{ "cells": [ { "cell_type": "markdown", "id": "b1ad79e4-bcc7-47df-9c42-97476f85ccbd", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "source": [ "### Fitting latent groups in data - Exercises & Answers" ] }, { "cell_type": "markdown", "id": "dd034d6e-23ca-4286-947d-43489e14afbf", "metadata": {}, "source": [ "### 1. Applied clustering - types of fertility issues\n", "With a clustering algorithm under our belts, lets apply it to a real dataset. We'll investigate whether sub-groups exist in a dataset of females having difficulty conceiving. This might offer some insight into different treatment approaches. \n", "\n", "First, import everything we need. This includes the silhoette score and clustering functionality.\n", "\n", "Also import `matplotlib.pyplot as plt` and the `zscore` function from `scipy.stats`. " ] }, { "cell_type": "code", "execution_count": 1, "id": "a949d830-ede0-46e9-a6e0-ce520cf3c828", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# Your answer here\n", "# Import what we need\n", "import pandas as pd # dataframes\n", "import seaborn as sns # plots\n", "import numpy as np # numpy for some functions\n", "import scipy.cluster.hierarchy as shc # clustering\n", "import matplotlib.pyplot as plt # extra plotting functions\n", "from scipy.stats import zscore # Z-score\n", "from sklearn.metrics import silhouette_score\n", "\n", "sns.set_theme(style='whitegrid')" ] }, { "cell_type": "markdown", "id": "1feefb12-77e1-47fb-a655-983d83247454", "metadata": {}, "source": [ "Next, read in the data from the following link into a dataframe called `fertility`, showing the top 10 rows: https://vincentarelbundock.github.io/Rdatasets/csv/Stat2Data/Fertility.csv\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "50efc569-61d0-4752-b21a-efdcfd866663", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " rownames Age LowAFC MeanAFC FSH E2 MaxE2 MaxDailyGn TotalGn \\\n", "0 1 40 40 51.5 5.3 45 1427 300.0 2700.0 \n", "1 2 37 41 41.0 7.1 53 802 225.0 1800.0 \n", "2 3 40 38 41.0 4.9 40 4533 450.0 4850.0 \n", "3 4 40 36 37.5 3.9 26 1804 300.0 2700.0 \n", "4 5 30 36 36.0 4.0 49 2526 150.0 1500.0 \n", "5 6 29 35 35.0 3.9 67 3812 150.0 975.0 \n", "6 7 31 24 35.0 3.8 49 1087 262.5 2512.5 \n", "7 8 33 28 34.0 4.3 20 1615 375.0 3075.0 \n", "8 9 36 30 33.0 4.9 60 1879 300.0 4800.0 \n", "9 10 35 32 32.0 3.7 36 2009 225.0 1275.0 \n", "\n", " Oocytes Embryos \n", "0 25 13 \n", "1 7 6 \n", "2 27 15 \n", "3 9 4 \n", "4 19 12 \n", "5 19 16 \n", "6 13 9 \n", "7 15 9 \n", "8 23 10 \n", "9 26 8 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Your answer here\n", "fertility = pd.read_csv('https://vincentarelbundock.github.io/Rdatasets/csv/Stat2Data/Fertility.csv')\n", "fertility.head(10)" ] }, { "cell_type": "markdown", "id": "0c4b2fb0-14d0-4bb5-b0f4-97acb4b5d37f", "metadata": {}, "source": [ "What do these variables mean? The dataset description gives us the following:\n", "\n", "- **Age**\tAge (in years)\n", "- **LowAFC**\tSmallest antral follicle count\n", "- **MeanAFC**\tAverage antral follicle count\n", "- **FSH**\tMaximum follicle stimulating hormone level\n", "- **E2**\tEstradiol level\n", "- **MaxE2**\tMaximum estradiol level\n", "- **MaxDailyGn**\tMaximum daily gonadotropin level\n", "- **TotalGn**\tTotal gonadotropin level\n", "- **Oocytes**\tNumber of egg cells\n", "- **Embryos**\tNumber of embryos\n", "\n", "These variables all contribute to the probability of conception in one way or another, and they are all on different scales. We will standardise each variable with Z-scores. Apply the z-score function to all variables EXCEPT the `rownames` column, which you should simply drop, and display the data to check it has all worked." ] }, { "cell_type": "code", "execution_count": 3, "id": "c8773b4c-4b26-4e94-985f-9ed505b50b15", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
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AgeLowAFCMeanAFCFSHE2MaxE2MaxDailyGnTotalGnOocytesEmbryos
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333 rows × 10 columns

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" ], "text/plain": [ " Age LowAFC MeanAFC FSH E2 MaxE2 MaxDailyGn \\\n", "0 0.994801 4.010100 5.119848 -0.327405 0.246933 -0.152764 -0.093209 \n", "1 0.355286 4.154807 3.704057 0.600475 0.773195 -0.954406 -0.742011 \n", "2 0.994801 3.720684 3.704057 -0.533600 -0.081982 3.831078 1.204394 \n", "3 0.994801 3.431269 3.232127 -1.049089 -1.002942 0.330787 -0.093209 \n", "4 -1.136915 3.431269 3.029871 -0.997540 0.510064 1.256844 -1.390813 \n", ".. ... ... ... ... ... ... ... \n", "328 1.421144 -1.488795 -1.554597 5.188323 -1.858119 -1.060864 1.204394 \n", "329 2.273831 -1.488795 -1.554597 1.786098 1.562590 -0.506769 1.204394 \n", "330 0.994801 -1.778211 -1.824271 -0.533600 -0.410896 2.601038 0.555593 \n", "331 0.142114 -1.778211 -1.824271 0.084986 0.378498 -0.441355 1.204394 \n", "332 -0.071057 1.405360 1.142149 -0.945991 -0.279330 0.153784 -1.390813 \n", "\n", " TotalGn Oocytes Embryos \n", "0 -0.095492 2.229576 1.539390 \n", "1 -0.752558 -0.819495 -0.178330 \n", "2 1.474166 2.568361 2.030167 \n", "3 -0.095492 -0.480709 -0.669108 \n", "4 -0.971579 1.213219 1.294001 \n", ".. ... ... ... \n", "328 1.875706 -1.158281 -0.669108 \n", "329 0.890107 -0.819495 -1.159885 \n", "330 0.671085 0.535648 -0.423719 \n", "331 1.766195 0.196862 0.067058 \n", "332 -1.300112 -0.311317 -0.669108 \n", "\n", "[333 rows x 10 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Your answer here\n", "# Drop the column\n", "fertility = fertility.drop(columns='rownames')\n", "\n", "# Apply the z-score function\n", "fertility = fertility.apply(zscore)\n", "\n", "# Show\n", "display(fertility)" ] }, { "cell_type": "markdown", "id": "ca52764d-f1f9-43da-9b88-0d84dfa3e35f", "metadata": {}, "source": [ "Next, compute the linkage array, which builds the tree. Then, visualise it with a dendrogram." ] }, { "cell_type": "code", "execution_count": 4, "id": "ec1ec2ce-d87e-429f-a255-613a148e0ba9", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Your answer here\n", "# Compute tree\n", "tree = shc.linkage(fertility, method='ward')\n", "\n", "# Visualise\n", "dendrogram = shc.dendrogram(tree, no_labels=True)" ] }, { "cell_type": "markdown", "id": "3b162064-d151-46fb-a63a-6cec2f9cd191", "metadata": {}, "source": [ "This suggests an the possibility of several cluster arrangements - certainly two (e.g., cut at 25), three, four, or even five. \n", "\n", "Evaluate the silhouette score of the two and three cluster solution by cutting the tree at an appropriate height, recovering the labels, and using the silhouette score function. " ] }, { "cell_type": "code", "execution_count": 5, "id": "34d676ca-3324-4731-af44-06c5721629bb", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "0.2146644461574779" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.18422376524085038" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Your answer here\n", "# Get two and three cuts\n", "cluster_2 = shc.cut_tree(tree, height=25)\n", "cluster_3 = shc.cut_tree(tree, height=23)\n", "\n", "# Evaluate \n", "silhouette_2 = silhouette_score(fertility, cluster_2.flatten())\n", "silhouette_3 = silhouette_score(fertility, cluster_3.flatten())\n", "\n", "# Print\n", "display(silhouette_2, silhouette_3)" ] }, { "cell_type": "markdown", "id": "0934dddc-0311-4d2d-aeba-d645f4520459", "metadata": {}, "source": [ "We should see that the clustering suggests two clusters, but that they are not particularly strong. Put those cluster labels into the dataset, call the column 'cluster'." ] }, { "cell_type": "code", "execution_count": 6, "id": "3694e903-22cc-4fdc-b884-b66e45daf892", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# Your answer here\n", "fertility['cluster'] = cluster_2" ] }, { "cell_type": "markdown", "id": "60607018-c4d2-419c-aade-7acfc9203f33", "metadata": {}, "source": [ "We will now visualise the clusters to see what the pattern looks like. Reset the dataframe index, then melt the data appropriately so it can be plotted with a barplot. Use `catplot` to separate the plots by cluster." ] }, { "cell_type": "code", "execution_count": 7, "id": "04a78fb5-27e9-4706-a6ae-7c583cd5b988", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Your answer here\n", "# Melt the data\n", "plot_this = (fertility\n", " .reset_index()\n", " .melt(id_vars=['index', 'cluster'],\n", " var_name='measure', \n", " value_name='score')\n", " )\n", "\n", "# Plot\n", "sns.catplot(data=plot_this, \n", " aspect=1.1, height=8,\n", " x='measure', y='score',\n", " hue='measure',\n", " col='cluster', kind='bar')" ] }, { "cell_type": "markdown", "id": "4eba12ab-5c79-4c32-8301-e2b1b361057b", "metadata": {}, "source": [ "There is a clear pattern in this. \n", "- Cluster zero contains females who are younger than average, with higher antrical follicle counts but low follice stimulating hormone (FSH) and circulating estradiol. They have low gonadotropin levels and high numbers of oocytes and embryos.\n", "- However, cluster one has females who are essentially the opposite - older, with lower number of embryos and oocytes, and higher levels of gonadotropin and follicular stimulating hormone and estradiol.\n", "\n", "These findings would need further replication and investigation but point towards (weaker) subgroups in the data." ] }, { "cell_type": "markdown", "id": "ff17ec73-fac7-43e2-b7bb-8f7de9f3e8f5", "metadata": {}, "source": [ "### 2. Applied clustering - grouping penguins\n", "We return to another dataset we have seen earlier in the course - the `penguins` dataset. This dataset contains four measurements of three types of penguins, found across three different islands. We will see here if the hierarchical clustering approach can divide the dataset into clusters that resemble the different species. First, we can load the dataset directly from `seaborn`, using its `load_dataset` function - refer to chapter one for a refresher! Load it into a dataframe called `penguins`. Show the top 5 rows." ] }, { "cell_type": "code", "execution_count": 8, "id": "ed580f3c-6139-4751-8907-be9943e484f2", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
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0AdelieTorgersen39.118.7181.03750.0Male
1AdelieTorgersen39.517.4186.03800.0Female
2AdelieTorgersen40.318.0195.03250.0Female
3AdelieTorgersenNaNNaNNaNNaNNaN
4AdelieTorgersen36.719.3193.03450.0Female
\n", "
" ], "text/plain": [ " species island bill_length_mm bill_depth_mm flipper_length_mm \\\n", "0 Adelie Torgersen 39.1 18.7 181.0 \n", "1 Adelie Torgersen 39.5 17.4 186.0 \n", "2 Adelie Torgersen 40.3 18.0 195.0 \n", "3 Adelie Torgersen NaN NaN NaN \n", "4 Adelie Torgersen 36.7 19.3 193.0 \n", "\n", " body_mass_g sex \n", "0 3750.0 Male \n", "1 3800.0 Female \n", "2 3250.0 Female \n", "3 NaN NaN \n", "4 3450.0 Female " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Your answer here\n", "penguins = sns.load_dataset('penguins')\n", "penguins.head()" ] }, { "cell_type": "markdown", "id": "2faa16b1-f6ec-4dd3-85f3-e9e325ba481c", "metadata": {}, "source": [ "Drop any rows that have NaN in them." ] }, { "cell_type": "code", "execution_count": 9, "id": "0f6683e6-2c8f-4459-9565-b6031e8629d5", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# Your answer here\n", "penguins = penguins.dropna(how='any')" ] }, { "cell_type": "markdown", "id": "f9fdf399-4662-44ed-ad5e-a9a6c546a59d", "metadata": {}, "source": [ "Replace the `bill_length_mm`, `bill_depth_mm`, `flipper_length_mm`, and `body_mass_g` variables with Z-scored versions." ] }, { "cell_type": "code", "execution_count": 10, "id": "b39d56aa-8d37-454f-bbf0-b9b54a7e0e24", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# Your answer here\n", "variables = ['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']\n", "penguins[variables] = penguins[variables].apply(zscore)" ] }, { "cell_type": "markdown", "id": "54dcb40e-92e3-402c-a604-e68b26851dfb", "metadata": {}, "source": [ "Build the tree on those z-scored variables, and show the dendrogram." ] }, { "cell_type": "code", "execution_count": 11, "id": "bd33fb8f-286b-46f6-a76b-192ea1fd4289", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Your answer here\n", "tree = shc.linkage(penguins[variables], method='ward')\n", "\n", "# Show dendrogram\n", "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n", "dendrogram = shc.dendrogram(tree, no_labels=True, ax=ax)" ] }, { "cell_type": "markdown", "id": "5940186e-099e-4634-8a80-9dc69a614272", "metadata": {}, "source": [ "That is an interesting and very clean clustering solution. A cut at 10 would produce 5 clusters, while 15 would offer 3 clusters. More dramatically a cut at 20 would produce 2. Try those three solutions and assess their silhouette coefficient. " ] }, { "cell_type": "code", "execution_count": 12, "id": "0c77ccef-5df1-4463-aa5d-0f308ba0c89d", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "0.36144263075820016" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.4520982949638114" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.5308173701641075" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Your answer here\n", "# Make cuts\n", "cluster_5 = shc.cut_tree(tree, height=10)\n", "cluster_3 = shc.cut_tree(tree, height=15)\n", "cluster_2 = shc.cut_tree(tree, height=20)\n", "\n", "# Silhouette\n", "silhouette_5 = silhouette_score(penguins[variables], cluster_5.flatten())\n", "silhouette_3 = silhouette_score(penguins[variables], cluster_3.flatten())\n", "silhouette_2 = silhouette_score(penguins[variables], cluster_2.flatten())\n", "\n", "# Print\n", "display(silhouette_5, silhouette_3, silhouette_2)" ] }, { "cell_type": "markdown", "id": "97e9f03e-48c5-41c4-9661-6531a4958a04", "metadata": {}, "source": [ "These are generally stronger clusters compared to the previous example, as can be seen both from the dendrogram and the silhouette scores. It seems like 2 clusters offer a notably stronger solution, so we will take that, and add it to the dataset." ] }, { "cell_type": "code", "execution_count": 13, "id": "e9754876-ca93-49af-a09e-c85f0dd6f0d7", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# Your answer here\n", "penguins['cluster'] = cluster_2" ] }, { "cell_type": "markdown", "id": "283aac96-0291-4236-8a0a-0f5fc5187ff6", "metadata": {}, "source": [ "Use `pandas` compute the number of actual penguin species (in the `species` column) that are in the different clusters. " ] }, { "cell_type": "code", "execution_count": 14, "id": "0692c85e-3a8e-42f5-a12b-74960fe4caf0", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
cluster01
species
Adelie1460
Chinstrap680
Gentoo0119
\n", "
" ], "text/plain": [ "cluster 0 1\n", "species \n", "Adelie 146 0\n", "Chinstrap 68 0\n", "Gentoo 0 119" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Your answer here\n", "pd.crosstab(penguins['species'], penguins['cluster'])" ] }, { "cell_type": "markdown", "id": "674a9b64-4fed-4bda-8fb8-3dee57c1052d", "metadata": {}, "source": [ "This is about as clean a clustering solution as we'd hope to see in real data. The Adelie and Chinstrap penguins fall in one cluster, while the Gentoo are in another, with perfect separation. To assess this visually, what we will do next is melt the data so we can plot it, and then see what the clusters *and actual species* labels look like. First, reshape the data appropriately, keeping the clusters and species in the right spot and the measures as the variable." ] }, { "cell_type": "code", "execution_count": 15, "id": "711ba7ef-0251-41ee-a694-b7ab4dffa252", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# Your answer here\n", "plot_this = penguins.melt(id_vars=['species', 'island', 'sex', 'cluster'],\n", " value_vars=variables,\n", " var_name='measure', value_name='score')" ] }, { "cell_type": "markdown", "id": "1ca6626f-499a-4324-9dab-1a5e051c5569", "metadata": {}, "source": [ "Now create a bar plot that has on the x-axis the measure, with separate colours for each *species*." ] }, { "cell_type": "code", "execution_count": 16, "id": "5a03005d-c8a0-45ac-8e94-e742ac2ee56a", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Your answer here\n", "sns.barplot(data=plot_this, x='measure', y='score', hue='species')" ] }, { "cell_type": "markdown", "id": "d6aef322-e18d-44ca-9fda-286d510bc0a1", "metadata": {}, "source": [ "This shows the actual biological pattern. Adelie's have shorter bills than Chinstrap and Gentoo penguins, but on the rest of the variables, the Gentoo's are very dissimilar. Now create a bar plot that illustrates the *clusters* in the same way." ] }, { "cell_type": "code", "execution_count": 17, "id": "a0e27da7-a51e-4d31-a74a-a75d7b9b1c50", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Your answer here\n", "sns.barplot(data=plot_this, x='measure', y='score', hue='cluster')" ] }, { "cell_type": "markdown", "id": "d91e52bc-5703-4bbb-8d6f-4b3c32bcb3e7", "metadata": {}, "source": [ "From this its hopefully clear that the clustering algorithm has noticed the Gentoo penguins are so different on the other variables and has considered them to be a 'different sort' of penguin - the pattern closely resembles the actual biological speciation! " ] }, { "cell_type": "markdown", "id": "635c11d6-7930-4fb6-a349-3cbc8d8b8a5f", "metadata": {}, "source": [ "### 3. Applied clustering - combining EFA and clustering\n", "We turn now to a more advanced task, but one that in practice is often used and is very powerful. So far, we have used clustering on datasets with a relatively small number of variables (i.e., columns) of no more than 10 variables. One interesting mathematical issue is that as the number of variables increases, clusters become harder and harder to find due to the *curse of dimensionality* - each observation is almost equally far away from other points when there are many, many variables. \n", "\n", "Fortunately we can solve this problem easily. We already know how. EFA allows us to reduce a large number of variables (e.g. scores on individual personality questions etc) to a smaller set, keeping a portion of the variance in the dataset. Carrying out clustering on the latent variables themselves and not the observed variables can offer some really interesting insights into a dataset. Here, we will use this approach to figure out if there are a set of 'personality profiles' in a Big 5 dataset we have seen before!\n", "\n", "We will read in some raw Big 5 questionnaire data from this link: https://vincentarelbundock.github.io/Rdatasets/csv/psych/bfi.csv\n", "\n", "We've seen this before - read it in, and drop any missing data." ] }, { "cell_type": "code", "execution_count": 18, "id": "cb69a6d3-e6ae-49cd-844d-3ad7491fbd73", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
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rownamesA1A2A3A4A5C1C2C3C4...N4N5O1O2O3O4O5gendereducationage
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5 rows × 29 columns

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" ], "text/plain": [ " rownames A1 A2 A3 A4 A5 C1 C2 C3 C4 ... N4 N5 O1 \\\n", "5 61623 6.0 6.0 5.0 6.0 5.0 6.0 6.0 6.0 1.0 ... 2.0 3.0 4.0 \n", "7 61629 4.0 3.0 1.0 5.0 1.0 3.0 2.0 4.0 2.0 ... 6.0 4.0 3.0 \n", "10 61634 4.0 4.0 5.0 6.0 5.0 4.0 3.0 5.0 3.0 ... 2.0 3.0 5.0 \n", "14 61640 4.0 5.0 2.0 2.0 1.0 5.0 5.0 5.0 2.0 ... 2.0 3.0 5.0 \n", "22 61661 1.0 5.0 6.0 5.0 6.0 4.0 3.0 2.0 4.0 ... 2.0 2.0 6.0 \n", "\n", " O2 O3 O4 O5 gender education age \n", "5 3 5.0 6.0 1.0 2 3.0 21 \n", "7 2 4.0 5.0 3.0 1 2.0 19 \n", "10 3 5.0 6.0 3.0 1 1.0 21 \n", "14 2 5.0 5.0 5.0 1 1.0 17 \n", "22 1 5.0 5.0 2.0 1 5.0 68 \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Your answer here\n", "bfi = pd.read_csv('https://vincentarelbundock.github.io/Rdatasets/csv/psych/bfi.csv').dropna(how='any')\n", "bfi.head()" ] }, { "cell_type": "markdown", "id": "604e8b5b-d367-4d3e-9313-a6a56b13e62e", "metadata": {}, "source": [ "Now select only the questions that are relevant to the questionnaire - e.g. columns like 'A1', 'N5', etc. There should be 25 of those." ] }, { "cell_type": "code", "execution_count": 19, "id": "48d770a6-db38-43c0-9d0e-ae4ca7141363", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# Your answer here\n", "bfi = bfi.filter(regex='[A-Z]\\d')" ] }, { "cell_type": "markdown", "id": "ae78bb80-daf9-4100-917e-a5058a0896a7", "metadata": {}, "source": [ "We *could* cluster this, but it would likely be suboptimal. So first, we will conduct an EFA to reduce the 25 variables to 5 latent factors. Do this below, importing what you need to conduct it. Check the amount of variance retained also." ] }, { "cell_type": "code", "execution_count": 20, "id": "1beeef25-6cda-4f3b-984b-4764a586a563", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "(array([2.71289544, 2.5194412 , 2.02988607, 1.57809897, 1.48183355]),\n", " array([0.10851582, 0.10077765, 0.08119544, 0.06312396, 0.05927334]),\n", " array([0.10851582, 0.20929347, 0.29048891, 0.35361287, 0.41288621]))" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Your answer here\n", "# Import factor analyzer\n", "from factor_analyzer import FactorAnalyzer\n", "\n", "# Fit the model\n", "efa = FactorAnalyzer(n_factors=5).fit(bfi)\n", "\n", "# Get variances\n", "efa.get_factor_variance()" ] }, { "cell_type": "markdown", "id": "9ec90566-0bd1-49c3-81a7-f97c36522b39", "metadata": {}, "source": [ "Check the loadings matrix and plot them, to confirm this is a good solution." ] }, { "cell_type": "code", "execution_count": 