{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sales ratio study with real data\n", "\n", "AssessPy can easily be used with various data to conduct a sales ratio study. In this vignette, we demonstrate this process using real data from the Cook County Assessor's Office (CCAO). The CCAO publishes assessments and sales on the [Cook County Open Data Portal](https://datacatalog.cookcountyil.gov/stories/s/9bqn-cfsv).\n", "\n", "## Basics of sales ratio studies\n", "\n", "A sales ratio is the ratio of the assessor's estimate of a property's value to the sale price of a property. A sales ratio study is a report on how accurately and fairly an assessor predicted property values. The CCAO has a [rigorous set of rules](https://github.com/ccao-data/wiki/blob/master/SOPs/Sales-Ratio-Studies.md) that govern how sales ratios studies are conducted.\n", "\n", "In general, there are four important statistics produced in sales ratio studies, listed in the table below. It is important to understand that these statistics are calculated based on properties that sell. In most jurisdictions, the number of properties that sell in any single year is a very small percentage of the overall number of properties. In order to characterize the quality of the assessment role in a jurisdiction, we draw an inference from this small number of properties.\n", "\n", "| Statistic | Acceptable Range | Interpretation |\n", "|------------------------|------------------------|------------------------|\n", "| COD | 5 - 15 | How often properties with the *same* sale price receive the same predicted market value. Lower CODs indicate more fairness between similarly priced properties. |\n", "| PRD | .98 - 1.03 | How often properties with *different* sale prices receive the proportionately different predicted market values. Lower PRDs indicate more fairness between low and high-priced properties. |\n", "| PRB | -.05 - .05 | PRB is a different approach to measuring fairness across homes with different sale prices. |\n", "| Median Assessment Ratio | .095 - 1.05 | The median ratio measures whether the most common ratios accurately reflect sale prices |\n", "| Sales Chasing (E.4) | $\\le$ 5% | Measures the degree to which the statistics above are *true* reflections of the quality of assessments. |\n", "\n", "### Interpretation of sales ratio statistics\n", "\n", "Suppose you have a jurisdiction with a median ratio of one and a COD of 20. This indicates that, on average, the assessor predicts the sale price of properties accurately, but with a high dispersion. To use the dart board analogy, the assessor's darts fall in a wide area centered around the bullseye. On the other hand, if the median ratio is greater than one, and the COD is lower than 10, this indicates that the assessor consistently over-estimates the value of properties in their jurisdiction.\n", "\n", "Suppose you have a jurisdiction with a low COD and high PRD & PRB. This indicates that the assessor consistently under-estimates higher value properties, and over-estimates lower value properties. Properties of similar value receive similar estimates, but there is structural inequality in the overall system.\n", "\n", "Finally, suppose you have a jurisdiction with CODs, PRDs, and PRBs all within the acceptable range, but there is strong evidence of selective appraisals. In this case, the sales value statistics should be disregarded, since they are based on a non-random selection of the underlying set of properties. They cannot be used to characterize the quality of the assessment role.\n", "\n", "## Loading data into Python\n", "\n", "There are many ways to load data into Python. Below are some example methods:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### jsonlite\n", "\n", "Socrata can also return raw JSON if you manually construct a query URL. Follow the [API docs](https://dev.socrata.com/foundry/datacatalog.cookcountyil.gov/uzyt-m557) to alter your query. The raw JSON output can be read using the `read_json` from `pandas`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "%%capture\n", "import pandas as pd\n", "\n", "# Load 100k rows of 2020 residential (major class 2) assessment data\n", "assessments = pd.read_json(\n", " \"https://datacatalog.cookcountyil.gov/resource/uzyt-m557.json\"\n", " + \"?$where=starts_with(class,'2')&tax_year=2020&$limit=100000\"\n", ")\n", "\n", "# Load 100k rows of 2020 sales data\n", "sales = pd.read_json(\n", " \"https://datacatalog.cookcountyil.gov/resource/wvhk-k5uv.json\"\n", " + \"?$where=sale_price>10000&year=2020&$limit=100000\"\n", ")\n", "\n", "# read_json removes leading zeroes, add them back\n", "assessments.pin = assessments.pin.astype(str).str.zfill(14)\n", "sales.pin = sales.pin.astype(str).str.zfill(14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### From a CSV or Excel\n", "\n", "Python can also read Excel and CSV files stored on your computer.\n", "\n", "```python\n", "# CSV files\n", "assessments = pd.read_csv(\"C:/Users/MEEE/Documents/.... where is your file ?\")\n", "sales = pd.read_csv(\"C:/Users/MEEE/Documents/.... where is your file ?\")\n", "\n", "# Excel files\n", "assessments = pd.read_excel(\"C:/Users/MEEE/Documents/.... where is your file ?\", sheet_name = \"your sheet\")\n", "sales = pd.read_excel(\"C:/Users/MEEE/Documents/.... where is your file ?\", sheet_name = \"your sheet\")\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Connecting to a relational database\n", "\n", "The CCAO's Data Science team uses Amazon Athena. Python can connect to a wide range of database engines.\n", "\n", "```python\n", "from pyathena import connect\n", "\n", "# Connect to the database\n", "aws_athena_conn = connect(\n", " s3_staging_dir=\"your-staging-directory\",\n", " region_name=\"your-aws-region\"\n", ")\n", "\n", "# Fetch data from the SQL server\n", "assessments = pd.read_sql_query(\"SELECT * FROM your-database.your-table\", aws_athena_conn)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Sales ratio study\n", "\n", "In this section, we will use data published on the [Cook County Open Data Portal](https://datacatalog.cookcountyil.gov/Property-Taxation/Cook-County-Assessor-s-Residential-Assessments/uzyt-m557) to produce an example sales ratio study.\n", "\n", "### Prepare the data\n", "\n", "Above, we pulled assessment and sales data from the Open Data Portal. In order to produce our sales ratio statistics, our data needs to be formatted in 'long form,' meaning that each row is a property in a given year. The county provides *assessed value* on the Open Data Portal. For residential properties, we need to multiply assessed value by 10 to get fair market value. [Assessment levels](https://prodassets.cookcountyassessor.com/s3fs-public/form_documents/classcode.pdf) can differ for other classes." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Pivot to longer, Join the two datasets based on PIN, keeping only those that have assessed\n", "# values AND sales\n", "combined = pd.merge(\n", " pd.melt(\n", " assessments.rename(columns={\"tax_year\": \"year\"}),\n", " id_vars=[\"pin\", \"year\", \"township_name\"],\n", " value_vars=[\"mailed_tot\", \"certified_tot\", \"board_tot\"],\n", " var_name=\"stage\",\n", " value_name=\"assessed\",\n", " ),\n", " sales[[\"pin\", \"year\", \"sale_price\", \"is_multisale\"]],\n", " on=[\"pin\", \"year\"],\n", " how=\"inner\",\n", ")\n", "\n", "# Remove multisales, then calculate the ratio for each property\n", "# and assessment stage\n", "combined = combined[not combined.is_multisale]\n", "combined[\"ratio\"] = combined.assessed * 10 / combined.sale_price" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sales ratio statistics by township\n", "\n", "Cook County has jurisdictions called townships that are important units for assessment. In the chunk below, we calculate sales ratio statistics by township." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ncodcod_cicod_metprdprd_ciprd_metprbprb_ciprb_met
township_namestage
Barringtonmailed_tot35322.10[20.12, 24.46]False1.04[1.02, 1.07]False0.003[0.001, 0.004]True
certified_tot35322.03[20.02, 23.81]False1.05[1.02, 1.08]False0.002[0.001, 0.004]True
board_tot35320.75[18.72, 23.04]False1.05[1.02, 1.08]False0.001[-0.0, 0.003]True
New Triermailed_tot8821.57[17.01, 26.59]False1.06[1.02, 1.11]False0.003[-0.0, 0.007]True
certified_tot8822.69[17.8, 29.23]False1.08[1.03, 1.14]False0.003[-0.001, 0.007]True
board_tot8821.66[16.93, 26.57]False1.08[1.03, 1.13]False0.003[-0.001, 0.006]True
Northfieldmailed_tot36418.32[16.39, 20.19]False1.03[1.02, 1.05]True0.002[0.0, 0.003]True
certified_tot36418.10[16.45, 20.06]False1.03[1.01, 1.05]True0.001[0.0, 0.003]True
board_tot36417.04[15.35, 18.97]False1.05[1.03, 1.06]False0.001[-0.0, 0.002]True
Palatinemailed_tot213315.68[14.82, 16.82]False1.01[1.0, 1.02]True0.002[0.001, 0.003]True
certified_tot213315.62[14.57, 17.07]False1.01[1.0, 1.01]True0.002[0.001, 0.003]True
board_tot213315.40[14.36, 16.47]False1.01[1.0, 1.02]True0.002[0.001, 0.003]True
Wheelingmailed_tot219617.84[16.72, 19.29]False1.03[1.02, 1.04]True0.002[0.001, 0.003]True
certified_tot219617.78[16.88, 19.0]False1.03[1.02, 1.04]True0.002[0.001, 0.002]True
board_tot219617.55[16.45, 19.03]False1.03[1.02, 1.04]True0.002[0.001, 0.002]True
\n", "
" ], "text/plain": [ " n cod cod_ci cod_met prd \\\n", "township_name stage \n", "Barrington mailed_tot 353 22.10 [20.12, 24.46] False 1.04 \n", " certified_tot 353 22.03 [20.02, 23.81] False 1.05 \n", " board_tot 353 20.75 [18.72, 23.04] False 1.05 \n", "New Trier mailed_tot 88 21.57 [17.01, 26.59] False 1.06 \n", " certified_tot 88 22.69 [17.8, 29.23] False 1.08 \n", " board_tot 88 21.66 [16.93, 26.57] False 1.08 \n", "Northfield mailed_tot 364 18.32 [16.39, 20.19] False 1.03 \n", " certified_tot 364 18.10 [16.45, 20.06] False 1.03 \n", " board_tot 364 17.04 [15.35, 18.97] False 1.05 \n", "Palatine mailed_tot 2133 15.68 [14.82, 16.82] False 1.01 \n", " certified_tot 2133 15.62 [14.57, 17.07] False 1.01 \n", " board_tot 2133 15.40 [14.36, 16.47] False 1.01 \n", "Wheeling mailed_tot 2196 17.84 [16.72, 19.29] False 1.03 \n", " certified_tot 2196 17.78 [16.88, 19.0] False 1.03 \n", " board_tot 2196 17.55 [16.45, 19.03] False 1.03 \n", "\n", " prd_ci prd_met prb prb_ci \\\n", "township_name stage \n", "Barrington mailed_tot [1.02, 1.07] False 0.003 [0.001, 0.004] \n", " certified_tot [1.02, 1.08] False 0.002 [0.001, 0.004] \n", " board_tot [1.02, 1.08] False 0.001 [-0.0, 0.003] \n", "New Trier mailed_tot [1.02, 1.11] False 0.003 [-0.0, 0.007] \n", " certified_tot [1.03, 1.14] False 0.003 [-0.001, 0.007] \n", " board_tot [1.03, 1.13] False 0.003 [-0.001, 0.006] \n", "Northfield mailed_tot [1.02, 1.05] True 0.002 [0.0, 0.003] \n", " certified_tot [1.01, 1.05] True 0.001 [0.0, 0.003] \n", " board_tot [1.03, 1.06] False 0.001 [-0.0, 0.002] \n", "Palatine mailed_tot [1.0, 1.02] True 0.002 [0.001, 0.003] \n", " certified_tot [1.0, 1.01] True 0.002 [0.001, 0.003] \n", " board_tot [1.0, 1.02] True 0.002 [0.001, 0.003] \n", "Wheeling mailed_tot [1.02, 1.04] True 0.002 [0.001, 0.003] \n", " certified_tot [1.02, 1.04] True 0.002 [0.001, 0.002] \n", " board_tot [1.02, 1.04] True 0.002 [0.001, 0.002] \n", "\n", " prb_met \n", "township_name stage \n", "Barrington mailed_tot True \n", " certified_tot True \n", " board_tot True \n", "New Trier mailed_tot True \n", " certified_tot True \n", " board_tot True \n", "Northfield mailed_tot True \n", " certified_tot True \n", " board_tot True \n", "Palatine mailed_tot True \n", " certified_tot True \n", " board_tot True \n", "Wheeling mailed_tot True \n", " certified_tot True \n", " board_tot True " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import warnings\n", "\n", "import numpy as np\n", "\n", "import assesspy as ap\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "# For each town and stage, calculate COD, PRD, and PRB, and their respective\n", "# confidence intervals then arrange by town name and stage of assessment\n", "town_stats = combined[combined.assessed > 0].copy(deep=True)\n", "town_stats[\"stage\"] = town_stats.stage.astype(\n", " \"category\"\n", ").cat.reorder_categories([\"mailed_tot\", \"certified_tot\", \"board_tot\"])\n", "town_stats = town_stats.groupby([\"township_name\", \"stage\"]).apply(\n", " lambda x: pd.Series(\n", " {\n", " \"n\": np.size(x[\"pin\"]),\n", " \"cod\": np.round(ap.cod(ratio=x[\"ratio\"]), 2),\n", " \"cod_ci\": np.round(ap.cod_ci(ratio=x[\"ratio\"]), 2),\n", " \"prd\": np.round(ap.prd(x[\"assessed\"], x[\"sale_price\"]), 2),\n", " \"prd_ci\": np.round(ap.prd_ci(x[\"assessed\"], x[\"sale_price\"]), 2),\n", " \"prb\": ap.prb(x[\"assessed\"], x[\"sale_price\"], 3),\n", " }\n", " )\n", ")\n", "\n", "town_stats[\"prb_ci\"] = town_stats.prb.str[\"95% ci\"]\n", "town_stats[\"prb\"] = town_stats.prb.str[\"prb\"]\n", "town_stats[\"cod_met\"] = town_stats.cod.apply(ap.cod_met)\n", "town_stats[\"prd_met\"] = town_stats.prd.apply(ap.prd_met)\n", "town_stats[\"prb_met\"] = town_stats.prb.apply(ap.prb_met)\n", "town_stats = town_stats[\n", " [\n", " \"n\",\n", " \"cod\",\n", " \"cod_ci\",\n", " \"cod_met\",\n", " \"prd\",\n", " \"prd_ci\",\n", " \"prd_met\",\n", " \"prb\",\n", " \"prb_ci\",\n", " \"prb_met\",\n", " ]\n", "]\n", "town_stats = town_stats[town_stats[\"n\"] >= 70]\n", "\n", "town_stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Median ratios by sale price\n", "\n", "Suppose you are concerned that an assessment role is unfair to lower value homes. One way to visually see whether ratios are systematically biased with respect to property value is to plot median ratios by decile. In our sample data, we can see each decile of sale price using the `quantile` function:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DecileSale Price
010%$130,000
120%$175,000
230%$217,850
340%$257,000
450%$295,000
560%$330,000
670%$378,150
780%$454,600
890%$595,000
\n", "
