{ "cells": [ { "cell_type": "markdown", "id": "gallery-001", "metadata": {}, "source": [ "
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INFOSCI 301 · WEEK 2 · IDIOM + ALGORITHM STUDIO
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Choose the form. Trace the computation.

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A second Colab for every leaf in the class decision tree: 38 rendered idioms, paired Python and R recipes, and a 72-package reference atlas.

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\n", "\n", "> **Teaching data only.** Every gallery example below uses deterministic synthetic data to demonstrate visual grammar. Replace it with your project data, re-check the action + target, and do not treat any example value as domain evidence.\n" ] }, { "cell_type": "markdown", "id": "gallery-002", "metadata": {}, "source": [ "## Use this notebook in three decisions\n", "\n", "| 1 · Choose | 2 · Run | 3 · Justify |\n", "|---|---|---|\n", "| Start from **Compare**, **Understand Variation**, or **Reveal Structure**; then inspect every leaf and its required data. | Select one idiom. Run the Python figure immediately, copy the paired R recipe, or optionally execute the selected R code. | Choose a package because it supports the transformation, rendering, interaction, scale, accessibility, and reproducibility you need. |\n", "\n", "The decision tree selects an **idiom**. A library implements it. The **algorithm** is the reproducible sequence between raw attributes and rendered marks.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "gallery-003", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ready · outputs: /workspace/scratch/81e4611d448d/idiom_algorithm_outputs\n" ] } ], "source": [ "# @title 0 · Prepare the gallery\n", "from pathlib import Path\n", "import json, shutil, subprocess, textwrap\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "from IPython.display import HTML, Markdown, display\n", "from matplotlib.colors import ListedColormap\n", "from matplotlib.patches import FancyArrowPatch, PathPatch, Rectangle, Wedge\n", "from matplotlib.path import Path as MplPath\n", "\n", "OUTPUT_DIR = Path(\"idiom_algorithm_outputs\")\n", "OUTPUT_DIR.mkdir(exist_ok=True)\n", "\n", "COLORS = {\n", " \"ink\":\"#18324A\", \"muted\":\"#657488\", \"blue\":\"#315EFB\",\n", " \"teal\":\"#14877D\", \"amber\":\"#D97706\", \"red\":\"#C2413A\",\n", " \"line\":\"#CBD5E1\", \"paper\":\"#F5F7FB\",\n", "}\n", "PALETTE = [COLORS[\"blue\"], COLORS[\"teal\"], COLORS[\"amber\"], COLORS[\"red\"]]\n", "ROOT_COLORS = {\"Compare\":COLORS[\"blue\"], \"Understand variation\":COLORS[\"teal\"], \"Reveal structure\":COLORS[\"amber\"]}\n", "plt.rcParams.update({\n", " \"font.family\":\"DejaVu Sans\", \"figure.facecolor\":\"white\", \"axes.facecolor\":\"white\",\n", " \"axes.titleweight\":\"bold\", \"axes.labelcolor\":COLORS[\"muted\"],\n", " \"xtick.color\":COLORS[\"muted\"], \"ytick.color\":COLORS[\"muted\"],\n", "})\n", "print(\"Ready · outputs:\", OUTPUT_DIR.resolve())\n" ] }, { "cell_type": "markdown", "id": "gallery-004", "metadata": {}, "source": [ "# 1 · Choose through the class decision tree\n", "\n", "### Three roots\n", "\n", "- **Compare:** values/ranks; change/groups; composition/profiles.\n", "- **Understand Variation:** one variable; relationships/density; uncertainty/models.\n", "- **Reveal Structure:** hierarchy/flow; cycles/opposition; arranged tables.\n", "\n", "The 38 leaves below match the tutorial exactly. The table also states the required data/attribute structure and the action + target so students can reject a visually attractive but invalid choice.\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "gallery-005", "metadata": { "jupyter": { "source_hidden": true }, "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Verified: 38 idioms · 72 tools\n" ] }, { "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", " \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", " \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", " \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", " \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", " \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", "
leafrootfamilyidiomdata_requirementaction_target
1CompareValues and ranksSorted barTable · categorical key + quantitative valueCompare → values / ranks
2CompareValues and ranksDot plotTable · categorical key + quantitative valueCompare → values / ranks
3CompareValues and ranksLollipopTable · categorical key + quantitative valueCompare → values / ranks
4CompareChange and groupsSlope graphTable · group + two ordered states + quantitative valueCompare → change between two states
5CompareChange and groupsDumbbellTable · group + paired condition + quantitative valueCompare → paired values and gaps
6CompareChange and groupsLine chartTable · ordered time + quantitative value + optional groupCompare → ordered change
7CompareChange and groupsSmall multiplesTable · facet category + shared x/y attributesCompare → repeated groups on aligned scales
8CompareComposition and profilesStacked barTable · whole + part categories + quantitative valueCompare → totals and composition
9CompareComposition and profiles100% stackedTable · whole + part categories + quantitative valueCompare → normalized composition
10CompareComposition and profilesDonutTable · few part categories + quantitative valueCompare → coarse part-to-whole
11CompareComposition and profilesAligned profileTable · entity + measure + quantitative valueCompare → multivariate profiles
12CompareComposition and profilesRadarTable · entity + cyclic metric positions + common-scale valuesCompare → compact profile shape
13Understand variationOne variableHistogramTable · one quantitative attributeSummarize → binned distribution
14Understand variationOne variableKDETable · one quantitative attributeSummarize → smoothed density
15Understand variationOne variableECDFTable · one quantitative attributeSummarize → cumulative distribution
16Understand variationOne variableBox plotTable · quantitative value + optional categoryCompare → robust distribution summaries
17Understand variationOne variableViolinTable · quantitative value + optional categoryCompare → distribution shapes
18Understand variationOne variableRaincloudTable · quantitative value + optional categoryCompare → density, summary, and observations
19Understand variationRelationships and densityScatterplotTable · two quantitative attributesDiscover → association, clusters, outliers
20Understand variationRelationships and densityRegression viewTable · predictor + outcome quantitative attributesSummarize → fitted relationship and uncertainty
21Understand variationRelationships and densityHexbinTable · many x/y quantitative observationsSummarize → two-dimensional density
22Understand variationRelationships and densityPair plotTable · three or more quantitative attributesDiscover → pairwise relationships
23Understand variationRelationships and densityCorrelation heatmapDerived symmetric matrix · quantitative associationDiscover → matrix pattern
24Understand variationRelationships and density2D contourField or table · x/y coordinates + quantitative surfaceLocate → levels and gradients
25Understand variationUncertainty and modelsInterval / forestTable · estimate + lower/upper bounds + categoryCompare → estimates and uncertainty
26Understand variationUncertainty and modelsFan chartTable · ordered time + central estimate + nested intervalsExplore → forecast uncertainty
27Understand variationUncertainty and modelsCalibrationTable · predicted probability + observed outcomeEvaluate → agreement with reference
28Understand variationUncertainty and modelsResidual viewTable · fitted value + residualDiagnose → model error and structure
29Reveal structureHierarchy and flowTreemapTree · parent/child hierarchy + quantitative weightSummarize → hierarchical part-to-whole
30Reveal structureHierarchy and flowSunburstTree · parent/child hierarchy + quantitative weightExplore → hierarchy and depth
31Reveal structureHierarchy and flowSankeyNetwork · source/target + conserved quantitative flowTrace → aggregated flow
32Reveal structureHierarchy and flowAlluvialTable · entity/category at ordered stages + weightCompare → categorical transitions
33Reveal structureCycles and oppositionPolar / BurtinTable · genuinely cyclic ordered attribute + valueCompare → seasonal or angular pattern
34Reveal structureCycles and oppositionPopulation pyramidTable · ordered groups + two opposing categories + valuesCompare → opposition across shared groups
35Reveal structureArranged tablesOrdered heatmapMatrix · ordered rows/columns + quantitative cellsDiscover → clusters, bands, and blocks
36Reveal structureArranged tablesFaceted tableTable · exact values + grouping attributesLookup → values within meaningful groups
37Reveal structureArranged tablesMissingness matrixTable · present/absent state for each cellLocate → missing-data structure
38Reveal structureArranged tablesUpSet matrixSet-membership table · binary categories + countsCompare → intersections
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# @title 1.1 · Load the exact 38-leaf decision matrix\n", "idiom_catalog = pd.DataFrame([{'root': 'Compare', 'family': 'Values and ranks', 'idiom': 'Sorted bar', 'selection_cue': 'Compare magnitudes on a shared zero baseline.', 'caution': '', 'leaf': 1, 'data_requirement': 'Table · categorical key + quantitative value', 'action_target': 'Compare → values / ranks', 'marks_channels': 'bars · aligned length + ordered position', 'algorithm': 'aggregate by category → sort descending → draw from zero → label values', 'python_tool': 'Matplotlib', 'python_url': 'https://matplotlib.org/stable/gallery/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"s = df.groupby('group')['value'].sum().sort_values()\\ns.plot.barh(color='#315EFB')\", 'r_recipe': \"library(ggplot2)\\nggplot(df, aes(reorder(group, value), value)) + geom_col(fill='#315EFB') + coord_flip()\"}, {'root': 'Compare', 'family': 'Values and ranks', 'idiom': 'Dot plot', 'selection_cue': 'Compare positions with less ink than bars.', 'caution': '', 'leaf': 2, 'data_requirement': 'Table · categorical key + quantitative value', 'action_target': 'Compare → values / ranks', 'marks_channels': 'points · aligned position', 'algorithm': 'aggregate by category → sort → encode value as aligned position → label', 'python_tool': 'Matplotlib', 'python_url': 'https://matplotlib.org/stable/gallery/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"s = df.groupby('group')['value'].sum().sort_values()\\nplt.hlines(s.index, 0, s); plt.scatter(s, s.index)\", 'r_recipe': \"library(ggplot2)\\nggplot(df, aes(value, reorder(group, value))) + geom_point(size=3, colour='#315EFB')\"}, {'root': 'Compare', 'family': 'Values and ranks', 'idiom': 'Lollipop', 'selection_cue': 'Emphasize endpoints while retaining a baseline.', 'caution': '', 'leaf': 3, 'data_requirement': 'Table · categorical key + quantitative value', 'action_target': 'Compare → values / ranks', 'marks_channels': 'rules + points · endpoint position', 'algorithm': 'aggregate → sort → draw baseline-to-value rules → emphasize endpoints', 'python_tool': 'Matplotlib', 'python_url': 'https://matplotlib.org/stable/gallery/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"s = df.groupby('group')['value'].sum().sort_values()\\nplt.vlines(s.index, 0, s); plt.scatter(s.index, s)\", 'r_recipe': 'library(ggplot2)\\nggplot(df, aes(group, value)) + geom_segment(aes(xend=group, y=0, yend=value)) + geom_point(size=3)'}, {'root': 'Compare', 'family': 'Change and groups', 'idiom': 'Slope graph', 'selection_cue': 'Compare two time points across groups.', 'caution': '', 'leaf': 4, 'data_requirement': 'Table · group + two ordered states + quantitative value', 'action_target': 'Compare → change between two states', 'marks_channels': 'lines + points · slope and endpoint position', 'algorithm': 'pivot two states → keep identical scale → connect each group → label endpoints', 'python_tool': 'plotnine', 'python_url': 'https://plotnine.org/gallery.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"wide = df.pivot(index='group', columns='period', values='value')\\nfor _, row in wide.iterrows(): plt.plot(wide.columns, row, marker='o')\", 'r_recipe': 'library(ggplot2)\\nggplot(df, aes(period, value, group=group, colour=group)) + geom_line() + geom_point()'}, {'root': 'Compare', 'family': 'Change and groups', 'idiom': 'Dumbbell', 'selection_cue': 'Compare paired values and the gap between them.', 'caution': '', 'leaf': 5, 'data_requirement': 'Table · group + paired condition + quantitative value', 'action_target': 'Compare → paired values and gaps', 'marks_channels': 'rules + paired points · distance and position', 'algorithm': 'pivot paired conditions → compute/display gap → draw shared interval + endpoints', 'python_tool': 'Matplotlib', 'python_url': 'https://matplotlib.org/stable/gallery/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"wide = df.pivot(index='group', columns='condition', values='value')\\nplt.hlines(wide.index, wide.iloc[:,0], wide.iloc[:,1]); plt.scatter(wide.iloc[:,0], wide.index); plt.scatter(wide.iloc[:,1], wide.index)\", 'r_recipe': 'library(ggplot2)\\nggplot(wide, aes(x=before, xend=after, y=group, yend=group)) + geom_segment() + geom_point(aes(x=before)) + geom_point(aes(x=after))'}, {'root': 'Compare', 'family': 'Change and groups', 'idiom': 'Line chart', 'selection_cue': 'Follow ordered change; do not connect unordered categories.', 'caution': '', 'leaf': 6, 'data_requirement': 'Table · ordered time + quantitative value + optional group', 'action_target': 'Compare → ordered change', 'marks_channels': 'lines · ordered x position + y position', 'algorithm': 'sort ordered x → group series → connect only adjacent ordered observations → check gaps', 'python_tool': 'pandas plotting', 'python_url': 'https://pandas.pydata.org/docs/user_guide/visualization.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"for key, g in df.sort_values('time').groupby('group'):\\n plt.plot(g.time, g.value, marker='o', label=key)\", 'r_recipe': 'library(ggplot2)\\nggplot(df, aes(time, value, colour=group)) + geom_line() + geom_point()'}, {'root': 'Compare', 'family': 'Change and groups', 'idiom': 'Small multiples', 'selection_cue': 'Compare repeated panels with aligned scales.', 'caution': '', 'leaf': 7, 'data_requirement': 'Table · facet category + shared x/y attributes', 'action_target': 'Compare → repeated groups on aligned scales', 'marks_channels': 'repeated panels · shared position scales', 'algorithm': 'split by facet → apply identical scales → render repeated panels → compare', 'python_tool': 'Seaborn', 'python_url': 'https://seaborn.pydata.org/examples/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"import seaborn as sns\\nsns.relplot(data=df, x='time', y='value', col='group', col_wrap=3, kind='line', facet_kws={'sharey':True})\", 'r_recipe': 'library(ggplot2)\\nggplot(df, aes(time, value)) + geom_line() + facet_wrap(~group) + coord_cartesian(ylim=range(df$value))'}, {'root': 'Compare', 'family': 'Composition and profiles', 'idiom': 'Stacked bar', 'selection_cue': 'Compare totals and a limited number of parts.', 'caution': '', 'leaf': 8, 'data_requirement': 'Table · whole + part categories + quantitative value', 'action_target': 'Compare → totals and composition', 'marks_channels': 'stacked bars · length + color', 'algorithm': 'aggregate parts within wholes → choose stable order → cumulative stack → label totals', 'python_tool': 'pandas plotting', 'python_url': 'https://pandas.pydata.org/docs/user_guide/visualization.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"wide = df.pivot_table(index='whole', columns='part', values='value', aggfunc='sum')\\nwide.plot.bar(stacked=True)\", 'r_recipe': 'library(ggplot2)\\nggplot(df, aes(whole, value, fill=part)) + geom_col()'}, {'root': 'Compare', 'family': 'Composition and profiles', 'idiom': '100% stacked', 'selection_cue': 'Compare proportions when totals are secondary.', 'caution': '', 'leaf': 9, 'data_requirement': 'Table · whole + part categories + quantitative value', 'action_target': 'Compare → normalized composition', 'marks_channels': 'normalized bars · proportion length + color', 'algorithm': 'aggregate → divide each part by its whole → cumulative stack → show denominator', 'python_tool': 'pandas plotting', 'python_url': 'https://pandas.pydata.org/docs/user_guide/visualization.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"wide = df.pivot_table(index='whole', columns='part', values='value', aggfunc='sum')\\n(wide.div(wide.sum(axis=1), axis=0)).plot.bar(stacked=True)\", 'r_recipe': \"library(ggplot2)\\nggplot(df, aes(whole, value, fill=part)) + geom_col(position='fill') + scale_y_continuous(labels=scales::percent)\"}, {'root': 'Compare', 'family': 'Composition and profiles', 'idiom': 'Donut', 'selection_cue': 'Use only for a few parts; angles are imprecise.', 'caution': 'Use sparingly', 'leaf': 10, 'data_requirement': 'Table · few part categories + quantitative value', 'action_target': 'Compare → coarse part-to-whole', 'marks_channels': 'arc sectors · angle/area + color', 'algorithm': 'aggregate few parts → compute proportions/angles → render arcs → print values', 'python_tool': 'Matplotlib', 'python_url': 'https://matplotlib.org/stable/gallery/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"s = df.groupby('part')['value'].sum()\\nplt.pie(s, labels=s.index, wedgeprops={'width':.38}, autopct='%1.0f%%')\", 'r_recipe': \"library(ggplot2)\\nggplot(df, aes(x=2, y=value, fill=part)) + geom_col() + coord_polar(theta='y') + xlim(.5, 2.5)\"}, {'root': 'Compare', 'family': 'Composition and profiles', 'idiom': 'Aligned profile', 'selection_cue': 'Compare many measures on shared aligned axes.', 'caution': '', 'leaf': 11, 'data_requirement': 'Table · entity + measure + quantitative value', 'action_target': 'Compare → multivariate profiles', 'marks_channels': 'points/lines · aligned measure axes', 'algorithm': 'reshape measures long → normalize only if justified → align axes → connect profiles', 'python_tool': 'Matplotlib', 'python_url': 'https://matplotlib.org/stable/gallery/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"for key, g in df.groupby('entity'):\\n plt.plot(g.metric, g.value, marker='o', label=key)\", 'r_recipe': 'library(ggplot2)\\nggplot(df, aes(metric, value, group=entity, colour=entity)) + geom_line() + geom_point()'}, {'root': 'Compare', 'family': 'Composition and profiles', 'idiom': 'Radar', 'selection_cue': 'Only for a small, common scale; aligned profiles are often clearer.', 'caution': 'Requires justification', 'leaf': 12, 'data_requirement': 'Table · entity + cyclic metric positions + common-scale values', 'action_target': 'Compare → compact profile shape', 'marks_channels': 'radial polygon · angle + radius', 'algorithm': 'verify common scale → order axes deliberately → close polygon → retain numeric context', 'python_tool': 'Matplotlib', 'python_url': 'https://matplotlib.org/stable/gallery/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"theta = np.linspace(0, 2*np.pi, len(values), endpoint=False)\\nax = plt.subplot(projection='polar'); ax.plot(np.r_[theta,theta[0]], np.r_[values,values[0]])\", 'r_recipe': 'library(ggplot2)\\nggplot(df, aes(metric, value, group=entity, colour=entity)) + geom_polygon(fill=NA) + coord_polar()'}, {'root': 'Understand variation', 'family': 'One variable', 'idiom': 'Histogram', 'selection_cue': 'Show binned distribution shape; test bin sensitivity.', 'caution': '', 'leaf': 13, 'data_requirement': 'Table · one quantitative attribute', 'action_target': 'Summarize → binned distribution', 'marks_channels': 'bins/bars · position + count length', 'algorithm': 'choose/test bin edges → count observations → draw aligned bars → disclose bins', 'python_tool': 'Seaborn', 'python_url': 'https://seaborn.pydata.org/examples/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"import seaborn as sns\\nsns.histplot(data=df, x='value', bins=20)\", 'r_recipe': \"library(ggplot2)\\nggplot(df, aes(value)) + geom_histogram(bins=20, fill='#315EFB', colour='white')\"}, {'root': 'Understand variation', 'family': 'One variable', 'idiom': 'KDE', 'selection_cue': 'Show smoothed density; disclose bandwidth.', 'caution': '', 'leaf': 14, 'data_requirement': 'Table · one quantitative attribute', 'action_target': 'Summarize → smoothed density', 'marks_channels': 'density line/area · position + height', 'algorithm': 'choose kernel/bandwidth → estimate density → normalize area → disclose bandwidth', 'python_tool': 'Seaborn', 'python_url': 'https://seaborn.pydata.org/examples/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"import seaborn as sns\\nsns.kdeplot(data=df, x='value', bw_adjust=1, fill=True)\", 'r_recipe': \"library(ggplot2)\\nggplot(df, aes(value)) + geom_density(fill='#315EFB', alpha=.3, adjust=1)\"}, {'root': 'Understand variation', 'family': 'One variable', 'idiom': 'ECDF', 'selection_cue': 'Show every observation without bin or bandwidth choice.', 'caution': '', 'leaf': 15, 'data_requirement': 'Table · one quantitative attribute', 'action_target': 'Summarize → cumulative distribution', 'marks_channels': 'step line · value position + cumulative proportion', 'algorithm': 'sort values → assign cumulative ranks n/N → draw steps → retain every observation', 'python_tool': 'Seaborn', 'python_url': 'https://seaborn.pydata.org/examples/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"import seaborn as sns\\nsns.ecdfplot(data=df, x='value')\", 'r_recipe': \"library(ggplot2)\\nggplot(df, aes(value)) + stat_ecdf(geom='step')\"}, {'root': 'Understand variation', 'family': 'One variable', 'idiom': 'Box plot', 'selection_cue': 'Compare robust summaries; show raw data when sample size matters.', 'caution': '', 'leaf': 16, 'data_requirement': 'Table · quantitative value + optional category', 'action_target': 'Compare → robust distribution summaries', 'marks_channels': 'box + whiskers · ordered position', 'algorithm': 'compute quartiles/IQR → set whisker rule → mark outliers → disclose n', 'python_tool': 'Seaborn', 'python_url': 'https://seaborn.pydata.org/examples/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"import seaborn as sns\\nsns.boxplot(data=df, x='group', y='value'); sns.stripplot(data=df, x='group', y='value', color='black', alpha=.35)\", 'r_recipe': 'library(ggplot2)\\nggplot(df, aes(group, value)) + geom_boxplot() + geom_jitter(width=.08, alpha=.3)'}, {'root': 'Understand variation', 'family': 'One variable', 'idiom': 'Violin', 'selection_cue': 'Compare distribution shape; include scale and sample context.', 'caution': '', 'leaf': 17, 'data_requirement': 'Table · quantitative value + optional category', 'action_target': 'Compare → distribution shapes', 'marks_channels': 'mirrored density area · width', 'algorithm': 'estimate density per group → mirror width → align groups → disclose bandwidth and n', 'python_tool': 'Seaborn', 'python_url': 'https://seaborn.pydata.org/examples/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"import seaborn as sns\\nsns.violinplot(data=df, x='group', y='value', inner='quartile')\", 'r_recipe': 'library(ggplot2)\\nggplot(df, aes(group, value)) + geom_violin() + geom_boxplot(width=.12)'}, {'root': 'Understand variation', 'family': 'One variable', 'idiom': 'Raincloud', 'selection_cue': 'Combine density, summary, and observations.', 'caution': '', 'leaf': 18, 'data_requirement': 'Table · quantitative value + optional category', 'action_target': 'Compare → density, summary, and observations', 'marks_channels': 'half density + box + points', 'algorithm': 'estimate half-density → compute robust summary → jitter raw points → align layers', 'python_tool': 'PtitPrince', 'python_url': 'https://github.com/pog87/PtitPrince', 'r_tool': 'ggdist', 'r_url': 'https://mjskay.github.io/ggdist/', 'python_recipe': \"import PtitPrince as pt\\npt.RainCloud(data=df, x='group', y='value', orient='v')\", 'r_recipe': 'library(ggplot2); library(ggdist)\\nggplot(df, aes(group, value, fill=group)) + stat_halfeye() + geom_jitter(width=.08, alpha=.3)'}, {'root': 'Understand variation', 'family': 'Relationships and density', 'idiom': 'Scatterplot', 'selection_cue': 'Inspect two quantitative attributes.', 'caution': '', 'leaf': 19, 'data_requirement': 'Table · two quantitative attributes', 'action_target': 'Discover → association, clusters, outliers', 'marks_channels': 'points · x/y position', 'algorithm': 'filter valid x/y pairs → map both to position → manage overlap → annotate outliers', 'python_tool': 'Altair', 'python_url': 'https://altair-viz.github.io/gallery/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"import altair as alt\\nalt.Chart(df).mark_circle().encode(x='x:Q', y='y:Q', color='group:N', tooltip=list(df.columns))\", 'r_recipe': 'library(ggplot2)\\nggplot(df, aes(x, y, colour=group)) + geom_point(alpha=.65)'}, {'root': 'Understand variation', 'family': 'Relationships and density', 'idiom': 'Regression view', 'selection_cue': 'Show fitted relationship with uncertainty and assumptions.', 'caution': '', 'leaf': 20, 'data_requirement': 'Table · predictor + outcome quantitative attributes', 'action_target': 'Summarize → fitted relationship and uncertainty', 'marks_channels': 'points + fitted line + interval band', 'algorithm': 'fit stated model → predict on ordered x → compute interval → plot data + fit + uncertainty', 'python_tool': 'statsmodels graphics', 'python_url': 'https://www.statsmodels.org/stable/graphics.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"import seaborn as sns\\nsns.regplot(data=df, x='x', y='y', ci=95)\", 'r_recipe': \"library(ggplot2)\\nggplot(df, aes(x, y)) + geom_point() + geom_smooth(method='lm', se=TRUE)\"}, {'root': 'Understand variation', 'family': 'Relationships and density', 'idiom': 'Hexbin', 'selection_cue': 'Aggregate dense points to reveal concentration.', 'caution': '', 'leaf': 21, 'data_requirement': 'Table · many x/y quantitative observations', 'action_target': 'Summarize → two-dimensional density', 'marks_channels': 'hexagonal bins · x/y position + color', 'algorithm': 'choose hex grid/resolution → count points per cell → encode count → show color scale', 'python_tool': 'Matplotlib', 'python_url': 'https://matplotlib.org/stable/gallery/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"plt.hexbin(df.x, df.y, gridsize=24, mincnt=1, cmap='viridis'); plt.colorbar(label='count')\", 'r_recipe': 'library(ggplot2); library(hexbin)\\nggplot(df, aes(x, y)) + geom_hex(bins=24) + scale_fill_viridis_c()'}, {'root': 'Understand variation', 'family': 'Relationships and density', 'idiom': 'Pair plot', 'selection_cue': 'Scan many pairwise relationships; avoid tiny unreadable panels.', 'caution': '', 'leaf': 22, 'data_requirement': 'Table · three or more quantitative attributes', 'action_target': 'Discover → pairwise relationships', 'marks_channels': 'faceted points/distributions · repeated position', 'algorithm': 'select variables → create pairwise grid → place distributions on diagonal → share scales', 'python_tool': 'Seaborn', 'python_url': 'https://seaborn.pydata.org/examples/index.html', 'r_tool': 'GGally', 'r_url': 'https://ggobi.github.io/ggally/', 'python_recipe': \"import seaborn as sns\\nsns.pairplot(df, vars=['a','b','c'], hue='group', corner=True)\", 'r_recipe': \"library(GGally)\\nggpairs(df, columns=c('a','b','c'), aes(colour=group, alpha=.5))\"}, {'root': 'Understand variation', 'family': 'Relationships and density', 'idiom': 'Correlation heatmap', 'selection_cue': 'Reveal a matrix pattern after justified ordering.', 'caution': '', 'leaf': 23, 'data_requirement': 'Derived symmetric matrix · quantitative association', 'action_target': 'Discover → matrix pattern', 'marks_channels': 'matrix cells · color', 'algorithm': 'handle missingness → compute stated correlation → order variables → encode diverging color', 'python_tool': 'Seaborn', 'python_url': 'https://seaborn.pydata.org/examples/index.html', 'r_tool': 'ComplexHeatmap', 'r_url': 'https://jokergoo.github.io/ComplexHeatmap-reference/book/', 'python_recipe': \"import seaborn as sns\\ncorr = df.select_dtypes('number').corr()\\nsns.heatmap(corr, vmin=-1, vmax=1, cmap='vlag', annot=True)\", 'r_recipe': \"library(ComplexHeatmap)\\nHeatmap(cor(df[c('a','b','c')], use='pairwise.complete.obs'), name='r')\"}, {'root': 'Understand variation', 'family': 'Relationships and density', 'idiom': '2D contour', 'selection_cue': 'Show a continuous surface without unnecessary 3D perspective.', 'caution': '', 'leaf': 24, 'data_requirement': 'Field or table · x/y coordinates + quantitative surface', 'action_target': 'Locate → levels and gradients', 'marks_channels': 'isolines/filled bands · x/y position + level', 'algorithm': 'interpolate/model surface → choose levels → trace isolines/fills → verify topology', 'python_tool': 'Matplotlib', 'python_url': 'https://matplotlib.org/stable/gallery/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"grid = df.pivot(index='y', columns='x', values='z')\\nplt.contourf(grid.columns, grid.index, grid.values, levels=10, cmap='viridis'); plt.colorbar()\", 'r_recipe': 'library(ggplot2)\\nggplot(df, aes(x, y, z=z)) + geom_contour_filled() + coord_equal()'}, {'root': 'Understand variation', 'family': 'Uncertainty and models', 'idiom': 'Interval / forest', 'selection_cue': 'Compare estimates and uncertainty on aligned axes.', 'caution': '', 'leaf': 25, 'data_requirement': 'Table · estimate + lower/upper bounds + category', 'action_target': 'Compare → estimates and uncertainty', 'marks_channels': 'points + intervals · aligned position', 'algorithm': 'collect estimates/bounds → sort categories → draw intervals + point estimates → mark reference', 'python_tool': 'statsmodels graphics', 'python_url': 'https://www.statsmodels.org/stable/graphics.html', 'r_tool': 'forestplot', 'r_url': 'https://cran.r-project.org/package=forestplot', 'python_recipe': \"plt.errorbar(df.estimate, df.label, xerr=[df.estimate-df.lower, df.upper-df.estimate], fmt='o')\\nplt.axvline(0, color='0.5', lw=1)\", 'r_recipe': 'library(forestplot)\\nforestplot(labeltext=df$label, mean=df$estimate, lower=df$lower, upper=df$upper, zero=0)'}, {'root': 'Understand variation', 'family': 'Uncertainty and models', 'idiom': 'Fan chart', 'selection_cue': 'Show forecast uncertainty expanding through time.', 'caution': '', 'leaf': 26, 'data_requirement': 'Table · ordered time + central estimate + nested intervals', 'action_target': 'Explore → forecast uncertainty', 'marks_channels': 'nested interval bands · ordered position + opacity', 'algorithm': 'compute quantiles by time → nest bands widest-to-narrowest → draw center → label coverage', 'python_tool': 'Matplotlib', 'python_url': 'https://matplotlib.org/stable/gallery/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': 'plt.fill_between(df.time, df.q10, df.q90, alpha=.18)\\nplt.fill_between(df.time, df.q25, df.q75, alpha=.32); plt.plot(df.time, df.median)', 'r_recipe': 'library(ggplot2)\\nggplot(df, aes(time, median)) + geom_ribbon(aes(ymin=q10,ymax=q90), alpha=.18) + geom_ribbon(aes(ymin=q25,ymax=q75), alpha=.32) + geom_line()'}, {'root': 'Understand variation', 'family': 'Uncertainty and models', 'idiom': 'Calibration', 'selection_cue': 'Compare predicted probability with observed frequency.', 'caution': '', 'leaf': 27, 'data_requirement': 'Table · predicted probability + observed outcome', 'action_target': 'Evaluate → agreement with reference', 'marks_channels': 'points/line + diagonal reference', 'algorithm': 'bin/estimate predicted probabilities → compute observed frequency → plot against identity → report n', 'python_tool': 'scikit-learn Displays', 'python_url': 'https://scikit-learn.org/stable/visualizations.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': 'from sklearn.calibration import CalibrationDisplay\\nCalibrationDisplay.from_predictions(df.outcome, df.probability, n_bins=10)', 'r_recipe': 'library(ggplot2)\\nggplot(calibration, aes(predicted, observed)) + geom_abline(slope=1, intercept=0, linetype=2) + geom_line() + geom_point()'}, {'root': 'Understand variation', 'family': 'Uncertainty and models', 'idiom': 'Residual view', 'selection_cue': 'Diagnose model error, structure, and outliers.', 'caution': '', 'leaf': 28, 'data_requirement': 'Table · fitted value + residual', 'action_target': 'Diagnose → model error and structure', 'marks_channels': 'points + zero reference line', 'algorithm': 'fit model → calculate fitted values/residuals → plot against reference zero → inspect pattern', 'python_tool': 'Yellowbrick', 'python_url': 'https://www.scikit-yb.org/en/latest/gallery.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"import seaborn as sns\\nsns.residplot(data=df, x='fitted', y='observed', lowess=True); plt.axhline(0, color='0.5')\", 'r_recipe': \"library(ggplot2)\\nggplot(df, aes(fitted, residual)) + geom_hline(yintercept=0, colour='grey60') + geom_point() + geom_smooth(se=FALSE)\"}, {'root': 'Reveal structure', 'family': 'Hierarchy and flow', 'idiom': 'Treemap', 'selection_cue': 'Encode a genuine part-to-whole hierarchy through nested area.', 'caution': '', 'leaf': 29, 'data_requirement': 'Tree · parent/child hierarchy + quantitative weight', 'action_target': 'Summarize → hierarchical part-to-whole', 'marks_channels': 'nested rectangles · area + containment', 'algorithm': 'validate hierarchy → sum child weights → recursively partition area → preserve containment', 'python_tool': 'Plotly', 'python_url': 'https://plotly.com/python/', 'r_tool': 'treemapify', 'r_url': 'https://wilkox.org/treemapify/', 'python_recipe': \"import plotly.express as px\\npx.treemap(df, path=['parent','child'], values='value', color='value')\", 'r_recipe': 'library(ggplot2); library(treemapify)\\nggplot(df, aes(area=value, fill=parent, label=child)) + geom_treemap() + geom_treemap_text()'}, {'root': 'Reveal structure', 'family': 'Hierarchy and flow', 'idiom': 'Sunburst', 'selection_cue': 'Show hierarchical depth radially; labels can become difficult.', 'caution': '', 'leaf': 30, 'data_requirement': 'Tree · parent/child hierarchy + quantitative weight', 'action_target': 'Explore → hierarchy and depth', 'marks_channels': 'nested arcs · angle + radial depth', 'algorithm': 'validate hierarchy → aggregate weights → allocate angles → map depth to radius', 'python_tool': 'Plotly', 'python_url': 'https://plotly.com/python/', 'r_tool': 'plotly for R', 'r_url': 'https://plotly.com/r/', 'python_recipe': \"import plotly.express as px\\npx.sunburst(df, path=['level1','level2'], values='value', color='level1')\", 'r_recipe': \"library(plotly)\\nplot_ly(df, labels=~label, parents=~parent, values=~value, type='sunburst', branchvalues='total')\"}, {'root': 'Reveal structure', 'family': 'Hierarchy and flow', 'idiom': 'Sankey', 'selection_cue': 'Show aggregated flows with conserved quantities.', 'caution': '', 'leaf': 31, 'data_requirement': 'Network · source/target + conserved quantitative flow', 'action_target': 'Trace → aggregated flow', 'marks_channels': 'nodes + weighted links · width + connection', 'algorithm': 'aggregate source-target flows → verify conservation → compute node/link layout → encode width', 'python_tool': 'Plotly', 'python_url': 'https://plotly.com/python/', 'r_tool': 'networkD3', 'r_url': 'https://christophergandrud.github.io/networkD3/', 'python_recipe': \"import plotly.graph_objects as go\\ngo.Figure(go.Sankey(node={'label':labels}, link={'source':source,'target':target,'value':value}))\", 'r_recipe': \"library(networkD3)\\nsankeyNetwork(Links=links, Nodes=nodes, Source='source', Target='target', Value='value', NodeID='name')\"}, {'root': 'Reveal structure', 'family': 'Hierarchy and flow', 'idiom': 'Alluvial', 'selection_cue': 'Compare category flows across ordered stages.', 'caution': '', 'leaf': 32, 'data_requirement': 'Table · entity/category at ordered stages + weight', 'action_target': 'Compare → categorical transitions', 'marks_channels': 'stage axes + ribbons · width + path', 'algorithm': 'aggregate category sequences → order stages/categories → compute strata → route weighted ribbons', 'python_tool': 'Plotly', 'python_url': 'https://plotly.com/python/', 'r_tool': 'ggalluvial', 'r_url': 'https://corybrunson.github.io/ggalluvial/', 'python_recipe': \"import plotly.express as px\\npx.parallel_categories(df, dimensions=['stage1','stage2','stage3'], color='weight')\", 'r_recipe': 'library(ggplot2); library(ggalluvial)\\nggplot(df, aes(axis1=stage1, axis2=stage2, axis3=stage3, y=weight)) + geom_alluvium(aes(fill=stage1)) + geom_stratum()'}, {'root': 'Reveal structure', 'family': 'Cycles and opposition', 'idiom': 'Polar / Burtin', 'selection_cue': 'Use only for genuinely cyclic variables.', 'caution': '', 'leaf': 33, 'data_requirement': 'Table · genuinely cyclic ordered attribute + value', 'action_target': 'Compare → seasonal or angular pattern', 'marks_channels': 'radial bars · cyclic angle + radius', 'algorithm': 'validate cyclic order → map cycle to angle → map value to radius → retain radial reference', 'python_tool': 'pyCirclize', 'python_url': 'https://moshi4.github.io/pyCirclize/', 'r_tool': 'circlize', 'r_url': 'https://jokergoo.github.io/circlize_book/book/', 'python_recipe': 'from pycirclize import Circos\\n# Map genuinely cyclic sectors and values; see the official pyCirclize examples.', 'r_recipe': 'library(circlize)\\n# Map genuinely cyclic sectors and quantitative values; see the official circlize book.'}, {'root': 'Reveal structure', 'family': 'Cycles and opposition', 'idiom': 'Population pyramid', 'selection_cue': 'Compare two opposing populations across the same ordered categories.', 'caution': '', 'leaf': 34, 'data_requirement': 'Table · ordered groups + two opposing categories + values', 'action_target': 'Compare → opposition across shared groups', 'marks_channels': 'opposing bars · signed length + shared order', 'algorithm': 'align ordered groups → sign one category negative → mirror shared zero → label absolute values', 'python_tool': 'Matplotlib', 'python_url': 'https://matplotlib.org/stable/gallery/index.html', 'r_tool': 'ggplot2', 'r_url': 'https://ggplot2.tidyverse.org/', 'python_recipe': \"y = np.arange(len(df))\\nplt.barh(y, -df.left, label='Left'); plt.barh(y, df.right, label='Right'); plt.axvline(0, color='0.3')\", 'r_recipe': \"library(ggplot2)\\nlong$plot_value <- ifelse(long$side=='Left', -long$value, long$value)\\nggplot(long, aes(plot_value, ordered_group, fill=side)) + geom_col() + geom_vline(xintercept=0)\"}, {'root': 'Reveal structure', 'family': 'Arranged tables', 'idiom': 'Ordered heatmap', 'selection_cue': 'Reveal table structure through meaningful row and column order.', 'caution': '', 'leaf': 35, 'data_requirement': 'Matrix · ordered rows/columns + quantitative cells', 'action_target': 'Discover → clusters, bands, and blocks', 'marks_channels': 'ordered matrix cells · color', 'algorithm': 'construct matrix → choose/reproduce ordering → normalize only if justified → encode color', 'python_tool': 'Seaborn', 'python_url': 'https://seaborn.pydata.org/examples/index.html', 'r_tool': 'ComplexHeatmap', 'r_url': 'https://jokergoo.github.io/ComplexHeatmap-reference/book/', 'python_recipe': \"import seaborn as sns\\nordered = matrix.loc[row_order, col_order]\\nsns.heatmap(ordered, cmap='viridis')\", 'r_recipe': \"library(ComplexHeatmap)\\nHeatmap(matrix[row_order, col_order], name='value', cluster_rows=FALSE, cluster_columns=FALSE)\"}, {'root': 'Reveal structure', 'family': 'Arranged tables', 'idiom': 'Faceted table', 'selection_cue': 'Keep exact values while arranging meaningful groups.', 'caution': '', 'leaf': 36, 'data_requirement': 'Table · exact values + grouping attributes', 'action_target': 'Lookup → values within meaningful groups', 'marks_channels': 'text cells + grouping bands', 'algorithm': 'group/sort rows → format exact values → add restrained grouping cues → preserve lookup', 'python_tool': 'pandas plotting', 'python_url': 'https://pandas.pydata.org/docs/user_guide/visualization.html', 'r_tool': 'reactable', 'r_url': 'https://glin.github.io/reactable/articles/examples.html', 'python_recipe': \"styled = df.sort_values(['group','item']).style.format(precision=2).background_gradient(subset=['value'])\\ndisplay(styled)\", 'r_recipe': \"library(reactable)\\nreactable(df, groupBy='group', searchable=TRUE, defaultSorted='item')\"}, {'root': 'Reveal structure', 'family': 'Arranged tables', 'idiom': 'Missingness matrix', 'selection_cue': 'Expose which variables or groups lack evidence.', 'caution': '', 'leaf': 37, 'data_requirement': 'Table · present/absent state for each cell', 'action_target': 'Locate → missing-data structure', 'marks_channels': 'binary cells · color', 'algorithm': 'convert cells to present/absent → order rows/columns → encode binary state → summarize rates', 'python_tool': 'missingno', 'python_url': 'https://github.com/ResidentMario/missingno', 'r_tool': 'ComplexHeatmap', 'r_url': 'https://jokergoo.github.io/ComplexHeatmap-reference/book/', 'python_recipe': \"import missingno as msno\\nmsno.matrix(df.sort_values('group'))\", 'r_recipe': \"library(ComplexHeatmap)\\nHeatmap(is.na(df) * 1, name='missing', col=c('0'='white','1'='#C2413A'))\"}, {'root': 'Reveal structure', 'family': 'Arranged tables', 'idiom': 'UpSet matrix', 'selection_cue': 'Compare intersections when a Venn diagram no longer scales.', 'caution': '', 'leaf': 38, 'data_requirement': 'Set-membership table · binary categories + counts', 'action_target': 'Compare → intersections', 'marks_channels': 'intersection bars + connected membership dots', 'algorithm': 'encode set membership → count unique intersections → sort/filter → link membership dots to bars', 'python_tool': 'UpSetPlot', 'python_url': 'https://upsetplot.readthedocs.io/en/stable/auto_examples/index.html', 'r_tool': 'ComplexHeatmap', 'r_url': 'https://jokergoo.github.io/ComplexHeatmap-reference/book/', 'python_recipe': \"from upsetplot import UpSet, from_indicators\\nUpSet(from_indicators(['A','B','C'], df), show_counts=True).plot()\", 'r_recipe': \"library(ComplexHeatmap)\\nm = make_comb_mat(df[c('A','B','C')]); UpSet(m)\"}])\n", "tool_catalog = pd.DataFrame([{'name': 'Matplotlib', 'language': 'Python', 'category': 'grammar', 'use': 'Foundational static, animated, and publication figures.', 'tags': 'bar line scatter animation', 'official_url': 'https://matplotlib.org/stable/gallery/index.html'}, {'name': 'Seaborn', 'language': 'Python', 'category': 'grammar', 'use': 'Statistical graphics with concise data-aware defaults.', 'tags': 'distribution relational categorical', 'official_url': 'https://seaborn.pydata.org/examples/index.html'}, {'name': 'Plotly', 'language': 'Python', 'category': 'grammar', 'use': 'Interactive browser charts, maps, 3D, and dashboards.', 'tags': 'interactive hover map', 'official_url': 'https://plotly.com/python/'}, {'name': 'Altair', 'language': 'Python', 'category': 'grammar', 'use': 'Declarative Vega-Lite grammar for composable charts.', 'tags': 'declarative grammar interactive', 'official_url': 'https://altair-viz.github.io/gallery/index.html'}, {'name': 'Bokeh', 'language': 'Python', 'category': 'grammar', 'use': 'Interactive linked plots and browser applications.', 'tags': 'interactive linked brushing server', 'official_url': 'https://docs.bokeh.org/en/latest/docs/gallery.html'}, {'name': 'HoloViews', 'language': 'Python', 'category': 'grammar', 'use': 'High-level declarative views across plotting backends.', 'tags': 'declarative linked data', 'official_url': 'https://holoviews.org/reference/index.html'}, {'name': 'hvPlot', 'language': 'Python', 'category': 'grammar', 'use': 'Interactive plotting API for pandas, xarray, and more.', 'tags': 'pandas xarray interactive', 'official_url': 'https://hvplot.holoviz.org/reference/index.html'}, {'name': 'plotnine', 'language': 'Python', 'category': 'grammar', 'use': 'Grammar-of-graphics implementation inspired by ggplot2.', 'tags': 'grammar layers facets', 'official_url': 'https://plotnine.org/gallery.html'}, {'name': 'Lets-Plot', 'language': 'Python', 'category': 'grammar', 'use': 'Grammar-of-graphics plots for notebooks and web output.', 'tags': 'grammar interactive notebooks', 'official_url': 'https://lets-plot.org/python/pages/gallery.html'}, {'name': 'Pygal', 'language': 'Python', 'category': 'grammar', 'use': 'Lightweight SVG charts with browser-friendly output.', 'tags': 'svg browser simple', 'official_url': 'https://www.pygal.org/en/stable/documentation/types/index.html'}, {'name': 'pyecharts', 'language': 'Python', 'category': 'grammar', 'use': 'Python bindings for the Apache ECharts ecosystem.', 'tags': 'echarts interactive web', 'official_url': 'https://gallery.pyecharts.org/'}, {'name': 'Panel', 'language': 'Python', 'category': 'grammar', 'use': 'Compose plots, widgets, and data apps across libraries.', 'tags': 'dashboard widgets app', 'official_url': 'https://panel.holoviz.org/gallery/index.html'}, {'name': 'pandas plotting', 'language': 'Python', 'category': 'statistics', 'use': 'Quick plots directly from Series and DataFrames.', 'tags': 'dataframe quick exploratory', 'official_url': 'https://pandas.pydata.org/docs/user_guide/visualization.html'}, {'name': 'statsmodels graphics', 'language': 'Python', 