{"id":6638,"date":"2021-11-15T01:46:53","date_gmt":"2021-11-15T01:46:53","guid":{"rendered":"https:\/\/researchwithfawad.com\/?page_id=6638"},"modified":"2021-11-15T02:20:53","modified_gmt":"2021-11-15T02:20:53","slug":"ibm-spss-amos-series-factor-loadings-and-fit-statistics","status":"publish","type":"page","link":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-series-factor-loadings-and-fit-statistics\/","title":{"rendered":"IBM SPSS AMOS Series &#8211; Factor Loadings and Fit Statistics"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"6638\" class=\"elementor elementor-6638\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4b0sl1n elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4b0sl1n\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d4acc06\" data-id=\"d4acc06\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-bee4950 elementor-widget elementor-widget-heading\" data-id=\"bee4950\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Factor Loadings and Fit Statistics<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-261642o elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"261642o\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-885cb99\" data-id=\"885cb99\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-50ddc50 elementor-widget elementor-widget-heading\" data-id=\"50ddc50\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What Will be Discussed?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-105f188\" data-id=\"105f188\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4918b41 elementor-widget elementor-widget-text-editor\" data-id=\"4918b41\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The tutorial will discuss in detail the concept of factor loadings, acceptable factor loadings, model fit statistics, acceptable model fit statistics to establish a good fit, and how to use Modification Indices to improve model fit. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-w17lsf1 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"w17lsf1\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-2b10226\" data-id=\"2b10226\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fc6092a elementor-widget elementor-widget-image\" data-id=\"fc6092a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"1280\" height=\"720\" src=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Factor-Loadings-Model-Fit-and-Modification-Indices.png\" class=\"attachment-full size-full wp-image-6639\" alt=\"Factor Loadings, Model Fit, and Modification Indices\" srcset=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Factor-Loadings-Model-Fit-and-Modification-Indices.png 1280w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Factor-Loadings-Model-Fit-and-Modification-Indices-300x169.png 300w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Factor-Loadings-Model-Fit-and-Modification-Indices-1024x576.png 1024w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Factor-Loadings-Model-Fit-and-Modification-Indices-768x432.png 768w\" sizes=\"(max-width: 1280px) 100vw, 1280px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-1e8e919\" data-id=\"1e8e919\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-755d715 elementor-widget elementor-widget-heading\" data-id=\"755d715\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">IBM SPSS AMOS Series - 6<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-da54622 elementor-widget-divider--separator-type-pattern elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"da54622\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\" style=\"--divider-pattern-url: url(&quot;data:image\/svg+xml,%3Csvg xmlns=&#039;http:\/\/www.w3.org\/2000\/svg&#039; preserveAspectRatio=&#039;none&#039; overflow=&#039;visible&#039; height=&#039;100%&#039; viewBox=&#039;0 0 24 24&#039; fill=&#039;black&#039; stroke=&#039;none&#039;%3E%3Cpolygon points=&#039;9.4,2 24,2 14.6,21.6 0,21.6&#039;\/%3E%3C\/svg%3E&quot;);\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cf39eb3 elementor-widget elementor-widget-text-editor\" data-id=\"cf39eb3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In CB-SEM, Loadings, Model Fit, and Modification Indices are critical to model identification. This post will discuss in detail all these concept. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-l14cibh elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"l14cibh\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-d34879d\" data-id=\"d34879d\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-26216b6 elementor-widget elementor-widget-heading\" data-id=\"26216b6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Factor Loadings<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-61ca281\" data-id=\"61ca281\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-338057a elementor-widget elementor-widget-text-editor\" data-id=\"338057a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>The factor loadings in a CFA estimate the direct effects of unobservable constructs on their indicators.<\/li><li>While unstandardized estimates can be insightful, they are rarely reported in the results of a CFA. Standardized estimates are most frequently reported because they allow you to compare the weights of indicators across a CFA.<\/li><li>Standardizing an estimate converts your factor loading to a 0 to 1 scale, which allows for an easier comparison of indicators.<\/li><li>Additionally, squaring the standardized factor loading will give the proportion of explained variance (R2) with each indicator.<\/li><li>This lets you know how much of the variance in the indicator is explained by the unobserved construct. For instance, if a standardized factor loading is .80, then the unobserved variable explains .802 = .64, or 64% of the variance of the indicator.<\/li><li>How do I know if I have an acceptable indicator? If you have a standardized factor loading that is greater than .70 or explains at least half of the variance in the indicator (.702 =.50), then your indicator is providing value in explaining the unobserved construct.<\/li><li>If you are not explaining at least half of the variance, that indicator is contributing little to the understanding of the unobservable construct.<\/li><li>Once we have determined the standardized value for each factor loading, we can also determine the measurement error for each indicator.<\/li><li>The measurement error for each indicator is simply 1 \u2212 R2. Thus, the lower the explained variance is in an indicator, the higher the measurement error. <\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4582009 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4582009\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-f125a78\" data-id=\"f125a78\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8091d24 elementor-widget elementor-widget-heading\" data-id=\"8091d24\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Acceptable Factor Loadings<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-cc40102\" data-id=\"cc40102\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9492590 elementor-widget elementor-widget-text-editor\" data-id=\"9492590\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>How do I know if I have an acceptable indicator? If you have a standardized factor loading that is greater than .70 or explains at least half of the variance in the indicator (.702 =.50), then your indicator is providing value in explaining the unobserved construct.<\/li><li>If you are not explaining at least half of the variance, that indicator is contributing little to the understanding of the unobservable construct.<\/li><li>Once we have determined the standardized value for each factor loading, we can also determine the measurement error for each indicator.<\/li><li>The measurement error for each indicator is simply 1 \u2212 R2. Thus, the lower the explained variance is in an indicator, the higher the measurement error.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-07ba454 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"07ba454\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-0a2f937\" data-id=\"0a2f937\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a9dbbc0 elementor-widget elementor-widget-heading\" data-id=\"a9dbbc0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Setting the Metrics<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-55378da\" data-id=\"55378da\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9688e98 elementor-widget elementor-widget-text-editor\" data-id=\"9688e98\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>In SEM, each unobserved variable must be assigned a metric, which is a measurement range.<\/li><li>This is done by constraining one of the factor loadings from the unobservable variable by assigning it a value of 1.0. The remaining loadings are then free to be estimated.<\/li><li>The factor loading that is set to 1.0 is acting as a reference point (or range) for the other indicators to be estimated. This process is called \u201csetting the metric\u201d, and the indicator constrained to 1.0 is often referred to as a \u201creference item\u201d.<\/li><li>So, which indicator should I constrain to 1.0? <\/li><li>Researchers normally constrain the first or last indicator of each construct to be set to 1.0. <\/li><li>If you fail to set the metric or constrain one of your indicators to 1.0, the analysis of your SEM model will not run and will give you an \u201cunder-identified\u201d error message.<\/li><li>Lastly, if you are analyzing and comparing multiple samples, make sure that the same indicator is constrain to 1.0 for each sample.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-248b257 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"248b257\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-820226f\" data-id=\"820226f\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-97358a2 elementor-widget elementor-widget-heading\" data-id=\"97358a2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Model Fit and Fit Statistics<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-6d43d10\" data-id=\"6d43d10\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e7960f8 elementor-widget elementor-widget-text-editor\" data-id=\"e7960f8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>One of the advantages of SEM is that you can assess if your model is \u201cfitting\u201d the data or, specifically, the observed covariance matrix.<\/li><li>The term \u201cmodel fit\u201d denotes that your specified model (estimated covariance matrix) is a close representation of the data (observed covariance matrix).<\/li><li>A bad fit, on the other hand, indicates that the data is contrary to the specified model. The model fit test is to understand how the total structure of the model fits the data.<\/li><li>A good model fit does not mean that every particular part of the model fits well. Again, the test of model fit is looking at the overall model compared to the data.<\/li><li>One caution with assessing model fit is that a model with fewer indicators per factor will have a higher apparent fit than a model with more indicators per factor. Model fit coefficients reward parsimony.<\/li><li>Thus, if you have a complex model, you will find it more difficult to achieve a \u201cgood model fit\u201d compared to a more simplistic model.<\/li><li>The AMOS software will give you a plethora of model fit statistics. There are more than 20 different model fit tests, but only the prominent ones seen in most research are discussed.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2fb3d56 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2fb3d56\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-ad94456\" data-id=\"ad94456\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b11be56 elementor-widget elementor-widget-heading\" data-id=\"b11be56\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Model Fit and Fit Statistics - Chi-Square<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-36a574a\" data-id=\"36a574a\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-55feb97 elementor-widget elementor-widget-text-editor\" data-id=\"55feb97\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>The chi-square test is also called the chi-square goodness of fit test, but in reality, chi-square is a \u201cbadness of fit\u201d measure.<\/li><li>The chi-square value should <b>not <\/b>be significant if there is a good model fit. A significance means your model\u2019s covariance structure is significantly different from the observed covariance matrix of the data. If chi-square is &lt;.05, then your model is considered to be ill-fitting.<\/li><li>Chi-square is very sensitive to sample size. In very large samples, even tiny differences between the observed model and the perfect fit model may be found. A better option with chi-square is to use a \u201crelative chi-square\u201d test, which is the chi-square value divided by the degrees of freedom, thus making it less dependent on sample size.<\/li><li>Kline (2011) states that a relative chi-square test with a value under 3 is considered acceptable fit. Some researchers say that values as high as 5 are okay (Schumacker and Lomax 2004).<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2009519 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2009519\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-0c6815c\" data-id=\"0c6815c\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-02cbd29 elementor-widget elementor-widget-heading\" data-id=\"02cbd29\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Model Fit and Fit Statistics<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-0bbcd13\" data-id=\"0bbcd13\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d6dc5d8 elementor-widget elementor-widget-text-editor\" data-id=\"d6dc5d8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li><i>Comparative Fit Index <\/i>(CFI): CFI varies from 0 to 1. A CFI value close to 1 indicates a good fit. The cutoff for an acceptable fit for a CFI value is &gt; .90 (Bentler and Bonett 1980)\u2014indicating that 90% of the covariation in the data can be reproduced by your model. CFI is not affected by sample size and is a recommend fit statistic to report.<\/li><li><i>Incremental Fit Index <\/i>(IFI): IFI should be .90 or higher to have an acceptable fit. IFI is relatively independent to sample size and is one that is frequently reported.<\/li><li><i>Normed Fit Index <\/i>(NFI): An acceptable fit is above .90 or above. Note that NFI may underestimate fit for small samples and does not reflect parsimony (the more parameters in the model, the larger the NFI).<\/li><li><i>Tucker Lewis Index <\/i>(TLI): This fit index is also called the Non-Normed Fit Index. As with the other fit indices, above .90 equals an acceptable fit.<\/li><li><i>Root Mean Square Error of Approximation <\/i>(RMSEA): This is a \u201cbadness of fit\u201d test where values close to \u201c0\u201d equal the best fit. A good model fit is present if RMSEA is below .05. There is an adequate fit if it is .08 and below\u2014values over .10 denote a poor fit (MacCallum et al. 1996).<\/li><li><i>Standardized Root Mean Square Residual <\/i>(SRMR): Like RMSEA, this is a badness of fit test in which the bigger the value, the worse the fit. A SRMR of .05 and below is considered a good fit and a fit of .05 to .09 is considered an adequate fit (MacCallum et al. 1996). This fit statistic has to be specially requested in AMOS compared to other fit statistics that are presented in the analysis. To request this in AMOS, you need to select the \u201cPlugin\u201d tab at the top menu screen and then select \u201cStandard RMR\u201d.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0c4a468 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0c4a468\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-49062ac\" data-id=\"49062ac\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7d81a9a elementor-widget elementor-widget-heading\" data-id=\"7d81a9a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What Value Means the Model Is a Good Fit?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-dc8604e\" data-id=\"dc8604e\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b28120d elementor-widget elementor-widget-text-editor\" data-id=\"b28120d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>There is no shortage of controversy on what should be the cutoff criteria for these fit indices discussed.<\/li><li>Bentler and Bonett (1980) is the most widely cited research encouraging researchers to pursue model fit statistics (CFI, TLI, NFI, IFI) that are greater than .90.<\/li><li>This rule of thumb became widely accepted even though researchers such as Hu and Bentler (1999) argued that a .90 criteria was too liberal and that fit indices needed to be greater than .95 to be considered a good-fitting model.<\/li><li>Subsequently, Marsh et al. (2004) have argued against the rigorous Hu and Bentler criteria in favor of using multiple indices based on the sample size, estimators or distributions.<\/li><li>Hence, there are no golden rules that universally hold as it pertains to model fit. The criteria outlined in this section is based on the existing literature and provides guidance on what is an \u201cacceptable\u201d model fit to the data.<\/li><li>Even if a researcher exceeds the .90 threshold for a model fit index, one should use caution in stating a model is a \u201cgood\u201d fit. Kline (2011) notes that even if a model is deemed to have a passable model fit, it does not mean that it is correctly specified. A so-called \u201cgood-fitting\u201d model can still poorly explain the relationships in a model.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-67b72ca elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"67b72ca\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-13bfd57\" data-id=\"13bfd57\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2230722 elementor-widget elementor-widget-heading\" data-id=\"2230722\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Modification Indices<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-d6e0938\" data-id=\"d6e0938\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7f2ff28 elementor-widget elementor-widget-text-editor\" data-id=\"7f2ff28\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>Modification indices are part of the analysis that suggest model alterations to achieve a better fit to the data.<\/li><li>Making changes via modification indices should be done very carefully and have justification.<\/li><li>In AMOS, modification indices are concerned with adding additional covariances within a construct\u2019s indicators or relationship paths between constructs. Note that the modification indices option in AMOS will not run if you have missing data.<\/li><li>In the output of the modification indices, AMOS will list potential changes by adding covariances between error terms and also presenting possible relationships between constructs (listed as regression weights). In a CFA, we are concerned only with covariances between error terms. All other modification indices for a CFA are inappropriate.<\/li><li>The modification indices will have an initial column that simply says \u201cMI\u201d, which stands for modification indices threshold. This value presented under the MI heading is the reduction of the chi-square value by adding an additional covariance.<\/li><li>A modification indices threshold value needs to be at least be 3.84 to show a significant difference.<\/li><li>In AMOS, the default threshold is a value of 4 where any potential modification below this value is not presented.<\/li><li>As stated, with a CFA, you are concerned only with modification indices related to the covariances, but to clarify this, <i>covariances of indicators within a construct<\/i>. It is inappropriate to covary indicators across constructs even though the modification indices will suggest it. Below are some suggestions of \u201cdos and don\u2019ts\u201d with modification indices.<\/li><\/ul><div><img decoding=\"async\" class=\"aligncenter\" 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VFXFwcXV1d8i5vThJhYP4QYWCG5ebmsnPnTtTU1BgYGPjuF8wT09PT+Pv7s2\/fPsLDwxfcQKr5YHh4mIKCAtzc3AgICMDHx4ddu3ahp6fHmTNnaGpqmqG2x3NDT08PxcXF2Nvbs3HjRqysrPDz88PFxYXk5OR53yNgJogwMH+IMDALampqMDc3R1lZmcTExHk9uGhqaorS0lJUVFTQ19cXDYXmiYmJCVpaWsjIyCAmJoa4uDjs7e1ZunQpBgYGhIaGUlxcTGNjIwMDA4yPjyOVSudMCJTJZExNTTE5OcnIyAgtLS1cuHCBxMRE7OzsWL58OcbGxoSFhREeHk5sbCyXL18Wd7PukggD84cIA7NEKpVy9uxZlJWV0dLSoqioaF614J2cnKSxsREDAwNWrVpFWlragroTstCMjY3R2dnJxYsXOXPmDCkpKYSGhmJiYsKSJUvYunUrLi4uxMTEcPr0aSoqKrh8+TLNzc20t7fT39\/PwMAAQ0NDTExMfGm7kwDx1deMjIwwMDDAwMAAXV1dtLS0UFdXR0VFBbm5uZw4cQI\/Pz8MDQ15++232bVrF15eXiQkJJCbm0tJSQktLS0MDc3NJl0PChEG5g8RBmbZ0NAQcXFxfPDBB9ja2lJQUPBAD9KSSCQUFBTg4+PDzp07CQ0NpbOzU95lCfeZTCZDIpEwODhIV1cXly9f5uLFi6SlpREREYG3tze7d+9m8eLFrF27FmtraxwcHHB3dyc+Pp64uLjbW1paGnl5ebe38+fPU1RUREhICBEREeTn55Oamnr7dQkJCfj4+GBra4u9vT3btm1j8eLFrFmzBg8PD4KDg0lJSaGgoIDKykra29vp7e1ldHR0Xt+lkwcRBuYPEQbuk\/7+fiIjI9m7dy9WVlYkJSU9UCPt29raOHXqFD4+PqxatQpfX1\/a2trkXZYwB8lkMqanp5FKpUxMTDA2NkZrayvNzc3U1taSnZ1NTk4OOTk55ObmEhISgpeXF76+voSGhmJqaspHH33Ea6+9xssvv8yyZcvw9\/enqKiI\/Px8srKyKC0tpampiebmZnp7e5mYmGBycpLp6ek586hiIRBhYP4QYeA+m5ycJCUlBQMDA6ytrbG1tSU+Pn5OjlYeHBwkMzMTb29vfHx80NLSIjw8nOHhYXmXJsxTpaWlqKur4+bmRmZmJgEBATg6OmJubs6RI0dEc685RoSB+UOEATmqq6vj4MGD6Ovr4+npyaFDh4iKiqKmpkZui5w0NjaSkpKClpYWHh4eHDp0iKeffhpra2u51CMsDJOTk\/j7+6OtrU1CQgJwY5zA9u3buXr1KuXl5Rw8eBBTU1NSUlLkXK1wiwgD84cIA3PA+Pg4Fy5cIDg4GC8vL6ysrFi\/fj2ampocO3aMkpISOjo6mJqa2RXme3t7uXjxIsnJyWhra6OgoICrqyve3t7Y29uTl5dHf38\/+fn5qKiokJycPKPHFwSAqqoqNm3ahJmZGTU1NV\/63q5duzhz5gxwY\/pqcnIyWlpaWFhY\/MvPCvefCAPzhwgDc0xWVhY7duwgLCyMmJgYYmJiCAoKwtrams8++4x169bh4uJCaGgop0+f5uzZs+Tl5VFVVUVrayutra20tbXR2tpKfX09+fn55OXlcfr0aUJDQ\/Hz82Pbtm1s2LABGxsbfHx8OHLkCCEhISQmJlJXV8fo6Oi\/1FVZWcnKlStxd3eXw19FmK9iYmJQVVXFw8Pja0f2R0dHEx8f\/6Ug3NbWRnBwMLt37yY4OPhrz1fh\/hBhYP4QYWAOqaurQ0dHh5MnT97+mlQqpa+vj6amJs6fP3978FV6ejpxcXHExsZy9OhR3NzccHFxQUVFhR07dmBnZ4e9vT2RkZHExsYSExPDiRMnyMnJISsri6KiIhobG+nu7r7jVRdra2vZs2cPvr6+Yklc4Z60tbVhYmKCrq7ut\/amKC0txdbW9mvHqVRUVGBvb8+ePXvIycmZVw2RHhQiDMwfIgzMEQMDA7fHDNwJmUzGxMQEEonk9hSvvr4+srKysLe3p7a2lr6+PsbGxhgfH2d8fHxGxiF0dXWhoqKClZWVGEgo3DWpVEpWVhZqamp4e3t\/Z2+Kvr4+tm\/f\/o3TV8fGxsjKykJZWRkbGxuxNsh9JsLA\/CHCwBwglUpxdXXFycmJycnJe9pXX18f7u7uXLp0aYaq+1f9\/f04ODhga2srWrYKd+zatWt4eHjcHgdwJ1MAp6enMTQ0pLy8\/Ft\/bmhoCB8fH5SVlTl69Citra0zVbbwLe4sDMiQTo4yMDDM+OStCxIpo0ODDA6O8uB1fpDSVZlLdEImdQNfvcCaorv2LEfjTlPdd69jvKYY7e9nZOIL+5GOMdg\/wNjMDh8DRBiYE4KCgmZ0CWRtbW2OHDkyI\/v6JmNjY9jb26Orq8vVq1dn9VjCg6+kpARTU1PMzMzo6+u7q9fGx8ff8QyC1tZWTE1N0dXVJTMz857DtfDt7iwMTNGeF4zyTmPCS28tl93KcTN1VAyP0jiX0oBskJozGZy5\/G3vxUOkmS1n0aPv4VX11U\/lUc7Yr2XRw29gW3Kv3S0lVMS6YuSbRZcUYJyKEyHYOCXSPAvNbEUYkLPMzEyUlJRobm6esX2amZnh6uo6Y\/v7JlNTU8TExKCsrExRUdGsH0948IyNjWFjY8P+\/fu\/92yUW0Hibh5znT59GmNjY8zMzGb1LtlCd6dhoC5Ilf\/7wXMoJLbc+JKsBsfFj\/DQS+ZclEwzMdxL\/+g40vFBWhsa6Rz+QkIY66H5Si21zd18OdpN0nu1gdqGLka+cGpIetu4UtNM9+jNL8qGaa2upKqxm1sf3dKRfnoHR5BOjdHVVE9Lz41PV0lrCnuf\/G+eWOXO5f5hJm7WP3KthZrqetr7J4ExTh9azQ+fWYr3hS66W5roGLxV2Sh5jhv4t8f+jn3J6M3Dd1J76TLN3Xf\/Cd573pflb+8g5PIIsv5CbHZtZp\/3RW6P2Brr5WpjM539t44\/jWRkgKGxcaYB2fQ4QwMDSO7gfx0RBuSovLwcZWXlb\/3Dfx8FBQWEhYXdt77rkZGRbN68Waz1LnxJbW0tFhYW7Nmzh\/Pnz3\/v\/bS2trJu3bq7vqPQ19dHQEDA7eWIH+Q24HPVnYaB+jAtHv\/PV1E6cfMuoqwW13ee5LG\/2lE3OUJJpA2b133O9s+W8+ofX+W9jY4Udwxz7UIMe5a8yR+f\/QMv\/OUfqPpm0DkByHo4E2jAB39+gWee\/xv7nTPoBzrzA9nx0V959ukXWaHozYXaiwTp7+Qf7y1hyXufouGRQbd0itacYPZtUUZTZSuv\/\/FFXl+iRUzReeL0VvPEv\/2Af\/vpY7yr6EJZZxfZ7lp88PpLPP30H\/nbp7okXKwl03kr\/\/HfD\/PKW2+x+OVX+fuag5xuHAEmyHfayI8ffxfXyhFGq5LQXr+af3ywhKXLFXE7VcddDXOd7iJBbyMrjaNJj3RgyyZzzg3e+Fb\/pQSMdPUwNDVGW8cY37w2oJ9MOz9CoyqQAJKGPLxVgrhwB48sRBiQk\/b2djQ0NDh27Nis7NvNze2+rkefkZHBrl27SEpKum\/HFOauhIQE9u3bR2Rk5D0v0DUyMsLBgwepqqr6Xq+vrKzE2toaVVVVcX7OsHsOA6870Dw1QMqBFSxa9Ave32+NvdZq\/usnv2drYCEtpSewtfTiyJEQTFY9y3\/97zrCLw\/TU+rDG0\/+nr\/stCDIywHPI9k0NZ9D88MX+PXfduLi789hJ3uM92\/k6edWYhFzmkTLDTzy6Mf4FbRSfVSHny36Ga9sNMLNQYvXH\/o5v\/ncluPR9qx99H\/4zasKeJ0ooquvhfQQb+y9Q4kINOXDX\/6aN7d5c8R5Gz\/6\/x7iTYUDuFso8uxP\/5fFpmlImKTQdRM\/+f0SPPPy8dz6Dk9\/oE10ahIW617nkcVGnOu5NVZmgv6uNhrr62lobKSxsZHGhnrqmtrpHf7nPZD+yig2vfoX3vhoDboRV5gGGCrHadN2tplEUFh6nkQXddZvMOd8TxMRn2pibJrLGDB0Poq9TxmR0fndz2JEGJCD0dFRjI2NsbW1nZXpUFNTU2zYsIFTp07N+L6\/TVFRETt37iQxMfG+HleYO7q6urC0tERFRYXS0tIZ2adUKiUhIeGezqvx8XFyc3NRV1fHyMiImpoasYbBDLjjxwShGjz6H39COfXWgON6PN59kkf\/Yk\/j1CBpZsv5\/55YRkAj0HuCfzz9f7xkmkxPXxNn4o\/gZWPEur\/8mh\/9cBW+ha0U+m3hod+sIrj21jFkXM+x4aVfPcv2wzdnlExcwnLdM\/zg57\/jzaXLWfrmc\/zsP\/7CwcRqqqK1+O+Hnkc9cxjoI+Dj5\/l\/f9Ejt70E6z89wcufxXKj8fU4rRU5RPt7Yam1lqf+6xe8uMQOX6t1\/PiJD\/GpB6jF5NXf8vOPPeiQSShy28JPn\/sIqzBPNrz6v\/z8d6\/xj2VLWfzcI\/zyqa0cq7\/1Qd9FZpAz+moa6Ojpoaenh56OBqpG7iSXfWHMwnQHkSpLeeydAxTdbKkxXRXC26sNCbt0M2g3JaOx8ROsCss4vt0YC6t8JMDIxXg0XjlIdpcIA3OOVCrFy8sLPT29WW2WcuDAAeLi4mZt\/9+kpqaG3bt3ExYWJgZvLSBTU1OcPXsWNTU1fHx86O7untH95+fnY2RkdM\/76e7uJjY2lp07d+Ln50dPT893v0j4RncaBhojdXh80VNsDqpkCpANFaL3ysM88oE7bdNDpJp+xA+e+5jQJqD7JCuf\/y2vGgUT57ibZ19cjenhICzW\/5H\/+veV+BW3cs5nAw89sgyfC0Mgm2JyYoKuLCte+tXTbPEuYQqY6juP2cfP8O+\/+we6jr4cDgggKPQENdf6qYpQ5xe\/\/iMaGX3IaMFj2fP821\/1yajLweSF3\/DCx8F0ANMdWaj846+8tsYAP38zlv32Yf60wgF\/q3X8+MkPcL8kBel5NF\/8DT\/\/xPN2GPiPZz\/CMsyTDS89wuPv78HeO4CAgEAiE87QMnorhEoZ7r1Oe2sb7e3tN7c2WjuuMzD2xQ\/vYbK8NHn5swhuRampyiDeWKVP4KUbPWJkdQlobFyP64UK4j\/XxOTQWWTAeEk0Si+ZiTAwF0VFRbFnz55Zn5KXnp5OZGTkHTcUmkm3mid5eXmJhWUWgL6+PgIDA9m9eze5ubmzsq5GbW0tioqKMxagGxoasLa2Zv\/+\/aSnp4ueGd\/TnfYZGKlLZt8rj\/HL51ehZWWH6c6P+O3DL7DFvxQZwyTpvMOiR\/6Bfz3QmcA7v\/0Fz2r5EKT7ET97+AMOhUVju\/Y5Fi36ALdzPXSXePKXRx\/lj+s1sdRXRccpmsvVuai\/9wf+99U1GJmbYaS5j10blvOHxxezy8qfqDBfnFyiudQzQG2cPr\/44c94bqUqlvo7ePHXD\/Oadgwt3WXYvv5\/\/Ptvl2MWmU1VaRybX\/wNv19tQmiQFUv\/7394+l0LPC3Xs2jRf\/PmFn3M1T\/lsf99nGWOZ5hikny7T1j0q7\/jnJ2H1+Y3efSVdVj6hRLs445n8Gla7nr2xABpTko8uTKI2\/O2+osx+3QHe01DOHP2DBEWuih85kD5WD95pkp8tt6Y2Nw8oi338c7vtcm49t3\/T4owcB8VFxejqan5nXOmZ0JtbS12dnYzfoV2p3p6ejA3N8fW1pZr167JpQZh9uXk5KCpqYmFhcWsLml9\/fp17O3tv7Vb4feRlZXF\/v37cXBwmPGBvAvB3TQdGqtNxUxpPcuW\/INlqxWxiyrhVrRrTvFin6E\/FdMA9bga6GF7uoGx1jzsdn\/CyhXb0NDTQ0k\/mIKbd9Cr4+1Zt\/wjPlq5Dev4ywBMVR1Ha9Nyln60CiWnZJp7W0l31mbNyo9YuvxjtprF0QF0px3g4V89xvvb97J35QrWavhz6WYebM1wY+2q5WwxiKZdJqM+0YGtq1ayaoMGeiYHOOSTS2FmJPoaB7A2UmHdR8vZcTCOW50tevJC2K\/tSvE4cP0criqbWbHsI5at3oJJ2EXu\/uHUKGWJvuyzyuCL7+b9l5I4sPlDFv9lMR9uOURS5Y0BttK2szgorOCNt5ahdMgZT8NIKgdFGJgzmpub0dXVJTs7+74cTyKRsHbt2hl\/87wbAwMDODg4cODAAdGcaJ4ZHx8nKiqKtWvX4u\/vP+vHm5ycvL1OwWzsOyIiAnV1dQICAmY11Mw3dxIGuru7sbKyxs7BCWdbcwy0NDAwt8HJ2RE7G+sb33N0xt3VGXsbK6ys7XBxdcPF0Q5bO3tsLEzR1zfBxtkVdzdnHGytsbK2wcHBDnNDXfRMLXF0tMPayhpbB0dsDxqio3cAWydH7O3scXK246ChFuq6Ztg5OmJrbYnqxy+z6Af\/zZsK+lgdMMHUwhYHO5sbtdjbcsjEAANTC2xtbLB3ssPMQB9jcxucXV1xdXbA3sEJNzdXXOwOYaBvhLmNHfa21lhZWWHr4Iy7mwv2NtbY2Dni4mCBvrYWOqbWODnaY2NlhdUXtnt7b5QxMjD6NQFjEsldNiYSYeA+GBgYQF1dneDg4Pt6XAUFhfsWPr7J5OQkhw8fZu\/evWK+9zxx5coVDhw4gKmp6X39N42Pj0dJSWnW9t\/c3IyzszNqamokJCSIAYZ34E7CQGdnJ+rq6qirq\/P\/s\/eeYVFlWd++E\/udf88zM9d\/ngndM++kns62HRVzaGMbMSsIiKioSBYJgqIYUEAMGAFRQTFiQDCQQTKSJedY5KIoqCqoqvv9QGi12zaBpch9XfsDxamz1z7n1Nm\/HdZaJiammJmbY2ZqiknXZz3\/e+Tvns9NzTAzM8PE5NFjTDA1M8fc7OFzmZiaYW7eebyxsTHGJiaYmZk\/8JkRBvo6LFmkwYo1xpiamWFmavLIeR\/8zASzrr8fqt\/EGGMTU8zMzTA1MXl8W7ra\/Kid3aU3Y8y8CANi4CWwfft2tm\/f\/tKzq\/n7+3P9+vVeT338rMhkMry9vVm1ahXJyckqtWWAFyMwMBA9PT3c3d1f+n6Q2NhY1q9f3+f13r59m+3bt2NpaUlqamqf1vW68zrnJhC3DCRbe5ABMdDHHDlyBFNTU5qaml563dnZ2bi6ur4yKV5v3LjBpEmTXrrL4wAvTl1dHY6OjpibmxMVFaUSG2pqajhx4gSZmZl9XldjYyPnz59n48aN7Nu3T2V7b151XmcxMMDDDIiBPiQwMJB1flGahAAAIABJREFU69aRm5v75IP7gKamJqZMmfJQ5DVVT32GhoayfPlyQkJC+mTX+QAvxqO76hUKBcnJyaxduxYXF5fHZg98WXh7e\/dk9lQqlX3+DJWWluLk5ISOjg7+\/v4DXgePMCAG+g8DYqCXUCqV+Pv792zYS0tLY82aNURERKjULltbW6Kjo8nJySE2NvaVGOEkJSWhra2Nj4\/PQ0sY+fn5r8wsxptIQ0MDOjo6FBYWAp2j4+PHj2NoaEhgYGCfBMh6VlxcXJgzZw5hYWHY29u\/lGUnmUxGamoqmpqaGBoakpmZOSBkuxgQA\/2HATHQS8jlciZMmICamhqnT5\/GxsaGs2fPqsQWoVBIZGQk3t7eaGhoMHToUH7+858zYcKEZ47v3lfk5uZibW2Nh4cHLS0t5OTk8OGHHxIeHq5q095YDh8+zG9\/+9ueDs\/Kygo7OzuVzwYoFApu3brFjh07mDp1Kj\/72c\/42c9+xltvvUV0dPRLs6O1tZXLly9jYGDAoUOHKC4u7vmfSCTi2rVrVFVVvTR7XgUGxED\/YUAM9BJVVVW8++67PRdz6dKlKrOlqKiI8ePH99jSXXR0dFRm049RVlaGra0tjo6OzJo1i0GDBuHq6qpqs95I6urq+PTTTxk0aBC\/+MUvmDVrFpcuXXolRsAKhYIDBw7wm9\/85qHneeLEiSqJYVFRUYG9vT3m5uZ4enoilUo5f\/48\/\/jHPzhx4sRLt0eVvJgYUNA7T5cSBcrH+u93\/neAJzEgBnqJmJgY\/vSnP\/VczD\/+8Y9YWFhQWlqqEnvi4+P54IMPeux5++23X8kXVV5eXk8nNGjQIL766quBELEqwN7enrfeeqvnPgwePLhnueBVoL29HQcHB37+85\/32Ghubq7SkNeRkZHo6+vj7OzMsGHDGDRoEJ988gnZ2dkqs+ll8yJiQCZI5qzDSeIqXyyRFYhIPuuDj288D2\/TllEceocLHjEMvFGezIAY6CVOnDjxg5HL1KlTX0q0wcfh6+vLr371KwYNGsR7771HUlKSymz5Maqrq1m9ejW\/\/OUve67Zz372s+fOez\/A81FcXMzgwYN\/MJM0e\/Zsamtrn3yCl4RQKGTZsmU99h04cEDVJiGXyx+yadCgQRgbG78S+yteBi8iBsSZF9D9+zyOJzW\/oBW1XNBahuZSd4of+ryN6B3bWD3Vg5eXv\/X1ZUAM9BLq6uoMGjSI3\/zmN+jo6ODr6\/tKrB86ODj0jLibm1\/0R9e7pKSksGDBAt5+++2HxIC2traqTXujsLOz49e\/\/jUfffQR8+fPx9zcHEdHR3x9fV+5Z6ampoZJkybx85\/\/XOWbcwGSk5N57733HhIDf\/jDH7h586aqTXspPL0YkFJXlEJUVAJ5VSKUQFvOVQwHa3E8PIv7CQlkFNfxfdj+duryU7kbFUNaYS3yrnPUFBVSUS9GCXSIaynOr6Kdeq6tWc2qlae6YveLKUq7R2p2Ev7bdmG2wJsHw\/p0SJuoKCynrraIezHx3C9v5PttzBKq7icSFZ1EgaBzxqK5PI+ckhokCgAF4poSsvJqkAOy+mKSoyOJyyijO\/9Qq7COqrJyyosySMooQfTg5JW0juz4aKKT86ht7a61g7qCtM62FtShBFqrCsgtqqLzECWShjKycgW0I6W2KJ3oiCiS8gVIu+qUiOqpKqlAUJHBrasB3M2sorNaBa0N5eQUPnhtf5xXUgzIZDKEQiGNjY2vRSkpKeG9995j7ty5nD9\/nuLiYlpbW2lra+tpR1NT0w+SBnV0dNDc3NxndjU3N1NVVcWcOXMYOnToS7se3R4BSqUSsVj8o8c0NDRQXl5OVlYWt2\/fxs3NDS0trZ6XaVxcnMrv60+VlpaWn1xPb29v79N721ulpqaGGzducO3aNRITE8nPz6e6upqmpqaee\/dTbe3o6EAkEr00e8ViMYGBgSxYsICoqKg+q+NBF1yFQvGjz3F9fT02NjY\/mFHpnonLyMhQ+f19sDQ1NSGV9m6gnacTA1JyAjww1TZjp5M9W04EUNgCilJ\/Vr7zMWNn6mBispKFS405HlMLyEg\/sw31ZUZs3rIRPR097C\/noKQcb62tHPXNRgkIIs5jO9edIhoJWKfP6lU+VNLCXTcTFmkZsNFKl\/H\/HIum1nkeDPgrzg1gw9ezWbZ2LUbrdZmvaYF3ciPQQvypreiabMFxqwVrjay5mCumLngXCy32E10HUMe1wxvROhJLXd4t7M2MMN22h21GOph73qVVqaQ04DBLPxjJ9EULWeV6ldzGzmdJ0ZCB10YdlhhsxHzlWra63aFOqSDrnANztQyxs7dET2cl22\/kUB65n2XmjtwuVwIiwk7aoOV6nbvXPbBYaYXLfgdWzVrDgYD8zmsR5oXO++OYqz0ZNbW\/8Xf1HUTUATRy230ji\/ZG8iQ\/rVdSDKxYsYLPP\/+cYcOGvRbl66+\/5l\/\/+hdDhgxh7NixjB49muHDhz90zDfffMOIESMeWuPcvHkzn376aZ\/aNnLkSD777DPee+891NTUXsr1+OSTTxCLxdy\/f5\/33nvvsceNGDGCsWPHMmHCBEaNGsUXX3zB3\/\/+d959991X\/v6\/\/\/773L59+7HP8Jo1axgyZIjK7XxSGTp0KCNHjmTChAmMHz+eMWPGMGrUKEaMGMHw4cNRU1Pj\/ffff+xId\/v27X3+DD9ahg8fzpAhQ\/jqq6\/65PzvvffeQy6LQUFBvP\/++z967Icffsjf\/va3H5S\/\/OUvfPnllyq\/vw+WL7\/8klmzZvXei5qnFAMd1VxwNGCklhuJhRXUtbTRoQRJ7hVW\/WcSlmdTEdSmcXDJclaZ3aSqMoS1E\/VxvJZFQ2MdCV6bWLrYmvCqHNwnm7LnWDpKoCLAA\/2vnMnuEgP6a85RWHAL3TGr2RdUQG1NNifXrEVr0ZmeJEIAzfe80XhnMpuv5VBTk8Tu2dqs3xJGdekt9Mbr43T9HjnZkezRXo6m4R0aq2+weII+bhHVyOpjcFiyDNewBC7amrF45VHisnNI93Ng6pfWhFU1kXvajgnvzmJ\/RD7VTS3I5ADt5FxxZMqETdwsrqeusoiiigaEhYGsnriaXVfv09BYT+IpOzQ1bbid4M\/amavY5V9CR1s6LkvmYxeYR5uohqK8QgrzU\/DUn8TkzeepByqv7GLyn6eyOzSf0tzzaI7RZff1IiTCezgumofd7bInbqJ8JcXAxIkTOX\/+vMqV9LMo7tbWVlpaWmhqavrRY\/Ly8hgyZAgikainncuWLWPPnj19bl9LS8tjR+i9XRoaGvj8888RCASEhYUxbty4p\/qeUChEJBLR1tZGW1vbY6\/jq1Jmz56Np6fnY5\/h7777Dm9vb5Xb+TTlSdd6zpw5uLu7\/2g7dXV12b59+0u396d+ay9axo4d+5D48fLyYubMmT96rEgkQiKR\/KC8is9wZGQkQ4cO7b0XNU87M6CkLvUShhrzmLNmF9cTi2iTgzTnCgafaHEqox1o5abROlbpniIl+jjDNJwJFnR2X7J0L3R1NDiQlMTp2Vbs8+yMQCkI9sZ4xD5yaSTAQJ8163xJjjjE1xMduC3orDflwF5M551+aJmgKeks+p8s53w+QBN+unqsM7xASqQbn346kYUGltjbm6M9TRtTm1BEtHDdSBODg\/7E+h9gnvoRCgSpbNGZzj9H62DrsAUroxWoj99BtEBI1smdaI3eRPxDE8EthLib8ZmpPw8uvLXE7OOzRXu4U9058ybLPMWKZYtxzagkzn4l+o6XiA85xrwpu0hoktNWmUGg+352OFizbMJ\/+cLSh1qg3G8v2kPNCGsGaCNokza6uy9wN\/Aw86fvJlH4mmYt\/O677wgODn6ZVfY5jY2NDBs27CExsHz58se+ZF9nRo4ciUAgIDw8nBkzZqjanD5h2bJlP5l4avbs2QQEBLw8g\/oQLS2txwqfVatW4ebm9pIt6lumT5\/OrVu3ev4+deoUGhoaKrSod8jKymL06NG9es6nEgM9S0xtpHmsZ\/jiDVwoaocSf9Z\/sgyvlFZAyA3DNazWO01G0inGTLLkclcP3hy2H525+vgVZ+H1nR5bj3eKgbKLe9Ec5kpetxhY60tmghdjhllwpQJAStAmK3TneXftJeikWwz45siBOi52iYGMOHdGfrmKI3eLEYmENNQ3Imzp3AgquLONcfNWordiHotPpIM0GwfNxUw2uECZTEyzsIm6hlZASobHdrRG2RDzULDKVmI8Lfh8vgfdOTElba3Uxx9l+PiNXCzu\/EwU4cby2cvxKVciTXRl0oKVrNBVZ+7hWKSKMk5sXIf6mvOUiSvxs5jCCKvTCIByP1e0vzElpMttoinhIFPVl6OpOZvFx+J4Gn+NV1YMPPhj7A\/U1tb+qBg4evSoCq3qfZRKJSNGjOgRA9OnT1e1SX2ChobGE8XA9evXX55BfYimpuZPioH9+\/e\/ZIv6lkffP6dOnWLJkiUqtKh3SE9PV4kYUMrqSbx4mJ07nXDatAq11U7crJCgzL\/E8nfmcfxeK9DI5eVaLF3gTklTPu6rVrBE24htDrasnq\/Lhi2B1CrFxO5cw4zJGmzc6oC59mKmfuNIDg1c1V6GpuYpyoV57J+\/gFmLjNnhaIPW+NksWuDFgw7ejXFeaL6ziNNZcqAG7\/lL0NI9Q3ljLsd1V6C9agM7d+5k684j3Eiq7Jxeb47B9It\/8Pa7M\/DOkwJikk86ofOdNuY7O1MXu\/rEIEJC2iFb5g42I+ohMaCgIfUSa8fOYpnpFrZZb2Lv5QRqarJxX6XHEi0jtjnYsXqBLuZ2V6lQANJU7EZ\/wG\/+MJ4j6a3QLsDbYTVqSzZw4OhxNkz9gPdNTiJASYnvLub9dy13un0oZXm4zBrK\/77zHR7pwqe6lwNi4CUxIAb6FwNioJMBMfD6oCoxgLKFrDunsNVfw0pzR3xjS5ADioZ8gjwDuV\/bAUgoCAriVkAmYqCjKh5P+\/UsX6aH9aFA8rs6VqUwm3M7TNFbu4mjl24RcS2ZRiQU3gni9u1sxEBzzm32Gq9itY0LPpeDiAvJ4sHuUFqdxW3P2+Q2KYFWsgNucycohzZAUnaXo5vWoaOlh+GWw9y+3x2xVUx2wEncTkVR3e01Kq8l9rwT67SXs3KtGc4XkhChoCEjjoAzd6n+wfb9VvJDvbBao4eu4U4ux5chB+TVCXhtXc\/yZSuwPnSDvB4RIaUgyIeDniGUdu3+ayuOwWPXRkxsnPFwP8rJmDzaUNKcm0Tg6QjKevaHSrjrPI8vlx0gs+XpQi4NiIGXxIAY6F8MiIFOBsTA64PKxMAAL5+2++yftwDjE\/FInjL84oAYeEkMiIH+xYAY6GRADLw+DIiBNwdFXSoXj10mqezps2wOiIGXxPOJAQWy1kaqKsqpENTR3Pak0KttlGemkZpWTE1dE6K2x4SZUNSTEZVMYa34CedTIGmuo6KsHEGz5Kniez+rGFB0tNIgqKCsopo6ofiJgTEkDUUkRGbRKJfT2thEQ5PkMfHNxRSnpJCWU\/OT55RLRdRWlFFe1fDUChp6QwwokLU209reHXhESXurBMkD90ze3kqzpPtvOS11VZSWliFoEPe0WdkhRdwiRvboRVC0Ixa1IOnoblQHYpEYSfuzR4N\/UTHQ3iaiVdbe9fwo6ZBIaRO39zxPio42mttkPX+3NQooKy2lqk70\/b1TtNPW0oJE\/sjJlR20iVpo67oASrkUYU0lZRUCmtufLyL9s4oBRXsbra2tdHzfINqaJci7\/1a20yJpo\/tWKiRCBGWllFbU0tJjowJZWwtiyQ+f1vY2MS2tMhQokbU0UFVaRlV9C49eiicxIAbeHJTydtqfMa\/I6ykGlDIaKnJISognOauQ6qYn7ZUUkhkaTUJSNrnZ5dS1\/HinqmwrJepqNIWNTxOYQ05jVRWCxran6iSfSwzICzm5ai6fD\/6CMYtXYbH1EDfiy36ic6sjwNKRbct3YL3hEL6hpT\/eUbansnucJSfv\/DAbXUN2BtmFDQAoahM5Zr2CyWrfMFZjC1cTBU\/srJ9NDEi577uT6R98wpdT57PSzJ7D52Op+0HP9j21MecxGeVCurCKmy77cNgfg+hHj6zm4mpbtm6J5MHAsB3NVWQndK8h1hJ1zpkVk0fy9dC52HjG0tzxdB3Ii4sBEZGHDdBzj6IFQFmC53oD9K2u03n1Gwn3MMfgUjbIxeSGe2K6aBqjho9hjq4Nx4MKkSqhLuYoS0aNx8w7le+f6g5yL9owYcRs9kTXIG2s4t51FzQnGuEeXPrMSVteTAzIyfSxQGfvFcpkAA34b7dAU9eLUiVAOxnnrFnldRexQkr1vQvYrVBn7PARfLfYGJcrabS0gyTPj3XjRqLjHPrQ\/RaE72PWiG8xvVIIdFAUc4YNCyYxdPhEtDZfIr\/x2XMXPKsYEKefw8JmEz5Zne+NhsTTaIwx51pe50KvJNWHDY57CSyDDmEKp3cZMktNjdHjFmHsconkahlQha\/BdMbPtyem5oFfWWM8OxeNY7zJSfKqc7nmaoH68K9Rm7wez7ASniXo8YAYGOCneC3FgLIpAYdvRzN46GhmLF\/HBltXrsX81A+jhJNLbHAw2oOVuSd37j8mja8gFKvPLbma9eiZFJRGRpBW2NmFyJsrSQ08wIqJSzHcFsrTBGx9LjEgScJhqBGH\/dKpLL\/PnePbWLZoA6czu86hFJKbEMzlWzHk13cADfgbbcZqlReh6XmUdY38W0pTCL4ZSFRiKrml1YhEyeweYYTTkRuEhoSRUtJ5XFtdNp5L56O7xpmQ3CrqC+O4HpFOTWMRFw1WslzrOAVP6EmeTQw0cnvjFky0vMlqFJAVeZ5Ni5dh4p3U4wrTXHoP\/+s3icyuRQHUx5zF4PPtxAnbqM7NIDWrGgUgbywiNugGQXcTySoopra1jMt6Vlis9iQoKoS7GVVIO0QkndiG3uiluEXmUlWVw924VEqbJZQFujLrP2u5XvJ0nceLiwE5Bf7bGTn3EPc7QFkbiuHod\/ifGQ4ktgDSDA4t0OJQbCnlIe6sWaTPkbulyGTN5Nzex8IphvjlCijy28WIX\/yW\/yzeToiga6zYmsP+OV\/y+\/8OweBWHlVR59i7eQ1z3l\/Jft9cnrV7fNGZgaZYN8bN2ElItQKk99k997\/8Um0d1yqVQDlntDTZeSWNytRrWCzUZufNLMSyNiriT6I1ZQWHI4oQxJ1gxi9+x98mGHAmv+v3qajirP5U\/vy3d5h6IgOUQu7HhxGcWk5DaRAbv1mI4+WcJwrYR3nmZYLGGMx11mPs2RkZL91dm\/\/96xBW+hUDCjKObsbM8ACZwkoumC5n5c4LZNdJkFYl4LRGFx3rqzRI83Ee9gV\/+f1gVpzP6hr1d5Dnt4dv\/\/L\/8XtdF+LuZ3A3pYRWeR2BVuuYOc6J+8+g7AbEwAA\/xWspBuQVwWz8zIpr9xtprc8n5JAN85facD6va9q7tYSwy14cOHOHnDo5UM6phZZs3+ZHVGoegpZ2QEpZvD9enie5diecxJxyhOURbP7KkH0nL3L2zCViCjvP15AXgv2oMSw13EdwWjm1hXc542qN\/qSVmBr48zSpXJ5XDOxQM+fEza74WfJC3IzWsNAqDBki0s4exdZ4LYv1DbF2PEuJSMDtDTvZou\/CHqtD+IUU0VQTj7OZEatXLGD61Dks23qF8sb77B+\/CI3Fa1i1fDEaJnu5K5DSkH0V468HM3SkOubuUdT0aCIZUc62rNf3oOgJc5PPKgbuWG5lg65fzzUsvrGT0ZNdSJVIqcsI5ZCFCTrrDVi53oHA+2VUx13C+JvdpLQ1cNftEHtdohC1lXFuz2bW6CxmwdzZfLfChcS6Cm4Zr2fByGUYmKxgia4ZJ6LCOGW4gBH\/\/Bx16xPcLRShlImoLcslzGM765cdJqXx6SZfe2PPgKToJpbj1nKtvJWa0DNsmzaU99Vt8Yxror3cH90ZLtwrL8B3hzmzTW894Ctcia\/ZXNb6RpB8+gSWw1ew2MAKB+9sFEBt7CGWLd2AsbUW+tfyUSiUIM\/l0JQN7D99\/5lGk9ALewaE8ThM1MMroYqmrCD2Th\/B4NlG2F+tAmkkBtO3EZBaSLjXZiZqn+X7pMRigu0XoXXwKim3LmLz5UpWmJqz3ikGGSDLPs1KHSvMNyxioU\/KI5Vm4zRTD1f\/vL4XAzQRsNEUc8tL1MiFBK5dgvrsmYy1CKVdKcDbcjMWjnEIyy8zc5otF7O+X8etub0bndUb8c9J5uhEMywN1jJVz4M8GdCWitu2LazXXcJM5+PE1YCyrYHSnFhOWVlgaX2D6gExMEAv8XqKgcoQrD635Ep6V8fakc7mpavRd0pC2l5JgOMOrKytMDDdwGYnXwpFZZxbZseuTfvZouvKrZQKSlKvYqFvjJWFLlO\/VWe1WwR1tbFs\/nwW2ms3YLJKg2Ub9xEraEdw7zSrP\/uEMbP02eOXilCuBMSEWm5j4+qrPE1G9ecWA8PN8Ago6ZrubyDA0godjctUCG6xbNZKtp5PojzND7Ml32EdnkKozW62rd2FzXe2HD+dTOJNR8ZaBANNeFgbs3h7ApDHnhHL2HQoDmFjGns3zGX19SpAiL+RHhusT5Hd\/P34UVYezNZV69h1pfiJo8pnFgNWWzFffonuBYvmpDNofmpHeEE2Xpv1mbr2NIUNxVy2mM53O85wL+QSG9ScSJPVccd0MxZrAinLP8u01YdJaYWCa9sZpXkeMU1cX22A7jJPitrEhLksRf34HTICj2M5wwC\/\/BbkSiXCnFC8nK3Q+G4ii3bdoeEpX669IQaUkkLOWC\/D0j+GkGNH2WpzBBejTTgdieBe4Dbm7r5Jc2Me+x1NmeGe8cA3hdzer80cj1vEexzDZsFBzh7YgqHVcXLEQkJsjbDzvsRhRz1WXenO15bNvonm7PdWgRiggdvbtdE\/HUzM5VNsWn+QI5u3YrclgKyYfSzY4k1OjYDrxy0YvifqgbVwOaknVzHF5Rwx186yacoefE+4YLx6B3HiVlJ3b8Dy2HlO7VvF4lP3Hqqx7NoOtFa6El3+7Olxn2cDYXXADnStnAhOj2C71h5Oeh7CeJ4L8VnB2G\/ZiONdEYr0I3yx3o1Qwfd3QJLsjuYGCzwTYzg21YajHr7s0FrJoYwGhOGnsLNx5sy57WjsOkyMQEJdwiWcrPSYNmU+Npfzn6ldA2JggJ\/itRYDfmnd2avL8NQwwtAihOL7J\/l2igFHQzLJCzrIsiULcU5K55qePY7WzpiNtONSUDo3vGyZtDMVKGbzKhPMfQpAGofNZ3q4hVbRIQjD0ngpNpEiQMC5ZVrYHwqhRtq9nt1AgOkWNur3tRgwx\/Nmd\/ysSk5amKJpGUpF6hGGqWti6HqKS2c8cTl4EL\/cbIIsd7F1jSO2s7fg7ptJXtQBxo1Sx8DImOUatngmNgEZOI6w4FRwDXQU4b5LhxVXKgAZoRZGbHW80bVuDcr6DE5tWYfVsTvUPMWg+XnEwAZdv55rKAh3Y+pkV5LyYtlsNJev1u3lytWLnDiwgz0B8eSEncd02B7SZHUEWdhjtf4W1YLbaE2dyaLVJhgsWY\/NuXyU1HJpxSbsbSORAMkn9VH3Cic37CwO862I6roNclE1effTiA86iclqffbFPc3d7CVvAmULcee2MmvFNrbs3YLtnWKKztpi4rgb1\/VrcLmVCx1lHLVbz3dbox\/oJKu5bLmANb7hJHkewnSON\/ml11mvb8Ne9wOsXO\/G3fR7eDnpoudX2PWdHPZP2sDBM9nPPFJ+cTGgoOj2HqZpObBrnzXrLudQcdOFjdvscDI3wfFMJG0dYm4dNGe4YQDfj5vbiNihgZbbVZL9z2A+3o20kgi2mm5gq9sRjI33cCUmk5vHVjHf6\/v03DXxp9m43hzvhMpn3mQHzycGlJU30NO3Y\/P2jSzZe5W07BhOWK\/Aco8zThu3Ed4CpB1FTX0HN0q+35NUH7IXvbUbuZ6VxKGJ1py6kULYmU1omB3igMtWLA4mUBHrxFz7g0TXKpHVl3A\/PYlbXg4sN9xCYPnTL\/oMiIEBforXWAxYcz2ra1lAmsIWLQNMDqdQGObAh7P12eV1gSvnTnL4jB8JVTmc197MLmsXNoy353JkAYkX7Bg6dBo6WnosX32A8CopNEWy6ctN+Od1QEMMW7eswjq8GWjCT0cPZ+97D7xIm7ltvg3rtTd4zA6Eh3g+MZDItm+MOOZfjFyhoCzyOGs0VrA3poX28ovMm2+Ey40MBDU11NY3o0TADZNtbF61A+sZdhzzvkfCdTvGz1+N\/a7DXLiR3pm5qiOZ7UONOX6jHKU0jyPbNNH1KwekBJutwXLLBaqUSpT1aXjaLGfFDl\/yRADKJ24+e+Y9AxabMV52niqFAnltKu5Gi9Def5dWSQFHNq1B3cqP0hoBNbU1iCRSqiPPYPDFTlJlddwx24ylQSB590+zaLEGBrbOeJ4MpbIDoJpzOhuxtghGBCS46zHLM5Sc4NPYzjTmVq0SpbiWivrmzjaJknDRns7GgLKfsPd7ese1UElD8lkW\/+V9hmuYclUAFJxFffx\/+eu\/V3I9vw2QEX98M4tn2hJeJUOhUNCYepaV0wy4cL+U9BMHWDfRnUrE3LDR4KP\/\/9\/MOBhLs7CAEzs10blUAEolCkUGzmONcfFKR6J8ti2EveFaKCu7xZr3P+ebqZq458uhJgiD+Z\/wp\/+dj2dMDaCg8MY+NCYY4ZcnQqFQICm5icmMFbgFZ1Mc5IPBN85kKaXEHzbhy9\/\/hRG2VylraeTOQS1me3YmFqqKPonJihXs9M9CrACesa3wnK6FygpOr9bkm3c\/ZsW5ZGTtzQS7zuF3\/zsEbdvAzg2rTdFsmroUu9MJiBQKFLISzlitY4X5eQTS+zgNN+PErVIai\/3R+vff+b\/fGnCmpIO2yG1Mt3UlOLOMurbOwUhL3DG40qDaAAAgAElEQVTWaerglf300m5ADAzwU7ymYiAI8w\/X4RFWRG1tBXe9bFmmZ83VIhntWaeZNc+SExHZlJWVU1bZhEJZzMmFVjhY7MZklC3ng9K46WnImGWWHDziTWBkfud6rCAEi0\/MuJjZBjURbLJezsYwIdCIn84yLJ2uU9oiRS6XIqrPwXuFCfpLT5Dd\/IBb0WN4LjHQkc2B6eP4bPA3jBgxmvELTThy4x6izh1zRHpsZvZiPQyMjFmv70xsTTmhW\/eyZ+N+tms54uOXTnKAA9+qr2Hbrq2Y6htivT+Yekk2B2ba4RNaDbIiTu5dh8GNckBJRaAr6mpDGL3xNKFXd\/H1P\/7MPwarMW7MSCYY7ye29qcb+mxiQEKSmwVj\/+8nDB0xghGTF2HidJ7s5g5ATm3SBdZparLU0AwjTWMO38qkPOEq1lMPkNXRQMgWR7ZtCqUk+yxLFmuz3noLW6xMWbPBi5y2SvxNHdixPRIxkHLGlMUnY2kqT2DvomF8Ot0M74hQjpouYPz06ahPnIGuzUnuNz\/dy7XX4gwIk3CYOoxhiz0746d3ZLF98hA+mO\/K\/a6oY3JhHtd2rmXKKDXU1NQY\/t0Kdl3Opk3RTtbpI5jP8aQEEEfsYugwTdziG6G9mNPOazAJLaY+4jirxn7F+3\/9F+9\/PJLV3glPFau8m16JM9BRjKf2OD6dsIM0GUAlJ7RH8+\/RFoR1TcYoJZWEH9rI7HEjOjNsTlyE5Yl4mqUKBGFnMR\/nShqguO\/F1BFzsLhcBIi5c3g1mr5ZIK\/i9Mbv+N+\/\/pMhamMYM3IC+qfin6mt8LxxBpTknzHly3\/MwKWrQcWXNvH539WwCOyORt9BZeRJzBdM5hs1NdTUxjLf3J24SgmQgfO31pwKqgJlMW76M\/hGz5dGoDnckSW7j3En5AIbF01h4szZzJkwHyv3cOqfYTfoKyEGlIrHuALDT480FMh\/ylPuwe9Ky7m53xr32Kofqf4xJ3kOL1SF4vFfUsoVjznlkwdUj2ur8gf1KfkJE56Z11IM0JTIrsmj+PzrYQwbNobpq3ZxI61rSlBRR+hRG2Yt1WPNqjWsNz5OZnMFAVb7OebqieNyN+7EZxN8wpxJC81w3ruNtRp6WB+NoLbuHq4LnAkulELDPfbvs8UpTggoKPDbxVS1b5jufIfy4ggc1UfwxSef8snHnzPVzpucH\/dv6+H54gzIaW0SUFpcSEFBASUC4cNr9u0iKgozSEzLo6y8ltYOOTJxK+KWVlqEYiStlQTs0EB792XSM1IIcDFCe+027tbJaW1qQSJTAHLaWptplnZNqCokVGSlkFokoLmliarKCspLiigoKKCwso7WJ\/SVzxpnoEMiRFBeQmFBAQUlVQgf8upsp1lQQHJaJjm5FdSLpMjbpbQ0ttKBAlmLmJYWKfd9zFi2YQ+3U1JJ8j\/A2kU6nMxqQSoSIxZ3+q93SEQ0tbUDClqqCkhLy0bQ2oZQUMb9e\/HcyyykrvXpJ5V7L+iQnFZRI\/VCaddLQoFE2Ehjy8PxExTSZiqz7hGbkERmST3dl6mjrRVRY2vXsy+lsbm1K+aAnDZxM6J2OXJxAxWFRZRWllNaUkxFU9vjX8g\/Qu8EHVIiFTdR19hdtxKZqImG5taHly06xAjy04hPSCC1oIbWLkMVsjZEDeLO518pQ9gspq1D2XneViFNkg5QdiBqrKWiopySokIKCgqpaGx9prbC8wcdUraLqa9rpq0rdoBS1kpTfROtD40U5IgEJWQkxRCbVkBtj5tzO+IGEW1SRWeb2lpoEnd0tV1Mk7gNqaSFmuJc0hLjSS8Q8BgP6ceiWjEgoyYvjmvnznD20i3Sqjpn5GQNxcQHhhARfIUz16IpFTZSEB1DTNgtLl26QmR+DcLyVAIu+uLjc4nQ9HLalABK2moLiQ8MITz4CmeuR1HcrASkFIZ6sObbT5li6EpAVE7nspNcSHbMLc6fPcvlW\/FUy9ppLs8h9lYwQTcvcT4oldrmGjJDokmIC+bi2XNcj0iluq6Me0GX8fa9RXZN50y0UlJPVoQ\/vmcvcCshD+ED8SzaxbVkRt7i4rkznLkcRnZll6JHSasgjVsXz+J97gaJ2QXkJCYRF3KHa34XCUyuBBQ0l6cScOEsPj6XCEkr62yrspXSxCAunvXlWngKNVI5sopMQq6cw\/fyTZLLm59Hy\/yA11MMoETeLkHcIkIkaqH1Ub90pQxhXTlF5Q20tkmRK5Uo5QoUCgXyDgXy5ixObpyP7oHbpKcmcnbTcvTsPMhpUaLokHfNLCpRKOTfBw6hg6bKMqpEEuSKDiTiFsRtbbRJxIglsgeO+3FUE4FQSu6t\/eitNcPOfhOmRlbs80vlKUNVPxeqiEAoSr+EpYkhxpvs2LTBjE0HAh+I0d03DEQg7GQgAuHrg+rEgJzGlKtYrzbA8rA3J3euZonRYVIaZbQkn2b+7z5k5IxFGB30I6Mik+MzJjPk36PRsdvOwZOebNZaykKb\/bg7GTNn\/hqOR1Z0uhlHuTP7tx8wetYSjA9eJEWgAFpI8nZg4bD3GL7AhEM+UdTRRvZ5J1astcHV4wROpjoYH75NrPcOvn37QyZp6mF1KoySokjsPvuWaXNXYGlvgcbcKUxZZMiO7ZswXjKNWZsvUi2Tkn3VlcVzLHD3PsxeH3\/Sa7v7HwWCe1ewX+vAifOn2KqxBF2rs5S0g1KYwr7l81hgvI1ddltwcXJm++KlDPvr1yw0tWJfcAHCgnC2ai1hvtU+3J1NUJ+\/Bs\/oMmrTzrF8pgF7PY5x4KQvd+8ncsjQkLUbXfHyOsDR4DyeM77WQ7ymYuBFaSE98AhGptZs22aLhY0Lfs+52ehpUWU4YrmomvzCMmpb+rKFnagsHLG0idLiIkprW598bC8wIAY6GRADrw+qS1RUh\/9OQ0ZP2sy1mBhiAl2Y\/g8tDoWUIog\/ydJ\/z+VwUncqoWycx0xFy\/YmbbQT627DpAXuFCkBWrhluZD5m7yplEND6CEW\/GcBx1MfCbkrKeDMhkVsvdOVq1CcgOWcpSw28eRubDRXXcyZ8pkZB3dYMfertfhXdnXmtaGYfTwd24vFQD1njHQZNesYpYAkxonPZu8kuKyG+HN2qM2y5GxcMQ+POZSAHImwiqx7cZyzm8dIbUtCaqAp0pXRk7cTVd\/VayvyOT5PnXnaxylSALQSecyaifOPU6gEEHPHZilLbL2ICDzE5Bnr2HsrgwYZIErAXEeL+XZnSCnrvffdGyoGupA2UiFoemZXq+dhIDdB\/+JJYmDWrFmEh4e\/PIP6kBUrVuDu7v6j\/9PT08PDw+MlW9S3zJkzh8DAwJ6\/vby80NHRUZ1BvUR5eblqxEBHGR6WC\/nTUG227HFkh70lRqsPEV0oRBB9ilVD1nCjvKuTVGbhMm4xdkeTgRYuuxrx9aaQnnd05nFtJmw4SI4MGkKOs+KL9dx8JJCqsi0fH\/NFbL1T0vlB1W0WTZnEF3ON2em8A1vLTWzf6stl562smLiNpO4evSaMjUMWcyCkFqjnnLkxszUu0ABI4g\/w5Rg7rhZLUDbd58wuY+YvXsUWd38Km78PKd2UH42n3VZ2uu7GfP43fKxpTahAQVmAPR+u9XkgTksNZ5ZoY2jZHXG0louuhnxtE9wjMO57LmeixUEyaiqI8LJj8RJdzB1PklIroyr6DGb6umivtuR0THGv9GFvthh4iTxODBw7dkyFVvUNA2IApk2bxtixY1m+fPlrX\/7yl7\/g7e39o+1cvXo1n3\/+ucpt7M3y+9\/\/nrCwsJ42+vr68uc\/\/1nldr1omTlzJoMHD+7V38FTiQFFDX72axk75wgFXR\/JZDJAgSDcA91PV3G1pGvniDIL57ELsHZLQEE7kW7mDJ99hM5uXcil1QtZbu9HvQJqg4+yfMha\/CsemSNvzcHTcDaW3cHaxLGYTlvKit2RfD+ObiblqD3LxtgR2\/1hTRgWny3A9Y4AqOOsqSEzl\/hSB7TF7ePzcbZczhUi7+rQxZFOjJmrh2N0lz+ZUkSwmxFfzDpKmRJyPPUZqWnBbYGS5si9jBhtR1gTgBJRTQzO0zUw3Hily61aRPghc0bMPkxxl31X1i5Gx+4cxaKuCvMuo7NUHY0T3TFHRNzaPo+PDE5T+uxRt3\/AgBh4SfyYGNDS0up3MwMAw4YNo7q6mvDwcGbMmKFqc\/qEZcuW\/aQYiI+Px9PTkyNHjryUcuzYMY4ePdon5\/bx8aG6+od5LAAyMzM5efJkr7fl2LFjL+3aPVrOnDlDU1NTTxsFAgE+Pj69dv6jR4+qpH3Hjx8nJCSkV38HT7tnoCnjNlsXqTNlhjrq6nPQtDxFjriduihPln+4mmulXR2e8j67RyzE+kAcMqClMAJHjblMmjKTmTO+Y9bCHdzObEQJVN48hNbH67hR+YgYkNcTvGcxH302kXVbbyJATP7lg6ycOpVpc9RRn6uDvfcdQg5vQ2vUFuK73U0EIZh9tIh9QTVALT5Ga5m64Ay1QFvMXj4dbcvVvEpSL+xES30xS5YsYJzRQUJ6AlvJyL+zn2nfzWLpemsMp33Nh9o23K4GRJkc0pnHmFFTmDl9CZuOHGLLVG1MN17tir6qpKUwkt3L5jFp8kxmzpjGzEXbCc6soSzCnbULlrJUYxHjVjngcfMmxy1Xo7FIk+kLNND1jEPUCyvAr6wYCA0NfZlV9jnNzc2oqak9JAZMTEz44IMPGD16dK+VcePGMWrUKEaOHNmr532W8u677yISiQgLC2PChAmIRKI+LxKJhLa2tpdSl0gkQl1d\/bHr6AMM8Kbw9N4ECppKEvHz8uTE6QsEJRXS3KFE3lJHUXoJTbKenJVUZRVQUfN9Zs6WyjRunD6Bp9cVEkubenbOy5pqKMooRfgjo2JpXT4hl3zxiyjqci1tofjebU4e9cDn8i2SCmoQ1lRRlF3ZGY8CoL2ZsvRCapo7gA7qS0vIza+nA1C0VJORVYFQKqOpLJ2bvj54ngngXlnTwxW3C8mNCeDMOX\/uJiSRWlJJdzBXSU0Wdy6cxsM7kPSSKqryiykrb3rIm0Zclc4N7862JpQ09bQl8up5TnhdIjy3lo52IXkJQfi6n+RccAq1z+AF9VO8kmJgwoQJuLu7U1BQ0KelrKyMioqKPq+noKCA+Ph4Pv7444fEQGNjI9nZ2aSnp\/dKycjIICUlhW+++QYLC4teO++zlqKiIpRKJcnJyfzrX\/9i2LBhfVbU1NQYPXo0gwcPZvDgwX1a14PlP\/\/5Dzdv3nyZP4vHUllZye7du0lOTla1Kb3C\/v37OXDggKrN6DMuXLjAkSNHVG1GrzAQdKj\/8EqKAWNjY4YNG8aIESP6rIwdO5ZPPvmEd955p0\/r6S7Dhw9n2rRpdHQ8azDYZ+PMmTO89dZbODs792k9T4NCoUAoFFJbW9tnpaWlhXv37jFmzBhMTEyorq7u0\/q6i1AoRC7ve++Mp7nGlpaW\/OIXv+DixYuqNueFSUhI4J\/\/\/CeLFi3qWlfuXyQnJ\/OHP\/yB999\/v8\/fBS+DATHQf3glxQB07kpXKBR9UgBEIhELFixg6tSpyOXyPqvr0Xr7kujoaD744AMGDRqEtrY2LS0tT\/7Sa879+\/eZO3cuP\/vZzzAxMVG1OS+dq1ev8rvf\/Y5BgwZhaWn5SgiU56Wqqopp06YxaNAgvv76awoKCp78pdeIpqYmpk6dyqBBg3j33XfJzc1VtUkvzIAY6D+8smKgr9m+fTuDBg1i6tSpiMViVZvzwpSWljJq1Kiem\/mf\/\/yHrKwsVZvVp0RFRfHpp5\/2tNnExKRfjLaeltzcXIYMGdLT\/lmzZlFXV6dqs54LmUzGxo0be9ryy1\/+El9fX1Wb1WsoFAqsrKz45S9\/yaBBg\/if\/\/kfzp07p2qzXpgBMdB\/eOPEgFKpZN++ffz+97\/vGYG87gpdIBCwdOnSnhvZXS5cuKBq0\/oEmUzGyZMneeeddx5q74gRI2htfTlBh1RNdXU148aNe6j9f\/rTn4iIiFC1ac+MXC7n2LFjvPXWWw+1x9zcnPb2XvCZegVwc3Pj17\/+9UPt09TUVLVZL8yAGOg\/vFFioL29HRcXF95+++2eRv\/1r3\/lzp07qjbthcjNzWX9+vUMGzbsoRfqpk2bXutp48fR3t6Or68v48aN41e\/+lVPe99+++2HXML6M7Gxsairq\/PRRx891MG4urqq2rRnRiQSsWPHDoYPH84f\/vCHnraMGzeOyspKVZv3wgiFQjZv3syYMWMeEgQff\/wxtbW1qjbvhRgQA\/2HN0oMRERE8Le\/\/e0HI+hdu3ap2rQXQiaTIRKJSEpKwtramp07d+Ls7IyTkxPNzc2qNq\/PEAgELFy4kI8++og\/\/vGP\/Pa3v31jxIBIJKKxsZHQ0FDWrVuHi4sL27Ztw8PD47WbHeno6KCxsZGysjJcXV0xMjLi6NGjmJmZ9Yt9A1KplMbGRioqKtiyZQsWFhY4Oztjb29PSkqKqs17IQbEQP\/hjRIDTU1NZGRkcPr0ab788kv++9\/\/8n\/+z\/\/B0NAQ5XPkPX\/VKC4uxsPDg4aGzgCXzc3N\/XJmoJu6ujp2795NeHg4WVlZHDhw4LUfaT0rGRkZ2NraIpVKUSgUiESi1\/aeKxQKzp8\/3xP9r7m5ud95FDg7OxMXFwdAW1sbbW3PmmD51ULlYkApp10q7bW8MgqZhEfz3r0pvFFioJusrCy+++47ampquH37NqGhof1i41lgYCDW1taqNuOlce7cOXbv3v3QZy\/Da+NVws\/PDw0NDVWb0SvU1NRw5MgRcnJyVG1Kn6BQKJg+fToZGRlPPvg14VnFgFwqQfbouEsho7Xtwb0h3x+gaG\/\/QUZYRbu0K\/06CDOu4rzJggu5UhSPDOgUHR2dGWtR9JxRiQLFA4cpZRK6s7cjryJgpz4OV9No6\/j+OyhktEn6x96Vn+KNFAOhoaH4+Pio2oxex93dHUNDQ1Wb8VKQSCQ4Oztz9epVVZuiMpRKJX5+fo9NIvS6kZmZyfr16x8KzNWfKC8vZ\/PmzVRVVanalF7jqcVARxOZYec5dOAgB4+cIzavASUgKonB+\/gRDrm54XklkpIWgGqifK5w44ov7kcP4nbiCil17UAHguRQzrkf5bj3BcIy0wl0XMm3al+yYMMxIjKr6VDUcvfMRS76eHDI4zgnT10l+EYqjQCKKqK8A0kpEwNtlMRf5diBAxw4dIbIzCJyI06xbvwHDF+yAfdLCTSgRFyYxPWTxzh83IsbyeXfC4d+yBsnBjo6Oli\/fv1rv1b3KO3t7Vy8eJGAgABVm\/JSCAkJwczM7I3ZI\/BjtLa2cvbs2V6PN68qwsLCUFdXV7UZfUZiYiKHDx\/uV8\/s04kBGSW3D7Ns0kI2HPHEzW4rnjczaapNxVVrCeqWhzl\/ei+rFizA7GgMInEKmz8ezcSZ+uw5tId16nPRdohGLLqH5aJVmO46xbXrPvgERXB95yqmjR7GUouj3EmuoL0lgY3\/\/YzPhi1k6xFXbBevx2jeaUoBWmPZ\/IUpPpEV1GZcYt0UdQz2eXDEYTtHzgcTe8cHk8mfMlZzIx4XYmloK+CI6To0Dd244e+NV3AGzf1r1eoh3jgxIBAImD59er9S59A5xXr8+HEyMzNVbUqf097ezv79+7G3t1e1KSqlvr4ed3f3fjOtfufOnX4dhjggIIBr1671G3dJeNoUxhV4b13PFJObPel5QU7BjV2MnOrEva79rkU+G5m33JbQ3Dgc1dSx9S0EIHXfZjQm7SO7NoUtq5ahvtGd6ILOL8lyr7LDdD2XuhIU0hCF5ZDpWJ\/r\/q7zA2Ignm1qlpwPzSLI05ox2md5OCpHAze3aGJ7vcvVvK2AIzYrGaezA\/97r2f8jmfhjRMDQUFBuLu796sfJHS6Fzo4OPSrUcfjyM\/Px9zcHIFAoGpTVEpmZiZLly5FKpU++eBXHJlMxoULF16ZfA99gYGBAdevX1e1Gb3KU4kBSSEHtxrwrdODHYySzIs2vL\/iFAVdU++1Nzejrr+Ba6nROI3UYM\/VYkBJqttmtMbbk9wOwvsBOFobs3z5Wg5FFVKfcZltRms52+100hCFzZdLcL1ZAUiJ2+OI0TxvygFk99gxwprzd1K5dMycr22CePiXU8VVm8VYXUrvSZIkKY3FY7clyzWWs\/lsNPWv\/9ayx\/LGiQEHBweioqJUbUavExQUxLx58\/qFV8STuH79Ops2bVK1GSonLCyMefPmqdqMXkEoFLJ\/\/37u37+valP6BLlczuLFi\/vNkk43TyUGFHX47zRm4oKj5MuB+gJyyqrJv3OASSM3cK1ICggJsluDzpojZAvusf2r+ey8VAAoSN5vi+a4LUQLxEgVgLyGK9sWMWTTdfLTLmCzSpvDqZLOuhoisfxsPnv8SwEFqW52aE2xJ0EKkoyTLP7vKk5HFRLvY8+4yY7cEwNNJeQUVtAsreaq5RxWeXamT0bSQrNYCshI81jJ+7p7iSotIzMmhpz6\/rde8EaJAZFIhLGxMUVFRao2pde5devWD3bW90eEQiE7d+4kMTFR1aaonODgYPbt26dqM3qF6upq9PT0+m0+jYaGBnbu3NlvlnS6ebo9AwpEubfZPHsCn6uNYPgkLfbdKUAmFnBzpwFThg5j+HA1Jsyz41pKE3JpMg4jdNjnXwLIST2yA\/3Z+0gqiMN5xXymfjuVUeor2XKrCInoPscMvuU\/Q2ax\/UIustZ7OIzQxe1WKQCS0nC2Lx7Pp8NGMn+tPlqjN3AltR5J3T32aU7n62+GM3zMPOwvpNCm6CDb14yh\/\/2KOevOUVyXi7elDlPHTmHsjKWsORlNcdp1bGbo4ZEsfCnX92XyRomB+Ph4Dh482O8C8UilUs6ePduvp1i7SUxMRF9fv98t8zwrMpmMS5cu9fjkv64oFAokEgl3795FTU2N2tpa6urqqKuro6GhAbFY3C9iDaSlpeHt7d0TA6S\/8PSuhQpa6\/KIDo\/gbkoRja1d8+2yRvLuRRF0J5rc2pau6XkZzTWNtEg61w\/axSKa6sW0d7RSU5JB1J0w4nKq6Py3EkltAQmx8eRUS1DSjqimEbGke9u\/ktbqbGKiY0gvraOpvhlJe1ctTSUkRoUTnpBLXUvnM6aUNlGYFE1MpgCpXEZTVT7xIaFEJhfSLFOATExteTVCaf9zYX6jxMCuXbsIDg5WtRm9jlwux9XVlbKyMlWb0uccOnSI6OhoVZvxSuDs7PxaBllqbGwkMTERf39\/rl27xqVLl9izZw9r167Fycmpp+zduxcfHx\/8\/PwICAggKCiIoqKi13KPhJ+fH5cuXVK1Gb2OyoMODdBr9Fsx0NDQQF5eHtHR0fj6+uLp6clf\/\/pXDAwM8PT0xNPTkwsXLpCWlkZRURESiUTVJj81QqGQ7OxsYmNj8fHxYffu3QwbNgxnZ+eH2hYXF0d+fv5r2WHU1NSQnZ1NZGQkPj4+eHp6snfvXj7++GNcXFzw9PTk8uXLJCUlUVhY+NqF4H0WWltbKSwsJDExkYsXL+Lp6Ymrqytqamo4OTnh6enJxYsXiYuLo6Cg4JWcam9vbycmJoazZ89y4sQJdHV10dbWxsvLi\/DwcNLS0hAIBFRWVlJRUUFVVRUFBQXEx8cTEBCAg4MD8+bNY+fOnXh5eXHjxo1XLlSxUCgkLy+PxMRETp061fNb9PT0ZPLkySxYsAA\/Pz8SExMpLCzsF\/EUBsRA\/6FfiYGSkhLOnTvHwYMHOX78OMeOHcPJyQl9fX00NTVZvXo1y5cvR1NTE01NTfT19XFzc2P\/\/v3s27ePw4cPExER8Ur+SHNzczl9+jSbN2\/m9OnTHDlyhD179qCnp4eWlhYrV65ES0urp23r1q1j7969HD16lEOHDrFjxw4O\/z\/27ju+qiu\/977y5Ob1XF\/fSXKTTMncmyeZ5GYm48Sxxz0z7th43KnGNlWoINQ7Eqh3CVQo6r2ihmiSKAKEkJAAgXqlqKPeddRO+zx\/YDPuRkdlq6z367X\/sdl7\/845Omd\/91prrxUSsmjHS6jVasrLy4mNjSUoKIiIiAjCwsLw9vZGR0eHzZs3s2XLFvT09B6+TlNTU\/bv3094eDhRUVG4urqSk5PD8PDS788bHx\/n8uXLuLi4EBISQnBwMH5+fhgZGT18L776mRsbG7N\/\/37CwsI4dOgQDg4O5Obm0tPTI\/VL4cKFC3h4eGBnZ4eXlxclJSUaLxve0tJCRkYGe\/fuxd3dndDQUO7duzfHFT+63t5e0tLScHNze\/hbEhgY+LXv4ubNm9m5cyfa2tqYmZlx8OBBwsPDCQ8P5\/Dhw5w6dWrJLj0twsDysSzCwJUrV3B3d8fd3R0PDw8iIyO5du0a9+\/f\/9G+5cHBQRoaGjh+\/PjDpkkXFxeCg4Ml\/yGdnJzk3LlzODo64uvri4eHB97e3ty4cYPu7u4fndd8amqK9vZ2SkpKCAwMZM+ePRw4cIB9+\/Zx8uTJRfHkgVwuJzU1FUdHR\/z9\/fH19SUxMZGysjK6u7t\/dHrhoaEhWlpauHDhAvb29gQEBODm5saePXu4c+fOAr2KudPR0UFQUBBeXl4cPHiQffv2kZuby71793405IyPj9Pa2srZs2dxcnJi\/\/79uLm5ceDAAUmW6a6pqcHV1RVra2tiY2Pn\/PtUXl5OYGAgdnZ2hIaGLujYgvr6enx9ffHy8sLZ2RlnZ2cuXrxIU1PTjwadiYkJWlpayM\/P5+DBg3h4eODi4kJAQMCSe5riyzDw0ksvSV2KMEshISFLNwyUlpZiaWmJh4fHwwvIbOem\/3IlOD8\/P8zMzDh8+LAkza7Z2dkYGhpy4MAB0tLS5mwUcl1dHenp6fj4+LB+\/XrJnnv+csbEPXv2EBAQwLFjx+ZkzMPU1BQFBQUEBgZia2uLs7Pzom0N+aqxsTFcXFzYtm0b4eHhXL58edYDXcfHx5XiVuUAACAASURBVCkpKSE8PBxTU1O8vLzo6OiYo4p\/WHBwMOvXryclJYX29vYf32EWbty4wb59+zA1NaWqqmpez9XW1vYw4Bw+fJhr167NOoTIZDKKioqIiIjA0dERBweHJTP+58sw8Dd\/8zds2rRJbEt4e\/rpp5deGBgdHSU8PBwjIyNSU1PnZTZBhUJBaWkpXl5eWFlZUVRUNOfn+C7Nzc3s3bsXMzMzzp8\/P2+jjwcHB8nMzGTPnj3Y2trS3Nw8L+f5LnV1dezZswcPDw+uXLkyb\/39dXV1JCcn89577xEWFrZoV\/M7e\/Ys2tra+Pj4cP369Xk5R01NDXFxcbz77rukpaXNW6vQxMQE+\/fvR1tbe95ey3cZHR0lMTERIyMjLl++POfHV6lUpKSkYGBgQEhICPX19XN+DngwoVZISAiGhoYcP358UbTe\/ZAnn3zyW0vCi23pbz\/03V00YeDu3bsYGRnh7++\/IH2FX\/bdrl69mtDQ0Hl9pO38+fNs2bKFxMTEBeui6OvrIzQ0lO3bt1NYWDivPz4qlYrc3Fx27NhBamrqgvXtX79+nT179uDi4iJ5189XTU9PExERgba2NhcvXpz3Zm61Wk1hYSG2trYcPHhwzlu8JicnOXToEG5ubpL1gRcUFGBpaTmngWB0dBRra2s2bdpEVVXVvIdKlUpFeXk5dnZ27N27l\/7+\/nk932y8\/vrrkl+4xDa322OPPUZZWdn3fuaLIgzU1dWxa9cuoqOjF\/zZ48bGRgwMDPDz85uXR5tOnz7Nzp07KS4uXvC7AZVKRUFBAdu3byc1NXVelgFWKpWkp6djbGy8oHeMXxofH+fQoUNs27ZtUYwlmJiYIDg4GDc3twUfANfb28vBgwfx9vaeszk31Go1ISEhuLu7Sz4At6SkBENDwx9s6nxU7e3t7Nq1CxcXlwWfn2RkZARTU1N0dHQW7TTj\/f39vPbaazzxxBNiWybbiRMnfjDwSh4GKisr+eyzzyRdsrarq4vdu3dz5MiROT3uhQsX0NPTo6KiYk6PO1NFRUVoa2vPSzNreno6ZmZmkj4OJpfL8ff3R19fX9LR2yqViiNHjuDq6irZj\/z4+PjDQDA4ODjr450\/f56dO3cumr7uY8eOYWtrO6vPeXh4GDMzM1xcXCSd5CogIIBdu3Yt6hYCYeWQNAz09vaiq6uLv7+\/lGUAD0Z7GxgYzNkEIu3t7Wzfvp1r167NyfFmq6ioiN27d8\/p6POLFy\/y8ccfc\/PmzTk7pqampqY4cOAAgYGB89IC8iiCg4MxMDCQvMtCJpPh4uJCWFjYrFqjZDIZVlZW8xIiZ2Pfvn2kpKRo9DkrFAp8fX3Zt2+f5DMgKhQKduzYgaen55KccElYXiQLA18O3HF3d180A8BKS0sxMTGhq6trVsdRq9U4ODgQHR0t2YXpuxw5coTDhw\/PyQ9PX18fRkZGhIeHz0Flc6O7uxsdHR1SUlIW\/NyNjY2sX79+0SyW1dzczNatW2f1VElmZiZBQUGL7kJ1584d9PX1NbqjLi0txdDQcNFM2NXf34+JicmiuWkQVi7JwkBjYyOGhoaSPCf9fZRKJf7+\/tjY2MzqONXV1djb2y+aptUvyWQyrK2tqampmfWxTp48ia2treR3V9+UmJiIvb39gk5QJJfL8fT0JDY2dsHO+SjCw8NxcHDQqE9crVZjZGREbm7uPFQ2O0qlEldX1xkvaqVUKrGysqKgoGCeKtPMhQsXMDc3X9azbQqLnyRhQK1Wk52djaenpxSn\/0E3btzAyspqVhdyc3NzQkJC5rCquRMbG0tycvKsWmNGR0c5cuQIx44dm8PK5sb09DR79+5d0LUqKioqcHNzo7W1dcHO+Simpqb4\/PPPOX369Iz3vX\/\/Ps7OzhrMgyGjIn0\/n69+i7dWrWL1R1akVT4IZi0XIjB45y1WrVrFqk2e5N3T\/KmHixcvkpSUNKNukFu3buHk5LToBu3JZDJsbGzmfS4FQfghkoSBoaEhvL29KS8vn\/nOaiVT48P09\/bS29vH4OgkDy9rSjmTU3Jm0zCvVqs5cuQIiYmJGu0\/OTmJo6OjxhcjlWKC4cF+ent76e0bZkKhBrWSydFB+voGkSlm90RCfX09Tk5Os7oLuXv3Lt7e3pp1p6gVTIwN0tfbS29vP8Pj06gB5eQYA719DI5NMdtnLg4cOKDx56cJf39\/LCwsZr6jWs746J\/ei5EJ+ddfu1rJ1MQE00rN35HIyEiSkpJmPFCuurqa+Ph4DQbqtRH3iQF6uiFcKC6m+HoNHcNyYISLey1Z84weAcnJJKbmc29Q81aliooK\/Pz8ZtQN5+npSU5OjgZnU6OUjzM00Pdg9cWBEf60eJ6c0YE+evuGGJ\/WPGCfO3eO+Ph4jfcXhNmSJAw0NDTwwgsvaDSP+cS9s1i\/+V88\/fwLvPDCq+z0PEWHQs5IVxWZB2zZ8ukRqmb5mPX+\/fuxtLTUaPBVU1MTUVFRtLS0aHDmAS74GPHKb5\/m+Rde4A9vm5FR28dgw1ncd3zISy++yka7RKp7NL9gDg8P88orr8xq0qPz58\/z4YcfarCnmp7iBLSff5pnXniBF154F5uYGwyO3ONEgBUfv\/gsL71rQdLV+8xmjPfZs2dJTEz80Wmd58LU1BQRERFkZWXNcE8VbRfD+ex3\/\/nFe\/EhDill\/OkboeTOKSdefXEDXuc0n12wvr6eiIiIGfevV1dXk5SUpEG\/fAsx66xx9bzCoEKB8uG1eoAzFnvYtSacqv4hxmc5TKihoYE33ngDhULxSP9epVJhZGQ0466FB9pJt9rM8\/\/xDC+88AJvrnPibKsK9XQnJel+bFv9Gi+s3sGRK20afy8rKyvZsmWLhnsLwuxJEgZKS0t57733NNhTTfflRHY\/7cqVfgVy+TRyhRKVsoOzh52w1N\/OR084U9g\/u3vLvLw8IiIiNHrsqLGxkcTERLq7uzU48z3C3zfHzbeIIbkcuVyOQj7C3VsXOFnUQHdrAa6vbWRvSAmaLQfzoIn\/2WefndWjWWfOnOGPf\/yjBntOURUdiMGrQVR98frkChWTHWWcLW5gUNbOcWsDPnorkLpZfISaX8hmrqenh8TERA1mrpNxPcCH3X8Mp\/Er78WXL3uy7Rr+W17kv\/\/tO5jGaT4r3tDQEJ6enjOeibKyspLk5GQN3sMOkjdrs\/bt3bgfPkJYfD4dcoAJSkM92fbqJxja2LDXK4nyDpnGF8+Ghgb09fUfubtraGiI\/fv3azYXhaICn5dMOXS0jkm5HPm0HIV6mqpEb8xMD1DUpQDlKH3Dk6g0fEF3797l3Xff1XihJ0GYLUnCwK1bt4iKitJgTzU9hUkYPGlFelkjjY0t9A5PgnqS0YkpBkozsXjGjYK+2d12XLp0iZiYGI3CQGtrKzExMRpOA9xE1BpjbPakU37nLk2t\/Xx9HHcn0Zt1cQkv1jgMfDmZyGxaBnJzc3n77bc12HOa2vggdF904szt29y+3c7gxIM7O4VsiM6mWyTvs8Ha5iT3ZxEGSktLSU5OXpC+4Z6eHhISEqirq5vhnuPcPOSD7uteXLxzh9t3Ohj+su1Z0cPlCB8cLZzZ+KkhBuHVGtenVqtZv379jO+IGxsbiYuL0yDUtpGwyQijXWHklZZyq7KZ0S9e1pRskI7WVpoaCgnY8i6fBp5nUMOvallZGSEhIY\/cejc0NERAQICGYaAKv5cNcDt4jtrbd2ntGkWtuE2gmTmfW0STd\/4cBaX3GJ9F\/+TAwABHjhxZdONOhJVDspYBIyMjjfbtL0lk+z++j5GbN75+0Zwvv\/9wjMDQzUzMnnGddRi4cuUK8fHxGoWB6elpXF1dyc\/P1+DMrcRt3Mra1Qa4BB4iNC6f+19pBR0sCUd3qxNZlZpPJlNbW4u1tfWs7kAKCwvR1tbWYBCinMaj\/mz41UdY7vdnv38y15qHASUtRen42+vy7ur1OJ6Y3cx9R48eJTY29pGbkGeju7tbwzAwSUW4O2v\/7zpsAwLwD0jj1n0ZoKK77DiubtHUdd7CZqsxu+Nua1yfQqFg\/fr1M54LYnx8HD09Pa5cuTLDMzYTvXYvgaHfqFnx9StlvtNHvOueSY8GH5FarSY+Pn5GE5UNDg4SFBSk2ayQyhoOvLqeTzZZ4O5\/mNhTNciHyrCx\/Izndu7F32Mvhjr6uJ+qRqZhIGhvb0dHR2fe1iwRhB8jSRiorKzkt7\/9rQYXWzU9V5LY\/ZQtWVVNNDd30D\/2p3vn4ZuZmD\/jxpX+2T3bHxkZiYODg8ZzBPj6+hIfH6\/BmIMmotaYYLc3i5q2Nto7B5n64hBjjWfwNDUkIKdG41YBgLi4ONLS0mY1\/0F7ezuHDh3SYNXAKWrjg9D7vRsXmltoael8eDcs67pN+c2rnAx3ZruZJ+fvaz5qwMXFZcEGEPb19REXF6fBYNhxSg\/5ov+mLwWtrbS0djM6rQJ5B+n2H\/PCRmcSwxx5\/dnf81\/bIqjq0mzAZ39\/P25ubhq1VNnZ2REfHz\/Dv5X7JH22lhf\/4x0279iBtoELx+tGQdHDhUgvzMzssDc1ZssGc1JKOzQaGzI+Po62tvaMumampqawtbXVbMS+ogq\/V3bjGXyJ261t3O8bh6EbWJkasDniwfEa0iz4d6MY6mWaNWnV1NTw9ttvL\/oFjITlS5Iw0NPTg5WVlQY\/UGq68pMwftaXsu+4oxi5lYnpU64UznIWVi8vL3x9fTXev7KyEmdnZw2Wlb1L+HvW+B2q\/Fpf6lBtLp4m2tgn32A2s6iPjIywfft2zZ7i+Aq5XE54eDiHDh2a4Z5TVEUfxHhVOM1f+a\/TA530fpHppm9Fs3vzVqJrNQsDnZ2duLq6cuvWLY32nym5XE5ISAhpaWkz3FPGdX8\/TD9K5GvPZMh7uXk8gpCYeBIDrfn9U8\/z3OYQKoc0G3lfXl7OkSNHNBo\/0dTUhJGR0QwHw8rpqiogIyGa8LAwwmOOcbNjEpDRWJRDRMBhgg\/Gc7a0HU2fJcjNzcXDw4PJyckZ7WdlZaVZi52iAp+XLIk89ZXvs+IuR4wtMXAvZAq4l27DH2xTuDuu2cW8uLiYAwcOaLSvIMwFScKAUqkkKyuLhISEGe87UnGUbf\/7\/\/L0f73Myy+\/yg7P43QoRijy1uPN3z3BP\/z1r3hqjTFxlZpdNjs6OvDx8fnB1Z1+jFqtxsXFhZiYmBnu2UW67lp+939\/x+9ffpnX3zcm7eZt8gI28zd\/8wv+7YXXeOO1V9h8+Dy9GlwrY2Nj8fT0nJNBSkVFRdja2s4w8KhoOR3AB3\/3rzz38su8\/PJqLKMLaak4geWG1byzZh1r31iLTegFejS8UoSGhuLv77+gc87HxcXh4uIyw73kNKS48\/bPfsPzL7\/Myy+\/i13iDUYfXkvUqAeKMPtsN7vDqzQeaHfkyBFiYmI0bgny8PBYVJMpdXZ2oq2trdH38\/Tp0xw5cmTmT5mobxP8wRs8+cRz\/OHll3l7ox057RP0F8VjsGoNG9Z\/wrqdVkQWtyPX4INSKBQEBQUtmtkrhZVJshkIr1+\/jp6e3owHeakVMnqaG6mtrqKqqorb7f1Mq5WMtN+htraO2\/caqW28R49Ms3EDXy49O9spku\/du4e2tvYM70RUTAzc525DLVVVVVTX3qNvbILR\/k5u37lNQ201VVVVNHYOzfhHJz8\/HwMDgzmb8VGpVOLt7Y2vr++MmjaVk8Pcv9tATVUVVVU1NHWPMD05Qnt9OSVXLnO9upUhDWe\/LS8vx8TEZFZBThM1NTXY2dnNuAlaMTFI+536L96LWlp6x\/j6X52cob5BBmWajX0YHBzEysqK4uJijfaHP118T548qfEx5srExAQWFhaEhYVpFG5GR0dZvXq1BlP\/KhjrbaOxroaqqipq6lsYnAKYov92GfkFxVS2DGoUBACqqqrYsWOHWLBIkJRkYUAmkxEUFER0dLRUJXxLS0sLFhYWFBUVzcnxLl68yKZNmySf\/rSmpgZdXd1ZzVP\/XZqamrC1tdXgGfu519XVhZWVlWQTt+zZswcHB4dFs84GQEJCAr6+vrOe5ragoIA333xT0vnzx8fHCQ4OxtnZeVYXzaNHj+Lo6Lho1ltQKpU4OTnN2QJpgqApSVctrKqqQl9fX\/KLJfxpfnlvb+85PW5mZiafffYZeXl5c3rcR5Wfn4+Ojs68\/dhUVFSgo6NDRkbGvBz\/UXR3d7Nv3z78\/PwkG4DV2tqKpaUlV69eleT831RfX4+BgQHXr1+fk+Pl5+ezefNmjh8\/PifHm4kvx4F4eXnNekXIkZERjI2NJVnM6rvEx8fj5OTE2NgsZ0oThFmSNAwA5OTksG3bNu7evStZDSqVikOHDmFlZaXRoi4\/Jj8\/nx07dhAcHDwvx\/8ufX19HDp0CHNzc86fPz+v58rPz8fQ0JDIyMgFXx++rq4OS0vLGU9NOx+ys7MxNTWVfIGq+\/fvY2xsPOetJFeuXMHQ0JCwsLBZr+z5qEpKSjA2Nsbb23vOFp+6c+cO27dvn\/OWspnKyclh8+bNGjyWKghzT\/IwAJCWlsa6deskG0ATFBTE7t276ezsnLdz1NTU4OrqOu9L7KpUKs6dO4e9vT1OTk6aTbKigYaGBoyNjdm3b58Gi9vMnEKhIC4uDlNTU5KSkhZN83xoaChr167V4LHLudHd3Y2enh6enp7zMs9CS0sL\/v7+mJubz2srwb1799i\/fz9WVlbz8n25evUqGzdunNFcBXMpMTGRNWvWiKWLhUVjUYQBeNC\/+emnny5o\/3NfXx+6uroYGxvT3t4+7+dTq9UcPXoULy8vtmzZQlJS0qymBf6qgYEBEhISsLa2xsPDg5ycnAW\/U+7v7ycqKoo9e\/YQGBg46ybd76JWqzl58iRmZmbs3bt3zprB54pCocDHxwcdHR1qa2sX9Nz37t3DwMAAd3f3eW+BOnv2LB4eHnzyySekpKTM2WQ5VVVVuLm5YW1tTWBgILdvaz7h0o+5efMmGzZswMXFZUEmqII\/jX3YtGmTBhM6CcL8WTRhAB40CdrY2ODi4jKvPwJyuZycnBzMzc3x9vbWcB0BzY2NjXHs2DFCQ0MxMjLCwsKC48eP09DQgEwmQ6FQoFKpvtX\/rVarUalUKBQKZDIZzc3N5ObmsnPnTrZs2UJ4eDh5eXnzchGeicrKSmJjY9m6dSsWFhZUVlYyNTWlcThRKpW0t7eTkpKCiYkJe\/fu5cyZM3PWbDwf0tLSsLS05MSJE\/N+oVGpVFy6dImdO3cSERGxIAs0wYOnFY4fP05UVBTW1tbY2tqSnZ3NnTt3GB8f\/97WGpVKhUqlQi6X09fXR2lpKREREZibm+Pp6UlqauqCLedbV1eHvb099vb2XL9+fd7GnKjVaoqLi7G0tMTV1VWyliNB+D6LKgwA9Pb2Eh8fj4mJCZGRkTQ2Ns56NDQ8+AEaGhriypUrWFhYYGZmxvXr1yXtZ56YmKChoYG8vDySk5Nxd3fnnXfeQV9fn4MHDxIdHU1ubi5nz54lJyeH6OhogoKCMDAw4LXXXsPHx4f09HTOnTtHTU3NjCdhmU8qlYqqqipOnjyJl5cXb7zxBl5eXly6dIn6+npaW1vp7e1ldHSUsbExZDIZw8PDDA0Ncf\/+fe7evUtRURGpqalYWVmhra1NdHQ0JSUlc9aaMt+qq6sxMDBg+\/bt1NfXz\/kgsS\/\/fjw8PLCxsaGwsFCSv+fJyUnu3r1Lfn4+CQkJODs789Zbb2FmZsahQ4eIj4\/nzJkznDt3jqysLEJCQvD39+ftt9\/m888\/x9\/fn5MnT1JWVjavXXXfZ3x8nJycHHbu3ImbmxuVlZVz1rIyOjrKzZs3CQgIwMzMjOzsbDFYUFiUFl0YgAcXku7ubuLi4tDT08PX1\/fhRaSnp+eRLnoqlYqxsTHa2tooKyvjxIkTD+8qL1++vOjuKqenpxkeHqajo4OamhrKy8spLCwkJyeH3NxcTp06RVFREdXV1dTX19PW1sbw8DDT05qvCb9QRkZGqKur4\/Lly6SmpuLi4sLq1avZsGEDPj4+BAUF4eTkxHvvvYe+vj4vvfQS27Zt48iRI2RnZ1NTU0NPT8+SeK3fNDw8zOnTp9HX18fJyYmCggJaW1s1vntXKBS0tbVx5coVgoKC2LlzJ8nJyYvm73lqaoqhoSHa29spKyvD1NSU5ORkcnNzycnJ4cyZM9y4cePBfBmNjXR1dTE2Nib54E94MDPqiRMnHnZB5eXlUVVVRWdn5yPfkMhkMjo6OqiuriYnJwcHBwcMDQ3JyclZkIWzBEFTizIMfNXw8DD5+fm4u7ujra2Nj48PycnJnDlzhhMnTpCVlfWtLTs7m9OnT5OQkICTkxN6enrExcVx+\/btBesbnGvLZc5ytVqNQqFgcnKS\/v5+mpubaWtrIygoiJ\/85CcEBASgVCqZnp5eNIMC58LExAT5+fmYmZlhZ2dHQkICx48f58KFCzQ3N9PT08PAwAAKhQK1Wo1SqWR0dJTe3l6ampooLi4mOzubY8eOYWNjw\/r16zl\/\/jyDg7Oce3selZWV8ctf\/hJnZ2dg6fwNT05OUlFRQWBgILq6uvj4+JCSkkJOTg6nTp36zt+cU6dOkZubS1JSEo6OjuzcuZOgoCBu3ry5YN02gjAbiz4MfNX4+DhVVVVkZ2cTFRWFsbExH3\/88de2devW4ezsTFpaGoWFhZI0Owozo1Qq+eSTT9DS0uLZZ59dsMcvpdLR0cGpU6cwMTFBW1ub0NBQYmNjiYqKIjk5mfT0dI4ePUpCQgJxcXEcOnQIU1NTzM3NSUtLW7AnRGZDqVSyc+dOtLS0+NWvfrVoWi5mSq1W09TUxMWLF0lISMDS0vJbvzkff\/wx9vb2JCQkUFBQwO3btxf8EVtBmK0lFQaE5enmzZs8\/vjjaGlp8dhjj7F\/\/36pS1pQ9+\/fp7a2luLiYk6cOEFmZibHjx+nsLCQ6upqWltbF82MeY\/q+PHj\/OQnP0FLS4u\/\/Mu\/1GAhJ0EQFpIIA4LkNm\/ezJ\/92Z+hpaWFlpYWv\/71r8VELEvY8PAwb7\/99sPPU0tLi48++kgMnBOERUyEAUFSjY2N\/PSnP\/3ahUNLSwtTU9MlOWBQeLDS4X\/7b\/\/ta5\/n3\/7t33Lp0iWpSxME4XuIMCBI6tKlS\/j7++Pn58fWrVuxtrYmKyuLy5cvi4FXS5BcLufKlStkZWXh6urKBx98gJ+fH+7u7mRkZCyZQYSCsNKIMCBIanJyEqVSiUqlIicn52vz6YsLx9Lz1c\/s2rVrREVFMT09jUKhEOFOEBYxEQaERSMvLw9zc3OpyxDmSE5ODjk5OVKXIQjCIxBhQFg0iouL2bhxo3gsa5mwsrISC\/EIwhIhwoCwaHR2dhISEiLpctbC3JicnOTjjz8WT4UIwhIhwoCwaMjlco4ePcrp06elLkWYpfr6eoKDgxf1DImCIPyJCAPCohIfH8+uXbukLkOYpcTERDIyMpbVlNKCsJyJMCAsKtevX8fZ2XnJTl8rPLBu3Tpyc3OlLkMQhEckwoCwqIyOjhIWFsaFCxekLkXQ0L1793B0dOTevXtSlyIIwiMSYUBYdLy9vbGxsZG6DEFDUVFRhIeHi6dCBGEJEWFAWHSqq6vx8fGhpaVF6lKEGZqensbBwYHs7GypSxEEYQZEGBAWHaVSibOzMxkZGVKXIsxQXl4enp6e9PX1SV2KIAgzIMKAsCjl5OTg6+srBhIuISqVisDAQCIjI6UuRRCEGRJhQFiUZDIZJiYm5OXlSV2K8IiqqqpwcXGhra1N6lIEQZghEQaERev48eMEBwczPj4udSnCj1Cr1QQHB+Pr6yt1KYIgaECEAWHRUigUGBsbc\/nyZalLEX5EY2Mjpqam3L59W+pSBEHQgAgDwqJ27tw5HB0dkclkUpcifI\/p6WlCQ0O\/tvy0IAhLiwgDwqI2NTWFvb29WK9gEbt58yY7d+5kYGBA6lIEQdCQCAPColdeXo6hoSHd3d1SlyJ8g0wmw9bWVswrIAhLnAgDwqKnVCqJjIzE29tb6lKEb4iPj8fGxoapqSmpSxEEYRZEGBCWhJ6eHszMzMTiN4tIY2Mju3fvFoMGBWEZEGFAWDKuXr2KsbGxWABnEZDJZJibm5OZmSl1KYIgzAERBoQlQ6lUEhYWhq2trZh7QGI+Pj64ubkxOTkpdSmCIMwBEQaEJWV8fJx9+\/Zx8OBBlEql1OWsSJmZmdja2tLR0SF1KYIgzBERBoQlp6urC319fVJSUqQuZcUpKSnBxsaGW7duSV2KIAhzSIQBYUmqqqrCzMyMixcvSl3KilFVVYWurq6YEVIQliERBoQl69q1axgYGFBeXi51KcteW1sbxsbGJCYmSl2KIAjzQIQBYUnLzc3FysqKmpoaqUtZttra2jAyMiI2NlbqUgRBmCciDAhLXnZ2NpaWliIQzIP29nYRBARhBRBhQFgWcnJy0NfX5+zZs1KXsmzU19djYGBAXFyc1KUIgjDPRBgQlo3Lly9jbW1NRkYGarVa6nKWLLVaTVFREUZGRqSkpKBSqaQuSRCEeSbCgLCsVFdXo6OjQ0BAAENDQ1KXs+QoFApSU1PR1dUlPz9f6nIEQVggIgwIy05fXx\/btm3DxsaGlpYWqctZMjo7O3F3d8fW1laMvxCEFUaEAWFZUiqVhIeHY2VlxYULF6QuZ9G7desWJiYmGBgYMDg4KHU5giAsMBEGhGXt8uXLmJiYEBgYyMDAgNTlLDpKpZLY2Fisra1JTU2VuhxBECQiwoCw7N25cwcfHx9sbGy4dOmS1OUsGuXl5Tg6OmJvb09dXZ3U5QiCICERBoQVQaFQkJeXh6mpKZ6enrS3t0tdkmRGRkZwcnJi8+bNZGVlMTIyInVJgiBITIQBYUVpbW0lJSWFd999l7CwsBXVPy6TyUhPT2fnzp0EBARQXV0tdUmCICwSIgwIsNbcPgAAIABJREFUK1JdXR3h4eHo6elx8uTJZb0cb29vL7m5uRgaGrJ3716qq6tRKBRSlyUIwiIiwoCwYqlUKqqrq\/H29sbU1JSEhAQqKiqWzSQ7DQ0N5OTkYGdnh4WFBcXFxUxPT0tdliAIi5AIA4IA3L9\/HwsLC\/T19YmPj+f8+fP09\/dLXdaMjY6OUlhY+PAJAWdnZyoqKqQuSxCERU6EAUH4iu7ubmJjY9m3bx+BgYG4uLhQUFDAxMSE1KV9L6VSyY0bN3B3d8fHxwcXFxeCgoJobm6WujRBEJYIEQYE4TvI5XIKCgrYs2cP+\/fvx93dnf3795Ofn093d7fU5TE6OsrNmzdxdHTE1NSUwMBAbGxsOHXqFOPj41KXJwjCEiPCgCD8iL6+Pi5evEhSUhKRkZG4urryySefcPjwYUpKSmhra0OpVM5rDd3d3ZSWlhIdHY2FhQUuLi5ERUURFBREZmYmnZ2d83p+QRCWNxEGBGEGhoeHqa2t5fjx45w4cYL4+Hh8fHxYu3Yt+vr6hIaGcubMGQoLC7lz5w4tLS0MDQ0hk8l+cBsZGaG1tZU7d+5w69Ytzp8\/T3p6Ort27eK9997D2dmZmJgYjh8\/Tm5uLpWVlfT19Un9dgiCsEyIMCAIszA2NkZXVxc1NTWUlJRw\/vx5srKyiIiIICAgACcnJ959911efPHFH9xeeOEFbGxsCAoKIjQ0lOPHj3PhwgUKCwu5desWHR0djIyMiKWZBUGYFyIMCMIcUygUTE5OPrzj7+3tpaur60e34eFhZDIZExMTKBQKceEXBGHBiDAgCIIgCCucCAOCIAiCsMKJMCAIgiAIK5wIA4IgCIKwwokwIAiCIAgrnAgDgiAIgrDCiTAgCIIgCCucCAPCkqVWq1Gr1UxPTzMwMEB\/fz8DAwPf2vr7+xkZGUGlUi2b5YkFQRDmkggDwqKmUqno7u7m7t27lJaWcvLkSUJDQ4mJiSE9PZ3MzEySk5NJTEz83i0pKYnExERSU1M5duwYR48eJSwsjNDQUPLz8ykvL6e1tZWhoSGpX64gCIIkRBgQFpX79+9z8+ZNDh8+jKOjIxEREURHRxMeHk5ISAj79u1j586dODs7k5aWRmZmJidPnqSyspKqqioqKyu\/tX05VXBGRgZZWVnExsZiZGSEtrY2fn5+hIaGEhERQUxMDIcOHcLe3p6MjAxqa2sZHh6W+i0RBEGYdyIMCJIaGBigsLCQgwcPYmdnR0BAAAkJCXh4eODh4UF2djbV1dW0trbOy9K8CoWC4eFh7ty5Q2lpKSkpKTg4OBASEkJUVBQeHh7s3buXxMREqqurmZ6envMaBEEQpCbCgLDgWlpayMjIYPPmzVhaWpKUlERycjKnT5+murp60TTX379\/n+LiYjIyMkhKSiI6OppPP\/0Ue3t7Ll26JFoNBEFYNkQYEOadXC6nra2NY8eOYWxsjJGREQkJCZw+fZrKykpGR0elLvFHqVQqent7KSwsJCsri8jISCwsLHBzc6OoqIjBwUExOFEQhCVLhAFh3vT09HDjxg18fX3Zvn07ISEhFBYW0t7eztTUlNTlzcrY2BhtbW1cuXIFBwcHtLW1SUpKoqGhYUmEG0EQhK8SYUCYcw0NDaSkpGBubo6NjQ3nz59nYGAAuVwudWnzYmJigqamJhISEtDT08PLy4vLly\/T3d0tdWmCIAiPRIQBYc7U1tYSHR2NkZEROjo61NfXS13SgpucnCQ7OxsjIyMcHR3JyMigp6dH6rIEQRB+kAgDwqx1d3dz6NAhbG1t8fDwoKmpSeqSFoW8vDxsbW3Zu3cv6enpKBQKqUsSBEH4TiIMCBpTKBQEBQVhbGzMgQMHqK6ulrqkRen06dO4ubmhp6dHXl6e1OUIgiB8iwgDgkZu3LjBvn37sLGx4cyZM1KXs+hNTk4SFRWFtbU1\/v7+9Pf3S12SIAjCQyIMCDMik8mIi4tDV1eXrKwsZDKZ1CUtKa2trRw5cgR9fX3y8\/OlLkcQBAEQYUCYgerqaiwtLfH29ub27dtSl7NkyeVyrl69iq2tLf7+\/otmkiVBEFYuEQaEH6VSqTh\/\/jyrVq3i6NGjTE5OSl3SsjAwMICFhQVbt26lublZ6nIEQVjBRBgQftDIyAjR0dHs2LGDa9euSV3OsqNSqUhKSsLExEQMLhQEQTIiDAjfa3BwED8\/P6ytrWltbZW6nGUtLy+P3bt3c\/r0aalLEQRhBRJhQPhOw8PD+Pj44O\/vLxbkWSD19fVYW1tz6tQpqUsRBGGFEWFA+JbR0VF8fX3x9\/dnZGRE6nJWlNraWqysrEQLgSAIC0qEAeFrxsbG8PPzE0FAQtXV1VhbW4tAIAjCghFhQPiamJgYvL29GRsbk7qUFa26uhpbW1suX74sdSmCIKwAIgwID+Xk5GBnZ0dnZ6fUpQg8GFRoZmZGQ0OD1KUIgrDMiTAgAA\/uRHV1dSktLZW6FOErwsPDsba2Fl02giDMKxEGBEZGRjA2NiYrK0vqUoRvkMvlODg4kJiYiFKplLocQRCWKREGBMLCwnBzcxPjBBap+\/fvY2xsTE1NjdSlCIKwTIkwsMLdvXsXCwsLysrKpC5F+AHR0dG4u7szMTEhdSmCICxDIgysYEqlkqCgIIKDg6UuRfgR09PT6Onpcf36dalLEQRhGRJhYAUrKyvD1NSUpqYmqUsRHsGpU6ews7NDLpdLXYqwzGlra\/P73\/9ebMtsq6ur+97PXISBFSw5ORl\/f3+pyxAe0ejoKMbGxtTW1kpdirDM\/frXv0ZLS0tsy2wrKSn53s9chIEVqrm5GXt7e+7cuSN1KcIMnDx5El9fX6nLEJa5Z555Bi0tLR577DGeeOIJsS3h7ec\/\/\/nDMHDjxo3v\/cxFGFih8vLy+Oyzz6QuQ5ihxsZG1qxZI7oKhHn1ZRh47rnnpC5FmCV\/f38RBqQwOTlJT08Pzc3N3Llzh+LiYgoKCrhy5crXtsLCQu7cuUNLSwtdXV1MTU3Ne22NjY1UVFTQ2tpKbGwsZ86cmfdzCnNrYmKCsLAwzp49S1tbG9evX6e\/v1+yeqamppiYmKC1tZXm5mZaWlpoamri+vXr3\/l3\/9WtoKCAkpISWlpaaGlpobm5mfb2dmQyGdPT06hUKsle10r3ZRh4\/vnnpS5FmKWAgAARBuZbf38\/1dXVZGZmYmpqyp49e4iIiCAkJITY2FgiIyMJDg4mPDz8W1twcDAxMTHExcURGRlJeHg49vb22NnZkZaWxq1bt+ju7p7TeqOionj++edZtWoVv\/vd71i3bh27d+8mLS1tTs8jzL3R0VHs7OzQ19fn9ddf55lnnuGNN95g9erVVFdXz9t5ZTIZd+\/epby8nNzcXI4ePcrevXsxMjLiwIEDREdHExERQWRkJPHx8cTHxxMdHf2df\/PftYWEhDzcLz4+\/uF3ITIyEn9\/f4yNjbGzsyM1NZVz585RVlZGU1MTo6Oj8\/aaBdEysJwcOHBAhIG5JpPJKC0t5fDhwxgaGuLl5UVMTAwxMTE4OTkRHh5OYWEh5eXlD++SRkdHmZ6e\/tY2Pj5OW1sbzc3N1NTUUFBQQGxsLH5+fkRFRRETE4OXlxeGhoZ4e3tTVFQ06x\/A8vJy\/u7v\/u5rA0oef\/xxjh49OkfvkDBfZDIZOjo63xoQtG7dujm5ME5MTNDW1kZBQQFRUVEYGBhgaGhIYGAgERERDy\/WcXFxHDhwAG9vb06ePElRURFFRUXcvn374d19U1MTIyMjyOXy7\/zb\/+o2MjJCc3Pzw+9LQ0MDV69e5cqVK+Tk5ODt7c3+\/fuJi4sjISGB+Ph4IiIiCAoKYvfu3ejq6pKYmMilS5dobW1FoVDMwbstiDCwfIgwMEcGBwfJy8vDzs4OIyMjwsPDycjI4NSpU5SXl9Pd3T3nzZkqlYq+vj4qKyvJyckhISGBuLg4tmzZgoGBAQUFBQwODs74uEqlko8\/\/vhrFxMrK6s5rV2YPz09Pbz22msPP7s\/\/\/M\/Jzw8fMbHkcvldHV1UVJSQnx8PBs2bMDIyIjAwEDS0tLIzMwkJSWFnJwcKioqaGtro6+vj+np6Xl4VY9uamqK\/v5+WlpaqKysJDs7m4SEBFJTU4mNjSUkJIQ1a9awY8cOEhMTuXbtGn19faK7QQMiDCwfIgzMgkwmo7GxkZCQELZv387+\/fvJycmhtraWgYEBSWoaGRmhoqKCU6dOcfDgQVatWsWBAweoqKiY0TTCqampPPbYY2hpafG73\/2Otra2eaxamGtnzpzhL\/7iL9DS0uKpp5760RUN1Wo1MpmMpqYmioqKiI6OZsOGDaxfv574+Hhyc3O5ePEiNTU1dHR0MD4+vkCvZO4olUrGxsbo6uqitLSUvLw8srOziY2NZdu2baxZs4aYmBiuXr3K\/fv3xbTbj0CEgeVDhAEN9Pb2cu7cOWxtbTE2NubYsWM0Nzcvuh+PL\/txc3NzMTY2xsLCgry8vEe6sHd2dvL3f\/\/3PP7442JhoiVoenoaExMTtLS02LlzJ2q1+lv\/RqVS0dXVRXV1NadPn8bNzY3XX38dLy8vLl26RGVlJR0dHUxMTHzn\/suBSqVifHyczs5OKioqyM3Nxc\/PjzfffBNHR0fOnj1LTU0Nvb29y\/Y9mI1FEQbUShSTk0xOTTE5OcnkxCRy1QJ+Vuox7l67zOWKNqYe9bTyCUaGhxmdWDyLiokwMAMDAwNkZWVhbW3N2rVrKSwsXHQB4PuMj49z\/fp1duzYwY4dOzh79iw9PT3f++\/VajU2NjYYGRmJH8Elqq6ujtWrV5OSkvK1\/97X10dhYSGJiYnY29ujra1NVFQU5eXlD\/vwVzK5XM7Q0BDl5eVERESwa9cuPD09SUxMpKSkRLJWv8VI+jAwzb3Co9jra7NtyxY+\/\/xzdu6Jp1a2gCX05aP39D\/zlNlRen+0p2malhuZOOptYNXrb7N+lwtZZe3IF8FPrAgDj+jSpUs4OTmxfv168vLylvQFsrKyEisrK5ydnblw4cJ3vha1Wk1jY6PoHljCVCoVTU1NDwNrZWXlw8Gnu3fvJigoiJqaGrHk8Y9QKpVUV1fj7++PtbU1hw8fJigoiIqKCqlLk5zkYUDZQazZm2j91T\/z6gfrWbduLZv3JXDz\/jCyqS\/+ruWj9PYN8eCh7En6unoYnlSBYgLZ+BQqAOUU4xMKQI1soI\/BCfUXu\/bS2T+GEkAxycSUCpAz2PPFMYDJ8ghe\/Kd\/Y31QMQ9Hy8iHaG9upWfs6wNV1eMtHHXbzh\/++BHvv\/xbfvL\/aPHTzcE0LoJ1xUQY+BHt7e0EBgaye\/duMjMzpS5nzsjlcnJycrCwsMDd3Z2WlhapSxLmyZkzZ\/D398fT0xNXV1cuXrwoAoCG5HI5ly9fxtjYGA8PD3x9fTl79uyKfT8lDwNDpdi\/82\/88o9e1H3ZoKWoI9LcCLvIG4yP1RDtYIZFeCE9\/XfJdN3J6y\/+ntWf6GK8yxKf9BpUQE9RPLbG5ljb6PLB2++ww+YQx9JDMH7vTd780Jyj1+5RefogZiZW2Jh\/zqrX32azUwINYwraj1vym797BZfcdgAGqrPxMtjMho0b+WSnG1m3uvgyEqgmhrjf0ooMmL62n+d+osXjm0JpnpTTejkWe+cwijuledJFhIEfcPbsWUxMTAgMDKS1tVXqcuZFR0cH\/v7+GBkZcezYManLEeZQamoqOjo6eHt7ExcXR3Nzs9QlLSvNzc3Exsbi4eGBvb09mZmZK+6JBKnDwOSdE+j92+M89je\/4eXV7\/G+SSSlLVWEfPY8P\/v3zVibfsZv\/vVj\/C9VcdHrU\/7x50\/zudUedN\/6Z7S0fs7a0FpAQaHju\/yV1p\/z23d2oLPxJf5frb\/iiVe2YbzjY\/7lJz\/jDdtAfNc+yX\/X+iVv7TRC\/70n+bO\/fhXfshaK3d\/nl7\/cTHrTFPKOS9i+9Ryv6fpzItGJJ\/\/6pzxvlUX\/Nxtfp1uI1XuZ\/\/V3\/4XT2RaU6kGO267i58\/u4nSbCAOLxsTEBBEREWzfvp0LFy5IXc6CuHTpEnp6evj7+yOTLWSHmzCXlEolZ86cYf369Tg5OZGUlMTw8LDUZS1r\/f39nDhxAldXV0xMTMjJyVkx8xhIHQY6z\/nwh\/\/1U57+1BxXVwccQs\/TPqGkLceJX2n9Bf\/P\/3oanehaFH15fPbv\/8RTRulMAvcSd\/FX\/\/Aa3tfGgQ7C1v0nP\/mXT8m6PUbraVv+6q9\/i078beQdWaz\/9\/\/kfQN7tv\/Hr\/inVZ40yqE71YKf\/Y+XsDtTQOSmp\/nZG06UjYxTEbuD\/6H1F\/zjy2vYsvZV\/u13a3A+UcfkV8PAVCvH7D\/gZ\/\/zX9l04BLDKgAFfU1V3KrtYFyiPCnCwDcMDAzg7OyMnZ0d9+7dk7qcBdXW1oa7uzv79u0TyxUvMUqlkqqqKjZu3Ii1tTWnTp3SaI4JQXMDAwOcPXsWFxcX7O3tKSsrW9Jjix6FtGFggpKgz3nsF3\/A7dpXB3L3ctr5Y\/67lhZaf\/0cjkUjyCsP8uwvfsv2qBrUyjbCPnuWn\/+7Ljk9wEghei\/9ht\/oJdKrHOOsw7v84old5I3B2GUPXvrFH9C1sOG\/nvhP3gsqBcbJ3fsej\/\/6U+Ivncb4lSd40vgovRPdHDV+hf\/5iz9i5R1ASHwal8o7GP9KD5JysIpo4w\/59f\/3CiaRhQwuoswowsBX3L17F21tbYKCghgZGZG6HEmMj48TGhqKubm5WKlwiejq6sLDwwN9fX1ycnIkXYNAgKGhIbKzszE2NiY4OHhZd89IGgbk7cTsfh6tv3wKXa8IEmKiST5XwLkQS\/7wxGvoONrw4T\/+Ey+ZnKD+Rihv\/O+\/57lP7TngsoNf\/dlf8A+vBnJPDfLyYF76xT\/zod9Vxqcb8H7tX\/g\/LwfShpLrHmv46c9eZ7fDLp7451\/wm412HPG3ZdW\/\/pZXzDKoLg7m97\/4F9b6X2Na3U+66cv81S\/ewjnpDBeyj3GysJaByQflqkcbiNZ5Di2tP+MfXttNUHw88amXaB6ZpLMwmI9XfYzHpfsL\/z5+QYSBL9y7dw99fX0CAwMln0FNaiqVimPHjmFra8vdu3elLkf4HgqFgry8PAwMDPDz8xOPvC0yvb29uLq6smbNGq5evbosuw4kDQMTLaQ5b+Cpp5\/h2Wef5blnn+WVTZv49O132OZ2mp6xNpL3fsrL2oeoaK0l1uhd\/u03\/84ftXexZeM6NvsUMgX0XTnMJ69u4+CFTpQTZfiuXc0G7ysoGeGc+y5W77DF3exDfv1\/\/oOXVv2BJ377n7ymd5Abg0r6rgSx4ZUdHL7YCajpvZ6A3htP88Rv\/4Onnl6NeUjBw\/ECk60XcHjjSZ74j\/\/kmedf5MUXnuP5z30p7hql8YQrv39Tj5gy6VrzRBjgwUCgzZs3i7n3vyErKwtbW9sV112yFAwPD3Pw4EF0dHQoKiqSuhzhBxQUFGBmZkZQUNCyWzhJ6jEDMzI9zP32bkZnnMlaObz6n3j8KUsudw7S09nD2A\/1608P03b7HvcHJ1hKnUQrPgz09PRgaWmJr6\/vgiwPvNQkJydjbGz8gxMUCQurra0NV1dXHBwcxOcyU4pJRkdlTCsW9me6p6eHffv2oauru6yeTFpSYUBTk3Uc2fUJG91O0LWMnyBd0WFgamqKAwcO4O7uLoLA95icnMTX1xdPT08mJyelLmfFa21txd7enpCQkDnszpqm5epRnM2NMHI9Qva1VmY0B+FwLUn+xyi6PcCkfPqLfXu5FJ9BWu4dNKlyuO4ixy8W0f7FZCyDDTfIiinky0ewp9tLSC24SdvozC7qU\/Un8Yk9yvXuhf9Vn56exsHBAUNDQzo7Oxf8\/PNhRYQBhYz+ni56R6aW1J3+TK3oMHDq1Cn09PTo6uqaxVFUdJafwsvgM9Z\/akXwyXIGH\/l3Rs3E8BAjE4t7+tehoSG0tbXx9\/eXupQVraWlhb179xIfHz\/Ho9RbiftkK2s+MsHRzxVzQyO8LzQ\/+u5jDWSEn+ZqWQmZzgcp6FQDoxSnHSPz7O2ZBYsvD1lykNVbbImvmQDkXAnYyi\/+zyek3nuQBuoijdnsEEbVDMf5Thbv50MbR040SXeLFxERgZmZ2Sx\/dxaHFREGVogVGwYaGxvZsWMHpaWlszrO\/fxwjPX12bM\/ioSwYA4eCudS2w+0MqiVyKflKNVq1Kp+zjs6EpZWyvDDhTXUKKankSvV39pPofiyo0qFXPHtHzOFfBr5d\/3GqVUoFKpZpdrr16+jr69PVVXVLI4iaKqtrQ17e3vi4+PnYSBaA4fftiMsvRNQkOu9nSd2ZNAn7+N6khvrP9jITodYSjsfrFQ41VdOgpMZWz+1I7W0C+ilIDWdcFsdVv3yCdYYO+Pt5YGbRxh5VY3cykkm\/XI9U4C8q4LsxBRuDABD9WTuN+O9z43wSbvxxfPWX5i6if0HxngfbWCaHjJ3v8TPnn0b3WMdQB9J2ma4H7yCDOi\/mcFenU\/546f2JF1tQskAxccyifZ3wsgylKJ7bdw66sOuXYZY7t7AG1YenGuX7h5PpVIRGRmJmZkZ3d3dktUxF0QYWD5WZBiYmprCy8uLoKCg2R1I3kjQDgN2u53jy3HcU6ODjEz2cCXjKNEnGlAB3SWJBJ26TkdbGbHOJmz6wITogmrqriag\/S+\/4tk3t+KaVsboZD9lR7359P0PeG+bM8fKO5gav0NmYChHvGz4\/H199ief5UK6H5s37cA6tphBFaAao+7kYXQ+X8\/7RgfIq+15MN\/2SC1HfQ\/gYmuEvm0yt2fZyp+SkoKHh4eYxGaBDQ0N4ezsTEhIyDwtInSH0PdNcfa7SFtnA3Guu\/jIK4OcSD\/0tvlyqqSYYweMWLsvhbv9fVxJcuAt43AuF9+kvrUfxWQFfm9a4Gpvj85\/vcuesBMUXErCbvUunHzzuZHjxmqLOO5OTnLv5BH01h+kbqyFFIe92LglkldwDHfdLXhf+OrcFjIuOehhdfA0TS03OLLWECtLG7aZnmN09Cpmu10IKehmuu0clh+bcfBYPgXHgthpZE900VUiNq7hnTU2xJ24xPnUQ2z9cA9xeXkk2n\/MM9oOnJfuCS7gwbwQBw8exMHBYUk\/ZSDCwPKxIsNAZWUlb731FkNDQ7M7UMcZNunb4Hzhm819rUTa7WWHcxEq4G6aGR\/6xJIR7cSbBuFcr7tHZ08v\/QNlHPzwXXRswim5d5+KrAA+W+vBufomKrPcWbfLk8zLJ7F\/bgN24ecoPu7N+y+9wKeHzlKeH8b6dx1IK++i\/Xos29Y5c7KymrxD5mwz9+bqENB3AeNfv8rn+2IpuHVnBt0X300mk2FgYEBeXt7sDiQ8MrVazaFDh9iyZcs8XjRaiVn\/Dr\/51ZM8\/+JrfLY3gerGYlzt9Pk0rvHBP7mXxgev2JNVc5\/aM0fYumkf8Xm3aB+cgqmbeP3ehpC4ZPzW7CatQQ70kr7DDge7fPoGr2C+eg9ZJWVkHLJgW1QD6q5sNmzayKceyVzOS8fdaA1bE8r4auNA7wVP3rc+RFpmMDp70ynMjsFltzvHso6wx\/0AhX1Kmk7a8aRJIs0AtBC42woD51C8P7LlQFQ1MMGZMEuec7iACpgsDmC9vTvZLdKPBBsZGcHLy4v09HSpS9GYCAPLx4oLAwqFAnt7ezIyMmZ\/sLZTbNC1xuVi7zf+RyvhdvvY4VqCGmjKtOAD\/6MU56dhq22NX8xpSu8NA0Oc1NHDJ+YG06p+ju7X5eUDJQ8OMVGC4fvmOAeGYPt7R041TkPPJfZYbmNf0QRQh\/fLloSnl3I2xohfrd9L0uls0v3N+cTOh3P3gd6L2Dxpw\/GauVtm+dSpU7i6us4+SAmPpK6uDmtra27fvj2PZ7lNyHsWeB++xuDkFAoVMHiNveY70E7vePBPmo\/yx7fsyah90Ek\/Wn8GN+0t6Dsdo3O0msDX9xAck4D3B4ZkdwIMkLnTHke7S4wi46L7DrbaO7JvqxGpbSpozWTNhs\/YFXyWmqoKyioraOz6xjTYvRcwfF+XbVvWoXO8DlnHVQKd1vL2OzvwDsimD6hNMuYZy3QeTLPUTpCpJbs8I9m\/1omQpEZgnOwjJjzvVAiAouQAa21cyGldHGsI1NTUYG9vv2S730QYWD5WXBior6\/nww8\/nJtHssYrcdnwCVvdLjxclUotG0I21UKc1R507YsBaIjV4U2PVDqmgaEKIqx12ax\/mPrpYbJ3aHPgaD0gI9NXmzd8rj04kCwf7Y1WeEXF4PAHJ\/5\/9t47rqoz2+P2nXln7kxm7tx37p3cmUlmkpmUSWZuuqljYjRqooklttijoiAIgoA0pXfpoHQBBZEiohQBpUpRQKp0pPfey4FTvu8fqFGjEZUmnu\/nc\/7gnL2fvdhnn71\/z1rrWSu8RABtyRjoy6GfNgiUYPu5JseCs7joo8wbm2xJLSmn\/HoZFQ3tDIlB0hCL5ltahBVN3NrmoaEhFBQUfvJikTIxCAQCrKys8PDwmOQjlWA7Tx4Tp5wfMv9FbVw8Zsjyjbp4eh\/HVl+OteZhVHT3UJYahn\/oOVx1ZNiu7cn1thzM31fB2e8MdmuXsf6gNwnXCzm1WRU1pSj6gK4UU\/7xpxeZ950vLQDD1zmmr84WdSdCwsII9TtHZs3dorWFU5s+5L\/+42McsvqAbk6rfMicOa+hETnm5+8rCmH7BkV0HE5w3E2PdcpmBOYk47hwH1aehYCI61H2LFsmi6HrSZz3fMnr23WInCFiAMDZ2ZkjR45MUghocpGKgdnDUycGjhw5wrlz5yYoG3uU6ign9q5dw9a9Bzigvh8dCz+K+\/soD7Bl04LNKB2yRGPDx7yp7UlMuD8OHt7Y6ciyRcWRoqFBskx38+VXmzAIuUrBJR82r9mDhpEFxtq7WGPoT2Z+NFpvqBFSNAzNcRxQ34R6Yj9QgPFbCjgPxNfoAAAgAElEQVQFFtNUFoHilr1oHPHG56gzPsGXaZOAuD4KpVdUCCmY2Bh\/eHg4np6eT+TN60misrISWVnZKegx0E768fMkZzTdmfnfV0aoozF79yqjYu1Hev0wMEj+RU8OHNJBw+gooTntQAuXvC5yrbKJohhndsup45VdTnFEPLHR18f6yHcVE2Rtw4nMH4L1I7WpHLU0QNPQDHN9VxKu332dSmjMCOKwTRgl3RJATFPqGezt\/clqv+nmH+Z6nA8ae1XYq2OGZ0Il0E2GbwxX8trGkmaH64n1MuD73ZrYWjlx5OIVKnpmjhhobGxER0eHgoKC6TbloZGKgdnDUyUGBAIBSkpKFBcXT+CoI1SmhGClo4aajileUXl0SQBBPbEn7Dmk50hQeBjh+eUUZ4bj4GSLhaMvUdfaARhuzMTd9CDGZ3MZEvVSdMEPEwMTDF0CSa3qg9FWMs9lUtMrgqEmMjOTyGweBbrIPZtGYVUfMMz1lCCs7B1xtPUgODJnzIbBBq6czaS2Z2If2s3NzSxZsmRWFU+ZaUgkEmJiYnBxcZluU6RMAYaGhk9kC3GpGJg9PFVioKCgACsrK2k3t8dEJBKhqKjIlStXptuUWcvIyAiWlpbk5uZOtylSpoCkpCR8fHyeuPbhUjEwe3iqxMDx48cJDQ1FJJr+TOInnfDwcIKCgmZ9i9bpoqenh8WLF9Pe3j7dpkiZAlpaWnBwcKChoWG6TXkopGJg9vBUiYEVK1YQFBQ03WbMCvLz8zEzM5MKq0mipqYGRUXFJ26mKOXR6O7uRktLa5JXjUw8UjEwe3hqxIBIJGLt2rXExcVNtymzgoqKCubOnStNIpwkSktLOXr0qLQfxFPEV1999cSF3qRiYPbw1IiBnp4eHBwcnjjlPVNpamrim2++kVYjnCRqamrYv3+\/1DPwlCAQCFBRUZng5ObJRyoGZg9PlRiwt7enrKxsuk2ZFfT29mJpaUllZeV0mzIr6e\/vZ\/78+dIWxU8J9fX1HDlyZFzdDIVC4YzpsioVA7OHp0oM2NnZTbMYkCAY7KK1vecBndxG6elso7NveMa2zOzt7cXOzo7q6urpNmVWMjw8jJ6e3rTPFIc76ygsrKR9cKrq54vo72mnvXuAmVMJYPJJSUnBy8uLvr57Fwjr6uoiKSmJffv2oaqqKhUDUiacp0oM2NvbT3CYYJjS8y7IrV7Jit1qWHqGklX3U6V\/R6mMDsRiXygtwgbOWLpzIvL6PYRBG1EGDrh5ZjN01yfCoT66uvoeqS3sRNLT04OZmZnUMzBJSCQSzp8\/P61160cbUnAx2stew1PkNvzQK3ig+hKuhkb4pt2Z+d5TEo2VrhkBuQ+\/AkIiFtDT0YuYHq44u2FnlsDTVPDa1NSUoKAgxOIxCSQQCMjLy+PYsWNs3ryZ+fPn8+KLLzJnzhw2bNgwzdb+wNMpBiSMDnbT1trF0F3506KRPtpb2ukfmanTuPvzVIkBW1vbCfYMNHJqmzxb1urjHhKEp7Uu8tv1CS+7LY4uHmFoaBihBGCUQm8HZD92olzcR250PCl5LTdmQCIEw8OMCEVIJLX4rFFFR+sibQLBD22JJULKznhgo+3OtUER4mm83hobG1m8ePF9ZzJSHp+SkhLk5eUZGrpbEk4OEuEIg4OCG9eqmOsBh9m3Vpmggib6hn\/wDDTHWvH+nP+Hl9Zak3fr6+\/ivOYCfvHMs6zzL\/1hUPEoQ4PDjPzkohMJA7WpuMkZkdRYS7SaHsqbA6gbETAifPJuqg9LR0cHenp6ZGdn33pvdHQUDw8Pnn\/++Vs36Dlz5vDLX\/7y8butTiDjEwNiuquuEBwQRU794I33usiNCiUk\/CrtM80FJBY\/wCs1wGV3Zd5ZKkdQ5e0eMzE1UYf5dMFqLNMmYknwMBUpMcRk1dwqE95ZnkFM9BUaJuGW8NSIAbFYzIoVK4iJiZnAUWs58Z02FoevIgJEA1X468nypVok3UgQNuURaLaXxau3oe4URcvoIKWnXFBa5EYVfVw5GUxUcjVCUSeXvEzYsWYpa7fvw9jpMNpfy6OwShEFuY1s0\/Qgv1eIoC0T6xXzePulD9liHsK15unLNK+oqODjjz+WriaYRPr7+7GwsODcuXOTfCQx\/aWJHFHfyqcLN3Dg6Flyyq7gvOkL3n\/1UxS84qi+5RgQUhXmieyLi\/lcRhWT83UAjNadR2X5NlZtXcqmoDHBPVqfg7+hLAsWfMsug+PkNLdRnhSKg1cGg8BgwVkcAqMp62gk\/rAcnz77Kqu0DFBeqcKeRXIoK29jg8JhEqr7Zmy4bCLw8\/PD2dn5R8t0u7u7OXLkCH\/5y19u3aR\/\/vOf8\/XXX3PmzBkaG6e5DzPjFQMiKk9p8c\/\/ncf+6PqxtyTlHPnqDV7\/7DD5d0c87jHLkYjvN\/kRI77ryS0W\/fhRLhGOjjXg+uGd2wf\/4a\/+XGzXLmKNXvRdnikJolvjDhCt9yVznl+Ac\/EI3Kq1IqbUV4H\/+K+XkY+687sRjY4+QthLTGW4MV+sN+NSGyCpxU9PmZ16MXTeNphIKJyQ38dTIwYA1q5dO8Fu1zr8NmlhZHSJsQmShPJgW1Z94kDZQDUndNRR0A0ktzwVhz1r0Tp3hVx\/d5QXe1JHM\/4bNbG0Tacq\/wRrFDzJrc7HU1eJ1WpHcN4jz+ZVZsTmpeOhuZ7tvtmIRV0kWuxj21IFjqfX0js8fWv88\/LysLa2ltYZmGQuX76MnJwcHR0dk3eQwWzMtiuh4XCR8sosPA1k2WQdxFlrVbZ\/uQff7CYGb2m+UcpDvNH50hgHU31UNfxpAMp8tNht6YHTYRm2hVSCqJpjavuR0w6iqK6UMFtFvjvohZ+jKat3n6MX6Ik3YYWePSltAtqueKP08bfYRsVzQkmJdZ9pEJaXS5DRRtbbn6dtlmrO8vJytLW17\/AK3E1iYiKffPIJc+bM4Xe\/+x0JCQloa2uzYcMGEhMTp7WD6HjFwPXjKjz3yzeRDb9RwlxSit28v\/Ln98woHhFQdzUa\/8CzhAW4Y6Cli0NABl2jYgSdZZw\/Zo3OfmXUDJ2JKWq+0RROTFdZAs5GmigfOExoZiOCwQYS\/KzR2HcAC694GgSAZJCypCAOH9TmkLE7scVtiICu0hSCA8K5GB2EhbYmJq4x1PR0kh9wiE9\/9\/\/yny+txOhkHLUDw7TkRONmcRAlRQ0sj8fTPNhJvOV6fv3CXOTM7Disq4vNycv0jIioDFLlv\/\/8JioXxhJBJUP1JJx0Qk\/zIBZHz1LY8XC5HpKuTMxWL0fJP5e6zJMofLcbr5vuuOEGUs+fxsfbB78z8ZT2CIEhyhKTuZJTzygg7KogJTSV2qEHy4WnSgwEBARw5syZCewL\/4MYGJs4SSgNtmHFYldKK2OR2bGGpTruRJ8PwlprO3uDk8n2c0NlyQ0xsF4TC5t0KnOP8cW6g5w8dxrz\/Tpon4jkpLwBxoYpDAPpnjIsc04FoMLfFt2d1lybqnyu+3D27FlOnz4trUA4yQgEAtzd3bG1tWVkZOTBOzwCwlwPFu3Rxatk7IlbdsaOr9c5EHrCAaMdVuTdcdhRyoPd2f+VOxlZJ1FUOoRfSiK2e40IiL9CgONutoVWQ30EmxX3cSipE4D+zOOs\/FQNPX091shH0g\/0JVmw0sCBlE6gJRnrb5S40NTEJV0TNHadpRuoDFZkyeEQ6qcmUjKlDA0NYWxszJEjRx64bW1tLcuXL+fVV19lcHAQiURCQUEBqqqq6OnpUVRUNAUW\/5jxioEKXzVe\/M27yEeOeZKQlOHw+Uu88JE11aJeYsw28p9zfs\/rH33Ox2++wC\/\/awGWseVUxrmw6tMlrFy1kvn\/fI4XP9MkoUnISEsiyl+9xbP\/eJ+Fny9it7oNdroyvPa3v\/P+p\/NZ9OU+wip6qLloz6K3PmWN0iEOrJ3PP+drEF\/bQ1mIHi8\/8wJvzVvMZx+\/yR+eeZkVpsfw0VrPP3\/zH\/zmD\/9kubIL1xqu468jw4KvlrNqxef849l\/sc3yHGGOMvxqzq\/40+tz+fT91\/n\/fvceaoHZFARr8Oxzb6ES2wGSbiLMdvDm2ytQ0tLkuw\/n8qncCapv6AGxSMjoyAijo6M3XiOMjI4iusMFIqQs3JTFi3ewV0mWbYcu0AUgbCfOQpmvNh3Awe0ounLr2agTQsdII6c2aGJoksIQ0J9zmn1v6ZPQ8uAHxlMlBkpKSjAzM5vAWVYtJzYcxMomf+xPcTUntL5njVkave0J7N66me8dLlJTU0llXR2dQz0U+TmjtMidOpo5tUELC9t0rl91ZfmCJazbtg9DiyjaaSd0uw4GBskMICbNYxfLXdPG\/gdvCw7KOFE9Qf\/BoyAUCpGRkSEjI2MarXh6aG1tRUNDA39\/\/0kZX3DNk6XbtfC9kVtbEmjE4j3HiPGzx2C7JTl3RKNGKQ92Q3GBGzWjzZzQUOTrj\/7NQv0QylpqOeuwgy1naqDpPNtl92KYNCaT+1Od+PJbA+xM9dmw9SyjQE\/MIb7UsCatC2iIx\/wrJVL6B8gyMuOATCgdQHmgEkutQyclRjrdnDp1CmNjYzo7O8e1\/dDQENHR0XeE5oRCIadOnWL\/\/v2cOHFisky9Lw8lBn77Hgrnb4QJKMdxwUu88OENMWD4DXN+\/zFml7sYLvbhwxf+xMemMfT0tVCcm0dWUiRm69\/iNz9bikt6C0VBSvzxfz\/DOLETENGa4sRnf\/sbc\/cG0gZIeptpbynBedeH\/PqFhRxwcMdJdRm\/\/dmbaJ0ppjj4AL\/79Qts8ilGIijGcOFf+dlnuqTVZ2Dyzou8ueYUY0+JYZqrSsjJzCTxjBlL\/vwsbyyxwM1iA7\/4nw\/Qv9QJPcns+defeHHbES6cUOdPf3kbtUu90BrJ2ree568LZLFzdUL569f55bPrCaqUAIMUJYTgbGWDg5MTTk5OODnYYX30BDG5LXecPUnfNayXvcXzH24nsHLsxyipCWXdYmWOJo4tPR7K80dh1Sbci4oI3aaDkUnqLTGg\/JYBiVIxcCejo6MoKSlNYKvQZk5tWc+8t1Yiv28f8vIKKKpbEF8vAEkbMc76rN2ug4OHJy5WniRV1HPtuAO75jlTRyPHV6lgdDiNojR7Fq5XwNjWATun45yNicZhjSp6ukkMICbZeQvzHZIQAR2JnsjO+4R1hifJnibfaX19PV9\/\/fUTV0f9SSYvLw8ZGZnJqaA5WIm34X5WbdbCxPwQW\/coY55SRsEJC1RX65N5x4N4lBI\/B3a8ZUUx0BGuznO\/fhUZ3xLE4mZOmqzmmxOlIG4l8ughVqxRwdjEGDkFWdSCr1JxOQjFL9ax7YAl+ts+5VV5K9I6gb48HL5dxFIVNXYsUebArjN0AGUnd\/NvkyDqBu9p+RNLTEwM6urqE7YaJzc3F0VFRVRVVent7X3wDhPEeMVAuY8yz\/\/8bfaeb77xXgX2n\/2NP39oRZWol2j9Zfz8n6vxqwU6Y1j+z+d4WzeI7DgvZNduYMeeXXzzzv\/yq9+sxjOzlkuOa3nu7+sJunn6Kv348u+vsMbqCrcee4P5mKx7hV\/8zyt8snAJS776mrWbDxFxrZFifxX++4X30cuSAN2cXDOXX81VJ6b8Evpvv8Aba07SDjBYib+JEqvXfM\/ubUt5+b\/+h3eX2+BuspZn\/rWCE\/UA5Rh\/8Cp\/WnmYcz5q\/Omv76Ce2gslPix57f\/jv196j88XLWHpN9+yS92VzDYJMERlRiyBPic46e+Pv78\/\/id9OX4qnNSSu5MP+7josJc3VvtQd\/OM5nnwwSYjTpaO\/TBENec5sGs1RqlZnNt+EGPTNATA8LUw1N8xlIqBe+Hu7k5wcPCtJTyPxwgNmVEcc7TB0sICK88wcptum0b1lBF5yg1rZw+8XELIrOmku7qM9Nhyhhii4lIWBSV1ZPpqsEPLiai4CwRYq\/H9lj04B2dTUNCKCGgrT+NiyY1VB4IWLvnZYOAQTH7X9MQKTp8+zYkTJyYw3CJlPGRmZk6aIBC15BHkYo+xlT0eUXn0Af2VBVxNyqPjjrQQMb1VJVyOLBxLsOqrIPpiBsXto8AQldeSSKi4EcPuvU6Uz1FMLKxxOpPG2E+jl7wobwz1HTgVepZzOddpFwAIqEoNwtTyMEcCLnMtq4ERoK86g5iCWgZnUWpKXFwcqqqq5OTkTOi47e3tHDhwABUVlSmrtDo+MSCh9ZI9n\/76D7yzw5mUghKyww8z\/8\/P8rZiKAOSfs4f+oo5ry7HuxJojWDZ68\/zjpYLzgqf8csXNuKfV0io8jx++\/OvcMlso\/zsfv7432+wyz2BouxkIl32M\/e5P\/OPtabEZeeSdiGSjLICvOXn87uXVuMcX0h1eR6Xk67S2N9PaZA6v\/v1cyw1Okd2RhA73vozv19pTU5tKnr\/\/F\/+9pkhSfVttOef5KsX\/8Sbu73JyQlm50vP8fZXlnhYbmDOM68i45pIdpIzS174I\/+neJwkXzX+8NybqCT2QFcs37\/zd15eYUhsfhkluZmkpeXT+tC3zV7ijuznzfUnuTn9ktSFsX7+LszPj73TluDKri8VCG+qI1xGBoUD5+gD6kMPs+L1gyS0PvgH9NSJgYqKCpYuXTquSl9Tg4hrfgdYKaOBo5sLhzUU2GvgN61hgJ+ir68POTm5n0x4kjJ5ZGVlsXPnzilYYSBlMggMDGTfvn2T1ppaKBTi5uaGpqYmpaWlD97hMRlvnQGJoIEoi+\/5x5\/\/yF9ffY2X\/vh33lmhydmKfqCf6EMr+M\/X1uJXDbREsOy1v\/OJ4WlS\/A\/y0V\/\/wrvzvmbZJ\/\/kP5\/fgMuVbkR9eZht\/IjfPfsC\/3jpLVap2eJlq8p7f\/0jL7z0Mq9\/vovAsh46svz4\/qP\/46U33ueTj9\/nozUmpDX3cT1Uhz\/+5ne8+PaHfPK3P\/O7vy\/BOKoCwUgLIcrz+cWvnuX9zVakleXhsvNTnn\/xbebNX8r7r7\/O3FU2uB3ezXO\/f5WPPvmEN\/\/yB\/7w9lZ8cxqoCNHmr8+9z4H4DmCQy57qfPryy7wx90M+nPsJazSDqHloYdtLrJMKb67z5WaQBVEv6Z7GfDd\/Hgu\/WMinC77HxOsqg4hoiXNm67wP+PiL5WzatYvNnxqQ3C4VAz9CJBJhamo6LfG1+yEa6qA4I4GwiPNcSM6nvnNmVBe7F8HBwVhYWEjrC0wjOTk5KCkp4e7uLv0enhB6enpwdXVFR0dnAsOU90YsFnP69OkpEQQPV3RohKbiy0SFnuZ8cgEtt4V+xP0d1DV23iimJqKtoYHWQQkwQn1uIpFhCeRX1VPb1M0t3+tgA1cuRBARm8lYrTcxDXmJhIddILOinZuPP0FbGclRZwg8F0dWRSdioPq0Ov\/z\/FsonUwkOz6WlKK2H4wZbiIjKYa4zNqxMYYaSI+JJOpSPtVNzTR3DDE80ENTQwstFdlcOH+RrOobxeZE\/TTUNdN3KwdQRGd5BpGhIZy9mE5F26MsBxcz0NlMRV33ncXmxL1UZscSfPI0F7Mr6bnl7B6iLjuOsxGxZFc00FrbzqBIuprgnhQVFbFo0aLJXa41C+np6WHPnj0kJSVNtylPPQ0NDejr66OhoTHpDxcpj0dhYSEqKiqYmprS1tb24B0mgJuCQENDY1IFwXjEwPDwMHl5eVwrLKasrJSSoiJKSkspLSnkWl4eeXl5FBSXcr28lML8PPLyCygtL6esqIBrBUWUlJZQXFxCaVk518tLKLqWT15ePoXFJZSWFFNcUkppceHYtiVj25aVllBwY6zi0lJKS4ooKi6lvKyYgvwcQgxWMedXf2HLsRRKS0soKSmm8NoNW4pujFtUSEF+PgXFY5+XlJZSVlZGWWkRRUUllJeXUXbDtpKSIgry88gvKKa8vIzignzy8vLILyiitKyU4uIiikvLKCspJP\/G\/5yXlzety0Lv5qkUAyMjIxw5cgR7e\/vpNuWJwtPTEwsLC2knvRmCSCTC1dWVFStWcP78+XFnpUuZGtra2ggICEBdXZ2AgIBpybEJCQlBU1Nz0nIIxiMGqqur+fzzz2+9FixYwILb\/h7Pa8GC+3z2o7EWsGDBgntvd9sY8z\/7lHnz5vHp\/LF9HjjuT9h8z+PdY5t77T+TJlZPpRiAsQt0165dM+rLmMkkJSWxadMmrl27Nt2mSLmL4uJiDh06hJaWFikpKdNtjhQgISEBPT09dHR0qKqqmlZbzpw5g4KCwqRULHw6exPMTp5aMQBw8eJFZGRkqKmpmW5TZjQtLS18\/\/33uLm5TbcpUu6DUCjE19cXbW1tDhw4IK0BMU1kZWVhYmKCuro6586dm6BVS4+Pra0t2traE+7Vk4qB2cNTLQZGR0c5evQoenp60kSs+zA0NISlpSU2NjbSpYRPAC0tLZiamqKmpsbevXunrTLd00ZOTg6GhoZoamri4uJCa2vrdJt0ByMjIxgaGmJubj6hJcSlYmD28FSLARhrBqKtrY2pqemM6RE+UxCJRHh7e6OtrT2jEl2kPJiSkhIcHBzQ0tJCW1ub5ORkqZibYIRCIZcuXUJdXR0DAwP8\/PwoKSmZbrPuS0dHB5qamoSEhEzYmFIxMHt46sUAjM2mZGVl8fb2njFuvelGIpHg5+fHwYMHZ0R3NCmPRkVFBVFRURgaGrJ3715Onz5NfX39pPU5mO2MjIzQ2NhIQEAAe\/bswcLCgrCwMGpra6fbtHFRWFjI\/v37J6zgkVQMzB6kYuAGTU1NKCoqYm1tzeDgLKt9+pCMjo7eEgLSksOzg87OTnJzc\/H29uaLL77g8OHDpKWlUV1dzdDQLCz8P4EMDAzQ0NBASkoKRkZGbN68GXd3d65du0ZXV9d0m\/fQBAcHs2\/fvgnx9j2yGBjtpDyrkIaex\/XGCumsKaeoqJ4f37XF9DXWUV7cgPQKfzBSMXAbjY2N7N27F3Nz8xkX85squru7OXr0KDo6OtTX1z94BylPFEKhkObmZq5cuYK2tjZr167l5MmThIaGkpubK629cYO2tjYKCwu5cOECR44cQUFBAQMDA5KTk2lra3uiQy5CoRBDQ0McHR0fO3\/gkcVAWzIH527G6eLj3mP6STHTZtdqO4p+9JUMk2Fvg\/rGkzwZfpvpRSoG7qKnpwcLCwuUlJTIy8ubbnOmlLKyMrS1tTEzM6O5ufnBO0h54uns7OTSpUvIyMggJyeHm5sbp06dIjg4mNLS0qcmsbavr4\/S0lJCQkI4duwYzs7OaGpqYmJiQmxs7KwTSdevX0dRUZHU1NTHGuehxICwj\/a2bgQioOMSmm9s4GhcHUN9vQzeHbUSD9HV3kHvrYJ9EoRCAQKhkLFaeiJGRgVI6OOSoRpblx2m4DYxIB7so3+og3Q7W\/Z\/e\/yO8u4SiYixgnwi+rp6ENwdGR7qoa2jd6zan2SE4eHb6v6JRxEMDP\/QDAkh\/R1tdN1yS4hv2DdKX+8Ao\/eIOo\/0dNLZf9c\/LBygva2TwRuHEg4PIRDc9g+JRhkeuLGPeID2lnb6bn4sEY31rUHIQF8\/A4NDjIzcJvLEowiGR3lQDUKpGLgHo6OjeHt7s3XrVs6ePftU5BFERkYiKyuLm5vbHS1SpTw99PX1kZGRgb29PTt27ODo0aN4e3tjbm6Or68vOTk5NDU1IZE8uLTpTEYikdDY2EhmZiZBQUEcOXIELy8vnJ2dkZOTw9LSkqysrFlfXCs4OBhlZWXa2+\/ukjd+xtuoqK8yg0BbSywOH8blfBbN9anov\/UFW\/YcwtzUAD1LdxJqx863oOEyLjYWGJuZYmTuyNmiLqCPjOOBhEYUMwyMNuUQYB5GzUgPqaYabF9uTaEIQEjT1WDM9Y2wtNND7ovNyG0O4Hb\/w1BTDicP2eLsZoPRIR0MjgaT1ykEJHQURWJ\/2BJLM3OcTp0jOfk0zr6nyesCEFObEYSRd\/JYk67+Ss77OWJsbomlhR3hxQ10VaXjrWWMmbEWmo6nyb+9QZCom2tRbhjqG2Gga4nf+WsMAIOVcdhZmGNmZoyRlQeJtQN0ZAVhe+IcJb0Ao5SnBnLYP5ninDg8zA5jY2OGvoEPGdW9DDXncFzbFHNjHXSc\/QkLOsaRgEjK+gGGKU72xywg54GhEqkY+AlSU1NRVlZGX1+fsrKy6TZnUqivr0dfXx95eXliYmKm2xwpM4impiaSk5MxMTHBxMQEDw8PPDw8MDY2Zv\/+\/fj6+pKWlkZFRcWMrX7Y09NDUVERycnJHD9+HH19fWxtbTl27Biurq7Y2Njg7OxMUlLSDGpeNnVoaGgQFBT0yAJvXGJA1MY5q\/0s2WTPpfQ4zl++RlNtMjr\/msuiDYc4GeKDxpp1bNWOo2+0gVOKMnyrYEdYTBTH9HexerstJb11BH6nho5OAoPAYO5p5P+hz+WeLi6ba7J9uQ3FEqAnHf2V65Ax8uFMyFFkPlzKd5sDuT0Fui\/nJN\/99j026LoQFOzA9gUb0PIoYKCvANvvFTjgGcPlpBCMFPeiuEueJev2YpPQAbRzWnsNK+0vI6CPTE9ztu8y4\/SldBLc1PhupyORJ6358pf\/YPk+U7xjMqjuvikGxLSl+7Hj8\/Vonwjj3HFP\/M9dpb2rFJdtW1mv5UH0hXDs9m9l84Hj5Ka4s3zFXpzTe4E6jquvZYtbIsVXYvD1iiQjOwGrdd+w0ymG8rQTfPsfr7BE1gCf2MvkXLRn5TJF3C\/3IBFV4qnyLd+5ZfOglGGpGHgATU1NuLu7Iysri4+PD8PDj9JoYuYxMDBAQEAAu3fvxt7e\/onJhpYyfbS3t1NSUkJ4eDhOTk4cP34cPz8\/PD09cXR0RElJiY0bN2JjY4O\/vz9xcXFcvXqVoqIimpubaWtrm7BkxaGhIVpaWmhpaaGwsJCrV68SHx+Pn58f1tbWfPfdd+zbtw8HBwfc3d3x8vLC19cXLy8vLly4QGlp6ROZ\/DfRVFRUsHz58kee7LYyMAwAACAASURBVIxLDIj7uRpkzIJl29H3S6dbDPSmovnmWmwujoVfrloeZNs3RyksD+PblRocy7rR+KcmEtV132J5NZ+I3boYGSYzCAwVnEP1PVMyen8QAyXAaNZRPvpEl6gbui7bzpK9a3zvyBnoyfJn9z+3crIcoJ+Q7bIo7AulJMuLT\/61EkULN074OiD\/xXds3WiJ0V4ldBxiaa6JR3WBPAElAhCWYfb9at77SgNPXx88zJRY+DdZ7GzM+f49ecJ+FGUdJNVLg\/d2BHC7H2Ygy41\/f2VAZO2Y91mQfYwd67fimV+I3+5d6Lin0FgaidICJc5WCYBRWoqTOe3rhsaat\/hY7wTZcX7Ivfk9pypvjlqL2+YtaLglUZN3DoWFyoTVPPi5JRUD4yQ7OxsLCwuWLl3K2bNnn1gX4sjICGlpaSgqKmJmZiYtXyvlsejr66OhoYHS0lISExM5c+YMERERhIeHExgYSGBg4K0ZuLW1NfLy8mN12m97mZqa4unpiaurK66urnh4eODi4sKePXtQVFTEwMCApUuXsnjxYhYuXMiCBQvYvXs3NjY22NjY3MpzCAoKIiIigoiICEJCQkhKSqK4uJimpiZ6enqm+1TNWKytrXFxcXmkpMhx5wwIOriWeArDPZvYrOtJRnEiBu9vxvFiEyAk\/bA2O1Y6kpMTxKKNh\/AqGAvCS2qi0Ny+GvP0XMJltNA3SGYEGC08i8q7PxYDQ2k2vPG5IdHNAEIybaxQWePL7TVmu7NOIf+vnQSViYEOQmR2obgviJykI\/zfexvQcD7JuXPB+PuGc6W4japzBqzca4C9nSqL9vpyfRQYzENryyre3mBGcHQ4IcGnCYtM5+rpI+x+T5mYlrtPQB8JHqq8oRjK7RK097IDb6w1J7ppTAwM5\/kgu2UjR8qGaT+jxddKpjhYK7Nofyht4h7SvG3Yt9sc\/\/NBmGz5mHkGvmTF+qP07h7OVd9KIqAqWJOlSiZYWyixTOMcreOIdEvFwEPQ39\/PpUuX0NHR4ZtvviEuLo6Ojo4Zn1kskUhoa2sjPj6eTZs2ISMjw6VLl+jt7Z1u06TMUsRiMUNDQ\/T19dHR0UFjY+Mt0ZCRkXHrlZaWRkpKCqmpqaSmppKRkUFycjKysrK8+uqrY01l5s\/H1NSUuLg4srOzyc7OpqSk5NaYHR0d9PX1MTQ09MTnM0wHXV1d7Ny5k8LCwofedzxiQDIyQEvVdeo7W6kMM+T9rQc5FheG3tvrsD7fAIySZqLGpiXWFLVc5eDyHag6xVPf2ECatykyXx8kpbuNxANybN1uT2Z9PWnumix6VYcrfd2kGu1n4xJLigBJXTjb3tuKUVAWjTUpWK3fyOo1vnfkDHRl+rLz75s5WSIC2gjYvI1dewKorLyA\/GcyHA7NoqGhgeqqGlp6RdCRiMKiT3npb++jGl4yljworufUASXWbXfhalMjDQ31NLQ0URruwJbXZIlouPs6HOV6hA1f\/Xs\/Qfl1NJZkkppXQXNJFLILdmJ0KoPGxmpirLTYuc6E9EGgLYrv\/\/0xr\/5jHjoJTTBaiY3y93y+\/wy1rdWcVPiMD3S8yLhwHNnXthNc8UMgQNKegPz8f\/Pqq5+hlzC+FRtSMfAI9Pf3k5GRgZ6eHoqKivj6+pKXl\/dYiTiTQVdXFxkZGQQFBaGoqIi2tjaXL1+edZnRUmYPAoEAa2trTE1NiY2N5dixY6SlpeHs7Iy+vj4ZGRkTWk5XyhjBwcGYmpo+9H7jyxloJ9HlAN989Bn\/\/modsi7xNDRlc2SNJn5p7cAoeR526Mj5UIWYjjQ\/9n39b9595z0+XqaCd2ItYmC4PAqd1Z\/x9vsL2aJ2EN2dbhQN9ZLrZstBhRNUSgBJH1e99Vj+9jt8vOw75OT1sDOOvCNnoLcwEoNv9ImpEQOdRGvrY2RygU6JiOvn7Ni64APmvvcBHy\/bg1tSMzBEgvFWPlmoTvQtd7sEQe1lHOVX8eHcuXzw0QJ2Wp4lMzYQ\/bWWpN7jUSAZqCBEdzvz3n2P9z75Fv3AHAbFQmqinNnxxVzefeddPltziJCs1hurA9qJ0P6Of3+ly6UWCUhElEU7sWn1lyxdK8ueDd+wwSOaossRmHxrTHzD7ZPSPqIPfsULH6uT0DK+yapUDDwGIpGI6upqPD092b17N+bm5pw6dYrU1NRpS0Zqb28nMTGRoKAgXF1dWbZsGVZWVlRUVEhvolJmNMPDw1hZWaGrq8vAwADDw8Ps27ePyspKxGIxMTExqKqqYm5uTkpKyqzJ35kJNDc3s3v37of2Dow3TCAWCehsqKO6roPxrFUSDXdSXVZNx9CdDzLxQCv1LR0MCn\/KAyRmsKOBus5BHmUdmKCnmarrdXQNjyIS\/3Ccex5RMkBjZRUNbT0IhOIHLt8DAR3NddQ2990x9mh\/K1XltXTfYy2i5K6\/BlqbaGjqRCD+iaOJmjm971vWmEfSMU7HtVQMTBACgYDU1FRMTExQU1PDxcUFLy8vAgICSE9Pp66ubsLDCRKJhObmZq5evcrJkyc5evQoHh4e7NixA3V1dTIzM6VlZ6U8EQwODnL48GEOHTp0R22DdevW\/ajNuL+\/P8rKypibm3P58uWpNnVWIpFICAkJQV9f\/6H2k5YjnplIOq5gu\/MQJ1PqxyFQxpCKgUlAKBRSUFCAl5cXxsbG2NracvToUVxcXFBRUcHS0pKYmBgyMjIoLy+nrKyM3t5ehoaGGBoaYmRkhNHRUYaHh+nt7aW6upry8nKysrKIiorCzs4ObW3tW4LDyckJLS0tjh49SkZGxlNfTlnKk0VnZyd79uxBS0vrR3ksvr6+xMTE\/KjWR09PD+7u7ujr66OhoUFBQcFUmjwrqa6uRkdHh7q6unHvIxUDMxNxfxt1ja30i8afQyMVA1NAZ2cnRUVFXLhwAVNTUw4fPoyvry\/e3t74+vreqnjm7u6Os7Mz+\/fvR0ZGBltbW9zc3LCyssLR0REvLy+8vb2xsrLCxsaGqKgoioqKZuwabylSHkR3dzeamprIycndc6lfdnY27u7u9xW4DQ0NmJmZsW\/fPtTU1KT5MI+BWCzG3d0dNTW1ce8jFQOzB6kYmCZGR0fp7u6mra2NpqYmrl69irGxMfPnz0ddXR0nJyfmzp3L1q1bcXZ2JiQkhO7ubml1QCmzhu7ubrS0tLCxsaG\/v\/+e23R1dbFs2bIH5uDk5eXh6OiImpoaTk5O0hoCj0h6ejoHDhwYd86TVAzMHqRiYJoZGRkhJSUFFRUVrKysSEtLu1XD4Nq1a3h4eDBv3jwcHByk3eWkzBq6urrQ1tbG1tb2JxMBRSIRMjIy4+4Tkpubi6OjIyoqKpw9e\/a+IkPKvRGLxTg7O+Pv7z+u7aViYPYgFQPTxM2sfyUlJbS1te+77n9kZIQdO3agqKiIoqIiMTExM24JoxQpD0N7e\/stIfCgBFeJRMLJkyeJjY0d9\/gCgYDi4mJ0dXXZu3cvUVFR0poaD8G5c+cwNjYe12oNqRiYPUjFwBTT0tLChQsX0NTURFlZ+YFLpIRCIYsXL6aqqoqcnBzU1dXR1NQkNTVV6gqV8sRRU1ODmpoatra2424AlpGRgZ2d3UMXFLpZbVNLSwsLCwvi4+Ofmi6Mj0NzczOHDx8mOzv7gdtKxcDsQSoGpojOzk7Onj2Lvr4+SkpKJCQkjGupYVtbG4qKinc8+OPj49m\/fz8GBgYkJSU9FV0VpTz5FBcXs2XLFuzs7B5qv7q6OjZu3PhYJcDDw8NRVFTE1NSUS5cuPfI4TwsWFhYcP378gdtJxcDDIGawrYaiwiq6f+QQk9BTkUXkyQTqBNNhm1QMTDqDg4MEBgZiYGCAqqoqUVFRD\/XwLioqwtnZ+UexT4FAgL+\/P4cOHcLExIQrV65MtOlSpEwYpaWlqKio4OLi8tD79vT0oKqqSkVFxWPZMDIywokTJ9DX10dHR0cqCn6ChIQEPDw8HuhJmZliYJDqzHjyWqbpqfoTNKadwszYh0IBDFVlkn7tOje6MFB1xoaNr8sT0zY9tknFwCQxOjpKQEAAWlpa6OrqEhkZ+UgFgNLS0jh16hQCwb0v7O7ubtzc3NDW1sbAwIDr168\/rulSpEwoJSUlqKurExYW9kj7j4yMEBgYSHx8\/ITY09XVhbm5OZqamlhaWlJZWfngnZ4yBgYG0NPTIysr6ye3eygxIBqgsfQahVXtt6oQCkcG6e3pY6i\/jerqWhpbexgdGaC1voa6tpueoFE6qorJK6qh98aOEuEQPV19DA91UFVZT9fwzQmWiN7qRCw2fcpO51iqm3vHiu4MdVBVXEBZ\/c3cEQmCwX76+\/vp62ykqradIaGA3q5ehgXd1JRV0NA15rkdaK2lvLqdm35cUX8jJQUl1Lb231nQRzxCT1sTnYNjlV5H+rro7O6\/sd8ofe31VJXlcTk1jxZBJ0lmG1mz5xAR+c2MIKQm\/Ai7P1bnQk0ndWUVtA5MbcVYqRiYYIRCIaGhoezbtw9jY2MiIiIey7156tSpexZduZuKigr8\/f2Rk5NDS0uLtrZpkpdSpNxGcXExBw4c4Ny5c481TmxsLJaWlhNk1RjV1dW4u7ujqqqKhYUFra2tEzr+k46Ojg7h4eE\/uc14xYCkowA\/Gx32HjTBSF2BQz5xtIphqDSag4vWsXnbNvYaG6C7QwX5b9awac9eTMJLEQ7UcM5Rmy1KhzDRlGeXrhtXmgWIm1Mx\/XojmzeuY+tBR2LKb9ah6CbNXYtlbz7Pm0t2ctgnkaqKVI4YabBfzxg9VUUsIwsRIaH24nH2LVjFph1bUHA6T0VtLp7b9rFvrzwKynuR26+Gqb0Dtrr72Ll5J+ZnMmmquIKLqiZ6RnroH\/Elrf62CZ6gkXBXbZSDSgEx144dZJeyPVl9QFcOxxQO4eTojr2mO3Fp5zFY9i\/+MfdTtuxzJaezj8Z4V9b+\/k2+lVdDY+8Odmi5cLlp6rwbUjEwQQwNDXHhwgXk5OQwNjbmwoULdHd3P\/a4urq6xMXFjXv7vLw8XF1dWbFiBa6urtKEKSnTRmFhIQcOHCA0NPSxuwlmZGSwePHiSemvUVBQgIuLC6qqqpw4cUK68uAGUVFRBAQE\/OSS5vGJgT4yfcz5bq0pkQVllCY5su4zVQJzWunI8GT5b99D7ugF8sqvYLdkIR9+uJdT6XmUtXZSeMaOtYvV8c2+Tk1ZAmab16DiGk99wRm2\/s87bDIJ4GppFa39N6+LUTrL4jn8\/WLk3RKprCvEX0+LrfKupBWXkhuoz7LPzbja2Ueptw6f\/H4B+sHJ5NW0MdgQj8rf5rHVIJi8wiTMtnzNW1\/ocaEgn8ueyszdYsLRozp8usqUiyVllNXU0T542yRN3EPiUVU+X+NHh7gNP5l3mfOrr\/DI76Ur5xgrvjmEm5U+295TJa6llVSr79mopE9EVjVdw0Iaoh1Z9fxi9EKyKCwIZs8nWzE8XsBUyQGpGHhMuru7SUpKQkFBgUOHDpGYmDghIgDGllV9\/fXXj1R\/\/dq1axw9epRdu3Zx5swZqadAypRy8eJFduzYQVhY2IQkuDY0NHDo0CEaGhomwLofIxQKKS0txdzcHBkZGcLCwp56UdDQ0ICRkdFPnvPxdS1sxFdnA39+axO6luaY6u1hyT\/kOJHaSPMVX3a9IUtYE0AHJzdsRUE7krEz38UZW2U+VI3m5vz72pGtLNE6ypXUM6i+sx2\/onsUYRPWEKS5GYu0HhCXcmjTMl6ZvxtTK3P0Vbaz+A0dEpu7KfQyZst8fTJv+v\/bEtF4ax32CV1APyEH9rFsQyDdALkuzF2sx\/GYYDRl1rNcyZaYnDru7pfUnnkK9W+VCIgP57Dxev79f5tw8o0jym0fm50iSPe3Q+YjLTKAxmBtVA\/7UH3j51ET5sj29\/cT3wvQgNtXm1EzjWdiniYPRioGHpHW1lbi4uJQU1NDVVWV5OTkxwoH3Iv+\/n72799PeXn5I+0\/OjpKXl4eRkZGKCoqkpiYKBUFUiad+Ph4ZGRkCA0NnbAxh4aGCAwMJCEhYcLGvBcCgYC8vDz09fXR19cnJibmqV7Cu3z5ctLT0+\/7+bjEgLABH81t\/N9yG9KqqrheWkJhST1DIhGtl7zY9eYewusBGjm+fhvKOhGMFZXu5Ky1Eu\/JneVmEOCy6SbW6nmRk34W1Xd24l\/w43uuZLAUH5W1mKT2A2XobFjL5\/InyK+rory0lJLyVkSSYfLdjdi+wICMm46P1kQ03\/oOx7hWoJ0ANRVWbg6iAxhKd+Tdz\/UIqxoEOrh8XJdVG3Zgk3ZnKXhJRw7OButYuFEeC8sjBLkbsEdTjl1f7MQno4LS0w7s+EiTK0KoPKGCgpkPNztB1JxzYsdcFWI7AKpxXbYRNbM4qRiYqbS1tREREYGuri5KSkokJSVNWonguro6HB0dJ6QdcmZmJpqammhra3Px4kVpMyMpk0JsbCxqamo\/eTN5VEJCQtDV1Z3wce+FRCIhPj4eBQUFzMzMSE5OnvCuo08CDg4OxMfH3zfMM74wwSCFwQ5sW6KAtX8IISHBBEak0zo6SkuCKxv\/uo2QWoB63JeuYZfKGcayN0aoiHZjy8KdGHsFERRgz96lcjidLaCt4DQyf1mPd949wqCCWgIPLGWRgiMXUrM4Z6XP1tUHcAkJIeR0MGfjihAwTI6TDmvf1STt5q2w+SJKL3zD4ehmoJXje3bx+fITtAJDqVa8\/K4iVidDOBseQZivOUu37UU\/ofnOY0v6Sfbczi\/mvMpum2x6WqJY\/c7v+eWLqmT2DHH9lDlrX9tHyii0hB9k1erNmBxLoaZXQNUZG9a\/IkdUG8B17OatZK\/+BaZKikrFwDjp6+sjODgYQ0NDlJWVuXDhwqTEL28nPT2d48eP09PTMyHjSSQSwsLC0NHRwdjYmISEBGmNAikTRmxsLPv37x9XsZpH4fz582zevHlKr1mJRMLZs2dRV1dHV1eX6OjoKTv2TCAmJgZ\/f\/\/7roQa92oCUTtpJwzZ8OUSvly6gl2mIVQMihmsSOO4nj\/53QBdpDp7E3DmGj8spO4hN8SKbUuXsOTLzRj4pdMNiJuy8dM9TkbjvSLqo9SleLFr5bfsMYujtb+W83bKLF+0hKXLv+OAWzI9CGm8FInX4Qhqbs7leks4redJQkkf0E96oD+Obhn0A6NV8ZhYB3A6xAO1TetZvmYnB47F03yPw\/fknsVISRff\/F6gjejDmmh6JdELtF+9iJfJGSqEQGcuHmqbWbbchKT6PjoLkvAxDqZkEKCNBLtjnI4qZaqK0EvFwAMYGRkhICAAXV1dtLS0iIiImLIZQmRkJGfOnHmkJYk\/hVAoxMfHBx0dHfT09Cbt5i3l6SE+Ph5VVdVJvZaqqqpwcnKisbFx0o5xP\/r7+7GxsUFdXR0bGxtyc3On3IbpoLCwECsrq\/smIj98nYFHTyR9vBRU4CHa+T5wqFnYL04qBu6DSCTi9OnTqKqqYmhoSHh4+JQ3CnJyciIiImLSxm9ubsbf3x9NTU0UFBSorq6etGNJmb2EhoairKxMTk7OpB5nYGAAX1\/fCas38Ci0tbXh5uaGpqYmRkZG1NXVPXinJ5je3l5Wrlx531DlzCw6JOVRkIqBuxAIBJw\/fx4FBQVMTEymtcnJtm3bOH369KQfp6ysDFdXV7Zs2YK+vv6EhSWkzH48PT3ZsmXLA4vTTBQ2NjZoampOybF+itLSUtzd3VFRUcHZ2ZnOzs4H7\/QEIhaL2bZtGyUlJff8XCoGZg9SMXCDvr4+EhIS2LNnD\/r6+sTHx0\/YEsFHYXh4GCUlJZKTk6fsmEVFRRw7dgxZWVm8vLxoaWmZsmNLebIQi8V4enoiKytLQUHBlB03JCQEPT29KTvegyguLsbGxgYFBQVCQ0NnnZAWi8XY29vfNywiFQOzh6deDLS1tZGQkICysjIaGhpcunRpRhTqaWtrw87OjtLS0ik9rlgspri4GDs7O3bt2kVkZKRUFDzljIyM3JG0JxQK8fDwQEVFhfr6+im1pa6uDg8PD65fv05tbS2FhYWTtppnvAgEAq5fv46uri7KyspERkbOKk9BSEgIMTEx9\/xMKgZmD7NeDNxvSUxrayvR0dFoa2ujpKTEpUuX7lv\/f6oQiUS3bmxVVVUEBQX9qEHRVHFTFNxssBQeHj6rbnBSxk90dDRmZma3PGU33eMTseR1vHR3d5Ofn09AQABbt25l6dKl\/PGPf2TPnj0TnmD7qAiFQjIzM9m\/fz\/GxsaEh4fPiInF43Lx4sX71oyQioHZw6wWAxUVFdjb29\/huuvu7iY0NBQDAwOUlJSIi4ub9CWC46W3txdvb2+8vLwwNDTE1NSUkpISOjo6GB4enja7EhMT0dHRwdDQkKioqHsmUgoEgqdyHfbTgJaWFnPmzEFbWxsfHx80NDRobm5+8I4TSG5uLm+\/\/fatm9XNl6ur65TaMV4uXLjAmjVrsLS0vGehJLFYPOFFyiaLzMxMnJ2d77mkUyoGZg+zVgyIRCKUlJT4xS9+ga+vL2Kx+FYrYXV1dSIjI2eMCLjJ8PAwu3btuvWFPP\/88yxcuBAFBYUZ0VktJCQETU1NdHV1SUxMvPX+yMgIZmZmj92MRsrMo6+vjyVLljBnzhx+8Ytf8P77709LQx+BQIC5uTm\/+tWv7hADk12R8HEQCoW3Opeam5uTkpJy67PCwkJkZWUpKyubRgvHR2FhIfb29ve8Xz6aGBAxKhQy3RVOROJRRkTTaYWYUaEQ0Y8WTYoRCkcRPmY\/j4dl1oqBwMBAnnnmGebMmcPrr7+OqakpBgYGRERETHs44Kdwc3P70exn9+7dMyYxqb+\/n1OnTmFsbIysrCzFxcXExsbyzDPP8MEHH0x5DFnK5BIYGMgf\/vCHW9fis88+y5EjR6YlTj88PIyamho\/+9nPmDNnDq+88sqMEMkPor29HVdXV7S1tbG1taW0tBQrK6tbv+3p9PqNh\/z8fGxsbCZODIiqCTlkS1BiNdOX7TFK1cVAHA2CuD5tUaZ6wgxtORlddlczoi5SjhzFzSt1yqoPwiwVA3l5ebz66qt3PFD37Nkzo0XATbKzs3nppZdu2f3xxx\/PyPX\/bW1teHp6oq+vz2uvvXbL3oMHD0rDBbMIBQWFH4nTuXPnkpaWNi329Pf38+233zJnzhzk5eWfiN\/0Terr6wkJCUFeXp6\/\/OUvzJkzh2eeeQZnZ+fpNu0nqa6uZuXKlfc81+MVA8N16YT6HSejCyAfo7eWo+2eM41iQEC2ow7r5mpxZWrLx9xGIZYfrkDVJvWuKoPNnFy\/kW07TjCVU6tJEwNCoZD+\/n56enqm9FVdXc0HH3zwoxvYqlWrqK+vf6yxe3t7p+Tmc\/MG\/Nxzz3Hp0qVJP96jcnPZ0e3n+be\/\/S0BAQEMDg5O+Xf\/uK++vr4JEzLTdf1P1Ku\/v5+qqioWLVrEz3\/+c1588UWUlJQIDAykqKiIjo4OxGLxlH\/PQ0NDZGZm8vLLL6OhofFYY\/X29v5k8uHIyAi9vb0TYvfNe8fQ0BBbtmy54zfzyiuvcPnyZQYGBib0XA0ODk5I6eampibmzp17Tw\/G+LoWdnHVR4vVSxay3yuNuroUzD\/fxCGHCK6kJZN0pZD2W6pglNayq8TFxHApp5L+H3nK+6jKKaSmY+zxOdBaTVF+NYOM0lx2narrpeSmJxGblE3j0M3\/XUJPfRHJF6OIjs+ifkACCMl3N2H7fA3O5eSTejGBvIbesdCFREBrWQYXo2NJL6pj4OYw4l4qriYRE5NAdkU7EkS0VlRQUVrE1SspXL5WSnH2Vco6bjwjhJ0U5RRQ0dpLf3sFV+IvEB2bxv\/P3nvHVXWl+\/\/Oa3Lr3PL6zcyddmcmuZPJfJNJjCaxlxh7713BBiIIqEhRelNEigoWlCKIig0VsCGgNBUQpffe+6Gcw+H08\/79ARqNjUQQcPjwWn8czj57P2vvtfd+r7We51k51U98RXI5NHUlJk6XuZ9wj+h76dTIAOq4sH4TW7aeo9NFt5X8pLvcuh1DWmkTvTW53WswsHXrVoYPH86YMWPeaRkxYgS\/+c1v+P3vf88f\/vCHp+Wjjz5ixIgRb7XvUaNGMXbs2BduipaWFoqLi8nPz3\/rUlBQgLGxMUOGDEFDQ4O8vLwe2e9PteVlqxyq1WomT57MF1988XQq5tnys5\/9jL\/85S\/v\/Nq\/bfnqq6\/Q1NT8sU29X7X\/nirDhw9n+PDhWFpaEhERQVVV1QsgHBAQwN\/\/\/vd3btuECRP4+OOP3\/rYI0eOZOLEiS8FQJlMxvjx4xk1alSP2j506FB+9atfvXDP\/PM\/\/zOffvppjx7rs88+48yZM2\/dlqurqxkzZsxPhgFFaw6nty9g5LDPmLHOibA7V7H77jtGfzkTPXNTNi1bjsHxRyhRUn7rGLpbTXBwP4CNviZmgY+QPAsEklTcplsQeKcaUJFz2gvTZaepoYlg7c0sGDUXHevd6K1aguaeCFrVKlqTgtiyRhNtK2ccjdayzNiPfHEHhWcdmPIff2WO3m6sTTT5btEuQnKaqE0+j8Hybbh47mfPibMkVKlA1UzccSvWGTly0NUWfT0jAhIyuLlbl4kfjmKR7jYcz1zhhNU6Vh\/vTMmtyjmD5jZ7\/O\/exdvcmj0uB3E22Yqmxn4SWtVAEYdnfMfXf5vMFitLdFctZIllGK3qJq5s3ISuwUXqlK3EHTNnnakTh1ws0dXdgc+Dxl7xt+g1GJg1axaBgYE0NTW90yIQCGhvb0csFj9XRCIRAoHgrfZdVFTE0KFDX1jSVFNTk88\/\/5xx48b1SPnqq6\/49NNPGTt2LBMmTOix\/f7Y8s033zBu3LgXrq1UKmXEiBH4+fkxbNiwpy+OJ+Xzzz\/H0tKSxsbGd37936aEhITw7bff\/tim3q\/af0+VgIAApk2b9lonWxsbG3R0dN6585WzIgAAIABJREFUbQKBAJFIRFtb21vtJycnhy+\/\/PKl4bttbW0MHTqU\/Pz8HrU9ODiYvXv3oqWl9fR+GTZsGJ999hl+fn49eqxNmzbh4ODw1m35bWEAtZLGpADM9XQIzJEgk6ezb9wctB1u0dIhJPGIFcumelDQko3zqk1ssrpAemYGtz2MmfyVA4nPJoDteIj919vxuVkFqMg4cQC9qb5UUU\/ggoUs2+RHSUcHZWEHWPa1NUnV+fhs02etSSg1EinS+ngcFi\/ANjydjJMOLBq+hSslYjo6cvBYNg\/DIze5G+TA2IU2hGSV0yrtfO0qiq6wetwmHM\/dJysznkPaOmhqunFg42rmzt9LUpMYiUxEop8FU+Z6Uq5WknZoJxsMPEhrViAXNlBZnEdSsDMr583FMbEDKOLQ1HlomgVT29FB1d2jbJi2ieDSUm7obGbr9mCKckNZO34zriEPycq8w76VGizbcJnGt76qL6rXYGDBggXcvHnzrYzrbxKJRIwePfoFGJg7dy7BwcE9dpy2trZ+EdOfl5fHqFGjXvi\/RCJh9OjRfeJV3pu6d+8e06ZN65F9DfT2f\/36dRYsWPDabezt7ftFauCfKoFAwOjRo18JA6NGjaK1tX847v4UGRkZsXfv3rfeT3V1NSNHjvzpMAAI089jv92A4AqALJzHr8T+VBYAJedd2fCtHQlZUaybPZVhC\/Sw2WPFzi06aK87Sc6zEZgdyTiOMsb\/dudiVbkBR9k+6yTV1BG4eC077CIRA8LEU+h8ZUj4wxh2bNuGVtATR9MazpvOZLl\/FMknnNCatoc0JYCce\/uXsGBfMBWVKRzfvYFZq7bjeTGWug7oSPTg\/30xlUVbTLGzN2Xzym04OQfiuX4L242v8GT8VJQbivmqTZy4F4PrFkOMDqciVzbz+MJJDu53xEx7Pl9\/Nx3r+2KgiENTVmNz\/HFnT78mEmvtxey9n8Et3S0Y7AgmNdqDT7+YzkrDXdjambBpsR42++Pojdy4vQoDYWFhb2Vcf1NzczOjRo16AQYWLlz4XobVFRYWvnRk4AkM9MXqcb2pmJiYHoWBgdz+Q0JCmD9\/\/mu3sbe3x8TE5B1Z1POqq6t7Iww0NvZGH+zdaPv27T0CAzU1NUyfPv2tHAhbHp3CTE+Pyw0Aeewftxybk+kAFAa5sH6iHQ+L77Nt5iq0Dt5HhBqFQopY3PF84J30EfbfrOXAjc6sqAnOFqye6U9tFwxstwlHCLTc92fzV4ZEZCbjsGkTq5yTu3aQw4FFczG\/\/pj0k3Ys+kqfiFaANs6sm8cGt3Bausbgm6PdmbVkLXbxQtTFp\/j2Sy0Ox1WiVCmQylVAGWc1NqC3\/RJP029JSji1V4sxUxaxxMicM4UylDm+TJ6wk4vZjTSl+KOxcA4W99qBYg5NmcsGq9udMJB7ni0z13G2qJgbOpvR33GJ7Id+fPuVLicf1aFSKZBKOpBIZW+\/guNLNAgDP0Kvg4FXZegayMrPz38tDFRVVfWBVb2nQRj4XoMwMAgDT1RQUICVldVLfSu6CwPSyjvYrBrDyMXWnLt9BfOvl2Ll3bneQV6AI8u\/MCGhvZXUE3vQnLOcTQYGGOibsP9sEq3PvvnUTUTYbGTqt4vQ2W7I+rlLWT7Plxpq8Zm2hC27biAEmmNPsPrDjVyraiM\/2IP10xeyVk+XzavXsHbdUbIb28j2tWHSL75hvt4OdLWXMH2KKVdTysm77Y2ZgTEm2zcxYaMNAVkikJdzzkiPFUs0MDA0YKvRfkIf3sN3pQZaOuf4vlukpCTMlk+H\/IKJJhdoBBSFl1m+cAmrrfayV3cZX02ajEmsCCjEdexkRn+zkHX6uqycvhCN7ZepVzZwbsUaNmifo0ZYTpChDsuXr8PQ0ADdHU5cSKgaWD4DA\/1h+DINwkCnBmHgzRro7X8QBgZh4IkyMjI4fPjw2+UZUIspjjvHPsdDhD3IIe1OEjmlnVMwwtIcEm6n0agGlHUkXj2KlfEu7JyPcjWhjB+ORyhb87nq4YC1iw\/Xo5PJSiqhAwklMQk8zqhFAcgaikm8\/pAqKUArWVEB2Jnuwnr\/aR5UdM47tJXkkRBym+AAN8ytnbmYUIEcaMqNxsfJEUsHTy4llvAk3kQhyCH0+F52GVuw99hFEoprKEtK5tHjSiTP2VdEzNUwYnOevCfEFN+7gKvzAXwCLhIaG0dGowIQkX83kbjrlznoYIn1gWAyGuWAjLL7iSQnVyADFI3pBB92YJeJJU7HL5FQ0jsprgdh4EdoEAY6NQgDb9ZAb\/+DMDAIA0+UmZnZwxkIB9Uf1YcwoELaLqCmuoqahmZE0jdFT7ZTkppGVl4lDY1tiF+1vayOtLg0Ktu6lw9AJm6nXaLo1hzMIAx0qlswoJbT3lpPdVU1dYI2Ot50eZVNZN5LpbSxHYVERFOjCPlLLopEUEJyfA6CN6QDUCtlCBtrqKyso1XWvUG1dwsDKmRiIR3yJydGjaJDilTyfcWUig6ETz+raG+qpbKiivpm8dP2qlZKEbeLeaGKKjliUTtSxTNfqOW0i8TIlG9u7T0BA0qZGLFE8jQuWq2U0yGSfT\/EqZIhkkiRd\/1DIW6mtrKCqtpmJE83UiLraEf8QgXVyMTtiCUK1KiQCpuoqaymQdT9PCBvBQNqJVKxEMnTc6lC1i5BJvs+tl0u60D49Dklp7W+moqKGpqE39uokktob+9A8YNLolbIOq9V1+6UUhGNNVVU1TW\/0FN+lQZhYFA\/Rn0HA+0ZeKyYwRfDvmbqWj0snP2Izqx\/zVxIJee07dm3fT+mhl5cT33FTSq4h+0Yc66k\/3AREDX1GWkUVHYOsaglrVQkX8Bs5WaM90XTnYGXdwcDalSoUHULUVQou7nlj9XbwEB74U2Mxn3N0DHfsUTHmD1HLpNZLX71waQp7P9uN2ciiyh+cAkTvbMUvCQfTFtyMEbj9vPoBcfmDsqSUqgQAaiofHwF63XzGPvNeJYZnya76c1ON+8WBoTEHdNH2yceEYC6nJOGBujuvkZnHEkzMX4mGFzOBWU7BbH+7Fw5i3GjJ7JwoyU+UcXI1NCYcJzVEyZjcibtmWxuCvKDLZkyfgH74+oBNe0NJcQFObJi6i6uZLw5UqUnYKAp9jBbbVyJrFIDaopueLBimh33mzq\/b44\/zNYDp0htViOpucdRi03MHDWaSTPXYe4dQUmbEuTZeK6ezKwtx8l\/Jk2bqiKcnbMnsHjfDWqqUzhpuYlpo0fwnaYjYY8b6E7qqLeCgY4yLu3fwo7g3M7P0jT2LtfB8lhS58taXMhFT2MsoupBJiA55CA6875j9OiprNrmzIWkzmddYbANcybOxzWi\/Jn22Ua8pxbjpmwkME8OqkbizrmycfpYvhm1DOuAhwi7UcGegoH4+Hj8\/PwGFyp6z9V3MNAQw+6hOwiMLaSq+DFXXU1Ztc6e6+VdT3llE9kJUYRGp9D5\/q7i9Kpd2FlcJC69kNo2KaCipfgRURFRJKVlUVTdSHttPDbDtnH0wh1iY+6TW9f5RhHVpuA5fz66FidJLmmguSgOf0cDNCatw0D7Kt0JknsrGFAIKUmNISwklNv3Uiiqfx1+tJAadpvoB+VIOmp5FJtF9SvufnlzLjdORlHU9nxiT3VX6Ty2iIq0eK5dvUZcXi3dTcX902FATV3MafSH2xJZ3kBFVgw+27VYYxZAlqjzgaIWlfEgKpybiQW0KgBlCntGGuFzvZg2USWPHuR3viQVbRQ9vEP43Qdk5eeRfPUEW7\/YxYX7CdyNS6GqcyMassKwGDeb3X4xFFRVkZ+aQEKJCLUgCv1Pl2AbmP3G1KfvFgaUFIY5Mm7xMbIVoG6IxnDC7\/mvuXtIbgekmRxbrsGRhHKqon3RW6HD0bhSJNJWcsIPsmzGNq7m11NyxYkxP\/8Ff1m5l7v1XT23jjw8F33Ff\/3l72iFVgFSUi8dY89ubeZ9qMep2DevONgTMKCuusaapTtwCa8BJETaz+Rf\/zwJ+\/utgJx79oYYO1ygpCGX49prMfSKoLJNgrAokt0rNdnhGY+wNQmTP33I\/\/xxMpZ3ntgtI\/noTob\/z7\/xkUkguTnJ3IzPpK6pkCCdTWzc4EdJN+j47UYG2og+bsq3GzqdxJR5p5n\/6f\/w0ZZTVKpBVR7F3sVanCtqJv\/cXjasM+V8ah1ScS3xgTYsmGdDYnMzya5b+fSDXzB8mz\/ZXawsr32Axdj\/5T+GT8UxqR2kJcQnpFHe1kFxiAtz\/6LPjao356HrKRgIDw\/nxo0bL\/1uEAbeH\/UpDFgMNyM4pStisiMVa00ddA48RqFuJtHvEGaGW1iuvR2Ho9ep6aji4npr9u46zL6dXkSk1dJYEs0eA120Ny5h5owlbPGMorb+IY5fL2b9Jn02rl7Keis\/MgQKah4GsOmzvzFqmgY2Z5JokspR0Uy0lSMm64PpzoKsbwMDiopIjIaPYuTUuazW3Y6RqTOX4opfMeRXSeAKE6ytYmlqfIyrkS\/3yl\/eq5bkh7Ht\/+0m4rml5ZUUh9\/kfkZn9Kuo4BZ7t2mxdO5UJs83ISCitFtDjW8FA7FnMfxmHw+fPGcrQlgxfRued2uQtlUQtt+WrQZbWKVlwtErybRIMnEdb0rA7WKqM27hpHuaUmkrKWHebFu3lnUai5m1ZAu2e+3Y+NdF6BpvY83ipWw\/HkF1RwcZZ2yZ+8eP+XbNLrwjS1Cp5IiaKsm5dwqzJeYExde8MY3nu\/YZkBTfwnTSVsIqxdRHn8Vh5gj+usiKk0ktyKuusWmOO48qizi\/14QFO27xfQuoIshoMVvPx5ESeJJdozeyQn83jmdyUQGNicfQXGWM4W4NdELLACXtEiXq1mT2jjLh1N2qN46S9IzPQCWn1m3B7nAcrdIyzq+ZzeQFy1nikgqU4L7Jgv2BmdSk+DJ1tjPxjd9foaLTxqza6cK9rHu4jNbHaLs+Cw2DO5OttMRgu9OaHTpLmeN5mbKnJ0ZGzD5zDPVOUtaNmaG38xlQURMbgOHU3STIFZScccNk1kj+svwAd8tE1D0IQGOxLxWt2ezfaoiW+8Pv21\/LYw5sXozN3RTi9xzAbNpG5m+242RMHaAk+4Ybmsv10bLWxTq2cxRHJRHSUFlAtM8e9NceI63lzRXsKRgIDQ19Zc6MQRh4f9THMGDKxeSuMUOqCNywg607IqgsvcTCWXocicqmLN4HrZVLcXuUybXNDjiZumLyrTXnb2UQGWTLNIdHQAWOOoboeeeA8jFWX2rgfDkPUeUdzA1XsjumFaghSHMN5q5hlAqf9BFbiNxtj\/GG3oeB9pxQjL60J75BhUpYzn0vc+avseFqmQwkRYT5urLb\/SyJ5RKglqC1u7C1iaGFVlJjU6jpUIGqhfQwb5yc3Qi4GEJ0RikNueHs+lIfd19fDh48SWxpM4KiKCxHjWSehjXn4gupLc\/mUUkrIOSazgY2bwmgohv1fXsY2EvCk5wt8hSsx+rhEphGZqQLk+baEFlcRUbgTuas30FoziOOTN7FqchiymODMBh1kKzKRHYb78LmrgREYcxbaMvJ08fZ8uVWLhS0IEz0ZIWBEUHFauhIxm3GctzCsmiTqlA1FxB5xp0dq2bw3XpX7tW8eUmUdw0DakkxZ8w12HU9gTsnjmNvfgz3bZa4esXx+JYDi\/fforW5AA9nI+b6ZD7zy1Zue6xjoW84Sb4nsFh2mLMeNmzb7UN+ext3rLdhFXiJY85abL76zKp+4iTsRxi\/QxhQURBozLo9fjx4eBOrta4EeB1gxzofsvOuYmhhz5msduqj9\/H5zjPkPTP10xztxDzzfYQl3cF1kjX+gQFYrNYnqLadhssHMN3jzfkAM5a6n6eo610uq4zARtuA\/SFlvT9NAMhr7uFutAnX+ylctj+Im4s3dhoW+IUkEnXWjOVeKSibkzDYZYJuWPn3PxQX4ee4lo1X7xFr54rtVj\/87Y0w9rhJbWsJF0x24HwhiD0OuljEdMJAS84dTrrtYtXsaax2uUtLN0Y+egIG1Go1J06cICEh4aXfD8LA+6M+Hxm49LjrxaoqwFXLED3XRIrj9\/HZQi1sjp\/m0ikv9h3x5nZJDle07HAydcNsmh0X7xTwOMSBMROWs3P7NjTXORGcJwRxAtZfmXM1SwKtD3Gy08I4qhloI0x7M\/t9HzyzQlQrUeYOmGy80uvTBOLca+wcZsnt8q5BesVjzBbqs8v1IoH7XbDf64KdgxW7rI+S1lTB5fXm2NvH0yx+hNO8vUTlVZMR5YfeFisO7jdk+qRl7Ax8jKA8EqO\/zWHzbkdstm9io80BLt8JxmrCCKYu1udgWNYz9a3nvMlOTBxu0PQKO5\/V28OAE0lPZkOa76A90wjPa5lEB+jx4XIzfM9dIOi4K\/a+waSWPuTQJLNOGIg\/z\/bxHuQ0F3Bk+xomLNrCLp2tbN5zhcSIcxiPcSVFBWT7s97SnMB8FZDH8fkbOPmgKxeYXEhVUTapyXc5sHMTBidjaXnDG+KdRxOoRSSet2f+JkdsD9hiFVFKcZAlO5xdOGSoh3t4Psgr8LI2ZLbD\/WdGNuq4vGsZuudieOR3FKOFpyksD8VgiwUHfA+zWf8I8RmP8XfdiNaVZ2HgIQ4jTAiMqXmJMc+rp6IJ5HmnWbrBjn2upqw8Ek1x6jXcLPSxdXRk\/56jZElBcMeZ4cuPkvLMO7nsojlrTFyJTY9l7zhbriU95sIhEzbbeuPsYI9zUCYF4RbMdT5HuRxozsTfRg9znyjqu7mSy1tHEygqueBpyaqt9hi5unA0qZwUT322HfDAU0+HY4\/bQJyC8RZDtP1yv\/+dMJ1j+suxuvOYWBsnduneoDgvAI1Njpzwd2fjtkBycu\/iaKeDRXTnnapsqyE\/K5UH4X5s26zH4YdvjnLoCRhQqVS4uLiQnp7+0u8HYeD9UR\/CQDS7vtjJhWQBIKfohhuaaw3xz2hHmu3L9IVmBNwroK6+gYbmdqCCM2stcdzpgslkG87dSudu4A4mLtvKnv0nCL1b0NkbEMRhPnQn5x8LQfAAe8sN7IxqBlq4smEdNofvPpPKUUC4qQ07NC7y5sdjD8FA2ZPuTyEeiw1ZN2spf1+wDc\/rD8mKOMaGNUtxTEzj+mYr7B3u0dyWiNVwC64nZXLW04y5B7OBFLauNWX\/7WoU9VEY\/V2f8zkSKA9h644tHMoqIXqbDnaekTQ846ZcHXccw83mnE8Tdsvh8G19BrYOc+BBG0AzCUcNWazrTkJdB\/lhdkxY6UZ8SS119XU0tkpB9Ii9o4zwjyiiLC4Iw7EHSSlLZN+OlczbYo2HxyUy6oU0P7yIwZf2JEhAle7D2l1mnMpXgTqbI7NWcfhuJSgkCBrq6ExpriDWbikr912m7g2DA+8+tFCNICWIlb\/9G2PWGnG1DtSFZ1k0+RN+\/xdtQgvFgJTE49asnGdNXF0nzbRmnGPzLD3OZ5WRcdKTrdN8qUbENfM1\/P1Xf2GO5wPaWos46bSW9cFF3x9O+ACb4Tvwj6p8RyMDgCIXt\/lz+OazMRhHVqCSluFvPon\/75djMT2WiAyQl1xj67caHIos7ryH23M4vHkT210iaah\/gNUwM67lCKi6782cX\/+Gvy53IKpJQW2YETOdz1NQlk6QrRba+y5Q\/EO\/4dfo7UMLFRSEHmLWf33IpJ3uJIhAlujCqGF\/5ZPRliS0AGoBIZb6rN14hCwhgJKSyMOsnbOL2IZq7lrYY7Q+hFZ1HSfWTuUv\/\/s1Gy8Vo6iNw9ZyE7timkHaRE1zV8Vak3DVmM2uW28O6+0JGJDL5cydO5fCwsKXfj8IA11SddBY04BQ3hvpgN6N+g4G2h5iP3YkQ78ew\/jx3zFj7S5O3c3unBeVV3PN3YR5GrrsMNzOjh0nyGyrIMRwHwfsD2OzdD8hMTnEnjHj28Xb2O9mj6H2DpxOJdEiSMZpqi2hWe3Q8gjXfduwjG4ClOSesWbqN18zx\/U25eX3ObhyIl\/+38d89KfPmWkdSE7bq82FHoCB4bbE1HX9QxDN9jUGrNNYzl9XGuIceJXwsMsEhd4mo6GQ4A3mnTAgTMJ2hCU30kuIObGdr7+ZzqplG9EzP09uhxJVWTgmw2y4Ww9UXMfIageeueXEbtNjv19Sl9OcGkHaVRxNTPC51x3s6dTbRBO0pV9g44ef8NXY8Yz\/dhbL9PcTnl2HElA0pXLYeDPzN+\/AWEcf8\/3XqG7L4vA8Gy7ElVGREMyueSfIrkxm\/05N5m2yxM3VGn1dW7yOHcZ0xiFS5UDOWbY4OBBUoARaiLRby+ej57DzbAxRPsbMmjGDhXPns2TlLq5kNvY7nwEAdWsyDtNHMHKFLxVqQJ6N47Qv+OviA2R1zYUrWvMJ2avLjAljGTt2LOPmbMLpUg4dKjnZgcfYucCPUkAU48SIEas5nNgM8lJOuW5GL7RzQij\/ghNrxn7Fx\/\/zEZ+NmI35tfzX2tVzeQZkJLhp8OnHGgTlSQAlSQc38PFHs3BP6ppDUovJCz2E9tzJjBk7lrETpqNpe5FcgawzOmisFWG5HdCWiOmiaUw3j0AGVISZs\/LQee5ecuKrP\/6GD78cy+RJE5i+8wjd6Dj3SJ4BWUkYm4cPZ7bJjU74bLyLzrBP+Hrbpaf56qVVSZw01WDS2M7rN2nZDo7fqUKBkLu2+zDXDUUAlJ7bzt9GGxJWqYKG++x11MfuQTt0pOK+ZSGT581nybR5bLQMILvtzRMhPQED7e3tjBw5ksrKypd+3\/cwoEKpkPdYNJVaIX8hzPNZKcUVxJ3zx8fHB19fX3x9ffE7eZLA056YTdvGueQfjLlKSwg74c3VpIo3OjD3tfoOBtRyRI3VlBQVUlBQRHm98PmHtbSZkrw0ktMLqKxqpEOpRNomol3UjrBZhKQljyDLlWh73iYzM4WLNhtZb3aEjFY5IoEIqUINKjntojaEXcG6aoWQ0vRk0ioFyGTtNJQWUVxeRnlFCSU1AiRveFu8DQx05Iag93+bOBaRRlZWMhf3GbDR1JOo2AtoaZpyMPQh+Xl55OZVIVFVcHrFTiytYxG0PcB8qBlXH6Rx4ZAOk7Ts8Q84T\/i9QtpkKiQFYRh8YsqtKqD0KnqmerhlVpBgoo2+uR+PqwQ0ZVxEd953rLA7y6PMPAoqG94c98\/bwYBa2UFzTRnFhQUUFJZS0\/K8y6K8rYbszDRSM4qpqWtFrlIgbhYhkSlRyiW0NYtpTjmD2VZtXK+lkZkWiZPGAoxPP6CxWdzZg1R00CoS0dH1XJS315P9+BH5jSLELXUUpifz8FEaxQ3t3Urf2TdJh5SI2wQ0tki7HmgqJC0CBELJczarpK1UZidzP\/EhmaWNTx1AFeJ22gRd50MlQdAqpnOhNSUdolZauxq1tKWOssJiyqrKKS0ppeYNeTh6MumQUtpGQ4Owyy5Qdghpamp9JpcAgAxBRR4pSfdJyCil6UkDVckQNgqRKNSAknaRkNau75QSIc3tHXSImqmqrKS8tIiCggKKqhoRd8NpoEeSDqnltDULaH7qh6SgXdBEi\/j5R7+8vYnS9ATik1LIr2rp8mlQIxWKELZ2XWuFmKbWjq5rKUMkakUoU4NaiqC6hMzkBB5lFtMg7t48SE\/AQFlZGQ4ODrS0vHxpnB8LAyqZ7MWXolqORPryC6ZWKF64d9UKGbKuHnhbVhiHbM25XCh7PsBaqUSl6oyp6vx7+uvnwUEu+z5Hh7KWcBd9nK5lIlG+\/Imh7Kgk9pwvR\/ebMXPct4xeaoanjzcBJ53Z+NEyjsc3PG+zrIKooDPcSq3uer+pkUllqF4DHDKJ9KXgoHrJuehJDeAMhG0kX3Jina4J9vYWbDO0wvt2frcTcvwUvQ0MKOsTcJ4\/hXHjJzBhwkxW7\/blYVXnMHD6xf2sXKeFjs5Wtm33Iq21luh9xzkZmIlInIX3Fh8eZGVz9YA+c9fbcOTIPrYsXstu3zhqKpM5quVNsgCou8\/BE4cIKhQjSvBj1dRxzHW8RPTV\/UweOZKxE75l0sQJzLUMeOMoCPSDDISCNHydjNiw3RpHG2MMdx8jvqLjzb\/7iRrMQPi9BjMQDmYgBEhNTcXb25v29pfPv3QbBpRt5N+\/is8xL7z8rvCopAU10F71iIsBvpzwOsHpG4lUdQDUk3DpBhE3LxPge5wTZ26Q1awAlDRmxHM5wBe\/c1e4l5dNuNsWpo39hpXmp0jMzuDBjQhuXjzNcd9AInKrqUtPIupGWufUsKqWBxciSK9qByRUpd7E38uLYz6XSMgrp\/DeGQynfMq4tbvxv\/qQhpe+edWoVSqULUkYrVnP0n1JKFUqVDVRmHwxF2OXk5zxPYHPxUgK21WAgKSLodzPbUAhEZB+8wK+3j6cu\/2QKrHquf1KBCXEXT6Hv98JvHxDeFwiQq2q4\/75a0Tcuoy\/9zF8zkeQ350kEz9BAxgGOiUVVJJXVIlA3PtzNb2ZdEgtbaXxNb01VdNDPLctRutIJI+S4vDavoat7leofk3SAGW7ELGim95UL1GfwwCAWkpTVTH55Y3IeiOz0jMahIHvNQgDgzAAcPPmTcLCwl66SBF0FwbkVN45wfoZS9nheZyDu63wuZ5JS1MmhzeuZr6xB2f8XNi0bDkmvg9pF6di+\/m3TJ2rxd5DTmxZuJgN+xIRi1KxXLWZHXv8CL4SQED4Xa46bmL6uBEsN\/bmTsIN7CdMYugnk9F19uRqRiEJrs4YLgygDECciO3XRpyJq6YpJ4RtsxeyxdULTxtbjpyL4