" ], "text/plain": [ " Decile Sale Price\n", "0 10% $130,000\n", "1 20% $175,000\n", "2 30% $217,850\n", "3 40% $257,000\n", "4 50% $295,000\n", "5 60% $330,000\n", "6 70% $378,150\n", "7 80% $454,600\n", "8 90% $595,000" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "deciles = np.linspace(0.1, 0.9, 9).round(1)\n", "\n", "median_ratios = pd.DataFrame(deciles, columns=[\"Decile\"])\n", "median_ratios[\"Decile\"] = (median_ratios.Decile * 100).astype(int).astype(\n", " str\n", ") + \"%\"\n", "median_ratios[\"Sale Price\"] = np.quantile(combined.sale_price, deciles)\n", "median_ratios[\"Sale Price\"] = median_ratios[\"Sale Price\"].apply(\n", " lambda x: \"${:,.0f}\".format(x)\n", ")\n", "\n", "median_ratios" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using these decile values, we can graph sales ratios across each decile of value. Here, we use the very useful `matplotlib` package to make an attractive graph." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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m6KaRuvoPHjwwujwqKipDfXzw4AHmzZuHsmXL4siRI3r3sLp///5Lv1wyi6urK+bOnYtt27bhxo0bOHfuHCpWrIhffvkFp06dwkcffYSlS5fqvWft2rVYuXLlK9t+3c/5deXPnx8jR45ETEwMpk6dis8//xyzZ89Wln/zzTd48uQJfvzxR+XeVjr9+vXDgQMH3roPuhijoqIMZgVEBFFRUQY/Vq1atUKrVq0QFxeHQ4cOYdOmTfj+++/RpEkTXLp0CXny5HmjvtSoUQP79+/HH3/8gYYNG6Zb79KlS7h79y4KFSoEb29vvWXR0dHQarUGiZVuG3d1ddWLO7PG1hR0d76vXLmyUmbKbSIr9md/f3+sWLECRYsWxd69ezMljjeV3vfei9uKMdlh+6H/8I7q76Dg4GAUKlQId+7cQevWrV96wz9nZ2e89957uHjxYoYON7i4uMDPzw9Xr17F/fv3DZb/9ddfGerjtWvXICJo2LChwU1BM9pGZlCpVHB0dNQr091dvVWrVgb1M9rX1/2c39S4cePg5eWF+fPn4/r160p5ejGICA4dOmTQjrW1NYDXmy0pX748ABh9dM2xY8eQnJyc7uyTs7MzmjRpgsWLF6Nnz56IiorCsWPHMrzuF3344YewsrLCkiVL8PDhw3TrTZs2DQDQu3dvg2Wpqak4cuSIQbluzHXxZtXYvqmkpCTMmjULANC5c2el3JTbRFbtz2lnX3VeN47MYCzG+Ph4nDlzBi4uLnqHKl9k6dsP6WNS9Q6ytrbGli1bsHnzZnz55ZevrD9kyBAkJibik08+MTrFHBkZqfcD3b17dzx79gwTJ07Uq/f7779n+Hwq3UzG4cOH9c67uH37NsaOHZuhNt7UokWL8PfffxtdtmXLFly8eBF58uRB6dKl9fr64uM+Dhw4gCVLlmR4va/7Ob8JBwcHjB49Gmq1GlOmTFHK04vhq6++Qnh4uEE7bm5uAP47NJYRXbp0gY2NDcLCwvTOn3n27BlGjx4NAOjZs6dS/ueffxr9gdbNgtrb2ytl0dHRuHTpEqKjozPUl5IlS+LTTz/Fo0ePEBISgnv37ukt12q1mDJlClatWgV/f3+MHDnSaDvjxo3Ds2fPlNe3b9/Gt99+Czs7O3Tq1Ekpz4qxfRM3b95ESEgILly4gPr16+ODDz5QlplymzDl/vzFF18YXYeI4KuvvgIA1KpV643jyAx79uzBrl279MqmTZuGp0+fKgn+y1jq9kOGePjvHVWpUiVUqlQpQ3X79u2Lo0ePYuXKlTh06BAaNmwILy8vREVF4dKlSzh27BjWrFmjPDPws88+w6ZNm7BkyRKcP38ederUwa1bt7B+/Xo0b94c27Zte+U6PT090bZtW2zcuBGVKlXC+++/j6ioKPz22294//33M/XZezt27EC/fv1QrFgx1KxZE15eXkhISMDp06fx119/wcrKCvPnz1fOBwkJCYGvry9mzJiB8PBwlC5dGpcvX8Zvv/2GNm3aYMOGDRla7+t+zm+qT58+mD59On744QeMGzcO/v7+6NevH5YvX462bduiQ4cOyJcvH44ePYpTp04ZHbNSpUrBy8sLa9euVZ6LqFKpMHjw4HQPZfj7+2P69OkYMWIEypYtiw4dOsDR0RFbt27F5cuX0apVK73DM0OGDMHdu3dRq1Yt5fl1Bw8exPHjx1GtWjW9H865c+ciNDQUkyZNyvCz52bMmIGYmBgsW7YMxYsXR/PmzeHv74/Y2Fj8/vvviIiIQPHixbF9+3aj59B4enoiISEBZcuWRUhICBISErB+/Xo8evQI3333HQoVKqTUzaqxTU9qaqryuWg0Gjx9+hT//PMPDh06BI1Gg1atWikPO9cx5TZhyv05LCwMkydPRqVKlVCxYkW4ubnh0aNH2LdvH65cuYJ8+fIpM29vEkdmaNGiBUJCQtCuXTv4+vri6NGj2LdvH/z9/fHFF1+88v3m3n7oNZjlmkPKMi+7Z5ExL3tMzbp166Rhw4aSN29esbW1lUKFCkm9evVk1qxZBpeyP3r0SPr06SP58+cXe3t7qVix4mvfUT0uLk5GjBghvr6+yt2Xp0yZIs+ePTNaX3fbBGN0NybU3d35ZS5duiQzZsyQRo0aiZ+fn9jb24u9vb34+/tLjx49jN4P5tq1a9K2bVvJnz+/5M6dWypXrixr165N9zLzlz2K4nU+Z2MyMuZz5swRANK9e3elbN++fVKzZk1xdnaWPHnySLNmzeTkyZPp3sjz6NGjUrduXXF2dlYubc/IHdV/+eUX5X12dnZSpkwZo3dUX7t2rXTo0EH8/f0ld+7c4urqKuXKlZPp06cb3IH7de9Tldbu3bulffv24uXlJba2tpInTx6pXr26zJo1K907l+vG7/Hjx3p3VC9Xrly6d1QXyfjYvuzze91HPelu/6D7L1euXOLu7i6VK1eWAQMGGL0thI4pt4nX3Z/T8+eff8qYMWOkevXqypg5OTlJ2bJlZeTIkXL37t23iuNlt4Z42WdvrK307qieL18+6dmzp9HbzmTmdwNlPpWISNakb0REOYNuRoCHXOhlVqxYgV69emH58uV6h7Yp5+I5VUREREQmwKSKiIiIyASYVBERERGZAM+pIiIiIjIBzlQRERERmQCTKiIiIiITYFJFREREZAJMqoiIiIhMgEkVERERkQkwqSIiIiIyASZVRBZCpVJl+GHApmhf9wBdczxqJSoqCu3atUO+fPmgUqkwe/bsTF/n9evXoVKpsGLFikxf1+vo2bNnlj8I15xjT5STMakiwn8/Mmn/8/DwQP369bFjxw5zdy/HGTZsGHbt2oWxY8fixx9/RJMmTczdJXoLjx49wsyZM1GnTh3kz58fefLkQbVq1bBu3Tqj9VNSUjB69Gh4eXnBwcEBVatWxe7du/XqJCYmYt68eWjcuDE8PT3h7OyM8uXLY8GCBdBoNAZtarVazJgxA35+frC3t0fZsmXx008/ZUq8ROmxMXcHiCzJF198AT8/P4gIoqKisGLFCjRr1gxbt25FixYtzN29t5KUlAQbG8vY5ffu3YtWrVph5MiR5u6K2S1ZsgRarTZL19m9e3d06tQJdnZ2JmnvyJEjGD9+PJo1a4bPP/8cNjY22LhxIzp16oQLFy4gNDRUr37Pnj2xYcMGDB06FMWLF1f2s3379qFWrVoAgGvXrmHw4MF4//33MXz4cLi4uGDXrl0YMGAAjh49ipUrV+q1OX78eHz11Vf45JNPULlyZfzyyy/o0qULVCoVOnXqZJI4iV5JiEiWL18uAOTvv//WK3/8+LHY2tpKly5dMr0PAGTSpEmZvh4dXcyRkZFZtk4dlUolAwcOzNJ1RkZGCgBZvnx5lq43PfHx8ebugslcu3ZNrl+/rlem1WqlQYMGYmdnpxfrsWPHBIDMnDlTKUtKShJ/f3+pXr26Uvbw4UMJDw83WFevXr0EgERERChlt2/fFltbW71tSqvVSu3ataVw4cKSmppqkjiJXoWH/4heIk+ePHBwcFBmeEQEvr6+aNWqlUHd5ORkuLq6om/fvi9tMyUlBcOGDUP+/Pnh7OyMli1b4vbt20br3rlzB71790aBAgVgZ2eHwMBALFu2zOi6J0+ejBIlSsDe3h6enp744IMP8O+//yp1MnrO1o4dO1C7dm04OjrC2dkZzZs3x/nz51/5PuD57EL79u3h5uaG3Llzo1q1ati2bZuyXHeYVUQwb9485VDry6xduxYVK1aEs7MzXFxcUKZMGXz77bfK8sePH2PkyJEoU6YMnJyc4OLigqZNm+Ls2bMZ6vOlS5fQrl07uLm5wd7eHpUqVcKvv/76yvfpztH6+uuv8c0338DHxwcODg6oW7cuwsPD9er27NkTTk5O+Pfff9GsWTM4Ozuja9euyrIXz6nSarX49ttvUaZMGdjb2yN//vxo0qQJTpw4oVdv1apVqFixIhwcHODm5oZOnTrh1q1br+y7sXOqfH190aJFCxw8eBBVqlSBvb09ihYtih9++OGV7fn5+cHHx0evTKVSoXXr1khJScG1a9eU8g0bNsDa2hp9+vRRyuzt7fHRRx/hyJEjSv/d3d0RGBhosK42bdoAAC5evKiU/fLLL1Cr1RgwYIDe+vv374/bt2/jyJEjr4yByBSYVBGlERMTg+joaDx8+BDnz59H//79ER8fj27dugF4/kXdrVs37NixA48fP9Z779atWxEbG6vUTc/HH3+M2bNno3Hjxvjqq69ga2uL5s2bG9SLiopCtWrVsGfPHgwaNAjffvstihUrho8++kjvxG6NRoMWLVogNDQUFStWxKxZs/Dpp58iJibG4Mf9VX788Uc0b94cTk5OmD59OiZMmIALFy6gVq1arzypOSoqCjVq1FAO0UybNg3Jyclo2bIlNm/eDACoU6cOfvzxRwBAo0aN8OOPPyqvjdm9ezc6d+6MvHnzYvr06fjqq69Qr149HDp0SKlz7do1bNmyBS1atEBYWBhGjRqFc+fOoW7durh79+5L+3z+/HlUq1YNFy9exJgxYzBr1iw4OjqidevWSp9f5YcffsB3332HgQMHYuzYsQgPD0eDBg0QFRWlVy81NRXBwcHw8PDA119/jbZt26bb5kcffYShQ4fC29sb06dPx5gxY2Bvb4+jR48qdaZNm4YPP/wQxYsXR1hYGIYOHYo//vgDderUwdOnTzPU9xddvXoV7dq1Q6NGjTBr1izkzZsXPXv2zHBS/aL79+8DeJ4g6Zw+fRolSpSAi4uLXt0qVaoAAM6cOfNGbTo6OuK9994z2ubp06ffqP9Er83cU2VElkB3KOzF/+zs7GTFihV6dS9fviwAZMGCBXrlLVu2FF9fX9Fqtemu58yZMwJABgwYoFfepUsXg8N/H330kXh6ekp0dLRe3U6dOomrq6skJiaKiMiyZcsEgISFhRmsL21fXmz/xcN/cXFxkidPHvnkk0/02rh//764uroalL9o6NChAkD++usvpSwuLk78/PzE19dXNBqNXl8ycvjv008/FRcXl5cevklOTtZrW+T5oT47Ozv54osv9MrwwuG/999/X8qUKSPJyclKmVarlRo1akjx4sVf2jddew4ODnL79m2lXHd4a9iwYUpZjx49BICMGTPGoJ0ePXqIj4+P8nrv3r0CQIYMGWJQVzee169fF2tra5k2bZre8nPnzomNjY1B+YuMHfr18fERAPLnn38qZQ8ePBA7OzsZMWLES9sz5tGjR+Lh4SG1a9fWKw8MDJQGDRoY1D9//rwAkIULF6bbZkpKigQEBIifn5+o1WqlvHnz5lK0aFGD+gkJCel+7kSZgTNVRGnMmzcPu3fvxu7du7Fq1SrUr18fH3/8MTZt2qTUKVGiBKpWrYrVq1crZY8fP8aOHTvQtWvXlx7O2r59OwBgyJAheuVDhw7Vey0i2LhxI0JCQiAiiI6OVv4LDg5GTEwMTp06BQDYuHEj3N3dMXjwYIP1verQWlq7d+/G06dP0blzZ731WVtbo2rVqti3b99L3799+3ZUqVJFOdEYAJycnNCnTx9cv34dFy5cyHBfdPLkyYOEhASDK8PSsrOzg5XV868yjUaDR48ewcnJCSVLllQ+I2MeP36MvXv3okOHDoiLi1PiffToEYKDgxEREYE7d+68so+tW7dGoUKFlNdVqlRB1apVlbFOq3///q9sb+PGjVCpVJg0aZLBMt14btq0CVqtFh06dNAbq4IFC6J48eKvHKv0BAQEoHbt2srr/Pnzo2TJknqH7zJCq9Wia9euePr0KebMmaO3LCkpyegJ8vb29sry9AwaNAgXLlzA3Llz9S66eJs2iUzJMi4FIrIQVapUQaVKlZTXnTt3Rvny5TFo0CC0aNECuXLlAgB8+OGHGDRoEG7cuAEfHx/8/PPPUKvV6N69+0vbv3HjBqysrODv769XXrJkSb3XDx8+xNOnT7F48WIsXrzYaFsPHjwAAPz7778oWbLkW1/ZFxERAQBo0KCB0eUvHq550Y0bN1C1alWDct0hmRs3bqB06dKv1acBAwZg/fr1aNq0KQoVKoTGjRujQ4cOerdg0J1/NH/+fERGRupdbp8vX75027569SpEBBMmTMCECROM1nnw4IFewmRM8eLFDcpKlCiB9evX65XZ2NigcOHCL20LeD6eXl5ecHNzS7dOREQERMTougHA1tb2lesxpkiRIgZlefPmxZMnT16rncGDB2Pnzp344YcfUK5cOb1lDg4OSElJMXhPcnKystyYmTNnYsmSJZgyZQqaNWtmkjaJTI1JFdFLWFlZoX79+vj2228RERGhnDjbqVMnDBs2DKtXr8a4ceOwatUqVKpUySA5elO6S+y7deuGHj16GK1TtmxZk6zrxXX++OOPKFiwoMFyc9yOwcPDA2fOnMGuXbuwY8cO7NixA8uXL8eHH36oXFL/v//9DxMmTEDv3r0xZcoUuLm5wcrKCkOHDn3prQp0y0aOHIng4GCjdYoVK2ayWNLOqL0trVYLlUqFHTt2wNra2mC5k5PTG7VrrC3g+cxpRoWGhmL+/Pn46quvjP6R4enpaXQG8N69ewAALy8vg2UrVqzA6NGj0a9fP3z++edG29y3bx9ERG929mVtEmUGJlVEr5CamgoAiI+PV8rc3NzQvHlzrF69Gl27dsWhQ4cydFdwHx8faLVaZXZJ5/Lly3r1dFcGajQaNGzY8KVt+vv749ixY1Cr1W88Q6FrB3ieyLxqncb4+PgYxAE8v7pOt/xN5MqVCyEhIQgJCYFWq8WAAQOwaNEiTJgwAcWKFcOGDRtQv359fP/993rve/r0qd7JzC8qWrQogOezOm8Sr45uhi+tK1euvPFd0v39/bFr1y48fvw43dkqf39/iAj8/PxQokSJN1pPZpg3bx4mT56MoUOHYvTo0UbrBAUFYd++fYiNjdWb/Tx27JiyPK1ffvkFH3/8MT744APMmzcv3TaXLl2KixcvIiAg4JVtEmUWnlNF9BJqtRq///47cuXKZXBlUffu3XHhwgWMGjUK1tbWGbrBYNOmTQEA3333nV75iwmZtbU12rZti40bNxq9gu/hw4fKv9u2bYvo6GjMnTvXoN7rzDAEBwfDxcUF//vf/6BWq1+6TmOaNWuG48eP612+npCQgMWLF8PX11fvxy6jHj16pPfayspKmaHTHe6xtrY2iPPnn39+5flQHh4eqFevHhYtWqTMaKT1qnh1tmzZoreu48eP49ixY8pYv662bdtCRAxumAn8N54ffPABrK2tERoaahC7iBh8bllh3bp1GDJkCLp27YqwsLB067Vr1w4ajUbvsHZKSgqWL1+OqlWrwtvbWyn/888/0alTJ9SpUwerV69Od6avVatWsLW1xfz585UyEcHChQtRqFAh1KhRwwQREr0aZ6qI0tixY4cys/LgwQOsWbMGERERGDNmjME5Rc2bN0e+fPnw888/o2nTpvDw8Hhl+0FBQejcuTPmz5+PmJgY1KhRA3/88QeuXr1qUPerr77Cvn37ULVqVXzyyScICAjA48ePcerUKezZs0e5pcOHH36IH374AcOHD8fx48dRu3ZtJCQkYM+ePRgwYIDRe2oZ4+LiggULFqB79+6oUKECOnXqhPz58+PmzZvYtm0batasaTRx0xkzZgx++uknNG3aFEOGDIGbmxtWrlyJyMhIbNy48Y0OfX388cd4/PgxGjRogMKFC+PGjRuYM2cOgoKClCS3RYsW+OKLL9CrVy/UqFED586dw+rVq5WZqJeZN28eatWqhTJlyuCTTz5B0aJFERUVhSNHjuD27dsZutdVsWLFUKtWLfTv3x8pKSmYPXs28uXLh88+++y14wWA+vXro3v37vjuu+8QERGBJk2aQKvV4q+//kL9+vUxaNAg+Pv7Y+rUqRg7diyuX7+O1q1bw9nZGZGRkdi8eTP69OmTpXerP378OD788EPky5cP77//vt5FHABQo0YNZTyqVq2K9u3bY+zYsXjw4AGKFSuGlStX4vr163qzjTdu3EDLli2hUqnQrl07/Pzzz3ptli1bVkmwCxcujKFDh2LmzJlQq9WoXLkytmzZgr/++gurV69O97AmkcmZ45JDIktj7JYK9vb2EhQUJAsWLEj3NgkDBgwQALJmzZoMryspKUmGDBki+fLlE0dHRwkJCZFbt24ZvaN6VFSUDBw4ULy9vcXW1lYKFiwo77//vixevFivXmJioowfP178/PyUeu3atZN///1XqfNi++ndUX3fvn0SHBwsrq6uYm9vL/7+/tKzZ085ceLEK2P7999/pV27dpInTx6xt7eXKlWqyG+//WZQDxm8pcKGDRukcePG4uHhIbly5ZIiRYpI37595d69e0qd5ORkGTFihHh6eoqDg4PUrFlTjhw5InXr1pW6desq9dK7o/q///4rH374oRQsWFBsbW2lUKFC0qJFC9mwYcNL+6Zrb+bMmTJr1izx9vYWOzs7qV27tpw9e1avbo8ePcTR0dFoOy/eUkFEJDU1VWbOnCmlSpWSXLlySf78+aVp06Zy8uRJvXobN26UWrVqiaOjozg6OkqpUqVk4MCBcvny5Zf2Pb1bKjRv3tyg7ouf48vaS++/Fz/zpKQkGTlypBQsWFDs7OykcuXKsnPnTr06+/bte2mbL+4rGo1G/ve//4mPj4/kypVLAgMDZdWqVS/tN5GpqURe4/gAEekZNmwYvv/+e9y/fx+5c+c2d3coC12/fh1+fn6YOXMmn2FIRAB4ThXRG0tOTsaqVavQtm1bJlRERMRzqohe14MHD7Bnzx5s2LABjx49wqeffmruLhERkQVgUkX0mi5cuICuXbvCw8MD3333HS/XJiIiAADPqSIiIiIyAZ5TRURERGQCTKqIiIiITIDnVBmh1Wpx9+5dODs76z1HioiIiCyXiCAuLg5eXl4me9bm62BSZcTdu3f1HpVARERE2cetW7dQuHDhLF8vkyojnJ2dATwflBcfTfK2dM+Sa9y48Vs9/NbcGIdlYRyWhXFYlpwSB5BzYsmsOGJjY+Ht7a38jmc1JlVG6A75ubi4ZEpSlTt3bri4uGT7HYJxWA7GYVkYh2XJKXEAOSeWzI7DXKfu8ER1IiIiIhNgUkVERERkAkyqiIiIiEyASRURERGRCTCpIiIiIjIBsydVcXFxGDp0KHx8fODg4IAaNWrg77//BvD86oDRo0ejTJkycHR0hJeXFz788EPcvXtXeX9KSgq6d+8OFxcXlChRAnv27NFrf+bMmRg8eHCWxkRERETvHrPfUuHjjz9GeHg4fvzxR3h5eWHVqlVo2LAhLly4ACcnJ5w6dQoTJkxAuXLl8OTJE3z66ado2bIlTpw4AQBYvHgxTp48iSNHjmDHjh3o0qULoqKioFKpEBkZiSVLlih1iYiIiDKLWZOqpKQkbNy4Eb/88gvq1KkDAJg8eTK2bt2KBQsWYOrUqdi9e7fee+bOnYsqVarg5s2bKFKkCC5evIiWLVsiMDAQRYsWxahRoxAdHY38+fOjf//+mD59usnvNUVERET0IrMe/ktNTYVGo4G9vb1euYODAw4ePGj0PTExMVCpVMiTJw8AoFy5cjh48CCSkpKwa9cueHp6wt3dHatXr4a9vT3atGmT2WEQERERmXemytnZGdWrV8eUKVPw3nvvoUCBAvjpp59w5MgRFCtWzKB+cnIyRo8ejc6dOyuzT71798Y///yDgIAAuLu7Y/369Xjy5AkmTpyI/fv34/PPP8fatWvh7++PZcuWoVChQgbtpqSkICUlRXkdGxsL4Pk5XWq12qQx69ozdbtZLSfEodEK/r72EABw9OoDVC6aH9ZW2fMB2jlhPADGYWkYh+XJKbFkVhzm/lxUIiLm7MC///6L3r17488//4S1tTUqVKiAEiVK4OTJk7h48aJST61Wo23btrh9+zb279//0kN6vXr1QlBQEPz8/DBu3DgcO3YMM2bMQHh4ODZu3GhQf/LkyQgNDTUoX7NmDXLnzm2aQImIiChTJSYmokuXLoiJiTHLqT9mT6p0EhISEBsbC09PT3Ts2BHx8fHYtm0bgOcJVYcOHXDt2jXs3bsX+fLlS7edffv2YfTo0Thy5AhGjRoFGxsbzJgxA+fPn0edOnXw6NEjg/cYm6ny9vZGdHR0pjz7b/fu3WjUqFG2f25Tdo1jz8UoDFt3BgLAzkowpZIWE05Y4Zn2+SzVNx2D0PC9Aubt5GvKzuORFuOwLIzD8uSUWDIrjtjYWLi7u5stqTL71X86jo6OcHR0xJMnT7Br1y7MmDEDwH8JVUREBPbt2/fShCo5ORkDBw7E6tWrYW1tDY1GA13OqFarodFojL7Pzs4OdnZ2BuW2traZttFmZttZKbvFodEKvth2Gcka/cN8KVoVUjQqqAB8se0yGpculG0OBWq0gtORjwEAp2/HoVoxj2zT9/Rkt+0qPYzDsuSUOICcE4up4zD3Z2L2+1Tt2rULO3fuRGRkJHbv3o369eujVKlS6NWrF9RqNdq1a4cTJ05g9erV0Gg0uH//Pu7fv49nz54ZtDVlyhQ0a9YM5cuXBwDUrFkTmzZtwj///IO5c+eiZs2aWR0eWZjjkY9xLyY53eUC4F5MMo7/f5Ji6XaG30Ot6XvRe+Xze7v1Xvk3ak3fi53h98zcMyKid4/ZZ6piYmIwduxY3L59G25ubmjbti2mTZsGW1tbXL9+Hb/++isAICgoSO99+/btQ7169ZTX4eHhWL9+Pc6cOaOUtWvXDvv370ft2rVRsmRJrFmzJgsiIkv2IC79hOpN6pnTzvB76L/q1PPDmNb/ld+PSUb/VaewoFsFNCntabb+ERG9a8yeVHXo0AEdOnQwuszX1xcZPeWrdOnSiIiI0CuzsrLC/PnzMX/+/LfuJ+UMHs72r670GvXMRaMVhG69AGN7hwBQAQjdegGNAgpm+0OBRETZhdkP/xFlpSp+bvB0tUd6aYYKgKerPar4uWVlt15bTjuMSUSUEzCponeKtZUKk0ICAMAgsdK9nhQSYPGzOznpMCYRUU7BpIreOU1Ke2JBtwoo6Kp/iK+gq322OQ8ppxzGJCLKScx+ThWROTQp7YlGAQVx9OoDRF88imU9KmerWxHoDmPej0k2el6VCs+TREs/jElElJNwporeWdZWKiXpqOLnlm0SKiDnHMYkIspJmFQRZVM54TAmEVFOwsN/RNlYdj+MSUSUk3Cmiiiby86HMYmIchImVUREREQmwKSKiIiIyASYVBERERGZAJMqIiIiIhNgUkVERERkAkyqiIiIiEyASRURWQSNVnA88jEA4HjkY2i0xh7AQ0RkuZhUEZHZ7Qy/h1rT96L3yr8BAL1X/o1a0/diZ/g9M/eMiCjjmFQRkVntDL+H/qtO4V5Msl75/Zhk9F91iokVEWUbTKqIyGw0WkHo1gswdqBPVxa69QIPBRJRtsCkiojM5njkY4MZqrQEwL2YZOVcKyIiS8akiojM5kFc+gnVm9QjIjInJlVEZDYezvYmrUdEZE5MqojIbKr4ucHT1R6qdJarAHi62qOKn1tWdouI6I0wqSIis7G2UmFSSAAAGCRWuteTQgJgbZVe2kVEZDmYVBGRWTUp7YkF3SqgoKv+Ib6CrvZY0K0CmpT2NFPPiIhej425O0BE1KS0JxoFFMTRqw8QffEolvWojGrFPDhDRUTZCmeqiMgiWFuplHOnqvi5MaEiomyHSRURERGRCTCpIiIiIjIBJlVEREREJsCkioiIiMgEmFQRERERmQCTKiIiIiITYFJFREREZAJMqoiIiIhMgEkVERERkQkwqSIiMiGNVnA88jEA4HjkY2i0YuYeEVFWYVJFRGQiO8Pvodb0vei98m8AQO+Vf6PW9L3YGX7PzD0joqzApIqIyAR2ht9D/1WncC8mWa/8fkwy+q86xcSK6B3ApIqI6C1ptILQrRdg7ECfrix06wUeCiTK4ZhUERG9peORjw1mqNISAPdikpVzrYgoZ2JSRUT0lh7EpZ9QvUk9IsqemFQREb0lD2d7k9YjouyJSRUR0Vuq4ucGT1d7qNJZrgLg6WqPKn5uWdktIspiTKqIiN6StZUKk0ICAMAgsdK9nhQSAGur9NIuIsoJzJ5UxcXFYejQofDx8YGDgwNq1KiBv//+W1kuIpg4cSI8PT3h4OCAhg0bIiIiQlmekpKC7t27w8XFBSVKlMCePXv02p85cyYGDx6cZfEQ0bupSWlPLOhWAQVd9Q/xFXS1x4JuFdCktKeZekZEWcXG3B34+OOPER4ejh9//BFeXl5YtWoVGjZsiAsXLqBQoUKYMWMGvvvuO6xcuRJ+fn6YMGECgoODceHCBdjb22Px4sU4efIkjhw5gh07dqBLly6IioqCSqVCZGQklixZghMnTpg7TCJ6BzQp7YlGAQVx9OoDRF88imU9KqNaMQ/OUBG9I8w6U5WUlISNGzdixowZqFOnDooVK4bJkyejWLFiWLBgAUQEs2fPxueff45WrVqhbNmy+OGHH3D37l1s2bIFAHDx4kW0bNkSgYGBGDhwIB4+fIjo6GgAQP/+/TF9+nS4uLiYMUoiepdYW6mUc6eq+LkxoSJ6h5g1qUpNTYVGo4G9vf50uYODAw4ePIjIyEjcv38fDRs2VJa5urqiatWqOHLkCACgXLlyOHjwIJKSkrBr1y54enrC3d0dq1evhr29Pdq0aZOlMREREdG7yayH/5ydnVG9enVMmTIF7733HgoUKICffvoJR44cQbFixXD//n0AQIECBfTeV6BAAWVZ79698c8//yAgIADu7u5Yv349njx5gokTJ2L//v34/PPPsXbtWvj7+2PZsmUoVKiQQT9SUlKQkpKivI6NjQUAqNVqqNVqk8asa8/U7WY1xmFZGIdlYRyWJafEAeScWDIrDnN/LioRMetzE/7991/07t0bf/75J6ytrVGhQgWUKFECJ0+exPfff4+aNWvi7t278PT87yTPDh06QKVSYd26dUbb7NWrF4KCguDn54dx48bh2LFjmDFjBsLDw7Fx40aD+pMnT0ZoaKhB+Zo1a5A7d27TBUtERESZJjExEV26dEFMTIxZTv0xe1Klk5CQgNjYWHh6eqJjx46Ij4/HnDlz4O/vj9OnTyMoKEipW7duXQQFBeHbb781aGffvn0YPXo0jhw5glGjRsHGxgYzZszA+fPnUadOHTx69MjgPcZmqry9vREdHW3yQVGr1di9ezcaNWoEW1tbk7adlRiHZWEcloVxWJacEgeQc2LJrDhiY2Ph7u5utqTK7Ff/6Tg6OsLR0RFPnjzBrl27MGPGDPj5+aFgwYL4448/lKQqNjYWx44dQ//+/Q3aSE5OxsCBA7F69WpYW1tDo9FAlzOq1WpoNBqj67azs4OdnZ1Bua2tbaZttJnZdlZiHJaFcVgWxmFZckocQM6JxdRxmPszMft9qnbt2oWdO3ciMjISu3fvRv369VGqVCn06tULKpUKQ4cOxdSpU/Hrr7/i3Llz+PDDD+Hl5YXWrVsbtDVlyhQ0a9YM5cuXBwDUrFkTmzZtwj///IO5c+eiZs2aWRwdERERvSvMPlMVExODsWPH4vbt23Bzc0Pbtm0xbdo0Jdv87LPPkJCQgD59+uDp06eoVasWdu7caXDFYHh4ONavX48zZ84oZe3atcP+/ftRu3ZtlCxZEmvWrMnK0IiIiOgdYvakqkOHDujQoUO6y1UqFb744gt88cUXL22ndOnSendaBwArKyvMnz8f8+fPN0lfiYiIiNJj9sN/RERERDkBkyoiIiIiE2BSRURERGQCTKqIiIiITIBJFREREZEJMKkiIiIiMgEmVUREREQmwKSKiIiIyASYVBERERGZAJMqIiIiIhNgUkVERERkAkyqiIiIiEyASRURERGRCTCpIiIiIjIBJlVEREREJsCkioiIiMgEmFQRERERmQCTKiIiIiITYFJFREREZAJMqoiIiIhMgEkVERERkQkwqSIiIiIyASZVRERERCbApIqIiIjIBJhUEREREZkAkyoiIiIiE2BSRURERGQCTKqIiIiITIBJFREREZEJMKkiIiIiMgEmVUREREQmwKSKiIiIyASYVBERERGZAJMqIiIiIhNgUkVERERkAkyqiIiIiEyASRURERGRCTCpIiIiIjIBJlVEREREJsCkioiIiMgEmFQRERERmQCTKiIiIiITYFJFREREZAJMqoiIiIhMwKxJlUajwYQJE+Dn5wcHBwf4+/tjypQpEBGlTnx8PAYNGoTChQvDwcEBAQEBWLhwoV47w4cPh5ubG7y9vbF69Wq9ZT///DNCQkKyJB4iIiJ6d9mYc+XTp0/HggULsHLlSgQGBuLEiRPo1asXXF1dMWTIEADPE6a9e/di1apV8PX1xe+//44BAwbAy8sLLVu2xNatW7FmzRr8/vvviIiIQO/evREcHAx3d3fExMRg/Pjx2LNnjznDJCIioneAWWeqDh8+jFatWqF58+bw9fVFu3bt0LhxYxw/flyvTo8ePVCvXj34+vqiT58+KFeunFLn4sWLqFevHipVqoTOnTvDxcUFkZGRAIDPPvsM/fv3R5EiRcwSHxEREb07zJpU1ahRA3/88QeuXLkCADh79iwOHjyIpk2b6tX59ddfcefOHYgI9u3bhytXrqBx48YAgHLlyuHEiRN48uQJTp48iaSkJBQrVgwHDx7EqVOnlBkvIiIiosxk1sN/Y8aMQWxsLEqVKgVra2toNBpMmzYNXbt2VerMmTMHffr0QeHChWFjYwMrKyssWbIEderUAQAEBwejW7duqFy5MhwcHLBy5Uo4Ojqif//+WLFiBRYsWIA5c+bA3d0dixcvRmBgoEE/UlJSkJKSoryOjY0FAKjVaqjVapPGrGvP1O1mNcZhWRiHZWEcliWnxAHknFgyKw5zfy4qSXtWeBZbu3YtRo0ahZkzZyIwMBBnzpzB0KFDERYWhh49egAAvv76ayxZsgRff/01fHx88Oeff2Ls2LHYvHkzGjZsaLTd0NBQPH36FL169ULjxo1x7tw5/Pbbb5g7dy5OnjxpUH/y5MkIDQ01KF+zZg1y585t2qCJiIgoUyQmJqJLly6IiYmBi4tLlq/frEmVt7c3xowZg4EDByplU6dOxapVq3Dp0iUkJSXB1dUVmzdvRvPmzZU6H3/8MW7fvo2dO3catHnp0iWEhITg9OnTWLZsGQ4ePIj169cjISEBTk5OiI2NhbOzs957jM1UeXt7Izo62uSDolarsXv3bjRq1Ai2trYmbTsrMQ7LwjgsC+OwLDklDiDnxJJZccTGxioXqpkjqTLr4b/ExERYWemf1mVtbQ2tVgvgv8NvL6uTloigb9++CAsLg5OTEzQajcEUo0ajMXifnZ0d7OzsDMptbW0zbaPNzLazEuOwLIzDsjAOy5JT4gByTiymjsPcn4lZk6qQkBBMmzYNRYoUQWBgIE6fPo2wsDD07t0bAODi4oK6deti1KhRcHBwgI+PDw4cOIAffvgBYWFhBu0tXboU+fPnV+5LVbNmTUyePBlHjx7Fjh07EBAQgDx58mRliERERPSOMGtSNWfOHEyYMAEDBgzAgwcP4OXlhb59+2LixIlKnbVr12Ls2LHo2rUrHj9+DB8fH0ybNg39+vXTaysqKgrTpk3D4cOHlbIqVapgxIgRaN68OTw8PLBy5cosi42IiIjeLWZNqpydnTF79mzMnj073ToFCxbE8uXLX9lWgQIFcP36dYPyiRMn6iVpRERERJmBz/4jIiIiMgEmVUREREQmwKSKiIiIyASYVBERERGZAJMqIiIiIhNgUkVERERkAkyqiIiIiEyASRURERGRCTCpIiIiIjIBJlVEREREJsCkioiIiMgEmFQRERERmQCTKiIiIiITYFJFREREZAJMqoiIiIhMgEkVERERkQkwqSIiIiIyASZVRERERCbApIqIiIjIBJhUEREREZkAkyoiIsqxNFrB8cjHAIDjkY+h0YqZe0Q5GZMqIiIykBOSkZ3h91Br+l70Xvk3AKD3yr9Ra/pe7Ay/Z+aeUU7FpIqIiPTkhGRkZ/g99F91CvdikvXK78cko/+qU9kqFso+mFQREZEiJyQjGq0gdOsFGJtb05WFbr2QLWffyLIxqSIiIgA5Jxk5HvnYIClMSwDci0lWDm8SmQqTKiIiApBzkpEHcenH8Cb1iDKKSRUREQHIOcmIh7O9SesRZRSTKiIiApBzkpEqfm7wdLWHKp3lKgCervao4ueWld2idwCTKiIiApBzkhFrKxUmhQQAgEEsuteTQgJgbZVepERvhkkVEREByFnJSJPSnljQrQIKuurPqhV0tceCbhXQpLSnmXpGOZmNuTtARESWQ5eMhG69gMfxSUp5QVd7TAoJyFbJSJPSnmgUUBBHrz5A9MWjWNajMqoV88gWSSFlT0yqiIhIT05KRqytVKji54btF58f3syOMVD2wcN/RERkQJeMAExGiDKKSRURERGRCTCpIiIiIjIBJlVEREREJsCkioiIiMgEmFQRERERmQCTKiIiIiITYFJFREREZAJMqoiIiIhM4K2TKhGBiJiiL0RERETZ1hsnVT/88APKlCkDBwcHODg4oGzZsvjxxx9N2TciIiKibOONnv0XFhaGCRMmYNCgQahZsyYA4ODBg+jXrx+io6MxbNgwk3aSiIiIyNK90UzVnDlzsGDBAkyfPh0tW7ZEy5YtMWPGDMyfPx/fffddhtvRaDSYMGEC/Pz84ODgAH9/f0yZMsXgcOLFixfRsmVLuLq6wtHREZUrV8bNmzeV5cOHD4ebmxu8vb2xevVqvff+/PPPCAkJeZMwiYiIiDLsjWaq7t27hxo1ahiU16hRA/fu3ctwO9OnT8eCBQuwcuVKBAYG4sSJE+jVqxdcXV0xZMgQAMC///6LWrVq4aOPPkJoaChcXFxw/vx52NvbAwC2bt2KNWvW4Pfff0dERAR69+6N4OBguLu7IyYmBuPHj8eePXveJEwiIiKiDHujpKpYsWJYv349xo0bp1e+bt06FC9ePMPtHD58GK1atULz5s0BAL6+vvjpp59w/Phxpc748ePRrFkzzJgxQynz9/dX/n3x4kXUq1cPlSpVQqVKlTB06FBERkbC3d0dn332Gfr3748iRYq8SZhEREREGfZGSVVoaCg6duyIP//8Uzmn6tChQ/jjjz+wfv36DLdTo0YNLF68GFeuXEGJEiVw9uxZHDx4EGFhYQAArVaLbdu24bPPPkNwcDBOnz4NPz8/jB07Fq1btwYAlCtXDosXL8aTJ09w7do1JCUloVixYjh48CBOnTqF+fPnv0mIRERERK/ljZKqtm3b4tixY/jmm2+wZcsWAMB7772H48ePo3z58hluZ8yYMYiNjUWpUqVgbW0NjUaDadOmoWvXrgCABw8eID4+Hl999RWmTp2K6dOnY+fOnfjggw+wb98+1K1bF8HBwejWrRsqV64MBwcHrFy5Eo6Ojujfvz9WrFiBBQsWYM6cOXB3d8fixYsRGBho0I+UlBSkpKQor2NjYwEAarUaarX6TT6idOnaM3W7WY1xWBbGYVkYh2XJKXEAOSeWzIrD3J+LSsx4k6m1a9di1KhRmDlzJgIDA3HmzBkMHToUYWFh6NGjB+7evYtChQqhc+fOWLNmjfK+li1bwtHRET/99JPRdkNDQ/H06VP06tULjRs3xrlz5/Dbb79h7ty5OHnypEH9yZMnIzQ01KB8zZo1yJ07t+kCJiIiokyTmJiILl26ICYmBi4uLlm+/gwnVbGxsUoHdTM56cloIN7e3hgzZgwGDhyolE2dOhWrVq3CpUuX8OzZMzg6OmLSpEn4/PPPlTqjR4/GwYMHcejQIYM2L126hJCQEJw+fRrLli3DwYMHsX79eiQkJMDJyQmxsbFwdnbWe4+xmSpvb29ER0ebfFDUajV2796NRo0awdbW1qRtZyXGYVkYh2VhHJYlJ8Sx52IUvtpxCU/ikzClkhYTTlghr5MDxjQthYbvFTB3915bZo1JbGyscqGaOZKqDB/+y5s3L+7duwcPDw/kyZMHKpXKoI6IQKVSQaPRZKjNxMREWFnp39XB2toaWq0WAJArVy5UrlwZly9f1qtz5coV+Pj4GF1/3759ERYWBicnJ2g0GoMpRmN9s7Ozg52dnUG5ra1tpu2Amdl2VmIcloVxWBbGYVmyaxw7w+9hwJqzEAB21s9/e1O0Ktx8koIBa85iQbcKaFLa07ydfEOmHhNzj2+Gk6q9e/fCzc0NALBv3z6TrDwkJATTpk1DkSJFEBgYiNOnTyMsLAy9e/dW6owaNQodO3ZEnTp1UL9+fezcuRNbt27F/v37DdpbunQp8ufPr9yXqmbNmpg8eTKOHj2KHTt2ICAgAHny5DFJ34mIiDKbRisI3XoBxg4pCQAVgNCtF9AooCCsrQwnOyhrZTipqlu3rvJvPz8/eHt7G8xWiQhu3bqV4ZXPmTMHEyZMwIABA/DgwQN4eXmhb9++mDhxolKnTZs2WLhwIb788ksMGTIEJUuWxMaNG1GrVi29tqKiojBt2jQcPnxYKatSpQpGjBiB5s2bw8PDAytXrsxw34iIiMzteORj3ItJTne5ALgXk4zjkY9R3T9f1nWMjHqjq//8/PyUQ4FpPX78GH5+fhk+/Ofs7IzZs2dj9uzZL63Xu3dvvdkrYwoUKIDr168blE+cOFEvSSMiIsouHsSln1C9ST3KXG/0mBrduVMvio+PV+50TkRERG/Hwzljv6kZrUeZ67VmqoYPHw4AUKlUmDBhgt7tBjQaDY4dO4agoCCTdpCIiOhdVcXPDZ6u9rgfk2z0vCoVgIKu9qji55bVXSMjXiupOn36NIDnM1Xnzp1Drly5lGW5cuVCuXLlMHLkSNP2kIiI6B1lbaXCpJAA9F91Ci8eH9K9nhQSwJPULcRrJVW6q/569eqFb7/91iz3gCAiInqXNCntiQXdKiB06wU8jk9Sygu62mNSSEC2vZ1CTvRGJ6ovX77c1P0gIiKidDQp7YlGAQVx9OoDRF88imU9KqNaMQ/OUFmYN0qqAODEiRNYv349bt68iWfPnukt27Rp01t3jIiIiP5jbaVCFT83bL/4/FwrJlSW542u/lu7di1q1KiBixcvYvPmzVCr1Th//jz27t0LV1dXU/eRiIiIyOK9UVL1v//9D9988w22bt2KXLly4dtvv8WlS5fQoUMHFClSxNR9JCIiIrJ4b5RU/fvvv2jevDmA51f9JSQkQKVSYdiwYVi8eLFJO0hERESUHbxRUpU3b17ExcUBAAoVKoTw8HAAwNOnT5GYmGi63hERERFlE290onqdOnWwe/dulClTBu3bt8enn36KvXv3Yvfu3WjQoIGp+0hERERk8d4oqZo7dy6Sk58/Z2j8+PGwtbXF4cOH0bZtW978k4iIiN5Jb3T4z83NDV5eXs8bsLLCmDFjsH79enh5eaF8+fIm7SARERFRdvBaSVVKSgrGjh2LSpUqoUaNGtiyZQuA5zcD9ff3x7fffothw4ZlRj/Jgmi0guORjwEAxyMfQ6M19kQqIiKid8trHf6bOHEiFi1ahIYNG+Lw4cNo3749evXqhaNHj2LWrFlo3749rK2tM6uvZAF2ht9THpUwowrQe+XfcHNy4KMSiIjonfdaSdXPP/+MH374AS1btkR4eDjKli2L1NRUnD17FioV7+ya0+0Mv4f+q05BANilyZ3vxySj/6pTWNCtAhMrIiJ6Z73W4b/bt2+jYsWKAIDSpUvDzs4Ow4YNY0L1DtBoBaFbL8DYgT5dWejWCzwUSERE76zXSqo0Gg1y5cqlvLaxsYGTk5PJO0WW53jkY9yLSU53uQC4F5OsnGtFRET0rnmtw38igp49e8LOzg4AkJycjH79+sHR0VGvHh+onPM8iEs/oXqTekRERDnNayVVPXr00HvdrVs3k3aGLJeHs71J6xEREeU0r5VULV++PLP6QRauip8bPF3tcT8m2eh5VSoABV3tUcXPLau7RkREZBHe6Oaf9O6xtlJhUkgAgOcJVFq615NCAmBtxYsWiIjo3cSkijKsSWlPLOhWAQVd9Q/xFXS15+0UiIjonfdGz/6jd1eT0p5oFFAQR68+QPTFo1jWozKqFfPgDBUREb3zOFNFr83aSqWcO1XFz40JFREREZhUEREREZkEkyoiIiIiE2BSRURERGQCTKqIiIiITIBJFREREZEJMKkiIiIiMgEmVUREREQmwKSKiIiIyASYVBERERGZAJMqIiIiIhNgUkVERERkAkyqiIiIKMtotILjkY8BAMcjH0OjFTP3yHSYVBEREVGW2Bl+D7Wm70XvlX8DAHqv/Bu1pu/FzvB7Zu6ZaTCpIiIioky3M/we+q86hXsxyXrl92OS0X/VqRyRWDGpIiIiokyl0QpCt16AsQN9urLQrRey/aFAJlVERESUqY5HPjaYoUpLANyLSVbOtcqumFQRERFRpnoQl35C9Sb1LBWTKiIiIspUHs72Jq1nqZhUERERUaaq4ucGT1d7qNJZrgLg6WqPKn5uWdktkzNrUqXRaDBhwgT4+fnBwcEB/v7+mDJlCkSMn6jWr18/qFQqzJ49WylLSUlB9+7d4eLighIlSmDPnj1675k5cyYGDx6cmWEQERHRS1hbqTApJAAADBIr3etJIQGwtkov7coebMy58unTp2PBggVYuXIlAgMDceLECfTq1Quurq4YMmSIXt3Nmzfj6NGj8PLy0itfvHgxTp48iSNHjmDHjh3o0qULoqKioFKpEBkZiSVLluDEiRNZGRYRERG9oElpTyzoVgGhWy/gcXySUl7Q1R6TQgLQpLSnGXtnGmZNqg4fPoxWrVqhefPmAABfX1/89NNPOH78uF69O3fuYPDgwdi1a5dSV+fixYto2bIlAgMDUbRoUYwaNQrR0dHInz8/+vfvj+nTp8PFxSXLYiIiIiLjmpT2RKOAgjh69QGiLx7Fsh6VUa2YR7afodIx6+G/GjVq4I8//sCVK1cAAGfPnsXBgwfRtGlTpY5Wq0X37t0xatQoBAYGGrRRrlw5HDx4EElJSdi1axc8PT3h7u6O1atXw97eHm3atMmyeIiIiOjlrK1UyrlTVfzcckxCBZh5pmrMmDGIjY1FqVKlYG1tDY1Gg2nTpqFr165KnenTp8PGxsbgcKBO79698c8//yAgIADu7u5Yv349njx5gokTJ2L//v34/PPPsXbtWvj7+2PZsmUoVKiQQRspKSlISUlRXsfGxgIA1Go11Gq1SWPWtWfqdrMa47AsjMOyMA7LklPiAHJOLJkVh7k/F5Wkd1Z4Fli7di1GjRqFmTNnIjAwEGfOnMHQoUMRFhaGHj164OTJk2jevDlOnTqlnEvl6+uLoUOHYujQoem226tXLwQFBcHPzw/jxo3DsWPHMGPGDISHh2Pjxo0G9SdPnozQ0FCD8jVr1iB37twmi5eIiIgyT2JiIrp06YKYmBiznPpj1qTK29sbY8aMwcCBA5WyqVOnYtWqVbh06RJmz56N4cOHw8rqv6OUGo0GVlZW8Pb2xvXr1w3a3LdvH0aPHo0jR45g1KhRsLGxwYwZM3D+/HnUqVMHjx49MniPsZkqb29vREdHm3xQ1Go1du/ejUaNGsHW1takbWclxmFZGIdlYRyWJafEAeScWDIrjtjYWLi7u5stqTLr4b/ExES9hAkArK2todVqAQDdu3dHw4YN9ZYHBweje/fu6NWrl0F7ycnJGDhwIFavXq0cTtTljGq1GhqNxmg/7OzsYGdnZ1Bua2ubaRttZradlRiHZWEcloVxWJacEgeQc2IxdRzm/kzMmlSFhIRg2rRpKFKkCAIDA3H69GmEhYWhd+/eAIB8+fIhX758eu+xtbVFwYIFUbJkSYP2pkyZgmbNmqF8+fIAgJo1a2LUqFHo1asX5s6di5o1a2Z+UERERPROMmtSNWfOHEyYMAEDBgzAgwcP4OXlhb59+2LixImv3VZ4eDjWr1+PM2fOKGXt2rXD/v37Ubt2bZQsWRJr1qwxYe+JiIiI/mPWpMrZ2RmzZ8/Wu0P6qxg7jwoASpcujYiICL0yKysrzJ8/H/Pnz3+LXhIRERG9Gp/9R0RERGQCTKqIiIiITIBJFREREZEJMKkiIiIiMgEmVUREREQmwKSKiIiIyASYVBERERGZAJMqIiIiIhNgUkVERERkAkyqiIiIiEyASRURERGRCTCpIiIiIjIBJlVEREREJsCkioiIiMgEmFQRERERmQCTKiIiIiITYFJFREREZAJMqoiIiIhMgEkVERERkQkwqSIiIiIyASZVRERERCbApIqIiIjIBJhUEREREZkAkyoiIiIiE2BSRURERGQCTKqIiIiITIBJFREREZEJMKkiIiIiMgEmVUREREQmwKSKiIiIyASYVBERERGZAJMqIiIiIhNgUkVERERkAkyqiIiIiEyASRURERGRCTCpIiIiIjIBJlVEREREJsCkioiIiMgEmFQRERERmQCTKiIiIiITYFJFREREZAJMqoiIiIhMgEkVERERkQkwqSIiIiIyAbMmVRqNBhMmTICfnx8cHBzg7++PKVOmQEQAAGq1GqNHj0aZMmXg6OgILy8vfPjhh7h7967SRkpKCrp37w4XFxeUKFECe/bs0VvHzJkzMXjw4CyNi4iIiN49NuZc+fTp07FgwQKsXLkSgYGBOHHiBHr16gVXV1cMGTIEiYmJOHXqFCZMmIBy5crhyZMn+PTTT9GyZUucOHECALB48WKcPHkSR44cwY4dO9ClSxdERUVBpVIhMjISS5YsUeoSERERZRazJlWHDx9Gq1at0Lx5cwCAr68vfvrpJxw/fhwA4Orqit27d+u9Z+7cuahSpQpu3ryJIkWK4OLFi2jZsiUCAwNRtGhRjBo1CtHR0cifPz/69++P6dOnw8XFJctjIyIioneLWZOqGjVqYPHixbhy5QpKlCiBs2fP4uDBgwgLC0v3PTExMVCpVMiTJw8AoFy5cvjxxx+RlJSEXbt2wdPTE+7u7li9ejXs7e3Rpk2bV/YjJSUFKSkpyuvY2FgAzw8/qtXqtwvyBbr2TN1uVmMcloVxWBbGYVlyShxAzokls+Iw9+eiEt0JTGag1Woxbtw4zJgxA9bW1tBoNJg2bRrGjh1rtH5ycjJq1qyJUqVKYfXq1QCef4BDhw7F9u3b4e7ujm+++QYBAQGoXLky9u/fj0WLFmHt2rXw9/fHsmXLUKhQIYN2J0+ejNDQUIPyNWvWIHfu3KYNmoiIiDJFYmIiunTpgpiYGLMcpTJrUrV27VqMGjUKM2fORGBgIM6cOYOhQ4ciLCwMPXr00KurVqvRtm1b3L59G/v373/ph9WrVy8EBQXBz88P48aNw7FjxzBjxgyEh4dj48aNBvWNzVR5e3sjOjra5IOiVquxe/duNGrUCLa2tiZtOysxDsvCOCwL47AsOSUOIOfEkllxxMbGwt3d3WxJlVkP/40aNQpjxoxBp06dAABlypTBjRs38OWXX+olVWq1Gh06dMCNGzewd+/el35Q+/btw/nz57F06VKMGjUKzZo1g6OjIzp06IC5c+cafY+dnR3s7OwMym1tbTNto83MtrMS47AsjMOyMA7LklPiAHJOLKaOw9yfiVmTqsTERFhZ6d/VwdraGlqtVnmtS6giIiKwb98+5MuXL932kpOTMXDgQKxevVo5nJj29gwajSZzAiEiIqJ3nlnvUxUSEoJp06Zh27ZtuH79OjZv3oywsDDl5HK1Wo127drhxIkTWL16NTQaDe7fv4/79+/j2bNnBu1NmTIFzZo1Q/ny5QEANWvWxKZNm/DPP/9g7ty5qFmzZpbGR0RERO8Os85UzZkzBxMmTMCAAQPw4MEDeHl5oW/fvpg4cSIA4M6dO/j1118BAEFBQXrv3bdvH+rVq6e8Dg8Px/r163HmzBmlrF27dti/fz9q166NkiVLYs2aNZkdEhEREb2jzJpUOTs7Y/bs2Zg9e7bR5b6+vsjoefSlS5dGRESEXpmVlRXmz5+P+fPnv21XiYiIiF6Kz/4jIiIiMgEmVUREREQmwKSKiIiIyASYVBERERGZAJMqIiIiIhNgUkVERERkAkyqiIiIiEyASRURERGRCTCpIiIiIjIBJlVEREREJsCkioiIiMgEmFQRERERmQCTKiIiIiITYFJFREREZAJMqoiIiIhMgEkVERERkQkwqSIiIiIyASZVRERERCbApIqIiIjIBJhUEREREZkAkyoiIiIiE2BSRURERGQCTKqIiIiITIBJFREREZEJMKkiIiIiMgEmVUREREQmwKSKiIiIyASYVBERERGZAJMqIiIiIhNgUkVERERkAkyqiIiIiEyASRURERGRCTCpIiIiIjIBJlVEREREJsCkioiIiMgEmFQRERERmQCTKiIiIiITYFJFREREZAJMqoiIiIhMgEkVERERkQkwqSIiIiIyASZVRERERCbApIqIiIjIBJhUEREREZmAWZMqjUaDCRMmwM/PDw4ODvD398eUKVMgIkodEcHEiRPh6ekJBwcHNGzYEBEREcrylJQUdO/eHS4uLihRogT27Nmjt46ZM2di8ODBWRYTERERvZtszLny6dOnY8GCBVi5ciUCAwNx4sQJ9OrVC66urhgyZAgAYMaMGfjuu++wcuVK+Pn5YcKECQgODsaFCxdgb2+PxYsX4+TJkzhy5Ah27NiBLl26ICoqCiqVCpGRkViyZAlOnDhhzjCJiIjoHWDWmarDhw+jVatWaN68OXx9fdGuXTs0btwYx48fB/B8lmr27Nn4/PPP0apVK5QtWxY//PAD7t69iy1btgAALl68iJYtWyIwMBADBw7Ew4cPER0dDQDo378/pk+fDhcXF3OFSERERO8Is85U1ahRA4sXL8aVK1dQokQJnD17FgcPHkRYWBgAIDIyEvfv30fDhg2V97i6uqJq1ao4cuQIOnXqhHLlyuHHH39EUlISdu3aBU9PT7i7u2P16tWwt7dHmzZtXtmPlJQUpKSkKK9jY2MBAGq1Gmq12qQx69ozdbtZjXFYFsZhWRiHZckpcQA5J5bMisPcn4tK0p7AlMW0Wi3GjRuHGTNmwNraGhqNBtOmTcPYsWMBPJ/JqlmzJu7evQtPT0/lfR06dIBKpcK6deugVqsxdOhQbN++He7u7vjmm28QEBCAypUrY//+/Vi0aBHWrl0Lf39/LFu2DIUKFTLox+TJkxEaGmpQvmbNGuTOnTvzPgAiIiIymcTERHTp0gUxMTFmOUpl1pmq9evXY/Xq1VizZg0CAwNx5swZDB06FF5eXujRo0eG2rC1tcW8efP0ynr16oUhQ4bg9OnT2LJlC86ePYsZM2ZgyJAh2Lhxo0EbY8eOxfDhw5XXsbGx8Pb2RuPGjU0+KGq1Grt370ajRo1ga2tr0razEuOwLIzDsjAOy5JT4gByTiyZFYfuSJO5mDWpGjVqFMaMGYNOnToBAMqUKYMbN27gyy+/RI8ePVCwYEEAQFRUlN5MVVRUFIKCgoy2uW/fPpw/fx5Lly7FqFGj0KxZMzg6OqJDhw6YO3eu0ffY2dnBzs7OoNzW1jbTNtrMbDsrMQ7LwjgsC+OwLDklDiDnxGLqOMz9mZj1RPXExERYWel3wdraGlqtFgDg5+eHggUL4o8//lCWx8bG4tixY6hevbpBe8nJyRg4cCAWLVqkHE5Me9xWo9FkYjRERET0LjNrUhUSEoJp06Zh27ZtuH79OjZv3oywsDDl5HKVSoWhQ4di6tSp+PXXX3Hu3Dl8+OGH8PLyQuvWrQ3amzJlCpo1a4by5csDAGrWrIlNmzbhn3/+wdy5c1GzZs2sDI+IiIjeIWY9/DdnzhxMmDABAwYMwIMHD+Dl5YW+ffti4sSJSp3PPvsMCQkJ6NOnD54+fYpatWph586dsLe312srPDwc69evx5kzZ5Sydu3aYf/+/ahduzZKliyJNWvWZFVoRERE9I4xa1Ll7OyM2bNnY/bs2enWUalU+OKLL/DFF1+8tK3SpUvr3WkdAKysrDB//nzMnz/fFN19axqt4HjkYwDA8cjHqFbMA9ZWKjP3ioiIiEyBz/7LIjvD76HW9L3ovfJvAEDvlX+j1vS92Bl+z8w9IyIiIlNgUpUFdobfQ/9Vp3AvJlmv/H5MMvqvOsXEioiIKAdgUpXJNFpB6NYLMHaHVV1Z6NYL0GjNdg9WIiIiMgEmVZnseORjgxmqtATAvZhk5VwrIiIiyp6YVGWyB3HpJ1RvUo+IiIgsE5OqTObhbP/qSq9Rj4iIiCwTk6pMVsXPDZ6u9kjvxgkqAJ6u9qji55aV3SIiIiITY1KVyaytVJgUEgAABomV7vWkkADer4qIiCibY1KVBZqU9sSCbhVQ0FX/EF9BV3ss6FYBTUp7pvNOIiIiyi7Mekf1d0mT0p5oFFAQR68+QPTFo1jWozLvqE5ERJSDcKYqC1lbqZRzp6r4uTGhIiIiykGYVBERERGZAJMqIiIiIhNgUkVERERkAkyqiIiIiEyASRURERGRCTCpIiIiIjIBJlVEREREJsCkioiIiMgEmFQRERERmQAfU2OEiAAAYmNjTd62Wq1GYmIiYmNjYWtra/L2swrjsCyMw7IwDsuSU+IAck4smRWH7ndb9zue1ZhUGREXFwcA8Pb2NnNPiIiI6HXFxcXB1dU1y9erEnOlcxZMq9Xi7t27cHZ2hkpl2ufzxcbGwtvbG7du3YKLi4tJ285KjMOyMA7LwjgsS06JA8g5sWRWHCKCuLg4eHl5wcoq689w4kyVEVZWVihcuHCmrsPFxSVb7xA6jMOyMA7LwjgsS06JA8g5sWRGHOaYodLhiepEREREJsCkioiIiMgEmFRlMTs7O0yaNAl2dnbm7spbYRyWhXFYFsZhWXJKHEDOiSWnxPEinqhOREREZAKcqSIiIiIyASZVRERERCbApIqyrex85Dpt37NzHGkxDsvCOCxPTomFcaSPSVUOpdFoAOScjR94flNWAEhNTQUAk9+YNatotVqoVCo8e/YMIpKt4wCy/3jo9hXd/xmHeeWUOICcE0tOiSMt3fewWq1GUlKSydplUpVDWVtbIz4+Hn369MGjR4+UH8DszMrKCgkJCWjVqhX++ecfc3fnjVlZWSExMRENGzbEoUOHzN2dN5ZTxkO3r3z44YeIjIw0d3feGOOwPDkllpwSR1pWVlZISkpCtWrVcPLkSZO1yzuq52CzZ8/GiRMnkC9fPnN3xWT+/vtvnD9/HiVLljR3V97KtWvXcPXqVbz33nvm7spbySnjsX37dpw6dQp+fn7m7spbYRyWJ6fEklPiSCs8PBwPHjxAxYoVTdYmZ6pyMK1Wq9yuPyfMVAHAw4cP8ezZM2UaOjvRHYoVEURHRyMlJQXW1tZm7tXbyc7jkVZ0dDS0Wi3UarW5u/JWGIflySmx5JQ40kpKSoJarVZOYTAFJlU5RHo/arpnKmXHHz1dEpI2IbS2toabmxtsbLLPJOuLcahUKlhbW8PJycmc3XptOW080p5vqFKpUKBAAdja2pqrW6+NcVienBJLTokjLV0sGo1GSaLUajW0Wi1SU1NNNvHApCqHsLa2RkJCAiZOnKiU3b17F7ly5QKAbPWjp6NSqZCSkoJOnTph+/btAICUlBQ4OTkhV65cRn/kLZHupPR69erhp59+AvB8Z86dOzfs7e2NfoFZopw0HsnJyRg6dKhyTlt8fDwcHR316jGOrJFT4gByTiw5JY60dDF17doVq1atgohAq9XC0dERefLkgZWVlV5ypdFo3ii+7PdLS+k6deoUpk6ditu3b2PZsmVQq9XKIwCy69Ualy5dQkxMDMaPH4+8efMq088pKSlKbFZW//1toFarLfIvqejoaPj4+GDQoEFwdnaGs7MzUlNT8ezZM9jb2wPQH6OkpCQ4ODiYq7t60l6heP78+RwxHocOHcJff/2FGzduIH/+/NBoNBARvVjTxqHVavVeWwrGYXlySiw5JY60bG1tcePGDcyZMweurq54/PgxPDw8lHjSTj5YW1sjNTX1tWPiY2qyubQbeGpqKnbt2oUuXbqgU6dOyJMnDy5fvoyQkBBoNBq4ubkhOTkZiYmJEBHkzp0bXbp0sajzejQajUF/jh49itmzZyMiIgI+Pj64ceMGypUrB0dHR+UKtFy5ckGj0SAlJQWLFi0y+/OkjN0q4erVq5g9ezZWr16NJk2a4MyZMyhSpAjy5csHlUqF+Ph42NnZQUSQkJCAdevWwdnZ2UwRPKcbj2fPnimznocPH8Z3332XrcbD2Ha1efNmzJs3D25uboiLi0NycjJq164NGxsbZVbO1tYWycnJ0Gq1+PLLLy3yR2PTpk2YP39+torD2P6xYcMGLFy4MFvFAeScbSunxJFW2u1Ml/SlpqaiVatWiIuLg4+PD9atW4cWLVogKSkJjo6OSE1NhVqtVt67YsUK5M+fP8Pr5ExVNqXbQNJ+MdnY2CA4OBirV6/GRx99hIcPH6JUqVKYOXMmnjx5AmdnZ4iI8gM5a9Ysi0yoYmNjsWHDBnTu3BkODg6oVq0aBg8ejIULF2Lz5s3QaDQoV64czpw5A2tra9jb20Or1UJEMHDgQIv5AX/27BmuX7+OJ0+eoGrVqihWrBg+/fRT2NraKolG69atceXKFVhbW8PR0REqlQpWVlbo3r27xSRU8fHxCAwMROfOnfHVV1+hRo0aEJFsNx6xsbE4dOgQmjZtCgBo06YNUlNTsWzZMhw4cEA5lHnt2jXY2NjAwcFB+YINDQ01+4+FLg7dYQvdvvvBBx8gJSUFP/zwQ7aKIzU1FfHx8UhKSoKnpyfatWuH1NRUrFy5MlvEkTaWnLJtZfc40tLFpPvjTqVSwcHBATY2NtiyZQtat26N1atXo2TJknBwcEDevHlhZ2eH1NRUJCcnw9nZGcHBwa+VUAEAhLIdjUYjIiJxcXEyYsQI6du3r/Tv318iIyOVOtu2bRMfHx9p1qyZUhYfHy8pKSmSmpoqT548ERERrVablV1PV2pqqoiIxMTEiJubm7Rr105E9Pt38OBB6dGjhwQGBsrJkyfTbcucMenGJjY2VurWrSulS5cWR0dH6dChg1InIiJCPvvsM3F1dZUDBw6k25Y549CNx9OnT8Xf319UKpU0b95cHj16pNQ5fPiwxY9H2u0qT5480r59exH5b5xERLZs2SLBwcHSqFEjOX/+fLptWMp21a9fP2ncuLH0799fli5dqtTZuHGjNG7cONvE0bp1awkKCpLKlSvLl19+qdT55ZdfLH480vYju29bOSWOtNJuZ8HBwVKjRg0pXLiwfPHFF3LkyBEREVGr1dKyZUspX768bNy4UdRqtdG2XjcmJlXZVHx8vPj5+cn7778vbdq0kYoVK4qjo6MsXLhQHj9+LCIiW7duldy5c0vPnj2V9+k2HEvZ+EX+2wFiYmKkSJEiSkJlzP79+6VDhw5SpkwZ+f3330XkeSwajUbvS8CcEhISJDAwULp37y4HDx6UjRs3Sr58+eTPP/9U6kREREi/fv3ExcVFfv75ZxF5HkdqaqrZ43hxPHr27Cl//vmn2NrayubNm/XqHjx40GLHI20c3t7e0rZt23Trrl27Vt5//31p0aKFHDt2TCnXxWIJ4uPjpXjx4tKkSRMZNmyYNG/eXAoVKiRdu3ZV6vz0008WH0dcXJyUKFFCOnToICtWrJAhQ4ZItWrV5NatW0odSx+PnLJt5ZQ4jElKSpKAgAAJCQmRtWvXytixY6VixYpSu3Zt+eWXX0RE5NmzZxIcHCxBQUGyfv16SUpKeuv1MqnKpr744gupUaOGiPy3YwwbNkzy588vM2bMkJiYGBF5nli5u7vrzZRYosTERClYsKA0b95cKfv+++9l5MiR8tlnn8mPP/6olP/555/StWtX8fPzkz179pijuy+1dOlSqVGjhsTGxorI8527YcOGcujQIdm7d68yXjdu3JAhQ4aISqWSjRs3mrPLBmJjY8Xd3V35q1VEpFOnTtKwYUN5/PixXlJ++PBhix2PxMRE8fLyko4dOypl69atky+//FK+/fZbvf6uW7dOmjRpIg0aNJC///7bHN19qeXLl0vFihWVfTsmJkbWrVsnbm5u0qZNG6Wepccxfvx4ad68uTx79kxERP755x+pXr263LhxQ27cuKHUs/Q4csq2lVPieNGePXskKChIoqOj9co6duwoZcuWld9++01EnidWISEhUrx4cVm7du1bTzjwnKpsSndSne6KDCsrK4SFhcHBwQFffvklfHx80KFDBwQHB2PJkiXo2rUr/vnnH5QtW9bcXTfq/v37SExMhJ2dHTQaDXr27ImTJ0+iSJEiePr0KdauXYujR49i7ty5qF27NoDnl/PrTp62JLdu3VLOYQOAPXv24MCBA+jfvz/u3r2LwoULY/369ShevDgGDRqEZ8+eWdQ9q0QEc+bMQUhICJYtW6aU161bFxMmTMCDBw+QN29epKamwsbGBtWrV1fOW7C08Th16hQePnyIvHnzAgB69uyJo0ePwtnZGdHR0XBxcUHHjh0xbtw4dOjQASKCRYsWmbnXxt25cwfx8fHKvedcXFzwwQcfwNnZGd26dUO/fv2wcOFCdOjQAVqtFosXLzZzj42LjIxErly5lKtCL168iCtXriA4OBgqlQply5bF2rVr0aFDB2g0GixZssTMPTYup2xbOSWOF2k0Gly5cgV37txRniry/vvvw9HREWFhYZg/fz78/f1RqlQpbNy4ESEhIShUqNDbXyn/VikZmc3UqVPF09NTUlJSREQkOTlZWfbxxx+Lh4eHPH36VESez2Q9ePDALP1Mj+4YvEajUf4dHh4uhQoVEnt7e6lVq5aEh4eLyPO/yOfOnStFihRRpm1FxOLOC9M5fPiwODo6SuPGjWXUqFFibW0tc+fOlWvXrklsbKyULl1aWrZsqdTXTTlbwrkuOlFRUUaXBQUF6R2eTdtnSxyPZ8+eybZt28TV1VXc3NykZs2acubMGRERuXPnjowcOVLKli0rx48fV95z//59c3XXKN3n+eeff0rhwoWVv7B1kpKSZMGCBRIQECCHDh1Syi0hDt2+raNWq2X69OlSvnx5GTt2rHz33XdiY2MjM2bMkD179sgvv/wi+fLlkwkTJijvsYQ4RAy365SUlGy/bYnkjH3EmHPnzkmZMmVkwYIFBudLbd26VQoXLmxwOoMpMKmycOkdq46NjZXAwEBp0aKFUpaYmCgiz38QCxcuLOvWrTN4nyX84Om+aGNjY6Vly5ZK8iTyfEdo2LCh3uE+kec7ccGCBSUsLCxL+/oyaT/LtOOUkJAgO3bskE8++UQGDRokHTt2lNTUVOVwx9dffy1lypRRDg+am2484uPjZdu2bUa3EV18YWFhUrZsWWXMtFqtRWxTIunvKykpKfLrr79KvXr1ZNOmTXrLIiIixN7eXlavXp0VXcyQ9OK4evWqBAcHS+fOneX06dN6y27evCn58+eX77//Pgt6mDFp9/Pu3bsrfzyEh4fL6NGjpUWLFtKoUSMZNmyY8h6tVivt27e3uNMVdLEkJibqffZarTZHbFvZbR9JK+1J8i8m8f379xdXV1fZv3+/wfvq1KkjnTt3Nnl/LOf6RzKg0WiU+/7MmTMHI0aMwNq1a5GQkAAnJydMmDABN27cQNeuXQFAuVmkSqWCo6Oj0ZtHmvsmoGkv3a1cuTK2bt2K6dOnK3euLV26NFatWoXmzZsr7xERODo6olixYvDy8jJX1/VoNBrlDr0PHz6ElZWV8iig3Llzo0mTJli8eDEcHR2RnJwMa2tr5XDHgwcP4OvraxF3uU87Hn5+fjhy5IjRbUR3qXTbtm1x69Yt/PzzzwCeb0/m3qYA/X1l4cKF+Prrr7Fy5UokJycjV65cCA4Oxrx589CgQQMAUG5i6OzsjFKlSsHDw8PMETyXNo4JEyZgwIABGDx4MBITE+Hv74+hQ4fi2LFj+Pbbb/H3338r7/P29kZgYKAZe64v7XZVs2ZNrFq1SnmaQGBgIKZOnYpff/0Vzs7Oerd1UalUcHJygpubm8XcrTttLIULF8bu3buVZSqVCsHBwZg/f3622ray8z6Slu7WInFxcRgwYACaNWuGAQMGYOnSpQCA+fPno27duujQoQN+//13JCcnK+/19PRE0aJFTd8pk6dpZBJpr8ooXbq01KhRQ+rVqycqlUrmzp0rIs9nFhYuXCgBAQFSu3ZtuXbtmkRERMiPP/4o+fLlk1OnTpkzBANpL90tXLiwtGnTRqZNmyZlypSRu3fvikj6M2nff/+9eHt7K9PS5qTrY1xcnJQtW1aCgoKUE2xf/Etp8+bNUrp0aVm+fLlcu3ZNFixYIC4uLrJ9+/Ys7/eL0o6Hj4+PtGrV6qX1dNvkzJkzxc/PTy5evJgl/XwV3XjExsZK8eLFpVGjRlK1alWpUKGC+Pv7v/SE2qVLl4q/v79FxJL2MvASJUpIkyZNpE2bNlK+fHlp3769Mg6bN2+W9957T5o3by5LliyRiIgImTdvnjg5OeldlWUuaberQoUKSZs2bSQ4OFhvVl1XZ+zYsdKtWzc5c+aMxMfHy/fffy958uSRP/74wyx9f1FG9xHdLHRalrRt5ZR9xJiMXBX7wQcfiKurq4wePVqWLFki33zzjdjb2ytXLJsSkyoLlpCQIEFBQdKpUyeJi4sTEZHhw4crh5NEnk/b7t27V6pWrSp58+aVYsWKiaenp/z000/m7Hq6nj59Kt7e3sr0/uPHj8XR0VEmT55stP6FCxdk6tSp4ujoqNx6wBIkJydL27ZtpWTJklK3bl2pV6+eXL9+XUT0p9gvX74sH3/8seTNm1eKFi0q/v7+erdQMDfd5e1NmzZVyrZt2yZLly6VhQsXSkJCgsF7tm3bJnnz5pXLly9nZVdfKjU1Vdq0aSMffPCBaDQaSUpKkrNnz0r+/PmlePHierd7EBG5cuWKzJo1SxwcHGTDhg3m7LqehIQEqVq1qrKPJycny5dffindunXTq7dv3z7p2bOnuLm5ib+/vxQpUsTo4f6spvt8dfu57vy7Q4cOiaOjo8FVrrt27ZJSpUqJn5+fVKxYUby8vJQ4LGH/EHmeiPj7+0vr1q2VsoMHD8qGDRtk27Ztynmtuv3eUretnLKPvCi9q2Lz5s0rH3zwgVJv8uTJ0qxZMylcuLDUqlUr076HmVRZKK1WK19//bW0b99e7yTz8ePHS+vWraVLly4yY8YMOXHihLLs999/l6NHjyo3Z7OULyUdrVYrH374od4PuIjImDFjpGrVqnLt2jW98idPnkhYWJjUqVNHOaHQUmI6cOCA1KtXT3799VfZvHmzNGjQQOrWraskVmlPjHz48KH8/fffsm/fPomIiBARyzkXaeHChaJSqWTRokWi0WikZ8+eUqFCBfHx8ZEiRYqIp6encmPPtDHp4rAUT548kerVq8vWrVtF5PkPnFqtluDgYPH19ZUCBQrI7du3ReT5+Xm6e9bofuQtYSxERH744Qd5//335c6dO0rZrFmzpHLlytKhQwdp3bq1XLhwQUSe/4V+7949OX/+vDJTagnb1bNnz6R+/fp6F2PcuXNHatWqJf379xcR/RndEydOyOLFi2Xp0qXKtmYJceh8+eWXolKplKRDt4/kyZNHihYtKqVLl1buDXj37l2L3bZyyj7yoqlTp0rJkiX1ytRqtWzfvl3c3NykT58+Snl8fLxER0crNzHOjO2MSZUFCw8Pl507dyp/Af3888+iUqmkY8eO0q9fPylevLi0bNlS7wvY0t28edOgbPfu3eLk5KScIJl2I4+KilJuCmhJX7Qiz68g0Y3Npk2bDBKrFw8FWqpRo0ZJsWLFpFKlSlKxYkU5efKk3Lt3T+7evSstWrSQIkWKKDOlungtaRxEns8mlClTRsaMGaOUabVaKV++vPzyyy9SuXJladOmjdLv69evy9WrV5V6lhJPYmKiHD58WElgf/nlF1GpVNK/f3+ZNGmSNGrUSNzd3S366quEhATZuXOnQfncuXMlV65ccunSJRFJ/6RpS9S1a1dxd3eX+vXrS+XKleXQoUNy7do1OXz4sFSpUkWCgoKUQ4CWsm29uN6csI8Y60NGroo9fPhwuu83NSZVFijtwOv+/fjxY+ncubPMmTNH+bE+fPiwqFQqizn/4EXG4khP9+7dJSgoSPmLz5JkdEdMO2OlmzlYvny5MrNgbi/7ERs5cqQUK1ZM/vrrL73yCxcuSL58+TLl0uM3ZWw8kpOTZcSIEVK3bl0ZPHiw7Ny5U0qUKCGNGjUSkec/6NWrVzd67ou5GBuPtLHFxMRI165d9R5F8/TpU/Hw8JBvv/02S/qYEcbiSFumiyk6OlqqVKkiw4cPT/eRIOaW9vzBF/vYrVs3cXV1laNHj+qVb9++XTw9PZUfbkugi+PZs2fKrXXUarWMGjUqW+0jaaV3BeaNGzcs6qpYXv1nAXRXjcn/PyhVpVJBRAD8d7Venjx58N1332HQoEFKmbu7O9577z3lZoCWRHd1nC62V10h1qxZMzx58gQXLlwAAIu68kd3ld++ffuwdetWXLp0SVkGQBmr1q1bY8iQIbC2tkbv3r0xYcIE9O7dGzdu3DBb/3V0V/4kJSVhy5YtWLx4sd7VYzNnzsT8+fMREBCg975nz57BxcUFBQsWzOouG6Ubj5SUFJw8eRJHjx7FjRs3YGdnh+HDh6NKlSrYv38/PvvsM1SvXh07d+4EADg6OuLevXtISEgwcwTP6cYjMTERK1aswLRp07B//369/cTFxQXz58/HRx99pJQ9fvwYnp6eKFWqlDm6bSDtdrV582YsW7YMZ86cgZWVlbIP62LKly8fqlSpgt9++w1qtRrAf/uOpbC2tkZCQgI6deqEbdu2Kf0EgB9//BE//vgjihUrBuC/vru4uMDKyspivod1VyvGxMSgfv36OHjwIADAxsYGI0eORNWqVbPFPpLWy67ALFKkCIYPH245V8VmWfpGRqV9OHK/fv3kwIED6c6MvFi+YMECCQoK0nuQsiVIe6XJe++9J19//XWG3lehQgUJCQnJzK69lrRXYFatWlXKlSsnXl5e4uLiIvv27TNaV+T5IZtChQqJSqWS9evXi4hl3NgzNjZWKlWqJOXLlxdfX1+xtbV95QUNum3sxfPdzCFtHLVq1ZKyZctKwYIFJTAwUHlIanJysiQlJSlXk+qMHz9eWrZsaZJne72ttNtVuXLlpHLlylKuXDmxsrIymBF8cbtZunSpBAYGWsSVWGnjqFixopQvX16KFSsmuXLl0rsJadq6Dx48EE9PTwkNDc3y/r6K7rMeP368qFQqKV26tOzateuVs2rfffedVK1a1SJOw0h7tWKRIkUMzl8VeX5YLDEx0aC/lrSPpJXRKzA3btxoEVfFMqmyAImJiVK9enVRqVRSoUIFOXbs2Et/hKOiomT58uXi4OBgUYdl0kpKSpLGjRtLvnz5xMbG5qU37dTtNIsWLRI/Pz+j512ZS0JCgpQrV07at28vt27dkgsXLsiAAQOkQoUKes+UEvnvS/mbb74RlUolv/76q1Ju7vMREhMTpVKlStK+fXt5+PChPHnyRKZMmSIFChQw+nlfvnxZ5syZY3FX/sTHx0vp0qWlffv2cunSJfn999+lS5cu0q5dO4mLizP4nC9fviwLFy4Ue3t7ZTwsQXx8vJQtW1Y6duwojx8/lpSUFOncubMMGjTIaP1bt27J/PnzLXI8ypQpIx07dpT79+/LjRs35P3335dp06YZ1NUdUmvbtq00aNDAYm5++6Jdu3ZJ//79pXXr1uLu7i7bt283en7kzZs3Zd68eZI7d26DG2aaU2xsrPj5+ek9t/PWrVty/fp1gz82RCx3H0nrZVdgbt26VXmiyB9//CG9evUy61WxTKrMTKvVypw5c6Rhw4Zy7NgxKVeunAQEBKSbWF27dk06duwoRYoUsahL81+0du1aadasmezcuVPCwsJEpVLJrFmzlOXG+hwREaH3KARz02q1Mm3aNGnUqJHyCBYRkd9++02KFCli9GTh06dPi7e3tzIDZAkJlYjI7NmzpUGDBnqPnzlz5oz4+PgoJw7r3LlzR0JDQ8XHx8eirvzRarUyfvx4CQ4OVk6cF3k+m+br66tXJvL8fJLFixdLoUKFLGpf0Wq1Mnr0aGnTpo1yvouIyODBg6V3794ybNgwWblypTIDffXqVQkNDRV/f3+LGg+NRiO9e/eWFi1a6N16o2fPnjJq1CiZMWOGbN++3WDm48CBA0ZPZLcU27Ztk+rVq4uISNOmTaVAgQJy8uRJmT17tixfvlxEnt9uYPz48Ra3bWk0Gqlbt67kzZtXmYkaNmyY1KxZUwoUKCDe3t56s+cpKSkWuY+86FVXYAYEBCh/5MbFxZn1qlgmVWaWmpoqu3fvlkWLFillZcuWVRIrYyeB6m6dIGI5P9ovunDhgixbtkzUarVoNBqZNWuWQWKVHSxbtkyGDh2q95dqQkKCFC5cWE6cOJFucihiWWOzZcsW6dmzp8FJqEWKFJFdu3bp9TM1NVWuXr2qnGBvKXGo1WqZNWuWTJo0STQajdKnu3fviq+vr1y7ds2gn8nJyXLlyhURsZw4RESOHz8uW7ZsUfqzYcMGUalU0rRpU+nYsaM4ODjIxx9/LFqtVhITE+XEiRMW+Wig/fv3y7p16wziqFOnjgQHB4tKpVLuQZd2zEQs88db5Pkse/369ZXv3jZt2oiTk5PY2dnJjh07ROS/qzTPnj0rIpY1JqtXrxZfX18ZOXKktGjRQsqUKSNr1qyR9evXy/Dhw8XKykrvfmEpKSkWuY+86FVXYJYrV84iTrJnUmUGxr740/5op6am6iVWuvrp/Yhbkhd/nHWSk5ONzlgdOHBAuWzXEj19+lTvYdW6ssKFC+vd3d2SDlnqGHsArK5cq9WKWq0Wf39/2bt3r1Ln4sWLBjM+liQiIsLgAdQ3b96UQoUK6Z1b+OKhWUthLKm4c+eONGnSRObMmaNsa7/99puoVCqLeyqCjrE4IiIipHLlyjJnzhxl5mrJkiViZ2dnEefkvYouDo1GIwEBAbJt2zYREZkxY4bY2toqd3rX7UeWJu2Y/PTTT+Ls7CxBQUEG59/16dNHypYtK0+ePLHI35O3uQJTd26lOfHqvyyWmpqqXA3z7NkzAICdnZ3y/KvU1FRYW1vj1KlTsLW1Ra9evfD333/j66+/RpcuXXDlyhWz9T09t27dUv599+5d5d9pn+llZ2eH/v37Y9asWRg5ciS+/fZbzJ8/Hw0bNkR8fHyW9jc9aeO4ffs2AMDV1RV2dnbKlT6pqalISUmBiCjP8lu+fDlKlCihvMfcIiIiADy/6uratWtKea5cuZTy1NRUPHv2DNbW1rCzswMArFixAkFBQbhz507Wd9qIhw8fKv9+8OABAKBYsWKwt7eHiChXlyYnJyuxAMCyZcvg7e2Nx48fW8RVpOntH7rvgQIFCmDhwoUYNGiQMkYFChRAsWLF4OjomLWdfYm04xEdHa38WxdHkSJFsHz5cgwaNAi5c+cGABQqVAiFCxdWtjFLkXZMdNu7SqWCWq2GlZUVypcvj/z582PevHmYPHkyfvvtN9StWxchISHYu3evxVy1mN6YdOrUCevXr0ePHj3g5+en9x7d81Nz585tEc/tfNHbXIHp7Oxslj6nZf4nur5Drl69irlz52L27Nn4/vvvce3aNQwePFjvcnUbGxuo1WrY2tri1KlTqFKlCt5//30kJCRg9erVKFmypBkjMBQXF4dVq1ahUqVKiIiIwG+//YYRI0bg/fffN6hrb2+PAQMGwMrKCsOGDYNKpcLKlStRrlw5M/Rc36vi0H352NjYwMnJCQBga2uLVatWoV+/fli8eDEKFy5stv7raDQaLFq0CAEBAUhISMCaNWswbtw4hISE6NWztbWFtbW1kuSvXr0affr0wdKlSy1iG7t//z5+/fVX1KlTBwcOHMDx48fx0UcfoUaNGgD+Gw9ra2u4uLjA1tYWuXPnxqpVqzBgwAAsXLgQbm5u5gwBwKu3KxGBtbU1ihQpAuC/uI4cOYJ8+fIp25q5vWo8RAS5cuUyuHz94sWL8PHxUZJFS/CyMdH9oVSyZEnUrFkTVlZWWLNmDRo3bozGjRsjODgYNjY2FpGMvGpMmjRpgvr16ysJre4PkdTUVAQGBkKj0ShllkLXny+//BIbNmzAxYsX4eDggAYNGigPoE/7Xabr+6lTp1C4cGHkzZvXLP3WY64psnfVqlWrpGbNmlKsWDH53//+p/f8tLRTsbppz6+//lpUKpXyaAFLnK6NiYmRhg0biqenp/zwww/KY3LSStvvadOmiUqlUqbXLeU4fkbiEHl+PkW5cuVkwIABYmVlJatXrxYR88eRdt21atUSDw8PWbdu3Usvv69cubJ06dLFouIQeX5V2alTpyQkJEQKFSok33//vcEJ9TpRUVFStmxZGT9+vMXFIZLx7Urk+aNEVqxYIfb29vLLL79kYS9f7nXGQ1d/xYoV4ujoqHx3WZJXjcmuXbukcePGyoPPXzwMZQnbVUbGJG0/ExISZOXKleLk5KScG2apsvMVmEyqsohu416wYIG4ublJ586dlQdApj2GnPb/69atE5VKJWvXrlXasISd+UVLliwRb29vGTx4sNJXEcNzxTQajRw4cEDy5s0rq1atEhHLiimjcVy/fl1UKpWoVCq9K2YsJY7169dL0aJFpUePHjJ79mylPDY2Vu8y9qioKMmTJ4+oVCq9q8osJY6wsDApWLCgjBs3Tu++YC9evBEeHq6MR9ormywljoxuVxcuXJBevXqJl5eXRdzf7EUZHY9//vlHBg4cqCT1IpYVh0j6Y5L2SsUXn+5gaTGIZHxMzp49K5988onkzZvXYsckrex8BaZKxEIODudwIoK4uDi0a9cOLVu2RP78+XH27FlMmDABCxYswLVr1+Do6IhPPvkExYoVg4jg7NmzePr0KerVq2dwh3VLMnjwYDx8+BDTp0/H1q1bUbhwYRw8eBCXLl2CSqXChx9+iPbt2wMA/v33X8TGxqJ8+fIWF9PrxDFixAjUrVsXLVu2tLg4Pv/8c5w7dw5btmzBlClT4OHhgbNnz+LSpUtISUlBmzZtMHLkSKhUKsycORPFixdH69atLSoOEUHPnj1hZWWF3r174/bt2/Dz88POnTsRERGBXLlyoX///qhSpQri4+PRt29fdO7cGS1atLCoOICMb1eJiYnYuXMnChQogJo1a1pUHK8zHtevX8eePXtQvHhx1K1b16Li0HnZmGi1Wnz00Udo27atubv5Uq8zJteuXcP27dsREBCABg0aWOSYpJWcnIxmzZphz549sLKywgcffIDdu3dDrVZjy5YtaNKkCZKSknDmzBk4OjqibNmyFhMTT1TPIiqVCrlz54ZGo8HZs2fRpEkTWFlZwc/PD1u2bMGDBw9w+PBhjB07FnFxcVCpVAgKCkK9evX02rA0Go0GT58+xb179xAZGQkXFxf069cPW7duRYMGDfDs2TPMnj0bUVFRAAB/f3+UL19eeb+lxJSROL755hsljqlTp1pkQqVz+/Zt/PLLLwgKCsL48eOxa9cufPTRRyhXrhxWrVqFc+fOAQBGjRplcQkV8PyCgMjISMTFxcHR0RH3799Hu3btsH79ehQuXBiHDh3ChAkToNFo4OTkhDlz5lhkQpXR7erBgwfInTs3PvjgA9SsWRPA8xgsJY6MjoeIwNfXF7169ULdunUBWFYcwKvHRKPRICwsTLk4wlK9zpgULVoU/fr1Q4MGDQBY3pjo6PbfXLlyISoqSnmETvXq1ZGSkgIHBwfkypULz549g4ODA6pXr46yZcsCsJyYOFOVxaZOnYqNGzfi6dOn8PDwgL29PUqXLo1vvvkGy5cvx+LFi3H48GGLu1rmZS5duoTGjRvDxsYG+fPnR2BgIEqWLInKlSvD19cXdevWxY4dO1C6dGlzd/WlMhLH9u3bUaZMGXN39aWuX7+Onj174ubNm/D29kalSpVgZ2eHkiVLomXLlqhUqRJWrVqF6tWrm7urL3Xo0CE0btwYnp6ecHJyQtGiRdGoUSM0bNgQT548Qdu2bXHgwAEULVrU3F19qZyyf2RkPP7880+Dq80sEcfE/G7dugVvb28Az6/ALFSoEAAoF2p169YNn376KY4fP47PPvsMmzdvxvz587F7925s3LgRwcHBFpFEvYhX/2UR+f+rGj7//HNUrlwZSUlJcHNzQ506dbBkyRLMmDEDzs7OcHBwQGxsLPLnz2/uLmeIRqNBqVKlcOzYMTx8+FD58b59+zZ+/fVXbNiwAS4uLsiTJ4+5u/pSGY3DIq4ueQmtVgtfX19s2rQJcXFxyswBAHzxxRcYNmwYrKysLH770mq1qFmzJs6fP48bN24gT548KFeuHA4ePIjTp0/j6NGjKFCggMU8xDY9OWX/yOh4WMIl7a/CMTG/nHIFplFZdfIWGZ48mNbgwYPFxsZGOUHV2JUOliq9uBYtWiS2trYyatSoLO7Rm8kpcaR3subGjRvF1tZWPv744yzu0ZsxNh4pKSkybdo0yZUrl3z55Zdm6NXryynbVU4ZDxGOiSXICVdgGsPDf2a2Z88eXLhwAdOmTUPr1q3h6uqKKVOmZKvDfy/asGEDDhw4gK1bt2LkyJHQaDTw8/NDy5Ytzd2115JT4ti6dSt2796Nbdu24X//+x/OnTuHIkWKoE+fPubu2mv57bffcOTIEaxYsQIff/wxSpUqBX9/f1SpUsXcXXstOWW7yinjAXBMzGHp0qX44osv0Lp1a9SsWRMdO3YE8PwkdXt7ewDAkydP9I4OiIXdV8sYHv4zs2vXruH69etYu3Yt6tevjxUrVuDp06coUKCAubuWrrQbtrGNvGrVqrh8+TLWrFmDGjVq4Pr16xZzE8O03pU4KleujHv37qFLly6oVq0a2rVrp3f3ZUtkLA5PT09otVqsX78eNWvWxF9//YXixYubqYfpyynbVVrZeTwAjoklOnv2LGrUqIERI0Zg69at2LJlyyuvwLT0hArgieqZKjU1VbkL7MvEx8db/A6s86qYtFotrKysoNFolMeGWOJfF+9KHLo+G4vHkmR0X3n27Jlyd25LjOVd2a50LH08AI6JJY6JRqNRLqYJDQ3FzZs38dlnn8HV1RV9+/bFrl27EB8fj82bN8PDw8Pc3X0tvKVCJtHtABqNBoMGDcLTp08N6ujy2eyWUGm1WtSuXRu///67QR0rq+ebVNod2VK/nLJ7HBqNRomjePHi2Lhxo0EdXZ+NxWMp0o5H+/btcfjw4XTrpn3ciaXFklO2q5wyHgDHxBLHBHjer/HjxyMyMhK9e/fGvHnz0KxZM/Tu3RtBQUFYsGABbt68afG3tTCGh/8yiW4HqFatGmxsbIyeI2VpO+6rpsh1MVWuXBnu7u7KM6YsTU6Mwxhra2totVpUqlQJvr6+aNSoURb27s28bDyqVKkCDw8Pi3gWpDE5ZbtKKzuPB8Axya5yyhWYxnCm6g1otVq9/6fnww8/hIuLC37//Xc4ODhkRdfemFarhUqlwrNnzwCkn/ANGDAAnp6e2LBhg0XOsOW0OFJSUpCSkpJuvalTp6Jo0aLYtGmTRd5eQLePpKamAkh/PFq3bg03NzesW7cOjo6OWda/jMpJ2xWQ/ccD4JhkZ7o/CD09PVG2bFnlIe6FCxeGjY0Nli5diubNm1vEQ+pfW+ZeXJhzxcfHS7t27eT27dtGL+1Uq9Xy+++/S0JCghl692YSEhKkTp06cujQoXTrREZGWnxMOSWOpKQkqVKlivLgaWPu3bsnycnJWdir1xcfHy/NmzeXc+fOpVvn0qVLEh8fn4W9en05ZbvKKeMhwjHJSX7++WcZNGiQ+Pj4yJw5c2T27NkW9VDxjOLhvze0cuVKhIeHK3eBTUtEYGNjky0Ox6QVERGBiIgIvPfee+nW0d1I0pLllDj+r70zD4iqav/4d5BFQQFZcwEcUExFcX/BJQUX0FASxaTQV17NpcztZ5laWvmWaWrmQr5qYuaCSaWouMXrnuaSoimiqJjiggsKA7LO9/eH773NIJIUMneG8/kHOHNn5vnynHPvc889z3Nu376Nq1evolmzZk895oUXXqhEi/4aR44cwW+//QZvb++nHiPdqSoZU+lXpuIPQPjEmKAJZmCWhgiq/iJarVaupSFlj0goba1UWUidmyTu3buHgoICxS5uLAtT0KF7oikuLsb9+/dRUFAg9zNj5e7duygqKkJxcbGhTSk3ptCvSmLM/gCET4yRkpmLJa+RWq0Wbm5ueO+992Rfenh4GNW1VEKsqXoGSls7VVxcLK+T+rO1VUqE/8s8lH6qVCpUq1ZNkVsalIUp6VCpVPJJtVq1arCwsICFhYVR9S/JD7o2m5mZwdHR8ZnSwZWCKfUrwPj9AQifGCumkoH5rIig6hkwMzNDbm4uxo8fjzt37gAA7t27JwdV1apVA8knBr2SkRZ4dunSBXFxcQAe1zextraGlZWV0WgxJR2FhYVo164doqOjATw+GVWvXh1WVlZGE1hJi+sjIiLkk2deXh5sbGxgYWFR6gVFiZhSvzIFfwDCJ0pF9/9d2v9eNwPT2traKDIw/w6mFxY/J65du4aFCxciLS0Na9eu1SvYWTKiNpYI+86dO6hXrx5GjRqFWrVqwcLCAoWFhSgoKJBLQOhqefTokSKzGG/fvm0SOoqKihAQEIDx48fDxsYGTZs2lYN16U5O91FzVlYWatasqffo2VDoPro8c+YM7t27hylTpsDe3h4kZX9INXR0bZZ2pVcaptKvkpOTTcIfgOmcs86dO2cSPpHOR5Ldf5aBuX79eqNcJ1UeREX1Z0C6YCQlJSEoKAiBgYFwcXHB4cOHMWDAAOTn58PBwQEFBQXIyckBSdSoUQNjxoxR1B5+pS0OTE1Nxeeff46NGzeiV69eOHXqFDw8PODo6AgzMzNoNBr5LjAnJwfr1q0z+KCQqgTr6rly5Qo+++wzo9JRmj+ysrIwb948zJw5ExERETh48CAcHBxQp04dAI93d69evTrMzMyQnZ2NLVu2wNHR0RDmy0j+0L0gHDhwQL4J8fDwwLVr19CiRQs5CMzJyYGlpSWKi4uRn5+P6OhovaKFhtRhiv3KGP0BlK7l4sWLmDt3rlH5pLTK5ocOHcKCBQuMziclyc3NRa9evTBr1qynzkKlpaXBxcUF1tbWlWxd5SNmqkrhaQvPfX19sX37dvTt2xfp6elo3LgxYmNjkZOTg5o1a4IkcnNzYW1tjWnTpikqoJIGdVFREfLz81FYWAh7e3s0bNgQ77zzDiwsLLB8+XLY2Njg5ZdfRkpKCszNzeWpW5VKhWHDhinm5JSVlYWvvvoKUVFRcHFxgVqtxrvvvgtLS0ssW7bMaHTk5+cjOTkZd+/eRffu3WFra4tJkybB0tISH374IZycnNCvXz/cvHlTPilLegICAhQTUGk0Gvj4+GDw4MGYOXMmOnfuDJJYunQpNm3aBJJ48cUXceTIEbkYrjQLN3bsWINfLEytX2m1WhQXF4MkLC0t0blzZxQVFWH58uVG4Q9dLQUFBfj999+RmZmJdu3aoVGjRpg4caLR+SQrKwubNm3CwIEDUb16dXTs2BFarRZfffWV0fikNEwlA7PC+OvVGEyToqIiko/rhnz00UccPXo033zzTb06J6dPn6aXlxcDAgJ4584darVaarVaFhcXs7i4mI8ePSLJUutXGYLi4mKS5MOHDxkaGspWrVrRz8+P48aNY2FhIUny0qVLHD9+PO3s7Hj48OGnfpYhNUm+efjwIZ2cnBgeHv6ETcnJyZw4caKidej6w9/fn82bN6dKpWJQUJB8TGZmJufMmUOVSlVmrRYl+OPBgwf08vKiSqVi3759mZmZKR9z4MABRkZG0sfHh0lJSU/9LCXoMJV+lZWVxcjISHbs2JFhYWGcNWuWfIwx+IPU19K1a1f6+PjQxsaGkZGR8jEpKSmK94lu33JwcODAgQOfsGn//v1G4RNdJFu0Wi0TExPp6OjIhw8fGtgqZSCCKh2kjpKVlcXGjRszODiYffr0oY+PD5s1ayYHICR58uRJOjo6yhcR3Q6vpM4vkZuby2bNmjEsLIxLly7lv//9b7q6utLPz4+nT58mSV6+fJnDhg2jnZ2dfCEvLi5mUVGRfJIzFLonWXd3dw4YMEDv9ZIXwOHDhytSh0ROTg5btGjBIUOGMCkpib/88gttbW0ZHx8vH5Odnc1p06ZRpVJxxYoVJCkH74buY7oXC3d3dw4dOpSJiYm0sLDg1q1b9Y7dt28f+/fvT19fX+7Zs4ck9W5CDIkp9qvGjRuzb9++nDlzJkeMGEEHBweGhIQwKyuLJLl3717F+kOXnJwcNmvWjIMHD+a+ffu4YcMGOjg48ODBg/IxKSkpij9nSWOkZN/SJTEx0Sh8Io0HXZv27t3LBg0aiKDqf4igqgR5eXns3r07BwwYwOLiYhYWFjI5OZlNmjRhbGwsyT861MmTJ1mvXj12796d9+/fN6TZf8pPP/3Exo0bMz09XW67fv06mzRpwhYtWjAlJYUkeeHCBb755ps0MzPjli1bDGVuqeTm5tLV1ZVdu3aV2xYvXswRI0bwn//8J+fNmye3p6SkKFYHSX733Xds06YN7927J7f16dOHCQkJ/PHHH3nv3j25/3344YdUqVRcuXKlAS1+kqysLL2ZHZIcMGAAg4KCnrjROHDgAAcNGkQvLy/u27fPEOY+FVPqV3FxcfTx8ZH7VWFhIQ8ePMj69eszMDBQvjHcs2ePYv0hsWzZMnbo0IHZ2dkkHz896N69O48cOaJn85UrVzh69GhF+iQ3N5cvvPACQ0JC5LaYmBhOnjyZ06ZN4/r16+V2Y/AJSebn57NTp07cuHEjSXLXrl1s2rQp8/Ly9GawqipiTVUJDh06hIcPH2Lu3LkwMzODmZkZGjZsCGtra6SlpQH4IyujZcuWiI+PR48ePXDp0iW0bdvWgJaXzYMHD/Dw4UN5DU5BQQHq1auHX375Be3atcPo0aORmJiIRo0aYdy4cXqLjpWCVqtFUVERSOLq1auYMmUKzpw5g6ZNm6KgoAAff/wxjh8/jnXr1sHb21uxOgAgPT0dd+7ckffrS0hIwLZt23Dr1i1cunQJrq6u+PLLL9GjRw9MnDgRRUVFcHV1NbDVf0ASX375JUJCQhATEyO3d+nSBTNnzsTdu3dhb28v16jp1KmTvOBWadmxuuuPjL1fZWRkICsrCw4ODgAel3vp2LEjEhIS0LNnT0RGRiI2NhZdu3YFAEX6Q+L3339Hdna2vCZqz5492L9/P0aMGIHr16/D09MTGzZsgKenJ8aNG4fCwkLF+SQ9PR1ZWVmwsbEBSQwePBi//vor6tWrh4cPH2L16tU4evQo5s+fbxQ+AUwnK/a5YdiYTnncunWLc+fOlddQSXd2oaGhfP/990nyielYaVpdKZR2l3Djxg3WqlVLb31Ffn4+SfK3336jg4MDlyxZIr+Wm5v71M+qLKRHTIWFhfJMYGZmJj09PalSqdi5c2eeO3dOPnbTpk2sWbMmly9fLn+GEnSURmpqKu3s7Ojn58eRI0eyWrVqjI6O5u3bt0mSnTt3ZkBAgHy81OeUsGZH4ubNm6W+1qxZM0ZERMh/69os+dHQ/pC+v6CggOTjfqVWq42+X509e5Z2dnZctWqV3CbZuHXrVtatW5c//vij/JpS/CGNdenRHfl4rVGNGjUYEhLCyZMns1q1aly8eDFTU1N5//59Nm7cmP3795c/Qwk+Ke1R3alTp+jq6kobGxt26tRJ3t/v/v37/PLLL+nm5qa3v6dSfCIh+UPXnsuXL3PEiBGsXbs2X3vtNTZt2pS9evViZGQkhwwZwrCwMEZERHDQoEHs06ePPNtYFTB8gRsDwlKqSbi6umLixImwtraGVquVK9xaW1sjJycHwOOZqm3btuHChQsAoKiKvlLWi1ar1dPn7OyMSZMmITY2FmvWrAEAWFpaQqvVomHDhmjevDmuXLkiHy/dWRjqjknKmMnOzka/fv1w8uRJAIC9vT1+/fVXBAYGIjIyUs44qVatGrp16wYPDw9F6dAt4Cf9ThJeXl7Ys2cPOnfuDGdnZ/To0QP/+te/5JmrYcOG4erVq3KxWWl21JD+kIrg7tixA4D+voNmZmayvqFDh+LcuXNITk6WX5f6Yu3atQEY3h/S90u1f6R+FRAQYFT9quQ5zMnJCf3798e6deuwe/duAH/Y6OfnB1tbW6SmpsrHG9ofgP5YDw8Pl+3z9fXFhg0b4OLigqysLISHh2PUqFFwd3dH7dq1ERUVhdTUVGg0GgCG94nuGNm5c6fc7uvrix07dqBdu3YYNWoUfHx8ADz+3w8YMAAFBQWK84mEbubinDlzkJGRAQByVuzrr7+OuLg43Lx5Ez4+PsjKyoJGo5EzMAEoIgOzMqmyQVVxcTFUKpVcYkAXqTPrXigKCgrk9jVr1qBPnz64dOlS5Rr9J0iDWqPR4I033kBISAj8/f2RnJwMc3NzhIWFwcfHB0uWLMGqVasAPNZoZWUFFxcX+XNKCzYrE92B3Lp1a2zbtg2zZ8+WfWFnZ4dt27ahf//+8ntIwsLCAnXr1oW7u7uhTNdDKs3x6NEj3L59G2ZmZnK/A4BWrVphzpw5sLOzQ1FREaysrOR9/i5fvgy1Wq2Iff90/aFWq3H48OFSj5MCv7CwMKSlpeGHH34A8Hg8KeECIflDo9Fg8uTJGDt2LEaOHImLFy8iNzcX9vb22LZtG8LCwuT3KLFf6Y7ziRMnIjIyEv3794dGo4GLiwuioqKQl5eHRYsWISEhQX6fo6Mj1Gq1oip16/atdu3a4ccff8Ts2bMBALa2tujTpw++/vpr2NjYoLCwUN6+CXi8X16DBg0UsbVLyTHy888/y69ptVq0bNkS69atQ+/eveV2kqhZsya8vLxQt25dQ5hdJrqavLy8cOLECbi4uMjXBy8vL7z11lsYM2YMtFotwsLCsHnzZnz//ffYsGEDYmNjsX79eoSGhhr8mlKpGGiGzKDoZvn5+PjwnXfekR8DlESa+uzbty8//fRTbt++nWZmZnoLDJWArqZGjRqxT58+nDlzJgMCAujl5SU/xjx27BijoqLYoEEDjh8/nvHx8fz8889pZWXFxMREQ0ogqZ9V5ubmxv79+3P16tVs2rQpjx49SpJ6WZi6rFy5knXr1uXx48crzd6nIflDo9FQrVazdu3avHLlCsk/NEocOHCAzs7OnDdvHpOSkrh48eInMgENha4/PDw82Ldv3zKPkx5/zJo1i15eXrxw4ULlGPqMZGdns1GjRuzVqxdHjhzJLl260MbGhvPmzeP169dLfY+S+pVutqK3tzd79+7NUaNGsUWLFvT395df37VrF7t378727dvzs88+47Fjx7hgwQJaW1vzwIEDhpQgo9u36tevz379+vHjjz+mr68vb926RfIPvRs3bmTz5s25Zs0apqWlcenSpbSzs2NCQoLB7Jco7xjRZcWKFXR3dy+zlIIhMLWs2MqkSgZV5OMsv169etHNzY1WVlacPHnyUy/WJPnGG2+wbt26NDc357fffkuScn0qpZCbm8suXbowPDxctuvw4cPs0aMHNRqN3Hbjxg0uW7aMarWaTZo0YcuWLRkXF0dSGc/xMzMz6ebmJtd0ycrKYt26dTlmzJhSjz99+jTff/991qxZk999911lmlom+fn5jIiIYOvWrdmlSxd6enry8uXLJPVPsLdu3eL06dNpa2tLDw8PNmnShN9//z1JZfgjOzub3t7e7NWrl9y2fft2xsTEcPny5czLy3viPfHx8axdu7aigiqtVsuRI0fqZWKRZMeOHenk5MQPP/yQd+7ckduV2q9ycnLYoUMHvvrqq/LFa+3atezXr5/ecUlJSZw2bRodHR3ZsGFDent7K0oH+bjGmZubm5xBmpGRwRo1avCTTz7ROy45OZlRUVG0s7Ojl5cX1Wq1nH1mjGMkOTmZn3zyCW1sbGQdSsOUsmIrkyobVG3fvp19+vThvn37GBsbS3Nz8zIDqyFDhlClUsmzB0oLqEhy586dDAsL46VLl+S2ZcuW0cXFhR06dGDTpk0ZHR1NjUZD8vGMz4MHD3j37l2SytE0atQo9uzZU68tOjqaHh4eT8wWZGVlcfXq1QwKCpLvkpSggSSPHz/OsLAwxsXF8ejRowwKCmKDBg1k/+j2tby8PF66dIknTpzg1atXSSrHH0uWLJFrZWm1WkZFRbF169Z0c3Ojm5sb69evz1OnTpHU1ySV6VASoaGhcrKGlGAyduxYtmjRgi+88IJ8c5GZmanYfrVx40Z27dqVv//+u9y2aNEiNmrUiEFBQezQoQN37Nghv5aVlcVr167JiQVK6VdarZavv/66XiBCku+88w79/PyYlpam156RkcEjR44wMTFRDtaVouVZxog0G5WRkcG5c+eyU6dOctKAEjSURKPR0MnJiV26dGFaWhojIiLo4+PDgQMH8pVXXqGdnZ1eQkpKSgqHDx/OnTt3GtBqw1Nlg6orV65w48aNcvXz9evXy4GV7qNAqbOfOXOGP//8s9ymxEFAPr6QSxe2hIQEqlQqTp48mfHx8Zw4cSJr1qzJvXv3GtjKstGtXi9x/Phx1qlTh0uXLiWpn2Xz8OFDOWtOab756aef9B69SoFVamoqyT+CkNIeDSiJCRMmsFGjRmzXrh3btGnDEydOMD09ndevX2dwcDDVarXsNyVkKj6N4OBgBgYG6rW1aNGCO3bs4ODBg+np6SmPfyX3q0OHDsl9Z8uWLVSpVBw/fjyXLVvGiIgI1qpVi+fPnzewlWWj1WqfCJxIcseOHaxZs6Z8A6vEsVFaXyjPGLl586YcFCupb5lqVmxlUqWCqpKOlk7+0s/SZqwSEhKemB1RQod5mhaJvLw8LlmyhGvXrtVr9/b25ujRo5+7fc9Kef6XY8eOZf369eULnZJ41rUDx48fZ8+ePfVmrFasWMGdO3cqol+VpWPcuHFs2LChXkVr8vENh4ODQ5nb6VQ2T9OxZcsWvvjii/Tz8+Ps2bOpVqv50ksvkSTPnTtHtVqtqGDkz/qVRqPhu+++y2+++UavvV69evzggw+ep2nlpiwtJQOLiIgItmnThg8ePKgM08qFbqmXkjeA48ePL3OMbNq0qdLsLA9l+SYzM5OBgYH8z3/+o9eenZ3NZs2acerUqc/bPKPC8GkTlUBqaioaNmwIlUqFK1euQK1WA/gjW0n6+eqrrwIAIiMjYWZmBjs7O3zwwQfYt2+f3ucZOpNJd3Pk4uJiWFlZ6W0ADQBWVlYYNmyYXIytuLgY2dnZcHd3V0yR0tJ0lAb/t1N9eHg4du3ahV27diEyMvKJja8NhaTj0aNH2L17NzIyMtCmTRu0atVKtl362aZNG3z66aeYNm0agoODER4ejlmzZmHr1q2K6Vel6QCABQsWoFevXk9snFpYWAhbW1u9EguGpDQdrVu3RuvWrdG1a1fMnz8fixYtwsGDB9G3b18sWLAAAHDz5k3k5eXJ2WWGRtKRm5uLmJgYpKWlwc/PD7169YK1tTUAwMbGBh988IGcsk4St27dgpubG3x9fQ1pvh5/pkXq+9I4CQkJwbRp03Du3Dn4+/srZqwDj8ts5OTkYMCAAQgKCkJUVBTs7OwAAF988QWCg4OfOkbq1KljCJPLRDcrdubMmXj06BHy8/MxadIk1KtXT86KlUpXAMrMilUMhozoKoPCwkJOmDCBq1at4qJFi9ihQwe9Qmul8d1331GlUlGlUj0x02NoypO5WHLmY9WqVVSr1Tx27Nhzt/PPKI8OXYKCgujv7/+8zXtmdLNk2rZty1atWtHDw4OWlpZPLP7X9ceJEyfo7u5OlUolLx5WQtHCsnQ8jejoaLZq1UrObjQkZekouUhbelQhMX/+fPr5+TEjI6PS7H0auvvG+fj40N/fn507d6ZKpeKyZctKPVYiJiaG3t7eiskoK48W3THg6+v7xOJ7pRAdHS0/Elu2bNmfFoBW0hgpDWPPilUSJh1U6Q7Qjh070tXVlbGxsfJz4acdO3/+fJqZmcnBl5KeeZPlz1w8deoUFy1axOrVqysq+6c8OnRTxR0dHXnx4kXF+CQ3N5dt2rRheHg4b9++zfv373P69OmsU6fOU09I8+fPV1ziQ3l1pKSkcPHixaxRo4acragEyqvj1KlTXLhwIS0tLRWlIycnhy1btuSgQYPki/bbb7/NiIiIUvvKhQsXZH8oLaOsPFqknR6io6OpVqt57do1g4+Nkhw5coS9e/fmkCFD6OnpyaVLl5ZaNVypY0QXU8mKVQomHVRJbNiwgWq1moMHD+aiRYvk9uzsbL2BUFxczBMnTtDZ2ZmrV68mqYyLXUnKk7n48OFDzpkzhz4+PvLzfKXoKW8GJkmmp6fz9OnTlWjlnzN//nx269ZNb4bjxIkT9PDw4MWLF584/vz582zfvr1c60wpfaw8OtLT0zljxgy6u7srqvwDWX5/xMbG0t/fnz/88ANJZejQarX8/PPPGR4erndBmzp1Kvv378/Bgwfziy++4JEjR0g+Xvj8ySefsHHjxorSQZZfi8T58+f5yy+/VLa5z8Tx48fp5+dHkhw2bBgbNWrEuLg4Tp8+nfPnzyf5eMP6Dz/8UJFjpCSmkBWrFKpEUPXee++xX79+LC4u5vTp07ls2TK++eabDAgIYMeOHfXqbdy+fVtOBVfKxa4kz5q5KJGdnS0vilaSpvLqUCrff/89hw4dqhcMarVaurm5cffu3aX+v5WY+VMeHUVFRbxw4QLPnj0rH2eMOiTS09Pl45SiIykpiTt27NArgKlSqThw4EAOHz6cDRs2ZL9+/Xj37l0WFBTw8uXL8iJ7Jekgn12Lbral0unWrRtv3LhBknzrrbfo5ORECwsLxsbGknw845aSkqK4MaJrg/S7qWTFKoEqE1S1bduWW7Zs4ebNm+ng4EBPT09+8803fOONN9iyZUv+9ttvhjazTP5q5qLS7vT+io7t27crUoeuFumRhdSen59PT09P7tu3Tz4mJSWFmZmZlW1qmfwVHefPn5drnSkFU/WH9Pu9e/c4cOBALlq0SB4X+/fvp5mZGffs2WMIU/+Uv6JFieVeSp6zpL/bt28vF4L+7LPPaGFhwXr16jEmJkaRWYvkH5mLubm5vHbtmty+detWo8qKVTLKSKd4zgwbNgzVq1fH22+/jblz52LIkCEYMGAAzM3NMWvWLDx48EDeLFlpSBttSpmLEqVlLq5Zswbz5s3D9OnTMXv2bISGhipmn6+/o6Nv376K1JGWlia3W1payu3FxcXyPmVSRmNMTAxatmwpb5BsaP6OjlatWuHGjRuVbnNpmLo/pKy42rVrY8mSJRgzZow8VlxdXdG4cWNFbegO/D0tStp492nnLOlc1KVLFzg5OWHx4sX4+OOPER8fj5dffhlTp05FbGwsioqKDGL309Ddy8/X1xexsbHyawEBAfjiiy9Qu3ZtOStWynpXWlas0qkSJRUaNmyIH374ARqNBiTh6ekJAJgxYwYSExNhYWEBR0dHA1v5JEVFRYiOjoavry+ys7Oxfv16TJs2TW9TTl1effVVmJmZyaUh1qxZAz8/v8o0uVSqmg5zc3OoVCoUFhZCpVJh7dq1GDlyJFasWIFGjRoZyPo/EDqMT4dKpZLPUVIgkpiYiOrVq8PJyckgdpeGqWgpS0e1atUAAHXq1EHv3r1haWmJ9evXIzg4GMHBwcjLy0Pjxo0VsdGzhG5A5ePjg1atWmHSpEny69bW1rL9ubm5cskOAEhKSoKHh4fignfFYuipMkMSFxdHc3Nzjhw50tCmPIGpZC5WRR3k48eZbdu2ZWRkJM3MzOTSHEJHxVBVdZDknTt3GBMTwxo1aiiqmKSpaHlWHWfPnuVrr70mb+pcsvK70tYaaTQauru7620tc/LkSe7fv59nz559wl6lZsUqnSoZVG3evJljx46lp6cn161bx8mTJ/Prr782tFmlYiqZi1VNx61bt2hra0uVSqWX+SN0VCxVTceFCxc4cOBAuru7K2pDYV1MRUtZOqTkmkePHinK5qeh1Wo5YcIEWllZcdeuXSTJ0aNHs3nz5nRwcKC5uTn/7//+T94+i1RmVqwxoCJJQ8+WVTbXr1/H1q1b4evrC39/fxQUFCAzMxOurq6GNu0JpkyZgpSUFMTFxeGjjz5C/fr1cerUKSQnJ6OgoABhYWGYOHEiACAjIwMPHjyAt7c3JLcaukq3RFXUMWvWLDRp0gSvvPKK0PGcqIo6du/ejVq1asHPz09xOgDT0VKWjry8PAwcOBDjx483tJnPzKFDh7Bw4ULcv38f2dnZKCwsxGeffQa1Wo2jR4/i7bffxrBhwzBnzhz5PTdu3EDdunUV5xtFY6hoztBI2WZK3KxTF1PIXCSrlo4zZ86Q1K+krrS7PKHDeHUoHVPRYgrnrJJ9/fDhw3z55ZfZvn37J3bWWL58OS0tLRVZbNWYqJIzVcZEamoqoqKicP36dbi5uaFNmzawtLSEr68vgoKC0LZtW2zYsAHt27c3tKllUpV0xMbG4h//+IehTS0ToUNZmMr4AExHizHrkPa7BaC33y0AnDx5EufPn0doaCisra3lvf/i4+MxduxYHDt2DM7OzoYy3ehRTnqCoFSMNXOxJFVJh1IymMpC6FAWpjI+ANPRYqw6/iwDs1WrVvDx8ZFLJEgZmBcvXoRarYaZmZm8sbXgL2CgGTLB30TJmYvlQehQFkKHsjAVHaTpaFGyjr+bgblly5bKMNOkqRLFP02J+Ph4jBs3Du+++y5Wr14Ne3t7rFy50tBmlRuhQ1kIHcrCVHQApqPFGHRIs0vfffcdbty4gZ49e+LOnTto0qQJAECj0UCj0cjHX7hwAcOHD8eUKVPwzTffICQkRF6ULvhriDVVRoYxZS6WhdChLIQOZWEqOgDT0WJMOsqTgZmQkAA7Ozt07NhRZPlVACKoMkKkhYVSlVxjRehQFkKHsjAVHYDpaDEWHVOmTMFPP/2EGTNmQKvVIioqCvb29pgxYwYOHjyIY8eO4dtvv4WPj4+hTTU5RFAlEAgEAoEJYcyZi8aOyP4TCAQCgcCEMNbMRVNALFQXCAQCgcDEcHZ2hlqtlgMqAGjRogVWr16NwMBAeHl5GdA600UEVQKBQCAQmDDGkLloKog1VQKBQCAQmDDGlLlo7IigSiAQCAQCE8dYMheNHRFUCQQCgUAgEFQAYk2VQCAQCAQCQQUggiqBQCAQCASCCkAEVQKBQCAQCAQVgAiqBAKBQCAQCCoAEVQJBAKBQCAQVAAiqBIIBAKBQCCoAERQJRAIBP+jQYMGWLBggfy3SqXCpk2bDGaPQCAwLkRQJRAIFM/QoUOhUqmgUqlgYWEBV1dX9OjRAytXroRWq62w7zl27BhGjBhRYZ8nEAiqFiKoEggERkFwcDBu3ryJtLQ0bN++HQEBARg3bhxCQkJQVFRUId/h7OwMa2vrCvksgUBQ9RBBlUAgMAqsrKzwwgsvoF69emjdujWmTp2KzZs3Y/v27Vi1ahUA4MGDBxg+fDicnZ1ha2uLwMBAJCUl6X3Oli1b0K5dO1SvXh1OTk7o16+f/FrJx38luXbtGgYOHAh7e3s4ODggNDQUaWlpz0GtQCAwRkRQJRAIjJbAwED4+vrihx9+AACEh4cjIyMD27dvx4kTJ9C6dWt069YN9+/fBwBs27YN/fr1Q+/evXHy5EkkJiaiffv2z/RdhYWFCAoKQq1atXDgwAEcOnQINWvWRHBwMAoKCp6bRoFAYDyYG9oAgUAg+Du8+OKLOH36NA4ePIijR48iIyMDVlZWAIC5c+di06ZNiIuLw4gRI/DJJ59g0KBB+Oijj+T3+/r6PtP3bNiwAVqtFitWrIBKpQIAxMTEwN7eHnv37kXPnj0rXpxAIDAqRFAlEAiMGpJQqVRISkqCRqOBo6Oj3uuPHj3CpUuXAACnTp3CG2+88Ze+JykpCampqahVq5Zee15envz5AoGgaiOCKoFAYNQkJydDrVZDo9GgTp062Lt37xPH2NvbAwBq1Kjxl79Ho9GgTZs2WLt27ROvOTs7/+XPFQgEpoMIqgQCgdHy3//+F2fOnMGECRNQv3593Lp1C+bm5mjQoEGpx7do0QKJiYmIiooq93e1bt0aGzZsgIuLC2xtbf+m5QKBwBQRC9UFAoFRkJ+fj1u3biE9PR2//vorPv30U4SGhiIkJARDhgxB9+7d4e/vj1deeQW7du1CWloafv75Z0ybNg3Hjx8HAMyYMQPr16/HjBkzkJycjDNnzmD27NnP9P2vv/46nJycEBoaigMHDuDKlSvYu3cvxo4di+vXrz9P6QKBwEgQQZVAIDAKduzYgTp16qBBgwYIDg7Gnj17sHDhQmzevBnVqlWDSqVCQkICXnrpJURFRcHb2xuDBg3C1atX4erqCgDo2rUrNm7ciPj4eLRs2RKBgYE4evToM32/tbU19u/fD3d3d4SFhaFJkyYYNmwY8vLyxMyVQCAAAKhI0tBGCAQCgUAgEBg7YqZKIBAIBAKBoAIQQZVAIBAIBAJBBSCCKoFAIBAIBIIKQARVAoFAIBAIBBWACKoEAoFAIBAIKgARVAkEAoFAIBBUACKoEggEAoFAIKgARFAlEAgEAoFAUAGIoEogEAgEAoGgAhBBlUAgEAgEAkEFIIIqgUAgEAgEggpABFUCgUAgEAgEFcD/A+OoWRWa/W7CAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.style.use(\"default\")\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "graph_data = combined\n", "graph_data[\"rank\"] = pd.qcut(graph_data[\"sale_price\"], 10, labels=False)\n", "graph_data[\"rank\"] = graph_data[\"rank\"] + 1\n", "graph_data[\"decile\"] = pd.qcut(\n", " graph_data[\"sale_price\"] / 1000, 10, precision=0\n", ")\n", "graph_data[\"decile\"] = graph_data[\"decile\"].astype(str).str.replace(\"(\", \"\\$\")\n", "graph_data[\"decile\"] = graph_data[\"decile\"].str.replace(\", \", \" - \\$\")\n", "graph_data[\"decile\"] = graph_data[\"decile\"].str.replace(\".0\", \"K\", regex=False)\n", "graph_data[\"decile\"] = graph_data[\"decile\"].str.replace(\"]\", \"\")\n", "graph_data[\"decile\"] = graph_data[\"decile\"].str.replace(\n", " \" - \\$9050K\", \"+\", regex=False\n", ")\n", "graph_data = graph_data.groupby([\"rank\", \"decile\"]).apply(\n", " lambda x: pd.Series(\n", " {\n", " \"Median Sales Ratio\": np.median(x[\"ratio\"]),\n", " }\n", " )\n", ")\n", "\n", "graph_data = graph_data.reset_index()\n", "plt.scatter(graph_data[\"decile\"], graph_data[\"Median Sales Ratio\"])\n", "plt.xticks(rotation=45)\n", "plt.xlabel(\"Decile\")\n", "plt.ylabel(\"Ratio\")\n", "plt.suptitle(\"Median Sale Ratios: Open Data Sample\", fontsize=14)\n", "plt.title(\"By decile of sale price in 2020\")\n", "plt.gca().set_yticklabels([f\"{x:.0%}\" for x in plt.gca().get_yticks()])\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Detecting selective appraisals\n", "\n", "Selective appraisal, sometimes referred to as sales chasing, happens when a property is reappraised to shift its assessed value toward its actual sale price. The CCAO requires selective appraisal detection in every sales ratio study. This is because selective appraisal renders all other sales ratio statistics suspect. In the code below, we construct two sets of ratios, one normally distributed, and one 'chased.'" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'Blue Chased?': np.False_, 'Red Chased?': np.True_}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from statsmodels.distributions.empirical_distribution import ECDF\n", "\n", "# Generate fake data with normal vs chased ratios\n", "normal_ratios = np.random.normal(1, 0.15, 10000)\n", "chased_ratios = list(np.random.normal(1, 0.15, 900)) + [1] * 100\n", "\n", "# Plot to view discontinuity\n", "ecdf_normal = ECDF(normal_ratios)\n", "ecdf_chased = ECDF(chased_ratios)\n", "plt.plot(ecdf_normal.x, ecdf_normal.y, color=\"blue\")\n", "plt.plot(ecdf_chased.x, ecdf_chased.y, color=\"red\")\n", "plt.xlabel(\"Ratio\")\n", "plt.ylabel(\"F(x)\")\n", "plt.grid()\n", "plt.show()\n", "\n", "{\n", " \"Blue Chased?\": ap.detect_chasing(normal_ratios),\n", " \"Red Chased?\": ap.detect_chasing(chased_ratios),\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ratios that include selective appraisals will be clustered around the value of one much more than ratios produced from a CAMA system. We can see this visually in the graph where the cumulative distribution curve shows a discontinuous jump, or 'flat spot', near one." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GINI Coefficient for Vertical Equity\n", "Another way to measure the vertical equity of assessments is to look at differences in Gini coefficients, a widely used metric to analyze inequality.\n", "\n", "The first step in this process is to order the data (ascending) by sale price. Next, calculate the Gini coefficient of sales and assessed values (both ordered by sale price). The difference between these Gini coefficients is known as the Kakwani Index (KI), while the ratio is known as the Modified Kakwani Index (MKI). See [this paper](https://researchexchange.iaao.org/jptaa/vol17/iss2/2/) for more information on these metrics.\n", "#### Lorenz curves\n", "\n", "Using the ordered data, you can plot the classic [Lorenz curve](https://en.wikipedia.org/wiki/Lorenz_curve):" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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c9+7dM32VKlWKc+fOhViGYPDgwTg4OFC0aFGyZs1Kjx49Qo2TmTRpEteuXSNt2rQULVqUUaNGhUjGgn/32rRpE+o1XLRoEX5+fqbX4Wv3ypgxI/369WPRokUkTZoUFxcXZs+eHeJ1vHv3LpqmkTVr1lCPd/PmTdP/V/DP3f+/lpaWliH+iPqSJEmS4OLiwpYtW0w/a2vWrMHCwoLGjRubzrt+/Tr16tXDyckJR0dHnJ2dTbOovvTzF1737t1D0zSGDx8e6jkHd0UHP+9ff/0Vd3d3smXLRp48eRg4cCBXrlz57hiEjLkR0eD/xx8E/2W4cuXKEGMKgllYhPyx/G+rz9cMHDiQkydPcuDAgRCDlz8nR44cAFy9ejXEX+mfYzAY0DQt1P7PDUb83NiLpk2b0q5dOy5dukT+/PlZv349lSpVImnSpKZzjEYjVapUYdCgQWHeI1u2bF+N93MMBkO4zovI2JH/cnR0pEqVKlSpUgVLS0uWL1/O6dOnKVeuHKtWraJt27bUrVuXgQMHkixZMszNzZkwYQL379//7D2Df25atmz52ZlEefPm/ez19+/fp1KlSuTIkYNp06aRNm1arKys2LVrF9OnTw81CPhzP3dh/f9/qw0bNuDn58fUqVOZOnVqqOOrV682DT7PmTMnt2/fZseOHezZs4dNmzYxZ84cRowYYTqncePGlClThi1btrBv3z4mT57MxIkT2bx5M9WrVzc9x8mTJ392bJODg0O47gUwdepU2rZty7Zt29i3bx8//fQTEyZM4NSpU6RJkwaj0YjBYGD37t1hvp7BjxVZWrZsyY4dO9ixYwc//vgjmzZtomrVqqY/jtzd3SlXrhyOjo78+uuvZM6cGRsbGy5cuMDgwYO/OBD8c78z//+7H3yPAQMGfLbVM/iPi7Jly3L//n3T67do0SKmT5/OvHnzQo0hFBEjyY2IEGdnZ+zs7Lh9+3aoY7du3cLMzOyrg/8yZ84MqEGowa0QkWHt2rXMmDGDGTNmUK5cuXBdU7t2bSZMmMCqVavCldwkSpQozO6w/2/x+Jq6devSpUsXU9fUnTt3GDJkSIhzMmfOzIcPHyL1NUqfPj1Go5G7d++SM2dO035XV1fc3d1Jnz59pD1WsMKFC7N8+XJevnwJwMaNG8mUKRObN28O8YHx/wOs/5+zszMJEiQgKCjom16Tv/76Cz8/P7Zv3x6iVSa8s7TCkj59+jC7FML6/QjL6tWr+eGHH8J87vPnz2fNmjUhZtbZ29vTpEkTmjRpgr+/P/Xr12fcuHEMGTIEGxsbQHURd+/ene7du/P69WsKFizIuHHjqF69uul3z9HRMVyv4ZfuFSxPnjzkyZOHYcOGceLECUqVKsW8efMYO3YsmTNnRtM0MmbM+MVkPPjn7u7du1SsWNG0PyAggIcPH5IvX76vxgrw448/kiBBAtasWYOlpSXv378P0SX1999/8/btWzZv3kzZsmVN+x8+fPjVeydKlAhQCdJ//f/vfnBLk6WlZbhe48SJE9OuXTvatWvHhw8fKFu2LKNGjZLk5jtJt5SIEHNzc6pWrcq2bdtCLC3v6urKmjVrKF26tKmr53NcXFxwdHRk/PjxBAQEhDr+5s2bCMd17do1OnbsSMuWLendu3e4rytRogTVqlVj0aJFbN26NdRxf39/BgwYYNrOnDkzt27dChHj5cuXQ3UPfE3ChAlxcXFh/fr1rF27FisrK+rWrRvinMaNG3Py5En27t0b6np3d3cCAwMj9JgANWrUAAg102ratGkA1KxZM8L3BDWz6+TJk2Ee2717N/Cpqyb4L/j/toCcPn36s9cHMzc3p0GDBmzatIlr166FOv61n5uwHtfDw4OlS5d+8bovqVGjBqdOneLMmTMh4vjcVO7/evr0KUePHqVx48Y0bNgw1Fe7du24d+8ep0+fBuDt27chrreysiJXrlxomkZAQABBQUGhulWSJUtGqlSpTNP8CxUqRObMmZkyZQofPnwIFVPwaxiee3l6eob6GcyTJw9mZmamc+rXr4+5uTmjR48O1eKlaZrpORUuXBhnZ2fmzZsXYsHMZcuWhUomvsTW1pZ69eqxa9cu5s6di729PXXq1DEdD+tnwN/f/6tj80AlYObm5qHGuv3/tcmSJaN8+fLMnz/flND/139/Tv///9TBwYEsWbKEWpZBRJy03IgwLVmyJMwl83v37s3YsWPZv38/pUuXpnv37lhYWDB//nz8/PyYNGnSV+/t6OjI3LlzadWqFQULFqRp06Y4Ozvz5MkTdu7cSalSpUKs9RIewWMuypYty6pVq0IcK1my5Bf77VesWEHVqlWpX78+tWvXplKlStjb23P37l3Wrl3Ly5cvTWvdtG/fnmnTpuHi4kKHDh14/fo18+bNI3fu3KEGUn9NkyZNaNmyJXPmzMHFxcU0JTbYwIED2b59O7Vq1TJNQfb29ubq1ats3LiRR48ehejGCo98+fLRpk0bFixYYGqiP3PmDMuXL6du3bpUqFAhQvcL5uPjQ8mSJSlevDjVqlUjbdq0uLu7s3XrVv755x/q1q1LgQIFAKhVqxabN2+mXr161KxZk4cPHzJv3jxy5coV5gfuf/32228cPnyYYsWK0alTJ3LlysW7d++4cOECBw4c4N27d5+9tmrVqlhZWVG7dm26dOnChw8fWLhwIcmSJQvzQyg8Bg0axMqVK6lWrRq9e/c2TQVPnz79V8dOrFmzBk3T+PHHH8M8XqNGDSwsLFi9ejXFihWjatWqpEiRglKlSpE8eXJu3rzJrFmzqFmzJgkSJMDd3Z00adLQsGFD8uXLh4ODAwcOHODs2bOmLi8zMzMWLVpE9erVyZ07N+3atSN16tQ8f/6cw4cP4+joaFoh+Wv3OnToED179qRRo0Zky5aNwMBAVq5caUpCQf0xMHbsWIYMGcKjR4+oW7cuCRIk4OHDh2zZsoXOnTszYMAALC0tGTt2LF26dKFixYo0adKEhw8fsnTp0nCPuQnWsmVLVqxYwd69e2nRokWIRUNLlixJokSJaNOmDT/99BMGg4GVK1eGq6vRycmJRo0aMXPmTAwGA5kzZ2bHjh1hjvOaPXs2pUuXJk+ePHTq1IlMmTLh6urKyZMnefbsGZcvXwbUoPXy5ctTqFAhEidOzLlz50zT78V30mGGlojBgqdvf+7r6dOnmqZp2oULFzQXFxfNwcFBs7Oz0ypUqKCdOHEizHt9blr54cOHNRcXF83JyUmzsbHRMmfOrLVt21Y7d+6c6ZywpjNrmqaNHDlS+++Pb/DU3bC+/jtF83N8fHy0KVOmaEWKFNEcHBw0KysrLWvWrFqvXr20e/fuhTh31apVWqZMmTQrKystf/782t69ez87FXzy5MmffUxPT0/N1tZWA7RVq1aFeY6Xl5c2ZMgQLUuWLJqVlZWWNGlSrWTJktqUKVM0f3//Lz6nz712AQEB2ujRo7WMGTNqlpaWWtq0abUhQ4aEmLaqaeo1De801YCAAG3hwoVa3bp1tfTp02vW1taanZ2dVqBAAW3y5MkhpvcajUZt/PjxpvMKFCig7dixI9RrqGmhp4Jrmqa5urpqPXr00NKmTatZWlpqKVKk0CpVqqQtWLDgq3Fu375dy5s3r2ZjY6NlyJBBmzhxorZkyZJQ07Y/99zDWgrgypUrWrly5TQbGxstderU2pgxY7TFixd/dSp4njx5tHTp0n0x3vLly2vJkiXTAgICtPnz52tly5bVkiRJollbW2uZM2fWBg4cqHl4eGiapml+fn7awIEDtXz58mkJEiTQ7O3ttXz58mlz5swJdd+LFy9q9evXN90rffr0WuPGjbWDBw+G+14PHjzQ2rdvr2XOnFmzsbHREidOrFWoUEE7cOBAqMfbtGmTVrp0ac3e3l6zt7fXcuTIofXo0UO7fft2iPPmzJmjZcyYUbO2ttYKFy6sHT169LPLL3xOYGCgljJlSg3Qdu3aFer48ePHteLFi2u2trZaqlSptEGDBml79+4NNc07rJ/HN2/eaA0aNNDs7Oy0RIkSaV26dNGuXbsW5vvM/fv3tdatW2spUqTQLC0ttdSpU2u1atXSNm7caDpn7NixWtGiRbWECRNqtra2Wo4cObRx48Z99XdbfJ1B0yJxdJwQQgghhM5kzI0QQggh4hRJboQQQggRp0hyI4QQQog4RZIbIYQQQsQpktwIIYQQIk6R5EYIIYQQcUq8W8TPaDTy4sULEiRIEO76OkIIIYTQl6ZpeHl5kSpVKszMvtw2E++SmxcvXny19pEQQgghYqanT59+tTByvEtuEiRIAKgX52s1kIQQQggRM3h6epI2bVrT5/iXxLvkJrgrytHRUZIbIYQQIpYJz5ASGVAshBBCiDhFkhshhBBCxCmS3AghhBAiTpHkRgghhBBxiiQ3QgghhIhTJLkRQgghRJwiyY0QQggh4hRJboQQQggRp0hyI4QQQog4RZIbIYQQQsQpuiY3R48epXbt2qRKlQqDwcDWrVu/es3ff/9NwYIFsba2JkuWLCxbtizK4xRCCCFE7KFrcuPt7U2+fPmYPXt2uM5/+PAhNWvWpEKFCly6dIk+ffrQsWNH9u7dG8WRCiGEECK20LVwZvXq1alevXq4z583bx4ZM2Zk6tSpAOTMmZNjx44xffp0XFxcoipMIYQQQoTDh1cfeHP5Bc55U+KQ8uvVu6NKrBpzc/LkSSpXrhxin4uLCydPnvzsNX5+fnh6eob4EkIIIUTku7X4OBmrZedFplK6xhGrkptXr16RPHnyEPuSJ0+Op6cnHz9+DPOaCRMm4OTkZPpKmzZtdIQqhBBCxBtuPm689n7Nx3vPAfC0T6lrPLEqufkWQ4YMwcPDw/T19OlTvUMSQggh4oyjj4+Sb14+mm9qjvmlUwC4p/lB15h0HXMTUSlSpMDV1TXEPldXVxwdHbG1tQ3zGmtra6ytraMjPCGEECLeMGpGJvwzgRF/j8CoGXG0dsTS9RkAvoXL6BpbrGq5KVGiBAcPHgyxb//+/ZQoUUKniIQQQoj4x/WDK9VWVWPY4WEYNSOt87Xm7I87KPLyNgB2VeLxmJsPHz5w6dIlLl26BKip3pcuXeLJkyeA6lJq3bq16fyuXbvy4MEDBg0axK1bt5gzZw7r16+nb9++eoQvhBBCxDuHHh4i//z87H+wHztLO5bVWcbyussx33kMgHMUIlc5Z11j1DW5OXfuHAUKFKBAgQIA9OvXjwIFCjBixAgAXr58aUp0ADJmzMjOnTvZv38/+fLlY+rUqSxatEimgQshhBDRINAYSM9dPXn14RW5nXNzttNZ2uRvA4DX+t0AnHCsTooUekYJBk3TNH1DiF6enp44OTnh4eGBo6Oj3uEIIYQQscrlV5eZd24eU12mYmdpp3YGBvLRMRm2H98ztPxxxh0uGemPG5HP71g15kYIIYQQ0Wvf/X0sPL/QtJ0vRT7m1pr7KbEBOHYM24/veUciElcrqkOUIcWq2VJCCCGEiB6BxkBGHh7JhGMTsDCzoFCqQhRMWTDsc5csxwLYRAMqV9M/tdA/AiGEEELEKM88n9FsUzOOPVGDhDsU6EAu51xhn/zxI2zaBMCuJK3pmDe6ovw8SW6EEEIIYbLr7i5ab2nN249vSWCVgEU/LqJx7safv2DnTix8vHhMOpL8WAqDIfpi/RxJboQQQggBwNCDQxl/bDwABVMWZH3D9WROnPmL12hLl2IA1tAcl+oxYyhvzIhCCCGEELpLbJsYgF5Fe3Gi/YmvJja8eAF79gCw2rIdMWVlFmm5EUIIIeIxb39v7K3sAehXoh/F0hSjdLrS4bt43jwMRiPHKEXm6tmIKSusSMuNEEIIEQ/5B