'category': 'statistics', 'use': 'Regression, diagnostic, time-series, and model plots.', 'tags': 'regression residual diagnostic', 'official_url': 'https://www.statsmodels.org/stable/graphics.html'}, {'name': 'scikit-learn Displays', 'language': 'Python', 'category': 'statistics', 'use': 'Model evaluation displays with estimator integration.', 'tags': 'machine learning calibration roc confusion', 'official_url': 'https://scikit-learn.org/stable/visualizations.html'}, {'name': 'Yellowbrick', 'language': 'Python', 'category': 'statistics', 'use': 'Visual diagnostics for machine-learning workflows.', 'tags': 'machine learning diagnostic residual', 'official_url': 'https://www.scikit-yb.org/en/latest/gallery.html'}, {'name': 'ArviZ', 'language': 'Python', 'category': 'statistics', 'use': 'Exploratory analysis and diagnostics for Bayesian models.', 'tags': 'bayesian posterior interval', 'official_url': 'https://python.arviz.org/en/stable/examples/index.html'}, {'name': 'corner.py', 'language': 'Python', 'category': 'statistics', 'use': 'Multidimensional posterior and parameter distributions.', 'tags': 'bayesian pairplot distribution', 'official_url': 'https://corner.readthedocs.io/en/latest/pages/quickstart/'}, {'name': 'missingno', 'language': 'Python', 'category': 'statistics', 'use': 'Missing-data matrices, bars, heatmaps, and dendrograms.', 'tags': 'missingness data quality', 'official_url': 'https://github.com/ResidentMario/missingno'}, {'name': 'UpSetPlot', 'language': 'Python', 'category': 'statistics', 'use': 'Scalable set-intersection visualization.', 'tags': 'sets intersections upset', 'official_url': 'https://upsetplot.readthedocs.io/en/stable/auto_examples/index.html'}, {'name': 'JoyPy', 'language': 'Python', 'category': 'statistics', 'use': 'Ridgeline distribution plots built on Matplotlib.', 'tags': 'ridgeline density distribution', 'official_url': 'https://github.com/leotac/joypy'}, {'name': 'PtitPrince', 'language': 'Python', 'category': 'statistics', 'use': 'Raincloud plots combining density, box, and points.', 'tags': 'raincloud distribution', 'official_url': 'https://github.com/pog87/PtitPrince'}, {'name': 'SciencePlots', 'language': 'Python', 'category': 'statistics', 'use': 'Matplotlib styles for scientific publication contexts.', 'tags': 'publication style journal', 'official_url': 'https://github.com/garrettj403/SciencePlots'}, {'name': 'statannotations', 'language': 'Python', 'category': 'statistics', 'use': 'Statistical annotations for seaborn and Matplotlib plots.', 'tags': 'significance annotation box', 'official_url': 'https://github.com/trevismd/statannotations'}, {'name': 'GeoPandas', 'language': 'Python', 'category': 'specialized', 'use': 'Geospatial vector data analysis and mapping.', 'tags': 'map geometry spatial', 'official_url': 'https://geopandas.org/en/stable/docs/user_guide/mapping.html'}, {'name': 'Cartopy', 'language': 'Python', 'category': 'specialized', 'use': 'Projected maps and geospatial data transformations.', 'tags': 'map projection geospatial', 'official_url': 'https://cartopy.readthedocs.io/stable/gallery/index.html'}, {'name': 'Folium', 'language': 'Python', 'category': 'specialized', 'use': 'Interactive Leaflet maps from Python.', 'tags': 'interactive map leaflet', 'official_url': 'https://python-visualization.github.io/folium/latest/getting_started.html'}, {'name': 'pydeck', 'language': 'Python', 'category': 'specialized', 'use': 'Large-scale layered geospatial visualizations with deck.gl.', 'tags': 'map layers gpu spatial', 'official_url': 'https://deckgl.readthedocs.io/en/latest/gallery/index.html'}, {'name': 'Datashader', 'language': 'Python', 'category': 'specialized', 'use': 'Rasterize and aggregate massive point or line datasets.', 'tags': 'large data density raster', 'official_url': 'https://datashader.org/user_guide/index.html'}, {'name': 'NetworkX', 'language': 'Python', 'category': 'specialized', 'use': 'Network analysis, layouts, and graph drawing.', 'tags': 'network graph topology', 'official_url': 'https://networkx.org/documentation/stable/auto_examples/index.html'}, {'name': 'PyVis', 'language': 'Python', 'category': 'specialized', 'use': 'Interactive browser-based network visualization.', 'tags': 'network interactive browser', 'official_url': 'https://pyvis.readthedocs.io/en/latest/'}, {'name': 'OSMnx', 'language': 'Python', 'category': 'specialized', 'use': 'Download, analyze, and visualize street networks.', 'tags': 'network street map osm', 'official_url': 'https://osmnx.readthedocs.io/en/stable/'}, {'name': 'contextily', 'language': 'Python', 'category': 'specialized', 'use': 'Add web basemaps to geospatial plots.', 'tags': 'basemap tiles spatial', 'official_url': 'https://contextily.readthedocs.io/en/latest/intro_guide.html'}, {'name': 'GeoViews', 'language': 'Python', 'category': 'specialized', 'use': 'Geographic visualization built on HoloViews.', 'tags': 'map geographic interactive', 'official_url': 'https://geoviews.org/gallery/index.html'}, {'name': 'Graphviz', 'language': 'Python', 'category': 'specialized', 'use': 'Graph and hierarchy layout through the Graphviz engine.', 'tags': 'network tree layout', 'official_url': 'https://graphviz.readthedocs.io/en/stable/examples.html'}, {'name': 'pyCirclize', 'language': 'Python', 'category': 'specialized', 'use': 'Circular genome, chord, and sector visualizations.', 'tags': 'circular chord polar', 'official_url': 'https://moshi4.github.io/pyCirclize/'}, {'name': 'ggplot2', 'language': 'R', 'category': 'grammar', 'use': 'Layered grammar of graphics for analytical and publication figures.', 'tags': 'grammar layers facets', 'official_url': 'https://ggplot2.tidyverse.org/'}, {'name': 'plotly for R', 'language': 'R', 'category': 'grammar', 'use': 'Interactive Plotly figures and ggplotly conversion.', 'tags': 'interactive hover web', 'official_url': 'https://plotly.com/r/'}, {'name': 'Shiny', 'language': 'R', 'category': 'grammar', 'use': 'Reactive interactive applications and dashboards.', 'tags': 'app reactive dashboard', 'official_url': 'https://shiny.posit.co/r/gallery/'}, {'name': 'highcharter', 'language': 'R', 'category': 'grammar', 'use': 'Interactive Highcharts visualizations from R.', 'tags': 'interactive web time series', 'official_url': 'https://jkunst.com/highcharter/'}, {'name': 'echarts4r', 'language': 'R', 'category': 'grammar', 'use': 'R interface to Apache ECharts with rich interaction.', 'tags': 'interactive echarts web', 'official_url': 'https://echarts4r.john-coene.com/'}, {'name': 'ggiraph', 'language': 'R', 'category': 'grammar', 'use': 'Interactive SVG output for ggplot2 graphics.', 'tags': 'ggplot interactive svg', 'official_url': 'https://davidgohel.github.io/ggiraph/'}, {'name': 'lattice', 'language': 'R', 'category': 'grammar', 'use': 'Trellis graphics for multivariable conditioning.', 'tags': 'facets conditioning multivariate', 'official_url': 'https://lattice.r-forge.r-project.org/'}, {'name': 'r2d3', 'language': 'R', 'category': 'grammar', 'use': 'Build custom D3 visualizations from R.', 'tags': 'd3 custom interactive', 'official_url': 'https://rstudio.github.io/r2d3/'}, {'name': 'htmlwidgets', 'language': 'R', 'category': 'grammar', 'use': 'Framework connecting JavaScript visualization libraries to R.', 'tags': 'html javascript interactive', 'official_url': 'https://www.htmlwidgets.org/showcase_leaflet.html'}, {'name': 'vegawidget', 'language': 'R', 'category': 'grammar', 'use': 'Render and compose Vega and Vega-Lite specifications.', 'tags': 'vega declarative grammar', 'official_url': 'https://vegawidget.github.io/vegawidget/'}, {'name': 'dygraphs', 'language': 'R', 'category': 'grammar', 'use': 'Interactive time-series charts for R and Shiny.', 'tags': 'interactive time series', 'official_url': 'https://rstudio.github.io/dygraphs/'}, {'name': 'reactable', 'language': 'R', 'category': 'grammar', 'use': 'Interactive data tables with sorting and grouping.', 'tags': 'table interactive arranged', 'official_url': 'https://glin.github.io/reactable/articles/examples.html'}, {'name': 'patchwork', 'language': 'R', 'category': 'statistics', 'use': 'Compose multiple ggplot2 figures with a layout grammar.', 'tags': 'composition panels publication', 'official_url': 'https://patchwork.data-imaginist.com/'}, {'name': 'cowplot', 'language': 'R', 'category': 'statistics', 'use': 'Align, arrange, annotate, and theme publication plots.', 'tags': 'publication arrange annotation', 'official_url': 'https://wilkelab.org/cowplot/'}, {'name': 'ggridges', 'language': 'R', 'category': 'statistics', 'use': 'Ridgeline density plots for grouped distributions.', 'tags': 'ridgeline distribution density', 'official_url': 'https://wilkelab.org/ggridges/'}, {'name': 'ggdist', 'language': 'R', 'category': 'statistics', 'use': 'Uncertainty and distribution visualization for ggplot2.', 'tags': 'uncertainty interval distribution', 'official_url': 'https://mjskay.github.io/ggdist/'}, {'name': 'ggbeeswarm', 'language': 'R', 'category': 'statistics', 'use': 'Non-overlapping point plots for distributions.', 'tags': 'beeswarm points distribution', 'official_url': 'https://eclarke.github.io/ggbeeswarm/'}, {'name': 'GGally', 'language': 'R', 'category': 'statistics', 'use': 'Pairs plots and extensions to ggplot2.', 'tags': 'pairplot correlation multivariate', 'official_url': 'https://ggobi.github.io/ggally/'}, {'name': 'ggstatsplot', 'language': 'R', 'category': 'statistics', 'use': 'ggplot2 figures integrated with statistical details.', 'tags': 'statistics annotation inference', 'official_url': 'https://indrajeetpatil.github.io/ggstatsplot/'}, {'name': 'forestplot', 'language': 'R', 'category': 'statistics', 'use': 'Customizable forest plots and confidence intervals.', 'tags': 'forest interval meta analysis', 'official_url': 'https://cran.r-project.org/package=forestplot'}, {'name': 'survminer', 'language': 'R', 'category': 'statistics', 'use': 'Publication-ready survival curves and diagnostics.', 'tags': 'survival model curve', 'official_url': 'https://rpkgs.datanovia.com/survminer/'}, {'name': 'ggforce', 'language': 'R', 'category': 'statistics', 'use': 'Geometric extensions, facets, and annotations for ggplot2.', 'tags': 'geometry facet annotation', 'official_url': 'https://ggforce.data-imaginist.com/'}, {'name': 'ggthemes', 'language': 'R', 'category': 'statistics', 'use': 'Additional complete themes, scales, and palettes for ggplot2.', 'tags': 'theme publication palette', 'official_url': 'https://jrnold.github.io/ggthemes/'}, {'name': 'ggtext', 'language': 'R', 'category': 'statistics', 'use': 'Rich text rendering inside ggplot2 figures.', 'tags': 'typography annotation publication', 'official_url': 'https://wilkelab.org/ggtext/'}, {'name': 'sf', 'language': 'R', 'category': 'specialized', 'use': 'Simple-features vector data operations and mapping.', 'tags': 'map geometry spatial', 'official_url': 'https://r-spatial.github.io/sf/'}, {'name': 'terra', 'language': 'R', 'category': 'specialized', 'use': 'Spatial raster and vector analysis with plotting support.', 'tags': 'raster map spatial', 'official_url': 'https://rspatial.github.io/terra/'}, {'name': 'tmap', 'language': 'R', 'category': 'specialized', 'use': 'Thematic static and interactive maps.', 'tags': 'thematic map interactive', 'official_url': 'https://r-tmap.github.io/tmap/'}, {'name': 'leaflet', 'language': 'R', 'category': 'specialized', 'use': 'Interactive Leaflet maps from R and Shiny.', 'tags': 'interactive map tiles', 'official_url': 'https://rstudio.github.io/leaflet/'}, {'name': 'mapview', 'language': 'R', 'category': 'specialized', 'use': 'Rapid interactive viewing of spatial objects.', 'tags': 'interactive map exploratory', 'official_url': 'https://r-spatial.github.io/mapview/'}, {'name': 'ggraph', 'language': 'R', 'category': 'specialized', 'use': 'Grammar-of-graphics approach to networks and trees.', 'tags': 'network tree grammar', 'official_url': 'https://ggraph.data-imaginist.com/'}, {'name': 'igraph', 'language': 'R', 'category': 'specialized', 'use': 'Network analysis, layout, and plotting.', 'tags': 'network topology graph', 'official_url': 'https://r.igraph.org/'}, {'name': 'ComplexHeatmap', 'language': 'R', 'category': 'specialized', 'use': 'Highly composable heatmaps with annotations.', 'tags': 'heatmap matrix annotation', 'official_url': 'https://jokergoo.github.io/ComplexHeatmap-reference/book/'}, {'name': 'ggalluvial', 'language': 'R', 'category': 'specialized', 'use': 'Alluvial plots for categorical flows in ggplot2.', 'tags': 'alluvial sankey flow', 'official_url': 'https://corybrunson.github.io/ggalluvial/'}, {'name': 'circlize', 'language': 'R', 'category': 'specialized', 'use': 'Circular, chord, genomic, and sector visualizations.', 'tags': 'circular chord polar', 'official_url': 'https://jokergoo.github.io/circlize_book/book/'}, {'name': 'networkD3', 'language': 'R', 'category': 'specialized', 'use': 'Interactive D3 networks, trees, and Sankey diagrams.', 'tags': 'network sankey interactive', 'official_url': 'https://christophergandrud.github.io/networkD3/'}, {'name': 'treemapify', 'language': 'R', 'category': 'specialized', 'use': 'Treemaps in the ggplot2 ecosystem.', 'tags': 'treemap hierarchy area', 'official_url': 'https://wilkox.org/treemapify/'}])\n", "idiom_names = ['Sorted bar', 'Dot plot', 'Lollipop', 'Slope graph', 'Dumbbell', 'Line chart', 'Small multiples', 'Stacked bar', '100% stacked', 'Donut', 'Aligned profile', 'Radar', 'Histogram', 'KDE', 'ECDF', 'Box plot', 'Violin', 'Raincloud', 'Scatterplot', 'Regression view', 'Hexbin', 'Pair plot', 'Correlation heatmap', '2D contour', 'Interval / forest', 'Fan chart', 'Calibration', 'Residual view', 'Treemap', 'Sunburst', 'Sankey', 'Alluvial', 'Polar / Burtin', 'Population pyramid', 'Ordered heatmap', 'Faceted table', 'Missingness matrix', 'UpSet matrix']\n", "assert len(idiom_catalog) == 38\n", "assert set(idiom_catalog[\"root\"]) == {\"Compare\", \"Understand variation\", \"Reveal structure\"}\n", "assert len(tool_catalog) == 72\n", "display(idiom_catalog[[\"leaf\",\"root\",\"family\",\"idiom\",\"data_requirement\",\"action_target\"]].style.hide(axis=\"index\"))\n", "print(\"Verified:\", len(idiom_catalog), \"idioms ·\", len(tool_catalog), \"tools\")\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "gallery-006", "metadata": { "jupyter": { "source_hidden": true }, "tags": [ "hide-input" ] }, "outputs": [], "source": [ "def finish_ax(ax, title):\n", " ax.set_title(title, loc=\"left\", fontsize=10.5, fontweight=\"bold\", color=COLORS[\"ink\"], pad=8)\n", " ax.tick_params(labelsize=6.5, colors=COLORS[\"muted\"], length=2)\n", " for side in (\"top\", \"right\"):\n", " ax.spines[side].set_visible(False)\n", " ax.spines[\"left\"].set_color(COLORS[\"line\"])\n", " ax.spines[\"bottom\"].set_color(COLORS[\"line\"])\n", "\n", "\n", "def draw_idiom(ax, name, seed=7):\n", " \"\"\"Draw one compact teaching example from deterministic synthetic data.\"\"\"\n", " rng = np.random.default_rng(seed + idiom_names.index(name))\n", " blue, teal, amber, red = COLORS[\"blue\"], COLORS[\"teal\"], COLORS[\"amber\"], COLORS[\"red\"]\n", " ax.set_facecolor(\"white\")\n", " cats = np.array([\"A\", \"B\", \"C\", \"D\"])\n", " values = np.array([5, 8, 3, 6])\n", "\n", " if name == \"Sorted bar\":\n", " order = np.argsort(values); ax.barh(cats[order], values[order], color=blue)\n", " elif name == \"Dot plot\":\n", " order = np.argsort(values); ax.hlines(cats[order], 0, values[order], color=COLORS[\"line\"]); ax.scatter(values[order], cats[order], s=46, color=blue)\n", " elif name == \"Lollipop\":\n", " ax.vlines(cats, 0, values, color=COLORS[\"line\"], lw=2); ax.scatter(cats, values, s=48, color=teal)\n", " elif name == \"Slope graph\":\n", " before, after = np.array([3, 7, 5, 8]), np.array([6, 5, 8, 7])\n", " for i in range(4): ax.plot([0, 1], [before[i], after[i]], marker=\"o\", lw=2, color=PALETTE[i])\n", " ax.set_xticks([0, 1], [\"Before\", \"After\"])\n", " elif name == \"Dumbbell\":\n", " left, right = np.array([2, 6, 4, 5]), np.array([7, 8, 6, 9])\n", " ax.hlines(cats, left, right, color=COLORS[\"line\"], lw=3); ax.scatter(left, cats, color=blue, s=38); ax.scatter(right, cats, color=red, s=38)\n", " elif name == \"Line chart\":\n", " x = np.arange(6); ax.plot(x, [2,3,3,5,6,8], marker=\"o\", color=blue); ax.plot(x, [5,4,6,5,7,6], marker=\"o\", color=teal)\n", " elif name == \"Small multiples\":\n", " ax.set_xlim(0, 1); ax.set_ylim(0, 1); ax.axis(\"off\")\n", " for i, (x0, y0) in enumerate([(0,.55),(.55,.55),(0,0),(.55,0)]):\n", " xs = np.linspace(x0+.04, x0+.42, 5); ys = y0+.08 + .08*np.array([1,2,1.4,2.5,2+i/3])\n", " ax.add_patch(Rectangle((x0+.02,y0+.04),.44,.39,fill=False,ec=COLORS[\"line\"])); ax.plot(xs, ys, color=PALETTE[i], lw=2)\n", " elif name == \"Stacked bar\":\n", " a, b, c = [3,5,2], [2,2,4], [1,3,2]; x=np.arange(3)\n", " ax.bar(x,a,color=blue); ax.bar(x,b,bottom=a,color=teal); ax.bar(x,c,bottom=np.array(a)+b,color=amber); ax.set_xticks(x,[\"G1\",\"G2\",\"G3\"])\n", " elif name == \"100% stacked\":\n", " raw=np.array([[3,2,1],[4,3,2],[2,4,3]],float); pct=raw/raw.sum(1,keepdims=True); x=np.arange(3); bottom=np.zeros(3)\n", " for j,col in enumerate([blue,teal,amber]): ax.bar(x,pct[:,j],bottom=bottom,color=col); bottom+=pct[:,j]\n", " ax.set_xticks(x,[\"G1\",\"G2\",\"G3\"]); ax.set_ylim(0,1)\n", " elif name == \"Donut\":\n", " ax.pie([45,30,15,10], colors=[blue,teal,amber,red], startangle=90, wedgeprops={\"width\":.34,\"edgecolor\":\"white\"}); ax.text(0,0,\"100%\",ha=\"center\",va=\"center\",fontsize=9,fontweight=\"bold\")\n", " elif name == \"Aligned profile\":\n", " x=np.arange(5); ax.plot(x,[2,5,4,7,6],marker=\"o\",color=blue); ax.plot(x,[4,3,6,5,8],marker=\"o\",color=teal); ax.set_xticks(x,[\"M1\",\"M2\",\"M3\",\"M4\",\"M5\"])\n", " elif name == \"Radar\":\n", " theta=np.linspace(0,2*np.pi,5,endpoint=False); val=np.array([.7,.4,.8,.6,.5]); xy=np.c_[np.cos(theta)*val,np.sin(theta)*val]\n", " ax.plot(np.r_[xy[:,0],xy[0,0]],np.r_[xy[:,1],xy[0,1]],color=blue,lw=2); ax.fill(np.r_[xy[:,0],xy[0,0]],np.r_[xy[:,1],xy[0,1]],color=blue,alpha=.15); ax.set_aspect(\"equal\"); ax.axis(\"off\")\n", " elif name == \"Histogram\":\n", " x=np.r_[rng.normal(-.7,.55,160),rng.normal(.8,.35,90)]; ax.hist(x,bins=14,color=blue,edgecolor=\"white\")\n", " elif name == \"KDE\":\n", " x=np.r_[rng.normal(-.7,.55,160),rng.normal(.8,.35,90)]; grid=np.linspace(-2.5,2.2,180); bw=.28; dens=np.exp(-.5*((grid[:,None]-x[None,:])/bw)**2).mean(1)/(bw*np.sqrt(2*np.pi)); ax.fill_between(grid,dens,color=teal,alpha=.25); ax.plot(grid,dens,color=teal,lw=2)\n", " elif name == \"ECDF\":\n", " x=np.sort(rng.normal(size=90)); ax.step(x,np.arange(1,len(x)+1)/len(x),where=\"post\",color=blue,lw=2); ax.set_ylim(0,1)\n", " elif name == \"Box plot\":\n", " data=[rng.normal(i*.3,.45,60) for i in range(3)]; bp=ax.boxplot(data,patch_artist=True,widths=.55); [b.set(facecolor=blue,alpha=.25,edgecolor=blue) for b in bp[\"boxes\"]]; ax.set_xticks([1,2,3],[\"A\",\"B\",\"C\"])\n", " elif name == \"Violin\":\n", " data=[rng.normal(i*.3,.45,90) for i in range(3)]; vp=ax.violinplot(data,showmedians=True); [b.set(facecolor=teal,alpha=.35,edgecolor=teal) for b in vp[\"bodies\"]]; ax.set_xticks([1,2,3],[\"A\",\"B\",\"C\"])\n", " elif name == \"Raincloud\":\n", " data=[rng.normal(0,.55,70),rng.normal(.8,.45,70)]; vp=ax.violinplot(data,positions=[1,2],showextrema=False); [b.set(facecolor=teal,alpha=.23,edgecolor=teal) for b in vp[\"bodies\"]]; ax.boxplot(data,positions=[1,2],widths=.15,showfliers=False); ax.scatter(1+rng.normal(0,.035,70),data[0],s=5,alpha=.22,color=blue); ax.scatter(2+rng.normal(0,.035,70),data[1],s=5,alpha=.22,color=red); ax.set_xticks([1,2],[\"A\",\"B\"])\n", " elif name == \"Scatterplot\":\n", " x=rng.normal(size=80); y=.7*x+rng.normal(scale=.7,size=80); ax.scatter(x,y,s=17,alpha=.55,color=blue)\n", " elif name == \"Regression view\":\n", " x=np.linspace(0,10,70); y=1.2+.58*x+rng.normal(0,1.05,70); coef=np.polyfit(x,y,1); fit=np.polyval(coef,x); ax.scatter(x,y,s=12,alpha=.34,color=blue); ax.plot(x,fit,color=red,lw=2); ax.fill_between(x,fit-.65,fit+.65,color=red,alpha=.12)\n", " elif name == \"Hexbin\":\n", " x=rng.normal(size=900); y=.65*x+rng.normal(size=900); ax.hexbin(x,y,gridsize=16,mincnt=1,cmap=\"Blues\")\n", " elif name == \"Pair plot\":\n", " ax.set_xlim(0,3); ax.set_ylim(0,3); ax.set_xticks([]); ax.set_yticks([])\n", " for i in range(3):\n", " for j in range(3):\n", " ax.add_patch(Rectangle((j+.08,2-i+.08),.84,.84,fill=False,ec=COLORS[\"line\"],lw=.7))\n", " if i==j:\n", " pts=np.linspace(j+.2,j+.8,18); bump=np.exp(-((pts-(j+.5))/.17)**2)*.45; ax.plot(pts,2-i+.17+bump,color=teal,lw=1)\n", " elif i>j:\n", " xx=j+.15+.7*rng.random(16); yy=2-i+.15+.7*(.6*(xx-j-.15)/.7+.4*rng.random(16)); ax.scatter(xx,yy,s=4,color=blue,alpha=.55)\n", " elif name == \"Correlation heatmap\":\n", " mat=np.array([[1,.75,-.2,.45],[.75,1,-.45,.2],[-.2,-.45,1,.6],[.45,.2,.6,1]]); ax.imshow(mat,cmap=\"RdBu_r\",vmin=-1,vmax=1); ax.set_xticks(range(4),cats); ax.set_yticks(range(4),cats)\n", " elif name == \"2D contour\":\n", " x=np.linspace(-2,2,70); y=np.linspace(-2,2,70); X,Y=np.meshgrid(x,y); Z=np.exp(-(X**2+Y**2))+0.65*np.exp(-((X-1.1)**2+(Y+.8)**2)/.35); ax.contourf(X,Y,Z,levels=9,cmap=\"YlGnBu\")\n", " elif name == \"Interval / forest\":\n", " est=np.array([-.3,.25,.7,1.1]); lo=est-np.array([.4,.25,.3,.45]); hi=est+np.array([.35,.5,.25,.3]); y=np.arange(4); ax.hlines(y,lo,hi,color=COLORS[\"muted\"],lw=2); ax.scatter(est,y,color=blue,s=42); ax.axvline(0,color=COLORS[\"line\"],ls=\"--\"); ax.set_yticks(y,cats)\n", " elif name == \"Fan chart\":\n", " x=np.arange(12); center=4+.25*x+.25*np.sin(x); spread=.12*(x+1); ax.fill_between(x,center-2*spread,center+2*spread,color=blue,alpha=.12); ax.fill_between(x,center-spread,center+spread,color=blue,alpha=.25); ax.plot(x,center,color=blue,lw=2)\n", " elif name == \"Calibration\":\n", " pred=np.linspace(.05,.95,9); obs=np.clip(pred+np.array([.03,-.04,.02,.06,-.02,.04,-.06,.03,-.04]),0,1); ax.plot([0,1],[0,1],ls=\"--\",color=COLORS[\"muted\"]); ax.plot(pred,obs,marker=\"o\",color=teal); ax.set_xlim(0,1); ax.set_ylim(0,1)\n", " elif name == \"Residual view\":\n", " fitted=np.linspace(1,9,70); residual=.08*(fitted-5)**2-.5+rng.normal(0,.38,70); ax.scatter(fitted,residual,s=15,alpha=.55,color=blue); ax.axhline(0,color=red,ls=\"--\")\n", " elif name == \"Treemap\":\n", " ax.set_xlim(0,1); ax.set_ylim(0,1); ax.axis(\"off\"); rects=[(0,0,.48,1,blue),( .49,.48,.51,.52,teal),(.49,0,.31,.46,amber),(.81,0,.19,.46,red)]\n", " for i,(x,y,w,h,c) in enumerate(rects): ax.add_patch(Rectangle((x,y),w,h,fc=c,ec=\"white\",lw=2,alpha=.85)); ax.text(x+.03,y+h-.08,chr(65+i),color=\"white\",fontweight=\"bold\",fontsize=8)\n", " elif name == \"Sunburst\":\n", " ax.set_xlim(-1.1,1.1); ax.set_ylim(-1.1,1.1); ax.set_aspect(\"equal\"); ax.axis(\"off\")\n", " for a0,a1,c in [(90,220,blue),(220,360,teal),(0,90,amber)]: ax.add_patch(Wedge((0,0),.58,a0,a1,width=.30,fc=c,ec=\"white\"))\n", " for a0,a1,c in [(90,150,blue),(150,220,\"#7895ff\"),(220,300,teal),(300,360,\"#60bdb3\"),(0,45,amber),(45,90,\"#f0ca73\")]: ax.add_patch(Wedge((0,0),1,a0,a1,width=.36,fc=c,ec=\"white\"))\n", " elif name == \"Sankey\":\n", " ax.set_xlim(0,1); ax.set_ylim(0,1); ax.axis(\"off\")\n", " for x in [.08,.82]:\n", " for y,c in [(.25,blue),(.65,teal)]: ax.add_patch(Rectangle((x,y),.08,.18,fc=c,alpha=.9))\n", " for y0,y1,w,c in [(.34,.34,12,blue),(.34,.74,5,amber),(.74,.74,10,teal),(.74,.34,4,red)]: ax.add_patch(FancyArrowPatch((.16,y0),(.82,y1),connectionstyle=\"arc3,rad=.08\",arrowstyle=\"-\",lw=w,color=c,alpha=.35))\n", " elif name == \"Alluvial\":\n", " ax.set_xlim(0,2); ax.set_ylim(0,1); ax.axis(\"off\")\n", " for x in [0,1,2]: ax.vlines(x,.08,.92,color=COLORS[\"line\"],lw=6)\n", " for y0,y1,y2,c in [(.18,.32,.2,blue),(.38,.22,.55,teal),(.62,.72,.68,amber),(.82,.58,.85,red)]:\n", " verts=[(0,y0),(.5,y0),( .5,y1),(1,y1),(1.5,y1),(1.5,y2),(2,y2)]; codes=[MplPath.MOVETO,MplPath.CURVE4,MplPath.CURVE4,MplPath.CURVE4,MplPath.CURVE4,MplPath.CURVE4,MplPath.CURVE4]; ax.add_patch(PathPatch(MplPath(verts,codes),fill=False,lw=7,color=c,alpha=.35))\n", " elif name == \"Polar / Burtin\":\n", " ax.set_xlim(-1.1,1.1); ax.set_ylim(-1.1,1.1); ax.set_aspect(\"equal\"); ax.axis(\"off\"); vals=[.45,.75,.6,.9,.55,.7,.38,.65]\n", " for i,v in enumerate(vals): ax.add_patch(Wedge((0,0),v,i*45+3,(i+1)*45-3,width=.22,fc=PALETTE[i%4],alpha=.85))\n", " elif name == \"Population pyramid\":\n", " y=np.arange(6); left=np.array([7,9,11,10,8,5]); right=np.array([6,8,10,11,9,6]); ax.barh(y,-left,color=blue); ax.barh(y,right,color=red); ax.axvline(0,color=\"white\",lw=1); ax.set_yticks(y,[\"0–9\",\"10–19\",\"20–29\",\"30–39\",\"40–49\",\"50+\"]); ticks=ax.get_xticks(); ax.set_xticks(ticks,[str(abs(int(t))) for t in ticks])\n", " elif name == \"Ordered heatmap\":\n", " mat=np.array([[.1,.2,.8,.9],[.2,.3,.75,.82],[.7,.8,.25,.2],[.85,.72,.18,.1]]); ax.imshow(mat,cmap=\"YlGnBu\",vmin=0,vmax=1); ax.set_xticks(range(4),cats); ax.set_yticks(range(4),[\"R1\",\"R2\",\"R3\",\"R4\"])\n", " elif name == \"Faceted table\":\n", " ax.set_xlim(0,4); ax.set_ylim(0,5); ax.axis(\"off\")\n", " for r in range(5):\n", " for c in range(4): ax.add_patch(Rectangle((c,4-r),1,1,fc=COLORS[\"paper\"] if r else COLORS[\"ink\"],ec=\"white\")); ax.text(c+.5,4-r+.5,(cats[c] if r==0 else f\"{(r+1)*(c+2)}\"),ha=\"center\",va=\"center\",fontsize=7,color=\"white\" if r==0 else COLORS[\"ink\"])\n", " elif name == \"Missingness matrix\":\n", " mat=(rng.random((12,7))>.2).astype(int); mat[2:8,4]=0; ax.imshow(mat,aspect=\"auto\",cmap=ListedColormap([red,COLORS[\"paper\"]])); ax.set_xticks(range(7),list(\"ABCDEFG\")); ax.set_yticks([])\n", " elif name == \"UpSet matrix\":\n", " ax.set_xlim(-.5,5.5); ax.set_ylim(-.5,7.5); ax.axis(\"off\"); counts=[9,7,6,5,3,2]; ax.bar(range(6),counts,bottom=4.2,color=blue,width=.65)\n", " memberships=[[1,0,0],[0,1,0],[0,0,1],[1,1,0],[1,0,1],[1,1,1]]\n", " for x,mem in enumerate(memberships):\n", " ys=[]\n", " for j,on in enumerate(mem): ax.scatter(x,2.6-j,s=25,color=COLORS[\"ink\"] if on else COLORS[\"line\"]); ys.append(2.6-j) if on else None\n", " if sum(mem)>1: ax.vlines(x,min(ys),max(ys),color=COLORS[\"ink\"],lw=1.5)\n", " finish_ax(ax, name)\n", "\n", "\n", "def render_gallery(root, columns=4):\n", " subset = idiom_catalog.query(\"root == @root\").reset_index(drop=True)\n", " rows = int(np.ceil(len(subset) / columns))\n", " fig, axes = plt.subplots(rows, columns, figsize=(15, 3.3 * rows))\n", " axes = np.atleast_1d(axes).ravel()\n", " for ax, row in zip(axes, subset.itertuples()):\n", " draw_idiom(ax, row.idiom)\n", " ax.text(0, 1.01, row.family.upper(), transform=ax.transAxes, fontsize=6.2, color=ROOT_COLORS[row.root], va=\"bottom\", fontweight=\"bold\")\n", " for ax in axes[len(subset):]: ax.axis(\"off\")\n", " fig.suptitle(root, x=.01, y=.992, ha=\"left\", fontsize=20, fontweight=\"bold\", color=COLORS[\"ink\"])\n", " fig.text(.01, .01, \"Synthetic teaching data · visual grammar only · not domain evidence\", fontsize=8, color=COLORS[\"muted\"])\n", " fig.tight_layout(rect=[0, .045, 1, .955], h_pad=2.3, w_pad=1.5)\n", " slug = {\"Compare\":\"compare\", \"Understand variation\":\"variation\", \"Reveal structure\":\"structure\"}[root]\n", " for ext in (\"png\", \"svg\", \"pdf\"):\n", " fig.savefig(OUTPUT_DIR / f\"idiom_{slug}_gallery.{ext}\", dpi=220 if ext == \"png\" else None, bbox_inches=\"tight\", facecolor=\"white\")\n", " return fig\n" ] }, { "cell_type": "markdown", "id": "gallery-007", "metadata": {}, "source": [ "### 1.2 Compare · 12 idioms\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "gallery-008", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# @title Render Compare gallery\n", "compare_gallery = render_gallery(\"Compare\", columns=4)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "gallery-009", "metadata": {}, "source": [ "### 1.3 Understand Variation · 16 idioms\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "gallery-010", "metadata": {}, "outputs": [ { "data": { "image/png": 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fA2jSOKdzTgfQeNSnc/oTP3yvD/75viTJMrCNLhg1mHM6AI+r95fa0rKPatLXnyoqIEhzrrtVEzp1019+maXvtyQ51ym22fTu6uVVbj+0ZSttvOshbbzrIV3fs6/Lsss6dNaxokItSdslSfolZZuKbTZd3qGz8kuKNX/Xdhlk0M/bt8pmt7tsu+imu/SXIedrRvImLdi1Q5Jkzc7Xmg/nqM15E9T74ut17+1PyXDouCTpkitGKSw02Ln9g4OGaf4Ndyot+6jeXLH0tD+Hly+6zPk6Qn19q/GTA4D6pSGdz082M3mzDDLoeHGRFu7a6bKM8zmApohzeinO6QAag/p6Tpeki9t24JwOoF6o90n06Vv+UKHVqifPv0AdwyP10ODhigkM0rQ/1jnXaRXaXB9vWKPcosJK23ubzYoJClZMULACvLxdlo1q3VZ+FotmbdsiSZq1bavC/Pw1OD5R81K2K7+kROM6dtHhgnz9vme3y7ZRAYGKDy7tdx7k7SNJMgX6qHmbFvL39VHW4Ww5TEaFxkfKPKStHJ2iXbYP8vZWbFCwvE1mJYSG6nRCfHydr8NoqPe/NgCopCGdzysqtlk1Z0eyLmzTXn4Wi2Zu2+yynPM5gKaIc3opzukAGoP6ek6XpGYnqsE5pwPwtHp/VsjIzZEkxQWXVnEbDAbFBAY5xyVpeGJrxQYF66P1qyttv2DXTrV8+V9q+fK/9FtqissyP4uXRrVup9k7kkuvWu7eoYvbdZTJaNTM5M3yMpn012EjZZBBM5NdT8a9pr6i66d/ocndemlwy9JJKcxBfuo2Yag2/vKZ/jft37KO66HHn7pLgwf31tyd21RktTq3f3bxfLV7/XllFxaqW+Tpe/rePes7tXz5X+p74pYkAGhoGtL5vKLfUnfpWFGhLmjTTgPjEjifA4A4p5fhnA6gMahv5/Tg5sGyTuwtr8kD9N7hnZzTAdQL9T6JHhNYOsnc3pxjkiSHw6GM3BzneJl7+g/Ru2tWyHrS7T8D41pq4U13auFNd6pvi7hK+7+sQ2cdys/Ts7/NV35JiS47cUvRrynb1bdFnBJCmqlzRGSlW4u+vPJajW7dTtO3/KH0Y9mV9lt28h+e2EbnJ7ZRblGRFu4uv7Xojr6DSmOKjdMTv/582p/DU+dfqIU33anvJt1w2nUBoD7ifF6K8zmAxoBzeinO6QAaA87ppTinAziVep9EH9+pm7xN5tIJLrIO6pVlvykjN0dXd+vpst4VnbrIbDQqv6TYZdxoMMjHZJaPySyrrXJ/rdGt28nPYtH/1q1Uc18/DY5P0K8pO5RfUqKle1IV/vyT2nTwgA7l52lphVuL2jUP19MjLlSR1ab31qxw2WeJzaY5O5IllVbD/G3+bElyqZQxG43yNptlMhhkNJb/GuwOu35LTdFvqSlal7HXOe5lMsnHZJbZaJTVbjvTHyMAeBzn81KczwE0BpzTS3FOB9AYcE4vxTkdwKnU+yR6y5BQfXnlFGXk5ujCT97V15s26tlRF2t8p24u65mNJt3Rd1Cl7Ren7VL3t15W97de1svLFlVa7mfx0shW7WR3OHRxu44yG02ambxZfhaLfppyixbceKemT7pBZqNRM066XbRt83ANS2ilL5PWq6CkxDn+W2qKsgsL9OiQ87Xgxju14MY7NSguQXN2JqvYVnoifmXZb+r/zuvan5ujFy8Y69y20GrVhC8/1oQvP9ZffpnlHH9ozkzn69hx+NBZ/SwBwJM4n5fifA6gMeCcXopzOoDGgHN6Kc7pAE7F4HA4HJ4OAgAAAEDteO29r7RnX6ZGDumjS0a5Jj9KSqz6+JvZOpKdo2YhQbp+4hhZLGYPRQoAAADUTyTRAQAAgEbsaHaOtu5I09FjuZWS6L8tX6/c4/kaO3qwZs1bqsAAPw0b2NPNngAAAICmqd63cwEAAABw9kJDgtwu274rXd06tZEkdevURtt3pddVWAAAAECDUa/v1XQ4HLLbHTIaDTIYDJ4OBwBwljifA0D9dDyvQH6+PpIkP19v5eUXuF23xGqV1WqTw+GQ0WiUt5eFczoANHB8TgeA6qnXSXS73aH12/aqZ/tYmUyczAGgNixbnaTFKzfIIIMmjRullrFRldaZOXeJVq7fomceu12SlLb3gL784Vc55NDQ/j00qG/XUx6D8zkA1E/+fj4qKCiUFKyCgiL5+/m6XXf2ghWaNW+pLBazbpwykXM6gAbhogesOnhUigiV5rxWr1MgHsHndACoHt5BAKAJy8sv1Pyla/X4PdcqOydX//tilh69e4rLOjm5eco8dNRl7MsfftXNk8cqJChQz735qbp3bit/P5+6DB0AUAPatYpXUvIuxbWIVFLyLrVrFed23TEjBmj00L6y2e1KTs2qwygBNCTzVtk1dbpd+YWejqTUoWxPRwAAaAxIogNAE5aanqG2ibEym00KaxaioqJilVitspjL3x5m/bpMY84foKmffC+p9Hb+ouIShTULkSS1TYxVanqGOrdvVWn/Zbf+2+z2Onk9AIDKPvlmtlLS9slqtSl17wFdNnqwtuxI1YXD+2tQ3y76+OvZevGtzxUaHKgbrrzY7X4sZrMsZrNsNs7pANybOt2u1P2ejqIy6j0AAOeCJDoANGHH8wudvXAlydfXR3n5hQoJCpAkZWYdUVFRsWJjIpzr5OUVyM/X2/ncz8dbeW5KjU6+9R8AUPeumzim0lhci0hJkpfFoluvuayuQwLQiJV9LDQapLAQj4bi5Ocj3fUno6fDAAA0YCTRAaAJ8/ct64VbqrQfbnlS/cd5S3XZBUNct/HzVX5BUfk2hUVuW7lw6z8AAEDjc6qWLWXtU8JC6EEOAGg8eEcDgCYsMT5GM+YukdVm07GcPHl7W1xauRw6nK0vvp8nSTqWk6cvf/hVk8aNkreXRYeP5ig4yF87U/dp7OghVe6fW/8BAAAan+q0bKF9CgCgMSGJDgBNmL+fj4YN7KmXpk6TQQZddflIpe/LdPbKfezea53rPvH8O5o0bpQk6arLR+r9z2fKIYeGDezJpKIAAACNXMXq87Jqc3ctW2ifAgBobAwOh8NR0zstKCzS6+9/LbPJpOISq64YM1Qd2rTUlzN+VXrGQfn6eOumSZfI38/3lPux2exav22veraPlcnEGzAANFSczwGg8eCcDjRN4x+zVqo+T4iWpj9PbV5DxjkdAKqnVt7tvL289Mid18hkMirrcLbe/WyGLr/wPBUXl+gvd12j5Ws2ae6ilRp/8fDaODwAAKgjxwoLNC9lu1bvS9fuo0dks9sV6O2tzhFRGhSfoIFxLWU08IUMAICG6FTV51SbAwCaklpJohuNBkkGSVJhYZFio8O1fVe6unVsI0nq1qmNflu+3u32JVarrFabbHZ66AIAUB/tyT6qV5cv1ldJG1Rit1Va/tP2rZKkxNBmuqf/EF3TrZdMRr5oAwDQkFTV+zw+iupzAGgI8vPzlZycfMbbFRQUKDU1VQkJCfL1PXUXEXc6dOggPz+/s9q2vqq1d76jx3L17mczdDDrqK6/cow2btkpvxM9c/18vZVXUMU03ifMXrBCs+YtlcVi1o1TJtZWiABqkc3ukMloqLf7A3B2bHa73lq1VM8vWaBiW+Xk+cl2Hz2ih+bM1MfrV+vNsePVMTyyDqIEAAA1If/E13aqzwGg4UlOTlbv3r09cuy1a9eqV69eHjl2bam1JHpocKAevXuKDh05ppffnqa+PToq/0TivKCwSP6+7iehGzNigEYP7Sub3a7k1KzaChFALTIZDXribZt2Z5z7tAuJMQY9c4epBqICcC4O5+fppu+/0rL0VOeYj9msQXEJGhSfoDbNwuRtMiu7sEBbsjI1f9cObcnKlCT9kblfF378rl67+HKN79TNQ68AAABUpWLblorKWriEhUhzXqP6HAAakg4dOmjt2rVnvN3WrVs1ZcoUffbZZ+rYseNZH7uxqZV3wRKrVRZz6a59fbzk7e2ltq3itGHTDvXs0k5JybvUrnWc2+0tZrMsZrNsNtq5AA3Z7gyHktNqYk81Pv8xgDO06+hhXfXVp0rNPiKptGnbyFZtdW333gr2cb3Fz9diUXRgkEa2aqvNBw9o6upl2ptzTAXWEt0+81sdzs/XrX0GeOBVAACAqlTVtqUiP/c1cACAesrPz++cqsE7duzY6KrJz0WtJNEzDhzS1zPny2A0ym6366rLRqp965ZK2pKiF9/6XD7eXrpp0tjaODQAAKhhu48e0RXTPlRGbo4kKdDLW38eOFS9YmJPu23niCi9fOFlenv1Mi1MTZEk/fXXn2V3OHR734G1GjcAAKiek9u2VEQLFwAAaimJ3jI2So/cdU2l8cnjL6iNwwEAgFpy4Hiuxn9RnkCPCgjUk8NGKyYouNr78Dabdd+A8xTuH6CvN2+UJP1t/mxFBwbpsg6dayVuAABw5mjbAgBA1bicDAAAqpRfUqzrvpumvTnHJEmRAYH614iLziiBXsZgMGhyt166qksP59idP36r9fv31VS4AAAAAADUCi4xAwCAShwOhx6cPdOZ5A718dU/z79QYf4B57TfSV16KCvvuBbs3qlim003fv+lFt10l0JO6qsOAABq3ukmEAUAAFWjEh0AAFTy2R/r9N2WPyRJ3iaTHh0yQhEBgee8X4PBoDv7DlL75uGSpH05x3TPrOlyOJhAGACA2lY2gejBo67/2U+8DTOBKAAAVaMSHQAAuNh+KEt/nfeT8/nNvfurQ3hEje3fYjLp4cHD9ec5M3S8uFhzd27TV5s2aFLXnjV2DAAAULnyvKzinAlEAQA4MyTRAQCAk81u130/f69Cq1WSNCQ+URe0bl/jxwn3D9C9/YfouSULJEl/nfezhiW0VnRgUI0fCwCApqqs8vxk8VHS9OdJBwAAUF1cZgYAAE5vr16mtRl7JUnhfv66u9/gWjtW/9iWGpbQWpKUW1ykR+b+WGvHAgCgKSqrQDcapIjQ0v8Soqk4BwDgTHHpGQAASJLSj2XrhSULJUkGSXf2HSRfi6VWj3lr7/7aeGCfsgsLNXfnNs1L2a7RrdvV6jEBAGgK5q2y6+DR0sdhIdKc1/j6DwDA2eLyMwAAkCT9ff4cFVhLJEnDElqrV0xsrR8zwMtbN/bs53z+6C+zVHgiBpSzO+zafPCAVu/box2Hs2R32D0dEgCgnps6vfy9gglDAQA4N1yKBgAAWrh7p37avkWSFOjlrZsqJLZr29CWrTQvZbs2HTyg9GPZ+t/albq7/5A6O359llNYqJeXLdI3mzYqKz/POZ4QEqrre/bVbX0GyMvExzkAgPtJRCXatzQmr733lfbsy9TIIX10yahBLsuWrU7Sj/OWqnlo6RwzN0++VKHBgZ4IEwAaHb51ASex2R0yGQ31fp8AUFNsdrv+sWCu8/mkLj0U5FN3JWsGg0G39OqvP8+ZIYekV5Yt1jXdeyvEx7fOYqiPFuzaoXt++l5ZeccrLUvNPqqnF/6imcmb9f7lVyo+JNQDEQIA6hN3k4gmREuj+pJEbyyunzhGW3ek6eix3CqXD+nXrVJyHQBw7kiiAycxGQ164m2bdmc4amR/iTEGPXOHqUb2BQC14etNG7UlK1OSFB8coovbdazzGBJCm2l4QmstTE1RTlGhXlu2WP8YcWGdx1FffPHHev159gzZTrRtsRiN6hgeqUj/AO3LzXH+vtbv36cxn76nn669RQkhzTwZMgDAAypWn5dVnhsNpT3QpdI2LlShNy6hIUGnXL587SZt2rZLHVrH69ILzpORYi4AqBEk0YEq7M5wKDmtpvZWM8l4oLYsW52kxSs3yCCDJo0bpZaxUc5lqzZs0cKl62Q0GOTj461bJl8qXx9vffjlT9q7/6B8fbwVEOCnO64d57kXgHNSaC3R80vmO59f172PDAbPfNma3K2Xft+zWyV2u95fu0K39RmgmKBgj8TiSd9t/kP3/fy983mn8Ejd02+wy89i55FDevH3hTqYd1wH847ryq8+0c/X3qowP39PhAwA8JCqqs/jo6Tpz/NVvynq3rmtBvTuLEn66KuftXL9Zg3s3aXKdUusVlmtNtnszLMCANXBJWkAaMLy8gs1f+laPXzHZN08eay+nPGry/JeXdrr0bun6JG7rlF8i0itWLvZuWzSuNF6+M7JJNAbuE82rFVGbo6k0mRtnxZxHosl3D/AWQVfZLPp2cXzT7NF47M8PdUlgT4sobX+OeKiShcT2jQL00sXXqrYE+O7jx7RXT9+K4eDC7cA0JSU9T83GqSI0NLWLVSeN13+fj4yGo0yGo3q26Oj0vYecLvu7AUrdP/fX9Oj/3qrDiMEgIaLy9MA0ISlpmeobWKszGaTwpqFqKioWCVWqyzm0rcHs7m8FVFxcYliosKcz7/5cYHMZpPOH9RLfXtU3f6DCpf6raCkRP9ZscT5/PoefTwYTak/deqmeSnblV9Som82b9S9A4aofViEp8OqEwfzjuvm779Ssc0mSRoSn6gHBpzn9s6AIG8fPTX8Aj38y486VliohbtT9NnGtbq2HvweAQDn7uSJQqtS1sIlLESa8xpf75u6/IJC+fmWzmuTvDNNUeHuW72NGTFAo4f2lc1uV3JqVl2FiCbOZrNpyZIl2r9/v6Kjo3XeeefJZKL9LRoG3mUBoAk7nl/+QVuSfH19lJdfqJCgAOfY76s26tcla+Rlseii8wdIkiZcer4C/f2Ul1+gV975Uglx0QpvHlJp/7MXrNCseUtlsZh145SJtf56cGY+2bBGmcdLJ6XqHhVTL5LVQd4+mtCpmz7ZuFZ2h0Mv/r5Q/xt3lafDqnV2h133/jRdWfl5kqT2zcNPmUAvE+4foHv7D9G/fiu9i+Rv82drZKu2TbINDgA0Nu4mCq2KX93NBw4P++Sb2UpJ2yer1abUvQd02ejB2rIjVRcO76+5i1Zp645UmYxGRUY00xX9u7vdj8VslsVsls1GsQvqxvTp0/XQQw8pNTXVOZaQkKCXX35Z48eP91xgQDWRRAeAJszf10cFBeXlTQUFRfI/6VvYkH7dNaRfd81ZuFJzF63UhLGlCXRJ8vfzVce2CdqbcbDKJDoVLvVXsc2q/65a6nx+TddeHozG1cXtOuqH5E3KKSrSrG1blHLkkFo3Czv9hg3Yx+vXaMGunZKkQC9vPTJ4uMzVrMrpExOn0a3bOSv4n1n8q/479k+1GS4AoJacbqLQqjB5aNNy3cQxlcbiWkRKkq4YM1RXjBla1yEBpzV9+nRNmDBBY8eO1RdffKEuXbpo06ZNevbZZzVhwgR9++23JNJR75FEB4AmLDE+RjPmLpHVZtOxnDx5e1ucrVwkqaTEKoul9Lmfr7eKS0okld8qarXalJK6T4P6VD1hERUu9deMrZu1/0Qv9C4RUWoXFu7hiMr5mC26vEMXfXqiGv2Vpb/pv5c23qTwgdwc/fO3ec7nd/QdqDD/gFNsUdl13fto2Z5U5ZUU65tNG3VXv8HqHBF1+g0BAPUKE4UCaGxsNpseeughjR07Vj/88IOMxtKLfgMGDNAPP/ygcePG6eGHH9bll19OaxfUa7wTA0AT5u/no2EDe+qlqdNkkEFXXT5S6fsyK9wSulLJO9NOrOur668srXx597MZKioqkc1uV/9enRQTVX8SsDg9h8OhtypUoY/v2NWD0VRtTNsOmr4lSXklxfpuS5IePW+E4kNCPR1WrXhi/mzlFhVJkgbFtdTg+MQz3kegt7cmdu6mjzaskUPS0wt/0ddXXVfDkQIAalvFiULDQqgyB9DwLVmyRKmpqfriiy+cCfQyRqNRjz/+uAYNGqQlS5Zo+PDhngkSqAaS6ADQxA3p101D+nVzGSu7JXTs6MEaO3pwpW0euLXx96huzH5P261NBw9IkuKDQ9QzuoWHI6rMz+Klse076atNG2Rz2PXq8sV6dczlng6rxi1N262ZyZslSQFeXrq198Cz3tfF7Tpq1vatOpSfp4W7d2pT5gF1iaQaHQAagrI2LkwUCqCx2b+/9PaaLl2qvnu5bLxsPaC+4pI2AABNTMVe6Je173zaySs9ZWy7TvI50V7oq6QNzvYzjYXD4dDTi35xPr+qcw+F+vqe9f68TGZd0bH8y8nrKxafU3wAgLpT1sbF7ih9zkShABqL6OhoSdKmTZuqXF42XrYeUF+RRAcAoAlJzjqo+bt2SJJCfX11fmIbD0fkXqC3ty5u21GSVGK36T8rlng4opo1I3mz1u/fJ0mKDgzSxe06nvM+R7VqpyBvb0nSj8mbtSf76DnvEwBQu+atKu+DbjRICdG0cAHQeJx33nlKSEjQs88+K7vdda4su92u5557TomJiTrvvPM8FCFQPbwzAwDQhExdvcz5+KLW7WUy1u+PApd16CyvExMMfb5xnY4W5Hs4oppRbLPqmQqTiV7brXeN/C68zWZd0q6TJMnmcOi9tSvOeZ8AgNo1dXp5UqlsEtFRfev3+zMAVJfJZNLLL7+sWbNmady4cVq+fLlyc3O1fPlyjRs3TrNmzdJLL73EpKKo93hnBgCgiThSkK/vNv8hSfIxmzW2fWcPR3R6IT6+GtWqrSSpwFqi99eu9HBENeOj9WuUeqJKvF3zcA2Ma1lj+76oTQeZTyTkv0zaoCKrtcb2DQCoeWWTiUpUoANonMaPH69vv/1WSUlJGjRokIKCgjRo0CBt2rRJ3377rcaPH+/pEIHT4h0ap2Qra8pXT/cHAKi+r5LWq8hWmlAdHJ8ofy8vD0dUPZd36CLjib7t761ZofySYg9HdG5yCgv18tJFzuc39uxbo33pg318NDAuQZKUXVign7ZtqbF9AwBqxrxVdo1/zKqLHrA6JxONCBUV6AAarfHjx2vnzp1auHChpk2bpoULF2rHjh0k0NFgMN03TslkNOiJt23anXHuye/EGIOeuYPbcwDAExwOhz7ZsNb5/NITLT8agsiAQA2JT9TitF06Wligzzeu1a19Bno6rLP23toVOnKiLU2fmDh1DI+s8WNc2Ka9lqTtkiT9b91Kje/crcaPAQA4e2UTiVbEZKIAGjuTyaThw4d7OgzgrJBEx2ntznAoOa0m9kQVOgB4yrL0VO08ckiS1KZZcyWENvNwRGfmio5dtfhEUvjNlUt1Q89+sjTAvonHi4r0zurlkiSjwaDrevSuleN0Do9UbFCw9uYc06p96dp99IgSG9jvHDVr2eokLV65QQYZNGncKLWMjXIuyzqcrY+++qn0jgiDQTdPukShIUEejBZo/MpauBgNUlhIaQKdVi4AANRfvEsDANAEfLJhjfPxBa3bezCSs5MY2ky9oltIkjJyczQjeZOHIzo7H21YraOFBZJKq9Djg0Nr5TgGg0EjEts6n3+9aX2tHAcNQ15+oeYvXauH75ismyeP1ZczfnVZvmjZOg3u100P3zlZg3p30YKl6zwUKdD0hIVIc14zM5koAAD1XK1Uou/Zl6kvvp8no9Ego9Go6yaO0Y5d6fpx3lI1Dy2tarl58qUKDQ6sjcMDAIAKDufnadaJvtj+Fi8NT2jt4YjOzvhO3bRu/z5J0uvLl+hPnbrVaC/x2pZfUqy3Vi6VJBkkTe7as1aPd17LRH26cY0ckr7e9If+MmREg/p5oeakpmeobWKszGaTwpqFqKioWCVWqyzm0q8CMVFhKigokiTlFRQqMMCvyv2UWK2yWm2y2e11FjvQ2MxbZdfU6XZnH3QAANAw1EoSPTjQX/ffMlE+Pt5K2pqimb/8ro5tWmpIv266ZNSg2jgkAABw46ukDSq22SRJg+MT5GVumN3cOodHql3zcG0/nKXkQwc1f9cOjWrdztNhVdtnG9cpKz9PktQzukWtt9QJ9w9Ql4goJR08oD3Hjmr9/n3qFRNbq8dE/XQ8v1B+vuXNln19fZSXX6iQoABJUse2CXr9va/1+6o/ZLXZ9Pi911W5n9kLVmjWvKWyWMy6ccrEOokdaGxO7oVOH3QAABqG2kmin/hALklms0kmY2nV0/K1m7Rp2y51aB2vSy84T0Zj1dVQVLkAAFAzHA6HPtlY3srlkgY0oejJDAaDxnfsqud/XyBJenXZ4gaTRC+yWvXmyt+dz6+u5Sr0MsMSWivp4AFJ0hdJ60miN1H+vj4qKCh0Pi8oKJJ/hczd9J8W6fKLzlOvru21av0W/TD7N00ef0Gl/YwZMUCjh/aVzW5XcmpWncQONDYVe6HHR9EHHQCAhqJW37GLios1Y84SXTC8v7p3bqv/e+QWPXLnZB0+mqOV6ze73W72ghW6/++v6dF/vVWb4aGONQ+WbPaan1y0NvaJ+oW/HeDsrdy7RylHDkuS2jQLU8uQ2unBXVf6xcarRWBpa7hV+/Zozb50D0dUPV8mrdf+3BxJUrfIaLVtHl4nxx0YlyCzsfTj3k/btsjuoEChKUqMj9HO1H2y2mw6fDRH3t4WZysXqXTq9wD/0hYugQF+yquQcK/IYjbL18dbvt7edRE20GjMW2XX+MesuugBq7ONS1iI6IMOAEADUmv3c1ttNr372UxdeH5/xUSGuSzr26OjNm/frYG9u1S5LVUujVOgn2QyGvTE2zbtzqiZ5GVijEHP3GGqkX2h/uJvBzh7X2/a4Hw8slUbzwVSQ4wGg67o2FVvrirtLf7a8sX6bMI1Ho7q1EpsNv1nxRLn80ld6qYKXZL8vbzUI6qF1mSkKys/T2v27VW/2Pg6Oz7qB38/Hw0b2FMvTZ0mgwy66vKRSt+XqS07UnXh8P66ZORAffrdXJmMRtlsdk2ZcKGnQwYalZNbuEi0cQEAoKGplSS63e7QB1/MUo/ObdWzS+lt1vkF5b0Yk3emKSrcfR9Qi9ksi9ksm41qqcZod4ZDyWk1tTcqiZsS/naAM1NQUqIZyaV3fnmbTA12QtGTDUtorWlJ63WkIF9zd27T9kNZahdWN5XdZ+PbzX9oz7FsSVLH8Eh1iois0+MPimupNRmlFfvTtySRRG+ihvTrpiH9urmMxbUo/VuMiQrXo3dP8URYQKNWNonontKuWjIaSivQ/Xxo4wIAQENTK0n09Zu2KWlrinJy87Ry3Wa1iA6Xj7e3tu5IlcloVGREM13Rv3ttHBoAAJwwd+c25RSVtmXoGR0rX4uXhyOqGRaTSZe176yPNqyWJL2xconeuGS8h6Oqms1u12vLFzufT+pS959/+sXGy7TKIJvDoZ+2b9Fzoy+WwVD1vDQAgJpzcgV6fFRpCxcAANDw1Mo7eO9uHdS7W4dK41eMGVobhwMAAFVwbeXS1nOB1IIL27TXN5s3Kq+kWN9u/kOPnzdSMUHBng6rkhnJm7TraGlP+rbNw9Q9qkWdxxDg5a3uUTFat3+fDhzP1YYDGeoZXfdxAEBTwySiAAA0HlwGBwCgEcrKO64Fu3ZKkkJ8fNU7JtbDEdUsX4tFF7froG82/yGr3a6pq5fpnyPHeDosF3aHXa8uK69Cv7Kz5+7CGxDbUuv275Mk/bx9K0l0AKhBZW1b8k+ak/fkSUQBAEDDxTs5ADRxy1YnafHKDTLIoEnjRqllbJRz2aoNW7Rw6ToZDQb5+HjrlsmXytfHW4eOHNPHX/8sq9Wmrh1b6+KRAz34ClCV6VuSZHOUzi0yKK6ljI2wfccl7Trph62bVWK36ZMNa/TgoGEK9fXzdFhOP29PVvKhg5KkxJBm6hMT57FYKl5E+WXnNj0xbJTHYgGAxqaqiUMrYhJRAAAaPu4nA4AmLC+/UPOXrtXDd0zWzZPH6ssZv7os79WlvR69e4oeuesaxbeI1Iq1pZNUTv95kS67YIgevWeKknemaf/Bw54IH6dQsZXL6NbtPBdILQrx8XW2qckvKdEH61Z5OKJyDodDLy9d5Hw+oXM3j/Yhb+7nr8SQ0kndt2Rl6sDxXI/FAgAN3bxVdo1/zKqLHij9r+LEoRGhrv8lRNPGBQCAxoBKdABowlLTM9Q2MVZms0lhzUJUVFSsEqtVFnPp24PZbHKuW1xcopioMElSesZBtW1VWlXbtWNrbU9JV3RE87p/AahSctZB/ZFZWhIXHxyixNDG+7sZ17GL5u7cJoccenfNCt3Vb7B8LRZPh6U5O5K16WBpViUuKFiD4hI8G5CkPi1itTv7iCRp3s5turZHHw9HBAANk7vKcyYOBQCg8eKSOAA0YcfzC+XnW36Psa+vj/JOauj5+6qN+sfL/9OO3XsVE1maRHc4HM7lfr7eyi8oqHL/JVarCgqLVFBUVAvRw53vtyY5H58X38qDkdS+qIBADYlPkCQdKcjXF0nrPRuQSv//+HeFKvSJnXt4tAq9TO8K7WRm70j2YCQA0LBVnDCUinMAAJoGLpMDQBPm7+ujgoLypHlBQZH8T2rcOaRfdw3p111zFq7U3EUrNWHs+S4JwYLCIvn5+la5/9kLVmjWvKWyWMy6ccrE2nkRcOFwODQzubTtjkHS+YltPBtQHbiiU1ct2bNbkvTGiiW6rkdvmY2m02xVe+bu3KakE3cCxAYF67yWiR6LpaK2zcIU5O2tnKIiLd2zW8U2q7xMfBQEgOoqm0C04oShc17jPAoAQFPApXIAaMIS42O0M3WfrDabDh/Nkbe3xdnKRZJKSqzOx36+3vLyKm2TERcToZTUvZKkTcm71K5VrKoyZsQAvf7PB/TC3+6qxVeBirZmHdTOI4ckSa2bNVeYv7+HI6p9rUKbq2dUC0nS3pxjzosInuBwOPTi7wudzyd27l4vqtAlyWQ0qmd06f+r+SUlWr4nzcMRAUDDUtbGxX7ihjwmDAUAoOngsjkANGH+fj4aNrCnXpo6TQYZdNXlI5W+L1NbdqTqwuH9NXfRSiXvTDuxrq+uv3KMJOmKMcP08TezZbPa1KVDK0WfaPNyMovZLIvZLJvNXmevqambmbzJ+XhgPejDXVfGd+qq9Qf2SZJeW75YV3Ts6pHk9clV6ENb1q92On1iYvVbaook6ecdWzUssbWHIwKAhmHeqvI+6EZDaf9z2rcAANB0kEQHgCZuSL9uGtKvm8tYXItISdLY0YM1dvTgStuENw/Rw3dcXSfxofocDodmbitv5TIsoekkSLtERKltszDtOHJIW7MOasHunRrZqm2dxmB32F2q0Cd06lZvqtDL9IxuIaPBILvDofkpOzwdDgA0GFOnlxcEMIEoAABND+/8AAA0EsmHDmrH4dJWLq2aNVeYX+Nv5VLGYDBofKeueuFEEvvFJQs1IrFNnSaxp29JclahtwgKrpcXMQK8vNUhLEJbsjKVduyodh09rFahzT0dFgDUaxWr0CUq0OFZr733lfbsy9TIIX10yahBLstKSqz6+JvZOpKdo2YhQbp+4hhZLKR9UDvy8/OVnHzmk9UXFBQoNTVVCQkJ8nUzt9bpdOjQQX5+fme1LXC2OJsCANBIVOwFPqgJtXIp0z+2pWKDgrU355jW7d+rn7dv1SXtO9XJsQutJXrmt1+dz6d061XvqtDL9ImJ1ZasTEnS3B3bdGe/QafZAgCatopV6AnR0qi+JNHhOddPHKOtO9J09FhupWXL1iQpKryZbpl8qWbNW6pla5I0bGBPD0SJpiA5OVm9e/f2yLHXrl2rXr16eeTYaLpIogMA0EiUJdENkobXwyro2mY0GHRt9956bskCSdJTC+ZqdJt28jLV/sed99eu1N6cY5KkDmER9boffa/oWH2yca0kaeHuHSTRAeA08gvLH1OFDk8LDQlyu2z7rnRdOLy/JKlbpzaau2il2yR6idUqq9Umm525i3B2OnTooLVr157xdlu3btWUKVP02WefqWPHjmd9bKCukUQHAKAR2HbooLYfzpIktQptruZNqJVLRf1axKtzeKQ2n2hX8vH61bq1z8BaPeaRgny9umyxpNILGDf06FurxztX8SGhCvb20bGiQq3cu0dWu01mo8nTYQFAvRcRShU66rfjeQXy8/WRJPn5eisvv8DturMXrNCseUtlsZh145SJdRUiGhE/P79zqgbv2LEj1eRoUPgEAABAI+DSyiU+wXOBeJjBYNANPcuT2C/+vkg5hYWn2OLcvbL0N+UUlR6jf2y8OoRH1OrxzpXRYFC3qBhJUn5Jidbv3+fhiAAAQE3w9/NRQUHpZ5KCgiL5+7nvNz1mxAC9/s8H9MLf7qqr8ACgQaMSHQCARqBiEn1Yy1YejMTz2jYP19CWrbQ4bZeyCwv06vLf9NT5F9bKsXYcztIH61ZJkixGk27o0a9WjlPTukVGa0naLknSryk71LdFvIcjAoD6Y94qu6ZOtzvbuBzK9mg4QLW1axWvpORdimsRqaTkXWrXKs7tuhazWRazWTZb/WznwqSVAOobkugAADRw2w9lKfnQQUlSq9BmCvMP8HBEnndNt15anp6qErtd76xeocndeqlt8/AaPYbD4dDDc35Uid0mSRrduq2iAgNr9Bi1pfuJSnRJ+i01RY8PHenBaACgfpk63a7U/ZXH/XzqPhbgZJ98M1spaftktdqUuveALhs9WFt2pOrC4f01qG8Xffz1bL341ucKDQ7UDVde7OlwzxqTVgKob0iio0FrHizZ7A6ZjAZPhwIAHjMzeZPz8aB6PKFlXYoMCNRlHbrouy1/qMRu0wM//6Afp9wso6HmOtl9tWmDlqWnSpKa+frp2u59amzftS3CP0DRAYHafzxXGw9k6HhxkQK8vD0dFgDUC2UV6EaDFBZS+tjPh0lFUT9cN3FMpbG4FpGSJC+LRbdec1ldh1QrmLQSQH1DEh0NWqCfZDIa9MTbNu3OcJzz/gZ3M+juCUyuBqBhmbmtQiuXhNYejKR+ubJzd/2etluZeblatS9dH69foxt71Uy7lX05x/TEr7Odz2/u2U++FkuN7LuudI+K0f6d22S127ViT5pGtWnn6ZAAwGMqtnApa98SFiLNeY2vzIAnMGklgPqGTwRoFHZnOJScdu77SYg+90Q8ANSlHYeztDWrtJVLYkgzhdPKxcnbbNZd/QbpqYVzJUlPLZij81q2UpvmYee0X7vDrvt++t45mWifmDgNbpl4zvHWtW5RMZqzc5sk6ddd20miA2jSqmrhQvsWAABQhiQ6AAANWMUJRQfFJ3gukHqqe1SMLmzTXnN3blOB1apbZnytOdfdKh/z2VeNv7pssRafmJQzxMdH9/UfUlPh1qmuEdEySHJIWpK229PhAIDHzFtVnkAva+FC+xYAQH1RVOyQ1VZ3xysodDj/zSuou2JTs0ny9qq/7ZpJogMA0ID9WCGJPrwlrVyqclPPftqUeUD7co9p88EDeuDnHzT10gkyGM78A9ovO7fphSULJUkGSXf0HaQgn4ZZqhjo7a1WzZor5chhbT+cpay849zJAKBJmjrd7nwcHyVNf56vyQCA+qGo2KHVWx06nl93x9y5vTRxvn67Q7mGukuiB/hJfTvW30Q6nw4AAGigUo4c0uasTEknWrkEkACtirfZrIcHD9dj82apyGbTd1uS1LpZmB4Zcv4Z7WdtRrpunfGNHCr9IHl5hy4aENuyNkKuM90jY5Ry5LAkaXFqiv7UubuHIwKAulPWB33PgfIxqs8BAPWJ1SYdz5e8LA551VEWt0P79np/2iq1TGgvH9+6SaIXW6Xj+QZZbZJ3nRzxzJFEBwCggZpRoQp9QFzDTubWtsTQZnpg4FC98HtpFfmLvy+UyWjUg4OGVWv79fv3adLXnym/pFhSaZuY67r3rrV460r3qBhN35okSZq/aydJdABNysl90BOipVF9SaIDAOofL7Pk4103Fdo+3v4K6VHX33UcKi6p40OeIT4hAADQQFXshz48gVYupzMwLkE39uzrfP7c4vn68+wZKrSe+tPaD1s36fLPP1B2YYEkqW2zMD02ZISMxob/MapDWITMJ17HivQamKEbABqQ/NL5oWU0lCbQqUIHAADuUIkOAEADlHLksDYfLL3/vGVIqCIDAj0cUcNweYcusjsc+njDGknSZxvXakV6qp4cfoEuaNNepgqJ8eSsg3p+yQL9tH2Lc6x1aHM9NfwC+VrOfmLS+sTbbFbb5uHampWp9Jxs7c/NUXRgkKfDAoA6FRZCH3QAAHBqfFIAAKABmpm8yfl4YAPvy13XrujYVSE+vpq6epmKbTbtPHJY103/QmF+/uoZ3ULeZrN2Hj6k5EMHXbbr1yJODw0aLm9z4/r41CUiSltP9Nb/PW2XJnbp4dmAAAAAAKCeqZVvgXv2ZeqL7+fJaDTIaDTquoljFBIUoI+/ma0j2TlqFhKk6yeOkcXSuL6EAgBQV2jlcm7OT2yjxJBmemv1Mm0/nCVJOpSfp3kp2yutG+DlrWu69dRFbTrIYKifM8Wfiy4RUfpm80ZJ0m+pJNEBNH5lE4oeyvZ0JAAAoKGolSx2cKC/7r9lonx8vJW0NUUzf/ldbRJaKCq8mW6ZfKlmzVuqZWuSNGxgz9o4PADgDCxbnaTFKzfIIIMmjRullrFRzmVzFq7UuqRtMhmNio+N1KTLR8lgMOilqdNktdpkNpvUIjpcV48b7cFX0PTsOnpYm060cokPDlUU7TfOSkJoM70w+hKt3peueSnbtTEzQ8U2myTJaDAoJjBIw1q20oVtOijIx8fD0dae9mHhMhkMsjkcWrGXvugAGr+TJxT1a7yneAAAUENqJ4keFFB+ALNJJqNB23el68Lh/SVJ3Tq10dxFK90m0UusVlmtNtns9toIDwBwQl5+oeYvXavH77lW2Tm5+t8Xs/To3VOcy3t2aauLzi89d7/z6Q9K3pmmjm0TJEm3X3u5QkNI3npCxSp0WrmcG4PBoH6x8eoXGy+b3a7jxcUqtJaoma+fLCaTp8OrEz5mi9o2D1fyoYNKyz6qA8dzFUWPfQCNWMUJReOjmFAUAACcXq32UykqLtaMOUt03ZVj9NWM+fLzLb3E7+frrbz8ArfbzV6wQrPmLZXFYtaNUybWZohArWseLNnsDpmMNdcCoKb3V1v7RP2Xmp6htomxMptNCmsWoqKiYpVYrbKc6PkcGd7Mua7ZbJaxbNJFg0Hvfj5TZpNJl4wapA5tqk7kclG0dvxYIYk+LKGVByNpXExGo4J9fBSspleS2DkiytkDfmnabv2pczcPRwQANaOsdUtZ4lySs40LE4oCAIDqqrVPDFabTe9+NlMXnt9fMZFh8vfzUUFBoaRgFRQUyd/P1+22Y0YM0OihfWWz25WcmlVbIQJ1ItBPMhkNeuJtm3ZnOM55f4kxBj1zR81XR9ZkjIO7GXT3hKZRwdnQHc8vdF7glCRfXx/l5RcqpMIdRZK0LWWPjuUcV7tWcZJKq9AD/f10JDtHr777lZ647zr5+HhX2j8XRWteavYR/ZFZeg96XFCIYoKCPRwRGoMuEVH6bssfkqTFqSkk0QE0Gie3bqmINi4AAKC6aiWJbrc79MEXs9Sjc1v17NJOktSuVbySkncprkWkkpJ3ORMxVbGYzbKYzbLZqFxE47E7w6HkGmk1e+5JbndqKsaE6NqLETXL37fsAmep0oucrt8o92Yc1Pc//6a7b/qTc1LFQH8/SVKzkCDFRofr4OFsxbeIrLR/LorWvIqtXAbE0coFNaNDWISMBoPsDoeW0xcdQANXsfq8rOrcaCitPC/j50MbFwAAUH21kkRfv2mbkramKCc3TyvXbVaL6HD96ZLh+vjr2Xrxrc8VGhyoG668uDYODQA4A4nxMZoxd4msNpuO5eTJ29vibOUiSQcPHdXH38zWHdeNcybOHQ6HCouK5evjrcLCIu07kKXmoVX3RueiaM2rmEQfntDag5GgMfG1WNSmWZi2H87S7qNHlJV3XOH+AaffEADqoaqqz+OjaN0CAADOXq18iujdrYN6d+tQafzWay6rjcMBAM6Sv5+Phg3sqZemTpNBBl11+Uil78vUlh2punB4f301c77yCwr14Zc/SZIuGN5fndol6OW3v5DFUpocv3T0kFO26ELNSc0+oo0HMiRJsUHBakErF9SgzhGR2n649I6RpXt2a1zHrh6OCADOTsWJQ8NCqDoHAADnjkvxANDEDenXTUP6ufY/jjvRmuXemyZUuc3fHrihtsNCFSpOKDogllYuqFldIqL1/dZNkqTFqbtIojcyy1YnafHKDTLIoEnjRqllbJTL8jkLV2jL9lTZ7XaNHT3Y7YTRgKdVNVHoySpOHDrnNb7yAgCAc8cnCgAAGoiKrVzOT6SVC2pWx/DyvujL0lM9HQ5qUF5+oeYvXavH77lW2Tm5+t8Xs/To3VOcy5OSU1RQWKQHb5/kwSiB6jnVRKEnY+JQAABQU0iiAwDQAKRlH9UGl1YuIZ4NCI2On8VLrUKba+eRQ0o5cliH8/PU3M/f02GhBqSmZ6htYqzMZpPCmoWoqKhYJVarcw6MtRuT5efro1fe+VLBQQG6etxo+fl6V9pPidUqq9Umm515LuAZ81aVJ9BPnij0ZLRwAQAANYkkOgAADcCP28qr0Pu1iPdgJGjMOkdEaueRQ5Kk5empGtu+s4cjQk04nl8oP9/yklxfXx/l5RcqJKh08tjsnOPy9/PVg7dP0oKlazVn4XKNv3h4pf3MXrBCs+YtlcVi1o1TJtZV+IDT1OnlF3CYKBQAANQlLs0DANAA/OjSyqWNByNBY9YlItr5+LfdKR6MBDXJ39dHBQXlDaQLCorkX6HPhb+frzq3byVJ6tK+lfbuz6pyP2NGDNDr/3xAL/ztrtoNGKhCxSp0iSpzAABQt7h0DwBAPZd+LFvr9u+TJLUIDFJccIhnA0Kj1TE8QgZJDknL0tM8HQ5qSGJ8jGbMXSKrzaZjOXny9rY4W7lIUrtW8Urbe0Cd2iUode9+RTQPrXI/FrNZFrNZNhvtXFD3KlahJ0RLo/qSRAeAmlBU7JDVVnfHKyh0OP/NK3DU2XHNJsnby1Bnx0PjQxIdAIB6rmIVer8WLT0YCRq7AC9vJYY2066jR7TjcJayCwsU4uPr6bBwjvz9fDRsYE+9NHWaDDLoqstHKn1fprbsSNWFw/trUN8u+vSbOXrp7S9kMhp106RLPB0yUEl++c0UVKEDQA0pKnZo9VaHjufX3TF3bi9NnK/f7lCuoe6S6AF+Ut+ODTOR7l10QP5FB+RtaXixV5epxKESRUmKPu26nkISHQCAem5mhX7owxNbezASNAVdIqK16+gROSQt35OqMe06ejok1IAh/bppSL9uLmNxLSIllVaY33T1WE+EBZyxiFCq0AGgplht0vF8ycvikFcdZQg7tG+v96etUsuE9vLxrZskerFVOp5vkNUmVZ46vf6LS/9IbVJe8HQYtW5n60el/o97Ogy3SKIDAFCP7T2WrbUZeyVJMYFBahlSdZsFoKZ0joh0Xrj5LTWFJDoAAEAj52WWfLzrpsrZx9tfIT1618mxyjlUXFLHh6xB6XE3KDfuokZdiV5U4lC2oupxHTpJdAAA6rUZyZucj/u1iPdgJGgqOoVHOR8vpy86gHpg3iq7Dh71dBQAAHhGkXeU8nwjZaujCx2eUFjkUFFB/X59JNEBAKjHfthankQfkdjGg5GgqQj09lbL4FClHTuq5EMHlVtUqEBvH0+HBaAJqzipqB+nI6BBYdJKAI0FSXQAAOqpXUcPa8OBDElSbFCw4mnlgjrSOSJKaceOyu5waGX6Ho1q087TIQFowphUFCi3bHWSFq/cIIMMmjRulFrGRrks+3HeUjUPDZIk3Tz5UoUGB3oqVCatBNCokEQHAKCemlGhCn1AbEsPRoKmpnNEpH7esVWStDhtF0l0AHVm3iq7pk63uyTOD2WX/sukomjq8vILNX/pWj1+z7XKzsnV/76YpUfvnuKyzpB+3XTJqEEeitAVk1YCaExIogMAUE9VbOVyPq1cUIc6V+iLviw91XOBAGhypk63K3V/1cto5YKmLjU9Q20TY2U2mxTWLERFRcUqsVplMZendpav3aRN23apQ+t4XXrBeTIaq66MLrFaZbXaZLPbq1xek5i0EkBjQBIdAIB6aPuhLG3JypQktQwJVYugYA9HhKYkxNdXLQKDtS/3mDZlHlB+SbH8LF6eDgtAIzdvVXkC3WiQwkLKl/n50MoFOJ5fKD/f8qtJvr4+yssvVEhQgCSpe+e2GtC7syTpo69+1sr1mzWwd5cq9zV7wQrNmrdUFotZN06ZWPvBA0ADRxIdAIB66IetSc7HA2nlAg/oHBGpfbnHZHPYtWZfuoYmtPZ0SAAauYoTiMZHSdOf5+sqUJG/r48KCsp7HRUUFMm/wi0aFR/37dFRm7fvdptEHzNigEYP7Sub3a7k1KzaCxoAGgku5QMAUM84HA59f6KVi0HS8ARauaDudY4ob+myOHWXByMB0FQwgShwaonxMdqZuk9Wm02Hj+bI29vi0solv0KCPXlnmqLCm7ndl8Vslq+Pt3y96eINANXBpX0AAOqZzQcztfPIIUlSq9DmigoM9HBEaIo6hUc6Hy/dk+q5QAA0emWTiTKBKHBq/n4+Gjawp16aOk0GGXTV5SOVvi9TW3ak6sLh/TV30Spt3ZEqk9GoyIhmuqJ/d0+HDACNBkl0AGjilq1O0uKVG2SQQZPGjVLL2PLq0zkLV2pd0jaZjEbFx0Zq0uWjZDAYlLb3gL784Vc55NDQ/j00qG9XD76CxueH5PJWLoPiEzwXCJq0cP8ARfoHKDPvuP7IzFChtUQ+ZounwwLQCJ08mSgTiALuDenXTUP6dXMZi2tReuH7ijFDdcWYoZ4ICwAaPS7vA0ATlpdfqPlL1+rhOybr5slj9eWMX12W9+zSVn+97zo9es8U5eTmKXlnmiTpyx9+1c2Tx+rhOyZr/tK1yqt4/zXOicPh0A9bKrZyoQ81PKespUuxzab1+/d5OBoAjVXZxwijQUqIppULAACof/h0AgBNWGp6htomxspsNimsWYiKiopVYrU6l0dW6KNoNptlNBpVYrWqqLhEYc1CZDab1DYxVqnpGZ4Iv1HacCBDaceOSpLaNg9Xcz9/D0eEpoy+6ADqUlhI6WSitHIBAAD1De1cAKAJO55fKD/f8numfX19lJdfqJCgAJf1tqXs0bGc42rXKk7Hco7Lz7d8AiI/H2+3leglVqusVptsdnvtvIBG6Iet5a1cBtPKBR5WMYm+dM9uD0YCoDEp64Fe9vGhrBc6AABAfUUSHQCaMH9fHxUUlCfACwqK5H9SI9K9GQf1/c+/6e6b/iSDwSB/P1/lFxSVb1NYeZsysxes0Kx5S2WxmHXjlIm18yIaEbvDrh+2lrZyMRoMGtaSVi7wrEj/ADX39dPhgnxt2J+hEptNFpPJ02EBaOBO7oFehl7oAFD3vIsOyL/ogLwtBk+HUmtMJQ6VKEpStKdDQQNGEh0AmrDE+BjNmLtEVptNx3Ly5O1tkcVc/tZw8NBRffzNbN1x3TgF+vtJkiwWs7y9LDp8NEfBQf7ambpPY0cPqXL/Y0YM0OihfWWz25WcmlUnr6khW7YnVRm5OZKkjmERCvH19XBEaOoMBoM6R0RpcdouFVhL9EdmhnrHxHk6LAANXMUe6GEhpY/9fOiFDgCeEJf+kdqkvODpMGrdztaPSv0f93QYaMBIogNAE+bv56NhA3vqpanTZJBBV10+Uun7MrVlR6ouHN5fX82cr/yCQn345U+SpAuG91e3jq111eUj9f7nM+WQQ8MG9nRbiW4xm2Uxm2Wz0c6lOr7atNH5+PzENh6MBChXlkSXpCWpu0iiAzhrZW1cytq3hIVIc17jKykAeFJ63A3KjbuoUVeiF5U4lK0o6tBxTvjEAgBN3JB+3TSkXzeXsbgWkZKke2+aUOU2CXHRevSeKbUeW1OSV1ysH7dtliT5mM0aEp/o4YiAUp0jIp2Pf9+zWw8MGubBaAA0ZCe3caF9CwB4XpF3lPJ8I2XzbrxJ9MIih4oKGu/rQ93gfjkAAOqB2Tu2Kq+4WJLUJyZWPhaLhyMCSrUIDFawd2mma82+vUwUDOCszFtVnkA3GqSEaNq3AACAhqNWKtFfe+8r7dmXqZFD+uiSUYO0LWWP3vt8pqLCm0mSJl46Qi1jo2rj0AAANEhfbdrgfDyyVTvPBQKcpKwv+rL0VOWVFGvTwQPqHhXj6bAANDBTp5dfgIuPkqY/z03RAACg4aiVTy7XTxyjrTvSdPRYrnOsW8fWum7imNo4HAAADdr+3BwtTi3tOd3M1089SFCinukcEall6amSpKVpu0miAzhjZZOJSlSgAwBwpoqtkuSok2MVFuQrLTVZLRM6yMfXr06OWfr66rdaSaKHhgRVGtu8bbdefOtzxcVE6E+XDJfXKW5TL7FaZbXauF0YANAkfLflD9kdpR+IBsclyGCgXx/ql87h5XcQLk7bpbv6D/ZgNAAakpMnE40IlUb1JYkOAEB1mE1SgJ90PN+g4pK6OebObdt074399MaHq9Wmfa+6OahKX6fZVGeHO2N1cg9dyxaR+tejt8liMev72Yv1y6JVGjva/Zev2QtWaNa8pbJYzLpxysS6CLFRsNkdMhlJvABAQ+JwOPRV0gbn8wvatPdcMIAb8SGhCvDy0vHiYq3Zly67wy6jgSQYgNNjMlEAAM6et5dBfTtKVlvdHTPQUZpb7NnOoB496i7PaDaVvt76qk6S6D4+3s7H/Xt10vc//3bK9ceMGKDRQ/vKZrcrOTWrtsNrNExGg55426bdGTVze8fgbgbdPaEeXwICgEbgj8z9Sj50UJKUGNpMccEhng0IqILRYFCn8Eit2peuY0WF2nYoSx3DIz0dFoB67uTJROOjaOUCAMCZ8vYyyPv0q9UYXx+D819/3/qb1K5rdZJEzy8okp9v6a87eWeaIk9MMOqOxWyWxWyWzUY7lzO1O8Oh5LSa2VdCdN30WgKApuzTDWucj4e1bO3BSIBT6xwRpVX70iVJv6ftJokO4LSYTBQAADQWtfIp5pNvZislbZ+sVptS9x5Q53YJWro6SV4WswL8/XT9lUwwCgDA8eIifbvlD0mSt8mk0a3beTgiwL2T+6Lf2meAB6MB0BAwmSgAAGgsaiWJft3Eykny4YPqrhE9AAANwfdbNymvuFiS1KdFnPy9vDwcEeBeYmgz+ZotKrCWaNXePXI4HEyCC6BKTCYKAAAaGz7JAADgIRVbuYxp09GDkQCnZzIa1TmitIXLkYJ8bT/MvDUAqlY2maj9RHdIJhMFAAANHUl0AAA8IClzv9bv3ydJahEU7ExOAvVZl4ho5+NFu1M8GAmA+urkyUQTomnlAgAAGj4+zQBokpoHSzZ7zU6eW9P7Q+P26ca1zscjEtvQFgMNQrfICkn0VJLoACqrajJRWrkAAICGjunRATRJgX6SyWjQE2/btDvj3JPfiTEGPXOHqQYiQ1OQV1ysbzdvlCR5mUwa3YoJRdEwJIQ2U4CXl44XF2vV3jTZHXYZDSTHAJSqWIUuUYEOAAAaD5LoAJq03RkOJafVxJ6oQkf1Td+apNyiIklS75hYBfnQLBYNg9FgUJeIaK3Ym6acoiJtysxUt6jo028IoEmoWIWeEM1kogAAoPEgiQ4AQB1yOBx6b81y5/NL2jGhKBqWbpGlSXRJWpS6kyQ6AKf8wvLHVKEDkCTvogPyLzogb0vjbV1oKnGoRFGS+EwENGYk0QEAqENL0nZpa9ZBSVJCSKjLRI1AQ9C1Ql/033an6L4B53kwGgD1wbxVdk2dbteh7NLnEaFUoQMoFZf+kdqkvODpMGrdztaPSv0f93QYAGoRSXQAAOrQO6vLq9AvbtvJg5EAZyc2KFghPr7KLizQmox0ldhsspiYEwJoyqZOd+2F7keXMgAnpMfdoNy4ixp1JXpRiUPZiqIOHWjkSKIDAFBHUo4c0i8p2yVJIT6+GpHY2sMRAWfOYDCoa2S0lqTtUn5JiTYeyFCfFnGeDgunsWx1khav3CCDDJo0bpRaxkZVWmfm3CVauX6Lnnnsdg9EiIasrI2L0SDFR9HKBUC5Iu8o5flGyubdeJPohUUOFRU07NdXbJXqap6vwoJ8paUmq2VCB/n4+tXJMUtfH3BuSKIDQBN3qsRKSupeffrdXB08dFTPPHqbQkOCJEkffvmT9u4/KF8fbwUE+OmOa8d5KPqG5d01K5yPRya2kZnqXTRQ3U4k0SVp4e6dJNHrubz8Qs1fulaP33OtsnNy9b8vZunRu6e4rJOTm6fMQ0c9FCEai7AQafrzfMUEgIbCbJIC/KTj+QYVl9TNMXdu26Z7b+ynNz5crTbte9XNQVX6Os18/cI54BMOADRhp0usREeG67F7rtWbH3xbadtJ40arbWJsXYbboGUXFujLpPWSJC+TSZd16OzhiICz1yWi/GLbb6kpemTI+R6MBqeTmp6htomxMptNCmsWoqKiYpVYrbKYy78KzPp1mcacP0BTP/neg5ECAIC65O1lUN+OktVWd8cMdJRW7fdsZ1CPHnVXwW82lb5e4GyRRAeAJux0iRU/X2+3237z4wKZzSadP6iX+vboWOU6JVarrFabbHZ7rcTfkHy6YY3yS0rLOwbEtlSwj6+HIwLOXlRAoML9/JWVn6f1+/ep0FoiH7PF02HBjeP5hfLzLW9S7evro7z8QoUEBUiSMrOOqKioWLExEafcD+d0VGXeKrsOchMDUGdOdRdpSYlVH38zW0eyc9QsJEjXTxwji4W0D07N28sg99/6ap6vj8H5r78vSW00HJxNAaAJO11ixZ0Jl56vQH8/5eUX6JV3vlRCXLTCm4dUWm/2ghWaNW+pLBazbpwysabDbzAKSko09cSEogZJ4zt28WxAwDkyGAzqFhmj+bt3qNhm06q9ezQ0gR7/9ZW/r48KCgqdzwsKiuRfYebHH+ct1WUXDDntfjinoypTp5dfVGFCUaB2ne4u0mVrkhQV3ky3TL5Us+Yt1bI1SRo2sKcHIwaAxoMkOgA0YadLrLgT6F86AYy/n686tk3Q3oyDVSbRx4wYoNFD+8pmtys5NavG4m5oPt24Vll5xyVJ3aNilBDa3MMRAeeuR3RpEl2Sftm5nSR6PZYYH6MZc5fIarPpWE6evL0tLq1cDh3O1hffz5MkHcvJ05c//KpJ40ZV2g/ndJxs3iq7UveXP2dCUaB2ne4u0u270nXh8P6SpG6d2mjuopUk0QGghpBEB4Am7HSJFXfyC0or2K1Wm1JS92lQn6orqy1msyxms2y2pnvrf6G1RG+sWOJ8fnVXvsigcegeFSODJIdKJxdF/eXv56NhA3vqpanTZJBBV10+Uun7MrVlR6ouHN5fj917rXPdJ55/p8oEusQ5HZVVrEJPiJZG9SWJDtSm091FejyvwLncz9dbefkFbvdFiy4AODMk0QGgCTtdYiUz64g+n/6L9u4/qPem/ah+PTpp+KCeevezGSoqKpHNblf/Xp0UExXu6ZdSb33xx3odOJ4rSeoSGaX2YafuOQw0FEHePmrVrLlSjhzW9sNZyjyeq8iAQE+HBTeG9OumIf26uYzFtYistN4zj91eVyGhEcgvv5mNKnSgDpzuLlJ/v7LlwSeWuZ+DhxZdAHBmSKIDQBN3qsRKZHgzPXj7pErbPHDrVXUSW0NXbLPqPxWq0Cd16eG5YIBa0COqhVKOHJYk/Za6U1d24U4LoCmYt8quqdPtOpRd+jwilCp0oC6c7i7Sdq3ilZS8S3EtIpWUvEvtWsW53RctugDgzPBJBwCAWvLVpo3am3NMktQpPFJdIqI9HBFQs3pGt3A+npeyw4ORAKhLU6eX9kK3O0qfM6EoUDcq3kX6/uczddVlpXeRzl20UpI0qG8X7TuQpRff+lz7DmRpUJ+ubvdlMZvl6+MtX2/vugofABo0KtEBAKgFhdYSvbJ0kfM5VehojNo3D5eP2axCq1WLU3fJ4XDIYDB4OiwAtaSsAn3PgdLnRoMUH0UrF6AuneouUi+LRbdec5knwgKARo9POwAA1IL31qxwVqF3DI9Qt6gYD0cE1DyLyeS8w+JIQb62ZGV6OCIAtenkCvT4KGn682ZauQAAgEaPSnQAAGrY4fw8vbp8sSTJIOmmnv08GxBQi3pEx2hNRrok6deU7eocEeXhiADUpLLq8/xCOXugU4EOAACaGj71AABQw15e+ptyi4okSQPiWqpt83APRwTUnp5R5X3R5++iLzrQ2JRVnx88SgU6AABouvjUAwBADUo5clgfrl8lSfIymXR9jz4ejgioXTGBQQr385ckrc3Yq/ySYg9HBKAm5ReW/ms0SBGhUkI0FegAAKDpoZ0LAAA16J+L5slqt0uSLmjdTlEBQR6OCKhdBoNBPaJbaF7KdhXbbFqatluj27T3dFgAasC8VXYdPFr6OCxEmvMaXx8BAEDTRAkBAAA15NeU7fpp+xZJUqC3tyZ16enhiIC60Su6vKXLzzuSPRgJgJo0dbrd+djPx4OBAAAAeBhJdAAAakBecbEe/WWW8/mVnbsrwNvbgxEBdadHVAuZjaUfK+elbJfD4fBwRABqQlkrF4kWLgAAoGmrlfvxXnvvK+3Zl6mRQ/roklGD5HA49OWMX5WecVC+Pt66adIl8vfzrY1DAwDgES8vXaQ9x7IlSW2ahWlsu06eDQioQ74Wi7pERGnDgQxlHs/VlqxMdY6I8nRYAGpIRKiYRBQAADRptZJEv37iGG3dkaajx3IlSZu37VZxcYn+ctc1Wr5mk+YuWqnxFw+vjUMDAFDnNh88oLdWLZMkmYxG3dVvkAwGg4ejAupW3xZx2nAgQ5L08/atJNEBAAAAD8rPz1dy8pm3Wty6davLv2ejQ4cO8vPzO+vt66NaSaKHhrhOorZ9V7q6dWwjSerWqY1+W76+Ng4LAECds9ntemjOTNkcpX1jL27bQa1Cm3s4KqDu9YmJ03trV0qS5u5I1iNDzvdwRADORcVJRQEAQMOTnJys3r17n/X2U6ZMOett165dq169ep319vVRnUyvnpdfIL8TM9H4+Xorr6DwlOuXWK2yWm2y2e2nXA8AAE97c+XvWpuxV5IU4R+gKd3O/kMK0JBFBgQqPjhEe45l64/M/crKO65w/wBPhwXgLDGpKAAADVuHDh20du3aM96uoKBAqampSkhIkK/v2bXj7tChw1ltV5/VSRLd389H+ScS5wWFRfL3PfWnsNkLVmjWvKWyWMy6ccrEuggRAIAztvFAhp5fskCSZJB0R5+B8jbXyVsrUC/1jYnTnmPZckial7JNk7moBDQo81bZNXW6XfmF0qHs8nEmFQUAoOHx8/M762rwwYMH13A0DV+dfBpq2ypOm5J3SZKSknepXeu4U64/ZsQAvf7PB/TC3+6qi/AAADhj+SXFuvPHb2U9cdfUBW3aq1dMrIejAjyrb4vyz3g/bz/z/osAPGvqdLtS90sHj0p2R+lYQjSTigIAANRKudwn38xWSto+Wa02pe49oDuvu0JJW1L04lufy8fbSzdNGnvK7S1msyxms2w22rkAAOqnpxf+oh2HD0mSYoOCdVPPfh6OCPC8ts3DFeTtrZyiIi1J26Uiq5W7M4AGJP9E102jQQoLKW3jQhU6AABALSXRr5s4ptLY5PEX1MahAACoc3N2JOuDdaskSRajSQ8MGEqiEJBkMhrVOzpWC1NTlF9SomXpqTo/sY2nwwJQhYqtW8qUtXAJC5HmvMb7GgAAQBnKCgAAOAO7jh7WXbO+cz6/snM3tWke5sGIgPqlT4WWLjO2bvJgJABOpWLrlrL/ylq4MJEoAACAK8oLAKCJW7Y6SYtXbpBBBk0aN0otY6Ocy1JS9+rT7+bq4KGjeubR2xQaEiRJOnTkmD7++mdZrTZ17dhaF48c6Knw61R+SbFumv6lcouKJEk9omI0oXN3D0cF1C+9omPlZTKp2GbT7B3Jetlul8lI3QbgaSdXnpdVnZe1bilDCxcAAIDKSKIDQBOWl1+o+UvX6vF7rlV2Tq7+98UsPXr3FOfy6MhwPXbPtXrzg29dtpv+8yJddsEQtW0Vp1fe+VI9u7ZTdETzug6/TjkcDj0y50dtzsqUJEX4B+jhQcNlMBg8HBlQv/haLOoVHasVe9N0pCBfy9NTNaRlK0+HBTR5ZZXnJ4uPkqY/z9dCAACAU+HTkgfZ7A6ZjCRfcGaaB/O3g5qTmp6htomxMptNCmsWoqKiYpVYrbKc6O/t5+td5XbpGQfVtlVpy4auHVtre0p6lUn0EqtVVqtNNnvDnyj6/bUr9fXmjZIkL5NJjwwergDvqn8+QFM3OD5BK/amSZK+25JEEh3wgOpUnlN1DgAAUD0k0T3IZDToibdt2p3hOOd9De5m0N0TTDUQFeq7QL+a/duR+Ptpyo7nF8rPt7zxqa+vj/LyCxUSFHDK7RyO8r89P19v5eTmVbne7AUrNGveUlksZt04ZWLNBO0Bv+zcpr/Nn+18fnPP/mrbPNyDEQH1W5+YOFmMRpXY7Zq9fateuvBSWroAdYzKcwAAgJrDpycP253hUHLaue8nIbpmkqloOGrqb0fi76cp8/f1UUFBofN5QUGR/Ksxm1jFFiYFhUXy8/Wtcr0xIwZo9NC+stntSk7NOveAPWBT5gHdNvMb2U9cOLigdTtd2La9h6MC6jdfi0W9YmK1cu8eHS7I14r0NA1umejpsIAmpawCncpzAJ5WbJWkuvnOWViQr7TUZLVM6CAfX786OWbp6wPQ2JFEB4AmLDE+RjPmLpHVZtOxnDx5e1ucrVxOJS4mQimpe9U6IVabknfpqstGVrmexWyWxWyWzdYw27kcyM3RNd9+prziYkmlE4ne3qdpTKIKnKtBcQlauXePJOn7rUkk0YFacnLbljJl7VvCQqQ5r/G1D0DdM5ukAD/peL5BxSV1c8yd27bp3hv76Y0PV6tN+151c1CVvk4zN3cDjRqfpgCgCfP389GwgT310tRpMsigqy4fqfR9mdqyI1UXDu+vzKwj+nz6L9q7/6Dem/aj+vXopOGDeuqKMcP08TezZbPa1KVDK0VHhnn6pdS448VFmvzt58rIzZEkxQeH6i+Dz6clBVBN/VrEO1u6/LR9q164YCz//wC1wF3bljLVuMEMAGqFt5dBfTtKVlvdHTPQUXrHbM92BvXoUXfziJlNpa8XQONFEh0Amrgh/bppSL9uLmNxLSIlSZHhzfTg7ZMqbRPePEQP33F1ncTnCTa7XXfM/FZJmaVZiWa+fnpi6Ej5eXl5ODKg4fC1WNQzOlar9u3Rofw8rdy7R4PiEzwdFtAoVKw+r2rC0DK0bwHgad5eBnnX4fF8fQzOf/19SWoDqDkk0QEAOMlTC+Zq7s5tkiQfs1mPDhmhyIBAD0cFNDyD4xO0al9pS5dvN28kiQ7UkKqqz5kwFAAAoPZQlgAAQAUfrFuld9YslyQZDQbd22+I2oeFezgqoGHq2yJOXqbSBqEzkzeryMrMW0BNqDhpaESolBBNxTkAAEBtolQBAIATfk3Z/v/s3Xd0HOXVx/HvbNE29Wq5d9wrxgUwBmyM6RB6IEAglRQS0kjvHULyJpBAgBB6D2CKMcUUGww22LjJXa7qXdqiLfP+sdZasoplW9KurN/nHA7SzOzM3dX6mdm7d+7DbUtfiv3+2UnTNBmiyDFw25OYNXAI7+zaQU3Az5Jtm7lgzPh4hyXSK7XVwkWThoqIiIj0DJUriIiIAOtLirnp+SeJmCYAC4aP5jPjJh3mUSJyOGcMGxn7+eG1q+MYiUjv1tTCpbQKItFTlSYNFREREekhKlsQEZE+r7iuls8+/TANjY0ATM7rz5dnzI5zVCLHh4l5+WS53FT4vLxTuJ2S+jrNMSByhJZ+eLAHetMEopo0VKTvafD6uP/xl/D5Awzqn8uVF87HMFpOnnnbb/9JVkYqAGNHD+XcM+fEI1QRkeOOkugiItKnNTQ28tmnH2F/XS0Ag9PS+f4pp2O1KDEh0hWsFgunDxvJ0xs/JWyaPLV+DV+bdWq8wxLpVe5+NhL7WROIivRdS5at5MTJY5g9fQL/efJlNmzeyYQxw1tsY7EYfOcrV8cpQhGR45cyBCIi0meFIxG+8uLTfFoSLe/LcLn4yWkLcCclxTkykePL6c1aujzy6SeYB9omiUjnNE0kCqo+F+nLtmzfw6Sx0XPq5LEj2bJjT6ttTBP+/M/H+Ou9T7JnX0m7+wqGQvj8AXyBQLfFKyJyPFEJg4iI9Fm/XPYar2wtAMBps3HbKWeS40mOc1Qix58BqWmMzc5lU3kp2yrLWVu8nyn5A+IdlkjCa5pMtGki0dwMmD9DSXSRvqrB58ftcgDgcjlo8PpabXPbN64lxeNmz/5S7nv0RX7+nRvb3Ncrb37A4qXLsdtt3HDNZd0at4jI8UBJdBER6ZMeXruauz5cAYDFMPjGzFMZnZ0T56hEjl9nDB/FpvJSAB5as1pJdJFOaJpMtIkmEhU5/kUiEf7wj4dbLZ84ZgRulwOfP4Db5cTnD+Bxu1ptl+JxAzCofy5JSXYavH48bQwei86YxYK5MwhHIhQUlnX9ExEROc4oiS4iIn3O8l07+e6SF2O/XzNpGnMGD41fQCJ9wJxBQ7l39Qc0hsM8s+lTfnHGQpIdjniH1Wes+Ggd76xcg4HBlRfNZ8jAfrF1r761ko/XbcZqsTB4YF6bE9VJzzt0MtHB/dTKRaQvsFgs3Pb1z7W5LtAYZF3BDmZOHcf6gh1MnTC6xfpgKAQm2O02qmrq8DarXD+U3WbDbrMRDkfaXC8iIi0piS4iIn3KzqpKbnjucUKR6AeG04eN5JJxk+Iclcjxz5OUxKlDhvPGjq00NDby2LqP+cKJs+MdVp/Q4PXzxvLV3Pa1a6mureO+xxbz/Zuvia2fOmEUZ58+E4B/PfQ/CrbtYuyooXGKVppoMlEROdTCeTO5//HFvP3+Jwzsl8O40cMAeOL51znnzDkEQyH+8Z9ncSTZiUQiXPOZhfpSVESki+hKTERE+oxav5/PPv0wVf5o/8ixObncPGNOnKMS6TvOHz2ON3ZsBeBfqz7gxukzsRiqrO1uhXv2M2rYQGw2K9mZ6QQCjQRDIey26EeBvJzM2LY2mw2Lpe2/STAUIhQKE46oarG7NPVA9/qJ9UEHVaCLSFSyx8U3bmzdv/yKC+fHfv7JLdf3YEQiIn2HrsZERKRPCEXC3PT8k2ytKAcgz5PMbaecic1qjXNkIn3H0IxMJuRG24jsqq7ijR3b4hxR31Dv9eN2HeyH63I5afD6W223eftuamrrGT18UJv7eeXND/jmT+7k+7++q9ti7euaeqCXVkHEjC4bmq/JREVERETiTZXoIiLSJ/zkjVd5a2c0YeexJ/Gj0+aT6tQMbSI97bwTxrG+tBiAuz9cwYIRow/zCDlWHpcTn+9g0tznC7SaZG7v/lKee/ltbv78Z9q99V+T0HW/pu82LAZkp0cnElUVuoiIiEj8KYkuIiLHvQc+/pB/r14JgNUw+PacuQxOy4hzVCJ904z+g8jzJFPSUM+7u3awpbyM0dk58Q7ruDZscH+eX/IuoXCYmtoGHA57rJULQGl5FQ8+9Qpf/txFpHjc7e5Hk9B1n6Y2Lk0tXLLT4dU79VFNREREJFGorEFERI5rbxdu57alL8d+v27KDKb3b7tVgYh0P6vFwrmjx8V+/+dHK+IYTd/gcTs5bfZU/nz3o/z7kRe44oIz2bOvhCXLol8uPvHCG3h9fh54/CX+fPejfLppe5wj7nua2rg0tXBx60YpERERkYSi8gYRETluba8s58bnniBsRqsmF4wYzQVjxsc5KhE5c/goHl33Mf5QiKc2rOVHp80ny+2Jd1jHtVNOmsQpJ01qsWzQgDwAvv75S+MRkjTTvI3L4H5q4SIiIiKSaJREFxHp41Z8tI53Vq7BwODKi+YzZGC/2LpgMMSDT71CZXUtmempXHfZIux2Gw88/hJ7i0pxOR0kJ7v58rUXxe8JtKPa7+Pqpx6hJhDNTIzPzeNL02fFOSoRAfAkJTF/+CgWb9mEPxTirg+X85N5Z8U7LJG4y06HZ3+vj2giIiIiiabHr9Buvu12hg3OB2DW9PGcctLkng5BREQOaPD6eWP5am772rVU19Zx32OL+f7N18TWr1i1jn45mdx09fksXrqcFavWcdrsqQBcedECRg0bGK/QOxQMh7nxuSfYUVUBQH5yCredciY2qzXOkYlIkwvHTOCVbZsJRyLct/pDvjbzFDJc7ffjFhERERERiZceT6KnpyXzna9c3dOHFRGRNhTu2c+oYQOx2axkZ6YTCDQSDIViE85t2bGHhfNmAjBp3EiWLFsZS6I/9eKb2GxWTp8zjRlTxsbtORzKNE1+sPQl3tm1A4DkpCR+dNp8kh2OOEcmIs3leJKZP3wUS7ZtpiHYyD8/WsFtc+fHOyyRHrf0wwilVfGOQkRE5Mh4vV4KCgqO+HGbNm1q8f+jMWbMGNxuFV9Iz+rxJHptXQN/uvtRkt0uLjv/DLIz01ptEwyFCIXChCORng5PRKRPqff6cbsOzl7mcjlp8PpJT02Orm/wxda7XQ4avD4ALj3/dFI8bhq8Pu741+MMHZRPTlZ6q/3HYzz/10fv8981q4DoBIa3zpnHwNTWsYlI/F0ydiKvb99C2DT550fvc9P0WeR4kuMdlkiPuvvZg+dITSgqIiK9RUFBAdOnTz/qx19zzTWH36gdq1evZtq0aUf9eJGj0eNJ9N/+8MukeNxs2LyD/z71Ct/+0pWttnnlzQ9YvHQ5druNG665rKdDbFM4YmK1GPEOQ0SkS3lcTnw+f+x3ny+Ap9kneI+7aX3agXUuAFI87gPrXYwdNZS9+0vbTKL39Hi+ZGsBP31zSez3G6fOYGr+gG4/rogcnbzkFBaMOIFXtxXgDQa5Y8Xb/G7BufEOS6RHLP0wwt3PRthdfHCZJhQVEZHeYsyYMaxevfqIH+fz+SgsLGTo0KG4XK6jPrZIT+vxJHpT4mX8CcN59LmlbW6z6IxZLJg7g3AkQkFhWU+G1y6rxeBH/wyzc7/ZJfs7eZLBzZeqN6+IxNewwf15fsm7hMJhamobcDjssVYuAKOHD2ZdwQ4GDchjXcEORg8fBIDXF61gD4XCbC/cx5wTJ7S5/54cz9eVFPHFF57GJDpOnzNqDOeMHtetxxSRY3f5hMm8tXMbgXCI/3zyEV+aMZuh6ZnxDkuk2939bITCooO/D82H+TOURBcRkd7B7XYfdTX4ySef3MXRiHS/Hk2i+wONJNltWCwW9u4vJdnT9jdOdpsNu81GOJxY7Vx27jcp2NU1+xqa3zXJeBGRY+FxOzlt9lT+fPejGBhcceGZ7NlXwsathSycN5M5Mybw4JOv8Me7HiEjLYXrLz8HgHsefp5AIEg4EmHmtHH075fT5v57ajwvrq/jmqcfwRtsBGBq/gBumjazW48pIl0j0+Xm/BPG8fTGTwlFIvzkjVd46DOfjXdYIt3Oe+BGMIsBg/upCl1EREQkkfVoEr2opJyHn1mC05EEhsE1nzm7Jw8vIiJtOOWkSZxy0qQWywYNyAMgyW7nC5+9oNVjbvnCFT0SW2c0NDZy7dOPsL+uFoAhaRl8d848LBYlI0R6i0vGTeT1HVuo9vt5detm3incztyhI+Idlki3aT6ZaHY6PPv7Hr9BWERERESOQI9erQ0b3J+ffOuGnjykiIgcx4LhMDc9/wRrivcDkOF08ePT5uNOSopzZCJyJNz2JD43+UT+tvI9AL63ZDFv33gzDpsSi3J80mSiIiIiIr2LPpmIiEivZJom33rleV7fvhUAp83GD049gxxPcpwjE5GjMW/YSF7dVsCWinK2V1XwlxVv84O5Z8Y7LJFOaZok1Os//LYA5dUHf1YbFxEREZHEpys2ERHplX7zzus8sX4NAFaLhVvnnMYJ2bnxDUpEjprFMPjqjJOxGgYAf/3gXTaWlsQ5KpHOaZoktLSqc/9FDkyPpMlERURERHoHXbGJiEiv84+V7/HX998FwAC+PH02MwYMjm9QInLMhmZkcsm4iQCEIhG+9MJT+EPBOEclcnjNJwnNzejcf0PzVYUuIiIi0luonYuIiPQq//xoBT9/67XY71dNnMqCkaPjGJGIdKXLxk/mw7172FVTRUF5Kb9atpTfzD8n3mGJdEp2Orx6pz5iiYiIiBxvVPogIiK9xn2rV/KTN16N/X7hmPFcPmFK/AISkS6XZLXxrTlzsVuil6n3rPqAxZs3xjkqERERETlW4XCYZcuW8dhjj7Fs2TLC4XC8QxLpNCXRRUSkV/j36pX8YOlLsd/PHT2WG6aeFMeIRKS7DE3P5LqpM2K/37z4WbaUl8UxIpH2Lf0wQmlVvKMQERFJbM8++ywjR47k9NNP5+qrr+b0009n5MiRPPvss/EOTaRTlEQXEZGEZpomv3vnDW5rlkA/e+QJ3DRtZhyjEpHudu6oscwdMhwAb7CRK596iNKG+jhHJdLa3c9GYj+7nXEMREREJEE9++yzXHrppUycOJH333+furo63n//fSZOnMill16qRLr0Ckqii4hIwgpFwtz66gvcseLt2LKzR47hSyfOxjCMOEYmIt3NMAy+etIchqZnALCnppqrn3qY+kAgzpGJtNQ0qShoolAREZFDhcNhbr31Vs477zz+97//MWvWLJKTk5k1axb/+9//OO+88/jOd76j1i6S8HSVJyIiCckfCnLjc0/y0NrVsWVXTpjCl2cogS7SVzhtdn5y2gKyXG4A1hbv54qnHqK+UYl0STy5GTB/hj5eiYiINPfuu+9SWFjID3/4QyyWludJi8XCbbfdxs6dO3n33XfjFKFI5+gqT0REElJxfR0r9+4CwGoYfOnE2Vw5cWqcoxKRnpbl9vDTeWeRnJQEwId7d3P5E/+lyueNc2QiIiIicjhFRUUATJgwoc31TcubthNJVEqii4hIQhqansnjl19LlsvNF0+cxaJRY+IdkojEyZD0DH5x+kI89mgi/aN9ezjv4fvYVa3ZHCU+ln4Y4ZIfhDj7lhDl1fGORkREJHHl5+cDsH79+jbXNy1v2k4kUSmJLiIiCWtK/gCevvI6TsjOjXcoIhJnIzKz+dUZZ5PmiM7cuKWijDP/czdv7Nga58ikL7r72QiFRVBaBREzukyTioqIiLR26qmnMnToUH77298SiURarItEIvzud79j2LBhnHrqqXGKUKRzbPEOQEREpCMOm05VIhI1PDOLP5x1Hr9c9hr762qp8fu58smH+OKJs/jRafNxH6hUF+kKSz+McPezkRYThzZpqj63GJCdHk2ga1JRERGR1qxWK7fffjuXXnopF110EbfddhsTJkxg/fr1/O53v2Px4sU8/fTTWK3WeIcq0iFlJkRERESk1+iXnMKfzjqfv7z/Nqv27wXgnlUf8GLBBn48bwGXjp+ExVAyU45dU7V5Rwb3g2d/r49UIiIiHbnkkkt4+umnufXWW5kzZ05s+bBhw3j66ae55JJL4hidSOfoik9EREREehVPUhI/nDufl7Zs5L9rVhOMhCmqr+Pmxc/y79Ur+d4pp3PG8JFKpssRa159fmi1+aFUfS4iPe2TdVt47tW3Ka+s4a7ffafNbdYX7GDx0uUAnH/WyYw/YXhPhijSrksuuYQLL7yQd999l6KiIvLz8zn11FNVgS69hpLoIiIiItLrWAyD808Yz7T8gdz38Uo+LtoHwCdF+7jqqYcZlpHJ56eexOUTp5Dpcsc5Wukt2qo+V7W5iCSKUcMH8ZNbbuDnt9/X5vpIJMIzLy3ju1+9GoA/3/0oY0cNxWLRF36SGKxWK/PmzYt3GCJH5bi9GgxHTKwWI95hiIiIiEg3GpCaxk/nncWaon38++OV7K2tAWBnVSU/efNVfv7WEmYOHMLZo07g9GGjGJ2drQp1aVdT/3P1OheRRJTscXW4vrS8iuzMNNyu6EzHWRlplJZX0S83q9W2wVCIUChM+JCJHkVEpG3HbRLdajH40T/D7NxvHvO+Tp5kcPOlur1ERI5PKz5axzsr12BgcOVF8xkysF9sXTAY4sGnXqGyupbM9FSuu2wRdruN8soaHnzyZUKhMBPHjuCcM2fH8RmIiMCU/AH8ddFFfLRvDy9u2cCG0hIAwqbJij2FrNhTCCwhxeFgWv4AJvXrz8jMbEZkZjEsI4sslxurKvWOWx1NEtpcUwuX7HR49c7j9qOSiBynGry+WAIdwO1y0tDOwPfKmx+weOly7HYbN1xzWU+FKCLSax3XV4Y795sU7Dr2/QzNP/ZEvIhIImrw+nlj+Wpu+9q1VNfWcd9ji/n+zdfE1q9YtY5+OZncdPX5LF66nBWr1nHa7Kk8+/IyLjjrFEYNH8Qd/3qcqRNHk99GhYuISE+yWizMGjSEWYOGsKemmte2b+bDvbspaaiPbVMXCPB24Q7eLtzR4rEWwyDL7SHX7SHHk0yOJ5mzR43hgjHje/ppSDfozCShzbmdh99GRKQ7RCIR/vCPh1stnzhmBOctOLnDx7rdLrz+QOx3rz+Ap50BbdEZs1gwdwbhSISCwrJjC1pEpA84rpPoIiLSscI9+xk1bCA2m5XszHQCgUaCoRB2W/T0sGXHHhbOmwnApHEjWbJsJafNnsqe/aWMGj4IgIljR7Bl+542k+hddZuoCbrVVESOSP+UVK6fMoPrp8xgX20NK/ftZmNZCdsrK6gNtK7Ki5gmZQ31lDXUQ1m0in1AaqqS6Amks9XkbTncJKHNqYWLiMSTxWLhtq9/7qgem5edQUVlNb4DifSKympyszPa3NZus2G32QiHE/Ma2+v1UlBQcMSP27RpU4v/H40xY8bgdms+FRFpSUl0EZE+rN7rb3HLp+vALZ/pqcnR9Q0Hbwl1uxw0eH0AmObBO3TcLge1dQ1t7r8rbhM1MEiyWimqrz2qx4uIWCwGswcNYfagIZimSbXfR1FdHaUN9ZQ21FPhbaC2MUBdIEBdwE/4wBiX6lA5ciI50mrytmiSUBHpzbbu2MOLS5dTXVvPHf96nHlzpjJt4gk88fzrnHPmHFKS3Vy86DT+eu+TAFy86LReO6loQUEB06dPP+rHX3PNNYffqB2rV69m2rRpR/14ETk+6QpSRKQP87ic+HwHS/p8vpa3fHrcTevTDqyLTmZkGAcnbvb5A7hdbU9y1BW3iQ5OTye9nf2LiHQ10zSpDQSo9HkZmt529Z7Ex6GTfh4pVZiLSG83avggvv2lK1stv+LC+bGfJ44dwcSxI3oyrG4xZswYVq9efcSP8/l8FBYWMnToUFxH+RlizJgxR/U4ETm+KYkuItKHDRvcn+eXvEsoHKamtgGHwx5r5QIwevhg1hXsYNCAPNYV7GD0gRYug/rnsr1wLyOGDmR9wQ6uuODMNvffFbeJJlltZLt1uhKRnpPjSWYEmuehJ3WmVYsm/RQR6TvcbvdRV4OffHLHveNFRI6Grj5FRPowj9vJabOn8ue7H8XA4IoLz2TPvhI2bi1k4byZzJkxgQeffIU/3vUIGWkpXH/5OUD01tAHn3qFcCjMhDHDyc/LjvMzERGR3uxIWrVo0k8RERER6WlKoouI9HGnnDSJU06a1GLZoAF5ACTZ7Xzhsxe0ekxOVjrf+fJVPRKfiIgcv5oq0HcXR38/XKsWtWQRERERkXhQEl1EREREROLi0Ap0TfwpIiIiIolIV6giIiIiIhIXzScLHdxPVeYiIiIikph6PIm+4qN1vLNyDQYGV140nyED+/V0CCIiIiIifUpH1+DBYIgHn3qFyupaMtNTue6yRdjtXf8x4bM/C1FR03JZ88lCVYEuIiIiIomqR0s9Grx+3li+mu98+WpuvPo8Hn/+9Z48vIiIiIhIn3O4a/AVq9bRLyeT7331s/TLyWTFqnXdEkdFDZRWtfwvYkbXabJQEREREUlkPVruUbhnP6OGDcRms5KdmU4g0EgwFMJuaxlGMBQiFAoTCocBCIcjR3W8UYNMkmzmMcc9OM8gHI502f66Y5+KUTEm0j4TfX/dsc8h+cZRj1VNLBYDwzCOOZZEZJrR1/hYXyMRkd4g0cbzw12Db9mxh4XzZgIwadxIlixbyWmzp7baz7Feo+dnRXDYWy93OeGmCyw6R4hIQkq0Mb2r6TpdRPqSYxnTDbNpxOwBKz/ZSElZJRecdQoAf7r7Ub7w2QtIT01usd0Lr73H4qXLcbudXHPFxT0VnohIXE09YSBW6/HZC7axMcSn2/bHOwwRkR6RaOP54a7B/3LPE1x76dlkZ6ZRXlnNw88s4ZYvXNFqP7pGF5G+KNHG9K6m63QR6UuOZUzv0Up0j8uJz+eP/e7zBfC0ce/mojNmsWDuDCKRCBgGDru9x7759QUCfP/Xd/GHH38Vl8PRI8c8UokeY6LHB4kfo+I7dokeY1vxWSzHb4WLzWZh0sj+vaqSJ9HfQ91Bz1nP+XjV08850cbzw12De9xN69MOrHO1uZ94XqO3J9Hez4pH8RxP8UDixRSPeBJtTO9qvfE6vSOJ9p6V1vQ3SnzH89/oWMb0Hk2iDxvcn+eXvEsoHKamtgGHw96qlQuA3WZrc3lPsFosBIMhrBZLwn7bnOgxJnp8kPgxKr5jl+gxJnp8Xc1isZCU1LueZ1/7G4Ges57z8asvPufmDncNPnr4YNYV7GDQgDzWFexg9PBBbe4nntfo7Um0v63iUTzHUzyQeDElWjzHg954nd4RvUcSn/5GiU9/o7b1bCW628lps6fy57sfxcDgigvP7MnDd4rNZuW8BSdjs1njHUq7Ej3GRI8PEj9GxXfsEj3GRI9P+ubfSM+5b9Bz7nvaugbfs6+EjVsLWThvJnNmTODBJ1/hj3c9QkZaCtdffk68Q+60RPvbKp6OKZ6OJVo8kHgxJVo8knj0Hkl8+hslPv2N2tajPdFFRERERERERERERHoT1eSLiIiIiIiIiIiIiLRDSXQRERERERERERERkXYoiS4iIiIiIiIiIiIi0o4enVg0kT3x/Ovs2L0fgCnjR7PojFmttlnx0TreWbkGA4MrL5rPkIH9ejTGT9Zt4blX36a8soa7fvedNre57bf/JCsjFYCxo4dy7plzEiq+9QU7WLx0OQDnn3Uy408Y3mPxATR4fdz/+Ev4/AEG9c/lygvnYxhGi216+jXs6H0VDIZ48KlXqKyuJTM9lesuW4Td3vP/bDuKccVH63hx6fLYa3bj1eeTkZbSo/Hdee8T7N5XwpmnnMi581v+vRLhNewovkR4/aRjnTk/HG86M54fL+J9bu9pHY1Hx6vd+0p47LmlWCwGFouFz122iJys9HiHJcco0a6LE+06OFGueRPtOjeRrmkT7fo10a5XDzd2d+Y9Ln3TQ0+/SklZJd/5ytXxDkXaUF5Zw6/vfICB+bmEQmGGDOrHVRctiHdYcoi9+0t55uVlBIMhQuEw0yeewILTTop3WPFnimmapllcWmGapmmGwxHzd3/7r1lSVtlifX2Dz/zlXx4wg8GQWVZRZf7+7w/1eIx19V6zsTFo/vB3/2x3m47WdbfDxRcOh82f//k+s8HrMxu8PvMXt99nhsPhHo3xmZfeMlesWmeapmk+8MRL5rpN21tt05Ov4eHeV8tWfGy++Np7pmma5ouvvWcuW/Fxj8XW2RiXf/ipuXjp8h6Pq7nKqpp240iE17Cj+BLh9ZOOHe78cDzqzPnmeJAI5/ae1tF4dLyqrqkzfT6/aZqm+enGbea/H30xzhFJV0i06+JEuw5OhGveRLvOTbRr2kS7fk2069XDjd2deY9L3xMMhszf/PVB8//ue8osr6yJdzjShrKKavP2fz4W+/32fz5m7isqjWNEcqgGr9/8+Z/vi33ujUQi5voCjbGmaZpq53JAXk4mQPSbbqsFi6XlS1O4Zz+jhg3EZrOSnZlOINBIMBTq0RiTPa7DViCYJvz5n4/x13ufZM++kh6KLOpw8ZWWV5GdmYbb5cTtcpKVkUZpeVUPRghbtu9h0tiRAEweO5ItO/a02qYnX8PDva+27NjDpHHReCeNazve7taZ9/77q9fzh388zPOvvkMkYvZ4jBnpqe2uS4TXsKP4IP6vn3TscOeH41FnzjfHg0Q4t/e0w41Hx6O01GScTgcANpsVq0WViseDRLsuTrTr4ES45k2069xEu6ZNtOvXRLtePdzY3Zn3uPQ96zZtZ/L4kcw+cQIffrIx3uHIYYTDERqDQRwOR7xDkWbWFWxn0riR5GZnAGAYRo93kUhUx/8n5CO08uMN5GSmk52Z1mJ5vdeP2+WM/e5yOWnw+klPTe7pEDt02zeuJcXjZs/+Uu579EV+/p0b4x1STIPX1+I1dB94DXs0Bp8ftys6QLtcDhq8vlbb9ORreLj3VX3DwdfM3U683e1wMU4eP4pZ08cD8J8nXmblJxuYPX1Cj8fZnkR4DTuS6K+fHNTe+UF6r95ybpeuEWhs5PlX3+Vzly+KdyjSQxLpurinr4MT4Zo30a5ze9M1baJdv8bztWlv7O7Me1z6ng/XbOTS884gNcXNnfc+2SfaMPZGu/cV8+e7H6W6tp5B/XNjraIkMVRV15KZrhazbelTSfRIJMIf/vFwq+UTx4zgvAUns3FLIcs/WsfXPv+ZVtt4XE58voMXuj5fAI/b2Wq77o7xcFI8bgAG9c8lKclOg9ffpXEeS3xutwuvPxD73evv+dfQ7XLg8wdwu5z4/AE8bler7br7NWzucO8rj7tpfdqBda3j7W6dibHJjClj2bBlZ0IlgRPhNexIor9+fcWxnB96q2M93xwPeurcLvEXCoe55+EXWHj6TPrnZcc7HOmkRLsuTrTr4ES/5k2069zedE2baNev8XptOhq7O/Mel77F6wuwvXAfDz/9KgAVlTXs2V/KoP65cY5MDjV4QD++/aUrAXj8+df5cM1GTpoyLs5RSZOM9FT2F5fFO4yE1KeS6BaLhdu+/rk21+3YvZ8XlrzLN266jCS7vdX6YYP78/ySdwmFw9TUNuBw2LHbuv7l6yjGwwmGQmCC3W6jqqYOb7Nv5xMhvrzsDCoqq/Ed+ABRUVkduz2kK3UUY6AxyLqCHcycOo71BTuYOmF0i/U98Ro2d7j31ejhg1lXsINBA/JYV7CD0cMHdVssRxtj9DWKXlgXbNtFvwOtLxJFIryGHUn016+vOJbzQ291LOP58aKnzu0SX5GIyf2PLWbK+FGtzvuS2BLtujjRroMT/Zo30a5ze9M1baJdv8bjtTnc2N30GrX3Hpe+5+N1BZx9xizOOHk6AJu2FrLy4w1Koic4t8tJfb3uJEkkE8eM4JU33+fkGZNi1yobt+xk3OhhcY4s/gzTNNWAF/j5n+8Dov0MAS47/wyGDOzHvx99kZuuPh+A9z78lPc+XIuBwRUXnsnQQfk9GuPWHXt4celytu/ax4ghA5g3ZyrTJp7AE8+/zjlnziEYCvGP/zyLI8lOJBLhorPnMmbkkISJLyXZzbpN23np9RUAnDt/DhPHjuix+CB6a+T9jy/GH2hkYL8crrxoARaLEdfX8ND3ldViYePWQhbOm0ljMMiDT75CVU0dGWkpXH/5OXHpU9xRjM+98g6bthZitVjIy83kms8sxGa19mh8/33qFbbv2kcoFKZ/vxwuWHByQr2GHcWXCK+fdKy988PxrL3x/HgU73N7Tzt0PLr5+kviHVK3W/1pAf954uXYv9sB+TlcddGCOEclxyrRrosT7To4Ua55E+06N5GuaRPt+jXRrlfbGrsnjhlBXYOX2dMntPsel77r9n8+xrWXnh1L+gVDIX55xwP84js36b2RQMora/j1nQ8wMD8XE3A5krjx6vNxOdUXPZHs2V/Ksy8tIxgKEQqHmT5pDAvmzoh3WHGnJLqIiIiIiIiIiIiISDss8Q5ARERERERERERERCRRKYkuIiIiIiIiIiIiItIOJdFFRERERERERERERNqhJLqIiIiIiIiIiIiISDuURBcRERERERERERERaYeS6CIiIiIiIiIiIiIi7VASXURERERERERERESkHUqii4iIiIiIiIiIiIi0Q0l0EREREREREREREZF2KIkuIiIiIiIiIiIiItIOJdFFRERERERERERERNqhJLqIiIiIiIiIiIiISDuURBcRERERERERERERaYeS6CIiIiIiIiIiIiIi7VASXURERERERERERESkHUqii4iIiIiIiIiIiIi0Q0l0EREREREREREREZF2KIkuIiIiIiIiIiIiItIOJdFFRERERERERERERNqhJLqIiIiIiIiIiIiISDuURBcRERERERERERERaYeS6CIiIiIiIiIiIiIi7VASXURERERERERERESkHUqii4iIiIiIiIiIiIi0Q0l0EREREREREREREZF2KIkuIiIiIiIiIiIiItIOJdFFRERERERERERERNqhJLqIiIiIiIiIiIiISDuURBcRERERERERERERaYeS6CIiIiIiIiIiIiIi7VASXURERERERERERESkHUqii4iIiIiIiIiIiIi0Q0l0EREREREREREREZF22OIdwOFcdNP3WPnJBpwOBxveeBS3y9li/Y3f/Q0vv7kCi8XC3JlTWPb+xwzMz+WjxQ8c0XG++bM7eHLxGy0e29YyERE5Nk1jaxPDMEhL8TBl/Ghu/eLVnDhp7FHtr7vG6j//6xFuv+dRAIpWv9Tl++/IJV/8Ae+vXqfzkIgkrFfeep+7H3qGrTv3EGgMkpWRxujhgzl//ilcecGCLj/eilWfsmL1OgC+86XPtli3fvN2Xl32AQBfuPpC0lKSu/z4ndU0fs+ePpFn7/n9ET22o+coItJduvoavbdasepTPvOl2wB45l+/Y86Jk+IcUUt79pdw0vmfB+DOn93CFd1wrhWRtiV8Jfp5Z54CgD8QYNn7H7dY5/MHeGvFagBOmjKO3KyMHo9PRESOjWmaVNfWs+z9j7nsyz9ix+598Q5JREQ64eFnX+Xz3/k1H63dRHVtPT5/gL1Fpby5fBUvvbG8W465YvU6br/n0diXm81t2Lwjtq62rqFbjt8TOnqOIiI9RdfoIiItJXwS/dwz52AYBgCvLHu/xbq3VqzG5w8AcN6ZJ/PXX3ybotUvdVm1XlfvT0REWvrwxfvZ/t4z3HD5eUD0C9PnXn37iPahsVpEJD7++fBzAEwdP5oPX7yfnSue4+2n7+bX3/0SY0YMiXN0XcsfaIx3CCIiPaYrrtG7QlO+pyfNOXESRatfomj1SwlXhQ4wqH9eLD5VoYv0rIRv55Kfm830iSew6tMCXn/3Q4LBEHZ7NOympLphGJx75snt3tL/3odr+dsDT7Bmw1Yag0GGDerPNZeczeevOD+WoG/L4Vq8/PXn3+KXf72fzdt3MXRgPj/71k3Mmz2tG18NEZHjj9vl5NpLFvHAk4sBKCotB+CjtRu5874nKNhWSGV1LWAwYkh/rrn4bK4/cEEPxz5W+wON/OPBp/nfkrfZs78EpyOJcaOH89vvfZkxI4e2G/e6gu3cfs8jfLhmIw1eH4P653Hx2fP4+g2XkWS3t4gDDraCae8WzK079/D93/2DT9ZvZkC/XH74teu64NUVEek+u/cVAzBhzAgG9c8DYPSwwYweNrjVttW1dfzl34+zZNkHFJWW43G7mTJ+FHf89Jv0y8nitXdWcu+jz7Nlx26qa+uw2WyMGTGEL372Ii48ay5wsEVKk/zp5wIwe/pEBuXntmhD0DTONj83bNpayJ/veYT3V6+nwetlUP9+XHXhAr5y7SVYLNHaouYtvF556E5+/Md/sq5gGz/6xg1MGD08dov/vX/8IS+/uYLX3lmJI8nO1ReexQ9u/hxWq7XD12zx6+/xr0eeY+PWQiIRkzEjh/CFqy7kkkXzDvscj7QtjIjIsWjvGr1JZ8ZUgCdeWMrt9z5GWUU1p82aws3XXcoFn/8uALd+8epY26qm8e6WG68kFArxxOLXaWwMUrDsSQCWvP0Bd/33WTZs2UE4HGH86GF8/YbLWHjarNix1mzYwh/ufoh1Bdupq28gKyONcaOG8eVrLuGUkyYD8Nyry/jnw89RuKeIYChEXnYmU8eP5odfv56B+bnttnMpr6zmj/98mNff/ZDyyhoy01M54+TpfP8rnyMvJxNo2Qrm3j/+kCVvf8CSZR/gcjq49NwzuO3m67DZ2j5P3Hnf4/zhroewWi2sXfIwWRlpALyxfBXXfONnADx5928YOjC/zc8SXp+fv97/BC8ufY99xaUke9zMmz2N226+joH5uQBc+qXbWL7qU+bOnMITd/0GgIXXfJNPN23jpqsu5Fff+SLhcJjRp12O1+fnWzddyfe+cm3n3jAifUDCJ9EBzj3zFFZ9WkB1bT0rVq/jtFlTCQZDvP7uhwCcOGks/XKy2nzsS28u54vf/z2RSCS2rGD7Ln78p3+xecdu/vjDrx1VTOWVNVz1tZ/QGAzF9nnjd3/DR4sfIDM99aj2KSLSV5mYsZ8z06MXjBu3FvLm8lUtttuwZSe3/eFuIqbJ5684/7D7PdxYHQyGuOprP+GDj9fHHhNoDPL+6nXs3FPUbhJ99boCLv3SbS0qE7fv2sef//UIH6/fzMN//XmHX9IeKtAY5Kqv/YR9xWUH9rWXL/7gd3Ht5ysicjh5OZnsLSrl4WdfpXDPfubOnMrJJ05iyvjRLcbA2roGzr/hO2wr3Btb1lhTy1srVlNeWU2/nCw+WrOR9z5ae3B9MMTH6zfz5dv+gNvpZMHck44p1k83beOim77Xoqpx+669/PpvD7CtcC9/+dktrR5z1c0/prq2vs39fe83/0dVTR0ADV4ff3/waex2W4fJhnse/R8/u/3eFsvWbNjCzT/+E/uKS/n6DZcfxTMTEek+bV2jQ+fH1GXvf8wtv7gzts2St1eyZsPWDo/5n6cWx8be1GQPAA8+9RI/+P1dLbZbva6A67/9K/76i29z+Xln4vX5ufrrP42NzQBFpRUUlVYwfdJYTjlpMh+t3cjNP/4zpnnweRXuLaJwbxHXXXZuLNl8qKqaOs67/lZ2HfjyGKCkvJLHnl/K2x+sYcnDd5Kdmd7iMbf+8q/U1kdbi9U1eLnrv88weEA/rrv0nDaPcdFZp/GHux4iHI7w2jsruerCswB4+c0VAORlZ3LyiZNinxeai36m+SkfrtkQW1ZZXcuzryxjxap1LHn4TnKzMzlpyjiWr/qUNRu2YpomgcYgm7YWAvDxugIg+hnM6/MDcNKU8W3GKtJXJXw7F4i2amnyylvRAWT5qk9jA+t5809u83GmafLTP99LJBIhMz2VVx++k3VLH2HO9IkAPPTMK2zcuvOoYvIHAlx14Vlseutxvv2Fq4DoN39vrlh1mEeKiEhzXp+fh555BYjeWXTO6bMBmDllHP/79x/Z8MZj7Fn5Aqte+g8Tx4wAiG1/OIcbq597dVksgT535lQ+eP7fFCx7gn//6Yfk52W3u99f3nk//kAjjiQ7T9z1aza99TgXLzwNgDeXr2LpgS95O+uJF1+PXRB/5dpL2LzsSX7zva8cqMAXEUlM116yCIhec7/74Vp+83//4Zzrvs3sC2/i7Q8+iW1372PPxxLonznndNa8+hDrX3+UO392S+zLwrNPn80rD93JprceZ8/KF3jv2Xvon5cDwEPPRsf8Z+/5Pbd+8erYfptuZ3/2nt/z1198mzubJcI/fPH+Fq2+fvGXf+PzB5g4ZgQfPP9vdq54jh99/XoAHn9haZufCUYNG8zKF+5j01uPc+4ZLT9v5GRlsPKF+3j/f/9mxJCBANzz6PPU1LWddK+ta+APdz0EwPDB/Vn+3D18+OL9jB4erdq//Z5HKa+q6fA5ioj0pPau0aHzY+od90bv7HEk2Xnq7t+y/vVHGT18UIfHra338teff4tt7z7NC/f/ifoGL7/+v+hYfuUFC9jwxmPRcflAnuiXd95HKBRmy849sQT6kof/SuH7/+PDF+/nrz//FhNPGA7AqrWbME2TIQP6sW7pI2x/7xmWPXkXP7vlxljld1vueeR/sQT6j75+PVvefopffedLAOwvKePv/3m61WMy01N579l7eOPxv+N0JAHw0hvvtXuMoYPymTp+NHAwcR6JRBPqAOcvOLVFhX9zz726jA/XbMBqtfDA7T+m8P3/8fpj/0dGWgrFZRX847/PAAeT4rX1DWzZuYdPN20jGAphGAbrN28n0BiMJdOtVgsnThrTbrwifVGvqEQfmJ/LlPGjWbNhC0veXsnvfvBVXj6QTDcMIzb56KG2Fe5lf0k0KXHpuWcweewoAG656crYjPfvfPAJ40YNO+KYbFYrP/7GDSR73Fy4cC533PsYAPuLyw/zSBERadJ0KyKAx+3iZ9+6kSkHLh7zcrJ44MmXeGP5R5SUVRIKh2Pb7tyzv1P7P9xY/db7q2Pb3vnzW8jPjSbOD02WNOf1+Vn16SYAzjj5RObOnArA975yLc8tifaKfPuDjzlr7sxOxQgHKz8Mw+DbX7iKZI+b6y49h/974Mk2q01ERBLBNz5/OckeF/c++jyFe4tiy3ftK+YL3/stK57/N9kZaby1IjrWJntc/OlHX8fldAC06OWal53Jb//+ICtWfUp5VTXh8MG7SHfs7tyY3x6vz8/KA9V56wq2M+vCm1pt8/7qda0+E/zy1i8weEA/ANJTU9jV7Dl+6bMXxdbddOX53PaHu2nw+ti0rZBZUye02v+qdZtilX03XH4+wwcPiO3n1l/9jUBjkA8+Xt+ieEhEJF46ukbv7Jh6wvDBfLJ+CwBnnjIj1k7lm5+/gnc/XNvqMU1Onz2Ny8+fD8AJI4aw7P2PqW/wAdEE/eMvLG2xfUVVDVt37qF/bhZWq4VwOMKd9z3O7GkTGTtyKOcvODV23mmqNC8uq+D2ex5lwgkjGH/CcL742YvaTVADvLMy+sVwZnoqX/3cZ7BYLNx01QXc/dCz7C8p4+0PPm71mC9fczEjhkTH+rEjh/LJhi3sL+k4X3Tx2fP4ZMMW3v1wDfUNXtZt3kF5ZXV03cK57T5u2fvR44fDEW649det1je1CTtx0pjYa/TJ+s2xwtT5p8xg6bsfsn7zdlav23wg5mEke9wdxivS1/SKSnQ4WI1eXFbBqk83sWTZBwBMm3AC/dupFmx+G09TYuTQnyuOssovJys9NqA4k5JiyxuDwaPan4hIXxcOR2IJBoBv/PR2/vvMy+wrLmuRQIdo+5POONxY3VTp7XI6WpwbOlJTVx9rEda/+bml2bmoowry5omhJiXllQB43M4WF6vttSoTEUkUn7/ifN5//t8se+pufvatm2LX5XUN3tidPk1jYv+8nFgio7lIJMJ13/olz726jJLyylbjZKDx2Cb1rKmrb3Psba6tti0dzYvRVCUP0C/34FhdUlbZ5vZV1c0+l+Qd3L75uUd3H4lIIjr0Gr2zY2pldW3sGj6/2TVtv8Ncc489ZOytqKo5bIxVtXXkZmfyi29/gWSPi1feep+f3n4Pl33lh0w+65pYNfe5Z57M5eedSSgc5j9PvcR3fv03Fl17Cydf/MUWX5S22v+B3FJedmaLZHv+gfG/rfF72OD+sZ8dByrRD/cZ5oID1eaBxiBvLF8Vq0gfOjCfaRPbrwqvqO74NWo6xyV73IwdGf3CePW6Aj5eV0CS3cZ1l517cNn6aHHPzCnjOtynSF/UKyrRIZpE//Xforfw/Ppv/6G0oiq2vD0ZaSmxn4tLK2I/N58QI+so+5c3nzToSPreiojIQR++eD+GYXDjd3/Dp5u28Yu/3MfU8ScwccwI3jxQuTh35lTu+s13ycpI4wvf+y2L31je6f0fbqxumsPC5w9QVFreqUR6WkoyFouFSCRCUVmzc0uzypKm/SYl2WPL/IFGnI4k9haVttpnXnZ0MqIGr5/6Bm8skV7cbP8iIomm+Xh1wvDBnDB8MIP753Hjd6OTldUc+NCemZ7Kzj372V9SFhsLm9uxe3/s1v/LzzuTX3/3y6Qkuzn72ltYu7Fl79yOrrvbW9d83L76ooXc/pNvtNqmeX/cJofG2VzzzxPNP2c0TS53qIz0w38uaTp36LOFiMRbe9foJ00Z1+kxNRKJYLfZCIZCsYIRaHnN3BbHIWNv8znn/v6r7/CZc05vdaymcfPGKy/gmksWsX7zdrbv2sdDz7zMqk8L+Nnt93LW3JlYLBb++otv84tbv8D6gh1sLdzD3+5/ksK9Rfzt/ie5/affbDOmjLQUdu6JFr5EIpFYIr3owHje1rx4tqPIGeXlZDJn+kTe+2gtL7+5gtUH7la9sIMq9ObHdzkdFCx7giS7vcX65ue4mVPGsX7z9lgl+rjRw5k1dTxWq4U33lsVu/tL/dBFWus1lehDBuYz4YRoL9zmkyWcO7/tVi4AI4cOjFWJPPPKW6wr2E55VQ1/u//J2DZzZ03tpohFRKQzBubncsdPv4lhGJimye/v+i+hUDhW7e1IsuN0JLH8o7W8/l7Xzjtx+uzpsZ9v/dXf2LO/hLp6L68ue581G9ue9MjtcjL9QCXIm8tX8d6Ha6mpq+dP/3o4tk1Ti5fmSfl3V64hGAzxr0eea7XP6Qf6DZqmyR33PkZdvZf/PvOKWrmISEI7+9pv8eu/3c/aTVvxBxqprK7l1QN3i8LBKrzT50TH2voGH9//7d8pLa+ksrqWJxe/we59xS3u5HQ6HFitFp5/7R0+3bSt1TGbT7jcNBlabF3qwXUF23bFfna7nMycGk0GPPvKW7zy1vv4/AEqqmpY/MZyLvniD9r8grMj9zzyP/bsL2HX3iL+/fiLQLTlwbiRbbeJPHHi2FgV/gNPvkThniL2FJVy76PPA9Fz3axpEw77HEVEekpb1+jQ+THVarXGWsC8sXwVH67ZSEVVDX974IkjimPG5LEke1xAtMf62k1baQwG2VNUyn+eXMwVX/0RAGUVVfz27w+yaVshI4YM4LwzT2b86Ggv9KZq9uUfreXeR5+nrKKaaRNP4PwFp8buJuqomvvUmVOAaMX5vx5+jvoGL/c/8WKsfXBX5pUuPjs6z9JLby6PfRZomnupPfNmTQOihUE//MPdlFdW4w80smbDFn78x3/yjwcP9mxvSo5v2raLfcVlTJ9wAh63ixOGD+HdD9fEEu4nTVUlusihek0lOkQnEF2/eXvs9ynjRzOondmTIfpt3y++fRNf/MHvKa+s5qzPtvyG9OqLFh5VP3QREela40cPZ+Fps3h12fu8v3odG7buYMbkcXy0diNL3/2QkadeimEYDOqfy+59JV123EsWnc5jLyzlg4/X89aK1S36P97/5x8zZdyoNh/3k2/ewGVf/iH+QCOXfeWHLdadNmtarB/6onmz+fO/HsE0Ta779i9xOpJoo9iRy8+bz1/ve4J9xWXc/dCz3P3QsxiGQVqKh5q6hi57viIiXam2vp5/PPgM/3jwmVbrJo8bxawDSZYvXHUh/1vyNtsK9/Lk4jd4cvEbse2WPvo3Thg+hMED8ti9r4T/PvMy/33mZZLsNvrlZMaq/A7ud2Ts5zOuvBmAb954BT/46ucYP3p4rNfr5771CwAuWTSPf/z6u/z0lhu55Avfx+cP8PnvtO4Xe6Qqq2tbnDMAvnj1haSmeNrcPjXFw/e+ci2/+Mu/2b5rL7MvatlD+JabriT7wKR2HT1HEZGedOg1+gefrGfW1AmdHlO//YWruOprP8HnD3Dhjd8FIDcrI7a+MxXayR43P/ra9dz2h7vZsXs/Z19zS4v1TX3O/YFG/u+BJ/m/B55stY+mJPfeolJ+evs9bR6nqQimLV+8+iKefWUZe/aX8Mu/3s8v/3p/bF1+bhZfu+7Swz6PzjrnjDnc9vu7aAyGABg3ahgnjBjS4WMuWXQ6j/zvNT5au5FHnlvCI88tabG++YTVTcnxpoKlqRNOAGDaxBNid4UNHpCntpIibeg1lehAqwlEOzPxznnzT+Hxf/yKk0+cRLLHRZLdxgkjhvDzb9/En370te4KVUREjtAtN10R+/mOex7lH7/5LvNmT8PtcjKofx53/PSbbU7WdixsNiuP/f1XfOdLn2Xk0IEk2W2kJnuYNW0CQwfmt/u4GZPH8b/7/sSCU08iLcWD3WZj2KD+fOumK3nwLz+NfSAYO2ood/z0mwwZ0A9HUhIzp47ngdt/3Gp/jiQ7j/39V8yaNgFHkp1hg/pz92+/x7gD1TMiIonot9/7Cp8553RGDBlIanJ0LByYn8sNl5/HY3//Vex299QUDy8+8Ge++NmLGDwgD7vNRkZaKvNmTyM7Mx273cYDt/+EGZPH4nQ4GDVsEPf96UcMHdS/1TFnTB7Hd7/0WfrnZbdKvgzMz+X3P7iZwQPysFpbfsyZMm4Ur/z3L1yw4NToMW02+udlM2/2NP70o6+324alPX/44c1ceu4ZJHtcZKSl8rXrLm2RpGjLl6+5mH/+7vtMm3ACLqcDpyOJyeNG8bdf3sotN17ZqecoItLTDr1Gh86PqfNmT+MvP7uFgfm5OB0O5p8ygz/88ObY/prfedOR6y8/j//+5WfMmT6RFI8bpyOJoQPzufjsefz+B18Foi1XbrzifMaNGkZqsgdHkp1B/fO48YrzYy1npowfzWfOOZ2hA/NxOR143C7GjBjCz265kRsuP6/d42emp7L4gT/z2YsX0i8nC5vVSm5WBlecP5+XHryDnGZfDByr9NSU2B1cABcdppULgN1u44m7fs23v3AVI4ZEP9OkpyYz4YQRfOPzl3PZuWfEtu2Xk8XgAXmx35vusD2xWc91tXIRaZthttUAMMEs372TX771GgXlZeSnpPClE2dzw7STAMj5/U9JslpZ/ZVv0y85hRF/+S2LRo3h7+ddwrS77mBPbXVsPz8//SxunnkwEX/T/55k2c5tbPrG97FbrdT6/Yz9vz9w5cSp3H72BXz+uSd4cfMGrp08nTsWXQjAH999kz8tXwZAisPBGcNG8Y/zLsFhszHtrjsYk5PLo5ddA8Br2zbz2acfITnJwaZvfA+nzc7u6iqm//MvACRZrYzIzOL2sy9gxoDBfG3xs7y4eSO7bj2YYFm+aycXPfZAi9ej4BvfJ8vddpWLiEgi03iu8VxEjg/NxzSnzcaE3H7cdf6lDMvI5GuLn+WJ9Wti2y4YMZpHL7um1djapMbvY+zf/kgwEuala26iX3JKbHxt7ooJUxicls6fli+LjZ8vFmzg9uXL2FldxdD0DG49eR4XjBkfG6Nz3B4+/uq3CYRCjLzzd3z35Hl879QzeOzTT/j9u29Q7m2gX3IKv1twLmeNPIELH7mfSp+Xd2+KFtt87plHWV9SzMdf/TZX/N8/eOc/LwNw6c2X83hp9A7ZFIeDRSPH8JdzLsRusXLb0pd5esNagpEwQ9IzePyya+mfmtbq+fz8zSX848PlzBs6gqeuvA6Axz79hG+8/BxnjRjNI5ddw8tbNnHds4/xv6tuAOCixx7AwCDZkcTE3Hx+MPcMZg8a2mK/Tc/dwMCdZGd0Vg63zJ7LOaPHAtHzbXP3XXQFWS43Fz32AONy8nj7xptZV1LEGQ/czd/OuZirJk3lzvff4V8fvU99Y4ABqWn88/xLmZI/IPY3Pf+E8Xzj5dbtyn582nx+/fbr/Gnh+Vw/dQbPbVzHF194it8tOJebps9s+80lIj2uu8b0Jy/+LPVl1Xz+3cVgGBAKY/loF5a90TnurvnutfyncGNcxvTm1+uHfi7QmK4xXaQtCV+Jvqu6iiuffIh+yam8+rkvcOm4SXzvtcU8t3FdbJvGcJh7Pnq/zcfPHTKctV+9lbVfvZXrps5ose6CMeOpCfh5d9cOAF7bvpnGcJgLx4zHG2zkjR1bMDB4ecsmwpGWs08v+/xX+d4pp/N8wXre3NF239wXCjZgYFDfGOCtHS17On57zmm8cf1X2FVdxd8/OPwkebeffUHseWS4XIfdXkQk0Wg8j9J4LiLHk9vPvoBnrryej4v2cd/qlbHlbrs9Ntb9/bxLOtzHy1sKCEbCGBi8ULCBAalpsccCXDlxKmu/eiu/nr+oxeM+3LubG//3JDMGDmbJ577ISQMHc9P/nuSjfbtj25R5G3js009aPM4fCvKdJS9wybiJvHfT1/n1mYtIdx7dePz252/mF2eczZMb1vLw2o95b9dO7vt4JX9ZdCGvX/9lvnDibKyWtj9yvbg5em55b/dOKn3eFuuWbt/K5vK2+7T/aeF5PHn55whGwlz11MMU19W2ud235szlxc/eSL/kFG547nHWlRTF1jW9pmu/eisLRoyOLd9YVsLr27e02E9hdSW/eft1bpk9l2Wfv5nvnXI6rkMmrbtw7HjWfvVW/n3R5cDBc90XTpzF7EFD+OO7b1LWUM+v317K+Jw8bjjkPC4iiaGrx/TnPvmYL37zl3heXM+AZTuxPr82lkC/9PwzyRlwsD2vxnSN6SKJLuGT6M9u/BR/KMRPTz+LsTl53HryPPqnpPLopx/HthmekcWDa1ZRF/C3erzDZqN/ahr9U9NITnK0WDd/xCjcdjuLN28EYPHmTWS7PZw8eBhLt2/BGwxy0dgJVPi8vLd7Z4vH9ktOYXBa9JadVIez1XEbwyFe3VrAwpEn4LbbeWHzhhbrUx0OBqam4bDaGJpx+Ft/0p2u2POwGAn/ZxMRaUXjeZTGcxE5nqQ7XQzPzMJiGKQ6mo/NRmysy3S5O9zHC5vXk+P2cMqQoby4eQMW4+BjIZq86Z+a1iop8vi6T7BbLfz89IWMycnl56cvxGaxtEiwDM/I4q4PVxBudvOtPxSiMRzmvV07Wb57J5P79eekgYOP6vnnJSdz+fjJABSUlVB74Pz1fMEGNpWVcvGYCeQlp7R63CdF+9hdU81FYycQikR4acumFuuHZWTy95VtfzGb4XJz4oBB3H72BTQ0NvLkhrVtbpfqcDAxL5//O/diDAz+u+bg5NxNr2n/1LQWyZPhGVn8feV7LfZT6w8AsHT7Zj7ev5fTho7ghOyW81K57Un0T00j2xW9u6rpXOe2J/G7BedS6fNxzkP/ZndNNb9dcG67SSgRia+uHtPf2l/I+QtOJSs9larqWrAYZA/K5Y8/+hp/+9m3WjxOY7rGdJFEl/Dv9P0HvoUblBa9iDYMg/4pqbHlAPOGjWBgahr/+eSjVo9/c8c2htz+a4bc/mveLtzeYp3bnsT8EaN5ZWtBtLpw51bOGT0Wq8XCCwUbSLJa+eFpZ8aqYpqbdvcdXPfsY1w9aRonD2k9OenbhTuoCfg5a+RoZg8aypJtmwmEQrH1v33nDUb/9fdU+/1Mymvd7/FQNy9+hiG3/5oZbdzaKiLSG2g8j9J4LiLHk5sXP8P4//sjJ2TltGiz5Q02xsbsP7z7ZruPr/H7eKdwB3OHjuD0YSPZX1fLqv17OnXsfXU1ZLjceJKSAPAkJZHhcrU4r1w+YTL1jYHYl6wQTQZ8ecZs1hYX8a1Xnmfa3X/h8XUHkzRbKspisb+27WAFX86wfJKunk3R6pcYNGpQbPnHRXsBGJyewRnDRzFz4GCeL1jPjf97gml338GG0uJWsb9QsB6A75w8jxSHI/Z7k6+eNIdnN35KUTsViRBNygDsranu8HVKc7rIdLvZ02y7h9asjj3H7ZXlseU3TZ/JR/v28EnRvtiyiXn9OP+E8bxduIOvLn6GqXfdwbKdLe/K6sj43H5cP3UGhdWVXDJuInMGD+30Y0WkZ3X1mF7c6OMLX7+a1S8/yK4Pnid88VTO+epnuPaSRa3mfOjpMb09GtM7pjFd+rKET6L3T0kFYG9tDQCmabK/rja2vMnXZp7CPas+IHTIbfqzBw3hrc9/hbc+/xVmDBjEoS4YM55ybwO/ffsNvMEgFxy49f/17VuYMWAQQ9MzGZ+b16oFwOOXX8uCEaN5duOnLQavJk1JmnnDRnL6sJHUBQK81Wxg+vKMOdGYBg7iR6+/fNjX4WenL+Stz3+FZ668/rDbiogkIo3nURrPReR48tN5Z3HL7LlsKCvhte2bY8vddntszP7CibPaffwrWwtoDIeZN2wEpw8bBdDqy872DEhJo8rnxRtsBKJJnmq/r8V5xWmzcdP0mfzjkArAX525iE9vvpU7Fl2IO8nOQ2tXx9YNTc+MxX7KkKEdxjDt7r9w4SMPMGPAIK6bciIuu53F19zEOzfezLfnnEaV38fzhyRTAF4s2MjgtHRGZ+dw6uDhvLer5e3/k/v1Z9bAIdyz6oN2j72jsjL6OrTRm7e5ar+PSq+Xgc22u2jshNhzbLobCyAvOYXLxk9u8XoZhsH9F1/Bh1+6hV+deTaN4XCL/sidcf4J4wA4d/S4I3qciPQsjeka0ztDY7r0VQmfRL9k3CQcVlt0IrqyUu5Y8Tb762q5atLUFttdPG4CNoslNuA2sRgGTqsNp9VGKNwyIQPRSTHcdjv3fbySLJebkwcP5fXtW/EGgyzfXUjO73/K+tJiyr0NLG/WAmB0Vg6/OGMhgVCYew8ZCIPhMK9uLQCiFY4/fuMVoOXJw2ax4LDZsBoGlma3vkTMCG8Xbuftwu18vH9vbHmS1YrTasNmsRCKhI/0ZRQRiTuN51Eaz0XkeJKXnMJ3T4m25zo0qdE0Zjf/4rLS542NjdsrK2Lj6ddfeo7TH7gLgBc3b8Rsdqt+e66YOIXGcJifv/Uam8tL+flbr9EYDnPlxJbnlc9PO4ni+rrY7/WBAN9/bTG7a6qZOWAw2W4POe7k2Pokq5XhGVkMz8jCbU/qMIb/XX0DG7/xPV6+9gukOJy8v6eQv33wLuGIyWlDRwC02DfAmqJ97KqpYndNNTm//ykvb91EKBLh5UNu///arFPYUVXR6phVPi+r9u3h1lefx21P4vIJU9qMrTYQYF1JEV9b/CwmJtdOOTG2zmqxxP4+jeGW56KvzTyFnVWVsd+3V5bzm7dfp8bvY86gYXiSklo9JxE5PmhM15guIu2zxTuAwxmSnsHjl1/DL5ctZeF/76Ffcgq/nX8Ol4yb1GI7m8XKl2fMiSU4mryzaweT77odiN5C84szzm6x3m1P4szho3lx8wbOGT0Wm8XKCwUbcNvtPHXFdbjsdqp9Pi5/8r88X7CBPM/BwWVUVg6nDR3O4+s+4ba5Z8aWv124nWq/j++fcjoLR40B4Mevv8Kr2wr49pzTALhjxdvc+f47DE5L549nnRd7rD8U4tLHHwSi31b+4vSFANz66gsHn9ONNzM2J+/oXlARkTjReK7xXESOTzaLleunzuC377zB6gOtWLzBYGzMntyvP69f/2UAVu/fGxsbv3TibN4u3M7CkSfw/VPPAODpDWu568MVrNq/hxkDOu5pO3PgEO698HLuWLGMJ9Z9wpD0DO698HJOGjiY3dVVse0yXG6unTydf62KTlxttVjYVlHO1U89TCAcYmxOHj84cPwjNTgtnSy3J/a7x+7g+U3ruX35MiyGhXNGjeXqSdNaPKZpsuqHL72a/JRUQpEIlz3+IM8XrOeSsQfPiacPG8mE3H6sP6R1wHeXLMaTlMS43Dwev/wa8g+5o6vJHSve5p8frWBkZjYPXHwlk/sdbDn2+LpPYu0OfrfgXMY264c7MiubRaPG8PLWaALIabOzfPdO/r16JREzwvT+A/nqSXOO6vUSkcSnMV1juoi0zTA785WgiIiIiIiIiIiIiEgflPDtXERERERERERERERE4kVJdBERERERERERERGRdiR0Et00TcLhSKcmoRARkcSl8VxE5PihMV1E5PihMV1EpHMSOokeiZh8snkvkYgGcxGR3kzjuYhI4vL5A/z+7w/x57sf5bd/+y+bthZ2uL3GdBGR44fGdBGRzrHFOwAREREREYkfR1IS3/3KZ7FaLZRVVHPPw8/zo28OjXdYIiIiIiIJQ0l0EREREZE+zGIxAAMAvz/AwPycNrcLhkKEQmHCkUgPRiciIiIiEn9KoouIiIiI9HFVNXXc8/DzlJZVcd3li9rc5pU3P2Dx0uXY7TZuuOayHo5QRERERCR+lEQXEREREenjMtJS+P7N11BeWcPt/3yUSeNGttpm0RmzWDB3BuFIhILCsjhEKSIiIiISH0qii4iIiIj0YcFQCLst+rHA5UzC4Uhqczu7zYbdZiMcVjsXEREREelblEQXEREREenD9heX8+QLb2BYLEQiEa644Mx4hyQiXcg0TUJhsFnBMIx4hyMiItIrKYkuIiIiItKHDRnYj+9+9bPxDkNEuphpmqzdCu+siVBWBTkZMHeKhcmjlEwXETlehCMme0rAFwCXAwblgdWiMb47KIkuIgnN2xjq8WO6kzQ0iog0Z5omjeVlhBoasHk8JGXnKAEjIpLg1m6Fh5eEaQxCqhu27YXdJWHAypTR8Y5ORESOVThi8v46k02F0Z+tFoOxQ2H2RCXSu4MyRSKS0Mb9dEmPH7Pw9+f2+DFFRBKBaZoU1dUSNk0ADCDHkwxVldRu3IAZDmNYraSOG48jJze+wYqISLtM0+SdNREagzAsP5pIyUqDnUXR5ZNHWfRlqIhIL7enBDYVQk66ictp4PWbbCo0GJgLQ/PjHd3xR0l0ERFpZfe+Eh57bikWi4HFYuFzly0iJys9tr7B6+P+x1/C5w8wqH8uV144Xx/ERI4DDcFG1pbspy7QiAUDiwVmDxpKakMDZjiMs18//MXFhBoacOTEO1oREWlPKAxlVdEK9OZS3dHloTDYlQ0QEenVfIFoBbrLGf0s7nYahCMmDT4oLFKLl66m06aIJLSNv1zY6W03l5eyePNGBqZk8IuniwH4xWX5lHvryElO5jPjJinR20lpKR6+edNlOJ0O1m3azguvvceNV50XW79k2UpOnDyG2dMn8J8nX2bD5p1MGDM8jhGLSFeIREwaQ2HyU5JJstrYXV1NKBLB5vFgWK34i4sxrFZsHk+8QxURkQ7YrNEe6Nv2RivQm9R6YeTA6HoREendXI5octzrN3EfqEQ3DCjYZVJZqxYvXU1JdBFJaEfSn7y4oQaHzYLTfvBTQZLNQk6Km2qfF28oEG1LIIeVlnrwdbLZrK1Otlu272HhvFkATB47ki079rSZRA+GQoRCYcKRSPcGLCJdImRGCJsmFsMCRNu5hM0ISdk5pI4b36InuoiIJC7DMJg7xcLukjA7i0xS3dEEepI9OrmoCktERHq/QXkwdihsKjRiCfPsNJOKGsjNUIuXrqYkuogcF+oDAQqrq8h0uVut89iT2Ftbw77aGiXRj1CgsZHnX32Xz12+qMXyBp8ft8sBgMvloMHra/Pxr7z5AYuXLsdut3HDNZd1e7wicmzCkQimGcHalFwxohUshmHgyMlVCxcRkV5k8igAK++siVBWFa1AnzvFcmC5iIj0dlaLweyJMDAXfAEDlwMafPDu2tYtXnwBfXl6rJREF5Hjwu6aamr8PkZmZhMMmS3WGYaBx55EQXkpk/rlxyospWOhcJh7Hn6BhafPpH9edot1bpcDnz+A2+XE5w/gcbva3MeiM2axYO4MwpEIBYVlPRG2iByDcCRCxKRFhWIoHI5jRCIicrQMw2DKaJg8ykIoHG3hEo8KdNM043p8EZHjmdVitKgwLywCq4UWLV6slmiCXY6Nkugi0uuZpklBeQl2ixWLYQBmq22y3R7219ayv7aWgWnpPR5jbxOJmNz/2GKmjB/F1AmjW60fPXww6wp2MHPqONYX7GhzGwC7zYbdZiMcVjsXkd4geEjrJYth4A+F4hSNiIh0BcMw4jKJqGmarN1KrBI+J+NgJbyS6d3HNE0e+9/r7N5bTDgSYcHcGZw0dVy8wxKRHtJWi5exQ6PL5dgoiS4ivV5RXS27qqvI7aBVi8tuJxAOsaWiTEn0Tvhk/WbWbdpObV0DKz/ewID8HCaOGUFdg5fZ0yewcN5M7n98MW+//wkD++UwbvSweIcsIl0gGGlZdW63WPCHg3GKRkREerO1W+HhJWEag5Dqjk5yurskDFiZ0nb9hXSB/SXl7C8p5wdfvxa/P8Av//IfJdFF+pC2WrwMytOkol2hS5Pod977BLv3lXDmKSdy7vw5Ldat+GgdLy5dTlZGKgA3Xn0+GWkpXXl4EemjCspL8YdCeJKSOtwux+2hoKyUaf0Hku5su/2IRE2fNIbpk8a0uz7Z4+IbN6rHucjxxh8M0rw40Gax4m1sjF9AIiLSK5mmyTtrIjQGYVh+9MSSlQY7i6LLJ4/S5KbdJT01GZvVQigcxh9oxON2trldMBQiFAoTjuiOUZHjzaEtXqRrdGkS/brLFrFp6y6qauraXH/KSZNaJddFRI5Fpc/LprJScjyew26b4XKxubyMgrJSZg0a0gPRiYj0LvWNjdit1tjvdqsVfzhEYzhEklU3MIqISOeEwlBWFa1Aby7VHV0eChOXFjN9gdvlJDc7k5/84V4aG4Nce+nZbW73ypsfsHjpcux2Gzdco+IYEZHD6dLTVkZ6aofr31+9nvWbdzBmxGDOP+tULO3cSqBvREWkszaWFlPj93FCds5htzUMg0yXm7XF+xmfm0eKo+2qDBGRvsg0TeoCfhzNkuhJVisNjY34g0qii4hI59ms0R7o2/ZGK9Cb1Hph5MDoeukeG7cUUl1Tx29+8EV8/gB/vOsRxo8Zht3W8jy+6IxZLJg7g3AkQkFhWZyiFRHpPXrs09Dk8aOYNX08AP954mVWfrKB2dMntLmtvhGVruBt7NmJ0NxJSi70tCqfl3UlRWR7PJ2+HTTb42FLRRkbS0uYqWp0EZEYbzCIPxTCbbfHliVZrQTDYbzBRlKd+uJRREQ6xzAM5k6xsLskzM4ik1R3NIGeZI9OLqpWLt3JxO12YrFYcDiSCIcjRCJmq63sNht2m41wWMWLIpL4TNMk6PWyu7CeAE48mak93uu9x7J+zftwzZgylg1bdrabRNc3otIVxv10SY8er/D35/bo8QQ+LS6i0udjTCeq0JtYDINMp5s1xfsYk5NLmnqji4gA0BBsxB8KkuE6OC4ahoFpmDQE1RddRESOzORRAFbeWROhrCpagT53iuXAcukuY0cN5cNPNvGHfzxMKBTm9JOn4UiyH/6BInLMwhGTPSXgC6AJPbtIJBQi7PUSqKnhk08bKNwdxJ+SBykmY4fC7Ik99xr3WBLd6/PjdkUT6QXbdtEvJ7PdbfWNqIgcTmlDPetKi8hLTj7iSpZsj4ct5WV8WlzEqUOHd1OEIiK9S10gQMQEq8XSYrnDaqO0oZ4RmdlxikxERHojwzCYMhomj7IQCkdbuKgCvftZLBZuuFIFXnL8SfQEdThi8v46k02F0Z+tFqPHk7w9pbv/FqZpEgkECDU0EKqrIdLYSFGljW3lHrIz6knKMvAmmWwqNBiYS49NotqlSfT/PvUK23ftIxQKU7i3mAsWnMzGrYUsnDeTJcs+ZNPWQqwWC3m5mVw8c3JXHlqklY2/XHhE23+wew+f//dGAK49w86U/v1ZNHpMd4Qmx8g0TT7ev5e6QID+KR3PxdAWi2GQ60nm05IiTsjJJdeT3A1Rioj0LiX1dTjaaFLrsSdR7fPhDTbitifFITIREenNDMPQJKIickx6Q4J6TwlsKoScdBOX08Dr7/kkb0/ozr+FGQ4R9vkI1tURbmjAjISxJDmxpaTRWAsRIjiSDEzA7TQIR0x8gV7azuVzly1qtWzQgDwALl40l4sXze3Kw4l06Eh7lIciB3uoZ3pc+MIB9TlPULtrqtlUVnJUCfQmmW43BeVlfLx/LwtHnqCqGBHp0+oDASp9XpKTHK3WuZOSqKypodrnUxJdRERERHpcb0hQ+wLRpLLLGc0txCPJ2xOO9m/RUfV6OFZ1Xksk4MewWLE4XViaTYjsTDKxWAz8jSYOwOuPJvBdrT++dBtlCEUOqPJ5Yz87bDYaGgMEQiEcNv0zSSShSJhV+3YTDEdIcRzbaDkwJZVNZSWMzcljSHpGF0UoItL7VPi8eINBst3uVusshoHFgOL6WvqnpsUhOhERERHpy3pDgtrliCaFvX4T94Hkck8neXvC0fwt2qpeHzM4wkkj/ZjeaNV5JBTCYndgS07FOKS9JEB+TnRujR3bDBpDJqREK+AH5XXTE22DsoMiQMSMUBXwx353WG00BP3UNwaURE8wWyvK2VZZwaC09GPeV7LDQUl9Pav27WZAaio2S+s2BiIix7uIGWF3TRVOm7Xdu3LSnS6K6+upbwy0Wa0uIiKSqEzTVE92kV6uNySoB+XB2KGwqdBo0eakJ5O8PeFo/hYtqtdtQXy1DWxfW0euz0//bAtWlwu7u+M2u1bDYNoYyHMYNLotJOcZPd4XX9lBEaA+0Ig32Bj73WmzUeZrpK4xQJbbE8fIpDlfMMhH+/bgsFlxdtGXGwPS0thWWcGW8nLG5R5nZzcRkU6o8Hopa2hoswq9iScpiYqaakrq6kjOSqBPKyIiIu0wTZO1W+GdNRHKqiAnA+ZOsTB5lJLpIr1Nb0hQWy0GsyfCwFzwBYyEnPy0KxzN38Lri2D6vbj8DRj+OtyhIFVBO422FOypnS9mtBoG+dkGSVkGjqyef12VRBcBagJ+/MFg7HeLYYAJNX5/B4+SnrahtJh9tTWMyMzqsn06bTacNhur9u9hWEYmLru9y/YtItIb7KmpIhKJdHjnlWEYJCclsbO6koFp6bpLS0REEt7arfDwkjCNQUh1w7a9sLskDFiZMjre0YnIkegtCWqrxUiYHu3dpb2/BUBhkdmi57kRCRP2erFXV+Os9RIwTZweJz6bG5xGrCVMW8KmSVEZ+BvBmRRt52KN8xeg+gQkAlT7fYQikRbLLIZBhbchThHJoWr8Plbv30u604mtjf5YxyI/JZVtFeVsLC1m+oBBXbpvEZFEVunzsqemhixP+1XoTdKdLvbW1lBUX8vQ9MweiE5EROTomKbJO2siNAZhWH406ZKVBjuLossnj7KoGl2kl+kLCere4tC/RYue5+EItkiAE3LqmTKoAYIBclPtDBvuYdt+K5HK6AShIwdGE+NtCZsmHxeYbNsLkcjB7aeN6ZGn1y4l0UWAsob6Vt9ouZOS2Fdbg2mausBKAGuL91PhbWB0djuj7DGwWSxkuFysLtrHqKwcUp3OLj+GiEiiMU2TnVUVBCNh3Pakw25vtVjw2JPYVlFOv+QUnDbduSMiIokpFIayqmgFenOp7ujyUBjsyoaIiHSJPSWwaUeIXJcXV6QOf20DOzeb9PO4GDggHcMwmDbOJD8H/I3GYSvLi8qidw9lpZo4HQa+gMm2vQb5WZDnBPOQItie0rXlnCK9UMSMsLe2Bk9SywRCclISNX4/NQG1dIm3soZ61pUUk+tJjrba6QY5nmTKG+pZX1rULfsXEUk0pQ317K6pJvsI5v7IdLup8HnZU13dfYGJiIgcI5s12gO91ttyea03utzW+Ra8IiLSgXAgQH1pJdbKPbi8+yHgxZniJuhIJYAjVpRqNQwG5hqMHBj9f0etWfyN0Qp0pyO6jcthEAkFqV21gm1/+QN7H/1vjzy3Q+m710N4G0M9fkx3kv4M8VTl81Ht85HsaDlRWnKSg+K6OsobGkh3uuIUnQB8WlxEjd/HmJzcbjuGxTDI9nhYW7yfMTl5ZLoO39pARKS3CkXCbKsoxzTNI5oLwmIYpDudbKsqp19KCikO3bkjIiKJxzAM5k6xsLskzM4ik1R3NIGeZI9OLqo7jUVEjp4ZDhP2+Qg11BGqr8deH8JiJOGzpOJyWvAFoi1YnIe/2bVNziSwWKIV6O5ABebHSxm+7k0afNUA+PfvY/hXvoYlydHxjrqYsreHGPfTJT1+zMLfn9vjx5SDShvqaQg2ku1KbrHcYhiYwP66GkZmZccnOKGkvo6NZcX0S0np9mNludwUlJexvqSIuUNHdPvxRETiZW9tDfvrask/irE13elid3UV2yoqmJLfX4kIERFJSJNHAVh5Z02EsioYOTCaQI8uFxGRIxVpDBD2+QjW1BD2+zEMCxaXk4HDbYxoPNDDvObwPc8Pp19WhJGBtdQ/9xqOPaswTLPFeltyKv7iYtyDh3TBs+o8JdGlz9tTU43VaLsaIc3hYGdVJScPHoa1iyezlM5ZV1JEXWMjA1LTuv1YhmGQ50lmfUkxE/LyVY0uIsel+sYAm8tKcSfZsVuP7n72HE8yO6sryU9JoV9KahdHKCLSNUzTJBSOtu7QF359j2EYTBkNk0dZ9D4QETlKZiRC2O8jVFdPuKGeSDCIxZ6ELTkFo1mebNoYyM/qXM/z9gRrayl/+03K3lwKJcW0KHW1WMg48SQy55xK5pxTcGb3fLGrkuiH2PjLhUe2fUkZl/7jYwB+8plc9tfVcsn4iQzLyOyO8KSL+YJBdlZVku5q+3b0NKeLkvp6Surr6N8DSVxpqayhnoKyUvolJx9+4y6S4XKxubyMTWXFnDx4eI8dV0SkJ5imydbycmoCfganpXdqe6oqMX0+aJp02e/H6XJhtdkoKCslw+XGYdMlpYgkDtM0WbuVWAVyTsbBCmQlUfsewzA0iaiIyBGKBIOEvV5CdTWEvD4ArE4X9nbmU4r2PD/y45imSf3WzZS9voTKle9jBoMt1tszMsk5YwE5p59JUmYWwdrauJ3LdSo5xJH2JzeNcLPH2rHbwCSiPue9xP66Gqr9PoZlZBIOt17vstsJhEPsra1REj0ONpWVUBvw0z+156ocDcMgy+3m05JiJub2J9Wpfr8icvworq9jZ3UluZ5kDMPA7/NRuG17q+0aA37279lLfmoK9rIyiIQh4MfEwHA4GDJ4MNljxrA3EmF7ZTnjcvvF4dmIiLRt7VZ4eEmYxiCkumHbXthdEgasTBkd7+hEREQSk