5s12Xv5aO5cLv/sYxQPoAEnbhxuN7lVnloBsRYFnaI5et9qU4AIYxBFahXVTicLv/uGK+JG0b9HCAPCTViyc1j+WwoTY6JaJaN3srNCdb1nNOQskm/peN+BZUueeeroJQmdRVfv9pFttfJvS9sH/fL6bF0dsA+AqM+gniZevCS+UfXEHo83KK5vZjx4/7FCQpr/D9aQ2eU+906Zyo2nm7l3Sv2r13VVDVn1rLFa6sDGp6XG6MuKr0xL4bVLZ/LZtVfwy223cdGQ09hbkk+BrYJdeblM6z+IRZPPJiYkjFlDvMGjb9b6orOmcssYM7ePncrsuNPYfcefuHHsWP53zdV8e/21jOodif7ogiVHS6iJCDhWBO9QPewuyGNF2j5UINxo5Iw+A1l+5TVMiIxG6w2J6wwoazIZjEyKigFARWHB+AnMPW0kPQxBRBiCiQoKxaN4SCstZm9xHk63m98K87GrLsqd1axMS2HywCguHxWHw+PGoTqxVtsxanTcPeEszowcSEZRBX0CQhkREcnfzpjJO0kWjlSUoSocDeWPUkGPBh0agrUBDDJFoNPq0Gu0GBUdEcYggnSGo/dIId9WweyhsfQNMVHtdvP5vmSWbLcw+7Q4tGg4VFBBRnHLdwhwu+H1zRa+TT3WUeLXjBymLn6XTYdyePNXC2syar9nNKQDxS3jzFw8LNZ/0Lb4aLnK4m3N71pRVyeQjsqX9DhZd6H2Mn9C7eeoq2nM511L6kyvz86o2Zlip9NJYmIiu3btYu/evaSnp2O1WqmoqMDpdBIcHExISAg9e/Zk+PDhjBgxgmnTpjF48OCWGL/oQI7PLp5spSWZCd6yWurApqX5XhO+INeXyYsIMVLhcODwuKhwOIkIMWDJy8KpehgQEcLKtJRamcCaKw4ef7r8tVmz/bXEd5xh5uuUFPIqKzhcXnq0lEDF7nH6rz+13yA+/t3VBAVBTPh0duXlcqi0FJOh/uUcP9pn4YfMVIIMWsKNgQwJj2DRWVPpH27iYIGVh1clklaZz6PTp/PkT+tJKynisLXE3085VO/NFH/92wHe3LKN3gEh5FdXUO124nC7+cemdcwYGMuqQ3txedwMCo3gLxtX8Xj8dOYMtfJrVhZOjzfyKXNU4/S4CQ40EKTRExEQAlo3h60lGHQ65g4bS69QAyv3p1LpcBBqCKSoupIoUzAbb76Vp9ZtQq9TmD/BzJLtFo7YSjlQXkD0FgP/mtOypUxZZVaKq+yc038Q8yeYySy18vvln5JfWckdq79BcWkZ1SuaW2q0gzzZpFdfEDhgQO3XwKyhcazel86sFuii0ZkynA1JerSXrl7WcrLXaWtq6uuzpc6mdPVMdZOD4qSkJJ577jlWrVpFRUVFg26zYsUK//cjR47k5ptvZuHChRiNxqYOQ3Qgx78JnmySXlecCX687rT8an18r4lH1ybWCnJ9wXLNpbo9HvwfMhoNDT7AWpmW4g+mJw+MYvLAKDJLrezKyyU5Pxen6kHB227NoGgZGhHuf+15lw2/2v9hUZ/rhpsxGuDy0+P8K9kB/GVNIjnWClYcTgZV5cmf1vParNncuXoVu/JzAAjU6jEZA3hr+zY+2L0Tp8eNzmFjUGgPDpR5y0UqXXY25RzC7fGWRRwsL0IF/vHzWi4dHst3B6sod9hBhSC9HrvLRe/AYEINgTw6eSY9esDV//sUjxsSsw7w2NnnsV57mKmDBjFvtJnvM1JYeKZ38ZCXZsz0f6DNn2Dm6z0p7C8twuqwt9jz7rM0ycIvuRn8bkQs/cNNPLo2EYNGR6jByIGSYuwuJ2nWQu6yHuuoc7ID5u05Ofx5TQL/N2cmUwcf65axOi2Fao+D1WkpTB0cVSsAiDaZ8LV6Npm89/tkH+ztFeyIzqWxk6NbKphsamnbqeayNDRo7uq14orayFUZqqqquP322/n444+PLuzQtJpQ5eh5zsjISJYsWcLs2bObtJ22Vl5eTlhYGGVlZYSGhrb3cE6pqgr27YPhw6k3K+t04m0d1rv+uuOurr0fg+a+YXb0o/eGvOE29T7Ut+3MUitXfPIZaSVF9A8NZ2zvaLRuA3+/YApDTvFJVvP1ALVfG779VbkcrD2UjlGnYXdePi7chBkDWHjGGWSVWfl4dzKhRiMmg5HIkBBKbDb2FOcDMDS0J1annQJbBUaN9wV38Wlx6FUDyYU5HK4optxpJy6sN9/dcAMv/ryJnzIzSC0uwOnxoCig12gJMRi5Ps7M87Pj2XYkh5kfvofd7aJvSAjVTjeKAteOHIXLDb6lrY9//G/98ls++S2Za0aOZunlFzf8gW+AXzNyWLhyJYNDI9ArBqqcDjKsJaB189vR5ct1ipYzY2LYcMu8U25v2jvvsvXIEc7o189//cxSK8+u34TTpfDXc6eg0cAVn3zGodIS/jBmDPeeOYUn125CQeGv501hYISJjOL6P9g7+v9STQ15fxctp+Zrw+Np3GdGe3/GnOw1D95uQV/tTeV3I2JPmoF+dG0iX+1JZfbQWJ6a0bE7izQlXmtUpriwsJALL7yQXbt2nRAMDxo0iKFDhzJgwABCQ0MJDAxEp9NRXV1NVVUVubm5ZGZm8ttvv1FV5W3DpCgKOTk5XHbZZfzf//0fd9xxR2OG0ySDBg0iIyPjhN8vXLiQ119/vdX339ZyKqwsTrHwQIyZoUFd76iuq2hu5ryjZ94bciq1qfehvm33Dzfx2qzZ/sVPJsZE+T/QmsOXcTlv0EAuHhbLnNPiePvXXbi1TkIDDMyfYObFnzcTqNPTN8jEtAGDeGjaFC79+GMAgrQGzFHRrE5LJUhn5NXzZrOvsJipA2J4btNGnjzvPP6WuJbUogJcLoXnf9rMTeOPdYc4XFZKVFA4E6OjCTEYuG64N2O0+mAKfYJCsLlc/HvGxXx/MA1QsDocfLR7F4oCOq3Kq8e1cwwxGAjSGQhphZV1VqalkFVRxp7ifJxu19FsvY7ooDAMWgMhOiN6ncIL53vHVF9A6vv9U9Nmcu93CTx77kz/ap3/3mRhQ2YGc4bGEhVi4m8/eBd8qXI6+CT5N4rKHaw5nAJAZIR3cZyTnYJurf+lzhRsi7rVzJLGhDVuhVbfxLyFZ5rpHVD/svCt5VQliw09Q3LrODM5xXaKKx1klVm7XLa4UUHxzTffzM6dO/1Z3smTJ3P77bcza9Ys+vbt26BtuFwuLBYLH3zwAe+//z5WqxW3283dd9/NhAkTmDx5cuPvRSNs3boVd41ZCbt37+bCCy/k97//favut70s22lhQ16qf+Ws1iRv+qKj8bWW+3j3Ln/Zg0nbvDfxmq3m+oebcLthTO8oihxWHluXyIXvfoDT40araKhyeuulo00mXpx+CTeu/JRAnZ708hLcqATq9By2FfPiJfFMe+ddduRnctWXn9DLEIJWoyGjsogluwrZnJOO2wPnDRpIoDaWG0eZiY0y+bNVvnE5Hfj/dtko7wqEd69M8I/d4VRwu2v/f941cQr2cgN3TWz5coFbx5lJOWIlISMFO966bofqIrOyBJPeSJ/gIDwehTe2WIgOm47HQ53ZLF8wctsEM4unziMkwMpdKxIoKHOi1cKUyEHcMdlMToWVzGIrEfoQbO4SCqqtbM3OZvaAUeh1x1aXvGWsNwC4pY6l7U/F4YDCQujV6+QrdB7/ftjVTzt3B8cfTDXm4Ont7Ra+O5SKXbFjVI1t/jo41cHeqYJm3+u5b5CJQI2R7zNSWWwx8FQrxxVtrcFBcUJCAitXrkRRFHQ6HW+++Sa33HJL43eo0zF58mQmT57MQw89xHXXXcfGjRtxuVzcc889bN68udHbbIzevvOhRz377LMMHTqU6dOnt+p+28vNY80UFnov69NSrWXkTV90NL4A1rfEckv0hq6vdv7vKzfx8Z6dOFXvQbcWBa1Gw6whcXg8EGuK4rvf38pH+yz8bkQc/925C1D8tYEvnD+TCz98j2qnk3KXDbfHjQs3qOBwubksbsQJJSI1J1hFm0z85az4Ew5K7540lfJycGmcuNzUyu643WCzgU7f8rXvmaVW/rPFQrXbidVdBYBR0eNWPXjwEBkUjkYDu4tzSC3Lo0eQHpPRyHeHUgkMhH9ecOwx9gUjBiPMGxTPu8kWPt23i3KHnTCDkdvGnkFMmIkn1ieyNjuVcof96FLWKvk2K78fPoapg6L8QYGvS4nSgJbvxwe3LhckpVlZt8PCn8869nxkllp54xeL/6BkS2YO96xO4NVZM5kyKKpTTeATdWtOvfm8MWbvAazqaLPXQWMSVacKmn2f7wvPNDMxMoZP9iUxrldMyw64A2jw2+B///tf//evvPJKkwLi48XExLBy5Up/J4otW7awf//+Zm+3oRwOBx988AG33HKLP/t9PLvdTnl5ea2vziQqxMSCuHiiQup/tbdUa5n2alHTVL7Trx2xnZFoGb4AdtHUKa3aHiqz1MrPmVkYFB1GRU+4IRC9RofT42bVgRSyrVb+lZRIaCg8c1E8kwdG8dqlM3nt0hn+oGrywCjev+RKegWamD5gCFqtxhtYo2Fyv/6nbJG3JTOHCz54ly2ZObV+PzDCxP9dPJNBPUP4JTe91v9nVpmVBQnL+TF7L+8mt+z/7eJtFlakprI575B3sRUUIg09CNYFoEWD0+3G7nKjRYNO0YCiMPu0OIJ0BuKj42r9X/rae9023kxgIFx9mpmehhDcqhtVVbh0eBxarTdYuXTIKIaYehGsDSBAa6DMVckTW1YRHn4sMGjMe9XxLbCS8nK4ddNSlu/7rVYLuMXbLHyTkspbWy14PPDQ2gR2FR7hobUJte5DV21RVlNXfW/1Ta6LbkZ9ze/jRjNjQGybTORsTPu2Uz1nS5O87RMXb7Pw713rKXJaef7X9Q2+fWfR4KB427ZtAPTs2ZPbb7+9xQZgMpm46667TthPW/jqq68oLS1l3rx59V7nmWeeISwszP/Vv3//NhtfW7l1nLlF/klbajttxbc6VkdsZyRaVmv0hq5pyXYLVoeDEKOBYL2B38Wezh9GmJl72hhum2BuUBDmdkPCgTScHje9g4K4friZ3w+dwC0jJ/NoA85kHR+E+fgyQAsmnhiULU2yUGK3EW4MbPH/21lD48CtIQAjBkVHiDaATHsBVlcVqqKSUVlIXqWVuPA+xIVHcvXpo1l1IIVSu42/rE8gMSWHR75LZGNaDi/8aOHWcd6sbFaZd6KjXqtFBcocVdydsIrMUisxYSaemjaTh06/hEHBfZjWbwgGjZ4hwX0oLT32gd2YAPX45+7hdQkUVpdzpKqE00Nj/NucNTQODRryqirItlp59tyZjOzRj2fP9dZLN+Q12JTAoiMFI76xZBR3zV66zeko9O4uCxsLUknISOGx+Hh6GkykpnrP1LSW5hz8He/WcWYmRw6kuNLBgJAeGBQdp/fsU+ftO9JrsrEa/NQeOXIERVEYPnw42hYuGB0z5tgkkuzs7Bbd9sksXbqUWbNmER0dXe91HnnkEcrKyvxfmZmZbTa+luCdaJdITkX9b04tcfQLx2qSYsKkdEJ0TZmlVv+iIjXdOs7MxYNHMPu0WLQaBVOAnhcumMnzF8wgJszUoAPGrDIrv2ZnoaJiMhp5dPJMXp99MW9ePqNBC/Y8d95MxvTqx3Pnzazz73UFZbeOMzN70AgWz5xLmMbUoh9iq9NSyLIVkV1dwoCQiKNBrIoC9DAGEaQ1YNDoGRQeTkpJHn/8dgUT+sRQZreRWlLA/WtX8e3+VO5Y+Q1v79nMkz8lotHAe6mb+fjArxyxlhGmC0an6DhUWsLibRaqqiClPIeHtn+K1VNJoF5HoGIk11rFPavW+BcoOVmA6nZDaSn+IHreaDPxkbHMG23G7Ya/T5pJgM6A2+Ph9h++8GfmVx5IIddWxo9HUliaZGFivyi+uWIeE/tFnbCP+mQUW/nrmsRGLaTS3r2KawZAvsDoX1s2N/qsYV2BVEsFVy21Hd8BZlNCoNsmmLlocCwLzGZMJlhz4ADT/vcqq1MONG9QJ3H8+87JHodTBdAxYSZ6Bhv5JTedviHBXNlvCg9Pia/z9jVfx50tQG5wUGw4OqOgoT2JG6PmNg2tMAO6LhkZGfzwww/cdtttJ72e0WgkNDS01ldn4ptot2xn65c0NOcNQ4jOoL7V1nwHhH87Zzo3nm5m0ZSphIfjP2XfkAPPpUkWbG4Hw3v14p7JUxr9/zSpfxQ//GEek/o3PAiLCTPxxLnxBAfDkz+1bGbv5rFmjFoDKiqV7mrCAoz0CTDx+6ET+HDOdQyP6Eu4MYCkgiPYVSd7ivN4ZvN6XLipdjtxetyM6hFNnq0Cu8fJ9twcHlqVSLG9Aqu7mjJ3FdUuO5HGHkzrM5wpveJ4eHUi96xZRbG9AofqJKuilHJ3JRvzU/n8oIV/bTn1nJWsMit//HYF095byi8ZtUtRMoqtLN+dwl/jriRAa6Da5WLRmgQcDjivXxxRQWFM7xvHvNFmDAaIjvY+fw0N9l75dRMfpVp45ddNta7ncNR9/ZwKK0tSEzlYYG23oKNmhtAXGIHa6LOGdZ25qy972dhA6/jttEeg5nsP6Bvk7Zt9z9pvybOXcfeP3zZ6HA0Zv9sNYRoTfzvnWKLqZNngUx24a7Xwx0lmZgyM5ZqRY+gR7m0t5xuH7/bzRpt5YeNmPt7v/X/rbCvwNXiiXUxMDCUlJezZs4ecnByiohr+xnsqa9asqbWftrBs2TL69OnDnDlz2mR/7eWWcWbKy72XQojmqa9xvi+ADQqqewZ3Q0671uxqERls8neVaKimtBLzTxLcsImP9uzGpbGf0LKtqTweODPiNPaX5/PP86ezLTeL60d4W1kFBcE7oXNZmmRhSv8YFn73DU6Pm8FBfRgVHkNSYRaVTgcHSgsJ0RnRBWqJjejFe79ZiAgIQoMGNx4cuMhzlLDfqmfRuhXkVJXSJ8hEbGgUL50/mzKXd+W8EL2Bclc1NRbKrtfSJAvfpCdT7Xby5+9WMTt2KIm5qYQnQ5nNzheHdnN2Lytn9hiGpSSdcCWUzYdyeHhdAjank94BIbUOfjKKrby83sLtE82c3t900k4UFXYnVS4HFXZvpw7f9a4YFcf/dqeccP1lOy0k5u1le0k6nw+Zy9CAujPfrdkVyFdrqtN5s6HFxXBTrJkIvQnT0f2fasEUqHvC9/GTE33bKKj2Tmqsed2Tbf/47TR0Urgv+ISmJXxqjsn3c0q2lfd2W7h95BSe/GUtQ4OiyCi2MqS36ZQtCX2/9x1ABATU/9jWvI/h2hMfz8emx9e6zfHdJ+rapsfjPUD7IiWZxJx0nJvsBGqN3H222X/7vCorP2cexuVR8aiqv+vG8ZNnO6oGZ4ovuOACwLus85/+9KcmL9pxvE2bNrF06VLA25li2rRpLbLdk/F4PCxbtoybbroJna7ZK113aP3DTTw8pfVqKYXo6moGtKeqC60vu9uQrG9r1z2fjEdV8Hi8ly3l39s283NhKmMj+nPhkNP4y1nxjIjx9natqoLIYBOPTovn4uGn8c0l85kTMwaNy8idE6byzkVXcUbPIUQbexHfL5aEq26lpyEYp9vFYWsJGhQGBPQk1hTFkNBeZFYWs788j0q3nTxbGUUOK8HBkFSYRa+AECaFD+PqwRO5e9LUesfrC4BuGWsmKsiECuwvLmJ0REyNDJqCR/WwsTCVn4tTqPJUk5ibwsLvVpBamkdRdZW/pti3vVd+3cSnByy88ssmysq8+6l5qrlm1s9k9PaMNhm9Z0x9AcVDPybw3aFU3j7uDMXs0+KodNsptleecDawLep73W74/RAz48MHUmh1kGOtoMJh5/Xtm9lfkcOTG4/t91RzOOo6fX989rJWeUZGKm/+avFnS0+2/eO309BaW48HUnOsPLG+aY9fzSypRgPlqndi67dpe/lifzIokFR0mCUWy0nvw/G/z7ZaedmSSPbRiL2ubKzvYMX3GLnd3gmqF/T3Pg5ZZd4WkvuOWP0HLjXfo+ra5jtJ3uXuParK+f1jcTgUvj/6OGq13gD6pU2bOWwtQqdomRk1hrGBceg8BmYMav4S7G2hwUHxzTffjOZomuPrr7/mwgsvJDU1tck7drlcvP7668yaNQu73Y6iKFx22WX07NmzydtsqB9++IHDhw+3SAcNIUTX1h3Kgu4YN4VZfczcMW5Ki23To6qoKuh0KrmVxz5gs8qsPPBDAo+sW0OZx4rBACNiTASHKHyfm8w7e70lDjsLM/mpYB9Go0pwMLjcYNQZcHhcqMCUfgM5t18sfx93Cb20YWjRMCikFwFaI0X2Sh5JTGCB2czYPlHstmYxY2Ccv690XeUMezKtPLw6kaIiCNF502VWt40nN63ljtPjKc008YehU+gX1AOroxqPx4NG0aCgcLC8iGq3i3J3Fety9vJOksUfVFTanbhVD1vyM3h47Rqyyqy1grSa9Zf3TJ7CdbFm7pnsfR5mDY0jQGPgz+azOa9f7AmtNVcdSMGo6LB7nEyPiqtVZtGc+t6GcLth3xErL/+yie2FWWzMSeOvGxJYnbWbFYeSeWxTAj8c9gbyDSlVqOv0/fHzVGqWZ5zfP5YrBpnrDRRrigrxli74ujA1dFJ4ttXK3RuWszp9b60AvOZjcMqODTWCxk9SLeRXVlBqt1HpdKJTNGjQceHAxgWMxwf19R1Q+B4jp9P7XC2xWLhmmHfp86VJFr4/nMrbOyx1Hkgcf+BWWgpTe3tX3ZzeYwxzB5kBlSmRg7h1nLfevqwMqu0qDtWDW3Xx/o5d/HNLAuVOG6sPpjTqPraXBqdJR40axYMPPsizzz6LoiisW7eO008/nenTp3PRRRcxZcoUhg4dWm9ZRVVVFYcPHyYpKYmNGzfy+eefU1hY6M84R0RE8K9//atl7tUpzJgxo8Uy3a1NFsQQQrQ2rdZbH9jc9xjf+5XRCLNjxvD93kycipPXt29m3eF0goK91/vm4G7Au8LcrePM/GujhQq7A1UFq93OvFWfkmrNQ6MoGAwK7yZb+DU/g/jo09hdkMfAwEhCAwysO7KXDTkpFLsqCNAZmNi3Pw6nSpo1n+fOm0lMmInMyiLSKnJ5dkcCc8zzTjj1DN5s6h+++ZQMazGJOanEGHuRouRhV721zfduWM7G7HTOih5MdIiJPWXZhOmDCNUHUGC3Ync7MGoMGNChVXSc0yeOt7d7g4rpAwdyWlhvDpYX8vXBZPqGG7h3Qjx3jo7HBLy4I5EfMlMJTYK/neP9ffTRkwUrD6RQ5XKwPjONAIPxhOfn5rFmVu1Np8RRwd82JPCyZy6RwSZCQ+H1rd5M4bmDB7Z4VyDfQcQtK5dzyFqIR1UZYurNX80z+WTvLqptClcMG01iTgo3jDRTWgrFTm/ZwN1n112y0JDFI64+zUxFBdw5yYzN5g3c+vb1bu9kfaBzKmqXZpxqX779vfmrhZJqG+GGQH8AXvM5qOu1VNPxfY1vGGnmm+R0Sh2FFFZXYNBpqXDaeHuHhemx9S+xfnxpyfHbrat/ckyYidvGezvf2PVxLFqTQLnDhsEIsVHxzBvtXZmu0ukg21p7ZTrfY20t954J2JNpZekOC6U2h7fne2oKAQHwS2EG0/vGEuQ24XTCgTzvAcmsfqOpsDtYk7MbJ05C9EYuHtY5MsWNqh345z//SV5eHsuWLUNRFDweD4mJiSQmJh7boE6HyWQiICDAv8yzzWY7YYKeb5lngF69evH111+3aJ1yV3GqfzohhGiu1yyb+eZIMiEWB/938Yw6r9OQA3Tf+5VW6112utBVxtrsMq7tMapWYJZbYkev8y5csnibhW/T9hKo1XNu79EYFThUXoRDdTE4sA83DpuCwQDlZXDlEDMeFZbs3oS12oFRp2VPUR4e1UPfwDBMAQZ+LkxnzrBYJvWPQquF+yefzYJV3xChDyUl21tW8do2CzeP8bZ4CwqCFzZuIrUsH5fqJq28gEMUMS58AEklmdidHlZn7sGlekjI/I3eQcEoKESHmDi7TxxpxcWsL9hLgEZHhCGEfJuVBxJX8e+LZlNWBn8+w0xZLDy7fhMmk8KsoXE8uTGRm0aZ0VaauGhQHGtS05l9WhzZViuvJVu4p4eZIQEmbhlrpqgQVNXBd+kn1mVGhZj4x9i5/PO35RRWV/DHtZ8yslckCt7q6XHhg7iq/xSG9zMRHta014bvedfpoKTEu5KfonhLO0psNvoHhzOu1wCmhY/m2/0pzOk/huWpu/j8oIVArYGKCtBpvafe12anEpJ0YtBa32ur5ucfQLDHxN1j44kKgyeTE/nhcCqGX+GpGfEnXVjj+IC5IfX3WWVWiirtnB01lKtPH80n+48F4DXHXVEBERHeg8rj+YJvo9F7P5xOGGSIooc2lOzqEmxqNcVqJTvysikrq38J+uPHf3xQ3zfIux5Br6P78f3+v795s8EbjqRxsLSYQWER3D7RjNUKpgATPYKMfHcwlaVJBv9z4sv4Bnu8pZcHC6zc89NyCiorCNTp8eDhl5JUbh92HnY7nN/DTFkZlLis/GntcsrsNqbFDGF9TiplngoUFIweLSsPpDB1cMeP8RoVFGu1WpYuXcrkyZN5/PHHyc3N9Qe3vsyr0+mkuLi43m3UXCRDVVUuvfRS/vWvfzFw4MAm3gUhhOhaGtoPtSVWo3S7wVat4vGA213/GbSGTE7yjeeyEXGUV9u5oH8coQEG7p40hd4BJv+H9UszZvoDoFlD41i6PYlql5M0NZvLBs/ka91urK5qSh1VvLx5M3eMm8KNo8y8s9OCJsDBmmxvpnlgeBjBWgMGjYHHzBfzXfpeNKqWCwfE+QOtrblZuFU3iTn7+MM3JUwf0p816WmsO3yAsREDuHXSaH7NzsKo0dHP2INiexUexcNhRwEuXGRXlhKsGPGgMLn3UG4xj+H5zRt5+KyzWZ2ShlNxoqJidVVT7XHiVN1kVBTzQfIuDB4jFRVQXQ3OSiN/GGfmmxQL3x1MxYGdAMWIQ3FQ7XGwOi0FRYN/Ut8/zotHUbyrDU7qPYRfDmd7ez8fp3eAiRemzOWunz4jpSyPtIp8FBSC9QYujBrFsl0WHok00/O45c0behbSF5iWq1Ze3WDhgfPN6HSQX2UlUGfg7xNn0ysghEU/L8fqsrG5II09RfmASojeiKqqaHVQ7XL6T7XXtY/SUm/QGBZ2bDw1X98A/9ppOXra3sSNI72ZzuJKBxnFVsI0Ju4eF0/fkBPvQ82AuaGT2d7ebmFzdgazT4tlQ25KrQDcd7vtOTn8eU0C/zdn5gkBn8MBvg6zvXt779+rWzaxsWw3PQ0m8u1WwvVB6NHjdsOOA1YmjThW3lPzcTg+4K8vqD9SbmXxNu/jFW0ycVl/b8Ba5KggvbSUUWEx9Ak0sbfIykfbLMwZFofLVftAIqvMypNrN6GgcP9ZU/hgr4Vim40qp4tSZzUVzmrcqoeX9n3LAwOv5oODm/g63zsnoajK2/Ncr1ewe7yTRXtrw4kwBnPR4C6YKfZZsGABN9xwA8uWLePLL79kw4YNOJ3OBt1WVVViYmK49NJLmTdvHhMnTmzKEIRoM1LC0vl1tuewoZ0kWmLpYI8Hzu89ho1pOcyNHVPv9RqyL9/knk3Z6RRXOpgxMJbHz42v9QEPte/b6rQUQvRGyh020qx5LNr2CQ9PuICnt/1AudPGF+lbSS7NZFJUf37KTOf8oQOZFTMKnU7hpgmj+WR7Chf0j+OJbStJKc3HqNHy940JLAufiy3fxPUjzKxMTSO1LI/D1hJcrhhM2kDSygvJtJaRYs2mvNrBwKA+ROp6UVi9l5igCP4+bTpPbkzErXqosLkZGTiEnjojo/r05dNZ81h2MJFVmbsptdtw4UaHBo+qojm6CuGmIxnYPQ425x/A6nCSWVGCYa+VK0aM4J2qJHIrKkktyeDcQQO5aLC3Xthm82Ye5432lhz8Z6uF74/s5X8HkwjRG1mdVne2LUJvYkRIDIdKi+kXEkZcSDQBgZBUlEFOZTlBW048A1BfIHq8zFIrr262UOapYGV6CoYtDsKDDKzNTsXjgXVHUryTyNw2IgIDGd6rFwdLi4kKCmNAUC++y9qDS3WhVTRcG2vGhMk/scsn22rl1e0Wro01M95kOmHlQV/ZzeqMvfx8JJ3HRs1l2nhvpnPVgVRe3KTirDAyb7SZyMgT/3GiTd6AORjv/a3r7OvxB33XDTeTngE3jzGj10NODpwX7q3P9d3uobUJ7C7xLpjz063zar3PbDuSw5++SWD+gJmcMz6E/2y1UGpzouKh0FFOlcdOMHoUFDIri7nmx7f5LPQahgRG+bO9vm352rn1ruc9Ia/KyuIUC06Nw1+m9PdzvN06SqrsONxwfp/RXDd4Cvtzrfx5/XJK7TY8KiwyxxNV40BiaZLFX95kMKoUV8CQoL7sdxYyytSLACNsKchA9cBfUt/FraoEFmvpqQ1Hq9Uy2NSLSb2G8LlmN243uBS396DvYArnnNbFMsU1BQYGsnDhQhYuXEhlZSV79uxh7969pKenY7VaqaiowOVyERQUREhICL169SIuLo4RI0b4l3UWp9YSmSDRPFLC0vk1tAVTZ3OyU8YNlW218uyuVeQ4S1iemsx5I+r+4GrIvm4d583e5ZZXUkoJc06LO2nABd42XpVVkFdZwWf7k6iy2/ny8Hbi+8XyZfoOFOBwRSFVmXZKqu2MDx3K6MjTGDwYevSAcX2jeP7XRLKqSkEFg9ZAqd3G/23dREmekUdmmHls4mwe2PANiqJwYd8xXBgylXcOJJJSkYPBE4gWBwFaHeuLk9FrtQwPieGCQacxuedppOZYeXFLIlsqd+Ou8hCx28FT02cwWxPHt3sPMMDQlwPWPMICAih1VqJHj0t1k1tVRqBeT6mjlDK7DZfqYUPuAfaW5VDktJJSWMiFg2K5c5KZ3gEmCqqtvL3dwrVx3trV+9ckUFrlRIeWKpeDyGATt9WzAl9yQQ6/5Keh1Wi4ZfiZpBYVowl0kGsro9rtwlPPHJqcCisfb7dwzzn1/1/8a8smPt6/m75BJv/ZhFvHmckutFNa5aTS5eCywaNRgWtjvf2ZA10hXDnEzKJNyylzVBJuCOK8vqO4caS3Rajb7S038E1gXZpkYXXGXn7JTeeTvnP9Yzk+w/vdvnRyK8uZ/8tSPo2+muuHew8e3C4HazL3srMsnc9j5p5Q4lBZCaVuK69stHDl6Lrb2x1/0JdjrWBbYTqp2XGM6xvFmQYzn6RaGDrUzCC9t9fww+Nn8khZAk+dM9NfdlBZ6f2seGhtAr+VZvJA+XvMdYxiU1YmQXo9fQ0RHKkuwoNKvqMMF95ZbnaHnQcTV/L69Kv5ONXCnVPMBLpM6PXe4LiuPtfgDZrf/NVCQkYqZ0UNZFrfWG4caWbfEW/ZQ1p5IRpV4fIB3tfOHT8sp9BWQaASQHGFt544OvrY4+D7H1ZQqHI4+D43BVQN5c5KbGoVX116KyUlMO+npdg9TjyoBKo6Mqrz0Wm0lOVbOWQtJFAJBB1UexxUuumaNcX1CQ4O5owzzuCMM85oic2JGloiEySaRw5MWk9bZXC76v/RqTJIDbE0yUJmZTEOXGi09ZdPnGpfbjeYMBGsNfJrwW48Hlifl8LMsSfPDsWEeVuzbdifw8/pRzDoFR49cyYf7LWgQ4NR4528llVZikN18bet33BB5Cj+MWQKfQ0mDAZvYJ1VaKesTGFm1Gj2uFPIqixmVf52DNus9Ak0UeK2Uu12smSHhWC9nrSqfPLspRyqykenaDjgceFBRfHo2VOexbasHIZFh/DRwU1YrAcodVeiUTRUVqkYjfDdoRSqXE5yHRWgUbG6qxgW1ocrBkzg5eQfmBI5iDvGT+GDpF2UOivZUpBBoMaAy61i0OgYER7F384+lkV/YauFH7JSQYHgIPgmfTceNwwIDUeHlklR/etdLfSZXavIsBegAP/6bS0mbRDnDx3IBTHD2Z2fz9Uj6j4D8HGqt8WWKenEvrXHeEsehwVHM0wN4Y5x3p60z5w7k8fWJvJjzl72lGTz4LiZvL/bwtXDzPxxRDx2o5XBoT1xuN38+bTZnDkwisBQK//akkh8ZByJuSn+YPzWcWa+359OSbWNN3+1+EsUokK8Gd4gFYpcVoYGRpFRXkKpo4oHf0zgiTFz0WlhREQkHzi3Q5WLpUmWWv/jHo/3PebT/d77uq0wnVKb44T3glvHeSfxXX2aNwB/eMMq9ldn8+gvq3hr0q2sLN7ET6W7Mf5s55VZMykrA6MzhNGmQfQJDvEnT3yeOmcmsz59D6fHRVL+ESqcdlweD+OCTyNS04cMZw79Q8LIKCvD7nFS7C5jRI9I/3MSGAjzBsVjMkF5ubeOW1W9ZRk5/8/enYdHVZ6NH//Okpksc5IQshMgbAk7yAE1iGVcU61WW1zrvrS1tVprfdva1bZva5e3ffur7au1Kqh1l1o3NLgFRUBh2LcMCSQkZN/PZCaznt8fh8kCCQQIEMj9uS4uYDLLM89MZu7znPu57xrjb5vN+PPVXJW6Fj+tbSauzzVqRD/4YTG1Hg+RcATdZKLG38zT7tW0+n04rLF0dpr4uMZN6cpqXsruPpDIiFe4J68QkwkeLSsiHImgmOMx2cBuieH1KmNFvyBtAitq3YT1CBaTmQg68RYbCzPzafYE6LCEmZqSwcd1JYQiIZadjjnF4sQbjJUgcWxO14BqKDhRq/Dye9S/O2ar7Gvw09lp4r4z+y/Jdrg850jEWCW7bZZKjaaxpbGehRn5B50qP5DFYrz+b7pLMGHigpw8JjmycNhisJjNBAlj1sGGFUwmOsMh3qvdwvjNtq5NZ9mKwo/VQjbt1ni71sWdZ6h8+5MX8OsBNjfW8O+rnSzfU0p5ezOfNe8ibAoDYQKRMCbdRFbsCEYnpLC1tZoRsbGUddRwx3svsWjKVN6s3IwWDpBoSWBa3Diamkxs2qOxKFfl9S2lNOoaGbHJnJWRy/fnF/Ct95fiCfuo72zn7DFZxLVkkZwMexo1XixfRYAgE2JHYQ7Z2NuiMSOpe1W0ptmP5g/wxdwZbE/T2NVWz/1zFvLWpirunqN2lf/qmV5jNsNYZQRbW/eRZI3n+/mXsbWlitunqDy/08XmhhqK9hx86rpa0/AE/V15vv2dTblnbgENjTotgQ7Kg2XUe/PJ0RRqOzQ68RNrNlbm/2dzEfWaj88byvn9mYv4oNrFzrYazkzJIy3OwVO7i9ErA7y7u4yX3RtxWO3Ex8N/zXNiDyj8evYiXq9y9aryYDIZ76u2Nnh8m4tNbRUUpE2kwtPEfVMKebnUxZoWN69VdOCLBPBH7L1aGnc1+ojmubfD1bPyeX1HyUGfBdmKsYkP3XjMaanpbK6rwx8K8+HeUj5p2UZQj9DRYWJPo8bvPjFK0XUEAzyzDX6Z7uy6r3AY4nUHcxImsaNzLxNHpOHx1WHBzLaOCsIRMFvDzEjP4Lezr+WjHTU811TEpeMn80FFGQWZuVw0Jp/frSnmpjPzeaOkhOvzVLJNRpWH2lrw2zRe22usKKfHKcRZ7HxYv4PSjmr+mF1IjabhCwXpjIQIEqK4toQvj5rDxTlTqPN6eLdiGyF0Yrwmntzo4hcLnWiakf8eXZW+Yfx83i+poi7QwixlPK2RduaPzOfJDS5KtBrOjJ9OcqydcUkpPFb6PrMTcwkGYVVzCXZzDFMs6WRb05iRnsm1k9TDfhYMBRIUD3GDsRIkjo0EVMfPiVqFH0gJpuEqW1H42VnGylBWcv/X6xmIHbjC3/N07qRMhbR4hUCohrd2lXD+1KzDfhHua9eo9WjYrRack3L425Zirps2k3YN2rxB3J5q9mhGi7+chJGcMyq3VyqB2QwBu8Yfdy+lyesjtRJmp2fjbm5idno2o5MV/nH+tdy2/Hl2aw3Eme2MUkZQ7W2lMxIi1mrj+1MvJcmssNtXw483vUSsJYaIrpNuT6Ij0MjMxHHsbW9hs3c3iWvhzukFmC0mTCaIMVu4alQBsbGQmzAST2KYH6uF1Hk1XqhxcWemisMBZZ01tPl9jEyIY0djgEc/t/H/Rjm7WoE7Yuy8t9dNrNlGkkkhGKnhjV072KY1UVabT1sbPLfDxc3TVebmd/++jIx1kGhO4OzkfFbVlmGxmGhvhy+PNlJTDgxIwmH4+xoXq/ZVcOmEPJLMCn9aV9znwX9OkpG7+3rVevzhEL9YVcS/LryVJ9a7+LyugjkpY0mOt3NmVg7/9fFbdHqD/Ni1lB/OLqQuBW6aqvJSqYtPG9wszB2LYo0jFAnjsMZx01RjI1hnJ4ywKlyWZQTyqandKSUPr11FJGziy7kzCAZh0XiVTh88v3sVrZ1B5qbmUtPuYUVnCeflTOxaTY8ecD+1zchz13W4aYyTvDSYEJ910Heq2Qwek9Gq+ruKyg/PdrKuvIm2oIfful/FHw6RYnNw6YgCntrk4u3KrYQjEcYpadx+QP3oSASe2eZih78cTJDpSOC8rCmsbiijRNuHCRMzRmbzzXkqnX6Nl9uL6DT7+NumlbR1BrhwdB4v79jMfyq3ssVbhuYP8Om+cl5MNUruNXRq/Nq1lPaQj4R4uH2Ck3mJ+bwS3khLMMLPVhaxvbEJfzhELDYy7SOYPCKDSMTEJRkqL+1dTZAIloiFkfYE7pitdh18VLVpvLzHxTUTVfx+iJjC+PUA+/yN6MBb7hIuyVBpSPHT1GSiME1lld8FZp1N7XvICiZhxYzZZGJLaxVNkQ6S4saQuD+gH+p7OyQoFuIwJKA6fk7UKvzRtEAeLsxmGDny4C+qylaNRz938a0z1YO67B24wh+JGO1r/7XDxfe+oHL5pHw+2ltKQ6fnoBqoffnr2tV80LAVmymGR9avpD0QIC4Ofn9+IVu2wOJ9RVR4GsEEZ6aP5SfzLqZHGiQWCzxf4qJO8+DXQ3xlaj4pNgeRdgc/KlAxmyEvS8FutRIkTCTSyez0KZzFWD6rqcIb9vGbHUv5/oxCPqwq4a/nXMuqxhK+pRpti2s62tjWsYfWsJcIOlUdzfxg9VI6wj7irDb8eoCiBhefhWBrSw3zM/IYE5PFP9YWs6LGTVKiUc6sze8j0RbHr88p5KlPSzg33ti4FRtrvA53nqHS1umnRvMQiRgl1TY3l1PireWuFS9yYdY0VteVsa6pnJcyFpGtGBU9rhldgNZkIxjj4e3a9cSYzJR5q/nZtEX85Bwnuk6vgCQSgStyVFpb4I4zVLxeuHWmsdHvwIP/Oq9GwOTngux8yttb+M25RrDvCfqZkZJNTIyJq8ar/GubCxtWmoIerB0Wntu8mfQkO9ZkD2Grn4KsXK7Jn0FTo1GF6rqxxlmJ/15bhKczSKzFRmcnbPWU49gI357m5KltxqavYDjC1uZqfjynkH/vceEnwLIqYyPXl+Kmo1gSuCBZ5acLCvqs5dzhhctHqehhY/W5rzMeFgu8VGqkLjg2wgNznfxk8iJ+tfMlmvwerCYrV6QV8JudS/nGnDmk25IYl5DJN/IWdrX1rvNqXfnA1+epNHf4MZtMfGnMDP69tYTbchfy8PY3CJvCqOmjmTxK4YHlRVR1NjDakcLDzkL+vc1YFX5k8wp84QBjHCOo0ttp9Bj1jC+blM/X17yE1WRmVGIihePyeWhVEatqKvCFA2RbFe6YtIBf1n9As6kd3aQzL2U8jhg7nza6se6w8dXcAnbVaOzs3MutExaAplAR1PjdZ6tYW1uFNxQgokMkDI2BNnQdwoSxYMHdVsendS8zPS2DkkAlf6zYy7iETM5Mzicp1saV42fw2/VF7As0Uu1twWQ2ETT7+ePaYu49R8WhK0N6f44ExUIchgRUx4+swp98/b2/n9jg4p0yNzEx8MvznL1Wg5uDxinp6Ap/ZavGvcVLafH7GLHZCDwagm18VN3GkxsdAzjg0bGZzMSYLXx37gJWV1ZxXZ6Kx6SxpHoVnZEgF2bMJN5q4/YpBTgcBwc1N01VeWNzORbdx7LSEh6c7+R7ZxiNMLxeI/C8b9KlfO/zF9AtYUYkxPD9GYWU1mn8bONS2gI+frPhbao8bSSm+fndBUbwZzJBdlwKNX5jc5SOzvbWGpJiY1FiYpmhZJKi2Lh1psrIkcaq50Vj8vn71mLOz86nIdH4/1P1m5kQO5oR8bauOfy1+2XGTLmULyhGakNmggK6iXf2bcFusTAqNoVAOIwJE75QgLUNe9BCnYS9EZ7a6OIXTicdZo23a1xcPV5lWctqYkxmghGdKk8r3/z8SZ7PupZR5qxeVReqNY0ntq+iw2eiRvPwxC4X3zpL7arQED197vUapcnW1FawMDOPn0z9KvE2jVvfNMqvJcXE4Y0EMIdsnBuvsi6uHJMZYrDycdN2krw2tn9cTovXWP18t7yETW0VzHCMZVmti2UtAd7YvRVP0NjomBM/grMyc43OfaH9qTjNfj6vqaLN7+MPm4vQ/AEW5o7loqzpeDQTFgtsai3n7JQ8xozofiNXaxr/b52LG6eo3DbOSaNf44ldxfwwV2VCet8f6D0/j2w2o/6wyWQiRBh0E683rqI14OUP61uxhOxU+RqJj+9+L0bzgSNWP/jtfDVzPql2hZfKilnd7MZigR+N/Dpb7S7umavuv50Ji9nMvMzRqNlZ5NqziI0FGzZizTbS4h1cO7aAX64p4pzUfH74QRHNnR5G2B38evYi3t7l4q29m2kPdgIwUclg5d4qmkPtePVOrFhISDBx9WgVLeCnqt3DoztW4PbvxRP28lTpStQRE/lPq4s3yrcSCkUYm5DGFzLy+XfpZubET2F3ZzW1gUZMQGV9PRF0zDEhOvHT4vdQ29lGYdZ0dHSWVrjItqUyypHIHk8jU5KySbDa+KDSDev8xGIf0vtzhkRQHB8f39XqORQKnezhDDsDrYkqxGCTVfih65tzVcwm+LpqHLBET68CPOU2Vvjj4uAHZzl5YoPR+SvRFsdts4w6trWtxg72gRzw3DN3Pp+U1eAJ+djQUNWV2/nHdUW8vm89drOVq3Pn8X3VaISQmNj3StOEuCwUxcTts1ViY2HSJKPKQXOzUWf37arNTI3JIzvNxnfUAswhGJeq8Kf5i1i8cxWfVO+mI+THFwqiKPD7NS7WNVQwN20sZnMOFU0dVHU2Mjk1lSzFgdUKn1SWc35KHhnxCmkOY4Xxt64i/l25mRX1O7Bhw7+lmQ9ryyBiJcUfS/mqaja1VeOPBHngo2V8kHUHMTFGOTZd14k1W4kxW6nubEWP6EyIy8IcE6Hc04jVZMZiNnHpRGM3//M7XSyv2sHHllImJGSSGjOCus5W2oKdhPUw33//bdTkiXx1ej7v7S3huwuMLmdvV2wlGISy96rxhgLYbPDAPCctLUbwnJpq/H3rTBVPB3xlrIojBpZsc9HsNVJAvj2hkOLmzexpa2bFvpf51pSFbGov4829W2kJeYgJJfK7877Kq1uMHN64OPB2QHNHgJUNbs7KGMtlY6fjDQYpaavDGwyg2G3oOvzZVcy3zlJ5UC1k216ND9tcXDs7n1e3lHB1rsrHzR6e9RVxQeYcVlftpcPsoaFTIz5eweuFx10uiva4CfjhsmyVH7uMMmSpG+G3Fzv7fB8emK64rNaFPxIgwRRH2BRhcmIWO9tq+cGc83ly43paQx5+7FrKSxMWYbHQlaMN8ME+N1oSfHuqk2snqVgtUJCSz3/aXdw8SSU/2yg9d++8AnxtNu7uCpKNVf0FiTP41FrNorwZvLxjC/s6m3hl5xZ+eU4h975dxG3ZhSgoqEo+VvN6LPtbjiu2GGbG5PMfy3piIzZyHancM7cABQV9B3zWsQXdpBNrtjDClsDtExfwz13FLJqeT+0YP82aUV73NxuW0d4ZYLJtPLFWM3bdSmZsMpOSU9nraeGMjAxWdlaSbI9DD1nBHuTDqhI6QgHizDbGWUZiMsFIm4ML0vNZX1uNNxDk49qKIb0/Z0gExbqunzJtl0+0ExGwykqoOFnkvTd0ZSvGAQt05wxXthqnh687I5/OTiNg0jT4Ym4+b24vZUx8atdtfzqvcP+/D/9YOUlGYPp6pbFJzuMzcju9weD+FWQrX5qYj3WExt8+c3FP4sErTf+3YTUr27by5ZQZjE5Wut5bFc1G3npzwMPbtZvRdciMzCU9TsHjMZ5bQkQh3mqnye/BbDaRYDfak908XaVqH9wy1WiG4PHAP93FrGx0MyM7hW/MVYlbZcOZmc9fNhZzz3yVJLuCt8PYEFjuaUTXoTnchi8cJBIJkG1O5FvjC/nv9jeo7GwiPymdmhrIyjJOvwNcnDmLwqwZ/HvvOso66rk//1JWNG+hpqMNu9VKgtXOstIS5uVkcfUElY8ry6nwNlLjbSPJrBAkRFpMIgnmOLJiR/BapYu1LWUEI2ES4o1qHTsrNTbU1POlpAVsb6/ihikqimIcQNTVQXKyUeFA9yp8e6qR95ycDF805bNsezn35hcyPi6Lte0lrGpajz8S4l/lKynIzCUcCWExWfhC1kRmZxirn2lpxuv0tckq/9i8ijPTc7l2TAFjU4zXqs6r8XKpUdlgyWYjD9hsgfvnOBmXqvDdXCeZGZCpZ1FVBU9WLaUitI/frW2lMximub6Nx9c5eOg8Y8PYTVNV6uvhmokqL5e5aPR56AyHODe9/82fPb9vIxFjrL5OGNmQzy5LCf5IAIe5nfW1NUxyZOH2VdHa6eOpjS7MFlhTW8GFo/P41hyV1hadtqCHP+54C7vJxp3TC1i8YzXvt27BURPgIotRN3pUosIDc52MyjBW5js7jfrRH1aXEDIH+LCqhJgYHZ0IG5sqeSC+gF+NvxWbzVj1f2dTCQ5LHCPjExlvG8NXJs7ghx8VERO2McqRwu/nLULB+F2xWkyYMIEOs5VJfGviRbxRu4rlNVvpjPi5JW8+D6xaSqW/kYgeJsYUQ6mpgk49wLiEdH4+bRGTMhV8PojP0Ijxu+iMBFhTW86IhBiumDAdT2eQBJuNq/Nn8O/tJXwxTeW9OhcdwQAWPYYvZORx64yhe2ZwSATFon8SNAghTrZIBErrNO7/dCktPh+JiXC/6iTZYXyBv7OnhBpfGzW+Np7a5OAhpxOzGRISBnZAX+fVeLPaxddVI9j95Ypi3q90s3DsWCYlZVDv9fDw+iLOasjuak5w4EqTyaRjMoHdrvd6zGjeemyMef/1IBLWqdY0/rpuFaGgiSszCrh6vIon6EePmPjeWQWEw+DQFW7McZKZYASEdjt86yyV+K3G39mKwv1znPx1czEfVLpRthjVFC6IL6A5Rac93MFebyNj41PZrddR09lCmnUEr+3azFjbKMZbxnF9/gz+7Crm2jPy+eFHRbR2+piRMJ4VdSUQsBPSw2wPl3Db5AJCHTYuy8tnRU0Jt87sDizylSyyExLZ1dpIWA9jMoEn3EGSxUGMbiOiRwhGwsxKyuUrY1XiwwppcQphatjUVMVlDicZ8UbKwc/WFLO1sZ7vdixkfUMVX0hQKZiuYDYb+cgvbjNWLd+udaGYHKgZOeTEpRAOm/ivmYVkJzlo8vjxek3815kF6Hp3K2Sz2Ugx+KyuglnJY3mhxMWiccY8piYo3DPTSUxMdx7wV8ao1Hk1nipzcd0kFYdHoSmo8eetq0gyJzI+PsxNYxayrqUMxWFs+trbovG4y8Wdc1TunOjEj4bH7yfeEodJD7CstIQLpx28+bPn5lEAd42RI392cj5vN5VwfpKRWpGQbMPvD+BqKqcgaywJVnvX2RFNM2o1Z8Qr2Ex2PqnfSkcoQKzJhj1WZ2NTJRF0YmK636NmM8TFGb9HTU3G78Kjm1bh14MUZOXytckqtR4Pr2zbQXuwk8c2raKpDTqCQZJbbcxLmEEwxViFXtNSwoulLvb664jRbdw5upCpoxWjfJwd5meO57V9nwPQ4G8nJ0khVGMEyaGQibeqXWgho3V3OGyi2tdCY6CNsfFp/PqMReQkKaSkGJ8HDofCnROd7PLUsKmhmisnzGSUOQur1QjW09Ig0eLg0Y1G+tMZI3K5boyxYp2ZMPDPnhNNguLTnKRGCCEGw792uGjq8JFkj+OW6UZAVtWm8ddVLi6dmE9ljp9I2MTts1R03TigdzgGtqHmyY37Nzg54FdjnF25nd86U6U+F+4tNk59g87FY/L6TMm4d958/O12HjhH7fWY0fv60qR8/v7JZjqCQcDEY5tW88burYQiEdbV7WVe5hjunDqfnCSF7OTu+rbx8UYtWI9J49EtRu7tA3ONU+xmsxEsf+tMFT1iPNaGmhoedhdx+6hC1Ows/rq5mA2amwRbDHFmO1WdjazxlWA1WRhtT+f5kgBFFSWsby+j2RdghD0OHRMf1G7FGw7gzM7jrnkqtXXGOHJTHeQpzq4V+JdKXWxoqSDRbqM5qBHWI4xPSCcmxkRrpwd3e4gMewqBcABLyIa3TqGkDhak5fPunhLWaFvZ3OlmYuPlfFRdwhsVW/GHQvz0s7eIs1pZGtzII1mX8fm+Kr4xV6WxPWCUumuppK7Dw1vVG7DpMSRaHKTFOUizK3xzXCE2G0zJMeaxrs5YCY+NNYLGUAj2tXl4t3YrWjDAXeMuRtcBReO5TS7uPlvlnhlGKsejW4r5pMFIg/ivM528Wubi/caNeHU/CxOmM2/ERM5QJpKVZaxsL95czPIKNwBfTFf58eql1Hd4iInEMi1uAgviVZqajKCt5/skunk02izjny4XH1S5+TBcSo2vjUp/LaXeWr6fdxmjRiSwvraar0ycSY4lC4cOSfFwzwwnPp9xcBE0+Tl/VD4ef4DSlhY6AkG8oSDjHKk8cM78rseOlp3bVauxZIuLkDnARw1bMZng+pEqCRGFt9wuYs12RsTEASber9tiBNtmK66Yan42dREftLpwaW7CWphO/GAysclXwnm6g//+fBXx8SY+qysnRBg7MYyOT6UppgazWefskfmYTHDFlHy8PrhukkpLC7y4dxWhkIkbxxcwe4KCrkN6evccmc3weWsJ3nCAt9wlfG1UFqNGGc9L1+HlMhfvVm8FHRaNNQ4WvN4Bf+ScFBIUn+ZkpVkIcayqNQ3N72da3ARuzS9gVKLxofK31S4+qHRjAn4ytxBNgyyH8UUP3X8fzoEbLqO5nckOsPrgL19YxKt7XHxnntHlq68SlaMSjduMSuy96he9r9hYuGtcFv+sWM57tVv4cno+l+dO59N9FZRq9dR0tpCaaOPHY5xdp8+rPRpP7nTx3TSV1zbvX3GONSojQPfna2xsdy7q1a++zM6Oah6vfZMzw2Np1DuIMVu474yFfLCnjDWNuwnrYTCBhw52NIeIRDBW5wIat+cvYMXeMrwRP62hDso8deQkKfzfmmLWaW6Ucj/moJ17xxhNGq4ab1SPWDQ9n6XuzUQiRmWH2Dh4YNVSGjt85FnHExcHVQ0B1sTWsE4rITYpQLW/mU49AAH44cfLeOWqayjZp7G2uobRcans6KigNeDl/g/fItkWjy/iZ1d7LXZLDDFmC/5wCF2PgNlEa8jD91ct5c/nLCIlWSEx0QjivV7jT8Ro3EZGvMJ3pjv51WdFmExgxlg19fvhjXoXy8uNXPVFuSp/2eCiYGQ+4RS4ONU42Cocl8+f165ER2djeyn3b17CTemFOLIcPFvq4sa5+fi8cEZCPj9au5Rmv5E2EaITt28vz5ZBVlYBI0cqfR6wRYPjr44zVqsrWz3U+NrY6ivHG/Hzv7vfYIwjhY6Ij+U1m9H9JdyWoDJbUbpqdb9W5cLVXMH52XmkxMDOpkaskRgWpk/hsiyVLMfBb+CXy1ysrHczY2QWCg7MJhPzRxpl+C7LVmlsNIL88blQWednR1sNAJ6Qjw9aXNw0XcVRAR9UutHR6aSTqRkpPLfDxQf1W7FYYKRNwYaNkZZkXFoJP19XTVWbRlqsQmOzhm2rHxsKjZ0eimpLOH/kTJZVbea12tWMH19Ayv5OfklJRkCclGTsOWjX4Ms5KorDOEjUNON3484zVDS/n0jExBcy8nlsRzGLxqmYzUM3KBlwUPzMM88ct0GEe/YvFEIIMaQ8udHFxzW7aPUFoDzArFkLmZBudCJraDDyNjvMGk+UunhgtEqqXeFIPtYP3HAZPcNltRpfvPHxCvePcDJyf7pGX2JijNXImJjeJeN6/nzUKDDXBuiMBAD40ZxCftS2nH1aG9lxKdw1TyU52bh+VZvGvZ+8REV7K8pOP/efPZ+2NiOPWonv/+zbtJEZbKmto8mv8XblFryhAGBiQ/tuUpPstNR40AGbyUxqbAK/LijkyZUlpNgDbA008eiOFVS2NxPRwWqyMnVkOtWaRqvPKGtmNsOH+9xYPzM6nqVYFb6R5yR/HOQnZuHxGO2vW1rggRmF/GZtEbPNM9nuLWGbfwebyrcS1iNcokwmzzqOrcFdWEwmpozIAE3hzklONlS/xMb23UxJyKE0XMs3JpxPlbeZcDiAFggyJi6VPzkLeerzzYQiJs4bOYN/lBfR4PPw3VUvcWbmGO47q4CODiP4iY83Vg537tP4+xoXBSn5BANw1oh82vxB/r5rOV/NKuDSMSp19XDjFJUn1rtY1ezGZDKCwjf3ucjIUFm6ews2rFhNVkbGKpR4qvhj50tcHDOR5XtLsCl+7playK9WFNPU4WNkgoPbsxfwSPlbtIdaaGpr45UyGwXTnb1et57VKpqb4bUKF2fE57O3djNzlXw6LR1saapmZuJYSjxVtIS9NHV2sLOpgoRSmD2hO2Xo5myjnfWi8Sq1modPq8u5ZNRMcqxZJCYac6FpxrxUaxr/vXYV+7QOzLoFXzhIfbgZW8TKipoSRpmzsEQUbh7rxOOBuJAR3GKqYaYyFmvYzpcn5/PvPS6+NlnlojH5XPHGk4QI8/+2fcjya++gpMJPomLii9kzeGlTCa1hD1v8JUS3ckUPXrc31ePx1bClvZwmT4B1TeVU+Zqw+iB1i417Zhr52opipHk8ttPFRWPysVqhPeLhLbeL2+KMgzWLxSiD+LOzCunshD+vL+bDmh1GOcHxixgbOzQD4wEHxbfeeiumgR72C3EaOVGtiMXQMdxaex/uPX7HbJWn1m+kLdTBB3VbWLLZ0dVNriPk52n3auLjYVVjOUu2wJWjVV5wu7ra+B7OgWe0ev7fZusen9XafzpYz9tEVyWhO8Bu6NR4stSFPRbiLDYSYmLwWTVirDpnxs7iG3kFZCYoXY/1xHoXVR2t+CNBTJjITFC4dZyRtnCoaik/PHshropGtIiHBEssIT1CXWc7JpPOpRPyWV5aSk5SCil2BzdOKGBmmsKNOVmMmqwR6rDhtXjY1VaPFTPjbNn88Gwn/1hnlEUrHJfHt85Q8bXZ+FKmSkcHmBI1ntp/MOKIVais7D44WN1cghby8ar/bdKsKfhNnQTCAYKE8IUCVIVrMekmkqwO7jtjIfX18Fa1i4ZQK4FIkOZwOyE9zLbmGu7JL6SGGj4qqeaucYXkKVncNy2LvXshKw1+YFnEr7cvpaqjkbqKNrJSbHxzstNobrK/ssY/1hpnFj6vL8cTCGDDRqWvAV8kQHHjNv4cex1fGeEkNghfylLZUq6xtrWMvR3NbPaUsdFTxsyMDGzWGM6Kn841+TP5wfqXsOox7GyuA8CEidoOjbZOP1PjJnDbpAJeKnURb7aTbHOQbc1ECwQOqp/95EYX7+1109LhZ2N9DVrIxycmoyW0I2wjYPYxwqJgM9kI6GFCepg9bc1cNCaPr002mqP4fEZ+8EhF4WvZTuwh+GifC28owPtVJdyam4XDYbw/a2pgzBjjcd/YvRVPIECMbsPiCRMmggkz05JzWLKnmHxTPpv9Jcy3q9g18Ib8zErM5YzE8bxQtZKl5R5ctdWYzMYGwzPsU9gV3Msv515GezvEmezcPMloiHLDqCw6YzSW1Tn4mprPvz4v4SvT8nnLXcItBfm8WVLC5fn5/P3jzXRGgmRaU8kaaeP8Ufk8srmY6/JUFE3hifXGfK2sKqe9M8CW9nI6wgG0gJ/EWDv3nWvkikd//748SmVNTTmtnb6DWnEPJUecPhGtEiEBshguTlQrYjF0DLfW3od7j6faFX497Vr+tPNtZqRndnWTW7LZxfu1W8EEiyZN5+JcI9/3kU+NL0xHAl3B87E4MEg+nAMPahQF/rjWeE0XjBrLpRkq98xVeb7Ehat1N1biujYFRufi1pkqVQ1+WttM3D2n//bXBxqdrPBbdRHLal0Uphmd7F7a5eJ7Z6v85bPVNIbbODNpDFekF/DmPhdxafn8p7WE65tVbhrjJHGURmdbDB2hIHaT8WSvHK3S0gzXTuzOpa7v8FBU4SI+OcCqxnKe3WaUxzObjflKSjJynT8oL8PdVsfeQC1hdBJMscSYrLhbGumM+HGYE/he9rXEhxXaMSpT1Lb4afIE2RusJhQJ42qq4G/u5VgsGPnE/hIuCmQRF2fkWv+r0sVCReUWxyJctlUkJ5u4bZZKxGdsWPvnehdXNqhcMMJIA/hyfj7L95YwxpTD3yrfIEyE1pDG/9tZxE9zbqW6GiraPXzato1AJEiFuR4dnb2eFgpycvhShsq58Sr5SQq3xV/La94ivjNtAZ9UVPHtM1Qed7nY6qlghj2Pjg4Imv1MTRqNNxBgU9suyqttPLG+u014OGzMbXsbtHUGaA/4iLXEkBmXiMfXwlWZC3i7bSWlvkZaWto4a0Qeld4mfjijkJxkBy+4XXwzUaWsHt6pd/Hts1S8XoXOTrhyjNFGvdbj4e+7lvPgqALSMSqZRCJw6wyVvbV+Wr1BtCYbMbEB9nQ04tM7+e2WN7ERw9vhjSRY7TRZ/DRpNbT6fZyfNYVX61bi7thHTF2I80flcc0ElWe2umigiXPizmC2MpG/uIp4t3Er7PDz4JxCbCM13q9zcUm6ytQRClckZzF3FCR2ZDEhHu5Xs4wGJ7YStrVUMMeRx/emOfnHzmI+qTdyte9XnF2VZ+Yl5fPuvs0ETUESYoz36/uVbpSN8IuFzq4zLw5d4VsZi9hgdp1e1SdMJtOgl1CTAFsIMZQMt6Yih1sZ9/shtjWL/5lyJ1lZkLF/pfTOOSq1rcYGuxsnFBi72dHQAn5mJ+f2qpBwIvV1UNP1mp6h4qtXyFaMQOiNbWXs7qjnp5tfZvr0a7q6k2U5FH40p5DqahiVCDUejcdLXPxXjsqE+N5z1HNDc2wsnDVVwWZTWVru4hqHyp2TjAoWOsb3ZkyMzrI6o/XxuuIyqjytNHZ6SDQ7uDklHyXWTshrojRYzotuG5dlq0T2n3J/dY+LFfU7eHvfRuzYuSR5PF8cl8edc1TqfRrPVrq4IiGfd3aXcO85KlNS0ilprSWCToQIug5zY2aiJATYp7WRZktkYqaDylajJNrdI1Vuyijkn+3FBMJh4mNsNAXbeb9uCxdnT+eMhDy+OVclxmq8Lz5qc7GmfQcra0sZEcgkLcbGtWMKSI9TaPbB07tWsbxpK+1+PzeMm4837GdphQtrxMb28G7iTXHEkYDNauWGMQt4qaaYK2JU/lJaRCASxGQy4TAlMMIWz7zMMdySV0DnCAWHw6g9/ar3bRrCrbxXXsYiRyEZ8cZrvWsXnGlRea/JxQ5vBQkxNna01eCPBIkJJnLTtO73ZiSyP2ib6qSsXkP32+iMBHi3diOekJ/NgZ384axF/GPTKrw+E9eNKiDZopBmh3/tKqa4xsiDbmmGj6rdOLbBxTbjACUjXkGx23ln/+a58aU2fprp7HrszARjY6LXC9u8MCJHo6S1lupwPf5wgFDERKzZis1sZp1vOwkRC2mxidw0VeUiTz6/WVfEg7MKOSPTaABz5Rhjk9w5ccaGU5PZaEUeCJgIBmGFx8XHtW60eJjoVXm10QV78/n37hLuTDJWk0MhWJCez2atnLlKPnVeI31neko2nmCAOq/G9DSF78xwUl9vBNAf7qvgwjF5XJ+nkhhr447ZatfBbFm9xpJyF2daVG4Y5Tw9qk9kZmZSW1uLyWTi9ddf5/LLLx+0QcTFxeH3+wft/oQYTMPtVLo4uIj/6W6gK+O6bmwkikSM1TUFhQfnFHalK4TD8Nhao+TW/JF5fW4oOhH6OqiJvqYpKRBMMk7pO3SF2Sk5uFvrqfQ08+RGl7HS7TKqTFgsCgkJxsrr4k0uPq5zk7oJ/vsiZ6/HOzD9Q1Hg9SoXnzYaq+XXZRlpBPfOnY+3xc79BSptbaBvgIZODxWRZj5r2YUSE8veteXUtvqw6jGcMyaXO+eo/HWlEUCjw41TVT7ZW04oHMZhjuPbZ8wnJcbYfPjL4mI+qXOzocVIT0jcAsnxdsxmM3pEJ4KJMBGqItXclb6ANY2leCOdFDW68FQHWF6zhUhMgHmhAgImP7McEyjMnkFx42b8BGkJdLDbX0VJSw6fVbuYquSwQSvFZrGwz9NMSbAWR6OdjAobY8eqPLrRhS8URNeNtIbXalfzXvN6aNFJsNr5Qsp0ZtunMCaSz07rZv65+0P2dbTianczJj6VCfHZXBq/kB2eKr6YqjIjRyEhAgkjITcXfvq+i4ZwM50E0DqDRPYfrNV7PWzuLGEzbu5MOx+bzegquGTLOko99Xxj1KVk9DiwCYeN9I5opz8TJpwjZ/Bh/VYidLK6yc1/xS7kp2cVsnMnWEIQxkizuSJHxd9ppC00NIKvEy7MyedZVzGXZ6ukWhSuGK3S4vNjtZq4edrBB4qRiPHY74dc3K6ofCf9WpZrq7A5glTWBeiwtGC2hPFEOojFwUMzFjE+TSHVrvA/028le0T3feUkKfz0XOP9putw2+QCzEEbX0xX6eyE22epVDf6iRDgiW2r2dRZzu5N5bR1Bhi5F+6c6KSyVePx8iJaAh7+VV/E3JhsNrRUkBRroyMcINFt4zdjndR5NRaXuTh/VD4trXBuej4vl7q4YYradXAJxudLce0OPjOV8+v0RcDQ/WAdcKGuefPmdf173bp1x2UwQgxF0YDhyY2ukz0UIY6LO2ar/ZY6A6j3abypFWNOMppLaBpdbYOjAbLHY1x25WiVeWlj6QgFqPFoJ/BZdIsGwNl9lN6xWIycYK/XKOF1zZj5fHnUHK4cP4s7Zqtdv+9LNrtQlO5NSLfNUvlCRp7RgvgwLBa4ZbrKhTl53DBF7WpLPWaEwo/OdjIxQyEz07jsskyVzNgUvMEAnmAn35m1gDhrDPXBVti/stwR8jMvLZfLRxkreT+duohzMiYxIT676zHDYViYlU+cxcZtExZwZlIeV41TuXdeAYUj53DeyNnMj53DqNhUOk0+ni1fSazJTpLdTl2HB1dTOaGwTiSi82mni53+3ZR6q4m1Q5zFjiVi4+O6Esp81fzXx2+xvNzNL9a9xS6tDrNuJis+GTMmMuKSuHmacRr/nYoduNtrmWbNxxsOsKmtnBizhdFxqVyUOYOChJn4wn7eiixjnX8LFR2NdEYCVPkb2aTt4cyUCYyJzcBkMtJmfD7juUbzcgtTVbJjRxJHLAkxNkaMMA7aflxcRFW4jqpwDS/XF4MJ0hMc3DHJyWxlAslWB5pmzFk4bHRq1DR4c5+L4satFDdtYWVDCb+fdT0plkQSrHb+U+mi0a/xTG0RS2qWU+at4ZmKYmJi4GK70zgwsSt8NcXJC9s28599n3H3hifY2VZDbCzEme18ObWALIey/6xDMTUeressw0dtLrb43bxctQoXLr6aNZ/UWAe79T00hloZGz+SRIuDH067jLRYhdoOjb9sLGbtTo3OTugwa/xufREPu5bTEtLIzoaWkMYru11clm1sfgVjZVqx29nqKUfXdRZm5PGNsYWcmWykXzgcsKzORVvQhy8SoNLfSMjs59yMPH54RiFnpY+lIxigtkPjP5XGgd+alhK+lu1kVWMJ71e6eX5n7+/KL+bm0x7yUeVv4JXKVUf8+3wiDXileO7cubz55puABMVieBlup9LF8HO4lfHfrV7Ba7VbsCR5+NH0y+joMAJLj8cIVjQ0/rHVxTfnGeWmHDY7H9a7WbLZNig5xYMt2ozk2e1GbuXdkwrJzja670V/3++co6L0yLHOcih8I99JluPw91+tGZ3/rp6g4vPBs3uKuS/DWD2L1m5estnFxw07cDWVkxubwR5PPYFICFd9FTOSx1Db2YbdbuKpTS5W11TgzMrrWt1MiVEIhUx83LyFxzbBw+cZ3dHecpfgDQXY3FLFV0c6iQ9DugO+k1eIrkNpKcSma7xev4qAHmR8nA1HAiyr3EIoHCHDksa52RP4/boPaA97CUYi/KOiiFZvgKnxYzk/dTolbfX8fP5CPiqtYlJcDi9WreQ7EwtZ0byF2g6NszJyyVYUbktX+WB3OZWt7biDmwkFw1gwMcGRxW9mXkOnxcN3V7+EN+QnQAiHKZb5GZPxaLDHV4fdYmFWbD6fd7jYFnSTFoQMvxPoXpVXUHhw4jW8UOLi+lwVe4xRE/nanAVsrf83ZpOZ0Y4UVjW6iS03Aub1HW7MZljocXad4Sit03i6zMVVM/KpqPUTMAUJmgOkxTv41dg72Bbr4q55Kn/5xMVqz1YA9kWqjdX4cpjeYtRUDoeN1dlg0IRPD+IJdPK91S9xQfZUNnjLidfgWrOTv65dxdKKrdjX+vmfiwtJSoKLc/P5tK6U9U0VeIMBNreX893JhWwt12g21xNrseMwx7O9rYqFoyby5FYjDaKsAy7WnbxS5uLN8q3oERip2Jg5zskLbhfF1W5afX7izHYuz1bJQ+GKHJVYOziz83ljZwkRzcFXU5yMtBmpDi0dfmYnTjC61TWXdKW4psY6cMTY+bDazZItNu6ap6K1GweM9WVw6xSjA+Bts9Rem2Hf2VNCWI8Q1ENYY0xDum/CgIPi6Eqxruu4XLJiJoaP4XYqXYgDbWusJaiH2NSwjydKi7k+T2VkxCjEn5pqpBZ82ugmtRzumuzkuklGh6/ohrwTra+mRQe28H3B7eKjamPj0B0TnF0rwj1/38NhulZ5ofe/DyXajCSyvyzdh9XdG4+iY7hzjsp7u8pp6PCRE6uTaU5jgiOTm6aq1NVBQoyNB84x5q++HqYl5vCtz5bwzSkL+Ly2iuaOACYTWCw6imKs0l8zUaW+zU9Dp4fFLcu5f0wBWftTQIJBMCdpvNW0il2+KjrDAS4cNYVrJ6m0ef2UtNdzaaSQf2wvotRTTwwWshNG8usFhfxr02Y6O00sSnNiTVYoyAZrcwbL6lz8ZPIixqUqjElxEPLauHmSMeYsh8L/+8IirnzzSQIEATBhZkpCDql2hfs3L6Ut5MGE2ch2NkcYGZvAnqYa2iMdWAIm1ng3E7IEsFigLeyh3F/DRztKeCBLJc1ifCCPjFE406LyVFkx7vZ67pt0Kdvaq7CYjKOZkfYEcpUUrpmgGmczAka9355l0V4udfFJvZvEvXB7TiFLG4v5rNXNW9U2Lkl0cn6+Uf/6y/n5fFhRyihrJjdMVlleUcJV41W21sGWco13G1wU2FSuzy2goc3Pxo5SLCYzaxv2EGOysjArf/9BVnQflYlqTeN/17uobwtQH2yDYIRYawyd+FjfUcJIu0JDpIY4Swyz4vJYNM5Yzb1ukpEOkV1j5O7eNkulptlPZ6eJb841OgF6gn7OysgFYHWdUU98/HiV16tc3DTVaH+9utlNaUij3lTHpPYUStsbqfO1c1HGTL6Zv5DMKgf2mAAfVrqJtcN1eSpmi3HwmBZrHCiCxr+qirl/gspdk51Myuy9Yfdr+SrrNvuJsZn4xoyCIb1h/YhWisHYFNfQ0EBlZSWjR48+bgMTQghxYhyu8+XvF36J77xZxOy0VN6vNALJbyaq/HOXi7uSVa4aZ1QVuGRcPn/ZWMxV41Tum93dde1E66tp0YEl267PU4mEjS/5URlGjnE0YO55m+jKrtncXerscO6YbQRgt0w3GmtEg4ieshWFP52ziMWbXbSGPDRGWogNmrBaYXyawjcs3avS38hzcvfaJexs38cvN7xFUkw8U+PGGqvc+w88qjWNV8pcxMSYeL+qhEgExuy2ccZEJ3Fxxqa4dbqLj5u3Eo5EGJuQxo1TVcalKozcodDWtIfHgk/zg+nn0dEZIt2agmJ2kJ3oYES8nXfr3Nh0GxdYnYRCRs60S3NjtcAPcp3YAgrXZTpJj+t+jhnxCr8741p+vPoNwnqEsZbRXJw1k6fLi/nWtAX8ae1K4oOJlITKyI5NIRQ00RbyYNHNzHHkYbOZKK5x4wkGaK1vY7WplKRYOwmfwU8WGBvZ/DaNVyJL2VtXTTAc5tHdRfz3GYvYvc+PCRM3TypgfJpCnVejqNHF2Tk5/G/pUn6WWUh8fBaxscbBhMcD4yw5/GTHEr42ZgHWGLhlhorJY5QCtFhgRU0JwUiE2JCDSY4sUrOzyFZglx1erzTm9s3wRv444lq+OepywvEaf9qzlNL2RiJhncf2FHH+mYu4d14B/nYb984z0nXer3IzLiaLNGsSecmZ3D5T5ZP6EiYlp/CS+32mJuRSmDWT/7SV0NTp4a3NLq6dqPL1SU4+bzHSSibGKfxgtrExND0O/ri2mDW1FVwwykj5+afLxkJF5ZltLlY1ugmV+EGHSQnZrGrcRXvEQ0VNLRbMgIlQSGd0ssI34p0kZGoQsHH1RKMrXfR3e2+LsfnUa/HwTn0Jji1+vj628KDfh/Q4hXOt81kXdh1R/fKTYcBBcWpqKmPGjGHv3r0ArF27dtCC4ptuuolgMDgo9yXEYJNW2eJ0d7jOlzPTsvhpzq1MUTWe2ebi+jyVJZtdrKhxE7MRrpmggg6vlGyhuLKcUAi+me8kHB5YEHmiVbZqPL3VxQU5+bxc5uK+bJWxyd0T0HNFOepIuoNmKwoPzHXisxqte6/PM1InDmwqkhFvNN54vKKIECFqA83834ZVWMN2Lk5ViW5IsljgoYJCvvduEZePWEB9TBWXZKjEh5WuSiBPbnTxwT43Z6aN5fxReWxvqueC0fldDSU6OowDgY6Qn6b2IPH7y2cpirFZ7OW9a/Dpfv65bTUvnn0f/ygpZlWjm6e3GjVqP64s5+yUfJJMsLvBqEYwIzGXy/LyeXSHcfbAbFboMGss3t+q2RRWUEwOZtjzmaMb412nFbOmxU3KSHgo91ZK6zRW+lK4foJKTAxsba6mxedjRIyDwkQVTDqNbUFqInU0+3wk2eL4aq7KvnaNp3a78OsBghYfY2NHEglauSWzkGSLwgLzfNbp3We1Xy41zgz8u3MjTZ0d/H5DES9n39r1c12Hv29fQZmvlldrV/L3M29l+lhjhb2jw7jObbNUKqtgur/7AKd4byk/2PcWN486H384RFuog//Z8TZTbRNZlKvyx4JFPLV9FZ/XVeEJ+ViyxcWPznZ2peLcMdso2bZmXxX+SJBUm4OpI7JQs7NY8OpfaIlo7PTuZWVjClv8bqq3lePTA5jAaOhh0nhki4vvjTDmN9o98I7ZRsm06/NUxqcp3JLrpKYGbp6lEgxCe2eAz+rLIWwhEAmQaImnIGMc5oiNSKeNS5IKiI83nn9OkpELb7FAY6NxoAjdm08TYi1Gd0Kzqc+zKQ2dGv/yLaUj4uPV3bBglnNgv0gnwRGVZPvss8/o7OwEICUlZdAG8fjjjw/afQkx2KRVthju6rwab7S7mGY2VoDBqIJQWQVfHavycqmLFbVuLnSM5cLReVw5Wu3V2neoeWqjiw+r3KxrKMcbCaBs7K660V8TkKOxeJOra2X9jInOXj+LHmy3tMBdM+dTXQ12u3Fa/cNqN2YLXGR1UtWmsWSPi0sm5DPGmoWrYyfx8fDKvtVcmFBAOGwMNhoEXTXOaJziC1Xx8PoipucuwmRSaPRrFO1z8ZWM+bza4eLzFjf/rrCRkWGcTv/22EtZvPdDfjj1MsDYMOnxGEHV01uMBhSrtM3EmksIuANs9e5mJHF81AgrqsuJj4OrRzl5pdLF+3vdWK1w32wnr+01coJ9Fj9W3c5d4/JJTDRW0d/5pIZnOoq4Mq6QsSkKdjv8YMIiXnC7uDRDJSGicN+UQpqajI1kr5S5+LpqrFb+cX0R/6ncynnZeRSOmcJXxqq07DWarzQ2wtqIi+0hN//aDg/Od3LN/hrP05NzeHTrSn54hrGiWePRuP/TpdRrPiY7cjDrFu7IW8Di3cX8cIJK7kil6yzCqESFuyY7WblJ45GSIjr9Jta0b6M50s5ztR/yvexrebbpbYLhCJ9rO4irgx+Nc3L/jEJcdo31uPo8Y+CIseMLB3BY47g0s/vnD8+/jO++/xY3JF7GBTkZeL1w5ZR8ltdsxhMMsLq+lD82v0WsJYYRW4zUpZ73G/1djUSMjaW6bmy0u3+Ok+agxuJNNipbPVRqrZwZN51fzCykvh4qmjU+7nAxM6CS7FC6al9H369Rt882Ovd9ZVo+L20o4dop+TxeUsw9qWqvjnXPbnPREfGhWOO4avzQ3ptzREFxRkbG8RqHEEKIIeq5nS42et28UAJXjFZ5dpuLwtx89P1B45VjVFrb4K5ZKokmo2lBKDR0z67cNkultg6cWfmsay8Z9E200YD39tkqXp8RsPa