/nTZ08fCi8szAf/DwAYDIbwJzZBQbBoEQDz6UKDBlEVacRJciOEEELEMw/eP6DUklL8fvp3brnd4q/bf0X8JocOwcuXvCUxmy2aULt25Mf5raRbSgghhIhHNt3YRPvt7fH08ySRTSKW111O7ezfkJmsWgXAehpTrooViRJFcqDfQZIbIYQQIh7wDfRlwL4BzD47G4CSaUvyZ4M/SeeULuI38/ZG27wZA7CSVnRqFLmxfi/plhJCCCHigYH7BpoSm8GlBvN3m7+/LbEB2LABw4cPPCAjl2xKxKjxNiAtN0IIIUS8MLTsUP5+/DeTq0ymWpZq33ezuXMBWEBnGjQ0xJhZUsGk5UYIIYSIgz4GfGTN1TWm7RQOKbjc9fL3JzYXLsCZM/hjyRLa06bNdwYaBaTlRgghhIhjbrndovGGxlx9fRULMwtT+QQzQyS0acyZA8AGGmGdJhkVKnz/LSObJDdCCCFEHLLi8gq67eyGT4APyeyTmVYdjhSurqZZUrPpQevWYG4eebePLJLcCCGEEHGAt783vXb3YumlpQBUzFiRVfVWkTJBysh7kGnTwM+PUxTjJCVY3jbybh2ZJLkRQgghYrnrr6/TeGNjbry5gZnBjJHlRjK0zFDMzSKxWeXxY5g5E4CxDKNKFQNZs0be7SOTJDdCCCFELHf//X1uvLlBSoeUrGmwhvIZykf+g0ydCh8/csqiNDsDa7K5W+Q/RGQxaJqm6R1EdPL09MTJyQkPDw8cY9rcNSGEECKcNE3DYDCYthdfWEzt7LVJZp8s8h/MzQ3SpYOPH6nMfm6lrsyjR2ARjU0kEfn8lqngQgghRCxz+dVlSi8tzVOPp6Z9HQp2iJrEBmDkSPj4kVv2BTlIJTp3jt7EJqIkuRFCCCFiCU3TmH9uPsUWFePE0xP039c/6h/0/n1YsACAbt5TsLQ00LFj1D/s94jBeZcQQgghgnn6edL5r86su74OgJpZazKn5pyof+BhwyAwkHPO1fj7TQU6toFUqaL+Yb+HJDdCCCFEDHfh5QWabGzCvXf3sDCzYEKlCfQr0S9yFuX7kiNHYO1aNIOBzm/GYWYGgwZF7UNGBkluhBBCiBjs8MPDVFtdDf8gf9I5pWNdw3UUT1M86h84KAj69gXgYObOXLxXkMYNibHTv/9LkhshhBAiBiuepjjZk2QnU6JMLKmzJHJXHP6SZcvg4kWCEjjR4v4YAH7+OXoe+ntJciOEEELEMNdfXydH0hyYm5lja2nL4TaHSWybOMTU7yjl4wNDhwKwOfcIXp9ypnp1KFAgeh7+e8lsKSGEECKG0DSN6SenU2B+ASYcm2Dan8QuSfQlNgBLloCrK4FpM9LufE8AhgyJvof/XtJyI4QQQsQA7z6+o+3Wtvx15y8Arr2+Fmqhvmjx/j2MHg3AunQD8H5qRYUKUKZM9IbxPSS5EUIIIXR24ukJmm5sylPPp1iZWzHdZTrdCneL/sQG1NRvNzf8s+ai46lOAIwZE/1hfA9JboQQQgidGDUjU05M4ZeDvxCkBZElcRbWN1xPgZQ6DW65dAnmzgVgWqbZ+N61xMUFSpXSJ5xvJcmNEEIIoZP77+4z4vAIgrQgmv3QjPm15pPAOoF+AQ0fDpqGZ40mDN1THoBff9UvnG8lyY0QQgihk6xJsjKrxiw0TaNjwY76dEMFO3sWduwAc3NGmY3BaITataFoUf1C+laS3AghhBDRxKgZ+e3Yb1TOVJmiqVXW0LFgDCjUpGkwcCAA72u2ZMZfaqW+2NhqAzIVXAghhIgWrh9cqbaqGkMPDaXJxiZ4+3vrHdInq1apUgu2tvT1HI2mQaNGkD+/3oF9G2m5EUIIIaLYoYeHaLG5Ba8+vMLWwpaR5UZib2Wvd1jK27fQrx8A91qMZPmi9FhawoQJX7kuBpPkRgghhIgiQcYgxhwdw69HfkVDI7dzbtY3Wk8u51x6h/bJ4MHg5ob2ww80P6eSnG7dIHNmneP6DpLcCCGEEFHA08+TOmvr8PejvwFon789M2vMxM7STt/A/uuff2DxYgD215/H2V8tcXRUS93EZpLcCCGEEFHAwcoBe0t77C3tmVdrHi3zttQ7pJD8/aFrVwAC23Wk0zK1mM3PP4Ozs56BfT9JboQQQohIEmgMJCAoAFtLW8wMZiyvuxw3HzeyJ82ud2ihzZsHN26AszN/pJrIkyeQOjX07q13YN9PZksJIYQQkeCZ5zMqLq9I151dTfuS2CWJmYmNm5tpnve7Pr8yfHpiAH77DexiUK/Zt5LkRgghhPhOu+7uIv+8/Pzz5B+23NzCI/dHeof0Zf37q1lSefLQ82IHfHygdGlo0ULvwCKHJDdCCCHENwoICmDQ/kHUXFOTtx/fUjBlQS50uUCGhBn0Du3z9uyBFSvAYOB85/n8udESMzOYORP0XCA5MsmYGyGEEOIbPPF4QtONTTn57CQAvYr2YnKVyVhbWOsc2Rd4eUGXLgAE9epNm3klADWuOLYu2BcWSW6EEEKICDJqRqqtqsZNt5s4WTuxpM4S6uesr3dYXzd0KDx5AhkyMC/1WK5fhyRJYMwYvQOLXNItJYQQQkSQmcGM36v9TvE0xbnY5WLsSGxOnoRZswB4/9t8fhmnVkgePx4SJ9YzsMhn0DRN0zuI6OTp6YmTkxMeHh44OjrqHY4QQohY4sH7B9x/d58qmauY9hk1I2aGWNBO4OcHBQrAzZvQpg3N/Jaxdi0UKgSnT4O5ud4Bfl1EPr9jwf+IEEIIoa9NNzZRYH4BGm5oyP139037Y0ViAzBihEpskiVjf/VprF0LZmawYEHsSGwiKpb8rwghhBDRzzfQl567etJwQ0M8/TzJ7ZwbS3NLvcOKmEOHYNIkAHxnzKPTYNUH1acPFCyoY1xRSAYUCyGEEGG4+/YuTTY24eKriwAMKjmIsRXHxq7kxscHOnRQ33fuzLDz9Xj8GNKnh9Gj9Q0tKklyI4QQQvyftdfW0vmvznj5e5HENgkr6q2gRtYaeocVcWPGwKNHkDYtl1pNZXo5tXvOHHBw0DWyKCXJjRBCCPF/Tj87jZe/F2XSlWFNgzWkcUyjd0gRd+UKTJ4MQNCMmXTo7YDRCE2aQI1YmKdFhCQ3QgghBKBpGoZ/l+idWGUiWRJnoUvhLliYxcKPSn9/aN8egoKgQQN+f1SHCxcgYUKYMUPv4KKeDCgWQggR7626soqaa2oSaAwEwMrcih5Fe8TOxAbUgJrz5yFRIu7/9DtDh6rdkydDihT6hhYdJLkRQggRb3n7e9N+W3tabWnF7nu7WXpxqd4hfb/Tp1V5byBo3gJaDEqNry9UrfppbHFcF0tTUiGEEOL7XH99ncYbG3PjzQ0MGBhZbiTtC7TXO6zv4+WlSnsbjdC8OVMeNuT0aXB0hEWL4k5hzK/RveVm9uzZZMiQARsbG4oVK8aZM2e+eP6MGTPInj07tra2pE2blr59++Lr6xtN0QohhIjtNE1j6cWlFFlYhBtvbpDCIQUHWx9kZPmRmJvF8hXteveG+/chbVpu9pjFiBFq94wZkDatrpFFK12Tm3Xr1tGvXz9GjhzJhQsXyJcvHy4uLrx+/TrM89esWcPPP//MyJEjuXnzJosXL2bdunX88ssv0Ry5EEKI2Gr0kdG0396ej4EfqZKpCpe7XqZCxgp6h/X9NmyApUvBzIzAZato9VMi/P2hZk1o21bv4KKXrsnNtGnT6NSpE+3atSNXrlzMmzcPOzs7lixZEub5J06coFSpUjRv3pwMGTJQtWpVmjVr9tXWHiGEECJYk9xNcLR2ZFzFcexpuYdk9sn0Dun7PX0KnTur74cMYcLxssHjiVmwIP50RwXTLbnx9/fn/PnzVK5c+VMwZmZUrlyZkydPhnlNyZIlOX/+vCmZefDgAbt27aLGFybs+/n54enpGeJLCCFE/KFpGpdeXTJt53TOycPeD/mlzC+xpzbUlwQFQatW4O4ORYtyvvZIfv1VHZo5E1Kl0jU6Xej2v+rm5kZQUBDJkycPsT958uS8evUqzGuaN2/Or7/+SunSpbG0tCRz5syUL1/+i91SEyZMwMnJyfSVNj51OgohRDzn6edJ883NKbSgEP88/se0P7FtYh2jimSTJsGRI2Bvj8/C1bRoY0lgINSvD82b6x2cPmJVyvr3338zfvx45syZw4ULF9i8eTM7d+5kzJgxn71myJAheHh4mL6ePn0ajRELIYTQy8WXFym0oBBrr63FgIGbbjf1DinyHT+OadTwzJn0m5OF27dVa0187I4KpttU8KRJk2Jubo6rq2uI/a6urqT4zApDw4cPp1WrVnTs2BGAPHny4O3tTefOnRk6dChmZqFzNWtra6ytrSP/CQghhIiRNE1jztk59NvXD/8gf9I5pWNtg7WUSFtC79Ai17t30LQpBAZC06ZsS9SW+fPVoRUrIEkSfcPTk24tN1ZWVhQqVIiDBw+a9hmNRg4ePEiJEmH/APr4+IRKYMzN1bQ9TdOiLlghhBCxgruvO402NKLn7p74B/nzY/YfudjlYtxLbDQNOnaEZ88ga1ZejVlIx06qmWbAAKhUSef4dKbrIn79+vWjTZs2FC5cmKJFizJjxgy8vb1p164dAK1btyZ16tRMmDABgNq1azNt2jQKFChAsWLFuHfvHsOHD6d27dqmJEcIIUT8tfXWVjbd3ISlmSWTqkyid7HepnpRccrcubBlC1haYlyzlrY9HXBzg/z5YexYvYPTn67JTZMmTXjz5g0jRozg1atX5M+fnz179pgGGT958iRES82wYcMwGAwMGzaM58+f4+zsTO3atRk3bpxeT0EIIUQM0iZfG664XqHZD80okrqI3uFEjX/+UYv1AUyYwKwTBdm7F2xsYM0akJEYYNDiWX+Op6cnTk5OeHh44OjoqHc4QgghvsO7j+8YdmgYEypNwMnGSe9wot7z51CwILx+DU2acPWXPylS1ICfH8yeDd276x1g1InI57fUlhJCCBErnXx6kqabmvLE4wkefh6srr9a75Cilp8fNGigEpu8efH+YzFNK6jEpmZN6NZN7wBjjlg1FVwIIYQwakYmH59M2WVleeLxhMyJMtO/RH+9w4p6vXqpit+JEsHmzfw0xJ4bNyBlSliyJP5O+w6LtNwIIYSINdx83GiztQ277u4CVCmFBbUX4Ggdx4cZLFgACxeqDObPP1l1MjNLloCZmRpnkywOVJCITJLcCCGEiBUuvbpErTW1eO71HGtza/6o/gedCnaKm7Oh/uvkSejZU30/fjy3M7jQtYHaHDECypfXLbIYS5IbIYQQsUIaxzQAZE+SnfWN1pM3eV6dI4oGL1+qcTYBAdCwIR9/GkzjEuDtDRUqwLBhegcYM0lyI4QQIsby9PM0dTkltUvK3pZ7SZ8wPQ5WDjpHFg38/aFRI5Xg5M4NS5fSr7+BK1fA2RlWrwZZ4i1sMqBYCCFEjHT44WGyz8rO8kvLTftyJ8sdPxIbgL59Ve0oJyfYsoX1uxyYN08dWrVKDSQWYZPkRgghRIwSZAxi9N+jqbyyMq8+vGL22dkYNaPeYUWvpUthzhw1gHj1au4ZstKpkzo0ZAhUrapveDGddEsJIYSIMV56vaTllpYcengIgHb52zGz+kzMDPHob/GzZz8tWjN6ND4VatKgBHh6QqlS8Ouv+oYXG0hyI4QQIkbYf38/Lbe05LX3a+wt7Zlbcy6t8rXSO6zo9fo11K+vFuyrUwftl6F0bQdXrqjp3uvWgYV8cn+VvERCCCF09+D9A6qvrk6QFkSeZHlY32g9OZLm0Dus6OXrC3Xrqkrf2bPDihXMX2jGypVq4PC6dZA6td5Bxg6S3AghhNBdpkSZGFxqMG8/vmW6y3RsLW31Dil6GY3Qtq1a0yZhQti2jTO3HP9bH1PWs4kASW6EEELoYvfd3WRPmp1MiTIBMLbi2Li/IN/njBz5qc9p82bckmSnYRU1G7x+fRgwQO8AY5d4NEJLCCFETBAQFMCg/YOosaYGTTc2xT/IHyD+JjbLl8PYser7BQsIKluB5s3h6VPIlk1NnIqvL823kpYbIYQQ0eaJxxOabmzKyWcnASiauiiapukclY7+/psQc7zbtWPUcNi/H+zsYPNmcIzjZbOigiQ3QgghosX229tpu7Ut733f42TtxOIfF9MgVwO9w9LPnTuqzykgQK1EPHYsmzZ9asRZtEgtTCwiTpIbIYQQUco/yJ+fD/zM9FPTASiSqghrG641jbWJl9zcoGZNeP8eiheH5cu5cs2MNm3U4b59oVkzfUOMzWTMjRBCiCilaRpHHx8FoE+xPhxrfyx+JzZ+flCvHty7BxkywLZtuHnbUqeOKohZpQpMmqR3kLGbtNwIIYSIEpqmYTAYsLawZn2j9Vx1vUqdHHX0DktfmgYdO8KxY2owzc6dBCRKRmMXePQIMmWCtWtlob7vJS+fEEKISOUX6MeAfQNIaJOQMRXHAGodm3jdWhNszBhV9dLcHDZuhFy56P8THD4MDg6wfTskTqx3kLGfJDdCCCEizb1392iysQkXXl7AzGBGm/xtyJI4i95hxQzLlqn1bADmzoUqVVi8GGbOVLtWrpQBxJFFxtwIIYSIFOuvr6fg/IJceHmBJLZJ2N50uyQ2wfbu/TTle/Bg6NSJEydC1Mekbl3dootzpOVGCCHEd/kY8JG+e/sy//x8AEqnK82fDf4kjWManSOLIc6fhwYNIDAQWrSACRN4/PjTLPD69WHYML2DjFskuRFCCPHNNE2j8srKnHh6AgMGhpQewugKo7Ewk48XAB48gBo11DSoSpVgyRI8PA3UrAmurpA3r1qg2Ez6USKV/PQJIYT4ZgaDgU4FO3H37V1W1V9F1cxV9Q4p5nBzg2rV4PVryJcPNm8mwGBF48Zw/TqkTAk7d6qBxCJySXIjhBAiQnwCfHjs/piczjkBaJu/LXWy1yGRbSKdI4tBfHygVi24exfSp4ddu9ASONKrG+zbp0or/PUXpJGeuyghDWFCCCHC7cabGxRdWJSqq6ry1uetab8kNv8RGAhNm8Lp02pe9549kCoV06fD/PmqCOaaNVCokN6Bxl2S3AghhAiXZZeWUXhBYa6/uU6gMZBH7o/0Dinm0TTo3l01y9jYqIVrcuRg61YYMECdMmUK1InnaxlGNemWEkII8UUf/D/QY1cPVlxeAUDlTJVZVW8VyR2S6xxZDDR2LCxcqEYI//knlCrF+fNqkpSmQdeuqm6UiFqS3AghhPisq65XabyxMbfcbmFmMOPX8r8ypMwQzAzS8B/KkiUwYoT6ftYsqFuXp0+hdm01BMfFRS3YZzDoG2Z8IMmNEEKIz5p4fCK33G6RKkEq/mzwJ2XTl9U7pJhp1y7o3Fl9/8sv0K0bXl5qTPHLl/DDD7B+vdSMii7yMgshhPis2TVmY2thy/hK43G2d9Y7nJjp9Glo3BiCgqB1axg7loAAaNgQrlyB5Mlhxw5VJ1NED2lXFEIIYXLx5UUG7huIpmkAONk4sfDHhZLYfM6DB6rfydsbqlaFRYvQMNCxY8gp3+nT6x1o/CItN0IIIdA0jbnn5tJ3b1/8g/zJ5ZyLdgXa6R1WzOburvqd3ryBggVh0yawtGTYUFixQhX+3rABihTRO9D4R5IbIYSI5zx8Pej4V0c23tgIQO1stamTQ+Yqf1Fwv9PNm5A6tWqecXBg7lwYP16dMn++qrwgop8kN0IIEY+dfX6WJhub8ND9IZZmlkysPJE+xftgkCk9nxe8ls3Bg2BvrwbUpErFtm3Qs6c6ZdQo6NBB1yjjNUluhBAinlpycQldd3QlwBhAhoQZWNdwHUVTF9U7rJhv6lRYtEitZbNuHeTPz8mTalFioxE6dvw0I1zoQ5IbIYSIp7IkzkKQFkT9nPVZ/ONiEtok1DukmG/9ehg0SH0/fTrUrMnt22roja8v1KwJc+fKWjZ6k+RGCCHiEXdfd1MSUzZ9WU53PE2hlIWkGyo8duz4tNRwz57w00+8eqUKf797pwYOr1sna9nEBN80FXzlypWUKlWKVKlS8fjxYwBmzJjBtm3bIjU4IYQQkcOoGZlyYgoZf8/ILbdbpv2FUxWWxCY8Dh6EBg1UUczmzWHGDLy81IDhR48gc2aV+9jb6x2ogG9IbubOnUu/fv2oUaMG7u7uBAUFAZAwYUJmzJgR2fEJIYT4Tm4+bvz4548M3D8Qd193Vl5eqXdIscupU1C3Lvj7Q716sGwZfoHm1KsHFy+Cs7Mq/J0smd6BimARTm5mzpzJwoULGTp0KObm5qb9hQsX5urVq5EanBBCiO9z7MkxCswvwM67O7E2t2ZezXmMrThW77Bij0uXoHp1+PABKlWCP/8kyMySFi0+TZbauROyZNE7UPFfEe4ZfPjwIQUKFAi139raGm9v70gJSgghxPcxakYmHpvI8MPDCdKCyJYkG+sbridfinx6hxZ7PHyoql26u0OpUrBtG5qVNV07q/X6rKxg61ZZpC8minDLTcaMGbl06VKo/Xv27CFnzpyREZMQQojvtOzSMn459AtBWhAt87bkfOfzkthExMuXqpzC69eQP79qnrG355dfPs0CX7MGKlfWO1ARlgi33PTr148ePXrg6+uLpmmcOXOGP//8kwkTJrBo0aKoiFEIIUQEtc7XmrXX1tL0h6a0y99OBg1HhJubylru3YOMGVVi4+TElCnw22/qlPnz1fhiETMZtODqaBGwevVqRo0axf379wFIlSoVo0ePpkMsWI7R09MTJycnPDw8cJQSrUKIOCLIGMTii4tpm78tVuZWgKoXJUlNBLm7Q8WKaqRw6tTwzz+QMSNLl0L79uqU336DwYN1jTJeisjn9zclN8F8fHz48OEDyWLREHFJboQQcc2rD69osbkFhx4eom/xvkxzmaZ3SLHThw+qK+rkSTUF6uhRyJGDrVtVK43RCAMHwqRJegcaP0Xk8/u7lhqys7PDzs7ue24hhBDiOxx4cICWm1vi6u2KnaUdBVKEnvAhwuHjR/jxR5XYJEoEBw5AjhwcPvyprEL79jBxot6BivCIcHKTMWPGLzZzPnjw4LsCEkII8XWBxkBG/z2acf+MQ0MjT7I8rG+0nhxJc+gdWuzj768qfB8+DAkSwN69kDcvp06pfMfPTy1zM3++lFWILSKc3PTp0yfEdkBAABcvXmTPnj0MHDgwsuISQgjxGc89n9N8c3OOPj4KQKeCnfi92u/YWtrqHFks5OOjEpvdu8HWVg0eLlKECxdUWYX/LG8jZRVikQj/V/Xu3TvM/bNnz+bcuXPfHZAQQogv+xj4kYsvL+Jg5cCCWgtolqeZ3iHFTh8+qIqXR46oxGbrVihThuvX1dAbDw8oXRq2bQMbG72DFRHxXQOK/+vBgwfkz58fT0/PyLhdlJEBxUKI2Oj/Zz7tubeHzIkykzVJVh2jisWCC0MdOwaOjqrlpmRJ7t6FMmXA1VUtznfggDos9BeRz+9vKpwZlo0bN5I4ceLIup0QQoh/PfV4Srll5Tjw4IBpX7Us1SSx+VYeHqpp5tgxSJgQ9u+HkiV59Eh1Qbm6Qt68ql6UJDaxU4S7pQoUKBDirwdN03j16hVv3rxhzpw5kRqcEELEd3/d/ou229ry7uM7euzqwY3uNzA3M//6hSJs79+rkgpnz36aFVWwIM+fq8Tm6VPIkUPlO/L3euwV4eSmbt26IbbNzMxwdnamfPny5Mgho/SFECIy+Af5M+TAEKadUmvWFE5VmHUN10li8z1cXVWLzZUrkCSJqnyZLx+urmpB4gcPIFMmle/EouXbRBgibcxNbCFjboQQMd0j90c02diEM8/PANC7WG8mVp6ItYW1zpHFYk+fqhabmzchRQrVNPPDD7i6qgWJb9yAtGnVun0ZMugdrAhLpI+58fT0DPdXRM2ePZsMGTJgY2NDsWLFOHPmzBfPd3d3p0ePHqRMmRJra2uyZcvGrl27Ivy4QggREz31eEqB+QU48/wMCW0SsqXJFmZUmyGJzfc4eRIKFVKJTerUKoP5v8QmdWo4dEgSm7giXN1SCRMm/Gp9kuCR/EFBQeF+8HXr1tGvXz/mzZtHsWLFmDFjBi4uLty+fTvMkg7+/v5UqVKFZMmSsXHjRlKnTs3jx49JmDBhuB9TCCFisjSOaaidrTZ3391lbYO1pE+YXu+QYrf9+6FOHbUCcb58al53+vShEpu//4YsWfQOVkSWcHVLHTlyJNw3LFeuXLjPLVasGEWKFGHWrFkAGI1G0qZNS69evfj5559DnT9v3jwmT57MrVu3sLS0DPfj/Jd0SwkhYpr77+6T0CYhSeySAOAT4IOlmSWW5t/2Pif+tW0bNG6sViCuXh02bAB7e0lsYqloK5z5Pfz9/bGzs2Pjxo0hBim3adMGd3d3tm3bFuqaGjVqkDhxYuzs7Ni2bRvOzs40b96cwYMHY24e9iA7Pz8//Pz8TNuenp6kTZtWkhshRIyw/vp6Om7vSPkM5dnWdJtU8Y4sa9ZA69YQFKSqXq5ZA1ZWktjEYtFSONPHx4cnT57g7+8fYn/evHnDdb2bmxtBQUEkT548xP7kyZNz69atMK958OABhw4dokWLFuzatYt79+7RvXt3AgICGDlyZJjXTJgwgdGjR4crJiGEiC6+gb703dOXeefnAfDu4zs8/TxxsnHSObI4YOFC6NIFNE0lOIsXg4WFJDbxSISTmzdv3tCuXTt2794d5vGIjLmJKKPRSLJkyViwYAHm5uYUKlSI58+fM3ny5M8mN0OGDKFfv36m7eCWGyGE0Mudt3dovKExl10vAzCk9BB+rfArFmZSvOi7TZ8Owe/53brBrFlgZiaJTTwT4RWK+/Tpg7u7O6dPn8bW1pY9e/awfPlysmbNyvbt28N9n6RJk2Jubo6rq2uI/a6urqRIkSLMa1KmTEm2bNlCdEHlzJmTV69ehWpBCmZtbY2jo2OILyGE0MvqK6spOL8gl10v42znzJ4WexhfabwkNt9L02DMmE+JzaBBMHs2mJnx8qUkNvFNhJObQ4cOMW3aNAoXLoyZmRnp06enZcuWTJo0iQkTJoT7PlZWVhQqVIiDBw+a9hmNRg4ePEiJEiXCvKZUqVLcu3cPo9Fo2nfnzh1SpkyJlZVVRJ+KEEJEK58AH4YdHoZ3gDflM5TnUtdLuGRx0Tus2E/TYPBgGDFCbY8ZA7/9BgYDT55A2bKS2MQ3EU5uvL29TdO0EyVKxJs3bwDIkycPFy5ciNC9+vXrx8KFC1m+fDk3b96kW7dueHt7065dOwBat27NkCFDTOd369aNd+/e0bt3b+7cucPOnTsZP348PXr0iOjTEEKIaGdnace6husYWW4kB1odIFWCVHqHFPsZjdC9O0yerLanT4dhw8Bg4MEDldjcu6fWrzl6VBKb+CLC7aDZs2fn9u3bZMiQgXz58jF//nwyZMjAvHnzSJkyZYTu1aRJE968ecOIESN49eoV+fPnZ8+ePaZBxk+ePMHM7FP+lTZtWvbu3Uvfvn3JmzcvqVOnpnfv3gwePDiiT0MIIaLF8kvLCdKCaF+gPQBFUxelaOqiOkcVRwQGQvv2sHIlGAywYAF07AjArVuqVtSLF5A1q6q0IMMt448ITwVftWoVgYGBtG3blvPnz1OtWjXevXuHlZUVy5Yto0mTJlEVa6SQdW6EENHhg/8HeuzqwYrLK7A2t+ZKtytkS5JN77DiDj8/aN4cNm8Gc3OV4DRrBsDVq6pW1OvXkDu3qhX1maGcIhaJkqngDRs2pGPHjrRo0cK0DkOhQoV4/Pgxt27dIl26dCRNmvT7IhdCiDjgqutVGm9szC23W5gZzBhWdhiZE2XWO6y44/17tXbN4cNgZaUW5/vxRwDOn1e1Md+9g/z51QLF8tEU/4Q7uXn//j01a9YkVapUtGvXjrZt25IpUybs7OwoWLBgVMYohBCxgqZpLL64mF67e+Eb6EuqBKlYU38N5TKEf+V28RVPn0K1amqEcIIEquWmcmUATpxQCxF7ekKxYrB7NyRKpHO8QhfhHlB88OBBHjx4QIcOHVi1ahVZs2alYsWKrFmzJsQKwEIIER9pmkabrW3o9FcnfAN9qZalGpe6XJLEJjJduwYlSqjEJlUqOHbMlNgcPqxabDw91SDi/fslsYnPIjRbKn369IwaNYoHDx6wf/9+UqVKRadOnUiZMiU9evTg/PnzURWnEELEaAaDgayJs2JuMOe3Sr+xs/lOnO2d9Q4r7jh7VmUtz59Drlxw6hT8uyL+1q2qxcbbG6pUUS02CRLoG67Q13fXlvLy8mLNmjX88ssveHh4EBgYGFmxRQkZUCyEiCyapuHu604iW9VEEGQM4trra+RLkU/nyOKYvXvVGBtvb9XftGsXJE4MwLJl0KGDmhFer54qIWVjo2+4ImpE5PM7wuvc/NfDhw+ZMmUK48ePx8PDg8r/Ng8KIURc5+HrQZONTSi/vDwfAz4CYG5mLolNZFuzBmrVUolNpUqqv+nfxGbaNGjXTiU27dvD+vWS2AglwsmNr68vq1atomLFimTNmpUVK1bQoUMHHj58yJ49e6IiRiGEiFHOvThHwQUF2XBjAzfe3OD40+N6hxT3aBpMmgQtWqj1bJo1Uy02CRKgaTB0KPTvr04dMAAWLQILqWAh/hXuH4UzZ86wZMkS1q1bh6+vL/Xq1WPPnj1UqlTJNDVcCCHiMk3TmHlmJgP2DSDAGEB6p/Ssa7iOYmmK6R1a3KJpMHAgTJ2qtvv0Ud+bmREUBD16wPz56tCECarygnwMif8Kd3JTvHhx8uXLx5gxY2jRogWJZBi6ECIeef/xPe23t2frra0A1M1RlyU/LjGNtxGRJDAQOnVSg2kApkwxNdH4+0OrVqr7yWCAefOgc2f9QhUxV7iTm3Pnzsl6NkKIeKv7ru5svbUVK3MrplSZQs+iPaXVOrJ9+ABNm8LOnWrV4UWLoG1bQA25qV8f9u0DS0tYvRoaNdI3XBFzhTu5kcRGCBGfTaw8kfvv7jO35lwKpSqkdzhxj6sr1Kyplhi2tYW1a02rDr97pw6dOgV2drBli1rTRojP+a7ZUkIIEVe99XnLskvLTNvpnNJxuuNpSWyiwu3banG+8+dVrYRDh0yJzdOnanmbU6fUonwHD0piI75OxpYLIcT/Of7kOE03NeWZ5zOS2CahdvbaANINFRWOH1eJzLt3kDkz7NkDWbIAakHiatXUun0pU6ouqR9+0DleESuEq+Vm+/btBAQERHUsQgihK6Nm5Ldjv1FuWTmeeT4ja+KspHVKq3dYcdemTWrtmnfvoGhROHnSlNgcOQKlS6vEJmdOdUgSGxFe4Upu6tWrh7u7OwDm5ua8fv06KmMSQoho99r7NTVW12DIwSEEaUE0z9Oc853Pkz9Ffr1Di5tmzVIjgv38VMvN4cPgrMpVbNigup48PKBUKVVCKn16neMVsUq4khtnZ2dOnToFqHUepGlWCBGXHHl0hPzz8rP3/l5sLGxYVHsRq+qtIoG1FCiKdJoGw4ZBr17q+27dVGVvOzsA/vgDmjRR077r1QuxILEQ4RauMTddu3alTp06GAwGDAYDKVKk+Oy5QUFBkRacEEJEh5cfXvLyw0tyJs3J+kbr+SGZ9H9EiYAA6NoVlixR27/+qhIdgwGjEX7+GSZPVoe6d1eJjrm5fuGK2Ctcyc2oUaNo2rQp9+7d48cff2Tp0qUkTJgwikMTQoio899W6KY/NMU/yJ8GORtgb2Wvc2Rx1IcP0LixKtltZqaWGO7YEVCtNO3bq7VrAMaNgyFDZNVh8e0iXBV89OjRDBw4ELt/mxBjG6kKLoQ4+OAgA/YPYHeL3aRw+HxLtIgkL19C7dqf1rBZt05tA56equD3gQOh1u0TIoSIfH5HOLkJ9ubNG27fvg1A9uzZcf53IFhMJ8mNEPFXkDGI0UdGM/boWDQ0uhbqytxac/UOK267elWtwPf0qRowvGOHmhmFynlq1IBLl8DeHjZuVFO/hQhLRD6/I7zOjY+PDz179mTlypWm8TXm5ua0bt2amTNnxtoWHSFE3PbC6wXNNzXnyOMjAHQs0JGpLlN1jiqO27NHdUV5eUH27KqsQubMAFy5ArVqqZwnWTJ1qHBhneMVcUaEVyju27cvR44cYfv27bi7u+Pu7s62bds4cuQI/YPrzwshRAyy995e8s3Lx5HHR3CwcmB1/dUs/HEhdpbyx1iUmTdPZS9eXlC+vFqo5t/EZs8etYbN06eQLRucOCGJjYhcEe6WSpo0KRs3bqR8+fIh9h8+fJjGjRvz5s2byIwv0km3lBDxy4brG2i8sTEA+ZLnY32j9WRLkk3nqOKwoCAYPBim/tsq1qYNLFgAVlYAzJ2rZoEHBUG5cmoWuEz1FuER5d1SyZMnD7U/WbJk+Pj4RPR2QggRpaplqUa2JNmonLEyU12mYmNho3dIcZe3N7RsCVu3qu2xY+GXX8BgICgIBg2CadPUodatYeFCU84jRKSKcMtNpUqVSJIkCStWrMDGRr1JfPz4kTZt2vDu3TsOHDgQJYFGFmm5ESLuO/XsFMVSFzNN9fb088TRWn7fo5Srq+qGOndOZSzLlkGzZoDKeVq0gG3b1KljxsDQoTLVW0RMlLbc/P7777i4uJAmTRry5csHwOXLl7GxsWHv3r3fFrEQQkQC/yB/fjn4C1NPTmVa1Wn0LdEXQBKbqHb/Pri4qH+TJFFZTKlSALx4oaornD8P1tawdKkp5xEiykQ4ufnhhx+4e/cuq1ev5tatWwA0a9aMFi1aYGtrG+kBCiFEeDxyf0TTjU05/fw0AM+9nuscUTxx8KCaEfXuHWTMCHv3QtasgJoRVbMmPHsGSZOq3qp/cx4holSEkxsAOzs7OnXqFNmxCCHEN9l6ayvttrXD3dedhDYJWVpnKXVz1NU7rLhv9mzo3VuNDi5SRLXYpEwJqIWIGzdWCxP/3yxwIaJchKeCCyFETOEX6Efv3b2pt64e7r7uFEtdjItdLkpiE9UCAlTBy549VWLTqhUcPWpKbObMUcNvPnwINQtciGghyY0QIta68eYGc87NAaB/if4cbXeUDAkz6BtUXOfmBlWqqHVsDAaYOBGWLwcbGwICVMHLHj3AaFRlFPbuhUSJ9A5axDff1C0lhBAxQYGUBZhZfSZpHNNQK1stvcOJ+65cgTp14NEjSJAA1qxRTTTA27fQqBEcPqxynvHj1XI3MiNK6EGSGyFErOEb6Mvg/YPpULADeZPnBaBr4a46RxVPbNmiup+8vVUf0/btkCsXADduqDqYDx6Ag4PKef6tiymELiLcLZUpUybevn0bar+7uzuZMmWKlKCEEOL/3Xl7h+KLivPHmT9osrEJgcZAvUOKH4xGGD0a6tdXiU2lSnDmjCmx2bkTihdXiU2GDGp8jSQ2Qm8RTm4ePXpkKpj5X35+fjx/LlMvhRCRb83VNRRaUIjLrpdxtnNmhssMLMyk4TnKuburbqhRo9R2r16qMFTixGgaTJqkEhkvL1VK4exZ+OEHPQMWQgn3u8P27dtN3+/duxcnJyfTdlBQEAcPHiRDhgyRGpwQIn7zCfCh9+7eLLq4CIBy6cuxpsEaUiVIpXNk8cCVK6q15v59sLFRA4jbtAHA1xc6d4aVK9WpXbrAH39IKQURc4Q7ualbty4ABoOBNv/+gAeztLQkQ4YMTA0ulCaEEN/p1YdXVFlZhWuvr2HAwPCywxlebri02ESH1auhUyf4+BHSp1fVLQsWBODlS6hXD06fBnNz+P13NUNKBg6LmCTc7xJGoxGAjBkzcvbsWZImTRplQQkhhLOdM8nsk5HcPjmr66+mUqZKeocU9/n7w4ABMHOm2q5aVY0OTpIEUCUU6tSB58/V9O4NG9QQHCFimgj/CfTw4cOoiEMIIfD298bczBwbCxvMzcxZXX81ACkcUugcWTzw4oWay33ihNoeOlQNJDY