21, "id": "bd9aea6d-9ffc-4f98-a887-3e504d4a7412", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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65Z1UWEBSYQERgcFsuRyrExOfn0dacXFdmoOFJX6OTiy6wSZwQuPJzS3Rm5xsYWnGI4/3p0fvAKJqegMcHK3p1MWblSsML6l66MAlZj29TCctqI0br7w+kv/7fAvnorXzU7p292Xq4/2JiU4lPU3buJRIIHRQG87HpFFZKfZbaUzZxaVkF+v24FjKzZgxsDf9A33ZdykRACdrK3r6erJ4j/G6eeByEqM6tSGghaJug7eAFgr8WyhYtEe7R0tCdj4Pdm+Pg5WFzn4OXX1aUVyh1GtICLeuOZdVDQ8Pr/f4zp36K6YBdXMP3N11J9e7urqSkaH//dHCwoKPP/6YDz74gB49eiCRSGjRogW//PILUmmDpyk3SIMaDFVVVezYsYPIyEiioqIwNzdnwIABRERE8MYbb/DEE0+IxsI1vjl9iF+HT2bBwDH8fimabq4ePNuhF58e24NSXYWtuZwgR2eSigrIUzZsw7UKdRWLzh7l5S59ySkvY09aPG2dXHm5az92psRxIMP4hBihcX1z8jC/jprEgiFj+P1iNN1atuLZzr349Mg15e1UU94N3GDPQibjs7ARlFaq+PbkYQKdnHWOZ5QWi96GZvLtscP8Mu4hFowYzW/nz9LdrRXPduvJZwf21pV3oMKZ5MKr5f3N0cN8MXQE+eXl/J0QxxC/AEYHteaFrRt1rt3G2QWASwZ6KoTmE306hVMnEnnzvbEsXbiLoqJyHn0qlJKSCjZtuDoM19vXBXNzGXGXMikuKqe4SLe+W1prF6dISc4lMV67q+/G9ScYNbYrH342iZ+W7UVZUcmYB3vg69eC11/6peluUqhzPDGNI/Ep/PehCL7Yvo+CsnJmDe5DUYWS1UevLnUd0EKB3ExWt0Pz1uhYnh3Qi8WPj+erHfsBeG1Yf2Izc9h+Vvuw4KeDx3mgSxuWPTmRJXuOUqxUMrRdEKM6teGzLXtEz+J9rrxc+55x/VwFCwsLCgv1hyNXV1dz8eJFunbtyjPPPINarebrr79m1qxZrFq1CltbW5PlrUENhoEDB1JSUkJISAjz5s1jyJAhdZl4/fXXTZaZe8WhjGRm7lrPK137sTh8PJllJXwS9Q9Lz2mfQHdwbklkxMPM2beFPy43fAfX/zu5n5zyUqa16cLjbbuSr6xg5cVTfH3yxpsGCY3nUHoyM//awCvd+7F42DgyS0v45PA/LI3WPoHu4NKSyAemMOefLfwRe65B1+zW0oOWNto69suoSXrH/+/4Af7v+EHT3YTQYIdSU5i55U9e7d2XxaPGkllSwrwDe1h6UrtfQntXVyIfnMycv7ax5oK2vNdcOIdcJmN6tx5MateB5KJCXtuxhc2XdffvcLHWjpsurGectNA83v/XGma8MITpswYjlUg4F53KR++u1Rky9NLsEbR0c+DRhxY0+Lp5uSW8+vwKnnpuEC+8Mgwrawtiz6cz95WVxJwVG/Y1l5dXbmTuyAHMGRGKVCLhZFI6r63eTFHF1br57pjBeDjaM/RLbU9SpVrNMz+u4a2RA3l/7BCq1BoOXE7i0y176pZoTS8oZuqS1bw6tB/vjxuCVCIhLjuPl1Zu5O+YywbzItx9jPUg3IilpXZVPZVKVff/oJ0GYGVlpXf+5s2bWblyJbt37677Xr5o0SIGDRrEmjVrePzxx28pH4ZIqhuwjEvnzp1xdnYmLCyMfv36ERYWhkXN8oLt27dnw4YNBAbeePOahvJd/l+TXUu4C1RKbnyOcM+QKE3bTSrc2QJXiyEW95O0gfqThYV7V8xHrzZ3FozKTW++kS/OrW5tSewzZ87w0EMP8ddff+Ht7V2X/vDDD9OmTRv+/e9/65z//vvvc+7cOX777Ted9AkTJtCpUye9829Hgz65Dxw4wIwZMzh//jwvvvgiffr0Yc6cOezcubNuWUlBEARBEARBEG5NmzZtsLW15ciRI3VpRUVFxMTEGNw02d3dnaSkJJTX9EqXl5eTmpqKj4+PSfPWoAaDra0tkyZNYvXq1WzevJnJkydz+PBhZs2ahVqt5scffyQxMdGkGRMEQRAEQRCEW6Ghutl+bpVcLmfatGl88cUX7Ny5kwsXLvDqq6/i5ubG0KFDUavVZGdnU1Gh7bkdN24cAK+88goXLlyoO18ul/Pggw+a4p+xzk2PDQgICGDu3Lns2bOHBQsWEB4ezvr16xk5ciTPPPOMSTMnCIIgCIIgCPeLl156iYkTJ/LOO+/w8MMPI5PJ+OGHH5DL5WRkZNC/f3+2bNGulunq6srKlSuprq7m8ccf58knn8Tc3JxVq1Zhb29v0nw1aA7DjeTl5bFhwwbWrl3Lxo0bbxxwA2IOw31GzGG4r4g5DPcXMYfh/iLmMNxf7uQ5DFfSWjXb73bz0N9o925nkk9uhULBk08+aZLGgiAIgiAIgiAIdw7xqE8QBEEQBEEQBKNueqfnpiAtvCOzJTQSta3YyfR+Igag3V/GLr219ciFu9P8tQ80dxYEAQCxBZ5piR4GQRAEQRAEQRCMEo/yBUEQBEEQhHuK+jaWNxX0iR4GQRAEQRAEQRCMEg0GQRAEQRAEQRCMavCQpPXr17NmzRoKCgoIDQ1l5syZ2NnZ1R3Py8vjoYceYudOMcENIMzHl9l9+xKocCavvJyV0af5Liqq3pixbdoys2dPvB0cSS8qYsnxY/x27qzOOUemP0cLGxu92F5LFpFTVmbSexAaboCnL3O6hxLk5ExueRm/XjjNwtNHbhwIdHBuyfqxjzDwt6WklhTpHJvSuhNPd+iOt50DaSXF/Hz+JMvPnWiMWxBuQpi3L7ND+hFUU79/PXua744frTdmXOu2zOzeC28HB9KLi1ly4hirY6IB8LCzZ/8T043G/h5zljd2bjfpPQgNl3SigsO/FJGXUoWVg5QOI2zoPsEWicTwFP681Ep+nZWll+7oYcajC1vWvT6/s5QT60sozKjCxklG60HW9Jxkh8xMLA3Q3Gzk5rwxOIwhwQHYyOWcTEvno7/+IS4nr964FjY2vDUkjH5+PpjLpOxPSObjHbvJLCk1eP6jPbrwZK9uDF64rDFu476mFiOSTKpBDYbff/+d999/n3HjxuHj48OqVav4+++/WbZsGZ6engBoNBrS0++9jSpuRTd3d5aMGcvm2It8efAgPVq1Ynbf/kiQsDDK8JeKiKAgvhw+gh9PnmBP0h6GBQTy6dBhKKuq2HDxAgAu1ta0sLHhoz3/cCIjQye+oEJsjtRcuru2YunQB9kUf4Evju+jZ0tPXu8RilQi4dtTh+uNbatowfLhEzCXyvSOTWvbhY/7DWXh6SPsT0ukSwt33uk9CGszcxY0sDEimF43t1Z8P3ocmy9d5MvDB+jZyoM5ffojlUhYcMxwuYwMDObLoREsP3WCvckJDPUP5NPwYVRUVbIh9gLZpaWM/22lXtxjnbowKqg1v8WcNXBVoSlknFey6eNcgvpbETLNnvQYFYd+KaJaAz0n2RmMyYmvBGD8Ry6Yya9++Zdd8/+nNpawb2khgX0t6feEAxVFGo6sKiI3sZJRbzs37k0JN/TV2JF0auXGf3fto1Sl4oX+IayYOpGRS36isEJpMEYmkbB0yjiszeX8e9tOzKRS5gzqz7KHJzD2h1+o0uiu2/NA+za8GR5GZnFJU9ySINyWBjUYVqxYwb///W8eeughAGbMmMGzzz7LY489xqpVq2jZsuUNrnB/eSmkD+ezs5m9fRsAe5MSMZfJmNGzFz+cOIFSXaUXM7tPP7ZeiuWjvXsA2JeUhIOlJS/36VvXYGjXwhWAbZcvkV5c3ER3I9zIy936EpObxat7tFu170lNxEwqZWan3nwffcxgeZtLpTzRvhuzu/enokr/OMDMTr3YGH+Bz6L2AnAgPRk/BwVPtO8mGgzN6OVefTifk8Vrf20FYG+ytrxndO/F0pPHDdfvkH5svRzLR/v/qYlJwtHSild692VD7AVUGjWnMnUfAnR0bcmooNZ8cWg/xzLSGv2+BMOORhbj4mfOsFcVAPh0s0Sjrub4mmK6jrXFzEK/NyA7oRJ7VxmeHS0MXlOjruZoZBFeXSyImHu1ceAaYM6vL2aRfKoC7y6WjXNDwg118XBnUJA/01evY09cIgBRKWnsev4ppnbvzHcHDD/4G9E2mLYtXRm5ZAWXc3IBOJ+VzebpjzGybTB/ntN+liusrXh1QD8md+1Ifll5k9zT/Ugsq2paDZrDkJqaSkhISN1rT09PfvrpJ6RSKc888wwlJaJ1XEsuk9Hbw5Ptly/ppG+9FIutXE5PDw+9GA97e/wVCrZfvqwX4+voiJ+jIwDtWrSgsKJCNBbuIHKpjBB3L7Ylxuqkb0nQlncvN0+DcYO8/Hm5a1++PXWYT6P2GDzn8e1/MO+o7rFKjRq5gd4IoWnIpTJ6e3qyLe66unq5prxbGajfdvb4OynYFndJL8bX0Qk/RyeDv+vDgeHE5efyw6njprsB4aaoK6tJPaskIMRKJz2wrxWVFdWkxxh+0pyTUImLn7nR65YVaFCWVOPXU7dRoPA2x9JeSmKU6DFuTqH+PpSqVOyPT6pLyy8rJyo5lQEBfvXGxefm1TUWAOJy8ojLyWNA4NW4GX170c/PmxfWbGT35fjGuQlBMLEGNRhcXV05f/68TlqLFi1YsmQJWVlZzJw5kwoxJAYAL3sHLMzMSMjP10lPLCgAwM9J/8tBoEL75Or6mKTrYtq2aEGhsoLvRj/A6ZmziH7+Bf4XMZIW1vpzGoSm4W3vgIXMjISi68q75rW/g+Evg6ezr9Bv9RK+PXVYr5u61uWCPNJq5jQ4WFgypXVHHgxsz4rzJ014B8LN8HKoKe8C3XHMiYUFwA3qd4GR9wQDDYYxwW3o3NKd9/fuRlMtBuI2l8IrVWiqtHMPruXgrn2dn264dzAnoRJVmYbf38hi4cQ0fng8gwM/FaKu0palha0EqQyKsnQ3rawo0aAs0VCUKTazbE4BzgpSCgpRX1f3kvIL8VMYfk+vjUvIzddLT8ovwPeauMiTZxi26Ed2XLysd65gOmokzfZzL2rQkKSHHnqId999l8TERMaOHVs3BMnf35+FCxcyffp0nnrqqUbN6N3C3lLbBV2iUumkl9a8tpPL9WMsjMVox8HayrXH27Vwxc3WjtVno1l24gQBCgWv9ulL5EOTGP3rz5QbGdoiNB57ufYJYfH1ZVepfW1rrl/eAJllDe+V69HSgzUPTAXgTPYVfjwnGgzNxXhdrSlvQ/W75m9EL6bSeMz0rj2ISk/jSFrq7WdauGXKUm1jXm6l+wWg9rWqTL8xV1agpqxAg0RSRd/HHbBrISPltJLja4spyVEzfLYCcwspQf2tOLO5BGdvM/xDrCgvULN3aSFSM6hUikZic7KztKBUqdJLL1WpsLEw/J5eG5eYX2Aw7tp6Hm+gUSEId7oGNRiefvppKisrWblyJZ06ddKZs9C9e3d+/PFHXn/99UbL5N1EWtOyrDayYYihp4XGYmoX4KiNeeOv7SirqojJzgYgKj2NS7m5/D55Cg+2a8evZ86Y5B6EhpPWrZLS8PK+WcnFBUzatAo3Gzte7daPjeMeZcyGn8kpF6tiNbXa8jZWrBoD6dKaP5Hq64Jq/3Ku/xvp7t6KDq4teXbT+tvIqWAKtUVjZDEkg+lyKylj33fGycMMuxbaj1iPDhbIzCUc/rWInpMqUXiZM3CmIzJzCTu/LWDnNwWYWUjo9qAtVcpqzA3MixAah4Rr38e1pBKJwXd0Cfr1+Po4Q4ESjH8nEIS7RYMaDBKJhJkzZzJz5kyDlcXc3Jxu3bqRkpJi8gzebYqU2jGttb0CtWxqni4Uq/THvF6N0X1yYW1urhNz8rqVkQCOZ6RTpKygrUuL28y5cCsKldqheLbm15V3Tc9CcaX+U6qblVVWSlaZdkm+k1kZ7Jn0DFNad7rhCkyC6Rmrq3X1W2mgfqsMvydYmxt+T4gICKagopzdSQmmybRwyyxstKN2r+9JUJXXDC2y0f9ib2YhMThh2beHBYd/1Q5XUniZI7eSEv6iE6HPOFCcrcbeVYa5pZTzf1/B3q3BK54Lt+mF0BBeDO2jk7b1fCx+Cmu9c63lcoN1vFZxhdJgD4S1XE6xkZWVhMZj6AGOcOtu+l2pdt1ppVLJ5s2biYyMJDo6GqlUyvDhw02ewbtNUmEBVRoNPjUTlWv51ry+lKu/hnN8zdwFH0fHut6D2tfamFzsLCwYERDIySsZXM7TvYa5VEZeuVhpoTkkF2vL29feUSfd1147XvVSfs4tXdfG3Jyh3oGczM4gqahA5/cVKitwtzG8nKPQuGrrt6+Do0567evLebl6MbX129fRkZicq2vz174nXB8z2M+fHfFxRue2CE3Hwc0MiRQKM3SHe9a+VnjpT2zOT60kNVpF6wFWyK2vThOsUmm/vVjaa9MSosqxsJXSqq0Fzt7atLICNcU5alz9jU+YFkxr9clodl/SbZwPaR1AqL9PTc/AVT5ODlyuZx+G+Lx82rV01Uv3cXLkTPoVE+VYEJrHTe/0HB8fzyeffEJYWBhvv/020dHRTJgwge3bt/P11183Rh7vKiq1mqNpqQwPDNRJjwgKprCigtNX9N80kgoLSCooICIoWC8mPi+P9OJiKtVqPhgczowevXTOGRoQgJW5OUdSxVjn5qBUqzl6JYURvrplN9IvmEJlBaeyb+1DQq2p5rPQEczopFvenVzccLK04nxetpFIoTHV1e+AIJ30iEBt/T6VaaR+FxYQERisFxOfn0faNaueOVhY4ufoxHGxjOodwUwuwaO9nLjD5Tq965cPlmNhI6FlkP7T5JI8Df8sKuDyQd2HOJf2l2NuJcE1UBtzdlspB5YX6pxzamMJUin49hRLqjaVrJJSzl7J1PnZH5+ErYUFof6+dec5WVvR09uTA9esnHS9A/FJBLgoCHBR1KXVvt6fYDxOaBxi0rNpNaiHoaqqih07dhAZGUlUVBTm5uYMGDCAiIgI3njjDZ544om6DdwEWHDkCD9PmMi3I0fze8xZurm3Ynr3Hny2fx9KdRW2cjmBCmeSCwvqega+PXqYz4eNoKC8nL/j4xni78/o4Na8sHkTABVVVSw+doyXQkLIKStjb1ICbVxa8HJIH3bFx3MgJbk5b/m+9s3Jw/w6chILB4/ht9hourdsxXOdevHp0T3a8jaXE+ToTFJxAXkVDesJqlBXsejMUV7q2of8inIOpCfh56DglW59OZebxe+x0Y18V4Ix3x47zC/jHmLBiNH8dv4s3d1a8Wy3nnx2YG9dedfV75ry/uboYb4YOoL88nL+TohjiF8Ao4Na88LWjTrXbuPsAsAlAz0VQvPoMcme9e/lsO2/ebQdYsOVCypOrCuh32P2mFlIUJVpyEupwsFNhpWDDI/2cjw6yNm3rJDKimqcPM1IPFbB6U2l9HvCHktb7XO6zqNt2fCfXPYuLcCvlxWpZ5Qc/6OE7hNscRBDkprVsZQ0Diel8MXYCD7ftY+C8nJeDO1DcYWSVSevzhUMcFEgl8k4n6l9gLP5fCwz+vVi6eTxfLF7PwBzBvUnNiuHbedjDf4uQbhbSKrrm8FTo3///pSUlBASEsKIESMYMmQItra2ALRv354NGzYQeN0T9dvh/39fmexazWVYQCCvhPTBz8mJzNISfj59mh9OaNdT7+3pyaqJk3h9xzbWxMTUxTzcsSPTu/XA3c6O5MJCvos6yvoLV5ezlQDTOnVmaqfO+Dg6kl9ezp8XL/B/hw4Z3CzqbqG2vfuXEBzuE8Sr3fvh76At7xXnT/J99DEAQty9WD1qCrP3bOGPS+f0YicGtefLASPpF7mY1JplVEFb3o+07cKjbbvga+9IgbKCbYmX+OLYPpPMjWgu0vKb7ti84wzzD+TV3n219bukhJ+jT7H0ZE399vAk8sHJzPlrG2suXC3vh9t3Ynq3HrSytSO5qJDvjh1h3UXd5apHBQbzbcQDhP+ynPh840Mf7iazh25u7izctrhD5RxZVUR+WhW2zjI6jrSh2zjtsMDUaCXr3slhyEuOtA3XLnGtLNVwdFUR8UcqKM1X4+BmRucxtnQYprsEduzeMqJ+K6YoU42dq4yOETZ0Hm3b5PdnSvPXPtDcWTAJe0sL3gofwJDgAKQSCSdS0/nk7z0k5F1d4ejnRybi4WDP4IXL6tLc7Gx5Z9hA+vr6UKXRcCAhiU/+2kN2aanB3/Pp6GH08vbUucbdJPbtV5s7C0bFpOjvi9NU2nnde73EDWowdO7cGWdnZ8LCwujXrx9hYWFY1CwvKBoMwu26FxoMQsPdCw0GoeHuhQaD0HD3SoNBaJg7ucEQndJ8I186et17w8Qb1O954MABtmzZwpo1a4iMjMTa2prBgwcTERFRNwlaEARBEARBEIR7T4Me9dna2jJp0iRWr17N5s2bmTx5MocPH2bWrFmo1Wp+/PFHEhMTGzmrgiAIgiAIgnBjmmpJs/3ci256bEBAQABz585lz549LFiwgPDwcNavX8/IkSN55plnGiOPgiAIgiAIgiA0k1teikEmkxEeHk54eDh5eXls2LCBtWvXmjJvgiAIgiAIgiA0M5Os3aZQKHjyySd58sknTXE5QRAEQRAEQbhl9+p+CM1FLFciCIIgCIIgCIJRYncYQRAEQRAE4Z6iFs/ETeqObDBINM2dA6EpWSoatvuxcG9Qx93dG1MJN8dSWtncWRCa0g13dhIE4W50RzYYBEEQBEEQBOFW3avLmzYX0V8jCIIgCIIgCIJRosEgCIIgCIIgCIJRYkiSIAiCIAiCcE8Ry6qaVoMbDOfOnePIkSOEhITQrl07IiMj+eGHH8jMzCQwMJCXXnqJgQMHNmJW7y5hvr681rcvgc7O5JWXs/L0aRZFRdUbM7ZtW2b07Im3oyPpRUV8f+wYv509a/T8fw0YwFPduxPw1Vemzr5wk0JbBvBKu0EE2LUgT1VKZPxxlsQeMHq+uUTKk0F9GO/TGTcrezLLi9iYcpYlF/dTWW141v9bHYfxRFAIrdd+0Fi3ITRQmK8vr4bW1O+ycladPs2iIzeo3+3aMqN3T7wcHEkvLmLp0WP8Fq1bv4cHB/Fsr54EKJwoVqo4mJzMf/fsI7esrDFvR7iBhONK9v1SSm5yFdYOUjpHWNF7ojUSifEvJHFRSg6uKiUnqQpLOynBfS0IfcwWueXVmIzYSv5ZVkJmXBXmlhLaDdSeY2Yuvug0Nxu5OW+EhzEkOAAbuZyTqel89Nc/xOXk1RvXwtaGt4aE0c/PB3OZlP3xyXz8124yi0sNnv9ojy482bsbgxcsa4zbEASTadCQpN27dzNp0iQWLVrE5MmTWbBgAR999BH9+vVj7ty5BAYGMmvWLHbv3t3Y+b0rdHN3Z/HYsVzOy+P5jRtZHxPD7P79eb5XL6MxEUFBfDFiBPuTkpixYQOHUlKYN2wYY9q0MXh+Tw8PHu/atbFuQbgJXRWeLOwzhbjiHF488ht/JkfzavvBzGjd32jM252HM7NNKGuTTjPz0Gp+TzzJM8F9+U/XUQbP7+HszaOBxv9+hKbTtZU7ix4cS1xuHrPWa+v3a6H9mRlST/0ODuLzkSPYn5jEzPUbOJScwicjhjGmbRudcxaMfYBzmZm88Ocmvty3n16envwyeSJymawpbk0wIO18JWs/KsTZU8a4tx1oN8iSfT+Xcvg34424y0eVrPuoEBdvMx58z5HeE605+3cFO74pqjsnP6OK394twNxCwpi59vR60JqTm8vZuai4KW5LuIGvxo1kWOtAvti9nzf+3IazjTUrHpmIg6WF0RiZRMLSKePo6O7Gv7fu5L2tO+nUqiXLHp6AmVT/69YD7dvw5pCwxryN+5q6WtpsP/eiBvUwfPvtt7z44ovMmDGDH3/8kc8++4y5c+fyxBNPAPDII48QGBjI/PnzGTRoUGPm967wYp8+nM/OZs62bQDsTUzETCbjuV69+OHECZRVVXoxr/Xrx9bYWD7esweAfUlJOFpa8krfvvx54YLOuVZmZnw2fDhZpaW429k1/g0J9ZrVdgAXCq7wxrH1AOzLjMNMIuXZ4H4sv3QYpUa3vB3MLZni14Mvzv7ND5cOAXA4OwGANzoO5YuzO8lXXf0yYiUzZ173MWSVF+Nu7dA0NyUY9VLfPpzPymbOlmvqt1TGc717seyY4fr9amg/tl2M5ePdNfU7UVu/X+7Xlz/Pa+v3rL4h7I6L572/dtbFxeXlse7RRxgc4M+22EtNcHfC9Q6uKsXVz4xRs7V1z6+7BRo1HFlTRo9x1phb6PYGVFdXs+v7YoL7WBDxij0APp3lVGvg+MYyKiuqMbeUcHRNGXIrCePfcUBmLsG/B5hZwM7FJYRMtsHBVTQSm0sXD3cGBfkzPXIde+ISAYhKSWPXrKeY2r0z3x04ajBuRNtg2rZ0ZeTiFVzOyQXgfGY2m599jJHtgvnzrLauK6yteHVgPyZ37Uh+mVhWXLg7NKgZdPnyZUaPHg3AlClTAOjTp4/OOcOHDyc+Pt7E2bv7yGUyent6sv2S7of71thYbOVyenp46MV42Nvjr1Cw4/JlvRgfR0d8HR110t8aMICc0lL+OHfO5PkXbo65VEZvFx92pOs26rann8fG3IIeLt56MXbmlkQmHGNXRqxOemKJ9gPGy8ZJJ31ux6HkKEtZm3TaxLkXbpZcJqO3lyc7rvvyvq22fnsar9/bL+nW720XY/FxcsTXyREJcCAxidVnonXOScjLB8D7uvcAoWlUVVaTEq0iuI/uU+XgvhZUlleTek6lF5MVX0XhFQ3dHrDSSe8+xppnv3fBvGZIUuJJFQE95ciuGX7Uup8l1RpIPKF/XaHphPr7UKpSsT8+qS4tv6ycqORUBgT61RsXn5NX11gAiMvJIy4njwEBV+Nm9OtFPz9vXvhjI7svie9Nwt2hQQ0GZ2dnkpK0FcfS0pL//ve/OF73AXbu3Dnc3NxMnsG7jZeDAxZmZiTk5+ukJxUUAODn5KQXE6hQADQopp+3N+PbteON7dvRVIsdcpqbl40TcplZ3Zf9Wkkl2nGuvrbOejGpZQW8f2orCdfFDG3VFpVGrXOtvq7+jPXuxFvHN6AROyI1Oy8HB+SG6nd+AWC4fgc4a+t3Yp7x+l0NzPtnL39fjtM5Z3hwEACxOTkmyL1wswqvqFFXgZOH7tN+p1ba1/npar2YrARtD5OZXMKa9wv4ekIW86dk8/fiYqpU2jpcqaymKEuDk4duJ7+1gxS5tYS8NP1eKqHpBLgoSMkvRH3dZ2xSXiF+Cv06fm1cwnX1HLTvD77OV+MiT5xh2Hc/suPiZb1zBdPRIG22n3tRg+5q3LhxzJ07l61btwLwwAMP0LJlSwCuXLnCN998wzvvvMPUqVMbL6d3CXsL7ZOoEpXuE6LSmte2cnnDYyq1O6Ta1hy3lcv5dNgw/u/gQRJrvmwIzcve3BKAkkqlTnpplfa1rbnx8a7XGtaqDWO9O7EyLoqiygptrJkFH3d7gPkx/5BYUv9EO6Fp2FuasH6rdOv39XwcHZk7IIyzVzLZE59wexkXbklFqfYLo9xa96NSbqXtFVCV6Tfiywq1ixas/6QQFx8zJvzbkd4PWRO9o5wtX2vnMChLtedYWOlPbpZbSVCVi4cDzcnOwqKuTl+rVKXCxkK/jtfFWVpQojQQp1TpvDfE5+ZTpTG8uIUg3KkaNIfhhRdeQKVScebMGSIiInSOnTt3jl9++YUZM2YwceLERsnk3URau2qGkaf/hnoFalfaqL7uWO1HSW36uwMHcqWkhGXHj5sms8Jtk9aUkrGP94b0Ag1v1ZYveo4nKieJL85dHb/+dufhXCkv4sfLh02RVcEEjNXVWobKu/Y9odrIX4mhmACFgp8mTUClVvPCnxtF31IzqdZo/+WNLYZkKL12ylJQiAUDnrAFwLuTHDSwd0UpeY9UYV7bUDB03Wrjv08wPQnXfG7XkEokBj/CJRLjdb82zuDvuEGc0DjEsqqm1aAGg1QqZc6cOQaPDRw4kOXLl7Nq1SpCQ0M5ceKESTN4tylS1jxZvu6poU3N04VipdJ4zHVPJ63NzetiBvn5MbpNG8b9+mvdm1Ltf2USCZpqY19HhMZU1xtgrlt2NmY1T5VrjhvzRGAIb3QcwtHsJJ4/FEmlRjvEYaBbEKM82zNh19Kr5Y0o7+ZWXHGD+q1qeP22kV+t39cK8fJiwbgHKFWpePy3P0gtLEJoHpY22p4F5XU9CbU9AHIbwz0EAP49dcvbt7ucvStKyUqowr+n9u/HUA+FqqIaC5t7c0jDneiF0BBeDNOdk7n1fCx+NtZ651qby+veAwwprlAa7IGwlssNfvYLwt3kljduUyqVbN68mcjISKKjo5FKpQwdOtSUebsrJRUUUKXR4HPdHI/a15fz9IeW1I6H9nF0JCY7Wy/mUm4ur/Tti6WZGdsef1wvPvbVV1lz7hxvbN9umpsQGiy5NE9b3jYKnXQfW+3ry8XGx56/03kEjwb0YkvqOd44tr6usQAw3KMtljJzNg+dqRcXM/5d1iad4q3jf5roLoSGMlq/nbSvLxtYo712TLOPoyMxWfr1+3Lu1TkrD7Rtw2cRw0nMy+epP9ZypaTEtDcg3BRHdxkSKRRkVAFXG4m1cxdcvPQ/QmvnN6ivm4ZQ2/NgJpcgt5Rg6ywlP0N3DkRZoQZVWTXOXmKFpKay+mQ0uy/rDvkbEhxAqL8PEnR7j30UDgbreK343Hzaubnqpfs4OXIm/YqJciw01L26vGlzuekGQ3x8PJGRkWzYsIHCwkIkEgkTJkxgxowZeHp6NkYe7yoqtZqo1FSGBQby/bFjdekRwcEUVlRw+or+m0ZSQQFJBQVEBAez9ZrVlSKCg4nPyyO9uJj/HTrEz6dO6cRN6diRKZ06Me7XX8krF0uzNQeVRs2xnCSGtmpbt0QqaIcZFarKOZOXZjDutfaDeTSgF8svHebT6B16x789v4df43Q3Apvk143Jft2ZsOt7nWVXhaajUquJSklleHAgS6Ou1u8RN6jfyQUFjGgdzNZrVlca0bqmfhdp190f4OfH5yNHcDw1jefWbdCb8yA0PTO5BK8O5sQeVNJz/NWN2mIPKrGwkeAWbK4X49neHHNLCRf2VBDY62ojI+6oEqkMWrXRxvh2lRMfpaTqmasbtV08UIFEWjOESWgSWSWlZJXobqpmaWbG8/17Exrgy96aZVWdrK3o6e3JIiNLqgIcSEjigQ5tCHBR1G3wFuCiIMBFwcL9RxrtHgShKTSowVBVVcWOHTuIjIwkKioKc3NzBgwYQEREBG+88QZPPPGEaCxcY8GRI6yYOJFvRo/mj7Nn6daqFdN79OC/+/ahrKrCVi4n0NmZ5IKCui/6Cw4f5r8jRpBfXs7O+HjC/f0Z1bo1L27aBEBaURFpRbpDEwb5+wMQnZnZtDco6Pju4j6W93+U//WayJqkU3R19uTp4L58cfZvlJoqbMzkBNq1ILk0n3xVGW0cWjI9uB/R+WlsTT1HZyfdpTgvF2eTVlZIWlmhTvrACu3T5rMFGU12b4K+BYePsGLSRL4ZM5rfo2vqd68e/HeP8fr97cHD/HfkCArKy9l5OZ7wQH9GtWnNS39q67dcJuOTEUMpValYePgIgc66q2tdKS4WvQ3NJGSSDb+9W8CfnxXRcYgl6RcqObq2jAFP2GBuIUFZpiE3WY2ju0y7ypGVlH6P2PDPDyVY2BYT3NeCtPOVHFlTRrcHrLB20D717PWgNRf2VrDmPwX0GGtNXrqafStK6DzCCvsWooehOR1LSeNwYgpfjI3g8137KCgv58XQPhRXKFl14kzdeQEuCuQyGecztT2Hm2NimdG3F0unjOeLXfsBmDO4P7FZOWw7H2vwdwnC3aJBDYaBAwdSUlJCSEgI8+bNY8iQIdjaaidzvf76642awbvRoZQUZm3cyMt9+vDdmDFklpTw6d69/FAzWbm9qysrJ03ijW3bWBMTA8CamBjkMhnP9OjBQx06kFxYyOytW9kSK95k7nSHsxN58chvvNR2IAtCJpFZUcx/o/9iec1k5faO7vwc9jhvHtvAuuTTDGvVFqlEQkcnD34b9LTe9R7d+xNHc5L00oU7w+HkFGZt2MjL/fqwaNwYrpSU8Nk/e/nhWE39bunKr1Mm8caWbaw9p63fa8/FIDeT8UzPHkzs2IGUgkLmbN7Klova+t3NoxUta95Tf5qkv3jE/AOHmH/wkF660Ph8OssZ+5YDB1aWsP7jQmydpQx80pae47Vj3DPjqlj9dgERL9vRYYh274We46yxtJVwbF0Z0TvKsVVI6TfVht4Tro6Ld/Yy46EPHPlneQkbPi3Eyl5Kj7HW9J9m0yz3Keh6Yc1G3hoygDcGhyKVSDiRms7LazdTdM0chv+MGIyHgz2DFywDoFKt5omVa3hn2EA+HDmEKo2GA/FJfPL3Hr0lWoXGpxGTnk1KUt2AqfudO3fG2dmZsLAw+vXrR1hYGBY1k/7at2/Phg0bCAwMNFmmAr76ymTXEu58Zr7iyen9RB1n29xZEJrQm2PXNXcWhCb0398fbO4sCE0o9l+vNncWjNqe0K7Zfvdwv5hm+92NpUE9DAcOHGDLli2sWbOGyMhIrK2tGTx4MBEREXVjOgVBEARBEAThTqC+RzdQay4N+te0tbVl0qRJrF69ms2bNzN58mQOHz7MrFmzUKvV/PjjjyQmJjZyVgVBEARBEARBaGo33fwKCAhg7ty57NmzhwULFhAeHs769esZOXIkzzzzTGPkURAEQRAEQRCEZnLL+zDIZDLCw8MJDw8nLy+PDRs2sHbtWlPmTRAEQRAEQRBumtiHwbRM8q+pUCh48skn2bhxoykuJwiCIAiCIAjCHeKWexgEQRAEQRAE4U6kEZOeTUr8awqCIAiCIAiCYNQd2cNgViqWar2fKLOtb3yScM+QuCtvfJJwz9ic3am5syA0IZv05s6BIGipq8V3SVMSPQyCIAiCIAiCIBglGgyCIAiCIAiCIBh1Rw5JEgRBEARBEIRbJXZ6Nq3b/te8cuUKGo3GFHkRBEEQBEEQBOEOc9s9DCNHjmTDhg14eXmZIj/3FBu5OW8MCSO8dQA2cjknU9P5ePs/xOXk1RvXwtaGN4eG0c/fB3OZlP3xyXy8fTdZxaV153g42PP6kFB6+3hiLpNxODGFL3buIzGvoJHvSjBkgJcvs3v2J8jRmdyKclbGnGLhqaMNiu3g0pJ146YyKPIHUkuKbvq40PTC3P2Z3SWMIAcX8irK+PXSSb47d6hBsR0Ubqwd8RiDNiwmrbRQ59gE/45Mb9cbXzsnsspLWBt/lm+jD1BVLR7KNKeS6Byy1sahzCjFzE6O00APnEf6IpHceFJltVpDwsfHkFpI8Z3bw+A56vIq4t87TIux/jj2b2Xq7Au3wNrCnFfHhzGwcwA2FnJOx6fz+R//EH+l/s/va30xfTSlFSr+/fMOnfQWDja8Oj6Mvu18kUklRCdeYcHGA5xLyjT1bdzXNGLjNpNqUIPhrbfeMnpMpVLx+eefY2NjA8C8efNMk7N7wJfjR9LJw43P/95HiUrFC2Eh/PToREZ99xOFFYZXipFJJHz/8Dis5XL+s2UnZjIpswf3Z9kjExi35BeqNBps5OYse+RB1NUa/rN1F6oqNc+H9ubnxyYxepHxawuNo1vLVnw/fDyb4i7wZdR+erh5MKdXKBKJhAUnj9Qb21bRguURD2Iuk93ScaHpdXPx4PuBE9mcdJ4vT+2lp6snc7oMQCqRsODswXpj2zq6smzQQ5hL9cvzidY9+HfPoWxJOs+8E7tQWFjzSqf+tHFswYy9axvrdoQbKLtcQPL80zj0aonrgwGUXSoga20c1Rpo8YDfDeNztiRSkViEdWtHg8fVJZUkzz9FZW6FiXMu3I55T46kg68b/7d+H6XlKp4bGcLilycy4cOfKCqr/zNWKpHw+sQBhHcJ4s/D53SO2VrKWf7aZKwszFmw8QDJ2QUM7hzI0lcmMf1/v3M28Upj3pYg3LIGNRjOnj3L5cuXCQwMxNHRUeeYRqPhypUrWFhYNEb+7lpdPNwZFOzP9FXr2Hs5EYBjyWnsfPEppvbozHf7DT99HtEumLZuroxatILL2bkAnL+SzaYZjzGyfTB/Rl8gol0wvs5OjPzup7reitisHHa99DQj2gWz+kR0k9yjoPVK9z7E5Gbx2u6tAOxJScRcKmNml94sPXMcpbpKL8ZcKuXxDt2Y3aMfFbdwXGg+L3fqz/n8TF47qN3Zfm9GPGZSKTPah7D0/FHj5d26B691DjNYnlKJhJc79WdfegKz9q2vS4/Oy+CvB56lv5sv+68kNtYtCfXI3hCPpbcdHtM7AGDb0YVqdTW5WxJxHu6NVG68MV+RXEzO5kTMHOQGjxefzOLKylg0FepGybtwazr5uRPW0Z8XF65j/7lEAE7GpbHpg6eYFNaZpduM9x4HtXLhzcmDaOvdknJVpd7xsX3a4+HiwBNfRHI6IQOAIxeScbS1YvaDA3jyq9WNck+CcLsa1F+zZs0aHnnkEfLz83n66af5+eef634sLCz48ssv614LWv0DfChVqTgQl1SXll9WTlRSKmGBxp9K9Q/wIT4nr66xABCXk0dcTl5d3Pbzl5m8LFJnaFOlWvuBIxdPopuUXCqjdysvtidc0knfEh+LrVxOL3cPg3GDvP15uXsfvj15mE+P7L3p40LzkEtl9G7pzbaUizrpW5MvYmtuQS9Xw0MzB7YK4KWO/Vlw9iCfndytd9zF0gZHCyt2pun+HV0uzCW3oozBnoGmuwmhwTSVGsou5mPfzVUn3b6HKxqlmrLYAqOx1VUa0n44hyLcC7mb/l4z6rJKUr49g3VrJ7xf62rqrAu3oU9bH8qUKg6dv+bzu6Sc45dS6d++/l6lDx8fjkQi4fHPI8kvLtM77ufmTGFpRV1jodbxS6l0CWiFnZV4+GoqaqTN9nMvatBdyeVy3nnnHebNm8e7777Lv//9b5RKMeylPgEuClLzC1FXV+ukJ+cV4ufsVG9cYl6+XnpyXgF+Cm1csVLJqTTtm425VErrli58OnY4eaVlbI2JNeFdCDfiZe+AhcyM+ELdMkusee3noDAYdzrrCv1Xfs+Ck0dQG1g04EbHhebhZeuIhcyMhCLdccyJxTXlbWe4vM/kZhC6fiELzh6kykB5FqkqqNSo8bRx0Em3l1viILfEy9bRNDcg3JTK7HKqq6r1vvDLXbWvVZn6XwhrZf8ZT3VVNS3GBRg8LpXLCPioDx7PtEdmZ266TAu3zc9NQWpOIWqN7ud3SnYh3q7GP78B3v1pO0999RuX0nMMHs8vKcPGUq7XMPBy0dZ9DxcHQ2GC0OxuqhkUGhrKhg0byMrKYsyYMZw5c6ZBk77uR3aWFpQoVXrppSoVNhaGu6cB7G8ybvHD4/jz2UcJ8fXiq90HyCk1/gEmmJ5DzVC8EpVuA7q0UluGtnLDZZ1ZVkKh0viY5RsdF5qHvdwSgJJK3TpaWqktf1tzI+VdXkKhynh5Vqir2Jx0nkdbd+ehgE7Yyy3xt1cwv/9YqjRqrGTiC2VzUJdph5RILXV7bmtfq8sNDxcsTygkd1syHk+3Q2pu+GNWYibFwt3GhLkVTMXOyoLSCgOfwxUqbC2Nf34DRhsKtbZGXUCj0fD5M6Pxd3fG1sqCiJ5tGNOnPQBWclHXTUVdLWm2n3vRTa+SpFAo+O6774iMjOSpp56iouLqh2BpaWnd5Of7iQTtGORrSSUSqo2cXF1t8EjNYQmGDkswHLdg7xEW7z/KkNaBfDR6KAprKxYfiLqp/Au3ToK23I2VaH1lLdx9pDXVvNpIiWuM/iXc2L+ObEOpVvNpyEj+22cUZVUqFp87gpXMjHK1/lhooQnUFqeRB2OGHphpKtWkLY1BMdQLK3/xtPhOJ5EY+fw29DksAc1tvqfHX8nj5UUbeHfqENa88xgAMUmZLNh4kLcmDzY470EQ7gS3vKzqlClT6N27N5s3b6akpIT33nuPTZs2ceLECVPm764wKyyEFwf00UnbFhOLs7P+uFUbcznF9axiVKxUYmugJ8FaLqfYwDCw4ylpABxJSsXeyoKZob1ZevCY3lAooXEU1fQs2F33ZNmm5nWxSgzdu5fUlretue5wApua17dT3mVVlbx5eAsfHPsLDxsHUksKKVdXMimgE8mZSTe+gGByUmvtR6Tmup6E2knKtcevlbU2DqqraTHGn2p1zfCzmrfjarUGpBLRM38HeTYihBmjdD+//zoRi4+d/ue3tYWckvLbf08/fCGZUe8to5WzPQDpuUWMCWkHQGGp6Fk2Fc09Opegudxyg0GpVHLy5En27t3LggULkEqlDB061JR5u2v8diKafy4l6KQNaR1A/wAfbc/ANeneCod692FIyM2nrZurXrq3wpEzadrl1jq2aomno4PefIXo9EzGdWqHk7WVGJrURJKLCqjSaPBx0B3X6lvz+lJ+rqEw4S6VVJxPlUaDr9115V3z+nJh/cMR6jPYI5BCVTnHs9O4VHMdZwtr3G3sOZcn1mdvDnJXK5BKUGXpvp/WvrZopd+jXnwsi8rcCi7M1J/cfn76Llo91U7stXAHWXsgmn1ndT+/B3YKoE9bHyQSdHoavFo43NQ+DIa4OdnRu403m4+eJz336r46bb1bUlBSTkae2GtHuDPddIMhPj6eyMhINmzYQGFhIRKJhAkTJjBjxgw8PT0bI493vKySUrJKSnXSLM3NmBnam9AAX/bGJQLgZG1FTx9PFhlZUhVgf3wSozu0IcBFUdewCHBREOCi4Lt92jX9BwX581z/XpxKzSCjqLguNjTAh6ziUnJFY6HJKNVqjmakMsIviCWnrw4FG+kfTKGyglNZYk3te4lKo+ZoVjLDvVqzJObqHhsR3q0pVJZzKjejnuj6TQ3qipOFFRO2r6hLe7JtT9TVGnamXb6tfAu3RmouwzrYkeIT2TiP8KnrGSg6loXU2gwrP3u9GK+Xu1BdqTuxPWPFeQDcH2uLeQvLxs+40GDZhaVkF173+S03Y3pEb/q29eVATCIATrZWdA/y5Id6llRtCIWdNf+ZNoysgpK6VZic7a0Z0b01/5yJu61rC0JjalB/TVVVFVu2bOGxxx5j1KhRREZG0qtXL7766itkMhlPPPHEfdtYMOZYchpHElP4fHwEE7t0YEjrAH6cNoHiCiWRx8/UnRfgoqCtW4u611vOxZKYm8/3D49nVPvWjGrfmu8fHk9sVg7banoUVh0/Q35ZOYsfHseItkGEBfjy1YMjGRjkz2d/77mNUdTCrfjmxCG6uLqzYMgDDPTy47Ue/Xi2c08WnDyCUl2Frbmcrq7uKCytmjurggl8G32QLi6tWBA6jgGt/HmtcxjPtgth4blDdeXdxaUVCoubK++fLh6jWwsP3u0+hD4tfZjdOYxZHfryfcwRUkoKGudmhBtq8YAf5fGFpH4XTfEZ7Y7PuduScBnli1QuQ11eRVlcIVVF2kmylp62WPnZ6/xILWVILWVY+dljZlv/pFmh+Z24nEZUbAofPxHB+L4dGNQ5gEUvTqC4TMnv+65+fvu7KWjt2aKeK+mLSc7kZFwab08JJ7xLIAM7+bPoxQmoNRoWbzls6lu5r6mrpc32czs0Gg3z588nNDSUzp0789RTT5GUZHxYamVlJV9++SWhoaF06dKFadOmcf78+dvKgyENuquBAwfy9ttvY21tzbx58zh48CDffPMNI0eOFJM66/HC7xvZeTGON4aE8umY4WQWlfDEL2soumYOw78jBvPtQw/Uva5Uq3ny1zWcu5LJh6OG8F7EYE6lZfDMynV18xKyS0qZ+uNqEnLyeHfEIL556AHc7e2Yvmodm85e1MuH0LgOpacwc8cG/B0VLB4+lnFBbfnk8J66HocOLi1ZN/4RBnv7N3NOBVM4lJnEzL1r8bd3ZvGACYz1bce8E7vqehzaK9xYN+JxBnnc3N4J+zISeGn/Bvq7+/LDoIcY4d2a/0Tt4PNTexrjNoQGsmmrwPP5TqiulJH67WkKD1+h5UNBuET4AlCRVETix1GUnLn14WjCnWf2ko38cyaOV8aH8v6jw8kqLOG5+WsovmYOw1uTB/PVsw/UcxXD5ny/ieiEDP718BD+/cgwEjPzeeqr37iSX3zjYOGet3DhQiIjI/noo49YvXo1EomE6dOno1Lpr9wF8J///Ic//viDDz/8kDVr1uDo6Mj06dMpLjbt35OkugHf+Dt37oyzszNhYWH069ePsLCwup2d27dvz4YNGwgMNN3GQq0//Npk1xLufEpXscvp/URiI1YBuZ908Utt7iwITShxpdhk8H5ycsGrzZ0Fo5bGhjbb734meN8txalUKkJCQnj99dd5+OGHASgqKiI0NJRPPvmEUaNG6ZyfkpLCkCFDWLx4MQMHDqw7f9y4cXz88cf06dPn+l9xyxo0h+HAgQNs2bKFNWvWEBkZibW1NYMHDyYiIkKs9iAIgiAIgiAIt+nChQuUlpYSEhJSl2Zvb0+7du2IiorSazDs378fe3t7wsLCdM7ftWuXyfPWoAaDra0tkyZNYtKkScTFxfHHH3+wceNGNm3ahEQi4ccff+SZZ57B19fX5BkUBEEQBEEQhLtFeHh4vcd37txpMP3KFe1CKe7u7jrprq6uZGToL6qRmJiIl5cXO3bsYMmSJWRmZtKuXTvefPNNAgIM7zJ/q256ZkZAQABz585lz549LFiwgPDwcNavX8/IkSN55plnTJo5QRAEQRAEQbhZd+Ok5/LycgDkct3FESwsLFAa2IurpKSE5ORkFi5cyGuvvcZ3332HmZkZU6dOJTfXtMu63/I+DDKZjPDwcMLDw8nLy2PDhg2sXbvWlHkTBEEQBEEQhLuKsR6EG7G01C67rFKp6v4ftHufWVnpr7xnbm5OcXExX3/9dV2Pwtdff82AAQNYt26dSR/km2QbPIVCwZNPPsnGjRtNcTlBEARBEARBuGVqpM32c6tqhyJlZWXppGdlZeHm5qZ3vpubG2ZmZjrDjywtLfHy8iI11bQLToh9swVBEARBEAShmbVp0wZbW1uOHLm6MWhRURExMTH06NFD7/wePXpQVVVFdHR0XVpFRQUpKSn4+PiYNG+3PCSpMckqmjsHQlNy8zXtODvhzpYX5drcWRCakHu7wubOgtCEzno1dw4EQUtTffet4imXy5k2bRpffPEFCoUCDw8PPv/8c9zc3Bg6dChqtZq8vDzs7OywtLSkR48e9O3bl7lz5/LBBx/g6OjI/PnzkclkjB071qR5Ez0MgiAIgiAIgnAHeOmll5g4cSLvvPMODz/8MDKZjB9++AG5XE5GRgb9+/dny5Ytded/88039OrVixdeeIGJEydSUlLCihUrUCgUJs1XgzZua2rt/iU2bruf2Idl3fgk4Z4hehjuL0NGHm/uLAhN6K8d3Zs7C0ITuvTmnbtx27cXBjfb736hjen3QWhud+SQJEEQBEEQBEG4Vbcz+VjQd9v/moWFYnyqIAiCIAiCINyrGtTD8MYbb/Dee+9ha2sLQHV1Nd9++y3Lly+nvLwcZ2dnXnnlFSZOnNiomb3bWMvNeT0ijPC2AVhbyDmVnM68Tf8Ql51Xb5yLnQ1zI8LoG+iDuUzKgcvJzNu8m6yi0rpzXO1tmDMijNAgX2RSCadTrjD/7wNEp2Y29m0JBoS4BPNc4FD8bF3JV5WyLuUIKxL2GD3fQmrO9MBwwt064SS34VLxFX6I28nhnFid8wa1bM80vwH42rSgpLKCY3lxLIjdRp6qpLFvSbgBG7k5b4SHMaR1ADZyOSdT0/loxz/E5dRfv1vY2vDW0DD6+Wnr9/74ZD7esZvM4qv1W2FtxWuD+tHP3wcHS0sS8/JZeugYW2Ji67my0FhyTxUS91sqpWkVyO3N8Ahvgc9YdySSG0+q1KirOfZeDDILGd3fa6NzLDsqn4R16ZSlVyB3NMetvzO+49yRmokno80p1M+HV8P6EeiiIK+snFUnz7D4cFS9MWPat2FGSC+8HB1ILypi6ZHj/H7mrM45ndxbMndQGO3dXClTVbLh3Hm+3nsQlVrdmLdz39LcxgZqgr4G/Wtu3LhRZ4e5FStWsGzZMmbMmMGiRYuYPHkyH3/8MX/88UejZfRu9MXkkQxpF8hX2/fz5u/bUNhYs/zpiThYWRiNkUklLHl8HB093fjgz528v2EnnTxbsvSJCZhJtcVlayHnl+mT6RPgzf/+PsDLKzeRnFfAT89MopOn/jq9QuPq6OjN510fJbE0mzdP/cq29JPMCBrGE/4Djca822Ei4zx783PCHl4/sYLUsly+6PoYnR19684Z3LID87pM42JROm+dWsl3l3bQ1cmPb3s+g1wqRhM2t6/Gj2RYm0C+2LWfNzZsw9nGmhXTJuJgWU/9lkhYOmUcHd3d+PfWnby3ZSedWrVk2dSr9dtcKmXZ1Afp6+fN/D2HeOGPjZxOu8L/PTiKsR3bNtXtCTUKYos5/cUlbDys6PRqIG79nYn7LY3E9RkNik/akEFxfJleeu6ZQs58fRlrN0s6vhaI51BXkv7M4NLPKaa+BeEmdPVwZ9HEscTl5jFr7UY2nD3PawP6MbNPL6MxI1oH8fnoEexPTGLm2j85nJTCJyOHMqbd1Qait6MDP06eQHllJS+v38zSo8d4tHsX3hs6qCluSxBuW4O+dVw/L3rt2rW8/vrrTJ06FYABAwbg6urKsmXLRC9Djc5e7gxs48+Mn9axNzYRgOOJafw15ymm9O7M4n+OGowb3iGYNu6ujPnfCi5naZcbvZCRzYaXHiOiYzAbT1/gwe7t8VQ4MHVxJKeStR9ah+KScbK24o2RA5i2ZHWT3KOg9XRAOLHFGbwf/RsAh3NiMZPKeNRvIKsS96PUVOmc72GlYIh7J/4bs561Kdq1lo/lxdPJ0YcJ3iGcLkgE4KmAwRzIvsB/Y9bXxSaVZrO8zyz6tWjD7kzdp1dC0+ni4c6gIH+mr1rHnrhEAKJS0tj1wlNM7dGZ7/Ybrt8j2gXT1s2VkYtWcDlHW7/PZ2az+bnHGNkumD/PXmBQkD/t3FyZ8MNKojO0PYYHE5LxcLBjep8ebIg+3yT3KGglrEnHztea9rP8AXDu4kC1upqkPzPwHuWGTG78uVtxUhmJGzKQO5rrHcvYk4Ols5z2L/gjkUpw7uSAqqiS5C2ZBD3qJXoZmsmL/UI4n5nN65u2AbAvIQkzmZRnQ3qyLOo4yir93oBXw/qy7cIlPtmp7VXen5CEg5UlL4X24c+YCwBM792DUpWKmWv+pFKjYU98IhWVVbw3dBALDx4hvai46W5SEG7BLb0j5ebm0rNnT520vn37mnxXubtZ/yAfypQqDlxOqkvLLysnKjGVsGA/o3H9gnyIz86raywAxGXnEZ+dR2hrbZy/qzOFZRV1jYVaUQmpdPNphX09TzgF0zKXyOim8OefzHM66buunMXGzILOTvplnVVRyBOHvmVb+qm6tGqqUVdrkEtlAEiQcDT3MutTdL94JpdlA+Bpbdrl0oSbExrgQ6lKxf746+p3UioDAozX71B/H+Jz8uoaCwBxOXnE5eQxIFAbV6JSEXniTF1joVZCXgHeTo6mvRGhXppKDfkxxbTo6aST7trbCXWFhoILxr/kaao0xHwXj9cIV6zdLQ0cr0ZmIUUivTqsydzOjOqqatQVGtPdhNBgcpmM3t6e7Ii9rJO+7cIlbC3k9PT00IvxcLDH31nBjthLejE+To741tTZ/v4+7I5LoFKj0TlHJpUS6udr8nsRQI2k2X7uRQ1uMJw7d46SEu246W7dupGQkKB33NC21fcr/xYKUvILUWt0e2eScwvxdXEyEgUBLRQk5uTrpSfnFdTF5ZeWYWMh12sYeCkcAPCo+a/Q+DysFcilZqSU5uikp5ZpX3vbuOjFVFaruVCURplaiQQJLS0deKXNaDysFaytaSBUU838i1vYl637NHmgawcA4orFXJXmFOBcU7+v631Nyi/Ez7me+u2iICFPv34n5RXgq9DGHUxI5r0tO3WOm0mlDAry41J2jl6s0HjKs5RUV1Vj7a77XmvVUtsAKMswvstowpp0NFXV+E/U/5IJ4DnMlbIrSpI2ZlBZWkXhpRJStmbi3MUBc1sx5LA5eDk6IDczI/G6OpqUXwBQV0evFeCsfXiTkFegk55coH3tp3DCwkyGp4ODXt3PKy+nuEKJr8LRJPkXhMbUoHelVq1a8dxzzwHQokULrKysOH78OP3798fa2ppFixbxww8/8MwzzzRqZu8mdlYWlFSo9NJLlSpsLeTG4ywtSMotqDdu06kLPNm/O18/PJp5m/8hs6iEAa39GN+9PQDW5vrd30LjsDWzAqC0SveLQ5laW/Y2svp7ex73H8CMoOEA/JkaxYm8eKPnelk782LrCC4UpnEoR0x+bU52lhaUqgzUb5UKmxvU78TrvljUxtX3vvDmkDB8FU48/9uft5Rf4dZUlWqHn5hZyXTSZTWv1eWGJ6sWxZWQvPkK3d5rg9Tc8HM5p3Z2+DzgxuWVqVxeqe2dt/O1psOL/qbKvnCT7Cy079clSt26XVvXDdVR+7oYpU567TVsLeTYW1gavG7ttW0txKiAxiAmPZtWgxoMu3btorS0lNjYWC5evEhsbCyXLl3CouaP/LfffmPSpElMmzatUTN7p5JIQHrdahnXv772XE09e+VJJRIMHZZwNS4uO4/nf97A++OG8OfLjwFwNi2T+X8d5N0xgymrrLy1GxFuWm05GytRjdEjWvuyznMqP5G29p48ExiOq6UDrxxfrneer00L5vd4GpWmirdO/0r1Da4rmI4Ew/XbWD2tby/M+t4XjMXNDQ/lsV5dWXzgKH/HxjU024IJ1JWJsdWQDKSrVRrOfZeAV0RLHAJtjV77wg9JZPyTg+94dxQd7CnPUhL/RzonP42l279aI7OQGY0VGsfV93PDdVFjINl4nZbUxFRTO+rM4HUlknrfMwThTtHgfk8bGxu6du1K165d9Y59++23rFq1itDQUE6cOGHSDN4Nnh8UwqzwPjpp26NjUbhY651rLZdTfN2TiGsVVSixtdR/imFtIaek4mrcwcvJDP1iGR5O9gCk5Rcxvls7AArLjHeTC6ZVXFkOgI2Z7hMia5m2DK/vebheXIl2aNGp/ESKq8p5p8NEOjn6cKbg6tj4bgp/Pu0yjbIqJS8d+4GMcv0hLULjeSEshBfDdOv31phY/JyN1O8K4/W7uEKJjdxA/TbXf1+Qy2R8NmY4o9q3ZsnBKL7cfeAW70C4VWbW2o/Iqut6Emp7Fsys9b/Ux/+WCtXgN74VGnXNF8Ga/2jU1UikoMyvJH1XNr5j3QmY5AmAUzuwD7DhyBvnSP8nB6/hLRvprgRjimrq4PU9CbV19vpehPpjtD39xUqV0XMAbMzN6/1OINy6e3UuQXO55YGSSqWSzZs3ExkZSXR0NFKplKFDh5oyb3eN36Ki+eei7pyO8LYB9AvyqXlyeDXd29mBuCzj67Qn5uTT1t1VL91b4Uh06hUA3B3s6BPozcZT50nLL6o7p51HSwrKykkvKNKLFxpHWnkeVRo1ntbOOume1tq5CwklWXox7lZO9FAEsD3jFKprVlA6X6gdluBqeXUOyjD3zrzbYSLJpTm8cnw52UpRtk1t9Ylodl/Srd9DWgcQGuCj7VG4Jt3HyYHL9ezDEJ+bTzs3/frto3DkTPqVute2FnK+nzKerp7uzPtrD8uP3H8PYu4EVi0tkEih/Ipuw788U/vaxkN/MnPWkXwqclT886R+me2edoy2M/ywcbeEanBordsDYetljbmtGaWp5Sa8C6GhkvMLqNJo8HF01En3qZm4fO1iBbXic7X13dvRkZjM7Lp0b8erMeWVVVwpKta7rsLKCjtLi3rfMwThTnHTDYb4+HgiIyPZsGEDhYWFSCQSJkyYwIwZM/D09GyMPN7xsotLyb5m0yUAS3MzZgzqTf8gX/bVLKvqZG1FT19Po0uqAhy4lMSoTm0IaKGo2+AtoIUC/xYKFv2jXYJTYWvNRw8OI7OwpG4VJhdba0Z1as3OGDFkoSmpNFWcyk9kYMsO/Jq4ry59sFsHiirLiSnUX1O9lZUT/+owAaWmkh0Zp+vSQ1yCAbhcrF39qo9La97r8BBnCpJ4/eQKSqvEU6jmkFVSSlaJfv1+vn9vQgN82VuzrKqTtRU9fTxZZGRJVYAD8Uk80KENAS6Kug3eAlwUBLgoWLhfW79lEgmLJ4+lY6uWvLJ2M9vOXzJ6PaFxyeRSHNvYkRWVj/dot7phJllH8jGzkWFvYMhR59eD0FTqDjG58EMiAG2e9sXK1YLqam1PQ8GFEly6ONadV5peTmVJFVYtxJj25qBSq4lKSWVY60CWHj1elz6iTRCFFRWczriiF5NcUEhyfgEj2gSx7eIlnZj43Ly65VL3JyYxKNCfebv21m3UNqJNEFUaDYeSkhv5zgTh9jWowVBVVcWOHTuIjIwkKioKc3NzBgwYQEREBG+88QZPPPHEfdtYMOZ4YhpH4lP470MRfLF9HwVl5cwa3IeiCiWrj56pOy+ghQK5mYzzGdonE1ujY3l2YC8WPz6er3bsB+C1Yf2Jzcxh+1ntRNdzaZkcT0zjvbHhfLFtL2qNhpeH9qNKo2HhrsNNf7P3ueXxu/imx9N83Hkqm9KO0dHRh0d8Q1kQuw2lpgprmQV+tq6kleVRUFnKybwEjuXGMbvtGOzMrEgqzaa7wp9pfmGsSzlCYmk2cqkZb7d/kDK1ih/jd+Nro/tUOquiUPQ2NKNjyWkcTkzhi3ERfL5zHwXl5bwY1ofiCiWrTlxTv10UyGUyztc8edwcE8uMfr1Y+vB4vtilrd9zBvcnNiuHbTW7OD/Sows9vT1ZdfwMGUXFdPbQXX3udJr+lxah8fiOb8XJTy5y9n9xuA90oTC2hKRNVwh82BOZXEpVmZrStHKsWlogtzfH1lt/qJrMUjt0yT7Api7NK6IlyZu0ZanoaE9FjoqENWlYushpNbhF09ycoGfhwaP8NGUC88eN4o8z5+jm4c4zvXvw+e59KKvU2MrlBLooSM4vJK9c2xO04OARPhs1nILyCnZeiiM8KIBRbVvz0vrNddf9/vAxRrdtw9JJ41h+9AS+CidmD+hH5MkzXCkuaa7bvaeJSc+mJaluwGyb/v37U1JSQkhICCNGjGDIkCHY2mqfrLRv354NGzYQGBhosky1+9fXJrtWc7K3tGDuyAEMbheAVCLhZFI6n27Zo7Ns6o9PT8TDyZ6hXyyrS3NzsOWtUQPpG+hDlVrDgctJfLplDznX9GI421gzd9QA+gVqhz0diU/l/3bsN7jC0p3OPkx/2M7dZoBrO6YHDsHbpgXZFUWsST7EyiTtF8JuTn4s7PUsH0b/zuZ07TAFa5kFTwcMZlDLDrhY2pNelsf61KOsTjpINdV0V/izoOd0o79v6eW/WRq30+jxO1lelP6QnLuRvaUFbw0dwJBgbf0+kZrOJzv26Cyd+POjE/FwsGfwt9fUb3tb3hk2kL5+PlRpNByIT+KTv/aQXdOL8etjD9HT2/gDmOCP7q73xyEjj9/4pDtcVlQ+Cb+nUZpRgYVCjudQV3xGaxty+TFFnPjwIm1n+NFqgP4yygDHP9Bu3tX9vas7/1ZXV5OyNZO0ndmUZymxcDRH0cmBgMkeyO3v3pXu/trRvbmzcNuGBgfwUv8++CucyCwp5ZcTp1h2VPve3cvbk1+nPsTczdtZGx1TFzOlS0ee7tUdd3s7UgoKWXQoig3ndJfF7uHpwdxBobRt2YL8snLWnzvP/+09qLc8893k0puvNncWjPr43Ohm+93/ar+p2X53Y2lQg6Fz5844OzsTFhZGv379CAsLq1shSTQYhNt1LzQYhIa7VxoMQsPcCw0GoeHuhQaD0HB3coPhg7Njmu13v9fh3lsCu0FDkg4cOMCWLVtYs2YNkZGRWFtbM3jwYCIiIurGdAqCIAiCIAiCcO9p0AAvW1tbJk2axOrVq9m8eTOTJ0/m8OHDzJo1C7VazY8//khiYmIjZ1UQBEEQBEEQhKZ20zNCAgICmDt3Lnv27GHBggWEh4ezfv16Ro4cKXZ6FgRBEARBEJqdBkmz/dyLbnkfBplMRnh4OOHh4eTl5bFhwwbWrl1ryrwJgiAIgiAIgtDMbrnBcC2FQsGTTz7Jk08+aYrLCYIgCIIgCMItU4tlVU1K/GsKgiAIgiAIgmCUaDAIgiAIgiAIgmCUSYYkmVq5u6a5syA0IfURsS7//eSZiTuaOwtCE1q6dlhzZ0FoQpVequbOgiAAoKm+NycfNxfRwyAIgiAIgiAIglF3ZA+DIAiCIAiCINwqtXgmblLiX1MQBEEQBEEQBKNED4MgCIIgCIJwTxFzGExLNBgaSZi3L7ND+hHk5ExeeTm/njvNd8eP1hszLrgtM7v3wtvBgfTiYpacPMbqmGidc4b6BfJizxD8HZ3ILitl3cXzfHf8CJUaMVG8udnIzXljSBjhbQKwkcs5mZrOx9v+IS4nr964FrY2vDksjH7+PpjLpOyPS+bj7bvJKi41eL6bvS2bZjzGj0dO8O2ew41xK8INpJ4o4/jKPPJTKrF0kNF2uB2dHnREIjH8AVWQqmLNi6l66Q4e5kz81kvnvKgVeWScLUcqk+DW3pJeTzhj72beaPciNIyN3Jw3wsMY0vpq/f5oR8Pq91tDw+jnV1O/45P5eMduMq+p3x4O9rweHkqIjyfmMhmHE1P4fNc+EvMKGvmuBEMGtPJjdtdQghxdyK0oY+XFUyw827D32g6Klqwb9SiD1i4htbRI51iAvYI3ewwkpKU3VRoNR7NS+ChqFyklhY1xG4JgUmJIUiPo5taK70eNIy4vjxlb/2TdxRjmhPRnVvfeRmNGBgTz5dAI9qUk8dyWDRxMTebTwcMYG9ym7pz+Xj4sGjmGhIJ8Zmz9k1/Onub57r14p//AJrgr4Ua+fHAkQ9sG8uXO/byxfhvO1tb89NhEHCwtjMbIJBK+nzqOjq3c+M/mnfx78046ebRk2SMTMJMarp6fjBmGXT3XFBpX5oUK/pp3BQdPOUPmtiRwgC3Hfs3n9B8FRmNyE7Qrx4z80J0HPm1V9zNo9tUVwkpyqtj0VjoVRWoGvupKvxkuFKRUsu39DKqU4oFAc/tq/EiGtQnki137eWPDNpxtrFkx7cb1e+mUcXR0d+PfW3fy3paddGrVkmVTr9ZvG7k5yx95kNauzvx76y7mbNhGSztbfnl0Ur3XFhpHtxYefD94ApcLc5mxex3r4s8xp1sYszr2uWFsW6cWLB8yEXOpTO+Yu7Udf0RMQ2Fhzcv7NvKvw9sJdHDm56GTsZCJZ7fCna/Bf6WPPvqo0adn11uxYsUtZ+he8HKvPpzPyeK1v7cCsDc5ETOplBnde7H01HGU6iq9mNkh/dh6OZaP9v9TE5OEo6UVr/Tqy4bYCwBMbNue9OIiXv1rC5rqavanJOFsZc1TXbrx4f5/qBK9DM2mi6c7g4L9mb5yHXsvJwJwLDmNnS89xdSenflun+HepRHtgmnr5sqo71ZwOTsXgPNXstk08zFGtg/mz+gLOudP7dEJf2dFo96LUL+Tq/NR+Fow8BXtl33PbtZo1NWcWVtAhzEOmFnoN/TyEpTYuprh3sHK6HVPrMrD3EpKxPvuddewbWnO359cISdOiVs747FC4+ri4c6gIH+mr1rHnrhEAKJS0tj1wlNM7dGZ7/bXX79HLlrB5Zya+p2ZzebnHmNku2D+PHuBiHbB+CqciFj0U11vRWxWDrtffJoR7YJZfSLa4LWFxvFK537E5Gfy2v7NAOxJT8BcImVmx94sjYky+PltLpXyeJvuzO4aSkWV/nGAV7v0p7RKxSM7IqmouUZKSSFLBz9IJ2c3orL0eyCF26MRz8RNqsH/mn369CEqKorc3Fw8PDzq/bmfyaUyent4si3usk761rhYbOVyerXS//fxsLPH30nBtvhLujGXY/F1dMLP0Ul7bZmMsspKNNXVdefkV5RjITPDxlzeCHcjNFT/AB9KVSoOxCXVpeWXlROVlEpYoF+9cfE5eXWNBYC4nDzisvP04jwdHZgTHsq7m/4y/Q0IDaKurCbjbDm+ITY66X59bKisqObK+QqDcbmJKpz9jNfR6upqkg6XETzETqfB0SLQgoeX+YjGQjMLranf++P16/eAAOP1O9S/pn7nXFe/c/IYUFO/t5+/zKTlkTpDmyrVakD7ni80HblURm83L7Ynxeqkb0m6iK25Bb1aehqMG+QRwMud+/HtmUN8euIfg+eM8Alm9aUzdY0FgOjcK/T+faFoLAh3hQb3MDz//PNYW1szf/58Fi9ejKen4Ypzv/NycMBCZkZCge641sTCAgD8HJ3Yl5KkcyzQSfvEOKEg32hMQkE+K86c4qcxE3i2aw9WnYsmwEnBk527sSsxnkKl4S8qQtMIcFGQml+I+prGHEByXiEPdHQ3HtdCQWJuvl56cn4Bfs5Oda8lwKdjh7E1JpZ9cUl65wtNo/hKJZoqcGilO6fA3l37uii9Errox+UlqHD0NOfPuWnkJaiQ20gJGmRL96kKpGYSSrKqUJVpsHU14+DiHOL3l1ClrKZVZyv6PuuCbQsxZKE5BTgrSDFQv5PyCxnToZ767aIgIU+/fiflFeCr0NbvYqWSU2kZgPZJdYCLgrlDwsgrLWNrTKxerNB4vOwcsZCZEV903Wdxsfa1n72CfemJenGnczPov2YRhaoKJgZ00DvuaeuAvdyS1JJCPug9lAd822Jtbs6+9ETeO7KD9NLiRrmf+51aTHo2qZvqr3niiSfo1q0b//d//9dI2bn72Vtox5yWqHR3uyyteW0r13/KaG9haTimUjfmcFoKS05E8Va/AZx59gXWPTSV3LIyXtmx2bQ3Idw0O0sLSpT6O5yWqlTYWBh/smxvYSROqRv3eEg3vJwcmLdjj2kyLNwSZal22J+5te4HkbmV9q1UVaY/LLC8oIryAjWFaZW0jbBn+HtutB5qx9mNhez9JhuAikLtE+VjK/Iozati4GxX+j/vQl6Cki3vplNZIYYbNic7S4u69/Br3ah+1/e+YGsgbsmUcfz57KOE+Hrx1T8HyCktu72MCzfFQV7z+V2p1Emv+yw20pOfWVZCocr4QztnS2sA3uw+ADdrW17a9ydzD26lncKVVcMfxspMLGog3Plu+rHVxx9/TExMTGPk5Z4gRftFotrIcY2BA9Ka7x7V1z29qv1KUjsE6eOBQ5jYtgPfRB3iQGoyXvYOvNKrLz+OmcAj6383OnZSMC0JIL1uPo9UIjFa5teXq861jMRJJFfj/JydeGVQX176fZPBLx9CE6opLGPzuQylm1tJGfEfNxw85Ni6aN9y3TtYITOXcHxlPl0mOlI7SsHSUcaQuS2R1Lwp2Lubs/HNdOL2lNBmuL3p70fQY7R+G6ioEuqv39dfpy5OYjhuwb4jLDpwlCGtA/lo1FAU1lYsOhB1M9kXboOk9vPbSJHWV9b1Ma+Z4J5TXsZzu9fVvecnFRWwbtSjjPdvx8rY07d0bUFoKjfdYLC3t2fgwIE6aZcuXcLDwwNra2tT5euuVaTSPpm4vifBpuZ1sUqpH6OsjdFdEcO65mlGsVJJSxtbprTvxMJjR/jqyEEAjqSlcibzCtunPsGkth1YEX3KpPciGDZrQAgvDtBdMWNbTCzOCv2/fxu5nOIK/TKvVVyhNPik0Voup1ipRCqR8OnY4WyLucSBuCRk13wBkUokyCQSvWESQuOR2xjuSags1+gcv5aZhRSPzvp/G149rDm+Mp/cRO1wJQCvbtZ1jQUA19aWyG2k5CYY/xsSTOuFsBBeDNOt31tjYvFz1i9D6wbUbxsDvcrW5tr6fb1jKWkAHElKxcHSgpn9e/P9wWOijjeRopqeBbvrP79rP4srb60e1vZQ/JMWr/OA6GROOoXKCtopWt7SdYX6iX0YTOumGgzr169n3rx5LF26lI4dO9alf/rpp5w+fZoPP/yQiIgIk2fybpJUWECVRoOvg6NOeu3ry3m5ejHxNXMXfB0cicnJ0o/Jz6WVnR1SiYRjGWk6sbF5ueSVlxOkcDbdTQj1+u14NP/EJuikDWkTQP8AH+0Tx2vSvRUO9a7TnpCbT1s3V710bydHzqRfwd3eji6e7nTxdGd853Y658wKC2FWWAiD//cDaYVFetcQTM/OzQyJFIoyKnXSa1/XfvG/VkGaiozoCgLCbJFbX21QVCm1fymW9lLs3cyRSLWTqq+nUVcjk4vVPprK6hPR7L50Xf1uHUCogfrt4+TA5Xrqd3xuPu0M1G8fhbZ+A3Rq1RIPBwe2ntedrxCdnsm4Tu1wsrYSQ5OaSHJRPlUaDT52TjrpvjWvLxXof343RFJxAWqNxuAkdjOpVIwOEO4KDf4UOnToEG+//TZDhw7F3V13ktd7773HiBEjmDNnDseOHTN5Ju8mKrWao+mpDA8I0kmPCAimsKKCU5lX9GKSCgtIKiwgIjBYNyYwmPj8PNKKi0kq0DZEerbSnWzu7+iEwsqK1CLxhbGpZJWUcjYjU+dnf1wSthYWhAb61p3nZG1FTx9P9tczSXl/fBIBLRQEuFxdKjXARUFACwUH4pLIKi5hwvcr9X4AVh+PZsL3K8kqLmm0exV0mcmluLWzJPFwqc7whIRDpchtpLQI0l83vyxXzcHFOSQe1C2nhAMlmFtJcAmwwNxKSsu22ute22hIP1NOVUU1bu0sG++mBB0G63d8Tf0O8K07r7Z+H4g3Xr8PxCdp6/P19dtFUbfi0sAgf74cH4G7vZ1ObGiAD1nFpeSKxkKTUWrUHM1MYYS37mfxSJ/WFCorOJWTcUvXLauqJCorlRHewciv2aOhr5sPNuZyorJSbivfgmGaammz/dyLGtzD8P333zNt2jTefvttvWM+Pj589NFHVFdXs2jRIpYuXWrSTN5tvo06zC/jHmLBiNH8FnOW7u6teLZbTz47uBelugpbczmBCmeSCwvIqygH4Juow3wxZAT5FeX8nRDHEL8ARge15oVtGwHIqyhn+ekTPNu1BwD7U5LwsLPn5V59SCsqYlXMmWa7X0G758KRxBQ+Hx/B53/vo6CsnBcH9KG4Qknk8atlE+CiQG4m4/wV7WTXLedimdG/F99PHc+XO/cDMDu8P7GZOWyLiUVdXc3ZjEyDvzOrpMToMaHxdHnIia3/yWDX51kEh9uRdbGC6PWF9HxUgZmFFFWZhoIUFXZu5lg5yHBrb4l7B0uOLM+jUlmNo4c5KcfLOLe5iF6PK7Cw1X6B6DFNwZZ309n+YQYdxzpSXqgmakUeLYIt8O4phns2p2PJaRxOTOGLcRF8vnMfBeXlvBimrd+rTlxXv2Uyzmdq6/fmmFhm9OvF0ofH88Uubf2eM7g/sVna+g2w6vgZJnftyJIp41iw7zBlqkrGd2rHwCB/Xlu3xejcKKFxfHPmIL8Om8KCAWP5/XI03Vp48GyH3nx6/J+6z+8gBxeSivPJU5Y3+Lr/PbGHVcMfZvmQiXx/LgoXS2vmdh/Iyex0/kq5fOMLCEIzk1Q3cBZPSEgIK1asIDg42Og5Z8+e5dlnn+XgwYO3lSm/b7+8rfg7wTD/QF7t1Rc/JycyS0r4OfoUS08dB6C3hyeR4ycz5+9trLlwri7m4fadmN61B61s7UguKuS740dYd/G8znWf7NyNRzp0xtPenuzSUvalJPHFof11DY+7kTzv3miN21ta8OawAQxpHYBUIuFESjrzduwh4ZplU1c8NhEPR3vC5y+rS3Ozt+VfwwfSz9+HSo2GA3FJzNuxh+ySUqO/6+J7r/LNnkN8u+dwo95TY3h64o7mzsJtSzxcyonIfArTVNg4m9E2wp6OYx0ByDhbzpZ3Mwh9sQXBg7VPjVWlGk6sziPpSBnl+Wrs3MxoP9qBNsN0JzJnXqjg+K95ZMUqMbOQ4NPbhl5PKLCwuXvX41+6dlhzZ8Ek7C0teGvoAIYE19Tv1HQ+2bFHZ9nUnx+diIeDPYO/1a3f7wwbSF8/H6o0Gg7EJ/HJX7r129vJgdmD+tPT2wNbCwvOXcli0f4jdZvE3U1UXnf/wgzDvYN4pXN//B0UZJaVsOLCCZbGaCefh7T0InLEVObs38wfcWf1YicGdOCL/qPo/8d3pJbq9vx3a+HB611D6dKiFeVVlexIvsQnx3bXzZ24GyU+Pre5s2DU8yemNdvvXtjtl2b73Y2lwQ2Gbt26sX79ery9vY2ek5KSwtixYzlx4sRtZepeaDAIDXevNBiEhrkXGgxCw90rDQahYe6FBoPQcKLBYNi92GBo8Dc1Pz8/Tp48We85J06cuO93ehYEQRAEQRCEe0mDGwxjxoxh/vz5ZGVlGTyelZXF//73P0aMGGGyzAmCIAiCIAjCzdJUS5rt517U4EnP06ZNY8eOHYwaNYqJEyfSpUsX7O3tKSgo4NSpU6xduxYfHx+efvrpxsyvIAiCIAiCIAhNqMENBplMxvLly5k/fz6///47y5cvrzvm4uLC1KlTmTlzJpaWYvk/QRAEQRAEofncq8ubNpeb2rhNLpczZ84cXnnlFVJSUigsLEShUODl5YVEcm92wQiCIAiCIAjC/eymGgx1QWZm+Pn5mTovgiAIgiAIgiDcYW6pwSAIgiAIgiAIdyoNYuSLKd2RDYZq26rmzoLQhMxS5M2dBaEJ9baOa+4sCE1okZ3Yq/h+Is0zb+4sCILQCO7IBoMgCIIgCIIg3Cr1Pbq8aXMRU8gFQRAEQRAEQTBK9DAIgiAIgiAI9xSxrKppiX9NQRAEQRAEQRCMuqkehoSEBDZt2kRhYSGhoaEMGDBA53hJSQkff/wx8+bNM2km70YDPPyY3TWUIEcXcivKWHnxFAujDzcotoNzS9aNepRBa5eQWlKkcyzAQcGb3QcS4uZNlUbD0cwUPoraRUpJYWPchnATrOXmzBkVRnj7AKwt5JxKSufTP/8hLiuvwdf4v0dHU1qh4l+/77il40LTOBsFG36C9GSwc4ABo2DEZKhvO5ozR2DTL5CWCDb20K0/jH8SLK7Z6/LgDtjxB2Slg4MC+gyBkVPBTPQFN6swH19m9+lLoMKZvPJyVkaf5rtjUfXGjG3dlpk9e+Lt4Eh6cRFLjh/jt3Nndc7xd3Lizf5h9Pbw1L6fp6Xxyb49pBSJ9/PmFObjy+x+15T3mdN8F3WD8m7Tlpm9asq7qKa8z541ev47AwbwVLfu+H/9lamzLwiNosE9DMePH2f8+PFs2rSJvXv3MmPGDF588UVUKlXdORUVFaxfv74x8nlX6dbCg+8HT+ByYS4zdq9jXdw55nQLY1anPjeMbevUguVDJmIulekdc7e244+IaSgsrXl570b+dWg7gY7O/DxsMhYy8Y2iuX0+dSRDOgTy9db9vBW5DWcba5Y9OxEHK4sbxkolEt4eM5ChHYJu6bjQdOLOwYL/gJs3zHwXQsJh/Y+wZZXxmNOHtTGtfODFD2HEJG3j4Oevr56zcx38+KVEe91/w5jH4PBOWPJxI9+QUK9u7u4seWAsl/PymLl5I+suxDC7b3+e79nLaExEYBBfDh/B/uQkntu4gUMpKXw6ZBhjW7epO8fd1pbfH5qCk6UVr27fwju7/ibIWcFP4yeI9/Nm1M3dnSVja8p740bWnY9hdr/+PN+rnvIOCuLLESPYn5TEc3/WlPfQYYxt08bg+T09PHi8S9fGugWhhqZa0mw/96IGvyt9+eWXTJw4kXfeeQeArVu38q9//YsZM2awePFizM3FUmq1XunSj5i8TF7btxmAPWkJmEulzOzYm6XnolCq9ZeNNZdKebxtd2Z3DaWiyvCysq927U9plYpHtkdSUXONlJJCloY/SCdnN6KyUhvvpoR6dfZ2Z2Bbf2YsW8e+i4kAHE9MY8fcp5jSpzOLdx01Ghvs5sK/xg6ivWdLylWVN31caFobfwUvf3j6De3rDj1BXQXbfoOhE0B+XfuwuhpWL4Ju/eCJOdq0Nl2gWgM7N4CyAszNYeMv0LZbNTPeuRrrEwT/eVZCzPFq2nVvktsTrvNS7z6cz85m9o5tAOxNSsRcKmNGj178cOKEwffz2X37sfVSLB/t3QPAvuQkHCwteTmkLxsuXgDglZC+lFaqeHTdH3Xv+alFRSx5YCydWrYkKj2tie5QuNZLITXlve268u7Zix+O36C899SUd1JNeffpy4YLF3TOtTIz47/DhpNZWkorO7vGvyFBMJEG9zBcvHiRadOm1b2OiIjg+++/5+TJk7zxxhuNkrm7kVwqo7ebF9uTY3XStyRexNbcgl4tPQ3GDfIM4OXO/fj2zCE+Pf6PwXNG+ASzOvZMXWMBIDr3Cr1/WygaC82sX7APZUoVBy8l1aXll5YTFZ9KaJv6d0WfN3k4UomEqQsiySspu+njQtOpVEHsGejaXze9eygoyyVcMjACISUOcjIkDB6rmx4+Hj75UTskqagAykokdA7RPaeVD9g6VHPGeHtTaERymYzeHp5sj7ukk771ciy2cjk9PTz0Yjzs7PF3UrA97rJuzKVYfB0d8XN0BGB4YCC/nTur84AoOiuTPj8sEY2FZiKXyejt6cn2y9eV96V6ytveHn+Fgu2X6i/vWm+HDSC7rJQ/zp0zef4FXRokzfZzL2pwg8HW1pb8/HydtO7du/P555+zfft2MW+hhpedIxYyM+ILdf+tEou1r/3sFQbjTudk0P+PRSw4cwh1tUbvuKetA/ZyS1JLCvmg91BOTnmJi4/OZmn4BFrZiKcUzc3fVUFKXiFqje4mVcm5hfi6ONUb+9bq7Ty66Ddir+Tc0nGh6eRcgapKCS2v+97QopX2v5kG2u0pNfvUmVvAN+/CrAfglQmwaoG2AQJgZQNSWTW5V3RjS4uhrFj7e4Wm52XvgIWZGQnXffYlFhQA4OeoX7cDFdr3+OtjkgprYpyc8LS3x97CktSiIt4fOJjjz87k/KyX+P6BseKpczPycrhBeTvVU94F15W3gZj+3t6Mb9eOudu3U10tNjQU7i4NbjAMGDCADz74gNOnT1NZeXVYxJAhQ3j77bf56aef+OCDDxolk3cTh5rxCCWVSp300ppvBrbmhnc1ziwroVBVYfS6zpbWALzZYwBuNra8tPdP5h7YSjuFK6uGP4yVmRgS1pzsrSworVDppZcpVdha1L+T9Y0aAqKhcOcoK9H+t6Y61ql9XWGgA6i4Zv7qd+9fM4dhMuzfBss/1x6zsISeA2D3Rti/XdtQuJICS+eBzAzqeWsQGpG9Rc37uUq3bpfWvLYzULeNx2g/N23lFiistH8wc/uF0tLWlpe3beHNv/+ibQtXfn3wIazELPdmccPyltdT3krj5V0b++nQYXx98CAJNY0JQbibNPhdafbs2bz66qtMmTKFxYsXExYWVnds2rRpSKVSPvnkk0bJ5N1EUrNMirFnB9VGj9TPXKpt2+WUl/HcrnV1V0kqLmDdqEcZ79+OlbGnb+naws2RSLSTkHXTJIZLVgIa8STpnlFblMZWQ5IYeARTO4Kwaz+Y8Iz2/9t00V5r3TIJYx6rxs0LHnkJzMy1E6FXfCVBblHN8EmgUoLcUv+6QuOT1r2fG67Dhuq2sZjavxlNdTVyWc37eVkZMzf9efX9vLCAtZMfZlybtqw6G22COxBuRl3ZGXnP1hj4O2hIeQO8O3AgGSUlLDtx3FTZFW7gXp183Fwa3GBwcHBg2bJlXLp0CVdXV51jly5dYty4cfTt25ft27ebPJN3kyKVtmfB7rqeBJua18UqpV5MQ9T2UPyTFq/ztnQyO51CZQXtFC1v6brCzZsZHsKsoborXm0/E4uzi7XeudZyOcUVt1bmwp3H2kb73+t7EmpfW+n/CWBppf1vx9666R16wLpl2iFLbl7a8x5/DSbPhLzMapzdtD0PB7ZDa3fT3ofQMEVKbd21vW4mu03Nk+ZipX7dvhqj+xlgXbMwSLFSWfcEe09Sgs77+akrGRRWVNCuhe5nrNA0GqW8VUoG+/kxunUbxq78ta6BUftwUSaRoKm+1UeJgtB0bqrfc8OGDXzyyScsXbqUjh071qV/+umnnD59mg8//JDnnnvO5Jm8myQX51Ol0eBjpzvW0bfm9aWC3Fu6blJxAWqNBrmB5VbNpFKdidBC4/r9SDR7zifopIW3D6BfsA8SydWn0ADezg7E38Q+DMKdrUUrkEqryUrXTc+uee3uox/jWjPfoeq6Ba5qq6x5zXeTM4fB2g4C20MrX21aUQHkZ4N3oClyL9yspMIC7fu5g6NOum/NRNZLefp1O75m/LuPgyMx2dl16bXXuJSXS0FFhfb9XGbk/dzISnlC40oqqCnv6yYq15V3roHyzqspb8fryrsuJpdX+vTF0syM7Y89rhd/6ZVX+ePcOd7YcX8/bG0MYqdn02rwv+ahQ4d46623GDp0KO7uuo+73nvvPUaMGMGcOXM4duyYyTN5N1Gq1RzNTGGET7BO+kjf1hQqKziVk3FL1y2rqiQqK5URPsE6jYa+7j7YmMuJyky5rXwLDZddXMq5tEydnwOXkrC1tKBfsG/deU42VvT09+RAbJLxiwl3FXM5BHWEkwd0G4bH94G1bTV+rfVjgjqChWU1Ubt1008f1k50Dmirfb1nM/zxve45O9dqhzl1uq53QmgaKrWao2mpDA/UbbFFBAZTWFHB6Sv6s9GTCgtIKiggIkj3MyAiKJj4/DzSi4spq6wkKj2N4QFBOo2Gvl5e2MjlYpWkZqJSqzmaaqC8g26xvPO05f2/w4cYu/JXnZ9V0WcAGLvyV/53+FDj3ZQgmEiDexi+//57pk2bxttvv613zMfHh48++ojq6moWLVrE0qVLTZrJu803pw/y6/ApLBg4lt8vRdPN1YNnO/Tm02P/oFRXYWsuJ8jRhaSifPKU5Q2+7n+P72HViIdZPmQi35+LwsXKmrndB3IyO52/Ui7f+AJCozmekMbRuBT+OyWCL7fso6CsnFlD+lBUoeS3I2fqzgtwVWBuJuNCenY9VxPuZKOmwtdvwuKPod9wiI/R7s784NPaPRjKSyEjGVq4g52jdqjRmMfg9yUSrO2q6doP4mK0+zaEj9OeAzB4HPzvbQmrv6umcx+4cAq2rpYwYnI1LcSQpGaz4OgRfn5wIt+OHM3v587Szb0V07v34LP9+7Tv53I5gQpnkgsLyCvXvp9/e/Qwnw8bQUFFOX/HxzPE35/Rwa15Ycumuut+fmA/Kyc8xLKx4/n++DFcrK2Z2z+UkxkZ/B0f11y3e99bcPQIP0+YyLejrinvHj34bF895X3kMJ8PrynvuHiGBPgzunVrXtisLe+0oiLSiop0fs9gP38AojMzm/YG7yNiDoNpNbiHISYmhokTJ9Z7zsMPP0xMTMxtZ+pud+hKMjN3r8PfXsHiweMZ59+OT47tZsk57WLqHZxbsm7Uowz2Crip657ITufhbZFIJRK+GzSOt3sMYmfKZR7/6zcxsfYO8PLPG9kVE8fskaF8PGk4mUUlPL1kDUXlV8e9vjNuMPMffaAZcyncrjZdYMa72iVUv3sfjuzSTmYe/pD2ePJl+PQVCdHX7J0wdAI8/lo1sWe0S6se2A5jHr06CRqgfXd45s1qYk7AN+/Bif0w5flqHnyqSW9PuM6h1BSe37wRfycnFo0ew9g2bZi3by/fn9D2prdv4crayQ8zyPfqfitrzsfwr51/0d/bh8Wjx9Dbw4vXtm9ly6Wr+/OcvJLBI2t/R4KEhaMe4K3QAeyMj+eJ9WvF+3kzOpSSwvMba8r7gTGMbduGeXv38v3xmvJ2dWXtww8zyO+a8o6J4V9/15T3mDH09vTitW1b2RIba+zXCMJdR1LdwMWAu3Xrxvr16/H29jZ6TkpKCmPHjuXEiRO3lSnfHz+7rXjh7mJzvv5lR4V7y8KZ3zV3FoQm9OSGGc2dBaEp6W8jJNzD4l99rbmzYNRDB2c22+/+ve+99znX4B4GPz8/Tp48We85J06cwMPAToiCIAiCIAiC0FTETs+m1eAGw5gxY5g/fz5ZWVkGj2dlZfG///2PESNGmCxzgiAIgiAIgiA0rwY3GKZNm4abmxujRo3is88+Y/v27Rw6dIitW7cyb948Ro0ahUKh4Omnn27M/AqCIAiCIAhCvTTVkmb7ua18azTMnz+f0NBQOnfuzFNPPUVSUsNWW9y4cSOtW7cmNTX1tvJgSINXSZLJZCxfvpz58+fz+++/s3z58rpjLi4uTJ06lZkzZ2JpKbYkFQRBEARBEISbtXDhQiIjI5k3bx4tW7bk888/Z/r06WzatAm53Picz7S0NN5///1Gy9dNbdwml8uZM2cOr7zyCikpKRQWFqJQKPDy8qrbtVAQBEEQBEEQhJujUqlYtmwZr7/+OgMGDADg66+/JjQ0lL/++otRo0YZjNNoNLz++uu0b9+ew4cPN0rebmkbPDMzM/z8/OjSpQve3t6isSAIgiAIgiDcMe7GIUkXLlygtLSUkJCQujR7e3vatWtHVFSU0bhFixZRWVnJc889d8u/+0ZuqoehqchyzJs7C0ITqpbd+Bzh3nFBKXYhu59US8WeAvcTi7xbeg4pCPeU8PDweo/v3LnTYPqVmt3E3d11PyddXV3JyMgwGHPmzBmWLVvGH3/8QWYjbgR4RzYYBEEQBEEQBOFW3Y07PZfX7B5+/VwFCwsLCgsL9c4vKytjzpw5zJkzB19fX9FgEARBEARBEIS7gbEehBupXThIpVLpLCKkVCqxsrLSO/+jjz7C19eXKVOm3FpGb4JoMAiCIAiCIAj3lLuxh6F2KFJWVhbe3t516VlZWbRp00bv/DVr1iCXy+natSsAarUagNGjRzNmzBg++OADk+VNNBgEQRAEQRAEoZm1adMGW1tbjhw5UtdgKCoqIiYmhmnTpumdv2PHDp3Xp0+f5vXXX2fJkiUEBASYNG+iwdBIwnx9ea1/XwKdnckrL2flqdMsOmp8hjvA2LZtmdG7J94OjqQXF/F91DF+iz6rc87kjh15onvXunN+PXWaH0+cbMxbERrIWm7O6xFhhLcLwNpCzqmkdOZt/oe4rLx641zsbJg7Moy+gT6Yy6QcuJzMvE27ySoqrTvH1d6GORFhhAb5IpNKOJ1yhfl/HSA6tfHGKwrGJRxXcvDXYnKT/5+9+45vquofOP5J2iTdI23pppPSsldZbdkbFBBciOhPBUVRQBD3RkEBfRwgCoIioogKKEuWrLLKprTQveneK+n6/ZFSCElKgQ7A83698nqenJzv5Vxv77n33DNuJabWUjqPMCNwonm9K8bFhZVz5NcSshMrMLWU0qavCcGPWyAzuTpJ9HKUmgOri8iMrURmIiFggAlBj1tiLLv7npTdS/q19mRO7yDaKDX1+c/hZ/nm5PF6Y8a1DWB69560trYmraiI706dYH3EeQBcLa049ORUg7EbIsKZt+efRt0H4eaYy2W8Mqwfg/19MFfIOZ2cxsfb9xGbVX997mBhzmsj+tHXu7Y+j03io+3/kllUopXvqb7deahHR5ytLEkrKOSnY2dYd/xsU+6ScBeQy+VMnjyZxYsXo1QqcXV1ZdGiRTg5OTF06FCqqqrIzc3F0tISExMTPDw8tOKvTJp2cXHBzs6uUct2Uw0GlUpFdHQ0vr6+mJiYEBkZydq1a8nIyKBNmzY88cQTODk5NWoB70bdXJz5dvxYtl68xGeHDtPD1YU5IcFIJRKWHdN/kRnp14bFo0bww8lTHEjYz1BfXxYMH0Z5ZSV/RV4EYFLnTnw4dAjLjx0nNDGRzs7OvD6gP6YyGd8Y2K7QfBY/MoqObk4s2XGQYpWa5wf1ZvXTE7nvfz9SUKbSG2MklfDdE+MwU8j5YPMejI2kvDw8mJX/N4EHvlpLZXU1Fgo5a6c9jKlcxhe7QknMyWdIO19+nPoQT67YwLmU9Gbe0/+2tEg1mz/Ko22wCX0fsyQtUs2htcXU1ECvhyz0xsQeL+evj/NpN9CUkCkW5CRXEvpTMaUF1YyeawNA/uVK/ngnDxd/GaPn2ZCbXEno2iJUpTUMm2HdjHsoXKubkwsrxoxja/QllhwNJdDFlbl9NPX50hPH9MaM8vVjydCRrD5zigNJ8Qz19mXh4GGUV1awOeoiWSUljP9tnU7clE5dGN2mLb9FhOvZqtCclkwcRUdXJxbv0tTnLwzozQ9PTGTM0hvU55PHYSaX896WPciMpLw8JJjvp0xg/Dea+hzglWEhPN6rK1/9e4Rzqen0b+PJO6MHUVlVzW8nzzfnbt7Tqrk7H7S89NJLVFZW8tZbb1FeXk5gYCDff/89crmclJQUBg8ezIIFC3jggQeatVwNbjDExsby5JNPkpWVhYuLC/Pnz+f555/Hzc0NHx8fdu/ezZ9//sm6desavRvkbvNinz5EZmYxd/sOAA4kJGBsZMSzvXry/clTqCordWJeDg5ie1QUH+3bD8DBhERsTEyY1bdvXYPhuV492XrxEosOHgLgcFIyXra2TOnaRTQYWlhnd2cG+Hvz3A8bORCVAMDJhFR2zX2KR3p35tt/9R+f4R388Hdpxf3/W0NMZg4AFy9nsfmlKYzs5MffZy7yQI/2uCmtmbT8V84kaZZVOxKThK25KfNG92fyt+ubZR8FjSO/FuPgJWPkyzYAeHVXUFUJx/8oodtYc2QK7YtUTU0N+1YW0aaPCcNnam78W3dWUFMNp7eUUqGqQaaQEPZnCXJTCWPftMVIJsG7hwKZQsLe7wrp/ZAFVq3E+sMtYWbPPkRmZ/Lyru0AHEhKwFgq5bnuPVl5+iSqKt36fE7vILbHRDH/0L7amERsTEyZ1asvm6Muoq6u4kyG9hKJHVs5MrpNWxYfOcSJy6lNvl+CYV3cnBnQ1ptpazdyIDoBgBOJqeye9RSPBnZm+QH99fmIdn4EOLdizNdriMnS1OeR6Vn8/fwURnbw4+9zF3G1seLJPt2Yv+1ffgk7B8Cx+GScrC0J9vUQDQYBIyMjXnnlFV555RWd39zc3Lh06ZLB2F69etX7++1o8ILJn376KV27dmXTpk10796d6dOnc9999/H333/zxRdfsH37doKCgliwYEGTFPRuITcyope7G/9ER2ulb4+KwkIuJ9DVVSfG1coKb6WSnVExOjEetjZ42toA8H+//8nCAwe08lRUVSE3EiPLWlqwnwelKjWhMYl1aXklZYTFp9DPz8tgXFAbD+KycusaCwCxmbnEZeUS0lYT5+1gR0FpeV1j4Yqw+BS6ebhgZaJo5L0RDKmsqCHlvJo2fbT/m/sFmVBRVkNqhFonJiuukoL0KrqMMdNK73a/OU9/51DXwEg8rcI7UIHRNcOP2gSZUFMNCaf0P9EUmpZcakQvNzd2xF5XN8do6vOeLnrqc0srvG2V7IiN1onxtLHFy8ZW77/14YDBxObl8P2Zk423A8ItCfb1oESlJjT2mvq8tIywxBT6tzFcnwf71tbnWdfU51m5xGbn1sUNCfBBXVnF76cuaMW+vGEbL63f0sh78t92N7647U7W4AbD8ePHmTVrFv7+/rz66quoVCoeffTRujG7xsbGPPfcc5w8+d+u7NytrVEYGxOfl6eVnpiXD4CXUvdi4WunBNCNya+NsdXExObmklZYBIC1iQkPdezA+PbtWHvmTCPugXArvB2UJOcWUFWt/ZKqpNwCPO313yAA+LRSkpCdp5OelJOPp50mLq+kFHOFXKdh4K7UPK12VYrhKs2lIL2KqkqwddFupNs4a57+56XqPm3OjK8AwFguYeMHeXwxMZ2lj2aw99tCKtWav5cKVQ2FmdU62zWzliI3k5CXprtdoem5W1ujMDImPl973HpCQT5wtW6+lq+ytj7P1z6vE67U53oaDPf7+dPZ0Zn3D/xLdY140V1L83ZQkpJnoD63M1yfezsoScjRU5/nXq3PA5xakZCTR6CHK388O4nz77zEntlP80hgp8bdCUFoZA1uMJiYmFBeXg6Avb09Dz30EAqF9g1MYWEhlpaWjVvCu4xV7X+TYpX2k8YStea7xXUv49CKUV8fU1Ebo/3fubuLC6dmPM+C4cOIys5mzWkx6bmlWZoodI45QIlKjYVC95hfG1dSbiDORBO35exFqmuq+XzSGHxb2WFpomBMZ3/Gd28PaCZbC81DVaIZgyw3036CJDfVfFeX6t7slRVoYv5akIdda2PGv2NLz4nmnN9Vyo7/FdS73Svb1rddoekZrpvrqc/lJvpjKgzHTO3ag7C0VI6lptx+oYXbZlVPfW5eT31eX9yV64CtmSmOVhYsmjCS30+F88xPGwmNSeS9MYNFo0G4ozW4wRAcHMyHH35IbGwsAB988EHdXIWamhqOHTvGO++8w5AhQ5qmpHcJad0qKfov8PqeHl3ppam57re6LV2XnlxQwKO//sasLduwVCjYNPkx7My0hzsITUci0Uxuu/YjNbA6jgT9x/wKqUSi9y9FIrkaF5uZy/NrNtPazpq/Zk3h2DvPMyWoG1/uOgxAaW3DUmh6NZr7egwthiTRU6NeGeLu29uEfk9a0rqTgsAJFvR5xIKoQ+XkplRS30Plmhr92xWanrSubtb/e7WedGnt34ah+vz6+qC7swsdWjmy4lT9q+gJTUNffS6pp16+/rhq/37j+lxmZITS3Iz3tuzhl7BzHItP5p2/d3MwOoHn+/VqnJ0SADEkqbE1ePD766+/znPPPceyZctYsmSJ1m/btm1jzpw5hISE8PLLLzd6Ie8mhSrNWOPrewXMa58qFal0xyLXxVz35OLKk+MitXZMZkkJmSWaJdrOXr7Mnmee4uGOHQyuwCQ0rucH9eaFwX200v45H4XSQrfRZqaQ6z3mVxSWq/T2QJjJ5RSXX407HJPE0EWrcLW1AiA1r5Dx3doBUFBafkv7Idw8hYXmQqC67om/ukzzXWGme2d/pffBO1C7TvDspuDQmmKy4ivqfruynWtVlNcg17Ndoeldrc+1z9F663O1/muAmaw25rr6fKSPH/nlZfybGN84hRZuygv9ezNjoHZ9vuNCFHZ2eupzef31eVE99fmVuBK1murqGvZHax/vgzEJhLTxxM7cjJyS0lvZFUFoUg1uMCiVSn777TcuX75MTU2N1nrjjo6O/Pzzz3Tv3r1JCnk3SczPp7K6Go/aicpXXPkek6O7hnN8rmbMo4eNDRGZWVdjbDQx0Tk5mMtkDPb14ezl9Lq5DQBJBQUUlJfj/B8fCtacfjt+nn0XtSv7we18CGrjUfsE6mp6a6V1ve9hSMjKI8CllU56azsbztcul+psbUkf39b8fSaS1LzCujztXB3JLy0jLb9QJ15oGjZOxkikkH+5Siv9yndla90q1aZ2XkJVhXZjoLp2E8ZyCTITKRZ2Up3tlhZUoy6twc5dLGzQEhILNPW5p7WNVvqV7zG5OToxcbVz0TxtbIjIzrwaY6M/ZpCXNzvjYuuW3BSa128nz7Mv6rr63N+HYB8D9Xk972GIz84jwFlPfa604Xyqpj5PzMlHKpUgMzJCXXn1fDc20jwUKNeziqJwa+7VJ/0t5aYeW23evJlx48YRHq69RvQ333zDs88+y/bt2xu1cHcjdVUVYSkpDGvjq5U+0s+PgvJyzqbrrpmfmJ9PYn4+I/38dGLiaic6V9XUsHD4MKYF9tDK09HJEVtTUyKzshCaR1ZRCRdSM7Q+odGJWJgoCG7jWZfP1tyUQC83DkcnGtxWaEwi3g5KfFop69J8WinxdlASWhuntDBj/oRh9PR2r8tjb2HG6M5t2RMR2/g7KBhkLJfg1l5OzJFyraEJUaHlKMwlOLXRnU/i1l6GzETCxQPaPUGxx8qRGoGzv+aJpEcXBXFh5VRe07CIDi1HIoXWnQyPmxaajrqqiuOpKQz3aaOVPtJXU5+fydBTnxfkk1iQz0hfP52YuLxcUouK6tKsFSZ42dhyUiyj2mIyi0oIT8vQ+oTG1tbnPp51+WzNTAn0cNNaOel6obGJ+Ngr8XG4pj53UOJjr6xbQe9Abc/C6A5ttWIHtfXhYnoWJXrmQAjCnaDBj62OHDnC66+/zgMPPICzs7PWb++88w4rVqxg7ty5ODg40KNHDwNb+W9YeuQYax6ayFf3jeH38HC6ubgwNbAHnx44iKqyEgu5HF87O5Ly88ktK6uNOcqnI0eQV17Gnpg4Bvt6M9q/LS/+rVlmrbyykm+PhzGjT2/yyss5nJiIp60tM/v2ISIzk9/DL9RXJKGJnUxI5VhcMp8+NJLFOw6SX1rGC4P7UFiuYv2xc3X5fFopkRsZEXlZ08Dbfi6KaQN68u0T4/nsH837NV4eHkxURjb/hEcBcCE1g5MJqbwzdjCLtx+gqrqamcOCqKyuZtmeo82/s/9xvR4y5/d38tjyST4dhpqRFqnmxMYSQp6wRKaQoCqtJiepEhtnY80qR6ZS+k6yYP+qIkwsCmnTR0FaZAVhf5bQ9T4zzKw1z20CJ5hz8WAZG9/Lo/s4M/JSqzj0UxGdRphh6SDewdBSvj5xlLXjHmTpiDH8FhlOdycXpnUL5JPQA6iqKrGQyfFV2pFUkE9uuaY+/+r4URYPHUFeWRm742MZ4uXDmDZtmbH9b61t+9vZAxCtp6dCaDknElM5Fp/MogkjWbxLU5/PGNiHonIVv4ZdU5871Nbn6Zr6fFt4FM+G9OS7yeP5bFdtfT40mKjMbHZEaOrz4wkp7L0Yy+sj+mMqNyY6I4exXdrRzd2FF375q/l39h4mehgal6Smvhk813jqqafw9fXljTfeMJjnzTffJCMjg5UrV95WoXwWf3Zb8XeCYb6+zAzqg5etLRnFxaw9c5bvT2iWnO3l7sa6hx9i3vYd/HEhoi7m0U4deSawB86WliQVFLD82HE2RUTW/S4BHu3ciclduuBhY0N+eTk7o6NZcihUZ0WOu4ki9944qa1MFLw6uj+D2vkglUg4nZjGwq37tZZN/eGZibjaWjF00aq6NCdrC14fM4C+vh5UVlUTGpPIwq37yS4qqctjZ2HGq6P7E+Sr6SY/FpfC//45RGJOfnPuYqOY+cSmli7CbYs+Us6RdcXkpVZiYWdE51Fm9BhvDkDyeRUb3sxj+Ewr2g++Og46fHcpJzeVkp9WibnSiE7DTQmcYI5EevXvP+WCmgOri8iKr8DUSkrAAFOCJlsgNbp7z5GFO+9v6SLctmHevszu1beuPv/p/BlWnq6tz13d+PWBh5m7awd/XLz64ObR9p2Y2q0HLhaWJBUW8M2JY2y8FKm13dG+fnw98j4Gr11NXJ7hoS53E0X2vTHfxspEwWsj+jPYX1Ofn0pKY+GO/cRfs2zqmicn4mpjxeD/XVOfW1nwxsgBBPl4UFFVTWhsIgt37Cer+Gp9Ljc2YsaA3tzXKQClmSkxWTks23+MPRfvvh7ji+/PbukiGDRw75wW+7f/HbTkxpnuMg1uMPTu3Zs1a9bgd92wmWuFh4czbdo0Dh8+fFuFuhcaDELD3SsNBqFh7oUGg9Bw90KDQWi4e6XBIDSMaDDody82GBo8JEmtVmNiYlJvHmtr67p3NQiCIAiCIAhCS6gRQ5IaVYMfBXh5eXH6Bi8IO3XqFK6urrddKEEQBEEQBEEQ7gwNbjDcf//9fPnll2RmZur9PTMzky+++IIRI0Y0WuEEQRAEQRAE4WZVI2mxz72owUOSJk+ezM6dOxk9ejQTJ06kS5cuWFlZkZ+fz5kzZ/jzzz/x8PDg6aefbsryCoIgCIIgCILQjBrcYDAyMmL16tV8+eWXbNiwgdWrV9f9Zm9vz6RJk5g+ffoN5zkIgiAIgiAIgnD3uKnXh8rlcubOncusWbNITk6moKAApVKJu7u71pufBUEQBEEQBKGliPcwNK6bajDUBRkb4+Xl1dhlEQRBEARBEAThDnNLDYamVuV4976ETLh55XJZSxdBaEYbg9q2dBGEZiR9XTzl+08Rh1u4Q4hlVRuXeMOKIAiCIAiCIAgGiQaDIAiCIAiCIAgG3ZFDkgRBEARBEAThVolJz41L9DAIgiAIgiAIgmBQo/Qw3HfffXz33Xc4Ozs3xubuCf1dvJjTpR9trO3JKS9lXfRploUfbVBsB6UjG0dOYeCm70gpKdD6bZh7G17sGIS3lZKsshI2xoezLPwIFdXVTbEbQgP18/BkTu8gfJV25JaVsS78LN+cOF5vzNi2AUzv0ZPW1takFRXx3akT/HbhvFYeb1slrwX1o5ebG5VV1RxPS+Hjg/tJLiwwsFWhuZhaKHjmvYn0GdkFMwsTIsJiWf7GepKiLtcbZ25lypNvjSdodFdMzRUkRKbyw8ebOHvwkt789i62fHvoPTYu383aT/9uil0RbqCfpycvB/XF1672/D57luXHw+qNGRsQwHM9A2ltbUNaUSErTpzgt/PhWnke7tiRJ7t1rcvz85mz/HD6dFPuitBA5nIZrwzrx2B/H8zlck4np/Hxjn3EZuXWG+dgYc5rw/vR18cDmZGU0NgkPtr+L5lFJXV5Hu7RiffHDNaJ/fXEOd7bsqfR9+W/Skx6blwNbjBs2rTJ4G+JiYls374dpVIJwLhx4263XHe1bg6urBgwkS2JkSw5c4AerdyY26U/EiQsDT9Sb2yAbStWD3oQmdRI57dgZ0+W93+ALQmRfHJ6H21tHJjXtT9KhRnvhu1qqt0RbqCbkwvfjRnH1uhLLDkaSg9nV+b0CUaChGUnjumNGenrx5JhI/nhzCn2J8YzzNuXhYOHoaqsYPOliwA4W1iyYeIjxOXnMXvHNhTGxszpE8SP4yYw8uc1qKoqm3M3heu89t1U2nb15Pv3/6C0qJzHXhnDwo0vMy3oXYrzS/XGSKUS5q9/CQc3Jd+//wf5WYWMnTaYD395iZnDPiY+IlUn5uUvn8DcyrSpd0cwoJuLM9+OG8vWS5f4LPQwPVxdmBMcjFQiYdkx/Q8FRvq1YfHIEfxw6hQH4vcz1NeXBcOGUV5RyV8XNef3pM6d+HDIEJYfP05oYiKdnZx5fUB/TGUyvjle/8MGoektmTiKjq5OLN51kGKVmhf69+aHJyYyZumPFJSp9MYYSSV8N3kcZnI5723Zg8xIystDgvn+8QmMX76WytoHewFODkRn5vDWXzu14rOL9dcbgnAnaHCD4f3336e8vByAmpoand8//fRTACQSyX++wTCrUxAReRm8HLoFgP1p8cgkRkzv0JuVkWF6b/RkUilPtO3BnC4hlBu4EXzQpyNpJYXMCv2b6poaDl1OwN7EjKcCAvnwxB4qa0QvQ0t4qVcfIrMymbNzOwAHEhOQSaU816Mn358+qfd4z+kTxPaYKOYf3AfAwaRErE1Mmdmrb12DYVavvpRUqHl84wbKKzXbSCks4Lsx4+jk6EhYmu7NpdA8Anp402tYJ95+5EvCdmueGocfjeaHkx9z31MD+OWzbXrjBk7shV9XT2YMml/XODh3OIpvDrxLt4HtdBoMY/6vP+5tnJp2Z4R6vdinD5GZWczdvgOAAwkJGEuNeLZnT74/eQpVpe75/XJQENujovho334ADiYmYmNqwqy+fesaDM/17MnWS5dYdPAQAIeTkvGytWVK1y6iwdDCurg5M8DPm2k/b+RAdAIAJxJT2T3rKR4N7MzyA/qPz4h2fgQ4tWLM0jXEZOUAEJmexd/PT2FkBz/+Pqc59v5ODpxPTedsSnqz7M9/lZjD0LgaPIfhzz//pF27dvTq1Yv9+/dz8eLFuo+pqSm7du3i4sWLREZGNmV573hyqRG9HFvzT1KUVvq2pItYyBT0bOWmN26gqw8zOwXx9fnDLDz1r4FtG1Naqab6mgZbrqoMhZEx5jJ54+2E0GByIyN6ubnxT2yMVvr2mCgs5HICXV11YlwtrfC2VfJPTLROjKeNLV42tgAM9/XltwvhdY0FgPOZGfRZ9a1oLLSw7oPaU1Zczsl/I+rSCnKKOX8kmsAhHQ3GBd/XjXOHo7QaBhWqSp7p9TZ/LNXuJXTysOepdyfwv9k/Nf4OCA1Sd35ff65G1XN+W1nhrVSyMzpGJ8bD1gZPWxsA/u+PP1m4/4BWnorqKuRGYi2Slhbs60GJWk1obGJdWl5pGWEJKfRvY/iltcG+HsRl59Y1FgBis3KJzcqti5NIwK+VPZHpWU23A4LQBBrcYPDy8mL9+vV06tSJsWPHsm2b/ido/3XuFjYojIyJK9Qe55hQlAeAl5VSb9zZ7MsEb/yGpeFHqKrW7cEBWHPpJJ6WSqa164mVTEFXexee8u/B3pQYCtTljbsjQoO4W1mjMDImPv+6412QD1B3838t39qhe/H5eVrpidfEuFlZYaUwIaWogPcHDObk1OeJfH4mK+4bh4uFZePviHBT3P2cSE/MprpKu1cvLS4TVx9Hg3HeHdxJjExj3LOD+eHkx2xN/4av975Fx75+WvkkEglzvn6Sg5tOcHLvhSbZB+HG3K2tURgbE5973bmanw+Al20953de/TGxubmkFRUBYG1iwkMdOzC+XTvWnjnTiHsg3ApveyUpeQU61+Kk3AI87XSP+bVxCTl5OulJufl1cZ52tpjJZXRxc2LHi09y/u2X2D7jCcZ2DmjcnRCERnZTjzKMjY15+eWXCQkJ4dVXX2XPnj289957TVS0u5O1XAFAcYX2GMeSCs3bqy1kCr1xGWXFN9z2kYwkvo04yhvdB/FG90EAhOekM/OQmAjZUqwUtcdbrf128pLa75Zy3Z4fK4VJvTEWcjlKUzMAXu3bj7MZ6cz8Zyt2pma80jeEnyc8xKiff6RMz1AIoXlYWJlRWqTbSC8rLsfM0sRgnLWdJSFju1OcX8rK935HVabmoZkj+Oi3mcwasYC48BQAxj83GCcPB96dtLTJ9kG4sRud3xb6zm8TQzEVtTHa14DuLi789ugjAJxPT2eNmPTc4qxMFBSr1DrpJWo15grDvflWJgoSc/P1xlnUxgU4OQDgYmPFwn/2U1lVzdjOAXwyfgRyIyM2nArXiRdujZ7R88JtuKVlVQMDA+smQY8ZM4aKiorGLNNdTSLRjJkz9Heqb/5HQ33cazjPtuvNl+dCeWTnOl45vBVbE1N+HPwQJqIbu0VIrxxvA4dVX2eRtHZYZc11fyVXRltW19Qgr530nl1WyvStmzmUlMjmS5HM2P43HtY2jPNv1xjFFxpAIpEgNZJqfSRSif5zWSKhxkAPIYBMboS5lRlvPvg/Dv19irDd4bzz6FeUFpXx0EsjAHDzdWTK6+P4YvZPlBaVNdVuCQ1w5fw2VKNX60mXcKVO0H9+X5+eXFDAo+t/Y9bWbVgqFGya/Bh2Zma3VW6h4SQSzWTlaz8SiURvnS6h/mt4fXFXhhIfi09h2s8befLH39kXFc+h2ERe+XMHobGJvDiwb+PslCA0gZu+yywrK8PExAQrKyuWLFnCpk2b2LhxIzKZrCnKd9cpVGt6Fiyv60m4MsegqEL/6go34mhqwSNturA0/DCfnT0IwNEMOJtzmZ33PcNDvp1Yc+nUbZRcuBWFKs3xvP5Jo3nt9yK17vG+GqP9N2J2TUxxbY/U/oR4rVuSM+mXKSgvp529Q6OUX7ixx14Zw+R592mlHdx8Ehsf3aFhpuYKSgoN3+SXFpeTHJVO9uX8urSyYhURYXF4d3BHKpUw5+v/4+BfJzi1LwKp0dVnOhKppuFy/TAooekYOlfrzm9VPef3dU+izeQyvTGZJSVklmiW3Dx7+TJ7nn6Khzt2MLgCk9C4XujfmxkD+mil7bgQhZ2dbqPNTC6nqNzwNbyoXKVz3K+PyykprZtIfa390fEE+Xhgb2EmVktqJNWISc+N6aYaDJs2bWLBggWsXLmSjh01E/vGjRvH33//zZgxY/jwww8ZOXJkkxT0bpFUlEdldTUeljZa6Z6WmvGL0fnZt7RdV3MrpBIJJzO1J7tG5WeTW16Kn7X9LW1XuD2JBfma421jo5Xuaa35Hp2boxMTVzu22cPahoiszLp0j2ti8svLqaquRm6ku7yusVRqcCUtofFtW3OAYzvPaaX1HdmFboPa1T5RvNqkc/FuVe97GNLiMpEpdKtdY5kR6vIKHFyVBPTwJqCHN0Mf0X7a+NjcMTw2dwxPdH2djGTdvyuh8SXm6z+/r3yPydFdk//K3AUPGxsiMrN0YqJzcjCXyRjs48PZ9PS6uQ0ASQUFFJSX42wp5ik1l99OnmdfVLxW2mB/H4J9PZBItHuPWyut630PQ3xOHgFOrXTSWyttOJ+qWREp0MMVFxsrNp/VXiDGxNiYyupqg0u2CkJLa/CQpCNHjvDGG28wdOhQnRe0vfPOO4wYMYK5c+dy4sSJRi/k3URVXcXxzGRGtG6rlT6qtT8FqnLO5NT/UidDEmobIoHXrbLkbaVEaWJGcrF4kVdLUFdVcTw1heE+bbTSR/r6UVBeztl03WXzEgvySSzIZ6Svn05MXJ5mImRpRQVhaakM92mj1Wjo69Yac7mcsFSxSlJzyU0vIPpMotbn5L4IzC1N6T6ofV0+azsLOvZpw6l/DU9SDtsdjncHd62lUi1tzWnX04fwI9HkpOfz4pCPdD6gabi8OOQjctLzm2xfBW3qqirCUlIY1sZXK32kXz3nd34+ifn5jPTz04mJq53oXFVTw8Lhw5gW2EMrT0dHR2xNTYnMEivoNJfMohLC0zK0PqGxiVgoFAT7eNblszUzJdDTTWvlpOuFxibi46DEx+Hq4iZXvl+J6+3dmgXjhtNaaV2XRyKB4e3acDblMhVVVY2/k4LQCBrcw7BixQomT57MG2+8ofObh4cH8+fPp6amhuXLl7Ny5cpGLeTd5qvzh/l5yCMs7TeODTHn6ObgyrT2vVh46l9UVZVYyOS0sbYnsSiPXFXDxijnqspYFRnGtPa9ADh0OQFXc2tmdg4ipbiAX2LONOEeCfVZGnaUn8Y/yNcjx7AhIpxuzi5M7R7IJ6EHNMdbLsdXaUdSQT65ZZrj/fXxoywaOoL88jJ2x8cyxMuHMX5tmbH96gT2RYcPsm7CQ6y6fzwrTp3A3sycV4NCOJ2exu742JbaXQEIPxLN2UOXeHX503z//h8U5hYzed59FBeWsfWHq0tltvZzRqYwJvZ8MgCbvt3D0Ef78sEvL/Ljx5soK1Exac5oampq+P3rf6isqCL6jP4bkisNF6F5LT16jDUPTuSrMWP4PTycbi4uTA3swacHDqKqrD2/7exIyr96fi89epRPR4wgr6yMPbFxDPbxZnTbtrz4t+bdPOWVlXwbFsaM3r3JKyvncFIinra2zOzTh4jMTH4PFytjtaQTiakci09m0YSRLN51kPzSMmYM6ENRuYpfT1ztbfRxUCI3MqpbInVbeBTPhvTku8fG89luzfs1Xh4STFRGNjsiNEut/xp2joe7d+SbR8fy1b6jlFdUMCmwM21a2fHEj783/87ew8SbnhuXpKaBs3B79+7NmjVr8Lvuqcm1wsPDmTZtGocPH76tQnn+tPC24u8Ew939mNU5GG8rJRmlxay5dIqVkZoxqb0dW/PrsEnMDd3K73HndWInendkcdBogv/8hpQS7Z6Dp/x78JhfV9wsrMkqK+HA5XgWn97f4IbHnUiad/fPfxnm7cus3n3xsrUlo7iYn86d4fvTJwHo5erGLxMe5pVdO/gj8uqNwKMdOjG1Ww+cLSxJKizgmxPH2HRRu5u6m5MLc/oG0cXRmbLKSnbFxfDxwf1650bcLfw+uNTSRWgUFtZmTPvwQfqM6oJUKuXCsRi+e/s3UmIy6vJ8unkOju52PNHt6oMWe2cbnnp3AoGDO2AsM+LCsRhWvLOBxEuGex93ZH/H2k//Zu2nd9+KaNGv+7d0EW7bMF9fZvbtU3d+rz1zlu9P1p7fbm6se/gh5u3YwR8Xrr6X49FOHXmmRw+cLS1JKihg+bHjbLrmPUUS4NHOnZjcuQseNjbkl5ezMyaaJYdCdVZYupvIiu6NmzQrEwWvDe/PYH8fpBIJp5LTWLhjP/HXLJu65smJuNpYMfh/q+rSnKwseGPkAIK8PaioriY0NpGFO/aTVVxSl8dDacPLQ4Lp3toFc4Wc8NQM/rf3MCeT7r6e44vvzW7pIhjUbdtbLfZvnxo1v8X+7abS4AZDt27d2LRpE61btzaYJzk5mbFjx3Lq1O1Nvr0XGgxCw90LDQah4e6VBoPQMPdCg0FouHulwSA0zJ3cYOiy9e0W+7fPjP6wxf7tpnJTL247fYP1oU+dOoWrnjdfCoIgCIIgCIJwd2pwg+H+++/nyy+/JDMzU+/vmZmZfPHFF4wYMaLRCicIgiAIgiAIN6umpuU+96IGT3qePHkyO3fuZPTo0UycOJEuXbpgZWVFfn4+Z86c4c8//8TDw4Onn366KcsrCIIgCIIgCEIzanCDwcjIiNWrV/Pll1+yYcMGVq9eXfebvb09kyZNYvr06ZiYmDRJQQVBEARBEARBaH439eI2uVzO3LlzmTVrFsnJyRQUFKBUKnF3d0ciEROdBEEQBEEQhJYnllVtXDfVYKgLMjbGy8urscsiCIIgCIIgCMId5pYaDIIgCIIgCIJwpxI9DI3rjmwwSI2rW7oIQjNSZDV4sS7hHiBR2rZ0EQRBaCLlDuL6LQj3InGnJgiCIAiCIAiCQXdkD4MgCIIgCIIg3KpqMSSpUYkeBkEQBEEQBEEQDBI9DIIgCIIgCMI95V5943JLaXCD4ffff+f+++9HLpfXpR09epRVq1aRnp5OmzZtmD59Or6+vk1S0LtNP2dv5nTsj6+1PbmqUtbFnOKbiCMG88ukUp7x78UDXp1wNrMkvbSIzQkXWB55mIrqq5PIOimdeb3rYDrYOlFaqWZTQjhLzu1HXV3VHLsl1MNMLuOV4f0YHOCDuULOmaQ0Pt62j9is3HrjHCzMeXVkP/r6eCAzkhIam8THW/8ls6gEgI/HD2N8t/YG4wcvXklaQVGj7otwY6bmcp6eN5o+Q9pjZq4g4nQi387/i6TYzHrjXvt8Ev1Hd9ZJXzh7Hfu3nq37PuGpfox8pBcOztZkpuaz+adQtvxsuA4Rmk4/T09eDuqLr50duWVlrDt7luXHw+qNGRsQwHM9A2ltbUNaUSErTpzgt/PhWnke7tiRJ7t1rcvz85mz/HD6dFPuitAA/Vt7MqdXEG1s7cgpL2Nd+FmWnTpeb8w4vwCe796T1lbWpBYV8d3pE6yPPK+VZ6J/e6Z26YGntQ2ZpSX8eSmCr04cpbJaTBQX7nwNbjC8/fbbDBw4EDs7OwAOHTrE1KlTCQoKIjg4mPDwcCZMmMDq1avp1q1bkxX4btDN3pXvQh5ka1IES87vp4e9O3M6DUCChGURh/XGvN1tKA94deTr8FDO5V6mva0jMzuG4GpuxWvHtwHQ2sKGnwY+ysnsVF4M3YiPtR1zOw3AUqbgjbDtzbmLgh5LHhpFR1cnFu88SIlKzfMDe7P6/yZy31c/UlCm0htjJJXw7ZRxmMnlvP/3HoyNpLw8NJjvn5zA+KVrqayu5pt9x1gfdk4rztrUhM8fGUNYfDKXC0VjoSW8+tkk2nZy5/tPt1FaUs5jM4awYM00nh21hOKCMoNx3gHO7Nl8ii1rtW/+UxOz6/7/0/NGMXZKED99uYuoc8kE9vfnhXfHUVVZxfb19d+4CI2rm4sz344by9ZLl/gs9DA9XF2YExyMVCJh2TH9x2KkXxsWjxzBD6dOcSB+P0N9fVkwbBjlFZX8dfEiAJM6d+LDIUNYfvw4oYmJdHZy5vUB/TGVyfjmuDjGLaWbkwsrRo1jS8wllhwLpYezK3N7ByORSFh68pjemFE+fnw2ZCSrz55if1I8w7x9+WTQMMqrKtgcpTne/9epK++GDGJrzCUWHN6PrYkps3v2xd/Onme3/9Wcu/ifIZZVbVwNbjDUXNe3s2zZMqZMmcLrr79el7ZgwQIWL17MunXrGq+Ed6GXOoQQmZ/BnKN/A3DgchwyqZTn2vXh+0vHUVVVauW3lpswybcbn5zZy4qLmgrpcEYCAK93HcynZ/eRqyplWkAfiivVPHtwAxXV1ey7HEt5ZSXvdR/G1xdCSSstbNb9FK7q4u7MgLbePLtmIweiEwA4kZDKrpef4tGenVm+X/8NwPD2fgQ4t+K+r9YQk5kDwMXLWfw1YwojO/rx99mLJOcVkJxXoBX31aP3UVBWziu/7xDdri3Av0treg0M4J2pqwjbfwmAC2EJrN77KmMm9eHXb/bqjVOYyHDxsOe3b//l4tkkvXkc3WwZ/38hfPPhZrauOwrA2aOx2Dtb0y3YTzQYmtmLffoQmZnF3O07ADiQkICx1Ihne/bk+5OnUFVW6sS8HBTE9qgoPtq3H4CDiYnYmJowq2/fugbDcz17svXSJRYdPATA4aRkvGxtmdK1i2gwtKBZgX2IyM7k5d2ah3D7kxKQSaVM79aTlWdO6ly/Aeb0CmJbbBQfhu4D4EByIjYKTYNgc9RFpBIJMwP7cCApgRf+2VIXdz4rg92T/o9gNw8OpSQ2y/4Jwq265UnPiYmJjB07Vivt4YcfJiIi4rYLdTeTS43o1ao1/yRf0krfnnwRC5mCQAd3nRhLmYJ1MafYnRqtlR5fpBnK4m5hA0A/Jy/+TY3RGqK0PfkiRlIp/Zy9G3lPhJsR5OtBiUpNaOzVSj+vtIwTCSn08zP8VvRgXw/isnLrGgsAsVm5xGXnGowb4OfFkHa+LNy+n6Jy/T0XQtPqHuJHWYmKk4eunrMFeSWcD4sjsL+/wTivts4YGUmJjbxsME+fIe2pUFeyc4P2kJeFs9bx0Ytrb7/wQoPJjYzo5ebGPzHadfP2qCgs5HICXV11YlytrPBWKtkZHaMT42Frg6etDQD/98efLNx/QCtPRXUVciMxtbClyKVG9HJ145847WO3LVZzvHu66B5vN0srfGyV/BMbrRPjaW2Ll7Ut9qZm2JiYsichTitPTF4uOWWlDPYU12/hztfgBoNEot214+npSWlpqVZaXl4elpaWjVOyu5S7hQ0KI+O6m/0rEoryAPCyVOrEpJQU8M6Jf3Rihru3RV1VRXxhLgojY9wsbHTy5KpKKVKX692u0Hx8HJSk5BVQVa39uD8xtwBPO8MvKvN2UJKQk6eTnpiTrzdOIoG5I0I4Hp/MzgvROr8LzcPdpxXpyblUV2mPPU5LzMHVy95gnHeAMwCjH+nFz6Fv8Vf4Ryxa9xxtO119kOAT4EJqQjYdAr358s8X+fvCx/zw72uMfrR30+yMYJC7tTUKY2Pic7XP0cT8fAC8bHXPUV+lpi6Oz6s/JjY3l7QizXBCaxMTHurYgfHt2rH2zJlG3APhZrhbW6MwMiYu/7rrd0E+AF42eo63reZ4x+Vfd7yviSlUq6ioqsLNykorj5VCgbXCBDcr60baA+FaNTWSFvvci25qSNLgwYPx8vLCx8cHuVzOokWLWLt2LTKZjFOnTvH+++/Tv3//pizvHc9KZgJAcYX2k9+SSs13S5miQdsZ4daW8Z4d+eFSGIUV5TiYmANQVKH7RLm4Uo1FA7crNA1LEwXFKrVOeqlKjblCridCw8pUQWJuvk56iVqNhZ64gW298XGw46Mt+26nuMJtsrA0pbSkXCe9rESFmbnhc9E7wAUAuYmMhbPXYWVjxkPPDmDhT9OY/dBSEi6lY600x97RinlLHmHtV7tIicui/+jOzHh/PABbfznaNDsl6LBSaI5lsVr73C6p/W4h1z1HrUwMxVTUxmj/fXR3ceG3Rx8B4Hx6OmvEpOcWYy2/heOtuHLN14658t1CLqe8spItMZeY0rELUbk5/BMXjb2pGe8GD6SyugozmehVEu58Df4r3bt3L5cuXSIqKopLly6RlZVFQkICVVVVyGQynn76adq2bcucOXOasrx3PGltT4yhYeXVDRhwPtLdn8/63M+xzCQ+Pfuv1nb1kSDRmWMiNB2JRPd4SCUS/XMJJLrzf66P0/fHIkH/38pjvboQcTmTI3H6x78LjU8ikSCRah9viVT/8ZZI6j8XN/5wkEM7znHmSGxd2pkjMXy/6xUemT6IhbPWYSwzwlppwYcz1nB45wVAM4fBwdmGR58fLBoMzejqea7/mFbrSZdQew247u+gbkvXpScXFPDo+t9wtLBgVt8+bJr8GON+XkfOdT34QtOT3OD6rf+cv/Jb/cf7zf27UVdV8cnAYSwaNJzSigq+PR2GiUxGaYXuvAjh9om7osbV4AaDi4sLLi4u9O7dG4VCgVQqpaKiAplMRnR0NL/++it+fn46Q5f+awrVmqeOFjLtJxHmxponF/p6CK71dNuevNZlEMcyk5h2cEPdcqmFapXe7Wq2LbvhdoXG8/yA3swY1EcrbUd4FJ72Zjp5zeRyilSGj01hmUpvD4SZXK4zP8HG1ISeXu58tuvQLZZcuBWTZgxm8otDtdIObj+HjZ6hRyZmckqKdHserkiNzyY1PlsrraSonAunEvH21wxXKitRUV1dTdg+7XlQJw9G0aNfW2zsLMjPKb7V3RFuQmHtuXt9r4B57ZNmfed2Xcx157WZXKY3JrOkhMwSzRLKZy9fZs/TT/Fwxw4GV2ASms6V66zl9dfvBhxvy+v/Rmq3UVS7zdKKCl79dyfvH/oXV0srUgoLKKus5MGADiQXiAdAwp3vpvrBNm3axIIFC1i5ciUdO3ZEJtNUgAsXLuTs2bN8+OGHjBw5skkKerdILM6jsroaDwvtsY6elprv0QXZ+sIAeLf7MJ7w68GWxAjmHv1b690KZVUVXC4txMNCe66CUmGGpdyE6ELD2xUa128nzrPvUrxW2pAAH4J9PZBItJ9CeSitic00/B6G+Jw82jm30kn3sLPhXEq6VlpwG0+MjaT8Ex51ezsg3JTt649x/N9IrbQ+Q9rTPcRPp0fBxcOOpBjD72HoP7ozhXmlnD6sPf9EoZBRmKe5aUxNyEYqlSKTGVGhvvrk0chYM+VMXV5x2/skNExifr6mPrex0Uq/8j0mR/fcvjJ3wcPGhojMLJ2Y6JwczGUyBvv4cDY9vW5uA0BSQQEF5eU4/8fnAraUpAL9x9vTWvM9Oi9HJ+bK3AUPaxsuZF899z2uixnk4U2BqpyT6WlE52rS7ExNcbGwJDy7/ne3CMKdoMGTno8cOcIbb7zB0KFDcXZ21vrtnXfeYcSIEcydO5cTJ040eiHvJurqKo5nJTHcva1W+kh3fwrUZZzNTdMb90rnATzh14PvLx7jpcOb9L6I7VB6PINcfZFLjbS2W1ldzZHaZViFppdVVMKFtAytT2hMIhYmCoJ9Pevy2ZqZ0sPTTWvlpOsdjknE216Jj8PVhqCPgxJveyWhMdpxndycuFxQJF7S1sxyM4uIDk/V+pw6FI2ZhQndQ/zq8lnbmtMx0JtToYYbdKMn9WbG++Mwll09h+0crWjXzYNzxzQrqFxZprX/GO2Xu/Ue3I64i2mUlojexOairqoiLCWFYW20X0g60s+PgvJyzqan68Qk5ueTmJ/PSD8/nZi42onOVTU1LBw+jGmBPbTydHR0xNbUlMisLITmp6qq4nhaCiO822ilj/LRHO8zGXqOd0E+iQX5jPLx04mJzcsltXZi+2MdOvFmkPYcz6c6d6equlpn9SShcYhJz42rwT0MK1asYPLkybzxxhs6v3l4eDB//nxqampYvnw5K1eubNRC3m2WXgjlp4GT+DpoPBviztLN3o2pAb355MxeVFWVWBjL8bW2J6k4n1xVKQE2rXg2oA/nctLYmhRJFzsXre3FFGRTXKnm24ij3Ne6HasGPMyqi8fxslQyt/MAfok5zeVScRPZkk4kpnIsLplPJ45kyc6D5JeW8cKgPhSVq1h//OpL13wclMiNjYi8rLkh2HY+imn9evLdlPEs2akZajRnWDBRmdn8c0H7xtPP0Z7YTN0nXELzCz8Rz9mjscxb/AjfL9pGUX4pj704hJKicrb9cvXlTq19WiGTGxMbqXlQsG7pHuZ//zRvff04f689jKW1GY+9OITiwjL++F6zxOb543Ec3RPBtDfuQ2EqJzEqg8Hju9GumwcfTF/TIvv7X7b06DHWPDiRr8aM4ffwcLq5uDA1sAefHjiIqrISC7kcXzs7kvLzyS0rq405yqcjRpBXVsae2DgG+3gzum1bXvxbswZ/eWUl34aFMaN3b/LKyjmclIinrS0z+/QhIjOT38MvtOQu/6d9deIoP499kKXDx7AhMpxuTi5M6xrIwiMHNNdvmZw2SjsSC/LJLS+ri1k8eAR55WXsTohliKcPY9q05YV//q7b7g/nTvPT/RN5J3gAu+Jj6evWmhe692LZyWMkFxYYKo4g3DEkNQ2cLdu7d2/WrFmD33VPTa4VHh7OtGnTOHxY/9uMG8r7l49vK/5OMMzNj1kd++FlqSSjrIifok/y/UXNmNRerVrzy+DJvHL0b/6IP8+sjv14qUOwwW09umctxzI1YxwDHdx5rcsg2tk6kqsqZVNCOJ+d20/VXTzpWRFh2tJFaBRWJgpeHdmfwQE+SCUSTielsWD7fhKyry639+NTE3G1sWLIZ6vq0pysLHhj9AD6+nhQUVXN4dhEFm7bT1Zxidb2/35xClEZ2cz5bVuz7VNT8Fyv+5TubmRhZcrU18fQZ0g7pFIpEacS+Pbjv7XmKHzy0zQcXW15ctAndWld+7Zh0ozBeLV1prq6mlOHovn+021kXc6vyyOTG/PYi0MYdH9XrJXmJMVksm7pbo7svvvecxP1rGNLF+G2DfP1ZWbfPnjZ2pJRXMzaM2f5/uRJAHq5ubHu4YeYt2MHf1y4enwe7dSRZ3r0wNnSkqSCApYfO86myKtD2yTAo507MblzFzxsbMgvL2dnTDRLDoXqrNJzN6kyuXuvRVcM9/JlVs++eNce7zXhZ1h5RnO8e7u48ev4h5m7Zwe/X7zasJvUvhNTu/TAxcKSpMIClp08xsYo7aGM97fxZ0aPXrhbWpNaVMhP4Wf58fzdvSpWwgt37kI3fr9/2GL/dtTEt1vs324qDW4wdOvWjU2bNtG6dWuDeZKTkxk7diynTp26rULdCw0GoeHulQaD0DD3SoNBaJh7ocEgNNy90GAQGk40GPS7FxsMDZ7D4OXlxekbrA996tQpXPW8+VIQBEEQBEEQmouYw9C4GtxguP/++/nyyy/JzNQ/mz8zM5MvvviCESNGNFrhBEEQBEEQBEFoWQ2e9Dx58mR27tzJ6NGjmThxIl26dMHKyor8/HzOnDnDn3/+iYeHB08//XRTllcQBEEQBEEQhGbU4AaDkZERq1ev5ssvv2TDhg2sXr267jd7e3smTZrE9OnTMTExaZKCCoIgCIIgCEJD3MVrwdyRburFbXK5nLlz5zJr1iySk5MpKChAqVTi7u7+n3/DsyAIgiAIgiDci26qwVAXZGyMl5dXY5dFEARBEARBEG7bvTr5uKXcUoOhqcmixTKb/yWeK6JbughCM4r81KOliyA0I3mSuGj/lxiXiuMtCPeiBq+SJAiCIAiCIAhC06murubLL78kJCSEzp0789RTT5GYmGgwf3R0NNOmTaNXr1706dOHl156ibS0tEYvl2gwCIIgCIIgCPeWGknLfW7DsmXL+PXXX5k/fz7r169HIpEwdepU1HreAJ+Xl8f//d//YW5uztq1a1mxYgV5eXk888wzqFSq2yrH9USDQRAEQRAEQRBamFqtZtWqVbz44ov0798ff39/Pv/8czIyMti1a5dO/t27d1NWVsbChQtp06YNHTp0YNGiRcTGxnLq1KlGLZtoMAiCIAiCIAj3lJqalvvcqosXL1JSUkLv3r3r0qysrGjXrh1hYWE6+fv06cPSpUtRKBQ6vxUUFNx6QfS4Iyc93yvM5TLmDenHYH8fzOVyTqek8dGOfcRm59Yb52BhzmvD+hHk7YHMSMqh2CQ++udfMotK9OZ3srJgy3NT+OHYKb7ef7QpdkVoAFNzBU+/8wB9RnbCzMKEiLA4vn17A0lR6fXGmVuZ8uQbY+k7qjOm5goSItP4ccFfnA2NqsvTyl3J02+Pp3OQH8YyY84eusSq+ZtIjdP/5nWhafV38WJOlxDa2NiTU17KuqgzLAtv2LnXQenIxlGPM3Djd6SUFGr95mOl5LXuA+jt2JrKmmqOZyQz/8Rekosbt+IXbp65XMa8wf0Y0vZqfT5/Z8Pq89eH9iPIq7Y+j0vio53/knFNfa40M+XlgUEEeXtgbWJCQm4eK4+cYFtEVD1bFppS3fX7muP90T8NvH4Pveb6HdeA6/eztdfvA+L6fa8YPHhwvb/v2bNHb3p6uuZ+wdnZWSu9VatWXL58WSe/m5sbbm5uWmnffvstCoWCwMDAmynyDYkehia05IFRDA3wZcmeQ8zbtAM7MzN+nDIRaxPdluAVRhIJKyaNo6OLE+9t3cO7W/fQydWRVY9NwFiq/3B9fP8wLOvZptA8Xl3+FEGju7D6o80smvEjNvaWLPh9JhY2ZgZjpFIJH657gd7DO7Hqw03Mf3oFxQWlfPDzC3gGuAKahsjH61/C09+Vr1/9lUUv/ICdsw2fbpxd77aFptHNwZUVAycQU5DDc/s2sjHuAnO79uOFjn1uGBtg68DqwRORSY10fnM2s+T3kZNRKsyYefBv3jz6D77Wdvw05GEURuLZTkv7bPwohvn7snjvIeZt3oGduRlrJt+4Pl/5yDg6Ojvx7vY9vLNtD51cHFk16Wp9LpNKWTXpAfp6tebL/UeY8fvfnE1N538PjGZsx4Dm2j3hOkvGj2Kof+31u/Z4//h4A67fj9Zev7ft4d0rx7u+6/d94vrdZGpa8HOLysrKAM17z66lUCgaNCdhzZo1rFu3jpdffhk7O7tbL4ge4irURLq4OTPQz5up6zZyICYBgBNJqex56SkmBXbmm4PH9caNaOdHgFMrRn+zhpisHAAi07PYMn0Ko9r78df5i1r5J/XohLedskn3Rbgx/+5e9BrakXceW0rYngsAXDgWw+rjHzDmyX78+r8deuMGTuiJXxcPZgxdSEJkKgDnj0SzbO+bdB8QQEJkKiH3d8fVuxXP9vugrrci/mIqP4bNJ+S+bmz/6VDz7KQAwKzOQUTkZfBy6FYA9qfFI5NKmd6hFysjwlBVVerEyKRSnvDvzpwuIZTr+R1gdpdgSirUPLbr17o8ycUFrBz4AJ3snAjLTGm6nRLq1cXVmYFtvJn6y0b2xyYAEJacyt4ZTzGpR2e+OVR/fT5q+Rpismvr84wstj47hVHt/Pgr/CID23jTzqkVE75fx/nLGQAcjk/C1dqSqX16sPl8ZLPso3BVF9fa6/cv112/X2zY8R69/Lrr93MGrt/dxfX7XmWoB+FGTExMAM1chiv/H0ClUmFqaviVAzU1NXzxxRd88803PPvsszz55JO39O/XR/QwNJFgHw9K1GpCY68uhZVXWkZYYgr9fA2/9C7Yx4O47Ny6ygYgNjuX2KxcnTg3G2vmDg7h7S26E2GE5tV9YDvKSso5ue/qxb0gp5jzR2IIHNzBYFzQ6C6cPxJd11gAqFBVMjXoff74ZjcAoVtPM3v0Iq2hTZXqKgBkctHmb05yqRG9HN35J0l7qMi2xEtYyBT0bOWmN26gqw8zOwXx9fkjLDy5T2+eEa39WB99TqtBcT4nnV6/LxONhRYWUlufH4rTrc/7+xiuz0O8a+vz7Ovq8+xc+tfW58VqNb+eOlfXWLgiPjef1rY2jbsjQoM0+vU7u57r91Zx/RauujIUKTNTe7hxZmYmTk5OemMqKip45ZVXWL58OfPmzePll19ukrI1+G5j0KBBSCQNWyrqVltW9xIfeyUpeQVUXTf7JSm3gPs6OhuIAh8HJQk5eTrpSXn5eNnZ1n2XAAvHDmN7RBQHr6nUhJbh3saJ9MQcqquqtdLT4rMY+IDhcYTeHdw4sv0c46YOZOzUgTi42BIfkcp37/7B+SOaF9qVFJZx8WQ8AMYyI9zbOPHMuw9QkFPEwb8adxUEoX7uljYojIyJK9Q+RxOKNN+9rJQcvJygE3c2+zLBfy6nQF3ORB/dBqSbhTVWchNSSgr4oOdQ7vMKwMxYxsG0BN45vpO0kqIm2R+hYXzslCTrqc8T8wq4v0M99bm9kvhc3fo8MTcfT6WmPj8cn8Th+CSt342lUga28SI6K7sRSi/crFu+ftsrSdBzvJNy8/FSXnf9vl9cv5va3fimZ39/fywsLDh27BitW7cGoLCwkIiICCZPnqw3Zt68eezatYslS5YwevToJitbgxsMM2fO5K233sLX1/eGkzkEsDRRUKzSXTO3RK3GXCHXE6FhpVCQmJOvG6dSY25/Ne6J3t1wt7XmuV83N0p5hdtjYWVKaVGZTnpZcTlmliZ6IjSslZaE3NeV4oJSvv9gI6oyNQ/OGMb8X2Ywe/Qi4i5oP1l+f+3zdOsfQFVVNV/P+4W8rEIDWxaagrVMM9a4uEJ7LGlJheZct5DpP7czyorr3a6dQjMX5bVu/TmbfZmXDvyFnYkZ87r155dhjzLi79WUVVbcbvGFW2RpoqBEzxroN6rPLU0UJOTm642zqCfutSH98FTa8vxvf91SeYXbc8vXbxMFiQaO97VxT/SqvX6vF9dvQZtcLmfy5MksXrwYpVKJq6srixYtwsnJiaFDh1JVVUVubi6WlpaYmJjw559/sm3bNubNm0fPnj3Jysqq29aVPI2lwQ2GsWPHIpPJmDdvHgsWLMDf37/RCnG3kwDS63pfpBKJwXkvNfWsuSUxECeRXI3zsrNl1sC+vLRhi95KTWhaEokEiVT7eEukEr1LqUkkEmqqDR9vmdwIcyszZo38lOzL+QCEH41h1bEPeHDGUD6Zvlor/7rPtrH+y3/oM6IzM5c8hrWdBeu//Oe290lomCu9rIZO4ZpbnO0mM9KMDs0uL+XZfRvrtpJYlM/GUY8z3qsd66LP3tK2hZtjsD7Xd35Tf31+/Xbq4iSG414dHMKUnl35NvQ4u6NiG1ps4Rbd1PW7nuOm+fnGfyfi+t2MbmPycUt66aWXqKys5K233qK8vJzAwEC+//575HI5KSkpDB48mAULFvDAAw+wZcsWAD799FM+/fRTre1cydNYbmoA9KhRo9i7dy+ff/453377baMV4m73Qv/evNhfe4WUHRFR2Cl1V7Axl8spKjc8072oXKX3yZOZXE6RSoVUImHh2OHsiIgmNDYRo2sqOqlEgpFEotONKjSuSXNGMXmudrffwb9PYeNtqZPXxFxBiZ6ehytKi1UkR6fXNRYAykpURITF4d3eXSf/hWOaG4hzoVFY2JjxyKyRbFi6S2colNA0CtWac9fyup4E89rvRepbe7PmlR6KfalxWte409lpFKjKaad0vKXtCjdvRr/evNhPuz7fHhGFl51ufW7WgPrcXK6nPpdp6vNryY2M+OT+4Yxu35bvDoex5N/QW9wD4Wa80M/A9VvP8TaX3eB4qxpw/b6/9vodJ67fgn5GRka88sorvPLKKzq/ubm5cenSpbrvq1atarZy3fSMyffee4+UFDEB71q/nTzPvqh4rbQh/j4E+3honixck95aaV3vOs7xOXkEOLXSSW9ta8O5tHScrSzp4uZMFzdnxndup5XnhX69eaFfbwZ98T2pBWKoSlPZ/tMhju86r5XWZ0Rnug8I0PQoXFPhu3g51PsehrT4TL0Tl41lRqjLNTeRfl09cHS305mvEH0mkSEP9sJaaSGGJjWTpKI8Kqur8bCy1Ur3tNR8jy7I0Rd2Q4lF+VRVVyPXs9yqsVRqcGUlofGtP3Wef6Ovq8/b+hCipz73sLUmpp76PC4nj3Z66nMPpaY+v8JCIWfFI+Pp6ubMgl37WX1MzE1qLr+dOs8+Pce7Ua/fShvOpTbw+v2luH4Ld6YGNxhycnKws7PDwsLC4HAktVrN7t27GTVqVKMV8G6QWVxCZrH2S1lMZMZMD+lFiK9n3bJstmamBHq4sdzAkqoAh+ISGdPRHx97ZV3F5GOvxMdByTcHj5FZVMyEFet04v6YOon1J8/z26nzZBbVP15auD25GQXkZmi/SEthKufR2SPpPjCAE3sjALC2s6BjH19+/Z/hIUNhey7w6OyRuLdxIjlacwNhaWtOu0Bvdv92DIBeQzvy8EvDuXgynqzUqxPqug9sR25GAfnZYkJsc1FVV3E8I5kRrf347sLV83iUR1sKVOWcydZ9sU5DlFZWEJaZwojWfiw6fQB1tWYVrL5OHpjL5IRlJDdK+YUbM1SfPx/cixAfTw7ULqtaV58bWGITIDQukfs66KnP7ZUsO6Q5v40kEr59eCwdXRyZ9edWdkRGN82OCXrVe/2+yeN9KC6RMQaOd931e6We6/czk1h/Sly/G9vdOOn5TtbgZVWDg4PJydF+ejZnzhyttMLCQubMmdN4pbuLnUhK5VhCMovGj2Ri1w4MaevDD5MnUFSu4teT5+ry+dgrCXByqPu+7UIUCTl5rJg0ntHt2zK6fVtWTBpPVEY2OyKiqKiuJvxyhs4HILO4mPDLGVRUi+EpzS38aAxnQ6OYt/T/GD6pL31Hdubj316ipKCMbWsO1OVr7eeET4erS29uXrGX7Mt5vL/2efqP60GvoR35cN0L1NTAH8s0y+1t+eEABbnFvL/2eYLv60qPQe14bflT9BzSgRXv/VHvmFqh8X11/jBd7F1Y2m8sA1y8eblLCNPa92Jp+BFUVZVYyOR0tXdBqTC8ZrY+n57aTyszC1YPnsgAF28m+nTgi5D7OJ2Vxq6UmCbaG6EhTiSlcjQhmcXjRvJglw4MbevDj49p6vNfTl1Xnzterc+3Rmjq85WPXq3PVz46nqhMTX0O8FiPLgS2duOPMxe4XFhEZ1cnrY/Q/LSu311u4fp9zfFecc3xrvf6XSSu38KdTVLTwLsNf39/QkNDtd4c17VrV/766y/c3TVjrbOzswkODubixYuGNtMgbT/4/Lbi7xRWJgpeG9afIW19kEoknEpOY8HO/cRfs2zqmikTcbWxYvCXV8ehOVlZ8ObwAQR5e1BRXU1obCILdu4nq1j/q+UBLr0zm6/2H+Hr/Xffq+W9l90bT9QsrE2Z+v5E+ozojFQqISIslm/f+Z3U2KvrKX/y5ywc3e14MvDtujR7ZxueemscPQa1x1huxIVjsax4/0+SLl19Wu3s6cD/vTmWDr19MbMwIeZ8Muu/2FH3kri7SeSnHi1dhNs23L0Ns7oE422lJKO0mDWXTrEyIgyA3o7u/Dp8EnNDt/J7bLhO7ESfDiwOGk3wH9+QUqI99KCbgyuvdA2hi70LZZUV7EyO5uMT/1JYcWtzI+4E8iTDq8rcTaxMFLw+tD9D/Grr85Q0Pt65X2vZ1J8en4irtRWDvtauz98aNoC+Xh5UVlcTGpfIx7uu1uc/T3mQwNb6398B4Df/7roeSu6R5xdWJgpeG3rd9XvXddfvx2uv31/puX571V6/4xpw/X679vp94O67fl96e3ZLF8EgzzULW+zfTpjyWov9202l0RsMISEhREbe3psp75UGg9Aw90qDQWiYe6HBIDTcvdJgEBrmXmkwCA0jGgz63YsNBvGaWEEQBEEQBOEeI+YwNKYGz2EQBEEQBEEQBOG/56YaDBIDL6ERBEEQBEEQBOHedFNDkubPn49Coaj7XlFRwaJFizA3NwdApbp7J+YJgiAIgiAI9wgxn6ZRNbjBEBgYSFZWllZa165dycvLIy/v6qoBPXr0aLzSCYIgCIIgCILQohrcYPjpp5+ashyCIAiCIAiC0DhED0OjEpOeBUEQBEEQBEEw6I5cVrXctaKliyA0o8rMrBtnEu4Z0nyfli6C0IyqxWsY/lOklS1dAkEQmsId2WAQBEEQBEEQhFtWI1b2bExiSJIgCIIgCIIgCAaJHgZBEARBEAThnlIjJj03KtHDIAiCIAiCIAiCQaKHoYn0d/VibvcQ2tjakVNeys+RZ1h27liDYjvYObLp/skM2LCClOJCrd8eaduJp9v3oLWlNanFRfx08TSrL5xsil0QbpKphQnTFj1O37E9MbU0IeJwFMtmrSYpMsVgzE9xS3HybKX3t8vxmUzxeQEAGwcr/u+jR+k+tDOWSgtSoi7z26LN7P/tcJPsi1C/fh6ezOkdhK/SjtyyMtaFn+WbE8frjRnbNoDpPXrS2tqatKIivjt1gt8unNfK422r5LWgfvRyc6OyqprjaSl8fHA/yYUFTbk7wg308/JgdkgQvnZKckvL+OXMOZYfC6s3Zmw7f57r3RN3a2vSigpZefwkv50L18oz3M+Xab0C8VHaUqRSczgxiU/3HyKntLQpd0doAHO5jHmD+jHEzwdzuZzTqWnM37WP2OzceuMczM15fUg/grw8kBlJORSfxEc7/yWjuERv/sd7dOH/enZj0LJVTbEbgtBoRIOhCXRv5cLKoQ+wJf4ii08eJNDJlVd69EMqkfD12aP1xgYoHVg9bAIyqZHOb5P9u/BR0DCWnT3KodQEurRy4a2eAzEzlrH0BtsVmt4b62bRtqcvK19dS2lhKZPfeZBFe97lmfazKcor1hvz3gOLkCtkWmkBffyY/tmTbP12JwDGMmMW7HgLC1tzfnx3PdmpuQQ/0Iu3fp2NTG7M7rUHmnzfhKu6Obnw3ZhxbI2+xJKjofRwdmVOn2AkSFh2Qv9DgZG+fiwZNpIfzpxif2I8w7x9WTh4GKrKCjZfugiAs4UlGyY+Qlx+HrN3bENhbMycPkH8OG4CI39eg6pKLD/TErq6OLP8gbFsuxjF5wdD6e7qysv9gpBIJHxzVH8jcWTbNiwaPYIfT57mQFwCQ9r48PGIoZRXVvJXxMW6PF+NHcO6M+f4/GAo9mbmzAzuw9pHJjL2x59RV1U1524K1/ls7Cg6uTjx6d6DlKjVzAjuzZpJExn13Y8UlKv0xhhJJKx8ZBxmMjnv7tiDsVTK3IHBrHp0AmO/X0tldbVW/vva+/Pa4H5kFOm/Pgi3SQxJalQ31WAICwvjjz/+ID8/n5CQEB5++GGMja9uoqCggBdffJE1a9Y0ekHvJjO7BhGRm8ns/VsB2J8aj7HUiOmderMi/ITeC79MKuXJdt2Z0z2Y8kr9NwbTO/fi77iLfHJCc4MYejkJLytbnmzXTTQYWlhAbz96j+nOm6M/5vj20wCcP3iRn+KWct/zw1j30Z9642LPJGh9N7M05Y1fZnF0y0nWf7oZgN5juuHb1YsXer5G1IlYAE7vOY9jawcemjdWNBia2Uu9+hCZlcmcndsBOJCYgEwq5bkePfn+9Em95/ecPkFsj4li/sF9ABxMSsTaxJSZvfrWNRhm9epLSYWaxzduqKsDUgoL+G7MODo5OhKWltos+ydoeymoN5GZWczdugOAA/GJGBtJebZ3IKtOnERVqXtjPzukLzsuRfPR3v0AHExIxMbUhJlBfeoaDC/06cW/sXG8s3NPXVxsbi4bp0xikI83O6Kim2HvBH26uDozsI03U9dvZH9sAgBhyansff4pJnXvzDeh+huKIwL8CHBsxajv1hCTnQNAZGYWW6dOYVSAH39d0Bx7pZkps/sH8XDXjuSVljXLPgnC7WrwHIa9e/fyxBNPkJGRgUqlYv78+Tz22GPk5+fX5amoqCAsrP5u2nudXGpEb2d3diREaaVvi7+EhVxOTyc3vXED3b2Z2bUvX585wsKw/XrzPLFjAwvC9mmlVVRXIzfS7Y0QmleP4Z0pKy7nxM6zdWkF2YWc2x9Bz5HdGrydye9MxNrBiq9mrKxLKyksY8t3u+oaC1ekRKfh4uN0+4UXGkxuZEQvNzf+iY3RSt8eE4WFXE6gq6tOjKulFd62Sv6JidaJ8bSxxcvGFoDhvr78diFc64HB+cwM+qz6VjQWWojcyIhe7m7sjNI+3jsuRWuOt5ue421lhbdSyT/X3fDvuBSNh60NnrY2SIDQxCTWn9UekhafmwdAaxvrxt0R4aaEeHtQolZzKC6xLi2vtIywpBT6+3jVGxeXk1vXWACIzc4lNjuX/r5X457r25Mgr9bM+ONv/o2Ja5qdEDTLqrbU5x7U4AbD0qVLmTVrFqtXr2b16tX8+uuvpKWl8eSTT1JcLLrTrmhtaY3CyJj4Au1xjgmFmguBt5Wt3rizWekErf+Wr88epbKmWm+emIJcUmvnNFjLTXjErxMP+LZnTcTpRtwD4Va09nfjclwG1VXaxy4tNh03P+cGbcPRw4GxM0ayYfFfZCZl16Wf3nOeL577TiuvkbERvUd3JyE86fYLLzSYu1Xt+Z1/3fldkA9Qd/N/LV+lEoD4/Dyt9MRrYtysrLBSmJBSVMD7AwZzcurzRD4/kxX3jcPFwrLxd0RoEHdra+TGxnU38lck5uUD4GWre7x97DTHO6E2j06M0pYaYMG/B9h93c3icD9fAKKuueEUmp+PnZLk/AKqrltmJzGvAC+l/mv4lbj4nDyd9MS8fDyvifv19DmGLf+BnZdidPIKwp2qwQ2GuLg4Ro4cWfe9c+fO/Pjjj2RkZDB9+nQqKsTbmQGsFCYAFFWotdJLar9byBV64zJKiylQlzfo3+jRypVzj7/EJyEjiMrL5oeIU7dRYqExWNiYUVqo27VcWlSGmZVZg7YxfuYoKlQVbPxi2w3zPrtkCq5tnFn3sf6hTkLTsFJozt9i9XXnd+13S7nua42v1AmGYizkcpSmmr+RV/v2w9Hcgpn/bOW1PTsJsG/FzxMewtRYTDdrCVYm9R9vC4We430lRqXSH6PnbwTAw9aGVwf0Izw9g/1x8bdXcOG2WJooKFGpddJL1GrM9Rzza+Ou/1u5EnftcY/LydOZzyA0PklNy33uRQ1uMCiVSpKTk7XSvL29Wbp0KefOnWPevHlUiUlaSKntijKwAHB1IywMnFRcwENbf+Glf//GUq7g77FTsDdp2E2pcPskEglSI6nWRyKVUqPn2EokEmoacGGQm8gZ8dQgdqzaS3G+/tU0rpi26HHGvziKXxZu5PDm//YQwOYmlWjOb0OncbWedOmVKuG6GXhXOq2ra2qQ1y5ykF1WyvStmzmUlMjmS5HM2P43HtY2jPNv1xjFF26SpO54G6rPddPq/kZ0N1Yboxvko1Ty8yMTUVdVMWPzFjFXsxlJ0ExWvvYjlUj0HgMJhv8WoPbY6/lZgu75Lwh3mwY/tho1ahRvvfUWc+fOJTg4GCsrKwC6devG4sWLmT17NpcvX26ygt4trvQSXN+TYC7TPF0oUutfXeFmZJYWk1mqGQZ2Ousy+x+cyiNtO91wBSahcUx+ZyJT3n1IK+3AhiPY6Bl6ZGphQknBjZdI7D6sE+ZWZuz9+aDBPDKFjFdWv8DAR4JY/+kmVr2x7uYLL9yWwtqnxtc/JTaXGz6/r8Zo1wlm18QU1/ZA7k+I17qtOJN+mYLyctrZOzRK+YWbU1S7Gs71PQl1x1ul53iXG/gbkclqY7SfQPdu7cbScfdRolbzxG9/kFKgvZS20LRmhPTmxZA+WmnbI6PwUuo+hDOTy/Ue8yuKylV6eyDM5PK6vyVBuFs1uMEwY8YM8vLyePXVV/n222/p27dv3W9Dhw7lyy+/5NVXX22SQt5NkoryqayuxvO6uQpXvkfn39rYVHOZnKGtfTmdmUZiUb7Wv1egKsfZ3OqWyyzcnG3f7ebYFu13X/Qd15PuwztrehSueQLl4uNEYoTh9zBc0XtMdy7HZRB1Uv8EODMrMz7e+joBffxYPudH/vh8y+3thHBLEgs057eHjY1Wuqe15nt0ru75HZenGdPsYW1DRFZmXbrHNTH55eVUGVjAwFgqpVwsqdoiEvNrj7etjVb6le8xObrHOz43ty5PRGZWvTH3BbTlk1HDScjN46kNG0kX8wGb3frT5/k3WnsI2JC2PoR4e9T2DFzlYWtNTD3vYYjLzaOdo+57dTxsbTiXlt5IJRYaTHTqNKoGD0kqLi7mww8/JCwsjO7du+v8PmjQILZt28bixYsbtYB3G1VVFcfTkxnh2UYrfZRXWwpU5ZzJurVemKrqaj4JHsFznXpppXeyd8LWxJTI3EwDkUJjy7mcR9TJOK3PyZ1nMbcyo8fwznX5rO2t6NS/HSd3na1naxr+PdtwIfSS3t+kRlI+/OtV/AJ9+eiRz0VjoQWpq6o4nprCcB/t83ukrx8F5eWcTde9KUgsyCexIJ+Rvn46MXF5uaQVFVFaUUFYWirDfdpoNRr6urXGXC4nLFWsktQS1FVVhCWnMLyNr1b6iLZtNMf7sp7jnV9AUl4+I9q20YmJy80lrbAIgP7eniwaPYLTqWk89PN60VhoIZnFJYSnZ2h9DsUlYqFQEOLtWZfP1syUwNZuhF6zctL1QuMS8bFX4mOvrEu78v1QvOE4QbgbNLjBEBwcTG5uLiYmJihqJ/7NmTOHnGuelkgkEubOndv4pbzLfHXmCF0cXFg26H4GuHkxp1swz3bsydKzR1FVVWIhk9PVwRmliWmDt1leVcnyc8d4pG0n5vXoR5Bzayb7d2HVsAlcyMlgQ/T5G29EaDLnD0Zy5t9wXls7k5FPDyJoXE8+2fU2xfklbFm+qy5f6wA3fLp4asVKpVJaB7iSGJmMPmNfGEGnfu34Z/VespJzCOjVRusjNK+lYUfp4uTM1yPH0N/Dk9m9+zK1eyDLThzTnN9yOV2cnFGaXj2/vz5+lDF+bflgwGD6eXjywYDBjPFry2dHQ+vyLDp8kFYW5qy6fzz9PTyZENCe/40Yxen0NHbHx+oritAMlh45TmcXZ766fzT9vDyZFdyHqT178M3R46gqqzTH29lJ+3gfOcZo/7a8P3QQ/bw8eH/oIEb7t+V/B48AmuVaPx4xlBK1mmVHjuNrp6SLs1Pdx8nCoqV2VwBOJKdyNDGZxWNH8mDnDgz18+HHRydQVK7il9Pn6vL52CsJcLw6XHBrZBQJuXmsfHg8o9u1ZXS7tqx8eDxRmdnsiIzS908JTUksq9qoGjwkSd9En7179zJr1izs7Ozqzfdfc/hyEs/t2cTsbkF8N2Q8GSXFfHx8HyvCNRNUO9g5sn70o8w5sI3fo8MbvN3/nQ4lq6yExwO68nT77uSrytkap3mbtEpMOG9x709YzHNLnmDqp48jlUq5EHqR+Q9/rjWJ+aWlz+Do6cDj3i/UpVnZWWAsM6Y4T/9k5+AHNL1KY54dxphnh+n8PlT6YCPviVCfIynJPL/1L2b17svyMWPJKC5mwaH9fH9aM0ytvUMrfpnwMK/s2sEfkRcA+CPyAnIjI6Z268GD7TqQVFjAyzu3sS366k3E6fTLPPbHBub0DWLZqPspq6xkV1wMHx/c3yiLJQi35mhSMi9s+puZQX1YPv4+0otL+GTfAb4P06xO196xFT8/+iDztv3Dn+ERAPwZHoHcyIhnenZnYsf2JOcXMHfLDrZd0hzvbq4uONY2Cn58eILOv/ll6BG+DBVz0lrSjD/+5vXB/Zk3KASpRMKplDRmbtxaN0cF4L3hg3C1tmLQslUAVFRV8eS6P3hr2AA+HDmEyupqQuMT+XjXfp0lWgXhbiOpaeAdvr+/P6GhoVqNg65du/LXX3/h7u4OQHZ2NiEhIURGRt5WoTy+//S24oW7i99UsdLPf0ns/3q3dBGEZiQtb3BHtnAPkIrpNv8pUW/MbukiGOT5bcsNkU949t4bbSMW9xYEQRAEQRDuLaJTp1GJRz+CIAiCIAiCIBh0Uz0MV15iIwiCIAiCIAh3LNHD0KhuqsEwf/78uhWSACoqKli0aBHm5uYAqOp5oYkgCIIgCIIgCHefBjcYAgMDycrK0krr2rUreXl55NW+mAigR48ejVc6QRAEQRAEQbhZooehUTW4wfDTTz81ZTkEQRAEQRAEQbgD3ZGrJElUYi72f8nwC4UtXQShGa3YIM7v/xJVa3VLF0FoRnILcbwF4V50RzYYBEEQBEEQBOGW3aNvXG4p4lGfIAiCIAiCIAgGiR4GQRAEQRAE4Z4iEZOeG5XoYRAEQRAEQRAEwSDRw9BE+rt7MqdXMG1s7cgpL2PdhTMsO3W8QbEdHBzZ+MAkBq77npQi7QnBnVs58Uaf/nRo5UhpRQUboyJYfPQQ6uqqptgNoYGST5Vy4ud88pIrMLWWEjDcks4TrA2+7DA/Rc2GGWk66dauxjy01I2ijAp+fTbV4L/nN8iC/i/ZN1r5hZtnLpcxb0g/Bvv7YC6XczoljY927CM2O7feOAcLc14b1o8gbw9kRlIOxSbx0T//kllUoje/k5UFW56bwg/HTvH1/qNNsSvCDfR39WJO1xDa2NiTU17KuktnWHa+Yceig50jG0c/zsA/vyOlWLs+D3v4BRxMLXRiAtd/TVaZ/r8HoemFOPowq/0AfK0cyFWV8GvcKb69FGowv0wi5Sm/Poz36ISTmRXpZUX8nXSe7y6GUlFTXZfvIa+uPOHbC3cLG9JKC1kXe4I1MQ27LxCEliYaDE2gm5MLK0aNZ0vMRZYcO0QPZ1fm9gpBIpGw9OSxemMD7BxYPfoBZEZGOr+1trLmp/se5FR6Gi/88ze+tna80isYS7mC1/ftbKrdEW4g42I5Oz/OxDvInB6P2ZAeqSLs53xqaqDrgzZ6Y3LiNSuJjP7QESP51UaFsVzT6WemNOb+T5x04iK2FREXWkLbIbo3GULzWvLAKDq5OrFo90GKVWpm9OvNj1MmMnrZjxSU63+JpZFEwopJ4zCTy3lv6x6MjaTMGRzMqscmMO67tVRWV+vEfHz/MCxNFHq2JjSHbg6urBg0gS0JkSw5fZAerdyY262fpj4/d6Te2ABbB1YPmYhMqluf25uY4WBqwYfH93AqS/vhQV55WaPug9BwXe3c+CboYbYnX+B/F/bR3d6d2R0GIpFIWH7xkN6YN7oMZ7xHJ5ZFHuR8XhrtbJx5sV0/XMysefPkFgAe9e7O+91G8d3FUEIz4+isdOW1TkMxM5ax/KLhxohwG8SQpEZ1Uw2GzMxMTp8+TZcuXXB0dGT//v2sXr2ajIwMfH19ef755wkICGiqst41ZvXoQ0R2Ji/v2Q7A/uQEZFIjpnftxcozJ1FVVerEyKRSnujYjTk9gyjX8zvAc117UlKhZur2jVRUV7MvKZ7yygreDxnM1yeOkloslidtCSd/zcfOS87A2Q4AuHczo7qyhrN/FNDxfiuMFboj/3Li1Vi0Msalo6nebRrJJDi2NdFKy4pRERdaQuBkW5zameiNE5pHFzdnBvp5M3XdRg7EJABwIimVPS89xaTAznxzUP9TwxHt/AhwasXob9YQk5UDQGR6FlumT2FUez/+On9RK/+kHp3wtlM26b4I9ZvVJYiI3AxePrgVgP2p8cikUqZ37MXKC2GG6/OA7szpGkJ5pf76vL2dIwA7EqNILRF1951iRkA/Luan80rYZgAOZsRiLDViWtu+rI46iqpa+3hay0x41Ls7i87v4fsoTQPySGYCAK92GsLi83vJU5cyrW1ftiVfYHH43ro8nhZ2TPYJFA0G4a7Q4DkMp0+fZvjw4cycOZPRo0ezceNGpk+fjkwmY+DAgeTl5fHwww9z+vTppizvHU8uNaKXqzv/xEVrpW+Li8JCLqeni6veuIEe3swM7MPXp46y8MgBvXn6uXuyNzGWimueQm6LjcJIKqVfa89G2weh4aoqargcXo5nbzOtdO++5lSU15Aeof9Jc068GjsveYP/nZqaGkK/zcHGTUaH+6xuq8zC7Qv28aBErSY0NrEuLa+0jLDEFPr5etUbF5edW9dYAIjNziU2K1cnzs3GmrmDQ3h7y67G3wGhQeRSI3o5ufNPUpRW+raES1jIFPR0dNMbN9DNh5mdg/j63BEWntynN087ZSsKVOWisXAHkUmN6OXgwc5U7Yb7PykRWMgU9HBorRNjKTPhl7iT7E3T/htJKNKc4+4WNgA8c+gXPj2/RytPRXUVciMx0EO4OzT4L3XJkiU88MADzJ49m6VLl/Lmm28ybdo0Zs2aVZfn448/ZtGiRaxbt64pynpXcLe2RmFkTFxBnlZ6Qu13L2slB5MTdeLOZqYT/NMKClTlTGzbXud3hZExblbWxOVrbze3vIxClQovG9tG3AuhoQrTK6iuBGsXmVa6lbPm1CpIq8Ctq24vQk68Ght3OZtfvUxOnAq5uRS/QRb0mGSL1Fh33kPsgRKyotWM/tARqZFYW7ql+dgrSckroKpGu887KbeA+zo6G45zUJKQk6eTnpSXj5fd1XNYAiwcO4ztEVEcjNWtL4Tm4W5po78+L6qtz62UHExL0Ik7m32Z4N+XU6AuZ6JvB73bbqd0pEBdzvKB4+jr7ImRRMLelFg+OL5HzF9oIa3NbZEbGZNQpD0PKbG49nhbKAnNiNP6LaU0n/dPb9fZ1jBXf9TVVXXbii3KrvvNWmbCMFd/xnl0YmVU/cPaBOFO0eAehvDwcJ588kksLCyYPn061dXVjBgxQivPo48+yoULFxq9kHcTa7lmrHGxWvvJcolaM2bdQq7/qXJGSTEFqnKD27VSXNmu7ls0SyrUWMga/rRaaDzqEk1vj9xM+1SSmWq+q0t1x6SX5ldRll9NQUoF7UZYMvJdJ/yHWXL+r0L2fZmtkx/g3OZCHAMUBocwCc3L0kRBsUrPuahWY64wfC5aKQzEqbTjnujdDXdbaxbs3N84BRZuSV19XnFdfV5RW58bqHczSospUBuuz0HTw+BkZsm57HSe3v0788P20tvJnfUjJmFqLKs3VmgalrLa41153fGu/W4ha9hcomGu/oz16MTPsWEUVmj/HXSzcyNs7Ct81OM+ogoy+UlMem4ykpqW+9yLGtzDYGFhQVZWFu7u7lhZWTF79mzMzc218iQnJ2NnZ9fohbybXFkVp8bAH0yNoR9uQHplu3pm8UgMpAtN70aHU6KnSS43lTDqfUesXWRYOGhOQecOJhjJJJz4OZ+uD1pj6371RiQ9spycODVDX2/VmEUXGkjC1fPvCqlEYvCMq+8clxiIk0iuxnnZ2TJrYF9e2rBFb+NCaD519bmB32+n3n3l0DZUVZVcyM0EICwzhaj8bP4YNZkJPu1Ze+nMLW9buDX1XWcBqhtw/R7uGsCSnuM4npXI4vN7dX5PKcnnsX0/4mhqxUvt+vHn4GeYsOd7clSiV0m4szW4wTB8+HDeeust3nvvPXr27Mmzzz5b91txcTE7d+7ks88+48EHH2ySgt4tClWaJxGW1/UkmNd+L1LrH9Pe4O3qecJhJpNTJG4sWoTcvLYnoUy7J6GiTH/PA4CxQoprZ92egtbdTTnxcz65CWqtBkP84VIUFlJadxe9Cy3hhf69ebF/H620HRFR2CnNdPKay+UUGVghCaCoXIWFnh4IM7mcIpUKqUTCwrHD2RERTWhsIkbXNFSkEglGEonOMCih6RSqr9S719XnsturzwGdlZEATmamUqguJ0ApHg60hMLaniQLY+3rrHnt96KK+o/3/7XpxbxOQzielcj00PVU6FnuPLO8mMzyYgDO5qaya8QLPOjV1eAKTIJwp2hwg2Hu3Lm88cYbbN68mZ49e2r9FhoayltvvcWDDz7I888/3+iFvJskFeZTWV2Nh7X2nALP2u/ReTn6wm6orLKCy8VFeFjbaKUrTUyxUihuebvC7bFyMkYihcLL2itnXPl+7Y3/FfmpFaSdL8O3n4VWg6JSrbkRNLHSXoIx6UQpHr3M9M5tEJrebyfPsy8qXittiL8PwT4etb17V7VWWtf7Hob4nDwCnHRvBlvb2nAuLR1nK0u6uDnTxc2Z8Z3baeV5oV9vXujXm0FffE9qgZgo2xySivI09bnldfV57ffo/Furd63kCoa39uN0VhoxBdrbkEmNyBXLqraIpOJcKquraW2hvTKZh4XmeMcUZhmMfbvLcB737cm25Au8ErZZq7FgbixnkLMfZ3NTSSq5Oh8muSSPAnUZzqZiIYsmUSOumY2pwXMYSktL+eyzz/jggw90fgsODubIkSO8+eab7Nr1317RQ1VVxfG0FEZ4t9FKH+XtR0F5OWcy0m952weTExjk6YP8mjW9R/n4UVldzeHUpFvernDrjOVSnNqbkHC0VGsoStzhEuTmUhza6DYYSnIqCV2eS/xh7S7ouEMlyEwl2PtcjSkvqqLwciWO/mId/paSWVxC+OUMrc+h2EQsFApCfD3r8tmamRLo4caheiYpH4pLxMdBiY/91RsSH3slPg5KQmMTySwqZsKKdTofgPUnzzNhxToyi4qbbF8FbaqqKo5nJDPCw08rfZRnWwpU5ZzJvnxL21VXVTG/zzCmd+ytlT6sdRtMjWUcTRf1eUtQV1cRlp3IMFd/rfThbu0oUJdxLle3VwhgTodBPO7bk9VRR5l17E+dnoWqmmo+7nEfU9v21UrvaOuMrcKMiwUZjbsjgtAEGtzDEBwczKFDh7TmKMyZM4c33nijLi07O5s5c+YwatSoxi/pXeSrk0f4+f6HWDrsPjZcDKebkwvTugay8MgBVFWVWMjktFHakViQf1NPkr49Hcb9bfz5YcwEVp49gZeNLfN6hbDuwlkuFxc14R4J9en6oDXb3s1gz6Is2g62IOOiinObCuk5xRZjhRR1aTV5yWqsnGSYWhvh3N4E5w4mHF2VS0V5DTZuMpJPlBK+tYheT9iisLjaIMxLrADA1l1MgryTnEhK5VhCMovGj2TR7oPkl5bxYv8+FJWr+PXkubp8PvZK5MZGRKZrnkxuuxDFc8E9WTFpPEv2aIYgzBkcTFRGNjsioqiqqSH8sv6bh8ziYoO/CU3nq7OH+Xn4IywdMJYN0efp1sqVaR16sfDEvqv1uY09iYV55KoaVp+XV1Wy/PwxZnYJIru8hP2p8QTYOjCzSzB7kmMIvSxWxmop30Qe4od+k/mi9wT+SDhDVzt3nvHrw6Lze1BVV2JuLMfXyoGk4jzy1KUEWDsytW1fzuemsS0lgs5K7aXTYwqzKKlUs+LSYZ4PCCFfXcbhzDg8Lex4sV0/IvPT+SPhTMvs7L1OjN5sVA1uMOibyLd3715mzZql1Yi41Um995IjqclM37GZWT2D+HbkWDKKi/n48H5Wnj0BQAcHR34d9zBz92zn90sNX1UqNj+Xx//+nTf69Oeb4feTW17G9+dOsuSYGPvYklw7mTLkVQdO/ZLPzgWZmNsZ0+sJWzqNswYgO1bF1rcz6P+iHX6DLZEaSRj2eitO/prP+b8KKcurxNJJRsh0O/yHWWptuzRf86RKYdHgzkChmcz47W9eG9afeUNCkEoknEpOY9YfWym8Zg7Du6MG4WpjxeAvVwFQUVXF/639gzeHD+DDMUOoqK4mNDaRBTv3i7kJd6gj6UlM/3cjs7oE8+2g8WSUFvPxiX9ZeSEMgA52jvw6YhJzD23l95jwBm/3f2cOkV1ewuS2XXnCvxt5qnLWXTrD52dEfd6SjmYl8OKRDbzYrj/L+jxERlkRn57bzaroowC0t3Vmbf8pvBq2mY2J5xjq6o9UIqGj0oUNg57S2d7k/Ws4npXIVxH7ySovZpJPD55o05MCdRnbUyL4PHwfaj1zHQThTiOpaeAdvr+/P6GhoVqNg65du/LXX3/h7u4OaHoYQkJCiIyMvK1CeS5bfFvxwt1lxsD/9jC2/5oVG0bcOJNwz1C1Fgsy/JfILcTx/i+Jmvh2SxfBIO//fdZi/3bcrJdb7N9uKuIVg4IgCIIgCMK9RXTaNioxzkEQBEEQBEEQBINuqodBIhFLVAmCIAiCIAh3tnv1jcst5aYaDPPnz0ehuLq8Y0VFBYsWLap747NKdesvsREEQRAEQRAE4c7T4AZDYGAgWVnaLy3p2rUreXl55OVdfRFJjx49Gq90giAIgiAIgnCzRA9Do2pwg+Gnn35qynIIgiAIgiAIgnAHEpOeBUEQBEEQBEEw6I5cVlWea3TjTMI9w1ue2dJFEAShiRibVbR0EYRmpDhi0dJFEJrTxJYuQD3EkKRGJXoYBEEQBEEQBEEw6I7sYRAEQRAEQRCEWyWWVW1coodBEARBEARBEASDRINBEARBEARBEASDxJCkJmQulzFvcD+GtPXBXC7ndEoa83fuIzY7t944BwtzXh/ajyAvD2RGUg7FJfHRzn/JKCqpy+NqbcUrg0Po7eGGzMiIownJLNp7kITc/CbeK0GfSyeq2LWmgszkasytJPQaZUz/h4zrfTv6xeNV7FlXQXpCNWaWEjoEGTH8SRlyk6sxHz1WSnGebuwba02xVIo3r7ckc7mMeUP6Mdj/6vn90Y6Gnd+vDetHkHft+R2bxEf//EvmNef3tZysLNjy3BR+OHaKr/cfbYpdEW6gn5M3szsMwNfKgVxVKb/EnmT5xcMG88ukUp726814z044m1mRXlrEX0nhfHsxlIrq6rp8w139mebfBx8re4oqVBzOiOfTc3vJUen/WxCaj5lCxpwx/RjUwQczhZyziWks3LSPuIz6z+9rff7EGEpUat76dadWuoutFS+PCSHQV3P9Ph6TzOdbDpKYnd/Ie/EfVyOukY1J9DA0oc/Gj2KYvy+L9x5i3uYd2JmbsWbyRKxNFAZjjCQSVj4yjo7OTry7fQ/vbNtDJxdHVk2agLFUc7jM5TJWP/YAbVvZ8e72vczdvANHSwvWPv5QvdsWmkZiRBU/faDCwV3CY28q6DrIiJ1rKti3vtJgTOSxStZ8oMKxtYQn31PQ/yFjTu6q5M8v1HV5ivJqKM6D0VNlTP9MofUxs2qOPRPqs+SBUQwN8GXJnkPM27QDOzMzfpxy4/N7xaRxdHRx4r2te3h36x46uTqy6rGr5/f1Pr5/GJbivG4xXe3cWB70MLGFObxweAObEs/zcseBTA8IMhjzVpdhPN8umD8TzvHsod/4Lf400/z78EG3UXV5RroFsDRoIhfy05lx+A+WnP+Xng4erB0wGblUrBTY0j6dPIrBHX3537ZDvPHLDpQWZnz/3ESsTG98LkolEl4fP4Ahndro/GamkPHdsw/QxtmO+X/s5fV1O2hlbcHqFx5q0LaFe191dTVffvklISEhdO7cmaeeeorExESD+fPy8pgzZw6BgYEEBgby9ttvU1pa2ujlapQehm7durF582bc3d0bY3P3hC6uzgxs483UXzayPzYBgLDkVPbOeIpJPTrzzaHjeuNGtPMjwKkVo5avISY7B4DIjCy2PjuFUe38+Cv8IiPb+eGptGXk8h/rnmZGZWbz74tPM6KdH+tPnW+WfRQ09qyrwNlbysOvaCr7tj2MqKqCfRsqCB5vjEyh/ZSjpqaGLd9W0KGvERNf1sT4dDGipgoO/1WJurwGuYmEtFjNk8j2fY2wdRRt+ztJFzdnBvp5M3XdRg7EJABwIimVPS89xaTAznxzsP7ze/Q3a4jJqj2/07PYMn0Ko9r78df5i1r5J/XohLedskn3RajfS+1DiMxPZ+7xzQAcSI/DWCrlWf++rIo6hqpK+8GAtdyER3268+m5Pay8pOkROpKZAMBrnYew6PxeclWlvNAumH/Tonnn5Pa62NjCbDYOfZpBLm3YkaL9tyA0n84ezvRv583zKzZy8GICAKfiUtnx5lM8EtSZ73brP78B/JzteWP8QNq5O1Km1l1SeHhnPzwcbBn76Y91vRXRl7P5562nGd7Zjw1HxfW70dylk56XLVvGr7/+yoIFC3B0dGTRokVMnTqVLVu2IJfLdfK/9NJLqFQqfvjhBwoLC3nzzTd5//33+eSTTxq1XA1uMHz99dcGf1Or1axZswZra2sAZsyYcfslu8uF+HhQolZzKO5qqzCvtIywxBT6+3gZbDCEeHsQl51b11gAiM3OJTY7l/6+XvwVfpF/ImOIycrVGvpQUVUFgNxIPJlqTpUVNcSdq2bIZJlWeodgIw78Xkn8hWr8umkfk8txNeSm1zDxZe3TL2icjKBxsmvyVWNigWgs3IGCa8/v0Fjd87ufr5fBBkOwT+35nXXd+Z2VSz9fL60Gg5uNNXMHhzDz9y2sfOyBptsZwSC51IheDh58ceGAVvqO5Eie9e9LoH1rDmXEaf1mKTPhl9iT7EmL0kpPKNLU1+7mNuSpSgnNiCcsK0krT3yxJk9rC9vG3hXhJvRt60GpSs3hqGvO75IyTsSmEBLgVW+D4aNHh1OqrmDyl7/y5VP36/y++1wMcRm5WkObrly/Zcbi+v1fp1arWbVqFa+88gr9+/cH4PPPPyckJIRdu3YxevRorfynT5/m+PHjbNu2DR8fHwA++OADnnnmGV5++WUcHR0brWwNbjCsX7+e7Oxs7O3tkcm0b46qq6vZuXMnRkZGSCQS0WAAfOyUJOcVUFWj3cRNzCvg/g7OhuPslcTn6g5aT8zNx1OpuYgUqVScSb0MaMbK+tgreXVIP3JLStkeEaUTKzSd3Ms1VFWCvat2L4K9s+YmPztVt8FwpedAJocf3i0n9mw1xnLoOtCYkU/LkMk127ocV42phYS181XEnKmiphr8exoxepocKzF/oUX52CtJ0XN+J+UWcF/Hes5vByUJObrnd1JePl52V28SJcDCscPYHhHFwVjDXdFC03I3t0FuZEx8UY5WemLtxCIvS6VOgyGlJJ93T+3Q2dYwN3/UVVXEF+VSAyw4u1snz3BXfwCiCrIaaQ+EW+HtqCQlp4Cq6uvO7+wCRnc3fH4DvPnLP0Rdzjb4e1G5irOJmuu3sZEU71ZK5t7fj9ziUv45K67fjeluqOgkGgAA2ltJREFUXFb14sWLlJSU0Lt377o0Kysr2rVrR1hYmE6D4cSJEzg4ONQ1FgB69uyJRCLh5MmTjBo1isbS4AbD5s2befvtt0lMTGTRokUEBATU/da1a1fWrl0rhiRdw9JEQYlarZNeolZjrtDtUro2Tt/E5RK1Ggs9cd89Mo4gbw+qqqt5d/sesksaf9yaYFhZiaZGMjHTvoGXm2n+V6XncJQUaGLWzlfTeYARIQ/ISImqZvfPFRTn1zDpdc0wpctx1RRm19BzuJSgccZkJdewa62a7+aV89LXJlqTo4XmZWmioFh18+e3lUJBYk6+bpxKjbn91bgnenfD3daa537d3CjlFW6NldwEgOIK7WNdUqkCwELWsDHnw139GefRkR+jj1NYUa43j4eFLa92Hkx47mX2X465jVILt8vSVP/5XarSfx2+Vn2Nheste2Ycffw01+8Pf99DTpG4ft8rBg8eXO/ve/bs0Zuenp4OgLOzdsO0VatWXL58WSd/RkaGTl65XI6NjY3e/LejwQ0GpVLJ0qVLWb9+PU8++SRPP/0006ZNa9TC3K0kaCY5XUsqkVCjp3UrQTOG3ZDrt1MXJ9Eft/TgMZaHHmdIW1/mjx6K0syU5aFhN1N84TbUXFnwxMC9u77DeWXIc7u+Rox8SnPx8elsRE0N/PNDBVmPV+PgJmXCbDkyuQQXH01vhVcHaNVawrevqDi1p5Leo2W6GxcancHz20D++s5viYG4a89vLztbZg3sy0sbtui9aRGaj6T2xK4xcLSr6znWV4xw8+ezXuM4npXIonN79ebxsbTjx/6Poa6qZMaRP+7Wodd3JYmk4ddvJA075g21fOcxVuw+zqAOvrz30FBsLUxZuUdcv//LysrKAHTmKigUCgoKCvTm1zevQaFQoFKpGrVsNz3p+eGHH6Znz57MnTuXAwcOsHDhwkYt0N1oRr/evNivj1ba9ogovOzMdPKayeUUlRs+iEXlKsz1HHwzmZwiPQf/RHIqAMcSU7A2UTA9uBcrDp/QGSohNA1TC82FRlWq/d9bXfugyMRcN0Zhqvlf/57aQ5X8uhvxzw8VXI7VNBg8AnTHs3q2N8LEXDMPQmgeL/TvzYv9tc/vHRFR2Cl1z2/zBpzf+p5Qmsk157dUImHh2OHsiIgmNDYRo2tuZKQSCUYSiTi3m1FRbW/A9T0J5sYKrd8NecqvF692GsyxrESeC/0NdXWVTp7eDh4sDZpISYWaJw78TEpJfuMUXmiQ54b25vnh2uf3zrNReDjov34X13N+36xT8Zrrd1hsClZmCqYN6cXqf0/oDIUSblEL/mc01INwIyYmml5NtVpd9/8BVCoVpqamevOr9YxmUalUmJnp/g3fjltaJcnLy4v169fzxRdfMH78eCoqdFcC+C9Zf+o8/0bHa6UNaetDiI+HpkfhmnQPW2ti6lmnPS4nj3ZOrXTSPZQ2nEvTdFV1cnHE1dqa7ZHa4x3Pp2UwrlM7bM1MxdCkZqJ0liCVQk6ads2UfVnT9dCqte6EZTtXTVpVhXZMVZXmu7ECyopruHC4Cnd/KY7XbKOmRjNnwlwsq9psfjt5nn1R153f/j4E6zm/Wyut630PQ3xOHgF6zu/Wtprz29nKki5uznRxc2Z853ZaeV7o15sX+vVm0Bffk1pQeDu7JDRQYnEeldXVeFw3CfnK95hCw8NP3uk6nCltAtmadIFXjv+lt7FwX+v2fBJ4PwnFOTx14BfSy4oadweEG/r96Hn2R2if34M7+NC3rUdtz9/V9Nb21sTexHsY9Ong7oir0lpnvsKF5Azu79EOG3NTMTTpP+zK8KLMzExat25dl56ZmYm/v79OficnJ3bv1p4PpVaryc/Pb9QJz3AT72HIydGe9GVsbMycOXNYunQp9913H+bm5qjVarZt29aoBbwbZBaXEH45Q+tzKC4RC4WCEB/Puny2ZqYEergRGmd4EmNoXCI+9kp87K8upXjl+5UVlwa08WbJ+JE4W1lqxYb4eJBZVEKOaCw0G5lcgmcHKeGHq7SGooQfqsLEAtz9dE8xrw5S5CZwdp/2DUTk0SqkRtA6wAgjY9i8VM3+37Qb4xFHqqhQgXcnsZpGc9F7fsfWnt++nnX5rpzfh+qZpHwoLhEfBz3nt4OS0NhEMouKmbBinc4HYP3J80xYsY7MouIm21dBm7q6irCspLrJyFeMcA+gQF3G2dw0vXFzOw5kSptAVl06ysyjG/U2Fvo7+bCo51hO56Tw0N4fRWOhhWQVlhCRkqH1ORyViIWJgqC2nnX5bM1N6eHjxuFLt7cIQf923ix8bCRONtrX76C2HmQVlpBbLK7fjUVS03KfW+Xv74+FhQXHjh2rSyssLCQiIoIePXro5A8MDCQ9PV3rPQ1XYrt163brBdGjwT0MwcHBHDp0CDs7u7q0OXPm8MYbb7BgwQIAsrOzmTNnTqPOyr5bnUhK5WhCMovHjWTRnoPkl5XxYr8+FJWr+OXUubp8PvZK5EZGRGZoVsXYGhHFc0E9WfnoeBbvPQTA3EHBRGVms6N2BaRfTp7j4a4d+e6RcSw9eJRSdQXjO7VjQBtvXt64TYx/bWaDHpHx/Zsq1i1Q02OoMYmRVRz8o5IR/ydDppBQXlpDZlI1SmcpFtYSFKYShkyWsW1lBaYWatoHGZEYUc2B3ysJGmuMhbVmGEq/icbs/aUSC1s1ft2NSI/XTIxuGyjFt6toMLSkE0mpHEtIZtH4kSzafZD80jJe7K85v389ed35bWxEZLrm/N52IYrngnuyYtJ4luzRnN9zBgcTlaE5v6tqagi/nKH338wsLjb4m9B0lkYeYk3/x/iqzwNsiD9LN3s3prbtw6fn9qCqqsTCWI6vlQNJJXnkqkoJsHFkmn9fzuWmsS05ki5KV63txRRmoa6u4uPAMZRUqlgWeQhfS3utPOllhaIB0YJOxqVyPCaZhY+N5LMtB8kvKeP54X0oKlOx4cjV89vbUXN+X0xt+KpW6w+fY2Lvjix7ZhzLd2qu3/f3aEe/dt68unab/rkTwn+GXC5n8uTJLF68GKVSiaurK4sWLcLJyYmhQ4dSVVVFbm4ulpaWmJiY0LlzZ7p168bs2bN57733KC0t5d1332XcuHGN3sMgqalvht41/P39CQ0N1WowdO3alb/++qtudaTs7GyCg4O5ePH2XjjjN//z24q/U1iZKHh9aH+G+PkglUg4lZLGxzv3ay2b+tPjE3G1tmLQ16vq0pysLHhr2AD6enlQWV1NaFwiH+/aT1ZxSV2e1rbWzBkYTGBrVywUCi6kZ7L80LG6l8TdTRY+uqali3DbLhyuZPfaCrJSarCyl9BnjDEhD2gmJcedq2LFayomzpbTfejVNvqJnZUc2lhBdmoNVnYSeo4wpt+DxkilmgZDdXUNx7ZWcmxbJTmXazCzlNBlgBFDJst0XgZ3N3n95yktXYRGYWWi4LVh/RnStvb8Tk5jwc79xF+zbOqaKRNxtbFi8Jfa5/ebwwcQ5O1BRXU1obGJLNipfX5f79I7s/lq/xG+3n+0SfepKVT5G96vu8VQ17bMbN8Pb0s70suK+DnmBN9HaZ7i9XLw4OeBjzPv+F/8mXCOme3782L7EIPbeuzfn5BIJKwdMNlgni8vHODL6979cLcwPaZn4tZdyMpUwSv392dQBx8kEglnEtL4dPN+ErKunt+rpk/ERWnFiI9W6d3Gjjef4kRsCm/9ulMr3d3Omlmjg+nu7Yq5iYLIlExW7D5W95K4u8n5JbNbuggG+X3ccveSUW/c+n+XqqoqPvvsM/7880/Ky8sJDAzknXfewc3NjZSUFAYPHsyCBQt44AHN+3lycnJ4//33OXjwIAqFghEjRvD666+jUDTum8MbvcEQEhJCZGTkbRXqXmkwCA1zLzQYhIa7VxoMQsPcCw0GoeHulQaD0DB3dIPhoxZsMLx55/53uVXiFbKCIAiCIAiCIBh0S6skCYIgCIIgCMIdS8wHaVQ31cMgMfBSMUEQBEEQBEEQ7k031cMwf/58rUkUFRUVLFq0CHNzzZjFxn6rnCAIgiAIgiDcrNtZ3lTQ1eAGQ2BgIFlZ2kuHde3alby8PPLyrq4aoG+dWEEQBEEQBEEQ7k4NbjD89NNPTVkOQRAEQRAEQRDuQGKVJEEQBEEQBEEQDLojV0lSuVS0dBGEZnS42K+liyA0o+oAsS7/f4nRRbEu/39JUUd1SxdBEIQmcEc2GARBEARBEAThlolJz41KDEkSBEEQBEEQBMEg0WAQBEEQBEEQBMEgMSRJEARBEARBuKeI9zA0LtFgaCL9Xb2Y2z2ENjZ25JSX8vPFMyw7d6xBsR3sHNl032QG/L6ClOJCg/ne7jmIZzr0wGPVp41VbOEWZZ4p5OKvlylKKUdhZYzHUHt8x7Uy+Hb0otRy9s2+qJNu7qJg0P8CdNIrSqvY/8pF2j7ohPsAu0Yvv3BzQhy9md1hAL5WDuSqSvkl7iTfXjxsML9MIuWptr0Z79EJZzMr0suK+DsxnG8vhlJRU12X7yGvrjzZpifuFjaklRayLuYEP8aENccuCTdgLpcxb0g/Brf1wVwu53RKGh/9s4/Y7Nx64xwszHltaD+CvD2QGUk5FJfER//8S2bR1cn/rtZWvDIkhF4ebsiMjDiakMziPQdJyM1v4r0S9Onv6sWcriG0sbEnp7yUdZfOsOz80QbFdvh/9u47Ooqqb+D4d3ezu+k9pJMeegs1IaFLL1KkWLCiqAhKsbwW8BEUENFHRYqIguVRKSLSpfcWeg0hvfee7CabvH9sElh2E0JIodzPOTmcuTO/2Tvcndm5c8vYOfLXkGfovWGF3u/3yXGv42BirhfT+Y9vSS0Uk0EI9zdRYagHHZu4sLLfKDZHXmVR6EE6O7oyq2MPpBIJ356r/qLTwtaBHx8bjVwqq3a7Lo5uPN8yoC6zLdRSxrV8TiyIxCXImubjncm4ms/V3xMpKyvDf5STwZicqEIAAmf7IJPf7BkoVej3ElTnlXBiQSSFqWL2sPtBBzs3lgWPY2vsZb68uI9O9k2Z3ro3UiQsvXrYYMz77fsz0rMtSy4f4kJmAq2snXijVQ9czKz4v1ObAXjSO4CPOw5m+dXDHE6OpJ2tK++2ewwTIwXLqtiv0HC+GDmYtq5OfL7rIHlqNVN6dGP1M2MYsnQ12UUqgzEyiYTvJzyOqULBnK27MZJJmdEnmFVPjebxFb9QUlqKmULOqqdGoSkrZc62PahLNLwW0pWfJ45l6LKq9y3UjwAHV77vM5rNUVf44sxBOjVxY2ZADyQSCUvOH602toWNAz/2G2Pw99ve2BQHE3M+ObGb06kJOusyiwrr9BiEcqKFoU7VuMLw3//+l5dffhkTE5PKtA0bNvDDDz+QlJSEn58fU6dOJSgoqF4y+iCZ1r47lzNSeOvAFgD2x0diJJXxattufH/xFCpNiV6MXCrluZYdmREQTFGJ/vpbmRjJWRQyiOSCPFzMLevlGISaC1uXhJWnCQFveADQpL0lpSVlhG9MwWdoE2QGKgE5UYWYOCiwb2VR7b6TTmZz8cc4SopKq91OaDhvtAzhSlYSs078DcDB5AiMpFJebh7EqrDjqEp1z18ruTETfDry+fndrAzTPjA4mhIFwDvt+rHo/B4y1AW83Lw7W2IvsejC3sptvCxseca3k6gwNLL2rs709vdm0v/+4kB4FACnYuLZ/cYLPNmpHUsPnTAYN7ClPy2cmjBk2RrCU9MBuJKUyubJExncyp9NF64yqKU/nnY2DF66urK1IiwljT1TX2RgS3/+OH2hQY5R0HqzfXcuZyQz/eDN32+5VMqrbbqy8tLJKn+/n23RkRkdQqr8/W5l5wjA9ugw4vOr7jkgCPerGg96XrZsGQUFBZXLmzZt4sMPP6RTp05Mnz4dT09PXn75Zfbs2VMvGX1QKKQyujm7sz06TCd9a9Q1zOUKuji6GYzr7ebNtPZBfHvuKPNP7a/2M97v3IvUwnzWXr9YZ/kWakdTXEr6pTyculrppLt0s0ZTVEr6lTyDcdlRhVh5mhhcV6E4v4STiyKxa2lOt/d96izPQu0ppDK6OniwM+6aTvr2uCuYy5V0dmiqF2MhN+Z/N0LZnaB7TYjM094cuptbA/Diwd9YeF73+qku1aCQiYbgxhbs40G+Ws3hG9GVaZkFhZyMjqOHr1e1cRFpGZWVBYAbaRncSMuojNtxJZxxq37X6dpUrNEAoJBV39Is1C2FVEZXJ3d2xBj6/VZW8/vtw7R23fn2/FHmh+4zuE1L2yZkq4pEZUF4YNX4l6isTLdtZ/Xq1bzxxhtMnjy5Ms3b25tvv/2WPn361F0OHzBNLaxQyoyIzNbt1xqVkwmAt5UNBxOi9OLOpSXR/c/lZKuLGOPbusr9B7t4MNq3FYP/Xs0In5Z1mnfh7hUkqyktKcPcWamTbuqkACA/UQXt9ONyogoxdzPm0PthZEcVIjeV4d7LlmbjnJEaacc9yJRSen/ZHHMXYwpSRLeE+4G7mTUKmRFReek66dF52vPb09yWQ8kROuviCrKYc2a73r76uzZHXaohMld7rbiRe3OfVnJj+rs1Z6RHW1Zeq74bhFD/fOxticvMRnPb72BMRjbD2jhXGxeVkamXHpORhZetDQC5KhVn4xMB7ZNqbwdb3unXg4z8ArZdDtOLFeqPu4U1SpkREdm6ZRaVq132srSt4vc7keB1y6r9/W5p60i2uohlvR8nyNkTmUTCnrgb/OfEbjF+ob6ILkl1qtaPrpKSkujVq5dO2oABA1iyZMm95umBZqkwBiC3WPdtl/nly+ZypV4MQHKB4SfRt7KQK1gYPIjFZw4TmaP/IyQ0vOIC7ZNAIxPdJ4EVyyUF+l2JVFnFqLJLQFpEi6dcMLFXkHYhlxt/p1CYXkzAVG3XJqmRFHMX43o+AuFuWMi15ZF3+/ldoq3QVXV+326Aa3Me92jD6usnyCku0lkXYOfGH32eA+BCRgJrxKDnRmdhrCRPpf8G43y1GjOloso4S2Ml0QYGLlcVt3zC43T39kBTWsrsrbtJyy/Q20aoP1YK7fmbV6z7gObm77fhsq7J73dL2yY4mVrwe9g5frh0Cl9rO6Z3COaPgU8y5J+fKCwRY9SE+9tdVRgyMjKws9PO0NK8eXPS0tJ01sfGxlauf1RJK2bFKTNctS29hyrvR137kpSfy8qL4gbivlFaXp6GJ0My2OnPyFRGtw98MHdRYmKv/QGyb2mOVC7h2u9J+I1yxMJNVBTuRxXnd1kV53FpFef9rQa6NueLro9zIjWaRRf0u3DG5Wfx1N41OJpYMLVVT/7q9yKjdq0iXSWeQjYECbdcx8tJJRLDJS7Rb33XXS0x+FMgwXDckgPHWX7oBP2a+TJ36GPYmpqw/LC43jcUSeX5bVhV531NzDq0FZWmhEsZKQCcTIkjLCuN9YOfZrRPK365drbW+xYME9Oq1q0aVxiMjIwYPnw4FhYW+Pj4UFJSwieffMKmTZtQKpX8888/LF68mEGDBtVnfu972Wrt00Jzhe6TRrPyJxO56tp1Lenj7sNw7+YM3bSm8sdMWn6XKpNIKC27l0uZUFtys/KWhEKNTnrFstxUvw+yTCHFoa3+YGfHAEuu/Z5ETnShqDDcpypaA8yNbju/jSqeTBbpxdzqeb+uvNOuL8dTonn18J+oSzV626QU5ZFSpH1ieS4jgX8HvcZYr/ZVzsAk1K3Xe3TjjZ6BOmnbL4dhZ2eqt62ZXEFuNbMY5apUmBtoSTBVKMhV6ceFxsYDcDw6DksTJa+GdGXlkVN6XaGE+pFT/vtscVtLwr3+fgN6MyMBhKbEk6MuooVtk1rvVxAaSo0rDGfPniUiIoKwsDCuXbtGWFgY6enplTXy//znP3Tv3p2pU6fWW2YfBDG5WZSUluJpYaOT7mmpXb6elW4o7I4Ge/pjbCRn16gX9dZFPD+LtdcvMPPgtlrtW6g9U0clEinkJ+l2VygoXzY3cOOfl1BE2sU8XINtdCoUGrX2pkBhIQa53q9i8jIpKS3Fw1z3/K5YDs9JMxQGwIftBzDRrzNbYi/x9olNOpUFMyMFfV38OZseT0z+ze6GMfmZZKsLcTYVs6E1lD9PX2Df9UidtH7NfAj28dC2DNyS3tTWqtr3MESmZ9LCSf9msKmtNefjkwBo4+KIm7WV3niFCwnJPN62JTamJqJrUgOJyS0/v2///ba4t99vS4WSAU39OZOaQHi27j7kUhkZYlrV+iHq2XWqxncm2dnZ+Pv74+/vz9ChQ/XW79q1CxMTE3bt2sXgwYPrNJMPEpVGw4mkWAZ6+rH84s2p9gZ7NiNbVcTZ1MRa7ferM4dZffmMTtqTzdvxZLN2DP17DZkq8YPSGGQKKbYtzEk6noXPMIfKCnTCsSzkZjJsfPWfShZlFHNhZRxSIwlN+9zswpdwJBMjEylW3tXPniQ0HnWphpNpMfR3a145RSrAQLcWZKsLOZeh/xQRYEbr3kz068yqsGN8dm6X3npNWSmfdhrKX9Hn+TB0a2V6GxtnbJSmXM1KrvuDEQxKycsnJU+3+5ex3IhXQ7oS4uPJgRtRANiYmtDZw41lVUypCnAoIpqhrZvjY29bWbHwsbfFx96WpQe1L/Ls7efNK8FdOBuXSGJObmVsiI8HKbn5pIvKQoNRaTScSI5loIc/Ky4Z+P1Oq93vt1qjYW5gfzZHXmXGoS2V6f2b+mFiJOdYUsw9510Q6luNp1UNDg4mPV23ZjxjxozKNCsrK3JycpgxY0bd5vAB9M25o7R3cOG73sPp5ebFjIBgXmnThSXnjqHSlGAuV9DBwRlb45rfGMbl5XAhPUnnr2Kg1YX0pGrfCC3UL//RjmSGFxD6ZRTJZ7RvfL7xTwq+Ix2RKaQUF2jIDMtHlaOdn9uupTl2rcy5tCaeyG2ppJ7P5eJPcURuS8N/jBMKc9HCcD/77vIh2tm68nW3UfRw8uHNVj15qVkgy64cRlVagrmRgva2rtgqtJXFFlaOvNw8iPMZCWyLvUJ7W1edP3MjBUWaElZcPcJYrw7MaN2bwCaePOkdwIrgcVzJSmJd1LlGPupH26mYeI5HxfL5yEGMad+afs18+Onp0eQWqfg99Hzldj72trRwcqhc3nopjKj0TL6fMJIhrZoxpFUzvp8wkrCUNLaXtyj8L/Q8mQWFLJ/wOANb+NHDx5PFowbTy8+bBbv2i4ekDeybc0do7+DCkl4j6OXqzfQOIbzcuitLzh+95ffbBVtlzX+/izQlLLtwnNG+rXmvUy+CnD14sWUnFgUPYXdsOIcTo++8E0FoZLWeVhVgz549vPnmmzoDnasbAPaoOJIYw+Q9G3mrQ3dW9B1JckEen57cx/flg5Vb2znyx+AJzDiwlXXh4l0KDzr71hZ0muHJtT+TOPV5JMa2clo+7YLPMG1XhOzIAo5+fIP2r7nj3ssOiVRC51leXPsziRubU1FlFWPqqKTtJHc8+j3akwY8CI6lRjHlyDqmturB0qAnSC7MZcH5XawK0z4xbmnjzK+9nuGdE5vYEH2e/m7NkUoktLV1YW3f5/X299S+nzmRGs03lw+QVpTHk76deM6/C1nqQrbGXuHLi/sMjnUQGtaUtf/w7mM9ebtfCFKJhNOxCby5fgs5t4xhmD2oD67WlvT9ZhWgfZ/C87+u5/0BvfhkSD+KS0s5HBHNZzv3V45LSM3L58mf/mBGn2A+HNgbc6WSy0kpOi+JExrO0aQYXt37F2+2D2Z5n/Lf71N7WXnp5u/37wOfZOahLXf1+/3V2UOkFeXzdLMOPNs8gExVEb9dO8uXZw/V16E88sSg57olKavhHX7z5s05fPiwTuWgQ4cObNq0CXd3dwDS0tIICQnhypUr95Qpj1UL7yleeLA80TG0sbMgNKC/w9o0dhaEBiS9atbYWRAakMpdf/pZ4eEV9dw7jZ2FKrX84MtG++zLc99qtM+uL6LvgyAIgiAIgvBwES0MdarGYxgEQRAEQRAEQXj03FWFQSKp6u1UgiAIgiAIgiA8jO6qS9LcuXNRKm++sKi4uJjPP/8cMzNtH1WVgRfRCIIgCIIgCEKDEl2S6lSNKwydO3cmNTVVJ61Dhw5kZmaSmXnzRUOdOnWqu9wJgiAIgiAIgtCoalxh+Pnnn+szH4IgCIIgCIJQJ8S0qnXrvpwlKXLwysbOgtCA/PY919hZEBqQJNa4sbMgNCDLCPGr/SgpKFA0dhYEQagH92WFQRAEQRAEQRBqTTyrqFNiWlVBEARBEARBEKokKgyCIAiCIAiCIFRJdEkSBEEQBEEQHi6iS1KdEhWGenLwOPz3B7gRBTbWMH44THoKqnr3XUkJ/PgHrNsCqeng4abdfnCfqj/js29hzVoJV/aLs6Kx9XDyZnrbnvha2pOhKuC38NMsu3K0yu3lUikvNuvKKK+2OJtakFSQy9/Rl1h+5QjFpaV625sbKdgy6CW+vniQ9ZEX6vNQhBro4enJWyFB+NrZkVFQyP/OnWPZ8ZPVxoxo2YLJXTvjbmVNQm4OK0+c4s8LF3W2GeDvx8tdOuNja0OuSs2RmBgW7j9IekFBfR6OUAOmxnKmju1BrwBfTI0VnA9P4Ivf9hKZkFFtnL+7A6+NCaallyMSiYSrUcl8u+4Q16JTKrcZ1ast7z3bTy92/d5zzF+zu86PRbgzU4WcmYN70LelD6ZKBWejE5i/eR83Uqov71t99dRQ8lVq3l+3UyddJpUwpV8Qwzq0wMbMhKuJKSzaepAz0Ql1fRiCUGdEhaEenLkIr/8fDOwN016E0Avw1UooLYPJzxiO+fYn+P5XeO1Z6NAadu6HGR9LkEnLGNBLf/uT5+CX9fV5FEJNBdi5sjzkCbbEXmbx+f10cnBnRtteSCUSvrt8xGDMBx0eY5RXG769dJgLGYm0snFkausQXM0see/EVp1trRTGrAh5Ajcz6wY4GuFOOrg4s2zUCLZevcaXB4/Q0c2F6SHBSCQSlh47YTBmkL8fnw8eyOrQ0xyI3E8/P18+HdifopISNl25WrnNNyOG8dvZc3x56DD2pqZM6x7EL+PGMGLNr6g1moY8TOE2c18ZQitvJ7758wD5RWpeGh7I0refYOz7q8nJLzIY4+pgxfL3xnItOoW5q3ZSWlbGUwM6sfK9cTw95xeik7TvMPJv6kBEfBqfrPpXJz4jJ7/ej0sw7PMJg2nj5sTibQfJU6l5rW83Vr00huFfria7sPqX1EolEt4d2pPHWvuxMfSS3vr3hvZiRMdWfLn9IAmZuTwbHMCK50cy5ttfiU7LqqcjevSIaVXrlqgw1IMlP0FzX1j4gXY5pKu2BeH7X+G5sWCs1I/ZsBWG9IXXn9MuB3WCK+Fl/PYXehWGgkJ4fz40sYOk1Nv3JDS0N1qHcCUrmZnH/gHgQFIERlIpr7QI5IdrJ1BpSnS2t1IY86RvAAvP7eH7q8cBOJIcBcC77fvy+bl9ZKi0T5T7ufrxUUB/TI3kDXdAQrWmBgVyJSWVmVu3A3AgKgojqYxXunZh1anTqEpK9GLeCunO9mthzNu7H4CDUdFYGxszrXtQZYXh9aBu7L0RwUf/3nyifCMjg7+eeYo+Pt5sD7veAEcnGNLGx5mQ9t5M+/IvjpyPBOBMWDx/L3yRMX3aseqf4wbjxj8WgEpdwptf/kWRWvu9OHklln8WvcTYfh34/Jc9APg3bcKliCQuRiQ2zAEJ1WrX1Jlezb2Z/NNfHLwWBUBoVDw7Z73A+G7tWL7X8IMBAH8ne94f3ptWro4Uqov11jtZmTOmSxvmb97H78fOA3D4ejRbZzzHCz06MXvDrno5JkG4V2LQcx1Tq+HEWXish276gJ5QUCgh9HwVccVgbqabZmMFWTn62y78DuxtYeSgOsmycA8UUhldmzRlR9w1nfRtsVcxlyvp7OCuF2MhV/Jb+Gl2xeveAEbmapu63ctbEizkSr7rPprjKdE8v+/3+jkA4a4oZDK6urux87ab9+1hYZgrFHR2c9WLcbW0xNvWlh3Xw3VjroXhYWONp401EuBwVDR/nNftbhaZoX0C3dTauk6PQ7g73Vp7UlCk5vjFqMq0rNxCTl+Lo3tbryrjohLT+WV7aGVlAUClLiElIw83BytA203V182esFjx9Od+0d3PgwKVmiPXoyvTMvMLORkZR0izqssb4LMnBiCVSHhy6e9k5Ot3Jezm0xS5TMauizevB8UaDfuvRtDjDvsWhMZU4xaGZ555BklVHfBvs2bNmlpn6EEXmwDFxRI83HXbwpq6af+NioXunfXjnhurbYHoHaTtkrT3CBw6AW9N0t3u8EnYtBPWr4Qt4kFEo3M3t0YpM6q82a8Qnau90fOysOVQUqTOurj8bGaH7tDb1wC3Zqg1msp9FWmKGbBtBZG5GbiaWdXTEQh3w93KCoWREZGZmTrp0ZlZAHjZ2HAoKlpnnY+dLQBRGbfFZN2MicrM4rN9B/Q+b4C/HwBhaWl1kX2hlrxcbIlPzUZTqntdj0vJYmC3FlXGrd+r/4SoqaM1Pq52nLgcU75sg4lSTmtvZ9Z91h5Xe0viU7NZtfk4W49cqdsDEWrEu4ktsRn65R2Tns3Q9s7Vxr63dgdhSVWfr95NbMlXqUnL061MxKRn0cTSHFOFnAIDLRNCLYguSXWqxhWGwMBAvvnmG7y9vWnbtm195umBlpOn/dfcVDfdzET7b14VXVKfHgWh5+Hlt29WykYNLuPFCTe3yc2DDxfCGy+Al/6Da6ERWMq1by3OK9bt05pfol02lxvof2bAALdmPO7Zhp/CTpJTrO0PXVxaqlcRERqXZXl/wjy1Wic9v3zZXKH/lltLZVUx2psCc6Xh74iHtTXv9OzBxaRk9kdEGtxGaBjmpkryC9V66flFasxMav5mY6XCiNkvDaSouIQ/dp0GtN2RAJztLfnq932UaEoZEtSSjycNQmFkxMYDYpKDhmZprCRfpV/eBSo15srqy7u6ygKAhbGS3CL9MRD5Ku31wEypEBUG4b5U4wrDa6+9hqmpKV9//TXLly/Hzc2tPvP1wCorr9FW1RgjNdAJTK2Gp9+AtAyYM6MMr6Zw+gIs/1lb0fi/qdrtPvsGHB3g2SfqJ+/C3ZPeodWttOzOjzgGujVnceBwTqTE8Pm5vXWVNaEeVLSyllVRrobKu+I7UlbF4y5DMT62tqweOxq1RsOUTf+IB2UNSCLRP6+lEonB8pMgqdE5DmBmrGDR1BG09HRk1rebSM7QPl0KvRrLtC//IvRKLKpibdelYxejsbE05ZWRgaLCUM8MlbdEIsFgsUpqdk2vjrSKfVdkoapri3D3xKDnunVXg56fe+45Dh06xFdffcWiRYvqK08PNAtz7b+3tyTkF2r/vX2cAmhnRLp2Q8IPX5QR1Emb1qW9dl9zv5IwZkgZ8cmwdQ+sXQGlpTf/QDugWio1XBkR6leOWtsaYG6k+9TJzEj71Di3uPrZNF5o1oV32/XheGoMrxxci7pUzIRzP6t4Mnh7q4BZectCrlq/vHNU5TG3tT6YKbQD2XNVujHd3N1Z8vgw8tVqnv1zHXHZBgYyCfXmpeGBvPx4oE7arpNhNHW00dvW1FhOXkH15ziAo605X745kqZONry3dAuHzt1sMcrIKagcSH2rw+ci6NrKAztLU9JzxLS69eXVPt14vZ9uee+4EIadvanetqYKhcHWgbuRU6TC3Fi/lcK04npwj/sXhPpy17MkzZs3j8uXL9dHXh4KTV1AJisjJl43PSZO+6+Pp35MQrL234A2uumd22n/DY/WvtdBpZYw/Dn9+DZ9JTw+sIzP3ruXnAu1EZ2XSUlpKR4WujcTFcvh2VU3T88O6M9E/05sibnMzGP/iMrCAyA6K0tb3rcNQvaw0S6Hp+l3IasYuOxhbc3llJsDWyv2EZ6eXpk2rEVzFgwaQFRGJi+s20BSXl7dHoBwR3/tP8+hcxE6aT0DfOjW2gOJBJ2nw25NrO/4HgZfN3u+nj4KpcKIqV9s4PS1OJ31Ac3ccLKz0BuvoFQYUaIpJacGFRKh9taeuMD+q7oVtr4tfejup1/eTe2siLiL9zAYEpWaiYWxEhszEzIrniQCTe2sicvIRlUifgfqjGhhqFN3/Uza0dGR3r1710deHgpKJXRqC/8e0L3Q7NgPluZltDUwPs6rqfbf22dQOlP+Tic3Z5jyHKxdXqbz98RQ7QesXV7GlOfq/FCEGlCXajiZGkN/t2Y66YPcm5OtLuRchuEX8cxs24uJ/p344epxph7ZKCoLDwi1RsPJ2DgG+PvqpA/09ye7qIhzSUl6MdFZWcRkZTGwmb9uTDN/IjIySMjJBaCnlxefDx7ImfgExv72u6gsNJK0rHyuRCXr/B2/GI25iZLA1p6V21lbmBDQzI3jl6Kq3JejrTnfzhxNGfDSp7/rVRYAOrdoyuwXB+LWxLoyTSKBPp38uXgjkWJxA1mvUnPzuRSfrPN3+Ho05sZKuvt5Vm5nY2ZCZy83Dl+PrnpnNXAkXBvfv7VfZZpcJqNnc2+dWZkE4X5T4xaGiRMn1mg7iUTC6tWra52hh8HkifDCdHhrNowaDGcuwarfYcYr2ncw5OVDeBQ0dQVba+jTHdq2LOPtuTDlefBuCuevwLKfoXfQzUqG622TM+wrf5Fw6+YNeXTC7ZZcOsya3k/yTdBI1kWeI8DejUnNu7Hw3B5UmhLMjRT4WtkTk5dFhqqAFtZNeKVFIOfTE9gae4X2di46+wvPTiOvRH/AnXB/WHLsOGvGjuGb4UNZe+EiAS4uTOrSiYX7D6IqKcFcocDXzo6YrCwyCrVPEL89coyFgweSVVjI7vAI+vp6M6R5M6Zu2gxop2v9dOBj5KvVfHfsOL52djqfmZSbKyoQjehMWDynrsTyn5cH883aA2TnFTFpRCC5BSqdmZC8XGyRG8kIi9G2JM14sg92VmZ8uvpfzIyVtPa+eRHPL1IRmZDBur3nGNmrDYunjWDFxqMUqYt5ok97fFzteHXh2gY/VkH7zoUTN2JZOG4QX2w7SFZBIa/3CySnSMWfx2+Wt08TW+QyGVcTaz4lbmJWLhtDL/HOkJ4Yy42ISsvk2eAALEyU/HjwVH0cjiDUiRpXGFxd9ecXv9WpU6eIjY3F3Nz8njP1oOsWAP/9D3z7I0z5ABztYdar8Pw47frLYfDsmxI+fbeMkYNAJoMfFsFX38PSNZCdC+7O2rdCPzu2cY9FuLOjKdG8fmg909r0YGnwGJILc5l/djc/XNO+3KeVrRO/9Xmat4//w/rICwxwa45UIqGtnQvrH3tOb39P7vmF4ykxDXwUQk0di4nl9b//YVr3QJY9PpykvDwW7DvAD6dCAWjl2IRfx4/l7a3b2XBJ231zw6XLKIxkvNS5E2PatCY2K5uZW7ax9VoYAAGuLjiWXztXjx2j95lfHz7K10eONtARCoa8/e0m3hrfk6ljeyCVSDgXnsD/Ld1M7i1dht55pi/O9paMmPUDRjIpIe208+r/37OP6e0v9GoskxesJT07n0mf/cHro4OZ+VRvzIwVXI5M5vXP13M+XLzIrbFM++Uf3h7SkxmDQ5BKJJyJTmD6b1vIuWWMwQcj+uBqY0n/havuat9z/tpNTqGKF3p0wlSp4HJ8MpN+WE9MenZdH8ajTXRJqlOSsnsckp+Xl8f8+fNZt24dQUFBzJ07FxcXlzsHVqM0yf/OGwkPDb99zzV2FoQGJIk1buwsCA3IRgx5e6QUONXsfU3Cw+HSZ281dhaq1Gb6l4322RcW37//L7V114Oeb3X48GE+/PBDcnJy+Pjjjxk3blxd5UsQBEEQBEEQakVUXetWrSoM+fn5zJ8/n7Vr1xIYGMi8efPuuVVBEARBEARBEIT7z11XGCpaFbKzs5kzZw7jx4+vj3wJgiAIgiAIgnAfqHGFIT8/nwULFui0Kjg7O985UBAEQRAEQRAakhj0XKdqXGEYNmwYiYmJuLu7ExAQwPr166vcdsqUKXWSOUEQBEEQBEEQGtdddUlydnampKSEDRs2VLmNRCIRFQZBEARBEASh0UhEC0OdqnGFYc+ePfWZD0EQBEEQBEEQ7kP3NK1qfcksLWjsLAgNSFN4X34NhXoikzV2DoSGVGokJjd8lBSbNXYOBKGcaGGoU9LGzoAgCIIgCIIgCPcvUWEQBEEQBEEQBKFKoi+IIAiCIAiC8HARXZLqlGhhEARBEARBEAShSqKFoZ4cOyFjxQ9yIqOlWFuXMXJYCc88WYzEwPi/LduNmLdAWeW+PnhHxeCBJZXb/vaHnPh4CXZ2ZQzqX8JzzxRjJEqyUfV09WJGQAh+1nakFxXw29WzfHfheI1iW9s58tfQp+m9/nvi8nJ01p0c9xoOpuZ6MZ1/X0JqYX6d5F24ez08PZkeFISvnR0ZhYX8du4cy06erDZmRIsWTO7cmabW1iTk5PD9qVP8efFildu/37MnL3TsiM/ixXWdfaEWTJVypj3Rg17tfTAzVnDuRgKLft9HZGJGtXH+7g68PrI7rTwdkUgkXI1O4ZsNh7gak1K5jY2FCa8/3p1urTywNDMmJjmT1dtP8e+psPo+LKEKZgo5b/ftQb9mPpgpFJyJS2Duzn3cSKu+vB3MzXjvsR509/JALpNyKCKGeTv3kpx783pta2rC9N7d6e7tgZWxMVEZmaw8eoqtl0V51yUxrWrduqvbzMjISDZv3kx2djYhISH07NlTZ31eXh7z5s3js88+q9NMPmguXJTy9vtK+vbW8PKLKs5dkLL8BzmlZfDc08V62wd1K2HFklKdtLIymL9ISUEBBHbTVhb+WGfEf5co6d2zhCmTS8jKlvDDT3LCI6TM/0TVIMcm6Ato4sL3fUexOfIqX5w+SCdHV2Z27IFEImHJ+WPVxrawceDHfqORS/WnDrI3NsXB1JxPju/hdGq8zrrMosI6PQah5gKcnVk+YgRbrl1j8ZEjdHJxYUZwMFKJhO9OnDAYM8jPj0UDB/LT6dMc2L+fx3x9+ax/f4pKSth09are9p1dXXm2Q4f6PhThLnz68mBaeTrx9fqD5BWqeXlYN5bNGMMTH60mp8Dw9dfNwYrvZz3B1ZgU/rP6X8rKynj6sY788PZYnvzkV6KTMzGSSfn2zVFYmipZ9vdRUrLy6NPBl/mvDEFuJGPrsSsNfKQCwOKRg2nr4sTC3QfJV6mZ0qMba54ew+Blq8kuMlzeMomEleMfx1ShYPa23RhJpczsE8yqJ0cz4vtfKCktRS6VsurJUVgaK/l6/1GSc/N4rJkvX40aglwm4+8LoryF+1ONKwyhoaG8+OKLODo6UlZWxq+//kq/fv344osvUCgUABQVFbFx48ZHvsLww2o5fr6lzP4/7UWlWxcNJSXwy29yJjxRjPK2xgQba7Cx1q0w/LHOiOgYCcu/KcLGGjQaWLVGQedOGubNuXmxau6v4annTTlxqpgunXT3ITSMN9t353JGCtMPbgFgf3wkcqmMV9t2Y+WlU6g0JXoxcqmUZ1t0ZEZAMEUl+usBWtk5ArA9Ooz4/ByD2wgN743AQK6kpjJz+3YADkRFYSST8UqXLvxw+jQqA+U5vXt3toWFMW//fgAORkdjbWzMm0FBehUGEyMjFgwYQEp+Ps4WFvV/QMIdtfF2JqStN1P/+xeHL0YBcOZ6PP989gJP9G7HD1sMVxQn9O2AqriEaV9vpEit/V6cvBrL5vkvMq5Pexb+by8hbb1p3rQJz8z7jctRyQCcuBKDs50Fzw7sJCoMjaC9qzO9/byZ9L+/2H8jCoCTsfHsmfICT3Zqx9JDhst7YEt/Wjg1YfCyNYSnpQNwJTmVLa9MZHBLfzZdvEpvP29aOjVh9A+/cSFRW95HImNwtbJgUmAnUWEQ7ls1HsPwxRdfMGbMGHbs2MHOnTtZvHgxhw8fZvLkyRQX6z81f1Sp1XDmnIyeIRqd9N49NRQUSjh7/s6T0KdnSPh+lYKRw0to1VJbCcjIlJCbKyE4UPdmxMuzDGurMg4fFX2SGoNCKqOrkzs7onWbkrdGXcNcrqCLo5vBuN5u3kxrH8S3544y/9R+g9u0tG1CtqpIVBbuIwqZjK5ubuy4fl0nfVtYGOYKBZ1dXfViXC0t8ba1ZWd4uF6Mh7U1ntbWOunv9exJWn4+6y5dqvP8C7UT2MqDgiI1xy5HV6Zl5RVyOiyO7q29qoyLTMzg5x2hlZUFgCJ1CcmZebg5WAGQX6Rm/f7zlZWFCtHJWbg5WNftgQg1EuLjQb5azaGIm+WdWVDIyeg4evpUXd4h3h5EpGVUVhYAbqRlcCMtg56+2rg8tZrfT5+vrCxUiMzIoqmNdd0eyKOurBH/HkI1rjBcu3aNp59+unJ50KBBfP/995w5c4a33367XjL3IEpIlFBcLKGpm+7TfjdX7XJs3J1fYvT9KjlSKbz8oroyzcK8DJmsjMQk3SLLyYXcXEhMEi9HagzuFlYoZUZE5Oj2a43KyQTAy8rGYNy5tCSC1y5nyfljaMoMtwy1tG1CtrqIZX0e5/xT07j09Jt803MYDibizUiNxd3KCqWREZGZmTrp0VlZAHjZ6Je3r60tQI1iujdtysiWLXl7xw5Kyx7SX50HkJezLfFp2WhKdcskNiUbD0fD5zjAuv3n+XlnqE5aU0drfF3suJGgvak8cSWGT3/ZrbONkUxKSFsvbiSk1dERCHfDx86W2MxsNLedg9GZ2XjZVV3ePva2RGZk6qVHZ2ThaauNOxIZw0dbbytvqZTefl5cTxXlLdy/alxhMDc3J/O2H7yOHTvy+eefs2PHjke+G1KF3DztjbuZme6FxtRU+29+fvU39hmZsH2nEaNHFmNxy1hXY2Po21vD+o1GbN5qRE4uRMdImP2JEpkRFBaKCkNjsFIYA9qnRrfKL9Yum8sND2ZPLsgjW11U7b5b2jbBydSC86mJvLhrPXNP7KWbkzt/DJqAiZG8DnIv3C3L8v6EeuVdvmxe3j2zRjHlLbPm5evNFQrm9+/PV0eOEFVemRDuDxamSvIL1Xrp+So1Zib6ZV4VY4URHz8/gKLiEn7ffabK7d56ogdNHW1YVUVXJ6F+WRgrK8/pW+Wr1Zgpqy5vC2MleSrDcebVxL3brweetjZVdnUSakdS1nh/D6Ma92Pp2bMn//nPf5gzZw4tW7ZELtfesPTr14//+7//Y+7cuSQmJtZbRh8UFQ+Lq7p9l9yhirZpi3Zw9NjR+t283p6uQiFX8NkiBZ9+rsTYuIynxhdTpJJgYvKQfkPvc5Lyaa/KqmiDLLuHp8SzDm1DpSnhUoZ2NpWTyXGEZaWxfshTjPZpxS/XztZ630LtSCumOauiXA21ClR+R25bV3GNqEj/sFcvkvLyWBWq+0RaaFgSyS3lXJkmMXiGSzBc5oaYGStY/PpwWno4MnPpPyRn5hnc7s0xIYzv24Eft55g39kbd5l74W5J0C9vqURi8BSXUP01/fb9VMZJqo57p28IE7t0YPnhE+wKE+Ut3JlKpWL+/Pls376doqIiQkJCmD17NnZ2dlXGnD59mi+//JLLly9jampKjx49mDVrFta3dYmtTo0rDDNmzOCtt95i/PjxLF++nB49elSue/rpp5FKpXz66ac1/uCHlbm59qKQX6B74SgoKF9vVv2Py979Mrp00mCoK6OpCfzf22refENNUpIEZ6cyTExg8zYjXJ3FgOfGkFPeSmBxW0uCmVz7NClXXfvZq06nJuilhabEk6MqooVtk1rvV6i9HJW2PM1vm7nArLxlIVelX96VMbe1PpiWP3TJVano7eXF0ObNefzXXytvOir+lUkklJZVVSUV6tqkod14ZXigTtquU2HYOprqbWuqVJBXeOdz3NHGnP9OfZymjja8u2ILB89H6m2jMJIx5/kBDOjSjJ+2n+Tbvw7X/iCEGpvSoxtv9NAt722Xw/CyM1DeCgW5VcyQBJBbpKq8FujEyRV61waFTMaC4QMY0qoZK46c5Iu9orzr3EN60ZwzZw6hoaF88803KBQKZs+ezbRp0/jll18Mbh8ZGcmLL77ImDFj+Pjjj8nIyODjjz9m6tSprFmzpsafW+MKg5WVFatWrSImJgbb8j65t3ryyScJCgpix44dNf7wh5GraxkyaRlx8VLg5sBn7TJ4eVR9Y5+SKuF6uIzxYwxfkA4flWFhXkbbNqV4e2nPhIxMSEmR4O8nKgyNISY3i5LSUjwsdfu1epYvX89