2maRPw+Qg0NhOrqiDQGMOxJ2JKTMSxdOyNz2O+jYvm7lL6+BN+uwlbrUydOJnf+QtKnnYhxlHfPdjVleo+R95BvSDDAG2yMTzByxHZWVWACNouFcDjS5jbJSUlsLi9lWv8B2Lp40JD21fh9bCgtIdvT9rec3SnL7WFLeRlbKso4ccCgHj++iEh38AWDFJSVYLUYsclEC7dt5/pzzjvifd3/h98wZtgwslNS2VZRTo4nmRxPz901JCLSHtM0eWdNhMYgDMuPVqplpcHOoujyyaM0qaSIiEhzZjhEyOslVFtH2NuAaZpYHE7saRldfizvnt2Uvb6E8vfeJuLztVhnTU4m+7QzyD1jAc78/l1+7GOlJPoxqvJ6W/xut1io9vva2VoSSX0gwNbKCrJcrg63y3J72F9XQ1FdHYM6ceu7dI2tFeVU+b2MzjrKmSiOgcUwSHU6WVdSxIS8fjht9h6PId7uvPcJdu8r4cxTTuTc+XNarFvx0TpeXLqcrIzoHQI3Xn0+GWndP/GriByb7ZXllHkbWpzLho4cwX9eXtxq28Jt2/j5N27h57/7FYNt9jYr0Q2Xi2SHg5qAn4LyUtKcTpKsurQUkfgKhaGsKlqB3lyqO7o8FEatPURERICw3x+tOq+vJeL3Y1jtWN2eLq/8jgSDVH30AaWvL6G+YFOr9Z6Ro8idfzaZs2ZjSXJ06bG7ki4fjlGVv2US3WG1Ue5tiFM0ciQKq6uo9vkYldXxZAROm43QgdvVlUTvGYFQiPUlRaQ6nFjiVCmU60lmR2UFhVVVjMk5isZevdx1ly1i09ZdVNXUtbn+lJMmtUqui0jiKq6vY1tlBdluT4tx1elyMWbihHYfN3TSFE4Y2L9VT3TD5YID87/0S05hb00NO6sqOCE7r1ufh4jI4dis0R7o2/ZGK9Cb1Hph5MDoehERkUQVjpjsKQFfAFwOGJQHVkvX5UXMcJiwz0ewrpZwQwNmJIwlyYktNb3L79QKlJVS+sZrlC97g1BtbYt1FoeDrDmnkjN/IZ5hvWM+OiXRj4E/FKSusWXrFqfdTl3ATyAU0iRbCSxiRthUVozDZu1UkjbT5aagvJQT+w8i2ZG434odLwqrKympr2NoHCfotVksJFmtbCgtYnR2NhbDcvgHHUcy0jvuQ//+6vWs37yDMSMGc/5Zp2Jp56QeDIUIhcKEI223SxKR7ucPBSkoLQFMPElJR/Zgw8DIzIJmE4w2JdCbLrKtFgsZbhdby8vJdieT1c5kQyIiPcEwDOZOsbC7JMzOIpNUdzSBnmSPTi6qVi4iIpKowhGT99eZbCqM/my1GIwdCrMnHnsiPdIYIOT1EqypJeL3YVisWFxOLF18570ZCVOz5hNKX19CzdpPop8jmnEOGEju/IVknXoatl72uUFZ3mNQFwgQCIZaLHPZ7JR7vdQ1BpRET2D7a2vZU1NDXnLnWlBkutxsqShjR1Ulk/rld3N0fZtpmmwqK8FqsWCP8+QRucnJ7K6ppqiujgGaWDZm8vhRzJo+HoD/PPEyKz/ZwOzpbVeyvvLmByxeuhy73cYN11zWk2GKyAHbKsop9dYz8FjGsapKItu3QziEabVhGQFkZsVWpzqc7PXXsKmslJkDB8d9/JYjs3tfCY89txSLxcBisfC5yxaRk5Ue77BEjtrkUQBW3lkToawqWoE+d4rlwHIREZHEtKcENhVCTrqJy2ng9ZtsKjQYmAtDjyIVZUYihH0+QvX1hBvqiARDWJIc2FJSMSxdWygYrKmmbNmblL3xGo3lZS3WGVYrGTNmkbNgISljxvXaL7SV5T0GtQE/gVDLiUUdNhu+YJDagJ/sXvaNSl+yubyMxkgoNrHa4VgMA489iXUl+xmXm6sJRrtRaUM9u6qryU2ACerc9iR8oRq2V5Yrid6Mx+2M/Txjylg2bNnZbhJ90RmzWDB3BuFIhILCsja3EZHuU1Jfx/bKCrLcbqyHuVA2TROzsgKzpJjIjm1NC6P/8/kgHMLIzsEsL4tWpB/y+H4pKeyrjbZ1GZ3d99pg9WZpKR6+edNlOJ0O1m3azguvvceNVx35hLMiicIwDKaMhsmjLITC0RYuvfUDu4iI9B2+QLQC3eWMnrPcToNwxMQXOLJzWCQYJOz1EqytIez3gWlgdTqxu7s2z2KaJvUFGyl9fQlVH67EDLcsNE7KzibnjAXkzDsTe3rXT1La05REPwa1gQCHNiiItgYxqfX74xGSdEKVz8vm8tIj/pIj15PMntpqdlVXM6JZ9Z10rZ1VFTQ0BhiUlhhJ6yyXi4KyUk4cMAi3/QjbIBynvD4/blc0kV6wbRf9ctpvu2O32bDbbITDauci0tP8oSAFZaWYmCR3ZoKeqkrMT9cQ2V9EZN++6LK6GgAMlwvTasMsLwOrLdrS5RA2i4V0p4stFeVkuT1q69KLpKUe/EBls1nbvV1YLbqktzEMQ5OIiohIQmveA72y1sRigNdv4j5QiW61GLg6cSlvmiYRf7TqPFRfT6SxEcNux+ZJxujiQtCQt4GKd9+m7I3X8O3d03KlYZA2aQq58xeSNnValx87nnRJcQzKGuqxtVHVZbUYlHvr4xCRdMaW8jKq/T5OyM45osc5bDYM02BjaTHDm/WCla4TCIXYVFZKehvJmXjJdLnZXlnBruoqxub0nQnz/vvUK2zftY9QKEzh3mIuWHAyG7cWsnDeTJYs+5BNWwuxWizk5WZy8czJ8Q5XRNqwraKckoa6TrdxMX0+TJ8fw+PBcLkBiPj9mJUVRLxejKxMzCQHFrc7NqnoodKcTvbW1FBQVspJauvS6wQaG3n+1Xf53OWL2lyvFl0iIiIiXefQHujRulyTsiqDiHmwJ/qgDlIRkVCIsNdLqK6WsNeLaZrRqvO09C6Pt6FwB2VLl1Cx4j0igZbFw7aUVLLnnUHOGQtw5vXr8mMnAiXRj1LEjFBUV4s7KQnwtVjnsSexv64O0zSVaE0wDY2NrCspIsPlOqq/TV5KMtsqy9lfV6v2Ht1gb201ZQ31DEmg23ysFgs2i4Ut5WV9Kon+uctaJ1AGDYg+/4sXzeXiRXN7OiQROQLFR9DGpYnhcmG4nESqqjB93uiyYPCQXugjohONdqCprcuOqnJOyO4742ZvFwqHuefhF1h4+kz652W3uY1adImIiIh0nbZ6oJdWGYwfbpCZGq1AH5TXelJR0zSJBAKEGhoI1dVEq86tdqxuD0YXF7FEGhup/GAFpa8voWHbllbrk0ePIXf+QjJmzsbSyZbJR6PpORMJHX7jbtKlSfQ7732C3ftKOPOUEzl3/pwW64LBEA8+9QqV1bVkpqdy3WWLsPfie+tqAwHqAgE8bbR3cCclURvwUxcIkOp0tvFoiZdtldHJ1UZltv3h8HCSkxzsr6tlY2mxkujdYHtlBSYkXOVittvD7poqKrwNak8gIgnPFwyyqbQYOtvGpUlGJsakKVjy+mE5MPG2abcfthf6oWwWCxkuF1vKy8l0echJgDkupGORiMn9jy1myvhRTJ0wut3t1KJLREREpOu01QPdNE0yUw3GDm191W2GQ4R9PoJ1dYQbGjAjYSxJTmwpaV1exOsvLqL0jdcof/tNwvUtu21YnE6yTjmN3Pln4R48tEuPeygzHCbs82GGg1icTpJy8rCnpHTrMdvTpVns6y5bxKatu6iqqWu1bsWqdfTLyeSmq89n8dLlrFi1jtNmT+3Kw/eoSp+X+sZGMlJbJ9Q89iRK6+up8HmVRE8ggVCINUX7SElydLoqry25nmQ2l5cxJX+AEgNdqD4QiFZNHmghkEhSHA7219eyu6ZaSXQRSWimabKlopRSbwODjvDLXsMwMLKyISsbSzi6zOJ0wmF6obcl1eGkLhBt65LqcOKw9d7Cib7gk/WbWbdpO7V1Daz8eAMD8nO46qIF8Q5LRERE5LjmckSrzA/XAz0cqzqvJRLwY1isWJwuLF18jW2Gw1R/vIrS15dQu25tq/XWfoPxnLqQYQvnkuTu3txNOBCIPlfDwOr2YEvNxepyd/lzPhJdeuSM9NR2123ZsYeF82YCMGncSJYsW9luEr03TFpU6fViYh6YSLQlq8VC2DSp9DUwrJ2eodLztldWUFRXe8x/k3Sni+K6OjaVlSiJ3oV211RTE/Ax8ijvEuhOhmHgsSdRUFbC5H75WIyj/xJGRKQ77a2tYXtlJbkez2G/MDZNMzqZqM8XTY63Nd9HShqWgf1bbNNZ/ZJT2Ftbw9aKMsbn9lOLuwQ2fdIYpk8aE+8wRERERPqUQXkwdihsKjQIR1r2QDcjEcI+H6H6WsINDURCISx2B7bkVIxjKAxti6+ygsLFb9Dw/uuYNRUt1hk2G+aY2ewdfBbe7NFYrBZqd8G0MSbWLr6+NyNhwn4/ZrARS5KDpMxMbB4PFufRtWTuaj2Wvq9v8OF2Rauy3S4HDV5fu9v2hkmL9tZU4+zg2w+Hzcq+2hqm9x/Ug1FJe4LhMGuL9+G02bqkVUiOx8OG0mIm5uWTkYCV073Rtspy7BZLm19MJYIst5vi+jpK6uvJT2n/C0MRkXip9fvZWFqMw2rF3Ua7uVaqKg/pdw4c2u/cMDAysw7bwqUtVouFHLeHLRVlZLjcaoMmIiIiItKM1WIweyIMzAVfIFqBPiCjkUidj0BtLWGfDzCwulzY3V1bxGmaJnUb1lGydAnVqz+CSLjF+qScXHLnL6Rx/DxWbE8lK9Uky2HgC5hs22uQnxWNuytEGhsJ+6N5YpvbhS0rG6vb3a091o9GjyXRPW4nPp8fSMPnC+Bxt387cKJPWuQLBiluqCOlgz6jKUkOiupq8YeCOG2J9UfviwqrK9lTU83gLpqdONPlZnN5GQXlJcweNKxL9tmXVfm87K6pItOVuK1S3PYkfMFqdldXKYkuIgknGA6zoayY2oCfQZ0815k+3xH1O+9U5foh3ElJNAQbWV9SRHJSEmnOzrWDERERERHpC6wWgyF5EcJ+H+H6ehr31xMJBrHYk7Alp3RJ1XnYNCkqA38jJIXqsW9cRvkbr+Ev2t9iO9MwCA6dRvnIhUxbNIX8fla27TWJRCI4HdHrfpfDIBIx8TceWwGkGYkQCQSINAaw2O3Y09KxpSRjdbq6vNK+q/RYEn308MGsK9jBoAF5rCvYwejh7VdoJ/qkRWUN9dQFAgxOS6e9jjMpDid7a6opa2jo9IdZ6R7hSIQ1RfuxWgySuqh3kmEYZLndfFpcxITcfFIc6n1/LPbU1FDr95OfHJ/JITor1eFgS0UZJw4YdEx99UVEupJpmmwuL2VPdTX9U1M7fauj4XJhHkm/885Urrch2+1hb20N60uKmTFwEElW9UcXEREREYmEQoS9XkJ1NYS9PkxMrA4n9i6ciy1smnxcYLLnk22kFiwhZecKLOHGFtuEnGmYU88kPGk+ZloO9SUmgVA05+FMAoslWoHuOlCJbrEYODtx42tbIqEgEZ8/Oimq04UjLw+b242lg0LlRNGln2L++9QrbN+1j1AoTOHeYi5YcDIbtxaycN5M5syYwINPvsIf73qEjLQUrr/8nK48dI8q89YTikSwW60E2smiJ1mtBMNhyhrqlUSPs101VRRWVzIgtWurh7PcHraUl1FQVsqMgYO7dN99iWmabKssw2m3JUSPq45kut0U19VTVFfLQP27FpEEsbummi0V5eR4PEfWsiwjE8sIOt3v/Egr15sYhkF+Sir7amtILktiYp7mlhARERGRvsk0TSJ+P6GGekL1dUQCAQxbEla3B6ML2g83Fw4E2L7kXfxLlzCoYker9d5+4yketoC9GTMYN8KGx2VplSTPz4GRA2Hb3mgFusViMHJgdHlnmaZJJOCPThRqs2FLTcbmScHqcnX5c+5OXZpE/9xli1otGzQgD4Aku50vfPaCrjxc3BRWVeHqREWz025nV3UV0/oP7IGopC2mafJp8X4w6fK2OhbDIN3lYm3xfsbn9etc/1lppcLnZW9NDVkJ3MqlidNmJxAKsqe2Skl0EUkIpQ31rCspwmWz4U46svOQYRhwBP3Oj7hyvRmbxUKuJ5mt5eV47EmMzDqCq24RkeOYaZqEwmCzkvAFJdJ77NpbzLMvv004EmHowH5cet7p8Q5JpM8zwyFCXi+hujrCDQ2YkQgWpwtbanqXj/++fXspe+M1yt9ZRtjbQPPeCZEkNzuyTsMyYz75YwaT6otg7oRdJQYprtZJcqthMG0M5GeBvzGaXM/PoVOTikZCISJ+P2YkhMXhICknD5vHg9WR+FXnbdH9tEeoLuCnpL6ONOfh23ekOpwU19dSHwiQ3EvfIL3dnppqdlRW0i+le9qE5Lg9bK0sZ2tFOZP79e+WYxzv9tXWUN8Y6PI7BbpLmtPJ5vJyTuw/uEsmqRUROVo1fh9ri/cRioTJ6YG5Gsz0DIysTCLV1VjS06O/H8HjXXY7qU4nG0pLcNrs+jJSRPo00zRZuxXeWROhrApyMmDuFAuTRymZLscmFArz7MvL+MrnLsbpVB5Ceo9wxGRPCfgC4HLAoLxov/DeLhwIEGpoIFRXG63EtlixuNxYuqjdcJNIKET1qg8pfX0JdRvXt1ofyh1GZNpCdqbPZsNeJyflR19bj8tCXmaE4QMM8jLaTpJbDaPTk4iapkmksZGI34dhsWB1e7ClpmBzuzF6eVvH3h19HBTX11EX8JObnH3YbVMdDnZU1VPSUKckehyYpsn60iIaIyE8R1id11lWi4WUJAdrivYxJjsXRxcPgsc70zTZUl6KO8neaz4sZLrc7K2toaiulsHpGfEOR0T6qIbGRj4tLqLW72dAalqPHNOoriJSUYkRDmFWVGJJz+hUT/Tm0pxOguEw60qKcNhs5HiSuylaEZHEtnYrPLwkTGMQUt2wbS/sLgkDVqaMPridKtXlSG3ftQ9HUhL/fvRFAo1BLjjrFEa1MSddMBQiFAoTbm+iN5EeFI6YvL/OZFNh9GerxWDsUJg9sXcm0s1wmLDPR6ihjlB9PWYohMXhxJaS1uVjeaCinLI3llK+7HWC1dUt1hn2JDJmn0zlyAUURkYSMaHeB1npJs4kE4j2OLdZLQzJMxiYe/SxmZEwYZ8fM9QYrTrPysLmScbidB435y9l/I7QvtoaTCPayuNwrBYLpmmwt6aGEZmHT7pL1yqur2NrRQX5yd1bnZfrSWZnVSXbKssZn9uvW491vCltqGd/XR2ZLne8Q+m0JJuNUCTCruoqJdFFJC78oSCfluynuL6WAaldfyHegmliVlZg+nyYNdWYoSCWnNwj6ol+qGyPh6K6WtYU7+PE/oPI6EXnABGRrmCaJu+sidAYhGEHKgGz0mBnUXT55FHReSNUqS5Ho6a2nr1FpfzkWzfgDzRyx78e55ffvanV++aVNz9g8dLl2O02brjmsjhFK71Fd1eJ7ymBTYWQk27ichp4/SabCqPVz0Pzu+ww3S7SGCDs8xGsqSHs92MYFiwuJxZP17YXNiMRatetpfT1JVR/vBrMll+GOfrlkzt/Idlz52FLTmGoaZJfBv5GsNtNSipg+z6Dytqj63HeXKQxQDjgx8DA6nJiy8nB6nJhsXf8nHvjnQdKoh+BYDjM9soK0hyHb+XSJNXpYGdVBXMGD1Xrhx62sbQEb7CRQWndW6Fnt1px2mx8Wryf0Vk5+jsfgb211TQ0Brr9b9TV0pxOtlSUcdLAwbr7QER6VGM4xLriInbXVDMwNQ2rpZsn6KyrIbI9OqFoNGluHlVP9EP1S05hX20ta4r2Mb3/IFI70SZPROR4EQpDWVW0Ar25VHd0eSgMG3Z0rlJd5FAet5MRQwbgcjpwOR0ke1zUNXhJTW45B9WiM2axYO4MwpEIBYVlcYpWEsHhkpk9USXuC0T37XJG9+d2GoQjJr5AYidVIZrQDvt9hOrqCTfUEwkGsdiTsCWnYHTxtXqoro6yt9+k7I3XCJQUt1xpsZBx4knkzl9IyviJLb44a9mOxWBgrkn/7CPvcd7EjESI+P1Ego1Y7HbsqenYUpKxOl2des699c4DZX+OQEl9HVV+LwNTOp/wS3e6KKqrpbShvsdutxYo9zawqbyE3B66Tbxfcgq7aqoprK5klCZL65SIGWFzeRnJ3dRqpztlutzsqq5iX10NwzOOrJWBiMjRCobDrC8pYkd1JQNSUrF1QwLdNE2oqsQsLQEg4veD1YKRnYNZVgppaRhp6dEEekbmUR/HMAz6p6ayr7aGNcX7mJY/UK3vRKTPsFmjleXb9kYr0JvUemHkQLBaTN5ZY3ZYqa5qdGnPsMH9eX7Je4TDEYLBIHX1XpLdrb/4ttts2G02wmG1c+nLOpPM7IkqcZcjejyv38R94BhWi4ErgS8PI8EgYa+XUF0NIa8PAKvThd3tOcwjj4xpmjRs3ULp60uoXLkCMxhssd6ekUnOGQvIOf1MkjrZavFIepw3FwkGCft8gInV5cKZlYnV5cZyhHmd3nrngZLoR2BvbQ3BcISkI6g8ddpsNIZC7K2pVhK9BxWUlVDr95Of3T0Tih4qyWbDahh8WryfEZlZWIxurgw8DhTX1VFSX9djX3R0JbvVSsSEwqpKJdFFpEeEImHWlxSzraKC/JSUI77rKZYc9/liCfA2EzBVlUS2bydSXBR9XEkJZn4/zLJSDJsdS798jCPsg94ei2HQPyWVfbW1fFK0j6n9B5CclMCflEREuohhGMydYmF3SZidRSap7mgCPckebdkSjkBZldlhpbpdn+SlHW6Xk9NPnsaf//ko4XCEz5wzD0t337kmvVZnkpk9USU+KA/GDoVNhUaLZP6gvC47RJcwTZOI33dgotA6Io0BDHsStuRkDEvXdiUI+31ULH+X0teX4NtV2Gp96oRJ5C44m/RpJ2J0Y0cE0zSJBPzR52q1Yk9LwZacitXpPOrj9tY7D3Tq7aRwJMLWijJSjqJqNtnhYFtlOScOGNT9t10LtX4/60uLyXZ7erRCIz8llcLqKnbXVDM0/eir8/qK3TXV+IJBXIfpk5WoMl0utlaUM2vQENz23ldNLyK9RzgSYUNpMVsry8hLSTm6NlIHkuOEQ5hWG5YRtDkpqOmLtm4xDlzvREpKMJ1JGP0HYAwffkzV522xWiwMSE1lb00NFMH0/gM1popInzB5FIA11vN85MCDPc+h40p1m7pHymHMnj6B2dMnxDsM6QU6k8zsiSpxq8Vg9kQYmAu+gJFwPbLNcIiQ10uoto6wtwEzEsHidGFP6/p50nx7d1O6dAnl771NxOdrsc7qSSb7tNPJPfMsnPn9u/zYzUVCISJ+H2YkjMXhJCk7F5vHg7UL7h7tjXcegJLonVZcX0dZQwN5yUdeNZvpclPaUE9pQz35Kd07yaVAQXkplT4vo3u4rYrLbicUibC+pJghaRm6xbIDwXCYgvJS0npxD9wMl4sdVRXsralhdLZa+IhI9whHImwsLWZzeTm5nmScRzkPQyw5np3T4aSghsuFabURqSiP/h4JQ0MD1NRgGEa3nNusFgsD09LYW1vDJ/ujFelKpIvI8c4wDKaMhsmjLITC0cR48zG2o0p1fc4Qka7SmWRmT1WJWy1GwrXyCPv90arz+loifj+G1Y7V7enyyu9IMEjVRx9Q+voS6gs2tVrvGTmK3PkLyZw1B0s33rkZrToPYDYGwGLB6vFgT0nB6nJhWI/sc0hHvfZ7y50Hh1ISvZP21FQTCIeOqmrWZbfjCwbZXV2lJHo3a2hs5NPi/aQ7XVjicHGZn5zCtopyiupq6a/2Pe3aX1dLWUM9g3rxa2S1WLBgYXtluZLoItItImaETWUlbCovJdeTfEx37jQlxw87KWhGJpYRYNm/HwCzMYiRngFWa7uJ965gtVgYmJrG3poaLBaDKf0G9No7lUREjoRhGG22ZjlcpXpPME2zzQS/iBw/OpPMTPQq8a5mhsOEfT6CdbWEGxqildhJDmyp6V0yFoZNk6Iy8DeCra4Uy5qlVLz9Bq24f5wAAQAASURBVKHa2hbbWRwOsuacSs6ChXiGDj/m43bEDIcJ+/2Y4SCWpCTsWdnY3G4sTudRPefD9drvre8pJdE7oalqNvUYbllIdTopKC9lWv+BR9zHVDpvW2U5pd56RmVmx+X4yQ4H++tr2VBarCR6B3ZVVxKKHNn8Aoko0+2isLqSWr+f1F5cVS+JwzRNSqugwWficRnkZuhDa18VMSMUlJVSUF5Kjsdz7AnlA8nx5j3R22IYBmRmYRk2AgBLVha4XBipae0n3ruI1WJhQFoae2qqMTCYmj/g6FrXiIgkgGNNQB+uUr07mabJ2q3EEvg5GQcT+LouETm+dDaZmYhV4l0t0hgg5PUSrKkl4vdhWKxYXE4stq4r7AibJh9vDLF/5RpSNy3Bs28NBmaLbZwDBpI7fyFZp5yGzdO1k5QeKtIYIOz3YxgGVrcbW0oOVrcbyzFeg3em135vfE/pk0kn7K2tobS+nsFpR58UzXK52Vtbw/66Woakd33PJAF/KMgnRftISXLEtfd8nieZgvJSJucP6JWTZna3QCjE1opy0o+DpHOaM9oXfW9tDeOOg+cj8WGaJlV+H3X19Xy0aguF+w3CEbBaYGh/E1eSnz27djFoyBCcbbzPRo4eTbLHQ4bLpUmNjxOmabKtopxNZSVkutxd0tqkKTne6dTHgSSJdfx4LEOHgtOJaZqY+/Z2PDHpMbJZLPRPSWV3TRU2i4XJ/fqr+EBEElZbifKuTkC3V6nendZuhYeXhGkMRicz3bYXdpeEAStTRvdsLCLS/XpjMrOrmJEIYZ+PUH094YY6IsFQtOo8JRWji/NKwZpqti9+neDbr9O/vqzlSouVjJNmkrvgbFLGjOvWLyzNyIGq82C06jwpIwNbcjIWp6vLjttbJw49HCXRO2FHZQUR0zymqlmHzUYoEmF7ZbmS6N1kW0UFxXW1DG9jorSelOZ0UVRXx8bSYnKHjYxrLIloX10NFd6G4+LfgcUwsFssbK8qZ1xugjfvkoS1r7aGj/fvZfP69Xzv8quO+PF/fPIxRk2YwJT8/prU+DhRWF3F+tIS0pwuPEcxoXlnmKYJVZUtKtPbvGhOTccyYCBmZUWnJibtCnarlfyUFLZXVWCzWJiYl6+J2UUkoXSUKO/tCWjTNHlnTYTGIAzLj54XstJgZ1F0+eRR6skuIr1fJBgk7PUS/H/2/jxKkjux7wM/cWRmRN5ZWVl39d2NxtW4BsAAgwEwQAOtIU1RlD0USY0o8lFe20+UZL/VW3meny3K3mdKWq5X1lrXeklTNsXTT1qKQ4560JjBDObCcHBNA+hGn9Vd95l3RmRmRPz2j6jqrvvMq6p+n/fw0JURmRGRlRUZ8f19f99vIY9rWyAUNMMgEG6sEVIIQenqJ8xcukj2h+8gXIflvnYv3s3CyfOc/I9e5dSDzb2X8+p1XMsCBJppEkh3o5kmahPuN/ZrcehWSBF9C4pVm2sLs6TDe5/CnA6HuTY/yzODR4g2oM1Wcp+a6/Dh1DhmIIDeATfaPdEoH89M82hvP+lwc6ff7DdGc1k8wYFxFnaFw9zN5cjbFgmjuVEHreSf/K9/wN3xaV594TP85PnnVyyr1x3+9R99jYVcga5knL/+pS8SaLVF6oBQcx1uLsyDAk8/9hj/9A/+lIlpHc9TUFXBQK/DwtQ1fu3v/Ff82v/8/+LYqbUDc0dPnaS86Fzui8YwGjjdUNJ6Jgp5Lk9NEA4EiDXzWiG7sG1RXAiBNzWJNzuN2tePqFabmo8OENR0+qJRrs3PEtI1znb3StFGIpEAnZHTvZFQLoTK2x+KfS1AOy7MZv3jWk487D/uuLTcGS+RSCSNQAiBZ/uuc6dUwqvVUAIB9EgURW2sPuFUysy//S1m3/w61tjoyv1AoX70ccRTr1MefIJcSSOcbs53g/A8vGoVr1ZFDegEEjH0aNwvCm2idrZfi0O3Qn79bcGt7AI5y+J0eu8Z211mmOvzs9zKLnCu75DOlWkSN+bnGSvkOsaFmTJMPp2b5eOZKV48drLdu9MxVB2HGwvzByLKZYl4yGCmPMdEsXCgRPS//qUvcuX6HbL54ppl3/vRZfoyXfyNX/gpvvrGd/nejy7z0nNPtGEv9z9jhTzT5RJD8TiaqvL0cw+TK6hYtoppeCTjHp9+5GfkHTt9mrOPPrLu64SE4G4+x91cljPdPa08BEkDyVoVfjw9iaIqpJqQP3739m0qpTIAYmYaMT2FkkwhclmUQhFl2YyakRs3/P9/+D7erRuI8TFEsQQffYyS6UHzBCzk7q0fjkY4cvx4Q/fX0AOkw2GuzM4SCYQ4cgBmMEkkkt3TKTndmzm133rfYz6/vwVoXfPf2xtj/nEtUaj45ab6wfDBSCSSQ4TnOLiVCk6xgFupIITwXeeJZMO3VRm5zcyli8x/9228qr1imR6Lk375FaaPnuduuRfPE6glhVND0J9p7H54Th3PthGui2qYhHp60CMR1GBrDL37tTh0Kzr8K7y9uJ7HxzNThAMB1AZcmKmKghkI8MnMJA/39MqpyQ2i5jq8PzlGSNM7xt2sKAqZSISPpqd4pLefLjO89ZMOAdOlIgtWheEmfFm1C1VR0BSFkdwCD2b2+bDqMlLJ+IbLrt0a5cLLzwJw7qFTXHzrnQ1F9Lrj4Dguruc1ZT/3M+Vajetzs0SDgXvfB4oCqYRHKrGz90tVFJKGwc2FefpjcWKhgzNQdViwnTofTU9RrtUY2kMHy0bcvX2bn33xCzt+3q995b/d9rp/+O1vNlxIjwZDVB2Hj2amiIZC8vtUIjnEdEpMymZO7fkcpJMKtyfEvhWgFUXhxcdV7k673J4UxMP+/gcD/qBFpzvpJRLJwcH1BKPTfr72TkVYIQRetYpTLuOUCnjVKooWQAtHUBqsG3m1Ggs/+B4zly5SvnFtzfLoAw/Sc/4CqWc+ixoIMCgE/bNg1xSMoC+ga5ucW10hmJwFu8am6y8ds1ezUVQNLRIhEIuhmeGGH/N2OIhZ+1JE34Q7+SzjhTyD8cbdzPZEYowV8ox2kGt6v9NpLvQluswwV+dm+Wh6UrrRF5ksFnA8j2CHDHY0ioRhcjeXw6rXMQMHP0qjVLYIm75IGzZDlCvWhut+7Rs/4KtvfJdAQOeXv/ylVu3ivmAkt0C+anNkm4NKxZLCxLR+z6G++ropaZjczWW5lZ3nsb7Bxu+wpGkIIbg2N8tEscBwEwR04J4D/df+6T/xY4GEgGIeYVehXkPkcgjXRUxPIeYXqKoqU7Nz9A0OYA4P401PoyYSKMkU6vAQxJP3Xnvkxg1+7W//l/e20WjS4Qhj+TwfT0/x7PARgpq8fJVIDhudlNO9uVNb4fOPK4zPin0tQD92GkC75/o/NXTf9S+RSCStwPUE378suDLCijiQ5x7dXEgXroNrWdSLRdxyGeG5qEEDPZZo+DnYnppk9s2vM/utb+CWSiuWqYZB+oWX6Dl/gfCRoyuWaYrC0DYnD7tC8N5VwY0xfOe66jvXnzx7X0gXrotrWQi3jmoYBNMZ33UeCu2b7539grwL2YRPZqYRQmDsoVB0NYau4wrBlZnpjhN99yOd6EJfQlEUeiNRLk9P8XBP36HPRhdCcDu3QOQAisyJkMGdXI6ZculAFKZuRSRsYFk2kMCyqkQ26Yz44iuf5bUXn8b1PK6OzG643mFjwapwe2GeLjO87Qub8ekAhHU0DY4P19Z1q3eHI4xkswzEEmQijS3FkTSP6VKRm9l5eiKRps9SO3bq1JpYIG98DDF6FwIB3LE7oAjo7uKcrkLEBF1Fefgh1BMnUPv6Ny4hbSL9sRijhTy3FuY5e4Bm/Ugkku3RSTndWzm1HzsNyj4XoBVF4fEz8Nhpte358xKJ5HAyOg1XRiCTFJiLxZRXRnzxeT13s7vkOi8W8Kq+E1s1TNQG6nngC9a5937EzKWLFC5/uGa5eeQoPecvkP7ci2gNiGecnF0ctI0LjJCCVRXcGFPoT0N/ouofq6KghcPo8R40M9zwY5bcR76zGzBVLHBzYZ7eaKzhr90bjXJ9fo4nSkX6mvD6h4nr83OM5nMcT3XmgETK9LPRL09P8vLxtYWAh4litcp8pUL8AMZMBDQNV3jMVQ6HiH7mxBEuX73F8GAvl6/e4syJ4Q3XDeg6AV3HdWWcyxKe8Lg5P4ftOmSi2xe6PRfSKZf5rIZlq+uK6OFgkKxlcX1+li4zLGPD9gGO53J9fg4F//fXDhTTRGg63u3bUKuBqsDYGETCqGfPAgrKsWOoDz7cNhFFU1W6TPNeZNFB6qCQSCRbs5ec7mYUkW7m1D5IArSiKB2f4S6RSA4mVtV3oJuGf/4MG35BpVW9fz4VnodrWTilAm65jOc4qIEQejS+7dLM7Ual1LILzH7zErPfuER9YX7FMkXX6frs82TOXyB6+oGGnvPtmu9AN0L+a5oBD2ybyrwDsRDBVAo9GkU1zIZudy9ROvtpmztFfiVuwMcz01TqtaZMq46HDCYLBa7OTksRfQ9UHYf3JsYwA4GOc6EvoSgKPdEoH834bvTD7AydtyqUalV6Ijtz5FfrOxdfa4637r+3Syiwc+ExpGuMF/I8NbCxoLyf+N//6GvcvDOO47iMjE3xF1/7HJ9cH+HCy8/y/NOP8K//8Gv843/+b0glYvzSz/5Eu3d3XzFVLDJayNOzyflACO4VjBZL/oWDqsF8VkPTwDQ2/lz3RKNMFAqMF/KyiHEfMFksMlUqMhDfuIeg6aS6UE+CVylDdsAX0WdmoKcHkUih1hZLkbILiEUXuhDC/9myoJBryW7GQwZ3rSwjuQUZWSSRHDJ2k9PdzCLS7Qjl7RCgmzFgIJFIJO3ADPkCasUWhBed6JrqF1R6tRquVaFeKOBaFqCgmSaB8M70lq2iUoQQFD++zMyli2R/9ENY1fMVyvSQOX+B7pe+QKCBMdDLMYKgqgpWuYqp2NhVUAImkcEM5lAEtQkz/XcbpbPftrkbpIi+DnOVMlfmpncs9u2ETDTKxzPTPNrbf+hjPnbLp3OzjBfzHO/wWJwuM8zV2Rl+PDXJqyf30TzOBpOzLTwhduyM/crvje9pu3//jyZ3/Jz/6Rd3LoRHAkGmSyXqrtuxgzo74Re/9MU1jw0P+hEKwUCA//Sv/sVW79KBoOY6XJ+fQ1MVQptMs8sVVG6PBnHdxRgXIBFzCQYEibhLIraxiB7QNIyAzrX5WXqiUQz94EUoHRQ84TGSWyCoa+htnDWgKAp0pdEeeRTP8xBWBY6fQD16FKpVvKkpGB/HuzuKduY0HD8J2QW8mzfBdfBGx1q2r11mmPFCgZOpbqKhUMu2K5FI2s9Oc7pbUUTaKU7tZg4YSCQSSTsY7oUHj8GVEd+BrimCs/0WPUoZa6yEV6+jBoLo0di2Xeer2SgqpdcsEbzyLWYvXcSenFj5JEUl+cSTZM5fIHHu8V1vezsIz6MnanMqZXNrOsBsIIFixjlz1uDYKQ21SeLyTqN09us2d0MHfOV3Hp/MTFGwbfq7M03bRsowuTo3yycz03z+2ImmbeegYtXrvDc5RiQQ3BeCZV8sxiezUzzS29eUiKD9wHyljN5BI4iNJhzwYzRKtSopM7z1EySHktFcjulykaEtnAqWreIuxreM3vAfyxc1ot0KCzmdRMxbN85liXQ4wmg+x0h2QeZHdzB522ahUiHVgLzEvSKEAEDp7UUBlN4+lK40YmIcZWICLBuRz+IpoCVTvgPddVC6M3Drdsv2MxoKMZfLMmeVpYgukRwydhKT0klFpK2gFQMGEolE0ko0VeG5R2Gwy6GSrxCs5emJWLhF0EIGgXXMqNuNZllidVRKeOEm2vcvMv0734N6bcW6gWSS7pfPk3nlPKEmaoUAnlPHs2yE56IZJk8/38dQMYwtQi2JOdlOlM5B2OZuaKiI/r0/v8y33/kABYWf+0vnOTrUt2LZn7zxXdIpf7ryr/zCT5FKdJ6YOF8p89H0FD2RaFMvrBRFoScS4aOZSR7p7ZOi2w65Nj/LZDHPya7udu/KtkgaJtOlEj+emuC1Uw+0e3fawnylvCtH7K///M6n7Ncc754D/R98qZ+g3nyHp6Hr2I5DqVaTf8+SdSnVqlxbmCUeCm05I8M0PLTF+BZ1cZxwO5noS6iKQtKQ+dGdTs62qbnOprMSWkZ2AXHrFrgOaDqKovjXQaaJcFzIZ1GSKdA0hGXdy1EXc7Pc+5C2iJCmMVsuyYJ2ieSQsh33dycVkTabwzZgIJFIDj5CCDzbximX6HGLeIEqihlEM6Ioy0yUy0XzQEAwPS+4Oa6sG82yHkYQNK+G+953CH/0BurUTVY3uMUeepie8xdIfubZphZ2CiHwqrZfFKrr6PEoeiSGZpoomsbxFl72bhalc5C2uRsa9gkoV2ze/O67fOVX/xq5QpHf/L2v8vf+5pdXrPPCM+f4yfPPN2qTTeHjmSlytsUDTR5ZAn9K8qdzs3w0PSXd6DvAqtd5b2KMWDDU1unvO6UvGuPK7AyP9vbTF2tj9m0b8IRHqVYluItZA7vJJ19OUFf3/BrbQVNVPDyser3p25LsT24vzFO0qxxJJrdcNxn3OD5cw7JVqPifqe1moi+RMAzu5nLcXJjjif4heQPdgeRtq2PKX5ec5aS7EXdGcEduowEimUI7cxpPAaGqKKqKyOfAMFBOnADbRm1xcbAZCJCzLFzP65j3TyKRdBZ7KSLdbxymAQOJRHKwEa6DU6ngFIu45TLC81ANEz2eXHMvszrPvGhBqSJ45IQgbKj3oln60zDUs3Zb1vgYzptf5+Rbb4FdXrFMC4dJf/5les5fwBwcauIRg+c4eLaN8BzUUIhgphc9EkFr44zLNVE6i/nkw02c4NyObe6Ghn2djoxOcPr4ELqu0d2VpFqtUXccAstGar7/7kd89Oktzp48wk+9/vkN83vqjoPjuLhea2/K5iplLrfAhb6EoihkIhEuT0/yUE+vzEbfJtfmZ5kqFTi1T1zoSyQMg+lSkR9PTxw6Eb3mutQ9j8CBFzsUaq7b7p2QdCDzlTK3swukI+Ftfb8oCqQSfmRLYcaP2RjsrTPY52AaHsn49r4fM5EId/I5BuPJQxsl1cnsdnCxGdxzlt8ZQczOogCe66GePAnHT6IlU3hTkzA9hcjnoVxBPXkSZXAIFnIt3degqlGp16m5LuaB/16RSCS7YTdFpO1ir2Wgh2nAQCKRHEzcahWnXMYpFnwntqqhmuFNnd+r88zvTHrcmQCrqhA2wAz5jnS7dv+86jkOuXf/nJlLFyl+fHnNa+pDJxj4C6/T/fzn0YzVnvTFfd1hZMx6CCHwajU820JRVbRwBD0eQw+HUbT2j3ouRekM9fjvZysiZNqxzd3QsN9OqWITNu9/yEzToFyxScb9dtzHHj7NZ596GIDf/oM/4533P+a5px5Z97W+9o0f8NU3vksgoPPLX/5So3ZxS348NUG+RS70Je670Sd56fiplm13v2LV67y/6ELfj+6z3miMq7OznOsbpO8QCVquJxBCoCr773e2ExQhcEVrB/8knY8nPG4tzFNzXXqDu3cUxKKCgV5nR88xAwEWrAo3F+boDkf25XnzIFN3XdROEXJSXagnwR257WeiHz0G83MIy0Lt8otHFcvCy+dRQiHE1BReJIyaan2kiqoqeEK03GwhkUj2FzstIm01jSoD3U8DBhKJRLKEcF1cy8IpF3FKJYTjoIYM9FhiW+et1Xnm0bACCEqWIJ3wS0JVVcEIQnV+jrlvvMHsN9+knsuueB0lECT93OfInL9A5OSpTbe92v2+nciYFcfsubiWjXBqvus8nUaPRFENo+PO1ZqqtLzQsx3b3CkNE9EjpoFl2fd+tqwqkfB9UX35v59+/EE+vnZ7QxH9i698ltdefBrX87g6MtuoXdyUmXKJT2am6Y3GWvrhVRSF3miUyzNTPNTTRyYSbdm29yM3FuaYLBU52ZVu967sioRhMFUq8snM1KES0SWSw8xMqcRYIU9PtD3n955IlIlCgcligaFEsi37IFkfXVVZ7PNsC0IIPwvdsmDRbaMYBmgqYm4WRQ+gLCs9VUwTxbbwbt0CBJgm9LX+SlcI7me2SyQSyQbspIh0PfbqEN+KRpaBNnPAoNnvg0QiOVx4tSquZVHP53FtG0VRUU0DNbKz/jQj6BsrrKrADCkYQUE6CWVbYXRaoCqCU7UPqfwfX+fH770Lq8xuRv8AmVdfp/vFl9E30WaWO89zJcH1UUF3AoyQsmVkzIpjrtooKGimgZ7JoJkmamDnnXHgmxRHp/0yzk51bB9UGiaiHz8ywB9ffBvHdckXyoRCgRVRLhXrvlP96o079GU2di4FdJ2AruO2MGPzw8lxirUqA/HWx2ykzDAzszP8eGqSV092iDWiA6m7Lj+emiAcCOyrLPTV9EQiXJmd4bG+gUMT4aOrKqqi4B1wl7ZQQDvgbnvJznA9j+sLc6DQtvLIgKYR0jWuz8/SE40S7IApghKfcCDIglVp3w5kF/Bu3EAU8ohCAYJB6OpCQUFJJFD7+mGZ01wkUwgzjFBBHRhGBAK+AN9i6p5LQFU7JgpHIpF0NtspIl1OoxziW22jkWWgex0w2Ggfm/0+SCSSw4HwPFzbwimWcMslvHodNRBEj8ZQdqnt9Gf8AcMbY0tFoiovPibImCXK3/8mtXfewJ2bIr/8SapK6jPPkHn1deIPP7rltlc7z3NFKFcFA93+OXC9yJjlx+zZNl69hhoIEIgn0WNRNMPc9TGDL6B//7LgyggrssOfe1QK6a2gcU70sMFLzz3Bb/yL30VB4a/89KuMjk/zyfURLrz8LBff+iFXro+gqSq9PV38zLOPNWrTe2ayWODK7ExbncF9sRgfz0zxcE/vocvL3i4juQXGC3mOJFPt3pU9kTRMPp2b5fr87KER0YOaRkDTqTk7i6LYdwgw2iSUSjqTiWKB6WKRvlh7Z56kwxHGCgUmigWOJVsfvyFZn1goRK2FhoG+qEJX+TbhOf/CXczM4M3egmoVb3oaFNCSjwJ1FDQUT4X56XvPF/k8IncNz5mDu1MomQyqrdFVnqMv2rqLdttxSIZMAlJEbyj/5H/9A+6OT/PqC5/hJ88/3+7dkUjaRiMd4hvRrDLQnQ4YbEYr3geJRHKw8ep13EoFp5jHqfjGC80wCTRAB9EUhSfPQn/ad2RrE9cQ37rI/DvfR9TrK9YNpLrIvPoamS+cJ7iDKMLVueuq5jF+AybnBYOZlZExK47ZsgCBZpoY6S40M4waDG64nZ0wOg1XRiCTFJiGQsUWXBlRGOqh46NQDgINVXteeOYcLzxzbsVjw4N+lerPfPFFfuaLLzZycw1BCMEHk+OU6zWGEomtn9AkEobJVKnEB1MTXGhxpMx+QAjBxzNTKIqy751niqKQMk0+mp7iXN8A4UBjTqadjKIoJEIGY9Vcu3eladRdF11TMXc5JUty8Ki7LrcW5gnp2oZinxCQK6hYlkrN8W+Yw6ZfHNrIrwFNVYkEAtyYn6MvGsPQ5ee0E4iHDL/AU4iWZKP/Z08F+amP/hv4aJ2FSx+Jq//W///NbbzgmP/fucXXbhV2vU5PV+v6aw4Lf/1LX+TK9Ttk88V274pE0jYa7RDfiE4vA23V+yCRSA4eQgg821osCi3i1aoogSB6NIqiNvjkVrUJXn6b3KWLWHdG1iyOP3KOzPkLJJ/8zKYlpRuxOne9v0thNCpYKCgrMtH7ugWubfvHqmkEEjH0aBzNMFAarF9ZVd+Bbhr+PoUNBdcTWFV5Tm4Fh94yOVbIc3VuhsEOcH8PxOJcnZ3h0d5+BuPtE/Q7kelyiTu5HL0HJDO+Oxzh5sI8d3JZHsz0tnt3WkImEuHGwly7d6NpWPU6ph4kHtp9caTkYDFRLDBdLjK0yfk8V1C5PRqkUFKZX9BIp1ziMY/jwzVSiUWH8mJm9uyCRm9e3bXA3hUOM5rLMZ7PczLdvYsjkjSauGEQCQYp12rEWnDu+Ffv1njqV3+DY6dOAv4gjrh2BffjTwABmoZ64iTqkaMQT6z5nInxUdzLH4HrgKajPfoIyuAwIzdu8q/+n3+bf9j0I/AjklRFJbEsq13SGFLJ9l8LSyTtplkO8dV0ehloq94HiURycBCug1Op4BSKuJUyQgjUkEEg0fgkgcroXWYvXWTuO9/CWxUtqEWidL/0BXpefR2jf2BP21mdu16tw1CPwpkjCsmoQkhz6IlaiJKHEjIIdvegRyJoTbyuN0N+bEvFFoQXneiaqmBKGaIlHOqvPiEEH0yNU3Vdoh0gfMVCISaLRT6YHGcgFm/7xVMnMZKdp1yrMtzG2QKNRFNVAprG1dkZznb3HIrfdTocwWtng16TKdVrJA2TaLD95xJJ+6m5DrcW5jADAbRNMu8sW8V1IWwIpl2FsOnhuv7jSyJ6seyfH2bnNG6PBlcK7DtAVRSioSC3cvMMJhLSjd4BhANBMpEod/PZlojoUyXBQuQ4mfSD9wpFveQZxPEoSiQKlTLK6QfRhobXfb5rxxB9CoQj/rrpB9G6h1mY9Jgqteb8XqhWiYVCdEkRvS3UHQfHcXG9g91xIjm8tNIh3swy0L3S6U55iUTSObi27bvOSwU820bRAmjhSMMd2F69TvbPf8DMpYuUrl5Zszxy8jQ951+n67nPoTbonnxt7rrC6SHBuaNV1HoNVBUtEiUQi6GZJkoLuqeGe+HBY3BlRFmRiT58OLyZbedQi+h38zmuz811hAt9iYF4jGvzs5wrDDCcSLZ7dzqCuutydW6WpGG0e1caSnc4wlghx7xVofsQZKN3hyOYeoBKvXYgI2yK1SrnevsPxYCIZGsmi0VmK+VNXegApuGhaVAoKeiaoGKpxGMepnFfoLJrvgifiK8V2HdKygxzN59jvFDgZFd6V68haSwDsTgj2QVcz9t0wKWhZBfwbt70HeWW5d/kVMrgulC18ebnwLZRTBNSXffOa2o4jJdI+s9LJFHD4c230wSKVZuHevpkQW6b+No3fsBX3/gugYDOL3/5S+3eHYmk4ezWIS6E2HGhZzPKQBtFpzvlJRJJexGui2tZ1IsF3HIZ4bmoQQM9nmz4+aE6O8PMm19n7q03cQqFFcvUUIiu5z9Pz/kLRI6faOh2YVXuuuUREha9yTqaEkJPd6OHw6iG0dJzoqYqPPcoDPWAVfUd6MO9slS0VRzaOxAhBD+emsARLpEGBfw3gmgwxESxyIdTEwzFE/ICBZgqFZmrlBiKHQwX+hLRYJDxQp7xQv5QiOiZSIR0OEzOsg6ciO54HqqiyBgmCbCUhb61Cx0gGfejWyxLpTa0MhN9CSPo/ztfUEn1s0Jg3ymqohALBrmVnWMwHpdu9A4gE4mSMk2yttWy7wKvUkHkcxCOIBwHolHIF1B0De/OHdS7dxCG6Qvpvb2off2Q6oJUF+pJEJZ1T2BvJbZTJ6jp9HeQ+eGw8cVXPstrLz6N63lcHZlt9+5IdsBuRN7Dyk4c4kIIPrzOvXUzqfvr7kRM78RolE52ykskkvbg1ao4lQr1fAHPtlBUDdU0UBt8TyE8l/z77zHz5tfJf/i+n0G4DGNwiJ7zF0i/8BJ6pLnXz0q9Rq9ho5gKWjiMHutBC4d3lbHeKDRVkSWibaIDv65bw918juvzc/RHO+9GbDAW59rcLI/1STc6wHghT931CLbxJNUMFEXBCOjcWpjnsb69ZXXtB1RF5Uw6w5u3rnPQjjZrVUgZJgMHbKBHsjumSkXmrQoDWwh990pFbRXT9OhfJ+tciJXXjKbh3XtstxpIygwzms8xVSpyLNlaEVSyloCmcSzZxbuTY6TNcEPELSHEvbiW9cRupVbFm50FZwL0AEoshpJMoHRncK9dRaCgJJJ4t26CZeGVK6gnQelKQ1eadslv85UKg/EEKUNGuTSD//2PvsbNO+M4jsvI2BR/85f+8pp1ArpOQNdxXRnnsl9ohMjb7P3rNHF/Jw7xD6/D71x0qdX9vPAbY3B32gU0Hj/Tun1uxvvYyU55iUTSOoTn4VoWTqmEWy7i1R3UYAg9Fkdp8CzKej7H7DffZPYbb1CbWzlYr2gayaefpef8BWIPPtzU85HwXFzbRtTrqMEgwVQKPRpFNUx5HjzkHCxVcpssudDrXme50JeIBIOMFwvSjY7/uxrJLRAJHEy3ZCJkMlUqUKpWOyKXv9kcTXURCYYO3PHOWxYvHDmGeUA/p5Lt43oeI9kFgpq2pQt9qVTUdUHTWDfrPFdQmZjxP1dTszpdszqWraIou8tFB9+NHgkEub0wz0AsLmMxOoC+WJzYQoh81SbZCIF4WVyL0HTUkysXi2AIJZNBiUQQ5TLEYlCxEHOzKGYYBYE3NQkoKH19UKv5gvze92zX1F0XUDiaSB3q66Jm8otf+mK7d0HSBDpF5F1Np4v7sLVDXAjBtz/wqNXheL+/z+kE3J70H3/sdPNjT1rxPnaqU14ikTQXr17HrVSoF/K4tgVCQTMMAuFoQ17fFYLJWbCqAnXsCuJHF8n98B2E66xYL5juJvPKa2S+8CqBZONLSpfj1eu4lgUINNMkkO5GM03UDtQNJe3hUH4djhfy3FiY29Il2E4GojGuz88yURw81BERxWqV+UqFeGjneejV+s4Epprjrfvv7RIK7HwUNh4KcStbZs4qHyhReSN6I1GOJVNcm5s9MMdbqlYJ6wFOdXW3e1ckHcBMucRMuURvdOuLy6VS0XTKZT6rrZt1btkqnuv/29mgeHQ3pEyT8UKB6VJJznjqACLBIMeSKS5PTzVERBeWBa6D0p1BzM36Py9jeba5kkii9Pl9DsKyUBb7R9TpKTCnENUqih7wHe1tZL5SpicSJRNpzI2bRHIY6ASRdyM6VdzfCY4Ls1l//5cTD/uPOy5NF58PwvsokUj2jusJRqfBqrKnjGwhBJ7tu86dUgmvVkMJBNAjURS1cUWhrhC892GZ2W9/m/jVrxPKja5cQVFInHucnvMXSDzxZEO3vRrheXjVKl6tihrQCSRi6NG4XxTaqr4iyb7hUIrol6cnqbmd6UJfIhoKMV4scHl68lCL6Fnbolyr0rOLnKuv/N74rrf79/9ocsfP+Z9+cXjHz9FUFSEEOcuC5I6fvu9QFIVHevu5Pj+LVa8fCOf2ZKnIQz29MqNXgic8RnILqKpCYBtt9KbhoalwdyKA4yhk0s6amBbT8Fi6ZtyoeHQ3aKpKSNcYyS4wEIu3rtBSsiHD8RS3s1kKVXtXA8fLUUwToemIuVnQ9EUBPHd/hXWyzRVFWeE0F11p6OtvW/75cuqui+N5nEh1yc+qRLIDOkHkXY9OFvd3gq75zu8bY/7+L1Go+PnhevM0H2Dr9/HcKQXXU2QMi0RywHE9wfcvC66M+P/WVIUHj8Fzj25fSPccB7dSwSkWcCsVhBC+67wJZpvKyG1u/8lFvD//NhmnumKZGonT88orZF55DaO3r+HbXo7n1PFsG+G6qIZJqKcHPRJBDR4Ms5+kORw6EX2qWOD6/Ny2XILtpj8a49rcLE/0D9IbjbV7d9pCsVrFFeJA3zSrikLOtrZe8YBwPJXiRFeam/PznEyn2707e6JUrRLUNB7vG5Q3JxLmKhUmi0W6w+GtV8YvFe1KOSzkQ+iaYD6rk4h5KxzmybjHYG8dgAdPVzl9vLameHS3dJlhpku+c14OArWfaCjE0WSKT2am9iyir1sAOn5/cFhRlE2zzVdnqotkCiW7gLdKdG8V81aFnkjs0F4LSSS7ZSuRVwixOHjb2muYThX3d4qiKLz4uMrdaZfbk4J42H9vgwE/UqXZ7+tG72MsDJ/eEfzPfyhYyCsdGZUjkUgax+g0XBmBTFJgGgoVW3BlRGGoh03LJ4UQeNUqTrmMUyrgVasoWgAtHEHZhiFoJ3i1Ggs/+B4zly5SvnENgOUKjzv4ADMnLvDgF59j+FjzzK5Lx+zVbBRVQ4tECMRiaGa44ccsOZjsg8uTxvLJ7DSleo2hROe7u+OGwWSxwJXZ6UN741ip13adwfrrPz+4o/VrjnfPgf4PvtRPUG+NcB/UtUMloquKypMDQ4xksxSrVWL7NNZFCMFYscCT/YMMHeLZIpL7jOazeMIjtM0SZEXxb7QTMXfDSBdFgVjUbxbt73EZ7HM2erkdE9A0FAXu5LL0RWPyxroDGI4nGcku7PncuJVIviWrMtWVdBfe/MLKjPWu1gyCOp5H3XE5Ll3oEsmO2UjktWyYXhD8D78FmZRoucDabgd3Izl3SvBzrsp3f+wxl1M4NXRfsG52aepG7+PELGRLoKqQiAgZ8XKImZ5d4O//xm/yf/3Pf57Tx4favTuSBrI8vmViTuC4voAOEDYUXE9gVdc/7wjXwbUs6sUibrmM8FzUoIEea2wfnysEo59MUnj769R+9E1EpbRiuacb1B58EZ56nUriCOWCghluzveQcF1cy0K4dVTDIJjO+K7zUEjeA0l2xKES0ResCldmZ3YVDdIuMpEoV2aneaxvgJS5PXfjQaLq1tntOW03GeVLBHV1T8/fCbqqUanXW7KtTuFoIsXDPb28OzHGme4M6j784pqrlEmGDJ4aGJJfvBKyVoXxfJ70Nl3oS5iGh6bBfFZD09hzTMtOSYcjTJYKzFXKMmu6A4gbBkeTKa7MTrd0gHG189yrVFZkqnu5HMqqjPVWnfUWFj+bfbHDaSaQSPbKY6cBtHvFk7EwVGxBodw+gbXdDu5GcL/QU/iFnkmFn35R4ckH/AHwVpSmbvQ+5kqQjMKJgf0blSNpDF+99D3OnNh55Kiks1kd31IoQ6Es6EkJomGViu1HupirLiXdJdd5sYBX9Z3YqmGibtMAtF2E67Lw3o+49ccX4daHa5abR47S/errjGY+z9isiecJ1II/CNmfaeiu4Far/rEqClo4jB7vQTPDmx7zTvPlG5VHL9kfHCoR/drcLHnb4oHuBv9lNpGUafLp3CzX5mZ5dvhou3en5XieQGnZrXp7UAHXa61w1m4UReGZoSPczeeYKhU7uuR3PWquy4JV4fyJMwdaePzen1/m2+98gILCz/2l8xwd6lux7E/e+C7plP+7+5Vf+ClSicMrco0X8lRdh97Azt6DZNzj+HANy1YxDY9EzCObV+/93IjYls0wdB3X8xjNZw/0Z3k/MZRIcju7QLlWa113yzrO8+WZ6moyiZhfWJWx3nxcz8NyHB7r60JvYqGURHKQURSFx8/AY6dV6o7gX/xbj2Kl/Vnkq8X95Q7u/cCaQs9xuDsj0BfjAFpV9rn6fTw+AK4rSK76St9vUTmSvXPr7gSJWGRTs1LdcXAc99Ddi+53Vse3lCoe71+Dm+MK8cj9TPThXr8007UsnFIBt1zGcxzUQAg9Gm94aWYtu8DsNy8x+41L1BfmVywTmk7x2HMc/4/+AsefeQBFUcgIQf8s2DUFI+gL6FoDvoOE5+LaNsKpowaCBLu6fNe5YW75HbfTfPlG5NFL9heH5iu0Uq/x0fQkKXPrP5xOQlEUkqbJRzNTnOsbOBBFjDtBVzU8RLt3o6l4QqAfwinqKTPMZ4eP8mfXrlCp1wgHOrfodzUjuSynujKc6xto9640jXLF5s3vvstXfvWvkSsU+c3f+yp/729+ecU6Lzxzjp88/3yb9rBzKFWr3M3ndjVbSFEglbifg57Nq9weDeK6oGlwfLjW6N1dQ9oMM14ocDxVOZQznjqNpGEyFE9yMzu3KxF9tat8O/nlwrJWOM9FMIR68uSKTHQ1mWp5yWjWskibYelCl0gagKIoKArM5RTi4ZXX1u0QWJeL+82MPGkGmxV6fut9FwWlZaWpq99HVfH4p3+kcHtC0J28v95+jMqR7I0/e/P7/NLP/gR/9Cff2HCdr33jB3z1je8SCOj88pe/1MK9k+wFq+oLtkvxLdGwykDG44EjCgPdvgN9MFXDK1pUCwVcywIUNNMkEG6saUYIQfGTj5h54yK5d3+IcN0Vy71ED+7jr+M8+jJTxThHB++fAzXFz21vFF6thrsYk6uHTfR0N1o4jLoDDW2n+fK7zaOX7F8OjYh+a2GB2UqZUy3K8GwkmXCEm9l5bmXnebinuQ3FnYYZCHDANXRqnkssuMcSuX3KQ5leRvNZPpic2DexLtOlIrFgiBeOHt929vV+ZGR0gtPHh9B1je6uJNVqjbrjEFh2zN9/9yM++vQWZ08e4ade/zzqOqPth8HhMlbIUapVOZpM7fm1LFvFdbmXkV6xVIol/30tlpTFArg9b2YF4WCQuUqZ8UJeiugdwpFkkrv5LFa9vvPB81Wu8m3llxsGWBW8a5+imAaKaaIsy1RXYG8Z67vAE4Jyvc6DPb0EtYN7rpVIWkknZpErirLvnNGbFaPOLACItpSmfnwLvv0B3BgVTMxBsSwYyEBxn0XlSPbOj6/c5NhQH9HI5jPHvvjKZ3ntxadxPY+rI7Mt2jvJXjFDvsu5YgvCi8JtQFM5MQDDyQpuqURtooRX953YejTWcNe5Uyox9/ZbzF66iD05sXKhohJ6+Elu97+O8eA5TEPDqgpU1XecNxLheYtFoVXUgE4gkUSPRdEMc1fHvHqAYqt8+Z2uL9n/7LNLlt3heh4fTU9g6vq+LKXSVJWQpvPRzBQPZnpQlf13DLslGvSDvIQQB/air+q4pCOHU7jSVJUXjpxgplzmbi7LsRa5G3dLpV6jUK3y+skz9O+zCJqdUqrYhM37gzumaVCu2CTjvnvhsYdP89mnHgbgt//gz3jn/Y957qlH1rzOQXe4VOo1RnJZEkZjBsJWZ6TXHRif9kXU8ekApwori0cbRcoMcyeX5WgyRSx0OAf1OokuM0x/LM5YIcdgYGfFxWtc5dvMLxcoCASsClHbjbO9EeRtm0QoRH/0YJ9rJZK9spPyyoOQRb6aZpd3rsdmgxEnB/2z6I1x0dKBiuXxMskolG2/XDRswANHlX0VlSPZO6MT03x68y43R8YZn5planae/8uXf5p0auU1RUDXCeg6rntwzS4HkeFeePAYXBnxBVtNuJztq5Bxi9jjFgKBFjIIhBvfBVi6eYPZSxdZ+P538GorZ8zqiSSZL5wn88p59HQ31at+74aX9wX0Rmaee04dz7L9UlTDJNTbix4OowY37xTaKr98vQGK9fLld7u+ZP9zKET0sUKesWJ+3+UuL6cnEmUsn2Msn+dIA9yO+4WUaWIGglTq9dZlw7YQT/g2+7S5f8puG03cMHjp2En+/dWPmauU6W7Cl30jcD2Pu7kcTw4MHegYlyUipoFl2fd+tqwqkfB9cXX5v59+/EE+vnZ7XRH9oDtcJgp5ClWbI4lkQ14vEfPoSjrkCxqJuEtAA29xVqTn+k71ZojosVCIhUqFsUKOBzOHa8ZTJ6IoCkeSKcYKeequS0DbvuqimOaKPPNt5ZfbNoppog4f8Z9n3//b35WzvQEUqjaP9fUfuhg7iWS73C+23Fl55X7PIl9it8ffCDYbjHjpCf98fXemdQMV68XLdCfh1oTgxCD86n+irDtbsBn7sd+ieQ4qP/nq8/zkq37k4v/2+3/KC88+tkZAl+xfNFXhs48IBuI2lVyJoFOiN1qFWhAtHEHRNFwhGJsR2DX2nDfuVqssfP87zFy6SOXWzTXLYw89TM/5CyQ/8wyqfv+67cmz0J9uXOa5EAKvavtFobqOHo+iR2JopomywbXyctE8GBCMzwo+vaNsmF++ZoBiWb78eux0fcn+51CI6NfmZnBcgaHv3xsxMxCg5rpcm585VCJ6OhwmHQ6TtSoHUkQvVm3iIYOe6OEu9Due6uK54aN849Z1woFAx+WjCyG4nVvgaDLFC0eO78sZLTvl+JEB/vji2ziuS75QJhQKrIhyqVj3nepXb9yhL7P+LIKD7HCxnTq3sgvEgqGG3SzmiyoLOR3XhYWcTlfSYalPUdV8p3qzSJoGI7ksRxJdB/J8u9/IhCNkIhHmrQp90R1kgqe6UE+yo/zyzYT33Trb90KxWiUSCDIQkzf8EslGrCm23GZ55X7OIl/Obo+/UWw9GNG6gYqN4mUSEVjIK7ieQjMvXds5oCHZml/+uZ9s9y50NFs5kzsN4To4lQpOsUiPU0aEPVTDRA0m7/29uULw3pIL3LvvAn/y7M5EbGtinNlLF5n79lu4lfKKZZoZJv3iy/Scv4A5OLTu8xuVee45Dp5tIzwHNRQimOlFj0TQQlu7zpeXfhbKUCgLnnxAEDHVdfPLNVXhuUdhqAesqrLlZ2Kn60v2PwdeRM/bFtfn58gcgLiMTDjC9fl5nhm0iTcoOqDTURWVB9IZ3rh57UBGusxXKjyQ6SFpbMMpeMB5amCI+UqZ9yfHOZXu7qiy1YligVjI4JUTp4lu8WV9UIiEDV567gl+41/8LgoKf+WnX2V0fJpPro9w4eVnufjWD7lyfQRNVent6eJnnn2s3bvcciaKBXKWxXAy2bDXXJ2JHtRhsLcO+P9PxpsnosdDBnfzOcYLOc50N7DlR7IrNFXlWLKLqWIR1/O2PXinKMrO88s3EN6FEFC1Efk8olRCiSe252zfI1nL4oHujIwWkkg2YLNiy+2WV+7HLPIlGnH8e2WrwYhWDlS0O+u+3QMaEsluWS2yrudM7hTcahWnXMYpFnwntqqhmmHUdTq6JmcXzwdxgRFSsKqCG2MK/Wm2FLU9xyH3ox8yfekipU8+WrM8fOwEPa9doOu5FyAUYnIW7DHREKf5coQQeLUanm2hqCpaOIIej6GHwyjb7MpZXfp5c8zjxihUbIWIuXF+uaYqOyoF3en6kv3NPr102j4juSz5qsXpaIPCl9pI0jS5Pj/HSC7Lub7D81d6oitNfNwgZ9ukWnDz3ipqjoMrBA92y7k+4AtGnz92grxtcWthgdPpdEcMmmQti5rrcv4Q5KCv5oVnzvHCM+dWPDY86H9ef+aLL/IzX3yxHbvVEVQdh1sL80RCwYYW4q7ORDdNj1jUj32KRUXDS0WXoygK8ZDBrewCQ4lkx80IOYz0RWN0h8NkLYvuSIOiroRALMyvzThfT3jPLuDNzYOmIRwXtTu9LWf7XqjUa4Q0jaEGRSRJJAeRzYotm11e2Ql00vFvNhjRqoGKdmbdd8KAhkSyW1aLrOs5k9uJcF1cy8IpF3FKJYTjoIYM9Fhi078ru+Y70I2Qv44ZUvA8gV3b+DnV+TnmvvEGs9+8RD2XW7HM0wKoDz/PA//JXyB28jSKojTM7b7mmD0X17IRTs13nafT6JEoqmHs+FyyuvQzHlEAQaEiyKRkfrlkdxzgyyvwhMfVuRnMQKChIke7UBUFU9f5dG6aR3v7Ds0FSToc4cHuXt4Zu0NyFyfPTmWiWOBoMsXRQxTPsxXRYIhXTp7hj698xFghz3CbRRSrXmemXOKlYyc4K525kmVMFAssWBWG4o2Nm0jGPY4P17BsFdPwSMY9phu6hS22bxjcyeUYz+c53b3/B5/3OwFN41gqzZ+P3yXlmY2Jkirm8W5a28o4F5aF4rkox477US+h5n8Hz1cqHE910WXu/xmEEkmzaLfzuN0c9uNfj3Zl3XfSgIZEslNWi6wbOZNbjVer4loW9Xwe17ZRFBXVNFAj24snNoKgqr4D3Vx0oquqn0u+HOF5FC5/yMyli+TeexfEyhmvbrIf74nXKJ15mflajP64QnzxOnAvbvcNj7lqo6CgmQZ6JoNmmqh76MZZXfpphgQ9XVCqKNyekPnlkt1xoL/SZsplJgp5Mo1yb3UA6XCE8WKBmXKJ3p1kpO5zHusf4NO5GeYrlca58dqIVa/jCsFTA0M7Kow7DPREonzh+Cm++uknzFfKpNtUNOp6HndyWR7rG+DpwSMHZvBGsneqjsPNhTkigWBDRE0hIFdQVwjnzSgP3Q6KopAwDG5m5xlMJKQbvQMYiMVJmSY529rT+dC2LACufngZLxmHRBIxOQEjI2gDA7CUP17MI+wqNQUmxifoE4LQrdugaqiuBwu5Na89cuPGrvdrxT46dXRV40hCDi5LJJvRTudxJ3DYj3892pV1Lwc0JPuZ1SJrO53JwvNwbQunWKJWLDI5XacmghjRKAO96o5Mof0Z/+/vxpiywiXev+iPcYpFZr/1DWbf/DrV6amVT1ZVAg89ze3+10g//igoKgbgTa90su/G7b7eMXu2jVevoQYCBOJJ9FgUzTBRGnCPtbb0U+X1ZwSDPQq1uswvl+yOhoro3/vzy3z7nQ9QUPi5v3Seo0N995bV6w7/+o++xkKuQFcyzl//0hcJNHlYejSXxarXD5QIEAkGGc3nGCvkDpWI3h2O8OTAIG/eukHSNDsqL3unCCG4m8/xSE8fJzdw/x12TqW7ef7IMb5x6zpmG4pGhRDcyvpFoi8dOykHOiQraLQLPVdQuT0axHVB0+D4cK1tIjrcd6OP5WU2eicQ0nVOdnXzo7FRksbu3eh3bt4E4B/++j9q5O6tIBzd26DnbLnM0USK7jYNnkok+4l2OY87hYNw/EKIhgverc66lwMakv3MWpG19c5kr17HrVRwinmcioUrBJfvGtyYCt8XwLNiRzEpmqLw5FnoT4Nd8x3ofd0C6/o1Zi5dZOGd7yHq9RXPCaS6yLzyGpkvvMqM08WVDwVWTWCGWNfJvl23+4bHbFmAQDNNjHQXmhlGDTb2nn/90k9FiuaSPdGwr9hyxebN777LV371r5ErFPnN3/sqf+9vfvne8u/96DJ9mS7+xi/8FF9947t870eXeem5Jxq1+TUIIbixMEe0wX+InUAkGOT6/BxP9g8dqguTx/oGuZVd4G4ux4mu5mayNpOZcomkYfLs8FFUZf8OBjSbpwaGWLAqvDcx1vKi0cligbhh8OohKhKVbA/bqfsu9GBjXOiwtkzUstV1RfRiSWFiWr/nVm/W6X+FGz2eJHIAv0f3G4OxBLfD83vKRn/pwusAHD1xAsOp4U5MQD4P8Thi5DbE4lCr+hEugSAj4xP83//o3/Jrv/4/cOyxVddrhRze6Bh4ru9QHx4iPDDIkePHd32MlVqNgKpxvKsz+jAkkk6nXc7jTmE/H78Qgg+vc28AIJO6PwCwX45hOQdhQEOyP3E9wei0H8uyG1fx+iJr853JQgg82/KLQktFvGoVJRBEj0aZnlO5MSX2HJOiKX62u2tbzH/3ba5euoh1Z2TNevFHzpE5f4Hkk5+5V1LaL8SmTnbY2u2+7jFXbbxaFUXTCCRi6NE4mmGgNNGwJks/JY2mYSL6yOgEp48Poesa3V1JqtUadcchsPiHeO3WKBdefhaAcw+d4uJb72wootcdB8dxcb3dO/HmrQqz5TLJA1REuUTKMJkplViwKm2LumgHZiDAC0eO8++ufMSCVdmXealWvU7etrlw6gF6ItF2705Ho6kqLxw9TtaqMJJb4FRXd0u2W7BtbMflJ06coe+QFYlKtmY8n2ehUmE4mWzYa64pEzXuf/cJ4YvnANfvhBCmjq43362eNAzu5nOM5rOczcigwHYT0nVOpTP8cOwuddfd1eyYZFcXP/3zP3fvZ7Ewj3fzJt7sNAwOog4N4d64Dq4DnosY8O84hkMhHhjsv19ACnjjYwhNRenOIOZmUYaPoA4O7ekY5yplTnVlpAtdItkhrXYedxr78fg/vA6/c9GlVvezw2+Mwd1pF9B4/Ey7927n7OcBDcn+xfUE378suDLCChf5c4/uXEhvlcgqXAenUsEpFHErZYQQqCGDwLIYO7sm9hyTAlAZvcvspYvMfedbeIuRfktokSjdL32Bnldfx+gfWPPc9Zzs/ZmVTvjtrAPgOQ6ebSE8FzVkEOzuQY9E0A6RUW2vgz2SzqJhlxylik3YNO79bJoG5YpNMu4LhaWydW952AxRrljrvg7A177xA776xncJBHR++ctf2tX+zJZLlOs1BuMHTwSLBINMlArMltuXF90ujiRTPDM0xFu3bxINBAnq++eq2ROCO7ks5/oGONe39stKspZoMMTLx0/x7z65zHSp2PQIo7rrMl4s8Lkjx3gwI2MsJCux6nVuZueJhUINLater0x0KSd9ek7n+h3/InM+q/JwUFCrKxu61RvFkhv9dm6BoUSSaPDwXOh2KoPxOH2xGDPlEoONiBJKdfmlopEwmFN4pRKEQihnzyLujkIu56+Xz+PdvLmigFQxTYSm+2Wjmo6yR8NC3raIBkMy4kwikRx4hBB8+wOPWh2O9/vXEukE3J70H3/s9P6NQNmPAxqS/cvoNFwZgUzSLwat2IIrI777utOcx65tL7rOC3i2jaIF0MKRdR3Ye41Jyf75D5i5dJHS1StrlkdOnqbn/Ot0Pfc51C2u7Zec7LtZx3edVxG1KqgqWjhMIB5HM00U7XCdJBo12CPpHBr2CY6YBpZl3/vZsqpEwvdF9Uh4aXlicdnGN1xffOWzvPbi07iex9WR2V3tz0ypiKoo+/YiZDMURQGhMFMucvYQCn1PDQwzWSzy6dwsZ9Ld++Z3PJbP0ReN8cLR4w2LgTgM9MfifO7ocf7DtSvEQwbmHhq6t2Ikl+VUuptnh47um8+VpHWMF3JkbYsjieSeX+vu7dtUSuU1jxeAaXz3+fh0gFxe5c4tv7RxYeo67/+5RzLhQaVOYUZs+PrhaGRP0RoAiZDvRr+by/JQT9/WT5A0FV3VOJPOMF+pUKnVCO8xZkdRFOhKo6a6oK8fpiYR4TAiFPJvcu6M+uv1D4Dr+DEvS09eFOCFZfkCemr3EWuu55GzbJ7oHyRuGFs/QSKRSPYxjguzWd+Bvpx42H/ccZFCtESyDayqL0qahn91Ejb8XHOr2hn3cMJ1cS2LerGAWy77TuyggR5PbnqfudOYFIDq7Awzb36dubfexCkUVixTQyHSz3+ezGsXiBw70ajDWxfhuri2jXDrqMEggXQaPRxBNYxDe2+9nwZ7JNujYV/Rx48M8McX38ZxXfKFMqFQ4F6UC8CZE0e4fPUWw4O9XL56izMnhjd8rYCuE9B1XHf3LrvxYoHILsS2an1n26w53rr/3gmhwM4F1UgwwESxsPWKB5CgpvPisZPMVypMFAuNceQ1mZxt4Ql48dhJksbBixhqNo/09DGaz3F5aoIz3ZmmfAnPlctEgkE+f+R4U4V6yf6kUq9xY2GeRANc6Hdv3+ZnX/zCjp/3td/6z3a0/h9++5t7EtIVRSFpmIzksgwnksRCUuBsN73RGCdSXVydm2E4EGjIjIjVYvqSMK4FFn/f+Rz0ZFa4zZee04gz8XS5RF8sxtFUauuVJRLJoaAZhZudgq75Geg3xnwH+hKFii+c6bLLXnJAaXSkhRnyn1+xBeFFcVJT/VzzduLVqjiVCvV8Ac+2UFQN1TRQ9e3dX243JkV4LvkPP2Dm0kXyH7znZ0Auwxgcouf8BdKffwm9yekFXq2Ka9soioIWDqPHMmjh8L2M9cNMpw/2SHZO45zoYYOXnnuC3/gXv4uCwl/56VcZHZ/mk+sjXHj5WZ5/+hH+9R9+jX/8z/8NqUSMX/rZn2jUptdg1evkq/auhLCv/N74rrf79/9oclfP+59+ceMBhY0wAwFytkXVcQgdwpNTdzjCC0eP86effkKxWiXWwZladddlsljkxaMnOJVuTa73QUNTVT535BgTxQLTpWLDs8rrrsucVeb8iTMM7INBGUnrGcvnKFTthrjQlxzov/ZP/wnHTp1ad50lJ3qppHJ33MHK32H42CCPPayQiIt1113seMQtX+H/8V//nXWd7jslYRjczWW5k8vySK+0S3QCp9LdzJRLzJVL9DQg4koIAdmFe+K5MjCIoigoi+dCpbcP9eTJPbnNN6JUq6Ki8EB3D8FDNr1XIpGs5aAVbq6Hoii8+LjK3WmX25OCeNgX0IMB/1gbdZwHeSBCsv9oRqTFcC88eAyujCgrXnO4DVU+wvNwLQunVMItF/HqDmowhB6Lo+xiBvpmUSr1fJ7Zt95k9s2vU5tbmdqgaDqpp58l89oFYmcfaurfvvAWXed133UeTKXQo1FUw5TnnGV06mCPZPc09I7lhWfO8cIz51Y8Njzon8WCgQD/6V/9i43c3IZU6jVqjkssvLepzp1MSNOp1CzK9dqhFNEBznb3MFEo8M7YHU6nuzs2IuV2LsuZdDdPD+18sERyn5QZ5tmhI3zt2hW6wi7BBrZ4jxVynEh28ZjMqpesQ7lW42Z2nkSDpyIeO3WKs48+su4yIeBUQeWT6yG8cJCuxGeo2CqJPpuzJ2sr1p2Y1iGsk065zGc17JzbsH0ESJomd3JZjiRSMm6jAwgHgjyU6eUHY3f3HOsihEDcvol3/TpoGko8gXrqlJ99vvhZV3p6UZqQVe56HnOVCo/29DW970IikewPDlrh5kY8dhpAuzdYcGro/mDBXjkMAxGS/UczIi00VeG5R2GoB6yq0pbCRq9ex61UqBfyuLYFQkEzDALhaEO3I4SgdPUTZi5dJPvDdxCus2J5sLubzCuvk/nCqwQaYPjZDK9ex7UsQKCZJoF0N5ppou4xZvCg0kmDPZLGcCDV15rr4nguAXXnItuv//zgzrblePcc6P/gS/0E9dYIuQFVxfE8am5jxZL9hKIoPDt8hIlintF8jmNNcMntldlyiVgwxAtHT2BscwqXZGMeyvRybW6GO7kcJ7oa8/su12qAwjPDRw/tgJRkc8YKWYrVakNc6NtFUSCV8EgnXW7dhWJJpWypzC9oZLtVknFvSePENDw0DeazGpoGRrCxhaPxkMEdK8vdfJZHDOlG7wT6Y3HOpLv5ZHaaIV3f/SBydgH32nWYnUZJphBwP/t8cVqwmJlGLPRDqquhAsxkschALC5naEkkEuBgF26uRlEUHj8Dj51WG+4WPywDEZL9xUaRFmULRiZ3H/GiqUrLc6WFEHi27zp3SiW8Wg0lEECPRFG2qT+5QjA5C3aNDeNaANxKhbnvfIvZSxexxkZXLlQUEucep+f8BRJPPLntbe8G4Xl41SperYoa0AkkYujRxaLQDjUydgqdMNgjaSwHUjFyPA/XE7vKCt1NPvkSQV3d0/N3gqqquMLDOcQiOkA0GOL5I8f54ysfUajaxDsos7fuuixYFc6fOEN/g+NHDisBTeOpwWHu5LNU6jXCgb2PeI8XCjzS28fxZOcNwkjaT7lW41Z2oeEu9O0gBFTrULFU7BrgQb6kcns0yPHhGqmEL5Yn4x7Hh2tYtoppeEzf2bhwdLd0mWHpRu8gFEXhdDpD1rKYLBYZSuwuhkpYFoquQSKFyGVRgsH72efFvL/O9BTezZuoJ/Ed6g1goVLBDAR4pKdPxrhIJG2i0+I+DmPhpqIoDT2mwzQQIdlfrBdpoShw9Y5godC4iJdm4jkObqWCUyzgVioIIXzX+Q5NNq4QvHdVcGOMFcWhT569L6RXRm4zc+ki8999G69qr3i+HovT/fIr9Lz6OqGe5tqZPaeOZ9sI10U1TEI9PeiRCGpQZpHshHYM9kiaxwG7FPFRFQVFgcbLCJ2DEP4XTyNKxfY7J1JdnOvr44djo8S6Qx1zcTiaz3E8leacjAhpKMeSKU51dXN9bo6T6b0JOqVqlZCu8UT/YMd8biSdxXgh13IX+hK5gsrMXAAj5KEHFDwX0ikP1wXLVu+J6Euu9aWfp5vwUY6FQixYFUYLOR42+hq/AcmOCek6D/f2URq9w3ylTHoXpVGKacJSD0QwiHr69L3sc2FX/XWSKXCd+w71PWLV61TqdZ4aHCJlhrd+gkQiaSidGvex08LNThsE6AQO40CEZH+wXqRFd0Iwn4eeVOMiXrZTXrqTglMhBF61ilMu45QKeNUqihZAC0dQdhktOjm7eJ6LC4yQglUV3BhT6IvXMG99n5k3LlK+cW3N86JnztJz/gKpZ59D3UX333ZZOmavZqOoGlokQiAWQzPDuz5mieQgcSC/RnVVRV+MO9EP6PQSV3hoioouT2QoisJnBo9wfWGe2XKZnmhjM8h2Q7lWQ1EUnhk6IiNCGoyqqJzrG+D6/By242Ds4f2dKhd5KNPHgJwpIFkHq17ndnaBeKg9g3OWrRLQBZm0y9ikjuPCfFalr9vFNO5HtgjhC+5LTvRmjSAnDZO7uSxHEymiHVzmfJjoMsM81NPHuxOju8tHT3WhnTp1r1R0RWTL4u/Yu3sHzCA0YAaC43nMlEs80J3haCK159eTSCQ7p5lxH3sRtrdbuNnuQYBOFu93OhAhkbSK9SItyha8/eHaiBeruru/q+2Ul2634FS4Dq5lUS8WcctlhOeiBg30WGLPf/d2zXegG6HF465MwQ/fYPoP3kJUiivWVQ2D9Asv0XP+dcJHju1pu1shXBfXshBuHdUwCKYzvuu8TfdBEkmnciDVvXAgQFDTqbl7E9g6mZrjEtJ0wjJnG/DFnSf7Bnnz1nW6I5G2O/THiwUezvTKiJAmcTSR4kgyxWQhz5Hk7oQY23FQUHi4p09eGEjWZbJUIFe12+JCBz/rPBb1qFj+dO90yiEYgK6UQzJ+X0TPFfyIF9cFTQO33JzPczwU4m4+x3gxxwMh2YbTKRxJJClWbT6enWZQ0wjsYHBdURToSm/qMBcIBMqeXehCCCaLBQZjCc5298rzrkTSBpoV99EoYXs7hZvtyvxut3i/HbY7ECGRtIPVkRYjk6CprIh40VRfYN8N2ykv3Wodd8l1Xlx0nasqqmGiNlBTMoKg4uF88iPCH72BNvIhBis9MObwEXrOXyD9wktoSzF7TcKtVvGqNoqioIXD6PEeNDPc0GO+t60dzAKQSDqVA6kwR4JBIoEAVr3eURnZjaTi1AkHg4SDUkRf4qGeXj6YGme+UiYTaZ8bvVKvEVQ1HusbkBerTUJTVR7O9HFrYR5P7K7/YLZcYiAWb5tAKulsaq7D7YV5osFg2wblknGPE0dq6FqAcFhwZKDOfFYjGIDlu2TZKq4L6ZTLfFbDrjVnBpaiKMRDBiPZLEcSXZhNnEoq2T6KonCmO0OpVmM0n2M4sXeXFABVP85FO3LMd6nb9hZP2JzZcol4yOCR3j45Q0siaRPNivtolLC9VeFmOzO/90th53YGIiSSTmC9iJcHj/mP74aNykuXO9vXXcd1Keeq2KrvOndqdaYLIapEMQ2V/p2n5W1ILZuF77zBqa9fgsL8imWKrpN69nl6zl8geuaBpuoIwnNxbRvh1FEDQYJdXb7r3DCbtt3tzgKQSDqdA3kXoyoqA/EEH06N00us3bvTFErVKmfSGVTlYMbV7IbY4s35t27faquIPl0qcTzVteuiN8n2OJZMkTJNslZlx1nAQgjKtRovHTuJdkAjnyR7Y6ZUYsGyGIy3L+pnKesc6riewnxWQ9NYEeUC/s+axr3lRtBb/wUbQMIwGM3lmC4XOSZn2nQMQU3nkZ4+SrUq06UifQ2IqFIM3womclnIdN8vHN0FedvGE/Bobz8Jo7mOKolEsjHL4z66EuB5oKp7i/vYjbC9VSTKRoWb7cr83k+FnVsNREgkncJ6ES97cSavV1662tm+Yh29jlUoYxSK6FmbuqaCYfDjO+FlpZ9iTennThFCUPzkI2beuEju3R8iXHfl+5Duofe11+l5+RUC8ebqB16thmtbAOhhEz3djRYONyxjfTOn+XZmCkgk+4EDKaIDDMYTvDsxuljAebAuHDwhcIVgICZF2tWc6srwo/FxStVqWzJ7Xc+j7ro8mOmVAxxNJhoKcaorzY8mxncsoherVeIhY9dRMJKDjSc87uSyBDW1bYMsy3POjZDHsaEadtXPPF8e5QK+Y/34cO1eJvr0nebVaquKghkMcCebZTielINQHUQ0FOLR3n7+fOwuWcsitYnoLYSA7ML6WeiLy8XSxygSQTlx4l7h6E6xHYe8bfN43wD9sn9CImkriqLw+ccVLt8QvPWeuCei96bg848r275nWi6C70TY3mskSrsyv/djYedGAxESSavYTnTH6oiXvbAdZ/tQxuNsn8X16yWyVgFdOJw8EmTwaAxd0xibEdwYE2tKP/vTvti/E5xSibm332L20kXsyYmVCxWV5BNPkjl/gcS5x1GaeD0tPG+xKLSKGggQSCTRY1E0w2zodrdymm9npoBEsh84sF+tA7E4sZBBoVol0YAyrE6iWLWJhwwG2uiQ7FR6IlGGEwlGcgttEdF94SLMUSnOtoTjqTTvTozvuER43qpwsitNlxneemXJoWPBspitlNv6+Vidc358uMZAr7PuukuOdd+1DtNNvhZNGSYz5RKzlTJ90YM522u/0huN8WBPLx9MjmPo+saRO9kFvJs3wXUQmo56EuhKr1guxsb8f1t+TuZuDAmu5zFVKnK6q5sTy19fIpG0D7GYvSvwuw6W/bzlU9cRwT//uEJ3UnBzfGthe6+RKLvN/N5rGags7JRIdkY7ojs2c7Z7joNbqeAU85xLVug5IagpBmYkRn/mvst8demnGVLwPIFd2/4+l27eYPbSRRa+/x28Wm3FMj2RJPOFV8m88hqh7kzjDn4dPKeOZ9l+KaphEurtRQ+HUYPN0Ui2cppvZ6aARLIfOLAiesoMcyzZxZWZ6QMnos+WKzza20dSToleg6IonOrKcHV2pi2zEBbsCk8PHCEcCLZ0u4eVgViClGGSsyy6I9tzowshqLkup7qae+Ei2b9MFQs4rtvW3ObVOeeWrd4TydtNQNMQAiYLBSmidyDHU12UalU+nZtlKJ5Yd7aAsCxwHZTuDGJu1nekr17uLU439tw1y7fLRLHAQDTOQz29ctaCRNIBCCF4+0NB2ICXnwRPgKrAyBS8/aHg8TObXzuvL4ILnn1IZXTG21TYblQkyk4yvxtVBioLOyWSndGu6I7lznYhBJ5tUy2XcEpFvyhUDxKMRjmaWH/kywiCqvoOdHPRia6qCsYWt/ZutcrC97/DzKWLVG7dXLM89uDDZM5fIPX0M6h68zqFhBB4VdsvCtV19HgUPRJDM02UHRTP74atnOaNzsCXSNrFgRXRAc529/DJzBQ1xyF4QEqsqo4DiuBMk0cu9zNDiQTxkEGxVm1psazreSgoHEtJF3qrMAMBTnR18e7E+LZF9GKtSjQYlDM5tsH3/vwy337nAxQUfu4vnefoUN+9ZfW6w7/+o6+xkCvQlYzz17/0RQIHYN5w1XEYL+SJGa23RSyPcKnVQVPZMAe93SQMg6lSgXItQyQoBw07CVVROdvdS6laY7JYYGid8mTFNBGajpibBU1fk3eumCaoizdbqrarPPS5cplIMMgjvX0YTbxhlDSOzc75koPB8lgSRVHQFrXfeFhsGUuymQg+OSf4q6+rvP2h2FDY3m4kynby0reT+S2E4N2rgt97w6Pu7L0MVBZ2SiTbp53RHcJ1cC2LeqGAWy4jPA/VMNHjyS0HvPoz/t/2jTFlMRNd4dSQ//h6WONjzL75dea+/RZupbximWaGSb/4Mj2vvo45NNyow1sXz3HwbBvhOaihEMFML3okgtbCmflbOc0bnYEvkbSL/a94bMLRZIrhRJKpUvHAZB9PlYocTXbJuJBNSBkmfdEYY4VcS0X0nG2RNAyZ+dpijiS6+NH4GJ4QqNtwAuVsi8FFB7tkY8oVmze/+y5f+dW/Rq5Q5Dd/76v8vb/55XvLv/ejy/Rluvgbv/BTfPWN7/K9H13mpeeeaOMeN4b5SplitdqSQZZiSWFiWr+Xc748wkVVIZ1yCAZYNwd9ieXC+2brNZpoMMjdfI75SlmK6B1ISNd5pLePYq3KbLm0tmw71YV6khWZ6GuWDw8B+P/fYR56pVaj6jo83XeElIzN2hdsdc6XHAz2EkuyqQieg0dOKjx+RtlQ2N5q25oq+ODaqqiYxxQePgEBfW2k1EaZ3/fc5+97fPtDQa0GD5+ArjikE8quy0BlYadEsn3aEd3hVqs45TJOseA7sVUN1Qyj7sBMqSkKT56F/jTYNd+BvjzuBcCp17n9zT+n9J2LODc+WvMa4WMn6HntAl3PvYDWxEQEIQRerYZnWyiqihaOoMdj6OEwitZ6mW87TvNGZuBLJO3iQIvoAU3j8f5B/v2Vjw6EG73qONRcl8d6B9BVGb63EYqicCrdzfWFuZZuN2fZPNrXL6NcWkx/LEY8ZFCo2tuKOKrU6pzs6pY3PlswMjrB6eND6LpGd1eSarVG3XEILJ5Hr90a5cLLzwJw7qFTXHzrnQMhos+US6DQkuiJ8ekAhPV7meerI1yCATbMQV9ivez0VqAoCkFNO1CD1AeNhGHySE8fPxy/S6VWI7xssENRFD8DXQjEwjziysf+4719KF1pf3k86a+8DefWclzPY6ZS5uFML0NxWYC+X9jqnL9E3XFwHBfX66zZMY1ir9nZnc5eYkm2I4I7rrKpg3yzbf/4xv2omJgJ730K33hXMNgNDxxVth3DshQ5U62BXYW648dKcAx6UnsvA5WFnRLJ1rQqukO4Lq5l4ZSLOKUSwnFQQwZ6LLHrc7imKOuWiFbn55j5xhtMvfEmlLIrlimBIOnnPkfm/AUiJ0819ftDeC6uZSOcmu86T6fRI1FUw2jr95Z0mksOCwf+EuBUVzfHu9KM5rMcT+3vUquxQp4TqS5Opff3cbSC/lgcMxCgXKu1xCXpCYErPI4ld+bWk+ydWMhgIBbnVm5+SxHdduoYekDmOG+DUsUmbN53T5imQblik4z7jtZS2bq3PGyGKFesdV9nPwkuNddhulQk3qKpj96qzHPT8NC0jSNc1nOdr5ed3ipiwRDzlTKVek0OHnYog/EEZ+wMn8xOM6TraweHsguIH3+ANzEJCJSBQbTHHl9ZMrpDJotFBmJxznRnDqQIeVDZ6py/xNe+8QO++sZ3CQR0fvnLX2r1bjaNRmVn7wd2G0uykQge0KE/rfC//J+C2azY9L3baNvnTgn+l/9T3IuKmckKckUoVmBaBU0T24phWRE5M6CQKwpyJXBdGJvx85llGahE0nyaLah6taof2ZLP49o2iqKimgZqpLHxccLzKFz+kJlLF8m99y6IldfmbrKf7OnXePgvfYEjx5s7i9Wr1XCrFgjQwyZ6JoNmmqgblci3Aek0lxwGDryIHtA0nhoY5m4+2zJBtRmUqlU0ReWpwWHpQt8GmUiEvmiM6VKxJb/zvG2RMAwGZc52WziW6uLK3MyW6+Vtm5Rp0hONbrnuYSdiGliWfe9ny6oSCd8XWCLhpeWJxWXrD2DsJ8ElZ9uUajX6Y60ZZFFXCebJuHfPkW4aHomYRzZ/XzQXAkbGVrrO1xPeCy3ZewgHg2TzefK2LUX0DkVRFE6nM2Qti6lSkcFVznBhWQjLRlnslBBWZdclouDHZRm6zsM9fQTbMJVYsnu2Oucv8cVXPstrLz6N63lcHZlt5S42lfULM3eXnd3p7CWWZI0IPgg9XfDOxx61beSOb7TtugOzWV+YF0IwNuML38koCOBIr8Ld6a1jWFZkvgNDPQqFisCuQq4ItybACMkyUImkFTRaUBWeh2tbOMUSbrmEV6+jBoLo0RhKg2eQOsUis9/6BrNvfp3q9NTK/VBUvNNP4zz+Ot7RR1iYUagFmmNiEZ6HZ9vUa1VmCgFqeoJwMsaRPgNdjgRKJG3hUNzhnEh18XCmj/enJnggvf9iHIQQjBfyPDEwxHHpdN4WqqLyQHcPt7LzCCGa/jufr1g82ttHrIUZ7JL79EVjGLqOVa9jbjIan69WeTDTKweitsHxIwP88cW3cVyXfKFMKBRYMa3/zIkjXL56i+HBXi5fvcWZE+sX5uwnwaVYtfGE15IoF4DB3jqDfc49AV1RIJXwSCV8l0s2vzKqJWx4a1zn/T3OCuE9GfeYbsne43cQKJCzK7ILooMJ6TpnMz3kRi0KVXtFV4himiimgZfNAgIlMbirElGAuuuSt22e6h+iS+ag7zu2OucvEdB1ArqO63b+7KLtsllh5m6ys/cLu4klWRLBz51SeO9T+M6HHv/nN6FWh4ePK3QlNn/vlsflBPT7jy+PiknGoFL1Y15qji+kq+r2YlhWR85kUvAQCh/fFoQCcHoYXnpCk2WgEsk+wqvXcSsVnGIeZ3Hmq2aYBMKRhm5HCEH5+jVmLl1k4Z3vIer1FcsDqS5Cz57ncvwVEv1dmCEFq+qXjxoN9pJ49TquZQECQgaX53v5dCqMowbQRhUeLMJzjwoZlSKRtIFDIaIrisLTQ0cYyWWZKZfo3WdRDjPlEqlwmKcHhw/kRXyzOJJIEg8aFKpVEk0s9ai7LgLB6fQGtd2SptMdiZAyTApVe0MR3RMChGAgJjN6t0MkbPDSc0/wG//id1FQ+Cs//Sqj49N8cn2ECy8/y/NPP8K//sOv8Y//+b8hlYjxSz/7E+u+zn4SXOYqlZZ2Z8SiYtPM89VRLSiscZ2vFt5bjaHrzJUrIE9/HU0mEuV0upsPpyeJBIL3B4pSXSjnHkft7QP8TPSdloguMV0qciSR4tguny9pL+ud8w8LmxZm7iE7+yDz4xsKv3/Jxa7ezx3/ZETwEMq6ueNbxeUsj4q5M+W7yLMliIZ9N7nC9spP14ucKdtwZhh+7jWVp86uLSiVSCSdhxACz7b8otBSEa9aRQkE0aNRlB2aoVwhmJwFu8a6RaGubTH/3beZvfR1Kndur3l+/JFzZM5fIPnkZxCaRumq4MYYeJ4voJ8cELjCH7xb7/V3dMxVG69WRdE0AokYeiTGaN7g6pxKpltgLpa0Xhnxc9t34vR3PcHoNFhVZF65RLIHDs0lYXc4wjODw3z95jVSZpigtj+cqDXHIV+1ef3kA6QbPNp60EmHIxxPdfHxzHRTRfTZSpneSJThRLJp25Bsjq5qHEt18c7Y3Q0HyUrVKtFQiF4Z5bJtXnjmHC88c27FY8ODfiNQMBDgP/2rf7Edu9UUXM8jb1uYHVJALYTv7MsXNUoVFU0VRCPQlXQI6mCavuu83Zh6gFK9StVxCHXIeydZnxNdaWbKJWbKpXszBxRFQUl3Q7p7T6+dt21CeoAHujMtm8khaTzrnfMPA1sVZh7WGfMblawud+6fGIB8yY9K8TwYmxFkUsqa9247cTnLo2I8T+B6Sy50we3J7ZWfrn6d1ZnvUkCXSDob4To4lQpOoYhbKSOEQA0ZBBK7K7F3heC9VaL3qSF48ixUx0aZvXSRue98C89a2e2kRaJ0v/QFel59HaN/YMWyJ89CfxrsmkIgIJieh+/9GDzPW/H62xXSPcfBsy2E56KGDILdPeiRCNpiR5M9J3A9D9PwXy9s+GWtVnUnpe+C718WXBlhRdHrc49KIV0i2SmH6o730b5+bmUXuJ2b51TX3m4YW8WdfI7TXd08sugSk+yMB3t6+WR2GttxMJog8HhCUKjaPDt4RApIbaY/FvdH8IVYd3m+atMbja2IMpBIlrDqdaqOQ9xoTanoVuQKKvNZHV0TFMsKgYBCsSSwbJXjw7W2Oc9XE9J1ipUallOX58AOJ6jpnEln+N7onS2jr3aC63lkbYvHevtJyRgXyT5ko8LM7Yq2+5GNBPKlZZu5xlfkjisKQz2CYsV3N2aLcHtCEAref++2G5ezPC+97gg+vg1vfyB2VH4Ke8t8l0gOCnfHp/m9f/cGqqqgqiq/+KUvkkkn271bG+La9qLrvIBn2yhaAC0cQdmj8XFydnGANC4wQgpWucbUt3/IR//m69RuXVmzfuTUaXrOX6Drs8+jBte/J9AU3wUOfmHxzfFlr18V3BhT6E9zb5318F3nVUStCqqKFokQiMXQTBNlVaeMGfKF7ootCC860TXVL2vdLqPTcGXEL1fei5tdIpEcMhE9qOk8f+QYk8UCC1al4zM75ytlwoEAzw0fkwVdu+RoIsWRZIrxfK4pU8znK2VSZpgz3TLLoN1kIlEiwSCVWo2AulYcqtTrHEt2yRspybrYrkPNdQm0MS9fCF88tyyVsSmdiWmdRMzDdRWCAUF31/0s9GTc89ddloXejo+2rqrUXRfb2TiWRtJehBDU5mZxymWS4TBHEwluZbMcSSYb8vrzVoVMOCJjXCT7ms3cyweJrQRy2No1viZ3PAkcg49vQTAIp4cUXnzi/nu307gcRVEIBhSeOAOPn95Y7N+K3WS+SyQHhUQswt/5G1/CMEJcvnKTf//17/ArP/8ftXu3ViBcF9eyqBcLvuvcdVGDBno82bD7NbvmO9BNexbtB2+QuvwNlEqB2rJ11FCIruc/T8/5C0SOn9jV6xshf3/NkILnCeza+vsvXBfXthFuHTUYJJDuRg+HUQ1jw2Me7oUHj8GVEWWFi3y4d/v7aVV9B/pe3OwSicTn0F1aDMYTfGZwiLdGbhIPGegdOu247rrMlst84cRJBuIyw3m3aKrK430DjGQXqDlOQ/OOPSGYr1R4+fhJEsbuitgkjSNlmCQNk3zVJm2sFNFdz0NBoUdGuUg2oO66eEK0LIqiL6rQVb5NeO7+9oolleJ0gHJFIT+lI8oaJU0QNTy6u1zUMY9eDXqtOt4dKE4H8FxwNIj31olFfXd6V/k2fdHWXBQvXfDXXbcl25PsnNrcLIVPPka4LoqmceTkSSYDAYrVKrHQ3mZe1F0Xu+7wWO8Aht4YZ7tE0g4Oi3t5K4F8u67x9XLHTx+Bn18nd3y16O7PGoR82S/63CrjXArhEsnOScTv3/PourZhZEfdcXAcF9dr3QxHr1bFqVSo5wt4toWiaqimgdrg6wjhuXD9fQbfvEho7H0UVs5WNgaH6Dl/gfTnX0LfZWyuEQRV9R3omxWNerUqrm2jKApaOIwey6CFw6jb0CY0VeG5R31nu1VVdpVn3gg3u0Qi8WnYZUm5YvFbv/+nWHaV4YEefu6nz6+5+PzK//gvSaf8HM4HzxzjJ199vlGb3xFP9A9xO7vAaD7L8VS6LfuwFWP5PMdTXTzRP9TuXdn3nOxKcyLVxd0G/77nymXS4TAP98ionU5AURSOJlN8b3SEtLFSLC/VasRCIbpN2SsgWR+nhTcPAP/ZU0F+6qP/Bj7aYkUXKC/+t8TlddZb9tiji69fLCkIQdMd6orS+vdPsn2csu/uMvr6sKemiDgux5IpPpmZ3rOIPl8p0xeNMRCPN2hvJZL2cpBF2+0I5Nt1je8kd3xJdL8z5fLhDUGpAsUKmAa8+PjBG6iQSDqJaq3GH/+Ht/nFn/3iusu/9o0f8NU3vksgoPPLX/5S0/ZDeB6uZeGUSrjlIl7dQQ2G0GNxlAYbWOr5PHPfepOZN9+gNjvD8rs/oWgoDz7D6b/8F0g8+PCeB0v7M/7578aYsiJzvT/ji/iubSPqvus8mEqhR6Oohrnj7WqqsqfYlUa42SUSiU/DLhMvvvUOn3nsLM899Qi//Yd/xsef3uaRsyunw6iqwt/9L36hUZvcNWYgwPNHjvP/u3KZgm0Tb2Lp5G7I2zaqqvDckWPS2dUAdFXjyYFh7uSylGs1IsHg1k/aAtfzWLArnD9xRrrQO4hMJArCv1FcTqlWJR2O7FkwkhxcPCFaGofyr96t