18pyUZPzxWSEp1s7V41WmToU4s417zlGx2WDJFhfFtW7WNZWzvbOJTj2AyatjwoQ/Ca7D2GSXrRjBjs0G10RUVlWX0+r3sXiTi5vGOFla7mJdu5v2drg0UyUcgTvPMF7LNc1u5ibm8fdJ9zE6BUaOhASPwg3tRi5tNFd5U1sFNd52zk4dhx8/rcEIOjoXj8nj66pKWix8fbRKp98IzhXFOIugaRAyB9jQ4qa4Gn54tpO2iMYf975ES9DDk+GXOD/mDtISFWwNRrpCW8TD8iYXt2epxMcrmDsVrk7vTs8w7c/LddjsfH+uk/ImjX/WvEmZr4ZcezYXjFSJ8flp7ghQVq8xKdOoqxsKQb51IvExRs744k0uWv0+EsxxXJfpxKErLG81GlWk7y+/1/MgyWfVWNy+lNpwIybdxMyUUbT4Ohkfm0Wy1cHc5Il82ryDlLg4rpuk7l9BNVZbzxlrpB0ceGL8mokqbe0wKzafd+pc5Oaq6F6Fc7Mn8r3EO1iPC5stg3tmOBkxAlY1lvDBPjev79lMc6SDZJODq8erdMZovNroYpJXZVy80nWW4MDfQ4sFJo1U+J7FycqyGjZXNzLbMrOrm+NH7S62BNw8vxMemOfsygGOphNFjU5W+K8zjXz8OyZm8e89xX2Wdrx5ukppGVyWpTIqcWivMB1x8w4hhBhKpA334DjUPN4+S6W5ycixfOxzFx/uc7Omtpw2f4DkcmOF6mvZRsDS2GgENdFc3KEoy6Fw+3gjgJw/LmvQc5+jq2qxsUYaRVqacVlVW++a59Eg46lNRn5uXA04VePUejQtIloNo3BcPn9+32i9WxWupjHYTlyc3is9Jfq42ZrCz6YuYnmTi5unq4Rb4bxEFbvdaGiRFqtw02gnOUnGAUJjIyxUVPT9ZfWysowyZbW1xn1mxCskWO3U+trpDAfZ52slzmxnhC2O78yZT6JJIS3ZSJWJje1eobRYYFKmwrUTVV6sLiY2xsJZI/Jpb4dn97qIt1rxRqzYzTEsq3NxVbzKH8qWUtvpYV35Rmy6naRyuD7LqJ1b1OjizmwjTeOuWQU0NRsl6+q8Gv8ud/Fp+1b8epCaYBMpMQ4SY+2sanbz/B6dlHo7X87prp/r9cKIEcbzb22DWSGVlBiFtja4LEfF7zeCOej+3YiJgX/sXMHuzmpGO0Ywd8QEAKxUstGzmyR9FdeNnY8/AN+cqzJtjNJV1zta77ovGfEKd0xw8kxFMaub3WSUw+0TjDlc5XexJbSDyo3l/OmcRfj9Cov2r7YuK99Bs7+DJLNCepzCs+XFrG5ys2QL/PI8Z9f7IRiERr/Gq7UuJnQYzzPq3X1bqA83sc6/hfmdDp6tW0XQFGR2Ui5XTzAep2cKYc9AOxLpvQp9y3SVYODg/PmcJIWbxzjx2zT+vq2Y+1OHbs1/CYqFEKc0acM9OIJBqKmBMWMOnsdsxdhlnq0Yq1qaBnOVfN6t3kyDz8OftizngvgCIhGjfW1S0sl5DkNdtAZyXFx3S2aPB26aZgSm1+6v3BBtARxtkPLd2U7adQ1sAfZp9Xxr8vmsaSjDYjFR79NQeuR3mc3G/I8ZoXBzghFcb6zRWN7k4obJKoEmhfh4aG83rp+ZoHDzWKM8WWOouxawx6TxQo2LMR0qsShcPkolaPYTCJi4fOwM3igp4ZYZxsqf1kc56nAYWlu7Vx4/btyJLxRiec1m8hOzuG2mSqcPplrz+bSxhK/PUXligwtP2EeQEAlmK3GROG6comIPGLVzN3S4UUqNBhOTMoxAfXW9G8Vm45qJKhUtGlubaxgVk0rIEmBu4gy8PrBaAxTXuOnshO/ONppOvFCxiuRqEz85v4B7ZjjZvds4u9HRARlBo95wdFU6+hmTkgJlWi0hPYzVbCbBauOc1HxWVVbSpDexq7OKcBgutBq3TdrfJKapyTjYjFY4sVohM9P4G4yg/ulyF4Xj8rHZjTMMDt0Yj4rKHks5bQEfL5e6jIPPFIV7Zjo5Mzmfh1YX8dV4o+TejVNV9lVzUIMQsxmKGl2sbTPSSaKbFM1mo7uiMSadt6qNknM6EUYH07o2xVksxudrMNh94GWxHJxelOU4dKWmZbUuXB43SRsP3SToZBqiJ7eEEEKcSJFIdzDWH7MZJmYo3DbeySQli8RYOytqS3incgtFjd2bmup9RmOB6r6ipSFC17tXuE6UaJOUW2fuTykIGXOe5VC4YYrK2zVGg4joZqVoMBaJwL92uFjRvJXdndU8X7kSR4ydDS3lLN5kzHt0NS8mBpKTjYAsenCzpGQVRY0uni9fhcViBLwv1RX3aq5is0FcusaS8mJKqjWe3hINolyYzTA2ReG/ZhVyzegCPqgq4cIUo3JFfweikYix2uzzwQ2TVcYkpGA3x6DrJjo6jIoE98x0MnVEFt9XncTEgCfoZ+aI0UyLncjsEbmMs2ejKMbzWZCWT6zZxlQlhyfLiqnt0LgiR+W87Dyuy1OZkqPwnelOppjzSLQ42OEtZ117CZfEOblxQgHnjcrjmolG+bKiRqNJx1sVW3hyowufr3tOvBajGYaudwexURYL/PnCLzFVGc3UpFGsbHCzsrGEG2KuITsmHb8eoLjdRWcnvaosWCyQmGg8D7PZmOvsbONvsxn+U+liZYObj2tL+MGZTnKSlK7XNCNe4f6xi7goZwrnZefzVFkx9T6j6ce8nCy+m3QriWYHj24vpt7r6fO1sFiM1+CsEcZ7LxrUWixwz9z5fCFB5ctp8/nKWJXzUqeTk5BCW9DHK7sHVn63vzSNqBqPxuJ9RTQGPJyVkTuka/7LSrEQSIUJIQ7FbD64Rq/FYmyyi0nw4wkE8foCVLVreL0Kr1a4+Lje3WsD21BS26Hxr0oXhWkq+fs3Eh0PB36uRGsgJzuMVdQoiwXe2GdUj3BshW9Pcx4UbN4yXWVbhUZJaz3fn1aIYnYQCsFXc1XC4b5bTTc2wq5ajQ2NVUQw8pCbgxr/u/FlqjpaGLkhwO/Ov5hIxFgFXN5sBML/XA/XnZHPe6XlXDohn5gYI6WitdXo+raywY0/Gb5gdh7y+df7NJ4tM1I4/jDvGh53ubhurEp4fzfE6LgdDli81cWa2grs2GgOBWjyWKjytPJnV4CUGAc1ngBBU4AXq1biCQZI2QaLRjr5zgwjPSU+3ggutwbcTLON5ewReVw0UqWzAdJiFb47y9mVBnHNBJX6Vj9JSSZumKzSWgmvVK7ig4ateEb4mWwtJBDo+znNysji4fxbsY4wNv6dk5bPS/tcXJVQyOZACV/OUana0Z1a0NFhBIyBQPcKa08WC9w1T6W1la50hSi7HUaPBodD4dxRTv62tZiVDW7spfDtqU6SkmBfpIbH214iKRTDprZy6loDPL0V/vvC7prYVW0aS0pW4Q13R/ndB1FGKT2HA9Ji4aaMQizJGs/tdHH1eLXX+7da653+ExUTA2lp/VeaeWabi09at2I2ww3pap+dJocKCQGEoPcpIXFqqdY0/uwa2quSp7JoIBEXZ6ycRVc497ZovLHPxc2T5pNsdbDNU86LJS68XiNQu3B0/22jT7YlW1x80uBmWa3ruP7eH8nnyvV5xpwd2EY6GryMTlb42RmX87tJdzBvdBZpsQpXjnASH1YOWt2PbuJTFHh6q1E5YnRsKrdNLmBZnYsqbzP+SBCTqTsn97GdxVyQk8+8pDwKU1Xe3W10x3u5ZDP/b2MxHpNGTIzx2k5zjCVAgEa/dsgFhVd2GwH065Uu0mIVLo13kh6ndHVDbG/vXlG9bZbx/B+YUchZI/KY6DDaO+9oruPdyh1s1/aijszlgZmFnJue15UiYLV2z/FXxqjkm/Jw2ubztVFORif3Dr6iZ0Piwwo3phfy9bEXk7F/Q5o1xoTJBDExpgG9XmmxRpmzT+pKcONme6SEq9OcpO2vutDzILK2Q+N/N/T/GZXlULhzojE3UeGwUVs6kqDxXFUxDZ0at85QWZCex1fHql25yks9RbRHPPjDQX61oJCzU/K4Zbra6733xHoXb1VsZUXTFp7e6up6jxh579AWX8Mv9ixhl1bT9dwuHqny73IXGlrXfETTf57cePDZiUO9zy+blE92QhIX5OTztclD8zMh6phXisePH3/Mg7Db7SQlJZGamsqsWbMoKCjgkksuwSIRijiO+jvqFaeW6Af1gTuexeCInsIPh42/qz1G/mOjFsAdLCd+N1ySrqJHjJzG6hLj1PjJbN5xONHasB2dAWo8GhPiT85Aw2FjJTEUMk6Tf3eWk/Q+VpGjC2sOh3HKPSEB9u07dOpHdAX2ukkq7T4/7R4Tlf46NmilnJk6gZE2B/fOKwDgzWqjoUNsqR+wA0aec1sbmAiwfK+b2Fj4ep6TMSMU7LqdTZqb/1TaOGuqs6vuMhiBksek8c8tLs7Lzifgh6+rKqEW6IzReKLUxZcyVRTFqFISPdjKdBjvmbY2iMvMAkXD5HXwrXPy+fmnRTR0+Ii32Jg6IotxscbmyBpP7xSHtFiFuSaVIm0Vrr0mFmSP57GmlfyitZB5ShZgzJnPZ8y5phlz3dCpYbXqfCFlBouyCjCZjJXn/s4gmEzG3FY0a3gCfmaPyGU+RrUQq9V4fRITu/Nun9ro4sNqN46NfX9Gmc3GPHR2GpU/oqkzXq+xMv9poxtHmZGHfudEJ1kZxs9sNrgtu5DF1UX899mFzEzL4sacrINakd86U8Vd4cfvN3HL9N5BqcUCT1QWsVPbx++2vs00+0RuSFZZ3uRii9/NiB5jjm76jB7sRt+b+yv19nuAtKy0hFAkQlqso6tV+VB1zEFxeXl5V19svY/fUNMBSTmHu84777wDGOXfHnzwQe65555jHaIQfZJg6vRw4Af1gaQ6xcBUtWn8bauLH2Wp5B3wxVXn1Xh2u4vLs1XeqHKxsmkHhGKYl5HLVeNUrD5l/255jeeri7k9TSVxCJdeitaGXVXtZvEmG/99kfOkjKPnzn04OIf1QGaz8T62WvfX7a13kTZeZQIHz3W1pvGXjS6uGqeSEGNnleZm7efbaQh0kNRp5VezvkrW/tXMq8er+DsBAqxtcxNXDX84w6iCEZ+hYcfG7bNVkvafHi9MV0nT4e6z1T7TAV4qNVJBQmG4c5LRpKK6HT7xutjkM9pffyfFid9vbOw0m43fU4/HCFbBCHC/lu1keir8avYini9xcVm22utxepYHi24w3GR1sTG4FUsjrGrdTpO/gz9tK+LF0bf2GqeuGwckHg+8U+diXWMFM+x5JJmNYD05uf/Pi+hK/PJGF5vaK5idPJZP2l0UxquMMSvExfW+7TUT1T67Gva8v/h4aG7uXaWi3qfhDfuZl5bLVftL25nNxthiYoxA+qwxWYy23sq0lO73x4FBaZZD4dsTC2ltpVfAHD0o+/70Qv5nSxHjlVTWNrhR9hkNXdI6e4+5Zwv0/p5HX5kRt81Sqas/OD1kKBqUnOKege6BAXJfQfCB1+vrOrW1tdx333289957vPbaa7JqLAbd4YIpcWo43Ae1VKcYmMWbXXxS5yZjMzyc5ey6vLJV4zsfLaXV78PnNcpUfVxTRrm3BX9gDHEhhSqvxjv1Ltgb4LOWcpLK4YcZzv4eakg4f1Q+K/eVc+nE/BP2mAeupG1uqOEH24v43zGFTIjPOuztY2IgPd14L79T72J9h5tXd0PBdOdB131yo9E4JBI2Uh7a2uGL03P44+qVfH9adyveOq/GsjoXX0xXmTIFtCZbr+Czq6JAsvH4JhOkxyrcPaH/MwHRRiJXjlYh1P28L05VsXr9dIQC1Ps0YixK10pjMAj19UYd4JdqXXx9lEpCgoLJtL8ixmE2RFosRnWIc0bmU9pUyuQRmVw4ajJ/21JMriOVOq9GKARP73ExV8nng+YSLkpRsVgUbpxq1BMeF8znuapiCuwqVqvSldrR86xisqW7y+CNU43ucJ16gM98bmLawNaazx+qikjaXUjhTOM17dkauT89N6tFD2iq2z18UFfCl8dNJz9LYUeVxpLtLv4rU2WEVema1+gBf71P49lKF4mjVKbH978JMioSMeZ+YV4W4+NupV3XWBJwsShXRUFhXsbhz/YM5IxrND0kKa7PHw8pxxwUf/TRRwBUVlZy//3309jYiMlkYuHChZx33nlMnjyZ5ORkdF2nra2NnTt3UlxczMcff0wkEiEtLY0///nPpKamUl9fz9q1a3nxxRdpbGxE13Xefvtt7rvvPh555JFjfrJC9HS4YEqIoW4wN4heMi6fZdvLuWRc7yDxqY0umr1Ge90vZamMtClMdeRQ0dqK16vj8RgrZp82uJkzYixzlTwW5Q79A80P9xk5s8tKS5g/7vAB6WCIrqRVNBuBxMfVZZRotfx8ZRHPXXwrYOzUf3R/kAF0BRw5SQrRRrIxMUZahM8L1+X1Pdc9O9wlmY2gZNJoSGmeSFZy94rskxtdvFe1g48j5fwutztYjm6u1HVjNTUlZeAtu6Pd1Lxe44A0urKam6LgCNr5qMHNiEobt+YaVSdiYoyg2OeDtztcrG134yj342uzk5Wn8sY+F6ua3cZGrSSVl3a5uH+UStwBK+QWC2wNlRAmQqrNQUHaRD6KrWJ7q5t/la1ifW0NjR0+XM3ltPsD2O1wrtlJhl3hrilOfvpxESvbt7LL0oSnTuPRCYV8QcnqdVbxB2d2153OiDfOkLSGNTpabHwpU+WvpUspD+7j158XcdH0Wwf61uha+bZYug9orFj2vxYmFAVe3WP8nmVsNUrLRTdA1hipwDy91TiwjdsE/zP64M2a/T1uNBjPsBv1knPToaFhYOMe6BnXaD63zTaw+z1ZjjkoXrhwIevXr+e+++6jpaWFBQsW8PjjjzN58uR+b/Pzn/+cnTt38s1vfpNPPvmka0W4sLCQm266iT/84Q/cfffdLF68GF3X+cc//sF3v/tdJk6ceKzDFUKcZoZz5ZDBbEH+zh4jSHxnTwlfyOsOEm+bpVJTC4WpRkAcicAV6fNpb7RzyQhjd/r1yUYFhItGqtj8Sld916HsmokqPh9dG9v6SrM5XvsOooHEGelZ+L0WfrWgsGul8H/XdwcZQK+AIznZuCwYNAKyy0epvFTqYtSog8cXDUyDQeO5paf3HdTeMVvl3Z3l1Gs+HlpdRIPmY+f2cqZOXUSiw1ip7Vmqr79T9H2p92k8t8vFD3ONesbp6XC1w1hdjR44RVMHNM14L982Qd1fQznAmmY3z+2EL49SiYSN3NiXS118Uu9m5NbebY2jLhyhEgrR1S75Cw6VnCSw2gK0BnwkxsTx9dGFfFRTYqxkYzw/44S1CXRwB/bi1b385JMiPsm7tddZRbvdqDFst3c/ZmaCwo05ThIT4euWQv7UXtRrNf5wop9hHR3dr4mmGWXo3ikr4fYpxjivHq/S0mLke/f1fr15f73ra44yTSFaOs5v0/hXlYsbFZUxfaTm9DTQM64nsvThsTjmoLitrY1FixbR0tKC0+nknXfewTaAQ4HJkyfz/vvv88UvfpGPPvqIRYsWsWHDBhITE4mNjeXJJ5+kpqaGd999l3A4zNNPP82vf/3rYx2uEEKIPtwyXaWqil4bccJhiAspXJPu7AqCwmGI6VQ43+xkxP5NRUlWhZvGOGlthVDk1DhQyYhXuvJdoe80m+O17yAaSNw6U2VfosLMtO6SXXfOMYL1aJDRV8BRrWk87FrF5zVVBE2BwzZDiDZfsFiM9IdoTqrVagTPfzx7EY+tdXH5pHx+u+ltKryN/G39Kn6sGoFddJOl3W4Eus9Xu7gtVWXcYQKmV/cYm8SWbIafL3Tur8hgVG2IiUC4R6AUXbEclazwjTwnCZkaIa+Nm6aqeGoUvp7nJCVdo3OXn7MycrltpgoHlE3TdUgyK3wpwejWZ7FAtkPhi9OcxKZp4LexUFEJBCBo9vPqvtUkJxdg8igkJ8PlqQWYzTr72jpos7Twm3ON59/XWcXoe7yx0VjhNZmMx4+LOJhgyWVk7AG73Q7hwA1rPVfasyZkdXU3zEwwVvyzFeP10DRjzqIb/5KSjJ+nJgz4obs0dGosqzA2Qb62w8XHdW5sNpib7zzk7QZ6xrXnSvhQdswfWUuWLKGiogKAxx57bEABcVRMTAyPPvooYGzYW7JkSa+f9wyCi4uLj3WoQojTUM8GB+LoZTmM1a4sR/e3W7QBQ3QlrdGv8diOYqraNHy+7tUfs9kIunw+aAoY1+lZyulUFW22caT7DqJBZM8GDj1FA4nMhIMjiejPshWl17973vff17h4Y89W9nmbSbbH9Tu+Oq/G37YU02HWeh2g9GweAUawdft4J/lJWeTFj8GMCbPJ1HVgE30ftLUZBwor6928VHr4xg7XTVK5ODePO+f0Hl9Dp/EeqfcdXKIsGhznjlT40dm9y6o9vdXFZ/UVjEiwYbHAXzYWU+ftvo/aDo1XGoz7jYsznp/dbrwv0+MUrpmo8vLeVfy65GVWtGzhw4YtPFu6iqd2F9MU0MjPVoi32KkNNTIzfgKzMvpPq4kGsjZbdzmycBjernGxPeTm3xUuajzaQWM8FtG5iTbHqfN2l8Tr2azlQNFUmP4abJjNRre5j/a5eW2vi1umG+Xxrj8gNedUONg9Vsf81F5++WXAWPmdNGnSEd8+Ly+PKVOmAPDSSy/1+pmqqqSnp6PrOrt37z7WoQohhOhH9HTsgYFcvU/j5XojaHir2lhBeqd1Fe8Fi6nv1Kho1vjbViPwaotoPFK9lOVVO7pqmQ5F0S/3w1V76CsoHYiBHqjVdmj8q6qYzQ01A661HYkYlQHOS52Oc+Qs/nj2on7H94LbqALxSpmrK2DS9e4AN/paN/o1XqotptGvcUlSAV/OVvnumQVdNWijucWaBl/MzSfeauP8UYffoNi9wUzZv3msmO0tNfx6+1I+btzBm/v6f4/0XFk0mYyA7raZ3cHa4k1G3u0L7u77eHqrC5fmZqXPuCxaXSH6Wrxc6uL9+q3U+JrJjk3mwowZgIm1bW7e2OciLQ0uyVDJI4/zk448BaHRr6H5/Yw35/LVsd1jfHkABxA9RStx9HdQtWSzi+Xlbp7faXRAfKXeqGPcH4sFAnaN11r6vp7FAjdMUSlIyePybNXYXDnHycSM3o1tjraefzQoj95+KAfVx5w+sXv3bkwmE1lZR79RITMzkx07dvQZ+E6YMIH6+nqam5uPZZhCCCGOUDgMz5e4WO9x42gx8jRNZqhpDuDGzUovrHfDRq+bcAga2kAL+UiPjePWGUN3s11041dS0sn9gn52m4s1zW72rCynPRAgPgF+dq7zsLfLiFe4a3whLS1GF7L+RFf6bpvVXQUifv+p+GgOLxjB4soGNzEx8OUkJ7c5utNKosFpMGj8P5p7vrKhhMvMfX/vRw86etZbfnKjiw8q3XxWW44WNHJ7Lx81sPdIdAxZSUaQHX1OHV56rWZeMi6fVzeW0hz0UNuhkR6n9Dqjcc1ElZpmP4GgiatHFZASo6A7jDSNK0ereDyQbOlODToS4TC8VeuixF/BRPKIDyvcPN0Y4zUT1CMKBiMR2NO4vypImsqEA7ou3jhVpanZeD7PbHVRXOMmfh38ZIGz3/t8ucw4oB255eBUm2j79i/nqLzb4GKKV8WxvzLIYJzt6Vk+b6g3yTrmoLh1/7u+YaBbFfvQ2NjY6756Skg4iuQYIYQQRyQcNk4z91ydikTg0gyVNq+fkDmArsOVyU6qLRpag43CVLUrwPzqWJW1Pg/bOsq5a3xhn6kBQ8lQyHG8aZpKdQ3cNj+fZaUlg14eMrpSm5FoBLU1HiMf+KZkFUePfOBrJhod0r4yViXceuj7vGW6Skuz0Zr4UHV8NTQe2eLi+jyVbJSuPOov5+fz9GclFKaqXd3fjkZ0NbNnSsA7e0poDLZRH2njP3sdXDVe5YOwi5EhFVDIiFe4atR8Xtntosnv4Y19Lm6aqnJdppNUe88NdwPTs0JHRwecFaPitcO0ThWv15j/6/NUXnC7uC9bJcXS//PtuakzLVbh9SqjoUooCBee1buSRDS3ODW+++Dgq7nqIc9MRA8e+nqPRVdw3641DtJG7m81Phwdc1CckZHB3r172bZtG5WVlYwePfqIbl9VVcXWrVsxmUykp6cf9PO2tjYAEhMTj3WoQggh+hEKQUtLd6ku6A6Ud2o1eNt9mAI2Lol3khKjcIHVyUibERDcMtaJzQbrO1z4CbCqqYSvcWLKnB2toZAfma0Ym8qmZcDkpKxBLQ/Zs6JBdKPdHz7b37luNzyQ4uy6brS0WKwd9niN60Y31h0oy6HwjXznQV3TDhQtKwZwxkRnVypKbCxclzk4740DT+ffMl1lS4kff8DETdOMyhwbvW6UOrgQ4/kuqzVSLEr85XQEAyRWwaXxxs+OtEJCnVfj8RIXhePyeb5pM5GIiQviCwj5u1/IF9zGPCgbD70Zsuemzh+d7eSKHKND3lXj1EO+R7Mc3avnhzKQWslX5KjEx3efWRgsB1bXGMqOOSg+++yz2bt3L7quc++99/Laa68N+LbR2+i6jslkoqCg4KDr7Nq1C5PJRG5u7rEOVQghRD/qfRrLAy4W+FSy9q8i7mvX+M3OpbSFPGQ4HFyaqaK3Q1tE4wPLKtbXm7gkqYDUDoWYGLh4pFGy6uKRar/5kEPFYJazO5Yx9OxgNtj33bOiARiVLTwdRqAVFQ1YGhp6p1e0tXUfMBzNaxktK3bgZq2opoDRpOPBiWqvNtvR1deegWBfl/Uly6FwU0YhmgYT0uE6k7ECfu0kI7A0m40V1Q4vnJuez6eNJVyRY6yON3RqvFTh4uKRxqpyTz0PoHquxkYD2TW15WzvaCISgViTjYuTuld2D7VCe+B8RSuNmM0wLlXhvmwnI0caed2RSO8GH32JztPhcuX7kxarcO9M44CnjxP3/T7m4Q4u+3ovDlXHfIx82223df37jTfe4Morr6QmWkn6EGpra1m0aBGvv/5612W33357r+ts27atK6Vi5syZxzpUIcRpqFrTBrxJSfTv+Z0uNvuNzTtRT291oYV8pNgd3J63gN+WLGW3r4b3W1xsjGzhvSYXbzev7ioJNdJmtOaNCylSDeQQBrpKfajrRTefJSYOfLU7Wuoro0cQGk1/SUw0SrQ5HN11g5ubwV2j8f+OooJCX48Vfbz4eHi33shjXryp9ya0ep/G4yW9K1McyQYvk8lof2w27y+7N9HJ+DTjoC05GcanKVyf5WRWehbXZji7UjheqzBW0Vdorq4WytF57e/xo9VJfrWgkAVJ0ylwzOD8JJURI7prQvfcbHi4+Ypu6oym9vSsbBEdx6FSfk5GStDRbr4bqo55pbiwsJBFixaxdOlSTCYTb775Ju+99x6XXXZZr452YOQMRzvavfnmm3R2dnatEi9atIiLL764132/8sorXf8+77zzjnWoQojT0PGqJTvc3DRNpbTM+Dvqlukq5eXwlTEqf3QvZae2j38Fi3hg/CI+byqlmVZAP+2+GI+36Hx5vQf/rGcg3N9qdjgMfr/RkCMl5djnPRpMeb29m2kkJBib8D7YZ6RB3K86j+2Berg0U8Vq7W6eEvX0VuP3OS4OfnCWc8CrxD2fi9V68Jz0fI/2rNsc9ZWxRk7uF8fn82h5MXmKSkzMwALZ5qBGnMXOBfFG++WTqc6rGTnMytE3nBkKqUUnyzEHxQDPPPMMbW1tvP/++5hMJnw+H6+++iqvvvpqv7eJBsMAF154Ic8888xB19m0aRNnnXUWZrOZyy+/fDCGKoQ4zQy0o5I4tMwEhUUjnWQesLdZjxh5xfdPLeT3m4q4NrmQkTEKX0+6ls/MqwiFTDT6NSYdppGDGJiBpnUM9qpgnVfjmW1G+kBWloLHY1x+zUS1199HEqT2PN1/4JmDVLuRx3xgbvIt01WCASPVQ1GMDYI9n+fhUgiOVlqs0czluX3FbA+5ebcBvmRxHnS9nhvioqu/z25zsa7dTSAWvjLi4NucSAPNYT6U6EHDYDpe3SEH26C8reLi4njnnXf4zW9+Q+z+mdR1HX1/1np//46Li+Phhx9m2bJlXbfr6bXXXmP16tV8+umnKCc7+UsIMSQdbS1ZcXhPb3WxVnOzvNHFlBFZ/O+sW5k+MsvoimYxGh1s95bzVvXQrUl8KjjSlbmB5iIfyf2+4HZRXO3mrWpXr9tkxCvcM7M7DeJIgvFDnUFo9Gs8VVZMjcdIk6jxGGkTQL+/z2azkQKRlGSkJxztaqbJZKSL9BXcXztRZVpMHl8d2/dBdvTMVM863DdNU5mbmIdTUWkJaSxtMmo+nwzX5xm1nI90keBY85EPp695G4oG7VjLYrHw4IMPsm/fPh555BEuueQSUlNTuwJgMALi1NRULrnkEh555BGqqqr44Q9/iEXOuQkhxJBzy3SVM5PyuHKMSlKScVraZ9V4qa6YzhiNi1NVVCWPy7JVtjTU8NPSJZR2HH5PiejteKWfDPR+zWa48wyV80blccXog4OpOq/GI5sHrzMbGFUgeuYUL95k1NF9euuhG3r0TIE48LlFNwqaTEZjlEc2925WYTZ316dOSek7uM+IV7gkztlvubi+uhxmJhit0EdYFd5tX81H7S5erlx9UtIQBprDfKDjnY98tN0hT7RBf6mSk5O5++67efvtt6mvr8fr9VJTU0N1dTVer5f6+nrefvtt7r777q5cYyGEECdXbYexwlXb0TvwMWF8WUYbXSyrdfF5q5tVfhfpcQpXpTpJtSs8tKYId8c+Fu8rOm6rTaebI82Xhf2noTcfuoPZkbJYIC9L4buz+g4Go3nFL7hdgxLoNfo1AiY/c1Jyu3KKb5ul8oWMPG6ZfvRBUzQf2mw2Uho+2Ofu1TXPYjFWmnsG09FAOvp86rwa7/j6n9/DnZkymXRMJuPv43WwM1iruidyk/KpckbvuB+/xMbGkpGRQWZmZp8pEkIIIU6+Z7e5WN/h5tlt3UHEks0uPmt181ZNd5vgSzNVZiaOxRcO0BTQMJmMoOKX8wvJd4zivqmFsuFugI5mde7JjS7erdzBL7cuHdSVWzACwid2HXxgdM1ElQtGGe2Vj6XVbzSYfqvGxaa2ChwxNrIcRpDUXf94cIKmm6YZYz5c17wDU1FeKnWxObCDH7uWHlGw2BzU+E9rMRekzuRLmSrXjZ1/LMM/pP7eNzUeo5X2QN8Xp0pKw4k0DPcWCiGEONBN01TmJOT1qj5x0zSVMxLyuDSju+5wqr07l7iowdXdgldxMEPJZaT9MF0dxDG5Y7bKCFsczX4P9396ZIEbGIHvw64ifrN2+UG3fbnUxSf1vQ+MwDglf81EozPb0a4q9gymr8hRWZjVd6rGYMlMMHKhj7Rr3rUTVeLNcbQHfEcULL7f4mKD182mzhJuzT3yxx0MizcZm+xecA9s3KdKSsOJdMKC4lBoENujCCHEfsO5fNBg6q4+0f1lnpNk1B22eBWCwe7rXpJh5BIXpnV/mT67zUireL1KVp2O52npbEXhT+csIsXuoNV/ZIEbGBvq3qzYyuu7txx022smqpyb3vvAKOrlUiPgOtZVxWhjim9PPT6B44HpEIe6Xl8pCBnxCjcnLOL87ClHFCxeOMI4qLwk4+gCzAM/x47mc+22WcYmu/4aphzoVElpOJEGpSTbgT744ANef/11PvvsM8rKymhrayMSiRAXF0daWhpz5szh3HPP5cYbbyQ1NfV4DEEIMUwMhc5kp4PNDTX8d1URmQ2FnJ9qtOE1m40mBj5f75JaI21GLrFiM34Gxqryvn3wtXwVX/1JeAJDyEBrZx/tAV1GvMIvpi/iwzbXEQVu0Q11mt+P2Ww66LYZ8UZZsswEKDvgttdMVDFbjr30Yc+ayMdDtGRbf+kd0WA4JqbvcdR5NVb5XXxvrHpEwWJKjHFQOdJ28OMN5DXu+TkWiRzZJsloC+WBtnwW/RvUtZUVK1YwdepULr74Yv7+97+zbt06mpubCYfD6LqO1+uloqKC//znP3z/+99n9OjR3Hffffiin6onwL59+7jxxhsZOXIk8fHxzJ49G5dLVjaEEN2G4+rzz1cWUda5j5+vLOq6rN6n8XJ9Ma3h3iueTQGNVxuLKe2o4eV6o/tYZoLCTWOcTMpUTsm5G8zV3YGelj6WjVjRlrxHErhFN9T97KxCfjLv4iO67dFWNTiRogHxoTagHS6P+8kdq1jld/Fs2aoBPZ6iGFVZolUv+nq8wdpsV+c1coY31tV05Q4P5v0Px8+9Aw3aU//DH/7ABRdcQElJSVct4p7l2HqKXu73+3nkkUeYM2cO1dXVgzWUfrW0tHDOOecQExPDO++8w/bt2/nTn/4kVTCEEL0Mxw5tvz63kEnxo/j1uYVdly3eZLS+Xd7Ue+HgnToX6z1unq4twqW5eaWs9w7/gdTQHWoGc9ORnJY+OQHWgQFvQ6fGs3t7t4zuT1dTkP2RrYnDl3aIfk7YbANL2ThW0cYcP/qo6IhyhwdqOH7uHWhQ0ieWLFnCj370I4CuLnX5+fl86UtfYubMmaSmpmK322lvb6esrIw1a9awbNkyOjs7ASgpKeHiiy9m7dq1xMXFDcaQ+vT73/+e0aNHs3jx4q7LcnNzD3kbv9+P3+/v+n97e/vxGp4QQpw0c7Ky+NOMW5mT1X3ZzdNVyivgvMTeK56XZqpggoun5PCH1Ss5b1T+CR7t4JPOiH2LBosdHUd2u8OlNfXXmW4wg+mXy1x80uAmtgzm5jsPO964OPjmzAJq99q4aeLJex/01/0tmit89cx8XtlcMuDcYTFwxxwUNzc38/3vfx8wAuLc3Fz+/ve/88UvfvGQt2ttbeVnP/sZf//73zGZTOzYsYPf//73PPTQQ8c6pH698cYbFBYWcvXVV7NixQpGjRrFt7/9bb7+9a/3e5uHH36YX/7yl8dtTEIIMVRlxCssdKi81+Rirk8lIUEhEID0OIW7Jjv5d1MxvnCAj/aV8IVJWYe/wyEsurqbNnwXd/sUDW73r2EN6v3Gxh68KhmtNTwYrs9TiYTpM3is8Wj8baOLBWn5vFNWws1xKmPjFEYlKlyddnD76YFoDmq8XenismyVnGPYRNhfTno0hSUtDUbN7vv37Xi1wR4ujnnaFi9eTEtLCyaTicmTJ/PZZ58dNiAGo8nHI488wt/+9reuVIu//vWvRA5skD6Idu/ezaOPPsqkSZMoKirirrvu4t577+WZZ57p9zYPPvggbW1tXX8qKyuP2/iEEGKoeb/FaPX8QomLrCwjfzLqhskqZybncfUEWbE6HfRXp/hUdWCL6p6i5ct+v7GIlQ29U4COVHTz3kqfizUtbpbVHltaw7GUSpMUiGNzzEHxsmXLuv79xBNPHHE1iW9/+9tcdNFFALS1tbFy5cpjHVK/IpEIc+bM4be//S1nnHEG3/zmN/n617/Oo48+2u9t7HY7iYmJvf4IIcRwYDbD5dkq8xLzuHWmiseksaS8mEa/ETRlJigUpqq8UuY6bQKpnobTxiOzGf5T2Xed4tNRtHzZD2cXsiDt2A7sLBaj4+OiXJX5I/OM9KJj0FdOerXWuzFHdNPdiehGN5wc86+62+0GjNzcgoKCo7qPG2+88aD7Ox6ysrKYOnVqr8umTJnC3r17j9tjCiFOrhPZyvR0Y7HA6GSF67OMTmN/XbuK16tcvFpl7Mw3m+HDNhcfVZ+egdRwWnWzWOCueSoLM/uuU3y6iZYvmzoii9snOEmPMwLQaBnC/ipYHOpAKdWucPNYo+35YHtyY+/GHNFNd6dKN7pT5QDzmIfX0NDQlUt8tMaOHdv176ampmMdUr/OOeccSkpKel3mdrt7Pb4Q4vQirUwHpq+mB9HLugME4x/6/gssFrhqnMoFo4dHIHW6i7Za7tnA5VQTTQGp8RzZQXC9z1h5bejUiI/v/0DoZB0o3TG7d2OO6/OM/58qG0NPlQPMYw6Klf3L+y0tLUd9H62trV3/TkhIONYh9et73/sea9as4be//S2lpaU8//zzPP7449x9993H7TGFECeXtDIdmL5KqR1Y4ureeQVcOVrl5okFXYFytF7uqRxInUqiK26HqsV7vB5zqK/yATy1YxWvVbr42/q+6wxHzxwdGDS/UmasvA7VMx7ZirGyHc2P7lk3ur/nFHVg6oXo3zG/xUeNGoWu62zdupWGhoajuo/333+/1/0dL/PmzeO1117jhRdeYPr06fz617/mL3/5CzfccMNxe0whxMklNWOPXrWm8fdtxTQFjC/TLIfC1yc5yR2pHLTiM9D2uuLYWCzgMWk8tfvEBTmDvcrXV5Ad3ax27O+fQ9cZjp45Wrypd/B79QRj5fVYz3iYTP038jhe+ntOPX9+POoan46OuSTb+eefz+bNmwmHw/zgBz/oVQN4IEpKSnjyyScBsFgsOJ3OYx3SIV122WVcdtllx/UxhBDidPDkRhfF1W5a4uAKnF2X91X2KSYGMjKMv8XxtXiTi5X1bpL3wllTncflMRo6Nf5V6eKmaSrZDO4BZTTI7nGS+LCd5gbq9ikFWEI27p6jQujgn0frUd82S4UezXTT44yVV0WBhmM4uIuecTmBjXp7P6d+nrOm9V2aTvR2zMdkN954Y1fDjmeeeYa77767qynH4Xz++edceOGF+Hw+TCYTl1xyCSNGjDjWIQkhhBgEd8xWcWbnUZja+8u0rwBmsIIacXi3z1a5aEwe3zpTPeL5NpuNSglJSX03znA4jLzcX2xeyvKqHcdtdfF4pWRkxCvcOcnYGNqX6Jmj/n4erZN8LO/jet/R5TUfrehzGpXYd4v1A1MvRP+O+e04Z84cbrzxxq7WzY899hh5eXn8+te/Zu3atQcFyFVVVSxdupSrrrqK+fPnd7V3ttls/OEPfzjW4QghhBgk2YrC3dOcjLTJl+lQMjpZ4UcFTkYnH/nrYrFAcrLxp6/GGQ4H/GuHi7aAj2RbXNfq4mAHsafKxqujsbTcKG3XXzrD8XKkc3oq5YqfKIPS5vmxxx6jrKyMVatWYTKZqKqq4qGHHurqThcXF4fNZsPj8RAOh7tuFw2kLRYLzz33HPn5p36rUCGEGM421dVw/6oi/jqikPnjTu0ud0frVA82bpmu0tgIN01Vu1YXD9e2+VRV79N4tszV1dFuMCzKVQkF96czDGE9X9Pj2DftlDIov7JxcXEsX76cb3zjG12BbrRLna7reL1eWltbCYVCvX4ORjm25cuX89WvfnUwhiKEEOIoWK2Qmdm7Y92RiAaCP/mkiG0t+/jRR0WDO8BTyKm+