KNxR2R5YKJQM8/DmTkkB4tu4+5DIZGaqC2mVYuCfRWeXlfdtTmYrl8Az9Odorxi54WFtzOTVVL+Z6ejpvBgVhbGTE9mef1YsPe+st1l+6xNuP+LW1oWw4cEHvhr5XBx+6tfIof1J8M929iRWRCdXPy+/ras+3b45EKTfija/+IjQsTm8bcxMF/506krbeziz+cz+//nu6To5FuLM/Tl9g73Xd8u7XzIcQHw9ti8It6R42VoRX8x6GiPRMWjrpP8zxsLXmfEJS5bK5UsH340fSwc2Zz/7dz4/HRXkLNZOcnMzGjRtZvnw5nTp1AmDx4sUMHDiQs2fP0r59e72YjRs30qRJE/7v//4PiUSCt7c3s2fP5qmnniI2NhZ3d/cafXaNKwwTJ06s6aa88sorNd72YaNUQLt2pew7KOPJcTdf1LZ3v/Zmv2WLqm/sL1/RViratNYYXP/XJiOycyR8v+Rm3/c/12kHSHcPNBwj1C+VRsOJ5FgGevix4uLN/qeDPZuRrSribFrtuumpNRrmdnuMzVFXmXFwa2V6/6a+mBjJOZYYe895F+6eWqPhZFwc/X19+f7Uqcr0Qf7+ZBcVcS4pSS8mOiuL6KwsBvn7s+2W2ZUG+fsTkZFBQm4u/z16lJ/PntWJG9+mDePbtuXxX38lo1C8d6OhpGXnk5at+1JEY4URLw3pSmArT46UT6tqbW5CgL8bq7ZW3e/c0cac76aPQqMp44UFfxh8yZtMKuHLKSNo5enIeyu2sCv0uoE9CfUlJS+flLzbyltuxGvBXQnx8eRA+bSqNqYmdPZwY1k14wwOR0QzrHVzfOxtK1/w5mNvi4+9Ld8d0r7IUyaRsHzcCNq4OPLmhi1svyLKW6i50PIuq127dq1M8/LywtHRkZMnTxqsMAwfPpzevXtXdo+9VVZWVt1XGFwNTBd4q1OnThEbG4u5uf5baR81zz2tZtpMYz74WMnQQSVcuCTltz/kvPay9h0M+fkQGS3F1aVUp+vRjUgpCnkZbq6G29GeGFXCW28b89W3CoKDSgg9I2PNbwqeeVKNq8tD2vb2APjm3FF+HTCOJb2Gs/b6BQKauPJy6y7MP7UflaYEc7kCP2s7onOyyFDV7MavSFPCsosnmNY+iLTCAvbHR9DCpgnTOnRnd+wNDidG33knQr1Ycvw4a8aM4ZuhQ1l38SIBLi5M6tSJhQcPoiopwVyhwNfOjpisrMob/SXHjrFw4EAyCwvZHRFBX29vhjRrxhubNwMQn5NDfI7u9Lm9vb0BuJCsO/2i0PDOXI/n1NVY5r40iK/XHSQ7v5CXhwWSW6hi3f7zldt5OduiMJJxLVbbkjRrQm/sLM2Y9/MuzEwUtPZ2qtw2v1BNZGIGY3u3J8DfjXX7z5OUmauzDcDFCP1KqFC/TsXEcywqlkWPD+Lz3QfJKizkjR6B5Bap+N/pm+XtY2+LQibjSrK2vLdcDmNy9y6snDCSRXsOATCzTzBhKWlsL3+L81Od2tO5qRv/Cz1PYk4u7Vx1y/tcvCjvutKYg4/79u1b7frdu3dXu74qycnJ2NjYoLytW2yTJk2qHEfs4+Ojl/b999/j4OBA8+bNa/zZNa4wVDULUl5eHvPnzyc2NpagoCDmzp1b4w9/WHUKKOXTj1Ws/EnBux8qcbAv4/XJap4cq52L+9p1KVPeMuH9d1QMGXhzfu6MDEnlGAhDunbWMOeDIlb/rODvzUY4OZbx1hsqnhhl+MVfQsM4mhjDq3s28maH7izvO5Lkgjw+PbmPlZdOAtDazpHfB01g5sGtrAu/WOP9fnXmEGmF+TzdvD3PtuhApqqI366d5cszoq9rYzoaG8vr//zDtMBAlg4fTnJeHvMPHOCH8ic/rZo04bexY3l7+3bWX74MwPrLl1HIZLzUqRNPtG5NTHY2M7ZtY2tYWHUfJdxHZi79h+ljezJtTAhSiYSzNxJ4d/kWcm95y/O7T/XBxc6SYe+t0k6N2kY79/77z/TT29+pa7G8smgdfQJ8ARjTsy1jerbV267jpC/r6YiE6kxZ9w/vPdaTt/tqy/t0XALT1m8h55YxDHMG9cHVypI+364CoFij4bnf1vNB/158MrgfJaWlHI6I5tN/91dO0Tqghba8J3Rsy4SO+uXtP1eU96MsLi6u2srGtGnTKl+WfCulUonKwBg6Q+bPn8/+/fv5+uuvKycwqglJ2T1M43L48GE+/PBDcnJymDVrFuPGjavtrnSkJxh+2ZXwcOq4Y2pjZ0FoQLJM8ZLBR4n1FTHl86Mk16OxcyA0pLAP3mrsLFQpYHLjVb5OL6vd/0txcTExMTFVrt+/fz8rV67kyJEjOuljxoyhffv2fPDBB9Xu+6OPPuKvv/5i9uzZTJgw4a7yVqtf7vz8fObPn8/atWsJDAxk3rx5uLi41GZXgiAIgiAIgvDIk8vlBrsQVbh27RpZWVmo1WqdloaUlBScnJyqjMvLy2PKlCmcOnWKL774giFDhtx13u66wlDRqpCdnc2cOXMYP378XX+oIAiCIAiCIAg117FjR0pLSwkNDSUwUDsdcEREBMnJyZWzJt1OrVbzyiuvcPXqVVauXEm3bt1q9dk1rjDk5+ezYMECnVYFZ2fnWn2oIAiCIAiCINSbh3AuGEdHR4YMGcIHH3zAp59+iomJCbNnz6ZLly6VMySp1Wqys7OxsrJCoVCwfPlyQkND+eKLL/Dx8SH1lum9K7apiRpXGIYNG0ZiYiLu7u4EBASwfv36KredMmVKTXcrCIIgCIIgCEINfPLJJ3z66aeV99o9evTQGbtw5swZJk6cyJo1a+jatSubN2+mrKyM6dOn6+2rYpuaqPGg5z59+tRshxJJraeLqiAGPT9axKDnR4sY9PxoEYOeHy1i0POj5X4e9Nzx5cYb9By64v79f6mtGv9y79mzpz7zIQiCIAiCIAjCfUg86hMEQRAEQRAeLg/hGIbGdF9WGNI0pY2dBaEBGaXU/MUhgiA8WHI9GzsHgiAIwr2SNnYGBEEQBEEQBEG4f92XLQyCIAiCIAiCUFuSms3pI9SQaGEQBEEQBEEQBKFKooVBEARBEARBeLiIBoY6JSoM9ST0hIxff1ISEy3FyqqMgUOLGTNBjcTAlOS7dxjx389NqtzXtLcL6du/RC995XdKNm1QsGlXbl1mXaiFHl4evBXSHV97WzIKCvnf2fMsO3ay2pgRLZszObAL7lZWJOTksPJEKH+ev2hwW3OFgs0vPM3Xh46x4eLl+jgE4S6I8n70mCnkvN23B/38fTBTKDgTl8Dcf/dxIy2j2jgHczPe69eD7l4eyGVSDkXEMO/fvSTn5lduY2tqwvRe3enu7YGVsTFRGZmsPHqKrVfC6vuwhCqI8hYEXXdVYVCpVFy/fh1fX1+MjY25cuUKv/zyC8nJyfj5+fHss8/i5ORUX3l9YFy5JGXeRyYE9yrhqedUXLko45cfFZSVwdin1Hrbd+qqYeHX+Xrp3y42piBfQqcuGr11F8/L2LxRzC50P+jg6syy0SPYeiWMLw8epqObK9N7dEcikbD06AmDMYOa+fH50IGsPnWGA5FR9PPz4dNBj1FUUsKmy1d1trUyVrJ89AjcrKwa4nCEOxDl/Wha/Phg2ro4sXDPQfJVaqaEdGPNU2MYvHw12UUqgzEyiYSV4x/HVK5g9rbdGMmkzOwdzKoJoxmx8hdKSkuRS6WsmjAKS2MlX+8/SnJuHo819+WrUUOQ/y3j74tXGvhIBRDlLQi3q3GF4caNGzz33HOkpqbi4uLC3Llzee2113Bzc8PHx4ddu3axYcMGfvvtN3x8fOozz/e939co8fIpZfq7RQB07KKhRAPrf1cwYowapVJ3eyvrMqysddvONm2QExcjZcF/C/TWFRXC158bY2tXRlqqeItqY5vavRtXklOZuWU7AAciozGSSnmla2dWnQxFVaJf4XsrJIjt164zb89+AA5GRmNtbMy04ECdG8h+vt582K83pgpRObxfiPJ+9LR3daa3nzeTfv+L/TeiADgZG8+e11/gyY7tWHrYcEVxYAt/Wjg2YfDyNYSnpQNwJTmVLS9PZHBLfzZdvEpvP29aOjVh9KrfuJCYDMCRqBhcrSyYFNhJ3EA2AlHeDweJ6JJUp2o86HnhwoV06NCBjRs30rFjR1599VWGDRvGP//8w3//+1+2bdtG9+7d+eyzz+ozv/e9YjVcOC8jMFi3C1H3kBIKCyVcviC74z4yMyT8+qOSgcOKadZC/50Uq5YrsbYto++A4jrLt1A7CpmMru5u7AwL10nffu065koFnd1c9WJcLS3xtrNlR9h1vRgPG2s8bawBsFAqWTJyGMdj4nj+z7/q7RiEmhPl/WgK8fYgX63mUER0ZVpmQSEnY+Lo6etVbVxEWkblzSPAjbQMbqRl0NNHG5enUvP76fOVN48VItOzaFr+3RAalihvQdBX4wrDiRMnePPNN2nevDnvvPMOKpWKCRMmICnvlG9kZMTkyZMJDQ2tt8w+CJISpZQUS3Bx073Rd3bVLsfH3fm//NefFEil8PTz+s2eZ0Jl7N0lZ9qsQoPjIYSG5W5thcLIiMjMTJ306MwsALxsbfRifOxtAYjKyKo2pqi4mIE/rOHtrTvILCis24wLtSLK+9HkY29LbGY2mtumaYzOyDZY5rfGRWZk6qVHZ2bhaaeNOxIVw0fbduusN5JK6e3nxfXUtDrIvXC3RHk/JMoa8e8hVOMKg7GxMUVF2i429vb2jB07FuVtfWtycnKwsLCo2xw+YPLLhyKYmup+Y0xMtf8WFFR/l5+VKWHvv3KGjFBjbn7bvvPgm0XGPPmsCle3h/Qb+YCxLD8H8lS6Y1Py1dplc4WimhhVtTHFpaUGf3yExiPK+9FkoVRWltet8tVqzJT6ZV4ZZ6zU+64A5KvUBr8rFd7t1wNPW5squ74I9UuUtyDoq3GFITg4mE8++YQbN24A8J///KdyrEJZWRnHjx/no48+ol+/fvWT0wdEaam2QlDV03/pHVoFdm6VU1YGw0bpdzdaudQYe4cyRowWXZHuFxUtbGVVPFIoNZAsrYzR25k25mF9PPEQEOX98JOgHbx6659UIsHQO6AkEu3vX1WkVfwQVBf3Tt8QJnbuwPLDJ9gVdqM2hyDcBVHeglAzNR70/N577zF58mS+++47vvjiC511W7duZcaMGQQHBzN9+vQ6z+SDxNxce1G4vSWhsED7r6lZ9TcHhw8Y0b6jRm+g88ljMg7uNWLxdwWUlYFGQ+UFTaPRXpCk4jV8DS63/Knx7U+PzMqXc1X63cpyiqqIkcvLY/SfUAn3B1HeD78pId14o0egTtq2K2F4mZnqbWsqV5BbxYw5ALlFKoNPpE0VCr3vikImY8GwAQxp1YwVR07yxb7DtTwC4W6I8n54iUHPdavGFQZbW1v+/PNPsrKy9NYFBgayceNGmjdvXpd5eyA5uZQilZaRGK9bYUiM197NN/XQH8RcIS1VQuQNGSNG6/dfPnxAjlotYcpLZnrrRg6woE//Yt58u+gecy/crejMLEpKS/G4bbBaxXJ4erpeTGRGRuU2l1NS9WPS9GOE+4Mo74ffH2cusDc8Uietn78PId4eSNBtKfKwtSK8mnn5I9IzaenURC/dw8aa8wlJlcvmSgXfjxtJBzdnPvt3Pz+eOH2vhyHUkChvQaiZGlcYJk6cWKPtJBIJq1evrnWGHnQKBbRqq+HoITkjxxZXdk06fNAIM/My/JrrT7lYIeyqdgalFq31t5kwUcWQEbpPIndskbNzq4IvluRjaSWq0o1BrdFwMjaOAf6+rDxxc8D/wGZ+ZBcVcS4xSS8mOiubmMwsBjbzY9u16zoxEekZJOSIF/Hdr0R5P/xS8vJJydN9L46xkRGvBXclxMeTA+XTbNqYmtC5qRvLqul3fjgymmGtm+Njb1v5wi8fe1t87G357tBxQNsdZvnYEbRxceTNDVvYfvV6lfsT6p4o74eYuC2qUzWuMLi66k8XeKtTp04RGxuL+e0jdR9BY59S89HbJiz4xJjHBhZz5ZKMv/5U8OxLKpRKKMiHmGgpzi6671+IjpQil5fh7KL/LXd0KsPR6fZuStplv2ZVt1oI9W/JkROsGT+ab0YMYe2FSwS4OjOpaycW7juIqkSDuUKBr70tMZnZZBRqW4++PXKchUMGkFVYxO7wG/T19WFIi2ZM/XtLIx+NcCeivB89p2LjORYVy6IRg/h8z0GyCgt5IySQ3CIV/zt9vnI7H3tbFDIZV5K1LUlbLocxOagLK8ePZNGeQwDM7BNMWEoa28vf6vtUp/Z0burG/06fJzEnl3Yuui8/PZegXwkV6pcob0HQV+MKQ1XvV8jLy2P+/PnExsYSFBTE3Llz6yxzD6p2HTS8O7uI31YrmDfbBDu7Mp57WcXIJ7SDlW9cl/H+TFOmzSqk74Cb72vIypRgZi6qxA+aYzGxvP7XP0wLDmTZyGEk5eWzYO8BfjipbWZu5diEX598gre37GDDxcsAbLh4GYVMxktdOjKmbStis7KZuXk7W6+GNeahCDUgyvvRNGX9P7zXrydv9wlBKpFwOi6BaRu2VI5RAZgzsA+uVpb0WbIKgGKNhud+W88H/XvxyeB+lJSWcjgimk937a+csnNAc18AJgS0ZUJAW73P9Z/3ZQMcnXA7Ud4PPjGGoW5Jyqob8n8Hhw8f5sMPPyQnJ4dZs2Yxbty4OsnUtViXOtmP8GAY8tusxs6CIAj1RFpy520EQXgwhb3/VmNnoUpdJy5utM8+vubhmwCoxi0Mt8rPz2f+/PmsXbuWwMBA5s2bh4uLuMkXBEEQBEEQhIfNXVcYKloVsrOzmTNnDuPHj6+PfAmCIAiCIAhC7YguSXWqxhWG/Px8FixYoNOq4OzsXJ95EwRBEARBEAShkdW4wjBs2DASExNxd3cnICCA9evXV7ntlClT6iRzgiAIgiAIgnC3xKDnunVXXZKcnZ0pKSlhw4YNVW4jkUhEhUEQBEEQBEEQHhI1rjDs2bOnPvMhCIIgCIIgCMJ9qFazJNW3X7K6NnYWhAbkeEq8eO5RkukvbewsCA2o0Emc34+SMnF6C/eL2r81QDBAnNqCIAiCIAiCIFTpvmxhEARBEARBEITaEoOe65ZoYRAEQRAEQRAEoUqihUEQBEEQBEF4uIgWhjolKgz1JPFMHhf/l0JOnAqlpRE+/W1oPtIOiURicPuceBXbp93QS7dwUTDoa9/K5Rv/ZnJ9Swb5KWpM7eX4DLDBb7BtlfsVGo6psZzXn+lJSBdfTI0VXAxL4Ksf9xAVl1FtXLsWrrwyIRg/zyYUFKnZezSMFb8fpqBQXbmNtaUJr0wIpks7TyzNjYlJzOS3v0+y+8i1+j4soQqmCjmzBvWgbwsfTJUKzsYk8NnmfdxIrb687S3MeGdQD4J8PZDLpBwOj+GzLXtJycmv3Mba1Ji3+gfTo5kXJnIjLsWnsGj7Aa4kptb3YQkG9GjqyYxu3fGztSOjsJBfL55jaeiJamMeb9aCVzt2oamVFQm5uaw4fYo/Ll8AwNXCkkPPTaoydu3li7y9e0edHoNQcz2bejKja3f8bOxILyrkt4vn+O70HcrbvwWvdexCU0sr4nNzWXHmFH9cuaCzzZjmrZjUvhOeVtakFOSz4dplvjl1jJJSMTGAcP8TFYZ6kHa1gMMLYnAPsqL1hCakXS3gwv9SKCsro+VoB4MxWZFFAPSa44FMcfPmX6a42WssfEcGp79Povnjdji2NSP9eiHnVidTUlRa5X6FhjPnzSG09HXmu18OkF+g4vknAvl69lieeusncvOKDMb4eTbhyw9Gc/J8DO8v2oS9rTmTnwrBw9WWt+ZqX45oZCTlyw/GYGGmZOUfh0nNyKNnVz/+89ZQjIyk7DhwpSEPUyi3aNxg2rg58cX2g+Sp1LzWpxs/vjiGYf9dTXahymCMTCphxbOPY6pQ8J9NuzGSSpk+IJiVz41m1Le/VN44fPPUcLwcbFi84xApOXm81KMzq196gpHf/kJ8Zk5DHuYjL8DJhe+HPs6W69f44thhOru4MjMwGKlEwpJTxw3GDPb154vHBvHj2dMciInkMW9f5vftT1FJMX+HXSU1P5+Rf/6mFzexbXuG+DXjz8sX6/uwhCoEOLnw/eDH2Rx+jS+OH6aTsyszuwUjkUhYElpFefv4s7jfIH48d5r9MZH09/ZlQZ/+FGm05Q3wfNsOzA7pw5bwa3x2ZD82xia81SWI5nb2vLJtU0MeoiDUSp1UGIYNG8aKFStwdnaui9098C6tTcXa05iuU10BcO5gTmlJGVf/Ssd/qB1GSv2hI1lRRZg1kdOktZnBfZaVaePdgyxp+7QjAI5tzclLVBO+LVNUGBpZK39nunf0YeanGzh6JhKAc1fjWbvkJUb1b8fqDYZ/aMYP60hmTiHvf7GJkpKbT5nef30gTV1siEnIpHuAN/5eTXjx3V+4eiMZgFMXYnBysOTpEV1EhaERtHN3pldzbyav/osDYVEAhEbF8+/MFxjftR3L9xl+GjmgtT/NnZsw/L9rCE9JB+BqYip/T53IoDb+/HPuKh521nT0dOWDDTvZEHoJgLMxiRz6v1cY0aEl3+051iDHKGhN6xLIlbQUpv+7DYADMVEYSaVM7tiFlWdCUWlK9GJmdOvOtvAw5h7aVx4TjbWxCW92DeLvsKuoSzWcTU7UiWnTxJEhfs1YdPQQpxLj6/24BMPe7BzI5bQUpu/Slvf+mCjkUimvBnRh5dkqyrtrd7beCOOTw/sAOBAbjbVSWyH4O+wqUomEaZ0DORATxes7NlfGXUhNZteTzxPs5sGhuOgGOb5HiUQ03NSpGg963rhxY5V/0dHRbNu2rXL5UaYpLiX1UgGuXS110t0DLSkpKiXtSoHBuKyoIqw9javdd48PmtL2mSY6aVIjCaUl4qxobF3beVJQpObEuajKtKycQs5ejiMwwLvKuGW/HuTtz/7SqSwUl2gAkBvJAMgvVLPx33OVlYUKsQmZuDpZ1eFRCDUV7OdBgUrN4fCbP/KZBYWcjIqjh79XlXHd/TyISM2orCwA3EjNICI1g5Bm2jhFRbmrbnZJy1erUZVosDap/hoh1C2FVEZXNze23wjXSd8WHoa5QkEXF1e9GFcLS7xtbNl+47pejKe1DV7WNgY/65NefbmRmc4PZ0Pr7gCEu6KQyujq6saOCN3y3nqj6vJ2s7DEx8aWHbeV99YbYXha2eBlZYO9iSnWxibsjorQ2SY8M4P0wgL6elb9GyEI94satzB8/PHHFBVpu1WUGXgZxsKFCwGQSCQ8/vjjdZO7B1B+cjGlJWVYOCt00s2dtMu5iWqc2uvHZUUVYemmZPf/RZIZWYTCTIpnL2taj2+C1EiCRCLB0k0JaP//1XmlxB/PIWp/Ns2G29X3YQl34OlmR0JyNppS3XMjLimL/sEtqoxLzcgjNSMPABNjOa38nHllQjBnL8dxIyYN0LYmnLoQoxMnk0kJ6uhNREy63j6F+uftYEtspn55x6RnM7Rd1S2tPg62RKVl6qXHZGThaa+9kbyenM6R8Ghe7d2NGykZpObmMbl3N4zlRmy9IMasNCR3KyuUMiMis3THpURlZwHgZWPDwVjdJ8O+trYARGbplnNUVnmMtY3euuH+zWnn6Mz4DX9QKl421WgqyjuiqvK2NlDeNtryjritTKNviTkSH0OxRoObpe6DREulEiulMW6W4sFPvRCnUp2qcYVhw4YNzJw5EwsLCxYsWICjo2Plug4dOrBp0ybc3d3rJZMPEnV++dNhU93GGyMT7XJxgUYvpiirhKIsDUjUtH26Cab2clIu5HN1YzoFacV0e9NNZ/v0a4Xs+SAKABtvY/wG29bDkQh3w9xUSf4tg5QrFBSqMTNVGIjQt3XVayjkRmTlFPLNmn3VbvvGxJ64O9vw7sK/a5Nd4R5ZmCjJK9Iv73yVGnNl1eVtYawkOj3rjnFz/9nL8mdHsmnaRABKS8t4f8NOzsYk6sUK9cdSqX1Ik6fWLev88mVzhX5ZWyqMDccUVx0zqUMnTibEczw+7t4zLdSalaIW5a0sL+9i3Zi8W8q7qKSEzeHXmNimPWEZ6eyIuI69iSmzg3tTUqrBVC6Gkwr3vxp/S728vPjjjz/4+uuvGTFiBB999BGDBw+uz7w9mO5Qo5VI9WczMjKR0vOjpli4KDG1lwPQpJUZUrmEi/9LpeUYVWXrAoBZEzm9PvagMKOES3+ksuudCPrN98bYWlx0GoJEAtLbZqWSSiUGX0MvkWhv9u5EJpPyzvyNyGRSnhgcwHf/Gc/MTzdw+lKs3ravP9OTJwYH8PNfxzl4MtzA3oS6ZLC8q5iVTCKh2ifEUonE0NcECTfjvB1s+fWVccRnZjPttwPkFakZ1Naf/4zsR1FxMTsuXtffgVAvKsq5qiI1dGpXXOJvb4mv+Mbc/v3o6OxC6yaOvLx54z3kVKgLFbMNVnUGGzx371DeFenv79+FWqNhQe/+fN5nAAXFxSw/cxJjuZyCYv1xEYJwv7mrO0wjIyOmT59OSEgI77zzDrt372bOnDn1lLUHk9ysvCWhUHdcQUn58u0tDwBGSimObc310p0DLLj4v9TK7koVTGzlmNhqKxZ2fiZsfSOciN1i4HNDeX5MIC+ODdJJ23P0Gu7O+n2TTYwV5BcYnjHnVhpNKSfOa5u6T16I5pfFzzFxVFedCoNCLuP91wfSr3tzftl4gmW/HbrHIxFq4rXe3Xi9b6BO2o4LYdjam+pta6pQkKuqurxzilSYG+s/pTRVKsgr0sZN7B6AVCLhxVUbyC7UdgM9eiMGC6WSD4b1Yeel61XewAp1K6e8LG9/smxWvmyorHPUFTFKnXRTeXmMWjdmkI8/WUWF7I2OrJtMC7VWUXYW8rso7/I0i9vK2+y28i4oLuadvTv5+NBeXC0sicvJprCkhCdatCY2W7fLqVA3xJue61atHkl37tyZjRs3MmfOHIYOHUpxcXFd5+uBZe6oQCKFvKTbmifLl2+98a+QE68i5WI+HiFWyE1llekatbaSobSUUVyoIeFkHrZ+JjrjI8ydFCjMZBSkiScUDWXTrvMcCdUdvBbSxZeu7TyR3NbQ4OZkTVRc1eMMgjv5kJtfxLkrN2dFKSkp5UZ0Kl7u9pVpZqYKFr03itb+Lny9eh9/bBYDIxvKnycvsO+a7s1c3xY+dPfz0CvvpnZW3Eip+j0MUWmZtHBuopfe1NaaC3FJALhYWxCZmlFZWahwMiqOAW38sTU1JT3f8OQJQt2Kzs6ipLQUTytrnfSK5fAM/XM7IlPbl93T2prLaSk3Y6wNx/Tx8mZnxA0xF/99IKa8vD3Ky6pCRXlfzzRQ3uVjFzysrLl0S3l73BbTx8ObbFURoUkJXC//DtiZmOBibsHFW+IE4X5V41mSbmdpacnixYuZPn06HTt2RKnUvxF+FMkUUhxamhJ/PFeniTL2aA5yMym2viZ6MYUZJZz+PonYo7rzq8ceycHIRIqNtwkSqYSTSxO49neazjYZ4YWo8zRYe4r//4aSlpnP1Yhknb8T56IwM1XStZ1n5XbWlia0b+nGiXNVT5c3YVgnZr38GLJbuqqZmSpo7e9CeLT2JV0yqYSF74ykha8TH325WVQWGlhqbj6X4pN1/g6HR2NurCTYz7NyOxtTEzp7unHketXlffh6NN4Otvg43Bx35ONgi7eDbeWMS5Gpmfg0scPKRPec7tDUhdwilV5FQqg/ao2GE/FxDPDx00kf5OtPdlERZ5OT9GKis7OIzs5ikK+/XkxEZgbxubmVaVZKY7ysbQgV06jeF1QaDScS4hjorVveg33uXN6Dffz1Ym7cUt5PtW7L+9176mzzQruOaEpL9WZPEupIWVnj/T2EatzCMHHixCrXlZWVMXPmTEDbB3D16tX3nrMHWIvRDuz/TzRHv4jDq481adcKubYpnbZPN8FIKaW4QENOnAozRwXGVkY4tDTFoZUp51Yno1GVYeGqIPF0Hte3ZtDuGUcU5tpWh+aP23F5XRoKCxmObczITVRz6c9UrD2VePW2btyDfsSduxLP6YsxzJ42mO9+Pkh2XiEvPBFIXr6Kjf+eq9zO080WuZER16O0T5R+XHeUxR+MZu6M4fy18yxmJkqeHtkFY2M5K/84AsCoge1p39KNjTvPkZKeSys/3Vl4Ll0XA2EbWmhUPMcjYln4xCAW7ThIVkEhr/cJJKdIxR8nzldu5+Ngi8JIVvmG5m0Xwni5VxeWPzuSxTu1Xcqm9w8mLDmNHRfDAFh9OJRh7Zuz6oUxrNh/gtwiFY+18mNIu+Ys2LpfPIluYN+eOsYvjz/BkoFD+fPKRTo6ufByQGcWHD6ASlOCuVyBr60dMdlZZBQVAvDNiWMsemwgmYWF7Iq8QT8vH4b6NWPKtn909t3cTtuKeN1AS4XQOL45dYxfRzzBkgFDWXvlIgFOLrzcoTPzj94sbz9bO6JvLe9Tx1jUdyCZRYXsirpBP09teb++42Z5/3T+DD8PH8NHwb34N/IGQW5Neb1jV74LPU5sTnZjHa4g1JikzNAcqQa899571a4/deoUsbGxmJubc+rUqXvK1IcXRt5T/P0g7ngOl/5IJTdBjYmtEb4DbSunP025mM++OdF0ft2l8kZfna/h0p+pJJzMpTCzBHNHBX5DbPF57Ga/+LLSMm78m8mNHZnkJalRmMtw7WpB6/FNUJjJDGXjgbDnP8GNnYU6YWGm5I1nexHS2RepVMKFq/F8vXofMQk3p9v7Zs5YnB0sGfP6ysq0jq2b8sITgfh5OlBaVsbpS7Es/+0Q0fHari1LPh5H+5Zuep9XofsTX9TfQdWDTP9aN2zeVyyNlbwzuCd9WvoglUg4E53A/K37daZN/enFMbjaWPLYolWVaU5W5rw3pBdBvh6UaEo5HB7N/K37ScvNr9zG096Gt/p3p6u3O1KJhBspGfxw8BS7Lj94g9wLnR78Ck5/b1/e6hqEl40NyXl5/HzhLCvPaFv6urq68fuoccz8dzvrr16qjJnQqi2TAjrhYm5BTE42S08d569rui9ZHOLrz7eDhtH3lx+JyKy6K9uDpOwhOL0HePnyZpcgvMvLe83Fs6wsfz9GNxc3fh85jpm7t7PulvJ+slVbJrW/Wd7fhR7nrzDd8h7u15wpnbribmFFfG4OP188x+oLZxr02Opa1OszGjsLVQoevajRPvvQ+pmN9tn1pcYVhqrk5eUxf/581q1bR1BQEHPnzsXFxeWeMvUwVBiEmntYKgxCzTwsFQahZh6GCoNQcw9DhUGoOVFhMOxhrDDc0zychw8f5sMPPyQnJ4ePP/6YcePG1VW+BEEQBEEQBEG4D9SqwpCfn8/8+fNZu3YtgYGBzJs3755bFQRBEARBEAShTjycY48bzV1XGCpaFbKzs5kzZw7jx4+vj3wJgiAIgiAIgnAfqHGFIT8/nwULFui0Kjg7O985UBAEQRAEQRAakHhxW92qcYVh2LBhJCYm4u7uTkBAAOvXr69y2ylTptRJ5gRBEARBEARBaFx31SXJ2dmZkpISNmzYUOU2EolEVBgEQRAEQRAE4SFR4wrDnj176jMfgiAIgiAIglA3HtI3LjeWe5pWtb6UlkkaOwtCA8ppKibufpSobMRF/FFSaqZp7CwIDcjmjLyxsyAIQj24LysMgiAIgiAIglBbYtBz3RKPdgVBEARBEARBqJJoYRAEQRAEQRAeLqKFoU6JFgZBEARBEARBEKokWhjqSdKZXC79nkJOnAqlpRHe/W1o9rg9EonhAd058Sp2vhmul27homDAf/0qlyN2ZRC+JYO8ZDWm9nJ8BtjiO9i2yv0KDcdUKWfG0B70ae2DqVLBuegE5m/cR0RyRo338eWzQ8lXqfng95066S42lkwfGkJnXzfkMhknwmP5cvNBotOy6vgohJro4enJ9OAgfO3syCgs5Lez51h24mS1MSNatGBy1840tbImITeH70+e4s8LF3W2GdemDc917FC5za9nz/HT6TP1eShCDfR09WJmxxD8rO1ILyrg16tn+e788RrFtrZzZOOwp+m17nvi8nKq3O7DLn14qXUnPFYtrKtsC/fAVCnnrcd70LutD2ZKBeciE1i4fh8RSTW/nn/x0lDyi9R89MvN6/l/nu7P8K6tqowZ9NFKEjNz7ynvglAfalxhWLduHcOHD0ehUFSmHTt2jFWrVpGUlISfnx+vvvoqvr6+9ZLRB0natQIOL4jFPciSVuObkHa1gIv/S6GsFFqMdjAYkx1VBECPOZ7I5Ddv/mWKm41AN3ZkcGZlIs1G2NOknRkZ1ws5vyaJElUpLUYZ3q/QcBY+PZjW7k58ueUgeUVqXu3fjR8mj2HEwtXkFKqqjZVKJLzzeE/6tfXj75OXdNaZKuWseGUUmtJS5q7fg7pEwyuPdeXH18fyeA32LdStABdnlo8cwZar11h86AidXF2YERKMVCLhu+MnDMYM8vdj0eCB/BR6mgNR+3nM15fPBvSnqKSETVeuAvBku7Z88lg/lh0/weHoaNo5O/Ner56YyOUsrWK/Qv3r2MSFlf1GsTnyKotCD9LZ0ZVZHXsglUj49tyxamNb2Drw42OjkUtl1W7XxdGN51sG1GW2hXs0/7nBtPZw4qu/tdfzVwZ1Y8UbYxg1bzU5BXe+ns8a3ZO+7fzYdFz3er5i+3HWHjqvk2ZlasznLwzlVHgsSVmislBXxKDnulXjCsOHH35I7969sbOzA+DQoUNMmjSJ7t27ExwczMWLFxk9ejQ//vgjAQGP9oXvytoUrD2N6TLVDQCnDhaUasq4tjEN/6F2yJT6PcGyIoswdZDTpJWZwX2WlWnj3QItafO0IwCObczJS1BzY1uGqDA0snYezvRs6c1r3//FwatRAJyOiGf7+y8wvns7Vuyq+obP39me/xvZm5bujhSqi/XWD2jnj4eDDSMWrq5srbiemMaOD15kQDt/1h67UC/HJBj2RmAgV1JSmbltOwAHoqIwksl4pWsXfgg9jaqkRC9menB3toWFMW/ffgAORkVjbWzMm0FBlRWGyV27sOXqNT4/eAiAIzGxeNnYMLFDe1FhaETT2nfnckYKbx3YAsD++EiMpDJebduN7y+eQqXRL2+5VMpzLTsyIyCYIgPfh1uZGMlZFDKI5II8XMwt6+UYhLvT1tOZHq29mbL0Lw5djgLgzI14tsx5gbEh7Vi5o+rz0c/Fnnef6E3Lpoav53Fp2cSlZeukLX5pGNkFRfzf6u3i1QHCfavGYxjKbvsWf/fdd0ycOJGVK1fy9ttvs2bNGsaPH8+iRYvqPJMPEk1xKamXCnDtaqGT7tbNipKiUlKvFhiMy4oqwtrTuNp9B7/vQZtnHHXSpEYSNMXiCtPYgpp5UKBScyQsujItM7+QUzfiCGnhVW3svAkDkEglPP3172Tk6X8/dp0P5+mvf9fp2lSs0c5tLzeq/smlULcUMhld3d3Ycf26Tvq2sDDMFQo6u7rqxbhaWuJta8vOsHC9GA8bazxtrAF4ft0G5h84oLNNsUaDQiZ6jjYWhVRGN2d3tkeH6aRvjbqGuVxBF0c3g3G93byZ1j6Ib88dZf6p/dV+xvude5FamM/a6xer3U5oOEEttNfzo1dvuZ7nFRIaHkdIy+qv53OfGYBUImHiF7+TmWv49/5WIa286NPOl0Ub9pMrWovrVmlZ4/09hGr9SxQdHc0HH3ygkzZu3Dj++OOPe87Ugyw/uZjSkjIsXJQ66eZO2q5ceQkqaGeuF5cVVYSlm5I9/xdBVlQRclMZnr2saTW+CVIjCRKJBEs37T7LysooztMQfyKX6ANZ+A+3q/8DE6rl7WhLXHo2mtsuFDFp2Qzp6Fxt7Pv/20FYYlqV63OLVJyLTgTASCbFu4ktM4f3ICOvgB3nwqqME+qeu5UVSiMjIjMzddKjM7MA8LK14VB0tM46XztbAP2YrPIYGxuiMrO4kXGzQmhlbMwAP19GtmrJ9ydP1fFRCDXV1MIKpcyIyGzdfutROdqy9Lay4WBClF7cubQkuv+5nGx1EWN8W1e5/2AXD0b7tmLw36sZ4dOyTvMu1J6Xky1xafrX89jUbAZ1qv56/sHPO7ieUPX1/FYSCbz1eAinrsey6+z1OwcIQiOqcYXh9kG1np6eFBTo1p4zMzOxsNB9sv6oKc7XPvk1MtFtvKlYLi4s1YspyipBlV1CrhTaPOWIqb2clAv5XPs7jYL0YrpO032KlX6tkH0fRgJg422M7yBRYWhsFiZK8lRqvfQClRpzpcJAxE3VVRZu991LjxPo74GmtJRP1u0mvQZPsIS6Y6nUVtpvL+t8tXbZXKFf1pUx6ttjistjdB8udHRx4c8nxwNwISmJNWfEoOfGYqnQtvrmFt9WduXL5nKlXgxAckHeHfdtIVewMHgQi88cJjIn847bCw3HwkRJfpH+9Ty/SI25cfXX85pWFgB6tvbG28mOBev23W0WBaHB1bjCUFZWRt++ffHy8sLHxweFQsHnn3/OL7/8glwu5/Tp03z88cf07NmzPvN736vsuVXFpEWGJjOSm0gJ+dADCxclpvZyABxamSGVS7j0ewotRjtUti4AmDWR03OOJ4UZxVz+M5Xd70TQd743xtai60JDkEi0g9puJZVIDPc9lUBpHXZKXbbzON/vOkGf1r7MGfsYNuYmrNxd/ew8Qt25We6Gy9RQWVc8bLm9W2flnm5Lj83OZsLvf+Jobs6b3QPZ+PRTPP7Lb6QXiMphQ6ss7yrO4dJ7mOj9o659ScrPZeVFcf42piqv51VsW5fX8/E92nM1NoXj12LqbJ/CLR7OnkGNpsZ3mHv27OHq1atcv36da9eukZqaSlRUFBqNBrlczosvvoi/vz8zZsyoz/ze9+Rm2paEkgLdloSS8pYFual+n3OZUopjW/1uSs4B5lz6PYXs6CKdCoOJrRwTW23FwtbPlO1TrxO5O7PKGZiEujX5sW68NiBQJ23nuTA8HEz1tjVVKMgrqrt+qacj4wE4eSMOS1MlL/fryo97T+k1nQv1I0elLcvbWwXMylsWclX6ZV0Zc1tLk6lCew7nqnVjUvLzScnPB+BcYiK7X3qBcW1aVzkDk1B/stXa2ev0ylteXt7q2p3bfdx9GO7dnKGb1lTerErLq5AyiYTSsjJxr9NAXhnYjcmDda/n/54Jo6mFgeu5UkFeHY0zsDI1ppOfO19vOlQn+xOE+lbjCoOLiwsuLi706dOnMq24uBi5XPuj9/vvv9OsWbO6z+EDxtxRgUQKeUm6zZkVy7fe+FfIjVeRcimfpsFWOhUKjVr7k6GwkFFcqCHxVC62viaYO9/ch7mTAoWZjMJ0/dkYhPqx7tgF9l+O1Enr29qHoGYeSCS6DyOb2ltx4y7ew2BIa3dHXG2t9MYrXIpNZninllibmYiuSQ0kOiuLktJSPMoHKleoWA5P1y/ryAxtdxMPa2sup6TejLHWxlxPT8dMLqevrw/nEpMqxzYAxGRnk11UhPMj3tWzscTkasvb08JGJ93TUrt8PSu9Vvsd7OmPsZGcXaNe1FsX8fws1l6/wMyD22q1b+HurD98gQMXda/nvdv6ENhc/3ru7mB1V+9hqE73lp4YyaT8e0aMQ6svYlrVulXjCsPEiRNrtJ1EImH16tW1ztCDTqaQYt/CjPjjOfgPt6vsjhB3LBu5mRRbXxO9mMKMYs58n4jUSIJXn5s/TLFHsjEykWLjbYJEKuHU0gQ8eljTcbJL5TYZ4YWo8zRYeVQ/w5JQd1Jz8knNyddJM1EY8fJjXenezJND5dOq2piZ0MnHrdopVWuiZ0tvXurbhXPRiTpzdHdv5kFqTr7BmZWE+qHWaDgZF0d/P1+dwciD/P3JLiriXFKSXkx0VhbRWVkM8vdnW9h1nZiIjAwScnIxNjJi/oD+/HXpMu//u6tymzZOjtiYmHAlNVVvv0L9U2k0nEiKZaCnH8sv3jyPB3s2I1tVxNnUxFrt96szh1l9WXdsypPN2/Fks3YM/XsNmSpxTjcUQ9dzY4URkwZ2JaiFJ4fLp1W1MTeho69btVOq3o3WHk4kZeaKl7QJD4waVxhcDUwXeKtTp04RGxuLubl+15pHTYvR9hz4JJpji+Pw7GNN+rUCwjal0+YpR2RKKcUFGnLiVJg7KlBaGeHQ0gyHVqacX52ERlWKhYuSxNO5hG/LoO0zjijMta0OzUbYc2V9KgoLGU3ampGXoOby2hSsPIzx7G3duAf9iAuNiOdEeCzznxrE4s0Hycov5LUBgeQWqlh79OZLerwdbVEYybgaX/MbwD+OnGdMtzZ899LjLNt5jAJ1McM7taRHS2/e+WWrmLe7gS05epw1Y8fwzbChrLt4kQAXFyZ17sTCAwdRlZRgrlDga2dHTFYWGYWF5THHWDhoIJlFhewOj6CvrzdDmjfjjX82A1BUUsLyEyeZEtiNzKIijkRH42ljw7SgQC6npLDu4qXqsiTUo2/OHeXXgeP4rvdw/rx+gY5NXHmlTRfmn9yPSlOCuVyBn7Ud0blZZBQV1mifcXk5em99Ti7wAeBCun6lU2hYp2/EczIslk8nDuKrv7XX88mDA8ktUOm8dM3byRa5kYxrcXdfofdzsSciqXYtVEINiR/HOlXjCsNnn31mMD0vL4/58+cTGxtLUFAQc+fOrbPMPaiatDEncIY7l/9M4ejCWExsjWj7jCP+w+wByIws4sCcKDq95oJnbxskMglBs5pyaW0KYf+kU5RVgrmjgoCXnfHuZ1u535ZPOGBsbcSNHRlc35KOwlyGW6AVrcc30XkjtNA43vrpH2YN78mMoSFIJBLORiUwc80WnTcxfzCqDy62lgyct6rG+03LzWfit3/w5pBg/m9Ub8yMlVyJS9F5SZzQcI7GxvL63/8wrXsgS0cMJzkvj/n7D/DDqVAAWjk24bdxY3l723bWX7oMwPpLl1HIZLzUuRNPtG5NTHY2M7ZuY+u1m90Rvj5ylNT8fJ5u357nAwLIKipi67Uwvjh0GHX5ezeEhnckMYbJezbyVofurOg7kuSCPD49uY/vywcrt7Zz5I/BE5hxYCvrwsW7FB4W01f+w8xRPXnrce31/FxEAm+v2qLzroT/G6u9ng+eU/PreQVbC9O7mlFJECqoVCrmz5/P9u3bKSoqIiQkhNmzZ1e+WPlOli5dyldffcW1a9fu6nMlZbdP0XEXDh8+zIcffkhOTg6zZs1i3Lhxtd2VjvfPj6qT/QgPhk2rQxo7C0IDKnAWT30eJSW21b/pWHi42JyRN3YWhAZ09pu3GjsLVeo1cEGjffa+7e/U277fe+89QkND+fTTT1EoFMyePRszMzN++eWXO8aeP3+eCRMmUFJSctcVhlo9ls7Pz+fDDz/kxRdfxMPDg02bNtVZZUEQBEEQBEEQ7oWkrPH+6ktycjIbN27kgw8+oFOnTrRt25bFixdz8uRJzp49W21sQUEBs2bNolOnTrX67LuuMBw+fJhhw4axdetW5syZw48//oiLi8udAwVBEARBEARBqJXQUG3X165du1ameXl54ejoyMmT1b/TZd68efj7+zNixIhafXaNxzDk5+ezYMEC1q5dS2BgIPPmzcPZufpXpAuCIAiCIAhCg2vE3q99+/atdv3u3btrtd/k5GRsbGxQKnWn6G/SpAmJiVXP2vbvv/+yf/9+/vnnH/bu3Vurz65xhWHYsGEkJibi7u5OQEAA69evr3LbKVOm1CozgiAIgiAIgvAoiouLq7ayMW3aNBQKhV66UqlEZeDFoaCtZHz44YcsXLgQGxsbg9vURI0rDADOzs6UlJSwYcOGKreRSCSiwiAIgiAIgiA8kmrbguDo6MjWrVurXL9//37UarVeukqlwsRE/z1fZWVlvPvuuwwaNIgePXrUKk8Valxh2LNnzz19kCAIgiAIgiA0BMkD+B4GuVyOj49PleuvXbtGVlYWarVap6UhJSUFJycnve0TEhI4cuQIp0+fZuPGjQCUlGhnruvQoQOvvPIKkydPrlHe7qqFQRAEQRAEQRCEhtexY0dKS0sJDQ0lMDAQgIiICJKTkw3OfuTo6MjOnTt10nbu3MmiRYvYuHEjVlZWNf7s+7LC8Mc2MS//o0QTUNTYWRAakCRVeeeNhIeGMknMy/8oCXn+VGNnQRC0Shs7A3XP0dGRIUOG8MEHH/Dpp59iYmLC7Nmz6dKlC+3btwdArVaTnZ2NlZUVCoUCDw8PnX1UvODt9vQ7Ea8HFgRBEARBEIQHwCeffEJgYCBTpkzhxRdfxNvbm6+//rpy/ZkzZwgODubMmTN1+rn39Kbn+uK74MvGzoLQgDRuooXhUSJaGB4tRgWSxs6C0IAGDBQtDI+SbwJ+bewsVKlvn88a7bN373mv0T67vogWBkEQBEEQBEEQqiQqDIIgCIIgCIIgVOm+HPT8MOjh5cFbId3xtbclo6CQ/509z7Jj1b+2e0TL5kwO7IK7lRUJOTmsPBHKn+cvGtzWXKFg8wtP8/WhY2y4eLk+DkG4Cz2cvZnRrgd+VvZkqAr49foZll46WqPY1rZObBgwkd6blhOfn62zbrR3Gya16IqnhQ0phXlsiLjItxcPU1L2EI7meoD08PBkRlAQvrZ2ZBQW8tuFcyw9eYfzu3kLXu3cmaZW1iTk5LAi9BR/XtI9v49PegUHMzO92C4rlpFWUFCnxyDcHTOFnLf79KCfvw9mCgVn4hOY++8+bqRlVBvnYGbGe/160N3LA7lMyqHIGObt3EtyXr7B7Z/p1J7nuwTQ57tV9XEYQg2knc0m/M948uKLUFga4dbXAa8RTkgkd+5eV6op48RHV5AppXT+qLnOuvj9aURtTqIwWYXCWo5LiB3eI52RGolnt/Xivutw/2CrcYXhvfdq3h/rs88ar9/Y/aCDqzPLRo9g65Uwvjx4mI5urkzv0R2JRMLSoycMxgxq5sfnQwey+tQZDkRG0c/Ph08HPUZRSQmbLl/V2dbKWMny0SNwu4vpsIT6E2Dvyvc9x7Al5gpfnDtA5yZuzGzXEykSllw6Um1sC+smrOr1BHKpTG/dc806MbvTY2yNvsJnZ/ZgqzTlzTbBNLdxYPKBql+eKNSvAGdnVgwfwZawa3xx5AidXFyYERSMBAnfnazi/Pbz44sBA/npzGn2R++nv48v8x/rj6qkhL+vac9ve1NTHMzMmLt/H6cTE3Xis4rEOJ/GtnjEYNq6OLFwz0Hy1WqmBHdjzZNjGLxiNdlFht+wKpNIWDn+cUzlCmZv342RVMrM3sGsmjCaET/8QkmpbsV/WKvmvNu3B8m5eQ1xSIIBWWF5nFkUjlOgDb5jXcm8lkf4n/FQVob3SJc7xkf9nUhORAE2Lcx10qO3JXNtTSyOXW3wf8qd4twSbqyLJy+mkPYzfOvrcAShztS4wiCVSlm/fj0uLi64urrWZ54eeFO7d+NKciozt2wH4EBkNEZSKa907cyqk6GoSjR6MW+FBLH92nXm7dkPwMHIaKyNjZkWHKhTYejn682H/XpjqhBTFd4vprUJ5kpmMtOP/APAgcQIjCRSJrfqxsqrJ1BpSvRi5FIpz/p3Ynq7HhQZWC+VSJjWJpiDiZG8fmhjZfqF9ET+HfYywU6eHEqKqq9DEqoxtVsgV1JTmbGj/PyOjkIukzG5cxd+OH3aYHnPCOzOtuthzD1Qfn5HR2NlbMy0wKDKCkNLhyYAbA+/TkJubgMdjVAT7V2d6e3nzaQ//mL/jSgATsbGs+e1F3iyYzuWHjZcURzYwp8Wjk0YvGIN4WnpAFxJSWXLpIkMbuHPpkvasrc1NeGtnt0Z16ENmQWFDXJMgmE31idg4WlCm9e9AbBvb0WZpozITUl4DHFCpqi6NSA3uoCIv5NQWOv+PpeVlnFjfQJ2bSxp9+bNl3JZeplyZNYl0s9nY9dWPACsc/ffnD4PtBq3g82bN4/nn38elUrFf//7X37++ecq/x5lCpmMru5u7AwL10nffu065koFnd30K1uulpZ429myI+y6XoyHjTWeNtYAWCiVLBk5jOMxcTz/51/1dgxCzSmkMro6NmV77DWd9G0x1zCXK+ni4G4wrpeLD1PbBrPk4hEWnNmrt97e2AxrpQm743S/E+E56aQXFdDHVTyRagwKmYyurm7sCNctl23XwzBXKOhs4GGKq6Ul3ra27AgP14vxtLbGy9oagJYODmQXFYnKwn0oxNuDfLWaQxHRlWmZBYWcjImjp49XtXER6RmVlQWAG2kZ3EjLoKfvzbjJQV3o7tWUKev/YW94RP0chHBHpcWlZFzOxbGzjU66Y1cbNEWlZF6t+twsLSnl4tJImg5sgpmz7kxwqqxiSvI1OAToVgrM3UyQWxiReka3K6og3I/uquPcrFmzcHNz45tvvqmv/Dzw3K2tUBgZEZmZqZMenZkFgJetjV6Mj70tAFEZWdXGFBUXM/CHNby9dYd4CnWfcDe3RikzIjJXtx9zVJ62/L0sbQ3GnU9PJGTjdyy5dMTgeIQcdRHFpRrczHV/YCwVxlgpjHE3t66bAxDuirulFUoD53dUVhYAXjb657evrfY7oHdNuC2mhYMD2aoilg4dxrlXX+fCa1P476DBOJjqj2kQGpaPnS2xWdlobntiGZ2ZbfCafmtcZHqmXnp0Zhaet8T9fuY8/Zf9xM5r4XrbCg2nIEVFWUkZps7GOummjtoKQEGi4a5nADfWJ1JaUobvGP1uS3IzGRKZhMJUtU56cV4JJfklFKZUvV9BuF/cVYVBKpXy6aef0qxZs/rKzwPPUqm9sOSpdC8M+WrtsrlCUU2MqtqY4tJSIjP0f3yExmOp0P6w5BXfVt7F2rI0l+uXN0ByYR7Z6qr7pRdpStgSfYVn/DvyhHdbLBXGeFvY8nX3EZSUajAxEl3SGoOlcfm5qjZ8fltUd37rxRQDYK7Qrm/p0AQncwvOJyfx0t8bmXdgP93c3Pn9ibGYGIn5KRqThbGS/Nuu6aAtdzOl4XO8Iu72cq+Iu/W3ICI9U288g9DwSvK13YWNTHTHlMnKl0sK9bsTA2TfyCd6SxKtJ3silevfVsmUMpy62RCzM4X4vWkU55WQn1DE+W8ikMgkaFSi7OuDpKzx/h5Gd/0r5OPjg4+PD7GxsWRmZmJjY4Obm1uNZg94FFT8P5RVMTy/1ECytDJGb2faGDHU/74lLf/aV/X+w9J76EP5/ontqDQa5ncbzELJEApK1Cy/fBwTIyMKS4prvV+h9qTc6fzWT68qpuKSWRHz9r87UJWUcDk1FYCTCfFcT09n7bjxjGrZkl/Pn6+TYxCqJ+HmNbmCVCIxWOISqj73K+IMBUqo+jskNJ7KsqzidsbQbY5GXd4VaZAjVr7m+huUa/GSB1K5lEvfR3FpBUiVUryGOqFRlyJTilmShPvfXVUYysrK+OGHH/j5559JSUmpTLe3t+fpp59m0qRJSKWP9hc/t7yV4PaWBLPy5VyVftNjTlEVMXJ5eYz+Eyrh/pCjrmhJ0O2zala+nFtc+6bmgpJi3j2+lf+E/ourmRVxedkUaooZ69OWmNzoO+9AqHM5lef3beVdcX6rDZzfVVwTTCvO7/KYM7fNjAQQmphAjqqIFvYO95hzoaamhHTjjZBAnbRtV8LwsjXV29ZUoTB4Ta+QW6Qy2AJhqlCQW8XMSkLjkZtqb4lub0nQlC8bmerPZhf+ZzxlZeA90plSTXmFo/yfUk0ZEqn2QaKRsYxWr3jS7Fl3ilLVGDsoMDKWEb8/DdOWFvV3UI8yMei5Tt1VhWHq1Kns27ePESNGEBgYiI2NDdnZ2Rw7doylS5dy5swZli1bVl95fSBEZ2ZRUlqKR/lA5QoVy+Hp6XoxkRkZldtcTknVj0nTjxHuD9G52q4Enha6/Zg9zbXL4dlptd53H1dfstWFhKbGc718P3ZKU5xNLbmUmVz7TAu1Fp1dfn6XD1Su4Fm+fD1df07+iPKxCx7W1pWtBxXL2ph0LJRKBvr4ciYpkfAM3X3IpTIyCsWYpYbyx5kL7L0eqZPWr5kPId4e5S0DN3nYWBFezXsYIjIyaenYRC/dw8aa8wlJdZRjoa6YOCqRSKEgSbcyV5CsXTZzNdGLST6eSVGamj3Pn9Fbt+vpUFpN9sS1pz2pp7MwMpNh08wCc3ftflTZxRSlq7Hw0q+MCsL9psbNARs3buT48eOsXbuWuXPnMmTIEIKCghg0aBAff/wxv//+O2fOnGH9+vX1md/7nlqj4WRsHAP8dWexGdjMj+yiIs4l6v9IRGdlE5OZxcBmfnoxEekZJOSIWVPuV+pSDSdSYhjgrjuuZ1DTZmSrCjmbrv/UuKae9OvA/3Xoq5P2fPPOaMpK2R0vBkc2BrVGw4n4OAb46p7fg/z8ted3koHzOzuL6KwsBvn568VEZGSQkJtLsUbDf/r0ZXKnLjrbPObjg4lczvG4uLo/GMGglLx8LiYl6/wdiojGXKkkxNuzcjsbUxM6N3XjcETVrX2HI6LxsbetnNgCqFw+FClaCe83MoUU6+YWpJzM1Olqlnw8EyMzGVa++hMQdJjlS9e5LXT+LLxMsfAypevcFjgEWAMQuyuVsF91z+OYbclIpBIcOljX52EJQp2ocQvDH3/8wdSpU2nevLnB9c2bN2fq1KmsX7+e0aNH11kGH0RLjpxgzfjRfDNiCGsvXCLA1ZlJXTuxcN9BVCUazBUKfO1ticnMrnxy+O2R4ywcMoCswiJ2h9+gr68PQ1o0Y+rfWxr5aIQ7+fbiEX7pO4ElwY/zZ8R5Otq78XLLbiw4sxeVpgRzIwW+VvbE5GWSoar5k+LVV0+xpu94PuzYj11x1wly8uD11kF8d+kIsXlZ9XdAQrWWHD/Oz6PH8O3goay9fJEAZxcmdezEgkMHteWtUOBra0dMdtbN8/vEMT7vP5CswkJ2RUTQz9ubof7NmLJlMwBFJSUsP3WKqd26kVZQwIHoSJrbOzCtWyB7IiI4HBvTmIf8yDsVG8+x6FgWjRjE53sOklVYyBshgeQWqfjfmZtjS3zsbVHIZFxJ1rYkbbkSxuTuXVg5biSL9h4CYGbvYMJS0th+JaxRjkWonvdIZ0I/DeP8fyNw6WVPdlgeUZuT8JvghkwhpaRAQ158IaaOShSWciya6rcOGBlrn8Va+dysYDQd2ITTn13n6uoYHDpak3Epl8i/k/Aa7lQ5C5NQtyRiLHmdqnELQ3h4ON27d692m5CQEK5fv17tNo+CYzGxvP7XP3jZ2rBs5DCGt2zBgr0HWHkiFIBWjk1Y98wEet0yf/eGi5f5YPsuuns2Zemo4XRt6sbMzdvZelX8qNzvjiZH8+qBDXhb2rG8x2hGeLbks9N7WHHlOACtbJ34a+Cz9L7LdyccTIpk6qG/CXby5IdeTzDQvRlzTu7k87P76+MwhBo6GhfLa5v/wdvGhmVDhzOieXM+O3iA70NPAdCqSRM2jJ9Ab6+b5/f6y5d5f/e/BDf1YPmw4XR1c2f69m1svX7z/P7vsSPM3rObnp6erBwxkhcDOvLbhfO8Xl6pEBrXlPX/sDvsBm/3CWH+0AEk5+bx7G/rK8egAcwZ0Iclo4dVLhdrNDz323ouJSXzyaB+zB7Qh7Pxibz4+196U7QK9we71pa0e8uH/IQizn4RTuLhdPyfdMNrmBMAOVH5nPjo6l2/O8G+rRVtpniRfiGHM5+Hk3Iik+bPuuM3wa0+DkMQ6pykrLopHm7RoUMH/v77b5o2bVrlNjExMYwZM4YTJwy/9bKmfBd8eU/xwoNF41b19KLCw0eSKp6mPUqMCsQMeo+SAQNPNXYWhAb0TcCvjZ2FKj3WfW6jffa/hz9otM+uLzVuYfD19eXIkSPVbnPw4EF8fHyq3UYQBEEQBEEQhAdHjSsMI0eO5JtvviE2Ntbg+vDwcL799lvGjh1bZ5kTBEEQBEEQhLtW1oh/D6EaD3oeP348+/btY9SoUYwaNYoOHTpgbW1NXl4ex48fZ926dYSEhDBy5Mj6zK8gCIIgCIIgCA2oxhUGqVTK0qVLWbp0Kb/++iurV6+uXGdvb89rr73Giy++WC+ZFARBEARBEAShcdzVi9tkMhlTpkxhypQpREZGkpWVhbW1NR4eHo/8G54FQRAEQRCE+4NEzERWp+6qwnArr1umDBQEQRAEQRAE4eFU6wpDfRLTbD5apMlims1HiSJLTLP5KCl0K2nsLAgNKOxlv8bOgtCQ7udZdEULQ50S/YgEQRAEQRAEQaiSqDAIgiAIgiAIglCl+7JLkiAIgiAIgiDUWmljZ+DhIloYBEEQBEEQBEGo0l21MGRmZrJ3716ys7Pp3r07/v7+OusLCgpYtWoVU6ZMqdNMPoh6OHszo10P/KzsyVAV8Ov1Myy9dLRGsa1tndgwYCK9Ny0nPj9bZ91o7zZMatEVTwsbUgrz2BBxkW8vHqakTFSlG1MPT0+mBwXha2dHRmEhv507x7KTJ6uNGdGiBZM7d6aptTUJOTl8f+oUf168WOX27/fsyQsdO+KzeHFdZ1+oBVOFnFkDetC3uQ9mSgVnYxP4dOs+bqRmVBvnYG7GOwN7EOTjgVwm5fCNGD7dupeU3HwAPh3Zn5EdWlUZ3/eLlSRk59bpsQjV6+nmyYzOIfjZ2JFeWMBvV87x3dnjNYptbe/IX48/Re/fVxKXl6OzbqCXP5PbdcHH2pZctYoj8THMP7GftMKC+jgM4S6YmCqYNK0/gT2bY2qm4PL5WJYu2k5MZGqN9xHYsxlzvpjArFd+4nxoVGW6nYMFk6b1p1OgL1KZlKsX41i9dA/XLsXXw5E8usS0qnWrxhWG8PBwnn32WQoKCigrK+Pzzz9n4sSJvPvuu5XbFBQUsGTJkke+whBg78r3PcewJeYKX5w7QOcmbsxs1xMpEpZcOlJtbAvrJqzq9QRyqUxv3XPNOjG702Nsjb7CZ2f2YKs05c02wTS3cWDygQ31dTjCHQQ4O7N8xAi2XLvG4iNH6OTiwozgYKQSCd+dOGEwZpCfH4sGDuSn06c5sH8/j/n68ln//hSVlLDp6lW97Tu7uvJshw71fSjCXfjiicG0cXVi0c6D5KvUvNa7Gz8+N4Zh364mu1BlMEYmlbD8mccxVSj4ePNujKRSpj8WzA/Pjmbkd79QUlrK0n3H+ePkeZ04KxNjvhw3lJNRsSTmiMpCQwpwdOH7AaPYHHGVL04epJOTGzM7hyCRSFhy5li1sS1sHfhx4GiD1/PBXv5899gIfr18li9OHsLe1JTpHbvz25BxDPtrDSqNpr4OSaiB9+aNoVkrV1Z+/S8F+SqentSTBUuf5eWxS8jNKbxjvIWVCVPfG6aXbmqmZPHKFzA2UfDT0j0kxKYT1KsFny9/jlmv/CQqDcJ9q8YVhvnz59OlSxcWLFiARCJh9erVfPXVV2RmZrJgwYL6zOMDZ1qbYK5kJjP9yD8AHEiMwEgiZXKrbqy8egKVRn+aQblUyrP+nZjergdFBtZLJRKmtQnmYGIkrx/aWJl+IT2Rf4e9TLCTJ4eSourrkIRqvBEYyJXUVGZu3w7AgagojGQyXunShR9On0ZVol+e07t3Z1tYGPP27wfgYHQ01sbGvBkUpFdhMDEyYsGAAaTk5+NsYVH/ByTcUXt3Z3o18+aVn//iwPUoAE5Fx/PvWy8woUs7lu03XFEc0MqfFs5NGPbNGsJT0wG4mpTKptcnMqi1P/+cv0psZjaxmboti9+MH0Z2YRGz1m0XMwU2sDcDgricnsL0vVsB2B8XhVwq5dV2XVl5/lTV1/NWAczoHEyRgfMf4I2AIPbE3OD9Q/9Wpt3IymDTyGfo09SHbZFh9XNAwh21aONG1xB/Ppj2KycPXwfg4ploVm96k6FPdOZ/Pxy44z7eeGcImhL9St+AER1wcrXhrRd+4PL5WABOH4/AytqUV6YPYPqLq+r2YB5l4mJZp2o8huH8+fNMmTIFhUKBXC7npZdeYvHixWzevJnPPvusPvP4QFFIZXR1bMr22Gs66dtirmEuV9LFwd1gXC8XH6a2DWbJxSMsOLNXb729sRnWShN2x13XSQ/PSSe9qIA+rr51dxBCjSlkMrq6ubHjum65bAsLw1yhoLOrq16Mq6Ul3ra27AwP14vxsLbG09paJ/29nj1Jy89n3aVLdZ5/oXa6+3qQr1Jz+EZ0ZVpmQSGnouPo4Vf1Sy2DfT2ISM2orCwA3EjNICItgx7+huN6+XvRr6Uv87ftJ7fIcMuFUD8UUhldXdzZcdvN+9ZI7fndxcnNYFxvd2+mdQzi2zPHmH98v956CXAoPor/XdFtSYrM0nZn87C0rpP8C7XTMdCXwgI1ocduVKZlZxVw/nQUXYLu/J6Jno+1IqCrDyu/+VdvXVNPB3KzCysrCxXOn46iVbummFsY3/sBCEI9qHGFQaFQoFarddL69+/PBx98wOrVq/npp5/qOm8PJHdza5QyIyJzdfsxR+VlAuBlaWsw7nx6IiEbv2PJpSMGxyPkqIsoLtXgZm6lk26pMMZKYYy7uXXdHIBwV9ytrFAaGRGZmamTHp2VBYCXjY1ejK+t9jtQk5juTZsysmVL3t6xg1LxtOS+4WNvS1xmNppS3TKJTs/G006/zCt4O9gSlZ6plx6dnmUwTiKBmQNCOBEZy87L1/XWC/XL3dIKpcyIiGzdMosqX/ayNlzW51KTCP7fCpacOYbGwPW8DJh3bB//Rus+NBjopR0XeC0jrQ5yL9RWU097EuMzKdXoll1CbAauHnbVxlrbmvH6O0NY+sU2MtLy9NZnZ+VjaqbUqxg4u2l/F5xcqr5+CEJjqnGFoUuXLixcuJC0NN0L2YQJE3jppZdYsGABK1asqPMMPmgsFdqLQF6xbuUqv1j7ZNBcrjAYl1yYR7a66jdcF2lK2BJ9hWf8O/KEd1ssFcZ4W9jydfcRlJRqMDGS19ERCHfDUql9S3XebZXp/PJlc4V+eVcZU1ysjSlfb65QML9/f746coSo8sqEcH+wMFGSp1LrpReo1ZgpDZ/jAJbGhuPy1WrMDcT1buaNj4NdlV2chPplVeX1vPz8rup6XpBHtqrq67khnpbWvNetJxdSk9gXG1GL3Ap1xczCmIJ8/da8wgI1pmbKamPffH8YV87HsnvreYPr92y7gKa0lA/mj8XD2wEzc2N6D2xD/2HtATA2Eb/ldaasrPH+HkI1HsPw9ttvM2nSJEJCQlixYgUhISGV62bOnElZWRk//PADEomkXjL6oJCWH35ZFV+Ye3lK/P6J7ag0GuZ3G8xCyRAKStQsv3wcEyMjCkuKa71fofakksoCN7jeUHlXnCO3f0cqzpyK9A979SIpL49VoaF1k1mhViSSW8q5nFQioaozuapzvyLOUKAEw9+Vp7q253JiCkcjYu4ix0JdqepcrVBdWd8NH2tbfhk8FrVGw6u7NlX53RLqnkQiQSK97fyWSgxe0yUSKCutunT6DWlH6/YevDzuuyq3iYlMZfZb/2Pa+8NY8efrAIRdTmD10j1MeWcIRYXit1y4P9W4wuDk5MT69esJDQ3F19eX2NhYMjMzsbGxwc3NjVmzZtGjRw+2bNlSn/m97+WoK1oSdJ9CmJUv5xbXvg9yQUkx7x7fyn9C/8XVzIq4vGwKNcWM9WlLTG70nXcg1LkcVXl5K28r7/KWhVyVfnlXxtzW+mAql1fG9PbyYmjz5jz+66+VN6sV/8okEkrLysRNRQN5rVc3pvQO1EnbfjEMT3tTvW1NFQqDZV4hp0hlsAXCUJy1iTFdPN1ZvOtQLXMu3Kuc8lZfC8Xt1/Py81ut31p0twKd3VnW/3HyitU8s/lP4nKz7xwk1JmnJvXkmZd76aQd2HUJt6b6XY+MTRTk5xluObJzsGDyjIGs+GonWZn5SGVSbcUDbQVEKpVQWl7ZOH38Bs8O/wpHF2sAkhOyKlsYajIDk1BDYrb5OnVX72GQy+VcunSJd999l5SUlMp0e3t7nn76aSZNmkTXrl3rPJMPkujcTEpKS/G00O2H6GmuXQ7Prn3f1D6uvmSrCwlNjed6+X7slKY4m1pyKTO59pkWai06K4uS0lI8bhuoXLEcnqE/J3/F2AUPa2sup6bqxVxPT+fNoCCMjYzY/uyzevFhb73F+kuXeHvHjro5CKFaf566wL5rkTpp/Vr4EOzroX3ieEvNzcPOqtr3MESmZdLSuYleuoedNefjknTSgv08MZJJ2XFRzJbTWGJyys/v2wYhe1ppr+fXs+5trMFwnxYs6jWIyOwMnt22jqR8/T7vQv3auiGU4wd1z7GgXs3p2M0XiUSi04rk4m5b5XsYArr6YGFpwoyPRjDjoxE66xYsfZakhCyeHf4VDo5WdOjqzZ6t50lOyKrcxq+FCzlZBSQnZiEI96O7qjBMnTqVffv2MWLECAIDA7GxsSE7O5tjx46xdOlSzpw5w7Jly+orrw8EdamGEykxDHBvxoorN1/sM6hpM7JVhZxNT6z1vp/064CNwoTRO9dUpj3fvDOaslJ2x4dXEynUF7VGw8m4OP6/vfuOjqpaHz7+nZlk0ntCEhIIqYTeEiBA6CBNEfAiKIo/ERSkeEW96vVaXrmKilhAIiBe6aigiHRFgdBC6J2QHlpCeu95/5gQGGYmDpAwBJ/PWlmL7Dn7zD7sOTvznN0GBgSw+NChmvTBQUHkFBdz/OpVnTxJ2dkkZWczOCiILTetrjQ4KIj4zEwu5+Xxxf79LD92TCvfmDZtGNO2LY+uXElmkTyFuleu5RVwrXpTteuszM14oVcXegQ0I7J6WVUnaytCfLxZuNvwfIN9sUkMaxOMv5tzTWDh7+aMn6szEbu0NwJr6+3BlZw82aTNhEoqKjh4JYVBvkEsOnFjI8YhvkHklBRzLE33/jZW7ya+zO0zhENXLzJx28/kld19b4W4fZnpeWSma99jFpbmPDGhJ53C/Dm0T/O31cHRmrYdm7H6W/1Lqh6IPM/Up7TncQa28GTGmw/zxQe/cua4ZlUkR2cbZr49nIy03JpVmJxcbOk9sDX7dunuwSPE/cLogGH9+vVERUXx448/EhwcrPXa4MGDGTt2LOPHj2fdunWMGjWqzgvakMw/tY8V/cbyVY9H+SH+BJ1cvZnUsisfHf2TkopybM3UBDi4kpyfRWaJ8V/8lp47xLJ+Y/hPp/78fvEC3Tx8eLF1Nxac3kdKfnb9XZCo1VdRUSx77DHmDRvG2lOn6Ni4MRNDQvg4MpKS8nJs1WoCXFxIzs6u+aL/1YEDfDxoEFlFReyIj6efnx9Dmzdn2saNAFzKzeVSrvausH38/AA4mSq9SaZ2KOkSUQkpfDxqMJ9ujyS7qIgX+4SRV1yitemav5szapWKs1c1TyU3n4phUs/OLHpqBJ/+phlqNHNAD2LS0tl2WvspZ5C7K3E3Lb8qTGPe0QOsHDqar/o/wo/nT9LRvTGT2nVmdtQuTXturibQyYWk3Gwyi41rzy1UKj7qOYiCslLmHz1AgJP28JcrBXnS22BCp44mcfxQAq+/P4pvvvyN3JxCnprUm/y8Yjatu/FgqKmvG+ZqFXHnr5KXU0Rejnb9W1lrhq5dTMogMU4zKuPC2cucOpbMtNeH8c2X26moqOSZKf2oqKhkxSLdJXjFnZOdnuuW0QHD999/z/Tp03WCheuCg4OZPn26BAzA/tQkJu/+iX+2DWdhz1GkFubx4ZE/+Oac5sljK2cP1gx4klf2b2Rd/Emjzxt5NYHpe35hautujA1oz6WCHN6N3s7SGJkUa0r7U1J48ddfmREWRsQjj5Can8/s3btZUj1ZuVWjRqwaPZrXtm5l3ZkzAKw7cwa1SsVzISH8o3VrknNymLllC5tjZPhJQzF99a/8a1AvXnkoHKVCwdHky/zz+03k3rRXwtvD+uLlaE//zzSbMZVVVDBh6TreHNKb//dIf8oqKtkXl8TsLbt0lmh1sbEmJlWW1zS1/ZeTmfzbL7zUqTsLBz5KakE+HxzYyTcnNV8cW7u6s+bhMbyyczNrY4zbK6WjuxfuNrYArBg6Wuf1zw/v5fPD++ruIsRt+3+vfs/z/3yI52YMQKlUcPp4Cv9940fy827MYZj6+lDcPR0Z/8jnt3Xu91/7nhdeHsT0Nx9GARw/nMh783/nWqrMXxH3L0WVkcs8hIaG8sMPP+Dra3hTouTkZEaNGkV0dLTBY4zhu1I2gvs7UabWvkydeLCos//eK6n93RR569/pWDyYmi8q+OuDxANj26F3TV0Egwa1+4/J3nvr8fdN9t71xeh9GMrLy1GpVH953N99WVUhhBBCCCEeJEYHDAEBAezbV3sXaWRkJP7+/nddKCGEEEIIIe6YbNxWp4wOGEaMGMG8efNISUnR+3psbCzz589n9Gjd8ZhCCCGEEEKIhsnoSc9jxoxh586djBw5kpEjR9KhQwccHR3Jz88nKiqKtWvXEh4ezogRI+qzvEIIIYQQQoh7yOiAQalUEhERQUREBCtXrmTp0qU1r7m6ujJlyhQmTJhQL4UUQgghhBDCaA/o0CBTua2N21QqFVOnTmXq1KkkJCSQnZ2No6MjPj4+KJVGj24SQgghhBBCNBC3FTDcrLblVYUQQgghhDCZSlMX4MFyxwFDfVKky7r8fyeOLWQ327+T/EOupi6CuIeUBX+9HLd4cAxZscfURRBC1AMZRySEEEIIIYQw6L7sYRBCCCGEEOJOKWTSc52SHgYhhBBCCCGEQdLDIIQQQgghHizSw1CnbjtgyMrKwsnJCYDU1FTWrVtHamoqgYGBjBw5Emtr6zovZEPU06cZM8O6EeDsQmZREatOHifiUHSteYY3b8Hk0FCaOjhyOS+XRYcP8cPpU1rHRD33PG42Njp5Oy/+mvTCwjq9BmG8MNdApgQNwM/WjazSAtYlH+R/8bsNHm+pNGdSYF8GerbBSW1DTO5VFsX+wf70C3qPtzGzYE33aSyK3cGvl47W12WI22CjNue1/j3p19wfG7Waoxcv899tO4lLz6w1n5utDa8P6El3Px/MVUr2xCfz321/kpZXUHOMl4M9r/YPp4uPN+YqFQcSU5izI5LEzOx6viqhT0+fZszsflN7fuI4EdF/0Z4Ht2By5+r2PLe6PT91yuDxb/XqxbMdO+H32dy6Lr64TclHiohakUNWSjmWDkpaDbKl4yg7FAqF3uOzLpax+sWrOumOXmY8scCz5vf4A4Uc/iGXrEvlWDuqaN7Hmo6j7FGZ6z+vEPcTowOG1NRUXnjhBc6dO0erVq2YNWsWzzzzDFVVVTRp0oSNGzeybNkyli9fjru7e32W+b7X0dOTRQ8PZ1PMeT7dv4+Qxo2Z2a0HCoWCBdEH9eYZHBDIpw8N4rtjR9iVuIuB/gHM7j+QkvJyfjl/DgBXa2vcbGyYtXsnR65c0cqfXVxc79cl9Gvr2JTPOo1j+5WTLIj5jfZOPkwJGoBCoeTbuJ1687zTdiTdXIOYF7ON5IIMhnl14PNOT/H8wSUcy0rSOtbe3IrPOo6jsbXTPbgaYaxPRwyhrZcHn/weSX5pKVN7dmXpU48xNGIpOcUlevOoFAoWj30Ua7WadzfvwEylZGbfHnz75CgeXbSC8spKbNTmfPvkSCqqKnl3yx+UllcwJbwLy58ezbCvDZ9b1I+Onp4sGl7dnu/dR4hXY2Z2r27PDxpozwMD+XTQIL47elN7PqC6PT93Tuf4UC8vxrfvUN+XIoxw5WwJm/+bTkAPa7qMc+DKmVKiVuRQVQkho+315kmPLwNg+Cw3VOobX/7Nbvp3yrFits7OIKCHNV2fdiQjqYyo5dkU5VTS83lp2+tFpfQw1CWjA4aPPvoIW1tb5s+fz6pVqxg/fjwdO3bks88+w9LSktzcXKZNm8bs2bP57LPP6rPM973pXcI4e+0aM7dvBWB3UiLmShUvhHRmyZEjlFSU6+SZ2a07Wy7EMGv3LgAik5NwsLRkRtduNQFDS7dGAGyNvcDlvLx7dDXir0wK6Mv53Ku8fWItAPvTL2CmVPGMX09WJuyhpFK7vr2tnRno2ZYPT//C2mTNF47ojHjaOfnwj6ZdtAKGXo1a8GrLoVipZKnh+0l7L0/6BPkxcfXP7I5NBOBQ8iV2THuWJ0LaEbFH/xfJQS2DaOHRiKFfLyP2mmY54bNXr7HxhacZ0iqIDSfPMbhlEM1cnBgSsbSmtyImLZ0/pk9gUMsgvj9y8p5co9CY3rW6Pd96S3se2pklh/+iPd9V3Z4nVbfnYd10AgYrMzM+HvgQqQUFNLazq/8LErU6tCYXV181/f/pAkDTjlZUVlRxdF0u7YfbYmahO/UzPaEUu0YqvNpYGjzvuR0F2Lmp6P9PZ5QqBU3aW1KUXcHxDXl0n+CIykx6GcT9zehJz5GRkfz73/+mX79+vPfee+Tk5DB58mQsLTU3iL29Pa+++iqRkZH1VtiGQK1S0cXLm21x2kNLtsTGYKtWE+rlpZPHy84ePydntsXFaue5EEMzR0d8HR0BaOnmRk5xsQQL9xFzpYpOLr78mXpaK33H1VPYmFnQwbmZTp7U4hzG7V3AlsvHa9KqqKKiqhK18kYMb2tmyScdn+BwRgJTo7+rr0sQd6CHvw8FpaXsjbsR3GUVFhGddJGeAYY3tezh70N8emZNsAAQl55JXHpmTb5tZ2N5/Ns1WkObyioqAE37Iu4dtUpFF29vtsXe0p5fqKU9t7fHz9mZbRdqb8+ve7NnL64VFrD2tHYbIu69irIqLp0qxq+rlVa6XzcryoqruHymVG++9IQyXH3Vf3luMwsFStWNwMDSXkllOZQVyQ5j4v53W6skmZubA+Dt7U337t1Rq7VvEKVSqZP2d9PE3gELMzMSsrK00hOzswHwddTtegxwdgbQyZOUU52nes5IC1c3ckqKiRj6MMdfeJGTk6fyxaAhuFnrzmkQ94aXlTNqpRlJBela6SkFmi+ETa11Nykrq6zgbO4lCspLUKDAw9KBmS2G4G3tzLqUG0+miyvK+EfkF7xzch3ZZQU65xGm4+/qzMWsHCpumVSXnJmDr4vh4QX+rs4kZmbppCdnZuPrrMmXV1LCsUuaIYfmSiXN3V2ZPfwhMgsK2XImpg6vQvyVJg5/0Z471dKeZ9/SnuvJ06NpU0a0bMm/tm2jSiZomlzO1XIqy8HBS3vwhYOn5rtPzuUyvfnSE0opLaxk3WupLHwshf+Nv8T+pdlUlN+o0zZDbMm5Us7Rn3Ipya/k6vkSTvyaT9NOlljayYOAelFVZbqfB5DRQ5JCQ0P57LPPmDVrFo6OjixZskTr9ZSUFN5//326detW54VsSOwtNENH8ku1n0QUVP9uZ6EbUBnOo2mcbNWa11u6NcLD1o7vT53k26NH8Hd25p9du7HmsdEMW7WconLdrnFRv+zMNT1sBeXa48oLKzR1aWNW+1Ci//PvxYtBAwBYn3KIQxkJNa+VV1XoBCLi/mBnaUF+ie7TxoLSUmz03OPX2VtakKRn4rKhfAvHPkp3Px8qKit5Z/MO0gtkYYN76S/bcz0PyGrylNTentup1cweMJDP9u0joTqYEKZVWqB50q+20n6WqrbS9AqUFup+ESzMrqAou5IsRRlh4x2xdVNx6XgJR37KJT+9ggEzNUObGrexoMMIe/YvzWH/0hwAXP3Ma14X4n5ndMDwxhtvMH78eN577z2dOQrbt29n+vTpBAcH8/rrr9d5IRsSZfUqClXojzAr9USehvJcX5Dhep7XfttGSUU5Z65dAyD68iUuZGTw4+gxjGzRkpUnT9TJNQjjKam9vg2lX7c79SxHMxNp6eDFpMC+uFs6MPXQd3VdTHEXFNy4R69TKhT6a1ZBrU+KFSj0PnxSoD/fV7ujWLjnIP2bBzBr2ACcra1YuLf21XlE3alpmw3UaaWeT4Gx7fl/evfmSn4+3x45XFfFFXfpejUbWAxJb7raSsHD77nh6GWGnZvmK5VXa0uU5goOrsyh02h7nJuYsysii3M7Cug02h7vthbkppYTvTqXje9d45H33TDXMzdC3KUH9Em/qRgdMDRp0oStW7dy+fJlQNOjcH2J1TZt2jB//nx69uz5tx+SlFuiedJ8/SnSdTbV/y95JbornNzIo/1/Z109BOx6nqNXtVdGAjh85TK5JcW0cHO7y5KLO5FXrlmdytZMe7KbtUpTl/nlta9eFZufCsDRrETyyot5p81I2jk25Xh2cj2UVtyJF3t2ZVqvMK20rWdicHHRXULaxlxNXi2rGOWVlGCrpyfBWq3W2zYcTrkEQFTSReytLJgc3oVv9h3SGQol6ke9tOelJfT19WVY82CGr1pZE2BcX7JTpVBQWfVXjxpEfbCw0XxpLy3UnlNQWqSpDbWN7pd6MwslTdrrTnZuFmLJwZU5ZCSUobZWcGZ7AZ0es6fLkw4AeLWBRoFqvp+eyrnfC2gzVCa8i/vbbe3DYG5uzu+//87y5ctJS0urSXd1dWXcuHH07du3zgvY0CTlZFNeWYmPg6NWerPqiW4XMnXXaI+vHh/r4+BY03tw/XdNngzs1BYMCgjg6NUrxN5yDnOlisyiorq7CGG0i4WZlFdW4G3trJXexEbTzRyfn6aTp7GVE6Eufmy5fJzSm1ZQOpN9EQB3KwfIrr8yi9vzw5GT7LyQoJXWv7k/Pfx9ND0DN6U3dXaodR+GhIwsWng00klv6uzIiUuaddzbNHbH29FBZ77CycupPNq2JU7WVjI06R5Jyq5uz2+ZqFzTnmfoac+r56j4ON7SntfkyeClsG5Ympmx7enxOvkvvPRP1p4+zWvbt9XNRQij2XuYoVBCzhXt4b05VzTDyZya6H5lyrpYxqWTJQT1skZtfSOgKC/VtAyW9kryr1VAFXi00A4iXXzUWNopyUzWPzdCiPvJbfWBTZ8+nS+++ILw8HDmzJnDkiVLmDt3Ln379iUiIoIpU6bUVzkbjNKKCg5eushDAQFa6YMDgsgpLub4Vd3NXZJysknKzmZwYJB2nsAg4rMyuZyXR1llBf+vTz9eCOmsdcwAP3+szM2Junix7i9G/KXSynKOZiXS172VVno/j9bklhVxOlu3XrysnHi7zUj6urfUSg9zCwQgJlf3MyJMJy2/gFNXUrV+9sQnYWthQbh/s5rjnKytCPXxZk98ksFz7YlPwt/VGX/XGwHm9d/3VufrE+jHnBGD8bTXfuIY7u9DWl4BGRIs3DOlFRUcvKinPQ+8w/Y8U9Oef3FgP8NXrdT6WV09pHT4qpV8cWB//V2UMMhMraBxKwviDxRpDUOL31eEhY0C90Dd3sGCzAp2f51F3D7t+zJ2TyHmVgrcAtQ4eGoCkStntHuksi6WUZxXib37be+hK4whk57rlNGf0vXr1xMVFcWPP/5IcHCw1muDBw9m7NixjB8/nnXr1jFq1Kg6L2hD8tXBKJaPfIz5Q4bx4+lTdPRszMROIXy0J5KSinJs1WoCnF1Izsmu6RmYf/AAnwwcRHZxEb/Hx9Pfz49hQc2ZunkjAMXl5Sw8fIjpXbqSXljI7qQEgl3dmNEljD8S4tmbIkNYTGVJ7E4WdP4/Pmo/hl8uHqGdU1Oe9u3Bl+e3UVJZjo2ZBb62jbhYmEF2aSGHMxOIzojjtZYPY2duRWLBNUKc/RjvF8665IMkFlz76zcVJnUo+RJRiSl8MmIwn/weSXZREdN6hZFXXMKawzfmEvm7OqM2U3H2qqZON5+O4YXunVk8dgSf/rEHgJl9exCTls7W6h6F1YdPMLpjGxaOfZQFuw9QWFrGo+1a0jvQj5k/b5ahKvfYVwejWD7qMeYPvak9Dwnho8ha2vOoA3zyUHV7HhdPf38/hjVvztRNmvb8Um4ul3Jztd6nr68fACdTU+/tBQotnUbbs+Hta2z/OIPg/jZcPVfK0Z/zCHvaATMLJaWFlWSmlOHgYYaVg4rGrSxo3NqCvd9mU1ZchZO3OUmHijixMZ9uzzhgaat5Ltv2ETuO/axZEr1JO0vyrlUQvSYHWzcVLQbamvKShTCKosrItdzGjh3L0KFDGTdunMFjVq5cyaZNm1i1atVdFcrvi7l3lf9+MNA/gJe6huHr6ERqQT7Ljx9nyVHN5LYuXt6sfmw0r27fyrqzZ2ryjG3dhomdQvC0tSM5J4eIQwdZf+5szesKYFzbdjzRph0+jo5kFRWx4fw5Pj+wX+/mQQ2FU1DGXx90n+vj3pLnA/rhY+tKWnEuPyYdYEXiXgA6OfuyqMtzvHtiLb9eOgpoVk+a6N+Hvh6tcLO053JhFutSDrI6cb/eidKeVo5s7P2q1jkaqvxDukvNNkT2lha8PqAX/Zv7o1QoOJJymQ9/20VCxo3lNJc99Rhejvb0m/dtTZqHvS3/fqg33X19KKusZG98Eh9u38W1/BtL5zZ1cmBm3x6ENPXC1sKCM1fTiNgTVbNJXENSZtvwQ5yB/gG8FBaGr1N1e37sOEuqJyt38fZm9T9G8+q2raw7c1N73qa6Pberbs+jD7L+7FlDb8GMrmHMCAvD77OG/fdvxuDNpi7CXYvfX8jB1blkXyrD1kVF6yG2tH9Us8vzpZPF/PLWNfpOdya4n2ZJ85KCSqJX55AQVURhVgX2Hma0e8SOljcFAlVVVZz4NZ/TW/PJTS3HxllFk/aWdBnngJVDw11WdUbw76YugkGD/V4x2XtviZ9jsveuL0YHDKGhofzwww/4+hrelCg5OZlRo0YRHX13q3g8CAGDMN6DEDAI4z0oAYMwzoMQMAjjPQgBgzCeBAz6PYgBg9FDksrLy1EZscuowtB6ZEIIIYQQQtwLVbKDdl0yetJzQEAA+/btq/WYyMhI/P3977pQQgghhBBCiPuD0QHDiBEjmDdvHikpKXpfj42NZf78+YwePbrOCieEEEIIIYQwLaOHJI0ZM4adO3cycuRIRo4cSYcOHXB0dCQ/P5+oqCjWrl1LeHg4I0aMqM/yCiGEEEIIUbsHdHnTkpISZs+ezdatWykuLiY8PJx33nkHFxcXg3ny8/P55JNP2LZtG2VlZYSGhvLvf/+bJk2aGP2+RgcMSqWSiIgIIiIiWLlyJUuXLq15zdXVlSlTpjBhwgSj31gIIYQQQghhvHfffZfDhw8zb9481Go177zzDjNmzGDFihUG80ybNo1Lly4xb9487OzseO+995g8eTIbNmxAqTRusNFt7RaiUqmYOnUqU6dOJSEhgezsbBwdHfHx8TH6DYUQQgghhKhXlQ9eD0Nqairr169n4cKFhISEADB37lwGDRrEsWPHaN++vU6eqKgo9u/fzy+//ELz5s0BeP/995k4cSKJiYn4+fkZ9d53vL1gbcurCiGEEEIIIerO4cPV+7906VKT5uvri7u7O9HR0XoDhsjISIKCgmqCBdAsZPTnn3/e1nvfl/uRVzqVmroI4l76Sdbl/zup8jF1CcS9VOkq7fnfyYYrbU1dBHEPzQg2dQnuT/369av19R07dtzReVNTU3FycsLCwkIrvVGjRly5ckVvnsTERHx8fFi1ahUrV64kNzeXTp068cYbb+Du7m70e9+XAYMQQgghhBB3rAFOer548WKtwcaMGTNQq9U66RYWFpSUlOjNk5+fz+nTp8nKyuK9994DYM6cOTz99NNs2LBBJ/gwRAIGIYQQQggh6sid9iC4u7uzebPh3dJ37dpFaalur21JSQlWVlZ685ibm1NSUsJXX32Fg4MDAPPnzyc8PJw//viDwYMHG1U2CRiEEEIIIcSDpQH2MJibm9e6AfL58+fJzs6mtLRUq6chLS0NDw8PvXk8PDxwd3evCRZAs7qpo6MjFy9eNLpsdbK00aRJk0hLS6uLUwkhhBBCCCFu0alTJyorK2smPwPEx8eTmppas2rSrUJCQrh8+bLW9/S0tDSysrLw8TF+UqHRPQzR0dEGX4uKiiI6OppGjRoBEBoaanQBHlS9Gvsys31PAh1dySguZFXMURacOmBU3tbO7vw85Gn6/LyIiwU5AHjbOLBn1GSDeX6MPcGr+wx3Y4n6Z21hzox/9KR3e39sLNUcj7vMnDU7SbiSWWu+oCZuvDiiO62auaNQKDiXlMa8n/ZwLvnGze3pYs+Mx8IJae6NuUpF9PkUvlwXSXJqdj1flTDERm3Oa/160r+5PzZqNUcvXmbW9p3Epdde3262NrwxoCfdfX0wVynZE5/Mf7f/SWpeQc0xztZWvNynO939fHCwtCQxM4tv9h9i85mY+r4soUdNe+5Q3Z5fuM32fPDT9Fl/oz2/bmCTQKa16Y6fvTPXigr4OeEUC07tp6yysj4uQxgp1DmY//MdjI+NOzll+fx6aT+rkw0PMbFQmjPe9yF6N2qPg7kt8fmXWZa4nejMcwC4WzqxKuw/BvNvvXKQT86tqfPr+NtrgD0Mf8Xd3Z2hQ4fy1ltv8cEHH2BlZcU777xD586da1ZIKi0tJScnBwcHB9RqNYMHD2bRokXMmDGDN998E5VKxQcffICvry+9e/c2+r0VVVXG/Y+2bNmS64fWlkWhUHD27FmjC6BPs2Wz7yq/qXV082LNwCfYmHiWXxJOE9LImxfbdOPTY7v56uT+WvO2cGrEsv6jcbOypce6iJo/MGqlilbOurPZn2rekWHNWvDEb6s5lGZ819L9xCXSuAk397vPpw2nVTMPvlwXSX5RKZMe7oqzvTX/eHspuYX6JyN5uzmw8j9Pci45jZW/HaGqqopxAzrR2teDJ95fSVJqFtYW5qz8z5NUVFYSsX4/peUVPDesCx5OdvzjHcPnvl/lPSCrJC18fDhtG3vw8Y5ICkpKmdqzKy421gz5eik5xfrrRKVQ8NOEJ7BWq/n0zz2YKZW80rcHBaVlDF+8gvLKSsyVSn58diz2lhbM232A1Lx8BjQP4MmQdrz6y1Z+OXl37eu9VurTsD6ft+ro5sWaAU+wMemm9rx1dXt+yoj2vF91e/5ThFbA0MOzGcv6Pc7GxLP8EHeC5o5uvNahF2suHOed6N/q+7Lqjb93wx5t0NK+GXM7TGFn2jF2pB6htYMvT/j0438JW1mV9LvePG+1fIpQl2C+idvEpaJrDPQIpW+jDsw8toCTOQmYK1QE2Hnr5Bvu1Z3ejdrzyrEITuUk1Pel1YsdfeaauggGDfaaZrL33nJpXr2du7CwkA8++IBt27YB0LNnT9566y2cnJwAzUP8p59+mmXLltUsv3rt2jVmz57Nzp07qaqqonv37rz11lv1s0rSsmXLeO211/Dx8eHNN9/E2toa0AQPDz/8MIsWLaJx48ZGv/GD7KV23TmTlcrLezcCsOtyAuZKFZNbd+WbM9GUVJTr5DFXKhkfHMLM9uEU63m9tLKCo+mXtdLauHgwrFkLPjm6q8EGCw+KNn6ehLf1Y/oXP7P3VCIARy9c4tcPn+UffdqxZNNBvfnG9utASVk5M75cT3Gppt6jz6WwcfYEHu/bno9X/8mA0CCaujvx2NtLa3orYi+ls3H2BPqHBPHT7pP35BrFDe29POkT6MfE1T+zKy4RgOiUS/wx9VmeCGlHxB799T2oZRAtPBox5OtlxKZnAHA29Rqbnn+aIS2D2HDqHH0C/Wjp0YhRS1Zx8koqAPsSkvFysGNiWEiDCxgaupfa6mnPFdXt+dla2vPmhttzgH/4t+FyQS4v7f2Vyqoq9lxJxNXSmmdbhPL+oR2UV0kvgyk87TuQuPzLzD67CoDozHOYKVSMbdqXtSm7KK0s0zre09KFPu4d+Pz8Wn69vA+Ao1mxtHLw5RGv7pzMSaCsqoKzuUla+YLsvOndqD1L4jc32GBBmIa1tTWzZs1i1qxZel/v0qUL58+f10pzc3Pj008/vav3NXoOQ0hICBs2bMDZ2ZnJkyeTmpqKl5cX3t6aqNnDwwMvLy+8vLzuqkANnVqpoot7U7Ylaw8d2Jx0DltzCzo30n3KANDHy58Zbbsz/+Q+Zh82bjON97sMJC4ngyVnDQ8XE/dGWCsfCotLOXDmxh+F7PwijsRcpHtrw5scJlzJZPm2wzXBAkBxaTmpWfl4u2kmKO04HMszH67RGtpUVl4BgNpMVdeXIowQ7u9DQWkpe+Jv1HdWYRHRSRfp5W+4vsP9fIhPz6wJFgDi0jOJS8+kV4AmX35pKWuOnKgJFq5LyMymqZNj3V6IqJXB9jz5NtrzI/rbc7XSjMLyUipv6rHPLCnCQmWGjbnusomi/pkrVLRzDCDy2gmt9N3XjmNtZkkbB917O70km8mH5rIj9caY8iqqqKyqwFxp+JnsjKDHSC5MY13Krrq7AKGtstJ0Pw+g25r0bGtry6effsq0adOYPHkyn332GeXl+p+e/F01sXPEQmVGfK72OObEvCwAfO2d9eY7nn6FHj9F8NXJ/VQYMUrsEd+WtHdtzHvRv2v9wRGm4evpzKX0HCpu2Yo+JS0HH3cng/nW7jrB8u2HtdKaujsS0NiFuMuaL5X5RSWcjNdsyGKmUhLo7cp7zz5EVl4hvx2SMe2m4O/iTEpWjs69mpSVg6+L4fr2d3UmITNLJz0pM5tmzpp8+xKSeXuz9nhpM6WSPoG+XLiWXgelF8ZqYnsX7fnPEXx1ar9Om3DdsvOHaWbnzKSWnbE3t6CDa2OeDQ7hj4ux5JQW1+2FCKN4WrmgVppxsfCaVvqlIs19523dSCdPWVUFMXkXKawoQYGCRhaOTAl4FE8rV369tE/v+/Rt1JFg+6Z8deFnKpG/36JhuKNlVYcPH06nTp3417/+xejRo6moqKjrcjVYDuaa8fj5ZdrjdgvKNOvm2prrH6+fWpR/W+8zqWVnotNSOJCafAelFHXNztqCgiLdtZELSkqxsTL+aaGl2oz3/u8hisvKWbPjqM7rX0x/lK4tfaiorOTDFTvIyC28q3KLO2NnaUGBnrWwC0pLsbEwXN92lhYkZmbrzWdbS77X+/ekmbMTU37YcEflFXfGQV1/7fn+1GQWnjnAm5368manvgCcyrjKjD2/3k2RxV2wNdOsY19YoR2wFVZo6t/GrPb5dmN9+jHBbwgAmy8f4Hh2nN7jRjftzcnseIOvizoiD1Pr1B3vw1BVVcUrr7zC/v37yc3NxcxMtnQAzaRvMPw5raqDpwmd3Lxo7eLBxD/X3fW5xO1TKEBZXc830hR6a1YBRvcA2ViqmfviI7T0ceeViF9JzdL90rF4YxT/23yQ3u0DeOvpATjaWfG/zTIkrT4p0K1vpUKh9x5XUPuiELeepyafwnC+f/UL5+nOHVi49yC/x8gXjHuppj038LqRa4bo9UGXh3jMvy1fntjLvqtJNLF14KV2PVjabzRP/Lba4NwHUX/+qr7/qi3fn36ak9nxNLdvwtPNHsLN0pHXjy/SOqaVQzMC7bz5z8kldVFkIe6Z2/qWX1VVxZIlS1i+fLnWeq6urq5s2LCBiRMnolTWydYODVZuqeZJhN0tT56uj0nNK737FUOG+ASTXVLEnxfly4MpTBzWlecfCdNK+/1QDM7u1jrHWluoyS/66zp3d7Lli+mP0tTdidcXbSLyhP5JcMcuXALg0PmL2NlY8NyQLizbesjgsAdx96b27Mq0ntr1veVMDL4ueupbrSbPwApJAHnFJdiodXsSrM3V5JVo51OrVHz0yEMMbdWcRfui+fTPvXd4BeJO/WV7XnZn7bm7lS1jAtvz1al9zD0eCcCBVDiecYXtDz/H6IC2LDt/5C5KLu5EflkRANYqS610a5Wm/gvKax8qllCgGTp6Miee/LIiXm0xhlYOzTidk1hzTE+3duSWFRKVIYsXiIbltgKG6dOns3PnToYPH05YWBhOTk7k5OQQFRVFREQER48e5euvv66vsjYIyXlZlFdW4mPvqJXezE4zPvlCzt2PQe7r7c/2lAuyioaJ/LT7pM4X+t4d/Onayqf6SfGN9CaNHEi4XPu6/AFersx/aQQW5mZM+/xnDsdor3jVqpk7jV0ddOYrnElMZVhYSxxtrWRoUj36/shJ/rygXd/9m/sT7u+j6VG4Kd3HyYHYWvZhiM/IoqWH7jhoH2dHTly+WvO7rYWaxWNG0MHbkw9/28X/ouTLoynUtOd2jlrpNe159p2151429igVCg6nXdJKj8lOJ7O4kCAH1zs6r7g7l4szqKiswMtK+///+u9Jhak6eTwsnengFMjvqYcpq7zRK3Q+LwWARhaOnL7p+K4uLdmbfpIK+ftd/2RIUp0yujtg/fr1REVF8eOPPzJr1iyGDh1Kt27dGDx4MO+++y5r1qzh6NGjrFv39x4mU1JZwcHUFAY1ba6VPsQnmJySYo6lX7mr8zuoLfG1d5ZlVE0oPaeAs0mpWj8HTidha2VBWKtmNcc52lrRMchba+WkW7k72bLg5ZFUVcGzH32vEywAhLf1Y9Zzg/FwttNK79bKh/TsAjLzJFioT2n5BZy6kqr1syc+CVsLC8L9m9Uc52RtRaiPN3vjDdf33vgk/F2d8Xe9MVn2+u/XV1xSKRQsfHw4bRq789JPmyRYMKGSygoOpulpz5tWt+cZd9aeJ1YHIqG3rLLkZ++Ms6U1Kfk5BnKK+lRWWc6JnHjC3dpopfd0a0deWSHncnXnDHpYOvNK8OOEu7bVSg91DgYgLv/Gcuh2ZtZ4W7tp9TgI0VAY3cPw/fffM336dIKDg/W+HhwczPTp01m3bh2jRo2qswI2RPNO7mPlgDF81fNRfow9QcdGXkxq1YXZR/6kpKIcW3M1gQ6uJOVlkVlSdFvnDnZyAyD2Dp9sifpx9MIlDp1LYdZzg/lybSQ5BUVMejiMvKIS1u66sUSfr6czajMV51M0q3C8OrYPLvY2/Hf579hYqWnt51FzbEFRKQlXMlm76wQjerbhi2mPsnjjAQpLyhgW1pIebf349+LN8hDFBA4lX+JAYgpzHh3MJzsiyS4qYlrPMPKKS1h95EZ9+7s6o1apOJuqqe9NZ2J4oXtnvhk7gjl/7AHglb49iElLZ2v1Ls5PhrQntKk3qw+f4EpuHu28PLTe+/ilq4h7Z97Jfazsf1N77nb37XlmSRHfno1mUivNpkp7riTiZePAjHbduZifw+rYY/V4RaI2KxJ/45P2L/B2q6fZeuUgLR2aMbppbxbHbaK0sgxrlQU+Nh5cLkonp6yAE9lxHM26wLSgkdiaWZJSdI32jgE83rQPGy/tJ7nwxvBtX1tPAJIK5B6+J2Sobp0yOmCIjY2le/futR4THh7O559/frdlavD2X01i8s6feal9Dxb2GUlqYT4fHP6Tb85oNnNq7ezBmoee4JW9m1gbd3ubbrla2gDIsnv3oVcifuXl0b2Y8Vg4SoWCY3GXeX3hJvJu2on59Sf70tjFnoff+BYzlZLwNpp1vf/9VH+d8x06n8Lzc9aSnlPAhI++Z9rIHrz2RB9sLC04l5ymtUmcuPemrv2VNwb04rV+mvo+cvEyM9ZtIvemOQzvDu6Ll4M9fed/C0BZRQXPrFrHWwN78/6Q/pRXVrI3PokPfttVs0TrQy0CABjbqS1jO7XVed+gWZ/dg6sT1+2/msTkXT/zUrseLOx9U3t+9qb2fGB1ex5vfHv+wZE/uVqYx5NBHXiuZWeuFRWw+0oCc47uqpk7Ie69Y9mxvHtqKc/4PsR7bZ4lvSSHRXG/8mP1fgmBdt7M7fAiH59dzbar0VRSxdsnv+WpZgMZ3bQPLhYOXC3KYHHcRn66GKl1bidzWwDyym/vQaEQ9wNFlZHLPHTo0IFffvmFpk2bGjwmOTmZxx57jIMH9e9yaqxmy2bfVX7RsLhE1r5UnXiw5PmYugTiXir1kS+/fyf+3ml/fZB4YOzoM9fURTBosNsLJnvvLdcevPm8RvcwBAQEsG/fvloDhsjISPz9/eukYEIIIYQQQtyJKplYXqeMnvQ8YsQI5s2bR0pKit7XY2NjmT9/PqNHj66zwgkhhBBCCCFMy+gehjFjxrBz505GjhzJyJEj6dChA46OjuTn5xMVFcXatWsJDw9nxIgR9VleIYQQQgghaieTnuuU0QGDUqkkIiKCiIgIVq5cydKlS2tec3V1ZcqUKUyYMKFeCimEEEIIIYQwjdvauE2lUjF16lSmTp1KQkIC2dnZODo64uPj87ff4VkIIYQQQtwnZM3xOnVbAcPNfH1967IcQgghhBBCiPvQHQcM9arg/iyWqB/ZzeUpwN+JqkRh6iKIe8j8kiyb/Hei/kDq+2/lrKkLIO4V+WYuhBBCCCEeLJWyrGpdkokHQgghhBBCCIOkh0EIIYQQQjxYZNJznZIeBiGEEEIIIYRBRvcw7N+/n9DQUMzMbmSJi4tjxYoVXL16laCgIMaPH4+zs3O9FLSh6dWkGTM79yDQyYWM4iJWnT7GgqMHjcrb2tWdn0c+QZ/VS7iYl6v1WrtGHrwZ1ovWbu4UlpXxc8wZ5kTtobSyoj4uQxipZ7NmvNyjGwEuLmQWFbHq2HG+Phhda57hLVrwQpdQmjo4cjkvl8XRh/jh5CmtYx5v04ZnOnWoOWblseN8d+RofV6KMJKN2pzX+vWkf5A/Nmo1Ry9eZtZvO4lLz6w1n5utDW/070l3Xx/MVUr2xCfz39/+JDWvQO/xT4W05/+6dKTvV9/Wx2UII4T7+vDPnt0JcHUms7CI1UdPsPBA7ff3I62CeaFrZ5o4OnA5N5dvog7z4wnt+7utpzv/6tOTVh6NKCwt45fTZ/ls9z5KK6Q9NzUrazUTXh1CWL+WWNtYcOZYEgs/2EhyXFqt+V7/dAy9hrTTSZ89czW7Np8AYMjjnZn2ru4mt5vWRDH/vfV1Un4h6prRAcOzzz7Lnj17cHFxAeDEiRM89dRTeHt7ExAQwNatW1mzZg0rV64kICCg3grcEHR0b8ziwSPYGHuOTw/uIcTTi1e6hKNQKPjqSFSteVu4uPG/oSMxV6l0Xmtq78DyYf/gSOplXtz+KwFOLrzauQd2agve2LW9vi5H/IWOjT1ZOGI4m86dZ+6efYR4NWZmeA+UCgULovQHiYODApkzZBDfHT7C7sRdDAgI4MOHBlJcXs6Gs+cAeKJdW94f0J+vow6yNymJdp6evNG7F1bm5kQYOK+4d+Y+OoS2jT34+I9ICkpKmRrelWVPPsaQhUvJKS7Rm0elUPDNmEexNlfzzpYdmKmUvNKnB9+OHcXwb1ZQfsskvYdbBfN6/56k5uXfi0sSenTw8uTrx4az+WwMn+3eS4i3Fy/36o5SoSBiv/77cFDzQD4ZNoilh46yOz6RAYH+fDBkACXl5Ww4o7m/mzo68N3jozhy6TIz1m/C39WZl3t2x87Cgre2/n4vL1Ho8a9Px9K8jTdL5myhsKCEJ6f048P/PcfzD39Gfk6RwXx+wZ7s2HCUjav2a6VfSsq46ZjGJMWm8vlb67SOyUqX+7wuVcmk5zpldMBQdctYsM8//5z+/fszZ84cFAoFFRUVvPbaa8yePZtvvvmmzgvakLwUEsaZ9DRe/mMLALtSEjFXqpjcoQvfHD9MSUW5Th5zpZLxbToyM7Q7xXpeB3ihfWcKykqZuOVnyior2ZmcQHF5Ge/16Mf8wwe4lJ+rN5+oX9PCwjibdo1XtmwFYHdiImYqFc936cySw0coKdetz5d7dGdLTAz/3bkLgMjEJBwtLXmpW7eagOGFLp3ZdO48n0TuAWBfcgq+Tk483aG9BAwm1t7Lkz6Bfkxc8zO74hIBiE65xB8vPssTndoRsdfAF8kWQbRwb8SQhcuITdd8gTibeo1Nk55mSMsgNpzS1L2ztRX/7N2dxzu0IavQ8JcTUf+mde/K2dRrvLpRc39HJiRhplIyqWso30YfpqRctzfgnz27sfXcBT7Yobm/9yQk4WBlyfTwsJqAYWKXEApKS5m8bgNllZXsik+kuKyctwf0YcG+KC7n5t27ixRagts3pUvvYN5+/juid58H4PShRP7326sMG9uVNV//qTefhaU5jX1c+WHRLs4dTzF4fv8Wnpw/kVLrMULcb+54DkNMTAzjx49HodCsqa5SqZg0aRKHDx+us8I1RGqlii5eTdiWcEErfXNcDLZqNZ09vfTm69PUjxkhYcw/coDZB3brPaZnk2b8kRRH2U1R8+a4GFRKJT2bNKuzaxDGU6tUdGnizbYL2vW9JUZT36FeuvXtZW+Pn7Mz22NidfL4ODnSzMkRgP9b+xOzd2t/FsoqKlCrZK0CUwv386GgtJQ98Uk1aVmFRUQnX6RXgOFNLcP9fIhPz6wJFgDi0jOJS8+kl/+NfC9070x336ZMXfsrf16Ir5+LEH9JrVLRpam3zr269dwFbC3UhHrrub8d7PFzcWZ7zAWdPDff3z38fPgzLkGrPd967gIqpZJw32Z1fi3CeJ26B1JUUMLhvTfqMCergJOHEgjt2dxgPt/mHqhUSuLOXTZ4jEKhoFmgB/HnrtRpmYUeVVWm+3kAGR0wKBSKmuAAwN3dXeeYiooKLC0t66ZkDVQTewcsVGbEZ2dppSfmaH73ddQ/x+P4tav0WLGYr45EUaGnG81CZYa3vYPOeTOLi8gtKcHX0amOrkDcjiYODliYmZGQpV0vSVnZAPg669ZLgIvmM6CTJ7s6j5MmT1xmZs1TRgdLS0a3ac2IVi1ZcexYHV6BuBP+rs6kZOVQccsfhqTMHL11fnO+hMwsnfSkrGyaudzIt+bICQZGfMf287E6x4p7p4mjA2ozMxIz9d/fzfTUtf/1+zszWys9+fr97eyEhZkKbwcHnc9CZlERecUlNHN2rJPyizvTxL8RVy9mUlmh/bf4clIGXs1cDebzC/YEYOiYLqzc/SYbjr/PJ8sn0bxtk5pjvJq5YmmtJrhdUxZvmcmvJ2axePPL9BveoX4uRog6cltDksaOHUtgYCD+/v54enqyYMECIiIiUCgUpKamMnv2bDp37lyf5b3vOVhodrnML9Uew1xQVgqArVqtN19qQe1jF+2vn7f6PLee29B5Rf2qqZcS7XopKDVc3zV5Sm/NU1adR3un1E6NG/PDE2MAOHn1KsuOyqRnU7OzsKip45sVlJZiY2H4XrSztCDxli+SAAUlpdi63MgXn6EbVIh7z+6v7m89dX2jTdD+G3D9HLYWauwtLPWe9/q5bS1kt2RTsrWzpDBfdx5SUWEJ1jaG68YvuDEAagtzZs9cjb2jNaMn9mb2d8/xzzERJMZcxb+FJqho1NiRxR9torysgv7DO/LK7NGYq83Y+mPtk+nFbah8MJ/0m4rRAcPy5cs5f/48MTExHDhwgAsXLlBUVERBQQG2trYMGjQIV1dXPvzww/os733vei+MoY/prXNBjKW8fl49+RV3cV5xd5Q1vW76//8r9dWXgbqsOdMt6Sk5OYxd8wPutra81D2M9eOe5NEVq8goLLyrsgvjKLi5njWUCoXeXmeFovZ78dbzGJtPmEZNu2vw/jac51bX7/vKqiqU1YfoPa9CIZ+Fe0ihUKBQateZQqm/DhTUXjc/L93Dnm0nOXYgribt2IE4lmydyZgX+jD75dUcj4rn7ee/43hUHKUlmvltR/ZewMHFhqem9ZeAQdy3jA4YQkNDCQ0NBTR/2C5evEhmZiZZWVnY2Njw+eefExISgo2NTb0VtiHIrX6qZHfLk2Ubc83veaX6V08x/ry6TzeszdXk6XnaKerf9Xq5tVfAprr+80p067smzy1PJ63V5po8t3xG0goKSCvQLLl5/MoVdjz3LI+3aW1wBSZRt6aGd2VazzCttC1nY/C1sdY51tpcTZ6BFZIA8opL9PZAWKvVej8rwrQM3avX7+9bexFqz1N9f5eUGjwGwMbcXD4L99ATU/oybmp/rbTIrSdx1DP0yNJaTUFescFzXUpM51JiulZaQV4xp48k4dfcA4DsjPyaidQ3i951no7dAnFytZXVksR96bZmT1