8dSv/gbHTp2891ihqHLjTpBiSSVXUNE1CIagO+lw6ljtntNcCJia0RmdDKBpgrAhSMZddB0cBz795Bb/6t3/gl+aDnCqoDY9P12wcReBpP3oET9T1J6aQtE09EiEo/E4d/M5ClV71z0RddfF8TxOdKXR2xiDJJFItsd2BPLtlqzu1Ln/2Gm4Pqryu1/3sKoQC/v/vfOxx+nh++Wim7FZjrtEIlmL47r8f37n33PhC88y0Lt+H9wXX/ksr734NK7ncXVktuH74NXruJUK9UIe17ZAKGiGQSDc2NnBQghKn15h5tJFsu/8AOGujBnUUt0EnnmN+OdeYehkatuln1uhKcqKolEjCL3JOl7RxkOgmSaBdDeaaaI2QIPY9X42wM0ukUh8GiaiX7s5yoWXPwvAYw+e4tqt0TUiuhDwG//y9whoGn/5J15ieHD9oa9WTCs6lkzxWN8APxi9QzQUQu2QizFPCCaLBZ4/coxjSZkv2iiOp1KczfRyeXqSBxqQXz5RLDAYS8jC1w6jywwT0nVq3soLp3K9zmPxhLzpkmxIqz8ZUyXBQuQ4Pd0P3XusoKvMzoawVBUvComUSzzqkUk7qAmPyuJOZvMqt7wgxYiK4yr0pOrM11VcG/JFjem6ylRJ4Llg2c0X0RWUtuSxS7ZHsDtD/KGHccpl9EiEYHeGkKJwJJHkk5npXYvoWatCdzhKr4zJkkj2BdsRyHdasroT5/7kvGC4F470Kqiq/727OiZmPbaT4y6RSFbieYLf+r2v8vjDp3nikY1HqQK6TkDXcd3GXSsKIfBs33XulEp4tRpKIIAeiaI0eNDdrVSY+863mH3z61ijd1cuVBQS5x6n5/wFEk882fBtL6EpCoPdHl61ilerojo6WiKGHo37RaHbcNq7nmB02s8ub5bAvVc3u0Qi8WlcnItlE14MVTLNEOWKtWadr/ztv0YsEmZ0Yobf/N0/4df+7q+s+1qtmFakKApPDw4zkl1gslhgsENyxycKefpjcT4zMNzuXTlQqIrKZxZ/3wuVCl3h3ZfK2k4d23F4evAI4UD7RpQla0mZJpFAkHKtvuJxIQTpXWbdSQ4HquJHn7QTu6pihASDfTXmsxqDfQ4DvWsLOy1bxfPgyGCd+ayG8BRcD9Ipl3JFZSmeXNXANFoQsyLomIFoyVoURSGU6SG0avx4KJHk1sI8lXptx99lnhBYjsu5vi7pQpdI9gnbFch3WrK6HYf4che8tkxPioVhekFQdwTBwPrP3U6Ou3SoSyQref+jT7l85SaFYpl33vuYwf4MP/+XXmvqNj3Hwa1UcIoF3EoFIYTvOk8kG76tyshtZi5dZP67b+NV7RXL9Fic7pdfIfPKaxhNNrx5Th3Ptv1SVMMk1NODHomgBrc/+9n1BN+/LLgywoqolecelU5xiaQT2ZGI7nke/+if/c6axx89e5KwGcKyq4RNA8uuEgmvjbiIRXzhcnigh2AwQLliEwmvdUA1e1rRvf0JGTw7fJSvfvoxtlNve3SKVffF2fMnjxKVsRMNpy8a4/H+Ab49couEYey6QPBuPscD3T0NcbRLGktQ0+kKhxlZyN17zPFcAppKUsbuSDZBV9WWu9FXYxoemgbzWQ1tEwF89XqJuMtCTmc+qxGLegwP+INIg711kvHmi+gC0JXOLOmWbEzSMOmPxRkt5HYsoudtm0QoJF3oEsk+YzsC+XajWnbiEF9TLoovqn98G0IB+Bf/1uOlJ5Q1z900x/19DyEU3v5QSIe6RLKKp86d5alzZ1uyLde2ccplnGIer1pF0QJoYT9KrpF4tRoL73yPmUtfp3z90zXLo2fO0nP+Aqlnn0MNNE/XEUIsus5tFFVDi0QIxGJoZnhXxzw6DVdGIJMUmIuln1dGFIZ6kM5xiaQD2ZGIrqoqX/lbv7jusmqtzuWrt3j2iYf46OqtNdOG6o4DAgIBnWy+SGWZc301zZhWtBFnu3u4Nj/Lp3OznEmvnxXWKkbzeR7MSHG2mTzeP8iN+TkmiwWGdjEqnrUswnqQZweP7FqElzSXvmiMK9Nz93626g6mHiSxy8gCyeEgqOuoqorjeeht+ttOxj2OD9ewbBXT8DYUwFevl4j5/y39PC18K3os2vycd08IFPz3T7L/GEokuZvP4nrejr7TilWbR3r72m4+kEgkO2MnWeZbRbVs5RBf/VovPq5yd8rl1oTA9eD6KCBgKAM3x2F0Zu1zN8tx//SuYGTKd6FvtX2JRNIcPMfBnp7Gq1qoQQM9nmzoIJYrBKOfTFJ4+w1q734TUS6uWK4aBunPvUjPaxcIHznWsO2uh3BdXMtCuHVUwyCYzviu81BoT8dsVX0Humn4rxE2/PJPqyoHAyWSTqRhd70XXn6W3/r9r/Kt77/PUF+Gh84cB+AP/vgSP/Hq89Qdh3/22/+WUDCA53l8+T++0BEuAU1VeWbwCKO5HDnbaptbNWtZhAMBnhmS4myjqNTWxiCoaJzrHeLPrl2hEKoS0u4LADXHW/ffS3hCMJYr8NzwMZJGZM3rh4NSROoE4iET3+PkYzl1EmaoIYWykoOLoeuENI2a46B32GdFCMgV1BXieirhrcg6X/7zdAu/WmuuS1DTMKWIvi/pDkeIGwYF2yJp2wjLQjFNSG3cyVJ1HAKaTk8k1sI9lUgkjWQnWebrsalDfJ2McyEEQghCQRiZhPkCBHV44iz0JP39We+5m+W4Fy1QVTgxsPn2ZdyLRNJEhADXQQtHUBs4sC5cl4X3fsStP74Itz5cs9wcPkLP+QukX3gJzWyufuNWq3hVG0VR0MJh9HgPmhlGbdC1rxnyY1sqtiC86ETXVL/8UyKRdB4Nu+uNRkz+9q+szS//Kz99/t6//9v/8pcatbmGMhhPcK6vn+/dHSEeMlqe7eoJwXSpyOeOHKc/Fm/ptg8yD/13F7dYw95wyd//o8kNl/1vXAOurXl85B/+5Db3TNJMoqEgy2sia45Dl9FYV4Tk4GHqAcxAANtxCLdJRM8VVG6PBnFd0DQ4PlwjlfDWfTwZ99YI6+34iFedOkbAf+8k+4+ApjEUT/DJ1avE52bBdRCajnpy4+fkqzZdpkmqyTetEomkc9nMIT6b9ZcvF+k/vA7/5uu+6H56CAqfgqKsLKZe77kb5bgHdIiZkIhsvH1dk4WkEsl+o5bNMvfNS8x84w3qC/MrlglNp3j0s8RfvEDowbPUQgo0aaKx8Fxc20Y4ddRAkGAqhR6Nohpmw88fw73w4DG4MqKsyEQf7m3oZiQSSYOQ1rFFHu8f5NO5GWbLJXqjrXVXzZZLdEeiPN4/0NLtSiQHkUggSEi/n0dnuw6pdToaJJLlKIpCdzjC9flZuth98fBesCyVQkklbAgKJQXLUkkl/JgW1/WLQ+ezGpbtz1ZaT3BvNZV6ncF4QpZL7mO6w1H0mo1br6P39CDmZhHW2nL4Jax6nYcyfagyB18iObRs5hA/NeQvX2K1a10IQW8XTMzB2Iwgk1JQNnguwLlTgp9zVb73Y8Fszl/n848rfPt9j5vjG29/J3EzEomkfQghKH7yETNvXCT37g8RrrtiuZfowX38NWoPf4F3R2JEigrJTwSq6v+9P3kWtAYJ216thmv710B62ERPd6OFw03NWNdUhecehaEesKq+A324V5aKSiSdihTRF0kaJk8NDPP1m5/SHY60LFLF9TyylsWFUw+QkMWHDeWT//5Cu3dB0gbCgQABbdmpTUA0KPPQJVuTMsO4Qmy9YpOoOTC/oDHtKuiaoDbkP75e4eh6wno7RPS669IdluWS+5mUaRKJJ7Hn5ojMzYKm+5Eu5NasW3UcQppOl3ShSySHmo0c4sGA7/Ze7tRc7VpXFIWhHsFcHmZzMJcTlKy1z71fXOoXh3YnBT/9osqTD/g9XQoKozPrbx/YUdyMRCJpPU6pxNzbbzF76SL25MTKhYpK6KEnuT3wOsaD5zANjck5j1wJjvQJBrpVrKrgxphCf9oXoHeL8LzFotAqaiBAIJFEj0XRDBOlRZqQpiqyRFQi2SdIEX0ZD/X0cnl6kulSkYF4YusnNICpxW091NPXku0dJmRG+eHEDAQILm9GV/yoDolkKxIhA1MPYNXrbYknCei+KB42PSqWem86+0aFo6uF9VazlI2dMOQg1X5GVzX6jhzletUmGgjcz0QfXxtrVqxViYdC8ncukUh47DSAdi8u5dTQ/biU5aznWs8kobcLPA80FU4NKWueu9pJ7pePeuiatliO6m//W++7zCzAyUF46QmNx07vPG5GIpG0jtLNG8xeusjC97+DV6utWKbHE2S+8CqZV15D785QvSq4MQZeXpArKiRjgr4ufwDMDCl4nsCu7W5AzHPqeJaN8FxUwyTU24seDqMGD0cYuesJRqf9YlPpfpdIto+8fFhGOBDkyQG/dNLxPPQmjzzWXZdyvcZLx07JPFmJpEHoqoa56s4oJEsPJdsgFgqRMk0WrEpbzslh0yMe83BdiMc8wqYvjCsKa4pENxLWW0mxWiURCpGUguq+pzsc4WoyBYnEpu5Mq1bnZCoto1wkEgmKoiyK2eqmxZ0buda7k/BXX1d55KRyL8JlKcsctnaS33t9FEAs/t9nJ3EzEomk+bjVKgvf/w4zly5SuXVzzfLYgw+TOX+B1NPPrCgoffIs9KfBrinkSoJrd6Fa90VfqypQVQVjB1VGQgi8qu0Xheo6ejyKHomhmSaKdnhODK4n+P5lwZURVuSwP/eoFNIlkq2QytIqHujO8MHkONOlIoNNdqNPlYoMxhI80J1p6nYkkv1Mpebs+DkBZdmpzVMRnrKj15GzGA4niqIwGE8yXiwghGj5VO97wrilUnOgYqn3HgfWFImuFtZbTaVe50w6IwXVA0DcMIgEg1TqNaIbOLBcz0NRFJJmezoDJBJJZ6Ioypau7q1c66sLQJ9/VGFmQWzqJP/41qrM83HB3ZmlzPPtx81IJJLmYU2MM3vpInPffgu3Ul6xTDPDpF98mZ5XX8ccGl73+Zqi3ItqcQUI4Q+OeZ4voJ8agv5tSCme4+DZNsJzUEMhghnfda5twwhyEB3bo9NwZQQySYFpKFRswZUR/72WsTISyeZIpWgVhh7gif4h/vTaJ011o9ddl0q9zisnBqVLViLZhIf+u4t7ev5vvmHxm298b0fPGfmHP7mnbUr2L5lIhGggSLlWIxpq7XTOJcc5wNRoEMcBu6bSk3YwQh4LWR3Xa2+R6BKVeg1D18lEZR76QSAcCJI0TWbLpQ1FdKteJxwIkAjJmQcSyX5HCIHjgqYKXE/Z0EXeKDZzrX9wTawtAJ0ShIJQrKzvJNdUwbc/EJs61bcbNyORSBqL5zjkfvRDZi5dpPjJR2uWh48dp+e1v0DXcy+sEbFdIZicBbsGRtAXyJdKQzVFWeFMX718NUIIvFoNz7ZQVBUtHEGPx9DDYRRte/rLQXVsW1X/eEzDP4awoeB6Aqu6f49JImkVUr1dhzPd3bw3GWemidnoM+USA7E4p9PdTXl9iUQikeycaDDEQCzBzYW5lovoSyyVhoaCgpFxDdtWUFXQdcGRgXpbi0SXyFoWQ/EkSVmIfWDoiUQZy+c3XF6u18hEojJ+TiLZxyyVdX7rfZdrd32ROhaGB44ovPiELzA3W0xf7loXQmwY2xIKKgR0sa6T3PVgNru5Uz2gby9uRiKRNIbq/Bxz33iD2W9eop7LrVimBIJ0Pfc8PecvEDl5et2/RVcI3lvKQF/mNH/y7EohfasSUeG5uJaNcGq+6zydRo9EUQ1jx+eAg+rYNkP+IEDFFoQXj0tTFczDEQcvkewJKaKvg6EHeLxvgK9dv4rreWgNdqO7nkepVuXFYycxZOGhRLIpn/z3F9q9C5JDxlAiwUh+AdtxMNowU8g0PDQNpud0hFCIhD3m5jVQFe6MBXA9hUzaQQjfvd5q6q4LwJFEsvUblzSNWMhAVdjwuqfqOvRE7s88EEJQm5vFKZfRIxGC3RkpUEkkHc5SWedcDqYXoFb3hemKvTwKpXX7s1kBqKIIfv41le9dFus6ybebeb6duBmJRLI7hOdR+OjHzH77LfLvvwdipcEj1NtHz/kLdL/0BfRobNPXmpxd/JuOC4yQglUV3BhT6E+zpXAO4NWquFUbBOhhEz2TQTNN1D0M/h9Ux/ZwLzx4DK6MKCsc9sO97d4ziaTzkZcUG3A6neFH46PMVcr0bnHC3ymzlTI9kZh0oUsk20Dmk0taTXc4Qn80zkQx3/RujPVYykYPGzpCCMandaq2AihUqwqZtMtCVicRa08m+nylTCYcJRORUS4HgUqlwtWrV7Edh4nxUWZUFTMQYOTGDQBuX7+O53nMVSoks0Xy4QgAx9NdOCO3Ea6LomnEH3qYUGYbd7kSiaTpLMW1LHdfL7m+qzVwXdBVSKchX4a6A3bVd6g/dlpr2YDY5gWgCk+d9f9bz0kuM88lkvYy/R/+lLv/x29jT06sXKCqJJ96mp7zF4g//CjKNg2Jds13oBsh/+/XDCl4nsCubfz3LDwPz7bx6jXUQIBAPIkei6IZ5ra3uxkH1bGtqQrPPeoPTlhV5cBkvUskrUCqUxsQCQZ5tLefN29dpycSbdjFmBCCnGVx/uQZwoEdVElLJBJJCylXLH7r9/8Uy64yPNDDz/30+TXnwa/8j/+SdCoOwINnjvGTrz7fjl1tOIqicCzVxUQxT9VxWt5bsZSNnozXQIFPb6noGmRzKnVX44GTNWo1pS2RLnXXpe56nOhKN3yWlqQ9XL16laeeemrD5f/g7/xX6z7+nT/595wOmxh9fdhTUzjlMiHZky6RtJWluJblJZ1L7u0l13fMhIk5X3AWAjwPxmchV4KpBfjcOcFTZ1sTfaIo2ysAXc9JLjPPJZL2UstmVwjogVQXmS+cJ/PKeYJd6R2/nhEEVfUd6OaiE11V/ezz1Xj1Oq5lAQLNNDHSXWhmGDXYWH1lt47t/VBGqqnKvo6kkUjahRTRN+FMd4Z3J8bI2hZdZnjrJ2yDrGWRMk0e6JZ3mhKJpHO5+NY7fOaxszz31CP89h/+GR9/eptHzp5YsY6qKvzd/+IX2rSHzaUnEmEglmCikGcwsTc3uhCQK6hYtoppeNgVC4BPP1pbtrSaYklh7q5gdGScrt4h6m6Y9ywXIySYH3cZT7nEIgKWXZcvOYibwVylTG8sRl+DZ2hJ2sfZs2d59913Ab8wtur4cT22bTN65w7DR49iGAa6qhBbViq65ES3p6ZQNA09EmnL/kskkvssxbWsKOmc9mNaHjvti+rXRyEcglzR/37KFn2Xd90BVYXff8ND11oX67JbMXyzslKJRNJ8er/4k9z57f8v0VNnyJy/QOrpZ1H3YDzpz/h//zfGlBWZ6P2LsokQAq9q41VtFF0nkIihR+NohoGiaZu/+C7ZjWP7oJaRSiQSHymib0LKDHM208MPx+42TESfrZR5bvgoCVnGJpFIOphrN0e58PJnAXjswVNcuzW6RkQXAn7jX/4eAU3jL//ESwwPHpwgPVVROdmVZrpUpFKv7WnmUK6gcns0iOuCpsGnH90C4Nf/b/91o3Z3XcLRxoqatuPgCTjd1S1d6AeIcDjMk08+uePnCSGohcMrMtElEkl7EEJQdwTfen/9ks5vf+Dx2Gn1nutbVX3RfC4PAt8paYTg7FHfObm0fqvc6HsRwzfLPF8v1mYjdrKuRCKBYDLFZ37nD3GLRZRgcE8COviloU+ehf402DXfgd6fAcV1cWwL4bmoIYNgphc9EkELtSZTZaeO7YNaRiqRSHykiL4FD3T38OHUBOVajcgepweVazUMXedMWt5oSiSSzqZs2YQXA/9MM0R50T29nK/87b9GLBJmdGKG3/zdP+HX/u6vrFmn7jg4jovrtT67e690hyMcSaS4sTDLkeTuz/+WreK6kE65zGc1nnjhi3zlHwuOnjyJYa4zoCqgWFawq76gMHn3Bv/0v/tb/J3/4f/NqQdOYFdVZuc1EnGPfEEl0+2S6XJXvEQ4GuHI8eO73uf1mC2XOJpI0ROVWegSX2QKZXpkhItE0kaWx7dMzws+HYWepP/4khAcD/sxLo4L504Jnh1V+fdve7iLX8tmEKKLX0U3Rv1IMdf1RflgoHViciMLQDeLtVktkO9kXYlEspJgMoVVLDbs9TTFF5t913kVUawiVBUtHCYQj6OZJorW2RLWQS0jlUgkPp19BuoABmJxjiZT3M4ucDzYtafXmikXOZ5K0x+LN2jvJBKJZPd4nsc/+me/s+bxR8+eJGyGsOwqYdPAsqtEwmvF3ljEn6EzPNBDMBigXLGJhI0V63ztGz/gq298l0BA55e//KXmHEiTUBSFU+k0U+XCvSiu3WAaHpoG81kNTYPeviRnf/7nNlw/m1epjAZxFZWFBQ0RDABw6oETPP3cw2Tz953tqX44PlxrejZ6oWpj6AFOpbtRFelCl0gkkk5geXxLLAy1uh/XkohCT8pfxy/p9B3WH15XeOcTl1Tcjyf46BYUK1C2/dzxoO7HuzgefHwLnnigvce3WzaLtVkdU7OTdSUSSXMRrotr2winhhoKEUin0cMRVMPYN4NaB7WMVCKR+EgRfQsUReHBTC/X5mZxPW/XU9gdz8PxPB7K9O2bLwCJRHKwUVWVr/ytX1x3WbVW5/LVWzz7xEN8dPUWTzyy8k6y7jggIBDQyeaLVJY515fzxVc+y2svPo3reVwdmW3KcTSTWMjgdFc3701OEAuF0HfxHZCMexwfrt3LRE/GNxe8l5zrYUMw7SoYIX99u6bu6vX2iut5LFgW53r7SDUo2kwikUgke0MIP6ZleXzLw8fhR1cFH98C9aSgaN0v6QTWrn9C8Pb7UK1Bfzf3xPhkFN7+UPD4GbHv7lvWe19Wx9osHdNO1pVIJM3Dq1VxbRtFUdDCYfRYBi0c3nNETDvYbRmpRCLZH+y/s1IbOJbsojscYb5S2fU09vlKme5wlGOpVIP3TiKRSBrPhZef5bd+/6t86/vvM9SX4aEzfjTIH/zxJX7i1eepOw7/7Lf/LaFgAM/z+PJ/fGHdG82ArhPQdVx3/8W5LHE02cVkqch0qchgfOclo4oCqYS3bbf4knO9UFLQNYFd9cUPI+jt6vX2ymy5RG84yolUd0u2J5FIJJKtcVw/piW+bGwzk4LTwzCT9X8+NaTciyZZb/10HMKmL6IrQDLmO9RV5X4ETKMiVlrFescJK2Ntlo5pJ+tKJJLGIrxF13m9jhoMEkyl0KNRVMPc14NXuykjlUgk+wd5WbANzECAB3t6+PbI7V2L6FnL4uXjJzH0QIP3TiKRSBpPNGLyt39lbfzKX/np8/f+/d/+l7/Uwj1qHwFN42x3DznLolitEmtykdE9p7mlUhuC0Rt1AGIR0dTtrkelVsMDzvb0EtqHbiCJRCLZCfupXFLXfNH8xpjvngZfCNc1ePFxhf/8ZxQCunLvOHRNrFlf0xTMkMAIwGcfAU31j/v2pLgXAbPfWO99gZWxNrtZdzfsp8+TRNIqvHod17IAgWaaBNLdaKaJusf+uU5ip2WkEolk/yDviLfJ8WSaH+qjVOo1woGdneDLtRpmIMDxVLpJeyeRSCSSZpKJRDmd7ubHU5OYgcCuYl12jAKJmIe9VBra4vtv1/OYKZd5uKeXvmistRuXSCSSFrIfyyUVxXeZ3512uT0piId98Tegw+cfVwgG/O+p5ULueuv3pkAAozMsPibuRcB06rFvxkbvy3rHtJN1d8J+/DxJJM1EeB5etYpXq6IGdAKJGHp0sSi0FdfUbcb1BKPTfumodKZLJPsbKaJvk75YjP5YnOlSkXBiZyL6fKXMYDxB7y5d7BKJRCJpPye60sxWykwViwwldh7rsl1yhfvFoZoGbrk9F9lTpSJ90Rin0jLGRSKRHGz2a7nkY6dBoPLWex43Rv2S0FgYvv3+YtyX8LPNl4Tczz+u8FdfV+89dmrIf2z5eqeG7gu++xV/37V7IvbqY1o+sLDVurthv36eJJJG4zl1PNtGuC6qYRLq6UGPRFCDh6dl0/UE378suDLCioz05x6VQrpEsh+RIvo2URWVs9093FqYR4jtl+wIIag4Dg9096AqB3+UVSKRSA4qQU3n4Z4+itU7ZK3Ktks2hfCF8eVFoJt9hSwVi6ZTLvNZ7V6haCvJ2zYBVeOhnl4ZQyaRSA40+7Vccsnt/PYHgut3YWLOLwRNxuDmOHx000MAYWO5kCv48gWNv/UlZU3MyONnDk70iKIoPH4GHjutrjgmIQQfXBPrOsRXr7tb9uvnSSJpFEKIRde5jaJqaJEIgVgMzQyjaPswI2qPjE7DlRHIJAWmoVCxBVdGFIZ6kJEvEsk+RIroO2AokSQWClGq1badiVusVokHQwwnks3dOYlEIpE0nS4zzIOZXt4dH8UMBDG2kRO+2ll+fLi2aSnoUrHofFZD0+4XiraKmuuQsy2e6BskE5EzqCQSycGmU8old5qfveR2rtb8ItFixY9l6e/2s3jfek+AgJef9F9vtZAb0FduQ1GUA1eiufqYNneIN+b4O+XzJJG0GuG6uJaFcOuohkEwnfFd56HQoR44sqq+A900/PcgbCi4nsCqHt73RCLZzzTsK/z9y9f4d//hW8wt5Pnnv/53113no6u3+Oob3wXgp17/HA8/cKJRm28JaTPMQCzO3Xxu2yL6gvX/Z+++4+So6z+Ov2a27/Ukd5d26ZWQQAihhN6JgIAaKYJKEayIiL2i+FNUEBuKiqh0VGogYCBSDD0ECGmEhPSeXL9tU35/7N3mLldyZe92b+/9fDwgd7uz3/nO7t7M7ns+8/02MKZkEIM6WbEoIiLZbXRxMZWRBt7fu5uRhUV4DjCW4/6V5ZGo2WGInppYtLFyfceGvptQ1HVdttfWMqZ4EOMGaR4PEcl9vT255IF0Z/zs5tXOo4fuq0KPW7B5p8vgInCc5FQajguexmYGcpDbVxXimX4/iWSCVVeH6fHgCYfxFpbhCYUxNSE9kBwD3WMmK9DDjZXoHtMgNHBGtBHJKWnbs00cV8H3rr2MH958R5v3O47Dv594jq99/mIAfvmHe5k6cQxmP5pIwjAMJgwuZfXuXZ1a3nVdorbF+EEaT1ZEJFeYhsnU0nJqYzG219UyorDj8dH3rywPBTuuLDeMZJAOyQC+tnFM9No6g607vJ0aEqa7dtTVMigU5qCy8gOeHBCR3NGZYphc1VuTS3ZWd8bPbl7tbBoQDkBVbbLPDbHkMqYJuMn7mwzkILevKsQz/X4S6UuGaWAEAgQKCvDm52MGQ3qP76eiHKaOgZXrjRZjoleUZ7pnItIdaQvR8/NCHd6/c3clQwYVEQ4FARhcUsTO3ZUMLWtd6ZawLCzLxnb69hL2zhhWUEieP0B9PE6ev+MJRuvjcfJ9AYYXFPZR70REpC+EfD6mlQ/l1U0b2NvQwKBw+1cb7V9Z3hSQd6T5EDBbdiTHJN+ywwdhb6eGhOmO6mgEA4Np5cPIH0ATPonIgYthcl1vTC7ZGd2tjm5Z7WwwssyltgGq6qCkADbugPKS5PAu67dDYdgd8EFuX1aIZ+r9JNLXDI+X0PARA3Kf0lke0+Do6TCyDCKxZAV6RbkmFRXpr/rsGpv6hkgqQAcIh4LUN0TbXHbBoleYv3AxPp+Xyy6Z11dd7JQh4TCleXnsaag/YIheFY1QmpfPkHBeH/VORET6ypBwHtPKh7Jky2aCiThhX9vHBMOAkiKnS6F3JGJSU2cSDrrUNyQrwp02hoTp6qSl7YlaFtXRGDOHDWdofkHXGxCRfu1AxTBNsrnQpSfam4iyI10dw7wt3a2O3r/auSCUnFAUA8oHwYSRBscdaoALL77tKsilbyvEu/N+Eumv9N4+MI9paBJRkRzRpRDdcRxu+v3drW6fPmU8Z592TIePDYdDNERjqd8bojHywsE2l5178lGcdvxsbMdh1frODZ3SV0zDZFzJYDZUVR5w2fpEgqMGDdGBRUQkR40uKqE2GmXF7p2MKPDg8/SslK0pFN+yw8uW7V68HqiuSYboZhtDwnR10tK22I7D9rpaJg0qZWyJxkEXkfZlc6FLOnRmcs3ujGHenp5UR+9f7XzYZDjuEINp45KPsx0DrwcOnWQoyG3U1xXiuThZq4iIyEDWpcO6aZp860uf7NaKyoeUsGdvFZHGIH3P3irKhpS0uazP68Xn9WLb2VnlMrSgAI9hkLBtklP2tJawbbymqYo+EZEcZhgGk0vLqEvE2VxdTUVRUbdDCteFDVu8vL8+QF29gWUbDC9PYNUkj4UjyhOMGGq1GBKmq5OWtl6ny7baGoYXFDK1rEzjoIvksJ4UwzTJ5kKXvtKdMczb05Pq6LaqnZv6lwyJ3R4F/LnoQBXi6bi6QERERHJX2s6Nr1m3iccXLqaqpo5bbr+fE+fM5LDpk3ng0Wf40ClzKMgPc/7cE/j1nx8E4Py5J/SrSUWbK88roCgYpDYWI8/XdjV9TSxKYSBIWX5+H/dORET6kt/jZXrZMOrjcXbU1TK0m/NgVNWYvL8+wJ5KDz6fg2G4JBIGeXnJULwg32V4udXiMV2dtHR/uxrqyfcHmFE+jKDX161+i0j/0JNimCbZXujS27o7hnlHelod3bza+a333LQF/Lls/wrxdF5dICIiIrkrbSH6xHEVXHf1ha1uv+DcU1M/T586nulTx6drlRkT8vkYXlDEmj27OgjRY0weUqZQQkRkAMgPBJhePozXN2+kMhKhJNS58YWbi0RNvF6XwgKH6hqT4kKHUSMS1PoS7T6mO5OWNqmJRXEcl+nDhlEU7Hp/RSR3tFcMIy11dwzzjqRr/OzeCPgHinReXSCSLV56fRkvvPoWBgYXnncqo0cOzXSXRET6PY3S1k0ji4p5d8e2du9P2DYjC4vavV9ERHJLeX4B08qHsWTrJoJeLyFf106ihoIOhY1V5z6vy4QxMUaPsFjd4Lb7GMOgxdAuQKcmF41aFlWRKIcOHc5wHatEBrz2imGkpZ6MYX4gPR0/uzcC/oFAJx8kF9U3RHl28RK+9cVLqaqp5Y775vONL1yS6W6JiPR7+ijVTUPCeXg9HizHbnVfvHE89CHhvAz0TEREMmV0cTG1sSgrd+9kZGER3i4MW1Zc6DB2VMuq8s58b+/q5KK247CjrpYJgwYzbpAmEhUR6ayejGHe23oz4M9lOvkguWj9pq1MHDsSr9fDkEHFxGJxEpaFz9vyzZywLCzLxnYG5hBdIiJdpY8E3TQ4HCY/EKAuHm91X108Rn4gwGCF6CIiA4ppmEwpLaMuHmNLTTUVRcWdDlUMA0qKnC5NDApdn1x0a20Nw/ILOahsqCYSFRHpop6OYd5bsjngz2Y6+SC5qK4hSji0b9jZUChIfUOU4sKW87UtWPQK8xcuxufzctkl8/q6myIi/Y5C9G4K+/yUhvJYX1nV6r66eIyKwpIuX8ovIiL9n9/jZXr5MOoTcXbW11GeX9Cr6+vK5KK76+vJ8/uZPlQTiYqIdEe6xjDvDdka8GcznXyQXJQXChKJRFO/RyIx8sKt53Kbe/JRnHb8bGzHYdX6XX3ZRRGRfkkheg+MKCxixc7WB5uoZTFCY8yKiAxYBYEg08uG8ermjdTGYhQEAr22rs5OLlofjxOzLY4YOopiTSQqItIjPR3DvDdkc8CfzXTyQXLN2FHDefTpF7Fsm+qaegIBX6uhXAB8Xi8+rxfb1nAuIiKdkWUf/fqXQeFwq/FqXdcFFwaFwm0/SEREBoShBYVMLS3jre3bCHq9+Dy9c014Z4aBsRyHXQ31TCst10leEZEcl40BfzbTyQfJNXnhICccPZNf/uFeDAwuOPeUTHdJRCQn6ONVDxQFQwQ8XiCWui1mWwS8XoqCrS+XEhGRgWXcoMFURiNsrKrs0vjo6ba9toaKwiImDSlVMCAiItIGnXyQXHLsETM49ogZme6GiEhO0YxiPVAcDBLcb9zzhniCsM+vS+VFRASv6WFa6VCKgiF2N9RnpA97GxoI+/0cVDYUv0fpgIhItnFdl4TlJq9oFREREZGspG/TPRD0+ijer+K8IRFnaEEhgTbGHBMRkYEnPxDgoNJyXtuyiUgi0eNJp10XqmrMFmOgt1dcHrMs6uNxDh9ZoZO7IiJZxnVd3l5Daizu0pJ9Y3HrqiERERGR7KKkt4dKw/ktfo9aFuV5+e0sLSLSPyxd9h4PP/U8u/dWc9tPr29zmXdXrWP+wsUAnHP6MUybPK4vu9ivjCgsYmx9PWv27GJUcc+GdamqMflgkx/bBo8HxlbE2x0PfUddHWOKS6goLO72+kREpHe8vQbuftomnoDCMLy/GTbusAEPh07KdO9EREREpDkN59JDxaGWlX2O61KsSUVFpJ+bOK6C7117GSVFBW3e7zgO/37iOa65ch7XXDmPfz/xHI7T/sSWA51hGEwaUkpJOMSehoYetRWJmtg2DC6xse3k722pjDSQH/AzpbQMj6nDvYhINnFdlxfecognYOwwg8FFBmOHGcQTycp0De0iIiIikl30rbqH8n2B1M9NH3bz/f5MdUdEJC3y80L4Ophda+fuSoYMKiIcChIOBRlcUsTO3ZWtlktYFpFojEgs1kYrA0ue38/kwWVEEgkStt3tdkJBB48H9lR68HiSv+/PchxqYnEmDymjIKCJrkVEso1lw67KZAV6c4Xh5O1W9w8TIiIiItILNJxLD+U1C8wtx8bv8ZLnD3TwCBGR/q++IUI4tC+cDYeC1DdEWy23YNErzF+4GJ/Py2WXzOvLLmalEYVFjCwqZmtNNSOKirrVRnGhw9iKeIsx0fe3q76OYfkFGsZFRCRLeT3JMdDf3wyDmx0Oahpgwsjk/ftzXRfLTt6nMdNFRERE+pZC9B4K+/aF6FHbJuD1ktfDSeNERPqC4zjc9Pu7W90+fcp4zj7tmA4fGw6HaIjuqy5viMbIC7eueJ578lGcdvxsbMdh1fpdPe90P+cxTcYPGsyOuloaEvEWx5DOMgwoKXLaHQc9Zlk4rsvEwUPwedpIYUREJOMMw+D4Q0027rD5YJtLYTgZoPt9yclFm4fkmoBUREREJPMUovdQqNlwB3HbJuD1EFKILiL9gGmafOtLn+zWY8uHlLBnbxWRxiB9z94qyoaUtFrO5/Xi83qxbY2X3mRIOI+KoiLWVe4lXJT+4b9219czoqiIsnxNci0iks0OmQjgSYXjE0buC8eb0wSkIiIiIpmnEL2Hmlf5xW2bQeEgXlOVfyLSv61Zt4nHFy6mqqaOW26/nxPnzOSw6ZN54NFn+NApcyjID3P+3BP49Z8fBOD8uSdgavLKTjEMgzElg9lUXd3tavT2RC0LDIOxJYMxDb0eIiLZzDAMDp0Eh0w02x2mZf8JSCE5/MsH25K3HzLRVDW6iPQ523HZtAMiMQgFoKIcPKb2RSKS2xSip5Ft25pUVERywsRxFVx39YWtbr/g3FNTP0+fOp7pU8f3ZbdyxqBQmJFFRXyQ5mr0PQ31DC8oojScl7Y2RUSkdxmGQXtzeXdmAtIO5gEXEUk723F5eZnLyvXJnz2mwdQxcPR0BekikttUppZGCccmX5OKiohIJ1QUlWAaBnHbSkt7luPguC6ji0tUlSgikiOaJiCtaWh5e01D8va2JiAVEelNm3bAyvVQWuwydrjBkOJkoL5pR6Z7JiLSuxSip5HluGm9LF9ERHLXkHCY0nA+VZFIi9tdFyqrTbbu8FJZbYLbufaqohEGh8KU5qkKXUSyk+u6JCwX1+3kjk1SE5D6fckhXPZUu3ywzW1zAlIRkb4QiSUr0EPB5P4nHDSwHZdILMMdExHpZbr4L40MwO9ROYiIiByYaZhUFBezbXMtruumgpCqGpMPNvmxbfB4wK7vXEBSH48zeXCp5uUQkazjui5vryE1gWZpyb4JNBUCH1hnJyAVEekLoUBy2JaGqEs4mPzXYxqEdFG+iOQ4hehpZBiGwgsREem0IeE88nw+6hPx1HBgkaiJbcPgEps9lR6i8eRFY7V1Blt3eAkFHYoLHZrnTpFEgqDHS2lefiY2Q0SkQ2+vgbuftoknkmN5v78ZNu6wAQ+HTsp077JfZyYgFRHpKxXlMHUMrFxvtBgTvaI80z0TEeldaQvRly57j4efep7de6u57afXt7nMt/7vjwwuKQRg6qQxnHXKnHStPmt4TY2QIyIinZPvD1Cal8+W2upUiB4KOng8sKfSg8cDQb8DwJYdPgh78XhgbEWckiIn1U5NLMqgcJjCQDAj2yEi0h7XdXnhLYd4AsYOSwa/g4uSQ5O88JbDIRM1JElndTQBqYhIX/GYBkdPh5FlEIklK9AryjWpqIjkvrR9DJs4roLvXXsZP7z5jnaXMU2D6z93cbpWmXVc19WBQ0REumRofgHrq/amfi8udBhbEScSNQkFHXZsSI4d7DSrTo9EzRYhesyyGF5YpCBKRLKOZcOuymQFenOF4eTtlo2CYRGRfsZjGowZluleiIj0rbR9ZM3PCx1wGdeFX/7xPnweDx/50AlUjGj7ep+EZWFZNrbjtHl/NvMYqkQXEZHOKwmFCHl9RBIJQj4fhgElRU4qJN/RmIubzarTQ8GWAbrf46UkeODjsIhIX/N6kmOgv785WYHepKYhOba3VyMhioiIiEg/0Kd1H9+65lIK8sJs2rqTO+59nB9ef0Wbyy1Y9ArzFy7G5/Ny2SXz+rKLPaYiQBER6Yp8f4CiYJCaWJSQz9fuciPKE4wYaqXGRG9SH4+T7/drKBcRyUqGYXD8oSYbd9h8sM2lMJwM0P2+5OSYuoJGRERERPqDLoXojuNw0+/vbnX79CnjOfu0Yw74+IK85HWcFcPL8Pt91DdEyQu3/tI/9+SjOO342diOw6r1u7rSxSygLwIiItJ5hmEwNL+AHXV1HS5XkO8yvNxqdXt9Ik5FURkezckhIlnqkIkAHl54y2FXZbIC/fhDzcbbRURERESyX5dCdNM0+daXPtmtFSUsC1zw+bxUVtfSEIkSDgXaXNbn9eLzerHt/jeci4ppRESkqwqDIQwDHNfF7OKBxHWhJBQ+8IIiIhliGAaHToJDJppYdnIIF1Wgi4iIiEh/krbhXNas28TjCxdTVVPHLbffz4lzZnLY9Mk88OgzfOiUOSQsi9//7SECfh+O43DJR8/Qh2cRERGgwB9IjYue5/d3+nFRK0HA46HA3/ZJaRGRbGIYhiYRFREREZF+KW0fYyeOq+C6qy9sdfsF556a+vl71346XavLWq6b6R6IiEh/E/b5KAgEqI3FuhSiN8SToXt+oPOPEREREREREZGu0QCqIiIiGWYYBkPCeUQSiS49LmIlGBzOwzR0OBcRERERERHpLfrWnXYqRRcRka4rCobo6kwgtuNSHAr1Sn9EREREREREJEkhepppOBcREemOPL+fgMdDzLI6tbzlOHhMg3yNhy4iIiIiIiLSqxSip5FhgKtKdBER6YZ8v5+Qz0fE2m9Il8bDyq69HiqrzdTJ2mgiQcjnI78LY6iLiIiIiIiISNelbWJRATBUiS4iIt3iNT2UhEJsramG4L4hWmrrDQB27fbwwSY/YyvilBQ5NFgJSoIhgl5fprosIjnigUefYd3GrQAcOm0Sc08+KsM9EhERERHJLqpETytXlegiItJtg0Jh4rbd4rZoPHmoLip0sG2IRJO/xyyLIeG8Pu+jiOSeE+ccxre+9Em+8YVLeXv5Gnbursx0lzrkui4Jy8VV9YqIiIiI9BFVoqeRi8ZEFxGR7svzBzAMA8d1MY1kBXrQn5xutLrGpGQYhILJ310XCgLBjPVVRHJHeekgAEzTwPSYmGZ21tm4rsvba+CFtxx2VUJpCRx/qMkhE8Fo3GeKiEjfsx2XTTsgEoNQACrKwWNqvywiuUUh+n4a4p2b0K2t5RMJl2jC7nIbYb9eBhGRgcx1XXZWwq4aP7H6IJFQgrzGsc4L8pJnZ0uH2IytiFNc6BCzLPwej8ZDF5G0evXN5ZQOKmbIoKI2709YFpZlYztOH/cs6e01cPfTNvEEFIbh/c2wcYcNeDh0Uka6JCIy4NmOy8vLXFauT/7sMQ2mjoGjpytIF5HcovR2Pwd9/+luP/Yfi+L8Y9GbXX7c+p+d1e11ioj0hqXL3uPhp55n995qbvvp9W0u863/+yODSwoBmDppDGedMqcvu5hTdlbC68vrWb92BR9U7WXwkAjDByXHOl///vsA1O1ZxfYNFtuBukQMA4PV9REOPmga4XA4g70Xkf7AcRxu+v3drW6fPmU8Z592DCveW8/i15fxxcs/2m4bCxa9wvyFi/H5vFx2ybze7G4rruvywlsO8QSMHZYMZQYXwQfbkrcfMtFUNbqICH0/z8WmHbByPZQWu4SCBg1Rl5XrDUaWwZhhvbpqEZE+pRBdRERamTiugu9dexk/vPmOdpcxTYPrP3dxH/Yqd9VHXNavW8WXLjuy3WVu+PJX2rx9yZIlHHbYYb3VNRHJEaZp8q0vfbLN+9Zt3MpjT7/INVfOw+9rf7LiuScfxWnHz8Z2HFat39VbXW2TZcOuymQFenOF4eTtlg0+fbMREeHEOYdxwbmn4jguP//93cyaMZmyISW9tr5ILFmBHgomT2SGgwa24xKJ6cSmiOQWfdTcz4ofndHlx8Rti5119QCU5efh9+hpFZH+LT8vdMBlXBd++cf78Hk8fORDJ1AxorzVMpm+9L+/yAsZjBk3hd/+7XUcHEaUJyguSN4XjUbZtGEDFaNHEwzuGwM96PMS8vqYMmVKhnotIrniHw8uAOC2vz0EwLxzTmb0yKGtlvN5vfi8Xmy77/fpXk9yDPT3Nycr0JvUNMCEkcn7RUSk8/NcpOtzeiiQHLalIeoSbqxE95gGoUCPmhURyTpKe/fTnfHJw3gpDmlyNxEZWL51zaUU5IXZtHUnd9z7OD+8/opWy2Ty0v/+pKwEZk/L46Bxh5EXMigr0SR5ItJ32tp/ZxvDMDj+UJONO2w+2OZSGE4G6H5fcnJR7TNFRFo60DwX6fqcXlEOU8fAyvVGizHRK1rX14iI9GsK0UVEBqgDjY97IAV5yWvqK4aX4ff7qG+IkhdueUIxk5f+9yeGYVA+CEAhkIhIew6ZCODhhbccdlUmK9CPP9RsvF1EZOBIxzwX6fqc7jENjp4OI8sgEktWoFeUa1JREck9CtFFRAaojsbHPZCEZYELPp+XyupaGiJRwm1cs5nJS/9FRCS3GIbBoZPgkIkmlp0cwkUV6CIyEKVjnot0fk73mIYmERWRnKcQXUREWlmzbhOPL1xMVU0dt9x+PyfOmclh0yfzwKPP8KFT5pCwLH7/t4cI+H04jsMlHz1DQYaIiPQJwzA0iaiISDs6O8+FiIh0jeG6rpvpTrTHth2Wrt7MzMkj8XjangxDRESyn/bnIiK5Q/t0EZHcoX26iEjnaA8pIiIiIiIiIiIiItIOhegiIiIiIiIiIiIiIu3I6tEEm0aa0YR0IjIQmKaRs+OKa38uIgNJLu/PQft0ERlYtE8XEckdPdmnZ3WI7jjJnfk772/NcE9ERHpfchzC3PyArv25iAwkubw/B+3TRWRg0T5dRCR39GSfntUTizqOg2U5WX/mNxKL8Y0bb+Om736eUCCQ6e5ID+i1zB398bXM9n1dT/SX/Xk26o/vZen/9L7rmVzf12Vqn57r78tc3z7I/W3M9e2D3N/GtrZP+/S25fp7AQbGNsLA2E5tY+7o6XbmbCW6aZr4/dk/bLvHNEkkLDymqdms+zm9lrlDr2V26S/782yk97Jkgt530pFM7dNz/X2Z69sHub+Nub59kPvbmOvb15bu7tMHwnM1ELYRBsZ2ahtzRya3M3ef1T7k9Xo4+7Rj8Ho9me6K9JBey9yh11Jyhd7Lkgl630k2yvX3Za5vH+T+Nub69kHub2Oub186DYTnaiBsIwyM7dQ25o5MbmdWD+ciIiIiIiIiIiIiIpJJqkQXEREREREREREREWmHQnQRERERERERERERkXYoRBcRERERERERERERaYc30x3IFXf96yl27NrL9Z+7ONNdkW7avbeaG2+9k5HDyrAsm9EVQ7novNMy3S3phs1bd/LvJ58jkbCwbJtZ0ydz2glHZLpbIt2i44v0JR0LpT944NFnWLdxKwCHTpvE3JOPynCP0m/psvd4+Knn2b23mtt+en2mu5MWL72+jBdefQsDgwvPO5XRI4dmuktpdeufH2Djlh2ccuzhnHXqnEx3J+02btnBfQ8vxDQNTNPkk/PmUjq4ONPdSqtINMav//IgXo+HeMLi/LnHM3XimEx3K2vl+t/0QHjPN9mxay8/+OUdfPWzFzFx7MhMd6dXbNi8nYeefB7bcRgzcigfO/ukTHcprVzX5b5HnmHj5u3YjsNpx8/miJkHZbpbabH/8dV1Xe5/9Bk2bd1JKBjg8gvPIi8c6pO+KERPA8uy2bR1J4X5YfZU1jC4pDDTXZJuGjViKNddfSEAt9x+P1u372L40NIM90q6oiES44775vO5T51P2ZASXNdlxXsfZLpbIt2i44tkgo6Fku1OnHMYF5x7Ko7j8vPf382sGZMpG1KS6W6l1cRxFXzv2sv44c13ZLoraVHfEOXZxUv41hcvpaqmljvum883vnBJpruVVp+aN5eVazZQWV2b6a70iqKCPL585TyCwQDLVq7lsf/8jysuOjvT3UqrgN/P1z73CTwek117qvjT3Y/ynS+PyXS3stJA+JseCO/5JvOfeYlJ4yoy3Y1eY1k2Dz35HJ/75PkEg4FMd6dXbN2xm607dvPNL11KNBrjR7/6W86E6PsfX5ev/oB4PMHXP/8JXn7jXZ5+7lU+8qET+6QvGs4lDZatXMsh0yZw9OEH89rSFZnujqSBbTvEEwkCgdzcweayZavWMuOgCakv04ZhMG3yuAz3SqR7dHyRTNKxULJVeekggGR1oMfENHPvK01+XgifL3fqndZv2srEsSPxej0MGVRMLBYnYVmZ7lZalRTn9onuosL8VPjk9XrwmEaGe5R+pmng8ST3J9FojJHDdAK5PQPhb3ogvOcB1m3cSlFBHiVFBZnuSq9Zu2ELAb+fv9z7ODf/8T7WrNuU6S6lXXFhPl6PiWXbRGNx8sLBTHcpbfY/vr63bhMzpk4AYMZBE3hvbd+9nrn3iTMDXntrBUcddjAzDprAu6vXZbo70gMbt2znl3+4lx/88i+UFBWo6rMfqqyqYVBx7n4AkIFFxxfJBB0Lpb949c3llA4qZsigokx3RQ6griFKOLTvC30oFKS+IZrBHkl3xeJxHn3qRU4/8chMd6VXVFbXctPv7+bWPz/IzIMnZbo7WWsg/U3n+nv+yWdf5syTcm9YtOaqa+rYvG0nV1x8DpdfdDb/+NdTuK6b6W6lVTgUpGzIIL5305/58a/+xlmn5N6wYk3qGyKEG08ShEMB6iN9t+/JnfKGDGmIxFi7fgt3/+spAPbsrWbT1p1UDC/LcM+kO5pfwn7/o8/w2lsrOOLQ3LgEZqAoKS5k6/Zdme6GSI/p+CKZomOhZAPHcbjp93e3un36lPGcfdoxrHhvPYtfX8YXL/9oBnqXHgfaxlySFwoSafYlNxKJ5VSV3EBh2TZ/uvsxzjjpSIaXD8l0d3pFSVEB3/jCJezeW83Nf7yXGQdNyHSXstJA+ZvO9ff8OyvXMmbkUPLz+mY86UzJCwcZP3oEoWCAUDBAfl6I2voGCvPzMt21tFnx3nqqqmv5yTevIhKN8fPb7mHalLH4vLkX++aFgzQ07n8i0Rh5ob7b9+Tes9nH3ly2ijNPPoqTj5kFwMo163n1zeUKOXJAOBSkri6S6W5IF02fMp4Fi17mmNkzUkO6rHjvAw6aNDbDPRPpGh1fJBvoWCiZYpom3/rSJ9u8b93GrTz29Itcc+U8/D5fH/csfTraxlwzdtRwHn36RSzbprqmnkDAl5Nf7HOZ47j89b75HDptYs5WaCcsK/W+DAX9BAL+DPcoew2Ev+mB8J7ftHUHq9duZO36LWzZvovtu/Zw1SXnMrgkt67wSr5f/4dtOyQSCWrrGsjvo4ko+45LOBzENE0CAT+27eA4uVVt32TiuAreencNMw+exLJV65g0vu/G8zfcXLuGoY/d/Mf7uPRjZ6bCuoRl8aNb7uSG66/EzNExs3LV7r3V3HjrnYwcVoYLhAJ+rrj4HEI5OvFELtu0dScPPfEcCcvCsm1mzZjCacfPznS3RLpExxfJBB0LpT/44S+Tk202Vc7NO+dkRo8cmskupd2adZt4fOFi1m7YwvjRIzhxzkwOmz45093qkf+99g7/e+1tDAwuOPcUxlQMy3SX0uof/1zA2g1bsCyb4UNL+cKnP5LpLqXVkndW8bcHnkz9rY0YVspF552W4V6l14bN23nwsWcxTBPHcTjntGOYOnFMpruVtXL9b3ogvOebu/P+Jzj2yEOYOHZkprvSK15e8i4vvPIWtu0w96SjmDk9t06MOI7D3x9cwM49lViWzVGzpnHKsYdnultpsf/x9XOfPJ/7H1nI5u27CAb8XH7h2X12NYVCdBERERERERERERGRdmhiURERERERERERERGRdihEFxERERERERERERFph0J0EREREREREREREZF2KEQXEREREREREREREWmHQnQRERERERERERERkXYoRBcRERERERERERERaYdCdBERERERERERERGRdihEFxERERERERERERFph0J0EREREREREREREZF2KEQXEREREREREREREWmHQnQRERERERERERERkXYoRBcRERERERERERERaYdCdBERERERERERERGRdihEFxERERERERERERFph0J0EREREREREREREZF2KEQXEREREREREREREWmHQnQRERERERERERERkXYoRBcRERERERERERERaYdCdBERERERERERERGRdihEFxERERERERERERFph0J0EREREREREREREZF2KEQXEREREREREREREWmHQnQRERERERERERERkXYoRBcRERERERERERERaYdCdBERERERERERERGRdihEFxERERERERERERFph0J0EREREREREREREZF2KEQXEREREREREREREWmHQnQRERERERERERERkXYoRBcRERERERERERERaYdCdBERERERERERERGRdihEFxERERERERERERFph0J0EREREREREREREZF2eDPdgc748g9u4cH5zwLw2uN/pWJ4eeq+2WdfxuZtOxk5rIzX59+Z1vVu2rqDI865HIBbf3AtF3z4tAP2sTf6ISKSK5rvz5uEggEOmjiWz15yPmefemyX2vvl7fdw85/uBWDbkifS1s/29GRf/+7qtTz13CsAfObicykqyO+NLoqISBc9/8pS7nzwcZYsW01NbR1DBpUwfcp4Lv3oXE455vBMd6+Fnhz3OjoO9fXxtLmm73MfP/sUfn3DdX223k1bd/DA488AcME5p7b4jikyED34+DM8sWgxK9asZ/feKgYVFzLz4Ml843OXMnFsRWq55vsLwzAI+H2UFBUwcewoPjr3RD4y9yS8Xk+mNqNDv7z9HgDmzJrOnMNnZLg3ItLf9IsQXUREclckGmPJslV85hs/5Zbvf5mLzj09013qFctXr0t94bjgnFMVoouIZIEbf/NXfv/3f7e4beuOXWzdsYvN23ZkXYjeEzoOtbRp647U8zFn1nSF6DLg/ebOf7J2w+bU71t37Gbrjt3896UlPHXXrS2C9Cau6xKNxdm2cw/bdu7hhVeX8tBTz3Pnzd8lFAz0Zfc7pelvnqsuVoguIl2m4VzS5Nc3XMe2JU+oCl1EpJNee/yvbH7tMf78829jGAawrzqks66/+hNsW/JEp6rmorF4t/opIiK56aEFz6UC9OHlQ7j7NzfwwUsPs+yZe/nVD65l5LD0hKqRaKzd+2zbJpGw0rKenujK8VREclNJUT7f+dKnWfLk31n13ANc3FjY0hCJcsf9j7X5mH/f/lM2vPIIT/ztZmYfchAAz7/yJj+45c991u/+JFv2+SLSPTlZib5yzXp++ad7eHnJu9Q3NFAxfCgXnXsan7v0I5imyZbtuzjx45+jrj7CvLNO4Tc/Sl42+Mlrb2Dhi69ROriY5x78Q4s2YwmL79/8J/71xCIsy+bDpx/Hj756FeFQEGj7Ev/mt/36h1/hR7/+K6vXbmDMyGH84CtXcuLRh/XtEyMikmU8Hg9nn3IMUyeMYcWaD9i6Yze791ZRVVPHT377N95dvZY9ldU4jsPIYeV8ZO6JfOnT8/D5koevti4/b37bgrtu5bs//yPLVr3Pd665jKsuPq9VH5oP3fWzb36e1es28vBTz2HbTqt9fXv+99rb/ObOB3hr+RriiQRjK4ZzyUfO5PILzsEwjFbD2DStT0OAiYhkzq//+kDq59/d+DWOPuxgAIIBPxd++DTmnXVy6v5EwuK2u/7Nv55YxMYt2wkFAxw+YypfvepiZh48ObXcsFlnAXDtFRdiWRYPzH+GeDzBquce5CNXfZOXlyzj6FnTufi80/nVn+9jw5btPHXXrRw8efwBv8O05/W3V3DrHQ+w6v317K2qAQzGjx7OJeefyac/fjbQeji1/Y9D7Q3nsmzVWm7+0z289tYK6hsiVAwv5/wzT+RLl83D7/MBLY+7z97/O773i9t58933GFY2mOuuuoiPfWjf83ggDz7+DL/+6wNs37WXww6ezC+/+yVGjxyWun/Ttp384g938/wrb1JZXcvQssGce/rxfPWqiwkG/AC8v37zAT9DNO8zwEev/lbq521LnuCBxxZy7Q23AnD7T7/JQ0/9l+dfeYtxo4Zz07e/wJiK4Xz9J7/l+VeWMmJoKT+49gpOOXY2AJZlc92Pfs1bK95jx669NESilA4u5qSjZ/GtL36KIYOKAXjpjXdS6/3zz7/Nk4te4j8vvErA7+Pic0/nm1/4JB5Pdg6JIbnp/t/fSF44lPr9u1++nHsf/Q8A6zdvb/dxfp+Pw6ZP4a5f/4CjPnwFVTV13PfIf/jqZy6mvHRQu4/bumM3N//pXp57eQm79lRRUlTArBlTuO0nX0/9Pc9/5n/cfs/DrFizHsdxmTJhNJ+56Fw+MvfEVDvN962Xf/xsfnH7PWzcsoOpE0bz0299nkOmTmzx9wbJivSmfcC/b/8pcw6fQX1DhFv+fB9PPLuYrTt2U5Af5tjZh/D1z13K+NEjgJZ/t82H/G3eh4f+9LNWt7W1zxeR/ifnQvR3Vr7PeVd+vUXFx9oNm7nxN3fy/vrN/OoH1zJiaCnf+/IVfOP/fsc/n3iWi88/nT2V1Sx88TUA/u8bn2dQcSH1DZFUGzfffg8791Smfr/n4aeJxRP89kdfPWCfdu+t5qIvfo944xnHVWs3cMXXfsLr8+9kUHFhujZdRKTfcl133y+Gwcat23nquZdbLLN2w2Z+8ce7qa2r5wdfubJT7V70he9SVVPX6X7c9Ie7qKyuTf3emX39E4sWc9U3fobjOKnbVq3dwHd/cTur123k59/+YqfXLyIifWP7rj28t24jAJPHj04F6M01DzCv+ubPWhyX4gmLZxe/wYuvvcX9t/2k1eP/9s/5qeNPYX5ei/tWvLeOa75/S4tjX2e+w7RnxZr1LFr8Rovblr/3Ad+66Q84rsvlF5zT7mM7smTZKj529bdaXMm1dsMWfnn7Pbz57mru/vUPU1eSNTn/ym9QU1cPwAebtnLN929h+pQJTB436oDrW/TSkhZB//9ef5vPf+cXPPH3WwDYvG0ncy+9lj2V1allNm3dwe/+9k/eXvEeD9z2E4w0foZo8s2f/T712WDFmg/49HU/ZtTwcpYufw+ANR9s4jPf+Cmvz7+TwSVFWLbNP59oOf/Ltp17uPfR/7B8zQc8ddetrdbx9Z/8NrWO+oYIv/v7v/D5vHz9c5d2qa8iPdE8QAeIxff97Q/tIAxvUlSQz/lnnsidD87Hsm0Wv/FOi7C7uc3bdvKhT32FXXuqUrft3FPJgv++TCweJxjw86d7H+EHN7esaH9r+Xt84bu/YMv2nXzpso+3uO/dVWu56ps/S+1bly5/j8u/+hNeefQvB+x7PJFg3me/nfq7BthbVcNjC1/kuZff5Im/38KEMSMP2E5b2trni0j/1O+GczninMsZNuus1H+bt+1scf8Nv/oLkWiM6VPG88qjf+GDlx7mO1/6NAD3P7aQFWs+AODSj5zJsbMPAeAb//d7vv/LPwFw9inHcPYpx7Rar2EYPHPfb3n76bs44tBpQPIS0HUbtxywz9FYjIvOPZ2V/72f6z5zEZC8JGrRS28c4JEiIrnNtm3mP7uYle+vB5KX0w8pKWLimAru//2NLFt4D5tefYxlC+/h1MYKr3sf+U+LwLojE8eO4tXH7mDlf+/nrJNb79v35/f5urSvd12X7//yzziOw6DiQp66+1aWLbyHObOmA3DXvxewYs0H/PqG67i1WQDy2uN/1RBgIiIZtHX7rtTPYyuGd7jsi6+9lQpl5550NCsW3c9Df76JUDBAPGHxg5v/1OoxNXUN/PqHX+H9F//FY3/9RYv7qmvr+fS8s1ix6H5em38no0cM6/R3mLYceehBPPKXn7P82fvY9OpjvPHE35g+JVnleNe/FwB06zj0o1v/SjQWJ+D38cBtN7Lyv/dz/hknALBo8RupAqTmjp41neXP3scvv3sNkDxOPrnopXbX0dzuvVX8+obrWPXcA5xwVPKK3TffXc3WHbuBZMX7nspqRgwt5dn7f8f6lx/htp98DYAXX3ub/7zwKkCnPkNcf/Un+PftP02t+9+3/7Td4WwqhpezbOE9qcBuT2U1e6tqeH3+nfz4+quB5JA9i15aAoDP6+H2n36TN574G+tffoQ1L/yT66/+BABvr1jD2yvXtFpH6eASXn3sDl5+5C+MH50M6v5076NU13a+EEAk3X59R/JqHcMwUhXXBzKmYt+VI1t37Gp3uV/efk8qQP/Cpz7K8mfvY+lT/+CGr34Gn9dLTW09N912FwDjRg1n8cN/4rXH/8qkxhNyN//pXnY3O6EGUFvfwDc+fymrnnuACxv7u3XHLt58dzVzDp/R4u/7q1ddnPqbn3P4DP71xKJUgH75BWez+rkHU0NO1tTV89Pf/71T29+Wtvb5ItI/9bsQvSMNkSivvrUcSF56eNS5VzJ2zvn85Ld/Sy3z8pJlQPJA8MvvfolwKMh76zaydcduSooK+ek3P99m2xedezrTJo2jbMggrrlsHgCO47Bk2eoD9svr8fDday6juLCAc884PnX71u27u7upIiL93hHnXM7IIz7MZ77+f6nbrvvMxUDyy+SLry3lw5d/jfHHfoTpp32CZ/73OgA1dfXs3lvVqXX86KufYdSIoRQXFjBiaOkBl+/qvv799ZtTXxA+dtbJHDJ1IkMGFXPtlRemlnnhlaWd6quIiGSn5vvx66/+BCVFBRx92MF86OQ5QPJ7R3IYlX1OOvowPn7OqeSFQ0weP7rFfcWF+fzgK1dSUlRAxbAyPB6z099h2lJeOpiHFjzH6Z+4hrFzzufwsz7NslVrgWQ1eHc0RKK88c5KAE4+5nCOP3ImxYUFLSqjn3/lzVaP+/aXPsWg4sIW1acdBWnNzZw2iY+ffQpFBfnMPfGoVo9/7uXk+rZs38UpF36RMUefx+e/s+8ExctL3gXS9xmiyWUfP5shg4o56rBpqdvmnX0KI4eVcdKcWanbtjWG/R6Ph5q6ei6//kamnnQBE4+f12LOlw82tn5Nrv7EeYwaMZQxFcO48sLklQP1DZFUkYFIX/vj3Q/zt38mQ+fPXnJ+m1frtKV5tfX+V6o099/Gk07jRg3nO1+6jEHFhQwtHcxVF59HOBTkjWUraYhEAbjs4+cwbtQIKoaXc/UnzgMgFk/wypvvtmizbHAJX/r0PIoK8jn71GNTt3dmH/TCq2+lfv7m5z9FYUEeZ59yTLNx3rv/eX7/fX5BfrjbbYlIZvW74Vxee/yvLWZOn332Zalq9OraOmy74+rE5pf1jx45jJPmzOKJZxcDMPfEo1Jj1O1v+NAhqZ+Hlg1O/bxj154D9rl0cDH5eckdZdDvT90eTyQO+FgRkVwXDASYOmE0V19yPueenjzR+KNb7+DOB+e3+5hYvHP7zykTxnSpL13d1zcf+mVY2ZA2f96zX7AiIiKZN2JoWernAwXNezuxr99bVdNimMapHRx/xo0aQcDvS/3e1e8w+7vm+zenQuL9dfZ4ub/q2rrUVV/Dm29zectt3t+4iuS4wU3jGQPEO9mHsaP2XREQaOPxe/arOt1fVU3ydUrXZ4gmTSfhm3+PG9b4GcHv2/d1uum73fxn/sfXfvLb9tffxkTnw8v3nehv+fljb5f6KpIOd9z/GDf8KjkEyryzTuF7X768049dv2lb6ufhzfYX+2vaf4ytGN5m2F5Z1Wy/W77vb2L//W5zo0cOS80fEWy2j+3MPqiyOtlWfl6oRcg9vHHd9Q2RDvcdHe3D99/ni0j/1e9C9I4UFeRjmiaO43DxeWdw8/euabVM8zOjry5d3uLywgfnP8tlF5zd5iQPTZUFANt37gtTyksHt1p2f83HU+zobKyIyECy/0nR5ppObk6dMIZ7fnsDw8qG8KNb7+APdz3UpXU0/xLfGV3d15cUFbS5/Lad+9oZ3BiqaP8vIpI9yksHMXn8aFav3cDqtRt4delyjpw5rcUytm3j8XgYtN++vmnf33xfv/88R4EOjj/739fV7zDNNR9G5PgjZ3LbT77G4JIiPvP1/2N+47G0SVeOQ837tK3ZieTmx8m25nbyej1dXlfqsc2/M7Vx/6DiQnbuqeT4Iw/lgdt+0ur+pueo058hOtlHTxuTurZ1W5MnFiXXHwz4efgvP2fGlPEsWvwGl157Q7uPaf5eavn548DjUIuk09//+QTf/cXtAHxk7on86gdf7vTfc3VtHQ8/9RyQ/HueM2tGu8s2/T2v37wN13VbraOk+MCfsfffBzXtf6Dr+6CSomRbdfURausaUkH61h3JdeeFQwT8PvzNwvBos1B9y/aWwww319HxQET6l5waziUcCqY+/D604L8s+O/LRKIx9lRWM//ZxXzkqm+mqtYj0RjX/ehWXNfluCMOYdqksVi2zVduuBXLslu1fd+jC1m5Zj07d+/lN3f+EwDTNJk1fXLfbaCIyADRVM3l83kJBQMsW7WWfz25qNfX29V9/YQxI1PVY/9e8F+WrVrL7spqfvPXB1PLHH/UTACKCvNTt616f0NvbYKIiHTSly/fNyndF777C/770hJi8QS7K6u5/7GFXPG1ZFB73JGHppa75c/3Ulldy6tLl7Pgv8lx0g+ePL7NQLmzuvIdZn+WZacqxgN+H8GAn8Wvv80z/2s991JXjkPhUJBZ06cAyfHP//fa21TX1vGL2+9OLXP8kTO7tqE9dOLRyXHSX3ztbe56aAH1DRFqautZ9NIbfPq6H/Ny49AOnf0MUdzs+Vi9bmPaJv3bV61qkB8OsWtPFbcdoAjgT/c8wqatO9iweRt/uf9xIBnaHTRhbFr6JNIZ9zz8NN+66Q9AMkD/zQ3XtSgIbE88keDNZau49Ms3UF2bnFj4E+ef0eFJoKahkNZu2ML//e7vVFbXsnP3Xv5876M0RKIcPn0qoWAAgDsffIL1m7axadtO/nzvo0Byf3dUJ4eYaa7p7/799ZtJJKzU7ccdcWjq55//8W5qaut5ctFLvP72CgCObzwONK+E/99rb+G6Lg8teI4t2zs3bJWI9G85VYkO8P1rr+Ajn/kGkWiMy6+/sd3lfv6Hu1i3cSvBgJ+bvv1FqmvqOOvTX+Xd1ev47d/+yVeajWcLyTFxT77wCy1u+8jcExk3akSvbIeIyEB28jGH89CC53hn5ftMPSm5Px49Ymivr7er+3rDMLjhuiu56ps/Y/feKk7/RMvqwYvPO4ODJia/AE+bNA6Px8S2HT75lRtSbf/+xq/1wpaIiMiBnH/miby7eh23/ePfbNm+i4u/9P0W90+blNx/H3/kTE4//gj+88JrzH92cYsKb5/Xyw3XXdnjvnT2O8z+CvLDzD7kIF5/ewULX3yNCcd9DMMwqBhexsYtO/bbnq4dh7735cuY99lvE43Fmfe5b7e474SjDuP044/s4lb2zPWfvYRnF7/Bnspqvv6T3/H1n/yuxf1XXXwu0PnPEGNGDqMwP4+aunq+fdMf+PZNf2D2IQe1mgi2q045ZjYL/vsy0ViM4z56dbvrb25vVQ1HnNNyyIyrLj6XwoK8HvVFpCtuveP+1MmkhxY8x0MLnkvdN3JYWZsTEX/06m+1uu2kObP4wVc63i9ef/UnWPTSG+zaU8Xv/vZPfve3f6bu+/g5ybkRvv65S7nhV39h7YbNHH1ey/auvfJChpQUdWXzAJgxdSIvvLqUR//zAo/+5wUANr36GB8762TuemgBb69Yw1/ue5S/3Pdo6jEFeWG++YVPAsnhnQ45aCJvr1jD/GcXM+G4j9EQiRIMBIjGYl3uj4j0LzlViQ5w6EETWfCPX/Hh045jyKBifF4vw8uHcOLRh/GL73yJ8tJBLH13NX9u3Clec/kFjK0YzqHTJnHZx88G4Na/3MfqdRtbtPvVqz/BZy4+l5KiAvLzQlx83hnc9K0vtFq/iIj03I1f+yznnn48BXlhyocM4ttf/BQfO+vkXl9vd/b1Z596LPf//sccc/gM8vNC+H1eJo8fzQ+vu5JffOeLqeVGDivjZ9/8AqNGlOPx5NzhV0SkX/rely/nvt/9mDNOOJLBJUWp7w5nnHAU3/rCp1LL/eXn3+Hrn7uU8aNH4vd5KczP46Q5s3j4Lzcx5/D2hyzorM58h2nP73/yNU48+jDCoSAVw8u55ftf5qiZrSs0u3ocmn3IQTxyxy847bgjKCrIw+f1MrZiOF+58kL+/qvv9/kwZRXDynjqrlu56NzTGFY2GJ/XS9ngEo6eNZ0bvvoZpk+ZAHT+M0Q4FOQ3P7qOiWMr8HnTV1t20bmnce0VFzbOixXi42efwo+vv7rDx9z07S/wsbNOJj8vRElRIV/81Mf46lUXp61PIr0p4PcxrGwwJxx1GL/98Ve569YfpKrI2zNyWBlP3fVrLj739NTfc+ngYs488WgCjfMPfPaS8/njT7/BYQdPJhQMEAz4OeSgifzmR1/l2isu7LD99vz4a1dx5MxphEPBVtvwrz/+H5+95Hwqhpfj9XgoKSrk7FOOYf7fb2bS2FGpZW/7ydc4etZ0QsEAQ0sH8esbrmPmwZO61R8R6V8MN13XrfWSxRs+4Lz7kmc8g14vB5cN5bZzPsbYkkF8cf5DPPDuW6llTxs/iXvnXcJht93ClNIy7p13SYu2qqMRpv7m5yQcmycuuZKh+QXM+uOvWq3zgoMPZVRRMb9Y/ByrrvkGg8N5PL5qOTcvfo4PqioZU1zCV485kQ9PmcbGqkpm/fFXlIbzePPz1xGzLCbc+lO+dsyJfP24k7nvnaX87MVn2d1Qz9D8An562lmcPmEy597zV/ZGGnjxymTA8sl/38u7O7bz5uev44vzH+Lx1SvY8NXv8vMXF/GLxc8BUBAIMHfCFH71oXPxmR6+tfBJ/rX8bRKOzejiEu6fdynDC1ufjf3hoqf5/WuLOXHMeP55YfLLwH3vLOWaJx/m9PGTuGfeJTz53ko+9dB9PHLRZQCcd9+dGBjkB/xMLxvGN48/maMrxrRot2nbDQzCfh+TBpdy7dHH86FJUwEo/VnLap47zruAwaEw5913JweVlvP8FV9g2Y5tnHznH/jNh87nohkzufXlF7j99Zepi8cYUVjEH8/5GIcOG5F6Tc+ZPI1rnny41TZ+94RTufH5Z/jFGefw6ZmzeXjFMq567J/89LSzuHJW31bJiEjbsnV//qlJM/jOtT8DwJ49miEHjdb+XPtzEclx2XpM0neMgXNMeumNd1JVvP++/adpORkj0l3aJ2qfmOl9okh/0G+Gc7n5zA8zZUgZ59xzB3cseZUbT50LQNjn4+XPJC+fD/o6nvH4yfdWkXBsDAweW7WcG04+g7c//1UADrntZi6cPpNvHXcyYb+fP73+cupxr23eyBWPPMinZh7OHz88jzvefJUrH3mQJy69gvK85IQXuxrque+dpXzkoOmpx0WtBNc//RhXHX4Unzx0Nqt27aA4GOrW9j9/+RdYsm0z1y14lFkjKpg4aAh3vPkqfz3vAqaUlvHK5o3tTnDz+OrlGBj8b+MH7I00MCi0b7bphWvXsHp322Ms/uKMs5lWNpTvL3qKi/55N6985hqGFrQe7/Erc47n7MkH8Yv//ZfLHr6fZz79WaaXDwNIPacAJaEwb27dDMCKXTt4Zu17lOfvmzBkfdVefvL8M9x4ylxOHT+Jt7dvIbTfa3ru1GmcMGYcr2/dxJWPPMjNZ36YU8dNpDgU4tl1a/j5i4s4a9JUbnx+IdNKy7ls5uwuPMsi0heybX/+zf88QfPRHrU/1/5cRAaObDsm6TvGPjomifQ97RO1TwTtE0Xa02+uJy8Ohhg3aDCmYVAYaH5pkMHwwiKGFxa12Em15bHV71IazuPY0WN4fPVyTGPfYyF5YBheWNRqh3v/sqX4PCY/POkMppSW8cOTzsBrmtz3ztLUMuNKBnPbay9hNyvsj1oWcdvmfxs+YPHGDzhk6HCOGDmK7ijPz+fj0w4BYNWuHdTEogA8umo5K3ft5PwpB7fYMTZZum0LG6urOG/qwViOwxPvrWxx/9iSQfzu1cWtHgfJne/hIyq4+cwPUx+P8+Dyt9tcrjAQYHr5MH571vkYGPzjrX0TGTU9p8MLi1rsmMeVDOZ3r/6vRTs10eQYYgvXrubNrZs5Ycx4Jg8pa7FM2OdneGERQ0LJ8QGLgyGGFxYR9vn56WlnsTcS4UN3/YWN1VX832lntXuAE5HMybb9+f77Ce3PtT8XkYEj245J+o6xj45JIn1P+0TtE5PtaZ8o0pZ+807/wvx/M+23P2fy4FK+cOSxqdsbEnFG33wjo2++kZtebD3repPqaIQX1q/j+DHjOWnsBLbW1vDG1k2dWveW2mpKQmHyGsfmyvP7KQmF2Fpbk1rm4wcfQl08xvzVK1K3FQdDfHb20by9fRtfWfAoh/3hV9y/bN8B4L09u1J9/8/77x2wH29uS55NHFVcwsnjJnLkyFE8uupdrnjkAQ77wy0s37m91WMeW5Wcpf76Y06kIBBI/d7k80fM4aEV77Ct2bbsb2xJcgzGzdVVHfavKBhiUDjMpmbL3fXWktQ2rt27O3X7lbOO5PUtm1i6bUvqtunlQzln8jSeX7+Oz8//NzNvu4XnPni/w3U2N61sKJ+eOZv1VXv5yEHTmTNqTKcfKyJ9J9v254OGFHPCNy7k1Wfvxh0zRPtztD8XkYEj245J+o7RWq4ek+YcPoNtS55g25InNJSLZA3tE7VPPBB9TpeBrN+E6N8/8XSuPfp4lu/awX/Wrk7dHvb5+O/ln+O/l3+Ozxx+VLuPX7BmFXHb5sSx4zlp7EQAHlu1vFPrHlFQRGWkgYZEHEgeQKqiEYY3u8Qm6PVy5awj+f1+Zxd/fMpc3vnCV7ll7rmE/T7uentJ6r4xxYNSfT929JgO+3DYH37FuffcyewRFXzq0MMJ+XzMv+RKXrjiC1w35wQqoxEe3W9HDfD4qhWMKipm0pBSjhs1jv9tSF5a1OSQocM5auRo/vTGK+2ue93evcnnoY1xv5qrikbY29DAyGbLnTf14NQ2jioqSd1enl/AvGmHtHi+DMPgr+dfwGtXX8uPTzmTuG23GHutM86ZfBAAZ006qEuPE5G+o/259uedof25iPQFHZN0TOoMHZNkoNA+UfvEztA+UQaqfhOil+cX8LVjT2R4QWGrHWbQ4yXo8WI7Tuq2vZEGnl+/lufXr2Xt3j2pHfeXnniYk+68DYDHV6+gM/OqXjD9UOK2zQ//+x9W797JD//7H+K2zYXTZ7ZY7vLDjmB7XW3q97pYjG/8Zz4bq6s4csQohoTzKA3np+73ezyMKxnMuJLBhH3+DvvwyMWXseKar/PkpZ+hIBDk5U3r+c0rL2I7LieMGQ/Qom2At7ZtYUN1JRurqyj92fd5cs1KLMfhyf0uLfriUceyrnJPq3VWRhp4Y8smvvrUo4R9fj5+8KFt9q0mFmPZjm18cf5DuLhceujhqfs8ppl6feK23XK9Rx7LB5V7U7+v3bubnzz/DNXRCHMqxpLn97faJhHp/7Q/1/5cRCRb6JikY5KI7KN9ovaJItK+fjOxKIDX9PDpmbP5vxeeZUnjJUENiQSH3HYzkDy798ynPwvAkq2b+dj9fwfg6sOP5vn1azljwmS+0TjZwr+Wv81tr73EG1s3MXtEx+NlHTlyNH8+9+Pc8tJzPLBsKaOLS/jzuR/niJGj2FhVmVquJBTm0kNmcfsbyckxPKbJ+3t2c/E/7yZmW0wtLeebjevvqlFFxQwO56V+z/MFeHTlu9y8+DlMw+RDE6dy8YzDWjzmsVXJiS3u/tjFDCsoxHIc5t3/dx5d9S4fmbrvksGTxk7g4LKhvLvfZUlfe3o+eX4/B5WVc//HL2FYG5NbANzy0vP88fWXmDBoCHeefyGHDB2euu/+ZUtTl1L99LSzmNpsrK0Jg4cwd+IUnlyTPLgEvT4Wb/yAvyx5Fcd1mDV8JJ8/Yk63ni8RyW7an2t/LiKSLXRM0jFJRPbRPlH7RBFpm+F25pSgiIiIiIiIiIiIiMgA1K8q0UVEREREREREBqKNW3Zw38MLMU0D0zT55Ly5lA4uTt1f3xDhr/c/QSQao2J4GReeeyqGYWSuwyIiOUSV6CIiIiIiIiIiWa66po6A30cwGGDZyrW89tZKrrjo7NT9Dz35HMPKh3D0rIP524NPcviMKRw8ZVwGeywikjuyemJR13WxbadTk1CIiEj20v5cRESyhY5JItJfFRXmEwwGAPB6PXjMllXm763dxIypEwA4ZOoE3lu3qd22EpZFJBqjIRIlGotrnygicgBZPZyL47gsXb2ZmZNH4vHoEiQRkf5K+3MREckWTcekvx9xJon6hkx3J+0+MnlwprvQK4YuWpTpLvSaEQ/dmOku9JrBX/xFpruQk2LxOI8+9SKf/PjcFrfXR6KEQ8mQPRQKUN8QabeNBYteYf7Cxfh8Xi67ZB6Tx2/E41GQLiK5K+w9rkePz+oQXUSkPa4LjgumARrmT0REREREBgLLtvnT3Y9xxklHMrx8SIv7wqEAkWiMcChIJBojLxxqt525Jx/FacfPxnYcVq3f1dvdFhHp97J6OBcRkf05LiRsiNmQcBr/tZO3i4iIiIiI5CrHcfnrffM5dNpEZh48qdX9k8aNYtmqdQC8u2odk8ZVtNuWz+slFAwQCgR6rb8iIrlElegikvWaqs4tB9rKym0XbBsMwGuqOl1ERERERHLP0ndXs2zlWmpq63n1zeWMGFbK9Cnjqa1v4OhZB3PGiUfy1/vn8/zLSxk5tJSDJo3NdJdFRHKGQnQRyVpuY3Bud7LK3CVZnQ7gMcDTGKiLiIiIiIj0d7NmTGHWjCnt3p+fF+KaK+b1YY9ERAYOhegiknVcNxmG92SIlubV6X6PKtNFRERERERERKR7FKKLSFZK1xjnbuN/ytD7h2XvbyWesDLdjZzl93mZPmF4j9rQa9S79Bplv3S8RiIiIiIi0r8oRBcRkawRT1jMmjoq093IWUtWbuxxG3qNepdeo+yXjtdIRERERET6FzPTHRARERERERERERERyVYK0UVERERERERERERE2qEQXURERERERERERESkHQrRRURERERERERERETaoRBdRLKO62a6ByIiIiIiIiIiIkneTHdARKSJ64LlgK0QXUREREREREREsoRCdBHJKNcFxwXbASfNbXsM8JhgGmluWEREREREREREBgyF6CKSEa6brDi30pycG4C3MTg3FJ6LiIiIiIiIiEgPKUQXkT7lupBwktXn6aSqcxERERERERER6Q0K0UWkz6UzQDcN8JmqOhcRERERERERkd5hZroDIiI94dGwLSIiIiIiIiIi0osyEqLv2LWXz37jF6z5YHMmVi8iIiIiIiIiIiIi0ikZCdHnP/MSk8ZVZGLVIiIiIiIiIiIiIiKd1udjoq/buJWigjxMjb8gIiIiIiIiIiIiIlmuzyvRn3z2Zc486agOl0lYFpFojEgs1ke9EhERERERERERERFprU8r0d9ZuZYxI4eSnxfqcLkFi15h/sLF+HxeLrtkXh/1TkRERERERERERESkpT4N0Tdt3cHqtRtZu34LW7bvYvuuPVx1ybkMLilqsdzck4/itONnYzsOq9bv6ssuikg/k3DAccFjgqlRokRERAasu/71FDt27eX6z12c6a6IiPSaW//8ABu37OCUYw/nrFPntLjvpdeX8fjCxQwuKQTgiovPoaSoIBPdFBHJOX0aop91yhzOOiW5k7/z/ic49shDWgXoAD6vF5/Xi207fdk9EekDhgEBD1gO2G562rRdsG0wAG9jmK5pF0RERAYOy7LZtHUnhflh9lTWpAIkEZFc86l5c1m5ZgOV1bVt3n/sETNahesiItJzfT6xaJPLLjwrU6sWkQwzDPB5wOsmq8gtB9KRp7skK9MhGaZ7FKaLiIgMCMtWruWQaRMYWjqI15auYO7JredgSlgWlmVjOyrUEZH+q6S445OELy95l3dXr2PK+FGcc/pxmO1crqt9oohI12QsRBcRMYxk0O0xk2G6ncbqdMsBi2RVus9UmC7psbm6iuueeozlO7enbvN5PHx29tFcffjRGHqjiYhkxGtvreBjZ59MYUGYW//8YJshuuZdEpFcd8i0iRw1axoAf3vgSV5dupyjZx3c5rLaJ4qIdI1CdBHJCqYBZmN1uu2AlaYw3XGTFeqKNqWnHlu1nOsWPEp+IMBlM2enAvMNVZV879mneO6Dtfz2rPMpzcvPcE9FRAaWhkiMteu3cPe/ngJgz95qNm3dScXwshbLad4lEcl1eeFg6ufZh05l+XsftBuia58oItI1CtFFJKsYjZXplp3pnogkNSTifOeZBdz99hLOmTyNW+Z+mOJgqMUy50yZxjVPPMwJf72N3511PiePm5ih3oqIDDxvLlvFmScfxcnHzAJg5Zr1vPrm8lYhuuZdEpFc1xCJEg4lg/RV729gaOmgdpfVPlFEpGvMTHdAREQkWzmuw2ceeZCHVrzDr+aeyx3nfbxVgA5w2vhJPHf55zm4bCgX/fNuXly/LgO9FREZmF59cwUHTx6X+n3C2JG8veJ9HCdNl7WJiGSRf/xzAU8//yovvbGM3//tITZt2cHTz70KwNPPvcb//eYf3PS7u6mPRDn2yEMy3FsRkdyhSnQREZF23LL4eRauXcO98z7BqeMndbhseX4B9827hAse/AefefRBnr3sc4woLOqjnoqIDFxf/exFLX73eb38+OufyVBvRER61yfnzW11W8WIcgDOn3s85889vq+7JCIyIKgSXUREpA0L177Hz//3HN847qQDBuhNPKbJ7R+eR8jn47KH7ydqJXq5lyIiIiIiIiLS21SJLiIisp8PKvfyucf+xekTJvGVOV2r5hkczuNvH7mIs+76C99e+CS3zD23l3opIiIiIiKSXdbVVqelnXEFuqpXsotCdJF+znHBIDkhZ6bbcV0XxwXTAKMHDTW1k+xPDzcsDVwXXNLzPEv2iyQSXPbQfQwO53Hb2R/FNLp+0dYhQ4fzizPO4ZonH2bm8JFcesisXuipiIiIiIhI70tXMN5b61TgLn1BIbpIP+S6ydDbcpLhLiSDa6/ZtaDXdcF2wW6jHbMLYbHruiRsiNvJ8BvA53Hxeww8XWjIaWrHcpv1x8VjGhkJ053G58ZuNi+Z1wSPoTA9l9304iLWVu7hP5+6msJgsNvtXDRjJq9v2ch3n1nACaPHMaq4JI29FBERERERSa9MhOXp0FG/FbBLuihEF+lH3MbgvHmo28RxIW4nQ3TPAYLetsLh7rRjOy5xOxl87y9hQ8J28Rgufq+RDPjbaMh1XWw3GZxbTtv9cWwXg6YwvfPV6R4juQ1dOyGw7/lpoztYDlh076SFZL+l27bwh9df4rsnnMrU0vIet/ejk8/kvx+8z1efeowHL/hkVlxZISIiIiIiA1t/Dcu7o61tVbAu3aEQXSTLHSjUbbU8+4Le5iFyW9Xr3WvHJeEkQ2+nEw3ZLkQSyQX9nmSgbhpGm9XrB+5PcsGmvrQVSHYm/G+z/caq/LaC/LZ05WSD9A9x2+LaJx9hevkwPnfEnLS0mR8IcPOZH+aCB+/ivmVLuXjGYWlpV0REREREpLMGUmjeGfs/HwrVpTMUootkMduBRCdD3TYf74LdRpV4d9uxHRe7M4l3O+KNobmB26kgv93+OC42YBgu3sahXkwjGWSb3QjPEw6dCvLbfDwtTzYkK+6715Zk1m9efpH39uxi4ac+i9f0pK3dk8dN5IKDD+X7zz7FyeMmMjS/IG1ti4iIiIiI7E+hedcoVJfO6PpsaSLSZzpbFd1XehKgN5eeVpIBuGlAwAN+T7IivKsBtkv3A/T9tTU8jvQPK3ft4JaXXuCao47j4PKhaW//x6ecid/r5Zv/mY/r6o0iIiIiIiLpta62OvWf9Ezz51LPpzRRiC4i/ZqGUZGesh2Ha598hLElg7huzgm9so6SUJibTjuLJ95byWOrl/fKOkREREREZGBR0Ns39DwLaDgXEREZ4P74+kss3baVJy69goC39w6L50yZxtmTD+Kb/3mCY0eNZXA4r9fWJSIiIiIiuUlBbmY1f/417MvAokp0EREZsNbu3c3PXlzEZ2cfzewRo3p9fT877Sxsx+E7zyzo9XWJiIiIiEhuUCV0dtLrMrAoRBcRkQHJcR2uefIRhhUU8s3jT+6TdZbnF/CTUz/Ev1e8w1NrVvXJOkVEREREpP9RQNu/6LXKfQrRRURkQPrLktd4bfNGbp17HmGfv8/W+7FpMzh9/CSuf/pxqqKRPluviIiIiIhkP4Wx/ZtOfuQuhegivcBxXBzHTUNL6Wgjne2I5IYPKvfyk+cXcvlhRzBn1Jg+XbdhGPzyzA8TSST43rNP9em6RUREREQkOyl4zT16TXOLQnSRNHFdl7jlUB2x2VOf/K+6wSJuObhu50Ns13VJ2C71cQfbSf7elce31R4YPWpjXzs919ROutozjLQ0IwNI3Lb4wvx/MziUx/dOPC0jfRhWUMiPTj6T+5ct5bFVyzPSBxERERERySxVLQ8Meo1zgzfTHRDp7xzXJZpwicQd9i8+j9sQjziYBoT8JkGfgdlO6uu4Lgkb4pbbrG7cxTTAYyYf47ouRidT46Zlowmb6oYEHtOkJM+H12N2q52eZt5NobnluCRsG49p4PfsO4/X2f408Zrg9xqp56a7TAP8HrAcWr1+XeExwGMq1O8PfrjoP7y1bSuPfeJy8v2BjPXj4hkzeW79+1zz5MMcVFrOhMFDMtYXERERERHpOwpUB6am131cQVGGeyLdoUp0kW5K2C41EZs9dTb1sdYBenOOC/Uxhz11NjURm4TtpirMLcelIe5QF3OJtQjQ9z02YbskbAfX7bgyvek+x3Gpakjwwc56NuyOUNVgsacuzvs76tm8N0JDzE4t3147Tf86LsQtB6sbCXNTO05jlX5dzCISt7Fsl1jCoTZqEU3YqefuQNXpBhDwGuQHDMJ+E28PA/QmTUF6wJMM5zvLILl8wAM+T7IdyW4Pr1jGn5e8wo9POZPDR1RktC+GYfCruecyoqCITz98H3XxWEb7IyIiIiIivUsVyQJ6H/RXqkQX6aK45VIXs7Gd7j0+ZrnELBuvSbIqvJOPc12wGkNmj2lg0lQhnrzNMAzilkNlfYKaSKLdUL8ualEXtfB5DIrz/BSHfXjMlu24gG13fGKg/X7ua8d2XOKW3WEAnzxBYGEaBn6vic+z776m6nSPCX6PgdfsesV6VxgGeI1kVbnjgu1AWy+zaSTDcwNVnvcnq3fv5NoFj/LRg2Zw+WFHZLo7AOT7A9x5/oWc9vfb+epTj/HHcz7Wq+9xERERERHJjFwLTZ/clLmrej9UkRsFSKpM718Uoot0UTThdDtAby4VgHcjMLMdFxswDJeE5ZCwXarq40QSne9YwnbZVRNjd02MgpCX8qIgAI7t9GgaUttxsd1kv7oSwieHxbGJJsDvMfF6DPweCPtNzD4u8TaMfcOzuO6+oV48ZvJ2ZZz9T20symUP38+oomJuPvPDWRVUTxpSyq0fOperHv0ns4ZXcNXhR2W6SyIiIiIikib9OTzPZFDekfb61V/DdYXp/YNCdJFM6mGQ57qwrSpKwu7BxKNATcSiJM/p8fjiAA1xu8dtxG2HuA2hPG+fB+j7MwxaVMdL/1MdjXDhg3exva6W/3zyavL8/kx3qZXzp07nza2b+e4zC8jz+fnEIYdluksiIiIikoVu/fMDbNyyg1OOPZyzTp3T4r5EwuLv/1zA3qoaBhUX8ql5c/H5FPtkSn8Lz7M1MO+KtrahPwXrCtOzm/amIiKSs/ZGGvj4A/9gQ1UlD1346ayevPOGk88gallcu+AREo7Np2fOznSXRERERCTLfGreXFau2UBldW2r+156YxlDSwdx5cXnMH/hYl56YxknHD0zA70c2PpLeJ4LoXln7L+d/SFUX1dbrSA9CylEFxGRnLSrvo6P3f93dtTV8vBFl3Fw+dBMd6lDpmHy89PPxu/x8LWnHydqJfjs7DkHfqCIiIiIDBglxYXt3vfeuk2cceKRAMw4aAJPP/dquyF6wrKwLBvbScNYpZKS7QH6QAnOO9JfQnVVpWcfhegiIpJz3tq2hc89/m9qYlEe/cTlTB5SlukudYphGNx4ylwCXi/fe/YpttXW8M3jTiHk82W6ayIiOeeciYMgEsp0N9LuodV7Mt2FXlG2bFumu9BrfnjmRzPdBckRdfURwqHkXFfhUID6hki7yy5Y9ArzFy7G5/Ny2SXz+qqLOStbw3OF5gfW/DnKxkBdVenZQyG6iIjkjEgiwc//t4jbXnuJg0rLuftjlzN+UPYO4dIWwzD43gmnURrO4yfPP8tTa1bzq7nnMmfUmEx3TURERESyWF44SCQSBYqIRGLkhds/UTj35KM47fjZ2I7DqvW7+q6TOSgbA3SF592TrYG6qtKzg0J0yXqu6+ICZg8n4XRcF9elx5Nnum73J/FsznaS29XT/phGcvLLnnbLsh0Mw+z58+y4GEYyCOyJdGyT2/ia97Q/6WpHeo/tODy7bg3ffXYBW2tq+Pbxp/D5I47B5+mfs8IahsHnjjiGU8dP4toFj3LuvX/l0zNn85Wjj2d4oT44iYiIiEhrk8aNYtmqdVSMKGfZqnVMGlfR7rI+rxef14ttaziX7sq28FzBeXplY6CuqvTMUoguWctxXOK2S9xO/u4xXPxeA6/ZtSAzYTvUxWwaYg4uEPAa5Ac8BH1mF9txicSdVH+6w3Vd4pZDXcwimkh+WMkPeCgM+wh4u9afJsNLQrhAXdSiJmIRt7r3IWhLZRTDgIKgl8KQD7/X7PRjXdelIW5TF7VI2C4G4PMa+D0mZhdPEvg8Bn6vB49pYLkQs1x8nq6dRHHd5Psmbu8Lv/2e5H9deY4d1yVhQ9xqOpGTbMPXxXak92yqruK+d97kvmVL2VxTzVEjR3Pvxy7J6glEu2Li4FIe/8Tl3Pnm69z4/DP84603OHncBD4xYxZnTJjcb08SiIiIiEj3/OOfC1i7YQuWZbN+83Y+fNoxrFiznjNOPJI5sw/m7w8u4Oe33UNJUQGf/viHMt3dnJVNAbrC897X9BxnQ5iuqvTMUYguWcV1XSwnGVra+1Uh2y5EEo0BrScZqLcXrLquSyThUBe1ie/XUMxyiVkWppEMsPMCnnarwV3XJZpIttWTE/SO69IQs6mPWVhOy/7UxWzqYjY+j0FR2Ed+wNul4NkwDAz2hd/RhE1NxKI+atHVQm7XhZpIMowP+kwKQz7yAp52A2Or8QRFfcxqUTXuknwN45aNxwS/18RrGu22YxrJZXye1sF98vUCr+ni9xh4OjiJYjsuccslsd9r5bot2wl4jcYK/tbtuG7yvRe3ku/F5hwXopZL1Gp8D3qMHl9JIN33pzde4bvPLCDs9/GRqdO55JBZzBw2IudOcJiGyRWzjuTjBx/CIyvf5e63l3DZw/dTlpfPK1ddQ0EgmOkuioiIiEgf+eS8ua1uqxhRDoDf5+Mzn/hwX3dpwMmGAF3BeWZkW5iuIL1vKUSXrLB/xW9HXEhVGe8frFq2S33Mpi5uH3AoEMeFmqhNTdQm5DPJD3jwe41UO9GEQyTRs/FE4pZDfcyioRPl6wnbZXdtnD21cQpCXa8GbwoOA16TssIAdr6f2kiCmqiFtf8ZiU6IJhyiiRimAYUhH4UhL16P2XhiwaEuahHrRNW77UAk7mDQGJQ3O/nh9SSr1Zva7Sj8tBywGoeKaV5V7ja9d2wXpxObaTlgxd1WVeVdbSdhJ1+z7l4hIT13/Ohx/GruuZw7dRr5/tz/EFkQCHLpoYdz6aGHs2LnDl7atF4BuoiIiIhIH1F4Lk2yJUxXkN63FKJLRjmNld7dHIEkFaw6TrLqef+q886KJBwiCQevaeDzmJ0KUTsSS9hURxIkutEfl33V4EUhLyX5/i4NZdIU5HrMZGV7cZ6fPbUxqiNWl/sCyZMNVQ0JqhoSBH1m6raucoGY5RCzoCjkJS/oxWwMr5v3+4DtNKsqN3Hp7gUCzavKTaNzwXlbmq6QAPB7klXuCtP7xpTSMqaUlmW6GxlxUFk5B5WVZ7obIiIiIiIDQqYD9GwOzxdsDvfJeuaObOiT9XRFNoTpCtL7jkJ0ySjLptsBenMJ2+l2gL4/2+m4Iroz6mJWtwL0/eUFvfSkJ03bUR/rwUDuzaTjuTGAgpBv3+89aC9dU+D09KRJk7gNAe1VRUREREREckYmA/RsCs/7KizvzvozHbBnOkzXOOl9Q3GPSJZLR1VzmjJiERERERERkQEjUwF6psPzTAfmXbV/fzMVqmdDmK4gvfcoRBcREREREREREWlmoAXo/S0470imQ/VMhukK0nuPQnQREREREREREZFGmQjQ+zo8z6XQ/ECab2tfBupPbgooSM8hfRqib9yyg/seXohpGpimySfnzaV0cHFfdkFERERERERERKRNuR6gD6TwvC19HahnqipdQXr69WmIXlSQx5evnEcwGGDZyrU89p//ccVFZ/dlF0RERERERERERFrp6wC9r8LzgR6ct6cvA/VMVKUrSE+vvg3RC/P3rdjrwWO2PWFiwrKwLBvbcfqqa5IhmvBSRJrz+7wsWbkx093IWX5fzw/7eo16l16j7JeO10hERESyTy4G6ArPO6/puerNMD0TVekK0tMnI98CYvE4jz71Ip/8+Nw271+w6BXmL1yMz+flskvm9XHvpC+4LlhO8r9s4rb4qe2TPAdsw3W7+cjWHMdNtmd0v0XXdTENsNPUp572x3GT2wT0qJ1sVRdzCfjAZ+bm9vW26ROGZ7oLcgB6jbKfXiMRERGRrunLAF3heXbrqzBdQXr/0+chumXb/OnuxzjjpCMZXj6kzWXmnnwUpx0/G9txWLV+Vx/3UHqL64Ljgu1AU3ZuGsn/OY7bo6r0/IBJ0GdQH3OwnO63ZDsuGCTH7Te6Fhg3Leu44DENDHpWae+4LtuqohSGfJTk+fB6zG71J2G7+L0mCbtnMbpB8vVr5wKSLvTHYe2uOorDfgbl+VMnHLoaOHtM8BjJPvXkZIwBGCa4TnqujHCBaMIlCvg9Ln6PgdndJ01ERERERER6VS4F6Nkcnq9b3nZoPG5a306o2lm9HaYrSO9/+jREdxyXv943n0OnTWTmwZPaXc7n9eLzerHtLCtTlm5xXbAbw/P9Q0rDMPAYydDZcV0cx6WzGbhpgN9j4POAYZgA5Adc4rZLXdQmkuje+8d2XWzbxTDAYxiYqb5CW9XpTVXVMcuhPmYR7eZ6m9pJnmhwafyV3bVxdtfGKQh6Kcn3kRfwthumN7UBUBezqYkkiHWzP03M1EmFrgfBTf1JBss2DQk7eaICqI5YbK6MMDjPT1lBgIDP06mTBH4PrYJpx3VJ2BC3On8yxtN4oiS1PjPZX7sL78EDidsQt108hovfa+BVdbqIiIiIiEjWyJUAPZPheXvheLofn6mwvTfD9L4e3kVBes/0aYi+9N3VLFu5lprael59czkjhpVy0Xmn9WUXpA+5LiQcuhCKG5geA9d1U0FyW3wm+L1Gm2PqG4ZBwGsQyDexHZeGuE1dzKY752NcFyzXBVw8RnJ9hkGLoUgcx6U+blEfs9vtb+fW5WI77W8zQG3UojZq4fealOT5KAr7WlSFG4aB7bhURyxqI4keB8GtQuZOav78WI5LQ8Immmi7Ct52XHbWxthZG6Mg6KU0P0Bx2Jd6fKovBh2G0KZhEPAmA3bLSQbXbb3mRuN2Ge1sl2EYeJu9B3t6hURqO12IJJLD/Pi9EPAqSBcREREREcmkXAjQ+zo872lg3hvr7qtwvbfDdAXp2a9PQ/RZM6Ywa8aUvlylZJDjdj5Ab66pOt00khXMtuOCmwxR/Z7Oh7oe06Ag6CU/4CFmuVTWJ7C7mYg2Vacnh3hJ/lcft4nEuz9EitP45HS18jluOeyojrGzJkZhyEtJnh/HdamJWD3qDySr7ZuGouluxbTluFiOQ0Pc7tLQOk0nCXwegyH5AUrCfvxeg8Jg+5MQt+5/8soEnyd5giNmJyvUzabt6uQ2Nb9CwnVdLDs9YboLxCy3S+9jERERERER6b96I0Dvq/A8k6F5ZzXvY18E6gs2h/t9kC7dk5GJRUU6wzCSYa7pSYaNgW6+Ww3DIOgzCPk91MV6FjI7LtRGLGJpmBHVajZkS3e4LlQ3WDTE7W4NtbI/n6fzIXNH9jbEe/T4hO2yrTrKtuoo00cU4DG798KbpkHAMOjpJhmGgcekR2Pti4iIiIiISPboqyr0/hig94fgvD379723QvXeqkrvqyBd1ejdYx54ERERERERERERkf6vvwboCzaHey1AX7c8lvovl/T2NvXG69Hbk8826cvhjHKFQnQREREREREREcl5/TlA7w25GJy3pTdPEvTGyQ0F6dlJIbqIiIiIiIiIiEga9IcAfaCE523pzTA9nfoqSJfOU4guIiIiIiIiIiI5rS+qbrM9QB/I4fn+euO56I9BuqrRO08Ti0orbuNslz2dZNJ1kxNnpqOd5JyOPWvHcV1sx8Vj9qwdy3FJ2A4+T8/OQSVsB9elx+04rotBz5/ndDGAdEzB2dPXqYntuJhG9jw/0rFl728lnrAy3Y2c5fd5mT5heI/a0GvUu/QaZb90