3B2DtWmjfXjXm5MihJkv9W2VBiBjnm9p3Dx48yMGDB3n9+rWpRSfYkiVLIiUwIUT8cu31NRpvaEy59OWYW2suIElNtDl6VNVKcHUFJyc1mObfKU+BgfDzzxA86qBaNZXo/GfYpRAxToRnS40ePZqqVaty8OBB3NzceP/+fYgvIYSICE3TWHxhMUUWFuGm202239nOW5/Qy02IKKBpMG0aVKyoEps8eeDcOVNi4+amin0HJzZDhsCOHZLYiJgvwi038+bNY9myZbRq1Soq4hFCxCNefl5029mN1VdV95NLZhdW1ltJErskOkcWD3h6QocOsHGj2m7RAubPB3t7AC5cUAOHnzxRu5Ytg4YN9QtXiIiIcHLj7+9PyZIloyIWIUQ8cvnVZRpvbMydt3cwN5gztuJYBpUahJlBSt5FuWvX1DTvu3fB0lK13vToYZrytHKlmurt66vG1WzZArlz6xyzEBEQ4XeRjh07smbNmqiIRQgRT/gF+lFjTQ3uvL1DGsc0HGl7hJ9L/yyJTVTTNFiyBIoWVYlN2rTwzz+qurfBQEAA9O4NrVurxKZmTbUYsSQ2IraJcMuNr68vCxYs4MCBA+TNmxdLS8sQx6dNmxZpwQkh4iZrC2vm1pzLwgsLWVZnmXRDRQcPD+jaVY0GBjXNe/Vq+HdZD1dXNab46FF1eMQIGDkSzCTfFLGQQdM0LSIXVKhQ4fM3Mxg4dOjQdwcVlTw9PXFycsLDwwNHR0e9wxEi3jj/4jzvfd9TOVNl0z5N0zDI6m9R7+ZNtUDN3btqavevv6opUP9mLmfOqF6q589Vse+VK9XpQsQkEfn8jnDLzeHDh785MCFE/KNpGrPOzGLA/gE4WDlwqcsl0jqlBZDEJjps3w4tW6oCUOnSwfr1UKyY6fCSJdCtm1q/L3t22LpVrWMjRGz2zQ2O9+7dY+/evXz8+BFQb2BCCPFf7z++p8H6Bvy05yf8g/wpm74sDlYOeocVPwQFwbBhqgkmuLLluXOmxMbXV1VY6NBBJTZ16qgWHElsRFwQ4eTm7du3VKpUiWzZslGjRg1evnwJQIcOHejfv3+kByiEiJ1OPztNwQUF2XJrC1bmVvxR7Q82N95MIttEeocW97m5QfXqMG6c2u7VC/bvB2dnAB4+hFKlYNEiNUFqzBhVPkp66kVcEeHkpm/fvlhaWvLkyRPs7OxM+5s0acKePXsiNTghROyjaRrTTk6j9NLSPHJ/RKZEmTjR/gS9ivWSbqjocOaMKnK5fz/Y2alBw3/8oaZ8Azt3qsMXLqixxHv3qgYeGTgs4pIIj7nZt28fe/fuJU2aNCH2Z82alcePH0daYEKI2MlgMHDL7RaBxkAa5WrEwtoLcbKRJW2jnKbB7NnQrx8EBKgFajZvhh9+AFQv1ahRMHasOr1YMVX4Mm1a/UIWIqpEOLnx9vYO0WIT7N27d1hbW0dKUEKI2MeoGU3r1Pxe7XfKpS9H8zzNpbUmOnh6qgE069er7Xr1YOlSU52EN2+geXM4cEAd7tlTlVSwstIpXiGiWIQbIsuUKcOKFStM2waDAaPRyKRJk744TVwIETcZNSMTj02k1ppaGDVVSNfW0pYWeVtIYhMdrlyBwoVVYmNhAdOnw6ZNpsTm1CnVDXXgwKdeqpkzJbERcVuEW24mTZpEpUqVOHfuHP7+/gwaNIjr16/z7t07jh8/HhUxCiFiqDfeb2i9tTV77qnxdttubaNezno6RxVPBK823LOnmvqUJo1KcEqUMB2eMwf69lW9VNmzq5xHVhsW8UGEW25++OEH7ty5Q+nSpalTpw7e3t7Ur1+fixcvkjlz5qiIUQgRAx19fJT88/Oz594ebCxsWFR7EXVz1NU7rPjBzQ0aNYKOHVViU60aXLxoSmw+fFBL2/TsqRKbhg3h7FlJbET8EeEVimM7WaFYiO8TZAxiwrEJjPx7JEbNSM6kOVnfaD0/JPtB79Dih8OH1QCaV69UN9TYsTBwoGm609WrKu+5fVsdnjxZ1YuSHkIR20XpCsVLly7FwcGBRo0ahdi/YcMGfHx8aNOmTURvKYSIRbrv7M6CCwsAaJu/LbOqz8Leyl7nqOIBoxHGj1cFn4xGyJVL1UkoWBBQ3VBLl6rWmo8fIXVqVUaqdGmd4xZCBxHulpowYQJJ/y209l/JkiVj/Pjx3xTE7NmzyZAhAzY2NhQrVowzZ86E67q1a9diMBioW7fuNz2uECLiuhXpRmLbxCyvu5yldZZKYhMdXr1SXU/Dh6vEpl071c/0b2Lz4QO0aaNWG/748VMvlSQ2Ir6KcHLz5MkTMmbMGGp/+vTpefLkSYQDWLduHf369WPkyJFcuHCBfPny4eLiwuvXr7943aNHjxgwYABlypSJ8GMKIcIvyBjEyacnTdv5U+TncZ/HtM7XWseo4pHduyFvXrUon62tap5ZskRNfQKuXYMiRVQjjrk5TJigFur7dzFiIeKlCCc3yZIl48qVK6H2X758mSRJkkQ4gGnTptGpUyfatWtHrly5mDdvHnZ2dixZsuSz1wQFBdGiRQtGjx5NpkyZIvyYQojweeH1gkorKlFuWTnOPj9r2i/1oaKBnx/07w81aqiFavLmhfPnoW1b0ylLl0LRonDrFqRKpYbj/KfYtxDxVoR/BZo1a8ZPP/3E4cOHCQoKIigoiEOHDtG7d2+aNm0aoXv5+/tz/vx5Kleu/CkgMzMqV67MyZMnP3vdr7/+SrJkyejQoUNEwxdChNPee3vJPy8/Rx4fwdrCmhdeL/QOKf64cwdKloRp09T2Tz/B6dOQMycA3t4qx2nfXnVDVa2quqGkIVsIJcIDiseMGcOjR4+oVKkSFhbqcqPRSOvWrSM85sbNzY2goCCSJ08eYn/y5Mm5detWmNccO3aMxYsXc+nSpXA9hp+fH35+fqZtT0/PCMUoRHwTaAxk+KHh/Hb8NwDyJc/H+kbryZYkm86RxQOaBitWQI8eKoNJkkQ1z9SubTrl+nU1G+rmTdVCM2aMtNYI8f8ilNxomsarV69YtmwZY8eO5dKlS9ja2pInTx7Sp08fVTGaeHl50apVKxYuXBjmoOawTJgwgdGjR0dxZELEDU89ntJsUzOOP1ULcnYv3J2pLlOxsbDRObJ4wNMTunWDNWvUdoUKaiBN6tSAynuWL1d5j48PpEwJf/4J5crpGLMQMVSEk5ssWbJw/fp1smbNStasWb/rwZMmTYq5uTmurq4h9ru6upIiRYpQ59+/f59Hjx5R+z9/xRiNarl3CwsLbt++HWohwSFDhtCvXz/TtqenJ2mlUpwQYdp8czPHnx7H0dqRRbUX0Sh3o69fJL7fmTPQrBk8eKBGBf/6KwwerL5H5T1du6pkBqBKFVi1CpIl0zFmIWKwCCU3ZmZmZM2albdv3353YgNgZWVFoUKFOHjwoGk6t9Fo5ODBg/Ts2TPU+Tly5ODq1ash9g0bNgwvLy9+//33MJMWa2trKegpRDj1KtaLF14v6FyoM5kTy4rjUc5ohClTYOhQCAyE9OlVBvPvSsOghto0awYPH6pcZ/Ro1Q31b94jhAiLFkHbt2/XSpcurV29ejWil4Zp7dq1mrW1tbZs2TLtxo0bWufOnbWECRNqr1690jRN01q1aqX9/PPPn72+TZs2Wp06dcL9eB4eHhqgeXh4fG/oQsR6j94/0lptbqV5+XnpHUr88+KFplWurGmqx0nTGjfWtPfvTYeDgjRtwgRNs7BQhzNk0LQTJ/QLVwi9ReTzO8IDilu3bo2Pjw/58uXDysoKW1vbEMffvXsXofs1adKEN2/eMGLECF69ekX+/PnZs2ePaZDxkydPMJORckJEum23ttF2W1vcfd1xsHJgTs05eocUf+zapVbdc3NT69X88Yea+vRvjYSXL6FVKzh4UJ3epAnMn28q9C2E+IoI15Zavnz5F4/H9PILUltKxHf+Qf4M2j+I30//DkDR1EVZ13AdGRJm0Dew+MDHB4YMUckMQL58qhvq3yneEDrvmTlTLUgstaFEfBeltaVievIihPi8B+8f0GRjE869OAdA/xL9GV9pPFbmVjpHFg9cuQJNm6o53KDWrpk4EWzUTDQ/PzWG+HeVc5I/v6oNlT27PuEKEZt9U3/P/fv3GTZsGM2aNTOVSdi9ezfXr1+P1OCEEJHn70d/U2B+Ac69OEdi28T81ewvplSdIolNVNM0mDNHLSV88yakSKFKKvz+uymxuX0bihf/lNj06QOnTkliI8S3inByc+TIEfLkycPp06fZvHkzHz58AFT5hZEjR0Z6gEKIyJE9SXZsLGwolbYUl7pcola2WnqHFPe9fw8NGqjFafz8VCmFK1dUZUs+VfIuWBAuXYKkSWHHDpg+HWSSpxDfLsLJzc8//8zYsWPZv38/Vlaf/uKrWLEip06ditTghBDfx83HzfR9ygQpOdL2CIfbHCatk6z1FOVOn4YCBWDLFrC0VKUUduwwVbR8+xYaN1bjiH18oFIllffUrKlz3ELEARFObq5evUq9evVC7U+WLBlubm5hXCGE0MOfV/8k0++Z2Hhjo2lfjqQ5sDS31DGqeEDT1IDh0qXh8WPInBlOnoS+fU2jgvfvV3UwN24ECwv47TfYt0+tOiyE+H4RTm4SJkzIy5cvQ+2/ePEiqf9dJlwIoZ+PAR/p/Fdnmm9ujpe/Fysur9A7pPjDy0sNGu7dWy3K16gRXLgAhQoB4OurcpyqVeHFC8iRQzXwDB4staGEiEwR/nVq2rQpgwcP5tWrVxgMBoxGI8ePH2fAgAG0bt06KmIUQoTTLbdbFFtUjIUXFmLAwPCyw9ncZLPeYcUPp0+rwTPr16vmmBkzYN06+HfK6pUrUKSI2g3QvTucP68uEUJErghPBR8/fjw9evQgbdq0BAUFkStXLoKCgmjevDnDhg2LihiFEOGw4vIKuu3shk+AD8ntk7Oq/ioqZ6qsd1hxX2AgjBunynMHBUHatGoOd8mSgKqwMGOGWt7G31/Vg1q6VI0tFkJEjQgv4hfs6dOnXL16lQ8fPlCgQIFIqTUVHWQRPxEXXXh5gUILVNdHxYwVWV1/NSkcQhefFZHs3j1o2VK12oAqAjVnDiRMCMCzZ2pBvkOH1OHatWHRIil4KcS3iJJF/IxGI5MnT2b79u34+/tTqVIlRo4cGar8ghAi+hVMWZD+JfrjZO3EL2V+wdxMqipGKU2DxYvVgjTe3qouwpw50Ly56ZT166FLF3B3VysNT58OnTrJSsNCRIdwJzfjxo1j1KhRVK5cGVtbW37//Xdev37NkiVLojI+IUQYNE1jxeUVVMpUiTSOaQCYUnWKzlHFE69eQceOsHOn2i5XDlasgHTpAPD0hJ49YeVKdbhIEVi1CrJl0yleIeKhcA8oXrFiBXPmzGHv3r1s3bqVv/76i9WrV2M0GqMyPiHE//Hy86LVlla03daWZpuaEWgM1Duk+GPDBvjhB5XYWFnBpEmquuW/ic2RI6pc1MqVavbT8OFw/LgkNkJEt3C33Dx58oQa/xkBV7lyZQwGAy9evCBNmjRREpwQIqTLry7TeGNj7ry9g7nBnJpZa2JmkDnEUe79e9Ucs2aN2i5QQLXW/PADAB8/wtChauCwpkHGjCrBKVVKv5CFiM/CndwEBgZi828dlGCWlpYEBAREelBCiJA0TWPB+QX03tMbvyA/0jimYW2DtZRKJ5+eUW7/frWM8LNnYG6upj0NH65aboCzZ6F1a7h1S53esSNMnWqaAS6E0EG4kxtN02jbti3W/yl44uvrS9euXbG3tzft27xZ1tQQIjJ5+XnR8a+OrL++HoBa2WqxrM4yktgl0TmyOO7jR7W63syZajtrVtUcU6wYoKZ1jxkDEyaoGeApU6qZUDLFWwj9hTu5adOmTah9LVu2jNRghBChmZuZc+PNDSzMLPit0m/0K9EPg0y5iVr/3xzTvbsaX/PvH3LXrqnDFy+qw82awaxZkDixTvEKIUL45nVuYitZ50bEBpqmoaGZxtPcfHMTDz8PiqcprnNkcVxAAIwf/2lBvpQpYckSUxXvoCDV5TR8uGq5SZIE5s5VVRaEEFErSta5EUJED3dfdzps70DhlIUZUmYIADmdc+ocVTxw65Zqjjl7Vm03bqzWrkmiuv/u3VML8p04oQ7XqgULF0IKWStRiBhHplkIEYOceX6GAvMLsPnmZsYcHYPrB1e9Q4r7jEY1rqZAAZXYJEyoZkWtWwdJkmA0wuzZaor3iROQIIFqzNm+XRIbIWIqabkRIgbQNI0Zp2Yw+MBgAowBZEqUiXUN15HcIbneocVtT59Cu3ZqrRpQ5bqXLIHUqQF48gQ6dIADB9ThChVUXaj06XWKVwgRLtJyI4TO3n18R521dei3rx8BxgAa5mrIhc4XKJyqsN6hxV2aBgsWQO7cKrGxtVXNM3v2QOrUaBrMn6+WsTlwQB3+4w/1vSQ2QsR80nIjhI78g/wpvqg4d9/dxdrcmuku0+lauKvMhopKjx6p5pjgapbFi8Py5aZlhB8+VGvVBB8uWVI15mTPrk+4QoiIk5YbIXRkZW5Fn+J9yJo4K6c6nqJbkW6S2EQVTVNJTN68KnOxtYVp0+DYMciWDaNRTefOk+fT4enT4ehRSWyEiG1kKrgQ0czNx43X3q/J5ZwLUONtPgZ+xM7STufI4rC3b6FbN1UbClRdhGXLIEsWAO7eVY05//yjDpcrp4p+Z86sT7hCiNAi8vktLTdCRKN/Hv9Dvnn5qP1nbTx8PQAwGAyS2ESlfftUc8yGDWBhAWPHqgqXWbIQFKQab/LlU4mNvb0aenPokCQ2QsRmktwIEQ2MmpFxR8dRfnl5Xni9wMrcijc+b/QOK25zd4cuXcDFBV6+VH1LJ06oCpfm5ty8CaVLQ//+qtJC5cpq5eHu3VVFbyFE7CW/wkJEMdcPrlRbVY1hh4dh1Iy0ydeGc53OkSVxFr1Di7s2bYKcOdWMKIAePeDCBShShMBA+O03tazNqVNq3ZoFC1QDT4YMukYthIgkMltKiCh06OEhWmxuwasPr7CztGNOjTm0yR+6TpuIJO/fq6aXtWvVdvbsKnMpWxZQLTPt2sG5c+pwtWrqcNq0OsUrhIgSktwIEYWmn5rOqw+vyO2cm/WN1psGEYsocPiwKp/w7BmYm8PPP6siUNbW+PvDxImqZFRAgFqEePp0VU5BJqcJEfdIciNEFFpaZykTj01kdIXRMmg4qri7q4EzS5ao7axZYdUqKFoUgJMnoVMnuH5dHa5dG+bNg1Sp9AlXCBH1ZMyNEJFo3/19DNg3wLSd1C4pk6tOlsQmquzYoVYZDk5sunRRY2uKFsXLC376Sc36vn4dnJ1h9WrYtk0SGyHiOmm5ESISBBoDGXl4JBOOTUBDo2TaktTPWV/vsOKut2+hd2+VrYBqrVmyRE1/QuU83bur0lGgup+mTjUV+BZCxHGS3AjxnZ55PqP5pub880StANe1UFeqZ6muc1RxlKapwcK9e8ObN2rOdv/+MHo02Nri6qoOrVunTs+YUdWIqlJF37CFENFLkhshvsOuu7tovaU1bz++JYFVAhb9uIjGuRvrHVbc9PSpWmV45061/cMPqrWmSBE0DZYtVXnO+/efcp5Ro8BOegSFiHckuRHiG43/ZzxDDw0FoFDKQqxruI7MiWVZ20hnNKr52oMGgZcXWFrCsGFqNpSVFffvQ+fOnwpdFigAixZBwYL6hi2E0I8kN0J8o0IpC2HAQM+iPZlcZTLWFtZ6hxT33LmjpjodPaq2ixdXRZ9y5SIwEKZNgpEjwddXFbocPRr69lVVFoQQ8Ze8BQgRAa+9X5PMPhkALllcuN79Ojmdc+ocVRwUGKgWohkxQmUudnYwYYJaadjcnLNnVWvNpUvq9MqV1fRuqQclhACZCi5EuPgH+dN3T1+yz8rOg/cPTPslsYkCV69CiRKqG8rXV40Gvn4dfvoJdy9zevSAYsVUYpM4MSxfrkonSGIjhAgmyY0QX/Hw/UNKLynNjNMzcPd1Z/fd3XqHFDf5+6t+pUKFVH2EhAnVgOG9e9HSZ+DPPyFHDpgzR02aat0abt5U/8oqw0KI/5JuKSG+YNONTXTY3gEPPw8S2yZmWZ1l1M5eW++w4p6LF9ViNFevqu06dVQWkyoVd++qNWsOHFCHsmeHuXOhQgX9whVCxGzSciNEGHwDfem5qycNNzTEw8+DkmlLcrHLRUlsIpuPD/zyiyqVcPUqJE2q1rHZsgXfxKkYPRry5FGJjY0NjB0Lly9LYiOE+DJpuREiDH+c/oPZZ2cDMLjUYMZUGIOluaXOUcUx+/ZB167w8KHabtBANck4O3PggGqtuXtXHapWDWbNknE1QojwkeRGiDD0Ltabw48O81PRn6ieVVYbjlRubtCnz6fSCWnTwsyZUKcOr15B/xawZo06lDIl/P47NGwo42qEEOEn3VJCAB8DPjLlxBQCjYEAWFtYs7vFbklsIlNw6YRcuVRiY2amaiXcuEFQrTrMmaMGDK9Zow799BPcugWNGkliI4SIGGm5EfHeLbdbNN7QmKuvr+Lu687YimP1DinuuXFDZSsHD6rtPHnUYnxFinD6NPTsqSZIARQurNasKVRIv3CFELGbtNyIeG3l5ZUUXlCYq6+vktw+OeUzlNc7pLjF0xMGDIB8+VRiY2OjpnufO8ebDEXo0EEtOnzuHDg6qnE1p05JYiOE+D7SciPiJW9/b3rt7sXSS0sBqJixIqvrryaFQwqdI4sjNE2V5u7XD16+VPt+/BFmzCAwbUbmzYPhw8HdXR1q2xZ++w2SJ9crYCFEXCLJjYh3br65ScMNDbnx5gZmBjNGlhvJ0DJDMTcz1zu0uOHxY9XPtGOH2s6SRY0KrlGDY8egZz01nRtUkctZs6BkSf3CFULEPZLciHjHqBl5+P4hKR1SsqbBGumKiiwBAaoe1OjRav0aS0u1hs2QIbx8Z82gVrBqlTo1USIYP17VxDSXnFIIEckkuRHxQpAxyNQykztZbrY02UKBlAVMRTDFd/rnH+jWTdWAAihTBubPJyBLTmbOhFGjwMtLzXrq1AnGjVPr9QkhRFSQAcUizrv86jJ55+Xl2JNjpn0uWVwksYkMbm7Qvj2ULasSm6RJYelSOHKEw69ykj8/9O+vEpuiReH0aZg/XxIbIUTUkuRGxFmapjH/3HyKLSrGjTc3GLh/IJqm6R1W3GA0wqJFqtDTUjUom06d4NYtHpZrS6PGBipWVDPAkyZVp548CUWK6Bu2ECJ+kG4pESd5+nnS+a/OrLu+DoAaWWuwvO5yDLIa3Pe7dAl69IATJ9R23rwwbx5eP5RgwgSYNg38/NRCfN26wZgxaoyNEEJEF0luRJxz4eUFmmxswr1397Aws2BCpQn0K9EPM4M0VH4XNzcYNgwWLFBTvR0c4NdfMfboxYo1FgypD69eqVMrVlRji/Pm1TdkIUT8JMmNiFOuvb5GicUl8A/yJ51TOtY2WEuJtCX0Dit2CwpSA2WGDYP379W+xo1h6lSOP05Dn1KfVhfOnBmmTlVL2kgjmRBCL5LciDglt3NuamWrRaAxkKV1lpLYNrHeIcVuJ0+qLqiLF9V23rwwcyZPMpRl8EBVKgogQQK1KN9PP4G1tX7hCiEExJABxbNnzyZDhgzY2NhQrFgxzpw589lzFy5cSJkyZUiUKBGJEiWicuXKXzxfxH3nXpzDw9cDAIPBwKp6q9jaZKskNt/j2TNo0UKtrnfxIiRMCLNm4X30PCMOlCV7dpXYGAzQsSPcvQsDB0piI4SIGXRPbtatW0e/fv0YOXIkFy5cIF++fLi4uPD69eswz//7779p1qwZhw8f5uTJk6RNm5aqVavy/PnzaI5c6E3TNKafnE7JxSXpvKOzaSaUraWtDBz+VgEBakRwzpyqPLfBAO3bY7x5m1VOPcie24IxY8DXV83+Pn8eFi6UsglCiJjFoOk8N7ZYsWIUKVKEWbNmAWA0GkmbNi29evXi559//ur1QUFBJEqUiFmzZtG6deuvnu/p6YmTkxMeHh44Ojp+d/xCH+8+vqPdtnZsv70dgIa5GrKq3iqsLaTp4JsdPAi9esHNm2q7RAmYNYujHwoyYACcPat2Z8gAU6ZA/foyrkYIEX0i8vmta8uNv78/58+fp3LlyqZ9ZmZmVK5cmZMnT4brHj4+PgQEBJA4sXRBxBcnn54k/7z8bL+9HStzK2bXmM36huslsflWT56oAcKVK6vEJmlSWLyYW4uOUWd0QcqVU4mNg4MqmXDzJjRoIImNECLm0nVAsZubG0FBQST/vzbt5MmTc+vWrXDdY/DgwaRKlSpEgvRffn5++Pn5mbY9PT2/PWChK6NmZMqJKfxy8BeCtCCyJM7C+obrKZCygN6hxU5+fmpq07hxqhaUmRn06MHrHqMZOSMRCzuriVLm5mp9vlGjpPtJCBE7xOrZUr/99htr167l77//xsbGJsxzJkyYwOjRo6M5MhEV3H3d+f307wRpQTT7oRnza80ngXUCvcOKfTQNtm+HAQPg3j21r0wZPk6aydQD+ZhYGD58ULt//BEmToQcOfQLVwghIkrX5CZp0qSYm5vj6uoaYr+rqyspUqT44rVTpkzht99+48CBA+T9wkphQ4YMoV+/fqZtT09P0qZN+32BC10ktk3Mnw3+5LbbbToW7CiDhr/FzZvQvTv8/bfaTpkS46QpLPNrxvAGBl68ULuLFIHJk6FcOd0iFUKIb6brmBsrKysKFSrEwYMHTfuMRiMHDx6kRInPL7w2adIkxowZw549eyhcuPAXH8Pa2hpHR8cQXyJ2MGpGxh0dx6orq0z7yqYvS6dCnSSxiaiXL6FLF/jhB5XY2NjAL79wYPZt8k9qToeOKrHJkEFNkjp1ShIbIUTspXu3VL9+/WjTpg2FCxemaNGizJgxA29vb9q1awdA69atSZ06NRMmTABg4sSJjBgxgjVr1pAhQwZe/bveu4ODAw4ODro9DxG5XD+40mpLK/Y/2I+dpR0VMlQgtWNqvcOKfXx8VB2E8ePV9wB163Kj03T6zMjA/vFqV8KEagHinj1lrRohROyne3LTpEkT3rx5w4gRI3j16hX58+dnz549pkHGT548wczsUwPT3Llz8ff3p2HDhiHuM3LkSEaNGhWdoYsocvjhYZpvbs6rD6+wtbBlVvVZpEqQSu+wYpegIFi1SmUsz56pfcWL86zPFAZtK8WfNdUuKyuV0AwdCjLhUAgRV+i+zk10k3VuYq4gYxBjj47l16O/YtSM5HbOzfpG68nlnEvv0GKXgwehb1+4elVtp0/P+0ETGHq1KQsXGQgMVLubNlUTpTJl0i9UIYQIr4h8fuveciMEQKAxkGqrqnHwoRp/1aFAB/6o/gd2lnY6RxaLPHgA/fvD1q1qO2FCPvYZwgTvn5gywIaPH9Xu6tVVUlNAZtALIeIoSW5EjGBhZkGRVEU49ewU82vNp0XeFnqHFHv4+qpsZfJktXaNuTkBnXswN9lIRv2e2FTIu0QJmDBBBgoLIeI+6ZYSugk0BvL+43uc7Z0BCAgK4InHEzInzqxzZLGE0Qh//qkGzDx+rHZVqszGUjPoszA3L1+q03LnVuOJa9eWVYWFELGXdEuJGO+Z5zOabWqGX6Afx9ofw8rcCktzS0lswkPT4MAB+PlnuHBB7UqViuON/6DdX/W5d1BlMOnTw6+/quLe5uZ6BiyEENFL96rgIv7ZdXcX+efl59iTY9xyu8W119f0Din2OH0aKlWCqlXhwgW0BAm43mI8xRLdpcyMBty7b8DZGf74A27fhtatJbERIt4oXx769In8+7ZtC3XrRv59o5AkNyLaBAQFMGj/IGquqcnbj28pmLIgF7pcoGDKgnqHFvNdv67eXIoXh8OH0aysePBjbyqlv88Pq4dw9rodjo4wejTcv6+Ke8t6NUL86+RJleXXrKl3JPpatkz1TRsMqpZcmjTQrh28fv3l637/XV0bi0i3lIgWj90f03RTU049OwVAr6K9mFxlslTy/pqHD2HkSLVmjaahmZnxvHIberiOZPv29ICq1t2nD/TrB4kS6RuuEDHS4sUq41+8GF68gFTxeN0sR0fVrGs0wuXLKrl58QL27g19blCQSoScnKI/zu8kLTciWnT8qyOnnp3CydqJTY038Uf1PySx+ZJXr9Tqetmzw8qVoGm8LtuAlvmukXbfErZfTo+dHQwapPKfMWMksREiTB8+wLp10K2barn5/xaI9+/VwDRnZ7C1haxZYelSdczfX/0epkypSpakT6+mHAZzd4eOHdW1jo5QsaJKGIJdvgwVKkCCBOp4oUJw7pw69vixGuWfKBHY26uR/7t2fbr22jW1boODAyRPDq1agZvbp+Pe3qrf2cFBxTd1avheD4MBUqRQCV716vDTT2oM38eP6rVJmFAV1s2VSzX/PnkSulvKaIRJkyBLFnVOunRqxmawp0+hcWN1r8SJoU4dePQofPFFEkluRLSYW3MulTNV5mKXi9TPWV/vcGIud3c1+ylzZpg9GwICeF+kCl0LnSX50Y2suZgTGxu1Rt+DB6pid9KkegctRAy2fr0qa589O7RsCUuWqEH5wYYPhxs3YPduVVh27txPv1R//KE+6NevV60dq1erAmzBGjVSXTq7d8P581CwoBoT9+6dOt6iher6OXtWHf/5Z7C0VMd69FBLNxw9qhbcnDhRJSqg3gcqVlSLUZ07B3v2gKurShiCDRwIR47Atm2wb5+qGffvBIMIsbVVyUrw6p4+PiqWRYtUd3iyZKGvGTIEfvvt02u3Zo1KwAACAsDFRSV0//wDx4+r51WtmkoWo4sWz3h4eGiA5uHhoXcocdqDdw+0hecX6h1G7OHtrWm//aZpiRJpmnrr1dxzFNX65jsYvKlZWWlajx6a9vy53sEKEYuULKlpM2ao7wMCNC1pUk07fPjT8dq1Na1du7Cv7dVL0ypW1DSjMfSxf/7RNEdHTfP1Dbk/c2ZNmz9ffZ8ggaYtWxb2vfPk0bRRo8I+NmaMplWtGnLf06fqjeD2bU3z8lJvCOvXfzr+9q2m2dpqWu/eYd9T0zRt6VJNc3L6tH3njqZly6ZphQt/Og6adulSyOvatNG0OnXU956emmZtrWkLP/P+vnKlpmXPHvI18/NTse3d+/nYwiEin98y5kZEuk03NtFhewc8/TzJkDADlTNV1jukmCsgQP2FNGYMwQvTeKXLxTjbcUy8VQcwYGmpusWHDlWtv0KIcLp9G86cgS1b1LaFBTRposbelC+v9nXrBg0aqFaPqlVV90vJkupY27ZQpYpq9alWDWrVUueA6nL68AGSJAn5mB8/qlH9oAbCdeyoupYrV1YtPZn/Xe7ip5/UY+/bp441aAB583669+HDn1py/uv+ffUY/v5QrNin/YkTqzi/xsND3ddoVAuAli6t3oOCWVl9iiMsN2+qFqdKlcI+fvky3LunWm7+y9f30+sSDSS5EZHGN9CXAfsGMPvsbABKpClB1sRZdY4qhgpegG/ECNW/BHgny8Aku9GMfdQCI+ZYW0OnTmpcTdq0OscrRGy0eLHqbvnvAGJNU+NEZs1SA2WrV1fjX3btgv371Yd2jx4wZYrqZnr4UHU7HTiguoUqV4aNG1VikzKl6g76fwkTqn9HjYLmzWHnTnWPkSNh7VqoV08lPS4u6ti+fWosz9SpauDzhw9qPM7EiaHvnTKlSh6+VYIEKpEzM1P3srUNedzW9surff7/+f/vwwc1tmj16tDHnJ0jHu+3+q42olhIuqWixt23d7UC8wpojEJjFNqgfYM0/0B/vcOKeYxGTfvrL9Uk/W9/k49Tcm18qpmaFb4aaJqdnab1769pL17oHawQsVhAgKYlT65pU6dq2tWrIb8yZ9a0uXPDvm7ePNWdFJY9e9Tv7du3mrZvn6aZm2vaw4fhj6lpU9UNFpaff1bvC5qmab/8orp2AgLCPtfLS9MsLUN2S717p948ItItFd7j/+2W+vhRdTF9rltqwQLVvR4Fn7ER+fyWAcXiu224voGC8wty8dVFktgmYWfznUysMhFLc0u9Q4tZjh5VTcC1a8PVq/jbOTE96TiSetznlxc9sU5gzZAhalLBlCnqjyohxDfasUPNhOrQAX74IeRXgwaqVQdU6+m2bao15Pp1dV3OnOrYtGmqhfXWLbhzBzZsUDONEiZULTglSqhurH371C/uiROq//jcOdV11LOnatl5/FgNrD179tO9+/RR068fPlQtKYcPfzrWo4calNysmbrm/n11brt2anq2g4N6XgMHwqFDamZV27aqNSaq2djA4MGqSXnFChXbqVOfXs8WLdSA7Dp11IDihw/Va/DTT/DsWdTH9y/plhLf7YP/B7z8vSiTrgxrGqwhjWMavUOKWc6cUavr/TvNM9DShsX2PzHEfTDvfRKTMCEM6qN+92U6txCRZPFilYCEtUZLgwZqKvOVK2qMSfBfFba2UKaM6joC1YUzaRLcvasWASxSRP0eBycRu3apZKZdO3jzRiU+ZcuqmUPm5vD2rZqu7eqqPvDr11fvBaCSlB491Ae+o6Ma0zN9ujqWKpVKhgYPVmN8/PzUNPRq1T499uTJn7qvEiSA/v3VeJroMHy4Gr80YoRaIydlSujaVR2zs1N/yA0erJ6vlxekTq26+6KxnqMUzhTfJNAYiIWZyo01TWPjjY3Uy1nPtE+g/poZPVpN4wSCzCxYZd2RIR+H85JUJE2q3o+6d4/W33khhIiVIvL5Ld1SIsJWXl5J3rl5eevzFgCDwUCj3I0ksQl2/Lj6a6tECdizhyCDOWssW5PDeIO2H+dikTYV06erPxR//lkSGyGEiGyS3Ihw8/b3pv229rTe2pqbbjf54/QfeocUsxw9qppeS5eG/fsJMliw1Kw92bVbtAhYjlWurCxfrrqo+/RRi5IKIYSIfPKntgiX66+v03hjY268uYEBAyPLjWRY2WF6h6U/TVOD5UaPVquFAoEGC5Zq7RivDeGRlpFSpWD6YLXye3SM9xNCiPhOkhvxRZqmsezSMnrs6sHHwI+kcEjBmvprqJCxgt6h6UvT4OBBldQcOwZAgMGSRVoHftN+5gnpqV0bVg2GUqV0jlUIIeIZSW7EF805O4eeu3sCUCVTFVbWW0lyh+Q6R6UjTVPTPkePhpMnAfAzWLNA68QkbRCvLNLSvLmaJZk7t86xCiFEPCWN5OKLWuRtQZbEWRhXcRx7Wu6Jv4mNpqmVRIsXV9MxT57kIzbMoDcZtQf84jCThn3Scv8+LF8uiY0QcUFwkWw9lC+vxuaJbyPJjQhB0zT2399P8AoBCW0ScrXbVX4p8wtmhnj446JpqipwkSKqrsyZM/hgy1T6kZGHTE83g/5TUvHsmVqiQmo/CREzvHmjSjelS6eqLaRIoaodHD8evXGMGqWqGRgMammYDBmgb1+1RM2XbN6sSs6JbyPdUsLE08+TLju6sPbaWubXmk/nQp0BsLGw0TkyHWga/PUX2siRGC5dAsAbO2bTgykMIGPRZPzRX61RZSG/RULEOA0aqNqSy5dDpkxqHb2DB9W6etEtd25VmiowUCVX7duDjw/Mnx/6XH9/ta5g4sTRH2dcEg//FBdhufjyIoUWFGLttbVYmFnwMeCj3iHpw9cXFi/GmPsHqFMHw6VLeOHAeIaQyfCIsw0nsfV4Mk6dUjX0JLERIuZxd1cr/0+cCBUqqMV9ixZVCxH/+OOn86ZNgzx51LIMadOqBTW/1qKybZuqp2ljo5Km0aNV0vIlFhaq5ShNGlWUvEUL1SAMqmUnf35VmDtjRnVfCN0t5eenFv1Nm1a1RGXJ8qniAagKDNWrq8oMyZNDq1bg5haulytOkuQmntM0jdlnZlN8cXHuvbtHOqd0HG17lN7Fe+sdWvR6+xbGjCEwTXro2BGzmzfwwoHfGMwP9o9402c8p+47