ZVVbFkyRKWL19OWtqNtYhdXV0ZN24c4eHhdV7AhiY5N5vyykp8HLTHtjar/v1CZoa+bH+pqLyMK/l5+Dg4aqU7W1phb2HBhaw7O6+4O0nZ1fVdPZHxuuu/x2borsl/fdyyj6MjZ9Ku3cjjqMlzISMDG3Nz+gX4c/zK1Zq5DQDJOTnkFBfjaWdXp9chDPv+6En+jE3QSusf5E+4n4+md++mdB9nB2Jr2YchPiOLlh6NdNJ9nBw5cflqHZVY1JXkrOr7u/revK7m/k7XbXfjq+/5po6OnEm9cX83dbyRp6isnKu5eTrndbayws7SotbPkKhbW344yMGd57TSwvq3pFOPQBS39PY09nGpdR+GXkPakptdyNF92nOPLCzNyc3S9Ai3CfWlUWNHdvyiPbRUbWFGRXlFrUu2ittUJcuq1qXbWiVp+vTpfPHFF4SHhzNnzhyWLFnC3Llz6du3LxEREUyZMqW+ytlglFRUcPDKRQb5BmqlD/EPIqekmGNpd/6lIDIlkb4+/qiVN/ZoGOIfRHllJfsuJt/xecWdK62oIPriRQYGau89MjgoiJziYo5f1a3vpOxskrKzGRwUpJMnvnqic0VVFbMfGsik0BCtY9p4uONkZcXZa9cQ90ZafgGnrqRq/eyJT8LWwoJw/2Y1xzlZWxHa1Ju9CUkGz7U3IQl/V2f8XW8sfnD995tXXBL3h9KKCqJTLjKwufb9PSg4UHN/X9G9v5Ozc0jOymZQcKBOnviMGwsZ7ElMok+AH+qb9twZFBxIeWUl+5OkPb9XMq/lceH0Ja2fI3svYG1rSaceN+rQwcmGNiG+HNl7weC5ho7tytS3H8XM/EadujSyp2UHH05Ea1Y7a9/Vn5c/eAzPpi41xygUCno81IZzx1MoK5NN+8T9yegehvXr1xMVFcWPP/5IcHCw1muDBw9m7NixjB8/nnXr1jFq1Kg6L2hDMu/wflY+PJqvBj7Mj+dO0dG9MZPahzL7wG5KKsqxNVcT6OxCUk42mcXGP01YeCyaRwKD+W7YKL45fghfByde6xLOqjPHuVIga3abylf7o1g2+jHmPTyMtadO0bFxYyaGhvDx7khKysuxVasJcHEhOTubzKKi6jwH+HjwILKKi9gRG0+/AD+GBjdn2q8bASguL2fhwWimhnUlq7iYfUlJNHNyYka3MM6kpbH21GlTXvLf3qGUSxxITGHO8MF88kck2UVFTAsPI6+4hNVHTtQc5+/qjFql4mz1k+ZNZ2J4oVtnvhkzgjl/aPbXeKVvD2LS0tl6NsYk1yJqt2DfQZaOGcWXjw5l7YnTdPTy5LkuIXzyZyQl5RWa+9vVmeSsnBv3974oPhr6ENlFxey4EEe/QH+GtmjO9PWbas67+MAhhrUI5pvRj/K/g0do5uzEzF7dWXP0BFdloz6TOnUokeNRcbz28eMs+XQLedmFPPlifwryitn8/Y3NV5v6N8JcrSLurGaJ1FULdjBr8bO89eU4fl25HzsHK56c2p/83CLWfRsJwMY1UQwe3Zl3FzzNivm/U1JcxrCxXWkW6M6/xi82yfU+qKpk0nOdUlQZObtq7NixDB06lHHjxhk8ZuXKlWzatIlVq1bdVaGaRcy5q/z3g4d8A3gptDt+jk6kFuSz7NQxvjl+CICujZuwZvjjvPLHFtae1/3i91jzVszpO5geKxZxMU97M7ZQTy/eDOtFS5dGZBYX8XPMGT49uEdneceGRFVwx9uB3DcGBgQwo3sYvk5OpObns+LYcZYc0uxJ0qWJN6seH81rW7ay7vSZmjxj27bhudAQPO3sSM7J4euog6w/c7bmdQUwtl1bxrVvj4+jI9nFxWy/cIFP9+zVWWGpIVGV6J8U2tDYW1rwRv9e9A/yR6lQcOTiZT74bZfWUpnLxz2Gl4M9fb/6tibNw86Wtwb2ppuvD+WVleyNT+KD33dxLb9A7/vMHjaQzj7eWudoSKp0N61vcAYE+TO9Rxh+zk6k5hew4sgxvj14BIDOTb1Z+cQ/+Nembfx08sb9PaZ9GyZ07oSnvR0p2Tl8vT+aX06f1TpviLcX/+oTTgt3N7IKi1h/+iyf797XoNvzgKWGh+w0JLb2lkz811DC+rVCqVRw5mgSCz/cqDVH4aOlE3H3cuKZ/h/XpHXoFsATk/vi29yTysoqjuyNYcmcLVy7klNzTGMfF/7vnw/RqlMzrGwsuHDqIku/+I3ThxPv5SXWiS1n79+FbgaqnzDZe28vvbvvwfcjowOG0NBQfvjhB3x9DW9KlJyczKhRo4iOvrtZ/g9CwCCM9yAEDMJ4D0rAIIzzIAQMwngPSsAgjCMBg34PYsBg9JCk8vJyVKq/bvkVBpaUE0IIIYQQ4p6QSc91yuhHuwEBAezbt6/WYyIjI/H397/rQgkhhBBCCCHuD0YHDCNGjGDevHmkpKTofT02Npb58+czevToOiucEEIIIYQQt6uqsspkPw8io4ckjRkzhp07dzJy5EhGjhxJhw4dcHR0JD8/n6ioKNauXUt4eDgjRoyoz/IKIYQQQggh7iGjAwalUklERAQRERGsXLmSpUuX1rzm6urKlClTmDBhQr0UUgghhBBCCKPJHIY6dVs7PatUKqZOncrUqVNJSEggOzsbR0dHfHx8UCplpRshhBBCCCEeNLcVMNystuVVhRBCCCGEEA8Go/dhEEIIIYQQQvz9yDgiIYQQQgghhEESMAghhBBCCCEMkoBBCCGEEEIIYZAEDEIIIYQQQgiDJGAQQgghhBBCGCQBgxBCCCGEEMIgCRiEEEIIIYQQBknAIIQQQgghhDBIAgYhhBBCCCGEQRIwCCGEEEIIIQySgEEIIYQQQghhkAQMQgghhBBCCIMkYLgPVFZW8uWXXxIeHk67du149tlnSUpKMnWxxD2wYMECnnrqKVMXQ9ST7Oxs3n77bXr27EnHjh0ZO3Yshw4dMnWxRD3KyMjg1VdfpWvXrnTo0IFJkyYRGxtr6mKJepaQkECHDh346aefTF0UIeqFBAz3gQULFrBmzRpmzZrF999/j0KhYOLEiZSWlpq6aKIefffdd3z55ZemLoaoRy+//DLHjx9n7ty5rF27llatWjFhwgTi4uJMXTRRTyZPnkxKSgqLFy9m7dq1WFpa8swzz1BUVGTqool6UlZWxiuvvEJhYaGpiyJEvZGAwcRKS0v59ttvmTZtGr169SI4OJjPPvuM1NRUfvvtN1MXT9SD1NRUnnvuOb744gt8fX1NXRxRT5KSkti7dy/vvPMOISEh+Pn58e9//xt3d3c2btxo6uKJepCVlYW3tzfvv/8+bdq0wd/fnylTpnDt2jUuXLhg6uKJejJv3jxsbGxMXQwh6pUEDCZ27tw5CgoK6Nq1a02avb09LVu2JDo62oQlE/Xl9OnTODg4sGHDBtq1a2fq4oh64uTkxKJFi2jdunVNmkKhoKqqipycHBOWTNQXJycn5s6dS2BgIADp6eksWbIEDw8PAgICTFw6UR+io6P5/vvv+eijj0xdFCHqlZmpC/B3d/XqVQA8PT210hs1asSVK1dMUSRRz/r27Uvfvn1NXQxRz+zt7enVq5dW2pYtW0hOTqZHjx4mKpW4V/7zn//www8/oFariYiIwNra2tRFEnUsNzeX1157jbfeekvnb7gQDxrpYTCx6+Na1Wq1VrqFhQUlJSWmKJIQoh4cPnyYN998k379+knA+Dcwfvx41q1bxyOPPMKLL77I6dOnTV0kUcfeffdd2rdvz8MPP2zqoghR7yRgMDFLS0sAnQnOJSUlWFlZmaJIQog69vvvvzNhwgTatm3L3LlzTV0ccQ8EBATQunVr3n//fby9vVmxYoWpiyTq0Pr16zl06BDvvvuuqYsixD0hAYOJXe/GTEtL00pPS0vDw8PDFEUSQtShFStWMG3aNHr27MnixYtrHhKIB09GRgYbN26koqKiJk2pVOLv76/TxouGbd26dWRkZNC7d286dOhAhw4dAHjnnXcYOnSoiUsnRN2TOQwmFhwcjK2tLVFRUTRt2hTQjIs8c+YM48aNM3HphBB3Y9WqVbz//vs89dRTvPnmmyiV8ozmQZaWlsbMmTNxcXEhLCwM0Cy5eebMGRmG9oCZM2cOxcXFWmkDBw5k+vTpDBkyxESlEqL+SMBgYmq1mnHjxjFnzhycnZ3x8vLik08+wcPDgwEDBpi6eEKIO5SQkMAHH3zAgAEDeP7558nIyKh5zdLSEjs7OxOWTtSH4OBgevTowXvvvcesWbOwt7fn66+/Jjc3l2eeecbUxRN1yN3dXW+6i4sLXl5e97g0QtQ/CRjuA9OnT6e8vJy33nqL4uJiQkNDWbJkic5EaCFEw7Ft2zbKysr47bffdPZUGTFiBLNnzzZRyUR9USgUfP7553z66ae89NJL5OXlERISwsqVK2ncuLGpiyeEEHdMUVVVVWXqQgghhBBCCCHuTzKgVgghhBBCCGGQBAxCCCGEEEIIgyRgEEIIIYQQQhgkAYMQQgghhBDCIAkYhBBCCCGEEAZJwCCEEEIIIYQwSAIGIYQQQgghhEESMAghhBBCCCEMkoBBCCGEEEIIYZAEDEIIIYQQQgiDJGAQQgghhBBCGCQBgxBCCCGEEMKg/w/xrraqdIX8dwAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Your answer here\n", "loadings = pd.DataFrame(efa.loadings_,\n", " index=bfi.columns)\n", "\n", "# Plot\n", "fig, ax = plt.subplots(1, 1, figsize=(10, 8))\n", "sns.heatmap(loadings, cmap='viridis', annot=True, fmt='.2f', ax=ax)" ] }, { "cell_type": "markdown", "id": "8a9ef687-6e0d-4c59-9bfe-186e748af804", "metadata": {}, "source": [ "This does indeed look like a solid representation of the Big 5 - factor 0 is Neuroticism, factor 1 is Extraversion, factor 2 is Conscientiousness, factor 3 is Agreeableness, and factor 4 is Openness. Next, create a dataframe that has the latent scores of each participant on each factor (refer to chapter 8 for a refresher!), giving the column names in that order (N, E, C, A, O)." ] }, { "cell_type": "code", "execution_count": 22, "id": "dd41de94-70ce-4b00-89ae-e9e8bb6a4a00", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
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NEACO
00.0264661.2439561.4252210.1508610.400030
10.578565-1.826086-1.283536-2.161121-0.574579
2-0.1664270.199434-0.180815-0.103906-0.348037
3-0.493933-0.0932990.545031-1.640684-0.285600
4-0.8695140.190820-1.3379560.6373780.304959
\n", "
" ], "text/plain": [ " N E A C O\n", "0 0.026466 1.243956 1.425221 0.150861 0.400030\n", "1 0.578565 -1.826086 -1.283536 -2.161121 -0.574579\n", "2 -0.166427 0.199434 -0.180815 -0.103906 -0.348037\n", "3 -0.493933 -0.093299 0.545031 -1.640684 -0.285600\n", "4 -0.869514 0.190820 -1.337956 0.637378 0.304959" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Your answer here\n", "five = pd.DataFrame(\n", " efa.transform(bfi),\n", " columns=['N', 'E', 'A', 'C', 'O'])\n", "\n", "five.head()" ] }, { "cell_type": "markdown", "id": "2f9ee205-010a-4a8c-b116-47e8bdf451d4", "metadata": {}, "source": [ "Now we have represented a large dataset of 25 variables as a set with five. Our next step is to cluster *those* values with our usual tree approach. Do that below, building the linkage tree and showing the dendrogram. This will essentially find groups of people with different personality profiles, which is an interesting finding." ] }, { "cell_type": "code", "execution_count": 23, "id": "a26f12d5-75b7-4983-a26b-565500da103a", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Your answer here\n", "tree = shc.linkage(five, method='ward')\n", "\n", "# Show\n", "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n", "dendrogram = shc.dendrogram(tree, no_labels=True, ax=ax)" ] }, { "cell_type": "markdown", "id": "6d8c1c2d-2e1b-435c-889c-9caff0c325db", "metadata": {}, "source": [ "An interesting result - again one imagine cuts at various areas (30 for four clusters, 38 for three, 42 for two), but arelatively clear pattern has emerged. Test the cluster results for four, three, and two clusters, and show their silhouette coefficients." ] }, { "cell_type": "code", "execution_count": 24, "id": "8db550cc-bd49-4dad-80a6-5897add92695", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "0.19988331032320203" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.11861979499537914" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0.12536850355775894" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Your answer here\n", "# Make cuts\n", "cluster_2 = shc.cut_tree(tree, height=41)\n", "cluster_3 = shc.cut_tree(tree, height=38)\n", "cluster_4 = shc.cut_tree(tree, height=30)\n", "\n", "# Silhouette\n", "silhouette_2 = silhouette_score(five, cluster_2.flatten())\n", "silhouette_3 = silhouette_score(five, cluster_3.flatten())\n", "silhouette_4 = silhouette_score(five, cluster_4.flatten())\n", "\n", "# Print\n", "display(silhouette_2, silhouette_3, silhouette_4)" ] }, { "cell_type": "markdown", "id": "48ebcc24-8363-4c13-ae1b-b9dc6856cfca", "metadata": {}, "source": [ "Two clusters seems to be a good bet here again, though the strength is rather weak - this suggests there are not particularly strong clusters of personality profiles in the data. Nonetheless, let us melt the data and examine the two groups, after adding the clusters to the dataset. Visualise them with a bar chart as before." ] }, { "cell_type": "code", "execution_count": 25, "id": "7030295b-62a4-4d3a-901d-35c51a188ac7", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dgzcMc5dXdXgNJtXE/Wex+kmySHJKsvx72jM1cf8Bkg+EEZvNpuHDh2v+/Plq0KCBGjdurHnz5ikyMlL9+vVTUVGRzp8/r9q1aysoKEgbN27U1q1bNWnSJHXq1Elnz551rat4GQCoKBarv0LCWysn85BCwlvLYvX6t1XA6/jEb8348eNVWFioadOmKS8vTx07dtSqVatks9l06tQp9e3bV3PnztWQIUO0ZcsWSdJLL72kl156qcR6ipcBgIpUu0kX1W7S5doLAiiTT4QRPz8/TZw4URMnTiw176abbtKRI0dc00lJSVVZGgAAKCevv+kZAACo3ggjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgCo8RITE9WvXz8lJiaaLqVGIowAAGq0vLw8JScny+FwKDk5WXl5eaZLqnEIIwCAGq2wsFAOh0OS5HA4VFhYaLiimocwAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAIBqwelw1Ojt+zJ/0wUAAFARLFar0rasVO65dLf65dgLS0wffvslhdjc+/MYHNZIUQNGuNUH/0EYAQBUG7nn0pWbccKtPnkFRSWnM0/KEuBXkWXhGjhMAwAAjCKMAAAAowgjAADAKI/OGTl//rxWrVqlzz77TGfPntVrr72mv/3tb4qOjtZdd91V0TUCAIBqzO2RkZMnT2rgwIH661//qoiICJ07d05FRUVKS0vT+PHj9fHHH1dCmQAAoLpye2TkxRdfVFhYmP7yl78oJCREt912myRpwYIFys/P1/Lly9W7d++KrhMAAFRTbo+M7Nq1S7/73e9Up04dWSyWEvPuu+8+ffPNNxVWHAAAqP48OoHVz6/s66/tdnupgAIAAHA1boeRuLg4vfrqq8rJyXG1WSwWORwOvf3227rjjjsqtEAAAFC9uX3OyIQJEzRs2DDdfffd6ty5sywWi1atWqWjR4/q+PHjeuuttyqjTgAAUE25PTJy6623asOGDercubNSUlLk5+enzz77TE2bNtWaNWvUunXrCi/S4XBo8eLF6tGjh9q1a6dHH31Ux48fv+LyFy5c0IQJE9SxY0d17NhRzzzzTImRHAAA4D3cHhlJTk5Wly5dtGDBgsqop0zLli3TmjVrNHfuXEVERGjevHkaMWKEtmzZIpvNVmr58ePHKz8/X6+//rqysrI0depUzZo1Sy+++GKV1QwAAK6P2yMjc+fO1b/+9a/KqKVMdrtdSUlJGjdunHr16qXo6GgtWrRIGRkZ2rZtW6nl9+7dq927d2vu3Llq06aN4uPjNXv2bG3atEkZGRlVVjcAALg+boeRsLAwZWVlVUYtZTp8+LB+/PFHdenSxdVWp04dxcTEaM+ePaWW//zzz9WwYUO1aNHC1dapUydZLBalpqZWSc0AAOD6uX2Y5n/+5380e/ZspaSk6JZbbtENN9xQapnBgwdXRG2SpDNnzkiSGjVqVKI9PDxc6enppZbPyMgotazNZlO9evXKXP56OZ3Oq553YvKSZovFoqCgIAWHNbr2wpWgeLu5ublyOp1u97dYLAoODlbj8LoVXdp1K952Xl6eR6+honi6bdP7kP1XPt6y/yTP96FUvvfBV199VVu2bNGAAQM0cuRIj7bt6fug014o6YRrOii8iYJt7v15vJ73wZCQELdrqyncDiMvvPCCJGnTpk1lzrdYLBUaRnJzcyWp1LkhgYGBunTpUpnLl3UeSWBgoPLz8z2uo6CgQIcOHSpzXkBAgGJi2sjfv+z7r1QFp8OhqAEjjG2/qKhQ33zzjQoKCtzuW7z/xgzrVgmVXT+nw6GgoCBj2y8qKtRXXx302X3I/isf0/tPqph96Mn7YF5enrZs2SKHw6EtW7Zo1KhRHu0LT98H8/LyZP3rL+RwOGS1WnXbg097tP1rvQ/Gxsa6vc6q4nQ6jX6odjuMbN++vTLquKLi/xB2u73Ef478/HwFBweXubzdbi/Vnp+fX65UGhAQoJYtW5Y5z2KxyN/fT4lv79TpzNIBqbI1Dq+rMcO6Gf9UdaX9cz0KCuzy4P2vwhR/qkrbslK55zwfQfNUcFgjRQ0YoVtuucXjn6HJfcj+Kx/T+08q/z4sz/tgYUGeHA6HpMtXT05fulX+Ae6FgfK+Dw4YMMA1MuN0Ol0fhN1VnvfBa5kyZYp2796tjz76qMLWabfbtWDBArVp00YDBw6ssPW6y+0w0rhxY9f3ubm5ys7OVr169RQQEFChhRUrPuSSmZmppk2butozMzMVHR1davnIyEj97W9/K9Fmt9t18eJFRUREeFyHxWK5Zpg5nXlJx05f8Hgb5WX6U1V1kHsuXbkZJ669YCUpK2D7EvZf+Zjef1L596En74OOwpKj1ie+vyirf6BH2/f0fTAhIUEJCQke9fVlmZmZev311zV37lyjdXh0O/jPP/9c9913n2JjY9WzZ0+1bdtW9913n/75z39WdH2Kjo5WaGioUlJSXG1ZWVk6ePCg4uLiSi3fsWNHnTlzpsR9SIr7cndYAAC8j9th5IsvvtDDDz+srKws/e53v9OMGTM0evRoXbx4UY8//rj27t1boQXabDYNHz5c8+fP1/bt23X48GH9/ve/V2RkpPr166eioiKdPXtWeXl5kqR27drpjjvu0O9//3vt379f//znPzVjxgwNHjy4XCMjAABUNqfTqdWrV6t///5q27at+vXrp5UrV5Z56KlPnz6aMmVKibaNGzeqVatWOnXqlKTLpyjMmjVLPXv21G233aZ77rlHSUlJkqRTp06pb9++kqSnnnpKffr0ca3n888/1/Dhw9WuXTt16tRJkydP1vnz50tsJyYmRuvWrVP37t3Vs2fPcj0o1+3DNC+//LLi4uK0atWqEg/MGzt2rB577DEtWbLE9UIryvjx41VYWKhp06YpLy9PHTt21KpVq2Sz2Vw7c+7cuRoyZIgsFouWLl2qWbNm6be//a0CAwN1zz336KmnnqrQmgAAqGgLFy7UqlWr9PDDD6tbt2766quvtGjRojLPhbwezz33nD799FNNnjxZN9xwgz755BO9+OKLqlevngYMGKClS5dq7NixGj16tO6++25J0p49e/TII4+oS5cuevnll3Xp0iX98Y9/1EMPPaT169e7DoUVFRVp+fLlmjNnjs6fP1+u82XcDiMHDhzQggULSj2512q1avjw4Zo8ebLHxVyJn5+fJk6cqIkTJ5aad9NNN+nIkSMl2sLCwrR48eIKrwMAgMqSlZWlP/3pT3rwwQc1adIkSVK3bt10/vx5paamKjw83O117t69W127dlX//v0lSZ07d1ZISIjq168vm83meoRL06ZNFRMTI0lasGCBoqKitGLFCtff+nbt2ql///7asGGDHnjgAdf6n3jiCfXu3bs8L1uSB4dpatWqpcLCwjLnFRQUGL9GHgAAX7Rv3z4VFBSoX79+JdqnTJni8RGHzp07a926dRoxYoTeeustnT59WmPGjNGdd95Z5vK5ubn68ssv1atXLzmdThUWFqqwsFBNmjRRixYttHPnzhLL33rrrR7V9VNuj4zccccdWr58ubp166ZatWq52rOzs/Xqq6+WeVIpAAC4uosXL0qSGjRoUGHrnDp1qiIjI7V582bNmjVLktShQwdNnz7dNRLy37KysuRwOLRy5UqtXLmy1PzAwJJXOYWFhVVInW6HkQkTJmjIkCG666671Lt3bzVs2FBnz57Vxx9/rPz8fD3//PMVUhgAADVJnTp1JEnnz59X8+bNXe3p6ek6fvx4mTdTKyoqKjH90zuF22w2jR49WqNHj9b333+vv//971q2bJkmTJig999/v9T6atWqJYvFoocffth1aOe/Vdbl826HkWbNmmnt2rVaunSpPvnkE126dEl169ZV586dNXbs2Eq94QsAwLt5ckv9woI8nf3yP9NNb6zn0U3PfF3btm0VEBCg7du3lzjK8MYbb+idd95Rjx49SiwfGhrqemRKsS+++ML1fV5engYNGqT77rtPjz76qG688UY98MADOn78uNatWydJpc7/DA0NVUxMjL777jvdfvvtJdaVkJCgnj17VsrfebfDiHT5DnNTp05Vw4YNJV0eWjpz5gxBBABqMIfD6dEt9bOzszXo/UTX9PQn7lZoaKhH27dazd3SvLwaNGighx56SG+88YZsNpu6dOmiAwcO6M0339STTz6pr7/+usTyd955p1asWKHly5erffv2+vjjj7Vr1y7X/KCgILVp00ZLly5VQECAWrVqpbS0NL3zzjv62c9+JkmqXbu2JGnXrl1q0aKF2rVrpyeffFIjR47UhAkTNHDgQBUVFSkpKUlffvmlRo8eXSmv3e0wkpWVpYSEBKWnp+v//u//JEn79+/XyJEj1adPHy1YsMDn74IIAHCf6SBgevsVYeLEibrhhhv09ttvKykpSTfddJOefvpp/eY3vyl1T5FRo0bp/PnzSkpKUkFBgXr37q3nnnuuRGCYPXu2Xn75ZSUlJens2bMKCwvT0KFDXXebDQ0N1SOPPKK1a9fq448/1s6dO9W9e3etWrVKS5cu1fjx4xUQEKA2bdroT3/6k9q3b18pr9vidPPyl+nTp+ujjz7S9OnTXdck2+12ffrpp5o5c6b69+9fKZf3mnTgwAFJKjFkVZan/7jVyO3gb25cX88n/LzKt1sdHXxjtpHbcQdHNFXMb6dX+XYrGvuvfEztP8nsPszOztagQYNc05s2bfJoZAS+y+1Lez/66CNNnjzZFUSkyyfI9OnTR08++WSZJ8QAAABcidth5Mcff3Sd8ftTYWFhunDB3IPiAACA73E7jLRp00YbNmwoc17xPfEBAACul9snsI4ePVojRozQkCFD1K9fP4WFhen8+fPavn27vvrqKy1fvrwy6gQAANWU22GkW7dueuWVV7R48WItXrxYTqdTFotFrVu31rJly9SzZ8/KqBMAAFRTHt1nJDY2VomJiapfv74uXryodevW6eLFiwoJCano+gAAQDXn9jkj+/fvV58+ffTmm28qMDBQy5YtU2JiojZv3qyHH35Y27dvr4w6AQBANeV2GFm0aJGaN2+u++67T3l5edq8ebOGDRum3bt3a+jQoZwzAgAA3OJ2GCm+HWyTJk20a9cu173vJennP/+5vvnmmwovEgBQffn7+8tqvfznyGq1yt/fozMI4MPcDiNWq1U2m02StGPHDtWpU0dt27aVdPkuekFB7j3cCABQswUFBWnw4MGyWq0aPHgwf0dqILfj52233ab169crKChI77//vnr37i2LxaJz585p5cqVuu222yqjTgBANTZmzBiNGTPGdBlGmHzAn6fbdjgcWrp0qdatW6esrCzFxsZqxowZatasmUd1uB1GJk2apMcff1zvvfeeGjRo4Hogz4ABA+RwOLRq1SqPCgEAoCayWi1KfHunTmdeqtLtNg6v69FTliVp2bJlWrNmjebOnauIiAjNmzdPI0aM0JYtW1xHT9zhdhiJiYnRhx9+qKNHj+qWW25xXc47c+ZM3XHHHWrYsKHbRQAAUJOdzrxk5EGrnrDb7UpKStLEiRPVq1cvSZcvbunRo4e2bdum/v37u71Ot88ZkS4/crhdu3Yl7ivys5/9jCACAEA1d/jwYf3444/q0qWLq61OnTqKiYnRnj17PFqnR2EEAADUTGfOnJEkNWrUqER7eHi40tPTPVonYQQAAFy33NxcSSp1bkhgYKDy8/M9WidhBAAAXLfiS6/tdnuJ9vz8fAUHB3u0TsIIAAC4bsWHZzIzM0u0Z2ZmKjIy0qN1EkYAAMB1i46OVmhoqFJSUlxtWVlZOnjwoOLi4jxaJ/fcBQAA181ms2n48OGaP3++GjRooMaNG2vevHmKjIxUv379PFonYQQAAMMah9f1qW2OHz9ehYWFmjZtmvLy8tSxY0etWrXKoxueSYQRAACMcjicHt8JtSK27cnt4P38/DRx4kRNnDixQurgnBEAAAwy9Vwa09v+b4QRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAADHI6HD697WXLlunBBx8s1zq4HTwAAAZZrFalbVmp3HPpVbrd4LBGihowolzreP3117V48WJ17NixXOshjAAAYFjuuXTlZpwwXcZ1y8jI0NSpU5WamqqoqKhyr8/rD9Pk5+dr1qxZio+PV4cOHTR+/HidO3fuqn2++OILPfjgg4qNjVWPHj00depUXbx4sWoKBgCgmvvqq69Ut25dbd68We3atSv3+rw+jMycOVM7d+7UkiVL9MYbb+jkyZNKSEi44vJpaWl67LHHFB0drXXr1mnRokXav3+/xo8fX4VVAwBQffXp00cLFixQkyZNKmR9Xh1GMjIylJycrGnTpikuLk5t27bVwoULtWfPHu3bt6/MPsnJyQoPD9fTTz+t5s2bKy4uTjNmzFBKSopOnjxZtS8AAABck1eHkdTUVElS586dXW1RUVGKiIjQnj17yuwzcOBAvfjii7JYSj8WmUM1AAB4H68+gTUjI0P169dXYGBgifbw8HClp5d91nGLFi1Kta1cuVINGzZUdHS0x7U4nU7l5OSUOc9isSg4ONjjdVeU3NxcOZ1O02X4JH6G5cP+Kx9v2X+S7+5DXxASEmK6BK9lNIycOnVKffv2veL8hIQE2Wy2Uu2BgYHKz8+/rm288MIL2rFjhxYvXqyAgACPay0oKNChQ4fKnBccHKyYmBiP111R0tLSlJuba7oMn8TPsHzYf+XjLftP8t196AtiY2NNl+C1jIaRiIgIbd269Yrzd+zYIbvdXqo9Pz//mp8iCgoKNH36dL3zzjuaMWOG7r777nLVGhAQoJYtW5Y5r6xDQiZERUXxicZD/AzLh/1XPt6y/yTf3YfwbUbDSEBAQJmHVYodOXJEFy9elN1uLzFCkpmZqcjIyCv2y87O1tixY/X5559rwYIF6t+/f7lrtVgsXj/E5i3DvPAcP8PyYf+VH/vQjOCwRjVim1fi1eeMxMbGyuFwKDU1VfHx8ZKk7777ThkZGYqLiyuzj91u16hRo3T48GG99tpr6tKlS1WWDACAW5wOR7nvhFqebVus5buW5YUXXih3HV59NU1ERIT69++vadOmKSUlRfv379eECRPUqVMntW/fXtLl8HH27FnX4ZwVK1YoNTVVs2fPVosWLXT27FnXV1mHfAAAMKm8YcBXt/3fvKOKq3j22WcVHx+vsWPH6rHHHlPz5s21ePFi1/y9e/eqe/fu2rt3ryRpy5YtcjqdevLJJ9W9e/cSX8XLAAAA7+HVh2mky5dCzZkzR3PmzClzfufOnXXkyBHX9AcffFBVpQEAgArg9SMjAACgeiOMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMMrfdAGQfjj5T+VkHlJIeGvVbtLFdDkAAFQpRkYMczoKlZN5SJJTOZmH5HQUmi4JAIAqRRgxzOkokuQsnvr3NAAANQdhBAAAGEUYAQAARnECKwD4uM2Hzumzk1nq2qSOBrYOM10O4DZGRgDAh9mLHNp5IksOp/TZySzZixymSwLcRhgBAB9W5HC6ToF3OC9PA76GMAIAAIwijAAAAKM4gbUCNQ6v63afwoI8nf3yP9NNb6wn/4CgSt8uAADegjBSQRwOp8YM6+Z2v+zsbA16P9E1Pf2JuxUaGurR9q1Wi9v9AAAwjcM0FcR0EDC9fQAAPEUYAQAARhFGAACAUYQRAABgFGEEAAAY5fVhJD8/X7NmzVJ8fLw6dOig8ePH69y5c9fd/5VXXlGrVq0qsUIAAFAeXh9GZs6cqZ07d2rJkiV64403dPLkSSUkJFxX3/3792vp0qWVXCEAACgPrw4jGRkZSk5O1rRp0xQXF6e2bdtq4cKF2rNnj/bt23fVvjk5OZo4caLi4uKqplgAAOARrw4jqampkqTOnTu72qKiohQREaE9e/Zcte9zzz2nW2+9VYMGDarUGgEAQPl49R1YMzIyVL9+fQUGBpZoDw8PV3p6+hX7bdu2TTt27NC7776rv//97xVSi9PpVE5OToWs67/Z7XZZrVY5HA5ZrVbZ7fZK2Q6uzmKxKDg42HQZys3NldPpe09dZf+Vj7fsP8l396EvCAkJMV2C1zIaRk6dOqW+fftecX5CQoJsNlup9sDAQOXn55fZJyMjQ88884xeeukl1a9fv8JqLSgo0KFDhypsff+ta9eu+uyzz9S1a1elpaVVyjZwdcHBwYqJiTFdhtLS0pSbm2u6DLex/8qneP8FhzVyu6/TXijphGs6KLyJgm3uv7UXb9tX96EviI2NNV2C1zIaRiIiIrR169Yrzt+xY4fsdnup9vz8/DI/RTidTk2ZMkX33nuvevbsWaG1BgQEqGXLlhW6zmKtW7eulPXi+lks3nE7/aioKJ/8VMr+Kx+LxSKno0hRA0a43Tc7O1ta85/D0dHDJnn0fCtJl2vw0X0I32Y0jAQEBKhFixZXnH/kyBFdvHhRdru9xAhJZmamIiMjSy3//fff67PPPtMXX3yh5ORkSVJhYaEkqUOHDho1apSeeOIJj2q1WCwMsaHSectQva9i/5WPxerHPoQRXn3OSGxsrBwOh1JTUxUfHy9J+u6775SRkVHmVTIRERH68MMPS7R9+OGHmj9/vpKTk1W3bt0qqRsAAFw/rw4jERER6t+/v6ZNm6bnn39ewcHBmjFjhjp16qT27dtLunwC6KVLl1S3bl3ZbDY1a9asxDrCwsIkqVQ7AADwDl59aa8kPfvss4qPj9fYsWP12GOPqXnz5lq8eLFr/t69e9W9e3ft3bvXYJUAAMBTXj0yIl2+FGrOnDmaM2dOmfM7d+6sI0eOXLH/kCFDNGTIkMoqDwAAlJPXj4wAAIDqjTACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAODD/P39ZbVefiu3Wq3y9/f6R44BpRBGAMCHBQUFafDgwbJarRo8eLCCgoJMlwS4jQgNAD5uzJgxGjNmjOkyAI8xMgIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKB+UBMG7zoXP67GSWujapo4Gtw0yXA6CKMTICwCh7kUM7T2TJ4ZQ+O5kle5HDdEkAqhhhBIBRRQ6nnP/+3uG8PA2gZiGMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjPL6MJKfn69Zs2YpPj5eHTp00Pjx43Xu3Lmr9snOztaMGTPUpUsXxcbG6oknntDJkyerqGIAAOAOf9MFXMvMmTOVmpqqJUuWyGazacaMGUpISNCbb755xT7jxo3T6dOntWTJEtWuXVuzZs3S6NGjtXnzZlmtXp+/AJ8VHNbI7T5Oe6GkE67poPAmCra599bkyXYBeA+vDiMZGRlKTk7WihUrFBcXJ0lauHCh7rnnHu3bt0/t27cv1SclJUW7du3Spk2b1KpVK0nSs88+qxEjRujYsWNq3rx5Vb4EoMZwOhyKGjDC7X7Z2dnSmkGu6ehhkxQaGurR9i182AB8klf/5qampkqSOnfu7GqLiopSRESE9uzZU2aff/zjH7r11ltdQUSSWrZsqb///e8EEaASmQ4CprcPwHNePzJSv359BQYGlmgPDw9Xenp6mX2OHTumZs2a6a233tLq1auVlZWl2NhYPfXUU4qIiPC4FqfTqZycHI/7w7tZLBYFBwebLkO5ublyOp2my6hSP/29ysnJ4XAqqqWQkBDTJXgto2Hk1KlT6tu37xXnJyQkyGazlWoPDAxUfn5+mX2ys7P11Vdf6cKFC5o1a5Ykaf78+XrooYe0efPmUsHmehUUFOjQoUMe9YX3Cw4OVkxMjOkylJaWptzcXNNlVKmfvt6vv/7aK4IhUNFiY2NNl+C1jIaRiIgIbd269Yrzd+zYIbvdXqo9Pz//im9WAQEBys/PV2JiourWrStJWrp0qXr06KGPPvpI9957r0e1BgQEqGXLlh71hfezWCymS5B0+TBkTRsZyc7OLjF96623enTOCADfZTSMBAQEqEWLFlecf+TIEV28eFF2u73ECElmZqYiIyPL7BMZGamIiAhXEJGkG264QfXq1dOpU6c8rtVisTDEhkpXE0cEHA5HiemQkBB+14AaxqsPzMbGxsrhcLhOZJWk7777ThkZGa6ra34qLi5O33//vTIzM11tmZmZunDhgpo1a1bpNaNm2nzonKZ8mKbNh65+DxwAQGleHUYiIiLUv39/TZs2TSkpKdq/f78mTJigTp06uS7rtdvtOnv2rOtwzr333qubb75ZCQkJOnDggA4ePKgnn3xSUVFR6t27t7kXg2rLXuTQzhNZcjilz05myV7kuHYnAICLV4cR6fI9QuLj4zV27Fg99thjat68uRYvXuyav3fvXnXv3l179+6VJNlsNr3++uu68cYb9fDDD2v48OGqX7++Xn/99TJPhgXKq8jhVPFZHg7n5WkAwPXz6kt7pcvHj+fMmaM5c+aUOb9z5846cuRIibaGDRtqwYIFVVEeAAAoJ68fGQEAANUbYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBIBR/v7+rgfjWa1W+ft7/UV+ACoYYQSAUUFBQRo8eLCsVqsGDx6soKAg0yUBqGJ8BAFg3JgxYzRmzBjTZQAwhJERAABgFGEEAAAYRRgBAABGEUYAAIBRnMAK/JfgsEZu93HaCyWdcE0HhTdRsM29Xy1PtgsA1QVhBPg3p8OhqAEj3O6XnZ0trRnkmo4eNkmhoaEebd9iZbASQM3DOx/wb6aDgOntA4ApvPsBAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowghQTv7+/rJaL/8qWa1W+fv7G64IAHwLYQQop6CgIA0ePFhWq1WDBw9WUFCQ6ZIAwKdYnE6n03QR3u7AgQOSpNtvv91wJQAAVD+MjAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACM8vowkp+fr1mzZik+Pl4dOnTQ+PHjde7cuav2OXbsmEaOHKm4uDh1795d06ZNU1ZWVhVVDAAA3OH1YWTmzJnauXOnlixZojfeeEMnT55UQkLCFZcvKCjQyJEjFRAQoLVr1+rll1/Wnj17NG3atCqsGgAAXC+vfqJXRkaGkpOTtWLFCsXFxUmSFi5cqHvuuUf79u1T+/btS/X59ttvdfz4cS1ZskQtWrSQJD3wwANatGhRVZYOAACuk1ePjKSmpkqSOnfu7GqLiopSRESE9uzZU2afunXrymKxaN26dbLb7Tp//rw++OCDMoMLAAAwz6vDSEZGhurXr6/AwMAS7eHh4UpPTy+zz4033qinn35aGzZsULt27RQfH69Lly5p4cKFVVEyAABwk9HDNKdOnVLfvn2vOD8hIUE2m61Ue2BgoPLz88vsY7fb9c033+juu+/WAw88oAsXLuill17S73//eyUlJclq9Sx/OZ1O5eTkeNQXAICQkBDTJXgto2EkIiJCW7duveL8HTt2yG63l2rPz89XcHBwmX3+9Kc/affu3dq6dav8/PwkSTfffLPuvvtuffTRR7rrrrs8qrWgoECHDh3yqC8AALGxsaZL8FpGw0hAQIDrJNOyHDlyRBcvXpTdbi8xQpKZmanIyMgy+6SmpiomJsYVRCSpWbNmql+/vo4dO+ZRnQUFBa56AQDwxJEjR9SqVSvTZXglr76aJjY2Vg6HQ6mpqYqPj5ckfffdd8rIyHBdXfNTkZGR+uKLL+R0OmWxWCRdPvfk4sWLuvnmmz2qo3g9xf8CAICKY3E6nU7TRVzNhAkTtG/fPj3//PMKDg7WjBkzFBoaqr/85S+SLp8jcunSJdWtW1c2m01ff/21fvWrX2nQoEF65JFH9MMPP2ju3LnKy8vT+vXrGd0AAMDLeH0YycnJ0fPPP68PPvhAktSzZ09NmzZN9evXlySlpKTooYce0p///GfXJcD79u3TwoULdfDgQQUHB6t79+6aOHGiGjRoYOx1AACAsnl9GAEAANWbV99nBAAAVH+EEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRXv2gPFxZnz595HA4tGXLFoWGhpaYN2XKFJ0+fdr1/B6U7cEHH9Tu3buvOP/TTz9Vw4YNq7Ai35Wdna1u3bqpVq1a+vjjj0s8ZRtXV1hYqNWrV2vTpk1KS0uTzWZTTEyMRo4c6XpAKK6sqKhIa9eu1caNG3X06FH5+fmpZcuWuu+++zR48GAecOojCCM+LD09XS+88ILmzJljuhSfde+992rq1KllzgsLC6vianzXe++9p7CwMP2///f/tG3bNvXv3990ST7BbrfrkUceUXp6usaNG6cOHTooLy9PGzZs0KOPPqq5c+dq8ODBpsv0WoWFhfrd736nAwcOaOzYserWrZuKioq0c+dOPf/889q+fbv++Mc/ys/Pz3SpuAbCiA9r0qSJ1q1bp5/97Gfq0aOH6XJ8UlBQEKMfFWDDhg3q3r27MjIytGbNGsLIdVq8eLEOHz6s9957T5GRka72qVOnuh4S2q9fP9WqVctgld5r+fLlSk1N1caNG9WsWTNXe4sWLdSpUycNHTpUq1at0siRIw1WievBOSM+bODAgYqPj9czzzyj7Oxs0+Wghjp69Ki+/PJLdevWTffcc492796to0ePmi7L6xUUFGjdunUaOnRoiSBSLCEhQa+99pqCgoIMVOf9nE6n3nzzTf3yl78sEUSKRUdHa9CgQfrLX/4ih8NhoEK4gzDiwywWi5577jllZWVp7ty5pstBDbV+/XqFhISoZ8+euuuuu2Sz2fT222+bLsvrnTx5UhcvXlT79u3LnB8eHq62bdtyiOEK0tLSdOHCBd1xxx1XXCY+Pl6ZmZk6depUFVYGTxBGfFzjxo01ceJErV+/Xv/4xz9Ml+Nz3n33XXXo0KHU15NPPmm6NJ9QWFiod999V3feeaeCg4NVu3Zt9erVS5s2bVJubq7p8rzapUuXJEl169Y1XIlvunjxoiSpfv36V1ymeN758+eroiSUA+eMVAP333+/PvjgAz3zzDPasmWL6XJ8Sp8+ffSHP/yhVHtISIiBanzPjh07dPbsWf385z93tf385z/Xtm3b9N5772no0KEGq/NuDRo0kPSfP6pwT7169SRJP/zwwxWXKQ58Vwss8A6MjFQDHK7xXK1atdSsWbNSX5zUen02btwoSRo/frxiYmIUExPjCndr1qwxWZrXa9KkiW644Qbt3bu3zPnHjh3To48+qiNHjlRxZb6h+Pf0apfnp6SkqGHDhrrpppuqsDJ4gjBSTTRu3FiTJk3S+vXr9fnnn5suBzXA+fPntWPHDg0ZMkTJycklvoYOHaoDBw7oq6++Ml2m17JarRo6dKg2btyojIyMUvNfe+017du3T40bNzZQnffz8/PTQw89pPXr1+ubb74pNf/w4cNKTk7Wb37zG8678QGEkWrk/vvvV9euXXXy5EnTpfiMvLw8nT17tsyv/Px80+V5tU2bNqmwsFCPP/64br311hJfTzzxhPz8/DiR9RqeeOIJNWvWTPfff7+Sk5N14sQJHThwQFOnTtWGDRv07LPPlrqpIf7jscceU48ePTR8+HCtXr1ax48f1/Hjx7V69Wr99re/VefOnbms10dYnE6n03QRcF+fPn30y1/+UuPGjSvRfvr0af3iF79QmzZtuAPrNVzrDqwLFy7kfhlX8Ytf/EINGzZUUlJSmfMTEhL0ySef6B//+Ad/UK8iJydHSUlJev/99/X9998rMDBQbdq00ahRo9SpUyfT5Xk9p9OpjRs3at26dfr222/ldDp1yy236Fe/+pWGDh3KHVh9BGEEAAAYxWEaAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgCUqbJuQcStjQD8FGEEQCnbt2/X5MmTy72ejRs3qlWrVjp16pQk6dtvv9WwYcPKvV4A1Yu/6QIAeJ/XX3+9QtbTu3dvrV27VuHh4ZKk999//4pPqQVQcxFGAFSaBg0aqEGDBqbLAODlOEwDoITiBwju3r1brVq1UkpKilq1aqU1a9bozjvvVNeuXfXpp59KktatW6chQ4aoffv2atu2rQYNGqStW7e61vXfh2mWLFmipUuXSpJatWqlJUuWGHl9ALwPYQRACTNmzFBMTIxiYmK0du1aZWdnS5IWLVqkyZMna/LkyWrfvr1Wr16t6dOnq2/fvlqxYoXmzZungIAATZw4Ud9//32p9f7617/W0KFDJUlr167Vr3/96yp9XQC8F4dpAJTQsmVLhYaGSpLat2+vlJQUSdL999+ve+65x7XcyZMn9eijj2rMmDGutptuuklDhgzRF198oRtvvLHEeiMjIxUZGelaLwAUI4wAuC6tWrUqMT1lyhRJ0g8//KBjx47p2LFj2rVrlySpoKCgyusD4LsIIwCuS1hYWInpEydOaPr06frnP/8pf39/NW/e3BVYuJcIAHcQRgC4zeFwaOTIkQoICNBf//pXxcTEyN/fX99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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Your answer here\n", "# Add cluster labels\n", "five['cluster'] = cluster_2\n", "\n", "# Melt\n", "plot_this = five.reset_index().melt(id_vars=['index', 'cluster'],\n", " var_name='trait', value_name='score')\n", "\n", "# Visualise\n", "sns.catplot(data=plot_this,\n", " x='trait', y='score', \n", " hue='cluster', kind='bar')" ] }, { "cell_type": "markdown", "id": "d7291647-c585-47cc-80ec-bae490c2e6c4", "metadata": {}, "source": [ "Cluster 0 are relaxed, outgoing, friendly, and conscientious. Conversely, cluster 1 are neurotic, introverted, unfriendly, and not very conscientious! Remember though, clustering is a *descriptive* technique. It is not an inferential model like OLS or factor analysis of any form, and so its findings can be very dataset specific - it is a powerful way to explore data, and we have touched only one approach to clustering here. There are in fact model-based clustering approaches that use probability distributions which can be more inferentially robust, but they too come with their own drawbacks. Taken together though, clustering is a very useful tool to have, as long as it is treated with respect." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 5 }