vEaSXrv3VnPjrXcyclgZlmUzumIoF513Wqa7JSLSK156fRkvvPoWBgYXnncqo0cObXHf4wsXM7ikEIArLj6HkqKCTHU1q/S3AL03wnNpW9Nzk65JSNM96WhfTDaqiUY7RyG6pLiuS8KGuJ0MrT2Gi99r4DW7FkA6rks04RKJO8l2TPB5Tbym0aV2LNuhqiFBZX0C23EpDHoYUuCnIOjpUjsNcZsNexvYsDeC5bgUh3wMyQuQ5+9aO3sb4ry9rZZ3d9ThuC5ji0NMHpLH4LC/020AbKuJ8vqmKt7aWoMJzBhWyGEjiijN7/wO23VdLMclYlnEHAcDyPN7yfd78ZpdC+U9poHPY+BtDK1tNxk8d1dpfgDHhYaERSRh43ahKQMoDvsoLQhQEPThui5xy8Xn6fp7sC5qU91gEbddvB6D/ICXUMCD2Y0w3TDA7zVxXBfHaTqpI70hnrCYNXVUpruRs5as3NjjNvQa9S69RtkvHa+RpN+oEUO57uoLAbjl9vvZun0Xw4eWZrhXIiLpVd8Q5dnFS/jWFy+lqqaWO+6bzze+cEmLZY49YgZnnTonQz0cuLI1QM90eO57eUuXlk8cPaKXenJgvRGmK0jPLQrRBdtxidvJAL3F7S5EEsm00O9x8XsMzHaqg5OhLkTiDjGrZcJoO2DHk0Gvz2vi8xrtBpmu69IQt6msj1MXbdmhmqhNTTSCz2NQWuBnUJ4XbztV3K7rsrM2zvq9Deyqi7e4ryqSoCqSIOA1GZIXYFDY327Vs+24rN3bwFtba9hSE2tRZb2uMsLaygglQS9ThuQzpiTYboCdsB2Wb6/l1Y2VbK2JYRrguGADS7dWs2RLNSOLgswaWcyU0vx2++O4LjHbJmLZ2M3SaReoi1vUxS0CHpN8v5egt+OTBD6Pgd9jYpoGruumlvXg4vEYyf41Xk3QFYZh4DFc8htD/ajlEElYJOz2G/J7TIYU+CnND+D1mC2uhohaLlELfGbypE5HFepxy6EmYlETbRneW7ZLVUOC6oYE4YCHvKC3S1cAGM3+beqf042TDV4TAt6unUwSERGRzrNth3giQSCgcURFJPes37SViWNH4vV6GDKomFgsTsKy8Hn3RTsvL3mXd1evY8r4UZxz+nHtfocfSHq7Cl0BetfD8u6001cB+7rlsQEbpEvHFKIPUK7rknAgbnWuqjbeWKG+f3W621R1nnCwnQOsk2TIGbeS1el+r4mnsTrddlyqGxJU1sc7DFsBErbL1qoY26piFIe9DCnwE/abGIZBzLLZuDfKhr0NRC2nwwFgYpbDluoIW6sjDAr7GZznJ+xP/knUxiyWba/lnW21RJq107xnTT9XRi1e3lzF61sNJgwKM2lwmKKgD4A99XFe31TFm1uqiTVrp/lz3vTzluoom6u387TX5LARRRw6vIjiULIdy3GIWDZRe78zHW1tl+0Qi8QxDcj3+8jzeVPBs9lYUd1Udd4U5jYPdZt+Ng0XD/uqrw/wsuzHoKnJoNck5Atg2Q4NCZtowk49d4VBL2UFAQobt7Ot/jRJOJCIu5hG8oROU3W667rUxxyqIxbRRMdvQheoj9nUx2z8XpO8gIdQZ65IMFo/XyYupsfAPcDJBgPwe5P97U4VvIiIiBzYxi3b+eUf7qWqpo6K4WWpoQyaS1gWlmVjOwf40CoikqXqGqKEQ8HU76FQkPqGKMWF+QAcMm0iR82aBsDfHniSV5cu5+hZB7fZ1kDZJ/an8Z7TFaD3dniersC8p+vtzVA9nVXp/SlIVzV6xxSiDzCu6xKzXOIHzmLblKpOb6zEPVDg3W47jVXrjutSG01QH7W7PI62C1Q2WFQ2WJgGNFgJ9tQn2gy6D9TOnoY4exriWI7L+qoYm6qjLarOO9OO5bis3l3Pqt31hDwGe+vibKmOpqrOD9RO031Ry+HlDZW8tKGSWSMLmTmyiO5kr44LNbEENbEEg0I+BucF8Jpmi6rzjhg0hekGhgmexm3s6lAmqQp306Ag4KUg4CXgMykO+/F7O9+f5tsVtVwiCZdYIhmId2d4leQJHYfqhgSD8pN96Uo/9i3r4mt8Xu1mz4/XBL/HwNPF4ZBERESk65oP53L/o8/w2lsrOOLQg1oss2DRK8xfuBifz8tll8zLRDdFRHokLxQkEommfo9EYuSF94XqzX+efehUlr/3QbshuvaJ6ZGuKvT+EKBnKjxvT/P+9Fagnq6q9HQG6b1NQXr7FKIPMLZLtwP0/duxuhmgNxeJ262GbemOqmgyLO6pdXsjbKlNDv/Sna1LDfWyu4H6WHJSt+4EvE0PGVEcbPytZyFsSTiAp4Mq7wNpeozT1bFd2mjDAMoKgz2ewNZxoTYN7x2vxyTg83T78c377/WY4Dr4OxiySERERHpXOBSkri7S6va5Jx/FacfPxnYcVq3flYGeiYj0zNhRw3n06RexbJvqmnoCAV+LoVwaIvsq1Ve9v4GhpYPabWsg7BP7yzAu2RygZ1tw3p7eDNSzLUjXsC6ZoxBdpJmmuDpb5o00jPRUMRtkXzV0tvWnp5q2J+A1u3XlgIiIiHRf03AuLhAK+Lni4nNaLePzevF5vdgHGoNQRCRL5YWDnHD0TH75h3sxMLjg3FPYtGUHK9as54wTj+Tp515j5Zr1eEyT8rJBnH/kIe22pX1i7hjI4XlbmvqezjA9XcO79JcgXdXobVOILiIi0o+t3eyyaadLRZnB+JE6gyMiA8+QQUXc+qNrM90NEZE+cewRMzj2iBktbqsYUQ7A+XOP5/y5x2eiW1lnoFShpztA78/h+f56K0xXkD5wKUQXERHpp9ZudvnL4w5VtVBc4HLlOaaCdBERERGRXpSLAXouhef7S3eYnq7hXaT/MTPdAREREemeTTtdqmph4sjkv5t3ZstgVCIiIiIi0p5sCdB9L2/J6QC9uXRua0+f/3SNg5+uEzrt6e0rOvobhegiIiJZZu1ml+fedFi7ueNQvKLMoLgA1mxO/juyTFXoIiIiIjKw9Wbw19uhZV8aKOH5/hSkd42C9H0UovcjrgoMRURyXtMQLQ88k/y3oyB9/EiDK88xufBUQ0O5iIiIiIj0A9lQhT5QA/QmCtKlOxSiZznXBduBmAUxO/mv5XQ/UM+6eCVdZwbS1I5hQNrOVaShT5bt4jg9a8d1XRw3Pe0AOD3cLrexLben7aTpNW9qJ13tifRUV4doGT/S4ITDFKCLiIiIiGR7FboC9OyRruFdsiVI702qRk/SxKJZynWTYbm9X3bikrzdAjwGeEwwu5CbmAaEfAZxy23Vdld4DDC9RjLk7WI7zcNKxwVccHAxDQPXdTGMzm1QKvwE6hI2NTGLkNfE5zG71U7CdqmuibG7Kkphvh+/19O1dhwXwzSIxeLs+GAzlfVxho4ehs/v63J/DMMgHoly12OvMWNkEScePZVgwIfrgtnJF9x2XDymwZ6aKAtf38DowfnMPXw0Pq+JAZ3uT1M7O+tiLN1cTWHQx6EjijAMutROc7tq4+QHvYR8yfN4XWmj6flxXfCaBlYPTw4ARGIWAb8Ho1n7XWWS/HvsxkNFWkgO0eJ2aoiWtZtdNu10qSgzFKKLiIiIiOSwnoa1fR2e169e1e3H5k2eksaedMz38pYeTzqaDZONPrkpwIcq0jfZ7P7W1VYzrqCo19rvDxSiZxHXTYbKtgNOJ5a3XbDtZJDpbQzTDxTgGYaBzwM+j4HtuCRsl7jd+T6aBnhMA8NIhp8BXzJkjVsO1gFS+aZw0nZcaiIWtZFEiyDfMJJhrdlYC95ekNnUTtRy2FwbYUd9rEWQGvKaFAW85Pk8HbbjOC6mabC7Ls7r66tYsa2GRLMOleT7KS8JUZwX6DBYdRwH0zTZubOSZW+9z9o1W7Dt5Cu4aulqykeWM2rSKIqHFOM6DobZ9gUgTfdVbt/FxhXvs2vjNlzX5Q3g7n+9wDGzJzP35EMZO6oM23bweNpux3ZcDAPeen8n/3l9I2+/vytVXf/zfy3l7CPHcPEJExlTXohlO3jbaMd13caKcVi6uYrn1+5hza761P3/fnsrR44u4bjxgxkU9qeC9s6KWw576+KYBuQFvOQHvXjM9k+iND/xEk04ROI2dg/CcwMI+j2E/J5Uv5Pv35bvQThwwO8xkn9/Cs8lXZqGaNm802VkB+F407AvVbVQXOBqOBcRERERkSzWHyqOe6InofmB2urtUD0dQXpPLNgcZu7Ihh6309tB+kCnED0LuG4yELc6k5y39Xgg0fjYrgR6HtPAYxoEvC4JG+LtVJUbNAXnbQeKHtMg5E9WbSfsZKDefGSMpgC0IW5TE7GItJPauy6pIN5jNlX1Gq2G2djVEGdrbZSqWKLNdiKWQ8SK4zEMigJeCgNevKbRqgJ+xdYa3txUzdaqaJvtVNbFqayLE/B5KC8OUlYcwttY5d7EtmxWrdzI8nfWsWd368tbXMdl+8btbN+4nfyifEaOH8mIcSMwmwXXhmFgxRNsfu8DNq9aR0NNXat24nGL/y5ezn8XL2f8mHJOP2EGxx01FY/HbKyeTlao1zbEWfjGRp5ZspE9Na23qy6a4P7n13D/82uYNaGUC46bwKkzKzAMA9NIPi8e06A6avHc+7tZvG4vtTGrVTv1cZtFa3bz3zW7mVyez3HjBnPQ0ALoQqU8JNdXG7WojVoEfSb5QS9Bn6dZtfm+Ey+RuE0s4fRouB1v43s14DPbDcdtx8UmecLINI39qtNdwOjSiSuRrmheWX7CYR2PuNZ82Jc1mw0273QVoouIiIjIgJXtQ7n0RLZWoaczOO/senorUO9pkN7TavT+EKQP9Gp0hehZorsB+v5sFzx0bexzwzDwe8HnSYaa0URyqJd9Veeday3ZjpGqcq+PJauF66IWtRGrS8Nu2E4yzDQNl0RjZfX2+ijb6qLEOzkOje267I0m2BtNkOfz4HXBNAze2lTNsi3VRBKde9JjCZuNu+rZtLuewQVBigNguC7vvrOO91ZtJBFvHTK3pa66jlVvrmLNO2sYOnooFeNG4DoOm1auZfsHm3DszvVn7fod/GH9Qv7xzxc44eiDOO3kw6iJWDz92npeX7Wj0xXaS97fxZL3dzHoX29y/tHjuPCkyeyuj/Pc+3t4d3tNp4Z0d4FVO+pYtaOOkpCPo8cO4vTJpUDXh3mJJhyiiThe0yAv4CEc8JKwHCIJ+4BXObQnOdwM+L0mIb+nzar79jguOLaLQfKKBRMXj7kvPBdJt65Wlndl2BcREREREcmcnlShZ2OA3lfheUfr7o0wPVeC9N40kIN0heiSYhgGHgOCPoNO5svttuP1GDiOxaY9kR71yXFhR32UDTU9a6c+YfPOhirWbK/r8hjuTVwXdtdEeeOd1VRu29nteUNty2bL2i1sWf4eOF0YS2c/9Q0xnnx2KU+9Wwm+7u+k99bGuOM/K3mnzsbXOAROd1RGEjy5YgdnTCnrdhsAluNSHbGI9uRN2Mg0DQbl+3vUhsu+6vSA11CALr3mQJXl+49/3tlhX0REREREpPsyXYXeE7kWoDfXW2F6poP0dNCwLr2j82WZIiIi0ivWbnbZvsfF62m7srypSv2BZ5L/rt2cPIs3fmRy2BcF6CIiIiIiuacnVejpDtDrV6/KmgC9ud7oU19PwtpcusbO782TP705dFI2U4guIiKSQU0B+fNLk7+fMNNoNZRL8yr1qlrYvLMnswOIiIiIiOSWbA71cmFC0WwMz5vLtoC/p0Pw9If3TDb/zfUWhegiIiIZ1Dwgt2yDYYNpVVmeHP8cjX8uIiIiIjJAZEsVejaF0weSzr729DnMhiC9Pw9FlI00JrqIiEgGdWaCUI1/3jUxy2JnfV3qd49hMKygsMsTHouIiIjIwDbQQ8j+FKA3qV+9Km3jpPd0fPRs0Jvjow+0SUYVoucY13WBnocEruv2OGxwXTdt7aSD67o4jgNGzy7ASNsgCmkKc0zToOdTcJK2CTObXi+FVSKd09mAvGkyUenYK5s28LnH/8XmmpaXF54+fhK/Put8hoTzMtQzERERERloMjEsR7qq0PtjgN4knUF6T/R0ktEFm8PMHdnQ434oSE8Pheg5wHVdLMclYTnYDkRNCPlNAl6jS0Gm67o0xB3qYjYJ28XnMQj6Pfg9ZpfasWyHzdUR1uyspSFhE/Z6KPD7CHo8XWrHdl3q4wkSrsPQPB8Ry6Eh4ZBwuhZj19U28MbLy3jhuTeJRmIMHjuaIRMnEC4p6VI7ibhF1d56YkaY4NDRWA21WPW1uFaiS+34ggFKhpdTPLQMXIfKTZup3LgZKxrtUjulZSUcdsRUDp45mXjc4s0V23hn1XbqI13rz6ihhRw3cwSHHzSMSNxmzc56PtjdQNzq2vM8bnCYOWMGkbCTj/OYyWC+q2G632MS9Jv4PSa2m3xfN7Upkqv2D8jXbnbZtNOlojFU3/93ac1ybG5Z/Dw3v/Q8s0dU8MszP4zXTJ403VZbww8XPc2Jf72N35/9EU4YMz7DvRURERERaV9PhwLpqf4coDdJV5De02p0Bem5QyF6FjAM8JlgOV2rcnaaAkbLbfE4y4HaqEMdEPIbBH0mng7KjC3bpS5mUx+3aV70nbBdEhEL04Cgz0PQ58HsoJ2aaIL1e+vZuLcBu1lDDZY77rGFAAEAAElEQVRNg2XjNQwK/D7y/T48HQSrMdumJp6gPmGlbjMMg5DXJOzzkLCTYXrEctp9vlzXZf3aLbzy4lu8+/Z7uO6+Cuk9az9g9/vrCA8qoXTSREpGj8L0eNptp74uRtWeOupqm0JuA8M08OUV4ssvwo5FsOpqsKMd79QKBpdQMmIoeSVFLSr0B48by+BxY6nftZu9GzdRv2t3u214PCaTDxrL4UdNY0RFGY7tYHpMfF6TY2eN5thZo3lv/W6WLt/Gxm3tT/Lg95nMmjKU4w8byYiyAmzHwWMa5AU8HFpRyCEjC9mwJ8KanfXsrW8/lA96TWZVFHPM2EGU5gewm53gsB0XGzANF9M0MDt4zY3G91jI58FjGqnnxwN4/V6CrkvcckjYDl05h+I1ARxMw+zS4/bnMcDv7XgbpP/LlqC6aaLRqlooLnA54wiDp19zU7/vP+mowPbaGq549EHe2LKJrx1zItfOOR6v2XK/fuKY8XzxiYeYd/8/+OJRx/DdE07F7OGVSSIiIiIikvsyPaxLuoL03jQQgnSF6FnCYyb/c1ywHWiv8NZ1XWwnGSjaBxjDwwUa4i4NcRu/h8bq3mR1uuu6RBPJqvPYASqOHRca4nayHa9JyOfB29iO47hsrYnwwZ569jbEMWj/RIDlulTG4lTG4uR5vRQGfAQaw2vHdalPWNTEEySctjesKXT2eUwKTYOCgIdIwqHBcrAaE9JoJMbS11fw0gtL2b2zMjnUyX7paVOYHqmsYsMrr7F5yVIGTxjHkAnjCRYUJPtq2VRXNlC1p45Ewm57gJzG/ngCQTyBEK5tY9XXJKvTHRsAr99H8bAySoaX4/X7oY2hTpp+zi8dQn5ZKYlolMoNG6navBU7HgegqDifmbOncujhUwiFAriNz5HpMVNtNDU5acwQpowrpbI6wpLlW3n3vR3E4sn+lA8Kc+yhIzlq+jD8vn0Bk8c0W/TFMGD0kBBjS8NUNSR4b0c9G/ZEUiH5iKIgR48ZxGEji/A2O7HS1skaxwXHdjFoCtP3rcfrMQj5PAS8LYOsff3Y96/faxLwebBsh3iz17zVywIUhDwUhrwt2rUdl7jtkrDbfFib/B7weYwOT0JJbtg/uM5kUN18otE1mw1efNvlgy0uk0bBriqDzTtdhejNRK0En3zoPrbV1vDYJy7nyJGj21xuaEEhD17wSX7/6mJ+9NxC8v0BrptzQh/3VkRERETSbV1t+wVkA006hnLJhSr0JrkyrEu69GY1+kCgED3LmAaYHvC6yfCxqTrddV0SdjI8784Q4XEb4hEHAxfDgJjVtYreVDuWk+rD9toIm6obSNhuKmTubJP1lkW9ZeE1DLymScy2u1SFbxgGBhD2meT5PezcU81Dj/yPN15bjtUsJd0/QG+uKUy3Ewl2rnqPnStXkz98BIHhY4nE9gveO+5N8v8eD96C4uR/WBQNyiN/UHGqv40/dLRRQHK4l9JJEymdOIFAvJ6DRpcweuwwXNfFbAq7zfarJ5uuFiguDHLK0eM48cix7NxZQ2lhgDHDi7Adt1OhcFPVdVHIy+wxRRw2qpB4wmV8SR7Di0KdbqeJy77q9LyASX7Ai9djdnrc/KZlPKZBOODFaaxOj1vJEwo+j0FRyEtBsO0rJjymQcg0CHqTQXrcdtv8GzAN8HsMfB6N6z6Q7B9cZzKobj7RqNfjsn4b7K6G3ctg6hiXkWWqnm7u2wufZMXOHTxx6ZUcMnR4h8uahsmXjjqOhkSCn72wiEOHDufkcRP7qKciIiIiItJfaViXA8v1anR9E89ShpGsTA94k9WwJhBLdC9Ab852IZLoXoDe3N5IjHV761NjVXe3Oct1iXYxQG+uKeR85aV3efWld1oE6F3S+MTGCBCJdn+azmRFuEHJsCHkDypO/d7FVpKPM02OnDWe0WOHYRhGKkDval+8HpOZE4cwelgh0Ha1eGfbmTNqMMMKg91qp7nisD/1+K4+P03Lm4ZB0OchP2AyvNhPxaAARWFvh0MONT3e7zXI8xuE/QZeM3kaxOeBPL9BfsDE38X5BKT/SwbXsGZz8t+RZZl7/ZsmGr3wVINjZhj4fQZzpsOQIjhmhsZEb+6ut97grreX8PMzzj5ggN7c1449kVPGT+Dqx/7FhqrKXuyhiIiIiPRXT27KfOWw9FwuVdana6La3nxv5/KVIQrR+wHTAG/bQ3ZnTpbN8+g4TpdD5t6WjhDWNNMT5nYvzG+rnZ5vV9Oj0xVSl+T5CPm7Nmlt0/q9pkHYb1IQNAkdYO4AyW3Ng+tsGHN8/EiDEw4zmT3VpLggOYzL2BEGs6dm134uk97cuplvLnyCT8+czcUzDuvSY03D5A/nfIziYJBPP3QfDYl4L/VSREREJL1een0ZP/vdXdz0u7vZsHl7i/sSCYu/3Ps4P7/tHv5y7+Mkms0zJn0vXYFnX8qlwLk39HS4nHRMGKsgPXP0bVxERIR9wXWmA/Tmsi3czxaVkQYue/h+ppcP48ZT5narjeJgiDvPv4i1e/fwzf88keYeioiIiKRffUOUZxcv4frPXswVF5/N/Y8+0+L+l95YxtDSQXz9859gaOkgXnpjWYZ6KpJdsunkgIL0/ktjoouIiGSx8SM1hMv+vr/oaerice447wIC3u5/lDm4fCg/Pe0srl3wCB+eMo1Tx09KYy9FJNuVz5+fk8O3lS3bluku9IqdZ56Z6S70mtWfnpHpLvSaqXc+luku5JT1m7YycexIvF4PQwYVE4vFSVgWvsbPQ++t28QZJx4JwIyDJvD0c69ywtEz22wrYVlYlo3tdH84U5GBqKdjo0N6JhrVGOl9T5XoIiIi0m8sWreG+5ct5YaTz2BEYc8/kF08YyYnjZ3A9U8/Tm0smoYeioiIiPSOuoYo4VAw9XsoFKS+Yd/nl7r6SOr+cChAfUOk3bYWLHqFL3/vVr5x422912ER6VWqSO9bfR6idzR+l4iISGet3ezy3JsOazdn2SQN0mvqYjG++tRjnDBmHJ/o4jjo7TEMg5vP/DCVkQg/fv6ZAz9AREREJEPyQkEikX2heSQSIy+8L1TPC++7P3lfqN225p58FL/+8bXc9N3P916HRXJUT8dGh/QM6wIK0vtSn4boBxq/K9u4bvK/dLQjIiLps3azy18ed3jgmeS/CtIHhhuff4a9kQZuPvPctA7BUFFUzPdOPI0733yNlzauT1u7IiIiIuk0dtRw3l+/Bcu22VNZQyDgSw3lAjBp3CiWrVoHwLJV65g0rqLdtnxeL6FggFCg94IzkWySTeOiN8nGIL03w/T+rk9D9PbG79pfwrKIRGNEYr0zJs+BOC7EbYjZyX8tp+tBuOu2bCdmgd2tdlyiCYe99Ynk7ygoaotpmjhpGMvNSNcZDzf52vWU47hpacdNU3/S0U7To9PRHwCD3BvLVA5s006XqlqYODL57+ad2jfmulc2beCvb77Gt48/ldHFJWlv//LDZnPEyFF8ZcGjRBKJtLcvIiIi0lN54SAnHD2TX/7hXv5yz2Nc8OFT2LRlB08/9yoAc2YfzJbtu/j5bfewZfsu5hw+PcM9HtjSMV51X8ubPCXTXeg30lGNDtkXpEPvVKXnQjV6n04s2t74XcWF+S2WW7DoFeYvXIzP5+WyS+b1Sd9cF2y3MehufjvJEN0CPAZ4TDA7yOw6aifRmPF2ph3bcWmI29RFbezGhkzDwWMaGEYygOxOFZ4BeE0D2+leHN+03rDPQ1k4yN5IDKsHYagBeAwD2+16f1zXTYa6uAwaPozi4SOp3LYNHLt7fTFNSgq8BIeE2VsVw7YdDOhyvwzDwHBdfD4PltX1YL9pnX6vSSJuE/aaROyeBYR7Ixb5fpOQz0z1sav9CXhMIo5FccCH1b2nGEi+/2KWTdDr6dEpIY8JIZ+J19ODRqTfqigzKC5wWbPZoLgARpb17GTK2s0um3a6VJRpEs9sFEkk+MqCRzl8xEiunHVkr6zDNEx+Pfc8Tvzrbfz8f4v4wUln9Mp6RERERHri2CNmcOwRLSejrRhRDoDf5+Mzn/hwJrqVkz5UEVNVrvSJdEw0CvuC9GydcLS/TzTapyH6gcbvajL35KM47fjZ2I7DqvW7erVPTmPg3ZmM0nbBtpuC6GQI3pRFpqMd13WJ2y51UZtIonX46rguju0mg2fTSF1GkOxD50IfwzDwGAamkQytbcfBOUCfmyqGXaAhZlMftbEcl7DXSyjfQ9S2qY0naOhCsuozDbymidn4BLqui+26JDrRH8dxME2T6roYi15bx/NvrKe6Lgbkw9AJEK2F+iqIdbzTMAwD13UJFuQxavpUhk+ZgD8YTK1j9646tm2rorY21vj6tNdO8r5g0EdpeSGDhuTj8Zip7YrFLKIxC/sAbw7TSL6PyooCzBpbwpThBfga2/l/9u48Por6/uP4a2b2zJ1AAoRw36cKouCBB6Ki4lkV77N3tVbtYftra1t72GprW7VWq9b7aL1RtHgr4oWoICAIIjfhyn3s7sz8/thkScidbLKb5P18PHiQ7M5+57OzyWT3PZ/5ju24FFfbFFVGCLe0gRoRtl32VNoUV9mk+EzSfBaWaeC6e3+Gm5KT4mNwZgq5qb5Y+G47LlVhm6qQ0+ogPMVnkZniJeA1Y+M4rovjuNhtONbg9xgEfSYes20HA6RnGVFgcPlck02FLgUdDL5rp4YpKoWsdJfL55ptGk8BfOf709uvsbG4iPtOPwfL7LyT6Eb26cuPDj+K377xMnPHTGBKfkGnrUtERERERLo37+LNhGcMjMtY8QrSIRqmK0iPvy4N0YcNzueZl94iYtsUl5Q3mL+rltfjwevxYLclWWsj26mZpqUdj63bVW7WfN/RcUIRm4rqaDjdmsdFl3MxDSMaqLexO90wopNgmJYVC7DtfdZdO17YdimvsqkM2Q2ep2EYBD0egh4PEcehLByhNBTGbiRxNgGvZWIZRoM6DcPAY0SDdcd1iThOg7DYdhws02Tlup28/N46Plm9DWff7WUYEMyI/gtXR8P0imJwnfrLuC59hxQwaOJY+gzKb1CPaZrk9csgr18GZWXVbNtaRGFhaSPrg6zsVHL7ZZCa5m/0eQUCXvx+D5GIQ1V1hFCo4cEGyzSYMDCdA4Zm0z+r4YElyzTICXrIDlhUhB2KqiKUhdr+++G4UFbtUFbtEPAYpPos/B4jVmstj2kwKDNIQWaQFG/D31HLNEj1e0jxuYQiDpVhh0gjBwksAzJSvKQHPHishsGXaRiYloFlutEDUXbjZySYBgR9JgGPgdncKRzSq4woiE9ovbHQZcsOl5wM2LIjOjVMa8ftaAAvLVu6dTO3vb+In86cxei+uZ2+vu8cdAjPrvqMK194ilcu/jb+Rt6niIiIiEjyGJ6e2SOmioiH8IyBHZ5mJHXM2KScP7yjusNUNQrSk1vXdqLXmb/LwODsU2Z15erraaTRu13iMYzjupRUNpwbvrWPrdudbrVnipeaANsyotOjhGwH13WpDEe7zsOtnErEY5pk+X1k+rxURmyKqkOEHAePaeCt03XeEtMw8FkWXjMa7hfuKce0TN5euoHXPviSwt3lrXtiXj9k9YOMXKgswW+X49oRCsaPpmD8aAJpqa0aJi3Nz8hR/Rg6rC87CkvZvKUIx3bom5dBn9x0vN6W5xMxDAOv18LrtXCcaHe64Tj4PCZThmYzcVAGgVaOk+qzSPVZRGyXouoIuyra97NTFXGpikSwDEj1WeSkeEjxWgzOSqF/WqBVYbVhGPi9Fn5vtJ6qsE0oEn1emSleUnxWqw7sRM+QiP4MO66LbUdDdY8JqX4Tr9XwwItI3Liwowi+2gYpgbZdu6Lu3OxrNhltCuClZdWRCFc+/xST+g3guwcf2iXr9JgWfzvhNI759x38+Z03uG5m4t6riIiIiIhIcotnNzrsnSM9maZ3UZAe1eXtVY3N39XrxeF6eNGpWVwaafZtNcOIzrfuRGBbUft/OaJzpnswDYPSDlycrTbcX/LZFh5csAynvXOvmyakZjF4/GSGjuiP0c6pADweiwH5WeTkpjfo2m9bOQbBoJc54/syMDvY7nE8lkHfFC+l1TahDsyZbrtQUm1z0rh+BDowwbjHMkizPGQEve0eA2q602u644NeA6+lQFI6mQG5WZCTAbtLWp7iqK54z80u9f3lnTdYu3sXL1/8LTxm110AYXxeP64+ZCY3LXqDE0ePZ3L/AV22bhERERGR4RP8cbvgZHv01G707iTZutJrrw8QzzC9uwXpnTexqIiISDcwKM8gP9egKhT9vy1BeO3c7POOMTSVS5wt276Vv777Fj84ZCbj8/p1+fqvnHE4Y/vmcuULTxGy23fGj4iIiIj0bvGYTqOt4tUV3R2mP2mtzn4uHZ1CpynxPJBS25XeUfG+2G53mopJIbqIiPRqHQ3CRxQYHDFFAXo8lVZX8fVnHmdM3zy+P+PwhNTgszz89cTT+HxnIb9+bWFCahARERGRxIr3FBbdTU8K0jtbZwbp8QrTF2xKiUuY3luDdIXoIiLS6ykITx6u63LlC09TWF7G3aeejc9K3IU99+ufz6+PPp5/friYp1cuT1gdIiIiItL7dGQqj3jO0d3dg/TuXn+tZOtKj3eQ3h0oRBcREZGkcccH7zD/8xX8/YTTGJHTJ9HlcPnUgzl9/CSuWvA0q3fuSHQ5IiIiItKIZJ5XORFTusRbTwmiO1tndaPXSrau9HgG6d2hG10huoiIiCSFxRvX86vXFvK9gw/lxDHjE10OEL3I9c3Hn0xBRiaXPPUoZaHefUqviIiIiHSdZOlGh+4ZpCei5s4O0iH+YXpH9KYgXSF6ArmuSzjiUFYV7vA4EduhvDrCjtJqiitChCIOruu2eZwd5VWsLCxmfUkZhRWVVEXsNo/juC47K6tZvbuMtXsq2F5eTVXEbtMYAGHb4bOtpWxyvBw8a3+GjxtMSlqgzeOYpkl2XjZWSpDNhWUUlVQRiThtHscwIMXvIT8nhcF9U8lK9WGZbZ/6wWMZ5GUG2Fphs3RrOVtKQoTtttfjs0wGZgQ5eWx/5o7JY0zfVLztqCfD72Fqfia4UB12sB23za85gGlAwGvVPLbtjxeR3m1zSTGXP/04BxcM5mdHHJPocupJ8/m597R5bC4t5vsvPI3ttH2fLSIiIiLdU3eeF70zgvTuEKYnus6uCNIhfmF6R7vSe0uQnriJRhPMZ0HEhkR8DHZcl+qwQ1XIxm5HWFnLdV1CEZeQ7VB3mJDtEqoMYxoQ9FoEvBZmM+FqdcRmS0klm4srCdUJcysiNhWRSjymQYbPS5rXi2k0P872imq2l1cTqVNQacimNGTjswyy/F7SfFaz4+ypCPHpllI+21pK2HYxMLA8FgMG5ZI/JI+SPWVs2VDIrm17mg17/Sl+svNyyOybiWEYGAa4LpSWhygtD+H3WaSl+gj6PRjN1OO1TIL+6HYEYstmpfrISvVRUR2htDJMZaj5AwVpAQ85aX4yU7wAOC44tsumkhCbS0JkBy36pflI85nN1pMZ8JKX6ifD743V47MMpuZncsCATL7cU8GaXeXsrmz64IxpQEFGkDF9U8lL8+O40e0MELGj29QyXUzTaPa1im4fA5/HxDJNXNdttvbWMgCfx8Cjw3wivcJXRXs4/ZF78Vse7jzlTDymleiSGhjVJ5fbTjydS59+jO/Of5JbTzotKesUERERkeQzp6Ci3SHl8An+DgWl4RkD4x7q1gbU5Z+viuu48ZAsIb938ea4H8RoSu3PR0fOXIBomN7e6Yde2OiP2wGndaXFSTlFU68N0U0DfJ5oqBpxwO6CxtmI7VAVtqkKtz+6d10X24VQ2CHiNF+040J5yKY8ZOP3mAS9Fh7LwDAMXNelqDLMpuIKdpQ3/0MecVx2V4XYXRUizesh3efFb1mxeoqqw2wrr6aouvmO+pDtUlgRYkdFtPs50+/BZ0VTUttxWbezgk82l7C5uAqDvb3Mtf8bNQcC0rNSGZs9nMj4CFs37mDbxh1UV4aiyxgG6TkZ9OmfQyA12GyoWx2yqQ5VYpkGqSk+0lK8WDX1GEDAZ5Hi9+CxGg+Ha79P8XtIDXiJ2A7FFSHKKsPUvjSWaZCd6qNvuh9fTZd2Y/W4wJ5Km92VlQQ8Bv3SfPRN8cQ63T2mQd8UP3lpAXyN1FP7tWXA8JwURvZJZXdFiM93lvNVUWXsYE2K12JUnxRG9UnF77FiByEaC8ptJ/q6GLhYloFp7F2PaUQPLvg8ZuznqW4d7eUxwWcZWGbHxxKR7mHt7l3RAN3j4alzLqFfWnqiS2rSiWPGc9cpZ/HNZ/9DtR3hnyd/LaEXPhURERGRvYanZ3ZaF+sJg6oTeiHFZAzSIXnC9GQJzvdVu827MkzvKUF6Mur1n/wMA7wWeNxo6Bxx4j8RRXXYpjJktxh6N8d1XcK2Syji0J5hqiMO1REHEygOhdleVkVl2KatMWVZOEJZOILXMAg5Lrsrw4TaWJALFFdHKK6OYBmwcWclq7aXURl2YvU0N2JtuOrxeigY1p+CYf3Zs7OEkpJKUtJTsTwWtCHUtR2XkrJqSsqqSU/xkZ+bSlpwb5d3S+PE6rFMctL85KT5qaw5cJGR4sVo5Ti1z7kq4vJVUTUbi6sZnhPggAGZ9E3xNbrOxtQG4tlBL9MHZXHgwEw2l1Th95j0T/Pj1lmmNdvHZW93esALAZ8H7z5BfkcCb4Po76DP03LXu4j0LJ/vLOT0R/5NViDIE+dcTP8kDtBrnTx2An7L4tKnH+OSJx/l7tPOJuDxJrosEREREUlyHelGT3Z1Q+yuDNSTNTzfV3frSq/9OW1PmB6vID0Zu9E1WUINwwDLBL8nOtWLFacsz3VdSqsiHQrQo+NAVbh9AXpdZeEI6/eUUxmOTj3S3uFKwzbbykNtDtD39eXOSpZuKqGypju/raNFp2kxCKQGSc/OiAbo0TvaVU9Wup+0oDc2blvVPi4n1UdmSnT6m/YGzI4LE/My6JPii43blrFql/daJkOygvRP82MYHQuqUwNePGbHg3OI7nyCXoM0v0HAaypAF+lFXNfl8eUfc9KDd5ObksrT517SLQL0WseNGssDZ5zLm1+t4+SH7mHlju2JLklEREREeriOdhiHZwzskiC3dj7yzpiXvDPH7mzexZu7bK50iM986e096BOvszaSbX70Xt+J3hjTANMCO5LoSjpBkl3v0Y1XQXEKYOOW4xrxmY7EasWc5K0qJ17bJ45j+b0G3ngdrZJeZ+0ml42FLoPyDEYU6OeoO9lUXMQ1Lz3Lq+u+4Izxk/n97BPIDna/jpyjh4/i6XMv4Yrnn2LWvXfwg0Nm8v0Zh2t6FxEREZEESvYpXTrajd7RaV2g86Z2aUp3C7s7WyKmeIH2H4Rp7/QuPXFqF3Wii4hIt7J2k8u/nnN47OXo/2s3JdnRQWnUtrJS/vzOGxx2962s3FHIQ187jztO/lq3DNBrTc0fxKuXfJsrph/Gn995g2P+fQdPrVxGdaQnHoUXERERkXho73zTtTrakQ5dF+BK02o707vqgEZHDr4ksiM9mbrR1S4lIiLdysZCl6JSGFXgsmaTwaZCV93oSSri2Ly8dg0PfrKEl9euwWdZnDt5Cj87Yhbp/kCiy4uLgMfLdTNnMXfMBH728gt845n/kB0IcubE/Thv8lTG5/VLdIkiIiIivUqyd6Mni67uSJem1X0dOvMAR0e60tWRrk50ERHpRtZuctm2y8VjRQP0rHQoyFOAnqzuW/ohFzzxMNvKSvn97BNY9r1r+cOxJ/aYAL2uif3688x5l/LO16/gvP2m8OSKZRx17+1sLytNdGkiIiIikmSSoRsdum6edGm9ruhQb29XeqI60pOlG12d6CIi0i3UTuNSVAoeC444wGDaOM2JnsxOHz+JaQMHM7n/gESX0mVG9cnll0cdx3UzZ7Fkyyb6daMLpoqIiIj0FN2hGz0Z5kevVRukqzM9uXRmh3p7u9Lb25HeUetKixmentnl661LIbqIiHQL+07jMqAPCtCTXHYwpVvPed4RPsvDjEFDE12GSK+xaUshT7zwOuFwhIhtM3XSGGYfcVCiyxIRiavyikruefR5KquqGZSfx7xTjsEw6r8fvu53d9AnOwOAcaOHcuKsQxJRao+XTEE6dGy+67oUpievzgrU131W3SVBek+Y1kUhei/jklwX4ItX/BW3GC25Ng+O6+K6boM3RoniAiRRPdK7DMozyErXNC4iIlJfRWU1dz8yn29fdBp5fbNxXZcVq79MdFkiInH30uvvceB+Y5kxdSL/fvwFPvv8SyaOHV5vGdM0uPbb5yaowuTSmd3oySaeXemgMD3Z1b4u8QrTu0uQnuhudM2J3gyv2fFw1jAM0gMeLLNjIxkGBLwm7R3GdaPpsM8yyQ74MNsZgtaOY+Li6cBzqn3kuH7pHDY8h1SfVe/2to4zLC+ViQUZ+Dxm+8apeUCqZdA3xdvu7Vy77rwUH/1TfR36+TENWLOznNLqCG5NmN7eegJeM7Zt2ssAqsNOh8aoHcdnQQfLkV5oRIHB5XNN5h0T/V9d6CIiArBs1Vomjx9JXt9sIPr+e8KY4Q2WC0ciVFZVU1ndvbugRKT3Wr12I5PHjQRgv3EjWb1uY4NlXBduuuMR/nrX42zcvL3JsbRP7Lh4ddXGa2qMeM2TXpfmTE9u8Zw7vT0HYTpyFkV3pE70Zlhm9J/jgu2A3c4uZb/Xwu+1iNgOlWG7XUGkYRj4PAZey8B2IBRxiDgtFeRSm7tWRxwqwhHCtovXNOkT8BF2HCrCNiGn5XpqA9zKiENF2CHc4rqbZhkwKDOFIVlBMvxeAOYdMJClm4t5fc1O1u6qwDSi270xBtGOaJ9lMDE/g0kD0slKiY4Tijgs31TMu2t3s624qtlxau9L9VkcODyHA4dmk1kzTth22FRczZd7qigL2bF1NldPqtdiXG4qo/ukEvRGDwpURxy+LKrki93lVEScVo2T4jUZlhWkIMOP1zIpqgxTWhUh1W+R7vdimUarutM9lkGa30PQb8UOmkRsh/Jqm/LqCK3N5D2WQdBn4feYHepAtwzweQw8Jupkl3YbUaA50EVEpL49RSXkZLV8/YEFr77L/IWL8Ho9XHL+mV1QmYhIfJVXVpESjAalwaCf8orKBstcd+UFpKemsHFLIXc//BzXX3tZo2P1ln1iZ3ejJ8u0LrXi3ZFeq26Qru705BOvzvT2dKS3VXee1kUheiuYBpgWeNxokG477Zv1w2OZpFsmqX6X6rBDZSjSZMDbFMMw8FjgsSwc1yUccQlFnHr11AastgMV4QhVYbvBegzDwGdZ+CwL23GpjESojNiNjhNxXCrCNhVhp13PuzYcTvd5GJqdQkFGAI9Zvw3ZY5lMG5zNtMHZbCmu4s21O3nnyz2E7L3Bs2FEj6rnpfvYb2AGo3JT8Vj1x/F5TKYMzWbK0Gw276nk/XW7+XRjMXadDVAbng/uk8LBI3IYOyCjwZkCXstkWE6QodkBdldG+HJ3JVtLQ/Wef21dAzMCjM9NZWBGoEGHv99jMrZvKmP6pLC9PMQXuyvYUlbdaJjeP83HkKwAfYLeBiGz7bqUVEUoqYoQ9Fqk+z0EvFajYXrQZ5IW8OK1jAb3eSyTzBSTjKCHypBNWXWEcKTxVzXgNQn6rAbbuK18Fngto8NnY4iIiIg0Jjsrgy3bdrS43JyjpzN75jRsx2HV+paXFxFJBMdxuPG2BxvcPmnsCFKCfiqrqkkJBqisqiY1JdhgufTUaBA7KD8Pn89LeUUVqSmBBstpn5h84hmkQ/zmSd+XAvXkFY8wva1BeldfaDSRU7ooRG8DwwCPEZ2GwnEh4jTd5dwc04h29ga8JhHbpSIU7RBvzzh+b7RDPeJEg3nbcQnZDhUhm5Dduo53yzRI83lJ9Xqoth3KQmFs16XKdqkIRwi1swXfa0bryk8PMDQ7hexAw3C4MfmZAeZNKeDUSQP4YEMRL68uZGd5mHH90picn05ueut+mQdmBzlt6kCOn9SfjzcU8d7aXZRV20wdmsW0YTn0bcU4hmHQJ8VLnxQvVRGHjUVVrN9TheO6jO2bxpi+qaT7W/41MgyD/ml++qf5qQjbrNtTwRd7KjCAoVlBBmUGCLRyfpPKsE1l2MZjRjvNMwLR7vRUv0Wq34PZirDaMAxS/B5S/B7CEYfy6gjl1TaWWdN17jXbPeUPRA8w+L1GdEokdZ2LiIhIJ5o0dgQLXl3ModMmx6Z0WbH6S8aPHlZvOa/Hg9fjwW7le2QRkUQwTZPrrriw0fuqQ2GWrVrHwQeMZ/mqdRwwcXS9+8ORCLjg9XrYU1xKRZ3O9X31pn1id+lGh/gF6dB5Xel1NRbWKlhPPO/izUkdpHfXbnSF6O1kGtEO26pI+8cwDAOvxyDd9LK7PNSxcSwDx3XZuruqQ+MEPBZhx2VdUcePIuWl+jlgQGa7Q9SA1+LwEX0Y1z+VwrLqdo8T9FnMGNmH6SNygPaHugGPyai+KRzQPwO/x8Q02tehneK1mJiXzojsAE4H6ok4LkWVYQb3CZLi87R7HK/HJMvjI+izoQP11OX3GPgsheciIiLS+VKCfi47Zy6PPLWQcCRCxLaZOnlsgxBdRKS7O+7Ig7nn0fm8sXgpBf1zY/u5x555mRNmHUI4EuG2fz+J3+fFcRzOP+M4NTV1kWQO0qHzutIb01R4q3C9a3W0K70rpnZpr0R1oytEl04Vjz/YhtFwWpJE1VI7TtyeV5LVI5JoPq+HJSs3JLqMHsvn7fiffb1GnUuvUfKLx2sk8TcoP4/vf/2sRJchItKp0lKDXHlZw/nLzz7lmNjXP7/q4i6sqPvo7G50iH+QDvG7cGMiwvR9xfMCpQrkW6+jXemt1Ru60fUpQEREksakkfmJLkFaoNco+ek1EhEREWmouwXpEN+udEiOMD0e4h0K9/RQvr1BejJ3oydCx64YKCIiIiIiIiIiIgBx767tjIs2Dp/gVzhaR3jGwHr/eqL2HihoywGXeB7wSUYK0UVEREREREREpMfrqnmUOyNIV5jedXpqoJ5sHfcdOWujs88qaYxCdBERERERERER6RW6a5AOndOVDnvDdAXqDfXEMF3aRyF6gtmOS3XEwWeZeEyT9l7b0WMaZKX4OHBoNiPz0kjzt326e9d1KQtF2F7e8R19lt9L36CfXWUhyqoiRGy3XfVUhCJUhmyCHguvabbrQpwGkBHwMjQ7hZF90uib4sNjtn0k04DcND/Dc1MZnJNCZtBDO4bBMCDVbzEoJ4XBOcF2j+MxDfpnBLAMg1DEwXZcXLft27mW1zLwWEa7ahERERERERHpLhSkN02BeuN6Spjenm50TekSpQuLdpDXhIgDbYkuXdclYruEIg5OzQMNw8DExTItHNfFcVzsVgSifq9J0GvhsUxc18UwDPqm+chN91MRirCtuIodpdWx9TTGdlx2V4XYWRki1I6wu5bHNMhL8dM/1Y/fsnBdF9eFqrBDVdjBYxkEvRY+j4HRzNGCiONQVh2hrDoSq9s0DLwmeE0L23WJtGL7eC2D7ICPrKAPyzRiAXPfVD99U/2UVkfYUxmiImw3O07Qa5GXFiA3zV8vYM5J85GT5qOsKkJJZYTqiNNiPakBixSfVedWg9w0P33T/JRWRSiqDBNqYZw0v4fcdD/ZKd56t9uOiw2YhotlNr+NG2MYBrhu7GfJcaNjtpXPAq/V8nIiIiIiIiIiPV28LzYKe4P0zg4s6wbp3f1ipPESnjEw6aZFka6hEL2DLDP6z3GjYXpzmaPjuIRth1Ck8YVqQ0/TAMM0sDBwXDfaYVxvnQYBr0lgn6Sy9vG1/we9FsP6pjKkTyo7SqvZXlJFRWhvYFwRttlZGaKoKtymgwD7Svd56Jfip0/QV69TfN8QN2K7lNoRDCDgi9Zv1aTSrutSFXEoqw5TGW48RK4dz2MYeExwXJeI4xDeZ6On+TxkB32k+T2xAwuN1ZPu95AR8BKK2OyuDFNcFdp7UAPISfHRLz1AesBbb5x9pQU8pAe9VIdtSiqj4b9bZ5ygzyI1YOHzmE2OE+2W95AZ9FIZtimuCEfHqbnfNKBPmp+8dD8Br9VsPY4Lju1iUBumN3zuTam7rUxcTMvAdcGuOSDSFNMAnyd6oKOt4b2IiIiIiIhIVxuentll8yp3RpAOXRemAw0603tzqF7bka4wvXdRiB4nphHtwI0GjmDX6U6P2E7NdButHc2ITetiApbHxHFdTAMCXgtvM2FsvVFq7rcM6Jfhp39mgJLKEEs3FbO+qILKFjqem68Q8lL8DEgNEGwh1N2XC1SGHCpDDh4TXCMa6Le16znanW7iNcHBJd3vISvgi3VSQ/OBbu19XsukX5qfvDQ/ZdVhPJZJ31R/m8fxeUz6pvvok+6jtDKC7ToEfVazBxYaGyfgMQlmBrAdl/JQhIDXIie1+QMUjXGBSM02bU93+t5lXbxmdFvYjlvvQJHXAp9lxA6GiIiIiIiIiHQXPSFIh64N02s1Nd1LbwrX1ZXeuyhEjzPDAI8RDa6j3ekulaEOhNV1gvC0oLdVoW5z41RFHFbuLGt3PbUy/V6GZ6W2u55aVRGHkNPx7ZMV8JIV8DbZdd6aMQwgNy2Az2N2eJwUv4nttH1KlbrjWLVznrdjapZ9dSTkrrtuj2ViOw5e08BrqetcREREREREureuDtKBHhWm76u5udR7YsCurvSOeWGjv1OuHdAZFKJ3EqMmSI9bg247Qt3GdGTalrpqy0iaeuI1Thwz4XgEzEacxiEO40Qf7+KzDLyWwnMRERERERHpGboySIfO7UqH5AjTG9Pai5V2x7BdXek9n0J0EWkDI64HGkT2teyLLYTCkUSX0WP5vB4mjczv0Bh6jTqXXqPkF4/XSERERJJPTwvSIXnD9Ja0NmyH5ArcFaT3bArRRUQkaYTCEaaOG5zoMnqsJSs3dHgMvUadS69R8ovHayQiIiLJKRFBOnTe9C61asN06H6BekuSbfoYTe/Sc5mJLkBERERERERERCQZDE/P7PJ1duWc0HMKKuqF6j3Z8An+ev+6Um2YLs3rLvOhgzrRRUREREREREREYrq6Ix26riu91r5Bek/rUG/MvkF6Z3eqJ2NXusL99uvSEP2xZ15m3YYtAOw/YTRzjp7elasXERERERERERFpUSKCdOj6ML1WT57ypSl1Q/XODNQ1V3rP0KUh+pGHTOHsU47BcVz+eNuDTJ08hry+2XEb33XBdsF2ot97TDAN2nwhRNeFiBMdywSsdo7jOC7VEadtD2qxNhcjGa7s6MZnmCR4JknNJY6vueu2/YdYREREREREpJdKVJAOXXPh0ab05i71zgrT63aAJyJQ70gHelumwunJUwV1aYjeLzcHANM0MC0T0+z4lOyuGw0aIw44+wS74Zr82jKigXpz+aHrRh9vO1A39nYAp04ob7UQpruuS8SBUMTFdgEMPBZE7I6lzq4LoYiN1zJj62lLsFq7fHbAy4S8dFbvLCO87wZrBYPo9rZxcQ2waLjd28JnGViWSVXY6VAuH4rY2I4Hj9XxkNhjGbEDMe1lmSau3bHnBGC7Lh4jHr8nLqZpdLgey4j+E5HEW7vJZWOhy6A8gxEF+sUUEREREekMiQ7Soeu70vfVWDDaU4P1ruhO7+ppXjSFS3wkZE709z76jNycLPrmNH6xhnAkQiRiYztNJ5l1u85bCgZtF2y78a7ytowTcSBC9PEeMxoo147juC5hOxqe7zuOZRqYRs26HLdNobMBWFb08WAQtl0Mw8UyauNsmg3TXTe6TGXIpqQyQkXIZmB6kAFpAXaUV7OhuJI9VWH2jtZ0HQCDs4OMy0tnQHoAwzBwXJfSygi7y0JUhVufPPs8BkGfhcc0YuNUhGxKq8KtDvcNINXnIc3vwedpf9BsGJARsMgI7h3HcVyqwi6VYaddBwlMw8DnsXBdF9txsd3WD2IYEPRaBLwWZvSFj43Tpp8dI3qQwmtF64G9B4rackzHIPp709IBJBHpOms3ufzrOYeiUshKd7l8rqkgXURERESkk9RebLS3h+l1NdVx3JPC9a7qTu/MML2jAXpnXpC1IxcVTcQFgOMeojuOw423Pdjg9kljR3DS7ENZsXo9iz5YxvcuPaPJMRa8+i7zFy7C6/Vwyfln1h+/HSFg7LHs7Sq3akLt9jQcOy6E7NpgORputjRri2EYGEa0Cz8aiEYD9aZYZk3HfiOppetCpCaUtUwDk2iXeW23ee3/tuNSWhmmpDJCZJ91mYZBv7QA/dIClIcibCqpZFNJVb2wtzZYD3pMxualMzo3jVSfp8E4mSleMlO8VIVt9pSFKa4INxrImwYEfBYBjxkLh+uOk+b3kOqzCNkOpVURKsJ2o9vGYxqk+z2k+j2Nbp/W8nkMMoMe0gJWg3FM0yDFbxD0GYRsl8qQQxPlNMswDDyWgeW6NT+7TXeney2DgM/CZ5kNDozUjuPWjOM4DQ/W1PKYNR3+ZsMDLKYBpgWeVhw8auxgkYgkxr5d5xsLXYpKYVSBy5pNBpsKXYXoSUJnCIiIiIj0XInsSofkDNP31dJ0Ht0xZO9uU73Eq/O8rQF6T57KBTohRDdNk+uuuLDR+9Zt2MKzL73FlZefic/rbXKMOUdPZ/bMadiOw6r1O+rdF7bjMx13B2dXAaJ1hCLtmBLFiE7xYpm1BwXc6HTV7O06b+1ULbbjYgOmEQ3OLQOqIw4lFWHKqluX+qb6PIzpm87InDS2lVWxsaSSkuoI/dP9jOuXweCsYKvC6oDXYkC2RV6mn+KKMLvLQoRtF68V7Tr3WkaLz8swDPweC3+ahe24lIcilFZHsB2XFK9Fmt+D39MwZG6L9Jqu84C35e71aD0Gfo+J7UQ70ytD7XvNLQMs08JxXZya7nSD6HYL+Cwss+XntHccIzZO7fERnxU9MNCa18owwFMzNcu+0yG1ZtoiEek6jXWdD8ozyEqPBuhZ6VCQp1/YZKAzBERERKQzLV22mqdefIOdu4u5/ffXNrrM8lXrmL9wEQBzjz2UCWOGd2WJvUKig3So38GbzIF6Y1obtCZj2N7ZYTo0H4DvG7B35jQtndmB3l116XQu9z++AIDb//0kAGfOPZohBf0bLOf1ePB6PNgdnZg6ydUNRGunXWlvOOy4EA7bFJZUt/sgg2UaDMwIMjwnlaxUb2z+9faMk5PmI+gzKa2MtPs5WaZBRsBLut+DCx3qOo+NaUBehq/d9aT5LWw7QqgdXem1TMPAtAzSvAaeRrrO2zqO67pYRjRAbyvDiB688VnRMxxqbxOR5NFY1/kRU0wun2uyqdClQB3PSUNnCIiIiEhnGjV8ED+/6hKuv/nuRu93HIcnnn+dH37nXABu+sfDjBs1NC7Xo5P6Ej29S13doTu9PZK5o70rwvTGJPPc5m3tQu/IVC6J0qUh+vXXXtaVq+tWOtJZXVc8uvSBdgfodUWnsOn48zIMg3hFEPGqJx5bOq7bJw4bSOG5SHJqqut8RIHC82SjMwRERESkM6WlBpu9v3DnHvrmZJISDADQJzuTwp176J/XpyvK65WSoSu9VnfuTm+PZJiTvSsuQtrV2tOB3tXTuCRiPnRI0IVFRUREpHVGFBjqOu8m9FqJdC/5z/4R0w4nuoy4u/74pq891Z19fvHkRJfQaf76708TXUKnuePeRFfQu5RXVMYCdICUYIDyiqpGlw1HIkQiNrbTs2cA6ArJFKTX6m2Bel2NBbpdEawnqjs9nrpqCpfu2IUOCtFFRESSzr4Xp1TXefeh10pEREQ6wnEcbrztwQa3Txo7gpNmH9rsY1NSglRU7Q2nKqqqSU0JNLrsglffZf7CRXi9Hi45/8yOFS1JNb3LvnpzoF6rbrDe2YF6dw3T2xug95YudFCILiIiklT2vTjlcQdFL14wSJ3N3c6+B0NEREREWmKaJtddcWG7Htuvbza7dhdRWROk79pdRF7f7EaXnXP0dGbPnIbtOKxav6Pd9Up9yRymgwJ1aBj6dlao3l2meulI93l7AvTu2oUOCtFFRESSSt2LU37yBTzwoovPG51r+/K5ZiyMVUCb3PY9GFL3tRMRERFpjzXrNvLcwkUUlZTx538+ypGHHMCUSWN47JmXOWHWIaSnpXDanCP4612PA3DanCOavKio1+PB6/Fg25rOpTMk4xQv+9o3zFSo3rsC9Y5O3ZKIAD2RXeigEL3DvJaB44LjurjtuNak67rYjovtgGmAZUYvEtmeC06apkH/TD9VYYey6ggRu+0F+TwmWSne2POynfZdQNMAgl6LFJ9FRcimotpu11heyyDos7BMg6qwQ1XYbtd2rldYEvBYBpZp4Lpu3C4qKyLJrbWhd92LU3osl4gNE4ZFv99U6DKiwKgX0Hosh0MnmUwbrzC9M7X1oEXdgyF1XzsRERGR9ho1fBBXf3Neg9vPPuWY2NeTxo1g0rgRXVmWNCHZu9L31VjA2duC9Z4eqMdrzvPe1oFeq9uF6B4TIg50JEeFvVlqh8cxDExcLNOMBeItZcWu68YC6rqBsOOCY7sYgGVFQ/W2BKymAYZpkmoapAU8VIdtyqptKkN2888BSA14yAx68HutWLBr4mJ5TBzHxW7lQQLTiIb5prE3IE4PGKQHPFSFHcqrI1SHmz/SbRgQ8FoEfWZsuwKk+i1S/RbVEYeqkE24jQcJfB6DnFRvmx7TmBSfievatLBZGzAN8HpMvNbe1zQe+bkJWI03F4hIkmhLV3Ldi1O6rsFL77us2QRZ6VCQF31MbUCbm+XyzjLYscfhk7WGup07SWtfv7pBe92DIXVfOxERERHpXbpbmF5Xbw7WuzpQrxXPYD3eFwrt6vnP60p0Fzp0wxDdMqP/oiE0tLXZ2mOCZUTDS9eNhugRhxaD7+bUDbot08CiJhB33HohfTRkb7m724VYF7llurFQurV11P7v85j08Vo4jktZdYTyfbrBvZZBetBLRsCDWdMV3dg4hgHeZg4SGNQG583XE/CaBH1+IrZDebVNRXWk3lgeyyDotfB7zUbHqeX3mAS8FhHboTJkUx12mj0Ykh6wag4QxCdp9lgGmSkebMelKuxQGXKbXb/HNPB6DDyWGdfO87o/yyKSOK3pUG5rV3Ldi1MO6ueyqdCloM74tQHt5xuiy48eDDuKULdzJ2nN69dY0F57MKRAU+6IiIiI9HrdOUyvq7mO4p4asCfiwqTJpiMBenefxqVWtwvRa5kGmBZ43GiQbjfTnV7bqWvuEzga0Wu14bOigbrtRgP19qobjsa6uF2XcMQhYrfcod6Y2tDdMFysmqC6tSFs7XKmGe0Er+0Gtx2HoM9D0GfVC3WbGrfu/ZYJFtHt5bhug67z1tRjmQYZQQ8ZQQ+VIZtQxMHnMVsdMtcdJy3gIS1ATZi99yCBxzLIDHpID0SngukMlmmQ6rdI8blUR1wqQ07s58egpuvcs3f71K29vQyi4fm+P8sikhit7VDuSFdy3UC97m2XzzX5YKXDok9ddhSp27k1mjvg0dx9rXn9Ggvaj5iiMwNEREREpL6eEqY3pjVhaXcP2mvD5M4O05NFR7vPe8I0LrW6bYheyzDAY0Q7cvftKreMaODYmrCx7ji1Xe4dubxGLCx1XUKRjk4aEw2trTrd4u2tJ+g18Xm97Q519y7v4qkz1Upbxqm7rL9OeN6RcQJeE5/HpDocITPoIeA1u2yuccMwCHgNAl6TiB3tTncx8FhGiwcoWssy9h4IEpHk0doO87pTtMSrKzkarltMG9ewU10aau6AR0sHQ1rz+mn6FhERERFpi54cpjentaFqsoftXdmdngjxmLolHgF6snShQw8I0Wvt21Vee1t7xqkNLKsi8agrvh+iO9zJHKdQN17j1M6rE496LAP6ZyZ2J+uxDNIsi1DE7dBBmLpMA7xWnAYTkbhqS3DaWEd5PHTWuD1Ncwc8WnMwpKXt3BkHSnqy9UW7Wbplc+x7wzA4YuhwsoM97wOIiIiISHN6a5jekpYC2GQK2XtSoJ4s4TkkV4AOPShEr0vTXPROSfWyG0bHr1pbO1R8hhGRTqDgtPto7oBHvLrIdUCjZa7r8uAnS/jZywuojITr3TcgPYPbTzqdw4YMT1B1IiIiIomjML1tmgpqEx2u7xtCd5dQPV4XDe2pATr00BBdRESkqyg47R6aO+ChgyFdY09lBVe/+CzzP1/BhfsfyM+OOAafFT3ValdFBd9/4SlOf+Q+vj/jcH502FF4LZ2GJSIiIr2PwvSOaSzETWSwnsyheryC81o9af7zxihEFxERkV6huQMeOhjSuVbu2M68xx+gIhzmnlPPZu7YCfXuT/P5eWLexfz9vbf5w5uv8tZX63j0rAvICgQTVLGIiIhIYtXtxFWg3jHJFKwnMlSPd2heK97heTJ2oYNCdBEREZF61m5y2VjoMkhd6XFRVFXJhU88TFYgyAsXfJ2BGY2/KbZMk6tmzOTwIcOY9/iDfPu5//LQ187DNMwurlhEREQkuShQj799g99kCdX31daQvbOC8qb0lgAdFKKLiIiIxKzd5PKv5xyKSiEr3eXyuWaTQbrC9pY5rsO3n/svRVVV/GfeRU0G6HVNzR/EHSd/jXMef5Cb3n6dHx1+dBdUKiIiItI9KFDvHHXD4ETPq15XV4firdUZU7ckc4AOCtEb5bpgx+mikCIi0rMoOO3ZNha6FJXCqILohUY3FbqNvs5tCdt7s5vefp1X1n7BI2edz9CsnFY/btbwUfxk5tH8/s1X2H/AQI4dOaYTqxQRERHpnhSod45k6VJPRr0xPK+lEL2G64ILRBxw4higG4aB32sSCjt0fFgX6NgHdBdwHBfDiNbW5se7LoZh4Lourgt1h2jPeKYBGB0/aGEa4PckT3jhMSFs0+HX3AAsncUukjTaG5wqeO8+BuUZZKVHA/SsdCjIa/z1am3Y3pv974vP+dOi1/nJ4Ucza/ioNj/+qhmHs3TLZr793BMsvPibDM/u0wlVioiIiPQMCtQ7j0L1zrtoaHcJ0EEheqzr3HY6Hng2xecx8VoGtuMSijjYTusfa5kQ9JoEvEYsvI44EIq4bQqeDcA0DcwOh+cQcRzcOus2jejYRp3lWmISfW6mEX1ejusStqPPqy2vg8cEn8fAaufz6izRUD96QMZ22n6QwDJqt0/n1Cci7dOe4FQdy93LiAKDy+eabCp0KWjmoEdrw/beanNJMd9+7gmOHzmWHxwys11jmIbJbSedzuz7/smlTz3G/y76Bj6r1791FREREWnRvsGkQvX46i2hemcF57W6U4AOvThEb2+42V6GYeCxDDyWieO4hG2HUKTplfs9BkGficesHw4bhoHXIhbKh22XkN30emsDbrNNAXO0471u17njgt1E+u+44NguBm50XXVi8H2Dbcug5jntW6eB3wM+K/q6hOzowYLGGESDc69FG59X1zMNMC3wtOJgjUF020QPdHRllSI9x9pNLh+scMFwmTaubWF1a7rF2xOcqmO5c6zd5PLBSgdcg2nj49vhXzvWxkI39v2+Px+tDdt7I9d1+eFLz5Hi9XLrSad16MKgGYEA/zr1LI6975/8/d23uebQI+NXqIiIiEgv0VhYqWA9fnpSqN7ZwXmt7hagQy8O0ZsLnjubaRr4TQufxyViR7vTHTcangZ90a7z1oTDlmlgmQZ+T00Xt+3GpqKxOtR1Hg1xXaLBeWunt3EB23GxiT4Xq6aN2sDFE6un+TGiBxvAY0W700MRNzYtimWCzzIaHFjoDgwDPDUHEBy3/rRB6joXiY+1m1xuecxm5fro94s+tbnqbKvV0620plu8PcGpOpbjr/5r7bJoGa1+rVs7ft2fh+MOMnjpfbfBz0ftP6nviRWfsnDtah4441wyA8EOjzep3wCumH4YNy96gxNHj2dsbl4cqhQRERHp3RSsd57uFKp3VWheqzuG57V6bYieDAzDwOsx8HpMHNfFZ0bD4/aM4/NEg9jagwMdCZldF8JtmXOmEbXd6eCS7jfa1VltGgYBb/QggUvyd523lmlEO+5rp8TpIU9LJOE2Frrs2ANeT/TMjh17aHXXd1u6xdsanKpjOf42FroU7gGfJ3qQtS2vdWvHr/vzsHydziZorR3lZfzs5QWcOm4ix48aG7dxrz7kCOZ/voKrFjzN8+dfjmXqgiEiIiIi8dZUwKlwvWOaCqoTEa53dWheV3cO0EEhetKIzg3esTGMOIyRjAzD6ODlVJNTT3ytRBJpUJ5BbjbsrHl/l5vd+q7veHSLNzcdjDqW42tQnkFeNuxqx2vd2vHr/jxMHG6weafOJmiNn778AgC/O+aEuI4b8Hi5Zc6pnPTg3dz14bt866BD4jq+iIiIiDStufBTAXv7tSXQbilwT2Q43pLuHp7XUoguIiI9wogCg6vOtvhgZfQsmLbMid7RbnFdPLRr7X2tHcBg2rj4z4m+78/DoH6uziZowYLVK3l65XL+MfcMclPT4j7+QQWDuXzqwfzuzVc4fvRYhmblxH0dIiIiItI2bQlIFbi3XzKH5E3pKeF5LYXoIiLSY3Sk47sjj9XFQ+OjNRd3rRV9vaxOq2XfnwedTdC8oqpKfvi/+cweMZozxk/utPX89IhZvPTFKn7wwjM8cc5FHbpoqYiIiIh0rfaEqgreu5+eFp7XUoguIiLSQbp4aMepm797+8UrL1IRDnHTcXM79eLfaT4/f55zCl979D7u/3gJFx8wrdPWJSIiIiKJ19pAVmF74vXU8LyWQnQREZEO0sVDO07d/N3XK+vW8Miypfx5zinkZ3T+G+cjho7ggv2mcv1rL3HM8FEUZGZ1+jolue3cXcwNt9xLQX4/wuEw6WmpnHHikQzI65Po0kRE4mrpstU89eIb7NxdzO2/v7bRZa773R30yc4AYNzooZw4S9cRkd6hpQBXIXvn6enheS2F6JK0XBdcIOJEv7ZMsAxdkFNEWq8t04N0lKb76Bh183dPpdVVXPPisxwxdDjnT57SZeu9/qjjeGXdGn7w4jM8ftaFndr9Lt3D4IH9ufqb8wBYu34zdz7wDD/9/oV4Pfq4IyI9x6jhg/j5VZdw/c13N7mMaRpc++1zu7Aqke6hqaBX4Xr79JbgvK5e+67SY0bD2Y4yiAa9HWUa0X8drcUywO5gQYYRDaztDm4fA/B5jDZ/sHXd6HOwnfrbNuJAhOhztMyOby8R6dk0PUj3om7+7ulXr/2PPZWV/Pn4U7o0yM4IBLjp+JM59z8P8vCnSzlvv64L8CX5jRg6kIEDcvlq4zZGDiuI3R6ORIhEbGwnDh8CREQSIC012OIyrgs33fEIXsvi9BOOYNDAfo0up32iSJTC9dbrjcF5Xb06RLcMcGrC2rb+2fDU6Yp23eg4EadtgbpBfLurDQO8FniaCKFbU4/HBNOIBt+u6xK2odp2cdswkGVEw3OPSZs+UNe+Fi0dBLBdsO269ao7XUQa0vQg3Y+6+buXN9ev5b6PP+TGY09icFZ2l69/9ojRnDVxf37x6oscOWwEA7tgKhnpPnKy0ikqKa1324JX32X+wkV4vR4uOf/MBFUmItK5rrvyAtJTU9i4pZC7H36O66+9rNHltE8UaZ7C9ajeHpzX1WtDdKjpuK7panZrQvDmAlyTvR3QdUPbuuO0Jghuapx4MQzwGNGAuTbcd5qpp6nObsMw8HmiwbztQijiNtu977PAaxlYbWgRb+8BCIguH3aafw4i0ntpehCRzrOttIRvPfcEM4cM5+IDDkxYHb89Zg5vf7WOrz/zOE+fewk+q1e/tZU6dheVMnncyHq3zTl6OrNnTsN2HFat35GgykREmuc4Djfe9mCD2yeNHcFJsw9t8fHpqSkADMrPw+fzUl5RRWpKoMFy2ieKtE9PD9cVmjdNnzRq1O3i3jfUbUtAaxpgNjGOJwFzeptGNNzed4qUtnTBG4YRDeV9Bk5Nd3oo4uLGxjfwWm3rOq/luHuD8I6wXXBs8OsnWkRqaHoQkc4Rtm0uf+ZxLMPgjpO/hmmYCaslKxDk7lPP5uSH7uH6V//H72afkLBaJHl8uWELm7fuYMig/vVu93o8eD0e7I7OWSgi0olM0+S6Ky5s12PDkQi44PV62FNcSkVlFSlBf6PLap8oEl/Nhc/JGrArMG8bRY772Lc7vfa2RI0TL7Xd6dY+HfRtZRoGfk9NMF/zfUfEYz75zhhLRHoGTQ8iEn+/eX0hS7Zs4plzLyU3NS3R5XDgwEH8Ztbx/GTh80wbOIjTxk9KdEmSABs2b+OmOx4hEo6Qlhrk6+fN1UVFRaTHWbNuI88tXERRSRl//uejHHnIAUyZNIbHnnmZE2YdQjgS4bZ/P4nf58VxHM4/4zhdfFskCbQmrO6MoF0heXzpnWUz4vW3Jpn+ZsXvORkk0dMSERGRLvDMquX844N3+O0xJ3BQweBElxNz6ZSD+HDLRq5a8Azj8/oxpm9eokuSLtQ3J5Nbfn1VossQEel0o4YP4upvzmtw+9mnHBP7+udXXdyFFYlIvCjwTn6JO/9WREREeqy1m1xe/8hh7SadJ9RTfLJtC99/4WlOHTeRr089ONHl1GMYBjcddzKDM7O46MlHKCwvS3RJIiIiIiLSgyhEFxERkbhau8nlX885PPZy9P+WgnQF7snvg80bOO2RexnXN4+/HH9KUp4anurzcd8Z51AeCnHKQ/ewtbQk0SWJiIiIiEgPoRBdRERE4mpjoUtRKYwqiP6/qbB+OF43NG9r4C5db9GGL/nao/czIbc//zn7ItL8jV+gLBkMz+7Ds+ddRlUkzMkP3c3G4qJElyQiIiIiIj2AQnQRERGJq0F5BlnpsGZT9P+CvL1dy/uG5h+sdJoN3CWxXv/yC855/EGmDSzg0bMuSOoAvdaw7ByePe8yAE5+6G7W7dmV4IpERERERKS7U4gubea4ELahOgIRB1zlHSIiUseIAoPL55rMOyb6/4iCvSH6vl3q0HTgLolTEQ5x/asvcfbjD3DI4KE8+LXzSPX5El1Wqw3KzOLZ8y4j4PEy6947+PfSD3BcJ9FliYiIiIhIN+VJxErvffR59hSXNnpVaUlOrhsNzyMO1M3MIw5EANMAjwkG0JZpUi0DHKL/OsqjQ0Iivc7aTS4bC10G5Rn1glpJvBEFjb8m0S51NxaaTxtnMG2cwaZClwK9jknh7a/W8YMFz7K1tISfzpzFdw46FK9lJbqsNhuQnsGLF36dX732P3740nM8ueJT/jLnVEbk9El0aSIiIiIi0s10eYi+aUshFZVVXb1aaSfXBbsmPG+O40LIjobolhkNx1sTphsG+DzR9USc6LrawqxZn9nK9YlIz1E7LUhRKWSluw06niU51Xap7xua67VLLNtxeH39Wh74eAnPr17B9IIhPHLm+Yzs0zfRpXVIZiDIn+ecwmnjJnH1i89w5D23cc6kAzhvv6ns1z8/0eWJiIiIiEg30eUh+vxX3mHOrBk8veDNJpcJRyJEIja2o9NuE8V2ov/a+gq47O1Ot4y9AXdLDAO8Fnia6HjfV1vGFpGeqe60IGs2RTuZFcR2D011qUvX21pawgMff8jDy5ayuaSYcbl5/HnOKZw3+QBMo+ec4nX40OG8cdl3ufXdt3ngkyXcu/QDJvUbwHmTp3D2xP27xVzvIiIiIiKSOF0aon++dgP9+uaQkZba7HILXn2X+QsX4fV6uOT8M7uoOqnluBCOw/EL2wXbBr/V+i5xw9gbkDtuTZhfk6YbRKdsUde5iEDDaUE0l7ZI2y3bvpXbP3iH08dN4rz9pjBlQAFGD/0jm+L18aPDj+bqQ4/g1XVf8OAnS7j+tZc4ccx4hegiIiIiItKsuIfojuNw420PNrh90tgRrF2/ma+ffzIVldXNjjHn6OnMnjkN23FYtX5HvEuUFiTLhUJNA8ya7nRQcC4i9TU1LYiItN6s4aNY/r0fkubrPSGyx7Q4duQYjh05hpKqKjICgUSXJCIiIiIiSS7uIbppmlx3xYUNbq+qquajZau568FnCUUibNm2k+dfeYcTZx3SYFmvx4PX48G2NZ2LKDwXkaZpWhCRjrFMs1cF6PtSgC4iIiIiIq3RZdO5BAJ+fnH1JQDs3F3M/f9Z0GiALiIiIiIiIiIiIiKSLBJyxai+OZlc/c15iVi1iIiIiIiIiIiIiEirJSREFxERERERERERERHpDhSii4iIiIiIiIiIiIg0QSG6iIiIiIiIiIiIiEgTuuzCotJ9mEb0n+N2fCzLAMPo+Dgi0jv4vB6WrNyQ6DJ6LJ+343/29Rp1Lr1GyS8er5GIiIiIiHQv+hQgDRgG+CxwXbBdiDhtfDxgmQrQRaTtJo3MT3QJ0gK9RslPr5GIiIiIiEh8KUSXJhkGeIxoGO64YDvQXJ5uGuAxo/+LiIiIiIiIiIiI9AQK0aVFRk2Qbpl7w3S7ZqoXdZ2LiIiIiIiIdL7HnnmZdRu2ALD/hNHMOXp6g2Xe+WAZb773MQYG8049hiEF/bu6TBGRHkkXFpU2MQ3wWuC3olO++Kxo97kCdBEREREREZHOc+QhU7juigv58Xcv4JPP1lC4c0+9+8srqnhl0RKu/da5XHbuSTz6zMsJqlREpOdJ6k501422O9t2Gyflli4Th2uPikgN0zQweugRKe3PRaQ36cn7856g9m+SY3kTXEnnsN2e+bPn+gKJLqHTeFNTEl1Cp7FtR/vEOOqXmwNE/86Ylolp1u+LXL9xC6OGFeDxWPTNyaK6OkQ4EsHraRj9hCMRIhGbiG0DYDt6jUSkZ7ONjv1NSuoQ3XGib3A//WJLgisREel8B4wpwLJ65ptX7c9FpDfpyfvznqD2b9LGwy5LcCWd46twoivoJN+/KdEVdJqLvp/oCjrP0s83aZ/YCd776DNyc7Lom5NZ7/ayiipSgnsPOAWDAcorqsjKSGswxoJX32X+wkWkpAQ4/+zT+OLLQZ1et4hIYm1i4oj+BPy+dj06qUN0j8dk8sj8dh0lqKyu5sc33M6N//cdgn5/J1XY+XrC89BzSB494Xn0hOcAjT8Pswdflbcj+3MRke6mJ+/Pe4Ku/pvUU9677KunPi/ouc8tUc9L+8S2cRyHG297sMHtk8aO4KTZh7Ji9XoWfbCM7116RoNlUoMBKiurYt9XVlaTmtL4WRxzjp7O7JnTqKio5Dd/vY/f/OjrpASS64yPZP5dTObaILnrS+baILnrU23tV1vfn37+3XaPkdQhumma+Hztm7bdMk3C4QiWaWJZ3Xfq957wPPQckkdPeB494TlAz3kerdWR/bmIiEg8dfXfpJ76N7+nPi/ouc+tpz6vnsY0Ta674sJG71u3YQvPvvQWV15+Jj5vwymphg3O55mX3iJi2xSXlOP3exudygXA6/HE7quoqMJjWUn3c5HMP7PJXBskd33JXBskd32qrf1q6+tIA0VSh+gd4fFYnDT7UDweK9GldEhPeB56DsmjJzyPnvAcoOc8DxEREWleT/2b31OfF/Tc59ZTn1dvcv/jCwC4/d9PAnDm3KMZUtCffz38HJefO5fUlABHzDiAm/7xMAYGZ58yq8Uxk/nnQrW1XzLXl8y1QXLXp9raLx71GW7tlXVERERERERERERERKSe5OuvFxERERERERERERFJEgrRRURERERERERERESaoBBdRERERERERERERKQJPfbConUtfON9Pv5sDY7jktsniwvPmoPHSs6J7pvy2DMvs27DFgD2nzCaOUdPT3BFbbd02WqeevENdu4u5vbfX5voctrknQ+W8eZ7H2NgMO/UYxhS0D/RJbXZLXc9xobN25l12IGceMwhiS6nXTZs3s4jTy3ENA1M0+TCM+eQ2ycr0WW1SWVVNX/91+N4LItQOMJpc2YybtTQRJclIiIineSB/77I9h27ufbb5ya6lLjZubuYG265l4IBeUQiNkMG9eecU2cnuqy42LSlkCdeeJ1wOELEtpk6aQyzjzgo0WV1WOw1y+9HOBwmPS2VM048kgF5fRJdmiRIazKGRH0Obk12cN3v7qBPdgYA40YP5cRZXfcZtzX1LV+1jvkLFwEw99hDmTBmeJfUVl5RyT2PPk9lVTWD8vOYd8oxGIZRb5mu3nbN/RyFwxHu+88CdheVkJOVwUVnzsHr7bqosrna3vlgGc8tXBTbVpedO5fszPQuq625DCfR26252hK93VrKjVrzO9IktxcIhyOxr+9++Dn3kxVfJLCa9tlWuMt1Xde1bcf9/d/ud7fv2J3gitqutKzCDYXC7k9/f0eiS2mTsvJK99d/udcNhyPujl173D/c+kCiS2qX3XuK3UXvf+rOX7go0aW0W1FxqVtZWeW6rut+uuIL918PP5fgitrOth03ErFd13Xdwp173Btu+XeCKxIREZHOEg5H3N/+9T7373f/x925uzjR5cTNjl1F7s13PBL7/uY7HnE3by1MYEXxUV5R5V5/092xz1qO47jLV61NcFXxse9r9sWXm9zrb7rbDYXDCaxKEqmljCGRn4Nbkx0kMldoqT7btt3rb7rbLa+odMsrKt1f3Xy3a9t2l9T2xPOvue98uMx1Xde997Hn3WUrG+7DunLbtfRz9Po7H7nP/e9t13Vd97n/ve2+/s5HSVNbovOT5jKcRG63lmpL9HZrKTdqze9IU3rFdC4eT7Tr3HVdXNclr5t1rgL0y80BiB5JsUxMs/u9dGmpwS49MhYv6zduYdSwAjwei745WVRXhwhHIokuq82yszISXUKHZWakEQj4gejvtWW28mhhEjFNA8uK/v5WVVVTMCA3wRWJiIhIZ1m2ci37TRjJjAMn8v7SFYkup1PYtkMoHMbv9ye6lA5btmotk8ePJK9vNgCGYXRZ92hXGzF0IAMH5PLVxm2JLkUSpKWMIZGfg1uTHbgu3HTHI/z1rsfZuHl7l9RVq6X6CnfuoW9OJinBACnBAH2yMyncuadLalu9diOTx40EYL9xI1m9bmODZbpy27X0c7R63UYmj4/WO3l84/UmqjaAxUuWc+NtD/LMi2/iOG6X1QbNZziJ3G7Qcr6UyO3WUm7Umt+RpnS/RLOdnn/lHRZ/uJy8vtndOkx876PPyM3Jom9OZqJL6TXKKqpICQZi3weDAcorqsjKSEtgVb1bdSjEMy++xYVnzUl0Ke2yp7iUOx98hsIde7iomz4HERERadn7H6/gaycdTUZ6Crfc9Xi3nJKxKRs2b+OmfzxMUUkZg/LzYqdtd2d7ikrIyeq6U84TLScrnaKS0kSXIQnWVMaQ7J+Dr7vyAtJTU9i4pZC7H36O66+9LNElxZRXVNbbdik1265L1l1ZRUowGiAGg37KKyobLNOV266ln6Oy8r3bKqWJehNV234TRjF96gQA/v3YC7y39DNmTJ3YZfU1J5HbrSXJst2ayo1a8zvSlB4TojuOw423Pdjg9kljR3DS7EM5cdYhnHD0DB55aiGLP1zGkYdMSUCVzWvpOaxYvZ5FHyzje5eekYDqWqel59AdpQYDVFbu/YNXWVlNakqgmUdIZ4rYNnc++CzHHXUw+f36JrqcdsnOTOfH3z2fnbuLufmOh2NHkEVERKTnqKisZu36zTz43xcB2LW7mI1bChmUn5fgyuJj8MD+XP3NeQA8+szLvP/xCg7af3yCq+qY7KwMtmzbkegyuszuotJYN570TB3JGDr7c3BHs4P01BQABuXn4fN5Ka+oSpr6UlKCVFRVx76vqOq6bZcS9FNZVU1KMEBlVTWpKcEGy3X2tqurpZ+j1JTa+zNr7mtYb2dpTW21pu0/js9Wf5k0IXoit1tLkmG7NZcbteZ3pCk9JkQ3TZPrrriw0fvC4QherwfDMAgG/fi83i6urnWaew7rNmzh2Zfe4srLz0za+qH559BdDRuczzMvvUXEtikuKcfv9+L19JhfnW7FcVzueWQ++08YxQETRye6nHYJRyKxn59gwIff70twRSIiItIZPlq2iuOPns7Rh04FYOWa9bz30Wc9JkSvKyUYoKwsebrg2mvS2BEseHUxh06bHJvSZcXqLxk/eliCK4u/LzdsYfPWHQwZ1DUXipTE6EjG0NmfgzuSHYQjEXDB6/Wwp7iUijqdpclQX7++2ezaXURlTZC+a3dRbJ/S2bVVh8IsW7WOgw8Yz/JV6xp8bu6KbVdXSz9Ho4cPZtmqdQwa2I9lq9YxevigTqulrbVFt000EF71xVf0r5kCKRkkcru1JNHbraXcqHbbNfU70hzDdd2unZwmAR5+8n9s2b4T13XJ7ZvN+Wcch8eyEl1Wm1x/091AdO4tgDPnHt1lV8aOlzXrNvLcwkWs/WozI4YM5MhDDmDKpDGJLqtV3n7/U95+/xMMDM4+ZRZDBw1IdEltdv9/FrD2q81EIjb5/XP57sWnJ7qkNlvy6Sr+/dgLsZ/9gQNyOefU2Qmuqm2+2rSNx599BcM0cRyHubMPZdyooYkuS0REROLs5jse4YKvHR8LTsKRCL/+87386trLMbvhdV3q2rm7mBtuuZeCAXm4QNDv47Jz5xIMdP950TduKeTJ518nHIkQsW2mTh7L7JnTEl1Wh8Ves/x+RMIR0lKDnH7CEeT31/V5equmMoZ/Pfwcl587F0jc5+CmsoPHnnmZE2YdQjgS4bZ/P4nf58VxHE49fiZjRw7pktpaU196WgrLVq7l+ZffAeDEYw5h0rgRXVJbWXkl9zw6n6rqEAX9c5l36mxM00jottv358gyTVasWc9xRx5MKBzmvscXsKe4lOzMdC4+64QuvZZec7U9teBNVq5Zj2Wa9MvL6fIscd8M5+TZhybNdmuutkRvt8Zyo0ljR1BaXsGMqROb/B1pjV4RoouIiIiIiIiIiIiItIfZ8iIiIiIiIiIiIiIiIr2TQnQRERERERERERERkSYoRBcRERERERERERERaYJCdBERERERERERERGRJihEFxERERERERERERFpgkJ0EREREREREREREZEmKEQXEREREREREREREWmCQnQRERERERERERERkSYoRBcRERERERERERERaYJCdBERERERERERERGRJihEFxERERERERERERFpgkJ0EREREREREREREZEmKEQXEREREREREREREWmCQnQRERERERERERERkSYoRBcRERERERERERERaYJCdBERERERERERERGRJihEFxERERERERERERFpgkJ0EREREREREREREZEmKEQXEREREREREREREWmCQnQRERERERERERERkSYoRBcRERERERERERERaYJCdBERERERERERERGRJihEFxERERERERERERFpgkJ0EREREREREREREZEmKEQXEREREREREREREWmCQnQRERERERERERERkSYoRBcRERERERERERERaYJCdBERERERERERERGRJihEFxERERERERERERFpgkJ0EREREREREREREZEmKEQXEREREREREREREWmCQnQRERERERERERERkSYoRBcRERERERERERERaUK3CNG//8s/M2DqiQyYeiIbt2xv8+Pf+fBTbvrnQ9z0z4c6obr4q32ura3397fdx8Bpc9m5p7jJZd758FPmXPgDRhx2BgOmnsgx53wvXuXG1WPPLuSmfz7EY88uTHQpItIJ6u7P9/2XbPulm/75UKy2rrDgtcXc9M+HuPPhp7tkfSIi8fTGu0u5+OpfM2n2eQyZfgpTT7iYi6/+Da8s+rDVY2zcsj223619L9ja2zrTxi3bY58l9v0s8tizCzv0OUVEJFns+z49/8CTGHfU2ZzzvZ/z4acrO229p3/jJwyYeiKnf+MnzS5Xd3/7zoefdlo9bc1jmqO/ESI9iyfRBXSFd5Ys4+Y7Hwbg2m+el+Bq4u/VRR+y//hR9M3ObPR+13W5/Ee/Z09xSRdX1naPzX+FxUuWMWPqJM4+eXaiyxER6TIvvr6Yx+e/QsGAPL5x7qmJLkdEpNVu+Ns93HbfE/Vu27J9B1u272DT1u3MOvTABFUWHxu3bI99ljhk6iQG5fdLcEUiIp3PdV2KSsp4ffFHvPvRZ7zy6N8ZPnhgossSEUmYXhGid6aq6hABvy9h69++YzfLP1/HD5s5OLBtx65YgP6jb1/AVZedjWEYHV53ZVU1wYC/w+OISO/0/nP3KIhA+1IR6d6eXPB6LEDP79eXP/7sCg49cDJlFZW8/Nb7vPj6ux0af1B+P7YueT4epdYTr/fwZ588W40fItLjvP/cPfTJzuSGv93LvY/Pp6q6mqdefINrvnFu3Nf15J1/iPuYyUJ/I0R6lm4bor/z4aec8c3rALjrjz/lpTfe5aXX3yUY8PO1E4/muu9ehMdjcfo3fsLiJctij6s9LX/G1EmxnfW7S5fz17sf46Nln1MdCjFiSAGXn3My55xybOxx3//ln2Mdgn/86fe44W/3snrdBq755rncePsDANxz0/8x56gZAJSUljNp9rmEwhGuumweP/7OBdz67/8w/5VFbNyynZLSctLTUpg6aSzXfus89hs3ql3b4ZVFHwBw9GHTGr3/sWcXctWvbol9/8d/PMAf//EAZ500i7/+6mrC4Qi3P/AE/33+VTZs3kYw4OfAyeO45hvncsDEMQ2221WXzSMSifDY/JcJhcKsev1xAF56411uv/9JPlu9Dtt2mDB6GFdccibHHTE9NsbHn63mxn88wLJVayktK6dPdibjRw3jW+efzmEH7VdvyoTFS5bFvr/mG+f2yDMIRKRx/3vzPe56+BlWr9tAUUkpHo+HsSOG8I3zTuWUY2fGlqvdv8+YOolLzzqJP/3zITZs3s64kUP4/XXfaXG/WlUd4rb7/svTL73Bxi3bCfh9jB89nN/96FuMHTm03rJrv9rMz/54B+8t/YwBeX24+hvn8LUTjo7d/8s/38Xb73/Clu07KCuvJCsznUOmTuK6717E0EEDgGgn40FzLwXgdz/+Np+tXsfzryyiYEAeRSVlbNpaCMCmrYWx/V/tvlpEJFn99Z7HYl/fesMPmTFlIgABv495J8/mzBOj+8rW7CcbU3ffecsvr2oQRlSHI/zi5jv57/OvEonYnHzs4fz6mm+QEgwATb+Hv/PG6xg1bBC//fu/Wf75WnbtKcZxHAoG9OP0OUdyxcVn4vV6uOmfD8W60IHY5w+ArUuer/deu+7B4fUbt/LHOx7krfc/prikjH65Ocw5agY//Ob5pKelAPXfpz9x5x+486GnePO9j8nJyuDSs+fynQvPaPPrISISLynBABecPod7H58PwNbCnfXuX7lmPTfd+RCLlyynvKKCQfn9OeeU2Xz7gtMxzejMwYU7d/O7W+/jrfc/ZufuItJSUxgxpIDTjz+Ci886Caj/nr42owmHI/zmb/fwn/mv4LouXztxFqOHD25QY7396D9/zyEHTgZg2kmXsGlrYb330vHMY87+zs94872PmTB6GC8/cmvs9tozswJ+P8tffoj5L7/d6N+IjVsL+dM/HuSNdz9iT3Ep/fP6cMqxM7nmG+cS8PsIhcOMOeIsqqpD/Pg7F3DVZfMoLatg7FFn4zhOLHtasmwVJ118DQD/+cfvOOyg/dr0PESkbbptiF7XNb/+KyVl5QCUlldw+/1PMHhgfy762gktPnbhm+9zybW/wbad2G0r1nzJ1b/+K1u37+TqfY607tpTzCXX/IbqUBiA0cMGM7B/Lpu37eCF196JhegL33qfUDgCwGlzjgCi891+smJNbKw9xaW8/PYHvLf0M9584g765/Zp83N/ZdGH5PbJYr9xI9v8WIBv/OQPvPj64tj3oXCEVxZ9yFvvf8yjt/829kGo1r//M5+ikjIAMtJSAbjvP8/zkz/cXm+5JctWcfHVv+Gvv7qas06aRUVlFede8Qv2FJfGltlauIuthbuYOnmcdvYiEvPBxyt4+4NPYt+HwhE+Wv4537ruRlICAWbPPKje8stXreUbP/kDrusCsPSz1Vx6zW9595l/4fU2/mcuHI5wzvd+zrsfLY/dVh0Ks3jJMr7cuLVBiH7q5T9i5+4iAL7cuIUrf/FnJo0dyZiaN/NPvfg6O3YVxZbfubuIZxe+xYefruLtJ//ZoNP8j/94ILYvHdg/t/UbR0QkiWzbsYvV6zYAMGbEkAbvGwEsywLavp9srZv/+RCFu/bEvn/oqZeoDoX5+6+vqbfcvu/hATZs2VbvfTDA2q828ac7HqS0rJxf/uDydtX01aatnHDRD+q97920tZC7Hn6GxUuW8+w9f2rwfC+5+jexzzObt+3gN3+9h7Ejh3D0Id17KhwR6d5c3NjXOVl7p4/9dOUXnHr5j6isqo7dtvarTdzwt3v5Yv0m/vLLqwD4/vV/4fXFH8WW2V1Uwu6iFQT83liI3pgb/3E/dz38TOz7ux99lrw+2R16LvHMY0497gjefO9jPlv9JV9t2sqQggGxdQAcO/MgUlOCjT5209ZC5lxwFbvqXNNu45bt3Prv//DJitU8dvtv8Xm97Dd+FO8t/Yylyz8H4OMVq3GcaG710bJVzDlqRmyueo9lMWXSmIYrE5G46hYXFm1JTlYGbz95J688emvstMznX3kbiJ4aVPeUo61Lnmfrkud58s5o4PJ/N92BbTscfeiBLH3xfr54679cdvZcINpZs+/FOiurqjn60AP55KUH+PR/D3LQ/uM59bhoZ+TLb71PuCY4f+G1dwCYMHoYo4dFQ5YfXD6Pt574J2vffoL1i5/mkVt/A0SD/6dffKPNzzscjvDWex9z1IypTU7PcvbJs3n/uXti39/yy6vYuuR5/vqrq3nr/Y9jHxzmHDWDFa8+ypN33Ugw4CcUjvDLm+9sMF5JWQV/vf4HfPHWf3n2nj9RVl7BDX+/F4B5J8/ms1ceYeVrj3LirEMB+PUtdxOJ2Kz+cmPsg8RLD/6V9Yuf5v3n7uGv1/+ASWOGA9HXZsbUSUD0TIHa10pd6CI900FzL6138aLai/ccf9QMFjxwCytfe5SN7z3L20/eSX6/aND8wJMLGoxTWl7Bj79zAatef4x5NR2KW7bv4KOaN5yNeerF12MB+syDD+DdZ/7Fqtcf419/+ikD+vVtsPzUSWP57JVHuOn/rgSic0S+8Oo7sft/9+Pv8N6zd7Nu0ZOsffsJbv75lbE6GruoXigc4f6//JK1bz/B7b/7ER/Mv5ezTpoFQMGAvNj+T13oIpLMtmzbEft62KD8Zpdt636ytQzD4OVH/s4nLz3AQftPAKJTzKzbsLnecvu+h5+23zhGDR3Eo7fdwLKFD7HxvWdZtvAhjqk5u/Php/+H4zhc+83zeOKfv4+N88Q/fx/bRzflT/98KPa+97Ybfsjnrz8e6ypf/vlaHn76pQaPGTm0gKUv3s+jt90Qu+35lxe1c6uIiHRcRWUVDzwRfe9tGAYn1DQMAvzqL/+isqqaSWNH8O4z/+LLd57iZ1dcDMCjzy5kxZovAXj/4xUA/Pqab/DVu0/z8YsP8OBfr2fOUYc0ud6iklLueSza/T5q2CDen38vb/z3H3g8VoeeTzzzmBOOPgS/zwsQ+0ywcs161m3YAkRD9qbc9M+H2LWnmIH9c3nl0VtZv/hpbv/tDwF46/1P+N+b7wHE/qZ9tHw1EG1UhOhrsaTmc85Hy6L/Txw7InYGloh0nh7Rif6t809jxJDoBS7GjRzK0s9Ws2X7zhYeBes2bGHD5ugVkl9d9CEHHH9hvftD4QgfLVvFsTMPjt1mGAZ//On36JuTFbvttOOP5Lb7nqCopIx3lizjoP3H89o7S4D6O8+0tBR+9sd/8OnKLyguLY91TQKs27ilzc/7g09WUFpeEXuz31Zvvrs09vW13zyP7Mx0ZkyZyAlHH8ITL7zGslVr2V1UQk5WRmy5o2ZM4ay5xwDRjqPXF39EWXklEP1j+eizC+utY9eeYtZ8uZH8vD5YloltO9xy96PMmDKJcSOHMnf24ZoLWETq6dc3h9/deh/vfPgpO/cU1TtTqPaNaV15fbK54uIzMU2Tk445LLYf2rJ9R4Nla722eEns61uuv4oBedHg/MSjD210+Z9ecRE5WRmcPudIrr3hbw3Gt0yT7/38ZlZ+8WVsn1jry32CHIhO01LbUV97oFVEpCdr636ytc455VgmjI42ZFx5yZmc//3PcByHJcs+r3cBvMbew1dVh7jvv89z3R9uZ/O2wthZpAAlZeXs3F1EXt+cNtf05nvR99gTx4zg9DlHAtH32nc9/AzhSIQ33l3KZfNOrveYq79xLv1z+9A/tw99c7LYubuIza34PCMi0hlqp9ECSE0J8ssfXMb+E0YD0XD9vY8/A2DZqrVMP6XhWTuLlyxj/KhhFAzIY/W6DTyx4DUqqqoYO2IIUyePY1YzGcaKNetjHe6XnHUSgwbkAdGmvT/f9Ui7n1M885jM9DSOOuRAXnx9MQteX8y3LzyDBTWNlJnpqRzdzAW1azvzN2/bwax532tw/+IlyznuiOkcvP94/k70zK0Nm7exdPnnBAN+pk4ay0fLP8e27ViwftD+49tUv4i0T48I0YcN3tv54q/pRK97qmZTdu3TZd6YojqnYQL0zcms9+YbYMLo4YwaNog1X27khVffoaKyisqqagzDiIXoGzZv4/wrr6e8ov6HhlrV1aEWa9nXK4s+xGNZHDF9SpsfC7C7znOrDZD2/XrfEH3cPlMctGYb7ikpZdyoofzq6q/zh9vvZ8Fri2OnOaWnpnDrDdfWO1AhIr1DYxcWdRyHi37w61j3yr6qQw33lUMKBsTmXQzUdIQAhJr5O7C7KHqx5WDAX2+f15Thg6JBTN2L0NWOv2TZKr7+49/VC/vr19ywjn2nixER6Y4G9s+Lff1lMwFEe/aTrZXff+8+vH/e3lPxt+/YVW+5xt7D//qWu2Nz/cazrj1F0ffY+f321hMM+MnOTKdw157Y36C6htf9PFPztywUbv92ERGJF9t2qKisin1fXFrW5P68Vu20hX+47rtc8fOb+GTFmthUKpZlcuUlZ/Gjb1/Q6GMLd+6OfZ3Xd+8ULv3aMN1K7bQntTojjzntuCN48fXFLFn2OYU7d8dmI6jbpd6YljKUopLo35AD9xuHaZo1B4ZX8dHy1ew3fhQH7T+etz/4hNffXcrmmjPCFKKLdI0eEaJ7rL2n9TQ2rUlTU53UDYdrL9ZQl+u6DR7r9/lozKnHHcGf7niQF19fTHlldMd84ORxFNQcNX393aWxHfYfrvsu55wym0jEZsRh7b9g0CuLPuTA/caRkZ7arsfnZKbHvt5WuIvsmu/rXjCk7jaCvQcpGrv/1t9cyxknHFXv/rrb8LJ5J3P+6XNY/vla1n61mQeeeIEPP13FL2++KxaiN/5KiUhvsW7DlliAftZJs7jhh98iPS2F4y+4qt4chnXVPbWzqf39vmr3XZVV1Wwt3NlikF67jsbGf/H1d2MfJB786/UcMX0Ka7/axJFnfafJ8QKNvLFube0iIsmiX24OY0YM4fO1X/H52q94b+lnHHzAhHrL2Lbdrv1ka22t0629rXBvcL5v2NLYe/jnX4lOlzJu5FAe+vuvGJDXl1/fcjf/eODJ+gu2cf+cnZXOjl1FbK1TT2VVdWyKl33fX0PLn2dERLrS+8/dg2EYXPbD3/Lpyi/41V/u5oAJYzho//FkpqfFwt1zTz0uNj1XXbVd3jOmTOSD+ffy+dqv+GL9Jl568z3++/yr/OVfj3L+6XPIb2QaxbpnABXu3HvNi30PjgL46rynrqpptqmqDsWuZVSrM/KY2TXznpdXVHLHg0/x2eroZ5jmpnKB6N+Awl17mHnw/jx2+28b3F+77TLT0xgzfDArv1jP0y+9wc7dRZx10iymTBoLUG/O+IP3n9BgHBGJvx4xJ3pLMtPTYl+vXLM+9vWIIQNjXZD/euRZ3lmyjOpQmO07dvP4/Fc49rzvt3odpx0f3VEW7trD0y+9Ue82qN8RmZYSJByO8Ifb72/X84HoxSg+X/sVs5o5Taglhx+8f+zrP9/1MHuKS3lv6WexLvGJY0Y0+ia/rmn7jSMtNRgb45OVawiFw2zcWsi/H5/P2d/5GQA7du3hd7fex8ov1jNiyEBOmnVo7NTbukdiMzOir9XmbYWUlJa3+7mJSPdUt+su4PdjWSbP/O9NPl35RVzXc9SMqbGvr/nN39i4ZTulZRW8+PpiPm4irG9K3ZrTUlMoq6ho16mmtfu/3UUl9TpwRESS2fcvPSv29Xf/70+89s4SqkNhdu4p5tFnF3LZD38bt/1kYx55ZiEr16yncOdu/nbvfwAwTZOprbjAWm1dXq+HYMDPslVr+e8LrzZYLitj72eJz9dtqDcFQGMOP2h/IDrNwdMvvUFpWQV/vvNhwpHodDEzDz6gVc9NRCSRCgbk8edffB/DMHBdN5ZfpAQDsQOmTy54jQWvLaayqppde4qZ/8oiTv/GT9i0tRCA3992H2++9zE5WRkcd8R0jpi+d//XVEf2+FFDY1O+/vs/z7NxayFrvtzYYOpYoN61jN5672MA7nzo6XrTc0H88xiInmF0/JHTo+t8+GkgOs3koQdObvZxR86IziTw1vuf8MCTCyivqKSktJxX3/mQi6/+DYtrrtsEezvMX347eu2QKZPGMGXiGAzDiE0dNnxwfoMzrUSkc/SITvSW7Dd+ZOzro+d9F4DvX3Y2P/nOhdzww29y6bU3sGtPMWd84yftXsewQfnsP2E0H3+2Gtt28FgWc2cfHrt/5vT98VgWEdvmez+/CYAhA/u3e321F2CadWj75kOH6Bv4Y2cexP/efJ/5ryxi/it