P6tUxhO\/Yzxq3bidSaGytclZGlPwWy9NqvcUjo\/N0Sz\/eMvGDd7M\/sO2LFuzlIMjqYjowDXcctx8E0gI\/IYi+eYEnD1LD4XQ0mufvacKql9FMo+Mw+Cb17GRXMl68yDKK56gPkn45mxbCv7DtqzYc4SdA8k0BtdogEPA+9SfZqOWNVGRmQAllZ2ODk5YO9kQ83nAAAgAElEQVRymvjCbnTv30L9AgbeoQZh4HsNwsAgDAA4OjoSFRX1yu+7BQOKas7aGzBz242nLzE1CopuODNhxn6Su05\/4SlTlm60ITo\/kX2jFmJxttNpMeWgNaune5Bbn4KVlgZLzP1JLO3ckzQvhL079LlUBZCPy7jZbNkf2zVCLOa+y362LQmkHDpDa0fv4sLdHKJOWjBR88zTKDKVWg00cdN2LZaheW8+McJHmK7TYqXro87PNZHs\/GIx+yMaADnRVjvRWOJHpbIIj29Xscf3AWmRHnw3ewv7b6TR+oq+q7SlnEexUfgYz2aMlg130u5i+\/Vi9lyrARTE2ZmyZo4neb3Q9x2EgR+hfrE2gbKdpm6uu\/C2GoSBn66B3v4HYWAQBgBmzZpFbGzsK7\/vFgxIijlsr88Ut+dfMFmXLPhEK5CirsHLhnBbFm0xJiT1Hq7j1uASUgqoSTtig+Z3dqQooDk9BAcTfdZvNMT7QRmCzMs4bNPjXDFANi4TVmHn87hrFUERD\/bvY\/vS01QCyB\/jNNacCxFpBJ8wZoRl5A+mlWsIsVjJ7uCMN8\/NvwQGTIau5licAJByz8GYDYsOU9RRhOekldj6pSKX1XEvyBktjfXoOxwnsebZoQcFdak3cd1mhcvxw1iuHsOXm+y5m3YXhxFrOXynHpCRsNeUdbOdyeiFmYJehYFXpbAcqBIKhX0PA+9Qb4KB2trupGoaOIqPj+9RGBjI7f\/atWvdggEzM7N3ZFHPq6mp6Y0w8CrHuYEgIyOjt4IBkUiEoaEhBQUFr9ymWzCgaiB0zzamrPDt7KG3lVNUVUdB+EEmjzPjRqUKEBNtr8c6nSNk1z5m7zfLcLpcBKhI8bBi7SRb7td3OcrSwFX7ZXxpGUZh2gUsdNbjkwOQg\/PYpVgeS+oagZDy2NMKzZmOpCpBkXuGNZ9sITCuiIRAWybOdCNDBrRXU1RWjVBaw9VdC9E91fmCF9fk8Sg5i\/qX9b2EyRiv3ciy\/cmdn2si2Pn35XhGNwISYm2NWLfgMEWSQg5NXIaVVwItok5EkaUHMH\/FGrbfrP5+f+pmbh7YyrBlflQrlaQf28DYDVbcTr2D3VcrORhRyxPIWDtzgMHA7NmzOX\/+PO3t7b1exGLxOzlOZWUlw4YNeykMXLt27cef\/X6u0tLSV8LA8OHDKSgo6JVr+a6u5w\/LzZs3mTRpUo+cu3fZ\/nujBAUFMWvWrNfW0cbGBn19\/T639aeWkpIShg8f\/koYGDZsGGVlZX1u508tOjo6ODg4\/OQ2nJ6ejr+\/\/wvPu2fVPZ8BFa3ZNzCf8x1fjZ\/AhBnrOBhRiFRYTZidLtNHj2XChHFMXmjO5YdNKKSPsR+pgXtXKu3UIw5ozzlAcmEi7tormDtzLhMXbcL8ehHitgyO6U7i4+ErcDtzAcfp2jj5pXSNDKjpKI7Cbum3fDF2Iiv0NrN6tBGXUxvpqEvCdeUsRoyewITvlmF3MQWxUk72mW18\/dfRrNnhxxWvPeiutSf+Zet6tadis0mXdW6POz\/X3GHX8PWciG8COohz2IX2cm\/KZEUcnrqeff4PeBx2gDXTZjN73ny+0z9IRPkzKTfVckqjvVg8bybzNA3QmTmSrwxdiEmPZs\/ojRy7Ww9IuO9kjtbCQ+QMpGkCbW1tvvzyS8aMGdOrZdy4cYwfP77XjzNmzBhGjRrF119\/TUfH8+4bs2bN4vDhwxQXF\/dKKS0tpbS0tNf2\/6oSGRnJ8OHDX7i2KpWKcePGMWLEiB4\/xxMmTGDs2LHv5Hr+sAwfPpyVK1f+2KbeJ+1\/bFfMem\/tf+jQoWhra7+2jr6+vnzyyScDrm5PysiRIxk9ejRy+YuBXFKplBEjRjBy5Mhet6O36vvJJ58QEBDwk9twYGAgZ8+efen5eaLuhxYqEdZkcSc8nKiEXBpEXfuU1JN1P5JroVFkVLV0hd9JEVTW09oVHyptbaahtg2ZXERV\/iMiw24Rk1FOe2cMJu3VOcRFx5NVIUBQ00iL8NkVSlUIK9K5e+cOyYU1NNY0P10Ou6OxgPjIcMLjMqlp7ez+qzqayLl3l9jUcgQNdVSW1fHSSE61jOaGRhqehEwrOmiqbEAoUQFqJM0CGuqEKNRyWqsbaBVJELdUkR5\/hxu37pHX8CKAohRTlZVA5J0EcotLqBC0IpF10FzVQFuH8vv91rb2Ss6CXoMBiURCY2MjdXV1vVbq6+spKSnh7t27lJSU9OqxnhxPKHxxtUFTU1NGjBjx9Kbu6fIEdnpr\/68qo0ePZtmyZS+9viKRiPr6+h49v01NTcTFxZGent7r1\/JlpaGh4QXQ+6nq7fYvEAh69Vw0NjYikbywTvRzksvlPW5HfX09TU1NPd62XnWsl40K9GYbf1mba2xspLa2tsf33dzc\/MoogO7IzMyM0NDQ127T90mHBtVT6jUYeFe6du0aa9eu7fP5a5VKhVKp7PECkJKSQnZ2dq\/svzvHf1dqaWlh9erVXL169Z0edyCqurqanJycvjajV5STk0Nubu6bN3xPlJGR0efPrx9KLBZjZ2fH48ePX7vdIAy8PxrQMFBWVsbXX3\/Nf\/3XfxEcHNzX5vSKamtrWbx4MUeOHOlrU3pVKpUKU1NTfv7zn7N8+XLU6l5OKjCAJRQKWb58Oaampn1tSo+rpKSESZMmYWtr29emvBPFxsYyadKkfueAnJOTw8mTJ6mvf\/0SboMw8P5owMJAe3s7S5YseWr81q1b+9qkHpdSqWTr1q0MGTKEadOmvXbubqDrxIkT\/Md\/\/AdDhgzhd7\/7HQ0NDW\/+0T+odu\/ezZAhQ\/jyyy9pbW198w8GiDo6OtDT02PIkCGsWrXqjdMUA121tbVMnTqVIUOGYGdn19fmPKcDBw7g5ub2xtHBQRh4fzRgYcDd3f2p4UOGDGHu3LkIBC9z+xy4cnFx4V\/+5V8YMmQIv\/3tbyktLe1rk3pFjx8\/5k9\/+tPTa\/mrX\/2KO3fu9LVZ\/VLBwcH867\/+K0OGDOHXv\/41Dx486GuTekx+fn784he\/YMiQIQwdOpSsrKy+NqnXJJPJ2Llz59M2v2bNmpf6I\/WFFAoFu3btIjAw8I3bDsLA+6MBCQPh4eH85je\/eQ4G\/vd\/\/5d79+71tWk9poiICH75y18+rd9\/\/\/d\/97uhxJ5QVVUV8+fPf+5a9seeUn9QSkoKn3\/++dNz9E\/\/9E+4uLj0tVk9ori4OH77298+rdsHH3zAqVOn+tqsXpFarSYgIOAp6A8ZMoRf\/vKXJCYm9rVpABQVFeHh4UFJSckbtx2EgfdHAxIGUlNTOXLkCHp6evzyl7\/kP\/\/zP\/n3f\/93Dh061Nem9YjKy8uZPXs2H3zwAR988MHTC7R58+a+Nq3H1dzczPnz57G3t2fYsGH827\/9G7\/4xS+YPHlyj3n2vw96Ejf+wQcf8POf\/\/xpmxg3btw7d\/TsadXX1zN69GiGDBnCz372s6d127Zt23s5NZaamspHH330XF2HDBnCsWPH+to0AIKCgrCzs0OlenMw+yAMvD8akDDwRImJiUybNo3KykqioqJ49OjRW4XS9Be1tbURHR1NYGAgEydO5He\/+x1\/\/OMfmTFjxmtDoQayVCoVc+bMITQ0lPT0dCIiIt6r+fC3lUQi4cGDB\/j7+6Opqcmf\/\/xn\/vSnPzFu3DiKior62ry3UltbGxEREfj4+LB8+XL+\/Oc\/8+GHH7JkyRKqq6vfvIMBpoqKCkJCQvDw8GDs2LH84Q9\/4I9\/\/CM2NjbIZK9bHaf3JZfLcXNzw9\/fv1vbdw8G1MjaW2kStH2\/XHC\/kAqJsJmm5nYU3fBXVis6aG4U0C5TAzLqcjNJy6hA\/IN9tlbmk5KY\/8r1B\/qrBjQMxMfHY2Nj09dm9KpmzpxJZGQkVVVVpKWl0dTU1Ncm9YrEYjGmpqbvrV9ET8rDw4PIyEhqamrIzs6mpqamr03qEXV0dHD27Flu376NQCDg\/v377217h04o8PLyIjs7++n93ddOk3l5edjb21NcXNyt7bsHA1IK7wTi7Hqe3JevhNxHEvIo+Bgux25S0Y0+pLLmHodt3blVKAGERJgZsnGVF8+juIwMX0fWTbDkwQDrtw1YGFCpVFy\/fp0zZ870tSm9pubmZrZu3Trge37dUUlJCYcPH6aurq6vTen3WrJkSb+ZX+5J1dfX4+3tTVlZWV+b8k4UHx+PhYVFvxrNvHLlCubm5t3evnswoKCxIInbkcnUytXI5Z31lbQ109Le1SVXSxAI2r9fIEitRK7o\/NTeJED0w9kiSQt1dU20P50hUyGTd+UvFLXR2i5BoVIDUgSN32fskwpbaO14MgwgpSI1jojYDAQqACVtTfU0ND8\/PSlrb6K2vhVJWylxt6LIbFQBrYQbG7JZ05cCSQctgieUIyfd2561Y3Zz\/4k\/qKKdxrp6Wt\/N+nI\/WQMWBjo6OggICODRo0d9bUqvKTs7m2PHjg3oldO6q9u3b3Pp0iWk0n5+x\/SxJBIJGhoaZGdn97UpPa60tDS0tbX\/YXxFAgIC0NDQ6GsznkooFOLp6UlERES3f9NdGKi8dw1ft2tUqVp4cNybA5Y2WO+1YbeFIydvXOfyEQdMd5jhfjaGRgWo27I5s8sZ9wP7sLEyY4f1IUKyOxeNas2\/hZvTHhwd7dlz0JcH9UqgmVivozhu24mptQWOHsc5umMP7h6O7DIyxdHnNNeCT+G5dzfGpvsJL2gF5OSGnsPv+F0alULSzp1gv6MDTgePEpLV+X1lfAhHHBzY5+7G0TOnOGHlzb1KOSDirvVW5nw+j53ObjiYmWB9OoFGpZzsk3vQHG9JohSU1Q\/wPrAXOwdHHPcd5FZx\/536HLAwIBAIMDQ0fK97ETdu3ODy5ct9Po\/4LnTkyJH3Mlqip5Wbm4uvr+97F0YLcOfOnX71cuxtnT9\/nsOHD\/e1GU+VnJyMoaHhj5qq6O40QcoRS5aPtCZDWs2pObOZOHYzPjevc3znIj4ZroHrqWCueBkzcbEBflkiaLiN9q9HsGj7Ia7cuIT1yvksNjhLjaCQ45v12eYZRmJiFB7bNrLBKh4x9fjPncxnHy\/GNfgKV47bMe\/fR7LJNYiQYFcWT\/iWaavtuXLjCvt15zPROgIx7dwx1WPVvMNkFd5izewt7Dt3h4TEGBJKRCDOYs9mLVbtPkdKShzh511Z+\/uFeD1oBTqItdFm3IczsDt\/g5tBdswepcHpjAqyT+1j\/bf2pAlrCbUwRtsygLjERC446rBk8zkq+qlP7ICFgbKyMsaPH\/\/eOtRB5xKx\/ygvSA0NDS5cuNDXZvR73blzh6CgoPey9xwZGcnRo0f72ox3IrFYzIULF4iLi+trU4BOx0EfH58fnem0uzCQ7u3Auu\/2kCmtJnDRSgzs7wJQG+bK2KG2PAQQ3mH5Ij3MwypRC+5g9PcVeCV2uuc1XnNkzUYTLt4KYO7IeawxdcPHz4MdC5Yya5of9TRwevEKdCzCkQHKlNNs\/HwTl6sBirCesY5NTp0vuIJLFvxtkR\/ViIizNmLjsuPkN2axf+tqZujuJ+xR11SlrJar7tv4duFWPMKyEZZHYfqlBt4JrYCICDN91q06QUVnTTitMxuds3GknnRm0xQnUgpj2DRlNtPW2+HrdxxHnTVM\/MyWhH7lN\/G9BiwMZGZmYmFh0a3wl4EqAwODHzVkN1AlFouxsbF5bQMcVKeOHz\/OlStX3rt0zTKZjMuXLxMVFdXXprwTVVdX4+7u3m1Hvd5WXl4e06dP\/9E+Oz8NBjQwdoxCAhQHu\/DdiH2kKIC6Wyyfo4dFSDkqwV12fr4a78Q2ABpuOaK5xZRzV72YNG4FegfOEh5xg6tXbhKXXI+cSvwWarDTLgIR0HLfH+2hOoRUAuoMLGZtxsDlIWog\/fRu\/j7Xh4ouGNiw2IN8QN6YRYjvfnTXrmTXlZxO\/4X2Gh6Gn8Z2ywqWrpzPlA9XcPKRiE4YMGDDSi9KAajhjOFcdM51wcBUJ5KzbrF82gIWmHlz8+4tQkOuExFdgKifvrIGLAzcunXrvV7QRiAQ4OLi8l5nYXui\/Px8jh07RkVFRV+b0u+1Zs2a97Ldt7a24uLi8l76QrxM2dnZGBgY0N7eP7qJQUFBeHl5\/ejfdRcGUo9asWK0LenSKvxmLkPfKpwOoPC8IyM\/syVJDtSGMX\/KJkwul0NbNPr\/N5pVtpfJLXiEn\/4m1m0NpKg6ActZG9l1NJy8wkLycvIorm0HyvGasQzd3TcQAs1x3mj8ZR2XygF1GsaTNNByfIAKSD1pxIfTvKhASPQufdbMO0BKTQ3FhcVU5CfgbrKScc6xiCXNlOZkU1ReTLy3Pn\/+9b\/xLx9M5cRjMSAmylSTUR+v5sT9XArun0R7\/EYCHpWQ4ePAqlHWpDTlcmStFpstgkgtKqQgP4\/8knr666TvgIQBlUpFQEAAMTExfW1KryktLY3Tp0+\/l3PDP1R8fDz+\/v79Jh1rf5VEIsHMzOy9jCSorq5mw4YN\/xDtHeDu3bssXry4r80AOqM4dHR0flKIavdgQEZBsC82Ov6UKOu5vsOGg77JyIDKKH+01viSowCa7mFquJcjMbWom2PY+dcxTJs5m5EjvmLiYhvCsloBNdWxAejPHcc3X41g9NR17L9RBjQSssMG9xOJiAFhWhgOy\/cR0wCoCzisb4+TfyYqoOC6J0u2X6UOMY+PHcTR\/Cr5RTHYrZ7PpPFT+G6DHVfy2lFLCjltsZFpoyYyfqkuNocOYLPYluu5nTCQ7OWJ8YINLF48ma+\/nobBoRiEaiUlof7YbDpBngokuTewXTOVb776hlHjF2Di95D+gX8vakDCgEKhQEdH571e5vT8+fP4+voO+Oxy3dHhw4ff+1UZe0IVFRUcOnTovQw1zczMZO7cuX1txjuRWq0mIiKi3\/hHWFtbExAQ8JOmXHsrA6G8OAy9j5ZyNLqc1uZGWn\/odKdoo6aiDpGyB6fLlO3UVtQilD+7TxnNNTU0vCYuUNHWQG2ziFefPTF1FTW0SPtPCOnLNCBhoK2tjRkzZrzXMek7dux4b\/LOv0kmJiaDMNANJSQkcPbs2fdyBOXBgwf\/MG1AKpVy7tw5IiMj+9oUcnNzWbRo0U8GzN6CAUXtAw5utCUk6\/1r6\/1VAxIGCgoK2Lt3L21tbX1tSq9IpVJhbm7+D+FdL5VKcXNzG1ylsBvy8fHB29v7vXMeVKvVXLp0iZCQkL425Z1IKBTi5eXV5yObKpWKnTt3cvny5Z+8j96CAbVSTFNjA63Sfupt9x5qQMJATEwMQUFB\/SpzV0+qsbGRgIAACgoK+tqUXldtbS2+vr7\/EHV9W+nr63Pw4MG+NqPHpVAoOHr06HudQOxZ1dbWsnjx4j4f4Tl79ixGRkZv5acxuFDR+6N+CwNKpRKJRIJAIKCkpITi4mIePnxIdHQ0lpaWnDp1ipKSEhoaGpBKpSgUigHZY1Kr1U\/rWldXR1VVFdeuXcPMzIwrV67w+PFjiouLKS0tpa6uDqlUOuD9CFQqFXK5HIlEQmhoKHv37iUpKYl79+4RHR1NTk4OJSUllJeX09bWhlwuf69DSH8opVKJVCqlpaWF0tJSKioqSElJQVNTExcXF0pLS5+eH4lEMuDPT0dHB7NmzaKyshJgQN7HP5RSqUQmk9HS0kJxcTFlZWWkpqYSGxuLn58f8+bNo7CwkOrqajo6OpDL5e+03rm5uRgYGLy1M+ogDLw\/6jcwIJPJqKqqIikpievXr+Ph4cH48eOZMmUK7u7uODs74+XlxdWrVzl48CBOTk64uLigqanJjBkzcHJy4sKFCzx+\/Ji6uro+X\/DjdXpS14cPHxIcHMzBgwcZP3483333HQcOHMDOzg5PT0+uXbuGt7c37u7uODk5MX\/+fObNm8eRI0cIDQ0lIyODpqamAbHMa3NzM8XFxcTGxuLr64u2tjbTp09n586dWFtbc+jQIS5cuMDly5dxc3Pj0KFDGBoaMnLkSHR1dTl16hRRUVHk5OS8d6sZqlQqGhsbycrK4vbt23h5eTF16lTGjRuHs7Mzrq6uHDlyhPPnz3P69Gk8PDxwcXFBW1ubiRMnYmBgwKlTp7h\/\/z4VFRX9JlztVVKpVIjFYurq6igvLyc2NpYpU6Zw7do1wsPDuX79OjExMaSnp5OWlkZBQQH19fWIRKJ+C8JKpRKBQEBOTg63bt3C1dWVRYsWMWfOHPbt24erqyvHjh0jODgYf39\/7OzscHd3Z+PGjYwaNYqtW7dy8eJF7t+\/T0lJCWKx+M0H\/YkSCATs3r2bwMDAtwaQQRh4f9TnMFBfX09UVBSnTp1i4cKFrFu3jitXrpCamkpxcTE1NTUIhUKEQiFisRi5XI5MJqO9vR2hUEhDQ8NT6g4NDcXKygpNTU08PT2Ji4vrVyu6NTY2EhcX97SumpqaXL58mdTUVEpKSp7WVSQSIZPJUCgUdHR0IBQKaWtro66ujpKSEpKSkjh\/\/jwGBgbo6Ojg4+NDfHw8LS0tfV3F5ySVSsnMzOTSpUsYGRmhpaWFt7c3cXFxZGZmUlNTQ1tbG2KxmPb2dmQyGXK5HJFIhEgkQiAQUFlZSX5+PtHR0bi7uzNlyhRsbW0JDQ0lLS1tQE8VCYVCUlNTuXr1KlpaWsyfP5\/Tp0+TkJDwdCW7tra2p23\/yYiKSCRCKBTS1NREeXk5qampREZG4uzsjIaGBm5uboSHh1NWVtZvRgxUKhWlpaXEx8dz8+ZN\/P39WbVqFaNHj8bc3Bw3NzdOnjzJrVu3iIiI4NSpU7i7u7Nnzx6mTJmChoYGHh4ehISEEB0dTUFBQb9I093a2kpKSgoXL17E2NiYzZs3c\/r0ae7du0dOTg61tbXPXcMnbVwikSAUCmlsbKSkpOTpNbS1tWXq1KkcO3aMO3fu9HhSIplMhq2tLVu2bOmRe2cQBt4f9RkMNDc3c+3aNWxsbFiwYAEhISGUl5e\/dQNVKpXU1dURFhaGsbExxsbGXLlypU\/XRW9tbeXSpUtYWFiwcOFCrl692iN1VSgUlJaW4ufnx+zZs3F0dOTWrVu92qvorh49esTBgwdZv349jo6OZGVlIZFI3urlpFKpkEqlZGVlYWFhwcqVK\/Hz8yMpKakHLe99PZkecXJyQldXl5MnT1JcXIxMJnurnppSqUQoFD5dDW\/Hjh34+vr2aeIqiURCXFwcQUFBbN++\/SnwpKam0tjYiEQiedrbf7buT6bPngy3NzU1kZGRwaVLl1izZg1aWlr4+PgQGRnZJ7kJ\/n\/27jOqqjzP97\/rpgezZs26D+b\/v3em7\/9OT9\/u23d67kxP5+mqDlVWWUErWVpaaplIkoNkJOcgkiQJgmREclQUBJEsIElylpwOOR3O+\/\/A0raSpXAOB+X3Wms\/KIuz9xfOOXt\/9v6l+fl5bty4gYeHB9ra2oSHhzMwMLDpx\/1SqZSZmRlKS0uxsbHBwMCAoKAgub2HoaGh6OjoyG3xM6WEgaVhqtPL6JlaZHFqiK6uIebXgbUp7l1PIToylXvd\/fR29jO1vIn3YmGSvp5eRude3huOF6GUMHDjxg2cnJwwNzenqKhIoW1lxcXFmJmZoa+vT3p6+pbfKZWUlGBvb8\/BgwfJyspS2HGkUinJyckYGRlhZmZGcXGxwo71LN3d3Xh5eaGjo0NgYKBC16OfnJwkKCgIQ0NDnJycXorx97W1tbi5uXHw4EFiY2MV2sTT0dGBl5fXk2aWqakphR3r26SmpmJjY4OxsTEhISF0d3fLbd\/Dw8OEhYWhqamJk5MTqampWxaC6+vrn4SAjIwMha60OTAwgKGhIRoaGkRHR2+q02F6ejo6Ojp0dHTIrT6lhIGRAoz\/yYSMhiF67sbh6hJLmxQWKvzY\/+kxTJwvU3AnHT+7QIoevsj3a43R5gqahh99jtb77xB03peMplerWfK7bGkYmJmZISQkBE1NzS2fUjUtLQ09PT2cnJy25KS4tLTEpUuXOHXqFAkJCVsWQqRSKRcuXHhy3K1sY83Pz0dLSwsHB4ctnVq4r68PbW1t1NXVt83CL98mKSkJFRUVwsLCtrRPS01NDba2tpiZmW3JdL+tra04OTmhqqpKUFCQQh\/nr66ukp6ejpGREdbW1lRXVyvsWAB+fn6oqakRHx+\/pU\/g7t27h7GxMWZmZjx48OCZP\/u44+nTLl68iLGxsdzf\/+cLAzJWlxdZXFpmeW6Mnp6xL6fkXWN0YICxr0\/WvzxNf2c7PcNfW4RuZZqBngEmegqw\/qUlGQ3jSIaaKClpYEI6S5HbYT4y9ORuzxiDrVXcKqhmcOnLG80VCQOdnTyUPPVZXJ2ip7WdnrFZZMDqVAMham9xyi+Lju4eOurLuXW7gq7Jv7xmXTJER3s3IzOPnxasMT87z9r6CtPDA\/QPv7zD3bcsDAwNDWFra4udnZ3SFucYGRnBycmJgwcPKrSG0dFRbG1tMTU1paGhQWHHeZbGxkZMTU3x9vbekiFM\/v7+7N+\/n+vXryv8WN\/lceDLzs5WWg3fZm5uDm9vb3R0dJQ2lbBUKiU6OhoVFRXKysoUdpysrCzU1NTw9\/dndHRUYcf5utnZWeLj4\/n0008JCQmR+\/4XFha4cOECR44cUejf71lWVlYIDg5GT0+P0tLSb\/2Zzs5ODh06hLGxMfDoBkxfXx8dHR2FDN99vjCwSm9BEnafnsHE0QI9NXVM\/IKI9HHCylgbTT0nbnQ\/ClZLnfk4mJ3F0MoaUwMDnFMbWAKWegrxtdBF66wpuiqf8eYP9MntmmLgTir+djGU1t7A4cN\/5p9+v4dTOoZonT6NhlYQ9Yuw1luIj6UeuqYWGGk5kts0zFhLIV4Glrh42GOkeo6wzAoqks\/z+a\/+gZ+\/dwRdI2P0VI5xzMaPgsF1QMbA3Qh09EywtjZD76wNkZVjwAiZNs6Yq2lz1toELQ19vK63b9v1B55lS8LA8MHzfLkAACAASURBVPAwtra2BAYGKn2ioJWVFfz8\/NDS0qK3t1fu+x8dHcXGxgY3Nzelz7M+NjaGmZkZZmZmCr0TjYqK4vPPP1f4XdnzqKurw8TEZNsEgpWVFRwdHdHV1VVqv5XHsrOzOXbs2HdeTDZKKpUSFRWFrq4uhYWFct33iygvL0dFRQVPT0+5jaqYn5\/H0dERLS0thoeH5bLPzcjIyEBFReUb72FRURG\/+tWv2LVrF++88w63b9\/G1tYWR0dHhXWkft61CRpDz\/Hm376HS045FVlu\/Plf9nDaLo7KiiysVD5ln999oJ8r2mc4rBvK3fpGypNcOPaRLlltnVx3NuSjz1y5Xl3FzTAL3v1HA3K7x2mKcOLwr0woHHnITfuDfKpjT2ZFFQUhluz7kQq53f1k2ury8VEP8mvrqMi7zr3uCaYHH3A7r4Lm9mbSzn3BPk1nrt8rI1Ttbb5wjaOy+h4VCXbsU1PFt34Npkqx3HsC3YDrNDTWkuh0hsMq3jROdxG6+x3e\/tCanOoqUpy0ee9tbxpewhXGFR4GFhYWcHFxITAwcFsNgQsICODAgQNyfdS3srKCm5sbtra222a9+dHRUSwsLBRytwSQm5vL8ePHlT6b2tOampowMjLaFss\/R0VFYWRktC0uIo\/l5uZy+vRpubYdR0ZGoqWlRWtrq9z2uVFDQ0OoqKjg6Oi46fb89fV1goKC0NLSUmj\/lxeVkZGBrq4uDx48YHV1lStXrvAP\/\/APT07m\/\/W\/\/lcMDAyorKxUaBPl8y9hbM+xP9lQLQMWi\/ji5yp4XB8HFkl21ea3urdYkhTw+WEDnPK\/fKI0fx8vtX3oXrmKveoZDrvXPfr36btY\/tKK3PYRHsR6cOI1a2qA\/lhjDC9cZQyQ3k9A85f63KgpwVRXD\/WYrz0Jls7SW5lLRNAFjA\/+kp8dt+bOxBR3XU9gnfnlE5SBTAxsjAhoXIP6i\/zyIzduffk1Xqm9zMmjXxB6v56Yj49w9nw5AJLCUE78sw45Qy\/fXBkKDwOxsbHY2dltu7HPi4uLODs74+fnJ7d9JiUloaqquuUdtb7PwMAAFhYWcr9j6+7uRk1NTWmPTZ\/l+vXrGBgYyPWC96Kqq6s5ffo0fX19Sqvhu8TGxmJgYCCX4WVZWVno6el9b1v2VhoaGkJdXZ3Y2NhN7aekpIRTp05ty\/cwJCQEKysrLCws+E\/\/6T89OZHv2rWLH\/zgB1sShp8\/DDhy8s+2VK4AEzc49ktNLuQNAZMkOGjxms5NlqZvceigDtZ5X4aBmSrcTn6CVWIqdqdU+dSl5tG\/D+Zj9K8WfwkDr1tTLYOOCH103KPpk8Js+RXUf6lPfu1dzDS1OBn+l5AqXZunLsmLQx87kXO\/jnTHg\/zutA0FD4fItz2MefqXYaA9CW0rQy42rsH9i\/x8nwO5Xz5gmS8P5vQRFeJamoj+6Bhm50tYBkbzgzj1c22yB2Ugk7K2JmX9JckFCg0DDx8+REVFZduuUd7d3Y2FhcUzf\/HnNTk5yYkTJ7h\/\/74cKpO\/GzduYGBgILcJe9bW1rhw4cK2WXnt2xgZGWFvb6+UuQhWV1cxNzfn1q1bW37s5zE9Pc25c+eIjo7e1H56e3tRV1fflsuJ9\/T0oKqqSn19\/YZePzs7i4ODw7ZYUOjbzM\/PY2hoyKlTp9i7dy8\/+9nP+MEPfsB\/\/I\/\/kV27dhEREaHwGp43DNQGWHHgF2aULgPj2Xzy4y9wyRgAJoiyOMnPTmSyQh+RWpocVfck\/WY+V31tUPnIgfLBAW45GbPvLROi8\/O55mfEn\/\/+DBmdozRdduDgv5pSKYPWYDVO24fTI4WZklCO\/PAU2T0D5DoY8NGHlsTl55N1LYmixnbyQ0341\/2O5JZXk3h2D\/\/7kCn5g+PccfmUd7W8uV3VzVRDHCpnNfCqXYXJu5jtPYG+Wwz5N7Pw09fmjFYUPctdBLz+MXrOt1kGhrJ9OPQjDW5MLNGVGY7\/pVz6FDfYRK4UGgaCg4OJiYnZNhOffJvg4GDc3d03XWNkZCS+vr7bdhKchYUF7O3t5bYyXHd3N8ePH2dsbEwu+1OE7u5uLC0taWxs3PJjV1ZWcv78eaX3kXmWtLQ0vvjiC+bm5r7\/h7+Du7s7Fy9e3LZTCOfm5mJsbLyh72V5eTmGhobb9neDR8MFPT09mZubY2xsjDt37uDi4sLu3bvx8fFR+Ln3uTsQ5icTYHONTikwW4evSQiZNZPAHCVXQ7EIqmIJWB+sIMjoEH9+7Y\/s\/syChKpHz+Wlo\/e4bHSYN958D3VXP4Js4mkan6G\/KJ2LVkl0A0MF4VxKLWJ8HRbbCgk0CKZ6GmSjVYQaHOZPf3qD99U9KeqdY2mgDB\/jY3xwVAcLXS30w1JpmV1HUhOD2p49fGaYQHN7OZEJkWR3P+oOOFZzDYvDb\/PH1\/7MAf0g7j5cA0bItfcjNrOVNWC67gYXTWJoW5rnfvh5XLzS6HtJehMqLAwsLS1x6tSpjaXydQmN+TG4Odhy7tw53AKzaPvynLo23kxeShGtY\/Jpk29ubsbHx2dTQ+FWVlY4derUtuhA9yyxsbGYmJhsujOhTCYjMzOTS5cubez1C\/0UJVzEwcaac+fsCLxWxYRUynhzAeGudjj6JFE3Jp84bWtrq5TV8DQ1NTe46uQCnXeT8Xayw\/rcORy9EqkdfXw2kTFck0t0SjE9c5sfMiqRSAgMDNzwcMyOjg7OnTunlLD1vCYnJzfUhLG2tkZISMjGVtNcHqYsNQQHW2vOnbPBN6aYwRUAKQM1ufjZ2eAYnEbj+OavEouLi5w4ceIbTYDLy8tMTEwofGixwpYwnv\/27\/9mos3Kyjdfvbr0bSFRyrN6ty3Pb88bvs1SWBh4vMzwhu4c56ux\/9177D2kg6WFBS4BGbRKVugpTsbb9DC\/+cEJwovl0zt2eXmZCxcubGoJ3dbWVjw8PDY2q9f6OOUJ3uhpqHL69GkM7ONomlljvqeUy7b6qBo6cbVmcFNfgscerxC42bZdqVSKv7\/\/hleZm2tMRvV\/vckhPXMsLKwJuFrJxGQrGZH+OJgbcvKjgxzViKJTDt1MsrOziY2N3dJx\/cvLy5w5c2Zjwwhl7QS8\/wnvvKeCmYUFdh7x1Ix8edGYKMP2kx+x67+dIrlDPmHYy8sLV1fXDb3W19cXCwuLbX3nvL6+TkpKCkFBQS\/0uvn5eT755JMNjQBZ6clD56e72a9hgqWFFd5RRTxcldJfFIqNqTHGZy2xuRDJnV75vIePFzVTBjEd8avD29tbMWGgsrKS+Pj4jT2CnL6Lzb9Zktn0dKpdoLOyiPz0YHR\/bkjkjX65XCABTE1NN9X2nZ+fz9WrVzc2gmC+Crtff8wxXReCgoK4klRC\/9RD7qZdwsnJHVcrbT59y5Kcls239S8tLREaGrrpiXmWl5f54IMPvjGxyfOarEzC4JdefGWC1aVx+kYffVYkty6y\/0f63JDDMPXa2lpiY2O3dHGjvr4+QkNDn6zC90JkjXi9bsaVvK\/3XJ+g0NcdzU\/e5u9+a8HVB\/JpfnByckJTU\/PFy5TJ8PLyIjg4eANHXaPvTjRGhz\/mww8+4IMPDYiuGmKu7y7eagf5+MMP+fCTs0TeGZDLd7ykpISPPvrohV4zODjI4cOHN9TMM9+ShdG\/OFP5dJhdecD5o9o4xD4174icMlRaWhoZGRlKWcTpcRj47W9\/u+XHFuTLz89PMWGgvLycpKSkjQ3dm63A9hefoW3uQ0RMEnklHSyy\/ujEIGvC8zUTIvL65BYGNDU1cXNz2\/DrY2JiyM7O3tgdkqQM21+Yca36qdAkW2RiYpJ5AGk9Lr8\/iM\/Nzc\/oJ5PJuHDhApmZmZvaz9LSEr\/4xS82PHZZUpeExo8PYXUxgitRaZQ2j7IOrI60cifzCg6aKhifv82EHM5ttbW1xMfHb2nbfUNDA1FRURt7UiR7gM+bRzmt7kJYXCLpN5tZAKbvX8PZMZCs5EDe2GtLfLN8fp+wsDDMzc1f+HVra2tER0dvsMf6JNlGRhzZc4743Fxyc0toHZ2lNzuQg3\/\/Pme9\/fD3i6ekdUIu18uWlhZUVFReKBC2t7fj7u6+oYC\/1JmD7k8OYuIVSmRUCneaJ1h7mMHHn53mpIkNlrpGuF7KpXtOPmmgoKBgy2dEfOxxGPjrv\/5rXnvtNbG9xNsPf\/hDxYSBiooKEhISNjakcLYC218eRs\/6IrFJ6dyq6OLJV3KpGud\/NybiuvzCgI6ODj4+Pht+fXR0NBkZGRsLA3NV2P3yU1T1nPEPvULKrRae\/ostNMSh+oktOe2bP\/lLpVJ8fHzIzc3d1H5WVlb44osvNrwWgKQ2CY2ffI7dpVgSErOpaHvUlDTbcpuYYHcM1Y5w0Dya5pnNp4GampotDwN9fX0EBgZubDia7AE+b36BupYnUclp5JV0sjz\/gEs2RhgFldBbFczvXtcnpHSQVTlcS7y9vTE0NHzh183MzBAaGrrBNTDGSNe2xkI3l79My7VOx7VAVH7uwJ3xWRbl2Czb39\/\/ws14IyMj\/OlPf9rQ3AJLHTno\/e9DWPpFEn81k7I2CdLORHYfU8Mg\/Cb376ZhZ\/AF6rGNcgk7cXFxZGVlKaWj9uMwILZXa5NrGOjq6sLW1paRkZEX\/4RJSrH7lTnXqr9tGt1Wzv\/BisQS+YzlX1paIigoaFOPzjcVfOYqsfvVMUydI0jPvcnduj4et25Lp2q5ZKyGdUw5Ejl8z5eWlggLC+Pu3bub2s\/jULHRDpOTVdcw\/LUb1U8340vX\/nJiHLuLs+onWN7cwGfna5KSkhS+INDXzc\/PY2JisrE+FbJGzv\/JlEtpf3kStN6Th92pvXyqaYGFyjv8v3\/\/a\/abXKVnZfOXkqCgIHx9fV\/4dWtra0RERGwwWE5x3cSA\/b89jUNQIEHh+QysrDBSEsuZX36IqpExZvah3H4wjjwywf379zl27NgLjSiYnZ3lD3\/4w4amLV9ozebszx0ofrq7VG8yHx2w4HLDKrBO7kUtfutQIJdpa+3s7BS6CNqz\/OQnP1H6hUts8t\/Ky8u\/8z1\/4TCwurqKmpraxpbenCnF\/CcfoHEuiMTERFLzKhlcXmeus4qcK\/Z8+P+8wUmTEIo6Nj\/db0NDAxcuXNjUdJ0DAwPY29tvbLpZSRn2v7Yi7f5Xe8+uTDST5GWKbWwZ8prLsL29naCgILlMoJKTk4O\/v\/+GXiu5F8+J\/\/4hVqHxJCYmklvWxsRAA7nXYrmalUNGiBOaR88Q3bz5NRVMTExISEjY9H5elIqKCvHx8S\/+QlkT7r\/dzxdnvIhLTORaZhGtQxMMdbfR3tnJ\/WuW\/ONPj+Kc3cZmx1s8XvVxo+slXLx4cYN9BibIMTJF5SNn0kpLKa1oYXJFinRVwkBHB51t1cSZHmO\/QQB1cnigU1hYyKeffvpCr1lbWyMoKGhD80Qstaej9nf7MPaNJjExkayiBiYW2gk9rY2mcTi5uTGcM9TEPHPzq2yurq5iamr6zJO3ImlpabFnzx6xvWLbs2YS3dA8A5GRkRsbg7w6QLbrWY4fOcyhQ4dQMQmidnaVkYIwzh7+nC9OHefYcQ08bnZu+jFbZGTkhntTPyaVSjEwMNjYnfJcORb\/+00+OW2Bm7s7fpF5dI70c9VkN\/\/tR7\/luLkbnl7+JNUObfp3jY+Px9nZWS5zIfT19aGiorKhELU2WkuY\/imOHD7EoUOHOOuXQ99wK4mO2nx+7Cgqp4wJymvddAh68OAB5ubmSpkit6ysDBsbmw2sTzFBcaAtqkc\/59ChQ3yh5cqNnqceoQyXcf58GlVDmx8dcevWLQwNDTc8O2hqairOzs4bmPJ3lDQtexzMv3vmyuEkI95Tt6FwdHOfeqlUSmxs7IZ621dVVXH27NkX\/r7IJM3EmKo\/+XzruF6lYxmW2nJx1TjDKQ0d7COLGZXDw6qsrCzOnz+\/pR1khZ1tQ2FgZGSEkydPKm2Vwu\/T3t6Orq4uNTU1m95XZmYmHh4eL75cq3SUkggvzM4aoKenh4VnAk0DfRQm+GNlaY6xoT56hpaElPRtKgxMT09ja2tLRkbGJvbyVNlSKWFhYXh4eMhlf\/Imk8nw8PAgPDxcKUPfVlZW0NbW5saNG1t+7OcxNTWFhYXFpqbrnZiYwNraegNNbNPkGqnwhx+9g4quLrp6DiRXd9FWEIO1uT0udrZofHQSl\/hSJjaZW\/v6+jhz5syGmiunpqYwMDCQ+3sorzloJiYmOHv2rNKGFQo704bXJoiIiODzzz\/f9IIh8vZ4Rr6NtJd+m+npaYyMjDY9bE9RkpOTMTIykut4+56eHtTV1bl9+7bc9ikvubm5aGlp8fDhQ6XVcO\/ePTQ0NDY8BFORYmNjOXfu3Ka\/l5cvX8bBweEFO2hKGWsqIibQBy8PDzw8L1HwYIjBhkKC3TxwdzjP5eQKRuRw53zx4sVNBdaKigq0tbU33FlWkQICAjh37ty2W\/dFeLVtatVCY2Nj7Ozstk0gWFtbIzw8HGtra7n2Mi8uLkZVVXVTsxkqQl1dHSoqKhtuG36W\/Px8tLW1t9WqhcXFxRw9enSDPd3lKzIyEgMDg221eNXt27c5ceKEXJ7Yzc3Noa+vT0xMjBwqk69bt26hq6vL6OjGJ6yQSqUEBQVhZGTE7Ozm+7DIS25uLqqqqrS1tSm7FGGH2XAYgEePs86cOYOdnZ3Sl\/ZdXV0lPDwcQ0PDTZ0kvs36+johISHb6uQ\/ODiIubk5iYmJCtm\/VColNTUVU1NTGhoavv8FClZcXMzBgwe5du2asksB\/nIxMTAw2NgkRHIkk8nIy8vj+PHjlJSUyG2\/bW1t7Nmzh+zsbLntc7NaWlo4ceKEXBZQWlxcxN3dHQsLi42NjpKj9fV10tLSUFVVpa6uTqm1CDvTpsIAwOjoKOfOncPCwoLe3l551PTChoaGcHd3x8rKamM9\/5\/D45O\/vr6+0pc9bWlpwcLCQuGLRa2vr5Oeno6qqirZ2dlbOvXvYysrK6Snp\/PFF19selIleVtbW8Pd3Z29e\/dy7949pfRhkEgkxMXFyT0IPFZSUsLx48dJSUlR+sJklZWVqKqqynVNiuXlZdzd3dHT01Paezg9PU1ISAh79uyhtrZ2y48vCCCHMACPTtgXL17k9OnTJCcnb6Cn9cbMzc1x8+ZNNDQ08PDwUPhdu0wmw83NjQ8++EApQ37W19cpLCzk1KlTREREbNmJq6KiAg0NDc6fP7+lbaxNTU34+\/ujqalJZWXllh33RchksicX4\/Dw8C1tSqqursbS0hJLS0uF9l8oLy9HVVWVsLAwpqenFXac7zI7O0tSUhIqKioKWZzq8QJdampqhIWFyf3J4rOOW1FRga3to4XbtlszpLCzyCUMPFZSUoKuri5ubm5kZmYq7OI8OztLXl4ezs7O6OrqbnDq1I27du0aOjo6BAYGbtmIitbWVi5evIiuru43VjLbCpOTk\/j5+WFhYUFISIhCh\/Xdv3+fiIgItLW18fHxUcoF6EX19PQ8eUIWHx+vsKdkMpmMsrIynJyc0NfX58qVK1uyvHZvby92dnaYmJiQk5Oj8OM9Vltbi7W1NWZmZjQ3Nyv0WC0tLVhaWmJtbU1oaCjDw8MKO1ZJSQkBAQHo6emRkpKilPUHBOFpcg0D8Ohxek5ODmZmZlhbW+Ps7CyXL7FMJqOpqYng4GAcHR2xsbEhKipKKY+u4dFUqI6OjlhYWBAUFKSwduO2tjaCgoIwMTHB399foSeo51FXV4eTkxOOjo64urqSlpYmlzupiYkJ0tPTsbCwwMXFBWdn5229hO53KSoqwtLSEjs7OywtLSkqKpLL3PJdXV1cvXoVb29vbG1tMTMzU8pnITw8HDU1NTw8PBQawmtqajA3N8fCwoKwsLAt7ZOUnZ39ZF0TT09PioqK5HL8np4e4uPjcXd3x8bGBl9fX6V\/nwXhMbmHgcfW1tbIzMzEwcGByMhItLW1MTc3JyMjg4aGBiYnJ7\/zC7a8vMzY2BhNTU0UFhZiamrK2bNn8fPzw8PDg8zMTKUs3vFtysrK8PDwwNraGg0NDfLz8zfdO3l6epqcnBzU1dWxtbXF29tbISMGNqOxsZGgoCDc3d1xd3dHXV2diIgIqqqq6OnpeeZoDolEQn9\/P2VlZVy+fJmTJ0\/i5eWFl5cX7u7u23LI3ot6PDmRt7c3Dg4O2Nvbc\/XqVWpqahgaGvrOYWNra2tMTk7S3d1NeXk5tra2aGpq4uPjg6enJ1evXt3UrJryMDo6Snh4OI6OjpibmxMZGUl3d\/em99vb20tsbCy2trZ4eHhgZ2enlImlHrt79y7nz5\/H3d0dJycn7OzsSExMpKGhgcHBwe88B62vrzM5OUlXVxe3b9\/G09MTIyMjPD098fT0JDY2dsuaIgTheSksDDxteHiYtLQ0QkNDiYiIICwsjICAACwsLHjvvfd4\/\/33n2z79u3D3Nwcb29vAgICiIuLIzQ0lOzsbKV1UHwe1dXVXLx4kcDAQExNTTE1NSUqKoqysjK6u7sZHx\/\/Rhv\/6uoqIyMjdHd3U1RURHx8PCdOnEBPT49Lly4RFBS07e+O5+bmqK2tJSIigoSEBCIiIrhw4QIeHh6cOHHiK+\/t+++\/z4EDB3BzcyMwMJCoqCgiIyOJiIigtrZ22wQ8eRofH6e4uJiEhATi4uKIiYnBz88POzs73n333W\/8fVRVVTl\/\/jxBQY+m7A4ODiYuLo62trYXn\/hKwUZHR8nJyeHSpUt4enqioqKCvb09ubm5NDY2MjAw8K2hZ35+nqGhIVpaWigsLMTZ2Zn9+\/fj6upKSEgI6enpSu+k+7SRkRFKSkqIi4sjPj6eqKgo\/P39sbOz47PPPvvGe3j48GEuXLhAcHAwCQkJXL58mdTUVJqbm7ekSUcQNmJLwsDTFhcXGRsbo6enh4aGBvLz87+y3bx5k\/r6erq6uhgeHn7pLhASiYSGhgYKCwu5du0aERER+Pj4YGxszGuvvcbrr7\/+ZPvjH\/+IpaUlvr6+xMXFkZmZyfXr17l\/\/z5zc3Pff7BtaGpqir6+Ptra2rh79+433t\/i4mLa2toYGBhAIpEovYf6VlpbW2N6epre3l6am5u\/8bfJz8+nsrKSzs5OBgcHX5rPwNraGr29vRQXF5OamkpycjJ+fn64ublx+vTpr3zmX3\/9dY4dO4abmxv+\/v4kJiaSnp5OXl4e3d3d22bOku8ilUqZmJigr6+P5uZmbt++\/Y338O7du3R1dT3z6YEgbDdbHgZ2EqlUytzcHBMTEzx8+JDm5uavbC0tLQwNDT1pMhGdiIRXwcrKClNTU09C\/9c\/993d3YyOjjI1NaW0Pj+CIHyVCAOCIAiCsMOJMCAIgiAIO5wIA4IgCIKww4kwIAiCIAg7nAgDgiAIgrDDiTAgCIIgCDucCAOCIAiCsMOJMCAIgiAIO5wIA4IgCIKww4kwIAiCIAg7nAgDgiAIgrDDiTAgCIIgCDucCAPCtieTyZBIJPT29nLv3j1KS0tJSkoiODiY8PBwXFxceOedd3j77be\/dXv\/\/fextLTk8uXLhISEkJCQQGFhIaWlpfT09DA2NvaN5aUFQRB2EhEGhG1hfX2dxcVFJicnaW9v5+7du8THx+Pq6spbb73FyZMn8fLyIjw8nOTkZDIzMyksLOT27dvcunWL27dvU1RU9J3b0\/8\/Ozuba9euER0dzYULF9DS0uKNN97AwsKC+Ph4CgoKaGxsZHx8nIWFBbGapCAIrzwRBgSlmZ2dpa+vj4qKCnJycvD09OTQoUPo6uoSExNDQUEBlZWVtLW1MTAwwOTkJHNzcywtLW3qAi2VSlleXmZhYYGpqSmGhoZobW2lsbGRgoIC4uLisLOzY\/fu3Tg4OJCamkpBQQEtLS1IJBLxFEEQhFeOCAPClhodHaWmpoaMjAzCw8PR0dFBX1+fhIQEamtrGRwcZHFxkbW1NaXV+DgsSCQSHjx4QE5ODqqqqnz22WcEBweTkpJCWVkZAwMDSqtREARBnkQYEBRucXGRsrIywsLCOH\/+PMbGxjg7O1NeXs709LSyy3tuS0tLVFdXExwcjJ6eHh4eHoSGhpKfn8\/ExISyyxMEQdgwEQYEhenr68PLywsXFxdcXV1xc3OjrKyM5eVlZZcmF01NTfj7+2NnZ4eXlxdBQUHU1dUpuyxBEIQXJsKAIHe1tbU4Ozvj6OiIlZUVSUlJzMzMKLsshVldXaWwsBA\/Pz\/Onz+PhoYGN2\/eVHZZgiAIz02EAUFuysrKMDU1xcnJibCwMGpra5Vd0pbr6+sjODgYT09PDA0NycvLY2VlRdllCYIgPJMIA8KmSKVSamtrMTQ0xNzcnJSUFPr6+pRdltKNj4+Tn5+PjY0NWlpa3Lx5k6WlJWWXJQiC8K1EGBA2rLu7Gw8PD4yNjcnIyGB0dFTZJW07k5OTlJSUYGhoiK2tLffv32d1dVXZZQmCIHyFCAPCCxsZGSEiIgJ9fX3Cw8OZmppSdknb3tzcHLm5uaiqqhIQECCengiCsK2IMCC8kOrqaszMzDh16hQ9PT3KLuelMz4+jo+PD1paWmRkZIinBIIgbAsiDAjPRSKR4OrqirGxMTk5Ocou56VXUlKCrq4u3t7e9Pf3K7scQRB2OBEGhO\/V3d2Ni4sLqqqqtLa2KrucV8b4+Dj+\/v7o6+tTUlKi7HIEQdjBRBgQnqmwsBADAwOio6PFEDkFycnJ4dSpU1y9elXZpQiCsEOJMCB8p6SkJFRUVLh165ZYnEfBqqurMTY2JjY2lsXFRWWXIwjCDiPCgPAN6+vr+Pr6snfvXhobG5Vdzo4xODiIubk5Li4uzM3NKbscQRB2EBEGhK+QyWRcvnwZLS0tOjs7lV3OjjM1NYWPjw9ubm4iEAiCsGVEGBCekMlk+Pr6oqenJyYQUqL5+Xn8\/Pxwd3dHIpEouxxBCl82gAAAIABJREFUEHYAEQaEJ+Li4njvvfd4+PChskvZ8ebn53FycuL8+fOi46YgCAonwoAAQFFRERoaGjx48EDZpQhf6u3txcTEhMTERGWXIgjCK06EAYHW1lYMDAwoLS1VdinC17S3t6OiokJxcbGySxEE4RUmwsAONzc3h4uLCzExMWL44DZVWFjI6dOn6erqUnYpgiC8okQY2OE8PDw4c+aMGNu+zV28eBF3d3exDLIgCAohwsAO1tjYiLGxMfX19couRfge8\/PzqKurU1hYqOxSBEF4BYkwsEMtLS3h4eFBZGSksksRnlNFRQUGBgbMz88ruxRBEF4xIgzsULW1tRgbGzMzM6PsUoTntL6+jr+\/P1euXFF2KYIgvGJEGNiBlpaW8PX15caNG8ouRXhBjY2NaGhoiNkJBUGQKxEGdqDa2lrU1dVZXl5WdinCC1pZWSEkJESscCgIglyJMLDDrK+vExUVJS4mL7HS0lIMDQ3FzISCIMiNCAM7zOjoKO+99x6Dg4PKLkXYoNHRUdzd3amqqlJ2KYIgvCJEGNhh6uvr8fDwEBMMveSioqLw9vZWdhmCILwiRBh4xclkMpqbm8nKyqKurg4PDw\/S0tKYmppiampKhIKXzMzMDLOzs6SkpKCrq0tVVRU3b97k\/v37SKVSZZcnCMJLSoSBHSA0NJS\/+qu\/YteuXezatYvf\/OY3HDhwAH9\/f3EBeYnIZDLS0tI4cOAAr7\/++pP3c9euXXh6erK+vq7sEgVBeEmJMLADjIyMfOPi8V\/+y38Rs9m9hFpbW\/nxj3\/8lffyb\/7mb6ioqFB2aYIgvMREGNghzMzM+A\/\/4T88uYDo6OgouyRhg1JTU\/nP\/\/k\/P3kvT548KWYlFARhU0QYUKC5uTlGRkaoq6ujpqaGe\/fukZ2dTUxMDPHx8V\/ZYmJiyM7O5t69e9TU1NDR0cHY2JjcTvIVFRX8z\/\/5P9m1axdvvPEGExMTctmvsPXm5+c5fvz4kzAQHBys7JK+QSKRMDExwejoKLW1tU8+13V1ddy8eZP4+Hji4uK+8T14vMXGxpKUlER1dTU1NTXU1NRQXV1NV1cXExMTTE5OimYRQZAjEQbkYGlpia6uLgoKCrh27Rpnzpzhrbfe4uzZs1y4cIGgoKAnF\/yrV6+SlZVFRkbGky0zM5O0tDTi4uKeBIXAwEA8PT1RV1dn37592NrakpKSQnl5OT09PS\/c1r+yssKf\/vQndu3aRVpamoL+EsJWaWho4Mc\/\/jE\/+9nPePDgwZYeWyqVMj4+TktLC6WlpaSmphIeHo6pqSlvv\/02Bw4cwNHRkQsXLuDt7c3ly5efXPxjY2NJTk4mMzOTzMzMr3wPvv6dSE9PJy4u7smWkJCAv78\/np6eeHt7Y2hoyNtvv83JkycJCQnhypUr3Lx5k3v37tHf38\/8\/LzoICsIz0mEgRe0vLzM2NgY9fX1pKenY2JiwltvvYWXlxcJCQnk5eVx69Ytbt++TXNzM319fYyNjSGRSJienn7mrH9LS0tIJBIkEgnDw8P09PRQX1\/P7du3KS4uJjs7m5CQEKytrdmzZw8ODg7k5eXR0tLC1NQUa2trz6zd19cXKysrMZXtK8LFxQVTU1O53yHLZDJWVlaYnZ1lcHCQ+\/fvk5OTQ2RkJLq6uuzevRsbGxsCAgKIj48nIyODwsJCCgoKKCoqoqysjI6ODnp7e+nt7WVycvLJ53p6evq5l2FeX19\/8jqJRMLMzAyDg4N0d3fT09NDQ0MDRUVFFBYWUlhYyI0bN0hOTuby5cu4ubnxySef8NFHH+Hn50dKSgpVVVV0dHQwPj7O4uKi6DwrCE8RYeA5LCws0NnZSWFhIQEBAezZswdNTU1yc3MpLy+no6ODqakplpaWFHqCkUqlzM3NMT4+zoMHDyguLiYqKoojR47w4YcfEhMTQ3V1Nf39\/ayurn7j9YODg0xNTSmsPmFrDQ4O0tfXt+n9yGQyJBIJPT09VFdXU1BQQEREBFpaWhw9ehRXV1eSk5MpKSmhrq6Ojo4ORkdHkUgkLC4ufm8I3SoymYzl5WXm5uaYmJigt7eXBw8eUFpaSn5+PqGhoRgYGPDmm2\/i4eFBamoqRUVFNDQ0MDw8\/NwhRRBeRSIMPENnZyd5eXlcvHiRU6dO4ejoSGFhIf39\/SwuLm6LR5BSqZT5+Xk6OzvJzc3l7NmzaGlpceXKFXJychgaGlJ2icI2NDc3R0tLCwUFBaSnpxMcHIyuri76+vrExcVRVVVFX18fs7OzLC8vv\/Tt8zKZjNXVVRYXF5+E6by8PFxcXDh69Ci2trYkJiaSl5dHZWWlmKFT2HFEGPgWpaWlREREYG5ujra2NsnJyUgkEmWX9dyGhoYICwvjwIEDODk5ERUVRUtLi7LLEpRsenqaO3fuEBgYSFhYGPb29ujr6xMeHk5dXR2zs7PKLlEpZDIZAwMD5Ofn4+zsjJaWFgEBAYSEhBATE0N3d7eySxQEhRNh4CkFBQWYmppiYWHB+fPnaWhoUHZJm1ZcXIy9vT329vb4+vpy\/\/59ZZckbCGpVEppaSnW1tb4+fnh6OiIjo4O169fZ3x8XNnlbUvLy8u0tLTg4+ODhoYGQUFB+Pr6cu3aNfGkTXhliTDAo57ZmpqamJiY4ObmxvDwsLJLkrvGxkbOnz+Pubk57u7u9Pf3K7skQYGGhoZwd3fHyMgIPz8\/rK2tKSwsFPMRbEBHRwdRUVH4+Pjg5OSEtrY2d+\/e\/dZ+OYLwstrRYWB8fBw3Nzd0dXXx8fGRS2es7a6pqQlPT080NTVJTEx85ugG4eWyvr5OaWkpVlZWODg44OPjQ3p6uphTQk5kMhnV1dWEhITg5eWFmpoaYWFhO7Z5RXi17MgwIJPJKC4uRldXF3d39y0fp61sUqmUqqoqLC0tMTExobW1VdklCZuwtLRESUkJOjo62NnZkZSUJN5TBRsZGaGwsJDQ0FA+\/PBDPDw8GBwc3BadigVhI3ZcGJicnMTNzQ09PT3u3r27o++Mp6enSU1N5cyZM0RFRbG4uKjskoQXIJFIKC8vx8jICFdXVwoLC0U\/gC22vLxMQ0MDISEh7N69m5CQELq6upRdliC8sB0VBpqamjA2NsbGxkY8On1Ka2srBw4cwNTUlLGxMWWXI3yP5eVlqqqqMDc35+zZs1RXV7OwsKDssnY0qVRKd3c3np6e7N27l+TkZAYGBpRdliA8tx0TBm7cuMGZM2eIjo5Wdinb0uLiIk5OThgbG++4ZpOXSUdHB+7u7hgaGpKXl7ejn2xtVz09Pdjb26Otrc2tW7de+jkahJ1hR4SB1NRUjhw5QkFBgbJL2fauXLnC6dOnqa6uVnYpwtekpqair6+Pu7u7mEnyJXDjxg2MjY05f\/78juicLLzcXvkwkJqairq6OjU1Ncou5aWRnp6OoaEhtbW1yi5FAGZmZvDx8eHMmTMUFxcruxzhBYyNjeHh4YGamhqVlZXKLkcQvtMrHQYyMzM5ffo0zc3Nyi7lpZOVlcWxY8eoq6tTdik7Wn9\/P25ubjg6OvLw4UNll\/PyWZllaHCQ8bnnWD9BNktPYx11bcOsyLmMlJQUDAwMSElJkfOeBUE+XtkwcO\/ePdTU1OR2J7W+tsaadOcMG5JKpVy6dAkjIyMxQZGS1NfXo6mpSXh4uJw7CK7RWxiG+vu7efMzdVzC8+mde94Fth6S4h5AQEwDy8Dc8DBTKwDzlEeF4+xRxPQLVrP6sILoEG9S2h79jrKxSnz1\/clv\/nJPo\/eIjLtMatsGRrv038TJ25nIxu\/7+0npyHBGXf8cgYmVKKIRpq6uDlNTU65evaqAvQvC5rySYaCvrw8jIyNSU1M3v7PFHnJDrDjw+r\/zx326BOc28vxzuC0z3N7F2Eu6GNra2hrOzs7Y29uLjmpb7P79+xgaGpKSksLKirzvU8fJ0jNG5aAnacVZ+J09wuHz+UwArC8yOTbK2PQiT0ff9cUZJkYnmVuaoOFOKeX1IywttHLlpDbB11uZmZmkpayY\/KJWZlZWWFt7HC5kSKUrSAHW5hkfHWNq4at36euTVdjrnuZYYCMAk4VO\/PRv\/w\/Hrzz674nbIRidMiCl71FFy3NTjIxOMr8ie+oY66ytLCKZnmVpDZAtMzM1yUT1FVRNtPCq\/vq3dgXJ+Bijk\/OPalvt5sqhTzG6kEH78AyKWoexra0NU1NTkpKSRMdCYVt55cLA6uoqrq6uODg4bP7Ltj7GdWdNPjf0Jru8gbqbKSSmZFI9MMvc0qPTxeriDJKFL0\/WsiUmuttobuljemmJmd7beOw9hndmPaMzj6YuXZ7qp\/F+Ex2DM49OttJl5mYkjPV30dY5yrJMxsJYLw86B5lb+8vpeFXykKaGZjpGvjypyVaYk0gYG+yle2AKRU2MOjs7i6Ghobib2UIjIyOYmJgQERGhqCOQrG6Lk+2jTqIj6Wb8eO8Fqh72UhJqzafv7+P9I2ZElrSzuC5jdayKQGt9Du\/TIjgrn+yYZDJyK7ib6s7Hf\/+P\/PnAGSzOmaGnaUVwagU1eeF4hGfRuwpIHpDs7sHV0joKLlryycFDHDENorxnir88i5jmhus51E\/HMIqMOk8N3nvt5\/xfk1zmZAtUhniiezqGh6wjqc7CTfswr795GH3POGrG1wAJpbGXcNBT58gJG+JL6ikOd+LU0c85eXw\/b5w+Q3D9X54qyFYmqI5254uPP2D3JwaE3CqnMjeQ4z\/+CX\/4VI+gEsUOCWxra8PExIS0tDQxSZGwbbxyYaCkpARVVVUmJyc3va\/1nnSO7T5LRNlTY+9X24l08+diRgcATanOmCTWIl1b5MHdKHSPHOfkSUdyWlopjrDivf\/+Q\/5w1JLI4m4knWVcMlZhz7ufcuiMHVfr+xhquo6LmgmWZ8\/wyTunsAsIJ9DdkH37T2IWU8cy68z315LgaMj+o8c4ZnCB4s4J5h6W4H5KnZPHT6HtkM5DBZ5TmpqaMDIyoq2tTXEHEYBHcwi4ubkREhKiwKOMk2VgxBcf25OQkUmglQaHzyeRGenKgc8ucG9MQt9NTz48bkNK0wRd+S787pgvZb1TrC80EXnqHLaWN+mfqsTj7Q+wDMmntbOaK7omaJ9Ipv1BDPuPO5DaMstUXTwae8\/i4GjJ5yrB1A53kn7uMz5zjKX7qaf+I\/kBGOrYUDDYzhUVe3xcnTjx0QWquxqJ8DZD40oXLFdh8Zk2tpFVjI02cdnmNPtdSlhlmnT1Y+zZbUZ6ZSuN+QF89P450ho7qE8w5+0jKgQ2PD6YlJ6bAezfa09+zwSjVZc5cdoQt5QMAg58irFXKg\/GFf8UrKGhAUNDQ6qqqhR+LEF4Hq9UGJicnERVVZXbt2\/LZX9z5f78UtOX\/KGnHtOuNeGoYsbZ4EePMMtDT\/Nu4F1kiz2E2Jxh77k8pteWWV1bQjJQgPPu\/bhlNCOZaiHEXI9jlrlMsUp7shXvqvuSkXCREz\/V4Gr7MK0J+vzbax\/gXtzLQFEgH7zlTe14H2meZ\/lQ7TKtU\/1kWu1nv2s89WXXUP\/HD7G9WsXQhARFt0QEBQXh7e3N2pqiHqAKAImJiejr6zM3N6fAo0xzw0yV3\/3od7z74ceoOyTQOtDBFQ813rx479GPLFWg9Y4+bqntTPcW46p1lnNe0dypqyFezRpby9tIGCTm01MEFwwCUio9PDA8dJVxBgn5XIfzcbfJjzLnpFs4ThaH+b9qnqRlpRLmeIbjfql0Pf0rDudjaGqFQ5AfZ+zDyKosI9pAHY+YGALM9LjSI2O97iJ\/0HIksfvRM4XmBA92vxvKIOOknrbE7lwxK0B7mjX\/ZJrCKEBfJoa2Rly49\/iJmoT8oDP8yvkmj3oRtOJ01IizF1O4oqGNf0qjwp6yfV1qaiqGhoZioi9hW3ilwkBhYSHHjh2TW1vcQmUAvz3kRnbvU3cKshacNSwxDXs0MU91hAb7AotZYYmu4qs46hpi6hpNSes0rDQT8qEK0fWLMFaE9tkz6N4YfbSfrlQOvmGKr58\/Rn\/0oxlYq\/LnsLUT2UPAYD6mv7Dn5r1yXEwP8i8aXqSkJXHJzZizsTdpKknF7DeulEvk8qt+r8nJSTEyQ8GmpqYwMDDYgkmfRkjVtMXe4s5f2salD4lzVeUtl7JH\/z2Zx9EDRvgUfDmCYbWX9PPmaJw6ic4+C9xsi5mmj8v7viCsVALIKHdzw\/BgHENAX4IZB3TUUN2nT2pFOUHWp3nz7FU6BwfoH+xneGqetae\/putDJFid5f1\/\/jUHAtPpnp3j3uUT\/J\/fv8uJEwG0SUHaEMqe41YkdD56yb1IC\/6kksws46SctsTGooAFoCvNmn9RiWUcoD2GE9oa+NZ92YFQNkfxJW1+b5r1KECv3cPktAFWV\/NJUDuDT9J9hQfrx2QyGdbW1iQnJyOVPm8HTkFQjFcmDCwtLfHZZ5\/J9bGbbPw2em98jFlsw5f\/soKkNx\/rg4ZYeNcBq+Q672ePz01mHp9VJxsJUDmIhmsKfdNNBO89QXTjGizVck5fA83YR2eypXJ\/3j3hTnxkCIa\/96IOWKsO4NA5e9L6ZdB\/A5NfOHC7qY5Aa3XeN0ulf2yE0fExZpaXmai+hv6\/2nF7dOvu1KOioggICBAdnxTk0qVLREVFbcGRRkg6ZcJZzSz+spLBCg8r4zj5yQlUdM+ip\/0Fh9xSaJ9eobfoMhauXjha6KJhbMDZvcY4WBUyyxy5hvv5zZ7juORWUeDijNbeCPoABtP59P\/+D\/7Xu0EMrizSW3SZE4fPYOx5gfPWLlzOaWL+K01bMnqvmvLTXf8fJ0JqWAfGrtvyd7v+mj+6lD66W1\/sJcbJkH2famNirsN+NUO874wBw8QdM8bCLJ85YLE7D71PPuY9NWPMP3uffzvyVBhAhqQtF\/3PT3JM2xhD\/eN8bBXJvfEuEg9+jmtszZaFAXg0W6G2trYYNioo3SsTBhoaGtDR0ZHz49UlenL8UPvoXd547z3eefdz7CKyKYz14egbe\/nwMzWOH3iD3UFlLM\/0kRpii7aZFWpHj2MZXc7M0gRZhh\/xT388hG1eIw9uhXLo\/cMcP3mKT06oYZddQ+uNaPRe96IeWK3y5YCFLal9MujLw\/CfrbjVP8NgeSyqh0+ifs4eax1LLl1v4mFNGoa\/tqd4fOvuKB4+fMiJEycYHh7esmPuFEtLS+jq6tLQ0PD9P7xpywzXt\/CgafRr4+nn6a7I4VJQCCHXCmgeeXRZnOwqIz7+CqHRWZR3DvGwuZ32tgmkwFx\/FdHBQSTd72Wsq4um6gEWAWSztBXd4k7ryKOOgtJpmkoyCIuJJzEmi9KmoW+M5ZfOdFNyq4IHDx+1769L+rlXUEzDyFOd\/yZauR4fSUBYNKllnY+OxRIP61toa5v48knHCkO1uQQHXuJa2k2KmlrpnXn6e7LKcEMBkcEhBMZnU9UzC6wyeK+WzsEZtjrquri4UFpausVHFYSvemXCgJeXF1lZWQrY8woPm8rITk4kKfcuD4bmkEmnabpznfScO9S3t9M+sQhrCwy0V5KVnUVOSTPDc49OKfNDzeSlpVPSPw\/M0XXvNilpWdyo6mRmVcb63BT9baMsArL5UboeDjG9AqzMMND8kJlVQDZPX9NdMm\/couBWFW0Pp1lelPCwbfgrIw4UbWVlBW9vb4qKirbsmDtFVVUV\/v7+SCRb1O4jbBtlZWWEh4ezurpVvRWeT39\/P7\/\/\/e\/59a9\/LbZXYCssLHzm+\/1KhIH19XXeffddMZ\/+Frh9+za2trbKLuOVExkZSU5OjrLLEJTg4cOHHD16lNnZWWWX8sTy8jL\/9m\/\/xq5du8T2imzfNzz8lQgDAwMDeHh4MDo6quxSXnkNDQ289957YlSBnOnr65Oenq7sMgQlmJmZ4Z133tlW\/QaWlpb427\/9W6VfwMQmv+37psJ+JcLAnTt3iImJYX7++ecGFDZmZGQEW1tbBgcHlV3KK2Xfvn1cu3ZN2WUISrCyskJgYOC2Wjp8aWmJH\/zgB08uJD\/84Q\/54IMPxPaSbb\/61a92VhhISkoiIyND3K1ugenpaYKDg2lsbFR2Ka+UY8eOkZGRoewyBCVYWFhAQ0ODrq4uZZfyxNfDgJGRkbJLEjYgOzt7Z4WB8PBwbt68qewydoTFxUWSk5O5e\/euskt5pXh7e5Ofn6\/sMgQlmJ6eZvfu3UxNKWJ5pI35ehjQ0dFRdknCBiQlJe2sMBARESFOpFtkYWGBq1evUlZWpuxSXil5eXnExcWxtPSSrmr1NNk8Q739jM2Kxa2eR1tbGy4uLtvqvRdh4NWw48LA5cuXFRgGpKyuLLO8svoc44+XGenuoKNnjKXVNaTr3zHsb32azoY2hiTb58v\/vB6HATEuWr5GRkYwNzenr69vC462RmeOHwa6juS2Pz0vxyy18S5oGJ6nePDFmtzWl2cYeTj65bj\/dsK+cCE6p0d+Jb\/CoqOjSU9P31aTeYkw8GrYcWHgypUrZGdny38FMGkfiSbH+N2vfsObx3Sw942jtP1Zj\/LGybP2xlPHk3NnA0kqHuBbK1quw\/t9G+KLRr7xv2Z6u+gdmpHXbyB3MzMzREREUFdXp+xSXjnnzp3j1q1bW3CkWW5ZHeBvd\/0V77rdYOzLa9DacClW\/\/437PrpXoKaVgAZi5Ix+gdGkCw+nrRnndWVFVa+nEt4fW2ZpeVFem5F4XjMktz+OZZWG\/F6TQ+vSxUMjo4yvbh9LnLbzfz8PO++++62+z6JMPBq2HFhoKysjKtXr8p\/NMFCNQ6\/1sQrppj6+3dJ9DDnxEl7sp6sVbDEcFcTVQ0djC0AjJOmaY2FegT5NS30frnc8PJEL0319bT39DMyKWFprhrnX+vhF11Kc3ML\/VOPJhtZmenl6pnTnLWKoGZYwso2PIcODQ1hZWUlZiFUgPr6euzs7LZg4qExMg1sOfOHD3j9jDc5bYvACo3ZAZx4+1M+MNXH\/\/4Mc\/ezcdU8xOtvfoaOaxRV46sg6yXRL5Tw3G4A2q\/7YH0liouqH\/GbH\/yMDyyuUNlaQfCHqqgcPsOJI59yyjaKxuntNaHOdhEfH4+Xl9e2Gwm1LcLA+hor8\/PMLyywsLDAwtwCK9ItXvJ5bZL7Bde53TTM8871ujo3yejo+FMBWnl2XBjo7u7GwcFB\/vMMLFTh+FsTYm5\/eQc\/V4+rhjpHXcqRskRndjS2Oid475gW9sF5jC+NkWvohL22H77OkeSVDzA3VU+IlQ5fHNrL+x98hoZXJgMTDfj++Qgnj2lw\/PB+TlhH0CxZZex+Ilr\/+lN+8e8fYRhazNjK9lvrvLGxkbfeeovlZdEerAgODg4KXr4YYJQ0bUecjYJw19TH5ep9FuY6SbA3wvpSCOfczPG9noPzSX1so+4xNdVGlJ0KHzuV8P+zd95xVV3p\/vbOzf1Nu5PJzCSTmzYzyUwmiWOaJWrUxEqssUWNBQFBqiC9Su\/FQhFFBBEFQVEsiIUqIE0QpArSpPd26HDO8\/vDEo0aQZGjeJ7PZ\/1x9tln7Xfts8t3rfWu9+0TXsdW3gBtz1urSZK95Vm89zRJQdvZMkeG\/ak19HRm4zr3RxT1g8kpTMBRcyUqoc+Pp\/zzQkFBAZs2bXouV+aIXwz0cCPWH22Zdaxe9QMrVqxgvZY3GS0j20PqLwxm4T\/+zkz7aB77xBtoJf30XjQ2rGT+rHmsVrbmdF7DiIe3vpeXTgx0dXUhKytLfn7+8FbcmYr1ZB0OXCy\/\/Yc2cFJLDxnpUKobolBYqoz98auUZ4SgtXYZNklZROnbY6XqgOF329h\/JIPUcEfm6EcANXhoqfKjXSpwHccpP6K3K4bq8svYbF3J1nO1QC0hStJo6Ptwta5TrBfRo0hKSsLe3l7cZoxaysrKkJeXJykp6RkepY5QFVPMjC9yNcqWNTp7CAvzY6uyNxnXwjCx08Pc3YYlOrYcKb7lO5B\/1InZ872p7cnHQcn4p6ydB5RY7hfDjdgj2KwwIE4AkMeOmTrsOVYG1BHoLM26oOvPsD0vHu3t7RgZGXH48OHhn94cBsQuBgYq8VGfyZg\/\/ovZy9eydu0a5KyOU9gqoLm5lc6eW+dsoKuZmvrWWzkpBtqorqylrWeAgR4Bgq4+hAM9dHT0AgO019fR2HHrqdrTUkNFXevdrJ393Z109YqATmora7nj+1oTYcWH74xHI+TWSBi9TZQWlVDd9uBIl7A5iz2Gm1mjZoSZ3Bz+8N9jGGsWPqKJr37OSycG4KelWcN6Y90VA3fm\/qs5qK3FBt1IbmZ4MnHZBrbu8uOYvyeWdrYcys7lgq49VqqOGC8y48DRHHKjdzB9jhxWNuYorDfCPb4ByMJuij6HYhqg7wZ77WTYfKoa6CFSWx0rp3Cahq8Vw0Z\/fz\/e3t5ERUWJ25RRTVxcHNra2s9w3XkdoSommOhG09qdxbYF85k6TQq54GL6ys+hb62Lrfd2flQ05XDBrSs\/7YAB32w+QedAMa4btTHYlQNApNNqlnhHcj3SH\/NlRqQIAfLZ\/o0W7keKEIqqOOwozfrggmfUlheP3t5e3N3dcXZ2fq5CEN+L2MVAcyqGUh\/z9nx78u7JaDVQFY217GbMg\/PprI\/Dbosq245k0FCZhrvK90z6cjLf\/bAReVk99sSWUnnlENqbVVDXkEbq21ms03Am8PAuFOdMZ7qUMn5xlQzQSdL+bcgpqrNFYQnTvp6NnGMo5V29ZOzewN\/eXoFfTiuNWaex2LSa5SuWs3y9CUEpVdwrCYTd3XenEurCDHn3T++yxD2SzPP70TN2J75q5KfKXkoxkJycjKamJl1dXY\/febB0pmA5SY+A+BYA2q4eQnmtHHbRTfQUH+K7pRp4XMyjrr6ehpYOoJbT6tZYKjtgtMDs1sjAaTNmLtqAjrEzh45n3FKJvWlYf6WFz4Vq6LrObmtpFE9VA12c36KImeMS9NDcAAAgAElEQVQ5nkcXwtraWmRlZSVhn0eAo0ePoq+v\/4xC1NZyTE4PHdVw2ugnwWo+v31vPaHVQig9hZq+Mk5xOVzYpc+ipUps1VVmmbwWO+JrgS6yfW1Y8e1y1slrILt8Gt94xtFYGI2F1ASmyTkQnp\/IzjlauAXdQEQVB+3WsfLwMI\/avaD09fXh4uKCiorKc+13I24x0FV4AoWPfsdv\/\/QvJs+cyxwFD1KbhAjbr2AycyzvTJZDQ3ohH36pQMCVTALVZvPmP2ahZWnKxqlvMuZXn2MYdZ0kx6W8NuYVPl2ihNqG6fy\/Ma\/y8Teb0FZexb\/\/8Cem65+hsauA7V+\/zpgxn7BWT5\/Ns\/7JmHd\/4EDBDUKVpvL38QacTzmN3tyJzFRy5ewRCz577Q0m6pyg8d6+Z08Fp\/eYsGHNEib\/+yOmye0lp6aSk8bzeXeyGmcrRj4o3kspBjo6OpCRkRler9zea9h\/PYXPxs9g7tz5fLdKFZfAWOr7gb5qTjrrsmSDOoZGRuhr7ya9sYJzenZYqTtivNSCA8eukXrGihlLlbFxssFAywSXw1do787E4Rtd\/KJqoLsAT1s5FE5VAAMUHbFkwYQvmG0ews3nbFr+1KlTuLi4PFdLoEYrPT09HDhwAC0trWcwp9xLbU4hBfkN9AM9DUUkZJTTAdBVT35BLqUdQFsRUSGH2XvgCGdSS+i68+DrqSbh5GG8\/UKJu3KFK5VtIOyi+HIo3gdCyahrpCLzBhV1XUAP1aU5XKt5vhzkxEFPTw+enp4oKSlRUPB8j5SIWwxUnbNj6mtvMlHGAEcnW2x9IikRiIA+rngr8Zcxv2LMO3MwC6+i\/8Zhvn3vA+Y7JwMQZ7eU341dz4nr1wlY+yWvfriBU8Wd3Dytxx\/\/NA6lwGL6K0+w\/KNPWGQVTePN82x456+MlfanHchxWsNv\/7KIXbERWM3+mI\/XuRDoupH\/HfP\/eG\/KQn5YNI1PJv6A7dnr9NwrBrpucnafJSqbVjD+jTf427dmxFd30lpZQHZhzU\/3zwjyUooBgBMnTmBsbDyMNfZQX5xFUsIlYmMvkZxTQcc970FReyXpiVGER10mPf0GTT29CGrqqauup6asjuamm4Q5yLLJ6QSZmWmEOqiwQcWG5MZu6ktqaO7oB1EPjXUVVNyegxL1NJEZHcbZtGIEz9E7t729HSkpKdLS0sRtyktDb28vwcHBaGlpSaZmXnAaGxtxcHDAzMzshRhZE68Y6CJx51p+89Y0bFIE93\/VUYC30jReGTOGMX9fhGdON02RRvzzjUlsu1CNqPUKJjM\/5O\/z7LlWnIjGjI\/5WPEw9QMCzhnP562xykR1QHu0JRP\/PBntwBvUpbrz6TvjkA0qA6rwkp7EH6Zocjr6CD98+SlSZgfYrTyT195dhOF2T3yDThKfXc29iwVEwgEG+m73\/PvK2L9qLK+M+RL7mOqHLy8fIV5aMdDY2IiqqipxcXHiNuU2nVwNtmCNgi42dhZoq27F7nAiLeJfcTJkvLy8cHR0fK6ipL0MiEQi4uPj2bRpEwcPHhyBZYcShpsrV66gra3N3r17aWlpEbc5g0KsYqCvnP3KExjzv+OQMXdj3253\/CJyaG2v4rztBj77dDH6Ntp888YHzLeIIjvCmi\/+8i6zFUyx0ljMq2N+x2TFUxRl+TDjzff53imRzt58bKe\/zzvTd1FBP0lWi3j11dm4Xm2k4Jgar\/\/xDSbKmbLdQp4v3\/uMH5yiuXbWjI9f\/5Kt\/tEE6szlL\/83EyOfk4SFBHIsOovGex6FrUWxuG\/Vxmn\/IQ44qzPxb3\/mrW\/1OHjAjkWzFmMVJZ7Ebi+tGAC4ePEiSkpKdHZ2ituUW4j6aK0sIC0zh+LqNkZ6mexwkJWVxYYNG8jNzRW3KS8tJSUlaGpqYmJiIhmdeUGoq6vj6NGjzJs3j8DAwOdy1cCjEKsY6LpJiM06Jk+ZwtSpU5gyZQprt4dRkBWOwYrlqO1JoaujEHf1VSzSD+TGzXRcN87mP+Omslp1CxvWyWJ4NI+qqz7IztuMZ2wtA12ZbF+zhPUulxmgjQh7VebLOJPaUsc5nVm8+dYEZi+czmfjvmLFtiCKe4WUnbZmxXw9Tua10JgdgvqCr\/hs3Gd8OWEBul7xNN3zd\/bVprJD\/ju++PQ\/jB33Gd+uMyc8v5bck3bMXrSFQ5niEYEvtRgQCATY2dnh6uoqblNGBQKBAE1NTfbv3y9uU156hEIhAQEBbNq0CT8\/v2fkXChhOIiOjkZXVxcjIyOuX3\/xllWK22dgyPS1UVvXwpD99buuoP+f13ljwQ7yBQKa6lrofdS+wg5qSstpEDzqKAO015VTVtcu1qmBe3mpxQBAYWEhK1asIDAwUNymvNCIRCK8vLywtramre15XN\/wclJQUICdnR0GBgYEBQVJUnc\/RyQmJqKtrY2Ojg7BwcHiNueJeeHEwJNSn4iNzBqU9ibwnIwlDysvvRgAiIiIYNmyZSQnJ4vblBeW0NBQFBUVKSkpEbcpEh7CxYsXsbe3R05OjsDAQHp7H9mnkfCMSUtLQ1VVFWNjY+zt7WloaBC3SU\/FSyMG+gQ0NjTQ0jk6BbVEDNzmzJkzrFmzhtTUVHGb8sJx7Ngx5s2bx9WrV8VtioRfoKuri0OHDmFvb4+6ujqHDh2isbFR3Ga9FPT395ORkYG8vDwaGhq4ubmNGuH80oiBUY5EDNxGJBJx8OBBVq1aJREEQ+DEiRNMmTKF8+fPi9sUCYOkvb2d+Ph49uzZg7KyMtu3bycnJ0ey+mOY6e\/vp76+nnPnzqGsrIypqSnBwcEUFhaK27RhRSIGRgcSMXAPQqEQPz8\/1q9fT0JCgrjNea7p7+\/nzJkzbNy4kQsXLojbHAlPQHd3N9evXyc0NJQ1a9agpKTE+fPnyc3NlSxLfEJ6e3upra0lNTUVV1dX5s6di4eHB4mJidTWPpiGfDQgEQOjA4kYeAiRkZHMnDmT48ePSzLuPYSWlhY8PT2Rk5OTLCEcJbS0tJCRkcH27duZNWsWjo6OnD59muTk5Bd+TvtZ09HRQV5eHpGRkQQEBKCsrMyWLVuIiIigvLx81D9DJGJgdCARA48gNTUVeXl5duzYQX19vbjNeW64fv06BgYGmJiYSJarjVLa29u5cuUKZmZmLF++nL179xIQEMCpU6fIzs5+6UcNhEIhFRUVJCQkcPToUfz9\/VFTU2Pt2rWcPHmSmzdvvlRhuCViYHQgEQO\/QHV1NU5OTigrK3P27FlxmyNW+vv7CQ0NRUFBgb179z4\/gZokPFP6+vrIzMzEzc0NFRUV7Ozs2L17N7t27SIoKIiMjAyam5vFbeYzpaenh6KiImJiYvD19cXDwwM3NzdMTU0xMzPjwoULL\/XoiUQMjA4kYmAQ+Pr6Iisry65duygrKxO3OSNOamoqhoaGaGtrExMTI25zJIiR6upqIiMjcXFxwcbGBg8PD3x8fNDV1cXBwYEzZ86Ql5f3XGfZ+yU6OzspKysjISGB\/fv3o66ujrOzM7t378bd3R1HR0cOHz5MVlYWfX0jn2b2eUQiBkYHEjEwSEpKSnBxcUFeXh5\/f\/+XIrBOZWUlO3bsQFNTE29vb8kyNAkPUFtbS2ZmJt7e3nh4eHDw4EEOHjyIm5sbGhoabNiwAXd3d8LDw0lJSaGoqIiGhgaxvkibm5upqakhKyuLuLg4AgICMDQ0ZMOGDVhbW7Nv3z4OHTqEt7c3rq6uREREUFRUNLwpz0cREjEwOpCIgSEgFAqJj4\/H2dkZeXl5goODR+X8aW1tLa6urigqKuLh4fFChkiVIB7ueNMXFBQQFRVFUFAQp0+fJiQkhICAgLu9602bNjF79mzk5ORwd3fHy8uLgIAAYmNjuXTpEjExMSQnJ1NUVER5eTllZWX3laqqKsrKysjLy6OsrIzq6mry8vLu\/j4uLo6zZ8+yf\/9+vLy8sLCwYPbs2axYsQIrKyvc3Nw4ePAgAQEBd+07efIkaWlplJWV0d7eLu5T+cIgEQOjA4kYeAIEAgEpKSnY2tqyefNmgoKCKCsre6HXaQsEAq5fv86ePXvYtGkT7u7u5OXlSYZCJQwLPT09tLW1UVtby82bN8nKyuLy5cvEx8cTFxdHfHw8586d4\/jx44SEhHDixAmOHDmCq6srTk5OODs74+zsjIuLy935+tmzZ\/Pxxx+zdOlStm7dyp49ewgPDyc0NJSQkBDCwsK4dOkS8fHxxMbGcvnyZdLS0igpKaGqqorm5mYEAsELlRToeUQiBkYHEjHwFHR2dpKbm8uOHTtYs2YNLi4upKSkUFVVxcDA8597uKuri7KyMi5evIiRkRHfffcdR44cobS0VCICJIw4QqGQ3t7eu6W7u5u2tjZaW1tpbW2lra2N7u5uiouLWblyJTIyMsjKymJgYMCMGTNwdHQkJSXlrih\/mTz6xYlEDIwOJGJgGBAKhdTV1XH69Om7MceDgoKIiIigoKDgufK8b2tr49q1a5w\/f56DBw+io6ODsbEx0dHRL4UfhIQXm7KyMvT19dm7dy8ikYgff\/yR2NhYuru72bVrF99\/\/z179uwhJSVF3Ka+NEjEwOhAIgaGGZFIxPXr1\/H29kZNTQ0rKyt8fHzYtWsX4eHhlJaW0tHRMWL2NDU1kZ+fT2hoKM7Ozvj6+mJiYoK2tjanTp16Yb2+Jbx85ObmsmXLFjw8PO5u8\/HxITw8\/O7npqYmTE1NkZaWZu\/evZKgWCOARAyMDiRi4BlTUlJCWFgYW7duxdLSkn379uHm5oalpSVOTk6Eh4eTkZFBeXk57e3tT5RNrquri6amJgoLC7ly5QonT57E2toaW1tbvL292bNnD4aGhujp6REbG0tTU9MzaKkECc+OzMxMtLW1H4j3kZaWhq+vLwKB4L7t1dXV7Ny5Ezs7O9zd3SkuLh5Jc18qJGJgdCARAyOIUCikqKiIqKgoPD09sbGxYe\/evfj4+ODl5YWnpyf29vasXr0aGRkZFBUV2bx5My4uLpw8eZKQkBDs7e2RkZFh\/fr1rF+\/HhUVFbZv346bmxteXl7s37+fffv2YWtri6+vL2lpaZJIgRJeaM6ePYuqqupDk2G1traycuVKbt68+dDfFhYWsnv3bmxtbdm+ffuozQ8gTiRiYHQgEQNipru7m\/r6eoqLi8nOziYpKQlfX1+MjY159dVXeeONN5gxYwbr1q1j9+7daGpqoqGhga+vLz4+PoSEhJCSksK1a9e4efMmjY2NL4TzogQJgyEsLAwtLS0iIyMfuY+iouJj02fn5OSwe\/dutmzZgqurqyRmxjAiEQOjA4kYeM7Iz8\/H2dkZW1tbYmJiKCkpobS0lJMnTyItLY2CggLR0dHiNlOChGfKwMAAwcHB6Onpce3atV\/c98SJE4SHhz92iaBQKOTq1at4eHggLy+Pt7c3dXV1kqWFT8moEQPCDioK8rhR3cYD3SlRJ1U38imsbKH\/KS8XkUjEg5ecEKFQvNehRAw8B7S2tpKWloaNjQ0KCgqEhYU94NgnEolITU1l\/vz5rF69Gn9\/f0pLSyUPMgmjjoGBAdzd3VFRUaGwsPCx+1+7dg1LS8tB+9v09vaSm5uLq6srMjIyHD58+JHTDBIez5DFwEAtl47uxcbCmROZ1Q++eJ8Bwp426uua6Pylg9VfQv6b\/zDB+AQPZNtoSUZj7qeM0zxE1dDduu6jpy6HU4eDiCm5HdFSVEP04QDCM0bmXDwKiRgQI3V1dZw\/fx5LS0t0dHS4cOHCL0Y0LCsrQ0FBgby8PBwcHFBSUsLPz29QD0wJEl4EWlpasLOzQ09Pj7q6ukH9pq6ujtmzZw\/ZMba\/v5\/r16\/j6OiIgoICR44c4caNG09i9kvNUMXAQHEoaya9zpgxY\/hE9TAVD4Q0GaCrXUD3PW\/Gga4OBF39P9uti9bmNu6+m0UDDPT3IQIQ9dHZ2cOdrtJ1f3UmfvAd9lFV97xwRfR0CmjvuG1AfQTLP\/4L720NpKGnh67ue47XeAnpz9\/gLzJ7qei\/c\/g2mtuGHqJaJMjHVXMjy03C6AB6Uj1ZsdSI4zktP+3UI6BVcK\/qEDIg7L9ru0g0wIBogOHsCkrEgBioqqri7NmzmJubs2XLFs6fPz+oACmJiYl8\/\/33dz+XlJRgYWGBtrY2Xl5ekrDBEl5o2tvb0dPTY\/369UPKhNjd3Y2joyNZWVlPfOz8\/Py79+ORI0coLy9\/4rpeNoYmBoRc99Xgi3Hj+GLCh7wxVonQ4p8itwrbizi125B13y9jw9Y9pFU2UZJwCO2NP7B0zRb2hl+nB+i6GYe7oTqy0gro2PiTXt8LokrCfbzY4+2Ls5EKa9arsiv0KlUlMVh\/909+M+aPjJsjj1d8Me311zlip430yqV8v3YLruF5tFbHsHHCu\/x19ho0FGVYv0kPnwuFdAM0xyM36R3eUfShVthDZWIgxoqbkJXVwNH\/EtVdQ3ktCyk\/68Kq79U4klfCCYNNKG4\/S0MfQCdFyaHs2r6TXS7b2XcmhbpeEDbnc8bzLPntt\/bJPRPO2fN5DOcidYkYGEEqKyvZv38\/tra2WFhYcPHixSEN80dFRWFra\/vA9vz8fCwtLTE0NMTV1VXSu5HwwlFfX8+2bduwtbUdchwOoVDI2bNnOXHixFPbkZWVhYODA\/b29gQFBUlWHwyCIYmB3kJ2rJrOF2t3EhW2nal\/H8d630yEAAPNXHCR4d13\/s3c1TLIrlLGzsWGxeM\/5h\/TlyG7djVaLmepbMrHZd1sPpXaioe7Ncu++JJvdcJp78nHaO5n\/P7VscxasYaFk97jN+\/PZ5v\/IewXfcxr\/\/UXPpsjz56L+dRmHEd19UbUt2oiM\/cTXv\/7GrxPHkNuxvv86o\/\/Ytaylcwe+yavfSrL0Zt90JmE3KT3+LuKD9mpR1k3ZSrfqe3Ay0aece\/PQjsw7\/YIRR+dAgHt7e0IBIJbpb2d9s4ehPc+67sKcNXazBKp+UhttOF0bhsgpDH9KKoL16LkHsyZQAc2LFmH45kcGnLPoPGRIReqAOo4KquPrvpZhtMNViIGRoCGhgY8PDwwMDDAwsKC+Pj4IdchFAo5d+7cLz7w0tPTcXJyQk9Pj+3bt1NVVfU0ZkuQMCIUFxejoqKCra3tE8XZAEhJSWH9+vXDZlNqaio7duzAzs4Of3\/\/B+IYSPiJoYiBzqwDLPz7v5mlfZz8rBB+\/ODPvLPQjTKApjhUpv6T91d6cuvJVceJbYv4w1uL8M67fayuDqpiXfjirb\/wkZQcxoZqzPvkDf40wYRrVdexWfxPXpmiTWon9Ca68PHv\/8bigGwqzugw7r+nYHPpttDsrSc\/PYW4M0ex2TiBP42ZitFOL9bM+IB3pffRCFQEqPPX333C5jP10JeK\/OS\/876iO8HO6\/njq\/9ASkEfY42V\/ON3f+Fr9eO3Xswt1zjsaIu5uQWWVlZYWVlhaW6KpWc4xS33z4c0Re9k5tv\/ZJHTRdoBRG1ccNdmxsaguz4LKXbrWWLkRWpSGEYTzImsBqjnhJIJxjrnGc6IMRIx8Aypq6tDX18fVVVVfHx8iI+Pf2KHv+7ubry9vUlMTHzsvikpKbi7u6OhocHBgwdHNOKhBAlDoaioCAMDAzw9PZ8q0VdpaSnS0tLDnkU0JSWFHTt2oKamRkBAwAudjOxZMXgx0EP6XlX+PuZX\/OH\/PuTTzz\/k9V+N4bdvria4QgjV4Uh\/\/g8mKQbd7vEOEGW1gL\/+az0n7vHvrLtgyidv\/ol\/TVvCBhk5NqtsxeFAPC3NuZh99wGv\/biTcoDs\/Uz\/\/fvM804i\/4gqH\/9qEmbn6wGoiNnNitkLkd6qh+LCj\/nj\/3yLubs3P874gI8NTiICGk+Z8I9ff8KGE1XQl4rC5L\/xj8078TH7nj\/+6e\/MWLoOOTl5VDW24ReZTydAVw2ZcTFERkYRFR1NdHQ0UZERRCUX0NT1s6ng2nBWLVBAL6jk1mdhPSE7tjDe4CJ3rrJsL1nmG7tzOe4UhuNNiaoFaOaUyjaMtSVi4LlGKBRSW1uLra0ty5YtG7Y46QKBACcnJwoKCgb9m7S0NA4cOMCWLVs4fvy4JPeAhOeK1NRU1NXVCQ4Ofuq6BAIBvr6+pKenD4Nl99Pd3U1sbCxWVlYoKSlx\/Pjx5yrfiLgZrBgQtV3DYsmX\/PWrzew\/c46zYec46izL67\/\/P6R2XUXUm4\/9ys\/5\/dg1uJ+6yNmDOzGUm8df\/vQf1joe5cIxX3xDY8m\/EszKjz9gwkYXIlOvkHDxApFJJQi78zFd+BH\/\/ZYUNgePc8B4GX\/685doRRVTGqjGR2PeY6VFMBmV1UTaL+M3r03HNjyZsxaLeW3MVxjt9GL11Pf4n7Eb2HssBBe5afz6nTk4pHZBdyIbv3yTdxQ8uOinz9i3Pkd25xkyMpOJOHmepIIaeoZ64qrPsuK7zWgHFd3e0EHqAVNmz7Mhob6b7s58vNavRXPHBW4WXER\/who8kurobriM2aw1bNaLoOUXDzA0JGJgmOjp6SEnJwd3d3cUFRU5dOjQUzk0\/ZyqqipmzJhBS8vQ\/v47y6i8vLxQUVHh9OnTknlQCWLn0qVLaGhoEBYWNizZBQcGBjh27Bh79+4dBuseTltbG8nJyWzbtg0lJSVOnz49JEfH0cpgxUBnXhAKU79B1vOeuBH96RjP+orp6wNoAroyj7Bx5md8NPZTPp+niG9kLAcMVvPhv8fy6X+msdkthk4g74g5i8d\/ycTJk5k8dTG6\/nlAOTZLP+X\/vfcZUlMnMfZfn7HU8Cg1ABUX0Jz\/KR98NI1tobnU5YaiMutLxk+Zx9LF8xn\/jQruh0LQW7+COd8tZ8nkcfzrw6nI74y5PYSfh8WymcyzCqetq4Lj26SZ8tkEJk+ZwmSpLfinP8FruTYC+bXamIeW\/nQ6mvM5rC\/N9C8nMGHCNFaoeZNR24twoIno7cp89Z8vmLVmAzLLNNnpFvfgEsinQCIGnhKBQEBmZiZubm7IysoSGBhITU3NsK\/\/z8\/P59tvv6WnZ8j6E7glCoqLi9HS0mL16tWcPHlSsrZawogjEok4c+YMWlpaJCQkDGvdp06dYtmyZcNa58Po7OwkNTUVPT091NXVCQsLo7q6+pkf93llsGKgpbGWi+cuEB2XSHJSEklJSSQnJxIXcZEL56OJT0rmSkoikScO4rbTnQMh54hPSiEpJgy\/3btw8z7Cueh4klJSSUmM4\/xRH1ycXfA4cIzz0fEkXTrMj5\/9hf\/6aj2u3j54efkTFpNIakoyyUmXiTx1CA+PfYScu0RS0mUiQg\/isdOTgFPnuRAZTUzsJaIiI4mKPM8JP0\/cvI4QkZBMakoSSYkJxERcJCI6jqSUFJLiLnLM1w17JzcOHD1LdNxlkm63KTMzc3AnTthPZ0cX3X33i2FRbwvFWelcuVpAw73rK\/vaKMvPJPtmA4KObvp6+yVLC58H2traiIiIYPv27RgaGuLv7\/9MQ5xevXoVd3f3pw41LBQKyc7ORlpaGjU1Nfz9\/SXLqCSMGIcPH0ZaWpqkpKRhrzsvLw8DAwMaGhqGve6HIRQKuXz5MlpaWujo6HDmzJmXMgnYYMVA+tVM5klJMW\/ubGbNmnW7zGaulBRSUnOZPWsWs2bPYZ6UFFJSUkhJzWPO7NnMmTvv9mcp5s2dw6xZs5g9Z+7dbVJ365zOxM\/GMvaLycyce\/\/+s2bPYd68W9vmzpn9s+PcOtbcOXf2mfezeu+x83Z999okJTWPubNn3W2TjIzMyP4Bw4REDAyRzs5OwsLCsLCwQENDg+Dg4Cf2gB4K58+fJygoaFhHHFJTU5GWlkZfXx83N7cRe4hKePkQCoUcOHAAVVVVioqKHv+DJ6C5uZl9+\/Y9E6HxOKKiojAxMcHe3p7z58+\/VE67z084YhHNlUXcqBxeJ9KXBYkYGCQCgYCDBw\/i4OCAmZkZgYGBI+ZEJBKJ8PPze6IliYMhMjISVVVVtLS0cHFxkThHSRhW+vv7cXV1RUVF5ZlOTQmFQnbv3o25ufkzO8bjOHv2LE5OTtja2nLu3LmXImnY8yMGJDwNEjHwGLq7uzl48CDa2tpYWlpy7tw5urqGHoLyaRAKhZibm5Obm\/tMj3P69Gm2bduGjIwM7u7uT+yfIEHCHQYGBnBxcUFfX39EpqOCgoJQVlYW60u4t7eXc+fOYW9vj6amJpGRkaM6h4hEDIwOJGLgETQ2NrJv3z60tbXx8PDg4sWLIy4C7tDX14eUlBQlJSXP\/FgikYiwsDDs7e1ZsmQJhw8fpq\/vgeDhEiQ8lrq6OgwNDTE2Nh6xlMF5eXm4ubkNOq\/Bs6SxsZHQ0FCMjY3R0NAgPj5+VI4USMTA6EAiBu5BKBRSV1fHwYMHmTt3Lrt37yYjI0PsgUYqKytRV1cfUeekrq4uoqOjcXBwQF1dnYSEBEkUNgmDpqqqCisrKxwcHEZ0+V17ezv79+8fltgew0VNTQ0XLlxAU1MTXV1dEhMTxdaxeBZIxMDoQCIGuDWnmZubS2BgICoqKuzYsYOsrKwRcQwcDNevX2fXrl20t7eP+LE7OzvJzMzE2toaXV1dLl++PGK9PAkvBu3t7fT3\/5ThLS8vD319fby9vcUyquTu7o6Liws3b97k+PHjVFRUjLgND6OpqYnIyEiUlJQwNTUlPj5+VAQCk4iB0cFLIQaEQuFDe\/cDAwNkZmayZ88eNm\/ejLu7OzU1Nc\/FUF57eztNTU0IhUIiIyO5ePGiWO3p7e0lKysLQ0NDtm3bRkREhEQUSKC7uxtzc3P8\/PwAyMnJQU1NDW9v7xGdJ+\/r6yM1NZWjR48iIyPDe++9xx\/\/+Ed+\/etfk52dPWJ2DAaBQEB4eDibN2\/G1taWqKioF9pp94USAwNNZF9I4UZtx8\/W6HdQGJtKdmED\/Y\/46Whn1IuBOw5MBw4cuLutv7+f2NhY9u3bh4GBAYEZ\/fkAACAASURBVI6Ojs9dVL6rV6+iqanJtm3bWL16NUZGRpw+fZrCwkKxPziioqJQUVHBysqK06dPP3TEor6+\/rkQVRKeLVlZWXzwwQe8++677N27FwsLCyIiIkbcjo6ODjZt2nT3YXanTJ069bldMtvb28uxY8dQVVXFxcXloUGYuru7n3vR\/UKJga5UzL\/Yim9kxc\/EQBEe87Ziv\/sqo2cCZ2iMejEQFBTE\/\/zP\/7B8+XLa29uJjo7G0tISLS0t9u\/f\/9wGCWlubmb27NkPPNw0NDSGHJL4WdDX18epU6fQ1tbGwsKCkJCQu9Mq\/f39KCgoEBQUJGYrJTxr3N3d+dWvfsWYMWN45ZVX8PT0FJstVVVVLF68+L77xdjY+L4pjOeR3t5e\/P39sbKywsXF5b4lxJGRkSxfvnzwUe3EwODFgJCe7m76BvrpbqunsupOPIBe6qpqaeu9f9\/WugqKS6tpu92n6O1opaVF8FPPvb+T1pYO+n82ACVorKS4pILme2eohJ3UllfR2piE3RR9DkZV3v6ij8bKKlo6cvFaYsD2PRIxMCrFQEZGBu+\/\/z5jxozhnXfewcDAAHt7e3x9fZ+7kYCHYWFhcd+D7Te\/+Q1nzpwRt1n30dnZyfHjxzExMcHQ0JCwsDAiIiL4wx\/+wIcffjis+RkkPF80NjY+8PL9+uuvxRL05w4FBQVMmjTprj0+Pj5is2WotLW1cejQIezt7VFTUyMxMREDAwPGjBnDxo0bn9uOy+DFQBfZIQex2LAFAxtjNJRUMffxJ2CXJfpb1dA09SStEaCac+7OmBlbYGliiL7xEW50Q8e1YMwcdnGh\/JYcKIvzRs31IrV3ByDrueTjhpm+GdYWRmhr+3KtZQBhw1X8bHVQ1TZEb+sG5vyfPIdTmqHnJud2G6OiYcA2403MfXs1zv65Q084NEoYtWKgsrKSr7\/++r4H1YIFC16oGOJRUVG8\/fbbd+0XZzCVx9HR0UFYWBi2trZMmjTpbm9x4cKFz+1DTMLTce7cOX7zm9\/cd4+99tprODo6ijVGRUJCAh9++CFjxowhOjpabHY8KfX19ezbtw9NTU3eeustxowZw69\/\/Wt27NghbtMeyuDFQDsxJopMf3c5OyLiuXRAhy8+WYTWjhDiInyRW70KuYBioIX0i1HEJ2eQdekgKvMXYprQjKjxIkpLpNHzK2CAZo7rLGPVzhgEd0YGRE1ci4khJj6dnLQT6C5agEFYGoleZny\/0IDghEQi\/U2Z\/zcFApKrKI7yZMU3iuyNSeRS+HZW\/ms1VgdyeT7cxkeeZyYGhEIhnZ2dtLe3j3iprq5GVlb2gSH2t99+m9jY2KeuXyAQjMjQY3t7OzNnzuS\/\/uu\/+OGHH57b9f4ikYiBgQGEQiEeHh4PnHcLCwuxXQvDUYbzxSYSiejq6hJ7m562NDc3o6+vz5gxYxg7diw6Ojp3M3VWV1fT399Pd3e32Ozbu3cvCxYsID09\/anqEQgEv3jf9fb2PpPrrbe3FyUlpfvuo1dffZVTp07R0dHxTI75pAxFDERv02D9MleKASpDWfhvJQ7kCIFKtmsqMtfkEgPAQEMBcaEH2G6uynffjmfDsTKgm4umyigYHya\/4CLqs9U5knl\/R6O\/tZTksEO42umyfM54fvQMwlVTmYX6cbd2EGZg\/bUhQVE5hLnrMGF10O00wBV4LdHFcU+GZJpguMWAjY0Nn376KV999dWIl4kTJ\/L222\/z17\/+lTfffPNueeONN\/jPf\/7z1PVPmDCBxYsXP7Td9fX1FBcXU1RUNCxl5syZvPLKKxw7dozS0tJhq\/dJSnl5+UOdFy9fvszHH3\/MxIkTeeutt3jllVfull\/96lf8\/ve\/5+OPP2bSpEliuR6etnz44YcUFhYO5fJ\/JEFBQXzyySdib9PTls8\/\/5zp06eTmJhIZWXlA8twL1++zEcffSQW26ZMmcJXX33F2LFjmThx4lPVNWHCBBYuXPjQ\/7K5uZnx48c\/kzaMGzeOP\/\/5z\/fdS2PGjOH1118flmfYz8snn3zyxEswhyYGtJBd7koBwI1gFo3V4HBWFwiLcFTbzALTZIQD6ViuU8fM4xSXLnij8P0MpI\/dymchSPZgubwmegabWGAYSOG9oU86M3HarIGubRCxcUHorP6WdZ5H2KGhyDy9S7f26UjCdJI+QVHZnHbT5PNVR26nAb6Bm5QmjnslYmDYxYCMjAzW1tbU19eLpbS2tiIQCB4ojY2NT113QkIC48ePf6DNAwMDjB8\/nokTJzJ16tSnLl9\/\/TWff\/45n376KdOmTRuWOp+m\/POf\/8TBweGBdh87doxFixaRn59\/Ny67g4PD3WJmZsa+ffsoLS0V2\/XwNGXSpEkkJiYO5fJ\/JHZ2dkhLS4u9TU9bjh49ypw5cx7ZzuDgYKSkpMRmX2NjIy0tLTQ0NDxVPYmJiUyYMOGhbSwvL2fcuHFUVFQMq+11dXWcPHkSa2tr7O3t795H9vb2WFpaEhISQl1d3bAe84svvnhi\/57Bi4E2LuqqsHqeE3kA1w\/x7bsK+FztAGEhlnLr+cYwib7GcyxdsBHtQ9FcjfBi3Zzx\/BBYcKuK\/kJ2rZfinT+MQ\/tkHveN2dRcZP2KDci5hZOeFITagvEs9YkiwcOMxd9o4R+XQMShbcx9eyN+iRUUXdjN0vGbcD0Xx+Ww7Sz7x3K27bsmmSYYbjEgLy8vVs\/iZ0lRURFff\/31A9u7uroYP3485eXlCIXCYSk9PT0IhUJEItGw1fmkxdzcHE1NzQfaHRISwg8\/\/DASp14szJ49e9ic4hwdHVFXVx+WusRJbGwsUlJSj\/z+6NGjrFq1agQtejaUlpYybdq0h35XXl7OlClTEAqFD\/3+RWLGjBkjIAY6yfTzZofVaaoAKi9hunk3kSU9IKrmqMcuTPzyAAEJ+41YvmoV0utV2WKoy\/bLP\/l65e6X5aMpihy9\/vNRynbSjtiwZs0KflynxlZdDWzjKhC15uKvu4avp33DOhM7nHW8SSxuh\/5qzjurMmva1yzVNMFW15NzcaUSMfAsxIC7u\/tTGfe8kp+f\/0gxMHnyZGpqasRg1bPHxsYGHR2dB7aHhISwYsUKMVg0MsyaNWtYxcBzvQ57kERGRj5WDKxcuXIELXo2FBYW\/qIYmDx58qhI6DV9+vQREANDo7+tgwe9NZoJ01nHSqPDlDwiSvyAoOMhL3QRfY\/w8xL29760gYbuRSIGnoDHiYGqqioxWPXssba2loiBp0QiBl4sJGLg8Yxo0KGuPPxNnDh8seghQkHC0yARA0+ARAzcj0QMDB6JGHixkIiBxzOiYqBXQHNrG93C0ZsSWlxIxMATIBED9yMRA4NHIgZeLCRi4PG8UOGIJTwSMYoBEX3dbdTX1lDb0Iyg+3GzNj1U5WeTmVtDr6iXtqZ2Onsf4bjTW8e1hGtUtj\/+Jh3o6aKzu4+h6EyJGLifwYqBgb4OmuprqalvpK2j5zHnvJ+WigKuphZS39pOm6D3Eft3UJKRSW5xI7+cCUFEf1c7DTXV1DV3PGbf+xlpMTDQ20VX70\/tFfX309N5zzUq7KWj59b9Iuxuo766ipq6FrrvvR1EA\/R2d9Lz81itiOjt6qK796cz0NfVRVdP\/5DugeEQA8K+brp7erhrtrCfns7enz6L+ujsuf25r4PGmmqqa5vo7LvXUiF9PZ109z34LOjvvtMuEX0dLdRV19AoGNqL+2nFgGigj+6uTn4yWUhvZy8Dd3u2A\/T09tArAuilraGGquo6WjvvHwQf6O2i6yHPKWFfD51dPbfW5vcIaKyppraxfchD6BIxIEF8YkBURoDaKr4Y9wXTViugb+NJ+JWKX3DkaOK8kTXGW8\/T1HENR8UdhKY\/IgFJUwLmU405cU1w3+aWG7kUFN\/+TX8HtXnncFBSRln3FHVDaNtTiYGBTmrL8snMuEZ+SRXNXY95LQ3UkBp+mYKadrpb6ym5UU\/XA09tEZ2114k9m079YzSVsFdAzY0crmbmUdk+NL\/ZpxMDrcQ6b2X6vz\/lyyXrUTdxJjAij+5HvoF6yA3cj+kCU2yt3NmxL42Hp2eqIEBmG\/ZOKfddO30tlVy\/WsidK0DUVcIZz22snjaRSTM34x5eSM8g334jLQaqLzihaOvFlSaAXjIDHFm71IXM2yegJsIZ1T3HuZaXxCFrZRZMmsw38zZg6HWewqbbIqExGdtlM1muH0TlPZdYd8FxlOZ+wwbPBDq726jJDMForRJ6jrEMJZnucIiBlmRvNM3tOHvz1ou8MsaLdVJmxNTe+tyesh\/NXT5cys0kzEOP5VMn8\/WMH9DYfpT0ytvXbm8h3vLf8Z28GzmCn\/5QUU0s25bNZIllKBXV2QRYKSM1ZSLfrDPnRGrtoB3GnlYM9FVdZqe5Bs6XbyUbElVdZMs8NfbF3IqNL6qIwWWHNftTCsk6vQO5+TOY\/JUU0vquhF1ruC2MBFxylmP2AhVO5N\/zL\/WWEqizlGnr7UiuqiT5iD0bZk5mwrQNOIZk3y8OH4NEDEgQnxjoTcd2sjquwencLMvivJcF0mt08c+5kwFPQGlmPGHRqZQ0DwAtnNU0RUf+JPWiTvKSMilv6QGEtJZeJSYqhrTsPEqqG+msicf0cw08j8aQEJ\/CjcYe+ttLOCS7ls0q24ktqqWj9iqBdlvZtFgamcW+lAyhbU8jBgS5oSh9\/hmfz5jLD5s12WbvR0px66N7ZV1XsJmii\/+FG5QkhmCoHkTRQ\/RDfXwAWybYk\/5Al6CDgsh4itoA+im5HIDujwuZPn0my7d4k17ZNege4dOJgZscWKmNqVk4N2qKSQr1QGWFAg4xt5YMtd3M4MKFGK4UN9+2p4csX3c0ZjtyLruAghv1CIGB5lJSYyOIu3KNG2UVNHeXErBWDyPNAC4lxZN2o4G+\/nZSvMxRmLMJn5QS6gX9COuziUjMo6GriXhnXRZOMCd1kNFFRnxk4EYgS743xOdKK9DIUa2p\/M+\/vsc9qwfoJEJfCV0zWywUNqHhHkZxUyftxVEYr9uImuNFWoVAeRgy\/\/sGb3y0DJfUO4mtOol1UODD13\/Lp3ZnqShKwt9WjXXfSKOmEMpQsnUMhxgQ1Z5Hfo0GVqfKETFAgstS\/vedrzGKbgQGSHMxQE\/XFGt1ZVSt\/cmsaqerKpUdWzaxQTuQyn6gMxmDf\/2Tv7w+FZ3z5bdr7uOa3zYmv\/Eb\/m\/LPrLz0giPzaCiroijGops\/NGTwkG+KJ96mqDrBrsNVfnBPIF+oCHCnLFvvcecnUkIgdpIX8xllbGyMEFVxYJT2fV0tZYS7qHPspU2JLWIgBoOrZXi7d++yYJdsTSLAEQ0pgWx4YPf8dv56oSm55KclkNVeyuZvuYs+ESXmObBtREkYkCCWMXAVeyn6LD\/7M1bn\/uL2a2pzAr9KHoRkHtsP2YaSqzavAVjxyPcbK8nQs8SfaUwmijBR3Un4RnVNJTGYKuujILsCr77bgXKbpHU1KViPX4FMpvUkFu7is12+7mYcByjrz9jwpTv0fKKpVLQRb+og8w9O9FY6E3RENr25GJARHWUP+qTHEhq6aalLI0jRoos1\/AireVWX6W\/IZtTAX74hqVR1wMMpGPzlTa+50poF1Rw9Uoxnf0t5Ecfwy8ghOiEJLLLG6iMC0Ljcz38zoYRFBJJYZMI6KUiKQD1z2aguusM6UU3KbyWxOUbzfS3p2E9ZTnmfhk8YoXOAzytGPBbpY+NfdLtRCACIt30mSftR3pGLF76GqxXUWWzpj3n8hoR0U+u\/14MFlrhtt2LfftSae8sI8B+G4obV7J86fcsUfEgs7GcU5uVWTVTBpUtG\/lR0ZiAxBh8Ni9h8vtfstL0EMmlt8YH+jtbqCm5xgk7Y7aqHqF0kGOpI+4zMHADjxUKOB3JpK0lhwMrZzJ5iTRye64D+Viv24aFnhYzlzsRU\/1TI0qCDPlRzYJLTUB5BEbjNrNFS521RudujZDUnkVP1QRt1RUs2nOe2q5+RKJmYrZZoyMTwlCydgyPz0ANRxRVMXGKoLmvmuMyS5i7ZDkLrVMRUcFeDStMtmgzb4UlgRk\/ZepsiN7FJjkVgktF0J2K9URVtNVVWKx65FYbBInYGZiiIb+SBTsDuX4nOR4DJLuaoyrnyY1BDg08vc9AN2l7bVFb50Yx\/Vyx1kB24TeMUwiiqredy34eaKzSQVpeF\/392XeFubAqFivZZWyLbQUqOPCDEQZr5Zkru5NLFQMgrOGMlzWb1sjwg40lAdf7oLedupt5nHM1Z4ucD\/kdg2sjjD4xMNBRQ056NpVtkkWDg0W8YmCqNvtOl9yev23hvLER0j8EU1kfidxSBcwCU6nIOY3e2gXoR18lxtgaA+WzNJOL41e6HAq\/RkSgOfOs0oByrBW3oOKVCwPpbPtsPfYh+bSVR2KksQHz5OtcNlFBW9+H7JY787HdpO\/ejvqikRMDNTGHUZ\/kQOqdG7UunB9nbcE1ooKOhjwOm5igY2KIkqo22w9foq4jC+dpevhHFlNxNQz7jS4cOuGL9mZVtNXX8u2sDdiF51J9JZjNHyxG2dAAxbWrUN4ZSomgm7xga5b94yPmypnhF3vzHlMKcF6+me3HB5+Y46nFwGp9rG0Tbg\/dD1BwbDfSn0izdrMiq3SCKW0q4ojmPL6zO42AAQoO78NoiTm2my2w1IukNM+PeUr7KewTkhlgxnTZUHpp5OgGReQVDlPW0cRZ+9X84BdLTqg7uks0OV0ioG9ABAipSj2Jm4Ua30stRt0va9B+AyPvQNjH1d3KyOw8SnLcSYylXdi\/0xEtpQAKcwPZbOqMm6cZXxkdIkvwUxe3Lc6Z73VMCS0Hbl7AaIophw7tQ2+9LmFt3VQetEbX6QBH9mmw3D2chgGANiIMLdGRFYcYgJIgIzZa7CUhIwLzdY747t2Jxo+eZBecRd\/KGqfddnyjv5vwip8ka1+2P3JaqrheG4CeVKymGuFz8CBma5U5eFNAc5gHBhYeBPqbsMrRj5zbOmKgNh47JVUsggoHfc0PhwOhIP0QWpp6BOeksUfZES+vvWgvteD8lRQO7DZhq4sHcttMMY66Jz5JSyY7TNYgc7IKqMBnhREujgG4Km3GOjyf9hsx7NDZxp5jnqhZmnK4sIf+wgi87LRY+d13bNqdNKSwuqNNDAir49ll6kJ4oeDxO0sAxC4GdPA+U3Z7Qy0BRjqs0Y6g8to+pi5fh+r2A4QE7sdl506Cc\/OINLRCX\/kszeThPE2fwIhC0k9aMWXGj+hpayK90ZZjee3QmYTZl0aE5nRDayp2lkqYpZeRaa2Hpc0pfvI06Obq7h1oLN4\/QtMEd8SAPcl3ZkN60zGerIrL4QzSwqz4apE5EYVlXD2giZSsFqE5V\/CYZcChqGJuxodg9Ik0awz0UQ1pBuJZvdSIgxkNtGYEs\/kTdU5VdtOT7sFqdS0Ci0XQe5Vd83\/E9fwNuu46WYkoPm7NRuWdJFQM\/pExHGLAxi7x9tx\/H1cDHZH6cjWz5FbyjZYboaHBeO+0wPpUBt30kX\/IC6Ml5tirWGNtFEN1+WlWz13Kxi06qK3VwDSkFKjh8I9G2Dqk0IuIy94KrAxIpOiCH5ZrzEi5Z9ijp6GM7MwrRAQ6Ia9iyNGiwbVdHKsJBOl7WLzRCqedhqz3TqY8KQBrYy1sLUxxcgsgPtyNr9e6k9z80yRPVagZ69QtiGkESs+hO8Ga6JwreFnpsNXRBysTK9xP5pBxXItFO8Nu+5e0EWlkha7c8RGfJgDoLwpi9SZz7JwNWLPrAtevXcDNUBkzBwdcbDyIi\/Nn3mobQgp\/8hhpveyJ4mY1AkpE0JWM+VfGHI+\/yilPPeS27WOHnRWWvhkUR1uwxOYA1zsBQSFHrNXQdT9L1RC864ZlNUH7FcwNjNlqsg05p\/2cy83njLkc2q67cdUx5nTKRTQUtNA5Vnr3J6L6RJwUV2IY1QxUsH+ZPk5uSVyLcmCVyi58ve1R3HaOypwA5E2M8C\/oh7ZK8q6lEXPCHWXFrfhmD94LZCTFgLCznsKsaxRUt\/3kLEofjcU5pGcW0tAlAkS0VN6ktvXOue2nrbaaqoYuoJfakjwyrl2nqr3\/7veC5lY6OtqoqyihsDCbxKhEbrTekvydjWVkpWfed8y+bgHNTW10tNWQn32dmvZ7L4weam\/kkFVQTus9m3vqSsi+lktZwx27+miqyCcz6wYNHS925APx+gxM0sTrdCkANcl+qK2XxeFSK32VIaxcqY7T6Wu3YnA3tiGinnBtM3Q2h90aGZiqh\/\/ZLKL9tZixUgUruz2ERhbccgxqisPoU22C0tuhKRErUwVMr5SQbr4VQ9NgKu8a0UmamzNq8\/eN7MjAV06k3XlJ1YYj\/Z02ey7mEe2rxN9WGeIXcpyjfp7sDL7A9fJUts+4LQYSjmMyWRM9I3mmzlqNkrQCCsbHuNHRR0tSMFsnOpIhAnJ82WhizOFCIZDHnkWy+KU03rWiKsEX\/a2GBF9rGJIH+dOJgTJ8V+piaZNAF9BVGo2dsgy6uw9hY6DKjxZhVNTVUldfS7NgAOgh228P+gtNsVW0xMowksJMb5b\/sI4t25zZ7xdLnRCggoOrdTG3jKeTPi55yrL8UAKFZ70xXq5P7O0h4v7maqoFt53rCo+jt3Yl268MrtcglqWFHWmYzZjFpElzsUhqBkE229Un8\/qfZ2N3NJ+OhktozdqAc3jhLc\/x7iK81ORRNg+lQQj9hWfY+p9txNYIKDzjxMzX\/srYTW4kt\/RTcESJeS6nb4uBFi7om6MlfUwsIwP038Dth2VM+nACW8JuMNBTRZDlPP78xmQ0diTS2ZGN5cJ1GPsm0w4grCHUSoONyvso7gXaEjD5XJeQq\/XUXD3E8rfe4p\/LjDlT00\/TBQMW2PiRfTOfUHtl5C39yW9\/jD0\/Y3iWFrYR4aDD9D+9z9KdJ6nq7afwqApvvfcJ86T9\/j977xlWVZbn\/zrzPD09Pc\/Mf+7t29N35naqmemurq4uK3R1lZaWVpVWWVXmhFkJKklABATJKCKIgSBRQSSqoEgGySASJUnOSI4HDpmTPveFobQrKZwDSPt5nvPCI3ut39p7n72\/a63f+i66x3u4fvggKnpXHrnqTVIZ4ciutaak9EiBZi6uM+DU+UKGx4swWbyQ197bwMm8Uaj0Y4\/pMXyyWxgce\/Qy6kjBZtc6bNK\/J8H6O5gpMTBcE4\/94f2oG5pwWPUoITntSMWdxLsfY4eWKbamWqgYnSOpvos8f2P2e6Y+3FVwuBQXx5M4RGYS53EGU0MrbC2N0FBz4m6HBBgiy9mWPR+vZqemBkeO22K13Za0pj7qUoIwUzfB\/qw1mtuN8ElsRAb0F9zi2Ge70DY14LCGMnsMXcjsmAS6SXYzRFnrCIbaelidjaQd6L7rh4GhCZY2FhgZmXGtpJGKSB\/MD5lge8oK+7Bcep93znUOMntiQFLNhdWfsPCtv7J0yTI+3arLhVu5DEgBqYDMS1as274fncP66GqcJauzjTQbB6yMkhBShdPX1oSmVpARbMKyjbo4nLFB98AR7APyGejPx+4zSyLLRkBQgOMpPazz+hhKd2PTB2+xyDiE6s4yrqh\/zbt\/+D2\/\/X\/\/yFLd89ztfr5X47TEQKo\/6m+ZkdgmQCBoIM5BnW0GnpQKJqiLtWfFdkdSaltpa22hvXcU6VABtu8f4UpSPQ+ybmD4+l4OmKiwVus4Fy+GklXxMOO4JzMQzYXHyZ0EaYk3O4yNCaiVgrQCt6+UOB1Vzcj4JC3pXhzYvgOL6wV0DYww8gLLKqcnBnqJPLyT9\/\/3HT5asoSla1SwvnSb7vEJurMD2b9zD\/v0jTi89wieiQ2AhOprl7HZdYpzRo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\" alt=\"\" \/><\/div><p>Source: Collier, J. E. (2020). <i>Applied structural equation modeling using AMOS: Basic to advanced techniques<\/i>. Routledge.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-390fd85 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"390fd85\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-4d70ab8\" data-id=\"4d70ab8\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-618bcf9 elementor-widget elementor-widget-heading\" data-id=\"618bcf9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">References<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-0f647f8\" data-id=\"0f647f8\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c4d18aa elementor-widget elementor-widget-text-editor\" data-id=\"c4d18aa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>Collier, J. E. (2020). <i>Applied structural equation modeling using AMOS: Basic to advanced techniques<\/i>. Routledge.<\/li><li>Awang, Z. (2015). <i>A Handbook on SEM<\/i> (2nd Edition). Malaysia<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a083ccb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a083ccb\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-3664cd4\" data-id=\"3664cd4\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fafd920 elementor-widget elementor-widget-heading\" data-id=\"fafd920\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Video Tutorial<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-d0ca5cb\" data-id=\"d0ca5cb\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0232284 elementor-widget elementor-widget-video\" data-id=\"0232284\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/JDF19KgsopI&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-08fd49e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"08fd49e\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-b0d7a39\" data-id=\"b0d7a39\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-98eb3a7 elementor-widget elementor-widget-heading\" data-id=\"98eb3a7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Additional AMOS Tutorials<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-90bae56\" data-id=\"90bae56\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a04fb94 elementor-widget elementor-widget-wp-widget-ccchildpages_widget\" data-id=\"a04fb94\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-ccchildpages_widget.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<ul><li class=\"page_item page-item-6829 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/\" class=\"menu-link\">Assessing Construct Reliability and Convergent Validity in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-7107 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/basic-first-structural-model-in-spss-amos\/\" class=\"menu-link\">Basic\/First Structural Model in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-6713 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/building-a-basic-model-in-spss-amos\/\" class=\"menu-link\">Building a Basic Model in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-7029 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/common-method-bias-in-spss-amos\/\" class=\"menu-link\">Common Method Bias in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-7064 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/common-method-bias-using-latent-common-method-factor\/\" class=\"menu-link\">Common Method Bias using Latent Common Method Factor<\/a><\/li>\n<li class=\"page_item page-item-6757 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/confirmatory-factor-analysis-and-analyzing-spss-amos-output\/\" class=\"menu-link\">Confirmatory Factor Analysis and Analyzing SPSS AMOS Output<\/a><\/li>\n<li class=\"page_item page-item-6687 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/first-measurement-model-in-amos\/\" class=\"menu-link\">First Measurement Model in AMOS<\/a><\/li>\n<li class=\"page_item page-item-7247 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/full-structural-model-analysis\/\" class=\"menu-link\">Full Structural Model Analysis<\/a><\/li>\n<li class=\"page_item page-item-6909 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/how-to-assess-discriminant-validity-in-spss-amos\/\" class=\"menu-link\">How to Assess Discriminant Validity in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-6415 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-lecture-series-basics\/\" class=\"menu-link\">IBM SPSS AMOS Lecture Series &#8211; Basics<\/a><\/li>\n<li class=\"page_item page-item-6443 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-series-1-what-is-structural-equation-modelling\/\" class=\"menu-link\">IBM SPSS AMOS Series &#8211; 2 &#8211; What is Structural Equation Modelling<\/a><\/li>\n<li class=\"page_item page-item-6538 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-series-4-introduction-to-amos\/\" class=\"menu-link\">IBM SPSS AMOS Series &#8211; 4 &#8211; Introduction to AMOS<\/a><\/li>\n<li class=\"page_item page-item-6561 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/introduction-to-confirmatory-factor-analysis-cfa\/\" class=\"menu-link\">Introduction to Confirmatory Factor Analysis (CFA)<\/a><\/li>\n<li class=\"page_item page-item-7330 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/mediation-analysis-with-multiple-mediators\/\" class=\"menu-link\">Mediation Analysis with Multiple Mediators<\/a><\/li>\n<li class=\"page_item page-item-7820 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/moderation-analysis-with-categorical-moderator-in-spss-amos\/\" class=\"menu-link\">Moderation Analysis with Categorical Moderator in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-7370 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/moderation-anlaysis-in-spss-amos\/\" class=\"menu-link\">Moderation Anlaysis in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-6944 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/reporting-measurement-model-fit-indices-reliability-and-validity\/\" class=\"menu-link\">Reporting Measurement Model &#8211; Fit Indices, Reliability and Validity<\/a><\/li>\n<li class=\"page_item page-item-7354 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/serial-mediation-analysis-in-spss-amos\/\" class=\"menu-link\">Serial Mediation Analysis in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-7080 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/spss-amos-assessing-normality-of-data\/\" class=\"menu-link\">SPSS AMOS Assessing Normality of Data<\/a><\/li>\n<li class=\"page_item page-item-7300 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/spss-amos-mediation-analysis\/\" class=\"menu-link\">SPSS AMOS Mediation Analysis<\/a><\/li>\n<li class=\"page_item page-item-6782 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/understanding-assessing-and-improving-model-fit-in-spss-amos\/\" class=\"menu-link\">Understanding, Assessing, and Improving Model fit in SPSS AMOS<\/a><\/li>\n<\/ul>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Factor Loadings and Fit Statistics What Will be Discussed? The tutorial will discuss in detail the concept of factor loadings, acceptable factor loadings, model fit statistics, acceptable model fit statistics to establish a good fit, and how to use Modification Indices to improve model fit. IBM SPSS AMOS Series &#8211; 6 In CB-SEM, Loadings, Model [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":6468,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"enabled","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-6638","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>IBM SPSS AMOS Series - Factor Loadings and Fit Statistics - ResearchWithFawad<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-series-factor-loadings-and-fit-statistics\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"IBM SPSS AMOS Series - Factor Loadings and Fit Statistics - ResearchWithFawad\" \/>\n<meta property=\"og:description\" content=\"Factor Loadings and Fit Statistics What Will be Discussed? The tutorial will discuss in detail the concept of factor loadings, acceptable factor loadings, model fit statistics, acceptable model fit statistics to establish a good fit, and how to use Modification Indices to improve model fit. 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