s2EDlCz55aK5Qgh9OTior61bVVLwOWZm8McfqqTU8uWqTNOgQZ8//59/VDWF3r3hxg3V8rJsGYwbF7H4bG1VC02we/dg0ybVFfVvQ3EorVurMld//AE3b6rHdnBQx9zdoWJFKFBAlbXas0e1VDVuHLG44pRIL9sZw0lV8E/ef3yvNVjXwFTJ+8c/f9Te+rzVO6zodeuWZuzWXQuwtjNV6X5CGq0/k7W86d5rU6Zomru73kEKISJq40ZVnNrGRtNKltS0IUM07fLlL1+zYYOmJUnyafv/i2RXqqRp48eHvGblSk1LmfLz9xw5UtPy5fu0fe6cpiVNqmkNG346bmmpaa9fh7yuXLlPBb5v31ZvT/v3h/0YY8ZoWtWqIfc9faquuX3787HFNhH5/JZG9XjsqutVttzagqWZJZOqTKJ3sd4Y4kOTxL9r1ARMmo7l/l0YUIPPLpKfyQzEo0ojuv1kycTqqvadECL2adBALZz5zz+qzNvu3aoG5qJFqoA2qHEwEyaoot+enqp7yddXjYexswt9z8uX1ZiZ/7bUBAV9+RqAq1dVK0tQkGqxqVkTZs36dDx9enB2/vxzuXRJvReVKxf28cuXVVHx4Jac/7p/H7Jl+/y94ypJbuKxMunLMKv6LAqnKkyR1EX0DifqffwIq1fjO3EGNveuYwkYMbCDWiy060PmjhUY1cMQL98IhIiLbGygShX1NXw4dOwII0eq5ObRIzUBsls3lawkTqzW4+zQQSUgYSUqHz6oMTb164f9WJ+TPbsaY2NhoQp+W1mFPP61Uiy2tl8+HlwcfOLE0MdSpvzytXGVJDfxyLuP7+i5qycjy40ke9LsAHQr0k3nqKLBq1cE/j6bwNnzsPFywwb4gD1LaM+eLL34sX9W/mwZ9l89Qoi4I1cuNQ4H4Px5MBph6tRPZVHWr//y9QULwu3bajBvRFhZRfya/8qTR8V65AhUrhx2XJs2qWnmMslBkZchnjj59CRNNzXliccT7r27x+mOp+N+F9SdO7iPmIrDxmVYBPljATwiPbMNvXCt1YH2/RLSq5wMDhYirnn7Fho1UlOu8+aFBAnUQNtJk6BOHXVOliwQEAAzZ6pWj+PHYd68L993xAjV2pMuHTRsqJKiy5fVTKWxY6Pu+WTIAG3aqOfzxx+QLx88fgyvX6tBwz16wMKF0KyZGhCdOLEapLx2reqGi4/d6zJbKo4zakYmH59M2WVleeLxhMyJMjOv1ry4m9hoGv4HjvKscF2M2XOQcN0CLIL8OUEJOifawNJf7vHT4/6s2J6Q8uUlsREiLnJwgGLF1MKaZcvCDz+obqlOnT6NdcmXT00FnzhRHV+9Wo2/+RIXF9ixQ1VgKVJELVg+fboaMxPV5s5VCVX37pAjh3ou3t7qWKpUKjkLCoKqVVVLT58+anXl+Fqs16Bp/y5FG094enri5OSEh4cHjo6OeocTpdx83GiztQ277u4CoEnuJiyovQBH6zj4vAMCePH7BoKmTCOt63nT7h3U4miJwZT+uTQ1akiTrRBCxFYR+fyWt/o46t67e5RfVp7nXs+xsbDh92q/06lgpzjXYvPx5iPu/bKEFLsWk8r/hdqHDZvsW+Peti8/DspBLSmJIIQQ8YokN3FUeqf0pE+YHgcrB9Y3Wk/e5Hn1DinSaL5+3Jm8jaD5i8jx/AB5UI2PL0nBwew9cB7elaZNkkorjRBCxFPy9h+HvPF+g5ONE1bmVliaW7Kx0UYSWCfAwSpuTAN6degGT0YsIuvJFWQ3fioQc9SmCq61OlLitzq0zGytY4RCCCFiAklu4ojDDw/TfHNzWuRpwZSqUwBImSD2L3Dw8c0HLg/bgOO6heTyOEmKf/c/N6TmXJ72JP+5HaWbZIy3g+aEEEKEJslNLBdkDGLs0bH8evRXjJqRPff28GuFX7Gz/MxSmbGAZtS4tuwc7ycvIv+tPymOFwCBmHMySW38WnWk2AgX6iSSH18hhBChyadDLPbS6yUtt7Tk0MNDALTP356ZNWbG2sTmzrHX3Bm7jsyHF5HH/4pp/0OLLNwr15GsY9tQpniKL9xBCCGEkOQm1tp/fz8tt7Tktfdr7C3tmVtzLq3ytdI7rAh7dvU9137dTKI9ayj84W+yYQTAF2suZGyI7U+dyNerLBnN49YsLyGEEFFHkptYyN3XnUYbGuHh50GeZHlY32g9OZLm0DuscHv70JNLv27HdvtaCr/bRzUCTMfuOhXCo25bco5tQck0iXSMUgghRGwlyU0slNAmIfNqzePww8PMqDYDW8uvVFWLAd4/9+Hy+B1YblpHIdedVMLPdOy+fR7eVmlG1mFNyFook45RCiGEiAtkheJYYvfd3dhY2FAhYwW9Qwk3t+d+XJywB8tNayn86i8c8DYde2SdnRdlm5JpSBNSVMipY5RCCCFiA1mhOA4JCApg2KFhTDoxieT2ybnc9TLJHZLrHdZnvX4ewLnfDmCxaS3FXm6lCp6mY88sM/KwaBPSDGhKxjp5yRDHVksWQggRM0hyE4M98XhC041NOfnsJAANczXEycZJ56hCe/E0iLOT/8Z84zpKvNxEDd6ZjrlapuZR0Sak6N2E9A2LkEYSGiGEEFFMkpsYavvt7bTd2pb3vu9xsnZi8Y+LaZCrgd5hAaBpcO2KkYuzjmOzfR3lXm+gDq9Nx99aJONxkUYk792U1I1KklxW2BNCCBGNJLmJYYKMQQzcP5Dpp6YDUCRVEdY2XEumRPoOtA0IgGN/B3J1wQns923FxXM9rXluOu5hkZjHhRuQrFdTUjQpRxJzcx2jFUIIEZ9JchPDmBnMeO2tWkH6FOvDxCoTsTK30iUWT084uMmdZ4v3kvzMX1QK2E2F/3Q5eVs48rxIPZL2aELixpXJa2mpS5xCCCHEf8lsqRgi0BiIhZnKNb38vDj25BjVs1aP1hg0Da5fh1Mr7uC/eQc5H+ygtPYPlgSazvlglYh3JWuRrEt9bOpWAxubaI1RCCFE/CSzpWIRv0A/BuwbwBPPJ2xtshWDwUAC6wTRlth4esKhfYHcWX4S+yO7qOK1iY7cDXGOa+IcBFSrTcpOtXEoXQIHC/mxEUIIEXPJp5SO7r27R5ONTbjw8gIAx54co0z6MlH6mJoG167B0XUv8dq4l4x39lBV20td3E3nBBgseZmtPLYNa+LctibJs2SJ0piEEEKIyCTJjU7WXVtHp7864eXvRRLbJCyvuzzKEpsnT+DwXn+erT+B08ndlPHeQw+uhDjng00SPIu7kKRtbazr1yRdggRREosQQggR1SS5iWYfAz7Sd29f5p+fD0DpdKX5s8GfpHFME2mP8fYtHNvhzpP1pzCcPEHO98dpzAls8TWdY8TA63SFsaxdjSTNq+FQrBgOMsNJCCFEHCDJTTRruqkp229vx4CBIaWHMLrCaNNA4m/l/l7j4oZ7uG75X3v3HtTUua4B/EnABAooCpKApl7wLigoQgEtVSl4KdWe45apHqQ33XuL1ha13mpB3QpYddxaqlVbcHpU0FYYRtlYtGK3SC0i1Btiy0VtJVSrHi623PKdPxyzjaISNImJz2+GGfKt91vrXS+QvKy1knUMsvxc9Pn9GMJwDlLoXite+1xn1AaEotPUsZCNfxnKzp0fa7tERERPIzY3RrZ4+GIUXCnAFxO+QIh7SJvWcaX0DxR/eQI13xxDh7PH4FF9DCNx7b64qx164c8hAXB+NQC2ISNg378/7PkJwUREZOHY3BjYrcZbyP81H0HdgwAAfl39UPpuKeTW8lbNFxqBin9fRnnKcTQeOQZF2TEMrD8Jt7veng0A9RI5Lrn4oNEnAK7/HYCO4wPQ2cXlie8PERHR047NjQGdu3oOk/dMRumNUhx/5zgGKQYBwIMbGyFQ/WM5yr8qQM13hZCfL0L3ayfQQ1xFj3tCr1or8Wu3QEgCA9B1cgCcgr3RW966homIiMiSPRXNTWJiIj7++GOo1WoMHjwYGzduhK+v7wPj9+zZg6VLl6KiogK9e/dGQkICxo0bZ8SMH04IgeSiZERlRuGPpj+gtFeiur5aN6ihAX8UleCXrDOo/XchZGdPokvVSThqbmDwPetrhDXK7TxwvW8AbEcHoMfUAHQe1B2deYqJiIjoPiZvblJTUxEdHY3NmzfDz88P69evR2hoKEpKSuDSwmmVY8eO4fXXX0dcXBxeeeUV7Ny5ExMnTsTJkyfh4eFhgj3QVdtQi5n7Z+LLU18CAF7uEYwvPT+C/EAlfj4Sg6ZT52B/8SwU1T/BFk3ofc/8eshQIh+Eq6qhsPIehM5jfdDrvwahTwd+EjAREVFrmPz2C35+fhg2bBg++eQTAIBGo4FKpcLs2bOxcOHC++LDw8NRV1eHffv2acdeeOEFeHl5YfPmzY/cnqFuv9DcDBz5/ghmfPs/KNX8AqmQYG6eC5ZnX4eNaGxxzv+hPUraeeA35SA0DRoKp5eHoN8kD3TuYpp7SRERET2tzOb2Cw0NDSgoKMCiRYu0Y1KpFMHBwcjLy2txTl5eHqKjo3XGQkNDkZ6e3mJ8fX096uvrtY+rq6tbjHtcZ1ZlIPfgBJSOAtyqgV1fC7x4sQoAUAs7nMVA/NLBA7d6DIR8yEAoRg5A39Fd4evKU0tERERPkkmbm2vXrqG5uRkKhUJnXKFQ4Pz58y3OUavVLcar1eoW4+Pi4rBs2bInk/BD2Ph7Y3EMUNnOCcPPvYTLzj7433AP2L/gAVXg8/AYIIWfncHTICIieuaZ/JobQ1u0aJHOkZ7q6mqoVKonvp3eo1QQ6mv41MXpia+biIiIWs+kzY2zszOsrKxQVVWlM15VVQWlUtniHKVSqVe8XC6H3AhvkZZKAbCxISIiMjmpKTcuk8kwdOhQHDp0SDum0Whw6NAh+Pv7tzjH399fJx4AsrOzHxhPREREzxaTn5aKjo5GZGQkfHx84Ovri/Xr16Ourg5vvvkmAGDatGno0qUL4uLiAABz5sxBUFAQ1q5di/HjxyMlJQUnTpzAli1bTLkbRERE9JQweXMTHh6Oq1ev4qOPPoJarYaXlxeysrK0Fw1funQJUul/DjAFBARg586d+PDDD7F48WL07t0b6enpT8Vn3BAREZHpmfxzbozNUJ9zQ0RERIajz+u3Sa+5ISIiInrS2NwQERGRRWFzQ0RERBaFzQ0RERFZFDY3REREZFHY3BAREZFFYXNDREREFoXNDREREVkUNjdERERkUUx++wVju/OBzNXV1SbOhIiIiFrrzut2a26s8Mw1NzU1NQAAlUpl4kyIiIhIXzU1NejQocNDY565e0tpNBpcuXIFDg4OkEgkT3Td1dXVUKlUuHz5Mu9bZUCss3GwzsbBOhsPa20chqqzEAI1NTVwc3PTuaF2S565IzdSqRRdu3Y16Dbat2/PPxwjYJ2Ng3U2DtbZeFhr4zBEnR91xOYOXlBMREREFoXNDREREVkUNjdPkFwuR0xMDORyualTsWiss3GwzsbBOhsPa20cT0Odn7kLiomIiMiy8cgNERERWRQ2N0RERGRR2NwQERGRRWFzQ0RERBaFzY2eEhMT0b17d9jY2MDPzw8//PDDQ+P37NmDfv36wcbGBp6ensjMzDRSpuZNnzpv3boVI0aMQMeOHdGxY0cEBwc/8udCt+n7+3xHSkoKJBIJJk6caNgELYS+db558yaioqLg6uoKuVyOPn368LmjFfSt8/r169G3b1/Y2tpCpVLh/fffx59//mmkbM3Td999h7CwMLi5uUEikSA9Pf2Rc3JycjBkyBDI5XL06tULycnJBs8TglotJSVFyGQy8cUXX4izZ8+K6dOnC0dHR1FVVdVifG5urrCyshKrV68W586dEx9++KFo166dOH36tJEzNy/61nnKlCkiMTFRFBYWiuLiYvHGG2+IDh06iF9++cXImZsXfet8R3l5uejSpYsYMWKEmDBhgnGSNWP61rm+vl74+PiIcePGiaNHj4ry8nKRk5MjioqKjJy5edG3zjt27BByuVzs2LFDlJeXiwMHDghXV1fx/vvvGzlz85KZmSmWLFki9u7dKwCItLS0h8aXlZWJ5557TkRHR4tz586JjRs3CisrK5GVlWXQPNnc6MHX11dERUVpHzc3Nws3NzcRFxfXYvzkyZPF+PHjdcb8/PzEX//6V4Pmae70rfO9mpqahIODg9i+fbuhUrQIbalzU1OTCAgIENu2bRORkZFsblpB3zpv2rRJ9OzZUzQ0NBgrRYugb52joqLEqFGjdMaio6NFYGCgQfO0JK1pbj744AMxcOBAnbHw8HARGhpqwMyE4GmpVmpoaEBBQQGCg4O1Y1KpFMHBwcjLy2txTl5enk48AISGhj4wntpW53vdunULjY2N6NSpk6HSNHttrfPy5cvh4uKCt99+2xhpmr221DkjIwP+/v6IioqCQqGAh4cHVq1ahebmZmOlbXbaUueAgAAUFBRoT12VlZUhMzMT48aNM0rOzwpTvQ4+czfObKtr166hubkZCoVCZ1yhUOD8+fMtzlGr1S3Gq9Vqg+Vp7tpS53stWLAAbm5u9/1B0X+0pc5Hjx7F559/jqKiIiNkaBnaUueysjJ8++23mDp1KjIzM/Hzzz9j5syZaGxsRExMjDHSNjttqfOUKVNw7do1DB8+HEIINDU14W9/+xsWL15sjJSfGQ96HayursYff/wBW1tbg2yXR27IosTHxyMlJQVpaWmwsbExdToWo6amBhEREdi6dSucnZ1NnY5F02g0cHFxwZYtWzB06FCEh4djyZIl2Lx5s6lTsyg5OTlYtWoVPv30U5w8eRJ79+7F/v37sWLFClOnRk8Aj9y0krOzM6ysrFBVVaUzXlVVBaVS2eIcpVKpVzy1rc53rFmzBvHx8Th48CAGDRpkyDTNnr51Li0tRUVFBcLCwrRjGo0GAGBtbY2SkhK4u7sbNmkz1JbfZ1dXV7Rr1w5WVlbasf79+0OtVqOhoQEymcygOZujttR56dKliIiIwDvvvAMA8PT0RF1dHWbMmIElS5ZAKuX//k/Cg14H27dvb7CjNgCP3LSaTCbD0KFDcejQIe2YRqPBoUOH4O/v3+Icf39/nXgAyM7OfmA8ta3OALB69WqsWLECWVlZ8PHxMUaqZk3fOvfr1w+nT59GUVGR9uvVV1/FyJEjUVRUBJVKZcz0zUZbfp8DAwPx888/a5tHALhw4QJcXV3Z2DxAW+p869at+xqYOw2l4C0XnxiTvQ4a9HJlC5OSkiLkcrlITk4W586dEzNmzBCOjo5CrVYLIYSIiIgQCxcu1Mbn5uYKa2trsWbNGlFcXCxiYmL4VvBW0LfO8fHxQiaTia+++kpUVlZqv2pqaky1C2ZB3zrfi++Wah1963zp0iXh4OAgZs2aJUpKSsS+ffuEi4uL+Mc//mGqXTAL+tY5JiZGODg4iF27domysjLxzTffCHd3dzF58mRT7YJZqKmpEYWFhaKwsFAAEOvWrROFhYXi4sWLQgghFi5cKCIiIrTxd94KPn/+fFFcXCwSExP5VvCn0caNG8Xzzz8vZDKZ8PX1Fd9//712WVBQkIiMjNSJ3717t+jTp4+QyWRi4MCBYv/+/UbO2DzpU+du3boJAPd9xcTEGD9xM6Pv7/Pd2Ny0nr51PnbsmPDz8xNyuVz07NlTrFy5UjQ1NRk5a/OjT50bGxtFbGyscHd3FzY2NkKlUomZM2eKGzduGD9xM3L48OEWn2/v1DYyMlIEBQXdN8fLy0vIZDLRs2dPkZSUZPA8JULw+BsRERFZDl5zQ0RERBaFzQ0RERFZFDY3REREZFHY3BAREZFFYXNDREREFoXNDREREVkUNjdERERkUdjcEJHZio2NhUKhgEQiQXp6uqnTMajY2Fh4eXmZOg0is8DmhsgMvfHGG5BIJJBIJJDJZOjVqxeWL1+OpqYmU6f2SE+qESkuLsayZcvw2WefobKyEmPHjr0vpqKiQlsniUQCJycnhISEoLCw8LG3b0gt1WjevHn33aOHiFrG5obITI0ZMwaVlZX46aefMHfuXMTGxuLjjz9u07qam5t1btRoDkpLSwEAEyZMgFKphFwuf2DswYMHUVlZiQMHDqC2thZjx47FzZs3W4xtbGw0RLqt0tDQ8MBl9vb2cHJyMmI2ROaLzQ2RmZLL5VAqlejWrRv+/ve/Izg4GBkZGQCA+vp6zJs3D126dIGdnR38/PyQk5OjnZucnAxHR0dkZGRgwIABkMvluHTpEurr67FgwQKoVCrI5XL06tULn3/+uXbemTNnMHbsWNjb20OhUCAiIgLXrl3TLn/ppZfw7rvv4oMPPkCnTp2gVCoRGxurXd69e3cAwGuvvQaJRKJ93JLTp09j1KhRsLW1hZOTE2bMmIHa2loAt0/RhIWFAQCkUikkEslDa+Xk5ASlUgkfHx+sWbMGVVVVOH78uPbITmpqKoKCgmBjY4MdO3ZAo9Fg+fLl6Nq1K+RyOby8vJCVlaVd3515KSkpCAgIgI2NDTw8PHDkyBGd7bamXrNmzcJ7770HZ2dnhIaGPrBGLZ2W2rZtG/r37w8bGxv069cPn376qXZZQ0MDZs2aBVdXV9jY2KBbt26Ii4t7aJ2ILAWbGyILYWtrq/3Pf9asWcjLy0NKSgpOnTqFv/zlLxgzZgx++uknbfytW7eQkJCAbdu24ezZs3BxccG0adOwa9cubNiwAcXFxfjss89gb28PALh58yZGjRoFb29vnDhxAllZWaiqqsLkyZN18ti+fTvs7Oxw/PhxrF69GsuXL0d2djYAID8/HwCQlJSEyspK7eN71dXVITQ0FB07dkR+fj727NmDgwcPYtasWQBun6JJSkoCAFRWVqKyslKvOgG6R0kWLlyIOXPmoLi4GKGhofjnP/+JtWvXYs2aNTh16hRCQ0Px6quv6tQPAObPn4+5c+eisLAQ/v7+CAsLw++//653vWQyGXJzc7F58+ZW12jHjh346KOPsHLlShQXF2PVqlVYunQptm/fDgDYsGEDMjIysHv3bpSUlGDHjh0PbSaJLIrBb81JRE/c3Xfk1mg0Ijs7W8jlcjFv3jxx8eJFYWVlJX799VedOaNHjxaLFi0SQgiRlJQkAIiioiLt8pKSEgFAZGdnt7jNFStWiJCQEJ2xy5cvCwCipKRECHH7zsvDhw/XiRk2bJhYsGCB9jEAkZaW9tD927Jli+jYsaOora3Vju3fv19IpVKhVquFEEKkpaWJRz2FlZeXCwCisLBQCCHEjRs3xGuvvSbs7e2FWq3WLl+/fr3OPDc3N7Fy5cr79mPmzJk6642Pj9cub2xsFF27dhUJCQlCiNbXy9vb+768W6pRTEyMGDx4sPaxu7u72Llzp07MihUrhL+/vxBCiNmzZ4tRo0YJjUbzsBIRWSRrUzVVRPR49u3bB3t7ezQ2NkKj0WDKlCmIjY1FTk4Ompub0adPH534+vp6nWs2ZDIZBg0apH1cVFQEKysrBAUFtbi9H3/8EYcPH9YeyblbaWmpdnt3rxMAXF1d8dtvv+m1b8XFxRg8eDDs7Oy0Y4GBgdBoNCgpKYFCodBrfQEBAZBKpairq0PPnj2RmpoKhUKBiooKAICPj482trq6GleuXEFgYKDOOgIDA/Hjjz/qjPn7+2u/t7a2ho+PD4qLiwG0vl5Dhw7Va1+A20e2SktL8fbbb2P69Ona8aamJnTo0AHA7YvOX375ZfTt2xdjxozBK6+8gpCQEL23RWSO2NwQmamRI0di06ZNkMlkcHNzg7X17T/n2tpaWFlZoaCgAFZWVjpz7n6htbW11blW5c7pmgepra1FWFgYEhIS7lvm6uqq/b5du3Y6yyQSickvVk5NTcWAAQPg5OQER0fH+5bf3UQ9Ka2tV1u2fefao61bt8LPz09n2Z2f+ZAhQ1BeXo5//etfOHjwICZPnozg4GB89dVXem+PyNywuSEyU3Z2dujVq9d9497e3mhubsZvv/2GESNGtHp9np6e0Gg0OHLkCIKDg+9bPmTIEHz99dfo3r27tpFqi3bt2qG5ufmhMf3790dycjLq6uq0L/65ubmQSqXo27ev3ttUqVRwd3dvVWz79u3h5uaG3NxcnaNYubm58PX11Yn9/vvv8eKLLwK4fdSkoKBAe13Q49TrUTVSKBRwc3NDWVkZpk6d+tB9CQ8PR3h4OCZNmoQxY8bg+vXr6NSpk175EJkbXlBMZGH69OmDqVOnYtq0adi7dy/Ky8vxww8/IC4uDvv373/gvO7duyMyMhJvvfUW0tPTUV5ejpycHOzevRsAEBUVhevXr+P1119Hfn4+SktLceDAAbz55puPbFbu3c6hQ4egVqtx48aNFmOmTp0KGxsbREZG4syZMzh8+DBmz56NiIgIvU9JtcX8+fORkJCA1NRUlJSUYOHChSgqKsKcOXN04hITE5GWlobz588jKioKN27cwFtvvQXg8erVmhotW7YMcXFx2LBhAy5cuIDTp08jKSkJ69atAwCsW7cOu3btwvnz53HhwgXs2bMHSqWyxSNXRJaGzQ2RBUpKSsK0adMwd+5c9O3bFxMnTkR+fj6ef/75h87btGkTJk2ahJkzZ6Jfv36YPn066urqAEB7NKO5uRkhISHw9PTEe++9B0dHR0ilrX8qWbt2LbKzs6FSqeDt7d1izHPPPYcDBw7g+vXrGDZsGCZNmoTRo0fjk08+aX0RHsO7776L6OhozJ07F56ensjKykJGRgZ69+6tExcfH4/4+HgMHjwYR48eRUZGBpydnQE8Xr1aU6N33nkH27ZtQ1JSEjw9PREUFITk5GT06NEDAODg4IDVq1fDx8cHw4YNQ0VFBTIzM/X6WRGZK4kQQpg6CSIic1JRUYEePXqgsLCQt0QgegqxhSciIiKLwuaGiIiILApPSxEREZFF4ZEbIiIisihsboiIiMiisLkhIiIii8LmhoiIiCwKmxsiIiKyKGxuiIiIyKKwuSEiIiKLwuaGiIiILAqbGyIiIrIo/w96ABWI1PmzBwAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Combine sale price and assessed value, calculate cumulative sums\n", "gini_data = combined[[\"sale_price\", \"assessed\"]].sort_values(by=\"sale_price\")\n", "\n", "sale_price = gini_data[\"sale_price\"]\n", "assessed = gini_data[\"assessed\"]\n", "\n", "lorenz_data_price = pd.DataFrame(\n", " {\n", " \"pct\": np.concatenate(\n", " ([0], np.cumsum(sale_price) / np.sum(sale_price))\n", " ),\n", " \"cum_pct\": np.concatenate(\n", " ([0], np.arange(1, len(sale_price) + 1) / len(sale_price))\n", " ),\n", " }\n", ")\n", "\n", "lorenz_data_assessed = pd.DataFrame(\n", " {\n", " \"pct\": np.concatenate(([0], np.cumsum(assessed) / np.sum(assessed))),\n", " \"cum_pct\": np.concatenate(\n", " ([0], np.arange(1, len(assessed) + 1) / len(assessed))\n", " ),\n", " }\n", ")\n", "\n", "# Plot Lorenz curves\n", "fig, ax = plt.subplots()\n", "\n", "ax.plot(lorenz_data_price[\"cum_pct\"], lorenz_data_price[\"pct\"], color=\"blue\")\n", "ax.plot(\n", " lorenz_data_assessed[\"cum_pct\"], lorenz_data_assessed[\"pct\"], color=\"red\"\n", ")\n", "ax.plot([0, 1], [0, 1], linestyle=\"dashed\", color=\"green\")\n", "\n", "ax.text(0.785, 0.1, \"Sale Price\", color=\"blue\", va=\"center\")\n", "ax.text(0.9, 0.15, \"Assessed Price\", color=\"red\", ha=\"center\", va=\"center\")\n", "\n", "ax.set_title(\"Lorenz Curve for Sale and Assessed Values\")\n", "ax.set_xlabel(\"Percent of Properties\")\n", "ax.set_ylabel(\"Percent of Value\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this graphic, the green line (Line of Equality) represents a hypothetical environment, where property valuations are completely equitable. The axes represent the cumulative percentage of value (y-axis) as the percentage of properties (x-axis) increases.\n", "\n", "The curves show that for the vast majority of the income distribution, assessed values are closer to the Line of Equality. This can be interpreted two ways:\n", "\n", "1. When the assessed value curve is above the sale price curve, the gap between the the two lines at any individual point, represents the cumulative over-assessment for all houses at that value or below.\n", "2. Gini coefficient for sale price is going to be higher than the Gini coefficient for assessed price (larger area between the the curve and the Line of Equality).\n", "\n", "In this situation, the graph shows slightly regressive property valuations. This is not immediately intuitive, but to conceptualize this, think of an exaggerated \"progressive\" policy, where all houses were valued at $0 with one house responsible for all the assessed value. In this distribution, curve would be at 0 until the final house, where it would jump to 100% of the cumulative value (a Gini of 1). Thus, a higher Gini represents more progressive assessments, where tax assessments become larger as property value increases.\n", "\n", "#### KI and MKI\n", "\n", "To translate these curves to a single metric, the Kakwani Index (KI) and Modified Kakwani Index (MKI) are used (as proposed by [Quintos](https://researchexchange.iaao.org/jptaa/vol17/iss2/2/)). These are straightforward, with the following definitions:\n", "\n", "- **Kakwani Index:** `Assessed Gini - Sale Price Gini`\n", "- **Modified Kakwani Index:** `Assessed Gini / Sale Price Gini`" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'MKI': 0.9350431709690498, 'KI': -0.02132444068329764}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from assesspy.utils import check_inputs\n", "\n", "# Check inputs\n", "check_inputs(assessed, sale_price)\n", "\n", "# Arrange data in ascending order\n", "dataset = list(zip(sale_price, assessed))\n", "dataset.sort(key=lambda x: x[0])\n", "assessed_price = [a for _, a in dataset]\n", "sale_price = [s for s, _ in dataset]\n", "n = len(assessed_price)\n", "\n", "# Calculate Gini coefficients\n", "G_assessed = sum(a * (i + 1) for i, a in enumerate(assessed_price))\n", "G_assessed = 2 * G_assessed / sum(assessed_price) - (n + 1)\n", "GINI_assessed = G_assessed / n\n", "\n", "G_sale = sum(s * (i + 1) for i, s in enumerate(sale_price))\n", "G_sale = 2 * G_sale / sum(sale_price) - (n + 1)\n", "GINI_sale = G_sale / n\n", "\n", "{\"MKI\": GINI_assessed / GINI_sale, \"KI\": GINI_assessed - GINI_sale}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "The output for the Modified Kakwani Index is MKI, and the Kakwani Index is KI. According to the following table, this means that the assessments are slightly regressive. \n", "\n", "| KI Range | MKI Range | Interpretation |\n", "|-----------------|-------------------------|----------------------|\n", "| < 0 | < 1 | Regressive Policy |\n", "| = 0 | = 1 | Vertical Equity |\n", "| > 0 | > 1 | Progressive Policy |\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "vscode": { "interpreter": { "hash": "81794d4967e6c3204c66dcd87b604927b115b27c00565d3d43f05ba2f3a2cb0d" } } }, "nbformat": 4, "nbformat_minor": 2 }