7L17k9Xj41dUN5zbbV1pqCj/73sVcd+M/WLdhC8eff1W9+2s78auqQ/z93sf5+72PN6yjzh/S/caPYsFri9mweTtjjox+KHvs9hv0YUOklxg1dBCDB/Zjw+bt3P/EC9z/xAv4vB765+bU6+jrqNPnHMUjzy7k3Y+W89o7S+rN+3jPTf/H/uNHtXqsWYceyJ0PPQ3AqZf/CGjf/n2/cdF1VlRWsd9x0dNbb/q/KznvtOPaPJaISFc57fgjWf75Om6//wk2b9vBuVf8ot79E0YP49Kz58ZlP9kYx3Fi7+9rnT7nyHrzoTfl6EMP5MkFr/Ppyi8Yd9S8JusaWjCAjLRUSsrK+emN/+CnN/6DafuN59l7/tTouNd+8zxeXfQhRSVlfPunf6x33/hRw7RfF5FuY8Lo4Rx3xHRefH0xi5cs492ly5l+wER+cdVlnP71H1NZVc2l197Q5OOfXPA6f7unYQbQP7cPo4c3fl2grIx0Lj37JG677wlWr9vAQSddAhA7c76uKRPH0D+3D9t27OKOB5/i/icWUB0K4fN66gXp8c5jap123BE88cJrsbOt5s4+HMtq/gKo137rfF5Z9CG79hTzo9/eyo9+e2u9+79x7imxrw/afwL3/feF2PQ0UyaOITszneGD81n71ebYMiLSNXpFJ/q0/cbzw2+eR36/vg1Ojzx25sE8eeeNHHPYNLIy0vB5PQzK78fxR87g1t9c26b1nHrczNjXhx20H32zM2Pfjx42mNt/+0OGD84n4PczY+okHr711+1+Tq8u+oD8frmMGzW03WMA/OuPP+NH376AEUMK8Hk9ZKSlctQhU3nqXzdySAtHUGtdfNZJ3P+XX3LI1Emkp6YQ8PsYWjCA044/kj/8JHqabnZmOpedPZfxo4aRkZaK3+dlUH4/Ljt7br3Tvy47ey6nHX8k2ZnNd8CLSM/k9Xq49+afM22/cQT8fkYNG8Tdf/oZQwflt/zgNvB4LB659Tdc+83zGDl07/5v+pSJDC0Y0KaxZh58AL/90bcY2D+XYMDPcUcczO2//WGbazp59uFcctZJ5PbJavNjRUQS6effv5RHbv0Nxx1xMH2yM/F6POT368txR0znuu9eFLf9ZGOu+eZ5fP3cU8jOTCctNci5px7Hjdd9t+UHAjf88FuccuxM0lNT6Nc3h59+7yK+duLRDZZLCQb426+vZtSwQXg9LfcgDRuUz/P3/ZlTj5tJn+xMPJbFwP65XDbvZJ686w+xDksRke7gqsvPjn395zsfBmD/8aNYcP9fOHn24fTNyYrt94+cMYU//ewK+uVGp2S59KyTOGj/CeRkZeD1eOjXN4cTZx3Ko7f9ptl5w3/87Qu5/JxTyExPJSMtlQvPOIEfNzKHus/r5Z6bfsbkcSMJ+H0MGzSA+/7yywYXhY53HlNr5sEH1Msu6s5G0JRBA/J48YFbOOeU2QzI64PX4yGvTzYzpk7iV9d8nUlj9zaB1p3rPL9f39gUlFNrpnTZdxkR6VyG29L5iAm26KsvOfWRe7n71LM5eewE7vjgHX7+yoss+dYPWLRhPVe+8BTHjhjNQ2eezwurV3LRk4/w9DmXcOiQYXyybQvXv/oSn2zfgs+0uGD/A/nZEceQ+4f6HTJ3n3o2fYIpnPrIvQAEPB4m5vXn9rlf4+ZFr/PY8o8b1LXkWz+gIhzm8LtvxcDgk+9ew4D06M4z9w+/4NIpB3HjsScx5fY/s7GkiJcv/hb79c/nyHtuJ9MfYN6kA7jyhacajPt/RxzDDW+8zJ+Om8vFB0zjqRXL+Maz/+H3s0/k8qnRecOrQ2HGHz2Po488mKfTyhpdv8+yWPLtq+mfls6Iv/yOOaPGcutJp8fqCXg85Kdncs7kA7hy+mGYRv3jKac8dA/vbFzPFdMP4xdHHktxVSUTb/0TVZFIbPsu2vAlv37tf6zauYMB6el888AZXDLloFgNAEGPl2HZOVx+4HQu2C86fUJtDbWuP+pY5o6ZwNQ7/sL1Rx3Ldw8+LHbf0q2b+cELT7Nm904yA0Eu3v9AfnR4ww83IpL8tD9vuD+vtWpHYZPr1/5cRJKR9unap2ufLtJzaJ+ufbr26SIt6xGd6AvXruHznYX1bttWWsJpj9xL2LF56pxLeOBr59InJSV2/7xJB/DJd67hk+9cw+wRo2O333z8yTwx72I+2rqZu5e8xw3HzOGT71zDt6bNAOCNS7/LJ9+5hoEZmTy7ajkGBi4uz676rNka//7u2/W+P2XcBD75zjX869SzYuv95DvX8PUDpzNj0BD++Nar7Cgv44Y3FjIhtx+XHLB32ha/z8vat59g1HEHNrn+kG1z5weLG61l5pDhvHbJdzhj/CR++8bL/GvJ+03W/Z/ln2A7Dk+tXE5VZO/pUF8V7WHe4w/QPy2DFy/8Ol8bP5kf/W8+T61YVm8b/++ib3LgwEFcveAZXlyzql4Ntdv/ogOanpLmF6+8SFYwyFuXf49/nXIWo/vmNbmsiHR/vW1/Xqu59Wt/LiLdlfbp2qeLSM+hfbr26SK9XY8I0Ydl53Dre4vq3fb4Z59QWl3Nn46by37985k2cDDfmnZI7P4Ur5f8jEzyMzIJeveeRpQVCDI8pw+mYZDh95MVCJKfkUm6L3raZb+0NPIzMrFMk2c//4z9+g9gaFYOz37e9M58WHYOz69ewfqivReKS/H6yM/IpG8wNbbe/IxMUrw+fj/7RHZXVnLCA/9iQ3ERv5t9IpbZ8KVqbv3Ds/tw38cfUlpd1eBxfo+HkX368qPDj2a//vn8e2njO/MROX2IOA6vr1/Lo8uWMr1gSOy+J1d8SlUkwi+OOpZxuf245tAjyU/P4OFPP6q3jcfm5nHjsSeSm5rGvXXW4/d4Yts/zdf0Ka2l1VWs2bWTF1evIt3v59RxE5tcVkS6P+3PtT8XkZ5D+3Tt00Wk59A+Xft0kd6uR4To3znoEJ5c8SlbS0tit20qLgKiO7XGPPDxEobcfANDbr6Btbt3xm7/7vwnmPD3PzKmT26901v29fnOQj7fuYMjh43kqGEj+GDTxnrrr2tkTl+OHjaS2/f5g9OUCXn9ufiAaawv2s3p4ydxyOChbV7/kcNGUJCRyb+XftDsuoZl57CpuPGrYntNi9PHT+LGt17l021bOXPifrH7ttSsa1BmdN53wzDIT8+I3V6Xx7QYnJlVbz2vrvsitv3fWL+2yfquOuQISqur+eVrL3H0vf/gqheebvb5iEj3pv259uci0nNon659uoj0HNqna58u0tslfYie5o8eLQvZ0VNaqmtObUn37z2Ktl//fKYXDOHOD9+N3VaQmQXAl3v2HoWs69RxE3nt0m/z2qXfZnBmduz2Xxx5LFfNmMlnO7bzv7WfN1lX7Wk8Rw4bwVHDRuLi8tznK5pc/orph/Poso/ZU1nR3NONmTsmenGIE0c3fpGI1qz/ewcfxp0fvkuk5krOjVm3excDMzKbvH/epANYunUzx4wYRd+U1Njt+TXzgG0qie6gXddlS2lJ7Pa6Io7NhuKieuuZMWhIbPtPGzioyfWfOm4iK674Efedfg7jc/vx8KdLYz8LItK9aH+u/bn25yI9h/bp2qdrny7Sc2ifrn269ukiLUv6EH1ETh/SfH4eW/Yxr65bw1MrlzM0K4fsYEq95b43/TDW7dkV+/7MCfuR5vPz4//N59NtW/loy6Z6O3vLNAlYHgKWh5Btx27vl5bODw+LniJzWzNHMGt3pqc+fC8XPvlIzW3Lm1x++qAhTOzXv9Ejhu3RmvWfNn4iHtOkIhyqd3t1JMIXu3by+zdf4dPtW7nogAObXM+kfgO4bMrB9U7JAjh9/GT8lid6gYsdhfz5nTfYUlrCOZMPiC1TEQ6zakchP3xpPjvKy7i4zhxcpmHEtn/E3vvHZt2e3byxfi1vrF/LropyfvP6QhZv+oqxuXmMyOlLViCAz/K0cWuJSDLQ/rz969f+XESSjfbp7V+/9ukikmy0T2//+rVPF+k9kv63Is3n5/aTTufXry/k4icfZWSfvtw+9/QGyx01bCQT8/qzvHAbAAPSM3jqnIv55asvccrD9+C3oleJrvXosqU8umwpAL+ffSLj6lw4wWNaXHzANH735iss2bKRqfn1j9it3rmDVTsLuWj/A2MXZ7j1vbd5asVytjWzs77i4MNiO96OaO36PabFt6Ydwv+9sqDe49/8ah1H3HMb+emZXDdzFt84cHqz6/vDsScC8MLqlbHbhmRl8+hZ5/Pr1xdy3P130j8tnd8dcwKnj58cW+bRZUt5euUyhmRlc/PxJ3PC6HH1atjv9puB6Glhl02JXgH7/o8/5P6PPwTgoa+dR3UkwlUvPM2eykoGZmRw8/Ent3l7iUhy0P68Ie3PRaS70j69Ie3TRaS70j69Ie3TRWRfhuu6bqKLEBERERERERERERFJRknfiS4iIiIiIu13y12PsWHzdmYddiAnHlP/VPFwOMJ9/1nA7qIScrIyuOjMOXi9+oggIiIiIlKXOtFFRERERHqwPUUlrFzzFXuKSxuE6G8sXkppWQUnzT6U+QsXkZ6WwhEzDmhiJBERERGR3impLyzqui627aCcX0Ske9P+XEQkcbKzMpq8b/W6jUwePxKAyeNHsnrdxiaXDUciVFZVU1FZRVV1SPt0EZEeQO/TRURaJ6nP1XQcl6Wfb+KAMQVYlpHocqQFjuuydUcxAANyMzENvWYiEqX9uYhIciorryQlGAAgJeinvKKyyWUXvPou8xcuwuv1cMn5Z2qfLiI9TmVVNY8/+wqLPlgGwJ1/+nGCK+p8ep8uItI6SR2iS/fiui5bd0avUt2/bwYoRBcRERFJaqkpASorq4BMKiurSU0JNrnsnKOnM3vmNGzHYdX6HV1XpIhIF1izbiP3PPo8u/YUYxgwe+ZBiS5JRESSiEJ0EREREZFeavTwwSxbtY5BA/uxbNU6Rg8f1OSyXo8Hr8eDbTtdWKGISOcKRyI8+9Lb/O+N93Bd6JOdySXzTmx2fygiIr2PQnQRERERkR7s/v8sYO1Xm4lEbNZv2sbJsw9lxZr1HHfkwRwybSL3Pb6AP97+ENmZ6Vx81gmJLldEpMu4rsstdz3OmprrQRw6bRJnnTyLYMCf4MpERCTZKEQXEREREenBLjxzToPbBg3sB4DP6+Xr553c1SWJiCQFwzCYefB+bNu+iwu+djz7TxyV6JJERCRJKUQXkaTlui4fr4EX3nHYXQw5mXDCISb7j4q+4RUREREREWmLnbuLKCouY+SwAgAOOmA8k8aNiF1kWUREpDFdFqLv3F3MDbfcS0F+P8LhMOlpqZxx4pEMyOvTVSWISDdSWuFyzd9sPlwJlgm2E/3/iddsDhwHN19pkZ6iIF1ERERERFrmui6LPljGY8++gt/n5ZfXXEp6agqGYShAFxGRFnVpJ/rggf25+pvzAFi7fjN3PvAMP/3+hXg9aogXkb1cNxqgf/R59Pva65fV/v/R53DN32z++WNLHekiIiIiItKskrJyHvjvS3zy2RoACgbkEg5HElxVz7Vlh8vi5Q5bdkB+LsyYaJKfq89tItK9JSy9HjF0IAMH5PLVxm2x06ikezMNg3HD+sW+Fmmvj9fAhyubvt9xovd/sgb2H911dYmIiIiISPfy8WdreOA/L1JaXoFlmZxy3OEce8RBmKaZ6NJ6pC07XO561mHbLpcUP6xYD8vXOXz9ZAXpItK9JbQFPCcrnaKS0ga3hyMRIhEb23ESUJW0l2EYpAZ1FfOusPCN91n41geJLqPTlJbD5FbM9PTPeyE9tfPraa3Zh09j9hEHJboMERHpIMdxqQ5DwKdrcIiIdFe27fDQky/x9vufApDfvy+XnTOXQfl5Ca6sZ1u8PBqgjyyINtc5rssXm6Kd6WccZSW6PBGRdktoiL67qJTJ40Y2uH3Bq+8yf+EivF4Pl5x/ZgIqE0luldUhiorLEl1Gp/K14v2VHYGi4s6vpbUqq0OJLkFERDrAcVzKq6Co1CViw4C+BgFfoqsSEZH2sCyTSMTGMGD2zIM45bjD8Xo1lWxn27IDUvx7z043DYMUv8uWHQkuTESkgxL2F+TLDVvYvHUHQwb1b3DfnKOnM3vmNGzHYdV67Wm7C8d1KdwVPbMgr0+6pnTpREG/j6zMtESX0WlKy6GyuuXlgv7k6kQP+pW0iIh0R47jUl4JRWUuZZWgtzAiIt1TOBIhFIqQmhK9UOi8U2dz+MH7MWr4oARX1nvk50ancHFcN9aJXlEdvV1EpDvr0hB9w+Zt3HTHI0TCEdJSg3z9vLmNXlTU6/Hg9XiwbU3n0p24rsumwiIAcnPS9Am0E80+4qAePW3I0tUul/3WbnG5e35msf9o/ZyJiEj71Ibnu0tcKqrAsiAtJXpfeWViaxMRkbbZtHUH9zzyHNlZGXzvkjMwDIOUoF8BehebMdFk+TqHLza5pPijAXr/PgYzJmoOehHp3rosRO+bk8ktv76qq1YnIt3Y/qPgwHHw0efRi4juyzRhyhjYb1TX1yYiIt2fXROe76kbnqeCZRqx+0VEpHtwHIeX3/yAp198i4htU1RSxq49JfTNyUx0ab1Sfq7B1082WbzcYcuOaAf6jIm6qKiIdH+aEExEko5hGNx8pcU1f7P5cCVYZjRMN02wnWiAfvOVli72JiIibVI3PC+vAo8nOi2YaerviYhId7RzdzH3PvY8a9ZtBGDy+JFc+LXjSU0NsuirL9leXkq/1HSmDxqCZfaMTuhb7nqMDZu3M+uwAznxmEPq3ffOB8t4buEi+mRnAHDZuXPJzkzv8hrzcw1dRFREehyF6CKSlNJTDP75Y4tP1sDz7zjsLoGcDDjxEJP9RqEAXUREWs12XMoqYE9ptPPc44EMheciIt2W67os/nA5jz7zMlXVIfx+H2fNPZrDDprM86tX8rOXX2BLaUls+fz0DH57zAmcNGZ8AquOj4vOnMPKNV+xp7i00fsPO2hyg3BdREQ6TiG6iCQtwzDYfzTsP1pdDCIi0nYRu6bzvDTaee5TeC4i0iOEIxGef+UdqqpDjBg6kEvnnURunyzmf76CS596lH0n5dpaWsKlTz3KPafN6/ZBenZWRrP3L16ynOWfr2PsiMHMPfZw/c0TEYkThegiIiIi0qPUhue7S6IXNPN5IFPhuYhIj+Hzerl03omsXreJ4448CNM0sR2Hn738QoMAHcAFDOD/Xn6BOaPG9pipXfa134RRTJ86AYB/P/YC7y39jBlTJza6bDgSIRKxsRu7CJWIiDSgEF1EREREeoSI7VJWe8HQavB7FZ6LiPQEVVXVPP7cqxTk53H0oVMBGDG0gBFDC2LLvLvxq3pTuOzLBTaXlvDuxq84dMiwzi45IVJTArGvp+0/js9Wf9lkiL7g1XeZv3ARXq+HS84/s6tKFBHpthSiS9yYhsHoIXmxr0WkZ3v2pbdYsWY9Hsti3inHUJCfl+iSRKSXqhueV1aDzwtZabp+hohIT/DFl5u459Hn2bm7CJ/Xy7T9x5GemlJvmd2VFbywZmWrxtte3vhc4j1BRWUVKcFokL7qi6/on5vT5LJzjp7O7JnTsB2HVet3dFWJIiLdlkJ0iRvDMMhIDbS8oIh0exs3b+fLjVv5yfcuYHdRCfc8+jzXfuucRJclIr1MOBINz4tK94bnmQrPRUR6hEjE5tn/vc1Lr7+H67rkZGVwybwTSU9NoSxUzbsbv+Ktr9bx5vp1fFa4HbfRiVwa6pea3smVd677/7OAtV9tJhKxWb9pGyfPPpQVa9Zz3JEH89Lr77NyzXos06RfXg6nHbxfk+N4PR68Hg+2relcRERaQyG6iIi02faduxlS0B+AnKwMdu0uIhyJ4PXoz4qIdD6F5yIiPdvmbTu4++H5bNpaCMC0KeMZNW0sz2z5gjcX/4+Ptmwm7Nj1HjOmTy6bSoopD4caHdMA8tMzmD5oSGeX36kuPHNOg9sGDewHwGlzZnLanJldXZKISK+gtEPixnFddu4pA6BvdpqmdBHpwfL75/LK20uIRGy2Fu5iT3EpFRVVZGak1VtOFywSkXhxXZfqMFRWwZ5Sl6pq8PkUnouI9DRl5ZX8/u/3U2hXU5xhYuZn8tKWT6j47wf1lhuUkcXhQ4czc8hwDhsyjH5p6cz/fAWXPvUoQL2+9Nq/Ejccc0KPvaioiIh0LoXoEjeu67Jh2x4A+mSlgj7QivRY+f36cvAB4/nLXY+R2yeL/H59SU9LabCcLlgkIh3hui5VIagKQVmFS0UV2A74fZCZrvBcRKSncF2XdXt2x6ZneS2rgjI7HL1zRwUAfYIpHDZkODOHDufwIcMZmpXd4O/ASWPGc89p8/jZyy/Uu8hofnoGNxxzAieNGd9lz0lERHoWhegiItIuRx4yhSMPmcLmbTt48dV3MRvp6tEFi0SkreoG5yVl0a9tB7weCAbAYyk4FxFJRrbj8O7Gr9heXkq/1HSmDxrSbNf3trJS3lq/jjfXr+WVL1azo6qi3v2pPh8zBg1h5pDhHD5kBOPz8jCNlrvITxoznjmjxrapFhERkZYoRBcRkXb5y52P4TgOqalBzj1tdqPL6IJFItIaseC8GkrKo187DngUnIuIdAvzP1/RaPf3b+t0fxdXVbJow3reXL+Ot75ax+pd9RssTOCggsEcMXQEhw8dzpQBBXgtq131WKbJoUOGtfv5iIiI7EshuoiItMsPvnF2oksQkW7McaJznFdVQ3Gd4NzrgZQAWArORUS6hdp5yN19bt9aWsIlTz3KCaPHsbW0hE+2bcFx9y5lANmOhz7VBnkRDxcdcRgnzzoUy1LHuIiIJB+F6CIiIiLSJWqD84oql9IKYsG5zwupCs5FRLod23H42csvNAjQYe+FPV9YvTJ224icPhxSMBSzsIwdKzbidw0G9OvDZefMZfDAfl1Ss4iISHsoRBcRERGRTuM40S7zymoF5yIiPc27G7+qN4VLU66cfhiXTTmYgGNw0z8eYefuIgKGwTEzp3Hq8TPxehVNiIhIctNfKhERERGJq7rBeUk5VIfrBOdBsEwF5yIiPcH28tJWLTchrz/5GZk4jkN2ZhqO43DJvBMZM2JwJ1coIiISHwrRJW5Mw2DkoNzY1yIiItJ72I5LdWjvVC3VYXDd6BznCs5FRHqmfqnprVouJxAEwDRNLj/vZPw+HylBf2eWJiIiElcK0SVuDMMgKz2Y6DJERESkC4UjLmWVUFQaDdEdwKfgXESkVzi4YDApXi8V4XCTywQdgy2frofhowDIzmxd8C4iIpJMFKKLiIiISJtVhVzKKlyKy6A6BD4fpKYoOBcR6U3u/2RJswE6Lkwq97FrdzG27WBZZtcVJyIiEkcK0SVuHNdld3E5ADmZqZrSRUREpIdxXZfKaigpdykth7ANAT9kZehvvohIb7N443p+9vILAJw5YTKLNqyvd5HRoG0wpTrI1XOP5/CD98PQ50MREenGFKJL3Liuy/otuwHIzkgBvUkSERHpERzHpbwKisuiU7e4LqQEINWjv/UiIr3RlpJiLnvqMSKOw6njJnLbSWdQUlbB7x57kk+/3EDAMZg2oIDLvzmXvL7ZiS5XRER6gC07XBYvd9iyA/JzYcZEk/zcrvs8ohBdRERERBoVsV3Ka+Y7r6iOHh9PCYDHUnguItJbVUXCXPzUo+yoKGdCbj9umXMqhmHg2DbVG3YxJOJj7rGHcvyR0zV9i4iIxMWWHS53PeuwbZdLih9WrIfl6xy+fnLXBekK0UVERESknnDEpawCisqi4bnXA2ma71xEpNdzXZcfvjSfpVs3kx0Ics8pZ5Pq8wGQnZXBpefMJTM9lSEF/RNcqYiI9CSLl0cD9JEFYBoGjuvyxaZoZ/oZR1ldUoMOC4uIiIgIEL1Y6M4ih6+2uWzd6WI7kJUGaUFDAbqIiHD3R+/z6LKlmIbBr6YfzT13P8mnK9fG7p88boQCdBERibstOyDFT+z6i6ZhkOKP3t5VFKKLiIiI9GKu61JR5bJtl8OGbS6Fe8A0oxcLTQkYuhCciIgAsGjDl/z8lQUAnDFgJK8/+QaFO/cwf+EiXNdNcHUiItKT5edCRTU4NX9vHDd6xmx+btfVoOlcRERERHqhfS8WChD0Q5ouFioiIvvYVFzE5U8/TsRxGGOmEV62GQODg6dM4JxTj9EBVxER6VQzJposX+fwxSaXFH80QO/fx2DGxK7rD1eILiIiItKL7HuxUNOE1ABYulhoj/bOB8t4872PMTCYd+ox9aZb2LGriH8/9nw0BDMMLpt3ItlZGQmsVkSSSWU4zMVPPsrOinKybJPRuxzSUoKcf8ZxTJ08NtHliYhIL5Cfa/D1k00WL3fYsiPagT5jYtddVBQUokscmYbB8IK+sa9FREQkeYTCNeF5zcVCfbpYaK9RXlHFK4uWcN33LqCopJS7H5nPj797fuz+19/5iEMPmswhB07inQ+W8eqijzjjxCMTV7CIJA3Xdbn2pef4ZPsWfA4cVOJnvzHDueisE8jKSEt0eSIi0ovk5xpddhHRxihEl7gxDIOcjJRElyEiIiJ1VFa7lFa4lJRDdQj8vujFQnXqfe+xfuMWRg0rwOOx6JuTRXV1iHAkgtcT/SiQ378vlZXVAJRXVpGe1vj7uXAkQiRiYztOl9UuIol154fv8vjyj7EMk8sHj+foEaOYOX1//Q0REZFeRyG6iIiISA/kOC5FpS67iiFsQ8AP2RkKPXqjsooqUoKB2PfBYIDyiqpYF+m4UUP5612P8/b7nxKxba674sJGx1nw6rvMX7gIr9fDJeef2SW1i0hilJVX8scnnuHOrSsBuP6oY/nWQYckuCoREZHEUYguceO6LntKo1cmy04PqjtBREQkQcIRlx1FLkWl0fA8NUV/k3uz1GCAysqq2PeVldWkpuwN1Z98/nVOOf5wpkwaw/tLV/D0gjc49/RjG4wz5+jpzJ45DdtxWLV+R5fULiJdb9mqtdz+n+d51tqJbcKZE/bjm9NmJLosERGRhFKILnHjuC7rNu0E4ICxBVgK0UVERLpceaVL4R6XympIT9EFQwWGDc7nmZfeImLbFJeU4/d7Y1O5ALhAWmp0Cpf0tBTK6wTudXk9HrweD7at6VxEeqLqUIj/Pvcar7y7lDczqwiZMDYnl5uPP1kNUiIi0uspRBcRERHpAWqnb9lZHA1FMzXvudRITQlwxIwDuOkfD2NgcPYps9i4eTsr1qznuCMP5sRZM3jgiZewTBPbdjj/a8clumQR6WLrNmzhnkfms33nbj5Oq6bY45ATTOGRsy8g6PUmujwREZGEU4guIiIi0s3tO31LwKfwXOo77KDJHHbQ5Hq3DRrYD4D8/rn8+LvnJ6IsEUkCn674gtvvexLHcdmSZbHRsrEMk3tOPZuCzKxElyciIpIUFKKLiIiIdGPlldEAvaJK07eIiEjbjR4xmD7ZmTh5aTy7YzW48OtZx3PokGGJLk1ERCRpKEQXERER6YYcx6W4DHYUuZq+RUREWs1xXD5a9jlTJo3BNA0Cfh8XXHwypzx2H7brctbE/fn61IMTXaY04Za7HmPD5u3MOuxATjzmkHr3hcMR7vvPAnYXlZCTlcFFZ87B61XsIyISD2aiCxARERGRtglHXLbtdtm6y8XjgfQUQwG6iIi0aHdRCbfc9Rh3PvgMr72zBICKcIhvv/AUuysr2L9/PjcdN1d/U5LYRWfO4WsnHtXofe98uIz+uTn86Dvn0T83h3c+XNbF1YmI9FwK0UVERES6kfJKl807ovOfp6do/nMREWmZ67q8t3QFv7r5HlZ98RVerwef14PruvxgwTMsL9xGbkoq/z79HF1INMllZ2U0ed/qdRuZPH4kAJPHj2T1uo1dVZaISI+n83okbgzDYGh+TuxrERERiR9N3yIiIu1RXlHJQ0/+jw8/WQXA0EEDuOyck+iXm8Nt7/0/e/cdHld95v3/faZoNKMuWy5yNy5gjMGAwaY302tohpAQYNN3k91k82Sz2f3t86Q9m32SbDa7m7IEAilgejMGQofYmF5s3CuWZcuyuqbPOd/fH0fVlqxiSVP0eV2XLkkzR6N7RjOjmc+5z/39C4+uX4vP4+G3V9/IpOKSNFcrR6I1HCUUzAcgFAwQjkR73TaZSpFK2diOM1LliYj0qrrW8MY6h+paqKyAJfM9VFZk1nsdhegyZDyWxdjSwnSXISIiknOSKXfx0MYWyA+o+1xERPpn07ZP+O19T9HU3IrHY3H5BadzyXlL8Ho9vLJjK9975XkAfnD+JZw2dXp6i5UjVhDKJxqNASVEo3EKQsFet33mpTWseH4Vfr+P2265fuSKFBE5SHWt4c4nHfbVGUIBWL8T1m13+PyVmRWkK0QXEZEBM8Zw/+Mv8EnVPmzHYelZizhl4bx0lyWSk8JRN0CPxNzxLV5v5ryQFBGRzJYfyKOlNcL4inLuuOlypk+ZCMCuxga+8MRDOMZw03ELuf3EU9JcqQyFOTOnsnbjdqZMGs/ajduZM3NKr9tect5ilp61CNtx2LizdgSrFBHp7o11boA+a7LboOsYw9YqtzP92nO96S6vg0J0GTLGGJpaYwCUFObrEHORHFZdc4DqmgP8w998hlgszvf+/R6F6CJDzHEMTWGobdD4FhER6b/m1jDFhQUATJs8gb+5/VpmzZhCIM+ddR5OJLj1kftoiEU5ceIk/u2iy/X/JYv8/qFn2LZrD6mUzc6qfVy59HTWb9nJReecymmL5nPvg8/wb7/8E2UlRXzuhkt7vRy/z4ff58O2Nc5FRNKruhZCATdAB/dzKGCozrD9ewrRZcg4xrB1t3sPX3j0ZLx6ISaSs0qLC/F5PaRsm1g8QUEov8ftNGtRZHCSKcOBJkNDM+TnQX5A/1NFROTwUrbN0y+s5s+vvsU//PVnmFI5DoBj587s2MYYw9dXPs7HtTVUFBTyu2uWke/TQqLZ5LPXX3LIaVMmjQcgz+/n85++cqRLEhE5IpUV7ggXx5iOTvRI3D09kyhEFxGRAQsF8xk3tpx//vGdJBJJPnPdxT1up1mLIgMXiRn2N2h8i4iI9N/e/XXcff8KdlXtA+C9jzZ2hOhd/debf+GJjevweTzcffWNVGohURERSbMl8z2s2+6wtcoQCrgB+oQxFkvme9JdWjcK0UVEZMDWb95JY1MLP/yHLxCNxfm3X/6JY4+egd/X/d+KZi2K9J/Gt2QOYwyxBBgDoL+BiGQuxzG8vPpdHn36VZKpFKFgPp/+1IUsOuGYQ7Z9afsWfvDqCwD86IJLWTxl2kiXKyIicojKCovPX+nhjXUO1bVuB/qS+Zm1qCgoRBcRkUExhEL5eDweAoE8bNvBccwhW2nWokj/JFOGuiZDfRPkBzS+JR2MMYRj0NQK++oNzS0wqcIiGEh3ZSIiPWtobOaeB1eyYcsuAObNmc6tN1xKWUnRIdvuaKjvWEj0luNP4nMLF410uSIiIr2qrLAyahHRnihEFxGRATtm9nTeen8DP/7vP5JK2Zx7+okdi1WJyMC0j2+JxqCwAHwa3zJiugbntQ2GxlZIJMDyQjwBWs5BRDLZOx9tYsOWXfj9Pq677FzOOW1hj0cwtSbi3ProfTTFY5xcOZl/XXqZjnQSEREZIIXoIiIyYB6Ph9uWXZbuMkSymjFuaHugwWAbKNb4lhFhjDtvvrFLcB5PQp4PCoJQXmyRSBn21aW7UhGRwzv/jJOoq2/inNMWMmHcmB63McbwtacfZ0Ptfsa1LSQa8CkGEBERGSj99xQREREZYSnbcKDR0NAMgTwo0PiWYdUenLfPnG9shViiMzgvK9IODBHJfB9v2sFzr7zJV2+7lkCeH4/Hw7KrLzjsz/zHmtd5atPH+D1efnfNMiYUFY9QtSIiIrlFIboMGcuymDqhrONrEREROVQyZaipNzSHoTCk8S3DxRhDNH5Qx3kC/D4oyIdSdf6LSJaIJ5I88vQrvLL6PQD+/MqbXHHhGX3+3AvbNvOjV18E4F8vvIxTJk8d1jpFRERymUJ0GTIey2Jc+aGL2IiIiIira4BeVABej0LcoRaJGZpaYX97x3ncDc4LgwrORST77Ny9l7vuX0FNbT0A555+Iheec2qfP7e9oY4vPfkwBsNnTziZz55w8nCXKiIiktMUoouIiIiMgHjCXUC0JQLFBeBRgD5k2oPzA03uiJxoW3BeoOBcRLJUyrZZ+eIbrHxxNY5jKC0u5NYbLuXYuTP6/NnWeJxbH7mfpniMRZOm8KMLLh2BikVERHKbQnQZMsYYWiJxAIpCAb1hFRERaRNLGGrqDOE4lCjUHRKHBOcJ8Hvd4Fy3sYhku0effoUXXn8HgEUnHMPN1yylIBTs8+eMMfz104+x8cB+JhQWcbcWEpVhUF1reGOdQ3UtVFbAkvkeKiv0f1dEcpv+m8qQcYxh8679ACw8ejJevXkVEREhGndHuETjUFKgcHcwjDHEE+5ioNG4gnMRyX1Lzz6FDz7eytUXn8kpC+f1++f+/Y3XeHrzevK8bQuJFmrcpgyt6lrDnU867KszhAKwfies2+7w+St7D9J7Ct0BBfEiklUUoouIiIgMk2jcsK/OEEu4I1wU8h6eMYZEyp1j3h6at0QMrRGIJyGZgpQNPq+7KKuCcxHJFQ1NLby/bjPnnX4SAGUlRXz/f30er9fT68/YjsOa3buoCbcwvqCIlkSMf33tJQD+9cLLOXnSlBGpXUaXN9a5Afqsye66aI4xbK1yQ/Jrz/Uesn1Poftb620w0Byh30G8iEi6KUQXERERGQbhqNuBnkhBSaHeEB4smXJ3LsTiblgejhlawu7XiRTYtrudz+vONw/kQVEIvF7dliKSW976YD33Pfo8kWiMMWUlHD9vFsBhA/QVm9bz3RdWUt3S3HGaBRjgtoWL+MzxJw1z1TJaVde6wbenbSe2x7IIBQzVtT1v31PovvojwILTjutfEC8ikgkUoouIiIgMsdao24FuO1BcMLpD35TdFpYn3O7ySNTQHHZHsSSSblhuAI8H8vyQ17YgqE9huYjkuHAkyn2PPc/bH2wAYNrkCYwbW9bnz63YtJ7bH1uOOej09u9Pmzp9SOsU6aqywu0cd4zpCMAjcff0nvQUutuO6fi6/fPhgngRkUygEF0kBxhj+GALrFztUN8E5SVw6WkeTpitw9xFREZaS9iwr95ggKLQ6HkOtp3OueWxBERjbWF53A3LEyl3O8vqDMtLCt1Oc/2vEpFscfAIlcVTpuH19N4x3pv1m3dwzwMraWxuxeOxuPT807j0/CX4vIfvwrUdh+++sPKQAL2dBfzvl57jirnHDqoukb4sme9h3XaHrVWGUMAN0CeMsTrmnB+sp9Dd6wGs/gfxInJktBjw0FCILpLlWiKGb/7C5p0N4PWA7bifH3nZ5uRj4Kdf846qEEdEJJ2aw24HumVBYTD3n3tjcUNzBJpaDHXNbaNYku55luWOYcnzufPL/T6F5SKS3XoaoVJZVMwPL7iUy+f2f/HPJ597nRUvrAZg3Ngy7rjpcmZMrSTl2ByIhKmPRKiLup/rY1HqI2HqoxHqo1G21tV2+/0HM8CelmbW7N7F6dNmDPq6ivSmssLi81d6+h3I9RS6z5oCGNhaRb+CeBEZvMEsBiw9G9EQvap6P4+sfIVkMkXKtjnpuLksPfuUkSxBJKcY4wbo721yv7ed7p/f2wTf/IXNb77tVXAhIjLMGlsNNXUGrxdC+bn5nOs4htYotESgrsnQ1OIG51gQDEBBPpQWdR6eLSKSK3obobK3pZnbH1vO3dcs6xak245DQyzaEYg3RKPURSPUR8JsbNrDuwVxisYUsavY8PwzD1EfjdAYiw5ZvTXhliG7LJGDVVZY/Z5d3lvoDqgzVmQEDHQxYOndiIXokWicu+5fwZdvvYZxY8swxrB+846R+vUyAizLYvK40o6vZfh9sAXe2dD7+Y7jnv/hFjhhzsjVJSIymhhjaGyFmnqD3wfBQG79D0ym3LEszWHDgUZojUIyCT4fhPKhqFChuYjktsONUGk/7UtPPsxx4ydQH41SH43QFItheh26AuQD4Qb34yCl+UHKgyHKg22fQwWMCYYoCwapj0b55Vur+qx5fEFRv66byEjoLXRXgCcy/Aa6GLD0bsRC9LUbt7Fg3qyOhVIsy+LYuTN73DaZSpFK2diOM1LlyRDwWBYTxhanu4xh9fyrb/H862+nu4wOLWFYMKbv7X7zOygqGP56ulp65iIdaSIiOc8YQ0OzYX8D5OVBfl72h8nGGKJxaA5DQ4uhoRkicTAOBAJQFII8f/ZfTxGR/lqze9dhR6gAxO0U71RXHXJ6cV4AT9LBlzIcN3Ma44uLOwLxruH4mGAB5aEQpfn5+Dy9B4u24/D4hrXsbWnuMaK3cEfMLJ4ybYDXUkREctFAFwOW3o1YiN7Q2Ex5af/2hj/z0hpWPL8Kv9/HbbdcP8yVifRfNJ6gsak13WV0k9ePnfd2Chqbhr+WrqLxxMj+QhGREWaMoa7JUNsI+QEIZHGwbNvumJbmMNQ2Gloj7pgWj8ftXBlTAl5P9l4/EZEj0d/RKF9atITL5syjvC0Y/+iDjTz69KskkylCwQBfWnQOR886snDb6/Hwwwsu5fbHlmNBtyC9/Vn6BxdcqkVFRUQEGPhiwNK7EQvRy0qLqd7Xv2MFLjlvMUvPWoTtOGzcqeMLsoUxhkjMDU5D+Xk5OdIlGMijtKQw3WV0aAlDNN73dsHAyHeiBwN5I/sLRURGkOMYDjQZ6hohmJ+dndnxpKEl7M5yP9AI4RjYNuT53eC8pFDj2UREoP+jUS6edTSLp0yjsamFe+9bycdt40uPnjWN2268lLLSoTlq9/K587j7mmU9LnL6gwEucioiIrltoIsB90d1rRmVaxqMWIh+3NFH8cxLb3D6ogUdI13Wb97BvDmHrhju9/nw+3zYtsa5ZBPHGDbsqAFg4dGT8ebgG++lZ5+SUSNK3t9suOOHdp/b3f1dLyfMyb2/h4hIOjiO4UCj4UATFATB78uO51djDOGYuwO2vtlQ3wKxOGDcTvrSwuy5LiIiI6mioKDjEPiedB2h8s6HG/jjI38mEo3h9/n41GVnc+5pJ+EZ4qN5Lp87j0tmH82a3buoCbcwvqCIxVOmqQNdREQOMZDFgPtSXWu480l3sdJQwB0Vs267w+evzP0gfcRC9FAwwB03XcH9jz3vzjy3bU5acHSPIbqI9M8Js+HkY+C9Te4iogfzeODEuXD87JGvTUQkF9mOobbBUN+cHQG64xiawtDU6nbNt0QhngCf110UtKKUIQ92RERySVVTIzc8+IeOAL2vESqbtu0mEo0xbfIEbl92GRPHjx222rweD6dP0/tpEREZOW+scwP0WZPp2MG8tcrtTM/1xYJHLEQHmFI5jq9//oaR/JUiOc2yLH76NS/f/IXNOxvA63HDdI8HbMcN0H/6Na8OxxcRGQIp27C/wV1os6gAfN7Mfm5tiRg+qTHsq3PHtAT8UJAP5cWZXbeISKbYH27l2uX3sqe5idljxvLXp57Bj19/6ZARKv/nvIs7Rqhcd/k5VIwp5bwzTsLnze0wQURERp/qWnf0o6ctZ/JYFqGAoXoUTOMe0RBdRIZeUcjiN9/28uEWeHq1Q30zlBfDZad5OH625tmKiAyFZMpQ22hobIbiAvBmcICeSBr21Bp217iLg5YXZ+fMdhGRdGqMRblh+b1sb6hjakkpjyz7HBOLirlx/gkdI1TK84NUf7STba+tw5l7LB6PRSAvjwszaPyjiIjIUKqscEe4OMZ0dKJH4u7puU4hukgOsCyLE+bACXPU7SIiMtSSKUNNvaE5DEWF4M3Q8SeOY6hthF37DA0tUBSECWMys1YRkUzWmohz04N/4OPaGsYXFvFwW4AOnSNUdu7ey93LV7Bvfz0Am7bt4pjZ09NYtYiIyPBbMt/Duu0OW6sMoYAboE8YY7Fkfu6vyaEQXURERKQX3QL0gswN0JtaO0e3+H0wvkyzzkVEBiOWSvLZR+7jneoqyvKDPHzjrcwoK+8437YdnnnpDVa8sBrHcSgpLuTW6y9RgC4iIqNCZYXF56/08MY6h+patwN9yfzcX1QUFKKLiIiI9CiecGegt0TcES6ZGErHk4aqGkNVLSQSUKbRLdKL1W+v5bU3P8DCYtnVFzBt8oRu5z/78hrWb96J4zhcvvR0jp41LU2ViqRP0rb5/OMP8fquHRTmBXjgxs9ydMW4jvNrauu5e/kKdnyyF4CTFszl05+6iMKCYLpKllHocM/nq99ey1PPr2JMmXvkxB03X0FZSVG6ShWRLFVdaw4bkldWWDm/iGhPFKLLkLEsi4ljizu+FhERyVaxhGFfnXt4Yklh5v1fsx1DbQPs3GtoanVD/rKizKpRMkc4EuPFVe/ynb/+DI3NLdx1/wq+/dVbOs5fu3Eb0Vicb3xxWRqrFEkvxzh8beVjPLt1I/k+H3+87mYWTpzUcb4xhrvuX8HO3XsJ5ge4+ZqlnLJwXsb9f5Dc1tfzOcAZpyzgsgtOS1OFIpLtqmsNdz7psK/OEAq488/XbXf4/JWjo9v8cBSiy5DxWBaTxpWmuwwREZEjEo27AXosASUFmRegN7Uadu0z7K9vG90yxv0fLNKbnburmT1jMj6fl7HlpcTjCZKpFH6f+1bg3Q83Egrm87PfLKekuJCbrl5KKBg45HKSqRSplI3tOCN9FUSGlTGGb//5aR7++CN8Hg93X7OM06fO6LaNZVnccu1FPPbMq3zmuospLy1OU7UymvX1fA7wxrvrWLdpO0cfNZUrLjwzI4+kE5HM9cY6N0CfNZmOhUO3Vrmd6aOx+7wrhegiIiIibSIxdwZ6LOF2d2dSgB6LG6pqDVX7IZVyR7f4fZlTn2Su1kiMUDC/4/tgMJ9wJEZpcSEAjc2tFISCfOOLy3hp1bs8+/IbfOrScw65nGdeWsOK51fh9/u47ZbrR6p8kWH3/Vef557338bC4pdXXMvSo+YA8O5HG2lqCXPe6ScBMHXSeL7+Vzeks1QZ5fp6Pj/+2NksPulYAO55YCVvvv8xS06a3+NlaceoiPSkuhZCgc4mHY9lEQoYqmvTXFgGUIguQ8YYQyyeBCA/4M+o4EFEhl51zQHue/TPAKRSNjUH6vn3//P1NFclMngtYcO+eoNjoKQwc/6H2bZhf9voluawO14mVJw59UnmKwjmE43GOr6PRuMUhDpDmIJQkGPnzgRg/tyZLH/ihR4v55LzFrP0rEXYjsPGnXonJbnh52+8xn+u+QsAP73kSq455jgi0Rj3P/Y8b76/Hq/Xw9yjpjJpQkWaKxXpz/N559eLTjiGjzfv6DVE145REelJZYU7wsUxpqMTPRJ3Tx9Kfc1dz0QK0WXIOMbw8fZ9ACw8ejJehegiOa1y/Fj+/ss3A/DOhxvYuPWTNFckMjjGGBpboabe4PNCUTBz/n81tBh27TXUNkLAr9EtMjgzplbyxHOvk7JtmprDBAL+bof+z5k5lV1V+5g3Zzo7q/YybkxZj5fj9/nw+3zYtroWJTfc9e6b/PBVd6fR9867iM8cfxIbtuzkngdW0tDUgmVZXHzOqYwfW57mSkVcfT2fR6Kdneobt+5iQkXv991s2zGajYGbSDZaMt/Duu0OW6sMoYAboE8YY7FkvmfIfke2zl1XiC4iIkdszXvrueicUw85XYeJSqZzHMOBJkNdEwTyID8vM160ReOGqv2GPfsh5cCYEvB5M6M2yT4FoXzOXrKQn/zqPiwsbrzqfHbvqWH9lp1cdM6pnLZoPn946Fl+8uv78Xo83L7ssnSXLDLsHlz3Af/w/NMA/P3p53DHwlN44IkXePEv7wIwbmwZt914GUdNn3S4ixEZUX09nz/3ylts2LITr8fD+HHlXHPq8b1eVjbtGM3WwE0kG1VWWHz+Ss+Ad1oNZEdXts5dV4guIiJHpDUcZd/+Omb18CZTh4lKJkvZhtpGQ30zFAYzY764bRv21cOufYbWMJQUQTCQ/rok+51xygLOOGVBt9OmTBoPuEHK7Tddno6yRNLi6U3r+drTjwPwhZMX8/ennc2P/vMP7N5TA8DZi0/guivOJZCXl8YqRXp2uOfzay45i2suOSsdZQ2rbA3cRLJVZYU1oMfWQHd0ZevcdYXoIiJyRN75cAMnLTi6x3UQsu0wURk9EknD/gZ3xnhxCLxp7vI2xtDQ4obntfUQzHdHt2h9ERGRofXKjq184cmHsI3DTcct5PvnX4zH8nDqwnk0Nbdy6/WXcNwxR6W7TJFRr2tX68ZdBo+VfYGbyGgZQzTQHV0jNXd9qClEFxGRI/Lme+v57PUX93heNh0mKqNHNG6oqTeEY1BSAB5Pel/IRmKG3TWGPbVgDIwt0+gWEZHh8FbVJ9z66P0kbJsLZ8zmGwsW47HcGa8XnLmI0xcdR0EomOYqReTgrtZ9ddAahfHlhqJQ/wO30RJgSmYaTWOIBtpZ3p+565n4+FWILiIig1Zb10jKtpk4fmy6SxHpl9aooabOkEhBaWF6O71TtmFfXdvolgiUFUG+RreIiAyLtTV7uemhPxJJJjmhbDzFH+7nrl0r+Ke//RyBPD8ej6UAXSRDHNzVWlxoWPURfLgVpk/o30KHoynAlMw0msYQDbSzvK+565n6+FWILiIig1YxppTvfv3WdJch0i+NrYb99QYsKClM75unplbDtirDgUZ3dMsEjW4RERk2W+sOcMMDv6c5HmOqP8SUrS3YWJQWFxKPJwjk+dNdosiod7jxLcUhi1mT3NOmT7T61ZU6mgJMyUzZOvd7MPrTWX6ww81dz9THr0J0GTKWZTF+TFHH1yIiIpnAcQwNLYbaBvD707tQpzGGmnrYUmWIJ6CiLP3z2EVEctnupkauXX4PByJhyh0vC2og3+fjmkvO5vwzTk77SC8R6d/4FgOce3Jn6FZda3jkZbvXUQ+jKcCUzJStc78Ho6/O8oHK1MevQnQZMh7LYsr4snSXISIi0sF2DAcaDXVNEMqHPH96x7fs2mvYudcN88eVKbgRERlONa0tXHv/PVS3NFOUsljcHGDmxPHccdPlVE7IwRRDJEsNdHxLf0Y9jKYAUzLTYLqzs9nhOssHflmZ+fhViC4iIiI5KZlyFxBtboXCgvQu1hmNu4cgVh9wZ7GnsxteRGQ0aIhGuOGB37OjsZ5Sj58lLX6uPvc0rlh6Oj6fRjmIZJKDu077Gt/Sn1EPoy3AlMwz1N3Zo0mmPn4VosuQMcaQSNoA5Pm9GukiIiJpE0u4C4iGY1BUCN40Hq7f0GLYutvQ0AJjS8Dv0/9HEZHh1BAOc+NDf2R9bQ3jC4u476plFNgWR02fnO7SRKQHPXWdHjy+pav+jHpQgCmZYCi7s0eTTH38KkSXIeMYw9qt1QAsPHoyXoXoIiKSBuGoYX+DIZaAksL0rdNhjGFvHWzdbUjaMK68882eiIgMjy2f7OH6++9hj4lTHgzx8I23cnTFuHSXJSKHMdCu0/6Oejg4wOxrjrqIZI5M3AGhEF1ERERyRnPYHeHiGCgpTN+bomTKsHOvYdc+CPiholRv0EREhpNtOzz90hv80xsvsDfPxm8s7r7iegXoIllgoF2ngxn10J856iIih6MQXURERLKeMYaGZsP+RvB5oSiYvjdDkZhhy27DvjooK4J8zT8XERlW+w80cNf9T/Fg/Q72Bmx8WPzh2ps4feZR6S5NRPppIF2ngxn10J856iIih6MQXURERLKa7Rjqmgx1jRAIQH5e+kLruiY3QG8OQ0VZehczFRHJdcYYXn/zQx586kXe8oepyrfxWha/v/Zmzp81N93liYwa1bVmxGcXD3TUQ3/mqIuIHI5CdBEREclayZShttHQ2AIFwfQt2uk4hj21hm17wHE0/1xEZCREojEef/Y13vNF2JGfwmNZ/PrK61mqAF1kxGTLmJT+zlEXEelN7wOjRERERDJYPOHOP29sgaJQ+gL0ZMqwdbdh0y53lMzYUksBuojICCgIBfGfMIUtwSQAP734Sq4+Zn6aqxIZXbqOSZk8zmLWZNhX53amZ5Il8z1MGGOxtQqq9hu2VvU9R11EpCt1oouIiEjWicbdAD0Sh+IC8HjSE1q3Rt0AvaYeyoshkMZRMjI0bMewvwG27oaaesP48nRXJCLtItE4DzzxAscdM5OTjz+G3777JndvfB+A7593Mbccf1KaKxQZfbJlTMpg5qiLiHSlEF2GjIVFRVlhx9ciIiLDoSVi2F9vSNpQUgBWmrq+DzS6889bIjCuDLyaf55VjDG0RmFfXfuHYV897K8Hu615bt4MWDArvXWKiGvTtk/43fKnqW9sZt3G7WwyYb7z/NMAfOv0c/jSKaeluUKR0SmbxqQMdI66iEhXCtFlyHg8FtMmql1LRESGhzGGpjDU1Bk8HiguSE9obTuGPfsN2/eAAcaXpy/Il/5JJN3u8n11bnf53rbgPBI7/M/tqDYjU6CI9CqZTPHoM6/ywJo3iVqG8eVFzD39eL7x3FMAfPHkJXzrjHPTXKXI6LVkvod12x22VhlCATdA15gUEclFCtFFREQk4zmOob7ZcKAR8vyQH0hPaJ1IGrbvMezeDwX5UBhSeJ5JHOPOyN9XB3vr3B0u++qgrsnd4dEXy4Kxpe6RBaF8OOdE/X1F0mn3nhr+8U8P8kryALHi9kdxnKdXvwDAzQtO5PvnX6wdmSJppDEpIpmnutboMTkMFKLLkDHGkGo7/tnn9ejFrIiIHDFjDJEYNLS4XegF+ZDnT8//l5aIYcsnhtqmtvnnaapDXNG46RzFUu9+XVMHiVT/fr4wCBPGtH2UW4wf44bnfp9FIuWwrw7mzdDfWCRd6hqa+eqv7+aNUBR6aWg9f+YsvecQyQAakyKSOaprDXc+6S74Gwq445bWbXf4/JUK0o+UQnQZMo4xfLh5DwALj56MVy9oRUTkCMQShsYWQ2Or+31xAXjTtIDo/gZ3AdFwtG3+eZrqyESJpKG+GaJxMAYcA47T5bNz6GnGuHPHHQOmh/MdY3r8OcdAc6sbnDeF+1efz+v+zdzA3GLCGHcET5GOIhDJaKUlhWwosaGXHWMW8P+9+CyXzZmH16OxESKSmdQRLCPtjXVugD5rMh3rFGytcu+H2tl1ZBSii4iISEZJ2YamVjeYTaagIOh2B6eD7Rh27zNsrwaPB8aN0vnn0bihrskdi1LfDHVNpu0ztETSXV2nsqLO7vLx5W5gPqZEOz1EsoExhlVvf8Qxs6czpqyENbt30ZhK9L49sKelmTW7d3H6tBkjV6iISD+pI1jSoboWQgE3QAf3cyhgqK49ksvUziBQiC4iIiIZwnEMrVE3oI3GID8fCoLpe3EWSxi27THsqYGigvTWMtyMab/tob4J6ppNl8Dc7TLPJIE8mFB+aHd5fl7u/o1EcllzS5hfLn+SVTu24x1XRMGkMbxZtatfP1sTbhnm6kREBkcdwZIOlRXuDhvHmI77XSTunj4Y2hnUSSG6iIiIpF0k5i4c2hIGnw9KitLb8d3Uatiy2w2Sx5Skbw77UHIcQ1Mr1DW3d5R3BuX1Tf2fJd5VQRDGFLu3UUHQ7db3WG2fPZb7tXXw6XQ/3eMu6OltO906eLuDPgcDUFI4Oo8IEMkVNa0tfLivmg/3VfPa5s18WLOXqOVAMRCLw7YD/b6s8QVFw1eoiMgRGI6OYJG+LJnvYd12h61VhlDADdAnjLFYMn9wo8+0M6iTQnQRERFJm0TSnXve0OLOuy5M49xzcDuya+phS5UhnnC7mz1ZOgqkodnw9gbD3gNucN7Q7M4hHwgLN7AuL4byEhhTYlHeFpqPKYaAOr9FcpLtOKzZvYuacAvjC4pYPGXaoOeOtwfmH+2r5oO24Hxf60Hd45b7fDOjpJyTJ0/h+ImTmD9uPF968mH2tbZgerhcC6gsKmbxlGmDqktEZLgNdUewSH9UVlh8/krPkI1f0c6gTgrRRUREZMSlbLfrvL7ZEE+md+55u2TKsHOv4ZN94PfBuLLsC4iNMezcC6s/Mqzf6S7g2RevB0qL3GDcDcgtxrSF5mVF6f+7iMjIWrFpPd99YSXVLc0dp1UWFfPDCy7l8rnzDvuzXQPzD/ft5YN9ew4NzHHfgBc7XooSUJbycNGC+XzxsgspCYW6bfejpZdx+2PLsaBbkN7+rPSDCy7VoqIikrGGuiNYBPo3n7yywhqyLnHtDOqkEF1ERERGjDGG1og7czsSc2dblxalP6RtbDFs32OobXQD5WAg/TUNRMo2rN0Kqz4yVPcwBcHvoyMYdz9bHaF5SaEW3hQR14pN67n9seWHdH7vbWnm9seWc/c1yzqC9P4G5hYWc8aO5fjxlSyYUMnxEyo5uryCn//qfhIkue2zlzN7xuQe67l87jzuvmZZj6H+D/oR6ouIpNNQdwS30yKPo1c65pNrZ1AnhegyZCwsxpQUdHwtIiLSVTRuqGsytETA64XigvSPSrFtw55at3s7kYJx5dkVKLdGDG+uh7c+dm/XropCcOp8ixPnaIa4iPTNdhy++8LKHkentJ/2tacfY/lH7/PR/r3s7RJqt+spMJ8/fgKFeQH21hxg3NhyvF73TfdXPvcpigtD5OcHDlvX5XPnccnso4dsvIyIyEgayo5g0CKPo91wzSc/3I6Z4doZlI0UosuQ8XgsZkwak+4yREQkwySShsZWQ2P73PMgeL3pf9EVjhq2Vxv21kJBCMZlQEd8f+09YFi91vDhFkjZ3c+bVAGnL7CYfxT4MuB2FpHssGb3rm7d3j1pScR5btsmwA3MZ48ZywkTDg3Mu3Ich2dfXsMTz73OFUvP4NLzlwAwbmxZv2vzejycPm3GAK+RSG5a/fZaXnvzAywsll19AdMmT+g4L5lMce9Dz1Df2Ex5aTG3Xn8Jfr9in95kQkf3QGvQIo+j23DMJ+/Pjpmh3hmUrfRsKiIiIsPCdtzu6Pomd5HOYD7k+dMf6rYvHrq92h0tM6Y0O+Z+O45h0y53ZMv26u7nWRYcO8MNz6dOUNe5iAxcTfjQUSw9uf7Y4/nsCSf3GJgf7EB9I3cvf5qtO6oA2F1dgzFGz1EigxSOxHhx1bt8568/Q2NzC3fdv4Jvf/WWjvNXv7OWCRXl/NXNV7Di+VWsfmctZy9Z2K/LtmNR6OEID8vrwdPlsW5Ho71fiMeDNzDIbWOx3hdzsSy8+fmD2zYeB+fQldX3HjD8bqVDdVN+R3C4fnOU2y6FiWN7fo7yBoMdXzuJOOYwK7b3Z9uOGhoDhPIt1u+Ej7fEuP0S02sN1fv9HSGqZSfwOTZFXsO+ags72j3k9AQCWB6PG9R/GGdfTYoJY+HUeZ5DLr99WwAnmcSkUr1eN09eHpbXO/BtUylMMnmYbf1YXt+AtzV2CifR+7aW34/HN5htbZxEovdtfT48fv/At3UcnHh8UNtWlths2mog4d4HbMtDJJ5HZYX7HseJxXq/XK8XT16ee7ldtn3jPZvaGsOcSV12zFR7eGNdfkdwfrjHcjY9R3R9XA6GQnQZMsYYnLY7qcey9OJYRGSUMsbQGnXD89YYBPxQkiFd3vGkYVe14ZP94PfC+PLMD5xjCcO7G+GNtYb6g5pE8/Ng0TGw+DiLsgy5jUUkO40vKOrXdp9ecCKLp0w77DbGGFa9/REPPPkS8XiC/EAeN151PqedfFzGP+eKZLKdu6uZPWMyPp+XseWlxOMJkqkU/rbQb/P23Vx0zqkALJg3i+deebPXED2ZSpFK2dhtAfOb118FsUMDrbJTlzD///6k4/s1117ea1BXcvxCFvz7f3V8/9bN15Fqauxx28K5R7PwV3d1fP/ubZ8mXrOvx21D06Zz0u/+1PH9B1++g8iunT1uGxg/gVPuf6Tj+4/+9iu0btrY47aL/SV8dOuTHcHhtEe+xY4HPmBHD9t68vM5feWLHd+v/5fv0vDmGz1eLsCZL63q+HrTj77Pgdde7nG7c4B3bn0W8kI4xjB25U/YsfzZHmsAmPSNJ1kfL8Exhulr/otx6x/nxLbzVt/VfdtF9z1MvXcCdz7pMGHVnSzYuRyAHW0fXZ141x8omDETgN1/+j2f/P7uXq/bCb/8LUVHHwNA9SMPsuN/ftnrtsf97D8pPcGtcN+KJ9j2i5/1uu2xP/p/lC8+DYDaF55j87/9qNdtj/7/vk/FOecBcOD119j4vX/udds5/+sfGX/xZQA0vP0WH//jt3rd9qivfYPKq68FoGnth6z9xt/0uu2ML3yFycs+DUDrls188JW/6nXbqZ+9nWmfuwOAyK6dvHfHZ3rdtuSKm1jwd38NQHx/DW/ffF3HeeOAG7tsu3nq1Uw48xssme8h2dTIm5+6vNfLHXfRJcz99j8B4MRirL7sgh4vE2Bi5dlUH/X9ju/bt+1JNj1HdH1cDoZCdBkyjjG8v9HtMll49GS8eoEsktN2Ve3j0ZWvYjsO0ydP4LrLz013SZIBonFDQ4uhudVtZirJgLnn7eqbDdv2GOqboKwY8vMyo67e1Dcb3lhreGcDxA9qmBlbAksWWJw4FwIZ0N0vItlv0aQphPx+Ir10/lm4C3r2FaA3t4T5/cPP8tH6rQDMnjGZ25ZdztjykqEuWWTUaY3ECAU7uy2DwXzCkRilxYXu+eFox/mhYIBwpPcuz2deWsOK51fh9/u47Zbrh7fwEdQShkdetvs1msWyuo/F8A5gqYV4703HA+axLJy2z74+JmYsOsZibY3F1irD2IgbgB5O+/iXY/q3n1QywEdbDWNrTb9GC00eB5e0jV1JNA5dDbbtjheS7ixjeuuFTz/bdnh/UxUL507uWIBGMpftON1DdC32I5KzUimb/7z7Ib782Wv6XBAM9Hw+GiRThqZWQ0MzpBwoCGbOPO6Ubaja7y4e6jhQXpw5wf7BjDHs2AurPzJs2MEhC/zNmuyObJk9tfNNnwy9RMphXx1cdaaH8eV6zjqYntNzT9K2+eqKR3lsw9oez29/trn7mmVcPnfeYS+rel8tP/iPe8HAVRefydKzFuHR+wKRIbFu43Y+3rSdG69yu0K/97Pf8Z2vfaajE/1//vgEl5y7mCmTxrN7Tw3PvvImn//0lT1eVtdO9I07a1kwbUyP7+GzYVRD+1iUmjpDKGDRYuczYYy7GOL44kSP41wef83m5XcNM6YHOzrRd+yKc+6JhqvP6jnJ7joK4pHnI7z8ts1RXUZgbNsD555kcfVZ3n6Nc+moYVo+Ho8Hxxi2f5LgvIVOrzV48vPZe8ANx/fuSzCx3O5xPAu4I1r+62HDzr2GKWNSWI47dmVPrWHaBIsvfcrbbVuNc+l7nMveA4Y31zvsOwATxvtYcnyAygpr0ONc2u8DXe9HW/f6OO/UPK4919v36JdeRrQMZNvujx+IxGH8WC93XBPsCPI1zsWlTnQRERmwbbv2EMjL47f3PUU8keTKC89g9swph2x38GGikntStiEcdbumo3F37nlBBnVGt0bc7vOaOigsgMJg5tTWVTJl+GirG57vret+ns8LC+fAkuMsJozJzPpFJHtFk0nuePwBnt+2Gb/Hy1+ddCpPbFzXbZHRyqJifnDBpb0G6LbtdOxQqZxQwa03XMqk8WOZXNlXj6SIDMSMqZU88dzrpGybpuYwgYC/I0AHmDNzKms3bmfKpPGs3bidOT28Pm/n9/nw+3zYbeGuNz/Yrx2jAwmhBrRtlwBsoNu+udWmuskwa7obRI7vtthmzw0/S040rKty2FplCAUMkThMGJfPkhM9ePvxerG6IUCgwECe20UOECgwVDdZeIMHzSbvZf2Ijhr2dKmhItBnDZUVtM2q7vv2raywWb8TbI8Pj9ePYwwtNkyoPLTOjnr9fmgLfPsyoG19PvD1L4YcyLaW14c3OBzbdt8ZUl1ruOvPnQtwrtsD63a1L8Dp7ff93fJ4OratbrIPuR/lBzsXCu26bZ+Xa1mD2nbyFLjjU4df4HbYHvcj9BwxVBSii4jIgDU1t1K1dz///He3EYsn+NlvlvO9b/3VIXNOc/Uw0dHOdgyxOIRj7sKcsQTk+aGkMHPmizuOu3jotj3uG5KxZZnTGd9VS8Tw5seGNz+G8EGNGMUFsHi+xaJjoCBDw38RyW4t8Ri3PHwfq3fvJOjzc/c1N3LBUXP4l3MvZM3uXdSEWxhfUMTiKdN6Pcp08/bd3PvgSu646XJmTpsEwKkLD9+tLiKDUxDK5+wlC/nJr+7DwuLGq85n954a1m/ZyUXnnMppi+Zz74PP8G+//BNlJUV87oZL013yiKiupWOxTXA/hwKdQWRPKivcTvXDBYeHU1nhLkbqGNPRQRyJD2wExpHW0B9L5ntYt/2gnQVjLJbM1xFCA9U+GmfW5C5d4x07a/qYw9OLobgfDYXKCmvQ12E0UYguIiIDVhDK56hpkwjmBwjmBygsCNISjlBcWNBtu0vOW8zSsxZ1HCYq2ctxDLEERGKGlog7B9LQtmhoBoXnALG4e9hq1X7Iy4PxZZlVH0D1AcPqjwwfboGDj+6dPM4d2TJ/JngzMPgXkdxQFwmz7ME/8MG+agrzAtx3/adZMmU6AF6Ph9OnzTjszydTKZ549nWef+0tjIEnnvsLf/eFg5cmE5GhdsYpCzjjlAXdTpsyaTwAeX5/r+Nbctlgg8gjCQ6HKpwe7vByqIL66trDdyqPBoPZWdMX7eTILgrRRURkwNxDSf+CbTskk0laWiMUhg49FOvgw0QluxjjBufRmKG5rePcMW5wXhjKzLniB5oM2/cYGpuhrCTzFt2Mxg3PvOEuFtqVx4JjZ7rh+ZTxmRf6i0hu2dvSzPUP3MumA7WMCYZ44MbPcvyEyn7/fFX1fu66fwV79rnJwemLjuOGK88frnJFRA4rHUHkSHSRD1ZPgfeRBPXVtYY7n+wcY7J+J6zb3j7GJP3Xdzh1vS1r6g0NLVBZMXRd45l8P5JDKUQXEZEBCwXzOff0E/nJr+/Dth2uvfQcLRqWI4wxxJMQjUNzqyHathZTnt9dLNSbgcE5uDPFP9ln2LXP/X7cmMxaeNMYw9ptsOIvhtYuY1uCAVg0zx3bUlqYOfVK7ln99lpee/MDLCyWXX0B0yZPOGSbJ597nTffX88P/+GLaahQRsrOxnquu/9edjU1MLGomIdvvJU5Y/uXADiOw59ffZsnnnsN23YoKgjxmesu5oT5s4e5ahGR3qUriMzEERjDEXgPxxiTbHDwbdnQAvXN8PF2KCsaup01mXg/kp4pRJchY2FRVhTs+FpEctuSk+az5KT56S5Dhkg84Xadt0QMkRikbPD7oCA/80eKNIfdxUP317ujZUL5mVVvY4vhydcNG3d1nhbww/mLLE6ZB3kZ1i0vuSccifHiqnf5zl9/hsbmFu66fwXf/uot3bZpbglTc6AhTRXKSNlYu5/rHriXmtYWppeW88iyW5laWtbvn39/3RYeXfkKAMfPm8Vnrr/4kFFuIiLpoCDSNRyB93CMMckUhxtTc/BtWVlh+Hg7VI6F8eWWusZHIYXoMmQ8Houjpozw6gciIjJoyZRxO87DhmgMkinw+SCQB4W+zH8xaDuGfXWwvcrtnq/IsMVDHcewZh38+S1DItl5+jHT4cozLUrUeS4jZOfuambPmIzP52VseSnxeIJkKoXf1/lWYMULq7nk3MX86veP9Xo5yVSKVMrGdjSiKxu9v3cPNz7wexpiUY6pGMeDN97KhMKiAV3GicfN4aQFczl27kxOX3ScRk+JiGSY4Qi8M2Xxy6HWV9d+T7dlWZFhfLnF39ygHTajkUJ0ERGRUSSZMsTi0BJ1O84TKfB6ID8PCkLZE4ZEYoYdew172l7cjivPrNr31hkee8Vd3LRdUQiuONPi2BmaeS4jqzUSIxTM7/g+GMwnHIlRWlwIQE1tPfF4gsmV4w57Oc+8tIYVz6/C7/dx2y3XD2vNMrRWfbKDWx6+j9ZEnBMnTmL5DZ+hLBjq8+eaW8M8+dxfuPaycwjmB7Asiy9+5urhL1hERAZlOALvXF38sq+u/VzdeSCDpxBdREQkx6Vsd1RLOGpojUA8CR6P23FemmGjT/pijKG2EbbvMTSHobw4s8ahJFOGl94xvP6hO0u+3Snz4KLFFsFA5tQqo0dBMJ9oNNbxfTQapyDUGao/9fwqrrzwjD4v55LzFrP0rEXYjsPGnTlwDPco8eetm7jj8QeIpVKcMXUGf7j2ZgoDgT5/7oOPt/CHh56lJRzBcRw+e/0lI1CtiIgcieEIvHN18cu+uvZzdeeBDJ5CdBkytuPw/sYqABYePRmvFhkUEUkbx3FHtYRjncE5uMF5SWF2dkInkoZd+wyf7AOvF8aVZ9bioVurDI+/aqhv7jytohSuOcdi+sTMqVNGnxlTK3niuddJ2TZNzWECAX+3US4H6hq5/7HnAWhqDrP88RdYdvUFh1yO3+fD7/Nh2xrnki0eW7+Wr6x4hJTjcNGsufz26hvI9/kP+zOxWJwHnnyRVW+vBWDShArOPf2kkShXRESO0HAF3rk4c76vTvP+3JaHm6kuuUchuoiISA6JJ9wXf02t7tgWg7uIZVHIXbsiGyVThoZmqNrvdqGXFpFRHd2RmGHlasN7mzpP83rgnBMtzj4xs+a0y+hUEMrn7CUL+cmv7sPC4sarzmf3nhrWb9nJReecyj/8zWc6tv3uv/6mxwBdss/vP3iHv3/2KQyGa+ct4D8vuwa/9/AByJYdVfxu+QoO1DdhWbD0rFO46uIzu+10ERGRzJaLgfdw6E+n+eFuy75mqo92ubiDQa+GREREspztuPPNWyKGcASSNuT5oSAE3iwNzsENpw80GqoPQHMEfB63+zxTrpMxhg+3wNOrDOHOSRlMm+B2n48ry4w6RQDOOGUBZ5yyoNtpUyaNP2S7H/7DF0eqJBlG//XmX/g/L/8ZgM8tXMSPL7wMj3X4o0TffO9j7l6+AmNgTFkxn7vxMuYeNXUkyhURERlxR9q139dM9dEsV3cwKEQXERHJQsa4c84jMXc2eCwOlgeCASjwZe8LE9sxNLXC/nrD/gaIJtzrVFEC3gzq6G5oNjzxmmHz7s7TAnlw8WKLRfMya8yMiIwexhh+9NqL/PyN1wD42uIz+aezL+jXCK95c2ZQWBDiuGOO4sYrzyeY3/fcdBERkWx2JF37fc1UH81ydQeDQnTJKcYYPtgCK1c71DdBeQlcepqHE2Zn5/xfEZGDpWxDNAZNYUM4Crbjdp0XFWTvuBaAeNJQ3wR7DxgaWsAYKAxl3vx22zG8sRaef8uQTHWefuxMuOIMi+KCzKlVREYXxzh85/mV3P3eWwD809kX8PUlZ/W+veOwduN2jp83C4CiwhD/8s3bKS4sGJF6RUQkPXJxzEY69DVTfTTL1R0MCtElZ7REDN/8hc07G9xZtLbjfn7kZZuTj4Gffs1LUUj/GEQk+7R3nYejbV3nCXdhzfwA+LO469wYQ0sEDjQa9tVBawT8fnfmeSZer+paw2OvGvZ0efFXXABXnmkxb0bm1Ssio0fKsfna04/z0McfYmHx4wsv47YTT+l1+wP1TfzugafZsn03n//0lSw64RgABegiIjkuV8dspEN/ZqqPVrm6g2FEQ/QD9U384Oe/Y/LEcaRSNtOmTOCmq5eOZAmSo4xxA/T2Rd1sp/vn9zbBN39h85tvezOqo1FE5HCSKXfWeXPYnbntGMj3Z1539kClbHeh0Jp6w4EmSCShIAjjxmTmGJRE0vDi24ZVH7l/AwALOHU+XHiqRX5e5tUsIqNHLJXkC088xDNbNuK1PPzX5ddw3bHH97itMYY33lnH8ideIBZPEAjkYbe/YBYRGeVGQ4f2cI3ZGA233cGOdKZ6LsvVHQwj3ok+ddIEvvHFZQD87DfLqd5XS+WELN8VIQBYWJQU5nd8PZI+2ALvbOj9fMdxz/9wC5wwZ+TqEhEZKMcxRONtXecRN2D2+aAgP7Nmgg9GJGaob4bqA4bmVrAsdwxNeXHmXq8tuw2Pv+qOmGk3rgw+dY7F1AmZW7eIjA6tiTi3PnI/r+3aTsDr466rb+Ci2Uf3uG1La4Q/PPwsH3y8BYCjpk/i9mWXUzGmdAQrFhHJTJnSoT3cYfRwjNnIlNsuHY5kpnouy9UdDGkb52LbDolkkkDg0AVrkqkUqZSN7agrIpt4PBazp47j+Vff4s7fPzSiv7slDAvG9L3db37nBjaj1dIzF7H07N4P7RWR9Ekk3T30TS2GaMKdCR7Iy/6uc8cxNIWhtt5Q0wCRmDuGprwEfBm8U6A1ali52vDB5s7TvB4492SLs07I7NpFZHRojEW56cE/8E51FSF/Hn+89mbOnD6zx20/3rSdu5c/TUtrBK/Xw5UXnslF55yCx5PdHWEiIkNlqDq0jyQEH4kwejjGbOTqIpJyZHJxB8OIh+if7NnHT351H43NrUypHMeYsuJDtnnmpTWseH4Vfr+P2265fqRLlCMUjSdobGod8d+b14/Hpp2CxqbhryVTReOJdJcgIl3YbV3nrRF3Nngy6c4ELwiCN4sXCQV3FE1dE+ytc0e3pBwoCsKEMZm9U8AYw/ubYeVqd5ROuxmVcPVZFhVlmVu7iIweNa0t3PjA7/m4tobS/CDLb7iFkyqn9Lq9ZVm0tEaonDCWO5ZdzpRJ40ewWhGRzDcUHdpHGoIPNoweSHA/HGM2cnURSZGDpXWcy/InXuCtD9Zzygnzum1zyXmLWXrWImzHYeNOPeqyTTCQR2lJ4Yj+zpYwRON9bxcMjO5O9GAgL90liAgQSxiiMWhsdRcMBff5qSCY/QFta8RwoNGwt859bvb53MU38/yZf91q6g1PrzJsreo8LT8PLj3N4qSjMzv8F5HRY3dTI9cuv4cdDfWMKyjkoRtvZd64Q0PxcCRKQSgIwLw5M/jyZ69h/tEz8fvTdjCyiEjGGooO7SPtyB5MGD3Q4H44xmyM1CKSuTJ3PVeux2iU1ldQoWA+ra3RQ073+3z4fT4tcpNlbMfhw017GDNuIv/3H7+MdwQPD31/s+GOH9p9bnf3d72cMEdPTiKSHsmUoaHF0NQCSRvy/FAUcsdhZTPbNjS0ti0U2gjxOISCMK48O65bc9hdOPSdje4YnXbHzYLLT7coCmX+dRCR0WFr3QGuXX4P1S3NTCku5eGbbmVmWfeZhqmUzZN//guvrXmff/rb2xhbXgLAwuO0MJCISG+GokP7SDuyBxNGDya4H+oxGyOxiGSuzF3PlesxWqVtnIvB7Yq94+YrRroEGUZO1/RhBJ0wG04+Bt7b5C4iejCPB06cC8fPHvnaRETAnbF9oNEdERLKh4IsDWaNMSRSEIvTsQBqfQu0T/EqCkFZUXZct3jS8PoHhr98AIlU5+klhXDVWRZHT8uO6yEio8NH+/Zy44O/50AkzOwxY3n4xlupLC7pts2efbXcdd8KqvbuB+C9jzZy4TmnpqNcEZGsMhQd2kfakT2YMDoTRqmMxCKSuTJ3PVeux2g1oiH62PISfv69vx3JXymjhGVZ/PRrXr75C5t3NriLvzmOG57bjhug//RrXh2KLyIjrr37vL4ZPFZ2LRTqOIZ40g3MI3GIxAxNrW54nki27bS03I768uLsWWzTdgzvboQX33Zn0bcL+OHsEy1OXwB+X3ZcFxEZHd6s2sXND/2J5niMBeMn8sCNn2VsqHNGoeMYXnj9bR5/5jVStk1BKMhnrruIE4+bm8aqRUSyy8Ed2tW1hkdetvsdDB9pR/ZgwuiRGqXSl+FeRHKwo24ybWxKJuz0kMHTQDzJGUUhi99828uHW+Dp1Q71zW6oc9lpHo6fnT2hlYjkjpaIoa7J7T4vCGZ2MGs7xu0uT7iheUvbYqexOMST7pgTy3KD5ry2xU+zJTRvZ4xh0y54do1hf0Pn6R4PnDIPzjvZojAH5tKLSG55afsWPvfocqKpJIsnT+NP132a4vz8jvPrGpr43fKn2bx9NwDHHXMUn73uYkqKR3aNIhGRXDKYsRtD080+sDB6eBYKzbzweaA7CzJ1bEqm7PSQwVGILjnFsixOmAMnzNFhMCKSPsmUoaHZHXOSid3nyZS7oGk07obkTWFDa8QNyxNJwIDX64bl+XnuwqDZMNv8cPbUGlauNuyo7n76sTPgosUWY0uz+/qJSG56auPHfPHJh0k6NufNnMXvrllGyN99ofjX1nzA5u27CeT5ueHK8zjjlOMz6n+OiEg2GuzYjeHuyO7p9w3lKJVMDZ8HurMgU8emDG5kT+bt1BitFKKLiIgMoUzrPo8nTcf88mjc0BSGSNQNzJNJwAK/r7O7vLSo8/DCXNDQbPjzW4YPt3Q/fep4uGSJxbSJuXNdRSS72Y7Dmt27qAm3ML6giJ2NDXzj2SdwjOHKo4/lV1dcS5730Ldvly89neaWMJect4RxY8vSULmISO7JprEbQxncZ2r4PNCdBZn69xv49cjMnRqjlUJ0ERGRIZAJ3ee2Y4jGIByD1oihsQUiCbe73LbBAvxt41iKC8DnzawO+aEUjRteedeweq27Nka78mK383z+zNy97iKSfVZsWs93X1hJdUvzIed9esGJ/PTiK/F63C61jzZs4y9vfsgXP3M1Xq8Hv8/HrTdcOtIli4jktNE6diNTw2cY2M6CTP77DeR6ZOpOjdFKIboMGQsoDAU6vhYRGS3S1X2eTLm/MxyD5rAbmsfikEi5Qb7f784wL8jPvvnlg5WyDWvWwcvvGqLxztND+XDeSRanHDt6bgsRyQ4rNq3n9seWY3o5//yZs/F6PMTiCR566iVef/NDwB3jcu7pJ45coSIio8hwzBrPBpkcPg9Ervz9hmunhkbEDI5CdBkyHo+Ho6ePT3cZIiIjplv3uWf4u89jibbQPAqNrYaWsLsQqG27vz8/DwpD7niW0dZlbYxh7TZ4bo2hoaXzdJ8XTlsAZy+0CAZG120iIpnPdhy++8LKXgN0C/jnF59hbl4R9z74DLV1jVgWXHDmIs44ZcFIlioiMqoM9azxbJEr4XOu/P2GY6eGRsQMnkJ0ERGRATLG0BqFA41ut/NwdJ87jrv4ZzgK4agb1IcjEE+BcdyFP/PzoKxIndU7qg3PvGGo2t95mgWcMAeWnmJRWjQ6b5/2+1A8AQawrLYjxazOI8Ysy/1oOxks9yiG9vPaT7esvn/OsnJrnr7ISFize1ePI1zaGWBPSzP/8Ns/Mjbppby0mNuWXcbco6aOXJEiIqPUwWM3qmsNj7xsZ3Uo25dcCZ9h5Bd5HQ7DsVMjXSNicqH7XSG6iIjIAAxX97ltG8IxiMTc8TANLe7XySQYCwI+yA9AUQF4PNn1YmO41DYYnl1j2LCz++lHTXIXDc22F2VDIZF0X1zH4m6onZ/nzr+3PO7OF8eAafvo+rVpb4M1YLedR9tH+1lOl20MbT/X9n3H+cZgcB8beT7w+dwjI/xe3W9FelITbul7IyBqGZacNJ8br7qAUDAwzFWJSKYKR6LcvfxporE4UyrHseyqCw55HfqdH/2aMWXFABwzZzqXnX9aOkrNOaOpezcXwudcMRw7NdIx9z5XHj8K0WXI2I7D2i3VABw3u7Jj8SMRkVww1N3niWRnaN7U2jbPPAGpttEs7bPM84pG32iWvrREDC+9Y3h7fZdgFxhfDhcvtpgzdfTcZrbj3h+jMXcBVb/PvW9OGgslhRaFIcjPO/xtYYzpDNPpDNRNlzC96+kHn9b1I5FyO98jMUM4CvGkezRFKuX+Hgfwedw6FbCLwPiCon5td/0FZ3LbuWcNczUikumee+VNTj7+aJacNJ97HlzJx5t2MP/omd228Xgs/v7LN6epwtylBR4lXYZ6p0Y65t7nyuNHIboMqZTtpLsEERlBX/3OT5kxdSIAi086ljNOOT7NFQ2Poeo+T9mG2gbY32Boibhho+2A1+N2mZcUjtyipNkokTT85SN47X1DItl5elHIHdty4tzcD2ONMcSTEI279x+PB4IBmDAWyoosikLuzpeB3A6WZTH0+xzcC0zZ7t8qnnTrTSR7DtidtoTeq4BdRpmtDXWHPd8CJhYVc+vZZ4xMQSKS0TZv281F5ywG4PhjZrF5++5DQnRj4Ce/vh+/18unLj2bKZN6XrcsmUqRStnYjt7D90c6undFhkM65t7nyuNHIbqIiAxaaUlhTne6DFX3eco2HGiE3fsNDU3g97tjNsqLwTvK55n3RyRm+Hg7vPiOoTnceXqeH85aaHHGAsjz5+7tmLLbus3j4Dju9S4KwrQJUFxgURjM3Ovv81r4vBDK73rqEQbsXvciunbMd3zd9v3B3fNdO+27fm91uZyOyqzOnVsiw+mxDWv51rNP9Xp++6P6hxdcqiM8RQSAcDTWMdIpGAwQjkQP2eY7X/sMRQUhdlfv5677nuJ///0dPV7WMy+tYcXzq/D7fdx2y/XDWncuSEf3rshwSMfc+1x5/ChEFxGRQWtuCfP/fnUfhaEg119xHmPLS7qdn80dLh3d583g8Q6u+9y2DbWNULXf0NDshucVZQrO+5JIGnbtg21Vhm173M6FLjknHgsWzYPzTrYoCuXebekYQzzhhuaJhLuIbDAAk8dBaaHbbR7Kz/6RNf0K2BNuoH5wwI7pXPDU8rQvaup+WJ7Or7HcMNzjcbf3dN3W09mF33Ux1fZgPZZwd5yJDIc/b93EV556BIPhpvknYKoaebxuFzFv57NdZVExP7jgUi6fOy+NlYrISHMchx//9x8POf24o48iFAwQjcUJBfOJxuIUhA79R1VUEAJgSuU48vL8hCMxCrr/swXgkvMWs/SsRdiOw8adWdYOmgbp6N6F3FiMUQZmJP7mIz33Pl2Pn6GmEF1ERAbtR//4JYoKQny8aTu/f+gZvvHFZd3Oz8YOF2MMrRE40DT47nPbNhxogj37DQea3ZEUY0rd0FAOZduGqlrYVgXb9hg+2ed2AvfkmOlw0WKLcWW5dVsmU4ZIzA1uMRDIg9JCGFPizjUvCo2u+8/hAnbbNh3feqzh2ZlgO24n/Gi6zWXkrPpkB3c8/gApx+H8yTPwvbeH1pYIl3oKmLN4HtNmT2FiUTGLp0xTB7rIKOTxePjO33y2x/PiiSRrN27n1IXzWLdxOwvnz+l2fjKVAgN+v4+GphYiXTrXD+b3+fD7fNgaydov6ejezebFGBX+D042/80PJx2Pn+GgEF1ERAatvdPl2Lkzue+x5w85P9s6XJIpQ32z2zU+mO5z2zHUNbqd53XN7tiJsSUK4g7mGENNHWzb43ab79hLtxnnB5swBo6aBPOPspg2ITduS2PcnTSRmLuYbHtgPGGMuyBoUcgd+ZPt3ebDQUdySDZ7f+8ePv3wn4ilUhxXNIbCD2toxWLi+DHcvuxypk2ekO4SRSSDXXTOqdy9fAWvvvE+kydUMG/ODAAeeOIFLj3/NJKpFP99z6ME8vw4jsMt116k1xJDaCi6dwcSLmfrYoy5GgSPhMH8zbNlh8VId78PB4XoIiIyKLF4gjy/D4/HQ1X1fgp7mHuQTR0urVFDbYMhFofQALvPbcdQ1+SG5/VN7piIMQrPOxjjjsVpD82374FwrPfty4vd0PyoyRYzK6Ewh0a2OMbQEnbD81C+O96nvNida14YVEAskss21NZw4wO/J5xIcMbUGXxl2gIe2vkCF5x5MldfchZ5fn+6SxSRDFdYEORrdxx6dOeNV13Q8fU//+3nRrAiGYiBhsvZuhjjUIX/Qx0Oj0TYfKS/Y6B/c+2wGFkK0WXIWEAoP6/jaxHJbXtrDvDHR54jP5AHlsUt116c7pIGrTlsqKkzGKB4AN3ntuOG5lW17sKhXg+UKzwHoCViOsazbNsDjS29b1sYhKMmw1GTLI6aBGXFuXf7OY6hOQLRGBQVwNHTYFy5RTCQe9dVRA61o6Ge65bfS0MsykmVk/nDtTdTkJfHnGmT1H0uIjJKDDRcztbFGIci/B/qcHgkwuah+B0D/Ztn69EK2UohugwZj8fDvJl6EyAyWsyYWsk//91t6S7jiDW2ugG61wsF+f17ceM4bmd11X539rmF2z090NnpuSQWN+yohq173PB8f0Pv2wbyYGZlW2g+GcaV5e7YEts2NIfdBTKLC2HmTIuKMgj4c/P6isihqpubuPpPd7M/3EqZ8fG7K2+gMODOKFaALiIyegw0XM7WxRiHIvwf6rEmIxE2D8XvGOjfPFuPVshWCtFFRGRUMsbQ0GzY3wB5fsjvR0ew4xjqW9wFQ2sbwbKgrCi7w3NjDLYDyZQ7mzuZ6vw6lYLkYT4nU4Z4Aj7ZB1W1YEzPv8PnhakTYFZbaF5ZAV5P9t5m/ZGyDU2t7m1VWgSzp1pUlGb3fUVEBm5/ayuX/O43VEdbKbAtzksUEW0KQ2lpuksTEZERNtBweaQWYxzqMSdDEf4P9ViTkQibh+J3DPRvnq1HK2QrhegiIjLquJ3khtoGyM/vuyvYcQwNLbCn1v0ZSG94bowhkXLnanf9CMcgEjMd33cNxLuF43b3oLyX7HvQLAsmVXTONZ82YfSEx8mUG547jnsfmTTOYmypRvyIjEY799Vy+R/upMaOEbQtbq+Yw9eWXUVpcWG6SxMRkTQYTLg83IsxDseYk6EI/4d6rMlIhM1D9TsG8jfP1qMVspVCdBkytuPw8ba9ABx71ES8Hj1oRSTz2I6hrtFQ2+TO4j5cuGtMZ3i+v94Nm0sLIW8IR3EY43ZzR+IQjh4circF4nGIRNs+t52XsoeshCExrqxzrvmMSkbdrO9E0g3PDe6ispMqLMYUa6FQkdHIGMOrb3/EV55/glpvioBj8a+Lz+em887M2dFVIiLSt5HqLB+I4RpzcqTh/1CPNRmJsDkdgXYm3qdymUJ0GVKJZIalOiIiXaRsw/4GQ2MLFIV67w42xt1mT62hpsEdU3Kk4Xk8YVi/AzZ94s7I7gjL427X8nCzLPB7wec79LPPC/7DffZZ3X+my9d+H4wtheKC0flCLZ4wNLaCxwMVZe4L2bKi3B9XIyK9i6dSfGfVn90AHQ9/+tQyzj766HSXJSIiGWC4O8sHaqRmag90ZMxQjzUZibA5XYF2pt2ncplCdBERGRWSKUNNvaG5FYoKeu4QNsYNRKtrDTX14BgoKRz8IpAp27D5E/hwi2HDziPvHvd6oCDovtAN5Xf/KMi3Djktz98Zhns9ubt4ZzpE44amsLsjoXIsTBxrUVoIHoXnIqOW4zg4GL684hG2xlsIeLw8vOxWFk+dnu7SREREejQSY04GOzJmqMeajETYrEA7tylEFxGRnJdIugF6SwSKCnvuEm6JGD6pMdTUuZ3hJUWDC88dx7C92g3OP94OsUTP2/l9XQNwCLZ9DgUgFLQIBdoC866huE9BeLpFYu6RBHl+mDrOfXFeUqi/i8hoFk8kePipl2mNxlhX5rBi03ryvF7+eN2nFaCLiEhGG4kRJD2NjPl4u+GXj9iML7eGpGN7qLrAh3qRVcktCtFFRCSnxRKGmjpDOA7FBYd2Chvjdp1vrXLnj5cVQSBvYC+UjDFU7XeD87XboCVy6DahfDjuKDh+tkXl2KGdqy7DyxhDOOr+XYMBmD4BJoy1Ru0IGxHptP2Tau6+fwU1B+pZG0qwLZjCa3n4nyuv55wZs9JdnoiIyGENNnweSNh88MiYSBRq6mF/PcydZoZkMdP263IkXeDDsciq5BaF6CIikrOiccO+OkM8CSUFh3YLJ1OGnXsNu/a5Xd7jywfWUby/wfDhFsOHW6C++dDz8/xw7AxYMMti1mQtMpltHGNojbgLvobyYdZkGD/GojCov6Nkn9Vvr+W1Nz/AwmLZ1RcwbfKEjvOefflN3lu7Ca/Hw9TJ41l21QU6uqIPKdvm6RdW88xLb+A4hp1lFts8KQD+49KruWzuvDRXKCIi0j8DDZ8HGjYfPDJmzwEIx+DoaTB53NAtZnqkhmuRVckdCtFFRCQnhaNugJ60e170sjVq2LrbsK/e7T4PBvoXGDW2GD7aCh9uNew9cOj5Xg/MneZ2nM+dqo7zbOQ47uifSAwKQzB3Kowrtwjl628p2SkcifHiqnf5zl9/hsbmFu66fwXf/uotHecvnD+bi889FYDf/OFxNm7dxTGzp6ep2sy3t+YAdy9/ml1V+wCIzyrng4bdAPzr0su48bgT0lidiIjI8Bpo2HzwyJjdNW6DyqSK4V3MdKBGapFVyV4K0WVI5Qf86S5BRISWsGFfvcGYQwN0Ywy1DbB1j6E1CuPKwNdHh3g46o5p+WirYefeQ8+3gJmT3OD82Jn9D+QlsyRTbngeT0JxCI6ZDuPKLPL195Qst3N3NbNnTMbn8zK2vJR4PEEylcLvc98KjK8o79jW5/Ph8fQ8BzWZSpFK2diOMyJ1ZyLHcfjvex5l/4EGCkL5lC86il+sewuAfzzrfO446dQ0VygiIjK8Bho2HzwyJhgwVB+AUL4Bhmcx08EYiUVWJbspRJch4/V4mH/UxHSXISKjXFOr24Hu9ULBQWM3UrZh117Dzn1ux/j4st7Ht8QT7ny+j7YYtlS5i40ebPI4Nzg/7qieu90l8znGEIlCawy8lntUwvgxFmNLB7ewrEgmao3ECAXzO74PBvMJR2KUFhd2227Ttk9oam5lzswpPV7OMy+tYcXzq/D7fdx2y/XDWnOm8ng83HzNhbzw+tuUHz+Nb7zwNAB/c+oZ/O2Ss9JcnYiIyPAbTNjcdWRM+ziY4VzMdDBGYpFVyW4K0UVEJCcYY2hoNuxvBL/v0G7wSMw9zHBvnTsfvafRHCnbsPkTd4HQjbsgmTr091SUucH58bNgTIlC1myVSLpd58mUezjpjAkwtsyiuAC8Hv1dJbcUBPOJRmMd30ejcQpC+d22qarez2MrX+Wrt1/b687FS85bzNKzFmE7Dht3jo5jm40xvPXBBgBOXejOOZ83ZzpVnji3Pno/BsPnFi7in89ZqjnyIiIyKhxp2DzYxUyHW6bWJZlDIbqIiGQ9xzHUNxtqGyE/cGgH8YFGN0BvDkNF6aHjW2obDK9/aFi3DWKJQy+/pBCOn+WG5xPGDGzxUckcjmMIx9yFQn1eKCuGCeUWZcWQn6e/qeSuGVMreeK510nZNk3NYQIBf8coF4D9Bxq496Fn+NJnr6aoINTr5fh9Pvw+H7Y9Osa5hCNR/vTon3nnw43kB/KYNX0yY8qKWbVrB3c8/gApx+HaeQv48YWX6f+CiIiMGkMRNg90MdORkql1SWZQiC5DxnYcNuyoAeCYGePx9jJPU0RkKNmOoa7RcKDJ7SjuupCnbRt27zfsqHa/H1feObsP3G7kl981/OVDODgTCuXDcUe5wfnUCd1/TrJLPOF2nadsKAy68+srSi2KQuBR17mMAgWhfM5espCf/Oo+LCxuvOp8du+pYf2WnVx0zqk88OSLRKIxfrfcHU1y4TmnsuCYo9JcdXqt27idex96hqbmVjweiwvPPoXS4kLeq67i04/8iVgqxcWzjuY/L7sGj6XXvCIiMroobJbRSCG6DKlYPJnuEkRkFEnZhgONhvpmKAiC39cZiEbjhu17DHtqoTAEhV3moxtj+HgHPL3K0NTaeXl5fpg3A46fZTFrMnj7WHBUMpftGMJRiMQgzwdjSmB8uUV5cfcdLSKjxRmnLOCMUxZ0O23KpPEA/M3t16WjpIwUTyR4eMUrvPrG+4C76OodN13O9CkT2VBbw40P/oFwIsGZ02Zw59XX4/cqQBAREREZDRSii4hIVkqmDPsbDE0tUFTQPfCub3bHtzQ0w9jS7uH6gUbDir8YNu/uvCyvB848Ac450VLAmuVicUNzBIxxu85nTYaxbV3nGrcgIoeTSCb54X/cy7799QCcd/pJXHPp2QTy/GxvqOO65ffSGItyUuVkfn/tzeT7/GmuWERERERGikJ0ERHJOomkG6A3h6GosHMhSNsx7Kk1bN8DjgPjx3SOYUmmDK+8Z3jt/e6jW2ZNhivPtBhbOjIBq20bbAc8Hje8V7B75Gzb0BqFaNw9mmB8OYwvc2edd92BIiJyOHl+P8fPm00s/jGfu+FS5s2ZAUB1cxPX3X8v+8OtzKsYz/3X30JhXiDN1YqIiIjISFKILiIiWSWWMNTUuQtEFhd0zrSOJdrGt+yHUBCKQp3h6Yadbvd5Q0vn5ZQUwKWnW8yfOXxBdso2xBOQSEIi5XZHez3g9bpBvm0DGCzAtP2Mz9sZsLd/9no7v9dsdpcxhlgCWiKAcUf2TB0PY0osCoLaOSEi/bN3fx1ej4dxY8sAuPKiM7j43MUUhPIBOBAJc90D97K7uZEZZeU8eONnKQv2vviqiIiIiOQmhegiIpI1onHDvjo3PC0p7AxKG1sMW6oM9U3u7Ov2kSz1zW54vnFX52V4PHDGAjj3ZIvAEI1uMcaQTHWG5Ym25SF8XrczuqjADe0Lghb5eeD3uYtc2o77OWW7gXoyZUgkIZ50LyOZgqTtfm+3bY8xHYG7Zbkf3h7Cdm9HEJ9bYXLKdhcJjSUgmAcTx8K4Urfr3KcZ9iLST45jeHn1uzz69KtMmljBt796C16vB7/Ph9/nvkVqikW54YHfs6XuAJOKS3hk2ecYX1iU5spFREREJB0UoouISFYIRw019YZ40u1AtywLxzHsrYNtVW6IPb7cDY2TKcPrH8Ar7xlSdudlzKyEK8+yGFc2+LDV6RKYxxNuAG4BPh8E/FBe5NYXzLcIBiAYGMhIke7b2Y7BtrsE7qnu4XvX0D3ZFuCnbHCSnZ3ujnE73bHcUN/ndUP89q8zNWRvv+5ddzQkk4Dl7pCYWekuEloQzMz6RSRzNTQ2c8+DK9mwxd3DGgoGiMUTHd3nAOFEgpse+iNra/YyNlTAwzfeypSS0jRVLCIikvuqaw1vrHOoroXKClgy30NlhV7rS+ZQiC5DKs/vTXcJIpKDWiJugO44UFLovpBKJA3bqw1VNZAfgIq2YHzzJ4an/mKoa+r8+aIQXHqaxYJZAxvz4TiGRIqOkSyOA1huCB3ww7hyKA51huX5gaHthvZ6LLwet5u9Z91/l+OYjtDZtt0u9vZwPZ5wR+BE426HeyzhhvLtIbuhe7jua/vaO8Qhu2PawnEbUm1Bf/uOgfbb1wI8VltnvRd8HvdvWJAPY0otygq7LyQrItJfb72/nvse+zORaBy/38d1l53LOact7Pa/IZ5K8blH7+ftPbspDuTz4I2fZdaYsWmsWkREJLdV1xrufNJhX50hFID1O2HddofPX6kgXTKHQnQZMl6PhwWzJ6W7DBHJMeGoYe8Bg2VBYduc8+awYetuQ20jlBdDIM+iscXw9CrDxzs6f9ZjwZLj4PxFFvl5h3/x5Thul3v7KBWnbfHPvLbAfGypO2c9P+COEckPDH3AfKQ8HguPxw3DD+XWaozbnd8+eiaZdMP2WNwQTUA05p4ejraPkHGHx1hdOtk7QnZPZ5htjLuTo1sw3vbZMe48+PbL8Xo6Lyc/DwJ57k6IgN/C7+sM87t+nakd8yKSHWKxOL9/+Fne+XAjANOnTOT2ZZcxYdyYbtulHJsvPvkQr+zcRsjv5/7rb+G48RPTUbKIiMio8cY6N0CfNdldA8oxhq1Vbmf6teeqWVMyg0J0ERHJaO0jSkqLLIwx1NTDlipDPO52ghsDr75neOldd8xKu+kT4cozLSaMOXz4mrINzWE3VA62hbkTyyEU7OwwD/hzJ8S1rM6guqD7OUBnyN51xnuyrZM9Ej80ZHfaQnZD50z29oC8IL+tQz8P/H4Lf/soGR8dX6ujXERGgs/vo7auEY/H4rLzT+OS85dgWRardu2gJtzC+IIiTpk8hb995kme3ryBPK+X3197M6dMnpru0kVERHJedS2EAm6ADu7nUMBQXZvmwkS6UIguIiJZIZky7Nxr2LXPHW8yrtxia5XhydcNBxo7tysIwqVLLE6Yc/jRLfGkG547DpQWwZypFuVFblf0QEa+5JquIXuX8cB0HR2TTLk7LNqDdtvpHoy3d4+P5ttRRNIvnkji9Xjw+bz4vF7uuOlyYvEE06dMZMWm9Xz3hZVUtzR3bF/gzyOcTOC1PNx51Q2cPf2oNFYvIiIyelRWuCNcHGM6OtEjcfd0kUyhEF2GjOM4bNy5H4Cjp4/D4/GkuSIRyRXhqGHnXqhpgLJCd+TK/X92WLutcxvLglOPhaWnWAQDPYe3xhiicWiOuKNIKkph4hh3gUp1RPef32f1MjJGRCQz7Ny9l7vuX8GJx83lmkvOAugY3bJi03puf2w55qCfCScTANx+4iIunXPMSJYrIiIyqi2Z72HddoetVYZQwA3QJ4yxWDJfuZJkDr0FliFjgEgs0fG1iMhQqG82fLzDYGExptjw5scWL75jSCQ7t5kyHq460+p10RnHGFoj7giSYACmjocJ5RYlheqWFhHJJSnbZuWLb7DyxdU4jmHNe+u49PzFBPLyALAdh+++sPKwr1VXbt7A98+/BK8aQkQkw7y/djOPPfsqB+qb+OX//fset1m3cTsrnl8FwBUXns6xc2eOZIkig1JZYfH5Kz28sc6hutbtQF8yX4uKSmZRiC4iIoNWU1vPv/zkLr75pZuYPWPysPyO9vDbsgz3Pw/7Gzqjj1A+XLzY4sSjO+fndZWyDS1hiCWhKAhzpsK4MouCoF6MSW4yxmA77pgiu+3DGHdWvbdtVn2mLYgrMlT27a/j7uVPs3P3XgAWnXAMN1+ztCNAB1ize1e3ES492dPSzJrduzh92oxhrVdEZKBmz5zCP//tbfzvn97V4/mO4/DI06/wra/cDMBPfnUfx8yerqPEJStUVlhaRFQymkJ0EREZtBUvrGbOzCnD+juaWg2vvAfb9nSeZgGL5sGFp1qE8g8NBBNJQ1OrGx6WFMKsKRZjSyHgV3go2c1xDI5pC8jtzrAc3KPAPIDH44bmHq+7KK7fB7G4u0BvONG2GKzVfRFYryd3Fs+V0ccYwyur3+fhp18mmUwRCga4+VMXcsoJ8zq2cYzD+v37+eOH7/brMmvCLcNVrojIoBUWBA97/v4DDYwtLyEUdBe2GVNWwv4DDR3jrLpKplKkUja24wxLrSIiuUYhuoiIDMr2T6opKSrosQN8KKRsw4MvGv77YXeOebtJFXDVWRaTxx36e6NxQ3OrGx6OLYWJY9155z7NO5cs4Timo4Pc6fIZ0xaSe8BruZ99XvAH3KDc57PcbnNPZ9e519N9XFHKdheETdmQSkEsYYgl2haHtd0g0uAuEuvztH3WY0eyQGNTC4+ufIVkMsUxs6fzuRsuobSkiB0N9by+azuv7dzOqk92cCAS7vdlji8oGsaKRUSGRzgS7QjQAULBfMKRWI/bPvPSGlY8vwq/38dtt1w/UiWKiGQthegiIjIoK198g8/dcCkPPfVSr9scSYfL/gb4zwcd4m2zz4MBt/N80THdO2YdYwhHIRyBQMCdjz5+jEVJweA6a40xmLbAsj24NKb9vLbvnZ5P77p9+weWe3rXSgxuyOn3ul3CWtR09Emm3EC7vaO8/R5gtYXk7fePgnzI87v3kfbO8c6QfGD3G5/XwtftCFkLYwwpm45wPZlyd1olU273uu203amt9vuqRsJI5ikrLWbZ1RdQ09qCXVHI/179Eq/v3M7u5sZu24X8fk6dPJV3q6tojsd7vCwLqCwqZvGUacNfuIhIDxzH4cf//cdDTj/u6KO4fOnph/3ZUChIJNb5/BaJxSkI5fe47SXnLWbpWYuwHYeNO2uPrGgRkVFAIbqIiAzYRxu2MX3yhD4PKT2SDpfKsRa3X+HhV486zJkK151nUdhllrltG5ojbtBXGIJZU2Bcefdt+mKMG2Qmkm6AaFltYWbbRVi0nWZ1ntf1a9q7gi03/PS0dQhbVvvXVsflebr8nG0gGnO7gMMxMG3jNfLaRm+o+zc3JVOGeMLtAvf53A7ywiDk+a1u3eM+b/tIluG/H1iWhd/n3u/aTgHcjviOYN12RyRFY91HwlhW5+gYv08jYWTkhCNR7n/seU448RgO+Gxe27md13dtZ+OB/d2283k8nFQ5mbOmzeTM6TM5qXIyeV4fKzat5/bHlgN0W2C0/d77gwsu1aKiIpI2Ho+H7/zNZwf1s+PHllFX30i0LUivq29k3NiyHrf1+3z4fT5sW+NcRET6QyG6DCmfV284REaD3dU1bNr2Cdt27mHPvlr21dbxhVuuYkxZSbftjrTD5bOXWIwpsdhXZzrC8UTS0ByGlAOlhTBrssWYEsjP6194ZzuGRNINzo1xw79QPhSGLPze3kNz6On0wQeGpYVWRy3JlDuKJhJzdwokbYOnrfPX/VAwmY3ad9LEk27Hud/r3teKQhb5ee5OkyO5Dw0nj8cikAeBjlPcOnscCZPsHAnjGIPfB4E87QySoRdLJXlozVvc8+pf2O1EafzkHcxBd7P54yZw1vSZnDltJounTKMwL3DI5Vw+dx53X7OM776wstsio5VFxfzggku5fO68Q35GRCQTbNm+m6eeX0Vjcys/+81yzjltISceN5cHnniBS88/jaLCENdccjb/ceeDAFxzydlaVFREZIhYxhjT92bpYdsO72+qYuHcyXgVzoqIZKTfLX+aM049ntkzJve6zZE8n3+4xWHVRw4Tx1q0hN3wekyJ26leXtK/oC6ZMm6QmXI7xgN+KAhCMOCGmZkSUhvTGfDHk+6YmvYueWN1jn/x+zI3fB3tjHHva/GEu5Mmz9+2kybYHpzn3t+t60iYWNzQEnF3HKRS4PV1Lm6aTfdZ23Eff9MnWv3eQTeaHMlzuu04rNm9i5pwC+MLilg8ZVqvXd+24/Dhvmpe27WdV3ds483du0ia7h2TM8rKOzrNz5g6gzGhgmGpRUQkVyl3ERHpH3Wii4jIEblt2WXD/jvcTm2orICJYyxKCg8/NsJp6/COJ8ExkOd1Z6oXlnZ2AGfiTGfLauv+zYMiLMaUtI2bSUE84Xaqx5PubWGMcReW9LvhusZopE/X+xttwXlZERTkW+QHMmcnzXDpOhImlG9RVty5I6E16s5Yj8TAsgwBv3v76P46Oq3YtL7H7u8ftnV/G2PYXFfbMZ5l1Sc7aY53XxAv4FjMLxnLzUsWc95Rs5lcUjroerweD6dPmzHonxcRERGR0UMhuoiIZLTCEMydCjMmWRSFeg/ekqnO0Sietvni5cUQCrjBdDZ2AFuWRV5b6FgYdMfWtF/PRArCUXfGdmvCnavuaV+sNEN3EuSSrmOBwO22HlMMwfzMOrohHSzLvQ3y86Ck0CKZcuf/R+Nud3drxB370n7f1tiX0aF9DvnBh8DubWnmtseWs2TKNLY31FPT2tLt/EJ/HkVhm4qkl5l5RXz9+suZf/RRI1e4iIiIiAgK0WUIOY7D5k/cmcdzplZo9pqIDInyYoupEzyHBOiOY0i0LQrqGPB7ID8AY0s6u7lzMUj2+9yu3wKgrMgiZXeG6pGYIRaHcBQc2w3V8/yQ51Pn71CwbbfDOpECD+59bGyJ232tGeC9a7/PFoUs7BJ3x0/72JdozL1ds3Xsi/SP7Th894WVhwTo0Lmw5xu7dwGQ7/NxyqSpnDl9JmdNm8lx4ydw15+ewuPxcPM1F/a5oLWIiIiIyHBQiC5DxgCtkXjH1yIiQ609ME6m3GUO8/zu4qKhfKtjTMRoC+B8XgufF0J0X6w0kexcrLS989fvc2+j0dwlPVAdOymS4PG43dVlRe48/VzdUTOcvB6LUP6hY19aIm63usa+5KY1u3d1G+HSm++dexGfO3ER77y3noXz53YE5nfcfAU+r3fUPb+LiIiISOZQiC4iIhnPtqGpxeD1ut2qZUWQn6fu3554PRbBgDsDvqQtVI8nOudTx+LuZ4/V1vmr0S+HaF+INpUCb1twPqbYnW8eULA7ZHod+xIzhGMa+5JLasItfW8EFHr9/M89j/Px5h2s37yTL9xyVdvMfb1lEREREZH00itSERHJaH4fVJR1dv4GRmG3+ZHo7PyFsmKLRNIN1SNtXerhqDtP3etzx76MtnEaxhhStnt0QzLVNhrooIVo8/NG122SLt3GvrTt/InGDa1dxr742o6m8Hq18yebjC8o6td2K1e8RlHYxu/zMXvGlGGuSkRERESk/xSiZwFjDB9sgZWrHeqboLwELj3Nwwmz9aZeRHJfYdCiMKjnuqGS53cXKy0q6Bz9Eku4i5RG453jNNpnqXtzrPvXcdzQPJGClA2WwQ1mfe5ooIC/czFX/Y9Nn65jX8p7GPsSa1tMFwDLPWLA4wGf1/061+632W7xlGlUFhWzt6W515F/QduiMJxi2uSJ3H7T5UwcN2ZEaxQRERERORyF6BmuJWL45i9s3tngvim0HffzIy/bnHwM/PRr3kMW2xMREemPrqNfyoq6jNOIG8JRCMfautS97tiXvCzsUrcd09llboNluaF5KAAF+VbH9RptHfjZpKexLynbfU1k2+7c+ljC3TFi227Y7hgDBkxbwN4esnvbgnb9rUeW1+Phhxdcyu2PLcfioLVz2r45PhrgigtO59ILTsPn9aahShERERGR3o14iP6Hh5+lpraev//yzSP9q7OOMW6A/t4m93vb6f75vU3wzV/Y/ObbWmhJRESOXNdxGk5JW/dvElrbun+bY4DlLlAa8Gdmt2/K7h6ae7xuSF4Y7FyA1u/T4qrZrP1+2qnzb5myTVuw3hmyx9tGGNkOJBIQNZ0hu2W5o2F8HvezG7TrvjEcLp87j7uvWcZ3X1jZbZHRAjycZpXxgy9cz8ypfw3+2AAAKkFJREFUlWmsUERERLJJda3hjXUO1bVQWQFL5nuorNDrOBk+Ixqip1I2u6v3U1wYoq6hmTFlxSP567POB1vgnQ29n+847vkfboET5oxcXYfjUZgvIpITPF261Evbun/jSYjFDa1Rd+yLbbuz1P1esDzgsdxQ0sL9PBILcCZTnaG5AbyWO4qlffHZ9tEsCkZHB5/XwueFQLdT3b+97bgBu+24Ibs7C98daZRMQSLpnmcc496XPGm4Ajnu8rnzOLF0HNtjzewPtzK+oIip/hDjxpYRyMtLd3kiIiKSJaprDXc+6bCvzhAKwPqdsG67w+evVJAuw2dEQ/S1G7Zx/LGzmFBRzlvvr+eS8xb3uF0ylSKVsrEdZ9C/6/lX3+L5198e9M9ngpYwLOjHOMjf/A6KCoa/noFY3svpS89cxNKzTxnRWkRE5Mi1d/8WBi3GlHTOqA7H3C5fx4aUAYy7OKcBMAbTNlLDahvZYNpmObghe2fg3vXDc9D3Fm4gb0xnYJ5sn2feNmqmpMANzdvHs4xEgC/ZxeuxegjG3ftJ+wKzXTvYHYeDOt7lSNi2wzMvvcGKF1Zzy7UX8alTFqS7JBEREclSb6xzA/RZk91mTscYtla5nenXnquxcDI8RvStwVsfrOe6y8+juCjEz+98sNcQ/ZmX1rDi+VX4/T5uu+X6Qf2uaDxBY1PrkZSbEfL68di3U9DYNPy1DIVoPJHuEkRE5AgdPKPacYwbnLd9dP364O/bv7Zt92dSNhjHDS47znMOugzckNPCnWeenwfl+RDIszTPXIaEZVltY37SXUluqqmt5+7lK9jxyV4Atu6s4gyF6CIiIjJI1bXuGkft0xA8lkUoYKiuTXNhktNG7K1CJBpn2849/PHhZwGoq29id/V+plSOO2TbS85bzNKzFmE7Dht3Du4REAzkUVpSeEQ1p1tLGKLxvrcLBjKvE703wYAO1RURyTUej8XAJ18cGnqbts713gJ50DxzkWxijOHVNz7g4RUvk0gmCeYHuPmapZyycF66SxMREZEsVlnhjnBxjOnoRI/E3dNFhsuIhejvrd3Ixect5rzTTwJgw5advPnexz2G6H6fD7/Ph20PfpzL0rNPyfqxIe9vNtzxQ7vP7e7+rpcT5qQ/UHAcw7Yqd6fHUZMrdCi9iIgMiGVZ7jiXdBciIkessamFex96ho837QDg6FnT+NyNl1JeqjWRRERE5Mgsme9h3XaHrVWGUMAN0CeMsVgyX+8kZPiM2L3rzffWM3/uzI7vZ82YzIfrt+I4ZqRKyDonzIaTj3FnxvbE43HPP372yNbVG4OhqTVGU2sMd0kuERERERmNauubWL95B36fjxuvPJ+//fyNCtBFRERkSFRWWHz+Sg8XLLKYPtHigkWWFhWVYTdinejf/NJN3b73+3x8/399fqR+fVayLIuffs3LN39h884G8HrcRa48Hnde7Ilz4adf82oOrIiIiIikneM4eNq6P2bPmMxNV1/InKOmUDl+bJorExERkVxTWWFpEVEZUVo+KcMVhSx+820vH26Bp1c71DdDeTFcdpqH42drITURERERSb8NW3Zy32PP89XPfYoJ48YAcM5pC9NclYiIiIjI0FCIngUsy+KEOXDCHO1hExEREZHMkUgmeWzlq7z4l3cBePLPf+ELt1yV5qpERERERIaWQnQRERERkRy3+u21vPbmB1hYLLv6AqZNntBxXjKZ4t6HnqG+sZny0mJuvf4S/P6+3yZ8Ul3DPcufZu/+OgDOXnwC111x7rBdBxERERGRdNGytSIiIiIiOSwcifHiqnf5+y/dzB03X87yJ17odv7qd9YyoaKc//WVTzOhopzV76zt1+X+7DfL2bu/juKiAv7m9uv49LUXEcjLG46rICIiIiKSVhndiW6MAcC2nTRXIv1hO51/J9t2wKSxGJEs5PFYObvOgZ7PRWQ0ybTn8527q5k9YzI+n5ex5aXE4wmSqRR+n/tWYPP23Vx0zqkALJg3i+deeZOzlxw6zzyZSpFK2aRsGwCv18PJxx/NDVedT1EopOd4EclJmfacPtT0Ol1ERpMjeU7P6BDdcdwn84+2Vqe5Ehmoj7bobyYyUAvnTsbrzc0X6Ho+F5HRJNOez1sjMULB/I7vg8F8wpEYpcWF7vnhaMf5oWCAcCTa4+U889IaVjy/ilAon1tuvIZbbrwGgK2764H64b0SIiJpMv+oCeQHcvcoG71OF5HR5Ehep2d0iO7zeVgwqzKj9/xG43G+/YNf8uN/+grBQCDd5aSdbo9D6TbpTrdHd11vD48nM5/nhsKRPJ/n2n1G1yez6fpktmy5Ppn2fF4QzCcajXV8H43GKQh1huoFofbzS9rOC/Z4OZect5ilZy0iEony/f+4l+//r88Tys/vcdtclS33weGg667rPlqv+//756+mu5RhNdjX6Zl231A92VGL6lE96a7nSF6nZ3SI7vF4yMvL7LHtXo+HZDKF1+PB683sWkeCbo9D6TbpTrdHd11vj0zdWTgUjuT5PNfuM7o+mU3XJ7Pl2vUZKTOmVvLEc6+Tsm2amsMEAv6OUS4Ac2ZOZe3G7UyZNJ61G7czZ+aUHi/H7/N1/FwkEsPn9Y66v8Novg/quuu6j9brnsuv0WHwr9Mz7b6herKjFtWjerK5nvRf+yzn83m5fOnp+HzedJeSEXR7HEq3SXe6PbrT7dG3XLuNdH0ym65PZsu16zNSCkL5nL1kIT/51X389k9PcuOV57N7Tw3PvfImAKctms+efbX82y//xJ59tZx28nGHvbzR/HfQddd1H2103Ufnde+PTLt9VE921AKqpy+q5/DSWY9l2leREBERERERERERERGRbtSJLiIiIiIiIiIiIiLSC4XoIiIiIiIiIiIiIiK9UIguIiIiIiIiIiIiItILX7oLyFbhSJS7lz9NNBZnSuU4ll11wSGrdn/nR79mTFkxAMfMmc5l55+WjlKH1eq31/Lamx9gYbHs6guYNnlCx3nJZIp7H3qG+sZmykuLufX6S/D7c/sud7jbY/Xba3nq+VUd94k7br6CspKidJU6In5+5wN8sqeG8884mcsu6H7/H433j8PdHqPx/tEfh3tMZZtP9tRw/2PP4/FYeDwePnv9JVSMKU13WUespraef/nJXXzzSzcxe8bkdJdzxHZV7ePRla9iOw7TJ0/gusvPTXdJg2aM4f7HX+CTqn3YjsPSsxZxysJ56S5rQA5+3jTGsPyJF9hdvZ9gfoDbl11GQSiY7jJz2mh+rXe46/7sy2/y3tpNeD0epk4e3+N7gWzXn//BTz73Om++v54f/sMX01Dh8Onruj/78hrWb96J4zhcvvR0jp41LU2VDr3DXffaukbueeBp975uWdyx7DLKSovTWO3Q0fuWgTnc7ZUOmfQ6OxqL8x+/fRCf10simeKaS87imNnT01JLV5n0mv2r3/kpM6ZOBGDxScdyxinHp7WeTHr9X11zgPse/TMAqZRNzYF6/v3/fD0ttWTie4knn3ud9Vt24vN6WXbVBUyuHDeyBRgZlEeeftmsfmetMcaY3z3wtFm7Ydsh2/zj//31SJc1olrDUfO9f/+dSSZTprauwfzrf/2h2/mvrH7PPPXnvxhjjHnqz38xr6x+Lx1ljpi+bo9Vb31kVjy/Kk3VpUd9Q1Ov13u03T+MOfztMRrvH33p6zGVbRqbWkw0GjPGGPPR+q3mt/c9leaKhsZv73vK/PTX95vN23enu5QjlkymzM9+c3/H3ynbVe3db/7fr+4zxhgTjcbMd36Ufa9LDn7eXLthm7nngaeNMcasfnuteeTpl9NYXe4bza/1+rru+/bXdXz9698/ZtZv3jHCFQ6v/vwPbmpuNf/zxydy7j1PX9f9ow1bzaMrX0lTdcOrr+v+4JMvmlVvf2SMcV+7Przi5TRUOTz0vmVgDnd7pUMmvc62bcekUrYxxpj9BxrMD35+T9pq6SqTXrNn0v+NTH79//YH680fHn42bb8/095LfFK1z/z8zgeMMcbUNTR11DaSNM5lkDZv282CY2YBcPwxs9i8ffch2xgDP/n1/fzHnQ+ye0/NSJc47Hburmb2jMn4fF7GlpcSjydIplId52/evpsF89zbaMG8nm+jXNLX7QHwxrvr+PF//5Ennn0NxzFpqnTkHK4zZbTdP+DwtweMvvtHX/rzmMomJcWF5OcHAPD5vHg92d+xuP2TakqKCnLmqIltu/YQyMvjt/c9xU9/fT9bsvx5qbS4EJ/XQ8q2icUTFITy013SgB38vLl5e+frrwXzZrF5W3b/jTLdaH6t19d1H19R3vG1z+fD48mtt1X9+R+84oXVXHLu4jRVOHz6uu7vfriRZDLFz36znLvuX0EkGk9jtUOrr+teOWEs0bbrG47GKCoMpavUIaf3LQOTaUcgZNLrbI/Hwut1/yfEYnEmT6xIWy3tMu01e3NLmP/3q/v41b2PcaC+Ka21ZPLr/zXvrefUE49N2+/PtPcSNQfqO46OKi8tpq6+ccTzgdx6tTeCwtEYoaD7JB0MBghHoods852vfYa//9JNfOqyc7jr/hUjXeKwa43ECAU7H0TBYD7hSKzz/HC04/xQL7dRLunr9jj+2Nl871t/xbe+fDN1Dc28+f7H6SgzY4y2+0dfdP84VF+PqWwVTyR44tnXufCcU9NdyhFb+eIbXJxDAUpTcytVe/dzx81XcPtNl/P7h5/FmOzdoRUK5jNubDn//OM7+f6/35MTY+XCkSihUJf/HdHsf07IZKP5tV5//wdt2vYJTc2tzJk5ZSTLG3Z9Xf+a2nri8cTIH0Y9Avq67o3NrViWxTe+uIwZUyfy7MtvpKPMYdHXdT9m9nReW/MB/+end/Pamg/SPoJhpOTyc12uyZTX2Q1NLfz4v//Iz+98kIXz56S1Fsi81+w/+scv8a0v38xZi4/n9w89k9ZaMvX1f2s4yr79dcyaPiltNWTae4nKCRVs2vYJqZTN7ur9NDS1EBnhfGB0D/Lqg+M4/Pi//3jI6ccdfRShYIBoLE4omE80Fu9xHmdRgbtnfkrlOPLy/IQjsbTvuRlKBcF8ol3evEaj8W7XryDUfn5J23m5PbO0P7dHu0UnHMPHm3ew5KT5I1pjJhlt94++6P5xqL4eU9koZdv8zx+f5KJzT6Vy/Nh0l3NEPtqwjemTJ1BYkDuP3YJQPkdNm0QwP0AwP0BhQZCWcITiwoJ0lzYo6zfvpLGphR/+wxeIxuL82y//xLFHz8Dvy96XfwWhfCJtzwvRWJyCYHY/J2S60fxarz//g6qq9/PYylf56u3X5tw89L6u/1PPr+LKC89IR2nDru/7fZBj584EYP7cmSx/4oURr3G49HXdH336Fa66+ExOPG4ub72/nsefeZWbP3VhOkodUbn8XJdLMul1dllJEd/+6i0cqG/ip7++r+NIhnTIxNfs7VnZsXNnct9jz6e1lkx9/f/Ohxs4acHRaX19kWnvJSrHj+XUhfP49zsfoGJMKZXjx474EVHqRD8Mj8fDd/7ms4d8XL70dObMnMrajdsBWLdx+yHdJ8lUimTSPaygoamFSJfO9VwxY2olW3fuIWXb1DU0Ewj4uz2Yut5Ga3u4jXJNX7dHpMsL0o1bdzGhy2HAo9Fou3/0RfePQ/X1mMo2jmO4+/4VnHDs7IzoSDlSu6tr2LTtE/7jzgfZsGUnD694ibqG9B6OeaRmTK2k5kADtu0Qi8VpaY1QmNVvlA2hUD4ej4dAIA/bdrJ+VNTsmVNY1/V/x1Gj+3/HcBvNr/X6uu77DzRw70PP8PlbruwIA3JJX9f/QF0j9z/2PP9x54M0NYdZ/njuBMn9ud/vqtoHwM6qvYwbU5auUodcX9fdAIVt9/eiwtCoORool5/rckUmvc7uOl4imJ9HIJCXxmoy7zV7LJ7AcRzA3Rmd7nA/U1//v/neehafmN5FPDPxvcQ5p53It758M0vPWsSkCRUjPk7PMplwnEIWag1HuXv5CmLxBJMnVLDs6qV4PBYPPPECl55/GslUiv++51ECeX4cx+Hqi8/KqVXb2/3lrY/4y1sfYmFx41Xn4/V4WL9lJxedcyqJZJJ7H3yGhqYWykqK+NwNl+b8KuaHuz0ee+Y1NmzZidfjYfy4cm659iJ8Xm+6Sx5Wv3/oGbbt2kMqZVM5oYIrl54+qu8fh7s9RuP9oz8OfkxNnzIx3SUN2rsfbeSeB1Z2zHGbNLGCm65emuaqhsbvlj/NGacez+wZk9NdyhF74911vLbmA2zb4ZJzF7PwuOzd4eE4Dvc++Az76xpIpWwWn3Qs559xcrrLGpCDnze//NlrWP7481TtqyU/kMftyy5P+5uvXDeaX+sd7rr/590Ps29/Xcd82QvPOZUFxxyV5oqH1uGuf1ff/dff8MN/+GKaqhweh7vuyVSKPzz0LPVNLXg9Hm5fdhklxYXpLnnIHO66V++r5Q+PPIfX48G2HW657iImTUj/vOehoPctA3Pw7fXVz30qrfVk0uvsXVX7ePDJF7E8HhzH4Yqlp3PM7OlpqeVgmfCafccn1fzxkefID+SBZbHsqguYkubRYJn2+r+2rpH/+eMTfPfrt6a1jkx8L/Hv//MAjuNQUBDk5muWjvgRAwrRRURERERERERERER6oXEuIiIiIiIiIiIiIiK9UIguIiIiIiIiIiIiItILhegiIiIiIiIiIiIiIr1QiC4iIiIiIiIiIiIi0guF6CIiIiIiIiIiIiIivVCILiIiIiIiIiIiIiLSC4XoIiIiIiIiIiIiIiK9UIguIiIiIiIiIiIiItILhegiIiIiIiIiIiIiIr1QiC4iIiIiIiIiIiIi0guF6CIiIiIiIiIiIiIivVCILiIiIiIiIiIiIiLSC4XoIiIiIiIiIiIiIiK9UIguIiIiIiIiIiIiItILhegiIiIiIiIiIiIiIr1QiC4iIiIiIiIiIiIi0guF6CIiIiIiIiIiIiIivVCILiIiIiIiIiIiIiLSC4XoIiIiIiIiIiIiIiK9UIguIiIiIiIiIiIiItILhegiIiIiIiIiIiIiIr1QiC4iIiIiIiIiIiIi0guF6CIiIiIiIiIiIiIivVCILiIiIiIiIiIiIiLSC4XoIiIiIiIiIiIiIiK9UIguIiIiIiIiIiIiItILhegiIiIiIiIiIiIiIr1QiC4iIiIiIiIiIiIi0guF6CIiIiIiIiIiIiIivVCILiIiIiIiIiIiIiLSC4XoIiIiIiIiIiIiIiK9UIguIiIiIiIiIiIiItILhegiIiIiIiIiIiIiIr1QiC4iIiIiIiIiIiIi0guF6CIiIiIiIiIiIiIivVCILiIiIiIiIiIiIiLSC4XoIiIiIiIiIiIiIiK9UIguIiIiIiIiIiIiItILhegiIiIiIiIiIiIiIr1QiC4iIiIiIiIiIiIi0guF6CIiIiIiIiIiIiIivVCILiIiIiIiIiIiIiLSC4XoIiIiIiIiIiIiIiK9UIguIiIiIiIiIiIiItILhegiIiIiIiIiIiIiIr1QiC4iIiIiIiIiIiIi0guF6CIiIiIiIiIiIiIivVCILiIiIiIiIiIiIiLSC4XoIiIiIiIiIiIiIiK9UIguIiIiIiIiIiIiItILhegiIiIiIiIiIiIiIr1QiC4iIiIiIiIiIiIi0guF6CIiIiIiIiIiIiIivVCILiIiIiIiIiIiIiLSiwGF6O+t3cQPfn4P3/vZ7/jnf7uTn/76fhzHDPqXR6IxnnlpTbfTfvKr+3h/3eYBX9bqt9eyd39dx/cffLyFB558cUCX8f66zWzftWfAv7u/Vr+9lv++59Eez9u5ey//88cnhu13A3y0fis/+dV9fW63e08Nb32wflhrOdgv7nqIfV3+fkPpC9/6MZFobFguO9ccqG/i6//883SXAcALr79NU3Nrv7f/u3/5BQfqm4axok6DeX4ZrMamFv7tl3/q9fyRvN4iIiIiIiIiIqORr78bNja38oeHn+Of/vZWxpSVALCrah+WNfhfHonGefblNVxy3uLBX0ib1e+sJRgMMHHcGABOOHY2Jxw7e0CX8cG6LUyZNI6Z0yYdcT0DNX3KRL5wy1Uj/nt7srt6P+9/vIVTTpg3Yr/za3dcP2K/azBs28HrzdwDNzK9vsF48fV3mDtzKiXFheku5RCDeX4ZrNKSIv7XVz49Ir9LREREREREREQO1e8QvaUljMdjURDM7zht2uQJALz70UZeW/Mhf/eFGwFwHId//L+/4Wt/dT0trRHuf+x5Zs+cwradVdiOw203Xsb0KRP50yPPEYsn+N7PfofX6+G7X78VgK07qnj+tbdpbGpl3pzp3HLtRQDEYnEefOolqvbWkkymmDmtkpuuXsob765jV9U+HnzyJZ768yquueQsWlojvP/xFr76uU8BsOrtj3jx9XcBg9fr5YufuZqx5SUd12Xthm18uH4rG7bsZPXb6zj39BM589TjeePddby86j1sxyE/z8+yq5cypXIcVXtr+dOjz5FIpkglU5yycB6XXXAaAKmUzePPvsa6jdvxeCxKigr5+udvACAeT3Dnn55kz75a/D4fX7jlKirGlLJp2yc88MSL/H/fuI0D9U18/99/x/lnnMRHG7YRjcVZdtUFHHfMUYAb9j/6zCv4vF6OnTuTv7z1Ed/9+q3drg9AyrZ54IkX2bBlJ6FgPrNnTO44r6m5lTvve4pYLE4ylWLuUdNYdtUFtEYiPPHn14nG4nzvZ79j5rRKbrn2In5731PU1NaTStmUlRZx6/WXDCjcjCeS/MMPf8n//uYdHT/35J//QjQW58Yrz+c7P/oVX7n1U0yZNJ6nX1jNm+9/jM/r3j2/etunGFNWwhe+9WN+/r2vE2q7D/7dv/yi43o/9NRLbN6+G9t2CObn8ZnrLmZC2w6V3nS9fQpC+cyaPpldVfv4+y/fzKZtn3DfY39m5tRKdlXVcOn5S7AdhxdffwfbtjHGcNXFZ3H8vFmAewTFtMkT2Ll7LwcamjjtpPnMnDaJlS+9QWNTC+edfhJLzz4FgO/86FeceuKxbNy6i4bGFi49fwk+n5fX13xIU0sr11x6dscOjP+/vfuOj6pKGzj+m5ZMJgkkpEAaCSmEEFJIgCR0EBFQEERRQYKsulaUd113df2ouLqru+LaX1SagCLNEALSO4QaAgRCMQRCDaT3OpnZP4ZcCMmEAX1fVvf5/jdz2znPnXP/eO6Z51iLe+NvpH98NMeyckiIDWdIv55N+pd58jRJa7ZjajBhMOiZ8MBQvNu7czL7nNUxeb31W/dypaCYiQ8OAyz/HHn9/a95989P4WhwsPneN8bmzPncZmO6rKKS735Yz5WCIjDDoD6xDEiIZtWGVErKKvj6uxR0Wi2THx6Bn0/7Juc9lJlF0uqtaNQawkM7NdmWcz6XRSs2Ultbj06nZdzIwQR38lXiNrhPDBknTlNbW8fj40Zw4MhJTp46h8lk4qnHRuHTwcPqGFGrVezaf0R5vtgaT7D+DMs5n8u3SeuY9vITTeI2pF9PfL09eeejuXzyztSb9vtKfhGLUzZRXlGF0dhAv/goBveJBSz/yBg9rD+HMrMor6jivrt706dnJAC5VwpYnLKJ0rJKAAb27s6AhO6UllWwaMVGCovLqKuvJzo8hNHD+tt874UQQgghhBBCCCF+C2xOovt4eRIc4Murf/+SzoF+BAX40Kt7V1zbOtO9W2eWrdrC5bxCOni6cTjzFJ7urpaEXcU5LucXkjhuOBMeGMq23QdJXrudqU89zISx9/DOR3N58w+Tm1wrv7CEl59+lAZTA299MJvsnIsEBfiwdNUWQjr5kfjQcMxmMwuWrWXTzjTuGRjH3vRM7urXg+7dOgOW0imNTmafY9WGXfz5hcdwaeNEbV19s/5FhAUR1TUYPx9PJRl56swF9h86zivPjUen1ZJ1+jyzFqbw9h+fxN21DX94+hF0Wi119fX84/NvCQvxJ9DfhzWbd3Mlv4jXp05Cp9VSXlGlXCfnQi5v/s9k3Nu5kLR6K2u37FGSlNerrqnFx8uTUff04+iJ0yxO2UREWBBlFZXMW7qaPz3/GF6ebqTuz6CyqrrFe7Zjz2Gu5BcpibmPZy1Rthkc9LwweSx6eztMJhNffJNEWsZxekV35f6h/Zq8gAB4eNRdODsZAFizeQ8rN6QqiVBb2NvpiIkIZW96JkMHxmE2m9mddpTnJ49tsl9lVQ3rt+3jgzefx06no7auHrUNf3cYNiieh0YOBmDfoWMsXrFJeXFhzY49h8krKGLaHy3x+Wz20ibbL+cVMn7MUCaNGwFARWU1vaLDUKlUFBSV8v5nC+j6+jPotJZhVFhcysvPPEp1TR2vvTeDyupa/vTcBErKKnjznzPp0ytSeQFQW1fPqy9MJK+gmLc/nMOIuxJ4dcpEcs7n8unspUoSvbW4V9fU4t3enbH3DmzWt7KKSmYtXMnLz4zH18uDvemZfDU/WemrtTF5vb5xUbzxj5mMvXcgBgc9qfuPEB0efEsJ9EbWxvSi5I2092jHs5PGUFZRyd8+noeftwf33d2H1P0Z/H7CqGbJ88b+zVuymleem4B3e3e27zmkjAOjsYEZ85eT+OAwwkMDyTpzgS/nJ/Puq79X4tbRtwP3D+vPzn2H+WTWEp6fPJaHR93Fuq17WbkhlWcmjm51jNzIlngCrT7DjMYGcs7nEuDnRX5hCZfzi4gIC6K4tNymfptMJmYtXMnvHr0PL083auvqef+zBQR29FYS+lqthr+8mEhuXiF//3Q+8THdAPjimyRGDe1Lr+6WvpVXWp5Zcxf/yPDBCYQGdaShwcTnc5aRdvgEPaK63PJvQAghhBBCCCGEEOLXyuYkulqt4tlJY8jNK+Sn7PMcPXma1Zt28/pLk/B0d2VAQgxbdx3kkdFD2LIrnUG9Y5RjPdxcCezoDUCgvw/rt+1r9Vo9orug0ajRaNT4eXuSX1hCUIAPh45mkX32Ihu27weg3mhEZUOC9cjxbOJjw3G5OgPa3k5nU58PZWZx4VIe7326QPmusqqGuvp66oxGli7fwPlLV1CrVBSVlHP+Uh6B/j5kHM9m7L0DleRqYxIUIKijD+7tXCyx6OjD5tQDLV5bp9USE2F5IRDo70N+YTEAp89ewsfLUylbkxAbwXc/rG/xHCdOnSU+NhytVgNAn54RpO7LAMBkNpP041ZO5VzAbIbyiip8OrhbLeGy7+Ax9qRnUl9vpN5oxMmx5UTqocwsMo6dIvGh4c229e4Rwfxlaxg6MI6T2edwcnTA18ujyT4Oejs83V2ZvXAVXTt3IjIsEFeXNi1e63rHsnLYsvMANbV1mM1mKqtuXgP9xKmzxMWEo9VY4pMQG8HOfYeV7e7tXAgN6qh8LigqYfbCHRSXlqNWq6msrqagqFS5FzGRoajVahwNejzauRAZFoRKpcK1rTNOjgYKi0ox+FiS6D2vJiE93V3R6TTERIYCln93VFbVUFVdg8FB32rcNRo1cTHhLfbtzLlcfDp4KPGNiwln4fINlFxNyNoyJg0OemIiQ0ndl8GQ/j3Ztvug1ZJDK9btwKWNEwMSure43dqYPp6Vw+svPQ5AGydHukd05njW2ZuWVGocB97t3QHo2yuSRckbAbicX4RapSI8NBCAkE6+tHE2cP5SHq5tndFptcrLNn9fL+zt7OgS7A9YyirtTbesB3ArY8TWZ1xrz7A+PSPYtf8IAX5e7E47Qlz3rs1K9Nys35cuFzDz2xRl/5raOi5dKVCS6HFXk+Renm5o1CrKyiuovjrTvjGBDuDsaKC2ro4Tp85SVn7tJWBtXR1X8ous3hchhBBCCCGEEEKI3yKbk+iNvDzd8PJ0Y0BCNJ/MXMLhzCzuHtCLfnFRvDV9FvGx4eQXFhMVHqwc05hMBksy3mQytXoNa/ubMfNs4hjae7S71WbftoQe3RgzfECz7xclb8TJ0YE3pk5Go1EzY95y6o3Gm55Pq7MtFlqtRkmuWfa7/QVcG6m49sJh4/b9lFVW8dqURHQ6LUtSNlFf33L7s85cYNPOA7w65THaODlyKDOLlHU7W9y3tVrRQQE+mM1mzpy7xO60o/TuEdFsH7VazWtTJpKdc5GT2ed47/MFPDV+FCGBfs3iYLwa78LiMr5fvoG/vJiIp7srFy7l8YENC6je6Mb3MfZ2dk0+z/wuhQdGDCA20pIAn/rmJxivi1nT360a3Q33uuG6e339viqVWvmsUqlQqaDBZLpp3O10OtTq21uUwNYxObhvLF/M/YEO7d1wdjTQsYVZ4QD339PvF7ne7S+xcLMjr21vfKnU2Bad7vrPaqVttzJGbO1fa8+whNhu/PWjuTw4chC7D2Qy5XdjWziD9X5hNuNo0Df7Z0+Tduqa/u4aWnmumK9uem3KxCbHCSGEEEIIIYQQQvy3sXklwuLSck6duaB8rqyqoaC4FA83VwAcDXqiw0OYMW85/eOjUatvfmoHvR319UaMxgab2hAdHsLaLXtoaDApbcgrsMzQ1uvtqa6pbfG4yK7B7E3PpKSsArCU0mippIuD3p7q6mvniOoazJ70TAqLywAwmczknM9Vru3a1hmNRs3lvEKOZeU0OW7TjjQlqX59OZefK9Dfm4u5eVzOKwRgb3omxoaW4xcW4q9sNxob2JV2rcRNVXUNbZ0d0em0lJZVcCDjpLJNr7drEsuq6hr09nY4GRwwGhvYvufQbbe/d49INqemk3E8u8nM10Y1NbWUlVcSEujHfXf3ITjAl3OXrgCW2b5nzl0CIP3ISeUeVtfUotGoadvGCbPZzJZd6Ta1JTS4I/sOHrPEp6GB3QeOtrp/VXWN8i+CPQcyqaq++Wz3n+PnxD2wozcXL+dz8XI+YClx49LWCZe2zrfUBi9PNzzaufDtsnUM6hNz8wNuUVhIADv2Wmb/l1dUkX70J8I6BwCgt7enysqYDvL34WJuHrlXx0Hq/gxlHHTwaIfJbObYT2cAyM65QFl5JX7enrfUttbGyO1q7Rnm0taZAD8vlqRsxtnJgHcHj2bHt9bv9h5u6PV2pO7PUPbPKyi2Wu6pUXsPN+x0OvYdPKZ8V15Zhd7ejtCgjqzZskf5vqS0nOKSstvsvRBCCCGEEEIIIcSvk83TC00mE6s2plJQVIqdTofJZCIhthvR3a7NOu4XF8WutCP0jYuy6ZyOBgfiY8N5+19z0NvbKQuLWjNu1F0krd7GOx/NRaVSodaoGTtiIJ7urvSPi2Lpqi1s3J7GmOFNF77rfDUh+8nMJahUoNFoeCZxNPZ2TRfijI8NZ+7iHzmUmcXA3paFRcfeO5AZ85IwmUwYGxqI6BJEgJ8X9w7pzZzvV7E77Sgebi50ua7sx7BB8SSv3c67H3+DRq3Bpa0TLz7xkE0xuZk2To4kPjic/52XhFajpWvnAOzt7TA42Dfbt29cFBcvFzBt+mxlYdGzFy4DMLhvD75akMxb02fh0saJsBB/5bguwQGs37aPtz+cQ1CAD4+MHsLe9Eze+OdMHA0OhIUEUFJacVvtj48N59W/zSAmojOOBn2z7dU1tXy5IJnaunpUqPB0dyUh1jJjfdyowSxesYnkdTuI7BKo1Ob29fKgZ3QY06bPxsnRwepM+Bv1j4/mYm6+Eh9/3w7Ki5aWPDxqCF/OT8bgYE+XYH/a2VBm5ucID+1023F3djLwxKMjmbPoR2Vh0acnjrap/NGN+sZF8X3yBqXkzC/pkfuH8F3SeqZ9OBvMMGJwb6UsyuC+sSxYthY7na7ZwqLOTgYmPTSCGfOSlAV2G38PWq2GZxPHsGjFRpau3IJOp+XpxNHo7e2oqGw9oXy91sbI7WrtGQaWkkdff7uCCQ8MbfH41vqt0ah5YfKDLEnZxMbtaZjMJpwcDTw5fiSOrbRJo1Hz/OMP8H3yRlZv3o1apWJAQgwDEqJ5YvxIlqZsZtr02aCylMJ6bOwwm0osCSGEEEIIIYQQQvxWqMxm88+vE3LV+q17yc0rVBZiFP83ampq0estSfODR39i+Zpt/PWVp+5wq36dGmNpbGhg9sKV+Pt2YNig+DvdrP8oC5dvoI2Tgfvu7nOnmyKEEEIIIYQQQgghxP+7X6zQ7VvTZ6FCxUtP/jIzroV1m1PTSTt8HJPJjF5vxxOPjrzTTfrV+tfXizEaG6g3Ggnp5MvgvrF3ukn/MUpKy/nwq0U4GvRMfXLcnW6OEEIIIYQQQgghhBB3xC86E10IIYQQQgghhBBCCCGE+C2xeWFRIYQQQgghhBBCCCGEEOK/jSTRhRBCCCGEEEIIIYQQQggrJIkuhBBCCCGEEEIIIYQQQlghSXQhhBBCCCGEEEIIIYQQwgpJogshhBBCCCGEEEIIIYQQVkgSXQghhBBCCCGEEEIIIYSwQpLoQgghhBBCCCGEEEIIIYQVkkQXQgghhBBCCCGEEEIIIayQJLoQQgghhBBCCCGEEEIIYcW/AdYHpMB0eXIBAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# @title Render Understand Variation gallery\n", "variation_gallery = render_gallery(\"Understand variation\", columns=4)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "gallery-011", "metadata": {}, "source": [ "### 1.4 Reveal Structure · 10 idioms\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "gallery-012", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# @title Render Reveal Structure gallery\n", "structure_gallery = render_gallery(\"Reveal structure\", columns=5)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "gallery-013", "metadata": {}, "source": [ "# 2 · Run one idiom in Python or R\n", "\n", "### Three things to inspect together\n", "\n", "1. **Fit:** required dataset/attributes and action + target.\n", "2. **Encoding:** marks and channels in the selected idiom.\n", "3. **Algorithm:** transform → compute/layout → render → verify.\n", "\n", "Python runs natively in this Colab. Each leaf also has a copy-ready R recipe. The optional R runner installs only the selected package instead of forcing students to install the entire atlas.\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "gallery-014", "metadata": { "jupyter": { "source_hidden": true }, "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Paired recipes ready: 38 Python + 38 R\n" ] } ], "source": [ "# @title 2.1 · Store paired recipes (expand to inspect all source)\n", "PYTHON_RECIPES = {'Sorted bar': \"s = df.groupby('group')['value'].sum().sort_values()\\ns.plot.barh(color='#315EFB')\", 'Dot plot': \"s = df.groupby('group')['value'].sum().sort_values()\\nplt.hlines(s.index, 0, s); plt.scatter(s, s.index)\", 'Lollipop': \"s = df.groupby('group')['value'].sum().sort_values()\\nplt.vlines(s.index, 0, s); plt.scatter(s.index, s)\", 'Slope graph': \"wide = df.pivot(index='group', columns='period', values='value')\\nfor _, row in wide.iterrows(): plt.plot(wide.columns, row, marker='o')\", 'Dumbbell': \"wide = df.pivot(index='group', columns='condition', values='value')\\nplt.hlines(wide.index, wide.iloc[:,0], wide.iloc[:,1]); plt.scatter(wide.iloc[:,0], wide.index); plt.scatter(wide.iloc[:,1], wide.index)\", 'Line chart': \"for key, g in df.sort_values('time').groupby('group'):\\n plt.plot(g.time, g.value, marker='o', label=key)\", 'Small multiples': \"import seaborn as sns\\nsns.relplot(data=df, x='time', y='value', col='group', col_wrap=3, kind='line', facet_kws={'sharey':True})\", 'Stacked bar': \"wide = df.pivot_table(index='whole', columns='part', values='value', aggfunc='sum')\\nwide.plot.bar(stacked=True)\", '100% stacked': \"wide = df.pivot_table(index='whole', columns='part', values='value', aggfunc='sum')\\n(wide.div(wide.sum(axis=1), axis=0)).plot.bar(stacked=True)\", 'Donut': \"s = df.groupby('part')['value'].sum()\\nplt.pie(s, labels=s.index, wedgeprops={'width':.38}, autopct='%1.0f%%')\", 'Aligned profile': \"for key, g in df.groupby('entity'):\\n plt.plot(g.metric, g.value, marker='o', label=key)\", 'Radar': \"theta = np.linspace(0, 2*np.pi, len(values), endpoint=False)\\nax = plt.subplot(projection='polar'); ax.plot(np.r_[theta,theta[0]], np.r_[values,values[0]])\", 'Histogram': \"import seaborn as sns\\nsns.histplot(data=df, x='value', bins=20)\", 'KDE': \"import seaborn as sns\\nsns.kdeplot(data=df, x='value', bw_adjust=1, fill=True)\", 'ECDF': \"import seaborn as sns\\nsns.ecdfplot(data=df, x='value')\", 'Box plot': \"import seaborn as sns\\nsns.boxplot(data=df, x='group', y='value'); sns.stripplot(data=df, x='group', y='value', color='black', alpha=.35)\", 'Violin': \"import seaborn as sns\\nsns.violinplot(data=df, x='group', y='value', inner='quartile')\", 'Raincloud': \"import PtitPrince as pt\\npt.RainCloud(data=df, x='group', y='value', orient='v')\", 'Scatterplot': \"import altair as alt\\nalt.Chart(df).mark_circle().encode(x='x:Q', y='y:Q', color='group:N', tooltip=list(df.columns))\", 'Regression view': \"import seaborn as sns\\nsns.regplot(data=df, x='x', y='y', ci=95)\", 'Hexbin': \"plt.hexbin(df.x, df.y, gridsize=24, mincnt=1, cmap='viridis'); plt.colorbar(label='count')\", 'Pair plot': \"import seaborn as sns\\nsns.pairplot(df, vars=['a','b','c'], hue='group', corner=True)\", 'Correlation heatmap': \"import seaborn as sns\\ncorr = df.select_dtypes('number').corr()\\nsns.heatmap(corr, vmin=-1, vmax=1, cmap='vlag', annot=True)\", '2D contour': \"grid = df.pivot(index='y', columns='x', values='z')\\nplt.contourf(grid.columns, grid.index, grid.values, levels=10, cmap='viridis'); plt.colorbar()\", 'Interval / forest': \"plt.errorbar(df.estimate, df.label, xerr=[df.estimate-df.lower, df.upper-df.estimate], fmt='o')\\nplt.axvline(0, color='0.5', lw=1)\", 'Fan chart': 'plt.fill_between(df.time, df.q10, df.q90, alpha=.18)\\nplt.fill_between(df.time, df.q25, df.q75, alpha=.32); plt.plot(df.time, df.median)', 'Calibration': 'from sklearn.calibration import CalibrationDisplay\\nCalibrationDisplay.from_predictions(df.outcome, df.probability, n_bins=10)', 'Residual view': \"import seaborn as sns\\nsns.residplot(data=df, x='fitted', y='observed', lowess=True); plt.axhline(0, color='0.5')\", 'Treemap': \"import plotly.express as px\\npx.treemap(df, path=['parent','child'], values='value', color='value')\", 'Sunburst': \"import plotly.express as px\\npx.sunburst(df, path=['level1','level2'], values='value', color='level1')\", 'Sankey': \"import plotly.graph_objects as go\\ngo.Figure(go.Sankey(node={'label':labels}, link={'source':source,'target':target,'value':value}))\", 'Alluvial': \"import plotly.express as px\\npx.parallel_categories(df, dimensions=['stage1','stage2','stage3'], color='weight')\", 'Polar / Burtin': 'from pycirclize import Circos\\n# Map genuinely cyclic sectors and values; see the official pyCirclize examples.', 'Population pyramid': \"y = np.arange(len(df))\\nplt.barh(y, -df.left, label='Left'); plt.barh(y, df.right, label='Right'); plt.axvline(0, color='0.3')\", 'Ordered heatmap': \"import seaborn as sns\\nordered = matrix.loc[row_order, col_order]\\nsns.heatmap(ordered, cmap='viridis')\", 'Faceted table': \"styled = df.sort_values(['group','item']).style.format(precision=2).background_gradient(subset=['value'])\\ndisplay(styled)\", 'Missingness matrix': \"import missingno as msno\\nmsno.matrix(df.sort_values('group'))\", 'UpSet matrix': \"from upsetplot import UpSet, from_indicators\\nUpSet(from_indicators(['A','B','C'], df), show_counts=True).plot()\"}\n", "R_RECIPES = {'Sorted bar': \"library(ggplot2)\\nggplot(df, aes(reorder(group, value), value)) + geom_col(fill='#315EFB') + coord_flip()\", 'Dot plot': \"library(ggplot2)\\nggplot(df, aes(value, reorder(group, value))) + geom_point(size=3, colour='#315EFB')\", 'Lollipop': 'library(ggplot2)\\nggplot(df, aes(group, value)) + geom_segment(aes(xend=group, y=0, yend=value)) + geom_point(size=3)', 'Slope graph': 'library(ggplot2)\\nggplot(df, aes(period, value, group=group, colour=group)) + geom_line() + geom_point()', 'Dumbbell': 'library(ggplot2)\\nggplot(wide, aes(x=before, xend=after, y=group, yend=group)) + geom_segment() + geom_point(aes(x=before)) + geom_point(aes(x=after))', 'Line chart': 'library(ggplot2)\\nggplot(df, aes(time, value, colour=group)) + geom_line() + geom_point()', 'Small multiples': 'library(ggplot2)\\nggplot(df, aes(time, value)) + geom_line() + facet_wrap(~group) + coord_cartesian(ylim=range(df$value))', 'Stacked bar': 'library(ggplot2)\\nggplot(df, aes(whole, value, fill=part)) + geom_col()', '100% stacked': \"library(ggplot2)\\nggplot(df, aes(whole, value, fill=part)) + geom_col(position='fill') + scale_y_continuous(labels=scales::percent)\", 'Donut': \"library(ggplot2)\\nggplot(df, aes(x=2, y=value, fill=part)) + geom_col() + coord_polar(theta='y') + xlim(.5, 2.5)\", 'Aligned profile': 'library(ggplot2)\\nggplot(df, aes(metric, value, group=entity, colour=entity)) + geom_line() + geom_point()', 'Radar': 'library(ggplot2)\\nggplot(df, aes(metric, value, group=entity, colour=entity)) + geom_polygon(fill=NA) + coord_polar()', 'Histogram': \"library(ggplot2)\\nggplot(df, aes(value)) + geom_histogram(bins=20, fill='#315EFB', colour='white')\", 'KDE': \"library(ggplot2)\\nggplot(df, aes(value)) + geom_density(fill='#315EFB', alpha=.3, adjust=1)\", 'ECDF': \"library(ggplot2)\\nggplot(df, aes(value)) + stat_ecdf(geom='step')\", 'Box plot': 'library(ggplot2)\\nggplot(df, aes(group, value)) + geom_boxplot() + geom_jitter(width=.08, alpha=.3)', 'Violin': 'library(ggplot2)\\nggplot(df, aes(group, value)) + geom_violin() + geom_boxplot(width=.12)', 'Raincloud': 'library(ggplot2); library(ggdist)\\nggplot(df, aes(group, value, fill=group)) + stat_halfeye() + geom_jitter(width=.08, alpha=.3)', 'Scatterplot': 'library(ggplot2)\\nggplot(df, aes(x, y, colour=group)) + geom_point(alpha=.65)', 'Regression view': \"library(ggplot2)\\nggplot(df, aes(x, y)) + geom_point() + geom_smooth(method='lm', se=TRUE)\", 'Hexbin': 'library(ggplot2); library(hexbin)\\nggplot(df, aes(x, y)) + geom_hex(bins=24) + scale_fill_viridis_c()', 'Pair plot': \"library(GGally)\\nggpairs(df, columns=c('a','b','c'), aes(colour=group, alpha=.5))\", 'Correlation heatmap': \"library(ComplexHeatmap)\\nHeatmap(cor(df[c('a','b','c')], use='pairwise.complete.obs'), name='r')\", '2D contour': 'library(ggplot2)\\nggplot(df, aes(x, y, z=z)) + geom_contour_filled() + coord_equal()', 'Interval / forest': 'library(forestplot)\\nforestplot(labeltext=df$label, mean=df$estimate, lower=df$lower, upper=df$upper, zero=0)', 'Fan chart': 'library(ggplot2)\\nggplot(df, aes(time, median)) + geom_ribbon(aes(ymin=q10,ymax=q90), alpha=.18) + geom_ribbon(aes(ymin=q25,ymax=q75), alpha=.32) + geom_line()', 'Calibration': 'library(ggplot2)\\nggplot(calibration, aes(predicted, observed)) + geom_abline(slope=1, intercept=0, linetype=2) + geom_line() + geom_point()', 'Residual view': \"library(ggplot2)\\nggplot(df, aes(fitted, residual)) + geom_hline(yintercept=0, colour='grey60') + geom_point() + geom_smooth(se=FALSE)\", 'Treemap': 'library(ggplot2); library(treemapify)\\nggplot(df, aes(area=value, fill=parent, label=child)) + geom_treemap() + geom_treemap_text()', 'Sunburst': \"library(plotly)\\nplot_ly(df, labels=~label, parents=~parent, values=~value, type='sunburst', branchvalues='total')\", 'Sankey': \"library(networkD3)\\nsankeyNetwork(Links=links, Nodes=nodes, Source='source', Target='target', Value='value', NodeID='name')\", 'Alluvial': 'library(ggplot2); library(ggalluvial)\\nggplot(df, aes(axis1=stage1, axis2=stage2, axis3=stage3, y=weight)) + geom_alluvium(aes(fill=stage1)) + geom_stratum()', 'Polar / Burtin': 'library(circlize)\\n# Map genuinely cyclic sectors and quantitative values; see the official circlize book.', 'Population pyramid': \"library(ggplot2)\\nlong$plot_value <- ifelse(long$side=='Left', -long$value, long$value)\\nggplot(long, aes(plot_value, ordered_group, fill=side)) + geom_col() + geom_vline(xintercept=0)\", 'Ordered heatmap': \"library(ComplexHeatmap)\\nHeatmap(matrix[row_order, col_order], name='value', cluster_rows=FALSE, cluster_columns=FALSE)\", 'Faceted table': \"library(reactable)\\nreactable(df, groupBy='group', searchable=TRUE, defaultSorted='item')\", 'Missingness matrix': \"library(ComplexHeatmap)\\nHeatmap(is.na(df) * 1, name='missing', col=c('0'='white','1'='#C2413A'))\", 'UpSet matrix': \"library(ComplexHeatmap)\\nm = make_comb_mat(df[c('A','B','C')]); UpSet(m)\"}\n", "assert set(PYTHON_RECIPES) == set(idiom_names)\n", "assert set(R_RECIPES) == set(idiom_names)\n", "print(\"Paired recipes ready:\", len(PYTHON_RECIPES), \"Python +\", len(R_RECIPES), \"R\")\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "gallery-015", "metadata": {}, "outputs": [ { "data": { "image/png": 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DecisionSelected answer
Use whenCompare two opposing populations across the same ordered categories.
Required dataTable · ordered groups + two opposing categories + values
Action + targetCompare → opposition across shared groups
Marks + channelsopposing bars · signed length + shared order
Algorithmalign ordered groups → sign one category negative → mirror shared zero → label absolute values
CautionValidate scale, labels, accessibility, and evidence boundary.
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# @title 2.2 · Select a leaf and render its worked example\n", "IDIOM = \"Population pyramid\" # @param [\"Sorted bar\", \"Dot plot\", \"Lollipop\", \"Slope graph\", \"Dumbbell\", \"Line chart\", \"Small multiples\", \"Stacked bar\", \"100% stacked\", \"Donut\", \"Aligned profile\", \"Radar\", \"Histogram\", \"KDE\", \"ECDF\", \"Box plot\", \"Violin\", \"Raincloud\", \"Scatterplot\", \"Regression view\", \"Hexbin\", \"Pair plot\", \"Correlation heatmap\", \"2D contour\", \"Interval / forest\", \"Fan chart\", \"Calibration\", \"Residual view\", \"Treemap\", \"Sunburst\", \"Sankey\", \"Alluvial\", \"Polar / Burtin\", \"Population pyramid\", \"Ordered heatmap\", \"Faceted table\", \"Missingness matrix\", \"UpSet matrix\"]\n", "\n", "selected = idiom_catalog.loc[idiom_catalog.idiom.eq(IDIOM)].iloc[0]\n", "fig, ax = plt.subplots(figsize=(8.8, 4.7), constrained_layout=True)\n", "draw_idiom(ax, IDIOM)\n", "fig.suptitle(f\"{selected.root} → {selected.family}\", x=.01, ha=\"left\", fontsize=12, color=ROOT_COLORS[selected.root], fontweight=\"bold\")\n", "plt.show()\n", "\n", "detail = pd.DataFrame({\n", " \"Decision\": [\"Use when\", \"Required data\", \"Action + target\", \"Marks + channels\", \"Algorithm\", \"Caution\"],\n", " \"Selected answer\": [selected.selection_cue, selected.data_requirement, selected.action_target, selected.marks_channels, selected.algorithm, selected.caution or \"Validate scale, labels, accessibility, and evidence boundary.\"],\n", "})\n", "display(detail.style.hide(axis=\"index\"))\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "gallery-016", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "\n", "### Population pyramid: paired implementation\n", "\n", "| Python | R |\n", "|---|---|\n", "| [Matplotlib](https://matplotlib.org/stable/gallery/index.html) | [ggplot2](https://ggplot2.tidyverse.org/) |\n", "\n", "**Python recipe**\n", "\n", "```python\n", "y = np.arange(len(df))\n", "plt.barh(y, -df.left, label='Left'); plt.barh(y, df.right, label='Right'); plt.axvline(0, color='0.3')\n", "```\n", "\n", "**R recipe**\n", "\n", "```r\n", "library(ggplot2)\n", "long$plot_value <- ifelse(long$side=='Left', -long$value, long$value)\n", "ggplot(long, aes(plot_value, ordered_group, fill=side)) + geom_col() + geom_vline(xintercept=0)\n", "```\n", "\n", "These snippets state the production grammar; replace placeholder column names with your verified schema. The rendered teaching example above uses the notebook's dependency-light Matplotlib renderer so all 38 leaves work in one runtime.\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# @title 2.3 · Show the paired Python and R implementation\n", "selected = idiom_catalog.loc[idiom_catalog.idiom.eq(IDIOM)].iloc[0]\n", "display(Markdown(f\"\"\"\n", "### {IDIOM}: paired implementation\n", "\n", "| Python | R |\n", "|---|---|\n", "| [{selected.python_tool}]({selected.python_url}) | [{selected.r_tool}]({selected.r_url}) |\n", "\n", "**Python recipe**\n", "\n", "```python\n", "{PYTHON_RECIPES[IDIOM]}\n", "```\n", "\n", "**R recipe**\n", "\n", "```r\n", "{R_RECIPES[IDIOM]}\n", "```\n", "\n", "These snippets state the production grammar; replace placeholder column names with your verified schema. The rendered teaching example above uses the notebook's dependency-light Matplotlib renderer so all 38 leaves work in one runtime.\n", "\"\"\"))\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "gallery-017", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R execution is off. The complete R recipe is visible above and will be exported below.\n" ] } ], "source": [ "# @title 2.4 · Optional: execute the selected R recipe\n", "RUN_SELECTED_R = False # @param {type:\"boolean\"}\n", "\n", "if not RUN_SELECTED_R:\n", " print(\"R execution is off. The complete R recipe is visible above and will be exported below.\")\n", "else:\n", " print(\"This optional path requires an R runtime plus the selected package.\")\n", " print(\"Recommended classroom workflow: copy the displayed recipe into an R Colab/runtime,\")\n", " print(\"or run the exported idiom_recipes.R file in RStudio. Install only the package named above.\")\n", " print(\"Selected package:\", selected.r_tool, \"·\", selected.r_url)\n" ] }, { "cell_type": "markdown", "id": "gallery-018", "metadata": {}, "source": [ "# 3 · Select and justify the algorithm/tool\n", "\n", "### Three implementation layers\n", "\n", "| Transform | Compute / layout | Render / verify |\n", "|---|---|---|\n", "| Filter, join, derive, aggregate, pivot, normalize, bin, or model. Preserve identifiers, missingness, and provenance. | Sort, stack, fit, estimate density, partition a hierarchy, route flows, compute intersections, or project coordinates. | Encode marks/channels; add interaction if it serves the task; test scale, labels, color, accessibility, performance, failure behavior, and reproducibility. |\n", "\n", "The atlas is intentionally broad—36 Python and 36 R tools—but your project should use the smallest defensible stack.\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "gallery-019", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Showing 36 of 72 tools · 36 Python + 36 R in the complete atlas\n" ] }, { "data": { "text/markdown": [ "| Official package/gallery | Family | Best fit |\n", "|---|---|---|\n", "| [Matplotlib](https://matplotlib.org/stable/gallery/index.html) | grammar | Foundational static, animated, and publication figures. |\n", "| [Seaborn](https://seaborn.pydata.org/examples/index.html) | grammar | Statistical graphics with concise data-aware defaults. |\n", "| [Plotly](https://plotly.com/python/) | grammar | Interactive browser charts, maps, 3D, and dashboards. |\n", "| [Altair](https://altair-viz.github.io/gallery/index.html) | grammar | Declarative Vega-Lite grammar for composable charts. |\n", "| [Bokeh](https://docs.bokeh.org/en/latest/docs/gallery.html) | grammar | Interactive linked plots and browser applications. |\n", "| [HoloViews](https://holoviews.org/reference/index.html) | grammar | High-level declarative views across plotting backends. |\n", "| [hvPlot](https://hvplot.holoviz.org/reference/index.html) | grammar | Interactive plotting API for pandas, xarray, and more. |\n", "| [plotnine](https://plotnine.org/gallery.html) | grammar | Grammar-of-graphics implementation inspired by ggplot2. |\n", "| [Lets-Plot](https://lets-plot.org/python/pages/gallery.html) | grammar | Grammar-of-graphics plots for notebooks and web output. |\n", "| [Pygal](https://www.pygal.org/en/stable/documentation/types/index.html) | grammar | Lightweight SVG charts with browser-friendly output. |\n", "| [pyecharts](https://gallery.pyecharts.org/) | grammar | Python bindings for the Apache ECharts ecosystem. |\n", "| [Panel](https://panel.holoviz.org/gallery/index.html) | grammar | Compose plots, widgets, and data apps across libraries. |\n", "| [pandas plotting](https://pandas.pydata.org/docs/user_guide/visualization.html) | statistics | Quick plots directly from Series and DataFrames. |\n", "| [statsmodels graphics](https://www.statsmodels.org/stable/graphics.html) | statistics | Regression, diagnostic, time-series, and model plots. |\n", "| [scikit-learn Displays](https://scikit-learn.org/stable/visualizations.html) | statistics | Model evaluation displays with estimator integration. |\n", "| [Yellowbrick](https://www.scikit-yb.org/en/latest/gallery.html) | statistics | Visual diagnostics for machine-learning workflows. |\n", "| [ArviZ](https://python.arviz.org/en/stable/examples/index.html) | statistics | Exploratory analysis and diagnostics for Bayesian models. |\n", "| [corner.py](https://corner.readthedocs.io/en/latest/pages/quickstart/) | statistics | Multidimensional posterior and parameter distributions. |\n", "| [missingno](https://github.com/ResidentMario/missingno) | statistics | Missing-data matrices, bars, heatmaps, and dendrograms. |\n", "| [UpSetPlot](https://upsetplot.readthedocs.io/en/stable/auto_examples/index.html) | statistics | Scalable set-intersection visualization. |\n", "| [JoyPy](https://github.com/leotac/joypy) | statistics | Ridgeline distribution plots built on Matplotlib. |\n", "| [PtitPrince](https://github.com/pog87/PtitPrince) | statistics | Raincloud plots combining density, box, and points. |\n", "| [SciencePlots](https://github.com/garrettj403/SciencePlots) | statistics | Matplotlib styles for scientific publication contexts. |\n", "| [statannotations](https://github.com/trevismd/statannotations) | statistics | Statistical annotations for seaborn and Matplotlib plots. |\n", "| [GeoPandas](https://geopandas.org/en/stable/docs/user_guide/mapping.html) | specialized | Geospatial vector data analysis and mapping. |\n", "| [Cartopy](https://cartopy.readthedocs.io/stable/gallery/index.html) | specialized | Projected maps and geospatial data transformations. |\n", "| [Folium](https://python-visualization.github.io/folium/latest/getting_started.html) | specialized | Interactive Leaflet maps from Python. |\n", "| [pydeck](https://deckgl.readthedocs.io/en/latest/gallery/index.html) | specialized | Large-scale layered geospatial visualizations with deck.gl. |\n", "| [Datashader](https://datashader.org/user_guide/index.html) | specialized | Rasterize and aggregate massive point or line datasets. |\n", "| [NetworkX](https://networkx.org/documentation/stable/auto_examples/index.html) | specialized | Network analysis, layouts, and graph drawing. |\n", "| [PyVis](https://pyvis.readthedocs.io/en/latest/) | specialized | Interactive browser-based network visualization. |\n", "| [OSMnx](https://osmnx.readthedocs.io/en/stable/) | specialized | Download, analyze, and visualize street networks. |\n", "| [contextily](https://contextily.readthedocs.io/en/latest/intro_guide.html) | specialized | Add web basemaps to geospatial plots. |\n", "| [GeoViews](https://geoviews.org/gallery/index.html) | specialized | Geographic visualization built on HoloViews. |\n", "| [Graphviz](https://graphviz.readthedocs.io/en/stable/examples.html) | specialized | Graph and hierarchy layout through the Graphviz engine. |\n", "| [pyCirclize](https://moshi4.github.io/pyCirclize/) | specialized | Circular genome, chord, and sector visualizations. |" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# @title 3.1 · Search the 72-package atlas\n", "LANGUAGE = \"Python\" # @param [\"Python\", \"R\"]\n", "CATEGORY = \"All\" # @param [\"All\", \"grammar\", \"statistics\", \"specialized\"]\n", "SEARCH = \"\" # @param {type:\"string\"}\n", "\n", "selected_tools = tool_catalog.query(\"language == @LANGUAGE\").copy()\n", "if CATEGORY != \"All\":\n", " selected_tools = selected_tools.query(\"category == @CATEGORY\")\n", "if SEARCH.strip():\n", " q = SEARCH.lower().strip()\n", " selected_tools = selected_tools[selected_tools.apply(lambda row: q in \" \".join(map(str,row)).lower(), axis=1)]\n", "\n", "rows = []\n", "for item in selected_tools.itertuples():\n", " rows.append(f\"| [{item.name}]({item.official_url}) | {item.category} | {item.use} |\")\n", "display(Markdown(\"| Official package/gallery | Family | Best fit |\\n|---|---|---|\\n\" + \"\\n\".join(rows)))\n", "print(f\"Showing {len(selected_tools)} of 72 tools · 36 Python + 36 R in the complete atlas\")\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "gallery-020", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Export complete: /workspace/scratch/81e4611d448d/INFOSCI301_Idiom_Algorithm_Gallery_Outputs.zip\n", "Verified: 38 idioms · 38 Python recipes · 38 R recipes · 72 official tool references\n" ] } ], "source": [ "# @title 3.2 · Export every recipe, decision, reference, and gallery\n", "matrix_columns = [\n", " \"leaf\",\"root\",\"family\",\"idiom\",\"selection_cue\",\"data_requirement\",\"action_target\",\n", " \"marks_channels\",\"algorithm\",\"caution\",\"python_tool\",\"python_url\",\"r_tool\",\"r_url\",\n", "]\n", "idiom_catalog[matrix_columns].to_csv(OUTPUT_DIR / \"idiom_decision_matrix_38.csv\", index=False)\n", "tool_catalog.to_csv(OUTPUT_DIR / \"python_r_tool_atlas_72.csv\", index=False)\n", "\n", "py_parts = [\n", " '\"\"\"INFOSCI 301 idiom recipes. Replace placeholder data and verify every transformation.\"\"\"',\n", " \"\",\n", "]\n", "r_parts = [\n", " \"# INFOSCI 301 idiom recipes\",\n", " \"# Replace placeholder data and verify every transformation.\",\n", " \"\",\n", "]\n", "for row in idiom_catalog.itertuples():\n", " py_parts += [f\"# {row.leaf:02d} · {row.root} / {row.family} / {row.idiom}\", PYTHON_RECIPES[row.idiom], \"\"]\n", " r_parts += [f\"# {row.leaf:02d} · {row.root} / {row.family} / {row.idiom}\", R_RECIPES[row.idiom], \"\"]\n", "(OUTPUT_DIR / \"idiom_recipes.py\").write_text(\"\\n\".join(py_parts), encoding=\"utf-8\")\n", "(OUTPUT_DIR / \"idiom_recipes.R\").write_text(\"\\n\".join(r_parts), encoding=\"utf-8\")\n", "\n", "readme = \"\"\"# Idiom + Algorithm Gallery outputs\n", "\n", "Synthetic teaching figures demonstrate visual grammar only. They are not domain evidence.\n", "\n", "1. `idiom_decision_matrix_38.csv` connects each decision-tree leaf to data, task, marks, algorithm, and paired tools.\n", "2. `idiom_recipes.py` and `idiom_recipes.R` contain all paired implementation patterns.\n", "3. Gallery PNG/SVG/PDF files provide the Compare, Understand Variation, and Reveal Structure overview sheets.\n", "\"\"\"\n", "(OUTPUT_DIR / \"README.md\").write_text(textwrap.dedent(readme), encoding=\"utf-8\")\n", "\n", "expected = {\n", " \"idiom_compare_gallery.png\", \"idiom_variation_gallery.png\", \"idiom_structure_gallery.png\",\n", " \"idiom_decision_matrix_38.csv\", \"python_r_tool_atlas_72.csv\", \"idiom_recipes.py\", \"idiom_recipes.R\", \"README.md\",\n", "}\n", "present = {path.name for path in OUTPUT_DIR.iterdir()}\n", "assert expected <= present, sorted(expected - present)\n", "bundle = shutil.make_archive(\"INFOSCI301_Idiom_Algorithm_Gallery_Outputs\", \"zip\", OUTPUT_DIR)\n", "print(\"Export complete:\", Path(bundle).resolve())\n", "print(\"Verified: 38 idioms · 38 Python recipes · 38 R recipes · 72 official tool references\")\n" ] }, { "cell_type": "markdown", "id": "gallery-021", "metadata": {}, "source": [ "---\n", "\n", "## Finish with three judgments\n", "\n", "1. **Fit:** Does the selected leaf match the dataset/attribute types and the action + target?\n", "2. **Trace:** Can another person reproduce every transform, computation/layout step, and visual encoding?\n", "3. **Consequence:** What becomes easier to see—and what may still be hidden, distorted, or unsupported?\n", "\n", "### References\n", "\n", "- Tamara Munzner, *Visualization Analysis and Design* (2014): [book companion](https://www.cs.ubc.ca/~tmm/vadbook/) and [official figure collection](https://www.cs.ubc.ca/~tmm/vadbook/figures.html).\n", "- The 72-tool atlas links directly to each official Python or R project/gallery. Use the selected package’s current documentation as the implementation authority.\n", "- Gallery values are synthetic and deterministic. Cite the actual dataset, revision, field definitions, transformation code, and limitations in student projects.\n" ] } ], "metadata": { "accelerator": "CPU", "colab": { "name": "INFOSCI301_Week2_Idiom_Algorithm_Gallery_Python_R.ipynb", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.x" } }, "nbformat": 4, "nbformat_minor": 5 }