CprlULhzohPglG8QcbgmF6/sdlFct4P/Wr100DYupscdOoVDDF2DdhwbHx/PY489xvr167njjjtIT0/v97oWi4WzzjqLf/zjH2zfvp3zzjtvsIYhhBDiKNhskJ1t/N1TvU/j+erD50dGA8Hfn1/ItBGj+N15hcdxtOJ4qvFoPFFazJKS1YPeIOJEN9N5ufTQTS6uHq+SaIujNeDryp8evEoY4lQzKOkTPc2ePZt//vOfAJSXl1NWVkZrayt+v5+kpCTS0tKYOXMmsbGxg/3QQgghBtmre1ysa3fzzDb46QLnYa8/OyOLx+ffyuSM4z82cXw8vdWobnF25thBbxARLR8WnwC/Ot85aPfbn2smqpgt9Psc0uMUfjF9Ee83u7ryp490pb9a03hks4uFikparILDAQOsNyCGmEEPinvKzc0dUKe7ysrKrnSKMWPGHM8hCSGEGCBdh+smqXR29g4qoqkSVquxonYia7KK468rp3iyytgUhexBzAKIlg87Hs10osHpxakq+RzY5KL/26XFKtw93Ulc3NGtEj+50cUH+9zUxftx2OzcN0bFYjl+qRPR37+OjuP2EMPWcQ2KByovL49AIIDJZCIU6qPInhBCiBMuEoGEiMLt451kObov77lBx+Ho+7bi1BXNKU6KG/z7jpYPSzsOMWM0OPX5YMEs51Hdx9GUFrxjtkpbK9S1BljZ4GbkNrg28+gefyCiv3+yGj34hkzGTHRTnhAnyqm+Q1yI4y0SMVaj5KNZnCxHkoN8x2yVC0blccVo9YR+vmcrCvfMdHLN6AIWpB17V7yT4UTneg9VEg6IYetU3yEuxPEUDoOmDZ9STXKQPDRFc5AHstkvGpymxRptyJOSjHrMJ+o1TbUr3D7BSWbCqVd14kjm+XQ2JNInhBBCDC2RiBEUH2qV+HTKbTxd6/Ce6o4lB/lEphnUeTWW7HFxxWiVvGNsi93fAVrP9uqDfbB6PHO9TyUSFAshhDgqktsojrfjmYM8mF4udfFpoxuzGc6Z6Tym++rvAK3n2c3BDopPlXk+3uREkRBCiIMYO/mLafQP7xzD4WQ4pZAM9nO9ZqLKOWl5XDtJHZLzN5xe22Mh0yOEEOIg0Z38b1UfOsdQvmxPHydzn8WJ3ug12M81I96o2DE+TRmS+1RkD83ASPqEEEKIg0TLTC1UDp1jKLm4YjCc6KYeUWYzxMcfe63twbofcXJJUCyEEOIg0Z381dUneyRiODhZG72idYkH635kJfbUJie8hBBCHCSaFtFz5ctqhcxM+eIXgy+60Sv7JJ92GIx0oBqPxuMlxdR4JB//VCNBsRBCiINE0yJ6Bgc2G2RnG38LcToajNzbp7e6+LjOzeJNw7vm76lI0ieEEEIIMawN5obRW6artDTDbbOGd83fU9GAg+Lzzz//uA0iEAgct/sWQgghTidS8aN/PRtcHE61pvGXjS6uGqeSk6QMWnWGLIfCN/KdZA1CrnJ/er4HhkvXyRNhwEFxcXExJtlWKYQQQpxUUvGjf4fb8NYzmHxyo4v3K91EwnC/6jyh4zxWPd8DEhQPniNKn9AP1e9TCCHEaaXGo/HUbhdfHqXCMbauFWIo6BlM3jFbRdPgqnGndpqDnDkYPAMOim+55ZbjOQ4hhBBDzOJNLj5tcGMCLsJ5socjTiCz2VhxPZ3XwrIVhftmOwkGT/ZIjo2cORg8Aw6KFy9efDzHIYQQx4Wsohy9Syfms2xHOReOzu+av2pN4xGXi3vOURmbIt/Ep6tooNXRcbJHIkA+x04UmV4hxGlN2psevWWlJfjCAVa3lHTNX7Tz2JMbpdyUECeKfI6dGFKSTQghRJ9um6VSVw/XTuzOuTxZncfEiRNNnTgVVyWPpPqEEAeSoFgIIUSfshwKd050khrffVm081iaZE6ctk7llsWyoiqOhRxLCSGEEEIMklN5pX24k5dMCCHEoJGAQAx3p/JK+3AnH1tCCCEGjQQEQohTlQTFQggh+tXQqfHXzcVUa9rJHooQ4ihIObeBkykSQgjRr9erXHxYNfASbPIFfOqLvoZW6/B4LU/396xsPhw4qT4hhBCiX1fkqMTHD7wEm3TXOvX1fA1ttpM7lhNBgkYRJUGxEEKIfqXFKtw700m2BLpiEJzuq7Li1CZBsRBCCCFOiKF2JkGafYie5G0ghBBCiNPS4VamByN1Qla/Tx/yEgohhBDitHQi8oUlJ/n0IUGxEEIIIYQY9iQoFkIIIYQQw54ExUIIIfoUbdlsMp3skQghxPEn1SeEEEL0KdqyWQghhgNZKRZCCHGQQACqq42/hRBiOJCgWAghxEFCIaivh7g4KTUlTg1SGk0cK3nrCCGE6JOUmhKnkpP9fpWg/NQnOcVCCCGEEMdoqHXrE0dOjmeEEEIIIYYxWeU2yEqxEEIIIcQx6BlURiInezRHTla5DRIUCyGEEEIcg55B5akYFAvDMF8oF0IIIYQQQoJiIYQQQgxRkusqTiRJnxBCCCHEkCS5ruJEkmMvIYQQQggx7A2roPihhx7CZDL1+pOZmXmyhyWEEEIIIU6yYZc+MW3aNN5///2u/1ukVZMQQgghxLA37IJiq9Uqq8NCCCGEEKKXYZU+AbBr1y6ys7MZN24c1113Hbt37z7k9f1+P+3t7b3+CCGEEEKI08uwCorPOussnnnmGYqKivjnP/9JbW0t8+fPp6mpqd/bPPzwwyQlJXX9GT169AkcsRBCCCGEOBGGVVB8ySWXsGjRImbMmMGFF17I22+/DcDTTz/d720efPBB2trauv5UVlaeqOEKIYQQQogTZNjlFPeUkJDAjBkz2LVrV7/Xsdvt2O32EzgqIYQQQghxog2rleID+f1+duzYQVZW1skeihBCCDFsSec6MRQMq7ffAw88wIoVK9izZw+fffYZV111Fe3t7dxyyy0ne2hCCCHEsBXtXCdVUsXJNKzSJ6qqqrj++utpbGwkLS2Ns88+mzVr1jB27NiTPTQhhBBCCHESDaug+MUXXzzZQxBCCCGEEEPQsEqfEEIIIYQQoi8SFAshhBBCDBLZNHjqGlbpE0IIIYQQx1N006A49chxjBBCCCGEGPYkKBZCCCGEEMOeBMVCCCEOUuPReLykmBqPdrKHIoQQJ4QExUIIIQ6yeJOLj+vcPL3VdbKHIoQQJ4RstBNCCHGQ22apNDbCLdPVkz0UIYQ4IWSlWAghxEGyHArfyHeS5ZBt9EKI4UGCYiGEEEIMO1JPWBxI3gpCCCEOIhvtxOkuWk/YYjnZIxFDhQTFQgghDiIb7YQQw41stBNCCHGQ6Ea722aqKA45xSyEOP1JUCyEEOIg0Y12OUkQH3+yRyOEEMefHPsLIYQQQohhT4JiIYQQAyY79oUQpytJnxBCCDFg0R37QghxupFjfSGEEEIIMexJUCyEEEIIIYY9CYqFEEIIIcSwJ0GxEEIIIYQY9iQoFkIIIYQQw54ExUIIIYQQYtiToFgIIYQQQgx7EhQLIYQQQohhT4JiIYQQQggx7ElQLIQQQgghhj0JioUQQgghxLAnQbEQQgghhBj2JCgWQgghhBDDngTFQgghhBBi2JOgWAghhBBCDHsSFAshhBBCiGFPgmIhhBBCCDHsSVAshBDiIGYzOBzG30IIMRzIx50QQoiDWCxGUGyxnOyRCCHEiSFBsRBCCCGEGPYkKBZCCCGEEMOeBMVCCCGEEGLYk6BYCCGEEEIMexIUCyGEEEKIYU+CYiGEEEIIMexJUCyEEEIIIYY9CYqFEEIcxGwGRZHmHUKI4cN6sgcghBBi6LFYjKBYCCGGC1kDEEIIIYQQw54ExUIIIYQQYtiToFgIIYQQQgx7EhQLIYQQQohhT4JiIYQQQggx7ElQLIQQQgghhj0JioUQQgghxLAnQbEQQgghhBj2hnVQ/PDDD2MymbjvvvtO9lCEEEIIIcRJNGyD4rVr1/L4448zc+bMkz0UIYQQQghxkg3LoNjj8XDDDTfwz3/+kxEjRpzs4QghhBBCiJNsWAbFd999N1/60pe48MILD3tdv99Pe3t7rz9CCCGEEOL0Yj3ZAzjRXnzxRdavX8/atWsHdP2HH36YX/7yl8d5VEIIIYQQ4mQaVivFlZWVfPe73+Vf//oXsbGxA7rNgw8+SFtbW9efysrK4zxKIYQQQghxopl0XddP9iBOlP/85z985StfwWKxdF0WDocxmUyYzWb8fn+vn/Wlvb2dpKQk2traSExMPN5DFkIIIYQQR+ho4rVhlT5xwQUXsGXLll6X3XbbbUyePJkf/vCHhw2IhRBCCCHE6WlYBcWKojB9+vRelyUkJDBy5MiDLhdCCCGEEMPHsMopFkIIIYQQoi/DaqW4L8XFxUd0/WgKtpRmE0IIIYQYmqJx2pFsnRv2QfGR0jQNgNGjR5/kkQghhBBCiEPRNI2kpKQBXXdYVZ8YDJFIhOrqahRFwWQynezhnFTt7e2MHj2ayspKqcRxBGTejo7M25GTOTs6Mm9HR+bt6Mi8HbmBzJmu62iaRnZ2NmbzwLKFZaX4CJnNZnJyck72MIaUxMRE+UU+CjJvR0fm7cjJnB0dmbejI/N2dGTejtzh5mygK8RRstFOCCGEEEIMexIUCyGEEEKIYU+CYnHU7HY7v/jFL7Db7Sd7KKcUmbejI/N25GTOjo7M29GReTs6Mm9H7njNmWy0E0IIIYQQw56sFAshhBBCiGFPgmIhhBBCCDHsSVAshBBCCCGGPQmKhRBCCCHEsCdBsejXxx9/zOWXX052djYmk4n//Oc/h73Nc889x6xZs4iPjycrK4vbbruNpqam4z/YIeLhhx9m3rx5KIpCeno6V155JSUlJYe93YoVK1BVldjYWMaPH89jjz12AkY7dBzNvP373//moosuIi0tjcTERAoKCigqKjpBIz75jva9FvXpp59itVqZPXv28RvkEHS08+b3+/nJT37C2LFjsdvtTJgwgaeeeuoEjHhoONp5G+7fCY8++igzZ87sajJRUFDAO++8c8jbDPfvsBrGKgAAI5pJREFUgyOds8H8LpCgWPSro6ODWbNm8be//W1A11+5ciU333wzd9xxB9u2beOVV15h7dq13Hnnncd5pEPHihUruPvuu1mzZg3vvfceoVCIiy++mI6Ojn5vs2fPHi699FLOPfdcNmzYwI9//GPuvfdeli5degJHfnIdzbx9/PHHXHTRRSxbtgyXy8V5553H5ZdfzoYNG07gyE+eo5mzqLa2Nm6++WYuuOCCEzDSoeVo5+2aa67hgw8+4Mknn6SkpIQXXniByZMnn6BRn3xHM2/ynQA5OTn87ne/Y926daxbt47zzz+fK664gm3btvV5ffk+OPI5G9TvAl2IAQD011577ZDX+eMf/6iPHz++12V//etf9ZycnOM4sqGtvr5eB/QVK1b0e50f/OAH+uTJk3td9s1vflM/++yzj/fwhqyBzFtfpk6dqv/yl788TqMa2o5kzq699lr9pz/9qf6LX/xCnzVr1vEf3BA2kHl755139KSkJL2pqekEjmxoG8i8yXdC30aMGKE/8cQTff5Mvg/6dqg568vRfhfISrEYNPPnz6eqqoply5ah6zp1dXW8+uqrfOlLXzrZQztp2traAEhJSen3OqtXr+biiy/udVlhYSHr1q0jGAwe1/ENVQOZtwNFIhE0TTui25xOBjpnixcvpqysjF/84hcnYlhD3kDm7Y033mDu3Ln84Q9/YNSoUeTl5fHAAw/g8/lO1DCHnIHMm3wn9BYOh3nxxRfp6OigoKCgz+vI90FvA5mzAx3Ld4H1iG8hRD/mz5/Pc889x7XXXktnZyehUIgvf/nLPPLIIyd7aCeFruvcf//9LFiwgOnTp/d7vdraWjIyMnpdlpGRQSgUorGxkaysrOM91CFloPN2oD/96U90dHRwzTXXHMfRDU0DnbNdu3bxox/9iE8++QSrVT7+Bzpvu3fvZuXKlcTGxvLaa6/R2NjIt7/9bZqbm4dVXnHUQOdNvhMMW7ZsoaCggM7OThwOB6+99hpTp07t87ryfWA4kjk70LF8F8hKsRg027dv59577+XnP/85LpeLd999lz179nDXXXed7KGdFN/5znfYvHkzL7zwwmGvazKZev1f399o8sDLh4MjmbeoF154gYceeoiXXnqJ9PT04zi6oWkgcxYOh/na177GL3/5S/Ly8k7g6Iaugb7XIpEIJpOJ5557jjPPPJNLL72UP//5zyxZsmRYrhYPdN7kO8GQn5/Pxo0bWbNmDd/61re45ZZb2L59e7/Xl++DI5+zqGP+LjjihAsxLDGAnOIbb7xRv+qqq3pd9sknn+iAXl1dfRxHN/R85zvf0XNycvTdu3cf9rrnnnuufu+99/a67N///rdutVr1QCBwvIY4JB3JvEW9+OKLelxcnP7WW28dx5ENXQOds5aWFh3QLRZL1x+TydR12QcffHCCRjw0HMl77eabb9YnTJjQ67Lt27frgO52u4/XEIekI5k3+U7o2wUXXKB/4xvf6PNn8n3Qt0PNWdRgfBfI+TMxaLxe70GnZC0WC9B9pHu603Wde+65h9dee43i4mLGjRt32NsUFBTw5ptv9rps+fLlzJ07l5iYmOM11CHlaOYNjFWB22+/nRdeeGHY5Ske6ZwlJiayZcuWXpf93//9Hx9++CGvvvrqgOf8VHc077VzzjmHV155BY/Hg8PhAMDtdmM2m8nJyTneQx4Sjmbe5Duhb7qu4/f7+/yZfB/07VBzBoP4XXDU4bQ47Wmapm/YsEHfsGGDDuh//vOf9Q0bNugVFRW6ruv6j370I/2mm27quv7ixYt1q9Wq/9///Z9eVlamr1y5Up87d65+5plnnqyncMJ961vf0pOSkvTi4mK9pqam64/X6+26zoHztnv3bj0+Pl7/3ve+p2/fvl1/8skn9ZiYGP3VV189GU/hpDiaeXv++ed1q9Wq//3vf+91m9bW1pPxFE64o5mzAw3H6hNHM2+apuk5OTn6VVddpW/btk1fsWKFPmnSJP3OO+88GU/hpDiaeZPvBF1/8MEH9Y8//ljfs2ePvnnzZv3HP/6xbjab9eXLl+u6Lt8HfTnSORvM7wIJikW/PvroIx046M8tt9yi67qu33LLLfrChQt73eavf/2rPnXqVD0uLk7PysrSb7jhBr2qqurED/4k6Wu+AH3x4sVd1+lr3oqLi/UzzjhDt9lsem5urv7oo4+e2IGfZEczbwsXLjzk+/N0d7TvtZ6GY1B8tPO2Y8cO/cILL9Tj4uL0nJwc/f777+8VEJ7ujnbehvt3wu23366PHTtWt9lselpamn7BBRd0BXe6Lt8HfTnSORvM7wKTrg/jcxhCCCGEEEIg1SeEEEIIIYSQoFgIIYQQQggJioUQQgghxLAnQbEQQgghhBj2JCgWQgghhBDDngTFQgghhBBi2JOgWAghhBBCDHsSFAshhBBCiGFPgmIhxJDz0EMPYTKZMJlM3HrrrSd7OKel6PyaTCbKy8tP9nAAePrpp7vGtGTJkpM9nEO69dZbu8b60EMPnezhiB4+//zzrtfmpz/96ckejjiFSFAsxAEKCwu7PlAv/v/tnXlUVEf697+ALM2+KLiiCLIYAVEhiCAI7qiIYTGi4j6ZcRh1OHGJk5964paMEqOZuOKKRM0YnXhcc1CCGhVxieAWF4gr4IKIiLLV+4en672319sEaZHnc06fU9X3qarndt/u+9Rzn3qqf3/J7VxcXESGRnZ2tqR2a9euFbV79OhRXVUniEbLixcvMGfOHACAl5cXxowZo2eNiMZKQEAAoqKiAADLly9/ZyZ9xLsPGcUEoUDv3r15+ddff0V1dbXWNvfu3VP64/3ll18kjZeVlcXLnTt3RosWLaQpShB4fzyWKSkpePjwIQBg7ty5MDIy0rNGRGPm888/BwC8evUK//d//6dnbYjGAhnFBKFAaGgoL5eXl+PcuXNa26gygIXGriaOHz/Oy0KDnCCaCqWlpfj6668BAM7OzoiPj9ezRkRjp3v37ujTpw8AID09Hb///rueNSIaA2QUE4QCAQEBMDMz43Upxq1QxsHBAQBw8uRJ1NbWamxXUFCAu3fv8rrQICeIpsLq1avx7NkzAMAnn3yCZs2a6VchCWzevBmMMTDGGrWH/n0mKSkJAFBTU4Mvv/xSz9oQjQEyiglCARMTEwQGBvK6Lkaxvb09EhISAAAlJSXIzc2V1E4OeYqJpkZ1dTW+++47AICRkRESExP1rBHxvjBkyBAejpaeno4nT57oWSPiXYeMYoJQgdA4PXHihEaP76NHj3Dt2jUAQHBwsKitNoNaeNzNzQ2tW7euq8oE0Sg5ePAgf1rSp08f+g0Q9YaxsTFiY2MBvIkt3rJli541It51yCgmCBUIwxiePXum0eMrNGxDQkIQHBys8pgqhPHEqkInqqqqcPjwYcycORPh4eFo06YNZDIZZDIZ2rZti759+2Lx4sVaM1b85z//4YuxnJ2dwRjTKC9kypQpvG1kZKRG2erqanz//fcYNWoUOnXqBGtra5ibm8PFxQVxcXHYuXOn1pCSulJfY6tbuHbo0CHExMSgY8eOMDMzQ/PmzRESEoIVK1bg9evXOul6+fJlJCUlwcPDAxYWFrCzs4OPjw9mzZqF27dvczlNadM6dOgAAwMD0Y1+wYIFojbClxRKS0vxzTffoGfPnnBycoKZmRmcnZ0xcuRIHD16VKdzlMr27dt5OTo6Wqu8unR9hw8fRlxcHFxdXWFmZgY7OztERETghx9+UNnP5cuX8Ze//AXu7u4wMzODjY0NevToga+++krS91mXBY7l5eVYv349YmJi4ObmBhsbG5iYmMDR0REhISGYNWsWTp48qbJtQUGByu+zoKAA8+fPh7+/P1q2bAkjIyO133d1dTXS0tL4dWxhYQErKyu4ublh9OjR2LNnj6T/hszMTK5Hhw4d+Pv5+fmYPXs2fHx8YGtrC0tLS3h6emLq1Km4efOmpM8IAP744w/Mnz8foaGhcHJygqmpKUxNTeHg4ABfX1+MHDkSq1atwr1797T2NWLECF5OS0uTrAPRRGEEQSjx8uVLZmxszAAwAGzlypVqZadNm8blTp8+zRhjrFOnTgwAc3JyUtuusLCQtwPAtmzZIjp+7Ngx5uDgIJJR97KwsGDr1q1TO9ajR49Ys2bNuHxmZqakz6GyspLZ29vzdtu3b1cre+zYMebh4aFVVz8/P3bjxg2N486bN4/LJyYmatWzPsdOTEzk8vPmzWOlpaUsNjZWY7+enp7s7t27WvVkjLFFixaJri3Fl0wmY1u3bmWMMdH7+fn5on7at28v6dqQvxRR7Pv06dPM2dlZYx9JSUmstrZW0nlK4fXr18zCwoL3f/PmTa1tFK+N8vJyNnr0aI16T5kyRdTH4sWLmZGRkVp5Hx8f9vjxY416KF4n2ti0aRNzcnKS9F1NmzZNqX1+fr7S97lhwwZmbm4u6fs+e/Ys8/Ly0jp2QEAAu379usZzOXbsGJdv3749Y4yx1NRUJpPJ1PZrYmKi8f9Dztdff81MTU0lfU4ymUxrf69fvxbpdevWLa1tiKYLGcUEoYagoCD+RxoTE6NWzs/PjwFg5ubmrKqqijHG2IQJE3jba9euqWy3a9cu0R98QUGB6Pi2bdtEx+3s7FiPHj1YeHg4Cw4OZq1atVK6SXz77bdq9YyMjORykydPlvQZ/O9//+NtLCws2IsXL1TK7dixg5mYmIh0cXJyYkFBQSw0NJS1bt1adMzR0VHjjVcXo7i+xxYaO//6179YREQEr7dq1YqFhISwoKAgkTEHgHXt2pV//+qYP3++0nfWoUMHFhYWxrp16yYylvft26dkuAoZO3YsGzBggOj8XF1d2YABA1S+FBH2vW/fPmZpackAMENDQ9alSxfWp08f1qVLF2ZgYCCSXbZsmcZz1IWjR4/yflu3bi2pjeK1ER8fz+tt27ZloaGhzN/fX2nisWjRIsYYYwsXLuTv2djYsJ49e7KQkBBma2srku/fv79GPXQxij/99FOl793W1pb5+/uz8PBw1rVrV5EhqOqaVzSKd+7cyctGRkbM19eXhYWFMU9PT2ZgYCBqm5WVxb9f4f9JUFAQCwwMZNbW1qJjLVq0YJcuXVJ7PopG8aZNm3jdzMyM9ejRg4WFhSlNsgwNDdmZM2fU9rtu3Tqlz8nFxYWFhoayiIgI1qNHD9H3ZGpqqvFzlxMeHs7bfPfdd5LaEE0TMooJQg2zZ88WGVmqKC0tZYaGhgwAi4iI4O9v3LiRt1XnwU1KShLdWBTZtm0b8/HxYd98841a78bFixfZoEGDRDek27dvq5RNT08X3RBfv36t5RNgLC4ujrdJSEhQKXP+/HmRURoUFMROnjypJHf06FHm5ubG5bp168YqKytV9inVKH4bYwuNHbmnvnPnzuzYsWMiuZcvX4qeEgBgGzZsUKvryZMnRQaml5cXO3HihEjmyZMnbOrUqQwAa968uUajWJW+UjyWcoR9y89z4sSJ7MGDByK5q1evMm9vby5rYWHBSktLJY+jCeH3PGzYMJ3byJ9iuLm5sYyMDJFcYWGhyBiytrZmBw8eZIaGhvzJivAaePXqFZs+fbroczly5IhaPaR+7mvXrhX16eXlxfbt26c0gaqsrGSHDh1isbGxbMKECUr9KBrFVlZWDACbMWOGkldb6HF/+vSpaOJkZWXFUlNTRedeUVHBUlJSRIa5p6cnq6ioUHlOQqPYwsKCmZmZMVNTU7Zs2TJWXl4ukt2/fz+zsbHh8sHBwSr7VHwqFRUVpfZ/79q1a2zJkiXM3d1d5XFFZs2axfuNi4uT1IZompBRTBBqOHjwoOgmpMrju3//fn58/vz5/P0bN27w90ePHq2yf19fXy4zZswYpePqvLKK1NTUsBEjRvC+kpOTVcqVl5eLvEV79+7V2G9ZWZnoseOBAweUZGpra0UG0/DhwzV6S4uLi1m7du24/ObNm1XKSTGK39bYQmNHbsSUlJSo7Xfo0KFcNiQkRK1cYGCgaBJUXFysVlaVZ/FtGsUA2OzZs9XK3rlzR/SYPjU1VfI4mhgyZAjv87PPPpPURnhtAGBt2rRhhYWFKmVLSkpEhpaJiQkzMjLSGD4UFham8XcpR8rn/vDhQ9FvKCgoiD1//lzrOZaVlSm9p2gUA2BLlizR2pdw4mZsbMx++eUXtbI//PCDqP/FixerlBMaxQCYgYGByv8HObt37xbJqwqTycrK4sddXFzUTlqFVFdXa5VhjLG0tDTed8eOHSW1IZomtNCOINTQq1cv0a5aqhbNKS6yk+Pm5oaWLVuqbae4eE/VIjsLCwtJehoaGuLf//43r//0008q5czNzTF8+HBeT09P19jvnj17UFFRAQBwdHREv379lGQOHTrEz8PBwQGbN2/WmGO2RYsWWLZsGa+vXr1aow6aaKix165dC1tbW7XHp0+fzsvZ2dkqd0DMy8vD6dOneT0lJUXjzoVffPEF2rdvL0m/+sDd3R1ffPGF2uPt2rXDRx99xOvqFoPpypUrV3i5Y8eOdepj+fLlcHJyUnnM1tYWcXFxvF5ZWYnJkydrzAc+ZcoUXv6z57lq1Sr+G7KyssKOHTtgZWWltZ2lpaVWGV9fX8ycOVOjzMuXL7F582Zenzp1qsa0jzExMaLPa/Xq1aipqdGqy/jx4zFo0CC1x6Ojo0XX86+//qokI1w0FxAQAGNjY63jSt31UHht3b59G69evZLUjmh6kFFMEGqwsrJC165deV2TUWxsbCzKbQyAZ6G4c+cO/vjjD9ExxY09/uymHR07dkTz5s0BADdv3uQbISgyevRoXt63bx/KysrU9inMChAfH6/S4BSu5h43bhxsbGy06hodHQ1zc3MAQE5OjkYdNNEQY3t6eoomO6ro2bMnDA3f/JW+fv0a+fn5SjIHDhzgZScnJ0RFRWns09TUVJRZ4W0zYcIErRtmCLOqyFMQ/hlqa2tFhlCbNm107sPGxkZkrKvC399fVJ84caJG+Q8//JCX8/Pzdc4sImTHjh28PG7cOLRr167OfSkyceJEft2pIzMzE6Wlpbz+j3/8Q2u/wkne3bt3ceHCBa1thBMJVRgYGCAoKIjXVV0/wg2TcnNz6zVLjeK1defOnXrrm3i/IKOYIDQgNFYVjeKKigq+BXS3bt24sSVHaEwpthXWW7VqBTc3N416FBUVYevWrUhOTsaoUaMwdOhQDBw4UPR68eIFAIAxhgcPHqjsp2/fvtyrVlFRgR9//FGl3KNHj5CRkcHr8g1JFBGmlJNvqaoNY2NjuLu7A3iz09SlS5cktdPH2D179tTap0wm47sYAlA5ITl79iwv9+7dW5KHS+o51QdSzrNt27a8rG7SpQvPnz9HZWUlr2vyxqujR48eWo15+RMb4M1kw8/PT7I8Y0xkVOrCgwcPROn1tBnvuiKcpKjjzJkzvOzp6QkXFxetbXr27Cm6noV9qMLExATdu3fX2q+260fYx5UrVzB+/HgUFhZq7VcKdnZ2orq2FJZE0+Xd30uTIPRI7969kZKSAuD/e3zljwFPnz7Nb+qqHkkqGsVjxowR1eVo8hLfv38f//znP7F7925JjzHlqLuRGxkZIT4+HitXrgTwJoRC1Q5iO3fu5GEArq6uIu+ZnLKyMtEW1YsXL8aqVask6Sf0nD9+/FhSG32MLTSQNCGcEL18+VLpuNAz5enpKalPqXL1gZTz1HaOulJeXi6qy2QynftwdHTUKiPU297eXuuERHFyKw9/0JXr16+L6lIMR12QEm5y69YtXvb29pbct7e3NzIzM5X6UIWDg4Okbbm1XT/Ozs746KOPsHv3bgDA1q1bkZ6ejtDQUPTr1w+9evWCv78/TE1NJZ+HHMVrS/HaIwg5ZBQThAZCQkJgYGDAE9oLjVt18cRyfHx8YG1tjefPn4tkhR5mQP3WzleuXEFYWFidvBqaHvkmJCRwozgjIwNFRUVKMZnC0Al1XmLFLVNVxQlKoS6euIYa28TEROc+5deKunGkekTr4jmtK7qep6pz/LPUpU9d9a6v71MKT58+5WUzMzNJccK6ICU2WeiRlYdXSUEoW1JSolG2Pj/T9evXo7i4mD8Fqq6uRkZGBn9qJZPJ0KdPH4wZMwaxsbGSY4rfxvVKvJ9Q+ARBaMDe3h5dunThdaFxKy8bGBigV69eSm2NjIz4Y+nff/8dRUVFAIBTp06hqqqKy6nyFNfU1CAuLo4bxKamppg4cSJ+/PFHXLt2DaWlpaisrAR7k0EGjDHJC7MCAgLQqVMnPs7OnTtFx/Pz80WLwtQZxfXlbalL7KA+x64LdFNWRnEh6fu2+Ek4Ma2Ld1Mb2uKJFXXQxXgV6vtnYqp1xc7ODpmZmdi6dSsCAwOVduarqKjAgQMH8PHHH6NLly6S4p3l7YRIXcRMND3IKCYILaiKK66qquKG4wcffAB7e3uVbVVt+Sw0rFu0aAEvLy+ldvv27cPly5cBvImDPXr0KDZs2IDo6Gh4eHjA2tpaaXW2LgvWhIau0CsMiLNS+Pv78xhcRRQ9mQUFBSIjXeqrLgvK9Dl2XRDqKzUetz7idt9l5Fscy9HmkWxsCL/zsrIyvUyMhItPdfl/eP78OS835BML4I2xP2bMGJw6dQqFhYXYtWsXkpKS0LlzZ5HctWvXEB4eLorbVofitaUp8wvRtCGjmCC0IAxvkHt8c3JyeFycpuwEqhbbCReIycMzFPn55595edSoUaKV26p4+fKlTkaU0CjOzs7GzZs3eV1oFKvzEgNvHrEKvVU3btyQPP6fRZ9j1wVnZ2delpq5oT4yPLzLGBgYiBZf3b9/X4/a1D/COO3a2lqtsblvA6HxpyorijqEuurTgHR0dERsbCxWrlyJy5cv4/r165g8eTI//uzZMyxatEhrP4rXlvD3SBBCyCgmCC0oxvxmZWVpjSeW8+GHH3JvWFZWlsjDDKhfZCdcmKWYUkoVZ86c0SkUwM3NDQEBAbwuN4QvXrzIc8fKF+Wpw9TUVJSyTpit4m2jz7HrgvA7zMrKkrRoUr7QSRvCyUFjC9MQev/0YTS+Tby9vUWLy1SldHzbdOvWjZfPnz8vCttSx7Nnz0SLBIV96Bt3d3esW7dOtDj4yJEjWtsJvcmurq6i9G8EIYSMYoLQgpOTEzw8PHhd0SjWlAzfzMyMrzrPzc3Fzz//LFp5ra6t8OalypOsyJYtW7TKKCL0AsuNYqGXOCIiQmtWgv79+4t0aMi4UH2OrSuDBw/m5aKiIrUbrMiprKwUbbqgCWF8ZF0zJegLYUaGvLw8PWpS/xgbG4smvevXr29wHYQT9tLSUhw8eFBrmx07dvBJm3BdxLuEcBMi+VoNTQg3SqrvLCDE+wUZxQQhAeHNLTMzk+905eLionXTAfmNiTGGJUuW8PdtbW3h4+Ojsk2rVq14WduuWmfOnBFtZCGVkSNH8lRK169fx9mzZ0WbDQg3+lDH1KlTeWzzw4cPMWfOHJ31qCv6HFtXunTpItrcJTk5WWM6uHnz5qGgoEBS38KJS2PztgonhcJczu8LSUlJvHz69GmkpqY26Pienp4io/bzzz/X6C0uKysT7Ww4dOhQSWnv6gNdnnLIc7IDULueQ0hOTg4v/9mNkoj3GzKKCUICwpt3Xl4eT7GlbbczRZkTJ06I3le3glz4x71r1y614QHnz5/HsGHDdMphLMfR0RF9+/bl9aSkJJ77VyaTITo6Wmsfbdu2xYwZM3h9xYoVSE5O1rpivbi4GIsWLZK0w9a7OHZdWL58Off65+fnIywsDKdOnRLJlJSUYNq0aVi6dKnkFFrCx9uHDx8WbZ38rhMcHMw93UVFRUq5fRs7AwcOFP2W//rXv2LTpk0a21y5ckXrFuy6MHfuXF6+dOkSEhMTVf5Gnj9/jhEjRvCNf4yMjBp0ovnVV1/hb3/7m9Zr4OnTp/jyyy95XdOTOuDNUxfh72zAgAF/TlHivYbyFBOEBNR5F6QYxb169RLlOpaj6c88Pj4ec+bMQWFhIaqrqzFo0CBMmjQJAwcOhJ2dHR4+fIgDBw5g+/btqK6uRv/+/XH16lXRhhZSSEhIwKFDhwCId66KioqSnFd10aJFyMnJwdGjRwEAKSkp2LFjB0aNGoXAwEC0aNEC1dXVePz4MfLy8nDixAkeV6spZvldH1tXgoKCMG/ePMyfPx8AcPnyZQQFBcHFxQXt27dHWVkZcnNz+YYwGzduxLBhw3h7dWm9IiIi4OjoiOLiYpSXl8PHxwd+fn5o2bKlKI/r3r1739q51RUTExNERkZi165dAN4Y9cJQpcaOgYEBtm/fju7du6OoqAhVVVWYMGEC1qxZg/j4eHh5ecHS0hJPnz7FpUuXcPDgQZw6dQqJiYkYNWpUvegQGRmJ8ePHc2P8+++/x/nz5zFlyhT4+PigtrYW586dw5o1a0RrGWbOnClad/C2qaiowOrVq7F69Wr4+fkhIiICfn5+cHR0hEwmw+PHj3HmzBls3LiRh0wYGRlh1qxZGvs9fvw4Dyvy9fWFq6vrWz8XovFCRjFBSKBt27ZwcXFRWsEtxSi2s7PDBx98oBQzqekxnkwmw/bt2zFo0CBUVlaiqqqK3zAU6dy5M9LS0iQtyFNk+PDhMDc3V9phSlPWCUWaNWuG/fv3Y9KkSTy924MHD7Bs2TKd9dEVfY5dF+bNm4dmzZphwYIF/DF2fn6+6LqSyWRYs2YN+vXrJ2orTK8lxMTEBKmpqYiNjcWrV69QU1Mjelz8rpOQkMCN4j179jS4B/9t06ZNG5w8eRKDBg3iWVKys7ORnZ3dYDqsXbsWFRUVPDzq+vXrSE5OViuflJQkKavD2+LChQtacxA3a9YMqampWmOE9+zZw8tSQsKIpg2FTxCERBSNWEdHR8leLUXj2dLSUuuq7vDwcGRmZqrdntXc3ByffPIJsrOz65w2ydLSElFRUaL3HBwcdH7EaGZmhrS0NBw6dEhjWAjwxrsTFBSElJQUfPvtt3XS+10Zuy7MnTsXFy5cwNSpU9GpUyeYm5vDxsYG3t7e+PTTT5Gbm4uxY8eiuLiYt5HJZErbDwsZMmQIfvvtN0yfPh3dunWDra2t5N2+9M3gwYN5arbjx4+/d6nZgDcZDy5evIiFCxdqjNE1MTHB4MGDMWnSpHod39jYGOnp6UhLS9PoKfX29sZPP/2ElStXSlrgW5/ExMTg73//u1ZPrpGRESIjI3Hu3DmMHTtWo2xVVRX++9//AnjzpEXVlvYEIcSANbYcPgTRxGCMIScnBzk5OSgpKYGdnR3atWuHsLCwet86tr548uQJTpw4gfv376OkpAQmJiawt7eHu7s7fH19YW1t/V6OXZ/s3r0bMTExAIDAwECl+OP3iaVLl/L41YULF4riYN835L/nvLw8PHr0CDU1NbCzs4OHhwf8/f0b5Dedm5uL8+fPo7i4GAYGBnByckJgYCDf6VLfFBUV4bfffkN+fj5KSkpQW1sLa2truLq6IiAgAA4ODpL62bt3L18bMW7cOK3x3ARBRjFBEMQ7SGRkJA4cOAAAmDFjBlJSUvSs0dujtLQUHTp0wLNnz9CuXTvcvn2bZ0YhiLrSt29fZGRkwNDQEHl5eSp3DyUIIRQ+QRAE0UBI9UFs3bqVG8QAGmw7an1hY2PDM4ncvXsXO3fu1LNGRGPnwoULPGvPxx9/TAYxIQnyFBMEQTQQCxYswL1795CQkIDg4GAlb+i9e/eQkpKCFStWcAM6KirqncwcUd+8ePEC7u7uePjwIby8vJCbm9to4qKJd4/o6Gjs3bsXpqamuHr1KlxcXPStEtEIoOdTBEEQDURFRQU2bNiADRs2wMzMDB4eHnBwcEB1dTXu37+vtPlG+/btsXbtWj1p27BYWlpi6dKlSExMxNWrV7Ft27b33kNOvB3Onj3LJ5LJyclkEBOSIU8xQRBEA/HZZ5+JdjXURJ8+fZCenq51q22CIAiifiCjmCAIooGoqqpCRkYGjhw5gpycHNy6dQtPnz5FdXU1bG1t0bp1a/Tq1QsxMTEIDw/Xt7oEQRBNCjKKCYIgCIIgiCYPZZ8gCIIgCIIgmjxkFBMEQRAEQRBNHjKKCYIgCIIgiCYPGcUEQRAEQRBEk4eMYoIgCIIgCKLJQ0YxQRAEQRAE0eQho5ggCIIgCIJo8pBRTBAEQRAEQTR5/h9Y8mp2YbihVgAAAABJRU5ErkJggg==\n",
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+ "